From 2675196b67adcfcc64e16892ef9b5f4de3d34235 Mon Sep 17 00:00:00 2001
From: Henry Pinkard <7969470+henrypinkard@users.noreply.github.com>
Date: Sun, 20 Oct 2024 08:31:44 -0700
Subject: [PATCH 1/2] finished config files for v10 and plot making code for
 LED array

---
 .../compute_mi_and_phenotyping_performance.py |   4 +-
 ...10_Brightfield_150photons_replicate_0.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_1.yaml |   2 +-
 ...0_Brightfield_150photons_replicate_10.yaml |   2 +-
 ...0_Brightfield_150photons_replicate_11.yaml |   4 +-
 ...0_Brightfield_150photons_replicate_12.yaml |   4 +-
 ...0_Brightfield_150photons_replicate_13.yaml |   4 +-
 ...0_Brightfield_150photons_replicate_14.yaml |   4 +-
 ...10_Brightfield_150photons_replicate_2.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_3.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_4.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_5.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_6.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_7.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_8.yaml |   2 +-
 ...10_Brightfield_150photons_replicate_9.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_0.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_1.yaml |   2 +-
 ...0_Brightfield_450photons_replicate_10.yaml |   2 +-
 ...0_Brightfield_450photons_replicate_11.yaml |   4 +-
 ...0_Brightfield_450photons_replicate_12.yaml |   4 +-
 ...0_Brightfield_450photons_replicate_13.yaml |   4 +-
 ...0_Brightfield_450photons_replicate_14.yaml |   4 +-
 ...10_Brightfield_450photons_replicate_2.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_3.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_4.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_5.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_6.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_7.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_8.yaml |   2 +-
 ...10_Brightfield_450photons_replicate_9.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_0.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_1.yaml |   2 +-
 ...10_Brightfield_50photons_replicate_10.yaml |   2 +-
 ...10_Brightfield_50photons_replicate_11.yaml |   4 +-
 ...10_Brightfield_50photons_replicate_12.yaml |   4 +-
 ...10_Brightfield_50photons_replicate_13.yaml |   4 +-
 ...10_Brightfield_50photons_replicate_14.yaml |   4 +-
 ...v10_Brightfield_50photons_replicate_2.yaml |  49 +++
 ...v10_Brightfield_50photons_replicate_3.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_4.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_5.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_6.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_7.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_8.yaml |   2 +-
 ...v10_Brightfield_50photons_replicate_9.yaml |   2 +-
 ..._v10_DPC_Right_150photons_replicate_0.yaml |   2 +-
 ..._v10_DPC_Right_150photons_replicate_1.yaml |   2 +-
 ...v10_DPC_Right_150photons_replicate_10.yaml |  49 +++
 ...v10_DPC_Right_150photons_replicate_11.yaml |   4 +-
 ...v10_DPC_Right_150photons_replicate_12.yaml |   4 +-
 ...v10_DPC_Right_150photons_replicate_13.yaml |   4 +-
 ...v10_DPC_Right_150photons_replicate_14.yaml |   4 +-
 ..._v10_DPC_Right_150photons_replicate_2.yaml |   2 +-
 ..._v10_DPC_Right_150photons_replicate_3.yaml |   2 +-
 ..._v10_DPC_Right_150photons_replicate_4.yaml |   2 +-
 ..._v10_DPC_Right_150photons_replicate_5.yaml |   2 +-
 ..._v10_DPC_Right_150photons_replicate_6.yaml |  49 +++
 ..._v10_DPC_Right_150photons_replicate_7.yaml |   2 +-
 ..._v10_DPC_Right_150photons_replicate_8.yaml |  49 +++
 ..._v10_DPC_Right_150photons_replicate_9.yaml |  49 +++
 ..._v10_DPC_Right_450photons_replicate_0.yaml |   2 +-
 ..._v10_DPC_Right_450photons_replicate_1.yaml |   2 +-
 ...v10_DPC_Right_450photons_replicate_10.yaml |   2 +-
 ...v10_DPC_Right_450photons_replicate_11.yaml |   4 +-
 ...v10_DPC_Right_450photons_replicate_12.yaml |   4 +-
 ...v10_DPC_Right_450photons_replicate_13.yaml |   4 +-
 ...v10_DPC_Right_450photons_replicate_14.yaml |   4 +-
 ..._v10_DPC_Right_450photons_replicate_2.yaml |  49 +++
 ..._v10_DPC_Right_450photons_replicate_3.yaml |   2 +-
 ..._v10_DPC_Right_450photons_replicate_4.yaml |   2 +-
 ..._v10_DPC_Right_450photons_replicate_5.yaml |  49 +++
 ..._v10_DPC_Right_450photons_replicate_6.yaml |   2 +-
 ..._v10_DPC_Right_450photons_replicate_7.yaml |   2 +-
 ..._v10_DPC_Right_450photons_replicate_8.yaml |   2 +-
 ..._v10_DPC_Right_450photons_replicate_9.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_0.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_1.yaml |   2 +-
 ..._v10_DPC_Right_50photons_replicate_10.yaml |   2 +-
 ..._v10_DPC_Right_50photons_replicate_11.yaml |   4 +-
 ..._v10_DPC_Right_50photons_replicate_12.yaml |   4 +-
 ..._v10_DPC_Right_50photons_replicate_13.yaml |   4 +-
 ..._v10_DPC_Right_50photons_replicate_14.yaml |   4 +-
 ...e_v10_DPC_Right_50photons_replicate_2.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_3.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_4.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_5.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_6.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_7.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_8.yaml |   2 +-
 ...e_v10_DPC_Right_50photons_replicate_9.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_0.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_1.yaml |   2 +-
 ...se_v10_LED119_150photons_replicate_10.yaml |   2 +-
 ...se_v10_LED119_150photons_replicate_11.yaml |   4 +-
 ...se_v10_LED119_150photons_replicate_12.yaml |   4 +-
 ...se_v10_LED119_150photons_replicate_13.yaml |   4 +-
 ...se_v10_LED119_150photons_replicate_14.yaml |   4 +-
 ...ise_v10_LED119_150photons_replicate_2.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_3.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_4.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_5.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_6.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_7.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_8.yaml |   2 +-
 ...ise_v10_LED119_150photons_replicate_9.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_0.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_1.yaml |   2 +-
 ...se_v10_LED119_450photons_replicate_10.yaml |   2 +-
 ...se_v10_LED119_450photons_replicate_11.yaml |   4 +-
 ...se_v10_LED119_450photons_replicate_12.yaml |   4 +-
 ...se_v10_LED119_450photons_replicate_13.yaml |   4 +-
 ...se_v10_LED119_450photons_replicate_14.yaml |   4 +-
 ...ise_v10_LED119_450photons_replicate_2.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_3.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_4.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_5.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_6.yaml |  49 +++
 ...ise_v10_LED119_450photons_replicate_7.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_8.yaml |   2 +-
 ...ise_v10_LED119_450photons_replicate_9.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_0.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_1.yaml |   2 +-
 ...ise_v10_LED119_50photons_replicate_10.yaml |   2 +-
 ...ise_v10_LED119_50photons_replicate_11.yaml |   4 +-
 ...ise_v10_LED119_50photons_replicate_12.yaml |   4 +-
 ...ise_v10_LED119_50photons_replicate_13.yaml |   4 +-
 ...ise_v10_LED119_50photons_replicate_14.yaml |   4 +-
 ...oise_v10_LED119_50photons_replicate_2.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_3.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_4.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_5.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_6.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_7.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_8.yaml |   2 +-
 ...oise_v10_LED119_50photons_replicate_9.yaml |   2 +-
 ...0_Brightfield_150photons_replicate_11.yaml |   6 +-
 ...0_Brightfield_150photons_replicate_12.yaml |   6 +-
 ...0_Brightfield_150photons_replicate_13.yaml |   6 +-
 ...0_Brightfield_150photons_replicate_14.yaml |   6 +-
 ...0_Brightfield_450photons_replicate_11.yaml |   6 +-
 ...0_Brightfield_450photons_replicate_12.yaml |   6 +-
 ...0_Brightfield_450photons_replicate_13.yaml |   6 +-
 ...0_Brightfield_450photons_replicate_14.yaml |   6 +-
 ...10_Brightfield_50photons_replicate_11.yaml |   6 +-
 ...10_Brightfield_50photons_replicate_12.yaml |   6 +-
 ...10_Brightfield_50photons_replicate_13.yaml |   6 +-
 ...10_Brightfield_50photons_replicate_14.yaml |   6 +-
 ...v10_DPC_Right_150photons_replicate_11.yaml |   6 +-
 ...v10_DPC_Right_150photons_replicate_12.yaml |   6 +-
 ...v10_DPC_Right_150photons_replicate_13.yaml |   6 +-
 ...v10_DPC_Right_150photons_replicate_14.yaml |   6 +-
 ...v10_DPC_Right_450photons_replicate_11.yaml |   6 +-
 ...v10_DPC_Right_450photons_replicate_12.yaml |   6 +-
 ...v10_DPC_Right_450photons_replicate_13.yaml |   6 +-
 ...v10_DPC_Right_450photons_replicate_14.yaml |   6 +-
 ..._v10_DPC_Right_50photons_replicate_11.yaml |   6 +-
 ..._v10_DPC_Right_50photons_replicate_12.yaml |   6 +-
 ..._v10_DPC_Right_50photons_replicate_13.yaml |   6 +-
 ..._v10_DPC_Right_50photons_replicate_14.yaml |   6 +-
 ...se_v10_LED119_150photons_replicate_11.yaml |   6 +-
 ...se_v10_LED119_150photons_replicate_12.yaml |   6 +-
 ...se_v10_LED119_150photons_replicate_13.yaml |   6 +-
 ...se_v10_LED119_150photons_replicate_14.yaml |   6 +-
 ...se_v10_LED119_450photons_replicate_11.yaml |   6 +-
 ...se_v10_LED119_450photons_replicate_12.yaml |   6 +-
 ...se_v10_LED119_450photons_replicate_13.yaml |   6 +-
 ...se_v10_LED119_450photons_replicate_14.yaml |   6 +-
 ...ise_v10_LED119_50photons_replicate_11.yaml |   6 +-
 ...ise_v10_LED119_50photons_replicate_12.yaml |   6 +-
 ...ise_v10_LED119_50photons_replicate_13.yaml |   6 +-
 ...ise_v10_LED119_50photons_replicate_14.yaml |   6 +-
 ...v11_DPC_Right_450photons_replicate_10.yaml |  48 +++
 .../config_files/make_config_files.ipynb      |  94 +++++
 ...11_Brightfield_150photons_replicate_0.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_1.yaml |  49 +++
 ...1_Brightfield_150photons_replicate_10.yaml |  49 +++
 ...1_Brightfield_150photons_replicate_11.yaml |  49 +++
 ...1_Brightfield_150photons_replicate_12.yaml |  49 +++
 ...1_Brightfield_150photons_replicate_13.yaml |  49 +++
 ...1_Brightfield_150photons_replicate_14.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_2.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_3.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_4.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_5.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_6.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_7.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_8.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_9.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_0.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_1.yaml |  49 +++
 ...1_Brightfield_450photons_replicate_10.yaml |  49 +++
 ...1_Brightfield_450photons_replicate_11.yaml |  49 +++
 ...1_Brightfield_450photons_replicate_12.yaml |  49 +++
 ...1_Brightfield_450photons_replicate_13.yaml |  49 +++
 ...1_Brightfield_450photons_replicate_14.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_2.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_3.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_4.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_5.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_6.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_7.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_8.yaml |  49 +++
 ...11_Brightfield_450photons_replicate_9.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_0.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_1.yaml |  49 +++
 ...11_Brightfield_50photons_replicate_10.yaml |  49 +++
 ...11_Brightfield_50photons_replicate_11.yaml |  49 +++
 ...11_Brightfield_50photons_replicate_12.yaml |  49 +++
 ...11_Brightfield_50photons_replicate_13.yaml |  49 +++
 ...11_Brightfield_50photons_replicate_14.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_2.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_3.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_4.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_5.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_6.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_7.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_8.yaml |  49 +++
 ...v11_Brightfield_50photons_replicate_9.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_0.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_1.yaml |  49 +++
 ...v11_DPC_Right_150photons_replicate_10.yaml |  49 +++
 ...v11_DPC_Right_150photons_replicate_11.yaml |  49 +++
 ...v11_DPC_Right_150photons_replicate_12.yaml |  49 +++
 ...v11_DPC_Right_150photons_replicate_13.yaml |  49 +++
 ...v11_DPC_Right_150photons_replicate_14.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_2.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_3.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_4.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_5.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_6.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_7.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_8.yaml |  49 +++
 ..._v11_DPC_Right_150photons_replicate_9.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_0.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_1.yaml |  49 +++
 ...v11_DPC_Right_450photons_replicate_10.yaml |  49 +++
 ...v11_DPC_Right_450photons_replicate_11.yaml |  49 +++
 ...v11_DPC_Right_450photons_replicate_12.yaml |  49 +++
 ...v11_DPC_Right_450photons_replicate_13.yaml |  49 +++
 ...v11_DPC_Right_450photons_replicate_14.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_2.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_3.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_4.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_5.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_6.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_7.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_8.yaml |  49 +++
 ..._v11_DPC_Right_450photons_replicate_9.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_0.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_1.yaml |  49 +++
 ..._v11_DPC_Right_50photons_replicate_10.yaml |  49 +++
 ..._v11_DPC_Right_50photons_replicate_11.yaml |  49 +++
 ..._v11_DPC_Right_50photons_replicate_12.yaml |  49 +++
 ..._v11_DPC_Right_50photons_replicate_13.yaml |  49 +++
 ..._v11_DPC_Right_50photons_replicate_14.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_2.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_3.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_4.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_5.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_6.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_7.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_8.yaml |  49 +++
 ...e_v11_DPC_Right_50photons_replicate_9.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_0.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_1.yaml |  49 +++
 ...se_v11_LED119_150photons_replicate_10.yaml |  49 +++
 ...se_v11_LED119_150photons_replicate_11.yaml |  49 +++
 ...se_v11_LED119_150photons_replicate_12.yaml |  49 +++
 ...se_v11_LED119_150photons_replicate_13.yaml |  49 +++
 ...se_v11_LED119_150photons_replicate_14.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_2.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_3.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_4.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_5.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_6.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_7.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_8.yaml |  49 +++
 ...ise_v11_LED119_150photons_replicate_9.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_0.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_1.yaml |  49 +++
 ...se_v11_LED119_450photons_replicate_10.yaml |  49 +++
 ...se_v11_LED119_450photons_replicate_11.yaml |  49 +++
 ...se_v11_LED119_450photons_replicate_12.yaml |  49 +++
 ...se_v11_LED119_450photons_replicate_13.yaml |  49 +++
 ...se_v11_LED119_450photons_replicate_14.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_2.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_3.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_4.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_5.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_6.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_7.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_8.yaml |  49 +++
 ...ise_v11_LED119_450photons_replicate_9.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_0.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_1.yaml |  49 +++
 ...ise_v11_LED119_50photons_replicate_10.yaml |  49 +++
 ...ise_v11_LED119_50photons_replicate_11.yaml |  49 +++
 ...ise_v11_LED119_50photons_replicate_12.yaml |  49 +++
 ...ise_v11_LED119_50photons_replicate_13.yaml |  49 +++
 ...ise_v11_LED119_50photons_replicate_14.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_2.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_3.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_4.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_5.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_6.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_7.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_8.yaml |  49 +++
 ...oise_v11_LED119_50photons_replicate_9.yaml |  49 +++
 ...11_Brightfield_150photons_replicate_9.yaml |  48 +++
 ...11_Brightfield_450photons_replicate_9.yaml |  48 +++
 ...v11_Brightfield_50photons_replicate_9.yaml |  48 +++
 ..._v11_DPC_Right_150photons_replicate_9.yaml |  48 +++
 ..._v11_DPC_Right_450photons_replicate_9.yaml |  48 +++
 ...e_v11_DPC_Right_50photons_replicate_9.yaml |  48 +++
 ...ise_v11_LED119_150photons_replicate_9.yaml |  48 +++
 ...ise_v11_LED119_450photons_replicate_9.yaml |  48 +++
 ...oise_v11_LED119_50photons_replicate_9.yaml |  48 +++
 .../load_model_replicates.ipynb               | 265 +++++++++++++
 .../make_phenotyping_mi_plot.ipynb            | 373 +++++++++++-------
 320 files changed, 8345 insertions(+), 420 deletions(-)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml (87%)
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_10.yaml
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml (87%)
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_9.yaml
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml (87%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml (87%)
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_5.yaml
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml (88%)
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_6.yaml
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml (88%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml (85%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml (85%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml (85%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml (85%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml (86%)
 rename led_array/phenotyping_experiments/config_files/{staging => complete}/Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml (86%)
 create mode 100644 led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_0.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_1.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_10.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_11.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_12.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_13.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_14.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_2.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_3.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_4.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_5.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_6.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_7.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_8.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml
 create mode 100644 led_array/phenotyping_experiments/load_model_replicates.ipynb

diff --git a/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py b/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
index ef0fc2d..43e12b8 100644
--- a/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+++ b/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
@@ -60,7 +60,7 @@ def compute_nlls(model, test_dataset, max_num, markers):
     return np.array(negative_log_likelihoods), np.array(marker_indices)
 
 
-def estimate_mi(model_name, config, patch_size, num_images=5000, num_patches=10000, test_set_fraction=0.1, confidence=0.9):
+def estimate_mi(model_name, config, patch_size, num_images=5000, num_patches=10000, test_set_fraction=0.1, confidence=0.95):
     saving_name = f'{model_name}_{patch_size}patch_mi_estimates'
 
     # # check if already cached
@@ -94,7 +94,7 @@ def estimate_mi(model_name, config, patch_size, num_images=5000, num_patches=100
     pixel_cnn = PixelCNN()
     # stationary_gp = StationaryGaussianProcess(noisy_patches)
 
-    pixel_cnn.fit(noisy_patches, verbose=False, max_epochs=500, patience=100)
+    pixel_cnn.fit(noisy_patches, verbose=False, max_epochs=1000, patience=300)
     # stationary_gp.fit(noisy_patches, verbose=False)
 
 
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_0.yaml
index 962c053..2ffdb82 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_1.yaml
index 0d6f061..68756fc 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_10.yaml
index 7a1230f..474d488 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 9
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
index 8b393db..8d54f84 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
index 969e06d..e866d8a 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
index 8f72543..5e59858 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
index fd4a4fb..6616de7 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_2.yaml
index 5fd7bb3..bf65c45 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_2.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_2.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_3.yaml
index 557e769..0eb3a9a 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 9
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_4.yaml
index ba520b3..3b3d39c 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_5.yaml
index 0d84d24..d443cbb 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 9
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_6.yaml
index 7bd1833..b37dd66 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_6.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_6.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_7.yaml
index 42731ef..a2c1037 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_8.yaml
index d265e29..8e58389 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_9.yaml
index a8e63a3..f912443 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_150photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_0.yaml
index 9846ea5..9c5eba1 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_1.yaml
index b2edc68..e608882 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_10.yaml
index fe9e73d..e1d93bc 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
index 26ecb6e..eb7c5d8 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
index 06fe26d..23f97a9 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
index 2d6889e..566eb1b 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 5
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
index c7891ae..cc07b2e 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_2.yaml
index f063f18..8648205 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_2.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_2.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_3.yaml
index 33bb820..2c159fc 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_4.yaml
index 4d9997e..0648a4a 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_5.yaml
index cef0092..5ca5816 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_6.yaml
index 55d5cee..21172be 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_6.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_6.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_7.yaml
index d5ecab6..fe94dc1 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_8.yaml
index e37eb1c..844e77b 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_9.yaml
index 1fdc60b..6280845 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_450photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_0.yaml
index 81eb95c..78644b6 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_1.yaml
index b21c1e8..4adc447 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_10.yaml
index 624f02b..99af920 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
index b4b5c3c..99fc7ef 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
index ae63555..dbb09af 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
index acff0fa..5b3ffd2 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
index 482e38e..5547ee5 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_2.yaml
new file mode 100644
index 0000000..960dd1d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 9
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_3.yaml
index 3b1c6bd..0dded67 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_4.yaml
index c000d77..7b7b1c3 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_5.yaml
index 2f34073..7bf06e5 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 9
+  attempt_number: 13
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_6.yaml
index f419f63..a4829dc 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_6.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_6.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_7.yaml
index 285af75..304afd8 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_8.yaml
index 71a2c88..125a431 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 9
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_9.yaml
index 53e0d96..2fb7bcb 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_Brightfield_50photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_0.yaml
index 9b2d371..29294c0 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_1.yaml
index b1deff6..ad5f430 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_10.yaml
new file mode 100644
index 0000000..05c49d0
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 10
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
index 9c0dc4a..1edd833 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 5
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
index f38a9aa..26975c2 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 5
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
index 228bb49..73ba0b6 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
index 91d5c45..d08c75b 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_2.yaml
index 0882487..2408091 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_2.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_2.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_3.yaml
index 00fae5e..9f52c12 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_4.yaml
index dfcfb6c..de5002f 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_5.yaml
index d81ab59..b42110c 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_6.yaml
new file mode 100644
index 0000000..3bdd5b8
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 9
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_7.yaml
index 87fbdb3..286a04d 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_8.yaml
new file mode 100644
index 0000000..01000a6
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 9
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_9.yaml
new file mode 100644
index 0000000..76193a1
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 8
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_150photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_0.yaml
index 7db9e28..8179592 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_1.yaml
index 10d73fc..25c3a04 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_10.yaml
index 15e3f77..4a508f7 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 9
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
index 3a91d2d..910604a 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
index 402a562..d41f955 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
index 8bea0a2..8b4e950 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
similarity index 87%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
index c65e825..37a66b3 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_2.yaml
new file mode 100644
index 0000000..cd08c38
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 8
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_3.yaml
index db2c234..3f5018e 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_4.yaml
index 2875992..abfbca9 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_5.yaml
new file mode 100644
index 0000000..9d42bfe
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 9
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_6.yaml
index 54debc1..36256a2 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_6.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_6.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_7.yaml
index 4e3bab2..aad1338 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_8.yaml
index 23a8776..f611b19 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_9.yaml
index 89537ea..06ef3ee 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_450photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_0.yaml
index 0875902..8dde5b1 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_1.yaml
index 6356207..189a911 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_10.yaml
index 87e9c91..c61b7f8 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
index c30673f..4e2a259 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
index e019371..30c3b4f 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
index a437d44..d3a9246 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
index da52d11..270d2c5 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_2.yaml
index b2ed2cb..ff332dd 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_2.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_2.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_3.yaml
index b5eed77..58c554c 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_4.yaml
index b9e8068..4a6176b 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_5.yaml
index c75ba78..5ee1099 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_6.yaml
index e310925..201554c 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_6.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_6.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_7.yaml
index 1291e01..1754fc0 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_8.yaml
index 050cb58..79e981e 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_9.yaml
index 2575801..be63343 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_DPC_Right_50photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_0.yaml
index 93868b2..7f4d2de 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_1.yaml
index adb38dd..8b4893a 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_10.yaml
index d9233a3..c57dd50 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
index 91eae19..3847a75 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
index 6673fb4..9c1eaca 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
index 743aaf3..ad3aa59 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 5
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
index cff2e00..ee623f6 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 5
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_2.yaml
index 1a3c8ff..1045706 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_2.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_2.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_3.yaml
index 1545e91..99f8128 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_4.yaml
index e1d2a81..7644495 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_5.yaml
index 3c0fbb2..bd84af6 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_6.yaml
index 41e9b80..f17c559 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_6.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_6.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_7.yaml
index d90f530..ccd1d12 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_8.yaml
index 1be9719..c1451dc 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_9.yaml
index 51812e0..bf1fa16 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_150photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_0.yaml
index 2bfcb22..b9094d6 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_1.yaml
index ad96eda..26524ff 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_10.yaml
index 54ecb3e..646ff57 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
index d7a4f20..b58429c 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
index cd7467a..71dfe18 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
index 0578b50..0fb7025 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 4
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
index bdb255f..c84e623 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_2.yaml
index 18e5e1b..501cf0d 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_2.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_2.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_3.yaml
index fa132fe..f474431 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_4.yaml
index d6e504a..97154df 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_5.yaml
index 07577f9..1dc5c34 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_6.yaml
new file mode 100644
index 0000000..1899ab1
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 12345
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4556
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v10_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 10
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_7.yaml
index f54c5ca..316321d 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_8.yaml
index 9923f17..965fec3 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_9.yaml
index 72d49f2..7a98d2c 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_450photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_0.yaml
index c4e63d2..57d97a7 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_0.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_0.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_0/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_1.yaml
index 666d5a2..62a710f 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_1.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_1.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 11
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_1/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_10.yaml
index b5f9384..01bf0d7 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_10.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_10.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
index 88ca6a5..5a36182 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
index 6a05266..9be3b9e 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
index b6c7712..5ab5a9f 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
similarity index 88%
rename from led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
index 7f35300..a3f537f 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 1
+  attempt_number: 3
   elapsed: 0
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_2.yaml
index f3708c6..5d6088f 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_2.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_2.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 10
+  attempt_number: 13
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_2/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_3.yaml
index 0af199f..4d9c04b 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_3.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_3.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_3/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_4.yaml
index 2927db5..dad7103 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_4.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_4.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_4/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_5.yaml
index 67ee025..9b85397 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_5.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_5.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_5/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_6.yaml
index b5ed335..56f07de 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_6.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_6.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 6
+  attempt_number: 7
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_6/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_7.yaml
index f1a6bb6..9012f50 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_7.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_7.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 9
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_7/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_8.yaml
index 55b084f..1c07e98 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_8.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_8.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 8
+  attempt_number: 10
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_8/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_9.yaml
index d64eed7..fef0da8 100644
--- a/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_9.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_9.yaml
@@ -43,7 +43,7 @@ patch_size: 40
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
 training:
-  attempt_number: 7
+  attempt_number: 8
   elapsed: 0
   start_date: '2023-12-13'
   tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Analysis_Synthetic_Noise_v10_LED119_50photons_replicate_9/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
index 404c654..3eb6f6f 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 3
+  elapsed: 54303.02652692795
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_150photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
similarity index 85%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
index b238aab..6aef090 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 109789.08147883415
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_150photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
similarity index 85%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
index 0433417..f5640de 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 3
+  elapsed: 52157.347143650055
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_150photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
similarity index 85%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
index 2885dfd..659d24b 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_150photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 47144.047199487686
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_150photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
index 10b379a..0c6d1d5 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 57837.24168610573
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_450photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
similarity index 85%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
index f457521..ef03418 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 28680.670346975327
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_450photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
index 4fcadb0..a39558e 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 60671.45425915718
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_450photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
index 95ce444..6104698 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_450photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 75608.14245581627
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_450photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
index aef49f8..79aeb4e 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 56238.15553689003
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_50photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
index 490333a..54cd54c 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 41325.606723070145
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_50photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
index 45c7a58..7b87424 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 71799.5281226635
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_50photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
index e1d6d2c..affb532 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_Brightfield_50photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 57821.93256545067
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_Brightfield_50photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
index 21a9557..5e43bde 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 45406.49319148064
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_150photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
index 47f21a5..c979272 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 51733.21886086464
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_150photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
index dc99e78..8bdb215 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 27383.145106077194
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_150photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
index f7b6e29..b586dab 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 51110.61753988266
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_150photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
index 6d34a52..9846611 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 4
+  elapsed: 41989.02222299576
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_450photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
index c6a59c6..2002b75 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 27912.706077098846
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_450photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
index f31435b..afc8ca8 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 51964.071197748184
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_450photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
index 4bde659..9fb4e50 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 36030.200966119766
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_450photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
index 37e63fd..f1506be 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 55225.909960746765
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_50photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
index 09383b9..d4d6f0a 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 102616.43532371521
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_50photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
index 370ad97..f1f7acd 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 82205.1024889946
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_50photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
index dcf6bd8..5a80cde 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 6
+  elapsed: 47125.386674165726
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_DPC_Right_50photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
index afc3d98..2fbb611 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 73992.15336322784
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_150photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
index 768a8f1..d3e8812 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 125912.67483615875
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_150photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
index 6df6ec2..b62a584 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 68613.00662851334
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_150photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
index c96a33f..03e9ba9 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_150photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 83420.1274998188
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_150photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
index 2519147..43d0428 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 53978.63049840927
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_450photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
index 34149c5..806dbdc 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 122981.54178571701
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_450photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
index 6625ed8..c9aceb2 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 3
+  elapsed: 53900.20036125183
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_450photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
index 2853f32..9ee5ae3 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_450photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 3
+  elapsed: 69781.20701622963
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_450photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
index b90f07d..5752be7 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_11.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 3
+  elapsed: 52995.120037317276
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_50photons_replicate_11/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
index aa3b933..5267c73 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_12.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 65270.41405534744
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_50photons_replicate_12/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
index e4a86f6..8150b92 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_13.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 2
+  elapsed: 60083.80492258072
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_50photons_replicate_13/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
similarity index 86%
rename from led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
rename to led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
index 528082a..143d891 100644
--- a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v10_LED119_50photons_replicate_14.yaml
@@ -42,7 +42,7 @@ options:
 saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/
 train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
 training:
-  attempt_number: 1
-  elapsed: 0
+  attempt_number: 3
+  elapsed: 71583.26620650291
   start_date: '2023-12-13'
-  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v10/Synthetic_Noise_v10_LED119_50photons_replicate_14/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml
new file mode 100644
index 0000000..3fc4e99
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/complete/Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 2
+  elapsed: 34025.674588918686
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/Synthetic_Noise_v11_DPC_Right_450photons_replicate_10/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/make_config_files.ipynb b/led_array/phenotyping_experiments/config_files/make_config_files.ipynb
index 0f839c1..624dfe4 100644
--- a/led_array/phenotyping_experiments/config_files/make_config_files.ipynb
+++ b/led_array/phenotyping_experiments/config_files/make_config_files.ipynb
@@ -852,6 +852,100 @@
     "                contents = make_config(changes)\n",
     "                yaml.dump(contents, f, default_flow_style=False)   "
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Synthetic noise v11\n",
+    "Same thing as v10, but different data seed\n",
+    "Replicates with changing model seed, but constant data seed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import yaml\n",
+    "\n",
+    "\n",
+    "TEMPLATE_PATH = \"/home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/config_files/staging/template.yaml\"\n",
+    "experiment_name = 'Synthetic_Noise_v11_'\n",
+    "\n",
+    "\n",
+    "def make_config(changes):\n",
+    "    # load template and replace stuff as specified\n",
+    "    config = yaml.safe_load(open(TEMPLATE_PATH, \"r\"))\n",
+    "\n",
+    "    # recursively update the config with the changes\n",
+    "    def update(d, u):\n",
+    "        for k, v in u.items():\n",
+    "            if isinstance(v, dict):\n",
+    "                d[k] = update(d.get(k, {}), v)\n",
+    "            else:\n",
+    "                d[k] = v\n",
+    "        return d\n",
+    "    \n",
+    "    config = update(config, changes)\n",
+    "\n",
+    "    return config\n",
+    "\n",
+    "# get the folder of the template\n",
+    "template_folder = os.path.dirname(TEMPLATE_PATH)\n",
+    "\n",
+    "\n",
+    "patch_size = 40\n",
+    "\n",
+    "\n",
+    "# create a list of all the names of the yaml files\n",
+    "\n",
+    "for channel in ['Brightfield', 'DPC_Right', 'LED119']:\n",
+    "    for photon_count in [50, 150, 450]:\n",
+    "        for replicate in range(0, 15):\n",
+    "            prefix = template_folder + os.sep + experiment_name\n",
+    "            name = prefix + channel + f\"_{photon_count}photons_replicate_{replicate}.yaml\"\n",
+    "\n",
+    "            # create yaml files with each name\n",
+    "            with open(name, \"w\") as f:    \n",
+    "                changes = {\"data\": {\"channels\": [channel],\n",
+    "                                    \"shuffle_seed\": 8837, # fixed data seed\n",
+    "                                    \"batch\": 0,\n",
+    "                                    \"synthetic_noise\": {\n",
+    "                                        \"photons_per_pixel\": photon_count,\n",
+    "                                        \"seed\": 4553356, # fixed data seed\n",
+    "                                        \"median_filter\": False,\n",
+    "                                        \"use_correction_factor\": True,\n",
+    "                                        },\n",
+    "                                    },\n",
+    "                            \"metadata\": {\"replicate\": replicate, \"experiment_name\": experiment_name},\n",
+    "                            \"hyperparameters\": {\"seed\": replicate}, # changing model seed\n",
+    "                            \"train_script_path\": \"/home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py\",\n",
+    "                            \"saving_dir\": \"/home/hpinkard_waller/models/Synthetic_Noise_v11/\"\n",
+    "\n",
+    "                            }\n",
+    "\n",
+    "                contents = make_config(changes)\n",
+    "                yaml.dump(contents, f, default_flow_style=False)     \n",
+    "\n",
+    "\n",
+    "\n",
+    "            prefix = template_folder + os.sep + \"Analysis_\" + experiment_name\n",
+    "            name = prefix + channel + f\"_{photon_count}photons_replicate_{replicate}.yaml\"\n",
+    "            with open(name, \"w\") as f:\n",
+    "                changes = {\"data\": changes[\"data\"],\n",
+    "                            \"metadata\": changes[\"metadata\"],\n",
+    "                            \"hyperparameters\": changes[\"hyperparameters\"],\n",
+    "                            \"train_script_path\": \"/home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py\",\n",
+    "                            \"patch_size\": patch_size,\n",
+    "                            \"saving_dir\": changes[\"saving_dir\"]\n",
+    "                            }\n",
+    "\n",
+    "                contents = make_config(changes)\n",
+    "                yaml.dump(contents, f, default_flow_style=False)   "
+   ]
   }
  ],
  "metadata": {
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_0.yaml
new file mode 100644
index 0000000..84ef70c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_1.yaml
new file mode 100644
index 0000000..63fb008
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_10.yaml
new file mode 100644
index 0000000..ae9820a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_11.yaml
new file mode 100644
index 0000000..9813fdf
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_12.yaml
new file mode 100644
index 0000000..fe6d05d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_13.yaml
new file mode 100644
index 0000000..701a0e2
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_14.yaml
new file mode 100644
index 0000000..4818a32
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_2.yaml
new file mode 100644
index 0000000..905c5ba
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_3.yaml
new file mode 100644
index 0000000..98bc190
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_4.yaml
new file mode 100644
index 0000000..cccd0d2
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_5.yaml
new file mode 100644
index 0000000..060e915
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_6.yaml
new file mode 100644
index 0000000..61fa4df
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_7.yaml
new file mode 100644
index 0000000..a6868fa
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_8.yaml
new file mode 100644
index 0000000..4ae49de
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml
new file mode 100644
index 0000000..9029b91
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_0.yaml
new file mode 100644
index 0000000..a04cca8
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_1.yaml
new file mode 100644
index 0000000..27c94f4
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_10.yaml
new file mode 100644
index 0000000..5956e57
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_11.yaml
new file mode 100644
index 0000000..7e4e5f1
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_12.yaml
new file mode 100644
index 0000000..a21a55e
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_13.yaml
new file mode 100644
index 0000000..d3f3a9d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_14.yaml
new file mode 100644
index 0000000..e3c836c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_2.yaml
new file mode 100644
index 0000000..c810711
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_3.yaml
new file mode 100644
index 0000000..76eec23
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_4.yaml
new file mode 100644
index 0000000..fe9c1e1
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_5.yaml
new file mode 100644
index 0000000..56df944
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_6.yaml
new file mode 100644
index 0000000..b9b0fac
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_7.yaml
new file mode 100644
index 0000000..89530aa
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_8.yaml
new file mode 100644
index 0000000..01ae193
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml
new file mode 100644
index 0000000..6417653
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_0.yaml
new file mode 100644
index 0000000..481861e
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_1.yaml
new file mode 100644
index 0000000..7423048
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_10.yaml
new file mode 100644
index 0000000..0cdeded
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_11.yaml
new file mode 100644
index 0000000..a547f06
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_12.yaml
new file mode 100644
index 0000000..63d5ca4
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_13.yaml
new file mode 100644
index 0000000..6e4f941
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_14.yaml
new file mode 100644
index 0000000..f34d0bc
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_2.yaml
new file mode 100644
index 0000000..25d0913
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_3.yaml
new file mode 100644
index 0000000..69c17fa
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_4.yaml
new file mode 100644
index 0000000..d1a959e
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_5.yaml
new file mode 100644
index 0000000..0c42b34
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_6.yaml
new file mode 100644
index 0000000..321b743
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_7.yaml
new file mode 100644
index 0000000..92b076a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_8.yaml
new file mode 100644
index 0000000..ee82c7a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml
new file mode 100644
index 0000000..96203de
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_0.yaml
new file mode 100644
index 0000000..6dc562c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_1.yaml
new file mode 100644
index 0000000..084f81a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_10.yaml
new file mode 100644
index 0000000..e88c2e9
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_11.yaml
new file mode 100644
index 0000000..a0056a8
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_12.yaml
new file mode 100644
index 0000000..0ada1a2
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_13.yaml
new file mode 100644
index 0000000..c2db49b
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_14.yaml
new file mode 100644
index 0000000..1328d8e
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_2.yaml
new file mode 100644
index 0000000..5033b6e
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_3.yaml
new file mode 100644
index 0000000..3a82adb
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_4.yaml
new file mode 100644
index 0000000..e9dd32d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_5.yaml
new file mode 100644
index 0000000..ce98f85
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_6.yaml
new file mode 100644
index 0000000..bc5eab0
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_7.yaml
new file mode 100644
index 0000000..ba8cf32
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_8.yaml
new file mode 100644
index 0000000..6b5bb12
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml
new file mode 100644
index 0000000..6abef83
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_0.yaml
new file mode 100644
index 0000000..a85c786
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_1.yaml
new file mode 100644
index 0000000..f661907
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml
new file mode 100644
index 0000000..8e29437
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_11.yaml
new file mode 100644
index 0000000..c64aa72
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_12.yaml
new file mode 100644
index 0000000..6811780
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_13.yaml
new file mode 100644
index 0000000..756a37c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_14.yaml
new file mode 100644
index 0000000..259547a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_2.yaml
new file mode 100644
index 0000000..f880937
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_3.yaml
new file mode 100644
index 0000000..1e7e501
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_4.yaml
new file mode 100644
index 0000000..3933c7d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_5.yaml
new file mode 100644
index 0000000..fab6e22
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_6.yaml
new file mode 100644
index 0000000..618c32a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_7.yaml
new file mode 100644
index 0000000..1bb7ba4
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_8.yaml
new file mode 100644
index 0000000..49bde50
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml
new file mode 100644
index 0000000..6a40a6c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_0.yaml
new file mode 100644
index 0000000..2779ec4
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_1.yaml
new file mode 100644
index 0000000..9291a6a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_10.yaml
new file mode 100644
index 0000000..1ee5c89
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_11.yaml
new file mode 100644
index 0000000..7ea3bc4
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_12.yaml
new file mode 100644
index 0000000..88c1a5c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_13.yaml
new file mode 100644
index 0000000..c2b8b59
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_14.yaml
new file mode 100644
index 0000000..0e521c4
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_2.yaml
new file mode 100644
index 0000000..e8a4e83
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_3.yaml
new file mode 100644
index 0000000..a77c501
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_4.yaml
new file mode 100644
index 0000000..c5bf587
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_5.yaml
new file mode 100644
index 0000000..6655e43
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_6.yaml
new file mode 100644
index 0000000..503f6dc
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_7.yaml
new file mode 100644
index 0000000..08982f6
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_8.yaml
new file mode 100644
index 0000000..f7a08ba
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml
new file mode 100644
index 0000000..27c166d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_0.yaml
new file mode 100644
index 0000000..279e9ac
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_1.yaml
new file mode 100644
index 0000000..4728ea2
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_10.yaml
new file mode 100644
index 0000000..ff3f310
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_11.yaml
new file mode 100644
index 0000000..fddac58
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_12.yaml
new file mode 100644
index 0000000..a490e86
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_13.yaml
new file mode 100644
index 0000000..b07d292
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_14.yaml
new file mode 100644
index 0000000..135bb09
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_2.yaml
new file mode 100644
index 0000000..12808bd
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_3.yaml
new file mode 100644
index 0000000..7c2f616
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_4.yaml
new file mode 100644
index 0000000..d38616b
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_5.yaml
new file mode 100644
index 0000000..03ff00d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_6.yaml
new file mode 100644
index 0000000..4111ad8
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_7.yaml
new file mode 100644
index 0000000..2190492
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_8.yaml
new file mode 100644
index 0000000..ae54255
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml
new file mode 100644
index 0000000..24ca3a0
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_0.yaml
new file mode 100644
index 0000000..92e08e6
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_1.yaml
new file mode 100644
index 0000000..1e9bf81
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_10.yaml
new file mode 100644
index 0000000..59d3f76
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_11.yaml
new file mode 100644
index 0000000..69a4c15
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_12.yaml
new file mode 100644
index 0000000..034aebb
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_13.yaml
new file mode 100644
index 0000000..0f706c7
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_14.yaml
new file mode 100644
index 0000000..da49c54
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_2.yaml
new file mode 100644
index 0000000..4dd5c48
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_3.yaml
new file mode 100644
index 0000000..b0d875c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_4.yaml
new file mode 100644
index 0000000..147b5d6
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_5.yaml
new file mode 100644
index 0000000..b721639
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_6.yaml
new file mode 100644
index 0000000..482931a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_7.yaml
new file mode 100644
index 0000000..82e646a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_8.yaml
new file mode 100644
index 0000000..6b8f306
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml
new file mode 100644
index 0000000..b307ba1
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_0.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_0.yaml
new file mode 100644
index 0000000..a9e58fe
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_0.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 0
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 0
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_1.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_1.yaml
new file mode 100644
index 0000000..f840899
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_1.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 1
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 1
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_10.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_10.yaml
new file mode 100644
index 0000000..d70d25d
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_10.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 10
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 10
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_11.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_11.yaml
new file mode 100644
index 0000000..912d1a1
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_11.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 11
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 11
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_12.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_12.yaml
new file mode 100644
index 0000000..56c559a
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_12.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 12
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 12
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_13.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_13.yaml
new file mode 100644
index 0000000..b348dc7
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_13.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 13
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 13
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_14.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_14.yaml
new file mode 100644
index 0000000..2370d66
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_14.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 14
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 14
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_2.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_2.yaml
new file mode 100644
index 0000000..59ddae9
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_2.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 2
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 2
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_3.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_3.yaml
new file mode 100644
index 0000000..f824997
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_3.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 3
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 3
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_4.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_4.yaml
new file mode 100644
index 0000000..ac021fa
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_4.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 4
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 4
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_5.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_5.yaml
new file mode 100644
index 0000000..d076380
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_5.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 5
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 5
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_6.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_6.yaml
new file mode 100644
index 0000000..740e131
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_6.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 6
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 6
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_7.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_7.yaml
new file mode 100644
index 0000000..23f6720
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_7.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 7
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 7
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_8.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_8.yaml
new file mode 100644
index 0000000..ed8e01c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_8.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 8
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 8
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml
new file mode 100644
index 0000000..d1d65f9
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Analysis_Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml
@@ -0,0 +1,49 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+patch_size: 40
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/compute_mi_and_phenotyping_performance.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml
new file mode 100644
index 0000000..725c60b
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_150photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml
new file mode 100644
index 0000000..0b91378
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_450photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml
new file mode 100644
index 0000000..5e87acc
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_Brightfield_50photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - Brightfield
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml
new file mode 100644
index 0000000..e28f80f
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_150photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml
new file mode 100644
index 0000000..aebab7c
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_450photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml
new file mode 100644
index 0000000..987dc1b
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_DPC_Right_50photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - DPC_Right
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml
new file mode 100644
index 0000000..ebeb5ee
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_150photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 150
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml
new file mode 100644
index 0000000..5d982eb
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_450photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 450
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml
new file mode 100644
index 0000000..fb6a381
--- /dev/null
+++ b/led_array/phenotyping_experiments/config_files/staging/Synthetic_Noise_v11_LED119_50photons_replicate_9.yaml
@@ -0,0 +1,48 @@
+config_file_version: '2.4'
+data:
+  batch: 0
+  channels:
+  - LED119
+  data_dir: /home/hpinkard_waller/data/
+  dataset_name: BSCCM
+  shuffle_seed: 8837
+  synthetic_noise:
+    edge_crop: 24
+    median_filter: false
+    photons_per_pixel: 50
+    seed: 4553356
+    use_correction_factor: true
+  use_two_spectrum_unmixing: true
+hyperparameters:
+  all_marker_training: true
+  arch: DN121
+  batch_size: 16
+  change_input_channels: true
+  density_output: true
+  learning_rate: 5.0e-05
+  max_epochs: 50000
+  mixture_type: gaussian
+  num_examples_for_normalization: 500
+  num_mixture_components: 19
+  optimizer: ADAM
+  overshoot_epochs: 20
+  seed: 9
+  single_marker_early_stopping: false
+  single_marker_training: false
+  steps_per_epoch: 4000
+  test_fraction: 0.05
+  validation_fraction: 0.05
+metadata:
+  experiment_name: Synthetic_Noise_v11_
+  replicate: 9
+options:
+  hog_memory: false
+  immortal: false
+  resume_training: false
+saving_dir: /home/hpinkard_waller/models/Synthetic_Noise_v11/
+train_script_path: /home/hpinkard_waller/GitRepos/EncodingInformation/led_array/phenotyping_experiments/train_model.py
+training:
+  attempt_number: 1
+  elapsed: 0
+  start_date: '2023-12-13'
+  tensorboard_dir: /home/hpinkard_waller/models/template/tensorboard/
diff --git a/led_array/phenotyping_experiments/load_model_replicates.ipynb b/led_array/phenotyping_experiments/load_model_replicates.ipynb
new file mode 100644
index 0000000..6ec504e
--- /dev/null
+++ b/led_array/phenotyping_experiments/load_model_replicates.ipynb
@@ -0,0 +1,265 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For eric to make a model number vs estimate chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-10-20 08:18:15.553014: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Opening BSCCM\n",
+      "Opened BSCCM\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "# this only works on startup!\n",
+    "from jax import config\n",
+    "config.update(\"jax_enable_x64\", True)\n",
+    "\n",
+    "import os\n",
+    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n",
+    "from encoding_information.gpu_utils import limit_gpu_memory_growth\n",
+    "limit_gpu_memory_growth()\n",
+    "\n",
+    "from cleanplots import *\n",
+    "from tqdm import tqdm\n",
+    "from encoding_information.information_estimation import *\n",
+    "from encoding_information.image_utils import *\n",
+    "\n",
+    "import matplotlib.colors as mcolors\n",
+    "\n",
+    "from encoding_information.datasets.bsccm_utils import *\n",
+    "from bsccm import BSCCM\n",
+    "from jax import jit\n",
+    "import numpy as np\n",
+    "import yaml\n",
+    "from led_array.tf_util import prepare_test_dataset\n",
+    "import tensorflow.keras as tfk\n",
+    "\n",
+    "\n",
+    "bsccm = BSCCM('/home/hpinkard_waller/data/BSCCM/')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "experiment_dir = '/home/hpinkard_waller/models/Synthetic_Noise_v10/'\n",
+    "patch_size = 40\n",
+    "\n",
+    "mi_estimates_gp = {}\n",
+    "mi_estimates_pixel_cnn = {}\n",
+    "phenotype_nlls = {}\n",
+    "phenotype_marker_indices = {}\n",
+    "for dir_name in os.listdir(experiment_dir):\n",
+    "    # check if complete.txt is in this directory\n",
+    "    if not os.path.exists(os.path.join(experiment_dir, dir_name, 'complete.txt')):\n",
+    "        # print('Skipping', dir_name)\n",
+    "        continue\n",
+    "    if 'Analysis' in dir_name:\n",
+    "        config_file_name = [d for d in os.listdir(os.path.join(experiment_dir, dir_name)) if '.yaml' in d][0]\n",
+    "        config_file_full_path = os.path.join(experiment_dir, dir_name, config_file_name)\n",
+    "        config = yaml.load(open(config_file_full_path, 'r'), Loader=yaml.FullLoader)\n",
+    "        if config['patch_size'] != patch_size:\n",
+    "            continue\n",
+    "        photons_per_pixel = config['data']['synthetic_noise']['photons_per_pixel']\n",
+    "        channel = config['data']['channels'][0]\n",
+    "        replicate = config['metadata']['replicate']\n",
+    "        saving_dir = os.path.join(experiment_dir, dir_name)\n",
+    "        analysis_dir = os.path.join(saving_dir, 'analysis')\n",
+    "        mi_estimate_file = np.load(os.path.join(analysis_dir, [f for f in os.listdir(analysis_dir) if 'mi_estimates' in f][0]))\n",
+    "        # mi_estimates_gp[(channel, photons_per_pixel, replicate)] = mi_estimate_file['mi_gp']\n",
+    "\n",
+    "\n",
+    "        mi_estimates_pixel_cnn[(channel, photons_per_pixel, replicate)] = (mi_estimate_file['mi_pixel_cnn'], \n",
+    "                                                                           mi_estimate_file['pixel_cnn_lower_bound'], \n",
+    "                                                                            mi_estimate_file['pixel_cnn_upper_bound'])\n",
+    "        nll_file = np.load(os.path.join(analysis_dir, [f for f in os.listdir(analysis_dir) if 'phenotyping_nll' in f][0]))\n",
+    "        phenotype_nlls[(channel, photons_per_pixel, replicate)] = nll_file['nlls']\n",
+    "        phenotype_marker_indices[(channel, photons_per_pixel, replicate)] = nll_file['marker_indices']\n",
+    "\n",
+    "        # print the channel, photons and mi_gp\n",
+    "        print(channel, photons_per_pixel, mi_estimates_pixel_cnn[(channel, photons_per_pixel, replicate)])    \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Initialize an empty dictionary to hold the reorganized data\n",
+    "reorganized_data = {}\n",
+    "\n",
+    "# Iterate over the keys and values in the original data\n",
+    "for (channel_name, num_photons, replicate_idx), value_tuple in mi_estimates_pixel_cnn.items():\n",
+    "    # If the channel_name is not already a key in reorganized_data, add it\n",
+    "    if channel_name not in reorganized_data:\n",
+    "        reorganized_data[channel_name] = {}\n",
+    "    # If num_photons is not already a key under channel_name, add it\n",
+    "    if num_photons not in reorganized_data[channel_name]:\n",
+    "        reorganized_data[channel_name][num_photons] = []\n",
+    "    # Append the value_tuple to the list under the appropriate keys\n",
+    "    reorganized_data[channel_name][num_photons].append(value_tuple)\n",
+    "\n",
+    "# Convert the lists of tuples into Nx3 NumPy arrays\n",
+    "for channel_name in reorganized_data:\n",
+    "    for num_photons in reorganized_data[channel_name]:\n",
+    "        data_list = reorganized_data[channel_name][num_photons]\n",
+    "        n_replicates = len(data_list)\n",
+    "        # Initialize an empty array to hold the data\n",
+    "        data_array = np.zeros((n_replicates, 3))\n",
+    "        for i, value_tuple in enumerate(data_list):\n",
+    "            # Each value_tuple contains three arrays with scalar values\n",
+    "            data_array[i, 0] = value_tuple[0]\n",
+    "            data_array[i, 1] = value_tuple[1]\n",
+    "            data_array[i, 2] = value_tuple[2]\n",
+    "        # Replace the list with the NumPy array\n",
+    "        reorganized_data[channel_name][num_photons] = data_array\n",
+    "\n",
+    "# Now, reorganized_data is your nested dictionary with Nx3 arrays\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Flatten the nested dictionary\n",
+    "flattened_data = {}\n",
+    "for channel_name, photons_dict in reorganized_data.items():\n",
+    "    for num_photons, data_array in photons_dict.items():\n",
+    "        key = f\"{channel_name}_{num_photons}\"\n",
+    "        flattened_data[key] = data_array\n",
+    "\n",
+    "# Save the flattened data to a .npz file\n",
+    "np.savez('reorganized_data.npz', **flattened_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['DPC_Right', 'Brightfield', 'LED119'])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loaded_data.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f33c0437c70>]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# To load the data back:\n",
+    "loaded_npz = np.load('reorganized_data.npz')\n",
+    "\n",
+    "# Reconstruct the nested dictionary\n",
+    "loaded_data = {}\n",
+    "for key in loaded_npz.files:\n",
+    "    channel_name, num_photons = key.rsplit('_', 1)\n",
+    "    num_photons = int(num_photons)\n",
+    "    if channel_name not in loaded_data:\n",
+    "        loaded_data[channel_name] = {}\n",
+    "    loaded_data[channel_name][num_photons] = loaded_npz[key]\n",
+    "\n",
+    "\n",
+    "# Channels are ['DPC_Right', 'Brightfield', 'LED119']\n",
+    "# Photons are [50, 150, 450]\n",
+    "dpc_right_150_photons_models = loaded_data['DPC_Right'][150]\n",
+    "\n",
+    "# N x 3 array with columns for estimates, lower bounds, and upper bounds\n",
+    "estimates, lower_bounds, upper_bounds = dpc_right_150_photons_models.T\n",
+    "\n",
+    "# sort the estimates and plot them\n",
+    "sorted_indices = np.argsort(estimates)\n",
+    "sorted_estimates = estimates[sorted_indices]\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(sorted_estimates, '-o')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "encoding_info",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/led_array/phenotyping_experiments/make_phenotyping_mi_plot.ipynb b/led_array/phenotyping_experiments/make_phenotyping_mi_plot.ipynb
index 866cb58..326df81 100644
--- a/led_array/phenotyping_experiments/make_phenotyping_mi_plot.ipynb
+++ b/led_array/phenotyping_experiments/make_phenotyping_mi_plot.ipynb
@@ -2,20 +2,15 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-13 20:15:02.257267: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n",
       "Opening BSCCM\n",
       "Opened BSCCM\n"
      ]
@@ -51,121 +46,178 @@
     "import tensorflow.keras as tfk\n",
     "\n",
     "\n",
-    "bsccm = BSCCM('/home/hpinkard_waller/data/BSCCM/')"
+    "bsccm = BSCCM('/home/hpinkard_waller/data/BSCCM/')\n",
+    "\n",
+    "def marker_for_channel(channel):\n",
+    "    if channel == 'LED119':\n",
+    "        marker = 'x'\n",
+    "    elif channel == 'DPC_Right':\n",
+    "        marker = 'v'\n",
+    "    elif channel == 'Brightfield':\n",
+    "        marker = 'o'\n",
+    "    return marker\n",
+    "\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DPC_Right 450 (array(0.25418964), array(0.25055031), array(0.25922421))\n",
-      "Brightfield 50 (array(0.02385941), array(0.02212839), array(0.02537801))\n",
-      "DPC_Right 450 (array(0.25578977), array(0.25199761), array(0.26045847))\n",
-      "Brightfield 50 (array(0.02387626), array(0.02213843), array(0.02553391))\n",
-      "Brightfield 150 (array(0.04802918), array(0.04656787), array(0.04945744))\n",
-      "DPC_Right 150 (array(0.15861891), array(0.1546417), array(0.16213365))\n",
-      "LED119 50 (array(0.23689335), array(0.23253616), array(0.2418918))\n",
-      "DPC_Right 150 (array(0.73992027), array(0.7385138), array(0.74112278))\n",
-      "DPC_Right 150 (array(0.15773767), array(0.15424336), array(0.16090265))\n",
-      "LED119 50 (array(0.23548756), array(0.23177325), array(0.23895764))\n",
-      "DPC_Right 150 (array(0.16136238), array(0.15728217), array(0.16507235))\n",
-      "DPC_Right 50 (array(0.31303717), array(0.31100151), array(0.31474159))\n",
-      "LED119 50 (array(0.23790667), array(0.23391881), array(0.24187933))\n",
-      "DPC_Right 450 (array(0.25284749), array(0.24832751), array(0.25671442))\n",
-      "LED119 150 (array(0.32781567), array(0.32401736), array(0.33291367))\n",
-      "LED119 450 (array(0.41068675), array(0.40657379), array(0.41532323))\n",
-      "Brightfield 50 (array(0.02335309), array(0.02169306), array(0.02477536))\n",
-      "DPC_Right 150 (array(0.16154055), array(0.15830374), array(0.16511833))\n",
-      "LED119 450 (array(0.41259301), array(0.40668307), array(0.41769568))\n",
-      "Brightfield 450 (array(0.1691666), array(0.16825343), array(0.17003147))\n",
-      "Brightfield 150 (array(0.05120089), array(0.04975188), array(0.05244786))\n",
-      "LED119 450 (array(0.41433554), array(0.40918965), array(0.41904045))\n",
-      "DPC_Right 50 (array(0.1024709), array(0.09885025), array(0.10574324))\n",
-      "LED119 150 (array(0.32475301), array(0.32073942), array(0.32903731))\n",
-      "DPC_Right 450 (array(0.25623348), array(0.25197216), array(0.26013484))\n",
-      "LED119 150 (array(0.32916952), array(0.32479711), array(0.33296057))\n",
-      "LED119 450 (array(0.41529451), array(0.41038279), array(0.42099706))\n",
-      "Brightfield 50 (array(0.02351716), array(0.02177756), array(0.0250352))\n",
-      "Brightfield 50 (array(0.02296235), array(0.02093863), array(0.02468078))\n",
-      "LED119 450 (array(0.4100511), array(0.40535004), array(0.41565066))\n",
-      "DPC_Right 450 (array(0.25102722), array(0.24664358), array(0.25517102))\n",
-      "LED119 450 (array(0.40139763), array(0.39725199), array(0.4063045))\n",
-      "LED119 150 (array(0.32229641), array(0.31721627), array(0.32674206))\n",
-      "LED119 150 (array(0.32647627), array(0.32277707), array(0.33039557))\n",
-      "DPC_Right 150 (array(0.16535925), array(0.16178643), array(0.16840709))\n",
-      "LED119 50 (array(0.2341485), array(0.23013804), array(0.23791259))\n",
-      "Brightfield 450 (array(0.16961375), array(0.16861629), array(0.17058063))\n",
-      "Brightfield 150 (array(0.05031139), array(0.04892494), array(0.05157519))\n",
-      "Brightfield 150 (array(0.04863559), array(0.04755394), array(0.04986452))\n",
-      "LED119 450 (array(0.40887062), array(0.40481196), array(0.41424911))\n",
-      "Brightfield 150 (array(0.0478517), array(0.04648407), array(0.04920668))\n",
-      "DPC_Right 50 (array(0.10195873), array(0.09814157), array(0.10620005))\n",
-      "Brightfield 450 (array(0.16484502), array(0.16395072), array(0.16565639))\n",
-      "LED119 150 (array(0.32536389), array(0.32150716), array(0.33073719))\n",
-      "Brightfield 150 (array(0.05203672), array(0.05076868), array(0.0531085))\n",
-      "LED119 150 (array(0.32518296), array(0.32125653), array(0.32985905))\n",
-      "Brightfield 450 (array(0.04258874), array(0.04088411), array(0.04424087))\n",
-      "DPC_Right 150 (array(0.16045568), array(0.1562515), array(0.16429855))\n",
-      "Brightfield 150 (array(0.04779873), array(0.04648827), array(0.04899422))\n",
-      "Brightfield 150 (array(0.04872365), array(0.04743656), array(0.05012899))\n",
-      "DPC_Right 50 (array(0.31902251), array(0.3172405), array(0.3207299))\n",
-      "Brightfield 50 (array(0.02426253), array(0.02268437), array(0.0260777))\n",
-      "LED119 150 (array(0.3274552), array(0.32291096), array(0.33219225))\n",
-      "LED119 50 (array(0.23601073), array(0.23194599), array(0.23984898))\n",
-      "Brightfield 450 (array(0.1620245), array(0.16114147), array(0.1628796))\n",
-      "LED119 50 (array(0.23494478), array(0.23088703), array(0.23909166))\n",
-      "LED119 150 (array(0.32463055), array(0.32017712), array(0.32962442))\n",
-      "DPC_Right 50 (array(0.09940273), array(0.09555007), array(0.10348714))\n",
-      "DPC_Right 150 (array(0.15935981), array(0.15610423), array(0.16297775))\n",
-      "DPC_Right 50 (array(0.10193878), array(0.09783635), array(0.105173))\n",
-      "Brightfield 50 (array(0.02413767), array(0.02245267), array(0.0257841))\n",
-      "DPC_Right 450 (array(0.25318182), array(0.24893549), array(0.25770511))\n",
-      "DPC_Right 50 (array(0.31747191), array(0.31557874), array(0.3189637))\n",
-      "DPC_Right 450 (array(0.25124461), array(0.24760913), array(0.25550881))\n",
-      "LED119 450 (array(0.4047561), array(0.40020124), array(0.40980283))\n",
-      "LED119 450 (array(0.410878), array(0.40606185), array(0.41565818))\n",
-      "LED119 50 (array(0.2355794), array(0.23190243), array(0.24040282))\n",
-      "Brightfield 450 (array(0.16368929), array(0.16267899), array(0.16465214))\n",
-      "LED119 150 (array(0.32540104), array(0.3211362), array(0.33092691))\n",
-      "DPC_Right 150 (array(0.16045431), array(0.15663409), array(0.16403722))\n",
-      "Brightfield 450 (array(0.16126228), array(0.16033726), array(0.16226189))\n",
-      "LED119 50 (array(0.232989), array(0.22870604), array(0.2368475))\n",
-      "DPC_Right 50 (array(0.09977146), array(0.09639807), array(0.10354753))\n",
-      "LED119 450 (array(0.41063997), array(0.40643772), array(0.41577877))\n",
-      "DPC_Right 50 (array(0.10193362), array(0.09884643), array(0.10604076))\n",
-      "Brightfield 50 (array(0.02316563), array(0.02132233), array(0.02511017))\n",
-      "LED119 50 (array(0.23487874), array(0.23119319), array(0.23956074))\n",
-      "Brightfield 50 (array(0.02380816), array(0.02168038), array(0.02537149))\n",
-      "Brightfield 150 (array(0.05285811), array(0.05148237), array(0.05422519))\n",
-      "Brightfield 50 (array(0.02389071), array(0.02198512), array(0.02566463))\n",
-      "DPC_Right 450 (array(0.25361247), array(0.24885578), array(0.25828704))\n",
-      "Brightfield 450 (array(0.04257636), array(0.04118086), array(0.04413694))\n",
-      "DPC_Right 50 (array(0.09871067), array(0.09511926), array(0.10229368))\n",
-      "Brightfield 50 (array(0.02375484), array(0.02201369), array(0.02522121))\n",
-      "Brightfield 450 (array(0.16967016), array(0.16874805), array(0.17065245))\n",
-      "DPC_Right 450 (array(0.25474686), array(0.2508757), array(0.25849187))\n",
-      "LED119 50 (array(0.76376976), array(0.76238425), array(0.76542412))\n",
-      "Brightfield 450 (array(0.15860618), array(0.15775586), array(0.15947017))\n",
-      "LED119 450 (array(0.41065648), array(0.40481655), array(0.41684153))\n",
-      "Brightfield 450 (array(0.15428597), array(0.15320666), array(0.15520463))\n",
-      "DPC_Right 50 (array(0.10198315), array(0.09789024), array(0.10605795))\n",
-      "LED119 150 (array(0.32598027), array(0.32186549), array(0.33044124))\n",
-      "LED119 50 (array(0.23663709), array(0.23262572), array(0.24081755))\n",
-      "DPC_Right 150 (array(0.74899682), array(0.74751649), array(0.75005827))\n",
-      "DPC_Right 450 (array(0.25583035), array(0.25147014), array(0.25926153))\n",
-      "Brightfield 150 (array(0.05137665), array(0.05026044), array(0.05263139))\n",
-      "DPC_Right 450 (array(0.25422885), array(0.25023244), array(0.25788117))\n",
-      "DPC_Right 150 (array(0.15949602), array(0.15609326), array(0.16308229))\n",
-      "Brightfield 150 (array(0.04959216), array(0.0484008), array(0.05109564))\n"
+      "DPC_Right 450 (array(0.25291766), array(0.24867277), array(0.25788165))\n",
+      "Brightfield 50 (array(0.02392648), array(0.02197748), array(0.02600087))\n",
+      "DPC_Right 450 (array(0.25447719), array(0.24920456), array(0.25885163))\n",
+      "Brightfield 50 (array(0.02426391), array(0.02214055), array(0.02610897))\n",
+      "Brightfield 150 (array(0.02946297), array(0.02752389), array(0.03119224))\n",
+      "DPC_Right 150 (array(0.74717106), array(0.74552726), array(0.74851685))\n",
+      "LED119 50 (array(0.23557321), array(0.23132512), array(0.24103507))\n",
+      "DPC_Right 150 (array(0.16015506), array(0.15603974), array(0.16409862))\n",
+      "DPC_Right 150 (array(0.76603205), array(0.76431309), array(0.76730482))\n",
+      "LED119 50 (array(0.23330785), array(0.22875839), array(0.23898797))\n",
+      "DPC_Right 150 (array(0.74883791), array(0.7470995), array(0.75005562))\n",
+      "DPC_Right 50 (array(0.32991038), array(0.32766693), array(0.33165547))\n",
+      "LED119 50 (array(0.2329319), array(0.22857813), array(0.23739754))\n",
+      "DPC_Right 450 (array(0.25405274), array(0.24874857), array(0.25908802))\n",
+      "LED119 150 (array(0.32432374), array(0.31960629), array(0.33029304))\n",
+      "LED119 450 (array(0.40940651), array(0.40366409), array(0.41548796))\n",
+      "Brightfield 50 (array(0.02331801), array(0.02126737), array(0.02572998))\n",
+      "DPC_Right 150 (array(0.15896287), array(0.15459999), array(0.16356781))\n",
+      "Brightfield 150 (array(0.04841236), array(0.04658275), array(0.04989001))\n",
+      "LED119 450 (array(0.40716111), array(0.40191701), array(0.41404885))\n",
+      "Brightfield 450 (array(0.16038104), array(0.15919913), array(0.16133515))\n",
+      "Brightfield 150 (array(0.04522449), array(0.0433785), array(0.04677115))\n",
+      "LED119 450 (array(0.41096949), array(0.40507972), array(0.4168206))\n",
+      "LED119 50 (array(0.23379112), array(0.22992394), array(0.23828092))\n",
+      "DPC_Right 50 (array(0.32326394), array(0.32124071), array(0.32548149))\n",
+      "LED119 150 (array(0.32775857), array(0.32288859), array(0.33406936))\n",
+      "DPC_Right 450 (array(0.24963279), array(0.24463545), array(0.2543668))\n",
+      "DPC_Right 450 (array(0.2511139), array(0.24594449), array(0.25574354))\n",
+      "LED119 150 (array(0.32437671), array(0.31915268), array(0.33012979))\n",
+      "LED119 450 (array(0.39842645), array(0.39308087), array(0.40515564))\n",
+      "Brightfield 50 (array(0.02336169), array(0.0217361), array(0.02553935))\n",
+      "Brightfield 50 (array(0.02395744), array(0.02214601), array(0.0259029))\n",
+      "DPC_Right 450 (array(0.25336619), array(0.24902422), array(0.25787734))\n",
+      "LED119 450 (array(0.39476322), array(0.38965814), array(0.40179855))\n",
+      "DPC_Right 450 (array(0.25203435), array(0.24673329), array(0.25649048))\n",
+      "Brightfield 450 (array(0.04290038), array(0.04093859), array(0.04473406))\n",
+      "Brightfield 50 (array(0.0236176), array(0.02154184), array(0.02573884))\n",
+      "DPC_Right 50 (array(0.10034244), array(0.09575595), array(0.10432148))\n",
+      "LED119 450 (array(0.40718931), array(0.40237724), array(0.41383669))\n",
+      "LED119 150 (array(0.32627264), array(0.32122302), array(0.33130793))\n",
+      "DPC_Right 150 (array(0.15950152), array(0.1550443), array(0.16295164))\n",
+      "LED119 150 (array(0.32220973), array(0.31692201), array(0.32742346))\n",
+      "DPC_Right 150 (array(0.74792916), array(0.74614329), array(0.74921323))\n",
+      "LED119 450 (array(0.40415416), array(0.39849696), array(0.40971033))\n",
+      "Brightfield 450 (array(0.16304883), array(0.16194072), array(0.16411536))\n",
+      "LED119 150 (array(0.32336132), array(0.31868455), array(0.32902183))\n",
+      "LED119 50 (array(0.23159834), array(0.22586185), array(0.23649695))\n",
+      "Brightfield 450 (array(0.15555314), array(0.15418568), array(0.15669777))\n",
+      "Brightfield 150 (array(0.04849388), array(0.04679072), array(0.04990195))\n",
+      "LED119 50 (array(0.23315169), array(0.22887044), array(0.2381489))\n",
+      "Brightfield 150 (array(0.05075511), array(0.04923693), array(0.05217498))\n",
+      "LED119 450 (array(0.40456142), array(0.40007625), array(0.41085818))\n",
+      "Brightfield 150 (array(0.04926677), array(0.04759593), array(0.05060102))\n",
+      "Brightfield 150 (array(0.04932284), array(0.047494), array(0.0510016))\n",
+      "LED119 150 (array(0.32246976), array(0.31637402), array(0.32802293))\n",
+      "DPC_Right 50 (array(0.32445922), array(0.32213516), array(0.32651913))\n",
+      "Brightfield 450 (array(0.14977246), array(0.14868589), array(0.15088123))\n",
+      "Brightfield 50 (array(0.02427836), array(0.02155174), array(0.02648985))\n",
+      "LED119 150 (array(0.32392199), array(0.3187577), array(0.32871046))\n",
+      "DPC_Right 150 (array(0.15806306), array(0.15350624), array(0.16225559))\n",
+      "Brightfield 150 (array(0.04968297), array(0.04824333), array(0.05111371))\n",
+      "LED119 150 (array(0.32394056), array(0.31862393), array(0.33004883))\n",
+      "Brightfield 450 (array(0.17322952), array(0.17213881), array(0.17429831))\n",
+      "DPC_Right 150 (array(0.73631551), array(0.73448806), array(0.7375709))\n",
+      "Brightfield 150 (array(0.05077093), array(0.04941384), array(0.05255425))\n",
+      "Brightfield 150 (array(0.05159232), array(0.05009658), array(0.05321246))\n",
+      "DPC_Right 50 (array(0.32814481), array(0.32615959), array(0.3302382))\n",
+      "Brightfield 50 (array(0.02351544), array(0.02120298), array(0.02547481))\n",
+      "LED119 150 (array(0.32030072), array(0.3156919), array(0.32616182))\n",
+      "LED119 150 (array(0.33122506), array(0.32606662), array(0.33732894))\n",
+      "LED119 50 (array(0.23765798), array(0.23354571), array(0.24249053))\n",
+      "Brightfield 450 (array(0.16113019), array(0.16011088), array(0.16236065))\n",
+      "LED119 50 (array(0.23803015), array(0.2326371), array(0.24282315))\n",
+      "Brightfield 50 (array(0.02391135), array(0.02217809), array(0.0260378))\n",
+      "LED119 50 (array(0.23345163), array(0.22833907), array(0.23879734))\n",
+      "LED119 150 (array(0.32231429), array(0.31702962), array(0.32767521))\n",
+      "DPC_Right 50 (array(0.10110742), array(0.0963921), array(0.10484167))\n",
+      "DPC_Right 450 (array(0.2548645), array(0.24955957), array(0.25950972))\n",
+      "DPC_Right 150 (array(0.15959577), array(0.15489379), array(0.16369719))\n",
+      "DPC_Right 50 (array(0.32104984), array(0.31885742), array(0.32294971))\n",
+      "DPC_Right 50 (array(0.10104172), array(0.09662661), array(0.10492446))\n",
+      "DPC_Right 50 (array(0.32791848), array(0.32581097), array(0.33012533))\n",
+      "LED119 450 (array(0.42198739), array(0.41657119), array(0.429024))\n",
+      "Brightfield 50 (array(0.02373592), array(0.02196609), array(0.02565009))\n",
+      "DPC_Right 450 (array(0.25245537), array(0.2480568), array(0.25704334))\n",
+      "DPC_Right 450 (array(0.25549258), array(0.2512871), array(0.26059834))\n",
+      "DPC_Right 50 (array(0.10044494), array(0.09627721), array(0.1047569))\n",
+      "DPC_Right 450 (array(0.2499912), array(0.24513703), array(0.25452563))\n",
+      "LED119 450 (array(0.40878256), array(0.4032377), array(0.41507445))\n",
+      "LED119 450 (array(0.40529819), array(0.40021379), array(0.41068413))\n",
+      "LED119 50 (array(0.23550648), array(0.22971268), array(0.24184154))\n",
+      "Brightfield 450 (array(0.15300917), array(0.15164968), array(0.1541518))\n",
+      "Brightfield 450 (array(0.15924182), array(0.15783928), array(0.16025933))\n",
+      "LED119 50 (array(0.23737765), array(0.23283394), array(0.24291443))\n",
+      "LED119 150 (array(0.32246839), array(0.31802289), array(0.32756556))\n",
+      "Brightfield 50 (array(0.02383189), array(0.02169398), array(0.02584785))\n",
+      "DPC_Right 150 (array(0.73460944), array(0.73132464), array(0.7375311))\n",
+      "Brightfield 450 (array(0.16247785), array(0.16132298), array(0.16358615))\n",
+      "LED119 50 (array(0.73132593), array(0.72952831), array(0.73342058))\n",
+      "DPC_Right 50 (array(0.10187962), array(0.09759672), array(0.10666612))\n",
+      "LED119 450 (array(0.41272303), array(0.40672765), array(0.41997562))\n",
+      "Brightfield 150 (array(0.04953266), array(0.04785211), array(0.05103777))\n",
+      "DPC_Right 50 (array(0.10175958), array(0.09743453), array(0.10615532))\n",
+      "Brightfield 50 (array(0.02326607), array(0.02146813), array(0.02544631))\n",
+      "LED119 50 (array(0.23408521), array(0.22803238), array(0.23923368))\n",
+      "Brightfield 50 (array(0.0229135), array(0.02095683), array(0.02486358))\n",
+      "LED119 450 (array(0.40540688), array(0.39950657), array(0.41238132))\n",
+      "Brightfield 150 (array(0.04727246), array(0.04537524), array(0.04857078))\n",
+      "Brightfield 50 (array(0.02389105), array(0.02188314), array(0.02617743))\n",
+      "DPC_Right 150 (array(0.15876406), array(0.15450607), array(0.16308049))\n",
+      "LED119 150 (array(0.32567552), array(0.3199436), array(0.33147863))\n",
+      "DPC_Right 450 (array(0.25518645), array(0.25008807), array(0.26039407))\n",
+      "Brightfield 450 (array(0.1691439), array(0.16789865), array(0.17022818))\n",
+      "DPC_Right 50 (array(0.10277255), array(0.09802813), array(0.10803341))\n",
+      "Brightfield 150 (array(0.05063403), array(0.04883315), array(0.0523128))\n",
+      "Brightfield 50 (array(0.02367607), array(0.02186995), array(0.02620871))\n",
+      "LED119 450 (array(0.40605767), array(0.39896803), array(0.4128251))\n",
+      "Brightfield 450 (array(0.16298279), array(0.16205608), array(0.16426002))\n",
+      "DPC_Right 450 (array(0.25442216), array(0.24953282), array(0.25885725))\n",
+      "LED119 50 (array(0.75786835), array(0.75617864), array(0.7599355))\n",
+      "Brightfield 450 (array(0.1600185), array(0.15897665), array(0.16100497))\n",
+      "DPC_Right 150 (array(0.15928895), array(0.15406532), array(0.16352019))\n",
+      "LED119 450 (array(0.4214226), array(0.41580939), array(0.42746741))\n",
+      "Brightfield 450 (array(0.16653045), array(0.16523205), array(0.16746964))\n",
+      "DPC_Right 50 (array(0.1019491), array(0.09738461), array(0.10548387))\n",
+      "Brightfield 450 (array(0.15394407), array(0.15266507), array(0.15522118))\n",
+      "LED119 150 (array(0.32558265), array(0.32039601), array(0.33050231))\n",
+      "LED119 50 (array(0.23785645), array(0.23264438), array(0.24275098))\n",
+      "DPC_Right 50 (array(0.10239798), array(0.09818676), array(0.10696763))\n",
+      "DPC_Right 150 (array(0.1593089), array(0.15512997), array(0.16254802))\n",
+      "DPC_Right 450 (array(0.25253998), array(0.24768587), array(0.25734488))\n",
+      "Brightfield 150 (array(0.05109391), array(0.04929655), array(0.05250044))\n",
+      "DPC_Right 450 (array(0.25389039), array(0.24870645), array(0.25883273))\n",
+      "DPC_Right 150 (array(0.74850908), array(0.74673134), array(0.74985058))\n",
+      "Brightfield 150 (array(0.05431824), array(0.05270303), array(0.05552727))\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_1660289/211269713.py:43: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "  base_colormap = plt.cm.get_cmap('inferno')\n"
      ]
     }
    ],
    "source": [
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "\n",
     "experiment_dir = '/home/hpinkard_waller/models/Synthetic_Noise_v10/'\n",
     "patch_size = 40\n",
     "\n",
@@ -201,7 +253,29 @@
     "        phenotype_marker_indices[(channel, photons_per_pixel, replicate)] = nll_file['marker_indices']\n",
     "\n",
     "        # print the channel, photons and mi_gp\n",
-    "        print(channel, photons_per_pixel, mi_estimates_pixel_cnn[(channel, photons_per_pixel, replicate)])    "
+    "        print(channel, photons_per_pixel, mi_estimates_pixel_cnn[(channel, photons_per_pixel, replicate)])    \n",
+    "\n",
+    "\n",
+    "\n",
+    "# Set up the colormap\n",
+    "base_colormap = plt.cm.get_cmap('inferno')\n",
+    "start, end = 0, 0.85\n",
+    "\n",
+    "colormap = LinearSegmentedColormap.from_list(\n",
+    "    'trunc({n},{a:.2f},{b:.2f})'.format(n=base_colormap.name, a=start, b=end),\n",
+    "    base_colormap(np.linspace(start, end, 256))\n",
+    ")\n",
+    "\n",
+    "photon_levels = sorted(list(set([p for (c, p, r) in mi_estimates_pixel_cnn.keys()])))\n",
+    "min_photons_per_pixel = min(photon_levels)\n",
+    "max_photons_per_pixel = max(photon_levels)\n",
+    "\n",
+    "min_log_photons = np.log(min_photons_per_pixel)\n",
+    "max_log_photons = np.log(max_photons_per_pixel)\n",
+    "\n",
+    "def color_for_photon_level(photons_per_pixel):\n",
+    "    log_photons = np.log(photons_per_pixel)\n",
+    "    return colormap((log_photons - min_log_photons) / (max_log_photons - min_log_photons) )\n"
    ]
   },
   {
@@ -213,22 +287,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_4072284/3192413729.py:10: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/tmp/ipykernel_1660289/357394396.py:11: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  base_colormap = plt.cm.get_cmap('inferno')\n"
      ]
     },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "450 (0.983196, 0.743758, 0.138453, 1.0)\n",
+      "450 (0.983196, 0.743758, 0.138453, 1.0)\n",
+      "450 (0.983196, 0.743758, 0.138453, 1.0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
+       "<Figure size 400x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -243,7 +336,8 @@
     "mi_estimates_to_use = mi_estimates_pixel_cnn\n",
     "\n",
     "\n",
-    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
+    "fig2, ax2 = plt.subplots(1, 1, figsize=(4, 4))\n",
     "\n",
     "base_colormap = plt.cm.get_cmap('inferno')\n",
     "# Define the start and end points of colormap--used so that high values aren't too light against white background\n",
@@ -326,7 +420,7 @@
     "\n",
     "        # nll_index_to_use = np.argmin(nll[:, 0])\n",
     "        # use kth from best\n",
-    "        nll_index_to_use = np.argsort(nll[:, 0])[0]\n",
+    "        nll_index_to_use = np.argsort(nll[:, 0])[1]\n",
     "        lls = -nll[nll_index_to_use]\n",
     "\n",
     "        ll_to_use.append(lls)\n",
@@ -344,7 +438,19 @@
     "    ax.errorbar(mi_to_use[:, 0], ll_to_use[:, 0], yerr=[lower_bound, upper_bound], \n",
     "                xerr=[mi_lower_bound, mi_upper_bound],\n",
     "                fmt='.-', color='black', alpha=0.75, linewidth=2, capsize=5)\n",
+    "    # plot medians, lines, fill between\n",
+    "    \n",
+    "    print(photons, color_for_photon_level(photons))\n",
+    "    ax2.scatter(mi_to_use[:, 0], -ll_to_use[:, 0], color=[color_for_photon_level(p) for p in photons_per_pixel],\n",
+    "                 marker=marker_for_channel(channel), s=50, zorder=100)\n",
+    "    ax2.plot(mi_to_use[:, 0], -ll_to_use[:, 0], '--', color='grey', alpha=1, linewidth=2)\n",
+    "    ax2.fill_between(mi_to_use[:, 0], -ll_to_use[:, 1], -ll_to_use[:, 2], alpha=0.5, color='gray')\n",
     "\n",
+    "ax2.set(xlim=[0, None])\n",
+    "clear_spines(ax2)\n",
+    "ax2.set_xlabel('Mutual Information (bits per pixel)')\n",
+    "ax2.set_ylabel('Prediction negative log likelihood')\n",
+    "fig2.savefig('/home/hpinkard_waller/figures/phenotyping/bsccm_phenotyping_mi_vs_nll.pdf')\n",
     "\n",
     "\n",
     "ax.set_xlabel('Mutual Information (bits)')\n",
@@ -435,34 +541,7 @@
     "# Assuming mi_estimates_pixel_cnn, phenotype_nlls, and phenotype_marker_indices are already defined\n",
     "mi_estimates_to_use = mi_estimates_pixel_cnn\n",
     "\n",
-    "# Set up the colormap\n",
-    "base_colormap = plt.cm.get_cmap('inferno')\n",
-    "start, end = 0, 0.85\n",
     "\n",
-    "colormap = LinearSegmentedColormap.from_list(\n",
-    "    'trunc({n},{a:.2f},{b:.2f})'.format(n=base_colormap.name, a=start, b=end),\n",
-    "    base_colormap(np.linspace(start, end, 256))\n",
-    ")\n",
-    "\n",
-    "photon_levels = sorted(list(set([p for (c, p, r) in mi_estimates_to_use.keys()])))\n",
-    "min_photons_per_pixel = min(photon_levels)\n",
-    "max_photons_per_pixel = max(photon_levels)\n",
-    "\n",
-    "min_log_photons = np.log(min_photons_per_pixel)\n",
-    "max_log_photons = np.log(max_photons_per_pixel)\n",
-    "\n",
-    "def color_for_photon_level(photons_per_pixel):\n",
-    "    log_photons = np.log(photons_per_pixel)\n",
-    "    return colormap((log_photons - min_log_photons) / (max_log_photons - min_log_photons))\n",
-    "\n",
-    "def marker_for_channel(channel):\n",
-    "    if channel == 'LED119':\n",
-    "        marker = 'x'\n",
-    "    elif channel == 'DPC_Right':\n",
-    "        marker = 'v'\n",
-    "    elif channel == 'Brightfield':\n",
-    "        marker = 'o'\n",
-    "    return marker\n",
     "\n",
     "def clear_spines(ax):\n",
     "    ax.spines['top'].set_visible(False)\n",
@@ -786,12 +865,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf4AAAH0CAYAAADc9E9DAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9eZBkW37XCX7uOXe/13ePPXLPty+17yUkoYVmBKhRAyPGZEI0azEzQiYbmWEtCXXzhwYwUIOV0dMjG5YRIBk2IEAUEhok1FUlqfblvXr7yz1jj/D97su588fxF6qaKsRDyqQLlX/N0iwjI8LTr/vx+zvn9/suRtM0DSussMIKK6ywwjcExP/eT2CFFVZYYYUVVvgvh1XhX2GFFVZYYYVvIKwK/worrLDCCit8A2FV+FdYYYUVVljhGwirwr/CCiussMIK30BYFf4VVlhhhRVW+AbCqvCvsMIKK6ywwjcQVoV/hRVWWGGFFb6BsCr8K6ywwgorrPANhFXhX2GFFVZY4fc8vvSlL/HH/tgfY21tDdu2uXTpEn/5L/9lptPpV/zclStXMAzja/65c+fOV/zsRz7yEd7//vfTbrcZDod83/d9H3t7e//lLup3CGNl2bvCCiussMLvZbz66qu84x3vwLIs/tJf+ktcvHiRT3ziE/zjf/yPeeKJJ/jkJz9JGIbMZjO63S7f9V3fxfd+7/d+1eP80T/6RwmCAICf/dmf5fu+7/t4xzvewfd93/cxmUz4qZ/6KTqdDp/73OdYX1//L32Zbx7NCiussMIKK/wexnd8x3c0tm03L7300lf8+9/9u3+3AZqf/MmfbJqmaT72sY81QPPTP/3Tv+3jRVHUDIfD5tlnn23SND3/9zd+/0Mf+tCDv4gHiFWrf4UVVlhhhd+zyLKMj33sY3zTN30TTzzxxFd87/u///sB+OhHPwrA888/D8DTTz/92z7mv/23/5azszM+9KEP4bru+b9/0zd9E+9///v5p//0n1IUxYO8jAcK83/vJ7DCCiussMIKDwu2bfPSSy+hlPqq7x0fHwMgpQTgueeeA36r8EdRRBAEGIbxFb/3yU9+EoD3vve9X/WY73nPe/jN3/xNXnnlFZ599tkHdyEPEKsT/worrLDCCr9nIYTg6tWrXL9+/au+9zf+xt8A4Fu/9VsBXfg7nQ4/8iM/Qq/Xo9Vq0e/3+cEf/EHiOD7/vTcIfBcuXPiqx9zd3QXg9u3bD/xaHhRWJ/4VVlhhhRUeCN75zndydHT00B4/iiKSJPmaxLkf/uEf5od/+Iff9GP9o3/0j/iH//AfcuHCBf7CX/gLKKV44YUXSJKEo6Mjfvqnf5qqqviX//Jf8uEPf5jPfOYzfPSjH8W2bWazGQBhGH7V4/q+D/AVG4WvN6wK/worrLDCCg8ER0dH7O/vP/T/52v9H/P5/E3//j/4B/+AP//n/zxBEPAv/sW/oNVqkaYp/+P/+D/SarX4i3/xL57/7J/8k3+SH/zBH+TDH/4wf//v/30+9KEP0fw2Yrg3vifE129DfVX4V1hhhRVWeKAQwmBrq/tAH/PwcIpSDUIItra2vur77Xb7TT3OT/zET/DX/tpfo91u85GPfIR3vetdAHiex4/8yI98zd/5oR/6IT784Q/zy7/8y3zoQx+i1WoBkCQJjuN8xc8mSQJAt9t9s5f2Xxyrwr/CCiussMIDxdZWl9v3/s4DfcwrF3+I/f0JW1tbvyOTnLIs+bN/9s/yMz/zM2xvb/OLv/iLvOUtb3lTv7uxsQHAYrHQz+XKFUDP+nu93lf87G83//96wddvL2KFFVZYYYUVHgDquuZ7v/d7+Zmf+RmefvppPvnJT35V0f/FX/xFHn/8cf723/7bX/X7L730EsA5QfA973kPAJ/+9Ke/6mc/9alP0el0ePLJJx/0ZTwwrAr/CiussMIKDxxNUz3QP78b/NiP/Rg///M/z7vf/W4+/vGPf83T+FNPPcWNGzf4e3/v752f7AGqquLHfuzHAPiBH/gBAL7ru76LXq/Hhz/8YfI8P//Zj3/843ziE5/gT/2pP/VVEsCvJ6xa/SussMIKKzxgNDRN/cAf83eC27dv87f+1t/CMAy+53u+h4985CNf9TPr6+t853d+Jz/xEz/BX/2rf5V3vetd/Lk/9+cwDIOf/dmf5XOf+xw/+qM/yvve9z5AM/d/6qd+ij/9p/80H/zgB/kzf+bPcHx8zE/91E9x5coVfvRHf/R3daUPG6vCv8IKK6ywwu9Z/Lt/9++oKt0x+Ct/5a98zZ/5wAc+wHd+53fy4z/+4zz66KP8nb/zd/jxH/9xhBA888wz/NzP/dxXeff/wA/8AGEY8tf/+l/nh37oh+j1enz3d383P/mTP/n17dPPKqRnhRVWWGGFB4Td3V329/fZ3uly4/ZPPtDHvn7lf+Bgf8rOzs5/FQl4X89YzfhXWGGFFVZY4RsIq1b/CiussMIKDxjN75qQ97Uec4UHg9WJf4UVVlhhhRW+gbA68a+wwgorrPDA8eBP/Cs8KKxO/CussMIKK6zwDYQ3feL/maf/e2olWFQmk8KkbdVIo6FrFwRmyfOTLi2zZtPL6LsJwmiYFy6CBoVBXFpktc48dmVNYJXsJz6O+K2M5KvtKXFpE9oFXS+m15kjRE1ZWlhWydrlfX7919/HTm+MbRXM45CNtVOq0mQRhUzjkEF7Rr8/AaAsLVwv4/lXH6XrJ7T8mCx32L6wj2mXAFSZTZ652E6BIWtUZfJrz72VuDK51p1wefOAL96+xlHqczuyOc0NBg6suxUCcKViN4hIK4v7iYc0Gi4GCS07p6glSWVR1JKWVXCQ+qSVJFcGwoDTTLLhVrStmroxGOUmX+75cDVMiCuTqjEQQMsqud4/o6hMxqlPXFkM3JRhsEAKxUunmzy9ccAsCVjkLllt8iee+3//7lfJ/x9+5b1/nKSwMUVNx0uRQmGbJVlpE+cucWkTFTaPDE7od6d87vZ1NoIFaWlhScWljSNanRnSrlC1IE9dJuM+kyjkNAmZFjZXO1PabkKvMyNsL5B2RTIPaQ2muIMpZitl71NPE3QWWG4BQHj5kOyoz/RwjaOjDV452+A73/kZmlqSZw4Xvvs59j/yNFIoaiW4ff8CTWPQ8WMcu6BWgsFwTFlYWHaJ146oCov5qEt/65TWU3fJ9wY0yqCYhyTTFtEi5Nrv/zT50YAqs/F3TkEqhFOBaEAZqMQmO+pjBin21pT8oE9TCYp5SF2a9N5yk+z+GovDIWVp0t065dd//X3s9s8IA53wpZRAmjWNMlBKUJYWR+MBllnhWQXTNGBncMoiDjiN2tyed9gJIrpuSlQ4fOZ0wF+79+EHvhYAnvvO78AwGv1c/JTezjHO+gRDKppaYFg1KnFolKCKPG588UnKyuTJdz+Hs33G7KXLNErgdheUscfp3ia7z77G87/+LjpBxO5jt3juM28jLW0GwQLfS7l3us6zj73GZNSjrCx2Lt9jdtonTnwss6K/fsZi0sG0KvLMIU58fC/leNJHKYEpaxyzZNCbkGYujl1y+Zs/y+j567R2TkEZHL96GdspMK0SQypMu6R19YA6djG7EaJXE39pkzsvPkrTCMIgYu3KHjeff4Jn/rtfw7jY5/Cf7NC7fECd2ZSJS5k62EFGowzqyqQuLFQt6F3dQ5Um8Umf129cI68srmzt090YUcQu9+/vIowGz80IwphX717Gswocs8QyK9LCwbO1kUxW2hzOO4R2gTAaamWQVhavz9tsexk9N0Uaiv/mM//soawHABpo1AM+8a9G/A8Mb7rwd+2ceeHQsUq6doEtFFGpi1qpBF27omVWGEZDUtpIoThMfK605vT8mKRw+NTJGqbREFoGllDs+AltO6NuBEUtccyKtpfSNAZFbbKIAjrtOUVpkWYu1uEavlWwc/ke3tqUYtJifLiGZZf4XkpZmXiuXvxlaZEmHnHss9mdsnVhH7ez4OSWdmxK5iHCaPC7C+JFiONmZLHP8eka73/sZe4ebDNOfX7j9cfJaoktax7rpFxTgllpsp+YbHsVrqyZ5C4DN2XNkcSVyUHiY2YufbsgV4J5abGoTC4EEUdJwGFq40rFt2yOOEwCpoXJ0C3YEoqT3CKrBYGpSCqTWWniiIbO8rFOohaL0mZa2PiyRhqKOHdRGAzdhFkSkJYWUijW3cVv95b+jlFUkqPUZ+BkPNLf42Q8OP+eNBRX144oCptF5jE/2uap7fuUpUVf1phSm3rECx1nmaQeo3mH3fVjAAInQxgNw+GI2azNyXjA3skG/dacOPMI5y28wzWaRuB4KVns0zQG4caI9GDI6P4m0qy5cv0288ynLiz87oLW9gmkijz1qCpJ0xjnG4BpHOKWBVvrJ3jdOb2hjtysU5twa0xXGYheDYFD/kKLRhkIq6Kzc8L6+quIXo03PAXbhCCger6gMWuE30DLRoxyxLikzhzK0zbuYxPU2MDqRtSxy/RLV2hdOqIXJtS5TZ3aDIIIIRSzeZvDWQ/fKpBC4ds5nptx93SduhHIyiQpHJoGXC/D9TIcu+As9Ukqi56RMAgi3vVQVoKGZeobfF44TBZtbKcgm4dIs0JYFe5whsot6tyiLiwuP3oLw6xZHA6JT7v0nrpNcdLDkDWmm9PpTZne3sa1CqSsyRcBl3b3KDKHupZIWfPOtz1HXZkMNk4xRINhNLT6M4RUqFpQlxbSrHHcnKYxiOIApQShkzEYjGn1p9x49RFMqyI0YwyjId1bA8AwFGY3ZeOJ27jXx2AY1CcW0e1t6tilzi3UaRcZ5QTPHhPc3mU87ZKkHu7mmKv1a5SHHYzTgmAwPS/6AF5vQZXZWH6GY9WoSnD3pUcoChtVC5rG4NFHb7B35wJHZ2scj4b02zNUI6iUwXzS52T/IvPS4lJLp9GlpU1S2CSFgzQUCgPXrBinHoWSqMbAljWXw5ij1GNeWjzeGz3EFQG68q/IfV+veNOFf1HaKMAwGqTRkFYmoVVSKEFcWnStEksoslqSViZtu8CVNaaokUIhjAZHNFiiwREKUygCq6DrJ0TLU6JnF6jGoKhMKiWRyxuf52Z4bk4a+9hmhTRrDENRFRZekFLkNgCuk5MXFuBTVZJaCarKxHVy5PLmlKQenaHuCBS5TT3qIs2aNPFJEw/VGHh+QuBkjJKAcWHjCIUEArNCmrpT8HJpsuYa2ELRdTIGfowta6a5y6ywUY2BNBSubHSRMRp8q6RllcxLE0vo1zGrJUktsIyGVAlqpX/WMhrmpYk0GkyhEIApFEmlOyd1YxBaJXUjOEtdVGOw3ZqhGoMGA1Mo+q03H1P5n4NKSfp2znq4wA8TrFmHtHColycpU9Z47TlJ4bDIXaRQpLXEskqkrKlrwXS+TLcqHKaZx1U3X97UTZrGoGkMRvMORS0xDBgv2gRORpJ5lKXF+vop7c0z5kdD6sLCEA11YZGkHkGQ4HYXDMI5tp8h3RxDNBT3O8wXIbUSGIZ+X/JKb5JMUWNZJd76BFWYqFJ/NMS6AFNCoqjvKdJZiCEagsEUu7dABAUEDggDbBtcFygwLAWOANfB8AqKeYhhNNi9BQx6GOkYYkGdOQipkO0URINILVTeZ2frkDTxyXMHg+b88wCgGoO6EXhWiVjeDA2jIYl9HCfHMitCq8AzS8TyOkM7/5rv5YOA62UA1EpQVAGGoZBmRdMYVJlDFXnk84CqsBBSEW6d0tSSbBpSlxZ15OnNlF0j7BLTLVgcd/DcDClrknlIWejuXVXp98Vbm5Cc9GkMgSEUTaNbZVmqC2yrP9Ovt5+iav1+z+KQpjEQssbpxLSDCMdPaZadSAAnTDBbKaKd4/gFSANcG+EVCKuimIUIq+INUzrLTBhePMT1MmolULmFM5zS1BJVmJhuQVMLhFUhrQqrE6NOegi7RBUW6bRF4CcUuY1pVvhhgtuO6HZmTKZd8soiL2wMGppGoDCwRE1gGtRKH5DeQFFLamVRNwa+VSJFgyf0fS+tTAQNrqyxDL1RWuEbF2+68O8nHn27RBoNcS05ymzes3ZKWllEpUXXychrk3FhE1cSz6y41JphSsUi80hKi90g0UVN1NjL06olK4pKcpJ6PLpecDTtklYWhgGuVXA07/Ls+uu0N8/Yf+0KjlmSTFuk85Djo3WuPfsyxzcvUZQWllkxjVrEaYMlK2y7OG/hzkc9mtM+++MBF67dxfEy8tTj9uEOT16/wcH+FrUS9NszpuMeZW1iSkV7uaGJlqMKW4K9vAEXtUAKxbX1Iyyzwo0DfKsgyD1qpXfd0lBIoZ9PWZt4ZsmmZ5Arwb2oxbTQRVwB9xMLVzb4UmFLxSQ32fIKLKGoG4OBm5JUFo5QWFbJuh8xTn2OUo+mMbjSO8MwGuJCx0QGfvJQFk2pBE9s79HpzhCixrELTsdtbFnTdhOmUYuNwRmuVWAXNpNFmyh39fXbOaZZcTDv4ZklTQPSaBBGg2lWZLnLNA6IUp9XJgO2g5jdzphR3OLycMRo1Mc0KzZ/35cwXMgXgS4MsYewKl3olEGjDPr9Ca2LR9S5TXrapUg8JsmyAMuKWgnyymSzPaXXmWGIButCxOlHH6PIHLpbp6AUJDXZ6z1OXr1MXUksu0Q6JcLLyfcGuDs5nEY0RYaxozDMGsMHXFv/vi2Zn/UIezPMSxkIH4DspEc86jJ862sYYQMVGLnCEA3DJ24zfu0SVSXZtQvSzCX0E6SsUUrQ92NCLznfwJSlxeFkQOhkWLKi7eSstWfL19KjUvK3f1N/Fwi7c1St6UJFYeP1FrjrE6rIIx11UKXJ7LRP0xi4XqaL/riN241oKsnpK5dp9Wd6g2bqz5ZhKFyvQNWCOAo4mfa4vLOPZZX6VF/YSLMmmfpUlUnQnVMkLmezLo5Zstu7SZXZCKkQiYdhNJxFLRQG4aRL0FmwcfEAaZfkkU+jDKx2gqUMRJhjeAZN3pC92sdqxzTKwfQyFgfrtLbOMKwKw1DUBybhuw4J032qM5fozhbB7gnWxhwUejQkJNItkF6ODFPk3Ec6JdHxgPt3L/DY215gcTTE789whzPS4z6dtTGun5HGHnHi6+5UI+h6MZc2DpnMOpxGbfJK31N8u9Cn/soirUy6bsqaH9FyUwBeOd3kJPO41FrQdROUevj0rhW57+sXb7rwXw5jXFlRNwZxaZPWks+PhhS1gRQNl1sVAz9GGA0nqUdUWuy0SiolqZSgVJKuk5GWFgCWqClqk9fPNnDNiif7Z9wbDel6CYYBeWUSOBn91py6luSLgAtP3SCfBWSLACFqHnn7i8z21pnOW+SVhW/nPP2uLxIdDyhLk0YJLLvk6HCD0byDUoKN9ow88qhKC9Mq2R2e8MqNawSOPrWcTPp0gghT6C19pQRXOxOOohZtJ8eg4VcP1vnuC2Mmucskd/jE3as8MTjlxbN1RoWFLRQts2bNTbFlTV0ZHCY9OnaBKyt6TkbdGOx0JhwvOowzfWNqW4qWVROYeoRwMYi4uWhhCcHmsui37JwWkFUmt+ddWlaJL2tKJThcdJkXNpPcWXZlLK49+DXD41v77I3WuHu6TsvNOFx0zm8yllXy+vEWnSBikoRMMo/HLt3lxv2LjOIQJ3fZ6o2olUHXi/E9fWP60o3rXBie4nspUlZMFm2utqc4y07NY5fusFiEOE5OrzfFCAyyl/uUmUNdSWZHQxw/pdOdIe0SGoONp29gXYiQC4GQNe0g43puc3o2QDWCq9dv4fXnunVbmhhmTRMZdC4fYsga2UrZ+4VniaOAWRySlxZvf+9nsdqJnt2nDu4zM+obBtHNywirovVohbxwxvlHy7KhZXLhj36JZmOD4uK3IP/1xxEBhE/vE5r74FnMPnoZb2OM2Y5RpeT2p57l8jtepCfuc/vTz7Cxfnp+WpVCkWYue+MhgZ3TCxckhcNGd4xSgix3KGvBP3vlMZ7uLgisktuL1kNYCRpef04+C2j1p3Q3T3HXJxSTFnVh4bRj/HdPkU5JmThUmcPRK1cIOgvODjZQSrB5aZ/WM3eZfuEqZeoSbozwIp+Dw03aYcTu4zd59JEj8r0+8dGAInExZI2/c0qZ21QLE7cbAXD9yh3N1SlNFmc9Pvvq49SN4GJ3zLPXbvDS7SuUpUVdmbQuHDO7tU0SBdS1SZk5xFHAcPsYpxNTZTbB1QMMT4FCz/lHXdwrJ1SnIZPXL+K2YtqDu9BxMMmYfnpA59nbGGsudLpY79+i+cVXyU561JmNIxXexRNU7NJ75D6dy4fc/NSzPPLBz9PUguRwyNHdHXYfv0k06RDFIbZVMJp3uLK7hxfGZLHP+toZqhHMUp+0snj8wl0+eeMxAC60Z1y9eI9G6dFBnjlsp3OGniQubaaZz8CPHtp6WOHrH2+68AdWQVaZev7q6ba2oCGuLBalRV6b2LUulpZQSKPhxmRAsdxZerImrUxsUVM2gv0k4HK4YKc9JSkcDuMWXSdDNbogeG5Gu73g7GyAEEqfdGqBP5hRJB5p4pPd9khin3aoF7FSgpc/+ywb65rwl6QevpcS+AmDwVjPHlOXorCZTTuYsmbz0j5x4uO5GU1jYKQeTSM4mHf1+MGsuDfvktWSk8zDEoo/dGmfW7MepRKEVsmmH5NXFrvhgk0lqJXAMSsqJWi7KevdCZ3xgPuLDrUQ9OyUrcEZrx7uYomaoZdwlvp07QrLaIgrk73YZTfIeKo30XPowsEXlW73lxZRqUcQrqzwfN3SNYWibWdsBYK0tJjk7kNYMrql2/ESstIizh3s5dxeNQZNI/CtgkUSIIyGwCrZO9rkS6MhF4OI0M45nvZ527UbeH4CyxZ9UWheyNm0y8GsS8fN2OxOmUQhh/PuOWHTczOkWZG+OCA6HjAdd5Gyprd9wvxkQNjTpyart6A46cF9SE96ZLMWg2duMLh0gN+OSOYhpwcb9AsTJ0zJE5dbt66wczTEa8U0yjjnD+w+fpOLlp5XO99k07g9jOMjmOUwWEdsOYTfHFC3hyS9S+CuYR5+Fvu156hfjKj+u2/G+cSvYixmyOkx6f113PUJhlmDVMirFqabM7lxgTxzsO2CdmdOGXnQGHh+imWXGEZDkdvMFyFnURvfKtjojelvnLFeSl69qUlhTQNSNLxnbQyAK0s+sHPvoawFgM/++ruRQtH2E9Y3TkAJpFPgdBeY3YhmUmMPpxjLzUCSenTXRwy3Na9DWBWjTz+K246xw4SmFvSv38cJE128akk9dakT57yzUOf6ABGsj/F6c9Jxm7qSBGtjpFfQlCZBZ8G7H3+ZLHWJk4BPv/Y4a36E4+SoUurWfWHjtyMcP0PVgs7OCVXikk1D3S14pAtHY5oC5KDA7S6IXt6lUQZeb44wK6afvkJ48RjZNvDDBMMFOl0a00J+6Ys0wwY5C5F+hvmMSXOoqM9sqsSlqSUXH7+J+UhN8YLFYtRlbfsYq53Q3TrF8VPqwuJs1uXgeIN2FDHcOOXkcIOiMqmVQakko0mPy90xeWlRKskrt68yCBbLrpZFVDiaQ+XokU+y7Ao+PDSgygf/mCs8ELz5Gf+SQCSFbkmWSrAdzukoyXx585eGwjNLVGNgGor7SYAjFMFy5jjJXcwloaqoBfVyLueYJQM3IXByHLOkHUaErYhaCYRQmGZFVZmM9gd0ogBVC5QSNI3+ftiK9Aw3tykrk7KwaBo9b5ZmTZq5uIAwKzw/Ic9cTebJXO7duEKtBGVl6sczGuLMZVEueQOy5k4UcMFPcKUmMuaVqedopj69b7annC7adNyUqpacpQFdLyErbVxL37Adq6Tj5LiyxLFK0syl46Sa2FjpNuzAyamUQCqhRyqVSbGcP75BRhunniZ1LQu9sRydgO6SNBJcq0Qa6vx3HzSUksvRhaRUkqoWFLVJk3kYeUPLzRjHAbPc1eQiDNbdDN8qEEJhiYrBxQPi0z5FYiNlfT6WEEaDLWt6foRj58hlW9y2C8bjHqasKQqb6HDIbNIlyx067Tn+zqkm8m2Mke1Et9tlzfzuFnVhIe2Sphb672aFF8aYy24CQtHUkmnqs1lJLKfAasW0vSPN3t61odWm8ftU3SHqsT9OtXgdOb2NCtaw1t6HJQNM4I3baR1eJ+3s4IYfp+4/QrPzCkZVIrIUd3OkZ8YdBR0ftbGNu3OXdNpCxR5CKkynoM4cqsLSKoJck7+yzGUWh+et3zRzSReBHpOUFnUjMGiggbVgQVraSEPh2MVDWQsARW3iiQLfSwk3RkTHAwKnANHQlCbqzKFOberMpiqWHT8/Q/o5TWkSHfVJogBDNOdqG2HVunOjBKo0Se5vYLViLKegLjRJkMZA+jmmn+EvN2bCKVGlSb7wsYKUdB5SFDZJ7tC2M6RQmng662DvreMGqf4/haKMPMLLh/qiEld3gPbGqIUFysBwK6x2THU00DN7p2RxOCQYTvQaKgVuK6bJwBiNMEwJeYNhgj2YYTglVC6GAOfSFHnikx73CR49BNNHBhlBd44dpOccE8spMIyGYWfKZNEmST3yxKOuJbZZYVUWSSmYZz49P8KSFUnhMM/0eMeSmnBtZB5RYdNxMwQNs8x/aOthha9/vOnCP8kdAqukriEuLYolkcuzC0xRUzcC26wIlqQ2YTQ0cYgna3pORsvJmBbOebF3paJUkkXmEjo5a0simmlWBGGM24qZnfZxnRzTqqhKk4NJj/GiRb+1wHU08cfzc7xORKMM/QFxCmaTDkIoPF+3kYvCJktdLLvEdApUInDskiT1eP5oh2u9EZWSiCXhZZr6ZLWkZZYEVsG8bNNxMqRoSEqL2/MullCEVkHbTWmFMcfzDrZZoRqDUgm8JQvboCFfEt+6boJrlQhDcTTrcmnthFkckpYh0mjoOSlR6eABbTvjudGQ0bLLUDUGqjGYFA6erGkvyVrSUOdkr6w2WZQ26yLGtQr63sOZ8avla23JGnfZ2WgaiEtbj0bWjjmJWsxLW/MiRM0jgxPySt/0u+ECq52Q3tlhEbXwvRTbKahKC9cuWG/NGQ5HFLmDa+kbnzQr8spCZC5SKsrcJooDDKPB9TLM9QxvPsPsRmA2NJkJomF21tdkzeEElf8Wu9pyC7zugnwR6JOdWdH1EnqbZ/g7p/oE9pb3kvUu6Yu2uxh2D7n/cRx7gDMYwOC9/9HXSAoHf/PbYfPbCYD4HR5iehNrdBfzrXNIE5q1Tcp1rTKxrx8RjkY0tcDy9HtbV5Iqt3UbujQoSosk80gKh54fMY5bzJKAsjIJlxsnA82XqBtxrgLQBL+HN9Pt+RGuk9PqTXG3z5gfaHZ8ndrLgu+cy9YAXKdA2CXCqigTh/HZAClrokkb0y4xzZq6NDGEQkg985+f9djcOqOpJWVuY4gGVUuM3EKEFc7uCMOHJjJQoxaqNLFaCdEiZLxok5U2lzeOGM87uiuyCKnuSXYevY2qJUXskUQBA6/QpE2n1JuSF3exghRhVRABosH0M/01MDrrM3jmhu5cRR7SzSmPO8goQ4QZRsugSRvkxnLjNdHvU3P9AqazhzkrMLZCiBJEqGhdOaAYdSgXmncgpEJaFYPNU8rKJM1dplPd5fLNiqIyUWiOj5TqfDOblRYGzXJ0pogyj1nepm8kSKFI6ofv3baa8X/94k2/+2mtWcWurPHMCg94dbRGVksUsOml7FgFCoOktBnlLj27YFLYWj6yZPlOckfPr8MFWW1ymISIJMQzKwolGLoJWe7QXkRYVoltF9w/3CItbR7Z2aMo9En8DRZv2NPSqzz2GY96gJbyeW5GWdi8tHeRpy/epj2YIq2K+VmPo9M1Ajdl0Jvw7VtHvHL7Kr3WnLrWm4tJ5pHVkjU3ZaM143Ic8qnTNdackoGTEVglaWWynwScpD6T1Kfrppws2jhmxRPrh8ySgFES0PcSWkFMUWsijjAaguUmoqwsTXgyKzxrQV6Z514HYWOw7uZ0nYykshilHmll8v4Ld1BKcBa1mBcugyDiLG6RVhaXe2f8xv5Fhl6CZ2lS48NAVUsCL6Xfn/DIYMpi1NXSycQnKRxMWfPN7/4Mi3GX0ahPUjicRG0Cq8AyK06nPYqPv5N2a4Fj55xOenh2TlVLTFnTCRc4bkYS++zuHuC2Yl5+4QkCN9NdAFnh+hl+mGD7qZ7HHvkkpz1OXr9E0FnQvnTI9PY2G9fuUheWlpe5Bf333aA6cUnub3B2dxvTLhGypnt9j3d/f0q5fZWm2iZrbSA2P4AtXeoqop69jHn4ecr1J/mdNEmD9lPQfgouQnL0KzT+Fvbtj2J/6VNQ1dTXHsW5+CKq1Nruw5sXGWyeYvspdm6ft3w7Sq/37vYJ4/tbzGYd4sylrCyG4YKTRZuocBgGEa+N1lj3YrZ6YzZ3Dx/sIvgybG8dsZi3uH/3AjduXuW93/tLzF+4THo8oCwtqtJksHtEa/cE0cpoHXc5fe0Sd/Z3WOQuXS+hE+hxXZJ6xKnPW7/j18nPOrq4Gw1hb4YMMgzRIN0c+8KUcr+j9fCJy+LzG5SpQ/fKAfbGlPbWnCaW9NZGWFap5/q1xLUKWmFE0Io5PNgkPtMyQpSBF6RU0xD76gKTlOT5dfKFj7s2QdgVxbjFF3/zXbztg59GejnlPGDn2l3yk9752AlgGrV4/JmXaL/tPqwNME5HsKE3Q4zGNLMKXrkPJrjXTlj8h11ab4swOgKpctIbbSwvw98agVCMXrxGuDZmY/uIyemAuycbbHan1LWBbVbstKc8+77PcvMLT5PmLsJQWhaNQZJ6mGbFRm+MYTRs9ke/1ela4RsWb7rwP9kbMS80me1ebrPu5viyJjBLDEPLRUyh2/1vwJUVnpSUjcFB1CKpJdZSypfVJkMvZpQNkELRMyseH5ySFfZ5q38+a2NZFWvdCWVlMpl16HVmzOZtDKNhMBgjrIp41CVN9DzWMBqmcUhZmXRaCx7ZOCDNXPKDDQDSTJ/4TFlTVSbT4w6mqIlTn/KNortsi05zl9HRDkkt6Vi1liiaFfPU560bB1jLzUxeWexsHTKddClLC9OsaLkpvTDSBkSVSVFJ1sL5uXZcGA03T9dpOzmWrJhnHhutGaNMF/hCmcvxSYMrK9bdjKv9M26drXOaeaS1ZDeIMWVN10swhWKShLx/+z53pwOiwuYtl289sIXy5Qh9vTnLxzbzeYv7ozVaTnYu5Xv9eJtp1GKRuUSFHgNtdybcnQyIK4uL7SkArd4M0y7x/JSzswHzzGfYmtNbGzE6XuNk2uPe6TpSNAzCOesbJ8TLE/rwra+xeH0Xb22CYSqykx5VbuOFMeki4OQTbyUMtfmNtzGm1U0wHt9AvSwwH4HWuyva9z8PW2vk199C0f0OkC5B961fdb1VqZ9v7XUfyOvnb367/stbniK+8iLy7CWs45sYHRP34imGVOzaJWWsSZ+DK3uowmJ+NGQ27WjlyiKkKG0ss6Tfzrl1vEVaWstuk77RW7ImLy3Gizb5LZuNB/LsvxqjUZ8Lj9xm26qIz3rMX1gSHTe0VjxfBASXj6gjj+q4i/vMguQLPsPOlC2zwnYKBhcPKGOPdB5imRWqNDm5vYs0azprY6RZE9/Zos61dNO5NsUcLqjOWqhSIs2K7tvvMH/tAov76wTrE7w/7DLsvUZnLyA9HDI6WEcIxSIKGU17CEORxh5RHABw5ZlXyc46mO0Yw1JUmc3p0TpZ7OP4qfYK8FJOXr+EqrWJkm0X+O2IPHO0WsQu2PJTZsdD4l/p4ncXhJcXyG4GRUHxmo8qTUxfk4kNs6b1HRHNHQOEAhs6V/cp574eDQDSKpF+jow92t05j/spRyfrqOXhxzAaPvYffh8GDZ5V4i830fdmPdb8mK4XU9QmtllxNB7QNMa5n8bDQwMP2sBnNeN/YHjThX+U+eenbNBev4FV4MgKSyqyyiSvLColMaWe6xuGnpE3tWRaWmy6KQqDaqn9X/MbWtZypkeDUgZZaWFnLqap5VaUJlHin+utF5E2fvG9lKA7J522z2e//f6EsrTIcwfLKmkag/GiTduPKWtJVet2vjQU1fLrojaJCucrri2wCgRa6yqMhpMkwBIKW9YklcVxps0ymlxzBfqtOWnsM4tDitokVBJhKJLcwVzK3S6tneA4OfNFiyjzsGTFZivXz0tJfLtgbThinvnMMhdL1PQc/cFZlDaj3GFnqYjoOTk9tG53koTnM3bDaEhLm1IJagzmD4nJbVkljpNTlpqo9YZB0xsndtBEv7SyiCuLrpcyiltEy+eWlRbCaFCViZKKqjTZ2d3HOtY35mQRMpp3lh0Qi6rQ/gZbuweE7QVOmCIvCpzTBeUiIJ20OD7axDJL/EC3Uh27xPVS3O0RIigxPINy+wqiO6AWkro9xPzWH0MKh9+OAlnWMZx8DjOdgJAo68G+pkH7KdLxq5Trl3AO90BlIMF/7Jj0xpByEdDUkkYJhFTYVkngZOfrVTUGqpaYQtFxMxyzxDFLysrUnhhL86lu++GYOQF0e1PyeQCi0eY3Xsb4/pbWpXcXeIMpKnG0iU9mk3zWwnFyDKNBKcFk0j1X2TTK0KY94zaD7RMMs0aYFUXk4/dnVLFHvgiYf+YSVphSZTZNJXE6MU0pCXdPEH6O3AVOJShQucVi3NEmOY3BNA45SUIudCbcO9rCNitafqxJfYsA67iH1U5wugvtmLjsCOSFQxhEOEuTMKfOKXItGbSXs/igO2dyvIbjpdh+hunmNJWkubvQDo5nHYLLRwivRKUWTSlR/SHcnKMi3UtqagGNOHc7BH0NdWnSKAPHT+l3ZqSZQ5Y7pKWNZ5baKbVq9GEgXHAah9q1r7SxzUqvi1oyyz2OE5/f99BWBLpGr5z7vm7xnzfjNytsoRgs9fymULhmhbVcVHllaZcoUWEKk6YBW9YoIKslnaXWPy5tcqWZ58GyJa1YfuArizh3l0YvEsMqSQqHpLTpetqZruWm2HaB6RZMTwbkhY1wM4RUOFI7m4FuSY+SkNbS1S+vLGyzwqAhzrzzm2daWVqlsJzxO0uComcVuFZxbiKjC6ykUgbTzKNW2kRl0yq1/jz1lyYagp4fUb1hUWw0dPtTqkoiYv1/+HZOrzNjNO2xyExabkrQWbC+mGCKNkVt4tv5OftWAYvcxbcKfFvzB26Mh8yWHYw3uh1x4WAJhaBhHD+cwl8UNv3hiKKwmUch/faM+HSDGgPRKE1gNEtsWWMKhWOW3JoMyJYFqlS6fVuW2qynqjSzPoxi4sTnbNQnLW2y2tImSHZJqSQoA8vLsTuRNsxpBIuTPqenQw5nPYbBQvNDgoS2O8FuR8hnelBVkCZUgys4T3wTebqH5W4ixX+6aV8sXteBFkJiVAWGfPBKCZlOqS59G9z6EpDRlALjah97vKDOHLJpS7tZ5jZCaNtW18sQmd7AlqWWsraWUsimMZhGrXPuB9RI+fDau2Fvzsn9LZQSBGFMyyuIIq3qMK2S8MoB5SykWPjk84B4ERK0IrJUj4fGcYuyNumEi+WGXZAvfLqP3sOQinIWQuRrtn4jMGKPk/tbdHpTPXO3S1w/oylNrOsZzc4OVX8D+bFPoyKH9LTH2emQcjnXPksDDlOPoR9zd95hy48I3Ixk2tI237GH9ArMdoJjF/jdhVZ5pB5BZ6H5La429BnvbWAHKZaX4ZQmVpBijkuk0CZG0s+pElf/iTzyyKe93UBhYNQ11AIjS6kWHmrZzXhDsVDnNk1pYoivrHiqMvGXVs5VZSKqhkF3Qnq2DmiCbOAnhLa+d+WVRTeIUcpAGA1FLbkXPxzFzwr/deBNF/6L4eJcR77RmvHK6SbzwqFuBCG5NqoxFI6pi/e92Gfbywisgrads+4lJKVNUptYhmLLz7gx73IhiDClomlg5/J9opc127Qsdfeg056zZVbkhU2WO8xKDwAxV8gjzdq/cOk+Z8drfOz5t/Cua69xMu1pPoJVaOZ8LYhzl3nh0jSw0ZpzezKgUoIr3TFtOyN0Mspakpbe0nbYIq0svNJmGES8dLau9flWwTuHKXFpcbV/Rq8z1/7q3SnTWM/1J5nHdv+MteEIVQuS1OPW3YtIoVjrjdncOSCetVBK4DkZBg1SKLLYO/dmP5n1aJan5u1wztsv3uaVg10u9ibkhcUkCfDMitAuqJVBviT2rQURgyCiXLLUHwY+ffcq37wkTp5FLdzluEIKhWcV+nRhF1xZP6JWgoPxkFmpjYd8WdFyMp55+/Nk84C6tGh1FnzsU+9mPZwT5y5nic8jwxM+c3CBp4YnPPHkK1rKlnhEkw7xrEVrNuP+zUuczruYouaxXS1X23rnS8iLDbgOBCHGd/wkabaP+fq/weg+oUl3wZt3Nwi6b4Vl+z+98/M05VeenNNsH8/dOf86nn4Ryghn7wvUfpvm8n9DHd0m+G2IgCKL8f1LpE98AHf0C6iTGuIIuVVgpwsOblwiST2OFx0MGgZBxOaV++SRTzIPiZTAMUsCP+ZoNORw0eEsc3nL+hFtN2WeefzSi8/yf3nTV/2fhzzymSzaVLW2h3X217j4+E2qzKHMbESnwDZn3H/uMW4cbfPet38Bf33M3V9/N/emfa4NTnjiA58D0ZCd9Di+t63Je7mt/RJyC399zOT1C9hejtuNWF8a74Budavcwn1sTLNzEQDzc5+BwGD+3A4ne1tMklDnW5QWk8ImqQXTpX1tPO8yznyeXJo+eesTzF5Medpm48qeVoykDnVlEqyPufG5ZxgMRgyfuM30xUfZedeLmMMMFQtOPv0kF//g57nzkXdy695FHrl+E1WZdC4cYciaLPah06b8XEGjhPZt+Nwpk9eu07l0gH1lRvryOmYrXr4GFnaQ4qxPsNoxyeGQvVsXMQx9svfcDNfJ6Q0mFIV2/3PsgjjR+v6sMpFGxtr6KS+8/sjSGVSx5T08lYfGyrL36xlvuvDfXLR5sjfGswoO513W/Hi5mzQ5ilqUy7n1TmtOz4+4GrrkSiKNhsAq8J2c10ZrCCCrTU4XLu7S4nYrWLDdH3Hz1es4ZsnxosOicHjHlRs4bo409Zz8eNFBYbDpTzHNilv3L7DZH5GlLkppk44v3b3Khe7o3NHs+qW73Nvf0aQxJyUtLeapR1FrX/1XxkPaVslxEpIvNfinmcfAzZYmQ5IkDtkNF2SVPqH2/JgNqTsdaeZQlhZZ7tBvzbmwcUTQWXByuEGeObS7c4aXDmgfrhHNWySpxyIKGcct1jsTLW8zK6ZxSBAFzKOQvLIInIzXR2tc6Y5RjcErB7vsdCZYZkleaNnWZClR2mjPkFLxwuEONycDrvVGdIIIx3zQOlqNa70R+8cbeFbBIzt7et6/NFCy7YL7J302gEXmMctdpKHY8WPiJavftUrO7m0hzZq6kiSpx9W1I5SS2LKi7SYUtckjPa1Dv3vjCmEQczrtcf3KbcKtM6b3tpjFITuDU7YfuUPw7Tn5E++h4tvI+48QdN9KHL1KALowP/MXaV7+B2ROC/fqH39T1xlPPovdfgJL6hlw47bxvvQRFB+h7g4pB1eQw3cST794zg2Q49ep+48gzw6RHJIFQ5zDV2h+6f+FunIV+YH/4av+H/Nt/1cA7O1vJ/19Jk37Mv4//5uU932SwwG1Elx/5hWSz70FU9bs7h4wOVjnxv6F81b+dv+MsL3gopvTn8+5c7rBjfGQndaclpOx5T88FvftuxdZ600IWhFOkGD5GYc3tBoibEVkN9dRpcT1MrbaU+7euELx6iPkpcVmOMc0K174+LsI/UTPzMMYaVbYl+Y0CRSvhaST9nlIUbHwEVbF5HTA5Xe9gP1sBUkGFahPHVInLqi2DjSqJbZd4FkF6aINQMcu6KDvQ22rxFx2L08XHaLMo7d7hPBzilmIKk2e+3ffhG1VXLh2hypxufLUqwhbB/e85UO/idorQIC46LL5yAHqdcHFb/48F3KL7LiHFabIdoItFFuDOc2tlHj/st7815Ltd77EbNIhiXzC/TnB2hThlMhBQVNAdtaljjwMU2cZ+F5KnAREuYNtVUhZ8WvPvZW0Mhm4KT0vYW/epeNkDP0YW1bcunvxXI4tRYP1ZeFoK3zj4c0b+JgVB1GLtp2zFkTYZoUpakZxi1lkk1YmgVkRFQ6Cho6bMctcpoVDXFpsGg2Bqf2ja6VDI3xTt3Adszo37Llx/6K+UXUmjKdddsMEY7lIpVCEy2JmyprN/oiy0rPtqjKpaokr9Wy/Ws7O57M2d6Z9XFkRWAVSNBzFIQro2gU9NyWrTGaFnj9LQ/MOQjs/t3QN7ULr1pVkXlicRC3WgogGA0/WbGyccPPeRfylxFHGmjiorVS1g2BdmbhLk6CmMWiFEVmuGfDCaHDLAsfNcUvrfDe/HWod9hv+/VHmcX/axzNLAjunbRc0GNRK4Do5j68dcWc8JK8s0tzFfUjabbXMHqiUJE4CBoMR06iFMBrNnlaCeeoRl3qmutOesz/vMnBTPLMkW3ID3gh3AU269L0Uyyw1MWm0xk53vJQj1UxmHSxZ65SyvR1UY3Bh61Ab8jx9QvbMd2Od3qKxXRrTIRYWqJKyjqnKKSq+D4OrBOvf8iavsQLDPC/6ydGv4P7rn2XxpQvaIGjjFubOK9Q7L1NvPUFi3cR0hliX/wiUU5J3/QkophhVTrHzNDZQt4eU+TGu87VpdlI4+Nt/gDTbp7l2BTO+hbOIGa6dYbViLmxpZr7tp1SV5NL60XknbBrpsU6c+lqz7qZ6np16VA9Rygf6xO352j54cjykabRj3Bv2uvZwyuLmDtVSm55XFr3WXHcIlNSt/coiaAyUksSLkLA3pz61oTEw/QxnOCU9HGL3Fli9BSpx6M0DyrmPvDVC7krotWgOE4pJiyqzyRYB9dLvw5IVF9tTxqlP30vo+DEvHW/TWcpFhdFQLefpe69cI9jbxPFTLKfAtiqqSjI5GmI5BUkU0B2O6bUTSHLERY9mbZOqr99XefNTCE/BpiJ4LIJRRFOBEQiMnQHG4SGWn+E3hj7UTEOkVFhOgWmXxKddVClx6mXCYWNoTwQlEFZNqzejPZgyOlqjKG2UEuy0Zkwzn46T0vYS1papoEUlETSEbkpSONou3dH3wocN44GT+1Z4UHjThd+VNaPcQRgNu6a24rXNEkvU2su+0Yl7RS2ZFy5tW8/ZSyUoGsk49XVoxDI4xK5qvQibGsNoyHKHVm9GWlq0nJReb8rB8QZFbp/P4mslCF09rzfNin5/wtnZkLrWxdReFpJaCSolNZElDokrHXZjGNoqOKlMWlZJ284J7FwbDgmth7eWbnimqCkqHatrywrHbM6DTkaZy25nQt3oECCx/HBWSlKUNiLVelqlJHnqEZ32WCxChmsjGqVn2rZTkKTeubOhMBocNyeOtS+3YxcMwjnTOKRp9OYoKhwmuYsta7wlARF0aE6tBL6Xsh4uqJY8A/shfbgtWRO4mR6hZC5rS621VkXoDVSpdAKeMBocsySrJW1bdzey0ibNXJRdYlva+MV1CoSol7N/3RmQssZcJj5OkpBBsGAUt6iU4MLglI2nbmJfXsDOBnJxjNy/A46LnJ6hRndRtouyX0AWKaaqKTaf/k9eWzx/EWo9xkDqsVKS3MW5+WnU1CGfB1h+jllGGEWBSLQMrUkOqaWL7XaQzgYsi7t6o9259W00KkcY/+mPnG0PUX6IDAzMMMFpO6ilEx2ANGtMsyZsRbiViZO63D/dYBq1yEqbshbLuN6SSe5S1JLuQwzpkYbSBkOpYDLrANDvThFSUZYmolWgap1xEAYx6bSHHySUS8fGctkJUo1BvUyoq0uT4qyL9DOswRw5rDDMY2181FKIoKRdHOvEw1Eb4Y0w7Ixy0iY67ZFnDlEcYFulJugJxaA9o6g1nyYMEurGwLNKTKFHjVltUdWS40kfNw5Z749oD6YYhqKoHcbTLq0gJs1cekaD7CcgBGprl3L9EnVbezJYj7wOaQJBSPbE+3Fe/wzG6EwnN25ewqpKrE6MdAuEU5Kd6dfMWpoCZYuAbK5JzIZU1KWJcApU6qBK/bkK10fMR12K0qZpBN3liLDlptoXIwnO3x/DaLCtitDJziW+/kNcDyt8/eNNF/56eRo2jIa8sjiKdCiLLSoGbrpM5MvJapN5YZ/b+Qam9ve/vWix5uasBQtdVOMWcWkz8GPK2uTG8RbWMtSmqE2S2GetN2E26+DYOXVtMso8rqwfcTQZaBOT3hSAOHfxrIJBd8Jk1jkPJJFCkRQOW35Mx03x7Vx/8K2SjWUrfJwGBFbBwM2WxDyDrDbxapOodDhOPY5Tj8d7Y65sHmI7Ob/64jNcuLDP4cEmZ1Gb5K5D20vONyhS1vRbEVnqMl+0uHeyQV6Z7F69R5E6RIuQ+by1NO/RJ3rbrDGdQpsNlTaBr4lFW+4ZRWkxi0OS0uJyZ0rXizHNmtPcpbd0C5xGLZLC5vHL2tNeKXFuYPSgMehM6fSmJFHAvaMtTk/WmCxvNI5ZkdQma5bObUhLi7OoRVxajAyfojaRQkcJG0ZD4McMLhzResse889dIo90sb2mBMeTPvOl9e9RHDIM53S9mFYQc/3bPoXxjl2q/uOYe7co/+EtzEfmNGmEIU+RfoOxEBiewhi4NGsbuMkC1j5IEt+E2Q387T8AQLr3b5GzA8oL78f+e38PQyrkdZP0/f8tzUf/Pl5doTa2qf7472f4/ldBedTdt1MMLmFufQucfgKaiqbOvuq1+vJCL+Sb+7hJ4dDcfo0mb/SNv7DI54F2lnwj9a6STCddAj+h1VkQzDskhcMgnGNZJTdPtgCwDIVr1w/NzAmgweDWwa7W2zsZ7SDCDxOSyGex6LJV6mIzuHyAYVWcfuw9KCXOi76UFZbUktrASxgMx+SxXgetVoy87oDTQb3vKcznPkn5qon1dgdbjHV7P3KYfOE6zs2I/VevMpp3zsmuYnnPMkVNx0vx45xJom2g86W1dsvRmvfb4wFN49JZ5nbM5m2CVsRo3iEqHFpORr87ZePyHq1n7qPe/hSNG6Bsj8Zpg9PDDi5j/MEPkt38OayTu4jND5ALiTm6i5yPse68Qr2+jb3zPGohaUqTfB5Q14K6kqjS1ImSVsX8aEieOXrc2c6o5gHxWY+z0wGtrTOaRlAvkweLJXHZtguaxuD+osOjg1OsJamzqiS72wfsH24xSYKvSPV7aFid+L9u8abf/aGXoFIt6VvkLlkt2e1MyEuLaebxts19pFDcnw7OQ3qudabcmXeYFhY9pyCuTNLSJnQyAjtnLZxT1rol7lsl90832OmPmEQtbh5vsdme0m0vyAuLaRwwLy0OxkMmmafdyO5c5n872OIPXLpHlLt8+pULJJUkrgUDu2LLj9lq6y5CXukWdFQ4PLtzjyx3SAoHV+rRwUnqIY2GTT9mL2pp90Eaek7OvLDZ6EyZL0LScZ/3XrpFWZp02nMCXxf8yaKtT7aFzem8w3s++En2PvsW0sJm2Jqztn6K3UowvQzby8kjn7XNE6rcXp5OQr700hOsdya0W/pkp5SgtzbSr880ZbMZEbQiitxmsQh5ojdmEM61o1dpUyrJ8zevUzcGPW/pm/4Q8NrBLt1Jn9DVmQP3TzfwrJK2m9AOIwbBghujdSxR03EzzfnYOuR43CcrbdbaM5QycJ2coBVjBSkv//MP0utNyVKX0UQbMc1yj6bRccSbQYSUiie/5VNYTzWoO5D+m4zJXkaeXkPKitkXn/yyzZdiMBhxejqk358wvHYfZ/sezUv/Pd7mkHp9mywd43z8V7CObRpl4G9+gua9F6jWdyk72zTSpbz+NMaj/0eEYVJHNyjrHHH1j1Lt/yrOq5/E+Py/xxzNSb7r+zGdIfUn/jryfX/ld/X6Jge/jLfIMK5uYm4leMmU9LTL6HCdvNAnvDujNbZaU5pGMJu3GS0jZ7f7Oa6XcZJ6bHoJm+GCqHD4zeNNfv/v9o3/j8CgoR8s6PWmDC4ckow6JFFA2J2z8egdTj/9BL3r92kqQXw05CwOsc/0eK+qJC8fbfEtb/0CaeKjakGR27hBQrh7grUZgzMkeff34L3wy2BKrGs5xBVnn3hMuy7KmiJxMe2SeRySlTbrnQlx5nG8aONbJVu9EVVlEucOLTfj0sYRTy3lg8ejIWdRi8Aqz4mxeWlRVCaTUZ9hZ8oFN6MznKBqgbc+ockF8oUXqZ9+CvvoHsnbH9MmTUs0Tpu6u04V38Y5vYWIZhiH+3rT8u4j6okeYwi/wOstCDfH5LOAdBYi7ZLj/U0uPHqb4WDGwRcfI7mxhdVK6D9+h/6Tt8hPegy2jxlKbW08WY4m0iggjn1+3xMv8unXHueJrT02do4YHa0xOhsQegktPz43QlvhGxNvuvALo2HLjyiUPnkL4PZkgCUUgVVQKa0pbxoYOBllI5jlrs6rt0sEOqjnNA5Y5A6+VbLWmXLndIOilnimlsCczHp6pi0U+7MerpNzNutylgasuymzXDPz68bgLA14ujfndClb2/ASPLPEkopBOKcVRtw63ME1K7b7ZwRhzPzLcuDL2sS3C+0z3xhYsmYQRHS9hLTUXYtdJ+eJt32J/deuMBzOcVsxJ3tbtNfHjO5vUpQW61vHtNoLOrsnlJHH/ZuXGN3dZmPtFNOsMK0Sy8tRlYDlSQQgXJ+QjDpYbs7Fb/484+euk0YB9dK7v1yOEcrMOdfk3z3ePI+VNYwGIbQtp++lhHnG54922A0WSKF45cZ1th7gYnkD1zYOOZn2iHMX18lxrYLdzSMWi5B7pxv0/AhzaeQkDcXGxgmLeYtBW7vO1UpQlC6TRZtFHOCf9XHsUuctFDZx7tINIjZbM8ZxQFLabLWndLtTzG4CrQ3E+gn5K4G2RTYUi6hFGCSYVknQWRBunWKIhvX/m4G8e4PiixbVJODsN6/gdxe4g3sI5yazg0uYXobpZxgnDbx1iDk+RmQxxdYTKNtDvPKzNFmMmyU0ro/8//ww4kBRTkNUbXL2+ltpf+7XUPJXObp1gcvv/0vID25TvudDOPaA9M7P413+nvPXL733Czh3nye79q7zrkNRzbBN3fL1t/8A8R/pIOITrOPXcS+/yGJ/DdOsEKJBiJq39cfnQVOmV9PJXdpLY6XRvIMra0I715basuap7sOb8zcYmGbFYhFy9sWn6HenOG5OXVrM7m+SxAH5C9eRZkWjBBd7I32vWPJTFAYH+9uAzmQIZEKyCLHPOlgbc9RwA+voSxjRApKMZqFo0oZgONWue42gUYIyt/HdVG/wU5/PHW8RmBV5bVKcrbPdmdD2UobdKf3NE4RUjA7Wl2qkinHqYSw7TLUSlEqws3aiR2e1RNWC9qUjDr74GGFvTufqPvnzY/zrR3jur5A8EtE4XYx0hEwnNKYNVYZ88TmahaKeO1SLAPGlCXXkI7wc7Ab/0hFNaWKGCW46J5+2GG6cks5CZscDzsZ9hpf3mdzaRZgVvcfu0iiB3Ur06DCzKQsLYxnXHYYxi0XI9eExpqyZnfXOA8uOJ33iwsF86OS+lYHP1zPevGVvaenY3arhrPbY8GPOlnIx1RgUy0AZW9aawFVbzHJHu/eZEFcmgaVnvdZSfpQXNmlpUTUCR1YIoYhzW8/+Za0jJKMWs9w9T/ZLl/N6gKIyuNSZMFp+WNtOTlkLBuGcTnuO7Wi9+0Z3jOdrP/j1rWMmpwOsZa65jjjVhDpX1nhOdk5Qc8ySXmeO3Z/j+akuGMMp7UWAkLXWUkuFuQyAMWSN6WuS4mTSZX39FNPRc/Z41iIUDYasz6MyQ2Vo/3JlYMgGfzA7T4RzvQyl9M1G1eJ81q0aA8tQ2GZF6KY6E3zZtnMcTUj07YLAyTiadR/wctGQUkcqv8FpeIOwWJYWSWlh5y6m1PLON55zUdoYy69Bj2HyyiDOXRa5x+7whCK3qd/IgHAzzKoiLWyqpe6/PZzQ1AbG8QlKE/51AEvqsUg9DENxYeeI8PH7GE9tUV58DPOR7yN5/Cbe/CdRc73pqgqLYh7SVJIidpG2DnYp5wF2sXRUq0rM2T5VZwdRpMiTA5o7ZwgTZp+/zPG9bUZzTTi8PR4Q3iqolOC1WYf3jAc8fecluq/8COW734n4F6+jvu1LqHYfMR/jvnYXLAPbD6k3vwUpHEwZfMVrHAzeCwNIBjdxkwj/5Rnh+gRVSsrE1XpxsybPHIpcG7R0u1NGoz6LzKXnZGz0xuSFjSUrrocPz8BHLN/XstQeHHYcnqtqlJLYTq6zMpQms3luxv3pAFtWuFbBVrBgnvq03PT8s/EGMbApBXI6onEDmrCFsVjQlNBUEkM0CEu7/BWJS9MYRKnPLNUdwePU5mKgloZhNpZVYlklnp8gHR2eZVoV/hv2tl+WaFkrQVZL9k/XtSmZqMlyl6YW3D3cpj3tUuU2o7M+O5mDdzzCff5nMR7dpNq8CEoXVjt7AXUGTWFTxy5l5FHnFlVm43RiRKDDjGQ/R6gcmSZUiYtnl8SjLvFCq3zyeYiqBYYhKachRexhLsmSVWFhuzmmqc2zRKPvMd3ujEbpz6VhNOdeJlll4j0kxc85GjAetJxvVfcfGN504R8XDl0vXRLkFNudyfkMN62s81Q1xyx1WI/KOUk92naOIysq5eNKPcMP7YJBe8b+aEjViPPTIegTohR6JhdYBftzLeGTRsNi6W1vL3er5ZI85i65AZasuDUb0vFSRNTCiBVdL+biEzeYHqwzm3S49M4XyVNXEwQzl0pJxpmHtZQdGkbD8aJDYGsjINvJufvpZ1i/cIjpZ6haMnjiNrNbO7Q2z7Rl8Emf6bhHlnrYToHrpZSnQ6JFiJVpctHeyQaPiDu4rRhVS6IowD3pkSa+nu196kmcICFN9GzTa0e4rZg88qlrSb8/0adZP0EpPdcLu3NODjcYL7Rj4GZ3rEmLXkKnPdeOYw8Bo1mXpLQwhSLOXGaZR7WvteyeWTLNPDyz1MVfKI6P11GNYBSHqMZguzPBtgusZUrhNA3Icpei/K0QH8fNUYmg7SU4SxMndzilPGtT3fVIpy287oLprUucLjpYomZ/PODx3/8pmvc9QfWWH8CxBwD4wTXiP/zncO58ks3geS0vLgzyg75OdFsWjzq2cPfvUl/Ruebm0V3KzWep+09j3XmFyeeuMzvrcTrp89G9Czw3lrRtg0XZsO4aNMBzs4y42uG1yYCdz7+NrX82Ico+wNbzp8uNYkhVPcv2Nz+PUf3Wzfc/Rvrzg2vET34bwd1/iLp2DTGfol49Jj8Y0n/0LtMbu5ye6et0gwQ57SJFw3owZfPSPkd3d6hqyfblvYeyFgDcZdSrbResOznHkz5ZYWObJZ6b0dk8w5lrq2VVC8bjHvPCprscDa2vnXHr/gU8N9OWunHA+toZ7uYIKoP6uRHlU99MY9pY8xmGnGIEBeUdD2HppL6jow0cJ+f10Rqz0mJ7yWmwl46blqg1d2BprVss7ZD9doS5NCGrleD24Q65MiiUQVYLjrIdXKmwjKUM7u4VTQBedLgxWiewinOXScesePt7Pof/h/ZBCIwkhtlcG+d0CppakC0CmsZgPm3Tjuf0li6A9naGYUqM2VLFZDQIs8I0a2yz4uxwnc0r97H8nPikT7IIz0N8HDenf/GIOrdIZyFV4hIspZHAMvio4uB0Xduju+lXKGpW+MbDmy78617KOPWxRM2aF/Mbe5d4tDMhtHMmmcenToZsLk0hpNHwSHdM0xh8+qyPAN6zNuK1WYfHulMsUXP7ZHMZbaulc4GTUdQmeW3SUFMrg3uLNq1lxKwla4ZezGHc4ihzKJVBx6r5/97fBaBt1ez4Ke+/fJM49SlKHYhzdzLg+jJgqGkMZnd089uU9fLUaXGpM2GS+iwKh5cOd5mXFs8GEZ3ujNZwAicDgqsHGGaNSm2qyKe1c4KwK4Rb4F4+I7gzJD7pE03bRHHIM7/v04xev0hVmZqFLWucMKGuTJKlH/mtO5c1UW4wwfEz7rx2FcfW0bUne1s4Tk4UB9S11PanXopSkum8xVnUIr1jcak3ouPHqGV785nt+8yTgGxkszE4e/ArBjBFzcYyTbFpDF6ddrnemeMtcxscWbHenpGVum2fljZnqc+V3oiWHzOad7jUn9C/cEgR+dy7eZmrz77M3svXyHIHKWtev3+Rjc6UTnuO5RTMph1ufPpZ1reOdQ66VAi75MLVu1wAvLWpjkq9pGiylPLoN+DiHzl/zvoE/V54BxhAEt/E+xc/yd5H3oIbJJh2SV2ZxL/exnr+JtbaHGMnxP9X/wtqJojvrTM76/HCvcv801t95lUFVMwqgxeMV+nMB2wz4PHQ5cWZ4tfODDyjx7VwnZbVMHrtIqEJl4KSd20e4H42ZRC8QnL9C7+tuQ+AOb6JOhM0R/dgUCAu+iw+NyRLNUFyfe2MJPZZTDp0u3okksQ+n/zs27WBi2gYL9p860NZDXD1bS/xyqfeSpS79FvzpXLDwhT65Pnq80/iu+l5t8c0K55aP+T+dMDh3TY77ZnOi49adMMFGxsnxIsQERSIXRdME+8L/5765QWNUwEGd//DO7j8f/gsJx97klduXCMqHEolCK2CjpNjiZrHOhEbfswgWNDtzJBWiSgsFvMW1aTL2uYJtpdzdrjOK4c7PD/p4kvFXmIyLRpsYdC1DZ6f6OtcdwXHmSI0Ba7U5pEHaU3TNOz4JmtOw7+9t8Mf+vQel7YP6G0rvJ0Ied2EIMS0T7BuZ9hhwtrbX6GOPNKTHuHVA04/8gitnVPcqwl2f878zjaHe9vMU5/1rt4oj/Y2CVox3Wt7DAYLVGpRjNrk0xbeeyPKFw3MMMXrRLz63JNcf+J1itilylxa/Rm9zGWRBFS1fGgeH7+FBtSDPnisjvwPCm+68K8Fi3NSyOG8u2TrC3yrYKs1R2EQVyaOUDiy5jgO2Q4iArPS3vFL0pUlalxbn6zbXsLxkjEblzaeVZK8Ed3qFrx75x7juMU8d0gri2mkA1+yWlAog9BU9O2aqoE1p2A7nHPjeIu3P/oqANG8xW5nwtm9LYJWxNrOEVVhMZ109cWbNT76ZD/PtLROGg0fvHJDB+tkDmXqsvmW15CDEhwTYTaYdgFFSfmqSTX3cbYr7LWZnjUunfpGr1+kyG06a2OCnVOm0y7JPMRvR3S3TikTF9sp8MIYIRXH97bpdmfINySJlUldSQI/IV1+YAM/oaok5bLNXirJ2vopdWWyWISM4ta5XEd+2QjgQaPXmTOdt84Jk0/2xrjL9VArg2G4ICvt8/HPjVmXx3tj3dbPdEejaQzK2KNRgk57zt7L186fe5br2Nlef3w+uzVlzda1e5SJSz4PaV/bo4p8vIHmDaAMspMe49cuYvsZrUv/nuJ7jrGf+nNf8xr84BpN2dAaTDm4s8vxtM849Xl8c5/BcIxzmGDdyMAIOb55iUUUsMg8Xpt1uNqCV2aCW+oUhWKn1m5xCSW3YzhgRCkKFIrbMXiNz1SMuFhfAnwuLtoMj4e0bh3jf/4XSJ6a0fgbXzMgCMB57E8RDx/D++c/TT2yEVVC75F7VLFLMQ+JJm0WScBGkGCaFUVhczQe0HZTBrLGknrT+LDw+Y+/R4+eHD2eyiuLrhfjODlCKDrhgr2zdXw7x7Nz0sIh9JLz7p4UilHmsdsb0R+O8Acz6sqkOOzhWCOMNZfyJQVo6V6dW2w+eYvxp64zn7Zx7YK0sgjtnL1Im/RcbE8plWQYzmm3FlhOQTxrMZu3iXOXrLTZHw25unXIywe7vDRr40vFSSaJS8hrWJQ1rpQ4wsAUBr4Jj7UNXKnIaoN5abDpSl6IF1Sxy7wwmVU17YNtHo9bXJ90uSZrxJnC6s0BF7c3Jx11seMFGA1WkFKMOjSNYLG/RjZuY/kZs7O+3uzbOQfjIU9ev4FhKOxAj6Lygz6IhjL2iGct7N9MMGRNPm0Rz1qsr50RTdokkY5tBn3YMUWNLbUUeoVvXLzpyqCWgTdNY2gnvmXgxRuymboxyGqtMVXLv/fcFFdWKLQ//5XWYplH3+DZhWb0o4l8nllyluownFoJFrlL4GQ6eKayGOUOCliTNb5UCEO3/6XR4BoNLavAsUpYMv6LXBubtIIYaymTKwobKRTttm4lG7JGLT8U9tkavpT0vYTNK/cxRIPpFpitGPORmvqRt6FsPf8TWYyIZljBQut1owZ51cKYVEi7Ym3zRM/tl3POYhpqY5rCoios7UC2nOM3tSTLHNLMpdVe4LZiytzm7LjNxcdvMt7b1BK/wibPdZvSMBoUesyRxj7iy7y8Ayc7jyG9d7LBow92vQAwX4TnGv0asM2arDJJK2t5s12wyDztR2BW7PgxaWWdczdcq2Ay6VJXEiGUfm9KGyH0CaFuhDYd6c21R/qyPSptPZetC4tkfw27lSC9HFWYFPOQbBGwd7CNQcP6rMVa8hr83//j15Hd6nP/5mXujNY4ST3iyqQ+2OWxytT8DLdAOiVVJVlkHidxi3EhGecGUV1RCb1JExjklCRGyhTFsXGHtJqimhJTOPhyQN2UpOTMS5+bixZb8w7hq5folft4s3+NOigov/ntyGf//Ndu+1stjItDZByB16c5rLAHc1Rh6SJZmeTZMteh1oS5sjIpKxPXNmibD2/G/4Zro7Ns+Q9bc6Ss9Mw/9Wn58XnRd+yCtHDIl6xy1RgsMpdFZZKVNnnmYiU5rf6UYuEjT3NMETG99Rjdq/uo0qRKXdzNMcnrFylKrb1PS4vN9pSocChqiVKCrpvgufoekqceeaFHhfUyJ2ReOBhHW5xmHoUycERD1RhUDTQ0SMNglDeoBjwBganwpcIWDZYQgCCrDULDoWwUs6rGFYJX5wZneZej1EPKmqYRrK2f4nXn2K2E1sUjiokm6xqi0UFMjUEShahZh8H2MWnmYJkV7TBCNYLZpINllUi7whosKI475OOONimqdHhPtZTUemFCmTmUhUVeOMTLPA/DaJZk2OacQPwwsTLw+frFmy78ce5wa3nSf2x4gldoMk1RSwwliUqLSgmKWqDQ6X1FbZIrSVqZ1I3BW3bvEqe+dpqzCk4XbbLKpOemdIOIG7MeW35MVNiMCwe5jPCdFjbjwqRjaYOXllVhK3FuO+lJXVDekAXGUUAUB0ySkGu9KZ2NEYuTPot5i3ZnzvY7XzqPvGxyi+yoT28vpusnXNjdJ7y+j3ikS+MHYIbUfnhuq1qrnPzk45iTezTv+27qKsL7uf+J6pGn4XM3ELKmdemQ/KyLKk3yyGN+1sOyKpQS5IlHXVg6DMRLSROfOPapah0t6nRimqnBaN7hiWcPGd3fIlnezNJMz+5Mof0TLFFzOu0ROBlCNFo+92Wt8ZvTHt/+wJcM3J/2WQ+XfgyVqW+klb6RZrUkKRyiQieGdbyUje6YT929ytBLaS/9FPanfWZJgGVqDXfLj6mWG0dpKJ0o1koQdoHlZ9SHa6jSxGrFGInL3Zce4eJjOna4zi3isy6LeYu9aY9SCSZJwN2DbT74b34Y4w//1Fev5/mLHL3wCP/h9jUWle5UmKJhlLfoOBn9/gR3c0RTmvhBQjUesp/4TAuDV+KEuaGNewSCqZhTUpAbKbEascgPKKtTAEw5wJcDQmOIh0OhGl5fmFxb6u6Dg20eG7/K/u0LbL2wT+fJv0j9rq+29jWiPZL3/gmQHka0R/XLv0E4WGBIdV7w40STbYXQpknT1GdeOARFSW85mnkYaLkpnpsti5zBYDhmPm0zWbQ5jUOuOxn99gzbLjBEQ17YTOOQSgkqJdiPW2S1dnsMZvpwsfHELeKjAfmoQ6ME07M+vcfuYghNzmyWAU9K6YTN2ZK8t9Ga6aRIJel6EUIo8twhTj0qJXUyp5Las6MxuDXrkilBYCpyZejsJxoMDDqWwV5WEEoTaRi4skEuQzxdoZB2w6SQbLs246KmVIodX/DZ+YKX8pKbizZwDctoeCIO2F0/Zu3KHu7bIua/3KJMHZwwocrsc3Jkknq0Um0DHgYxfjvCCxJev3tZh4Z5GcaaiZkmTO/skMYellNgtROSsy5OmOKtTZje2cEQCqU08XoWhzhL8zTgoSV3rvBfB958Ol/m0V/u6O9N+/S9hIOohS8l6+GCb7pygzj12Zv2OEh96sZgPw7xzYrArEhqyWtH29SN0IlrZsVaOGc6XmMvajPLXb710Zd5/u4VFpXeRNRK0HKycxOgx3tjXpn0mZYSy2houRXTwiJbpuCZomYvbp3nUdfKQAiFM5wS/DGH3pVHcZ97BeO7/h+kd34e99c+Qn0Cwqp56x/8GHKjoLl2hbr7TrKNx8Ewoam+wuZVCkfnqW/qr2szJPuO/xZzdoC9MaVObRZ3t7h/+yIXr93BCVNtSZo5ZKlL0wjKwmK+aHHt6RPqymQ67XC86LBdHTG6s01ZWKx3J+z/8rNIs2KtO8GJQgaDMTfuX8QxSy6tnZzfaA2joV6+BrcPd3CtAkvWPLN+9ICWyVfCFIpF5mIYmp0/DOdsWSVFYbPIPPbnHTpORlLaHCcharS27OQYuiBP++y2p0wzH1kq1sM5iyTQQT9SS5LaYcRrn3uay4/eov3kXQDs/pxyFlKlLrvX7/CFz7xNyzCX6ZC/sX+RmwubUd7QNG3+T1dHvPa/Psmj8V/A+N7/51dcg/8L/zO/+ur38+rc4ixXlKqha0v++OVTrKUTo7mececj72TvZIOXxgM+O7J4IT/DwWYh5oyqWyTFCapJ8WwtR0vyO1/x/1T1CNd4G7v1DgPTwRYGHavhC+MuuYK0Npi9cIVv2Uh4LA65lrqsr78GH/jK1/wN2R9AXC/jaEuBWhZA26zY2j7SnhBRgC0rnrx4h5fuXSYutfLhYeFw3mHQnVDXkv3R2lL+Zi7NmhouPHKbV770JEVlEjoZF6/eZfTCU8SlzWnucJJZ7Hg6dTIMYnrbJ2TjNo0SFLFHldlceOY1VG4THfU52N/mzs3LeG5GGERIWXFn1uVjt6/z1OCUnbVTWr0pH/3C29lpT6mVYJrpgJ7bUUCpdLt+28vYCSLmhcMod7iXWdyJFNIwCE2DoduQ1Rahpav9jbnk0XZFXEkUIA24EuTs+gbHqc1xJpmXkBo5mZFwpAw+ddblnYOSw0V7mVZYkf5SG2lV5JnD3sE2V6/fItw9oZWekE3anOxt4XspgwtHuOsTspMe/uEWpqhZzFtY//4yXm+O7ae4rRjTy4j21rG8nHjS5ujuDrZdMJ116PcnbF/YJ5q2OTxbw1u6eb4xcnuoeOAz/hUeFN504fesilHmUjcGrtR2toFZElcWr08G7C4JXNJo2PFjvfP3Y8bLU8cbrNid1hh/ySotSmtp+aslgB956Rmk0TAtTKJKMC4sNlNftwFrcV70bdHQsSradsH9xMURiqyW5MvQjZNIz/YfvXqb9vYp1lsk+fY1VLBBefkxqoNfRuYLmquXkNzF/s4d5JHS9pt+SO13cO5/kaq7gdp6/2/7ukjhIDY/gHVxAM+AMX+Rzs3/QOsjn8K6kNDEDfbegGLhow7WsR0d87n11E2qyOPkZI0o8/jAuz/L5GiI344IunMtOcscqsI6j2I9OlmnHyzwXN3OH0+7hH6C62Wa2bt+xv693XP3rnkS/LbP/XeK0M6xlyzopLB57XSDi90Js9TjIG5paWRt0nJyBn5MXplcGJ6ek7uqWnLrdPPcu2GaBnS9mAtX7mH5GUXscefmZR55+hXsVkJ+0KeIPaRbML63tbQ/PuMoajFf5gFYQvFLBwbv6DdsezVlYzDKPK41BuXrNsZz/wvWW/4S2c2fw/6Vf8/xR7V9735as69mdBufd7Tg1rzDY90xaeIx++xldt/9Avd+YQsBDB2DOq84lVPsxmZDPspdMcVoBGlxQNOUaOpgAxgYhoMwHGb1ER1jwACHwIS7MVwOoVQGWQ2OhE+c+eynu+zPu7wr9uj9+v+E9WPv/YqCf44ywvmbf4jc7uE/93Nc/d8+z+jFa8wmHXprI9rDCXdfv8LB6TrzwmFe2Hx6/yLveCirAbbb2kgIoOWkTKMWnlXQ8yNaTsrHPvFebKEthIva5N6tS3S8mFHq48uadw4iOm7GoD1DKcHBjUt0elqKViut0c8WAXnmMJ11mKf+uSfER197guPU4S2DMb+0t86W77NR6QjnoR9TK0FcOIwyLQm2jIaTQjLKDQQur80DxoVBXEHdNHQsQWiBJWBRGkzKEilMerZB11PciU0WZYMjDDwTpoWLMDShuWs3TAqDJ+0BdTPAErDuGtxc2Gwu/QXuH25x/cptysqluzFi7eoeR69fpn1tD5VbNEqwceEAsbTBLiYtTD/j2fd/lvneBoasaV8+YPLaJUynIJqHTG9dpNPR8uX25plODbyxi2lVhL0Zlp+RRgHdcIHvpRiiwYoeNrlvha9nvOnC37YzosImqyUCPdc9TkL99TLGNqslptHQNOAuveXr5Q0hrkzcUreFi8JmNNdmJe6SzFYqiSMUtyIHR0DPrmkamJcWrqzxZU2pBKNc4knNKdBcAgNL6B25YcCV3hm+l9JqRfj9Gd7bJ5TX3qkv9uwVRJHh3H8ecXas5/ODNkaRnetu39il5pffC04P+Sby1y2rc/73oP0U8ZUS/533ITcwiLF6C9JRB9spznX96WmXRgl6vSntakGROueeANIu9WknczCtiqapAQvbLGm3FgipqCt5rqMngXI5MxVCUVUmSolzF7sHDccqmacedSMwhcKzKorKPNfbB8v3tLN0CRvNOxxP+tp4yKwI3IxR5hKYJqahqBodhrQZ+Vh+htuOWBuOqAuL0e0dHYrSn1DMQtrDCe31EdIuGfoJdl4TlTaj3GHTtQjMGlM0VLVBucyln7x2ieFHP0+8+0nEP/gc915+B3cPt5kUJn1bQtHBlwLVNAzdDOfLpE4y1IoTz6wITZunrQ2Oiy6WIVE05O4zLOoTkvKMhpqOc5FJegPH6uGbusWfNBPqpiKqK6aFTd00TAvBvGyYlRWmYbDmmMwLwUHic2P/Av7pOu/+x/+K9E/meF+mTgDOO1Bpto+RxBhS0do8I1nG1AqrotebMt3bZStYMPTkuefGw4BhNKSZgxAK18mJ5y62rGgwyEqLtp0xyTyS0l6aT9WcpT6FErhSuztKQ3N7ytKiKLU+vtPVxb0sLKrSZL5ocbbsDha1Jo5WSuDKhnnucDkocc2KNHOpT4dIQzHPPKaFw7y0KJXBvNSdsdA0WFSazJvVBuOiJpSCoQehqXk0ljB4XFqkNRQKpoUgqaBeUmoswTLZD5wlz2ZuaCXAtGgolf491cBprjtkLTunrsxzKbAqTXobp5hbOU2lR12mW6BqQR75mHZJ67F71HOf1tYZVWYT3d8giXw2d49xuwstaW7FeqToZUg/J0s93CBB1ZJk1GEy6VJUFlIo/CBhuDZ6aOtBY2Xg8/WMN134NSNUYaNbvaaoSWtJ1RgEsqZc+uNntSRfzt/nhbMkokFWC+LKIspdFpnLXtSma+d03Iyy1ulcfSfnS1OXwFVsujnz0iJXBo6s8WXForIY5Q2e1OTBefkGg13fTAKrYHf3gPbO8W+Ze1y6gnJDzOkxYj45104398ZQGRiPdxF3b8EiB8vgDcqL2no/jcpQoy+SdK7TNBWUC5DeV1hzgp77f7kPe9B9K+kTt3Bf/iS4OXKtwLjZIM1lCE1mMzpeo9Od0dk4Q0jF5GAd06yQSxOOKtft2a47RdViOfNL8IJUk3YqHZhUlCZVpXPQVSNo+TFZ7pwbED0MmEKbK9WNoONkdN2ErLQolzfytp2R1yamqM9NhfaiNgIIrIJeLZkVNpXSGwdz6ale5jZNJZFBStidk8xDjo42mCQBtl0g7YrO43eRmzVqDDuvntKKQ/ZnXe7GHutuQ1Eby4AgvRZP4xbW3jaqFmwGP80XP/Ft3BqvsRcH3E9Meja0LIkBVAoGboIp698iUaYWoZ/QtXMGjs3AgVuRS6GgqEFk19kTHWZOD8twuFJf4mXPosWArurRN3zuGDYCQdZUzEtJ15ZEZcOiqlk0OWVTsys75MrgKDOxpz2k0eD8u2/iyfnHSP7Pztc++QNGkYNocHdP8Y+GNLWgTFytT5c13aVxT/MQ75mVkjS5JqO9IUctam0Wk1cmG60ZJ4kOy6obA0coXlv4DOyKtqUjc4vKpFombRqGYrxoY5kVpqlJglUtGcctTpOA09zhMPEplYFv1oRWybRwuN7REcRZaTOOW8t1apEuCbxRKZmWBtKArt1QKANP6gJdKoVhiuUcX3cnpQEX/YJX5g6jXP+caWj+kiMhMBsEEFr1cr1pmZ9lNEyBrFYsSknPhnlpYho6Ha+qTOxuhCpM8mmLYHMEbQ/hlHrj7+YYhUVd1NpnYlNSTQXO9hnirM3RrSf0iKsbgdEQZDbSLqmP18gW2i8hS7VqSFUmaewxT33yykI1evzZ3Tp9eAtiha97vOnC/+LZOg3QtkoGnr7Rby1baQAKg8CsSCuTsjHIld4UpEtTljVXp+DtRS3qxqBaWmLGpU213BR4ZsUH1+fnznxpLfFMRVqZTHKbpBZMixrXM8lqOMvhLb0KXyrW3JTLmweEa2O8J0YQOpAVFL/coJIp4tIJxpqDOioR17oYLjRFA5MJxUsOi/sXtQ93J8LbvoH8N3+HKnFRtSB437+ivvYYRpZgVCXxkxOCtQ8CuuiXp5/A2vxKGp0aPkW9fg8RtmmEoNs74t4/eZRGGdhOcZ6ghxKYrYS1R+9y9vpFhFVRZTb37++ch4zUtck0Dun3J2SJS547lJVJJ1yQF5rYZVsF67uHCKsiGXc0eTB4OMEsZW2y1Z5RVCZx4eAAh7F+XwOz1DbImcnerM/dyYDj1Ofp4QmuVZBXFnenfcpl1HDfyRj4MU8+9TLB7gmGWVOMOjz/4pM8cukOa8MRXpRx72iL973rReQu0BtibFlc2LvBy595lpPU5+WZ4LPlXZzG5apY45mu4F5s40qPopbcmQz5wq9/gCthyhfGPl9aJCyMGc84QxwJoQlDt+TlaZ+n+yOK0mJ0sE6R6s3r5f4Zu50Jvpfywv4FbkYhJ5mkbVus5VvUagspIPAM/OQtVE1DbTRkTc11Y5schSMEbUvyaLvmdiS5GEpC0+PXTxWWgJNMbwbuRDaqgaPsKseLDu8ffwrvj/wqxh/8m1/xPnjuDvzBv0mz+78i927ROT3k7PWLzI7bBH7CsDPlbNZlnAZMsv905+p3iqIy8e2cpjFIM5ft4Rmfv3MV01C0nJyP3b/MbhCz5WtC5HES8o7+lGTJAWj5MYbR4NglShlLt8Gaj996hMCsuNCechqH3Ji3mZaSeWlwnDZsegZRaeGZ8O1bY2plnCuBRnFIy6lxzYqOk9FyM/7Ja5cQBmQ1/z/2/uzHsiTP78Q+Znb2c/fru0d4rBm5Z9bW1dXdxW52z3BTD0DM6EEY6VUA51nQg6AXvQjQH6AHSdCDAA0IQZwBQYLiUGQPu5vsqu7aurIqKyuXyFjdw3e/+z37sWN6sBNe1UNJk6QqBgV1GpCIRMLTw/3ec83s9/t9v58vSS04iDX/7UWJRBBJh7IxHCaC2JHXX6eNy1lmmFa2Uxcph27r44+dhnkpueHWHGcel7nkjX7JYeIx8sGXisui5kFPMPIqOm6FKxvSJGL2+QFBf4XfTzj/5C6b1SEqLIgPznA2chhHxGEFjoPx9nAnzxGbPp47J3yYURY+s09vka1jzi832dm6oNtfIFRDlXuEUcbjZ7fY27ygO1qwVXqcz0Z8fLlNPBvxe4Mlg1f2RGDJfb/qGf+XBf+vbH3hg/+gu8R3amotWRa+nc8Kc02HK7ViUgSWHOfYG/is8AhUQ88rudFZWmV67bKqPK4ql7JR6PIXVXvoVFSlz7TwmZd2rv9yFY1gXgq6jkE3MAwMXx9bb/JOmLI/mBJ3E9JJn7gzw5znpI92CW9e0FQOsw/uky47CNmwNX/C+sVdTg/3+dnJTSSGvE2NC5Tm8arDG/3FdRrh1y4/5bPHt7i1e8Jw74Kjzx/x4Nv/d9x7BWJ/H3njgW27Hv9bRKOpB7eJvveP4WwOEoQvKR92CTsJ0WiB20tJ/tLacCYnW9SHexgj6PWXrC+H+J2Ud//m98mv+qyuhgjRsDOakKUhQZjTH9oW6Hw6YDCYo2tFWXo8+/zudWiQfIUsblfVTJMOyzKgaiTb3QWhU7Mdr7i9f2yphG7FxarHpLQHzl+c7uFKQ98ruduf0fFKbg4m7O2fMHzzGenRNunJJmXmU6QhX/v6B2TzHko2bO4k3HzwlPOfPmA0PyF8cAG393H7ayptq8i9EObVGWNxg6ku+HgRcLcjmBUex2nAWSZ5uM75b6YpC3lELtfEDFnXI5aVoXAVY1/xoLfms9mIz+dDNoKcr8vPWafRdfTxxeU2wyDjZqMIpI+ShsZ4nGYN00IzLQV9R7HWDUMluWXTVfn6eE2galaVx6zwudOBXAsWpWQnUBhsJRkpyVYgOMsMs1LyfNVj65PX+No7f0R29t9aYel/9/14978gvfcc72f/OzrDBbpWXE7HlG14U9/PeGPv6JU9Dy+WA5aVS98ruTOc8NnxDXY6S+pGUdQOv7N/yDyLOU9jlqXH2M85a0cPxq2YLPu8dvsZJ6c7+F7JzXvPOX12g9/tz1msurxYDK9Hhp409Fz72vnSEIf2AJ4VAUoYDmdjvHZPejwfETo1kzzk5GqDpLYVu2gnYMepYsv1r1v3VWMYepKy/eiECp6tJb4y3HJ9IgeOEs1XRzWL0uGjmWToC/7JCwVoXGm4yB2+Omptqb7gIJZc5L/YxyTw50/v81vmMd4qJssDG6O7PUP2G0xukdDd1w9B5tCAUIbibISalIDL+M4L6izg+af3qWvFqL8gzwLm8wGuU1m8eHfN3uYFYZwhlWbrxikbuxfsnG1StYyQV7u+BPj8Oq8vbuerPIZRQlG5rf3GwZeayC3xHM2L2QiAqpE0tcu6clHSVqzGCJLK42jd5XZ3ga8s1Cdusa7L0m6GwyAjqR0WlcO0FeCMPEPeCNLatis9Jeh5MPI0PbfiKg9IKpe88tC1QghJ86IEJG4/ITvaomwhF7PZgItlH107nF1ucrIYMCl8zjOP2oArDYEyXOYKJSzQA+A7/+bbGGDrcovhZyVZ7TBZ9rmxdcHW7Rd0/safwcc/pD64T7n7Fk7/TQgCyuOetaD110i/IhotrG//YkhvsOTxs1v0ooQwKChKl+WiZ7267Yz5pcBHORrHrXl4eAvfqXBbL/zO7rkF+lSB5dXn4S/55yuG8tV8UGZph0keXbspPr3cIdWKqAhYLPpsbFxxOR9ez1aNERymDht+Q8+13IZvvP4J0WBlkceHO+jKYTXtt1GtDucXm4yHs+s276Yw9LamuL0UU4J4cky9HjPsLhnPh/xsbjsfl+YZKzklq2/SL4ZcFg6NgUw3PJQPmVXPqXSCkj6xN6RoGgauYuzDwKt5uo7JtUQKw7pWuM/uktYuvtT4Ts1pGuO3FtNVrSgbwaw05Nq+V76U9D3BhlSM/YbbccbLK5gjNbvxiufriEmhmJewqgxl8xJKZaiN4TRr2AokW4Gm6760nSrCP/2v0Ld+RH7vW6jVKcG9//z6PfF+/l8jHI0/WNFrtR3rJKZYDEkrj9PJBm+8kqcBdjor9jDXl81bGxfM19ZdM4zWJEXAeRpTNZKua+mODeC3nvJ5HvL8xQ0C16r6ab/PS4RzxytZlx49tyapPaaFbdcXjSB27Nc+XIW8P1yxLH3qli/SAFnttDokCJRt0VcNpDVc5DVj36FqDGVjBZmutJV+phtWFSgh6LrW5reqYOQpFqXkshAs6hpHOjhCIITAFVZvVDYQqQZf2jHnopLMCklS+8SOy05gmQd+i+sd7lwx+9k9gt4aJ7bFTD21djvTCBte1fr+da0o05DpZMg8iYm8AqVq1kmPdR7itXtHGKUMd65wgoJGKy6f79MdzmkaSZqHrI5jbr6i5+HL9eu//j0OfrsB140V1EhVX0NkJHaT7Djaiu7a9n+3DZYBWJY+61rhOZrQLSm0Q9+3bO5aSya0XvVWwZ9rwbxs8KQkqe0HEWDoSTxp8KRl9EthSLXDugjIswApG7Ln27hd2+ZeX45YLbrkRUBW+syLkOPzbQ4XQyaFT6klZ7ndSAR2w7RiHpeuaxDAP50fc2C2ARB4jDyHZ8keB9MN3rrY5jfkD5Guxv/7c4h28b0x1dYNpP8ROg2oFh3c4Qq3k5FfDciWHZRbUbSUQtcrcdyK+bxPVTu4bWiMbuEsAFJp0sptkwtt8I10aqokJG9JZC/BN6V2Wl/wqyH3ZbUVcr6c105Lj1JLlAgJl32bApZ0WLc/rzb2lX250a8L33Y+4gyd+5YvXjtMZsMW8evy+WLI+5VH06a39fpL4q0pMiysyzIXCKUZjOZsT8dI0ScQXS6KT8nVAtf1OcwiDAZP2AtKwZq8OgMkjgoJTUSDab3bkNSKRysHgRVuxY7ElV1KLem6Nb02ZEpCG+pk3SSNASEEgRCMfcnIN2wFFVtBzs3eHCUbLpOutXMpzbRUHCWGSVWwJKMWms2mhyskEljXDW/60HNrHNmQlj7Js12csxL36SHRZ5+Dp0jGbxL03qQsrwjOjtGNRLo1XpzR0Yqi8AnckkoHTF+huG9vZNHQeeGTlT79wYLpqoduMyWyyoqCA6WJnQptpHX5tHtDXjucrXrs9+dUlUs271372oUw9MPUuj9K22FxJQTK/qmkad+7X4wPq0ZisFqksqWGKmHou7aIaF5W+MbO8SusAE8J+2euDalukEDXUbjS/vdSG7ZCwWUhmBaGyjTUjSF2FJESeKr9ugYiZQuJ2lgBYIOg1IbGSEaebDkeOWGUId2axck2ZRoQD5c0WpJdDmm0dTQA1xV6XXgsFz0myz6hW1pAkfxF51U30iJ5Oxlef23BPkufNAtpGsEqjVlkEVm797zK9Stv9X+5fmXr3+tkOFzYnPSeW/L61hlniwFZ7ZJUHiOvsshepem7JSM/J3SsfSetXRalx3ujCZFnb7ldv2B3fGXDZnyrpD5JOlSNvUz40pKzLvIGv1XtH1YrdsM+s1LgS4dAVQy9glw7pJXLZD6k0rbt7Tg1xkik1HzatvMjr+TNnWNeTDdIaoekVsxLZUU7yn5gLwvNhU6oRE1sAmLhEhLxVBxzrh+SFCd49ZC96l12Zzu8PttjVXybW6Mr3vvadxD3jiiDHaqDb+L/7ZLwk48oH3YxlaLRCuFopNQsp0Pu3zy0Hmy/pLtzxfqjmFUa4yjNRiMo1hF5FqAbievUPNg5odNbXYdvnL7YIykCZDuSCN2SO7cOWc57FC1Z8VWsjleQVQ5CwDDI2BOGPz7ZoTKC2In44c/eJdWSsV8z8Cqmpcu3txZoI8hqh0fLAdEH79ELUzY3rxjePebH/+ZbXCYdjtOYp2ufyxyerG+yE9bc667xw5zJ0xtsxznydoPZvYH/+BE9I9mb93hjsskPVntM1TMAcrPmA77HO3wTF2gwvGu+xnfVC0b+PQ6a14iNh68kJ3nB07yGBSgUM7FgYHq8EUc86NUsKx9d2gvBVpCzHa/wnJpKKy6SLoHqcZhYQeGDbk3XrbnXn7M7nDDemBL21mwe7zBd9Dld9rnKBR/oJyzlBaVesy5O+E3/P2MoAkKl6HuCrlNTN5KLPOAqDzj707/BezcO2bl1TDc8pP57v4NcHlKk5wAUb/0G4i+/T7WKbUxr5nMxH7LZnzPuLTiZbrySZwFg++CEx5/eZ5HFOFJzdrZN3Sgmacx6NuZmd8Gd3gLdWOGlNoahX1iwVyMZBAUdr+DRZJNmssluvObejSOkbGwcrhHs7pzx8x9/gy2/4u3+kqJRtrWfRCwrxXuDjOdJyF5YsBWmuLLhcN1FtVTRpJZshzXfv1IoIRj7gpHvcpQ05K2jp+soygYmVUEDvNv7hS7CAK4UrR0Q8qbBFdLqOpTgVqzZ8CuOUp9cCxoEQjQ2PlfBVlAStBeRJ2ufjRe3uDe+YGvrksPP7rKxOcGLMhotWVyOSNLIdrta0fTuzhlBnKEcTWMEt/ZOGN86oU4Dpqeb3Lz3nOhki7zwCYOc3m8fkf5kk8XxFlkasbl1yXc/eveaodJp9+Ev11/P9YUP/rKRnGY+Y7/i9dEVPzvb57XxJb5TYYzgRiP506NbBMreUOdFgK9qLrKIZeUSOzXLMuA46dJ3S+6ML/lnn77Fa70l4yhhHK95tOwz8Cpk5bKqJNOqZOR6xK4gUIKx36frWp/2VlDiKY0QcJQGHKYBh+suSS0JzvbYCHIGXsGLxA5Zi0ZSNYL6fIuNoOTxKmRZCWLHsKoaHmUpPg6bno+jJVuyT9U0pKZmT/b44+rPyMtTQJOXCU/KF7xwdphmv8X+ZIefTIfs/9ELxs4/Rt/5iOisTUMbDvC+UdM8K5n89AGuWyOdmrPJmHfe+zll5rOe93jy/a/Rj9f04jVhlKFzz44GjCBPA/I84HIxYJ1GdOOEMMoI/IJed0V/c4rXSckXXY6f37hOG3tVc/609LjRn3Ox7vLHJ9vshVWrjLYH+7ujGevSQwg7nikbyfN1zGHqUDfwVr/kdNW77gxlacQkjekHOevKo2p8zoqSi0KwrFxy3ePsz3+H/+TdnyKDEqSHPD/B/M/+jwT/5T+g83TNjXjNHXdIWP0+5/KCq+Y52hQsZcKRnDCrj7gp3+Gu/9vsNhtsej5Dz16MNo0iULAdaM5zxXFqxwYG+HAW4UtDoKyA1WAvO7GfM+isGHWX/P43f8Dh49ucLQftV8DucEIY5EwnQw4/e53ILTlbd/lo3mNdGxwcGlOjTc1B9NvMzRK3sR/HVWpwhEvkwNjX3OkkhG5NEOTEB2eYv2EZBHLnd2hO/g3Bz74DQO2XZIsOyaLLcOeKr//OD5ke7rCY9xnFrw7Z+6ff+yZxC4ZZFhEfXm3x9viKzXhNv7XdxX5x3eHqOAV9H4o2QGgYrblqRb+etGOs+byPlIZ+f0F3NOfxZ/f59s1nnC/7nKYdBl7B45Y+13M1J5nP7TjjNPM5y312goKBV1K10duh0uzGCcb0WVbqutof+ba7mGsrrHzQkywrj6KxHYLLQrAsTTsKBO0KOi5sBHZ88KfLC/5mb4tFKVmUPluBBhcucsVRYp+ryDF8uvDQBvqu4SCueJHEZPUeB1nE7viK+XRAcbZFVTvXKYW+X+A2kiQP+fDRa9zePKfbXRPHKZdXY4I4RaoG1y8p1iF17bB/54j41hk//y+/TRgUhFFKt7+grlx+552fsZz3WKy7TJNXw/j4K+vLiv/Xdn3hg/+6LacV8zxiUvio6QaBqnHaWfRGUFjVqnY4zXze3DzHVQ3LwreVrdJIDLWRfHq5w8iryGvHtn69kqqxQT+plhgDtyKfpDacZHZutR86lBpyIZmXLi/WPZsPAJRactkIlpUdBVwWHZSwDOx1bWd0st2UG34h6JmXNnwj0tYHXzUGiSSQtu1aaYknJZveAy6BvPxFvGlZn3HsfMwPp1u81VOcHu0Tfm9JcPIR5u1d+PjEkvXchuJ4g8nVmMAviLtrBp0Vdelapnbl0osSRqMZqg1ZOfz0Hlt755ye7Nh2Z2/JZn/OKo1tS1dpev2lBc1cjpDTAZ5X0u2syfKAsvLwX9GtfhyvW59zw9i3VMWxrxm4Nb1W3f1yHNEgcGRD7Gg8aQ/+pJYcpxENgnXp4ywG3BpdsUjjdoQAr3c9nLYFPykk68pjNhsQfnSX/voU7zcV4h/+F5RPuhS5z6wI8BUEtUPH9CjUFplZ8MJ8SlWnaFPxwnyCLzr4IoASrkrB/Y7P2DcE0lA3gv2wRgnn2q/9+arCAJFSDD1FPLSjql5njR8UrFYdvDjj4N4z9koXLyzIVzFl4dEdzem9/Zzmn3/7OpRqM05o2CVKDzjJd7hQc1Tj0DMRCjuXHroOL9Ka3dDBlZKn65hNv+Tmos/mpE90dYZczsi7PwIvprr7Ju6zzyguhtAI/KBAVw7VOiRLIsrSe6Viz6erLl/fOrfz5tzwhldej5wABlHKIovwnYrQLZkkHSKvxFHWFtwYwYfTMb407EYpsZ/z09MbBKomnmzQPc7Z6M85vNoiqTwiVVM1irFfolv2visNZ7nf7h2Cs9xCw4qWwd91a16sO2gD80owyQ0PetYaDIKqgUWT872JRyQVHSU5TgVFY/CUQBlDoe3h/zzRREoy9gVfCbaYFIaeK9j0G3ZCG6LjKY+sljjS8HDpMvDM9cjgydpj7GuGntUiTBYDHPmLQ3KdRWyPr0iz0I4E3JLYLSkKizEPwpz7b39G1aYzdoZL0nmX3XuHOFFONe+wc+OU8+MdwihFuprZxSZgbdlxmF5fUL9cfz3XFz74tRGETtP6873rQ1obgWpP0aBFyBYv51KNZaC/XGUbj5tXDhd5wK3Oiqx2yWq31QrQgnmwLTTHCmrypqbGkNSK2BGkGrRxSHXEyKsRWLFQWVs1tCvtvN6VgrFvWJS0+QGWtpVndi5bNVbIo4RAm4aCGl03KCSuBF9JPGkvBDfqm5RuSlnNaExy/Tutiuf8NPqI2/orHM9GdB7fZKNyiL/qo5chdRqg/JJqFeO3Lfq6dK/hJGBzzLu9Fd3tCbpyqCcuy6TDMJtTaoc4yOiNrXq/PrWv4cslhKFpFE2j7Dyv1QQI0VDrV6PcDdySWdqh1Iquo8kbycirGfs5/SBn1bo+lqVv5/zGwnQiZfCkrdCs57lGyYZlGbD9S4FPkTIM/YbG2EtCrgWhY1ikMerMhhbtxJ+x+PwuunRYJzFlI2kMNMbQCPv6uATM6yN0U2LQVDqhlGsc16cStvW9V+9xEDeMvOqaAKgEpFqyrgTHGUzMmnXtAhHz0qJoq9rBMyVxlKIrB6+TErg1KizxR0uqVYQT2Aev211TFh6ea/Pp3153CVRE7Hj08w2OqwRXSCrTkDc1sVFcNAlx1UMKwaJUeNLhfNln48U24cOfIcaKgD/HeD6iaTBBSJ27ZGlEWXiUhcdq3SXJ7ahIvUqXR5uwB/ai7KqGvLax2J7SVFq11X1jP//aoStym9CoHaaJFYC+pHiCBX5NCo/Y0WxqRdTGfwOEjgX1uFJbMR8QOzUXeUiprWpfaUFiJKW2fntPSSaFQ8/VeK3IflXL65GiEoJF5XPKjLtig0AJUm3QxlL6AkfQcy3cx5eCsjHMSxj5gvMMlGfdBblWeLIhUtpqWoC6cVHCOhJyLVjXdm+pjCDwSgLXkjaValDtZyKMMxbLHusiYNhZ4baivbKy4teN28esroYWyDOwUC/l/0IbFA6XRNO+hX2VLnlhu1hxmOEoff39Xtky5hXY+b68rPyq1hc++HOt2G2jPRelh6cahm3utW4FNWWjrufK22HB8XLAaRqRasnIq9BG0HErS9wyrVq38khqt+0IWEFNx7GV3rxU1MYQKxdjDJOyZitwKRvBvIR1JfntLdpqHioj+LyaEhqPoQzY9B18aTeCZdlQNA3bOJzmFb60lbyvbGU3EUtm8hIHn3uNjVkdeIKOYzhM4JYfsy5vMncOKapfHPzG5Jwm32Xpvc/TZZ/ofIe4uyamxDTCYjhrW4Xs3X9ONuuRJSG9rSm6cPGinKCbEI4XuIM1+Zkl3MVBTll6jHoL+uMZvTsnNvQnDdG11QssFz0GgwVuqw6eXmxwPhux0Z/juRXrV9TOq7XiKo1ZV+51UNJGkDGOEjp+zjwLGQQZV1nEWe7hScO6UsSOHdHcH044XfV4sHMCwMOzPT4632Po5wRKsxHYBMbLwsGXhrFf80Z/QVJ5sOqyykJenOzRCVOK0mOWdlDCKrNTShZywqI+JlRDXBlSNxm1XiGES61XTGkonDVjcYNJWeNJw+3ugq3uks+vtq6f0aR2WNc+2TrA8FJxrzhPOtfdoztf+5jkbIxoHRR1FhBszvAPphTPxvz4n/zHbI4mrJOYulE4UvPu3hHzZza8pesqjqaGtSnQNDQ0VFXDXM64KgPytmrejySPlgPkk7s2H+HdR5hHM6plm8j2hx6NllxNRpyv+sRuyZ+f73AQpwz9nLqR/9238Ve23h1f0hjBxbrHadu1GXgl4yDDUSUvFgOb1VD4rAof3dhsgXURMM1CTrOI98dXVG2ITlIE3Ois+el0QKAaRmHKx5c2HMOVNsr3xuiK09mI8yxiUTlsBQVVA6ta4Ek7IslLZVMKDawqxayU3I5zAtXgSo/Plw1v9AX9VhjXdRzKeY+wFeoJYQl8AGPfcKdjXUfgcpjASVFQNh6hkgy8BlcaPl2GbPq1LZLaLmnPg7IR1O3dq+fCpBDshw7dKGH/3nOWF2OMsd2awZ1j0vMRSREwSWNG3SW6kTiOpq4VJ4sN/M9K5qsenlMxbiRhJ2V1uoFQGte3secbB6dkiw7r1hbruhVKabRW5JX3yp6HL9ev//rCB/92mF0HXphVj1yHdNwCbWRLcROcZAGRaui4FY4wnGUh6S958beCjNCtbDqf8vl4NmI3zK7ngxI7UpgUDieZVdrHjuC8qKlMw90o4OlasxPa9v60eon7tS1SVxh84/Jm3EFgD/TK2M7BvIRVU3KRrWxKmpZExiFQDstK0zddaGAizvhI/Iz7xZsoEdFz4StDzXHmsFsOmXj3OKlO/sprI2WXeamZlA5Z5SJdjf63Z7iva8xX30be/U8J/6v/BckPxjh+yaCT0n3/Bc//+devLT2ffvI6/XjNeGNKNFpwd++Szz94myjMmF+NmF5sMN6+JB4scUP7/yw+eQ3HrVjN+iyWPdLS5/6t50ynQy7nw+sc7l/1+vRqm6aF9QyDjEGUMkk6XCRdjlcWX/zJdIPGCHaCko5bcdCfoRtpw2S2Lsifuzw63+U0jfnJLOAyb1i0M+BQwv2uR9nAQVzzznDK73z7e+TLDsN3HyPv9cA05H/hsj4bI5/f5MdXG6xqTSoySpNRNglpdcVW8BY35TtIJJ8Uf0wvuIsrQkLRZ0tv8ol6THT1Gkm9ybuVx2ESMykVq8py9KsG3uvZStMR8Pu7E96/+4jxrRPC184p/s5/QhiNiH76r+D0HHoxk3+2x9l3dknyEGMEJ5dbfP90j8vCIakFzxJNt4XEVA1syqgVnCm6rs2C3ytvkdSaqS64lFe8Wd9kUXqcZtv8dLLBu5+/xo3xFVkecJV0Wf2RT8/LGUQpu705WeXxe/svSAofT2lubF28kmcB4L33PuKzT15HCsOd3oJxZ8npYsiq9FkWPjf6cwadFUdXmzxb9SkayedHHcrGiu4uc9gIYnpeiRSGpPDZ6iy5U3rMS48fX24xK61TZSuwBcQ/+uRNi2Zu7Cjvg6mibAxbgUAb+HCmeLPf8Dyxlsuea6gbOM58BBBIw1t9wdivmZb2UrCqYOB4xI5g4BnGnnVg1I3tRk4KryX0CfYjw92Oz2dLaxF8tpY8Q3IrNnx774SLdZezLGJeOfzP3/s5Pzu5ybL0iN2qxUtnKGn45PQGSRbhSM14PCUcLmm0skE9XsEbu0tuvP6Eq+99g6dXm/a9HE745Pgm4yhh0F0S9dY0WhFvTpm92OHw2QGbGxM8r7x2Vihl48iVskE/J4vBK3serteXM/5f2/WFT4auVzDPojbO0jKu08q7RuUuS59INRx0VnT9gp9NNrjdWbGqPJLKJdWKPI251V1hDExfsuWFIak8ktqh61Y8aXnjW4FhXgpiR3DXscKYtDbshAqv9doGUvIXlw47oUBiSLXgzaiLJ2k56A25lhwVKSuRokWNb2zLK5AOA0+xExp2QsmzdYBbSTomoit8XCWZlDWzEt4bKor2GY7o4Tk7lPUvku+MKTgxSy7yEc9XfUaP7/DezXNA40xPKd1/idPvE71+hnyywepki6P/51cpCp/VuotSNQ8ePGI17ZOnAXWt8EMbDes4NVorysJnOR3S7S8o1hFZGlJWDuk6th2CyNoXk3XMeDxle+/VJPMBfL6KOIhzlJFcZTGb3SVZ5ZJULmW70Tjypa+94fZgyv7OGX5kO0Z5EpFrl0kecpz6HKU1F82KT/R3qJuMyN1kufgWvzUKeK274o1bz4l/4xLv/dswN5i6orj1Lu7+IeP1gtFP/wTxTw3P1g8o1j0KkVO5OQmXSKEYNn12vYDI/CEY6ODjIllTcb+5g5CCw0QwKYZc5a2VSwhCJTiI4bVuxrAdY+wOJ9z8w59g7hzQ9L6Kszijduwz1cwF6x8M+eyz16ytqlGsS4+zNGZeKWal4CzTnOs1h02Bb3zcFhL9ILQi1GVpbWZJ3Vah0iM2u8xL6zH3lHVwfDSzz5qStrIcBxndIOfm3glBN+HTT163OhKtWBUBPz864MEreh5m5xu4qkZgeLHqkVQe8cu5dNviP7zaImshP9YG6jIvHTxpeK1bMS89lDDELihlx1S78Zqq6XGRh/S9mpFXUTaSkyygNpBWEm3s3HzgCVaVjUjyJOxHtJqNhtCxGpPtQNJzbQu+MYKj1OModek6DbuhxpOKTV/gKUPsNAy8qk3hM2RacppZYqi1A9oLRt5ofC0Zeja29zgVfHCxQ6A0gdLst8jhQNXkSlE1koFboaQdgwSqYtRfoFSN61Zksx6zky1mqx6usoVNOrHMkM3SpSw9kiJgvz+zseiVS7rssPnGM6YPD9C1YjSYc3G1wc7WBX6Q4wUFytFcXY0BKGr3Wpf1qpbgV2/nezUepb+e64ur+rW6ruybNqFvXvrETkXo1lzmPgPPwkZscl9Nx7N+/Vw2RNfJbNaXLYHaCMp2Dq2NYFF6ZFrgipehF4JJ0dB1rG0m04auawEZdWPtNau6Ia4UgRLtHM3a8goNZdNwXtQcqxMc4+CbgFpoGmPxqYGy8ZxdpwEUQeZzksN24DItNXlT0wDHqYXASCGITIfA6aObDN3YOb0xJS/EQy7z32QSukySDvnFEKf3Apl/SnB6BLM1ppBgJLpWnF5sEQcZda3w3JLO7iWm+cWcW0hDXTkEobXw6FqxWnfpDufXQUcvl3K0nfGphryw3G4vziiTVxO9aduWEuXU+FKjVMOq8rgsPHIt2fSr6xGAIxtcVVNXDn4jLGHweJ/zNOLTZcizdcOJmXMiH5HnFxhTsNIpJ9Et5uVdaiMJo5T67a9Q3/4DqnIGdQ7hJkW8Rfjxv8bkDoFfsBM2PEsUAolEETpDFA6yBbPsOjGuFHhSYDCkec2m5zKvNImGbd+haGw4S9eRbAR2zLARZoROhaQVLC4F8vwMtV7RjDYILr4LWYrsNnj9hP2tc47Od5jlIRdZyGnmMa8EpaYFvFQoHBQShUKjWdUNgbL6lnmlqUyDQhAqRcf5xUjK6lIkhZZk7Wu9FSfstTjhwcEp/u6U+7mHUA2uU+MtBpwu+/897+p/+MrygE6UUtQuJ0mXUis8Ka+fZSkMvmOZBGFjyZ9KGAvaATpuZTkhjaTUCldqns5HNO2eUDUCVxqqRpLWilzb2XxS2/+/+aXDv2qgNtBXho6rCVSDLxs82UAAfbfCkQ3aCHaMaHVDGiUMSW11Il1H40nDvHQtl1/aWT9AUltNAO3fG0jJwLPuIEumFJzlLmNP0HVr+3tq1R7ylnMyDrLrPImqUXau72iqykVniuWqyyoP2B1OiTsJWRrheiXd/gJdO1bkGuQkaYQxgnQdc/bRfUz7+SorF0dqqtK1mQ1BQbLo2hAtXuqxvkzn++u8vvDBP8lDqnZOKNsEtuM0pAE8pTnOXPajlFXlMclDtsIMJRqKNkFrI8hxpQ3z0Y2g55WWulc7eLJBCcPzxMVXlnbVGKvEf1LPGdcd+o5D1TQktd0cjbGivEgJktpeEmIH5jWUjaFsGmrTcCFmZGZBn01cPAqR0xibix4oLCrXrRn7Jb4MOc6tAC3RFTUGF8mjbE1X+BggND6ujIi8LfLao65nGGqm6aecBu9zswiYFwGnz24wznz8bmrTtuoOunSpc9+mCpYevlPheSVRnCL9iqCX4HQsYrNaRZwe7dMdLpBSI7IArSXK0dYS2Aql/KBAtvnxQZizPt3GaEWVBUzON9l5BQ9N0M6zfanZ6qwQoiGpHU4zl2UFG351DU2RWGjNfNEnywNWacx3z3ZZVZLvz9ccqedoUTHNHiKF3773OUsueJ7c5DwL7Wa29x5y9inRjT8EIFl/hloeUf3JjOOfvM/lYkDX0ThCUFFSNGsGag/X+BRUJLVHx5FsB4a8gUUpUAgiR/C8yGkwPPC6KCkZuIatoGY3yixh0i1JKo+TvIuUDcd/9j5BnBJvzAn/MKP+wRLnXoW5fZNgJ+PW+x+R/l9Cns5HnOUu80qwKO2oYOBJ/MpjR9lLSGMg0Zqjas1WE+NLwUWzokcIQraBMjD0GmalpGwE69o6V5SA7TDl9d0XbO6fIVVDeP8K8+AWm69fUf84JeglBKc5RfXqgC117dDrrawVbTGk4xYU2vkrosK90RVV7bDOImZ5SGMEhZZUxtL1ui0cSWiFpxQfzmKqFvwUKEv/PM1s4QE2GEe1FT/YDuBe2HCc2QS9rcAw8qpWrGlQoiF0arqezQzRRjL0c7Sxl41l23HItSRsaYCPly77kSbAHv4Dt2FaSKrGHv6utBe5rUBfg6rudQser3xiRxK08CCDIHZLVCs6vTG+YrLsX6eXNo2kKS3UJy988sojqTw6cUJva8LF831ctyLamONEOeFkTZkEVJVLVTtkecC/evgmf+f1j8nygLPFgNf2X1CWHkI2KK8iSSM8p0JKg6obKvdLZO9f5/WFD/556bIbZUgMy8rjsvB5fbCw0bmlzxu9jGXlMfQLhn7Oi3WXJ8uerfiEYVb4FI3kndEVvltxmXTpeSXPV12OU5+iEfzt/Qu+c75JY2gtOPCaO6RqDIESvNmXJLVgVnDtq/0oXbAhY+YVFI2m77gMPEmqYSJWqPZXzESCwmHUDNhxQ3ZDQeQYcm0vMq5sGHo1d+OAs1xTULMUazKR0jMDLsUVlSgQKPaa+zwxP7YvjHDA1IyiN7hkws8W29SmR+TsMZgPGfcWbG5dEg5XfPLh23TClH5/ye74Cs8rKUuP5bLL6s+/yiqNOdg/JhqsEI7m7b//pzSJx+rxPstljxu3j8jWEYt5n0XSYdRdopya7r5N2vr5X3yNYX+JdGqk0sSd5P/T2/n/03KkYeQXDMLMgmwql1vdJbkeoI2LrzQXuU/ZCA4iw7v/4z8he7zN4niLZWKFeAa4E8T4+T0eq6cIJJ7Tp6gmGHKm2UNK/ytUjWIx77P1f/hHyKjAbP1jkAL3ScR3/8UfcJX+ob1cGsEfnUFiSnwZ4MoQgSIyHQpRcVYn3JYdtgLNz+YOn+ULFJJHiWHLibgRSd4dpCS1Q+jUbRR0TT/OLUCmO+f13TXLdYeHh7cYRmtuaEVUXuF8LaLeu00T9VDrGcU3f4M30/8z29854+NPXufpfMRp5vN47fAwyXgj7BE7kNWQasOe69LkhlO9ZG1WFCqjo2/RlcoeIMrQd21VWmhJbeBeJ+P+6IpRd8l455Lof3kHNT/HLPcxno/8vf8NxR/9r7l8vsds0b8maL6KpZTm+GyHRRbiyoZBlFJULqFXEvo501WPebpF5JUWJR1k7HWW7Mc+89LnIgs4Tn3udVNix1b/3xgvSWqXeekyKx0WlaIBpqXt+H011Pixpf81wKRwGHgaTxm0sXqfH0wiDmLdCj8Fv7d3Qlp6dP2CwK347GqLzTCh1PZ791zrStKNLTp6rv1eLzM7lJTEDsxKAIMvBGlt+OOLio50GHoOV4Vl+t+OU/a7C5tg2Vlx595Too0Z3uaC5Mked/trVGStf7OHt5hPLaMjLa31ebdnu4n5okvTSDbvvrBdwDRAKI1QDaskJgxydm+c0Du9welkA0dpIq/k6HyH3fEVjx/ex5Gad/7Wdzn6/rs8Od3HGLi7e/rKnocv16//+sIHvxQwKXzqxtqrYqfhKvtFK/lGZ8Vp0iGrLbBDtjnXu17NMMivbX3PlwNCp2a/N+fhdIPYqUm1ZJU7vFh3edBNOct9zjL7oylh7EYvRNtqBUeCaA/+b3T7bYoWaKPaFr/BFYJt+rhtBRjqkKGIuRl5bAYGgWHgat7cnHO47jL2c2pfEDsuc23FcwPTY2wGZJQMzNAqrkXFSi5xTYivOkjh4BFhaBg0fca+Yz3tQcbN7TNcv6SuXKRbM+otEMJQFj7Hk032x5e4boXTeveHXevLB4g35tTTmNnDW1SVw87+CX4nZTXtEwY5nU5CoyV5FlI+3afRijDIcdwK3VZ3YffVHPyFltSNJC09VoXPdtcGMA08O7s8zQIaYzfSrluy/PC2bUMWliYYqYZcSQ7zgiN1iMKlH9xmln2CMSW0c++P5E+4Of8Wt8/22H1xTP+dZ6AN9VHId//FH/C9820eLRVXpaZsGr7f/BsAOmxx0Lxuq2bgt8c+Xx3N+YtLGHgVZaM4kc/R2Hbntr5FkwzpuyFvDVa8u3fUtlJjfK8g6iQEcYYb5mw3kuOnlnJujIDZjOrdb+BcneAcPgIpkf/1c1arferCY9RdUtQu88plKzB0nJDXugVHqQcIOq7gvWHKP3xu2/6R6VCKkrlYs2wk0yxgVQUESrGqJH234W43w1Oag/1jNu4f4r+1Jtv7n1BvzPCffAd1dUb9wf+e8OCCXb+ic7rB4bODV/IsAEyWfU7WPZLascyGILtOn8wLn0kaX4t6X0J8pGy4yCImhcfQL/na5gVPlwOO05hAadz2QPdUQ8+1leO8UtyObXeuwYKhACKl2elnVEZykQWkWiIEfGNsL3KrSlFqySeTTe705jbOuqWMfr4YIlq636y0dr+LwkEJuB0XRI7NFbE2PcOksEXIrKx5XKTc87tU1HQdn/3IXtB6ro0wrxtFP0wYbV8Rb01xB2uEp20C5aKD2whUWPKzhw/o+DlZ5bEu/WsGQn2xTeQVDPoLTCPIZj3yJKTIQrLcp9IO+arHbNVj4OfcufGCsvC5mg+Ig8xqf8KUppE8+c7XyAsbC5zXDk9Od1+Z5uPlEs2r1RF8uf7D17+X7FsbQdUIUm1bja5scGRzHcITKI2n/qoXVwpzPdffjNZMioBVHlA3IwKlmRUWtLEV1EwKlxtxRuxo+q6FX4x9WFbyOkrTlQbZttk2/YYG6/W2S3Ca2vS+2JFsBpKBZ9gt9trK3tpyAAaeZuBV1xnyuXYwxrbt+iuPrqMQreq6bFzWuka2s72e7jBijG9cMLAWKZlI6QifrivotxtVkkZ0hCGIctbn1q4TRhlhN+G21ARhTprE1LViMJoDkLaYXmcVIZWm0ZJGK+rCI1sIBlsTyjQkS0Ict8YPcoyR4NZ0R3OWkyFV6WIacR3y86tetYFUKyhtFyepPC7a2NeX7f2XFsvaSKanm2zdeYHWDnn7nCih8IXCNyENDVI4uGpEpecYk9M0K5LqkkfNmh9OBhx8fpfXOynBzpRq0eEqjflkIfk0n3GpzijEmqyeYkyDFC612GPs+Ghj6Hs1/SAndAxnudVGbJkbZCJlyRVrseRYK5p5D0d26Hlb7Pbm9HtLbnz9Y+p1iNtLUYOU1Sc30dpBSk2eBkz+6DbDs++jM496HVGtIp78/AHOL5ETXwrwhl7NTmDYCjNCp2ZRem3QTM1vDLssq4BlaXhcKjr416/3Std8snDoOtBxBI4wDPyc4Y1z/FtzzPY+UXyPpPwRxe1vIfbXuJePkGFj2Q6FR1r6/877+KtaQSvYe8lBqCoX37FZFGnps9tbsMjC64hugSEt7XPQdW2Ut80+0Nd5HaMooUGQa4UjXAQQOhpHmOuRo81IsGNBT2ny0iF2ND2vIlKaXCtcYRh5NtMDoNQOqrZajV+2o4K9qL60HVeNoGwkeasxeLn6nh3PeFLSMwH7EUxKHyXtKGfo1W12gENUueyNMtazHm4L9mFpaKo2S2MVsz4fsy49ZnnIuhVBx05N7NSUWiGEYQCcPLxjLyhakWThtTW0rB3meUjXL1gseizTmHkWMeotrscAaemTll6bc6BR0rAoXl1M85fr13994YO/MTZx6uUsK28kntSEbo0xcJrGjPzCkvzagz+oXGojbO62MIReiQSWlcOkcHlvOCfVIa40DN2Sp+uQXCt82bARVK1aOeciCznLXS5yyaZv8amBMuyGJZPSRdIghG3ZzwpbLfY82PA197oJWe0wLV2S2n7vRaUYeZWFfmQRgdIsK7u57Ic5tzsxG35zbRWqjeI8s5uNryBQHrkOUcIGbxyWEJmQruMQKOs1ziqXybKPlA1xf8XkfNPa2fySeGtK9+Y51TIifRxTVh5hb01duojEojer3MdM7N8phLGdgDRk/72HyKsBySom9DO8OMc0AuVXRAfnJIuuvSjUDg6vBtIhhZ3L1m0ltKgUmRaEbUs6ULYz47Yiqul8wI73lKqydkcbniLwhSQwEUs5p6zXdL1dViXXjolKJ7zwjvhw/hpvXe4wfLJgo3Yok5BCK17kBYfyM5b5MbrJcFR8Lf2tRMnAs89q3TRcpZGt6nKJJ+GuMyDVPY6Nx0ouuVRnTLjAmb1G2WzwfuXxm+Mpzu9u4Hz8HBELGPQovh+T5JaYts4ifvj4AQeP7tIJraviYjHkZN3Fk821vU5iNTE9r2HgFSjZsNtdsMxDZrltj//u9oRJHnKUBlSzLl4LiUl1w6opOdJnvGluMNYWkDWO1/g7UxhHmKDtvNU58ea3AUgBx/sBTemSZ8H1ofsqlu9U+MoS+Lw2VCj2cxsuVHnc3jlhfbrPqvCpGknPL5gXQRvt3fAsiblMOzbts+1+ha0Iz6utwK5qJFtewXnLhhh59tnWjaASglw7lI2k55V03ZJekPHR1RZhe4jGbsmsCP6KmPiy8LgdJ22Ajg0RGnlFK/SzYWGTUuEKO95ypWE3tFoLYyQDz+NmVHKauQRKULcCwKLlmkSqxnVrrqYjlKMJkvAXgkfVkC47XFxuUGrFsyRmXSkqA0NPMXBVm0aqSbOQo8kmm50ljtLXHnyvfQ3y2mEjXvP8aot56aMbyb1GskpjVnlIWrkU2sEY2I7X+E4Fr/rgN/zqZ/xfjvh/ZeuLH/zARpC3t+uArFac5SEDXdF1SwSwrFwu8sDOqKKUk8yj52p2wpz97oJHk037AQ4Kuk7F41WXgVdRN5Kr3GcnsEE/7i/d/k/TiNip2QngIvfxVMMNryFSDYHSJLXtGGTaeoIP4oYbcU5SO1SN4O2dE37w4oDXeku6fsHZukvsluS1Q1K7LCuH48yj61jc5s3ugp5XsK68a1HQcRrRcy0uWEm4EVkBz4u0IdUNd4IY3UDHtRafz5YBsVNzt+W1O15F3EkoC590HaGf7dHfv8AbrugO51SVc90+VrKh118yuHPCk798mywPGPaXbNw8IZkMeP7jtxhuTth/7yHJ2ZjJiU0N9PwCIQzDG+dUSYjyKsKbr8a73XU0VRu4UrWt+3vdzHZ+WnZAx60YBxm9IEMbyeMP3iEtfBypqRvJDyaav+T7pPUEX/VY5g/pBvdR0kMID2NKmiZHIuk6Ckc2XM5GzBZ9FlnMsyRk05VIrWhMgRAOXW+Xg+Z1IgJ8o/CVTVw7yx2Osz5JBb+1mXGYBJxkkhuRZFRs8jD3mcsZHdNDOXCcCnbaw1RcntO8dx/x6SPm/3zMbDIiqzx+fLnJZ0uXxsDx44i3+y6345LYqRl4BWdZxIskIlr1+b27nyOEoRtkjMdTHh0dEHnFNdluGK0ptUPoVq2+IOJF6vLBIudCXuIKD98EPNIXeOk2b/ZhYzilnseo9QKx2dD82f+W8Nv/K5LZj1Czp7D9NRCC8M45O151TW57FesHxwfc7c8ZApdpzKoIqLUi8EpuBBkX0zG6Eawqr81ecNqDNURJw36U8eGsx9ivGfsFd7pLni777WWpYBhk/Pn5Jjcim6NwnApUV9F3NUUjWFeKZRXztfEV2lg3QVL6bIUZPS9HSdM6DRqS2mVReZTaBnTFg4phlKAbyccT65MfBxnDliSZzvuMfbtHnecOr3UzAuWTtzkPq1rx1WHd2jUlf3EV8K0NW93n2uFsMibyCiaTEe6yS6eTEIQ5nz68wzyPSCqXx6uYRSkJFPTchnWl2j0t4rIIOFl3+d3XP8bzS6rKRa00WREwSbroRjAKs/aS5dJ1SyK34uPjmxwmHTpuxW6U8JV7n/LseJ9ukOE4msD9UtX/13l94YP/zcGciyzClQ0P+jM+no05iNeMwpTIKxiXPmnlcZHGrNrN/83+Gr+t/h/NxkxLl66jiZ267RQILnL/OiYzxN7glbLAjI0g5/k6ZuA1bIQZYz8nb3MAptrigieFYCewMb0JgstCoqTfxn7C949uMSlcNoOMrp+hG0k/TFkXAV4e0AB54zP0S5QwvFj3AFtlv0yy+srmJevSxga/jMUNlGY78EhexmUaG+cbKMOGX/MsCamaTeZZxOZsRBymbGxfUqQhy0WPZB1z7+9+j/BqjZoMWa16bAynVhNQO5x89Brb+2fo0rVkv+0pnfvHBB/fxjSSYt7FDQuiOEF5FX4nxeuvWRzuoEsXv5Pyasx88Hp/yXEa4wjDVpBxlkWsK9farbQkqyXDRrLXWbE5mJFmIRsbV1SVS3cd4zmag8keH2UdpuVH5LwABOviCEyNQRP5tynrBeflp/yxM+HZ43f4B7e38GXDvPS4zBUP6wuW9Qm6yVAyxpiGibog0gfcijwmueWsH2U5j9VTItNlcbLHwBMMPNgKar61kfDfnvX5UVrxQjykU73PsLH+7o+e3qX/x1OQBRfPv8Xjsz08pfmnz3dJaq7to1u+i8SCp85yl6+PCla1YlYqKBxuXuzwzp0n9DZmuHHG+NYJJ5/dwSSConL53vEBoVMzKTzOM4fHa8N7g4YHcchWeYOqMTwy59xii/3ItrWNsfNhHAV1hfF8yqP/BsIRurOJ9+SPoBciVjb2defmyX/v+/ofurQRLIqAYZBxf3yJbiSTpMNVFluip1bcH13hKtsJ6/o5qyIgrV2y2mFW+ChhNROzwueqHRtN04Bu5TLySq4KQYO1AW6H8DxRCBRv9StGQcnP5hEvki49tyJyKnzHcgV8p6YXpkRhxvFkk6s0tmp4I7gd2+6LFdTB7a4V1L1Y9VnXFke9FxbMSxdPNbw3XLEVJcSOPZCXlcMbvSWHSQdXGsZew72OpjaCcdv9PF72uTu6wm+tzqtVh7Lw2N+8YM9IitJlazZmkkXXQVaXueFe12Hs1fite+fDZ3f52oPP6AyWrFYdstJjUQQo0dAPcnQjudG3+oWXiOQ3BlMWRcCy9JnNBhyv+nSKEE/WmP8hXPFfAnx+bdcXPvh/eDm+hlrMixCnvT27ha3wq0bhOzVbUUJY+lxkIQOvZFV515a/jiPbdrzPYRJRtQEaQtqD/qXP1WkroYGfcZ6FbRVUsS49QqdmN7SbjRCGgecSOw2VsajOl5eIorF/193+nNdVTeQV+F7JXpgRhRlxFtLNQsaVh242McaGyex0lizzkKRyCZyavp8z7i1YXW4TqJowqHBVzW634SrpMC8CykbS90o+WXTbBK6aurEwG9kKbMrS4/TFHq5bIYXhcj5k9MF9srX14g66S+LuGqTBaGU3wYux7RRkPpxu0OketXM+Sb6MydYxWRrSHyxQbo30K/q3TymmPUyt0MtXk8Gea4dI6VZRbTeQ49Ra8QLVsBVUjP2cYbSmN1gy2r4iXXYQwtqyjlc9G1iCBl5uDgZjckAhZQdjGoRwKKsZRXVF6a/5lyd/i/3IglLAjle2vTdYqDNyPSepL+m7O4TCwZOw2zNUDUgCVH6PmUlQyjLXd1q7Xs8vGLiGkekwlV3O5DkT47GajUjqTV6sf5+dKCGvHealT2UEF7klxO1HNa4wbAbWyeKpmly7nKcREnAFLCrB01WP22sbGKVPFa5XsVx3KNqciqdrj6KxwVFlY2fgP5tbG1/sSC6LmnecXYaeYOhpPNkwnQ/ofH6TbnqJP3mB3Pbwm4Zy/3WaaIhcz8EPqOeCctnB770aoad9HuxIal16nCeddl5ft971Gj+sSUsrDFayIfIKVkVwndy4qhWTQuLLho7b4AjDX1x67ISCuhFkteRmrCm1JXS+XL7iehYfOYbn65C+59JxNF7WWAR0y6R33YrbOydE0/F1+/vluAEgq10u0pheG2z1y4DjXw4oW+QBsVuhjf2KtEUqh6rBcewe9tkyQBur3TjLfDbjhHkWtxHmLr9x5zEAVeWQZiFJ5VG34KtQGV7rwroWgHONOY/cisW8T5ZEaK1448HnpD9/m6ppcwza8C7l1ARuxTIPyWqX2K3oeAWuW7ERJlbjIA3b/dkrehpeLoMwv2qN0Ze9/l/V+sIH/2Eiudu1wpplOy8stSKt3esN3dp1bHLWpHSojLiGbdztLQFIKjtrnxSKgWcfDIkN+gCuYRuebOgGOQPPxu82vxT+Mw4sAW5VefRc69MtGqv6DxxbiTXG/rkznDAczilyn6aR9DfsA++nBXGcUlcu8zxiUfhETsWouyQtPTwlrVCxjbjVjUS0aWI7owlKaeujVzXaSDpewWkWtuEzmthRhEpfix+nSZd16TEMU3phyjyL+PTTB3Y+6lV0OyvK0sN17UYlhGGx6lqkbw5FFhDMZ9Sli2k32iL3KVqQj1BWyOVu29e5Wkbo/NW0d19eupr2/SwbyaKyl7ih17Afr7k1umI4nOOGOcFwycnhDZSqWWcRJ1mAJyFmiBTxXwk9enkRqHSCFC7GFBhq1sVzvud8ypuL++yFLq6EHSemp++yZI8z74TL6hE9M6DnOAw8w9v9FbPSp+u69DyPo8Rh4An6bkPXrYkdmw7pKegqF8+EzDmj1GsuVYfz5IDP1xu81Ruz4Ws6TsO8VPRcwU6ouR0nxG7FwfiKTrxGqoYi93ny2Vt4qqGDPSxOM4+jq02iZUlRu0RewTIPyWuHWRFwlMJlmRNJx4JvgE+bE96p9okcQWMM7wzsZ6XvahzRWFjO0T7DdcxgERMXZ6jFE/w8RY+2kOslNA3F1YD15ZDO5qvb6HVrgSu1x3EaWnCNbwicgtgr6Pg5k8RG6IpWsW6zEFxWlcOyksxL2Axs3r2nNEeFZiuIrHgPwX6Y2y6atsx7T9p/LBK8VePnDpQOWSs+DpRmiO3ypVnIcDhn1FsQuCXrPKRqrHiu1A5JC6DylL1YKdfgyYZV7VzvTcvKpdCK7TC9tkdOCv86ATBQttqflRDlLrGjOM8VZ+suRSNb8Z7kzSSiaaT9e4uAReFfj8gG7RjzsrDhVhZMZui3xUNVuThOzfC1I+LP77Nsg4uUsC4Cqa31MNcOuVbc6CwZ92y890ZnxTyLUaIhanNXvlx/PdcXPvh/cyMnam/PRSNZVg4HnTWxW9IYcW3vmuQhk8JDN4IPpl6Lz2zoeEVL/3PpuTX7Ucaqcnm4ClDCMPI0q9rnZpShhMERDd0o4ZZWXCUdZnkHVzYcpSH3WufAorQo3+PMvUZp7nsabWDg1WwFOWkWomRD3F3THS3ovv2c9NEubpTj5x6r6YC93oytRlFrxcl0zLNVn5udFQLDPIuQwtD1C66yCN0Ivvn+pzz/4C3y0kMKw3ZvxgenNxi9TN9rb+kPxpes8oA/efIaF7nLrY4ldi3zkLR2ebTss+kXjIIUtRiwKALuji7x/YJVGrMxmBOEGcZIqixg/WKL1axPd7igd/Oc3sEZq+MtwuESFRRkpxv4hYuQBreTYZpX087b66z48eUm60oRqIZUS7quFVve6y3YiFe8/be/SzXrsnyxzWo64IPjm6j2EqSEYTPQfD27QxM3PFv/y7/y/ZtmhcGhET6mveULXEqTMjUpurWRfmXg4kuHhg7L6nU+WO6z58S80Td8dTTn3uYFl8s+J+suSgTshrCsBI40rCqHRdWzh1ar0l7qC+qmIK8XlE2CdBU+Ac/WfXqu4EacMPRc/ubenKx20Y3k9ua5TUY0gkbbAyZouyFdp2bgSj6YBfzZ+RZbQcVWkNPTiqerHueZx0kmeFoumMoJWlQIo/BNQCpWvKjWDHXI0HX46viKiyxqFeeS/a1zPj26xSKN2ckCtrViPesRdlLinUPUOwkkFauLm5yc7qDOt/jGK3kaIHY0Se2ihGE3yrgzmPJwatMPu35OUbvc3LigrDzmScyj6ca1uG5SKK5ygTZ2NOcpOwq8H/a51y046KwZhQlHywFJLci1JXRGyrCqBWkt6XuC+92MsV+yLF2kgKFXUjSSTpDRGMnn57tUpzd4a/cFxghqrbjRn3G67HOVhywqB4kdNwx9qwswBg7TAVu+7QrMSoeJdlhUDlktmVeS2LFdpdixxUeuJfe7mlmpWFaK2DF8MO2083vN2C/53otbCLDMk8BCoq4Kl47TELg1P5r4JHXDva5gy6/wVY3nVGxuXuG4FUUeUFz1ry+Qz2cj3tw5ZZ7ETLOYReGzqFy6Tk3k207n47M9boyuGMkV6zzkx8/uvnI735et/l/f9YUP/soILnMfKWzra1VJTpOYQAXXm9Hrwwlp7RLUDvtRSseNKBuJKwxHyz7zyuV2Z4WnNMvCJ6kV9zpW5OTIhq/tHJOWPoFbUjeKf/jzt/iN8Zxp6TMvXVxpeK27IqldJnVAbQS/u3vKd892SWrJwNVWMYuN6Rz4Ofdef0T/vaeIsQvdLqZzi+g9hbg8w5ynhIdjdt56DNJQLWJW52PeUw1Xl2OyPKBurB/5wcFzDrLAkrE+vsfVYsCt3RN6GzMarXh4uc037z+kaSR/+fQ+sVtyOB8iBBx0Vhx0bIfiOI1YVYqdsKDnVtcq259fbbHVag9K7VBrRdMI6sq9ri6Onh5w884hXpyRT3ucPb3JzbcfYRpBMe2Tr2LSeddGI+c+0/mAb76Ch+bJYsC6UiS1YFU7llmuIVC2ctp1alaP9+kcnLPx9U9ZfXbAxQc2K1222gubrdBg0DhqSK3/akVq0CgZ0HFvEKohRbNmv7nLpTrjnIb3eMDNyIrh6kYyLx3e6Tu8NjxFiYZSO9z/+s/YORuzc7bF6HKbPz7d5LvzJS/kE7JmwZa8Q6fp4uLg4vC++BoLMrb8GCUElTG81nPYDWveHsy4t33KIungSM1mYFnpn57t28vvdHz9s3uy4W5/hjaSR/Mhb/fLa8jMR3ML78k1nGSao2bGWtouzabeoYNPQc0mYyLh0HcUN2J4uuox8uz3mZc+3/38DW73Z6wLnw+ObnOwtNz+m3sn+GlA+ecd8nkHxy/Z2TlncjXmVa3dKCFp5/VF7diuhrJ2xenVFr+5f2hJk8q2oaUw3BtOSC+3WVaKdwYVqZZt5R8RO4Y/2F21B7QtKLLa4c1+yqL0uCwczjLreogdgW7gNPNZlpKOayzGOEoInYrPJ1sM/YwHOyccXm1Zx0zbcTid2dfEFh4Ve/GKbmDdCK7UbPTn14XMqpYktaUCJo5kVsC01HxlKDjKJDdj2Asrem7JYRKzFdTXncwfT11GPvjKosp7rWMhqx1OZyOWlWLg2jHnvHSIHZD2GsKsdLgsBmzEa8zFJmGQ0+svOX10i63NK/b3LYjnez9/GyWtLmkjTNnraD6ZjfjwYofNZZ+7m+dMln16YUrglnTbkcaX66/n+sIH/8v5u2oV91LYVlSu1TVG8zTpMit8VrXClR6uNESqpueV3OwuOE+6FotZO6xql8vCYT8qeQmPLLXDuLfgcjHgcDlg7NuM7XXL5841XOa2gq+MVfOW2iJ/M6ywbOxX9N2K7WjN3miCG2eIBzvkb/4OzeA+cvoZwWffA9dDBCm6+Ks2J7+TUqbWdgPgqpphN71O0Xu5Ngczgiin0Qpduvzmg88Iwpyy8Lg/vuCzK6u2VxiqRjErfM5zj3UtKbSgbAJuxzkXaYw2dk55b/OC6arLJOngSk23v7IXGae24j2vJFl0KdMA5VUMx1PKVXTN+HfDHF05KLcmGqwY7V7+hz4X/1+XBG7EVlCUacXjtUugrId5p7MkDHLyVYw8HePGGVXucae75tk6ZlI4rGublhYpGFabnKv43zn4wTAK7rFn7jFsYko0K5HSb8ZIJMcsOEyGbIeW7dAgyNq4YLDt59mzPS4vN7hY9jlPY6oGKlGTNQvS6oorT1GoTW7qA3a9gP1IsB8KUm2xuNNCcJQ07Ie23Vxrxa2bL3jy/IBkEVDUDo9XHQLV0HMr+7nAsBWm+G6FIzVvju1h93CyyaxwyLXhRVqTG01FjRSSUmS8zxu4jiXGedLHlS/TKaHr6muxmGktc3vdBZdJh3XlUTSSi3WXne6SsvAo1xFCGKLxAulq6tyj0a8O0fpSbAigtKKsFbl26HslG1FCXnk4pe0WOlJz0Jvbw8epWTsKJc211KPrNtzppFxkAa40pLXDqvKu0bsH8Zq9SKJEFwnk7RjZtvZhLyzouaXV3mhF0I7izucj9odTitJlkUUsS9tpnBY2rc9TmkURUGiHtHbxpSb2c2LHBvVo4zFrlfdpbV1OsSNxZEPkvKzmczpewUUeXl/WSy0Z+XA7rui59ns9WUe8PVgQBHULOgs4Sl18CaHTsOk3zKUd5+Xa8kyq2kH4OUGYEw1WdLcn6MKjKjzyJKTnF/SCtGUnWBz4QWeNxOCpmumqxyBe82I2Zln6BOrVWH3/yvoS4PNru77wwR87VmFatzaXnquvP/CVsbM5bYTF3778d2h9tBVCGHpeTlp5LCqPZelSNqK9FdsPtSM1tVakpUeurdf+svAoGztDbLB2NyUMvmzQjvXfdhxtYRva/hwbYcrOYM7W7jnh7QuKu/8R/v7fQQqHVAbwyZ9DkWNyMFqxOu3j+m2bvnSRTk0UZvheiVKaqJMilEYqjetWeH5Jb2N2rW2QoSbemLG+HFGVLo5jD4BcO9c/76KdZSa1DRJZ15JN387hmpevQbtJ1Y3ElZpk1SGMUqRjQzw6mzNmJ1v2jfMqlGN3S60di+jdv6SY9O3P5NT44+Wv+HH5q88DgKodPOkSKdvadpSmqh2UU9NUDuWyg5CGd288Rx/eZl11uagFq0qwqms0NVL8v38MFS49E7Hte7jSVlaxsr7uo2bBWQ61cek4pqWvSU7SyIa2AI8PD7hKOkyKgEnh2k6DEYSyDy4IJJqavrJCsv2w4p3RhBfrHpPCQ6CYlxZYdZGFqMkmrmM3a20k69InqSWz0pLhuq0+I2g1IaFX0I0Tqtq+z6kW5NpwxgIEeMbFNQ6BidkJVct+t/yJQks8ZcE/jrTAHkc2uNLqTmI/53AxpG4Dsy6yiJuDqc1taKS9cKUBTlBiGvHvXFx/lStsxW4BGredNTfQdncaytohyUOUsAr1bpAhZcMwyKnaMeESRSDNNe3x8Spm8EvgndrYGOhOW6mO/ZCk/kXRYStpbYXAzstLRnNt3Z1mERvdJVnlUdSOzQvxi2s3St1IEuMiBCxb3YxaDq7Fq540DFxD6DTMS4mvBGCtrFuBIHY0tZFoI6kaSza14kbBhpbc6Vo78arwkcAw+MWMPdeKq9x6+D3DtVi51JAjGDsNszwk8CwFVNcKJ7JjM126lLnPVn+GMRKDuM5IGIUJvlNhEFyuu4x7C5tL0P7OX66/vusLH/xDPyPySlZFwPNVj4M4bdnhtpKInZqNMGHoWytJbSQXeYAvG6pG8XA25qCz5DIPmZQuuRZEytDzSjzZ4IiGzY0Jf/n5A4QwHHSWNt0s966hQapV6a8KC6m44VY0RjBsoRvz0sUAm50lm1uX9O+coP/WbyNu/u3rwyWKbqE7PeThBfVVjAoKnnzwLrtbFwhhSJOIm289IhotkE6DcGvqdUi+jDGt8taPM3r3X1AtOphaXvO3k1WHy9mQi3WX3e6Sn17aqn/glbYilxZ+k2u4LCp2Q4eBq1vaWMnji11b6fsFoVvygyf3eW//kP5ggdGSzu4Vg91L3G6KE+Usn+7RvXVKdj6yb+bv9BHfm7F8uoeuFdJpeBWYjlwrykbStJ2KTb+2oSmy4WrdJSkC7n7959AIylVE0M+5/43PMf8Pwaq+z7SM+NPVGVfimNwsEfy71agQHrPqOXPnBrdkwM3IMPYDPGn56VEyZFpqLgtDpBTbgSRyYFIoco3dNPUeBkhrG27TcQwKxUFzDxeHiZhRmZINX7EV2CS3Uv/iMtZ1G74+NpxmDhd5xE9nEeHZBv/TNx4SuCVlrei5modLC0iJKvt7LCqHt4UhcO1l8uPTfX4+jzjNDIu6JhVrdhr7bBgMd8w+voLdULMb5tztz3g4G7PRHg7nWUTklvTClH7Puj8+f3KXQNVshAWe0vzxyS7/UW9F1FvjdxOCnSk/+ue/TzdKiKMUx3t1vu1ekJJVVnAZu5qscvFlw1UecJxGfGXjgknSsZwC12bEd4KMne6C2C25SGPmpcM4qAmdmnlhq/FNv2AjTPCU5qeTTW71FmSVy1UWESnNhzOPgWe973kjuBHlLEsPTza8f/PZ9Sx/vu6yKAIeXWzjqAaJoecXbHcXRG7J4XLApPCJnZpxmHKVB1zkLpeFy6ZfcVm4BKrh3eGydSIo0tohb6RljIQ589JrrcceF4XDbUczDmwHQDLg/vYpvf6SIgvZvnvIxdMbTOf2YhGoCiUMZ7nPspJ0g5pAWSEpGu7ENc/XXbSRFJVLXtiLievUNI20EcY3T/j4k9cBiP3camlkw2gwt3jkVY9F0mHQivqO2s7YK1uGX33F/6Wo/1e2vvDB//3LTfquFd50XRtROgwyrrKIpPB5MJzws8kmsVPTd0uWlcvAK9mMErrtBlZULpFTEzk129GaJ8sB3ssqRjasVh22OquWWe1xkYd0WvytEPCgt+Tj6ZitwAIyPpkNOc5cHq80m4Hi66OcjlvxZLqJ75X09s8Jzp5jpv8nslvvo3a+jef0yd/9+/jBH+F+8hHVTzq891s/Ip/1KNPQJuMlIavJgLCb0Lt1SvD2EufnPTqA00lx7hme/d/eozNY0t2/QI0r9ENl0budFbduvOD5ixu8t3HBedLlMIlIta32V5WNXV2YnFXVpdfO9p6uY3bDHF82BKomq1ze2T2maexh0tm94uyzOxx8+yfoZcTsswPyLKRTXeJ1U5AG8WzK7PPXKXOfRivypxHdX/EDA7T2K4UvYSOoEMJaMMG2xHUjSM9HeHGG8ircTsbRv/4aAK8Nphwm9jqS6RlpdUXT2PezG7yGQFI2CZ6M+Zb8PV7ruuyEmlwL9sOa+/0loVPxk6sN5pWtyJ+sK/7h8l9R6iXbwdsArPUVMlf4osOG2WOLARuuy7txj7S2l4cH7h4/Sq/4aT5lWvb5nU3Fh7MhkWoYtNX7P3mhyE2OQuAIydLknH7wgI5jL3K5hr9Mp4TGoyt8Rp7D2wPBT6ZD3LlNDDzNPJYVJLVmYTI69HgsPyNmwE6zzU7gshtq3hvOGIQpT+cjNoKM/f4cKRpCp6IbZPS6dnwT3z3h/fGCw0/vAeAozR8Am7eOcYISp5sgv7XD1l9M6I3nVhMy77yCJ8GudR6ijSStXSZ5yEnm883NS0aBYJpHPJyN6Xsl43jNoLPi6GqLh9MNtqOErc6S2zsn7F1sE3oll+suP7y0s/e/uOry3sDhWwdPOX2xx34rrs21Yl45VA0cp4ZLKdmPbJex61ox3CqJ2dq84tHRAb5T8ZsPPiNLQ46uLKQn8gvOV31e2ztGG4kQXbpuSVa5vLd5TuznuG7Fk8sdBp7DbnfJa3ee8W8/fI++X9gLSmk7l4l0KLW0HUlh6LkNG0GOxPDDiy1+a+eU+brLbNWj1A6fn+yz2VkStNCtf3t0i6FfUmrBohS83iv5fBUTKNj0a3bjhNd3X+B5JWGcEY0WXDy7gdaSqnZJ8oDv/+R96raKT0uPO9tnvLjaYnocUzYOkzzgT842eNDNCZRmUvx70dq/XP9/tr7wu59rgSslY19zf3TF49mYaauu3gpyztZd3h1fsihCssrhtcGUSRpTakVeesR+zsPZmKZtTa5Li+98GZNZ1A6j0YwnlzsYY9uEq1oxL61ytutoZkVAriWnWYgElDSMfc2qUsQOpLXi81XA725PMEayPt3A+8FTzN99F5nN0C/+NenW15Hzx6jnj6A2hH/oIR4dYT5TeHGG20+4/PQ2W288xdlYIXsGun2ka1vXOgmofyq58Y2PqVN7gJkUoq0p9+4dUy1jJo9vIIShqB0c0dB3a346szjfs7zikhVv+ENmJTjCIXasNc4YQehUdLySjp+zsXEFgHJq8lmPrbtHmEohw4LewRm82LYdiUWHKvNxOinRcEk3KjCVw/Jk81f+wADsBAW5Dsm1YFnZFu1L/4ASDa/fek7nxgVOL0GEGhELhvMLzp7cJK1stGolSoRQ+M4AV4b01Q6begeFRCpBR/iURnOaKbRRjDxDz6vo+xmRV7AbxTy+6BI7MHQdjNbU9Yzj9XcwLao48m+zrA8p/RTFG1RlxPthQMexMbk/Sq8wNDjG5UKn/LMzxU0/pOsqeq7DwGu4HSvKxo4JfAWFdhl4hlVlgU0HsWZeDkm0pjINk7Lmj88bboQ+PRd8qdgNa1LtAg5xbQFRvTJC09CTLm/2rctlGCX0ooTXlGWwL9qDziA4XQ7Y3Tsj3JmgtmpC75ydpLVyeRXd3orkaoDjlwSFi/zhCXf+s0PSD7fIZr1X6uMvteI0iTFY6Fbs/KK7EDqWfT/wM5IiYJbGPF/16LgVWe1yldhK9mTds5z/RrITWqfQQazZjdfUtcPfvXHCzmjCOo04WQ45vBwx8KxLozE2pOvDWcSDnmUqAByf7VDUNoPj4mqDz6622O8uyWqXF+se89JjpzcndEsLm+rNma56FmkLZHmAbgTHaYQ2ks3pgGXlEbTdzd3ugh+d7wK0AlfJVLjMS8lpGjH0C/ZCu+/diFfXQUWlVkyTDkOT0I0TdqOMgW9po5eFz788iRj7VhjoSJt94QcF/c0p/mCFinLCixHHp7t4TsXe9jlHD9/AGHvxzmuHw8ttVoVP0SiS2uEi94mUIWs7tPth+cqeB7sM4stY3l/b9YUP/pFnW9KxY0VLgdJM8oCeVzIKUp6t+oy0je19GXwxCtPrvOsyjWnaeZxu+f2xU7c+dzBKIKRhM16xygPWpY8nGyaFompa7UDtMCkcRp7GU007S9NsBjaXe14puo4dLayyEG8yQn1eM3rzIaY/pOn0Kbp74PiYzR3oF1Q3XsP96AVGW5++CguCOMO7tYDYhbKGyQLvpsGUYDJFeTnAuZUjLwqazHrlZcvNbgo7c/OdiqrpsKg8znOPVdVwmtdciQWFyJFiyGVRAw4NduwxDmzqWuiWbA5nRIMVjZbUpUudewTDJcXFELef4I5WxL/k0xfC0BQexSomjjNkUCCdVyPgCVu9R6YVy1LSuC/V1XbG2duYoaIc2df2NfRcGzjUSFalz1kmCEzEUN1Eq8rO8psBQxERSIkUNof9sF5TVDW5DigbByUCem6XYduO96TdHAMlGDg3SYsjfgEEgqy0zH8lXIZNzK3I5yCq2vmzy7pYkrNGSkVlcjI9Y1q9xXYxYj8I8KVkM2jwpMERBiHAGCvAWlWKshFs+hWbQQC5ItGQNzVrCpLaJVR2/KCk4XZckgeSZaW4LAQ9NyDXNtjFFRVea3UE6EYJRWHjWVG2dbvMrWBPZz7yrKS8GtBohVQaIQ1BN0GqBuWXqKhAhpV93f0K5dSvLLAJIKk88ta94ymNavPsDfZz+/Jzvy495kVAWlvGRQ40xkdgq3WtFVUjEUDXrRj6OeN4zWg0Y2vLClWDMEcKw4+vhgw802p77M/hylbUWQQkk036ft5yAxRnqz5p7XCVxqwrl0nhURnBKreBN8YIjJE4SpOWfhvD29hOZK2o0pDh5TZNO3KUskEKQ6Q0HbfiKg+ojMARlh+Sa8vr343WPFoOWFc+ZWPdBB23sMwTr8JxNOMgJatdslqR1XBVluxHPj3XFg0dryAIc9w4Q/oVRiu8KCcKMpRskEqz111wvOxfg4BKrXCUhayVWhHIhlwIVrWkMbD5CjUf1+tLcd+v7frCB/+d7ppA1SjZMEtjhkHGs3VM7Aq6Qc56NuLz+fCajz1JY97aP2KVxFysezxf9bjRWTPJg+vUqY0wuw6i6AY5yarD3btPOTnaZ3K5w8CrWqa3nau5wnCaCTZ9+4E7qzw6TsXQq5mVDrNS8h/vTpgWAaz66EYiZUP4nSXBvefIB5aHT/8+5Te/gi6u8J7+G9Kn2xSr+Jp+17t9AsMIyopmLqivuqj/9C7UJeriFD+eQBQh4hQpc4QH9Tpi8ekW61WHovSIo5R6NuZFGvCzmUQbzYfiLxFCMmKP86LikiWyGBAoh55r2OvNrwNONm8d48YZ5SpCtqE7xbzL4nLE6MY57nhFsHdFPe+gggIVFAilOTm8wR4QDFbX4r9f9TLGip0A5pVohVcSJRS1ljhBgXC0VVxJCXlJOuuR5QGL0uPzNGVAh5HuUNGwEimpWIPpXycgTuuSC2UxsxPT5SLp8zQJSHWP/SjkInfZDDRJbSNY7zcPOJcfopsFQngE7g5ZeYjv7nGneYP3Bz6/tTlnGGSsSw9H9Nieb/HD8gfUeo69MAhW4inz6Ddwi7fZCR22As1BbAN4Cq3wpBW1qvb3X5cekfLpOAKDRBvFlohxhSBQcBCVlI3k7Y1LJIbzpMNfTnvsRxWlthaxeaUY1w7rwsdzagbdJe5Lgalf0NuYsbwaUlUOi6MdONrh9GSHOLJOD8er8LySzb/xqX2DHGjeeED+T6YIp8EfrCjmr2LoY9e68q4PfWNsIqOnNKINrbHuH5dl6bOqndb371yLg5elLSCyyiE3ilWtGPr2YOpGCRt3j8BIFi+2iPsrwihl6/jAxnlX9tIPcBCX6EbweBVxnkv+3g17aK4Kn7M0ZitMebjskdSW5++phlkWUWgbOFVqReSWXKYdGiPY6SzxnZqyEUwKh6oZsxMWOFK3FkOXsZ9zczDFnY/RSUzHrdruqC2ARnFCN+2wqlzqwqZZfqWzZG/TaoryPCD2Cz6bjzhMHRZVw4Zns0PGXsV2lNKPEhy/RChtC4tFB9cv2do9J1vHJKsOr919wsWH71M2NpOgH1hw0boVKu5HKfNFl1VltTlD8z8AsvfL9Wu7vvDBf5VbwU3cYmyTShEpW6Us85CDOGWvZV3rRhJ5JVI2PJlu8CyJ6bs1EsOi9Iidmrc3z/mLkxvsRylv7x1x8M7nfPyD90mzkPNln0XpcZT6DD2BwaJMP144jHz4fOUCLpEDq0oy8BqGXs3Yg6OkgwTWlcus8Lm5fYbbX0MNYjFDBJuYaoVwB5Bf4vzsx9ThAJZQrCOKdUS8MSP9I5/4xgXOmy7eVgoXx7BawrpA7PZASExSUJwPaUqXz37yNt04wRhBXvj0eiseL3uUWvL2wPB/nT4jFH1Kk3JlDpmLc77GVwGYl4Z7nYbn8zFv7Bxz681HxL+3onmSkV4OUW5N9+CMctZFOdaeVZ73KaZ9wt2r60PIGSUI0VAkIX5vTf+N57/ap6Vdh0nM/d6S2Ik4SnwEgtPMZXtY8PaNQ+rcR3x1DyMl4vKc+rnD8eENns/GLCqHHddhXlekpqRqK3QXj6fimFUzIdUTDuR7XOSf4qiA0tliJaeMzA6fLjeZFB5KwGlmiB3QpqHB8D+K/nMemlMMDcNmSBXWvBsO+f2dNe/vfcpHp/u8cecJ61UH73yX30i2OFq/y4v1n7a/mcGYkkV9zHNnhLvY481+SeyWKGErvP3xFWkW0u+uAPhXD99kUQnGvmEjELxIHGoD40CwH1Xc6S14sH9E0oqptodTXt/yyEqfrPLIKpeksj51gDhMufHO5wR/4KI/mNLkHu7thGH1hLN//Q4A/VunuEGBkA3LqyGTqzH93tIe+DNFU7g4W1f4+3N7uBxt8tFHb/E3X8nTAJthgicDtJHktUOnBVkFTk3HL7i3dYbr1BxPNnGSDhthytGqd81g+Gje5RsbM85yO8K7111xmsbsxzXPLrc5/bdjvvLez+jfbFMbk5Dfv/eQf/Tx2zbZ07PV/r84EXQdRexIYgeerrqsqgEjr+ags2JWBLw/mnCSdPl8FbLIFZ8u+hhj0/cCVZNUlt4HcLrq8eG8x3EqCBU4wvCvTkJ+d1sx9gt8qdnqrJgkHbbiFRthwp+d7RBIQ6e1d67ykMvCY+zbACenkSzyEDXZYJGFHK17TEuXRysHR8C9rqDrNpRa4kqr7xDC8Kc/+gZfv/s5USfh/GyHza1L8jSgLD3KyuODT95kHCXcCVPiKKVs00FDx0aPr2qXv3frkEnSYVYELF5hWuP1+rLi/7VdX/jgf574vDdcEaia0zTGlw1bYYYr9bVF5MXZLvtRysFgxrg/J0kj7o6uGIUZP5+OOU1jjlMHy6DeYzfM7INZu6STPm7bmt7orHBVw6LaZDfUPF97nOWWzHeWwaKqCZXiflew6WsuC0XZ/EIZvhPUeNIQOubaxiQ8QErUi+/QxBuox3+K9+GHnP/rtwkHS/zeGreNfHX7CcH+FWqjBn9ov+nJBfVZQJP18LwE/UJSXo0pVxGNlowGc9IsZJ1FzNKYn5zv8cnSJdcGJSSe8ahFQW0KqiZFSEmDwVeKoSe4113R8Qo6HTuL1Z9VqK+MGY0eUx1FJC+2iG9c0NeKYHOGu5fQlB7OVkIz9yknfYrPevR6K+raIZ32UWHJq4D2vjOa8pdXY85zReTAg15F2Ui2ozWj7SsbL3x+DI2huWpIj/e5df8pFx/0cAR8c6PmxxOXjcCjbODRuuBz+YjSpCT1hEonLIMZUjr0nX1uNHcJ8Rg5Hnc7goO4ZDdK+f5ln6IRZFpQNR4jT7GRjehKj9f6DgJ4b5gw9HOSPODv/vafs571WSUxy8JnUYLCwXf3KKpfhNjUOmftzEmbLVzZsNVZsjGaEvdXJIsut97+nMXxFqdn23x164xNf8DPF13OMknsQseBq8JQaBcY4js1NzYu6A2WBP0VbpyxOtkiS0IWyx6fXOxa7HEREMyH9J7uM/zatwiiP8bJM8qDb+L9xZ+w/Q9WiMUM/TBjsD2nOB4xO9/kctW3OOrv3bFIZyPoLq/Ip7eIb1wg3JqtwatD9j5ZDgiUtdJ5SnOaxgQt+jZwahZ5yFsHz/Ba73jdSE4yH1d6xI7mdpxz1ep2ci35cGYpfTuhxGmFxLPzDTrtWC1LI55c7OBKw1kmybXhayPNwHVwJZTaMC0bvrVRIrEt/RdtUl2hHVzZsBNWjP2CrHbouBWDdqyQVy5na0t03I1X9JKYC+mQabgsHP72XkpWW4b+KEzpx2s2BzMW6y4Xqx4DVzOv1DXT4adXG3SdNoNENtzozTFGsMotMyB2ai4Lj0DZAmBRwlsDSLXgJPPRxl5MstrhZ8/vXl9Cj6Zj9vozxuMp2wcnbK0uEKJhNhlx1sKktJHX1sZ15XG67HOYdEi1ZNN/1TP+L9ev8/rCB/9eaAUvVaNacp/NVfdafG6pbURrUrssspBhd0ndtvQ9VRO2t+jQMVSNYFE5DFtPbl0ryjSg21kD4JYedaOIHU2pJetakOuGsS8pNcwrO0d7eehnWlBoqBsbm5u2vPyuU1G3lRQ7A+rbD1DpEmd6iqhtvJqUGuXWYASmVhZz2whM4aIvBGI5R3Ybe+gXHjIsIPBoCoFpBFXucX6yQ69nWfurPGReBDxZB+TasKgaEl3hi4A1cwTS+sdNRY4mRGGAaelzd+OC7miOE/8SR3sQo6YZQhpUnCOujNUThA7CramO+zRa0VSKfN6hrhzGN84It2aoQforeET+3ZVVbptzby9jxlithZIGXbp4UYaZVQgHTOnSVI49kLDzUU8aVrWm11hldk6FQNKgCVSPnrPDjt5HeS4jPWZLxeyHig3fsB2W7IUpNwYzG2m6DqlySdeVrOqGgfIZuIquY3izv2YzSoi8gtArKNOQw5M9ns5HfL6KOMwKOqLPvvc+lf8GpUmRKJRw6TdjIumwriw7Iu6v6N06Q53UOJ0MNygIgxy/KdmsXXppxKqS+Mr8v9j7s1hJkzQ9E3vM7N9/3/3sWywZkXtmZVV1Ld1d3ezmNEn1cMghKQgEAWkudClII91IgHSrC90JkEYCRpAgjQAJxGBIamYgDslhk2z2XtVZW2ZWLrEvZz/Hd/d/NzNdmGcUKW7ZYgVRUOcHJDILqBPhx938N7Pve9/nZdUI5xwwMK9dsMsgj+kOJyT7V3ivSczvTlgtUuZ58iL/XQhLt/EIgorg+AdU+69jN79Ckr5CHnaww7cQVz8m4ndgvmTxx1vMFy2shYtFF+/xDYwRKKVpyoDe4QWm9DGlT6f78pgOG1FBZaQDUWk3khuVLiEz9Roi1fD4bJ+y8V6E92xHzsYbKL2OvA0J152rVSOcJkJ7xMY5GrI8pqoCyipgtU6c6/gNvlRMa8O4UutgJIFSEFlJuZ53y3VbuzaSVR0g1lbBO8Mrnk0HbKULNrtTgqBiNO07IWIdcJm18IWlH1gK7USEhVb4az3GsgrZEJaqdvhmIRxPwhfWZZLUPqPK4yDNCFVDK6hoRzkX8y6l9qj/mctKs74cCwHzWq5BaQZPGqZlzKrxnH1WKzqBswl2O3M6G25NBcsli5NNgqBi2JmRFxEXi+6LALWHi4T9xD1v3fP734Gq/8sb/89t/Qlm/HMu8wQDJEozM5JV45F4NbHnMJyfn2wnZUxv6dCx2kgao2j7NYvap+frF8CfTHu0cHjeqgxo992oIF+m5EVE22v4JEuYVc5+lXrun2mtiJWk7Vd8byTp+O6LXRpLKgWrRjIINLHfUJUhVkvM1i7V4bcJnv8RajpGb2yjtjdpH7qTcr1IqcuAuggpp213CFiH0aS3T52orp2hWiWYEHCHhDKPeHq9xZtJRl4HZLVDl45KF66y0jVP1DEd08Ous8Wk8DG2JhMlbeuvbUkRf2FjRNSf4/VWyLSGq2sXu+pZvLgAaZ2zIA9R5RzpN8yf7uBHFdJvHNO/9kmPLlCvR1C8nHbek6Wj1fUCwWXh3u+duEAJw3LeZtjK0OMEmRYIafCiiuWk64AqXkOhFXNTExSS2lrmYknLdmkoSUWPA7PDfhSxVad4nqDjS17rOPJZa502lkQ5NzpTzvIQkPhCcFqXHMYRHd+1bt/cPqVuPOKoIPBrPnt4m+9fbvN45fN0pTmRFxzaHbpq60V7eFHDvHb6hLYv1xu4xAtqvN6SljQ060TFKCyp1pS07ah0kgYBPxj7ZFpjUeSNYFRGtBZdNstrvI2C+o3v4H/wAass5XjeoxVUNNbdhKWwJBtTvMfH1H/uL5CkzrIXH/1lADLhUb2REfzOP+bhkxtMc/danizbL9rUqe/GbFtf/5R60kZXPklv8VLWAsBhd8L5okPe+NRWEqmG+drx4hIbG35wtclmWLGbLoj8ijeGV2vni8/JosOyVniBQ26nnuXdzQuezPrURhH5FVorZqsWy9LFf1srSJSm7Ttl/3kuaKwTYMYSYiWYlME6KdAQK02+powmStOPcoa9CVkZsrdxTW9zRFMGjKc9ekmGWQk+GXXxhXMOFVqyrAUnWcDNtKQ2kqsiZr/2mWStNSHPXW48aRlVPpV2EB5favpxRjvKicKSrA4ojaIxkkIrVo2k1KCEwBcwqeAgMQzDiq6/Tji1DmBWG0ne+NzZP3ZWze4SGZcoI5hNei6Fb/uK6fWAJ5ONdXiP5MlKknouJyVScFX+O2j1f1k/t/WFN/5PZ10mlUcgLdtRxd3ulEnpvtxCWHaTFZd5wnaS0YsyHo032Gkt6MUrAq/heNHhe6OIxIODpOG13pTrPHGRkapBa49kOCPoLwjOhuhG8UoV8qNJSjeAlhU8X1mGoSCSglHV8J8/h5Y0KOHwplK4QKCzXPJw6bNsehz2Rsyf7tL//XvE2ZLql/5H+O9s0zz8m6iTp4QHS5AQVDPs/X0ePrjN9MEd2lHB4e4ZW68/RrZrllcD5KSDfzFg9fttfL9hdD2gqgLeuvGY6/GARREhBPSCklEZMW4qTuU5V/UDjpsxm9GbNLYkr8ck/gaVqEmUZDOy7MUly3mbdNomGM4RNzeovlshwwqMj5cULD47AmDxfBv7dIf+G0/o3Tlm+WybYp6y8Y1PiT49pDgfIEcNpk7o/vWf/aJ5svLZTxr24pqd2LVn/8ybH6GUZjQaUjw+pD9t09m5Jtod0Xr7hNZ7J3S+P+Lg0RHPrzf51XybR0tYVZqWTdmSCY+MpGc73EwibrY0tXGHCheiY3i4jOkHPo2RhPOGf3iyR6EFlYFnecl+FNELYD9ueLU7w/drkjhnlSUcjzb40WjI71+BoUEJwR32+M427CcrtJE8XMa826te4KBnNbw9GGERDuNc+sio4uKHr/P8YoesDnjjxhP0rMc3bz7EGMnvP3kFbT3GNoMGWLa5KBJ+Dbhb+xAKvH/8e4hD3GtrPLphwdc2rwi9ml5rgfQbxF/53/MvDVWe30ctJ3z2t3+Z41mPx8uUpyuPs1zz9WGbW62MQbxiY/Oa8Ue3ae9fEe9eo1cvA+W0Xg/TAW2/ottaEKiG3zndX9t2HXnwleEVD5e3qK3AWOG4H0fPuP/8iOs84bA94+kqZlYrBkHDW5vXeErz+sYFxkqKOmCzP0YpTTddorXiatHlH51u4wvLfiI5zT6PznUdRV/Cw6UTzXZ8d+Be1oqtqHIiZWH44Oktvn73HmlvjtGK4+M94qjg4eUOz1cttIXHS4+WB6GyBAp244rjLCT1NNtxyf3rbXzpul2+1LwzvOL/8eCASEHPt2xEmv/q+YBvDlPeGl4xGEy4Objme6eHPFmFnGaCT4oJR16X2lq0tdxuebzaWaKES0ONVMNX95/zfDzkeOm4IN9uL7l/7w7eA83OzgWepxnNegCE0x7LMiJQmm7olP+/biTPsojISDp+zavtl6zqt/ZnH9Jjv7Tz/azqC2/8R2nG7ZYh0x7zKmBehdzsTigbn4tVm8Sr2U1WGASTPMGThk/HQ45aAck6Wvetbs2icSfcH4wG/PLOBbMyptIentcg/YbRJ7coi5Cm8WhFOV8bLJlWAeeFz/1Vyf1micXgW5+haLETe3R8d1M7LzS/c1UTCEXf94iVZFVGnDw9QCpNL3yE2vh/oZdTwuUclhnFkw1+9Aff4O6dhxSrhGmW8OrBc5JWRtRdODBO39l7mjKgKkKurzawVnA663OZJ0yf3uKX9o9fCHWUsBw3C0J8+maA773LY/vHzJtzsuoEawtif8BcTqnNgNSzvDa4RilNNJgjuxVMpgSHDWYh0fOEZhXjpzknn94mjErS7oLxR7cZvP2IsLdArmKacYugu8Rbe7aL85cTzDIMXXSoa+NqNpMVf/jpGxx0p9y+8Qw/KmkfnePfXMHuNlQVzOboyn8RI/xo6W7Wc1vwTD7kuZXcsLfpygBr4dX2kg+mHTbDho6vebIKmFaCee1zkXv8ZNYi9dyhr+PDe32BECU3Wwt2OlO2ty/pH1xw+tktnk2GfDjpkWtJaWuWtqQlQr418Pn1w8fsbF1ireDG8QFHO2c0jUdRRizymNivOLr9lGR7jG0Ui6c7rLKERRlxlqUc/+QdMq2IRpsoaVjWPqvG4KNYiYLHesyvJBvc7E7p713CsI/w5uhTweaNE/5CWNLUHjtvPkLnAX4nQ/z33mI1eR+VHBKF2y/e9+z8t4j//t/k/t/6Nv/tg9c4z32WjRO/fnPD8RXAAVyS4YyrJ/tsfP0evHuEal4em/3pMmE3Vi/cOr6wbCUuhCpUDe+fHdDxGzajnEG6IvBqmtqnG2dktc+Ho00XshVoh1qed0lWLW53J0RB5ezDccHZteNSeEpTNh7f2RpxmSc8XsYsmgZfSi5qN7tuSZ9zO+f1oE8VuXHarVbGWebSE2+2rvnGaw+RfkNycImIGoyWfPTp6zxedLhaA24iBS3PEiqDJyz35iGvtCvCdbv/qDdGCcPFostllpL6AQeJIdeCWS14nnnsxk7hv6xCzi63+PHFLk9XIVnjnB8+HsfNgoiADS9kN9bsteZ0kxVR6Kx851ebHA5G7PfGzLKUs9Nd/LXLajrp8eByh8PemKwKOV90UNISyDUCvHGZBakyLzJXRuXLOwh+WT//9YU3fm2dRaUTVHSCikg5XO6siLguIt4cLLlYuejc0GsIVMlG4jagaR7z8bTNUVoQKceh/rwGsZvBJrGzrqXDKS1pqLOIh5/doR8WjKuAQgs2/ZC6bhjJMY1oqE3MuFSA86aGUjDV7qG71O7m//7FLm8NRrQuNgke5KT9H0DeQCgxY1heDFkUEePLDYKg4sbmJZs3T6izyLHmJ22CbIEfF4gioCwiZlmKXMNGXMCO4HrZZlT6nOZuLpwQUlJTi5/y6PP6EmtrQGGtpmN6dCNJPyjppSt27zwluj2CbghFhc0ExfEm9SrGGoEXVWzsXSCVQfoNdRaRHW9RTFsYo4gGc3QRUM1a6EahX9Icr+u7B3SxftAvqwAhnJujyCL8qHT6AikgzzCbO8ggIGhlKKWZVxGVsZzaORN1BRZa9Ijw6QWK3cQS+w39wIFttpIV83qTUCqWjaA0ArAMA+NmusI9WEsj8eTaIjbrEMYF1Vq9HEonnLqZBCReQOpZUk8zXqX4owFxVLA7vCZtL4l6S6TSNEXI7GqAF5VUkzaL51tcXmzx4HKHvPEJpGHVeHT8mlEZrANzBKVp2PUTIKHQBl+6REP1/nvcOD2ltzVCNwprJElnifIborsT7AJEZMmHtxDlFLu6IJMeNuzin39I+I++y8kfvMOPn9/kJPO5NzcUxh1+Vo2iNhE305zQa8inbeK1VkSMrmD0cvQe4KydvjQvsis2IsfAj5SmHWiyRnGQZvSiHF85zOyD0711mpx9gUd2tj9JKC0LKzhedl58tmmcMytiulFO5Lv5f8t3qO7YMxwmPsvaoq1PYRoy0xDis2gMFJLaKGIVraE45gVboFwkcLyFEJbp9YBVHayTAl2mhgM3uYOuG1u4joJE0ljBR1fbDIIStSZuPl60SD3NqPSZ19APfqqPus4TrvOEZ1nIqBSMSsN189Obd0f67MSSQVCQhgVxVBAnOWlv7kYg3QW6UfgX7gC0KiOyKqQxzj75ZLLBqvYptCLxGkoTryPOnfbglc6cxdpB8jlr5WWW+HLG/3Nbf6KNX1tJJCvaYYEQlkURMasDCi1JwpLVrE/sNcTCzf33Nq64mvZZNR2uSsGNlhP7yPVCrBpFL17RSldEcUE5T2kfXuL3FwR5QPPJay9CObSFQShYNCFjJDUVJQ1nTQ2khFIg1vy43DZk2rLQEn0d0g3a+JcarSWHSQlG4LVy6lmLq3MXepPlMWFYsnvzmHBrglom1IuEOg8xUzfrF8ogpHFJcJUDrHjSIAScZSnTWnFdWJ5VKxIRkFlDLjIysUCbCmOWgETJFkIoNumwm1i2ogLfq0nfPYMtx92nqtHLgGLaRlceflw6Ed3OCGsEpnLzy+VVn+WihZSWbu2hy4Bs2qZpPMI12vhnXW7DU2hhUUIxr0J6az74KktIuwuEMiA9EBKTdLBRStC/h79GnWoLUzlmaUbEsktX90iURy8Q7MafB7FUbCUrBumCftBHCaitImsESv70MOqtg5Dyxs0/50XCqozQ2qOogjVGtWHRSA7Smt04J/Iani7anGUt58dOVuxvXpIMZ4RbE2RauY3sIwFGshh3OT/f5nrV5vGiTSdwmoNCK7p+xVkeMaoktQFPOEV6rOwLDsVns5jHy306Fzt8a+uCbrIiiXNa7SXtrTG0Qijd56UW56j5NXI5Q0xG2Islou3x4B9+kw+e3+SzeYuTDB6ZK4wwhDbipGxQYote4NMYRbZo0R5MMVmAeFZQnm28FHwzQNtv1owPi7WWXlByWYQEtU/bd6OTrWRJun5uKGU4XrUZvsi9d4mDuZYvMjmkFZxkjkwYKUN/vMG8CmmvUboAlXHqel9YdmLDlZAIoZjXMDI5QxlTG8O0thRa4kuf/dhZCFdlxGLUYzbrIISlrn2eT4dc5zF54z7HQruNu+1rQmnIGsVmqMm1pDEuXvrpMuAo9V4gek9zjxtpjbYOY7EXN3R8xyOZ1R75+lBRaLhuSh7J+xyaW0QEbIYeh0lDPyjpd2cEUYnvu4CuzTvPkGHtckNWCXUVUDceizJk1fhsxitOsoTaCEJpiaxgWfs01kGiukHFMFkxn4YU2rkOXnp9ufH/3NYX3vg3ouwFeWqcp07IVQVESnOr/VPh0KrxsDbGSwxPLnZYViGeNPzG7pTzPKYRlkAaYq+hMh7HswHDOsBfB+6cf3ybwf4F0WBO6NXEQcmtKqLSXf54pMisizIFjwbDSF7T0RGFESxMxbUcM+GMxpZI4TGvX6V7PeDePGFvtMlvGInvN2gtmSw63B9t0g8LkjinszEh3hnRzFP8v7ZDdPKU/A8jiuNNVjP32FTS8MrBc/7xx2+/iCUelYpJpTjPBZdVyak6IbFtJJJMLBhVjyjrU4SIsLbEmBVDeYO9MOAr/Rlv7JxQVgH6va+iLk9gtYR+D55ntI/OQViEp6mnLWZPdlFBQ9heUechUdvNp6siZPZ8h+6NM8LBDCEtMnw5wSyVkRRGrMl57qYzKmKEgIGdE6Q5zThFfvsVmrf/Bv4P/m+YVg+vlxGGJZMyoDaGfbPHQvSYMWIu57zitdmKXATtsgrYijOKxuPhaItVo7i/8AgVBNIhpO8t3Pz1RlrzzvCK3z3bZVQ6P3ns1Ryf7TOMciqtSP2aX+lO2WzPiMLSBcUEJefr8JPrVYvLVZu/8r9cwh9eoGcxqpsT9hbUCwdJuVh0WawpdcnniW6NgiJmUkpWNXQC3EYQGnzprJzfn9Rs+O4hXBjBpNrjIKleUN++snjCjf6C+dMjmjJgc/a7jD94hSAV1MUel2fbbO5c8v6TV/jRpMXDheW75seEskXbdPCth0RwVTa8P/JZ1Fu8fuuRi0Z+svuC+PayKvaan6bYKc15ljIIajKt+HDSY1QqlyxnFI1RvPGVn3Ax61FpxaSMeLryCZV16GfhnBBHacG9eUzbN+zFFT9c2+I6ZURSlNzojXgw3mTReGRakjeC/UQTlgprFbNK0fEVq8aw1A0jkxHmbcalx7xuURpJXvt892oTf43FfbryKDV4kjUn37IbV+wmKwrtsWxibrWX/HDcZa4h9SwHqbMFXq6DfVqepTSCm2njOqTrTX9ae1TavUfp2tnUkT5dNvDx2PJDbrU1b/RmbKRLDr/1Ic0yIb/qMb0ccvSdE8qPWxTTNr5fk60SoqBiKCxJ7fPheMhOXNDyq3W2Q7Eew7aYVy7W+Mmsz5NVzLySxN6XrP4/zfWFP/0kqFiuHF8/lJqd3ox27fPZeIPvXXd4p4xeMLK1lVxkKcMoJ/ZrRGOZVi6OUsCLrGpfagTWJWjNuty485juu4/RU+dbT+KcwdY1RR1wkSXcbCnCPGJSSrRo2FIpS7vkvnyKZz18GXCqP6ZopiT+Jons80j/gN7yO7SVz8OFzx9dv81h6lp4zm5k2IgdAa2pfKaP9qmKkOHfOaEo2iwuhlxfb3D4yhPGZ5vMF21uvfaQ33jnx3z6+DafTgbUxn2hH+c5IzmhY/o8NR9gMazyU4x1Iw8lY5QcEKiUSuQcpYbEq/GUZu/GMepHjzALiWwb9Lt3EFsa+/ee0ixSvPaKfNRzlqZZF3Oxwd5rjxFrXYDVimzUdYz+sw3KIiQIK/Z/tuvF/R7Cshm62+7jZcxVmfDr2zOUMJxNBhyap6jf2IeqIPyd/x1WKvTfveTe+9/hJ6cHZNo5MU6rgmtxirY1vxa+xpu9mpanKbTil2494GS0yfmyzagMiZThvX7J718F/KB+zqV5zG3epRQlnVmLj2cHfHMj4053wqC9IAxKrqZ9l1zWndDvT4nSjP5rT2nmKcvzIXXto4Qhr32HTK19fu9/doc0PHjhBPjk5JDLPOFub8xXX/2M+09uctSdvOC5b6xavH+9Sct3efDLRjAInAAWoDYB7/V8vjspeK6ekds51eQ9fjyRfGvo80vbc+rG48Pf/hajVYt5FWE/egdfOtzqovaZ1h7+/dcojKAXGL69YQnHXyHThpmtmYolnlWEUhIr1wUZj/vEUUmRRyhPEycZLyumZyPKiLyGSiuu85SnSxeu1fI0w1bJ6103fnG/X0jw0WtIYXkw75I3kj+/f8njeZdMSxojKI3ks1lM7Nl1emXNq92Sjydd7NottNd1XIKjdMlhIni4aLMTFwwCn91Y8Uod82QFHV+xFSlSL+SysFTGcpIJpnVKME2ZlM5Cpy0c5yWxVLQ9RWMEq0bwcBGwHTt+wLyGQrdfkAIL47oNT1YJu3HBbpzzw3GHx0uPXuAOMueFR6wsg6DBeDAqPX40WXvrbUmLFn91z+MonXFreMXO7gXJYEY16tIUTg+TdhfUD0NUXBGkOddnW/QGU0bzLvl6lHWjtaIX5tRGMa8irouEg9acw86USrtY4uNVSqUlgYKe/+WN/09zfeGNv5eumBYJxgpCryEOC8arlG5Q8V7fkPo1vjR4a4VrrBXaSOfh92pqo1608Txhadbc9u10gRCWSdaic7FBe3WCjEvi7THb8IIDkHoNvcBwXSi2bJ+FLThjzkieUOmMQCaEooWxhthz7fKlvqbj7bLSFbU2+FqSSI+TTHGUuvzuxHOHj+Wq5SJ3o5LR9RDP0xgtKfKI4XDE0/u3UEqTxDmj0y2OL7e5yFIy7Vjh2kJXBaD7LEVO3ozx1T8ffemrNrHXJ5F97uo79HyNJ7W7sWcxtgIM2KVAPfwMpEB0KqyRVBPX8q/LgCRdEXeXlPOUqLfEGoOpFXUesrrqIYQh6SyJOsuf5Vp5UVtRzkmWcFV6XJfu0DOtQgZxzuHGJUF/gfz0KfZoHz3YQj19gEoNvtfgf8449yR7dQff3GQmpzTWte4FEEqD79dUjSJQml5QcVWELBqFtbBpNujTx0MR25C2dCS/yyLkbs8ihKGqfTpJRitd0h7MSLfG+L0lIqwJwppuUqD8ht0i4NGTm8ymEVnjcZm1WM7669lxw48nHdqeJlp0kc+OGLQXbnwgDdpIzlZtJJYHC8miNtxquajeULn2cLoGqEiRcqd4jUVtWTQNfd+jMPBg1uc6dwjsURFTG8mNzoyTRYes8dbxxyCkZV4533fsWQIlWDSWGqchscJwlEq2Is1e7DpYynOiuCBxnYuXVZHniHdl42GAlq8JpaWxglXtk6iGh5MhhXZe+3mekNc+kTT4vkXgRoD90G1GhVZc5CGZloxKj0y3OUozYs/N5q+KmEJ77Ldn1Nojr32O0hWVUWzFOXHtMyoTfCHwpDvk5xrGVYMFtFU0VrJsDMZaEuWwzwCZaYisJBaCxANfCpYO+UHHd/oWbQVWugPDs8zpjyodk3oGi+sWLGrBzAqkgNoItPUotODZyo0iE+Gx7SUMQ8mb/XOONi+Jk5y68nn88V06bQfi0lrRaMXZ832OXn2In5R0+zOCJGezNyFYtplmCZHX0E9WhEGNMYKn4w0AyjWr331HnSYnVU7f8mX96a0vns63VmP7a1Z503hYBP0oZz8oWZUhgdLItWgkUA1ZHeCtsb7/7KYvhEXCC0ypMZKsCrgcD9k6GxIM5qikoHXzjPKqR7czZztrMSpDAqVoKRfSciFHTMrHKBlibA0KjK1peZs01lHyEttmIsfENiGxMcpKSq0w61N7s9YuzLOEplH4fs3Vokuz/rJYKzhonXIyHdCLMwbtOWfXm/zoeotKS/K1B3dWu9/bx0VjChTGNkgZYHSGECGx1ycSHbpmyK00JPEKfOVEUU3j/j4RGmgE5rgBabGVh20kuvIxxn1xo/aKeHPC9PE+ytPIdYRs2MqwZh0P6jX/PAjoZ1iB0mRrsFJtYDd2gSahV9PtzwgPxq5zsVogPQ/9qMbkCUlrxW5nttaFOEZ6XcZoo1k1hnntoE2eNKyyhLxxQqVCK0aVx7gUlMYQCQ+Fj8bSUh4tT+Kt2/957bPME6rGI/Rr0rXA1GpFs4hfLHgZV/R/8RHNeUT7chOmfXKtKPKYs9xfW8IsjxbwSlvhFSGNHfD1dEW1XrfVOvWsNJJVY1loJ9zsBTWJauiEJZ01Mz2QmlHp2sH3F07LoIRlWnuMK4+3vBpfGNKgZqs7IasCPBkga5/a+HgCaivIGlg0Am0tM+OQx7GNSQg5SGpe7864uXnBzmuPMZW/Zk/kiOjlqfqLxmNWRtRrDkG0VsCX61FIpAKmtY82zuJXNB6r2nfPC3CwLr8m9V2X5HOEcWkkuRZkpbuxV1pipYXGo9SKm/1rysagjXu+RDg40Kr2WdSCxgIG1h12NJbaGtQaxjNpKiKhkGs7sC8knnBaISmg41uyRjCpwAjoB3atineJf56wLGr3s6tGUBhFqd3/Zh3ZPK9dW39RS5a15aIq8RDESrGXSF7rlBxuXLFz4wTdKBbjHuNV20UdG0m1zhHQRtK9mNHdvibuLDFa0u3PXPBVEdNUDiAUBiVKGdqrEt9rKIqIeRmyaHyUtBjz74jRb+3P/sb/pZ3vZ1ZfeOP/g5NDtqKCTlAiheV83nMnzDVLumx8tvtjijJkVUYgLGlQ4ivNoog4yWK2Iwe+iL2GfphzlrVYViFKOMHc1arN1ZN92vMWyXBG/N6YoFmw/+49Wp0Fj9//Jm3P51IIDBYjDHVzhZZthCfRtqFqZij/BrHsAlBRcVZ+yGb4KqE+YGJX7Hl9RiXMa59EWTbDACUMl8s2p3lC6jU8/NwTu07rmlcheeMzylpUWvH7lz57iSBSlkxb7utLFzUrHZ63HeyyqM7wVEonPMJYTSL7JLZNz3bYilyUra8aorAkCCowILYTWOZU99rIsGb68ACpDHF/jjWCpLPETwpHGAQm5xvuRrtzTevtE0wuGb3/KvNRH/8lbfzzKqRZz/i3Iss3NyYMkxW9dI0b/s43nVbh2Qnldz3OfvIeWiuGu5e8vftjjs436H36BlfFgMYaYkLGTcVZFpF6Hnux4cHlDss64NEy4enSUflqYzjXKyZyTC1K3rKvsBFKQuUessOw4SpPucpTsjVWtT3aZPt0ySBdMstTvvWdPyIbdQnbGeF//D8l+Dv/K7o/ntG63iJbtvjJ1CFgF7pmxJLQ+gyqNp7wkMDppO8IhJWh0P76Viq5kYIUEdrCdpKR+hXD1py9wxOm1wN66Yono03mdZdXO07g1fE1w7Di8TLGWsHdjUu6nTlaK77+xidcnG9xNutTz7tMa299c7VclDWRVJyoUzqmxy014LWO4HZryq/88h/R+quC/O2/TPTsfTAaWeSI0eVLWQsA92d9DG50Fq1JfKHXUGjJZeFRG8F2VLEwHotGoa1kWrn2eaAc9XMQZ2RVQNb4LNdkyEgZfOk26eMs4DIX7CeWo7RAW8HVssNGusBPNI/P9/j3XvuE+6f7XJUB89qyagyZ0bSVx04s2Qx8rqqazDRkBgyWRCmydaRy1/N5tSO4yN13azNsuF+5Vrq1DhSUacVZ7g73PR8GgaW1bpvnjeQ8d1z/V9oujvgfXRi6nsd1XVFSE+PT8wJ6geROu+KX9p+xc+OE1uvPKU8GFIuUo60LfvT0Fnnj0azdB68Orrm42qTII3ZfeUax6BK2MtIywJ/1mNUB41WLVRkhhKUVFrSTFasy5KqMuDcP+fpgxcNlzFXx04vPl/Wns77wxt9de/FrI1llCQZH5avXqVaR13B1fES0jtrNG5/d9gxjBZ4y3O3M6YYF4zyh0B6TMuagtXjRhhomS472Txi+cozOA+osJJo1yNhSTdpYK/mFvedoc4Nu4PN01SbPSm60foPL6h61XmCtQQiP09UfEHib9MIjBJJh8Aq1LbhQp7xj73JR1HR9j8MEvjpYkmkPUUWUWrGsFd+9Dig1WCwCQesiZVxpNiNFz7eMSsFpveTHi2sa0dAzAxKbMBIZOXNqUTDkABFKAhLaDFiIMZFN2TfbHMUhtbH0w5JBe053Y0I8nGJLif7MYMo2Qlq8X90guloxPt5mPuqxcXSG9BrmZ5tcXm7ieQ0337yP0Ypq3kJdFzTLhMWsizGCcHP6UhZN6tccpIKTLOI4k/Qjd8Dodmf07zxn8Z9s0P9LK4oHA57/+DWi2Km5V9MOTREQd5a8vntC7NU8XXS5t0j4B/NT/GKDZePzbNXnm8OMll/jS0tpLCf1ig/0b1PrFYHX5lX1ixjhErr7gWE/rtBW8JNZSm1czHGgnPtgXAWkyw53e2PKeUr/9aeojRL7f/6fs3qwi+83+FLzO5eGV9uKD+clORWHqsuf323YiqfUa8pa5DUsqxAhLalf8a3NKx7MewyC6sVIqxMUbPfHBEHF+HJjnRKp2WrNnaag8fnedZfHS48Ha4His/WoaVd7DDoz+rePWczabDQeh8Mr/ut7r/Pp3InUAI7tnLv2Bm1P0Qskr7Qz3tg7pv3VY7Kv/sekvfdYAXgRopwSBj98KWsB4LLwud3OSFRDbSVvtuZMiphuUPF6193C52XIovZY1oqPJ33GlYs19qVlN3a3/2G6JK5CrosB09rFcc9ryaKGw8Rwu23Wt2yPXlAzLSOKxkMK5yT49PiQT2ddCi35te2Ch0v3nf68xlVDYRu6MiBWij/UH3JlBrRtmxYhhTH81njBkdcl8QT/4AwsNZmtUbgx4ZOVZBBYDhJn0/t4FnBReFwWmqWuudMK2Us0H0x8rsqage9TGctcZNRUeLZD6knaPmyGJRsbI1RQc/YHb794nfNFi9R34VC1UcxKl9jZSjNanQUqKtn4xqfoaUJdBLTCgq/vP2eybNEYRejV7Gxdcn09ZKszY6sz441lh8ss5d3+nEIrjlfxS1sPL+rL08XPbX3hjX8QFmsPqLODLBrFrRb0woJuVBCohtCryeuAovaRviX2K+Z5QlY55OTzRZdCO9Z/LygpG49JGbHXWnCwc441ktX5kGQ4I9q+RARQn7UQ0hL35+x5DV9tfH54vse8ltythjyrA3J/zlJIqmZBo0cIEdCYFaP8M7SZ4SkHsvFVyseBz1g/oaN3OZ3eItNtDpOGju/mqZmW9ALBqLBMak1lNd0goraWe6ucmgYP5Tznps9CLriWZ3iEZHZCY50VLZcrGl1S2jmFnHPA6yzFnArngd+KGjaSFcYosnmLeDhFKIu3X2IXBcvPDpC/d4X0OgRhxcXFFvapZOPwlLQ/Zy+s0JVPU7ibk1Aak4f43eUL/Gg9bfEyMB0SZ8HqBQ2DACZFzDBZUeQR86c76NrHPJwjRIfhzhVNGSC9BtN4NLVPVQXMVi12ulMCTzOptjiwm05UZS2nuWFUBpwXIc9XinHlhHTGNhhbUTUzHssfE9pfYKBbrBrJVekTSstJBsY6wmNeKJRwgtKW57ObhFRZjPAb2OohpCTtXpD/oza1UWyFPlJAIj3XIraWceUBMcOwYDdZ8mjWZyvOiP2afmvJ9t4ZNy43yIuIOCo4fPMB9SrGi8oXHICkveTybJusChkXMU9WMZPK6RkCCasGHi59hHAP/JtHz6kXCbt3n7Cxihmfb/Fuf8KqGXJ/3uZZveAVdri0K2jcnDhRmiCoKB/2SP7x/4Xs238JWa2Qe3+GhsfIzz6Fb7+ExQDcaOXMax9fGu4MrhmtWnTWsbraSOalW6O32gtu8VPra8d3zodFrfhk2qOfJ3T8mhvtOcW0x+YajbtqPIZhyUkWE3uatteQa4VYI3gjpdlIMo4XTo/hCTjNQ+7P3TpoDCwaw522jyd8LK5D9NX8LTLdMBErLsSYbTNAoVhpTW0lUgjO7ZyYgK4M6Acep0XJrBbopeQiDzgtanwhCKRk4Ac8XdVo67oEg8AjUgIa2DEdJDCMPL46qNiKCo66E6wVXD45ePGdLfKInZ0LgqBiunROovdeuc/g6AyrJSopCXcmZI+3XS5GXHLz7iNOnx6wO7xmuXL5DyfnO2wNRlRVwGje5ZPJgHntMQhqQqUZvkSXx5f1819feONvhyWjIkZbQS+oEMIx6UPpE6+V6eDsbq59h0OmmhKDC7SYVg5wEkrHtRbC0glKOlFGGJXMpx0AgjQnWCv/denjtTJUUrC67iHWTOzUs+zEEm1bzM0uwpOUssU0H4E1WJtjWKeB6dH631OeNzO0mbEUxyyja8zi69SmxSB0N4NZ5WaDn0+nDKANhFKyMJZM5E4rgKRFCAauxTOU8Mn1hMaUCCEp9RJja7SpKFkwj3bY1Fv0VEikHFCmHeZIqTFGIqSFwEINtnSvJTsbovwGL6yIo4KmUdR5hB8XhK2MOo/QtYfRa12BtHilj24Ude2xvBi+FO92ob0XEc1Oq+ES4RqtaMoAIS35022skch15rppPFaLFK0VUVwQ+RVhUNFqCnbjkre6TmE9rjQLXfN8zXrPNcRKYXVE7DlHhLGGrL7iKrxit0mpjGDZSHfDxbVlVw3kjWWpDZF0D/FlFXB9NaT1fIu0fQbbHfSJz+RqyKyMSD33c74QtKRH6ilaXuOChZQmCSpa60NI4DVEYUEQlxy9fZ9yluInBcl3VtiTC5pxir4YoBtFsUooypBGuzmsErAfuxWmrSO85dq1iivtvpLlIiXsLN17WTj/etvT+FJhsWxFHk2e0PUdlrY2kqIMGT88pDWf0C7+NiQRenKCV6zQl94X/7L/CUsbSSQNiWrwpOYiS9mKMyeAtIJF49NeZ3oEnmZehUTKW5PkDIWWTGsFBEigHZS0/YZwnfiXejX9KKc0Lnwr8WpMGdEYp9fJGw9tBOdFyDCsiICTzCNc2/KsAoNECRiE2mGDGzd3l3jUOkauWfgWS2k1tdaUNCzlnL7dJVYKKSCUikw3ZGtt3GP1nNgmDJs+HemT2YZSu8jwSAli5ZxDjZF40jIISu50J7Qj9x3IVg7M7AX1GuokqGufrd0L/CsHO+rtXhEdXKEXMViByX2uHh8QRiVhmrtDZuOxuXNJu7sgnXY4GW2SFxFZETuSo9JMK6eN8qShG/w7SOf7UtX/c1t/AjtfibaC1Gt4Y/OcWnv88GKXqyJGCMtQGkZZSicsScOCKo/xlGZr45peGfDkYgdwHvDKAITOprNzShQVZMuELI9RSlPMU9Rlnzi9wGqFN1xia8nx6R7PpgOsFQyCBiUUiSdZTLZo2zaN0HzsXVM15/+K30KjjQsCMnbFNP+In0Q1efY19osunhAsdY22hlT5hFLiI1k2lpYnUSKirQOUFJyYGUNatGxIY0sshrweo41TT1tbIUSEFCEWw7PsD/jl7n/EZuRIZ7URhEHtEt6iErE+OOX3NmjyiKCzpBx30WWAH1Zs7F5SZRH5IkVXPn5cYLREKkO+TKmrgLT2WcwOKMoIvd5kdv9/Xhr/6prXPt2gotKKqzJgOzI0RqGkcaAhI7h+uodUBs9rMGuS3mjexVea28PH7KUZo4tNBJY7g2t6YcE/OR9yVVoySj6cw24QkvqC7UhSasl+8RohIbWs+VT/NiPzlMweUhtJbQST0gFXauNEVQAXesk2LSKluCxi9OkheRlxdN1n8PoT/uD//ec5XnY4zyOkgItc4wlB6kk2I8Hd7vSFoDXya7559zPuPTvCVw2+XzM+2+TWN/+YWAlIQvT+K6h0BH+0oJi1mc07XK4RqgD9sCD1agZxxjhPOMtSUk9xVbocDG0Fq0WKXwbMRz3mi7ab8xvHi5jXGoNhK7LsxO5nEmWYVAFX0z4X0wHB80NeGXeRXkOYjPGSgqD/8kJZjrOQX9gY0w0LLhZdHi9jR9vzagyOdZF6TuybN84tkSpDJ6hpezVCgFkleMIp+s+zlJZfM6+dVPbzuN9XB9cANOvN6/E63Mf5+CMyLeitDwyBgnf7mkDaF5CgH018er4g9QyhhMdLSSAlB16EtRHjumFCSWh9chqeyfvEokvf90k8waoxDALFs6KmpqGm4Th/H1+lXPmbDNjjhtilF0DXty80HN88eoy1AikNceTGXlp7LFcJ40WHW4fP0c1PRxLPz3b5xl/4p4TtzCVuFgFId7CvJi1WDw54cHLA3mBEnLsDct14JP054daEQe0x/q3vcP9il0o7Z8xXd4/h7IDKSJc4GL0c/c8/V19u/D+39YU3/kXhZvKjIuYfPbvJG70ZB60lnaCgE2csiphbw6ufLmwjGS86bCqNXIM5XuuNOVu1CZTmaM35X2Zu8deNxzd+5btUi4R4c0JwOIPUQ3/mM//oJgB3XrvP1qhPU/tkeczFrMenkwGlDjjOPJ7UM/air3BWSmq9wJh/s4VpXnzGB/KU59EdApGgRU0sO3xV3AUg065rkGvLsZ0ylhf0zSa5yDjmHpVZIoXPOPvx/9efrLC2QNuaJDzEEyHrpgJSwFZcMF2l+F6NX9VMHh0QXg6YX/epa59wzV3v712STfs8eHqDYWfGIktJwpJWusQPK5Q0LBYtrBXsv/MZix+/DkAUlgy2rr/ox/snqkAajlcJmZZURpBryWsbl9SNx9nzfe584wMuTncYDCZ09y/Rle8sfHGO1h6TqyE7t56zf/cJpvbI5y2mn75O6lnmpuYj/dvc9X4RXbWgAoVkLwzpWdcRCq3PG8Gf5ZYakGuNsfCLm1N+56LHsoZAwTAUvD9f8Fj8hLk9oJ7t8ulcokTC1+Y3eWM8JP74Lf7h6ZCtSJMqw37ibtRPlpqrqiHXHk8XHf6Dr/yQzVefEOxPEfsdDp//AD1N0HmASgtGv3eXdGtMsDtBVZ9hd3fxD1b07TOE0uQPbzPKWpytUq5Kn69vjPhkvEFtJL40vNpZEGcp6fqQdDUZMM9jPhoPmVQurvfTmSDxBPuJxx3V5zAp+WQeMq3cWnu1Y/j7x3uknmEYNFTaI6sC3jx6wuatY2T6ciiOALfbGb99PsQCN9Oav3jrCdMsoTEKg0ACy9pniTt8KGFJPGd31NYd6n7z7mf88PgGx6uYUeW9yLXXRri0uzLkVntON8rxlGZSRnT9Gk9YhsJyuztFG8FkbfW70854uow5SlfEXo22kqPUstVa4ClNoxU3Wj0klrPcWVN3tc/rdotP5zWlrXnTvM1RHDKtDZV2YUDzCnaDiFndsLAlfzb875LZBt9IYqmIPcUvbs55Y+uMdmvJfNFmc+uKZDgDYbFa0fqVEcwKbAO2lFz/8esUC2f9DcKK/a0L8osB2bhLVQUEaYFZBayOtzh/us9PTg95deucrIgZzbtoK7m5c8oH779H7Ff0ujO2BiO0kTyeDjhfvyeXRcCNVsYwyr4UyP8pry/e6o9ysiqks24RVVpxvErZS1yYzqKMKBuPTh2QhCVJULIoYk7XwRqhV2PXClWJJQlKNrpTqtrHrr3gCENT+dSLBHHeYGuPePea5ZNd8nmLpLcgbq2QyqAmHc6nPWKvoRd4NFYCXT4t/4BApdTN7Au/CcYsGOef4Kk2AkXhdfltcY4nQwKRENkWY/McT4ZIq3jQfJdaL9B6gcXiqQ5CBFj70/aZkg6XkgRb7Kk3+YXggP3YEChLJF1YhpKGuvGpSgfbGZ1tMdy9RAjL+HyLwc4lbYd/PQABAABJREFUZw+PMEZysHVBlscUVcBo1SKY9fjmt95nerZJEud4fkO9SCmrgO2dc7yoYnr5ckJ67i9iQmnxpKXnG97sT6gaj3ai6Q1GXN8/ehERXK9i4u0x1gg2tiZYI7n69CbVKibdnDptwqSDFJadqObVNGaSfYtPyt+iE7o0Qk+ERNV7TMWc2MY0QvPM/gSveZct2SbXlt+56KEtfHVQsWgU9+aKHS9FN2+xEiueiyuGpk8sPE5zHyVbHCUFw8BwWShAsREaTjKDFIKu55F6ko2oYHjrmPBrFdU7f5bw7n+fPHtK9N3/K+KPL5g/OHDBNL0lMjbo5z6qvXAzImFRnqbXWpCGBYFskGJ9eFGa0kiyRuEpw2vdCcn6u3U667kwrFpxUQguC8OZXrBtWrQ9ibGCZ1lA1sBZUXEuRxSTHbYigUDiC8XzeZftZElZhiwvh5TzFlsvZTWsO0C+oeVr7nan3H3tPmfP9hnNu0yL+AUe9rwImVaS/cThjvthgZKGs1WLz8728YXhdntFqBruzzvEyqCloDaCszzAF23KxmOYrHhv/xmt1oq4tcJqxZNnh5zOemynS/f9yRPSnhNtSuF8fXnjc7roOnucFYRKs9+Zsp0u0VYySBf8rft3+fM7EKiIaeWxF2eMyoBRpbgqPo8ZFhylHoeJBSyZ9l+gg2+tBc3WCuIkp799Tblu56vYfb7Tf7jrumFJgQxrijyiv3dJ+yvH2Ds3EE+fYReWdHlJPU9ZnQ8pzofMrgYopfnG3c/40cM7FI1H7Dd0goKHpwcM0wVxVFBVAc+utoj8+kUU9l5rzlkWk6wFqA/mXf47L2k9vKgvxX0/t/WFN/6sCqka5TZt1axnlS6KMvAbVrWPWsNZfK9GCEXgNSwKpx7dbi9YFdE61MZl3ddrQIUUhnQtNvHDCoTFlD66CPEAL6qI7AqjJU0Z4EclQVjRT1Z4ygA9tI0Yl4KWt4XCp1QLqmaFp4YvZvz/urK2oG5KQNKYFcYsEcJHyTZKhlTNGF/18FREXj3753620ZN/4c8LvC4df48Nu88NNrjdMvQCTWPdjb+7phxKqQnC0nnwJ12Up0EahDAUi5Qsjx2kpIjwPI3vNaS4hLLlVX/92gVN7VGtYjY2RzS18/x3N/7F1/WzqMq4RLgA8Nd6jW66pNubkQxmLK/6+OsEtmKREg2n+EmJN1yCB92J+zyKaYtmbZdK/MoBoASENsRXbbJmhEASqg4ZDY1sqKgpREZWXXMSPmLFNstiCES80nJrUglLrGBcWbZkSmljamsYeAFbkeRuu2Y/yfCl4Ze2F1xkDuHa9mouiy6VcXzzXuAeXMuLIfHkGqucSM2aAtPpow7OiCczinGH4qKPmtZYrYjLOfgCb2NFN3yGtcIJOKOCvf4EYwS1US7Jz0in3l7rNKwVlEYxKUNq6zzhiZIMTIK1ltpAY+CqEKwaS71Woywazb6UayGboqcVsV+zzNym0x++nLUAcJL5hO7ls6wC5td9jFHEQYW1gkXtGCCJMhRKMCk9fOFonr5w4TdPli124oJuUBCvWfy9wP07bzwqI1Dyc12ExFpBnsVEcYEXVvRaC8radwcsv6Ed5YxWbaSwVFqxqgNqI/GkoRVU+FITeg17m5fUtY+Ulo3DU35l3uVg6GKBPzvbf6HvCFSIwPEUYs8wDGp2khUb6ZLQq6m1hzaSJCi5XradkHPcp2skV5ebbDaKVn9O0M6Yj/q0B1M8QPoNGzdPCHfGcLBFeeMdgihBfvYpKq2QnRKpNNnFgCKPKMoQKS29KOd8Lf5LwpK8Cigbn8C4LuCzZYfBejzb9mq2ehPS8caLcKKO/6W4709zfeGNf5q7Wb6SlnBNI+uHJZ2wXG/0zpMbhwVhWKLzGCWMO3FLQytdUlQB2rrQk0p7LBcRkV8TBRWtdOmAI60cGVRO7AZkFwOijRlBf8Hi+TarZUpLWDy/Zmfngh2ApzdZNRs8WwXcNW8xF0uqIGNma4bRXa7z5sVs/19fFtAvRgTWVjR6RLMW8lTNFfU/Yw/6l76hqk/sbzDwbnBb32Q7DNiMBFtRSeo1zGsHh+nFKyrtEfg1cXtF2FsQXAzXsz4H6plMeu49KEPGecLNwTVJUBJ3XNb8k+MDDnbOqaqAunHjgeFrT3nyx2/TaMVrf/a7X/Tj/ROVEhZtoEIQGEGlFb3+lGQww4sL0uGUsggpy5DVokUyb+G3nQ1NhILklTPMIuLqj98iy2Pa7SXBek19fkc48r/Kk+qPsY7YwFw46V4pCvL1f4+K+1zZn3AV3Waj+SV6gea8cLevbmD5dFlzGEUu+lQ6IMurnRVf33/O5tYV52fb3HrtIVUeUhchZREyr99iVPp40rIZuhvasydHpBtT4lc/o5n9J0TZen3s7hJxRv7bXcbPd7FW0BlOiJsJBB6iFyO7ffreI4Inu3hRRTCcMX+4T3XP5yJLmNaKNCx4MN4gqkN86Xzt09p5rdu+pRfARuNzkhm0dUS8SdlQWoNEMDA9hIBYWRoDlQZjhTt4lzEWwfbh6UtZCwBnOewnkGvJw2WL1rObdKOcVpSTxhnPF921krxEiYD7i4BIKaZ1gi8svaDmvPDYjARKOOBXKC29oEJiyZR+ISYN1mLRy1mfSRGzu+iw2ZvQ7iwc7bEK8P2Gdn/K4t5d4qBiWUZM1sLkTlAwSFckkcup37h5QpNHWCNo3TnhV9sZwe4Es4gQf2g5GW1icLCl9vrv34pXBEoT+jW3bz+me/MM20iqeYvJqeurXC/bjLIWrVmfk0WHG2XEXn5Nf/sKoyVhZ+UIl+2C4BselD42aSHrjGawS5A8BmMRCQTxnNX5kEYrlkXMPE+4dXBM/viWG6FFOYPujPPRkKbxUEHlAnqMJFaaflyQpBn90AV9SWG52/83X4b+rcoC9mcN8PnZ/nF/muuLh/SkS6ZFwiBecePgmHtPblFlilkR0S8ivvPWh3z6+DazVYuiDJkXCRerFrcH13TTJdeTAYsi5mZnhpLGKcCNIonmtNtLojjnsx+9xavvfoLfXSI8TXHdI5+3CHsLVKfEjws29i4Iewu89czy+pNb9OIVr3Y9Uq/F4TLm/bFPJY5QkY/lX1x87eguZbP414gA/1WlsfZfjrqUIuVO8mfZMpv4SH5jQ9HyNYVuqIzk3c0L+u05k0WHUZaSVSHb/TF14zG5HOKHFctVStpdUKwSPnx6i1/91h9z8WzPiSc7M+ZZShKU7uYf1KRhwWg0IAxLorCgyCPOP7pDGJakQc3q2Tbhn/A3/CKVa8FrHXeQsVbQjXKeHh/Qm3YZDCbE3aXDjRqJLgOmZ5vs7X9MfdVBziu8tyPK3/OoqoDJss35dMDxss12nPGuFRjb53tZjTY5jZ5Q1qd8rM7Yj7+Osj4d0+et8D/k76/+TwCMsx/zd71THp79GpGNkAgkDvK0bAIiJdmJNd/eumK3P+LgzhNar56wffSU5//Pu2zcOnFZCGcb/Gp970X6W137KGXoDCbIsEZ8+gh1sAGzGXZSY3OF6GoW4x6D/Qv89orV+QZ2/xA+eoQ9Nci9HPNn3iL5FY26PMM+K4mHMzb6Y+6WIf08JVAND+YphRFr7zocJIbKODKiL90/Dv9qWTaG79rvvYBUBTbml8PbTCq41WrYiws86YBUNwbX7B6c0nnt2b/y8/y3rTtty800pzKS8yJgkKy4WrnbaFdpPp6lfHtzzFke82QZsBlpImV5nnlUxqXvbYaa/fYcgPuTAQfpkss8oV4Lin/zrQ/4Wz/+KluR4tZ6tDgpQ2IvQUnD+XjIq7cfk7KiKgOu19G1xgokLsFRGEk3znk43uDpMuE3bjwhfeMc24BZ+cieJfxqCz4YY7Xk9q9+n+6Hd/js4W1Wa6iQBHb7Y2698xnJe1eUn3YIbswhDQmbJeG9Oc0P3yCvA6rM43TZxpOGk0WXWnv4QUUUOwCXjEvkUYLeu4H65EPEco6ocpr+EdU73yL47IfoT0qufvwW3cNz0us+ntL0h2Oslex0p9RriuTemw+4MfwBk5/c4tmTI/76t/6Is9MdFkXMooz4ex9+hW8fPHWplevOxJf1p7e+8MZ/vuwwXNO1/vjTN7i9cYknNWXjcTbvcTwbsNeZrFtrHrWWfOXgqWv/S8Puzjmzx7cd7EdY0rDAa3yejzYZFjEHe6fs7lxQLhNUVBIMFgTdJVUeYrXCNor0yM1UzTp+VgjjvuTrL9fXdk5oTg9JPY/T/IAnq11+YD9kL/k62tZUZCyqMyq9otELhAhQMv2Xtur/deXm+ZrQ3yb2Bijh8x31bbYiSce3dHzDe8NrPpoMaHmaw9aU00UXT2n3xY0zhynOY9Ikw/MaLp/v0uksWE47WCt4++gJv/+9X6CfrFxI0ar1YjzglzVSandoyFKCPCENC7rtBWl7xfXVELNUjgb4EupbGwvyxmNe+1RacrrospGsyIoYMbG0t0essgTfd66F+bzN5rRFMe4glaGz8xS/F3HjK5/SP97m3qNbeMK+yHMAmMsJsb/BYv3ZNHrCdf2Yt+Uvs+ulXDcl7yV/g/v6e6zKJ9TNFR82/8WL1yhFynbyHju8Sy+w7EQVe4Nr9m89B2D0/qs8+C9u8eqrDzj++BWyPCYKS4SwZHnM5tYVG7/8Kbx5G3F2gr51h/yt/wlSRQR/9J8ikonLo4sTDv/cD6hPulSzFl5U8vR/u814ept2uuLwnXuEVx+6dv+zTUZPv0oQVjw53yP2K15tn3M177IbVzzLQi5LuCgbHq0alJC0lMdG6Fr4y0ajsSgEG+KIghUCRct2KLTzrK8ayaz2+eWDp9z96k8opm3qImT84SuuO/YSKpSGtl/RWMmi9vBVw1H/GrWe7f/m0TEPpwNe7U756mbJ+xc7PFn5BNLS8eGq9Gh7hufz7jrt0Yn3fGloK00/ylks23xn/xiAWivGa+2AEu4SUdQ+3/v4LQ66Y/YPTtj9+ie0v/8G47Ebh+3IOQ8mQ3zV8PrmOXcGijCo3Ka/CLC1h721x+rvNKhwE1Mr5ucbSOUEiL40bMUZtzauePU730d4mupeG/E/+ArZ4BW86ROCBx+QX7Q4vd7gfNnBE4a3t0+ZrlrsDEbs3Dih8/Wn2Ewg+gK2t2h2jpDLqUvjPL1GfrAg3v0jis/6ZGWMqdukG1O8llPhC2EJOytmFxvukN1eEbYyvvfbv8Qbr90jTAqObj7j6eMbRGHJbDbgIkvYWqv4P9daeS+d1W9fwoz/yyv/z6q+8MYfrRXHn5P4rBVkdUBt3NwfYJanL5L3Iq/B9xrmyxbGSjyvoZ8sGa8c8naStVDC0Iszkij/5/LjTe1hSh8Z1k4tPZwjuxU0bt4f37xEhIby2QCpDFudGUpp4qhkEJYE0iAEWDyu56/watzmqmw4tVMmYZfGltRBQWM+t+GFNHqBsQWgca12H2trQON7myT+JsbW1Can0Tk78TtENkXh4VnHi089aPuGXtDwYN4j124WeZknbEb5etThSIfaSHyvIZuGBF7D5mBEU7uPQ2tFUUYoaVDr4KNINaR+5ZIMl22myzZl47O1bvsDFGVEzxvjKU3ReCznLyeB/bA9ozGK6yzheZbgCUP8OWc9T7h8fOB0B41HUYaEQUU1a6EbDy/KYNjBEwtUqyT+Zwhil0XESe4zqQxfl3f5THZ5HNZk5RP3Z5dPeJB0yPWrDGnTsck6ovlf7OoYu2KpL2mEJfVgN1mxvX9OtDFd/x8kvtI8fHCbz663uCoDfOmElwetBUoa2k+GREdzSFNsEGPLK+ziFFGVUK3T9155Cx8IpOseTR/vv4D5BEFFtUjwlitkXGG1JM9izHpdqLWW42zVYlJ51MYFv2SmWf8OhkxrJpW76RdWk1NRi4bYJow4JhJtgjUwphdod8BJlwwGE6KDK/zOCp2/jL7PTyv2DKVxmQq1FZzPe+x1JzRakVUhUlj204XLeKgCJpVH6jkOfm1gEBq0dSLBYL3m88ajt6YhBusDRCddMl+1yKqAvdaC02WbcZEwLhIWa4BQJ0jozLpEJ5sopakaH09qNjfHGOOECL5f026t8Pya8tkQr5Ujwhr7/WOmZ+8gpMFoxXKZ0uvNSIISiaUV5ewdHaPSAtsohN+gppcEDz6E2RxzbikW20zyhE5QMFx/Dr5fs7l/TrJ/BamP2Iyx7S7N1gH15l3i8T+FyRSzkjSLmKsf36V7eI6KSmRYo/OAxdNd+nsOu6wrn87GBF17eGsdRBRU5Etn9W0aj7Lx6XfnHHTHRKrmOk9fdAd8r6HdenmhTV/Wz3994Y0/9mq0EShp6QUZ2khWtSPyJV6NLx2WM1Ca2K9J1vOkrAopG9/N8ZOMrApZlBGLMiT2anaH1/h+TV35BGGFVJUjvC0TvPYKv7dEDSuIPeysIdqYIm913PzoGURpxnZYUVe+U8euT7baCqyFadXiF4Yl9+YhetGlrRNKagSCUlTM5IiO2qGxJbmZkTdjar3AV22sNVg03fCIW+ZNtNDM/RkX8h5v29cojaZC4yPxBGthmXu/PpuHbEea0giOs4gbnRlF41Fq94+1ENcBsyIi9BoOD064utwkCgu0VkyWLRK/wpMaGViGOGfFsoxYliGV8Qhkw2AwQSlNniWcjzpsNR6ep1GNZr54ORt/GhYvgE2jMnLRy2sLZ1n7FFdbtKOcRiuMkXS6c5rKRyqN18qx/Q1I24hsRTSZvhBtneUBJ5kTqv3ihkJMt4ntd3iSDrlefR/QjLIfsvQv+Jr358hFRVFP+ZfdBAQeZj1jTJRhqz2ne/c5auBanKqVc2MZ83ff/yb3FzGXhWBeG4yFb2700VYiPrDcPfgDxGaIml7hez/Euz7Fej4iTrBRjH7l3wf5D/ABL7+k+DgiiXPS7gJ/Hc0rlEHEBhVVBEGFVIbIr9BGMlm2uS4izgv3VVQCNAaFRAK11VzXGrcSNUu5ZMwZN82r1DYnVl0SQlJPsBuX3OrM2Btc0x5MEb7B669QvcyFP72k8oUlbxwcpjaCJ8s2nShHCktWBfjKMEiXzNebtAH6QUPW+DQWUuVAP5l2PIbEc4l2n2uKlDAEfoXv18xXLbSV3N48Y5QnzKqARe0xrRW3WjnaSiazDtmnMe3WgqwM6cQZ7f6M3cYjy10ughCGKC6YH2/T3rvESwsuP769FtBFVLWHsZJ2e0mvtUAbSRwVdF45xjYKrEB4GvGjT1h+vE9TdGkqn/m0w6oO2O3M2Nq8Jkxy/KAm3pyiOjngYTZ3MEmHpn+ESA8R2Qq7aGB9MHn8/JD3Ds/xOhlIixAp4+e77L36DBE1TD+4TXJwSX7Zx9Qe1vps9KYYLZnPOiyzhLwOUKphOBwThiWjk2T9fhrCoCJKXp6980V9qer/ua0vvPF/HrwRejWhV6Ok4ag3ZlHEXGcJJ1nCrx09oZWu8LyGpvGQ0rK/eUlVBVzNesS+T+TXxL5TnuZ1gNaKao1xTVoZUhnKRUIxbdGNSlbHW0TzuYtU9S3qLxzC4weYUwOmx+CNJ4w+eoXr6yFn8x6toCRUDcPQ0PJrRmV7HR5i2Qw9Wp7PcdZgAEnCLTEg9QRPi5yKhpYX8jw4o2U7pDYiEh5Y2Itd9GtlehwXO+xEHpPKIX4TpThKnVXPWLgufLYizVu9GdpKrouIi1WLxHP2mlAVNEaR1z7dqHBWyWXCPEuct19p2lHOhxd7vNIfkQQl7Sin016ijWTQntNqOZfD6cU2VeNR1D6XeUrrLMfzGura53r5cjb+59MB8yrEAL2gwljBo/EGnjR0Aqdc972asgowRmKNJN1xTAEVV4izE6qv/jLi1l9CDf6PtH8vJ140PFlCZSx32z6Ftrw3MOwXXTYWv8A/8p5RN1cAlPUpf1j/3/+F1yXwsGtao1JtDry36fqSjl/Sa89Rb4Rk3/7r+KNHeKeP2bz5mI2fvMNh27Hlf+usy/1VwW9fKE6yIdbCK7mPva/wqueIwTbljXeIb/619d8HMcAb/0Pq5j/FO7twYrEscgfUuEQog9qpsTOBSgq2XnmO9BuWy5QPTo54sEjp+ZqscWr90lh8JD/kD7kh3iG2IVfyio7tEduAkoJFc05HvcueeI1NPeBOGvH1QcHXdk4Iw5I4yUl2RzSjFsV1DyEsya2zl7IWwLH6b7UaeuvR0qJRrKqQ7faMrf4YrRXvP7tFJyi50Rtzd3jJjy93idadQyEsw7Dk8dJRGNt+Q9tvmJShE0wGBf3NERiB5zfsaElnY8IbVcDVvMd1lqLymK/uHuP7Nas84ZPzXd7cdeFfWRVydb7FdNlmd+OKVZZwcrXFZjVFKc3lxRarImJRRNw9cKMgY2LGq8TxMV59TLlIuTjZwdst0RcB1ahLNuoyux5gjLPvlVXIw6stOkGJJ7Vz2hQh7bW7xlqBGA5pBrsIo5HFHIofoH8yQ/ziHeT9h5gzj6/9mT/k4fvvcHD7Ge03nhG+m3P49kfYSY1Z+XReOaaetphdbBBGJZ2DC7yrAa3enLPne9wfbXFdRAzTBZV2SYK/9NonRHGOH5fUecjZ6S77L21FrOvLjf/ntr7wxq/X0bnXWcqsCiiN5FZ7ziBZ8Uq8QskNHo03ec2v6fTmnJ7s4vv1C1iGMZLjWZ8b/RFSGkbLDnnt83g6oBNUHPZGpMMp1br1G7ZyglszjFZEb0whChj9vdsM4k9Y3dvFaEXrNTfzi7oLdrSk151xPRngKc3T6YBnq5ivDJac5xGDQNMLBIUWXBSSXGtCpdiOJC3P0guc7SmSljvNTQIJvcCQKMNPZoph6IhwtnYCH2thO5KEShIrd5KOpBNkrYzk1VbOg3kHISzt9TzNrCOAlTDs98Z8erVDoR2gZUNY7k+GSCBSDZHXMK19Lpdt9rqarcGI6azL5bJDuw6oa5/719vcHlzR78/x/ZqjKmCy6EAVIoRlkC5/povl83q4aLMXFwRKkzceH4773G6vWDUeZ3mfXpKRLZxWIfIrwqjA660w2U9bzsHv/GOK/+xHTJ9v4Xs1v/TmRzxefNup3D3N967hmxtO0JYoya8Gf4V/av/Wv1aP8fmmD04TMOWCT4uUYNRHitfw/7OaG8V/DqsSDJS//pv85l//r7C1R5NF/JnnW/z973+DylhWDXw46TH8L/8cb7/7EzgB87tXBP/r1yke/k2iV/4GAGU1IgyG1K/8Jmp8QRKdY85r5N0BxVu/Tnz0l8lO/wE27BJ//Fvw2xcsn2/zwckRH0zaXOSCjUjycFXwRB3TUPK2fI3f9H8NAG1hSyeMTUlOSUjEnvcWqVR809+jE8Aw1GxEBTs7F26E4Gm83hLZNS6SN9Xob//CS1kLAP2wcWjeKuAsD1DCMi9D5uUWxYVHJyh5Y/OcOCowRvLoahsJ7MUZjZV8Mmuzn6zYiUoui4CfzGKUcOl4hoDVZMjz97/BLxw9dhHWWqGmztp6sHHJVu0TXe1wMh2sO44Vr22d8+PTQw7brlVf1z7XqxajrIUvXfTz8WiT2K9eOEoq7fF8TRittcekiFEn+/T3Lkm2x+z5DfbGAfZkhIpK0p0RP/n4dQbtBZNli6usxXkekyhNKyjptJd0tsbkkw5eVKFnMXx4hfzeHO8dD4zBni3JTnYJf/sU6/sEmzNkXPPmf/SHkDfYCuykhIYXT+vsZBPlO0u1UBqkxfdr7t+7Q9V47LdnHHUnNMaNFN1IybJatFB5TFN7L2LHv6w/nfXFffyNT+pXhKohkJ7Dbq7nurX22ErdXNRTmrr2GAwmhFGJ9Bowgm53zrPTPUarNkoakqDEVw2h19AKC9IkY3yyTdxa4QU1KqywGXitDLodiByj3uaK9BfH0G6DGWCfjPGiyqFirWCjP2Y87bHTWtBZK1e34hWjIuG6iJBItiKPQEpSzzII3Jd+1Uhq6yJAd2NDuNYJ1Ov4W4eBhUJb3uxEtDzXRbAWprWkF7gc8sRzbG67jhUFN3bIG5erHUhNEDZ0O3Oi8QbBWqA0nvbwpUEJSyuoGCQr7s87NFZSNc7Hn1Uh3bUNqagDhnFGUQd4heP9N1qRRgVV7RLLwvDlKHeHYYUSBiUMLb9CiYjLInKHFmlYFDFZ7dMKKtpxTtxf0ExThGewRlJ95KOLLhf3jyjLkCTNUF7DfpLjFxGlEYDgk5mk0IZMG4y1vBf8RX5U/d0vLMacVs9oB0POizb35hGvnO3T+ycjouGUYHuGf/UE8Y1tTG8DFaUMihX//v/hjzh+csh41WZehtyfDNl6vkucZvh+Q++P/kvyb/4H1HqFr1KsXdsQl4+xUYJIU8yv36HcvIONNlhNf0Qweoz6ve+R3dvl6skbHF9u82yVcJIJToqCq9JjSYlnPSSShal4Wk+5Lbbp+hKQbIoIbUN82SX1JL0AbrUqF84jnW12MW8TRiWeX2NWEfIIxDLHLHy8H7wP772U5UDHr9mKM6wVtL2YReO5Ucm6tVxoD09pyjIkrwP6ccZeZ8qijLlYtdDWBffMa2djzBrYjlzcrScsxgpir+GD4yMK7eFLwzv2+QuXi+/XbLXmnM27bLdntBL3Wr5x4xHWCqo6oKhc+z2vHFPAU5okKBmv2izWKX+TMqI2EiU/9/77yCzl7OERw50r4uEUPhpRTbaoVzG69rh9+Jw8i9kZlBxsXZC0Mr738ZsM2guCsOTs4RFZHtNbJvhhhRSWjTcfUXzXQ2chdbaJVAZT+lB7YASYHNU3sLeB8Dy4usZOGsw0xDaKZP8KGVdU69dQTdpIZRh0ZsyXLppXSUMaZ8RRgR9WeJ7G8xqylWODFGu2wsusn7Wb78v62dWfqNUvhSVUDZ2gxJcG32vcaRJL6DkFd6MVs2mXjc0RflzgRRVeXNBLC8bjPosyQhtBJ3I/G/sugEYpzXjcZygsiXI3VbMMkXEFJVAW+EkHEWqaN75O094ifPhDMAKhNMprUJ6mPZhRliGBvz7F1x797oz2eIia9bkuIu60LV2/IV1HCM9rn0h5lOuWXcdvXtxmx5WHLwSVcQIrgIPE/dm+cGz1wgi6fkPHd0EkkddwnSf0w5JKO9FTtZ7fBdLd/uvaX6uFKzylyaqQSGm0cT7bOChJlAv3yKqAKE8wVtAKC8rGJ68Dl4nQeNSNhzGSvA7oJCui9Yb/uZjpZ12R0mgr0dYSqYbY06waRaIMnaB6gQMNvZooLFwbdNwl6DtB0eL5Fn5cUpYh2jjhJ8DeOrZ2VEb0gpDzosas5/cFNVsyJfQGX3jjr+pLmrCh1Jpx5XGy6OB98BaH2+cMF+ek3ieImxvUm7dRO9/B87oM/+L/mOh3l1w92editMHZxS7Pr7bZqib0+lOqH0Cw+2PsxT0aoxGtPlVTEWZzaGoIAvSrfxVhG5h+Qvjwe8iLU57/46/y9HSP61WLe/M2T1ce01ozJ+fKlhhpaESDpuZCXnKpH7AjhrS9YP0+C5SQdAMYBm6sdJisaAXlixwAYyRynZcg4xIa1+Y1WUg9adP6Ga+Dz2snWb0I3Yr9musscel2Vrw4lHzusXddoJphfwITGOeunX5Z+KwaQWMFXd9FKnf9GmMd678TFDyY9ZnXikBa7pQRZeO7gCDtEa6xvHFUECc5y2XKcHOEtYLVrE1Z9WklGXFY0Kzfl25nzqKIyWv/Redt1fgvSIPg2CSzZZtoVhAPpyzv71NMW+RZQlP79LevENIQpTnJxoT41WvujPp0unOksIzmXYrawXXAdU7T/pzx2SZVFeB5mv72FUgHLTO1h2rl2FmD6INJWkg5wqwC9CpCeBqvlyG6jgWQjbpk07YTC/s1vtfgNZ/TCh0QLUpzjJZURUjTKMrKf/F6vqw/nfXFVf2qQRtJoBq6UUHZeGRVyE5vzHA45vh0D2MF59MBkyLm3fWXp785orcxxf9WwuofRhysgzZmqxaLMnJ2tTIkLyJ8r+HiapNh7aG8Bm8REyQVxQ8TqlmKCmrEYQfv+BHe6gPMaYWt3K8glUF6zYtAm+WkQ1FEvPLexxTTFq3Ogu3FNZ8dH7HTmZKubwWTRYdH4w3aXk3q17T+GX/rqg5I84RSJ1yVgl4gSD3rKGJrEV/qaX6pN8OXml6c0WstaHUWfPr4NnLd8rxe0wv7YUHqVwgBP3h2i7Ms5k5X01crhGfx84TrImVauQ3x9cGIR7MeZeYe2Xv9CVkZsigilmuS4o3NS7SR5EXEJEsp6oCbO6dIaXhwcshX/u3XyL9QL273StPyXehHz3cHOQn0koywCugkGVJann12m1a6Yug3qLAmzxJau9fsyBOKRUq+ivHCihu7p2xkCaN5l0WzgxJu1p9ry6RZ8aF4TmNy3HT93zw/tDT0zICNIKDtw5NVzAeTlK3zXQ4eZLz96THt9pKjv/C3ab75lNXeu6j3/gxJ/yO2//4TPv6tm9zqzLg/7TMrIm5rhfdAU/xvIrzACfWEWjH82n1EG+jE6Bt3kCrCPvwvCZ59BlfXFB92eXh8yE9GGzxcBrw/WyKRpMJnR7b5WIzI7Yxp+Yy6mSFlxH78VWKrqA1M6hpfSGKl8KWk4wt6SuMpQz9Z0e/OMUYw3L0kSHPCrQn84ivU//AU4QvkS45gfXX/Ob9z/3UqrbjdnbKZrng06wHQU5ob/ZFjdaQZTRnwg89ep2oc6nsrXWDGXUotqK1gGGre6C74/qhL6jnRKJUTDb+7ecGijKi0wlcNW90Jp+Mhl6s2/SgnbzwWmWPe17XPDz56i1/59d+nvXuNvG84v9rkcO+UqgxYrpwD6XD7nMWyTVEFBF7NRxd79KOcyK9YlSGvHj1zqXlWsDjdoioDLq82Xmyc19Mer7/zMX5SIqSlOU94/S//HoufHDI932R7MGY86zLNEgrt02jJf/O732EQZfQS97xoKh8hDUiL9BvUTUH9aQs5XyDiMVZYlo9vEG+PQVjyx9skb10gfcfRmM9c1snVvEcrLNjsTTBG8vx6k9G8S7+1YOfgjJOzXdJ11kHRvKysxnVZfvYz/i8lAz+z+sKfvpKG2kgqHWIQDOLMBVxMBjy93mK7PXMe9WRJEpSEYckf3n+drxjX8ss+aPFoMmSnCtkbXPPaa/e5d+8O/babT1sryIuIra0rN4873SYc98h/GNPtz0iHU3djFD6UBVSNs/Qdb2BqRXJ4QecuVB+ERNKSbEwop23uff9thLB46+7ETmfKTy53Sb0aJS2zMmRW+3T9mpgGT7nYzutVi0BpDtpzKiNJPZ9m7THXVrAVVmzFDvt6mTt9wNmqhT/eeJFKdqM3Ig0LYq/+Kd/AuLSsm70xey2PbrKilWZ4fs33zw7YS1cEsuG6cGASbSQNMC4SeqWj39VGkWmP2K85Hm3Si1d02wsOD094+OQGUhqipOBo8+Jnv2KAea3w1zeKz5PTNqOcRR1wWUR8+PAWv3l0jDGCogwIg4rHZ/sknSX9155y8Ks/4uF/+y0+PT1gmKz46i9/j4c/fJv5Gi8rhDtcfXNY8L1RxA/rE6w0PF/+k/Ur+OJPgEfmB4z0PsP5Nr/UHtDyLKNKcVm0+b3LN3mvn/OteYfNf3qFEH+ECmp6r10QHVX8+b/233D+41dZ3XudQLlxyvPj/Rcsdk9q+t05/sc3SPauUO0c7n+Gf/9/QfNYMXu2w9XxL3Dv9IDfuxxymQsKbUlEgLaGwmpq2xDKiKfZP0HJFE+1qfWYlZ1wauf065RQKhIluZkKhmFD7LkoW19qkjin3Z8ilaF1cIl/uIStAbZpkFGFjGtEy6LSn/Uq+GndOzlkGBU0WjLOXTJf+Dle1whHsVu1efXoKZ2NCQbBNE84X3YotOIoKfnRJGInNvjC8pNph28Mp6wan1XjkTUK5m4U1goqAqX5/tkBvaBivzNlkC55ONrk4SLhR5NbRMpylJT82p3PuHp0QJxkDA/P2LztNEHCb9g2gunTXeazDkXl3EnDjRHfWKdlhlGJH5Vk8xaN9VkuI84ut3j/fI9R6dELNHtJRj/KKWZt8mkHKTXDr93DlhI/zelujgF3ONhoLRxYK83w/RqpDEGSE3ZWjlIqLCoqUWlB+Qt/nnDx95i9f5PL57vkRcTr3/k+AKb0EdJS3BsyfbpLMphx9+v3ePZPvkaauOeDtYKqCnjn1ft4YYXRksW4y43DY/JVTFUF3A7/pPCyL+v/n+oLb/xF43F7cI2ShknWYpq7W2w3KthOZkyylFUZ0YkzNrpTrBW8u/+MJM7JlymLRYvD9gwpDWUZshi7iNYwcjGuZRky3BhTlQFlEWLWrSsdKOLO0uEtgwbb20Y8eIRZSEQIQmmCVoZ3WKFvvIH5/jlWK2RQke5fcdB4LCcdWn1HBfvw4zfoBSWtoKQV5bzeWlLVPtn6Vu5JzfWyzc11Z2JRxKwaD7W26wGUBhKvoTYKXxru9Ec8nA7YjHJCr0EbQezX5LVTtfvK0I5yfNW8IBa24ozz6YC8CinqgGfrn49UTaF95lVApDSdoFqnmdUsitiNRvzazdfDkmmekFUhft7geQ1FHVAUEVUV8Phihzd+psvF1U5UveA15I3HwdoTrIQllAZfWs4WHWrtMWzN2dp1BxDTeCyf7KLCmt1XntHtzyjyiJOP71BWPklQ0hjFLE8IpGXRKAaB5e16n4fNhButf4+r+gF5dY61JV/kADAvHpCmG3RsQm2cWDBSxinyjWDVeDy82mKybBEHFZ10yex6gB9UBGubaD/KWdUBz6cDisbDINhvz+i1FmR5TDxtEw7mqG6O8Buy9zeYHG9zfr7NZ6MtPpu1uDe3TJqSmgYfjys5ZswZy+YKZT2ECNEmR5MDkk2OCK2PLySDQLEbQ2cdhDNYq+cFFk9pos4KLy7w3wUyDxYLynd+FW68S/TZH8FsCu3Oz34hrOvhvEvqNXT9in6U82jeI12Pb7SVdOKc62Wbp6d7tEYDWkFJoyW9OKPSHo9mPb42yCm0otCSSBlaQcmojMgaRWUEH00TGitIPOvCe6zg9cE1WRVSNCmDOGerjEgbRaAMvaBklSUvxh9hvSJ994zlj/ZpZilSGdKNKXkW01mLYKsiZGPvAi+qsI2iWKT0dq84f3TIbNXictleh/Q42+e89glVw+h6gO+5mGb10W2S7TFWO+x2Pm/RTlb4XkOrvSTdmHL9bJfdNx7hrTd94TfoZYxq5cgdQXDyGYSSaDilu0zITmOWx1u0b5xhao+TT26z/8Yj2jvXmMZj9tFNVpmz6xkj0VoRhSVBkr/4XVazNmenO6RJ9oIc+NLryxn/z239ifs9Qlg86VSrtXEtN39965uVEa0oJ05ydKPY3j+nKQOKLMJaybAzw1qJEObFfBfWbXppsEZQ5BGNVsi1L1wq40JrpEHnIWo+xcwktvKQWxavlbt5ZuTEKrr08TsZMqwQvqZ75znxqIvXyjGlz9bJxMGCgoo4yWn15uTLhLZeUlcBi2Xq8gjCEr22whgLHb9Zd68EHWEJpKYdlCR+ReTXbEY5nSjHX3cMOsmKeZY6IeN67BGFJXXjQ+VakVntu/nn+iEZ+27TLxqPSGkkFl+adUhIw7IKSIKSqlGMi+TFZ1I2HvM8oVm3QFd57OyCL2mONwwLaiPR9qcaglUdsGo8J44SUGjPzU7XIiKlNEUWYbQk7iyJBnN63SXltM35o0NaSYaxgipLWVYBqaeZ1x4t33K7LVhM2sQ6oPCW1HqFMeEXzF/QzJtzlvIGhY7whctJdw4Ml9L2bNXiLEuJlKYXlMR+TRqUdOKMKHRWylpLJmXMsvZdoI/vDmGV9mgVIavzIdUspSlCTp8d8Hw85HjZ5uEy5N7ccKaXLOSCmoqBGTLmjFl9TFVPEMJDigBta8DgqS4d06YlQtqexzB0rPiW55LWovW4LQlKF1ITFwSDBba/g2guoGowUQf8FjQNzFyH7GWVJyyesE5lLqDQio2oQGJfiFM9qZnkCZdrlO9gPRYDWFYhEst5nmDwUMKyWq8bX1pqKxhXzt9faAFIOr6m0h7GCnypaYUFw7CgEzj+gS8N8/ynHSQ10sTHY1ajHotZ2+XXxzlZHuMpTbB+HljjbutIS1P5eC1nj5XrsJ79JHsRSJZ6Nf04w/caorggWN+um6WDNNVlQL6KkcKilEYbSZ1FeH6DSgqnXzIC2dY0U+XogYsSPnoEscDfmtPRirIICTtLvI0Vcu4+x3KWEnZX6EKxnHTpdBbOPlgFSGlI0ow6j1BroXSc5hxfbtPrzoiTjLKIXtp6+LJ+/usLb/ytoOJ03gMczOe1G0+Zz9usiphlEXOwdcns+ZFL5wtq/KAmbGXOftMowqDE8xqSVoY1guWi5WAveUS7s6C3Meb0+b7bvLyGMKjIVwlSWsplgpAWU3t4nz2nmbfwBkvMjTt4+T3sUsCiQJ08pV5tkn7tEnKDHod4tzTeuzWcztHjkDf+w9/9/7D3pzGWpeldL/pb75qnPcYckXPW0FXVVe12u01j44HGF8GxMZg+PoihjQ++V/I3hAQXPoAljnXh2gLZ0oUjhOQJ+8qWMS0f4GDAfbDBxnbPXV1dU1aOkTFH7GnNw7vW/fDu3G1fT9VdmXbTXj8plMqIHXvt2Ptd7/A8/+f/kL65TduIVS/4k5MNrj99m7o0mS1Chl7MLFaCGdkKQrNmx4/JapOqEewEC2a5y6XxGZZZcetol6e2D5jHIU2rMfBjev0Fhi7RdYlll9x+eAkAoTW0aJxGPdLaxDMrBnbM1z79Op+7c4PzZaj0an9GLQWzwqWWgkZoxJXFFbPkzckav3Hu83To8MxgRikNstokKhxubB5xOFkjLS3Wn1A5X9/JmecOvl4xcFPuzkZMSotcCtW5r4XQLJGtxjT1mZ6NOZsNCZyM0WiKtzkhOtjA35hgjxZc8t+kmIWc7m+TFA6z0mZklcSVTmg0jKyWqLJ4JapxtT6euU5ez97mwg9JcZtXrYLt8lu5GSoVemhW5FJnVlrcjmzOi5ZCgilc/thaxZ4f0zSCTavEMipCJ1+5Ls4rg9tRj+PMY8PNWKtmvHXrBqdxyHHqc1aYPEh0pmVDXNe8Kt5E1w3KNqNoYwxhENdntG2DEA61vMA01tGFj9BMevYuZmMwMEzWHZX3vhfrPN0rGdk5TauxOZjQ60WE4xm6XSF6Bdp8CpYFfoB5dgvz85+iet2knG2hiZbeX3kiw4F3jc4ppUEpdeLSwhANa26yFK1aHEU99voziiTgKPU4zk3+9KVoJcTzvZQ39i9jLhvxmFrL3Shk3VEunA0mjg4v9POVaG3Hj3l1MuaF0TlbowtmccjIVeZd5dLS1ygdNK2lqE2mUY9X79wgdFT04WESEFUGzwzmjL2Y9cGU0dMPuPuxd3N5PMPenqI7BXXs4voZG+0Fw3CB4yqRsmWVhGFMMJyriOOyxa4mWorzPsU8IIuVgr4sLWqpE8UB+dE2z7/3Zap5gEwddLdA82OayqA8XEPeMykTh/F730SMWpzehO1egv7+MYhL6Gen7E7e4uHnn2KQTRGmxPYyxu+5Rf5wnSpRmw4rSHnw2k1GrUawcUGwcUF4tEk4muP0YurjPwBxX3fi/7LlbS/8G4EKleuioWk1Pvr5d3OlpyZeuczlPr11SNNqLGY91nZOOL5zCdspqCuDg4t1QjujqkzlXiZaPLvgZKqazIyfesDkdI3bJ9usBZEqd/NyGilIFwGyNFl/6RZiR6A3GW1mor9xi/bqJZpPHqO1EuFXVJlN+ZYSwzWFicwq6tikldsIU2JpM6zRAs2QNJlNXZr0gpi6NHEHEe96/2d44+MvYRsVgZfihzHD03VmmU9Rq9PIMFA6hrbVSDMXU0jlTa/XeG5Gfzjn3oNLPPXMLTStZX42Yp47S9VziW1UbIQLPKvkNAk5TgI2F32GXsLe8AIhWsrK4HOn28hWUyVbusQSkjfON9lwU/7S9RmnacAkd6mXjUyurx9wOFkjsHMGbvLElLtbwwuuOTkX0yGvnG2xF0Qk9WDVVc1YlnD19ALbqJlGPU4ftRDNMvLzAWXqsHjtOlVlUlYWb51uYi9PVutOxqK08Q0V0o1qnVzCpumQVmMKodr7appAaAZlHa1aLwsRrrorqp4KKiyelw/5xfb/YP/sT/CCPWbLgbNC45LXcpK1TKoaDTCF4NfPTW4UA7563JKdbXKc+tiiwTdLGuAkMzAE+IZqrXuePcWsNEmlwNBaPnpa0dfVvDdvcyJ5yjXtJSQ1F9oxhZZhCJumrZAoMekjcyKh+Qh0PM1gUdeUqaBqdP7nqyeUjcHITdjbOma4c4rpZ8z3tzi8t8dTX/8pmNQY7+9RXn0O6/Mfoz0pEV6DO0gRoycyFABWG8+sMkilwdguGPeU5qcvdd69fsFrt2+wEUTs9qe8eb5JUtq0rcAJE/y1GaPJGF20RIXNrLS5HCS8tQjYTw3O8pZn+w0HmcW6XbPmqEqRgVXyymSNz16sM7YLbo7OOZwPSGpTlc2KmovUJ6lN5pXJaW6y45ZcCiJe2jjm9nSMKSTj3pzR5jnV3Ge0cU58MqZcBHjb57hfE2ONF1TTEFmYCKNh46n7VIlLlTp4GxPS0xHR8RpVteyQ+cJtysSlrpR97ubmqdISFDZFZXL01hXmUchp3GNeOLzv6m2CMKbIlWHR1nN3uPjU0wRbE+ytC/RRjpZEEKdUt22OXr9BLXWCrQmaLilmIQiw15ee/o2GuTtnO3HJI5/ze7vkucOgP2dysoZxMXhiLbt/C50Y78uWt73wT1Of9XCh8laFw3PrJ6rD3GDCxs4Jp4ebPLxYp7/03v/s514gsHOyC0u5/G2cEA5nFKlL0wgMsyYuHG7PhpTSwPUzpUD1Evp+jGFVNFJgOgX+cIFul+THI+LPjWgbDd2ose9nBIt9NKHcv9ppgTeeIzMLs5eiBxn5wRr+C0fIc5tqFtDkFvFD1TpTVgbxTCli3aUG4OLuHk+99CqL4zXVYa5WZT6CdpVb1/Wa0C8ZjGYIo8Y9y3HcnDTxyHKH4sRme+OUdBFQZC6LKCC0CyV8dDOaRvDJ/avcHJ2zHc6JcoeDJORZJ+d00Ue2At8quNZTi3tSGxylPs+MzjlNwmUpnVjZEz9qOdq2goGbUDf6qtTpSTCNerxxpHy/tr2YT56tobLmSsh7lJn8md2IRWlzLw4ZWSW+qXqm67rkM59+kaeu3sNxc3RDlYHt9GdcvbxPVRmcna6TT9YZWCXpo+5jruB+ozGsfWiu0GiXmVpnnJSv07Y1lrFF01Y0bYmmObRttVz0NYQI0IVDUR3yWvt/ca49y1Z+iQCX/QwqGix0NiyLqJYIDc5ywa+ejthwar5q7RzfzpdVLZK9IMLSlYFKvizL7FkWWW0wr0yuew6yhUkpWYiIp5r3oreCqYhJmylC06malLKeI5sYofk0bYJprNOz9vC0IWuWwdDSWHcku27KftzjUrBgZ/2U3a/5PPqVhnZasbEzYcNo0XZ7NHdiOMqwkhimMdpQoNc1OAbNlWtPZCwACK3lNHMxl578r07H1AeXCcySnl1gmdVqo1xJg00/Jq8N7p9scf9ki6Sy2OtPGHoxgZ0TFg73ox6u0bDhNHi6xo5bsp/aJLXArQ1Cs6RqBFtuxpqbsLdxyoPTTVJpcFHYzEqdLddWB5FK5yQ3qFsY24JJ7lJKncu9GQD75xuczwc8/dRb3L17lbXBjIGxbFsb9tCvL9DjCc1C4+ITTzN6/i66l2NEPkYvpRcubcITh4tblzn8xHM4S8HuKyc7DGcjXLOirHXOc4+60clrA0uX3Byf4bg59tK1FMDem6gOpY4aY/VJwOzXtlc+/IZZoes1+5+/SdtqWFaJPVpQzn1VPh1kVEc9osmANPGoahNNa+ivT5C1QZ64HO3vsPHERkTHlztvv5xv2QimljpVbWAtJ8KisEkXAeXSGKOsDbTcwV6K0Xw7x7MLpBQUqaualCwFN7IRjO0cS6+ZTQZMMp+NYKGsXuc9bKtiMe+xvnOC4eUkp0OSKEAIieMqr+nqVImWNKNBmDXulRNk5KL3MrSgVY5rc52mMGkbjTpxaJbCG020+P0IP1Qh8TpzVhazTpgQTQYsFj2Gw5kSzhU2LdrKHOdRmU8QxkipuuE1rYaUBsFwrn4/CjiPQ4bL+nrTrKgqc+n+p9ThoHKSs8yjlDqGaFQv9UeCQ61VGohlPtM1VEteobVkpYVnVFSNIMkdRr25mmBrg6x4Mnm8syRgUtroWkuDhqZBVqnqDUu0+IbyN2iWf2NSG2wFEZrWUlUmVaOrdqlai27UOG7LulErg6bWXY4NjaGdE5iCrDZpWodJaVI2JnoVELUFRRvi6AP1+SMYmzfImjlVk9G0FXl1imftYgoX2RZU9Rm1nHOSvczEuEvP3EFDYGoOw2adXrO2ik62qHzyotJZFDamXuOaJWM/xl92VyzKL5igVI3OorRYVAaeAcdZw1yWNKJBQ0PSUGqleh1NQy3zpfmPpGkTfPsGjt7DE0M25SaY4BstA1PpDnJpcGntjNHuCSIoQNhofQNqCULQ+iFiI1X/XyRgGzTXrkPToJUFYjF7ImMB4Dz1iCuTvlXi28XK9KptNUqpk2buytSpaTUCq1QCv6Vt9TR3eGozJy9sQDXzmuYu9rI8VNd0NJSozhItAogqi0LqjB01v6SZy3EcklQmRaMR1YJerdzp6lbD1ltGRsO6Xay8NoZBxNliQFxaxKVFsL/LeRziOxl+bpMdjzGzC5jmNInSFblLL4o6dknOBrSNhr0+oylMynmw8srf1M4QomHNSdFFS1aZFEv/gHnuULcCoRVKoGmoBd7pR+hWRX3uY24l1OcOxemQ9KLPyfEWvpdgmDWNFDhuTpKqyh/XyyiXgsU6dSgWPs4gxuvFyFqnljptK8gjn6oyKXKbqnryof4/EAFhx5fE2174x705We5QLY0uFrlapB7ORxzOh6wFEWuBMsRIS5tnbtzhY68+z7XtA8LRnLu3rzFZ9GnaL0wKll7zrh0V4owSn3lhM3CUP0AtBRu9OXcv1jHNmrFZk8Y+rqd0A06Y4GxMyU+HlJmNJlqcXoz/vgBxeAq+DY6N7hXMXr22cgOUlYHlZwhdojsl5iAGrSW+u0OZOLheyvRwg/7mOXVlcrboc/Vdt/AWMXniURY2Ugp0Q3JxMaZpNLa2T8hSD8suMK0KWevoy4hFXDhMc5ebuw+pa0MtfLXBTrBANoJZ5pFLgzUn5SQNGNg5gVXg2QV3p2PaVsM3KjyzZF64SuRl5wzDBWVlEhcOll5jCI1J6rO7dYxpqaZHvL0U+BfNee4uzU4MjjKHy37KZycButAIjYanwpxJ4eDokk1X1VcP3ISiUCewrcGERqoWvEI0OG6G3q9ZnA9/i6vY0EswdUlemeRyTGgaCE1D1wzOixjRCkbiErHwmJcP2WuuI5HMjCmz9pCeucOATXItIWrO0EWftq2RzRxZzsnLh+iij6YJFuYGTvt+eppDIXVcndUG5rV5n8u1ybXBhL4fqw1pFLDIlfmMuRRenuYW+4lO3cJRlTITC+zWYS5m6BhIKjQEdZPQtAVoAq1V/QWeEV9LrEXQwlUroJBfiJQ2aDw1PuXqu9/EWpshFw56U6DtujBNkVMDfVzQ7u6iTS8gzWF9jfzm1wNgHb+C8esfezKDAThIVeMdUzQIrcU3JOtugqmrDVxa2uxHPRbLxeZKEHFjfUZd6ziVhW3UuF5Gkrk0rcB3U3Z7M5LCxhAeDQ5lIwiNZmmSVXOeO8hWQzYaceHw1sU6+6mLvTQzErQITUWgPF11zLzkx2yGCyy9RogG30u5e76pImiNxqeP9vB0tcGP5iFp7BEe3Se9tUUZewi9IXzmAeXZgMn9HY6Ot1ifX7AmBfHZiPOzMYvUJy1t3EVOGMa86+o96srg9YeXKRqdwCypGp1C6rSFjSEadlqNujQJ1uaY/ZjJK9cZh2+yuL3H8cNt8sKmkgbrG2cYdsn8fIQTJgxbDdvP8LfOWTzcZHDjIYt7O5webnKld4v+9QMMq6I9WidJfQ6PtqikoTpPeskTGw8dX/687YX/fD5AFw3rgymbVx/y8qdfxLNU/3LHLLn69B2SaQ8vCihKm+nFkIGT8uB4m968z9PPvUGROsSzHtHSVnJzOGH/fEMNRDtj3U1Wp2BLl2xunTIaTalKk/nZiOHWOcm0hx2kyNrg4affxSIK2Luyjx2k6E5Ja3nI974P4/AeHJ7jfE3A/F/blKlSuAq9ocpsws0LhF3RFCazt/awewl2kFFmNv1NVcpnLcPzR7cvq7pesyLsz7H8nIvDTaQUWFaJvzYle9RjwMmx/JyHb10lL2yGQcSlzWPC0ZwHd65wtBiQVBZPrZ2otEllIVsNy5Bc7U+VUUjhcGc+XLWHdc0Sxyw5mI04SAIaNGyj4iTqr6xGy0aw58dUpckb968SFTaDpdjpcfPS5hG/drjH3dhiVqoOiLNK44ov2fNTPnURUjQaXz1KeGbtlGnqczgfYuk1nqWcGqvSwrRKZK0zmwzZvvGAg9efJi5sPKvka27cYv9ka5WykK0yTXp0hmi0loWYMmzWcfGozYID7R47zRUAsnrKN9h/nFebh0hqbC3AszaI8ltomoPQVFXAI4Fg1GTccT1CsUEpU/rZGleyDQQwtAySOmBRWkS1zleNz8mlSSl1toIFv368TSYFuRQYAn4tOuUpc40rwueirCmagENxTEuDr6/R0qDpIbKtaZEExgYxEVvNBju2zfVA6Q+23ZI9P2a7N+fZ935uVfYl7Art2U3Ic9jxEDd9GsNEnBxC2KPZ3FGdD//VjyP6EtZ7yPe++MWX8LxNnhnMluWrJq+db/DMcMKduSrpGzkpnz3domeVXHezlaeF7yUcnKl7/49/43/nzmefxdAlQqgGUw/nA64MJ/h2gW8GPIyDVSQBVCOgq37KrLTJpcGLuw/onW1ylrvMS4vAVBuAqtVWZaa7/Sm2VRL4CY6XM5/26Vk555nPtLS5Hdl849YFs0xV41x96g7FwxF15jA9Wefu8TaXHuxwOFkjWm5i10cXHN66yvl8gGwE7/3AJ/jkf/8aotRnngQcLAY8tXaiWnEvDz3PbT/k1sk2AKZQjqWuU1BkDpZTYDgln/n5P8nm5im71/aJLgZEUcDx0SaOXbC2rXoH9J6/r+6FxFINjAYp4e4pmtZib02Ibu1RlybhcE44nJMnHoNtddA6un35CY2GJS2PX9zXaQYeG1rbtm/r7bz7oT8GoFqrGpK61kmigLKyKEqTpHC4ceU+Xi+mbQTnRxscT8as9ZVLXtMItp65S5W4lIlLnngcnmyqSb35Qn9yxyx5MB1zmru8d+uQfhBRViZS6njLhawolSnMxqUjGimU25VZo3sF1uYMGtD0FowWzQBsDXlsUk2VwMzantJmJjJxqCKP2dE6G8/dQV+W/OlBwWv//usxjZr13WOEWZPPQy7OR0SpzzBcYFklvbUplp9RpSp9oJs1sjLIYo/FvLcq4zGMmjhRgsOyUr2yp6nP7mCyKin61YdXeG50ga6pkL4y+qm5Mx8CMHZyLKHEb1ltUkiDa8NzLpbPaxmS0M64SALqRsc2asZ+xPv+y//5OMcLAD/94l8ntAoucpfPzwJ8o+E01zGF6gl/2cv59NRnaDVsOSWeUVO3GgOrYOhkjMIIKVVq4NFGb3vnmKPDLUqplqcHsxGmkJznLpPSpG/WNK1ydzvNTf7LRYyLia4JBBq6pnHaRGyIkKZtSVrVevlCTDnjAVF5RC0ThFDRhLZt0DRBVZ9hGus4xoCmbQiMdRwtQG9NTCy2mzViCvqay5ZtcclXTZeKRtC2sOWW/NKJzUlZkFKgI9gXd4nkMUIzGeg7RM0Z0+wNNM3ENTfQNQNXH1K1GU0r2dWe4aa+wdAS+IbyGrgWlLxn7ZTN4QTHzbn8Zz5FdRDQ1vqq6x+iRd9paS9dRktj6q3LiHiGmE3gbAptS3t5F4RAe2sf4//xZHo3fOyb/ifO43A17gBcs2Seu5xnHqZo2Fq2tq0bgWtUbA6VuY2K+OT8xpvPrmytTa3h+uic+7MxgpaBmzFwEy6SkHo5X5ytdC0astUoGnVqf7TQB4bkdmSz7dYMl2ZbX/fHPsbidIysddUa2U+ZnK5x53SL/TgklzpnhcElr2DbSxh7CU2rUTc6uqb6kHzqZJuxXag2wss8/bqTYQiJZ5U8de0ei1kPw6iRUmcW9RiEC85mQ2aZx6K0Vx4dvlkxdDI2BxPGaxPm0z6zOGRtMMO0St64f4WL3OPF3QeYRk1eOIRBpDQeQUFxPFTlgHZFWwuMXkpTmNSxi8xtitjD6UcIo+Hk1mX66xNmp2OqymQwmrHzk59+7GNhb2+Pg4MDdvtw93+zf/9f+CK49vcLDuawu7vLw4cPH+tz/1HjbR8CHplDtI2gkS3TyQjHzrHMEikFSWUxn/Upc9UZLi9s5UoXJAjRcH46IjhaI192h2pb5f1fLxd8XTSqVr1Q6nffqEkLG9fOVc6/FZChampFg2WXaIakrXXKxMVyC3SvILu3gSxN3I0pxnpMfephbKVU05DkdIQ7XKDpDU2tIwsTWRlUhUXbCKpJyPTOHuvveRPHLjGXvdNnx+vYjnLdMnSpvOcLG1mpt8/uJywO15ed0Wr8foSxFK2VhUUS++SlReglCCFXm5y8srCsEl1vVqYsj94LTxTEy3D5o4YhgzCl56acLvrMCoessmjRVL9yvWY8VB25jGU/gOwJNeKoG8Fp6pNLnXW7Jqp1PKOlkBoXhQ4obUEuNc4KE6fW6Zs1F4VDVpurHg/W0gY6KlyaA3UCckxV8eGbJYdJgGw1BmZNtZzgS6ksgp/3Q5KqxRAajg6eAc0iwNQ0TF3Q1wzOyxK/8Un1IZmYUdYTbH1IWc9pmpLA3qNtGwzhoiGwdY+4PgMDTM0haxcIIYi1OYvWJ86H1E2AbxokVUsqG/zI5uX6gFSPaGnQMTCxadqGuomJtDMWxUM0zcQQPoawMYWKDjlaD0/rsduOcXWN0ITQaGlQJYc9V1WVWFZJeb+PzG3M4QLjSk1z3iC2THBstNkEZI2IZ2hlAULAuA/nM7Q8A1mr0tYnMhpgninvC1NX4tesNrGNitDWVmF0XWvIpElSWehaQ1WbmIbSDcWxj2dUeEDVCHJpUEoDZymg1LUG18mRkdLz2EbNthevvCxSqXM/sRlZEgGUjeB+YqALtYmSraZSMfd3V30t2laQpS5FaakNutYyK3V6pmRoFYR2gW1UzDIfS68paoP9qI/2qD+H1ElqnUVlrCo+9LplcqHSVcHSRa+SOnnh0Pdj1Yq8Nll3MtWca1khBbCY9ZSPv5NhLPVUjlkxJmU0VlHPIIyxvJzyoo/3QoHVzmgSC82U6HtQvOJTRT6yNGmk4Gh/F+88w/MTXD8jmfVIM1cJBBdPpmX3b6HL8X/Z8rbngqpUp27LLjGMmsPpkOtbR6sOcG5aEWceUerTLBe29eFENecoLKLC5fBgZxW6fdSIRjYCQ0gcu0BKnahwcAxlUFI1OkVpUUljpVCvpc5waUKhaS1lqupWNdFiVgb5PCRPXKVuHSakR2OcwmS+v0U0D7GDlOqsh0wdZKFyjmVpUc19kvMhn799k6+7dkg4nKmfZTan52vsbh/RG0/RRMvkaJ35ooc9DzGdEn/jmPLuLkJITLvE6sfKR1vqVJVBXthoPGqkoitvADsnqyy01McQkt1wrk67OqvGR7UU9KxClUothUGBn5IUDtYyvwysxHKunzGuVY/xLHc4Odt8bAPlN6NrDQ+Wzo1rTsm0NHD1lqaFaalxmAou+WrSLaQgk4K+WTMtLGLRsl7a2Ea1aqwyzx3mucN2b45l1uh6Td/JuBv1GNsFa27K7fmAaWmQ1moc3AxrHqYGjmhxjRZdg75p0LTg6Bq+AWBBCXWzTmM2lHKBKTzy9oKmTWhp8Mw1yiahbgt6YptF8QBdM6iFR0vDKSl1U5BoJpGYkFV7jMuQCy1iIk5oKslFeRtL99E1m5aGsbiCb4wpm5S6USI+19zANQY4WoiBjaQiaPuMmyE7nupJYGqqI51vSPqm6gVgmjWGUzJ/sIW/NkMf5TTXnkaIu1TPvxdjcoJ2sA9CINKYVui0nk/TG6GffhrmCyhqZPbknPseRD2u9ObK0Kg2SCqLQaOrhUvERLmzEvo9OtUnuYNt6BiGVH0G/BhTrylq1T43yh0GrjJ1qqRBVZtq0ygaQjun78fIC/VcWmlTNhrmspQ0LUzuxvDiUDnsJbVBVht88v511twUobVUcmkepqlOfJrWMqsET/cStsMFPTfFtkqSwiF0MrKox73Y43qQkkud6jdVzBSNgMpSmp04ZJr6ZJW1dDn1qaXO1ugC38kwox5rS3+NojYpa526NpjFIYMgYjCcYRiSaB4yDCJsq6S3c0Y+C3CGC2gF2aSHMwoRVYmYp7QlNJduUv9GQjYNaRodwy45nA5ppyOGXsLNp25zfjambVV09fB8jaef2Ijo+HLnbS/8j0JyQle9nZ+7dhcA06rw+xGmWREMFpS5Tbo0rkhSnyT1kVLgmiWVNOj7MUVpcTQfsj6csrl1SlWaLBYhp4u+quMOFvSCmNcPL+HVJklhK4c0qyCvDRV6MyT9q0eUiYvp5ZSJy9mdPXbf/wqnn36GMvIQhyMevHmD4UiFFfvjKe6lU/Z/9SWE3uAGCeH2ObIR3H/tJrpouL59gNlLaB9sUeQ2QpfcfOYWVWEpe02tRUqdaRowGk3RdEl6sM7Wi2+iGZLyos/x69eIk4BJFKrohZ3juRl3TrdUa15dsh4sSEuLWeqpE4doiUuLtoVJ5vMgDnhudMFF7lEv+x380uEOf1waDL2Yr7p0j8svvsGn/tvXklUWdaNzf38PS6/JKosodzhbVgU8bnJp8DUbZywKm9fmfXxD4hsNuqYTVTpRXbNuaxhCnY7M5YlqYFUEZsV6OCdbNiJSimOl6WhbjWkccJH6HGYeH7zxplIsF+p9ef3hFnKZmDrJDDZcVUUwKwWvLgp6hsm7+hqm1pBKjauB5JdOBIPWY6O5yY65xwNxm1Sc0TTRKt/ftjlC85HGLk1bk5TH6MLF1H1KuUBDxzZChG7wkNc5Fz2S5oK8mCE0g6atyKoJsklo25zGqbBEgCv62FqApQfomPRZZ12u0dCwYXikjaSmwdEhl0p97ukN79s6ZBgusJ2CZrlArX/dG7TPXof6EuLuHdrNTYzjB7SOS/PUu9DKnPqr/+/IaoY2fR3r/mdpr19Cm17QRvUTPX1NCws/9dn0G3puyq8db+EuT/NFbfCunYeczQcMnYwNI2J9MOWT96/TswrGfsz6cML81F+l/SxdYuoNw3DBLA55sAiUnmJZTuyYJZefucP8U4HqVFmb6FpL3YKrqVbb1wITT2+YV0rZb4uGeeQTV+bKGnholzxIHHqmZGRVvH8cYelS2UanPpOLdZLapFc6WKLmfWvnnGU+d2OXoVVzLYwopc5p7nJSC1yj4fraKYvc5dWLdeJaZ90uiSqLi9SnbgVxZfL6vM+ul63cOePcVS6EjU5dmhwcbaNrDXWjNgXpRZ/hS7eJ39xV7Xe/7i2y/2MN99kUGpDnHvW/m2KNcsx+gsws8mmPZy49YDobEBcOr7/+NGv9GUI0JPILDopPkk7V/+XL2174k9RbWVLGUcDZbMhzz7+G0BuK2GW0fUaZOJyfrXEe99gZndM0AstUHbbMqlId+MwaP0gYjaYksc9i1lM2wLrEMSvS0iJZlqE9tXlIkrl4S9c/06h55eAyu84FdWXyuf/6fq5cu0827eGECdsvvsnhx58nGM7RREsZeTz1/s+weLhJmdu0UqdJbfwwxu4lOKMFxlrEXnIPoUvisxEPH+4QvHYVYdQEQ7XRySN/ZSSkiZa9F26xPjvGHi0QuiQ5HjN98wpOP6KVOoZZYxoVjlWSL/+ea0/dYRr1SEulYr50ZZ/NXBkI5bnD4WSNp7cOlUioFTzdn3KSBHhLW9Cem3I3vglAWtrMM5+7v7iFbxXooqGoDU6SgNBUpVKeVbLNk3Huu5e4zCoTRzRsOQWT0kTTWjadkpEliGuLXLbIWqNuwTca0szmkpcTmiXH8wFlY9C3M1yr5NJgwmncI/QSdNFgmxWvL3orM6fATdkazHhx2dQlqXWOc4ObYcbtyKUFvnZs8b7xlE9cDCkajUteRVTrXPENTnOdWNY874fslS+R2C8wN3P29ftMq31cY6AcAduQwrmObCuqJqWoZ0BD3UTUMiLRjmmaCKH5tFS0bQW0S6MgtdBpmkNc3Mc2N9DNHfwmoBYFTSvREGhohJrNrieYl4Kkbli3G756lBOYFbZecx6HbK2fkaUufj9i8L8UyE/Y8N8PVCmfrxH/vEb47odoe2tIy0YsZli/8L/RHixoMx3xTF+F/OMMTYD1XPpExgLA/+3mm5zOh6o+vbLYdgu1eAuJY9Tsn69zZzGgb5VseAlFafHM2onq45E7/MqtZ/mGZ17FsCqqwmI2V9U/5/MBs9ylbTXVxXNZPllUJp/71Iv0vFTVp7sJrlGR1SYXucOs0pmWGrbQyRuNgSnZdAs+O/XZdOCiMLiXCN4zbNlxS9xlvn3kqvfIXgoQh06GL0vi0uYo6XOUWUS1YGxLNtycvpPz5nREz6zY9ipMIfnc8S5bfsy710+oaoP7S7fTSWlTSp0GSGqBo0vWvXhVlqvXDZ6b4fcjtkSLZZVYXo4dJji752h6g7etvAXaEnS7Qp7+1hz67JZyB9X0hmQecvlbPon3a89yeLDD1afv8NZrTylPEq3l6vD8iY2Hji9/3vbCb5kVSewjRIu+PPXXpUkrBfPpACNSdryPOkQ9alph2SVS6pSVyWgwI0l9ZosQXTQYRs1kNqRdlnUtcne1iIFD6CfkyxCaprUqfeCpmvk4Uc1pDKsiTjxkrSNrXXX/koIqt5CVSbHw0a1a1dg3OtGDLZIooGl0WqnjNBrCqCljj7KwMHRJMg9VHmzZ5tdyCvI0QOgNplmRT3pUhYVZmGAr/UO8CMkSVxnSiAZdVz2xx705g+GMeNqn78d4llokk3lIUdgUpUVeWZRS52w2ZF44pLWpdABaQyENktJGFw0vjWa4Zqna32oqQmBJg0oqK1lBS2AVmEsHPNd8Mu1YR1aNvvw8HF3i6DqOLslqg1mlcyNoSaVS4ZuiJakFtmg5zm0yqbPhZAzcjLbVmKUeSWVRNTpZ7mBbFZZe8761M0Inp6wNzucDfFstjKZolmJhFb7dciuqRom8enbBZa8gqtUEe9lPmZUBVSNwapOLosESGluOwWYbILJrXBc3WLQpGSmSmk3tOoWWM9eOlwY7GRo6LRLaCsvYopYRIBCah2n0qWWCJnw0zUAXNk1T4+gDPG2I13ictPfYaW8Sth62piM0DUe0+B6AxsCqCcyKgZ3RtBp3FgO+8fIhVeKi2xW8eghYNIWJ8EoY9wnfe0B7aRctTdA//yrNTEOEjRK22sva/gfnaLYGtgbRk2vN+8igJy8tssriSm9GtUxpaVpL0wouBRFtq5HXBknuchL18JKKuhFEtYFhVcymA+LMW93vUeFQNTr2slfFUd7H0iW+WSIbQVrYCK1Fo2UznLM/G6GLhp4pccOG0JDLzpr1ygr4rLBIpYYpUE6LosEUjao2oGVn7Qwp9ZVHQ1abq7lHFy1jW7K1tBKOS5sb/RmeVVDUJmlpoWkt06VnQSENLgoLz5DEtY4pWsZ2gaNLpXlYmj8JWjaHE/wwRl/W6WtCCV9lYZEfjjGDjHIeoOkSx67QeyltpaOJFuEW6FJgusVqTgaYfOIpTk82VHl0adKiQas0Dy1P+jSuPYEo09trx93x+/P22/Lqkqoy0fUKx83p5TZNbVDkNmnmKkFWGBOEMYZR0yzD8UJvkFKFrBw353QyJsodDL1h4CbMC5e2BUuXJJXJ2nLjIJe5KKUJUCYxppCMQmWgUZTmKscNUBaqtebazQdMbl+iLNSNW1UmazsnaE5DXZpksUddG0TzUIWQl4MzmfWQtc6gPydJPcrSwjQrvKXRTFWZWEIJ8IpEiYrq3EJfNuNoGo0o7mOaFcPhTJkUaQ2BnzDYPuOtl9+FYagNSNtqJKm30i8US1ObKLVpWyWey6XO2M4pHrl8VRbX1k6YRCpCYhk1rlERl2rRFLT07C8s+gDGsjnQ42bDyUilgYBVlz6BmkgXpeCyXxElFrrWLsVW2vIzU53WbF3Sd3IWlc28sDnLbTbcXAkTpRJF3tx9SJq5TKIei9JZLiTKodHUWhxdpRG23RRNg+nSZOdKOGeSe0xKi8u9GW8tfFoLGkvj8/OanmniG6BrcLX18A14kBqcNhqJSBg1AzIKpF4RixNkkyGERdOUtEDP3mVePFCiQN2lb+4R1cc4ugrrA1TkBNoag2ZET3PoscEWfYaWgaNrFE1LYLZ4eqM6yZnqhGnoqqKjQdmvmkGGzG3yh2Ps7cmyYVULWUb91V+DVlfo8zeQJxZ15GKvLdBsVPF6lkINWC1IaCL9iYwF9blay9etIRvBwI84jXpkjblyvBw7MbPMJyps0tJmUdpES/Fp36yoS3NVA6+LBoFabM32C2NYLKtAGjSCpT6oWuqFHglafaNeWVw/igLomlq0e1ZDVqtqDN+AUmpkmg6WmlsALLtczR1lrdpoA6qBkykJzArfqGlb1bH02prqPLlIfbJKpRxOlv02mlajbDSaWqUWQBkS7fkRumiVB0Gp7oG9jVOEkDTLiKHtKye/ttWoEhdajXyhTHqsQYwxjpVGSUi10UtadFu1N6+lhdAbDu7vMU99lUI7HWMI1SiolAZJ8XgV978jbRfq/3LlbS/8iyhk9/JDvPEcYVfYvjqxmVLQCyO8IMXyc/LIp64NktSDWDUBV2U8Omdna8hGEDo5gyDidDakb2fYphJ6NfMhO6Nz6uWO27JKDCEZ+enqGudnYzw3wzJNosJlcrxBbzTFDlMMNye/GPD63evYRsX22hlrOyeYXo6mN9iAWxn4ecTpw22iKMBxMw4PdvC9lMHahHD3lE/+0h9nrT9TjXbskldff4bt8TnTqarVferdr2OGKTK1SS/67O/vcfNdt7jz+k2y3GGsN5zOhgyDCCkNzu/vME0D5rmzEiftrp/R60UqhZC5JGeb+GaJs3TxUmp3m9AslZjJS8gLm9uzEa5Rs+1HDL2EVw8u4+qSLS/h0viMO2dbS5W/xDXLJzJoHKOmQYm1HrnVZUuXtL7VcJqb+EZD2WjMK+Vff1FoXPYloSm5G7uc5zbpsqlP08LlQK4sfYvlySnOlP4hsApuz4ZMSpPQkDi65KqvIgAjN1NVEIZSwY97c8I4xIr6rPdnOEfbuAbL05/NzbDkbmwxL+GPrRXcjm22HAO/GjCpPFpaGkz6zRhpP81U7FNKpe4HSKozfHMToeloCCzNY2Bc4kpzndFSrX8kFwS4BMKgZ+o851wnreFqIHl+MONBHBKYFYvKpJSCvdGCi8wjq0z6TsYHb7xJ8mCLujSVKdWLp2AbMOwjPz/n8GefY/dvThC3b4Ns0Tc19I2C6qu/HvPhW3DvmHof5F/6Fqxf+M8UD8fYexdPZCwA3DnbpJAGvlmyHkRcJCHTQtXTF43gq9ZPuEgC4speNvKx+aq9+xxOxpTS4NrGMcfn62yOJmhawyIKSUubrdEFiyTgYD4gqyxevHqH88mI07jH5Y0TLLsgTTxOZ0M+e7rFpSBahevj0mYznHMS9Wlb1Sb7WpAyLWzmlU5SC6pW46zQGVgGa4BjlXzqrafwzJK60TnNPHpWiWw0HF1ybakxuB/18Jf34DwJOI57BKbq6rgfhzxITDYcydAu2bNKFsuNUVLrfG4W8OeuXbC1dkZZWhxdrPGLh2Ou9NdYrwz8IGHjuTtYzxWQl7RJS1sJmkiVDLe1+gKoUwfdLhFBQX42JDkfqE6nyzlkY+MML8qYLPp8+v41vvraHabzHrPM42H8B6Dq7/iy5W0v/P3eguODHXpRwMZT9ykSFyl1FdY2JB979Xm2wxmGrk5sllUSBAnVMmTm+QmyEdgLJXhzvQxvaTnZtNoq3D2JlHd+Vpucx+EqXJ0XNknq43sJaeaudtiTeZ9P3btOz8rZG5/zK/du8J7NI3St4fhijTRz6fcXaEKd+OeL3ipS4Do5wlSissHaBE207C+NROLUI0r8ZT/xlFrq9HsLDLPmYn+L08mYrfUz+usTnnnP53n5E+/hxfd9Bmu0IDsZ8dS1e/SvHJGejHh47xI39vZXQq2qMnnrcAd3WbJlWSU7/SmTJCSpLGSjxG6BVTD0EmaZzycOL/H82inusi3rI13Etz73yvL9sXh4sYajK6W8Lhr6T6g735vzPqGpwtNbdo6jO4RmyWHmcT9W5inmUnSXS/gTGwkfuwgQGqu2uLLVkC0MTMnT/Tmb4YJ7Z5tEpU3ZCMZOxrOX7hMnPqdLM6MbRs0sc6kaweXBlKbVeDAfquiIkxMGMdN5H9kIelbOp/evsukWyvZVNLw0lMSVybWgJDRqbgwmfHq6R9m0eIZGaNq8nE5JhFL895sxa/o2J8YDNAQBA7aaDT7RfBRXH7KmXWZNjjARmJpOaAheHEruxkOiSkVCBpbGn79yQF4bnGU+b877PN2fU0iDy+GCoRdT1CZDL8Z1cvwgob91TnI+INy8wBpGyAsTfa2Gh+foGxqX/td90uf/Ms7H/z9oZos2Nsg/28O+eh+iBW0Jmlljf+7XINRwnpnAePBExgKApoGhNao183TMu3cfIM82uDa8YHvjlEYKPvLye5ah8oK2haPpmIGXYhkVTSMw9Zpfv6M0LNcGEyy9Zv98g2JZKz8KF5xPRixyD1NIdL3m3sEuR1GPqDZVG2Ct5fZsRM8qefHSPQ7P1wntnKZRts9jJ2Na2NwIEza8hF853mRgNuRS5zRTvUgMveFhojpzDu2Co2Wfe1NrGVgFH3z/xwlefRbfztneOiHPHDyr4NWzLW5H6qDzdG/ZtroR+HbOXrBgknnUrcAxah7MhhxHPUZuyvb4nP/X132M41tX6Y2meOM5Dz75HNeCTyLjkGLSI5v22Pi225hxRnw8pniwRfnmFaxlCXDzpo7tp9S1gaWXGFaFUdRkqcv+2SZxafHV1+7wuQfK3CqwSr7+2ltPbDwA0D4BcV8X5X9svO2Fv64NhqMJhiG5uLtLtQyHNY1OXlgEVkHgpavBGMUB+0fbDALVfjOOAk6nI0ppYBsVo1bDczPOZsqgxtAlVaOrfFSjOpINnBTLqJmnPk0aMPIjpvNHO/iSvh+zc+Uh1u2KOFdh4Uf5QH9Z7lfVBmVh43op4WjOaO+Ew7eucDobssjUTZ2WNm/euolv5/R7C/p+zJ3TLQKrYGMwJclcstzBdXJsL0Mv1A4+S13MWYjpWPS8hNnROsbZiCxxOTzb4JoUql+4WWEYNUVtq8Y/UmfkK8tM06zQhcpaD70Yp7JWZT7WMlQ/9FQ3wLcma1zuzbDNilrqHMxGbCxLHdPSZl6oiAKoEj+WwqLHzZabk9YGi9JaqaT3UwdPb3imn1FKnQepRQvYOtyOVGokqwVNa1A2GjeCjGTZgCepLNKlyt8xahxgUdh8+s5NXLPC0iUa7SqH6i83g3LZIvlRWqiqzNXYCJ2MXJo0pUXPKunbGfPCZdeP0Whp0TiOQzacFkdvqBqNo0zwtD0gl33ypqFoJTuOTVM05FqK1waMDIt+s0uPNXaadTZtC1toGAJMAae5gauDrmlYAjYdFR05zz0sIXlp6foHrOrTx/4EoUvq0lQbaauiv3eKc+kMLYS2aGkv7VL+FzVm7Ooc7xf+d1q9VXnUpML5Jh32H4LQ0DYd9P4AzpYCLsdQ7XqfELrWYJn1qm1xljuM/ISyNrj7cA/PLrD0hg0nZ8OLmWQegZ1jGRX2smSxKC2Gdq7SXJW6vx5FexyrVM2vlt0mDb1BSkN9xpWJXraYQvldVI16DVVtsDmccLHo02otQ0+5gm4vfQRko/HBvYdEhUNo5/S8FMfOcaIeI0tQNDpZbTC0C5Kl1XCDxuRkDWvpQ1HXBhvX9zn9xHsY2jlfZZWMvZgH86GyL6YlXxpSaVpLaBZs96csMg/XVA6WtlNQpw57L72OZirzo93n1KIsM4sqdShyG3mvBdFihylV6qieJb0I3VBzRDIPkbXOIuvBrEdvsKBtBbZRUUnBbBGy25+RlRZpZfHKwSXe/cRGRMeXO2974U8zl9HmObLWmZ+PlJVuZZLFDnHm0XNSgl60yoerul1Ve/7IwSoqnFU9+zwJVBc+XdIsc7fOMj+tOuA1hE5GLXWyWuXAB27CNA0whcSzC3yjVg5cdkFZm2SlRd8sV6YYplkhl7X0Zm1gA/YgQiwXR9kKilKVCqal0gj0ewtsu6CoDUZewmjtguzhrnLwqg3qwkJKHWd5nTxzaFpNiQKjYOVGdxqHDKYDdXNbFXWtynUeGYiEy9C9EjlKfC+lrExVwiN1dKEmMA8N26ywzJr8XCd0MgxDkhY2DRqnUY9quVHSNNW8xBQNetNQyieT1x3aOUWjQtNFo1rxPhLwOcvNStOCI1o0DSalQNdUyVrRqPB+UhuYQrXdPc0dAqtcaT1soyavDe7EASOrYtNLCKyCeaFOf9ZyzFhGzWDZ/Gceq0jSI9GSrjcEljpd9iyVKgGwlmHQojI5zzzGVk3Pqshqg7PcYmwrF8CkFkxLwciGUR5QtA6epjYzo3aLQdtjaJr4hsam066MXaoGdt2avFGirZ4piUubqhH4VkXPzagSHc+oMJcud0KXOL7aCMnaQNMlRpCh9QHPRqP4gm9/ZSAvLMo3BlhrMzRDQtqAF6AZqmwVIWiFjlZLcCwwdLiYPJGxAOCb5bLvvUGxLLcFVae+KGxkK9hwcsZuim8rfw5DSPTl51iUJsZStJdUFovCZqzXq+dvW41oWfIGYAm5cn70lo2x0spSJ+plbv+Ra+ajuUBoLULIVTdM2QquXdonzxwcN8fycoQuSTMXFn2a0kGglP0XmvLMcPSayaK/0hPkmao+0pZCWkNINkcT8srCXL7+Re7S9xLkchHuhcvS5yBZNeeRtY51aU6TCepJgNlLaVKVgze9HB/Ij8agtStNzepfXaIB2TxUh4vSIisc/CBB12t8O19FVMe9OYaQZJXJUeY9sfGw4gl1B+1457zthX+ShOwsQ9V1bTB+9h7xww3i2CfKXS6vnxCO5qSzUJ2EzYorm0eA2jQcR/1VKiCvLKapj21U7KyfUlUm8zhkK1S+6WJ5gnDsnIv5AA21GYgLh2gpSpkXDrPU4+hijaw28cySYRBT1MsmP0vLW7GsHpjP+8SxjywN0sxlGMSYRoWuS+rZgK3eDMcuKApbpSmsgvXhlP71Q06OtzCNirxwiBMVzhuNptSVqQwxGsE8CRj15gRhjG5V2CfbVJWJY5cYZsViEWL9JpW9bRfEqWqZadsF61eOufP5pzmLQ0qp07MLTtKAnpuRFjaz1ONqT70/UirTo8vDcz57tEfVani6MgH6/MU6ji4xhUZePZmF/1HoU9eUcc6mm+HoFlFlcDvy6ZuSnvkFo25TCGQLVaOR17CoWn41t3j3QKJrcJgZbDjWalHXtYY1L+FB4jEpTXTN4/L4bBUZecT6cMJg44K2Eehnql1yJfVluaM6Ve32lduirkt6bso0DRCoCXTTiymWqm8BBIZJVGtcC2qyWnnvFxIGhoVn2PiGxstxxDoDBoaFqwuSumXPK8ikas6z6RS8a/2Y48WAi9xlsew+t7XceBzOB/SdnKEXI0RDXtjMjwKuXt5f9WPXjEZ1k5yDJnMl0nv9APtZAUVDfeqwOFwnqHSsYYRwSur/OEH/X96L8fAO7VsXVC8bWDc12vEaWjQn++yA8K88keHA+mBKlPpKqFZZXBWS+9MxpmhWOfd3bR2QFY7K6W8dcXi+thKfHs6HPLV9wCwJiEuLaWmz25+RlDZJ5VIlAt+skI2Ga1Toy7E1S9RG29Qb0lyJTTe8hDU/wvUyDk6UgZVsBbPUw7NKdjdOOJ+MOE9CTKdg/NQDZGGqJl83LzDdgovfeB9VI7g5OlMbElu1FNa0lqRwGAeqhXeSetz7/NP0/ZgodzlJAkxdcm37AFDz5EYjWN89pn82UuJoQ+L76aofSJ3bOMMFbQNN5JCf9ykTF92s8TYm+LuHCLfh7L89u3q/21YwGE1pl1UTullTVia2o8zU8sokiX36wxlyuVl65JkhRKNSjE+o4qfjfwzetld/R0dHR0fH78XKq7+ncfv/+XjdIm/8vxccLNrOq/8x0MViOjo6Ojo6/gjxpPp2dHR0dHT8EaWF3+Kz8ries+Px0C38HR0dHR2Pn07c92VL98l0dHR0dHT8EaI78Xd0dHR0PF5a7QkY+HRe/Y+L7sTf0dHR0dHxR4juxN/R0dHR8fh57N35Oh4X3Ym/o6Ojo6PjjxDdib+jo6Oj47HzuMv5Oh4f3Ym/o6Ojo6PjjxDdib+jo6Oj4/HT1fF/2dIt/B0dHR0dj53HXs7X8djotmQdHR0dHR1/hOhO/B0dHR0dj51O3Pc78+EPf/gdP4emafz4j//4l/z73cLf0dHR0dHxB8RP/uRPomkabfvFuxA++r1u4e/o6Ojo+PKi5fGL+75C3Ho//OEPo2l/uNGQbuHv6Ojo6Oj4A+LHfuzH/rBfQrfwd3R0dHQ8bp5Akx46zcDjolv4Ozo6Ojo6vkx4/fXX+Xf/7t/x+uuvM5/P+dmf/VmSJOFf/+t/zV/+y38Z0zTf8TW6hb+jo6Oj47HTqfq/ONI05Xu/93v5qZ/6Kdq2XYn4AO7evct3f/d38w//4T/kP/2n/8SNGzfe0bW6Ov6Ojo6OjsdPIx7v11cwTdPw7d/+7SvF/3vf+17W1tZWP8/zHNM0uXv3Lt/wDd/A+fn5O7reV/a72dHR0dHR8WXOj/7oj/LRj36Up556is985jN8/OMf55lnnln9/H3vex+vv/46zzzzDMfHx/zTf/pP39H1uoW/o6Ojo+Ox0zbaY/36SubHf/zH0TSNn/mZn+H555//HR9z7do1fvZnfxaAf/tv/+07ul638Hd0dHR0dPwh8rnPfY6bN2/y0ksv/Z6Pe+GFF3jqqae4c+fOO7peJ+7r6Ojo6Hi8tE9A3PcVYuDzO5HnOb7vv63HhmH4Jbn+/Wa6E39HR0dHR8cfInt7e7z55psURfF7Pi5JEl599VV2d3ff0fW6hb+jo6Oj4/HTqfrfNt/yLd9ClmX8o3/0j37Px/2Df/APyPOcP/Wn/tQ7ut5X9rvZ0dHR0dHxZc7f/tt/G8dx+P7v/37+1t/6W3zyk59cnf6zLOPjH/84f+2v/TV+6Id+CNM0+Zt/82++o+t1Of6Ojo6OjsdKC49dif8VnOLn2rVr/Kt/9a/4K3/lr/DDP/zD/PAP//DqZ0EQANC2Lbqu8y/+xb/4LaV+Xwrdib+jo6Oj4zGj0baP9+sr3av/O77jO/iN3/gN/tyf+3NYlrVy72vbFiEEH/zgB/nlX/5lvuu7vusdX6s78Xd0dHR0dHwZ8OKLL/KRj3yEsiy5desW8/kc3/e5cePG6uT/OOgW/o6Ojo6Ox85XuunO42QymTAajVb/tyzrdzXyAfi5n/s5/uJf/Itf8vW6UH9HR0dHR8cfIi+99BL/9b/+19/3cXEc813f9V1853d+5zu6Xrfwd3R0dHQ8dtpWPNavr2QODg744Ac/yPd93/fRNM3v+Jhf+ZVf4cUXX+Qnf/In3/H1vrLfzY6Ojo6Oji9zvuu7vgspJd///d/PN33TN/Hw4cPVz+q65u/9vb/HN3/zN3Pv3j3W1tb4mZ/5mXd0vW7h7+jo6Oh4vLRAoz3er6/ger4f/dEf5ad/+qfp9/v8yq/8Ci+99BIf+chHeO211/jar/1afuAHfgApJX/pL/0lXn31VT70oQ+9o+t14r6Ojo6Ojo4/ZL7zO7+TD3zgA3z4wx/ml3/5l/nQhz6EaZqUZcn29jb//J//c77927/9sVyrO/F3dHR0dDx2Hn8d/1c+ly5d4ud//ud59tlnASjLEl3X+ZEf+ZHHtuhDt/B3dHR0dDwB2kZ7rF9/FPgP/+E/8O53v5s33ngDTdO4dOkSUkq+9Vu/lb/7d/8uZVk+lut0C39HR0dHR8cfIrPZjA9/+MN867d+K/v7+1y+fJlf+qVf4o033uB7v/d7kVLygz/4g3zVV30VH/vYx97x9bqFv6Ojo6PjsdOV8719nnvuOX7qp36Ktm35q3/1r/Lyyy/z9V//9TiOwz/7Z/+Mf//v/z0bGxu89tprfN3XfR1/5+/8nXd0va/sd7Ojo6Ojo+PLnOPjYwaDAT/90z/NT/zETxCG4W/5+Z/5M3+GV155hT//5/88Ukr+yT/5J+/oet3C39HR0dHxmHm8+X2V4//KzfP/yT/5J3n55Zd/T0e+8XjMv/k3/4Z/+S//Jb7vv6PrdeV8HR0dHR0df4j84i/+4tt+7N/4G3+Db/7mb35H1+sW/o6Ojo6Ox84flRK8PwyuX7/+jn6/W/g7Ojo6Ojr+gPjwhz+Mpmn8wA/8AJubm6vvfTFomsaP//iPf8mvQWvb9ivYCLGjo6Oj4w+Kvb09Dg4O2PJMPvkdv3tb2S+Fr/43n+c4rdjd3f0tXvb/oyGEQNM0XnvtNZ5++unf8r3fbzl+9BhN05BSfsmvoTvxd3R0dHQ8Xloev+nOV8gR9dGJv9/v/7bv/UHRLfwdHR0dHR1/QPzYj/3Y2/rek6Rb+Ds6Ojo6Hjtf6aY7/yPTLfwdHR0dHR1fJnzsYx/j53/+53nzzTeZzWZsbGzw/PPP86EPfWilCXindAt/R0dHR8dj549KY53HxeHhId/zPd/Df/yP/xHgtwj9NE3j7//9v89f/+t/nR/6oR/6bc5+Xyxve+G/8xc/gKa1RFHA/fMNpoXDe3YfkOUO50nIizdvcXq6zlnUY1a4GFpDYBUYeoNsBHFp8cFv+FV+4Ze+gfPc4dnRBc9eu8N/e+XdzEqb0Kx4dv0Y2y4QWktVm+xPxlxZO8VzMwyzpmkESeJRFDZR7jLJPDaCCID1wZTNaw85u7+LEBKhN5SFxafvXwPg8mBK6CXcP98gq012ezN00XBvOuYbv+pT3LlzDU1rubRzyPiZ+0zfukQae9S1ge0UxLGP52Y4bk6a+OSFxcWiT9tqXN46wrRL7u5foqhNBm6CbAWLzCV0cka9OSfTERuDKZZV0jQ6d4+3KaXOpdEFg8GMphHcfniJka/+nkkSomktu+MzLKtESoOiNDmejuk5Kf0wYhb1eON8g0vhnO3xOYZRczYd4ZgltdQ5ifp82yf/v+9ogPxO/Oev/U5MvaZudIra4KmdA6LYX9XtnkR9Lo3PyHKHrLLouSmukzONetRSZ60/I81cLpKQojbQRcPQSwi9hCTzOFr0iSoL36gYOhkDP2Zz65SHD3fYn43IpcH10TnT1McQDbZR4ZgVaWFznvk4es1mOKcfRphmRVWZlJWJoUvSzEU2KgSpi4ZSGhSVSc9Nufr0HT7+yffgmBVNI4hLG9uo2ezPKEqTWeaTViaeWZHVJrUUDNyM4zhkrzfD1CWvX6wTmhWWLgmsgrVwwTQO8O0cXTTUUuc8Cek5GYMgwnMzZvM+ty82GDoZvp3TthqyEfT9GCl1Thd9jpIQ16jZ9CO2RhdUtYGmtUhpUFYGTSs4jXpshAtG/TmmVXL/cAfPKrDMGk1reOk//efHPhYAXvufvgkvSEhjn/unm2gapJWJpUt8q8Ay6tVne5EEnKY+T4/PMIRSJdeNzt3pmGvDC4bhAsfNiaKAqjJZZB6L0mE7nHH58j5F7jCf91ikPrXU8e2cwEtxvYzP3L7Jdm9Oz48xzYo0c6lrg7X1czaeu0N2PKaRgpP9HR5erHNzd5+qMrGdAoDDk00OFgOGTsbW8ILLz96mzm0e3rnM2WJAURu89NSbTKcDgiBhsH2GuznhlY9+gLhwMHXJ+nDKrcNdLq+d4tgFtw72+OD//O/JDtZJLgYkUcDnHlzB0iWWLnHNko3hhLxwEEJimRWGUbPz7ltc3LrMyckGdaNzHPW5Oj4j8GOaRmc67yFbgdBaDCFZ3zhDFw3zWZ8o9dneOma4d4ImWqrUZnE6Jol9/CChrgweHG/zJ3/9Xz+R8dDxxbNYLPiWb/kWXn/9dTRN4xu/8Rt58cUXCYKA+XzOZz7zGX71V3+VH/uxH2N/f59f+IVfQIgvPZXythf+B0fbeJZalDfDOc/uPUA3JI5dEngpmtbSthqhk2PoDfcXfS4NJqvvD9yEIvbU5GaW7IzOKUuL3d6cTanjWKrd4CIJqKQOwGY45/bpFpeHF6ytn6uJurQQosG1SszCYZZ5bIZzmkYweahqIoXeYJoVAJYuWfdjBkGEZZWMvATbqGhajbS0MbSGs+MNNK3FdzIMsyLa36SudUyrQjck0+mA8XhCOJ5huDntkcb5TE0Eli7RDYnlFGwOJzSNwHFyojhka/0MgLKw8KyCqjaIU49KGgzchNO4R5S5CKHeR9/OMc0K06ixrJJFEjBZ9On5MWvr51i5w8HFGlHhomktoZdwbTBhrT+jN1hQ1zpJ4aBrDaZRsxEsvuSB8Xux3puzvnFGXetcnI+5f7LFem8OQF3rXF0/oSgtHLvAczMAvCAhyx0WtcfpbMjDxYDro3PGes0i87h27R5Z7GNbFb6bcvd0i56TMQoXeG7G0eEWaWkzcDN0TS32vqUmbFOXCK1hUToMnWy1KSkrk7PZkFrqyFZQ1AazwmHbj+h7CdM0wDYqosIhq0yc+7ucZj5eWeOaFZ5VEtoZaWFT1gZCa7H1mvFycwZg2wW3Z0OiwmEznPM1l+5xOBmvrnc8G5BUFpPMQxctutYgW8Es8wBoGkFRm+hao8au1iKERNN0wjBG01ryysLQG/bWTjHNiqKwyXIHXTQI0WDokji3eP+LL6ObNXVpki4Cxr05UhrkpcUk8XnpiYwGmMch06hHUjgsSoe9/pS6EYz9mLXhhLcO9jCEJK9MZCPoWQVnSYhrVAzchJ3NE7LKQjaCNHMBcJ2COPOQrcA3S3pBzNHhNnHuklXqfrKMGqG1lJVFObfo2zlZaeFYFuOtM6L7gXp9sz7lp96FbkhMs8Ky1Pzj9WKy2Keu1DS4t3NIVlkMvZgwjNGdkunBJpZZMfRiitokWJ9yeLRFUdjUlUF95xJCNISOGufTeY/t/hQpDeYLm9DJie/ucHx/l1msTmkfeOEVjo82iXOXptUwjJq+M6eRAikNqsokPlwHoN9bIKWObAS6XpNmLpOox9PX7/LqWzcxhGR3fcp0MqJpNHRd0vNjiszlzU+9gBDNak7pD+boVoWW2/Tc9AmNhi/QGfi8fX74h3+Y1157jWeeeYaPfOQjPPvss7/tMS+//DJ/4S/8BT760Y/yIz/yI3zP93zPl3y9t71lWO/PsK0Sw5C4To7rqYEupXoKy8sJ/ATXKnDNki0voahNhNbiuRmjwYyL43XaFhyjRoiGRgoGQcT6YMogiChqk6bVaBpB3ehYZo1sBHllkcY+aexjLnfEhpCEy9ORZdZkucPdo12aRiPPHBbzHnHssxUuCJwMIdTEGnoJw8EMxy5wzIqRm3I0HQOgaS1p7DM5WUNoKsxS5DZtq54zuhiwOFpnOh3gLX9f01qy1MUOUoIwxvNTXD9DCImsdWStr15jVZnUjbqJDV2yHkRYuvp+UZoANI1OszyR9vx4tdlJY5+ysNFFi2uWhH6CrksCJ1NREiExzRpTrymlQV5ZtE/I27rfn1PkNsVykj5JAppGoyhNzpeTWy11pNTVYmlV1KWJ6+QMgwhTlxiiIXBTBv05494c0y6pK5O61lcRgMDOqSqT2bxPKdXkbAiJvdzUCa0lsHN6fkwviPHNL/SqNoQkzV3S0iYubdLSQqMlrkyy2qSSBkVtkJa2WkC1ljj1CM2SuhUklaU2A3ZBWto0jcCzCjyrJKss6kZH01qaRqBrLZYu1XNkHlltklUmUWlzmvlqoS9tFqWFbAWWqKkbQS11qsokKRyc5Zg2hKQXxGrMWCWOmxG4KaGdYdkq8jONerSoqEBVGzSNYK0/wxvPVaQrt0lSjyTz0PUa186xjC+95vf3o1y+j02rMXQyDCFZ8yM8J6OuDXTR0rQCU1f3rGtUDJwU26ho0Shym7LWKWpTfV6p2hTposEQcnV4iHOXUqoIkS4ahuEC06yoa52iNBmFEaGTYRoVjRQI0RIGCU2jc/9kmzxziKMAKXUcu4BGwwtjTOsL4ym0M3S9oSpN0rMhZWFjO2oDawhJOulj6HIV/UtSDyFaNScZNY5d4Do5s8TnNOqpjUniEKceZW1gGxWaLnHsgsDJCJ2MptHxejG6Iclym+PJmGjSR0p1+LDsEkNIytIiy53V/GDpNW2rkWYuvZ7a5Ne1QVlZxInH4XzIIvOoKpOqNtCNmiJ1qQqL4WD2xMZDxxfPT//0T6PrOj//8z//Oy76AC+++CIf+chHAPiRH/mRd3S9t73w3/i6T7O5c4zvJTSNIFqEaoGNAyZRD2cQsbZzgmMXaFrLu67cY5L5FLWJadR4QcpbxzsklYVsNOLEp6pMfC9VN5UhSUtbTa52oU4IpcWaHyMbwdlsyNHFmnqsLtXpPVxg6TWWWZIUDq9P1AI+i0MeXqxzNB1z7coDTLOirEzK0iIMY2xH3Zw9P2ZzOOE4CWhbjabROZ+MOJ8PMMwKWetMFn1cJ+foYo3P373OJ954ltePdxmPLxiFCwxdcjobYvoZplNg2SXCUDfk/skWxxdr6qbT1SbG0mt6ntptX9o7oB+q02OxPFXmpUWcekSpz9r6Of0gopY6D463OZ8NsI2KzfE5m5cPATAMlQKpKxNNl4zCBbXUmaUek+SdNXL43XCDhFv7l/ncgytcxD1SqTY3i9zjftRnEvVoW42stIlSH8sumC96eEHCxuYp4/6MS/2p2kD6GRubpyTzkEUUcD4fcD4fMO7NsaySk0WfV8+28Gx1wmtbjby0yCuLUhpqUzmesrZzwuZgwqJQi7xjF8xSj7b9wsljqz/D0SWl1IkLh6bVmBcOnlky8FJkK7g8vMDWa5LK5DTzMHRJVqlNWegleFbBg/mQo0Wf87jH0XREYFaM/QhNa/n1wz2OUp+LwmFS2FwUNtbymm2rYYkabxndehTSn+cOoZ1jmxWmWdEfzqikTl0baHqD76WYRs1s1ufgbJ0HsyGmXqtxU5uU0mD7xgOqxGV2MubsdJ3z+YDbkzUAxuMJu+OzJzIWHmEIycBNuLR+Qt3obCwjdEcXa3hWgWwErlUwCheYesPV3QPW+zNkI7h3ssVhEi43WxbzzKeqDNzlewKQF2qD1nNSNvpTHLNkvHWGZVarTdhgMGNt7QLPT0nmIaZR0V+bYJklZ4mPYUiixCcvHNpWkKUe/sYEf7BANySzWZ/QT6hrnclswOGDPZpGw3YKlaJrBbfvXSHwUzb2jhjd2KcXxgBqkysaxmMV9TtNQg6TkGY59kyjpuemjMcT9u9fAtRpfjyeqOiimwMwjUNuzUZEcbiKRLSNRt3oRMuNT89NOT8bMwwX2GbF0XTE5rN3CIOYShqcLfrMU5+qEQR2zmAwp6pMZG0wmQyJk4DR3skTHQ+gxvfj/PpK5t69e7zwwgu/r3jvxRdf5LnnnuPVV199R9d726H+xd0dWikoS4uDyZivfvEVknmIsTxJXNzfwXEzTmdDjuMe167d48bGEVHqc+9ki/N7N9j0Y26Oz6ilzv3zDdr5kMBSGwXZCmxDneZdJ8dvBA/ON7ixdUhZmavQ5r3jHZ5/6k2EKXnr1g3emI1499O3WN89Zv1wqm7SpOb69gG98Yy6sNjcPWZ+PiSKA0yzVid2N6OsLG6fbfAtX/UpZpOBSlUEEdPZgDT2KQp1innzeIfd/hTPKlaTzNHxJr0gZnvtjEUUcu/lZxkM5jhhghWkbLQatlUyj0JOZiP6bsLW1gmWVYJoaZfpjEd/r2WV2HlB22q4Tk5/bUqV2+h6g+dmuE5OECQsFj0AitglLxw2t47JM5ck9jELC8/NMI2asjLJC/sdDY7fjeODHbYGE5Lc5SIJ+JbnP8f9wx2GXsyfvnyfqjQxrYrTszXi3MV2cjY2zogWIbNqQNsK3rxYZ5L6jLyEUW9OEMbEhUMlDRyzJM1cHs5H9O2M53tHHM8GKo9fWWrzmGj07ZzT2ZCyshiOJtw62eaicAilTpAEaBrcmg0ZWCVbQcRF3GNo5/SWJ60dJ+fe6SazpV7EFA0NGrrWsu1H+HbByXSEKaQK1x/vcJx57AUxlqhJK5PX5312vQxNa3Hsgh0v5Shz8Y0aWzTYumSaO4RmhSEa0soirmxen4cMrJoNJ0doLZPM5/LwHNcpeOvuNfLK5O7DPWyzwtJrbFuNjb6X4FoleWURLLUTVWXyG7/+Pt799C0suyTwE5pWY0drSTIPKQ0cO38iYwHANipKaZAUDhdJgKk3fOqtpzBFg2tWRIlDVFk4eo2lS+pG8MuvvsCmF2MbNbPC5QNXb3M6GyJbwciP0HWpNo16zfrmhKAXIWuDOAqoaoO9q/u8deuG2lCYJcPBjMlkiGlW9PoL1q7vU0U+Jw92KCuL91y+x9Vv/jhHv/4CVWHhuDn9vVPqzKHOLdpGRRrHaxMGzKgqgyJzGW6eEU0GSGmwNpywiFREa3Y6JpoM2Lz+gNc//QL1MlIXLUKSzOX6SGlzPDdjcj4mDGLaVpAmHhvr50RRgG5IhnvH+LHH/GSNxSLE1CXv29lHSqFSE1bFZDJUUZ9QaRfKwuKtg0tc3z5gOJxR1wa/8dE/gaa1DIOYzfUz6srg2d7yUJE7CNFiBynbxhFNo5Mvnsyh4DfTifvePmEYkmXZ23qspmkYxjvT5b/t356djWkajVrqhE7Oq689g2/nGLpa+GenG+ysnbExmDIKF0xO1/jU/lW2/JitwYwbOwdczIY8ONsglyZtCzc3jziejqEFU685SwI2hhPaVqMobHyr4N7JFp5V4lnqlP78M5/jrc89y3kc4lklzw4nRPMQEQUs4oD7p5vEpU0Qh4zmfUaDGUkUMFuExLnLLAkY+DFVbSCEZK8/5fBgGwCxFF8J0TBfqFOrZxXs6DU333WLaNLn4nyMEC3D0YSqsKhrg35vQTBc8PDeJab3rlFLwUZvThjESmBm1MySgHT/Ek9fv0t/75Dbn3g3w+GMorCpapPBaEbbagjR0LYaF8frRIkSzBnL0GAUBTi2Ci/O5n12do84PNhhnqpw8kZ/Spx56KLBsUrW1y7e0eD43RiOJjRSp9eL2GrPOD1b48blB4BKjdS1QduqdIZv5+SZS10ZeEGCB2SJx9DJGXkJhpCcTEccXKwzcBPW1i7obVzw5ivv4sbGEWVpUdQmO6MLstzBMmrGWoxr5zycrJGW1up1bQTRKo/etBqy0XhudL4KFdtWyaKwqZapkHnqE1c2a26CEA0Xqc+itOlZBZauTtN35gN0reVKb871jWOKh1fIKoMMg6oVmMvnziv1Oo8yl1IKHt1apRTIFp4bKg3EorTY9BJuhILQLAntnFnuMnIT4txl8Sj3//93ynlwvsHltVMMQ2kiBn6C6xScTEYcLAbshHPS2GO+6JFVFlvjc+ysxHUKpBRMFl9wCXvcRIXLPHcAcI2Kcqmp6Fs5e2unDNYmfPKVF7B0ydCL6fcW3DnaJbBzTL1mRzQcTccEdq5C2rVJHasTrm1U2IVNqM9pKw3XTzEKi5c//xzPXL3LxcWYWeLDbKBSDcMZ4doUI8g4ePUmm5cPKTObyamKfrSNQOgq7ffw1RtsXDoC0SL0Bncp8itym6q0KEqTZNajLCyKUn25To5plQhdImuDu59/hvuzMb5Zsh4s8IIE06xoGh0hJLZT8PB0U21O+gu2rhxw/9Y1XCfn5HSdz925wWY4R2gtg/6c9Y1z0tgjST2q0qKuDMraZOSnxLFarIMg4f1f+wmKyKPK7VVkwLVK9VlPhkySkBvGPkWu0lSbW8cks94qOriIAraf2Ijo+GL503/6T/OTP/mT/NIv/RLf9E3f9Ls+7tVXX+Xzn//879m+9+3wRW0bitKibVVefhr1MHS5CleOwoi8cBgMZjh+yuH+Llt+TGDn6HqNHyacTsZoGliiRhctVWXimCVCa9G0llKq0LrQWiqp4y3FW02rUUoDWyvIpkrt7lkljlnS92OEaFe5YduoccwKsZyQDbMmihyVD3bUyflsNsQ0alxT7cgPJ2sEdo5rFZhGjWkoEU3dqFO552RUhUWZ2+RLEdJOkJJEAWVlMdo85+J4HcOosY2KeR5S1iq18EizIESjBGeFRVOpEPXZ2Rq11DF0SVlY5IVNECQAxElAnLur1+y4OecXI+ALYcWyUIuNEA22Xi3D0ha74zNcLyOKgnc0OH432kawWIQYuqQ/mNMLYjVZLiMzeWWt/i5Lr4migLK0sHIV4p4nAT0rxzHLlfAoKRwMQ42nKnMw9VqlXJbKZwDZCjXeWo0kd1kUNiM3Q2gt0zigqA0GTkolDWa5x8BJCeyctLSVgK82cYyarDKJS4uq0cmlzqJ0VqK7gZ1TNTqzQukXemZFUhvUUtC0Gj2rQNca4somKXWEBpaQXCQBuTSoGo2BVVE1glwKUqk0AOeZS9UIklpnaOc4eo0u1DWHbrqsfFERms1wzkXqK6Ffbag0VKsRpT6G+ML7EcU+eWUpZbcumS96K11N24qVgYph1Hh28UTGAsAsc5cqdZXG8O18JVgsSot41qNqdHyrwF8q8HXRUEkdxyy5tH3E0ekGllFhWyW6LilKiz4q7y6lweH+Lr0wwjBrtKX+RugNul4rkZ9oVIrQqKlzm/xsqNJuZo3ZajhuTnG4pg4VuU2eOYS9CFkZtFJpcabzHnWt7nnTrBgOZxwcbuM6OZrWrnQ/k8kQ49E8Udg4eo1vFTh2QV2a6IZEtGpzURZKD9KiUVUG6SwkKRw8N1NCVlNt0IvcRtNaqsqgaXT8ZTqwKGzSwqZX62S5g6FLdKOmzi3yxKNpBIZZc2X7ECkN0sxVr8ksWSx6OHaO56fUlcnFxQjHVmmXuHCe2HgA4EmE57+Cw/3/+B//Yz760Y/yoQ99iJ/4iZ/gz/7ZP/vbHvPKK6/wHd/xHQwGA77/+7//HV3vi1r469pQk4ib0TQC2yopSou6sNncOubg4S6WXeIOF7QP9nj68n3i2KdphBr0pU3fTRFaQyUNThd91oJoKdBRIqCTqIchGhyjxrMKBn5CkjuUywnwwYNLhH5CGCQUpUkQJGoxkGIV6vK9ZCVoUT8zME01qQw3LnjtaA/XULlD30spagPb0HFhlbpQ4kODsla5xunpmHkUEuUu9VJck+UORW1i+Rm3jnZ5dm8f30uppY5lVOSFTVGbFJWJb+fUjU5VmuTTHr3xjHvHqtzKsQvKpVLbczOE3tA0GprWYpkqzOt6KWIyJM48XEuJjaIoWJayqQXUcXIELcP1Cwyr4u7DvXc0OH7XcVAZXCyUyMlxcobrF9y/e4WsspQIq7QpaoORm2I4klkcqp/FavHOKpOro/OlGKrEX5swmwyopU4UBcSxj643PFgM2PJjRuFiJY5ql5vAuLTIpYEppCpjyl2mhcMz3imgqkMeVTWU0mBR2CS1yfX+lPPMZ1rYGKLBFA3zQk26ji4ZewkP5kOiykQXDU8NJhxESlyYZB6+WeLZBWVkUC7HgSkaTjOPxVIL0LdKpoVN1WrIFkwBh5lN2ahNy2nm4Rs1ZaGjVxa7vRmzzGNR2uhai22V5HJZMSKVgHDgpsxSD88qGffmpJnLIveQjUp5CK3lIgnxrEItvKVJWtjouhKchUH0O3+Yj4FJaXMlXNB3U1w7ZzSaUlUmURRwsejzcLJGKXUcs1qJglWUxCSwc9auHZBnjrpvrJKgFzFfpuOk1MkLhzvnG7zLKn9rlVBuK7W6H2NZJdbyxJ7FHvGsRzBYUGU2bSNwg4TFUimfZkr0eemFN1kcqAqeujY4T9Si3HNThnZBsD7l6I13sc1MnfTNCl00HC8GGKJh4CYIrWXoJfS8BNfJSTNXzUmNtiojlY3AEJKqNlVFQK3GSeDHhEHE5vO3md3eYzHrES3FsesbZ1TlciNdG8hG0KLmBNOsOX24TVWbOHZOfzwl2JiyOFwnL9Q9uNafcT4fEAYRbpBwcbLOyaLPwEuxlvqQji8ffvAHf5APfOAD/NzP/Rzf9m3fxs2bN3n/+9/PaDQiSRJeeeUVPvGJT9C2LXt7e3z3d3/3b3sOTdP45V/+5bd1vbe98L/+8BLXNo4BuHu0i21UrJkVrlMgRMPrb91kd/2M+bTP8dEmUe6iTVtqqZNXFg8u1pGtYHMwoWl0HkzHnOYutlEzMit8L2WUpqyFC2aJz3kSsChsTNEw9mM2BlPCXoQV+xxdrFEuS+Jsq0IIyWTR5/WLdfaCiCB3VAShNvj4/lWeGZ8pBbQ02D/bZOSmrPdmCNFwNhvy1PYBUeJzPB9wdLTHhpsy9mM8uyBYqv73xme0aOiiYWsw4e5bqu7fEJI3PvucOsFUBr1+yrM33wLRMjldo6hN2lZjNJhRlhamVSErg2ja52u/4b+Tng+pMht/bUb86lPEiU/bCppG8OzTt3jl9WeZJT5XjQN29w64e/8KSeGoNISbsfP0Hd549RnuXqzz/mdfY3t8ztnxBuVvCoE/boLBgqt6Q5a6XEyHK9W9qatF2NAle1vHTKcDjucDysbA0auVv8Mz2wesrZ9zeLBDVUm80Zyjw63lpktVMdybrPGBG7doGo0k9VjkS6W31mDpNUNHcmV4wf5shFFZPLV9wJ3jbWaZT2DnvLB5yH++e4OBVeEsqwhM0ZDXBrrW0DNL+naOZ6mIU9NqVNLgzckaudTZcDNujE957XSbBlaK+7hUXgHnucO8MhDAfurjGzWBWTEtLN5YBJzmOoYGu17Fw9Tk6zdmWLrkJPX4lVOfHbelaCCqNOb3+3zbpUgJIqXOGyc7DO0MXahImKFLbl67y2wyVAuJNJimPmtBRFrazDKP06iHISSyEctNecXx1KGoTGxTbQ6fFIvSZJq7BLaq7HlwsMtpHGLpEs8skY1gw49YH10QDOdki4ChFzPLfEppUKU28zgk9BLywub03piyNlREyY8ZjibcBIJehGFIitxmkXmsiQuGGxdYfoZuVZzf20UsxYBtoyF0ydnBljq9b52TziwcN8OyfNLSplwE3L53hZ31U8bbp+i65N7xjhLSxSGzV/rs9KacRH1kNGDgpLz40ivkhbp+UjjYZkVVGyS58gx4dJo/ON0kKhwGbsqd+YCBm2BqFVHhEtoZRWkp1b5Zk52MePPONZWOMitmqYdllvhhTBAkhHHItfe8xuLhJmVhqZI8rSUtbGyrwPZyXv3YS6t7MHBTbKdgXZtyPhlxcLrJ+mCKY9ScRD1co+L69tETGw+P6Cx73z4/9EM/tGrS07Ytt27d4tatW7/jY/f399nf3/9t3/9imvy87YVfFw1J5iGWE2+UO/R9g7NZyP5iwE6wQAhlkBLnLsMg4nQ+ZHMwYdif48z77M9Gq1pWtWg2nKUB1jKKMM8dNgeTVUht7CWYuiQuHCapjz8Zs7dxyvb4nLxwmMYB54e7VI3OXn/Cn/2aj/GfPvE1XFoag5RSZ9uPSQqbjd6cwWCOaVYrc5808ZDLsirbKtkdXbA3PudkNiIvLS6SgLhU9cG11BFag641HEzGq7I6XTTLHX2D4ygB1eRcVRcky3I3384JBgsuTtZXpThBL2L/ladJM1eVMe5fIlxWLNQS0tKjqQ02B0rzUBY22jL/X0tVrx/lLoO1CevDCZZec3qqTjS+l2Lokrx6Mov/8cEOda3jOjmXLj2kaXRmE5Vj1UWzKsdy7IK1IFKbAaNmuIyAHE/HBEGC76X4QYIZpgR+Ql44ZIVDXpm4ZsVkrvLSWWlxf9Hnj197i4tFn7yy2Fs75Xw+oG/nmEaNXGpPjqI+Z6ky8fnA9iGvXaxTNQJblySNYCNccH865mIZ6nTNiv2oT1Ib+EbN06NzJplPUpm8fLzLwM6RjaCQxsog5+5kzPXeDN8uOFgM8M2S+1GPRaUztisOUptnegXrds6mH1OebJFUJlFlkkqdngmTUsPRoW+1JLXGee5wJYwIrIJ57nCee+hai1i2JJu/+jxX10/ICofbkzWeWTshLVWk4pFZUZq5q7SSrteMgwX28pRc1U/OpPNykJBUJhdJQM+PaVqNwCox9foLgsxSlRhai5LDoy08N8OT6iR7fPeSqmgpbAyjJnBTfC9d3aez6YC1tXOOjzdplqWxlzaPCcczyszmYn+L8+mIG0+/tcpje0GCFabsPnsHTaj3MLoYUBaq4mPHzTCcYqUpmp6sczIZrSoQpnFAVDhcHp9zeXyGprVKXPzmdd482wJgYGfsrJ3x8HyDaepjGzU3Lz3g8GSTu7MRVSNY8yN2/ZjXzzexhGTTj5mmPucXGwRWyV5/gjdX8xyoEr2d4ZRseXjR9QbPKjh54ypeL8b2MqJJfzWPNo0gj5T/hSFVqXV/OGf9va/zyv/5DUxTH120DEZTXj24hGtU+HZBnHhPbDx0fPF83/d93x/o9d72bLDdn5IUDqDycrpoSJflJc4yz1aWFnWtVO9CNOrEHvXQk4D7sxHPrh9TLm/2jSDCXdbxxoVDebqp8rvLEGtgFepfV9X71o2Oqdc8ON1kLVxQlCaHcY9rwwtqqa4Zz5Rqe144WLrENmpkLdBFu1w8LYrcxgtUmDDLnVXeOFyW5eSZyvdWjb40/0kQtCxSJayxjBrfzqmWdeWV1Mkrk5GvygSF3qhoQ2VhWyU2Km3QSkGceui6xAuhLC3KysKySqXMrlVodqu3QF/mLAFcJ0c3JG6QkMU+WWWtcsEbwYLTI2VaZJrVyvWrXAqRHjmjPW6GownzmVqUm0bHsEs8P10JotpW4+x8jG0pv4GmEVhWydliQFpa7PSn3H54CUNInKjHdDqgaXSkFFRSJyltSqmTFjYNGkVtEJgV++cb6m8VkrIyKeplGLVVJ12htfhmiWtoqi5el+z4MUllUTeCy8GC8zhUuX5dsual1I3O0M7oWWJlrOPoyxIyqaNpEFiFMuSRBkeLPkltIjPBonRYVCZXhxc8jEOSWscQLT1TYmgtuTQ4SkJkC6e5CuMDhGaDJVrmpWBealwNGtacnKwyKKXOyE2pUh3fLNE1pQ05TAK2ezOaVkPQKhV5q9EuN5J+kOC4OXnmUJQW09kA2y7IC5t6WUHypOhZOWtugmkoP43TOGTkpqqGvTYYeskqTVPkNrZVEYYx5bIsE6Dvx6rkbFlj7wUpZaF+pmktshF4brZM3ymjH2cWspj3yHIH301pG0FvbYqsdfLI5/T2JbwgxbCVGK8qzaXwVFPlw9MeYT8iS1yqSmfcm5PlqrJE01pso2YaByvvANuoOI1DDE35CKjxL+g5KXGuNvl55pCXFsby57NMVSa4Rk2zTIOptFJNKXUezkeqzDB3CJ0c2y6IUp/R0hCrqtRrSVJv5TLoBSm7drny0UgiH8ss0Sy1wTs/G9N8/Dn1+41OUukkUcB7rt1GEy1lYT1RsSeoDrrNY87Jf4V05f0d+YNe+N92LKbfWyAbgWyEMp9wU9LCVtaiTq4WZ6krIctS2FFKnUXucp4EHKQu/Z6quzeXau++mzFemtScpcHKpQzAWYbsDF3ie+nKhvM89YmWObq4MgnclPHyJjk422AznK8m2EeEjiq3SjOXk+mIs9N1ktSjbvSVY5pu1KsFW9mmgmcVbA4mjMJoNXnaRkXoK/c/QyjTFtkKekGMbqjcmRCNMtQxaqyleK1Y7uDbVkNWJlmyDOd5KUEvotdT9fe2U6id/TKcZ1oVbpDg9uPVRAio12crd7OscJDL/KjlFGS5o8KQS2OSx024NsWxVeg4Tx2qTJ3UdF05qbVoTJKAbJmSeERWmeRSVUEcxyGnScjD2ZBXj/ZIcgfjkROb1HGNCtkKylotcJ5RcZL6yEaFQx+VKrYtKo2z3DBausQ3y5X17XZvxthNsXTJyI85SQOq/x97fxZrW5af9YK/Ocbsm9Xv9vQnzok2MyM7bK67pApTpQtCLiMjEI0oWYjOCN4smRd4NIgnxAuIRmDBpSQkRCOquGVhuLbTxs7GkRmREXEiTr/7vfq5Zj/nmPUw5l6RQPneSGcey9fEkI7iKM7ee6291lij+f+/7/cpwcAu2OmtKLtKwTjY4JkVs24Bt7pDU9vqBUy1BpWSnKUBeSO5LByebQKSb7tJ1y1sKkmvE/ctSpvT1KNRBrNSsqwkTWvgypaJU2EYsKlbRnaDK2uS2mJZ2lokZ2rsr9a6lKwqk033evacYksSrJVupRlGi9P1odtWsMy0ODAtHda5v+0rv4hhSsW4t9Kbd2MyK9ztxphVNo5ZaeqnUNSdm8YNUmy73AJ6PLfANBuEaLGsCm+4/rafrz9nUU8jjg1Dscl8zRLp5kEQpNr+6haYbkmW+lzMx1xeTFhNhySriHijrYCNEppyuYownVJfOGqTwWiJZVW6n94aOGbFeRJxloScJyHLzKdsJL5VbjVCV9VCy6xRaD4JQGBVeGbNprSRoiW0CnyzomkNpKHwu0PdqnCYxxFVZxMWRkte2gRhgmnW2+eS5S7LuEea+Lj9mP7ejCDaYBiKvHC2F4WytJnHPT54chvHLnE79sU6jti995zB7gzHLbbY6k/G/5jjY9/4Z4sheWXhdhtZmnlUner9ymesy8sWy9zX5W27ZNDd2PtOzpOzQ0ZBjDAUx8sR69Lh93/hKzSVxaOnN7nYRBSVRVGbGlThZlsBXZy7HCcRn949JS0dmlbw2viSo9mEa6OZLsfWFtd3z7l58znHR9d4NN/h7uiSg8MzssRjvhxwtunx6PSAHzg45XAyJc08TNlwdHJIVtkIWt54/T2K9+9vqXP79z9g/uxga4V5er6Pag36Xkrg6EOP6+Vkqb8l9e3sXnJyfEjdyK2v/rXf8w02F0MuTvdoGsntlx+xng5Zr3q6uuFmqEawvBjzzvOb/J5X38Xp2gfpvM/FYsTeYM4dL8O0K7LEZ+/wlPOTA85XA5LCxbNKFmmIJWuGo/kLmTSr8wmGoXGxSerz6OQ6+8MZUbRhMFqySkIiN2eWhBwth+xF6+0mfFXBuD++YNFVUYJuI9s7PMNf9NkULl/8/G/wzW+8gSV1m+nxcsSn9k62Cuuy0mCoqjYpaovLuMe18ZQnizGbyiIwK+5PLnQ7wU/ZKR0eXu5SKEHPqph0veOHl7skcf+jA6DQ6N91aXOSOQhgUeqN1e/46rUSNI1ESsWhl/L1s0PWlYlvKqQBI7vgUWnRtAa7boHCJs4sqk7cZxkt00L/uy3gnZXFWT7kXpSx62U8Xw+YdL3hpjW4Fa0RwCwN2Ak2XB9NWSUhpmwoaou8NpnNRjSN1Btbq2+hRandFd/rm9d/O9LS5nI9wLcLHLNCdK9jUene/yL3uNZbMgnmOG7BL37zMwyHei4A2/yE8XCB2R1Ww9eOyJchWerpw87BJeVG33oBAler1W9+5n0Avv5L30fg5CRxqFHS6z43Dk6ZTscsShs3L3hwuc9r+8dbAW2WevilRbzRDpodpuSFw2USkVUmgVVxnnnsuhk9p6DnZvzA7/kq73zzdVa5hyUa3eZKApZ5dyjoxJVNK5CGiWdVCFpWlYtnVdzuMixO4wG2bLjlL0hLm7u7Z/qxVwNNGpUKpbToEGCd+1iyJmgNUIL52c7WtePYJetNyOWmx9BPuLF3pm3GQ61j6m1ClBK8++ufYZUFGEbLTm/5QucEfOLj/508PvbGL4yWRe5B7iGF4uWXP6R+eJu0Kzv7XsY3n95lJ4h59fA5641WVesgF61qv3PvMVXuUBY2w/6Ks9mEt997lazDqO6Ha3p+ytlyQFzpkvAyC6gbgWGgFdmbCNtsdAm0kcxzn0GuDxeRm/GtZ7cZ+gn9YMMX7n6AIVriVcR6E1LUFq8fHPGZ640W6MThlul/ZZOyZE26DvViqQTrOOSD//yD3Bhfbm+vb9z/gOfPr9Gi++0tBs9ODilqC1M0DENdxpzsTFkt+0xXA24dnLB8rsU5VlcZ+MZbn+JwMmWyM6XIXZ5c7DEYrIiGKz5tPMYQLUWub3mG0fLKKx+wuBjrw0qpbYTrmVY/v9xf0d+b8u//849ssakPn97i1vd6xqDLmWWlbYSuU9Iog/PliKqy6PfWpB0iVwrFoLPbzTOfs01ve3u/t3+iD3SFyywNefPuh/zS1z9H3QrGXsov/9oXkUIROTlBJ9arapPZdJdGGex0DH+rkTSpIG8s4jTg/uScorZYd9qJnlTUtW4bHCUhn9s9Y5V7fLgY8ytnBxx6OT27oGhMnm4CbgYJp6lPUgss0fKFG094dLnPLHdJahNptOz5KXUjWFU2X50PeDnSZe1KCWzZ8CuXA3KlN/WkFjzeGAQmuBKaFqQBA7umaARrQ39dWhs83nicZg6NMviFsx6+CT1LUbV9PrujQ23KxuTZbIe2NdiLVvTclKFQ7B6c89a3XtsemO/cf8L0fIf7t58g7YrNsvcCZoIen3npQ5bLAZfrPo+SkLv9BZdJhKBl4GgVf1FbPD05xDZrvnj3A+J1xDrRG9d4sKCuTfLcxSgcLKti+fW7nJ4cME8i6kawnx3j9BJkVwHLE1/3u493yFIf0QGUso4j4VkledbpOFxNr/uilxGNuhJ65rBYDBi9/gjDUFyc6zbSoBdjWzXnqwHvzMfc7y+5u3tGf7DCCbXF7ubhCVLqSsUHT27TtgZ3d87wvYxnZwdcpCFjL2XgpSSFQ8/LeLm/BGCT+VxseuwEWph5kUS8dnDEbN1nngXktcl+uGa16BMnwRYp/enX3kXVJunG58G3XsF3MwI/JRqt8CcL7Ic3uXHrOSfPr/HNp3d4ef+ELPXx/GzrpHh2ckjkZgReymRn+sLmw9X43U7b+z/z+Ngbf6ME17vJa8mG2ZlW6ddKklUWReGwE8REvi5R2VZNFMZczsdk3U10eak3rby0t9zquOuzu7JmlXv0/JTIzTEMba3z7WLbw4+8jGI5QBpqW/6beAnrzEMKF8esePX6c85mY62gN2uKUpfSFLo3usl8Ij9BiBZQW0/wlep+lYRUpU3Y2e9qJYmcnDgNqDr2vL3Qtp6sa0tEXoZrl9S1Lrf3ejGrZZ/JzozAT0lzj6Jwtphi1Rrb3jdo9etq1cM2tZ/ftm28IKOutf1PCIU0G86ODmi6lkPbGtSFTZp529ZC20iu95bsDhbat5y/GHLfo/MDdsK1tpCt+9yYXDKPe1S1SbyJuEwCrveX+Lb2VQPayulmWxBTmnksM5/L3GNdWfSPr3GZe0SdAv3KV98owTrziTvdBujSct1ITmcTkspGGoqxv0Eaauu0cMxa+/c3HxHKXhnMt6VTz6wZo/vHfkfVMwyf08wn7fInfKF47+walmgY2AWRpX3Xj9d9TaUza94crmlbKJWNQvfOznKYOKBaeLIRDG3Y9/RzT2pBpQzOM5vLwmBVtrjSwDfRzH/Rkrda+DdxGvpWgyUU88wnqSwsoeh1tMuy+cjjf35yQM/LdOnZrLZkyDQJUPFHJegXMZ4cX9su8kM3Y575TIKNbkV0PIeitigaXY2oa+03j/wEw2hZrXuMx3O8MKFthE75+/AOvpdxravwZKuQMnHxJ0t618/J3ruLNBtUo/Uj4/6S0d6Uk2fXSQoNepqv+wyjNV5HVixLm6a08Ecrgv0ZQirWD6+DaBmNFqQbn6MLHdhliYb7/SUjP8G2S6rKIj3fQYgGL9AbaZ66rAqPVw+1wnqThDo0yVA0SkOdstrCqWrKWosOs45vcBb3SGqLvJEs4h5pqauNrlmTVxbHsx19GbFLBtGazbK31QVpNohkuhhxPpsgHypuXD8mXmj66CTYIGWjb9xdRT9LPQInx3U0fjjv9AGfjP8xx3fU6LFkgyUbDFrWSUjZRYNaXfSuY1ZdcInEMBSun2N3vXDVGsxXfS7XfS43EbM02IItAN0fK+2tbuCq1yUNtVXe69Qytsr2orI4GM4xDGiUVpR7HfjiaoEvK5NNqUNAroYU+hZY1hZVpcVonq/DbholKEoL09S/Z6MEgZOjWoOittgUDpfLoe5Hdj25MNhQVia2rXv2XpSwyfztxuvaOiY3K1ytKxAKu+sLGoYOK7oSHSml/17XUgfzlLZmEpQWZ8uRBnZ0wrUs8bZBOEIq6tIi6kJ7dMDLi8G0qtZAGC1FbbHI/C4cqKFpBWnhYBj6MOS5efc76sNV4OQEXZJjUet+f929TyebHr5Za8FnaxB2VQvPLgmdfBvfawkNirHMmnXpklQ2RSe0LGrNbrjSmSSVrWNdM5+8tPHtkrjQWovIKnHlR+JHWzaM7IpVJamUgWp1z37RYZtt2dCzcwK7QMH2Z9wc6PlnCoUrm62+xDRaTKPFEuCIloFVE5j63x3RkjZ63jsSWlpcqed1qfRr27daIqshMGs82ZBUFlkjKZRAYWwPRU0XaBVnHv1go1832ZBudM5A04itl/xFjVkasOqAR5Mus2AQJNvnojoCpmt1a0H3nOlex7IxEbLBDjLsQGszitrCD1IGkzmDyXxL2xOmQnolQiia7jMtzZow2mC6Oqzqilx5pTWSssHyiq24sG0NTD/H7ceUqa4K2L4+YJwnIbM0oGzkVn8EaMFgYbNa9zAMDQxLM4++kzGYdM6ATUjVmDjmRz55v6sorruWx1Vmw7p0SCqLWglWuUfS6TSutC3rTrvgdYmd8+WAOAkoSqtrQer22DwNOIv7W0eVbZfsjeZa/Fo4VKVeO4rS2SYTCqHbHC96fMLq/507PvaN3zJr3r04oG0NDsM1vqO59aGT0w9jpFSczCf4lU2vA+iUhU2/t9aBKZuIsja3xDRbNlwmEZZotmVT02h5uhhRtYJKCfzUZz+MOdn0yLsbTK0EtdLpabPc5Y994TcwH2k4hmVWfOvRSwCaeOfrVL6k0FUFx6o42L3A9TLOPrjPNAuYeLpCcTWx2tYgyXyMTjldNRIpTAZhDBuYpSEXG5udwQKjqzwMdmf8p/df595w1vWwa+pGcnK+p5GjjnYoWLKmF8WEvZiy1K+TEApDaFznk/mEG0JhCMX0csI8iRh3EJqkI8+FQULYORCm04kOdenKkGXqMd30mCcRltQApDvf0+mix+dffY/lfEAW64rHhyeHRB1z3jBaXt07RQgdiHJVFcm6bPV5EvH+YsSnJhdahNcJpY42Pd48PCIvbZ097qekZcuN68cM7x5R/2/fTz+MSVJtwRwOl6zSgJ2gJqtsHi3GOLJh6Ga0LSSVBvyUjdS3r8akaQ1KJbgdrfCdkneWAw69nHnmEVgVr4wvOcmud4p8wXlp8pnRbOsKuBLTfWZysU30qxtJVpsM7ALP1Na124HPqjIY2Ir/y2jDfz6PNMxH6QXsephxkTvsui0COM5Mrnk1z1KTOJXcDBRDu6ZsBKkh2XVzmtbgIIi3m0KpJKNvm7uDMCYIE1Ss5+/pdIeb146RnQU0eoF2vpGXct7F7E5Gc0I/xXEKfUOtLY5XA774ynvUpUWeuwTRhrxwOF+MUBgcjqaoRpKvIppaJxbevfsYyylpapO6sOnfOMMaxlSrkORoF8fPWM00nc+2S6RdsTzb0XHUoxnRcMVm2ePJ2SEHrcGdV57qFl4jSKYDqtSlSDwGN85JZ33K1GW4PyV//3XWlSBtrlIFDXw3oz9YsTtY8/TxLVQjKDKPNPP4wg/8OnXqkuUuszQgb0yuRSu9iVsluztTFosBm1IfOGyzJi6dLbmxbCS1EqxLh4GTd24mfUgbRWsCP+VyPmZTuESOPkhVjclwZ6aBZbWpNUSljevkRP0Yyy349bc+Q+RoOuYVVr2ubexO/Pmicjw+Gf/nGB+f1Z+E/NDrb1NkHt86uklWW0yCmGUW8HA+4dWdMxyzYpn5LNKAl/ZOSROfJPOoOjvMMIy5tnPBat3jm+eHWEKx4yeM/YRRm6IwSEubuLLJlOC09NkJEj5z7RmOXXG5GGosq5+yYyh2S4e3vvYmo2hNWjicT3d45eCYrOuLX07HTDcRltTxnlUjeffJbeyuhXDb1ejQ0WiBNK/KyFqguD+aUXQKWVPowJBRtOZgcsnpdIcsd7XtKEix/JwvvfwuoBX9ySri5fsfatWwXeGFKUXiaTLdJiTNPAbDJZOdGUI0FLnL8cUeX3zlPRbzEVVtcvOlJwznAx4e3UAaikl/yf54SlVZzOdDDKPl4MYxH37w0hblmlc2kZvpVobRskpeDLI3iUPtIJA1lZLc/DZLJcA8jjicXGqlfaPYH0/hsrPiyZpXhnPS0mYvWrHOfN6eT/i+g2OS3KVqtI0tL7X3++jokNl0xC88v8kfuPV4C6z5YKaFeiMnJ3IKXts50xRFWRMXHllmEdpafzAKcnpuSl7ZvDefcJqGtIlBUguepx7XvByFwcPzHgO7Zl2ZuFIxcRvO0xBbNCgM8trkwC5QSpCUDllloTBIahMFbCqbtDbxTMW0kDzZCB5vQmwBF7lFpWBZGjxJPOZlQyAlA9vAkfA8NbnMW9JGAZLdccWssJiXkmlukdQGnxlqOuWiA1s9XIzZ9RPGwUZT3IKU957c5sFqyOd2z0g3Ab/x7Dalknzx5uMXMheuRs/WTphnZwdsSofP3n9AUThcbiLu7Z5R5g5tqxkDWeJTN1JHW9sl/eGKJNYtNj9IOLz/hA/eep39/XOy1ONyMeKeWyCXEVag/ffvf+sVbhzqhMpGCSgt1useVie0DfdnZBsNczKMlmLeww1Sdn74fcrnPS6+dZc08THPKprapMgdTt+9z53BnJ3+EikbZquB/t16MXVpcXRyyOHeObPZCN/LuP3SE9YnO3zt/VcZBxteOzxilYT6UlRLssrmrUf3ELT03Axp2BwlEV88fM7pasjTuMdJZjPxMkolSGsLWbRc5h6+rDUvZDlimvnc7C94uhxTNpKhm5E/uM/eUIt3z+Zjqss9Xrr+HNMpMe2K7//813naQcaEaFkmwdZW6zoljv3igE5X45Nb+scfy+WSwWDw2/Z43xmrP/OQsubO5JxvntxgEug8eVfqLOpxb4WXldvs9CuAjSVraiW5WA2R6z55rXGkN/sLIi9jHkc8Wg2plOD+cL69pTStwTQNtOgur3m2HHIQ6RvwlTDmlb0TktzDljWvHBzriFzZELi6rOw6BSfzMatc9zgP+8ttH1RKpXkAp/t4br4lhT293OX5pYaF1EqwzD3GfsI87iE2WmvgOjlFaTObjZhOx9vyd6O0H/yNz3+Dhw/v4tkFB3bJw2c3idyMRgnyyiY/c3jti99gfaLVuTuDBVni4zo5pqkFhnEccn1yoTdZp0SaDbPpSNt3jBZp1gzCmDjVIiDXqshKm6FsdALiYsQPfC9nSzeUMgBJ6KfcM2scW9PZTNkghMYxz9d9fKdgZ7AAtD3ToN3qQkZBoiEuXobvFMySkHGgKxm1kmSlTdlI4kJnJowd7Va4Aqk0rYEva6ShQ3kcu9we8vSGDNPM43Zfhx9dbnoEdsH9wZzncZ91afO50ZKHcUTe6ArUvd6Kee4hAAU4QnFnMCfrbvyNEhzHfZzOgmZKRd/JaJSBYehqVNZI7kUb8iaiyQSlMngpajS61zCwXL35x5VBS0vTGvSslgfrFsswiExBUisebxxUC45sMUWLQnCWuSggbwQv9zaUjWRZuNqS6BS889VDXFnz2nBOz09IUp+hm2EYbO1eL2IETs4w3KC6vnZa2axXPepGEtoFXzu6xQ+//C6G0ehMDakY9GLqWmdOVKVFEG2w/YymMpk+PeTlz79NsQ60XTjYcPTkBjs7U0QcoBrB3njKetXblq6LxuT6nacIq6YpLRZPDvHChNthghOluJMlzmhNs7Boa4EbpDw+ur5FdCtlMOyvEOseVW2xSkKO1wO+cPcDPnh2i3XhIEXL2Yc93M6TL45vcNBf0LQGZ3GPs7hH3pj07IJrgzmBk/PubIc7vRXL3KduBGMnZ7bpaWKpU1ApQd0I4i5oxxIKRyiu9VaUjUlWWfhmxSIN6Nl51+qEG3tnCKnXSc/NWa17CKHYLHrkuYtl6fyLSX+J6+XM4wjfKUhzj7xwsK1PsL2/k8aP/uiP4nke//pf/2tGo9ELf7zvqNSvhWSNtoo4OS26dDV0r5jazrZ3d7kaIIXalkQ1w93e9uMd0eCYFUrpfqVhtARWhezoeLZs2JW1hvAoQdVo1nqtJKbIEYZJ0gE/8tLG9modRtEF7siOR23KBrOD6lwFCgWOjsEFMAyTeRJSKw0mMs16qz0wOmTEvBOWeaa2MualjX0VHNNt5Ga32V5pC9KFVlHXjYZnLHOfnf6SqraocpOisShjnzQJtNApSLavddsaqEaS5B6+p4VEWepjdSKjK3hQmXqEvZi8cMhKG0vW5Ia1/b37LyiKVXWaC9BWoqu/a454hWcXW1Kc6hCyeWVpfchVD1w0bFIf1ynYnUy53OgchKt/9+ySs02EKTQp8ma41pkEssEU2iZ2NQfLxmTdJRQK1eJZFY5Zc7KJ8KySorKolE3VmNscewVI0RKZDb6pD6+BVerSa2tQdDY426y3oKCksjhNPcZOSWSVWKLWvfZWkFQmSW0yLyWmcBHo/n3eQFYLbKmQoqVpDCwBrhRYQnP888agUorIFpgC1lnDshRElnYAtGjhX1KL7XNvW6PbJJqt6FHQdmS2nLYVLNIQKVocsyJ9gWKuWkkGXpcFkEBgm8zjHr5dMAw3nG0iNolOlrSsquuR6/wMaTaaAWFVVJlLkTsUhYPoNibbLegZLY+f38CLQxy30IArqUg6YavjFlS5iTtaI5yKahWwuhwhpCLan2L1N8ggp5pHqFSjerONtrXNV31E17Lr99asM7/ToOg2kVKCRe4RVxZ9q2ReOrh10x3wDSInJ69N0q6VdNWPDpJwCx5qvs3W1rYGq/wjoapn1tStwDe1FmLdWUcdsyIt9YFzN4y5TKLtz1CtgSHabVBW2IuJwoRkE5DlLmVj4jtaAOoHWnMkTvXBzzYrpGy2NsEXNwzU9xzZ+7u3gvD++++zs7Pz27Lpw3ew8XtWyTr1SUubvLF45eCI+bqPKRqifsIqCXm8HBF04JHnm4iXhx/5yB3zI5iMFC3QkhQuRW2iWoOb0Zqd3opp3COrLQQtNwZzfC+j6NjYrmyIC4ed3pKgE9ZdxppAdRWAEzq5hg01WnyTdwCRkacJaLM0ZNJfajhGI1F2SbaysKvuw5xp8dSkK5dvCpeikVykAfdGU8a9Fe+fXsNA24cCLyP0U20PrDUG03cKPnx4l3FvRaMEF/MxjTLo9dddbK0+IDz54O6WZLiIe/oWE0dI2TAJNZtgHUdaRJcGTIJYp3x1C9JsOmLv2hn+JiOvbB3U05i6jOplfPbV979H0+S/Ho0SbDLda7dksxWOBW2+JRH2rIrlqs88HhM4OdMkJHK0z/tKif5wtkPPKfjMcMnAS3kwn2AJxWEYMx4sePdyD7tpsM2a/bFuiwAkqc+0qxCsc5914XCeBux6Cb5dMozW9Ppr6of3toLTSkmqRjDLdNyyIxSP44hrfkpoFbpSUFsEVsmmsslaAwXMk4CstlgULrPCZllJom+7LT1f97ksbI5Ti1VXPf2NhclLocA0IGsUX1soPjuUCGBa6OOkLbTFz5HwPGmJLEFogTDobvUtYwesTm/gy3b72XGNlnlps+vmTLoQo7K0tzG18ziibQ3Ok5ChmyENxfkLJLVNNxHDaK1L7ICUDY+newij5frOCb8n2PDg6AaHwwWTyZQi1w6Xfm+N56c4YUaRujx/fk3neYynnL9/G8ctcCMNrzJPDlltIva8nKAfc/zkBqoVSKmwrIrc0CI94ZRI39S33NmQcHeG8EoMS7F+ts/g3hHJdMCT42vsDha8d3pNC3TtEiFa3l+MCSyd72DLhufn+0hDEVl6XetZFatuc3Zlw6ZwmBU6/dORDS1QKMEHyxGqO5zNcp9dP6EUkmexFrHOOxu0NHTV5zBImOUu09xBocFH69JFtQaHe+dkRzbLzOsOCRUX0wkP5xMiq+Ret8F/eHJIi6HbClITRv3eBncQE7kZqzRgdzLFtkuOTz4J5f2dNCzLIghenPPmvx0fe+N/vhxhCsVuuOba4SkfPLnNeRLhyJqo0KlYYzfrwlAaXh7OMYVetNvWIM71JPakVq1uShtXagGN0cKmtPnyB/cZ2jV9qyIwK72odwlkjlnzhVuPeHqxxzzWt+mJl3L/xjMctyCJQ85mE6RQPD0/wLcLfKcgzjU5Td8CKzalzWoT0Q9jPD8jCBOS3GNvPEUpwdlsQl6bjHor3eNPAnpWxfXeUsNdkpBr/QVSKN46uUFSWXxm9wzVGuz0NVJ1Efc422h7m2G0ZJXFsyTk2ukB48GCnd1LlgsdOeu0BmGYcPD5d/nG//pD2JbOLRCi4cbBKV6UkCce/nyE6+RUtYnnaOX/Oglpjw6wzJqd4ULb/xod3wlsdQvf6yGMlsDNyLq8gDsHxzw8uU5ZmzSNYLrpkVYWe9GaQbDh66fXudtfMO6tkEJxsRwy7K+42Zhklc2To+vsjeY8XY62dryz2YTbfd0mKGuTy8WQJ8vR9ma+F61Z5z57gznj2uT9i33KxsSooNdpSkyh6EUbpssBq8LV/ujC5e5wRuRlvHVynR1/wzzzWRQmvlnxK5c7TJya0Gywu9jcReGSNhJXKn54NOM0DXm0CZkVJrPCYOy0tOhN3BIwLyGptW9/1xWcZfBg3SINA2lo+96mVlwWDVUX33rXdthUetO/H5n85/WUkyIkFCaelHxhrNhUEpOWgV1TtwZvLyNWpc19pYWrX3/wCpGTM+6tGAyX7AwXRP0VbSs4OX6xC/37p9fZCWKu7Z9t1e5FZfH06Do7gwVffPObbJa9bX/82vVjohsXSKckfraPYbTs759rRX6U8OzhbW7tPUbaFWXiMYzWOgdkE5BnLknu8bnf/8tgtBTTAYvFgGzWx5cNKAPTKTm4eUy58bE2KfIwJdhdIAcp0f6U3WWfd05u4Mqai8znwapPfr7LrlPxeOOzLCVNC//TRFckZ4XD89TipbCg6g6OWW3yLx7v8X1jvf4tCpu0EfQtfUBQwFHqct3XIsGBlzLp/PuaUmqxLl2muUuvs4uWjeSi0E6j/XCNag2enhxybXyJsxqQdC6T03WfptWi1bP5mOxC5wdcgczufP5b/Np//EHc0112hOLeZ97lq7/6RX7tg1doWxh1lcQXNtoXAPD5Xczs/dN/+k/zd//u3+Vf/st/yU/8xE+88Mf72Bu/YdBRyyTT6QRTNtwc6Bt9o8T2RlfUJovcI6lN+lZJ3823dr62NThPQ1xZsx/G27ZArQSVknx+rG0xqivpJJWNLWqkaLe2Gls2Wkvg6nJmlnpkqUecBiwzn91wzeFoqu0+HUlr3Ftpm1et1flZ7upNf6D1Aq+9+Q5P37vHNO5RdCjUsrKwrYq93opV4bLovMm2VfJkPmE3jDkMY+LC4Z3pLq+NL3l4foDCoGfn5I1knvnsRWvu754jRcsq0xWBXrCh14s5Otsn8jKq0ubo1z7FaLBkHUes1r1tH//sQovibLNiFUeEfsrk4JxgsiRb9Jie7m7xw5asmfSXnC9GJIUWH76IcTKfsNNbMYjWDKI1T84OsTrrHUBWW5hCMUtCaiUwjRbfLraJgcNww4PjG4AOUrpMdC93x09oW1292R0smK37LDN/2yKaeClpZXW8c43VvXqdrnXYZt/Wv/NsNuJgONMWJ1lzczCn6loSs1TDnG5EK12WtUosoUgqiwOvotdZsIpGsilt9vyErNYhO4vC47izgDmiJWsgawyWJZRNS2gZuFKQN7pMH1kty7pkYNpYBljCYMdtyWqDuhWYrUFgSvIGllVD27YEpsk9c4QrDWxhIA14sjEJzZa6FZTKxpeKShnbeTZfOdwfzOn7umX04MkdrSlpdKtlmb2420TPzUg60WV1fI2bB6dMu8O5KRsul0NkZ8cLg4TJrRNOPrhNvOrjeBlBP2bwmUfE797UJMujA7LSocwcPLvC8vOtMG0R9yhqi0+/9i7ZxRDLLzDDjNufeoB7OKWaR5SxT1NZOFHK6mxCXVr06nPK2OfJv/9B3eeuLColcCWkjSSuBT2rIWskA6tBGvA8kSS1ZicEZs01Hx7ELrbQlkxXKn7vJKdQgqrRpX+AJ4mNKy1UC7PCYFYEPN54HHold3tLjjY9XNlQKMG6shDAqrOZ2rLBFSYPFmMmbsbQzbZtVq9rdeWVhU9JaJdUjWBTOsSVzf3xxTYQS1g1fT/hYjlkuYm4c+splZK4Zo2g/SSa93fY+LN/9s/yta99jT/2x/4YP/zDP8wP/dAPcXBwgOf95i26n/zJn/wtP97H7/F3XHpLajBPWZldv137qmW3uIvO++6atQbtiAZTNlw9/bTrT1/FtzZK6F5a1y+70gQ0SgemXNnEGiU4mu4yDteMJ3OEaFh26varvnDT6jjSK3hNlrvaOx5tKHNHh1x01kKlBE1lUuUOZq7DhaShtpqCq559Wjq4plavp6XeZPpuvs3Y1sxuj1kaUnYby7p0iSz9wfQ6LUDkZixTXyfP1SbDINVwolqySXyEaHE7wEbdSIrCYTRabJPVrnpyhtHSlFofUKYuSaaRpqZott+rrYPNNp/8ez1M0VBWJlKaOhVMNFuPdqMEoV2wKR0ELYbBNlo2KVwaJZj0l1RKbv/dEoq8sjCl2sKZQHMjAqvc8vv7XoottXgsrSwGbr2FtfSDDetU2wdtVRP6Kfu3jslWGvt65fQ46PQGRRfPuykdvC5nXWHQsyqcrlrQ7967stHP1Zc1s8JlUwukocu0Q9ugaTUQwxT675FlEJpgdalwfdOmb+l2SN1CpQygJZQCKWBa1By4FqEUKKBSMLC7fn6rLzp1C7kyMFtN/2taSa0gbQRJbTGyi62K2pRaP5NXFkEjceyKSbTmRY1hGKPWGk6TljZ1ZRE52ZanUDc6gElKDaKSvs6TMM0aIRrK1KOeRhiywXY0cc8xK+rCJl8HtI3YhvSsk5CqEUirxnRLDKtGOiXOvq4ONblDnTuYTokziPESD9UI0osRy4sx8+7g0ChdhtdgL7BFS2g25I1EoVssO66iUoJ5qTdnVyqab7t1KgwKJchqQVxLygZ6tv6aeWFQKt2ykYbBGoElLII0JG8kZdcek532qFACpxP2eeZHMdI6pMraZgdowbHs1lmDrLZIaouBXVDUFp6lxY6X793W2okuPTRLfWxZU3d0yd1OdPuiRsv3XtX/u/jCz2c/+1lAR/L+4i/+Ir/4i7/4f/g9vy0bvyab6b52b7JgkwRs1n1cuyT0Uny7JOigK45VEXop87iH1wVxlELna7cY3WZvMvCSLYAk60A6kZdhmTooIy1tBqHOKV9uIj5cDrl/8yleb0OZ6ZjPfm+N7eoNz8v8j8RwuS5D7w3n2w3QMFqkqbGnVzjP5bJP1mExA0cfMgQtjlOwWA44j3v4Vqk3/krDM/YGc6arAU0nXhk7Oc82IbtujmkoFrnLncGcUceony8HWvSHscXvCqmIwoT5qk9euPS89KPfB6gqi6Af09+Zs1n0OD/fJfRThFCsln0uLnbIK01ADDswThhtOD7VbY7AT3GDF1PO63kpRW2RZD5K6dKy6oJyaiWZRGuWF3t4VkVoViSVpR0UtaV99Y2pS8GdvsMz223Yjm1p/kBeOJiiYRBsGADrzMc2Kw2EKhqerYYMyIgLF78t2R3NWKeBVj+7Gfu9c8KXjvEzm83zPdrzHRwvI4o22uaZRCwLj2nusk+K092Eou62H1oFoyBhloScpUG3IFfMSpO0NhAGeBKu+TWnmUlg6U26VDB2Wsa2VvKf5yY3Ax3ME1cGi6Ilb7R+oGdpod/bxYobYsLE0geHuIK+DUkFhWoRhv7aSoGunhoktW4rZLUgbySf2jvhZDXEKvSGujNYcDYfI4wWP0gY7V+8kLkA+hafdayMspHEm4DRYEleOCSZj2uXHW9A6CyLykQpgefmXZsuoHzvDsFgjeUWGqblFlSVRZ65W8iW6+V4dkFaOhpWNV7R1hJDtBh+S/m8Rxn7qEbgRCnWzpqgkeTTPuvZkCdnh7jdQTJttAi2bCTSaAlNhS2U3pQbgTRabvgFTWtwlFpYRsvNoMKVLb6pcEWLNFqebGz6lmJWGMRVS98GV7adc0NhCYPI0vM7rQUnmUvUHTA82TCyS2aFQ91t/LZsiMyawzDGEk0nHLWxhNkR+wSr3NWH5UYLnJPa5OXxJZebiKo28ZXk2XSXg/6CXkdHLHIHuxMu+3bBcPxiN374xM73nYwbN25gGL99r9fH3vjPkpBXJheskpB3fvX7efPG0+0GMI97OGZFkmuvetmYxLnLg9WQl/sLel7G6bqPZ1bcnFzQtgZHswmP5ju8NL5gEq3x7YJ15mGZFRfLIcdxn9AueTbdZRLE7AwW/N/3zzDNmq9+7bOo1uCNlz6kyB0sq2Y0XjAYrAjGS379179A30u5uX/K+89vMjm4oO02JtfLuTa+pKpN1mst9JJC8Xg+xhKKG4M5+6PZ1gFQt4Lnmx4TN6Pv5Aih+JWnd3l9csE0CUkqm9Au2VSSizwiMhU3g4RaSR5d7pPX5rYEuCptXhtNCfyU99+/zyr38KwKzyopa4vdyZRnp7oXe2PvjOOn17e3ZNusufW5d1k+PuT8fJfzdR/bbPj8F75OMhsQryNdNfET7RyoTdL1i/Hxz5OIvcF8G5ITBClHZ/s0SuDZZZeg1rIsXLLK4kZvSVo43N4/oWkkbz+/xevXnrPcREw3ESeJz41orRdhYeK3BrZdEqcBSepuU/2eroaM3KwD/1SsCh2UYoqGqrK4d+cx08sJvpfR251z9OXPsHP3CG9nidNL2JyPOTvbo2kF42BDYBcUjaRuBdQmhqH96Dcml2xSn7cv9xm7OWZXUcpqi1khWZa6ny+7D+rYaViWkkrBvttyK8w4SlyS2sASLT1L4UsFmCxLuB9VSMPiKFXM6oJbcsi0UASmgYHBrKzp2yZp09K0LUNb0LTQs1oqZbAoYVk2HHgS6egN6HzdJ6stTqa7qMs97g/n2LJm1WFke4eXL2QuABxf7OnDdG2S1R9RAic7U24MY1QjOH9ynfUm1HhuXwtwh3tTLD+HswlZ6tMu+jS1JEl9Xv2//hcu37pP3rhYVkWc6N7+Kg3IKoudH36f+a/c098PTH/9DZI04PDmEd4gpkpdyvM+2eWA2ekuTy/2+WA55P5gQVGbnKYB//HM4bMjhWW0VK3BZeHRtPBaLyWwKp5tgo62CLaE0KpQqUUgFQO7whKKd9cWd8OaVWUTV7DjVCxKF1eCNATCgBt+yfPUImsMzC6hUVeLGiZewpPExzNrqu51OwhiDkdTjmcTloXHrr/R8bqlw7pwOM187vbW7Aa6XZqWNq5d6rjr2S7PLnf5wu45eWXjosW2rpczPzvk2mCOadb8xnuvcPjCZsQn4zsdT548+W19vI+98d8bTRGGwrMabg3nPL7YJ3LyLTP/wXzCGzvn9IPNtlRbNiaBU+BaJfvRmrcv9+h7mWaxi5asNnk426Xv5IROTqUkH5wfMnBTXt/TQRi9Xszz0wPeP73OyEt4shoydHJGfsImDvGClCJ3CIcrgsMp8wc3uT7Wi1xVWbx8/TnxvI8hWr2ZrCOS1KdsTMraJC1t3luOGHf98A/nO9xsTBZpQFpbZLXJsuwSwaySgVNw4Cc8Ww1xZU3PLlAY3AyyrX1uU9qcbSKGbkboF1vu/J1hQT/YbO1wbof2rBrdnz+92GUUrXXGQOHQ763Jpg4NOgHx5O37NI0g8FPu+rpC8OE7ryCMViOGNwEPzg7Zi9bYUnK+HL0Qcp9jVtteIsDR2T5ZZVPUGo+8t3PJyEs1mtisWaa+5sjXJlLqw1XdoUt9u+RW1yY4GGoaWdOYeG7OyXyCLWv2opS48Jimgcbwlg5JbfLG5EK3F1rBxXJIEG3YPTzH8nKEbDifTfCCDNMpaUqTi/NdFmmAa1VaP7HpsSxtJm6OIxVKaTRvknkdP0Bb+B5tfFypGFg1eaNFe7fDhn23YFlanBUmt8OSgVUxK2zOMoee1SCF4FkiWZaSsSPoWYrfM1bMS8lppljXFY4heSmS/If1CeN8yFDaWIbB06QhMgWRpW1/0tD94rYFW8DNQIvP1qXAMmwOAz0/R166DZva27tgNh1zuRxy+atf4EdewFy4mg/9YENR2qyygHcuDvh+PyVLfdJNQG+ywDRrykYnKZpOCRufKnOoMofLywmHN45xewnFxidJfZLnu0ynE5aJRnsP/Q3z5YD90YyXB2uWv3YHy8+RdoWwag7eeKgrCZVE2BXBaM3Xf/6HEN2hzbf15xYgrXVexv/tMEN2baRSCSKzYVXJbdTyvpeTNpLX+qm2c9YmP7g75zfmQxaliSMVr/Qq0kYwsBW+afA4cYhMRVwJ8qZlYBtcFiYjuyFtBBe5gEJyzW9YVxbNpscbg+U25le/njWXy6HWM3lpp2lpqZUmCq4qSV6bHMd9Aqti5CWsUw1F8syKe/01k/5SY7VLm6J0CKMNkZN3LcWS124/eUGz4aPxolMhPxm/9fGxN/5xf6ljPrveqG9pUI9rlTqYZznC7qxTV8EcAHHukhQOSWXTt0vKrrzbtuB0HO+rHv9etCKvdKqbUoJ+b81mE+hwCatECsUr4wvKRgf8eIHm8ttOSZ3brB4d6lunn2nlb+ojpaJpurKiUxD1Yi6Xw44dXmKbNZPcx+60BSi42ES6ZFlbbCqLAz+j6jDBpFApgdNpHtLaYpq7TNycWgny2mRROhz4icb+NoLGELQtzJJwi1ed9JdcrIYIWizZ4Lk5SUcKu8rftqyKqtsgGyW4XAy3wSbCaOlFMWezydYzb5keNwZznXvefd+LGKrjnwNb54Ita8pG6tvUxa5WZ3e4UNeqyEubo+kulqzZHSw016FwaVuYhLFGFxttl1Wg40h9u9i2gnpuui1xghaErjKPcbDZCpUcL8ftbZBuCaJlbzylqnSiWZp5zONom/VQNCYXucv1Lkzm6pbadP73ojYJuvL/oVeQNpJFqV9nR4It1EdWLK9mYFUEXZtAVhZjp2BZ2jxPJKXS5V+3e+6XucCTABZNqx0AN9qdrU15YEuSWm8kABe5wsCgbxtULcwKRWBeeaQN3FoyzX2k0TKgi4l2CqrC/q/YEi9q9MINSeqjujjgz3VCS9UITEvDqETHY2hbA1Xp93c5H25Z+m1rEF+MdAXPrEnnfZpGbG+0A8/YrgtlYeNG+nPQNoKq9MiXIaZbYgcZwqoxzIbAzcgLp8PnNlumQc/WcLCkNnG7x26BwKxZVR5Vq0WTeSOxuvJ71Ql+H8c92lb35oUBTQca0wcMg1VpoCzd8qmU/q8+BOgchkrpg+Os0JkQV9kOupyvuSdx4ZI3H62TAD2nQHVugn23xJE1adc6W+Ve1xLohIhWyToJt4Jq26wpcodRtMZ1SqSsqasX7eP/ZPxWh1KKr33ta7z33nssl0v+8l/+y1RVxdHREXfufG+uch+bsKA/nIKqS9gaRWsEWm3vexk9u0DKLu42DVDdByKtbC6ygMdxxNhLKRuTtHS2oSdXcB/QCXn74xmOWZFVNqatwSO9KObatVOGwyX3XvuAQRjrTXy0olUCN0poapOT40Pd04w22HZJUVucLXVO/Sb1KQoHN0rYdJzqq0jeXS/56PbdCqa5p/tntcmmFkzcVIdwFC6nScS6dDClolKSVWlznNk0rcGycFmUDptaEtolpTLJG/3hzGqL8zTQWd+5y3gyp240M+BKmGcYrc44qGyK2iLL3a0IssUgLR0deqIESeZh2doTn3c37XXusbd3sVXzfzs74Xs5rsKEqsoizT1dcZA6KrlSgqPVELsTFoGm9lVKcp6EnG00YayqdRRz02rRVr+nxWdVZW3Jjz0/QQq1FVVWXcCRb5eEVsmi0Aue42jrpttLsKIUs5dgDWMmt49RjWS56nMyH3PRCTCzymKVu2wqyUFvRWiXmFLbUJPK5jINWHQqa8OAw2BDZNbEtdRlX6EX/KwTgt0MEr05KEFklew4BXt+wo6T45taCzCwa2ypSBvBpm4Z2DB2BIFpMC9arvsWfctEGrrvbwv9magUbOqGpGlQLVRKuwTOipJatVRKQ4IWha6CrAuHpNCalXUcbXHZ/gtyeAC4Xk5aOuSVRRQk3Hnj/W6OSGynpMwdbKckChICJ6fI3Y6a6ZAXDmGQUKQeFxc7zJcDAOJ1tD20VEoDvvo9bW+L4xA7Sqlzmzp3KGKfy4sd8lg7FwxT0VYmg+ES1ym2REkA26wJnYLIKlmXVicw/Ug2JrqDYdW9v9v/j97sH20sDAM8U+EKRdlZ1lSrg51AuzyabseulD7YXeQG80L/fFfCZd6yKAVld1lIutCqRgnyxiRv9NqyKB3y7vMguhCfa8FmK4otG8k085nlLqvSJqt1y2XeuZyKrrrRtgaBn2J14UXx5sW0Ab99fBLS852Pf/yP/zG3b9/m+7//+/kzf+bP8Ff/6l8F4OnTp7z88sv8qT/1p8jz7x7M9rGPfV/+8BUMo6VnlxxES0zZMOnrKNvz2YSD3opVEnIS9ymV4EvXjukvhzhWxSrzeHel++FJZTF0Mu7fOOZiOuH8Up9MJ0KxWvfY3b3USNhEM+Ff++I3iM8mZIlHf2fOejrUmNfeBm9/xupyhLc31wtBbWL7OaPPf0B4NKb6psXjxZibOxeczcecrQf4QcppGnKahqwri7eXNp8bFkzcnIvc5e2lwxv9kocbl9BU7HsF7y1HPNpYuFL3a0NTMSv0Zl+1WhU8zV3sTg2+59YcxT0UsO8nHPQXPJrucqu36nz9NkI0HCcRfavEMStEp8SPc+1/kEJxuhjz8s2nOrp01SPyE66/9pDkYsjJ8SHz+ZC4cPHMCtfWmfAfPLmN23H0gxdE7nOsipP5RB9cWoNN5dCzc4rGpGoFFjU7oxlJ6jNb9ylqk6GfMPQ3qFYwWw14MNthz98w8FPKyma4MyPox8zOd1jMJ7xx/y3mzw+oKotl5vOrxzeZlSZj22Xs5Fpv0fEEgiClVTpxzb6zgnEPDIG11lqDOPc42vSIa5PFMqJpDWypeClMOY97HPaWHI6mFKXNf3xyl6rVHP9lKfjCKMHqwEG2aOl1kJ11Jclqwdip6TkFj9d9lpXJgZdzt78g6nIgbme6mjR2cuLaYppbDGyDHbchrkTX74XnacXAMnGlwbvJBguToqgYCo/PDk0GdsP/5ywno+TQDLmoU+71TCqlN5qhUzCwC5JKUw2HqU/aQWJss8Z7QXMBYL2OyLqciMFoyckHt9mkvq6yVSb9yYJgf4p3OWR1OSLPXHZvHW+jg00v5+2vfBbP1mFW54sRgZNzsh5QNCauWdPvrbnxe7/J6oMbXJ7sYfU3PHv7Pn6Q4vc27F8/ZfD6Y9pa0mw8sosxReZRVZaGcNUWnllRdHbQWe5tHREAeSP56tzlzaF2R1hGy41ozbp0sIWC7uDwRr/dVn5sqRg6JRe5gzKgbymu+Q2PN05nxYTAhNNMf29gCsZO2x3gYFXCrDCZuPpyYXWE0YmtUejrzGddumSV3swDSx9QAR6tBvidJXBTWdhCcTtaMc19HscRnx7PGAcxptkQBhsO3viQ42++wnIV6MvbC0xr/GT81sZf+2t/jb/5N/8mbdsihEBKSV137dSjI5qm4X/5X/4Xjo6O+Pmf/3lM87detfnY33l3OGOWhqxLm9nlPp/ePSUtHaquJOXbBV+92OeGn3B7POPh01tsSgeFgSUVr/U31I1g10+QhuLBMx30c5U4drHpcRAtKXKHvHDJKpud3UuS6QDVCIRUrC5HbJKQvf0zDKPl6a9/iqgfk18MMf2cyf1n5NM+s6+8zGbZI04CIqvkvzx5iaajaL335DabyiIwdXn2zWHLWW7pE36HRP3/nkle78N5Lvn63EVfvhQ9S/MGskZwlumbWc9queZXLCtJIA16tsKRDfPCwRSKD1cDvjEf8XJvTVZZmEKXFt/54GXa1mDkpYyiNUXhUNYmq8JD0DL0E+7sn7JYDFBKYFkVvf6a+bMDlBJEYcJ0OWDoJ/hOgW2VGEbLRdzDMmv8MGM4nv/vvqe/1TEZLEkzj6K0aDHYFWvi3MMxa8Z+wsUmIt5E2FbJ3nBOWdkkucuot8K2S5arPtejNeNwjZQNWeFSdiLN/mCFH6TEZxP2P/0BfPM+F3GPz+2d8r8+u0nVcR5aDJyuxG8YLV6YUqcuBA6sNxTv9Vgf7dO2Bsdxn1+d+nxhlJM2BmOnYdctcGWNI2vO4z7ncR9b1rw+nDPPfWaFgyUkWSN5MBtqy5dUTAtt5dtxWoZOTWDWLAsXz6yxZYMA4sLl4XJE2inGAc66A11oNTxLBJanS8WqhZ4FcSW3N3pFy5cmNseZy3ne8MvzjE9HPge2ZFWbzOoC37B5vDHoWQa+bPnqLOLVnsXNaM2wawdtSkeLGLt43Bc1FpuIG5MLstzl1771Bl94+T2K0tHizdWAV156xOZ0h8XFmIvFiE3haCpfkOKEKc54zeHeOaoR5Lm7rfDcGk2RUtuBg2jD9Jv3EGbN5OCCzbM97nzxbfLpAISi90NntKuGdiVpCos8DnC8jHQ21m1DL8UUDSerIT034/bOOZ9Xgq8c3aJWgr5d8qW9nFnh0LMqgq4CuCxtLnKd/RCYih2nZGjr9zowa9oWXu4VJLXFrHB4a+EwLxR9S+B1q+u9yODxRnMeAJLaIGtqhKH5DReZzluwhF479kXDu9NdHeGLdlQFGLqKWEuOkoiRrSuEeeGSNoLrwQZLKiZuSmSVxF3V5zIJqFrBm6lP22q4li1rXr334QubD1fjf5Rb+vdi/MIv/AI/+7M/SxAE/O2//bf5E3/iT/AH/+Af5Mtf/jIAv+/3/T5+7ud+jr/4F/8iv/iLv8jf//t/n7/0l/7Sb/nxPvbGL4Vi7G8YecZW0AedRc5oySubQYe1rLqkNil0YArAsLtxuFbZcfudLpPaZuhm7Ee6LyikwrELlDKoS4sydxBCIToP8J1PvUc6HaBqk/27zzGEoqlMveinLtk6JNxZUGQuVWNSKcFZ7uCIltCqWBYelTKYFXaXwJbzdGPzpLK3Hl1HGDyMWzZ1TdrWhMLCEVc9YC2yuhJc6Z9l8iwRDG3JjtJWoCeJw02/ZOwUXDerLiK2pe9oj3VRW7iyS30rPorstCpdMu8HGyY3TjDPdigLWy+ATsnibJe6NnWfurIYRWtdci8iTNGwG63xXX3TXcxGvAhe28l0gvVtG4nRlUuL2qIoTC5zj2g12Cajna77DN2MsrP1uU5BE/fISgdL1hi0qEYivJy2tWhqieMWZBe6B9z3MqpG8lpf8+BdsyZycupGh7y4Xkbv5hlmlFE/NGhrvcmadkVorxi5KYEZMSstIkuruK/cHJZUzHOftJEEZk1Smwzskl03QxouH8QulzkMbNiRkFQtQ8fAkfrnXBY2AyUYOgV+11pZFC5Na2AaLQqIrEpbtlq6KlFLoQQGuux7NXQ5v+XADJiVWhwWSsHE9jnLFMLoeAEY+EKS1jrYRxoGZ1lL37JxZUClNHjoem/ZIZIFSWa/gJmgx05/+VEpXdQsFgPCYINhKB2g5WfkcaBR1P0l+2aDFyY0lUW+iqA72BaNg+MUHDg6AXMQxrrN1Ro8f36d69dPaBuN5sYDa29FuQqpMl3ZMG5OEM+mMIM8c2lqSeDk29vtMgsYeCmG0ZJkPr6bEZg1ZsfvkIam8V0PYwaeduY8XPe45us2Sd5I+nZJUptbrUne6MAxy9DzoVF6/YCuzK+0+yMw6fz9+r/XfBNPgilanqf6YNG3akyhSEqHvlViuoq6EZxkfjdnwZKKga31TmltseulvDa54OFiTNMKQqtg6KUsc19bIM0au9VrTNMK7YKRmoHyCbT3d874O3/n72AYBv/oH/0j/ugf/aP/f7/mT/7JP0kYhvz4j/84/+yf/bPfno3ftUqUEphmg2MX29APUzRYsiGrbCZeiiU0+CZys45Dr4UvjlmTVrYW+KC97I5saFodhnMFf8lSb8sLqCoLpXSUp0WF6+c4O0vSeR9h1PgvnaISm3odUCwjNoseZe7gVyZZ4jNPA8pGsqkk0tYn+HWX3JY22iI1bA0KpX3R1bcFmC2qigpFS0uh9E2uVAbUBstSMbAFUuoP8WUuWFcKaQhMQ6Bal+MUxrZk4iqGbsZpElF1yXSqg3F4Hbf+StAohRZN2maNbVVIu8KyKpq6IxwW9ra/flVlER2go6xNGiHo+YnO7K5NlpuIFzGWuU+/ywavG0mJub1RJpVF1khWVzkMGGwqG9esma37BEXOaLDU4TZKYAq9AdeVSdMdEi2nRDWC2dE+SacXySuLO8MpyyygVgLXKtk0ngYxdeJG4Zcsv6nFL95Y6z+aWiJFS2i2xJVgZGuR15V1qlFai1Ir7YdvWwNX1jTCQJQtF3nLqlJYQhI1grpT1btSi76aytyKu2xZ41oVZ5m2ZzUdme2q/6shPy220D3gq36vpvwZVK2iaRVDYbIsdUxUaEFkwbRsMdH+f/jISli3+vvrtiWuBRe5Q9YJ0+6PSyyrQim5DXZ6EcOyarLc0Wl8jiY09nrxlqlhBxmryxHSbPDDFNFVvcrCpqklVaVpmUWnvfH8bHswk1JRVSabrmKiGkGROxgdHMmQDUIqqBtQ+tVRpUUch7StPlCYrUHRUSMDJ6dqTNLCwe4Oao5ZE9oFTSvwzJrIzXTiXRKigInbWQZzF9es9fuNFtiWSmz1ILoHD2Bwpb3UvX6DyIK0hnXVIoCRD6Glf4cP4xa/K9s2ndh56Ob07JymFVzmHp5Zb2FYnllRd/PXt0vG/SUPZjtU3XOwZIMlGspO5S9F2yVMauCaMFrWvw09/k9U/R9//Mqv/Ar7+/u/6aZ/NX7sx36Mw8ND3nnnne/q8T72arC3f8HDJ7eIYxdpKEadLc21tao/ag3mcY+q0SrvyWRGnHsM+xtcJ2eTBFymIXudelsaitf2j/HcgvP5iG+eH/LazjnfPL7Jrr9hfzTDNGsct2C96lFWFtFoxbNffZPJtTO8gymGVMhBjnlQwPtw9Ogmvpfxjbc+xcPliOPM4aafI42WvqXLsg/jAF8q3A55+uE6IKl1L25TweMsZ0PG54IhAItS8aRa0SiPNK+paVkbCb004CXPI7BgVbbcDMQW3PHWqkGiuMhNFAHzwuF2tGJdusxyj3Xp8Jlrz8gqG2koHeZhKJLCZdxbYZk1Sepz9N5LxKnG2RpGCwu4ee14u2j2JgsevPcyoZcy7K9ZriMWm2gbnBMX7nc1OX6zEVglfT9BKcG0iMhqi1uj6VZMGNcW0lBcZD5Na/D9N57y4cUeH64G9KyKHz04w5YNw866mGYeaeaxWvc4vHbK3qc/5INf+jyPL/ZZFC6FEozsgtt7Z8SFx6pwGbcaVSo2EVnhcnK6z+s/8DWmJ3sADEqLxeWYh+cHPI571O1HJL2rUSlJktkMnZwdryGrLV7eP+FyOeTxesCjjcPANlhVeh5o8Z6OynWlomdVjJ2cspHMcpeksnhj9xRHKMZuRlLZPEk8ZoWJ15WJx27Gqhpuq0uClqQ2iCyDujVZ1CWPypjPhj12XS0e+2DdcsOXzIqWTaNIKLEbyaFnaTEacCsQ2KJlVkqSWnCvp2+sUigss6ZpXlxP98OTQ1yz1i0mu2AymVFXJlVl6eqg1ZDlLmGYaG3L0bUtHfMqJbNtDc7mY1bdnL23d6oPvV318NV7D8kzd8t/n01H+O9fx3RL/IMpmJLk5x2kPyJd9Hg83cMzK8ZBvBXv3b/1hNl0jCmaLdp4mrtdG1DbQl3ZkJc268zjG/MxoA+zoDeyutEhOWbnAkoqi6/OA8aOYuxU7HsNJ6kkNFtMocv6+15N3ghUK5gWLZumxi5sTAEDu+HDekmUTpgVFmDhSngptNh1TUK75HoYc9hfkFc2ce6y6nQ9YzclLhz+t4cvs+NmDL1065a5imyeeCm2LDmNewRdaiXoi9yLHp+U+j/+WCwWvPnmmx/ra69du8Zv/MZvfFeP97E3/geP7tDz0u1iHUYb4nXEYLQgPJjy/q+/uS3/KiV4/+lthNHy4cUedSsYeylDN9M9aKnoexm/+uwOgVnTd3Je7rz3QzdjZ7BgvH+JagSqs4uZssEdxBzuLChjn+x0grkK8V87J/7aNabPDlhsIo7mE2ZdvOs1ryCpTSZuxby0OM9tIqvBlw0PNy5pbTB21Barualb1kbKS6YGqqzqkqWRYiI5dBziWjFvMgojp2cM+LXihKao+Zx5g6+sNjiYZFRcikv+5/AuoaVvIJUSCKPlMFpxvO6zKB3iNMC3C2ZpSJboD/g891gVOuBkGG5wXV3OLkp90+9HMW998DLSUPS61ELXKrlcDyjmWkCny9c14/6GT904/a4mx//eWKUBy9zjOAl5dTTjdKUPSgYtO27GLPeQRsvYzfC9jM/f+4BXEp+ytLm8nAAwGi/o3zrF6iWcff0VVsuPEuRcp+D+wTGOq3uZj4+u8+jsANtsGHkpj+dj1pXNONgw6q+wnQKrv2H/7nPSeZ90HbJ7/ZTn010dlwoMrIbrQcpF7nJRWCwrk8+M5p0bQVI2kv/y9C4A89IibwxqBUlTYWAAFpumZuxYPN3YnGY2P7A75TzzcWSD2+kFqk6pHVcWSW3wRj/lNHN00E9l8+5K9+avyr8PsphXvYhKKVIKEpFwnPpUqotfVg1PEoUrBCPLYoLFplG8na5wsBhJB9uXDGx9IOlbNbtewjcu9tlZ97d5GS9qDPx0G82sWoPlcsBgsKQoHC5XA+q3XmWyMwO0YDerbO7eekYSh9S1xAsy3nr/ZcbBhsPJJVE/Zr3UrH/bKbHtktGrT6jWPul0SLKK6A9WZIse409/iLxtQFrj3TqnvByAMtiPVlSNxOlU/XUjee/xXWxZb9uQgZMTWRU9u8CUikeLAQb68yqNlj23YMdL+GCl0eDX/ISeU/BoNaDoMihCq+KaX2/Ru0O7ZsepSGvJspKsKwORmaxLTWG0hcG6zrEKgTRMTEPy/5iMeLqBZdkiDbjmK51E2uF4pdHqYKrhnP3JJbdKm68+vcvQzXBM3R4AeLgcEZg1PTsnrmwebjzKrg01K1zujGb0gg2OU+gqySfjd8wYj8c8evTo//Dr2rbl8ePHTCaT7+rxPvbGfwWWsawK06rJM5eysljOhxS5i21rX79nldRKcrLoYQpFrTR//CINth7TqxP4p3bOO3hNgyVrqkaX/JQSlKlLvI7YuXbGwWiFMGsM0SKcCtfRegCUQfm0T1tLLLsicjNOYx2AkdUms8LCMxVRd8qtlWBklzxPPWzRIi2olcG+p0NWTANuyQFNq0/lNS1ea1OjmJc1SVuRGDmRijgylggEJi7PyoQHxjcIxZhQ9dlRO0zzlllhsOOaXPcrnm30QubKhgM/YdNFefbsnIGrQTdXGd361jTl2fE1bFnr1wS4WIyolCTycobRGimVjsGtSgRa4X6VRwAwPd7nRaQ7XzEVQiUZ1kWn17BxZUNoFfhWte2rGgYcXexyOLmkKBwWaci6cNgJNtS1JD7eIXv/Nv2dOaqRNLVk+eH1Lp5YbZMG94Zz4jRg090IX9s/pa4lRW2RFzaTa2cIr0JVJlWpEa/SqRh4CQe517HWNTQlMGtGdsnITfnmfMxLUfxflXoBQiXoWyZpY3DLc0mblkq1RNKkUgZ7bs3Y0WmPQ7vA7RbgojZ5kric5TqroGfpFo4tWtaVyaI0+dyo4SI38aXWRoSGQ9W2GIaBiw0KDKk9303bUrUtaVsRd2t1TUNqZPTQ5dqLJiWOLZrWZWAbVMqgXg255ie61Ptt+QcvYgRuxqbLsXfsksBPMYRiNJkx3rtkOR3hBKn+/IqGII6oSgshGoQwyFOXcbChH8UEYYIwa6JezPn5Lp6b40cbFh/oUCdVm0hTfyYGd44BaM9LjD4UpyPKdUhTm7h2yXI50PQ6q8S3C0InJ/RS2lZQdtjg0Co1RroVHPgJz5MQabRYomFV2kRuzs1Ga0v6Ts7b8wk7To4UOnHPNWtmhYMrPoru/fpcH2DLBpJaEVc6nAn0+zk0PHYck8jq3k9l4Jlsw8kqpRkDQ7tk4OSEdsmtgxOKwiHeRJSVyVnmstNVLTalzV6w4ZZVITqivS0UlvjIqpg3gtN1H9/NsJRgsRy8ELjXdrwIC97v4grCD/zAD/Cv/tW/4l/8i3/BH//jf/w3/bp/8k/+CdPplD/yR/7Id/V4H7v+Z1sVZaUBPrZTMl8OyAqX9SZk3oXlpKWj1dYdyCerzU79Wm0ngTR0EAvAwe4FVicGvHIHSEN19DbJJvVxegnuzgJ3f4411F7vthb6jzLIpwOqwta9XiW4wh1f9ZcEV8AdRWhVSKMlqwVWl65VKK3WrZQ+kUsD0qZBYuAYEheLsfBxhMBC4LQ2IQ4tCqd1kJhciiktiqRdkBkpFiZHZcasrLnMW04zi7PM4SxzaJTAkbVO/Ms9slrrGMpaH5psWevyqFSsM125sG0NwlGtTv5zu8OT+LZbnGG01LUOTlJKUJQ2Z7PxdzU5frMhDK3JuLpF5p3Y0BINoVPQKANTKkypdJhIKygrS9uTCodKSQZhjGokRerR1BI7SvFCrUZPVhFJ4WqBZyOpG8lgtGTUX21Dj4TQ0KOqkbqk7OcUJyOt8+jEkum8jxSKgZOx62b07VK//kJ1tMiCdfURG8CVdfd8DepW61DSWuNafWngCIPAFPQtxcCu6XXPJbR1IJM0FKtKV2fWlQ5ugS6DQGrgT6W0VaxnKUyhb4kjy2Ja1CRNRUuLg4UjDJJaHziXbUZGQUbB0lhzKS5ZiwUSgYFBRc3c2JDULUVjUDSCo9TuUi3bLTznRQ1TNtuwLscucT3dExdWo9kKHS+j6aAxu7uXSNngBRlekJJmHlGQ4HpaDFoXNtKutpuWIVpUbbJZ9shTfWA2rQoZ5Bh2DWYLrk2TO7TK0Oz+rorTqI/yMVyr1NqZDiS0zAJsqVs869Jm6KW4stFtC6H0PGgkvlURdi2JdaXthT07Z+BkDNyUQRfydKUbKRu6gyb0unAm0f1pWuhbJqoDN60qg7zRuQuW0AeDZSlQrU4i9S19oRHdITjJXVQriKx6G2imH/MjlWjTamDYdT/X8cAdUyWvTbLcJc28rW34k/E7Y/yVv/JXaNuWn/qpn+Lf/Jt/89/9u1KKf/gP/yE/9VM/hWEY/IW/8Be+q8f72Df+qjY5Xw0Y+hsmh+c8WUx0ZKTUKE5PCU42EYfATm/JQRBztOkx8bTd7CLu4XeLo+z6/EE/5uzhPbLaYuhmDP0E28nx3Bzb0WEazs6StltAzU+55F/Wt0SlJN5gTZk5pHHIYjFgnoT07JyTJEIKxZ5X6P5rabHjlERmxaJ0sKU+W9fKIGsMHAGlaombmqJtaGi464bUqiWuG17u6ZdJc9otmrbFbockVKzZkLDkfvsmz43H5GxYGwlnxmNeVZ/msmw5L1reHGiUZ6l0itksd8mVwBGuTuIy2m0wR9WYLOeDLdzGsiqy3GXfmyFES17YrDchUZBQVRZVR8ybJxF5bdLvxG6z9MVEsTZK4Fpqm1PetyoCs2ISbBhFa54f3dIUMkNXMg5HU7LcZV26VEqyH67ZPTgnT3wcP2PvU9pa5FWSbBmRpR5paTPpL3VZViqC8RLTKalrSbUc8uhyvxNH6uRDVZl88PU3KGoLx6wI/JQPHt/uCIIVe52DomgkZgc+OV73mbgfPe9GCZ6shswKm3UliSvBUVoTmBJfanKeKeDlnr5plUoydHLCzn+e1x5HqcuBVzIvTZJasCoFE0fiCEXg6g3nly583hyVzAqT89xg14Vfax5hIIlUj3Hbw5UGj/OESzGlEBkDNcZqLWqjJmONjYdCC/58XJq2wTJ0GJApWs5Swbq0NWVOqBcW0Qz6kH3Vrw/CRCOSM5c88ShTnYpYJD5JHKKUwZ0f+jrrh9eww5Qmdzg922dnpFsBRe5QFA7DnRnjif5/TWnRv3HG5Vc/jWEoet18KC4H+G+cw/V9KEvMMEM6FU2t9QWWaJiEWmQohCLNPMrKoq5Nssrmg8WIa8GGtJHUShA6ObteRmgX1I3AFIrzJCKw9LqVKJuBdWUh1YCsYRhT1BZP1n2eZw4D22TiNkRmo7VFtuQotSgavdGrRguDnyQVVdvgC5PboYnsrJ150/IkUbzcU1vnQKMEJ+d7LDMfUyj2B3M+t6+1PkW3dh4nmjYaWZUm91U2P3TnQ2brPs/XAyzRElgV69wn6cSIL3K0fO/Ffb+b0/l+5Ed+hJ/+6Z/mb/2tv8WP//iPE0URZakPm9/3fd/HgwcPiOOYtm35c3/uz/GjP/qj39XjfeyNf7buM/AShNHy+IO79J2c0yTEMhQ9p2Cde7w8vsSgJbmC0BgtllkjDEVc2bx6/TlPz/cpG5P9YM7Z0QG3hjNsuyQMk22cbpr4pBcet+49Jn50iGokTpQQXL9EOD5OmNHUEtMt6b35nGf/4fO4TsG93hH/6f3XebzxCC3t1Y6sinWlKXyFEpSN4M3Rkl+5GPF4A9DiSXgpgteEiSUkzxNJ325JawOzg3UkNQxt2HEEZzl8q1gwk+c0bcWkPeTADBD1XdbGhtxIeUm9xpm4YF/tcj9wWRRwJ9Sn71nh8spwRuDkzJKQdeliiYa+p5ngjRKaQeDkHE13qRqd7Df0E3ZHM67fe4LdT3j7l7/IjevHhHHIYtVjmkTcHF/S68V4YYI3iL+ryfGbjbs3n5FnLoP+ijsHx/y/fuNzvDYoqGqTyw47PPF0hn3RmMzjHuPeivvuCVnuMksi6sJmcv8ZVreJykmBcCrSWZ/n5/ssCw9/NdiWqC+/PuT2zedEPQ0lkfMRvl1weO2EsrT59f/yRfaHM/3a5R6XcQ/HrLn30iP84Zo6d/jlX/sihRLUrYHAZFlZfHHvlP3xFM/P8IOUnpdyshrywWrAo43gdqjff112h4mlM9MDS1cPjpOQ+3bJ8arPsrTZcSqOU4fzXIN5Dn39/J+nDq5stxtHVguy2tim+d1obtLSYiMJpcl/Kr/JxDgkbHvkRsLMOCNtF/gM2Wmv87D+Cj05xGotKqNCoZiWHoUSBKbgwFPc7i94d7bDcRJuKyUvYqyTkEGoRXSXlxP2D86pS4umcxJE/RVF7mIYCpCcfOV1Li4nGrnbGpS1yTtP73BtMEdKRZK73P7CO+TzHkIqTD+njH3W6UftBHe0plhGtDkYSoFpkpz12Sx65Lne2JpW8HQxppcWOlTKqnh8sb/NvR/Y+jDU67IbysZk4KYkHfjoXn/BW7MdvthbYsmG03WfW9GaojY5S0JWlUVydsAPHpzw0mDBOPNIah3hOytNykbQsxp6lqI1DXzTwBKStIbPDEzAJK4M0hpOsoodx+K6L/gw1mLA49Tn2SagaQ3+n7/vP/Otd1/hOO4zzXzGbtrRIiV5Y3E9jPnVix2yRjJuDd5ZebyZBhSdRuBub81pGjB0Cg6iFTcOXpz+55PxWxs/+7M/y507d/jrf/2vc3HxUZrmV77yFQD6/T4/8zM/w0//9E9/14/1HXl8PDfXN1CnJFhnnUdY+89dqyQKE+JNQJbrtKheVXQZ0JJZ4bBJfJ2z3UhWSYht1gyiNaZZ09SSLPEQUhGEejOI5wP2X39IcjYmnfVxni6QYYa58RCViXQLLn/5FTabgHncY5n73O4vOzFMRWgVPIn7DB3dx8sbwbKSvLvsE5iK26FgVhjsuxWW0GXYZSVZlS3CMFiWLbOyJrI0U31RgkDf0M6LkEqV1EbNuO3xpFnyfvPLVE1CaB+ghGLRnrARS9bpdfaNHl+ZOQxsmDgNu56LZdZEbs7AT+kHGx5d7DPyk63eoR9smMc9pKFwrEr7/wuH5dkO1mxA4KUs5wOqWqunr4+mmLJhteoznw9pnxp88bueIv/9ePfRS/TclBaDVebzSn9NvwtZWqQhH8YB16IV42CDag1sWeuIWKHwrFLDXlKPfmXS1hJVWIhegeFV+JMlh4tL1NQgdDPMTi0+3JlRFzaqNXSZ39JRryfHhyzSkKfrPkN/g+dqol+tJGllc3p0iHOhhTADT2c7XN12bOkwS8MOIV3jWHo+zztx6BuDmoexycDWoqyy67EvCoe8kdhCcRAkmKLhsnA4zSz23ZpX+hs80wfgwMspOpvgsjRIapsfPYhJapO8EcS1wbME+tJm2RQsjISFgl2u01CTGikSk42aIg3dFF6JGVWZMDVPcITu88/rJ7xq/n52XIPAbElqwWUSstv9znH+YhweAHvj6TZcyWlLpF3RbnxMq9LApvmQa68+ok5dyszBH690FSvVFwTPz/gv776OZVUMh0v2nJLVs32SVaTXmn5MXVoc7lxQdG2cfDogeuMpxp4PdQ2rNaoZbp9T27UNg+7As8l87tx5wsl8onNCzIrQlhz0lmRde2Z3NOP9o5uUjcQwwFSKG76ODtcck4R5FvBgHSENGDslgVnzwWJM0Ug2tbYOD+wGy2i166g73Himom6FTvBz9XuuoVDwYbbh0Apo2paLHAJT8mFs4EoY2opXezGbhdYI7fgJe/0lTaPzF9aZz2UZcLQJuxZSRd8peLVnskx9KiURRsuqcKiUrgL1SwcvSl7YfPj29+CT8Z2NP//n/zw/+ZM/yZe//GXefvttVqsVQRDwyiuv8CM/8iP4vv89eZyPvfFf4V/L0qaqLIrSZtiFqwhDC13qWpJXNnllETo569JhpHTyXGDW24hZgLqROGaF7ZRdFGdAv7+irkw8P8P2c5JVRJ26uMM10qmQg5y20N+vGoHqeoZta1B1saA9OyfoRC5pZWsrmFMwV4JSGQSmXr0toTO4DQRStFhCoTrJQ65alqW+4QVSbv390qCj+MHIskjLkIwS25Bkhl5ghWEhDYvCyGhbxaa5IDUWhO33EWcl1xuPwBRklU4q8zpkr2XVNK2g6gKLksJhEOobu8LYlmvrRm7hJK5TcLkY4TsFvpdt2eZp4ZCXNnn9Yrzbs0wf4BolWBUulRI6orfUyWb6dWqxzQohWtapj8Kg7GxRO54u4apOpyGDnGbub/uzvcEaZznEc3XP17Iqwv0Z04c3qEqLqtLRr6KRnK/7rEuX0KqYJRGTbtPPKovAKplteloPQUtea2591IGF1qVDaGtxYlGbOFbFLAmZlzZZrfUiLZrf4MmWntUSmA3z0iJQgr5VYYmGuHApG92XVa0WEfpSoVrNUz/PHDxTYbc6h6dnF+RdTzZvYFbWWIZBRsG6E406rYtAIDExkFQqAwGlkdK0FYYhMBB4rY/V2mRySEtL3hiAQdHAprLpOwW20HPrRQ3DaCk7n7zr5lSZ3pwdt8DrbShLG3u8QlUmKg4QToXt59S1uWVUjP1Eb+iZi90aLOYjlDKI2g1ukJGsIqTZICod1INQNGsXKVIQKfWFbmuFwzVukcN8wI7R/ld42jJ3GAfxNguib2TUSm7/vermpy0b3V+3C9oWbYtUSn9ORUPParZfF1kls1xXWmplaFeFXWLXJmkjqFoNe2pavZ74pt7QLaGrSGnddrQQfdkwMFhXNbYwt1THnSChKBwcq9KQL7PCMCSzdZ9VB4uSRsuuW2IJxaa0Ca2KRe5hGFrUvCxtbKHodTjv1XTE3gubEZ+M72ZYlsWXvvQlvvSlL72wx/jYO8OgF5PlDou4xzLzUa3BK9eO8AP9gZ3Ph8RpwCrzKLoP22nqses7jMOCa0HchYe0Wx+/KZvtorFMAvb2z5gnYzw/w/Jz3FqSXAyZ/E/vwafuYuQe7bsXtMrQ6u3EI9ybMT3fwRQNkV2wLjVnYFPZxJUuu/lmxbxwqFuDG16OAhaFDQKu+SV5IxCITqCmgRvzsmbimNzwDealFurYQm/8cQVD2yCuXVAghIHd2kzse7Qo7NajJEMYJqVKKKsFqfsZZvICv7rLtcYkri3CDuErhck6DnGlJvqVjWRdOlxrDZpWUDda/CeEoint7WZouwVx7tEPNoTRhpPT/a11qVKS4gVBW+quHaFTAw2eJT63B3Odctca7LrF1t5lGIpl5rMXrTr4jvZlu0GK6g4m8qAke2dMlegboBslOj3Ry2lqfVuWfr49IKaFQ1o6eKrkPNU33td2zvlwtoNn6XCmdelwbTDn0XR3K7pKOtLZVaxwUpscDGc8vdylVCahl/LW+QGLQqv5q84zvizB8+DQK3BlwzcWAa2jN/g4DXC7dkRkKWypurRJUEBWm5xkJi/3Cvzu65alo8NZlBbwrducPi6JSNgwwzN6pKzZVzdwW5dC5LQ0lGqDaiss4WPLgJABYzWkL22ixidHcZlrclxgGcSVhS0ahNVu/dsvYuS5DsFynYLeYM1q0dfuH6fEGa0Ymg0i0Ama62UPf9Sl9ylBUTgkqc+1nQsWqz7rNEAKxTL1GQVJF5TUMp2PCP106/+3gozlg5u4vQTpVCSXA4RUhAeXqNImT116/TVZ6lOUFmVpM72cMOivNOky8wjcjOP5mFLpxFE1m2B3QkXfLuiFm04/Y1F1h0aDlvuDBUVtkjfahnvF2XCEZGgXDN2MRe5B4TIrTYZ2zXFqUyqDXVcT+PZdLeybFYqx8EgbRWSaBBa8nyeMDe0MEKCra61B5GtNz3IT4ZgVz+M+aW3imzW7Xkpg6c/Dceqx6xYsa3ubPjgvTW4HGbtBjGeVfHhyyMsvbEbo8cmN/7c2zs7O+Lf/9t/y4MEDlsslu7u7vPHGG/zhP/yHiaLvDZTt44v7KpNeL6auTWaLMbf7C0yrwhAtTS1YpwF5ZbPfX2JZFb/w6D4v91dMU5/zJKTv6NjaW6MpllWxiHucrIYMh0v8IGWne5zBaElVWsyP9nD9nHBX3w6N8xPaWY647uNbZ9TTgGI64Oid+9sgGGkoBkFK5Cfb2+LRxR5vXe7hmTW3gox5aXPNTzG7cm/fKdiUOnAnbyR1a/L9E4NpbmJLTWh7khiMHYOkNlhXmqiWNQrLMJhID1cKrNLCah1W7RlnxVt41oRN8ZS2rTAMhwt5wqo+5rF0sOMb1K3PjpsRFy7TJGReOlwPYjalQ6MMenaBUoJREFPWFnHu8f9+/3V+5MYTNpnPxWrIm59+m3EQkxcOydkez5cj7owvmfSXAGzS701Z6L8dnllxHPe7UmrFy731NhBmx834pfMRbWsQJtE2L/z69RP2SgtDKHq7c+ZHe6jaxN1ZUH3h98KP3iT8F/+S8nKAcCre/L2/wOXPv8JqMQCgyR1G189ZfavH08WI56nPa4Pl9jmtMp9740t2RjPSzOtaAi11qzPMdbm1ZjdacxoPmOUuI7vgZD7haawX2d3Q5zizuyQ2WNbwzWTNjvCJLJNlabGuXMZOw7UODPXvj12+tGfQtOCKltthzJNNtE1tiytTk/WUYK0Em1rwen/N+yt9YLnuC85XkvuRRRPvkouEss2YtIf08FCAQGAJn6JZU6iSui0YWbdZtZeEREwMh4F0mDcZt6yAm4FOAzzObHp2iSka0u42+yJGknla3Fg4PHl2g8DNsayK9WzAejZgfHhBeTokjwN9uO7KzMsnN7iM+4yDmOezHTyzYhBs2NmZ8up4SZW5yM7Gu8wCzuM+N8dT9u48x9lb0OQOzniFOUxwb1yyevs2x998hbPZmCerIfdGU/bGU8KwJM/0gfitJ3cJrJKdzp48CWMGvZiqMvkPH7zKj770gOlqQFx4DPtrbh2e8Pz0gFka8Dzu881lwBdGa0Zehm+XHMc9QqvkTm9J4OSUjcnRaoAULfteytgRnGY+lmgxDZ338DiWXboeXPMF0hAcpYp52TAvYSIC1pViaGsr9Nl6wOcPzvTrMB/wdD7hxmDOZ689Y7GJeLoakjcmp2lA1V1ejjNXk0ltMI2WdaVZIu/PJ/Ttks/efPzC5sPV+ITc952NOI756Z/+af7BP/gHqI5C2XY2XwDHcfiZn/kZfuZnfua7CuiB70jVb3FyrotDr07OiYKEB89uYkrFwEvwrJLj9QDVGvS9lNeGcxyzYhI2rDOPr88mvNpf4XX4y7S0EUaLNGuCfkw4XLFZ9BnfOkE4FaqwWBztEXz6jDYFdVQiJgJWKcbYwTITiumAqBcTJwGepWM/bbvk7Wd3ttQu26y5FemSedlIqtwh2Hp3tQVNl6dbktrkPDMZOorAbFlVgotc8lKkPflp3ZI3LUlTk7X6BiUw2DQGEokyFBKLnnOTRfYutjnBM0f4YojfRpjmXQbNkMpQvLNuuB+F3Axj+t1rUnbAlsCq2ItW1LVJv7fGcXUWd+DkOE6BUbaUtcmvfu1z+ibg5Dpu1C6w7ZJN2nG6X5CSO6strveWmtewibgerrnobt6BVfI/3zzlMglQGGSVyXHVZ7Lo65u+l2MFGdJs6F07xxykGA/fwbjTIN4Y455e0kxN6AU4YYad+mw2Ad/68ueRQvEbZ9f4MPbJG4PIjIisSiN2lcHuRPeawzDhnqeZBrv+RuODG5Oxl3K8GrAuHTaVxVHq8kevPadRButSh8Pcj1IerAPOM4PLomZH+BiGwaqEY8PEMlo80+DBOiCuDSaODtiplYEULc82EcepzTVfZ1IcZxZ9q6VuNYt94tZUSvf2I7Nl7NXUrd+JBxVuG7Cj9vmU3+ftdMVczAjbHofiFYShK1O1UfO0+jp98xq10bCsSxJKhoZHqVpmheDQU6wryYN1wKJw+HQHyHpRQwiF5+a4bk7Qj0njkCz1qGoLd9HD9HNEx50QTkV+HDAcLulFWhTouTlhL6YqbJ4cXWc/6zC4XWLdnf1ThGiIRiu8nQVityUcndBcWFSzCOnnPHz/HmVj4tsFP3j/PZ6f73NyuYslGzwnZ3fvQusQ7IKoF2OIlndPruPYJf3+mlthTJa79LuQo3UcakV93Kdt4TCM6TsF+9GKTeFytomYFQ5DJ2eaBZwmEfdGl1StYJbZ5I0gV2JLaaxamGcSz9Ri4VnWsGoqLEMwMK2t9W9W1lRKUxvzxiCubC4udtjkHqvcI6lsLuIenlVR1GaHhzYYOzlpY7KpLCqly/8ju2Qv2HC3Z/BrlzvsOBWmoXh+ucerL3RGfDK+k1EUBX/oD/0hfvmXf5m2bblz5w6f+cxnCMOQ1WSyH8IAAQAASURBVGrFW2+9xfPnz/kbf+Nv8ODBA37u537uu3q8j73xF6Wls+FFg2VVnC80GuYKOZtXNj1b40vnScDA1zGxwtBM84Glk9CqjsTnd0ETReZhuwX+cI2b2x17uwFL88/bjYHhtxiuol0YGP0u/zqWLE52cb0M26pRysCyKoJo81Hgj1CYbbP1ZqvWwJeqywbQvcJl4ZI2krIRJLWgUAZPNpI9V/PdhaFv+mOnxTQMhAG+afJerqNTPSxCabJpCpzWpcQhUwvatqRuEirhUQqH0+aMQ/k6BgZ5q0u+Z7mNJbTS9orbrzpG+VVZ8yoTIYg25KWN7xRYVsWgI29Joag6e1Lk5gRBSpwGFJX1wjZ+txNs1o0mnDldr7tpBU0rOk+3/iMNHYIDYJo1QjbUqYtSgraRtJXAWG9oTRuSDWpt0Gw85DrG9ANsu6RtQ+ZJyKZ0OM1c1pVWzJ9mtlYxO4XuESsDQ7aYZo3pVWSpT+DkJJVN0cBuf8HF6XUAQqvCM2viRGcBeGZF5GTMZxMcqXCkJFMNoTQJTIEw9GJtGnouXI3A1CmOY1sRSK132XFqBJArYxvEE5oNbhfmIkXLNe+jZMGe1XKWGfRNG9nsomhZV4pdETBqfVogUzUFFQ0NCoUr+9iGh2iFxkiLNaYSpJVWeY8ci551pWd5sQAfgPFkhmk25KnLxekerlPi+RkDd0kwXlKnLrafYTolm+f6AlHkDkVp43V0RsuqaTshnR+mVB1zf7mJ2Bks8IMctx9jTmLUS/cQ5yeIYgOtnjO10rqhMEiIRiv8xZBFGiKMFoOWeNXv+uO6vdjUkp1go1toteTWUGPGI1dnBcS5xyhaby8KVSMYuCmWrKkbQVKbjJ2ii3nW8/087lM2sgvggrPcJDBbFqUgraFt2d72bSGwlCCSJpGlgU2laolMSd4obKFv73kjyUqHWRp0OqYC2TEgTKkIrYJSmUwzT+tthMIRH1FDr7Q+O07F0CmwZcO6y0V4keOTUv/HH3/v7/09fumXfom9vT3+6T/9p/yBP/AH/ruv+Xf/7t/xkz/5k/zzf/7P+bEf+zF+4id+4rf8eB9b8VM2moblOAVNI3m2HuBaFYGT07YGq9xjFCSdglQriBslqBod4HKnt8SSijT3KEub0M3oBRuy3KHo1L12kKNKizp1aQoL288pz/vgCBj6VLMIhn2oG8pZn8upBtRYZoVpahSn5RXcvHHEZDhHygbZebav1K2eqYVOpqFoW4Np7pLUknUlSRu9sU8LRd2CZyoGdsMsb9l1anZcRWgaTBwDffcysA1J0CVyOO3/j70/i7ksS8tz0WeOMfu5+vX3f3QZkZF9VmVRQNEbc2xsc9hHaIOQjyWjc2HLsmQJX3BpWbKvfMGFbyxLBzdg37jTFmy2hTkuTAlT7DJUl1mZlX1G+/erX2v2c4xxLsaMBWw3SlwVPhycQwplROQf/4pYa845vvF97/u8IZ4TWCEWEm1KymbBpr5gXbxP6RQUjs0CCBzJWS54mIac5xHKCHzZ2PQsoXFdC6hZbLqs1l20lqyqkKKygT6dZMPx7iUH4wmeax9EnSgjCG2eeKVc1B+Aenw7VzcoySqfvPGIXes2SLwKXyqMsd2cJwl63T8g0HM9S1yr2ll+U/joPLCKOMCc5NSXPVTuoye/f2mq9vM7yRJWdQs3EYbzQnBeuCzbNnaaxTbsR0m0lki3IQzKbZhKFJbI1so1CgqeG065WPfJG+v5DvyaSeHR9xQ77eYunCc2LCiV/eE41qrqYAVb57luU/gaImmxwBqojUO3BfVEUhG0m6/SDjeSlK7X0GgHv80QGPqCa6FPz/WY1BWHkcvdrsfIc9lxA1wEtVNTOgVdsYs0HhpNSU3hWM//pbji1Ky4v7H0toHXbCEuT2u5UtHbmxGPllSVz9l8jNYOSX9N7/iC6Nh2G7y4IOimnD08tkK+MmSZdsjyyD4rajuS6UQZ0WCF51sXyyTtUFa+Jf+5GhFp6v07ADiRgxPVVMuOdRYlKZ3uBjcs6cTZlhlicJgsbEeyKEMm0xHzZZ+DsR0lbtKE8XhGpWwRbbU2LlFY0g2sU2RVhdscjEJ5Vjgc5mjj0PFLxvGGq8KOZwZ+ycCvyFWbLVAYrkpFbWxR52CFfl3p0vcEvoBGG9JG0fMcQimIXTtqLJUkqwJKZVMB97orpHjSCrYR1Z5Q9gDTpj52PIUv7IFnU/nMiohnB3O6ftkGlz29tMZP1h99/eIv/iKO4/DLv/zL/8VNH+DHf/zH+Vf/6l9hjOEf/sN/+C293sc+8RvjkJUBxoSUtceN1tu6zBNWpc3PzsqA2KsI3IbJpsuiChi3YJ6j0ZT3zo/sTRIURH5JWQZbvGc2723tO75f4ScFg+ceoksPs9Y4ZQb0yL/rx4i+8K8BuH7zEUZJm+alHVzfEsKE25AXIZfLIb0wo9SSw2RN6Ff83tkxXc8jb1yyxiarTUvJnY7dFBa1y6cGFdPS52HmcpJpdgKHohWICQc+3Cg+lfRZ14Zl3XCv3DBwIh47GxwkN7zPoL1PUzslhdmQqzklLg82v0Ec3OTYfYmejng3rUncmLEWvDkf8AOHpxwOZ1bMtBiwN5rawqXxyDYxn779Ad/46A5V43K0e4lSEtdtGHVX9GJJHOU8enwMQCcoKJ7SzW0V/Q5auxZL291sSWBSGDvrbKxrIW88CiUZ7k84fXCNuva4+xmbLCWkRkQl5uYN3N/6bXQW4I3WiK6iPutw7xvPs2pDivY6K95fDvCFYeBDz1M8TCWHkaHRgt88OeL5TY9nR1c2i76I2OkvtkXDtAz44gfPcbO3YFMFNv9cKivQ8kpqLfk/H97iuV5K5DZoYg5D+/4tK6u69oXDrY61fd5PBffyHIFD6EjmlUfiehxFVsvSaEEsNftRzpenPdaNay2mfslvnO7xyiCj1IK0keyENWnjsagcKgP7oeDQCW07uNSkSvFiz2O98kkpkLikZoFDycw8otY518QrzDljwD4dE+EJOMs97nYLe8J7iqS2vb0rynVsiXtS8/1/5rcwjaBadVid7BOuOvQ/94D09QMevnOHogxYTofsH5yzZwQPH13j+rUr0nWHurYHhYfv3aFpLGv/07c/tIJgr2ZzPqJYdBgm/x7SBjX3qSYDJg8P8f0KKW1Bb5TE82pevPMhQii0liznfYTQnM52+Gg54PnhlLQMmeV2NPaS29AJCjZliC8bbuxe8MaDWyxrn7RxabTg2fEl6yIirT0qJTjLEm50Vlsa6WsHJ3w43SNwGxpjN+FKO7jCITQC6Thc1gWl9jHGsNQVXzEPeE7faTmMthsw9K2TBKxO5OG6xyu75+yMZkhX8RtvvcooKDjPEt5ahrzcL/js/hknqwEfrDrsRyWJC4fxhn6UU9YeO/0Fv/b+89xPXfbCp8/q/2TG//HXBx98wAsvvMDnPve5/+bX/fAP/zB3797l9ddf/5Ze72Nv/AfjVjSlHSK/5EuPb/LZgxP6kfWdX98/560Hz7CqAhzHcJisGcbpFrlqjMNOsiGtAs5WfUot2Yky3p/sMQxzru9cMdqZWutPUhCMVuSnO3R+XKFGtxHZBi+7h/+7/wdUDf7+gp6jmX14nbIICMKSuLdhdr63TSIbd1YUtc/DNOYij4haF0HHKznPIxaV24b2mJbPbVP7LgsfXxhuxA07geDeBj5YSW52NK8OCkZ+wElmBWCu47BwlnjaZWB6dOhQOhWPeYcQ671VpsHQ4Moxoext31MHi+sslEMoNG9Pd7k8PWLoN/zQzXssV71tgt9602EwWP6hSj8IS37v/eetGDAoub1voRxPlM9P7IDf7tWLMiZph2mbnNe92rVtSGUhSZvZGGUc9qOcvTil45d8+Wuf5sXbH9Hfn5JN+8TjJcHBDNFVcO8hzSxE9gr0JmD9jUMev38LITRn6z5vznv0PNs6Hfm2I5K4Df+vZzfWr9949H2Xlw9OAYijnOvXH+OGFfOLXcJVTSAsNTFuQ1vKxhIPDzorTlZ9zouIeenyub0VD9c98kZwI7GfTaFgXsKiVkxLl+txw6cGNS/2Pc5yewuta/hg7ZI3MXe6GQ9Sj0lp8ByfP32wYV17nGUxjhPxbLe0qv72Og2E5npcM6980kZzp2u4LGxRsxc6TEuPga/pSpcrBQsuONa3aRyFcK6hpWblLNBGUTkVBRVpE3BZgCAgayTPdJ/OtQBwfr5Pr7tpN1iH82/epq49ojgnGayo0hA1cakz2wkMg5LVpoMfVPR3Z7z6A7/L/a++zNVyQNl4bYx3zU5/scX4zmZD9r2asJvidzP0ucH86c/Ab7zJ6nTXkvrchvPpGDkfcqzO6e/M6d99RHayy3tvvAhAv7tmEKVcU5K88Uhrn45X0m/tsIPOGt0S8S7mIyuiG87J8oj7V/uUjYfvNjy/c8ErXs0mj/FaBHBWBXzl7BqLymNeuYRS82eOpvzH8xFBG7/8OC8JHGmRvq6kS8S63sVzBHOdMxVT0vUeL3Vi5pXDqpbcShpe2T3H4LBJE0ajOWnj0vUkt/sLXt6xTpZp2qFSkp6nmBQBx7G1K5a1RyfM+b1Htxj4Nd8ZloxbXdEn64/H8n3/Ywv2Op0OFxcX39LrfeyN3/VqAiVw3QY/LPl0FdiZv6twpeLe2TH73SVHbT58WoZEXsWqianaqF7hGAZRRuLLrdc/cGsir8LzGjqHE4p5z4q/wpJqmWA+OEGG560ENoIsh9DHCUp05eOFJX1XEXZTosMJycGU2QfXmc8H5FWAKxQ3koymtdxIx+C7iq7btNnrileHOYFsSGufVe3RGAe03dhjqXm2azjLpQ29wGagA+TKBqjsmx1KalwkoCmcjLLZEHtDMCAdF+vg1qyqx+RyTuk9y8vmDmljOM1dbiQ160ayG9QkbsMHl/uM45T9waxtiwZIt6EX5vakWgaIyue4t6BuW/qrTYcoKHCwmQnVUzrxS6k47C0JsoaLLGFdhgSuZYfLxrYjV22ioNIOvciGo8S9DW4np9tNUUWACBroB9DvYx5ZooncK+kMHrOzSvjoo2dIaw/HgfRJMec39PwKz9GMkw2nqwF5O8MsK484yvGDkqCTU2aWFteLco6UtUl+MLX+kSeOhPM8ptECzzEcRBVFmy9h262Gt5YehTI4DnRdydfWazzRZRwopEPbsjV4zu+HrHy0iQmk3bQBLoqAUNpipePVhG7DvAhJXDsaeX8dIjAMPMPQd9AGbiY109IGAo0Dy28PpWFX9TDmOqlIUTQEJkTikpslm/qCyO/jIVk1NXthQM9XDPx6C7J5GiuJM4RQ25HOk00/TDLcsCLopaT3D1hejahrj35vRVEGlEVgSXvrhLrxtlZWX6otJ+IJ5KfXXVuPf1wgghq1ifAuT2hKDzeoGO1PuDg5QGlB2Xg8PD1isFojvYa68Lcunyfcfk8opq32xGsFhKezMWntc3M4ZdBZM1kOaLSkasdr42TDpgwxBmY6oVLSdrja7IxNFfA4DbmWFDYR0vw+Qz+SgO9g8MiURjpOO+d32K/7FLohwuO6PkQ4tthUxnaZmjZXoBNY9sFi0edWZ03ktZkotU/ZuKyqgLS9F8ZBxVUZWPiX23CyHHKWh/hCE0vN9CkCncDyLwyfIHs/7vrBH/xBfvVXf5VvfOMbvPrqq//Vr3v8+DHf+MY3+NEf/dFv6fU+9oxfq3bjdBVBVHDj+ATXVQihCPyKj5YDep0No9GcfndtgzvaokBgqGvPiqj8kmFnzU5nzbi/YH84Y9hf4fkVblLgRSUyLHFcBVpQ3N+h/HCAvnQwwzFmXsOyRK896izAEQY/zvG6KbKb4x0uifqbbaiNwWEY5PSDkp5fMQxyBHbWn7SJardHV0RtljxAIAyNcdBY+431b9v/V2lhxWUCaq1pjGbXCwiwc2aFPXW5wgb4CEfiOBLP3UHpnLqZkJWPWKhTNJA2hnllyJWg0oK+XxG5DY/a2FvPbfC9Gm2s6KkXp0TtzHGZdtgdzjnamTDuLVFGEPgVyliIzzp/Ou3duvboRBmjOGUYFOh2xhi5Nb2gZD9OGQcFkVtvmeae29BUHqYReDs2bMmJDQyHqNEuIqrs1TjswAs36V27YF2EFEoSSptu5zmGxG3oehVSWMTqurYCP09o2x0RekuCbEofpSS+tKEq/aBgWgZMy4B5FXCex5xkFkCUuA17rajLvobNXC8VLFuC08B3mIopiwoWlWX5G+y8P3GfJPEZznJB4mr2woaep5lX7lYAFroN0rEc9lgqQmmoNFwUDr6E3UChDIz8mkbbInPgazaNnQnveB47pk/pFC0ZcomiIVdzGpVi0GgMG0o8AYnURH8g+fFprOHu1ApTtSAIrfg0GayQXkOVRni9lOXFDpvUQnakq1pBriBdd3j8+IiysvePaA8IrlQWFtbY50bSTZGuQnoNwmvQlQtXExxXEY+XxGMr6JVCo7TDIo/Ji5DT+9dYTEbEUU6nk1K3QVZAuzlbwt+TGf5JluB5Nd3uxl7rSpLlEXkRott5eVrbmflJlrAsAy6zhEVu2/8a6Lo1I7+m46k2qAxCadHgkRTEUmzT+qyn3/7bE+lxGPp0pWuLTRxi19rxztMOVWP5KGkeM042bTCab5kBWG7Ek8PNwC8plNgG9qyq3xfz1cZh9RTtnZ+sP/r6e3/v75EkCT/xEz/Bm2+++V/8msvLS37qp34KKSV/9+/+3W/p9T72iX+56tEoiZspijxECE0c5dS1S5pHeI5hnSY8uNxnUwW8dPzIzmaHM8rK58Fkj16YUzUePrYde/jsAy4+ugGA59VM3n6GqLfBjQtM7dJUHr4wuN3ctoSrktlXnqVMrU/bD0uyVQetBeGqQzjvMb/YZbh/xXDXZgC8++gGj9MuoVT0/IpuWDBJO6xqO+f3W/LgSdplU3tWCIUF/PjtKW1Ve6xrq8IFmJeG/cghkpLGGDwB18OQk6IgdQyRiUmcF0nZUJsNxii0rjDGttdcOWAsbvJ75nd5ufkOBl7IVSEZ+ZoP1wmx1AyDiss8Jr1/h55fMk42dqThV3T7a7yw5NEbuwRuzXg8o78zo7PqUFc+H812mFU+d3rLb+ni+K+tadptw18Muy2W9zLtEspmi8W90dLxnrhA/vVbr/ADB+c833xEzwiE18DRDmowRs6u4NUhTGegNU6WYlqCYd2enI7jgt0oxZOaWlml8tvT3e1n9sxgZrG7QWkBLRe7BFFOlkdWb1JGXO/P+fTuBdMs4SxL+Nos5rVhTserCYTClZq9zop1EfF40+Us97nd0fzOVBEbwW5g+DPOLZSxbdtaO8SuIW9sISgdtl0AzzFU2uEkE7zUt6mQl3nI/U1MpR3udjPqtgv1EzfP+JUHh0xLG8fa8zSPspB14+ALGHgNoZS4ThuIUwsGesSj4kuYUNNnTFZP8N0+ytTMxRyA87zD0Jd0a49Z/nSYDgCjVz/kwf/+IygjuLv3Pl5k896np/ucTnb4juGK9bqDKxVxlLNYDEhay1zZwpiUsSE5AHntk7UdxZ3Ogp3jc7JFl4NX30cENUY/kcV7yGcg7F2y+NptRqM5WWGfDcfjCTdffp/f+a3vBeDZ48ck/TVnl3tbPcqzowkPFyPL7ndrbgxmJJ79e+ZZRF57DOLMjhrSLu8vhvT9ytpt4w3HUlM1kjfnIzquYhSUfPfOjJO0Q+za9MbaCHaDhovCJVd2XHS3K9viUVNoxWtDiTYhlTaUygKYamO4nsD1uMQXmosioOPFuFLRjXJO50PenFtexmGc89rRY64WQysCbFwqJbnT3dDxSgK34UZvwXF3RVb5ZI237ZI9zfWJqv/jr1//9V/nJ3/yJ/nFX/xFPvOZz/AjP/IjfPd3fzej0Yg0TXnzzTf5t//235JlGXfv3uXv//2//599D8dx+KVf+qWP9Xoff+PPEkbdtSWxbbpM047FV0Y53Tjlpd1zLtYWpBO0Cvt1lpBVAY0SBE9a636FUoKHV3uoFvUaJRleVFKkEcJryK4sc3v0Zx5w8ssvsvvcA+R+hfnoEiETot6GuvRJl12iJEe0VDKtJDe/7+tUsy7Ca0j2Zpxe7XFTmG3m9roIGccp2jgsnIC8cVnlsaWt/YF/76ySFMolkB6fHq5Z1pJ5JZiUEldAxzUoY5ibnLR0edv5Ki/yHXRMxEScgQOTyqbOSSdA6SWuHOI4FsG64hJlagRWJb6qYS+0KtxKO1RK8qm9c8raIw5K+p01RRFyeOsxFw+PeP/s2BYDjccHj27gy4aXXn6byek+d8eXVMp9alGs3TAnLcNt+t7rF4f0vIogsO3+s5UF4rhS44sGL0/47M6UF+++z+iVjxBRbU/77oHd9E8vQcPqa9eRXkN4OEVGPi/cfED2gc97dZdhUPBg3WcYlIRuQ20E33PjHh9dHXCZR3zjap9aC55Z99nrrujGKf/xg0/zyu45vlSWKqgFl2mXSWGJasrY8Bxf+Az8huvJhrcneyjjoLTgOKqotOA7hh7r2uEks59V4toNvuMaXuxveG+VkCmHUtn4075vWNQtijawoxzhWMBP5GpyJfjqrIPrGFwB9x8e4gk4iCzqd90IQmEIpaBQ8OEmYOxb50BjBImUTBuHcfQ86/qcb6rPk/j7lGrFZf4mUxHxgv+neEudEm2ukbjeltn/NNbpl17luU+9jZCKcp3QPb6kXHTZOT5n5/gcVXlcf+4e6WRAnkVce+YB99+/zeHROd3RkqIMeO6zb5JeDaiLgLr2+O13X2QYFOwOZwSjJV//2qe4nkUM9qZEuwvcuIBliZoFNOsB3ZvnXH7xMxS1R6Mli02X9e+9xvWdKxoluZjuMFsMEI5hGG8wxuHBYsxevCHwaqRUHO9OOHnnRR5NdumGBQf9Bb9+7w7fuXuFJxuGfsl5ERFUvnV1OFZYGghDp03F84TiONlQtymcl3nIZWnDeLSBrit4lBoKrelIwUHkclnAvGo4iFx2AsNl4bDvOQgMF4WPMlbMWiiXtLXhdYKSH7lxn00ZcrLu82vvvsReWND1LLBpXoZc7y55vOkxXwbc6KyRjuEsS1r9zdO7HuxynoK4709uIfE3/+bf3IJ6lFL8+3//7/n85z+//f/G/P6g47333uO9997b/tpxnC3o59u+8Yd+xTqP8GVDN07pRNYqs84SHk72KBqXvc7ats6EJghLgrzecvxdoRj2l4RRQZGH1MplZ3dC08ZkpssucXdDvrDiN+nV5F8dEiY5jqswqUEtYnovPmDz3nXyTUKaxaRZzP7RuQ3+6KWoNGTx8JDe4RXhkU3Au2iT2iK3bq0tsZ19CU3lGEK/Yi/K2LRs/2npMw4aVHvhzsuA3aCm0j5p4zAKoNSQuIJdHZNrxS3zMj6SGk1i+gQmZClOKJoFjbH2vkbNEU5CEhzhExPKLjO9wcsl12MP1zHstDP+TnsDy0AThzlhVHBxtUtwtktZBcR+xboI2emsEYHlJVRZxGrTIW3tlMEfGF98O5e1C0oqZduMfb/CGDjddCmWA3ptdrnjGBrkNnZ5Ph3BmxCPl0TPXOB8dI4TOdANqd4IWE+GJP01kd9gWiHYXmdNYwTzMsRg27MCwzjMcRzDUW9OKGserPsEXo3GYZp2OF/3SRuX05UF9ixrj45XknjVFuEL1hbYdRUDv2rZ6zWRWyMdGzv8n67GRNIy8KfKIB241dF0XU0kFaFs2AvrtjNhr5dvLDx6nm3vKgPT0tmm+wlHMvQdVjUcRrAfNpxkLo5jhYRp43KeO+yGhkgafGFV4Y4DmJbvrhQHosuCMY1b4jhtfoVu6ATH9OQBvvLwjYcnbDs5a55ea1c1knRikbkAxaxHlUbWZ+7XDL/vEcsv3rD2yqjg8vEhlXI5OTm08Jzeiui1Gc5bDen5mGblcq1jyXp17bE52+XTr32DYp2wmfcpNzEHP/oW4CB6JX5c4XRh79oZjZKsMksIfP3ikBvdldUVhQWOo/G9mtWmw8W6bztKWuLUVlBY5OE28a6sPZKw4Dt2JgCsyxBlBAdhjhRmm3EPkDYuO2HGKEk5WQ62Ix37LLTXTNczdD1bMA48xTsrd/v757nD0HdRGi7bVMe0gYFvr83zwsUTgnGYsdNZsy4iPloOGQUFpXKZlwGLysVrDzK+VOxGKZM8schfr+Yk7XCzu6LvV+SNy+VT7AB9sv7o62d+5me2G///iPXxxX3CEvgaV9Lvrun216yXXarGZV0FBLJpE9PsLDHPom0KmnAMSZTT6a0pixClZKuEFdS1h9EOorXhFFmI69dIr6YpfOLxEhG2G4nX4EhDU3mURUDTuERhgevXOH9ghunHBY4w6NxHtjnsyggabdjUPldFwDCoCIQmlPbfJdrZmNICZWzYRuzYU9ZV6THyGxu00bYFVrVD4oLruJwVhh06FEZRUhOYkJ5JcBA4SKQMcGVEUT3GUNPoEiM1XbND7VQsdM6B9tA41FqgjUPY8gZEe7IAO29cLK0laRhvWOUxvl/h1N4feq+fNqzFad+nrPFQxqHnW1FcbRzSRuJLSdwqnVULxwm8mqr2yTaWyKeWEbKf4wQGXBcRWb+9VhKd++jSIwhLDsZTfLfha+dH7IW5FRAKzSDKyPKIbsuOmOQJZQsrKXAplbRjmspmNCTt+zmIMhsopYV9r7Ez+iegE+loIs8WTOvSJW0EA9/O4rueg9L2wT0MSmLZWHKlX1G2xYQnNMZ4rbjp98NYCmXIlfVxD3wP6UDiaoZ+xbR0bSegEWSNffCva4euZzcNzzEEQpM2NjCqMgrhuCSmQyVsd0wbheMIXCcgMLaVvScSxoHBF4ZV9fR820q5ZKmL51c2g6F2KfMQ4Ri8oIJOTDBY23t0lTBf2+J+U1jozt7hBeqxwLShW6qR7PQXrThUsJoOOHzxI4BtSJPJDU4ITt8FV0JV4yc5gV8RtfeDARat8LSX2FN+oySNllsh5roM6Pjguw1FGdBtxw2hVyNlw8FgxuVy2Cb2WQZELyiplSCr/W0R2WhJ1Qpby8Yl8morUhSaxLUhPdKBUGhiVzHybUcobLNBYtdeJ7UG4zyB/BhCqQmEveZ6UY7vV5SbLsY4XOYxtRbbcZh0DBorKIz9ilkR4wqNdDTzymdT+Xjt/bMqnjLAxzyFVv+fYHXfL/7iL/4Pfb2PLe4zxiFvPLLap6o9/Djn8WSPWZ4wDHOePzwBQEqbHvX26TXyKmCWJizaHG3VuDw6O+T+xQHrIuLD+zc5v9pFa0F/f0K67OK0MBMhNd3PPCa8foUcVzhjD/msy/LNW2Srjr0J/Yobr71DMFhTLrpM37mFd7hk/H8/IdhdMHv3FkpJdqIUpR0us4RpGZAq0WItBZHb8LtXuzzcdG0qm5KM/LoF+li192Uh+HDjc1U4bBrDZW44zxWxC3uRfcAIx2HqLJiLGQJB7Lg0psSTCUfeqwy9m3TDu4Agrx6yas7p6S493cVFcFU2FMrhw43PB+uYdRWQliF55ZMXIWURbFngrtswGs159uYD4ii38JPGJeym7O9fcv3wjL3h7KnN2AK33p52Ere2oxzZcJxseGk4Y1n5JO3mqbTg7vFjOlFGv79keDAhuX1KNe8i9iTsj2GTIX/giMH1C8t0eLRPk4X0ji/Yv/2Q48MzhGN45foDdpKNzWTorLloGfu97ob9ZMNFHjArA0ol6XgVN3tLfGHhUd9z8yMSr2LUXRG6zbYN6TmGy9Ljw03Eh2vLnliXASfrHl+ZDtt/g8PQV7zYq7mRaNvWda0n3xcNoWy2N1IoFTc7etuUHPoNXQ86rkPXlfRcl45rOI41O4HdHCJXc6ezoePZAuNTwxJtYFk55MrG7A79xkKBtBVxfcQpgQksCtoJcEWALxM29QWnzVvkTs6tjsutpGLg11yWT+/EX5Q+qnX8xMMVQT8lT22Ql5fkVF+VhN+n6Nw+hfb+9mRDEhT0kw3xcMXn//WPc/H+Deo8oCwDut0NgW+xx+tNh0ffeA43LBndOGNweMX8K8+i5xI6MfT7qFOXzdUIrQVxlLO/f8lnD04YhxkCs00V/fDykKpxuT6YYgycZAl54231BvvDGTcOz9jfvSIKC+umcTSRWxO7NfMyJAkKIq+mMYLLIiJvXD5Y9Xlzssd+d8ms5URIofGFZjeoiKT9uS8131iEdDzDOFC4whDKNgnUhXEAs0rR0nsRjmEvrPnM3jlxUJIVEZsq4FP7p+1YUNB1GxJXc62z5rAV3JaNR6ftvCkj2AsL7qcJiypAtQXvJ+t/3vWxT/yBX/PMzhVZGfB4ukMSZ7itkltph689usVnb37EYt1jmnbY71rltrUuRfzqOy/x3fvnFsfpV9vo2edvPqC/PyEcrbh+6xwR1Wzevcb8dI/kwwHCb8i+uYOqXQYv3WPw/ffpL6F4PObsnTt8+Qvfy95gjmjRtd33DxD3NOuzHSZXY4Qw7A9n5Ff7LKqA270V7y76eEK3PxSNtkz1jqu5HhcWcSpslrbrGJ7p2ELgCc3to7WkMYJpCdJxGHkuk7pm1wxojKGkZknBgfsiS3POSfU6VTPBGIVwQlx3l7JZMA/nVE5Foju8FHW4lWTbE0SlBV47o+8mKQe3H7G+HBEkOavpgIurXQbdFVXtbfMP7r93mzuvvkOdhTQXO1xuev+FT/JbX+vCktYCoej4FYMopWxbyY2SW17CXmeNFJo3HjzDzeGUy6sdsjTm5nBNuD9Hn2qEn1H80P8D5//9BZqiY6NXvYbo+Ipq2kMrSdhN+dFX3uAL33yFRSvmu3N0wg9+/5dIJwPm8wHS0XzP/gWe21A2HusyoGokodvY4KPZDnv9OaezHS6zhFXtcV54RFLT8xTrWvK1mcv/7aBmXdsZ7p1Ozv00olAOqpb40nIeGi2YliF54/HS/imNklxteuS1R+TVfOfOFF8qi68uIjxhiKVCtgTBL1wE3EgcskYSSclzvTWVkiSuYlNLfuvC5yiGnmd57R9uHBZ1yLq2Fq+h6+OpAy6dBRKXHrs8rl9nx392e0fXpuKqMBQqYOAZriVPZ+wDEAYVeRFwdr7Po9Mjbt++h5SK+WzExcUe128+Yve59wHb+ldasC467HZXRHFOUwR8+u572xN+GBaUhY32bRqXtAxJy5DBtI9oI3G9qEAcOFCUcLGino+ZXI3tgcCr286d4PbhGWXlMV312e0v6Ac5ygiWWcJ+sqEXWPFbWoa8v+ny2vFDPK8mL0LuXR6w01nzystvs5oO+J0Pnuc7rz2kaSTrIrL3qNBcSzYo45A1Hm9eHfCdR49Y5TGrIqLnV3y47iBbK+GicjmMFMtKUAhJKK3pzXVgUhg2ShO32o55KfCF5G5vRehVVLXLuohYVAHPetaFsq79lkiqbFBZy/lQ2uG3L0aE0nAQ1jw/nDEtA2aVR9dVfNfe5VO7Hp6sTwA+f3zXx974kzjlpA28eO7aI4oiZNxbEgYlfljy+PSINIupWnFfL05Ji4haW6vVq6PplkXfaMmmCuj5BbPZkLry6Sy7dI8uCW9PEFIh3Qb/cAFAkAdU65jqasD89yz9ry59NmnM/nCG71fEvQ3dwwn+/pxm1iVPI6arPjcOzqhaC86q9thtZ8N9v6Lnl5SNa/GW2qJgT/IQzzE2Tau9iTa1tW6F0hBKw15kqDNY1ZpaW0/uI+ecUuQASOMx1jsszClpfYXSJZ3gJpvyEdoUGKXYjT/FhgWh6RCZiFwZwpb3bYyDAB6vBrywe07cSVGVx+nZAd0kJS9Citrjcj5i3Ftuo0u73Q35oovr13SHS150HnzbLxiwVMAnG30nKHi0GLU2Kts+96UiajGxqnHphwVRWDBb9zibjym+9Bq3X36P7quPIYrxH72J+F6B//AxphQ4nsbZDfBZ4ZY59bLDR+/d4aizpteGO13OxvhhSdN69+/0V5yf73O6GuA4MIwyhp01l8uhDYHxKjZ5zF5/Tlr7TIqQod+0MczQ9RTfvaPwhKbWArdt6wKWvd8+w/ajnEFQsJOs6XU2+H5FXdsci1UeM80SlrVPtxV7HXXWTKc7ZEq2Qk7D9+/WLGtrU4zchsSrUDogcRv6vqDjepQKcsfBFXAcw8ONaU/8hlprDgIfVfTYmJLcEfS8I3uftvoSRUPXs+OojmcYeE9v4++N5vipnRkHcU6+SQijgt54gRcVzE726b49QGUBdR6yO5jTG6zQyo61yk1M07hMWgT3oL/coqfzIqRsPD718jepspCq8nFbCx++C01bCAw2PPf9X2X14JB8naAalwfzEaJV7IdezTLtsNNfMF31OVv3kO0IRDqGUDYcJBsul0O6ZYgrFONkw6i35Op0H20cXjt+aK21nmCa2oCek8xjP8rwpSJ2axoteDwfU7SR2MMwR626nOcusWu4lRS8s4pYVDbmu+vZcZAycKMDkXSYlg6Vhjvdkltt7khR+1ylHTZtUfpoNrYdJ6lIaw9jfN6Y97mWFDzTW1IpyQ/tT1lVwXaMtRMWPExjppVHL+08tevhk/XHf/0RYnntwy1wa8KoIAitbUq6Da6rSMICIRS+cu3GXHtkbcvryU1UKdfO/VpAR9m22LR2bIV/so+MKoRf099v43hjcHspCIPwrNjHCgItkvPJbPvJf9EOqghQykUZYR8cZYAnLLSnUC6eMFs7iz3VAy3ZrVAOvmeV1dKxITRS2JN/oyE1FrwRSodCGUqsn79juqzFDEVN7AwpnYqsmaJNje92qVSKbdJqMA2VzijMEt+NkQgWleI8D9mLCpInefG13waIuGzmPdal1U3YrknJqoipG4+gTeULwoLNoocXWCBS3Hk6yt2sCrbag1pJ0v+LcMxr54rr9lS8E6UsNjZHOvZtIItpY2upK8Rsgjo4RuYZzrzAVIArMUrjCINsueuLPKEXlLjCMho2qy55EeL7FTvJlDAot+3NyKvwvdoGqmi5hZw88XkDpI3kKLLJh57QjMOcWksmRUgFdL2acSu2fKJXAOxDPsqJOymrZY84yul2rD1tXYY0pUBpKxyTjmYnLCiUJG1cikYyDir8Vl8C9mTkOJbg1/caDmNLfVPa2bZkS21tXtb/LSiUIZEeWkGDosOYihxFg6JB4lIq2AkMA7/Zjl6exso3ibVutu6aPIsYjmeE/TVeJ4eTfZpNhCMV0q85n43Zu36Gql2a0rcFgBZbfz20DPr2c3KFIjmYUH10zY4QtCDqbYgSDXkGosbx7LjFaGerA+gFpW1ra4Hf5nlEYYGXdlBG4Bi97cxUbZbHqm3t2/wLi8RebToEfsXO7tQG/JQ+8aokloqeZ5HQwzAn8GqclK1WB6BWNp0vkJZDYTHNdqPfNIZcORxEBm1shyeWmrRxEI5Dv7UfL/KYsvFQRrTMCJvK14lLlBGsap9SOySuxnMMReOyqXxuDOYtWMhmZzzBSGftqPNpr0/sfH9818fe+KerPv1kg+/V5FnE0Svvs3xwRLaJ2awCtHbodrJW+NVhnnWY5jGBbPDb9lwSFAhhCIOC3mDFW+/fpdvZWNa+cTg/20cIxeDZx0TPTqhOeniHK0RS4Qc1TqLYzR+yPt2jLAJct+HiYo9ed0O+SWge+PSVpFjYajb0Ks7nYxsVHBTEXs152iEUmtM8wBM+t5J0S+Kz2F4LT3l7GaFx6LqacdDgAPNKktZ2FhdKGPgCp3I4r3Pu+gM2zZo1MyITkzkbqmaJKxL67jFn6RcBiYN9uC3zd5CyT989RqGZ6py3V10Gfs1+bGOO/dKe/jebhKryaZTAGIc4ym3hdemSFuF2EzJGsFp3EanB9yri5Ols/JebLrFXIVpaGdgUsCcBOFbg5jDJY6alT+JVPFr3eWHngqPDc5LhymJXc4GYrSH2EUUOrospHKqLPoG/oJruI7wGGZUc373PW6fXGUUZw47Fw6Z5xMW6b1XUQUUUltwMz6lrl6IMqBsXTyrWZbgNjpoXEaqlt53ngrtdC9QJ3YZBmJPXHo/ThFrbYuBOb0mtJZWSFEqyrHyGQY5SLmUecTrb4fbhCUGU22u3bcV6bYExrWPuDKdcpV1O04RJ4VHpgJf7KxojKJUEfIxhiyK+EZfsRRmXuT2dTUvZplzaolMbh7ezNYduQixcSu3RocfMyVlxhesEHOjrTKuGWx3BoBWaPa314PSIg9EU123I5wOUcnHDCkcYVOkRdzIcqfCHa4ySfPH8kOfvfIQXFUi/xqklpggI/QrHsRS9Ig9t50sYhp01jquRfk1RhizShCjO6catwWuVY7Rg8d4NVrMhmzSmajxeuP6AxbK/jajeObxks+jZDpBr35OBUKS1T63l9r9gR1YXaZeDeroVzHphSdDbIJddBus114qIvUhSKpdelDEez5BXmqwMUNpqoiZFTK0djiPLBznLfSplT/nr0rBoSl7o2cew40DWEioHnrbgszYjQGmHnl+0oj/FsgyRQlM0LuctYvzToymVdrnIEtLGZb+2gkZfKvpRSl57NglUSZT+2PKuT9afwPVHCumRbZRtmsWMpgNUI/GCCtdrOHs05mrdJ/IqQq9i3Cnphjl55W/jWo92JwRJjmok+Sbh+//8b/Lgyy/z4PE1lBY8+8x9xt/5Hs2ky/z3buNFJe/89me5dusR/buPcCqNDGr7YM/tSS+vfVR76ur1VzheQ9BL6VYeSgku5iN2+wsu5iPWZcD17pKvT/Y4ikqGQUHHr7gsIiK3YVV5XFYuN2LDpnEold3QhoGl+cnWmlhpyP6ARX7tZBQ6JDQxG2fBhfmIwOngu30cBJme48oxwvEwKIyx57hecI3cLJmJgE+JWzzXtRbCsnG5Nr5ifRVa7r5xEEKTNx4Hfm2DSLTDuL9ASkV3uMT1ay4fH3Lz7j0rVMpCFpPRt/2CAbg+mLHIEzaVz6b2OclCPj2esShDHmxinuutbbfCsarkvPZ4YeeCfsuLr0uf9P4R+4dziH3IKpyTR7CuUZvQpjO+u4+qPBypaDYxH731HMe9hU1uC0vq2kUbwau3P8QPKtJ1wvjaOavLMUGUM46u8MKKd956gWUZMi0DbveWlI1LrQUj3/B9uzW3RxPbUq59pllCrQUdz0Yke0Kzk6xbgEuHKk/4joNTbj1zn2iwRtUuRenTNC7NxqX+A12kWgt6Qcn1wYxlbuNU98KcoyTlN8/G/NrpkHFgOAgtxvfJn2u04LL0+Np8xAu9mq6rOMsls7rCFwHjwGE3aPBEl29uMiSCoYjwtMsSlzHHRDpCtHLDdS04yUPOi4CfeCpXg302TJcDesmGnf0rxp/6gPJiSDHvUechO599h/u/+V3UtUV3/68vvmUdPGsbiey6iqIIqWoX32twhOFkusu1nUuGoxmuq5i9d4PRcw/xgorO5ZjezhzefADXezBIqN4Ouf/hLXZGM/q9FVezMXFvg3QtjOfJZ5O2I4leULKpfN5fDnh1POH20WOkq7j3+BrP3nyAdBXmgzuUVcDR8RmqkTy6Z2Fj/d6KLI9YlbbAe+nwMdNVn0cfPsuN4ZR35kf0/QrpaB6l1jf/3trHF3AQNqw92/bfCx184XOzs+HeJkFiSFyF5xgO45x5FVBpyQ889zaT2Yh5lpDVHmBphl+/2kcAz3ZTBkHB47RL2tiEw72w4CztMg4zxklBWoY4jiGtPXIlGfpPD+EMVoCvP0H2/rFdfyR8k+fZ3a5Rkscf3WCZdujFVq3dDXIu1n26YU4nzthkMaP+kqK0zPZOkvL1958j8mp6YcZ4OOfe775qRWCjKdJVPHx0jcGdx9TrmGzVwS98XvxTv4s7yMA11Bc9/OfWdGdzmsqjKEKmWWfLyk6SjM3pDgCbRY8sj3jm+iO++t7zOA6Mooyd/oKdTQ8BzMuQtxZ9AmEz0cdBhXDgYRYSSAgkJK7hMMotva8JabTDo6zGdRzucUnuZNzlJtOmZCNWlGZD1ky376w2DVl9RaOmgCQJbpG4Y2bFh9zgJXo6pi89jmNB5NbcHcyIg5L7V/tW6BSUDIcLvLDko8keOzsTHGFI1zbOtNdds5oNqWuXTRbjPDpESo3j6G3r9du9orDgctMjdBuuD2ZE8x1en44Y+g2vjaeUyuWb8yFR649vjGBVxCgjqGqPnlrjBxXNrIPfW2OeuYFTlZCeIzslUXSJqVyaVYxRknIR83C+w6byOah9DuWUncNLzr75InkW4QcVnYEVkwZxDtp66h99eAulBTf6c46U5D9d7vNMJ2UY5rhSM8ksQtYVikGUsttb0DQuvl+Rtd2Ey00P4RjOs4Rp6fPpKOPqfI9o0cMYh4+uDgAIpZ0jR55VoodPkL+NxyhZUyvbyq6UpDFwHFmhoDZsx06FEm0b1s7m140gawQnmeZ6FKINPEw1ry8UGTkRHgsn455zgScC1vqKi+YdhOPykvgBjiMLDlIGDqPyqVwLAOPekp0W27ucDjFfe357mg96KU6kGR1cMjvfY7IYsMhi9vKIJM4IowLhV+xdO6PKItJ1h8l0xK2DU4yxxYsf55SbmOJyyHo2YLXu2jFT4ZM8WCDDEqMkN595QLbusF53WBcRJw+vsbt3hTEOF4sR124/JPBrtM5xhUJph1fHE8adFWUZcHkxZNRd8fDkmGUeMS+tgr6ofHzXFoOnyyHnKysmHYY5lXJ5PN0hdBvGccos7VpUuHFwhGHk14RSs6p90gZWtSRXsKmtfiN2HR6msSWIYl0mz4+mvLMY8Ux3xThOeefRTSoleZR2qJSg16aQXuQ+PU9xrVOzKENrTa48VrXkhUFGoyXdsMCTDQ8XQ3aTlHFYsG43/6e9Pmn1//FdH3vjv3ZwzmLRJy1Dax0LSkKvQmuHPIvoxBnr0vqHq9q3qVCVR96GQcRRTj/MWZchmyIiKXLWWcK4VeSXRYBwNPnFiLqwp8Vkb27hPcaB0kHlPurURVUeZRlwNh+T1y49v7AnBcegG5eyCJCuYjhcoBqXw96SWZqwLEPUYoTSgnntkSnr2Z9WAk3QMvohawRD387bEldxWYRbol5jbCJfbQza0QgjaIxm6awRCKTjYYxmx7nBibNp4T2/v7Sp0Sgib0RkfDrCpesJEtee9uatIPKJUO5yOURrSa+3IlMu65V96GV5hBCmbYcqpHRwpWKdJvazCSqCKP/PPsdvx1q3FiiBYVXEpLVH5Gpit9nyECJX4bZ+/8CrSGufXpiRxBmd4ZJ02cU0Aj0TsHqEc6sDnoPTBQeDugRHGOp1SLmJ2euskGmHXpgRBCVNW1D2Bivi0ZJyY4uEdGk9zq7XkBYh55seoWyI/YqRXxNI6+dHQeJVNEoShhVhUBIENuGxajUiGjtn7vgViduQNi5XiyFpbccXrlQUjWUGFNKlZxyrvai9rdtl0Ua+VtpFaTtiOI4a0kZaX7fUzCp7G1baIW8crkqH49iQNlZzshPYLlOjDbU2KAwbJ2Xs7JBrD9MqARpd4GC9/A4Oq9rgCQfPMeyGT+daANjZneIGFdmqwyZNth2YTm+Nl+SYXLQsf8vF7wQlcZRvQ3PcsGIz7xOEJa5X02jJat1lOFzguoqmspCv5dUI1cgWFe7hJzmq9GgKH9NIijRGNdZv/0RDtFlZbckoWTM93cMPSpQSlJXPKNmwKSKK2qduXNIqYHLRwW/ZH8Y49MKcOMxtdkDts9ddIRzNpohYlhb402hJ0YBQdlwwDErC1pHjCo1p3Jb0abs6h5ENXVIGtIGz3GUnsPeLFFb87LfC4kbJbdDPrc4aZRyKxiVTLkO/2QYw5bWLwfJHlHHY1FY8rbRAad+OESqfde2RNu7H93F/sv5Ero/9+e/cOmGRdni4HDLLY3yvtvAUYVilHYKgpBvYWf0qi+3muu7ZjaEMSbOE470LIremUpK8CBGOxm3DOvIiZNBbs5oMKdIYN6gIb1xRTfs0ky46tQVEev+QprA36vmmS20EoV/huXUbJGSLiDAqGB1fUOQh16+dEHk1V0XE+4sh68blNPeYFNZCtagcHqYujzIbpykcGAc115KMwzjlYeozbbGbjTYMWvjGQPcZ6CEbSlZijkDgEuA4gl29gyciC1UREUJ0ESK2ISqmoSsPkEgcx2mTugyldniYdjhPO/gtAOd80+V0PmSx6COAyXLAdDlglccIoWgaFy+w/H7fr2i0bBPI9LZD8+1eZ6t+K1bzON10mZY+1+OUnl+yquzndByn+EKTt2lrlZJWfDdcEQzWVIUNWGpWCdMvPwdXK6vS7gTgOlSTAUY7VGlEtom5ef0x10dTxsM5UZJTFgGxXxINVnjddCsEW656zJd9ysLGQz9OLZc/q3yud1b47Wa9qQISz8aZSqkIowI/qOgeX9nNppEo7eAKTTfMGUcZA7/i3mrAB6s+91YDzjc9Ki1I282/VK7dbBr73izLkMs85nzTY1PZ+bEvFc/11ttAlVBaa9e8Df3ZNA6LSuELSwt0gFsdRdYYSm2QjsPQ9REIPCGI8emaIQEhBk3sjhm61wG4qkqUsQTBwVNEtMajJaqy47ey8VCNZLYYsF72UUVAs+hQZRG6RXTv9BcMd6d4nuXuu2HF4/MDijxsQ2Y0FysLqrJjqwjVSFYrW9R1uhuEMCTXL22uRyNpKo/JZIxSkjCo6MWWLDpd9VFacHh0zkdnx8jWatpoybC/JG88FllshbMY3pj3Wdc+oazpBwV7gznjnRlJd4OUDYcH5xweXBAHJavKCgEBNnXAtIjJG4+eb7HSohUcbxpB31PsBoqOp7iRFIx8TeIahAOzEtJG0BiHWlswzzgo2lCgAFdYEeKzB6e8cPSYYZjT9WqO44ydKLfobKlaWFbDTlgyKcLtZr9otVZXRcS09Nui8+k3znXr4vp2/fifYVVVxdnZ2R/6vV/+5V/mJ3/yJ/mJn/gJfuEXfgGtv3UKw8c+8Z+/f4telJE3Hg/XPc5nY7I29tWXiuz8ECE0jZY0ylq6VlXInb0zorDg/vkRStkb32tb0EmUc3qxz/HBOS//2d9h8d4NqiwkSHL8JGfxxm3i3blV7EYl0V5GJCagofPBhE0e83AxpKh8smpM2bh86tn3GexYeE0+7zI+uOLLb7xqT5yeRQj/7qRPriwhbVNLnu3WnGQeq9oS014dVHiOtfqs8LnbtRac81yzbGqOwoDG2BS0xrE6aoCJeUihlihd8q73BtJ4BLJHqVZovWEv+S567BKYkNTZ0BMexhjSRlMoh+txTeQ2hFJRKZejvp1pD4cL+seXXFs8Ynaxg2rFOZfLIa5QHLsNMrYdlP3xhMWyz+VsTLDqcfQtXyL/+VpUAb5U7EQZ+90l4cxG3RaNS6WtCG4c5ugS1rXLqgw46KxZZQmb9++g3hWMe0tkUiD3SkZxgV5KRF9CUVF/FPP5z/8p/uyf+w9Ir0FrSdNIHEeTZjF+UHH9T32N4fvHfOOrn6JSLi8//y7BaMXB4QVaCXsCzSMOo4zdJGWvP+ed82NCadHNwjHkT9gDbavdjwvyqwFaCfq9FXdcRVYGFLVHXnsY4xAIzbx06bWCua9M+wgHrsf2NHd2tc+tTrq9xg4j+0DeDXN8qeyJNy754aBkXkSc5RGL2mFdGzKl8RyH53qCeeUw8DR+i/19sgygjSEwAffUjIiAge7xNl+h1jldeUBPD9mIDZ9NjrjTqbmWpCTB08tff/jubXb3ruiP57huw9lkl1F3RV27nLx/i9HehGiwItvEzNc27KtXrXD9GqMFy/MditonzyKE0Bgcnj065fJqB+EY4ihnMFrQHS1Zz/pMJjv0+0vqlR3V+P0NUVwwnPVJ+jaKN1onLBZ9lrMd4spHuA3LMuT9+7e2p/lOEZL4lhgZuDXdUcped8VXz4+p0i4/+pmvcnG+h1h2qRuXxabLZDngZN23bo0oYxBnfO38qA0Bs+OUb84t+ClsqY6VdiiNIHEVO0HNeR4wrQTr2tozDyKHvqcYBzU9r2JV++wnG/pxajMF5mNWVUB1dow2DpMi4lZvyYN1j5MsRtPjuf6KoqVV2gLcOqqalrA5LwNWteQ4Luh4NsHzk/XHa/2Tf/JP+Lmf+zl+8id/kl/4hV8A4B/9o3/EX/trfw2wvP5f/dVf5dd+7df43/63/+1beq2PvfEXpY82DsM4ZRinhF7FC9feJ112uZyNGXZXLDddRv0JvdEcowTSb9gseqzXHbqBxU0+mT8HfmXz4/2S2WzI8j98jsFwwe4L96lXMcWyi5/khLenmAZ06lGdJLi9lOpqQHo5ohunvBgWrPOIrArQxuHhyTHdOCUM7BjiweXBtm2+qnyuSo9cOUTSMPA1Y7/mjUXIQaQ4iuwTdlq6uK2/23UMz/XWxDK0Fj4tCSUMPY+e8ciU4pw5rzl3ec/psnAvcVxBTI+1M6UxJQ6CneQ7cBDUTknjNMzVI0rnBiPPo+tZEPtzwymVcrcCtG6cEvg1m3WH2TeG9Hpr+sMFAGURMlkOON69pDde4Cc5z8YFb33zBY53rxgHM5bLpwPweX58xaqICForZ88vrHVNC2jgURbT8yuuJWuuJVBpl0q5LeLUnqS/53/9dZpFh/KtAY4whH/OQ79+Tj3r0aQRd/bO2ZyPKXPbQfD9ijjK8fwa16tZvnkLN6w43rugKELyTULfO6NpLDEt6m9oHkhuDme4QlGUAQKzJZfVxiFv3G0gj+fV9HdnVGnI8NYpxazH9N0hXzq9xvODOYVymVU+odC8t3K4nyYM/IRIGnaDhloLZpWPANa1x51OyaJ2Octd3lnG7IcRe6FiLyyJ3IazPKJSglLb8JZM2dCWUDo8Tk3rOrAt/lVT4zqC2ihyajInZ4c+E0pWYo1G4xMjHQ/PCZDG5dAMOYwUqRLc3yRos89nn8rVAEUbHONHJX5UcvNHvkL+YA9HKtykID3ZJT6cghZUlc83Tm4QhQXTVR+tBTcPT7lIO9w6OMX1Gk6mu2SXlqKn2m7grTuPmX9wHekqxmOrCWo2EVUaEfQ2eD91zH5xj/WjPdZr2y2Ko5xhvCGJMxvfLRWdMEc4NtJ5uurjSrUNszqZ7lIpySu79kR/dbnLIu0QBiVaC7IqsN+viHAc28Zf5jHXOuvtBjspQn53Knim43A91mjjtBY9yaKWzKqIgW9lbwMfui5Aa7l0GyKv4bi3ZFlEbBZ2815UAd9768NtDsET58FelBMIzbL2CKU9NCjjsK59+kGBMpaTkHg1twYzfuvkGr6wrptpe189zfXJjP/jr9/5nd/hr/7Vv4oxhsePHwM2rOdv/a2/BcBnP/tZvud7vod//s//Ob/yK7/Cv/gX/4K/+Bf/4n/36/2RVf1P/NubImIxGVHVHo2SrNOEWtmTWVP6VJWPV9dUpUVlpmXIzmhGVfnoFjH7pC39JCvbaMH5m88SdVKCTmYJdJMQlYboysfrbyzcRSrr725tY55UGAPL1rY1yiPCNiFQOIZey3gHm0UdCIPfBopMKxtksqgsBz1XMPQNk1TQ8WA3VHxz2WNRO3a+agz3s4pY2JvPAWIToY3BMz6JMyAyMbmTUZsc1wkY+EcM9S6PnffY6Cm+E3MknmejS2IlGfqSvbBhUUR0gxJfNqzrmMWmy9HuJY4jWG06OGvDatW1/nSvZtRds1p3qWuPbm9N//iSfpyitUNVBpRPic9et0x7Vyh7Cq+tuj9yGzp+RVyGTItwC7HpB3ZGGrR8AqUFl19+kaCT4Sc5Xm+NU/g4tyP8ZIH+0M7vpdcwGFwBUKURg3CKql1U7bGZ94m6Ka5f42tBVfo0m1ZjUvksznbRRrA/nthNouXAn68GnGUJs8rDGLiW5MQthGd2tksU50zvHbNZdVnmFjX9cN1jVbvMKknfU0SuQ9eDrmtY1g6Lyl4LGhsi9GK/sp59ZQlsB5GN6J1WEmVsGmCqhBXztWz+WmsaIciU4V61IsSnpKZ2GrSjyZwN2tEEhHR0h0hIAm033MqpiJwePTPkD7JY55XEb0NinmYMa1qGFHlEWYSsVl2i/Rnr8x3i0ZLgcEaspzRZiPAahuM5RytrsYs8qyzXWjAMc5arHqJlQOz0F9SN5XUApK1o13Ubyw4JKxyh0UqQz3tE//FNvJ5H52BGXC+RYcW9r7+INoKyCkiXXXaTNWXjUTYeVSPxpA1iKlu9SuyXrNY91oU9SKzLwIqCa5+i8rlqtS1gu1tp7bEXp+SNt2VDRG7Ds12P2NUUSqCMz0VhVfyRtJkLQ79hUQmEA4HULCuBLzQ7UUovypmmnVY7IEBJlHG4mI/w3IZ1ETIpQh6nMQeRjb72HMPjTY+r0mccVPSDkoss4TDZYIwttDdlyF5oOxJp7X1i5/tjtv7BP/gHGGP4G3/jb/DzP//zAPzWb/0Wl5eXDIdDvvCFLxDHMT/90z/ND/3QD/HP/tk/+x+z8TeNi9ICKTSeV6PymHWatK19SenYi6mqfTbrVuTjV7ZYEJpaS4LQVs5FaaM3nyhfPbchTDLyTcKD0yNuHJzR2ZuTzfo0qwRVeja5QmicjkHMbYVeNx5F5W9Ttta1DUdxnAhRWvDOXrIm9GpbbLSFhvLagI7aI20EXdcwKR3mlWFZN7iOx6xSSGEz4d9aODRGkynFxpQsxIp9NcJzpN348ZmrCuU0BCYkNhGlU6CNIhY99tQhCT6njkSh8Z2IfTUip0Y4DoGE3aBiVob0g2IbYbxuhZFCaCsuqmxOeeyXdJKUOMp5cHGAzBKKMqCzO2cwWFKVPnkRUNRPZ+OfFxGyPUEp9fsed6cloB0nGx5uuihjxVz73SVCaKLQgp8cx/DevWd45tpjRkmOiEu4SlGvvoroTvHXlyRxRtBLCfbmiKhGPB4jg5rsYkSZh9SVh5NGW6DLk3Ao11VURUBeRcRhTme4QroNqnFxvYasCjhJO6SNNbz1/ApfNtTK5Xy6wzXvnMurnW3CYSwV99OQdW0T9kAyDgwj387hT3MPsA/xWkPewJ2OJG8kuXK2YJZJKckah1q3YwVhSBuHtLHhLQCV1pRacyLuETgdSrNBOh5dM+SseYdAdhg4RwzooYEADww4CHzjc8CQlSnInJzU1Kxr201Shi186GmsRkua2kUpyWzdI78YsZz3rV8+neEONmT3DlGVhxtUHO5csVz16CYpnlfTNC69MGeeWf5G7JckScbVZEzZeLhScf7oiF5/1Rb7dtNynqQBpjGTr7zAzmfexd+bI4IGp2Oofte3eOA8Ii1CPNkwzzrkjUejBK7WPEk7DWTDuLekWgzZtBbkeRmwF2V23FP5FO0BZxiUVtCpJEdCbel/T1gJd7tPXEBiq+LvuLb1H0sLbgplK8x1rO0tlIpuWNCJMj6a7XBzMKNpRX1u7fNo3WcYFBTKZVVLHqQWRJa0Y9N14zIpJF1X4gsrRA3aTIq09pnlEQdxyqIMyRv3f4g17pM8gI+/vvjFLzIcDvn5n/95fN8+t//dv/t3APyFv/AXiGNrQ/2BH/gBbt68yVe/+tVv6fU+9sY/aW1Nw3jDeGfGcLggGS+YPDrio7NDXn7pHR7fv46UVqx3se5zPLRf53o18dyqirM8QimrzF3lMbduPGK16PG1t15ip7PmYDQl7qRU65jVssf+tTdxbu9A05D+hyHye1zUBynLyYjzxYC09ltspXUC3B3MbKxm2y5vtGSW+mS1T6Hclpzlkrfq3+txSakF0nHp+w6l8vjKMuP7RhEdV7FpBB82cybiDOXU+CLiurrBB/JDbqpnGMgApQ0fOu/iIPCcgMY0DPUQBEhcBA4zZ8OAfY7MHiPh84Ge8Zlwh53QtoqjlqzlOIbAq9mJUgZxxnLV21r6qtLn8Ojcivpqj7PzfZsahmCRdnjvjRfZGc2IWnBPNn86G//DtIMxDkPfRpV2/IqdOOPxusdbiwH/z89+mbM3XkNg9R9CaMKgZLnpUi2G9OKMcc+iSOssxNtEmLLBPb6k2buG+Qs3uHXjq5irErUKUauI6IdBv73BnI9pKg8/LFkte7x1ep1ANnzvp18n/tSlPf1ph9HhFcmNc1YfXqMu7ftgjEOtXPuQdTWLWvLRqkelJB2vxOCQpTGuVByMpvT6K+5/7bOsnmSpe/ZxOfKt6nvenvQLZYFO0rEPu6/MYg4jRcfVZI3kC5cN1yJJJO2MflYaYtdp/05QGRj5LldVzSULANb6kkKt8EREVwyp1Ipj9yUaGr7J77HLMxywT4cAx4TE0iUUgkVtmIsr5lzxOe8l+x5r56lu/KNkTdJNiYYrjl/8CBkXjAqffJNw8pUX2bv7gKbwmZ3vsVp32N274qP7u3zHMx/Q353x4INnWBURlZIE0nbq3n5wi8QvqZXLVdphkcUca0EUlkjZsJgNuNb70Hb+pMKLrIZBRNbqpueS4XBBmsbcuzzg67Mh+2HF9c6KUZTSKMlvnx/Q9xTHcUo3KYhCS8Lb6yxsd2ox4rg/b5HUhufHV7w93WVShPR8+71ki3j2W/bD6/OY53sljbG8/HXj8ELP/p1mleTdpcth7OELY6mcBm4kFTtRSt0WTvvJZgs76zaS2Ku4yjpIoQlp2AkrpqXkqnRZ1pJYan7w2kP+w6ObLGqXx2mXzx09tgmoaZe0ffY93z+nmO6yrHyy5unb+T5ZH39dXFzw6quvbjd9gM9//vM4jsOf/tN/+g997e7uLq+//vq39Hof3843vtr6U68ud1nnEc84hiDKubF7yfTcCrzSPKaoPSolWWUxcZTjthnYWW6VvVFoyX2X8xHvfnib0K846C84WYy4ceshjjBs5j18v6I6G6AfCOqsi1ES842HBEeaa/IdHEdzNtm17S6hqLTgy1d7VFpwPc650VugjNhaW/qi4N6qbwVa2mVRuRTK4yRT9DyxJfJ9bhCRNY49xQKfjUe8n8aotoadO0skLhfiklT3OHB6dBkhcVGmIXWWLMQF++YmBs1UTIlMzE1zyMC3Odw9k/Biv6HnNXhCk7aErrz2CLyaYZwySbtUSrZzuorv/YEvMX14tKWaHR+f8WzvfcospMwi6soj7qScPD5mXUSMks23dHH819Y4KPHbgKO09nm46VAogdf6ln/jrVcB6Hg1kVdTNy4P5zuoloYHVm0shGUNOMLQe+U++b/voJvHuHGB3qmpJwPcQYrsF6hvNMhrDt38HC8q0bXLfDbiu+68x/j6OZ3PnFK+16d7fMnq0T4P3n+GV+4+QlXulnnwzvkxlZJ0vYphWJDX7jY7XTgWm1q2XZXJpsf88U3eWUWkNUwqxUKVjGTArY7btm0Nz/UU01JSt63e3cBQG4eLQtJo66G/GQesa0PaGDzHQQrIGsOqVqx0TUmNQrEQcyonp2d2mPCQvnuEdDyWZsJR+Gka09AxPW7wvWx0SUd4CGyxYYzBYNiTCR31LAU191P7esexw3789FT9z376m6RXI6aPDqhrr8XxetsZr5sUeL0MGdTEE6vpeO3mPQZ7U1TjMtl0ebzpcru/QBuHb1we0PMqItcilxPP4SLrIOaG6ztXeH7FG49vcv2V97Zuju7Nc8rzMV5/g0gKEGaLAt7rrvg+v+Kq7VDmtY8vG/7MjYc2/KbxWOYxX3j/ee4OZmRVwCKPLZmyiCzlz7VagIMoo1AuiVcRug0Pl0POcp9DYC/KeKVfcJxsmBYRXVfyfL/i7WVna586jg2f253y9mJA5CquJxuexLA/GX1uKh+ZdraHgBsHZxy22Rwnqz7T0udaUjMpPBoN0jM8Xg5JG3sPNlpwshwQuTWlcvGE4ni04OF8B18qDuOU85Zh8TTXJzP+j79c1yVN0+2vJ5MJX//61wH+s43/9PSUTudby1r4+K1+Jalrb3t6k2XA+fk+nlfjthYZxzEssphFGTEMc7RxOLnaxZWKQWeN1oLz5QCx6aK0oBdnrPMIXzbEUc5N95LFZESnuyHqZGglLXe/8jBK4kYFTqDRaw/Hazh4/h7vn1yjG1px2aKdaftCU2rBLI8ZxymRW5M3XstONyReTdZu6q5j8ITbtnGhMZA2zrYV5mBPctfCgKuyYWFyjhjzwFQM9ZAAj1MW9M2AqZggEByrG3xD/xbH4jYdOoxNj1i49DyJMfaU992DEFBIoUncmtBtWNc+pbIJXLFfMoxTfGnT5rIq4ME37xJH1jJZVj5V6WMW3T90gy3nA4Kg/EM5Bt/u5QtN7NbEfkXslxgDl3lM2rgsa5cXBwuaFuH7RBsCIIWxAKc4oxPmpHlMXoR0Vl3C8YL0aoDr15bvLiC4s7Bs08ZBFz6y3OB4Ar+/QQQ1h08470qgLj3qdUJT+GRr25FQm8jOgjc2llVgWem+dBDY1uxJltD3rDDKl4qPJns8f3iCSA1fudpBYL3VoRB0jY8G0sYWiKHUdF3F48zeRolr2AsaMmVb/9pYb/4HKzvOGfiCgWe4Kp32z7s4pcO75gKJy4YpStf0GeM4At+JSbQNcQKonJrEhPiOxDO2rVtpQ6YUietSaU3VWn1GMmIcCHYDw370dClt6dWIunaJOxlhf42qPIIkwwtLtBKkJ7t0bp0R7s4B+ObXX2HUW7KZ92lqFykMh3FKrQRSGG73F3ywGDIMc1CSVRUSuZbF8GS9evyQqw8tSS/qpDhBjQxLZC/H6RhoQLqKKCwpK5vTcNhdcbbucdlqN54bTVi03H6lHbpujd+mOa6qgEJJ7uyd83i6w1WacL7pEsrGun3a9n7kNhxGFZ7QbGofZRwbjGMcm0siNHthTd5IauOgjbEocK+h59UMwpxlaYWyYIWwaW1DxUahLdY+OLlO7Jd8OB/boLGgYhzmQNfqCFoL4EFYb0cOUhgMDt2gRDqaeZbY67/VelzrrJ/qNWH49lvwzLeZBPjHab3wwgu8/vrrnJyccHx8zL/5N/8GYwx3797l9u3b26/79V//dU5PT/n+7//+b+n1PvbGnxYRReUTeDWBXxG4NbVyoc3+MEZs0/fAZrZrLbjMOriOZtS1s1aDVVMvNl12B3OyMtgGfPSHC05PjhDCIP0aIRWbizF+nOOGFcJrLAyk9HCEITic0QmsYKXRgkbbile0v15VgQ3qMIJ17ZM3LtIxKONsY1Jdx9AYh1UtqFqFdaVtchbY1qwrwBcOnuMQGI+uK/Ean44T4DmCUzZ0zR4uLp6xvw+gHUOIZOi5hNKe9Gpt4QmHUU3VEuaeVPzSMTiObREWtc/x+IqmnZ1LoSnKgP7AtsiVFjS1u2UWeH5NU7usNh26SYoblKRPqapv/oAwSGmrHN4Jc2QZsm5cukFhKXWNpFaCSrmEsqbWtnuhtUMUFmwWEY2yp5xiOqDMQ5zWTWFyDw6AtETPJUYL1JWPbnPljRKEvQ3lOkFVHtXE2vCKdULTRhXXqwTpWiFooyUaZ4s01Z5jI6OrJ5x06x+fVYHlssuG+smJ1bGENeG0G21jCKUtBhJX02hbn/jCMAwq/PbhaoxDpgQaSSQcOq6h7zdclR6htJ58ZSRBESIQBKJDQ4lGEzpdurpH33TpS59Ka3LT4CFwHQeMtYKVWlMbRSBdmj/woHUdh+NYsRfUdL16C4F5GqvIQyvK82u8JKdYdgk6GQKFVoL1bEA4WuENNgQDG9X8BOwFdqbfDXMu1z0aDYMow2ALxVoJO0tvI561cRBSsXftjIcf3iKJM6JOSrNItu85hcHkkjDJcN2GLLeFUy/KOFv3KJTdhOvGtcz79tASeU2bHmrHgYvK6gsq7bbkPZdxUFIoSdEy9W95FQO/ZFEFLCoP4cC0DNriuMETipFfUUhJ1bqL0tpj6NtAKSk0vmjaAB699f47jsF37cFgtolotGDdWOTxYbIhcBsGZcjacamUIG3Dn56sUNaktU/s2dc4z2MOomwreI6eYmjTJ+uPvn7qp36Kr3zlK/zZP/tn+fN//s/zj//xP8ZxHP7SX/pLAEynU37pl36Jv/N3/g6O4/DTP/3T39LrfeyN/3JjZ6F+paiVZG8wZ7+/oiwCFss+V+seSVCy31tyJOesspjaiO2Ddp0ljPpLbu1ekBchmzJEa0FZe9uLPeil+FcVs2Wf2bLPtaNTvvnBs7x450Pi/RmqCJh+9Tn6t09xeyk6DXnltW/w9hsvo43DOChZ1x6VFkhj55qnmy6LyqdQAg0kruK8CFqUpiJtLV3SgUjaOF5lLMtdA5WyG/9JZgiloOuGFNrg4bfVu2FghpTUHOg9JA4ZFbves2RmgzId+r498a1rGPrQ9zTzyiWQ9kGWNy6nWUzPqzlM1rhSMcsTBqMFX/7mS/iy4bkbD9m7+wBV+JTrBG9TkbWpaMlgRdBLqR4cWSpilNM0LqfzIa99S5fHf3md5yGu0Kxrn4s84qp0+bGbDznsLZmmHQtx0c4WUZstfY76Cy7WPWZFRFZ73BhOCdyawK3x3IZ82UEria4l1bJDczGi01yQX1wjn/cYv/Ihm3tHNgSqkSyvRpb0FlRIF6p1bANfhCFOUuvjn/Zx/boNjhIsKp/LwkPg0fM8nusvt4WqwG40u2HOfN2jalzudDK+NOnScQ3gUCjb/VnVilwJlpWg1B6xC56wkc2B0BipWNUehXIotWDoW4FdrpwWBGVwcIhdOxp4Re0zrxsO9JDcNDySD9nRh3aE5El84VAogd8WXJ5jxQQfqgkSl4QQXzhYl5ckU7BsKp7v2W5M1Qpfn9ZK+muMdmhKn6sPb5CmMYPGdgjzzG668/tHDK5fEBxMee3P/Ra//b//KEdBafM1HGO7ges+kzziLEvYCQuG8YZNGRLWAdd2Lvno/JAkLAgTG8rjeTVCWMDP5bvP0Bku8bMQVXmk0wE7L95j83iPbumz30iEMOzEGVKYbSGkjbOF2VRKUradwUJJLguX+5f7TIuw1QEZFu1J3xNPYpJtDsll6XFZSG7EDVe1y62kaLNAbELgfrJpk/ICTtOE1/YtpGWRxYyTDQ8WYyK3phsUjMKMg/6CwK/a5EZDWgYcxymJV3E8nvDe+RGJZ10ym9pjXbsUrQMAYBBnPLzsbQuKkyyk69bst+O/s7T71K6HJ+tP8gn9271+9md/ll/5lV/hS1/6Eu+++y7GGF5++WV+7ud+DoC33357+/Mf+7Ef46//9b/+Lb3ex974Xzh6xGrToVZW3f/B+RH+1R6u1G071HAwnjBdDDlddtnvLgncmue8Ck8qesmGd0+uEXk1sV8ySFIeTXYZxinj4Zyku+E3v/i9jOOUG4dnDI8uWZ7v8P0/9huoLKTeRLhxQedwgn+8gn6AKHJ6R48YPzjGFYooLLhYjHhzNmYnLDjorPjtM4uwqZRD5GpeGU34YDkkcRv24pTruxf8nx/d5VEWEkvNtSTjN8/7+FJTKUGmHISGm4nTsrYN2hiOnB5LU1GYmmtuF4O1ZGnAM4LYdHlOHHAjERyEDbNKMq8Mu6HDtaTgYRq2RC9B16vZj3JC2eBKxe5gzvPPv8/8cswwtnOf+XyAfkewXPWQUuO5Ness4cXveAN/uKbZRMznA3pRZk/RjUc3fDrQlju9FZFrTwyDoOC1FogjHc042fD2ZI9RULCufWotOIw3vDvZA9gCdD5//zafO7APv6+cH/O/7E7oDJd0rl/gXc84//+8TL+X0xk9Jinthpdcu6TZRNRpRHe0JB4vmd4/oq48OoMV777xMpdpl45XcjyaIoRm/9iq9F+/PCCSir2wZlW7rBvJvXWP47jgdn9BL8zQRvCpH/4SD7/yEtPlgKPOiu8yDl+adJiWhkprQil4sS8JBDyJDckNhA7E0vLbH6x76HbKYowdC1zkhmlpT/l3unZMUGmHVe0wr2s8IViqiqWzxkHiGskokIwD+/C8tNMPu9kYg4fLC94uApvqlrhwIzG8u3Q4bXJcBN9cdHmxv+Yg2WzV609jNaXP4NYJAOWszzg8RQY1jlSoIuDrX/osYVgwe3iAvndMnkWUjcvJdJfz2ZhKSZ678ZAbwwmCMW8vBlzrrLlY93GFfU9/9/4dhkFhMcyPD20EcpZw4/mPiJ85Y/HmbVTtsj7foWkPG47UzC92EUJx+6X3mJ/usc4jZBlQa89a8hqPvD1J70U5b0x2GQclidvgCZ93FiOGfkUoFdPSp+sqorYt32jBwzTBE4auqxl0FcOg5JU23rnWgmUZcmM43Y679vpzfvR/eZ3T159jthgQ+xWnqwE9v8CVFoBWKpe0DJluekhH88y1x1xe7TCfRaRNgjODB5su15IUpQUXhc/Ab7jdX+DLhrz2+J2T60RScW/dodQOu0HN/dR2RUZRzl6c/pc/zE/W/09WGIZ84Qtf4J/+03/K66+/zt27d/krf+WvkCT2M3v++ed57bXX+Mt/+S/zsz/7swjxrdkxP/bGX7UnBm0cysZjGKc8Xg5sjnmccjy62uazV0oy2XS5uXtprX6Vz+ViSNQqX8v2ew3jlH53TVkGTOYjxnFKJyhYLPqsVj08twbj4O8uaBYd7n35FY6efYBaejA3pPdvIIOKvetn9DcLNqsuoVcx8isSr8J3G8Z+zVnu4wrrd/3mbIdMCbpu3YJdQm7156SNFSe6juY4VsRS4wuD44jW+mVPfcoY1qqm7/qszIbKqbgpE9LGbgp2VKB5SR5yPXHYCxu6ruLDjct+aBP/PlhH6FbNe6e3pBfmrIqIXphTNh5XiyGOY1iuu6yLkE5Q0uvalLisCvBlg+NoqsZl9uiQcDKwF09Q4rqKLI8oK2/7sPl2r35QIISmqD3WVcC6CghkQyB/H1P6cGMzBQZ+xV53xe9NdjiMSgZuQ9raDN+e7pJ4NXthztfee55hnHJwOWZ0esV61WW8iJFJgaldyqsB1TomX1nR0/D2YyYf3GA6HdnsdiPw3YbDtuB0pYX2fPTBbdZFxFGSIh2NzmOCVomdKcGr/TmDZEPg26Cn+QfX6Q1WhFHBYjHg0brP3W7NyHdZ1JJSwchviFxNKDS7Yc669lnUHpUS3Fv1CaWiFvb7rxtBKA0dz6GDne3HrrIneGEY+4ZTKVHGsHFyKqfiVZ5hSU2pYNV2b9eNzX4vlGHRVPSlZ0WinkPXM1QaznNBz4fP+BbcdJqDKzocNR47Tym3AWC97NJ8cKMVSDoMji6RUUm9SigWXV5+9Zuo2kW0djchFeN0Q60sbGkc5rz/6AbPHj+i31vRDQ7ZVAHHg5mN+i1CHq97DNpWdVpE7AwWnM3HPHjnDv2zXVQjtx0gRxiW8z6bh/sk3Q1+nBMM1oSLHt0o31IML1fWIqd0TKUFiVexFxYtg0Ey8BR+K7wVjmHgNZwXPnHbVh8HRdsttLa6yK3xhObu0QkPLg5YlQFHvQV7uxMuLndJSzsSufe7r1p4Uat/KZVL0bh4QhN5NTdHE+onIynjMJ2OuNz0kMKA1kzymKFfbcWyB2HF/TRg5HfotzN9X2h6fmU7AC2i9253Tb8N7anV0+M6PFn/s2B2v13L9/0tpe//unZ3d79lC98fXB/703faTOpGy5YNbcVQvlS40gbpXEx32o3J3uBeG5xTVj5pFWxBOo2SrIuIfpTZVl0lycqAJCiIwoK8CMkrj3G/QFcupCHlosvlYsjN7tugBLr00EpQznskOwtb4a87eFJtW2yusH+/xNXtSckhq6yHNVOW1+6kVjn7hCefKZedoGZRuTgO1vZVCcI2rS+SAr+RLbDHFjBpo1HGoDUEwmHou4wCSFy9bSNKB/ZDi2AVWIFP36ttII0WOBgir6KorffYGBu6E7q2C6CUpGlcIq/aMsxDryJLY8oiwHUbojgnCEuK0gc8Qu/piLrcloBmTMJq4+E5hkA2VNqlUYLEbdjU9vf9FtjTdTWB0DZP3NEMAwu5ofbo+RVp7dFVcgskEsIGNvn9FOE16NpFSL0VLDquZtMGFhnjsElj+m0bU0q1TSZsMkkSFARuzSKPCaVCyIbaCIo8JAkKPK8mCEqS7oY8i+nvzvCigrrykC1PP3IFWdse7nmqbfdq+mGxnZuua49p5XIjtk6NQAhiaeh5CuFICmVb/k8cAMKx14FwoDYgjIOPjxAOniNYNw2NkUTShkKF0sETNvBnN7T2wHGg2+helwKHSELcBsKUyr5OrZ8Urk9nSako2zm6EIpiaR04VRpRZSG9oyv7TDACVXr4eYUnG5QWCMfa2uZFRFX5RHFOP06Z5fHWx66MIGmTD7MqoFIuflASeRVpEWGMoJNs6O5PEX5NteqwnFtIUFX6dhwUVUS9Dd0sREjLItFasC7s/LxUri0Y26Kw1g5Jy4bYbu5eTdxGC0vHELjNdoN9gmMWjsEPSnphhisU/a5Nonzy79Da4Wo5IKt8DJZz0fVLai3xhCLxS7qdDWmWUCmXRrms8thmXcgG6dliPq89lrVP1bb3B77a6geeXLOxW5M3LlU7Iur45TZbwJNPJ8fjk/X/H+tjb/xRWLQ3qcGXFoRy2FtuL6TT2Q5vz0ccRRmHveX2AW1aC2CtbXU/6i0pypDzyx5KO0Rt9R0H1kcdhgVSKiIlGezMAFh+dMz0wp7Iw2dnqKmHIzW95x8yf+s2TRZSF5b25grFXne1vbCVcbiWZFzkEbPKel4LJZiWVoFbKUmvFQhWWjAtA3aDko82AbE0jIOGVQ1dDwJhSFzwhMdl0XAgujTGMFE5fRGSqRrX8bmZmO2GlLcioCdCq4M4pR8UXKTWjnGVJ+isQ88vt6lioVfR7a1x3YYwsKrk2aqPcAx7OxOMdijLgEFvTVl51GWIbBp2didEwzXhqovWks5TsvNp49Drrqkbl03tsRcWDMKcrLZq5/04xXV0K0o0XKz7vDqesKl8jIFhYt0Kkzyh1oJKSfaTDdf3zy1Bb9XnxvEJk0dHdDcLOntz3E6O10vxh2tUFlDNu9SNR7ezQSnJdDng8OiMot2A/KAiSnKG4zllEbBc9vlwPmYQFEjHtLY+65iITEEYFfRvneE82sdLcnTt4oc2Ze0ki1hUYuvX73o1WeNaHkTtkTceO1FGz5ec5CM01vkwDCqGAZYbUHs8ygIepgCCnqdR2iFTDqW2hWOPiNJ43FMz9ukzMRuCxuOGSJCO1QT4AhLX5TBS9D1Fr53zTqsOY2naax4K5fDpYYon7OnviWr8aaxOb01V+Zi2YL083beiNb/CCyqWJ3vsvvIhuvRQpb13TYtKVlpAGyIzWQ4I0gRt7DVxshzag4XQ7CVrWwzWPpvKx3UbdgdzqtoK8JL+muS7riBvkI8qosmQ5GDKww9vUVc+g+ceEu4s6CuBVhIhFc/0P+Teu3cI2gwPTzZUy6HNB3AViduwqHw8YaE7sWx4treiUC6BUNskyr1kTVb5rKrQunPWHUaDBXtegx9UOI51QqlWAO0KRdl4zIuQTe3xqf1TG+UclAR+hTECz61R2oZLAVvbaTfMGXRXvPnoJvPSZ9368T+3e8WyDMmUuxUv+63bSht76KmURWcHbsMgevqtfv0Ux0t/Utcbb7zBF7/4RRaLBU3TYP4bM7q//bf/9n/363zsjX+17tIoie81dJKUi3Wfg96CurWfSUfzfccPAVpFt9vCekR7wdlOwWLdw8FwZ3wJYAVAtU/ZePRCy9TuDxe4YWWV/EFN/5lTuseXXMtC8nd2cFyNyn2yWZ+wl2IaiR+VjMIrjBEk3Q1Xl7t8cGkBN4nbsBMWRNLjvPDZC2t6foXnaNLGY1EFnOdWib8T2FSu53pyq17/znHJppbshBVdt+Yki7nTtZGplXYAl4Ow4aoM0AYiV7OuBSNfEbmKWCr2osymA656FGrA7U7GD7/6Oqtlj+mqT6MkR8enaC1ZLXq8+cGzbKqAUZQhhEZrYQlfqx5CKOra43w1IPFL9gZzfL/i/Y9uc7hzRVH6xFHO3p1H/90Xxn9r1crlP334nA3jCUp249SmncUpB70Fb5wf85037nG1GPJ408Nri8NO206fZh0meUTPrxiFGbFfsS5DvvHgGa4PZjz3wns0hc/O8TnRwRQR1Hz4W5/l9vd9neDaDDRkHx4A8ODikEYJQq9mOR+g282kng9YpB0u0g7X+guO9y/4DI8Ig4LL+YhNGXB3/4wvP77JD46n9I4sGrh7fIkIappNhOc1HPaW9MOCB6s+j7KQz+1e8TjtUhuHnlfz4vUHzJe2KFsXEdfjkrM8JBS2y1QowZ1uStZYJHQoHXqe5qoQeAKEA5lqiITEwUEgiEzAnMzGPCMptOHlvmRRQdbYzT9Xgo5rI1j9FkSVNZJeK1rd1B57UcYkj7msfC6LkD/3VK4GuLrcZWd3CsCmTdDbZDE7UcHg8Ip0MuT93/4ORuM5vaMrejfPuS4MTelTVx5VGbC/e0UY5bYoUIK88jkcTyztUwkWyz5hUJLPPR6nHcYPbm67h/04pX/rjOV/vIb0ra3v8LV3cW8WfPr2b2LW0MwTismAeH+GVhLTCKKXp7w4XrB8YNkYO7dO8N+qGAyWeGFJXQQ8Oj1isukihWYQZSzziO979Q2qymc6GXNtNLH3r5a4jmVb5EVIFNpOklaC/R9/j53pO6zfusnXv/YpFk2ML1XLHgn4/INb7EclB3FKNyh4tBpwazDbFgmVdpm0+Rd57XGyHPB70wHP9TKe7RckXsWqDKwrAcsfSbyKSkmWlc+8chkHDdMiInJtrsQTW98n64/HapqGn/mZn+Ff/st/+bH/zP+QjX/RAiWgIPCdbTUZhzmhXzFZ97becR8YeDWX8xGXmy6FchmGOVFY8Hi6Q1b7BG7DXmfFeDin07gs112K2rciNiUIm4Lp2R7e432KIqRpaX+98YJouMLrZfR7GWdv3GWwPyUYrfB6KW5YsjrfoZNsuLuvmT58ZtuWtelqtvVVKEmFZN0y1T1hq3fbLvMZB8W2fTcpYuZVTKEkgdCk/1/2/jxWs/S+60U/a57XO+655uqq7na320M7AWMTMpgMIiAuPgJdpkQhEJKgIMBcJbm5kYgZDvzhECWRMggQ6OgQkgOJInJzuHDJYMhop+223V091Lxrj++85vn+8az92r6JnYq7ys7prK+0pe6qXXu9e+/1rud5fr/v7/OtZCREyVYCRkbJzVBDlYRjv6+V7FjihGUqFY5WUCOxacVs28KQExUav/PKU6hy1eJvSz7y0jP0rRhNqXCMjJ3BDMvMCEKH41WP5669JkbTSoW8Le8DWHaCaSWcVyvC0GEwWGA4CXlgf8E3xufTrWWfZaFiyA0DI+PGfAjAwMgYmQm+nnG8GBJkBorUrANDkkKjRqKsZLba5LGmTUrb7c1JCx1NK8hCkWEfHni4sYk9WDHYmLJ4/RxWP0DzYvR+iO+vyAuR+WAbGS/ev8SOtyRITe4GPXacqJ1prqlKhSzXOF72ydsRwv3ZmIv+kqJQmdzZo65kLr3/d6BukO43FHdVcf+25VJDrrm16jPNVaaZQt0Y8NpT9I0UQymJC51JppFVMkHz6eL6qysXRRKY36EOUamwYQoPSV5LqJKMoypsKCpF03AzDVGQqRFQnrJp6OsNIBMUwmSqyRK6rIj7iwZfKyjbUbSwllkWKoQerlaw+ZhMnmdahB7jjakY4VNLypMxdSMTRTbmZIA9FBhpWa7Ilg754Zg80zHMFNDYP9nEt2Ixqic1aFrBzmhCVSmEoSOmf0oNXwvY8RaYSsHADQgTm4EbYlsJJy9dIY5sBhtTbKWiLlSyGz6re9sYboJ7/T5aZFJlGmViiorO0QoAzUqpSoXgaMzT/8v/IL01JHiwQbDy0JWSSxvH9PpL+jun3P7Uky0cSJy0t3aPMPScvNDWKZKHqz5haqGpJWWlYP1SSBo4TKdDVrnIsXh6fMKGv+RiqbBKbA4jjyA3SAqNqpHQlJJlYjFLbapGYmQmDKwYWWqYJQ7v3pyxzIw1POtdGyfcXfVJK6VtO+nci2ySUtBAdbmmb6SM7AhFrtc48MepztX/8PqRH/kRfuqnfgqAwWDAtWvXsCzrsV3vD9TjB0gLnTJQsPWMMLXW/c2mkUgzY83mPxuhOuv3J4UmgnuUGrWu28hIhTxve9ot8CErNbLMQFFFn3axFKdhRa6p2tJhU0s0pYzqx3jDJd6TotKQHw8wxkvMyEK3Uxwv5NJiiCw1TFvAzDrnXFLXgJmklHG1CqOdIT9OxM74bMMwNmPmmYHdlh0dpSauxAy3ITf09WI9RmMqNZ5WMLZi0btvqYJVI9O34nVIjdTO2Mvtz69qy5uC312jyBKOHa/72orcoNspqi7c9EWmkx/quH6ApgkW/WrlrUeAktAmiWzGj+hG+UzVjYSnVqKEreXr33PfSPGMlCDvsUgssnacb55ZjEzxYK8amUaGpNTaXALx/ZQtoTDLdVZLQW2MYgfLTkBqqEuVJLIoMx0rNcRGTyvQtRK1rnCdEE2uWCQ2y8xkVahoiYWuVGSFJrIhEGaqXjvbHGamCESKHbJcx3NDKBuaQHACvOGSkR1xZzkQISpmzklqEBQyRS1KmTdDCz/T6esFMrTkNHGPpbUwczqq1G4sxQl/VUgM9IaylkhKsXmsG9AV0JGw0aloyCjIKYkqmcPERJfFaKnW8iB6WoUErAqdnp5jtHPuDaItZSgVmlRjtJMUj1NVqVCkOkVqCKiXXIkFOzXoDwLMlSPu5fY9phs5ZaGRtilxrhOt24JlpZAXGovQE3G5TsSwRTxraoljZCLfQ8txnRDDzIhCRwR/ZTp1qSIpFemkR1WqSGqFpDQiF2S0QglzqthEtgrUJsFIRUBYHpvQM5GVClUvGO+cEC580sQUMLFCxTRSNCOnyjXxfeoFppXieiGS3HB8tIkmV+vnX1JoHN/fJct1FpHLKtcxlYo0F/RAw8iwawU5cmkaUJQaTxZehgYJs0UYG0qJplSC31HJbHkr0SoqVZJ25K8GkaXStrLSSkJXaixFVIYstcA1hclz9rjJfc1jMPe9iVsH/+7f/TskSeK7v/u7+eAHP/iGXfu/nx564VcV0dPKCo1larHXn/FgMVwz8gdmQpSZaC3+dBmLGfNNb0VRKdxdDAlSC89MMLWcRWKTlyrzQDiQzwKAqlomy3VhtuoFTOZDbDPBthJkpUbvhRSRRRlZKE6Kf3Uf6R3nke7eI/rEkP7oDmY/hLa8/JbiJrPZgElsr0uhR4lBWskipU+uAQVVEgCdGjhOdCRJpajFA/v66JS91omuyDXblsRBYuKpFZ5WYKkFV3oLwjYDW5FqBu3J/syoVzWyoBc2EnmuM3LEKb+qRSskykRZv9+CSsLYXs+sgwAi5bGJ7iSodopaqCS3L7B98QFFqhOuBtw63eLtV18nSyzCyGYRuTz1aO8XgHXqnqGU6GrFyI6w9UzgmdWKO8sBSRtGUzUSh7HFritOV2Uby3u4chhbEZaWkxQ6i8ShaVgDfaxKIW03hTQSq4VPkprkuU7Zjl9VlYKuifaB5SRseSvuzocErcHpJNVx1Bpft/ASi7qRcI0Uz47QtBJ16bNIHBaJjWekjMZTqhON7ETkqdt7p1wLb3Hy4tvayk3OYWKQ1xK20qDIcJrKTDOZkaHgaxWOKpIiRSUJgqJBkyWSSiKvIGsTHnVZpmoE28GQZaqmIa8kVBl6qkZQldBoFJRE5LweKFxyNExFjO7JEuxYMXGlMs10fE2MnNWNtPYYbFoxUWsWNR8jsEWRa7LUIM90wsjB9wMMI1sfCBQrW29YJalBNXIMP2S+v02amfTsiOHWBFUvqEqFcN7j/uEOp5HLyI7YsKZ4wwVH93cpShVNqVDVCttKsJwERSugZVoUhUaZ6jSVQhbaOOMFxnAlNhW1hHJBRolWVKcp0paOPM3Q6yWyViLPfOqDnCr10d0Y69wp6q1d7r16lWRmUleCHaA5YvF0C01sLOQae7hENXMePNjBa53zdZv8d5asl7cTT7tOwCRxSEuVYR2R5DqSxNozYOs588RGlet1WmfWhqTllcggAfCNTKSTykYLyRKHLEVqmGcGigR9TXgVVoWGqghEdt1uyDv94dErr7zC5uYm//gf/2Mk6fFXSh564b81H/GeazcwrZTpdMh01WPkhOipRZjr7A4ngEjMi1KTB0GPC705aaGhqyXvunSTVw/O4Rgpti5MVdPYod9mZntuRFGIjGylvYHv3rnIxnCKP54jSQ3H93c5fV1gOs/6gae3zjE8PMa+oDL6qptEv7PJ6e1z2G6Mf+6Ynfd9gtlPfwWXh1OuDCdoasmrp9uULUUrqRSe8COOEpOsVPDUij+xdcpR7NA0oqXxYNnn0nDCwXLAaWpxtT/D1y0W7QTDE5tHLCOXSyOxuMWZgWvFHM5HjJyA3b1Dbt+9gGmm1K2T2XMjZLliufKJMpOiVujrMZaZomolilIRxTauG+G5IU0j8ds3nuap1b4oh2cG+8sh+u2zaFOFKxvHBIGL60ZsbEzot5S/Ry1FqlnlBqqs4dU5ulXysaM9tuyIK5tHPH/+Dr9+5yqOWjAwE84hFvx5KkpXF/tTzlUqn5huoEgNl/3lOo/AMxNcJyIvNKaRy+2XnqF+SeJSf0ZWaGzpOapaksUmk8mIi0/cRjVzZvtbAJzrLVCCHtOV1+agy9wOPOapuebza+1oqa3l/PbpBufthBrx8/3q4ZIsNmkqBWXus/Hn7vAeueHenQscrPo84UXEpbfeKC6R2DBqZrnMSSoz0BtkSSzojgrP9gtuBjpDvaFsxGn/QVwhIzwbugyXXYWwhNO0IqxKVEmmamqecExMBRZ5w6YpNhvCUQ7Pj2acpha2UrLRSzhNLfp6tobTTFOTvFKYZcK74urZY7kXAF6ebKLKFZpasowdylJlvDFB00rqSub1X3kX5me0G2SlZvj1+0i/IcA9ktSwnAzQ9AJNL7DciM3BjN2NE/JctP/SxOTX715hp73HdCPD8iLilUu29MQ9ketUpcLiZMSDOxcwjRSrFwoOyNLB3JvQzAuqiU0ZmyieTPGSgbaxQj2fo95NCF6+gHv1AU2mcfO/fzl+f4VtJRhWwuDcMWWqQyPhXTmg/8dvc/pLT7Fc9HAHotVoGhmTqUdYiJ+7o4n0x43+nPNWypXM4GN3L4v+e22KSN9GxtdTbq36PEh0nu0H2GrB/dAjLBX27ERQRpUaQy3YcCJenY3RZDE15GoFVS3h69l6QqFBMCSKWkwkXOstWCQWealgqiWjxzzHf3b9R/0136zSdZ3d3d0vyqIPf4CF/2JvzvF0jKnlmEZGVOi4ZsLICbA0g9+6e4UnhhNRtq5lLvTmGGrBaCAW7buHu6L8VOjYuhh3ubPsoypiNFBRaqLUFKWsUqUsVfqe2Kl/6lNPk5UaT168gz+eE8x6ZKmJHllc+NMfZfXiJWYvXkV9NUezMvbe+TLadgSOSvw7Wxh6zubWCdYgII8sTlY9wtzAUht2dWGCGbR96KqWcPSMkcCgYSoFnplyHPTwjJQtbylOrY2E1waJlKXajtyJh5gqV0yWfd5y5SZ5pnPn3nlcKyaMHJpGpqpkFqG3flhuDWZoWsls2WMVeOJUoZbMAp/hcE6OCI5515M32shjlzTXGVoRulYQtuEjO9tHPDjcoSxVZLkhzgyu/D6/1y9EvpGxygw0ucbVM2aJw6WeSDRbhB6z2KGvZxiqQJE+iFyu9Wf0jBRFrjH0grEbsMoNFEmYpj5ytMtFL8DUcxGzq1SiNSKJSGdNKfHsSEQ9qyW9S4e8fvMKi5MRbn+FN1wShB79/oIrF+/x5bnGaiVGA4PUYp7Y3AtdTKUiKVV0uead2w/oa+V6DMrRCl5/+Rq+G6JpBUQW4f/+DPNFn6TQUeWak9REa8vVNaLs/jvzhrEOY7Ohr1d8ZCrjqBI9SeCjlc94L8ttgt80EyN6aSWCdK77DaaiME1l0rrB14RnpKhBQmJkVKJ9UAki5e3Ab9tNKnFrnm0aCUWq8HVRkVlkpnD9S/WanfA4dNEX74mmlDC0gr0L+xwfbKNrBcOtCdff/WtkB0PKlqqXBg4P/o/rLBc9ktQU0z7DOWlismonUjS1wPVCbDem10hoZsaXFzpRanI8H7I63qVvxrhGim0lmFbKxqUHBMcjVkufqpJxvRDFKARVMDWQjZJ6YVAXKtQSwS+apKsRfaVCMyOaSkbRC4qpT1NLjHePkaQGd0MYAuNpjzhw2XzyjphQuG0yePoOs8mIYNqnSHWS1OR+6K/bhEUtt7HUDUHgcrwY8o5LN3nx7mWaRsLVc/ZDj56R8szolLc0EvM2GCitFFaFwiW5RpFq0f+vZXSlZNOKmaUWedtmO00cLvbmLTVT5p3bB/zO0S5Ki/uOCp1db8nAW6Eq1Wchkzt96fX2t7+dj370o+R5/lkJfY9LfyBr51mfbRW66xPEGYRifEaMayQxm0yzjl7Nc526NXrUZ2Mlpco5d8XAjjDO+taVgqXnos+/6mGoBWVrVvHa3pTmRchzf13uqhYmZa7RNGLRrQvR0zuLw1LMTESFmpno9ck1tp4zsCPBJagUgkzshKtG4DfPyIJ5oVJUKq4VszGYk2Y6ea7juyH5GWADiYPlgJ6ZUFbKuifpORGHR1vrxK0otchLFbPtf+alysbGnCzXRAm6Hc2rKrHpqWsF10jFf1fiNa2WPppW4johji1aBoYpyn15oRGuPIL2VK0rJapcvZF743OqqkUUryIJr4ZMg62LkqS4dsUyMwVOWMvZtBLKWlnnnMs0zBJhWpIkiSA1GRkZq1yH0KeqZbS2bC04ETVGG9CSpOLruonO9mgiQEKRvQ4qsrwIWRGMeMtMyXMdUysYSSKR7CxYxZBrslLjghuwyA1MpeT69gOOZiPMtoRalCp5rqPrOV77AL3cyBS1L3LWS4F6fltfFv10uaFpBFJ3ZIjQnkWhYCoNs1yc7vtaw1JTUGTIStHbN1WJtAZfE+Ois0wmb9lLSkvlqxralpFABwNoksiakBFl4mWhs9GOcUWFQt9IiQodTa7Y8R9P9QcEa1+YVGtUtSIJHdI2g6PMdIqpJ0rpZzwGtSRZejh2TK+/RNNK4tBGawFfq9DE91doVoZm5EhqJSZ42tFfVa7WACmzTX88Pt4Up/sW5WsYmcgGqSXKVCdZurixTnR/E0UrUcycKtMI5n3yTxkYt1IkucEarMgWHsnSZTYbsHf5PvFUeAUAosjm7gtPYzsxTi9A0US7Lo5s8swQG3k7bmE+FWmporY8gLp100exgyw1xJVKGjscJwajdlz6bAy4amTRXlIlUZmsVXpyhibnGFpBXqls2SGLzOIkNdkw0rZCWdK3BNyrp+eUtYwM2GqBY6S4biQOFl8EVn/dmfseWt/1Xd/F+9//fj74wQ/ywQ9+8LFf76EXflMr6A2EO/d4PqRnR+SlRtEu9ueHU05bGpUsNRSNjKaVZJlB0p7kFblGa2E/UW5wYXyCZYrFLYrFw1tTStJCJ8xMYtkgLxVGTojnRCSxhSQ36yTAupIJbu+K3redYg5X5IFNnelU8wo5LpH1EuPygjqQKRcuTS167b3+UgRgzAY0SExLdx2B69ox/eGCohCRrrJcs3lpn+n9bTGqYyeMqwV5rrOMHU4SMZ5T1cJkY1opthfyqX3RltjtLVjENmVbRXANYf5x/YD0dNQ+JGtcNyKO7DXAyHdCkthCUWp8L+BoOmZ7NMG0UlQjpyo08kxfPzDngchTKCtFAJa0xzO7rbRz4U0jkbZZB7LUoKoVklTjmQn7oYfeyKhKzVCPWKZWW9aUWOUmR4mNo5bINESFzpYT8vpisN5IKHKNpRZrY5OuFUSxvb5+Mu3j+gFloZGlRrshqJGVSqBbW7hPkFpYeo5vx9itYVNqT2NZqYmR1MUQV8/ZuXqPRSgY5kWpkmYGTSMJf4kb4WQ6lpZzHNuklUwuS6g0vHM0J68UglLjJDUYGAKookiwyGUcteE4kdB06Oklo/KMmy8ejK4mSrKOXqHLDaBwlAjYjyaDpzUEhYzSJv5Vbc3TVgVZ7oyWmFUiiKhGRLte6Is0PFWuGbX//ThktgbPs777bDYgr1S0UoRILe/sCMhWy7Q4A+icTeMAhJ+8hmEl1LVMWSuYVoqsVMhaiWIUZKn4Xehajq7lWFXKwWyE1mKujxZDBqdD6kpGlmtsN0JSaupCpUhMktgin/ZYnYwwnRh7tKTMNcLI5mAypmkkMT7oxsQLj9PTMUfLPjsXHrQjijKGlVBVKrenY3wzYTefILdGvrJ932lawba3xG5hZWf35WfqeNkXYVyNxDQzCEqFpFDXkKWykUlLFUctUWVhzItKFV/PqJHIW5KgbWTklQqYOFpBUmi4RoatZkwiD0Ou1qZjvTUJrn/+xuNNbOz0B9Of+TN/hg984AP803/6T/nkJz/JN37jN7K3t/d5T/9f/dVf/QVf76EX/re+97e49dFnyTKDvbb3FmUmaVtCrCrhYHX1DN+Ksa2EW4c7OIYIqdgPPS77CwbeirzQOY08DL0gjFzmocs0dtYjgiN/yeZwyt3jHXS1YpE4TNpxF9cL11jOMw53FNtsGseYV6aoJwlVopMdDckDm9nRJjvhbdKFSzjvkSYml559lWzlEK9cYdbbPKE6EiUzU8vZunBAHpl44zmjq/vc+9hTvPri0+vxs1v3z/PMW25w/EDMkl/0lxxHroiqbZ3MUWRzrk0fnEZiU2GphTj5ZiaukXL//t46/EhVKjY2JuuKSJCaLBMLW8/ZGszYunCEN1hyerTJcuUjyzUXrt7htduXRLa91IiQpLYVkbeZCo9DT1+8w0devy6+9/6UD+9fwNVzikqhqkV14pwboMkVRa3w0ek2V70VT20cYVsJeSEicgd2hKGKTcvBqr8uW+pKSVJqbYBKjQUYZsa90y0ubBwz2JiShDYvvPYk77j2ChuXHlBXCvODTZanQ4pCIy904nbK5DMrH6pcs2qR0dvuiqzUGNkRnplQpjpv/eMfZXZ3lyhwMVv3eFmqjDZOsEZLfu4/fz0Az/SXWGrBjcWQpF3IFanB10pMt+ZOpNEAY0MQCy97AhkNYCgNA70iKQXSt27gqpvxINE5iEWK37bVMM0k8hpGSsPH57BjCRJgX4d7kcZzg4isljlKTKoGrvsBVS1xHLlMM4PLcs3eQECwposB1x7L3QD93pKjiQBs9Xo5q0T8d9NI1JUMtcTprXN4wyVWa7Tzx3OMfkBTqBy+chlNz8VkSuBxuOqxmxqkiYmiluhmRn/7lP3XL1HXCnUtMQ2FKXhn9whvZ4L+as5kOlyDxoLQ49zlu+SRRZULEuTprXNoWiHChG6d49bBOQZ2SHqGEN+cMrm3w2zRp25kLm8dEcz67Dx9C4DgcMxoNGVj85TZdMDxbMjzX/VrrO5vIcniIHK4v0tRKUxXPYEM7y2IYpuiEPRNVa7bFFEBBGp0ES6myMLU5xopp6HHLLUYGGnLuDAoarEZWGQmDxKTd4ym3J1ssuOE/JlnXuT1B+c5ilyySmVgxUg0nKQWEqICd5I4RIXONPSx9QxTy7n4mO6HM31mXHinz6/PHN37+Z//eX7+53/+836+JEmU5Rd+sHvohf9T//NdbIxF2MSN/Qs0jcTzT79MFLjcPtphHrvEpYYqV9jtyTYpNcJcPHw3rZirF+/ykVeeYpEbbNsRi5XHPHbR5Ipz/TkngS/KXJVK04gwn6G/JMt1ikLjspVgehGH9/YoS5Wt7RP83VNUN6aMTOa/fQX33AnLu7sYboy1MWfLSVjc3yJNTJpGpj+eMd/fwhvPGZw7xgkCXr1xXYwGtbCcPDKpSpXJ/V2S1wz6gwV5oaOpBZpWYiYWJwdb2FbCRTsmTiwmbRk5LTQeTDdwjZSLl+5SZjrT6YiyVFDVCl3LaRqJG4d7vP3yzTWiGMDdmFOWqhhnlBuun7tHb2PG4njEr//282y4K3Z3jphOh+zPxsxe9BjYIbaekZUaQWpy/bnXqXKNOLSZLfpf8I3x+fTgeItn9+7RNBKzwMdRRbVjGrssM4OhmQg8qFyjKhVvGcywtJyXTrdpGol3nr/DhdGEg/mAw6KHTMOmG5C1/PC6kbi6ecSLDy4wMBN8K2a18tnwlkyWfWarHrtbx/h6ymLeR5Ya/O0Jo/OHyFpFmQq40627F+i7gfAdLB2+7OItEQoTilbRwFthGhnzZY+iUpkfb8DxBkHocBr4HMcuX/vOj1CVKlloE69cLg+mgukfOywzkyf8JaephacV+HpGr1L4rWmfS06BrVafTv87Qw0DW2bGNDNYNaIl9nQ/4V5k0tcqztsCA/uxuY0iCVhP1UBfk7nklOgteXLDyNdc9k0rIW0nHba9FY6ZMg9dhr2lSLqMxGv944/lboD5oi/K3bnD5I7I6NifbnC86hFlJudVMR6XRZb4SEU2x5UnbmH2A2S5wrQSFrOBaK3pGSenY1SlYjBYoFsrVicjpqsetp5h6AUnscO10QmLWZ/5dMDpsi8W8MjFM1N2Nk84fbDN6WJAWSmYes7AW5HEAsyk6TnPXHuNolCJc4PTyOXurYtYZornRChKjSxX3HmwR1NLWE5CVSq8cucyca5jaaJ0/qv/51cxdEJGgzm2G+E6Ma4TU9eSIG9aKVFsMwt8gtRknlktCbBc3+vzzOCZ4ZSk0DiNHDaciAt6tkahW6rMdSckSE2UwmAHOIkdBm3r47X98xzHgrOyyg3SUuXZvfvoakVZyUgS9K2IjfGUG3cvcmcxZGzFj+lu6PSF6PMR+h7F5///6+HJfakNE7GLs7SC48glClyKQhVAiMzAVguySuUoECV/V89J296Yq2doWsm2t8JtT7zLxCHIDAy1RFNLxm6AYyZiljseYWo5RalSFOL0J8s18cpFb81vWWqwOhCnfkmpUfUCdSOkeVWizDWq1KBqTw66mWG2Yziy1JAuPdQ0R3cStjdOKQtNlIrlmsVsgN9fUdeiN++VIY4d0TTiBK1rOUlqrntyUWLj68IcqCsVQy8gzgyoJVQjx7Ej7h9v03dC5BYkpMgNVaXi2KI6UtcK6cIlS4UbWFNK0dqYDEQrZXRKXSvISk2vjTKdBWLeXWvL7rZuEa/c9U677wdv6Ob4XJrHDn7784hzgycHUyaJQ1VLGErFKjcYOWISIUhN7oU+tlIxSU2sdiw0r2SSz0hGq2qZJ0YnJIXOKrWYBT69tkd943QbTa7x9VT0/dWKOLLRWoBRnunEcx93ayp6yYVCU0tobVCPItf0DFFpECClCEMt8byQslAZDeZkmcHxbMiVi/c4nYtStavlyFrF6ckGRaGtjZvD/kIw4xObnpyKEU49w9IKLK3gspOhyTV5LXLSL3thm+H+6Y+6EcwHGZmw0GgawYb3tYK4UnDUBhuR97BpFBS1wYNExWyrBXGliP6xVAuio1pyEDlYbdRx00iczgfU7fv1bOPxODQeT1kufZJMbGBtJ2a3mZCkJmUtwnOy1EBWRd79dD7ANhOCWY8iNfBHC1S9wG1/xqaeY+gZWW4gyzWqlaIbOQM3RJYrAcppW4dn97prpJhaQd1I2G1/P8uFX0RVKgy14PWjXTbdFT0vQNPE6GBdKWtOxll8+Li/QDcy8syg74RUlUpVKmhm1la0cjG+amTrsnsU24SRw+mqT9+KsEzRc18FHooi2kpZa8K8F9ltCFgt6IMNHIQeYyvmXE/kjiSFTlzoIiK4VLH1HEVu1huGovXZnP2uJalhZCaosqAHVm0WSc8Q2QdqixIG8LSckfN4ng2fqS6k5+FV148nUO1z6Q9k7lulNjIiZKZpJIJQQCDOeP2GWhIXGnmlEGUmtp5R1aJ03TQSWWpgtnPXulIyix2CQm/feDWuFaOpJavYYZlaqEpFlNityatE0wqOjzdx7ViYiGKLIHAx9BzHjdCdBMkSiy0g+ntZy7o2Mww3pi5U8tgkiSyUTEdWKnobM+ZHY4pCQ9dzstygyLX19xZF9nr3fZaYFWXmOmQkLTR6VkKcG6hKhaYWxIFPFLhYToJuihAO0TcWPb+ekVAUKpqeo2kFea6TxDZ569wFCNuetmUnDIdzptMhRaGitotWnuuCiW4Ip7ui1OuNgyw3qI/JwKPKFWUpjI9xoXFhsOKgLb06as5pagmASyNTNxKrXKNSZbJaQpUFzjVvT/dnIUZZqTJQKyzy9ainq+eEuc4y19FbOI2plih1Qxjba1hUVakE8z66laFaKXWloKglvhMSRA6OmTLUs3Xi2VkqJEBZqphWiimnGLGN3G4mFFlMmxSJwSIUuOozEptfquSlQlSqSKlNceZxaFsKvp6TlGLeOq2lde9WzGHLTDPBGRBl2FqU+9ufbdVIJKWKKtFuBCp8PaeXq0wzFZAw21N/Xssgi0RJX085jBySUkwxSFJDmJlYbUrl40pqBFDVkqaRRbXPSpCVCr+3QpbE70mzUlYLH8lqUJSKODcY9FYEoUeWGVx8x0tUqYFVyahagZmaqGpJMRMLbl2qKHqB64h7Ps51FKlBbw2fdevDONsEKEpFU4vpmc8sN4e5zpbUiPeLXpDFovpQtuwNVakIYmv9NepaZFJkmYFaaFh+KEyzbTtKlit0taSsFcrWvDtLbBH20/pd0kJfeyBALLpHqUFWC2Or3EgYcsM00+kbKbaeEaQWulKSV8aaGpkUZ8z+s/Ku+hnwLwlNqrE1sSER7SmFpgHfiun3l6xWPrPAE0bpdhLicevNPH73f3U99MI/sIVjdpk47Ac+YyteL4KKXHO+P2OROAxa5GxS6OLUWmosMpOg0DGOt7i3EHCUPX+Jo+W8tuzj6xm7W8esVh7H8yFFpaLJ1ZqRPbAjXCfCHaz4/954hj0npG+J9Ku8Urm2eySY/Y1EeWhgDVY0tYQkN+Ik0W/hMamOc+6E43u76/nhOLLZ2DviZDZaA4Z2t46ZTEYYRsZoMOeV/QsUtULPEJGeaaHzYNVjbMf47UiRZaaosXio3Tnd4kHkokg1O6MJ/fGM8+MTDmZjppGLqZZc2ntAELiEoYPaehs+DddQ1337vf4SWalZznssI3c9DljVMn0/IM30NeQnSQ16vRVVKYiIQdh/9HcM8MzVm8Shw2TZJy417i9GOO3DLW0jTWWpwTQSsUmohdnw9qpPUCrcmGzRMzJ6ZsoAUaYOc4NfunOFC07I07v7HN27TNXI9IyUnXasMyk0FFk87Fapjanl6zHK2arHfOkz6K3ojWd450+wR0uOb13A9QO8nQlVpjH5rT6+G+L3V+zv76JrJbNVj74b8Nav+g1e/Z/vxG3LoCehx8HhtgDg6GIe++OzXe4sBywKjWmm8mBq8vZBjtourNPY4W74aVSyJjW8uvJQJAGvyWuZj0xrLrkqrtpgKk0bGgTTXCUqFdJaIigl9uwSXytY5iJW+pJb0ddKNq2E/TZb3ddTxm7AeDRj5C85WgxZZhYXBhOaRiLODeLcoKgeHwnsUzefYJmZuFrGRTMlDgUDI0os8kpFNXOmyz7DZonnB+I9lOksIoeBW2NcXZDd7GMoFYpRwAJkTZzs54s+aWoyGAlzYpxYzGMXV8/xeyvmsyFBYjFqg8GMNnBLlPQLokKnzARI59m9+/T7S0wvEjkgSs1i0SduU+7WiY6lQlUpyHKDolYsph5ZrmN7IY4hRnuzUsNvJA5XPapGZtMJ8K2YcWuszXINTS1Fy2EyErG+ZsK4t+DS0CBILWaJzTwz2TQTDhOb09Simm4gSQ3vevLGOscjyEwRxWskmO1rnGcmVWxTt8jrM/9Q1QgwmaKIeN+d3UM0K+Pm/nmSQsPWxUYwTh4fDrbTG1Mcx/zyL/8yr7zyCkEQ4Hke165d40/9qT+F53mP5BoPvfBfeeZVfus33yVuQDNlwxUnJkWusfWM/eWQc70ZPX+F6weY/ZD7r17GMzIMRZTxrz71OurL1wgyax2TOjIyTLUkjmwsM0WJXNJCVAkkqcE3E05Cn1uzMe7Bef7sl/8mTaWQRBaLZY9p5LKYDDHbka7l0RjDTmgqRTD/vQhFrdZZ4LNXLono2sREVSv8oeAMvPWPvUAe2JzuC8NeUuioaoWqlSLZrISgLWW+48teYO/+NpPZkLJWGPYXyHJNlus4UkPPjriydcjpYsCd42043uaZqzd5whELSlUqLBY9VomNb8WoSkVRqtS1TJwZVLXM2A0YDeYUhYbalHh+wDwQu/a+E7IxnlLkOnuX73Nyf4fT+ZChvySJLTb2jpC1kmn7vTxq3Xuwh62fUcPqNa0wKHSSSuHZ4YSNzVPi0CHPdd5y4Q5RbBMVOk6pcak3x7djZoHHPLWYZyZbdsRlLyAtVX7zzlUstSQpVaapSdFImErFU8MJI1+Mf00WfW7NhBtbV0uC1GTYphFqRo6+sWD50iUkqWZ/f5f09iWuXLjHlfP3cforFL1gnMw4mYzpu6L0e+s33obvBySxhWOmnFcL7HYUMcpMDlKT48TgLYOliHmuJS5vJtwNLczIxUwtDhKT637AvchFlWuu+ksWmcm9yGaVq6SVxCUXNs2KuJSJSol3DEPKuq2OFBq3Qp0ts+Y001jkKpZaYys1G0aOp+W4esa4Uth1A1GRykyMwCXJTHZbSNXpYoBnJWy0bv6ziYjHoTNyp61n1I1EWWi8crzL0Iq5eu4+uh+iyDVxYmHZCW/96l9n9solxhtTmkbizs+/i923vUq5EqOavSsHnLx0BV3L0VRRxj49ERW5SeiRlBpvu3KT4+NN6kaMfi5CwdXX9VwAsKqa37l3ns2WKilJDfcmm8SJhbuM8XsrNp+/QZEYHC36LHKDZeTyzJOvQC1RtdWh/s4pvY0ZWWgxOxlzY7KFr+dMYoc7yz7n3JUYqYs8TiKPtFIYlyrbvQWmkXE6GXEa+lhtSf72yTYvL/qMjLMo34YXFz59rWKe6SSlyjs2j6hKlfuTDW6t+gCsCoWw0PC1HK2tfmmyaPPklcKGE3J+51BMUQQecW6wt3EqfBOlysgViOt7k01mic3wi9Dj70r9f3D90A/9EP/oH/0jlsvfPX5r2zbf//3fzz/8h//wDV/noRf+m598EllqGJkxplYw7C2JE0uUu7SC61aCZSckscXBg1300zaDuuXwm0bG9GBLjLEZKYZesO0vWE22SAqNybJPkJmobWb7mVHlrDfpaDmaXPE7n3oGrwXBhJkpuOyhi6JUmFZK2o7unJV13dTA6QVkkUWe6ShqhTdcYLRlvvnpiCi2W+674G5XpYKmVChKiaqWXNg85mAyRldLbCPj7o2r6FqBoReYUoZlJ0ynQ+7NxyhSzcXxCXmh0XNCXFOcHIZX73PvY09TlMITYRoZeakRphZRZuLbEYvQWztu81JjdP6I07u7ZKmB5cQMvJUI/TBT1HazVOaaYIW3bYQL129SpgbJyiXPHw8Iomkk5rGoaFzsT5lGLpNUZCGklczdVR91X5RBNaVE1YQz/qxVkpUap6seadsf7bebiLRUMZSSvpFQ1ApVYzAwMpF4Vmo8WPW4sxQVI0cteBBb1O1/V43MSeKwGQnfyY5cMz3c5HQxICs1ZBqClYfrhcyPN0hSg7JUuXjpLsG8TxCK/ufetTtopwNmswFh5mCZKVmhEeY6aaWyYwneQNYiT09Sg6qROEiMFtUrM8/76HKDrwkTVlopFLVEUQsgzzuHEXdDiwYxp79sS9ebVoyvZzxIBqiyIACaigjgmWY6miy3WfHCJ3AQei20pyErVYZOKNIJK4Wt4QxFKZEVUYnQH+P4lmOkOGZC08gEsdPCpWI8I6HIdcLDDZaJvUYy+4dz4tAmz3VkucbzAw5fvIbthxhuTLGyqWuZ7YsPSFYuDx7sUDcytplQNTKrXOCBh30xUpsX+pqHUdcKcaRzMBsTlSpOO99+HPhsugGuLRa81dJH/th1ikLj4uYxm+mSvNR4+ZXronqh5zx1+RbHt8/heuJ1bV++j3+0x6a7EkFbmcEkcdh1VySlxjy1WOU6F/yFAGglFnfmY3w9pd/+bpJSw9NKdpyQolY4SSx0uUGTG1RZTIAsU4v6cAdLK7jgBry+6uFrIuFTkRqmqcXt0OJP7qxQpJrjyBUxv/M+y9hhkVpcHE6QpJqyEuFBvhdQ18o6s8F+jCTHTl+YPvCBD/CDP/iDNE2Drus8+eST+L7PfD7n1VdfJYoivvu7v5sHDx7wL//lv3xD13ro+l+YmWhKuZ7fznJtPcPaNNI6Ia4oVWaRx9F8JAhyUiPG5Iyco+mItNCRpRpDz6hb6E7ZyL9r/EyWGmHgkWp0uVyXUh9ELkFmkhQ6SSlKv2p7mq8qBdcPqNuvc/bnTSWLEa8W6VnmGnUlU1cyRamSFDqnqz7L0KOuZNEikEVPvyg0+htTLC2n5wiT3+F8KNgElUzTyKSJSZjYmEpBr80VSDNDbCSMDF0T8cJJapK2J3rDyASJrv15li2DXFFqFKVClQVsKM0M0kxAigAcJ0ZRRSwvQBpZgiGullSVjGZnVKVCEtuPjc5Vt2V3gJ4XiOqEmTAyUvotjCkrNZJcEAfTxCTKTGrE6e0zy86SBKoiqgamWrbmtHL9+1elGlfPGZgJcaWyynUWuc48M9GVmqjQSEoNSy2ICo1FJoyB84NNwshBaalnIMrEktRQ19L655enBmUpNmeqWiLJNYpaYhqiX1qUmhhP1UQQ08AUZj5fKzhviwexodTktURUisU9KkX4zlkfvmrBOyAWclVqyGqJqhZ90EmmkZ2hVhuJviZgQGdmvw0zwWwXcElCIFdbR3dWy0Rtq8Q0MrJcZxb4ZO39crbZaurHd/oqW4McQFaIBfCs15ykBkngoLUplFUjkyfCtAcCapPnAoxFLQkPTmiTZ/ratKbINUWlUJYiS97VchaBj2FmeH4ggnp08V6TpHodZOOoJVmhiVZHreCYKZadoCiVCM2Z9SlyHbtFXGtKSZgbzDKTeWqRxDZxYlG2BmVFK9FkMfJraeLErsq1MCO29+7ISEUpPTNYJA5RoYmKIeJ9UzcSulyvn2ditE/gnDWpwW8rOmlrdLR10UbytAKjhXJVjUTRSBhqgamLCkBVSywil6xUUWXxDAlCl6LQUJQSzcgpSwWjRVUr0uM1kzUIQuWj/HgzewZ+6Zd+iQ996EMoisI/+2f/jNlsxsc//nE+/OEP88lPfpLpdMo//sf/GEVR+OEf/mF+9Vd/9Q1d76FP/LvjCavAZZE4LFOTuNA5N5xQ1wpB7OAU2poGVTeS2NmWmhi/UQsMK+Ek9DDUEkMtsN2I37l3GRBUKUMt2Budrnv8Eg26XLHdXzAPXRapRVErDPUM/wyOUWhstMY3gDiy2X7bq2vKluGKdLt4IfoimlZQFBonh1sCs6uWuE6EaeTMl8KclqYmvf4SVRWO8LpWGF08wDQyTFO4i5P2pF63D9VVbBNkJm+79irOeEEeWRxONgSMpFLIcp34YGNNIdTUEsMUIURWkBCGDmFis7t1zHQ6pKw0el7AbH97vXinqcks8Lns3ydLDYLQw7Ejlove+nekKDXxtEeZ6ZSl4AU8DmVnc+vtA2Z354iNYkoc2QSxQ1Ep9JyQybLPSXsqncaOMOcpYgHY8JfMQ7GJi0uNspa5PjimrBVOQp+0VFm20xyWVrDlLUlLlc3WW3Iv9Hi6P2OSOJhKyV5/JloOckNa6BxPx8hSw87mCUHgcrIUo2KyUtMfz/D7K7LU4COvPMXl0Sn9/lLkvh9u0NQyjhfiD+fcvX2RJ55+DeveLqexcM3ris610Qkb4ynmrascxg7LQiWVZfQaNs2Ce5FOVsm4WkFeKRwmOqbS0NcqPrV0SCuxEJeVAO/YSs1+ZCNLMNBL8lr82w0zYbc3p0ZinpoYSsmGv+SSHfHgdJNp7LDKhaHT80Kmqx73Vn2soMcT42PMtuf9OHu6Ry1tUVcFJGaemZyT5tS1TFroeF7I9nAqNrx6gWbmeMoSuwyJVy4Pjrd4+i03xChvm7AXJxb7ty5gmRnD4ZzsZJNp6NOzIrb6Mz51cJ6tjVPcgejZ17WC1pr9TGC7v8BLU27OR8jAthugqQWGmQm2AKKqmKSGqOzpBbqec2E0od+aTw8mY7YGMwwzpUx1lqfDNRo5zg3mqcX53py8VMVYpRsw7i2YrXqcRt4aA+0YKYvIJSp0EbbTSCwzs60EyShSQ1LJ9HXB4b98bp+jk02CNgdl2C7+ea3SNCLqe9sUz9qzPBFJgrRU6dsxPSdkEXqchB5b3grXiWlqWRgtlXIdINTpD49+9Ed/FEmS+KEf+iG+/du//Xf9ved5fO/3fi+DwYDv/M7v5Cd+4if4iq/4ii/4elLzkAOBv/Tu9wOiz73KDb7+T/waH/7td4nFuDXa2XrG1tYJhpXy8ivX2ezPcVxB3Pv4/Ut85Ts/SjDvEcUOeTsGeLTqI9HQt2P2dg7ZP9glSE0kCZ66fIvfefXJNhCm5lJ/xix28NrRHYlmHQsMtOU3k7e+9VOUucZiMiSInPUUQFGo3Dnd4umLd9YnCsPMmM0GWGYqEsWUmhdvX+Vcb9ZiQXWmkcvF8Qm9/hLDSqkKjbv3z9F3Azw/QDVyPnnjKRSpXufOXxhNCBILS8vp95bUtYLjhUSBSxA69PwVWWYwXfVYppZY+LYPcP2AJLK5c7zNpr/EdWKyXLRCXp6N+fNf9luYvYAyNYiWHmWhCjCIWuINl2hWyuzBFmEkStdv/6//5Qu+OT6Xfu0r/hwbgxlxYnF3ssm5wZSXT3bQ5JpNJ2DgBkSpJUbeSmVd9RFRoRpRoXEzcHhuuGDbW6LKFfPYpWdF7aifyuGqxzwzcbUCT89IS5Vtb0WcG0SFTlqqRKVKUop45ZGZokg1aSX+LK0UrviLFvec4loxrhMRRs76FCnLNa4X0ts7oS5U7r1yZe230LUCzw8I2xGoZeAxbWfEo8zA1Ar6TsRgOOPXX36GpoGqkZnlOrZScZQaRKV4qL9juOBB7DAyMs77C27OR5ykBjVipG/bTAhKbV1+O1v0V7mOJtds2TGTRLQ1vDak5fLOAyazIYdBn+NYUBC/8q0fZz7vMw8EpMUwxMy72rawdv+3Fx75vQDw4ff++XVJPWqrca6RiqpcofHE9gGeH3BwuM1x0GPLWzIJPapGZsMNeOptn+LGx5/BNhMcO8YdrPD2Tkhnvqi+mTnRaZ+bN68IDLSZ4PsB7mDJ8nRI00jsvf0Vbv7mcxh6gTdY0LtwxP/nF74WXS4ZOhHjwYzJfLhOzJSkho3+HK8XUJYi9tvdmPPqi0+ja6XwF7Q/t9lsIFqUbsRkOmw3vDV1LXG4HBDkBllb9TjnrtDUcs20qBuJ9167wXLlM49dwlzn8viUm6ebrHLRJhIpihqaXGNpBX5rAvzYvUs8iG2e7C/474cbAoNuVFzxIg4Ti00zxWirrj0z5Ynz94RBejHEN0Xg15l7/3CygaZU62emItc8/99/8ZHfC+fOnePBgwf0VYd/8cRffqRf+//x+v/OoozY29tjf3//kX7tL7V2d3epqoqjo6PPG9TTNA1bW1s4jsPt27e/4Os99Im/asRojG8mbHlLXn7pKUZ2tEb07oxPWa584tChKlWG3gqznWWVpIYtJ+DenQvr2du6kVGocfQMUyuwzYRX71wWLtx2R37r3gWeOX+PyaLPqo1V9YyUo9BHkyvO92c8WA7QlQrPSBl4K8bDmZj1NzNG26eok7KNchU92ZETkiYmjhcKd/jSxzAyMcaXGcwCn6RQ8T1hFGtCiS1viapUZIlFEtmCFDicAqxLxqpc0bMjlHZ2XMzmS+tZ3rJS0I0MfzjHGyw4PthBUUo2BzM2gbzQMAzB428aiSs7hyxWHpqeI8sVXpbwJy+/jrMxQ3MTqkzj8P4uG5sTDg+2mUUeu5nB1vmDdV939ZgMXX03YBWIk/xef8Ys8rDb3mGQmaxSixoJX09xjYx7ywGunnM3EBjdP7azzzQTo0qzyEFTalaZWNAVqUZXKq5vH3A0HwECCXvBDbg/2SCrVOJSY5KKABpdqTBavK+lFZCamEopEMqNjKUJkFBZqgShx+snWyhyzciOeOLyHepKJpr0AfD9gDsP9hi2XoqTkw0BkDFyqkohKzWeedsnefnFZwRhsVA5Pdlg0w45iV3qBi57K8Jcx5Q1FK2hp4m2RVgoVI2JIYuRquu9JUGhM88MbocOvtby3BH9N02qcdSSGli2gUjPbh2gaQVhYnM8HRNlJrpccqW34PLOA2azAXGb1ZBXKnIh7oMs11gseuw+lrsBnrh6i2jpEScWeqVybzlgq7dgYzCnqmQmyz5N+5xwtJxJ6HFl+xDdEKO90/vb+E64fk9GS4+6UFDNnLoSfAuzH7IxmBMnFmlmkE10gsAlSU0aJJoXnsZxYnQ9p8o1Dj5xjQuDCUluINGszY2uFa8d++eee5Wjl65gOQmGE1OmOq4do2kFqibGh72tKVHosApd4tRa5wNUdU3dbvJNtSQqVeapwSQ1eao/Q5FqgdhtJD5+58qaFaArFaerXsvyFxtVscHNmaQWaezwHm9JFNuM7RhHKzDVkoFeYcgNI0O0va4MTwlTC8dIGfRWAtQ0G6CqoiL0qaNdnhie4tgxhpkx7i1aA7GCJNXoXwRWf6eH13Q65R3veMfvm84nSRJXrlzh4x//+Bu63kMv/LaekbYjZlqbuqYrZdu3EnzsrNSQEmGsCxPxRmsaed1binMDz0xEn7Utk5ua6FMpsjCp9IwUFAT7PDORZQHf8EwJ3wk5XQzauEmFODdE77UWI4VaO6ai5jp1pbTz7RVVKmAfZ3CQph1BbBpJ9OL1HN0Uzl8CGJgihvPM2Vs3oidfVSp5u9AXLXTlzJcgAkrK1vSX43ghZSn43QB52X5+oa37rWLmWbwOWW7EKF8uuAaSmeK7AjDTNDKOHYvQmNCGRqKpZYbDOZJSidANpSSMbZwWS5y2886PQ2lmULSejKqWMdpFv6zkNuhIaolhzafLv6lJ1UjoUkNWamwYAq2aVSqyJBbweWaiSTWDtpVj6xlF+/M7821Ubc/cUUvGVizY/lKDq2dsDWbIs1GLQK4pK1F+zkt1Pdte1DJR26o4l5jM531sK1mzFIqqjYZu75eh1JCl4uRm6xlFYuLZEXFqkZUauiLeC6ZaotUCMVwjAE3UoMsiwtjXhIM7bvkFqiwALppcUzQKfT0nbvPazXbToivVmgGw1U4s5Ln+6YCoFmbkGilOL+BwsoEi1yKXwEqo2g30GUf+cSlvsxLOzKQX+zPyQgUs0V9WKvJ2Dv0sl97rBSJApxTtF9uJ10AsTSuIAhdXWSG1SYhZYFNVMopSYpnV+nqeEwno1spjYzxFszLqQhGZGmaKoefrZ03dSPQHC6LAZRW5REcjNCOnqSWKRLRLVLUSXqD2+VHE5vp5QQ2emZAWOhViCmORG/T1DBlRvdHkGk2poYKST3MEovb9L/r1OTUSUaGtGRVpO8VkKmL8VaJpq6gNlpZz0Ulw2zRQXSnXVMwzb4XU3ieaWiJJYlOhyDV1LfxNZwZmpeUQGObjN/d1rv6HV6/Xe+gqxv7+/hse63voRo9rxRhaQVXLrBKb87sHIiK1ZaEHoUeQmsLMlRk8WPU5Wgw5WvSZxy5VLWNpuYjQNDLxMGrjVmVZpOTFlULPTMS8vJajytX6tGyoBcPRnKJWsFqTzTyx2RlM6VkJhlogyw23T7ZZhS7zeZ/Tkw3yNlHv7MF3Zkgs2oCbslKoKhXdzEQEqBOyN5qgmxlNCx8KMxPDzJDPAC1ewGzVI4gc4sTieDFcnyqbRsK0Uiw/XE8KOHaMppRUpcJy3uPkZANdy3EHgsCXZzpVJZO2sKG6lglCl/54xnLlE0Y2ppVyf7rB/HiD6f0dlscjRtfuUWY6rhuxO55Q1grTqWhv5KX62N54Z5z0rNC4v+oz8pf4Zozdmpx8M8HSijU0xtNyojYtbGBk3F/1GdsicldwIBq2vGW7qMsocsM88NcPtTg3OJwPMdVynfh10V+y15+x6QYMrBhLz9m++IChF6zNVKpSt9MfBgehx3Ek5r/LRuIksTmZjPnUyQ7H8yGzRZ/7ba78QehzEPSoapm6lplMhyIJ0ci4ffMyji1aW1mpYehiVtxSC3wjIy50ojMzmCw2GtPM4IIbsONE4ms2kqhs1TKqXONrFbvual3B8PWcopbR5BpHKxhaCZfP7XOwHHBrtkGc6+itV8ZQC2SpJk+FadQ2MkajKVtX7tEbLLHsRJT7lceT1Ahwd/8cR/MR89ilqBSefssNlonDnekGx4shnh1R158Oi/FMYUwMA5cktgS3307E+7FUsfsBZalSFRqyUqE7KYvJkFXoik2eG4lNs1yzee6QnSfuCohOoaKoJZqVtUY/EYTV7y9x2vvN35xhGBmzyOFTLz2F6SSCTzLvryuDi9BjEfgUucbsaJO89S/1/YDRaEbdbvibRmKWaywL0ZLZtmKeHp3inBkvK5WozYIQn6tzGFsUtcwq13kQW7wWmNRAWGg4asmuE643qWWtkJfClPjEcMITm0ecG03Q1ZLjVY+8UkkLncmiLzafRoZhio8LffF9FoXGctljfzkky3U0tcSyEyy7Q/b+YdLzzz/P4eEhP/uzP/t5P+8//sf/yMHBAc8///wbut5Dn/hfO97h6d19ktTk5ZMdLpYHIgs+sTkJ/TaaNcezElwnxDJTikJbR5zOAp9rz96gLlQWJyMWLbDi2mCKUugkhc6zG8eiHGidYWyF4/fMTPOxV57k6vYB90+3OI5c4kphEPhi0a0VFisPSysIMouhGuDYETcP9khKjZ6R4ppi06HrOSenY9FjHMxoGpkH986J8qjUiBPdykWShTnMMDNu3xVtCseK0XRRvdgbndIbLClyjcWizyq214vi5ULEb4JYyG0r4fbhHp6Z0GsZCCcHW/i9Ff3xjDLTSWJrbVRcLX1++xNvpWnbG5pW8K7nPkGRGMxmA06WAz518ypxobPjLxn6S/puINCd26doVkqy8N/QzfG5ZGo5aaEx8pe85alXWM0GmEZGUalkmcqyNHl6d5+8EJAWgGfHJ+sKRM9KuDHdoK9n61TDt371r6P+ypdzsuqRtPTHM+zv2QIpfucqalsxClOLZWqtT/3+3T1OV8Ls6Bgp9xZDwtzA1TMu92fcWw7oWzHLzCQsNE5DD0ctOIk8iDwUqebScMXexilppnOyHDCZih5yUamkkThtv3L/AkMnYsNfcLIccGEw4WjZZ5IJsuUiFymEtiLc2OfcAM9IeRD0eDWw0eWGPTttPQ/C1S2wy7RGtBVxrjNPLYJaR5UrZrMB88zE13OubB9iOTE3715cGy2zxOK93/wLJK+PWT3YIll4xKGNP1qgpQW3j3Yey70AAlp0ktiYSsV5b8lHXngbityw6a6wjYyD2ZiBLXDVktRgGhm/+DvPc9Ffcn7jGEmpyGKLqlIFdtsS46rB0kMOXBSlxOsFxInF4XxEOZF5Ym+f08WA/nSAlUVtFURmeTpcJ3gaZsbpyQa+F7J19S7jSw9Ilx51LbM3nLK5dcLxg21sK6E3WKA7Ka/dvcSGv8BqvT+y3LCKBSzKsWNWK6+l6qkUpco1f8X9yAW1xFJFSt8isdf35MBIeXk2Jm0DevpGwW9OBjzhJexaKSMj51Zg82QvZJXrHCcWz41PWaU2t1c90kqhb8eiahZ6aEpF3w0wtfyzDMenp2Mmyz5Pbr/G8Mtfw/gfb+H0ZMzBbExeKTxz/g5R7KAbObYfYm/NHtv9cKY3swv/Uetbv/Vb+S//5b/wzd/8zRRFwV/8i3/xd33Of/gP/4G/+Tf/JpIk8Tf+xt94Q9d7aHPfL7zr/8750Sl5rnOwHPDH3v5x7t66yCxyqRuJ5669xmt3LgGgtycux0zFab5UmYY+QydYL2zT6ZD95ZBNV8yilrWCpWd4bkjaRvnWjSxm9VNTjFAZGU478w2sT5QDb4WqiKrB6WKAbWSYbW98EXps9OdEbSzu9uYJqloymw3akbuSza0T5tMBRamhqQWOG30az1uJzcdo+xSAKtdIE5PVyidILDSlYmvjFNNKmE/FmF9RqSwTmycv3hFQoWWPvFB5dbLFOX/Bxd0DDDNjOe+J9oCZoZsZdamynPcoShVVqbg/2WC7v6DXW+L0AuKVu+7j2VaC5SQcH21S16LVYrUnKb8vyIWTyfixmPt+6d3v58L2oXDrzwcocs1J4BMWoiR+bXy8jrNtGokX7l/CUgsUuUGRRIysItXMUxFYMnZEQlpeCSa5ZyTsL0VEb42Ep+V82TOf5Dc++Vam7Wloxw5ZZiYDM0GSYJZYFI3MjiPoamf31Cx2GNoRfTfgteNdzvenrBKbZWahyRUPIhdPLdaeAF8X+QBnbumztMOwHfFaFRrPjE+oakGDU6Sa15cD5plo0Zy3Y6aZial8ume/aSWs2pKurRa8uuyR1xKWKvr4itQgI0ZYe0bGU7v3efnBeXSlwtZyPDNpcb/q+qQJov12ZlL0vYDJfNiOyopFyh8tAChSg+W8x1v+37/0yO8FgF/+E3+Bw8BnYCZcv3CPB8dbaEolHORtZPQycjG1HF0rSXOdoiXcWWaK7wcEgUuWGWxsnrLzxz/J/v94O6uVzyJyyEqNt73lJfF1pgPCyFkDos5GNLe2T7i/v4dtJuv+9SLw2RhOsZxkjWIOFz5xYolx3EZEdINoh4SJTZwbWJpg4Ot6voYMBUuPo5kg8JVtmqaqVESZyX7gE5UqRfvM6Ok5IzNFl0uW7RjrSSpaBn772k5SnbIRuF5LrXjr6FSAogodXRavNSk1XD3nuadf5uVXrrM9nGLZicg9kBvBLmjf+6oi2h+GnmPZyXqCKWwhWo4d0x/PSFrMem9jxujHXnnk98Jnmvv+yZW/+ki/9v/z1v/2pjX3AfyFv/AX+Lmf+zkkSWJ7e5t3vOMd9Ho9lsslL7zwAkdHRzRNw5//83+e//Sf/tMbutZDn/idNi2qaPtwAMvEIcwNrLYFYGjFmqk/Dz0UpSTLDEHBk8WbxIxsdD3HdSL6mUnPCVlGLkeBz+XRKUlqsoxcoszg/PiUvFRF/7Q1cO2OJ8yWPdJCWy/2Z/3YupbXkcBneeqbgxmKUmGZ6bp0C6K/LkkCspFEFvvTDapaYstfous5vfGcLLKYh32RBb/00LRiXZo39GzdLzOthDh06A8X9BqJ1cJnGrnEkS0iiFc9NntzMV8r19SVTLjyiGJb9MEzA7eKCAJ3bYas24VH13JUVbQJTk7Hbd9OIcsMTCtlNJ4SBa4Io1FqgtBBUWo0Pcdt+8KPWpLUcDobtYlsIn9BU2r0uhJjeG2lIyl0ilJlkevseUuqRkamod++rqjQUeSGvhPxycM9FKluW0c6ZtsLllsXchyKUbqBIUxiulohtUwaTa7oGSmvr/o4qoEiidnqRWKz4y+QpIZlJFIgD5aD9cYjKnR27IiqEXPfaaUg5wZBW7q1NZEbcHaCA+Gqj3ODMNeJCh1fzyhrmbiSW266cPLXiBwCBThJLFa5hq2KgKJnh1PC3BBeAKlGkRui1jtSVjJ3T7bX3pWyFqbColIYuME6m2Aeuqjtfa3rOYaZEbehMpaZsVj21gt/lhoczMa85bHcDQigl5VgaTl5puMYKXW7MUoLnc3hlCi12h5+jSzVOIZozWlqiW7kqHFF0kjEoUO6v4HTC8gzHTPXKdu0T8NO0cMcObGEh0fP8fwAzRJl/Z4rGB5NI4mFr1JYLHtUlYrrB8ynA/TW43N20FhFLnrrUZnHDr6ZiPu2UtiyY7LUWI8pN40kyveFjtNmIEwTm3Peiv3A515sklYyvlZwEAlwU1ZLeKrwd6hyTY2Y1z9DOCuSYO2fUUGbBjwz5ST0sNRCxPQebba9e/FeMK2U6XQo7n1NtHHmgY8qVxSxzTISLZGeG6y/19PFQEQSlyqy3BDOfUaP6X7o9IXpp3/6p/l7f+/v8eM//uMcHh5yeHj4WX+vqip/62/9LT70oQ+94Ws99MLfdyJ0XaTlyVJDkbRjVa0pZbnooSslPX+FphfMAh9VqVgUOlFmYOuC5R6188S2E2NpYncaxjZR+5AoU4tp7JBVKs/2l0Ttjl6WGhwzZbR7TBA6xJmxHv0q2p5qmhl4ToRppRSBS1GpeL2AxayPppVig1IqpJWJolYYesE88AlCj4NQmCU8M0VLCzb9kCpX17vlyXS4/tpZruP7AaaVoOhid75c+fS3pqjtnLAxLVlFLkkuRppcJ8LTM/Gac50kNUUYR/uGLguV48WQrf4M08hJM5EkV9UyZduvPw567PVnVJVMltsoSsVwa7I2ISpKSZBa4tThr+iN52/4Bvm9JEsNt2ej9oSctT+3BE0pWaUWx5GLpZWUiUxciZCRcW9B1Bo+t7ZPiAIXZ5WjKjWuE65dz3mlQN4u3qoAN1lawcHpJr6VrMcCQSz4QEuDzDCVirR9MEtSw1Hs8PTFO6xWPsdzH0VquBd69PUcR8tJK4Xrm0ecrnpMS7tFD4tNra5UWFpBXqkscgNNqvGNDEMVkJdpahG0M9muVjDLNYpaWsNVaE9iitQwzTXyWqYoJCRM3nHppthwZiZJrgsjnyxMgkmp8cpiwLu2D1gl1jpu9ywcxjRTZKVuzXPCjGa1qZO+FeO5IZpesDrZpCqV9Ub17P5+HNLVgoEdUjfyOvcgTizSdnO0MZijK+X6wKBrJZoqkvQUpUZpZ+iJHILI4ei1S2xfu0PVnmglGqpCo2gz75tGosh1ND2nf/kAbRAQ3tyjN1gSrjyqShaGQT1jHrstYCzmeD7kwvbh+nUMBgteuXsRx8iESbM1qqaFvs7ymM/7WGZKvjbzCuMqYp/GMte5vnHEPLWoaou6EffOvcjkKJXRZNg0JTYNYbgrapkK8LRy7cGJSoWTxBaeDrXAtyMOg96ay//q0S7XtkSFrSxUUS2MHZw2bMeyE145PMemK6ZR4lxnkVlca7+ntNA4icREzchfoqo5UTum+jj1xc2b+7++VFXlh3/4h/nu7/5ufvEXf5EbN26wWq3wPI+nnnqKb/iGb+DcuXOP5loP+4n9/oK6ltFbmMqDgx3O9WbsL4fshy7n2tLZcuWv3dx5ITLil5lJ0vYiVaVCL0vC0OHuYkRS6Dxx8S7PffWv83P/x5/F0QoUqaFvJISByzJxyCuFvhVz6enXeP0TTxGkFq6ZsLtzxC9/4m1rx26Y62hBT6RPtejbT752jZ4Vo7fc9d5ozsdfepp+a7irG4mqknn+/B2RrNfO7saTPllq4tgxcWKxtXWCN54jyQ13HuzxxJe9SDrts5oMCEOHvYv7nO5vi36+03INzITtzROc/ooy1ZFoWLTpXc981W/wwn97L+d3DxhevY/qJSS/ZOK6EUqbrnV31cPSchxHzDbv9uYcLfvsDubs7h2wmA042t/BdSMGo7lIHJRrppFHUujY7uMx8ChyzYYTEWQGJ4mNX4lytG/FeGayNnMaZoGmVJwrNFH5af0ak9MR96YbDO0IrzU+mUrJXm+BppTEucGvH+6ya6Vc6M0ZeCsmyz57W8e8cPMJXl35bLQI2o22enBjNub5nf32wWewzCxMRfTGF4lIgWxagBAIx/GWHVGWynoj4bcAmD1vidIyHBSp5ry3FPeTkXF7ssWOt6CqJaLSFZuHwZS0UphnAr17L9K56ooN0Ulq8PzmCcvUpKgVNLlituiLknIb7/rJ020u+ks2exNUpcI/3SQrNDwzRZXFBvX81Tu88tKT3D3dRFNqhk5AVmo0gUucWCwih3Obx5zMRiximy1fsL7jwEXTCv708x95LPcCiOpZFNvEqUGUGzhmQpBaJKVG1cjc2D/PprdapzraVoLtRkxOx8wDn8VKhHGdVQgmocfGxQeoRo5hZMSJxdHhFqvEFj8zMyGMbDbsmJNXLmFaKeMvu8HR//nu9v2ssgpdrly/yflaJossZrOBqOyUKppa4pgCze1bn24rPX/xFkHkcG4s2oFxYnG07DOuAiahx8emI57fmPDMuXuoakmaGVzrz3h9skVSqlz1YjasiKxSuezKbFsKZTuB0jRSm9YoKkK9toqQ1WJ6I69lznsrzo9PiBOL65tHzEOXWexgqCVbu0cUqcFq6bN/ssXFHTG2q2kFsiLopmcbrqTQOd+br83XuiY25NPY5eqlu9jDJfdfvfzY7odOb0x7e3t867d+6+f8+/l8zp07d3jHO97xBV/joRf+o5NNZpGDrlYMnYC8UpkGLq6e8c7tFdO2vJRWgot+fXefk/mQc6MJ18yU6ox4lVhEqUndSLzriVc5nYxYLX2U18/haAVXtw4pSo1Z4PHrt65xqTenqkU+9cmdc6LvZmSixGmn/Lmv+2+sDjY4OdmgWvbxzJSyUkgKHb0pOTc6xTAyjqdjToMeO4XGKjdZ5WLe2zcF+U+E9pQ4XoSiF9SFGOdR1ZLJZMx8NiRrswmaRuLWR58FxCLoODFyOyIznQ55/WiXp87d/6zkPHu84K1PvE6WGqI3/vJlBp5w9c9vnePu/jku7D0giW3SxFz3sYtKEX6ExGQwWOC5EVUls5iJ0l1RqoShQxTZyFLDk0++hiTX1JVC1WJbH7UWiY2t5Wx5Ky4MpgSpxavTDRG0s32IaWREiRh3k6SGK+fv84lbV7G0AlWuuDvZpKhllomIMtVjh0Hbx+65Cbt7B2JkqhYl0CAzGTkhr92/gKmWvGM0EShTRSQ45pXCOSfghcNzXB8K17NZFjhazv5yiCZX7LoBMg37oY+uVOtwoTAzCdpMiKujE25ONzmJPDS5wlILbLPgzmKIr2fsyAtGdkhZC0JgvyWqVY3Mtf5sDW+66mrivlALzrkrXpxssGmmOFqOoZTMY4fDyGsd/BnXhxMcIyVJTTEpoJac3zoCIMsMgtjh/s1L5C2ZbeCt2L5yn/nBJnmmo2kF1778Y2SzHqaVspUawiSnF3jDBWngcO/OBR6XvS8vtLXJUFcq7k03ePu1V4lCh/unW+uIYPH3JaaRrRP8HCNlOJzz6r2L6/HgsDD45AtvpdfG8C4SB9+MuX7lNrJaksUWr927yGCwoCw0TgOX/Z/9aq699QbpwqUqVXQ7palljvZ3xHXsmGGpcjgdA8L82d+cUtcKGwMB6yoKjc2NCa/fv0Cc6+z25th6ziq1yWuVLSsjLjTiRMCpTkKPgZmwc7ZRzHU+ORtz2RORxBLQ13NMpeIosehpBe8cH/PL9y9yvTfHUIVJsG9FvDLdJK/EGGK/zUEZ9xYMa5kotahLlTQx0fWc61du81svPbM+4Z9GrqAlVhP6veUaVHXh6dfJQ5tw3uOSXImW6uEW1ryH4zxeV38DnxWJ/Ki+5ptViqLw3ve+l1/5lV/5fT/3a7/2a9nf3/9drYA/iB564S8qhZ4lHs7TFqkqwDkJlpny2nSD895SpHPVCqvQpe8GInFPrWgamcmih9qyrlW1JE1ElSDNDKLAZWBHAOSFSo3EeX9JXokyt0yDLIuozjTXiRKbMHLRDz8dPuK2Y4K+FVNWyjrh72Q2Yhq5pJWKGzvs+QtOI4+iljG1Arn1CpSVgqJWmCA4/oVGWYgTim5krFYeSWpycedgPXFQVoqYm3+w3XLDI9TAR5bFPHCaGYSRw2g0E8Y8pULTSvYPdjm3e0CamCK1TM+YTkeUpbKGIl3ZPsS0UjS9QJYrstSkrkX/UjNyDCvFtIShryxVZrOBKIXmKpJS4zymUr8q1/htuleQWvSdiEVqiwd3La1LmFmpkZUqHG8xtCOKShE4ZqlpU/UqLD3HMlKq2hUxzqlJdrSNZyYsYntNWatqmXlqMbJihk6AbSWs2mCdtFIJCp20/Z1Xhcw0tbk6FPz1tNBYthWpQUuYyyuFqNTomWJ0tGpklrFDz0jxzGTtzj6797JSbSsZUpuqVmMoVTtxUAkuRCnMXU+PTlmkonWwaCsPVmsglKUGUyvYdQPCXCfMdSytaDerGrpa8cT5e7x+/wLnxic4biQCqyJ3/TOPE4sssEliC9MUM/zHN65gt2EyslJz59553nrxo8STgehTq+Xv/ct8BGoaiZNQgK8srcRUihZwpLFqW3Ljz/CbZLmO68Tr3r1u5IycgKqWcaSUsbvitcnWOkhGkWp8N6TINerUoCoVzo1PAKhrieqsynM0Rm3H+azRgngyQG/NhSDGcHU9XzM4kpW4f6p2wZ0s+zx59RaWJnwAvheSlVqL3pZJSpV+T1S0ZBo23YCeI8zIZxuf6705aaWhyxWorCNzNwwJSYJJ5OFrJbae4xipmD7Qc95u3luPA8eJJXggZkaR6xzOh8ynA2btBJPcsvkdMyFr2Srv3L1PWaqtwa8Qr33lEi09lssep6sel7YPWAXCW/TFmOPv9PBqmoaH8dlHUcTBwQGLxeINXe+hF/6s1NjwFxSFMDtVjYTbBnEobS/TODO/VQ11C50BKAuNJBW54K6RYmrixH40HVPVwhQVt3jbs5OiKlds9BbcOtlGkys0VRgF01xnmdjiAZyZlJWIsJXlGl0VJwbLFKenvFIpK4V57JC2G4gwMzk3OiVITXJUbFP0R88IfE0tIaslVWlQFm2P341wByLyNmxkbFewwaWoEdcpdOahi99bYTlinv2s/RDnBqvURlNLlpGLbwu6WJwZolfbbg48J+J4LngAmlIhSxV+f4XZC1D0AhqJ8Lbo06paieXGgi9uiSmHPDFgNmA1G1DXEpYT458/fkM3x+eSItdr13Da/twstVgHDp2BcM4AR/PY4fLmEZNln7xS0NtyuypX6xZMmNgock3ehvt4bRJb1Ujr8b0zLOqZU7ysxf/XwDTT8bSyNelpRKXaAlBqikolrxTyWmHTCSgqtQX5iPvjLLAkLVVcI6PnhGS5zrLFDmuteTQpxJhedGb+Uwvi8tMgp7MQolFbZl+kNsvMwNWKNVnwDLziGingMUusFmssQnp0tcLfnLJ87TrDxBLjV47oTztWTN2eAMOFT5SIhV/RSu4e7HL9ym1UXfxsppFLUynksSmMoMbje9CXpQhPSkoVSYKhFZElFnkuzJtJi+c++zlFmYnnhkg0NO3PbDicE4aOaHX0VtycbqLruei1ZyaOGxG1jnRNK+iPZzTtprtpJPpOxHLpM96YojsJkip+p44bUVUKklyjmxluLa2rankmUv2qSiVtxyfLUkGVxURCVYlNXS59+jHpGCnHQQ+nDe3y/YDgyCFtAUUb3or7i5EA+bT3Td+KsbSCrBT+k5GZoirVuj+fZwZ9LySNTcJIuPDFYUMcaopa5H2cPReLQluD1CSpwTMyLly9w/7ti1SVSlVVYhxy1iNNBcwrzHUBKQuF96ksHk818DPV9fh/b7300kt8wzd8w+9a6H/7t3+bCxcufM5/1zQNs9mMNE25fv36G3oND1/qDz36VoRhZOz25jh2zGzVI8pMykrhTz7zCU5PBDmsp0bs7B4SLHutEc1gFnhc3DpkFXgUhYamlhwHvjB31RkSTRvQ4jGwYkb+kqLUiNp+lanlvHD/Ej0jRZMrPDNl3FusY3Sj2KbITJ64fJuT400RyNIa6UZOiNc+5JNS43TZR5EbRmbIYLBYtyFsK2F88QBzZ8rpx66jaqUw86WC6rV14YB+OOfjN57imas3sZwEVROVC0KXLDWQZHGiK0uVYX+BXwran67nbPTnmFaK6cQ87QccHuxgGhmeEzEP/DXV0LYSBptTjh9s40QWthuhmjnTxYDtzRMsL0JWKpanQ0wnpspF8qDnhrx8/yKekbJZzfFmPo8D2lu2GFZTz9n0l9w83SQptfUI1GTZZ5VaDOyIvdEpcrtRSAqdIDfY8xfsOCGzwCctdHypWU99OEbKcGPJ6Xwg6IxtpO6mu1qnAh4vhtxd9bjaolFNpaKvF4wtEefbNKLEGucGca6jKjV7/rIN/MnX/o+k1DiJXVaFhqcWPLt3n3ngczwfrh/Yr8/GbNkhti5c3HFu8FvHm+xYGZd6c8ZuwP5ygNNS1XpmwmTZF//ejOkZCQ+CHpZWMO4t8LyQ1+9foFQULC1nRxWbgZ4TErfBT8vjMe968gY3bl/haNnn2t4+03ZSomdHbA6nIhJbakQrYNYXiGwvIlm6rJY+e/0Zt158mroWVEhFeXwn/lnkMTbFCb5GwjUT5kufnhfwZbtHfPyV60wjt8V+i4TBnhPS8wJUtSTPdCw3WudnNI3El1+/gb85ZfZgi/2ZKM83jcRgY4qzOWN2d5fNZ24y//W3E2Um7/yTv8nBS0/g7x2jOinL27tiY2ynaEaO3gsFoS+y0KwMt/V13H95W5iMjZTz/ZnwHcQO89Ti5mLIl52/wyIRffa3DCdkpcbICalqEUHc6y+5vxxgKCW2VnCwHGCpBRkqllbwxPl7aFrB0dEWZa1wvr0/zkBfo0sH3P/UE7x667LwyXghjh2TpMKArGkFT2wf0B/O2ZEP1tWy4v4ljudDbD1jszenKlV8f7WuVL724Bznx6e4biRYId6K2/fPr8csw+jx4Lw7/f56y1vewnve8x5+6qd+6rP+PMuyhxpTlGWZ7/u+73tDr+GhF/6vecdHuXXnEkFq4Vsxd0+2uHbuPlFssz8dM2tDYRwjQ5ZqXnzlKZ68eIciFyeindGE2aKPoRdUlczxfIihlpwktshlR4xCXRhM8dwI3chIYouxHTHqLfD7Kywz5f5kk7G7ErP4qx5ObqCpBcvI5d5yQHNLEj3Qdl7/9bsXubBzyGLRI8kNLmwfrt9Qii5K5mWu0UxGLEMP6fWLbLRZ7auVxyp28KwE6bTBtFM0M+Pd7/lNDl+/KIw0uUGQmuyNJkznA0aDOW/7mv/JC//tvShSjWvHuG7E3YNdxr0FJ6djskON8zuHnKx6nB+f0h8u1mFBZ/29X/no8zyze19gUEPQC40gtZBPxxRH22SFJjYSTkyw8shyg8vPvEJRaGJGuZaJpn2Gb+j2+L0lSWAbgkN+GvTomSlunVNUKveOdtgZTTi+71OFHqvE4kHkYSoVTwxPubq7j6qVvHbvIkUlM09sXptuMM0M/sTefapa5u7xNiN3hZWZbDgBhlrwieNdtuyQ15YDAP7scx/j3uEOea0KY+BYjO2VlSJ6/HrO6ydbAFwenTLemPDfPvE2elrOaWoRlApDveDJ/oynrIQgsfjvN6+zaSac6y3EaFqliuAhb8UqsXl9Nuap8TEjo6BoJE5jl3O9OZpctbTCnGnk4RopQ39JXmjcnWxyZThZg6Km0yGT2OF8b45nRzh2jO2HREuPRSgolZYbUWY6b3vLSwDUpcq7n/0k04nYkMgtkfDa2z+FYhRUqY698NCcBEmpkOSGYH+XJ55+jfs3LzEP3TVN7nFIkWoMQ6TQeW7Eawd7eO31ZLVk01utsbmKXDMazTg43sLUcspK4dZszLXxyRpVe9ZD3y0VDk43ub3qcW42YGNzwsnhFrNXr/Hssy+xvLXH9sUHbPOAyc3zbF25x+nNC0SRLcJ6ekvi0EHVC+xSoSoEICiLbKLApapkNvtzQTc0MySlIk8NBoOF4GJ4Ea+/dpXnnn4ZWak5OdjiN+9e4eve9gKmF5GFNvsHu/h6KsiBtQgdWiY25wZTBoMFTi/g9GCrpfCppIHPKjd51+Xb+LunINUEkYOl54SZKSqSm8dMln0UucYYZthuxK07l0SMd1vtenLrAKOdsspzHf/iIfUtmbpUkeKGg8jj0uYxaWJSlCpFoaGrJQNv9VkJhY9NzaPv8b+Zmvwf+tCH+Lqv+zpAnOS/5Vu+hevXr/M93/M9n/PfyLKM67o899xzXL169Q1d/6EX/tdvX15z6VeJzdAJWSx7FJXY2S4zq82RFrPb50anTKfDNV1K1QseTDdaSEvGZn9OORtzQV1htqew41WPWeStGejzWEA/loFHXmi4biRMV6bojdWxhKYWuL4o9Z/N9Oe5ThzZ4qSpVCwWPdFr0zPKUmW58ikqUQp2nQhVFSARRalw3Ijl6RBFLRkO5/QHC8pcI20BHHUlk4SiR+Z6giyXxDaLlYeulVSVyuK182SFRlJoTCMXXa3YGUwFaMUN1+XqQRtyFK48ktRY5wGYRsaze/coK4XR1il1qbKY9bl67j6KWpJENmkmWOT3757HMlP6/QVVoeL5gfA35DrzeZ/zb+j2+L1labnIR6/E6RpgdzhZZx/MVj023WBdYdHkmqc2jlCUmjByRV+2FjPRRS2IZmMzpW4ktHYM65WTbYZWsoY06UrFcey2CXXi9KrINdveEttM6PWXfOrmVbb8JaaRCXytlqPIDaeBz9GqLzLN1ZK+nqHI7YhdqeLYAgF7uSXQnY2sJoXgD0xCj7qR2LQiJqHH4DPK5ocrUfYFEYxzlh3/qf0L1EgMzERwDvSCupZRlBpfT9sESjGXXVUqslyxPZpSFCr37lzAd0O0tFi3TsLYZtgXm5s0MRmM5pzcOi9GBtWKPDNoKpk0sUhiC8tMufPqFRw7Zs9KHmssb1GLyQhDLbCdiLdcut0yLEQE9lnlZcNb0e8tCQKXMDPJSxVDK3hy80iUplc9stY7NO4tyDID3475sp19NrdOWhRvjhLV7N++SFkq2FayTtWU9UK8P1OTKLHFuN/WVGwIcw3DiyliE0Ur6G9MMfyQ1z72DJ4fYPYCTu7usXPtDsHReJ078MS1m1S5RhQ61LXMWzaOW5ywiqILfsnuYI7nBqh6wXw25OKFe2Tt86IqFfrDBf3hAhDeoRdfv8bJyQaS3OBuTsXhwA9YLX1WkSuqd2ZCEDksQo+8VNkdTygKlbqW8fyAwZUHBPc3mU1GgjXx+jkmp+P1ZNLV/oygTaI8e6/ILeskiS2mqx5PPLY7Qqh+Ey3Uj1rb29t80zd90/r/v+VbvoXNzc3P+rPHqYde+CeRy8CKxcJaqVhmyjwQSFhDK+hLzTqKs6plTDMlapO06kbCbE1QulKiqqWIRDVSZEmMmyiKmLOPCx0l16lUmWlic8FIyUqNPBJjOJparjcgAElq4vdW6K1ZxjSy9SagzAyKSkEtVZEDrooH6artJ1eyTBn46z6zYYoHSJUo4oFq5Oh6sWavgzACRaEjuABuhGbmyErNvZMttsyZiOo82MExxCmgaHfW/eGCoIUAKWpFGLgtplUiyz896ijLlRjPMTMW8z6yUlOXgizmj+aUuUZViiheSWoIUwtDz1GUinjWW7ct6kYEoDwOnS3GqiJMbcuWHaCpJXWtMF+69K2o9XsoaJIo9de1Ql6I3n9aaWtIka+na0MjgKF+OmTkTAMjYZWba7d8mpjrueszn8kqNxnX7YYjM0UfVK6YJTaL3GBsJeJ0bghU8CI3hI+gUtC0gpETCmhQu3hnpcqWtyRrZ+AlCVaZydiOWCQWQQvwUWRRaTi7L4tKZZYbaFLDjrekKFWSuOVRyBXDltmvSPV6gVFkBctOUJSSk4WIiVbPxsBKVeRaGDlNLdOkEoYbc//+HgCmkYn3WS0+t6qEIXW66rVf59MG2MchS8vXOGZFLVHUkjiyiVOLILUoKpG/cLbJEQFHKrpSrul9WW6I90utIElgGjlxYlFVstiU6yVFpqNqxTpvXmo5HmZm0PNXxJMBda1g6CL1T203iFWpUKQGcustkZUaRSuRtRJVbYNuWvCPrNbrDT6Aszljtb8l2Bx6jueFa4+JJDW4LTDMdiMUvSQOHdydCfWDLZJI/M4tP1x/PoCt5SS5oCnKciVCwnQxtnv2+7bMlCB2iHOjNbjWKIro3WtmhrE7I7i/uSY5BsseWWZQqRV1LWPpOUmhY7YBXiB4C3Ib3HP2Wjr94VBdf3EdEQ+9MnhGhqZUa1f+Z948ulJy6cL9Nd42Sw2m8wFbWyfc3T/H7ekGllZwdfsAy4nJEouDyZjd8YTFyhN52ErJzmjC0WwkssT1nMOghyLVqJoIRLl9vC3KrpEIA5Glhtcmm2uyVlLoXLh0D4Bo6TGdD1ilFkNvha4VKG2aVVkrXNp7gKYV3Lh9BbXQRNks10kTE88PiCNH9O6BVeiyMZ5ittnWQeByMBlT1xKOG5EmJrM2MKiuZU6DHm97y0vMT0eoaok3WGJ4MVHgCkNcanLjcI8NJ8Q1E1wnxustWc772G5MnunsH+zi2RHTow0UpcaxY1F5iGyCwGUVO/RqmZ4tzEunp2OKFvZS1sKg5LmPh9xXVAqemawrL6/eGpMf7zCyI3xLpKytUsErMLWCKnF48cEFLvTmjPwlmlbyynSTgZEytCMG3orpqid8Hu2G5j3PfXxdZdLVgvPjUywzZbnyiTKTRegRZCZRbrBMHMxln7DQOFqJ/nrW/iyqpkRVavp6tk511JVKtCd0EQS1CHwBiallnrt4hyQ1WEYueRskdWHjhKPZiNcWQyy1ZOytuLvqcTe0+F+evsN+OyKmyhVVLSA8e06IreVYWs79xZDDoE/fjNnoLdgcT+hvTlmeDpnNxCJ/ONnATsW41ri3wNALehszATaaDtjcOaYuVfJMEOvKXIwMVrVMlutMI1fMbOs5hp6hGxl7G6ccz4akhU7Pih7LvQCwMz5ltuivkdH37p0nyMTinpYqaaXy9nN3iRKLeyfCrV81EgM3xNAzbh3vYLX0PKulf5alQpBYTCKXVW7w7nYj0O8v2D53SHNfwtBzwkgY64zU5P6NbYbeivHGlMGVffKly/1XL69RttJkyM6FB1SFSrQckNzbZWv7iLLQWJ4OGW2fsjrYIEsNAfhyY/Kli2rk9CwxnZTFJs54QZnqZCuH3Yv7TA83OT7apq4l+v0lReAQh7Yor1sZipkRnQ6pSgVVK9bfd5KavHLzKpv9ObPJCNuJGY1nHB1uoaklnh3hOyG6nnPvaIdLew9w+yuxOTjusZgNxPemiqkf20pIM4Ppqsc0dvCMDMdaYpkp0/mAnhdgmBm6kbN57gsfBXtYdVuLL0z37t3jP//n/8wrr7xCEAR4nse1a9f4+q//ep544tHUaR6a1d+pU6dOnTp9Pp2x+nuKw//r0l9/pF/7g3f+Hcvqzcvqr6qKD3zgA/zoj/4oVSWqU03TIEnCKyFJEt/2bd/GD/7gD6Lrbyxy/fHUgjt16tSp0x9pPa5Y8Der/spf+Sv8zM/8DE3TsLe3xzvf+U5832c+n/PCCy9weHjIj/3YjzGdTn/XRMAfVN3C36lTp06dOn0J9bM/+7P89E//NJ7n8RM/8RP8pb/0lz7r75um4d//+3/P3/7bf5uf+Zmf4a/+1b/KN37jN37B13vMMx2dOnXq1OmPmhoEwOdRfryZe9I/+ZM/iSRJ/Ot//a9/16IPosz/l//yX+bf/Jt/Q9M0/Kt/9a/e0PW6hb9Tp06dOnX6EuojH/kIu7u7vP/97/+8n/f+97+f3d1dPvKRNxa61S38nTp16tTpkeuMwvioPt7MWi6X7O3tPdTnnjt3jtPT0zd0va7H36lTp06dHrk6Vv/DazQacevWrd/385qm4datWwyHb4zJ2p34O3Xq1KlTpy+h3v3udzOdTvnxH//xz/t5P/ZjP8ZkMuHd7373G7pet/B36tSpU6dHrqZ5tB9vZn3Hd3wHTdPwXd/1XfyLf/EvCMPPhq+FYcg//+f/nL/7d/8ukiTxHd/xHW/oet3C36lTp06dOn0J9TVf8zV853d+J0VR8D3f8z2MRiOeffZZ3vOe9/Dss88yGo343u/9Xsqy5Nu//dv5mq/5mjd0va7H36lTp06dHrlq3tyGvEetH/7hH+bSpUv8k3/yT1gsFrz00kuf9feDwYDv+Z7v4QMf+MAbvla38Hfq1KlTp05/CPQP/sE/4O/8nb/Dhz/8YW7cuMFqtcLzPJ566ine+973YlmPJmWzW/g7derUqdMjVcOjj+V9k7f51zIMg/e97328733ve2zX6Hr8nTp16tSp0x8hdSf+Tp06der0yPVmd+I/ap2envKhD32ID3/4w5yenpIkyef8XEmSuHv37hd8rW7h79SpU6dOj1ydue/hdXx8zPPPP8/h4SHNQ+yYzqJ6v1B1C3+nTp06der0JdQHP/hBDg4OMAyDv/7X/zrPPfccrus+tut1C3+nTp06dXrk6kr9D69f+IVfQJZlfvEXf5Gv/MqvfOzX68x9nTp16tSp05dQR0dHXL58+Yuy6EN34u/UqVOnTo9BXUjPw6vf7+M4zhftet2Jv1OnTp06dfoS6iu/8it5+eWXOTo6+qJcr1v4O3Xq1KnTI1fdPNqPN7O+7/u+D1mW+eZv/ubfFdDzONSV+jt16tSpU6cvkr7/+7//9/zzt73tbfzX//pfuXr1Ku973/vY29vDNM3P+XV+4Ad+4At+DVLzMEODnTp16tSp0++jc+fO8eDBA1zF4Tt3vuWRfu0fPfzXhFXE3t4e+/v7j/RrfzEly/LnnMM/W44/35x+0zRIkkRVVV/wa+hO/J06derU6dGqgbp5xACfN8kR9Su+4iveMIDnjapb+Dt16tSpU6cvkn75l3/5S/0SuoW/U6dOnTo9enVN5D+86hb+Tp06derU6UugF154gd/4jd8gCALOnz/P+973PjY2Nh77dbuFv1OnTp06PXJ1AJ/PrVu3bvFN3/RN/Nqv/dpn/bmu6/z9v//3+YEf+AEURXls1+8W/k6dOnXq1OmLpCAI+Oqv/mru37//u5L4sizjf/1f/1em0yk/9mM/9theQwfw6dSpU6dOj1xN82g/3iz6kR/5Ee7du4fnefzoj/4oBwcHRFHExz72Mf7aX/trNE3DT/7kT3Ljxo3H9hq6hb9Tp06dOnX6IukXfuEXkCSJn//5n+fbv/3b2d7exrIsnnvuOf7tv/23fNu3fRsAP/dzP/fYXkO38Hfq1KlTp0eu+hF/vFn06quvcvHiRb7iK77i9/z7b/u2b6NpGl588cXH9hq6Hn+nTp06dXqkanj0fP03S7V/tVpx5cqVz/n3Tz31FADT6fSxvYbuxN+pU6dOnTp9kZTnObquf86/P+Pzp2n62F5Dd+Lv1KlTp06PXG+WE/qXSo8zRqc78Xfq1KlTp05/hNSd+Dt16tSp0yPXo+7xd3p06hb+Tp06derU6Yuo5XLJr/7qr76hz/lcUwEPo27h79SpU6dOj1xvJujOo9YnP/lJvuqrvupz/r0kSZ/3cyRJoizLL/j63cLfqVOnTp06fRH1OI17D6Nu4e/UqVOnTo9cbybozqPU7du3v9QvoVv4O3Xq1KnTo1dn7vu9dfHixS/1S+jG+Tp16tSpU6c/SupO/J06derU6ZGq4dEDfLoCwqNTd+Lv1KlTp06d/gipO/F36tSpU6dHrq7H/4dX3Ym/U6dOnTp1+iOk7sTfqVOnTp0euTqAzx9edSf+Tp06derU6Y+QuhN/p06dOnV65OoAPn941S38nTp16tTp0ap5DOa+rnXwyNSV+jt16tSpU6c/QnroE/+vvuf/RlZq5JUCwJ9492/x0d9+J4vEQpIaBmZC3wkpSpWqkRl4K24cnOPy+BTTSLl5tMuDyOWd2w/Y2jpBMzMWkyF3T7bpWTF7O4dkqcFHbl3D0zM23BUAUWaiqyUbgzkX3/MCv/ZzX8vOaIJppRS5xs4zN1nc3uXkZINp6LPZm5NkJn1vRW80J1p6JKlJUWjUtYymFezPxpwfnWJbCWHkYhopd463ySsV30zY6C2Q5U8Xqmw3Yj4b8urJNkeJxa4V4xkZrpFiajmaVnCyHDCwQ5pGYhp5KHJN00hoSomtZ8hSg6YVhIlNUuhc2j7AtFKqWiZPDY6mYzwrYTye0DQSh0dbhKmFY6T4bojfX1GWCnf3zzHuLRhsTLl16zKbwylVpbAKXV483uUrn3yJk9mIRWzTIPFnPvLvH/EtAx/7019HmuvUjYTy/2Pvv3osy9I0TezZe6+txZF2zMzNXIaHykpVVdmd3dNVgx72gOBcDGcIEARvBrzjv+IV/wHZJNiYRsvqmtKZGZUihIdL00dvrTcv1rKTWQNeBIFwIIGKBTgiAuFmduyctdf6vvd7hdZzFY85DmNCLwPg3379Ef/DD39OHEds04Cn5xdc3pxg6D1dr7PJfR6MtmSVQz9omEbHl+sj/vjhazy3oG0Fuj5wcbfAFC2G3vPV+gjb6Phwfovv5Xx1fcYwaBh6T+QUnMxWXK+OSCqH0C45ma0pSpvLzYy2N3DNhk+evmS3G/HZ5SNWpcNPH1wAcHpyQ9cK/ubFRzS9ztPJmmHQuEkiTsKYz9dHeEbLsZ8ijA7HrMkqB4CPnr3i5vqYrtdpOsE6Cxi7Oevcp2wFhj7gmzWhXRJ5Ob6XUZQO63jEMGgIo8O3Szy34M3dMW/TiLg2+dF8xbZ0MbSekzCmbgWWaOl6nao1mQUxWeWwzgPWpYMO/OHZO/LKJi5c0trm49NL4iygbgW+XfJP/8P/61vfCwCr/+snLC9PcL2C+Qfv+PKvf0jg51S1SZL7dL2Oa9aMR3sct2S1mnO1nTDzU0I/wzQbstynqk2E0eG5BRerBZ88fUnTmNyt5lzux/zoyUvaVpAXLoGfc7uZss4DikYwskuenVyzT0KKxkLXBjyrout1puMdswd3vP7yGXHhMfYzoigmjiMss8YPU4ToiHcRq/2YYdCIvJyTsys+//JDrpMIy+g4CWP6QWPkpwDUtUXbG2wyH8+qmQQJpw+vqDKXLPUZBg3XK2gbgWnXFJnH9XoOgG027HKPZeHzdLzhwdEdbSuI04BlGhGqs8W2anwvR5gtq9UMXe85Pb8C4G9++X0Cq+KjZ6/45VcfUrQmhtZj6h0A8yChak0ATuZLdvsRQrTo6mz64b/5t+9lP9yv7xr039/1jS/+fekycgpGboah9/zsr/+Q08mawPa5SyJM0ZKW7uEg17QBVzR0nU7TClzRMAwadScoco+mMRnNtujLY3a5R3d5xl0aMnNzjqI9jl3y8uaU7z19xX4fsd1H2H/3CbbZIERL2wiW2ynh1Z67uyOyysEWDXHuMw1jDKOjzDz8MEXTBkptoGlNdL3nONzj2DVtK9imAaQB/aDhmg2O2XC3m/Dk7JKycFjuJlhpiIYsbhyjZR4k5LVNP2iHy0dnIKscul5nGMAWDasswDM1pmGM65Qst9PD11WVjTBbhkFD0wZm0Z44C9jvRlhWTRSkLNOIuHbY5j5hPGIa7dG0gZvNjNvtFN8uqWuLfRaQlC7PRlsu7o5xzIaT0Q7bqt/Lpvnl9RnPZ0t0bWCVhXx0csUuDdkmEQDfn655eXmOLRqE0fEXX3zKWbTDFQ1Z5fDFbszIKUgqB1u0HE82/KFTso5HXG9nDAN4Vk3RmjS9gWvWfHp0Q9WarNKI23jEzE95s51y5Gf4dimLzzDGFg3doLPZjxiHMcfRXl6woqMqbV7cnpK1gshscO2Sm92U5vKMtjNoep25mwNQt4KmN+Tl72X0g8a+cshbk0ejLWltU7Qmo5sFV/sJVSsfJddseLWbEpg1vtnQ9DqGJovIuhGQ+7xeH2HpLSO3IPQyHLvmi8tzTL3jcRizKVwmnty3dWtQNBZTP+EuHgEQuQVhkHKxneEYDR9OMt7uJ6ySiK7X6Qad0K54tzrCs2qE0bHNg/eyFwCWlyds4hFaEhHHIbZVEycBllUzCWO+uj7j0YeXDL3Oei0L6JMgRhgdZWWzTSKS0uHBdI0wOuI0wBYN1zfHGHqPabR8+uCCzW7MNg8oWpMn+vLw8x3Rcj5bkRfu4dKfhDHT2Zbl3RFZ7tO9O2WvzgbPLdC0gb7XuNtNmLQCzy2oG5OysTCNlrK2uHp3xiRIaHuDttPpetnQ6NqAYXRYZsPr2xNORjvq1mSfBZjXCxy3pKwc8somL1wMoycgPZwVlmhxrBrPrliEMW1vcLueE/kp89kG3y2oaou6NclKl6YxMc0GITpM0dBWFpvNhLPxBseuqCsLy2gJbNVIdIJV7vN4cctItDSNyXIzQ+gd2zQ8vD/frX+86xtf/JbRqUtNkxV6Y9H1OkLvsIyWbe5j6D22aDGNjq4zSGubsLHQtIFu0HEMWYkWpU0Vhxxpg/p/BvvCZVm6jJ2CvteoGxNLdHStQdOYVK1JvI8wtJ6qsul6nbK22C5ntJ0h/7sxKVqT4+kGgDzzKAuHIEypa4uu0wFB1Zo0jaCqLa7SiIWXMSAvYF3rEep1atqAMLpDt2UaLULvGPmp7KgHDQDTlJdNVspDxxUNoVuwzgPqziAtPDRtoGpMbNHgmDVVbeGTURYOVW1jioayMWmSCFs0uE6JKxrKUlAMJqLpaTsDXb2XXacxiyr6QUPXBiyjxRItdSvoeh1dH/D8/FveLnJ1g0bXyylR2QrazsC1KjZZyCr3OfYTLpMRx36Kb5ekjck6D2SBpA3M7Jqu1ykaE031BW1rsCk8yk7gGC2nkw1dr6tD10A4HWnpklQ2PRozLcU1ZQecVQ66PqDrHY5d0TQmeW2ziUfo2iALrdak7zUMbSAyG2zjH2ZZa9qAoQ1MvBRdGxgGjdCqWJcugVVj6D1VK7jJfZpWoGsDOgPLZISpd2SDxTDAzKwZ9cah8wqs7rCP+kEnKVzqzsDUO5rOoCgd2laoS2HANFrm3oBtV4yBupOPqGNXAIdiM0n/4UV+X1h3vUbTGzS9ROYCrVLo03vZCgDkhSsLjl6nbiM8q6LtDfS2xxQtkVOgawNlbVJWNqbesZjIZ3QYNFynpGisA0qW1zaRm5OULobe45qygI1Lj6SyaQe599pOvs+6Ls8RXR8IneLwfYVdy+dKXcAjL8MyG/m1jaBsrEPhbYgO26oJnQKhd6roMjmZbtlnASDPgGHQQBvoOoNGddO/u7pe5241P7wfeW0j9A5d7xBGR+TlOHZJVVu0qlj0nIJNEjEM2gFpHAaNQiEKg1XJ12j0NK3J7e0CTRvw3ALHLQ/nj2tVch81FpFlUDemel/k90wrh7IxMfSBurbe34ZAdvvf9oz/OwTh21vf+OKf+QnrLKQtDQy95zTacbefoDNgi5YvdlNOvRyh97LDrmzuSpdAbdqiMYmsCqF3FJXDdTyiak0sQz5MWWNhagNtb7DLAjRtYDHacrVcULUmQv/tZbzPAnW59dxsZsxHO9re4M1+QtKYfNpr9L3JJom4TSL+xQ9/IS/e1mRoNN7tJ+rBFrzNPJ5O1nSFKx881X2XhYNh9EyjPWnmy4NMG0CTh3LRmtT9cEA46s5gXzkSEfATfC8jSCLS2uYujchrGx35vaNRzPXNMZres09DNpnPPEhoOsGutDC0nnFrMg0TSnXJhHaBofcYeo9nycNcohYGkZ/i2oJ1PGI+2rFJIpLCZTTav5dNcxbGNJ1B08nX9mp1zB88fM0uC3ibeQitp+wMul6OAo69jHdpKP893PPHD1+zS0OaXqdoTZLM56v1grIzMPUeR7Q8/OAN1tsHbOIRSemwyULexSNMvcc3a6rW5MhPuEtDtqWLX3gSTndzhOgQXccXqwVju1QXoc6xn/JgtKXuZLFSVA6eVXE83dB1OknlEAVyXCVEh6H33OU+dWcwtioiJ+dtElF3Bp5ZYxkdd7nPJ0c39ING3QtCp+BkuuZyfQTAfLQjL11MoyWvbXaFx8guGQZIKodt4dH1Oo8ma6rWpO0MQnVRBn6Grg90nRxRhU5JXtvktc06C/CsmqoV3KUhUzfHsyvSyqEoTValy7PR7lBsee8J/QF52d0jLXUrWKYREy+j7QzywuXJ2SXxPiIrXNre4NFkzeLhNWUi4fDowZLms99+JkVjcr5IyCqHtjOodcFuMyepbAA80WCKhqoVWEaHqXfEWcA02uN6BU1tcrlcMC22GEaHYfR0nc6Dsyt2m8mh2EpKiaz4Xo4XptiOiRCtQgN0ysrGH8f0l2cA+G5OnAVEfkpROsSlh29X7HMfS7T4donv5/zN26echzG+XbLOfQAMvWfkp4xGe7wo5eL1Q1ZpRNUKfvDsa5rGxNB7mtpkHY8w9J68sWh6uR8MQ17ece7xej/hJ49eHYoWyyupWwMhZDGraQORn7LcTfDsCs+RyNLd7SlC7xBaT1K6720/fLd+/9c3vvjjwuPDh28Zeo3VZsp8uuFiP0VnIHIKFk7ByC7lpaTJ7vR/8+Hn3G2nxIXL2M351WrBIkiYjXbMJxsu7o45ma55tzzmxT7i//DDn7PeTnDsCseuqWqTWbSnKB2S0uXru1O+/+gVdW3RtIJh0NE02SW0nYErWp5PV6z3Y5rOoO0Nxm7BfjvGMDoCN+fruxNmbi47c7PmnyzuWKUhizDGtUtAHmTreISj4OAX6wVzLyNycgy95zfvHvN0tmQ2X1MVLv/hq4+Z2bJDn7o58+mGX799wtjN5TgkSsgSWcyYZkNZOGSVw5uLc3XQl0zHu8ODHNgl5w+u+MVXH2FoPVM/5Wi+xnYq3i4XWEbHNEyoahPfz4njiDj36AYdTRVPTSeoSvu9bJqz2ZKL1YJ96dAOOidBzF9+/REnQcJ//8mv+bu3T/kf/uTPSLcjlqsZj2ZLzsYbegVD3u6meFbFWbSn7QxWaUjb65x4GefTFUeLFX/3sx/x7OyCsM0oGotd4VJ0BsdeyjxIiEuPujIoW0HZCbJWcFd4dNsZC7fg0WTFqZ9gGR1Fa9IPGh8/ecWf/er7RHaFKxq+2s74ZLZkF4c0ncDQenZJROhl5LXJxW7Cw2jPPIwJgwTLrnmUynHGfSc+sQsud1NCu8Sl4avVgsCqWQQxVWvys4vHCL3n6WRNVtlcpCGW0fHJbEndCnaly76xsMyWs7Nr+k7n5199dECe9rnPy/2E//G//s/sY1lAdr3GB8fX/NWbZ/hmw2m4xzXrwyw9sEv+4PwtN5sZ60wiLXM/eS97AWCbBwTq2elVp7uYrgFIs4AkCWgak7fbGcvS5afnb/jNLz8l9DLKyub/8Tf/hD95+oIvbx5Q9QanfsJ6N2EcJDSNSVK66tmAsVsQ2HIsBPDs9JIgSri9OaFuTLKVR1K6rHMf491DOQ5rTXa5x+WvpkQKEdC1gePRjsvtlMv9BEMbmPkpd2nIo8mayWRH5MS8/PIDjkY7AMrK5mS+pK4tTk9u+WCccPNGjok0bcCxa2YPr/ng5uQwfhL3KJcaOX359jHPz94xDBoTLz0gOQ/Org7n2Y+eXpAup2zWE+rawvdyLlYLTidrTudLIi8/jDXW8QjjtufBZMswaGySiLyx+KNPf01WeBS1RVFbeFbFpw/fEE72lJnHZ18/f2/74X59Z+Dz+7u+8cUvjI4v3z5CGD2BXbLaTHk6v6Wo5KV8GsacHd0xDDpVbRJnEooc+SmWcChqi6fRjry2aTYzPKvCEi0XqwWm0fKn529ZbyfMZxvS1Od2M8U1a84eXcByxiYLuc58zvcjhOjoOv2ABDi2RBX6QcMWDXdJxNjLeTCWc7+j8xva2sTYh5xVWzZZQFo5uGbNUSQ7ZNNoEYqb8PXtKa7ZABIWfDJZUzYmaelStoJN6fKD6ZYsCYizgA/HG+bqcGgak5vVEUdBwjiM0fWB/W7ExXrOyC0kFD/oLEZb7vYThN5hGh37OKIbdCyjpekMbm6OeTS/w7Yaysri9eUZSeVwEskurm0NLLPm+m5B1Zj0SCJhVngEdomu9TTt+1FrGkaPb5d0vURqksph4hRUreDV7QmnfsKbF0/Ja5ukcsgaiyeTFTfxmLw1OYt2NJ2BYzZ4VsVY74lrh7mf0HUGb9+dU7WC67sFQu+InJzALmk2Opbo2Bc+f3ZzzA/Ge6ZugWtKKH5fuOSNRWSV2FbNz9ZzHvk5jtFiGR1X1yf88PwtTWuSVzYzp+QuDREKZRi5BWVjkm+nEjo1Gy6TiF3hEu7HhE5BaJf4dsltPCKuHZ5OVsSlR95I6PT+9ayykKY3GNkVaW0dCrzn+laOfyqHwC45G22x0oib3RhNk9B4N8jxxZUqMj6arrh5c0ZceKS1RVzbsIJPj26xFMS/SqMDVH7/LDSdYOJl6NpAXHrvZS8ATLyU6XhHUTpcruc8nt+x24/Q1Chvk0iEbeSUTN0c1ynZ5z5tK4iClH/18W/Y7iMejjcMaPSDhmPVOLbkJwjRsji+4/MXz1nnPldpyMQumfkpTWOqorrnz98845PpimkYMwlS8sqm7Qwcq+ahl9G0JrtMogw9cLMb41sVY6PDFg3j0Z7ZaEff6yxXM15t53xyfC35H41JWjncxiOeLG7JUp/NZkJROZIT0FjUtcV5mOPbJcfHdxhWQ9sZPHx4wecvnrMpPMV7EuwKn9AuiKKEIndpaotdHJLXNk/rd6yW8wP0X1YOx+MNWeGS5D661lO1JvPRDsPoyQuXm92YpjfwzZrjcE+yH2EaLaYrEYy2M9juR+xjuacejLbvbT98t37/1ze+GSTMLGef9yxhWdFKlrNltMRJqOaMOkVjsU9CBjQ5e2/MA8vUNDp8tyDNPYTeEToF49Ge7W7M5a0k9FhGS9Wa7FZT2s7AsyomdsU6C5n5CbZdYVk1b1cLjpCkroWXyrlaY+E2klioaT3xakLbCMnu7wRjLyevbHkJdwZVK8hrG9NsCIKMttfJawvXrDFFS9N0eFZPXssOemKX9L1OXrjUrWAaxjSNiecWkoVudOh6j2XXpKnP3X6CJeSowlJwXN/L38l3CoTRsUkiCQ1WEaV6PVbpshjLB1QYHULvcJ0SXT3I97N1TRvoep2Rn5IrgmU/GGTF+znsi1K+d5oGvlmTNRaRLZGebtDZli5ZY1F3xqEb3xc+tmhxzQZbNLIIqC08qyZyc2xDsvdtSzKZi1rNfNFgANcu+dHDNySZzzoLOHYkdN31GkVjUbWCmZ8y9nIMrSfJfWy1X3VtwNQ76k6wzwKGQaPpDHQGOjTaXpdz984gcnOWyegwh586Ba3iMwyDRlI5ct6qD3iiObwGCXe3nM+WVLXFXhWJjmjxzZq6EwytRtkKTMWL0RSXQCj+QFVb6HpPZJUklUOtUCzPqnh5d4Jv1thGi9DNwwz/vsP21Hy3HzT6XicpXUKnUHN3gaa9v/ZL1waqyqbvdSK3YDSK2WwmtJ1xuLza3mDqJ4RBRlHakjNjNhiKTyMUh6hu5exdPpv6QS0xrq0DouWLhqmXMR/vSFKfNPc4mq/xREvdCZrGxLYrVulcEXlLXD/ny9dP6QeNwC4J3Pzw9/LClZ/PoOH6ObvtWI4Zeh3DkOS4e4TnwWRLoz6rsrGwRIvr/BYp3L48l/P53MUZNI7ma+raQhgdrmgw1UVtGS0DGnEckuQ+J/MltlUfxmeuU5JkPlVrMg4SyQnoDVkIBym7JKLtDHq1Ny2jw9DvuSQaq92Yrtcx1blh6BLeH/sZhtGSF+8f6v/OwOf3d31jHb+h9xxFeyInp+4ErlOSFh51J7DVAfhuN+M2GRGXHlUrWGVyfp1UDkVrEng5oVvgOQWuV9ANOoFd4ns5tlNh2xVfrCVDfxQmcga/PCYtPGzR8HC0Ja0t6k5IGZSfc5PLh8OxK47Gcn7bqMs/TgPaVvDu+pTL5YLVfkxSSqmfMDra3pDM2cYiKR3q2sI0Gyyjo+l1+kHDUrNRYXRYosWzas4na4pczisNvcd1KilN63UsuyaKkgNBsKxs9qXDxEsRuhw3TMKYvLLVzC/GcUrKxpLEtE4nayzyxuRtPGaTRPS9wchPOZuuMZUcR9MG0sIjChMCV3YZo/Ee1y4ZBo2ytthk/nvYMhCr99zQeyK3kIQirZddmllzW3js1WdeK+LlvnQInYLj8Qbbqslbk03psS08ysbCMxu6XseyaqbztZzVK15H2xu0reDs45dS/mX0/Ghxg2V0VJ1gmfu8SiIcs2Y+3mFbDZvM59gtcIUk8tmq4Hq7m7LMQvLaohs0PEX2KhqTrLZxnfLQOQMsgpjAqnHNGqF33OQ+Selg6h2RU1A08kISekdgl4ynu0NxBhLuHDsFbSffg5Uqxu4v6qRyGQZJmGx7eVE66v3xzYaxLd/fN0lEj4Zn1YRmja4NJKXLrvAP3Z+liLVCjTfGqtBOaxvPqt7LXrj/fNbxiKJypLLCKQ+KkroTOGaDZbSMophwsiMtPFyrku+T3rOPI1ynpGpNktJhX0ruwz732Rc+m9xnvZpRtQLfrDkJYxbTNaP5hqo12eYBbpDxWHWxiWLC72qbAQ3LrrGcijfxiGEA16oIw5SjxRLfz2l7g7hwJQFYXeo9GhOnUI2O5F5YouXR81eUlS1HTZ2QxbhXEAYZwuh4+eaRlPjtxux2I9wg41pB/8fRHs+qyCubQI0crjYz7tIQTR/wPXk2CLtmNNkzoJFWDobRESsuiGNXjOcbLNGQFR5J7tO2BrbZyNGB4r/sco+klMqCqjUP5GdHSUfvm5jv1j/O9Y07/tDLmM7XFLlHdnnGzWbGnZIIOaJFZ2AYYOJljPyUdTxiU/i4osHRa0zdIFakvE3u8+pVxJMgOZCOhJBQ+w8W14zCBNfPGVYaoVNwtR9TdoJn0xVnox1vd1NebubM3Yyn0e4wC1yn8vCPbMl23+Y+J+MdjlnT9vICss2GNPNVNdwSehnP7ZKLzZyL/ZS0cjgKYgy9RygpzHy+5t//6gfM3ZxHsyVRlLBczZiNt2jawNVywbNHb6lKm+12zLvdDFc0PD97x+JoRehn7NOQuhXqoNfwrIrReM96PWUdj+h6jTT3eLK4pW4sLrdTPpnfUneCrHRwzJrTBzekcchyO+YmiViVLh+3gkmQSN104fLwgzfEqwmr1YztdvZeNk3VmozdDM8tcJ0SoXcMg8a73ZTXacCP5itK1YH7TsHlZsbILbhLI67jEY8na3786BXvlsfywq0cnp5cs9qNuVnPMXcT6lbws7sTvjdd8/j4hv/01cf86u6Uj2ZLHh/fECcB8yDhLo0Qes+jIGGVRuxzXxKeRIffN5h6x9jLCdyc292U751esNqPiWuHmZcxCeODrj8yG1avP+BhuGfs5iSVw9/enLFwCjTNpu4MbL3nbLpmm4bcZZKweDbaEfkpdWPxnz77IR/MlpStwDZazkZb+kGqE3okxPxwvjzo24dBI61t9o1FYNXUreCz1YIfzJYHdvzFfsp5kLIrXGzREirSWN0ZWIZED6JxzP/81Scc2SUnQcLILg6InG++X/mW0DuS1lWXY4O2HXO3m9Ap9v1lPOZ7pxcUpcNuPyKvbRyzZnS0wXQrkjTEtitm0Z7QLShrqRg6nq0AKEoHYXRMmuzw8/LCZVjpOIrb8Itff48H05VEHgYNIVr+9JNfs9lMSFMfQ7R8PFtyfnJDkgS8vDzH0Hr2lcu+srH0jk+jhM9fPePx4obj4zu2myk/v3yEqfWM1MWdbEYUjXVAKi2rZr8foWkSrYrChH0cEYXyTPoPf/fHAJwEMWM/Y+btDl16reSqWWPxdy8+xDNrQqdEiE7C/orMuNxNyGqbyJGfaVtZB+k0QN/bfLWb8tOHb9D1Dl0bOJmt+OryXMqf9Y7fLE+IrIovr86xjBZHFbzvc/XfDfl/b9c3vvgdp+Tt24cklUvVCn6xnvI4yCXDVu94dnzN9XrOXRpxl0Y8mqywRcM6C9iWAd09c18x059HO67zgKmC3nSjJ9t72FbD1eqI+MrhSBGSPjq5om0Fv759wKeLaz45uaSuLbZKLveLi8eSBOcWHE83PHIL9rsRmyTCcwuSQmreTbPhZjvjx//1X/D13/yAdTzCNFtut1OE3lO2ghfbGT99/JKqsmkak6YxuVwf8aPTC/peZ58F/Pr6DE2D59rAeLRnMdmwXk+lxKcTjOyCT56/4O52IRn2lcOTo1umirDYNCaBn7PZTBhPdhwtVqyWM14vjwlKF8+uOB1t2ajOtO4M2kHnJh5zFMSYRstJGDPzMqZhzD4LeLNaUHaCP2jkRypEx2n0flj9VSuYeB1F6fD13SmfbcZMrZaxVfPpZItj1jSdlFu2imQZehm7wqPtJNHONBtGbgb4ZI3F7WbK8XSDYbRUlc0vLx/x3//w51SlzcXdgrwzOA9idE1CpXltE3k5UV1i6iZCyb72pUs36IdC9DoLiCubeeUwC2K+vHlAj4ajRgu/uT7nyXjDx4obcpGFeLbsyn6zneCLjrmfys++crCNlj9/84yJXbHwUsa+JGjpei/lgvrAu90U36zRNLhNpKRwXbr4ouGTmbz0b5IRli5HH6Fd8dHJFXnpklc2P5zfyY63crH0lpmX4im5ZNNJguRptCdwczoF927WEz4eS/7AbRZylXv8M3W4d4PO5fqIT9/LboA32xmBVTMLUk4fXNM2JsPqiMjJmc826JdnrPZjZtGeURRTdwLD6Hn79RPJ2u8EtlXhuBIy32U+s2jPb949xjUb5mHMi9tTPnlwQdMKdmnITTwmsGSxMIv2tNspSe6zmK7RtIH1doIwOm7jEZaQhek6D3hkdMyP1nhuwW/ePeZBtMVMpf+Bofc8nN9hOxVtbVI3gvMgpukNhgGWScTnyxMmdqEUFSb73Gfsp3hujWF03K7nUnIZS7Twx49esUsiNAba1qDEIQwS1ltZ3B4FCX/6L/6CLz/7lLKRz8vfv33M48mao/FWkSLnWLrcr3EW8OL2VPqOqNGBpg18NFkzGu2pSnludZ3BzE9JKgnp/9MnX/Pm7piJl+HYFdV7lvPBd/K73+f1jS9+1y/QtlK7bGg9kdnxMNqhaQONmiGOvEwSkxqLvpfzONPoCa0K36oOM11TzaECu1La9J66srhLIo6jPbbZ4PZS269r8ucNaGStoOnEQUMeOTlZ5TB1corWZFV4nFYW4WiP7+W0rcAwWkK3IM492iyQJJmbObWaIRtGS91J3bOhD8zdQuqSB/3gnLVKQ3ZZIB3WrArfbIhrm13hKzZvRVnZik0vL7qulfPkTeFJSU6YcnV7TKMgQ7NuWMcjfC/H8SXsdw9tl4rJ7NslhSLuAYR2SdMJfKdkHCXkyjXx/v0IzIqmNalqk27QDyOYb3tZRicNRQaNwKr4IMww9V7C6qJlV3iM3ZxZtMc0GzbvnlCUzgFuXykTkbY3pDOhk3MVy4uhqk2WuwlFKygLh6KUs+4nQYIm3wb6Xs7aPavCNht0vafpBJvClyoTq2QaJhLm7ORMvR80rveTw+vv0fh8fcTMKUkrh7oVBHaFnklkBmBsNQTq8ixbQdkKns6X1L0gsCopnytdCiXTKxrpnBZYteQFDGDqHcLoCZX8r+ul3to3a8pWkFQOht6zjkcHvb8tmsPzcz+bzyrnsD8aNfvWtIGysaRqpjdIa+vgFHjqolQDHsMAi+D9sfod0aJrA2np8u7dOacnt4z9FNcpsZ3qoMnvOuPgWRDn3mGuv819Qve3kjVLtKz2Y+pOktVMIYujqrbpe4mWzUc73q0WZKWL7xTMR7uDgZSmuB03yrWxrQ0uNzN8s2a3HeM6lZRIqtm3b1U0nWC1mkvXwJsTYuWa6Fm1eg4t4tLlg+mKorZoOkHZyOfs6WSHaTby0lXFv/wMDe620sVzrcjOEy9D1+WY8X6lS7kvfbvEMPoD50goXpChSWXAWvFTQPJNjlVhv80DLGVqdo8m7JOQ0M9wnZK2FTStYBHtCZS3R1l9B/X/Y17feMZ/D2U5Vo1vV5wHKbNoj69kPMOg4Xs5oVPgK/gN5Kb1zJrjyQb5CWoYAAEAAElEQVTTkF2ZY8rL/8HRHSNPOgGWlUPaSBauabTSha922FWuZE1XNq5oD4zWphMHNv/JaMfEKSjUgX1vTnE/Z/S9jLoTxLXDZLLj5uL0YBjSNNKEQ9cGAqtiEcQHnTeAYXQ4ZsOm8ORF5eUsQjn3vTfoaFpTQrnqoRS6dIm7t8v0RINpSvQjLl1pPJREtL1BWTrkcSCNTYweW100+1I6EQZWRWBWeKIhUGQtTesRZoOud/S9jq5JL4WpIu4UjUXVmAep0Le9hN6pA1ySyp5O1pyGe0ZOgaH1xLWNrg04bokXyIMmr20MrUdjYFNKyVVZS8+C6XjHpnLYpSHreMRtFmDpneJkuAijZ+anDIOcJ5eNRaJmlPeqCLlf5NzesWqiKGbkp0RWiSNaul7jJpcEQ8doGAa4LWwCqyKpbNa5jzA6DG0grWy6QefUT5moWWzbG3SDjikapm6GZbSUjcldGnKXhrzYj3mdBpL0Z8lRU90ZB7jbMxs0BnLFI/FtyQPIFdy7zn3JO+h1BjQCp8C3SyzRyi638GShqvVU3b0JlUlZW2SNJVUnjYXOQGiXUirZG8S1RdJYNJ3x//vD/BbWzE8JnYK6E7zcHNG2gsDPEaKlKm05ErJqhkGjquXleE+mGwZNcnIqm6KUz78k9sp5/v3zMPFSksKlUS57J0/fMQxyX1W1hSlaiTQ1JlVpq9/9t8XmtpKFdJz7JKlP3+t4Vk2pzgHPqhQaJ7jYj3mlVBuG3h9so5te5+z0msjNpQ+IakJMs0E3+sPv0w8altmi6x3r3CetHG4Kj7WSJd5f3sLo0Bi4vDpVvhwVnlsw9dPDiK9sLEy9Q9OQn3MnCKyaptcZRTGjSBKbDb0nSQNqZUq0K3zp/RCmh7HD/XnYtsY/KDzex7o38Pk2/3yHIHx76xt3/FeXD3Dsimm0R9cHfLtkk0SsskDKtc4uaRsTTzlf9YOGbdVsMmmHaxg9V2nIuvAIrYrQLiW71aqpKptd5jNxClrV6W8rF1PvSRqTqNOZhCn/dL4kGseslrOD//4yDzg7kozYbtB59PFLXvz9J8SKRHUyXWOaDWezpSTqVJaS78lu46vrM6Zexlh1REXpMJntuF3P2ec+WeVwdnQn/d4VHGmaDUfTNX2v07ZCdWc2I09W2I5bIsyGo2jPsd7hezlvLs45iXbKCdDkL6/O+d//6Gdc3y34/PoMS5kAjdyMyE8JbGnW8vT8grJwuFgtDnrxTRJxs5vK2a6bH1QQGgOz+eagXhiN3w/UPwwaXy5P6AYN35SX6Fz52PeDhicavlwfcZNE+Mp1rRt0doVL3glMrWcW7Xm9PKbuDKJAep//cnlMKBpOgoTjyYYvrs+Y+ylHox0/e/eEEzXTzxoTnYGjxZKv3zym6Q0eHd1ylYykc1oaERcuU1+68BWtoOoEtt4TWBVpLZ0f/2i+Oujfu16qVRyjZVN6RFbFSbQjLV1cRaYz9J6/ePOMqjPYN4K615ioEUc3aOga6Ays84DAqtiXDj9bz5haDROrwlSkzG0SKelpi6H1ZI3Fg9GOq/2Y2zzgyWiLJVpJOO0kmtYoKDqtbN5lAY+0FLN0idyc+WjHLg059lI8q8YSLUUtvQ9MrSduLP7Nu3P+5XvZDfD40VvsKGN7tWD/6gPiOMQyG5IsZF/4PH/4Vpr5lC5xIdnpZ7Mlt9spVSv44OiO2/34QKAzjZY/+uEv2S1nbPcRV5u5yiawsbwWxy2pYp9d5XLsJ/SDzt+8/oD/7k/+C+++fiK/V6/z7OgGYXQkmY+m4Px7kmEwlr77P7t4zKmf8uTkitNPX/J3//mniswp1Ri+U3K7H5OqZuLq+oTZZIvrlOxSKb/7/NWzgya/G3RmfkLgpwiz5WOj489fSc38sZfy/OlrguM1r3/5EcOgEfgZV8sF5+dXZEnAdh8RBSn9oLNMQ0mKNis8s+bhqDww9H+5mXG3mXE02fDw9Jo88/jy5gzfrBm5GfvK4WY9xzJabKthNtvwb/7+R/iiJbIqgvdI9vxu/f6vb3zxN53A0wuaVrBNIkZ+ym0S4Zk1j6cr4jjiby8fMnNKTsK9JPloA0dhjGH06HrH49FWdqh6f7ClXW6nlOqhWihW7vFshe1UZKnPV9dnbEqPTelxlGUE+/EB3i5Liz96+oJUwWgPpiuuXjzmxXrBxCl4vLhhNNvyi19/imM2HI12RPMt5uqIuhUYyrc/qxzeLBeA9FrXYxkmcw+7DoPO84dvKQtHkvGyULK1Pck83+YBUz9hHY9od1OE3vHhk9eEQUKa+VwtF0RehqYN5KVLWjl8MtkcoP+RIz22p14mO6DGxHVK6k5wo7TsEy+FPCCrbSxDEotChZYYRo/ZSBnQzz//mKkvi4f3ZeBTtCZPJmtcW7Kyl7vJwaL0Xp7mGC272iZtLD6e37EvvMOl/8Pzt5imtHJtOsFqP+bIkXPTrtfZVy63F48RWs91MuIuDTkL93y2WhCYDVO7ZOwUvHz7SH1mNfs05GXi87998op+0Hi5OSKt7cMMPaTiK6XPD1Ro0m92U47s6uAoGZfugc3d9Aa/ujtl4pS8SyQM7xgdI7Nm2blMrYZu0LgpLD6dbBhbFUljcVd4ZK3A1H0svedpkB0O2rS22VYOJ3pPUTlsS5e4MclawZGf8mC0o6wt3iUjPpnfyudOITojR8pnF2HMIoxZpZL/0fc6tkKW+kE7IAMzP+XDMCYtPDaK8Pi+1vLuCLGeous9P3j0hjTzsEMZLPVQ72gbUypOSlnsr2OZRzGL9uzSkK+XCz46uaJQHe4u97j5xQ+JnEK6+TUmL1bHPBpvcJ2SsnDY7UaM7YKRlxEGKb6bs72ZYxgtD6YrxRWaE7m5VPyQ8IvbU56Ndji5RAp3acg/e/aCzX7Exd3x4Vya+VKifG/Q9ciSI8m6tlilEdfJmGGQI6OZn+I5hdT55x6nkzVf3jwgrZzDz34YxtzmPkVrsrybc311Ikd2bo5l15zOl3z58ilJ5WAZLZ/+5DNWf/lHzPwUy2hlTkThE7m5HGHEY348v2MSxiRpwM1+zMP5EktvSWuLtLY4Dfe82U1xlKLl6+WC7x/d4jslut69d8tegOG7Hv33dn1zAx9dhmfcm7IYek+PRtUJ9rmPb5ecqo5DY2Cf+1StIHILPKOiKB0p61GGI2Vj0TSm9PhX7nT3zlX3axi0Q7JZ30t9+H0CnhAdkZdxvTqS87RBJ849HLPGN2XAy+vbEyZJiFCzw10aknwp5UGhXWKLBstsmU2uWW/lnC30pdWoXtvsC5e7NjzM1VsVmNL2Or5V4Vi19IdvanmpK+16aFfc3i6wrJquMzCNjjBMKXJX+cYLvo5H/Fdnbykai6IxOQ73TMc7VtspuyxAL3omQUJZ2egKBozV7NzQJWSeFZ4Kmhmzq1zOop2CL036QWYnvI9laD1FbUm4vhV8fHbBi6sHOEIiEMs8YOoWnCljnV0u0ZcHQSINipQ8yjVld2poEka1jBZNSM29oVkKhpfBOJaQnbWld1hGh23KwJ+X+zGO0fGjBxc8j1Iu9lPaTqdoBd2gMXYlAlU2JnWvsyk89fMMPGXY1CMDeF6lPt+b7DG0HiF6LE9qrXNlmwyQNiYjq8bWuwP3IlVwetaYNIPG89GON0lE1goco8MREro29Y5jL5M8D6OVbpCtwZtEMsXtQcLaoXrfNA0MXXJcVrlPEY+YOCXnow1NbzBVxWTdCt7sJxypAC2QPJG0cjA0OR64H729j3WxnjMPEny3QNdbhGgPfhyOKZnuF3fHMlNCtIy8jFfXZ4fcio9OrsgKD0s0OEjPg9AsqRqTUn2O/aChIa3Ah0Ej9DO2pUvolLitPB/63qDrpP6/Vy6GvSpGda3ngZ8ycjOaxmS1kYiZadWYRkutCwwhNfJZ7snxynZMnPuq+NcOz35klQf4/y6JEJmPI1pssyHJfQxtkOMnq8a2K+rO4Pl0hWdXdJ0cVe1VvoFjVxhCJjS6Vo3nFPSNHDVmlY2mceA5yUyCTpoO+RlBmGKaDXUreTFx7TBzc2ZBzF084qP5rZQdtgZPZxt0fcAUkhNzLzf+bv3jXN/4NLgPOqmURep9JOu9YU7TCRZhfAjJKBqpj5dJVq00sVAue4YhzXASJau739xZ6VBWNlnukcQheeHiWDVjP2N03w134jAftMyGbe4fSHVpZZNVjuzoGNiW7iEkw1QbfZ0FVAqeH9BoW6WdtissU85Tm/twC2Wwkte2hPsq57fzdJXyZYoWy2jJlMd4peZ+pXJy0/UBjYGhl7bCQpdGHkVrSD94FebiuwWG6Chqi7iWYRrDoB9mxPdEn/vOrRt0GeLRCtLaJm6kVjdyc0yjO8wR38eyVViJZJgbCLM5HHymSqQL7JLAKXBUEeaYDWM3U3NW71DM2KKRs9JBP5g7jf0U12ywlG/6/cEv9B5LzUWzyiGtLTa1ya62qBvBkZeyLR2WpUszaNTq0G86IW17gVKZCrW9jqFJEx4dGXzU/k7IzTCAafQUjXm49OvOIG2VcY5ymIvMBk0DU+sx1aV7OtrK9D9l3tMryd590mBW2woZkUXB2Kroeqnj17SBkSJx1p1MhRsGjaIVFJ0k8MWFh66If4c92graXsr77gN7EsVV0N9jtw8SAapak6q2qGoTy2zIKpknsMsCqsrmMhmxzn3p2GhX6tmU50M0krB72xkIo2Mx2hL5qXy2RIdttJh6R6HOGZABPRNH5lfUjfzZcRKQqzNgm4aSLCm6g1HS1MuIghRd7ykai8DL6dX7a+g9bSMOTYWh92SlS9lY6tyTvCJXSH7G/finVmTeujOoGlOSRC25r+89/3VtIPQyfE9mL5gqICqtHNY72XDo2oDv5kRRQltaar+JQ9Sub5f0vUaliIX3xkK60RP56UE+Gjk5YZDKZ86WscT3/w7SZKjv9cN58j7Xtz3j/259e+ube/WXHlNfGtOktS2Z8qWsWg2tp24FoZdR1/JBcUQrPehHe4TZHLS4bWeQVzbrwmNbupyPtujawK7wuUlDpk6OnvtSEoPG2UQGqJSNRWhXvN5NsfUOTxlV3If86Eq7nda2nNnrOaGt8+zRW758/ZRpGBOGKc5mwm08omhlN2EaPT+/OePj2ZKysfjNdspDP2XmZZxONnhuwd1mxm9WC86DmGkY4/clp48uqVKPNAnoBp0vNypnW7mwPV7scP2cPPVJCpd3tzKPfhbtmc820tymEUyCVJL0Bo3lasYyD+h6jSM/4WY3BlCHvCQPXewn8iAwaxxTHvCBCu0xjY4wSAmGHE3rsZz3M8fzlFvgXGUqfH3xkA/O39F3OpvdGEPpx2/2Y7aVy9guOZ5I7sE+99kUHh8+e0mRe9IadTclsmpWuTRqOlncsU4jdoVHXNsUiuW8Kh1OvZxu0PliM2EYNGZ2jWd0vN3OmLrSnheUa5jes1IZBpW66A1tIFLzzbv9mA/GG6pWMHdbnk7W/NXNA6aWRHCKVrCpTR4pJnTWCjyj5zp3qXvpHb9wKj6KtgijkyOj3RTTbHg8lumCgV3ycnPErrZwVMFxnfscu/mhAHk82lI2yn7abNC1npt4zLp06QaNSJn2fDTbk5QOL/YTno+2rLNAFlyi4XG4Z1n4RE7B0WhHohA3y2gBGWbzvtYiSOh6nbR0sVvB+fkVi8I9pN8VpUPZGURWJbvwQSOySgx9IK9ttpspx7MVL6/OmXgpH/zk77n8+485nm6oapO8cNnmPndZyKPxhul4x2/ePeZf/jf/mdXrM26WR3S9zovNnJkqBpLaZuGlPJ5cyejqzUwqSMbSRrvtDKaLFbdXp1StvLDf3Z5Qd4Lz2ZLAT4mTUBIGVVHlmDXH0w27OGSbBxJSj6TN73I3kb4h0Q4nqOh7GVBUJRFHQcwuDTEL71AA2FbN5WbGZ3cn/MvRnrskYj4kRKOYprAPBk/C6Dg/uZESxfWUq3jCmyRk5GYsdxN8u2Q22TJyi4Pd8HY/wtAGfnV1znXhUfUalnHKzE8PskVfNWjfrX+c6xtf/IvRltl8Q13Jh/lyO8VTxjhNp7MYb3mzXDD2pIyraU1sqyLNJGxm6D2eW7Dej6kak4ejLWcnN5SFQ92YmOoCNw2Zzx6NY25vFlS1edCB/9F/+2dsvnzMly+fcpOGLAufp2MZ7zkJY54drbl6dyaDcCqbsrFYr2ZETs4+C7jezrjNfXzRqFQv2aH98ydf8/B7L2gyF//XH3ObREzCmLq2eHl9Rt6YHCup4t1+wqPFLS+//IDrZIypd/zw+VcAvNlOMfWeB6MtP3/7lE+Or6lqk9skouwEzxWhKb2VbPJh0MhKh0qhI4sg5jTcK0vjnKox8eyK0MswrQbPrvg42jMMOnUjyCqHVRry5X5C3Ei278XdMfPRjq4T/OL1Mx6/h02TVNJy9CYeS8c8o2MWSIbxbLKVHZdCYhwll/QCeVDtSpdFkPDu3bnshPyM/+b0M/7d3/+IT+a32FbD7d2RTCh0pZ+BaTY0jUmronyrVvD8XspUOSSNybqyEYYksjlGx7Gbsa1ceTEaLaYhfRr2lcN17ku5YKcdxjNC7xF6x2dbiz+cSpThy9ji1O14lfqY2oArOn44v+MiljwTnYGbUkpOv1yeEDcm537Cn7/+gMiUBLG8tjjyUpJGdnZC7bm7wpNcDbNhEsiC+vXymN+sj0hagydBdtijUzenbiXpteoEnrrE/+DJK7LM42ozZ185PAgS+l7nYn3EtnR5OJIGU/vS5U36/ixa//L6AT85viFwioO0NwoTROFSqgjtPz57h2VKZv+71dGBzGroPSdnV/yHv/tjfvLsK4TZ8lf/7k9w74299O6QX/BPPvyCthFs9yNcs6FOPLbbMdvc52yy4UGQKESu4STa8XY7k14craBqBc8e3fH//Jt/wsSueDxe85d//0PaTlc20jJyO1cqHz9M8UcJf/erP+Ak2jGgsUxGzKI9l/sJY6fg07N3LHcTdF0a5oyDhG0S4bsFt7spWWNxokZ4t+s5u9xjk/mYRs/UTzgZ7Zj6GX/76hk//egLac5Tm4yO19SvxCFp8uLmhHEgbcpHdkGzj1iloTQUKl3+8uuP+OTohqVCD6rW5K9WU/7V2RUfzu+wrBrPLfj12ycsQukDcrOb8qP3tiPk+s6y9/d3feOL/6/ePuXJfsLESyVxrDFxrRpXWUCaVsMslzKUXRriO6Wck2UBaW1jC+m97zvSJ7uqLTabCYbRSX1vZxA50j6zaQXJPuTV+ojHk/Uhivc//uv/lomXYZuS+Z03FqfzJZvdmKJ0MHcRceGx2XiqWpYsaEt00uvfk2SZ+/ATXRsQWktVW2wvjulaacHpmY1kEvsJD+d3/NsXHzO2agnnNibGUhLHtqWDZXRsNhOu9uND/rymDTwcbdH1DkOXCYGG1jMOY1CGPhIeFAcv88CqDiE9ptJF30e6prlH2xnMJxvaVpCrbqobdMrOxNQGfAXdzaI9XSeoapO5n76XTWMZLW1nEFgVP5xLxcM+99H1gcBPD9yOu1QWPH/48DXXNyeAtK+tWpOsktB3t5/CDfz06QveLY8xm5ZJkByiZyeDxki07HOfpLbxREOPxot4xPcmGzyjPcyrVsp/3FWZAMvCZ+wUpLXNKvMIzZojL2MYIK4dPt+HXGcBmbqs9pXDD8YNoWjJWoEvBqZWK6OCjQ5L73ixndH0OpFVY+gDTa9xHY+pOoO213mXRlwVNnOnoOs1frGbMTZbmkGTHAWj44GfMXZytoVH0lhSyqlkZ77ZEJoNN4WLDkTqAgzsiptURgL3wEsVLW0rO9yyE3S9hmO3eIaEmneFJ+fBbs6fnrbvZS8A/NOTa549eouwa4rUp+901tsJVStHZpGXsU1DIleO1O6RKt+ucMyaPAk4CWJ2+xGuU/Lw+IaLu2Ouk4iJU3A2WzH3E758+/hwsS9GW1ZXx9zEY+LK5lzrCZ0Cz66kTl5B30KNpSyj4+LqAQtXRocXjcXT+S3bNDwQjiM3J6ttWZznHoGX41sVt/HoMMKrGwtNg2UesK9cXNHwv3z9ISd+ysxPDhkacWUT17LA79Zz0sqRWQP81puhbCy2uU9g1dwsjySyx0BZOgral4Fn/aCx2ssQnrwxiayGojX5er1gZJf8wcmVHLGofJNh0PhXZ1cAB0mf4+c8P7mibiyy0jkUOO9zDd859/3erm988WetYJVLg5SZaCVRpdfplD7YtCQx6X7jOWbNNpVSP6F3HIV77uIRgZtLL/FBp6gFptqsWSXZ6oFiyOalS6Nmnm0nVKRphKZJTa/j1YR9gTAlASspXTJl6rJUVsIg549TP8OxajRlhGFkoQy0UF2erg9UhUvX6YfM+0559WvawFTpZi3RI4yede7TDvLvmoqhDXIGbPdyhmkPGq3KdZ94KUnpStkfw6G7aFqBrn7evd/9gJp1KrKSJSR5Z5uG2FbNej8GpMlJ2xmMbAnZ3RvMNMoC9z6M5H0sQ+8PngX3ZLJh0KhVEBKA7xS4pUvdGdSKqS0M9X4PA5omWdFpbbAqXZ7WFk2nI3RZCLlWTVJJlreuYGsJa0uiXNFKA5YejU59TnWvY6nXVrUCV7S0vUHRmqSNqUYQkifgGC1CHcB5p1N2OnWnc+7nvyUGWj0TuzrwAQB2tYll9L/9fpok9+WtUHtEXrC6Ssire41tLVg4snDMG/PAadE0+b5VrUmhvAnqzmBsVZiayYDkJGwKj8fjteIiSG5B3hnsSpeRKhx/t7vSGHDMhqYTaibeSlXIe1r36NigHBnTJGBX+HS9zBboOoNAZUgUpXPYM/f/7FpJgN3mAVVrcmS2h/enV7p322poM/1AUhRGJ+H10qVRZka6NtD3GrouLzxLSGvfurYo2/t5+f2lK3kBE2X3LSXHnfKhkDkTA9IsKFHPt2s2bNMAx5C8kKbXSWtLZYOYWEoVUinugqH3pIrPARxCrPLaInJ02k7GSruiOZxdjllTVjZCtAcekmM2h9Aq25Ak18Cq2RQy58G2KjRNSkWNuj+cYaVy8Otag66WRVjbSj6Ubby/QvC79fu/vjG571GQIJTmeJ8FOFZN0Vjc7Me8uz1B02T0573FbNWavNxPqDuD42jPh3/0K+pOKAKQdXBHKxuLdR7wKh7zKh4jjI6qNdlkPnNPenMnKu3tgS+hPKFm2cfHdxS5TMhbZQFfbuY8OL3hfLTlJIiZuvLrzx9cEajuV4iOyCqJrJJAOfF5bnF4cEzVjcwD6W2+3E340fkbLEN+3dTNyFqTYYCZU3DkpWgMHIcxF2nE290EU7TktU1aumhaL5MHC4/b7ZS2Nxi5OZ4l2b4gD8C2MzCV/jpVISPLeETg5zhWza70uF7P+ezuhKR0mI6kw+HZ0ZKPTi55MlmT1pbMPM+k7O99LlfBtpfxmJ1yGQSpnLBFi2U2zPyEyCr528uHNL1BWttktY1j1Qc05Xy0xTE6/uPrDw7ITNOYhF7GxMuoO4PreISmwfloI1UBvc7YktkOZWewqS3WlY2l9xhqNn+dhUzdnNssYF06tIog9yaJuM19ssbE0gdmToGpDbS9RtZJxnbWCppeY+FUjO2SpteJG5N15aBrYGoD68rkdSb9/QGS1qDqNc6DmJndHC6oM7eSXAA3xxEtN7nPdebzLpbacKGKzDtls3tT2mStyaMwZmTV1J3B5/sQU6EYQl3yI0Uq7JEXo9BkYmCj+BB1KziK9nS9xk4ZJr2v1TQmv3nzhBdvHlOVNnfbKalK0wOIC4+z80vp4phJ10bLaA9JfLrek5QOcWVzl4a8ujlFGB0Pwj1jT6bo9b3k+0ROIS/x0uE2iUgaSdqUSiGTXRaQly6uI5/t6+2MN9sZm9KjHzTi2lZGOCafr47xvZzxaI/vFocCoFWk1bqVYWQjp2Di5sz8lHfJSBqYTdY8HG/Y1TY/WFwT2hW70kVnOPCRJnZJ0YhDgJMtWmnWVLoHcy9DHw7mX6GXcXx8RxhkVMrMSehy7DcNYx5MV5xN10zdnAfTFadhjDB6VvsxUZTgeznT6ZZptOfF+gjTkM3Fbj9is5lwsVqwVU3R/D06Od6v/lv+89369tY37vg1DaZuzthPCYOUn73+gB+cv+FiteCv747pBp2HR7fsk5Bt7pOULn94ekFR22yykP/07/8Ey2iljaRXEDUJb29OD7PSUBGi/uLiMY/DmEfzO6420kIzUO5V54s7NvsRnmLAp4l0vEsqh3bQ8UVDvI8OcBvAzMv4N7/4Qx6pB8dxMsLCZZUFWK3JBydXbPcRr7ZzQqvi4/O3hGVBXHgqfrXn9fJYMqeVSuHZZC3hQ9FiiYbAz4mTgB+fXMoI0s5gHCTc7Sds01B6Z/c6n2/mnHgZx2FM2wnOVAjJvQPbXTxSSESnWMQ6b25PMI2W03CHY1d8qg24pvT8d62Kn795StbIJLeffvQFLy/POYp2tK3gN3en/OQ9bJp7lzFDxa3eEzG7XqdpBbdZwHy0O7iDPY12HI23FKVDVjnsco8Hky232ylFYzJ1CoTeE9glbW+w2ocA+FbFcShtf5fxmLjwKFppk2rpPVfJiOfTFc+Bi/2Epte5LVyaXsPQBpbLIzxDwvS+cuULlOFQ3gnyTudVEhFZDSduiW/WWKLje9PVgQ/w57dH/OF0R9JYvM5cslbjzBuYOzWO0WEq+aln9NS9zkUasXBK9o2F0AYmtiwkfrMbM7FrTryMd1lAZFWMnAJbNDK0RnVg3QC20ZHUNjoDpt7T9hrrNCKpbSKr4sNwLyVhylzItSreXJ9y5Gb0ulQmAHy1nfFstGUR7Q9Wru9jDYPGs+NraTerrGCfTFdSk194lK3g8uIMTRuYBVIN8255fFAKvbh8SD9oPJ0tAVinEW+2UyK7Yub/1nlutR/jmlL/r2sDH51c8bB0ZAOymzLxUkI/o+91rldHhGrcNPMynpxd8utXTzn2U2who6GfWA1fXjyUkkm9p24lOjTzMiI3x3VK4iw4dOZJ5bBwM+lCqfcEbs7MKRFGd0A09pXDcRizzmUy5o8evqEoHVZppKyTYxZBjNA7LLNlEsZUlc1tMpK8pMJhs5dGVIErSaWv7k4YK6S0rC02hUdoFweTr3UecPvVRzwcbxiFCYYhi8O0ku6frlnjeznF8pgeDUshm9+tf7zrG1/8T+e3B8Jctp7T9Dqr/RhbNPx4vmLipeSFi201jMn51fKYDx5cUG/lBWZoEoJ9c3csM9i9nIs0Osww7x3eHocxSW3zi4vHnIV7rtdzKf2yavLC5evNHC8esQgSzk5uyHJJkrKNlqoTfHX7gEeTFYbRSVONyuEDRQBc7scMOwnfhbY0VPnbN8/4YLpibBeyIm9MRkGC25jc7SdcxAFzN8MVjQzGUczZe7ORtjOw3YK/f/GR7GT0Dtds+eOPPj/4ZPe9zmm0px104tom3854GO0ADrGfd3mAb9ZErozvTUuXAUl2tIQcZ+yzQEqKzBrbavDcAmc7w9CkT/5uNyKwS5rGRIiOH569/Za3i1y5SkC794v/4PSSzW5MWkm/+qlbkOQ+vjIZygqX18tj4trG1Huez2/RdanFN5SbnWs21K2gaCQsO3VzdqXHXRbSKQb9RebxJEg58lJ80+JVPILdFFsRCKteV+Q5+bo8vcfSe8ZWhWs2fLGbYGgDPWBoA0+CnFQZ6AAceQ3v4tEBpm8GDaEPXBUejtFx5pVcZA6O3uMZHULv2SsjFEMb8IXkA7xMfNpBY243zN2cstN55GdEdoXGwLYWfKRLhUvVSja/pg1EZoPQpZQwaU3GVs2pl3HqZVwkEXN1EXy9PsLQB1xVNDSdwb96+I6qkRG8dWdgi5YjR1rltp3BVTx5L0UgyPFeGav3Qe8JnIJMjWk0Bk6j/cH/o+kNHnRbHh/fsNqNDxD3rvB4u50TWBXH4w2G1nM03tJ1grvNTJLb7qWfuc/J0ZKLmxO1d2omQYIpWq7Xc8pW4Fk1b9dzhkGaj90t57hmQ9XKSO/QzxCiY39zytzLmXgpTSdYpiHLPOA2CxFaz9gt2JfOQQ76owfvuNzMDgjKabijbCyu4xHrymFml9StUDHTMi/AsWo8S45G7y2WH4y2VLVJUkV4qsAFWG8naMj9p//O5fwuHqu8h5bIKrGthjj3pLJJb1kEOW92M+aVw8l0zfePr/ji7oTvnW5YnN6yvFlIe/MgxhLNwdn0fa2Bb3/G/x1j4Ntb33zGX7pUjTRSibwMO4mkb7ZdEXk5vpex3Y9oG9ltT+1S5lv3OoY+EDrFwcgFZNCKoQ04hoQs217HNDomXnaA5KpW4CsuQaX01LYuL/l+0NjvowNkZhn3ftYmaelKb21lnpMqwyGdgaI1idycsfJbb7uQXe6RKbLcPg2lF31vyDAiJf3ylalKVVuHPHGQh0qRef9ALtV2OnUlDYrazqBR+l5PNAczjpGyk+0GDUs0PBxtaVR2fdfrB3OYaRjLMUoWsM59Zp6Mw7Vs6WxoGS2WIbX1TfvbACNaEN/40/3/bzW9gfk7HWSaBaSlS1rbtJ3O4/mdLFIUgSivbYrWPAT5tJ1BXHjK0El63kdOTqMCdQKrJnQKOctvBHknGFsVjiFzH1yrZl+5LNziEMAjZ/IVQhvIWkHV6wRmcygsk9qm6TVcs6NTM9dSsfkb9btIkpxBrWb6jkIJaqX11rSBwOxoBu0APZadwZkaSfVo6AxYuk3bacoXQFfIkTy22t7A0QeyxiKupVXwsZ8qo50BHenR4BjSqMg1GwytP6gA6s4g7wSiH5jcx7T2BpGbc5eGlK04BPWktU1RW9hmc0i6fB+rUr4Xht5jwEFea2g9vS75PsVgE9qlnFNbtcyauPekUNwXx5BERU0b5ChRWU9XjUnTmoR+JqV1rcAQLWlly27d6XGdiu0+4jYLaAadU6OnU+9/2QrM2mYx3nK3myheT0/bGoxsaYMr+RAGjmhxhCwkd5VD3crwLsvoaHr9H2R76NpA4OXStEfvVUKk/L1MvUPX5Gdvi4ZS+ezH91G7SYSh/9aLwTQb2lb6Wdy7SN4TGW3RkjQWPdJj4j4zolaETs+qDyZYaW0TZwGhl0lP/8Yk20vU8Xf9HO65KN+tf5zrm3v178f0aJyGe2azDZebGcLosITqPN2SYTcmVsl2z4+vibOAqjEx9Q7ProgLV7LyrYa+1wjNmrGXS8iv+W3nM3ZzLKOjagVnwYp1PCKtbSzRchrtCb2MthVcbOYyaU2Zuwi9J60t1soHQNcGHkQ7NoXLyC7xrFo+OL1O4OXyAhUtX2/mFK3AalX6WW+gI1/H4/ndwRe/qC32yn/g+eyOrpcEnX0S8jDaqYhRydZfbaYH1n4/aAcGsGfVuGbNaBSTpT7DoOE6JafnVyxvFqzjkepK5NUSRYlMM8sCdpXDo8maIMgwjI7lfiwlX8pEyNClMZJrSZnlLvHfy6Zp1UWpqzHN57enDANKldAxm29Icp+8tukrh7ssxNQ7RrZ0PNsXPuvC4yzayTlkJ3DsmLaQPAdHr3GdEr+xKFqTZtAZOSWW0TFyZVDRqnD54fEVeW0Tly7toHE22rFMQ9rCI2mkPfDYyVllAVe5HN146sArO4NVaTF3pGb/nvSla5KpbxgDvmgwdQtHwfh5azC1GvaNwDEMTE1CqqfR/uAyBzCxa/TaQgfKTsiv7wz6wWYY5P/fVg55Z9ANmjKiGQ4Hcw94Rosr5KVfdwam3h8uG5CkN8+qaDqDXPFCNpUjiytDhmndqdjqmZfx7NH7QX9Afu6ukA54QplH3RfGfaOh6wOOWTOKYly/oMwdqtI+kGfrThYr8yBWbpdyLn6bjABZdGelw2i0xxk0utag7wyqTmD0A5a6jFdZyE4FRNWKwJb2EgHpe53pYkWqUgF1fSBOPU5GMo9hk/l0g37I7Whak+LuWAYmqW69agXbPJA8I72TUkNTBoeFdoVlSFe9+8wKHTm+EqKlUTyluLbxzYY3yYiJXfFovPmd0Ct5ZjWdYK3IzSMF6Xe9hmn0dL1GVUtS8H1REjoFuVK8NL30+Nc1aducVg7JrUuoxml5JRGi95Xc+bvru2HC7+/6xhf/3E/5cjtjtz4iKR1Cp2SmtNRJ6vP27hjLaHFMOVM6++ANv/nsDw4xvW8uxzyOdozChKq2+OJOZtonpcssiDmabPjszVMso6NVbn4fHl+xjuXDvwhiHpxK3f8w6BSDDDmxjJabNJJSJtHSIyVjvl0eTFW+d3pJo6JuV4XLl/uIM69g5uT4dsVZuD84oQ2DxltlkhNYxqFiv4jHOEbL1M356Uefs9+PyFOLpjck78HPyAuXuyTibRqyLF3O/ATHbDCNjkeTFVnlMKgu5PXl2UE9YOg9RekQhQmOXZNmHtf7CXFl88WbJ4oY1HDqJ5IgtZnIC96sySqHcZAwHu8lxB93jEZ7hGhJ4vC9bJqdsoKd+imz0Y7Pv5phaAOnXsaD0ZYvvn5GN+gcRXsMo+UXy2O+N13TI10Q217n2XTJy80RttHy/Pia18tjTL0jayzSxmKe+6wVnOqLhtPJmq9uH/DZ3QnbWnb02t0pU7uk6Q2KVqArF8S6M7gsBFfFiOPUV8x/mFktf7sJmFg9M7vh1K14kXj0QGQahGZAN2g8CVJspR33Rccn07X8vQuXq8Ija3VmNjKDftC5jMe8TX3KXufULfnlzueh12BoAzeFy5FdomlQtIJEKQZmdkXZGcStwZf7CY+ClMCssY2OYYBf7yN+ulhimw0XaUTV6zSdrjo+iSYUjXV4TZ7R8YPFNZomXQ1fbuZo2sCq9NjUNpo2vBdPB4BPnr7k9nYhI4edkvF0S576DPsReW2zVM9wGGToouXd7YlMbzRrJl56SLm0rJpoFOOPEl5++QGnahxWd4IvVgve7iYc+ynTMJGxvnrPyCkQRsev3j3i4ViS/xpVgO0rh5F6dhyz4fbqlFUWMvPTQzTuQVWh/pxGO+n5EY/46/WY/9NHX7FMIupOqPNNGoftK5fbLGRe+PxqPefcTznyUy7jEUd+RmCXmEZL1+v8z19/yLmfc+wnnI82/MXlI55Ge3yV4nhvIJTWUgK4rWx+tLgBlKOfU/Lg+BbTbLCDHGcS8/Ln32NTeozsklEUU21mzBTyBLBMI3aVw/PpkpMTmfvgxiXvVkdcZyHDoPFP39N++G79/q9vfPFbamZ4D93bojmQs6pOOoM9mK5U9KbF2y8/oOkMjsdSe76/OWNfOWTXZzii5fFkw4vNnMVoi+/J8IkPju5ICvfgWKUpLXvTCrrOYLmc8+vbB8xdOQ64zgOmtiRl3RujbCrn0PHuCx+hd/y7V8+JzIaxVRGYDd9f3PBifcQXu6k0n7FLLnOfuVPy/dMLNstjfnJyhWW0hwTC+wz3bemSvXqGazaHkJ6iljahVWti6AMfT2QVb5tyBrstPH78wS39eoptV/h+jiFa/vrzT6WE0apoDGm4U3YCW++YujmhXeKYNbZVY5kNVWvSq8PKFg0PH7/j+uIBvp/TdQa/ePkBz4+v+friIZ5V8YOf/uy9bJqLzOPJeINltlytjvj+/E6GnhQev7o7JbIqDG3gSuUfnHr5waOh63Vs0fCru1PKziA0JVckayyeTlZYVUfWmozdnKS2sQ1pwPOzd08IrJojpyCypGZe2kYPjL2Us2jH56tjJnZJaDacuYLHQcab1KftwdEHJnbFf+3mJI1F3Ji8zR1+NN2TNSbdIONhGzVmkfGpMLZq3qmLy9R7zrwMX9ikjckXuzELV6oZPNGhdxINWTgtjtFJO91e51XqH8iAY6vmpnC4Kxx80fHElxGvP1tPsHVZkHww3uKaMkr4Mh5zVzg8DRMCuwIqXNEQ2CVfb2eYWs8H0R5bKUlus5CsFZyoSyBU3Jn3qfK4uTk+EC+XacSJ8lO4t602tB7PrqSTXW0yCRI2ScTiaIXj51y8eciD+Qpd78hTn+ubk8NY0BYtnlXxONrxcj9hVfg0vcG2dPno6IaskoTR4zBmmwf8ZjuVbohBwtlod2DGm6Ll9fqIqZsh9I7sd8aOQo2XJipauGpMLKPjoyjnNh5xMpJo3ioN1WuSzQ1A3Qo+HG2VC6FF3Fici52MHm6l5/6jIMPUO3L1fvx3f/AZv3rzlLIxmUV7bKthu4+YhjGmaLlcH/2DuHOAIndZ5rMDWvbpySUfzW8lyle4eHbFL6/PGNklx+GepHL45x99Tqrez5FCMR4d3fFYGw7F0ftc/Xc6/t/b9Y0//VwdHPds0FxB75o2QA1pI733TdGqMImeXSnnuJ5T8HS8kWliSuN/NNnwxWpxmAVrquPOlL+4pZLx7oNu2s6gH3SyVhDd+3X3Ek0IrBRNG0jVa8wrm7IV5I3Fk+mKR36Kea/Z7wZuktGB4LWp5aHfDTI/fZuG+Eojf+/xv1Pf11W/W9ZIgk7b6ThmQ9/rmMo69T5N7B5KuyfeLVczqtbEtivpLFg4zP308LWOXUkbXtEi9B5d7w/8hUF5AhQqO1xjYEDj1asnMuNeG6SBkicZzVM/wXOLg+f3t73GVsMyC6kaE8equU0ijsOYUGXMG1p/yEFvewOrl0XcPRwuC6QeX838u0GnGzSuk7GENLWeXeERWeXhe+SdQVfZ2EaHrcuRTFxb5K0pL2plWbCtHOrOwBcdd6Uj3xtdwo5xbTFzSppBo+o1TEXi60Gl77Xknc6qdLCVa17Ty9dmKD5G1poINf+3dMkrKDuJQFS9xkjv+SBM2VY2ZWcoJUGHqQ2KuwHPgpQ7NcPtAQP4MMwkOVEbZB5GKzDMXkkJpQd/XlsIo8c1pZR2XdlEZsPckJyVd7up+nxkjGtSOfRIfwjtPVKjxqM9qdK63+/Hfe4f5tUyL6DHFA2m2VKUNpGb07UGTWnjezlVbWLo0ryrVLK6uheHUVaZCR74iZq3tzyZrA/qHaF3jPyUuzTiyC4PHIebZCRjn62SozBm5qU06jlqe4Nd7kkOhd5ja7Kwnrs5eT2h7XWmaj+v0wihd4y9HEOTz2WuSKgAYzeXipZO8DCIqVoT16zR9J66E8zdjLh20JBx5kka0g0aZSeVCpZoD8+2ZTYyz8MpD0ZdRtfjqNfSdDp7ldExn0nSclXamGYhOR9KTjjzVYBPbZFWDuMwpm0N6sZ779kN9+u7dL7f3/WNL/7i3uRCzSLLxiR0CmwhK9Jl4bPNfTyrPnSp29Ihsko8p2AW7dESKbXzVK69oUsznKKxDhflfbCPxkBceISDdugU72f5IA9qocvksftZ5/2DeH/pl5000Hm+uKGqLfLapioEb5KQiV0Rmg3rWhqvRKa08d2VHr4iDmmaTNkqO0NCkyrMI28ERSt91t2mwdR75laNZ1dUtXnIeweJlNhmw+V+gmVInXpdWezj6JC+1w2SBPRwvpRs8sYkLryDSU7VmuTqe+a/4/L2xfqIT+d3NLUlI1snG+rGZDZfYzkV+WbE5FvaKL+7HgYx28qVJEUvY1M5jBWD3DXrgwPhPYyZqXCVQv270UqJnausk0EWNxepRFbGVq0SC+UhWlXSpnZZ2czsClO5FHaDDOKpKpt9YzIyG/Zq3mtqA+8ymwduQ9NLk55dI9A1eSEPg4YrpBGPTAbsDk58+0ZgdwaO0aEBjnqNrTIGso0OV7RYuixwssY8SOhsvecs2rFdHlN2OqHZEpnNwV8g7wyejbZs1OdZq5/zeLwhr23yxqTpDeLGPJisNIMm7YpbE1fIiyFRKXWm3svZuuhIGgtfNEzdHN8u2RQyp8DUwbHeH5nLD1Pc7eRAiJWfjVTyuCotUtcGdF3Gcw+DjucWZLlHmvky1KfwMJUj5L0MVCbSSXOrqhM8nq7oFfn1aL7m7fWpfM9NWVDUncH5aIdr1iSly5tkdDA8itxCRonvpqDm6dvKxdAHRlaOxiCzANSzpWkwsiXB9DoLGFuVdNIsZMLmvnQOLP77dFBdG5h4FevstyO2ujWY+TI0xxJSTfDm7lhxjTTqLpDEToWiilpym+7HjkUjCdLzyUZa71o1kVlTNhamKYOA7uO378cc/aAxHe0pco9UERSFkM9jVjnU3fvv9r9bv9/rm0P96vAzNNk5z4KMupVyuaSysfQOYchOLYllUIup98xHOxy75n/5+kMeRTtqFePb3ejMnOLQHR9PNvz1m2ec+inn8zuCMOWvPv8U16qpFVvdtWpGil1vaD0nXsaTo1v8IKMsJHP/Yj/mbLomLTyu4xGf357y4fxWEnhUJvzYqvFFI+fzZse/OH8jYXTlEva3dycMA1TqAYmsmvMgPkTvrlP74OaXVw5FK5j5KYGfMhp1eMqrWxukCsEUrSxGWhPiMYlKJqs6Kfsx9Y5t7vOv/sf/D9nFguXFCWnp8sc//gXDoLG+XvDm7oQPH77lFy8/QDQ9Yzfn2WjLbLTj4m7BvnL4YHHD0WLJfjsm3kf4Klzm217PH1zhhynxLuLl7Sk/PrlklUbsYgnx/sHDt+ziUEnNBDoDb3cTDF121ZFT8Jv1ET9c7LBFw3U8pgdCZYbT9Ro/+ehLttvxYc89HG+4efsES+8l0a8xsfXfJq9p2sB/uQv5wbhC16Rk7llQEpgNq9KhbqS87qqwmVotC6ciaQVlZzBTM/g3ScjYavGVzWvWCmZ2haH1ZK1J0soCRCj2fdUZ/Ho7xRctJ255eEaKxqLsDLpBkvDSxiQyG3qgaA3+w9UD4kbn1G146FWYes/f3Z5y7BYc+wmhU3CdPSRT0PCpW/M6DXkSJHSDzk0acRbteDxdEQYphuj4xcsPsPRepRwKnp6vSEuXbSkvqvsi8n2sr14+YxLGGHrP1X7CpvD459//pbLw9ahLm+1+xNvlMW2v89HZO9a7yUG+uSo8jn0pY81qm+ssILJqHo/XFI3F1+sFJ0FMqVxAAX71+iljNydU5luXyyOlgpAQ/Dr3OQ8SbNHSdjrX8YhhP2YRJDhmTT/opI3Jp4s94/GesnC4uHyEeXXOIkg4Hm9wnZL/8tUnBGaNq4qT19sZJ0FM0+tsapPvH93SD7osvvSON9sZHx5fs0tDbtKIt6nPj0XH3E9wbJnPMfVT3iQjLL3noS9joNveUIl+PbvK5WK1ILQLLKPlZ+sTHi9usEx5Th5PNvz68iH7F/J5q3tB1pg4RsvD8YYHpze0reD15Rlx6aJpgzTF8jMmoz1Z7vP57el72w/36zty3+/v0oZvKLb8f//k/8w8SKSkrTWZhDFNK6hrS8lKdJLSYezleHbFMh7R9RrdcD+LlazkuHYknKv3vE4Dno9iRnaBbUqYfDHZsIlHrNLwwEoFyBuT3+xG/NF8RVw70jrYyygaUwZPqGCer9YLdrUlI1FFS9EKIgXHZq1gV8uO8MyTVrdfJz5nXolnSA22offkytrzPtb14/kdrzazQ2jKsnR5FMY0nezqPatmlQWcj7foWs8mC+QlZ9YynrT0mDkyVW7s5vhOye1ewtp5Y8l54oML7rZT6VzYmNykIYGSoo2cgkkYSwMTlR+uaQNp6SKMjut4xLZy8EXDsnL46ekFi6MVVWnzyb/+j9/6pnn1f/xnmGbDajXns+szHkU7xTzWKRRiMw4SVvsxd3mAqXechPFB2ha6BX2v8XdXD9nV0nt8W1nM7JqJXRLZFV/tJpx6mYRQex1HtGSNhStkBLDGwLs0IjAb2l7nqnDQ1ee6rGw+31v8787W3OQ+21qa9TwNCtaVRd3rdIMsME7dmqw1aAYNW8mrVqUcPc3sFkchTM79Z1/Jos/QBvLO4Dd7jw+C6rDfToKYt/H4APNP7ZLH0xVLxVC3jJa8sQ6S1W1ls2sEkdliK0+DWiFM93LEkV2QVA5ZIyWRoV1xPNrx9XKh/ruUjHQ/kWz13ONv7074eLSVSpZOcJWG/F9+9X/71vcCwPX/9COur07pe9nJX23mUpmi99iiIakcZn7KbRKxqRwehTG+XZJVchRzOllz9uFrXvz9J1zHY0n+PLr9Bz+j6wzebObSEU8RAh27oihlB3vPtB/QDlD57Xb621GHNrArPY78BFMpYFyn5MXNAyKnYOxn9L3Gm82cp/Mlmtbz6+szzkc7Rb4zqHsZlfx4vKYbdBI1romcnKKxyGuLQclwXZV7X7UmD6YrbrYzul5j4mWsshDPrBl5Ga5T8hevnvN0vMES7UEp9DYeEym/EctoCZwC15E5IGXlsE0DIlcpk6yary4eHtz47omIoyhGmC1tI5MHezQ+efoS0614/fUTfvof//W3vhfOz8+5vLzE1nz+hfs/favf+78U/3eqIePs7IyLi4tv9Xv/Y1vfuOMP7Iq49GTYTRizV0QXDQn9bxUDu26F1L8rMleloEvLkJC3p6x+95XDiVuyCGLZUalDYJ+EtJ1xmL2ltXVI0XOF9L7uB+0fZM6vlVxMGFIydlU41L3sljxD6nIzlSHw0Sjmy33EXengi5YPwxRDH5T/uaBpdB6FMavcO3z/63ik7EelhGem7ECXWUjdGYzcAs+sKWoLS7RMvIysckhKh7oXUg3gp8Slpxy7JJs5LV0Cu8ISLXEaHBAHkESy69znyClIK5u8ntMN+uGhvic/PnxwJT3+d1PKTvC96RrXKelaA/s9xfJ+ffGQ0C4oGkuG5GjDwelQKG5Gq+BKU+9UgIwsktrOYJVEtJ3U2d93yb6Q8DlArGb5/aBRdrIrD6yaXP3T0PpDx1R3BkVn0PQaZ14pLxu9Z2QN3BUeu0ZQdjo6Eoa39YG4kR76J04r4X+k897ErrjIXCyjx9EHPAXpx7XFurLoBo3QbNk3Jjqyo3noNXSDhmN0eKIhq23GVsWydGmVhvzeRbLrNWrk/L5WoT6+aHka7flsMyXDwDN6ZnZF1orDiGFTeJSdLGCHQSOpbNLlQnoi9NLLf+rm0lyrckhri7HZKMMaCbN/qFzx3sdaLeeYZiNtcoOcJPO5SRc4Rqv07AOrNMQyOs7vLycViAWwzwKyzz6l7Q0WQSylnF5OWTq0ncEw6AfnwaIxMQpPStjUmXP/PLzdyuLcFdIyeV85nEZ7NGT879jJuUkiOU5ycxaLJdzIcZylPPItoyNWxD9TuXBWCnG09BbfrNkVv5XJpsqi/N5zQfzOuQSSCCiMjrPZUsp9a0uej26OaTZUyo3RUzJAANcpD85/Xa/juZUkTyp1Rtfrh4jzqrYP703dCeUHIRGEzW5M4OXYdsXReMvlei49ELr3L+UbgP5bnvF/xxj49tY39vHsB41CGdLYTiXh6spW1rLyYLNFS9MJ4tI7dPrAIYTGFg2BU+ApGcvILhn5qUz3Ey11J6NmgQOr9V6+pGkQmQ2J6ihtlWIHHMhQnZrVBuLeeEWaXdjitwluUzcjFB06ktx3GsbM3Uya6agO/z5M4541vlZw2aD4BpbeKsau/Dt1K2QgSGNRNBamaKUCoHKJlWf5vYFQN0hbW9uq8e2ScZDguzlZ5WCqy07TBkZOefDyb5XPvYb8nYrGlOl8vY7tFwReTmhXTJ2cx8fXEpWp3h+Le5X7rLKQqjXxzPowq647cYhgbtUc/V5TLf+O5F6scp9t6eKbNXMnJ7JqJnaFY0gJXdGaHDkFTW9QdvJPWkv4vGyFzItoBL7Z0AwaeSv3iC9ahNbji5YzV2qaTW3AMXosYzgY9cjPXnbxZacrYl+HLxoMDVyjxxcdgdngi0Zq+DtJ3pPBPMPBBOjYLegGDVvvsIz2sFdM1b0nrQxNuke/8sYkbSzWlUPWyk515qfKuEcSDV3REKixR9kK0sYiUwW1pg1krXnQq9/vy7oTbNX7miuSmlDKAqF3hL8j9fq21y6V8jBDdBgqrMsVsnDvBp3IKaTnvFUxcnOJdKnQmXtnwdfbOVVjYlsyTe5+fKPrPZpy/QysGlORBYXo2GS+1KRbDabZkDUWRSNleVnlUHUCx6oPZ8swaKxKl1SF6HSKGDgMmjQGUo6HeS2798CqqNTMXOgdluiInJxcRUTbalx4/5xaSl5837kXjXUILbPt6vA6hNEfUgNL1dgIo8O2pH+FZdWM3QJbFdUAeW2xL9yDJLhVZ1tVmyS5VDrc55+YChFLKpeidGhbgWNXCKOnrmyK1D/wKL5b/zjXN+743+wnnCmIrixkd162AlRq2sfnb6WOfT9hmflcpSH9oB3gcUPrD3Gz0slN/mhNG4iihCDI+OWrZ9jqwLhfviKLdb2GrffsaouxXTJyC8ZBwjoPGDu5tLUdNNa5z0eTDWltc5t7vM48zsI9vllT9Trv4jEfT9YHO8xh0JiPdtxmAYbec+zkvNxP6OHgngay+k8ai7I08ERLmRo8Dvd4Vs2m8BkGWJcupt4fCpHL3CVrDUzFMja0niwLWGcBYzfndL6kri3yQkL294eFZ1WcHV1xl/uUrZDRpOPNgeV/74mf1xZffflchomYNQ9Pr2lqiyT3ZQBKZfP029opv7MejTdUjaniTgeWecDz+S1Z5XCnkKBAXdx5K1UTwuhIcoedspRduDltr6MbAzMvZayCamrlpvdotuLP3jyl7nX6Af5iNeLDsOJFEtAP8CzImTgFcW1R9jplJ+V3mj5w5GY8Hm0PnVBcO6xLh9eZi2P0LBxF5KxNfNFhK8Qnrm0e+Rk3pStlgvfBQ7VBZHY8CVKWpcsnkzVx7bCvpGlK3vlYhuS4XOYu17nLR6M9vtB4k4ZsCu+AWgGsK5uql0qSTW2yuzrnxC3JW0Hd69wWHj9a3PDZ3QlFZ/A4SChLl03pHZ6Zp+Gel/sJM6dgESR8vpmjAxO7ZOSWmMoS+GSypmlMfnn5kB+8h70AMrBpl4aUlc24trjYT/jBozfs4pB3uynPH77F8XNWt0dcbeaktU3eSVvd0JPZHc66Zl/4LOMRmUrw85wCx5Yuf81mcuiah0FjNIr587dPeAKM/BRTtDybrogV+U7X+0PIV92aJJXNro7IO4MnUcHJfMm761MMfeAmiSCJeDJdEdoFO/U8+7a0Ej4KkgMxzrErbKNlHiQsjlbEXz/nONwzIEOCCqX2WZeeLOyQaaKv747JG4t20Jk4BZUqfLpe5zr3mXgZptmgA2+uH3A83ijHT/k9q07GDDtmzThIeHF7ykOnQNd74sIjra2Dp4FQ6iND77HtiqYVXKwWRE7Odh+p8/T9ZTcAMLwHOd93Lf+3tr7xxf+TR68oK5u8trmJx9SdwaPJmihMMETH5e0xqyzAMxtOgoSX+wnPRlts5cF+l0myV90ZhHbJv3j+JX/x8jmv744x10eSBFXLEUCuYNBjNz90/CBJXn/y+BU3uzHX8YjreMTUlSMFR2vwvYy/unkAwMgpeRI1vL0+5lfrIxZuwbniKBStiW/JXHCNgc8uHxFZcpZ2v3SQUq5eZ2yXXGYBY6tmbJdc5z4a8GI/IbJqfnz2FkPv+Zu3T2VynJsT+Slp80TlureEdnmQK9adwFeWxkkmq+/T+ZJfvH6GZXTojcXF3TGXucsjXzobbvNApo3FI1whPQSOxlspAVJcgFgZ9uia7CDy96Td1pXbWqu88f/0hz9nt5ng2JL5/MX1OZErO6NtZXMepNJOtpNuesdhRtGaTNxcBqokkg+SdwJb73GU5vo8SBk7Oa5Z82JzxKvUZ2bJuXfRCv7d1QmmNmAZAydug2u2WHrLpvT4+XqGpsmZ/9zJOfMT/nzp89iXnvqO0XKZS3SpM3pc0fIw2vN3yyNGZks3aPxiPSUyO/7J0ZrQLjGNjry7N1qx2NQWXyUen0TygtmXzsF2914BM1KfTd6aB9h7WUk3v5nVMndKQqvis82U+3Oy6gV/dnnO4yBjpg1cZAEfjzcY+kBaWyxLl692UxZuTqA6ZlMbFDoi0JuBvPUOnJVeyRDf59K0Actscb2cRZBws5bOng/HG67vFhzPVlSVRCnOx1uOpmtuVkd8efOAshX8yR/8Emc7Ji9didyJhuvtDFPvcKya63jEE21F1+tklcMXtw/4aLIhtAtaVeQupmtu3zxlWbp4Zcf3Ty8pa0kI/Ojkiq/vTlhoA8MAL6/O2ZYuj8YbjsI9uj5QN4LbZCSlcEZLXHo8GG3Z59IEyhYN19upRO06QVk4fO/Ra764eMTMTzka7Xi9PMa3KtJEFrz/1aPXZKXzO0ThUoZEKTm00Ds+nq7Y5j5ZZePbFb5d8svLh5xFewJldb5QxYdnVYRhyizJuNpPsIxW8jgGXRUdBsLomEV7+l7ji+sz8k7wp5/8mtHRhip3qHKXIn9/aY3frd//9Y0v/q7XMZSGWNcGXm1nxIUn/dWNludPX+FdPqBsLBolWdpXLudOge9IcpKloKu2N7hZz5g5xcFd62QkCWLX8UhBmlISNPcycpV5HdkVo9GeOPcoO3mQ3ZMF89rCLjymVs2xIpLFpcvzMOcHx1fEhUdcO4fOu+lkx5w2NrFixLpmg2+XGFlIZFWHg9PQZIZ71es0lUPaGnw63hHXttSfb2fM/ISRXdH1Gk1nkBUex15KXEtS1lsl57uP910slnz1+gm22RB6GcJsD1JJz6rkIZaFfO/0AlNxAG6T0cH21zJaktznwfGtlP3kLpebGQAfP3yLF2RsVrP3smlSBV1Lb/Kat28f0vYy57vpxAH2L1s5Xy8ULO+KAtdsmI92JLnPOgtURoIs7GI1O8/VRaZrA0Uj0DSpwdcZKHsdaosB6cR35EizoPugHZm53nPqVmxVVoSuDfh2xQ/HNaYubXaz1qTupaOfrUPdGbyJR9S9TtkZKgpUBvVc5z63auZ66qcSydJ7IrPBVlbR99kKY6thV5vq52jsa5nS1/Q6gQkTp+BpUHBd2NJPoDNIs4CpgsibXidrwTF66QXQ66wrwU+ckst4TKEKSd9s1PtsMHYrfvLoFf/+5Yekv2NGBLIACeyS7/2vyHLf5jo7v+TN20dkpYMVh0zDmDfLBZbomPoJ/aBjiA7fkyoTITrSzMc0WhZBQtfrfPbiQyKnOCCCmjYw8dLDOOPJdMXVfiLDcgaNyCoPfgZJJU2/5rMND6IdoV1J3f14R1Ob5IXLPguwDfmM3RdYH8zupE5eweyGIXhkrqTeXV3UVSu9KmqFsPWDJnMQKofdzQNmviQalo2JroyLdG1g4eYS9dtOEXrPRAUs9b3OfLQ7uHVu84CyFQcX0q7X2dU+J0GCMKQ5WNGajPWcu3hEk4zY5gGW0UpSs9Fimg1R4WMqBZGuDdxupzw5u2SaBfSFT1NbXL8+x1bKguo9x3bDdzr+3+f1jS/+vjfoe+1gtKNpcl5d1Bad0LGDAiFausqh7Q1Cu2BfuRSKwewIOfsyemlqkVQOR0FM3U3U97GxRUNoV9KyV22a+2zsHlnt3mtWJYHlt57xTa9TVzYTu5TM6c5WGukW1ylJSpe6MxgGC9uQwTB1Z9D0OiOrpuoNMiUXsgw5RzQYEAw0vUGmtL/3M1VT74isilrNnz1LXkyG0WMaHUs19/zd1yf0/uBpnyYBu9JlDPiO9B93DGkt6jslwmw5i3bM5pvDz7xLIyzRYYkWXe8xegkfN7VFWdk4QrJ/daNnGHRcpVz4tpfOb33tNW1gnQU4oqVoTeJKepFnlYOhD8zsmpFdEtcOngCQhkdtJ9/vttcJrJq0tqSdMdKYqR40XNHStCa1Yvb/r9fErrHUzLfpddpOZ6cSACOrYqcuwayx0DSY2RVVr0vSXWfgGj11r+EYw0FnP7Ma/r/s/VmvJVmapoc9Ns+25zP6HHPknFldVd1dTbLZlNAiJZESBOpKd/pvggQIkC4ooEWJKFRTxRq6KioyM4aMcA+fzrxnm2fTxVrbIluQgGh0HCGJjpUIpLuHx9nn7G1m61vf977PmzZi/hnKk39UiWQ2Q+35SIbhgBR/dcIWVsnvT1N6FlaFJd+Prhd/hvy728IhNERx0PcKmdREHDoDba/g6OL180aEsIRGKzgV8nrVlF5oBjpl0ISoUnugyI7D4T0AUbTb0uZ2H0u3q8EyeBihzTwB1dK0FqXRUdRuEJs2rUaWiyCtw/f1YjsTuh87J7QzdrnH3I/QtI62VUWIjXyPbb1h6qVkUsfSS199HItx3dgRanlNa+kNBXJBETzkPHSSx+E638ZvW3aJWvWAoOgdRMp9LxIye02hbnWmTsI+98gksVSQBSth/5TwqKbVcPR6KDTaTiGwczStI5ZjvVqCrAqp8wjsXCZ1it9P/ISyMohKl6wxKGrhNKqBpLI48ktMvUZVe5pG58iPRNiPdPzsc3Gqb3sVlZ4kdaUYUBQ697/6713c90Ov//tb/w4bv0LXqZTSuz+xhEjvID7ZL6dcb6fkjYGiCAzmzBUt3rrTOA93QwRr1ys0isrx8R2q0nOxn/Lp3QnvjHaMnXQQiY3clL+5fETRqQR6wznw9fU5hhTkxLXJqZeIgqA2iCqbuR8LjGctugQ3hYN5fT58n8vCYWYV7CoLXel5FO4InYzf3p5xlXrsS4uRVfIm8TGUnsCoKVqNi1R0GDy9Z27XrAuXMz/CMmrWqU/ZGDKAoyL0En59d0LaCILcxCqH2N+sNrnLPN58855Ifyt7iYdVcc2Ko8kGTWu5W845nm5QJR0xCGNOc2ewN6pqz3S8I459Uhmx+cE73zD70Qt+9+d/zPriAU/OL7/Pa2VYoZPR5d5gPdLUHkPa7dpeZe7HfLOZM7Fznk2XjEd7/i+/+Rm21hFWFpvcJalNNKVnZAlnx5erY2wZdXuA9ZwYFUUr5t6K0lP3CjOtJTQq0sYgMGouUo99LexvoanzNrMZGaIoazuFotW4zV1ucxdTXn9Zq1G1KmeOUM8f4E1Np/J0vOGvboTH+aGX8Db1aVGwtY6xnEk/38zFPdEr3Eg6ogJYak9oNPzq9GJIJBybNXMnRVN73sQhv9n5fDxKObZL9rVB3mo888SowFQbDK0bxl660jOzWo69RGCj5aYa1QaXmcNjiYIta4NP3j7B0VoC6TkHOJ9s2MQBd0k4gHXuY+3uZpS1gWcVTI7W3F0d8/5Pv6BKHVbXR0Io22pihq12pImg+rWtKoOHRJGyL0VH7v33nvPy73+Fa5ZoakfV6HSZx1gKgx1bhD09Xy94NNriGzlv4pCXq2NsrWbqJYSjiGgfAlDIAuF4tiJJffF5KR1p5pLVJnOlx7QqysKiqg3i0qFqtIHU10tx38hJefb+C/78r/+YstOY2eLfT4OIJHfZZJ5I0esVHL1m5AgtwfPLhzh2ga63FJXJNg65SwKJr26YezGhn5BmLlWjM/VibKugrMThJG90osLhONiL+6y08eycohSo8KLR+cm7z4mjAF1vUSVG/LOrh+SNLgTKScjZdMUmDmk7lePJ5t6uhx/WH/76zhv/b2/PCKWqVqHnwWzF17en+GbF8WjH1e0xutaxSQXQ5tnihrwQN7IQ+YkLVtdauk6EjTSVgWlWwsKj12wLh7ZTcIwaS54ad7XOyBDs8y9WR4zNkk0jWmqhWQpkMMIFMA1iTh9e8n/7qz+l7RVCsyI0anyzGjLWJ3aOpddEstUV2Dn/6uUzfjzZMbWFBews3NK0Kq5RMfMSvtnM+Y9PMlx5KnsdjdCUnri02ZcO68Jm1gsrmW9WBEHCnz19znInWpMAiyDis7tTDKVjZmfYRs3Ej9kmgcidv/Vx9Zqjicg2eLubss08zLsjjicb5qd3ZJXF+eKOu82ML25OOZZEsIUfMZtsUdWWT//VPxtm+5e3xzz7/q6VYT18+obReiysQVrHn3/9Ae+NN4zsHM8QBMO00UEmjP3l5SMCoxmU6oeZddurg9WqkMrobWnyplH5+WQvbIC9GClsCgtNgX0tSIue3nCTO0zMCkfTuMy/tRbua423qc+5m/FW2jJNTTTjn4Y7TmRwVFSZPPCS31PN99wkISNDaD2yxuDdcMcy9yhajbQ2+IfLR5iqoPyp9Dz1heDUkz5+Vxf5DC92U/aSqPg3ywXvhwljs+Qn445Mnt7TRqNoVZ7vQ1RF0AVrKWL9yXTLsZFQtTqvozF1rxBKX//czrAlETKvTXaFSH7cZR6BUQmnRa9ysxvLyGKVTXF/M93L5RGBFJpdvz3HdXJef/kunpsyGu/58vqcxWSDZZdoestmM2GbeVh6w9hJWRwtWczX7PcjltGI/+ff/REfLm4Gcl0mtT8TOxdUzHjM1En5cHHDKgm4jEZYajeIfMvG4LMX7+CalRCVFg6r3EW5ORMjQdlWv4xH/OzBa3St5e3FOX95+ZCPJ2vxbOpULvYTfKPkwWzFPvX5fHVEVln84tlzuk5lux/xmztRJM7CPbPRjk004mI/IaqElfeQhNe0GkVpscsEkXNTCjvxsZNxdnpDWVhYlnA7bdIAxy6YTzfMJluO9yOu9hNu45HMkZCvN9tQ5Dbr7YTdbsRqP0bXWsZ+zAcf/Y4H+4CLqzOuoxHr3KVdLXg4X2LbBbv96N6uh8P6/k/8P6zva33njf/jxQ13cSjgIyjskgBDtq6jzB1uqJFRCTFbkPD17SkLL0HXWn63PuLpeDPM5drK5nY9F/MmaY8JzFKSyYQQbGIXPPFSPOnp35U2utYx1XMxHqh1joNItEwrS3AGVvmAci1ajZldDJuvirQ22Y3w61YmX6+OeBakbEub0Cz58eKGq2giwB8yQ91QO46DPdfxmNvMZW7nJLVJ2Wo0ksymK6JdXDQ6q9WMWNq6QKTZffX2CTMZ3dn1QpNQ7qaMnIwn3pKq1ilqk/V+TCU98O+cXaKqHbpZ09UakyDCcXNO9bshmvj67lT43WsTcyOwnEfjLZom5qj3sb748n0MrRUWRaVjZhXktYGhCapjITUZ69JiN5xcC3Sto2h0OkS7skO0hzeFOI3fFWK2e+4IOuMuN4U/XhOsexFnaxI3GudegqaK1zHUjoduya+3I65ylROn48TJucxcbK0bPPd3pUG+nTEyK3Sl56awiKXyWpGtcoBTNyWuTV7EHkFhs5AP5G2li1hfRZz2614lqkyS+lsB6tjKeRON6XuFiSk0BdvS466waaQt8KPRHltv2JU2+0p0DLpe4cgucPSavDHYFI7IhqDH0Rseuwmb3BX20M7mQRDx5XohmAVWIYKeOo3m9+6fx+GOXIoKH4+293ItAEMn0NJrNK3mcrkgLm3C1GPix8zclIu7Y5Rlj23UjIOIWWOQVybrNODuRcivfvpboihAUzomloDVCOeKQFp/fHqJ52ZEccAyHvFqP5FQpXaw+dlGjSpP6AChm6HI3zedytFoy2o/Jqks4lJ05zS1Y58ExIXDnz14jWk0FHKWb2oNt/GIu/2EplWZWQVZZXKzWtB2KklpDTbhJHdpEo3LaEQor5ff77IkuShANbUXowv57IwLm+VyzsV2RtVqaGrHzE2JU4+60TGNmvl8TVIKp0NRm9ymPm9XR5jbKY5Z4do5XadKQZ9GXRtcvnpIGApmQmCVfPj0G1bLOetohJl5nJ9e39v18MP6w1/feeN3nZxAeuiL1mCTezStStYY7EqHmZNhGYIT3qHQ1MLmdxDHjSV8BMDQWmytpqhM7MNss1Olp1aVMaoKaW0IUdgB5dor2FqNpgpveNKbRLmDbdTCi9uqbPehaKHLjVhTuoH/L5jY3eB3BchaXRYH30Jmcvm9V63GXRIK6l9pE1Umca0zt/lWPMW3LPcDe75sDG7TgMAsKRrB9bfUTii+lX6ge+WNjlba1JLUdoBzgIh8zQub0XiPrreUhWjxr1ZzVFVsuiLdTgT7gICiuGaJbRdDMXQfq251+Y8quAZai6F1Q6Gj0BOa1cDEVxRBWbSMmrhwuMrEXDWvDcpOwHU8vcZQTZqeYRPtEEK/Ss61Xa0lUnqQfAVXa4g7k07pcbQWRbE4d4UHf10IvzOKCMIRc30VTQFFzksBilbF1joMRLu+bLXBh68qENcaJ3aHqzcSFCQzGFQRFjQ2K+reYmzUjGVxcyAUaqoQhU6sRtABOwVVYSgoLbXF0VoiGVIFQv+xkcWSpbXYeoOj1gPc5bD28nooJajGMyrOgj3L1B/GWIrSf2tHvce5rqp2OGaJpolceV0ToKuuUylKAZg5CD7TyiL0kn8rNMjQOpJtSNtq2DLzAgStD5CjPRXdaND1b503B3COKQuAutWoG4u6E+MnVW2pa0PO/TNMoyaUIru0NrFlGNeBQ+LYBZEcBXS9QtLYOLrg4Ve9+OyrTh/sxobWDV3HsjbIa4O2V3H0ehB7GtJdYxsCx6urLVFpERgViiIYHVHu4poVWqNLhr+4r9pWo9Na0sQb0hXrThs0HQemv21WWKaw7R0OEKbeoKficxg7Kf4k4vJaHBIUBML3vtcP4r4/3PWdN/6m1Rh5ifDs5h43STAknmWtysPRFtuo8OycvlfZ7MYcB/shCev9swuu1/Nhgw+dXIBvtIZGE+K9wxzeM2psrSGpzUHA1PdiozS1drg5AK7TgFMvHkJWVlKRf1hlow8Pc1NV8JSKRM78Dj79vhfJex0Kt/EIRRGb+MH/bagdV0lIUovY3bZXh3Q/W+2G2evEzodCZlXYIje9E+lu74237AsbRREbY9ur9D3cpr6Ez9RM7BzXKgeBzqvlMR9aFb1VUuQ2cebxzWaOZ1TM3JSyMXg0E3GmAE2jY8giKC++9dR/32vkpGxSn30pfPnnXjIAl8rawNAbjt1UEM96hawxGLkpozDCSz2+3E7xjIpV4ZC1GoHeEFolcWOwqwx2tc5pr6CCJO8pjA1BKnT1dojP1aRC/yD6OrErHgd7lrnHP2x9PhplpI1OLT3zB0Jf0YrPxNU6ul4E+hyAO0WrfTuesSqWpdggHEVsOPvawOhUNKPH02rmXgLRmDP/WwxtYFTEUmOiKT1HdsGL2ENVYGYKNf5ehkABxI2GLRkVVafyIjF4xxcZDoFVYBk1Ue7Q9CqmKlLu1qXNiZNxl7u8zRxGVsF780vy2iSqLKZ2LuFVtSyy7iepEYSPPwximkYnyV0W4y1u5kohnUpSWQRWITb+WghRq1Yfvr/jyYbLu2Msvca1SkyjoqpNilqMSlyzYhmNMPSavLDpe8ErOHQzDpCoqtXYlQKju3ATmkYnLhxMvWHsx7SthudmmGaFX1o0nSDpHSBH2zjkJgnxjZIehbvM4yfHV4Cg4sWNRCa7qSgOM3d4fwupbxnJz+sgIhT/rQAF6Xor3C+dxrEnTuN5bZBWFh8/eUmWutztJmSS5aFLcevVdsJN5lG2GqbaceSImOtMUvvSwiYMI9KNS1S45LXBwo+JMw9Tr7GtkmwntB6nwQ7HLrhZLu5lDPjD+h/H+s4bv+dmvL4+4yIJ2VUm//OffEqSeAK3KTfSqjHoapOyNrhKQj6QedFFZfLi5gxLdgDE39UJ7JyvV8d4RsWT2RLfS/j01TNsvWHhiaz7tLS5SkKWpUXdKfz13bEUUdU88CNOxjtudmOiSpyA1oXNzC4G/v/VfkzdqTh6Q2gWnMkAn0Ceig+Y3EOkr2jPGRStwYPRlp8/3pGkLps04FiKfCZ+wjbxKSVF7jZ3OZLWxKIRYJIjR8BlTK1lbuc83034xckVrp1TVSaf3Z1y5seCQKh1nEzW7JKAutWYjvY8+tNPMf7ij0hSFzV3aFuV55s5gVENXYXAzjk6viNPHYrCxvYybtYzWmlFcu9JyS1eu8DSG/yqklz2Ftsq8R2oKqHWP5zI/tHTF3x+8Yin0jb13mjL3I+pO419aVN2Ks+Or7l7+S4jo2FkVuSNwbrSUYBA71hXOoZq0yEYC2ljcJfbrCudtFGoO/jRqORKJqN9GOaE8gFsqaJIGxsNoVmxLS2iWqPp4cwpWJYW68KkalWeBZmci8t2sdEQWKUEwJjDSf4mt3mTOjz2TXTJhqg7jaw2GNlCPHrgUXwV2Rw7DYEuCs2L1MPRWtJGI6o10kbhkSfwwIEiOAOFJLOZesPJYkl1fUqUhGLk4Qvb24s4ZF3qVJ2AD/3Dm6eEZsHjcEdSWSSVxcjqsIz632JUfN+rR+FuM8PUGkIvIUp8ZpMtbatTlCY/nq8ocpt+M6MtFVRV6DpiWfzYRo1rlvLPnKFLdIjF1jURef3N3QmmJlr61PD3+ykP3IKRUXElrby/evwNhlHzu4tHVLE4WJiacMHcbqeoSs/IS5hNtqSZyy4JiEtboIBVkax4OFRM3JS4EMp41yz51ekFn1w/4O1qgWeVGJpw2Ji6+P4P5M9VGnCXeeSthqW1/Or8LZ/dnNH2Ck9GW96d3QnCp9YSOhkv1keC8ph5vNpP0NSOX/7oMy7fPOAuDjmfrnkwW3G9naEqPeezJetoxAsZw3zkZNjbCt8TEKAo87iORjwcb1DVnnU04r/9+kN+eXzNLvekBsHin9zbFfEDsvcPfX3nkJ6/+Kf/K94eZpd2jicVt20nONqWjIi09HrIgV4lgUBUqi1TL6VqdCxd8MOXqU/fKzyURLpl6jN1Mk5nK9Gmzh0ud1PmfjyE0Dh6w23ucOpkjKwC16xExSur7V5ap6pWY+ameFbBn799zEM3Q5H4bFtveDgWWd5x6XCVBIRmRd8jfem1sIWVQgylyzld2hgD2nUq87fLRh9avwcym2+WTIOYv7l4xAOZH543BqtczGyPvYSJm4iI4MaQKNAGTel4uZ3zdLISkaW5i21WFJVJLoOQ3j1/y9XyiKbVMPUGzy6Yz1fkmcNyO+Xz5TH/6ce/ZbmakVUWnlXwx3/+33zf1wx/85/8F+xzQRVsJCbZ0WtWuceuMvlPP/icujb45vaUm8zj5yeXXO6m4pRt1DyYLfnk7RNiabcD0XEpW42s0ag6Bd8QKXgHbPK5l1BJQE1cG1zmJid2PbTse0Rs7mHMoys9viEKiKg2SBoNX29ZleI0b6k9Y7PmpjAJ9A5HEiZvCkts9tIrnzci3OmgmN9UFmOzEmMfKQjUlR5TtvUPeoKoEq6FmV3Q9QqrwiZrxcZw7ubYWiMAUb2weYZmwbZ0Bh7BXWFx6hQERk3bKXwd+7wXJJhaSy5piO/NluK0K++rqHBpO1HEfh0HnDkFmmz3T+yc/8lf/5++92sB4Kv/5X/EcjsRXHmzxHVyAavKXLaZzypzeTZdCcRsbfxbKGHLFOCtptHJ5fjCMiuWe/GsEfe1sMMp9AMGFxjuv0OhULUaz6YrbKtkE4c4ZsU+F/kiA+1uvMGQo8NeOgsUpadpNbaZaPP7VoGutgPmt2rFCG0x2ZAXNns5DjC0VszelW6A8eS1SVGLaG5d63gyWxKGEVEU0jSi87jNfM5nS9LM5fV2RmiV8ntQB2Gho9cEdkHV6Pzd3TEfTbZ4pig2FKXneLbi+eVD+h7GbsZdHAoEsl5jS87HZLzjdj1nm3mMnYyT+ZIsd4gzEaH+X/yb/8P3fi0cQnoMXH7h/G+/16/9Sf5/pCb7IaTne1jf+cQfl7aYYcrErV6elJtOG2ZNh9N83yuCN+1mxIVDXhvcxCNCs6CRbfeJnYvkNr0hqyzWpY2q9JzC4FuPa5NQIoG9tiZtDHS1k2CdfnjtSvpmA7vg+WaOozfkEik7sypCqxTdBin0qVudpLTZ5C67SgjIul6h7bohR/wwdtA18VAO5GmulOCftDaGYqLtFdq+G8YQu9TDlQ9oz6gIzYKoMtlXJn0fUDa6YJkrHYEt+AdJ7mLIln1VmSSljanXmLogHza1im7WBE5OKRMGVbWlkuIwS69xdEFOc2yPqtXp+vvBcm5Sf2CU1502nHgL2W5NM5co89AU0ZaMchFOVDcGSWVyuxVFgDgVgC499IVMO3T0jrwR6Xau3mJrLVtpYyzkzN9Ue+JG45GMHn6Tuiws0eEoW41to+HozbCR+nqLAuStiqd3QyjQ2NAoO0VaL+HEFkl7B6/8QSuiKj0dCnmjY8j5ua125K2GbdSDFbHtFdG2B6lF6PCMBktryBohYp1Ly6olYSuHzkdUmeTy/giNZigmFQVURfx/L+E8B1bCyEmZBBFpLlq8h+LI1TpMtcUxmkHncl9LVVscsySvLJH/LsNiDoFBmtpjWyV1bVDWxpDiaeiN0A5N9nz19buczpfoRkOR2yxGO5LMHfQwSWkT/h6aW1F6fKsgKe2h47QqHKLCHTIhzMP72+hEqT/M60HoByopMHTMCvX37vlc8jx8OZ44zNSz3MFzM+LMQ1dF/LahNcSFQ91qQhCoN6xSH98sCZ0cTWto5Dy96xX6ThWckdyhlM8H26h4vpkzsQsWXiy+VqcOKYITqxqyD2yjYhRG7PYj4tLCNYS4jzgkrqyhW9HqKsp+RNupBFbB8WyFovYDxtoz7yfAa1gKdN+3hVThh2P/97T+nUJ6Tkdbjkc7EZIjlfZ9rwwtLhFfqdN0Gk2jE3oJIydFV1vucmfYLBSlZz7a4UqbS9NpEmZisItDslxgO4tWo6gNeRJviH/voXug9R02H1tvmIV7tnJmlzcGm9zlxE3xTBHlOR/tmIV7qkYXwI/fo5ypSk/TqySVKcSA9CIsxRQY2kWwZ+JkeEZF1WpsK0uw5pVDCI2Cinjda5kAtpU8AdusUBEn0rTR2RYOeSNOPoZRY8hQj5FdyGJG0OdqObO3D5z/2Mc0KmyrHIqspjboexFR+jDcyV9XuGZJcU8CnttMjDkO6zBfVRG6iW0S8PLQhvQj9nLuasrkspfReEjfa+WJ3tRa6l6I3wK9oewUolqj7ISe4nXqsKlMdpVB0arMrZqo1lARReJ1rg/pfkmjsa4E/CauRciKpwsYj6n2uFo3dAbGZkUjXytvNY7dlLkj7HIHO+HIFKAgcfpX2ckNdtCk6A2hWeIZFSoil0GXosK6ExvCzE05D/aceMlgCz0QIZtWZVnaxI0oQuJaE8wAQzzwXb0eippavh+hWfAmDtnIzlkiY3vTxqDuVE7dTI7MYkmX+zYx7vtemi7GPLraklYW69QXXvhWQ1FgZBU4dolllWL+3X+b1Ni2KoZdsUx9TLvE9jLqRmc6XxN4KYGbMvJjGZYjIUmqgGTZRoUjuQW2/HyLRojb8toYRLy1TAmdegma9u1mlJUWu0KwMQRxVJz+49IirYS9TlPEa7WdSpR5Q6EtQnVqPDfD0NrhvnWtkrLTCGwRQFaWFknqkhb20OXz7Zy9zNMYu5mILpawJk8mf4YyZMnQGp6NtiLNT2sxzYpwtmOVhOxrk6oThYlj1PIAosjIYJubaCxcAuEeTW/JM4ckdylrA/e+N35Eq//7/OeH9f2t73zif3Z0w3i6I9qF7NZzHs/vSAuHkZNyNhWEuFfLYx7M7xjPN/z6i4/INgbnoy3PTq553N6RFzamWdF1GnHmcRzshRjKzvnT0ZbfXD3k09tTHvgxk99LEzuE3oSS3mXrYi6e1BZ1pzJ3xOkhTj1+Mlth6TX7wmFb2uImtAr2mUedhEJAZJZCiKc3nAB3mcd7szsss2IdjbhKQgy9Ja1NtqXNvrC5zV3eGe04H294vjrGVDsWbsrMi1HVDsOoB3HQm9Sj7BTmlkjv2mUul5nDT6ZrHs6XhKOI3XZMkrvc7qbUrfiZFuGOu/2EvDaw9IbL9Yh3Z0tMyeZ/eXfCcbDHczMcu+RqNedI24riqdEJ3YxXbx5i6aKVaer3M9e1pJXPNytGTsqL9YLQKinbjro2xGlF6diVDrvSYVtaBEZNaJY4xreK57eZSdPBY79nZIjNLaoNXqU2odGianBXGLyITZ75Na7crFXg2Mm4zCxeJoKjbqo9SW2wqYRi/6mfU8mch6JVqXuT69zgnx2vuM48rnILVzO4zA3GZsvcrpmaFb/ZTDDVHk9vCYyaq9zmmZ9Q9ypZo+PpQgS4rXTKTuXEFpt91QorXdwIZffcLiT4yWVi6WwKB1MV44QX+zHOwQ6JKADO3XQgRF5kNkWr8cAXCY2fruc89kWCZN2rQxaAiAoOBxHZ1BE6k1p2FZ7vJ5yPtswm23tN5xstNhRvzuhRhs7axE/wpAjuH16+QxT7PHj8lidHG25+95SitHizOuLtbkqcevz08Uv2uxFFaVG3OkfaLYZRi99XFo/md6S5yzbz2MnuzzvTlZyxNxhaw//05Jo0cykrk8DOqVuNmyTgNIj48P2v6RqdaBcOboEOhYmToamiyL5IA6ZmSdHqKIpgAAg3jz2Agf7+zVM+OrqmbAzeXjwSm7VRDZ0BQ6/51eNvKEohxi1qQd2zjXroQAR+gqa1RJnHLnM5n6348XRFh8IqDrmMQzy9ZmQXggHQaVzsp7xzdE3XqfzFv/kVf/LR54yvT4gKl1fLYxyjZmLlzLyEySiirAx2ScB8vENVW/71lx9z5sfEpYUui4Ef1n+46ztv/J+8fYJ/K/yymtrxenXESSgUom2n8s3yhNAsuFwveHl7QtNpuEbNi/UCZbPg2XTJ5X7CkR9T1gbf7Ce8O1mLtmDTYZviYeW34jS1SgLOvZjbzOdV6hBVKqdOg611XKc+daeyrXSOnVKebk1+t17g6fWQXX7mx+S1wdtoPBDNssokKgQuVLRYLd6ZrLncTyhbHV0RgBVba8SIQek48iMey0S/vldYuAnPpjnbzOd3q2Nuc4e5XXDspoRmwbkr2t+NxMNuS4cPxnuRS74fs9qP2eQutt4IjC8KF0lIWgsxYCgJZWMn5ez0hiK3uV4tOB1tRS53beIYFWfzFV9ePGTipsxHO/wg4fabd+gtgSSN8vuBtrw7uxs+96xweDTessk8XHk6/Xwz5z96+py+V4gyDyv1+fDsgsv1gl0uFOhfbqcsrAZT6wj0hpvcpu3BVHtObFG4iKhei7UmlPnL0hwEepraEzcKiiLa/rbWs6sM0kZlbLSERs03scfYFEK5qNY4sRtexiF3hfh7utXj6R0P3IKxWYoujNpjqCITIM1tfjZb80CKqardlFDt+HwXMDIbTp1cfF5Wia52IpQo8Sj0lpEh9AcLW8xv16VFXAv9wsJqeOjHYvSEMsxlDyf3vNFZlyavL8/w9JanfkrVqawKG1PtmNsFqtLz/ihiX1q8ikc8DiJexwGhUXPkpozclJ9ZBVfRhFfbOSOruLd0vrcvHov3R1Icj6cbkQpXC05+YJVUrc7Xz9+h/vID6k5Abc4nG7pO4Xo/YTFf46kZ+9Tn39wK29npXGgYVonPVRISyphcTel5MlmzST1WMrHw3elqmL9nMgrbkoVvURts7oSH/d2nL4n3I2630yF619BaXKvkvfGG0MkoapNd7vJXL9/lF+dvWWhbIbzbztGUjq9Xx8zdlIfjtYByqZ3oDnUKV5s5b+KQUzchsAvqVuc6DfhocYsraXuXy6MBKGZqYly3kqx916hZOCljN2ObeawyF0WBEz8ChP7h4/O35JnLZBQxnwoC39++eF/kfNQVbmUMLf3PLx6xl+JrlZ5HkzUAV5sZP72n6+GwRM/rh/WHuL7zxv+Ts7cUpWhVZZWJrouZWJq7xIWDrQmbkq51uKZowRnSC5/WJkkhHvi1bAvbsj1magI6E2XCy9qW6tA67lAoOxVP67BtwX3vEDNcTel57Ge4/x8xvhM75y73hAAqV8kbnaZXABEGY2otVasJMIgUpq0zD00RLVoh5tFIG4NQLTH1lqtozFm44zoasy1tHviREC9J4ptv1Bw5KY5RDcIjW28GQd/cyYRKvXCFBVBvmDoZ68wjkwr8wKi4zTwe+NEwx9TUjiT2ZU54jWMXFPW3wJe8sIbgo7rRSRMP3yoG69ZYhqJ83+vtbspMniCT0pKfdz0IHjvEg6XrFSGuqyyixGdf2OxlNGnRqkysiqlZElgld4VF0QqrpyN/fhBiTVMVj5BzpyBudNalha21PPFq6k4E6WiK+LtFq5CqKmWrocu4XUAWBy1xrWNr4tee3tH0Ikjn4EBwNdF+P3QW4tLi7WpBKlGshtrxyCvwZNF2nTuDHVNV+iFed1lYuHrLkZ1zk7sDL2BqCp7DIfRHCADzIbgI4L3xlr++W2BpHaYqTvmHcB4QyN6JnZPXYhOcmKLwOHTEVplL3ar4VinANlrN8Wh3L9cCSPiR1groldJhO8IuZuk1gZ+SFI5o60sxY9OpuGqHonQYRsfETdlsJoDgeZzYubANS7qcY1bYWiNZDNKxIlPrZnYucjtajXU0EoV9JcBIvzi5lPd4M9iRr69OyWVKaNHqvD+/QNfFzP06GlE2OrZRY0lxp6q2Q6chqkz+0cNXlLLdX7c6x24q2ulGgq4Kz37fK5StjiHvw58cX1E24gQODBoAXY4d9pk32IoPIVPCjSA4Dr5ZsRjtJP3PxtBrdKNmtZ7SdMIqeBbsJbehoZRWxVXqE1glMzdhl7vMRzvywiYuHbb3SHL8Yf3hr++88c8Xa/LUIUn9Ifqx7VSy0iKpTMZ2zq5wsPoGy67x5aYTSH93VNkc+RFZJQAbpvzvTUPclEWr4UkRXt1plJ1ox/W9QmA04qTjZCTSKiZiVHfsC4dCzuUBXLPClXnfsUx76+Xsq+40LOmRL+RMOpQnPdcq0NSOplVFUSKtfqrSsyttZo3BKne5Kywe+JHUNoj59NQqmMi2ZtsJcWBxgGtoDY4hQmgOQrO2V1h4MY1ULOtqJza/3MXSReZ2JVPI4tQT4Rt6gybJeCBmjGnhDDCYqjLZxCGm1AsAGPdk4VrKh4aiCNiRqbfCBqmKTcBSO94k4QAtyluNXe4NtMNDQp6h9MISKK8RUxPzd08q/A8CQKGcF3yHXW1IGI5KaNTUnSpO542GKkVwbSfCb2z5YBWwHwH5WZUGhtpjyyjetlfIWpWyU5ip4s8OiXuW1rApbTaVNaj380YnlNCkutNYFQZtp3DiCOjSAeiyrgy6Bo6AqNYw1Z7AaAhl/sC6lNer3pLWhiCuyRb0Ithjr2eMDFFcpLXwj2vyuk1r4aaoOg1TkutAFL2r3GUnqYimtA8aWjOwHu5jHaA9h5VnDluZMT81hXgtKtzfs+mJQ8OBO+HZOctoDIiNP7RKdL0V3n1NXB8ju2CXO9+Cs+QzRNcEfa9uNVJZzDedSlyL7ymwxRiyrg0CP+Xz5Ql9D5Y8cNhOgaJ2aKVFKRMlT+Q1KVJCe7LSopAMkel0S1WaJKmwMjuS/ufLcWNaCu7HIbTI0mtOTm75+tUT4tLGN4UGwOlVaBlU/JbWDEVRi4qqdsLLr7Y4ZoWmtaS5IzqpgENO2RgDMXEsI8fbTqWuDfFsbsSodTFfYywX6NIGnFTWUEDd3/ohpOcPeX3nT//vvviIR7MVmiYS4JpOnJp1reXIj1mmAWsJZNHjnvcbg7LRGTsZUzfl7X7CdTTCkxzxqDLxJL73kFiXlhankw3Ndsoqd5gHGVmrE0n/9NRNqVqNiVriGzK3enVE2am0nYKh9iSlxdTJcKXI8Mnill9fPmJkFYxscbP0NJyogr1/NNri2AUXd8cklcgLf3+6Yi1PX1WrceqJiMwHQcTMNrAMYVlceAm+tM4c7HOeVbDoVf719Qn/4sEFbafyZj8RcJ5GPBzbXuGz1REPvJinxzc4bs7b61M+mi05W9yh6S3b7ZiiNrGNClViPrPcoetVYdcxKtLSHir3sjb4dL3gyC54Ollj6TW30f3wuM+9ZBBqdiiEdsaljB0W77HOurQ5dhPmfsxrmat+sOY9DPasCptlKVTswo4JT/10yDF4sZsOAs+mUzj3M367DfGNlhO7RKXnNzuXf3q0wzMqvthOKDuFc0eMCYpOxVKFTe+wxlbBl5FLD3i6wtioOXbyIcpWk0WLrQmYUyST/uhUXHkC+2QzFu+BU+EZNVmrkGQGD7yM0Cz4Zj9h7uTMZADPpcxyfyfcU7Y6t7lI51vYJb4U7zWdxvO7Y3yzxDZqksLhvTAisEqSyuKrfcg/Pr3lb2/OWJfilH+XeRy7CVWnk1QmnlFzHOwHmFEoBa37zOMuDfh0eXxvrd3f3p7xwI9wrZKq0fm/P/+APzq+xjYqttsxoZ/wdicCvEyt5Z3ZHZs0EB2UVh8KnrwW3aF1KU7kItVT/LupF7PKvMH++XYzYyTjvutOY+HFeGZJ3eqDre3Nds5ZKFDF+8wjLhzucodApoAGlPzu9RNh5dU63l/csEkDITx28oFu51olvp0TeClvr87IKtFpM/WGF5s558GeN+sFW6mv6HsxvjmkBK7XUzGiUDt0reVMdvU2mcd15jGWhdvIKrDNim3moWkts9GOttW4WC+4iUeYqogON7WG3W7E6XyJ4+VoRs2LF89wzJIwiHHmG3abMWeNjmk0dK3KYrrmb1+8z1mwZ+HFpP9/IPf9sP5w13fe+ItW52or2nFVpzO2MzyrYJ97rHOXR+MNoVlwnQbkjc48iPjs5kza71raXhFz8PGGLBfBNnWncSoZ4mlpMw0i/u7tE2yt5XG4p+sV/vE7X7GPQraZz53MB3g8EnOqt7uZEJopPadBytOjG6JURHPaZkXbqWziEM+oGDtCPXsZjchanSPpxf/69pTQKlllLkWrD0ptU9qzVKXndTRCSQPGZolvVqxTn7+5OSPQG+mr1vl4fscm9ckbYfn7aBQPRU1oloydjLQ2RStUb0Q4TW3yZnmMpnYsU4+q01hnHkd+zKPzS9pWo64M4sQnyl0mfsLpyY0ggDUalfQ0m1qDrrX8YnHL+eKOthW4z4fz5fd9vQDwIhoxMmpGVsncSVkmoSDVlSqr3MU3Kn52fE1emdzGo8HmeObHNJ3KrnD4cLzhIg1IG51NaTExGwKjQtc6GilyW5cGI6PlyK64zhz2tcoDr2Du5HwThewr+B+WY+HD1wX73JNphnmjs6kM2t5gYjZMzYq73OW9IBdxur3Cq9ThoVswl6fCdWnzVeRRdAqGAiOz4b0w4m9WUxyt450g5b965wV/dfkIz6gJjIonXkXbC5vfbeqjKD1vEp8ju2Dcw3XuYsiukaF2TKxyoLqtCgdwOHIyHKNmVzpUuYenC+X7LneEOFVv+WK94GkQceroXOcubzNROM2sirFZElUWQWXhmaJrVjYCMw1CPJi192PtBASIyqgFxMmt+Hiy4ToeMalNFuGeO0lqfDxZMx4JhfkyDokKB1Oeyucy4Gbhx/zSj/mHN094NN5S1gY3SUhRG+RyrBaaBT0Ky9SXnZCOy2jMqrAZmRWnXsx7Z5fkhcUyGlM1woFhqx3vT9bDuLLtFUI7Z5e7pLWJbxXcpv4AO3LsgigOuNiJ7I6jyuJovGWu74gTj1UScuwlxNLL/8CPmAYiWa8obbpOwTRqPrt4xJE8kafSlpiWNgs/5t2TK94sj6labXgPf72ZcpN5Q+jTQTDpmtWAAD6abOh7hfVqyi4JMHURP54XNpvdWMKQbNpepahMDK3h549e0rYa+9Qf+Bf3tXq+fzvfD+f97299541ftKxrSaszeDAquItD2l5lbOXEhcPxaIdv59StTtcpfHR0jS0fdHWnUTUaq/2YXFabYzsjryyyymKVuYMH3zeEjWaV+bz96iMeBhGOUfEyGnHqptzGQkgU1ybPZOHQdBpXmzkn4w1RJgIuBBxFFAbbzMPUGp5O10Pr7uD/v079AbgSyPneVRJgqh2BWeJJlbyhdrSdwrpwGBs1D4II26hEZKrWsGp8ul5h7qZ8vpkxd1M0RbABsspi7qTidTud0CwET1wRKW+nQTSAfJpO43evnvKjD35HkdvYlqjk69qgazXi1CVJPbLKQlO6YY794OiWvLBFaEoldBX3sR75CfvS4jZzucsd/qN3vkK5ORXZ30rPF9vJkEPe9ioTO2fqxVxJYpiltuwbm0AX/veq1fjJ0TWvdzNu9q7IINAbDGnzUxFdkodujaWKMYqudvx0UtP00HTKMEOPpP3TUIQy/zDqWZcWba8ws0pKSRU8tiuZkmfgag0zSyjxt9W3t8Xb1GdiCs3Bbe7QL0+kfa/C1mp8o0YF4fNuxTWlKz2rwhbze0vE7xoyWyGqDY6dnKrVhpn915FIBEwbnaaHEwepRzBlPoVo2RdyVHTqZNQS36tLNsWmMhnJWGhTEzCZfeHgmyULL8GR1/B9rLwW3b3AKpg7OyZuAohu2eV2imtUBLYY52S5g++nLIKItLSl970eePfiGup4MNoRegltq2PoDXWjY2sNEztn7ke0nUpa2kJJLx0S7Ka0ndDotK3KzW6Kpde0qsgzaHsVzxRiYEemhva9UOU3nc1/9+YR//m7z1nFIW9XC+a+6PQdBHGq0lPVJlebcCgczkdbwB3gO1lpsYxGMlq3ZB8HtL0qKX/iM2g6QWVsO5WqFqMiW29E1kCrcWKXw3zfNSsCKyeXGN+0tPlmP6FpNTyrEPj01OdnZxeUkj5qGsL/f5j/d3LE0rYarpeJ4lTabX9Y/2Gu73wMEGIU8QAKzVJYygzhcw/sgqZT6TqFwEuZhnuK2hwCNVSlZ+RkmHo7MLpHUgi3z51hPh83uoh1NSsUpSeqTW4KS4oG26GNuSttNrKtpslZGMAqd+l7kRGf1iaJHBEcKviuV7DNigdHd4ycfADmHLj6IGbUmtLJDUE8yOduxsTO6VDYlg4dMLELbPmQHcvRR9mKkA2VXsyn+2+907U8cR3CgkQQjAjRMPSGwM6Zhnt8V6CKy0anzBzKyqSqTepaRBjXlUmWC+/xgeCnSuBR0+hkuUMjxxyb3P33uTb+f66pk2LJFnrTqXjSaqbK+a2m9jKcyJDhRwppabMvbdalxbJwKFodx2jw5EPfdXLS2mBdmuwqIRA8dF9UuYmfOMXwuoEu0L4jo8HTOwypdSg7ZeArGIoQ8SlKL/68F2FArUzJ842aulcoWm1I+ZuaFWOjxVR7ilYlb8SJ29Nbmh4uM8EkyGudXekMm3JzYBKoQqNQ90J0aKpC4V92KmkjcgcOfusOUdDc5vowBlGBXSU4Eurvncq837NB2rqIqfaMCktmTBStKsYpCNLcAW51wP4e37N9S6EfuBRNpzFxEywJ0nLNktBL0HURmtM2Go68f1SlJ8pdCrmJ163KRs7OB/S0WWKbYrQiwmmMYcx42PRtsxK8AENoL263U0GV1JvhPge+LRIQqYKm3mDoDYoC61LDlnqTXL5GXll4jmjzG0bNKg7Ja8FKOPBLDs+fXHJMotIaxIxpKYJ+qkanksJfwSFoJKhMxdRbXBliVtQGx27CzM5w9BoVASprOo2uF5wBV2vYlza9zDdpegVNa9ilHlWtY1sFtlViao1AHqstmib+USQHxbpHhPNhdd/z/35Y39/6zif+sZeQljaeVfDg6A7Hzfj4eEUW+Ww2E5za4C4aMW4MHKsgLhxebmfYmlSlBnsWVvFvpUL95eUjxjJf/Gy25VkvNoiDqj1tdH403jF1xcYytcoB4XmIwf1iveDYzeh7cbJbyrl2WptC/exkeFbJVBenkJvdmD9+9yWa1sBqwavdlF2l8djPh9MngCXbwXWncTZdUTcGv70+503q8JPplsAquI1DOhQejTcsk0DEAbcaF0nIQzeVnQ1DzMHNgoskZCzb/mUjbIsTN8W1SgqpFC5KC0Nv+ODBG15dnqMqPZvc4zZz+Zc/+4Tt3XhAozbSFlW3Onll8tnFIxZegiELrvieaG261gqaoVniGhXr9Yy73KNqNXyj5o+Or0lLi1XukTVig/xkPZfvj8JtofLPT/ZiBNSJEKXnt6fsakOgdI2Gu8IarHkLu+RcnlrbXrTJPUN47mdWzcQq8XSNu8LEVPshNOmQ6mfJDkDeahz5MbvKIm+NoTAThYBOJ611E6DMba5LnR+PsyHEJwTucpus0XlVOmSNyq9me6lDEIXdzCpw5WmykgK8hZPy6WZG3SkERis3dpk82Ko0vcK74y262rIrHD5ZT3jit1h6Q1qbrAqbU02QHstGJ6osNpXJkV1gayK611B75n7MLvdYyUAXTxf2VYDQvh+HBwgB79hLcZ0cVe14vZ3yq6ff4NgFujriaLHCskuyxCXLHYrSom2F+K4tVT5bL/iwWxPa4jT6ej9BUUTHQFMERns+2lE1OjdJyO+2U9JG4yfTNYYmOl77XATmOEbFJvf4N7cn/PPHL4fPd2QV+FaBY5ZEuctdGrAtLf7s6XN2SUDfw08nKW+XIizoyI9pO5W7JKDtVDyrQFF6Xuwm/OLkkqbTWKc+t/GIR7MVu8wlqUxOgj1TJxPFl9R3LPyYZRKILo9RcT5boasWhlHj2CULRcz+l/sxeWNwFu4w9IYod9kVLkkS0gGzRuc42PNnj97wyVcfcLxYMqv1gUZ4HYc8GO3w/JQkPmCFGwFX0lrCccR2PWGf+kyc+7seDuuHzfoPd33njf+/f/uEP3v4CtfJ2exHbK7OaHtVVLmtxvvzW2yr5NXymPXymF+cv6XpVI4DMav/5PoBM7sYLv6TcMcjP8EzKqLS4pObcz6e33Gb+pyqEWMvHZTVBwJXYJa8iUM+mKzRtZY3+wkfTFcUtQhIGVkFEzcRM+LaJKkNTL3hNg4BoaC9zFzMT35KVNlUrUZolvzRXMx/i1ZjJsN7nk3WRIXDKnf5u7dPOPNj4YW2Cj44v+DXr59QdhquLuZyp+EOQ2YTWHrDg9mSvXRAdL1CWlmktYGpdritzjp38YyKUhZCh3yCQtrGVLVnGkRcb2eM7YwPTi8wzQrHLtglAXlt8vT0kq8vHhLYBdMgYnSw2BUOu9wll0XM971so8K3xNd2zRJDr/nx8RU3+zG3kneeHpLV9IakNngviMlaXbzHls6b1CUoLepeIap1Hng9Y6OmOiBF9Zak1jC1DktreZMEAsXc6JJ5D396fCu6CIXDrjJw9A5XajMMtWNu51J5b7OUjP7/4fqMDmHv21UmttpR9wrI1vtnO1E4+nrHT8cZN4XFUz9lXVqsS4P/+uPP+Ozq4cDgv85cjp0cz84Hb/Zt5rMsxeZuqh2v4hFjieDVlJ6oNngQxLSdKCx/MsuHkBpd7fjToyWfb6d8MN5xJNvm12nA3MmYuSlHasd+eYxKz7q02VWiYGo6jVXmcVfYPA0iHKPmJgmIKmtwg9zHikubmyREVzpGdsGffvAlceyTFwLDfXV7LPI6xjum0y3r9ZSstDg7vuX0wRXHyw2q2vF2eUxam5x4CYtgT9UYZKVFXhvkhc0H77xgtpzzZnVEWpu4ZkVc2kTSQvfT8zc0jY5nFTyZLWlblbIRCndV6UlKW9p4he3wKrf4q9fP+NHilh8/esXl8khoCWTBrCo9j6cr9pnHPvOwjJqfHN3wYr3AMRpmbsI28ygqk5GT41uyM+FkrPZj2l7lfLbkej3/vY5pQyc1HnlpU5QWtlWSZC6GJhwav12eDHoi3yxpO2GLfThbYugNv37+Hk8Wt8Sxz+1uyvPdhLLRuZGaCW85Z595PDy+IS9s2lbH9lO++uYpmkSuu979k/t+WH+46ztv/AurYJMGRLlL3eo8Xtxxt5vIWZXCJvV5FsacjbZYesPbzUwIjixBnxpJZn3RCAFdXAikZ167eEbN+5M168zH1ht2hUtaWfzi6GbIqS9rgxuZeJVUlgDU1AbHkw1VbbCORryNR+SNIU6GnfBW3yUh15nHxCwJrZInvngAhqYoQhJp/Zs7Ob4p2mNfrBfMrIIOZbDovdqP6RCdgJv1jKmTEVW2aNfrLS+WR4PCf1/atMsjktrC1hrGTsZxsKeQedv7wubYi4cxRdupvN7PeHd+K2aSnSpbdiJLWz+0qyX5cJ36PI9GBFbOIohw7AJV7YZQFN8qmPvxfV0zbNKAplXRpE++bAx2uYhhndk51/GI83BHXptscpdN5jI2S+mdFgz9c7Mc/O2BLqBFcz8mlsWW1uiEMqin6VQ8veGuMOXm3nGVG/zt3fFgyzt1cy5Sl1VhYGkdI6PlMnM4cwocvWEGwyz+oIkw1I7LzGJsitN1VBu8E6RibHWg4Zk121IwBjSl5+/ePiGS+Q6O3jCxSppOcANEXrvK1Co5l3jiDtBlG7/tFepW45EfM7Yz0ampDeLSYhHsySqLqLIEtc/LpNffw5dCwrdJQNMLm6SrtQPFz9Y60kbj+XqBrTU8DSKO/Ii3uym62gmw0j0iWo98Ma93rZL5ZMNXbx7T99Jaa5as00Bs5JVJVZnkpc3VfjwI3Xw3oyhtJm7CkVHjOjl3m5nk/ssOnFnx9u0DVKVn7sWQBtwkIWfBfvhZ7/YiKMi3Co6CNa9vT4dcjarTmFgFJ+GOvlew65qfIg4DRS0EtE2nMXYzQi/BMGrq2qCWavyktIgqmwejDRM7lzHgHdvSZi67A1Wrs0s9odVRO6pa57OLR/z44WvWe4HPnY53fHnxaPjvdbXl+GhJentMXgueRGDUAhduFfhOxtm0YZcErKORGC0YFVeb2RAkdu4lTNyUH8u2ft3oPDi6Jc1cNnFI1eqoquCVnMpxomneT3Ln768fAD5/uOs7b/yhVQ7zproVc/S+VwTJzxRz3SR1ySQcQ4hm6uGGCCzx4LHlTLLpDnYdIRp0zVKK4MzBcx8XNr5Vys1FlwElIrzH1hpCmT3fyA21k976Q9vVkLYeT6rvTa1l7GTEhY2lN0JB3qtMLFEECJylEIfFtTkUHbbWsJPWLlNtBcBI6zBVgQ5WlG4QVTVyxp/JkzswiGtMmQBn6Q0jN2WTBoR6hqHUwxxcnKDFqaCQII62V2liAf+oapHoNzEFUrSoTUyzEht/acu5nhiVHMhl3/fKpHYCWjRFJe80IfCSmQuOLvIFklLkIfS9QtOrtL2CCoOoTYThiAd7XNp4nXgY2ZroEozNemAuVJ2Kb7S4WksHuLomWAByExxbBa8Tl7oHpRPXiab0QwhToDdD4Xaw693kzhC0VEr/91RukJV8TQVRLAR6y8houMtt0lbF0VRGvUJoVES1SSnnvabaUbYijrWnp2gMDLnpH67JutPY5q4M3UFYVnOXqLSkDkB0Hw4RxJYm/PwgNAFlJ97Lc7PCUERDte8VfLMatAFVq+PKdEJN7TCN+53pjlxhXdONhrwxsLV6mCcbcjMC0KTI7ZCLcbhHDkuV+Om8Nn/vPpZanCQQuG69ISotFEUApA7QrKSycGQHTlU7HAnkAlAaBCOk1QdR30kQEUsxbCqV+Y5VYMlnVVWbaFoj/PQH7ohZMXFFyFJRmYSyU3MQOFp6A6XQRBlqO9j7DiE+fS8gVwcL5sxLUCQRVFdbVL1nXTiMLNG+bxohbjwwEJCf9a50qDoVT68J7Zy0tLGlbmqXexwtVhi1KEDUrh+uD0vmo+w2M9691yvih/WHvL7zxn9QwzadRts5XG+nQziOJ2MsL3dTNoWAbHwwW2LpNUVtCmqWk7FOfVxDBJ40spI/DiIpljI5m654cXuKKyvmv7w55hezzUDTm1gF61KAcGyt4elkTZT6rFOfotEJjIqRXVC3qth4UXh6co27mVJLyMjYj1mn/vDQ7Xt4srjlr18/G2bEP5uv+GY/lrPkegCkeEaNY9QiCKQ1MFWhnq5rg2en16x2Y5pSwzUqylbH1SpMXTy0olwATMZOxiSIsMya56tjxk5KGOT4ZsVdEnAadnh2jqa13O0nMgBIiHoss2aXesy8hKfHN7h+yl9+/iNOW42xl7LJPd47vhJ2tuLb09L3vapWwzFqdK3D0BsK2dbflha7WuefHN8IK2XucJtbOLrAF2tKjy4DbV7HIadSu7EsHF7GIbrSE8r3O651HvmC2xDVBrtK5yfTrRgv1To4MJX0M1NthvfZ1zt0Rcy8j52c3+4CQkPhzCkoOxXfrDiSAKYv9wEzS9DuUhkHvK9N8kYX7X/EQ/bYyQnlg/x3uylpKciDba/g6A1xrVN1CrbWs7BzXiU+oSkU3GmjM7NKUlmA+EbNdeaSNiqh0TI2RejTy92UXHZENKVnU1qMpKDN0Wv6HuZ2IcYDvcpvdz4/nubUrUrdqwR6zQenAot8GQv887vzWy534j69T4BPVLg8XAi2fhz7hKbgWYCg2x2KXM8qGJkJnpsR7Cfy/YV16jN2hcWtblW0WHR5FHpsXQCI2lYkGCa1EMdeZi6/XNzxOhqzLk1O3VyAdKxC4KRbnaPpmqbRKSuLrBSMjpt4RNsJgudDP0FTO/JK2GJ9qxDFdGGT5g5X0YR3jq5xnZyJVeL6KfvtGENv2MYhUWPwdL7k1XrBWt5vY7MkbgweqS2hk3OixKz2YxE2pnVEsaD2vYzGGBL8kyUeitIz9sSo7tP1gmM3lYJAnW3ms8sdns3v0LSWN+sFpXweBlaBbdS83k848RIJCjN4mDmMJnshMMxdTKPm4fxOFA1JwKd3J/yTe7siAPrvP53vB0Pf97a+88bvmiVJ4dAhsrEnQcQ+Cah/T72aNwbnQYRvFVzux7yzuMO2Shy7YDTfcvPJL8hrY6jam07lNg6HSvQvX77LT46uqVqdtLT4xWzDXe4ytQpmbjp0BVR6TsM973/4FZ9/9hHbwhGimfGGrld5s5uS1ga21uB6Kc1ywSwU4Ta7/Yh3jq/JC5tVErAsbX53fc6pl3DkqDKgo2cmSX6WPMW/v7hlm/ncJAHL0uKPT654vpmz3FqMzZqfc0lWWewLQXoD8dB/f7Tj6cNLPn/zBEBEhuYOjl3g6DXbzCcuHEG+Q+Pr9YKRVfL+yaVQ/GsN0yBiJqEcBx9vvjV57BSch3s2uccq9TkOInZJwPsffoWi9Cw/Ce/jmuGucPjVeEPoJ8KOlKioiskjP+aZ2vK3d8f8ZLrmPNwzdXL+4vqYtNF4P4xx9JpvotEgpDS1lqmkmU2snKrT2RY2nqQBfrhYMgojXlyd8zIaE8gs9cvM5qdHN+wy4cHOapMHXs7LRDgZHL3hv7sZ8fOJsOh9HXvMrJrr7RhjN8LUOsZmw2VmUsnuwNjseJ04xI3K2Gx5xxfK6pMgYpkGvI0Cfra4ZRaNWZYWaaOxLi0Co5HY3oa5l/BVFPA8dlCAqdliqsJHH9Uam8rA11seeKUUFWoYSs+Rm/J8PyZpVJ74GTe5jW+ItLltYeNIrUTRik7Hj8cJll6zzkdcZw4nTkGcerhmydzJ+Gw7IW/O2dcGttYylbjY+1hFo3O9ng9x2qEUt2WVRdOqPD66Fcr9yiSrZuxyhyM/ZpN7FJ3OSRBR1gZ1q1J1OnTwaLIa5uojL8EwGqpOQ+t7RlbNT2ZrPnz3BY9Th91+xCYNGDkpRW1ysZ4TVTZ/9qPfcHV9QlI4FI3OqnAxlI65mzH1YorK5M1uymmwZ+pGvFkvSEobS6+F46i0+OzqIYbaDm6jpUxDPA33/OjpN+h6g2lU3G6n7AqHmZtyFY8kHU90IhZezNjNsM0K3xOajXfPL6hrg30S8Ga94OfvfUW0D1nux/yLZ1+zjkY8PL7B9TPevj3H0muhDehUfLNkI0elidQ3/Iuf/gNZ6hEnnkgabHRUvSEvbK6jEXHh4EkaYeCm/Ff/6G/u7Xr4Yf3hr++88TvSh3uw0lxdPGLqZBxPNoynO5Z3C4HTbcV89PFkzeVWeEUtvWESBzh6PSA048LhQbgbZq5tp/JktBNULL1h7sdYVklzezrQzV7vZuSNzsIRlfHV6wfitcYbysbgUqqBVXomdoFKz7/69BcEciSQlzZx4fBqM+fBeMuT4xs+ePyalxcPsIyarlfIKovfrOe8E0S0vcJ15ssTpzX4ycdGzRerI3yj5oPxTowRzIpjq8TSA2LJI3C1lqi0+IeX7wCizXsQWX3x9jGeUeHbOZZZUVYmUeESWjJC+PSOV3fHzLyErHC4e/EOgZVjSftb02okiUdSWuS1Ti2jOP/4l//A3cUpUerf25z/oR8PBYxh1OwKl+vMw5Etd1PrZFyoUFI/cMuBV3CY1zt6Q1SbFIVA8x4EeQev+5Gdsy5totszuD3ji8jlT+Zb6k4jqQXY599cn4vAHqUnqkwuc5OpKcYgY6vgRyORoOfpDROz4q6weOiK66+UVMCfTyLWpU3dK4RGzXVus9AaQkOMhg6isLwWX+vlbkoHLKySI7tnX5k4ekPdCXX+m2hMaLT4Rju0matOJW9VJmbDu+GOpLbwDdlO7nRWuUMuf+bQEKTKM0c8pPNGZ1cbOBJMZGotltrxR49ecrOZMbbygQnx2d0pgVENLeGs0TmRQUJX8f1QHAE+eviasrRYRyPeRGMeqR2bIXSmYp8EWEaN72Q4dsFRYVNV5qBvKRuDZeYLGqd0RPzu7oTzcC/4Cdspd5nPh/NbUhl1/Whxy5fP36FDYnEnayyz5pvrU5a5R9mpbDYT9rlH0ehoSicFcwKpHOUunhzxqUqPY5ecjbaC7NmLr/lssuZkthr0BrG05vlWiaE13C3n/MPtGa7W8nSy5oPTS57fnDGTinnBFNApa4O8MegSBWM3Zls4zDN/KJTOxxu+fvVEdrMM/vrmlJ/NVry5OcU2K46ma9bbCetUuA8Cu+CXD18J+19pkZY2d3cLwiBG0zryzBSvWdqsU5+6E+6IxWTD5fKIuyTkdjfl7N6uCAnw+Z5n/D+c97+/9Z03/ix3iAoXQ2s4Gm0JrFy2/VXy1GEZjYYQjqrRJUREAG/aTgBs8sZAq0QAhStHByDa4HFp887xNXe7yfDgutnOGEmRXXXgs5slZasPatptIU4PfS9m/67WkLU6Ni221gzQnQMwo25VdK2jqA2S1BtaiH6vDnaghVUS2jmZtF2d2PnAxAdw9JpbmUZnS8vVxXo+/LpoNcZWgapo5I3OtrIoO5UnfkxUONwkIbe5w5+cXYgxR2lTt0KRHciHYRb5TD0xNy1Ka+iqjL0ESxHWrr5XJENB4HNNvaVMXaLUJyltFuHue71Yhp/fqKkbnVLt0PWGk2BPXgvPeN1pzKwSRxd/p+40PFl4HeyFmiTrmZKJ33QqutpxlztScd/hGTVRbRJLq9zYaEXLuxanc0drhwdB2ysUcu6tqx1tL3C7527KN4lPrYj8hbpXaHplUNdXUqB4EN6JoqSlalXqXtD4os5kU9roEsajSbHhQTNStOIzdvQGQ+lE211mCNTye6rkqd6QrIORVcgwI6FJURSRWVBWFlWn0oHw+HdiU5qaFZYm/OhCMKjw8u5EEABRUOkZOQmTxkBFzNXfCWLSxhg20rvCvpdrAUDVBJfCNirO/IikFGCpwzVa1Aahk1FLdX6PQuCmpLlDLumTEzsXmQKyqG17lX3hiJGSKgJsDn79g3YgqURA1EEpnxdi7m9JrsL1dsYy87C1hiNZBOvatwWZZVYitEvyL9pOxXcyitKi64W+IMsdKilIdo2KdeYRSOdP26ss7JxYpvmVtcG6cHgsNQGd/DsjN6XLfKpGQ1FEpsMBxnTQLxSNjqGJZ+OmEp2w0MlQ1Y7dfkRUuAN/IC5sfCcjyd0Bd3yxnfFYa2lbdSB5tp2Ka1S4RsXITdGNZjh4/T5j5If1H976zhv/IdFp4SYsjpbodsWbF0/ICockd/lmP+bIyQaVt6k3jJ2UUobNAENryjcZ6F76wbNfG4SjaEgoyyqLzzczfnl8PVD4DLVj4mTcSESspwsmfGiV1K06JNpVlbgZbK3hzBeMb0XphdBF6RlZgtkflzbNTmVdONSdRmgqhE7O4/EGx6ho5Kb1bLokLsSprOk0AqsgrU10tZUkP5u00bHkppPJG/wA6ik7lYvU4NzVWFcmV7lF3qoYmhAbHRwO29JCVzri0oG7BfPJhkY+IMa9wioNmKo9mlZjAobeMB/tRPu/FFkBl5enYp5JP+SOf9+r7xVaFFHg1QaL6Zq6FWLMshX6C0uvSSuLuLLEhsi3SYmeVNA/9kXrv2x1FAU+WU+wtY4nXiqY5GpHKbUBH4xiGhncdBBJHgrEotVoO4VAFw/1uNGpWpWfzxOaKCBDxVDF5ps2Oq6EAFWd2NwPQJ0DMXDdmhStyqYyqWSAz8KqeSALTBGaI0YRrtbwtnTxZJrbIfykPohUewWtU3H0TsyTG4PAKklrg/r3mBSGKtDPu8rA08WvNaXH02umTk7eGPiG0JrkjcHfLOe8E6SiEOgVTsIdD0Yb4brpNB7P73i1PEZTOqrWoLpHZG+SeNS1gWMXzGcbPnnxLhM7HzafrlOxrZK73YTrOMRQO3423nK3m7DOPCy94dmRsJ4dnhe+WbGVyOHTMGUibXYHJ0xR2sNp39Ab9qlPXpsYasvYyul7h5vUZ1eZzCyw9BrLrEkLWxZawkM/kdkSy2gk5uxhTCcJfGVtsMtc2l5soKGT8TaWBxxVWAQ/On/LV1cPWOUumdSG1J2GoTeDkHc82g+HH1XpGTs169SnlcVFWtpoqiicQIyHbLNiNhORu7/55h1aOV7oeoWL/QQ9GhOXIjzKMWrWhYMfjQT2V2uFdqBwCOwc06xwnZy20XAd8fsDHOk+V8/96Up+WP9+67vb+cIdj45uaRqN128eUUnlraG26JKX/87RDVebGbeZx6PFLbreDNzovlf44/e/pCotltsJv709I210HgcRYyfjg6Mbrq5PePzoDddXp7zczJhYJc83c069WIS/JMJa83C0JSltrpKAHy9uuZbQnmM3Ia1NQrPCN0pcsyIqhPp1lXlk1bfe35knZqRJaTOREblJZdH1CkltSapex9TJMIyaMgnYFC5xbRDXJlElRGC21nDkZIydjK82c+LSRFN79qXFezPByp9lHpoyIq5FSMlDt2BbmbzazDkf7TgabdmnPv/yyUs2m4nMJQjx3YxE5m4/ePcVu7/9FV2n4LklXpBi+RlVauMHCXVtUJUmuzhEPQBsmvsJ4shrY7AwXeynfLk84d3Zkg8evsG0Kv6vf/9HGJmHrbVY8mRMK+JmAb7ZjzFVGX+r9tS1YDs88XICo+JMRsgaqc/IqIfMhL9ZTfH0jjOn4MP5LZ8vT7gpvrXanTgi3hcgNBrexqGIZO6Es+JPTq75fL0YwD6eLhgBji4gOLr2bTrjpjJZFQa/mG15lQTEjcbX+zHrUudPFmtMTRDiNLXHUoU3/zBWOCQLbiqdl4nGP15kpJVQ93e9wmfrOXO7IG900kbn1Mn41zfHzO2amVWxqQyeODmOXsuNW+NNImbLE6vkzI/548WKl9EIR2+YWgWrRKBhU+lGmZQWdaeyLgICo+J/9v6X93ItALxdL8TGCFimEGdaRs3RdE0w2fN3v/0xXpBgSp2BpTfEiU8sN+/TcIemtcPp39QaHL1m6qSYcsSxCCJh9Ux9ssbgyXhD2yncJQF9HJK1OqvC5pEniIF1r3IeRDi5y7qw+dcXj/gvf/xrPrl8CMDDcM+7P/0Cw6x4/vYRb+IQFeT7LVT329Lmx0c3hF5CVZtsEzFu0NQOTSY/3slx5uPRFtcsuYnGwo5aOIRmwcP5kiT16Hox0rjNPFytwdIbGUxmklSWGOHoNWMv5b/+5d+x34/YbIQ98TjYc3Jyy83NMVVj8O78ln3m8ccff068D/jq6gH/7P0v+O2bp+zjEV2viANP7g5x46fhjqS0JddAoUfh5/d2RYj1A8DnD3d9543fNGr+6pt30ZSej46uURUBgqhanbrR+Wh+x9e3p6SNgNQoSs831+dDW9Q1S/7iix/xwI+wjJrHoy0TP+bl6phd7tL1Cl+sF/xufYQrW/RvE5d9pZO3GsdOhqWLk6SutZRS6DQN90NhEdg5UyXhzXZG2eq0pcrzaMTErEg7ceI85HDHtbDinI83NK1GLMWJhwSxqZOSVSZXScDf3h3xy/mKhZsyaoWTAcRJM2t1lBoez+/4ubT2xIXNdeYP4KBCKrsnVi5GDr3KgyASEI/KpKhFvOY/vHiXIz9m4iYErcY3dycs/Jgo9ln95mNxI6sdUe7i7Cqevf+C1XJO2+oiNdFP2d0KcZuiMJyQv+/VofDNZk5oljwcr7mJxlSNzvPLh2S1wT9/5yvywmadBsSlNVjY7jJPxI72CmOzGja1uDZYFTYTs2RTWXz19iHvh6I1G9cGm0pnX6n8r995zavdlIvUZXnxiCd+gq01pI3BrpI4VKUnb1RWrcHH44LHksDYdCp/fXMKgKH2ND3c5jrnMk89rk0uY4eZVWFrLWdOxjthzV3mCea+UzK3M67SgN9ux5y7BXMnE0hnqX8BBgZA1ytMzYbRRIgYp2ZN0Wr8bh9y7hYERoWhdtiy+PlglIiQIrXD0S3WhY2tGUN341fH12wyQUdcZR6f7wL+aL4hkEp0x6jYZj5RZYkRReHimxVvUp+7wkK/PeNH93I1iEJQU7rBqbPJHdpOobw9wVjP+ejxK0bHa9arGVWnsS1tZrVJJEdgANfrubCttip57XA+3pCWNoWMmD07vuXXn59jai0TK2eZBjydLdG0lrIyudxPeBTuBoT1zEuEPqOyeODHzLyEvLD56cml2Jwzj6awuLg5oe0UHgURi3DPzW6Mpvacj3b8crHk6vaYdTQaOg0HgfMhlrvrFeaHTlBpE1gFR0HEOIgIRjHOOOLVl+/SdmKU+EiO3zRp4QPw7ZzXmzlZZdL3Cl/dnXAe7gacdxjErFcz4lKIgG2rxLZKXrx6TFKKCOY3N6eYasPUFqFoD2ZL9lcPh4yVKHdFtLfaoangGPfv4/9h/eGu77zxR4lPYIiZmK43+F4m7GutiGbNKot1aaOCmLPnDn0vTloC1CNO4pvcRSlEFaprrQDByCKi7xVcraHpBRBFJI8JFXdSmyx0kQQ3cVPqTiNtNL66eoBj1JL73bFJfWZuKtGmNqFRy41HPGA0tWNhpeSNCARZJyGKnL0eomNtrZYWOgbxWNuJm/SQJlY0OjP597Na3LC/PzcTJz+Rm37QJgCSaS8KoUOrXzw8Ir64O2WfOyKuVGtZ+DH73BWqYqsQ82X5frW9yvZ2wTYOsY0KWxVRvpV0JfhmyVyenL/vNXMTNMUdQEymPK21nSJn+warRMSumlrLtTz9d3IU8ziISGtzEGE1EnpzwC4DbCqLsSk2YVdT8d2ON7sJrl7zNEi4ycUD2Janu7Y3mVkFUWVQyI9hL33iRadSdwoTq2JXCU5D16qsS4Wv44CpWcvNviCuDRHna9Qs/Ih14eBqLYZ8zz29xtNFG16hJzRLXsYhpipa+YdCoJLs/7FZ8vk+xNU6VAUcvcPTa+Ja2FLbTsU3KurOkCOPBltrWPcWY61BU4Vw0ctdNjJ0SXQpxDy4bHR2hTPgjA/jrtDOUJWeh6VF2Wkc+dG9XAsAD2QnB2C7F8WupvbDfWXZJfvbGV2ncuwmhE4uSH52hqb2zGYbkqszJk5C3yvCvpYKAmTdaWSVib2ZEBiV5Nx3nIxWg2A3Ky3S2uTZ4pa0ELN6xyxZxiNGVj6IYZtGp2k1LJmOubleCCCQH+M6ObZTsImFEBHg4uZEZGtI2p1pCp98UlrYekPglqiK4Gcc2vam1rBO/YEVoCg9d9EIzxKaJlXp0fV2GCW0vUpRm8y9hKbVqGQLfpN5YtzVCLtsKUFPltbQdipLmRgY2MVQ9IlxaIttCAHw1BG5H6Yu/ptpEFGUIhRtn99PgNe3q7+HE/8P8r7va33nwV9em0ycjLnMptckSrdqdZpWZVs4EugjHoBJ4aDLdljVipt34cXUnSpPJQZlJWZ2pibGBb5RM3NTYaHrFRZ2zuMgwpYiqqbTuE59dpkr1bjwIg4Hv3rXi+8jdDLcg/deF61iz6gJzRJFgcDOsbWaplW5iEP2hS1CRmQV7prVcFr2jIqnE8EEBxkY42R4MnHMt0qaXhXCpdokLS1B7JMnD03pBpEhIDDEsvK2rW9paqMwQlF6qk6nbAwUehyrIKkEzct1cnRFvFemLvgBWe6QVaaAIXUakQzlOTxwgiD5970+/r+uwMkZSXpZKhGoVathaB2BLR6el2kwwGMyCS+pJcBn7sUCciRn2rtKx9VruYErmKqgJZpaK+A7RsOzMOJl4tH1CkdejCfxvYUEO5lah29W2FqHrQm6XypnrkUriHpHTo6lHsJ7OkwNbnLxPXS9wtTOaeV7pyniOj6MJOpeXLeKgsgGMCo0VcxX40YjboTbYFfrw5jI1FqRaCnFgprSSwCRQlSZRJUxYJXTRieqDRJ56rMlvtXUWrJW400csqsEKEhVekYSlLMtHO5yl8tUMOU1pcOQ7HffSzkN9zwMd4zvkeQ4n2wE7U5vqGT4kqF+S6arK4PVck7V6kzclKPpGt/NmPsxcz/CsksMrcVzMzxXxGevZPZD0wkmxzIOCaxyCJcZj0Wnb5e5bAuHslPF6zdCRa9pLVlt4FmF4I/I97lHhOqM3Yy1FBJ7bkYQxvSdgmcVeBLH/HI3pe1kwqcmxk261grLIeDaOeMwJqtNqkYk4dlGRV4bRLnDJhpxc308EPZ0TVA+DXnIEZZHk7ZTCd1UbtACilY24t8lpcVtHLIvncG2muUOd5kvrHlOzni0F/AfpUdTe0yZZhjaGYYmmCmq0mPJlE/XLImq+xN7/rD+8Nd3PvGfzVesdmMAVLXj6wsxKytag7zWyVqdj2ZLYV/5vYKgbHRqeXpuO5XTIBIq9EZsVE/nS4rK5C4acR4I+87EyRjbOXltyKAOoby+y102lUHVjRiZFT+arqlkEp9g9eecBhG1DACZOymfrhcsrJIfn17iewn/+quPhvZyUhvEjeD1m3pLXqukjYFv57SZSiIphIbWcD5bEqU+eWUyHu9Z7ce83M5YlyJM5sS3qVvRZbjJHRZWwdzNmPkRYRDzxdvH+FZJ6GaYRsVqP2a/nZHJGNlxYXPkphhaQ+hkhEHM/+vrD5lYhQjycXKKVud0thpidx9/+Jzi1x+TVwKSNJEP96wyySqTN1dnPPneLxmIMpektDD1lpmX8MXymCM3RVF6YfVMAppOZVdZjHqF//jRSy62My5Tn00rbJlFq/HRbMlFNOb//MYDxtiaSNGrOoUPxsLqeVDHnwR7LhJfsukttqXJthLdjYXV8PFkwyZ3mFkFgSFAOADHbiavEYOmVYeTeaC1/JeP9ryIRlhqR9WpfL0X17ehdCLkaX2EKsmBeSMSA6dmxcgqGcuH6svtjHeDRFo+DdJGFAie5FS8iUPO3RJHa4Xws9X4Yh8wNlpsuZmkjUHTqWwbjS1gah1/dHxN26nEpY2ttbyIHX4xjXBlt6DsVD7bjWUIUU/dKTwOxQYQVTafXD/gF6cXwzjudjvl43u4Fg5LVTtGYcSRvcS6PhlOoF2v8ObqbLCuVb3Oq5szfvmrT7h4/oSrzYzVV6E4LKRChNtJoeVE7VF68XkJq60pDxMNX7x+IuO+RZdjZhdEqT8U7GVlMrIKadUrmIz2xInPg7Mritxmtx+RlDbrzBPCu9zhaj/h40evKAobs9F4Ot5wlwQc+bEoMgqXsZ2xLYS9E2A+2lE0OiOZlzGbb5hNtvS9QhQHfHr9gKdjASHLKwtLr4lzR8bmimvlF+//jlcXD0SOgCwwz/x4IJEKF5GPJu+Hl9s559IKnZUWhbRGWkY9CIr3qY+utTzfzFmXFh+Mt5Q3pzw5v2Qy2WFs5vd4NYj1g7jvD3d9543/4u6IotExdVHJh04+xIZ2vUJZG3y1mfPx4pZ3H77hzdUZY6dkt3aoOo13wiV1q1NJS1dgF6wzH1XagI7CPct4hCUZ1bre0kQjksLi2EswtYareETa6Hw0XeFZBcskpO8VFm7COvP4ZHnMPz1/w+/WR1INnfE0iAUpa7UguzkjNEsiOfcDeBWN2FUWt7mLgvDa/+bmnGM3GSw7tlHz4vZ0mN/+xWc/YWZn+HJOnTYGi9GOf7h4zLa0JG5VkTN8i2Yn7HaWXlPVOnHusEx93qQ+R3bBxMq52szpe7D0nrS0WcYjfnJySZS7AsO5H/Gn7/6OzW5M3QqR4+rN6WDnaTuVZTTmvcevAKgKi310PwCfUo5J+l7Ei/5nP/0HPv/mHZJKiCKP3YRFEPHrm3N+sxsxc1NeJQFFq2JrnZztm1zHIZ5R8b9/X1iMDvHMB4tT1WqyXV6h0HMkBW+H5MaR2XDmFCzclLGT8vVugqM3A9Z2bFZcpR5xrVN2CucuHDmZ4APUBufBnp/O7/hivWBb6Xh6x2VusK8DDGnz63s4dUqSXuGmELfLsrCodyP6HmaWcB+k0n3xxBNt/riwsdSOIydj4SX8enlMLR0NtqpxUxgiZ0DryBqNczcTGyViVHaxH7OVYtPAqHnilUS1MbyOpvScOwW72iCuNX4y2bGSjhhLazjxIzayC+AYFceTzb1cCwAXd8dD3K1Cz2yyZZUEjL2Uo8WKv/7qQ96d3eG5wt++3E344tc/Ii5sdqXDqrD5X/zi70hjn7erIz5dz1hY4t7SpY32bLrCdXJ2+xHLOKTuVByj5tjOsExxXx08+AdWxtt4hJ4GzOyMiZvyV1cP+dNGxzJrdL3l6dFbwtWMqjHYZj6r3OWz1085CXdCRyTtkJ/enTCzCv7o2XPaVmMx2pEXgglysPGWjc43yxN+fflIFLXzOyZBxLvTFevM58Ozt9hOwX4fchuPBJGyEojm5XIuhH1+jGMXNI2OqvZkuUNZGShKz5GMVe4k9+BkvuTt7Ql1q2MbFbvcZeqmtKjQw8gThMT5eEfTCNt1VZkkiXevVM8f1v841ncn91mCIa8q/dBSzGuTXeFSNDq/evocgKbVWK1mTIKIqjI5chNB21N6AjdFL1qqVsw2z8ItyyQc5k99D+vMo+k0HKPiLvPYSnWyq9ek0i99mwa4Mr97WTjSyysIaU2nEZrV4Hnue8HwTmthr5r638ZR9j1S0f1tgIuudszsjKmXULc6+9zhcj8mbQwCoyK0Spy2HqAYoSXCf+KDX9jpmEgNQN4Y9D14VsnILsgqa7CgjeyCh71g2Ge1iW+WnIx37FIPeoWT8QZda3lnsSKJfS7XC4ra5Gi6RlU76tpgvZsMqFHbrKhqnd12jGOXlJWYs9/HOiBP207hJhoLLHMnULrb1ubpeCM2HL1hbDasM4+RURPoCrbWMJWbb9qI8BRLa9mWFjNbQGuyRnRjFNnpsbVWMCTUdhBnJrXGxBRt31Xu8jYOOXJyIsleCPSGVWHT9eDoLR7wMnE4czSyRtDvdoXDcRAxsUqBTzVqilZlZlVSlW8wNhu2lUnRihHEXWHwji8KlQ4YmxVFq+FqLa0qOAKH773u1MFueuJkpI1B1ug4ektoNEMWhKV2AmbUapSdGIfMrApHjrguM5u0EUmFE7PhzMkYGRV1p2I0Oq4mbK67QkTDlq2YBUeVjS3bx/nq6N5O/Klk1WtqR2CVOHmBoXVklcVyNePReINjF+SFTVraJKXFcbhnI8Wec7sgS0TRMnIynvnCmdH2Km2rkNYmN9sZXlaI1rt2GJu1XEVj0tqkAz6Y3Q32wQO/39YaPKtkMor4M+0VmzTAakQ2yGo5J8pdQifjeLwRY4lWxzSaIdP++c0Zc1tEUEcyL+PQldCkFuSQO9J2KuvSYmKVrFOfuLCpWp3TYCd4ALWBKtkVYydj3icUteh2umaFbZUYVoU3itmtpkSZ4Jvoasf7j17TtWLzJnO5vDtmm7tYWkNgtyL9T22H8eHL1TEjS1gqD0JCTYquNbVj6uT3dDV8u35Q9f/hru+88Sv0wwXUdKL9DWITKFuNorSY+Am71GOb+Tz0Umo5YwMwzUpgeuUcMKtMHhzdskxEelTT6Jhayyo30KpenrbFfDZvvv02Xa1lL610x25CUhvk8kEbmhV5beLotSACylO32DzEXN2UgT25tLWYsuVmSm+3pvS4ZiW6E41O02nsKgtdUtMO0KFd7mLpDaYm7EZrifI8eNhr6ZvueoW60Zl6MTf7sZi1SXfCkd/IUCIV16xQlG44OblOLtC+YUJVmuwLm6wyeXR+SdtqpJlLJefbjlUMOOK8tGllSNLhZ/q+1+HrVq3oZNxIb3aHmOP3vUJUuASSbx9VllCsS66+pnYYsgBSJDynkG1bVAHk2dc6Y7NGRSjl17k7UB4rKSgVrylcE+vS5GEQDRnunlHzPHYZmYKgKCx6+rDpq8BaiuI8o5KBKmLjPdj7ilYT4TithqaAq3UUrcrYLNHUThTAEhBVyZ/HUDvSRkVThHsAYJ27w8+qKj0GDLG+lWxV151K3GikjUrbw8SqcLVDsI5O3QOtKuBCncrCS1mmQjRpaq2ETclgl17EuCYSjy3U7ea9XAsguk1Nr6LRifetFDHAeW2QVBaP53cYRkOSucSF8KzrMpyr7VVMtSHNXHRdpMvNvYRMfr+D1702h/v8sDytJK5N7nJ7EAGrSk8vDye6KmKCXbNEUTqm0y2rJByispfxiLrThObHKgnsnGUckhY2mtYwHu+ZRCmOFK4e8gYO7ARFEfN0AZWCXjsET/VkjUHRCmKg72Ys92O6TpX6oxLHLNG0Fqeu+Hp1hK6JYr4uTYLp/ttwIUTXCYR2p2m1oajRlB5Tb3GsQjwLEIFlfS80JKJjJq4PR6+FGBChUziAfH5Y/2Gu77zx73JPiLWUDt8sB89p6Ihwjf/2+Qf8yemlLA5aktTDNCtyecqdTHZst2PyymKXu6xyl595OXNPzM8OVLp9adP3ULcqD4P9EHzhmyW1TH8rpWLal9XttrQJjIq5nbEvBFZT1cTdYusiFteXGM3r7XSYrTWyjRrXBq606FW9wjrzeJv6VK2KofZMrJKZnePJB75C/y0NS8JDLlN/EKRlcmb78fEVVWOwST1m8zUX2xkgFM99rzD2EmbhHsNoUNWWT1894/2TKzwvI4pCNnGAcSEiOZPaFPax0uRqecTzzZxfPnhNkrtCEGWX3F6MeDS/EyhdveFHH9+Pd3vsJWzSAF3rOHYidplL3hi4uhBQrjOPuDb5YH7HNNzz3794H4CRJYJUbpKQuSNU56YmBFGe4bPKHVqtxdNrVGyejXbsC5vLzKMoLKZmTWhWBEbFVMbgenqNaYhCwlCFruMQB7u5nHPsdNI21/Avn7zkN3enqIjT+nVus6lmfDjaY2otn+1G/P1GwdUczt2MYyfnTeryzE+Ia5N1afLjyUZYohRhUbzOPDy9IZPe/YlVsix1sgYWdsePxxlXmcttoTMyWk6dkl1tSNW3sIS+Thw+GiWkjUbdgaowiEEDo+LITblMffJGJ2tVXiQ+x15C3YsixDFq/n4956mfDkTBQ+EcmgUjN8WT+RH3sRyj5jjYY1slqtqRF7ZwOHQCPzxOfTz3TtoOa05nK3ZxyNl0BcCru2PKxhjEqbrWDgJb16gYH+5dOZY7pBfOPGGBNJSOD45u2KTiRK5LZbutNcyDiK5TeHVzRtHoTN0U26hoO5Xr1Mc3auJCOGm2mc9FEtLGI46SgJ95GT/9R5/w6d/8ckAeL8I9qzgcRgqWVNAfqII/dnL+/uaMEydjKsl7dWOIqPHaIKpsni1u2MivIQS9olg9jPXmj64xjJqzhQjViROf2+WCqtFJSott6fCf/yd/wdXzxyJIzMu42MzRawGIKhqdIyelaMWv205l7sW83U0FdEp2B+5z9Xz/sbw/aPq/v/WdN/6Jm8jKV1Tgu9zj6eklcRIQFQ7/4tnX/P3FY47dhJmXcB2N+dGTlxSlRVI4XF2fsEoDAqtg6qb4Zsnl5Sm3sbiZU9kq/JOHr1DVjjjzeL2dMrEKQqscgjO+2Y/xjRpXr1lLj3XRaqiYKAqcBBG73BWWNqvA0jVCLyHNXbLSInRyfnT2gs9evMPLaMxFZfIsjDDUFoUeQ+t4ML9j8/JdRkbPwklFFHGnyZwCk79fz/H0Fk/y+D2j4mm4p5BoT0evUdWOZTwaOgtl7nAUREOozMKL+c3NOb4cH7hmydPZkjR3We3HlI3Os9Nrfv36CabW8rOzCz65fMiTwmbsx7yv9GyTgMdnV+SZw2o7RaFnlwS8+/QlqD1ffPk+x/dw0SzjEfvSJqpM4kbno/FmyCEwtI6y0XkcityFq7eP+WLv8L/70Rtu92NWiYtKz/l4w+vNnFXuiqjjVnR8DgTDtlf486sTQkPE4RatKh7StUEpT+KuVL6D4OHfpj43hUPVqjJoSZAUl4XFEmsQmBWycPzZdMNFEvDVfoSrt/xitsFSJxw5BSOrILALrjKXS7m5nzgFv92OAYaI4LzReR47nDs1M6skrg2O7IaqU3jgFnx4fM3+7WNOnH6Ih76JXPJG5dQpOfcSTpyM59FI8AKslrtC5zJzsKQl7rDqTpGOhZZP1wuKVuWdIGHuxXzU6FxmHm2vMLNK/tmHn/Pf/PrnXCQh28L5t5DT3/d6/8lLulbj+u6IT29POfVSOUarmY5SwbZIAtGF0hv2ccBv7044ls+BtldZTLa0rYqmdWhaw999897gZRf+fg1N7Th2Uwy1JaksotwRsCCt4fPbM55NV6iy+/bp8pj/zR//NfutyLB/dnZB1wn3zTYOWac+p17Cwo+4iia8icYcuwm7yuDEEQFaN8sFr6/O8KyCj08vcOwCP4zpOgVNawmCBNOsKHIb3WhQ1I79bsSPZkvy2iCVgLNNHAiNj9oysnKuNjOiypYK/o4P5ncs5ViuanW+/PRHIh7YrND1BtsqhdtIhqM96BU2F8cYRsPr2xN+8/wDfrG4FeRBR3ApLvZTZm4y6LB6FB5N1oR+QttqvLg9vbfrAfmK3fcu7vth6/++1r9TSM+b7Yy6U4f50JdvH2NqLZ5ZcrmZcewmOEZNXpsklclXbx6Jyls+nEM7x9BaVKUbPOyPZisedApFbbLNPJ7fnYg8d114mBPpkddUk2XhDKK8otFJapOxWRHKCrZH4dd3J8O8tmx0UkkDAwbIxos3j6g70UkwWwH18YyKtlPYFg7V7enQCfgmGuPoDY/DPSo9VWtiqx1jeaIA2JU2rvT+gmjpNp1G2ejktU7aGPw/fvcRmiK6B3MnJXBTHsuH2WGEMpuvub45oe1VArvgzd0xZyOh3o1zh3cma5pWwzRqZqMdN5vZ0P5TlY73zi/45vqcLPHQzXr4ub/vdWCGh2bFAz+ikmLDqtPJG4EWfbGd8WS05aOzC5pepeuEQtlSW07DPS/XC9LGoO5Uskan6gQaV1cEq39s1jzwalTEyEZTelaFLa432U69yYUlqe5UvopdHrqiA6Qp/dDOP8zes0bjqyhkZDQkjUbeGrzNbB55OUWnkjQab5MAW+tIawMVd7BthYaA7yxLk7Q54KB7kCOEuaXQAevSompV+h4WluBHvFovOHIy2UHSUIGR0VJ2KutS6E7mTs6RXZANuQQNu1pnYlaoSs+mMplZJZ5eDyFFD6WLou1Vvl4fkTY6Z07GvjZJaoO//foDJlbJkZsO47b7WraX8/qbx6hKzz9/70te352wL22svsHUG2yj4nI75XS05fzkhs1mgvZ744epm8oZuD6cgkeWGIUMeSC1ya60GVsFIzvHs0rKxkBXWwK75myy5VJGhav0vDvacXN9TNupJIXAjZ+PdoReIk7qesPcj4SVziywtZrQyTl1cyw59skqi0W442Y3Jd7O6FB4Z3bHNhNjvW0SCDW9WdHIk7UlyaATNx08/bZZoeYuRauzzjw2lcUjP2Lux3huxsXqiKl09HS9ymfLY352fEVdi/tX11tebeZ4RoVnlQROTpwEtK2KbVR8MBYuAsuoCbwUx83peoWvVsccewkTNyGvTd7s5xwXovCO7rED9MP6w1/feeMfrFVSqKcpHbeZx8QqGDkpaWVh6SIcAkSr8ve9ok0nfK6HAAmAojZxnRxF6dALG1XpudyPBXWvV4fN+LDRqAgf/OHrK/J0fYB6KEpPVOvMbQGDiStLis6EOlfXG7peYVe4Q6iMq4lktbQ26Xsxt84bl5FVEsjXrDoV1yyJC4ftIclNnlSKVpOBLsYgelOUnqwUDzVTEyeWXWVgSlGSovRUlYkhWeZNqw2c7oNK37ML8trEtUraViWvTGYyrUzTOnSjxtAEtOcwa1bVTsCFCpsm9YjL+7m59QPoSKYobjNfzLl7TWJsBTCpknCi0CzQNNEN0LuOsjaIKmuYh1aVKTaCWpdK9x5Tiix3pUNSGcztnF9vxxzbPWOzGubZh0AbFQYKnKl16EpPJXUDAs7UUzQqE7Mka1yiSh2uI0Pp6ZVv4UtpI9r2HdD0CBZAp8rZfU8hYU627PbYmjXQAfteQevBN2ppQXWYmqXMN0CGEPV09FSdwq4SsbmhWdJVFrn08NeSe4DUqGhKL4rhToQEuUYl2rrSaqgpPSNJEIwxZRcMDE1svPeFbwaoc0HOVOhR1X7woh+EwJ6X0cWjIVYWYGbnMsRIbOrAAL0BmHnxwO3X1I6ZF4u8ArPEswoMo+btekGFGP95TjZs+ofOU1ZZGForoo1Lm1Fpo6stjbxGD//ekM8tTekYy8S+vlfQlA7TqKlajbITtuJNGoiv0Qs2SdcJAM8hWMiSP4vIAiioWlHMjB2RCVA0Oqduii6fV4ZR03YKdasxCSIsq8TbzkQxLfPsq1KEXSVysz7AzgApqCyG1M62U2lq0Xk8HEwOxVMlI9Q1qUu67/V9t/p/WN/f+s4bf9NqhFYpHvgHgpbyLdRi4UcskxBLr/GsgnEjRHdFKwQ+aWPg6TUPxls0raWVApum0WhakyR3GXkJT7SGZRyyKx3Ow93QNgeRahWVFo5eY2oNgaGgKGKD9+QscGJVPAoiikYnqiwh6lPE/M2zBcUrKhx2lUXdK5w4OW2rcp164ibSazKZo30a7HHNks/uTrH0motywsvEJWtUbL1hWzikUh+wladRQ3Llo8pi7qSEtihM5o5F1eroqnA1XO0nQgAlEcFjX2A5M2mJs62CU70egks0tZOkQ41ObjyOWfLV8oS5tLNdLY948uCC29sjrvYT1uX9QDpEh0L82jDqYe6ptWLOHhcOU6tglzts8geAQD57Mtzod9sZgVEzd1OBaG10bK1lWRridC9Fe12vsCpsrnOT98YbttWUqSl0FJrSsbBLIYDT4FkASW3QSojU2CwpO5Wo1nD0jtCocfSG9xe3pNcPuM51ZlaLCgMoaGIVXGYeaatRyA2qaMXJvJZq+4nVcJMbaEqPb1achVv0eIQti8pV7sogIFlQNgd6o4AMiYQ+8PSWuhOb+6YymTsphmKQ9gpxbbCwCl6lHkWrcmxXwv5VCqb7yBKJjK+TAFPtpAYlw5aRvIEcHX1yd8K0MjH1hri4P1LbajVnFMQkqcevXz8BYOpkdCjEcjR15EfktcnFzQmm3vy/2/uzWNvS9CwTfUbfz37O1e4+uoyMiEzbme7BduEqCk6VMLjQkRBcWFxyDhIWN/gCCYTEPfI14oaLIxUccS58TIHBYKfbxM4uMvrdr372o+/Pxf/PEeGyfRTg2FTaMb/UliL33mvtueYcY/z//33v+7zcHs8JU4d16nIeBRx5kRDOSgHncLDmajGBVsExc24/eIx/HncbX02rWJ0JcI+livHS7tRe1RqXUY87dtLpCjxd0Dh3952qtDzZTnhN5mnswsUcvezEo66ZU1YCyDRzY2b9FU/mM+5Ol2JkIKN1F4nX5UlcxT7H/rbr5B2Mljy9PuB0cg0Itv+rr37At95+nattn6w0sXThfDkcLZjcPeOt3OJqNaInc0IuVmOGdspGRhK7MqxIhISJDfbJ7IowCggTj/lmQNVovHxwwTbxWCdCiHzkhRz01yhKQ1q+uI3gvr7361Mv/G0rwiJ2u8daKouvYp/3ntzn2E048EJUpaWoDAZOTJiPupPIXXfOuzeHLGIfrxTpfbs86XXscxX7/PDsmmXYkyI+eLIeMbTTbkdryE7BjvluqjXfWvf4yaMr2hbeWUx5Y3olSFsy8W6R29xkDqYuvvY3zk45cRNeHgrL2cPNAFsu2EWj8iwRAqOZkzKPfcL1CFNthHdeaQj0BlNt+dZyxImbcuTFcuxg0JMnu0frET/28ru8//wWVSPAOpraCLSuFEBdLscEdsomEXnhu1TCV++InO1nF0ccT6/RtBpij23qigfXcszEDxlZQjh56G8xpOc4K022m56Epyjcld7fz7pqOe5RlZY4c5iNFuSFQc9OsMxCUgyHoDaY0sXw4eWxoJHV4jRyI22atlbhGyWWVvFDUyHiqhoB3ElKk0Mn4dBJWKYuPzTZMrAyLL0iKize3njcdgsCeSqztZqJnbLKbd7ZBHyhH3KT2wR6xdhOxWavFrkSp77N+5sh1zKtrW0VFrnJl8ZzvrmYsCw00spkaFYMzJIGuuz2sSU0B++s+3x31cc3anTlY0V9Jdv4htLi6fXHuGIpArvjhzwKA/pGxVjGTm8Lu9uwrAsL3yy4jYjTfRxb/K93n/LhckLZqjh6yeOoz5GTsC1NrlKHsZ3wdDNkbCd4Vk6Y2UytjKLRuY56L1TVb+glqtriezEvGyW+F+MPt1w+P+I7F6dE56f07axzAl2GfV49ek6c2QychDfvf8TDZ7dk507gZf+/b7/FiRdx1Nvgewm//XvfR08q0VWlZdxfc5NbvDFacDq5Js1svn15gq4J8drUjbpIa8csOO2v+fbNAWWjMrZyjv0tP3j7EQBX6xFJYXJ3ekWa2SxTV2hOYp8iHGCqFT0rxbFzZsGWKHVJCouosOiZGYGVM3BiLCuH+YzzqNdF6p7PPwblaFLI+vVvvclBsMGxRSesrARw7OHlEe+e32LkxN3XlBLVu5HiUkvdJVLq9MyckRtzPJpzfjPjYLREURqywmSeuDwPhYh24CSYWsW90+eEoU9RmNya3Lyw62FXTbsH+Hyv1qde+DWtIitMwSf3oi7ycxc2otJyk/jcHc4ZD1esN31O+qsuitI0RQCJSIxquHfrGReXh7Stgirxpu8/vYOll0J5q1f8+7ND/pe7IkRnHgv2u2cUjG3R1qtblb9467nw28ucAN9JuN4MiSQF68SLOB3NO5oVCKV/0tldGnpmTloZcuHIqBuRcV7I9t694byDeZx6MZep08WcGmrNuCdgP+tUoIT7Vs6zq0Oi0sJsatRtn0fbASdeyDTYEvgxrplzth7hmgUTP8R3E5FfXxhoes3heMHXPniNH3v5XY5PLvDXPd49v4VnFES5TXp1gKWXNFJdvPPobmIf2yjo2QmjwfpFXDO0rcJN4tEAQ2kf2ok+t5nLR+shEzvtOOdDN+IqFLGnhtKQAkdOKkl2YtP0+mghMslV4Uk2NdFREfnqlQgdalXxuRcqcWnw5w7mcjQjTvo3ucF5atE3al7qxTyOxWZqh8n19JKzzYC6Vbt2btOCoUCjtGSNyvubIVmjYKgtptqKU7ocXeza/8dOxrY0CEtdWNMUMZpoQES85jo9o6ZoFG5ii4EpqH1Tq2Aoux5jqxAsAQlxeXWwZJ073OSiw1FIDoKr1Zw4YrN1p7dmlTk8i3oMJX4ahKZh5EVcpx5xKUSutl5haGIjVTcKqvniWrtVrXG1Ggn3jh8xPL5meXZAlLoYasOmNLk7mlM1Wtd5mG8GHIyWWE5KGrsMvI9jum21YWJnfOHkGf3hGtSW08yiLA0Mo6RtVd45u8VAbvhu1kOSwsQ3CoGwrlUsvaJpNHw3oW5U8tzi+w/P2aRuN9J7Np8ycBPySnTiZkdXfPO7rzNwUtxK4yLxmUn3SVVrRLHLo+WYV6ZXaIroMjzaDrjXW1PUOkUiDgfH/lZsDHKbWCbvRbGHISNz780uhTupMEkLi+so4KWDC/LKYBPbTA7XnC2m3fsB8Mb0kuuoR9Wo9OU4wjVF5zWMPQInRVVr2lbtMix2bgFN8k1+890vcBRs8a2M9QtifOzrT0d96oW/rnXhKUUhTl3BIK+EqKunZZQSlZkUFtViwiL2OQg2nd85yRzqVjx8baOkKkTqlmuXjIyyi7QEceLLKmF/yisDFWH7aloFXT7ExUOvZhRsud4MMbWasZ1yvRnKjoTwRu9GBeso4Crsifmv2nRq4lLOvuxPzL0aRZw4G+QGoBZUsFTOHB/0Nry3Hor3Rc74NKXpstgdOW/TFOHxjiQHYPd9zMKQPvuP0wYryeOva63z4ceV4BskkctiM8DRSwI7paw1ykrH0kt6bkwuef27WW4lBVLuJ2BFn2WdhSJi2FBlcI2d8WwprIqOUXLsRd08sUBsBnbvi6E2JLXOgReyzhzSSlD1rhOPTWFiaTUzJ2Hmb0VMsvysPamByKQQsKgFannHzM9qjZEJ15lBUqtEpcFZojOza+JKp0mdjlYWyPl7LlXyWS0WdUf+t6M1OBJsZqlN106e2SmKArn8PqbWyLAeoQOpWoWyUchqBUcTq3LdCi5B0woRYi6dH5raoO2un1q0a1UJ81GBpFbxjRZDakYerUdMnKS7ZiduQtMqWJoIgyqlSNWWYKWktDnurdHURpAfpVXuRZSqNh11MahS2lojzy1q2dFxdCH43c3Nd5wNkDPqfohhlBSFSVEaxKnD2EloGpVoG9C2KqZRdvdG0witxZEXitm3BAI1Mp5bUaTAttLICrHZF0G0rdDAFCaxFCD3nbSbpSehjyU1Q5rSCMqiBCAlhSljyD+G4JhaLa2hct6uNHhWznSwYh32CDOn+95Nq3SvfThdsFkMu/fPNwtqqQUAiGTXMStNtFrggC2z5Li/EmFDjdb9mwCGXqEoDXluoWmVFPOJ0aoqu1mOmXc5H6ZZdOPTF1ftC5jx71X9n1V96pCerDBFQEWl83Q9YuhGXRCJaxY4RknfTtlmDu/MZzyU8ysQmMlFLNrPB8GWnpOwWIkF2jRKhsM1JycXWJJvvkxdVrnNq/0ta5nCFtipQPlKQZwQ0lXdwySwMmZ+yPsyH1tVhAiqQeFmPeTD5YSnsS8S3/SKoR8xCbb0LNFateRNnlYCi5rVOrW8Ea9kENAuUe325BpNPriKWuN52OM68cVmoVVwDOEj96UqO6kMpk4sgCWSC141GiMvwjEK8YCTFsTdSWATBczslLwwuVpM+Gg15mi4wLGyTiBZ1jqem9C2ClFuY5kFri0UvaVM6XsR9TR2OnW7pjT0+xs2uU1UmFh6yZdffh9NFWr8stE4l6cLxygJrAxXq+jLMCZTq7HUluexy8PI4iwR38d3k26zV0n1sqmK6yMuhRvgIvFQFPDNXDgvgi0TWzgBLlOTVSF+/qRWuUgt3g8tFoVO1QrCXtGoeHolNhKN0gGDxlYh43lFSM8O5HMUbJm4MUsZlmOpu5m92HjsRIEgHlG60uLpLbYqQoOEtVBE1u7EdwIxDLGcuXp6xdCsOmKhq4n/fmfrci07So5eMeuvmAYbbg2W3B0uWKcudSs2qjuNiWWWuE6KZ2cvDOYEYEl4Tt2qVI1Gsg4oK6NLibwzuWaVeGLh1KsumTKMPKLQxxuv8YdbbCejbVWWsY9r5lytRjy+PObJ1SFFaVDVGllpkhQWQzfmaCgY+Hmtc3L7Ob6V438iBa9uVC7DPjdRQF4ZrBJPCD0l1CiTmhvXFHCp55eHBHbadc9mbtQt3GllcJV4BFYuRIqtgqGJEVLbKkJMZ+X03JjBZIltCr2FromcgV3VjYrdE618VW3x7IzTyTXr2JMdr4azzRBLbpaS3GLa24hRWhAyGqwxtKrTCgH4XkIpN0y6VjMcroUzyBJdoaLScZ2UO8MFvpNgGiX93otLa9zX93596hO/bRbkhcFN7PPuJuDQ3/LavYfUlc563WcR9Xh/OeHYC/ny4Rll/XGCVdsqXG+GTOyEnrQhRZnDuLchSR22kQjXCGwR16nFPhQ2d4YLnm9G5JVO2zqUtYqhNdwkIs7V1So8uRnYtZv75h/MmU4Kk0dhD1urueOHvH7yDID/8uQ+i9zixI2p5eZhh1CtW4Ufe/A+Serw/s0h56nLS8aaYz+kqDR+4/EDBmYhWt6Zw2VmMrZK4krjtldzPL3m6w9f5rWDC+LM5rFs6d8+umC5HHKxHdC3U3RVYZWKxLmD/pr/8N7r/Njdj/DchKeLKf/j//CfOH//Hl6b8QO3HrMMe1yEfZEMaGU82wyJPiHgW4Y97t16RhBE1JVGtH0x7bw3RysebvtsSwMtdfmt915jKnPt21ZB02tBDFPFZmp3GowKS4jyvJhvX57Qt3KOvIjbvTXz2OcLg5pZsGU8WPH7jx+QSU9/WGldS/yl/pp7wwU3cUBcGpxHAbWE1WhKy4PepnNWPAh73QItELoePUN0ANJKZ2BUBNKG2bYKvlGyyE36RkFS61xnBp5fMbUy6lblncWUd7c2faPB08WCEFca3z+e8zjsE1UGfaNmbNXc82MstWYpldh3gi1hbvEw8nkc99AVGJoVhtry7lbnlV7DdeawKXUObYEQNtSGqlVRgZ88vMa38k40Np3NefbsRFDY3Jiy1jhuFXwzZxRsefOVD/jad94krXRGdsob9x6+kGsBwHJSaeM1KOTzYB17DLyY4WjJYj7GlU6MtlWw9JJ3bg6xtBpXLzmfT3nji+8IEp5RMHATvnl1xC1/i6mLe/tsORYUQrWhaVQu4oC3/FBgdiuDaC2u/SI32Wx7PF+N+cKtJ+KzRSzSO9vrycGcB07Gv/29HwBg4IdC47Hts4yDDs4U5iJq/Ja/pW9lWFrFw80AxyiEXkdeO1WjifFbYeJZGV/7xpfpWTl1o/DOesRMui0m/pbDwyve/9YXCHMHU6vouQmK0jKPfUHsNAouEp+vvPouda2RpbbsdKh868k9AO6NbxgFW4EJNwsMs+DDxYyv3P+APLf44Nlt3ln3eWu0lOmVFq4p7I+q2rCJfb55fcj/44VdEcj3fT/j/16tT73w70Iw+nbGVyYlv399yHSwEha5RmXsbxm6kYiP1WvO5xOSwiSTQj5LL7mKh6TXhlS/1xxbOesowDFzZtM5221AJFuSIprSYJ469MyCsqlZZTanwZY3j5+RFyYfLGad1W+TOSwzh2M/pGcnuKZO38rwbZFqty1MFrnNQwmuMLWal/pr7h9cMFmNWKVuR9zLao0PLk4EnUtp+KGj58zjQKS76TWvDBe8vxoLBbVRcifYYOo1H62HZLXGxXyKq5e8fXmMpdZMZNsyTWRWuKR9nW8HzPyQYbBF12v+wmtvs1oPiBKXBwcX3Dw65WwxRVMaJv01PTcmzAR/3TYKjoNNlzWuKC2OUbBZ91nIlufRCxLwjL0I38w7ToJvFhwOhNI5KSzi0OMi9hmYOYGdMbMynm+GaGpLUWtcJT5HXkhcmgIuozQ8jnxMTczSgc7zfupFXWt4kbmUjcYiFpS/sZ3iGSWG9GVHkhlfNhqOXtK3cqpa7a6RlwNBfVzlJqHE6oJQ7se1Shw6PPAzNqUpLXLwPLF5IEcmhtrw52YrnsY+VSs2C4d2zuOwT9UquFrDTa5z7IhFblOaXKSWoAoqAansChzYFfeCLTeZw0Vq8eVhSVzJlEZDII0dvRTEwlrF1Rqi0sK3xEm5bRXe/fAB496GsjS4Xg8FRlueAjexj2Pn3B/ddJax8gWK+95/fE92uoTm5Hw5ISkNylqnqjR0KazdIbTj3ObPf+FtPnp+i6iwOBrPWV5NWG77qErLqL/hS0AmuyCG3Nwstn3C3CbdIWvVhpEXs0ld3nt+m7EXEeW20BDNLvGCGC+IRRrfWkCnGhQ2zz3qRuHVyTU9PxKhNZXBpL8myRzCzKFtYeIJLolrFqwzl0dhwA8enmNJh0AtraJRYTGwE8a9Db3+lji3mfTXogPgpGxTscjHuU38/JSem4gkvkYlzmzywuCLp0/JC4tV5KMCT89OyCUEKK90Zn7IzA9FGFfYF9HCfkhRmDw+O+HPv/VNVL2imo8wtYoHQfQH7JFJIRb/8WTBGDpNzouqFmjaPbnve7U+9cIP4Jo5LjlFreMnPquwh2eLhb4odQIvpmlUwsgTATkoGFot5thqzYEXsZHJUJ5RkGYSwFLpbLcBpcyxtvSKgRTlHHkRivzIJxLdWUvwzdBKeboaCy+4XPyaViTqZaXJNrewjJJ1YeLpFYFR0LTCa+8ZQmy4CnvypA+WWnez/qgwSeTcdFwIC+EOGdz3YvpxgKVV+GbByAvJSvFv7Nr1x/0ViziQ3m6xkJ0vJ50vue9FhJlNz4sYjNYALOYjGVmsU4U9AjfG0kuqWhMbJKPgdDwX0J5Kx7My+r0tVaWL8UDso0r3wC4W9EWUqghGeS6DiGy94mY76IREm22PreSqu5lNXwKfMpm5MLAyNLWlZ+UUtcY6txlaRZeGuElddFVkKKQyce32cME6d8ikPW5dGNzpbYgKk6zSRetexi3bjbD7GWpNVYu2rKq0mKroBHh6jaG2+EbJtjBkToCYw98JNiLfvdaw1IaJXZDUQuTp6BW61nSEvkI+PMvd1wOB3hBVGgoWhtpw6mZC4S83H2Or5K4firyGwiLQG47dBEOtcWSMLQjRn6G02GZJoIvNTS05AbpkQ+jSFrt7b496a1IJrHp+PaNuxYZcUVrWL6j7AzDyQrapK2bPjcrYCzEzh6Q0eb4Zcdpfdp9DpahCkyHhS6ZaoeuVcPckLi0fp+vtnh2ukwq/+2YokOFGTovCOgqIc5ukNMhrnam/pag08trq4nmLwqSUoWC3BkuhHyoNFEU4NOabQTc3T3KLvhszdD9eNE1duAwMtWZglNzEAce9ldAutSo9mTho6hV5bvHk+anggzgprh+zDQMs3cAxBSY4zGxmwyVuYXSvcZV4BF6MqtY4ZiE7i4IzYmpC7KooLQPpDooSl6XMBgGxAdque5SVTpy61K3K3eGcbeb+AS3FIvbRb2oRUyxBaPv6fNan3vZZZoHviVx4Xa0ZWRmL2O+U8klhoWmC071JPExdiHo8O8N1Ukyjoi/n3IaEzWzl16alyfVm2OXZm3rFqL8hLw2O+iv6ToqtV/TttGNaZ6UQ5lxLPUDeCGFTWhlkhRDubKWydpWbuHrJLBBzrYET4+glVaMyj0R06U5A2LczembRnU4XhcFZ2MeSbb20NMTDzUk46m2Y9VfdezK0UwKjoKo1hoM1B/21JHgJEtgqc0ilWNF1UgI7w7EzDCfD8hPW8rXUjcrFtt+d4jW1IStNVFUEjTh2TlVr6HqFP9jiB5EEjehUtVAyu07asc8/6yprsTkpal0AZEqDyyhgk4uOxjwK0FUx045Ki0LOLnfe/74kydlGgSVVy7eDLTNHLICJPOk1QFzpbEoTS+ol0kpnK/36AGWjSSGh0gX4pHJDUkuNBwjBl2D21wSGwOueSCW5mKe3DMyGQM7DNaVFV1sOnYRIYoI1pWUrBYieXnXpeXmjUEjq4G0vJqtF4I6itNwOtvi6CEnpmTljK2fgJNSN0Bn0JH66ZwmhoqXJUJ3SwDNKDp2EAy/CNcouIAsERKnagWakh7wXRF2IzLOwz3kUEGUOeW6xSvwXci0ATKZzek4i2u6NxnQmWtFNq3CVuKSlKQOJdLLawNJLVmEPxxCOFhC2tai0WGUOq8QnyUUksao2GEbZkfEMTWR0TLyQVfKxi8HVS0yjwtBEm/552GO+GXC2HnEd9igqndFohWun9JykW9xXiUdSmpS1ynXid7qZnh/RojDwQ0wpUJw4CXOpzwGxAfbcmL4XoSoNYebwzmJKVaso0nZXNwIfvZvLA5hWgeukOFLrsM4dosSlaTRMveRwsBT6AFWE6RyOFqILZGe4XiKSDiuDReKRVwaumXM+nzLfDAgzB5WW2XSOKRkmmiLCyVa5w7P1mPlmgPkJ0uiLKoGp+ux+7euzq0994j+5+4x067Pd9Igyh5vM6bCb69Rl4CSsNj3SUogA75w+J0ttbEdEaSY3Do9XE+4MFgBd67dvpd18fufzzUuDbejz3eWEH/UiDK0iym0+3Az50uyCeRxQNhpjN+Irx8/4xuUJZaXTc+NOAOUbOXd6aza5zW1PLPRJbrHKbd6495DvPLrPtrB4dXIFwHkksut9M2dbmPzArccstn2+dXPAOxufqRuzyhyuUodHYY+3JtdMhksMq+DqakbPj5hOFtzMx/zuxSnTwYowFTqGydE17777CneDK/LcEqeyXNgc56sRceIyHi85Obhive6TliZVqzKZznnng5fRtZqXbz9h9vpDvvufv8rFdkBRa5z017z/wUuEuU3TKtyfXRIlLkVpoijNC0O1bjOnaxXOnIStnN0DJKXJWezzE3c/YhP7XW7CYW9NlDlEhcVV7NO3MuaJsNb5ZsHBYElRmGxTl+vE53ns0jNLscDqJZfrEbYmonbzRuWN0ZJ3ViOmVsbUixl5IaPKwNgMWBeWwPtKX3zPErS3WL5P20J0JDy9xNNrhlYu7E+1xr99cpueUXds+6dRwKbUCCuVdaFznuqMrYYvDrbc6a359ctDnicaR07DK70Nf+4Hfg/jv3w/N5mDCt0Cv9tIbguTf//slJekwKsB3t/0eX245CL22ZYGB07KgZNiaRWWLnQsrpMSpyKRMcwcFKXlg6sjKaAruD25Zhv6neiskeS/p5uhTIys/qiP8jOrB2+8R3g94u2PHjA4umEtMw10tWGVeEz8kHUiugIP7j5hs+pz96vfQR9FXP3mF3l4cSLgYHKxAnGYWCQ+6fWh2PhbGdvCZpPZfOWV97h+2OP2cM5sdoPpZrz33stMextGXsg86jH0Iw4kryPNbN5+fI+Zv6XnRzhuiqI2eKuUtlWpa5VBlfB7l8e8NlzgWRnXYY/T43NMs6BdjbiKfb58eEZR6x1tMMut7vCzww3rWsPTCzFS3Lkq7LpEocWzcqrSwLZyPC+hldHWglKoCuqg3OTVrTgEGEYpDjxhwDZ1eW854cBNJMI3E6mGWs3920/JM4s4cTGtgnXq8jz20ZSWuweXeGEfTWmoaq3rtu7r81lK27afanTyH374fxPtRVVE09pWTprbuHaK78eEoc/1Zkhgp3hOQp5b3Lr3lGePbrNNXaaDFYttn7iwcI2Co/GcbeRzFfapGhVFgW1hsi5MTtyYo5448e/aWbuTW17pQhlu5hhGyWLb71IDPUsIm94+v0UoT42WnJfuaFyBUXBnIkBBT7cD3t54/E+nF533t+ck3QOnbcVJ8UkY8OZYzEuT0mSROXjSmrbjpU+9UAh8ZDpW3Sq4esmtwZKDg2t+/e03GNopAzfB0KqOVX/sh0x7G3wvYrkeAOIBUtQ628xhnrr0zJzbwwV1o/L+/IC+lTHzt1yFfQIrE1GfTorjpmSpzfl8SlyajNyYH/u1f/OZXzS//NX/O/PUQ1NErremNlS1yEnXlIZF4uEaBUWjC12EjBouag3XKLkzu+Q/ffhql7twmXhYasOyMMlq0c5+EISEpSm87HpJUhkdoRDgyWrMtrCYubFwg9Sihfxk22ddGJStwveN53y4GTIwC6ZuzFXiMXNi0sog+UR+gydHQIvcFkE5dkZUmDyMXAKj5p2NiaPBgV2xKTWmVkVgVJiyq3GeWlLBL/znO4LgTpnftArXmSVHATWvjRa8vxqJRVptWBcGEynosyWZ7731kJ+6/wHjyZL1ckCa2ZytR6wLi6LWeGU0p+cmXG0GXCUeD4YLFrHPLNji2inPFlOm/pbL7YC6Vbg1WPKV//hLn/m1APDO/+0nKSud+WbAB+sRtlbz1TsPuxHVMNgyGq+Y34xZbPscDJd86/ltJm6CZ2Wdve6TlVYGtwdLPOmjf76Y8njbp28Kbcu4txE2QtlRUJW2U6yXlc4y7BHmNsf9FWWtcx32GHsRZa0xCrb0eiFX11PRZrczPDujrlV0vRKjs8ogzm0svWQpr/XAylilLgM77Rbll06eURQmy22f6yjgWezz5viGpDTR1YYHx89RlJbVWtD0TL3k6WrCK4dnuJ5w5Hz47DbHo3lnsesNxHjm0ZPb3MQBp/1lByPK5ehr1Nsw3wxYJR7rwuLIi/jC/Y8ASGKPKHYZDDbUlUbbqjhuwrPz406fsIx9/uff/X995tfC6ekpZ2dnKOj0nS98pt97k75DS8XJyQnPnz//TL/3560+9Ym/QWHsb8XseBefqQrOddsofPv8Fq8fnIsYSWmbuTo75DrskVYGVlxi6hVnYZ9tLoQmrpPi53YX8nIYbPiNs1tEpUmc2x1WchezamoVzzcjgb6ta3RdzGzFfF9DrxpuNmLW3JctctEiBc8Qi7gpZ/ib3CGrNe56OUkhsJmmLuaNVep2EBlTrzoPOUj2vhT1lI1IeRvYCZeSOGioNQMrRZdc/rQ0Ob845NAXvvUm9nCMEt/M6dsZB4Ml/YEg7G0zV9gk9QqlaAksBUuv8C0xEnjv4oRDP0RTBGd84CT0vYh+f4tpFWzXPVS1ZdJfM2o0DP3FtPN6TkosH7aWXrLOXBz5QCkajUpmqJtqBeiE0v+8E+ldr0Y8GKyICou2hUM3ZuKFPFpN2JaCP3+TOQRGKef5ggW/TF0x85VWyVrOxJPCJCxNjryQninAOFmtoSgwtHKqRhVWLMm3383Kl4XJyCyIS1Na4cDVKja5SPJ7rR92xL2iVmmAsSWU+GEpbp1DJ+PEzVjlJnkjqH07J4GuCp9/38oFSwBh3wtzwfY3pG5gaOVCoLhr8xcifGq+EQt+1WjEuS1jdgtsrWSTOd17sUtgdM0CVRHOisDKmE2FHiSvDDz3xTAdAC4WEyyjFDNvo6C/y6xvBJZ7HQVYZsk28dgWNqPKYGinWHpJUelcxT6a2v6B7PiDYCPusdJAVVvSyuDYiwisDNsouFqNGAWhhH9VQixXa6TSK2/qFVrRcLkdyECekOuoRytFmW2rMOxvqVtVMDgKk+uwxw984R3mN2Oy0mToCrGgqYrAMEOrCaxcPCckNOtyPu1ChBQFTqRrKZUckOV6wKAXduAi4XIS79t2G3C1HnXv43wzYJ26vAS4fiz1QmKT+/LBRZfSd7EdoKkNAz/E0GqasE/dKFxfT9E0AfGxrZyb+VjoI/SKq6sZR7NrlsshcW6/0LTGfX3v13+VuM+xM5pGZZX4hLkjcp0LizSzucktbCsnSlzCzCGwU67WI3IJJilrjYEnWNtxrTOPA247KaYUxKlKy2S4ZLaYSuFdTVK5uJ9YvCwrx9bKLqClrsUNvpuhp6VBUdud+GwX6OPJWF9VadmmYpaWygf3/Z7wvBpS+JUXQkUb2AITrOs1rp0SSWRuK8WBO3qWrjUMvJhH6xGOXmFItkHfjYlzW4R3NKroJJQmLWJO65qFeO2GeF11rYlTq9Zg6BV1rTG0txSFKTYCSktcmjyQ1K84c/CdBN+PsewM5I2cZhaem6Dp9QtTcpta1XU7BHoZ2hYJlxHzT0fOELVaLGTidCtmnJdRj7vDOWFuiZREO2U6WRDnNnrqEpYmq8KkJ62ZbSOgNU1pEMsRiSGFmCDEdVmtoUvRpkpL3mhEhUlgFGwLi7A0BLZX2h/rViGtNDSroWi0zk1gajWXmYOnV5z6Wza5zS0vIi5FN6pBhPOUUhSYSxZAVBpktYD4dC1faQ+1tVKAdeTmYis3Mob06QeW8J3v4EzPox4TJxUK9tLoxhCWJuyRlhSZ1a2KJXMr8upjZPKu2lbBkC3+XefsRdTutL4LbjoZLcgLk0KeTuPcIoy8jusBYvMo6HdigTSVWoB3ED770WDNetOnkNAsU60Ye5E4aLSKYNYXJqocC5h61fn8dyyNvpOylChsSy+FCFRpCHOxmbplXnVjhaw0OIt9vr9R5GvXRQdBHkpAtO0FOrzCkj79VeJT1uIg4Embn6HVXcR0nNt4pQjoaVqFUrI+CunR3+YWIyehqjViOYoMY09aD1tco2ST2904NK8MosLEzhzGgxW6VpNKgFcqnQIAR5OYVeJ19s+8MrCdlLYdda/lRVezn8t/z9anXvijwuJyOcYySnp2Ilr0tcpV2COpDN4YLVhs+ySyFVnV4sE78SJ6boxlFthOxknks0g8trlFLJnXO6X7cj3gp770DVS9Iotdokf3OeqtWcQB8yjA0kuOhsvu5k8zm8CNGffXbMKAJ+tx58Gt5WLxOPb4n+4+pN/bkmY2Dy9PZBBMy9jKGHkfZ1YnhcVCzuvy0qCsNdSiZRhsOT64YrsN2MQ+dwdLjg+uyFKbstIxZUTvoQznWKcuIy3kJvYZOQm3Di55ennE3elVx+Y+W0wIc5t14mIvS0bBFs/KUeXiqEkQx4dP7pCVBrZRCnuiH9Mbr9DtgvXFFCeI2cxHZLnJyb1n/NbXv59JHsoTlssrL+CiiXKbsDSxZNjJQbBllXgdGa9nZvRd0ZIvK126GvSuCwLw0WKKLzHOi8TjILWxjBK3KqikmrmoNQ7ciMDO+M7NAWM37ux8Ra0JEpm0hu5EbpZe4ZniVPbbFyfc9iJsraIBVplDUmsYkrw2tMTptG6Fe2Ce2RLPK1LznoV9rjKLQyfD1mrGVsb7oc+XRhGeLvQGjyKHB34qKYYK29IQ319eU8IeKhwAWa2RKSKauWxEJ0HXGi5jn2M/FAlzpcF1ZjKTjPVd6ppPimUWbBOPy7DHSX/d/f04t1mnLq9Pr7GdjCy1eXhzwOKjl7A0IXgLc4fXX8C1AHQBPHlhiRHTaMW7j+4DEEi+flVr9B2hxVAkMyMtRbrd7f4KSy8pa51cWkIjeR/uRKQvHZ8TRl63EZjJOOh1GtC2Cvdmlyy2feaxCM456lXcvf2Mw8RmvelzuR1wHGyw9JJF7PM87IlOg9IwlETGbanz/qN7bAubuhEz9kmw7e7JtlVYLsf0xxFZbhFlDncPz7lZjdCUBsssZW5BQ+DGf8Ayp2sNSWFyuR0QVwbPlmOmfshbdx6R5RZJ6tBzUnqO0Dw9m08ZexG3p1dYdk6Rm7xzdou4NDmQ0cKpzJnoOYIF4HsJV8sRV2GP6WCFZ+WYZoHjpvT6W66vZgBYesVTSUnd1+ez/ivS+USEZlJYJIWJaxakpSGEWcGWvBQ+WNMsUGXkZPHsNgM/pGlU/sMHr3G/t2bkRZh6xWXYEznVSkMj52XL2Kd6dtotxL4prIOCdKbzeDnhMBAZ2o08MZlmwVqqdieOEOCVjcrYTpj1NvyAl/BbD18iPruNq1e8Mbvk8WqModaMnIRBL+Q/vv8aR+7OcVATlyaOoRBlDlmtMxsu+fDZ7Q4+YhkFF9ezT+zALf7HH/kt6kpnfjHj+WbAyd2nFKXOKvF599kdDoINrpfQm6zQnQzvacJHz29hS4GOorSEmc11JISLqtLylmRxF7XOKnP40q0nXFzPeHx2AsAoCNG0Gk2rqCqX//ibP8TMF2pnJRXAkBdRvpWRrsbE6PiJiydV1kWtd/jiD+cH+IYQzS0yl6exy5dGC+7OrnjJS/jau68LpHGrsilNPro6EghUpWVgpdw/uuC3Hr5EUopr7fsPz/j29RG+UWLrFVmtUzYql6mLprRM7ZR35jNO/S2a0hDnNlMrJ5I6gaGVkVc633frMU9uZjyPAzSl5UnYx5XJepncuLwxXHKTery/9ZjZJR+FHp5eMzJLekbNO+s+ttbgajWv9HIWucXYyvGNkg+3Hl8arbuAqLPU5GdfumDmbwkzh1COEe76FY5RYmo1bSvcImlm4zkpP33nMXEuHupJYbGWbeLdJsVQBQ3u/ctjjnobTg8vseZjDl55THg+YxsGzPyQstawpbf+Rabz2VbO3QePKRKbR89u8ez8mMOhEPG2rcLB9Ia60pivRtxseyi0HPbXgOgS+E7C+5fHgOgUDZyEJLc4nN7gZRbLbZ+b1RBb5lRsMpvDYMt0sOJgtCTNLB5dH6KrNXdHc0yzIMstzs6OODi4ZtAXo7SDg2uurmZM/LCLk1aVVmpyVL46u0JVG46CddcpscwcEBuXsjS4f3ghcNlmgWlUXC/HXIY9DFVoAE6mgp0RxkJxb+klN1GPqb8VwWWZg6423Du8AGAbBpS1znvzGUMr47C3xncTilonzB0WsYCb9exUAMqsjMPhgl9/9BI/PbuiP1mRxw7/+3/5KidugooIxZrduiBJHTEqqnTGE/F5GIZApO82ZC+y2s/Yx7+vz64+9cL/1p3H6EZJHLucLydEhYWtiSzpdeJi6jVniymBleLYGaUU5l1K0M1rwwWb3GYeBZ2H23NjktWoA9C4ZsGt43Oi0Ge+GQDwwWLK1ImZuhHnUY8wszkaLlHVhsW2T1XpZKWIrfXtVO7ihTVP1yvSzGKVWwytnNu9NYeza75zfcjYFl2D+XrAkRszC7YYWkVZ60xU8VDQ1AZPLVhu+xQSUWtoCv3eVqBFN33qRuX2cE4eu1TSwnUUbPngvZeZDpeoSsuz9QhVbagrjcX5AVWlYcibz3NEdOju5zWkAlyl5Xw+xTYLhm6MX+mostW4YwWoak2Rm0SxaKPeHi5ICgtFEVaj/AVlsF+Ffe73V9KDL5T6Ey+irHWiQoiaJk5M343RtIbrxOdLo4V4L+YzmAv71bOoRylb5X07JSlM6lZFoeXJ1SEHbowhZ6ma2vDq+Iaz7YCrxMXTK3rWx7P+slEZWhlXiS8996VEL2vdr7JRefv8Fpm05tlazcAUm5Oy0Shl+7NoRCvU0RtOvYis7jEwS24FovW/q6LWeJY4HNq56DC1CkOrYpXbOHolNhymylXYxzUKESVtIcFHBptE8C4mTsrYi4hzm5uoxyJzuNtf4Zo5WWmS1zpvnjwlTDzCTCCsV4nHYbDF1CqWyyFZafLR778h/ONOyk3YF9hX+Z7WzYtr7eaFSRY7FJnVYWRdJxXdiMSlLEzSTHT2XKmXaRrBa8grXVjtnKTr4jhmjmNnRJFHkjlsUod55nJvsMQ1cxwZP7wOe8LWKC2dd6dXeG6Cbpb0GpUw9Lm4PASEHulbH7xMXyKMDb1klfjdxrttRYTwYRBiGh+z7M/nUwZeJGfnYoxTSnwwCOof0B1GHl0eyYRN8axKVItXTp5xuZjQtgqzwUo8P2Ohk8or8b2O/RDPyrAtcS1NByuiWHzeaQl3js8xrg4oKp2mUTnyYpLUobwQG8YfPDxnJEmoSWGRheJrd8+ATeIx9COaRmi0PlyN+coLuyL29b1e/1XpfJpkXNeNQIOqCB573aiYmsiUVmm7mVJRayi0aGqLJhPW8lrHUUox804dAc+oNVSJe9WNjxc2U6+oWgVTr4WlKXPEQ0zedG2riJmenL2DOBVpqpgTFoUpSHl6hatVQggj/z8I5XAkOf2OlaGqDVUqvndZq13repvKf7dVaQuFojAxjApV+mN7QUi4CQRas1EJ7JQP5gfMRgscW6A+dyeGstLF18tEMNsSJ4q1BG9oSkPPyBl4kTxZlFSV8GubVoHXKjSxJ7gJasN60xeefrUm8GKy0uy8/zvf8GddWaVzMlySFwbz1OOT48K6UdHVRsxTcxtdtuIHklYm6HqCqhhXOmWjMJBwk6zeBTKJZL5DfyvFexbbzOHW+IbnmwFxpdM3CxRabF2E0mQIZ0eWaZhqQ08y9k2plLelN38tdQ+aRDOrighTUtSWQC+7oCUQCvypFzHPHFzZjagbhakXU9Uqq8xhXWg8CAQkKioNmhYSydP39AoVMQMXIkHR4naMkrJR2ZYmSa1xoIjPKmks1rnNdWozc8wu4Cowcyypn9llF7St4CAYRimJiSZhbnNQibhXgIEXU5S6xGe/OHJf06qsVwM5G9dwjIKiMDvOQCWV5a6V41o5VaUJy+onOAuWTNor5ax6F++clQZFIxDL69SlL0WBuyAvUxdfH1hCLKxqDa18HhRyVq8oIqCnarTOMpzlVsc/2G2mo1xQ9FS1xpDPoAYFVW1RpaZlpyPIS6ODKDlmTlnpMogIqlIXm5FGhwYMs+ycOnlhoqs1mQwtKmsNV3YVdKkzUpUWTavxvRjDEKJIy86ZDleEkcc28TjtC1JmnDlkhSm7f8Lrr+sV4SboMgSaVgSFHRhL0swWQKP/DjP+PbL3e7c+9cL/7rM7DJyEXII2vv/0Qy7mU0ytwneFOGWduqgSzmNoFVmlc2uwBODrF6e8PFhR1iqelTPsb3n3+S0svepmtMvU5eL8qNtNO2bOy8MFugwYOe6tSQqLS3k6doySMBM4zKQ02UYWX7n/IWVpsI4CFnFA30l4qS8U5Jdhj7w0uCetT6tMKPsHZs5YkubWqUdaGVS1SliapLXGgSQG7uJflZuZSPWSJ/Cq0lmHPbnoicU8rXTWmz6mjN1NCgvTqITtzs44u5l1wsa2VdjI1q5nlPTdmJe+/F3aWmV1dsD1jcAf+4MtVmaxiQLmsc+4t+GD+QEnvTWT/rrL9XatvJs1vojyjKJrgdpahW/m+FYmfkZNxBd/czEVVjm94lieZi29ZORUbBQHQ2twtBpTFWLJcxk56ukljtRMVLWI3F1kDo8jl4NAtGw1KaRKShOlajHURsQVFyam2jC0M2bBlvc3fe4FEUNXxCDHuc1N7BOVpgDv1CKFcWjlHUTnMvGwtVrENRslvp0ysVPSyuB52ONZYnPoh7Sq9Fm3dFS9TamxLVUe+MKa5xs5E7dmnQqc9I4a+dLoBkMTgr2mtUSegWQMCN0EXMQB16nH2E65O73iajERNDd5ctfVpoPN+H7Mo8UU38xZpx5RbhPYKUe3zmhqlSx2ub6Z/LGf52dRz5cTIXaV9/7VaoSh1Xi2OE3bVobjJRhGRRx6XJ3dEhY5J2Pgh4LCV5qklcFSnlpn/RWOUbDNHIZWznXqklYGfRlU5Zoiu0DXa/peRJ5bNJnS6R5WicedyTUAUeryxTuPRGDYesgi9rvn2c6dFNgpD5cTDjwJQkJh1l9hWwWNfN/TzO42sHFpsE4dbg2WQqTYqNyeXrNcD0jlyKttFa6upx2B88P5AXdlzPfOmjednPPekztUkkgaSOLfZDpnMFli3oxZLweMD2/QjZIPPniVP//WN7m+mgk3Q6vyaD6laRWOehumwyWLtbBW754BWWnieuKwZWgVXzq4eKHXA7QvoNW/dyJ8VvWpF/7rxOMg2DAbLThtVZ5fH/D+asy93oaT4wsWixFjT9Cw0sIS0bGNUFr3gpA3K4N7t55xfnFIUgiV70//r/8H/+9//b9QtwqvTy+5c3zO+09vd9CSTeLRd2PSwmKTCr72ndkVKxl5mZQmbzz4kKurGVWj4ZsFYSRQljt6304tvWs7m9IKNPO39O2UR+sRll6JE0gtWoYvH5xzsRozdmMso+TRasxLoxsssxSxl8M1Uejz7vktFpnN1E558/5H5Jkl2p65xY+//C7/6f0v4OgVr00vuXtyxtX1lCS3qGqNp9sBf+6175ImDquwx8hOiEqLsRthWznLp0dcXYtMbtdJeW06J1z2SVIH2yy4NVjy/uUxB16IYxQkqcP5Zsg8dTlwhWDxLA74bJ20ou4fXXA+n3AdB1ynDm94EVvZsbD0CtssmFq5OEkr8HDb5yfuPuzyHsZeRFHpfGH8cZbAWdinb+X0zExqHhwORwtWckP1F24tWCU+Uy/mKNhiyq7Nh8sJWa0xbdMumtk3c0GRaxW2hdVR7s6jHrf7K7xciNAcveQq8aka0d3pGyWvjObdSbWoNX7p4UsEes2JG3N/tOYgcQlzu3MBHDtV5xToG6KrMHNSIcS0hD88KUxGTkJUWNxkDs83Q35vMeTUzXlpsCKrxIgklG3Zl/sCPOXoJVN/y8nLj1E+vMP7lycM7IS3Xv6Ab37wCmWt8a1ndzlLXN4YLejZCXllEOY2H61H9L2IZdgjKazOUfEiqpBUvV27u2rEqb9F3IeK0nC1GhFfmPhmzg//z7/Kcj3gOuqxTl10rcaXqXh5KTgYm8wGhnhWxu3xnKrS8L2YQmohBCin7ixui21f8iNKBk7MwWjJmRSw1bVGmNmMCpMPL49FTK1RCg2EWYjFM3cYODEjJ8U2i+5eX2z7rK5dfLPgeDTn15/f4SsHFxwGmUi9M3OerMYYUlj60dURB4GACIHoSjp2RlGaOHbG6eyaR5dH+JYAm60yh/u1yq3JDY9vDnh2dcwPuR9iGCVNrZFGHvPNgLunz7k6O6QoTF47uGCz6qNpFdPBCk1r+M8fvMqbswvqVuXx5TGGVnEyXbHa9Ikyh56T8N1H97pIXzXbL6Kf5/rUC/+RF7KIRbzlwA9JCpOpbLttNz2i1JUiIpui0TkK1rx1+gRLxlhqasPl5YFQ7lYGq5VL7xuvYqmCRd0LIpbLIbPepove7Lsx19t+1wL3rJw4cVnLvOqj/or1aoDvCsjHIhY8ck9y/utW5Xg052YzIMmF22Ceerw8viavDNLSZGhl9O2UReJ3EJrff3a3YxKsEh9XL3HsrIu5LUsD28l4MLvgKHVIS5P3ntxh5EX4boLvxdS1zhcm1ySFxU3Yo6xFnPHOp+zpJdc3E9ZyQ3NrcsPTudjBq1HQtRYDL8ayxcPt/GZGXhmCUjdYM48D0tIQWgE34Y5R8vzxA1yzYDZYcWd29QIuGVGOUTCSiN3rOMCRJ3VHL9mmDq9MriTYR8PWBzSN0ok4d4EhF2EfRy+5O71ilbqotN2pb+AkLGS0867lfTyaE8vTYCEhK0deJIh4uUVe64zlrPjxcsIXBhtsrSSrDTaRjauXXciRqaVscruzX2pqw1ZeI2tpn9LVBlsVpiSRnCd4BJ60mHpGwetOzDp38KSILpfUSEOtMbQK28rYyhjZYydh4odchT2+b7QW4lIvQlVbPrw+4La/RdcazqOA16eXXIV9zjZDkq9/mdPZFUfBmrwyePTsFqYmNjMnvTW3B0s2qYtjZ8QbYQEcf+J6bVs6m+WLKEVp6dnCmus7Cc8XExyj5GgsTqzvvv8y494GN7dFVO63XyYpLKZ+iCGtiEVlCItio9CzchyEfkjwABQeLyc4YV9kHEhrbuDFxJIGOOmvifKPNzhlqfODL7/HditIo3Wrsgp79GVstalXHE5veHx2IhTxVkYviFjEAputaRWTgxvWEj+dVTpXqxH/w72PyCVnv5Ge+p4c12WVznXqcn922ZHxprMb3n10v+tObGKf49GcutYxZNfi2dUhX3jtfWGxm8+IExfbykUAWqtQVjqr1QDHzqgqnXcuj3jr9ClNo1IhNhfH8pmQSbvr3XuPef7kFpZZ4DmpcEDZGXFuoakt097mhV0Pu9pjdr9361Mv/K5ZEOY2uRS2VI2gsOnSnlbWmpwdqfKk11LKk8Aute0q73UZ64AMsKiwjZK6FlneqpxL2UaJbeVYetUBfjwrIyvNLp4TxKzONERbW5UzuJ0GYGd5aT5hq8kbFcssZTqW0CoAAi5jtJ3ft6gMVKXBMQqiwqJpNDQJWCkLk7IUbXXfTVDSljC3u4UuXI7w3QRPPnzj3GYZC/pXzxSkvbrpifdKbVAVhTwXWoO2VchLg5u1OO2UlU6TiJNUVpqSLSBGBJZeiVCSysCpNYbDNW/EV7hm3o1LXkRFsSvgSPK9Owt7DO0UxygwJPRIlwtTUet4RiH44luLrDS6n3PXsq1rXYQkSSZA3aoEbszzhWhP72iAPTcRzHIp7spKg8BJO0Z/JnUTRW2yLizGdtq5QkDoS8pGw9BEkJFVVUJzIhfyZeawkKd3S22wFWH5y6VWwFKbzi4KoEtXgvhzYS109RJTgqU+WXllYGi1AL8oLYomFpNN4qFrNWM37u4lIdxsOj5FmNsUhTgxtpkQ0zaSFSBmwg1pJUORrLwDG5ky3wAEe+FFlWvmlLWOQkvTaAzcBFOryHKbq/ND0dYuTNLCFHPuzBI/k7yv80pn6MYdQVFXa8l+0HHNAtvKhVPHEOwLTY4TDENwNupCpax0xl6ELkW9u2eMoYtnlELbUes0qeuoJCDMlF2qplGoG7HQNo2KbpZoaiNcJJXIETl1rjmT9j1PhkyN3LizMXsy1GcXLKTrclMp1fyBLYA65Y4sqpdsUo9o1RPdAb3kfDPkzuSaIjc7FkNRmII/UlgCDBT2MDVxn+2utc22J0K6zIK6FJsTpWq78WslXR6GVlPXLzadb1/f2/XpyX1SnCRm0kKU40jQzm6hjUsTWxMxlgBPJYxHUVo2mc1Z4tE3SoZWxsSLOlwqCPuLoVXihG3mAp4hT65h4nU30679rygtUeagazV5YYkbVa1pWpUw8boH4xPZsjc0wbMe10LRKx4eYjOxiH0OehuhCNZFRvaz5Zi+nTLubVinLklu4dkNml6R5SZX6xFDN8KycnStZuCI8Iww9vjG1TE/cPS8Ez2absSz9YiZH3IwWuJ6MalcxF0rJy8MrsI+A6klSAqLi23AFw/PWYa9LpENYNJfizTEQoj4QAiY4tRhcnTNl37o9zh/7x7n8ynL1OWtz/JqkbWOfXw7xdQqKk2cfDwrw7VyNK0iCCIen52wSt3uFB70Qq5WI9LKYCjhRqey1X+9HtK2YgHZiaA8NyG5NNG1BlsruUg8zFXN2IvwHLEBWCU+pl6iqi1+XnSBKABFo7KQTPvAKPHNgqvEEyf2WsM2ILAzktLEMUopLtS4zEyOnBzfKPClnTIsBUxo5IjPbZ664nWisshtMvnA19SWkZV0uo1GClGHdkpUWOSV2GSYWsWZTG5UlBZLbfjK7UfdnNs3CjbJxxtFRWkJYw9f4p4VRTgDTL0mzkXaZSzDowbBlp4XMd8MMCwp8tQaes6LI/eJmbRPlNtsM5eXbj2lbRU+en6Lby8mvDmes0qEpqZt4WWrICxNLiUUS1MbJl7IrL+ibUVUbSL1NONWETZYK+fO8TlNrZLnFrYUMLpOiqIIh8+do3MA6lqnLHXOrg44nooZf5zbDPobNhfH4tCgwfl8InQ1MkgsSR1ahHajLA2KxBEQHbnB3WQ2UexyFvsMpNtGU8WGIsxs6lblpCdO3UeHV5huRrTqMe2tee/qGEOtOR3fUFY6kbRX7iiA3316V4yotJpHyx4ngyVxIq6z3QFgvhbQpiMv5CLsM3HEKFLkT9gkS5OZHzLprzk7OxIkzcomK00Gfki+7TPxQ3S9ZvmCkjt31QJt+9mOl/bDic+uPvXCfxb2u7APTasw1YpJfy0ypVMR/3jaX3bJVcP+phOzqWrNMPa5U2md8Oj45JwPH93Dt1M2qcfDZY8v336E5yZcLCacb465P7kWp1tabKOgP1zzHz54jdfHNzhGwTL1OArWLMOAcW/DGy894uzxbVaR2DyMgi1xYXE8mXO5GHMdiQ5Dmtn0vQjLLLDsnP/w7A4/bHzczr/90iOMxyVlZaCqDV9569v8/re/yDpxhfdaFy06S7b4LjcDDqSVJi1NekbBw+W0ix8eOglfOHnGYjMgil0UpeFwPOd6OSYwYkaDmMCLiRLRru15EX0nJvBDVLXGtgpcL+adhw8wOteDTs+LMDKbni/sRt96+3W2ucXMD8UDIX0xyl3XElkNy7DHh6sxd3obvntzwK1gy4Pj53z30T3yWierxEneNgouLw867/A3r484cmO+fXYbS6uYeBG+KVINN7nDPBV+d8comacu55XHDxye8Vvnt1jnNgMrI6t0PgoD7uY2vpGT1zrH/XU3I1aAN2aX/Ob5KdeZhaW29IySe4MlmtpQ1prYmDRaR787rjW+dBByEwWd2vujzYChmZOWOmdln6kXM7RSNFUo/J+EfTy94m5/Tc9OiHbUyrBPUgqy3I/+yO/w5N2XCFMRbhQYJfe0mnnqycjosvPkv3R4TiNDZb55dUxUGhy5CT969zFpIlLcilrj3uQGQy+JU4dt5jJRExw7R5fdU4V81AAAKZ9JREFUr5enCz786D6jYMvB9AZVe3Ezfk0TXTnbKJkNVtzMxwz7G6a9DW8pLSMvpKx0fCujrDXefXKHl8fXWGZBXWusY5/pZMG7T+5QNDozf8vYTjgeLTD0iq2MFN5ue/T7G0b9OeFqwHobkBQWZS3sl1HskUvXhmUWnG0HXaZHYKdcLSa48p7dxfPWrcptP6Q/XbJ+/wFTf4uu1WhazdXljOuwx2unz2gaBWM55uj4gv/ttQ9ZX055cn6MqVa8Nz9g4iTcHc6pZHT377//KklldEyKt249Zhv5PLw+5CDY8Gw7YGBlnLhL3rk5YF0a3HJjbg8X/PDxM6bTOYvFmG3i4tup7CiIDsitg0saGdBUSsJfz8y4d3CJH0QoSsvVasQ7ywnHXixihLWaL732LquFsE/fO92z7j/P9akX/ruDpeRRt6SZzUXiYy5rBk6Ma6eQOVx9YgZ3djNjkXic9lcYugilOe2vGPeE3agqDXpuzNlyTNGIdvDz+QzfylhnDsvCYhAHbDKbW4MltpXzu2+/wdDMyWQbS1ME3EZVG+LM4ezxbbLC5EzOjkdFwtiLUJSGk9kVQxngMx4vWS6HrOQD/rYncLqXyzG6VrNaD/DcBM+NqSqdX/+978M3C2xNtKg3qcMs2HKzHgrkrJtQ1jqOlTEyC3pOgmGUokUnxUhBPyQvTDaxz/VWvE9RYXETB1S1SlLrHHkRuWTJrzOXp6sxvpmjqaKlezK+IUmd7vR/vpxwOFhyuRxT1jqnk2v8IMKwc/L0xaVvOVZGKHGgb84uRFZ4ZpNXOleLCQe9TadSN7SaYbBlG4sTYVFpnHohIy9mIK2cZaV3BDRycRLaFhY9M+fQi6gbhV9+cheAiQ09O+Veb8Ns2wfoRk9h5ohcA12AcRRavm96RVYK2+aZjItOJCr41aOz7nSeZjamVhPlNn05rza0iteGC8EWkJ2EyyhgZCed8+Neb42p1awyh01mc9pfkRSWeJ/0ksCL+c7vvylGXrVOWgqXwkujOUM77fDTt4cLbsIeHy0n3f126MYM3ZhXX3+P//1Xf4I3plcosk1tyk7Z2WbIB9seP3nrCe+dndK2Cq5ZcOfggidroawHWIY97ryg68G0CqGqL0yK0mQ2nbPZ9GgahZEXEuc2h+M52zAgLz1svZLETqH5iAqLd5/ckfeq6OSNvIjFto9jFgSuSNd8vJhyX62x7Jx3n99i7EUSpyvGGHWtcbkV6YwAB26MbeVd+9vQS5Zhr9vU9cYiljmMAhYrwbC3jBLPSdG0miy3BSdh06esNdaZy+++/QYnUsUfFZZcdHMCO8W2csLEwzAqjvsrGun7T1IHRWkw5e8bRslJsBG2Wzk+fLW/YuTFmEbFs+WYnh9hyCyGTwpnTa0iSR0ebwYMrYyRk9D3Q4HuzWyS3ebSTvnK0fNuI2I7GXHoU1bCUphmNvde0PWwqz3A53u3PvXCv05dMUNTazGvlDP2KLcht/GtDF1tGDgx/d6WqtK5joMuvtKWyvnxcIXtZBSFyZP5jLJRcfSSvpOSlialDFextZrAStlkNptU+NazWsc3CwI7E7Nx6fmuFE2OIcQNPrJFW7NqNNaJS8+LhKgsdbC0qsvDBrFoeEaJaxToEtJR1Drmzv8rrWM7FW7TKkz8ENMssCoDpRILR1bpTIZLdKMiDH22sc+4v8ZzRXJeGosF27OybuFOS6PzkVeSu77jEfRMEUGqKGLMUtcqRWmSSe69KgVJn/xel8sx99yUMvTJUvuF8bgX2z49J0FVm65teXc0F0mF0kO9o5bZhjjJN62Ca+YEVoNt5VSVjip97YnMe0gKC5WWoZXhGR9/zmlhMjYFEMeTbfndhsGWc2yzqkhKo2MvjO2M8+2gw/oOpP2ylM6Nsvk4OW73nhtqzbawGbsiQjUpLNLK6MJ2QAjZikbHUOuuba+pTYfsTUuTcW9DUes07DYUFWkj7F2OUVLLDpmllwSW+Ld3r2HHxihrjam/xXNSNjci8U6TC5ipVThuSpo4QvlvFfhuIrIhZMdtuR5wIK/7KHE73vyLqCj0u/Gaa+VcXU87YartZKyfn2LaOe22R1Fr3J5eC+urXKyKrUiZFCdYnbQyGJsb8kpY7TSp+o8KSwB9Vv3ucwY6HYOqtp2jQ1PFs6FpFarSIM5sArem58YCwNNo9MySstRJS1181qVB3xWdtxahNRi4SbdhVZHfXxegrx2XoWfnFDKd0DYLNHlP6mqLbWVEsQdo3SYzkiFncW4T5g73hwuR70Ar4oGdhHXYo6yFfmeT2bw0uyTLLflzCv3HyEkIbBErLDIRhGVW1wQnwNSqLpQsDH3y3KLf2+K5CYvV8IVdD/v63q9PrfBYZMJG5tiZWPzsRKBRC4vrWKjQXanO709WHNx9LhjpUl0/cBIS2YZT1JYiN3kaBRhqQ99Jxa5eLhKmWjGxE8bDFVMvIqt0Fokn8qeNgp4X0fMi6VcXu31dAi98L+Kwv2bgilnrInPJcovFts9FOMDSKorcxDQLAj+m50fYmgAKBTI0yJULUyHpg0fDJb4k7KlKy6AndtieLaxnOxCIF0TYnuCnb1KHvDDR9BovEJG7uRRoTYdLPDvF1isGTsLM3zJxY2wJ3jH1ioEXMfVCAimaM/WaOLMpa3GyXcS+iBKVC23ZqDwL+ywWI27mY1Zh78VcMcA89VDVRpARSxGpOp3dMJnORTdHwlV2r20Hc/HslPFwxWi06h78msTPXm+GRIXA2Y6chMPBmqGkKWpqwxuTa14eLuiZ4vtvJIdhBy+yjZK81juy3szfcp26zFPRGnfNnBN5Gq/lg/LRaixiXSXURKSqib3wDk+9ypxu0cwrnbGd0LYizMeXxLei1nGMEtcoRVhOfys3yGJT1POFvXLnFXek+EzXRMS1b2WkpYmp1QztVP4bCoEntCzvP73NSbDFsTJMo8BzP9YRDN2YBxKPO+5thO5EL1klHidSQ5EUFtYLSmoEBFI3Em13Tat4sh53p0yvHwrrnV7L/AyN4XjJYLChP9wQ+OKU33djAb2p9E5L5EhAkaqKe84xSrLSYCNHdrs2ty4TM+tape/GHA9X3JpcCzdIrZPmNsvEoyhMer0QwxBEw6rSyGXAze455XsJYe5wGYrckcCNSUuDRC6kk/5a5I6YhQja0gRDoGo0lnJBB0EmbBrxHjQ7eI/07q8Tl7IWYsGr2Gcqw3YqmTEyHaxYxh7r1CXMxeZzPBbRvLtnzcROOBgupYNIXKu7YB/DKLvMAxCb1cW2L5gndo7XD7vn5ourlpbmM/21n/J/dvWpT/yvTa7IC4O8MHCMgtPDS37vw5dJKwFs0bRawHUWY8pS5/ilJwRWJlS4SkNc2Kxyh+8+vUtgiRCXH7/ziOnshjyzmC9G6GrNZdpj5CTMBis0veb7fux32J4dsF4OAAHQyHOB/1RoKSqxyNpGwfBwTlUITKhaiR1/TwpzRJtVJ1N0vvHRSyiKOFXfOrgUqWBKS1VpJLnF7VvPmd+MxcmgEm+RbYmTRFJY/N7j+3zx+BnrWCBX799+itcPKVOL9XxEnNv8uZ/6GvNHJ4Shz2IxYpu5nEcBB27MNNhwve0LdLEnfPvJtcXldoBrFh1r/s7BJYB433ML34spKx0iWCQ+71weYahNlyP/k69+l9/+6BVeGt1wfHCF7b0YQdf90Q2/8ewumtLyxvSKL9x6wqNnt2gaFcsoccyco96aKLeF9763IQztbgEtS4Nl7DPrbbBNwV+fJx6B/KyiwkJPa1aLCaZWMXATDmfXbDY9stzqrFS7VDNNbZgNVlSNxsCJcewMw6j4Aa1im7rilF7pLHJf2K36KwI75b9cnKLrNVebAUlp4MogJFUR185UAoPe+uJ3WVxOee/ilLde+YCziyNuooB54uLJFEJTE+6UwE65uZ7ydDtAU1oOe+suM941cwI35rY8yeky5OgyCjgMthhSXT7wQ5LUYRMFlLVIgExLg7I0WEUBz2Un4yVpmSxqnd99cp87Mnt+h75NMxvHzFGUlvPNkB94IVcDXEcBTaswcBKGwzWvNmoXAxxvAg6GSzYrMZYJrIz5zYSiMDtq5VFvw/PVmFmw5VTy57/7zqsY8pmyiX3eeOttnl4fYOoVfT/E1EveuT7ii4fn9HpbvvPoPuvMwdTqbtzo2ymGLsKqlplDYGX4mUWUutwkgoHfd2KywqSoNY77KywnZRJsabd9zsM+496GWFIGR5qwmAp2fsLhSJzUq1rj9uyK00bhYjHprklNbbi6nqJrddeNEel+QlQsAota/o/3v8CXp1fYsutoOxmOUeJZmRB0GiVJ7LHY9kVXojKYBVu5oarw3ITfPrvNT9z/gE3s8+xqxCuH552jameJnQ5WXF3NBPhJiiNfZO1b/d+79elDehpN7OgVYUl5fHaCprbkjcpNFJDXOjM3oqo1rtdDyncNPCvjbDNEU1q+75X3uJ1ZXC/HZIXJJvFYJB6P5lPGbszheIEu5+KWWWDbGXlm8d7vfkmgNCujS8y6Pb4RSWWRz7evjvix+x+QZja/9Gs/zsvja+pW5WA8x+uH/Mf/Ih53om0vfpahkzD0xa43jHxuTa6J5C7cMkpurid4bkIhH569IOR6OQaE8vxQrfno+oj700t8P+bmespiPpagIcEVb39DkLAtvcQyS4pK425/xdHkBi+IuQn7nEcBnpXheQl3js/JM3HibRrR1l9tegz7WwaDDbpRcn01Q9dqjqfX3DbPiEORYJakjjgtFSa3emvRQk0cmhdk2ckrg5968D5lZbCSwKTT2RXL9YDL7YBerYlZu1HgmjnT2Q3zsMci9gkzm+PRgsPBGs+NiROPm9gjq0WK39BOOepv8L0EQ6tZxh6XYR/HygQSuVUpJWDncLTo7JplpVNIFGwviBgdXvPu81s8OLjgYjXmo9WQmZNgyY6K78X88O1HAnBTGuS1jq42vHR4zvP5jLPtgLpRuD1Y8ezRbcpap2dmPHp2C9fMMbUaW684CDaEuSMWgEbEsD64+wRTImdNs6CudYYSqpQXZjfSqRuVqhFJhGfbgZjdy8Vu5IWkmfhcPSvDNcWseuiHDLyYplH49uUJx37IremVCMMZLkWwT+rwdDUhXo+ZeqEcKby4B/0PffFt1ssBul6jag0fXB3x6tEZvi9iuM8WU7748geYZsFyPWAdBRhajVY1VJXGddRj6oVkpcH1ckyceBia4CAErujq/eff/kERt1zphJlQ20+chHnYYx720NSGsXQuWHpJv7flYj4Vbh+t5law4fbRBZc3U8paYyQ7kNNeRU8ROOmz9UgwCYIQQy9Jr45IM5sv3XqMITd4H52fdkjyddjrXCUPLyZcpx6m2nB7IDI6stJgmzm8fHRGVprUpUohO0sgxjua1vADBxf0vKjLOPmPb7/JmwfnGEbZIc0n0wW9yAOEE8I0Cy6XY1xTUFAPnIRl2KPvRQyDLWeLKQMn7nglRaXRrkbYRoEjN9j7+vzWp2f1K03XgiukSOm0v2TsmiwSH0Otu7lv06iCnJfbncjq2fkxvpN01jRTrzjQan738piwNOl7EXXsskk8zMLCloS766jHyI3xrYx5FOAYOasoEC1xrRIAF2llmjgiCraqNbLMxrQKJtKiN3Bi4RlvVIZ+SFXpxJlDnNu4RS7DbcTcdRP7tK2KoZcCidoLWa4HHR60qjXuT8VpPE0cBv0Ni9WQUi5MKmIuH8vv2bdCDvvrzlvf1CqGWnO7tyZwY9pGIYwCTKNA0xrKSmWbuJh6xTb0STMLVW1Yhj3GvY3wvVctg9GazaovfLoS9DEMthSSHmh+Ipv9sy7DECwFyxCRoqoquhRhaRJYGYZRUmQObfPx7LqoBTVMUVrywqBpfPFQcxMWqcvEjQVHoTQpNgYX2760RNZspIVQlSzzSi72O0qkURn4Vi4yD+YqVaWJ/IitiI8eWjmKAr7sMGhhj+lowaP5AXmty1O+sIa6Zi755oLJPgy2kNkUtctFHHASbPCtjIETixOZXnVZ6L6dMr8Zc7keUTcKAzchlOMCSy/FOKvRuIl9bL3CNQr6dgqZuMdMvcbUKlS1ZegL8Zmq1iSZw1pGzlpGKU600iqbpI54bxsVwxD+dkMVDIBIiixfpJ1PlVyGqtKoZVBVmHjUtY6q1t1GpyhMosxhnnrYWsXt8RzPTRjkYoRVNeJXWppd5seu69Yzs86zrkmewjZzO8vsUX8lrhvZoctyq1P0A9S6SpqIEdluTFNUWqeaH/ghdaMyGGxoG7Epc4ySrQQjiZ9Pl+FXukgrbdTO/384EMmjT1djpsMl2zAQdNHKEHZjKfQraxVVaWXMuHAHGVrNKuxRFCa6XhEYRSekNiQrpa40NK3pND2JtDJrqtg8OfK+cUsDV3YtMrnJ9J0Eyyx4eHkEgNWWtPz3YPXvT/zfq/WpF37byqkaTQhOSkPEZ/Y3+JXeZWnvZo9lpVOlLklhdieNp+sR9yVVS5cgE4WWpoWyUclyq1PK2rKVu7vJdLXGdVK0RPDp55uBUNEbBWNPZGMrStt5VPPqY6zn2N92C7aniAVpJ26Jc5tMPig0RbahK2FD09QG32mwrBzVqGkRUBVDF77ZXi9kuRySVnpn69spaE25sQklqQxgMNiwXvdFq7oSnQVPzuyS1GEV+QR2imXlwkMsw2IKdJpcnGoLGfTTNBpKZmEYZddqLGsdrWroBSG53DTtxhSfdQkveYOiCAHUMuxhGSVRYYlgHAko2QWZtM3HHIK6UahrnW3mChudnHunpdH5+OPcRldrwtLs2P2bTIxzdLUGHdJSbG52D9S0NOnZCddRQFz2xKaLlo1s/05dwY2wDAFvinKbiWRTWFqFJjcUq8jH1CvJSIAGAcIRjACoGpWsNDrAVNMq6HqF9okkxFUUsMocDLWmL+FCqtQ7tK1CmNusC4sBovVtG0X3eetq3eGIfalyjyWwR+Q5QK9VGPghU09sDFIJjyklxEdTG6b+lm0m7kFVaRm/QFJbvAmIU3EKV1WRb59LfYyll0Lxn1lkkowYS3iNKhX6tszcsA3xPoe5LcSPRknTiu+z2/QpSoumNcJ3X4ixj6aKcJpIdgKaViFOXXkabzrYV5S4xKWJpmRdgFVeGXhthqFXwnHkJiSRGOEZmriH08ymrAwS+ZxRaLvxYF6IZNDx5AbTLLgK+9hORpaLcZWuNGSFKUZ0gKE11I3SiRN3pMFE6mBss2Dmh12qntMq6E5KVenCRq0JXU2Y2cyCLaraEMbeH4CaVZWO54rZv2kWeF6CG0QU57coarFZ+D8Dpvb1+apPvTLMjq4on54KhWut8dU3v831xQFJJu0jbixSoSKftLAY+CHL2OuCaGy9YjxckWW28B6nLr97fcBffPAhlpkTxh5DyWC3TaG63yX17QI36lbl4OSS8WRJlthkmc3Faswk2FLXKmHmMHNWWLpQ7Sapg+ukIh54PSCtDN66+xBNF8Q90xAPWEMvGR/cEK76fOvJPRHpq9ZkuUWcOZSlwfP1UPjj5Sn67Uf3GbqilfkbH77KvcGSpRSS9eQCNpaq6uvViNlQWBKv10O2mcPJYMnZaiQsb2qNZ+UC4lMJ4t/J+IZvPrvLbRlyFGYO02DbWXvaVuHpYsqDg4tu4QPQEq+7qdcvSOA37G/Q5XunKi3vLCe8Mbnu+PbDYMu3ngvz2NhJ0MxStsddslpnHXucRwEvjeYotCxiH0uvuJD2PM8ouHNw08FNFKVlsxqzjH1OxnM8reZsMyCwNY6n1zSNyI4AcI2CZebyaNvH1moeDJZYRommNEwnC4LRmu18yGo94GI+5fXbjwUGOgpYSFBUUuu4WsXMD+m5CZttT8QtDxfcnl4x3ww42wzYFhYDK6NsNAnagUerMa8fnDMKtphGieMlHNca1zcTFrHPRTzko9DjQRB3cKtEdguS0kBRYKxG6DIGOstsnsxn9O1UUAdV4Qy5/eV34RuvdWMeV55s08zGtnJe/9Hf48nvf4EnNzNBoixfXDrftx7fxzUERneV+Lxy9xFXVzMAHDtjNJvz7MktACZ+2CFjXUc4E56vhwyclNsnZ7SNyqPnp0LMa2rdJijKnK6baOiVEJMWIowrzGyWF6c4etlBoS5WY8hthnL8mFcGplbhGQX27nuWFi/1r0gLi6w0uX//Eev5CMMoMeX7KbpLGuvQ4+FmwJ1gy/FwhaELzsfvn93i1ck1RWbR1Bq+mXNxeYDnpByOFnjy4LMtbEy14tbkhjh1mfU2qGpL0yhsEq+7txexz63RgjB1Ooz4QbDllifcQU2jsk49rhKfu7MrktThOzeHfGFyzd3bz4i2ATerIZZecnh4Jbp/qU0cu5hqJbUoRaeveJH1WQN89vXZ1ade+J8+vi1OEHIeevb0lI8WMxy95GSw5Oj2GdfPRSvJlujNTW7TIjCUnlFwfjNjK0U2L999LNT+gzWNtNo5dkZd69S1yjYU4puh9MunlUHPTvnwgweAiH/dRf8mudAe7FpmO1tZmDl89+aQ16eXHA7WKIp4cDw7O6TnJNiWED49ujoU4wHp774zXHSAj6QwebQaY6l1F6v54PZTnp0fMxstsOwcz86Yhz0httJqotQlzGxePn1GUZgiqcwoUbUGbdsnqYTgZuKFAvmrtJwtppwMl4Spwzr2sAuTrz54nyj22MQ+Ra2xjAMOBksMXbglwsRjG/tdezPMRFt3F8sb5S/Gy//w4oRV5qArDWM35sfuPOThzQFDJ+G102e8d3bK7cFSBiVZfO0bX6Yv4Uia0nAR9vjK7UcS4yq80B/Jh6pnihNgkoo0u123xjdzhm7MYtunbRXujW+43vaZPxFuZGGrM3HNgpEX41hZ9znsRFXX8wnzxYisFBG2F7HPRdgXQrhS52nsMTBLfujWE2wrYxMGhKlDmNkYmtC2HB1eMpJjBk1tOQrWbDOXVJ5iT3trLjZDrhMPzyh5ML7m9y9Oud9fiXGC2vD//Iv/jqcf3e3ez7P1qEuRA7Ctgt96+BIHcUBgpxwEG8bDFZZeChdFbvP1X/0R3njju6yuJjy5OuLO7JLlto8nu0Y379/hg6tj7k2ucJ2UxfrF2beOehtOji6IQp/vnt3iN9/5YnftfnB1jD2fYhulUKEHEc2lSOurJHFyU4oQo3ce3kfXGgJLWPcerUdMnITb4xs01UahFa6iRuP/8/WvChuuVjFwUu5PLigrnfPlhKzSCayMV1/+kA8/uk9SWDgSG/3FyYLLmykPlxNGTkpVazhyo/7hR/e5CnvElUHfynnr3kdczyfYUsU/dCNmsxuK3BQjiFrjyBPJm5ttr+vABV7MNvIpap3ASXEd4fHfxD4fXh3xpfsff9/RaCW8/oXF4WjBYLQi3PTZJi6OXuK6OS/de8RvfPstvnj6VOSQLMfM3JizhbBN3u+vmA6XrBZDTLPg9PgcVWm5vp5yve1TNhqv335M0egkRdt1SPb1+a1PvfDrkrFvaRVDJ2GV+My8EEOrKSqDt9/+ApdRD1sXu2q3zOlZuSD7Sb7+LpnNsTLCrVACn18dUFQ6WWkQZo44GcrTZBg5FOshaWmiS0b7OvVQaaXIS+VQRvVmlUmDwsCJOTy8Is8s1ps+hlaJNmDikhQWy7AnLHhaTVGabBOXg96GutYw9IqXxtecb4ZM/JChH3JoFmS5LbIEpHd8u+l17POyFC3AvNLJShO9Fu/T3YNLisJE0xpOZ1esN+I023Njid2t+M2PXuEoi+jZCXWjYBoFrQwgCtyYLLPp97dYZoGyHpKVJp6b0DQq2zDgOuzx0uF5JxTrPP3Snua+IBFPJTPiAcE8MEomEqYSxS63xzecLccytlbhOnMYuzGaomKoIoSllJGkojXdMrGzbtyi0BKmDqWcqep6zf2jC/EZbPukpUnTqLhmgVKKEBpdazqITJQJO9b96SUfLicdYEU4QZSuXVtILoOpVniuQPRucpt52GNQabhOSrweoqki4XE8WKFJrYWitIxdEcp0FYrNnC43lgMnYeCIoKhlHPD69KrLoQ/sjM3NCE3OqxWlZeZvKUsDTRPz2meLKbeCDX1XOBRMs2CxGkrOhaDUTdyY+fmBQAK7EdvIF1G1TopuVCwWI3xT2FLjxGPzCavZZ12GtMg6bsoXT5/yO48fdJHUD2YXTKZzri7F5nq97rNJPYb9DZpeM1BD3kR42qeSqRDKUJ3vP31C26psYp+0NOk7YpNsGgUvD5cUldZZ4JpGlaJB0WGMC4twNegcGqZeMTu6YnE1ZZs6ZLXexYEHfoRp50TbgEXic+Js8a2M7VZ0zFaRj2dlTCcLwm0gccwKbat2yF5drzGNknFP2BQdNyWJXeYyQnybuCLgTNIrT4/PCbcBj89OcIyC6UCAfaJtwHw9YJM59O0Uz8q4uZ7imwXnc+EQGLhJt6kWn7n4nMPEo5RdPksvOd9KFLZR0jQqniESBg1DQIlebLXwmc/49+OJz6r+q2TfhlrTd1KB5YyF8lZTG5Lc4jLqEUlcalmrXYiMpYu89llfeLfVTwT47G48QPLHDRH0U6uCdCUDLwDhX5XQnt3JUVNboZ6udJHqVgrqnao1qJrwmc8GK6xP+P3D3MY0KnlqVthkDp6boElMp+ukFLV4mKhqg2XnDAZrMTJAzJ+jRIiKotgnDH0JYxH58UkhtAqeH9M04udw/YSsNNnKh6/rxzhuiq3VZJWAh9hGSSH5/bYhUKyb2KcsZJCIJmyRtVQFm2ZBKVnvpllgW4V4+Ej0bVlr3Qbqs65drrwrwUZNo4rgj0YlK01hS5M2TkVBcuzFQ2An7jOlR3sH+fENsZEwtBrHzthmDmkldQK1it8LKUqjE3XuPMqGWqNrTTez3Y0bouJjDcDuNdeNSlqK4BNTr3G0GksTr9WWjIhDmTy5lZCdBkW0R80C3ajYrPusU7cLmNldm7tZbSRjgHcBTfPEZeCH8hoX6OmNXFCaRmg2dKnkL0qdotbZ5hajYCsWJOkOiKXOQ5ELmW3lLLd91lFAWYtNp2GU1LVGmjhiNNRbizAjOed/UVXVGlHsUeQWlp3TM4sun94yS3S7QFVr4lQshFs5K29qIcLczZxtK8d1Ulwzp2+l9HohnszlyCuRZrf790ytYuTFBFYm0vokTMo2xFhJoWW+Hog8DLlJ1YxKdJkqg0K+l4ZeoRsVpp3jegmHwYZZf4XnJISpI1M9pf5Cr4ik3qKUUCBL5n40MtynLA3KUqeuxCEhyi1hEy5M6kZArNLEQTPFKCGUXVFNwsNK+QwTtkThGIhSF8coiEuTKBfX104npWsyOEp2/eLc4ioUiYSdfkmKJHcHnqZRSCQMaF+fz/rUJ/4wc4T3dLAmGG549M7r2HqFpVW0KAztlIkbCzqZWTAerHh8ecxBf810dsPw/nMufvmnuIl69KqUo8kNitIyPbymrTWibUAYeYSZg6KI08+4v0ZVW5abPmkhHti29LmDENNtJchF7OAbrqMe1ROto+zdPrrAsnMcL6UXO3x0fipOXr5o89etKh9QBXlhsgp7zPyQtlVEaErqEkia1yL2hQjNyklyi+ebIYba8Mrxc/p+yCrssU0d4tLsYjRVtRHUPrUmbU1utgOi1OVods1PfuXrPH14h0UccDq+4cnNjFdvPaUoTP7zR69w6odcRz1MtcI1hcf3fD7heDLn4OSSMBFZ3YGTomkVZ4sJJ+M5eSXakE3zYpS7hbTTzYKU0+Nzrq+nLGK/Sx97fH3Amy99yNXVjKuwz/ffesxi2yevxQYtKky+/Na3iVZ9itzCqcRCXzYqvpMwO7rid57ewzcEQc+uDLLU5oOrI477a/pexIfXh92sVlfFwn8hNROa2nISbPhoMeXYD1GVllCGotwkHhMnpuek3O2vBApVCv4ebfv8xbe+wTsP74uNZGYL8Z+c90aRx9vnt+R7UFPJTenU35JXBmFmcx4H+GbOPBYCv5vc4mg97ER2O68/QJzZbFLxuupW7bgXh36IaZQYZkkSu3x0ddQFOO0W/arWyAqT68QnrnRe2wUerUZspLDw9P5Tbp4fUkXaC41hnccBVu7AVmy8Xj4QHvIodTlbjmnPTwisjHXqsi4sVCRUZjXkcjvgeezz5vSKLLcI/Ji7Dx5TphbnF4fYVs7p4SXbx/foBSFZbjHfDHh3NeYn7n9AIDcGQT/Ej/xuEzH2Gp5vhrx+8ky8xs2Am7NDVFVsgLJauA+Gw7VotW99VK3h5S+8z3Y+ZLPpU1Y6z8I+t3trTKMikuNHxxbI6qfrIV88fs46CthWIh74PAq4ndusUpekMnD1UmgGtLrb9F6vh+SFiaq2HEhWxGI9pO+HBD3x7DkwKqLYJS8MPDtlFQXC/YHojoifMep+3qbR6PshaWGyyhwhhpS2w1pmP/S9iOfzGUkhgqledO19/N+7pbRtu++f7Gtf+9rXvv7EdXp6ytnZGaCgqp9tAmDThEDLyckJz5/vQ4b+JLUPZd7Xvva1r33t63NUL8bova997Wtf+/qc177V/71a+xP/vva1r33ta1+fo9qf+Pe1r33ta1+ffe3Ffd+ztT/x72tf+9rXvvb1Oar9wr+vfe1rX/v6jKulpflMf/1JAT4/93M/h6Iof+Svf/Ev/kX3995++21+5md+htlsRhAE/IW/8Bf42te+9id7O77Hat/q39e+9rWvff2Zr29+85vcv3+ff/SP/tEf+rMf/dEfBeCdd97hx3/8x3Ech7/7d/8uQRDwi7/4i/zUT/0U/+7f/Tt+4id+4r/3y34htffx72tf+9rXvj6T+tjHD4ry2QZDta0ISPtv8fFXVYXv+/zsz/4s//Jf/ss/9u/9pb/0l/jVX/1V3n77be7fvw/AfD7njTfeYDwe8/bbb/+3/wDfQ7Vv9e9rX/va174++2rbz/bXn6Dee+898jznjTfe+GP/ztXVFb/8y7/MX/krf6Vb9AEmkwl/+2//bb773e/y27/923+i1/G9UvuFf1/72te+9vVnur75zW8CdAt/kiTU9R+MDd4t6j/8wz/8h77+h37oh/7A3/nTXvuFf1/72te+9vWZV/sZ/+9PUruF/5d+6Ze4c+cOnufhui4/8zM/w0cffQTQjQ9u3br1h77+9PQUgEePHv2JXsf3Su3Fffva1772ta8XUC8mHfTi4qJbiD9ZP//zP8/P//zP/5Ff861vfQuA3/zN3+Qf/sN/yGg04jd+4zf4Z//sn/G1r32N3/7t32azEYFJvu//oa93XZGsGsfxZ/Vj/F9a+4V/X/va17729aemmqbpBISfrO12+8d+zd/4G3+DH/zBH+QXfuEXsCwRSfxX/+pf5Ud+5Ef42Z/9Wf7BP/gHfOlLX/pjv36ngVfVPxtN8v3Cv6997Wtf+/pM6vDw8IV+/yiKSJKE2Wz2h/6s1+v9sV/3t/7W3/ojf/+v/bW/xq1bt/i3//bf8uM//uOAmP//n2v3e4PB4L/hVX/v1X7h39e+9rWvfX0m9fWvf/3/6pfwX10HBwecn59z7949gD/SKvj/b/7/p7H+bPQt9rWvfe1rX/v6I+rq6oo333yTv/7X//of+rOyLPnggw948OABX/3qV1FVld/5nd/5Q39vp+bfgX7+tNd+4d/Xvva1r339ma3ZbEae5/ybf/Nv+MY3vvEH/uyf/tN/ymaz4ed+7uc4ODjgp3/6p/lX/+pf8fDhw+7vzOdz/vk//+d86Utf4vu+7/v+O7/6F1N7ct++9rWvfe3rz3T9yq/8Cn/5L/9lXNfl7/ydv8Px8TG/8iu/wr/+1/+an/qpn+KXf/mXMU2T73znO/zIj/wIQRDw9/7e38OyLH7xF3+Rp0+f8u///b/vdAB/2mu/8O9rX/va177+zNfXv/51/vE//sf82q/9GkmScP/+ff7m3/yb/P2///c7pT/AN77xDX7hF36BX//1X0dVVb7yla/wT/7JP/kjwT5/Wmu/8O9rX/va17729Tmq/Yx/X/va1772ta/PUe0X/n3ta1/72te+Pke1X/j3ta997Wtf+/oc1X7h39e+9rWvfe3rc1T7hX9f+9rXvva1r89R7Rf+fe1rX/va174+R7Vf+Pe1r33ta1/7+hzVfuHf1772ta997etzVPuFf1/72te+9rWvz1HtF/597Wtf+9rXvj5HtV/497Wvfe1rX/v6HNV+4d/Xvva1r33t63NU+4V/X/va1772ta/PUf3/AC+5v8PrdIV9AAAAAElFTkSuQmCC",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAf8AAAH0CAYAAAAzNiR9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9ebCl+XnXCX5+y7ue9e4398rKWqUqSWVJdtmSbQGibRls3J4AGgft7qabCBoCgjDRjIMBZrqjAwiIDiCgB2IYOwjaTDMNYzMe427vwpJl7VJJqiplVWVlVmbevPu5Z3+33zJ//E7eUrlKcsrKNMI634iMvPfc9773nHd7fs/zfJ/vV3jvPUssscQSSyyxxLcM5H/oN7DEEkssscQSS/zeYhn8l1hiiSWWWOJbDMvgv8QSSyyxxBLfYlgG/yWWWGKJJZb4FsMy+C+xxBJLLLHEtxiWwX+JJZZYYoklvsWwDP5LLLHEEkss8S2GZfBfYoklllhiiW8xLIP/EkssscQSS3yLYRn8l1hiiSWW+H0P5xz/+B//Y975zneSZRkXLlzgv/qv/ivu3Lnzhu1u3rzJj/3Yj3H27FnyPOfZZ5/l537u595ynx/72Mf44Ac/yMrKCv1+nx/6oR/ihRde+L34ON8wxFLed4klllhiid/v+LEf+zH+l//lf+GP/bE/xoc+9CGuXr3K//w//89cvHiRT33qU/T7ffb29nj22WcZDAb8pb/0lzh37hw/+ZM/yWc+8xn+5b/8l/zoj/7o6f4+/OEP833f93089NBD/Df/zX+Dc45/8A/+AfP5nE984hM88cQT/wE/7T3AL7HEEkssscTvY/zMz/yMB/yf//N//g2v//N//s894P/O3/k73nvv/9yf+3Me8B/96EdPt5nP5/6pp57y6+vrfjqdeu+9d875t73tbX57e9sfHx+fbvvSSy/5NE39hz70od+DT/WNYVn2X2KJJZZY4vc1/sk/+Sd0Oh3+zt/5O294/T/7z/4zfuInfoJHH30Uay0//dM/zXd8x3fwvve973SbLMv4S3/pL3F0dMTP//zPA/CpT32KF154gf/iv/gvWF1dPd320Ucf5Ud+5Ef4xV/8RXZ3d39vPtzvEsvgv8QSSyyxxO9bWGv5yEc+wvvf/346nQ4ARVFQ1zVJkvC3//bf5kd+5Ed4/vnnmU6nPPvss2/ax3d8x3cA8IlPfAKAj3/84wBfdVvnHJ/61Kce1Ee6L1gG/yWWWGKJJX7f4vr165RlyeXLl/mZn/kZ3vGOd5DnOXme833f931cvXoVgNu3bwNw4cKFN+3j/Pnzp/v6erf9ZoX+D/0GllhiiSWW+P2B97znPezt7T2w/U+nU+bzOZubm2/62Y//+I/z4z/+4296/eTkBIBf/dVf5ad+6qf4K3/lr/A//A//A8899xx/9+/+Xb7ru76LT3/604xGIwDa7fab9pHnOQCz2Qzg69r2mxXL4L/EEkssscR9wd7eHjs7Ow/877zV3xiPx2+5bVVVAFy9epWf/dmf5Yd/+IcB+OEf/mHe/e5384M/+IP8jb/xN/iBH/iBr/r3/GIoTkr5hu/vZdtvViyD/xJLLLHEEvcVUgrOnOnf133u7g5xziOl5MyZM2/6ebfbfcvfa7VaAJw7d+408N/FH/2jf5SzZ8/yy7/8y/zJP/knAZjP52/ax93X+v0+wCl34F62/WbFMvgvscQSSyxxX3HmTJ/rN//Bfd3n5Yt/mZ2dE86cOXPac78XXLx4EYDt7e23/PnW1hYvvPACly9fBnjLff/2Hv9XbvvMM898zW2/WfHNXZdYYoklllhiiW8Aa2trXLlyhZdeeomyLN/wM2st169f5/LlyzzxxBP0ej0++clPvmkfd1n+3/Vd3wW8zv7/atsKId5yEuCbCcvgv8QSSyyxxH2H9+a+/vtG8Gf+zJ9hMpnw9/7e33vD6//0n/5ThsMhP/qjP4rWmj/5J/8kv/mbv8nHPvax022KouAf/aN/xNbWFh/60IcAeO9738vjjz/OT/7kTzIYDE63ffnll/nZn/1ZfvAHf5C1tbVv6D0/aCzlfZdYYokllrgvOH/+PDs7O5w71+fajb97X/d95aG/ys7OkHPnzn1dZX8IpL8/+Af/IB/72Mf40R/9Ub73e7+Xz372s/yzf/bPePrpp/mt3/otsixjb2+PZ555hvl8zo//+I+zubnJT/7kT/LZz36Wf/Wv/hV/4k/8idN9/sqv/Aof+tCHePjhh/kLf+EvUJYlf//v/33quuZjH/sYjz/++H39/Pcby8x/iSWWWGKJ39dIkoRf/uVf5m/+zb/JJz7xCf7iX/yL/MIv/AJ/+S//ZT7ykY+QZRkQeAEf+9jH+P7v/37+4T/8h/zVv/pXieOYn//5n39D4Af44Ac/yC/90i+xtbXFT/zET/D3/t7f49lnn+WjH/3oN33gh2Xmv8QSSyyxxH3C3cz/7Lk+r1z/W/d1349c/mvc+V1m/ku8GcvMf4klllhiiSW+xbAc9VtiiSWWWOI+w3/DJL232ucS9w/LzH+JJZZYYoklvsWwzPyXWGKJJZa477j/mf8S9xPLzH+JJZZYYoklvsVwz5n/4Z99koPbQU+52xszmwa9ZK0NOjLk3Sn7O9s0TUSWlpx/+8vc+tJjSOmIk5o0K0k7MyaDHtZooqihrmI6qyN0GowXqnGL3sN30N0ZaE91e530ygEiE9DOcFtnkdMxGINPM+zmOZq1y8Q7X0Id7cPxAJyHlR4cDTF7KapdYacJ+kINa6swGuFHDUIDvRR75UnUtRchSWE2wx1YZOawowjfKHyjKQ5WmB33GY+6NCZiff2I8aiL9wIpHUlaYZqIsooxRiOlZ339iLJI8V4ghA/bGE1Tx1griaKGg8EaWlqUdBirSJOKaZGjpKPfGeO9oDGaSBuStOLwaI2zZ/ZQ2uJsWLcpbXBO4Z1AJzUrT7yGNwpvJUI50r9+cp8vGbj9p96NMZokL+icOSJ99wwmJX4qsNOE6rBPevYYrMRVEUI5ZFbhG42rNb7RxI+MwXoQAlJN/eWMatAl6sxJtk9AesTD6zAawaSBs6vgHDQ1ZDnFM/8JyfVPY7tr2N5ZXP8RxPgGPltDzvbRx69h1i4hqjFqPkJOh8jBEUiJTzPEdEL1CUjeUYQPpRVkOZwMw9dxBFmO66/hdYScjhDjEXb7HPWFd0L3UXTcp5leJ+48SqRabzpOjZ1hbv0iqhjidYxZfYS4/zSmGeJtia9PwJSgU1AZ6vAL6OE+XseIX34O9c41MIb6txpUq0Rt1eA9biRBgh3muFojpEevTnBFTLm3hiljWucPkFlNcWuT6eEK41GXt/3Cr9/3awHA/NIlODyCdo659BhqsE996SkARDPHXfogfvAlZDFA1gXC1KjhES5v4+MUUc5QB7vhvMwm+N0xzQe+l/jV58M5j2O4sYd/+ByiLKCYQ6sdrg1jQSv81jairvBxEv5uXcFsClrD4Zhmp010YQoOyDTkKRxPse96Bz5O8VJhe2fBVLj+w8jpDukn/3fs5ccx3XW8SkBq5Pb70LpNOX4Rffil8Drg0i4+CtdA+spHKR/7AKiU/Dd/GvP9/z2RalGUO6jrv4icniDqCmEaxHyGf/UI8wffjzA1eu8m7OyDFuEa1CocAylpnhfYeUq8dUJ5ewOdl7hG08wy0vUhAN4oXKPxTlCedEn7U3RnhplmDG+eIWkVOCsZHKw/sOshvBHw7j5n/suW/33FPQf/2bBL3poTRQ1pZ0ZdxSRpRVmkjMcdkrRkde0EYxRSeGyZkLfmKGURwmON4uDWGaazFs4LsrSk1ZqjIoNOa1RaEfdmRGfGUAvcPEbGNeL8KkgJxiCHA9zqOnI8xKcZ9dYT6NFt0HF4k5XD1yCqIWjQ2yVuJNGXbdimqfHnLiDW5lDX+DTDxSnKWBgNw+8qIBKYYYt63MaUMU2R4rxAKYuxism4w7zIiOMaITxVmaCUI0tLjNFUdYKQniip8S4EaSH8GxYDTROhhCOODEI4KhMRRw26sngE9WIR5b1AKYcQnjwrqKsYZe3pcUV6pDQobUm6U0YvXyDpTlFZjRDuAVwy0Luwhy0TdF4Sb53g9x2iIxCpRxQOldXgBN4oAGSrRD7cxu+MYeZQl+ZQedxMIpRHSIPuztCr4eHsG4lvNOpkCDODHST4gxL9aHjQ09Qk1z9Ns3kJ4Sw4C6ZA1DNkOcZLheltkz7/EezmudffeDGHSYkw4B0kl8DeiMELRFIi10vclUdoNi/hWhv4qI2oRvjuQ4jpLfTJTUzvHKKew9FzNNkqrY33v+n4OG+QQocFwYXvO10YJIuf3/2+SWfU4xcR1RCfahD6NBCpVhPe77zAVj1Uq8Qe6dMFFYBMGmTS4KqIydWLJP1JeD0y4AXlnTWcleT9Cfnq6IFcCwBMxiFADSbowWfg3BbxzlVc3sa2V1DXfoFm41G8ShBmhChnNJsXQCpwFqFjhGmQgyN8p0f1ju8BqcNCD3D5Kv49z6D2boVjYiw+byEWPyfLsetnsK011OwYNTyCk2PcuUvIg13oJESPVSATqJuwYBjOcDOJunYVv7WNXd1Czo4QzqJmn8K21ii+/79D7n0SNTvGpW3MxlPI6z+PdZbYhL9db50n3n0BH+f4ZAUxvY1ZPQO2RDRTXH8VJRPms2swuY6LM+zWOnp0By/D/aFfOSb63MdhpR8+V7+FO3MBMR0jRicwKcGALVexVYSdZMikQUQGCUhtkGlNc9LBVhF4QdSbkckR88MVmjsb5KsjuttH2EZjyoTeygO8HoAQ/ZeEv29m3HPwL+Yh2HktsEaTt2eo2NA04UHknEJKSxxbkB5bRyRpCdKDExTzHOckUjokoKRDSod34nSlGnVfd0jyTiC0g8aA0uFp3Rh82oLxEFFXyDJYOHqp8HGCSCQY93ozwxG+ljLc8K6CusLlbaQbI+oKPdgP20pCNWDxtZmnNPMUaxRCOLS2JGmFlJ6iTLBOIoQniprwf1LjbLiZjVUI4fFOIqQjigxSmxCsCdWSNCupF8cOINEN3gvyrEAKj1KWeZGhlUUIh1SWJG6QyiHkVwR1J06/VEmDsxJnNMo1yLy519P7dUElDSppkFmFiBy+kYjG4UuBtxKVVsiswflwbl0RIweTsJ0OAdzuKrxR4QEma0RiETlgwBUSO07gToGQoapR7K/S2b4NvRSiGKTCZavIYoBoZoj5Pmp2jJyP8WkLm/dgNsXFKcJZZF2FqpAL+8cIaFn8ooIiJJDG1BeexrXPobIzxPEaxfDzCJni03VsK1yfwlRf8/hI8fpt9VYVga+EiFfwzRQRdfBxjmvmeB2H67YM7zneGCGzBjNo4RsdFnxJjcgafB2uORWHcy0jg4wIi87F8ZeRIV1/gA/7aREWZdpB47D9tXB+8i4u6aDMcVjYpF2srYjGA7yOQxWgLhHOYtbPE4+Hi8pMl2j/JajrkLlLieltow52wr0sPWI6AbMILnUNzuHjHHl4A+oK31sB00C10HLPMxhPw9eLbFpEDmZVeK9pGzU9wbZXAPBxC6XbuDjHF0NclCPilbBtlEMCXieQn8Glr+FaW8Sth6htgcnWQGV4oLry7fjJVahOEBCu2cUClUXwF2sKKhMqmnECK2s06+eJzHXEZBSqYxFIZfGRCffQXUiPyupFpc8ipMY1CjtPQ+JSJtRVTDRPybcGMCdcE/Ir9rHEtyTuOfjXTURjNHUdkxrN2qU7mDImqWKsUTgraeqIOKnRusY2Gp2G1bGpI6xRdHtjWotMWC8uYFNHNFUMTpBDeKhFNvwzFnezQHYctBKII7yOwo07GZG89gXqs1dC9qc19LoIN4I8gVGJPYmQrQZ/XCFiQAlEeQfOnQ0PhekccXgMsYaVdniYzCtoPM6EQxMlNUJ6mjIm1YY4qU+DcqQNcVqRZCU6bpiNOtBokrhBCIdzEiUdKm6IspJ0VuKcJI5rOtuhLVCUKc4p8qygKFNWV09I8gJvFS+9epl+a0aSVGhtMSr8fSE83gv8IvA7p2CRBOVr4SEvlEX1ivtxjbwJrtGovEQoh68UIrG4kcIVCa6K0KsTxIpA2AY3bmGmGcVuRLI6RndnuCNHcWeLZG0EUmLHKXqzDIs1DbLlcLsR5VGPdHWMahdhIXYcozoe11+luvQMPukjJnvIcoYsp6jh4WlVSPQbaLVptp9GD15BlzdCGbUD0lW4icIOUvR6CZGAboY7dwnW3wXlIc6WSKER1RAxuYNwBmEr1GQfH2e4bAXVffR3PFbWVSiZvOn1+ewaCI1O1mlsiYr6mLiFqOe4fCUcC2MhT1GPaygKGIRFscoq9HqJd+BLjXeS1mM7mEELaVVYEDqBbhXUswxbR6Ea8IDgZz4EsFYXn7eozz2Fz7cWPzS4/sPQTMMCKmoT33gBNQ8VAFkWiHJOdfG9qOE+cjwkufZJ5PFB+PxxjDAGWQzxOkK02iHYH55AvAiaqkB1B6E9s78DQlI/8W3Ez38yZM1qcZ8MQPY9pBq0RqQGjMW1e9jWBnqwi8tWcP1HwBm4/vOQrWBba7h8NQTvC3+AJNnCe4sxU7RKqVcuEXefJFItVP9db1j80X079fP/DLN2Gd++gBAavfNvcXkXYWpEOcM+/jbU3m183sLlXXw3ej0TkRLyGLIMPRwjZ6GV2QzbuCpCxgadl9hZGqp90tNMcubHPWajDlHUkKQVttbhulgkIHUdP7Dr4fS6WBL+vqlxz8G/3ZphrcY5QVPHeCuZHfcBaHXCijpKK+JWgU5rhLIUi59HSc3m5dvYRjM77lNXMc4qVs4e0BQJSluidoGtImY3t4m7U+K1MXpzhptEkEhYWQmBH14P3HVNbBp46VZ4WK5EIfCXNd6AkA5fKOSqwxceX4SsQa4VuI1tRK9CvPoaTBrcgUUoh2gJ5i9uIZUl7U9CaVVZ7J2N01W0EI4Ll29TTHO8k6TdGTovqecZ1miUrshWJvQfuY2ZpRTHfepZhveCrD0j7c6opzmdlRGcwLzIaIymbGLG4w5plaCjhktn7zAed2iaiJackrUKmkaTd6ck3RmDm2fI+xNkZDBlzK0vPsbl73wO7wTeKuwwv5/Xyilk0iCkx4xzyuM++Zkj7OK4yNjgihh3C5qTLs08Ids8wQ/byCSsUJqDLlFnRvTuGLt5KfRqTYPY34OyBjzJ2QHuNc1sbw1nFe0zh6jNBvvou6ge+k5EskErv4S58VvI6YjiiQ8AnPZ8XZziP/g3aKkWs2QFl3RxrU3yz/4czPYQkUOftZgn34OPUwCEqfGDL5Fd/RjoiPLc42Sf/BX8SYPoafy5cxRv+0Mktz+Hi/J7erj99sA/n10jb10hb105fU13WhSjLxEdvoyaDjGrFeJ8n/Lp9yOrCdGH/z0iE0QXarAeb8DNJHYczq/KKnChH+ytOq2m3XjxUS4+cp24N+X4xcuc48FA9DRMQjXEr2+hJnsw2cPrGJetkp/9PmaHHyXa/SxqfIxPM9TwiGbzAmY1Jjp4DaTGdtfD/pyj+EN/kezD/+S09O91HNp06Rou78LDNpToj3YRZYHL26jpkOqp9yGrCfGLn2b+XT9C/omfg6Mh/rjCTXLkaok/LHGzCPnMJtWj7w0le5VSPvYHSF/6dWpTgU7Qg12qS9uI2qCmh/h6hj7zAcpiBz+7hSwGJBd/CDs9xPamRKqFsTOUTN9w3uO3/1nM7BrYkqT9EOad/yUAphnC5DpqtEP9gR/ENxP04ZeIf+vXQYoQ+J3DzxwiTRDvfgg9n+JfKohXJwxefIgkL+k8eRO9Oae8sQpOBN7M+oj0qEc5bocqbLvALKoB1miS7I3udkt86+Geg3/WKpiOOyRpRW9jwPy4h/NhRW2aCGM0vY0Bto4wZUL77CE3X73ESn9I1ioYHqyRtYpF+V+QxXNMGYfSuK4RUciiVavETDLqoz5pdogrYpQLGayYz9C3X4VWG9/pgZSI+Qy22qFEWhqIfXg98YDFFxJ7pJGdBnlWQquFe3mI7AUnJnMnD+Xru2VnLZgdrJC0C5AO5ulp5eJupt3K59g6BGGpHLaOsHUUCHcrY7KtAeXBCvWwjUoa2mcPmd7ZeJ2kFzdkZ47Yf+4x2t0J3bUhxaRFUabEUYNShtksEP+E8GhtEMqR90ccXD+PqSOyIiXJC+oiQS/2m+UFropQ3TlYSbm/Qna/rpSvgKsivJHIpKHz6C3MuIVKK1wVYaY5yfkj5tfOUJx0wmIobvA2VAZwMmQrKzNwPeR8CsYgdu7ghgI7bFGfdEB4onZB1J0hIoPqlzTv/U5saw199GWiO/8OGyeonZvgPJn5ZXyaUV55FlSKnB3ArV/E6IT4+DXUYJ/qyrux62cQeRvhHDbNcHkXOR8jy1loJY3DdSGGA5LXfhE/c7hnn6E69zSq9yStZAs23v+7npHNW1eo6mOS+HXHr6YZIaIOzaXvoSkO0YNrmIuPEO++hCiLwHs5PgkZoLEwMdhJRnRmijfgC4VvwjUg4xqhHa6KuPzUl7FlQj3J6Z7b/0ZP+1dHZaAVQZaBadDDA1zewSSd1ysAUtNceB9GaNLP/q8UT/9R4tc+hhrfotl+iPTLv4KYzxAmtC/0zX+P3TyDy7vYzhbJtc/g+uvgLHJ6gmuvYLN+aO8IjW1vkF77OD7OsXGO2tgme35BaBMCN9PoSyVUDhGDahmYjEhf/Biuv4ZLW6jxAPndfx1tZzTlHvX6u8jTc8znr+G9Qeo29sbP4TfeAckKTmpm4+dR574H7w1FuUOWvvUS6ysXeypOaOwMqbaJWldgG8pqH9tMQuXh8Sew3TXkfIwaHCBu74VkZ3gdpECcy/BXHatP3sAWCfNXt4l6M1wVnkPeKKLOLCQmvQnOaEZ762htyFdHRHlJNfna7ahvHB7c/W47Lnv+9xP3/AxTcYOUFu8Fpg4M7s7qCGsU9TzDNyIEgFmGqTW2ilhbG5C25kgVetTOSoRwKAXybv87rU4JfzI2uDq8JZVWiB5oOYN2O5T8yjIQfpIUkaZ4KSGOcf1V5HCAHw0CT8B4/MzjpgkiCpmozBYXotaYYZtITUD7UDrLKnylAIvIHSp+PaNzVmKqODD7lSOiCZ+5jpGRRac1Oiuphh2S7hSdl6E0mzSovAzlVulI+xOclXgvqCc5ul3Q3Ryc8gCUNtRVclpFaRZtlnQxKaEiQzVpEcUNWpsQUJ3AVDE6Dj14rQ22TEJmri06fzCre7noOwrpQ99eWew8BenR7Tm+1LhGI5RDq1CKlDq8J6EtSIfYiqEoAmkryyHW+Jpw/oUPn6c/RWQ2THuc20aNj9EHtxGTEf61CbIHtDPotPFxEohWcR90FjoIR1eRoz0AzPYlXNql2XgYUY0RzmI720SHryDqIrCvyzm+u0Kzfi4w7p3F9M7T2vzAfRXE+MrAf/p9vIbzhjrq44oBwlnE8DCw1uM49KmNDYG2FsikgSxwLTAOV0SorAps7yqiHHSJWkW4V6Uj27z/Ux+nMATGfbeH2bwYeuetNXzceiPpqzgEW+LSDGyJj1Nc3g5VFB1BmuFlCx+nyHKG7a7jspXAhWh3Md11oqPbyMEhwjl8X2E72/ikByqlPvMYLumBNzSbl4gOXgvHTwJWQtXg5wJiH4i9ZYVf2wRA1KF6IAFrwj2Ypeeo6uPwGRZleLv1baEl0EwCGdQZovYjlCefQw2vU5z7Hlx5+FUnQO7it/8sTbaYNUMAmvXzIDSiLgOXaSUkN34SCL6iLQGHSBpEFWEbTbJo60hl8cLTTFpE7ZA0eesR0hHnJVK5Uy7SEt/auOdnml6QzUwTMRt16G0dkW2eYGYp3kmMUaisxpYJzimaWcb6w7dwjcYUKemizBTFzSnjXUYhiMm4RsYGERnMcQ8Z1+j+FNZWEO0yZPk6CmWw2oCsQ3YPuO4KtrsWRoBqAR2Jrwx2mNOM2sRrI/AikM2KBvQUV7XwViC0X7BmHWaYwwxUVZJ0pzgTSIhSudC/txqd1WjhmU7aKG3DKjsridfG4WbrzhHSUQ+66FZxyl53RUyyPThthZSTFlFe0nr4DmbYxkwzkrRixQt0WodjZhR+2iJvzYiyCqkco0GfvDMlbhWniwHrJFK5sIDSBltFyCJGtUrirQdD8lKtEpQ7ZZ7LpKE66qPbBao/p97vo5KabFHmF8qGBYNyoBwycviNs4gbt8KExZaETgchR8jYECcTVKcI/dk4jGWZ7Yvoz3wKX3jsLKI5WSGRA7i8TX32Cj5q4VYfDw9mgGQlkMrKA5qNh3Db347wBt8CN99F2BK58jTy+qdDaVVKfN6muvQM+fYHTz/rm7v1Dw5SaKKoT9naRJ/cDgx6CFyUWMOoxM8F3grUWh3uCedB+zCxEBlsFVOP2sxGHXIvsEYRJaGl9UDh/GIB9gi4q9j2BhAmb8oqVB2io5dQ42Nsdw19dDX8Wt5FNnNsdz20XeLsdFHmskCwE+UY29/Cdi+gB3dgNkVqjcw7WAAV2jby7Pfi5regmWK7F8LfdA55cgLCY4/jEDytBeMQGMz6WWQ5A1PTbDyMqfax45fD+GUL3N5vQpzjozY2WaHVfpzZ8POo0S1kMcT2ztI0Q/TRVaI7r1JFLaLJPvV2Cf2n3xTk706CfCXu8kKkbofv+5fRg7AoRSr8yhri+BBvLTQg5iVCydBeqzUqMqhugZynp/uc7a2RrI4CmdoLkqwk3zgJ0wJ1dJqQPUgse/7f3Pg6M/8QCL0XxJ05xd4aMjLkGyek/QlCOKxRNHVEVCYc3TpL02ikdPRWRsynechspcdUMSpuOLx+nrw9p395B+8E8foQfzeoHAwCSc9YyDJ8t4cAzPmH8TpC792kuvAO0qsfQxweQOrBOZrdLkJZ4s0T3Dxk/yJxgfgzrVC5wo5zVLsI42Mjj5lmVMMOziqy1THlKCNKaqLOjGqeIpUjXxuh23PGx33ybgjCUhtmN7ewRlHsr54er3h1THFjE6kduj1Hb5ZETcgo4lZCduEAX0XgxCkZq3X2KLB0gfbKmLRVkK2McY2mOOnQ6k3wTmDrCCE9+daAuFUgVCBPds4dYqsIldWofonYSH/7abwvCMfTItYi5LktxPEBrdYObhZjhzmTnQ3W3vEKvtE0wzbFcY8oqygOV8Ki5x27cHIMWYRQBo6nkEi8U8i8QrYWPfaJQVgDsynNx27g2y2iR0rkE9skUmLWH6e59D202sE+cz5/DaE1rjxEDl9FOEv58HcibIk4eg7X2gRbQrqBB8TNX0YYg9c6BP2z38eDYUncO8r5DUR2BjU8DAtaAo/B91cR+y/hqtDC4vzmYsTOh+pI1TB58RLWaITw9LaOiVcmoQIjHOJBEv4c+DsN4vgl8t3b2MuPEx18FNfu0aycg53fgPYGNuuHMczVK+Sf/fnX5/RjjbtwGTk8xudtXLaC6WwimtnrGg1HB7hnVqjPPYU48wQuW4NkBXn0PPrkU9jeWYw34AyyGqFPbmF6Z7Gr26AjVO8Qd3Xw+nSME/gJRC9/IUwTxTE+bSEO/1dEdx3TO09R7pC++LHwnto9THedYnAVXZwQ37wKO/tEZzbw+edCFVJK0s/+CvPv/lFkcUxTfRS23v+GBYAUGucNxeQqUX6eWPdO+QFSprD5LFm8Fngq0fPo0R5qMZEkz8Ywr3AHDnUeiue6uDoi6swh9qSPnmD3Y8q9VaKsxHtJ1J8S9ad4txEmAxaTOshlCf1bHfcc/Ie7G7RXRngvmI/bDF87Q9ab4qykGnYYH62QtWdhzEwFEZpOL2SeMrJh5t2Hkb80K1m9cotq0KPVmRK3QlVAtUqakw4qq1DdOb4E8fBGeEiMxog4MFT1i8+F/r71ZJN/FwR64ghkg5854ktj/DRoBUQXZ/gJuFkEE4FaqYkuF0ETwIA/CbyAeHWCbpWYWUo9bhMtKhXNLKNz7pDJrU1mh33koEvWKmifOaRZ9M06b79FdXuVaGWCiC2+iBhfO0e2NgqseG2Zv7hF/tRBEBsyHvKY8W9shkmAdhGqHpPWotpgw6ij0JTDDjpuyNdGDHc3WL24h3ehdTDcvUTWnqHjhqhVkG6eoOaBcW93+vjbkvYfv6/XCwB2lhL1ZmAs4tp16hsdmvE2KquJujOy3pTi1iYIj5CeuFWQnjnG2zC/X76ySvreGr+xGcqyxwMwHnVRLsawVMhyK0l9q0s16OGsJOotRrWkxGxewLbW0fE61lWU4xeJdj4V2gJ1hU8z+J7/C+7WL+A6Z9+QzZ9+jvYjyEfDg/c/dNC/i9OFzHv+dCCV1TMA0hd/E1YW/IlS4F86XCwEDCITVLur5GeOAx/DqtBGS2rqk84i+5MPrIohJIgrXXy7g9dhIkcc7qHGw0VGfRlZz8JUjlSoyS6cnCyqeIHY5tIWbrsFUqIm+9jOFvr4JjiL7a5Rn3kMgHjnS8jpGP+9f41y+goQyIAu6SEnd3DZKq77EGb9XWTpOWYnnwb3OcR0jLyYgnO4OzXVnXC/Kucgi/FxgpwOKZ/+TwGQg6ukn/r70NTYsw/hdUy0d/1UKIrZDF95uHGAyA4RWi2EojLSL/8aoq5w/XXc5rPMZ9fQyTpatU6z/lb37W86jlHUw3tLWe2j9j+L3XgHzea7Ec//G0SaIYeDsFiSFpwjffgIaoH3AjeKqQ5Wgr5IZ062PqE56FLuLbQezhxjixgUCOnfOC74QOBfr8Ldz30ucd9w76N+VcJs1Anz+cJjnWR60iVtFaTdKe2VEbNhFwgse9tEQXxChRl17wVZd0pTR9RVTHG4spgKcJg6ohp2aK1PsIf900zYzSLUbLqY0fdQlfidMWIlgm4W5ovrBsazcOOt5LBTQE/jJzaMv8gakXskBt9I7CBBZA0iAWpBc9w57ct7J7FlwnzYobU2REgfJhD21kI/Ow4s99mogy2TMOse1/gqBDXfqNBLU47W9jHR1gjcYhHSm4YRo9rgC4+ILXEvTAnIVhkyEi+wRYJ1YapBLxYGCIdrNO2V8SmHwRqN1gZnNE45bJkwe/Us6eZJuLGdDL3YBwCZhN6pn4EvYlS7CEx+J3G1Dp8pabDzNFQikobZza1F79+EzGM6x5+5gE1byG4/ENsmo/D6zGEHKWaaBwJjWpF25+jNOX7rHM32Q9jONmqyh7/1S9hySn7nOv6lPURPwtoqrrtKU+2TXfyhr/o53moE75sFQqXhUWcqhK1g/yCo0kEImhpk3AR9gsoTrwaBH9UuTsmeyqrQYtMWlX5tbYJvBN6AKAqEd6A09tzj6FZ7kTGvoIph2NBZZF2i9m4FRb7RCUznMC7Qezdx3f7p7LvXcRh7kwqXrSC334epj2icReX7mL0Pgw6qey7pgE7xOiFde+8bzmtr5T3MqyHRzZehbnB7luLmJtODNZJBl46/g7pwhNhYD4TDhUokUoXxYa1D+8XZ0GbxLgiGpQlihUBy1DpMIBmDz1vI3dvQC1WOZvRiqAK0tmhgIei0Tqv/rnBIvqINIEUQerIqpeleCEJHQHXp3chyTBS/iowTZDrC708X2hQejKe4sXqq+GfLhLg1wlsVWquJD0lFbKiOe2H806gHQgZ+/aLg/gf/Zey/r/i6eExVkaGUIc1LlPDMJi3itAr97VbBbBgkb5Xwp8x4CIIjTaNpbQyJxm3Kecpk0Gf1/F5gyxuFKWPEIlN8Q0nq5HXhH8ZTzGGbqFeGbN850Bq3F9SuRBqIecJYfKlpphlxMzrlHHknacYtxMSh+zN8o5jvrhF35kTdGd5I7GJx0nISGdV4pRgfd8g707BYkY6ySLGLcpuMTRi5Eg5XJHijApt9fYJY0TAz+KlAr87ASKh84B/MDNHKJJS4Y/BzgcwX41JWYYpw4+r2HFcHCc+4O8UbeapBkHZmVNP8lLwzO+kSr0xC1hcZeF1D6L5CpCZkG7OY5qRLeuEQsgY3i3HzNDxw2kUQ+JknKOEY7a+fqkPG/WkYX6orfLuP664i4hnq5Bg/cpjjNrZIqIYddFaSrI3R23PY6AYlt842SIU+2gmZ2GyGP2mo7qySroyx2xeoH/sBlHwwbY/fCwihkfUMWU0QzuJHDtG7O5ttEK1FlaRo8CWo9Qo/DyqLvgZzY9EHVxYZB3ngBwYjgljOvA7kOm+wqxuBqd9aJ969eqpcKMoZ7B3h3vUMqixgPAs6AfuHyIVst3AW0e5je2fxOsGrlCxeI4nXKKM+TfYyyc1PY/pb+KSLS7tBRChqveWCzkdtmExg0jB79Tzjg1VmkzbxLIzm9sVrKH0CdUO8+yLNxsP4OKd65J1hDDEcSEx/k3hwGKpKcQIdwvWY9wIRcXyCW91EHR4EImPaIjp4Ify+swhToWbHmN4c13sKKTRNM3oTATTWPWznMq48xPsJsvsoLtrFFoG0KfIWYvel0LoK64XAq9oe0Iza1KOMzIXniEwa1Cn3xtEUCfNxG+eWti7f6rjn4N9ZGRJnFd4Jmiomac9JexOiVhEyQavobR+9Ieg3RXgAmSpmNmnR2hygooY0BxU1mDLcqGl3RtKfYIY58doozNtHDvn4Kubz07AgUA7fKGRaY17TyGSE3Bb4tQ1ENKXZ72KuZWSX59idhHJ/hXqak00k1Z31IHtJqEqovKI+WKEatRjsbZKN5/TV7ikBcfX8fqhaRAaVV6ylFdODcIPKuAlCPd0prtHURRupHdmjB1Sv9TGjNiqtsId9UneIr+NQgWhX2Dsatd0gz/ZxV0dgZRjTqjyuiDHjFvHWCTJpKAZdqmkexG3qIK7UlX7Rw/Wk3SnJ+oj6xcvotCbdOCG/sI8ZBxU4L/2DY/Q6kFe6yIMh0xtnaObhPEatAt0u8E7THHfxVhHlFUIGb4MoK0lXx+i1aahKfP46muugfRBhkmDHKcXBCvmZY8w8CJfITgkbXfxKIIXF+y8Frf1r13FDARLkZoT6wNuYn/k2Wv13/UdvV5kmW8ziFqIaY/qXidY+cTr3jSS0jprmdRVLB+JSJ1ROCodOq1MCKgu1yQeG2Ac+TeNxQ0nywqeo3vZevEpQk33k4JDm4qP4OAMpkee3US9+ET8xYEDkC6GgeIJYKPLZzlbo6wOimTI7+TStlfeE4yKvY/pbRAevUV16BtG6gK9PEPmZN721+ewa6Usfxu01+DJmcHuLqkrQOshjD/aDtkB7fIjuO+SZY7SOqa/8IdLtD1K5n8arBNu7QNR7Er93A4zB9dcwK+fwOiG7+EMU53bwgy9B/wnYCi0KWY7Ruy/juqvIaoyLckzvbFAiXPT+bTOkuPPvyR76kTe8b1fsBoGpeo7a+wJSKvQgaBrgHOKhdbhzFDbuRHTf9loY93QCnKTZbQcu0OJ5rDdn2EFK2p+S9qfED3L6A1jK+37z456fkeUsD6NuSU3SnoOTTAY91KCHTurAOI+DvKw1mqZIGI+6C8Oamq2HbxKvLgRp5iFzrqY5OEHSDZmyOepgplkoCwNqMsEVaZCAjQ04ibpkQvncRTCvaH59jLOh5JVd2cdNYpphm3hlQnb+kOrO+umF3px0uPOlR2l1prTWhkStgu7qCa2NIZM7m1ij0FGDNZre+QOaSYv5sENn+4ju2UMQDiE9Dz/7XKg0VPGpzG15Y3UhrWooj/vorERkDtmrkb3gUaBG41AydA759jX8KwcAuCKi3Fuj9djOKdlRRWZxLBVxq6Rz4QCVVpSLdonuzBa8AI8pY+pBFxlZ0isHuEmETAzisY0HcMlAdWedbP0IWhHdJ19DthzFK+uBTNQpsOOcZGNMc9yhPumgUke+OmJysEo9z9jYGDK+epHeUzdOpU39nFMzomzzBKSj9dhOIGZ5AcdjzHMW1bmBSBxunKDe1kPqMe7yw4j3/TVi4MHrlv3eobX5AWb5VdLn/j/Q6cBoDCt97OPvQP7yZxCJxVfBfEpkDWJnDEbgZgmmTCjHbdLeBJXVmEnrwfX871YhpEUqj9s8Q3R0O5gudbbQ8U1c2g498+MD7JkLqP2DhZRzhDtOgu6DlPjeCnb9TCgZJytI3UYIHYL+4UfRg2sk0xNMf4tm8xI+36LVugJvMVVnPvePyA528K8eML9+Bt0qmBcZkTZ0eiPqOmZeZKdS4iK2QTdESuTR85QHX4CLfxj9hZ8mfu1LuPZvIl67CRtrqMEBOIt+718BwlggZ8OM/2x6nWzj/aGM/zCoxftx3lBV++QLLoIavEw0HyHqEnP4P2G762F0MVsLioj5GfyCjJJd/KFgFLX7YZKrH8eub4WWaFlDbbDjjNntwCGKe1OGr5wnW5mg2yE5EzEUu+uouAnJyfibheWyxH8o3PuonzaLERFL3Kopx23iuEZGFqUNpg6ZtUqaEOCrGCkdkfAhoDaayatnT+flhbI4o5GLvnVz0KU4XAnz3e0C2apprraIzo1wwwRbJKh2gR8Z/EHIaEW0GNWzgbziZhH1UT98XUWBbT5PEMfd0AurI/obxwwP1yjnKUlaEecl5TCM2ERpRdKZh3K1CTr0aScQrmRcn7YkZF4FxvWRDFK33TL0s/MaV4VDqruzMBrcSYMkqVmQm+rmtF3hrUR2HCpv0CcF3spTVb7OkzcxRx3qUfs0gxeLbKWZJ0G3e57S2higkiaM08Um9AEBV2nU7uE3cGl8dai8xA1ACIM3GnAkZwa4WUpz1MXVYQTJWxUWcl4iI8Pa46+FWXQnSfqT0KIpDN5As98LTn+5RJkKc10hWgI3FphBGzGylIcrqFEbnZfo1QmQYd75Hnjbf376gP39hijdxvY3ghhSOw+BaTxAnteh570/D5WlpMEOcoS2wdXRCfL1k3DdzxNk9AAJXo2DwuEdiDg46jWbF5DljHjwJezmOeKbXw7bZjnq+iu4A4HIQPWa18lnkwqRTvBnL+PTLjoOWbmpj5hNr4PUmNUrmBUD+RnS/CHS31bmn42fR0xvo2aHxJ//3OmiMrsUGPPbF+4ACy+Eqefsyu3wEUYtoq0xHI/QvIROb4Jz2JMd1LWX8KMGaaDa7ZNwjLtyherSu9/yAdra/MBbHiYp9KkIkEg28NEdXFwjAX3tBdTBLq6/g8872PZKoOykG7jOWeZ7v4KXGmmq4DQZpwvPEg/eI5KG/OxhIPuWCVFWnfJ/vBOYO28M9jJ5ML4fXwlx3wl/S9xP3HvwX2TEtnn9V3RanwZyYTTOSnRkUGmDTmripF4I+3jqWRYWBMohtSFKa6S06LhZkOrCja7FIsjC4iKewyiYAwltsYMcW8QI6cNEgAvzzUJ6fKNwjQokwoXUaVOkpzOtUlmy1THj4z7Waoyx5GnFfFG90HEw6RHKghdhlRw3p+Yvp05aiUH0IsSkQbhQdhZJg+iAyhsgGA6hRLCFXd1Ajk+CVKnzgcBoFuOHiQYpFkp2YSRLRBa5HSFGhrg3DXK9fuF0lzRQRcHKs0zoXNxDpjVIj6+iUGlzAt9o7NHXSeq4R8jIBE15L3D1Qj2x5fCmwU+z0AaYZqgkLA5tFeGsJD17hFyx2OMwjuhLjRcNIg2LOLqtYMjiHOK1I/zMY4ctyoMVTB2qIadOhg/lmIuPYB/7T0l/B/Oc+4mvVHH7arr99xNCaFy8oGY5B8Uc6VyoBFQlIgaZ1cFgyQm466QYGeLVCfVxD1tFCFU/sPfoS0LfX/uFf0bg6chyhjw+wGxeRJ8c4ze2sf115Ct7+CZBtCwkIgj2FBLmIObB7MdHbWwz/AqNe43KLyBV+gbW/Feiqo+Jdj6B3nsNMRlRX+8htD1drItMkG8OcI0GL/FWBTXOox7NPA2aHEOBbIaQhHK5moxxexY7bmOrGFvEJIxw7T6q9+Tv+pgl6TZl5xAf54jj68GsqiqR+3fCXH85w0U5TqWIZkq0/xJm7VJY8DkblC6lBB+elULbIIc+THB1TLo+CgTZMths2yImahVB50A/2NHPJf7jwD3HBhnZ4BvvBXbR84dg8tIUaQjw8wypXGBn9ybYOqIsUpo6phnErG4fnBL8qmlOvjpCJQ3TgxWGR6tc+rYXApu9inD7PdLHT7B7IYOXC/Wy8mBlwSYPDPn5nQ3al+8gW8HZKl4dU58ENr6pI5oiobU5IFobI7Sl2l1l9cwhKqlRaY1MGsYHa+QLBb7BzhZR1NA7F8rs3kvMNDx8ZatE9hZ99E4HtTZAjCxuFgU+wmoFeYJSNeXLK6izY3ya4aXEbF9E6UXPrqmDYcnF9aBYOC2QXY8410WfY2EwVDJ69RIrT7yGSBrsJKMedEm3B4jI4I0iPukEgmGvRiSC8moX2SpxZci85VfwL+4nXBUR9ef4SmGHbSI9w01kmGA4c0I56IaF4iIQ2UYzn7RxjSZdHxF1Z9QnHXSjieQEtQHyqfMwDU6LuNCnnrx4gWqaU85y9g83eOo7PxPGsx4SVB/6v5EmW2/iNM7nryEGXwqOa4CcHWJXH6W18p778tnFVwSd34tpgabcIyqnQYhoZwoaxBYwLzGvaVQf1CMJTC3KFsHlr4qCva8IiosqVQ90tMtXEpE5RCeYa2FMmIOvw4JDDfeDsub6WZq1h8mzz6PiClzo9fsqCNbIVo0oDXrvJvWZt6Fv/Bpm5SKts9/3Nf9+bUaY6ggm10mP7gRBnOOS8ugSyeoIbxTNiSJ9Zh74ST6QJOLOHG9e50LYYUgstJkhkxAcRWyxkwwzC0TW/PI+7tFHMWuX+EbuLiUTos6jwWhq90Xm3/7DpK98FHntFfy5NnLnNaK0RQMkt16geNsHaa2FsUH1uV+levz7Qf1WcPxbfATRkkhXETNCnWsgzxHHc0RliVYniMyGySSjfm8U/paZ/zc17t3Sd9IK9rTC46ykLlohazc6SMwmNa31E2yjKY77zEYd1i/vIPZWqeuYzuqI4zubrF/cJW0XSOUoRu3AIchLzj56A6Et4xtnA5lw8wQ3EGF29a5CnHaoBbnQTDNkFZGuBVEgXym8kwxfvhB0BbKg1BflJbpdUB/2sGVCsj4kvXiEaAl85alurpK25pg6ZKdJWtG7sIeMzKlFb/u9u/hRcLBzI4l8KMe/eoI4lyPPd2Eyxq+swegkBK6tNdLVGveqQ4x2UFsnoUQ3nsHaCvbcQ7i0FfTkTYMo54jjQzgcBwJXL8FdeYR1fR16PTgZYieQrI9Qm4uHZmlJNwc0N1s0Oz0gVCbsJENvTBEx2JMHQ/ePn5hBBb5ZVGiOY+w0W3iJS/KtAdWwQ9QqUHkJXtCPDKodphma4w7xygS9OkVuR9grjwc2eJyi7tzEXRvTjPrcfOkKkW7orw55/B0vkD58TPWHPoR++I/zlTz+2eFHyX/r38D+mCwSYMP7Kr/v/0Ty6hfh1S/SbH6S6J1//k2f5a0U174W0mTrd3/gfhfIW1do3rZNI/9fRCcnQd+iDAskvV4Gg58XDRDjqsXYZVbRLHq6rta4On6wIj+1RiQ1zAzMBtQf+IOvm9Os9FGDQ3xvBX1wC33nOnQzZh/pofIqVLySBvXsxusL42JOcuvzVBffQ2vj/W/5N+eza/hmgkw38M6g7vwW8Uufh70BbiJxC7Kxq2PitRHxVg0jT/Rtq0STSTBr2kiZ/Wbg0MjOnPluIBi6Rge1yk5B80JO/NgEfW4Vu36GurWGXX/bW87pf72IdbhveeYvYutjysc0avMSeriP760Ec6q4RXXpHeAMs/HzIDXzZ3+E9Iv/X8zFRxBna+R8in3vWcTBLfjya6FF2suwL87BhwWqnSdE6zPERgyTktkXt39f8WOW+Ppxz089IR1KOpyVFOM2TRMRxzVZb0q+NqIYdJmfdEPmrw2d1SH1JMc5FXT8pWPt/B7eKGy1sHedJ6HcBqc9ybQ3Ie5PEUnD9JVzRK2C+dEKTZGQtIMZUNKdoSKDKVK8UegyOfUGyNdGeCdDX3gxbqY356huEURRkjAjbXYTzDTHG3kqXQxAVhFvnQQGeeagE0FvFSGHiJU+5uxDlGffQXTwItGd61DMQ5nONOH/+QwOj0EK5KP9kPnHSdDo7owRpkEd7SHzNi7NsN016K6hjUGMC7zxiFmF3LmJHzWIvIZYo9dn+EpiDxaKhZHDDSS2iok3hsiOw41UUHuLCYz85ME88N2ex7tQ7VBZhStj9OoEOUsx02zRj4dmklNPctLVoE+A9HgracY5zuiQfZgCefwFxLlu0PhfsL3Lox7nL78WSKbtguxdA8zb30P68OuqRbOTT5O+9Ovk11/BXFeotsA/9Sjl23+QVvftYY75/B/BeYP/8r8IWdPtj6LmY3A26Pebmmah6Bavvfdr6rH/dsyGn4d6SHrt45jVM/goR00OaR79I+T5pft2vCPVomitId7+DGqwHzLbjU348g5C+WDva0C5cOx8I8FL7DxFdwqgoFnwWh4UzFErlNhbNdEv/RqkQEsHAZ92NwjUNDVM59SvtE9Fn1wV4YsE8ZGTYODULREXusj5lHjvS/BVgj+jV5CmgukOsp4T7d0IVTQHvoqoDlYoF4TddhXRyu/QHHfRkyEiccEvotMlu7SPrzWuimCSE7WKU2MkWybo9hwKhzg8QM9niM3AAZo1E2TrAkmy9XUtHmfj54lbD+Fs+YYRvyReI1lbYxZ18MkNIkAf3UHv3cTnLcrHPoCY7+JVisjPUF1+N8mtL4Rjv7pFdPsVKEvoaCI3xl6NELENdugSOAA21uBkiDtRp63VB4elyM83O+697C88cV6G4D9tEUVBox+C3Ovd3r5cSM16L2iKOIi6RB5bR8StArswyblrPemsOl0QRD0X+slGYacZ5aiDTkPp0DqJKWOqMkXFJvTzy9fXrt5KlK1ReRUITkkT9K4bCb3QX1S1CTdCQyBFLfgL5aRFvjpazPF7ZGtxw7Si0FuVEr+1hTl7mWbzbaBSzMpF1Pg4GIGsbiGnwyACYgzCERQEL3RC0C8LZDnHp3kQC6nrMPfrHMIserFSQqwQ1gS9+5MysKgX5ECxlsBhhR1HCCeQug4Ln7xERGHh4o1CGBd6rxpE/mBuFt/oUwMmRDBH0pFDtkqUEyETjEwYMbPh3LpGBx2EhZiRd+L0HHgnUfMiHJd5BQSPA70wJtF5ibv0EPXZd7yhzJ9++VeQV1/GHkS4IkH1S8orz74pK5NCEz/5Z4iBUn8SMZ8gx8OwcNvYxqUthKmwZvo7Bv/i9r9D1HOSlz9D9tIh5e019m+eIWntoOMGU0f0HvvbmD/8dponf+Srurx9vcge+pGgKZ+/SBQnoUqS7+BrHzgnCznku8JOetGW4y7f5QESvLwN/XO8wJcOW0Xo9RmiI4PF8kIRLxgTOZpxHnwv6sW9X8Q0s4ykPwn8hbIKXgtSfVVexWlbpxyjR3uI4QBqgyuClkc5bgf7bblw3RxngTcUBdtu2hmu20duDqCqERODGgRjLAjZfzVPyVfGmHFoD8pkjmq/QNJ7AbqtoDUym2KuPEn92A+8wbnvrdDYGerwC5jhNZAKe/aDb/psKlnHcwMAMb6rbBoRdb+IcBYXZxipofsQLr0WXkvbCz5IAVohVgTMCM+xVgRaoUwJdILuiImJVybf+In/WvAg7veo3zL231fce/BXjmxteEo0624fMT/pYhtNPWphqpjO9hF4QTNPGR+tECc1rbUhKjJM9tdOs3YhfFD0u7hH1GjqSU49y0jlcbAgHbdPx9xMGYeZ9vacctxaTB2EVkNVJiRpRVOFVoSziqgzx1YxETNE2y98ATqvz0h7B/MCvTpDSE990uHgYINLvWlwEry7Io5EIOctXATrR96JXX0UhCa59mF8nGK7a5j189jWBqI3Izq+GfqA/RZ+dxoUw+azsNo+csjH10/dy1w72Nmqch4WDHUVFNzMLLgSlgL3ridRu7dASPxaDzHagYULIEYgOzUia3BDSXMYeupyaom2RohMBsnjBwARGViw+YWygYxYKURi0f058+tbJGtj4v4UlTQ0s4xmnlIvPBK65/dReYXMKmReIzpA3oHBJPSAjSa/sosd5sGBLGlCEMk2KF/91+jRHjbvEpUFvgimNsEBUHxVpvVdpFf+FLPVT5O8+lFwFrN2Ebv+NuLWQ79j4Hcf+R/xPz9mdrTC7uAixyfv5OrxJrdnGefykpZuGNYJT73yMBsfP6a38VPoi/vopzW+1QnnuJjjzpxHfedPfN3HvdV/F/N4hXLtSeJbH4ezm4g7B6EVtZHj92ZhARgbonPTRfk7DiOyi4XUg4A3oQIUvhGnVapgvNUPC63ydcJh1C7wRQTKBTa6VajIEK2PQ6Y6qYJC4+aTCG9Qv21Isbj5c4j1d4I3ePN8EA5aLC6agx7FYZ96UVFs9SborMTMU5LtE3jHwzitEcbg2n3k2ioYg8wmqP2Kk1fPYYymqWOG4w76tiWKmlN7beckcVwTxQ1COo4O1zl77g7t7X+Gu3xA/Z/8YdIrf+otj1M9/CLpUZguwFkqqcnfgs+gRneQ09FCvMrjKkdUfhzaGb63gnCW5sL7aLYeQ5TjYIy0vo3aPwwGUJ0O6uEyyGRLGRQIz7fh8Bg7ioJa6ZUHz/Zf4psb9xz8u2cPwxx3VrPWvkk5CCIuRZngRp1A+JvmZBtD4pVJkMC1isnRCs4qptMWeVYQZRVRa0K8MmF+a4uoXZCuj5CRYXL9LFI5ktUxUW9Ka56GUbpFUFZJw+ywv2CZS5o6pphnaG3RUUOalaQbJ2TbNQiPuRPkYeOzkvqxd+GinPSTvxxWxx2DKBrKYYcLD78WxmHmKcnamOKVddKHjxAXQ5kfKfFRC5FskOeXKN6+EXTDp1eRw1fwUQu9+Sy2+rdh/res4LFzMBmFm29tFZlOMWcfQpgmCNTUJWI6DpKmaY4YHAai39pKMLM5HKP2drAvVmHC4GLo7wLgAmnHTxSqY5Ath6oqyqMecW+GHaeIwgLugbD9yzvrRL3pYmxP4I2kPu7hG306gphuD1D9GmXnNK9kge3fmYVKjgwmJEhOH1a+3UEAQk6QzgRJ5EfKMD9eGvzHXyDfvYXv9PB5G2VqfJzQfN+HcPkKwlS4/iOk98DAb628B94dCIDNjZ9BzHepXUMtI/Tup/FRjm1voHpP4myJfu1Xia5+Hvu8Y3LnIX7rS0+xX+QcVRE3Z4pe5Nkr20TCs5ZYPr17jt1XL6Ol5+H2HPGvPLkyrGZzzqwd8dC3f5HykY9+1X727wRRHqGvvYDv9BDb68jOFIREdha675mGjU3El/cRKkhD13urD2zOP7pShRHcSuKriPL6JtnjBxBHqL0d/M0htBZcDAfx5RH2OKLaW6We5OF+tgp5x5I9ekD9Pd+Ly1be8vjMxs+jqzHc+nBQeJxPYTTE7VmqOxunglNxXqK0ofv4TeS6C4Fz7WJYRAKiHON/9gV4b4q/OaDeCU6UZZHy6u45Duct5lYRCU+mDYkyKOE5LDNWk5LGKYZ1zEkVM375Uc5kNZe7Ix79P3Z46Hv/LPZD34m/8seQKj1dVLbWnsW+9GGQiurSM0EQCMJz5CiY+Oi6oNl6FDU8xB9Yqp01ov4UcgXzEhHNsK31YGA0O0QP98MkkZRwZguf5ngpkS+8Ahc3wzNlMsEfThZTNQZXaexr8gELYflQ5bzf+1zivuGez3964ZDmsIudp3gnaF04QMUNpkgxdRSCZx0xvRNG9qTwqIWZT2WCQt361iFCelwdISNDunmC7s5Ox9RMHVzuktUxslXjypjyaEFmUw6ko5i2QpbvJHYxgqejhqy9MJTZXyXKK3R7jkjqwA04/w5wFjU7xl5+BB9n6BefQ0hH5+IeemWGb8IonasiknMDRCbwaYZZu0R2/o+8odx8t5Tbaj8OCyOWstrHdbYxmzO0McGCVt8JbmD9LZLf+OXwy6aGOmQ2cv8O6CiQ/05exp85g13dQtQFyr0GJyPUaqgC2Guecm+L9NzRqZVutD2DIpxF2a6I8iqw4XsNaPBfoYx8P5FsD2iOu9SD0HaJOnNGN86QdGfk5w7JlcOVwWiknuQ0RYI1+rRNpPPytDrCrEHoOfYLc2RWgxQI5VCXQruDSRkmCRKH2zoXsmfT0Fx4iuziD6EII3dCKPi1/x4xOsGeOY9973/7OqHqa0CP9tC/+vPY4xgRG0Tm8O94DJd2cbZE7XyE6PYruJtzxtcf5gtXH2N33uKwihg34fob1IJUgZCCw0ozMYqbMygtvDRu0dKCfux5tJPSSkpmOxv0/+1PMfvj7VON93s+9uk2Kr/E7Hv7xLc/jb5zI2j9uyKMp7XDZIr/8j7NcQfdn6Kz5oES/uwthVo3iNTh5xXZ5RFuTyLMNIzE9iTNqzl6ZRZEnWS4DvyiWqciw/HuZpgg0uCSDi5bpaqPT/vis+OP01p7FqHbuCgnufUCmAZu7FHf6mHmPaLujGLQxVQx2cqYzvccQWftlHfj2n30jZdgb4g5SkEopv9HwnzwNopZhtKW8aRDZTSlVQzriFw5unFFogyRcjzZnjAuMjDQjRqU8DQ+ZbeI2SvXuTnp8u5xhysvv0L3qf8O/54nmT3y3aj25XCsnvx+ANTgZfzRc8xau5CsEJ3shBFHKUle+wLi5AgHRN05arXE74pQIbOG+NaXKPtX8EmXZqMFW4r41heDHkQ5C4uBi5vh5MQxtFr4gzEiE4iOQs0azN5S5OdbHfdu7LPfC+VdJzBljBy3aGbZqbjP3X6n9wJvFXUVk4iQsUvpiOOaeFEavCs1KqTDL8bBXK2DCY5TNJMcuW9pJjnWaFyjgmpgE1HMs9e5BsJjrAqz+YspBBUHs50w+x/636azEXTFdUVz9hkA5HyMzPfClIzoIYoC5jWyqhDnV3HrW5SX3/s7lpHvQqoU230I40zwJddx8CI3DWp6EmRMy0AW8mmQOSXLcXkn9Ow6oW/n4zQsBuZTRJLC/jEYj8gaZGSRrWYxWqiDu5sXQS9AQ9SbIuLFatssiF8PAK4IvugyDgI+tkxQ8V2tBY2QDWaaU5x0qecpeX9CPQu6C96LkMkcV8FpEZCypDnZJEmOgbA4VFrht87ChkUYg10/E1omLExfWps0doazJUJqYt3DHx5Qv5Ajnt8huvUT8Cf+ye/4WfRnP83o0w8xOe6jtWHtiRtEh3tE7es0UoUxNefwRcR02GV31uGgjBg2ktkintbOo4QIXSILcwP7TcmcQMA7Y7qUVtGPY07mLYYHa8RX5+S/8v+geM8HcetvP3Xz+51wt6qR9p6myS8AP4s+Hry+wbQIpDcrgh6GylCdAr354LT9Xa1RVBBJRL7gGHRcuAZPGnyhguiTF6EtVjTgYmwV05RJyPzvyg8LEbT9mxlav05S1INXqHtP4otd4pMdqCvE7i7mIKeZZkEwrIqJWwU6rUKFyflAttURwhj0nRv420PKG+vMj1aIW8Wp3G/dxJR1zKTMmDXhOutFhngxEhcpRyctyNMCYxVKehJr0CKmMJraRowayXWXoPbPMi5zLu1ucObONfJn/gX28mOYlXOItItYfQp98uGFY+EJ0cGtU20Er6OFv4FDdhwimSNWEvxxBXrx3CwLxDS0D3yc41WKy7u4fGGsVldhf8aEtuW8DOTlBmhc0GX4PcBS5OebG/du6XvjHO3N45C5N5rJzgZ1mQRm/UK0R0UmOOM5QVmmKG1RKvzLs+A77xfjc0I7bJHgan26T6THG8F80KM46Z4G87qOmU9bnEy6xMrgvEArS5pU1HUgEDqjMWVCtnlyKnoDIBKHbW+ASiHqnJLBimfO4CbXAYj3voQcD4LYSJxQXH4v6fp30vo65rhj3SNu96jidWqpUZO94GI2PEbWuyE7Hw/x7S4+7wAE85M4C8Sm7XOoo/3AQo8zXHcVe/YK8f6vB/OOjZi4GCFSEE0I8GaYL4hcgf1/V9TDlwLfSFzxYAq9xf4qSX8SRve8oNhbI1sd452gPOojdZjEKBfGKclCQ8GUcbgm1ivKV1dRSVikuWFYVIqWDZKvsxRMQ3XpHbjuRXR+IZi6vPqvcWkXnyyqQZ/+v+MXErKz1hbZccPo5jZ1mZDeKOhf/2/R/+evvgCYz19j8mtv4/bt8LBWwtG7sEd0fIRq7YS/MR4GG9gqYjJpM2wi9kvJuPE0DlIlqCyAx3k4rg03xT4zNaLxJRJF1z5BIgXjRnJQtGgdbRAlNcX/3iX68HPkZ34N/+1d5u/9E/dcCVAyQSVbzC7/AfTLXwovOnBjga8Vaq3B31ZUR31iq4jPPziRH5k0wdlPeUg0/rgK0xuDCc1OkHhO1ocA+InBzxTeyuDmWSYYo4ii5tTNUzRzlA3tG+sqqr0Pkxzdplx/mfjO51G3rkGWY15LT0cahfCYaRZU7RZaIfY6qHQUFtGzKe5GRXlni8neOsNBnySpGE/CvdgYze64x9xEzIxGAutpgRSeximsE2RJiRCeVlKSRjXGKfxcsOZKGi9ofMRxJXhlknFcxdwa93jkeJ0nDq/Sf/cXSM7exHd7FM+cR925Cd6FMb2P7aMfJWh/RDHN+UeJhwPYjBFaB9Kw21tIKEuEaYgOr2E7C/luZ0+/dmkL4SzqZKH7fzLFz0H05II/oMFK+L2Y81/imxr3HPzz1RG2jk6zNx03tBea+aaMmR6ukPcnFNOc6aRNFBmiuGE+zTFGk+UFJ3c2WbuwR9ybhtHBrAqOdZNgYNM5c8Tx9XM0RQjo1irmRUZjNB6BlhbjFPMqQStLEtc4L5hOW0jpSHsTJje3ybcGQV0urRFPnIV0g6zz+BtGcrL0HKTnsK7C9J7EwWmJ8RvRi0viNZqt92Pch6nPx6EnNzyGhzqIV28hVk6wFy5jVs4R1TeJX3oukJXyjPId30O8fw29dzPI/+oImuDSJmoDStDcaqPXpsRPldibDvVYhr81xk1i5Hu3ELu7UBgoQ8b8IFDPMupZhljYHGcbQUa0GbeoZ6G/n6+OSOuI+bjN4SsXiaKGzvYRydoYexJRDbrE3Snx6gS1XiHHDWIlgTRBnzH4lW306A7GGWx5RFGOce0N0uufQh7shjdyMqL4/Ba2fo1sa8CXP/Z+tLKMp22OZh3e7QStvV8h3/7gmz6D84bDH/v/8dGrzzBtIgSwklTMj1bIkiFm+yLN2WfIr3+Z5jnL7hce55WDbQ7KiJkJQf7EzxGNYCzHyEULygnHvrtG4woS1WZdXGRGzWNJSjeylFbx0mCd/UmXyimMk5zrjMl+seahi79K/sF/hPgvf/Kez0WcnQ8ksMkIzmwg0xOYNbDWJV4bUe6tUR6sYD81o/PHf+f9/W4gtMWN7rZNDGIjhemc5maL8Wvb9B+7iYgszUE3aG1sDKmPeyTdKTpuGB2uorQ9nUjQw+B50Xzxn+J1jHYW+fLL5Id7MC/C54smqPUwQmzGLWwViIRqPWTKKhbYlzz2pQLf1Jhxi8nORcoio6lDu6ooU/KsYDprMatStHSM65h6UYWoXY6WDgnUVlFbzfmVY5RypEmFlI7hPKcVNVyJGrbTiE8e90ilpzCSvTIhHq2wtrMdCNM7J+jVW+T1vwRroG4Q5R7qu7YpLz8TsnipQaX4xxQ+6eJ1gqjnpCe/FKYLijnu1SkqzdAHt/E6wnVXaDYeRo/uhCqVVOGaGI/wjzwMgPvMDXwVo7Zq0AK793sgiH3fe/5L3E/ce9l/ltE+E7Tiq0GPctIKZj5ZSdSZ0wZOFuY4aVayenGX6f4aeXseZHzTmmqaU4za1LMsWI0uXPTutgEmu+tI4XEuBH0hHBsbR0ElsImIogZjNFpaKhMxmHRR0lFWCdHckIxbdC/ukVweQieBVhvbXfmaghxKJqj4/mbIkWoRnf8jzGfXsO63goiPc3B2PZj6TMe4hzaRr30R4hift7DrZ0iuPxeETgBMExYBkoXjnUBtGhQG0gTiCJlNYRzm+qWuF77jLji8xaB6Dyb4x1kVGNuLFhDS04xbuEYTJTWTQQ89zfFO0Fkd0v/u17B3NHae4qoo8Py6U8qTLs2kRVYdB1GlSw9j+puhbzkdogb7qIMdiBOqC29D95+mPjMmkhLhHOZTnmaWMRn0eeX5x3FeMq8SaqtQwrF/Z5u3f+oX4AffIvh/+h9iTc60iTisIpSAVtQw2F+nd30Hfe4O9soP0Fx5O+rGJ0jSikwvgpOAWEpyuyCXuVUGcshEnFC4EaUdsh4/Qsv30F6zJvNg62AFM6NJpOPAZkyNonaSmdGsJhXqtiP9xIzsfT9N8uifvqdzEakW7g/+X6lf+VfEn/tNaLWgI+HOAFf0Qqld29Os+kGgvLOOSiuizTFiq42/NUPkQX67e2kPnETIhTjXomWYbJ5gp2EB2dsYkJ87RK3PQ2n6E1eRV/pItxvG1+Y1/sJZxP4+bs/TDPoAjF47S7FYhPY3jxnvbrCRvYyqK6q9FvHGnGp3lfnRCpNBj6OTVVrZHGs15WIBIIXDOIX1ktvTDtYLYumIlaWlDVJ4Jk3EsI45KFOuT7rE0tGKGvpxxXp7gnWSsomxXvBop6RxkrmVNE5wc5ZzYdRnrQ7PThE5yiffR7zzArbdp9l8kvSl38C1NkFqMAutBpXg2udAZ6idj4fALyV0usgngcP94GhqGtT1l0CqhShQhtcxZuU86W/8HOJkFAjO2womFb4Cd6SpHiABdIn/OHDPwT9KaszCqz3uTammGU0Zh9l+7dBpTas3oZxlp79TlQnWhJ68rmPqMqGtQy/eexFK/RDY64vZ/6aJqOoIYxRJYpiMO+EBtujr102EcYraaIomop1Ur7/HrCI+N8S+6x2Y/hYuW0VvPnv/jtbXibx1hdlWiZyPQ78xThb9uILo4MWwkQu+9rKcgY6wmyuBnDg4xHVXkMlBME6BwIyHEPyTFHGOoBrYSxBxBIcH+IkLOuuxR+gHs7pXaXU6u2/qCLtwafQmVIXSVhGMUyDwAEYOO82CHbGyCG0xC88FZyXT25t0LoU2CVLh0hZqeIiYTuD4BCpPLCVV3MKnXapL7w7H98K/oDzqU8wzBrN2KM96icQTK0MS19irM9wjP0X85J85ff/2N/8W5n8/4nj47bR0CIpaOvpxRRwvvM9HJ8hbv45wFtn1tDcGXDg+5MkyR9KmGyvmRjFtPA5IG83UdbA4BtExW3aTXMQ472lpRbo4FTOjsFLQiQy5ClnlpNF0o4bDaYf45cu87X/7OOYHTtDP/MV7Oh9SaFy2EqYm0nD/CT1AJg3ZVuADnN5rDwA6L1HtAqEcDGdBTEs3COWDCVQZn5oNeavCaPA0C8qd2hF1Zqhu4Cq4QmLHOfJgAFkUxm1lOB/+2FLtbTDZXadpNMUsp6pj7IJjFEUNrkjChEMV4WYpbiHxPZ21iXVD00QYq3BeYJ1kVoW2QeMUsbLUVtGJGtpxTTcusV7ifODWODjlG3kvqK1iVqX0shllE9M4xWZaMDMR0YI06DxMqpSTgzV03KC6c/TJLWy7j8v6CFvi2v0Q+FUGQiOnO7jOWcQ8mBHZ8+/H1AVeLnwb6hJVzPF5G+ZTRG1wcYoaLoy8dIRt98NzQyze70mDSGFBQyHqzh7Y9RCwFPn5Zsc9PxFUWlGOOkRZSbY1CDeY0TSVD6YSWUnam+CdoCkTymEHa0IG75wijgLzH+lR2sJCfAMnQz+4jnBOYZoIazV2UQ04mXRJdEMc14gmYjRrY6yiNJpxndJOKrS0JGkVlOQe2cY+/Z+TfoV61r3AuoqmGaJ0++tSefud0Oq+nWLlGvr2q6+/aAzRneunY0cYgxyf4NpdTH8LYWrkeIjLO8i7+gTGhp6fsaG0pyNYWUOcTCHLIE1xN+cIsSAB4h/YnL/OS4rDoLrofejle/c6CTNfG51ua+uI6s7qwtq3QmUeb1TQdVj4P8yO+3S1DQuju2RJ56CpcUOBOeoSZ68SpznVw+8n678rBLwnPkr08jQ8xJuY/SInUZZUWTpAklbMb27R/rWPYqYn6Pf+Ffy/+K8Z/sYVrr/yXm6crNHSDb2kIpaGdlKRtxcPxcGE+KXPYzfPQL9FeuaYrUmLdzqJEueZNhFzq9grIoyDbhNT2phEwbDuEEcCJQRSQCyhox1a+rvTbrS0QQlH7RR3FlK0J1VKdbSJ/PffzhPy4/gv/tfMv/8/vyfSqes+hN18OYhHmQbRSpCtMpBBjcAddu/zVfA69OoEkTmoBXY/DloUd1vK0geynwsOmM5KTJFQzTPy7pS0PyHqFRB7qINAlKs19jhCtpogyBNr3I2Kan+N0e1N9vc3mZQZeVzRLJ4VZRNz8dxOWGBWETJpgnV4ozFGY4wizwqG0w52kWzUVnNUtBDCo4QnWZT47wb+VhIiZdFE2EUC4r0gVpZYOrRyzJqYtXYw8rJOkC3MwZTwJMpivWDWxAzGPdLjkmzjhPjaC9gLDwdCcDMP/fq7LUlvQpn/7AepDj6CMBVZ6wrzKxrvSqhOUMPryN4RflEBwy1IloOjoKKodSD8CQF5DFrh9zwitackYNV/QKNAS/xHg3sO/tODtcU4TBtTJqjInLqseaPwVjHZC37ROqm5c/M8F67cIBl2KOZhdZ23Fmx3L0h6M5pJjoob5sc9jvY3sFbTymdEuqFuNEejPivtKUI4mibieNzjxmiFTlRTOcVBmfLE1i79/pDe9hHJ+WPEH/6pN5Wz7rLCAcz8FnHnUZwtMeUe6vALuNYG0f5LSB1Rbz5JnawQtx6iqY7w9Qmt/rsoq/3fva775rsp3p2TvviR8L2UMBkjolDy991+ePnLL5Hs3g663v019POfwx25UDatHaInsXc0MpshVsLNa4+jIAgih7hJjn5PGzEZQxzTXHn7A5nljTbHlMf9MK+fzwJZT3qizhTdLhCRCdbKTr7uqBgZpHaYacbB1YfobQyI2gURRbiOIoNd3cK21pHVGNfuosoCmU3D/l7q4D7wKHH3yVPuRrN5geT8R2i9ep7jKuXjRympgrXEcyGvWNvfIk5qyk+14BMWFf8trr/yQV4drHNrnnNSa57qT1jN5uSLCpJQjtGr52gXhyTdMpBAOz3Uyg7Z6pjL/Qnb53c52t3kYLjCqMo4KjLGTYTxkCpHIh2vTlOs92ymBu8F/bhBS4cWnm5ckUcNkbQ0TjE3GiUccxNxUiUc3HyIm/9mncubezz80s8x//PVW4rBfCVa3bdTbFxDNDOEqYmHA0R2RHltk3qSo9Pqa/7+NwLZcdiTCDvNMNOM7NIBIgU3E6cVn/q4TV0k2DqiaYI1uDWB+KdXZ6FVtaFRRRjxBZg8f2Exvjrmzmee5uh4lZN5m0GRsVdmdLQhVpatfMY7H32B/iO3MdMMO0+JVibUR32qaY6Qjn5vxLU75xlVKdYJHKEFc1zFRMITq3BuLrQnSOGZNTGjKuXhjX3ENGScWniaRcafKks7rlDC0RhNGtWspIKb0y6bWUE3rnAIrBMUJizC6yphdHObTnNE9MqLRI8kVE9/J3blMmn3SaqDjxDvvkh1+btJZfIGrspduegqPobBK9QXnyB57jdhFJ4D0XOfCsnBIlHwWzniTCABeh0h3pvAazewwxwzS4nXxg/segh/FMT97vkvE//7inuODb1Lu3SqKEhlVhFSOcpJi2KWUdcx/fVB6McnNUlnzuWVq5gyIUorpHIYo4jzkqQzw1nF5NYmpolIWsHkZ3XthMODDYzRRJGhq6ZUJuJo0sXYUHpT0jMzmkwbunHFSlKc9u50WiN6ktnhm8VT1Mf+PlJKXHeVSCr01f8ntNrExRzzvMMcd5kVaeAkzJ4jTmqSVrHISqY02mKPe7gr+4iHN/E6oviOP03WunJPut5Zeo5y9huI+Qy3vkW9dYXo8Hpg/y/KtHI4gLObMBohDg9QZQF5htwsQraPBGNRmxbaoezvbw9Rj+Qw91AY9PYc9gv8zIMsidwX4amv42q4R0y/fIG6SHBGMztZjBcJT0dZ4q0TROZwe5KmipmP2+y+8DiJbgLprzNl64lXg5pbz0EWEecepgJxsIMa7IcRJbmQhn34HPoRic/b1OvvesPxdtkKbhZaT4/0TvjcIOekCmN3m6nkYNqhuf4QjdWMypRb8xa/sa8pXcjwWhqgw1GZcrkz5umHr7F/Z5vV1RPmB6uYjxQkm9cRytGMVmmmWSCYVXGYYIkrRlXGQRmTaUcqPeNGYz1calW0o4ZUWbbbY2qr6aZz1lZOmM1z6jqmMhE0cKY1Y1rHmEW1K1GWblrgnGR8c5uNf/f/hj/7tYM/QHbxhyjKHfzeJ7DrW8ibR0QrY3RnhowfYM//lbVFO8eRbAzDuZkEwR8ZGaK1CeKOZz7sMBiskCxaK+2VMfHKJJxDbfGNRXYF0btj/M0pncdvU97aYOdTb6eqktBXd4JUG87lc5wXdOOSTlowG3VY0RaVVdgiodxbxRQpzmicVTin6GczrBNYLzFOMq5jCiOppcd6wUpSM64T5CLKOAS/+drDJMoSSUcsLcYLelF4/0fzFokyfPF4A+8FSoQWUNFo2nFNpsIx914wq1LkqEfTaHoX90JrY2dE3HmBYu1JrCuh+yhV1EaOb8Jvm/qYDT+PmO8THV5DX/1i6N8b8DOFnaVE74qCeqmQICVyeEz5+LcT73wZuXMLyho/AtWdI5Sj3F17wD3/pcjPNzvu3dhHOISyqBikshTDTjDhyQvqeYZZ6Gj7xUhX3CqoZyl1HSOFJ13M38q4gRq8l6fz3wAqatja3uPkeJWm0RirOJq1yaOGYZVRWUUvqUiVpZ8UrOQzsrQMqoFxg1AWpCT78oepV57Gmil++CI+XSc/PkDUDXL3dtD/PnLYoaQ+6TDZX+P6rQtEylIbzbRKaCcVsTZoGS7e3XGfdlyx8oUprXyGtYr1s/8b+XuuY97zzD31Zn3SDf7zrTV82kWWBWJ/H5GnwdAG4PAo9DjTOFQHqrtC7er1/7M8zO4eDUNp9XAafiaBTNO8kgb/8lYDwwfT15ORoS6DcE+SVpTzNEglzzLicY4oDFF3znzQYzptoYTD2MU4l7JB++FshLtwCdfu46UiuvY8/rlg/CRSgxm0EcoGGeYzOT5vk6XnqM0I5w2RagUv9ChkkEdFi8PSc7uZctQkQExLZ1gnqZziuEp4cRTzqj3CYmj7Fl3RZmYEpZXMTcR43GE8bzEtcrK4otOeEu9ukLXnp54VpkgoZmECJZSBoXKSphGohcdrN7KcyWec6Y7YXD9i5ewBzTzFVDFlkTItck7mLY7LjJMqobSSVAX52GhRHbg77VIWGeWra7i3WNS+Fdz4ZZLD68gFcVT1S3yhKO+sP7CHvcqqMFmjbSDvyiC5LLIGSs/kxYvBg0J49GL0t2kiilE4x9nmSRgXrAQiKhFShkpAB6KVMe2VMWrSIp3n1FZjvUSJELAj5VDCUZYpxUJgzC28BnRawaSFaTRFmWCcIlKOutFMmpiTWtONQmkeQpCWeIQAu8jw50YxNYpcOfpxaF0aL/EeaqcYVMnpos36oPUQfcUYXTspT6sNAHFcUw66tC7uI9aiYHo0vomd7uD7jxD3n6Z2n2M+fw1fnyDiFfL8EnJ8EzXZR9QFfmMTMboVjlFmiToziDdhNg2JghTQagfhIOdCW9BYRMfCxAfuxV1dhSW+ZXHPwd/VMc7IoH2/eDjptIa0RirHZNALJj1e4p2knmV4L4OvO5DpRZBa/BxAZ2UQf/ESHdXI1OGPBMYqqjpmXCcIAeMmorSKdlTTjhq6WUGvM6HVngX+AGDmKf54AJNXEd1/SWRq5PA4sOcXs66+kdhJBl4wun6WyUmPokx5ZbBOJB3WC+ZG0ykbUmVCabCJ+MKwxUpsSZVDLyT/O688znffPsPlveew87+Fet9f+9rHL+lhevZ0dEfMFzdqWYVAL+TrgT9dPKbrJrwGIfBrHQRAJhPcSAZRpEIG977YI2ob7Dy789Ne6YOA9+HhIaVDJzWqjjCNpilS6pMOMm6IuvNTAmeeFVRVshBiUtSjNjFl8EfIFyp80zmzG2coxy1wgskovL66fUC3uglnLNZVOFvi7wb/dB21aWj1gkmJxzOWE+a+IJ2vcbEVY52kdpJBrRnWnlLMcTgSn5AqESyqgZnR7A1XmS6uu0wbtuYTWklJdzYla81JshJnNEoH3QqtLPm8TT82zBZiVS1tudiacqE/4Oy5XdaefgW13eAnnvm1bYZXH+b2aIXdec5BGTGoBbWDiy0beAHK0izuDymDcNX8cIXVX/znVN9x43ecAlDTQ+R4iDBNWBAmAt/4MJXxgKAW59o7GRKARoaZfwBtg3HWYrInTUuUDsG/aaJFWd6TbQ1wVRTUHUcLzkisUP2S1vYxUlk681Al0zKhsZrShOu7tprGRMwH4ZpRcUPcKsL7WXhhGKNpbJjXr61i1kRMjOJCXoXF2yI4O4LplPXhXyR9YO4vvq+dpDAatQjwU6NIF8RNKTwCyKOaSDkiZYiUQeJxXiyIg5Jq3KKTNdBavN/JPnI+ppYaF6+H1s3BZ4hPdnBpC578M6jJPmo6BGdx3RVUZy/4lmgFeRaqZforqmLdPmK+uC/aHcTdqkA1BAjJ0gPGfS/7L3Ffcc/RYX7cI1rI9Zo6Il8ZU5x0T/u9cVpxtL9B2pqTdKfsX7vE+oU71Itspxi30VGzIP0orFHU05woCyqAttHs3T4DcEqsSZTl+qTDzCjSxfjNajannc1Js5KsO2V0uIr3ImRUC60B8fwe6foQuTHFDFN808Y7iTcSM8uQkaUqUsoqCQ8FL9ibZ8yMpHYCSDif16TKLmQ+4Ysjy4mf0YiGNd9FCcWr06d5dvcc77/1Gfruf0R+919/y2PnvAGdok9uhxti4exn3vlu1HiAWDjMmXe+O5Ts5hPk0X6Qbb0bwI2FskZM59A4RALNbo/oXHhQ+kLS7LZJHzmGjS5+ZR2zfvaB9PzHB6u0+uPgg64saXfKcHcjuKgd90j7U1wd4b2k056eLtCaJuLkJLDzL7YL4jtfIln9IqytMPvUBuW4xa3XLvDFgzP044phnXBxf5unioT+h9bx1/419sx7T9XwWv13UXzPH2FD/gLfNurw/PDbODlcY0pF4xy7RURpJYUVDGtBN4LNeoOKhjYJq7HnXG5wHkZ1TDFc4bjWDCpFrj0Xq4SnNvYZTTrUTUTbaHobA/qXd1CtMkgsf65hJZ9yOO0ya2Iy3fDMY1fJV0e0H74D73sMPx2H6YGbhuNxj08f97gxhdI6HGC9px8pYukRQjJqNE/HFXkWWmLzcRv/61dY0x+hmY+pL3578Cd4C9j2BsQJzCaQyWBHKyA/d/gAroQAeTZi8tGNIPv90B52nKHyBa3cC9be+TLlnTWSPAT+u/dr1g7W3HdeucSVrQH2ro1uOSe60EBlEDEk58PEwkqj6a+eYI3mzu42FC3GVcK0jsnjKlRkmojeypD247e585F3EMc1aVYGzZA64bjMKRdCYxJoFuQ/LcLEx0m1cAtckPbO5HMmTVAudV6wX8a0tGUtqenHYeGQKkskHKk2rLemSOHptqYo6TiZdENFRzmqJmLncIPHOlPcPEYdDJGzGVx+BDEdk84/Dtc+RXxyBMehJ6+UwD/3CaJe79QhUZQF9spjuLyLnI9RB7twMMA98VggAktFdeUDRLufRU5PkCX43go+TpCjMTKrUeWDE31a4j8O3HNsMFXM6GiVKK7prp+w89Jlur1xGOlyQd632xtjqpjJ3jrt7oRqmmONojGa/f1Ntrf36W3thuzv+tnQi2sUMrIobcjygvG4y7xKmFXpaTmupS25Cg+NoomItEFrQzlpobSlLFLqIqKsVjm59ghKOtpJSSef0euPQum3yLBW0u5MKeY5w3GHWZUyqVIkEAlPrhypEqeSnnfLeRup59W541Dtcdy8ypfqPfL4HHeG7+CoOsvZ7hWe/tVPwPZbz2dbVxHtfQF1tBsIfnFodei9m7h2F7p9xGwW+t2Lm5uTIfQ7X3ECDBzPoaMhjRGZRQ6rUPpLQXQk0dMdcKEtIA73iE6O4G2/yyvja6C7OSDdOMFbRTXono6RRXFzuiA42dkiSUvWzh7QVDFZPufOzln2xz0qq2mM5vEPfBJJhb8xILskaeYp/e6ES2XOv71xltU4ZFTp9cs88revs/FDN7Cd7VM/BQBx5n34C19k67EbXHnlcY6rDntFzMxabs0cB4VASVhU5FlRMZtpxtnMcb5V0I9rVtJQIj+a53RjyXaqGTWam/OE5mCbs9k8MLwHhgvzjIvdKdHGGHUZHvqOm1y8+lmqnVWaWYaKG/JH9xAPb+L6j4X3eOP/z96fBVuaped52LOGf97jmU+eHCoza64e2I0CMRAkBJImJSEMOkRbYYkM02H6Qrxw+EJmyKFbX9oXjpDCQftClmyHHaQoUoYMyhABCQQIogE00Ojuqq65cjzz2fP+57XW74u181QXhkaBqmQ0zfoiTnRmV2XlOXv/ew3f977Pe0x7kjF5cJOLdZ9EdQwCCUhs1zEOFWsDVaGvRwe/eXqT26s1w6gk1IYvHX0b81GAOpr+kRt/3UzQs0eeeQ/e7VHWPsXt4PkxXe3DjmjLb1Zdq1Bp5e2fZYitIqqlZ4JIZQnSinhnTrnySaBtFREEhtPfe+U6/Gv/lYcErLEXmxCeqEWnFb0bV9gypFmnDHprzldDP4tXhqKJGMsOKTvWyz7NZuMHrq16O8M5SdBwshhzUaZ8bWvOZZXQbG7+szrkIKmoNweCQDquqpi1UWgBibLc20Ql94KGLGiY1DE7cUGoLVpakqBhWabkZYoUjtYqtpKC45W/5d/oLXFWsvzoiGQ5J9yf0z36GPlKQHe+xpz30OMcu4690FdbRNIiZleImyO60Rad1tiRzwBxvTF2sENY/7ZH/MbeqaQv30KfPfJvkJTeUdTUHu/bhph1wnOvL27+P9T1mTd/HTWETU0QegzncLxABe01WrcuY5/LHvkP3OnJAcPBEucUgTYE2jC8dQbgmeOyI9ubUk78uMBZRRTXyLX11h2jKa/bev7DmLcB49hQNxHdQtC0IdZK3wJsA4ompHG+HVi2AY3x7UDnBPO8R2kCdtYrlHScr4bM6hjrJHYjJsQJGitYtZr92BBpg5ZeyLUfRqybA3I1oegeUjTHHAvNd/Mev/LkDoNvrnnphf+OMhmT3PzZT712gcrIb/0Z9Nkj5NUFlCXupEV87ZZPJXMOd3TrWgDY6QAx6MG68GOAwH+5VY2khd2Ibv8G8pZEPHroZ3xxhMhX2Fv3Pf50UdA9p8O9rX22QLtOWJztoLXHOjvr2/rxwZSRk9etxeXV2AuilCMNG2wtCbQhf3xAOF8jZEf0wuQ6TlkJx/2+/+ZLq3g03yJ4bMm+MSPt/xJ5NLreAN3su4jFDFMOCISjH3SsW0HlBI1zdEha46g7R9E1BGiCRhApyXakOUgKtrIVWVpwsxPEScXpxR5X6z4XZcpxGWG7jEg6Qulv0dsXlwSjNXrX+vfqq7dIXspJKHGjLTApHR612u6/TPK9dxDa0hsveGn/lFgbHq8GnJYh00YyDDoua0GxkXgE0vMArsqExioGYUUxGxCOV3Tyj57Vat2j2vEHo/Db34BVjV0EuDKku1TPLcXNFptxT2AIo4bmavSJ9VNZwqwk2Z9SXY6p5j3flo8agqTyoLCg9aLKJsQ5P+ZA+mAwk8fU877PDbGSrpMI4RjtTHkJyIuU2vib+XQ69n+n6FBFRqBbkrSkriIW6z5JVNFYTRK07FDSWMVeUpCbwFs3jSI32of1OMl0k1syCluizYWgsorWSVZtQKxiDtOcXlQTBw1xVNPr5QzaNes8pWx8bkHRhgzCGtsJZlXCZLJNvM7YVY5wf+5fxJmPpA5u+ShmfVRD3eFKSVcGuJVClitENEemENyraQ9eoAtShK0h6+HiDJft4MIMhEYtrzxivCoRx48hDukacHVIuegxek7Pwxf1L0f9idaDKK4QymHrgDAtcVb5EBHAbgRQQvgPSWs1zimktCjVeaBLq6nnfdoywlmJ/b45pI/1lDjnQTHGyWv7VNsJnIPcBGS2pWr8pl+1Ia7zGoGyDVi3Xqm7bgOM84reemOzmZQpudE0VqGl46pMWRmN2Zz6205QWUFuJK4D04HaxHluR4ajNCQ3fSZilxmarqupzIKz+JjfnrzKl88PuPnONsn+PyGPhn9AnJX1XqHdOUSbFuoKnPC2nMZvcnbrYONz39zaktT/s2Aj/sPTwQgEBCFdGGF7I6/k/b6S8ytYbjQO9jkF+2zm9k2eUBYJaS/3GQ7aIpXd5DU4Oqtoy4jFYkCalIRBwyiDUBsGQ38DalcZSId4YqnXKW3rn6MXeiuKzXsXSUtrFfnViPi9D0jiX6B4Ze7/2aNv0R0vWZzdo3GKZz9x6xxt5zf9urMUVEzVhL4b4No+EDEKArajiBudoNdf09+foNIarQ3RxS5CdByXEfPGbwipcowbf1sFNqTFCLO1jxnf9pkDzqCnHyGcxUV9j2sFRGCJt5bsNeeEQYsSjlD1yaoI28HKaAQ+9RZg3SokHjVrnSBf9ElnfbLzExqz+EMTC+vqDGyFMA1UjY+Atj6tsp73eV45bv4w7zzQySjvisALQ30MeI7eylGLnmd5FPGnZs4ysES9grqMr0eC1dWQaLTCVhF1nlKsvUMiimqSrCQIDcPtGUpZ8iKjaTXLMiXYUPlcI4CELfB6gE07v9nYKhPdUhlNL2hR0rf4axfTbg6tcjN6VKKjr1uSDSHRVP7fqaz/2ks8LyDUBiUdcVIh0k1ITydojOcQRNpgnT8I5FWMtZL+KiNbxZ5smEVemKc1MllsRH0z5KKgEw31yRZic2lCdoT9cwKpcBu3UKcDOh3S6eiaGeDDxSSdc7DqEDR0tectPMtYeW7Vdc/B6veF2v/zrM+8+dsmIOoVmCZgcblFb7zENgFSGy/6arUX9xmNVI5Rf0mSFtc+b2cl5x/dYZ2nKGXJ0oLj9+8yHC/QUUPnBMvFwNP9rMI6yaTWRM984njhXyBjkqC9BnA8q27jqW2Rmw+nYtkGZI0XmpUb4lZuNOvWz1cdsGwVk9pvGa2DxsFutFESS8c48p5dJQYUJmKyvsE0vsuq+hAhJIWb8a3uAU/zAyYPbrKffUi6+L/D//QPKrObuz+DGX+Inj1Bh+/CeuUZ30mKi/qYgzsED9/1cJvtPUScemhLU/tW/q2MbrztEcHTS8+430B/kIJu/wbdtx7iKu2RruPn03brOsHqYoumDlHKJzYK5QiTGhU1zD+4RV3FWKOo64jj6TYvHT31YyJtsEYzuHHpkb6tpl5mvPeNr+E2h7WuExwOFrRWkUUV/d4aHRgfhvNwj7g8I3a/5l+X00vy9w/58MltLquIykJlO2ZdSSv8gm2FoRYVK3eBlS11V1G3I9Sqj5YZw2jE9vaU9OVTXB4wPLrAdYJlmTIKLGdVgOs8sCcLGoK4Ru9U2Dtfwwx2kE157cMvzn6JTofYgz9N166IPvhlH0msLXqQ0wPCrGS+GuA6QaYNT/OUcejoOq8YXxvBtBEUVrMyktpJJjPfPdlXjjT638HP/h8+9Z6UT38BvTpHzS887KXusAsf/CQCQ5M/vzavazXR3gyxwTx3ncC22nMNAkO4OwcnfGcgLT3Qy2jyZwI+bcj2pmTDFW6j0m/LmHaVIbQlGayZT0dMlkN2hnOy/ppi2SOMGn/LV4ZIOK7W/WsSaGs1eRsSbA4ZtpOsKv/3uc3lQooOKTqGUUmiW5aNR/Qq0aGl4yDx4sskMAzCingzRlg3IctNp+Dxus84qj0IyElGW3Okdgy2ZoRRTXEaEW0OBoHsiDe/7jpBsU5JTrcZfD3F3PQMfmFazCs/irA1+uIxMpoidhzmwxgV19BJmlVKePQQzj9GahCZpDs62vz5GtEUyHKGLJbY0R62N/KBgFLQlb5rF6blc3sevqh/Oeozb/5hWvrkrDpkte7TbmIvnZP+ZBu2COEojf+ABUFLb3dGZ5S33nRe+X10x0dR5os+7Ya8pQKB3FidorBlWaWYTvJjuxOO8x6lVZjOC99XbcDJuk+vjhjGFZKOUbZG15blxnZzu7+kdV7RazvBe4selfXAq0BCYQSR6jazV784PMN2mg4qK5g3ikh61ffNoQ8wquyI3PT5qBmQi5S6Pac1C0Qq+c2re7z09CbpYM14/NH1jPn3l8yvUFenXuU/HPoQHueww1t0qxPEzXuIxh9smr9/TPzGDMYj3wFwDjfYQp09gadXXtAVBj44vqgR6yX2Z970gsH5BM6fn8grTCuirPSxyXjOQpMnLK/GjA4vmX6wjesEgTa89sJDkl7O6OXH6L0Kl0uWb9+hWmXMZiMeXu7zZN3npAzJjcfz/tmDKff3Tjm6+4Teq0+Qt1O6/hBzcJf64OfItj22uf3u3yG++hY7/SXF2T5nFVy1La0wtDRIJDvdkC29y9wcEHUS03VIKUiV4KISfLAYYT56kdlsxHCw5HK6TVFHtE4xjmpu91aMk4Jb+2fc+VvHFF//OcT2/xbNpz9AZXVM9OhbyPfexZ7+KjaPaa2iurrDxdNDyirGWMXpasSijiiMpnISKSCQHX3t6YS2E5yU4fX8f95o3pvsEc522Dk/5PYH59z9+G9SPdn1TP3dJfHLu97qBf4wGIjrdryQHf2ji+f2LKSvnGGnMfXliGIyJNubEozWdEZhqwgz73H57gvEWUk8WFMte3z86DaRbsmSkiiqca2md/vckz/zmHhnwfLRAfmiT16kXCzG7A1nSOmunSBXV9sUTcS6jriqUg6zNUUbUrYBizZkNy55MN0mVPZ6rZiWKVr5m/8wqjBOsqj9mnVvOOe9+dh3B7Wht6GShnJziHTyU0AnCcxbTawskbSUbch0MsY5P9aKopoX73+MEB11GW8oppLhzgwdtkRbC8KjOZz458jceIF2+841qKlL+zhATi5Iji49/Eh0RAcT39WRXH/+XG/gb9r1ElUs0N/+Jva1L+HCBFmtfTNgOECFJcnOBeHk+bk/ruuLmf8PdX3mzb9c9rxFpwkIdItSn2yaSjqy/pq6ikjSAh03LK/GVMuMbtPGs1YhN4AUZyVNHdLvr+mcpK0i3AafebUaUBmNFB2nRYaSjrATaGAYtCQbFrsUHdZJ5nXEsolprKKyHgA0qxJWGytPZSWF9XaqZ38OQAk2SM+O21nFvAkwznu1J43CdlA7QWk0D2bbHtNp/UJ9371Blzjm1cd0OGLRZ944vnO1R/phTe/mBWb+eyTDLyGFxjofTyo3+eSi8mElJM31pp5+4+/jdvYR641wKs2I/lKKHd2mkwpZLJHnJ8j1wtt4XkwQl5sFfTz0QsI4QRYrL/iS8rlZ/c6PDxiOFmTDFdFoxeTBTbQ219jU1cUW83WfLK4YDJfsvPjYhy0NYyBGNBXr2ZDzi10+nu7wrdmAh2v4qJkDsCf77CcDDvoLwuEafuwVxI/9bQQQbr6eVfDlf49G/ye8vP5dfnI1oLSH1DagamNWwnFDjLidaW6kltZ5QV3jBI1lY/GDwkrOioxlEzGebZO3AYF0jJKS13fPuH3vEenujPBwRvETf/vabfD7S+keZuuQcPAELmqq6YCzB7eo6oh53rtOhntvPuSyVkQStOzIjWA3srROIIRkFDb0N+MP8N2Aqyom04bGSVZ1xOV/PqaXFAQboexR921k5vxrrDV0FSqpCXfn3np7+QfHBJ9bNQIR+PAgITuEdpSnO3Qb+6MMDPtf/hDXatpFxmo6JAkaGqtZ5D3CKqb7ULB37wkqaqnnfW8BFA7nJKviE9z2ct0jr2O/LmhD2YYUbUggHB8vRgzDhlS3HAQ5iW7ZztbEQUMYNuRlev2aAsyrhMOBd8tUbcBV3uP1rQnG+jb9wdaEpg2Q0hNGl2XKOKrYjksqo1mZgG3RESpL7RSLKmYfyKuEdJP6V+UpcVaQ9Ao/GhEdKvIIYFPEcDwi+lIOkym6ba75/OLyHPoDXG+A295DvXqBquoNyld6Vv925H+ft6iLU9SDD/0oSmnMoxhdfwcVSW8HHMceAmSshwM9x6wH8ALbz7vt/0ddqL6of776zE9AXW182k6ilCWMa/JVz9/Wo5qm3vDdpW+7PbvJoyxyc6t2nUBuwDlam2tLn8/01te/fxamoTYtvGfQDCU6YmWuXQClCTYtfk278XOrjQ93ZRTLVlEaD95onZ+nKiGINx7kQHbsxSVZ0BKriMr6iNXaCSa1Yt0qJjKidoJYOYqNHmAgYsbiBk24pnUlrrMU1jGpNRfrgQ+qOf5tymaO6N0lTo6omwlSxp9w6/2L4G9r65qu7uhu3KEbyOu5v+sNrsM8utDDgERVfPIhiDcf/mciMB34G/+zKN/s88so+P5a5D36/TVSOVTUko2WrKbewlc34fV7GwYNcVYS7i/g5p6HE81z7DRlOh1zuhpxXKScFILzpuJCneA6i+lucFzssCh9MqDZOuQHTSjbvddJ7/xT9kdTti93OdUBQStJuohYSVLtwTvBZqE2TlJsng/XfQJoAVi1IYF09KOanf6SXub58x5b7Age/Sr5jdV1vsD3V6iH5DuvwhsQVr9GOMuR0rEsU9ZNRGX96GnSKCor6LqOEEHrBCsjUQKCzTy5sJJM22sPebyhzAXC4TrBJO9dW1574wVy0NHduYU5uIPNdtCLE4LhW9BIWLXo4vmp/V0ZIEKDSmpvB2411bznR0FZ6WfoWQVlCGS0bUASV9gNtAcFOjD+olAJmjym3ugqyiqmbgMkHavNZSAJGxqjkcL5UZ+TCAGJNkTKC3XTsN6EO7Uo5XUASVRtxjcdgo6yDUhjz2sI64i8iXyOiBKet7ARHOvNRSc2LbE2iI1vP+0kaqNxsp0/+D7rIEpp0doQBC36+1wwQnkKos3jT2bvZetvI5MlYrKEnRHupEEeLBBpDzvYQjiHS3tewLecI9KV9+3nHo3MZIabCsDzHWQi/LjJOEToILCwrv0i2HyxjX5Rf5JI3w1GV4iOIGiJkorZdIs4qkh7ORfne2Rp4cN68G3/MCvprMJZH9vbFDEq9LG0QnSUmzlkXUeUVcygvybSLWUb0HWwHZfkbYgWHaYT1Bvr3TM17roJr39dWUVp1bVNp3GSynrVd7jh59TWh6xsR37RSJXj7tbEz+qSgqIJmVUJphOcV4ppI6idprKCfuBvaP7vF4zcGKPvUXYLLC2VM1QuYlbHnD09ZOvpe8gqp34hRqZ3kO/8Xcwrf+WTjTvcQHsmFfYqRQSGdvcendTIaomePEbNJ37z15ouTrF7R77lv1hAY+iObvguQtsgrPH+7tnM6wDikG5793N6TD5dxvqkRqSjc4LBi09ZfmPMIu8xKzIS3TLK1qRZQTxYI8bCk8wmU8xxwvrxPsfzLU7yjEmjKa3D0SGQGAquxAmn5TbnRUa7ytDND55PqsUTMH6RTrUlUgGBUAQo5Petc4k2DIOGym4U3Z1ACJD4A+ZBtsY4yTjNGfeXjHcn2DagzRM6J9BliPr5b5F+6R3MC9+kvPXjZL8Pw5qN34TxmzSmIS7/KXuLY86mvnPUbg6m1kE/6K7FibHquKjkdfLftFYMw09GAYOwYTsusJ1ES0cSNCjp2Ds8Z/zKI/Rrmua1P0fw8l8j/L4DSX7r90i+/f9GuGP0/vpzeOf/8HJ1gE5aZFoT9HNMGVOVCXHi0x2DXukdB0YhlENrgw4MxiqoPfVudHiJUNaPCTbs/+ViwKrIaKxP3JvkPfb7C3a2puRF6gXFwhP5tHAcjebXz2YaeuEmcJ3kt7M9pW5CorAh2WQdJBvoUBg2tEazrFIi3WKs4ulkh15UM8zWaGVJogpdpqyb8Pq9qK26vqQI0dG2AVpawqAl7RVkOzNk2CK0QwiHDA3qK33UyRVuoXz64Ewhbwe4kwZz1SeM1th5ihzkHuoTZ34tuf0mSE30+Lext18hfOebUPmf0V3JTbaCl3UmP76mOy8QmR8Ndpe1P6RtmBvP/vf51Rd43x/2+hPgfTtGWzNUaHxa36rHYLAk7a+J+gX25JD+1sKrva1CJjWmiiiWPZyTpP01YVp5X29cM7xzQq+KWJ7sUlUxxilW64xQG9KNsEZLS6h8+EllNLnR5G1AvFH0htLy1vwTtrwEbKfJNg92pj1+1XSCSEE/gDeGBSujuZXm7PVW9NOcvEzYHq7pOsFgNeDdR3c2YwH/s1cWWicIJGxFPkK1rRICe0AptqhFRU7DvIm5KGNOptvc/K8iBn/+Q5L1Aid/CXPrdaJf/zuIyZVv+WtB9/4ZaDyTHxDH38OlfVyYYLZvY77y15Ef/jzBB9+F40e4n3oTe3ALMdryYjeAyxmMMj8++N7HsN/D7d+gC2PfBXgOdbA1QUhHOR9QLfok4yWDnRlKW+LpmKezbQ63r/ziKzrY3/vUn6/zlGUTMW0C5o2gsJYAychtMxEtuZtw3tR8tMp4/OFdvvSNX6F99d/9VNpi/cH/g/Dt36L9Diw/PuLkyZ/nN57e4XemEfPG0deKQAi2Y8E43GSz42/2D9YJl7VkEHQ0ThBKwSg03Ni64ujuE+K9GTJqvM86aunqAFPEmDzBNSG8tUSvftd3aL72p/7Q16i7/1dwywn94h0OHh0xqvNrfsVhGmyS33yW/LuLIYFUXFSSad2RW4sUmkxLMi05DNrrbphxEmMVb/75Xye8u8C9/CLVj/wNkvjoD3wP8eA17NY30MsFwiz+kO/y86ngRk7XQLf2M/62iOlvzZHKOz5MEROP1pA0CNkx2ptgNnHgSR2xWmecfXyL7YNLv350gtl0CyEc1knWTUQvrBnEJbMiY1ZkvHbvYyZX2/Tjin5cMeqt6A1WzGcjytprK/I6pheXNBu1/yu3T+iNlqT7U8LtBdnvvkoQ1xTLnnetJCUX6wGDuCCJq0/Z96oyZroaeE7Auk+76UxeVBF7m4NEZRXH8y22szVaG+LhivTeGeLe2He9hiOql96kzfaQ+QXB7Bh9dUoXp/Dux4BEDQraD2OCW2u6O7d8HsnJx4iqRO088Ra+sydw8Mnr70rJ+sOb9F58SvA1jT04grcvEbvxhhoa4X7yddSTj3AfrzHTPqaInzPb/4v6Ya/PvPkHQUu+8ladttX0BivCuPaq1emQ3d0rH/pDgDWKfL2BTWiD0hZnNLYVnheQlQjtcwDaNqCqI9Z1zCjJfTstrBnEBf0s53y2RWu9Pae2isP+kuVGtXvYX6Jkx7LxlijXCZatYhB4q45XU8trJK+SmxteXNKPanpxydbOhN3Q0BQxZZEQ6Zb9uKFxEavWk+EC6TsGwcaHvh0JYhVyUWlmRlN2mpFImNSOi9Czx+tlhv34DKVntPff8PnczTegaqDtINaIbeUpflrR7e8jijX6ymd4d70+pl6hlhPIeohb0scCO+etglUD4xHdrRt+E6orRH/zdn7/WOE51GLdJ+35NL887xGmJYvLLdo2II5qbgxn3i7XKwh6Jd37C+R2gDvraGZ9jFEE0pEbSWkglpLaWcbdAIGkk463u9+lP/0xbj29zf4/eZWtr/wd+NF/n6o+pzv9deJv/Tq/93d+incvD3iYpzxYK97PSwJqhjpgFEpGQcfXt5eMo5Ik8KOUh4sxWnYoAceFoHEde7G/lTdNSLw3I3xxDa7DXUnka2NoG/R0RrcCcZTgHjeA/CNvNmV1jP7gv0IeP8IsY0Y7U5bTMcYoamkZxP62lgQNUdiy11/y0XSH7Sjmqgo4LjT7SUfHxo1SxZRG0w9a9tO1P3wFhu6FW7Rv/k2SPyJtUsmI6u7PgHPo8rvP41EAwFzErDbQrmS8JMxKr0zflM4qyid7NHmCbTVhVtKWMabVWCsZj+f0t7w2oS39lnTn1Q/Jr0YI0dFYz+0InSWLat8dzFPSpGRra0YUV8jA20ybahMzbRSRbmmMd2pkUYUpY3TYYtYJXavpH1xRLzPydcblYkRrNVupz8No24AobBgMVjR1RGs0WlmOlyMuNiOJRFtGgR9DWicJlWWUFGRxRTZckezNELcy37nb3celPWS9Qt38C4iz/xdyOfUjuu98jJmnFMe75LMB2XgJokNXT1D7E8z91zHjm+jZUw8CqyvUxTFMV9grTTvxDAhkR/dwgXiwpAs6xFYIRQmXS+Tptzarve831cvnMxL8/hLP1qEv6oeyPjvkJzDM5yOcE/R7a4LAL+6dVTQlSClomhCtN37vzUb5bA7WdQKpDXJD6rNliKkipLLX7TnjFEr4BvCz2ZmSjnazxmbaL+D9yIeEJEHDYX+BW44wmw+fEjGhsqTKEGuD22gInvHS+0HDKClQ0vnvUXYk23Nss+Pn1GHDrd6KQdAyqSOmjSZWjtJIr7zdfE+BlIRSMmhjzivFfhwwCmE79DntYVb5cB0XoS+eoqfn/vSvFWjojm5R33rdp25Nrz7ZqLWm0wEu7aGmZ3RpHxvGyGKNmE9xWzueB1DkdGmGG+0gihVSSkhSujDyh4Gmoj14geBzeEh+fzVGU6wztDa0rSZf9K+xrc/GQ97zv1EiryNU1mDz1N+gm4C8DWidH1cqIWhxWCytaPwYxS656go+WPX44ON7/OQH/w35nV+BcISyNe6p4YOrPd5e9Hicw9Oq4qk65sjeAAJCCTezlv1szf5oSq+XM5uN2Gn8wh2rECkCTgqYNR1PioAPrvbZevs+u+0jwt05cmy9Avuln/U565O3CU/fQ5qPIcuQ6wXdP/4PKb76lyAcIYpz4vf+GfHlBd1ljVnGtPMenZWejNlfE0U1Udj6mXBgPDRLG+KwYWcx4mTdB1IGgeWiUlxWitykZNpnTwyjAL3pbFWv/CTpHxMzHcQHXi+yen6RvmaZ0RTJtZjOWUlXxr4bFxgf+vNMA4S3Bl5/vp8FGiW13/g3qHAVereQkB3OKerGkz2f1bM1xTnPBnkGSAjjerP+OJZL70rS2pAmJevZgHSwpq1D2svo+nuwThIHLduDhWeMGH1tRy6LhMW6T7VB/AbScpiW1+tJJB3tRgcVK0ukvcYAJ7zg0bWwWtINRrjUK/LbxTvExQqxmNGdLlm9cwvX6mvdzLPPUtJqdLtCr38H+dISuV569kcU+7UEr/YXyqL7JWa+ERQrixqWiKKEssXlG1BQo6ETuFZ7MusX9a90febNXylL1YQEypD1c4RwqMDQKYe2EtME2E0cr44agmdoTevDNaS06NDP/k0V+ozv3Kt2tfJozKoN6EUVxikfu2kVgbK0xhFIS5Y0OCfJoopAG5yT7PTmLCuf3tb/Pu9/L6jpxxVpWJPXsRcWAWlYszOaU5QbvUGR0AOc87PCOKq5vX1J10muVgPO1n0GYc1HqwHWeRJgIDoSJdGxV4pLAvaTjoOkZTeqPQq2X/i2m5SI42PcFYgdoOcta9VLP0568BfJ018h/uCffVrkF6e4dIC+eILTAZ32SGDV1Jgd396VTYloKsxgBw10zuHiBNcboy+eIIzBbN3/HB6RP1hCdBRl4me3ylKsMwYj71Ko6gjrJGoj+nNWebFeqK6fB+ckK+O1FLbr6OioaalFQ02FwyKFpBAlj/Meb13t8eb7KfHdb1DffRPhLPXZNrMm4qISnNcNM7GkwxKiiJUgVnCUFiRBQ5YV9MYLlss+29maJGgZVDGSPssmYNpYaiuJ5ADef5Uv5yk3X/2I7MdnNDe+QrbJUie7T7v4O4jtHUSRIz5+xOSfvkT0//nl65vuYt4HbtCWkRd39QucU8RZQTZcoQKDjmtU3FxT8FTSoMOW+LQiVIZyM0e+rH2uxGnZcTtT3qlglU/YlB3Rwb/2md8zt3p+KW5t7jd+oSydfRbqJfwsPSsRsUFGLYErkYGh3RwMvID4k8uB28zrg7jBNgFBVpLYJWOjsBvxXVkklFWMc5J2w4QwbYAOfGS01pYoqfwo0ijaNiCMGpJezmIyJkoqqjxlsRhQ1jHDnid+jvpLdm6cs5yMqcoYKR1pr+Dxk5tM8h6uEyRBSy9s2M3WlG3IuvHi0Ot8AOXXMik6H9k87xNOnoLGH9idpQPCk9/bWHGX1A+3mZ3s+ZCjJsRaxXrZp1inOCsJ1wm2CRjwwSbEJ6YbjhGXE99BBA9XcoLyfMvTE4de39EtWmgEXaWxG1Ki1AapHUH2nH3+HZ//zP+Lkf/nWp8d8mMVhwdnhGFD1CuZX2xTlcn1Yh4n1adO/lobj+6MGoKoIeiVzJ/sU1f+xC03Cv6mCalq//9Ny5SuE2z1lxsboCAKW/obW0oU1ZxPdiiaCGUCBmnB/u0TD4dpQqTsmFcJe70VdRuwrBK2BwtqE7AznBMnFWWREEZ+oS6riIfHR6xWPS/oSUv6gwW9ZsVqMWTQakJluHV4Ch/fpx9VZFHF+WpIL6yvqYK7kXcKJBscsBAdzkh/Qg9DwHueMQa3f4PqxZ8iGH/Zv7DhCLN32//yW78O2zsI06IuTmjuvUH43rcgimleeA3ZGxA+fAdzcJt29z7h+fvY/iHBxSPE+Qkqiql/+l/HhQlqPUPPH8Knx+2fSw3SnCSuUWpj7RId8+kIpSy9Xk6gDW0dYo1CRQ3qjsO88XUC903Sdoo1/uY3b+BpVbMUBQiIupCOHq2scdKg0KyM46NVxPv/7Ou8GvwW8c77dKXk8vGXMJ1AAG3nmKlLXncvs58EHKUdL2QlgbQsyoQnH7zCsg3o65adDX0t0S23ezmN6/HuUrNsHU8KSWUHjONdtq4mZKuzT5Eai/wj4otj7DdmnH/3RR4f/wQX6z7vLIYcJhWH6Zqd3oobh2dMJlukSUn/6IJotEImHgzEjS2v0NbKj2fWNfWHQ2yrCYKWflyyFdVI0XFkFZEMuKol8wYO4o5xXHqf+FFJWZ2RPjuY/BHVzr5L2FTInc//OXhWTZ4Q9/PrSN/ldMzWwQXRMEfFNS4PaVfeZqfjhnCY08z6XF1uU9URd/Yecfno6Pp23+uvUWHL2ce3CMOGbLiiLmPyVY/5us+qigk3ND8lI5Twm+6gv6KqI7+ZmznD3akHD1UhdRkz3ptcO5YAhr0VQdASRN4KqKKWZDPOetYVAMjC2rsMNtz+3sYhYJ2gF9Xc2L1gueqz2FgSXSe89iD3bpXBm0/he4/Qw2Psa18GZ7HfnlOdHWDKmP7WgicPbmM339cHJ0dkYU1epGRpgdKWQXzhhbzrAtG05L+3j5DeLljM+5RFgpSO3niJ7vmN3Zz3EIEBJ1mf7CKVRYUtMmyQ0Rc3/3/V6zNv/tt3n3L23l0uN9jTu699wIN3XkIpx2g0JxmssUbTlhFtG9MbL1lNRrRNQLHsUT2O6fXXXuikLXFWcHm2h3mGvwxaXuyfsS5TtLIEQctq6UFAYdAShg1h1HBj/5zlsr+xBjrefftVf9I2mqIJ0dKLhIZpzmFSEicVw/GCZLAmHK8ItxesP7yJNYpBq9lpJxijrrGiq+mQg9c/IpqWHLz0EN0rmbx3hzdf/x7g241HnCKlxWxiSasypqxifyiRjmG25vLBTdp8QjRaEW4FqPsCdIRczkne/RU6+atUhy8R1CtPZbs8w521yOYUUh/8Ezz90Lfy4xRhGpqj14mv/jv0yUPUcoqLE9TqFGEM9AdUr/xpoo/+WzAN6PDaJvh5VxQ2FGWCEI4waCmrmCBosW1AUSYkcUUU14RZiUxrqIynGw5vEI9/FfXzfk6aG8eJPONJ8y12o5cZum0kkrTrg4SyKzi3inaR8n9961X+8mzMjfEMIRwfXhzwzUnEqnW0GAo3Y9aV7BMQbnCtHy0HnFWaSPpwqAcmYVykfH33kpeOnvoD7XzMXry7wfj6SNfvTrbhvVd5M6sYRv9L2N6C8wnivS0evnOP949/jLMiY73hAbyQFdweztgeLAgCg9KGso79s/yeYXzjgt4db3e0e0c0+y8THr+FOn6EWzZUkxGT813KKmZdx9csCgn0AstO7BgFLTf7S/aGM0wdQtehNtyIH1SizZHr+TUB7nlUMl5STIbX2OP9+4+YPT2gKRKy3SlRUtMZRTBeEQzXuMofDAeDFUFhOD8+YJH3vPofYLJDEjYEylCUCZPZmChs6DpJ2YYsm5hhVCFsh7GKUBv6WY616jr0C/yNWCcV6eEValSwfv+I+Zlfv3Z2vRhWR77rosIWFbWEreb48U0med/b/rQhUJZRb8XujXOuTvfYPrjENgHFOiWKa8oipTVe69NaTaYLgqBFaUs5H5AeJ8ikRowFnVSob3wL8bUDUqkQ5085/vnX2d6eslgMuFiMmVQJiW6ZrfvkVcLe1gTyGpIASod5pEjvnvv0xLgm6JXoyxFRv0Bqgy1Dyod7qKilW/sDSP/2GWpQ4PLYOwzq5zEQ/H31BeTnh7o+8+a/Pt3BGH870cpSTId+kY9q4qzYhPP4k2vnJOWih5DO8/+BtJczmWwzHC7Q2lBuEryWK59cF+mWXpajlUUpn/f9rI3XttpzveOaMGroZTntZgZX1jGrKqHacPqF8JavZ1Gew90pyeEVql8i+w62e2TuhOpkG1tFBLtzzj+4jXMKpQxJVqKSBhXX6LRCD3IGB1eotN5ATBydUciopV2mtLlvy01O9zzoxio/rnCKpkiIhjmyV+OeSuxSo3oTZP8CtwiIXnjqT/MA6WZmaoBlSZd3cF7AYQ8B6IunBB+/gz04oouzDcc7RFZrujDaxH1qXBhDuOEJPKdSyufZu2dt6+97v/q9Nb3RkmS4Jn35FHFv03qoZ0SPvkP3eIoMMgZBS6o1QRPiupZp+5CVvCCT2wzZIekyKpFTqZxVl8Fyn/14m6d5n75uCZVlL+6QSCoXs8896LwwcxhY9tOc/RTU0sNtAtGxMorcKOoNU2K046Ni52XKsg04KxWnpeOi0lzU+5wXf4Y33r/H1tYMIW7S1BEnVzvM65hYWfpBSy+ouXdwymhnStT3XYX8aoR1fqM6v9xlNhvxYmBIzDlKSlRvjFzOYZ5jrgacP77Bo8s9FnVMbgIP8mn1taBMio7DbHU97hLK4hYS+c7fpXzlr/yhSv9nJZoc4dwn7pDnUK7V9G5c0a5S5qe7tHlClJbXan81qAj6+XXol1mnxKM1q+mIVZ75qN0iYxDVKOGojOY877OT5kS69YehxmuCwCOWQ2VorM/qCIwmDprNeLEjsK3njCiLjms/614khP2C/dHH1wFS5eWYaGuJLSJsHXrmyDq91mbYzahQKUMYNtR5wnC8INhghWMrNzwTRy/1wKXWaIoyoa8swnWsFn0Gy5QwMHSLBv3edz2lyjlEVUFuiJKKzkrEso/rBNtxSesUvaAlDhqM0TSPBoR3lhAIZFYh+uAuA+8+EY4gbtCxf307Jwi3F5+AfETn0wEjh2gMwkpc/bwJfx2i+7wFf1/0/T/P+syb/9Xljm99bdq9y8WA/mCFjhqkcthGI5RDbU7dRZ4RhM1m9mdIegVPT24wHC487rKKSDZ86WtBoHKkWeF/74R3Eziv3G2N9mrypCROK1RjMa0mUIbGKspnqt7NwhBsABvxzpzgfg3DEV2vj93aRwcfkugLXB6jBiXZxRhTh+igJd1eIDakss4JOieJD6aIpEUmm2CdtoNMI89q1LzFWUVvnRJUEW0b0BpNlJSftA4l1Cc7rE53iPs54XBNORnSKy5AKJ+vfbumswLROroGzDTzN5ftFiKDaBo4m2Nf/To220U4Q6cjwiffpgvj6yhPl4zopEI469O+nkP57k3rSY1NiDGaqvVwn7RXkG4tSF86o/vKi9S3vkR4+h7xh79G962HFB8d0ZYxiTb0tCCrMqQIKJsTys5RhwcEYUTa9bEYmq6gZEnWZXy0HjNtFNtRyEv9Ffd7NbEKCZUmrQ+ocIzCju2o5nAwZzRc0Ds/oDaaygbkxh8E3AY1rUM/J/ZC0Y7KwolZI43gtAp5kvd4nN/n6KxiP12zvclq344L0rChF5cM+isO3/gQPfJtb5fHzE72EBsC5TTPWLcR/Q/X7LaarDkhNIbu4RX18Yjl4wMeXuzz4XLEpA6orCCQHfNG8kLWEYctSnSM0oJAfQKNscuU4He/iVpOyV//i9e44z+0nIXV8rk8CwBtGZHcuALAPNZUq4yo53M9uk6AhnB76cVmdUBbRARZiduEc63qmHUbEmtDpP0Cv24DtjtBpFuSuKJpQorGB3c9Y+XXtYd72U5ed92eQXciW9NZHxfeWUW7iAiGa78WOEc367BVhE4rzDqhLmKkclRlTG+woi8cdRWzWvWIwhZrFfP5kMFwSb1OfECR0deK9iiqiaKaovSahKL0bfimCWmWPS98rAPEVYfeA84v6VYdzemIzkrqKt5caCBUlsaqzYXIHyirizHB9goxBBE5iL1nXyhPVtRxjQwbXOP1VDJpsK32YsvQ4MoQvk/2IdUXt/J/1eszb/5FmbC7M/HIyo1gJ9ueY6qI9XyAlI7xzTM/Y8sT1qses9mINCmJpaMpYm7sn3vUr/HxvVXp/bhFE9FYjdaW8db0WrELsJyMCMMGaTSTyRa37z1CWLdBBEceOKT9AcA6yV5vuekYbAKHeiX0UtoXXqG99ZNkgzfI774Hf/oStTpFnn/E+M/10e982wtkNodVcdX5D1LZordyRIw3/msBvQj6A8TiApF7K+Po7onPL28COquIRiuWT/cpJkMQDlOFVGVMVcYEswFKW+YfHxEPcnRSIecG1/b9Iik7ulYT3FtCNsYNRrjBFnJnhSyWiKakCxNstouaXmJ3DrDJCIAu9GFAOOvT3Z5TlUVCVUcUTcRV7tvPWlo/100ryr/6P0MNX8PVV8h3/hmL/zJlPfsSxTpjuhxiOw9O2hIpsR5RtycAVM1Tjm3JzeRNhm6bUiS0okahmNSOUEpSJTkpU760dcXNXkjehqzbgGUbsB3l3BnOOLxxxvjeU258+QPaVcrqfJvgg5cRdITaslj3CS8bTi73mJYJlZV+4UVTUrOmQzeSB+uA3KQk2vDijRPG+5dEgxxTheAkvRefIvvW5yzUjuYypqlDtPTK73pDoTy+3CMvMrJHN9h+74yzBz/J1WLEZd7jwarPu8uAykJHhxKSRMGTImDbSu73c/qppwU+U8KbdUr5O2Oyy6ckzS+Q/9gYsXxIl2wjk0PCcAclI1y257UmJ8/vWWjLmPXDQ+p1ymrtMeCsff6Djmva4yHhyyvsmaKZ9bFNwPxsF6kc4/6Sk9WQUFomVUKmW3azNTeAl24/Ynx0TjDMmbx3h/rkBkXjBaWlCz2OOS5Jg4aqDcmiig6BEI4kK4mHK6Kbm0PJtOdv/GkMl2tvj9tekD/ZY3mxTVkkxEmF0oZuA3+K0pKLyx3yMqW1itoEvHd6xFZSoJV/1rOo8i1+6QOudg8uuDjd53S2RWkCRnHJxZNDksmIMGrQUUOyWNKuE+p1wmox5ORqh7yJrg8uH8zHvNBf+INp9AmsyBUhkgZXhqhtQfByTUDtfczvguqVlMeZFxBuLJPp3hQ9yn0XwPn45c5IT9183vVF2/+Huj7z5h+FLVFc0zlx3do+/ugOWVaQ9r26tM19mzsarYgnI+arAT1ZEMUV8WjN5aMjBlszOiu5uty5TtVKwoahznnlz/42ACqr6Iziwa99nf5wRVXEtEbTWM2TB7cZj+ckvZytwZogreCdF5GLEXkb8nQx5qX9U0/X2puiXtSIn/s/fooJn/Vegd4r2O0ac3NNFG5T7P8Swfn7qOk59p9NiG9d4ooQs0oQsfcy66Paz38vJ8AScTQgOAKWa3S+9j770tA8GXD+9n2KPCUMG0wdMrnaIgzaazb5/niK1t6XHCUVQexbiTqrCA7XqDcCmCmYzZHtJvZ3tEtw8sDT/kY7CNPgBiOQCmFr7M4bxN/9h9edALc5EHzelfXXGKMpmojT1RAlHIeDBUc3Ttl69SH6JUtpamx9Be2KLu2xnvU4O9tnWaU0VnF7MGdWR0zqkP3qPkvxMQBd12LslJl9wi4/whHbJFLRSocWglndURpJ7UJuZTHjpGC/v0Ar633ZwyX9vSnp3VPkjQCGQ8LVjPjjCTsnh/w/379PaT0nvOMelYVRCKHsGEfwZ9MEJWIa5yOef2RrzZf2T7jz4gOGX3mAuLcDbUM3jBFVyeTv3UNpQ5CVSO1wrWLv1ik30sp/VqoI0wQ8eXCb88WI9eUe7YMXuai8pz83gu3IEkg2X4JQdkQSdmLvHjnMVqRZwWrVo8xTwrgGJ6nXCfVb91DvG+L/+j8l+coFjPp0wzH13a8RHn8PvXMTu3OIzo6fy7MAEA9XSOXIdqfc350yfXJ43QG0rUZZRfXOCN0riG9MkJcGFbZ8+PYrnC1H7KS59/ErS7DBf3/ttXcwbUCTJ6i0Johrbt04oW0D8jzl20/voIQjDRqyqKJqQ5Rw9LOcKKp9BkkV8dF/82MMRku23/iIxYe3iCcrmvUe69nwWvznrPRdwsR3In2suESplihsqOqI2gRcFRmNk5xN/drVC1rujqbsZ8X1CCwZrhkWCcsio7Ga2mi++/QOR4P5dRbDYVpRF/Enn6WLfRLdbjQDkp24QskOaxVlkVCUCS8M16j9Bg53UXUFkyXc2IPFAvPA/93tokdb+gvR6Ec+pnjvgGbZw7Wa+N4VQvqcBVMnVE92n4sN+Iv6l6f+BGp/SZknBFHDcLQJwyj9Laeptwg3sbxxnqKjhiiuGPZWDLZmJGPf8kuz3ONSmwCtLGUVe4temrO1M6GZ9QnHq2tM5XA85+J8D/tMNxDV12MDZzREDWaTOSCl8x/IsCGJK3r7E5LXJrj9+38kF17JiCr/LlG4Taf8hinTHvroKV3ZIaLWo4XPUtSg9F2B1Qqk8L78jee2/dqPETz+AMqCbmVwdUDSy2mbEB0YpDaYjVWp3SwI57Mtdobz69GIeGYPVA47C2G6Qr3irYJEfqHQ03Mwhi6McHEPffGY5s5Xccm2z5E//W26MMYOdnDRANHm//xPxg+opgl9xoN0xOqTiNK2DbBliJYVspwi8ktksYSmZrDXkK96vsMj1cbO6WNye65PHOxRtWeAQ4gA42oyGbIfB2xHEErBrBHkpmNlHKsVHCR9+lF9zV5XypKOVqioxUz7BGqJe7CgmQxZne5wsR4gBazajmVrWTtDKjWx0uzFjlcHS26PvRAsr2MuC2/xKpqIapXRX0eo1r/nYjahO1/j7Jj1bAsuPdsgjBqy3Sk6rVCDArXdUry977tXoqOymkfrlMtakxtvTxdCsR15hn+oOo6SinYzChOiQysvkE0HGwuXE16TYhSmCWiKmPV8wF7UEr8wRShN+OQtT4Vbz3xKZPL8glyk8p0tqRw6rZDKi2FlYFGBway9ME0oh8xagnaNShru3HtE76zgeLJLrA39uCQOG5QyrBZDss2lwqwTwqQmTGrqdYJpA+6OJ9dZIK3VhNow6K29al973VBTxCRpSZQVdK0XI6c7M1TYXutznumUhOgoixS1caoobRn2c7KNOwR8Z+vRYkwgPXOhNJpAWeomoNfLibOCctFjsRgwSHP6SUnVhD5FMKxJ4trHnGtDlHpbsrOSmzsXTBYjGqNxm1FhbTRlEyGlHx9FoxVuKZDthQ/m+cqL/rV3Dn1rSreqKD/aJxrkZAcTulx5rHKrfScxV9gyopn16axEP2+rH3wCG/uifijrM68IcVR/AuYIfSpVIv3svq6iTba7pcwTRBETxTW9/torvrVfHHTUUKx6WOMjM1ujCbQhzQrS7YXf0DuByWOalWdUt22AcQqBXwS1Ntd2wTAtqVfpdbKgwIN/dGAIhmu6OzepXv9L/CCWlVydUIQfIevFJylUu2PE5QyRdSAN5pFG7Gpvz5ISksRv/hG4wQizfRdZLFFnx0AB0nuVn23sdZFgNt2S1ipap5itE6KNeDII2u/TGAhcEdMuMrK7Ux/O4xxyPvUAoDTD9ka4ZAzyKa63yfFurxBNiUsHuMC/dojns+CbxostlXAkQUuk/ZezknaVEa3m6OkJLh3Q6YCuNyB78T12Cn/YO59vcZlnzBtFbSEiIFQ96jaAa5QSxFKzHcHtrGErbJg2IWelt74tmo7cKFa1nwO3TrELLK/GBIse1knSszX1OmW96jFfDZhWCZl2RJsgJEdHLH2LfStsuT2ecPelj3FWUSx79CdbPJzuUJuA2WxE8P5tRuIRcrjETmOqsyPyRZ+L6bYHwCjDeLggTEtU1KKlg0hfC8wqo5nUESelZrlxWsUKIgm3s4pYWTLdstdbYaxiVcdoacmiiqhXEO/4Q7ct4k8IeusUU/uRUtdqOrMhq0lJl2be+WHaawfJc3keqtAjvZXnD2htvQXYSuxGWCYDA5v1Q0gP/+nfOkeHLbPVAOskgTJIaf08vwmIjEJu/tthVn7CRVCG7eGcZkMHba13DIWhV+6HYUOYVTRl5A+DcY0tI8+ekB0qMARxje4EofXEUms0dpNC+ExMbJqAKK5xzrsIUldStQGlCaitTx4NlNkwCiS2Cag3up8krgjD0lMIWaAD3zlNRkuCQYHUn2yMw3pGVcdQxdcgIS0tdet1TMGzBNVG4fAHFbt1gJqe+XVoOECYhT8UJg1qUGDzGBm11/ZL9wxRXfnRpK3DH7guflH//1+feXfY2pnQdf4DXRcJptVeST9YY5qA5WREb7SkXGes1xmrdZ+jFx7TljFNnlyLf5ra3xqDoKWxmp2tKdlwBaIjHKw3FCpJ5wRXlzuMR3NW6z7zPKPIvajMGE2alGTDFdPJGIBQGZTsrmdxMjTYnUMftPIDStZLOPkNhGn8PL0q6Hp9hNY+TnOwhY6/gzu6S6cDkAqXDlDTM5pbX8JlewQX3/MK/DRD9NcE4xXl5YggaFkuBlwtRhircHgMqHGS4yL1rT0nUcqRDNes5zv0Dq9QiSeiuacGeTP3MZwLg3jxgObmy9j+IQQ9mltAZwhOvoVaz6nu/QSizdEL3+Jtt+/98z8ZP6C6Dc70WYDK7niGlJ7oV0yHhI9WhONHND/+cx5kNPsmkZRsZW8jftPx1vEt3poPeJjDtDEoFEoExOEurc1xrkEJTaYUe7Hhfn/JjeGMXpbz8PyAR6shq1aTasvjdZ96OaCyktfriPzs6Dq5TYmOnSSndYp1E7JoA3YjQ9cFRErTOM0ohKOkZSeuCANzrTUJgpYoqtHKIek4nuzw4OKAuyf7xFGNMZqyijldjLkqU5ToGITen1+UCQfmFFuF8MT74Os64rzIeGsesWp95sQwFBwkjpf6OS9uX7K/e0nS890xFbQUqx7OeoZGvLMgPFrCMILQ4T4u/cJuFW0Zk/XXJC9dQKZBa6rX/nXk8jFqdY5y8+vu0fOo1XREf2vub/0buFcQ19g2YDUdMtib+g2njEA5bB5j64Bwa0V6eMXB+SXzxRCzmatbJzncuWS97KOUJckKb59c9nw0uHLozjBZjEg2Hvyz5Yhdq0njNdnOnPj2JW4Vg3S4MqJdpaTbC+zmMBH1C3RSEQwKiuNdVtMhShuc9ReTrpMsrsb0Rksy5a4PHjv7l8wut/3nVjpvNQxa5osh1VVIP803IUE+v2H36Iz+nTPqKy82jfZm6IOK9onf/GXcEK0TxltT5HyIy3v0uoZBXDArMpZNzCguyC+22LoxRRxGEIao5cQT/8oCqppuBSpqwQns0tv7dP/7bvdOghPgJMWyx9OTG/yZ5/ZEsIH8fM43/y/E/p9rfebNfzbZIt0oT9smIIxrVtPRdcrfzu1T8smI4f4Vuy8/Yn2yg44b2iL2IhzpSIYlpg6ZTsccT3bZG86I0wprFOuLbZLRkt7Lx9h5Sr3w59LzyQ6N0SRBw6svf8B6PkAHfp44Pd9hmfdoNh5bKTpCZUj6OWqnprjxp/7YuVb00l+nrI5xZo28epuo+B7V1/8duuoS3X+JKNyGL0N98osEkwcI09Ju30MtJ37jH76GiXcRl99GXZ1Cv4d60TBwT6nOtrwdsYqZ5n1O1n1y42NdGyewTjApel7xvM544eWPqed9VNkSjVYUjw7ojY/pXriF+epN9PQcO7gFiU/rc6M+waNfxfb3aQ++jKgXqNUZtr+PG7xANnjjn+OR+ONrnfdYFilKOvppzjr3nYbhYEk8XKEHOURjOukfr3DwGmbrA9T4Acn2gqPBgl85H3JVt1x1aybqggEHHPEykYyINu/a68OO+/01R6Mp+/sXpKMVw/GcVzeEtw+e3Oa9+ZhJHTBrBO8tR9QWlsYw6dYoFHtql91IcZB03EprEB2HScN2JJk2itxI5IacdzIbM/+2D5eq24DzvM/7yx65kdjOz+QnVcIgrNlKCtKwxjrJ8WZ+6/KUX7/Y5mtbSz6+9HPcflxRtiGPlkMuqpBMQ2UF08YyQDEMLC9uX7IznpL0ctLtBdHejOLpHmFSb27QivDeChAwHNK88Brh5J9g5oqgnzMar3x79/YedmuXLs5Q0w+ww1sACNMizelzeRYAhrtTj2y+GoETmM0NWmpDEtfeCvjCKYiOrvX/TEUtxfEurtXsvfiE5nsh/cEKYzTT6Zir2RaBMvR7OUFgWE7G7N57iq0DVhdbPHh6kw8XYx91LByNU+yveoRRTZjX6GkP9bO3UI8+ojtfoLIKtd/QPkmhEwjlPOPfSoJ+zjDwN/jZyR7bNy6IthbIcIMdz/37KwNDNRmhtSUQLWFcs1wMWK694DVQhslySBS0jDYAoZNHN+lPR2zfPiHanSOjlvKdPaIbV8jMb45JNSGfDbxFWRlOV0PioLl2L92//Zjt//GMLr7p0d9xgnryALKe/5KS9vsyvJxVdK3Glo52kVHO+5TrjLJIyPqex/IMKPRF/atbfyK1f5KW17NHaxRKF7RVRNsEyMAQ93MPvzjfQm1m3U0RY4xm6/Yppgo3LTVJpNtrip9UjniwINpa+pl3HdKWETu7VxSPE5/4Vyc8fnibKGzY3p5ijOZitsWot+JqMULSkcWlv1nXIeYiJnnrF+Gnf4AF6tmLEIwI4iOqYEQtNfrxP0G98tdQ8pPcq/TGX4Yb/tf1xa9QvfwzhIPXfMJf/gRpa9qbL6HWM9TJI8xSI7W75nQvqpizMuaiUhRWMAo7LuuYu+GSflz6mePCK5LDriSSHdH2Am4eYLf2Eab1Ir82R/TvEoY72Hf+bwjT0oUpIt6lA+zoVbryFFGckrcrBjt/7U/8UHyW8jc0QbPRMuyPpyRZ6YVvX96leuXHiXd+AoBAZYjlFfYDy/l7L/BwMaa2YOlIiLhjPaXuThITSGgdtK6jdvDRqseyCflTnWC/E2TjJfFozfx0l4PRlGmZMGs0j9aS3zYPaEVNwYzCThBCcqle4eXyDpkOqJ3kMCk3gCHNolWcFh3zJqAfBIyCjL24ZRzVxJtsiDd3rpCi46LMOC4SKqs43IjMnmkNTCdonP+aN4JvTgbc79UcpDmRMUj8bDrRjgNhUEKxHSm2I0svaJnmGYcHZx7SErWYZUp+NSIZrv1trgbzJEHfb7Fbu5idV3B/eYfg/ANEVSCqEnF+jjn4U7S79+kGLyB1jyw+ou5PMO6foEbT5/IcALR1SJUn17fm/taCep34OG/REWatv/U7gSliqnn/U3++mAx9QFieoZRhNFpwfL7P1nDB7p1jei8d05wPEZvxobXK80BER2k0Syc9G6FKkNOxHwn2C4LfepvmfIitBiAcUTCjmQ6uRxEqaohuThDKQScRgeHwq+95C11gkZGhnfjv1TUB7SrDtpqdWyfo2CcUmg8DorBlthiwrFK0tOR1TKQ9o2Q4XCKkw1YR1dm2F9/tzbz1jsZ775XDdYKqjpiXGWdlwu3RlL3+kkF/zc7rH2NuvIaLM+zwiGD3J+DJfwCXV5usEIXqWVyrsYVPVWzrEB22rKYjqjJmtDW/Tlxt6vD62X1+1X0yRv0c/5tf1OdXnz3YR9trj7HSFtvoa26/UhbbBF4L0Gq6zn3iP438h8Q2AbbxNzqlDNkmnEfIzvut+4WH6Gy6k9ZoT9/aPKRFE1K2Af2oIgob5KbFDxCH3ts/6K+p6tCnp12MSd96hBn8R+iv/a9+4M8WqIzW5nSd8dYoIPy+jf/3lxq+RhCMrg8HsloiqzV26E8HSj5BJRUmTyjzhFnRY95ETBrFZe3JdkpIJCF9nXrWvHAESe1Ro8M1we6SrpV08R5uA/URziKc2fwdERaw/V2EqXH5E4h36epLUDEdXs/Ac8C6KmXQ0uI6j+ldNxE7TnrmvHa4wZjk9s996s+IKseUEUWZMKsjsgCGRpM6Rab9LeTlgcV1MGvUZoOG3GjmjSKQhyzLlFvlGWnPB67Um1x16wS18wtD7iZUdoHrDEqE6E6jhSBWHXf7K26NJuR1zMW6z2kZcd40uAYCIYmEZC+OeHmgOEpL9pKco22/+Q/yknHUw3aSg9GcXuZplY0J2M17nJYhhRHYDlat8Ax35ef1SjpuOQ8Xap0kKzIcECvLIGhJAg+yedZa1v2S/sEVwcB32mwR+5t9qnHpAKRG7L6JLWd0I0UXZASDDzDDAxi+SJZ9kunQdZvb69b+c1N3t2VE5yTWKKoy9iyLbPP53njR20WGrUOaMqLKU5SyBHGNVN62G0U1bRsghEQrwyDN2bl1Qu+lY8SL20SHa7rLChk1SGXJ1xnrOuK86FFtMgEWdYKSjihvyC9HAFTznj+UhC2dVZTzPm0VYa0iGy2J5qmHdRURQebQowJXBnStn6+vnuzjWuX9+lVEnFSEWYnulcioIR2s6azyxMvar0m10X7M5wRRXIHsaMqIrojprCI5mICTdI0GvEDYWYVz3m4abdDnWVow3JkS3C2p+7vIeoUwtV93+j7KnKKCvEX2wZ0F17AigHqdYlo/fpDaII2iaTVN+y9I5/+F4O+Huj7z5p+lud/IRYc1CtMG2Mqf9IO4pskTwqwkHK69naSIkYEh3V54/vR0iNTGOwKimjQpqauIOCsIspJgkPt5fySRymKdpFn1rhW9ldWURnuYzwbfuzOaczUf0U9zer2cpJcTlTGdk6zOtzFVxPDoA8rdXyC5+bM/8OdrL38D0RSQ7RHd+jd/4L8b/74kNdHmqPXcW+s2p119ULB+vM9kNubRcshlHTKpfbu3cpakCVm2AiESbCd4WRn6RxcEu0vErqbbv4V89JTOWTodYne/grr6HjhLtzkAtDe+hoh2kWe/RTh7QnvvFtE7/4jqlf8BMruFnH74Wd/eP1FFoWfQPyOuneZ96jrCtAE4gaiKT/37ZXXsb8CBhy/VTnCYWASK1kGqQdJxOyv8e2xjJPDx2ncBAgmTusfROuXlvM+N3hKtLOerAZd1RG78jP/F7hbvS0cgEhQBQ3Z4UexzlCqO0oY//frbDG6eszreRT+6w7vLAROxZC4nNF2BpWVQ7RHIe/R1yI3eiu2dKU0dMt6e8WLUsJ4PGGzPrmlqSVayrmPm7R629vHR/cDbwAZxyfZ4RpKVHOmnHn+8Ttm92mVdR3QIEt1ytHvp7bNVRDRaoV5SZF8HGg2rFd1kSVcruvEdujBB1HO65JDo4fewO4fUd1+jzvZAapLfx/p3k99D1Ststv1cngXwzIe0V2BazdViRBTX7L3yEN0rwEnaRY/12RZl4TcjwPvXrQTlu2PZcEW+6NNswm329i8YfO0J3D7A7hxiBjuEj99FVyV6ccVBFaGUxZzcpLKKVBsuyoRQGsIiw515t0SV+0yBIGwp5n2s9QeU1miEdJj3bl+HBiVWEazXXpNQRbhW8+Cju7jOd7hqE3A4nng3QVyjR2t6ezPaIqKfJzStxnUSvUmz9PAySZQUVKsMYxRK+s6mihovyixDqqsR7SYNM9KGw9RHm4dh451S+3t0UqEvnqCvTqikJtw5RKQ95JNHmEcx+n6LKWIfBpSVhL2C2dODT3ECqjK+hnJ90fb/oj672j+pmJzv+jlUVtAbLb3wqwmoS5/OB9AWMVJZgqykuNi6bnt3zqver6ZbJHHFrZcfcP7gphfn3LxE7klUWdM+yMjPtslXPd45uek3e/wHyXR+sZTSsSxTHs+26YUNSjgf7lNFhFHD6cUe28M5w5sXICX68F/7gT9b+fQXCCaPEKahW5xQ5xeEN//yp9r+f1QV+UeEyyvkxSnh775DV0s61bF65w6m8U6FaRPw4UozrR1d16EQzFvLUaKIlQ/GWdcR1XRA+HqBu7MRF375y7Rv/E9QuoddP8D2dkFo4vgA4FrMWB3FNMUTdDCiuvujqOkHiPbbzw3yEyUlh/0108mY989uMI5Kj1rdtFPbgxf4/lcuiY/onjymmW2jteG10ZxvXm2zFTocbNL9BG/Pe0xqwZOiJZCSqS2JCRhov2EsW8lFFRMrwwtbV5hOEknHTmxRUnFcaFJzDwSkWjAKBW9u5fzIrYfc/cp7RDevqP6d/zm73/x5wp+v+M3TI+6pHX7Nvs+0eAuwyOxP8731Aa1Lsd0Or93/iMM3v4dMWh86dXcAs4W3e9YO9zuaUZLzpfGM7Sjjo1WCFJ9QK3XYsvfnvkf34i2gRMwecOsdKM+3sLVPwqzyBK0t/TunBF8WNG/8ONFLfx3raoRQSKEpTn4RpKJTMQQe+dyFEerkEcn0gvJH/io6vYX8PodHPvkGYvgiXbUk/Af/Nfz7z+VxYLYYejGwE2RRhZSO8nJEpizBvj/8I7uNDc9gjcY5Sb453CdZSdtqrqZbGKfopznz6Rj1Gy/SPz4jePG7mB/7GZrbrxKcPUAuFvRfe8zFk0MvBA1rHixHlEbz8XLEsol5NWx4691XScOaXlIwGs8Z7E2pVynOSZo2YHK1TRJXXlCZFQhlyR8fIJTFVBHzy22meca0SnFAqgxyvkUcNcTDFaFRRAcT7KOD69dCCscoyUmTkiBoKdYp62WfrYMLAqtYXI2Jr4Y4q0i2loTbC84eHaG13YiBFQeD+XU8dn4xRv/aKfq3/lvEKw6yHsmv/BceC952dA2ogQE0/Tce4Up/AJf9BpU0NLM+1SpjPR/QdZKslxObCjf/F7D5f3Hz/6Guz7z5C9GRpP6BjrIS22hkYD1Fb5MN3ZaR99CGLdH2kvXFtk/7UhZjtI/RbH2rdvLkgCQrfLa3UaAlbMcEQ8No62OiQU7ThjyZbvNk3WfVakZhy0WZYpxkGFUcDWfkdYztJGUdk1cJ56sB/aimqmPWZ1vEp+/S5A8J/gjxW75+D2lqmjs/CToBUxL2X/qBr4V1Nc3TX0TWS8L5Oeqj931071DDwlA/3WJ+sc3D4yM+XIy5qDSF6eiAQEpC/N5xkDi2w5btuOLu/hnZrQvsK1+ivv2jIDU4A2ZNN32L+Ml3aA5fodt+4/pQUj78B3DwY3S2QtRzgu/8x4i9W7ioj+0f0EVDks/6Bv8JKgwbinWGtZpBVOM6QRw07L/ykODfvo27/z/6A68Xd14gfPSU4Ok+UnR8bXvG03WPlVGkCj5aa/IW1tbSdo6eVNxUGbESJApGYceP707YTnPG/SUHt05YlilZ0DBuQoZBRE8HrI0klB27UcsobPj6rYfcfu1Dktcv4Wif5Hu/DGdXdK7vFfqB5L75CmlvzNpe0biClSiY1BFPioDLi122qkeoFzXu7itYQHUfwWSJnQUE/ZytrRlFE1GagPubcXbtJNMyY2fVY/mtOwyixzAeQn9A87/+GyQf/TLqV3+Hq995meViQK/nk+70+Rnyvve3/wHNyaaK/CNceYp49Jhu1YGG5Hv/EeJOH7t7gOuNkOs5vPE/pCtOoTPwY3efw5PgazRY+YhvBb3MQ71ME1BNB7hWY4qY/uEVzSrFVBHp/pTzd+4SRJ59UKxT3EbX03WCXlIwWw0Ybc18N7A0hI/fRVQl5GtYtZjpgOF4Qd1EOATDoGHZBgSiwzjJ2XxEGjaMeivi2Hvq86sRShukdCRxRZKWtG1ANlwRbyyBMjTMP/YWTiEcSnbXl49n7pYkLVCBASuRNzqy5BidVvQuPEL5/fdfvE44fSaIFrIj3ZkyfOkJaqvGXMW08x6LD25RVjEPpzus2xDXCVwnGCU5QnSYNmD5ZJ980Sf9YE06XhLthNgyJDycI/Zjut19aGrcS3uos6d0T6d0taRZZH40qyx1HVE3IakTBEHLeDR/bs/DF/UvR33mzV9pg3I+yEIFxoM8muA6uEfH/lSP8/5Y12pU4HkAUjmSfs5y2b/2rD4L6tE9b1li1ULm/Il2AzEZDJbI2RahdITK52bHylKYACFASseiTuiqBLHB6C+biFFcEujWi3Ju7/xA1bvUPVz/BqK68ur0oEd7+RsweAmpYoTQXvH/fVUt3yG6/Bi5nCEWM7pLg9gPoTa4ZUR1NWS5HHCe95jVIa0TtK6jsBYlBIEQtJ2nBSvprWTGKtRWTT08QPdfQuserjOYdo7TES7tI+slVmhcZ5BCe6pf/sT/HNUSUZXIKseFidcGtOs/0cPwWcs5xXwxZFX5MKJE+0AfldSIpkL8Pr6AMWs0IJQjCAxp0FC2AYk2NE6yaL3fHyCWkkgG7ET+ZpJqGASOo7TicLBgezSjP14Qj9ZsDxawHGKcRLUhxgl62tEPLNtRzWt7p+zdOCccr8B2uHcuKB/usb54g7PTfYyTHCYdgRxwq3mNmW25EgtuihGHieJG0iKEQ8YN9tZL2Df+XZyrkPP/C6IokUmLDAxJWrA9WBBqw4HRnK0HXJQJF2VCb7pDnFT05k8QRxnVGz9N1nuF/OaMdP+7RGlF2wacX/q0ORW1xLtvwZf/6Nc/Sm5StSvQCrdW1xCjreVD1M776L4GKZCH79HtfhUbjVHr2XN5FgD6wwXIDpxvc9dVjJAOnMC1nnOg08on54Ut4faCJCuv/fEAy1WPSLcEG6tuGtWEWYWMWroGxMMndA10ucLMh5SXY5o6pGpCVnXEqg2JpSOQjlBZ4qC93kj936No65CsvybOCpJ+56PG+znBaI1MPDa3azU6bNGBoW0C+nHJrEqorfKdHGlp6pB6laHSGmVmYDykK0gqXKtJQ89EKcrEI8d3rz55sZTP7gC8TbPy9MB5E5EbjRadDyvapBM+W2+bJkQVHrIWOn/IELdH2Jt3sb2xz/tocuR6gdBT3DwAJ72yvwkwm++/3XRd/oXUFzf/H+r67BQY6VG8YvMVDgvcdEBT+xmSjhtUaK7Z/WadXKecCelIxkv02a4HeTwj8ilHMMgRqqMrBEK2dAXYtRc4JWmB6wS9oCWQjssqYhR25EaTG03ZalYmoNocQCLpCKXbnLYN0SCnfvlr/CCHcxIf0egevPf3kFWOHewQfvgdyq9arI6QTU5z8FNYsyYMd3CdQV++5X+uxcxT3ooIJQVuIWkmQ/JFn6vVgFkTUVjpVfhdx7qrCTpNLBSWjtoGGCdprCIvUxhGuHhAHAyRQqM2zfM2u0WzD8HlB3SuomkmdJ2hS7aQ5QScRbYFXZx4mhuAs6jFMRz+iZ+JP7bKPOFqPaA0Ab2wJosqsn7u0w4ffUz9pTNaIP0+4ZkwLZ3s0FHDuLdmermH3jxL69af3LJAoIVH3B4klsZ6hfxW2HJ3sCCJKnqDlZ+DAoPBiqJKmFdehZ8bwVFq2QobtuKCOy8+IN2fIrSlPe5x/K1Xee/pbSZVwrr1ITr3ehV3MkG9SdI7LffYCjsOk5p7gzm9LEftNpS3vkq6OQSa0Q5qtUDkrZ/pDnJ2AsuoDqmriPmDlMpKlq2i68aMkpzDRYLc2iM9+Iv+9UgOoe+JllJ2XKwGxGFDerkm+Hj5Az+YSkZgK9CKZtrn/IPbnF7tIrUhW8wJxkv0QUUweYy883NIoWnC3/z8H4RNhVmFTvxzZ6uIxWzoCXrGp3EmvQJbB0jtkGGDCBzp1oJ6nfpuobbMih67gwVR6OFFUewx4TJpwAjsRNPVAWad0KxSqpW3rq2qhHkdM6lDxlGDFh2hsvSiiknepyh97yuOapom9Lf8QU7QzxHaEX6pgSQFGyIerWjOY8KsxLaastgmDhoCaTFO4vDPaVEmyIXz483xCjPP/Ghjs9n2eznzZZ+8jqmNZsdOPQSoDJFBgl0ndEZiqpCmDlmWCeUmlVRuwonSTRx5lBXEW0vi2ZAoLa9dBnpQ0Lz8E5i9N8AZsu0fp3z882AMOHzHRHoxpTEarSxdJ2lb73pp7PMjPn5R/3LUZ34C6iIhiit03CADg0or+r2CZt6jnA18gpRypOMlwTBH9UrsOqGZ9Wly/wE8m24jRMe4t2a8N2H08mOq8zHx/gz1pYxuMEKsl+jLCUJ2xGXMbm+FXQ1ZNCFPioCVUXQdKNmxVAFHaUEgOiqrKKxCS8eHs22UdGytLhj99q+Qb730x8J+XDJCnz1G/e53cKUkDv8pdu+IdvsuoR6ST79FJR5AZ0hOPvJ419M1brlpzS4qXJ76D3QV8Vvne0wb7+evLeTWEmxe7qazCARnlUDLkF7QMuytYDym09Gn5rZtdUbXzCDexWZT5PxDRH6FqnLsq3+V7vKbfjzgLBiDqAoPAYp3UfnzWfAfnt0gkJYg9Le0G7eOfYcnqekOj0BodDiiOPlF9OIY8dJfpb77NeLjY3rrC+72c9755UPOyphl6+FHW5HPJ9kKHTeziu2oYq+3ojYBZRugpWVdpnC+R7IYIqXFOYWWFi0dlZX8xO6cUFlub19x9433iHYWqJ0Cc97j6p27/MLbX+GsCpg3AtfBncxwbzhnfzRlMFgRpSVVnnqGfuXHScPdKe7111BbX7v++Tup6I6XrN6+RbXos/3VDxDa0pyP+fB3vky1WVgLK5mvY+KLQ15ZJ9fZEgBpege7tYtOHzEcLviRvUtGLxwT3Z/TvXDrj30P0l/+zzj9hy/z7oN7PNjEFhureNV+yNbOHLYHtLv3cPlHBPEB4Wv/i8/zEfhUnT++wd7NU1TU0pSRj+yu4g0Zr2W4PyHen9FM+9TTIZ1VpPdPiVcxzaxPfjHGOoFzAq2Nzy6Aa8pnMFojf/IIcXqM1iUxJel7l5hvv0w0b4mVIdWKqyqiHxhaJ5lUN6itYq+O2GnXbPWXzNd9xtszhLKopEF/WdONb2AHWwhnUevvErQr6umQ9XzA+WyLi7xPYxWhsgzCio+nO3z11iOkslSrjPr3XsZZSTJYE/YLf/mx8po6mkU133v8Aq91gt2sRG+t6RqFXabkkxHvPHyBXz/fo3bQDxyHSctef8nRax8TDNeIDRnxxo++TTMZYMqY/Okeg688xJkGgj5x+gLFyS8SP/oOYnJF5yA4yFk9OiRMatKtBdva8cHvvkGzST997tV1n3+wT/eF1e/zrM+8+TsnN619S1tGFNMhg5vn3strNNnOjCZPsHWIvdLUDw/p7c28JSYw5BdjDrevaNqAugl5772XeAUfCuLqAPl4Cq9mNPfeIORtxHRK0C/YGXt/cmMVUiTkRhBIUBvP52mZ+txrZUm1QYiOSR3x/nQH8903eHOYk47/PsWXJ5+amz6ronhE/Jv/ib81n5+zfuuI7MUT0Jp2+y7B/k/RGI9VDS7eQV+dIC7P6SYVdh7TzgbklyPaKmI+H1KUCas64azSVBamdcdlW1PSUIgCiUShaWlIyh1uZ4K9JGfnxjldGBFu/+gnr3lnQGjk6oTo0Xf8/5f2MOMjmv2vEqiYtneEqK5wOqL8+hse6WsraFe0B195LjP/XlQR6ha1adHaJvCMhzxGvfUxyeX/HvFv/ceE38dGaG78NO7+ewSTd5m+f5uLKmHZKlZGUBioLAxDz7bfCmt+5id+k2Lu1d+mDVitMx5MdulVMWnYEGqDFA6tLEejKXsbB8BwsGT37lOyN6+g34NFtYmatmTa4LqAUEKqO25mJS8cnHDnz/0u8rUxXW+AqE7h+BxznFCeb9H/ywUm3UX/9v+Z7vQp3WVF+3CH1ewe1SpjPh+SPN6n3fD1V2XCvAmZNZpF67MLDrK1j1mdf9L+zSffQI/20H8Rbn35I5iuMF/7EdyX/+YfKzTt/t7f4r3/9E0+Oj/kokwpjWYvLtke+HGIGjfYo1dBKnS4g1YZZXVM73k8DMD+7ROCrKTb5NuDH2c9y9woFz3vvR+syV449WTCMmD16IDVdIQ1fnPV2tC2Aat1j0F/RZCVqNSHfPHWx3Bz5HG2ZYGrQ7T2VspYZ5ws+qxaia4USvqgJoDWSQqrWVYJR6Mp62Wf4Z0T9Js93GibducmnYqQ9QrVSxHZkrqIWec9lHTc27q8VsYr6RilBUJ05CuPkN67cU4QNRTzPovLLQY7M9Jezmw1YF6muI2jQweGtoipvn2fKk9YLgacTHf4cDnCAaUVBNIfGrK0QAQGfaeiOzoCHSAePSQKp0SAHPrRgfrHv4mSv+k7sUWIUw5XRJj1EKEs0WCNrSKatT9E7R9cUOaJ7wRsHABf1L+69Zk3/875uZmziraMELK7PplHvcLbV8LW+5GtJNuZE+7NaCcDmlVK04QI0RGFDYE2RMYHAcnAbDKngSIHqWjuvYHauSJ++3vEDyqY+Qz2TDvmjaR14DqJEt4ipqUCZQmAVRtQGkljI2y3xda3X+UF8z7Z07+L+fr7f8DzH77zD+Gtpz5TYOVnlSLuNrcBQzP/LjK7RXD5gd/4J5e4pw3FgyOaPKFcp0yuvIr9ZLrNRZlxVcUU5hMkhaOjEhVLOSPqEnrdgEHXYxQpXshy7t84Zvj6Q8qv/Htk6hPidtdZuvUDpKlpD15ALa+wgx2ErdGXb9F2huDsOz6+11mYPKILYzod0qnNBrL13+fx+MMrSwraZ5atoKWuvNAzKGJ0HqC0pjELQj385HXWQ8qj14n7717/2baD2kJpO2oLqRMo0ZEGDaYJGN059XPRdYK+2GZRZLROUTQhRRMSB95yGEc1SVzRdYLB9pxwtIbaQD3HLSTVxZj1ss92XFJYRaoso7Bmv7+kqiPsOkHO5ojViurH/w3E/Rq9OKN/cYw5uIcZ3iCaX+FOGlbvvMDyauzzLNqAsopZX/rfL5YDZkVGJB17ccsg8JvGqo44/eAFjv6/H5Jc/i0IA9J1Aft7NLdfwd75Gl28Qzb6Uz/wdW9tjv6Hf5vLf/QC3z2+zYO1z0cwDmwnuFOmtEWMK6Ufs4xfvdarhOFzAD5sqlplntHxzJZbR9c3X609Mlkqi9DO+/4ji10nXgOgDVeTLbKo8l56/Gd9nfeInxz4WOCwpZc8hcdzALpa0S69z11s2vwHm9e7tJLGSlrnR0gAlVXM6pi06BEELSZPiC7PkU1D4CzCGMTsCvtOTXlyxGo2omk1oTLXt+RAG5K4otzw941VmI1Tw1nfTrdW05YxxTrbxJT7Q00SNFSlHz4K6SjzlCdXuzzN+8waTSg7Mg2JcsTK0h+sCPfnsLtDl/aQV+d0t24hboEocric4OYh1fmYdgNXija8gSZPMHVIurXwULU6pLPKM1Oihm6d0hqNeu6Qn03GxBf1Q1t/osHPM3FO2wbEWUG16BNsoBe29XP/dpOUFW4vkFlLdylo6/BaGPhM+fqpObzcqN/CEGEa2r3XMYdvkiznRL2CULdEyjAILJeVpAU6BFpItOxQZmMFlI5lq69xrMs2ITq+TVnH3LrYZmf2FvkL3yQbv0lZHSMf/RLh29+lfOJxuTJsiA+msD+gHR+BqQnOvkt7APHTj7zSeF1ipgOmx/s4KymrmLPFiGFScl70eJQnHBeKyoIWXtUfIGlEg+lqAiJ0pxjLmJsp3B1N2bv3BPHVoz8wmlAyQhZT6AxmeJNOKhAatbpErue4aIDcCLmEaZHLOQB254AujJHFCu7zuVewuaGBT9JrWw9wchu/tD28hWnmn9r8AVy2D6HXhDigdQLT+Xa/FB5LPwoM/biizhOGLz/2P1tgiJY9sqjiKu+Tt76BXtkAKTq0Ngx7Odb4RRDRYS8iD09pNOWiR1lFHA4W9MKG7d6S0cjnpc/nQ8qLMUJNEIHB/aXb6PQW4oUepjM00295tTw+WKWY9ymLhKqOMBu4TL7OKMqEZZlSmIBxVDEIvQvCdpJFG/LuyS2myyG7703pOsnWwQXDLz1Epz3M1v0/duMH0P/F/4bZLx3xwYf3eZqnPC00q9brSYTQXK377E1GZBcDwvWSIP7EgibEH5Vt+d+/6ioibjVC+UCfuvaxskJ0KG3RcY0MW8T3bTi2jFCB8c6RJqIfl+R1jOsESdCwKhPcxS5R2Pjgr1vntOdj7w6yyj9vViKlPyzeG8wp2pCLKmHVatpObBITvYBu3QZo2WMQF+RXY4L3C4LdBXKxpFs5mvMBk/deZj4fssx7tFYRB+31rT+SHVFcX4v4nlk581UP0/iN3zlBmSes8oxm4+mPtAeaNW1wnWlS1TFXVcqkDimMZDcypEowCAy7SU66tUBuQZf26KSEsqB59Uew/UP07BHh5NeweUybJ1SrjKYJr8WTVZ5i2k0ntkiwRl2zWaRymI0O419IfbH5/1DXZ7f6Sa/aD6KGaMPjn5yOUauM3mjJzo++h537QIk2TzzK8lgSDnP6R5fIs22CwFsCyyJhOh+xZRQqMHRWEbkZ9c/+m+j5Q/T0Q3+bLXJ6NxfcaDVSdiybiKdFsFnwIJQerWqcZ7Qr2eE6wZNcUFqHEoJF2+OsjLk/H3PvbI/7x/+Q7vX/E+q0RzPvUZsjbB0w+gsn2Lsv0sUZ5f1/gyg+oLr6DcLjd9Fnv4B75wqhOgg7VFYRxbWnZilHeRawXkY8yhOuaoXtYN5atBC0ztFtegB9toi7lJiQvlZ8bWvJrcNT0vtnlD/6H/yhKVvi8M8gP/4Fot/5RxQ//m+T/tbfp9MBdu8IN7hNd/QXaObfRc0fwN4dwqfv4+IMpEKsl5/LQ/L7qzWaKGxQynqBkrYESY1OKmTWUr7402S/DzYDEJx9B3flvFug0cxqn+F3kPgb/58/POdoa8JgsKK3O6Od9bF1QLXoM7na4uPpDk8KL6a7lVZ8uOyRrfq8Op5y59WPMFUInbhGuDrro1DDpGZ374p0a4FOK4LxCtlv6FrJ9lWf6mpEeb7laXLVFaYzROM3UUQEuz9F/d5/hjg9BisZ3T4lXa42h4CUNMspC7/xr+qYnTRnf4PS7TqBMZrvnd/g/cWA70xH8Pg2thP8W9W7tGVE/MGacPAPcH/hH9P99H/oY6Y//s+JvvNr0LT+q3TYacTig1ucPT3kMu+hZIdxPv8wUIJlCw/XfcSTO3Sd4O7X36ZZfUDTGUQ9RxYzgvt/47k8D7v3ntIW0fUBsGxD0qREbgRnQVr79SM0iMDR1YpyMtxQATW9qOLhdIdAOtJwAxJzkqoNMVZhnWTw+ABnJU2RYIyivzPzh46o4jDyn8eT831yE6Cl4yAp+Gg1oDSKeauYN4JYWU6WY6oHIeOrLW688BSdVOSXW1xe7PC90yNCZWk2IVxZ0HJz6N9LIdxGnJcyVs7TLEVHWflrzLNOgKxjqjakH1eMpCMOGgb9FeAdTrPFkJPlmNZJlOgIZMf/j70/jdUsW+/7sN8a9vzOZ65zqqpr6On2nUmKVxQpkpZtzTIiSDEUOHCsIB+CwAmQL3EcIICRyf6QIHCAyIgRAwY8RbacaGJC2aLFQRRFXuoOffv2WHOdeXjHPe+1Vj6st07fy3spt+gu6grsB2ig0HXqnPPud797rfU8///vv534RMeD4ZRb+4f07h5B56CpoTfE3HkNM/JWTadDGI9QvRVZcEZc+ATU5x+9wu7BsQ8+azXBMEdMB8RrIebl8TZJVlzT/5bFZ5l+f9Drnwjyk8/7pP0Vw7uHzD68ycbeGVJZZNBRPNhFRe31JmF2vMVg+5Lls+1rstfBl95j/nQXWUVsjKdIZWnLGNNquiqk/92/S3Pzc7gbP0US75Nv/gbx9q8y/nsPKVcZ48tNXusHvLeIuKgcq9Yno00bv+hHyovrGmsIpCCUglkDjdUUZshZlfD2yT6TXy3ZyFakUY1SHTv7J7jLGhU+gSSl3foQ3v6rxNZgeyO6/TfQm3uoi2PE6SnmMGbjzUdcvHOXw9MdVm3IeRVxWfsTvxRQWcM5CypR0MkOiWTJJQH7bAcRP79bcWd8wdb9p/C5m8TpKz/0urvzr2OSEfwL/xuC9/8qdrSBk76tGz75dZqDCqIxZmeMnHmin1pcgnx5J70XMcRNE3I5HbO1ecnlyRa2VUQ7U4LfhZMQvPN1Fh/sU1YRlRFEah3+5eCP7niWeb+/YrA5RScV8d1LzFVEV/p8iEFUE1QJp6Xk4TJbz3Zh3m5hfv0P8aXXPyDdufKUxD6YswC125L0Cq/o1prix/9l6q5CLY8Jzh8jf+mRdwMUMfPvvEJ6+bc8Wnkrwm1sIo6Pse+OKNshKq2I714gnlp0UpFVEfnUw1P6cYmSlrbT13NbayWLZR8lLItWcV5JVp2jpwWPLnao6ojN1RU3f/oYvjNHHv7PcK2l/eY+V2d3rufnbROwWPS91UwZ3rzxnODkBpf15HoMFkh4sIyAAdnpLpP/6pzB8K/R3bhDN76Fu/FTL+1+mD3bIduYEWYlKmy52RxzcbFBGDbEScXqZIJzEnmyQZiVZLdOiUcrEJYgjRlXEZ1VnC0HXBQZF0XGrdEVcdD4sYG0TE82ieKauJ/T63kX0fa9Z1SzPuUqpS4T7r/6gL35GatVRlElXiTaRpSdpug0oTIc5RnTKiZbDfjW89skur1GRDdWoaTjuExpreAgKzhdDn2srjRoZbkqUzqrPLtfGYbZimWe4daJnbMy/XhUII2P/Y0a6tJ3i1ZVwgfzAfNGo4RjEhpCafn5P/SbDF57htruoHbQixH5EtXUuDjxOp7ABwjRNsg/fID87kPEhT9YTCZTosHKn/7LmPJ0QtTzHBXTaYbjGcUq82RD4ZgM5i/tfriuz07+P9L1iRf/qOd3jUI6uqXfNSpt0GmFTitPK6sDgkFBtDUj6OfYJiBIvHK3WGVUFyNsp1HaIKTFGkU8XKKCDqEs4nRGEKc0OqLYqIjHXwF+FbVZMzk4YeN0h0UTsRVrWqs4Lg1FCYU1WOeQnUAhMDiU9W2/USApOli1gkAGXDUBs3WbbGMNp9FxjWsl4nyFq3KS+V/zJy7rkFrBF76Kfv4QlkvIO1RWkT/ZZbXsed+sE2Ta4AjWs35P8dNOodbKfYVmxA67TNiIfCbCxnjqE/BIadsZ6ndggwFEUyDagu7sN0iePwApsWkPGyY4HSCXR9h1el5w/hA7mKDODsFauoO7L4XnrpRdq7klYdBRVxGjyYzhK8fI14cgf9Bc2ZocvebvB7rjjUHBWRWx7CRm7ceW0vr8iLgm3FhQPZ7gWv/apHRMK69YG4YWLSWL1os/jRNcVCnnZ1sMbx/7001uUa8ID27Kej6euTfy6ujhF7CTjpZfIHrrkCw6pjraJD8bc/ztV/28WnsB2tXVPdo2oJflTHYuGKYV0cEV5iqlOhtjnWA6HzArMzorSXRLGLSkvRwVdvTHc5SyZEHLSelbveAFrMZJhHRUR5sefVz4kYVzAmMUZZFeK8e7TrMo02vYzKKJKDtBs36+BhLu9GruDeYcbJ8x/OpjzOiLXv8R9Ejj/ZdwJ/hq6hC9Sj0/30qEtIxHM+9RV4bZ1RghHP3hEihpLkbYVuGs54HosGUynCNw1wvrxsYVRZ5SNyGNldRNSLIeNQnhCNIKcESDFWFW+q4P/pDirL9ecq0HAFDCsVzb3GorWayZ95n1919lfDeqp/3cfRwasqBFS0v3QvAnLOO4RK1P/VoalkXGskowVtBaRd6GRKpDS4sQ0HSa1aLP1XLARd7jWd5j0SoybRiHHQe9JV957X3GP/cUhmtef9vgTle4vMV1FWp8Qdr9HU/9BFx/iLg8w+YKpCUYL+kVMSpqfTiUssQbMx9E1PhxgAz8dQiDFqc7pPxMOf8HvT652t9IoqxASEe7SnwSlvQBPkIbWPt4VVIjBzUqj7FNcO1LtVbRlBHWSJQy6KClLFIv+kkahLBQNIhihZ4+p9URKrtHM9hAbg2Id6/Y3TznPO8zDkMWjcQ4x8K2VDR0wmCx9F1KiEIJgXUOJaE1jsoK8s5/iIP1HLA1ikj7VEGbh9ilpJn2kSetZ28DCEdgv45bWmwe4OoUWwecPr3BdOm97qVR1NafwBrraK1DC4FyisCFaDSBC0iJ2QwDNiJHqgxZf+U3PVWJqS/gdyz+1nU4qZDWoKdPYT7HbW3jwginA0TXovJL0BFOR9A1uDCBxv/u3Tpo6NMuKQ1Nk9GtYSRtp4l6BcH+nPaVP/xDQ5Ga5YdoKYhGS8YbV3wuz+jPJhyVKZd1wKLxp6JekZK1GhF3lB+N6Cqv9i/qiMIojBUE0rEVedCRBFJlkcCiSKnWueky6AjT9dijqJBlATqAeorqvY4iwuz/Ubr8Ej37LcSpYbXo8/xsh3mdUK1917WVpKrjVjel1195odq4QUQe8FOXCbMyu96YJLr1drWsIhot0b0SFXT0shW7sxGnSw8laq26xl4vTzb9Z0sZ9KAgvXVKW8SwgKaO6DqFlI5ZlbJoQlorOS5jCiNQAlLl2IwNX9k+4c7NZ2x+4SPan/5p2o27EG8RpAcv5T54UVUVI5Ul7HzL3llJnFZI6b3l3TqFzxpJW8bUq5SoV9BVEabRnsmRTJHSk0DDqGGwbutbKylbv1lfVcn1yVUqi4prgjUkTK15/G0ZYYym6QIq45MnrRO8OIM2RtI6b/WMpN9otfbjVMZ5q8h0Rxa0KGFR0l47zF5sTL7XI7+qY/8ssYrGKMq17iVSFVI45pXXCBytBpyWCUel9g6Q2HDQW/Lq3iHbX/su7Vd+CqxBX50iZle0hx5k1DUB6cacuDpFDIFRHzfehOkKtETqBqGsd1Wtseo6bpBhdw03cuuQIWslSlmsFdTNSw73ce7TP/l/ZvX7VOsTL/6zsw127j9FSMfqbEyY+DmeqXxb1llBsj1FBB3mKuXw26+xcePMMwHClnQy992BF0KxsMMsFG2eeGhFr4RQ+RYXIJocgPDNv0xX/d9Ql99g/yfe4eRii8sqJlQhSghaOi7UOaWb47C0Yp8Dt8Uw0KTKK36VEFQGBIJ+4BiFLaEy17Ah0wTUZ2O6KqKY9VHaU7zCtUL24je2ufnWR7RFRDXvU5Ux33z6CgCV0RyVIWeVZNn6hb9d36RyffqXBAxFwkgHbMaCrajl7uSCMKuwdYCazqA6h4FH4RpbEWoP+rH9GwhTX3P66ztfwoUZanlC8PwBLk6QTYXpjalv/xjJu78MYehPuvHg07pPvq+UNnTG41hNKdldz7hFIug27vA75URVfUry9t+CKCb6fM7mzfdIN2fE77wGp3vMmgFPi5i822FVRwRhQ3bzjK4JODvd5mQ24ST31qpFq6is4CBt2I09/GkYNtwczFDS8uSjO2RHBf3hku49RdIrkMqg44beF99F7tyjHeUEKiOOdijHN9Htb1JcjPng8CZnRcbJeoSzaAVvDRsC6ReBIKkxdUD50SbBoEDFNSeXG6yaEOMEgbQEa3W7TirCrRnyQDOIntE7OGN8vMnkZBNrFd99fsufCvOM2XzIzs4Z0TBHbc2ofv7PMPwvfpFsltEsesxPNqmuIs7KhCerhNNK8LRouZkq9hLLftLw6viKr/7sPyD409t0P/a/Iwo3fuB9eFl1uRh+DP+KGh/3rQxSd2jd0OvlXiy3SplORwjh2IsOyed9TKcYbV+S7l0ilaVY9OhajY59II11gqKJMFbweDnklToiXGuHhgclIuj84aLVJDcuWJ5NuJiNOJqPWF4LQxWlUewkJWd1QGsFEnwEc+s1OrF03OuVPCtiCqMQ602WEhaHoDGKqtNspgVlG9C96MgYed1daIy6Bo4NQjBW8Gg5IBCOx3nEovWbjmHoGIQNr2ydcvsnvoN8c5tq/yfBtrjwO4QfPeTsvS9zeTmhNZqNyyk7VpB+/gyCELFa4BqQ90a+S3C48hqEoEMFPiWwvhxiai+KNJ2irlKqOkIKTxNdlenv093xWf2o1ide/MO4xjQBUhnCpCbZmiKTGluHdCu/gNeXQ1Tu208HX32X8mxMtfAjgqhfsLwaEQTe3gOw99YDVNSgN1aI/R50GhdntFv3EJPPX//s5rU/RSAV+utfp5/mDBdD+jpiEGiUyNDdDRZiQCkKSlHwgGfIThK1Ma+qbQIpWLYOKcA4yUMXsxmXpGFNELScPr3B5t4Z1khWyx6dURxdbXJRpsybkFBaZu+/yVZcM4lL6k5zXnvLT2Ek08Yv/HlnaZ3DOkeiFC+OGwI/juhpwVZs2Fzb0jwYp4FeCkJTv/8fejvf1h145c/76/b0t5BXZxBGkCQEl08RXYsoViAl7Q0v5xddg1oeY3tD7OYNXJiglsfwg5OE/85VFglR2PqFThnSpEQogysd4fG72J2f+z5QkXrvr3nxodaQZQi7IvupGfv5M+ompDCaxysfntJZxWqV8fQffJHNW8cE5x0XZco3pz3+wWxJKVaMXI/KxOwlHV8dX7EznCGloaojH+267PPsfIezIqMxip10xd3dY+597hC7+RbB2k5Z1ackB38aJ/8abRmRhTWHlxu0VjAJDXd7HZ/bOCcOWgbZ6jqSOtqZ0lwOqa8GfO6td+m/+px22sd1kvjgErOKkFGL7BuIM9RbIfLxBYmVmFbzznffoOo0F+WED2cTbvUWJHHF8OYJjHvYzbdo/wLYyevEz36d4D95n+iwpH+2iyUh7xw34oDWesdEqjs+d/8jojcWlHf+1DWJ8Per0rAmDP18XgiLkF7l/4Jv35/MrvUL4N1Cx0/3PxbLdZpk3vfo53Ubf3U+YbHsU7Whn5trw73hjM3egjipMFayOhsTVyFSdzSrlOJqyOnpNrPC++v3siWXVUpjJVpaLqoYge8UKQGHpSZRjkxbYuk4r0Mi6cg7xbT2jgHo01qIJAzDj73xlfGjx73BjIuVd6A0RnG7P2fRxDxeDpm3mspIpHDMGkFpvEZlL2nZTnwSqYwbz/d49MvIKvefk3GP/Z/5FuOHuzR5Qv/2yRr246CucOMNup9+leDDt3HHK9rzgT+IFTEyagkmS6If72C6wlVglyHl4Rbz043rMKWt8cvDPV+X/eyk/qNcn3jx3/3K+ywe7NOWMVJ3iIsROq1821866nmGkI6gn6MGBbYM6d0/pJv1MIX/kMdZQZWnVKVvE0aDFaYMfWs9y2FvC7oGtTzBAOXE+5OFijG9MaoQ3P3Kdwne7lBHB1RmzEkl6ZuUWRNxYmLO1RmNKKncks7VrNycQTtGIJFIDpcBQxFTmgkHyz43koqtNL+eN7ed9nYYo7isI47LkJtpzbJTVEXCURkzbRTj0FB2kmXnNQV5Z1mZzusNEEy7EoOhE4aWFkdy3XoMhCXSLfHmDLXZ+Rlem2OzTWzUx2bb1/z+F3M+URV0t+6jLo6xow2aO19ClTPPbLfG43y7M1wYY+MewhqCsydw/9O+ZaCqI8omQktDElcMNmYEaY2rJerkGZ2tkcrfWvniHeTr/xJy9R8iL8+gKNe+vh69W6e8YhT9LKd9dB/nBIsm4unFNq9snXL5bJdF7gExL54jtag4Fw1vzyeEMuK8yEiChs3hDCkcZROxqmPmdcxFFftrXvRIrza4Mw8Ri8c08S6hHhJHOxT5A+KVT1BbVIk/BSrrA2Kcb5XGYXPtVAFYPbyBjhvSvQv01oryL/xlH8SkYtzgTcS3/h9YqXBdgyxWiMszrxG4GFLnCePeilUT4Ry0TlJ2fv68Ot4keOcp/NQxwtQE6QHm3p8i+uefU//Vmq0s52YTYV2EFo5IWbails3E63HsmSA4/QDWCOHfrwqC9nohz/orkrQA6TBtQFtJojXHX0cNmbBcXWxccwA6o1jlGfNln43xlNhI2iri9GSb4WDBcLCgriMWeY/JcI4OWqz1ljYpHNWC6+TQYuEFcXJN/FzmfWJlSHQHnWYc1fTawF8rIJCOzsGqVVyuA6GMExRrLYVxkHeOUAoyDaCJlX+dL8YHj5YDGiOxeOfFsvVjQC09ljpWhss64gujjkx3DMKaXthwd++QjdtHqN3WW3il9KM8wB2vEBuapD0jzGPUqEDcHGBuvoXpjRFd4zuBUYzoFwQvWP8vDvNKYPduIs/fxcxiTB6j0wpnJVobtDYE4ctJ/Pys/tmpT271i1q6KsR0irif05QRKq4BdR3eIdctOAAZtQjlkFGD6yS2CQmTGmcUdRV5DGYTEA5zVL+ENPQ3f1MjqxwXLLC2oyoe+6CYaIBIHXqQM9q84saqx0mRoWTgbX5CUZUJCxdTs6I2C+puSW0WXIiAQCZoGSGFIhQpV/MD9vM+9/s9bjV+1z5KCiLdUrUheRt6nreDovOY3tL4RSjv/Fm+sX6cYJw/5DcYGjokgpX0oTp2veS31hDKgFg6kqBj9CIMBe/R1/NDP6MNMwh6GFvTOQ89cnHizYJSIpoaURXIeulPCoW3ESHl9feSTXltlXxZpaSn62ndEfU8kezFSa9Zfkgw+rJPTJw9gO0fw4y2/OJftiAE9C16t2CUPKJ345xHp7uclxmLNmDRertWpFuWdUxrJal2bMgEaQU1LStqDouIrSghVh2h7qjagKIJvQ6j0xRG+vaukhRNSHM5IHn7v6F6s6a58bOEeoh++su05wOfdW4UjZEEwqCUI1ZrglxUEYQ+5Of69cc1emsFb9wk2/rp7784X/nXqZtLzMXXid//dZiu6FYb/nMiHHFUMUlzsqBZ/0wftmI6TbtISd7+u9jJNu3zv7se+bQ0ZUoSNIzDmsoolHCE0rIRV2RhTb7sMbrqEz1/SH7wG2QbX3tp7/3vrChssNaLWIWymDYgCGtsp6mriCgr6dZdE7OeiesXQrx1R8C24hoEpkMfnxzFNSpsiZra2/0m/rRq2gAp1571OkSsPfjGvPjellR1VGhGUeUjv50gDRsmVtF0isZqUmW4akJKI8F6hX7e+c90Y6C2jsY6EuW1FZ2DRatp18+CohMooTHOCy5D6ciNJBCOnbBjI66YJDl3gHF/Qa+XE2cFXR3S374i2r2CQYbTGlGtP6th5B8mGxOkniOLAkKNG2/Sjvdx8QA9feoZHtb6Z0JoPSSj3wPTgbW40G9SbBnRLjNU1F7b/KQ0RGuE8kutz9T+P9L1iRf/8smOT+JLKtL9c9qH+wS98lpkk2zNEMJ6EmAZEuzmVI8/xsvZVvk5XlIRlTHFrI9pAqJbV4iDCXayiShyv7hZgwtTn2N/9SEu3sSFGdzYpH1fEqUVW5uX7C+HjMKA4zLFWM2y1QzaAZfyOca1WNfStjOcqwCBlD2k0BibcywSPuImF9Of4KJKebLa5Hav4c3xFZdlynTdQp6EhstG0xjWbUDoB47Dwp8GtPSQlUAIOgyFKKhFhUJTiRyLJXARLZZJ5NiMazbSFZP9U9pFip6tkPElAdCNbwHg1qf+6vK3CK3BDibYMCF4/hFYizw9Inz2BKzD7ez4zYEOsL0x+ugRqvOnVNG1v/Nt/FQqCFriqF6fINo13c0ieg6zfYPw2W9Qzh6i1xsU5kd0wxsE6n1cBXQg0hy2R4i7fXScsP8PLzk/zLhqAs4rxWGxw17SoISnJOzGLXk/ZNH0WHWOWdfyUb1glA9RokdlNEpYGqvonMC8+A/Wf5ZcPbnB6P93Tnb+N7Cvvo9dLQgeH3L+9HWWeYZFUBiBlpKeMGzFJdtbF9fajyCpUElDtHeFzCxspJSv/xQ/bHoahRvkQYY4P6N91sNUoe929QqqMmZ7cgn4lvfFfETdhES9AhW1FH9bk/2xJ8hf/wbt+YCyiDk73vHgGd0xCFoGod84ZIFHHR9dbDI+Oyd4/wmp/avUb3wEt/8kpp2B0KQ/hLvwaVWSFZhO+9Z/2HpL8GSONRJre0hlru147XoDlKTlNTRMSK+9yVceWJNkJcPBWqxpBUp39Pt+HPDicBGNlpx89x7LVZ+uU4yG8+vNWaRbdvo+ITAOG7I0J+0VzKdD4qSiqUPyIuVkMSLvNIPAkKw3IWfV+ncSnjw5CCRbsSNW1i/6nWTW+g2CJ41CaSyJkmTa2wXv9Gr2syU3Jpdsbl2y9dPfRewNcFnfkzifn/gFPtEQhLgwRp08x44m2N4QNZR0u7cQkxJZldC1OK2RbQFt4UmjixlMZ7jc4mqJ3Aj9WA3ANh4lrcG2miZPkFW4do0owtAQD5cv7X64rs8W/x/p+sSLf7XoESWVF/w93SEerDB1gO4VDDbmmDxGJjUmjzFFjK4KPxYIOpyRPo0rT4j6ufdIC0c4XLF8+ybh44Lo1gfwxk3MZId26x6ud4B49ndg76cQs/cQTU718/8LktP/LUkvYjB9wvAfXfHtb30eJR2t7XHVaG64Hpd2m1CnNLrgIv/m+hU4rC2w+BOHdZa8PeWj6F0W1U1uNRuc1xHvL/bYCC0bUUdrxdqfLbiVGc4qxXFpmbYtqdKc15bWWSpaVqKglAU5M1bmjM7WPgFQJgz1DV6JU0aBPw0M0pzp0TY7rz/GNRrKBqcD9PQpTofYakFdXhIdv4u6OIHLK9x0TZmzAluE2CYkuO2T/Fzax0mFvjiC9w6RQwlhAPnL2d23bYCOK6wVFHmKe3KDGz/5HUgC1NUZzSufI5ge4qTygSnPH8KBxW7uIMMQDk8hyzC7B55EuLji3lff4eH5DudXPX57XtBhuBf32IgEG5HhXq9gFDbMmpBlpzBOcV4NkQJmraIfaG5kK8ou4LKKOS69CG/ZCQ4LxTvzmO9Ox7w2nLP3jTmT4ZzBxDC7+Bpn0wnLOkYJy37iN0xaOFqr6E9mjP74KdUf+pMkB38agGIdlhOoDPnwP6duLgnWSYzfV8kW9vZd3HeOvHof36LOi5TtvVP/fhrF7t1nXDzdQ8eNb5dXEdWvN8yfvMX52RYnsxFfv9hiYy1UDaTl/uYpl6sBl2XKRZlyazDjv/nNP8Tud99ge3zFYHTCxpf+TcLXEtp7b1G/+udJ//GRAb/nenq4Txp6gV4wH3D/j/1DbBkRDlYkoyWnD2/x/GybqtMEyvLK7hG98ZzsxgWuk5y+d5fB5hTnBGWRcH62yTzvsTmcYaykKBOfcpcVRP3CM/97JVt3n7OtDM4olsebVGXMoL8irEPKKmY8XJCkBdnGjGTniuDJHr0b557rsMiIv3ufuzvHTJcD8jom0i2TcJPz2t87g6BDC8ey9af7WDsGQUebR8QSlPSLf6pgHDUMghYJfHH3kDd/5us+MdA66n/p/0Qc7SCAfPZN+PIKXEdw/iHB8wdgDXY0WXf2Kuydu9h0AOmAF0xE098levR15IOPaD7oo7KK5cPbnpa6McOdSnRvherViNhhH7a0lxs08x6mU6xmA5K08NHs60TVz+oPdn3ixT+IGmZnGyjdMdi+Wu8mfUsJfBs0ilrC/RkiBruUyH6FKwMfwrEO+HmRB6CilnrWp3fnCBF33mcvJWp2jmhKzOAEG/URUiOqBfryKUwPYW+L4it/ArU8pnf1K0we3qLuNNtxQG0F+UySmBQjW4xrSaObVM05WvXRKsY5i7HNtehr1jylUJcs5V361YC4jBmJhFAqtHgRMev47kwy7zrmtqKm5UOOQIJxLYvuhLq7QquMQCZIEZDqHhvigC27yQ2d8IVRRz/ouDGYsv/KM5oiIX7tEjr8KcBagm/9Q8zrn4fUEDx5G3F85Bdx8APFTiD2E9S0wB5pyu9skfSOsDs3IIwQqwW2lYgXvNzk5dh5rvI+5H1GSc7W9rmPbj0fEskpsjklXC78GCJJvWbBWmSxoNl7DfY0Yfod7GDihYtVDlISvXLJTn/BK6s+qy7l3XzFad1SdJqiU2yEIYXx7PZlKzkpBZ113OrBKPhYXDaMPNGsNIpZozBOMGscpfEizOJqTN6G3LGSKCm9fc8owqJjWSVkZUq79n0fFil1nkLTEu1/HAr1vVHF8d2/SFkdAt+PMgYIkwOa/c8Rf/UZ6v0jbOPHYzc6RVvG17z3wcEpGwcnRJMFKqnRaUV1MWQ+HXI0HfP21QbWQWO9sjxShpP5iEUTc1bFTBvNVROynxbr6FbB7GrEprRQ1eiz57T6b8Eb//OXcj8YK4kjL/oTwnk7ZK8EoCtioqT0ugndEaqOokxQVyO6JiCIGvoTzwVJ1omA1gmckyzyHlJYorClKBNs52FgQjjCnSnBcIXeL6EXET29RE4sbgnt+YD5011621Nmz3a8tXC0pHfjHDUowAoCYO/uU7CSG/EjpLaYOuBzVUi56FFXPp8gLzJmeUbeRCybiGkdsZe0xOuRy63RFVlSEIXt2toomOydo5IaqhZ2tjGLDymyijS9TTb6MkX+AGcqbHyM7Q0wX/of0c3e9hCfoA9dSdC7gzUVtlvhXEf8zt9AHj6FzhHemWMuA5+vUkbYs4kHBO0aCENoWqp3N/yItYip8tRH+7YBYeg3mMXVkNFLuRu+pz4T/P1I1yde/FVck/ZX3l8bttTLFBWWYKVnAKQeroEEIo0oOtrzAW7trVdRg4oabKs9n9sKuiZAJi0i9UNzJ5UH2KQDnA4JLp/S7v4kLkxBh4imotu95YVV0QD1quKVkw9x77xOYzSlUfSDmEHTQ1pJICLQoEWEcR0Og8OiZIh1LdZ1tN2ctptjnWWheggh6ckNIpdcJ/P02j5WOGpRUaqCxhXM2mcIFA5D3U5xrsPJBCUihmqXLbPLvuqzl0n2U8M4athOfAiNWfviseAsCNYt+jCArvEze4C8harDlQ67iJBpA6sSLMikQTsBTYssVrh1i03uKsjW6M7u5cz8tTR01mNXnZUkvdzTHRMDaewBSVUHTYuI/XHTxRnoCBtmdNu3kNXK6xKa2mcS9DW9pGA7rthLNO/kjsI12M4hRcBxGVJbn362bOGsbqmdQRcxAk2orI9/tpLOSjIdcdUoWguxEoxCwY2kZTOu2O/P2dm4YOuNx+jJitHpiPJ8xOLCR8vO6gTjBLHqKPMEmg7njE9M/CFll4+oZu/ipIZwdM3pd67Dju5T/OwOSf+voy4vYFmTrOZ0x35jZo2kWaaooAMrMHlMfjFier7B2WzMqo0IlUdVv+DUGyeY1hGzVrNqFa2DTFkkjqKJ0HnGeDj3gUWd3wyp4uWgnv3rFNdUO6ksXR4j09r775OG8cEpdZmwXGU+orleq/zbwG8MMn+/X9uHm4DRaEZZJB7oE7TIIl2PFf3zRiiHiFqItc8ECWrY20KEV2i3ZAhUVwP0WqhZXY4IstJrkdZhYsnOFXqSI8ba37ddB1Iymj/CLiWu0jSXA5ZH28yuRkyXA3bWBMdBUrC5ecmNr70N0uE6hWsVrtUIZemWCeajiLA7JQn/LjbtUW7dQd74WVy7RKc36Tb87xaHG0TbP/eDF1YPIdrx9t/eCFk1uNIhMoFrFTqpPCclapFJ6zkWUoCU6LSiK2J02BIZ3wE0nUKFnc9a+Ozk/we+PvHiL7Wlv3927anVYUvYL64Xcz3KsUWEyxVCdqChOhv7RT+t/QcuaQitxBQxbeFn6s4IhMILVpqabv91zNhzrKMPvkH3eY3p7dN2NSq/pNu4g5o9QjYlzVs/QZa8w43lc+omoOgCNqOI7Tol7gIqlyKlJNY9ZuaIvDnFuY5A9+lMjrG5f6hjKJsTKqFxWHJ9Tqo3qM3q+kSf6g1w0JmK2izpTI4QEpAIIYmDXTK9wZg9Duw2r2Qh+6nlRlKykxaUrebe9jGB7lhNB+y8/hhzESGiFtFfi552bvgkrCrHxSnCgqscZhHTznpE0RX2OPDXMmv9Tj9OoWn8v5MSc+81/36tFojl6tO+XwAYJAVNF6CVoW0CNu48R28uETspdv828vgZbtoiXO3nfmGA6Y394qhiuq3PEz34e2CN37g8vUAkgiSuGEclW1GMxeGxTQ7XOh6uojWvwZEbw8rVnMsLFvWARTskkgH9NL9mlivhaK0XaO4mjju9kldHV9y//YRsY0Z28wz+uftUt3+M6PBt4uePGD54TPSbNRcXmwhh6fdXdG0AxlGtPiIbvPUD18K6Dn31gOD5R4impn3lDRh9GWNrrKnIeq8DUP30TXj+S0QP3yZcnhEtU8+XqCLK2YAoK3BW0lUhR8/2eTbbIG8DjBPsxiXLLlhbSxWLOqDoJBe1QAD9ALK1tW62Dp65efsZ3SoloMLFic97eEnliYSarjMobbBNgGvVmt9RoF+p2MgTzLM9innkNRk2pW0DoibAGkWQVJ4JYqQXxG3M6I3naxDSGhoU+HRAqSyu9YcKN28ReYtZxWhroTPIxBHeXXD10U2S4Yq2Cpmdbnh4kJGEkwVqWKKHDfbHv0g32sFFA2yyAdJHaMtyimxKkqPHhN/6iPjxHvFZxWj7EqEN8eaM4H5F8xf+bbrZ2+j5c9TiAn1xAs/PKH57i3LRY1CcE56eoCY1ausJ5vIpdudVotGXicIN2t8Fhf29pWREtf0mgfwtXCl8JHKriTYWyKRGZAaRCH/SbjroDOHOHHekCCkIkhohLcvTDb8ZGC0Jtl/eZtC/MfiTzaf9PT+rT60+8eK/ONpi8vpjT/hbpox+9hnmyIv7nFHUJxOC4YpukeGuJHqU03vtGWaZYOvA2022K7RcoZIalUY+5csKMH7h4oNDgt4A098j2fwa4k/+uE//i3aww89TP/kb0L+Dawqs0JjeFqF+n8kf+Yhs94Lt9+8QfPQqgRxxUkZc1gF113Ls3kMg6YV7VGbmyXAiArmm6Lka63JwEOpdOlNSMkXLiDTYpurmSKGJRR8kXLkaISRtt0QIGEav8AX3VfbDmNuZ4/Xhip30ir2xF3W1bUC/l3PrD72NrQOaeQ8ZtXTLBC0cBC3twT2Co0fYtIesSsThERxs4j68QmYNye4F9YMRelD4fPMSxHBt6bk4hcsriENUvsJtbOHiBLH1cmJcO6MYZCuStCQdrAgGvotBENKNtgkPn/gvtPhT87xFraboe3/xOqu+fGuL4O3/DPfwhOlv3b9WIvfjil7RckP3eGguWEr/kKrrXe7EGT0t6GnNDj0o4V1+m8cGTi6+wuPf/AJfHLW0TvC80Ajgbq/jdq/g1Y0zPve1bxB/YY55/XPUN3/++pRut36a7os1plux+/r/md3pe7hlh8sVcmxgY0Bw+A+p493riFzwnIA42kF0DXayjemNsTd/HvAP7O/FNcfRDtz7S+Q7X0V84RmDv/mfoN5tURcj0s0ZxdWQpw9e4XA24aJMUdJirLeTrWzAJKoJpGFax0xrzarzGxvnYNbAu4uYLGjZSXN2R1f0tqckP1WADRFdh7v3L72UewHWuF3dopSlriKEsv4EHDfIpMVMQ9oqZDBa0OuvaNuAPE/Jy5TVYshs1b+e90tpGYwWdE1A1Ctoy4imiBnffe4PGT1/el+8f4uwX1C+O8BZweRLH8HpJe7mDexkG6cDdifvsfg7Q6rcbzSene5yo0jZrE/oZc8Rb+7SvPFnv2+MA8D44z92zf8VOTwj3pyxETVknzvGfPkt2q0v0219ifTFqX3bf7395X8Lt5TEmzNk0LE6G1M92mf3ix8Q3Aipb/046Jh88Q7Z4GPmxD+uqvqU+KNfgzRG3qghjNDNCnFrhEvHICXdZAcbZ4Tv/jbt2w7Xato88TqSoCM/m1BXEYnzBwJXvmTC32f1I1+fePFfzvv0pn2CXomKG/9wbGO6ZUZbRITDnOB2AbXD5hLXKURg0Zu575YGArSk+WhMM+/53WunSPYuEEGBCBrEXo92sodoc8rzXwOhr21UUmjs5HWCx7/kLW5dS3D8ISwX0IEe5Wy99oSfygruHe3x7eN9vj3tsW/GvNn8i5zXLVeuYBHMWXJFqyus6zCupbM17fokH6oMgcS4DussUsBm9CoOi0CiCJgEr9B3Ywaqz0DEjALNawPHKGjZimtu9ue8sn/oAzaqGGMl/fGM1dOda7hPHDXo1zrQCmRE8O43aL7wk4QffAsu5z7s/ugCu0z9tQpqgs0FrlPrUQm4Kcirc7icQu1we3vY3z5Bnj9BJus84ZdQd159SLXWegjhqC+GhFYgL2aE8m24LHClwsz8XFLtNbQbt7Hfc3pO4n1s5UcY6c4Vx+/cI0lLslVFIC13epKjWcyFOyI3FxRqSlx9kdf7Ebeyjq2owZ71WFVvciwe8t3u13l7VfML1RZ9uc2W2eZe1OfVvuXO+JL7b71P/IU5bm8PdXVGevK3vLf66N9FSNAWVCHoFgn6DnQ//YcJ3/qfXL/mH0bLi6MdiuIJbvcLyPwMANutyKdfh3aF6N25Vtk33Rxz9Muk7/+GH2+Vkmhj4ef7lyOW0xFPp5s8WvbX/AiFdZBpy25Sc7b20U8bzUklvXiscwQSBtqLIp0TlF1A12migwvM7S95BgTAo5c380/DGiEcdm2XWxxtMemViNjbP8uTCcl4gWkCmjyhLFKisMWYhlC3DPpLpLSkWUHXai4vJiRxhZwOSHs5w4MzgolXp4vIvx4ZdJTTAd06K2H5/i1Mq5n8kY8QPR/JLfYr+q+/TTjM6VYJvf6K4c0T4oNLxI4XnorpezTRJqEe0nQ+7KatTqA4RhZXRLNLhIZgZ054Zw4bA+rX/8QP7QIBtLuvEG4+QywMdp1LsfuFD9GbOa63+YO20N9RTTe/jsIu8ge44hiiMW7/cwQ6QF2cwvklaGjvf55ufMsnNh5/CIDdu0mgjrEf+utVXQ0wTUAyXpCMFwSDAhk12OafKM3991DuJcz8Pzv6f5r1ySE/d59hO+0XbmXoTnt+zmV92I8Qnophc4lZpMi0vhap2VYgOouZRdhWe2uYMhSzHsEyQ2jj1eN7NzHZFqKrUcsTn0z3PR+WbPAW+X6LKE4RTY6sl4SzK5wtENISDFekRcSttEKpjkzf4LRMuagDJnXAVT3kssmY2jEaj+Fs6KhVw0ovaEWNRNK6mkBENK6kpWLT3SBxEQEaiUcK7wUpSSjJNEwixyAw7KcFm2nBIPF2rrbTdEbRGk2Vp7g1+lOFLTJqITcgO1ACehH64hDKElc6XKkQkVmTvQQ2D/wMN+hw2kBj/btXFhCHIFvE8TEy6fyJu3bwktTdOm7QTeCZ4cbP/U0RI44ccjlDRGByv1jJrIKNMXZwC/09+fIA9Z2vEF+eoa8qqjqiaUKqNiTTLa/2K86rAVH1OkdqwIV5yBN1SJLfQglNIByZhrEbkIttWlmSlw9o2illOMUELaPmvg9viUvizTn2zl2qe1/zKuujR3B5yeIbN7l8vkteZMg1tOjAvEsw/pDizpP/Votcmt6G9DZmXFPN3yb5xn+OjRNkVWLTb1Lsf55o+2fo6gtUOYWyQEQxtlE4I3FGUS0zpvMBZasx1lMjjfMNsdJIZk1AZXwAzaKVVGtSXCAhUR5ZvRF23Ogt2R5O2d45Q0SWZuPj392FLw/nOuit/Nx/DZGRytKsiYcqq7CdJugX1/d/EDY+ea+OUMKuPfqKMGqQyvP9Aeo6IowaEBYRWETs/CHCOFTQUVz5e8Z0ygsc984wZxEqeJ/46txb37Y0kbxCXWU+hKxXIvrAcAjMCc4f0khNm+4gF08Jzh8SV4XXoZxe+u7gXCEyAwe7mM09VPSDHbUif4C4fIf4/W/QPQuRUUt294ieNsh7A8yNL1Lf/ekfGtv9vdU1M7qz34DRG7h26eFRgO3foI4HBL2HBPa7CGuxUR/w721z+0uo5Sny6DHu+QzX+U3R70Mz5gABAABJREFUi8h022mi0RIZNcikQfY/m/n/Qa9PvPhnrx2yev+AJk8I0or6cngN9dGxp0W5AmweY8rIq2oBW2s//1OW+myMDMx1CIU928BZ4TcRDXSjbZyO0PND1NUJdjD5wd9j9GUY+ZS4rp1hr46RZ1e42iF0h0prwvGSfXx6XHKyRy9ImYQhs1hzUmpmTZ9Ue6FdY6HsHPNuRG38qWJOwYCEnIZCFNyUY4aBXIcFeV//fupJYrF07CYNAthMC8bpijBsmC4HxEGzDgYxlEVCGDXXqnQAMw1xRvoP4yREHj7z9rxOYOsACYiwwzUaW0R0q4RgkOPqAGzn8bELD0giDLDPO+TEPxz9xf893BGfoEwd+LnrOoHOGklXxN7LPu8R3z7D1gG6XyLHaxuT676vZQ6QHPxp2ukz5MPfBuB8PqLuAmLdcZCt+EKrmZQJ49VtfkU84aR7FxO0LBa3WLUJSkIkFD03oFW7zPmuVwq4jtqtWLgKCJHSB07Vd36cbPvnqIZvUse/RGS/RTXv897zWzxb+QfpXpqjg44d+4BE/hX44//2J7omSkbo7A7iyWPU1iYsF6goJtAhavefJ0oOaLJNXNb3li5pMXVIM++RL3tc5H0aq5DCM+fBe81b60/7Xu0vyDt//zkEiRIMA8c4NBz0VhxsnrF79xnZK8eIYYAd3UOsiXRS9z6ld/8Hq9dfsVz0MVYSiBYdNVSLDOcEIfg5/XrD/wLIc3U1puk0oe4wRl2PfbQ2xEnlmfRW+r9bbxpI1hbWokGGa0x4E9I0IVfLATfuP6FdZNi6QR4tUAcW4hAxdmiZo8n9Z6IX49IeomlQZ4dETYWNM9TVKeLxM1wDZhlQHe4S71+AkcjI0m0f0G3cxnWr7wvhsr/8b5HMp1CUNO8ltPMxye1T5Csp5uZdmsEm8u5/j+wTtPgBwsPvUKc7yHgLKWOsrXC2h0xv0oYjRONFnLgOUS1wYUaw89O4+X8Cp5fUTzdQ6VroGfvgn66IUVkFyiIiC7s/+Gz91Osztf+PdH3ixb+77Pm0KGVAOHCCNk8IByvCrTndLMMsPIpS3yhACdxcIKRFJAaZWZLwlO6qj7MCmdS+BXf/0p98DeiLQ2SVo598AJcL6j/5P6AtfvjpK1AZgcrI3/qzxPIXkI8+wjyHYLxk+eEB1TJD645+XLHZW1K1Ias6Yl7HHJepFxuuE70WraJ1CoEiVo5VO/JpgDbGugE7cbemffmHsXMwCCzGCVJl2UkKDoZTjJN0VkEdsSgTtieX9EYLgrihrTyJLMxKdFJRPt8iff2E5tmIdtYj2Z3DssUuFUiH3q4o398mGC+8o6LRxDcukH2DmQe0V33CYE537B8oMm5QWwZzEaA2PEXPnAWf/A3+J6jDxzfp91ckvZwwqZmdbDHcuvLRyFZ4l4dRqL0G+6oXIKb/1f+T/I91P0ie+9z/EPmbv8l4POPB2R5Vp+mHNZXRfG485bWh5Omqz7vHd3hY/wpPmxOeiYDf6vq8Ev4EUkikk/TckL3sj7AyZ4Syx5a7yUDErFpBXqbYRpOusbcv5u/c+0tsH/1l/nBasvPBPf7B4S0O8x5X3/08dw/3+cLxOyT3/1Pie3/pE12XKNyg+ov/B3j+S6jVlOb2T10r/5WMSA7+NPngFeJ3fwE5NphnEReHu5xcbnBRpsyakFUnqY1g2nihYigdy1YSKYfEEUhBrASx8h+bzdjwan/JV159n/5kTv+rh3Q/9hNUN778fa3pfPEO/GDS8qdSQVxjZkPaNvBahUaTbs58+FGvRGUV3SpFRS3pxpz8fATAztgHQj0+3GdnfHWt7g/XxECt1tHfrcbWGjVYJ1YaR7g1Z+sLV5Tf3mT+fJvxxpRwsEKu+fumiFGyxD6tkEMLn7+LmF1hDu54R81qAcsVSIE8v0CuFyq7lD7hs4x8DK4VqFcVdv8NuuGuT957+ss09pd8qubiiuoXcnTPIEPJyXfus/+1txHKQddRv/ovED751U802wdwtsKMdgh6d67b/wD56n3c1Xc8sfLeH0POPiI4fg8z2oEbP0tbX5C8/XWfr7E3pTkeo3slerJEKOev3w0H9bozuPz9gPx8tvj/KNcnXhvaReY9ttKhk4rF4Q69rStU1GLLkHrWJ+uXni8tBPk3d8j+uEWcnuPm4DooHu15LGqvRCYNQWD8aKAXQX+ASz0By25sI4ZjRFdj82dUKvYP7B9W9RQnJfT7qM0pzeM+0TCnWmYUZcKt/UPOz7b8Q0UbYtOxGVeclDHGCbaihkz7GM9A+pzuftDQWolxEmMFQsDqe7LEAQJpyXRLrDtCZXAIRr0lYdigtGFz8xIh/dFbKINpA6KsQIUtQlvMMqI9HhBsL/zO/EGKGvjTvF0qVt/eJ7l5RjfL/MlHev90+byH1D5K+eK3X2P8+hP0bgWRxhwFqHELWQTzmvzpzkvp/A+HC6K0JFh3MqS0tFWIaTVSGd/OtpL2cUZQf4C7f5Pu9S+A7a4zC5puTludoE7/EXrsI157YU3eBpwVGYnumNcxQjhGYcN/f3ybv+n+BA/Mb1HUj6ntkvfbv46SQ6T8eCKvZIRxLcfqI17X25zXkm+f7ZL+/a/yxt//P6L+yL/5fa9F/Gv/AeN/Dd6qT/nq3/l3uPgvt5ldTCjKhIcf3OPmv/WM6M/8T2m+9DWi1//V/9Zrc72xAH6YpEpevQ/A8d/5As+ObnCx6nNcZDxcxdf46EDCrcxeb061cNRm3Q0AtIBR4LjdK9lNCjZ7S6xRxNtT6EV+UZo9ooi3iJIDlIx+1xn1p1HlKqPXywmjhrifMz+fUD1LiJOSaJATDnPCjbkXTwK2k9wwR5yfbbEqU+8eaQPyKkEJSz/LfXiPUZR5ij1SJDcu6N5p0Dcb2J1QvROSJifEt8+Ib58hhhJ75YVszgpkYCj/0RgV18iyRk6fUDzbRqVPCAY5pA2mDLwuIbK4UjL/7isESU3Qz5Fhh0orzCqBmwfUr/wkRGNkOKLtVojVM8LjdzG/coKpvEJQRS2j3XPaaZ/4/hVuY4vg8B8imori6BdheJ84vf2DMKh1tSZHLB4TvfqvYF33fX+X9V6HtXMEIAcaHXkHzfJD0l/8D3Bzv7CLwBKMVrSLDNlqVFohhw00Avciqbz5/u//Wf3Bq09O+Jv1iQYrn58ddDgrfEtpPZPuqgiRrLntWpC+egLNCEKN6HegJb2fPMVdGj8K6JRfqDpwlxViXiEGIx9M0xvhdICaH2Fv7OBcR2u8Z/137qBFuocZ7Xgf+YEluHmMfvuYIRAkNW0ZsbNzRlXGFGVCv444WowIhCWQlklScFFmjOMSJSzVet7YjyrCdfrged4nkAbjvL0oVh3JOu9bSksctGyOr9CBz9A2nW9T6qCjLmPaKqK3NfWBSNKBFSS7lzTTPiqpEQNvhWzPBwQ7nvkv1qcelVWYPKYrYrpVsn7X/OI/vHWCzBqPzK06IMBchsjG+3rTvcvf423xjy8hrfdkK0NXeTCLkI6uDqnKmHDZkG3OEMpCIjGbe3TDG+jhm9cPPtOtwHa4IMNtbNG/c8T92YD0dIfjxZDGKPJOo6VfBDsHt+QGC3mfY1PRdCf++9g55nq8oegQCBFhg45jVzBoejRG0XUa8c2PqHb/U4I7f/7adfCi4mgH/uz/hcEb/xGbv/2rNO8lzB4cMLsaM3r/GWH425TJmOTWn/s9X7d89T7pb/wii1/b5fHzz/N0Pua4SDirNUeFoLEOCQxDQWslw8Anzsn1JCdRFonHFW9EDfdGV2yPpow3pgwOTgnvLnFbtzC9MWbri2S/U8X+kuoF/lgqgzXSh2SFrVf9G4UpQ5yV0BkIBNHWjGANAdKXE5Z5Rl57jUwUtNejJKUMYVyTDlYsH94gyEqEukKVV0i9jc1DhDY+I2NpwUrkqEaEgIXQzpCJ/36uDHyA2NCr3c0qQg0quqsMGXbYRmNaTdcEVIsMFXSEvYJ498rnZeRnOFORDN7yI8eLd1AXx/C6JRsceT1CIgl1AU2HvXcfM9pCXZ0gnz0hXs2x6XdwXUs92aPbfgvaFbK4IlkneLb1BU77+/J32yBcV7vESY3IbhIlBzAeIfIL766z/rDmWo3emCO3nc8MqBpcrrxo2L78YJ9P2+n3WX269YkXf+eEP3EGnd8V687P8daqfamMnyU5PLt9J4Wy9Gr2ngLrsDdvI+Uj5HmHLQMIBG4Otgj9ZuK1DqhwOsTp8GPYDWBNRVedIHv3v+/BHcS7NFufB1N5ccydhHT+V4iDM4LhitXTHcJ+QZon9PKYrg0wzsdshrolTUrSZU22TkYrq5jGaJKguSaWlW1I06mPF3/dMYi9pkErQ5YWpL0CqTtspynzxOcgxDXWCe9nHi29vmF9ile9kub5NnqZEEQtSOctgEmNUBaVNLhWg/WisK6MEcqgwvb6+4T7XpBEI7zv2Uj/d61EBBY1Kj6Vm+SHlV2H5jjnBZ/OCtpWU5WxD3+qc5yR0Dls3MOFKXI9f25Njl0+AkDWC0RVovdrtvLH6PWG62LVZ9GGlJ2msZJpIxgGku1qjzyacmVzrP2drUvj7cWuo+kkR+Eht40fO1grWXznFQb2v0aM/y7m3n3UH/43fuB1Ra/+KxT7P0My/iuM7CGrRZ/8+Tb98Ig4+BX4PSz++eybRI9+lfTtb7L4zQN+67d+jPMi46SMOak0J6Vg1Vlq6xd3LTVaePRFTzpS5UdM47AhVAYlHJtpzu7kkvHWJb0bF8RvznC379BNdjDZ1ks96f+wCqIGoQ1dFa61LR4hK9YBPq5V2NIgrEUNKmTSkC5T6ipeL/4RWlnUuvsGXI8Awqzk7NEBg40ZOIFelKi4xtYB1AE4AcIhww45rH3nqzOo3RZ6CaJpcdOayM5QNzpcbrHLANEXcIW3zjqBVH5k0bZeixOPVsh+g5MKVc4gv4RdaMrnRJdPYT7Dbe0gwilEsU/b3LmHnp/QDXcR1qBPnmKegyqfoAIJtSW0HxC+8j4u9M+xPNtGDV71NNPBD/r+XzgAvu8ApNYHAaF9l2CyhTy5QAAo4Z/HcY2cWBiPofYaABGtN0ufLcx/4OsTL/69vXPqqyGmDog25mTbU0wdILTxtKytqSfWFeBaAYsKua0gWZ/UL+fIJ4+gM36XXDvc0mHL8Bp7auMEdXWOXMywgxHVva8hl0eQ7BGFG5hu9QMntkBlBOu5aj79OtngLZo3v0rQ/wh9eclo+xG2lMTdWljYarKNGVgJ67b8fvqQep7RNcHaueBYXg2p64jOKA42z3h4skeojMeTtiF6nWOf9nKGO5fMjrcYTeaoqCVeJVwdb5GNF8ig80SyoPMkP+l33M35CGsUzaJ3nYpomoDyeBMdN6i4prkcUs16dFUI0jF58zHNVZ+ujP1Dtp0iR2tVf2kwq4TgNZ+0SOWwV/K/4+3xw0tJy+XxNnFSMdy98FGqdYg10of+JBXVMsM+VWTNBcGbxzip6Lq/h6kXhEcPSLrWC9+Oj6je7hN/KSS6P2czq4j7OaPneyyfvcKTVcqzQvPRqmYShGSEjNhlpU/pjKYzPzyX3Nol0+4ZMvCL/6JM+dY3v8Dwg7s4J+hlOff/3F/Gfu7+D4wC0vQ23eYuevQOk90zTp/dYH4+YfPkiCz8Nym+9C8Snr6HOjukeeVzuCBD7/+x33Wum/79/4iL/2KT50/+OZ7PJ3wwH5B3CrP26a86SywFrYXaWWatYRQoOosHFamPr3s/aBglBVujKYPRwrPuBznmzc/TbtzChRmud/DpvdmfoObTIUFcE8U18ciPvWZnG8SuIhqsCEcrr1tZxojSIHs1ZpFe5x0Meiuez8ccpFP6WY7WPnWurryan8shV7MRAMWiR5yUTN56hMljTws1CtsqVOo7XnJQI/oSegl2/zY27fvkzItjnxxalaiyAAKCgxw6sLn0AJx+jgwMwXiBvuegEHQDL1QNHryDvfrfkx4feoolID56Agfb2NEG7eYBySt/3lsGj37ZI8mlxJYh7QcDkI5gkNMtE6KNBW7/Nt1kj+jp1xFf+YkfeLa9qPbqG7TxFtgW2hVdfka2/jndxW8hH/zHiK7FNSA2YuzeAWH6HLexBVUC+QrmlV/wP3fTo8CfPH6Zt4Q/BH7aM//PJASfan3ymf8yI+jn4OTHSn9t6VYpdton3rtExGAuA1wdoH+8h/nWDPWGxQ3HiIsZ5qlA3VKgLe3lgOjGFSLuCMYN9APcb76D++JdnNY+kjLdw8ZbsPiQPH/2Q2NK89X71xS1bPzjVPUpqmuwgzEiThEfPESOnE+/yuU6aEgRDnz7b3W86dPWhjlZP0ePCg5/9UueYBj5hbosUpKgJYsq+lmOtZIg6EiznCCpafKEbOR9zEIZ4u0pvTLyrU1hoYqoL0YE/Rw9yhGhoTsbe2KiNp7stkro3Tskf3iDy8c3aOqIzZtHAOv2qaS56vssBWlRQYeZJ7RnASqrUL2a4H6FPXKIxCBCrmesn3Y1TYjWHaZTzE82fZSrtNdc9sV8wCtffefjeOfvvEtYvYu4NaK7dZ/q1a8RPfhN5PFz3LRFpxVUDd1hQn0xpGsCJtsXvFHFVGafk0qzF4e8XZ97mx8hUviFX6BxGH7Yk6FoL/iOvISLDa6aXd4azXlvukGiDP2g4YN/74CvvPY+k//v/5Loqw3Vn/pfk8T7/jXuf57k/mP69jlCOoKsJL5xQfVLI8Kv/1Vk1kBkCd87RNzdpIgHBN+DaG26Oe7R3yR8+zc4///c4tmzAz642OGdeY/WegSDdWAR7MSCR3lL6ToMhtootIhIO4mWklhZetqf+JOgZdRbEicVgxvnxPfO4P4+5c0vo4Zvkvxu2piXWLu3DzGd707puKZaZvRGC+9oaAKCmyu6oxQ1KBFjgctBrbM+orgiGy14S3gXgDU+yKfrvPuiKBOqOmJRpTSXmkl/gVSGi7fvk23M/Iig9S37937rK/TikvF4xuTmMfHOFDV9F7k9onvlNcqf+h/7TuHyQ/Tpt9AXh4iNFqxFWMvgrdpbZxcr/+bEfdzOrm/dn5/Qvdvx6O/vsHPLUBcxq9mA7TvPydQJsutQa4qiVhkGkIspvPeMYBd040/tIoX2X/4XaGrftbLJmOjVf+UHrumLSOjg/AHp2XO67QP4/L/qN5hbUD79G7jNL+GCnqeBrua4UsJ5hZRHuJ0biMsz3MY27d23MMN9kl/5a4jpJSLrYV7/3EsRA39W/+zUJ2f7Ry22CZBhS7w9pb4coHueNmeqyLf8TmIvPttI4PQctQtUAmEvYdzDHVloSggk4dbMt/4BpxpEZxB3N+HyDBGEuDQjfudv0Ox/DtEUOKkos0OE0Aihr1PUvtfCVDeXPsI0HeJ0iOga1H0DXYd4doRoDXLkSFZT9Gjl219OgrBE+1cIZbFlwN6PvUt1OsZUEaZT6KDljfGMKk/pWk3Wz32GvTJg5fWMP96YIaMW12oGN0/Ro9X16eSFLdKsEq8gjuvrjYgpQ5pl6qEg0hJnBaZT1IsMoazvRkhDeTnyOee9Ap2VlKcTPwddg05E4XwXpTaIwHje90uo3miBc4KmiFku+sj1XF4pi5SG4Wh+HfgEoC4q1KsK4hi5mhHad5HFCrt3gBiX6NNT0CF6t7jeDE2PtsnrmElU8fmR4NEq4aqZsLQNFstI+8AmhwH3wzc5nbniRDyiX6WEMmUrSrmR5QyjkihoqZqQw+M9nwZ3PGPwnX8H+0eGuN6AUAeYvZuoy3cYZI+xy5DmfEQz71Gcj0k2ZgSjFa7VBK9pRFeTL95BXXyX4PQBwaOHNB+kPP3OG/zDD15n2kTM19CX0gjqNaqhMo5Z27FwFS0dVjgsln0ZE0ov7jNOECnDRlx4H//eKVG/IL55jthKsWkG8Rbh77BS/n5VtcwIkgrTaqplhhAOHdf+s9EE5G/v0pUxYb8gWKwQUUs3y4hGS6LREtuEbGvD9GSTVZ5RN76zButxo3AEqiMJGqwTNFWEC1svpC1j6lVKU4fEgXcJLJc9eLbHTq9EfS6hu3WfbngD5h/R5M8ILh+ijx4hjo89WVSr9X8+Yped7evX5nSAfPYE81RQPNtD645i1qdpQrrOx+VmaXidytd+6/9Oc+sPoesFtjdAfPVNxNkxDIa4MMJJRTA9pH3zz2ObGeriuz5jpJ1fW2HL6hD55L8mXFwgmhoz2cb0tzDLD6H/KoHKUDd+Fvfob6Jmpz4G/clTbB0i+7UXT2uNCELE7IqwWGEmFxBHoPQ6bOvlHAy+rz4bLfxI1yff/AmLs74tLpPaz/+lA21QkU+KsnUIXQVdh1s6xE7sRT5VDbHn2GMBCSIxdKcZMmpwRiJa65G0+coHwpQFYjlHp/4EYMOEbvnI2waCHibaIk1vX5/U6uaSrngGpsJmW9hkrSyOe8hqha5KZLzE7ewS2ueslVNE8gJXaQ+9UAKVtqAEkZ3RznqIMr62OArhaKuIMC2JRku/eQCwAmeFp5Bp9zE6Uztk1F6HbASDwqOO68AjcQGcRWg/H33hphDSocMW02mUbJHCIpXxOemDFWIdY9pVEUFWXucrYAW6V2LLCGp37Tb4tCvMSkwT0NU+Ya1dh51YawkC0FFDt86vV3ENykKUwXKBLAu6wQSs9Q/DMPI8g64FuUDMW58DbyRKWkZxSaQ6Vm3AzTTgsIDGdvTFhKtgTNstPaKZH6ZedizbE06DLYbVAWd1wBuTks3h7DopblkmHF9scXK5ifzwHq8+/Yjhaw9QNzoYj0AIRAZuJinOx5SrlK4JMI0mLmOCrCSYTok//A3/MXnylNVv32B59iZnZ1t8eLHDd2YD2u8Bns0bPs4osC0LkeOExa6flpGLEAIi6YiUJdOGUViz1V+wsXFFtjUjGK48pS7reRjWul64Kf5p1AuhXjJYocIW0wRYE1LN+wRJRVtEdFVI0C/oVgnx9hSZVdgyRPcK8ukAWSZEYXOt/H8x/6/agFGWI4XDOUkQtrCmZQplsFbSz3Ka1mcg1FVEu0wJ6ymiKZHlFD07g65Fnp/gDlfUx6P1Z7ZFBOtRwdYmtjfAhfE6dbLw4VomQkUNo+1LTKtRuiPp5cSjlWcP6MC7LJ58iI0zr1fSIVYHsA3dZA8nFbJaoRZrIa7UCFNTlYe41SNMdhMdbSKf/TcEZ8+waY9uc9/DfHZ/EmE7TLciUBnt/F3iq2PfXWgaGPVRdukZBvpFEqgE00HTIIsVKB+ChJTe6vhZ/YGuT/6UcNIntymDyWNk2GDyGKGNP52qtYjt2RD7WBEdFFDUEK5/xHSF2hZr+pwP5jBF7KMvLbgKxPQS+gOfXz/vEPsh+ugxdjCCHqjFM1yQYYMeQn7/r246n5Ety0vs4BVUtIlUMe30bYSpMOkQ0TV049vog+eEH30blkvkRuTTvKYNjDPa179M8Gu/hur5kwtOotKKxZNddNiSjJZ0VYRKamRae9TuWPsAm3a92E5a2kcJolOIqCWYLFkebpPeOUWUfpHSuwXmMkQEIKMWFTV0eUx+NqGrQ4LAf53SBhV4TGrXBMTjBThJM+/5/68tpgwxTUA4zJFJjSlDDwkKXo6dR0Ut1cKf7AejBbOrEWb9IJbSW/0AdFoRbsxRB8DllO5ZguyvaP7o11BXp6iLU1wYYSf+pKXyFWaVsLwYE6cV25NL3/LNewzylns9gXGatkhw1tELdlg661ORXYlzBiHWDz7XIWVG082Z6iPO3JjHqyE/4wRxVJP1c8LIz4gfXWxxmPc4rwM+utrkj1yOfCxr1JDeBVt6Lcb8YszVbEQUtswXA6KLhoM3HlB9o4czJe0qYX7+Zb754D7P8h7TRrNoJOe1IJS+1d8YOKkbjLPMRMFMXSGdxIgW5QJ6bsCu7GMchApGYceNpGBvMGf/1nOyrRm6n6NvFLitmz7JzRpEcUyXHtA1M+Jk/wc2ANZ1qJdDeyadzDGNv+7JYEW8OfcJhU1wnfjZu39IfbTJ6mSCqT82QcrMIkc1Lq/ob18RxDXOSN758DWSNSSr6TSnRY+98RVKGU9iHKyolxnRYEWWVjijiOKaYpXinI9rXp1P0N8sCU/fQW1FkNfYuaQ6HZOf7bG4HLH72hNPENTWjyF3JC6MfRCVDokevg2DBB236P0TMuPojjxyWw5qxEYEMvKbBGtAaYLnH2FHG54J8OBd6q/+PE4q9PIMdXaIHW2gHvwCSiqcVIir7yC6GrE8RhRz9NUpoiqp3/g5sq2fpsgfkK4POeBZ/+nf/Pexr67dHGdXFH/xf+W9/6cfoi5OvJhQa1ya4eIUG2foIr+O2BbTl+ME+r767OT/I12fePFPXj2jfjKiK2Lkun0ppENHrVeVS5C6Reg1g7svPXa2aqC02FwivrSP+9Yhrg5R2/XHtptlAktQX73ld7Kh9kl3Uexv1CJHWus/QHf/4g892bzArOZ40Z+xNXX5HNHmuGiIzfAfzqCHaErfZVhHeLavfpF28zWEqQiffcvH7O5F6HGFmpa4VjK4fYLrvPJeKEvwReC8xVUCtMIeWWQfSDSEmuD1DnPvdeTsEt479Na4LY2qWoQ22KVEvSJwpw3V8w0uHx2w/bmHjO4/xxlJt0q5eLRPmJY+9rVI6I0XfvQSGIK+j9E1VeQX2dEK22jKZ9sE/QKVNNSXg5fDdRGWdGNOmycUM9/2l+suQ9OElOeb3PuJt7Gtpj4fIYJLRChQkwKhIfl//buIGyPMwR264S4uTIkefB0uC2QasP3WA7plRvNoH9UGxEHDZlJSGMV+IklUwKIdUld3mYQ3WDFj2j7B2AatYsQa3axlRNle0dgVJ/o5qpD89Sf7fHU55M7oCiUtz+ZjtLCMwoZlpzivIh4cHbDKe/SyFZM6RKcVrlOkvYLeaMF7H3hFdtIFPH7nNdo28DQ656l0V3XMYRFyWQsaCydVu/bn+9W3ch01LYYO7TQX9gmR7JExJnQBgZKMQ8H9fs5Bb0mkW27eOPKi2E56NPTmiPLLfxZsh5o/Q3Q1oR5+Hxjme6uuTwmSl3EzQH45IhktUMJRzPqspkN2v/QBwWhFnMeoQcHqI794xaMV+cWIjTceewvgiSB4vcOeOcLhiqCfI6Rj53yTuo4o25DOKP7om++wWPSR0hEnFcurIdlwiaki2jrk8mLCYLjwrhtpkIHh5Pkes6sRANb51EhrFW2rMVYyHM0prwZkuxeEO3PERnCtwLdRn+j2n6PY+SrJb/9n/n179WswvI9zHfLqfYLzh9T3fg6d3kRIjelWiMNfQXQN+urYI4KlJPr2r+Ke5d6FMzSorSX2W74zGWzniL0B3Z03wFrkOtGzOXgN2ffppi+Ch+rmEnP1DaJHX8deScR3PoJxhLt/F4pj9PQZpjem3fH3px3dh3aJnD8levQtqCvEdIabd7QX2cuf+X8m0PuRrk/8/jeHntqm0wo9KFCvCMxTu7bwBAhp6RbZNTvaXoDcbPwp34LcBJoaMXHQ1qB8NKUp177WoEN++13Q4FYCWwYoe+4Z9Xf3MZNdZJVTH/4iNhpCskUQ7+Js932zsmz844CHvbh2ibSdB2GsZ1xB/1Vc+N5aEJh4nsDsDCcVSIULE8SNkZ//2SXOtdQnG8S3z3C1wqyS9azfws4AUdWQ14gE2FjrDy5XuAaU/AA6g5PQf/Mp7tJnccttR/ckxrwvkJFD90qitGT+eM+PEJzEOcHuFz6kmfZpck8+i4dLyukAFXSeYbDMCMd+s2XKCGcUzkpvFwyMt0K9hNJZxezhvs8rWJ/4F6senVUoYemlBfOnu36jJCzBoMCU4Zo4liNuDqCuUEdPUM8fwXKFm3Zwc4AKlpiHKW0RsXn3GbbVlNMB8yJD0kMIT7ZTArYZMbclsUjZ1gecief+XhJ+8S/tnO34DQCsM5zLc35l0fJoNWTv/Baj0DEM/H0hBATCUVnJSd7DOdi2gmy49ALLuKafVlSzPl/80ncwrfajiaAjHq1wVlBcDnnw4C55p9lNWvqB4rJWxCpACVi0jtO6pS9Dlq6gEx0SSU9tookY2BFDkTAOJTdSQz9o6UUVk8Gcwe4F6d0TxH6C3b9HM9lD1DOwBhsP0Ns/KIb93kq+5+T4aVdv6wprFM5C3PeALaTFWYE1Cq3NtbtGCOdFsjMPq5JhB0XjN4e9EhF2yMwyeTj1ccdFQl4mlEXCqvSneoDlKsNZSRRXqLBj/84z8umAtgno2oSi9F9ftwGNUTRGs5GtaDpNPy4ZDhZ+M22+xxHTdIgPniC++CY226YzOVnvdczP/Bs0zQXYDnH1HdT219AHr9DtrmD5Ic51RHoD9JDaGsLH7647gQ1MC8r3t5HrDqg91STBJTIK/AjwKkYPS/Sj93DDMWayQ7PzBiK7eX3aL4onuNUjgvMPiVYzAOTrQzifwrJGcEiw+ZDm4McR0RYA4uJbiNUzRLUgOH+E+OgZ7v5N6FtEeIlcfAb5+YNenxzvu0o8LSvyJ1c6gex3uNrgGoUtfWiPHuTIocWthJ8vtb61Sm1h7lOzUP60LHseB2tLz623dYCeLHG1/2DIZQMaP/s/8TGxbu9N9OwxbvGMdvIq+uI9mjt/FtOtcLN3Yfd7HnI6wQ5eAeN5AcJ2tMVzZDRA6gVIRTfYJHz6HvrqGJsOsOlg/W81JAly0KD7OSIGkVnkcIVdSmjWAqE1a1xkApIUmgbXAZ2AVQ3G4Wrvu7erCClrRF8g+xXN6ega3JNtzsjPPCnMGunDTYTD1Gtynu78KT9s/fhFWpoyIhwvEdq3LW3rEJ30D1r7scf60676ckhXh3TrDpBSBrs+9TohaLuAcpVdw16aqY9sFdqi6gphrb83Sj8a8qyHCCXWCje4HmmIdYjL5nDGRt6jMJK8E0ghGGqN7FLqteCvFpsk1l/PThhaMaZnerSioxQFlciZinMKt+JJnrKx6jMOAjItSRQMQw/VOa0irBNoZZkUCU0VkQ5WpNtX9G/l6I0VdhX58VdSX/vEoyYgCWsvJ1lfK+u4RvGWnX9txjla4YWLgQuJyei5ARv02IkC9lPLXlKznS2ZDOaMJlPS+8fwxbvUN+5h+nsQjhDhGFceI9rV73ri//0ooSxKWZwyUIfE69O7bbRnVQAqrunKmLYJPuZdaIuzEjsTHocbdgjlcBZ625fEZYy8HNF2fo5vnaCoI7pO+T/nKUoZgqRGxTVtG1wnaS6rBCkcZecXfyGglxRIaen1cnrjOWG/ID8fUc/6OCdR08aP4C5OCeNvY5bHcNfHUEfRDlXxBKcj7Mnfpw5TXDRCX32EqefkyQZ0FfHZUzi/8PN2KSBaszecRKzHC27pI3dxYv1Z7a41MCbbgNgfbIytqasTwnf/3x40tJhCVfk5vtLeJ5po3Mam5/0LjZAaZz9e2FU58/P9RCLmU/97NeswsZdczr78n/FZ/d7rk0N+jG83yqj1Cv9nArVjEKHFzsEsUnRaIYcGxj1EUECW+UWoaz15rvPzfaHxlLqhRMkKW4dUVwNk0JEm682CcNg89OEclys4Xvn52k++ijz9ALW4ou0agpPHNPIXEEKjyhnN5hxnO9+a0z2SeJ/8/Ne8KMp26NNv0E3u4/IL6Br/IOW9dYtOeX53WeLSDHp9RBgShFdQA5M+bjhGnp74AB7wCtqNoY+HDSOEtZ4wph0gcJUPO0Latdc5RHYtciIQZ5bicoJzgvFbD71VUKz9/vMe5enEq6eVIUq9d354++h6zi+kf4jItEIm9fVJ39YBrNkBL6OuDr2dTAhH2wZo3SGFRUmBEpa6CWjqkCTzLdj8ckSUFehY+03dLIedDShKj67T/v1mvoDSg6TC8ZLqYkS1yOjqkM2tS/ZXfQqjWbWKhRCMQkkoBaVRLLqWkR2wrVOscxTW0JMaJLTWUtiUYwkOy4xTSjvlSA3AwI3uPlsMSEvFG0PJspU0VhJIy9Z8QF4lbFVXxKMV0RdLSEaoyzli0SInUL49wbaargpJ4opQGWZVxLSWrDqfwNcCnQOFYOlqWtEQEBK5CCssW/TZjQP2Ese9XsletuTG5gWjrUvi0RK+eJfyC38Gnd5Ew7XbpQlHPgXun2LZVhP0c2yraYvYz/yFvze7KiSsAy96LWOaIkG+AIQFHVhBd9Un2Jv79Mpa41aK5OAcs0qwRvpQrLAhWS/qeR2zM7qirGKy/srTJcuYpg5Z5hnLOmHVRGykK6wTRKpjmJTsv/KMZGuKHhSIuMOVAfPn29TrTUbXaXbefIh4MEOffR3dj8nHd1DpTaypYPkIF/RIfu2/9ELmsd+suzBCNLX30y9K7EL4515iEQcTot0rbBGBcKhhiVl4WqcMOp9+2hlIUsxgA9PbgnbpoWZHv0z86FuIx88881krqDrslURueoGy29+n/OKfRp+9g6gusK0fpQrASY0sFoimxt67j/hHH3ihMyD0y2GAfFb/7NQnXvzD8RLVLxHBmlc/lNA6upOY5nJI+tVLCDXuWNAdOfTIIXqN3wBkoG74m81+OKM5HPguQa9Ejwr0ZEWmDOXpBNdq1KhAJl4nQG6hHyC2M9xgSPDh3wb8By58+j7MZ+i0R/3KTxLf/Yu0b/97qLf+NQDaduZ/+aBHuo4GzlfvI2RMd2sL10xRVx9SvvXzyGqBWp4SHn9A86Wv4YKU4PQB4vLcq77nc28ZXMy9jgEgTbA7+1T3f4bw+T/ChTGyylFSek3Bwye4WiG0QW3WcGMT9/iC6tE26rzGNiHRmjfQXA4J+oVHpK6tgdWsR3/vAhk2OCfp3X+OiAx2FeE6yfith6w+OiAYLxCZoTsfEd+9xMwDbBHh7Mcq8E+zsuGSi+Ntysqz96s2pG4DBknBaDj3D+VejnWCqvRphtUyQwYdwRiY9LHvTj2HQAmEdsisoXpnA6EMweYCuYlPvcsTuk5jjGKQFtzqfNrhssuIlaO10FhJ0UWMwpDjEowVTELFsrMoIUiUInOKrtmgpSMQIXOdctU+JtUbzOUlhVthneXZdMQ9PWYSKS7rPpf1fe4O5tRtwOVszM2jQ5LRgrCvkEFH/VGfaLKgWfSYnm7x8HSPkzIi7ySdE9fJkSdlR+ccUghaZ7hp92gw1LTs2g1eGwSMQstG1DGOKybZimBNt0vfPMXEbxEc/hbdxgXR9s9c6160yghv/PGX8j5/0lJR6x0na0hWt7az1rM+q+mAtoiJ+jn9V58x3HbYK5CJpXo8oStiknunNIcjPwJYh4YVJxtEG3MG958zuP+c5nLAYDrl4miHi/mIpgmZTKY0VcTsdIPxjTO2Dk7gOdRdQGN8hPHeYEYvyxlvX9K7dYr4F+/QbdwCIDx8n43mEbMPb3J+tsU877Fx+4j6o77nOnxhTvqL/z5k2Vo4lyDf+whzEdEtM0wlkEFHuHfoHS2AzT2Qyyy8dVcet7SLDVTUItdsDhl2dEXsn6nbHeZMo+QFgbWo1Qwz2EB98PcQVek3FdsTum9WXjOTCOTEUvy5fx2V7BFHO2RAO3gTU19A/gw9e4yanaKePICs5wXT1iL2e1BUUHZ+J/oyy/HpC/4+0xB8qvWJF389zhGxwzX4BW1nC/v2Oa7VhBtzL+7rzLW9zTnhT+v7A1Aa+2AGP3YX8aWMqKmhaWi/uf7m1osHg9SPFdqzAaaKSG6f0V3EiLlBZjPEToOeXuKGY+xgTHPrdbqdLxG/8wuER9+m7N/BHvw4onzu2wuuo5i+DdGIsvKMAHn1PuHBH6dafYRcHqEXF3Dwz2GzFe3oHm27JHr8Gzid+5yBvQP/AYwTRFV6TGaoYWPDbwZWC/TF++h3vglbm147sFwgpITdTWS2xJ3XXtm/vAIk4eaMdtrH1IH3+0tHeT4iu3FBO89oixjnBMl4iakDT/hbV7gxp7nqk59NSKuIuohRZ2PCdkkwWlE9nuA6idAW3Xs5eN8XtscoqkmTkmenu+RtSN6GzMqM/Y1zzs83r/UAWndMJlPqVUr34AD9rGXwlYUfn1gQgUOkEO5MsWWIWSbILGfxdBeAOCl59uwAKZwPfgka7vT0mpInqIxACUk/sPQDaIw/cWeBZLlGHUjncDgkAoUmc0N08Dq1W5G6PpGLMXRYLEdtzqwLSaWiMiGX9QaxsmTaMitStLIUTUhlNIlumdYxqzag6BSFkXx7+uJU5X9mbR3n1r8XodPUoiZwipiATR1zkEo+N6y8KE13TJKcJK7YvHNIfOcMhjHd5AbCGlzv5veR4H43W9/vp+VvcbSFNRIdtESDnGhjTrdKPbJbWopFj6if0171kXnr8yqmoW+DK0N33qPLE8zMW1ZNp4l6Be0iQ1uJnqxI3ryk/LURo8mU0WTKxfkmZZGsXTGW5cWYxXxA2wakYc29jTPyOiYIWoLAx//qtyTNaAdhamSxwAwmBAcPiU5z9OWEzijOHx6QDZd+zPZc4oxETWa4fEnxZJcmv39tu53PhmhlSD4qCYKWqFcweO0ZACqrkJ3yFuBeid5c+q7eKqJbJthWs3q+jT4bM/i5E9y0RZQXyNNLpBaQxn6hbi1EGn1PQNzDbWxT3fsJstGXKZ//baqmwExeJR68SZrepgDExQeY0Q7CWuTsEnl6BFpjHxe+G6H52Kb8Wf2BrU/+dAid9+eHgDQwn/nYy6j1p1obw7zGzGJsHRJuLNbc+QbsGrt5/NzPepsG5hVCx9hae9tMVmEvB9etayEd3czv3rESJy2iaaFsoe8hPkiFPv+O961WOerRL0K2idOXuHQHEY6RxRTX1ZCf4gDR1dcPT9HViKbC1BeI4hixFgV6z7nyPyNVuLTvQzy0xsVjRNZ6y4xUuDhZf10M+QqhtPdeT6fY/ZvItsHVnT8NLBNk0iCzhlDPcCcbvjW/jvr1rUCDilqKqyHxaIVtFVif/y6EpZv1wEnCtb8/25xhW00z7fuTRFzjWo01im6Vflr3yfeVVJYwbGiakLqOaI1GCkdrFUUTkhcpdReQxRVxVPlgFbe2fnUa0ynMfP0+a/OxyHMNRMIKbCnJtq4w6w1OFDZ0ncbiF5TtuOK0TAikpXOCVavYiPxKXxlFz0gqI7FOUnZQWVBILJZWNNSiIrM9BozInPdE5MISOb/Rqp3xASlWcVRKYiXJtAMmWKDsJLX1GRGHhcI6fzBpLZy2JYnQBEIghOCZna6jhwU1DoWmLyI2I81mJNhPW4ZhQyQNvbBmYzBnc++M6MYFYn+A2X8FWa3oNu6QfMKwnt9Pr78QDrW2/DqjEEGH7aSPsE4rAifQmdfd2BdI8KxBU3jGh7KotEbUFqsssvVkQLPuJKheCXRIZdCDmiCtUWFHV4deud9p8mWPvExpjaIzykOojCKLNHUdsbwaMZo+Inj+od/Ez+fepTOXvgupDePeirYNcEb6+3SVIKMWuwwwi5RqkWE7Tb7MqOqItg1ompAwbDzd0ijaad8TK/GjUtcpZFLTXXmMt2s19TxDxw1h0HldTtHgSrnmjXmdC1T+QDUI/Xw/zyGKMYMJjNYi1mwHogoR9FEy8ryL8ti/KdZ4+2HT+EPZtPA47cSnRHWXvR+aOvmp1mcz/x/p+sRPCBECnf8XItPYowZnPLKTcQpVg5mGdIvMCz0C4U/Iy8ozpxW40woRV7gK2tMherK8XhDVsF3T7vyGQocF7bxHMMhxdh2+0XXYuUQ2taf3LS5Qh4+xO/vQtQQPv4vc3sOmAzqpIOgjTI3IC2SVI6yhG+76IAxTIkztoR+zB6jlqedjr33T7gU4xRpMb4zKP/QttN4AdIi8OltvBlJcNMBtbCOOD4EWhkNYrjUBXYfJY/SowCxSCDtvi9y0qEXlFzsgGOZUF0PiycJDfC5GHg0sJEIZr4yOvHhOaks0WtIVMdHWjOp0TLNKEcoS3zrHLmPsStMsX87i76wgyUqcE8wXg/WJtUUZS2UCLvO+92irjiiufSpfp6+7AEHU0F4O0L3Sg4g66KYZ3Sr1rzVqsXlIeueU9nxAd7RFL8vJi/Ra+DWKSuZNyCBoUNKxUCEbcYmxktZKWidZNCGlCWmsAAM9qVlYT9BzGAJCNuihhKBxhhUWjUStrYKts3QWZq0llIJBIFi0IWYN7HFA3jqeNwUKH3TUYlmIFbi+/7A4x4U4YtfeJCLA4ohcwCTS7CVwI2nZjCti1bGR5kz6C0aTGaPXnyC3JXZjm/rWjxM9/wZy/IV/agCff1zpqLlmUQC4Vl/rV16w8vUacGXrANcpRGQR0cfJcgErbBXiOoVtNdXVgK7xrApdRIiZ3xTrpCIYrQi3p7TTPtXFkOXViLKKaY2iakPK/z97fxorW5ae54HPWmuPMZ/5jjkPNReLZJOgRMqUSLXbasmyqGrZbVlFS2b/UKOBNgxYbf+wBRhE2y3BBglYAgwDFClTAAmKKrAlW5ZENlgqEpZYFIeasnK+ecdzzxTzHtfQP7594hZNUspi3ltVyooXOMibceLEjtix1vqm93u/NmbZJoySGusMVZ3StAn7X9knnd7HrjOa6Z6k4auUet1Da8fe3jmua+UNXm1ml4QixXbZuBAUi6UIjyVJQ1llxN1UQ6Ukgze4cSJExjrGVQlZv6I6G9OshYzaVinD7ELadfMGdxbhCzn3lKkhQThEO3uSstcGc+8WYTDCDQ+IkglV/ZBk9MHfMU+iPPtnmMUdMfzeoVZLIQd6jztPMHuNcJTW9RMLDLb41wfv/iQZ93BvCdNfX1H4dYLOus07L4SQohXpU+eoXOHPNMEq0J1/6RXm4xMAVF2RjJeQxLhXhemttCe/frqRvw3oDbkQEKMJ+DJBvXGOvn0GQHjhOvrsIWEwxD71Aqpay2aZ30NNb2MW51TPfQ9m+QCzPodgKS9+nfj4C5jFOSFJic/f2Yh66KpL92d9otkJ+uE99HAqQkCnZ5jzc8LRFYgi1HyKKdbYneuo1QLyHM5m1J9vSD8SE371Nm2VCPnJSWujbyJYBlgHdHf4uSKjOBsz+vA7+KVEoTvP3aU42SXq7rGrJKKKOkET3yQkkxXTrzxNlLTEeS2R9TqhnQ9oV/lGdvhxYz0f0h8vMXGLtRGD/lpkWW1Mai33VyOezUrOF2OOp3vsDRb08pLheEk+Wm3aE6uTHdpSVN/yLmsR7ywxBw31mxOJDveXDCPHajail5fES4sLOedVj0h7EuOItYcEahdxZbAgNo66jXmwHrK2hkGkCMBJFfHWCnrhCBcCx1ywCjVRZ+zTkDLTC6LQtWXhOa09Omh6tke/SXioL9jzO12fvuNQD3moT1gzow4rrK841C8wCgPa4Gjx3AzPkhNzlKQc5Yp5I3IQ/ciTG0fWdWUkkZUJkbtz9HccwHIuUSpQP/t99L8Buv3vBpfrLB2tyI6mLN+6RlummMQ+EgGLHNQxIWiiSUF9a09EdcYQlgFzpcW/E4uRdZrBUw+lS8RpmtmAcD4mGa0IQUtm8cOgbs0AaOsEd7ZHbBxGV6RRSy9pOBjNRPAnqcl7Jf/iV74bo710pQTFqJvkGUUypGt4eEG6P8NVqWTP2oiL154mnywIztBUKcWqz3i0IMsrorTh9PiQusypSzHsWjsm3/EW7qxHdTbm4t4RR0lL7+o5ySpncfeIfLRiev+QJG3o781Ixivq2RAdWZJ6RbxT4Z9/cdOe3B69SPi2/xttfYbSkXR2fFV3hw+W8vRXMKtTzGqKLha0h08TBkNh+FcWX/UxpsXdChBS8m+7eOLrYsv2/+bGuzf+ixJziETzWUq0v4KDAWgR4gnDMXp+D7UTEw4OUU9r9HIujO61sP3Da6f4MhZVvKeHECdEk3MZ6dtGYLwo12X1Rv42PlgQaoOvY+w7GdGVAoYpNC3+AuxTL5O89UV8b0Dz9PfR+8d/AzMc0Tz3Edqr34lZ/C8iB7z/IXzvAfHD19BNiRse4YZH6HKKWU2Jzu4K4386JRxdQa/mqGINTSP/XdUiudbvYw9viPTnYIyqCtLPf1amfHUdAOnHSvzNZ1Enb2Aihx46QgO6V8thmHrw4L3C1zFtkaJ0oD0ZSSuQV/gmoX/lXMoFZUK77NNMh8SDkvXphPV8yN7NY3p7c5K9OSp2VPf3WL59jWxnQbo7x66fjKpLue5hOlGfKLKsC2nry5OaNGoJyzHn6wFGyXPuzXZ5ypySVCneadbTEcPDC4nyjaeuMgbZGfFkJffjDcn46KEndLSF4e6M4zvX0Cqwk5X004qikfJNGrWM+yuaJmE0XFE3MdPliP28wKjAvElYthEfmzRcz2MumohFqziyR1QusLKOJjhiUsZB7lkdHCtVUlKQklGpinN1RsWKpZliiFAYzjmhDTVGxUy4xkCPyH3aSfVqDqKMzCgaHxjEikniud5zjGNLZhyJduRRy4efeZskaYjzmnSyRNkU++wHaI9eItn9BPbeL0E3vfKbDQd/4hbhYUNwasMm7+3PMF0Zpp4N8e9ckdKV8dT3dynPx9gik+coT/bUmdTJu1Ziveupz0eUsyHeRoxuPBSS3GRFdLSCeB99JSZNLzBZjYkct958hiRyhEihmpR+r6CsxJn2XmO9IY8bCpdStAnWG9Ko5enDu+w+dYzp0vXJ3hxfxyzfvsZ6Kdod3mmKVZ/eYE3WF+GttkrZPzolSlpRtzSeeFAy/ZzwAlwnez27fZWy6KGUJ++VXBwfkvcK4qzupn4aek89lLMh94TrTwNg92/g+gcoW9Osb5ENXkApQ1U/RKkIW9wh6t0kTfaIT18nJDm+Gy6U3H1Nzq2uoya+ucKdJDRnE7zVJM3yCYv8qCeQ9ldsWX+PD1+DvG+AQU8GX0QRHGmZ1reYw2yJspagEaGM+bQj/3WRZx5hbnjsOynKeCGbLEQT261SfJ1setJ1N+ymXfREWndd4SsZ+6u7w4R5jV9r7GxA8tYXoSwwJw/Iq0/LdZcLzOIMN3wg5YB7nxPxHttgFhf4LJfUmG2Jjm8D0Dz3EdRoj7h5RRj+jZXXAuEpgPx/2xCd3Uc1NSHrgW1ldsF4/KgLYDhErxaE1KP6wDAl3LVS2vBAowhOSZtebEmGhZQ8qlTuQ9D41pA9P8ee9IUMOSg3qchstCafLHFthDKKdjZAx5bsygXhnnk0RKh9Mts7zWqaRkb4hqCpO2lXpYIQFaOWWDuijgE9r2K09lLrt5KGjbOawZVzed9Vwuz2VXZjEfWppiMp9xQirRu8JttZ0L+Y4FxEUac0NuLqzjnWGSIjw4RMZFHGU676m06Eoo3JjPSFR9qzk1jyyLPXcQJWraLyMbWLaTysrccFME7RhhhHQqFW1KqkCRKFu+AxKiIJCXFIWOk5vTBk5Ifs6RylFRo6noAcgHupYi/1HKQt/cgySSvGaUU/rYiMI8srertzkp0l0e4KSCX1azJi06cZPYXz9e879vUbiTCtReyrjmUvd+OpdWwxeYNtYvKD6capdXVMnIvhQ3kIGrfINmdDsAZ7L6Vd5zIwKmlxVUK1GGwUNqPoTMqJEUT7a4Y8YH82QnfTJXW39qLYorRHq8AkX+O8Jo3bjfPaS2p0V66I9yQjFZx8Z9lkRTwdM59KlJ0k3TjuIiPOavp7M0zSsjzeJ+/UCZuFOAttmVIWPRbLIfOFvK/hYEU2XGMii/cG10TUi76UCeoYPWxRV0e4wRi7cx03vgk6wpx9hbC6S726KzyjeADpDjQzrKtw2QzV3yM+vy0a/k0thr+uHvX0j3J0UZDoKTi96U7Y4lsXX5t10FoMv9aE/lBGSS7mhDqgdM2mHFk1hKVFpUqi5UQGSoTWYwZrKYVOA2pQE2xfPEStpN4f247hLhGY6WZ+q04NLFRiEHyR4ooM7p/I4vZr1MkF5DGsaszJfeIkA62Jzu5vhp+oYoVuavRqISnV6Qx6GT7uQToiGt1H3VsR5laY6Ali3C8J3E0rzk3ToKpK7okPhF4flYiIUUhS1HIuf5tG0irklzDUqLKVFsbLW5pLh4MtMlQnfhK8klRp2nnOOqC0xcSW+mJEdjAlvrKmePWI4DTtOsckLenTM+JFJ7DSzTl/EojjlrLIcR2xygWNdWZT8427dPzltL8ssoSgNjK4TZNQrXvk9ULquEnLxck+kybBW43v3rdfy4wCvCLqV2R5hXcarR2z1ZDxZI7rxitnY5kQ5yoxjqMqpWljqlYct0Q72qBJtaePDKGpvaZylx0DmmljOK00pQ3YoIldhELTqAaHRaFJVE+Mvk/IQ86QjNgnTEKPSZQwSbTo8mvoRXTtiIrrectO2jBJahLjGKYVO4Mlo9GSNKsxSUuysyS+ukANI/xkD9cbQyzGJB48S9vOMN+Eqf/2gQhjBavxTbLZw76Ju6FVjmh3iS8TaX+LEG2KrpTn6gRfJ5s5H75KaKZDvDPEaUPcq0S5smuDbaZDglfoxIq+fgZmWDLam26GSUW9ino2JHUG28S0pWQCijJHeekacUGTpjVKiWwyUUBlEJad0FRWE0WWpk3Q2tG/LNM5Qy9ryI8uRMvAihMf9Stmt68SZzV1lVEWOcsqx3rDIK0IQaONSGOXs+FmBoaUBYWM5w+uYCdHuP0PEWVX8L4imLfl/SyPAfD9fckrVQvQa7yrUICqikckP3g0sdD77t9IAGIh1F+HPv+wTft/M+PdG38PrAoxhEmMsm4zOEJd9ZIRmM9hZ4fQG6DeuSUGfg8xju8s8c0Y1RMD0T7sE4/XRIdrQqnx60yMlfIQ1MZw6dgSXSsgj/HHjvbheGMwI7XePK5iL1MEGyuKYeUZ0XpF+8FPbIQuVFMTohh17z6hDJArwrPPErQm+/w/BaUJk13C9euo5ja08l79sYjykMSi2mWtOEGXnnVtN7VZkhiSVDZc/GjzmSsOhhOopsIAHjboqJKDp46wJzv0P3QPP0vxTYTpV5Sv7BEN5HWbeV/qhqMV0eEanjqkx0Oqt/bQSYvJG0ID8e6S8u7BZrb6k0DTJMRJi7aeqspIjGVR5igFibGs25hJXmC77/D5K/dZrfuUXib19fKSpk558PozRJElyysObzyQ1kQlWQ4VW5rzMfqSAFjHtG1Ellf0hyvGE3G0xkfnxMM1OrGYUYFe5vRgQ0Dr9wrWRY9V2ePhcsQgaSjbmMIKIe16f0ViHPMq4zcvJuwmgdOg0F3SZ6FnHDkxuC2WnJSjONtM6JskmhshZRRDLwrEKjBrFfupZxw7RnFLYhyH+ZpRXtLPOrVJFUjiVtogP3CL+myMyWtUrghHR9hP/CWyrxrR+7+v835N35edY88+x/Dqn/8D/f2/CnaVY6vkkQMzXlGfjakWA9zZDqMbDwE2an/plXMxtLGUv9Qi2/B7fB1j1xk6dvQOptJN1KsIbcTgWoE7j2lOx7TzAfmzD2VA2CyhnY6IexVRv5QS2ionP5K6dvlwl+nJHmlnzC9mE87XA5LIsTuak/QqdOSp3j4kvXGGzj3YlvXtK1RlxnCwJEpaifx1IB0U5IdTov019nTAzo1jsisXqMxSVynn53sk3fhhkD0xL3s0NkJrx/5TD8hGa5llcLCgfOeQ/MWHcLCPG+3hr/4h+r2nH93g5/4vNHYOhzLAzK/voNcnxOfvUN/4BP2D7yX8w79C+9SL2P3r6KaUgUG2Jbp/C07PCQ9WtA/Gcp+9wq5yErb4VsbXQPjLJP2tOo+xn0h6PE7k5+4ZaPD3zwl+ivnoPqq3gnkhWYCxwt+NsCcZZlATHa2wd3pC8OnagpRxmF6FKwLBa6KesOGrN3bBK1G6e2GOXwjhkKBwd0FphcoQg2wd+oqBKhDOK+Lf/OdigHs5pBmhN8C/+KI4Ad14T/cFC8MKPQqouqL+rYz02QCp2jgA7n6EzhtUL3SqfYlwFw4i7Ld38wSiGLVeoh7eF6dgb1ccheWKsPT44yW6HzDXLGGNTDPUCqU9yeWQo67dTUUS5bgiw9WxkPy6aH795ev4L2iy/RHx3kJYzyc7rO/vk47WMuhnZ7mJrB43dq6dUFyMsa0w+Pf2LojjlnWnvhZrz+l6sJFVzRYNWgV6neF7ON3l+sEp8+UQoz27xlMc90nHa3SXWtUf3EH/8wZXZLSrnOXxPknSUFcZbRsz3JWaskgdB5mw2PdUdwcoHRg+e5/VravknTFI4pbd0Zz+YM161aeqM7R2fPnBDYZJzSQv+b4rNZ85PuLZgafxmtMq56h5nqADrQ944Om+IVIi3OMCjOJArMGoQKJlVoCk+Gv284IroxmHh6e0bbzRkc/yivGNk02pS6UtvZceoMYRjHfwowl2/gp++Oxj0eRPojHJlR98z6/z+0FIqDL1M9pdy/yKfkUvfYDKFfZByuqN69SrHiZy5C+d4c6lfUilnmi/wi9Np+hnMHlDu+gRJY20fVYJ8TNr7J0ewWmiQYlOW9wiY337Cq6NmHzkLVTuae5M8G1EfuMUlTtUrogmBcl4zfLOodTt85I8q7jxwi1RJSxTlg/26R9cMP/iM9huXHVVZixXA8ajBXFsibJG2m+tRsUWlUDwGltmFHcPxajaiDhqOV+MqW0ssxnGc/rDFb3dOfnVM/AaMylQBylh7xq9m3P81Q9iBxNClJC8+v9lff2jqP5NQrske/WfoKMYOznCT54lHn8Q178JD18n/+I/wQ7+Oc3HfwBla9J3Po965xbYQCgDbh3jS2H2q8iJHLXXqOKJjPx6hPAECH/bcv9jxbs2/v7qDVGcKtZQlp32fU+MW13BkYj5aDPFLwLcPcGvNXoIahzLgJuuXkcSNint0Eao2GKymvlb1zGxJe284nbZw9WP+n2z/RnVq7vSIpa2MkCoTDD7wgUI91biJAxklaiIR2N2lajuhSSV1P/yWN57nGD2C/FpIilrRMNy46SooQjVBB1EstcI6cTsNNCPoJdjzh7gRxMRA7IWfMCfeLSeb+Q4VY70ALcFRleEUqPH8t5CBbqUfmJ0IHhFOx3hrXw9Orak+YJgNdFojTKOdtmnOtkhLmS8cro7pzwVuVFXyZAfnawewxL53Yiypqv3C1N6terT7xXkWc2gTjDzCWdln0HS0I8bGhvRuohlN6O9n1aczSYkxpIkDbaNieKW9emEOGuI+yXh9kPMQFL+bj4gShu08aS6JE4bhi/e7dZPV87RHl/qTSeBPBZQyjPcnZH0S5Tx0tMdt2RVRtorebbKsP5RyeLZQdcuGBST2HDRGE4qLVorBnYS+c5ckLr+XmoZJw2x9sTKk0WWPGrppzW9RESQtPGkRrIwJm4ZP3evO4gbdF8ULOlPCL0+bv8q7gN/Fn366+jxEz6gHxNUbImHhZBTZz1YCLHPryNcmVDPhrKvO9Gp6q1dlPJEFCjTECJQucM9jKWE1Y2itkVG1KswWYM/k9Y5dBBOS+Qo7x3QlCl4RXn7cNO6p7SnvRiSPj2DSKF6nnhnyaCNyKoV/a4FdvSdd2nvdON50xZ0IJ4NqE52ZP9oTxy3NE0i2ha9kmR/1p1ZQuJNri+ozsa0RYZrImlljVuOYov3ijhuyfsl/f2pOOR5Q6hjGQOexKiqxN18jhAl2J2nCKNnCA9/Az16Ed/MUMVD3GgPO76O6iY4cvJl/M0/jB/sEJKckGSk7/wm+uykO5sN4eoh/jeFS6C0lBXaRR9XJ5t7u8W3Nt595B/FkEg6jLaBshDCXxTJsIm2kRG8gxTtKvxchv3ovJCmaBtEzS6xG26AzmuYD6QvGPA2wtuIbLLCjAraVf5VJQBpGWo7BnukPWbc4haRjNVFuAAqsVB1gkQaMdY2yDjLVTfwZjmH84XoFux0tfXQNW+vCvTIU9+WlGs07FLnThNamUwWWo06iKW1T2vU+ZlQAupqQxJUsYcmEGrROFe5I+pVqFjEY9AIPwFQqmtjLITwZ/IGX8fEnZAPiAPw1QJIyjhoI9oiJe4Ghmgjimm+iXEuQa+fzAb3ncGVMb4e71O5tgrEkaaX1pgqJ4/bDSu/dcK2NtoTR5aiSTkaz4gji7UGY+xmSqBJWtp7Y9Rl7biNiJIWbaR+HPcqdL9z+Gonugm5J1gwu4Wkguc5cScskwwL4p2FzKSoUrKgSNqKZFhwrU5YLoa0NiKOLFdWQyoX0ThDYQzDWKNV2tXxA7GW1L5REOvAKG65MVgSa+E45HEj+v5dSv+yBzx4JSOwk5b42ppQdXySXAsnZLJLiCJ8b0SWHlE1Bd5X7+l7+noRBJUO0K3PZjXcqFbaIhPSnlckwwKdiJaHXeWS6QqKUBtC2Ul/d5kthwhJNescbyPpgLHynMtYUsWy1uO0EUGrMsOv5DkmcpisEXLtUpw1nVqioWSWoqwRyeynbxAPTmE8wQ9GqKokuSNOJdoT5xVm5jDGEcXd+hs24CUjgQdyjek+F4j0dZS0cg3jZPLg/ky6GDIrsuXeSiYy7xGiuBMs04TeEXn/eWrzJXkP9RRdLfBJTsj3UOuH6HJGdPEQu3MHNzxCNQW6nKFnF7BcyfmjlahB1vFm3fkmIjiNK7+OyX6/nR/wzYx3bfz1xSmh1xfRicEI/eU3YO8AO7mKsg3+f3mT+OYp9BLox6jCoqzFzWNU6VBpID5cSKQDhDLIEKCHnvJ8Qr3OGV05k5rvzgLdb0n3FpvFGrqF1Lt+SnHvAFfHRPvHuDqhfmMsY3GvTFE7Cn+hwSpCFNBDA+uW8LACX6N6F6AhNICFcL9BKYToswK3yIiul7hKiEu0FaE1tIs+auk3+uM6M5BlUFWEaY2q7gsfoJvmFb7tBdSdd1CuxeyKAxF950AchFWQyXY+iDKglslfrkyJd5bo/npjGJo3BrSLHq5KMWlLfbKzIcTlV89o5wOq6QhbJaSDUpyA4HB1TPFwlych5bE+29kchm0T08tLGb9aZZRNShY3DJKGSDsCIoySRI5ctVhveHu2y35WkMSNlAuKHk2bMBotNhF4PRtSzYTspownyYXEZasEVydUt/aJJyuJnoceruyj6kq4FqfSPZE9dUrS6SbgNe1cuiLS3cVmsMxu0hDdvkLTJGT9kpPZDgOvWTQZ66LH08MFozjHA85rvrLocS1vGcUtmbE03nA0nske0dJ1EEVd50pkyUZrkYrtFOuCl3Y4dZTBshKde70i7B+CNuhqxXr2W8TljHbxOuvqFJoZ/cPv/32/j6828nVzjmtn9PrPU598lt4TTPdfwnc98XaVUy367HzwFsXdQ2yVoFSgd3QhtWan8U1E1KuIj+aEVrg+7aJHezn74bJmX6QszidwDv3ZgHxnuSmN+TomGhT0P3xMe7+P7YZfNfMBdSFdHtGgJKyN8EZiS7S76oxggulVxEcLQu+qzPUYvUiaHqFVRPvbf5PE/TpmUGLGLdkb+yQHc0Irr6U2/nSQst1cRlbHo0LOC+1lTU6CkJ1LS/NH/hjmS78GKwuDAVqvwES4w+u0e0+TfelXsNeeQcVDtIqIz2/TAlFboJqOSzR9G9UWKNvis5zo/G3am38IPXuT+O6bktV8dhf94C7+dkW4N8XXWVc+NLTLvqh/XnKpovaJr4stvrmhQgjbSsoWW2yxxRbvGTdu3ODevXtcHyne/H+NHutrP///WXBvEbh+/Tp37959rK/9rYhtXmaLLbbYYostvsXwzScUvsUWW2yxxb/WCIiWxuN+zS0eH7bGf4sttthii8ePLeHvmxrbb2eLLbbYYostvsWwjfy32GKLLbZ4vAjqCYj8bAf7PE5sI/8ttthiiy22+BbDNvLfYostttji8eOxj/Td4nFiG/lvscUWW2yxxbcYtpH/FltsscUWjx2Pu9Vvi8eLbeS/xRZbbLHFFt9i2Eb+W2yxxRZbPH5s+/y/qbE1/ltsscUWWzx2PPZWvy0eK7au2RZbbLHFFlt8i2Eb+W+xxRZbbPHYsSX8/d741Kc+9Z5fQynFT/3UT72n19ga/y222GKLLbb4OuGnf/qnUUoRwteuVnj5d1vjv8UWW2yxxTcfAo+f8Pc+Ufb91Kc+hVLf+KzI1vhvscUWW2yxxdcJP/mTP/mNfgvA1vhvscUWW2zx2PEEBvvwjY+W30/YGv8ttthiiy22+CbBV77yFf7BP/gHfOUrX2E+n/NzP/dzrNdr/u7f/bv8+//+v08cx4/lOlvjv8UWW2yxxWPHlu3/taEoCv7yX/7L/J2/83cIIWyIfQBvv/02f/Ev/kX+q//qv+If/+N/zPPPP/+er7ft899iiy222OLxw+vH+/M+hveeP/2n//SmE+Dbv/3b2d/f3/y+qiriOObtt9/mj/yRP8LZ2dl7vub7+45uscUWW2yxxTc5/tbf+lv80i/9Ei+++CK/9Vu/xec+9zlefvnlze+/8zu/k6985Su8/PLLHB8f89/9d//de77m1vhvscUWW2zx2BG8eqw/72f81E/9FEopfvZnf5YPf/jDv+dznn32WX7u534OgL//9//+e77m1vhvscUWW2yxxTcQX/jCF3jhhRf4+Mc//i993kc+8hFefPFF3nrrrfd8zS3hb4sttthii8eL8AQIf+8TkZ/fC1VV0e/339Vzh8PhH0gd8H+PbeS/xRZbbLHFFt9A3Lhxg9dee426rv+lz1uv13z5y1/m+vXr7/maW+O/xRZbbLHF48eW7f+u8cf/+B+nLEv+6//6v/6XPu+//C//S6qq4gd/8Aff8zXf33d0iy222GKLLb7J8Z/+p/8pWZbxoz/6o/wn/8l/wr/4F/9ikwUoy5LPfe5z/IW/8Bf4sR/7MeI45j/+j//j93zNbc1/iy222GKLx4oAj52h/z4u+fPss8/yP/1P/xN//s//eX78x3+cH//xH9/8bjAYABBCwBjD//A//A+/ow3wD4pt5L/FFltsscVjhiKEx/vzftf2/6Ef+iH++T//5/zb//a/TZIkG5W/EAJaa37gB36Az3zmM/zwD//wY7neNvLfYosttthii28CfOxjH+PTn/40TdPw+uuvM5/P6ff7PP/885sMwOPC1vhvscUWW2zx2PF+F+Z5nLi4uGB3d3fz/0mS/L5iPwA///M/z5/9s3/2PV1zm/bfYosttthii28gPv7xj/NP/+k//Vc+b7Va8cM//MP8uT/3597zNbfGf4sttthii8eOEPRj/Xk/4969e/zAD/wAf/Wv/lW897/nc37lV36Fj33sY/z0T//0Y7nm+/uObrHFFltsscU3OX74h38Y5xw/+qM/yvd///dz9+7dze+stfzn//l/zh/9o3+UW7dusb+/z8/+7M++52tujf8WW2yxxRaPFwHw6vH+vI97/f7W3/pb/MzP/Azj8Zhf+ZVf4eMf/zif/vSneeWVV/ju7/5u/tpf+2s45/j3/r1/jy9/+ct88pOffM/X3BL+tthiiy222OIbjD/35/4c3/M938OnPvUpPvOZz/DJT36SOI5pmoarV6/yN//m3+RP/+k//diut438t9hiiy22eOx4/H3+73/cvHmTX/iFX+ADH/gAAE3TYIzhJ37iJx6r4Yet8d9iiy222OIJIHj1WH++FfAP/+E/5KMf/SivvvoqSilu3ryJc44/+Sf/JP/Zf/af0TTNY7vW1vhvscUWW2yxxTcQs9mMT33qU/zJP/knuXPnDk899RS//Mu/zKuvvspf/st/Geccf/2v/3U+8YlP8Gu/9muP5Zpb47/FFltsscVjx7bV793jQx/6EH/n7/wdQgj8B//Bf8DnP/95vvd7v5csy/gbf+Nv8D//z/8zh4eHvPLKK/zhP/yH+St/5a+852u+v+/oFltsscUWW3yT4/j4mMlkws/8zM/wt//232Y4HP6O3/9b/9a/xRe/+EX+nX/n38E5x3/73/637/maW+O/xRZbbLHFY8bjrfdLzf/9W/f/Y3/sj/H5z3/+X6rct7e3x9/7e3+P//F//B/p9/vv+ZrbVr8ttthiiy22+AbiF3/xF9/1c/+j/+g/4o/+0T/6nq+5Nf5bbLHFFls8dnyrtOd9I/Dcc8+959fYGv8ttthiiy22+DrhU5/6FEop/tpf+2scHR1tHvtaoJTip37qp97T+1AhhPexaOIWW2yxxRZfL9y4cYN79+5xpRfzL37o9x9J+wfBd/y9L3FctFy/fv13aN//6watNUopXnnlFV566aXf8di/yhxfPkcphXPuPb2PbeS/xRZbbLHF40Xg8QvzvE/C1MvIfzwe/67Hvp7YGv8ttthiiy22+DrhJ3/yJ9/VY08aW+O/xRZbbLHFY8f7XZjnX3dsjf8WW2yxxRZbfJPg137t1/iFX/gFXnvtNWazGYeHh3z4wx/mk5/85IYj8DiwNf5bbLHFFls8dnyrDON5XLh//z4/8iM/wj/6R/8I4HeQ/5RS/Bf/xX/Bf/gf/of82I/92O9SAPyD4F0b//ZvpqgEQqFoz4ckzyxxDxPcOsM7QzxeoTKLij14sBd9zLBEv7wLWlN/tiF9aU4oAzSK4BSrN66TjNeYtAEVUJHDDGqqu3tU0xGjF+7iqwQzKtBDD0D52j4mbTGjArNvCXVAaQgeaBSqF8AoCIFQQPNgQvptFaSZfBBnwXvC3oHc1Hv3oLZysyvwy4TgNCp2KO0J1uDrGJ1YzE0H4xHui2vM8wqqBnyAQU7YO0DNp2AtxAnh1hy1o6D2+LVGfeIZ1Okx4bQglBp9xeBe/gjmzpvwcLHRWgxV92X3IKy6z5Nq6GUwHMGtY/zSELzGPKVxtz3mBrC3B/MZNFaeayJYr4n+r6v3vEj+97A/vwPnBaEbMKX6ClwgFApfxrhVTvLsHNIIGoe9lxNdK6jfnNAsBmT7M3TWoIcNodY0pxOSgxkq9YRWE6po8zsVe9DQPhijIoerEwB5fuxxi0zWYBuR7C6pHu5g8obkYEb9YJfgDFGvQuc1vk6IJit5TcAtMnyZYvoVqCDfc96g85bQauxsQDRao2JP83BMcbLL+EO3WL76FNWyj9aOve98jfLWIdmVC1Rmqe/tUp5PANDGEWUN8XBNvLuEKIBVtBdD0hfmhCXYWQ+dN+AVetyAh+L1K8SDEhVbokmButnHv1Wi+g48hFajDw32zRiVtuhhK2vqxQmh10fZFm4dw04G82qzpuL/R/3Y1wJA9f8es759BYBsb4ZOLK5OiHeWmCsOfwH6mQHhwYLmzgS7zlHGU82GaOMYvXybh5/7EHsvv4OOLdXJDvnNE3S/RUWyt0Mpa0H1AKMIZaC9N5Z1NsywrwNeE7ySc+epjHCvRD09JOzsE5IU/fAeYbyDWi1hOoPaw41D+RBNA+s1/r5HP5NCmhHuXqAOerAqCesg9/2pHngvP9YRTi2h1ai+Q+2kuOdfxrz+CmHeEtYGt8qJX6ph3cp1hinh6nXU6bH8v9a4V1tU3qJzD7GcXdy4AqdnhLlFReCmCeY5DUlCeLAAC+3pCJ02RFcq2WuRkR8gPChQz+3jRxNZD19+R/bpqA/W4u83JP/P8omshy2+diwWC/74H//jfOUrX0Epxb/xb/wbfOxjH2MwGDCfz/mt3/otfvVXf5Wf/Mmf5M6dO/yv/+v/itbvrazyro2/m2VEhxVEAWUc9BJQgWiyQg8doQE366H7FXroiW+s8UstmwxQug8e/DIm1HJojb7rLmEdQIPSULx6hF61+DYizmtUYonGnYXx8m6VDkS7S/RugCwhLFpU36M0oANc2YXTKWGl8EWCyRuYVpA30N2s9s0Und9H5y1k4lBkT52hMtB5i68jzL44BLQOLhTmsAav4XSKyhXhoRglNChKVL+EqpZDQWuqOwfk2cnGqKuH90FrVKrkw+hYDL/30I9w9xXmWkBh5W/GPdSVHhyfEZYeVRbgPb7UoANm2EI2Quk1/iSg5nKYqGGEe61BRSXq41fe0+L4fTEtZOVcHsqJhrUVUY+g0HmNPc46B0pji4xIlWKUWRFfX8laKDXBGqJB8TsMLZnFng7keUYR6oBOW5rpUAx52uLXGWZUEqxB6UA8WRG8Ih6U+DaiuntAsrPEt7LEgzWYQYlb9FCxBa8pHuyRjNeoOkYZL87FogcUci+No70YoiJPCJp0tKZ4+yr9GyekRYavY0IZE48KmvMR7TqnuBiT9CqU9kRZQ7KzBK8o7x6gdEDHsq7svRwVOXTaglfYRY8IUJkl3VtQPNgj7pcoHTD3VuhdRViDykAdZXBeEN20YpRqjb4Wg/eo5VzW1I19wltnhLZbgE+w/NrOBpt/+zYi/cia8BWNL1LUWQEa/K0VoYkx/QqTN9giY3DtVD6/Duy9eBvTq0AH0v0Z7XRI7NboSY0aRuAtWIWfanwdo3Qgudk5mEUNpJhRCTqgxhp/9QZ6uIB7D1HnC9Qwg7yHWsyh7c6COOC/dI7ecdBPobLol3fkg5SyBqgaSCJUAkprKCq5vwA2QBLQg67lqmgwb78GS3Fa6DuMLghzj+rrjWFW5yfyutaBVpirASZDCRxs91rn5/LcnRiyFBOvob8nj6UKhoZYd0GDUoSlRT01gSiCppHz6PQcXRbyWK5gkMP5itCA3n3y9fityM+7x4//+I/zyiuv8PLLL/PpT3+aD3zgA7/rOZ///Of5M3/mz/BLv/RL/MRP/AQ/8iM/8p6u+a5XgDJ+8xc6saC7L1aL8Q5Oo/MaZbpUhVGEMibMLWFp8c5ApKDTaVaRRDEAWGRBdgejji0mqzcRWqg1fq279+HkNcoA8xql5HrBijGhrqAVLz1YIwd9rCRCbxxhbrGrHF8m+DLGLzXBGVSCRNhRAK/kzkQKYoXZrWFvJJu3DSjj8WUMVonT4YHpFJadh79Yk9083bhWl5+DqhFnIVXQtFCUnbMg94XaQh7BMJPDqSwIRff6qYLGonOPyjvHQ2lU7giNwc9SwtrAoIdKLKGNUKcnX+t6eFcIjXy/KkG+i6WFVKGHHj2qiQ4r7KqHLyVKj8crMeB5TTQs5bvsR5vv34wqotFaXrs7MILtIpg6ENYGFTmCE8dHdesktGIIvDMoI1ma4BXoQNSrNs/zbYQrMnRqaaZDfJc9aEsx4K5M8XUszh0QWkNoDQSFq1JcIVkjk9XYqssMKY8yDrfOcGUiz2ti+b61x0SSOfJ1TLvsUc2HNMserkqwRUY7H9Au+vgq6T63fJZQRRLxZw06tgSnsecDwpIuKwKcF/ilgSQSw7pOZD2BOKBFBVFEqI2st9hv9smTQLvso4wjysWwU3Xfj9eEOsYvM/lcicOMKnS/IrQROm3RfUlLRJMVdN99NCnQxska6HxwPJIFi7r97hXBAo2cIzoVp50ICQqiWCLchZIg5NKoai3GsJfBZEhoI7mn1kEWQZLIc5IEtZPKPc5S6PdhOJQ9miXyEylolDgOsd68fmi6PeKR8/DynIsMJLF8R53hJzJilONE3lf3vHBq5Vpay+8SI05L28i/exnqmX3UjV0Y5d1e9I8+Yxs2zgZNg1+oznFhO9HlmxA/8zM/gzGGX/iFX/g9DT/Axz72MT796U8D8BM/8RPv+ZrvehmYneZRFJu23eINhDrGTWOJ+nc9aiiGOJQBu8wljb7uFmGWoBKLTqQ8UL2+gz3rYy/6tKcjkqtTkr058agg6g4FP09wsx5umYOVTW5nA5o7E5o7Y0hkc4Va4+Y5/nZFqNTGSKjIwaQvm9UF3EUPk7ZywOQt7dmI9GAmKdJeVxoInXNRe0nBXdsl7OxDL+8iXgWucxRiBZHCnzj83ODnhrAMNP/n/xPkMcEiG3M8ESPpkUOm7Daq9+CDZBzmX5Xer2rcWy1+laIy4OqRPPdgIJmHBqgr1F4qaWpr5CDb2Uc/P8Ls1lRfeO91od8THjkUYzHG9uFAIpcr+6irA8Iz18UpcAaVO8wHMjHguUP3GspbhxvnUWmPGmv0C31U7AmNITQGnbaEQuEuMprzMcEadOxQxqEih0pbfJniKjHcoY3wdYxd55i0IXluIV+l1dhVTrPsgQ4U52OJHCNH8ApbZDTLHu2yJ2n/tMXXUs6y64zgNK6OCVZvIvf1vQPqizG2zGiXPYqzHUJQJIOCfNyVWbTHVimr4z1Wp7s0RYZtYoLXVIs+zbJHMx1Sn0uvr8lqCApfJYQ2Irt2RjRZgwq0iz7F20fgFW6Rsf7yVdwqh8qKY1ymuHux5MebFtYW1iu53/0uVZ48OeNfr3JM0pJMVkSTFc1bI3wbiaMO1GdjVGbRk4Dai1Em0BaXDlfn7HmNW2eSFRxCtL9Edc4PpRVne5yhd8FMKnS/oX04ksxhpNBDRzsdijH2ATM7h/tnhFKyOvT7UFeEXp/QH0Lew11/BnNYy/4EKZ0tF1IWHI7x12/KXkwzyDJClhMqCDt7hJ09yBJ8kUCedw5BhLv5rGyRdYxfxwTXOck2yJlpuvS8VuIIXNZul0tYFfL9WUd7PpTzoGpk3zcOLpYwW3bBgqd54WPUH/gOwsEVCU7KAtYrqCvcNIHxCPoD8IHm4QR/BqQKNYwIa8+TRgjqsf68n3Hr1i0+8pGP/CsJfR/72Mf40Ic+xJe//OX3fM13T/iLwJ3HhFaiLIYDzOF08+twJ6a5NZQywL6C8Yi4XqBUQA0C2cvgX78sEgfq+7sAuColHq8wO2va4z6mXxFdLyExrD93lf6HHmByCz4Q5or46YLq1R3wivSlC/xMgwoSdV5z+PugrqaoZYU7lcu5N2oxGEahhxVmtwCrcLMe9cWY9NkZRAOwFpWBGTsgIswtfpFi0gVqvoBBDw53YTVFj2pUrsSQ93I0KxgPCYMhIctJf/3/R5i3wm+wAVUWqL1MIos0gx3g9Ky7twbVs6hre7J5ywJ/82mMvwXDHMhhOiXMPe4dj4pjdN5iX9VEH41QN1OivRKWLdy7g3/2BcKV62Txe18gvxfcrIfRks7FS3YlvFNLZBYr/Bs18Y4YMnfRI7ohUa3WYoB05Gm+ksu/YwtnFeF+RTvfIRoURFcq3Dm4RS4GAone4/GK6LCAXOPPHNX9Pdp1jneGdi2vF2U17aJP+8U+6eG0S9krbJVizwdkkxW2yHBVQjoscHVMvRigVKAPXLxyhSiyErkbR5S01Kse2njivJJylG5xdUy7yqmrjOHeTN6jjXBtxOx0j7JKiYyj1y9w1sg+UIFm7QlOs5qOGB+dk18/pTmbgPKkVy/QQ489yWhOJ6TXLjr+gWxTFXtMWpGnJ+hrGqxC555od0loIsIdcXjQoFYFvHQdbt0T7sjBk+P2msQSjwpxVM5GJFdn+LVkSFyZ4mpx4P3aomKHHgaGH7wjTv/FEJ1YdNZQXYwwsZX96RUqsV2pUAuv4YE4AcFposOKZHclH1Zr2ElIlnMAwnmNmh9jv+s7UU2JujiF+ye40wT1fTmqWMNygXvmg5jDXTG8gO8NaJ/7KOmv/iLh9Rn6RoJ7M2CeBfIcVU4JVhG+9EA4SMMMParxh8+hF1MoC9qDZ4lXb9Au+pi0IX6uwL7Tw0wKqFs4n8GHbgrXCKTm/4aVYKXLoqq4JnmuwV8o3HGMiVb4mULvy/PDqaU+HpLuvUnIel0ZocbffBp97w7utkcPWzidSnm23yf+oRvo+x7KEormUTbiCWJL+Hv3GA6HlOW742AopYii976f3/Ur+KnBLXrovMbsNYS3ppLy714heEXy9ELS/etA+4ZH6R5mVEAVCK/PpN5qPHpYkV2b4U+UpK1bjT3LpAY6dISlwheG7Oo5bprgHyTYVY+qqy3mBzOi0Rp3HHcRwBjOA9FojS8z1HlBWEskbK5Z2BvLBl/XKBtgrwfnBYaS0ZV3JFNxq0CZgBqLV00kpQCVtoS5xy8TVLRG5U6e44DdIWE4llq+tfi3F+AX6LEnNMJvMDstHE5wrxQQFOaD4PavoD77RfShFoJe20VlD8+7coNFP7iLP/HocoZfa+zZkOT/kGOimUQS/ZTooMV+yWN2K9QwwpcanVr0518hVApXR0+knSO6UsBYShNq2KCbmlAGSZ9WAZ3aTaTpq4Rwf0Y72ycxC/SwIXv+BLWT4o9b/DrBrzOiw4J2qqhPJ7RTL07A0Qo1T/DzAb6NJH3/ToKKPDq2uCbGpC1JUqDTdpN+T8Yr0msXhKDw80iId0nL/O1rRImkx0NQeGfQxolhNg5U4OCldySDUCW4JkapwO4Ld2hXOc2qx+Aj92jujFneO6Qqc/rjpXAN+sI1OLt9lThpaNsI6wzL5QCjPcY46jqlPd8lMo69o1OadU7z2lNkkyXtekDUrwi2pjkfk904xU77+CZCRZ523ie+OkflCpU4wqmHKEiEWSZCDEzoHDKEZNebSnmmFx6lvZ8ABtdOcUWGyWriw4UY+i7yN72K/tPHuDIllCmqdULk6ysiX+DrCJ1aqrt7pJMlJheiZ2gi3Ep4EWZSwAvXUbfuCdclMYQ5qL4RYl0NelTDH/4AwbaEJMWN9kluv0p44xgy4MYVzH4FZyeE0Rj39PMkb3wBd+UGalKgixV6tSD9wv8m2QQULCvMPoRTIOqc/ShAq7pSXIb7Qx8hJBn65AHhzoL0zj/Aq4T0xoUQ7FBE1wopDWQpxAm+N5DhtEUDdobZs/i1Rvc9pFoMftbSnO7g65jec3P0bo0/E76Q3oXsaQvTc5R9KGeI9+jFDIZDzFNL6s9PSL+92ZQPzfFdmC9gNIB+H1VXT2w9bPG149/8N/9Nfvqnf5pf/uVf5vu///t/3+d9+ctf5ktf+tK/dPTvu8W7tg0q9lLT1wG/1pJOS1uUl0M+mhTCsk8ilGtpFz105MXYIwvNrjNM2kottm4IbSrGvga76KNjiz1jw9rVeSO1V6TWH2XNphbq64RgNXFiO65Bdxi2EZEGcofWFWHtUXoGtSU0yAb2XtJfWjoDlA+ES8KgV4TSS8RopWYnh2pnoD2StnMWHi5QDxeQS7o+NEbKAV5SiTq18hrTOaHNCU5jThcYbuGaSF4zieRA0Yowb+VarSWcO4KNCEVAxV7ITedK3n+X9iOJgUuyowKrYGcEyxV4i3qP2s+/LzRCsrpMXwa5R36t8euE6GYDSYKmy7hEbMo4+K47YF6DNQRrcFWKunCYvEYZh28jKWOUWgiExuGdIVhNQKOxqJ7FJC0mbzBpI3XyQUF9OhFDfhlJwYbMV5fZo8dUIM4rKU10PJJ2nWPSdmP4vTN4p2lXOd7KVrFnGeXpDt5psrxEG8d6Ptw4E+uiTy8XD945Q2NjYmOJIotzhhAUkZH3ErxGaU/cq2jKFHM+xqyFV3CJy1KDTlr8MoF1kOxb7FCtly6BRJyt4BH+TKvxywxtndSHG/eozPSEoBP5DrAKtMe3EUb7rkQj9XsVOVQuazzUEjioICnuZHe52fN67PFLSxSt5Hs0HnXvvqy7RNacX8eYG32UXciiSiLwjpCkAJjVlKCNrLUskQi5q4mHKCYkOaHXx06OMKspyraSEajqzoFS2OOM6GaDQjqYaB85W0oVqLpCV2tYzWA6xy2yDYERD9RBsn79r9qzdYW+OH1047yHwwl6OocWKD1mP4CDeLQW3kMhXRoq/aqaft4TntG6lnMtVTCfi4OR56QvzIFc6vyNe+T8NQ1oC6snzPR/Eqn693Hq/7/5b/4bfumXfolPfvKT/O2//bf5E3/iT/yu53zxi1/kh37oh5hMJvzoj/7oe77muw8Mk4CZVPhlQvNgh2hQovKOZKQDaicSgoq/bLVTuDpGlwkqtpi9Bn9bohiKDF8l+CZCD2p8KSQoHVvCsofp1US9SgwpoHs1uldj8pr4+gp73KNd9AAITSSRb4JsuDMlh0quUZnHnSYwBxC+gRpq2UxagaGrGSJeNwhZcG1ERjoKsuGMks6AiEf1QaVo7g2xq5xkZ0n0otSiiZwQB5MYpRrCGtyDpCOfRdiTDDUt0f0WfNQZ/0iY2roV/kIF7fkQ06sIQaH3EvzTz+F/8R2im92mLVupbY6EbCedBC1+/xCdZahkBudPzrsPcyCS+xmKrjWxirCrHtHeQD5P0wJyP8yokNR0bVCZo7k3QhlHcNJK2ZyPSfbm6Kx5VL+/GKJice4oU5ztSJ86oDJLPCjRaStOaOJQqSdppeYenEYnXohjOtoQ3nxrpFsicsS9WtZoE+O9oa0TuBhRr2VtmbjFNTHF2QQdO0xkWbx5g+XFmN5oRTZZ4eqY9XJAU6WEoKkaWSDGCFOtsRHOa9Kg0NoTRZYkaShWfXFA4pZmnWPrhOJiTJQ2RFktXQdKSJIqdsSjgnY+EIJjG9F7+li4C7F0DKhI2nCDUxI1lwlRNX/U+lU9uYOzXeWkewvplljlmEmBWnf8mahLZactZtzCMIYq4KcRetiiVKC+GDH89nv4C73pdNEUHadEQ+1p3hqS3FyIEXOB0GaEwRAFqKSEfh99dkIYDGXtnZ/hbz6NuipT0/AO6gp/cIUQxaimxO1fJZi0+73f8G+UBm8N9ckO0dVjGKbQtPgLcKscu5ISU5yt0c3rEBn8mXAWzKiSFr9FtlmrJqvlzPE1uADxuiP7yXfi9w/RwcPDhWTvntqHuyeYq1ImDesgXR7jSP6uEU4HVUMowJcxJmmFbGy7duSPfxvm1S8Kz8giXURZJByCrv14i28e/PW//tf5nu/5Hn7+53+eP/Wn/hQvvPAC3/Vd38Xu7i7r9ZovfvGL/Pqv/zohBG7cuMFf/It/8Xe9hlKKz3zmM+/6mu++z//BiOSlAu0byjfGjPYW6Gtd/bquaF8xxC9K9GpP+qCCGMXRGj10kEckkxWqi9rr8zHp3nzTa62MI+pVUibojL656XD3O0JQHVM82GfQEy84HhXSVjj2wub1HtUXh6N85VDSxjtrzAcyIdKUVvr4TxBOQuuFSayhemuf7JmzTT/tZY+5HgWYDKh/XZjJ8bU17AxgsYYsInlhRaKLjiioUXFNqA1h6lFHwCBFDcAcBMLDFrXXbA5otRdD2eLPPH4Nvk5IPpJBY1HaEu8tsdM+8Q3pBtCvv4p+3uBux+hhgxpq2jdT4puuKxs0+D/0CcyXfpsw951WwbteB18bruyjjs/k/kUGtWeoviDM6Wi4JmS78Nod/ExIXWZU4JY5wRkheC2kRVSZIAxtr0heWDD71WfQsaV/8wQTW6oHe6SHU6KrNfYBmFFBc7JDPRtKOnhUUN3fQ8eW/KUzFr95k/5TD+X+REI8lSyT3Ijx0Tnnd65gIkeSNpQXI9LhmihpUXlFOlly+tozZP2CpF8S90tclWLSluAVrolx64idK2eSbTAS4e4cnFMXOW0TMx4seTjd5WA8YzxaMBysOJ/tcHAo0V657qFUYDAUJ8U5w3o+RGuP7bILg2unTF9/it7unHRvjhnUMGzhwuNNCjqgn0lpPpdj8hrdb4Vh7hR6EiDxqOMlxecPSa9cYPYC6ih9QotBuA71+Ui6dFLpV4/3O/6BVRS3rpA/dSKnTWOxD1Ki/UpIqzow/Ohd2jt9uddZjc7X8NQh4bVTVM/CtX2SfYt/3UAU0MNA9LSFqoS1dIm4a0+DbVC2lfR31Ugk/1A6XlQvg0WJf/ollG3Qqzkuycm+8CvCoG9awrSluTcRoiXg6oT6jbFoMtSB6vaROKhtRH02YXXniGyyIr12hlvnuCpl9eoNTNJKySNviPeWEGvqr4xQkSf5QNlxFHZgPiOcVuiLM8KDBeraBPYP4cuvicOxrqXbp9tzPDwXAvIww75iwefovJb6fixOkjD9G/ilLxKGwjlCBzjae9S+GBn0Qf7E1sMltvK+7x4/9mM/thnsE0Lg9ddf5/XXX/89n3vnzh3u3Lnzux7/WgcDvfuafxuJF4nU3H2R4r8MKIk6zLCk/uJI+niHJQnymIo9wYMqLdHOmvp4h3bZEwb12YRktCLeX5C8sKC91ZfoWXtpEzp3lHeuEQ/XmH5FtjeTyGLQpVUXPfSVGm0sOOkwSPYWuEJSqr6MMXFCWC42BJdQx7j7gXY6JDhDsjcnOZgLo36YYY4S7BcidF7jzgzhgSWa1JidVmraD1ZCpHrjXpdiBTvTYsxyhAjUS6Gx+OMgHIGDjBAUaq8PdkWYJYSDfdRb91DGY0YVJqoIO9dRTQ3TGf7EEB2scPe7/u0E9MSiIi1pxL1d4vpMuAcaaUH8zG/DFYN6aiQ1xfnia1oM7xr3z3BnqZAed3bwr5yRfXBGWHrp3vjslPgDKabfotc1KlXo4Rp7koHXRM+3lL+xT/b8CabXEuqW+rUxvavnQgDsBJ96LzzAr1LKLx8wu3PE0fd8ifTKOdEqF0JVI8vXVSnrL11BG49d9GAhNed4VNBMh+jYkoxXrI/32H/mHsEZXBPTlCnVYkCcSoRVnOwyOjwHr4V4lrb4Jt4Yflsl5DsL5vcO0cajI4u3EdlwLa9pDU0bM+mvyTLJujRNwsHuOW2d0DQJrY1Is5rpdIL3Bq2dpMdVIO/Jul7eOyTfWUgmzEtXSfGVq6IZoAOuTCCNyF46J6yk3BO+/Xn0m28SlgGWDr0PvZ2Hsu5j/YhV/gSQTpa4ryJmlq8fopOGaFiiB7VoL6wz/BrQHjOpaB8MiA5Wm2g2qgp02fExKoU6v6A9HxK5FbptYLHC1wl+kaCXluhlj9s/wrSNkN2SjOjiRAhxvQHh+RH6zTelja4jR4Vnn8KcPcAdXqe5eZ34H/wj7DITDsvBCBUZ4nKJXfTxdUwyFnIob4CKPFGvwtcJOrabTgaT1dT39zf/zp9/iD0doLOmy4SkqFKcRL+Ocb+Rkb98Qv35imgYMDcjEROKgOVKKg4fegn95hvQT1FDJVmDtoFBCvMK+6oWTYMoyN91p3h7v4/OG8xhif7ENdRb9yDreFnzOWHaoo560gZ67wmdDVv8gfBX/+pf/bpf810b/3i0xi9jlPYkBzPasxHx/qJrMTOo3Es6tmOsAp2YT90pA4KvI5QS8ROTSEpVJ1YM8gMwg7JTCFSEWmOnfZT2NIsBatkXZb+oKwcYj+7VUFn8TIg1Kpa+6vjgqxb2ciltZqlD9cAkNcFDkk2lPgldf3WLUjXomnaxR9qvutR8i7voAe3mfek79yEBElDeE6cLKSeUQB0gcdL6o6tOCKgj8yzWUvPOW9R6KW2CVjQJlA9S1xzkEun3GtROjLaiIIZXuDMj7ylLJUUZK/xFF+0NE7Sv8cegr6whiQnzJ1Pn9WtJcarYoqzFzgYku8KYDq5r61xKLTLUGqyXkkncpWynLShxaDBSIzZ9OVh9HUNHFov60uqG8vT3Zigl3AwV243YzyV02mLGJfXdfWyVoI2jXfRwVYqOhK/Sv3ZGfTESFbikZThZYgtRB7RViu36/03SErymXfaI+qWUJjrGfTUb4i4/IxHWdryANqJtY7T2aOVYFz3aNqZqE/o2xna6BVp7lssB87WQV2PjyNOKto1JEnFCfGtET8AriSDXMcX5GNOriUdrzKAk3GrFYaWL+BczIV12a4WlEzGgaSUiM/b8iawFAFfHUs7zSgSWZkMG107Faerq4PXZuMsEloRWE+1IdE0p2hZqR6HCJWcgCIEtbR7phmSJlDdiJ4TSAnSxEnWwJCa6eIhq6k3NH62l/bRpNqqbqiphPsckKSFKUKnHqFLKdErDeIJbrWjnfVwbbdoXXZ2gfMD0K9w623BAoqzB9Cvq2ZAor4iGZZdxErIxQeHrhGYqjlcIClcnJGfyHu0yx79hNyRo7S2wQk12Rdjraiy1/eWJaII0HYcobaU1+oWFlA0r0R6Ir3X3FIXy4pwEKx+N4RBVXsh9aRr84sllgkCm7/rHXKN/n0z0/T3xjTD+7zovo0dSmw9thJ4EQhCDpvMWvBZmfGylvcOKiI8rUzmMLGK8Vjk6EWJWNBCylDIet86ojncfCQYF6aO36wyTNfg2ol71pF5LZ6xbI21QS3DLHLfMuzYggxpr6SfuK8J5LaSZCFED2+uh+iKPqq8oVN91m1UT1gF/IURAFYsqlxpH0mvcwdcx7qFEXuQxDFPU1QGMBoRL1bq5GKXNwWWl7OFnSgx96mUzf9U3EJySOnolBkANAvRy1BiRddVBiEZ5t6HKQsRAbCeeNBzCMKZ5uEM4b2FeSG/0E0BoREwneEkxBqvF8NRa+uFNp3mwSDffi9KgcyF72rO+iMFYNqJJut/gyoR2leM69TyaTjEwtuRHF5t6Nl6LQMywIpqsiUYFpl+h9zXeajHUKuCbGO/0puUomqykvl7JwRdPVhJdB4XrjLhtROzHVgnFdEQIIgK1IfTNh3ivNwebVgFbJTRVSmsjIuOE5V/0mRd9iiZlUfQomhTnNcY4lkWf2sbUNqZsEuomoXER3huUEoetWAyoVz3adU47HWJb6YlXsUUPW6rb+0K8vTxgzy/E0fKKYA32oi/tsVZqwu7sybX6XTpGyjiJijuin68T7KKPSltslYqzOEbIwj2ROg5FR4bLOjKNDp0ugXTyqEEQkZu8hx46zE6LGiMZDWuldTZOUKuF/D+Ad6hiTRjvSH28qCRyXi5EGOz0mOjsPmonRe0ZYcBnmXQJlKmQS53Bltkmyxi6e3+ZBQpeCfm416Aji8kbVN7KGdB1plzyM+Ts6u6/9rSzASry+CaheLAv51lj5OxYWtT5CX4dPxJuMp1ImZNI3+y1kpFIpM34MsDgcCLlAhCnJ1aSGTBKuBCqIx1a6WzZ4lsb7/5E0EhKXgUIQSKr4z52lWPLjOFhgV3lmxpp/sETqtf6rN+4RpQ15B86pX59RO/GCSqRaFEETHrCaE5ayneOSHYXsmmahGi4FsJMXm/U23wbYVc5yjiSdkWwhvj6HD9PWN+6wvCjd0QGNRVHon04Inlm2RHrDOQ9/N0KZdqORBMTNQWqJwclLWSfWMEaKD3Be6K9Fez0COsKX6bE1+egY9mQTQPrmvbOADOR3uv6wR5pc4Fb5BLVNjVqEAhVsmEv08uxbzdE+xXqSixG/+hAjHpRiWG0Fg72UPM5oXTEL0vEE85rySA8NcE8U0Eqet3hvOs/Px0RTVaYa09mg6vEER8upMShc5LrM/xSWNCXSnU6b7rvMSK+soZnrqCWC9R5ga+FHHlJ+jPjEns2pJqO0MaTjFaoyGNnPSH/tdGmfui6SF3HluxZUTsLD2qmv/08+fFCskNdjd7kjbDkm5jywb6kpJd9Yd5bQzkfMD/fYThekvRKkrxGx5ZyOqJc5zRNwno+pDcoaNuIct1D64C1hiRpiKKSwcGUxckuTZPgnDDcb58fEGtHYhz9tKKX1iRxQxQ5lPLUTUKkHY2LqG3Mw+WY65MLkrTGJGI8T08O6OUlrolpy4zh4UXXyppiT2WfhTJGD2pUHyhBTxR+JjML2nkfc/8heggmlxkYTwrxoKRd5SSTFfFzC7LynPL1Q3xrMFmD2WsZJrdFu76ViNetJSNgJgXs7eG/dIJbdK3B1w4JUYxe3oNhJun9kwdwMIDZWgS4XrhO88yHiGYP0au5aPc/uEvI9oW5f/seHO6KwmirUetCovIGeFigpm/BTg+swx9dx+5eJXntt4h3VtJGvM5YPTigOZ3gqgSdiN6CXedk+3M2SqU7iv7gGD/vOqBMvXGCQESmentzfKePEvUqfBtRPNiTc/JgSnx9jkrAnSY0DwfEyyU4jbsVMDsn+I+8hCrWkrkAQpbTU/fBGwkiIkN4WKCiQs4wQB1ouH4kSoeAmk9pbg1Jri/gygT93eMnth4use3z/+bGuzf+VpS1QFL4Oq9pp3J4926cEF56mva3JWpJshrymORgJoppbYS9lxP3y40qoF0JAWzVManznQXxWIx9fLiQoS2zjHY2oFkM8E6T782pZ0N6V89QsaWdDSjOdhjqgOlV9K6f4i4ydN6I8Z8MSb5Dw3HAzzzBKpSecvobH2T89AOSgxksHXY2xJRVp/rWSYHGGkxAuSBs+mmB3tUkowXV67tk3y2tfu5CSG3R3kruZgzJ1SnquR0i72G+IEwRPsFhT8oQcwgPFkTPxhBlsKpp3hoRXZyhbyQiJtQsJeIpCyEBpgGylPCwkXTvOIX1GvtmjBnNUYOAXyf0v3eBfQXqB7skzeKJ9PnroRf+QxoRxju4L9iNQxaPV5gbHnfHYNeZGKm3DMnyVLIUIRX1Pistdpdqj6Zf0b9yjqtjidjbiPzGqcgURw6dNSzfvC7qjHmF6VW4u5p23sMVO0RZTXE+prc3l+jYOJIP1ZCBf2PK+tZV+k8dY5KWtkxpy4z1ss9k/4Koe+zibI/DGw+oq5S2jQlBUZQ5UWxJeyVZv+TurZv0ewVxKhFfs86py3yTCSirjMPBgnWdUdmI0sacrYZE2pFEjsRYiiZhmFUb5bJpnbE+vcLeakQWNzivubJ7zmo9YLUe0O+t2U1amvPxptac3zyR1rmudGJPUnEqgyKarEj+DxHhtu9kZoNI/T4h6LTFnU2oziKU8dhCSkJxVoMOVG/s4spEFAB31qRPzyBCDGarUfMZ+opB90V3PrxxAh+6iTtP4Nxjqldovvv7Sd74PPSks4OmJv3y5zYRu2pq0e73vkvza8KtCxl+E1tMXGCPe7gyId5dilaHdZTf/2dJHr5GfPd16g98B+nxP6F+W4YP9fanYvhjR9Sv0IOa/nefCbv/dkt1f1/+fSFteLpfY88yGVwUSdlCx5ZoshaOUefIXparbJWglz38bUO8s6Q5H1FNRyIOdHXVzb5AOD/TczmXfECdTyXa35nIZz2fU9/eI95boFMrZ8HuAfrN1/F3jDjg35mTfIfFvZLgj1viZ383Yexx4/2uyvevO961bbhMV6u+EkLbQggu0e4SfcXAa+/Qu54JWcYZ8AG3zkSzW4M9G8qhX4tCV/CKZp1jEks6XAunoImwbQZdK41OWxnkk9UEryWlqIJwA3JJ1+ddPdbPBwSrSa9e4NcZypSopJCe4KXU/PVQDsPdl97Z6L7rsSdiJRrkpaTM/FklKcm2S+8ddsxkK85NPF7BedNp3HvZzBGiBd60+GPD5YAfRgNUUktXxHIpKnwW1EEqswC0HATxwULaoi6Vm9pAuCuCPnIAIPKeOoiuQJ7D+Vxa/bQI7Oi8hcoTXbEiLLIz4EmgvSvETEOF6p1j9guRfO48/frLA+L9BenRFF+mrO8eklydQqukvVOFTmtfpjn6dbyppZq0JT2UvwvWbO5vcz7aRFyXmguXync6aTFZQ344FaJnG+GqFPt6Q3S4wK370gXyYJ9q0ada97CttP/ZOqFa93DWoLXn/js3hFvQHVx7exdo4zcKfUdHJ5Kp6lQAo6xGa4dzOXUTY72hdYZRXjDRntZGPFyOKW2M0YEktVyUfXypGOclo6zgrOgRKc+0yknahKvDGSfTXbK4IUtrTORIdxc0876UtVRARV5KBGegEytDkAB3lsqQpFfn6F0l66h5IsvgdyDpZI19G0lrG11LZiT6DcneXLIW0z7R0Qp/Hj9S8SvqR+1sudTwOT/FPBWg38fv7hPfflUuNBzKvuqGdIUkBW1QqwX22jPYnRtE8/tEt+6BBtOrpNx0kBE900O/MkVfUfjnX0RVBWZ9StCG0BugmxI3jSVj1Bno4BriqwsZzKMjuX5ZiO5J2uLe0Zgj2+kMCHk3HHdnlRG+UnDSXhq8ol3JVEtnu/ZTFYgG0rViO+2H9b0DxgcrQhnjl4bonVv4MxH3AUTt71oic0y60kA0KIULEQWRQT95AKXFHDlM1MLxGmKN2fNo24imwBbf0nj3xr+JULlMuCLvoeIlylqpX2c92tdSor2VpMZWPVhbXDUS5bFMFv5lKgzApC1NkTG+cSLp/cjhV9Jf7Wqp1SZ63RG/JN3lndnooyvtJEvQCWH4Nuq05D1hofFFgk6EbGhnY9EKT0HFgThadRGRko0y9vipxTdR1zttpQ7XEbvsCTJ2VSNOgdOiu41o0xO5blofHYHPiIwmyEHW78thtW5l+BCgslRkSCNEcOh6DufFZjOHy+6ER7dsI4xy2c4T6oDa0SIm0gBRIJw7eWw0ELLQE0BwQmiSHuYVKqIjR0KoDeXpDsnRHHKP1hVRVstEtiaS6Aewq5xYe4INhDYStngQgttlZkBEPS778yOy3alwLuoEu+wTDToZ2C7zY8Ylfp3gykSG6DQxSach4WxEU+Q0dSI8gKCxVmrm1kbSjaECq7JHbCxaBbT2wuTv6v0hKPLxSngBHZQOeP8oqr50HKLIEUctxjiitaNyEc4rVPd5XNA0NtrsQBs0izbGqMBB3zAre+yoQJaKIfW1pP+DV8RZgyvEUQ1eY9KWeGxh9ei5zfmYbHf6iFPin2zbVTR4JBqjYnFIcXoj7qP3PeHYYosBked3OHHUnSiO6vTuLwmt4wl+tIPP+kTnJ6KmGcUErSXNX5WES42MssANdkB3jjcyAdEkjYzRPbpGc/1l0vkvwt4edvcqZjVFVyuUdwStUbbp3pdHR90AqSjIWN80E96AEy0TlcnZ084GmKcXUlIsakIr3wda3oOOLO06J8q6nv2gaJc92jKVwKZTnGxWPVy3p5yNCKV5dB7dcbSzMVn/XEi+6xR1dA314J6UCyOZpKkG3ZwTC6qu5L/9CJIYf7fajAxWog32xLGN/L+58TWdCKoTk2G9xuy10t40ywgPO2GVVuMbOcibeyOC1bhFjpvLwJTgjIh9TAqSgxnWRuQvnhBfW28iP500pHtzsv25tDSBkLoma5rpkNVsJJru98bU5yP5/e6S5GhKejATQ5S28j4uhA5dXYywZ0PC0sNA3s/lsJPi1SNII/SOGB27ztHjRoiJozXRaM35l56XPvXG4MuU6SvPSAajY3Cr2MPuEH+/pb3dF37CtMbfbwkPVo9a7vxX/RSlpO9TBYMcf/WGELjOayElaQgfewF10BNJ10zUCVUqU/T8XRETYTQQ0mEmzPr6eIcwDXC+ILx2/F7Xx++J5KVCopjZAD81NO+MCWU3QS51cvhFIpbky4T+J46pj/ewS4nA7SqnXedUZxPqswnNfICOLenhtIvyxxtN/1BLViDZWeIbIfIFq7m4fQVXpmLkO46BPRNWtW9i5qcSJi1uX2F9uou/nMSnPaP9KZODc44v9lgshcAH0LQxu6M5yWWGyThsExPnNVHS4ltDWyX4VsiBzhqqRZ/FcoAxlvFwyWSwZJiVrKuM88WY+XrAIK2JtaPxEbOyzyQr6McNF2WPr5wfsGgTHpY5x2XKvSLjzekeLijWdSrkwCrlnS+9SLEYiMHIpfymY9HGiIZrGQZzpycp3p0lycFMJgEWQjK9VMp8Ergkw+l+JWOWnRbtAWsoHuwJmdeDSrppnRmYZ5T0p9O19l3tPRKhOV3QfPi7hPH/4A7R3bdw158RUZ6Ozd/c/Dh+MCYkmcyrLyp0U5L9xj8h+tJvwcEIxj3CczdxL36I5vrL+GyEe/kjuN1DdNVlKrIBqlpLt0SUYG547KonJZbcSSdNUUnWznvCg24v72SYq5LWxzpYVriTiPVb14h3FtK5UiX0P3iMNm7TvZHtzbi4e4XlYsh8Oub8/iHV8d7GuVQqMLz5EDsbbIKP4t4By3sHomfSBvCa8uN/RoiSLjwiS+4MpHPBA+OJ6JbcBfuqF8OfKvyFxp9p0fzf4lsa7zryd6sct5IhKqGNiHdkE+i8QeVKalRLg+42uCsy4p1FN9Pdo/OG5nQsmt9K5qNf+fhrVG/t0Sx71Is+OnbETSLRlDMUZxPivKanQyeb6bnykTc2LUXByYz2yz7baLjGvZORPLfADDpHZaXID6ciyLJIae9mJHsinhGclhR+43CnMogkf/ahKPx5hev6fUfXH8rn1B6V1+x/91fwq3RzeFVv7ZPWU1Qu7YfV/T2ai84x6ZfE5RI1PcPNekQve/z1p3CfvYXZQdr9ViXhs7cwN7RELl1Ks/r0kuxajR7LwBx3HGOuWdQoR3Vzu4m7Tdy0hCYiHq1liMvOjswffxKwjuRojlvk1A9F7dGXKWEtUfjgxXtgFXbRl7apdfloKE8E9jgjHhUbNvRlHdSuekSDgvTGBatXbgDSX+3biMXb18nGS/pPPSTbXbPjDOlT5ySlaD9U9/bRiRAeo37J1Y+9zvzWVWHrBwXOMLx6xvmta1TLPj4oRnnRqexZorgl9YqmSUiTGu81rY1Zz4dESUtT5BSrHkcv3dqUFZoyxduIyXhO3i+x1vDO3RvMq5yDwZJIO6ZFn2mV89RkivOaZZ0R2phZnbNqYypnMCowSRoSHREAowJRFzkWTUo73eVwMuXiYofFYshgOmKwsyAbL4l7FTqxFL++S3E+Znj9BDMucfMcMyzRz49QWhNeWT6ZtQDYZZ9sV8R02tMROm1wM6n7D567L1mHLEIfBvRuJ4rlO+ewjomu14TTAj70HL43wJwdE/39z8A1TTg4xB1ex/UeEdR0sSR989dEzKeupO6dJcS/+c9gb5cwHKOqEnfzeXzWx+UTfG+X7M1/Jl0AgxE+66N8JdH/agGnZ5gvPICJjIO2q5z2ofAF1GUJoPVUx9fpv3RPAp11TvI9MeGWF+fXaeJhQfR8IJwucIsMe9Ij3Z/TLno0iwGz+4dMpxOsMxjtcV7TlimL8wlx0tKfLCSbqOkynEEIsCpgZwNMU2EmBb1f/SmYdyN6VzXtxZj0ORkBHEqF/40pKn2kiOnmMeaDQ3R9IVnCrP/E1sMltpH/u8dsNmMymXxdr/mujX9ycw6xjLp1F8LQN4MSZYK0eZVaBHhGBdHBiurtQ1TsZKZ7UEQ7a9qlTLoyWUM0KCUd+1Xp9bZMN4z+S4a3ji3tsocqPCZppY9/b0GkPXrRBx3EgNOlGy/T81UDrSesFLpXE1o5aJSS1sIoflS38zNpQ7xk4uu8RUei4KY7eVk7GxDtL6Xu1gLrgF/G4GSDBmvQiUP7lqhXEe8uJTWei+JZdWuXeLSGRtqQ9DOKMAUVGVHcyutH6U+QmQOXM81xMMgxV0oY9GFVSB/wXgp3zyR70EHFFn8SZNKXBv6Pj2Wd/E64gC9SbJERXDd+t41Elz1vJdKs444AuJZWQKdpHww2NXm8Ip6sMMYT6hg9rLrJi6KjPvjwXVlTyxxX92jqhFwH4YvUQhYMrRYuSNaQP/2Q+v4+3mradU6zznFNzPjGCTqRTE781JpJHVMt+tg64eq1Y95+5ykmkzm94QpvI/J+SbGSHv1eXnL08i1R0TvZESJg99rpsGBwRXrns2WP9fmEYtWnl0jq/Xw9oPWGRFsO+mvKNpGxv8bhvEJfyg0D/UvBGC0TCI0K5HGL6dr+LiPC0XCJiRxae8qlfIZR5Il2V0S9it1rZ+hdD4lBmbVkltaSFVD6yR3E0XANEfJ9lYm0x3kpjwWnia5UhPNOGjsVedowtegx6DRAkqB2cnwUSRTvPfqZdFO20osLuc7x7Uc1/nt3pP5uu3KHC4SXnqO98gw+7mHKmejuA0QpJBOqD/8pzJlMulSuxnzh1x+l65cBX2boSS0E5SIjrGVUcbOU9xFljfCU3jmkLVLqRZ/e2RzX3pTvyRmaMgWvML2om2vhMLu1nD3GkfVLnt55m9nDPdo2Joosq+mY3qAgShtM5MQJHq5Fp6KJ6V0933TR+DqGeSBeLzDPSHkkTFuSmzM4FQ0OXyeYQY16ZiylikUprZ7rdccfUrBYPbH1sMXXjh/8wR8kz3N+4Rd+gd3d3a/LNd89GbwfdbW00KX1RMAmtBq/ioUElbabxy4R2mjT8nEpiXrZo02RSZ1XBWlxMt1h17XROGs2YkDUmmRnKfKfqdQUTS7aA5ez2X0dY4blRmEvNB4SaSMMa0/QATMqpCXNqc2gCHsxlAlvOBlI4b7qoNQedKCZDzD9Cnyzqff7IhUCW8cR0I3U9XQqIjEqFe3yUClCp0sflhaVnBMODmF6R1KGWssUvDSSIRxBenqTvYW8H4tE8R5JQZa+EwbyQmb0bjN8SA+dXK/VqPzJFfYuZ7FHvaq7ZxpaQ4jcptccpPf7ktXv1pnc59jK/TCShveXM907cZTQRphd+Z4uW0mjyJLtz1Bxp8vgDKGK8FUia2rcYLKa9mIktXGniS4H/kzWoivfQro/E2OqAyayZHGDiayQ+nT3fS37eK8x3Vr1nZJgHLdU8yFNnRB3NVxbZKIsaCPKKmNZCVl1VmdYrxklkESOeSUlqFh7ChtTO0PblRvyyLLupH2NClivsU6jTdhwCOom3URSKg5CWNQe38p9kNS7l7Sws5IdWytU0ZFGnlybvwwWajoNhqAx41bmyXcIK4WvI0zWyt5MEvz9EnOgYTyG9YowGotRrwrpcImTTQZM2Zbo+LY8rkWzI5y3qKiQ/RMpGKS0V57B9Q9AG0JboC/eJuxfJegIk18ljifUq7vo8gJdLOSMmLf4eYIrMrmfswa7yqlXYnjTQUG17AuPo242QlCuiSiLHuvlgDSr8UHhbCTE0W78c5TJmtQD6XowscVWgWRYkK96RK1kFC4VHoHNICmdNN1QK9FM0Mbh2nTDnXKrHFNI9jW0WsoTtbQ367QVHYWk+w6ytstSpqIT0oZHk0SfGBT+scv7vn8zCa+++ioHBwdfN8MPX1Orn8OdRNjZiHbZp7+3klG8swHNfEB+7QxztcHeS6mO9x4N5ukm5fkiIT2cSoTYq9GjgD5zeOO7bECNjvzGUVA6YG1nWG1E8OI8xDfW0g5UCpnIrkTf3DfirefjEoYjWK9QpZXaX1GidEvwDnVjF3X3Aj8XwSK0p7oYkQwLVCoRmFvKKNGNkelqu1GvQsUzqvv7JHtz7DrbSATbRR81c6JAlliqe/uk+3OC09hVj2RvLg7NRYauG7gpYz3D2qNq6dvftO7UFRQ15hmFu+1F7MZaERcyHpV0QkHdbAIaRfDCrOZohFqsZJscXXm8q+WrEKxE/PHBAns+wNUJlCmselJ6iJxIunb3Rw8dGI9xWgSjlokIOXWDUlTkNpyAEBTqZEem69lI2jzHK+LnCsLa485ENMVXop6mjCdNW8yowN4/IHhFb38m42G7VkGGMetf2yO7dk66u0AbRzkbcnR0golE6CdKWmwT03TCOwDFgz3argvERJbp+S5ae+JlH9vEnB8fMNmd0TYJ6zrjzdkuo6Rh3Q308UHhg+Kscwoi7TmrUhLzKMrvRS3TOiXWHqMClTOclH1GSUNmWowOnC7GGOXppxXD/lrmAyRLXBvRnE3wrcG9k26kZ3Xa0s4HjzTmv1r18nGvhW5/+yYSo7XXI5x5Ef9KW6o7ByT7M6lHjyf4wQj1xpswHON2DzBlsanlq6oU4mtcwqQPw7H0qn/xHdRLVwhZjqpK/CrF9CoR2tqZ0LzwUezkGXQ9R69P0cVCRgAnKe3RS2TpEXVzjlmfEp3clpLBqI8/qake7FEv+/QOpjTTIeuzHcp1TtvGjLusjHOGej6kbWXMszaeOG45mR7Qt4X8vlszea9kNR0RgmK4OyevUjk7jKcpMpGO7vT/AfLxivnDPdo2IoocWjtCUKST5YZI6bvsqI4t8WQlBNDb/W7wVSO1/2GM7l+Ob1bw8FQeV0rmBtSV3FPvn/xUvy2+JsRxTL//5EsxX413X/N/kLC+cwhek+7OqW7vb1pi8mtnBK8oPn8obOThmvRjJf5+Szyq8YuU099+kf2PvkGwhuZ0DCea4nxM/8q5TF7zimiyprq3T7Ps4Z1h9+YDmWD21EN0alm/ca2bdNbgy4Ti/gGjj93CL0X6M95foJ+f4L4wBafRPYWqapqv5EA3BMVfoPZStK3xpXi/l+TCdiE3Px6t0f2GUMa0swxX9ekfXhC8oj6WQTKrW1fRxoP22DtHDD/8TqeqpjG7BamdywbtyG2+NSS7C9r5gPZ2zqB8C3ItY4AvxTA+v5TWtrxFDRX2TUX0MZlSFm7PaGd7pNcuhNWbRrAzQR9FhNeOaR+KsE+4s+gEmYDTs8e9XmQtnEsLpELU48yg3Nw7nbasbl1l8Ox9glei52A1aXSB2Wkhj6A3oHp9QLPqoY0n3e0IUl12REeWeFRgikxatdKG5nzM8teuSZYoq+k9/4DFl54myhpcG7H8rZfIhmuGTz/Y8DP0i0M4OSEUCncXsmvnnP/2i4BI+EZJy+5H35QOgnVGsxhw9s51ev2C8WSOiSy9q+eUD3dpK+GEpGnNfDHaHPTT1ZCmTTieTXiwHnJeJyTGsZdWlDbmTpEzSSuiLqUfa89e2rC0EYn2ZMZxXknng1IBrQJGe1ZtTGkjiSiDIjOOjxwekyYtq6JHZBwTHWjLTMobH7pF/WCX7KVzONzFv1pJpiqSUpJbZDypOU920ZdsXtoQD0vq3xihdBC1xipl8IE7tCcjVLJAc4K+9xBeHGNvPIdPcszxPfQ7b8PeHjTSQusvEvTTOyKNfX6C/d7vJPrtf4Fuj7vOgITwwnPY3SMh7XmHatdE5+/gBjvEH/+/Uw1/DoJFVQuK418k9K4S2wb95pusf+uIcnqti8YTbBtTrnoEr7n/8Ii6jTnaueArr73Ild1zoshS1SnORfiZ8EGKOqWxEcsqF0dOewZZyW+//hK9uCFPGlbrAdnJHkfP3kUbhzae0UdusX7tOm2nJnhx72gT+bde0xvV1KsevatnmP0Cd9YjWFGvpDW4Y+F39A+nJDtLVOoJz9wk/OZ9kd3WAd2vpEMrs5uJpdVv9jBpQ7RXoF56coEBAOEJiPy8j0UJ/8Jf+Av89//9f8/f/bt/l09+8pNfl2u++8E+TUT/meNu8I6IWtS39nClDLooTnYJXpOOVpIqPW67/n5RARwcTDcqfQQtDG3jqM7GUlc3nvpiTLY/w1Yp5bxH3JNJa27Ro+3mrjcXI9LDKWZcMuzfkYMgSMqfOkbdugBiGTozljaXaHclE/p6CeGhIjxs8IVIeeJVpysfoyKp11+mUEMlLYrp4RS7kKjUNTHlbETSKyUr4TTVsk98ex/fxOhEJGwv5TyTyYrsaLppH8xunJIBxdtXGXzbPUAEc/w6Rn/iAHV6IpF+lhBdreF+1WnkS6TtVilRVsH+oOu8WKF2IpL+EnoJ/m6X5o29aOk/AbTToXAvxivMFYc7NsJ76DZ77+oZ1fEu3kboyJJevwAPfqlRlUX5QoiMJ490E8w1i38lopn3hTiF8BeUcZvXTkYrua9B0zzYoVr2yZBoKEobvNOUD3cZPHcfvud5vPf4Z14iaINZnMP/9iY7L95mdfeQYjGgN1pRH+9hslpEhg4X7F46MV3qVhlHOlkSLkYszneYL0acLB6Rz2Z1jvea0sZoFZgkLZlxGB0YJA0fiBt8J+bjgqJyhtJG9CMrQ1yAxhkS7aWTNCjWbUyq/cbwXxZvztcDksqhVCDSjp11j8HOnGxngTLS+2+PM0xxgT7U+M73UyZgRk9wvHOXir4s+11O9It2l3JWqED6/EyEsyIDvRhOZpjePXSvL90wrcePdnA39tBXhISnqrLLglXoz/wGrolQiUOPAuoPP4WdHNDe/EOYdB9u/xNCOqa58e3gKspbfw/dFtK+p0soZ0Sv/zPU9Ax7L6WcjmirlKRfYquEukopqwznItKoJTFi7K/uneG9Zl30qZqEXlbycLpLZSNcRyZNI0sIomVfNAmtN8yqnFWTMkwrlhd7aB0Y7U5JBwX1Xckm1asedSXlnCyviNIGbTzOisxz8WCfeNGXHv7Y4rsWU9OrSSo5p+rzEZyNydt7Uusfl6K8CdhjTVhmuLmi+VImXKSn1tBL8V84fTJ8oC3+QPiRH/kRfuM3foN/99/9d/m+7/s+vvd7v5erV6+S57//9MW/9Jf+0nu65rs2/koHzLjTrp6lGxlLtPRMm0T0u22ZbcQ9LtPmOrYQtNSGw6P+eG08bZVggkJlcngL6U4mnQGb1wHEMDsjHQTGQyztbzqq0b7evFez04oedodLw08So8aNCJ+sFEp7zG4hnrEK3RhcJ7XrWgg2BL2ZmS7T4NgIvijjiGJL3FRSh+s+fzjpSHBWo7s2KKyRiWBpi0olrbc51T1CHLSt1CHLgPINZBFh3Uo9PZJe9sv5BzgLOoPpSvqjMxEg8fVqw53wJ+aJlHrtKn80WMcHVCJ1fZNZzKCk6Vowg1dSsqmijW6CMo4oWqOHnsTPpMbvtLRhAsmwwOQ1totggjO4UnTW40Ep7P/aYKucpHMOZaiMFnGZoNBDR3X9Q0Tz+9jhIco7dLVG5w5DSTpab/re26KrozqDrhNM5PBO452mWedknc77JenOGMe6lag/1p5YO1ZNSmUjjArspDVGeTRho+pXNJINaL0Yi8uvvfGa1mvibq27zthflgMu/98FRQEsm5TUWHxQHA6WVGVGPljLPSkS4p2lOOcANuAWfZku12+ktfUJoZ4NpeOjjnEXmSjoxV7qzqnGn2hZk4Me5D1CFKM6PQvVdPt2fyKa/E2F740gStAXJ6i6gtLiZgPMqBDDNhngBxN8NgDf4l2FintEZ6/ixtcJ6QTfj4iWJwRt0MUCvZiilnPcayXN+a6w6Dup3aaRiYvC87DEUcB5TVWnJGmDbWNC6FT5bNQNbwoEAv20wntN4wzWGzwKozxrm1BZSIyjtDHLVZ84EeNeXYyEU+A1SdoQxS3KyGC0EFQn5OSlnbSJYSUZNZM1G/4MQLvuxNCMx14MhLOw1ISmxezLc1QkzqJJW/m7gRApVX7x5BYEEqQ/brb/+zjw59u+7dsAGef72c9+ls9+9rP/yr/5+hn/TsWOlaI5HxM1a6JhuRHYyY8uKB/s05QptomJBiXtvE+ys0T3G3yT4OpkQ3zTSYt2BvVVgimmU4kzsSXpVWjjujR/vREkULETec9LTPqoOIEkkUPl/FSILlUtjH/vpc5lrfzs7YCz6PmaYDz62RE4i2ElffaNaHD7dbbpNcdpTK8SY17HZMO1OCpBoYwl35sTDWX2e7vOqZc9+ocXm64FW2ToyMskO+3RrUjUhkraDS8H5XD/BD81j7oEEhH1kUEpEOJmM6iDogKt8VMjB63u2nysvP/QRjTnY57E7C7XxKT7M1RiCXPROdCxxYwKzE5L8eoO/SvnEBS2SmnOJgD41qBjJy1ok4A5bAlLmVDm1pl0AOwtMFcc9efGj/TRvaIpU1JnNprqvo3oHUxFORCEfDhag9ewk0lPtxXDYpan6Nk5aqgJdZA1mbZUZ2M5XAGKjLrrXlDKY9uYukrp7c1p1zmujUjSRvT2g94Y91FWcrYe0AaNUYHdvGBeZXLg6oBRnjSyjFA0zshPZywqp3FBkUdWhI+8xnalgVh7KmdwQWEDtNZQO4P18jc3jKWsMgaVyEtXx7v0P/RA2PQO/IWmmQ1IJitRfoyfHFlqNR2TH0xRbURxssswbzBpIddMYkILYVmjdiLc7gEAOopkv9oWsgT71AtEx1KL95M9+d57fdR6SagQUuckwO5IWvlsQzApenUPuEfIRiSv/zrtDUd7/Sr9ne+kvf950Aa9mqPeuQsa1rdu4JqYOG0wacPy4R7lurcZytTrF0KwrFKqWhzDKG6J4pbMV5xe7DHMSpLIUjUJu8MFq7JHW+V4FCHIpMZLx27kNUZ5iiYlWfU3BM62SUjSmmyyIJusqOd92jrZrEcTSXeTNo5qNqR3MJUSWPJVe6IQSe10UGw0US6zA/39Y+Fc5BV66DF75yKXrCPhTVx9MuqfX41tq9+7x82bN1Hq63u/3rXxN88YmLcEp4h3FkT7nc5/A2ot9dB4UJLuzoXZXSckO8su1S/99NPXb9LfnwkZEIj68l9Xy6x010bS02o8UVaTXbnAlakseOPxRSp6AQ92MHlN/DGkHp7lG2Zw89HvJvnS5+RNjySFG8Y7qNUSliuYX8A4k3pw28DxDL/WlLevCHlvvJIJcXmNii2u8FQPd+SzdBGg6z7T+Z2rAOw9dZ/FF5+ntzsnGa1IJ9L5kN04pz0dMb99BRM5dj7+pnj2ZUw8KnCLTDz+xGJ2WvxSdwqDnRTxeQAkEgw1cvhN+lBU+BPP4jN7TL5rIYesC/hXzoj2fTeCGPKXT97zAvm9IBP2DG7RQ8UWs1sJ36CNaO8NyC81IGJHEhdEg0KyH153U+k8YQ1+JaWX4BXJ0VymEHqFP1Gke3Oa6ZBgZEDP6MZDdGJpZgNpyTuY0swHxFWE2WvIPjqH2hJWinAe6P3yz+KefYHm5sdhfoyaT6WlctziSxk/mx9Mmb9zDZO0pLtz+s/d5/avfBuTw3OSfkW0bjl/55oMAnIG20YUZc710YxVneG8YmewxHpD2sa4oCnbmNab/z97fxZsWZafdYK/Nez5jHf2KTw8IjIjI1PKVBZSIqmEpKYEdDEJgSgw6KbN9MAoU/MkM/EijCeB8YBh3UaBYUhCxdBYG2AmVN0qyYwChKAgSSmHyMiYB5/ufM+05zX0w9r3eGQpVRVSpmNqKlaYm4e733vOueesvdda///3/T4y3ZPonizqSHzPfrTEWMWmyVDliLM2JVaORDomUc95kz4pBHkRFo6427YGrnkA1+NkPeVosqCuctyDQxZXM+70mnhcbTdN6c0LXB3jqjhUw57S2Hv+fhBXOkG2v0AfVVtbsL/sUXuO6pVDks0CtXwl+Py1RiQpXkf4+e4TNb/SYHrk5dCzGNIvXa/DqbXrEOsl7Sf/W9ToHu7iV9FX7+F1jE9z7PgQoVKa9gR79zvJ/90/hAfn2MtkgA1JoixULcuzHd59cJtxXjIeb5AybM7jtCUblcRxx8XVHCUdWpttX77qEpR05EnLu+cH3Nm5YFHlnNcZsbJUJmLRxWjhyXRP2ccs6nDS916w2Iy5uX9KNERH61FFfTmh3gSg02zvMghcxyXeKtrjPfL9K2yV4vuwyU6mJdn+Fe1izPJkj+nhefjZRjXxwRXcOqT/skNUKbFbIl8oUKwgyhHLK/ovSfQPPLUp8eH4DY533nnnP/tzfvCq8LoJtLDBjrX+4m3yZ07CKVx62uNZSFPzcntDFyODNwpbpRgX1NTeSrrliL5OUHGIA05ma+L9JSK2uPUgWImCNUwVzRYXGvK8XbjRawWrDkZh4fdphjCG+N/9a/zd2+Fmvyjx955BrJYBt+scFFGI+UyATc36i7dRcU80qnFWBUvfUKK+LltvzuaI4z3ynSVR3tKsCqRyFNM1zkpWj/cRwuN6HYAd53PmR+dbNW8+X5HeOsdcFaGSEZuwsdhkoSoQGdK9K0Tn0EcB3OEbcN/+ceRnX8avgsof38PZBhKBPFCMyhO6+xPiOyuYpnDVw0dvoc9OQ8LfefJUyv7xx2rseyI4N5Ke/vEkeLuHm6O4Zi8MiFdbpvSrUAUSqdlWPK6Htwox8ts8d+/k8D7121ZKurfAdQF/CoTPKelpHu8iz+2wITWIW1kIG5rtYkdz0lf/LeLkOJATjaB841aA9BhFsy7IJiHmt69T0jLj5je9TnMxoytTzODDBoiTjijueXB6yChpmI3WxHHwZU8mazabgrpJ6a3mQK4wTtEZzUU5IotCD1krS5609JsJB2lDaTTLLuZhlXCUdVvrXz5Y/64X/GsYTHlt9ROedR8TlyNaE5HHLTs7V2FzpWyowEx7yCSqLcNcunx6eN9ovg6Wy8iQ3Lyk/NINiu+4RNDha/DP3SF39weaZdiQt5/47SRf/o+IR6ewOxkqcxbokY8fQNPhy8HRAiEHYt3Abjj5J2/9W/rDU1QdKj8um9M8f4TXCTRnWHlF/v/5O8FBM03gKnD0V+dztA4akbrM2J0uiJNg97RGU28KVJlvF/v5dEkU9wG8A+zqS5ZXU86XMy6qglla8+bZAbGy3Bmv2HQxUniKqMc6weNyzCxpUMKxaHIerGZ8+s47bMoRxmimMrQUo7xhOpAlo1FNsxhtqwBC+NAOFQ7bRrTvHRIVNf26wDQJWhvaTY7SFtV3ITr9Kyekd4dAJ+0DMyHZwPkC34HMn5b888lwH578f0uPD742qMHO00YgPOl+UNTbZRxifAdbFZahvB0wlO+3qNheY3uNigxR1qKSnn6TBaBPGwUwTBQCU6QJUB7vBDKxwdvbhkVD7O7g8xHi4hQ/nePzET5OAx0tjxFVGSoB17aW5QpaFwAXXeBywwa/9nRlRkwoZZsmplqNGO8saauUtsro2oTJzhW2D0K/Zhkuwr5LBhKhpKlT8lE4cQltiZOOdpPByQ5RURPvrIJocZ2j8jYs/qlB2hblBEK7YPlLCUlnbRRU/9UGMZaoooM0xl+BORkh8xY17ZFZEMK5pUI0DaCe9FAlT9oW3+DhHjlcmwZOfxsN6XJh49YtRkSTKtyspMPbQdyZhI2fbzRi2mHOnpQdZdIPUCOHRyKw6HGJzDr6iwndOg8aAO1Cz1NJ+ioImDASGXfoGy3upZewoxkum+HSCXL3W/CXPxlsj0bQ3N8PEblNHAJ96pR8tt4KCrsyC8mSwmP6iPVqzN6NU3TaYpqEcjmmNRGRssSuQ0qH0gatLckQ6SuFZz5b0PcRbRdTNhlahk2gsQrrJLO4ZT1Af5TwxMqTKRvEfkbR2TgsAsN6HQGlk0yiHuMkmz5irHs2XYIQwSd+dr5LkrZBI+MlruqIPtaD6fCtpH6wz9MCuqrdDrG0gYM/FsTzddicx6F9hZTw7FFY4J0Lp34zpA0lGrd7gDx5FDIwnAsb9dpt7bxYSX81Qe00CKVxkzmia4gu3g3zMS1w45vI5Xv4bBdMQ/zoV8NrOD7HXSnMakx6eMWkewIVU6UlK2qclRijMX2E8wIF28TFNGu2ojylLVqH1sBseIx0wDSH9k6/BTjVJlSAJEE/0Pnw2WvpOF3MAZgDY6foLqa065xkXAUNS2RICal/XZlhjKa9nJDuLrbiv2412sKqzBASlORNOMBcjbf252vNlby4ABNsfxhPfzF5Ki3BD8fXP5xzfO5zn+MrX/kKi8WCH/7hH6bvex48eMC9e/e+Yc/zwRf/PqT0uV4H5v2tFns6pKdtMrJbZ6GsBsiBnOetDAuEkURpOxC/FCLtwilQOfoqIFJNldItC4pnToK+QAX1s0z6ALCJBDLv8Fbg8xFm5xAN2MnONrMaQB4ehb5/FOPHwSZHf62oj+nOZ8QHV4i1wyymdFX6JN2tTqmrjDjuKNcjlqsJZZsy27/AW8vVxQ5VnbG3dxFK/17gnMIOAjFnZbCuFTX1JodVoILJrGP94CAog1VgGUjhEZFFDwslnYAsMAbMJkMXDersJIikihEuHyFO3qS7GqM7jYhDCqAqmi1aV01rOLsItLMhZ+FpjObRXgD1DL3368/S9Zpuk4eMBenwTj4BPKVtiPc1CjnuaC8nREUdLGJDmVrEhJAWDcxzVFLj+xJTpfQDbe06La1vY1IIHva9Ff7eMzQv/i7QGUJopB6RJoeYOMB1XKtZPjgIrpImwQwESX9dkXIB0tIvA9DFWkXdpOi4Jx5XmCZhsymQwrNu0iDmi3qStH3yMypDpHtmhxd0ZUpTZ0Ta0BsdwoMG3/8sq6jM0NsVnkJbIunQwuM9XPaKaWSxXoByZMLTWYkcpnnrBL2XNL1GK0dmO1Z1xl6dIrUZENqe6Ln7oR1UJ1TnM55agvs4eZ/gVqAPN6AnIGXYeDU15uazyM0CuVnh05zo9H5IrxqPcJOdUOaP43C9tg2+GU6N15WjTUbaifDvzgYK4OoSHyf4fEI8eQl3/hWcysA06EfvwHyOfe+C/mKCbSPiOwtyd4ark5AgqSxxXtNVGbZROCeItNme+q/nQdsmOC9IaWmqlKyomUzWaBWEu9kmsBik8IySFmMVizajNppZ0tANQKdIOiZxw6PNhEnckrQ9dZnBo326NiYu6mDPVI5Iekyzg+0irFHUyxHJbI1MKmTWsXm8T9cEfZVzEqkCTdC2EX0bE083NOezYO0ratRFFa6vLIiqr6mFT3N82PP/jY+f/Mmf5Md//Md5+PDh9u9++Id/mHfffZeXXnqJP/bH/hh/9+/+XdI0/bqf6wOvDuWXQn9bF3XwwJcemfXEehEgLgceee5wJiB54xc29G/ldOs8iLWahHQ2YHiHEr7r9LbkrnfWnL/+TIhlrdLgDb+3wL9wF+7fh9bhvuvTqJc/D4tLlNZ0N59HNht8nKFW56jzY+zeESJOkItLxPIKe+9FpJSIdx9gz1LO3rrNnhNcDX3SVVWwM14xmqyJoh6XSC7Pd7enNIAvv/Ii47Tmshxz1WScric8d/QYO3yNVpbVcsLl5RwpHUkcBELj54O3t75/QFvmZLPVNiq0vxhvKwUy6RF3x/iHK6LbJWpdU79zQLQ+hnEK5Qa5XuGlJ95ZofdKxGGOfatHzXvkvAofkhB0b423C7Ntns7ePjm6xK5ykJ54f0l7vLP11kdZi1kVJM9cIIzDS0f63DnNmwchuQ8CF7/MiIa0RrPJ0G0FR7OQWdB3AT8aK9SsITELmrM5D199jv1bxySTkq5JqC+mTF54gDyQeGOQi7fw+58iyW6jZEJ59kvkj97DvuNZv3mb4+NDTtZTtHTMspK798LJMZmtg410NeLha/eY716SFhV70rE82yFZFywuZ5wt5uxPlnzh8S16p0jinv3bx1wdh2yJ0ahksnfF6N4j1m/eoqkzrJO8d7HPM7tnpElL0yY0fcwLu6c8Ws55cz3hcR0hgVg6ZrHBIVj0CusgVeGEGSvHwzolEp5pZHhQZqQqtARGcctHbt9nNHDhhbLkt87o3hihZ5tQjp+UT2UuAPirkJ7pN4L+/iQkaEJI4ZtKxOPHqDTD6wiXj0BHiM+9AvspFCP0y7+Cu3MX+ZU3grhvLLCrDH2vw69b+lVBenAVFpMHC+SjRQgZu7EfHjObk6iC5pnfhfQGXz0eHAUtarcHVohFQfvObsiRiAwyMrTD9dG1Qek/Gm8Q0ocFGRjvLHn5yx9jb7pgNCpJ0pbHj46Ikw5rFL3RjIqSVJtBzKnZnyy4WE1Jh3ZR74K1czqEO3kE86Qm1iH34fFqym5e8syNx0EcezEluX1O+dZNVudz6ioLpElVU19M6dY5ari+88mGrsoo1yOUdJTns60rpTrZwXQRed6gx3WonHaDeDiG8YsPntp8+HD85sZf+kt/ib/6V/8q3nuklCilMCbMowcPHmCt5R/9o3/EgwcP+MVf/EW0/voOdx/4u4tPPsaeBmWxXeRBVR6Z0JPPLPZRjMobxOBvd+dQPd5DZ4NXv42wTUK1GKO0YSQc8c6a6nxG0kWMdtZBODQLzH1cyCZX42M4PMDrCPX6l2GUY2/fo5/fAp1gin3ih18CZzFHzyCbMpT9+w6WFev/foE1Ka5/EaRnun/J5Xs36NokxLpaxWIz3np3O6fJdE9tIhqjMU4GwdXVLs0g+DtIBe+cHJFGPUo6WhOR6J7eaJKoZy5XSGk5/vJzoRyXtuy8cJ/qZIf6bIZe5+S3zoLCvdd4o1DvrhFTgV97ROQovvWc5uWdsHOfh7Aeu0oD4XAj6F8WyERgTtOtFsL74Lf2NvDu8296OoI/u8q3JxSsJNlfbFHHKm0Hdv9QOnUSv1S0y2KLOxUyVFpk0qMnZWgXaeB4EcSLowTyFHd4E/n6m3SXEx6/+QxSOtaXM0wXsffCeyR3F8MM1ghncdNnKIrnt69T2Ib6lzSrhx9hvRzzxsUB87QOIrwhSlUlPc3lZJuqtnt0hhCetsy4utzBGEVrIjZtyrJNefVql1wbiqhDKcPDt++QZzXj+YJ0UqKzhvKdYaOsDV0Xs5OXGKOJtGE6WVGeHXCynjLLKr5jvOILp0cc1wm1jXAeEunZjS2R9MTSUWjDqo84StutBXAWG4wXXLQJV21C3Ud8Qr490OcsKu6D0n8PcD3qseFpDTGN8GeBkhjfWcLhBDYVIopxO/sIKRAPHyLGodXT/4oh+mgQ+qE1otwg331nG7PtSz+4ixRiKkifvYTbe3AlIU3xu/vUH/l20ld+ie7Wx/B7n6JcvYxQKd4bZFcFCuDeIXK9Rm6GlM7DRaBDthG2jZgdXLC6CCX4NGsYH52H+1Ha0veB3viRZ9/ZRj63TUKe1SwW0wD7cSK0eqQLrUMveevskPEQw9wNLZrnJgsebCYI4blVrKlNRKwtiQ62zdeugoMginpUZOhfu8XohbA4u9NQnbo43+HWvfukO6swVy+mOKnC/Owi4kTjnCTLK+KiYXGyy97dRyFN8GROcSvcC9wl+E4FTchTHh+e/D/4+Jf/8l/yEz/xExRFwV//63+dP/En/gS/9/f+Xn75l38ZgO/93u/lZ37mZ/hzf+7P8W/+zb/h7/ydv8Of//N//ut6zg+8+FcvHxJNN9usdW8C2OUarhP+HFTUnoHrLvwWN2rqMaaLUDqUbU2dosqedLZBpx2uTLcCORwgQe21QIKPE9xoigR8mmFHc9z4Jnr8EezlrwRfMIRIzssTxOPHUDvsVcz6cgYQWOgioIbLTcGyHNEN9p7OaNzAjbZOcFlnVDYs/K2TnNQx09hs7YaLLsF6ia0CulUNpLZM98HnW46YyaB4t1ZRVjnjzXkox2UtKu4p7x8ElfnuKuQcSI+vYZtzLsNNzw/3bH/VIxMX6GftkGi4W1K/FkiLalSH3ntkQl63DIFLT2VIv20peBs+d1snwcM/qgPPoQ2OCN+HGOSoqINPPzKYTcbk1kkQ3m0y1KgOp5JEBB5DMQoCzq7FrUIvPo47oqRDa4vSNvRtJYF0mMZgDNnsWyhXLyPjGVl6C9FVoN22hPv+YYyirVPaOqVcjxDCM50vA1RIBLR012vON2M6pzF2sOc5ya2kwSFYVwX788uwWagynNGoKKceLF19F23Jb2pgL/R9xHIgwlVdzKZNME6SSE9poHOCaWTpvSAWwU/eDSXj9cD/l8CyV3gglp6xDuXn47N9psWG2c6CeL7GNglRvYFIkOwtn85cgFDe31WIWAdhWV3BsNDLyzPIs1DJ8Q4/nhJ9R4+b3AHTI0yPn+8iHj/G12HOqLRHHVnQUQjlGY8HoJWB1iDcCfHkNZqXvgtmHyPNbiFFeG+a9gRRX+EfXIbrVQqIQ2CXW0eIpMdtMrrViPzGReDxdyHwyxlNtSlI0naLfLZWESfBydB1MZsqp8hq8tE6aHuahElW0ZmIpo/QUrNs0kB5TGsKHRbZ6997pxAC1kMroRs2nZflmOiqx3lBnLaMdCBZJmlL30VMpqstw0HnDcmkZHW6S9cmJHHPaL7c6quclUz3L3FWoSKD1I7ujREqa4egsT5stD4cv2XG3/ybfxMhBH/v7/09/ugf/aNf82v+5J/8k4xGI37gB36Af/AP/sF/vsW/vpoEZntktv14oS2uC6IvlXXYOtqS7bxRIchniGRtNzlSm9ADV+GG7KwinoRWgKlSVPJkRgpN4PJ3XRCxOYs5uoswHS7KIRqTxLuUUmOLPYRpEX2FqErsscIsJrSLMXWVhZv5cJEBrKuCsg09k1g3VF0yZOlIeqe46hJ6J+mdoHGSk0YQK0l0Hb5hwo13YxTGCQodFn9J6PsZmyOFY5RXGKuC+Gs1Qiq3ZXp3ZUYW99skO98qfC3Dn6WHuodpgjAWaoNbDnnsnYCBqMY4wTYJQgdmvhvS8a49Y+8PWPpGji11z0pwAtvH4e/0IPSUFrN6wql2vUbnTXAESA+bjPT2Be3xHNdpdGG3mgfSBJ8XeB2FjHUThFZp1hBlbTjVDot/KBGrAI7JC6TQyNU7eJ3QzDWyXSHzQE5TyqGF25LYnBeYLqKqM1ZlKJtmeR1u9ITNYtPHLNuU8n39+UQ6sqhHEuZUFPc4J7FG4ZwkshLT62HzEGGdJEuaYBUcYlxrE5Eqs60utU4SSc918J4YWtvXww6v95oLIPFsTOinqyhsEByCi3IU2AN24Pg7gb2KkUUXgrie5phOQOuwSF8twibgOqdiPAFdgwiunPbOZ9BX91Hnj0OV7vr7CCJR9PB43oEY5rALG186EKoN/f6PPEuc7G0X/vDeBTGhW8aoaYgx3j5ElaAmoUVmh2CkbL7CVCmmi+irQPiDFiUdUWQCCCzqA7hM+u3fZ0VNnNfbaoCvBL1VzLKKk3U4jMTDgaDsA+TJ+oEEiad3EutDVVEIz7JJScsRcdQjhccuQnJqlIYqQrG7CBCrAaAWT0vi5ThkUgzBVFFRD8LlhHRnRXUxJcoaEJ7uZCe4qqIFJMP99SmPD9X+H3z8u3/37zg6Ovp1F/7r8f3f//3cvHmTl19++et+zg88BeafeGtLRZOJgXFIw2JISJNFQ3M6D2IjbTGLApV2+CFitS4zdu8cB8hN3BNNKkTS49uIfpXTlxnjF6+CEj+TMMqDCvj+u/DwBFVXtL/je8i+9P/FTG8CUJVvQjzDxTPk6j2ikzdhvaZ5uMfZO7c4v9qht4pIWaRwKOlY9hGXZcE0q4mUwThFbSIumjRAVLxg0Wki6WmsZNMLcg3VNZxIQDaEslzfn1sngCGlrY9oneJxOeal/ROKrKLIK7oqZXbvEb7XmDplcuck3IispD+b0C+L4IvfXYIz2NWw4MwMQoNILP3JlPjOMoSndD3IBJW2QfWuHNW7R4y/+V0wgyVs9HRO/jLrUPMebwLt0ZzNSJ85w5Ux7dkMqR2b0zk67kkmJfHuMjDHdVCEq6RHzAVq0QatwLMHuFfPoXTBHhonIXil3CCPBOPkXbrPfpR0stmebkRksKsUPTe42Q7NC9+F7i5wk2eRl6+iv/A/4NMCMdiodBSqMnYgTCrp0HEPdejvWiepypxsXNJVKevNiOPNhN5LTpugky+0Yy9tqPuIeV4yycttiltaNNu8gKSoOXl4FOAvVvHcR9/kK6+8iOod02KzxfbGyoYNRZew7gMhUAm46hQHiSGRDi0dkfDcr1KmkaV1gqtOk0pPph3WC87bCC0zZnHHqsnRZ3vIVx07L75DfzXGX4iQEPm0RtdD5mC9wp80iKmE48tgq51O8XGCSBPwDlGVqPUJ+tUvwHKwD/cS+VyGvNmC8yCjsGkoRnBygXmYob91FIBXhYT9Pfqbz5O8/j/R3v40cvfbgsbj6rOgMrSOkdMubCh7j6uHRE0Xki6j3RVpr7n4yrPsfuydsJFejqhXI0aTYN/1XpAXJfnOEttFqLinOLqg2F2wPNkN9L2hHRBF/fZgcfvomEhZjtdTzqucSdJuF/3KKhqriAYSZKG7YVOnqKymN6F077zg4ivPkoyqMHfjntE3PcQ3AldHuCZG5w373/5l3CaherjPydu3ufXSW8ioRkhPuyw4fXRIEvcU4w2j/UtU3mBWBVpu8N/23NObD8P4sOz/wcfV1RWf+tSnPtDX3rp1i1/91V/9up/zAy/+csdhT1WwYmlL9epRIPINoz8PKVbRuEQXDbaNWd4/QmpDnLXsPfuQ7PYZ7ckcU6ehJDkukbHZPk7zYJd4Z4266/C7B4imxt+4CVLh0gy5fkR383m8Tral3f6L/z3dnW9HleeIL7xG/eYBj15/lsvVlE2bEivD/uxqa7syVnFn74yr9YS6zxknNasu2K6cF9RG8eyo4qIdqgFasm4E74+TXPWKSXQdxAG9ExymhleW4+uOBbuJ4eWzQwptmMQNR026PfkrbXBWIaokeHx318TPrqm+dIAtr3v4Bll0iLEMboVSEj+3ClbKofTPm57s08stErh47hGLX3mO4ugCVTSYk+yp+PyFtgEj2kaYdVg8z/7DS6ioJxvEc/l8tfX9b965QbZ/FWyBLvjQfemIviXCj3YC1GXiYTaCKEZuVrQvfAq1uUI/eAvRXrL3Pa/j12BXwQ1hq4SLN29zc/IF5PSc/N//U6rv/O8gGuPjUHXQr3yebpNRXU45Pttn0cV8+tk3UUP5NE5bjiaP2O81cdaS3zlh/fbNEM/bJSy6mFxZbmQtiz7iotUs+4KPTjxzCC2jLmbv5gnlYkJ5Mcc6ya1799nbPydNWq6WUx69exsIcJh1k3Fvfs67i13yqGeWVZzWOY0L1jIpwvrnYLshKLRlYyS5elIOOGkke4nYgn8aG2xlrdFsmoxplVI/3iO7cY7eKRHF07sRu1OB1CXkGeJODEkaEuXmc/pnPkr08meDR9+ASNbIyQx35x72pRlqs0D86iuwKLGnEerA0Hzv7yN594uhKpBq9M0KN7uLfE5C04AxCNNhPv7fkUYzjNnQPfo52PsEAHZ6C/VffQL18F38yQYcyKJBFy4k37mQ4SGVQyhHfHhFvL8gur43tREyMmQHV+9bhMMme/Noj2K6pl6NuDrd5eDuQ7oyo2lSNm3Kg+MjpPBE0kIEedRR9TF2KPE3VmIE9E1KIh2xdHRWcdXGbPqI87rgaLTiaPciOAmEZ/TMCatfuUt6cIWebdBFR/naLfRQLU12VqTHDcsHBySjirio8Upx89kHNOtwLcQ7K8y6IH3+FDHW+M+9Cd/11KbEh+M3OHZ3d3nrrbf+d7/Oe8/bb7/N3t7e1/2cH3ht8KXf+saFdCEkYm+FK1P65Qg9KcmyLpzme8X6rVsh1GVUERU1erCkXSu9nZVbHr6MTLC2TSqQ4K8conkbJhnVd4WEI2Ha4OGNxojVO/jFKzRzjTY9ydv/BnV8H1dLmsUI5yTjvGQ2WocTXR3CV5Rw6MiGMryJKPuYdZswi1vKflCiD8EqQFBTC8dCPimfOw/xtf9aeCIFay84afTw/dA5aFzExCj2k45c91yWY/bNeegt9gq1zmnOp8Tjimi6QcclycFA8bISmVnsIg9K3ShE+Pb3C6Lb4UYu6Wne2yO7cQGtx5cK30aM7x6H0j+ghkyEb/TozmbhZ63CZzm+e8xEWUydYpoEoRyLx/sU0zXp3pJEBToiTiJkH0qXhQQdUKNmdvdJ4lxdwdmKpPmP+NE42L8OdqAsEb7FL8Sg2A75D66OUH2Hn85Ri7dR1TIgY6Wi/W3fTXz1C0zqE+5pwzgvsVZtRZjZdBPm8mA59L1GaUsUBTpfqiyLLg64XeGZxZZUWVJtiJUhSVqK6Zp2k5MW1bZqUC9HqLgnzRqyNglCP+Fp2gTjFOOi5BkEAh+0I14wiywXTlDZ0AIojQrlXyc5bTWNFRw3GiUgkp5xFDYIrRX0TuCI0CJjjxA0c7UIwjWVBq6EKp5ek1ceDRuLgcaHkDCfgnNE770WyvpCwG6GO7xJ++L/Gf34s8Hr72zImL9xiDoMVZ/o7G3Eu++FMpsJmxuvI3yaB55F2yBMh23P8d7gncGPb6JOPgdCo9ZnqIfv4t6psOsCPasQdwr84w1c9NhVOHzIQZHfncwx19wICJbPNsZbRZSFsrtpYkSUks1XOKOJ0pbEaKrLKVHWkiQt4zT8/GWbMknrcO/pYurhfpJpQ6HD752TQ6XHhwOD8LRO8rDKKfuIabEhisNnJpI+lPydwFyO6TfZ9lRtNjnVxZS6ysjyGt1rTJNge41O26A96TXt+Yz04Cpcd8bSnUyfWspj+MDEN/7k/19wJeE7v/M7+Wf/7J/xj//xP+aP//E//ut+3U//9E9zfn7OH/7Df/jrfs4PnupXR+9b/EP/SyQOSYPqdWDu6y6ozofQHiDsmAdKn9nkAyzD4F1IyLu+ABEeOQ1+fMyg+B0NJx2V4q+bVM7gdYJwBrt6HaUj1OlDuFrgO41p4pC3Hhl01IeozqEvBlA3KWWb0g8CrlUXc6PY4AgWmVTZ4OmVoaQqhGAWB41D6wTGgZbD6ex9c3Fjwt/54dS27AQgiaUmG3CgXRvKx0J42nWOtxKdBjiSWwdYztY/PwCO6D0kAhG/D9rjhyf3El8OpX3pB3Ruh0gBA757OopeN5yMIJRS/bCRU3YIa5Ee00WYLsIbico6+lWOGm6kInFBzDVsqkTXYA5ug5So04eIsxV+vUBMS0iTgHzVCopQKbnWF4yPBgTsqkQUI2RXoxbn0LVBRT6aIwpJvLtidP2+16G/a4f2AcKHIJrC4hYJKum2Fs9C9yy6cGqLtqLOoPQPds5Q1araeJvRLpVldT4nj/sAqxIePYjH9HV+ex4WibpJqZsYLR1tr4OvfxjWh7Jp4wQXrUCJsNCnKqyJQaMSqk6dg1UvGWvNJAr2084ONMQh8VKun6LCazwK/Xk3/Oq7cPqvq5BBMQ09cLIcu3OEjGfIpkStLhGrBWgVWgMQQphWi+B6iT0UGqYTfJzC6irMmSRFdA3YGimPQnqobZDNBrU4C8yAq00Qvg6LkFAaV0aYxZDaGHfExZNMeyFdELL2eojetVsGxHWQVwj6EpihDZSkLcurKbt5EzaTXUPXx8TaIIXDeUlrou29JREeLYP2xAqx1QhF0tFaRe/EoCESbOo8aAm8wNWhzO9sAJ+ZOiXbvwrVvXXQNMVxR5I2W/BY+JkGjZJVmCYOeqw2OKlc97SQTx+O38z4kR/5Ef7pP/2n/IW/8BfI85w/+Af/4Ff9u3OOn/zJn+RHfuRHEELwZ//sn/26n/ODn/yvkbdWhWQ+J0IMbdYT3Vjh1oPiX3lEYpk+95DFG7cDkW1wCJhN8HZH4wrXRnTrnOh9LHKEQGgPsQ+Wrywne+Vf4SY7mMkePpkgy3PMrW9H6BHi/Fex+RTdvRVEcc0IazRZXtN3EU0dAD6z+YJo8PU+vtrFDDd3JRydkyzalGUXI4BJ3G1LqZs+ousjbmUdqbKs+mirsl73kqaXeK5vwrDuHUoIZrHY3pjPW01tc/bSivVmzMhvSNKWelMwmi/RedgY9ReToJafVAgfwpOSo4sQg2scIhPoSRXifWuJXWdE8xVulSAnbeD+04e2QKJDpG/9dCJ9ZdKHaoWVqLSjPt7FGkVc1CQ7K3CC0XwVFNKrEVl2iW0SbN0j0y6kKHbhlCe6Fv34y9Tf/gcQO9+ES/8l8cNH+LXALQUiHlIXj2YQgZwuiGzIEUi/rcO9Z7HHEYoTuP0CXkpkVcJyRfTldyETgaYYGYT0zG+fsHq0z2Y9orqcko5LRGERhyNUVqLXNW2d0ZqIUdwxMxFlH6GlYxz1pMqwU6wp8hKtDX0TE6UtfZ1impi4aLYnHmcl1smtVSzNGvLxBm8lbZOE6lOXMI06/sN5QaJCtam1gkyFOWgdtJaQIKfDvCqNYN3DZNgLShHmX+uClkAKT6L7gKMeB4Fb+2jn6Z30pMTtHODjsCjLxw9gvYayx1cCMZdht3JdQTv+X9DvvgYnq+DymErE1QX+4Sb09W9Mtglu/vCQ+pv/G/TyAXp5FdDNO4eo1SWojCQOIUDi7HXs9CbRK7+Cf9xCDP5bXkB/7s0gfD1eYBYzVg8OKfYWpLfPti8/vnEVHEgXo+CN312iR+F9a85n6DRsnFyvKM/nVJucrKhJior25CBAorwgbhLWZcE4L2naBGcls6ykNXqIdQ5vw1kbWmWRdMQDKOhaPJwqz61iw6LOmXVrbK+pH+8FbsPVGNfrINz76DpslusFelyTHJfIuKcvM7oyIxk+dxhcV8pRPtoLAsZRvf35ntbwfOMFf/8lp/p993d/Nz/6oz/KX/trf40f+IEfYDwe03Vh3n3mM5/htddeY71e473nT//pP833fd/3fd3P+cFT/SKzRUYCqElDdzbd2syah3sUnz7GPo6pHu7jjaI4uATpt2Kv7OZ5QE72Gi/DyUSPqpBbvzvFvSlwdYIatYgxcHGFO7iBzSfYyR2K/dCkcs1D3Op1dHlO9OAN/Hvn9A+n1GdzsumG9cUMITxZXrN79xGrR/tcXcxZVwWJ7rkzu2JVjqi7mIPRmtPNmMmoJVaWWBk6q1m3CdYLWhdOW7VVjKKeedJineSNdU4iPcbDRSuIJBRaYr1n03t677loYSeRpFJwVhfE2tJ0MeO85OjuQ2RkaC4ncDkhHlf0izGJVeghnKO7mNItC9K9JfHHW/Dg1hGuHkqSd6pg5zMCvya8Zzf34OISaoecPJ0yma1juqsbIcjp6AI9qmlO50MsqaM+3WXy7CP6ZeCTR/tBbKZGNXLch6S3ix50RH/0LP7Ox4ku3sY4gz59L1R9Mk93NiLeX8A8hbIcvOQhHrZfpMg7N1HNK/izHrcQRJ//XwIfPo5gPkO4q2HuOlTRkE42nL11Gx33THevBseJxF6mKEJv2DYJeVGillOOyxFHxQbjJI3RIbAn6pmM18RpizGay8s5u3uX9F3EajXm9ZMb3JgsGdUZcdRv4TBdG5C9TZnz+nt3maQV1kkEnssu4WZuWfeSdS+oDSRKcNmGxX4cwaqHs8Zh/XCaI9jGEgVawDT2tDakAgrhiaKeyweHyMf7pEXF+Mb5U5kLAKzXyOUgKJQiYHqjGKYa4gSvNaKuoNwQvfca/tmXoKqf2M2mE/yDRWj5tRL3lQZ1M4h+xXpJ/q//CRQj7J3ncPkEF2eY+W3it/8V9fRV3PQZxL3fTZ7fpf3mc/SdU+xohvr5/4CXApFYkBAdrJgN7Z3uZE67GJPM1vhVoN2ppGd86zSclDc5rg9upOZiRjtULXdfepu5lXRXY6qLKQeHp/RtzPpyymo9Jk1alLIo5Wh6ybpJ2S02rK52WfcRkXTspoH6d60DeHuT8eJ0E2KEreJhOUIJj77YxzrJ3v4F0aqkW+dI5UJi6tsZeIFtY2yVICPL6et3aZoU5wXFuuLwY6GHLBYj2k1G26T0dcr4xjnZt2+e3nz4cPymxk/8xE9w7949fvzHf5zT0yeMls9+9rMATKdTfuzHfowf/dEf/YY83wfXg0kf1LIyROzqoyZEz0YGEVtU3mLuBzGWEJ5ovg6e/2uef9pCHgAvDGx/oSyy6MJ2eL1B3o7hzj3MaIaPM2S1wkyPcOObkMwpVy8T5beJ4z2adIFa/ScgKIBV0SAXhqio6coUIWO8kywfHhClLXkRbHeLKqdpEzqjsV6ivOOFg2PqNg03Y+F5uJ6yGMpiO/GT0rlxktYqlr0m0y70WofNwXkTenfOg/FBtd17z6ILoSzLfkRpNNO4Y7fKmc6WKG1QsSEu6qCI76IASooMdpVj63gQJVloOuTE49bx1g7Yv1eg52VgqmvCqf/ROSQyOCZ692s/x2/AcEaTHlxh65iLL7xAOilRcY/rNZsHB6Es+ngQpEhH8+4B8cFVEAhWCeLcoe8YmnufRux/K94biGb45hh38S46hv5hWPjlkQo+8KpEmFWQXaaS9DsmiOMHuGOLGSA93WsF+Y0LZNaCXCBf3A39YVmhXENsVuxxbfPyQ2lXBuqig/5kQjPYQ6857MtrS6iyjOLgEMlH1bayNBqFeOdyU7CuCiIZGO+Xg90rj1tWTc5OEQiSAM8ePeJL7z3LqktorKI0motW0dpQRRIC7pds2f6LznPSdiRSoYbYT+s9jfVoIcgiyFTw+5dGc381JVaG+XyxTaKzTynnAcA+AJxETjvEzRl+NA6IbR8HYuOqxLz4SdTiHLEZNgnzOUIP/x/HoVUlBEiHK2UI65rOEU0NV+fYNz1q5wuowwn21rO0z347/d6nyPO7AFSPfp76/PMkD95ALK+Q8j5MLYwTqDrsaSjZy6xDFM0WPa3yBjWuEZHDrsJnLYyCwXYsIoNa96TTNTrtQtKkEyA8yaTk+O07pFnQCiRxx9lyxs44/FyJ7lG547IsyHSPlsFqWkQdkgjjJYmyfHqn5bILtMFIOpTwLPoIKTxZ2gS7HpDfuACCLVoIF/DUw/118Xj/ScXJSaKo5+EXXyQvSpJRhXeScbEAQhywe+8pWz/5UO3/mxl/5s/8GX7oh36IX/7lX+ZLX/oSy+WSoih48cUX+e7v/m7y/BuHZf4Nlf1tFS4OmfT4jXji24Zg6VsGsIfOG6L5esvUBoaglh4z5E7LooNNjlBuUC4Z0Ip+/9nQ0+/LAPMpDtDjjyBVSrd6Be9MkNNLDXLo2zofeNhFjW3iEBoUm6/qfaVD/6xpE6KoJ7WKWBti3RNH/SCMiYYeXRjXfV4zWLOus9i9Z6vS7YYb9vWw3lNbS6YUnQuWQBG6mVx2EUp4UhVzdTVjMl2Rp902wTCebLbgpGshpEiCx9ivPUhCNvvgsTfnwecrYw8qXGiBjja8Lcun4/O/Ztm7XtNWGUrb7Sn62opnuiigRIWk34TYUtvGSGXRuxvY38ONblEkh9vH7YXGjvdRz95CXp2Gcv94hpvMkFqHheTkApYNzFs4vsSuRluMsYp7bB1j6xjvJentDf6kwfcSb4JmRaVdsKAOfVCdNYEoKcMcdlZibUjkq41m4wXxUJ7N45ZIWfprnvrQXzVC0w8sgFlWUbYpxoX3YdNknFcFs6zchsVEcU+iDELE9E7iPDR2cLmJUN70HjobKkuNc3RY5DAPHR6F2OrhGhu+vvcCrKSTkrofwoGyFh33SP10NoLAFtQkJGBNKO+PJ080AN7hpcJLGT5DqQJ+V+vAApAyJPtdrsGBGrWg47Dw19W2QmCXEUqvkMVFyKV3TxYw4QzR2Vvhe7o+JHdmetAgDBeocqHyZIA2RIpf47ZxYWOAE/gh6MmbIFJVaYvyIUDKVOl2vnkv6I0mHwKeIGz2vBfEUYdSkq4q8Agy3ZNiBs6/wnhJ50JbaBx1WwtgpMIGDqAxmqZNwmm/aJD5ENzUadrTechKcRKcDAJFL5DSIb1gsx6FbIqoR/fdNopcD5t0O7h0Phy/9UYURXzP93wP3/M93/NUn+cDL/62junWeTitxx1mk5E8G3aibh3hughnVVD2T0pk0SMWdhtLKbMu8L8HIpyI3BOsqxy40y6AemS9QK0uMDs3INvf9vX6eE4S72KHEBE72UOdPtqK3qL5mvLVuyTXCXsqCOg2j/dRsWEUr7Y2Oz2Q3IpRydXVjDTpQlRqkxEP/V0IYBchPNJJ6qENUGhH64IYq7JBcBUrQe88HdB4i3aC2hscihSJQ9DZcPP2XnC+nFEU1dZ2Vp3uMP34O9hV/qTkuBOskN4JzGWBGtfIHQ9xhDAWVQ83PwnEw0aoHfQTraM/nz2dJDcn6Vc5tosCFMcL+jrcpKKkw4gIFZmv2gzUx7uotCU6vIJvuku/d5NtUs0wvGuw4xs0n5iTLf85NB6fF9jJXlgMdASvXtI8OCA+vcCbAlsnIBwq6cnuLukfjuhXRbgxPr5i/cpdkMFi2ZdZyLtPegSWZlUw21uEDaxjuxA4J2mNZtFFWC8otGXiBJGypHHHej0iiXuktNRNSjrgXBPdkyQtb57eYJZWSOFZNkN/NwqbTO8FfRdxNF1s1fy9jwKwR7C1+qVKsDE+LPzOEaNw3tPj6DGMREIsBR7PpocmETSdYi8x242pdZI8a8LPfK2reQrDG4ma92GxHcSX9uBW6P8vLiCK0Zcn4Cw+TkIwj9bQtGGRHjvc/hHyZAVGIPZ1aN0M7Ss6gbrR4RYi3CeWV4h2gbx6m9LUyHQfFxeIZuhjSxkOFFrhz1p8L4PuI3OQSfzS4eoEmXYhDGdYCPVBGYSyToIJGifXSnTRhGjyQejaLMfhYOHCYlvMVngniaIA22nqlDRrsEZxstgJrAZpA2fCSdaD9a/sI0qjqK2is5IkMkjhWfSaSWRYtinnyxmH/XHAnoshkfSmwD6I6MpQZVVDEmrfxdtQoscXe+xOljgr6YfNymY9YjxZo7R9aomfXzUvPjz5/6bG8fExP/uzP8trr73GYrHg4OCAT3ziE/yBP/AHGI/H37Dn+cCLvyoaiiz0IYS22DKl+soNonFFtL8i2Vtu4129UTRvHxDvLdBlhrMKOe4pXzlCZR1COJp39tDjEjFPQp+2Mfjn7hA9eov+9kdoPvZ96NOXSfNnAWi7C9SDX4IXn0fJMJldlMOjc3ytELFFFo7d//Y+9/9fL4U+560zzt94hiRr8IhwWu4idNyzLkNC2+6NU9IqZ70paPoY4xSnTUqq7JboBzCJ2yDQ6SN6L3htFZNrtn7/2gqOa+idIxUKIQQVDY2XuD7ltHfcyFLWvUbLhDuzS6xRXDw6QEeG6eE53dksvIfSE03LUKbMO3wvMXUyIJBr0H2oAuQd/dkEsXySsSAOc/zjCrcJMctPY/RtHE4Racf4xjnNYhRaQtqihjjjuKjJd5fItKM9nbM52WW6s0QdtHigv/VtyMWbVJsHqL1vJYl3KUYvUglN/Nr/iJ/v0t4L0Au9PkXdf5P+85b65FbwVS/GRHkT5t+oDvbGgxlRskKvN2FO7CqSnSUnX3mO0/M9nrnzAGcV9SanaxKKcYkpU/pVEehteUNT5lR1Rmc1qXL0XnAjD6f2Ny72uTe/YJRVXC0nGKeYjdZ0XUwcdUipKOucjx49ZFPltCZimtbMsopNnQeE8GxJWwfexDUaejdpeXEieFRr1r1gHMFx7bmZCyIhWfSKde9Z9oZECAqV4PHU1lFoySwNp//dxDKNDKkyrLqEcj0iTtuwOORPr8ybfHQZXBlaDQuvQ1TrINTduUH03qu40RSbTxCmI37lP1F9318g/8X/J/YBqHSJeOsU4gFMtb4+jXu8A28FIlbIF+e4yQybT9BX7xG/9EMA1M1Doou38PkYXn8Te64BjZAGb+Kw8I8d/oXn6H7+Eu8l0XSDiJ9siGyVYt4sSO+dYoeY8Xh3SXu8u92A92VGu8lpq4xsXJLtrbBGY7qIZFSRjEv0umA0W9GWGU2dbuOcV13KpospjWYSdU+Cf7xgEvXB8jcAf+7kNcd1yoKYoskol2OmjQZth9CXlvzWGf3rd+ibJNhLq4z57iWmj9isRwEs1KQ4F8ime888Rg8HnL6Nsac7FDzd8SHh7zc21us1P/qjP8rf/bt/FzcgPr33iKHVlyQJP/ZjP8aP/diPfd2hPvAbWfxnDeZ8OH0+k1L+4iycqKzC1RG2jjFlFtT7WRsiOO+dkvrLIPKrJSrpifcX27JV9AkPSRGUwTLs6JGS+J0voxYnmN1nUDLBupYomiJf/L9tX4/UI+LHr4VFcGwhEZCndF+KOfrka+GNs4rRfBV2zDpYd5xTxHnNTfmkDLoeWOxKhv78x3cueLieoIRnErfM85KLaoQSofxbtjG7iadzgs4JcuWxPvRoIykx1vLQLRkCTdkQTobnbUZjNWsj2V3OMU6xM1lSjEukcnTLgnhchczuosHVMWaRB6V8lZAkS8SugtJgL5NAr9tf4ao4ROU+Mw0iyToC5dB3no7aP52uKc+G1LAB2azi0J5wvUJGls3ZPDD5BzvV+Mb5lllgdw6JHn+Ofvc5SPe/+rHzu7T7z5FcnuLTCdHZG+h3XoNNxeWrL3J2us+6flKy3J9dsX/7mMkn34G6xr70TQCo00dwdkF7OSWOO44OToObIu6JB/+0intOXnuW+dE5Ku7ZDJHU1smhvA6LTpPKnFncspOGk+XFarq1fz242CeP20BnQ5BFHYvNGCn8Ntc9j1vGeUmWNqGPm1c0bcyuF4ySlk2bUBvNQRpS/E4byUEaLKZKeGIl0ULgvMbjSRQsOsdYK9ywVmYKHlYK6xKqJFRbzpcz8qIiGzI5ntqINf6sReTAzYNQqt/ZR1Zr1OIcN9tFnT+GHYtLC5CS5PX/KajVd3soQTyzg38rJPfJowiyDOYx4uoKX1v8RY8YG3yc0u8/T37z9+C8oW1PcOV9RJyhH72NW4htG0LelLjjkCVCFmF/6RHJvSbkY6wS6ncPiedr9O6G6E6FWwmatw+IphvUqMa1EXpUsXlwEObmfAVOkk5KpLKhfO4kxaQMLbAmQQ84XhWF0J72/IBYGWJp0DLYOY+bjMlQBYqEpzSa0oTsEQlctBGpCvNHiGAp7M5npHfOQHqat3bwg4//OuFPacPyakbXR3QDtCzShjyridOWZjHi5PiIvb1z8tl6mxPw4fitMdq25ff9vt/Hv/23/xbvPffu3eOTn/wko9GI5XLJ5z//ee7fv89f/st/mddee42f+Zmf+bqf84P7/FdJQPkqDzoIvoSyQTl7MUFGJpxWAbMq6OoEt45DWfY67zxvwAlEbEj2F4GGYyzUPb5UiFUJuyFlS3Qtdn6P3pZE6tfuUZ3ZIDcrKJKBuZvj04z4Y6f4iz6w8o1i9OxjqodhgfFWhQs06YjzBmflloBlrKK31/GpZgvfcAiKpOGiGm2BLEp6xpEL4j4nWPaSWMIkCv392lrmvmBNg0CQENHSs+w8SghSJTitc1JtyOKWvC9DxOhmQpQPuehOBG1FZLf+f5m1+KXFrRL6VUE03YTTgFEBrpQXiLJExH1A/LqnY47ZVhRcQDuruMd2weppB7CQ0gbTxjgrKQ4ug8dYeEhjzPgAVS8g3aeYfOKrHlsKjZu9gLkZysRys4CLJf39nLrMaLugy+iMonOaSFmyq4riMkeZGnm4xOso9JK1QkaGdBzmZVdlw3tp0c7irKTrYvomxjtBtSno+4gk7smHtk+uHFed3n7u17nteRT476smC1YuBKnqyYqWs/U0EN4Ipc9YGdTAvrhu84xGoZrQWY2SYSORK4cWofxvPewmPZHwrHqNJKCnr7UBEASl/SA4UUIGu6mRpEoyiSzLJgv6hCH34akNrRBTG5DckxlyvUI4F8r7XYvbCzhuH6cBwNQENj8QbKmEwC4xXyCMD1WEvoOyhHWPN1HQf1iDHc2Ru9+ynStKj3BqEOqtl3gJKmuDNma6h2wugojCWPrFFJl1WzCZUA6Ex9URore4Ogbh8C6odGTSQ9JvN7a6aOjLLHAA4j5Ely9HSG1CNarMKJTFGo3rFX0bb+dBbSJKo+mdpLESSbQNnGqdYCfu6V3QAWTakQzCP2Mlm9U4ZBAscuQA/Kkuwz1Xxc2Wu1L0JapOUTJkp0jpkdKitQ0JpkbR1OlWaPy0x4dl/w8+/vbf/tv80i/9EoeHh/z9v//3+V2/63f9mq/5F//iX/BDP/RD/MN/+A/5/u//fn7wB3/w63rOD6wI6xdBzOe9gKomvrUiuhP6p/26QMaGaDegXW0TVOr91Tj0hoee0/XpQ0QulH8veihbfDPAYmqPm+3gJnN8PkbldzD94mu/oOpxuMGPx/jdfdzOHm62S/uZ70OMg71HFh3qrguqbqMCeKYPcCH1/v5c0uKGeF8I5apYWSRBdOOcRA4u0+vd+iQy7MSGTDuWXaCuTSJPoUELwUEco5BoJJnQSASltVuB4EUXsWoTVnVOXQbgj+0iXK+eCHmkR0179EFDfGsVopMv89DTNgGu49poG7AT3uRg9fJO4sunI/KSyiLEk8eWUbj5tZssRIg6QVw026S0aFIFwp8XoDWuOMBMb6HTo6/5+FF6RH/0SWR9iVxc4i4ly7dubUE5sQq90c4qqjaUt9vzKeaqQNy/P/jMNxCHQKF4XBGPK6xRqLgnykLYD06gtcUZTV8n9APXIYnbIO4bPufSKC47zUUTSrcAkTKkcYf1gkWX0BiNEJBnNdYJyj6ms4o06ojj6zRCs4VaFdM1cdRjnSSSgfGfacM87ni2qBlrxyzumMUt48gwjw2TCGIp6B0IIWiso7KOxjoa6wcHADRWkipLbSLqwd51LdZ9OhNCwuEBbv8owHikBNMHsZ3WeKnobryEGR8Eql/VBXTvsEEjHzYF8xnMiqAFqBrco5DMKbQNsB8hccmYON6lbh4CEOspIhojmw1sAhNC7bSIsQivY2ccDgi9Ay/pzye059PAGJmETWF/NaZ5sI/Z5CGVEsBKRGZB+kDhLGoQfoDohERTlYWkSbzA9Sos+lZh2phqU1A3KZE2WC/Z9PGw+AciY2PDJqB1gtIoJnFgiQDsDKwRIUIA0Go9wvUasy5wTRzgPtdaqsgEVHFkKOYrinFJmjbs7l4ixPUmYIh4jjuM0TR1hu3+MyT7fDg+8Pipn/ophBD883/+z7/mwg/w+3//7+ef/JN/gveev/W3/tbX/ZwfeAboITLW1xHmbYGQMsBlxg35nRNEZLGrcLJKb51T7B2z+uztbRnbt6EULZ+JwGnse8ETLuYCcXsPMZkjvvQK/cEzmOltUCnUjylm3/I1X49wBntwE9FUodd3/fd9Rf9t/zXR61/Avlaj1n2wITYDdGM/iBSF8CEwYzZ40C8sYj3GNIpR0hBrw1VVcFoVvH5+wO3pgizquahzHtcZd/KS+1XBeaPYGM/cBU+2B2axYtFZIsKiH1h/gtZZzjtB7yWRlPResmhy3HlQmO/dCJoK7yQy69m8epvRSw8QN0aQ5Yj3TlF7FTquQAncebhB9ScT+qsx6ckx9efmyLgLsKCndH07q2ibFOcketURj6rg588a0tmGzfEOtteks01oAxUNxccfYi9T/P0SWV+SPfMHf93H76sHqMvXSd74PPblJavX7tKUOaPZCqUtWZ3S9TH2agetwslGRiEd0T6Mgip6LqBqQeZb0plUbgt3cXUcQk7Sjr5O6bqYJG2Z7l5xebqHdZL9tOaiTZkOp6TOyVDK7yOUyBCt53FVsJO0RDLQ/KxVfPT2fd58dJtYGZ69/YCrqxl5UW5RsV0bMx5V5KOSHaOJo5D8FsKiBHWvUSKw3uMh3KdzIWwKBM3QD6ydJZGKSEpWvWUSKWoDl0KS6ZiDrOJyPUE88tzKnrK1y5hA1qtrzEufQi3O8XGCnewQv/cq1bd+Ah8VuHoRAr9PLsIuWBKEeW+dImKwVxHlO0dMPnUfb2LUbod/7g6sl/jRmPjxa7iL/wc6Tuk//n/F9AvExctEr34+WB+OdkIVoakxn92gv1lDluGvWvSoQk0qooNwzcvMsfr8HQDiaUn6wiV+IzCLHNdppGsp37qJzppgYz3eZXzrFNskVGdz6rdvDXPPMLpxzjTpMVVK4jbI8xldF2Od5KQcEUtLqiQXbURpBAepIR0oqNYLHpTF1u2RKMtxnVJoS5bV7O1dINQQ+qUczaM98r0rutUIK5JAUBWero1ZLSasNiNu336EEB5rNc4p8smSA3eCigzxqCI9vHq684EPe/6/kfHGG2/wsY99jN/+23/7/+bXfe/3fi8f+chH+PznP/91P+cHt/o5EQRnkUGNa9rHO6gyJbq1RD8bY19pqR7vEhU1QjvOPneT3Y+9E8pqvcZVKfH+gv6VGNeG6N/k8GpAdybB/7s/wUsVOP6AHn/ka76WcvUy0dV7iK7B52PsaI7aXCEvT+l375K+8suw2iDHLpwYnKArU5o6o5iuKZfjcPIqavL5iuoqhBLlWc18Gm4MZ1dz8qjj2WnH7mTJa8c3SbRhntZYL1j2MUp49lLLKJIYd52kKygNpEpSO8mahgtqbopdHogzRnYEFDyuFEoUPFMoMt2T5TXHD25wePM4lBRXKeneIgSyGAMXl9jLBIRHzTuIBM2jvSdM+ryBNCH75Dl+GSKC7XnyVIJ9APY/+k6YFzaooK1RiCbGNDHJcHrSg43RlSlSNugXJX5+m/j+l6gPfhtZeutrP7itSb78H7GvVJx/4aM8fnTEbLpk8egGzgmUsiRxy635JUe3HhEXNZvTOeJsxuyj7+FajXs7JnpxEFVVCX2dsl6O4bMvMT46R49quuWIuKjpqoxqU7DajNg1V4wma5QKi7nxgf2w6iLWJti0xlnFsk1ZdQmZsmz6iBtFy/5kyd6NU7oq5d7RY+zg/18OUa2p0cRJRz7ZsDjZpW0TvBeUVfDuhvaAR+J5aR5uzlo6sqjjeDPh5cWEQguUUDQWppFk2TuWpqNQEdaHIL1Ce1Z9SJmMdWg5rE532XlKc6H75m9Hnz9AGIM/SEJ/X0fI1RXy/ITqM38opO2dvkz0zlfCoj+9rhBYuKoQY0n7lQmmSkn3loEW3MSI0iCXV/iTDSLL8WS4fEL80g/RtCeh/ScVjCe4d1rE+gr0Zbh/HAGXAYDVX0yIbqwQzx3B2TnmXY1daIpnj7GrHDdERJurIijrk+AyiCebIKaNLOnRJcvXnkFFhmRcks7WXD04xHYROu1w1tGuihAVvSnoBqa/xHPWpk/yGob2YTdENCvhaZ0YRH/w+asJxZDY2FlF1yZEeYstU5AePaqJdte4t6JteFa9HJFOSibXh5k4AKbSocq1Oduh78J1Gq8LvFUkT2k+fDh+4yOO4w8s4huNRpycnHzdz/nBCX/aoqZ14ELXcegzaYtbxoh2g0jYBviIyDC9fYpQLuSIS7DLDHM5DpuHokErF4R66zViYIG73QOi47fpj8Ae/lc497VPKzKeYbMZ+vwR8vRhyPZOc8zRM7h0EvqOdYXoN2CCMIcB6tJVKUna0jcJ/RDuESUdadbQNgnGKKo6o+5jJJ4k6oOaW1mKqCNPWqwLxL5IeFLl6JzAeEFtn/RkBYJEKjoX0dFzxpqWBi00axuTGMG6l2z6iE0Xs9kUSDlEBXcR/dUk4H3PZVCy74+Qsw32MsXVEokLoS1Jj9pdIgoLXQKtC7hUKaBtv+4J8jXf/8hssweEdsjIMDk6pyuzLTK3Xo6I2pi0VyQHV/hODTHEkv7oHs78+oSxYv6t+Pj/TfnemIvzHa6qEdZJdudX9H20ReUePvMoQGF6HXIilENNGsRYoNoe4lHgC9QxXZmxXI+Joh55OkevCrwLmg8V9eSjktVmFEr/XYwZSvaJMkQm9OWVD2XY86ogUYZR1HFS5wigs4p1nREf72Os2iJ9vRfkcYtzEtOHy63a5HRdHE6N0nG82CEbWgDBBx5+18qhhijqadIwi3OMi5AizLN1H4h/Skgqa5BoKqOIpWCsg92vbNOA+i2eHs41futL+DTDpzk+TpEnD/HzPeg6WK4QtkEme6Hkv17jDYiqhf3dsAFYnuIu+SoPfXRjhT7chCAapRHTKLACdITXMZ1ZkiaHdCrFulehrpB7Dnc5UDUOYyhbyGOE7on0KnAILi7wVwZXF8G1tMqDYC7prnfvgVQZB6SynlSBSNoGKFAy2Ww1L64N9ua+Dq0VgHRcbmmAYhB9xioEQoWFXtBrQapCXoi1gtpKMuWIZNAASILWJJKOfggmK89mZLM1Ku0QyuJavY1Yt20cEMNGIYQjSQPb4TpquG9Ca+y6DeDel0/wtIYHPN/Yk/9/yXjf3/E7fgc/+7M/yxe/+EW++Zu/+df9ugcPHvDFL36R3/27f/fX/ZwfuOcvlENmwZ+Pk0GRnnaB0X8yQ0SOaFIF4l9kB2WqC+E/Y4ca15hrSFDWbU+vNCaEf/TdlvUunEUIjWvOfv0XJHUIcLm6QpydILoWM38G8hv0h8/j57sQSdyQAgchOct7gdRBIdu1cYDUJD1J2m4vls4GFa8bJu+6yciinjTu0NISa4sewC9KeKwTeNgS2lIVACwSiFEkPmYlFljR04qGDTWVdZQmCLRWXcKyHKGkw1tFX6WBqRA5urMZdhlBFCMSgYhswPk2ApV16L0SueMQYx02OuWQOpSnsDf7zc6L/93heo3rgsvD9Ro1CBXrMsN0EXWVUa1GNKsRro3BhlOe1xF2/5MI+b/dg/Zphu3CQtwYTdmmZEVNltchLW9UMnrhQXBJVBlmAKOIlJAotz8C51CjEMW6DTlxkmpTsL6c0VUpm9UYIT1x2hINHunrxRsC2S+STwRYmz4It5TwjOKQxy6Fp3eKsk04udrhYjVlWRWs6pxNmzIelRiraNuErk3YlKNhcXBIGWJ4ry1hdmgtNFZjBx2HEJ4s6phFPdPYMNYe4zytczgfgD8N/Zb611hQ0tM5tfX7x8nTC/bx75wGuA6Aed/zDO0JtXyIs03o6xsLjoCllhKfjyARuHUaeuijeggNE4gbE9jdCcmOu7vh+wGvEvrN2+Hz0VN8PFDPRtkAbAosDK41bWmMGEtcKfEXPW6TPIFRDfckoRyuDK4Zb2X4XgMiHTgbVRqySSYVKum2wT9yYPPXZUa5LkIw1/sS7ZR0FFHHLO4Ya4MWEEtPNPT0IWSARNfz00Omw1wL7UJP1SasLue062ILTQvVChkOC230JEjLy+BEsDLoWoC+i+h7Pfxb2Exe//wfjt8a4yd+4icoioI/9If+EF/60pe+5tecnp7ygz/4gyil+Ct/5a983c/5gU/+Mutp350htCPaXyHmGn9lEKlBz67hGg6zChM02V+EG64R+C4kAEbTzRBZq4iSFTiPuL0Tdv9tg3r3Tarv+9PQb1DH/wGEps3vbCE/AOXVZ1FXbxOdvROsgSbcNMVmRXTxNtHN30OXHtF5Q3J2wurlG0FJX2V0bcLenUeUFzOK+QqVttguqG510pEBSltmOwvuP7hF3cW0fYT1kjTq6IxmVWdcNRm5DjeidriIRtpt0+BSBfcrwVln6HFkxCSkXPgLelqQkLuU40YwjSRlrLmqiiAyWxf0vSaOO1wdYaoUlbWouhryznuwHrcMNynxwhE8PMEde+RtjSs1dqlQ0wrxiRtf9wT5WqNfFugibOZWj/e3C+D1abdajdCRQSqLaWOW79xg+swxan9Kd/dTCD3aYlmvR9tdfNXnjFRMv+kdDtcF6yZjklWsl2PG0zVJEQRYIgtVnaZOAzt/QMPKdagq+KVBzCVq1aG04YWXXmd9McM5iTWK48tDdnauaAYWRZq0TGYrklHF+mLGG/efIdM9LhH0TrLsNW9uUr7n6IxM91gvuVVsaK0OEJfhtFd1cUBHC0cW9WR5xeVySm8VedySJiFqdbmaUPcxt+cXXJZjWqNR0nFzvOTxakaqDXncUqQ1q6pgErfIodr0xYVmL9FUxrM010CqoDupDYOYTJJHHfPpknxn+VTmAhCcOsYgmgoB9M9/gujR25Cm+MmU+LXPY7oa2ZQhAfBqEU7Z6wHXfOMQ8fiK9FNlyAWoK/z8ZujdOwddiz24hf7sf0BJSXfrmxDRE51PtP8d2BufR37uZcQ0YK3Nyw6hE7gE8HiTsnnvkMkn3g0RuUlHvyq2wVqu12zevkmUN6iiwRlD/WCf9OZFCDPrNX0VqJurh0fouCeZlkwOLvFGkQyVlWuqaN9HGKPJ4jZUXroONhMe1gnV4MiQhMNCrhypdKx6TWkkR1nHcR1zlAUegPeCTZmT5RVxUaOKhvXbN8NCHrV4J3jjzee4d+d+EJUKz+J0l93bxyGlUIb2krMSKd12I/y0x4dq/w8+fv7nf54/8kf+CD/1Uz/Fpz/9aX7n7/ydfOYzn2FnZ4eyLPnSl77Ez/3cz1FVFR/5yEf4G3/jb/yaxxBC8NM//dMf+Dk/eNl/XyPOPP0qx7URaXGJ7yVyD/yNm4iLc/ylQihLNDJB2a99QNGWiu5shsqbkKAVuUDySj1Ygx/NcQc3kdUGfRKEDF7HpM/90a96Dc4b5PI97ChY95LLMyjCCc/NdrDFLubBz5Hc+j00R5/B3n2ENR3xuGKyd0VXJ7SbHJ10dHWCr1K6JmE0XyFVUMQqo3j48Aax7hF46j7GDjvsgAM2DMyFEPSiLEdZx69epWQqaI7uVyFvXQnJytds5JpT9zZ78i5GGGpKeubkKmVjBIte8/G45dFyjlKOdBDOdRcT4sFzTOOD3QkB4wQ5Bv9ei3u5QihAe7ovJ8R3B/sjEbz38ANPhN/IiHdW1Cc79HVKlLY0ZU7vJGnWMN5ZUK9GzJ55jOsChcx0EapooPVEJ6/j1icwAFquRxLvfpWtsz+4QyTeZnxwybNGc3a+SxT1TG6ekd49RYwF7lJugUMAsxtn+E5hHwXkrIwM7WtT+irE+HorWSxmRAOJb3f3ks2mGHCsPbtHZyhtSHcXqLjnZpnzxvFNtLSkylBozc2sCbGsypKqjjxuOV5P6F04YdcmbBZjZbeL92o54Xg9DUI+GcrAs4EZYJzCWEWsDEaFKN7zzZhn909CC8JEXG3GtCaisZpFF3PRal6aCl5deY5NyUIuiX3MmT9n0k3ZFSO0jIhEysd2wnx6mmVeObXhOtYR5uAZ1OocmiYo/UcT3O17iK5BdC1+NMZ95hb+F99EyQZhLOQZ8lv34OwU/+AyJIS+egXyAlmETAt9cQ7THJwjefs/4UZv0n3iTxLrKZEqKF/6/SSAevgu/vEm5GIkPWpaIyceRglj/Yj67UOa1Wjr7jj8xJvbAKrZN72FvBlB1eHXkOyukOMe30b0RtLXQY8gZADlmLMI20dksxVi2PgVz5wEDcAA1JHK8eqrH+GyLmis4iDpmY1bKhvw0SCZDKmAk8hQaEHrJPPEUGiDHmx7k3Eg83Vlhrt/SF+n1GUWNCSzNc/cehgWW+nJZ2tmd044f/tW4JcMLoKyyplM1khtttfM0xviKQj+/svdTPzFv/gXtzAfay2/8Au/wC/+4i9u/937Jxu21157jddee237ZyHEFgb0VBZ/6h69s0YNqMvm9TnRdIMvO8Tjx9A6VGG2WgB5JHDnPkR0Sose1aH87gis9Uaj0g7OVohNhZqXodQ7DvYvH/1ab3/fLxG2JTp7HbW6gLaBTYV7/gXsJJwa1dF3YWyJrx4jTEjp6pYj6tWItknIx5vQ7++D+KXvI7S2RGlLV6Wcne1te8tllUMfbF2J7rcQIDOEvijhSFUgqj1TRKx6SdOF7HXrwHqHRJL4lLE6wAx1yMRnuKGDpYfGy1WTBTGhlTR1Slxm6KxB760RicMvw/smegdVCEOyqynx3XCiE84T3xoW/q6H2uHWT+cC7y4nQ/WmQyUdzmjqMqMbfP2b9Qj9eI9svg7q/9M5aqcFEkTXYG689Guww51ZYh/9K2xfgtCopkRMIX/+McneguS1uwjh6DcZ6mRKPF0jC8f0hfvERc3lg0O8k/QXE1yvQwJaZMBJuiqjLjPyyYbZbBFS17RFKMt6PRrQzo7TR4fc+ejbyPd5oG/NLrmqRhSuRw0npt1iHZTUTmKc4mi8YtOmWCcYpw2Rsmhp6axmURdE2mCspHMKMVzE761m5Mqwk1XcOTxmsZwSa0NnNIs6pygqrq5m1F2gTrZGkyrDLO7oneBhHSHwTERK5iJKOgSSCRkTrUkVTCIbNiTVNy4M5GsNb0A4h1xcEp8d07/4LXD73rZHrzYL+v1nEaMKtTpHnZ/AQRMCqMZj+mdfRFYrpJSIYo26WNLfj5GJHcr4Aroed+cuPk7xcUp37/9E+r7WkVi9g1wtQntsLFFZR7/K8b1Glm3Ai0eCeL5G5W3QgWzyrfjYDUl/8tIEjDQD5vcyPIfKOqKuQQxwsGusrmljVGSwLhwQvA0C0XwnbBKqiyk74xV53HJVjXi4njBJWkQXVP79sEDqQe+jAOUkuQ6skdbobeBYnNfotNsi0+MktJ1W5/MQL91HdG1MvSkYDweWtklwTpIkLePxJlTHjN62BD4cvzXGn/pTf2q7+P/nGr8xMbgkBM1kPeZ0jh5V+FYSpO7BWy9cHzznzm17WtffJ2UIUMEBPvSt0R6BARaB6Q0hsEf/2p6wVClWaGRTIroWlMavXeg3TsDrBCVTuu4c2YZFUc829JsMqQ1KB3+utWob4CKEpx926tYqmj5mN+7p+2hL/Xt/+cojUCKkc/VO0Q0BLsEPHgerjhS0ziOG/xSakZ/S0yGRRD4sfdb7wQgIpYnYlQH8YozeXuBisAO5OkLEIZqU3uO7IWCpI1j6NAExGIeTi6slrn46el7TJNiBMBaNK7IBnnTd63QuwHMSK9GRIZ2Uw+cegl68/rWvK9ZTaqmCa6MpA6hnUiAnILue4vKK6mKK6ULoEWYFkUAfNeTyjGYxCryBdU5XZvRNQjQIn4Rww++e0XwZlN3+Sb/WWglI+j4AcUyZBrzr4M/vrUbiie3g0c+roA3pY+hhXJREpaG3mnFeEg1eflFnrHxG08db/Yh1EpSlNppC94zSmmK8YbUehz4wPmwc2piqS+is3moAlHC4YcG4toXFUiIclB5iHxEJhR6ma67Dz9f3msg8PV+3byViwPpSNbg4G1w7HbILol07vYNcP0Kbx6HsP3+yIRHOIpsaN5og0gypNXp9ESqD0RB4EEeYvdvgLMJZsuJ55Pu8rD6ZhrwArSFPUTvVkMBncHUSgrMOlwg1CGWHrANTpWEuDKS85nJCVNSoJARouToJKaZ5s2VbqKEUL1UAN72/f+7qBJX0qKQP+p0mIS8q0jS8D4s6Y5w0Qdw5HCSskySDKwPASkemAyFQq+D2yIp6m9HgrCSbr2jXOU2Zh8NC3AUGgQv8j67KBqLpULUc7m9myGB56ou/fwpl//+CFX8/9VM/9Z/9OT/4HWGaYx8I8AJ9uCHZG3r6DojA9wIRe0RscWVM+5UJyd0F3oSbA06gb7XbhQt6XBWjbvVDzz9cjNHF23gdY4sK5lBuXqUYvRj+TRV0o32EbfE6RuoI8d4C8c59tJT0Nz9Ne/w/4/NDhE5w+QQJRJOK6aTCtRH1xRStDfr6Bj3Q1ZQ2RHHPKKsoh0QsCCld55vJV70V47Rh06as+oiLNqaxkkQG4l8kwSk4ay2ZVPQuYiVWZD5n5Ed4PEZYPJ7KOqwPAKHeSXo7iNauE/K6CHNZbE+isgjhKX5t8L1Ef0LS/McdovkaNe/xVx6RmhDx5sIp5GkMGZktGVEmPcWzj6kHu2Sc1xijKaZrAHyvGb34AHsVocY9wjn01btw8GsfN7v9+6j3HuIvv0T88Ev48RQ3mYVT5WcvqKuccbpApS2+9UG9XWjUTsv41hkqb6ivblJvCqxR5LM1ttfku0smRc3m8T7JbE27GNOWOSrqsVZxWeekUcfRjROqyyliMUFKy2i24vJ0j9l4RaRyVnXOfLQhGlIgo6gn6mLm+xfko5K+jbcb3mv73v54xdWAhr6uFsTKMo077swuuXXrMU2d0Q2pgALPLK94cH7ApkuQeMZJQ200zguOm4S3N5qDNCwUjbM03rCSYY5VvkcZQTaIEgPkxZE+RbW/r4Mg1e3sbz9X2ZSo88eI9RLz/MeRxR3U6ZcDgKk2sL8H6xUsV+jPh7xyeedOyADYO0RpDXU1hP/0+N09bLGPXj5ErS7ougvS9yVCFrvfTv38Bcmr/x6hNeKZHcTbnvjWAl8rmkd7iNTTvj0BL1Bph9SOejUiGZehIjCrkKcT9LgOxMxOh6yNyCKTFjWr6B7PSKYlZrC16qSjXedD/93QbwLi3LXREA+dk2Q1WhuytGGctEzyEC9+vSFcdcE2XEQdsbIh9c9KsqhnJy85PDxlevdRyBgYMjXyF0+wn72H34R2w2I55fDwFJUbvA+alq6LQ4Jp1jDav+L+a/dwTpHE7VMVgH44/v9jfHCf/8MafeARWWDocyJQdyx+6egezpBJTzQuh7JYR78Y4Q24dRwutoMWEIMaHUTmkEULcRJEPnEMJ6e4Zz+OLfZBKuoHPwezF7avoVz8KkQjXDIheu9V+n+zJvodt8BZ7M4hIp6Tz7+V8vR/Jjp5DX38Hk56bJXSVwnNcsyjhzfY27vA9Jp+8OE++9u/wMUr9zBGc/PugxCkMV/SVClllXM0u6RsMq6qgrqP2M1LTqqCQhtSZblqE1onSJXHOKi8YBYrLruwyCc+ZSNWGGmIfRxO/iJm7TpAowSs+ojD2SXj8QapLG2T0Lxzi33hyW6fIQ889RcOAugDBSokoaWfeAd37jDHgRLIwgMFaloTfcfoGzlXtiM7vKRd59hO055PUXnL5JljbJXQlRnZKJDT4mmJHlVUb9yg+G0nIBWiqXDF3q//2Okt+sMZdn2M/Nb/O+3iV8m+8HMgHLODC+rVCP9on51v6cMpb7XBLiO65YjRjStGN87I965CDLJV9FVCeT6ne3jIwcfexllFfuOCpCp554sfpe4SEt2TpQ3eCZaLKUpZrFWUdUaWNlytJ7jBsjcqNlxczZmO1+wcnjP92LuoaQ+RwJee6o0bmM7+Jy8AAQAASURBVCZh1K0ol2NOz/e4uXNOvJyRtimdVdycXAVEcFbTtSGJ7cbRMZvVmHVV0BlNZxSxNLRW8+ZiJ3j2laXQlv1EcdZIJhFEImJjFXsiIHR754ikZDfxtEO5WOtAs3xaQ+21+PQgCP6WV8RVEFyKqyF++XlQb/6PyGoVon5XdYj0XdVhE/fcES4fId9+C2Xeg/05fjJDnAQLoG804l5G9iu/AHEcKgTv/QJ85P/y1S/EtEEEfFVBItGjHF8rkD7AnZ49ohhf4C967CoNcb15g61S2ospXEzReRO8807gmhg9D3MZ7REpxDcWuDLGPtynWo2C06OPUNoQpy1CWYT0lKdzmjojyWr6NlT6jFXb070ckv7iwUXSO8llm2IHguhBXjLPS6ZFeC9tk+AGuqRQlvUXbzN69jHZ/hWrB4dkRTWgtg067mk3GVHUD0S/FHk5pevigfznefDgJr8OZeMbNj6E/PzWHh9c8DcllJTLHvOqDDa/Yx/6aHcW+FKF3p8M5Kzso+f4SoSTqAvZ8rJwyBmQxhBH+JMN9k2Bmlwibo0CIjSZhH5/Mkfnt3HVA6rqXYTU0C2Q1SV6eYwoA0pYvPcQ99EXQiXg5HOUew16+QDR1WAMdpWzOd7B+5DVPp2sUMqG039k6LuI+mQn8OmdZHEa4CvXp7Sui1nUBfN8wyyr0DKl7qMA33CSxqrBrie57ASd89uef+UMNS0WgxGGmdvZ/n/hUvZ1SufgolPcyBznyxneC4q8IitqnJWkh1fIAw+jnPT5QEHzTej/y5NHEGuEaEPIUtEEOIkEMdb49On0epvTOVEaTg6216ze2qPYXYALEb7F/oJ2WQQLEjnVxZR8/RjRNxBXuLj4dTMbIFR4GEKcdHEPc3CH5NYv494ebE6dxj+uEC8cQG5Q84ri2cfYZYaelTBgj6MXGvj8DCE9GdBcTtBpR3m8y/pyStsmOC+C91866iojLyrOzneputCaKLIaJR2TrGIyXdHWGbduPmZ8eEF6dBkW/kzirzx2kxJNS2wbk81XFAeX7D//HutHB8Rxt+UHbOqcpovJ0oY0q8PfdxFRZMiShkWVo6THDAv+jWLDeZ2z7iM2vcJ4uFNYzhq1ZcMvTEcsFL13g11dYQb3idI2YGif0vCVQCyv8OMpfr6HSzPU4/thc3b7AJtP8SoJOp31KsxP08MkC4I/QK4W+Fu3QruwqeluvUjSVEh3gl/20LX4fJgvzm21QdfDuhYfF+E5HVA79LwMKYFZFO5d7xzjO/CtwrUxF6/eZfbMY2TcgXBU53OaVUFSZqH0nzf0F2NU3iCLYb4vwiZbKEuctpTLMWnWEOcNesi4qC6ngSZZVFuvfzdsALS0LMqCRZMj8UzihmRIYYylI5EWJR11H7EvLXlRkRQ1Km23eqt+OUJnDZt3bmwxv0nW4If43uuW13hn+QRb3caMipIkbRHCbWFmH47/447fWF24M0PJHvCC/nIcetGa0O+Ph0eUwNFX88R8G+Ed0HtoOjAGkQ2+dTcIevIRevkIWZ5Cv0YIDVLj2zNceR/iGbIfypdJiprWmEc5oqlDf7Fdgw1+Y9nUUJahLz4s/ABR3G/709cq2HZVUK1GdM2TXvT1RSuHC7G3wYaVRd3g65b0TuI82z5saTzr3lGZUNJv6ekHbz+A+1/9dz2UgEIbHCEfPEo64rxmfHCJSHpoHVxtEIkAEQACQgzvY2cg9kEVPQ1wElFIGOX4b0Ds49cabrj56bRFpx3r1TgE+wx9dGfktqfqe42MLL5W+E3IhRDO4OwHw80KqTHTW8h7E+L5mmyyIZmUYdtaV/iTCrdU4edPemRi4PqUOx6hso54siGZrbc6iq5K2ZQjWhMRKUue1RSj8ok90Sl6qzEuaELSuCPPatKixjlBsbcgObhCHXbw7FGweMCQaqhRaRvaScoSTSqy+Yp8VIXeb9JtYT51k9I2KVFkAmci7oi0IdGGWVYOHAlHEbe0g///WhMkCeFSowhyLYiFwgORkBRKkUhQ75vDKn06wKftWJSIco1Ls3DtOYefzumefQmvEoQLjAeKEYyyQK0cFn6vI+i7rY+fOA7XMkCehIOHlIELECeBB2JaOvPEvmjMBlWewWqDq+WwASZcXFpBFGOXURAbG4U3wfPerUa4LkZoR1zURGm3zV9wbYStUmTRhayASCKLJugGkp4kbwYhXhMsgklHlLXBNRT3QQ8wtGuuMbtKOloTUfeabtAKKOHRMkB9tHIhIyLqBnS1IdtZIbTD9zpED1cp3WpEuRxj+4gkb7aVHWfldqMhhiwCITxJ2iJl2IRYo0my+unOB9hqgL5Rvz4c39jxgRd/v/a4hQAH+qjCdRrbJJhVjluqsPCPU5BB/esns/B9nQoLv1EIDfY8wTxM8BcNzKeoZ2SIqXUOdIR+8Bbx49dQy/t069cDDKbfIPoN8fgjeJVgR3Pszj5yT9JeTBAX58jNIoiMkn1cMgknhasWtdeSjkvUcEErZVmtxwPNT2OMpq5yjs/3Wa4mW5BMPgoBGdfgn3WT0VtFPNycm6GkGsnwKx6U4K1zrI1hbXt6DJ3o6EWL9JKVXFCLCo+jFh1ra4glzGPDTtwyScPpspiuibKW4mMPw07/4YjmjR3cKpSW6YaLwTkoDSIGuQNM87A50wqSNNxUn8LQaReCkeKeaBIANs4Gm6fSlvJsZ5uEBpDvXQVVdRVD3SO66qs9/V9jlBf/nqp8E9Oe4/ND2k98B9GdDfmtM/JbZ4j9HP94Q/XaDap3j8AJ1M2wEdqOukYmHapogl2SUDKVygXngFVkScN4vmR8cMl4Z4EdgDtaWiQerS2jYkPyvsUzGleoUQuzgvqbfye+9ogxyFlLv85RWYdpYpqLGfXJDvFk8wS4MgCnnBes6pzLyzkq7sl3lsRJRxT17E0X7O1cksdtoPxJx6KLSJRlrC1awLJXFNoxjTwjLdhLNJEQzCPNfirItKMYoEXei6fq7Rb5cG9YbUDHiItTKEb0N5/D3Pp2kBpVXuDyCebms/jpPCz+6xbWLT5OAg3w4Qni7BQfJ0QP3ghVAq3hcB+XB0uvjxPcaI6+eBf7v6JERsfvYB9pzOUo3NkmWThsbFpog1JfKB+Cs7xk56PvUi9HtMsCnCC/dUbxzDHpzYuAf16Ftpm4kcONQ0hj5M0ImbWorCUa1aR5QzLZoLMGlbfE8zXjG+dEWUgNrcuMvo2pm4Su14NjSGC9pLGaqo9prKaIemJlsS4gfQ8nS5I42AWTowuEdDTnM8rTHZp1wen9G3QDoTSerwfgj9yW2q1RdNUT/kW+s6TtItbrEVVZoD+M9P0//PjAR0NzWRC9ZMPu+6xCf+eM6OGjQNArY+Tc4s+agME0CvEr7+DrJ8Qs7yQkEjlrt0ItzhfhJJtqKApE11J/6r9B1peo8gI7vkHy6i9gRzPM7j0A0uf+KOXFvyc6f4BfG4pPnuCvPNK8RWQM5lZDtHwUEL8fT/BffisI0YomRBBbxY0B2VuXOcvNGF3lFGmDtZL7D25x95n3qKuctk1o24Rx0tBZTdk+qQzURrI2CuvD4SKSIXEtVwolBMd9j0RSuIJM5GzEio2/IBdzRnbMlJz9JKLQHi1CdHASd4GOtxqh655RCv4y8Luj6YbmvX3ybzqF3uOuIuxDgfqICieoqtsy0ql7cJfI/On0/G0XhZvdkNK4s3NFXyd0VYq1iqrMQ2VgOP0A4CXZt1zi7r5AdPI6PPO1H7u3JaZfIMtTfL9BNCui9Snq/DH9/RH16RypHKNbj3BlRP7cMSIPUHvziU/j4gx9+Zjo3bdBKtrTEBGt0g7vJN1qhE46Dg5PObx5TFunjI7Czb49n8IF3DgK3GxjNPObpyFRbYjG3b99THy4CBtWIPu5fwjzKJAqO0EyW/Pg8x9j/+5DktkaU6XIpGfy/AOyqwnivSPeOj0iVhYhPE0f8/Dd20jpUcoQacNoVPLKO8+yW2wYJzXn5ZhbeU1tgvL/MAu0uId1ihlEpo311M4yl5pceRobkimNVazXI9xbt5l87bf86x6+A/ncCD+eIqo13Sc+g1cJXifIy1cRfeib6+P3ECcnuFMHn7qF0BraBnl5TveFBKE1Mm9R5j7u3nPIs3OINT5Ogj1Qa+RmhdysEGenRJ/680Dgf1izQewcIv1Dqse71Cc7TP64R7iTUB2TEvHpm/hfeRSoj0cN5lHO+OgclYUMiuZkDl4QjUN1UQhP9tIpkMPDE7p3AlgoOmyxFzHLBwfM7j0CIN5fIBLH8lfvISMTKmGDq+RyMaPqEvqB2micYj8vaY2msZq7swtaEwV2RNSRJi1dHzOZrhjtX2E3GdGNDc35DNcr4rzGX85QOtzP2ssgtk3HJbJOaMogQKyaJGSYZC3Lk13yrKas8u0G9GkOD1tB4zfyMT8c37jxgWeBa6PQZ8196Du/8TD0+BOPzhr6B0UQ9o1qZBYENTLrAv9dOvROQ//WaIv/FaXFLCbhZDZpEN0S90pF6iz9zefpDj+GrC/pDp9H3PivyQdlr/OG5L3/iHz3LbwBu4xwTYxc9Kj2LaKdfw2ATwu86fHLmPzGOd1iRLfJA6hjusZchPjWTRtU/VpasqhjnJecn+0NFqkhO95qsqijs5p1m/DOZoyWntIIVl0IUzHv01MtTE+MIhcRle9ZsMIIw8Tvk7mcCE2HZScRTCJLpmwQc+UVs8OLQK9TFnelkEWDnDqYj0j8AnsaIacd6lkRFvxGQawh1bhHLjD+CbHJT6vsL5WlXYyDwjlt6fuIbFSi4yGyWTrSSYltw4JZ3DxHKAu1CTqFYkS5efXX2LUAlEzobYMf30S0C2S7Rq4uERdnqEnHaBJiW5mMULtxyHwHmM/pd++RvfyvoNxAmmBf7Un2ltg6xpRZ+JzTFp01eKsoL2bsPv8APapg6I/f/G1fxrUBWeyNpF2Mv6r0KCODnA0+u81AdTsNrSvfabwT3PrmV2kvp5gqJb97jPhtt+EL72HLjmK6Zn+0preK3miqLglVBudphhQ4sfKB7IZADGp/5wXGSRygBtX/ug9B04n0tM7jCXjfjRFk2m/LypE2W/fF0xhiHsFqg9hUiFGOu/NNpK/+e9xkTnv30/g4R1XLsCGXEtk9gLfuh3AfYzFvOuJvi/GPNvhS4U4F0rwJtw5DNa8aPuPh5O/THJFmCG+QQiOFRukR+vQBfOsesxfP8PdL+ALw3BwyYL0G55BzG9C9DuS44f/H3p8G65al913gb6215/1OZ75j3sysyqrKylINmiwsI8sG40HGEW3RgnYDHwAH7WDQF9HhIGiBQgQR3eHAOGzxAWwIbLrbNEa2RDS2OwQu25IsCVWVskpVWZmV453P/E57XkN/ePY9NwvZJkXVVSvUuSJu5M17hvecd6+911rP8////u3bN8n1mqhoiScNwQsoxw+RnKZ74LhGJZB8dMvmV27QXcxQKowBUbWIm/sIZaRK6PqY1ekuwxAznW0Il2K5G5ym9xGJtqy6jMFrzOgO2XQZyWg99V60SfETAaHx9A9mtMsJbZOLYj/tyItaPlZleG9o1hPsCO/RxjHdXRElQjPcvfOQ5d3rdF0qrcVn3QbiQ8Lfb/fxwSN9pw2+i1BOwBuqVE9LrFZ6u2ZWSwY24KpMTv1DhE4HaQs86UElTpj/zbjwF0BkJL5TG3S9JuI+Pi7w8+co32fpCcHhkwyT56isY7g/xeTdVZ/XbJfY3evodisPjcihi47YGlwfUy1nsJpSbUuGISYxlt5FI7kvUDX5VW48PAX8OK9phpjNkNA7eQhnJuDjscwWJAhEK+m7bsMAASwOq8aWAxEKjSdg0Fde7cw49ootXTf6570CDH7keKu8FR+1EZ5C6DRq1UGqBLFcOsTcreRaOC2BO+vl/+aJ8Y8bthXMqYktOvIkaU+UDERZj9IesynFPplK2IirM1FVX8YYX0OaQXeJnvzG6adVhI4mONcSsgjfrqXadN4CWjZCZSoWsDSDPIcows92CNHYD/aeEMX4biXgn01Js5oI3GTsxwdriLNOhFSTDpJAsV+jbiwId0XRDdCcL4iLVpIL+5xuU1BeKNBiaR2WE4ZNSbq7wuT9VY4ESP/VLick791lWBYCRiobducr6ian7lJ6G11pD5o+oRt7xIuiZnjSEx758Os+pfOC7dXISWjwit5DrBSxMrgQaJyERj0p+YegMLH9De/1t22UJazW0oZSmmj1kKAN2IHo8i5BG1wxR2sj2R2JkdbUqiXUSgK+dCYVwdiLSK+Qa/kkH4AogtUK3feE2Ry3ewS+Qxt5v+N4jqorQlES5jsoa+GyFRCYlo2dWl1Ka7LtofGEXkBV0aRB5x39pZzszSiKM8kgOpsQrkSEyWwrOgGnRTPktbhKLqewCpjIsbmYj5oigYItGxEqxsaj1YAPwgrxShECdFaATbGxZLG0fub7l6TT6pvmVFK2Vza+NOvo2kzEy7HomLK8laChPqHwmijrCM5g25Qklpbck/Aw2z3bYJ8Px2//8cHZ/osOv0zF9xo5zPWxT193hO2T1L8BIlGjP7GmKONQ44PHzBsRoYxl2khLWp1Ee1rCzZv4yRzVN0T1Grt/C1Xc/g0/i915DrN/jr5cYeuMaFah8wGMQrVjya5vUdUGcoGC4FriNsVZw/lml6bN8EFTJB2uFXb/YCPOqwlF0jPRLUnSk403y7IpqYaExkZXToBF7CgjxbrX9F7AKwCZ1pz7AYhxo7BPB00Unr7dEx0xeAnyyIxlb7ai7aR0bmL7TYK50GjUtgbEPRGamGEVEd+osBclka9RE4/KR+BPGwgtqNOz/w1T4n99DG1CeTD67b0in43WLiPZ5iYSEIk2HhVb2uWEDAkDwlWYgx7eJ/j7X3L943iB684gygnZTLQLjUaZIAt/EhMebFF6A9MpfrbALg4hnojls5WToorP6U92aFYTmq1sSJI+xg2SQZDNKtlgRQF1fYa7/RF8VhJvfglNL+AqLYuAHyKGIWLYFuTvHYEKDFXO5fE+TZtxw90n218xbEr6Jh0XW8PmwSHxxQy8iCBNOrA4OMdczImjgarJWTUlzluaIaYZ/f7X4p5VXV5VnZSSqF/rNZU1xEpcJRsH6yFglCJW6ircp3GKWHs8CuvMVQLcsxihKFFNA1oRJlPMyQPhM9iB6PF7+MmM4eglgpe2Id4TDq4THj7ArQviTwxweomvtBD9diYiDLT2aZhPFKM2HVQdaujx156XOTQ6RrSKCM6ithuxDc8XoNewbmRjnCViARyFyGHVSjl9WokeJJLrqVR4Oo/TQb42kYRAd54Q769lLlS5POO6GNeLp987TTqt2a6n5EVDHLc0dcGmS5mmwvhXKrBpM8q4p3Ux1RBT9wlJ5CiSjiJvSJKe8trZFd8jjHMn3V2jY0tzOUNHltVyThxbpvMVTZ2T5S3OGpo2ZTYKnG0f44dvJn16r+naZwMA+6bX+fDk/5sefd9zfn7O9etPc1n+xt/4G/yVv/JXcM7xQz/0Q/yr/+q/itbf+v38gRd/d5Hhqky8pJHDvgbRNdm522WBii3dewuiaYMuetJDEXmZaYOeephmsiAnyOn01KLmCsadO9ah7t2DV3bpnv9dkO6Qf+mvoV/6F3G+w/mWJJpjdIrqK/TyglAFyo8+ZDifYu+X4ufV94i//h7q+gy/d4h6b4O6nkLlBflq/FUsrPOadoix3rCqS8GouojMW4q8IU0linVzuYdWgWnSYb3m6+uc58uejTXUVtN6RW6gstD5QO0sh6YkVoqNM9jgmIYpGQm5iigjw2GmKSIJ9TA6MNiI5z/xJnHRSexx1hHd7qHQgJxWzN4grYBJJxstqwhDJCS0/QWqqug/97uJ77+JOj4mrJ7NaS/OerzV+G2OtxHxtGKocoIzqHRg5+V3Wb15e0SfDiw+ep/gNK5JsZscfW9F/vrPw+EPAsL192MJF6T0Hx1/CbO9BO+E+Pg9L6KWF/jdfVTfMfyaJmaNigwsdnHTa5Q73001bMne+Z/R77xJWAw0b0yJ057J4SXbx7ukM/lZ203Jo7eek1hgIGGJ3lkJkvbmTZR+iH3HkM4qqrMFq/MdlpsZsbEM34jJclF6V3VJ1WYsT/aIV1NOTw7Y3z/n/HwXpQJ7++dX74NdTun7hOufeV1S15IBYxxn2ynH1UJ+dzVGudqIZZvT2oiZ19zdzBiCorGG9WBYD8KG3Ayw7B1d8NShJyMmMwkfmw6076sc+GcEfAKEtqkVTGfYw1tXVL8nSX/6u3+U9H/6CfxiT0R/b04wD04whx493dL86iEm66TV4gzhHUXx8mNCpQleWljquQVM06cOgbjALb+CNRnEU/Lpx2E6H10BRubNo3rEiyM5AxaJD1+1uKXYYH0f0z7YByXtS5P39KsSP0REWc/6F29T3nmMua4wn8i4+NlDOdVHskGvj3fZXs6vkNGrx/vEsSWO5YS/3kx46ejRVQux7lMmaYfzmsGLJqCxMUlwrNsCHzQ3rz+6OjyZoiX5WE37lTmui9HGUeyuOLt7XWBgXlFvS5bbKSfLHaZZw2y6FdDVEKMjK62BJqVrU4ZRN/Ih5Oe33/gv/ov/gh/7sR/jh3/4h/nP//P/HIC/+Bf/Iv/6v/6vA8L3/+//+/+ev/k3/yY//dM//S2/3gf3+WvJtiaoKxGf32j0biB+PoazCv+elEHDucEPhvyFY+zZlHCmSD822nIsBC/9IHW0L2W5uoFqAA3m3lvkFyf42YL+9idxl79KufPdGC071ebdnyb76i/C4wtCY+jP5wybQqAzQ4xJe1k87Rq93EjS2qYluAwzZlxHkeXRaofeGfbLLW8tZ+ymLcnYez9rStRlYF5U5Fl7Fdhy0uRcdgnXMosLitYpeq/IdKB738kfYGI0F4PFoLip5zxyayIUmdaUkSY1cC2z7KU987Qd4T5eNBJeSIr+DPSiuUL6Sp9yRPx2AvZxXYw9yzD9kjBokq/8spxUphNU8WwWfx3bKyubju1VX9xMakzeUd87wvUxaVmTzitU5Fi9fZM4bymun6Ne3CH8+tvUL/0cxbV/+psWfhDRn3rhn0X98k+hH9wTVXisCHeeQy8vYOiJ/8kp6l052en0DH20pn37v4X9T9J96o/DnXcw/+n/m8VH78vprIvZ/fRbPPqVV8inFfliTd8l5EcXJM9vYG8B2zXReglVRVgN+C6nPp/j+nhEP8cMzvDW+SGxdqRGIDxP+raxsayaEnUuscC9izh/9wV6Z7g+v2SxWFFMt1y+cUc0Hc7Q9QlaBU7aTAKDVGBrDRd9ykmbUllZtE9aRTYe4AaPxPaOQtO9NKJ1gXmIRuspvLWNeWXuRVTWpTTbfzhT4dsxQlHKouo9uq3obn+W+PQbaOSeb6q3yLIcXW/BO6JP9XDZQSox1PHOGjNrxYbXxfg+uoKBPSFVqqFn+I7vwSxP0BdnxMdvkX757+Ov36a78zn07BXqj/9udLtGdxvM+hxdgDtJ8cex2PMW4N7sUFq0NGFdYLJeKofpgF2XrN+7dkXGdDai2L8keMXwXkH/pVI0LAdLXBfTrksRn+7I7zl0CdW25L3TQ/bKLXkmWo2T1Q7Oq6v0S4A8EQ0RwCTpyeKeg8Ulu0dnFAeX2DoTYFqTMLzpRTgaW/xgaNcl+889YvnogKGP8V4zyWviETvc9TFx0lNvC2Z7S9JJTfOkpaE9cTxcVeue5fiw5//Bxy/+4i/yJ//knySEwP379wEJ+Pn3/r1/D4Dv+q7v4vu+7/v4K3/lr/AzP/Mz/NW/+lf5F/6Ff+Fbes0PLvjrI8y8EWBPL/19PR1T5lZr/OV4ysi7K1Gf32SoSFTN/kKhp+Pq6JFT67snqKPJ0387mEGcQJISslLS+/w3L2B+/xXsrcdEzqLqzRVvWxtPcI5upHTZbYFSnnh/jTtNcFWG62M26ynbpsAHuRmrLmU3bRm8obURQxARzrZP5cTkNZsupXuSjT6q+5e9obGy4CdGrPipVgxaAxGJUWRO03lP7z3X9JTcGDKjKCKYxZ5YB3krxohZpf1Vvng0RLguIU0vJKPey6bpyuaHCJZ0k1zZuFTqxdaUjeXV9tns7l0nan8VeXwXCxfdGbr1hHY1FZV/ZKX/uM3xVotDIBkkovj+hQioHvw6VTwh2/nc2K8Yv7/d4up7RN4Tjq4Rbqfo9RK7f0OiYs8vUOdrEYylGWEylQf+8kS+fnKAtgJFMfud9JW3KUp7ysUaN0R065IosmMrIkAUCVb2ta8JgroyuCbB2+gKzvPkwa0JdC4a/xiulVuc1/gQX7WEXNDUfTqSIAdO1nN6GzNrMs7Xc6a5hDjVfcqjakrtNNopPIplr1n1ERuraJ38eBedl9K+hsworudCk+y8GjcCisGPITFKvibWXtIF0+6Kuvgshjo/xV+/hS/ET5A8+hohinGzXeH2L9/Ez3YF97u6fHoKt060PrstKlf4RzGuET1JaOKrQwYqoPc74ne/Lq1GZ0XPkhf4rATvqNZfRS0+IXOnu8Snj0in76BWPXQSja2nLe2DfZKdDdHRFtVIeI7vEuy2wNXSrolnHUp7hjojXmxFuNxHRGVD3KYiXkVSIrtWgGDZtCLJO7o+IY8HrDfUbS4MEKcJKCLtSYxl2RRMko7eGTzQ2ohrO+fM9y7J95ayuR5JgcEZhnWJSQeptg0RtkuoLyK8l+yMJO1Fv5T01E1O3yfs7l3inZa46kGQwGUiFYkn4Wsfjt8+46d+6qcIIfBv/pv/Jn/mz/wZAP7e3/t7nJycsLOzw+c//3mKouBHfuRH+IEf+AH+8l/+y791iz+I9SUgnmE99ZLK1Xn8RmGXExH2jaAVNQn07xRCnYuFtKfLLaTy9aGC/t6CdK8V+lZqxf+rDb6Y4LMJyna/wd5RTj5OdUvCX/Txa4AoW58ImmwryXJ2BPnMy1YW/jbF9pLU11sJSdFKUw0JO1nDRVOwcTGVjdhL5cbcdimdjalsTG0jWqexQTEEWI1Ka6O4ClKJtABXovHkkBmNC4HaWw7TjDKSk1qqA9PIXwmyBm+wNsI7I1HDQYGWzcGTETyoBHwltkmVOPQumEreXyJQuSJsngQlQXhGAm/vzJjroARqE1s56dYZdVWyf/OxXIsuYWhToi6R8qMKQv07nhDf3mJOHmIW72D2vu83nP5Vc46b7WJ3buEXH8GcfQ2fzYhO7qMaz/BoQvxZTVjsjh5wh7KW+PRd8ZRnE8z1HnakHWKiFrfMyHbW1Gc7DG1MFA/YbS6QoL0WP9nBdPKeB2ukPdBkNHV+hYIOY9iKCSLm6pUmjyVdzTuDGTejIYhfuxpiYuWpbMm2Tymqkou2YN60GOXpfcRJm9K7J95vuOhFCDaEgPOyL17ZgVhpCmNItWIeO4Ygwj4XFKWWtpNRMg/NKCSNjcUYJ+FKz2pYh929jk+nRKvHRCf3sdeewxczEf4Bdue2uDasHUvzyA4FJ1CqwWM3uWwO0+EqaCc4wfPGTUVYnaHmEewsoO9xN57DZyWqr1Drd4mu/yAmv4ktKgYdwXyBOpW56LsYt8rpNiU6dphZTbAGnQ4Ml5m0rbwiynqS+VZO+3X2dGOdWeJIciGeOAEACQnzmjTUaCP33f58SdelVyd7kHaOGaOgWxfBmOqXGUesHTs7S4rdFVHZ4vsIHXnZZOgg8cSxxbcJfZPS9wldmxJFlixviVIJoHrCLXHvIzs6G9F5TTyGE4EIUf1vgd3v2QGlf+eNX/iFX2BnZ4c/82f+DEkiYsy/9bf+FgB/+A//YYpC2lS/5/f8Hu7cucMXv/jFb/k1P/AMMPMGt03Ba6HOTVP84wGsiIn8EBFfX+HXKe29A2ybUD7/6Erxb2Y19iwj+lQmntvtpdzY2w6O9vBHN9DHDxk+/hl03xI/fIuk/jLD85+46g0730n530t2OLmmPV+QzLZoZ2hWE2Y3Ttk83id4hUkGNu/coLzxVPiWZy1F3nB8uUvvJa1t3WcUcT9ytzM0ARc0F21B5zXTeODtbUHjFG48bfUeplEgMTLJ9ZPwsfFk9rhxZEYziw2LUYU9iSCPApkRMFCqPWU0MEtaosiyPRUPe1rWZPsrkk80oFMBoTQa9fw+qj6RjdfBHjQ10c2GsAnyce1p7+6TvXiGmkbAs9ndJ2WDrTNJRuxjkrKhrzJMZNk5OsVZw+ZyjnOGOB5YTCshno1aBrOoab6+T84Z+jkpP2oVMbgxEyDZQ9/4g3ADGH3/2Yv/e6r1V5/Ox0mDfzeglYZigiv3GG58juJXfxpVb7EvfIbu+/9pouUx5uIEZU/gQpT/6aSm2Fth24S+ykmWE+JHK2J+DW7tg9Zoe0K3Lbj/+BpKSRm/s5HwHoaYm9MVu9M1F5sZ1pmxfyvYirlx1H2KVoFZ0vPWdjIK9AJGB3aTnmpIqGzEeogZRkLkelCserjsLcMY/esJ+BA4Uyue03sUkUIp+Po64jCTz8kM3MwHHrcRg1doBYvEMUs6BhdRNzm2fXbq7vZ3/1HM5pj4/C6qraXvv75A1xvs7nWyWz9EtX2dLkox+zdJ3n0NlWZwckFYAZGnfedwTGQU6uHsUCyvUdmis57QIk+rg3365z5OfP9NfDEjOnsAUYz93L9CCBatSpJojh22DLc+ivnqCf3llGY5ZXhwyOL2Md2q5OLuNSY7a7K9JaZor0r/3eM92osZQ5NRrycMTUq+syEqWvn4umRoMrRxJGVLsbdiqDNp4WwLDg9PKXZXtKspTZVTNzlt/zTVsR0Sbs0vebhakMcDz+2cc3h4yt7L7wiN1GnoI2ybUDx3jNkbcKcJ7aM9qrMFbSOVQesMxaTC2ojN4wOSpOfB6TXKtGVnvubsdI/5fM10d4mJHJeP91E6YMdNbJz0/OMxWx+O38pxfHzMd3zHd1wt/AA/93M/h1KK3/f7ft83fe7BwQGvvvrqt/yaH1ztP/X4CvGdP5dBOcG/JXxoM2tJDpa07xwybMVPnV8/Q8UePR+bky7gG/BvrdGlR80V/j3xpxNFIg6qW5QdcIUovA3HmIvHNHd/luTWH7zq+0fzlxkOLjAXJ8w+/S72dAJBUyw2XN4/Ip/Ugpl1hu3lHNsmsvNNBha7Sy7OdyiSjjzpydOW8/Vcfg8t+NR1n4D3GOVJNVx2KeedJtZQRp7IQ+phOSi6Vh6+WkkvdvBi+ZvFhmu5WP86pzjKLKddBARmseXji0vyuGeSNUwnWxaH59g+pthZY4pOTtUnbozs1bLgjycm9yghPNgS3e4Jt2+hVpeo8y2hVuSfWYJVIqC8/qwifQUR6geDHaOP850Nfohw1hDnHdc+9h7BK2ybcPHgiMOP3LvC37plQXWyS3KwIn77Nbrkv0a/+L/Dv/3X8fkOXP/BpxYuU15lAOhkQfeR7yIpJui3vgE7QopTfYtfvAg6xi320PWW5PgtQhTTPf99JMmvEY3Oh3o5Ff1K2hOnPbZL6JZT+tWE9ldK9v4Vi3pwD79JSIuWw50LzlcLQf56w4Nqwk4qi+qmLqn7hEnase4zQoAbsyVvnh0xibtRKxKxm4j4LtFC3csjSxn3uKA47xK+fKnHRMhRO6A1M6MwShEIdA4+VRxSWZlfANNY5pzzcoCuxpNo60btXeRZ9yn7Y++5Wk35R8cpfWsj+5v/Hf5THyNEMfriDH3vPfAB/5GXsAefIrz936KTgvj4G5iHd+FsiTuPUdqImC9iDKx63/fcXY+sBYNvEx796ie5+U/9GiErJPXzUGJplPeo9SXqGz9N8sqffPr1Rz+If/AX0Ddisv4cN0Ro41k/PECP3IOhTejvHxElA3HeSVKpcSTTmmRaU147x3cx2+M91GpCsbMePfYS1rO9nFNXhZT6s/ZKBLp8fICJLM5FPL7co0xbyqyh6xOON3NeuH7OYrIhihxp3jA5uCTarVCZVPaaR/sEa7j49RevkNSTg0vivLvij8SRJY4tA2M1Kra88smv09cZm/VUnC1pP0YOS6zvZL4eqyka77/ZAfAsxoc9/w8+oiiiqp5W587Ozvi1X/s1gN+w+D98+JDJ5FsHuH3w2s/+Ar1ZyzF32xAe1QRbolMR6rkqI711RjLifAFIxvje0Xtudp8QAKVEnexsZEFbLVFtT7h9m+jxXey157B7dwhRIqpdoGvuU5QfAUCbDDt7DnvtIdFmjV4P+KVm6BKSrEO/z6pTBMX6fEHSJyRJL+zrXqJXIy16hMbGRCN20zp5YOvI0nnNdojpvGI38SQmEKvARW8wWhZ9o0Rg1TtBvFsfRupaYD2ISCszgdZJdG9hPPN4INaO3kVYZ3DO4IYIvMINEQbpVw8nM0zeoxInueanZ6BBT0ZAh1Wo0xNYdYRWiT/6iS/aBdh8MH7+b3Yk01p6kpG81rApaNeliDhVIMp6ulV59eABoQI+gRe5JiXJO/m9gsesz3Bv/3WCNvikxNktQ/sYEy+uLIBN+wATTRiyGT4r0Xu70PeEopRes46hPUXZAVVXqLah/s4/it48FOtfElE/2pP+/RDDBpK0wxhHdbZAG0+U9qjlBbSWMAiW9eRyl9NqQuuExX7exxSRpXeGLCiuzZdsW9nwGhXI0o5Z0pLFA4OLyAeLcobUOFLtyYxkODgv8yEznpuF4q2Nox8vXes9t4p4hPcotgPczC0nraF28kDdSTwuKJIosGcce+lArGIKo1HjnFQqEBtHkvQkzxLqUkbivilK/P4hOluPDP4Mc/4ayjtcciB2vyQjWv0qw+VU2nVli046zPWexFyik54oy0VTNBjstsCuJuw9/xB6hbo8I05kUxuSDDUmCA7XvoP31zasq4gf3oXIoLIWpYNUqGo50QPs3DihWc6wgHkCqDKOYVNeLbreaZTyuD5m+eiAvk1JigbXxzS19PRn0w3R2HastiVFWQs+N2+4tnMOgFaBKG+4k3ZEsaWY1Fc47GS2JXSG4AKhN2jjUMkA2l+V57ttcQXR6i5SjHEo5Ykiub4XFzvUVYHW0k7M0p50Ul+1amd7SzYXc0zkSLOWYvebN1vf7hFQ33arX/g2EwN/O41PfOITvPrqqzx48ICbN2/y1/7aXyOEwEsvvcSLL7549Xl/+2//bR4+fMj3f//3f8uv+cEX/+BRxstOfAluVTwVjljpzempBxUIzcBwPEM3gvplXAB02UEsqXPYgC5b8EZWzkHoXWol/Xy8w6dTCFbgLa7FB4t1FUpF6OyAYe8FKS8+ehOzHNAjmEcbfxVoEecCxujbFO8MUSSilyiMITqRRREIQSaXUoFkFEp5Rp1D0ExiR6ylR68VtE6sVrEGBfQKcBD00/W3d7JBKCOPD4p54lkklnL0+wavr3bHbizH4eVho/KBsJxcCSbpAnRWdHG59BjCxqNcJ0EmHlQ8PvWfXLJn6OZRkRfaXWyxWwEjBWfAOLnmCF/8yYkLRp//EEmrYD6qjesGvTxHb1cM155HeYtrHkGwOKBDNnu+uoeZvwwmwxcz/GIPVW8JWT62ke5jqlOpIFVbQbrGE+LLL2GW57BpOb9/jb5PGGyM94q4j9ndu6StM7Tx5DtrOFsSWvlZXR9zXpes+5R+hOsMo2r7yYMtTcSPn2hLEjmSeOBgumZwIsjKjL26xtE4r4YxF8LoQGYch5nlbqUZfLjqk8Z6bBEFiLVikQy0XqEG+RkyE8b55ZnEjsw4Ih2RK49mbEUh5EoTyZ9nNopMXDtJit89EthOFKPaisg77M5NlO3w+R59UhId3UV9vZVnh/FixStSzLwFX4vdr4vllDpE+CEiv3GGb2LM+VpaPVkmqZXWyhyIp9/0Iw2XXyHejPwJFTDJIN8vKPo+wT+B8/QxqRZYj92UhFHHYvsY2yVYa4gih/OaenRMeGdkbnstJ/AnuQ1dQtul5EXD0McY49g/OqWtCpw10tLLOrR2pJMGFTm0cUTTBruWfAHvDDoWNkoE+HED7WyEji2xCsRpPz47noaVGePExjfqCvKiEvGpldyNpGzoHx8Qh4Ek1UTZh1a/307jn/vn/jm+8IUv8Af+wB/gD/2hP8Rf+kt/CaUUf+JP/AkAzs/P+a/+q/+Kn/iJn0ApxY/8yI98y6/5wX3+X29RkfTv3TKTnXkqDzbfycR0J+nVqbS/nNJfTgWikY83x+OS6LBCpUpyvA3CoZ8XUE7QX3+D8NEXUX1H+tYXcLtHIhqKRpvf6tcF1jN/mSTZp509T/cC5KePSTnH5B1nr71AUjYEa+i7RDzes40EbPQJWVlzeP2YoZU41yTrOKxKzqspKgR285qDcosfyWhHLuK9zYzKSj9VRDpe4lSRxX2RiDhvM2gaNyq0PeQR7KWOm3lH7zVHeY15n4hvltXMJhIaY60kfpkxiEbnnuToEl16fKUZ7s1IPlmLz3lSgIlw91qi651suixS7s+iKy+0yr/l+fEPHZvHTwvIWo+9z90Vrk3pGwEpzT7ygPZ4h+p0lyTpMclAczkTiFHkKF98iFsW+MoStQ8Jd24RLU8w9RpXzIg/9n+kufhVvMlIkz16kzHU91HZAfYgQ9me4cV/gvjkNczylPzxXcG+bjfi4z5PyL76P4i97O5jtl++yWv37lAkPWk0jES9lOvPPWAYT3lR1tO/M0eng+CsRzGm0V5IbkGxm1jKeCAbg6KOL3ephoTdomJWbsnylp2Dc95++wW2YyxwZiwnbT5a+TyTpCOLB4zyDF6z7GP2Ui0towBD0Fx2sujvJI6j3JFHlp1EY1Qk88xqZu/bkJ62Kas+EvyvCiximQNRJMlwzj7DMq/W4tKJIqmyvPkAHQF7E/z12/hsRnz6lgRyzW5Tf/cfp3j3P4V0hO+0SrQ/GhG4tQmbR/tX5fhkvqU93iGe1XCi0NsV6maPWi2hnBCynOSdv0uf/DLD9e+EYUv+tf8RIkM47whOk167oL57JCXzWDbfp/ev4ZwhSaUiePr2LeJ4YHp4QRpbqtWUvkuuPPFPSulhdFXEkSUva6rNhH6QjcKTaOi2Synyhtsvvk58NseNcDATWznpOyPVybxDFz3Ve9doN1I9m99+jI4t/aqkW5fYIWbvpbvinAEW109xXcLyRKpi+aTi+ZvHV/+22UyYXTtje7rL0McSaKQCk+kWN+YNuCfV2Wc4fief1L/d40d/9Ef5mZ/5GX7pl36J119/nRACr7zyCj/2Yz8GwGuvvXb19z/yR/4If+pP/alv+TU/8OLfn8+IZzXBaWydMWwKyu85RXWW4X7O6u2b7Lz8Lm6d4buE4oVHKBMkyU0FzF5P+/YueikPzWE5kZPjtsPUNWre4jea8Mv3xC1wPcX84P8F/8b/neTR1/BZib/zT+Ore7jHP4+zHTpKUb3gYlVZoaueycEl6WJDv5rgtzm9T3n08Jp4WzNhuue7K7TxuF5K7QeHp6Sr7krRbZ1h3Rb01uCC5sX5ksfbKeddynlneNBoygj2U0eqAxur+czukuO6YNnHtF7RO00ZOcrIY1RgP2vJo4Ei6YlHQdiN648xkZPo17THpIO0QrymeXOf/OVziDS61CSHlQQIjAs/zkrGeJGMEcnjpiKJYTNIa2Xn2dzgB9/7Gv2jHTaP9rk42WduV2TjqcUNsYTldDEERZT2TJ97zMUbd5gcXDB7/iE6HWjeO6I+n6Njx6Q6JXX38J9+BVfMJP71V/4sSVbiJu9QlweY6pRgUohS0BEuX1D8yt8QrKxWMJ+j3nxbvONGofOB8MvvwEdLfCXCxO/99Je5/95ttl3GYCOyRHqi+y/dJZ5V6KIf42MV+qQjX0341M27vPH4JoWxRMZz3mb4oIi0o0xbGpVybfecwUpi2/zggvXZDmk0sFNI4uG9zZzTLpZTmwrMs5bbR4/p+4TJcoftELOXRiRjQmRlNZWF2ip8ECGh9RmzeCAzjmkUcdpFHGYd07iXNLigOG2lAuODovWa3azGGIs2nnJ/+UzmAiCcDt2hrEVFMd0P/TDZz/+sYJm1Jv/8f0f/Xd8PKkI3F/jZc3AwIdyrCMsB/dIeDD39F6E92cE7zfTmqbTv8g6d97hNjqszif8GeHvAXHNQn6OtpfvsP4+OJpj7/xPJu19D3XuIf/kT6PIYihK/f53Jz/8K+fUz+vM5zeUM2yVo7Vhe7GDPdrnzyje4uHud5aMD7BCxXM052czYK7fE42av66W50HcJ20oqAfOdFavLOV1fUhY1+zeO6bYFzmu6ixnpvMIU7WgRhPi7c8K7x/QPFlQPDkjW5VV7QSmI99bockBpTzKvSG9cUL91jXhW45qE5d3rlHtLTGSpNhOWyzl7bToKlqHIm1GEKCLgKJKQrWZbkJU1Sgc2J7ssnt2M+HD8JkeWZXz+85/nv/wv/0teffVVXnrpJf61f+1foyxljn384x/ns5/9LP/Sv/Qv8aM/+qO/tYQ/k/dE12qJ6/Wa9myOu6/RJUS7FTP3GHPDEjYeLjR+TPTT6QBB0b4pu9T+fD6G/QSi65eC08wQ1rf1mOc05HNIEtTP/ThRMZF2QBTjN+9AukPIDgj9JXp1Fze/jdu/i05SzFFHPlwwrAt0bCkPL8WKZjxtl14t7tWmZPf6KcXRBVHR0p3PiCJ35dnVxpGvZiy3U5ZNQaQdNyZr8iinjDLyKOGyN6x66a+mGt7bTMkjyyIZqGxEPT6j8shyWFREWqw+eSzZ8GXWcnq6z8HBGeW0uuqX66JDl4O0RPbm8HhJsB6VK9idSvTptgYfUJ97AR7ck/cuQvoM24bwBI0QPZvTXvPOEcPII5jMNhJ5fLEgyxvyxRrbptgqo9uUdFVOfLYgjHoGV4vtMtnZgAp4G2GrHHXPk/BVzDSFbOzp3nweN7tNtvc9DMMSv30H1a2E8Nht4OGFpENm7xM2Vha3ihkup8TzLe6NBp0GZp9+F79JMLFlc7Fgs5mwqiYUt4/RZY9eBPxLL6HG3rXOjkkvVhRnO9yYXdIOyZV1S0r88iY/wUKHoBlsxNmDayRpx2K+Imszltspe1lDO564Yu0p05bVWlwCPiiuFxWVjdhaEf7tJJbKxmQmsEg8h1l/daKPVSBNepJxPikVSIzETJ+3OeW4GThuCgJS4nZP7sNnNZIYto3MN61J7n0F/8JHUX0nfIZXPoup1wRdXcVMh/kOqm5hY+G+iDGjXc1ksUWZgL0siY62+GVK9fYNsYkOEfFYLkcF+q/k5B87Iyx2UZdfx+uIqG+kJVSUqO1agFeAaiuG3/97SX7+76LLnuzGGc29Qy7uXyPNOibxwFDl4rrZTGi7DKUCdw5OiMd+vnNyje0QE0WWncVSfu60ZzZfkxcN+UScLZODS9nUTxrimyuUhtAqfBMDOfZ0gq0z4qKl3xR0VUHwijjv0On4el3CsCnQp5Z40sizLO85/Nzr1PcPMdqjtUdraSFGeUtStLIBdGIflvaip6szuianmG3J91bMimejB3r/+BDv+5sbSZJc0fz+l+Pg4ODbYu97//jgPX+vCOOKptOeZF5BUKBFvGeqEemZCHzGrXLp1wVF8FItSPeXV301HVtU7PGbBJ300lKwCsqSUJTgPer4GH0ALkkJUYJyLcE1hO5SfqTpDekBT4QPoNoas3/GsBZP5BMKXVlUqCbHPqGdtRmT2YZ0sZG+W+Qx8UDsJA87ji151tK02cgDCORpi+cpYKeyGZXXEMTD2zpNZhRGBXLj5EGtPYukZ5rKexONwJUs7VBKFgClRDfhu7E8l7gRgRykr+mRBm6kCOUUFXega+jHh7nWUKTCMH28lN87Gq+sfTZ9XjdEsnlLBokVDQo7iqi0GYNDmkx8/kNMX+UiptMB10nUKV6JnzvyuC4WIeBljK57VNHBjX0BuADDsBznoEXZDj3UKNsL+0ArEVmsVqK+NKDzAdN06HmP34zs/lKhdU9xeHH1e3S9bFBV6mExw+7fIkISIbU2pJ94l+xBzazZYuoCNZbxi6SjzBqSeBSJjfCfJ3S1uskxxuLH3v5+KaTJxsY4r67S+6wzNENMPQpOA5reaTLjyU1gGntmsSV533x6ku53rahwXpNHA+XIjS+jgTy2ZGZgZ2QOKBVEo/Asy7zr5uqvyloRTWYZWAvW4qYHxKfvogBfTAW/qzVMCpTd4M80KpWwL5UqKBIivUXNY1Rlv8mmqLsYFScEq/H9099JN5f4uMDlC4I2RBePJBfAe0IxwS2OsPufILn2FbSzoDRpsyI53SUpGpKJZIKYSsJykrgnKXumuyvaJ4mQkaPayDz33ghELBlIJzXx2DqQZ4onKkb+RlAC6BrhXCqzkjCIVMaGOsP28dWBSBtHcAqVBJQS4uewKonKhmA1QWlUYhnqjL5P0DqQpa30/WM72q4N3purfA37BFRljeg/ihadf9jz///38cEJf0NE/c4RJh2I99ZkH72QPnMsGwDfxfAQzM54iqpkQXPbAm81UdGKnWWEVgCETtM+3iULl5gDLQ3PppYEuygiHB0JNzxK8PkOIV2gV3dJjt/CzfZIXv5XqB//HD6dovoW3Z6jDgrMo57+corrxYa2ODwnWU+otyVNm1H3KavLBQDpphB86xDLQtUldGNf3mhPMgqlIuNIo4E8jrBBM42SK2tWogNlJEIutKeMBxbKs5s35HF/dUqcTzbEsWTcx/HAQq2Ixh6ixMWKuC/04NY5kbUihsqAmfQ23f51lO3R60vUG2/DToHfOwRAvbsCHVBzLaew5tmc9q5sWM7grcakw9UC6PqYoUlHq6WW380r8p31SCzT9FVOczmlPLwUMJTVxLdr/IXGNzGaAXfjedz0CLO+hz55Tf4+VCjby+/ftwKHsQ42A+4ixdw2MmfsQHx5CeUC0zSwAn+mIQqYvCcpG/I+Zh/Y3jticfj2mATXY/dvoes1w+Ft/J1PU375F2mrgmGEp2gVWEw2FGVNmrV4L+mA3mncEFNvC7707i0yYymSnmnacHhwRmQcl/WEi6bg4XbGUbmld4bLNudulZNHnsEraqeIrOEglSpSrL1gqPOGMhY+wGWbsV9uGWxEmbZMS2kvHJVbhtHCdXO2Egtm1mK0p11OmT2T2QD2QU50u5Gef72FrsW9XgmI6lqM2Zyi+o6QpPgkx8+eR7V/X9pXRQpI6mfogShAEqM+tgPVFhVXJJP6ihwZgsLVKa5LKG6eAqDWS9R+j5/fguI6XLyOvvdLhL19VLXBTxaoF/5ZuPgSw/Mfv2L/x6tfZr59TLy3xuwNhBa69YTpzgoTW9JZRTyr8G/eAh2YHJ2jjy1dnVNXBW1XcHBwRrq3EiG0M6TXLiVrI5Y0QHce093fFdZJ0RJfXxOOe6KdHrstWJ3ukpcNcS5aKaWCJEGWa0lS7RP6TSFCwJFI2b89YXMxp6oLknhgtliTTStsH1OvJ1Tbkji2snkICmsjqrqgyBvME3Hi+YxnHe3zftz5h+ODjS9/+cv8wi/8AsvlEmstIfyj38Qf//Ef/5Ze6wMv/tnvdoR3L0Tcl9oxVrXHLxVulQsJKyjsmVCxomst9s2CaFLjuoTzt26xO574gzOyeBzvkD9/QugN/a9lxDdr0MW4Mwb/kY9jvvEaarFLSAqS2cuod/8B7ct/BFxD9Hd/gvDJfwqfiSjQTXZIv/IPiPfk6/vLKe26xMSWrGww2tO0GdYb9Lhwd9uCB3dvsZiv8N6w3MxY1gWzvKFIO44WF3RdSpZ2kraVtsy6jNRYivHk97iaiHjQOFn4s5qdyYY4sjRtxqbNeeH2PWaHF9g2YWgykqIhB4YmpVuXFPtLsjsnYvnpNGbWwHkLuYbG49+s4M13iG5FhOs38Yt9zN6l8NTPT+T0O4Xw5BDWW/zq2YS5dEtRVusxa7y9mJHOttgmo68yDj77Bu3jXfpaetDaeOlX1hnBB2m3TGq2716nfSin+3RvTXc+E0vW3grdVvhsJtG1tkcd/S78+a8RohSzeUT8hV/C3supHxzQrCcEr7h2+Ouo8zM57a0G6l/JKD6yFF9zF2PijvrBAVHRMrt1zNQZ4p0N6tYudnFAiBL87d+H3bxDtHyX9L0v4+Zb0qxltZqxakqaIb6aR3nWcuOj70nVoo/p1prVesadxTnrtiAxlvl0w3ZbigVwtPfN8przako1JHjgO/fP+PWLPQ4zmU+PmoTLPqL1mlls2Us7miFi3SfEyrOf13Q25rnDY4YhYl1NUAQOFpds6pKqy7DOcLC4ZLF/8WzpfgB/7FPwlS/CxQYW0ndW/8Qd9MUZPF6KewdQ2zWRHQjP/yFClqPefg+/0ajS4xuN3kVaCHUrG4OltMOSnQ0m78dMifH7Gw/aMzyYoC83RJN7qI/98xid0kUT2u+14oQpDwgmg4svobyFj/8IQ30f2lOSf/FPot7+b9HvfBnqCvviy+zYX0IdRBAZ/MMe/fIhe5Nv0J/PwSt2Pv6ebFhjDzpgzye4JiVebEmOesKdW7BZwXIjm5h/8qNkxw/A93C5Zbg3IX6+wp1EBK8oZlvSSY2JLa4Tgt/lm7eZd49lIx9bylsntCc76NFa6y0Uk+oK2rO6nI/viSNOBiazDUnWUa2mXCwXVF1GElmu33hMX+WYoiP73LNn+384Pviw1vIv/8v/Mv/Nf/PffOCv+S1b/BltM3iF26YY3TKcziT2NLYw3pS67FHG405STNbh2pRgNXsfuS+hO8YLAjgfCIOW9oAzqMgRGs3wazHRbou+EaPXl9Ba9IO7ZHWF332NoA3p63+LkOR0L3yO6OIt7P7HCbMd8JbhpS2x/RLJfEPcrFFfv47JeongHMuE+9M1Wjvqbcm2Kokid1XGmxYVgzNXEA7vFWVZXynW8YrV5QLnJQ5YqyAqfeU5XFwSxxY72oDmOyvyriHZ9pTzDdn+EjNpCF6zfesmto/JFluS2Zbk+pLheE602KJLYSeESsh9wUqMsn7lUCoj5yeY08ewqmHvfee5IsGfecw8wHyCnj2bYJ/86ILq/iHdphAWet7RbwtMMlAeXrJ+8xa2TUjLmsnhuSSlXU6vsKL12YJ8Z3OVjua9uVJQm6zDTDrU618n8250e8SoN3+W5OJ4RD+X2M99D1H0RcroMfrhPstHB+Ch/dKEbjnF9jEmsnT394gXW8ykAx1Id1fSDootOq/oT+ckr62Iuq/RfN8/S5HdhOwmVX5AfP8bsrmopY2UxT3OKy5GoVdWl0Tvuivdg7WGsqg5Xe4A4LymqkX4tW1zXJA2QN2nxNqRR5L8dtaUfGS2prYxl11K78Tm5wKS4qekUnaz3BJph/XSLgBIkp659mR5S9emzMst06JCq0BRis9bR5b8+ZNnMhcAold/Vf6SRVLqv2zR0WMp7R9M0GfHhPsXqIOCUJREX/t/0XzqD5Lzt9H37oGOUImFvX38bEFIUogSjH4PHS4I7SALbuRwVYbd5gxVTnH9HF1IwmXQhuhv/fu0r/xuAT5Nb4g+xHYEHUF2QHAN7vHPQ1JCPKG5+7OgDfbGCwAMN38X4Z9JpKUUJbjfc5P0nV9F3zgn27+ELME/HsRdU6ZQlsTPQ7xaid4mUqi6gvWWsIIweNTqNdx3voJ5dI/Qi16qf3OG62LRMKQ9wRnqbYG3EVo7dl68L4Cyh/u4IWbx/AOpuM632DpjffcafZtiIkec9CQq4J1mu5wRRZY066SCYCPRh8SWOBrIphXxpMFkEo38rMeHav8PPv7CX/gL/NW/+lcB2NnZ4aWXXiLPn5FdaxwfePEPl5YQtIhtgsKtRfAVvCJ0McoZTN5JfwstG4I+wjUJwZmRly0hHXQxbjAo47GbEp30mFmLWxfC9p40QqjrO1Gzg4BbioowWaC8B9uPmN8E0h2UzgjDKW56RDxfQGlR1pJXJwwXU8EMB0Wa9BRljR2kD+acobcxzmmSpCdLO6Z5MwppHHHiiONBIjyNRyeO3aMz+vEECKC1FxHfpBp7bOINToqGOOsoZltJs4ut7OZHsEcStPzuZStXQgchnmVcURHlpOMhAl9M0NZKf7saxEuYF7BaXgF99HS8rEMvD99nMOw2v+onRlnP0KRXNiYdW2wf42yEcbL5iIpW5sH47wB9lRMXLSYZ6CtRcutIoCbD+ZRkZ4s+fgjXDXZxSPz4XRGLaSP5D0kOkwKzvyZXp8TTCoywBprVhIuLHXZ3L6/Y7JGvcFVGCBodi4JcaQ9BxKlmVRGfv8NwVAlRcAyUctaQJD2TsiIyDut22I7oXhc0p5c7V9dboCv2yg3gguaimpJGA62NsN7gUcQ6Io+etmSUErLkEz3JLJHevpIrzxAUxcgHePL5WWRpOyHLPXnYP0msfD/dLSkb6T+rZ1eD9ScKlTtUHiBDnCbrCia5zM+hl/ZVnBCyUnj/rpVNXZZI66ZMpYpVbwl9h1/syxw2CrPX484Twgi8MXkvfxa16GNSJe2G1Zr0nVdxu49xkx3is/vYxSF+cpNy9gp19RZeRTBsYdjis5lohspD+aG7S9z8pmwWTCbi4uhVmM+hEU2AfiEnzOYCMYpiVN+hshy1WUnFwnvcY4Nbi4AvGhrMxSmstygN8cGazWu3MckgG92soz7buTqYEMl16i8lKfNJqR81HprG9yBOBoY+vgr2CUFdxQqbyBGcljmgA85poshi0p5oUqPzHr95dhHPAIRnIPj7HdxG+Mt/+S+jlOJP/+k/zU/+5E9+W9T8/2vjAy/+9qLETBspd0UD9mRGNKslbGK07Zm8u6L7md0OuzZ4K4jOJ0KvJ2X/YZuTzLe4LsZkHXra053siB0mKGisgEPmczlNRBEhkQ2Hm+1Jz+7yAcPeHUy8wNstujqWj+8ejmIfh3YWtR5LheNpqJxvWJ7uid0r7VhWEyBl4jVF3lCMiWtPoBwAOnYipHGa6fUzduuMrk3xXpMXDdPdJXa8gcuyJh9jOXVsiRdbosOW4WGJXU4EIXqwQucDvokJgyE0WjY90QiIT8ZLU4y7P2tH/YP4+EOtUNdm4m0/PhFcauTQL07EDbCqv+XJ8Y8azflcFpjZlnhWU795W4hhSgR94pjw2C5BbQvSvTUm77FNRvCKpGwZ2kSSAXUArxmqnHha4+qU9nJG8mkL51vUdA2LQ1Rd4W48fxUWo9stpBmUDWYnknjoBw3xfIt+tM9yOyVLe8y4GNo6o76YS3hK0aJTK+/9GB7jN5rora/R7nyefnITVR9DFEvfN+/I+5i+ygTpOyTi3lCe82qC85os7kcxp/RUjfb0Q8RZU7Cfy7UYvGY7JCTakY46kBCgiAa2fUI9Jgdey3pOuwRNuIoLLqOB7fi6s6Rjnlf0fUIcWZKso2ty0ry58pNn0wrbJiTzimja4NY5z0ry57Y5qrPooUczgqi6IOmS0VgNOJgQJlNCFDPsvUB0+Z7co+N8JollgV2tUd6joxjWlWhX9mZwvsVVGTodMPMt+loMyUSEr94L2CmJUPfuEz16RDSfwckF5tMx/s6BXJN4QVp+hLp+D3Xx66Q3/yDDsMLZLaE7JX74Jdz0iDB7DgB98TrK+6eRxcsN7Q/8fnyxi+4rdHNJdHKXMJldiQtxlv5kRzDn2gsM680TqZAWoG5OGL4o8eImHST07KFodrQRgWZ7MWNzMaeYbUkmAj2SlD8BayWZoMrPHx1eAYz8YCgmNVo7dOywXUJe1CL062P53sZjJg0qdfjts+74fzh+M+P111/n8PCQ//A//A9R6remYvLBF/+tqPdN2Qp9LjzdmejYkhysZIInFjTYs4xgNdnBJTqx+C7m3hdeYe/GMenumuzwErwiu3ZOtN/CXkE2nAr4pYvxDyP0eYt5sScc3ZDX+dUvo168hlvsQZSg+pbs9V+iXnwEnSzwSYnZPKK7/Vl0XxFd3kO/9w7J9++RPLhL8SilO12wfHiI94po9N3eOjyh7aTCMIw2njjpUSowDBFp3rL72Tep3zni4t517Ls3SMsGayMS07Pz/ENZ9KqcuGwoXjjGdxHJ9SUAoTP4laE93cGNEbj54aVY+vIBUqkIuHWBr9OnrZFyQFUDfmUYLqak3y+iKrIENWlhs0GdXBB6JC8hAXfzeeGnN0vcefKbi238gKO8cSaizSC99MXzD7Cjha9vUk4fH3Lj+fvC9u9jlm/eIptVEtSSt2KpSgZO33pOhI+3jzFZJ2FMWU8WOzaf36W4ZeHyhOgrj+DFCdGrX4CjA+y15xiOPoayA9FqzFK//hIxoB++zezWMZ+ab3j09nMwQnCGJuX8bJe+S9jRnlR7VOx476svMZuvmR+dU6qHZPXP4F7+Duz8GsO158l/6Buo48e4u4r1m7do+gRF4KwpqGzELB5wdUkeJZQ2BrY83s6ItSMxjp20obEx1mk8ikQ7jBZ7nh6PMo2NqZ20ihLtmSWSEhgQemRmHOddxq3J9urzF7MNUTzQ1DmnJwdMyorgNU2d47yWjHktKYrDckJ9ukP2DOYCgK0y0psX4t5ZavShEepfXhC0QZ0t4dY11Pkp0fkpwx/+A9jiCN03GO9h6uGNB1eJfX73AH12DEcHsnFwFv1dB5jjY1lgk1hyHTZrYVxEBn/zDvZvPiD5mAXrqP7HhPJ7Y6FBNo9oVYSJRKlfFHeguEN18nmUt5jNY8zyGLM8x718g/Stz6O3K8kqWF4Qshy/d4i/8zF8eYhZ3yNan42fE6FPHhFmc8hywhfuk93Zoh8t6JZT6kd7pE1CenQJrcO/2Ql2VwdWd69xebrH/vUTuioXENkQ0z7IuHbzIUOTMTSZaAKSAd/Igp0WLc4apvONWGj7GO80JrJEWS8n/7GNlo8uBmcN0aQhDBEq9pjbzzbSNyAb22/39/ydOpIk4caNG79lCz/8ZtT+fUx26xxVBELLaEnR6KIjnTRXEzMMkex4jcdWOUlipb+qAre/66us37tOOJsz/cgD6vuHxM7I196DaL9FGX+l1C9ffEj7pQUmPRl7e+D2r1/x/u3ikMgO5K/+ddxin/7Wd5N95P9A/fBvg+1w5R76hY+i6i3+M59Fv9yS/f03uf78CfZsyrApcF1CVLRXKvR6PUGrwOTgEv3kdOYM66/dIco69l+8x1Dl4DXza2dysxUCD5q++BCA7uGulPzK9mnM8aFmEj9gOJ1h64zoaMv2q7fI9pdEOxVh0EQ7lSiFAd9oVAL2sfSbk6MVDFMp+Wcp3LoG1uLv7Eq5dLsmvHmCeecNKCcwz/APn43PX2mPbTLisiF78Rx7lpFcX2LPJ/gHB+wfnOGdxIimczmBdtuCtsqJIsfs5jHDpuTwpffkOtaZ2I9iSxgCzivishH74xMs7fFaQo52esz6gugbPy2tjp0JmIj0i38Hf/M59CuHZIdnJI+X3M5GGlsQxsB0umW9npIWDTp2NBczyrLG2Yh2XZJelphQo9cXJOsL1IMHcvKcTjG31szCA17aFtx9IKEyRSRW1nT02TuvuX++T2UjKpuRGce1vMb5p9hoE4l63zpDY2PWfcpZm5FoYfU7Z1BDTABaJ9dPq8ALsxXLLiPVjsNyw2ZbclFNyeKexWRDlrc0dU6S9MSJOEqKHQlyGar8mYasRGVL/2iHaFYR3ZHFl20t1apyAjsT/GxHgnzsgDr/KmF6Q+y8283IslCESwubM/TpOaEKqL0Udnbwk3345TcJgF1OcE1CevNEiJZaoYxDN68R7ymBABUp5fdd4m/eIX77a8R338DtX6P7yA/i8utk6REAyd734N77H4jvvo7arHA376CrM8ERz/Yx6zPoHuJuvYjqG6K3voau1+jTxwI2Aobv+idh9zrxyT3Ug3u4JqV+8+Apt2OsdOp9L9XLZUy6v6S6fyiAqesnLE/3mO4sSUv5ntXobBiC/D46tsTTivrRHsOY6pfNtmQ7a5rzBeePD9i7JtwS1yR06wnZrBpdN6NI0GlM3mH2O2kX6g/78b+dxmc/+1m+8IUv0Pf9NyX7PcvxgRf/qGyu+lF4hWtTqQLop9x2M2nk/3UQ/78R1r/vYvrzOfGsIs5bzOh/dV2MSWJU5q8UtN3ZXBj8WQ9eSRm37FEFkEaYt14j7OzhF3vS/41iaGvpI8ZT2rf+n0IJbbco7+huf1pEP1GKbtekL8vpIbnRkqxW2McFfogkZCOTPmlcNqQHSzk1rUuioiIK+sq7+4QfoJ9sbNKBYTXB9xHqiY82sbgmxVUZqosJfY8urfT3dUBFUDx3LOE2OqCigLo+EYVzFa56tGa/Fu/zJIXzNRzuyum/76WXWleo1SVsG1SpcG97zN4ZxIpo99nslbtzURe7NqF9ew9lHH6snOQHl5j1RMqRWScURyunkji26EgCl4YNV4hRP4Ya2VqEdCgpb/ouRgMqdoRWif0xW0oJNpVoaMqJnA7PO1TfYQ9vYZIU3b1HxpksFl2MGyLqSjZSbVWgzz1x2pMXNd4b0SsUHW6VE68uhZtQWYK3qOlUSspjWIpkswdSLLtlRZb0tH3Ctsuoh4QyslcP/94ZEuOYJB1ae6yXaODBG1ob0TpDHln20pbtkFwt+JH2ZON/p9GA85pZ0jFNW3YmGy63U2JjKVOJqLZDdJXulk/HKsukkYqdF2HlsxoqttAlcr9PRt7+oxrVtOL32nRo/UA+2UREq4f4ThJBQ1GioiVEgdBpGJBNQYYwP6JY5vg+uAfCg4gmDaGJcU16dR/SeSH+FRmkmbBCohhG3YmyA8m9X8IVc+rrv5uiuMNw+g/IHryOevSIsPEY82C0AZ4JoKjeQj9glqdScbAOvbyQ+7BMCddvEkyK8jWqraEacE2J7WPioiXKemH0Fy2hCaJR8ind2QLbx9ISHdkQk1EzpWNLVOWYdMDZiKAU8bQWvdXI1tCx+PxVKyjtLG+Jso7mdAyoyrorHY4yTkS5ZYNKB3FXtFbswM94+A8Ffx94/Nv/9r/ND//wD/OTP/mT/ORP/uRvyWt+8MV/2kiAz/uKLyq2ctP2Am1RkUOZIIu/V5iskxjXLqE+n1OOcZnKOHwfYdIBP0TodBABVgL1+Zx00hBNK3wXE13vYFZcLXjuXYfhHFWU4J2IwLICny8ASP/nz2Nf/oz4wfuW8MLz6HiBbx7htaF7+XcRrR6LHmB5TuQfYE8KiSoe7UTx0Qo1VaiLAdekRLMas9eLmLFVRK66Uo8/GTq2siHKOsy0kQjkhxFuSCWkZF2SXruAse8GYJ43guJtBcXrj26g19+Q9zILIpw6mgi/PIrh0X3ULYkbpmuh7VFth3/o8U1O9B0Rw+sxhC1mt0MdPZtCb7ctyGZbXJewOd5jcnBJX+Vkiw3p4SUhaFyTyMKfDlDl8r7mIvC7Um73ogPxTuOdYahTecBlPX4EARGe2rtsncGJJ/I14cUdQiYZA3orC4mqK8L+DSFCxho9dRjbjLAVobMpFWjHPPbpzRP5OTpJHNTlgFsXsN4Ic14j3PmmlqAgzVVATqwdaeQ52D0nSXvOz/eou5TIeHI9ECmPHVtjk6RjPgatWGfY9inOS26AC4pF0rNXVOgmQC8P7RhwI9hnkvSs+5Sb0xW70zVF3nCy2mG33DAdsyHqqpT0vrQnSgZ56OcdrhbrbVw2v+E6fruGip1s9PIB4hmYiNB1soinPb7SaL8W22qRYS6O0dkGn5XSL89PUMoSOp7ie+cFYTKV71FXhNu34eEjuUdTi68TXJNIsmRQMs+KWJ4TWsMoxkNp+X/viO6/jUlS2nyXId0nfedXUffu4s/ANxmGJXqxK5CirhU6oA+yuVZaKm5DT6hBzQ1290hIk95dpXnJIu0xySDiuqxHT3tx7XQxYYiozhdXLcW+FfFoCFLBMMlw5dB4kuIXTWrsupSN83j/9NUebZWjVSArxSp4+fCQvGzI5hvq9YTJyNbQsSOeb1G5x56UBGeIo/Uzmw8fjt/8+KEf+iF+7Md+jP/oP/qP+PVf/3X+6B/9o9y8efMfWwX4/b//939Lr/nBg30qEWvpvEdPPfnLJxBrwsYTmhhTttiL6dVDW8d2rAwElNMkZYPSQUSBXnrF0+98QPXqNbqLGb6LKfZP0LEjKlr5fssJuBb/bodvwCxazL4CHaO3a0xWMuyJOAcdoS9ex778GdAaN9khxAX67KuooUbHBSEp8cUOgzak731FbuppjN506EUAbTHzFewV0A/ohSXNz0VRfOsaHJ/gVwYza2XnnIhS2T8OZB89x1ea0Eb4JgHdY+YNZiohQ3o60L69T1S2RHtbiOWUHoYCs6ixL39GThjXj1AHVh4m717A6RbaDjWfw47C/uoSs9uibizk917VuK30Mt3tj5CtX5WHXSZhSc9iTG+egA7o2pGOgTFDm2KPE2ydUT7/iM2DF5jElmhR0Y4+/BCUCJ1mDZm/xMwb3Cpn+dYtoaWVDQQBA7WnC/I9ERGig3ivHwu1MbSK8Opj1OduPFVdT+TBHP/KPyDU4AexSJrnFSaPibs1d7q3eOPLL5PEA5OdNcmLa9Q9+1SIOmiSOyvQkZxYpylqL8a90QMalXhM1pEmA0XekGctwxDjXMSk3FIWFU2b8fBij0nSERnPJG25dnjCcrkgS1vmO0uih46qy+hcROoMeTTQ2RilYBL3lHFPY2NOmoLGGSLtWfcJiz4laQpCUBzOL9Hjpsh5TTndMjk8Z6hyum1Bs5owDxqUJ11siBbPztetIkd8o4Iyhq4lHG/R1yJIUrlHXroG996TFkqcoN67i/vMd8kX20EyAF54DvX198CCOpDTuzo/FYfAZIrqO8xBj32c0V1OyZ6/wB/voIOHtMfsdnCJnGxNQB2sCAeHYlG2Dr1aie9+syF77efxj15Dnz7GPVTgI2lTnk6IvJOv0YrhlU8Tf+V/BqUJszl+Msc8vo8qwN9t0W9+AfOxhP6V78Ud3sR4T2rPaM/mUpFIB6FMLlPMbUdYdazfuomJB7omk7S9rGO+fym0zGQYKX8SIZwfXEqlEOH923uHuDYimzTM7jzCv3mLrs2gS7BtSl42NFXO5fkOPiimuwJ68lYTnBYSa9li1yXt2/vPTAD6ZDzLVtPvtPF+W9/P/uzP8rM/+7P/2M9XSmHtt1bN+8CLf3JnRX9vhqsyol5EJCpyhCHF99FYtrcor1BxID5aETojpy/doYxHl2Lnc3XGUKfYV3N0MpCOvdnmawdk841w3yNRjnfvLFCRRxmHPZ8Q+Ypw0aHiY8zBOepOhT47wR9ep33xn4DL+wRtCFFCSKTMq1ePMf2xeHoXR0QXD0cojnjIdXNPyo1lRLh5JKSyd+4SGinPc20f98VLdBGEBLZVAt65GKTqgWLz6nVsm5DtrMk/ccbFL3yU4uASPdr7ANKbF+hyjPurLOaWB3ogIvrKF2XR1gqylDCdo6aKsAmoagC9wV8glZDFRMqaeoo6r8XypALqta/AnmgAGHq4vPyWJsc/avgxZtWOaNI479j56D2U9rguobp7xORQbHb96Zx0UkuM77TGZB3D6YzV3WtXD7xid8Xl27cwyUCc9kRZz+T2MXZTYjclw2pC97XyyrYWRmS0Xl2K7TPNpN//4C7sL1BVRThxtA/3KPePoe7xaxGXfvrwF7HbHNfFtK/vYoqW5FMiKvVZDu+9hf2GbHZN3mP2t5jnxsfkxrN+dMDRjUdj3GtEOd1KpWPcBBVjhnqetVgbsd5OuPvwBtf2zinKCnQgiizzSAh9nYswytNYeY0ssiyKmn3t2MlriQYOilnSslduqbqMu8tdPnn9PtEIdcnKhnxHqIvpYkN56wQzbyTSdZ1L++piyrPqJOq5hzyWxb0f8KsEbXuILCppUfMdmdfWQbvFXyjM2SPCZIaf7dK98F2k73wBXcopPewdoDYr/P0WPWtgMoXjE+zDQoSmXlG/fkSy2IIKUvaPFe5ETtYqcujzjnDyCN8bfF1iq4zkYIV53qDaBnPyENoO87GccG+NPZ+I8v4Xj4muO1iUxG/+ukRnP1yiNlt4Se47ZS6lwhk5uNySfOWX4LzGbwyqhOlL9zF7A0wz8Ckm6fCPDf35Lm54+siVipccjJKdzYgsTih2VxIp3aZXVVQVOeJpJT7/966RzcTtkRYNk8NLVvcPUTqQlw3FbMvk8JJ+U2Cy7kro5880bpsTRirnh+O3z/jHkfy+HZ//Dxu/KTG4MpJApzJL8+4hyc7Ixk8s5qDHvZNLGT+2qAKG0xJVOSn9DxGqi1HGE03qq5SrMERX1YJ4Z81wOSM4DXYUzBUdjJhYFY/c69xJ0E1kROg2m4MdyN78eVTf4RYH6L7Bqwi3/0nU6dsM1z+Bz/dI3/0liBIBicDYN07l4RRFqL7DFyXKjQt/rPDfuEDFsosNLfguQg1ecuuDgtH21q6PxKZ2upZKhxn7+drLn9QTaoW/VOBSuAyYnf6K4sfUQGul1FhO4GAPpS/kFOpFREkSwbpCLSuY5VK5YJDkuvciorkfNxH6mfE1o0kjJVctgqYnYqJgJfI5ynp00osjZDzxuyG6UiB7ZyhG2A4IKyKbVmT7K5Ty4hq4mMmpH+Q9Hkd3PsPUGem1S8IjgSGpPY8vZqi9Ayn5FyVan5E1lxCNeRRTj150EBmiqr4K/4kWNUwPCFGEOTsmHN1AvXuP4AyuSYiSWq5JEOxyubOWFk8fM7QJQ5uSFO2YXSBWrd3xd2qbbEREC2rVeQ19fKUHmOU1aTxgtGfVFPK5xqIIotgfMyF8UGgVsN4QR5ajckNZ1CRpTzatSMqG7fEeUdpTFC0qctiLCcmdNaobUE4/8+6rfygOFXVzgvmEhjoQVgN+pdGnx7Lwz+eyuN9foqsNJCkqydDtmpAVsLO40rGgNHpf2gRSyUqIbtSYbYPbpvTnY/RyL1ZiQATIeQdRwDcxdllezTGT9yIeLeeELJf7aScSRoZGgoLO52TPnUJiYFtj7yVErySoW3KIMGfH+MUualaidI1qHPZhgWlrQqdF7NyMbI4nqu0khrZnuJjSXcizTcKW5NGrdKC+mGPbRLIuRix2eecxw3JCezbHDxGT545lQ9DHKB3Ynu6QJD35fEu8syZdTmg3JdZG4unPOtgUUmncrXDrDJU6tO+ebcjT++fEhyf/Dzz82Db6rRwfHPLz/hwIKwriZGcjN5cKMM3k5upieegbUaq6TvrOOrYM61J6g+lwVc71gPIKIiS683GMTiKMloQ7vJKbW3vMfMBdJhJlm0VSUmxbQjER2MbluZQLizFkBnBOep0+naOyA1Rb42e78jB88oZbB9PJ2OOTHiOxQHZwgf54h/SanKJDYwhdjG0SoqmIaII1RLOaJB8Z79tcbr5xqFFxGzpNaGJRsRsJ7dCtRHcGDyoyEAahhbWtlDuzRPrNTxZy6whNIFQGnUnJlFSheo89LYiaelz4/TNT9Kp0wMDTxbtN8H00boQ8JpNSuijtZePmnZQe/RDhupj0YEkYIlyX4OqUKBMAyZNRn+wS551soFQgzjv5XGuIbERysMSeT0UARoW+XQk7frKAyQJdlBj7tsyCyMAklmpK20BUYXQHrkJNkY3gEx1Fkso81gHfJ2DBN6OK3CviaTVucBV6iKi2pcCfIkuUeIllDg22TUR8FdkxyEmS1+wY9uO9IU07yqKmbnImqYB4noTxWG9IowFjxvTAtGOznRBpR1wMpFknWfeTGpP3OGuIcy+blirD1hkJa0Fxe/VN+pRv9wi1qPB1OhDNa8Lt2ygQm2ofjXoJRchyea/DahTQDai2Iu4F4OQXuyKyq7aC951OJNwqimUznCSoeIsOPeFUofJB8M9+BGLFDlW6cU5GDJtSRJB5S7K7QeWiuAckNjpJUa1oeVTkRvKjF9Ff4xkuZ0S+xu/totoGdXqCSlJhC1jR5AyricyX8R63y4m4fxqH0h1kATo5/IDwQuxWfPdoseC1TcYwROSTmmTUZuhJB+sS18fYNpHqTZVjW8nNaJuM+cGFWGTHzY82Hh3c+L3DU31NBsZ3hGGEtEXuqbbiGY7fyda83wnjg1v9qhjXpkJqO1uQziviGxWhBbcsYFMTHYE68xLyoxXx9ZUseNagtGf19Ttki+3VwjhsCuJ5hcl7gtN0D/epL+bMihZdeoL1PP5730E2rZjcOoFrC/wjCxcK7TvUCMDRT2Jt0wyaWihh3hNsT/a1/w8hSUnf/WUhw00WKDuISKxrCeUU+05C9JHqqq+nf/Er8PwcLlcM94unpXsv5ebgNPXJLrPFPfQ+hM2Ar2KmL92Tj3cx3XJKqJSUuifS9+8e7pDsbIivS8+fWQ7nQQRRiyCbj9zJw/Jyhepa+fuTa7BN0XoMACmd8A/euIfay1A7Gam/JJwGfBWjU4u6M/12zpWrEQaDii2hSWiXE9JZRXu6QzLbkl6/oD9d0JzP5WEUCxkxSga8M7hNQbctyJ8/wS3HB2IyUJ3sChJ4sRFOetlg2xSTCCFQxw3n33iOZIwibd47oq9ygtfkmzV5/nXIEtwrB9i9FwgmI/Mefe892QSVE4ZbHyV541WoOkIP5gXASsKcnyzws13CX/8awU8IVjPUKebBU4S1ToS2Vl/MheXfpjy+2KNuxEqXZy3X7jwQqErsSLOOrk1Z7F7S9wnBa4xxTCYVZ+e7ZFoWqotqyrWdc/o+YXDRFcIXxOanVKCcVhSTmrbOrpDBqXFX1Zf9l99BpwPt412ai33Ka2f4jUaXsml+lsOeTzATsWZWXzgiP7mPujHSCCNHuHlL+vfeo9oG7xX+SKx+er0U++rODvbwFiFJMdUWmuZpcuVsR8rtly1ojVIO16boWYBpJ9W4SmN2xs/3ghkfmpTtpcBy4sUWtZfB6dlVFUQSIQOhM1Iy/941w2slka5QuSK7cwKXoHksX9BawlcfYZcFdjue1hMRBT+pfG3vHzK5cSrth41Flx1unZHsbohntcCm1hNm+5e0m5Lzs13mi5UQIrW0N7OdDc07R/L3xZZhm7N85yZ9l6C0J83bUeEv4Kzt8R7Ls12Onr9Putig04EwROQ3T4VzUSvUnmHzi9fJ9lfyPHqWKY8fjm9p1HXN5z//eV5//XU2mw3T6ZSXXnqJ3/t7fy/T6bfvmf7BT/5dLAruyJNcu4CgZNHKPdHNBn9h0DuO6LDGVxr7XsawmpBeO8fsW4gUi/gdSMTSYy8mZNcucE2Kzjv0rqf70pw4F8+8u0wgKA4+9ZYAZLoE886S+LkgtjcysdwA7C+kx/34AvYmqNNjVJJALF3O5nP/DKpdY6pzlHfoi2OxylmLOj8j+nQOnYevv4c/KTHXAvbLPZCLcv/FCZwDCszUojeWqXmMed4Q9gReYsoOFlPY1rh77srGF9pIdA5dTH5HhHLiV0+ltxldotdbGMZ9cmSkVJgX+NcuxR9cpGAd/fmc/M7lFfXQf+kh+uO7+N190AYdPO0/iEmOLlE7ShTKz2AoHVCZRXfSs49nFaaP8H1Ce/+A9GCJSXv65QTbZCTTmvpiTqgKlApEac/yyy+S7y2JJiIETZvsyvqn0+GqN/qkN3n/1U9w41PfEGGe1UKJPNsRBOqqJHq3pl9OyN75Ivn1XyLcvIF69Aj38Zfle27XJG9/FXftJiZJUNWWsLMnm6fzU3QUM1x/ifTFWuKUNeRa0b8xxXUxdin/TecV3kqkcVa03Ng/I82bq8TId17/CIv5iiQVUE+S9PR9wnY9pe1SurG3v6wLmuUuWgXu7J5RN8L+t85wUZXc3D3H2gjnUozxdG1KVtYUk0oQrtrTbQvcEJH2MeVHHxLaiPz2CZnXDOczNq89R350QbS7vao+PYsRX9/iLlIIinguugb/0EMkjgsePYTeomrZuJmZRz26LxRKo3CvfAe+mBGd3JP0v71DujufJvviz8Fqja4qaAbceQxeo6LA9DPvMdwVe+2T9lL20XPJxKgV/fGc+SffJXu0i20ymkf7lOkj9O6TKlxgeDAnvrlC39CAx9936DTCHkv/P7rewSQl7B0KzKdcM7zZklyXjA6zLoW6V3SycMeWxaffJvQGX6f4IRNhbN4znM3Q6UD20QtuzLes3ngOpQIHh6c8fHCDnZ0lbZ3B2Q6z/UuKw4sxAE2NJfxS+A2RJck7wtgKBYjTnoObj0UwGHmJ68170SfVcmjwlzHTl+89Tfxsn53748n4sOz/mx9/7s/9OX7iJ36C1Qgve/8oioIf//Ef59/5d/6db8trffDFf5xowSt8m6CzHl8lUrrLHSp3+JUWNn3uMaaR3j0QNuCbCN8moIJM6jG/PlgtkJ91S7q3wj/ek9foYrlpLmSno3SgP14QNxVqKT0IXxWSftcuRZFvlCBBF+PuyFrscx/FrB5gtpcSg3t5zvDxzxBxX9T+fQt5DquW0GhpScQKXbaSLz4XPKk7SaWnmXrsshQV7tajokvCzh6qfgjrLQzS23ebnGhaoUw/UrXs1TuuUgXTGeq9u8I/39uVj51fjDYlIxWM3fe1JZKY7PYptEDRE8op+lYjpylrAQsXG7KPBlFdRwaqZ5TmpuRaBCv55XZbkH/8hFArhvOpECCrjHhWy2lnm5NNK7o6E1Sp9mQ7a1wr1r7k6BLbJkSZhJx0xztj/C/4waC8kvAcq3FDhrcyr/Y+9RbDcjK2EhJBBFcNZuikt7zy4tferuH4FDxClOtaeU8Zr0Xdok8fE2tNuPM86vQxrBvCKjBs86s+a1y0DNucvkuuGPrDEPHg9Dk6G0lwD4HOxmRxL71X7YldxMVmJgS/yNLbiEkqFRwXNGkin9v3iWxmVGBdl0zzhiTuicf0yCcCyXTS0G5KkqIRV00yoIzkIritVMNM0ZKnA9GsAg92VT4zdbevNGa/Q9c9w+kMZQLeGrQZIFVPraiHNexkKG0JB9dQ+kQ24G1NtF3R3/4kBEty9+tkX/15catoDedr3OkYAz5E+C4mnlbEH21x9zXDakI02mexiKhyUdGfLuS6jUJTveNgMZFnhIfkswP4DKYz2Uy/M1rqnlhUXZBKw/JC2BLbhvQjATz4yoiNMrbS2nhya+iAzjt8L6LYYLUIliOPqzPaL+0IxrqP6LsE7zWzqZD6lA6SL7LYohPLsE4lkGyIiNIe10uMdldnmERaa09es7x1gh8iolmFnjtCi4iOjy5I9lbQR6iDUdvUW+EOfDh+W40f+7Ef48/+2T9LCIEkSfj4xz/ObDbj8vKSN954g6qq+NN/+k/z4MED/pP/5D/5ll/vgy/+QySQGx0EPzuGRwRrYJBe2ZPek8Jf9RjDEBFswLcJrknHHHhN8FoEf6Ow6snmQo1oStkcBBjtSk/+LVhNsrsReNAQoQeNW2boJzvdClQxChTGcATdN+jtShb7yxq0kX6fGT3Bm61oGkYaHJqxDB/Jn1UN70u/Dk+ohOsWHbaoOJGbavBiVYrl/VEJIkhLB3TknkKSQhAwTZZcKfNDOUX5gDxZxh5zJKJGIilNqyniQNjW8pplCZdLlBbbYagC6uYIvektdM8G7PIkIhdA5T39eUEeK9QUTCcAFu8MWtvxGuuxdC9l8ycWIO+0ZD6MEJ5ssQUli5gay93eGXCSuNivn1oXlXHoWUcM8rOogLMRwzYn2mzQq0v8oIXG1g9SWckjWfhBbF99J9oRH8TaeXaC3z+U9zMEQtDEk4ZY16N+JcZ1iQTpjGhoYxz1kAiONyg2Q0I20v5i69DKk/kO6wxJZClS+bvR/moDoEafN0gmQJl2GO3HoCBHknV4ZySpMBlI5lvQfuS1i6XMb0Wr4Lciuk0PluiFlK8ZAm7zTKbC0+HF2uWdYTifjiFfCjrROoBsEnTUwlwEdAJq6qX07z0hSkEX4z25wd++g4pi1LaWE3Q54CsJBgse1M4CvTrDtD1oDx653xLQehDr3xAJe6Ns5XDQju95hiz6K3ltnMVVok/yzqC6GN12qH6AbSPzxwMHE7HfIpyTfvVUVPiE6KfGxD6C5EboVDz/wWpJwkwGdOygha5NKSaVgM0iScg0WXcldvVOY7uEbD4mR3YG1yXgRRxo0tEimA5E44EFL0hxNwpwARG2JqWQFxsvYuZnOMYn2bf9e/5OHX/n7/wd/uP/+D8miiJ+8id/kn/r3/q3KIri6uObzYY//+f/PP/Bf/Af8Of//J/nj//xP84P/MAPfEuv+cHZ/nVG9typTC4rN5hK3ZWa229EiOerBLeWE76tMoyVya/TAdcl2FY8qd5p4mklpd6QQJ3BqIImKLGRVTnxXARWTxabocrJnjtDlQpOAB3oL6eYtkfnF/gmRvtWiIB5TPTeG9gXPiFCr20toJalxJuGokQ5i3+7QU8hRIHhvMQc9XCwIw+FyxX2YUF0o4Z4dCHMKpp7h8RDhOkb1GYpPPPBEyzgFdFhK5+vAno6yM2WakLlRQGdrum/6/tJvvLL+He36GvdVXlfTv8RoWqF+hcnskHoRgrapUX157A7xT2MMP7yabUD4LIltBIk8ixGsIaoaKWHX2f0VYY/0+i5Q5cD1ddvEhWtpP8NkdiZior0UEST7aM9qpNdEUjaiPrBgTAgsm6E7ojAyY+bS2cN2/UUvS1Js06ilYH8dHIVVqKnDvdGTHM5IzhD1pyjS3CvD+hZhzoqBKI0stoBEYgmsSxC3kuJ2dkxT16hF4HkRkc4OEI9eED/9RKoyPaXV1ZHbyMOtmsi42j6lJOzQ+JIcNbtEOO9JgRFmbaUeUNR1nR9TNVlzIuK2XQjbYFGWiJpNLA7F9dD02ZYa5gmPXaI2VwuwCuK6+ckB0uae4dy8s86mof7lB97gK4y3FhhUweFVKX6jtg/u0hfXXqGRxNZlICLN55j/5W38XVCWBuiF3roG+yjlOEsI/3OHnX8UE72cSIVr/mM5MHXCEkmosCja9jd6yjbE2mN6e/C3gJzvsJfyDMIa1E7MZHfMhzPCb5F5aPWoPEkR0vqt67L5nPaEDYdvlKYvQAHM3hwLKf7uoMu0K9vUdw8pTtd4OpUQoSybmQHIK00awW9nVmSZMnZN54jSXuU8ujYUeyt2B7vSWtmrMpERSuU0tiRV9KTF2umYbORDW0cD0RpLxG/XqHeB/3p65y46K7U/sMQ09QFu/kJyWxLtKgYzmdkH7+AAdyFuCH2vvMbhEbsj/qlHLoW99iAizEf+bAk/9tp/NRP/RRKKf7cn/tz/Kk/9ad+w8en0yn/7r/777Kzs8O/8W/8G/xn/9l/9i0v/ip8QMOg/ZkDwnsbwjD28WIFIeBOE7oTiaSM8pZkb43KLP2jHbLPrLHvyCk5+diG6ouHYrl5crobd8xPfOPp/lK4/m0K2lPcPMVVGf1aEK1x0WKKDqU8pmyJ9lv6BzOi3a0cmJuEaL+leXOfeFYT3WxEH9Bb/Nl4MvieF0VU9Oge9L08HO89xK+V5HTfuiZEt7qBRjz99rIk2tteneRDP54cylRO5ePJwK/M1SZFp4PY/ExAFQH36Vcwr30NsoSwt0/IcvTpY8KjLW6ZyXv0yfqpwG+SyyJkg1QAskQelsfnhA0Ep9AvLQh3L0TFm3rU9Zm0HrSWikWaEf2x429pgvzDRvd/neLq0cWR9uiyx63yq5Kn3vfYByn+qtIjKY7DuDjEeQte021z0IG0aPFOM3numGE14eLuNQCcjZjuLskWW7YnO/RtSj4Rmtn6bIf5wcV44tPjqVPT1znWSjpesdiQ7q6FtDa6MtBBhGF7E9ztFzFf/DJcW0AmYjB/53nCP3hPLKwv7xPePoFPvQiAujwjPFozPJpJu2qIaM4XKO1pNyXVtqRuctKkp+ulNVAWNSEo+j7hcjPjvJrw/MExbZdKSyAeyNKeuskpi4rJbMPk2gXdqmR1Ku2g+cGFEAhHtDQ6EO9swGl00UtFqAuyIAJ4he8ifJ1iZrVUsfbmz2QuANT//gE67UVXMAZUxTdF7OtWOdFhhf/uT2Me3iW8uxJM8/fO8LMdeV/rLeryXJDAGfCcXP/2pe9GeUd8+i56fYn7kmiN1KgD0dekIoZ1hEuLujkRR8fqkuGr6qqq+OQUnu6vRLg571FzuU+77/lBdLslOrkPb9yju7sriN3FFv2cwIboWqhbaSMVMNybCLlv3tO+dcjZOzfp2pQk7Tn6xNvUJ7tXCZ9x2uNsJNTFscLTrCakkxrbx7SjDubiYoc06ZkvVizuPJJDzaqkr3K8jUgnNX2TysEi61me7HFw56FEaNcZxc1TQYvvdjBPCecd9qxEJ1bmyI5UEZ88j+ObK+L/U/cbL+a3OG7dusWDBw9YRCX/t4/+iW/r9/4/v/n/YGkrbt68yf3797+t3/v/1+PGjRs453j8+PE/NtwnhMDR0RFlWfLOO+98S6/5gU/+/RfBNXsQNOrUkd644Ak1RMeW9NrFaFnT+ErQruFyQMUQpVuYFOS3TulPF9h2pBk94dfnPcneCl32JIDdeCkHW3OFJo2nNfF8SxgihnWB8RquLdAnPfZsKpuBQzmxZbfOpe3guCp/6ynivb33npz2QErpq0sYAvowgjbgXj3HbXOS2wMYweyaSSMPpTyS8uQGVOIF95mOCN04QU8bdNtdlQiHx6WgR5se886buMcx5rBH7TjUg3vyFmTIzz0NMJ3D6bmU9odaVLp3pnI66lqxQI3laN8k2F92RPujqK9XhEdr2QCMQke+RQLUP3IEITg+SR7s7u+S7K1wVUb7cJ/UX2C3xdP89azDD4ahS/BOj33YLc4a3BAJn3yxpTufoXRg59Yx3bq8iiId6pTL0z2yvGVoU/nTJ6A9fZXjhnHDpR06spRlTVw2mLxHG4ddC9JUxZYwRMTDGr2p8V9+G30EaiXCUffxT2K+8RruiZjq4QlqqqGtpTLQ96hcyWbTigXV5D3Vw31M5CgnFXkpdsVF5Ma2hfy+JnJo7Znktfj5Y/H+R8aRF1INcM7QNjk83iUEyWM3kSPKepLFFjeGZ8kmwI/tlwitB9THrhHefny1AVCJI96vZOPYiIA1+mPPZjqYscctYjwnsK8E/Cqiv5gR364xX/4yfqUJfUz02YzwxmP07RryAh4dM3zX9xHN70Jdyby1luTRG+IIqLf0z3+S5Pzvy3UoivF6DFB1YtU7SJ9qBMoJ8UcusO9FAnQaMdLZ+xf+soTzFbrdolvRxqjrM4avFkR5C0xgCcnv0vibd+R9X57D/RMhdHpFaKQlqZSAmwDW94+I015EeV7SJJOyHWN3I5rVhMePjzg6OrkK3SlmW4rZFhNborGlZOtM2prGky8uOb93HTvEJGlHOqvIR71Hsrcif+EY38RjyxRU1cmBoosx0waSQP/GjPj6CjMbMIsWVT57tv+H44OP8/NzPve5z/2vpvoppXjxxRd59dVXv+XX/OBWvyG68mwrA2oKfi1gCx1b4XoDfh1jtxLLGw0byarHoC+3BJuM/m+ZeFfBI+MiIbQ8QI2e2U15RahS6YCeDvhNEJ43QNdKjGidoa2Rnl4WQTeI0D1WIySkG73ySH+3bmE2Gcv60u9Xoz7A12Nv3wOxfA8Ve/mc2EnASzzI/29rcQ1sOzjIZDPgPXQD5Jpov5LkMSCcB1QivWRWS4kizRJIx4StJxYOrcbTHag54ne2Yym6GUNKYlH0Kh3EEtnLz6ty9ZRdAGKXegZDpQMYARfhZXFXuUP7HrV1+DqVa6o8SgdsnaFHep+zRhbFsa9vYulzqtgSuhxwYw7AE2+8wruYLG8xkQQDKRVI0u6pd1o7vDd0bUY+rQSWEou/PbzvZgpDJJuQ5QS19fSrkunRVgBLfoXR9wWwNFYJQg3qZgHn51DkMF8QTh8LvyX1Ak2Z90QXM4lRHasPbpCTnu1j6u2MKJagnyTtyfIW7zWJ7XHOyNcEiZc2Rr6HHWQjkBYNcdrjnbSPlHGoyIuPfLyHcFoqP+/f6OkAgyJ0ATUW9p6clp/FsJtcVOaplLfDUt473z45HYA/E/6A0gGcZTidEUdr1F4PSYTZLkeYTyYxwNZKiE4UEbICs33qcpHv6eUeejLdkxjaFqV7+T47C6LqbNQhTUS0mVh5LoxtNSKF8k6SBu0g6ZC766ssCdcl8npjiihawzxD6ZZQB0Ij3nvvtWzSIktX5+Tz7ZW+ZegS6uUUEznU+FyL4+EquAtEK2EiK5s64/G9PCe18VfV0SxvGSJLknUk03rkYjRPffzZMLYcpTJpdlt8k8oBqg+yKcvGj1sIm2cPlflQ7f/Bx3w+/8DVjPv3739bLH8fePsXz7dEk1qS+XY2cPMIX0lvVsVWRHydwY/Qlvp8Dlbhu4RhOaF7a8FwOSr3zfiA3xEVkuti7LrEbXMpJ4/hGM3FbBT6tJj5gMoVOrVXnIDwqBPL2aikD12AJCZURhbEJJKTxc7O049Pxkb4KPbzG7EJ0fbgAjrria+vZeOgldC+ooBfp1cKWVUq4RecO8KjDn+hx/CXHlqL34hdT714iPrYAep2Kd77w9H9cDqKzvRYzs9S+fqmljZCGUEeE154YcT0rggri7uMZVOQiSDRfHoq1sZIrmS4fUuUzE19FfzzLIZeBLExeo1vYqJDEZbp6UCyt5ZToBLgjU4HwYymA/FIo1Pa02+EUR9lvcCixiS/4AzDmGqmjcc7g3fm/8ven8ZatqVnueAzxpjt6ncffZwuz8m0s3GmwfdatlwGW4AlSrJkBKK1ZQzGBoEM/oGRi5KMKAFSmR/Gkg0CRP0owBayhXD5Ur5VNEbgDhLS2Zw8edrodux+dbOfY4z68c294uR1Q7hO7Mx05nylUOaJvWLNudcac4yveb/3ZfvmEXFaEg9K0tmK8c6cpogxYUs0LIkG4mynTZdx1yG2jPBe1AeDUS5BpfI0qyHl6RSchlrhCo09jKh/WVzhVCyBjW+0SAc/tlBW2O192pMR3nbk1EiMl8JOnjXeXhKN8o3zmmsDlqsxRZ5iW4PWjjgpGU5XjLcWpMOcIGhpOhJhlFREkThLOqeJByXhoHwS5EStjJht1Z8v0uIUPDrd3NNlSN8eD3ELDaHCvJxyVShOtsAp6WvvxaiwpT0finlX0hlidcGKdwr/zop2ndIeD4WfsrePfuNzogXQtvjRRN54tQLnsJNtzP03Oqe+jrS5zoUfYxTEnajVag2LJRS58Dru7BPeyIj35oTjfBPEUzfy+tFADv66grKEIie6uyTYWWNmubDkmxp9eow+fAAnp6KtgZBe8UoqVE2INpYgbCSwm4qWiTIiuHN8vMfx0T6LixkmatjZORdFyC4AyBZjypWU+JsspVmnYg7U8VvK5YjJrSO2bh0xvn5KOFuTvu+IcGclY5UrDVsD+e4dUv15+TY6rfBViKsCwptrGYk2UrFojiZXth56/M7xtV/7tRweHvIzP/Mzv+3r/tW/+lc8evSIr/3ar33P13z6zL+MCJ/L5EBdNXB4hG8HnXytsP2b0ynBbE24t8S/pike7JEcXKDjmovP3mX7A28LS7+MqOdCdElvyKaF8rTLgZTnXjxGHYyw/3sgEqszuuyhmxIIrKh5DTx+pbB5ggtb1Kqh/OwEHcjMrTmvCGYn+K97P/riAn9uUWUlB25VSklxX/zaWa3wmYhjqOuJbC5VC23HFTBOsoa6pX5THhwzyTH7Lf7F56WdULYSQIwtrCz2rQV6VqFubWOea7sWwfpJlnbZ3y9a2uOEYLeE565Ja+H1Y1T2ppT+B2L3q4/WUikoWnzpZfzIOexFRLMYkYwfCvFOl3CwT/viV/3O9JufFntb+OO1lBWnBdW9bZIXzvGFpjqWMb3k+WPsPKFdDje9fBW2hHGOLUXRT4etyOCeThm98Ij2QUJTxNiu3Fl3fuSXmv5RpxDYLCWTs60hXw9JhwVbL7+DNk6sUq0mHmdiLBV2WZtTxLOco//y1ZjAEqbCM1h++i62CVDaiVrea+dUxwcblngSnqJnCn9S4d94jWCvhFbRng9p1ynqTU+9EGlV7xWD7QXZYizGQYOCnd0zJvvnosq2HLE8m8kUQVSTDHNGW0vK1ZDR/hkgkyTeKwYIsdJZQ7p3Qfi+GvtApmGIFTptpNefeex8QDsPhTOiNaq1qKhFf2AIKyvVrvSK2J/A+IVHXeXG4O45zEshfE5EkfSowmdgdhoM0krzd+8wGL0jhNgkkax7PILjOawrtNb4e3MhrOYnBEcn2A99DXre7RVBABcljLuRVufxJzn+az6AfvgO1HKg+8EIFZ5gdivMzQYKREq79aIJsj/DvPO5TVXNl7D85HMMbpwQ7Ir6I1rDYok7h/Z0TKTvk3/qQNwnRzkmarFWCyFVO5JUbL/Lo22ysxnr1YiDg2ORuLaGYj0UzwWn0d3h/8aD27x4675UgRrD9IVHnSLqgCpLyRZjBjsLkhun+CZg+cZN4knG4MPHKA3uWFH+aooyEcEsE07Lw4cE131nJYykeau1JEAIafmq8eXMzn/W+J7v+R7+7b/9t3zXd30XTdPwR//oH/0Nr/mX//Jf8uf+3J9DKcWf/bN/9j1f86nPBp2I9K78R4jZERtel8dS4k+VzEAvRgSDksHdx7TLYVeu9Iyvn8rB30nbps8do7ehvW/E9S5uCPeXNMcT3DxGuzXpgaddp1SH2/BIFNcGN09o1wOUcqhoia8Dov0LKdUFnsH7HmPnUl5UcYNKPOroIZQtygCrSpi+l5nDKJXsumigVRItv1OgXtqR33WxwL4RgnZU7wxxTUC0vcLc7g6VKJbMoWiEAJimEEW4z55idiuZuW9q/EmJSkrYneGvj1Gv34eDbenj1xYzK8VN8MFj4Ro8JwY9qqklg79Yw8u3pSc6RVTKOma6Tj2hXuFOEcGSspXWwv6t97xAfjM0/61Cp17GhwIZb2ofD/BOEU4ylHHY8wHNcoCrQ2nbhC3adGOcrWb4/ofkn7uGt4bhbRE/CgYldD4Al+Q272XETilPtRyK62PUkExEuEZ8IwyLN29SrIc0dcR4uiCerbj47N3PY12bpGK4tZQpAq/QxlEsRxsL1Xo9ICrXtGWErWVcK8qW2JNEAszGELsFrgopzyeUC6lkmaghXw+oqpgiSwnClnw+pq4iVusxy8WEa7cfEY9ydtKSajkUNzYnv2tThxuS6yXHpclSwlEBytFmKdHZHLucCqHSlth1QrDTohJFMMgpXt3F3lOYrQoCcBcGfzKXQDm2qPb0StbCJVQoz59yFn/YoEIx9WIcYu8pWItKooot6s13qN6YdfLYNb5ayLNy5xp2ex8fJejJTNj883M4W2A+92l5DiZD/HgKWzu4wQg32kLnS/SvfAL9xuckYHdAfYJ7PEdPPepgJJMe99+BNMU/mFM/nBHlC6pH2+jQSCB/zTL58NuoWzP84CasFsKfaTNU6Al2V5z8v15mfPNE5u3Ppmx95I1OvVJj24DTw33W9w/I5hPybEBZff4o32CyZn6yw/Y1mb6o85SXbt8jCFriYUEQNZy/JkqWzmpM2HLjo6/KaOe2g5Fi6/Y9/EULDfjWA4bko2vhP0wHMNzC/voF5gNj1ONz7FmEsi2sHEopMSUyV38092X/p8d3fMd38O3f/u387M/+LH/8j/9xfuAHfoCPfvSjTKdTFosFH//4x3n8+DHee77927+dP/JH/sh7vubvaM5fpxW+CWjnA8zOHOh065WnuT/ApFJm1sZKQDBbS/ZSRBthn8v5fRVY/EUtPalAzIJAyH++NbhljE5rAq9olnLoJttLXBVJaTeUh9w3AXpYCsM5izDbHfFP05lrKJivpL83vSQB1qKf730noPOk++GdRidWKgOX5jjaiY1vaPGNkVFAjPAJTCAjYy2beXyytbj37XQz9xdr6ctGIjCjtJYM5OJCrm89bhVhbgby8Donpft0IBte1cp45cmxlCyjUP4sMzH/mbaY1Rr3WHX3C+QVwec+CV/zntfIb7oWnFfCNp9URF/jsK83+EJ6vK6bc77UIrdNQDgsPt/cJwvRgcNrLypt3Yy1GVQYKmweE4/yzfz7xiSpm/9HSbDgmgBaTZjUZIsxxrTS/z+fymu1oykj6iJmlFRywHqFVg4T10SDonud8At0NzftmoC2DvFWSwC6HNIUCco4yvl44+JnWyObdGCJqfBeka2Hm7l/gNlsQVteTjqIJr/3GhMJwWvSjS6Gg2pDSkx25xthKFeJk50tpaROHGBGFe5UyfMXiGOkCp20jAIDiwYVtdIOshp/fHU9Xu8ULo+kOhZZ3CoR3kRtUVmDCuTZbzvBruhgIb3qtJFSfIWU7ssSc/q4k1ueyMhfN44rWv9jMf7JVtjrtyGIUK3sOWovEXnsWkrhRN2cfaVR6xydzqX9d3YmPJW0pr0YUi9GxNsL2Y8yJ6VzLzLELDP8Itv8bjptiIblpm2poxpvFcnunGad4tcDwrClXA3JswF5kdI60WeI4pogFgJqWUVi5zsspB1WRtSXrSFjiUd51/KKZG2GrXyXlxgk+NMchUPFSsavo1D2Da2hrtHjBk4ucAvT7ZG1jEFPG7yD+v7syi19e/zO8FM/9VP8wA/8AD/5kz/J4eEhh4eHn/fzIAj483/+z/OjP/qjz+R6vyPCn9kWJnObJ8SOjT60jlryh3sybhK2+NbQXIxJdkvcSknAUEbdw2LEjjBLRFRDiyWnUh5XBaAdrorwRUS4vUInNSpLMMYS3V5Q358K+757GC7dAkUEKEJnDeq5WEqDbQvrQsRvxkrm6NeVqPldsqJNLdm6UVw68LGVdGN2cjDrVBzAzJYHWtg7kAx9HMh1jueX345k4xc5bA3w0y3U4kJ6cpdYVzJXPBvj3lzLgwvYPME4J5K/dSXvrzV+1XaBBfhHDb4O0OMKNW7wK49/cQZBiBosUWePxCFYI8Iub9srKfvruKHNEwmURhXVh76e+OQXpL/YGGwZy//WIW0VUecJ02GBa4TsGQxKqsfbHcFJiJ1NHosPQudKV51PhHylXCdzKiQ6MS1xuDaQfmfXD4+3l+jTLZK0AO2p1qkopXXl9iaPhbBqjcxPhy3BsECHXTWim6HmXX4Etg1EpMpqGcsqEszFhGwxFmOdpAIiqjLeCP/kyxHrvJMxNpY4qhnvn7M42sG2QnLUxhGmwgAPBxVmUFKdTwjGGTqtqY+3hFHefXnGSavBtboToZJWUP3WFBU4GUt7cSIEz6SbCBhXnahRi1tp2iu09MVp2izajHraIhKSY2XwVSd65SyujkSEaZZhpoWM1hol9r9RBMdzfCuqi+q2wY8m0uePYnwU46ME8+gduJjDdVB1iV4vwInPhQ8CdNt2ZNoYPVkLN2fhUJziXnwJ/fBIJmyu5ZRvbktgPxQvEbsI0cMGNRcukpsrynt7gPhLqGDF4LpUUPSgQqcN9eFM/n0Z46whTguqMqZuImorglZNEzIYZTLD3wWF3isR6BmUcD6R4LDTTBleO5NKQUc+lspqpzhY1hBYmagaljLKHCdwsehkeyvQDer6BPtqiSujTYCotINxiCoaqvMpw6taD5fL4orf/8sNQRDwYz/2Y/z1v/7X+fmf/3leffVVlssl4/GY97///Xzbt30bt249u2ruU58N4c4Kt4rwVhNtrbBnEfnDPdCOaJox/thD7KnIi7o6ENvXc5HENaMCfz7Z2GqquEFPHW4lc8DtKsUvh0Q7C7L7N7oRr84K2BqCUSEjKwFEtxcQKnzmaY6mXX9NDHOil3OaNxL0Insyg994VAT2oRBfgr2S/LXrRFsrzCQHHOp6gi/m4ix4Zywb0cmpHK6NRt8IJUDo2PN2ex9Tll2m0TGOtxIpEToHaYCfCsmQdIC+g/icP2hFCnkrBNNZkTqFGlqijwHzTPTPBwn+hbvwyXdQkYgc+kqjv3qf4n9vifQCc2eMGpTY//hAqhJbAdweysOfSSZpnjPPbKG8GzqtSG4XYD32LCR+7eOdK6NojptBSXs+IRrlJFtL0e6/c0Z7KqOP4Qs581+8K6S+oBWfiE4PwDdy4J48uMa+sQyun6HihvNPvMhgd46JG7w1rI+3CJKacLoWC9v1gMmuiAiFw0LGQjsRosHOgmS6Yn7/gOnN443iWnCzovzMNsX5Fk0Zo5QnfLxNvRqI0cvWkno+It6/IBznxOcTLh7ts/vcQ3TgcK0mbAOiKpQebZGwWE6YjldMt+agPcV6yDuvvkgYtDKxUDXgFNGw3GT6izdvkMzk99DjhmR8TPG5AwYfOIKtAawL6tdlDNIWMeoNh6sGBJNcev8DL62r1uIfLoUf83UfBWcxj+6hmWOuWMvd1RGu1egqJHmhIx/GMn3jjj3mjiYZHxMcD2nOJkR7c9zFk/VpP2do823xifhATHvtDmiDamtUmaHaRvrzJoCDfXRZCAu/bYUzALjtPZpXPoKZn6A//TpqJxaFvs7ZUR89wt++AVEMzpLwDtFiIe2JWEmvvLPuxoEeO5Ibp0JEriKqR7u4xpBcO5fXxprqU1OCMqYtRaXUaEfehAwHGYO0IC9S0mFO242jhmnFwY3HTO4eikR6FdLkCfFISKPh1goVWWlpTcWau31Hqq3hWAjBftEQ7IqdNa2FoKV9KKJEetvCzhQ/mWLuPMK0OT5zlG/sk/6vawkUgpzRS19ec/JfTrh58ybf8z3f81v+/OLigrfffpuPfvSj7+k6v6PEUKcN7XxAebLF+GMPGdgjGSdxCnsc0i4H6KiVXl5XvqyPtqRkv7uQ6kHciLVmHOAeRdLrv5bBNMY9gNHL93FZspH8DToXN1dEgLQE7HLwpEQ7LEm6MSZ37AhvZXBtV8rmy0IO4osnJbP2ZMTgpUMhEzmkLPapY/Q1eTj9o7lsWpFBbWmU60aKFpUc8OMJ5v6bMM9g0DH19y41yM/xmaiP1f/LVxH9b7+As098yfVWBWkoY2MXc9TXvyDjTFkmm9r1AwkmqhJ1+AjGCkYpKh2gggD/+mOSW5L1UeS4288TLF/FF+BXLSpxshlsDSTYuHc1412uCnEPpQJgthphVq+lchLsrakfzkj25CAWm1/P8hPPEU3WKO1Z/rvrjG8eE94U8wp7OkAvrOj4h4ZglHP9fW8Tba0kI1+MoPM9nz53iBmsCOYjdFTTrobd+KmlzhLiSUabJ5TzMYODc+qVzG1HszWzwJGdzEhnMl1Q/Nouo1ce4K0hLCOSvQtsLkZEylhMKpMf9f0Z3hqisRjrxDtLmsWI/HxKsR4y2bnY6A0cXDvu7kfyniQt2b55JNmeUzJh0EnzeqfwlTgAhsOC+myKP5kRjgoGrxzhMo3KC9QUwv/TiPDVh+JAl1rMyzdRD5f4rHNtCwu4foCK5rAqMf/jv8P2GI6WMn1yhQhuy/Nen86o5mPS4bkE3quG9tDgigHRZIlbhRtxqHY5JPpwK9Wxtx+i90sCJ2JZ/n6Ffu3XMLeQcnbnbYFz2Nt3aLduEv/H/w1/+4ZwDAYjile+kWDxAJdu4dIZkXOow4dC7m1t96cVu+6zU1hU2PMYc9fhTxzNQ7GHDm9l+FqCbdpO0Axx5azXqXgIKI89jUF5Jv/njOZXS3yn57BajnFO0zThpu2zXEyYTJc4Z1idz0hHGW2W4JdDmkyY/eE4w8Rd23RXQxDKvlO06KEQk/2Zxy8teijjemrciRB5h9kuuwphp3D4+jv4ks0OH986xT/0EORSRSqubvoDhOznn3HP/8uZQGiM4Ru/8Rv5D//hP/xPX/sH/sAf4MGDB7+hLfA7xVMf/nYdg9PYPJEZ6E4rXIWteHcHVty1OrSd4xVIT7Bdp6TvO5IxvEqjohZXDSWTa0EtKnwbbV4Pcrhc8gx8p3dfn0wliEhrzLCUftasQmnwpZLZ7KITZdHgL1rsUrT/g5Fkpurla7BawrIQAtKwW6S1lNjtPMJ8IH7SNrh7B3X/vpTr63PY2cbfz2SmuBMM8sMxarmWiYAI4k//Kgw6nwOHEAzTcKMF4FdOMpaqFKJO1gUGw6H0+nUpxL2yI/zVnVfAWMn7lTV6eSGjf92oH85JiTOyMEjRt569gpd85xY6fX9/GmDih7hGDJr8UnemPNLnvRTXicb5JrsP41osYOcyh+y6doEZiB58czEh7CxivX/iXhYPSlHWq0I55DtLX5NUhJOcaFliuj65CuwTtcXAddwUhwlb6o6LYKKG9mwkgWTQbvQmlJZRwbZINsRDVxvJ7rzuZIsNQdQw3p6jjSOZZDKHr73MlHdaBnUB8e6cdjXENcLed02A1zK3b+KGgfaEHT/GO9V5rotsri/BHoeo9w3RYwWXnhFnp1BJVYvEYw9jzKwjsTqk7x0neCcCRmbnasY+Qcrj3mp0VBMORcJaTWXc1maJmNqcBqjAEu3NO78Pgz/JUetCnn/jN2qcPusKxoGBspbW1/uvoU6O0PMzwlayXj+adAJMFl0uCc4PsZMaF4+pb75CvJxLKy5bSzVsEkm1Lsu6WfgKMkTye1TQzmU0UpnOvKeIQcu8vOvaRcEo3xz0KmolyVCKYFgwCCWAXS2mGCPrJmxa0kHO5Nopyjiq5ZAwrcCLiVg6KkS1b7beVCDIPAwRMnJQ404DdF2hplr8Pyxy0JdS7aFuRbSn6nr+toU0kH0tE00SPfHS/gzAO795pnp8acB7z9OI7WZZxqNHj5jP5+/5mr8jwp933UasHe3FcEPWu5Sy1caKa59TYv/buWMpY6mXI9RQ4zPwZYBX4u+uG4NbiaGOq4PNwQFsmMpoD7WUg/EaHTi5VkcW0wjDmcjjM4O6yIVABKJ8ZZz0F4chqmyxu9elbE+n3JcEcvBb2UxdFWLiBFQNNsfO9gjOTvAnuQQE12Mh8Gm/IdioSyGQAIgD/IO5lPc7+VH5WUcIdA5aJeqCZQ1tJxnsGnno9bs4Alm7Ee9QW4FkMnUjgcHJ6ZNrgmyUTVetCCNhRV8BdNziGy2GPFmCOpcRSVtE4rMeN9BclvKFsRxuLWkuJlhrCEfi+LhR3jNW+qWDEleFVFlKMMq7zbczS1Fe2O/IOFw4ycgPdzc2piqwJB0J1XuNcU+mBNQlQRQ2lQFnJWhoO+GZyxYTgGtFqMfWIdXpVGxqgSZPUMaKsEsXKCRbS9oikf5tUmFGBfXZROb8a1E01FHbaQxodNCKg2Habu472hVWvu4MqVwV4NcKtaVQOOxJSrScbz5/XwJlJQd/pzrZzEeY8/PuC0JG6pJUgmK6110RLsW5TFoL6bfuDKm0VJ6UsbTz4WYMzdcWt4xpj8UUx0wKMb1ynY6GcegtB7ojsTlo928TrleoPBMy3iDGTnYwzqLKApOdigIf4Lcj7PiaWF2D8ADI5XnQGjUoUWULgwh7T4h8etygiwZXRJ0Ov0PFDa4jol6KRintpV1nWiFYnuXoSNa8tyVpFVLXEebSeGmUMdhaktzsWiHaY7p1bpKqM9zxItA1Goio0f0lamo2bcRL8zR2ui79PJPPq/VQNfgS1F4Ebf1E+VArSSTcGkULSYBqpK2hEML1VaPv+f/m+PSnP823fdu3/YbD/ld/9Ve5c+fOb/nvvPecn59TliUvv/zye76Ppx/1G1QisFOEmzEXZ7X0V8MWez6W3n5nxhJtr2hXKeH1JSpW6PsNzVupHAxe/LaVctRn004nQDKvaHeOCewTudChQlmHW1jKR7sMPniMLzwuE/awGRWUb+4Ka3Zvid6ytI8TVCj9U7PViFb4pZVrsZTe4dlaHpoBNK/FmJlFz4DdKWEwh0oOdUJN8ODNjXKetxp9cgyhg60t/GiMeuse7sGxMPzHIUynqCTvHASBYUzz1R8j/K+/JBvccIjaq+BsKdyEayMZ3QsuXQaXuEc1+s5QTIemFv/SXTh6/IRXQIt9pDB3urJm0eLOpU/JQDQU1MkVmbkEHqUtJnSYUQWBx87Dbva5oDwWDoYOW1RiiW7O8Y0mjs9wRUx5vEX22h3GN487y1lx9zOzHNO1c2wREc7EPc1VIUk3l2yGpbjYFTHK2M7hzlM+3mb4kceyNlaiexBOclz7RIHSpDXhwVKyn1JR3t8juXtMczLBNQHh1gq7HFCeTzBxw+j2EfmjPeLdBT6qCZKKOGqo1wOq9QDrtNgQt4bloz1M2LD90ddpVkPSa2dyn42hPpviWr0RPWqLhHhvjiti2vWA5GBN9faOjEQGlvp8jKtDhl91CEOxabW/XoALsd047ehrH+FeeT/69Aj/9rm878OU4JqQTSlrOSS1fF9kV7fZm4/swL3Hm+cJhxBX3THmvJZAL0+kMmgzzPWa9mG6CfxU3GAONO5Rp9l/I8YfXEO9+Q4ohbqzTbt1B/f+lPD4HTFn2tnDpjP0+gKlK+n/Oyc8h+UpZnlKc/AiupISpQ4Cqvd9HdHDT+MGI7hpMJ/5JHY5BnLMVkP4YoV9oDeTOnq6RfPvlkQvy3u07yRUJzNR1tOuMxFrMfsiMexWGluHTA/OKC7G6NCy9aE3QEsVxy0MTR6TPH9MezLqXP8Ueq8rwTsHeYGaatzN2+j77+BPWoJdD9OkUxv0cpDvjWGxxC9aXBFi6uaJZFtZY48D9HAuY4s3p9JG7IjAgGiK9Pii4Ku+6qv4hm/4Bv7Fv/gXn/f3VVU9lcqf1pof/uEffs/38fSZfxVSPN7uDHmcqLJdavl3hi16WuMr0fb3rSHcXmFPB51DmxH9/qmFcYxJHNm/H4iJxrDEzHL0rQTG2zLyc7Zg/fEbFJ+7zeTWEeHuknhvTnN/gC1jTFIR3V1QvzMVi18Qw5ADS3C7FlGO46HwCRbz7oBs8DlUr0fEN2SjcheG8P0NJKON6p9/7jZqvYLxBH8wEn/3IJAsqlXQWtSeZFyqruBgB63PRGltkGKv3cb8t493pXjwZxXm//PL8OKWWJV+bk5zOiE6WKBUV46NQvy9Ob4SRUE9812Er6QsenKEv6jgrJJy3s4Uvvomrm0629qVHPyhFtIXoHbi3+SbfO+oH842ZkyXG3gwKIluzkWM6FMnwvpep9gixs5l01TaE07XjL7hlPizc+rzCfVy1Gmpg1tLG6A6m1CvByT1miaPacuY4bVTYYvPR3KI76xIqiX50TbeK9K9OeuP39gIB+EV4bVMevFVSLhOmT/aZ/BoTTSUCkJ+Nu2ycLEbtssB4fUFqVN4Lxn74MaJrPnAkR6ci8pe3GACS1NFUl3QfuPIRqsIRwXNsstqk5pqPmb8gXsy1lYYJjsrXBVgwhwzyWmPh0Jq3c5RWyHpV7X4R6e0pwn+MJDA+/fegE+/I25yzy8AhX7nTfxZhV0mRM91qpujWyJw8z/uo6tjaQuFjubx8GoEnwD3qWOUUaiRhzSk+PiUpL6PKwxtPhRl0FGxqdSp05boYIGrpBWgrxnIKpTppLAvKlR+D5TCzRXtm5bkwf9T2lvjWFpnb56SlIUEzCDGPM6h8gy9XkK2xpwfY2c7+GSITYZ4HVC+8PWY1SHhydsQGKLf44EUagN5hatidN6gwgzlHNH7u/GZ2mJmOYMbXftu1WAvuvmJVMSG9NgzHb6NKzSj4UMA7GmMnlUy1fBczPij0P6nIeGHES8CRvhkgDo7fuLoOR2iX3sdQo066IL9KJLWoevG+h6fi7pnneFOI8xIPUkMskxk1RuLf2OJiudwZxv7Toa5K//evtlc2XqQL/HZ9/y/nJr+P/qjP8of/IN/EJCM/ru/+7t5+eWX+aEf+qHf8t9orRmNRnz4wx/mxRdffM/38NTff9OJ96iwU+6aSNnR5wq7jrFZQns8RKdPXN58a6Q/FljiPTmQMAryGn9WoYOEcJLL6FYWodcFLHIh3GQGpR2zFx5iZrlIrpoajIzmEHh5Xde7UmmDGVaQKwjEjlXPctkcnJfeVwvq+gBzVku5Pw5QaQvBpZ6/kw3k4qzzGtfQNpsNhlSjsVABOxPc9q4c/q8/lgM5klEb87lP43MlGfJWCC9cQ+ed7G4So+84gjoXp63L+dyLNerla6izM1g1+ALUjRl69K4IvUXEf2wLqzVm9VkpFToHk7RrQTSoTv74cuzrWcOkNa6OoBtXivYvaM4m1A9nBMsCM6uk/dD5zTcXkw1Jrl2nBBen6NQR719Iu6CK0LS0qxST1iQ3zjBnMk6ojSOeSAVACKANJpV5erRncP0MV4XUi+HG6tfbbsxwlZJMpQJh4obpwRl1xxO49A+4HPO7/L2aowlN5ymh4gafJZvXXHJNor25XGc+JkgqdGix55ONf3qzTjeSxW2eiMbBIpU2lnHo52L0UY4vROBqM/YGsk4v5qiXrhHox9iFGLaoxQXcnKC6AJWLufBEADMpZYw1W6PrGmyLSh1+Le5+SjvCG1en6Kbibj5eKahbqdhoUSj0rUaFlnCywK1FEEzFVqotVYiOG6LhUvg6Vng9vtKYHQN1ix63hGaJGgdPiH9xgtrKN1oYlxbN9UsfIpgfo+ZnIhOsDT4Zykjg8gJ/5/cS3/tVaGt8EGJffBlzfNjJBoeoo0eEvycF967nJoxEMjgJUAdTkRzOKtlLklbIt62VNkcUQtqgt6Ju33HSBhnPRPhrNMFNttHRsYzkDsEnA9lj1rnohqQD/L1TMR8qGigq4QHoSp7zxkFViYbIMgPrCfYzOEPEy8oKVu+aBjBKSIBZhh5VonJYtujxF0Lk58ov8bsW165d4zu/8zs3//3d3/3d7O/vf97fXTWe+vBXYStz8IEF4540dLQXT/W0lj5/3FlXWsPlML0yDpV4XNGRUhz4wqCj/wMJqZLXiGdARDjOP99BC5k42GTUjd4ICykjI3ZoJf3wyDxh+nbkPxUBcYIZLTYEOUB6ZHnZ6eoPu3GYjvxVV1L+jyKYTmDUykPnHAShBKONRuE7qVEn8seoJ6Y+USzkvrMLmIk1rx4+hulMDvJSyoY+GaA42/T48U7upam7+0PaDHkGrjOkGb3r8zPBpgeM81fm6mdmeaerIFmvHjeouRVhnFVKmDZiZRw3aKtps1SMZZQTzf3TAToVMyDfye/WuVg566jFdO936VVv0rpThpRrUMQY0xncWPFm0N0apGP+m7jBe028I0Gnt5p4Z4HS4ruujCUa5zRZircOArGZxok5j/F6YwGstEdZ2cl8xwAPqkxaFd2kyaVnAUpEoS79JvAKk0gA55sAV2lM2/XrW1l/KnQwTmStZo0IXGmDSjW6bEX/AkBpiCJZTxfzJ89gQGdsU3Vrqdt1tcc3Rgyqdq6O3a3G+sk1rd9MOsgte3TcosZKes51sClPXwZdvlQSLKZCVvTrLsBJItE0MK08W/CED5NEwpdJB9LHX69AG3yU4INQ2mjdc6fqElVXqGohugCAG01pZwfd4T/CDcYE6yX1C1+NWV+gynzDz9FRhE9S4f6885r8XqoG7WQ0MAqlSpikqDB6co+d6I7b3kU5h3IOMz+FUZekJLnc42oh/Xsto32+0qitLahP8LlHDU0n9uUl6AgQ6+Kztfx3KhUSHa+h9eLsFyD3NkolgDlbSLJWAgjxr8eXDty7Ddm+QHjqJRDfOad6e4dmMdwIowSdIIsKW8xujs4qaQPkYvhjBqVI+jYBdhHKRrTSG8/3oCkoHu2K4cvt+Ybh7FbC6E5eOcceR9RnE1wdEk4zopsyuuSdFg7Cdo7LQhmBylrs13w15lOfxi9aWLVCpNEKdiZdFN+1Hlrway8yqVGJPYtkZnoI5Ud+H/FbH0ct58ITuCjhxgC7fx2fDAne+DScrVHTNW40wdyIZPMLDGBgqlFhLTraqwb96uu4lUZPvWzObae8NhrLgw+wP0O98RbN/QH4mOBgjX9nJZmFkw1SbWm4OJNNYjKCrQB74y7mnc8Jc3rH4Q9uiILgagVn+ZUsGnV7InP0q0y0HzKDGZaEcYNKLdW9HZIXztC6AasxSY1dp50rpKY6m5DcOMMuUzngZzmrV58TCd64wUxyqvMpg9tHYj8aQXucoMKW9f0D2jJi9tIDwjsZ2f+4hq0iRi/f70xMQvGwv2Zo3wgJbuTYk4jqbJtotmbwwmPsMsUWEfFsTX42JUhFta1dp6Q3TwA5mJqLsbDTW4MZFYTQHaYKM6qI3RLXBFTnE9LrpyLR7CDdlzFHVwfo0KKjlvBGhlsYigd7+E9o4hcrKZPnVg4+kMAzqlGrEl57KPoOVtNcjFE726j/9gYqmaM6/wdRxXO4LMRcv1SjlLaUPY8xBzW6qYW9Hl8N+ROArZlkx3UXyJ3OiKNTGf0cFd0aFndMHTf4RhPdXKKPxfmOwEsycD2VZyNzMqZ7fQ/CGsoF/qxCTYMnAU5ZdyS47qB9fE7U/Ar25l38aCLiWqMp+lx4L240Ib7/CexkW8i5zqLrAmyLD0La8T56ci4BRBDhJwnN3kvo7AR78DFwLebkEwTpgPqVjxKcPkS/8QYAfmeX5s4rtLPnMMv7MukTdSX4kzX+9gB9/AhOzsR744UR/nCNOlvK9360hGnnCLpaomee5s7LhHWNcgvY2YYHx7THA9CO4Pa7CMYtYgFuPPZxKIH4OKB9KxL+R5xIUHI6xztkL7Eal5mrLfvzZVWl/4Li3r17/Jt/82/47Gc/y2q1Yjwe8773vY8/9If+EC+99NIzu47yTzNf0KNHjx49evxPcOvWLR4+fMjUDPm/PPdnnul7/623/x8sbMbNmzef2v72dxOstfzgD/4gP/7jP461Etx571GdLblSiu/93u/l7//9v08UvXe9zr7406NHjx49njl6Y5/fGf7kn/yT/PRP/zTee27evMnHPvYxJpMJFxcXfPzjH+fw8JCf+Imf4Ozs7DdMCvz/g/7w79GjR48ePb6I+Jmf+Rl+6qd+ivF4zD/8h/+QP/bH/tjn/dx7zz//5/+cv/AX/gI//dM/zZ/6U3+KP/yH//B7uqb+n7+kR48ePXr0eHp4hI/6LP98Ofen/9E/+kcopfgn/+Sf/IaDH6Tk/yf+xJ/gn/7Tf4r3nn/8j//xe75mf/j36NGjR48eX0T82q/9Gjdu3OA7vuM7ftvXfcd3fAc3btzg137t197zNfvDv0ePHj16PHO82774Wfz5csZiseDmzZtP9dpbt25xcnLynq/Z9/x79OjRo8czR6/t//TY2dnhzTff/J++znvPm2++yfb29nu+Zp/59+jRo0ePHl9EfP3Xfz1nZ2f85E/+5G/7up/4iZ/g9PSUr//6r3/P1+wP/x49evTo8czh/bP98+WM7//+78d7z1/+y3+Zv/f3/h7r9frzfr5er/m7f/fv8lf+yl9BKcX3f//3v+dr9od/jx49evTo8UXEt3zLt/AX/+JfpGkafuiHfoidnR0++MEP8g3f8A188IMfZGdnh7/xN/4Gbdvyfd/3fXzLt3zLe75m3/Pv0aNHjx7PHI4vb5Les8aP/diP8dxzz/G3//bfZj6f8+lPf/rzfr61tcUP/dAP8YM/+IPP5Hr94d+jR48ePXp8CeCv/bW/xl/6S3+JX/zFX+TVV19luVwyHo95//vfzzd+4zeSps/OoKs//Hv06NGjxzOF59lb+n6Zt/03iOOYb/3Wb+Vbv/Vbr/Q6fc+/R48ePXr0+ApDn/n36NGjR49nji93hv6zxsnJCT/6oz/KL/7iL3JyckJRFL/la5VSvPPOO+/pev3h36NHjx49njl6wt/T4+joiK/92q/l8PAQ/xRR06XN73tBf/j36NGjR48eX0T8rb/1t3j06BFxHPNn/syf4cMf/jCj0ehKr9kf/j169OjR45mjL/s/PX7u534OrTU///M/zzd/8zd/Qa7ZE/569OjRo0ePLyIeP37M888//wU7+KHP/Hv06NGjxxWgN/Z5esxmM4bD4Rf0mn3m36NHjx49enwR8c3f/M185jOf4fHjx1+wa/aHf48ePXr0eOZw/tn++XLGD//wD6O15ru+67t+g6nPVaEv+/fo0aNHjx5fIPzNv/k3f9O//8hHPsIv/MIv8OKLL/Kt3/qt3Lx5kyRJfsv3+ZEf+ZH3dB/KP81QYY8ePXr06PE/wa1bt3j48CEjM+QvXv/uZ/reP374T1jbjJs3b/LgwYNn+t5fSGitf8s5/cvj+Leb4/feo5TCWvue7qPP/Hv06NGjx7OFB+efscjPl0ma+k3f9E3PRKTnvaI//Hv06NGjR48vEP79v//3X+xbAPrDv0ePHj16XAH6hvKXNvrDv0ePHj169Pgi4OMf/zi/9Eu/xGq14vbt23zrt34re3t7X5Br94d/jx49evR45uhFfn5rvPnmm3znd34n//k//+fP+/soivirf/Wv8iM/8iMYY670HvrDv0ePHj169PgCYbVa8ft//+/n/v37v8HBr6oq/s7f+TucnZ3xEz/xE1d6H73IT48ePXr0eObw/tn++XLBP/gH/4B79+4xHo/58R//cR49ekSWZfz3//7f+dN/+k/jvecf/aN/xKuvvnql99Ef/j169OjRo8cXCD/3cz+HUop//a//Nd/3fd/HtWvXSNOUD3/4w/yzf/bP+N7v/V4AfvZnf/ZK76M//Hv06NGjxzOHe8Z/vlzw2muvcffuXb7pm77pN/35937v9+K95xOf+MSV3kff8+/Ro0ePHs8Unmevx//lUvlfLpe88MILv+XP3//+9wNwdnZ2pffRZ/49evTo0aPHFwh1XRNF0W/580s9/7Isr/Q++sy/R48ePXo8c3y5ZOpfLFy17U6f+ffo0aNHjx5fYegz/x49evTo8czxrHv+PZ4t+sO/R48ePXr0+AJisVjwH//jf3xPr/mtpgWeFv3h36NHjx49njm+nIR5njU++clP8vt+3+/7LX+ulPptX6OUom3b93QP/eHfo0ePHj16fAFx1WS+p0F/+Pfo0aNHj2eOLydhnmeJt95664t9C0B/+Pfo0aNHjytAT/j7zXH37t0v9i0A/ahfjx49evTo8RWHPvPv0aNHjx7PFJ5nL/LTFxKeLfrMv0ePHj169PgKQ5/59+jRo0ePZ46+5/+ljT7z79GjR48ePb7C0Gf+PXr06NHjmeNLYJS9x2+DPvPv0aNHjx49vsLQZ/49evTo0eOZoxf5+dJGf/j36NGjR49nC38FhL++jfBM0Zf9e/To0aNHj68wPHXm3/7zEe0bId5qzCQHp9DbDiIDraV+fUJ5PiEcFoSzNTpusFlCeC2DUFG9PsU1AeEkR6cVaE97PgbtCfeWqJsj3L0cZRxqACiFPYpwVUg9H4FXpM8foVMHGnwLvtDo981oP5HjqggzKGkWI6K9Ob41+MYQ7JdgFPY0oDmTexwcnBO9tIS9HdCa/BcUwagAp2jWKcMPPCb/7AHJjVP0h/bg5BT7QGPzBJwimK3R+x60hqKlPU7Qwxp9I4Kypnp1Qnk2Y/I1bwNgTweYSUFzNsY1AdpYoucXUhcLlXzAlcdlGn3NQN1SvTYlujnHng9QgUVPKtrTIcFuBq3CrmPa9YBwayX/3ilcFRLsZqgE/FpRvLPP5P9+79muGKD8v02pTmbYMkaHLcOXH9IcTXHWoMOW8PoSHKhUgYb2YQxO4xpZbko7jj/9AjvPPyTcWaICy/r1m8SzFUp7eZ32hFsrzLSBAOp3Jpi0wjtFuxxy9tZNhtMV2ji55jhDxw1mWOKdxuUx1dmUYFCiQ3G/qhcj0psn8kt4hXeK80+/QDLOiLcXhPtLirf3GX7sGAKDfSD3qqcWX4NbRdSnM8ygRBmLGZboV6aU/xl8E+BajWsD6ixldP2EYJah04bqwQ7BJMNVETZPiG+cUrxzAICOGkzc4KzBxDUoj28CbBXirSEYFUS3l1z88gvymqAlHJTE+xcEt2t85rCnA8rjLar1AIBksmb4e08of32KDltsGTO/f8Dtf/Frz3wtAOT/1z1sEWHiRp7lgwh/UdEejagvxgxeeIyrArAa7xTKOPIH+2hj0aEF5YhvnlO8vQ9eEe9f4KqIcGcF2uPWMY8//grX/9dPomKHy0LWb9wk3l5ikgqd1pitBrcw1GdTcErWzk5N82iIq0JU4IheWEEUQNbgVho0KOVBewhADTV+5VBDBaMUwgh/by77kQOfK4g81PL8uSoiemmNOwVXRLgixpYx0e4cnTZ4q6ge7ZK+cowvwRUhvgqxRUT8wQzKlvYwJnjebtYagccXIRiHbwxKe/SkQt3ZhuNz3ELj6wAVtdhVipkW6FsJDEdwdo5ftfhCY5cDdNxQHm1h65DxKw9Ae+rDGW2eEAxKhn/76ErWwyX6RP1LG099+DevxXIwG4ddpdgyJmwy9KxCjQPMqCDRHqUdvjXUyyHeGrw1ANgyJrlxSvb2dXTQMvzqx0TDBW5l8FajLtbobY19FKFsi04dKmwxYUsctrIhLlLac0N4fYmKFb6A8j8pgpEmmK1RqYMLOUxVYMHqzcGMU5i0It27ILy+wK+A7BSVQDCaAKDTisHeEl8qgkEpv/jhEfZQgpBwb4kaK9ypxl94VNKCRg7+iYdViS/BDEuiZi2Hn/XotEI9N8UUOaxTnDU0D4b4JiDYXqFCR304I355IQFFFBA/P+f8v7zE5PlHqLjBZSHBLEeNNTiPiUpcEaPjFu8VODCBxVcacBB50hev5uHWac3gfY9xWUhzMcatY8rzCSZs0dMWakV7McQ7jW81bZ6Q3DhDa4erInxr2Hv5HbxTuDzGjAqGd44oD3ewTYCJGqKtlfxupcJbhQ5bOfjXA2wVsnXrCG81bRnTlhHeK2wVEo1zop0F4fU17XoA2qPjBp1WhAcLmpMJNk9wrUZpz+z5h6iwRQUWX4QUFxOCV0uCcQHK0y5HqLWV14SW5PYJ5YMd8FqCrU9mBAMjB4w1GNeglAQw7XyIWjtsGVHNxwRpSbyzxLcGHVpsFUpAPFtj5yO8MehUDrPy7Wu4JsA1AcpY0p0FtoxQxmGSSn63dzRo6awGowKlHdH2EjMtcI8luCnPZW3Pbl/dRh/urDCVbCXeKjiq5bnaW2NmuayZcdMFgwr7KCDZneNq+Tc6aqFVxHtzcPK9RHeX8n7yz5kenNGejwm2V+i0kTWyswDt5bm/tQ/ZKcGgxLsuoNYQ3s3BenwJjAawLOT/O4VvNQQWrAILtA5fGdk7KORPAPY4RoUtetdBBQy87A+uhAL0EJSR7y3aXmIfh9hlincKMyg3e5AKrCQkswx/0uKtBEL+rEFpgwrl8HeZQin5GdrjG416fL5pots8wfhKAtgixB/m2DlAhNlu0dcMKlxDAIPJY3yjsesY3wSE+0sitcAu0ytbDz1+d+CpD39bxJLxBBYdeOqLsWRUyqPrBhU5yMG7d3USlGRlKrCSzQUeHbSYuJHsvQTfGlTQglFQOHn/1MEwQJVWovIqfLJRxI28twY9dBJgOAVOQa2eXFsDyuOKRDaUuMGMClQVQqvwjZb31g4zKmjnQwg1agzuXDIU3wT4osIWMeHBUg7ewOAd+DxAednofRngTY2dD/BOoeMGFTh5WJVCxRZO50CECiyq25xcE+DrAGhRuouT81r+1yiCRDJdXwfgFWqMBAcaVGol4Bl4fKbwVfjkYzc1auBh+OTvniVclmDiHD1sCN0avJIMPGrQcY1dptQX483rvdP4KuyyPouZlNh1Cq3Bt4Z2OdysJRO26MChuk3POw1OocIWu07xjXwW4CVAyBraIqHJE8JBKZ+9cfjO6lqHrRzcsUUNFfZehGuMvK+XAFNHcm28IkorXBNgi1gyMcBWEbo1aCp8t5ZlXTs59MNWrmstrpbD+vLgvgx+nZWqgC0itFO4xsjB7pVkrpV8774JwGnqLCVKK5Sx2DKWgySpZc0CwaDEFhHea5Ry8t95LOvLONrFUJbR5ed6GcxeAdTAYwaNZMct8mw5j4pAJQ5acIVGTQ0Mh+jFXIKyRA5EFTpcEcphZ2QPINRQOXyjcXVAeuP0yaHuFCZu8E2AHlWoCFgsUMqjwhaaQJKFM4/ZaUAp+dzOclwh6+nyfUD2IAA1dtjzBD3NpEJQNKgAeU/Ar7xU1VqgffLviaR6oEAqXoHFW41vIvm+47r73UBVXgKhACg9vuqSldiiEsAodNHgnd6sP1+EeFXjbbcXgKw7Iz93RUh5vCVVrnGJcl4SqsDJZ6Mdfh6A8qi4W9NFdCVr4RKeZ9/z7ysJzxZP3fM3aYW3T8pQeIXSjnY5oLy/B07RrlPadYpvNeFsTTjJCW4XmBeVlOJrQ3rrhPjGOb6E8t4u7XIgi3iU0hyOUEMLOwPYmnUPFdgsoV6OcE1AsJ+huuSWrYTk+WNUYLFZQjuXTO/yoVSBxTuFLeTQ1WPZGKtH292m7VCxQg8b2ayrEBqPK2LaPMEWsTzMxqJeugajAX4hwYctYlwWYdcx9emM+nBG/nCP6nQmH5hT+MyDl01w+at38U2AMk7K09NCWg3dtxDeXODmCnscYo9D3FIx+dr74JUclMbBIILagnMQBehrCmItbYAsoZmPcFXYbb5AYJ7ZQnk38sMd2pMRvtGY/QaMI95ZbNo91emUajXENgE6tASDkvpijM0TdNSin4spu8/JO0V5vEV+uEM4Koi2VphBKVn9cogrIllrgaVZSzaF8mSnM4KdNfHNc+LZCmc1g1vHJM+do9OG5miCa4xsvFEr30fhZQ0bh+mChHadbgJLjGP43CHRzhKlHc1yKJm46dZRllAdbuPagGBYSsY7ybsA1qLjRg57r3BtQFskVOsBJm6IJxneKfLjbdp1im0CnJXHTw8dwfZaSvRVRHU6pSljwnFGtLWSdtRKAiSlHLaMCa7lKO2xeUw1H6PCVqogyyF2mXTPpyfaXnYZtfrNv8xnAQ2MY9hKUNMAPXRQqy7DBsYh7ekYsgaiCLUVoCKL2arR+17aBFbjSvlfFVt85nCZxmUJrgoxNxuCmxUqdNh1jI5qqpMZvvsMm1dDCHyXtcvhVj7cpX00wJ4GuEVE8fouzfEE3z37gOwBuvszDbFlDGkAW1sSgBiFvqFRqaW8tytJS2ZoTiaUD3bwVuFWIb4y+EZTvzlBxRYzqtBhS3kxwYwq1AAJhsIWNdRwax811bJ2up+RBjAbyj4VWAjkuGuWQ7xV2OUAu04lwC6kGqHSBqUdzmqUkeqVO3a0p2N8oXErjVuFoDzBLAcHbhVSzce/8Xvs8RWFp878fWtAezn0qpDBy4eoCOmzncwoHuxh0ppglKNCS3M2ITq4wJ0rOJW+rW8MHoNqpZxly5j44AK7TGjuDUg/dI5fgbtX4tsG3yQEuyXBLJONroyp7m0T7c0lWzh2mGuKICqxZxHV8Rbp80e0ZyN0UqOHNd5rop2F9Npah3lOYdctrojlwVtaysc7DL/qIbRQfG6fejVAGQvLAa4rzapfm0t5fwjmpRj3CYcKrQQ9ZcT4q04Iz1e4IsaVkVQoNPhaNovhc4c0F2MpQcc17elY2g+VbF7q+oTm19SmfJ7cPIVQKh0+bFGRxR+1uDzC7FcwnVD9MvKZv+KIPpTS/NKK+nwC8xEmbggm2ZWMc3hrJGsLHT6D5mJM3WWa2jjaMmKwd7HJesOdJeXDXQhlHbkHJeOPPsDn0J6NcFYzfuER9fEWJqkItle05+PuMNU0yyG2iBh+6DH2OKZZDtl6332Ktw4IJxloTzQs8FZT35+C9phUNl+zXdI8nLJ6uM9gZ0Fy64R2PqI6n1DOR4xvH0v51Gp00OCRzNBWEfVqQHr3WO65jMEp4uvnmPlI2l/rmOp4i3CaUZ2MsHWINpb8Qkrt8aBkfPNYSretllKx9pSnU0YvPRSeRhHTniYSFKYVYdyglGNy7RSA+mJMcTFh9vI9lm/cwntFMl2x/sRN4t053mmysxlNkWDrkCCpMGlFmyUS4GiPTmuCm9UVrIRuPeSg2gpiBYMYfyHrVMWNZP4gHJ/HA9TFBS5P0ElNeTSSwFz7J4Fwa6QKEFnqwy2UsQSzDJzHF9L/d0VMuL3CVKVU24qG8KtaWCvcyoB2RNfnmHmNmcj7uiog2lu8aw0rzFaNdxJ8AdgHmuR9FzAaQ11D67FHEWa3Qg0V6ftPaR8N0OOS8PqSKJAqQHN/Ip9z3BDsrrDzjqeTVsw+9BZqx2AfKVyXhHBew/kpvpD+f/0pqaLGd86EXxCA3te4Uye8qUm26eE7oHy8QzjOpOKWOtgbMZneh1jjFw47F+6HrwN02qDHDeYW+DMJJvS4Yfyh+1e2HjafcZ+qf0njqc+G6O5CyvRWssr2ZERwsEbFVsg1VUh4O5cHdBURXb9g/eptmirCO0WY1IyeOxTySvfQmKRCpRYzlKzJnYO+FsNJhV0OMKMCUo0JGlSwwh6GRAdz9DaSyWTg5goVevS0ZrB1JO2AQSUPmYZoZ0E7HwFdlvkokd+ny1Lximi6pj0egleYpGLy/DEuE3KOawLCrRX16Qy1lPtUDx3hnWzTS0yvn9I+TDGTAh2UuFUi5ezTIWZcoMeW9jTclGzpWiOX1/BWw5troucdZlhKBcIp3LFkm2iPCq1kQLMcXwNvL1DBGJ1W2DcCUOeE7/PUv+KJ9+aYUSUkqyvA+AP3yN+4viGYTe4eMryzRqcNBJ7q3g7BqNhkZSqy6KihyVJsFZIOS9xKo0KHHlRE41wCo0jK2i5LJKOuwk0ZvziZoV/dxdXSytBhS5OltHmCSSqSbekRm1GBbwKa5ZB2nWKWA4JhyeyVd+QQrgN0XBNN1mSnM+rFkNFLc1TsqB/OiF9a0B7G+FYTjXNU4mkWI5SxRDtL9LZHLaXcb6uQ+eEe43eV903UkE7XxLtzdFpvPrP6ZIpJa8KdFcEkozmdEExyzG6OW4Xk9w7QxmIGlWz2ZYwKHCaoSVnSrgdMX74nlaAswVkjmZxXHUHSMbx2RrSzQI+t8EscXXstoPjUhKvK9dxaslC/0PhKs773POP3PUAZj88Vaijfi9JOMu3AUtzbJ9paER3M5fM53Nq0+GyeEB1cSBA3LDH7LWiFzzS2iLFViGkCCRqurwEo/+sO0fWadjnAe41Oa/SwlGfAKamExI3ck1W4LKF4Z0p69whqv1mn9jhELddyqMYKlTbYs0h69nsx+eEO49l94bWcx5hZ2QX0jbwmAbtQBLsVjGP8RQXOYbbk927PxwS3a9wpko1PcoJxgdmupOKZDqBuQCtwddfWtNJ6fC7D2IrieEuqP5Mc70BdrClf3xHyb0dkNaMCs1M/GbZvrQROlVQBzPDq2kA9fnfg6TP/jiRzWWK2RYwp8i4rkkzXZx5XhLLAqlBY2NrJYaclilVhiwfsOu36dnrDuPW1gUUl5fQqkqmCxkGo0cOuD1wb/EIyQleFmKSVXplS4D3t42TDC/CNlOtU1jWAOwJiMCokc41afBUSbK+kl6Y9Om1QNyfoh0ucU0IdCCzR7lzKxxrcKpFSZ0c/MKOKdj7YkGi87QiIsZWKh5OM18T15ucqsLgikjJ4WskmvVab/qMwkP3mELTLgbC9t3OoFS6PCCYZesviLqQ1oo4LBs8fSb/fKMyoee8r5DdbC7WRMqPyBFFDux5gnHAotP78a/rW4AthW+tA+vnSw7e4LJQ+vhNCn2sCtG6F4FhE0tPVDhU3RCNhfoXj/EkmnUg2qwPXkakC4TsEFpNU1IshLks3WRkIkx6QknjXB5eqlpBS28OY5mKCrUJpa50mUm43EnzZNxPqizHBoMSkNaOtpfA1nN9ct82l7H556LSrFFfL8+CtZPvVfIyrI8JmjYobwnGOUg4VuM/rbcsNKprVQH6uvXAnlHvCidCOaj0g2RYyocu8/E5W+uW+4yhcFfRAOCY+dzRr+bztcgDDUsrS8KTPHSqU80TLtXxntaz3YFRsfm/5+yf37DMxiLXrkUzbDMsna8RJyTzcWUr7ZFw8eYa66wL4SuPLAJX+xmfikjCLBj2uN9wFFXhcJpUGADOsCYddhSLymFmJCp7wkHxlpJ0xLPE1qFVXbWk9TBO0LmHuZU9zBhW1qNRJG6ACyhrcEn9WdS0CuimRkDZP8AsggmR3IfyrqJXKYS2VLrlJ94QXBZvqo7sw2EKmc9Bu8ztdJXqRny9tPP2o3/lQDvgm6Pqk4WaDbtcDwnBJ9WhbNiWvsXlMtLWS8jlCUGnW3eHY9TDjnUVHZnlSH2qPB0JGuSTj5KC2NMQSINh1il0Ksc57TbCdQRyAc/gLyO8fMHrpgfzbMkCFtTyMrcFbS7QlmZdK5b5869AzD00tY3eTLvrWy03/0DuNudHCIAGt0SdrfIGQfugIT01JdbQlvIRBSTi10sOrbdf7FyKgb4WgqKNWDuxpIZMC3lO/NcV2QZOZ5JIZpw57rmlOpzIW5TpGNaC3LKQh2jV4V8uY2rcBFxeSPQyuhtHbzocESU00zjGDkvzRHrqMCJuAoMklMxuUuDrA1ZGMABqLGpRyuLUGFVvsciB9fGu6PmYEMei4pXq8jTJdpSWqiffmuCoi2F4Lb+PBDuE0e7JOuskClBz0ZlxgzqWH3uYJ3nXjnF4CLNcExOMMHckhoiqP0p7VWzckGEUOBf9YE22t8K2mPp+wPtkGYBKfYEZLhvFjmosJrtXowKGjlmqdYuYjwlEB2lOdTzek13Y+ojydUq0HtGWMawzx/gXxtQu5XhlIQGQ1XstasVWEbQ11tkcY1zLWGLbCieiInlUuEziXuPzMm5VwapJbJ1eyFgDUFEhilKkxuUzUNN1USxTPITDC5dF6w0MJr6+x5/HmWY6uz/GVEVLw2NE+GjwZ7TwdSta8HhCMcswsR8UOvzK4RYQe15gXQiDEuEtSX1f2vqVBa9Qip806km/3/ITjvFs/lxU5UNMAilbadTnUZ1Ph/AQWNW+JdpZCuB1qmR64kIqXy2XUD12ixxZ7IfyFYLeU+necgPMSAHYTDMp4eZ+DffxrjyG3oFuao6lUPLYr1MDij4UfUj3aJpytiW4uYRziz4Qo6a0m2FtD+4TX0c4H6LpLNipNfbyFCluC2VrGb89HV7YeevzuwFMf/vXFWMqWk4z05VOW//WuvMFWRrCVdeSX7mALHMm1c4lYV0NU2JI8dw7vzGQTnuTE719Svz7CbFf4GumThW33YDdCyollpp+ixbcQjHLa5bB7GB0mrGnPRphKRnIumbC+NVKCTlrs+YA2T2SmOG6EtT9UcpAHAaZtcY/spj+mkgb/+mPUXoKKK+ypsG7Lz8yEVT3LMB+5Bq8eoUZeSEGtR33kGsF/ntOuJEha/NpztHXIcP+c+LkzOfidwkwK2SyjAH22EDbzXKFij7NGGLqBwxUR9jSRew9cF0iJBoJyDqcb7FlIfbxFMMrRg0o+809JdQIHrnBX0vOPP5jhz+ZSwTCOOJfWhOky8eJiQjgq5Hf2msU715nePdwwjJV2nP36S4xvHBPN1tgiJn+4R5DU0uM+3iJIapT2qLiREmzbcPGZ5xgVp1J+j4Wk6VstY3fd4euaAN3NdQfDAh3JNEC9HEkFIGypFyOq9QBbh+z+3lepj2YApB84xrwjM/zNYsTy0R4masgPd6jzlLqKOPjg61RnU+rVgHo1IEgqwmlGPFvj64DFa3fwXlMtR0IMTEuS3Tk6rWjmIxZvX8e1AVpbBnsXRNcvqA+3RCeglvaBDlvK5YgwrjFJJVMN78rcLw+j7N416RXPVly/fczFq3cZ7s0Jp2vKwwnDO0cSRGp3pYQ/v/I0ryeoMCC8kWE+OCR8+zH14Yzi3j7DV1pYnNI8HNAshyR3TgANRiZt0B47Hzxpr7gL2nVK8tIZvoTmaCrBchUSjJBZ/1WI0p7qeAv7ICR8kBPfOad5OBX9iUjWj1kswCh8DcH1CgYxHNWdJsaK+nBLgu3usG3eSLHlBKUcJq2pVwOGt47RaY1dpVSnM0a/byHJxkMZp0N7zG6Fb6H43AHx3vzJup12pf97c9FGGSvUzREcrYUoeN9gLo4gAnse08xHm7HW+uFE/k3ckB6cC6F6lQrh+qJBDRXatbQXQ5lE8d133Crh/nQJh4ob0g+eYI8Dqcgo0Va5ari+6f8ljafP/Aspf/rW4EsYf+gdiZIzT3M4ojibMbpxsik5NfMR4c6SeJJjVylnv/Q+pi88RMUNWE392liymvNYSlVphXea/I1rpLdOMK84WED7eNCJdFiCnTXBtRJ7FuLyWMZoopb1mzcwYUs4zQhHxYYpq7SHzEm59HLkcBljkgp/VuIzg12nhDdL8nt7ACTVKa5KaN6aoKOacHuFfi4mqHIp800D/GuPZeSnBuqONXx4CKSy6QQWEzUEqfQDfSG9ymC2lh7kMZgbHrUXoxYVrtD4Rm82eB3XmJEcpPFLC2g89kL6jvY47srUDj2sCbeWmO0SFYso0vr1m6QH5yL+8a6e87PE/N/dYnjzRGa4dVdyTaWF47KE8c1jgu0VdiWjeelsiW+1iNk0AdnDPfa+7jOoLQWBIawz9JsN7TqVjWlYEE5ymRBYp/jGsHrrBunWSg7yt0Y0ZcToxunmsEx2hZCJBjtPqI63iPcvWH7mOeJRQTxb8cZ/+2r2rx91xDzH7PmHtOcjOXgbQ/aJ62SnW4xvHKPDlsFshYkaITHWIb6Mmb9+CwDnJINNgNXRDul0TTgsSLeX2CqkWg/wrSGc5DSLEc4a8Jp4UFJ1YlG2jGhPx6wf76BNx9juyIuzD7y9qX7Ed87xmWH95g1wUolInjsnXsvnCsg43NaSeiXVlPT6KTptaC+EiBneya5kLQC4LCK8uZA5/iDAPzjHFaH0socl7jMNKu76/mGLyyLy+wfE2wt01FKezgi7KhJOUR1uE4wK2uNExm2dQg9q0tvH+MZs+DkAyY1TVGyl3TiPCbZXBHq1yYLdQqpMatxViM6qrmIowXQ4W2+4AM1j4eh4a/CttOZmX/cW9kI4OMF2Rngjg7nCLRV2MSS8luEqg8tkyiMYFbgqRCsPuhXNg50Av2ilqjENsJ9tgLgbUZbxYg52CcZzdHxBuxx2LSyL2S5QX32d6v9bSEA7LlDTAHf3OdRrr2PXXQu2yqWCZRwqaoX4fDrGKY82DqoGs9tidAuV7CdXjf7o/9LG04v8lBHRhI2oiG80yjm8A5RnfOexZGO1jMzpbi5bx7UQsIp4I3KB090svJURNqdwTUjTEfNU6CCKsBdWSrathirEt4bopSUqsqhKMuR6PiIcVHK4a0d861TKtg5olSj/DUsJBJRHD2tR9+KJJoFbSBnYJBV6alGtlYcysNK3L2uUNrKh1Hbzqbm5EGj0oEK3MualAum56bAVbkFcb4iDl/P6AH5VyXsjKmOukrlwnIxQEpXSn55oKGv8cYCv7JO58mGNTh3taYw/CjZBl+5mf31tcPnV9PUG17tKRhngay9iPI1+Iv40ybCrtGNx57hGDsHLyQndMeWDpITA4XMIZmtpFVThk2BvUIpIUDMQkZsqxAQOF7TYJqA8nRLPVpi0wlUh5eNtkoML4ZgoTzMfEST1JpOaTpdEaYWPa7xXG+Ed7xTeaWwdUmQpI2tQgazPej1AdfoDg8maOk+IBiUai1KeaLKWg96pjYIhwGD3AqWFLGirEN1pHETTNd6rTWvBljHp1nKjlqi0w1YRzdmk00WQaZHy0S7aSBCLUzLVcKkXUUe4OqLJUnQnAuStoXq0LVlpXOOWVznq56WnXoGvFXoMWPkOzKhGjcFndJwamRJJr52hEwlOg7QknGToQY3LI+rFSPr6xqHCSqoDkccvO62IwApnpIhEZTQJsZ8W8afk1hlq5PG5cGvsIsXoAhUZ/JlM4NAK70IFlnaVYlojn6tX6F0I4yW+EA2Ky70Cup7+XoB70OJd13IM2IwNXh7YIJwmci9l9jO5V5W0KCdVAnsao0KLmZZySp6di85JqAgPlrhM2jlKedTDR/h2Rz6TsMUvLDpfCxcqrdFFjJ55fC1TVMp0fKGu3YnV+BbcIkAPO36C7ZXdv9Lx9HP+oYjzqFAELJrjCf7C4zOZ/Q9vr1GpcAJsGYu8b57I6Js1BB3ZrV1LT1/FTcf8t9KHbYLPI0VxvtocXiqQh6uaj6GSg12EVWpsHRJO14TTtfzdxEvvtJFs2jUBZtIRjwKLioQLsOmPKU87l3KjSSVzVLHCbOdyfxGQtSK3abvZfSdZf7McUs9HcrivYxkJ1A4VW4JRsSHh2ELUtWyeSGm3lV6lWyoJniIPVsRabCdopHRHRHNOSoxWC/NfXSrWOQikt1tfjKlOZqiwlfZAF2TZ6mqi+3Bn1X0eIqPrrYgMicZA0EmTCgNdDyrJ6mYi66zDlmBQCqnuPMaeRDQnE/TQYfY6fobXXetGsjRXhUR78831deCkn11FGxEfW8bMH+3Trp4QCJvVkGiUd2vKMNk/IxwWxNOMaFhQXExkYqCMsHWIs1rWjhX+gGsCsvmEtqsURMOCIGwIooYwLYnHGcGoIIjrjSyx9PIjTCpBRzmXEUB7OaUQS0Uomq7RocW1mmi2JpquCccZZiBr5lKZTwUOu07JTmeYRD5LZSzFyZZkjl7Lmmk1dZ7ifUeerEJ5jy5AaE+vbq5bRVa4Cgvh4yjNE9Ji4GEUd1NCSvrcY0V4c71RvYu2V0IanCKiPdqJyE3USisutcKuz5ONToMQCC0kAUQhzWogkyCRhzTsKnOSjaMRuebLETinsUUkxOVSKoiXRFDhJyhUKgRSuxzIRE4TSE8/rzfTQ2or6PQ05LDFuE2p/VI3RIWO5qyrYBUhVC1c2xbBnajTNAH8hch7A6i9REb0OnKiPYw6XkzTtUgSODySdqPpyKyBkuAi6oTRWqmWqMDKPtIq6pMp9nywCSyuEpciP8/yT19JeLZ46sx/9rVvyohermjOxpy/cZv0ZItwUGIGFXpVdgQ72dxdE6Aj2cyDsGWrEzmpL8YyqwzEuwvcUg47HTcM/5dTmtdisjdv4JqA0YsPCbZkjttbTbBIqR9ONhu+jhtGLzwSAlnUomNH/skDyeDjBh2JKIrai1FljZtDczgivL7Glwq3DuV+rCG9eYKrQha/8hzJzpxwV9jDhCIOo8dNp1QWUj7aIZqtN0pjelgKG78K5XoJRDeXVO/McHUEyuEaQ3M6JUhqgkGJcgq3HBC/OIdhjDsOCHeXuEdRx52QMql70LUCkpr6dEZy+0T0v0uFK0LiG+e05yKlrO+ERHWOOxbOwOUY1bPG+nOiq647LfrywR7JrRPCUPQTcKoTLYrlPr6mgbXoMZhRhcsjyqMt6rOpELrKiOjuEr9W1CczVg/3GN980kK6bIfYOiQ7GdM0AUFg2fnAW6Je1gq5LU6Fga/CFl2FoIUdf8mQb0vxGJBDtiWIa84fHuC9wgSWOKnYvn4iM/5ZSrkacna2zXM78jmWyyHT5w5ZPdwnGraYpKY+nxAklQjsVBF1FVFXMeVqSBDXUuVwnWLku6oDl+x2W4oHQbJ/QXm8RbEYdcFF1+uPWinrBpZwkm/m1i+DDVsavFcMnz8iP52Rz8fUWcLo+inJ7gKlHG13yA2uZDWATh1eNehpjZqGsGKjPucLQ/sZg29EiEjUDBu86zQe6oDo5pz64YwomqMSiPcvOqVDJZM/eYLNu0mhVsSAzJ4EXP6kxXsLakK8s5TrD4fQLikf7TB4n/B3iCKq0yFJ1/4LJjImuVFnDCzN+Zjsl8cEw0L4BPMxw+cOqR7viGBU1FAcbWMiUQvFWdzCbMR4lHa4OiC8vkZPz1Cxgr1t3AP57tEetWrQO60EQ7URhn+oUSMH5WXFUqoHOCWtglmFP1ISXHSiZ+71mGadCrl4e0X2Xw9Inz+SKkQre4Me1ps1p7uR0Op0iklqwp2r7/n3+NLG04/6ZYb129cBiLeXbL0gIiVmUGLGhejur4YU5xPqPCG5ZCTnwhUwcUOwvWJ49zG2iGmWA9xnAvLTLdKtJendY7JfOWDwymPGz2X4wrP69dsM7xzJoao94e2c5r4QA/W4Rm2FuMdW+s1e4bJQ5pxjyRZUIplE/ekJ4V6FvhahbzlYK3whpb9yMWa4fy6iHNqT7Mw3rGxTVaA8qzduMvu6t/C1wjeGwYtCZhL5Vk1xb1+4BtqLMuBnDhi8cvR5IzfJndMn4kNpg12mUh05idDrmvD5BrQmiY+lalFJ5rMR6rjRkgyPaU4mT0xl0ob2fCia/yOPP5KZaz2w6GFNczzhKnL/IC0xqcgel6fTzYiZXQ4oT2eyIZWZVDysIXh4QnsxZHXvGrYOmL7wSIh+VQTdPH3xmX2CsQR68STDVuFmpM81gSjX1SFBXBOPcoKkkorHcohSnmR7yWBnQXG0LZl8EzC+eUz2eFfuOamIxxnKOLLjbfLVCNsatHakwwITyvjo2aN9tg5OZZrBGtKkxDYB6c6c4a1jVu9cY3G6TbBoSNISHbSkW8vNnH8yzsguJgRxTTLJiHcWLN+5zuJkmzipGO7OacuIej1AGyf3tbPYtMriQUldxIxudcqV3WccjzMZ6Xu8xfrRLpO7j3FNQJMnG6JXXUcY7QjTinCSUx5vEe/OCSY57fKqjn4kY9VAoXBzDSbElR25s5PrDT9i4CyjuT9i+dptJi89ILy+hlRDFOPvGZqH040PQ1tFlI92CQYlwe6K8PqS+r6YSbkmwCzyzcjx5UhlsxjhX8vRcQEqYPDKY3Bg37a0c+FKuDJ6MnETN4QfUnBSYy8iottzzHkp4l1AErboGxr3QGObgHh3QXLndFMv9RnoKTSPhgTbGWo3IIrn4kfhFbQO5c9Bb0sLyxqqB7tyDS06Be64kSQjAFpFOx/QvDUinK4xY5lOaY4nhJNcyvc1BOMCPaiFzBlZ9NgRVxdyU11F87Jq4NuOv2DcZsw5GJTSlrli+D5X/5LGUx/+1eMdIT91CnW+1WCQ7Np42tWQYJyRAvE4Y/hVh9T3p2JqYxyujCjeOdiMZ7m206i20mt1eYQtY+r7M8LtlYgHDSrpo2mPHpYwSUWcIvD4wtAcpTIO1G0wKrAb1TTVtuhWymTh9SW04E8q0QTQIgtgJjmz4b1NudoWMW2WYgaVlAJb4SYMbz4Zk9JxI+W+QzYOZZel9svDXgVWHPqiTjceyXLUplTeqZ/BRr8b56FocdklmTHGdJ+dT1pU62nPh10JPEPve3wmPVTvFeTCmDaTAjXyKDxhu3omi+T/CPPuOWKnyR7vMOqqMfH2QjbnUUGwIwIsvtGosN2o/um4FjLiEkg8ZlqQv3GdYCQl68tZ6uhgLizw5RAzKkj35jK738kGe6eo8xSlPPE0o1oOqfMUrS3xqKDNUumhN+K0F49zlNNEXTtmeb7FYNIZyFiNbQOKMiFZjkgna5LpimQxJhyUtEVCdrxNsRYCXRTVBHFNlaWY9UAInsZuAhSlPbaWylI8ymWtdCJP0eWsuJLpA53U2C6LCwYlURPIeN98JNoDdUi1HKIj+dzDpKY8nRJNMyJy2jKierxFkQ0YjdeYuBGXvaQSEuX6ik1cOpMcFVjMdoXLtIgtdZwanTZwVG7K2slsjZ7WUvIOI7gQaWhXhdIO3HXoqkYVkegtzAeE728wp/XmeWpOJwRjIde1RYKtIpJrj3G1PMcgM/tuJaZcOmrRg4ri/j4ga9hbQ7g878yjZDxwY9JlvbQdVw3pnWPsKqVZSLk/2M838/M4RGwpAIpOByQLMXs1jEVQ7JKDoYwl3FqKqmORYJwmiBvsIsRMu3HBKmTwyhF2IaPUl/wod+mNgWg36FkFhRj7ONcQvKKxnwshbbrRZakihNcy6fdn4cb58jJ56vGVjafP/C8FOJQ4+7kmkF688tJnU26jGuWtgYMJ+lScrpSROfcmT8T8JWwJLsk+kZjj2CImGJRSKYhajM4JLjfNbt6evBQHLyelr/J4i8HtIxR6U1azebLJojZlvanHrxxuFUEhvTZfG+nPjxv8udoQFW0TEIYZtr0cS/PoYSEPezcjTttg0lpKuF5vjD9UVz5UxtOcjTtFs1YOcNdF363BgxzajRFDo24qYUPy6aL0dj2QbLhV+Mo96Uvqzk64ddLjg424UjDIJbKxflOOfNZw1qA7IRUdyly7q4TdrdO6E+7RG46FO5YSZNTpHOCl93v5+arYbYhSOq5RWoicly6SqnOLjHYW+CagXhlQjraU/r5HbbJ9ZzUmbDBJJSp5quOAdGOotpH+vem+MxNY2iqi6T7bKGyw1sjYpfaEYYMOWur1gOX5Fm1rJOPv2PnOSwASd6zqtuvty7XkPcNhQTyW4W6lfLce1Eadz+ai2Hcp4iMa/yFNltJUonlR15FM3BgrZj91+GR8r9M4iOKaMK0I0nLz3bhajIxQV5yFWQ2RhVSLfHfbrUEnPfdLdrlOagLlxZ8jCEBr4dFcfteBhSTa+C/YsrOuZtFN7EgJvb0YEx0suvUkXA0ujW66fnu4sxIdhE15XxwwN62XgcWdd6ZYTqpteuwAGQ20RYxZiUKnrxvakwgdRZi6O/wbjVsFMmXSCtnRd/wl2u5wjRNUUG4qQyq0wlsCqWp1B7wrpDevtBchpE4EzDdBR6QWGeTLvVDFaqNZoeIGhiNcncm6DjQ0rQggxQoVgj8PpdUBT/bTK8azNvbp8Wzx1Id/cueExSefQ60GDGOZnQ5SUZlS05Bob9E5pUkW4N5eYrMJrhJXKz2sGRycSyQfOJLrUsIeJpcuapFEvN0m4VuDHlSb6YB2PsIeRqQfOBM7Xida6ZfjbC5LaBYj1sdbxKOcsCNOoT2BXqNCyUCakwltnsiDZCzq3G7Iid4awkG10YXXqTDy68MZ8XCOLwOaizE2b4iek2y/mY/E4z2pOi952VzK4y3QDhM1hJOc6O6S5v6oCwLkwa5OZ0IMTCr8uSgMRu8vINHoe6cUb+8/ERrKDOHBgvZshF2l3WYRY6aF9AmtlnEyjRgErQYik/pMl4ugvhhv1OhMUjHcb/G+y/I6Ilb+1nX5DOMGnCLZPet8DjTNxYgQIUwCRNoJhwLZHM2oYvHWDfQ9cc5TYUs7HxHdXRCs840db3Y2I0wqlPbk51PS2ZJkkok4UODwYYttJQBQQZcxFjF1HWG7Wft8OaKpQ7zXxGnB/u1DOdStITudSWBah6KuaFqaJiAd5BT5gKqMme2eU2YDnNXCxleeuoowxhIPC5EuzmOZSOkCnHoxoliONsY+wWnDYGdBtRrSlDFBXDO8diaHXxvQ1DFxKjyatoxEP2J3Tnk+2ZD8xh+4x0HnULgZt10OCadrkbU+2bqClSDwTm8OXiqHSmXs9LIy5ubSttMzIdD6x1qy3XAFgcLOE3F2jBtUHchcvI83xLkmS3GPG7ztSvZWghk18qhcPtMgqageb2+EyMr5mPTusfACrIzUEXgGN0+oTmbUqwHJtTOqw+2NAqSqQrgB7RuK8vGOkIlna+qH4i2hA9EDsEtxCvVWUxxvEc9WG46RkHU95dvbmMcV4Uc6USstwXtTyqhxOFtjs4TicJfBrWPsfIAeVARpxfl/epHJiw8+z6SoyWOaPCZIauL9C5ikcNxKpe/2EP/2MSAjgv5MPE1c9q6nXzvxMqhlaqE9kWewx1cunj7zrw3DG6eb/lo4LDrddclC9bji9L+8Qjpdk+xf0J6P0FEr+v9pR5qLG/GyXwzJD3eknG68+NA7jSs0elzTHE4pTraIxpL5x/sXhLs5kQZ7FAuj2xqRcD2ZYeJaMgDl2P7A25txOKU95dEWwURmnH2jqeZjkt05y3eu4xrD9IVH3YRCRDAsiO/OyT97IH0xkM1Me/LPHkhJerbGfGyL5r+I69ul37rPExkdS2ria2cMbh9hRhW+EXe6/FMHohTntAQaTjH66CPwHnchM9yTbz6BvMUdW5rjCYPnjyS76NoS2lUEO2thoxchBMKydmVEPR+xPt4BQBth/G7MUp4xoumaNkvRoZXe5HZO8cYB9fmY0Ek2Ek3lMKcLsur7TwRL4jvnqHFAkEmlxzdCFK3PprizKQCjG6dienIxwDYBk7uPqd4SAqW3hiYLGMxW2CaQzL0OGWjJ8lWnb14sRjJG2QbY1pAtxkz3zokn2aYEv3i8SzooxJI4ELJqOR9T5wl1HeGs5vjVPSbjFVvXTsnnY9bLMUp5BqOMwfYCnKLIB9TrCGuFRxAEbbeuJTsv5yOpOEQNaEc0eFdZ3Ij3weDgHKUcro5EVCupSKYromEhrwktbRmJ4mRSERtLPFtJ7ziPOH/jNpNrpyTXz4RFfzAXxnsVEs+upgUEUD7aIbl2DoC9CDG3wLyohMxWOJGXzSKx3NYeM+kqhCuF91IF01W08bRQgRXRnFyhhyURF6LoN5d1YoYixMQwRk8qgioX0uTNU2mjaU987YzmbEx1PiHeXhLeycj+x3Xi3QXxtXOSOyfoXU1xmGyeF1eFVL8iI7cmbkhvntAuB+LBMCiFL9CtVb9Z57KWqtMZbRkRz1YEw1KkvL2i/fWW+mJCuRgTxjWD20e0XdCL9sSzFTZPCPeW0k4MAibFI4LbYizklorqjRnp/sXGXA3t8Q8LVCTEPj7bYD52DVMdb6YPfBZgZiVuFWHXqbTKhrVUNgKLO78a3493o5f3/dLG0x/+nYyozNi2hHFDfT6GszFBk9GczxhsL4h3lphJgTuZYJuIoEVG4zJDsxiJkMcAYUdnCeFs3ZUtRQPeZbJxX85fRzsL6ZPPRQxo+cZNTCDz0tHBohvpC3CA6kprbZZsSntKi0WvSXPU0BIOC2wVMdw/3xxMKvSE5FJqW4ncatux9y9NO+JJjl2nokXwP85QRloLl/P8xdF2V9LtynfdLLvSMusb3V6K3ndX2q/OJ9Rvjwn3luhdJ65cF0j/sZBMVA087rTjPExEilRdH6CWBbaQsclwIn28eGcppMOOzaxCi46vRs89uMyuoxY9lg3mkn3edI6C0QtL2kNxvYt3F6iglu/ZKZrDCUGWPRF9qUIRaZpk2Eo22HBrhau2Rba308lvVsNNtgxgm4AgaojHGeOooVkNyedjyZz35rRVRBCKS542ijipaOuQqNu8zx5cYzjOqKsIV2vCsOHiwcHm/ZXy5EVKHAlrv84S2jYgjJrNONqj155nMMre1RbzzLaFfKWMo14N8F7RFNL/1UGLbUJmzz3s2PrxpifcrIYbIyzVBTJtHZIvR+y/8hZtllKtBkJWrAMhJQ4LzKCSjFN52jIW/sC2uMsFO2scMH/1uStj+8c7Syl9OyUthqzElw12Ls9Q9DGLmWfQeHwr/ApfBhurZbtOMQOxSL40v/I1ndQvYmW9qLBVhO5K48Esx5+JiRjKk94+puk8Fy6164NLHoF2YD3pXWHDu0zElfSq6SpzshZtlsC7JJJ9Z9jkykiIl+cTku0ldj4SN81BSXOW4rt9wHhZo+Fsvfn3toiIZuuNF4SZPTHUuXxGL+W63bHDt2JU5hcOXxlcERNN1wRbmRz0iPw12st+WUbCAXj1CL2tcOdQ3ZMkIGhkbwhma9r5SA5+pURm+N28nSuC7xX+vqTx1Ie/ihuUfaKdbjPZzGwRCeu9CWTMKrAi+NL13X1lADkQq8WQwaCUstfwUknLSDmsM3hR2qOjliheSXm5i8hdazCjLkPrjFbQXry/u366UuK25upwU14PRoVk56sIFclBXV+Mpdw+KEXoYz6SmfImoHq8I0JB+RNJVBU3G89v321W4qQm/VkVSFBx+XeAZOud+pz05EKUaTfGN+bS56AyEFhUrGgfPyFmmaTCXoRixhHX6NBhlwm6bkQr/FLMpfOSV3EjRLzuO4InHgBXAR0JwU8pL1WTUb5hUUt28q61E7afp3/uW73JYFRgUVY2OXXpV99qYTZbLfoSnemP/E7mSYldO+mxOr05LJuup++doipjrDWYy3J7FWEC8b23raFpQuoqoiwSXBeYBJFUp3xXPWjbgCCuxGDIONpGDn9nDbY1VHXMSK2Fue+fBFvOGXBmE7jY1uC8wliN7e4XhEeitKctwk1gc0kWDKJGevydC5urIwmSwoYiHzAwruNfaKmEBfJceKdEXbE1XXAs93pVUHHTsdWFz0Aa4C6UkGatgflSRIa6bLmdD2X9dIqGlxMwRB5qJQJQu6VoYFgPdYMv1SZIEmGtJyZZIq5l8a3e6AnIupMqgHcal2kx0YkVFH4zTWISERFSscWdS+XBd4JMOq0gFzKhLaVVdAlt7Ibr4+oQZRxBUuMa2c9sFXUkSCn7X46g+kLamCpuhK8TiNeAt9L/d0UsZMlK1Edt1T1bXm0EwtDCpbD5kySnOZkQ7WRAJ3Edtly6h4oraNvpk4gGgBlfTVWwx+8ePPXhb3YafCNRt0ot9YMx4agQhv7FkGT/QnpTXUmryVLSg3NRusokYGi6sRw9qomCBc3FZHPwFxcT4jYgHBZEszUqtVSnM5p3SV0GUcvoxYcbG9z2bESwlckh0LHm2/VAyqJxQzDKCfbW1A9nVMfiDZ7cOsMdiwGP8Qo1tFRvjxmMClxhWD7aY3f/YmMqAk+4Ar7VEvFvr1l+6i7J7gId17g6IL51Rn04k83OK7GTTWthka9SVJoJ+SZWmKghdheYrZr2NKHtSt3V+UTKhpMMM6zJ3rwhmUmnzd4uh6h7K1Qg7mHhTkb91lScB43M9l/aFLtCWgVX0fP3TSA9V+3lcGkN4U3xSveVqKrZw24jS2oRH9lS+LXwIcLpGr2vad8SkqIZFZSPt4VE2bG36/OxbHxJhUlqmiwlSOpNzzuIJGurs1Sshc+nRIOCoDsAXROwzkaEQYMxMsZ5Op9xd1BSLlLaJmA4XrOYTymrGK08QWDZ3j8iO51R5KmMzhlL04p4UzQsKA6lMtC2Ac5pxqNVV13wYsmrHevlmLqOCMOW4XhNXUVSFdJOgomgpTyfSObfBiJYlCdEw4I4WYHXzO8fbH53E4ldbOjW0gqYrTl96yZBx/53l/a2ndmSjhuKw12xmj4fY6uQYadVcFXwlbg86rTBH9xAPX68UdbMP7lPk6XSKgzbjY4D3URL+FIJlbS/mvMx5dmM6e17sHDYRbQZGwz21rhlTLMcovMGc71GZRafadpz8R4BNpUyWZsa50LcKkG7EjV8MmEj9s8GFcoUTn68TZDUnQNng973mHVN2yU6UdfqNGnVGU5Jm6iajzG06Lgi2Z1Tn84oOpGm0a1jiqNt0r0LaAKa5QAddgZB3efWzkdSTbsk9HWHvO1aIWpL2oDe600y4WsxWLtU0bRFjF/Jgf/uxMY7LYI/UYtbmI0Il9m/OpfHS/Rl/y9tPL2xzzuTzchcczRh+KHHZL9+jbYrZ5bHW8TbC4JxsclKbTfj750YTWy9co9gX/zo6/mwM7rJNxH08EOPqd6Y0S6HmKYivXlCdu+AaJyLKEprqI+3cJ1OfDDJqI+mkkVpjwoc7bIbDzJW1PtOpER3KX+qHokrm81jmtWA9o2I0a1jYQQHltH+GXY5ILq96MyCkJGZgyXtyUgy8Zln9NyhKJTFssTt6WAj7asu1RD1k+WvNDT3hpvgaPBNBe51KxnDKKddDxjcOCF8Xw2tpfjEbteiEP9tNYbo+gVulWxmld2hBB3qzgx8QPb/lvGv+MY5WldUD7afxRr5DQgO1qKyGAGxRhcNbi7jic1iSH42Zfur3xJ+R5bCkScOz0VpMezc/H5dqiS+CajmY0YfeIBbiRNaOF3jm4D4+n3KB3ss7l/DhM1mpv1SwjhIROHRWy1mOtlAlCSdYnG0wyAtGE1WXJxtcf9klzhoWM4nhGFDEIpE687uOdlqRNMEOKeYH+7RNgFlFVOUicz524C6FE2IMGgpyoTRKGMwFqneIGqg9qg2oKlD0oFkVd5rqjKmriJmu+eEiYiurM5nErB0zpTlckSZpRTrAVFUM9y/IBlnVKshrIYo7Tj/+EuksxUoj7eGG7/3U9SnoupokoricBfbZXxmWpA0866VIJyJy+mGq0BzNtmMparAwn89QY8twa6DQcxg+4jyzR1MKln27q0zFp94nuGtY4LtDHs/xrcRelwS7i+lynGssOvRxuzINYHwXVoj5LuwRZ12gj3jgupk1hGAG1Ayppc93COeZoSzBWZfDMTcsVQOTFpTH2+JWdCgxAxKJi8+3Bjr6LTCr5wYjXXTTXQiZUBXxhf75snveUD+yX3O3rjFtY9+lmCcMd2/wFch89dvkUyyDclYhy3xzXPa0zGqtJhJLu+zHBJMMsL9CtIQ90iMzJyJye8d4FvD6KUH6C0rkwjLSFp+SY2e1oQ7LfbNLkiarTdtWj1sOhJwjL4VYO85XB6jTvuj+SsdvyPWR3s0Er3utMIex5telrfCwF093Gd885joYLGx6ryc8/eXXvShgpIn5aqLsVQDwha30sR359IXbKSUH00zms5BLZ6t0FGNrYbUi5g2Txh98CHtqQQgKrRcduya5UCYtLePqE5mhF00nD3aJRrlIprhFO3jHaqzqdgLd0ziYLameTwUQZEq7HTHq409bP36hPBgIepji3CjINcsB+Lktqo3lrbiFib+3q4W/wAVOMpfDggnYlriugg/vnEupKjGE24txXikG1Pyhac+3N4IJ7VlxPD2Mc3RlMAuUcrTFAfUmbQOwq0V8bWLZ7dS3gW3iLpxPiUOhJea/XFDtL2UQ385JJxkRHtzfB0I8agrU+qwxQxKbJ7Q5DHVekB63smcWjFBquZj1OkMHbZMbh4R7S04+W+vyChb1FBnMh4XxjUqrShXQ5aLCVHXn9faMds7Y3U+RSnP3mSBtZrZ9oUwoTsp38eHBwRBSxi0BN1EQBC2TJKKnd1zYXiHLflyxOnJDnUdkSbl5lAPwwZrA8KoFuIYUhVIBwXeK8oioW5C8tWI1OZEw5Lp3jlVnlDkA2xrGG8tiKIaE8m1quWQaj3ABC06tBjjcFZcH70T/oB964YIz4wzlHKgHTsf+xztxZD8jevCVB8WUjlI6s/zHXjWEMdKjU5qqWYdD6Bt8Avw5zW+jmRtI60ol0WMX3iEqwMRvoobzG5OcyjZcrC9xi5SmuWQthQ+yeirxaZbBSLD3SwHG62DYFCKeM1sTX20Rb0YSXUoqYQUfDYlf23K7ANvS9WwK+sDDJ4/pJ2PhI/UsfDpWkouC8VHJJCAtTibSaLgFM4a2iwhObgQvY24YTBZkz/cI71+iiuiTWYe784pjraxdYiJGqJCnheaQEYad3PM+wzUGgoLqwbfRtRnU9oyItleUp5PaM7HqKWT56jV2DIm3l4Q79RQ1rhyIBoH2hPfOqe6t4PpJqLaVUrzcSMmXJ3ewVVa+3iefc+/ZxA8Wzz1jnCpFw7gvEIPRP3u0oJW6QpOZjSr4UbpTqYBHCppCSaZmH8UavM+lxrqOmwxaYtbJagwl7n2siuRGbvppV0eHJdyr96L7eXlg+yKGFvGRPsXMit+oURCdmM1bDdWsZc9eRNYqvVgw4z3Xnr65ako+KlOiMXVwWbOvlmn4mIWCMu/Ph8SjvONn7qOaoJJJn3sS9OTjqxzyYuozidS/h5KSVeHYo5D0bkEhhY9bvBV17dzYMtoU3I0TrQJ0F4MdoAwrsV4yYkSIVdE+NOpiI4IATLdiPIoJfeug7ZjrQdo5dHjWmaom04PQYvvvdJSRo3G+UbExHu9mYn2Xkl/3z7pV2sjjomhU1SZHJDeq43KXdsGBLRo7VjPJ2T5AKMdg/RJj1MIgHKfVRvS2ADva8KwIRnmFOshtg0Iw5Z8NdoQBb1XpEmJMVbm/42jLJInWgJO7J/rMt4QAq01JHGNs5psNSJfD5nsXogEctigL42y/KXZVEfyy4aMt+bSEujK3m0ZCbcgajZ/Z5sA3QQyGWPVZj6+KZ4Q15SxG97DVeBSbltFYr2t4kbIaZ0+gwqs+D90JjMosWpWnasdxkH9ZP7+UpNfxw1RKOQ2twqfkI2na2BEWySbPr/wSDrvhEGJDluKi7Gw8Y2VZ+NdnApANDTo1mM3lmqzRHr9ppuj7+b9XVc9EVlfJVyANpLAvBJuTzTKcW0gPhudrHS6taS+GG/2EhM94UttPrdUiW5H2eIWco/l422ZqupGlpX2MuvvW3EONZ1uQOA6QrWMxPpMdE58YbBltOEgKe2xZUeCDuxGMKrHVy5+x4Q/W8T4Mia9vpbsr+2U7JKaeJzTFJLJhWlJMhJ5SlqFmZQ0R1MpY3stevfWYIJKDsS4kSoACFu3ili8c4Pp3UcE46wTvUmhe0h1RwI7f+0Os7uHoD31xZi2DomvnWFmORFw8bk7DLYXmxGxeHuJ7cbyvFNE45x8Pn5SSlSe4nhLFNVCSzwSYYxmNdzIFNsqhFYIRcpYyuUIk4jSXpBUhPvLzninxBch7floE7hcEn10l2XqsYM0RKc59cMJAWshy1kt5KTabw42HYomgU5qTBLSrlOivcXGvCPaWhFOheHsWyP2wVcANYZAZzIatR4QTbON6t9lYKejVr6v1ZB06xhwaF2iShFgQXnMdk4g+yDl69sbe1pgI1TTZClFZydtgnZD+IzjhrJjxwNYa4jjCuf05jA+PN6Xgz8pSExLOiyoygSlHWFHJASo2wCFJwpDorRiPZ/QtlKVOTnfIY1LAmOJwobJbEmZJwwma4KoochTku5evVcooGmeOLIBm77/ajViXQwwgSUZ5sSRBCS2NdRVJC0NbXHOUJQx0240sFwNCcKG1kaEaUU8zaiLTsCoq7pF23Oqw+3/H3v/2WtLkqVpYo+ba/etj74yIlJWd1ZPVc90c7oHFACbBAkQ/BH8ifxE8NuAM5xGy1JZWZkZ+oqjt3TtZub8sGz7iezGgNFkHCKLFQZcZOQV5+yzt7ubrbXe93mFKXG6pa/i0fM+WG9UoD/XOsYpD7Uv2GlX0XsuRa6/nY02PuVm1soFaY0seidWPAZ6hbNSxIQK6q8uiE93+DOB7qAOmCoWQZ2VKjjofIJVQbAqGJqAx3eX5G1ItDwQnW1HUSlOzBmcNmM3M1xIMFnxu5dELl7YUwNDJ0UIg+f8/K7wcCAheLrmg0xCqbZfvSCdF8SrPfHJjo//+k9JpyXJrJCI530mwU+xC++JAobbArNOMGVCMK0p71dMX9wRrQ4yivCNgNSSlmBVYsuIoA/k3m88bCvvp187MNI+F6dMHzA4HLgXGDmM+cOoOXjO9eNg4Y97ff/NPzC0TpiWnG8YNE+iOKtG9GV6thV1qmsD1x9PMV1IdvUg87W8YbAKXaSEjnrW3C3pypTJi3vaRwnoCM/2TOs7hkFu7EELBjg+29JvJ2jXQp6cbSTKNNBE8wLzOGfz68+kQgwMybSkK1PCwXMdBKERDlZmx9HJjmg7pd1PiKcl0fLA42/fsvrJu1E85zl3wTAoN6LwqL+6kMjeUDO5khCaiXuAqNSy+w+fkF6sx67EkV5IkeJPavJ/cisagN9NR3jLd+Nn69sVk/AjIBCVwQak/+QB9MBwkK5J8uqRoXdUsnBA5dJalQrs+dTd5V9dkL65I1iUTLqNVKeuxXlMR+y2E1Qgh6PqtxeEs3J0JviLCr2eiDDxPqS+W5K/vkNlcoA60hf3v30zJup1uwnT13ci4OpCuoPkR/RthG4jUdNbxeL8ka5KeXxY8fNf/o6HjxdUdcp2NydOWtbrpRMAWrouou1DXp3fopTl3e0lm8OMs+VmbNsvpnvmJxt0G1FXGSrQ0gloI7omJopcWmUfOI//wGy+x1hFEBiirKYtMrJZwex0gwo1uxth81fXJzx8vBjZALVDFQeBZrHcjpt7OitGkaMKNbqJsDqgqRJOlx/IfnErKO1Qj/dh/skNXmRo35+K7mJQJM90PVz/xS+4/NPfE5wVeLHh8Ou3TH76XoR0tczYdZGKt34xsP0fP2H6kw8AIzgr+d/48Ncb9DYXR1Haifr9IM6a9O2tzK7rkKFQ+LMG00ZEywPB0ln19plE6AZCFH3x538nzP0IBg3NN0vS13fYKkKXCcE/juh/nwqeO+1pvj0T330TYzYiIBwcUOrID2hvlmAlDrpvIk7/RY/5C0v58RTdRJz82e+Zaye6c5G6fRcyCbQEoMUdg5nixx26SqhvV0yz92z+7U/FtrqS8eP5//F39J8nlN9ciiMhkW6iciLiwcgz95hRkPxig30Q2mh8spPPQtnRneUvO5QWvPnQgN5nxP9vPtcf1/9/r+8v+LtdukhfOfkWv3lFkMhsW8UdukiJFwea9Yzy5oT5Zx+kukvaUXwXX2zcXF0RfqcN6XmWYFIT/TMP8/+wtI8zgjoinIn3vnx3jm5ipm+vBQSj1dgShoBwUtNXMQcX4qJ8SzDpJTY17mU+uJtSb2Zkqx362DLVPrvPX5OdbihuTygflgSHfKzYus2U8n6FCjSz17eooEMXGetvr7j657+WGFt3yNGHXMRXbcdQ9wKfKVKZRc5K1NzgbXpHLHRWJKeF8NTA9E/f0XxxTnMn7/Pks494uaG/lrFGcF5CD8VfvJRK5vVejtbRkQA40H2QXAS1sqA05uZ5GF7Zz26Emd5Ektw4q/AnNf1mSv04J8prmv2E/PKRcF5w+OKloHKXe7zQ0N0KEdGLe+gEqtK466t+nLN/WPLqX/4Vk0+uKb+9ZP+wJMkr+jYiO90Qn+4I24qdC5ryA0PstRKqs5vS1rKJPnyUeX6eVfQ64PFxxWq1wVpFUyccmpSrkwcR/w0eF8s1+jsjhyjuqMqM7cOKNKuZrTaiCi9z+j4QXcFqI66AwSNKWqarHUHWUD4sxvFFttrR7Kb0dYzyLZPljuZuiacGTq7uZIavJfbad1S3cjcdBaNdlWK0T5TXIylwPq1IdzldkdH928/Iztc0jws352/p7uWgHp1vCOqIx998ynOF+r76V/8R7ySBA/Tvc2Z//o28hx2jDTP99FaeNi0s/pff0H+ZYVynz88aeF9iClG0e4ERUZpvxRufN66rEApYqw0J+4BoXsg91ItNLrzaoe9FJOinHfoglsJB+/T7jOR8I2O2WDqV3b9LCWcSmGTLaGR6VHcrTB8wiR8BGbeEWUt0tqX4/CXRvCQ925AC5jctXuiRnuzoDhnF56+Y/Pwd5pCKcDlrePu/+g+YIkUXGdWHM8KsRRcZfRWjm5j+w4TsbPPEfUg7ml/PHBlzS3B6QE0tQyOMfr3NiX+ylXS/XnREmAEvNygrhyaUC1Vy4xizC8eOiop7opfbZ7oanpb90ef/R72+9+avq0QiRuPO+VhDwmnFoBV9NZPqelpK9erU2KaQisNTg2yEi0qsJ716yr3ugjGlang/4GczvDbEdtH4Pf2of0JwVjK/VKFG+YauTPH1kZQmlDZPDY6kNowRwqYLaZuY0D2AAWzvS/jKfjL6x494VdtJdeV5YvHqi1SQsdYjyUUsWN+u6KuE/PJB5nBqEFFgmZC6dEDbB1KF26fDzmAU5i50QjkBAmEhnBd4VTIKx+zOoZFdS13fJMQutdCWiqHzJWrY9/AC0VkEi5LhIFWKFz2jncfNuMf5vBG2g+kDqVKaJ11A6NgOto4ZSmdjamIJaXH58wxSTZk+IJuUoru4W9LVsajzo2NoktiyDh/OqatstGMOg/DvPW8YwT5aB0+/5xvqPhyra4DVdE/k5vHD4AmVz7c0bYQxAv0JQ43Wvlj/Wnl4GqPwfUMQCNbYV5ZOi5e+OeRjhW2NojnkRFlDVWSEcTd2K0wXYqxCeQNxXmN0ih8Y/FA/kQldxkDfByR5TVemo2jMdKHoV7xh9J8P1ht977YLZRTl6JD+M8547SFE0YhYFbC1XJteZFB5j37I8aedQH4awNpRmOa7kKf+Yy4jxGPGQyLPmeOcf+hcPoGVezl0WpzBKOFudMGTPgbGTfHYkj+OFs1awEPH7AQ/lGhmFQvsR81a4l1JX6ZPkcv7HKwi0vK8CGelOAqaiPrj6agXOlpRj8hg1ADO8YQ3SAJq0mIci8RTg9wbs8qxANoxxMdUsczl1YAtI7y4EU2Qq/T1R3EXBbHoZoYGcct8R9jZ7zOikz1eahkOPs3DguzNLf7J/2/m/T+m+v1xr++9+VujJHQlNPIwccIa2weYQ4huI4ZBjVjc443j+QbPF7EawNBL5T9YNQpUiHu8yUD16wvi843gYDdTmt1U/Lah+Gm/m5zHEQjUxM7u5UvrP20xjqWN9ajWc+JJ5UReBt2FhHHnHqzy0K93EweNEdV+lLbSWu2FohZmLc12Ig/nuCNdim6guF/S1AnJ4jC+PhypLX97IwREcL57ga0cg2104YJOQpnlDbUvVicncPJigQ8FpwexE1po75ZkP78WiNI2E+FUZMBK9eMd4R+OLKgm7Q94qXxn6WOgjATm2FoSGQfjy8M0bYlcxkFfSEqinzd0j6L5iJYHmaO65bvwo65MUKEhXW5p1zPW12eCRJ0VhGkrYTYIwfHh+pw4bun7cBT8lXXKyXIj1bO7sutGBHm+svi+oW4SgkAThT3L2QFjfLo+RmsfzxuIog6thcqndUCc1pgip24SOh0SKMNsWhBGndv8vfHg0XYR+jtplX0fovsArQPaNiaMO8Ks4bBejL/v+5YoazAOLGSN2AOjWESuAEFgCNOG8nFBWydEUUd5mLC8fHAHWacfcfZFM254kupn+4Bs+XwzXr3N8fsGzxchp15P8JTFDypJ0QRQHiBcCLsXfc0RPuMpK1wMF4yFVahZg9m7wC4HApP5/+CyGswYbGON2P/8+uk1HJ02pkwkg2JS4ylLu5lK7Lb7vEUMrAjUQHhywMs9OYQ7wd6x+2K0L90Ib5BCxXiY3YSuTAUIdKR9As1mRjSppPvgXDGhGwN4cY+5XklXw5exmFoMmOuQKG/wVy3mnXvTBjXmnnxXv6HSjvLrS5LVHpUJb2BolViAHdwMjfAH0k4S/By8yJ/3sJzA5nk1ID+uP/71/YN9VntsG+Fpg0o68s8+CtxlVosH/i8iwuUBvc3pDznx6ZboZIeataPP1BTx6KONLnbYNqB5WMDDgshdjCqXmRaDR5g2GHdBA6ipwd6H47wfYPon3/Lwb39JsZ+ilGGV31Lt51jjk3gHaZdOK+LVnkEL2//bLz4hS2uiuBtnrKKIlgNCkDWUd0ux2iUdd5+/ZnEhvHTjkJ+6TgjCnjww6CqhuD1h9cuvBVFbiUUw+eRB5o0dmG0iaGCkCkk+W2M2oQThRGAeQ0EYA+FVgflv/pzgv/9LUfK6ikZFPe37J2Fc8skD1e+uONye4KmBi//tr2l+d0K4PIwCrGdZx7RA9/Du1jPaQ06yODB7eY+pEuKXa4YmwBQp3W5CMBGeQxBqwY0WKc2DfE5+qIlXe6z10ZWQ7ppSKuV8fiCel9jepy2ykQIZxy2nb66pNwLUCQJDeNA8biTAJk0a4qhntdpw2E/ZFxNWiy35tBw32SN4xxhF08Z0OmQx3XNy+shgFV0bM1ttKPZTgkATRx1FlZHlJVrLjH96vubj528pqowBj9h21GVK0ySEoWY633F3e87JyRrlWzYfLiiKnCytWaw2qNDweHPG8mRNX8cYq+SwE3eSFjipxms8ncmm1DcR2aQahY3HjSqal0+2y1Djz6oxfyJ9ffdMFwPE/+sEvt0yNMJ+0N+khFd7ufYbCM5L7INi0O5QMmnx//kJ+v+5pfz2knhxIP3pgwjX6hBTJfhIe7pfT2nXc9KrB8w2FzveXPItlEsG9VqJTvZ8i5d3cjgtE9p7sYp6ZhiFyeDsgsoSZi3pz+7Qd5nYdNuA/rdiPT7yE5I/L8irG8xdSP3hjPyVaAb6fU5/yJj/8hu5FKkNAADfBUlEQVQhKJaJCO2aiPmvvgaLhHdVS1QkIuHjoYxBkf7kFrNNqN5dEO/XFNenpE1M6t3L986ka2GrWIS+c0P/IUWXicCMrBqpqMG0lgPGTxuGjaa/n2GLmL6O2X19RXS7IpoVzP7VBh7BflvxfA+Hp/Wj4O+Pe31/wt+sQk177CGUII8XjwxtSO/8/PNffU17s0SFWoJ9qkQEL7XcGPXtSmw3DoQDYPYZ6aXM1QarCGYlzben9IectkowXcjkbEO9mzCs5yPsI7l8xFMD9cdTvvi//y84fXHL6VH81cTMLx7pypTN+0uiWB4Q3WZKc8hp6oSf/OnfjcCYMG1p9jm6jSDqiSeSjz4MCqUErWpMQLWVKt4OHkFgJMfAUbnCaSUtX0cR9HxLcFZg1hnV9Qnl44KTX3xD+ieP2L0n6v9SoWJNfz2TB/W/PMH/5j3mNqK/nuD9334D4bGDAaiB9OcP6JtMDkcOYJT98pr07a1oKToEZ+y6LP6L52n7H/76Lcm52NV0kcoowo1mbBMRzEoRmlXSDQgSQScftQ5DFxBkDaYL6fcRxXZG3oUk05IgFQa6ujnBDyT62WpF+bCkbWLqUtrj87M1xf1ytNi1TSCJe0lDGAgC11MDH68vUd5AmjR43kDbxGNV3vUhUdhzKHPsoMiSmrvNilfuUNi0EVkbsTp/oC5y9vsZrRYbHohWpS0y4rhlOjsQxJ1wB9TA7n5F10bsNgvaLqIq85EFkCYN09XOMf9j4riV8UGgOco0m0NO34dScf7NJ+Rna4q7E/ywJ10cWL+/xBpFEHcEUc/2q5dMX9wRzkpsF7B/f0H1OCddHFChpvzyxbMJvPr/qcALExlflRC93KIfctlkXbiVmhvsTmy0GIX362vC1wPzt9+4gwHjqMofWvr72RiAFWQN/W4iB/S8wUs0dD4q76VjNnjkn32UlnhuRR9gO/LLG+zBh2DACy2HX7+Wg+PiQHi2h2iQjpwRcqf/CpoPMUFe4wVWgD6/PQCi68l//oHmm3PBDLtxnt7mBLNKnmuhJp2VFL97Sf7JjQQY3Q/EV2tsmWBqGWWGs4rq95fuwFZQf3VBujwQTGr0PuP+d59w8avP3YFZLL/1705Hi2K/y5n8/J0Aj4qU5mZFfLqj/dvJKEpu1zOWf/I1ppS00vjFmuFashD63YTqdsX5M10PP66/H+v7B/v0PkMprfRoUUi72Sg8KylqXiIXpqklfzxcHsZZnJyIXazpMXgmkZmYcXnlkpKlnMjPSioW4GctcR/Q1bHM6KKe9n4xvq6rn3wrs3VXyR356GHWMPENbZlhtRp/P5sWlA+yafhhT7IoSJd76s0MqwPq3UQOAt9Zq8u70TetQvEMty6wRfnC7s4vH586EoPkmVcfzrBGMX95J5v4MAACGDG7dGQNAATfvMeuhfmtvAG9z5zQST1hhqeNeJBB3rt9ht0+pRzG6SPBi0o8yoEP6nkU/+FUvNOml5GLvxDXhmmlItJlIijl1R6c3qPbTFGR5A8MRknrtRUF+/xsTTQraFz6mSoMfqDp24i2lMNltjjQOo6CtYpqP6FrJJ4XIEkajFVMJuLd7txnOMmqMWXvyP23LiwoCnuiuCOoDb328LyBSVKLpdAbSOKOqszxG00Q9ZycPBJHLUqJkyQItMT/2qc437bMSBd70qwanQBNm4iGAAhDTdfEBFHvQD8pTStjmuMYyg8M1ijCuCNwPAFPDfhhP77PUdyRLvYSd92FcgiY1OMBOZ5U9HVCtBJiXvPx9FmuBZDsBn/VjJU+lrH13j864WJQQTAIMS+0wr44CMxLbydicXWbtBdLZgVJKzHVo0DWSPfRxQN37xbCGPHNqCHSd09xu8CTNa/2hZjZCByMx4HwQkSz/hH49UU85mIc00tV2olIdxA9RTAt6TbTcQRlGmGLHNv+XqiJphXdESd+sUGvp3/gty8/nhKmLX4sZMFjDLhyI7PZ6UagSY6YaA/SzTHO+RI41oCt5RoPXfqpdfeCF2rSqwchkIYuPvxmSXy5ETbHIXvWrAcQII/9gWf+PyoIftj1vXs/to4FTjF4Mj9zormjEG/QT5AXa6RilUCMeLxR7Hd83EOHADpcG/0Y6KPSlnBWEs5LsQJaDz+W9LJRhV9ktC4PPX3xMEJ//FCPAS8q0MISV2bMY/c8AaTUZSoVoA6wvS+WNPV00x9busYJfOJ5KTO6SBOmjWQa9KEgX8uU5iBVjuebEe6iNzntkfB1vhEhz8YXIVRgXABRNIJQ9Id0jDSFP4S+iGc/wpbq6QHpD6M4yLr3cOiVzPOyWGas+nnALkdQkXKzTnDOBa3QjXzmvhMy+RNpSbaHHF1La9SUQii0DrIUr/YjJ8B04ZiAVxc5VZGJmj7u/uCBVR4mGONTNwlNG0sr3/qEYT+Cf4JAk6QNSdoQhBprlXym7sCl3Gcehj2hOxykyVPqmu9rrPWo6pTBKrLFgcXpmiRtiL6jzNc6QDsXS9+FYBVh2hJljeQ4RC1BoF1Ijx0Jg1YHUt2bgK6N6LqIvg/dgcInOEKpvGGEzHhqoKtjlJI/Q1mMltEJgzdeC9G0GmfjXmwcGOd5lhcaPAV4nhwAWl/m8+Cqd4U9JHLtewNeIN2fY9CTNY457/rEXvBEAD1eY8GklvetDWWOr4S9YdsQBk9APJ2kSva7yViVHzUZxiWIHvkg/T5jqH2GLsDz5Z7qdxPR7jjtj590rvOmpIjZpXLv6mA8tB83ZcbnoC/jiiqmO2SorBvRwEe8b1el40bvBUaufRcAhTcQryQnQyBDMjob32tfPktbx5gqkedj3ox/JmjxjuC0EQS3gyzpIn0K0/IGgR79uP5Br+9d+TfrGenFWtjiLr2u305EoTpt0PcTVCRZ21hF+fUVoQsZ8Vwy2fGG0UVGc3OCbiLSsw3BrEJNO7yrCeZ3vpDrrEd1u6LeT0hnxZiTXd8tRblsxfdfvjtH+ZZ4ccCPe9rtVGJTuxDThaTLA+XjYpyP7h+X5PODbPxGUdxLFG/XRmSzgunba+rrUw7rubT4PUv1OJd42FhmbbqJSKYlzSGnbWJ0F1L/xS9Yvbp11UXE4999QpiKLat7nJF88sDh16+l5Xi+F73EuQv3aAK6hwXpn63pP0/ptvIQ8hcNdu8ejlrRflgRX61HeIdatPiqJYxLsNC/y1BA9zeCFk3fPs+cVxcpoS9c8mxWUX99jgosxnUy0je3qLTH7KXi78uUtkzZPSzFHnfxSJC0slENQnwsNyck8wPxrMR0Ic0+5+FxxenJmuWLO6rNjP1hyny2xw80TRuzWOwIXFXd9yFtF0rkbtiTTUrmF4/cff0Kz7OEkdD0qjolcNfjdj9FeQOTvMRXlo/rU84DTVXk48+6PHvk9uMlTZ2QdiGT8w2296ke51RFTpI1IvhrJBxostzRtxGhA/+UuymzxR4/7IX+1iR4nmV3L4rwNKtJqamrlCjq5LDiLItRJimWzSEniGUjskbRVm4zuFsKB0NLdWy+vSSIHOEu7tFtRPH1FcnpjuST9bNcCyCunubjCX7cE57sXfu9E4qjbwjP9nTXC4xL6QznhSjRz3YEpy2BrbC1GmE/wzZk+8Ur/KgnmR9ILjb4q5b2b+cS2pW06AdxvhyLkKPd9gjCOYZFBV0JRokO5dUjgaP1DdajvZUxpVeIdiV5fY+XDOi7TAKoTve0H0/FWmykEIimFUHaYINA3EC+ITnb4jtxbfXlJaYPCLPWOVxCQVw7oa/SPrOXt4IXV3Z0Tpk+wDws8LZTGX2pnn47GRHaftq56OQGNbXUv12MCZrKYcnD5XcikS30D7OxMxItD3gRhFd7wSdvnyvg2a3hGax+P5b+P+j63pv/5KcfUJ9NoNHo3/tUH86IZoWLw7R0TswWn+zxVxXTl7VUs32ARdwB4XKPdRaf9OU9j3/9U6JGWsbDbkL361yicTu5GbKLNbM/+YbqqyuK2xOy5X6cIeNJkIrnWALNZsb27oTLX3wl87oio9zOOFm+J8pq2YiVpf9wgW5FlW1cNvjpJx/oDhm+q6S7OiabFXRVSl1lLC7v0a1Y/3RnyVc7dJkyzWtmzpr08OVr53CI6KuE6eUD0fKALlLKuxUPX77m9DMBB5ldSvHugln6bmSH+0kLnSF8Ldnd/f1MNn4XjawWAgzxUuvaq4b1v/kZyz/9AmrEKvi6wn6pCK8MoZUgoudw+h9bqiCVRXKxobmVUUqQNqhpR/3FBfqoq8hrpp99pLlZUT4sWV+fcf7pe7oqFb/8dkpZ5CyRTkdTykhltdgyWI/dzSlJXnF+fj/y8vd1xmq1IUtrPM8yXe44ubynb0SRHcQdH37/CVHUjQe/E/c5Fvsp+8OEfZPxq1/+nVRyg8fyZE1V5MzP1qI1MIr1hwtefvKOvo45rOeyAWQ1fiSwn+uPl7z57Btu3l+x2c15mzV88+VbXr68JkxaorgjTMSBogJNEmi6dkmc1CRT0Yz8/jc/J08rtA6oK+lK5e4gpJuI/XbG/GTD7NWDdIvuIJmVokfpwtHB0n0H6Xt0UwyDR7fL0X+TsniGawEg+lVHeH14yptPe7xlgJ+14Fv0WkSHyat71KsIpjP8390LmroSd8qgfcLLElsOFF+8JJmWMl5SA916SuRLRy379Br1KqL8H5ckLx9GNb4X9xTvz8Uvb3yar6/k3wfmacz0+RXhvBTuhqvS/U88hvsWvc4lSvjEIXeth5da0n90T/lXF3i+YfqLazx/wOxTZ+FV6DKl+PqKMK9F0Z90o+PCCzWRS+58iteVjbzfTcTyd0SGt5FwHKbV6ByyW7EgB5NaoEdVTHe7xHwbklxs8NJ+FNWOBNFAmCr2IOFFukqgDYkuNhB6eEmAB6jpj2r/f+jr+/NfrcfwzQHbBtg+JntzK/M0NTDUcjJOzjfCJn/I8ae1bFzOqtfvJugio13P3DzYY3LxSHy2FcufVZhK5mdjRdgHdG6+H2dCBowXh1E1e6Rx+WlHbEomXcj6mxekk4og6pmdP9LtxaLXu/bi9GRLkEhIT99GdFUiaX/uxGyamLZOMAdXQU6L0SMt9iBJoTNdiB+YEa07DGIrDOKOMOnYfjxnUicC+2kjZqcb8fYGBf6swb82DG0o72FuCcM9KDA3EoCDGiS61OGMPX+gf8iJ4h2DHWSGGXeo3EoQUu1jHmSkQCcVQXD2PDe4cQ8sW4t4SDcRyelOKIxNTH87G8cBXZlQ7yaEs4pwWpHj5vHeQDIt8d1nMF9uZebtRkbH3IVj2M7NhxdkaY3vC4L5bL6lLPJxrr65P8H3LX0vTP40q5xH371W67N/XBAnLdYKo//lm/fjrL3rInQfEkYdm9tTmjam70Nm04NLaBywVkZB/VGnMnjMZ3uGwWM6LUiTBt1GXF3ejtdL5BL+4qynq2OaMmN+usYahXHdp/nkQNeHhGFPmtcjzrl1wTbD4GF1QHW3ki6Q8cdD73GFrhOlOyvX4naKMT7Zakf0jC1/QMZLLqFRFynxm0eGnX7ypUda3DGHFLUpGW4bbBXjr1r5d5sA00YMH3w835C/upNUPesy7tsQf94TPDZ0t0u8Bzlw73/3egzpCmYl859/K6E3bUg4qYlWBxls+pZoeXCtdyujhjomOD1gvg7oNyeimXn1gHkXPmWP1AqzjqQLlzeouaV/NxFf/j6jL3PSt7d478/GUUG4KPCzBn3IBRJ2c8L05d3TTN8FEJk6Ejtk3Emh454P7XpGt8vJX98RzES/ouIevZ1QfDwDJCZ4sB52m6HLRN5blxjpHT8T60lgEJXEBq8nBKqAvREHkQ7/y1Ld/j9YP/r8/7jX9575mzLB7FM5ZbYh/kmPlw1jXv1xZsvgSSvLYTmHI887ElTmcPSGG/+JdR9aF/bSj6InwCXeheNNcgRkmHFeL1jco5dZ+RbjstdlJm3RnQSC6C6kc+phEZ6JSvrYVtVdKICOfU4Y6ifhoLMT2e9Uu4Px0V1I30qV3xzyP4DNmD4QOEsTjXPqIG2ktVcmwuXOGjn0GE9yAuZAPzy9v46XcNQAyNdW2DrE7mVuHi0PEjkMEAzYOsJL7fjJeifPB/A0VYIuBDqDVeMhRYV6VGkfZ9fwXeZ/T5zIXNWPRf1ujE88k/yGvg/Eq+wNY3iOHTy6PsA4cR1AHLdotwl63kDn2v5lnVLV6SgO9H0R+3metPz7Phz5/+m8oD7kGPf/j17/po2p6pSyTYQJ4Q47R8GdbiOs9QUP7TbgINASEtXEhEmL78BEftTLhj146DaiKjOSWUEyK0dLYzYpJTsg7saD5mAUbZHRNglpVqMC/QdsCkCCbRwkyfYBKhBhoucbyiIXncExPOYZI31BLH6e8+4PvcKWIUMTjHbQcFrJAapU2MKJVDvkOnWvzVSiKQouK/c1jQuAcmI53ziAUYQK5Z5mcAl9ahgDskQjUIFvJVDLKPy0lahbdz+quBctRfMkklOpJFR6vkVlLZ4/jJhylXcQB5Jt4r6GwLmeEgKHQUKKVNqhItFjdNUTVNn2wTinV6FGRb08i6J+BAV1LgxMH2S046mB3gkGx4hwpx84QrZMGwpkLDDjM3MwSjQUGglK00q6BLsUc0j/4LnyPGvA/sC/fuz7/7Drex/+2ocFiQPw2D6V02MDZpvRbSfS+r1ZEeQ1wbQWgheM8JdgWtPvc+LFQWZYLoM8XB6wRqH3uUsHHMAOoOxon9GlCK6i1YGP/+4fiagr6unKdLy5tGvdzs/WpGcbut1krMSPQsG+D+nuVyx9O1ZeKtAcNgtSU2EHSVS7+MXXeNeijrZGiVDN+NLCTVuCpKUtMqwVkdlhN5WqzVVY1XbK/Gw9/ns/MCOIqLg+g2uYffIR/6xDfxTiWHRSYV3Gtp81+Jc9Q99g6mh82BwtT8cHTny5ob+fEcxKvFgUz17qMTDgRcB8/gNcIv/5CrOW6n6J8g3J6U6yHI4zzVALOGcvsaoq0OSnLlu+DbE6IMolda1rMnQT0bUxfqjHqnoYPNKsJsoaeqepWM53BO7vaB1grSI6KuG9gSjsiZOWss5ougjfl+tisdjJIVCLOFD3Ib0OJIDl+ozbuzMuzu9J8mrcVI92QYB0Uo0QKD/qxw0+8jv8sKd1yv1qP6GuUqwLjVm9uh2Jb9Yo6t2Ew37KoRR2fXr1QFemFEXO2eWdWF1jqd6qIiObVDR1gh8YFlf3gKQQmiamOYgmYXt9RpJXZMs95f2KdLkfeRZ8A9niIPdglYwb1LOsQBIk/ZMOlW7Qbp6sIo3n3sfkT7YMB4nJVZMWc7/A7BN81RCcNyJoBUkLXWYU//GE7KX83IP1sAefYVBE5xv8eU/z9Yrp1YNE1E576Dz62zl+3hAcuwZVjC4y0R0sCtTKYm5iotMt/psBe+MRnBXiNAAIPfxZJaO11IMowB8qzC4VwiAuunszJcga4vMN+9++cbRRiw8MbQhK7MzJak80rSTWPJSgq+L9OdnFGj8Rmt9g/LGzdFT4559cs/u7tySLAjzLw5evefkv/opZ2tLvc5qHBfns49gZ0E1EuHKfdfHEArDXMd1OQsWSc4n3PlIT/eWPgr9/6Ot7b/7piwep0mNLcF5iHmKaGyFVRcuDkKm+EQU3QHS1ZegVg1Pnlu/OSVZ7/OUBs8/YfvGK+ZsbGDz6zYz9xzOSacnkJx/Q21za9UnL4Zsr6kIeJtYoTj/9IBaxrCH+5JH69xfSfXCq2yMIJswaZq9upVPgQlHy+YEg6kUd7Wxnpg25+sVXmDakc8K0/fuL0TEwWEXXREyWuxG36mct1iiyhXDBmypheflAW4hGoKkTZpcPoy4B1531fEPgXITdejYKJ63x2f8Pl8z+9zvCDztRIddHW5Sb4wUD/mkFa0tw1cLZkv7f52N0sN0H+GlL/3Uu1jttMf9hT/B/+GEvGIDkJ3dEJzv0IUVXCd1mSnK+kZS/7ZRqOyU/2RK74CbbB8SnWwD0IWd7v5SIXBfhG0Ydt1+++YPv4QeacidwnSju+PbDS5Kww/ctvq9lBBAYmjqh60O6LqKsMmZ5QdPGPOwWfPLyA/vdDDt4hIFmvtjxcH8qLXPfsNvNuDi/p65S9vspgW/kAOkODEb7TF7dUbw/py1TmjpBOXX942GFtT6npw8M1mO62pLPD5hOMtrbQ0azz6WdPKnorMditWF18cDh5hRdJ3RVgvIGqsOEbCrgmr4XxsT89Q3pbkL7HZKkHxiStCGeVFTrOZOl5FRsr8+Ynm5GVwDWI0kdStY5bZKLzQ9/IbjV/8ZHpQEq1niJAIdMHUmrfDFQ/uUl/r0I4FSkMduM9O0dg/GwZQgHj2BRYMsEW8UMv7Hkn9wIi94bCBaSjRGna2wZ0n6zYP/tJdOrB5qPJ8BTol7vRHzRmWCwo2zjSJSKoR1If3UPUQBKuYRN6D/mdOsZANlnNxIvXCpMGdE+LBzWXIuosonJ3t4A0g2NphXtPsfPa4JpSfCyRl/HI40vmNRS5OS1pJnCH2CHQdDpcdoKGG0uaanpyU5gXdOOV588UP7dS6J5ISOIpJUCILaEV3vCqz3t1yfEL9ZyeAk1/qxhaH0JPCpSdl9fsfzZO7E1AmYdP3vb/4e2+v24ftj1/VP9Qo1xIiKVtU9+WOQ0Wf7mkrZKyJZ7PDVw+M3rkTgGkL++G1tZwaJgEb4fE8hU1DG9fBCLzOOMYFKTTe85fPlC7HyTSiw/vqXd5fJw6ULav5iSv7pz4hkhASbzA7vbU5K6Jp6V1NuZsPp7XwhybcT08mEE5YR5jXHRtKYLCcKerotI8powFatWs5sSZg3aAWKwHsm0pNpO6fuQKJZ2Y5B05KFhdiI+3b4W4pe1PvO31/SHDE9JJVQ+LDm5XI8dkvz1HeyFg27bkBDhJCgnIBoOvszxzxt5eO32QIJ6oeCuYdilqEXL9t+8ISt2gih9plavvstFaxF3pKt76nfnI+p3sJ5wDXyDaeKxO1PvJkRpix/1TM/XbK/PiJMGo/2ReOepQar/PqQq8pHLPwwevfFJo4HFYkuc1aKGjzt83xA78WbXRWwOM3xlOV/IZpi4EYPnDaLCD3v6PpTPLerou3AM8xG0bzDqAJo6ofx4SjwrCLOGZp9T7qbk04I4aWmbmKqU15lmFahhzBtIskbieY3i8LgYOwvHcJ/jtRPHLWHY05QZh8Nk1BmUd6txpAESDzxZ7KVD4jQxfmCwoSEcNOn5hvZxPtIfA+d4Ub7kUbTXK6L/2U/0/7sVviqPhGEIpFKPf1VCaejf5cSnO6oPZwSZoJ2DFxX6JpH2eNqLvXab4c9rzC5l9+VL5p99kGv/yO53hwkAP2+YvrhzIw01bsrp2zv0Jsd2wfj3TZEKe6JKSPvH8WvaOqbdnokjyanhJRkwBddeH9xoUjurXWAK2iJDfTyVYKpA/l40kZGGqRL8rYjzVN6Ji+dxRnr1MI4uwuWe8ttLEQjOJI3QC2RU4IUyRuhu5/hZg21E/zNoRf5LSUG0h4j2+kQw37UPw4DnW0wToTc5KmvxJy36cSKHgEmLF2nC2xXBpx32ZsCU8t7/uP5hr+8P+bFKgDwgatI2lDZVJDzuvkxJZqXQsVwbzPOe/PO2CyQsp5a/PzgBzLFToOKecF6M8+Khc2Elg/cH9qWuTPEGKzzvLiSHUVcQKPl+QeBmj24zAvAjjRoMXZUISatIR9W6cACUeLInFWymbpNXqECQv6aNHALYjCp2T8lcOp5Usvk71jg8+ZRRgxPYOOhIoMeoW/mLDmASaoEouWWKFJW1gveMe2mVbjOwPZSaoRGoCXUP7nsOrSLKa4KJtBqPtp8femmncRDNhqiLvVDj9W7j7IMxfCl0Vjjb+5he3Bkq1BwOE3zfSJvc/fKVMPmN8WkaN84x0uZf5CVZWhM7+1vbxkwW+/F9bOqEIBALn/IskWPv++7PjZHD39HPr62PbWPSpMFXFqWsRPUWUrEZq9A6oNjMmQC6C2nKbBQQHsdJxWHiqnItuQxAmtcj8wKQsYZvCMJekvtCDXWCHTyUUtjBo67EgpgmwiWo9hPSiajdtQ5I81qik5uIpsyIk0ZGJNaTEdhGYDrH+y2ZiobCAr5vnjQ5z7WUxOYOB/Hio2tQjEz65Hwjry/UECpHpAwASd3z1MDQid//qK+xjYy8Rj2N9vEi7TQ9Fl0lqMDC8b0+eu2tGoOjVNIRaAEfeb4d8wCO75ttQyzhSOb0Qi3PNle5+0krzxGtsGVCMiueGASuXe8piQn3fAHqqLhn6Hx5/cfgKzen90JDcrJ1FjwBhAWzUg7PTiDp5zIGGbR64n30SrRRkQQR2dJ1DjoYbODignuBr7n3atA+/qxBOQAb2jD0kYzn8ucP9/lR8PfHvb634G/4Tptq6EV5DEjwTtaifEv25lYiZeOe5PKRYCqtMC/ULgEvFtrb45zi4xl+Ku2r482m8o5gVTD0Ac39UvQBDlTiJwL/Ub4RAZWbe49iv0ATzSUzPj/ZSpiPb4gnFX6gifJaNmnf4juQh+kDepeUpnxLPKlILh+JJ7XAZtpoDFdpq0Q2+7wWHLDz+k8vHkmWe9n8U7HodHU85tBHaUuYtHR71+UILCrqyJZ7EQ9ZD88TodFQ+lI1xD2mjvFfDOhCNAHeWSwVS6kw65j+fobn0r2O8CX9MCX/6UfCzyrUCyWBQM+wjBMU2T74jr2rE2iJb6jW8zHqOF4cyC8fid1np52Hv6wzIe65ef1Rs6GUJQx7Wkcr0zrAWMX5xR1Tl3HfVQl1k8hnGRi0DtgcZihlmKQVSSxzFj/QY+UsX8tBgHxLb3z2TSaQobglTlqCsB8PIoOjAFZlxvbuhM39CbudaCiaWgRTQdwRhj3GqvEQG4Y906sHAfAgSZPGKIrDBKMD0sWBaFIRT4QAKN59n7LKmC93nH36nihrRsEiQN9FYxpguZuy283wfEtTpuPh+P7rl6PnHTWQXYij4LhRBaeHZ7kWAOxOCbSrFlW57SLMxwA0+OeywUa/6gg/KUU419rRgmcOKXqbo1yVbppYuoeBGSt2T0kiHmrA8wcIhBrZ7KZ0hwzbiR3WluIi8XwpLvQhxT/viV7viE92+KsKlUlEcDCrSN/eCTOgSijvVlQfzvCXnQh6nZZI/m45ArWSlw9jmqB1dj48K8/BuB+dBN3DgvZRrpd+OxHBdB1jq5j4VyXhmfyMpkrwcoF+9Q8zusc5/sxpfQYR0vpJS/PtGXYfg4LwZD9GNmMU/WZKdLLDP23xfIutIjnEHAuQQK5Du/bGTqOX/Gcf44/rH9j63pW/qSPhQ7c+9btzie516l6MEuV2rMUK2ETYTixx0dxt2C4hL5jU42bePAh8J8ga/LRl+5efkZ5tAaQttihGi4xpYh4+f0OcNnLjWRfBW6boJiJIOnw6hsEjcRZA3UQkyz2Hj+fSbg0N6awQG10lLelkVjJ9e0P14Yy2yNBfR5K5frYRj3SZclgvOP3kw5hYmC4O4A1PFbxD2IK0Z+NpRXvI2N5fMJkdWHz6geZhQZB0dIeMtjgliDvBtM7FGjRon8O3lyMWN/nFmuFR5oN4A8OmJbhoJTe96fDuNPvfvGX2J9+I3a+D+qsLwngHkwlojd0/T3hH9uk17cdTSRsLU8JZNVLIktMdYdZie398T46bUJB0mC5k93DG2eqRJG3wfMMsMNRliu4dWdH4nJw+cnNzweXlLYsXd6zfXdF3EX0fYAePLK3ZPy5ETKl9plnJ6ZtrNh/P6d24oCpyNtsFk7wkn5TsywmrYEcctXiepW4TiiIndETA9XrJarWhLHKUA/Ao3zA7F/ywF1i2X7ziq2/eMM1KptMCYwImk5JsVhBEYud7+OrlqCsI4o7F6XqMjN7fnBJlDbb3ebg/5VDlfPLmHYv5jq6N6D+eU5cZy/NHcSJonzDquPnmJSAuh7PzB6wOmJ2vhWTXhSwvpbV8TM/08prBKqoiwz/kMHjP1vY/Zs17qSVaHbDbAnUZQqcYdi7L49DIAaFX2NoXAt00lCfQpqS/kXGXijqiE7EJJ6/uhQTYhqh5J2E5+3hMywzjbvS1H7sDtonGDmV/yIkOO3HJdAHNV+ekP7vD1j7t3ZL4bEswlXFT5A6r3YcZupCkvuhiJweO7ChuTuju54QzKTJMG0oQ2NmW7n5Bt54RZu7g6dw83WZKNC9G0bLnG+r/YUl29SgpnllH+/XJaJW0WtF+XMGg8BMZIWAU8fmGoQ/obyVQaPKL99gycoFCEXqf49ua+t05h7sVF//sbwl/oRluPcx9yOxf3UMx4Oua/nFG9W9OOfm/PNMFwY94378P6/sT/h4XAISne/L/6ob2cwnaMXVEv8s53J6QXK4Fg3nkqv/0g4h4+oDJm1uCTzvMO59+n0ubS/soZ5UatM/8l9/Q3S/k5K4UD3/9U7LVbrT1RVHnKjPXQveFKnecuXW7nN29dCSsERKfbmKS+WGEbISTmubD6WgN1E1E+zijbyPiSUX+04+0H1djnrcfGNJJSbOdElTJaKf5rqff8w3pxZrt71+LNWtakp3siNKWro5Zf/6a+as7bO8SBhcH2u0UlJXqeTeh2U6ZvrkZ5/T6OhYFdB8QpC1e7KE/pPhdjZcNqJXH9LOPeKFFPySCL50X2F2EHxQQK/zzZ6r895lAkaIeFXeU316SXqzH6szUESqy9Id8BM/oNqJ7XOIHmtM31xT3S7o2wvcNYdKSZA377Qxr1diqDwPNdrOgLjOStEG78UYYaCbTgm/ev2KalSRxRxz1/O1f/IpP335DOhFC3jfXL8jjhl4H1JXoVeompmljWh0SBz2Bb0SBDyznO3QfOGugaA0+XF/R1imeZzHGZ3G6ZpJW+MrSu+6EWBLlujBdyOx0Q7GZ0TmYVJy0hGmDbiKqIidMZCQzm+9J4paqyKSLcfy5w56uSoiTBj+SrlXqwqeaOmHzuCTLK7zthDBtRZMwqyjen48tc317QrbaMT3eX/Z5DoLw5PqRtLwWlQ/Ym97hekVYNzRu4y8TyvfnpGcbUAN+2uK/VOhDju39sXthmhh/UTH0AfXtism8pvriinozpW0SwqgnW4ng0Q98kss1KpXcCKV9jFaE03LMFvACi95HDJ2k+gWTWlrjRqLF/UBGe6aK8ZMO1EDz7oz4xQP2LpTDft6RLB5ovjqnPx72VYE5pAxaxobR+Ybmw3dyFJSlL1PSqwcAmhsRKJbvz6Wqj3rRSJzspdugozGc6ZgbEC4P1O/OSV/eE79YE9YH2o8r6YSmLYHLBghjTfb2hvh0iylShq968MTqbL822DYW9PSJFBc/rn/Y6/ur/S/WmDrC7DO8VKJFB8OoYE3nBV7a46e9QD2cuG+cuS8qhgPYViAcpgvxk1ZUusbHNDEBxehbBZicrUegjOcNhGlLX8eEaSMUrzIlT7px7idWLC0Qnz5Au1k+yA0PyE17nI0ameUeAR22lyCSwXryMPcUg5uh+k51P1iPejsTbQByyPCsJ1oF914cRwV+0hIYhenD7/AF+vF98XxGf3zquNyeEj1Fv8/HnPBB+5g7+Rp6m0l2eiqtQjWRWR9WHmJq3jFUHkMJ3jNld5g2om9k3jwc3+c+ACdOssfQE+2PGGWQNryvZM7ZNsLjH0KPwPTEeUVQZBjji1hNyyYszH6FpyzT2WFk33ddxGJyGCE/fR8ySSvB5yoB3SRRx7bOGfAIgz1p3GAcG+C48UdRR6wD2i5it58xn+3HEQTAJK0wRqGNiAT9zZw8qwTw5A1UjkY4WDUKxZpDLjqCuMMYn2RairthENtnWyfyfdOGKOqoq0x+JhuN2RRNnaDaiCRrhPyWdIKT9Qbs4NF3Eco3hGk7+vxHPoYLnAJkFNYH7D+ePRvhjwD52X0rmo3HUEJn3OsJTgqJ2dUeKEFxd7uJu7YV3MgozAuVCHtTacUPjRz6bR9Qf3WBH/dEuRwwJ+cbyvsFYdK5Z4iSrqMrPEKnaj8y7X3HUDjqfI4cfF1Mx3k8ahAqobv//eUBWyb0e6F/hule4rUdQlgFVpJIb06eCHtHXom7B8K8Hu/ho63PVjHRrBhBWLaTtD9TxSLmtB7BokK/TyXMLO5G/v/Q+gIHCzVe2uNpD1vH+HGDbQMJYHPhTqaVTIzjc8ULDV7UidagfK6LwS0PrPcDFx8eP5b/P+D63uVA9FrCV3SZ0F/PnpTknsXPWrKrB9TUomaSuAVgy0R8pb7Byz30nSS+mS6kr5LR3woS+jO4jd504gdP396OgpqjlxYlh4Ag6unbSFLi3M0bTmritBnBGaED+RhnK/O8YaxEPW9AHcVgfUAQyby3vpXOgQqN+57ykA/SRqx/xqepEtKzLZHTEEjXQSJHo1iqBt1Eo+89TFp3E/fjAeL4oPZ8g581xFdyEpd2YkRfptL6SzsYPLqHhYi/ihS9zwX040hzXmrwZzJO8XJ5CJpthtk/z2BPwpiEk2DqWA5Kju7WbqfoJh7dF55n0TqQqihp8QNNvZ9QN8JOOHZyPDXgByKo9LwB3YeyOYc9oYPrZIsDaSaBNWWZsTp9JJvIRqyNz9n5PU2dUBUS6LKa7Ti0MUUr70OatCg1kMQtE5e6F4SaOOpQ3sBjKe1kAQbFdH3IcrkVnYCyKDWwP0zl3yQtobtm5JoNMK6bVB6ECxAmEujjO7yvNdLVqMtMDjeRpEqGTvNgTDByDLQOOBRT9tsZnetCHQ9RgW/o+++c2wdFf3hitSvfClnQyjgOz7LbPA/zARzg5wih8T2x6PF0LXsRsjFpHy80xC9Ej3BMoqvfnYs+KGvwJzX+pB1hUUe89ebDBX7WkJxsmVw8kvzkjrrI5d6c1DJrr2JMLXZjlTdjuJDAxSQTQIA3IjD0Ii2IXXd49Tw7FiOD9fBPNf0+oz+IuwUYgWbRoiA62aHyjrbIRB/kXAR+3Lvnh8FPO8KVYL51kY7wrvhqTXSxk7GDcxR0ZSr3TpmMKam6ien3+TgaNKXAtYKZhI0dOzoqF2qi3ucytnA6ItM+iSZVrMfI4+568WzXw3H98JCfH9cPub535d+9mxF9usNufLZ/+ymr/9MN9t8ZUIpwVgqxqwIYsHWILkWI58c9/rSGixNC9Yj5bexuPivz731OsijIP7lGnVpCXRBM5VhafP6SyMVV9mXK/mHJ4upeWvVOeT2e/pNW2mfTiu23V8R5Tbray4Pv3SWBSwbMz9eYJsaLeuJMhD/l11ekVw8uQnfASy3Nl6cMRtp92clOqGl3K5oyY/XqVk7+vhEXQqSpb06Ij1Q+BM0az0oJINI+2/sTLvN61A2EaYtuIorrM4YPcniZvb0GJMksXhwIZqVY+yyoTSdwkdw9BOqY5L9usF8rzEbmicGqoPt8Ig/QRSWV1jOsdpeTnezAG8RCdbLDtMIST2clj3/3CZOzDan7HMOtbKq7+xVtG5NmNWGgySclcV4TZg2//+tf4rvo3TDQGKsIfONS+gytS9YLAqlitsUUX1mZyytLnkmlN1vsZXbehSxO17zYz/Fdy75pwxERDIy0wLLK6EzA5WJN10U0bUwUaiZ5SV1lxGlNGHekbm5/e3fGgHQWzi/uMNpnt1kAsDx/ZHH2iOkC+iamayPu/upPePH6A54a2K3neN5AXaWEaStZFoOH7xsmiz3Kt9x+uOTy1TW3Hy7ZFxP0B3EpRHEnwtSkpa5SJsu9hFPdrIgnNYf1nDiRNMHjoaT8cEbfRmR59SzXgryR0K2nwjTot/j/uxd4WuPd3zB82KMfEnSRSeBOLhVq/vqOfp9jqngUpulCDjnhrCI623H4/SvCrCF/c8tgfOrbFYFL1bTbmCDQI/AovXoQtLRL2AyyBpR1McfiLjha/zxHFtXrKfHZVjpnasAcUtqHObqJ0d2c/beX+JF0Ev2khWgY7Ygqkq/Rvj9l9vZaHAZ1RPXhTBj/WcOgFYd3F+SXj2IbjHux9q1nktRZhrQPElakQo1vFH2dcPhwLp3QrGHy4p5mPaMvUymwMkkC9FLD4a/fys9++YjZZ+N7HF1sUKfQ/W5KsDrghZbDb16TnO7wNha8QYqKH9c/6PW9N//d11fMESHL9O01/V95BLNKKsBa+Prd9WJsffuZiKpME2PvfeLwQRK7tFQjQWbJfnFL+ZvLsRW+/9dv8OMO3cRjha58aeFiFbPzR7bXZ8xON0zO1sR5Rd9Go3VGFymmDZld3WO6kHo9I56WzF/fPEX2qsE5ByRVq99NSByApnuc0zzMR+SvqM8N8ayUbPS8JjvZYbqQ6uPZ+CBSmatUOrEMhVk7tl4t4AdGeO69zPG8UBNkDcnlI3ovtrn45RpbRpLkZXxRIbv0wyBrhGQW99guIFiU+JcGpucQCB3uaCXEVSzMUob753ngh1kjM9BQo7KGYFEStCG6SqjvlrRNjFrPRxuebiM5DOSCMG2bmNXFg2yOVYLpApK4ReuAwBeQTec8+cYEdFWEUhbdB/SufX51ej+y/7UOaJqErovo+lAcA4Hmyy8/ZZ4XYjtUA0pZ2i6k76VKnk0PDIPHbHpAKct0taPYzji9En3GbrNgMi1k9DA8HaQ8byBLatKkodhPWV7cyxwfEaEdmQPlYcKhzDldrWnKTKiEUUeUtFRFTu0sk9b4XP3jLzh8OGP3sCJNGtoyZbHYsVjsxu95tM+CjJuKzUy6KZFmfX3G6uWtpE66sJ9jVyr8TvzwsywL8YUI0gbt0/9fH8WeGltU6hGcN3ihQeU93lRBpOj+JpHrZ65RdU+/z8eNuttMpShY7oVU10QEaUO9mY0wL79I6dqISbYX8fD9kuRsQ7REni+X1RP62qUFmiIVmFAd0z/mojeYtJhCqm0VacJ5SXK5Fuut05j4Zx3mMWT/F5+Qnm3wJ7XAuerIje8stpOY3yBrCCYVppVMgnhSEV9uJL7Y8Qem/+0N9mbAS1uyn17TXS9ot1NJMTzdEZ7tRXdUpARZw+yfv8fchKO4WhcZQx+MeQWmjmnWM3GYeBbWU+y9vBas/Ox+qMf7sd9N2Ly/4O3zXRGAAH5/XH+863tv/mHcESwKgVDcrETh7bChKu4Y+oBgVtE9LGi2E6K8FhKeVijEEtberGRzdMhLW4ofF5e3bbTPMMjGb7qQ/HQror0uHC1NxoXoqNAQ5vXoK6eT5C7dhXITOKvWkbMPjB5z24sTQflWNn4H6OgOGW2ZSRiK85MfgzfEDuhunjqR1nQ3pSsy/PVsjA1WvqWvEyZX93T7yR/kklujXEs0JlntRy/xYHzMLhU8r0McH/3B8cluHLFEL/d07+airP/Y4Otb1HlAlO4xu5D+bobtIoZywBsqhvJ5hv6mD4imArWxXUT17YRoVmCqmHafE8UdfR8QOEpiEHeUDwup5qOeJJMAnLpMhbGfW5fOJ+9fEGjCsKc4TOgckAeQ6F3f4PuaNK/ZrhfEcStjIefT97wBa5XkBQQS46vUILG+oaHXgXj7fcv+MCWJW/JpQZLXRHmNvj9Bu7lxnLTus/DG6yc4hjwZmbFPl1taR6BUvsUaYflLuJB0JdK8FuZ/KJRJ2/voKBT2/rGSv18Qxh2Ls0eq/QRjfCbLHZ4aqFznRKiTdsybCFM5ZFrjk08Lwky6SXThOKo6XkffDQH6odegGd0e/qRGtaHzrQcMasBajb8U0Z+58VFxByp+SuQL7JhkB+KfD5IOBqmmGRRWB0xcBW21Ij7bsnTV8XF2riItOgPvKS3QthEq7mSUkHeCvVYNqg0pP5wxPVoyIy1M/7j/TtCYaFhUKv759GJNMCvlsN2G40gCZVGRHgsfvEF+/pETIHbmo7U3OJSYYiLR5UgmQLw4YLtI0hitsPglpc8yHCSDQCWdHK4OGf0hk3srawim9Rj963liHa3vF0IJDIzkDYSGbj0jWh4IZyXT0+cjPv64/n6s7z3zD/MalVu8UBLXPF88qsd21qB9ubl8I5ulm0cf7XBDL1AeL7CuKugl/MeIR3oMfgmfvNmhw8PqNqJrnrzznm/AsyK4SVqJAG5F1XoUTAGjxbCvEvoqGVGn1iiZO+8no9CvO2ToJpYbKumIphXRvJDX4Cw6pg/oa/k6nrKSO3/IqdZz6irDuBZ/U0pb8Jj25ykrBwn3kDrOFIWHr6TaWc+wXeAsk8L8RgniV+WdwIBSByWqEvrNlKEcYDqFaYgXmdGbbMsIu/HR+6dc+h90uffD88Rt0exzOXRYJRTCsEd5wxg0E0S9CNi8gdjl1fd9QN+HGCMPwDSvSfOayMFzlG/HTdwOHp15En6K2M5R2BxL3/OEyhc6jYDnDaRJg7UKe/xzK2K/KOoIo45OhxirCENNmEqrXGufqshoXTTwUbAKEl88uHGEUk6ZH3dUZU7fh+NGfARGJXnF5DvRqX5gCNPGXZtPQB4/6ik20nFKV3vipBkDezwXfGTciOJIK5SMic4hfX3CVA7a8rWNC07qR9FokD+NpH7wy6F8mi2jBvxcbG4q7vGc9500YOjFk643UnV/l0Dpz+oRZX0U5+FZd//GIwzHT1oZJU5aktOnlr0f97LpumfHsZrXRSoiwMBZ9ixjmFBbZqPOyAs1atqgMtlgbRWPzze9zRg6n2BVoKbGpU5KbO8RYsTRshx38hk5sJIfC8HQOOcOg8I8ZJhaRhTN42IE++CJM6C+X8p7krrn6T6Rg4K71m0f0JVSLKhIo7KO6Gw3HvBMHUnR5DQTANHJjr5KnMOqI7t6fLbrQdaPwT5/7Ot7V/5+2mF2ISrtmfzqA/bgjwlV1uVS2zIiutiwXB5orsXSEi73eKHBVjH5Tz9itplsUpW0/bKLtaPgiWI6/+wj/vsz9i6+MrtY07cR7X5COqnI5wd5MAxi5TuK/aJZQXS1pbteiPLZ+PhGUW1mKF8ofSrUYo85WmxcAI3tA7o6JnYjDdPEeO7k3hcpugsFyuNigFWgJUo0bccY2nhSEbm2dl8lbL98KalugRmTxsJ5IYcC69HeL0gu5We3+4x6M2M2rQSd7G5wALNLXfWp0H+XocuUIK8Jz/fwy5dwfY3+JqHfLSXjO+4ZOsn47nfPs/mH0/Ip6S7umX9yzaAVwaQiPtlx89c/Y37xgO8qqHo3YX62FoFkYOmLVCpgt1GCEOkOjwu0+7p1k5AmDZPZAeUNI/QmcBvGYTfn8tN3NLspVZHR64Cvby95efLAZFKSpA3b7Zyui8iziiDUfPvxnBdnd7RtTNdHzPKC+XKHH/W0RUZTugNcG2NreV2L5VZ+ZrcRX79/wWx6IEkblG+5e39FrwOm8x3JopAHrHHRylbIfcb4DiscYB4XkkfQRbRNTNxG5PMDyje0RYrRPsmioDxM2N2eiphy8KiLnNWF2MX2D0uyWcHju0s5UEQ9+8cl0SEnP90SLw5UD0tMEwm1Tgks6rlWe7MidsEx3f2CcCZkQv+qY3j7Bv9vvpF8C+tL98yTP7NbD7PzhQQ6B2rDcJAxYvmwYPmzdwxGCSMjqym/vRy7d8P7E3foj0BZ0os1ep+NB6qhD0aYjxcYhlbRvl88pdmpgfnrG1TeCMDMG1CfTGB9wNynoxsh/ad7ur/NaNYzVNSTfPIgBwn3vqpQU3x7Qf7qjuBFhd2IKC9cHSRps4zwUtEgDO5g328nI7wHZJSq9/mokTJtOPJQsKJXCM/3ov1BnFfdZkp8upPE0m1GcFpi9hntek5bpCQLcROYOkYlHeq/vSTZip3QGp/oZPds18OP6+/H+t6bv/KN+OONL4jaUIttxc2xo/ONoGgBL9HE5xt0kdHeLSW7/WIDVtqCni/z+fBqT/XbS2llLvfEswK9lc3YDzSHd+eo0NBW6XhTx7MCFVhs74s6to2YnK1pt1O2X7/EGsXi9a1LlxO8ZtdG5CdbgqRj++VLplcPMDyppz1vIErbkbjlpy3BssRWEd3jnPRkO252YrWScYV2HYD0ZCff55ARJC3Tzz5SfTiTqtEqcRsMHvZBDibWKEKX0Od5lvhkT/5nt3SfzxgcPc/UkRwOBk8qotRgtgnh6V5SxwIw//7eqXftCAPq7hfudVrC+fP4eVSo2b+/oK2EMGe0z8nrm5G+uHp9La6I/QTdRsR5xeTNLfXtinqTESYdh/VCmOpxh+dZdvcr6iolSRqyWUHXRSM/v24SptMCa4W337tUv3e//5TIUfm6LuInL4R/flTWP+wWmEFhrEKpgbev3ktmQ9yhu5Drdy/G0YHyLV0bMZvvub09xw6KPK3YbhbEUYc/LYjiblT/G6tGu2JRZ0yKXNTXRn7fj3r8SFr/Wgd0bYwylngiIsIw1MSpuFOOHQPdh7RNwm43480vvqR8WKB1QD4/8Pi4Ij1ITC8gIzAjOGU/7FnMilEvU+xPJD1xtZdUTa1GDv1zLNOF6H1G9HJL8l8F8L7FPMQMO/C++Zb2ekF4ukdNDWG+o/nyFO+DkQrbRXQX//6FRFRrRbebMH15T7/PUKEmXu1p1zN5TxPR13T7iUC8FpJhsfvy5SjMVaHGTzv8VUP/QZL+/KsO/VVK9vYGLzd4Adidj95OZPzgDfCbln67wk9b4pMdpo7p/jaTQ8TqgOcPmHUmmSSO6tc+zmSEZ3zMXYzKOmwbog85eJYgbyh+84owrxmMT/shJ56XRJ89ou8y6scF+T/6QPHvLwiSTkaiQURwXtK9W9A8LsRpVKSjJRkkaE0f5Pf8vMHuBQOevbwndTqs6GqDF1s8BXz+DcnPhclgD/Jcey7oEzjIzw9s9fux7v9h1/fe/Ov7JUHSEqQNfto54YzYaobeE6Z0LZSqoRcql+dZ4tMdg/VobpdESyHvHfOszUPmWujSamt2U9r9RFrlDo3b1bHY55B2fbWeS4ZA2jC5upcWW2DoNlO09jE6kNSsPkA3MVFWP4nvXFu0vFsRxp0j+CXMP/vI42/fonzLLG1EUGOVs8qExKdb6tsVbZVgdSCe9GO61uBhGml5+5FYfIwL9DnGzapQE80KDh/OAYjSFhU6/cBBEKb+qpKOhX2CGB0rGS/UKC2zZi+1MrfUUt3YwvuDmE6VbkRY6b7Wc6x2K8pu34UtzX9+LVa/JhL0r/bp6nj0pSvf0ty5COBZiW6iMWgnCGoml2uar1K08SnKnLaNCQL52mleC3NfBzR1QtPGKDUwn+/YbBYMgR6RwNcPZyynAs7xvIHT+ZaqFma+1j62zEQT4ub2ofu3IAcGY+T6iaOOtos4VDlNF/Hm8hpjFeVuSl2lkMm1OAweq9PHp8ho151I8mrMizh69sOwR/cBj7dno+hPeQNWByTOFaHd+7VYitbFWIW1ir6J5d/EnYysqpRc70fXgulDhqTjcHsi2fBRj581oo53ECpTxf/px/iDrTCvpW2tPbi+Z3Dd/MF4UMs83Aut/L72iM52UoFbsXgex4DdZirArMtHqcQjqVx1IZAcUyYjercrU9LlU7ZDfralr2LCrHWefQsawqsdQ68w1xHx6RY1N5hNSP84I1wUwtUPBtAe/XoqKZnHFj3CBfFCjacFaKbi3oX6CCfAdyI/XcmI4dj1kI6kkmTBmXBRLNIp042DDcU9iYtdPuKgB61ILjbo2wkq0qQX69GGqCItegYjoDQ/7kTs9+6cyWcf6dbTMS59MD72kDDsvHFMG/6JwdON6CKe6dnw4/r7s75/qp83ELibXMU9ni/EK/+40fvHJCw1zt2sEwF6aoANDv4jrWgvbend5ja4VC5rlDsMyLxYBHshftiPaWZjEI6Sh4NyTgEV9URRR3NsuTqYS5C142uTjoJBtxFh2qDU0+s8QlFsJy1AqmSMAwbc6MAymOEP5rW29+nreJy/6iYS3vh3RYY4MZJ73Sg5iISZe3h3IXYfixbgGHQUavff8rPYVkBFnmShMDSetPgdUMjzLYQeXigpieju+W5wN/MPfEOUtsSXG5p3Z+Pr9tQwwn2Obo1qPR9FlLLpa7pWPnPPN9IFCHu6PqRuknED9zxJpdN9gHYHjsCxAI4Kft83BFaN6XzKaSzCQLu5v0/XR/i+HrsEx78XJ+3YvRBHQUjkuAK6SbEIk0F34ZgyeFyeJ2CYKBILnnWwnwBGH34Y6icWgPFpO6Ea+t85dPRtNHZNPGVJ8hp9vO6UlU5RoEcGwDB4coA6XoNGCcOii4jVgBcL+KbZTkediW6eb/M/ZmUMvWLYC39/sB6e9YTy596zoVUMnWR4DK3TBwQGz83O+0OGN3hj8p6X9nidQH680DyBxdwKvmOt9Wclg108RWCDqNxPNVhLf0gJFqUggmu5RwNnxfVwvI9GqmfbB6PgLpxoBoedHlxwlUrbsR3vJy1e3KPcv9Fl8rT5O5CQjIHkgBM6AqbZZmJFzBr07eTpuhrUyEcIJhXqSMyM5TV6gcGf1rQfTyFpx8OPrWP3fBPNgWlDvCKVZ4hnxd3U6fFgdhRJPueyP6r9/6jX9978s9e3wuMOPegH6s9PSX/+ALpGl05o5rzeynnRmy9fABAuCtJP7lCLgdDusXUsilnji8+5D+h2OfGkGgV1XRNT7qaS6Ha6FmWzVaRnW1TUYfuA8sOZiPPmMt9KlwesdWlWcTe2EcNFQb+diGUoafFDLaFBrhV699tPOP/ZNwzWo7g9YXIhYhhrxDbV7ybEJzuCSS0Urjair8SRoLuQtkpZvrqhL1MJXtnOefnpO8kecMLHuAvIz7bjgaIrQpLLNZE7+OhDitVqDBmKlgf8tP3PqvdBS4CKLROClzVD2UugyeAx1ANeAt40gCyBfPKDXCT/6YpXO9edMSSnO0wR0xdCIotOJfeg2U7wlLz2rkyoioxiL6p1z7MST9toujbi8OGMMNQsVhvaJuFwmLA+zFiebKirjP1eQnsC35DlFb5veHw8IY7aEQCklOXF6T1R3BFFHX6guX84IU0EC9zqkJP5Vl5PH2KtT5bWZLOCphSlehj14r93osE46pjqgKaV9r7nDZxd3mH1E4xnfXc6HiKCWLILyt10VPGHaUPSxDTOITKbHqjqlL4Xtb8dPG7eX3F+dSvXkw7QbYRShsjltg+DWATX66Uk/2U1TZ2Okb/Kl00kmxZYHUh0cdaIJRPGjtJzLS8wYpWLe/zTiqFVT9Za9/wXpn8k4ruj0HHZyxD7DrxUQqBsH2CcHgjLKFxsb1aC7o16wom00J/IdRo17Uizu6eulwPiqHgnB4lA4nLNWtxF8eIgcK67pXTwlDAr+jIdu5sq7gl/oWn+0sVsz0q6zZQkbzB1SH/ISF+L1TY82xMqaL4+dWFEghT2Qsvh+pTp1QPh8kCY9tgqovj6SsBX84KPf/ELlpcPhJMaPMvhm0tmP33PYBT9PqfZzMjO1/TbCeGkJnjd4a+lug+mNeHpnuJ3r4nmBcGkwguNdB7d+xPkDcEvLOYLHy8c8OKeZ4J//sH6cfP/417fe/O3bYStO7zOyqkdKH8t83o/aenvZxTXZ6TLPZGyApyIO1EZW0X1xRW6iZi8ucUan8ffveLFf/fX2ErCKQbroevkSSk9LUlOt2y/kkCT43y/L1JUKH76aFpRb2Y8vrskijvy5R4/7GkeFiPad/7pR3afv3bYX2mtpZ/c0N3PsWVINCuYtSHdboJxvt7obEd7sxIr4Kx4enBasRgmLz+y/pvPmFw8Eq0OYqWZ9qAhX+dk7y4IEvHwHi2OwaLE7OVBFXolftpRfPVirI6jmcz+t5+/wuqAcFrin7eUf3WF51uSN/eS2/0gDHWVN5iHAP/FgL+00GrsQeEllmGn4VDgLZ8ntrN3/nTTx1TXJ2QXa+Golym7r16M9D/l27GDs3p5S7ObYrRPnNd8/cUnkujn8hryaSFEtF42+dP5luuPlwSBHjf5rot4fJSc+5PTR9JJNYJ0fGvIJhVtEwtnwA9Hit8weLQ6pO0i2i5iklWk08K5QSQYx5iAOK2xVtG2sess2NFn3/ehzOTrRAh+rnJfH2aczrfi9nDWxDjqCNC0TUxTJdyvT7g8vyPOxM6YpJLad6imaCMAH91GknKoLJPzRxg8mv0Ea0Vjs3xxx9yhlHUb4QeatklGImJxe4LnWfLTDSrqefzqFdmsIFkciJYHskn9LNcCyPgpWEhg1lD6qLmBvMXufPr7GdHLvWzyVpTxZp8Rvd7Rf3Sb6vkeL7Yo5wDwAkN9fUqQNlIJW4/kzT3d7YLmYY7uQhY//3bUDOjNlOZ3UyYv7t31mVHcrzj/737N0Mqhw1MWL9HyfXQrEbmxJVncYQ+CI84/+0i/mY5OElMl6H/noyIJMes2U1TU09wunYC3BDVQf3Mu7IGoJ/v0WuKNa7EC9vczFp99cHG7GqIBu3GI6t2Erky5+tXn47jPtqEbpQYuCTVh+uaG8HWB99Wc/pBhfx1ILHkluSl+0pJePo7R397UY7H8it2/+0RcWnEPh5b640vRPKQWvX2+TtCP6+/H+i+C/KSbgnBajq38scXrrHLzn74b22HRyY7em44X5OSyYv0//dRRtyqmpxua9yejMh8gSFrixQHTRPRVQn2/xBpFPHE3X5UwDB59LQ9nPxaW/+x0I6z/Q06xn3L+yXuSrJENqpZgH9PEkuznW/Ec3/j0VUI4rca5qDK+tKl7mZEdfZDHr2OPWNt95kYA/vj79sbF3LqvoZtYHjBKrI16K3Y42/vS3j0M+I4yqEI9zgxnb2/kfQ015DGJs+R4oUXfT0RbEIhYqvlwSqru8EILSGXav88JX5UQBxKu8gzraGOKk5JgWo5I5OP4JD3bPmUl1DHNISedF5IxP0iO+mxSjOI1rQPCtKWrpAKfTAsXdCO+ZaUsxgSjRz+KOnxl2T8usNYXrYdVzE83NFVC0yRo4xOFPXfrE3zPMs1KorDnfr9AW7FgzuZ7qiKndKE/aVZJYI43jKAeP5BkPrP3abuQ6XyHP9/L+6AGpnOZ2XrOkbArpmyKKUnYE/qaINDM8kK+XhPjK4txn2GimtGtks4KzGYu70/aUd6cUO6mcn2fr11bWDO0keQaLA5kp1t0ndAWmXtfDbqJ8dpIdAfap3Xx01EdP1+qX9Lhuba0hPcoVGohGJynXzpWXmoIwhJbh/TXE8mvUE8jgfZuiedb0rd3qECP6vdg0qIfpuJtD4XxgFVUH8+IZgXBpCZ12gGAKNyznFYyhuhkZKDinubbM8JZKZCeYGD/V58QJC3hVBwBzccT6TQ4zVD9uCCeFUSnwrRQe3EqRYtCxmzKYg6pJHPmDf6qZfjsJf1/Xzicbk+wOrjgH1/Ceu5j2rXAitJ5QXK+EX2Bi/ZWcU+U16i0lZ8t6fBnFeYuJljI99GHVEYHR4zykSXSdaguwNsb2rslQdoQTGrwLUPhkb54wEtldBiePF/E83H9CPn5417/BWp/+wSdcFzuETXbyMzbXzXYnYT/HG02g1Eye3NCJKykfCWnO2FQqwFPOUjH0QfvAD2DkQejzIU94kklG4+bj3235XlEAIdutimqX5nJqtDBP5TMD4fKGzMD+kM23uzCHFBjCMdRvyDBQ676d0hbPxDLnqmS0XI4fGfOH05qjFPjKt/Qu/nbMIi/v6kScgcYOuoDdJUQuyhQ20So2xIGeWTbMqRzbT8Qz3l8unMbP46CKIAUNGA16Odp7h295t+9NnQTjSOOcF6MYslxHl2m+M7yqEJN4lTuR6HdURtgB29kBESxY+Jb9Z0Zek8Qapo6oW3jkbw3DB6FSwXsdci+yrg6lUow8A2JcxUopxm2g4cxPtud4HaDQCr10I0NrDsgdM7a6SkroUTOPeArOyb/DdbDDmp0j5RdTBTIxh+FPakTneoupDUBvi+o3iM3IIo7uiodccPdZjp2m4wORrb/Uah41AH4zrFijXKHcB+NBCgdxbK6EVHuMWr2WZf1GNpQ0LkPyRgmo+8SPCdwxXojiGecV5diSVSh8DuG/gk/MjgLsJ+L4h0Qu+hugu5C/CZ2+p9uvF9VpPHmNfbwlG1hnXXX1LF8bzeXP7LxPd+6IKJBRHNBRzyVDt3QByLumwmcTOUdQ+cCduIeL2/kPlTgVaXrTjo9ksI9P4KRwhk6S3CQNahpA4eEdj8TIfV3dAyeGoR1UEeSSuruORVpuu1kfE5aNz498kF0kWKNL50T6wmJsJdcBbTHwDCOXn5c/3DX9978py/vCFYHUOI9H4yPylq5MJtY2tLKKdA7aeUH5yX99Yz+PkPdazerkxS88EyqJxWL0v94eq3vl5g+wFOWKK9HsI41PpOre3SZokIj4CAj4KCjAjc+2Y+o4GPKVpC2aJ2IN14NNOsZ3e0cFXUEiU/5sGT25gZThvJ9ev+JW2CeZpd9mRJNKpk/bqeOQCbCq75KSE+2jkQmWobk9T31N+djJWGcLcyPhCB2FBLKjaoIs1Y2yLjHq4wEBT0s5O/7Bi+wtPvJmFaGGgj+mwkcrGSlH+TB638K9qMQyPzV8zzwjyClvoolpnh5oHt/TteIlsOLe2yVSDBRKIS/YjsjSRviSSUdj6ShPEzGDXwMejE+2o1CgkCj+4Cui9BOwKmUwH/W2wV5Kg9R5UmozLfXV5wvNmjts2tSXoeaxeRAEBiCsBcrX1rJAcI39F3IupxyNtsRBpr9Ycrc25PkNb4nh7vNZiHdG28gzYTUV1fpKPI7HCZjFoExAaEvc/xJWjFf7CR1UA2ChO5y9ocJWVqPDhaAIO64fveCNGkJo46HD5esLu9Gq+PmTpgZ0/kBFWjirKarUroqHQ9PQaCpqpw0r0XtH3f42qfXCYOV1LznWraJZEM0QuoMJy3V52eEeU18seHw9Suxw1kPBuFBBMvySc3v0L7R+QaMonucY3Uwtv11kRJ9diDWe0F4NxH1dkrk8jFMF4p2qIvkXlE1Km7pvp0+OX/qhORs4/Q1MrbKX8oo7QgbCl2OiAo1Km0Jr/bYQzjqAqLXO5grqAbs2mL6gPC0woskydA8pqi7Bl0viIJKFPdlNGqa/FAs0P6qwX+3cDoEUOlT8JeaNoSBBGUdbdT2PiB9dU93vxizD0wTi1bBPQODWYm/ajAPmegSzjbjAXxwGgrf4X5RA0PvEz7bFQEw/PCpfj+a/X7Q9b03/+ine+pfC1gjnEpFr7e542EXmF1K+16U/H7SEr4oaT4/kUNCYMeNcf/NFWHckb28p9/lqCgaK/7W8eB9cGjMDt1GRFlD7OwyxzTAMK+JTnZUj3O8zZRwIrOv4n7Jyc++Fc7844IsWH8Hl2vIX99hu4BuI+Kz+duPfPybn7G8eBgPONf/+les3n4UD7Fvaa5PCFPZSHWVUG2nnPziG+kiODDLYHzCmbNsFRm7v/lk/LlE/a+YvL6VTkGVcPZnv+PwxUuyq0f8SU3z4ZTQccFV0hEuD3+gikZJRYgjAw7Ww/ybkvB1i93JXNOftKBi1GdO6Fc8D8/dCzXdo1TMx8jSKK+FetgHPP7lzwhi+exkbCMP1boUHnt4yOm7kKKU1zmdHPjydz9htdgyP1uTLAquf/spUSxK/CjqePsnX7L5+gVdG2G0zySr6PqIKOxGhO+L0weMUaRJw9vojv1+Sp5VaB1QFDlR2BPH7egkuH085Sev3tHUMiaYz/aUVUbTJEL9izr25YTptEBFPZ4ZuLs/JU2euhb7Kh/jgYfBY1flvF49Yq3PZr0a58fH0cXxvzuHELZWsX9cEIU90+WWZCYApcZtUOmkZHH+SL2X90q3Ecb45Is9bZHJWCLs2a6XMiZx1XT5sMSPevKzNSqwtOs5z8R7RBcZ4VWBNx0I1IGh9Zn91++wG5/25oTY2dmikz3+soNUYW4cJGpSyya7i4T+59LtJq/uRopoX2XUvz4ZKYXH9MT06oGhD2jWMz783We8+bPf0K7n1I9zoocaFRr6Q060PJD+9IHyb69ILtfSfm9iQREvDebefe8qERBZIQcSVUt4T3S+wYsMg4Xur1JsPyWcVYRne8q/fUk0LySGfJBwrTCvaXc53iGTLICkJXW2YFvHeDsnBvRlfDe0IbNffCubchNQvjsn/+SG5LNHhg6qLy7RLuIbJYch3YUSIORyBkyZEJw2+IuKNNSibzgMzl0k1myzz5zN75kuhB/X36v1/QV/W2FHq8HIhe7WEeMJ0D4spP3sWYZOWtC2lRZ8sCjpN1PCphNQR9qO7bfj3F+Fmr52mdZG0X84Y3rxiDU+fZnS7gXI0R4yUfRnzVhBD4OHaiLs4FHdrsaI0W6Xk109SiXfhrJp+QbTxLJpW8XZ2490pVC9/JlQBBmUOAT6gPaQ/4EVa/H6lur6hDBr5LDirEXF+3NpwaWitO4raX0GSSvQo30uIh+nE5j+5IO8pvs5XSlULtu5FuOiwYtAPz4x0+PVTt43pMOiy4SgLvHiAT+Wv28/dnh+A4FErT7H6jZTQle59oec3btL0nlBPCvFr73L2d2eii7DN4JArlNOzx5IFu5BScbJyaOo2/tgtO91VSrdlD4gcC1+zxuo7pcY41PV6cgBWCy2MtduY/ZFxmJ2wFqFUpYkaSirbPz3x5b66kQ6A10Tk0YtXSspfta1UP/TjIErJ9QbjI/RPlUXk6c1YdQTBJrsIIeJNKvxA820LtnuJTNAMMCWosw5OVmTTkqCqKerxKOum0h+Xu0ThJq6yGnKjCju0NqXyOGoB2U57KYsz4UpYLqA/MUD+ssXEofs2xFp7AdGkNdu3HLU5Tznik63gs0tB4YmFBy1Bi82RKdbmusTVGjEEnwwcBC7nakSUANxvBkDt/y4Iz7Zsf7dW87+6d8RXpbE3SO2iNl/8XKM3568upOfK5AEzFf/6Auil1vC8z1DHWLKZGy/Yz3MRkYL7f1ixPBe/5t/zNkvv8ZPW/H8T+oxRGzQCt2mBMgzbmgCzE46gvGFvF6zTwiPjIZaxn/RyU5yClzL3p/U9JspfiYWaeJegoK+SrFakORq2tDdLtyooCPM5d9gyjEEzE9bSU7VSqiaP/8W/7JnqAf6D3MZRd5lT26JqBN7sUMNe6lFtT1q1jK0ivqrK54n8FvWwA+v9v+x7v9h1/ff/MtI4BGOl2/6gCjSYF2G9aISRS3SFtb3E0wdjRn2xtG6wokwvAfXlsfN3o+/dBMRuJz0tsjE2+++pkI24OE462xDkmk5qvSBkZiGdRx4J6aTNprvZnxyao5CPcIzPGWlugvMKACUEJFgfAAfg4LaXU40rcZDT9C1Tv0eYLXYqqYv7+S1an9U74Ikjh33ZOMiTBkUQdKJPceNDbBK5otxL4rm7YRwdZDXbz081RMlHV4CeB4MA0MFQxPgTTsZwTxTiqsXmBFiFDgAj+kDIhdx3DzM3WdmCcJeRFxJg39EG5culCS2BL1PPWRMJwXZrJCNWgesTh857OTr+L6mb2J835DEAsc5buh+YIiRA1EUtxxcxvuR7e/7BqMd0z/uUMpgdIwxPtNJQd+HYyXfO4dCnLQy5/cGGUnogK6NaJ3lzzjS4DB4Tx7+o1MkbplNPJck6FTjLvNhcGMkz5MDrNWSUth3IWHUj92AJK9AC/HPWp/A+Eznh/GgC9CuZ/LeRL3EDff1SBzEG0iWQvc75tA/p6/bX3boh2ScjYsbyMgMHkn4DJcHMEqueS0sjsF6eIhIUMVa4naVJTgRcajeTvB1jefGA0HSjb+Eg6HGeFoVaYbax0sN0EMdj06kwXp0jzP570GN4T1x2qCLVO6n4yFJe+Pn5KcyprS92yZdeM5gFR4Wz7cEswrbyOd21MGo71p0j5uvGp46DuetezYo4QN0ZvyMvNCM2iHbhtCG9Id8fNaAjDSjVxZUAKX8vh/3mDoimNYEqwKzzeRnwmkHtECFvAC8yJK+vnu26+HH9fdjfe9gH1vHqLQbM+91I8xoQJKolIhm/Ew2rMahbL1QqFT1/VIoU/NCZuB1PJ7KjypXLxQ7XpjXUkX6lq5MBfzjDaNv+SgOO7b0hKynCdKGxP27oxtBRQIPOQb3WKMEyjMtiS83gh1NOsJ5KcpYN/M7CmIG60mkbtaKyt4oDuuFzO6ObO4RyOPYAG0k4p1UkspMK2I9zzcEeS3q4LgTfYOb3UWzgq5M6ctU0gUfZ9LqS2Xz7A+ZhIoE7lfa41/24HsQeOB5owXTyz3IJUnxOZYKBLgkok+pgq1RoxviCPEJQy2I28VBrHy+qNEPzgcfOCKiH2jyaUkyK4V+GGgml2uMEaGftQqtffzAkE9LFqvtOH8PAk02KZkvdwSBpjcBZZtQ1SlJ2kgwkJUqOE5aEdAdW+euassmJdOZxPoOVmKcg0jAUtaIOLNtYoxVxA4nXJQ5u2KKHTzaLqJukjF/YH6yIc1k9u6pAV9Z+j6kqdMxAXB9d8rjw4qykK7IUdhorQMjWSVdiSqhKVMm5490LgnRGsXm4zm6l6RJP2lJpuX4+hk8otUBP+7p65iuTMdxwLOsPKB7nI+jNNuG4rdvhPYZnezwX8nmo0sZe+ENI2fDtqFY4PpANtbcY/LpR7rdhOZmhV5Ppds0rSS290SooaaNRp//YJXMxHv5rLHSgTxumv0hF+Rv3MmmmGjmn32QgqM6BgCF2Fo4HgBq2ki6Xx1JuiByeBdegUTsKnd/Hg8l4z0SOXG0EwWCQISah7kc0t0h33aBjE9DGTF4kRys/bh33zum2ecjYEgOHx7MZwy7Hrt3mRdu5KgWLerTmYw04l5Eft6ALeKnp32s8H/xXEOgpzVgftBfP64fdn1/tb+7MIfGxVNqHzXtQMuJU99PxNoykRQ6P23xfxFifqtp7xfEiwPBuVygQ6vQa5lh1o+SZhYvDgKtOPqRrUe2Eo91cb+kqRMmiz3zzz6gi5R2O2Xz8Zz5xQPJ6U7ELGqgvVlxuFuRLQ4kpzvx5rqWMjCifrdfvJLN5PJBHgxOGNS9uyBMW8qHBVYHKGUcJU0eVGHaMrl6oLk+4fHdFW0Tszx7JF3uOWzmGOOzunigfZwRLSTRTTdyGOj2QvLyfINpIyaffhzFN9bZF63xiaYV6U9uQUssKcqSvHygv3mqAOQ9jKhvVxJqdLbDP9WYOx/MAAH48+ex+h1uTll8+lHGNPuMro1IAk1xd0L//pL55QPtQSp408nYpKlT4klNNK1YDp6IlKIe04Xc3p5zdXlLvZmNm9SH33zG8uyReFqhAk3tAppkQxaF+3R2IFvt0E3M/fsz6iZhlheCCa4z+j5kv58RBr1j7B87P7LqMiWOpQrrXQJj0yTs9lJVB4FmOpXuQBR3zBZ7TlizeVySuda/tYrru3NWSTOq+uWQ6NM2MWWVsZjvWF4+0NUCrlq9vGX/7Rt2dYbyBt5e3BBEvYtCDrn9cEkYaGaLPaFr4R9uTgnd3zk6DO7vT0n2U2bzPZOzDUGgnYpdRlbFzYrsZIefdJjm+Uju9s6SvHiQKte5T5pvzgkmFeHLAtIAe2McR38gmBQSRPNtInyNJiLSe6KzLVgP/SEluKyYnH9kaAfsLsI+LKhuV0R5TTARnYmpYoHaRBq7E7txdLXBy4TC1zwsRKM0L5n88h3t+1PK+wVRXpOeb1BZS7Iqn8LG1MD93/xEDqZxR7ieEeY1oVP5e7HB1pLn0T3Oadcz8jc3IsJb7gmtoltPaTYzZj99j5p21F9cEExqbCujhCDpqL+6GIFFXmDpd7mo82t5TsSLw4glHuqQWajH/48Wh4L5DTQfL7E6EC7K6oC/Elpg/ddT8k9K0QcZhSkTyQhIWqLVXlwB7xqC//OzXRJyXfwoLvijXv8FkJ9wVNr2VcLk4hG7jwVacyoQD/0+o71f4D3K/I7PS/A8opMdpkywu4j2bslgPaJFgb+ombm2me0Ckn9paP61ottM6dto9Fv7Uc/EzZjXv/2EbLUbuflhXtOuZ3ib6djiO/nZt2LLibW0oB02+DgvfPh4wemLW4EE3S9FkDQogqwhPtnTPs7IT7ejbz90f08U6Yr6cUEQCQXODzTZyQ7bB8xPN6Pda/P+krl+GCOC6/sFk1d3mDZCFylB1lC9P6c9COJ49vaaMGtITrcEZwXe1YTm30bElxtMEVN+fUm8OFDfrvAdYCT6ZY2ffcSLBF4y1NBvppiqx5/U+KfPQ3Vr6oRulxPNS8JZxfxMkhf9oCWdFdg+ID9fywO6ifn420+ZL7dsbk7puogw7EnzmnonOQ4vrm7IVjvavcy8exfqc8SUmjakb2KKw4QgEJtgfrJl8/Gc3ZdvaL/jBrjbLlnkJW9efuD29pzlcitiO+1T7KcOB2wZBstmuyD/DghI64DV+T3FYYK1Eg/8/uaSq9N7/EBohDd352RJzWR2ID/fCJWvD0mzitChgm8/XBJHPZ5nCXxDnLRU2yl1lVGUGd7HgZPFhsV0P+oYZqsNbS2wq9Vqw343+4P3vG2OugSFUoZ8Wkh3QlmirCE53xDNixEcU2+mTF/fSfb7evY/80n+MEutBuwWhk46T+aQOtKcZqg8+OlLlPpIoArsIUHlDfp+gp+2pMf5uLK01ysRj15tGRqoPr/EtJGMyuYFOFuuH3e0j3MXWWzHijY939DfzVCRJliWJOcb6Sz6RnQAbcjyl99ItV2kDEcR3aBkPj9rufyXf4Ot5HnkBQaVdnixxawzyt+fk5xsCWbVaFltbk6Y/OoDgwW7jen2ExZ//iXeMoQDNNspizf39HczPDUQn+zo97lU407rlFyuaW5WLpCopVtPMetM4ryvOnw6mt+cEF1sUFPD0Bm8qYf52sX1Lg/o7YRodUBlElJVfvmC7PWt5JRoxfyffs3QCfLYVjG6TJ515v/j+uNf33vzD8/29I/iP45DsZ6ZOka5+R2Iyj9wgRe42fRxDYNyMz3x1+oiIwDx3boWff8Xhm5zKht+IKmBejMbLXd+2Eurt0wJop7URcuaNhwFfio0LidA4kOP7Xg/EGHeMHjMl9vRFxuaRir+IpOqYiHdCeusNroLpW3q8uIHo7DWJ54VhEmL6UOp7EOZSx/nt1Hc0ewFWRzNS0wbUt8t5dDiy/ggnFZPX9fK/8cbZHZ5X2DqS6n8gXhxQMWCQFWReI5RoQSYuOXlHqETDQ69z3B4njnv2afviU/2MtKopTMR5fXY1bBa9BzDIO9XPinxIz1WrZ7nPNauCxO68Ym1PnHSkk5KqoPrkjjOAyAHAjfP7+tEOkZxK3N943+H7W/GKF/dS8DUMAj7f9AhWVoTBJqiTcav5/uG2B0ClBoIw5Yka8DFnitvwCrLLC+kvT4oaRd38vW1DlC9xQ978lzshNb9rNvtnNXJBt/XaOPTNAlJ0oz5BFHUEaWtXD99SH/MN2gaSdD03digTVDKkKU11rqN1sqYSVcJwaTCb2J811VRvkE5kNLxf59lWYFQHbkHxwx5P9QSf/tB0haPn7s5pPiTGlNIJr2/KDH75Ikj0voumEqJc2hWyQHfiYN1KY4fPCtfo3qKBB8GuZdQgyjhQf6sFyqe7Zz2JhXOAiD3iwN2mTIRmE7aM9QhXuz8+6EmcAdSz8V0H7VB9iCvVx9EnOulHhx6zDomzBqab89G378XCrwocJ1KrOcIp8fXLhqgbjMlDmSkQxqIZgLJ9EANmLuAxFXx/qzGPk7pvpyKRXp5wFQJto3cSFU6tt7SQ1ktLIZn9/kPz1D5/yj5+yHX9575e1Mk6jZvCKYlnmel3V+k6IP88kI5cQfnjYhe+sCBM4TnbhrpFBwz3XWZ0KxnmFr8udvfvx5xt+Dsfg5sYo0icLNl04XSPciPwBt3oHAz176K6TbTsVqXgJ9GZu1Rz+RS7H+2DVFRD4NHV6W0h1yyvZGwna6O6V3GwPH74EKH/LgnSoXnfvy+Rw9410ZEWY3VARwfYHlNW2Ro1361zhoYn+xGK5QfCzvAFCn99cQBOzKwimBRotKO8HRPcFJIlK82YIRihvZgkqImrah8rcJWz9PqTV484s9qd8gTdfTxPVGBHQ9dtg0ZrGJ6vh79/knaoHyh3AHjWOWox/B8QzwVpeJR2Hls1UdRJ2I8346Ux3xaMFvsSbOaKBRyYBT26D4kSRv6Xtj8fR8wDIqmi7DWcxu4YH+7Phz5+U2dYK207sOkJQ3lQGCMj3LfL3at+LZKHIdfOgtdGzEMHpPFniAWCqHyLWWdoZ0gUHkDxjkLQMSMidOGHK/7tovkdbXRH4j8jl/Dd2K5wUpnqnc+8kEfQ55k0zjCo5Rv8NPn8/kP7YAXgZcN0oEy/gj48QIw7xXUTwFHukilgtWy4XoZDNofhbemTOi3E9HCLA8Ei2JMgmTwsF0o196g6IuUfpc7UqIdK3KsJ/P4yDirrBUr7iCAr2BWEpwUci/F/Qjsqu+X4+FlsB62DAWapQb8rH2C4yhhJ4SLAlMmks3ROduyGTDrmH6fE6QN5cNC/kncPbmbnNYJNchz0TejLRRPQGq2DaVzYi0q7zD7DL3JwXp093M5pKyq8aBT367EATQT8iZWaJF+3mD2cjAhGkauwY/rH/b63pV/9/V0PCkPxhdLl/Meq0A2dF0l+HmD51tsG1K+d22yRUl4vqe7llhXox2VLLCO/25Racf8s49Enx1ofrPi+jef0bQxP/lnf01/yDBtRHr5yN37K2bxliDpaIuMxZ98jdlnNA8LDus5U28gXu1EXLOd0jcx08uHcU54nCmvv3hN28Ssru6JHHrW9AHFtxeyGdeMVerq5S19HZPMSonsdO2+cFoSTmoaR9sKQ3mIKN/QVSn5yVasiQ8L8l9+ILyu5WdpYsJpKZ2T2AmFnF4hnItOoFvPpA0YC7lMb6bElxuIBuGV73z8ixS8RqqkSOOFEXrtjSKoY0fmh16H378S7KoLQOnbiLZKpMsxrTBFSrOfkMwPxKdbgkXF4Tdv5NoZJKGPPiCMevzAYI1ifXtKEGjqKmP7sOL0xS3FZi4bl5LEvHI/lc03bfA8xWE/JckrSesrJZAncxChwXrcXV8IN78PqbuIKNR0OqBzM/zXFzc8bpZoHdArK+E52wVJLFkC1ijyrKKqUyIdjNCdNHuqKNPlXtT7jVP/Gx8Vd+weljRtTBhoThYbrm8uCZRhNpXPt6pT8qwiyRoOuylh0qKUYTo/MFeG25tLjPFHV8F0fiBJRfDq+4Yoawjd4Qh38C2d1fTInchWO/paumBBE/M8MU9gdxFq2uFNFd4ywb9tCd+UsgluhHE/3CnB7KYdXpUwNB59kWKamGizFW1O8HTob/c5k9e3+HnD0AVEEzkQ+rGIe9utHMgH7buNuCdYluM1dqSDHi104ckB2wbyuuQvwXICWjPcBpR3Mk7Lztc0NwJVSi4fWf/NZyx+8n6kgYbTClMm4yHCi/sxtXOwDd4+R98lcphxz7rJ+Ua4HWnPYBzp8fZEdB6TSpgBcTeOGFTco6YGWypsG+DXPdUXlzx8/ZI4abn4F78GkI7B3hf74b/KSbbb8T0ZjE94tZPyToPZJ+hvEvzTCi+R9++5rofj+lGk98e9vvfmD47h7aJphy4ge3UnaXKOY61WFrtWtJ/PGQbF5CcfRuSn2WdU90smr+6ITvYEeUN9t5SsgLwBo9h+8YpsIz7ZF7/6Pd1+Ms4wPWXBG8jyakyUm729xuwzio8SJzs7X/Obv/zHfPLmHfnphuxsw+7dJd0hk9fnTrx+3pAtDviFEViG9Sg2M0mW+/QD1vhYo4jSFj/qqdZzwrQRrLEWsuH2+ozpyZZoWhGmLYc7CQKK4o62iUmcMMnUEc0+J/j2hGY9I1ntSS7XtPcL/EmNrSP0Psc0EcnVo4goFQSLavQSS8VfQS1CPk9ZBm+g/0uDqaaEiwIvtNiv9vi55x6Ig+SUP8OS/IUYBoWKO5L5gf31GbqNaIuMMO7ks6lj6utTqt9IOqPnNtjzq3vpigCmDWkOudNPGKK4Ip5UxKs9u/sVh4MDAU0LpwEJaGtpty9O17S1TC6zSUnbJPR9QN8HKG9gNjvIGCbsmU6Ft75crZ+Qrt7Amzfv6LoIowM8b2CSCZnPWMX9zTmLxU5Ce4yo75O0Ickl28J0IbdfvSJ116Q1YgHsXQRsmjRkk4r/+He/5NPzGxEXuvZultYYE7DfzgjDni+//JRh8Ah9zWqxZblaUx4mrLcLqi5mOTmMIkOA+48XzOZ7OQRM/tAFE2gJK7J9QLLcj9qL51pq2mHWGaxFIR9+Bvt//YpoXhK/WVO/Oye5fETFEmwTssfWofPbi5U1udhg6ph+n9HuJ0xe3GOqRKpiz6LrhNk/+RpvHkCrqf/NkmhWkLx5wIug/NsrmdW7eHF/VtNdL57Q3/czgtWB/l02btq+LtAPEo6TzEq6MhXrXiepne39guUvvqF3QsXs1R1eYOjuF/Rliu0DssBIjG9ghVHw5hF7iOi30rmLlgd0lch9baG/nTP7yXuqD2dCujzbOh3UVDo2DvGrVjUqt5h1yPbffcr8z77i7Z/ejO95+uc77I34972rGebf3hJMJebb8wdsIyLsY5x5v8/kvcoV3lQx+7Ovn+16+HH9/Vjff/O3HnqfjTOvwSpR2PsWz9nsh3JwG1lM+rNbmi9PpXrNG8KLHep2JThfo2gf52Os5qClWln+7Fvax7kokx0P3rh0vv9Xe38Sa1uW3vWivzFmveaqdnn2KaLOzMgk7QT7Xuw0cOH5yXoXCwHCDRAdkOB1EBLS85MQ0KNDA4QQggYd9xCiZ2RZPFFIF+naeYF0Ac7KmREZ1Sl3tfaqZj3HGK/xjTV35DOgSHy2CV6sTwplZOx99ll7rTnnGOP7/v/fP4g7+m1GXaXig3Zi9YmPVrinp/TennNysBha8aaJyeZr0sM11gQ0VzPKm6nw8wMzBM30VUqcipXP1InwtE0gvn0/b15eHJGNKqL1GPN+xPT0WhTdq3zIco+iDh2KcMx2t/nfyaQcZoQ7vriOepoXh8Pbq/1D3SwktS84bCAPUZWRDVRhMIt04IpbP3IJxxUq6WRTgEVNAGtwtez276LikxX1syNsryXtsQ+HzgnI6cz1Ac1yQrmciN7CieZCBVYenCYYPrMwaSmLfNAEtEWKCgxZXg2n3bpKCQIr3Pq0IclqSr8xsEazXU84OLphu54QRh3xuKBeTgdgj9YWi0Q078SiOjBsVjOxEzpFoC29CSiLHK0NSew1JlFHNuoIow5rhf2wW+yDwJCNS5aXR1R1wjgvJMDHd5jK7YhJIr/DrpO0Wo9lzh91g9hwGAP4eOCbm7noCJRjnNSM84KiFAdDNqpI09pvNCKSJibJS6Af7s8QaMpU8uZ9HsedlYbguIRWYZsQV3TEs0Loe+8dy5iuC+lvPLMj6sU6nHQo3WKKlPieiIJ1aMmOluikEzStb5FXqzHx+6ckZ9eoHOLpVq6zJsBWMv6wbTgsxMrzPXR82+LepQACniIq91x0tJaZ+tMT+rVYMXcsgd3iuRtjRQctUbcdNEE6bYmTDlMlntYn7Xod9Tjt6NYjyUDxSn2Abjm+ZYl0IfmPvaD61pE8IyaiS3CN37h7l5ItIlRlPTfA0T8WQqHSwHqLKUVAiVXDZHyHLlahId5xEQorB6nfg87/Xu3/6a5PvPjvIiedATygQpjz8pBTiQh1ZM6rUCG06zHxpBzmjcmsQEUGW8W0RTZgMXeivOyNc9qbiSwiVlP7RWL3ADf+obqzM5kqFl+wcmhtUNoyOVh5Gp8P0chEaGNLWcx7Hz6TTAuCpBX1fR2TjGrQokVQoZEHlvPAD2Wp6nSwC27WE8b3rmk3o8GVsKsgNGTjQmxu/iGw0y2owGD9iUxHvXQkAjkxBGlDtxrfMg+qDj1R8v5agRbtHpj4eWBXZMRHEoeqNH5m7iBSKGuHGfDLLp1IUNIAKgmMsBYC8SDvwp6MPwGHSStgEh/utPscJHrXkIQ9XRcyieR9atsYVViy6RYVClRqs574AB4zBNzs4D3WitjP9MEQAORMQNdFwyjGKUUQ9sMJX2lL18QDFAig8XTAqpZ2feLxwoEXy5k+pO9vbxmlrdcwmEFbkMTt4BSwVpIADyZr2SzYgCDoCf098/FFPwpkJh4EIh7c/T1R1BFHHaOxX/z7kMTrX1bLGWnSeg1CMORagICYduRJ20SDLuVOyoJKweFQnSwsQdrQ91ra+pNSrgmPwN2NtlRoBu+6Sn3YjJK5/QDJgaHF3qxycRf1W6JpKZteAKuGrA0QD71tInRobxdvK3kXzmnZUJhAWuCRkesZCdrZsUmCQLQCu4Uc5JoS5J8kCEYwnOidFXSv8y6BwP/ZbjNCTUtcLfonHff0RTYkCaqoh6ODW0FgJmMEV2kRUcZyaLA+wU9FPTpv6dZi+3W1hdZ6roG3M/eBLPzeni3/bjDrFNUa+W9qL577rNcnX/ydJr63BAv9YkJXJvQbPUSSJmfXXhQjbbbqu6ci3ovltLv5zivC+17JzacDw+bp6SDwi5IWs5TZvgoNaENVjBhPtj/wkDx6cCECrDZCBZryo3uCz51uicYVm6endE0sYry0YXt5KC38WUF2eiMkPY/dNV7Nb40mHksQR7XO6bsIs3MJBJp4VJOPSrK8IhmXhElL4eNHnQnYrCfESUNTpaR5xfjBFdtnx34DIu3ubjOiK1O6JpZ5nBe+7VK5nNNcvfMq9370HXTWUn7/PiOey5gh61CzEL1sRChl5WS3+vABI3MpGF8NzdNDErtAT0ShrO9I5GW2CV2ZDtdFNCvozw+H7sbqySljr47OtaVvI7omZnywlpFBE9FfHnroTkCxmtD1Ecm0AKvpbyJsH5IcXki0ajmnaROsldm3Uo6b82PySUFVZARBz+m9FRfnp4Ak9vWLuaj7B2GdYX4swkPThdTFiKubQ6xTw+L8bHHM8WQ9LLxx3DI5XNGWKdfXR5yv5syzgsO52AeDEPLjG5YXRxi/CV1tJpI70MaDsO/45Jr1cooxmjBy3H/lKaaN2K4nbIsxs9mKLK19emHEkxdnvHL/OV0ri1WSVcKXGBf0XTRcM20bc3CwZP76U+rrOVFeYepYYn2VY/Lac4my3owGuNBdlKsC7DqEwKLzDpUp3LnE8cYnK4IHjvZ7Iy+Y7UV8d7LGbOW0HN1bQyiCuJ3obtcV1JF0LmapbCD7rcTZZq9eoI8BraHtSMJrjO+E4UTdHx8vCQ5aXAtukdOtRxLMtRnRFhnJg4WQBdfyM4NRLZsCjx53XUj59IT89eeoVBC/dqWFFpi1JA8W3tInMB3V+03EcY2tPN2wa2UU4UejwbREb0fyv5MOlSjMtywwEgppEROe1nTPcsJ5icpEve+sFnBP3qFyRcolZjnCtqHorELjw5U01Ipg6nUpO1hZp2ku50TTknBaoCZ3eyp3vPxI3/125eXWJ1780zcXmGs/UwsN9XrM7M2n6IlYlggddhPL7ta3+m0nCl6dtYzffIa5jNl8/yFdlZCMK/JHzyQZbzuiWeX0ZSqRtUqCgI4fnDO6f035/IimEG514WEvYdKSThtunt7DOQlA0Z5+1daJzN8nJcd/4Hv0y3ygaSUnS7ic44ymrTOWl0ccnl1QrybCAugD8gNZDNoqofUnpjgRJXhVZKSjmtkrL9g8PaHcjoiThqM3nlDfTGnLjKt3XuXg4fntm6ec/L0XB5heTsNB3NHXieSFz7foUcPs7IpuNSZoGu9RRvQSTYQq+ls19/0pwesp0+U54cMGt1L0yxEqtNgiBS1tZlvdjdrf7KxW+FOWjchOboZxUH68HDznVjnmbz0hvRSxp/Un8/G9a6JpgWliCi+wqlcTlJKW/NXlEVWRScenlxP0eFTS1MnA4+/7kCCwRD45bzZb+/GAIYo7ymIkVL64I4rbwbURhIYgMJweX9HUybAJGSc1xov8jAm4ujkcxJ6z2YrZbCUL/GrKdLohSWuef/CIxWbKfLxhPt1Q1QlFlQnv34N/mjphdrCiqRM2mzFX14dMchk5RaFY8sLAEGcVSjuqMqNrI4oyJ4kb8pkshHWV0vUhWdoQqp433v4+yeGacFKRhZbqXMZIQdyRHK0IJhW2kTjtyYO7w7nqBxr6DnsF5Tu34JlwJFoe80TU9Wab0a1zqs0xk6++IDxocRtLdz6VyORpSRzdyO+QtcRf6eG6pHrnlOTBFdt3H8rGRlv080N4pkXVDpTPTsQB5EdszioRtU2eiBMhFD9/s5xI4FFecf0bn+fw97/r0d166EA2Pj0vHHlUuFO4OqS9movCf1p6ZHmKPU+G615pi846uhcy+rRNRLsZYfqQ+Rc+QsW96AXKhO6DsyHuO314RfLGElcquusJ5//HW5z+b98asjni19bw6Az33pr+Iqf/ICP74hX9lYj9cIrwoBCyZ2NxlWLz3UfEk5Lk1WvUQYS7MOR/dIt7usUW8YAUvrty2Jcu+Nsv/y+zPvnJv5X24U44dPCj36e7mora9bjHXMiJfae47a4n6EjEMIHVREcbzDYjnpSkxyuSBwvqj45J7i+kla2kdRWOS7Ew+db45vE96Q6EvWBjk1Zm9qGhLSQDfX52OWRkZwcSFayU5Jur0MjsL+qBgO0H90Xkl9XSoh+Vw2gh8GCWzWJG5tXFO458klf0q5C6STEmxH14nzBpOTi7Ip6UFBeHQu3ShmzSUl7PiLKGMG0Fs3ozRUeGzAuwoqM17cWBEBGTTt6b6VbsTp7x3S8mhPOtUABbCeyIwgLWW1yxpl68QVJdY9YS4xlkLcFhiS0iXBfc2clfaYErOXP78Gk3I4KoJ0hboklJ8eLIjzS6YVFySmazbZER5xVBK2FLdZERJy1RVtNVKZv1hKpOOTy8EbhO6+2R/hQfJy1HaUPXhcRpgzMB2+UU5+S01fdi65tMNywWB9Q7bUHUsdmMJd3PMwPGky3r1ZSiEivUKKu8199yNL9hfXE0zOoBNhuf8hcYDyzquX986f35obTos2oIFOp3OGG/ccvSmpPTSy7OT4kjIQ82VUZZZeLtTxpm8xUXFydUvtvRVQlBLOOCYUwQd0P2RbzNiCYFfRuRTrdEs0I0M+cH8rnEHdkbV3dyLcgbG9B8M8d2Ej3bLSZDtny3zolmW1Ri0LYhUo4k6aDoJeu+0dRXM6+HkVFiMBLmfvT0AlBEsy2uichOb2Ru34V0RSaYaY/Hnrz9EbZI6VZjukLyA+KDDeU7Z3K/zbdEB2vc9WxoseenC9rLuYwa+4DxK+fSiq9ilNYEeU2ev5DnXiXx1ckDAZq5SlDAtguJ51sZd/YBxI5uNR4svfGkJBzVAvZBLM/NOifOa4K4Fnpg3mFuIjCygRgdrsRB4U/y9UcnZNtz+psxfSFR2cxGgy7CVAn9djRgj10vqYrN9z3uuO2h19j3Ksx2LGOL/O6sn/v6n6M++eJfiX1kx6SW2Zb1an6HintcG8oN4BS2k4hd00S4XkPsBqFbOJIEOuXFKyQdQdZ6HYEkYRE6XK9p1vnAf9eBpVqPRbTl6XsgJx0V2iEsKD9ZYppIRHubTKAzoQW/E42yemj/7lrvwDBnDLTFtLKAKOXom5jEbwa6Phw84lHaClr3cMPiozO0coSJJLG1/vXtUg67TU6QtIR5RTip0EkvvATflgtGNd1KEMkEduANEDqZW1oPLLJAZ3GNgI9c49uLmRcohdItcEYTTOvf9QXyX6puM5KUtk4Wch1IlK9w7IXu2GxHhElLHBi6KpUUu93Yw9she6/mjqJ+SFi0Rlrlk7yQtnknrIDZZIPZ6T+0fI5pXvm8AOUX/B1TQsJzrBOdSt/7RL+8oG4StLYkiP89THwksJIcsijq6PuQMOxJ0obtZixCw0DEdGkiFMndhtFaRTZq6LoIY7XkGYSG1IN4is2Yrg/p+pDAawRGRys4P/WQI0MQ9MRxS9vGtF1M34deMyCvu9zm5LONiBATIQYGUSeAKR+bvPtZOy3MLkJbwqkctrg7vC+rmr6cAzJbD/IanQkD3/UC3FEpaCQ4SyUWuxFHimuigW8gATsOFbawzehvcnTaovMas8mG+T07foH39Ou4R2UWt5GAnsAqnGcdmErGbK4LMU0sjp1ADSE9km9v0YG/dgI7AKqGMB4/qtuJcl0VCSzIh/a4LsQ28ru6RjgkWC33u0/zM6vEx5Q3A9QsyFqCUY1KwTz3YxnPLLF1jM68BmYzIvYjEhValOpBa3RWEfjOIHALGdIOV3v7YRvgNp04hzbZ4AS60+vB18tu++/r5dYnlgCbbTb8e1/H3Hzn9SFMons6Q58Kwa98csr2g/sCmJiWPtXPojI1wC36MqV9OhXiVhUJccrbXGwXog8t+vOHxEdrsoMNcV4R5RXZ2TWb1YTF+TGbxdwvBJZmPaZd5bLgdyHpaxckx0vJRb88GMhiANm9BfnrL0Rwox299+P2TUxTpZg+ZHxyI+35XmAlWy8228Fe0qwWlnpWD8piawI5mVqxPmazrX8YC7Grb29FVyrqsWUsHmZvHwxeV7Sr3MccW5rrKeF8O5w4d8x059dznVuykxtcGxBMK5IHC4JxNWQvuD64s0jfzfkR9XJMtZywvZkJ5U7bodW7U1gL21/CZ+KjFV0TU21ysVCmLdXNlKZMyY9v6PuQpsjo+5A0q3nwuQ8pNmNububUVcrpK88lKS8wWBuwWXn7pRXi4k41b6wmCCxx2lBuc0/3k81BNtvSmlshnfYRuJPphvlsRRK3w9fCSDabvZGRQ12n9F3E0YMLtkVO57sRTRt7MNCt8FSQ1D1aG8oqo+sirNVDuh9AFIrAr28jRuOC43uXftygWSwOODy64fDwhiyt2Ra5tNJDcUDkR0uCqCceVcI0MJr1xSFBLEE+2+fHbJ4few2GZFbcfOf1u7kYgPbDGdFUhL31syMvXBMwzW4eTaRQqQiD6RX9cky/mGCqhOR4KdjecSUM/cASjiu6dY4p0+Ha78uUdjkWv31eESSdLJ5Jh1mkrD94QJC0pI8uSU5v0HFP5v/dtiGrD+9Lt9CLbtubCdHBhvyVC0YPLmVz7dQQAGSqhPLZCaaKB6BPv5SwIVMnhNOCIK9pLufUVzOa5YTuYiqs/3FFNCvQWSPJgV7ICDA6XBH5joDOOlDyTNwdlKrl9HZzEVkiv4EI8ppwXBJOKihr9MwQTMtBc2W7ENvGmDph+ZtvotMW10T012PhB2StHC6sovXgoX19duuH8vl365Ekhs0Kpj/xhO6DnPpqjmki4i8WhMfi/258kpXrJM4WA24F6auXVO/fwxQhyXxDs5rRbHKBXcy2BFlDtx7RfzNDfdthmhOUtkOL+eo7bzCdS1s/TFqywzXFasLV81OyUcXsZIFrI+oPT0WQ2EbkPjBI+Xnz9sURk4cXdBtpU05PF4An7qWSHLhjzoeJYFfXyyldEzM5XDI5XA5pg84q7GWIeU/gG2HSEqYN0UhOh0na0BUZ2xeHHH75fZrLufxdVSJBIgjVT4UG+6SX37MTtXD26FLEfqcOdwPle2fouMMsRwRhiTpKiX9fCdbhrsFuE1TSSZKfRx27+m7Y/sm4lPTFrCGZCFSnuplSXBxQXs+Yf+EjTv7A98SOWSXUFwds3n8gM9mkZXlxhLo85ODBBaaNePq9Nzg4WojKX1uyUUWQNhyeXg3I25sXxxzdv6CvY5o6JYpbnrz7OpPJltgr3pPUcxnWY168uMfBbM0025B6wVxbyMw8CnuitGG7nBJGHZHnOURJO8xuA21lhJPKeKgsRlzeHLAtRpycXPnQoAmX6zmjOmMyKkgTsYuGYc/6ek7bxmhtiEI7nOzXmwmR16n0JmC1nlKUIw4OlqRZLe4Ep7i8OGE6XTMaF6hixNXlEbP5Cq0NjRelzh5eCLVOOerLg2GckkwLmrVc39GsQMcd1ersTq4FgPi1Ff2LkYztjtb+RK8xSxlHJac39O9JMp7SjuT1a8LDDdt3H1IuJNhr+uhc9ESdjIJ2Ebu2iegXudjVTpb0yzHdSlro3TbDNBHhuCJ6UKC+Lyd1VcW4PiB8UNC8Pxcs9htL5kCzmH3MktoSPOixV2A3gh5HOaGW+vtzdN+PS5QTB9LFAfFsK+yEIh2IfVFeoULL5ukpB1/6QFwAiEVvxwRIDtckX1yTzMC+v/ZvnqP9YCrd0NCgk46Tn/gOzZNj7DYhOGzIf+wFm197KPbDWCiBPCsJZqLr2Tw9IfXPo/RwLYeGwMj7qKVz2F+OCU+2dFt5LfHR6s6uh11Zt4f8fJrrEy/+4b3t4M91VlF/94BoWjB6eCmn29bgqpj49Ib0jQvULKT7fkLy6gKVa+gcbqMk8rfz+dVxRzIV248zAeXzY7o6Jj9Z3oYBdSHdJse0kVi/tCOelCjfNh5Nt2RjaZ87Lzasl2PJeR+XbP3sOZluCbKW0dGKZjkRDkAbYm3A1IvzdnakAeerHDbuOH5wjlKO1qN7dzYxHViyccHBqy9oV7kIeLwt7PrDB5y+/b7wtwNL/eKQMK+GCNFuM2L89hNsISLKcNyQ3LtBHxiINPQOu3a4lYiR0rOF6Ctyiy00/XcCgllL8/SQaL5FZy390vvedyMD5biLUN/5//Ie3dPZwEToioxkXPrsg4Dy6QlRXtFX6TD7nP2+D+iupnSbnNRs6KpUNlBOcXh6JbG/gR3ClGwnbfww6sQq2IfcnJ+QeC5/VWacnF4y9g/n9dN7tH62HyctJ8fXXF0fkiYNXRsTlkLse3B8ibWa9eKAycGSYjWhaZJBra+Vo2lj8e+nDaG3H2ajSjgP/vUslzM2Zc5bjx5TFiOxoPruUF2l1I10edKkZTJbUXgmQRBYwrDHOiU+/7zAWk2SV3R1Mowujo+vRNXvFFlecpBLRHBXx1TenrjTskTzLZOfeMb26/eHeXZ2tBL4U9IRZBF5dXcxLrbQsnDpW2tdfzUZHC5xJ44X6QIaVAzld0/Bi0OT+Ybo4Yr+wi/yr1Q035v5hb0nmFS0l3OSeT0cELYvjph/7olgvJ8f028zkumWZjEjKDKS4xXrX3+N9HiJbSI2//kROpREyCA06MigAkP77ljsqnFP9GaJu3G4652byBAebenOZ2IB9KMEYXXIRibIGhkTmACcJj9dSIZJKFbBfpkzeniJHrUQO+jAfLsiOLYQKFwDWEX8ygq7CTDbjOi0IRx7kiAQvGZJDteyKSkyglFDlPTYQhNMKo7+wDs05wfDGIHQCVEwb7HbhH6T0W1y9KgZxjA7W+C+Prv1yU/+rTDcweN9IxGm2SaSeVdTy65by6RH93JD2FUMG4dZT4hO1iLo82FAug/ot5nQsdKGrkyI0lbEfyAEvNV4YHaHWU1XyPhh18aP84quTLFG01YJMXiCmw8b+phH13kwRxD1EsRTpmxv5MZ2fTAECI3na7o6IYg7Ah87uwtw2RHWmkra/1HS4rqQrkoJQgntsEaT5qVHfspJsvU3rUIY3oL0vX17bSUUxHiygkSD61GBzEqpLQ5QgcP1sts3pZD/TJ0QdjUkEq0c5vXQXryr8A6Vef6AsrLpMoH4+dOGYFYMm49daM5Oe7FTUzsTDJ+bJDPWmC4kiivppnTixQ89O0ApR3EzpetCRrk4PSgzoqyR2XYnPzdJG8E3WxHoJf60vZuhR2E/tN/pJb9BKSfWwD7EWk2W1t5aqr01j+F3cU5RlSOCJsZaTehb/NYpgcY4NaTvBR6F3XUhhQ8pimPRF9RVSuTTCZNRRVNmEsKjjdcRyHWocIRaFqooq2WOHPVk40KQz21E6E+xdLdtZUAsb4EdNDWRz0u4i3JtMJzUVSB+fWtEw6EDi2niW698H2A3mnY7EkGsz52nVX5230EsPvxwJAwFmVVr0bsElnBUE/nNzLAY1wnJ0QpT+bm8VTJ+yeW55PqAYCJjyEHbM6qFIRBb3w6X+2X3rMEpsdN10oIPspYga+n96Rnn0GlL6A8NkmmgpevpgULtekw4qQTy04FdhtLSv3aeluqtvi23XI4klBGe1ZJNUFjC6S3/YHfQaS+FYBg+rEi4ud30W8Sh0HhtRNzL9eB1Dsq6u4U+ySdwBzP//YblZdYnXvz7m3xQ2touJL4vVjRTpDTLCdGRtNe7VTac9tLDNeWTU+p1Tt/EnN37xg+qTHsJ0wiSlnBeEn8MRGKqhOhkDTdKBH+pzNza9ZiuTGQBTzqhzBlJNtuFxcRZ4zPfY5JpMQB7ujKRDsLRyuN+jcysq5Sujj1voGZ6ckPx4ogg7ognJavHZ7IYJS1JLK6Cpk5IJwXRqKa8nlGux6SZfK3vIo4+/5FwAPwNbT0hTKV2mN+5Rg9gkh2meDauCGglhSt1cDBC3ZSY81Qe5p0IG0FgO+GoHjzJOC0QEG/Du6sAD7cRyJIKb2/uepOTx52Q0oweglmCpKNaTFl/8GCA87RFyuh4OWRF6MBS3UyGjVazHZF4Rv8OkNR7oWUQd0RpSxR3YotbTEWcaRXZXLpIfRtJR2e64fLqiL6Xzz+JO1abnCTuCIOGzXoiJ/q0RitHUWVEH3vPAg8tUoFESzdNwmIzZRQL+388Klks58RRTxT2GBNQ1SlaO8a58CnWmwmrYsz90wsZA3UR223OeFyQ5iVR2g7EQLSkC1orKGEZY/TyvnkxZJi2pAdrTJ1QrcbDxrV/NhoWNdfLSdx1AS42qMDJeOCOynUBKhchn4oB60OqrBo23fiFyzkZB+wS+3b43fZ8RnxvhRo7KOR9jw83soAupuhYaH5KW8JJRQo4owmnJeG0FO/9vCSYl/JcKVLivEJPG1TXEZdbST0cN5h1RrceCUtfWx/8A3Yhmp0gF92KaWLK50eEaSublKxB5dJO321kVdyjs8YTBiNMG5C+UmFLIXC2RUZSJZjFZPgzyenNEOEbjmXUZ5aj2wXZWvSBhcAK3GsRQOgIH5TSLagcdhVTXc5JpgXhKxX61Qj3gaeB9gH9JpPkwkgSQANKCTvyh5XdM2Rfn936IQh/HjJhNP12RHcxlRNaaEmPV0PL3VlFV0naWf76CxLtBqxt+3xOdLjB9QH1i0OcCcgeXmK2GctvvMHsix/KWMFoaUtp2Dw/FuZ7LsE825sph689Iz6SmVnxwf3hhGhaOTE2W7E3RZmcgnctZB1YooM1z779OWIfBWutplqNvS1PrHn1YkrfRrdkPuWY3L8aTqVDOlkbEaYt+dk1k4cXt/PHRwWb33xEdm9BdG9F/MYK+5uPUIHM9IJJJRCeswj7rIM2JDpZM9s9CG4gOGohC8HKAwDtsGVCdH+Ls410T44C0vE1dqNxVUR0sMZWHvvp3Rd3UXabEIxqwli6E6ZMcKsx14/P4PEZ9770PhfvvCbxvNMtyXRLux0NmObpqy+4evdVLxR0g1gvGlcERtDMF4/vMxoXpHlFnFccPLjg4oNH/pRsOXr9Gd1WTszEotI2fTC4DuJRRefZAHHcEiWtoH8DgzGapk2Io5ayGIkCP5eT8WI142h+I0LSJmGUF8IWaGOaNmY6KggDQxT1WL/YjvMtk0NhANjnAhpq23gg9t2bXZGNC5oqZbvNefjWhyxfnLC4OKY3AeNxQTbdSpJg0jLSjnI5odjk9DczAA7vXRHnFa4PKC4OyU+WjA5XmDaifH7E5O0n1O89lPsktCy+9QaHP/Ie9Eri6j9G/3vZ1V7P0FvZ+AUPHOaJJnqzJLIFbiUHBz1qhs2eSgzz8fu3ABpvIVb3EwhD7HsV8dFa6JpRT3K8orma0ZwfSGcrFeGaCNgkLyA7vcZtoHj3AQD5F57S3kxwnUZnluTVa5onhwTTGp3XhEZTfHBGPPNhXVk3RP/qpAMfv9ttRIVfX82ov/+IyYMLovlWkvKqhMtf+9JAotSBIT1eSqaGEnJhGHfEr28ov3mK0o7s7Qu6p2PSB9fCQPAoa53Xg2W3+fqM8R+7wX7Q0K9z4jc3LH/1dcavPyc8rlEh1M+PiL1NuH+coSc1wYGFSGig7WVI8sdSKLZw2WIWiWwmkGe5Ke9uDLSrfbDPp7s+8epguxC2IipxXUg42xIdr3FNJOCOD05JjtYkJ0vJk66TAUNrez0gfpvzA1RoiedbqhdHFB/dEx/y0Yrr3/ock/tXmDoWLj8we/U2zMJZxfR0Ia27LhQbj1MkMznV9HXM9eMzHvz+70kWQZkSjiu2HiKjtMMsZhyeXQwWv66Oh06BDmT8YLtArH1WoCDpbIPtAkwT01YJ1TZnNNmSzTfouKO9mZAeL+Uk7BR2pYm9lsGsU1jKic22Eaa2MvcMLPZZh1mPhAveeVKXP3maawEmqaTFtbG3JIFZReisQ+crqv98LB9iXhOMK4IDg72R9+auAD8gbHJBO0sr0pqAgzefDIyC8KzmpAtFXGQ0yiN1A99+ju8viT86I/GUQ4BqO/oB1PO9V5+JP7rIqG6mYt2zimxSkE63bJ4fD6LMIJQxjgoM1gSU6zGbzZiTswuiuKP3NsLZfMXUKbabMU2b0PvwnbpKabuYpo2wTnlMb0ea1bRtPGCC47hltZ4OroAw7Hlw/4V4+f3DNBtVbLdekNZF1F1MEPS3mwFt6arUR7o6Am3ZbvPhpK/97+CckqhfP27YjTaCpCUJZV69y2XXgcGsM8JYkhbDccVYWQ/4kXjb9nrGXV0ROur9jLmDBnQO7btTf9IUqp3rgyFoqn0+J3ltKSOsdULx4RnRpKD9rQSdNejMUT09IXvlYoj43VHunNVig0s6gpkAdfpNTjiRcZNYTwXAkxytsZsUs9SYKmHz/HgQ1jkrlrydtsgVKfGba+xSWvY66QkfGEbROUpbbCWY4nAq0doqkORMZxVB0hIfrgkmFSpwdM9noCVGPDrY4BpH9toFZp1RfPM+QdqKpTCviaYNdiMCRduGIjzOK+yTGpVbonyNXWgmbz6jPj+gX3ekX1qQPrzCVrFA1KYN6s1T7HevxMJnNdG0hA24yxJzNRo6QcG8JgxrbLG9o6tBygHW7Ql/n+b6xIu/jnrfTpQLst+OCPzJRweGYL5F57XM/ADdtMOfQ7tBwGLbCFppTeqoJzlayU3UB6TTQlS0TTScrk0tAkMd9cQnS2wby8OxUthWuPpB2ki7O24ZjUUMaPtIlOEfnski4VvIkVfX7zYxfRMT+hbyrgX4ccGZzKQFtmF6CXVJ81LGDiMR+3QbsRnKPFHTLyaDUtn6tufHtQ6AgHH8zC6+f4PORMi3a9vtgErRbHs7GmhiaWsicBQVWqKDzfC6XYv8bx/cdk/uoIqLQ1lo0gYdd+iwl42PHz+0H05pN3LSV0peT3q4lra0VfJgnhbSWXESR5vmlTgwjMa0spiOkO4KQH56M8B+TBMTxJ0f7QhCOQh7uloWwDSr6LuQ7XI6aDR2QjwAazXWKoyJiKOOKOo8kCegamLKKkMrR5JVwiXw10EY9SL4C/vBgrnbGHycM9B4sZ9SjiQUv74Kb8cJu6yK3eJeV+ltXK+2TI5vWJ0ff0wUKEFUpolwkSIaNTKvhmHD5IxsOJXPh8cz7HdaC1PeHds/PlnKe9xoXCc++GBcYdYjmuspadSLZc9ngYgPXdHf5PTbEdGkGMSAEo4TkT28lHm3lRROlRjZ/C7kdAyQRldexxPQXM0lPjptwHr7bC5x12hLoBy5t/8qo3G9bMh38bwA9TsiYjZVgtlC2BW4JoGoR+c1ybSiPZdOjPb3ejIu5T77mM13pzPAE/8AiRgOjaCwt5lYfJsI11vMNpORgnbD5s0sBQGsMjtog4JE/Pr2Sg8bP9cHmGVK+OwC10aYQtgZydlCwD6biL5Mie8tRUrVK2yjMJuMfX226xMv/rubWUc9waF4W91OAZs1BPNyCJgB0NrP6EKD1k7U6J4vbk2Aq7XsjF/ZinbgOiY9vk3zUq08KLoyGaw0emLg3ApMw4VYD2/RuweHCUhnG3kgtMI43yxmTA5XYufyrTxTJkKb68BYUZeHQedhOoq+jof0t9D7qPHCNYDRwVoWoETaj0EqwjO87add5UIjsxo6he31D7Q9ndW4LpD54rxE3UshjuEDoflhZePgvNAIfJJinaAii+s0thCtRHDUCQa4BlcEPrRED7S0u6hd0iJAHBmicSWfSS9xtdXlnL6OCeJe3hurCacF/XaEKROxhs4KOaF5e1c6q6lXEz9jt6yvpyLsw+NqT29Ib8RD3bcRUdJijZa8ehv4xT8hzivSRD7LZ48fSgxuJGOhpkk8+MePbUyAtVqEm7Es0ssqp7eBAIaspqpTAm19qFA/+PN3VZUZ4bQfUv6UdvJnkXjeLK3pTUAU3W5CdGCG16SUjCvGByu6KkEFluz0huJ6jnXyXuy6SPXNFKwektp2FD3rJNQlyISNb2sJdrFdiC2ly7CLMb6LUpkRqmQTiDahC4lfWWOrnmY9Jjlaizred4q0t6Q21yK2Hb1yLqftWE7Stg2JPt9iPZFYJUaEr1mIKkSw1xUZycfm1l2VkMW3yZlqF2ltLTqyqHEjoTqbDEKPD2YrmiPfDVi//5D5G08FzdsFogvwMJ4w3qImYJ+EqEZO12hLdrQaOpW7TUAwrm61vBpJO/SduOhoIy13JbbEnQh595mGoRmopKqwaDyqu4mG7IF2hx8eV35jl8q4Im1F0BdIyBmbDrOdeuuk1w8UscT9Fr8Xbf895OfTXJ988X/gcF0zxFbubmgJ2xABT3RvjW30kGCFch6LKeQ9Cc9oiHxiVni4xZUSOmH9+CB77YKw8ela2iN3tZOH6k1Mt8mJxhU6bglSR3F5SGav6BYTrt57ReJ+8QjaUc29kxs5DXvAkLJiN4wONgAkl7Ph5KQ8XWuzmAtRLTC0lcyfH335nWHB0ZEhmiylRR9YskeX8vpvpjSrnLbMmPzoY9QkFAvkhoGqpTNJ2uovYpL/xwjVR3B5RfvNGOyE8FhO8n2Zir0vFMGWTnqhjS3Tj2V05zT/acbojefoY+jezWkWU5L5hiCvhxnfy67T//u3ad+fyUnmoWze6g8OiaYFwcNOgD6++4F2mDKlX+dU1wIESsYl8Xwr8c1eWxHPpcOx+2ycU2TzzW135WYyhCFZo6lXMo4Jd7jbUS2bShOgdE8yKbl3dsHKz8yTtCHNKpo6pW0SOm0JAkMct/RdSBj1HN+/YFWMOTlYoJRjuZzzeHHEm6cvBo5DEFhW6+lgDUyThuvrI6bT9ZD/cHJ8TVXKySpJGkahPIzjuCUZVxJskzWU6zHbbU5Rjbj39gckJqDbZizffST21LQhGtWSaLecMDpdyALZB1TPjyVJMeqJxyXRWcHN198U3cCkID7cUL84pF6PCeOO8evP7+RaALj6v77IwdsfobNGWvSvrbErmSsHUY+e1ETeBqhCg60STJmSnl1L9PPlnORoTbcWKFM4LXHXneRU+EW2f5KjAkN0vCb/0ReYy5jg98/hGze0qzHpwZry6Qk6FBGszmtcHdKtxh6MU1O9K+Cj7K1z+MJDwusr+u+r4X6a3L+iPD8kPVxL0JfVwhRwQk1U547sS9fYpZycXZOQfvmG9b9/iFmFBJWM6rqnJ4xfPZeYYyuMjtg/b8r356QPrtGTDleItS/9YkH/fsyusV08PiV/VbgHrT9k6biV/IFeD3jjaFqCs5KhMmrQk47giylulmO/vqL45qkAk0YiLu6X3jmVdKRHd9v239envz65IkzL/M5ajVll4BTRqczU2psJwbii/uBY5s/zEhVZaUdNC3Teok8Dth9lcvpOxRakH8RQNpinMdX5Idm9Bd3llL5MsX1I9ex4+OtNG9FezWnKlLZKSCYl2b2FCLs8SzvNKkYH64GrH+UVzXLC+uqAky98SPpoQf3BMVjF6rdfo6sTQaZOC9oio/OhMfN718Iy2OSURc7ByTX1zZQgNIwOV4STYjiluOb2FKyinmRWEE9Kyu+ekZws0b5t119OCLJGksw8apT/8wY9agCh8iUPFvRLASllr1/In01E3WsWKeG0RGed2IacInvrXGAqm4zmWU55PWP6+rMBrnNXJ//u8VhoihufKTDys+omwr4XDR2eejmmq1LG965xNiQ9WA+nv/WH94mSWzyyWC0DKBOUshKxrBymin23QNrvrY8Bnr32jOUHDyVYKW3ITpbMzhbc/PZrVFcHWKMZjQviuB3SGJ+89ypHRwvyubAXqtWYxeLglra3mDEfb8gnMpLI8oq+D5jORFxaeq9+HLdYGxCFHQdHN3RdyHo1JdCWs9eecvX0jFFeDqOGrgiZzdYUmzHPn90n8vx/YwT5ezhbcf3+Q4krTlqyg80gVFWBGWKhTZ0M90ZyuBYmPghpMw+Zfu4J3c0EU8d06xxrAkbHEpRTPTvmrhr/Yy+G7dc5/TYjPC4wm4wga8inBSqyVM+PSU9uCGetJAD6CFxANvN5LVhef5K19cdGXFVEdT0jCEVnEmo5sevvXeKaTBY3EzD50Sdimeu0iF21I31zgSsV7dMp2YMraekvRqhfvxxEb7aT1xNOCtLXpN3gOj0gtsV6Kotm/9QnAOY14XFB996YZL4RkaBTbD46E3R4FaM2HXrSEc0KGVUoRzQtcU1Eu8zF4pw1uJuebj0XYmFeS1ds1mIv5O9PX7nErDNPC5WxoIxTrWwqvcMGCyw3qPWW9vKQaFZIQmFg6ZdjwsOtFw9LBPOdlnO4lw35cfup/8usT672X3X066mAL44Lmb2dzz2P34gSfb6VFrxy0N+2Rl0dYp8ZRveEprc7ndEb7FIu5nhSSqvbiQVol3ffVSmRz7tHWfLjpWgAQgHCzH78fczVCL3Nhhny5OGlD50R/vr8/qUsTuuE0M/jYg9NCXat5aiXYJ7ACrbX87ejrBH/vglQoYT0SMbBLTJYBRY9qdF1O7TdQWyQpkglba8Lpb24w/QWKS6Qnb+Oeknfir1WYjcrb8FVYjULj2vqDw6J06VwBda55InPG3QjQrv87JrwsBgeXOaOWnvVi6MB7tQupvIg8loFeT+ksxPnlSTopQ3Fi2OSSUGgW0wlWQlKSyRyu5jQFRL6ZLuQ+nou7593AwBDS72rE6zRQ5BTPimIsloeyElLVyeYPiBJG+K8FtqhcnR1zDgviEf1AGsKw57pdEPvuQKj6ZagEKeIs4q2iTk6WtDUyTDTT7OaMOrEAdBFrG5mjMYFgc97aLYZfR9QN2PPGuhIkoa+E6DUyNtBlXbEYcMov73GIj/HL69nTF65EEV7YAYRqE7kelWBlUU2bb0WJ8BetbguJpwWHo07klNw7tvG7V3gnqTS16+wmxizlceJiiVOWsUGnVnMTTzoEewyob6akd1b+Nl3IKK7TTboAsKJuBpkPh2J4j2vht+79/eMM4rgsETnNdVHpxApXOHtegctzYdzMJWIUOdb+sXEdxQtaAkUmnzpMbaKsGUiWGqrcEba9N3NVKxyqYwMdpqWYMDkQrscEx9svFjXMks/oluNiY7X6InFlTIGsI2QN8NpQXs9kw7HfIs+hv7DEfHhRn6+0QN2uL2ZiO7nYIOe1MJTCA16ZqnfPSQ8rWVkukjkGrHehmsEURw/XGML7XMRPITKasCJK2Bfn+n6xIu/uRJ8px51qIlGrQ3tKhf73EREMvFup25kwdrNJ0HsQPG9JWZ1C3uhN0PARDgtaC7nfiZoUGGHLmXWys4ipB3RdDPsgFVgUffGBG0h/03LSTG+f4MtYtElOEFeul7TrXPx+gay6IhSt6NeTAHEU629UDHpSEc1qV5hPHc7HAlhzPXB7VxR2yE9b0f1UlZJ/vfVxAv+/ATQKfEFJz39Oheho98sqFzhNk5EThg/+1e4NhArG3YQbe3EiqZK0GPJHg/ShmBWoWZaWADbVkRHd1DO3eaF91t5sBDcfj30IBUdCJ8BoG9iolEtMBwTDP5m22tsl9AW2SAKlMAlMwj4dvHB1mh63yHYpUumsw1B1NNXQruzTgliN5LXECWt+P77cBjldG00iOzSvMQVI4HwZM2gZzBt5KN4lyyujrCeE7Cb3Zs+oOtD2i5irNwg+mtq2XB1XUQY9oRRN2weQ9WLvdSpIQQqSnwgk3aEeSU++HN/0oz7oVUOXlg7LdG5xPdGuQBylHbDfRVOC5l1r6RLYZoYpezwOdxFqZMU1bWo2rsQemS8lQKBZ3nk9RCUAwxwKGckzGtH89TgbXKiyN+BxYJRg/IBWV2VSFu+D9ATK92k0OIKh9ml52XbAdEbzguCSYd57kVyVkMnICE1UWgERQ7+xN8H2CaWbuK4lI1MKB56U0o3U2boarg2CR1q7AhOHOrFEj2Rjp9tQnRyy96QzajAsVRkYTzGdppwIps0t0lpypTUb4yck+6izjrQ4vghkTEqiYbOPxsjOQy5NpBEz6Qdnu5KO7F6hkIYdH2Aq+7O+rkru5/5f6rrE68O24/uMf+J9yGPcDfyoUajZqC8mTqUnGgfhhO+UmGvQ+LTEiJFt/Iq56wdUufQ+va0GBlMKwp327boyBCOarJXLqifHdFuRyS6EOVuJheyiizmuwl6LEKb8vyQ0b0FOrco1RAWNeViRrgeDboB28SEh1vszcTjUVcU13M5ocYdQWCpVxNG0Q3BbEtw0oLa4ConLTOrsKVPpks6gZscJGx/9XhI8NJRj561xK+scL0kInaLCaZIiaYN6iAiuGkkb72KsU1M90FOcz0jPb0hOCxRE40rrNj3rmD7zYeeqiibjbCXUJHufDaIv1RkYTqFskIF/Z1R3SZvf4QaOVwpxLBuNSZ741weTL1CjR2uFGoh24zN43tDHoCOeoJRPbRcg6RDR9WQvaCjnvGDS7bPTlgvDhhPN+S5kP/KxYw4bj0d0fpQJrkW64089DOPGbZ9yPnjB0xn0uLX2hClLdVqTJIKWW+7nFL6mbhWjo3vODQ+T6DtIorNmLpJmE42zE+vacuMqshwTpOPSkbTrVhFnUI14hjZcQN2yN/r60Ne/33voAJLvZxI4JNTMrMPLOViRtvEjJuIZFYwf+25dK5ahuvJtJLeFloFkaJe5/J+JjJW668mA3GTSkvnYjuifHZKHLccfOmDO7kWACgbr0vpCPqA7vmU+OFaBGYbNXyuKq8J5w2T04Lu+XRYEG0TSaqnE7eL2WaEhxvQEMw6wocV1XeO0Il0kZS28n5sMwEMaUc02w6ZHrYL4ekJ0agmnBcyX7cM+Nt+PaJZTxndv8JtRIvjuhCHJ4I6hQqMYId/LMQ92WKLCKyivpoxnhYDGCj9sS31f87lABABbY8+C7EvBNwlJ+1q0Dw1z47J3r6geX9Ofz0mtAXBOMRuUtACNwpCGaGlZ9e3tEAv1DNNjHsSkpxdY56F8t+qhGS0kG7rjgDYB2x+8xGjRxeEr1TQ+Za5NbgioPng6E7Q3/v6n6eUc59skNL944TufHa7mx7LyU8saTHx4ZrorYb+w5Dy6ckg4uq3I/paNgXTP/SY7v0M1wnUBhAxntHQK/SBofit+yL6mm3p1zI73j0U+jIdNgOdj5IdPbykfC78/vhgg6li0jevoFX0yxHWhwzt5uDFxSHpTGaqQdJJ6tbaJwL2IaaJSA/XrJ8If2B0tCI+WlE8PpXwDu14/I0v8Pk/83/J719oSfIywe17M6pJ/2BF9y0PDclE8b47wamoRx9b2vcmdH5jkr12ga0iISBasUGpwPqwnph+k5M8uBLRjrdMFR+dSZLeuBroZbaMhQTohYLRX3v5sb7N3xsLd2B3bazGRAcbbJlIy3JS0m2zge2w+vA+6US0EOFYKGzXv/F5stlWyGl+0doFoFijifNKkMhxJ5tMoLicD4v9oNfY/fkupGti0ulWyIr+VNZuRrJQ+PjmtkwHZr5WjuVydhvp7BRv/a/fpFlMh+7DB++8ycFcwpxMH9B1ksJnetk3Hzy4GMBWXSUwoOPXn3L94QOhQGY1k6OltyUGmDbi8uKYs0fPSadbglEjY5Encn1JOp4I57rNSPIv7i+wVSwb5PWI6vJAtBJHaz8CGlFeHXDwo98fQDXKbxRV6garWPj/bF76tQCw+n+9Jrn0STucMs0mIzwtUG+eQVXC+Vpehwa7krFUez0jSBuiPzbG/vrlkByqs+Y2jjoEFYLdiIU2PN6gp47it+5h2oj0eEU43w4WQe3HHGaTDSFZzjsQopm4aXQi9uP6o2OShzKK3I0YVGBRcY8tUrYf3WP2lfcxy9GA9M3+dI792lPq8wO6ImOzmHPvy9+XDYnPlqgu5+QPrghGNc3FAaP/dUH9Ddn0xV9poGygc3RPx2w+OiM7WhLtfgcTEB1tKN87I314RXDcS2S6f+8IFKQh3fcSYfU3Ed1qTHy8RKX9rcboIsU2ggG3vSYaV7IJSzq0dynF/++Xfzh49OgRT58+RREyTj//Un/2tn4HR8/Dhw958uTJS/3Zn8X65H1hq4hO1gRNiVmPCMYVzYvDgY8OYK8EhrFb+FVgCUY12kNRuvez4eHQ3+Q/EBSkQkv8qCF9eIXZZnSrMUHS0lzPSI5WBPOSJBCbm/aiMaWdKIlXzS2vO+kwVyNxIrQx0WxLMK1EMGMCoqwmGlfDQ6Hfjrxd0RGqepgxz996Mtwk/fIHRT0HJ9eYq0SoYMiJYheysgOe0Dp0onChQUUGrMXWsSz8kQXjcL2W9ynqh4XfbqWdHR4UdJdTeW475Wd6gudUkRkidJOjlfiBI4stYmnvaemy7KAqL7ta75AAz0GfFgRHnbymKiG6txp4684qJmdXg3DN9QHdxZTR4cpvwFqCcUV7PSNMW5RvZdsuJLKSUy+2wY4g6kmmhXQJLg7pm5h6kwt2ebolHNVikQoNUV5hGkEFx1lDdnJFkLQs33tIXcimMhkX3H/1KX0bYbqItonZPD6V8ZEfDdy7dyEjBCdMgSRtaPzIIYzE72+6aEBIp1lNX8eSJRB1ZLMtncdd71gR+agknW5xVtOuctLIW9RMQLfKvRVMWvXhuBKvd6FRsbeWxh3Jw4W4anaBOcrRXs6FeAjDfNkZWfx3WpO7qNHbLwRV7XU+O1W5azTq/AI6i62EtEcg2hCVdEQzCR5SpRq4FrbX6N2GXTvh0Fsn4jvtpK1dyfPFtqEI2nztNsq2kbAs00Sk924gsOgmQqU97fMDglFNkNf0VUqwGAsRU1u6myn1ckx+dk2Q14zuLbDbZBgpOqtwv/4Bts2I51vi+Vba/lahxw3hQUG/9JkFh1thdzwNcauOaFqIJ/89i61zwpMt4dGWWfqRHFCyFteGOGMg9nkY2wylBV5kilS6GHmHMh3B2FI9OSHIWtJXLmXTD7geoYcmHf06H55/cEta3FER9/XZrk8u+DMKnVvv3xe7mvNQix3YxyxHIvTyCxpWy2KVeRhFFRMdrz2pLhtm5+BFYnWLzh22inFlihrVgzVF5/7BsZCFMIiCAWKzw9r2W1H+DrnWTSStrdANrzWIPwYT8XSugYFvtaTKtaFPRLs9qUfzEtuE2DKR5K4ilYd/YtCTGsp0aNGiHe6mw5kUFRlR6DeBB4MoWehDCQlRnlFgqxhCH+6h5cS28/6iLWFgxbvvf56KkWjRUYPKhK/g1hodOB9Mo6C5G1FPvyPU+Y2SzlpQ8oDUUY8afSxUyImAyhk9vDd2B0Ta8dFzg1rY28Utr+kWk+Hk3tUx2cFaNgd+cVPakYxLjIcxORMQJLKxc1ZhmmgAr4BApbomH4KCrA/JSY9XtKuctoBEOVY3c8aTLcZq+j5kPr0W6JPRKA+x2PqgntC3460JaJsY0weMku1gXwz8JqTZjlBKQExJXjHyZMi+Sr341fp2thu0HrYLCfNCwFkxAxVSebrf7qQ6XJ8+BAdEnW6aSNTd+I3gHY5flQYSC4HMzHeYb3qFKwyu0qjEytPGye+nMksQ+ZPn9QpcPmh5QFgYOhIXhttEQr/rQliPcE3nXTKInVUjanclY70dQOzjQUcys9+9XtkY69Cr8n0nzfaaZjsiM0uC3YHGh5UFnr5ofDdy91lEk9KP3XoZhV2LnmVnHwzHFa4IPKdfDR778EC0GSru0U6erZZehMCVCFpdr+nX+cDPcF2Aqyyukeep7UMC28mzwgjbwnWyCdtxHVQomwmVKYJOwoKcUajfA17eHu/76a5PTP5wfYBZRSLQ0wKMiOdbIg+vUdrRrXOCUU10fzvYeFRk/A1qRQE7s+i5IzqTGN743or4lSXRWUH/NMFVwXCzOaskj3zks7EXwgPYCcl01GOWI8KHDcG0pLqZYupk6DZYI/5cW0TDHG8nOmpXY0nEu7clPK0JDgX00VzPhCLYRNhtgt0mhIcF6mEm/vsiJZpvBy65ikHPnRdVSaqcWY/ons/o17k86CYKFVmC+6LUbq/muEYTnpYDAUwFsoDrvEZFBlcraZtmHTpvxT6prTxkJgqORkT3V8NDzfXyM5zxD90qol9MXvb1MlwLOrDoUB6wKrHYtSikVWhwWzn1y2nfcPnBQ5bPT+RUHhjCvJZT+SoXS2LuU+zCW3GbM5IU19WxRCw7ReQpgMXFIQDTLzxm/OBqGBH0ZUr61gXJyVJy4mOh4/VtxPKj+7zzm18WYmPcSfysks+tq1IaT/8ryhHGauK4ZeotgaHvOgCYPqTfCbGc2EGdU7RNQlll2F4QrUFohiCjXUpgMi6ZvPqC6Y98MGx8wqxGjxrCvCZ59Zr0S0uSB4shB2IHZ9Hj5nar7hTFB/fpN7m0trUT2+ubkuUO0G5H6KlDn0WoA/VD3Ok/fBXfOYNWSagPEL5y21lTWtj96uEU8kQolICaRXIda+ifJ6Cc14M0svHWFiWIBtqrOSDi0vpqTn1xgAqNtNm9s8a2Mv9W2hLkNdH9FfEXC2wb0i9zYWRsMqKjNeFZiT4NSM8W4DT9xpMGxxXppCAcy6Zk++zEI7Y7wnlJdH9FdH8LPk65W45lrDdq5IBRKprFVCiByxFmkZA8WsizxEN+woNCnh9Af5VTvn9fNj15KO/FNqO7mpK8KvN+U3pi34OFdMQaSf+sXxySzDdCA1xF6JlFpeKsai/n2FKeg/0moz8fQxigDwxmm9E8P5S01Tsth3P2pf6zB/y+3Prh5OBejNMsJ+RvPkOnLVEkiuTgkaUvO7qbCd0N9GVKNCkIo15OvrHBVRH1uwLyiA43hK/VuBuwq1gSul5pWX/tlaFta8vUw3csgSvRpw61qQmmNbaKBPzyagHzCcG9lKMv39D8qo8Z9vQv24Wo2BAmBeG9LepkhHnHEE5LTJly8/XPMfvCR6i4RyUdo9deoCcGcxMP9K3iwzOijwqiKST3bujXuTxktcz83SrAtrHYlu7doN+a0v1mj9lm9E9P0BeySAY3DdEXGtKfGqPevcDeyA2plKN5d4SetfKw17JgKKTNadYj+jJl9OPXUDhogE4AIq6JMGs5GTmnSV9fSAdha+m88vlllzUa0/vI46zFbmKCgxZbQPn8mP7D+0xeORfldxcyP70eFPftzYS+SgmzmsgnupmncnruV5mInB6FBNuG8GgrUbeBpVpOBBgTWLEQRr2IvZRsQoKoZ/TaC1SmsOcR1TZn+uicajUmiDvyoyVvHKwplxOCsGd6uha7okc8mz5kcnzD545v0JGh3YzYXM9RgWH66FxU+N5tEXtLnjWacjMmHlVMDpYkdUKxGTOyYil0TtGsc29NFQJlt5aFZvX0VDYiWYOpE5ptxqS6JDpaY7YZ2ZeuMVch9YenFJdz5p97Ira3MsV0Ifmr55gyJTpZow5C6m/MKH/tiPR4RXSwke+7CpGY2nDYrN5FuZ2jRUtHzVzsNrMWVyvZ/F978M8ml2CrymKuI9/atpKPcf9a6HbLsYCAXiTouCU+XuKMJvJ8DRVYyvfvk57eoOfSAehWY+Kj1XDiNssU81RcBtop2puJjKhANgzX4igIx+XQCrdNyPyNFeQJ1B3j7SXBtKQ9P5Bx3r0Vzosww3mJSh3Vu8fDBkSdhEy+2NP+aiKjAIBeEd5vMBdC7HROEZ7WVN87RoXWX7OG7vu5CKXHlTg5jteEpwXhcSGHFz+6seuY1XsPCNN24ALopIce2qfetZQ1lM+PiScl8fESPWtxN6BGEmO8EyTv67NdnzzSd5mLvzVryPxsTQXe5qah+35GfLKUdpQn/0XHa1wbYstYTrDThqANh5jP9t3bBDrlF4HRw8shNlhHPSoVn7OrQ+rvjOm3GaO3nqMnHWFTYy4i2u/E6KiVzkI88q1UN8xRm6eH4PkB8UFNcK/FXIh6d/zgUvjyi4m0nA9LqndO5UGSSOs2u3816At0ZgnDLa4N5NQeyIwTJVQ+Luek+YJgHBD5CGS0k5+XGdw1uBcf0Fwe+kyCEjV2JG8sxdffyHtjm0ioiffX6GyFXjeQxlD1copTCrMU8EcwrgiA+tkxzoLzKX8fb3u+zErmG7FX+XZpvxYwkW1iorwif/WF13LI757MNyRIC3QHR+pupj74J0BveqLZVuaiVtF9VzYJN++8SjIuyQ7XFJfzYe4fjBrie0voFd1yjGkj32rWmBcSwDQ9usF2IdMHwiOwfUi9zoQT4YVV9YsjgaSMKoKoo68TGg8o0oFlcrRk9OCS5mouiYRRT3y4Znt56K2MltFk67kAWmBD45JoJJ2N3UiibWOO7l2LT91DquavnNPXcp+Mv/CY6NmxALGSHhVvsUuPhFWWOGuoLw6IZ6JrGD5Xn5VAGJB+7proanvLmQd03uEqCTUKZndn7Rr/2DPcRjQNOpeRlLnxWnKfmeFaeb1DtkWgCI579GSNazSpb+M7qwcBX3i8wawyyienJPPNkCKqIh9lDXRPZ1gTiNjXMzVcr4Vnf7DxY7KWbFrKTL0L5VmUWbbffChpkpNSkv2iHnMdoVt5LUFe30Zy+9jd8EGJ6kSj47YiJDSFH4MVBp5c4/pcFuWslU7cRj4T24V0Tw9FZOg0tgnEAfSgRRfC97A+nhoNdiPvYXDmxzxrRTCqOfzy+3TLMdHRGj3pUIkCJwj2XcXTLd1GdFWR3qCzDnMdDc+W7r3jO1f77/G+n+76oVL9dtxoANfFBFkjPP9M0Z4nA3rUmcAz3T82c+4VKse3rhgoVUrbQanbrUcCCvLzuuCwpL/KBx/9bufs2kCUzFFPez0VgInTkj2QtkOLX+/Y+FbRbUbYbkR0vha198fmpbaVBLoICDQDX1wnHQR2AHjsBHR67jDPtbDMQycColFNey1JZmaRyO+XNYLZxduZdlz+KhmIYbYJUc6gpw56z/33LHYVGGi9JiIyuEuBeqiQQYCktBtgPvHRyuepW6AjzO7oge804agUa5JXEO8wzrvX45wePl+cllbqjk42qYn1arCm9WUqHQRPWnNWEaQtUVb78KCWKG2JRvLvOulE+NXI575LtlO+uwQMrgBg+HoQ9zK39XoP5x/IfSsbQRX1OBP/ABlxl3tu2oi+TogP18PPD+JuCIXa6Q6scuIc8aMBgDSr0b5LopOOZpthmkggPGmLrWI/MhJQ0O53+LioUgUGvWNMNHJiHh72q0YspVbhenm4K21FJe95EK4Lfsg23w9zPXhHgVGeHy/zaZUJk991/truQhH7BQ67VOhMXqOr+EGXQm4wV6l8XnE/zNUDkOdFF0pi57QYgo2CB4LC1uNG7rFnkngnVkCLyg22kD8XGC2iwZmwAJSywh9oInTa4ipxF/0AJ0NLp4CeIXjLfiyF0hSi3zB1TPbKhdzHnjJoNrHXBbRyP/TKW6SF1ElvcJ0IFdnpkpSDUMKMSGO4qQE1KPqV18/Q49HhWoBhSeu5BFb0TMqC0QNrROe1JCdWdxf0tK//OeqHeh7swltEjCSkvCBs4GhG8LihvZ4JVS+wJGcLeq8BUEknu+7+NtHOdgHx6xuZFS8TunVOV4gbIBjVhPMSvvAQvbmUDUaMtOhi2SG7KgDPPM8fXYhgpxF2PqFD961c8J4HYE1As5hSvn8PEIW+UMLkdG7aiKCTxTI+WolwqBNngorsx7zrGn1vjHvaYreZnEJOa0g0sV6JU2Ep+Nvs0aWEESFz0YTlYN+LfBfBLBLMMkEF22HEgZaFP5hVgvtFNk3NR4eS4hd76IunjjUvDnEmYPyHr6APvbYBeWjcQfV1THwkWg7XhfI6rybDZm/95B7je9fStdFO1O8Ha7rlGJwmmdQEpw163NAvcspnJ+h1T7se3zoYxhuSBwvMNqHfjsQmOBG2gbOa5oXM/XcdHtuLqEwFDbpI6duIWDua5UiihPOKNLBiJ/QExTATxXdTZjinmM+38ue8grxvI4oXR2QnS0wbsVlOyTY5WhvCtCXy37eL3G2bGFulaG1J0lpYFXFHdrCW6zpriO6tqS7nlIsZ43vXRAdrth/cF/qlX/jbxUSEYj64SgVGUNF5i92k9FVKV2RM31jhat/u9SLHHbxKR3KNmCoR62ITcVdE1/bDKdHRRsKEipje21d13kIewcYDcqoE20SEpwXN90+kJZ3L5meHhI6mBSpl0DME05Lk1YUkBnqAjSlSqpsJ2YMrOZBoJy3ttIFHp6imJqrWqKinX0wkVTMq6NYjTC2EQR1Ypn/kKcHjeoBm1Vdz8jefDd3L3p+cQaKU9ajFbmTztXMn7MSCYs3M6ZqY/MEV9pmlX4uToFt5CuC0Qo1r6S5EvViYAbtAhH0mEJdH1kLs0LtRTd1iK38teCGiPrQySmhk09q8OKLdjEgO18RZK+OySSk248CK7iLr0HMHaYS+YzGeg5eO991P/F9ufeLFv7g45ODL76OzDrtNCN5SXP9/XiFbrRlll0QPLJEuaD+c0q1yguMeqAgOOogU5gXU7x0P/vx4tsVtblX/OunormfEnu/d3+T0/7ohfbPFbiLsZSLty6yVXfcmG05t/SaX4AsTsH3nIdm9BXoiMaB2m6DzhnBcDqf9aLa9VVmPBSUajmo5yVYCHgoPt8P8EA32JqC9nmGqmFHyAhX5FlqV0D3z4q1Dadv2y5F/eAe41qBGSGjNuMGsBU+avH6NrTQq6wh27+nnPSbvuqR7b0zx5FSgRUmH60KJ5YwsdiPAj/BIdAf50QuhfYX+8V702EqBvZu5XnZvgcrkdKJCQ3cxJb63Ek95CNmXLih+6z6mtkSzLaOHl6Ac6aNrnFG053M2v3Eii998y+Tzj+Uz3Snnl2NpAztHvx0NYUViebQobQaIzfiVc3CK9Yf3CT7oSB5dy4Kx3dKuxrfe+XGF2WbUywlhKrkPQdJJOArQlanY9tqIztP+8tMFyavX2I1YRsdvPKN+fgQgUCifQBnEHfnpDTuFhQoM1fWcMO4YvXKOKVL6IqN7eoI+75l98cMhaMr1AcmsIDzcYAvRuKRvXtE+nss1lddDB6p5coyz6ja4KdIoBBOLVdjLOfHhUvLdQyDTREebIf73rqp8fsT84VqQsQUkry3lxBopaHuaZyeyofYn9uB8LPkYsxaVa8K4Jjyu6Z6PZWxxPCd5dIWeOuxCU71zj9GXz+keS2cvvr/kcFLRLibimNGW4htnMhJ5KjP88GEPxqG3PhBrm5D96AI6i7mUDYq9sphCFnidtuSvP5eoW9+h2tlTddJKANGzQ8Enn5XwAprlhOlXn2JeBCTjiuThArPKaL6ZizYhEt1P9toFtowx6wy2luZyLq4UA831DLUcS4jXLrTp8SkJIuK164Ty6QmTP3KJu2yk24i9VesrcYjYXjP5/GM57ChH/CM17TdTyVWZWLrHY3RgcZsOqmboKO7rs1ufePE/+vF3AImE7DcZ7rstB19+T062VtG8Pyf0D9noaAMnB6j1EnMjStfmak40KWQhsKLCJ3bouKfz+NzjP/YOrnADzUppR/PRodzUaSszxaSjW0xwThPNt4xD68cBWroMgRH7TtSjZi0Elu6ZPJaT0xsh9iHQELMcUX54Ji1Xzw4ABvqcjuX/95uM0C8SOEV/PiY82g4jiwHeM/MoUlehM4uzMrczlxnZG+eoA0WYym7cruJhM6LjnuCwwV2CWaR0S9kkzX7kA+rHJzKSGFeEDytc5W1fUU93OSV+fQOhgs7SfUuhdIAzY6/HuBuoi7MKs8oGvLGpEzbffUR2ekP0aoG5CqU97sTaNKQQlqlkQSjL/K0ngiguUmyV0CwnQ5ANTrF555GIrEYN6dES28bCOm9iyWnoJZ+hvZngTIAODFt/+olnBenZguV3X5PPI+nk1H2yZqLtYBFtF1Ouv32f+ek14weX6KRj9NZz2vM53WYkm4dCBHjGf1bZG+cY3ykyy7GI73yevXyWLcULH0ilLf12RHJ2g3sWoHYjkm02vAa5cCy2SAc2hakS0h9d0b8vQtjgtGH579/AmoD87Ir4iwXduynNf5qL+GxUyym3lI5AtKxIvyQLHRrfFbu7Ce/o/jUkt4tJf5UKFz8wBKOa+HiJ2WZEsy3Jo4Uw8Z/Pqd87JhxXRF8ytN+Mvf7HYN9fo18d4R4XmEIstK5mwP6C4MJNE8nGbir8fp23wziofXeEzmSUMlD+CrElOqPl1L4QBLLrQiwQzlraJxN5P0OxZkb31vSXsilJHixkpLDRqKQjf/Uct7EorQZAUfQVoK1wl41wN2aVkC6Rv7e7mTB664WgeOtwyA3YgbJ00rO9PGDUPMdu0iGjoX8nwDYz+bxnpWQGHEhHwKwycS7gc1S6ENYQHm6EraAU4b0t9AyODOvufvEXhf6+Pq31yWf+Hmm7E8AM4TWrSMhaOyiHvznt95f0V5NBdR9NCsJ54duTPpq3Wg8PQVMnME5wC/k5etTcon9DI7Ouna//cCMnwAMFj5FZGdICDvJabIZODTnapk78wu6tV63PtW7DQTjkeg2ReNYj3842hWf6j6tbn/pIbEX9QkQ9KhUxom0itG5glKIpoQJ6bql399fYK40K5PS6o5uB1xgUolkwVSKM9rQVBOyoJtRW3o8NECPagwY/T3fQyUjCmQCd1KgI8Q9P7ubm23ESdq4KFQgaGcBVAmTRsXddeJ80IDCiUKyQOukwJhhGBenxcsh37z1Ex3Yhtu/QsbR0282BbCQiuQ7Nx3QjcV5hmtg7AqQVOzpaDSFD/SYjVM5zFLyX3gRk40LCnbRwFbqLqbhR8soLU32OgG/Htufz4XfFauKxjCNwMiM2dUKcV+LW8EEwu89Re+LcADXy0bOmTgaCHICKHFQ9KhT2vblIZLRhtJz40xidtlh/DemJaEF2m1cVGNwKsdJZBPhzfHenfxUYMMIScJ3E6A5WRZ93YU1AoN2w+Dgfv+xMAG1De3NE1OuBfWE/KgdULYA6SlDL2zayinoCf42w3dmK/fVuFTTRrd14xxIJjbSjP+YE2mlMlAkIqmrQngS6Izm7QWVqOHg4IxRSt1UC0wH5na32OQQKbsQRgEY2I0bsyUFeozy/AQ1u6zf+mYwxdWBEy9EFjE9ucHU4UCojD0zrbyaoJiLwzBEdSrjQTuCpIjv4+4N5KcCwvIE0RLkOs4nQiCDzLqFP+/qfoz6x+7e5nNNvR15M5AY6X3s5o3x+RHhYAODaELPOqN6/R7fNMJ5uFj9ciuLYKUwV01WJtNhgaK/R9iJes0pANv5BqUeth4RIi0sfWtTDDHf/PjprCbJmAIsE4+ZjzHB5GOvA+AQ5jV1p7EZiZwWluR7aokpbVGbREwH/2E7+fDCrBoGi6A56mqu5nPp9oIapE+gNBCGMR4Ny3zTx8FBvzw+ExKWdJJMl3RDMYot0eE27B6ctYsL5lvBkS3DQYbYJKtzZqBzBtJS/pxS4SjCu0FmHnkirj/xuTnsfD5sxdUyQtUJNDKzYkkASyqzGtrGILU1AcNChz4JB6CeiNH/Cui+oX7TFdKHM+L1bw7axn/XLYqACgzWarvG+7rQVB8BsSzTzqu0iJT69ufVUF0KNNHXiEcKysZw8vETHQhR0XcjqwwdD8FA4KYQW1wXSGUpaNk9PpCORV0QT6TAEeS3vfdKJyny2JZqUwqFoInFeeFuWinr0zAwLkErkIdyX6SCYDEc1/YV0n5zVEsd7tLqlTLYdKvZpczsWgJYNUjQVXUS/HAlFstNylx/cYYarU7gav+hJvnw030pqnRfr6kA6hLtFUwWGcFwRjGrcRmBJzWImuOvQULz7QLoWu0CcNBEIVy8bxnBUE6SNbBa3IxmxdaLlUHE/dL1297BZj2RWX93mjyhlbzchXYgtUnGcGAmPUichhAqdC5/DbDKBWYVuAPaguQ0Fsoru8Zju6UyIh6GcxIdxVSTPObvxkcut2JBNlcj1bTSmSBm99kL+jHYE44rwfjNY82wbidOgicTJ0YYe7hMK7CeWja+6PxIRop/8uRr53Ts9UADvthwO+1L/2U/9X2594pO/zMSd3ExlRHZ/hd3ExIcb4qM1agT1949ksUxaRl98jjpKYdvIqWCWYj/qUEkn36+dzOqbluh0TfKVCnfTE5722E1Edzm9ZW2fdoBi8StvMX3rCdq0qKqE5yV6rugeh8PpXs2ArfWiJysPw9cs7qYT7+96BMoNJ36MJnpUoC98NOoqZv3uI8avnBOfLIn9DG23Q0c5gul2EC+p0KDzlvSsFtVysQTAbiVmM/bZACr0HttxI9CfwBCcddD4C/pkTHhzIQzzqwnbZyfMDjZCT+zloRneb3CNtO6Chx2cHOPeu5BTxszC8ZT218XqpEJBK9+FwlvFPdoHueikIzwr2f7WQ4KkIzm7Rh+W8vDbKrptxvWzU17/37+OKxX2Wk4p3WpMcraQsJTnR7heE822hONSgCuzrViSVmO6MiE56wdhp+sDORGNasK8GiKTQU6/7c2EzYtjDj73+Jb58DEaZDD2C8NOsW0Vtgqpr2bEo0o2Z2VKuxLvdTQpMWVCeX7I7PXncp2FZviZ3WIyOF0EbqNEYxIkbJ+dEPjZtAQz9dhVQDCq6VZj6osDwlE9ZGXsuhbt9WwYYyVHK0+19NfoM8P2vYeMHl7S3UzZfHif7HBNfCzX3s7OVXzrjPRsgY46zAfm7tT+ymE3MXrSEr7lCO+/wFUOW8giFcxLdLL2TgaNA9GIvD4DPab7jYbJ2x/RPD/0oyQEIJVbXGTR2571v54JUCzqCeqW8GhL/+xwaJW35zPMeTKMQJR2RPdWQ2qoSnvsJpUuia/o4Yr+Ih9cBuErFeYiJPQRvNVvHJD9+A34DarrQq7/z88xfe25dKCUQ6XQ3UxvnTd1QnKypPrw3sBjiO5vsZsAjCY+WrH5/kNvl5UNdHV+yPQPfECgmyGCN5yXdJdT6EKC+3PUZk3Wn2Mr2TSbLpTxpt+UqshIDkJioe/lWRtq+kWOuwyor86YvC08fFtIhPGdW/32bf9PdX3ytr8JCKIGmohmnROeT2+jLbXDrvQgWsEq6veOia5K9EShEgeXNSoH2h8ESzonnnR302CuRsMNEYzq2zzqc2nRT159gYpEYKZCS3R/Tf88kdZqF9KVCXEnqN8gr1G5oX86JqjXqFwRxjXmJmL7/Yfkr75A5y3t84MB9auzluCo4+DsfezCjzhqRbsa+0CaWpTIL1LpNiTi7zZXPo976zDbRDoiqzHp2bU4AY7GcLMlyGvMOoU1jN44xzyPB3Kb/W6HyjR2I57h0ckNwVGHufCty6SjfXc68ADssxB9c4k6CqHq5fT//ppm8YDkcP0Dnt+XXbaKh/Cm7NElroX07BpTpLSXc+J7N/IZTktGuwz6EJqnM/oiI/bRvViJOE5O5fubiwP6OgarpRWvBHoSpA1mmwkI5mxBcFyQaUdwXNKfjzFNTDQtqJ4de5tgw8GbT+hWucTkNsLez7/0guI7ZwTrDhUYASc9vBzU3rvgk249GkYv0bSQ9/LEkfYCXFI++c0ZwS2rUGb7XemjiY+XEqpSJwS+gxSfrMSKWIesfvs1pm89vVXo+/jjcCKfWXs9I3v7ArIQe2FZ/OfPMf/cE7QP/GmentCsc5L5Zthclk9PCGs/N0+ls5K9ekG/HNPdTIS3cEfXQ3goRDtbOIKspX7ngPTNBRTQ3kzIDkvcj38e/fhDzEc+gjbrYLWGytLenBE7Rfr6FSqWDsLo7RfyfSECMgoM4bwcrL62kojtfpn7cVdHOCpFYxIqgvst9iogeNXhVuKUiU+W8mcbQX8rv/CDH1d8OCI6K4YwHbNIcdcd/fVYxMbTklFzQ/H0hDBtSY5WuK1s9NqbCX0dMv7CU1RiyaIL2aQetLherIC7A8r8p59A3UPnsJUmu7fALpPbXI7Q0Lw4IJpt5cT/bC3izhiUETurdJoETGS2GYFuMBdy4NhhyVsvJAwOpCuLH7vosBvARPv67NYnXvyTs5uBEhasxhKoETrh3Vcx/Ta7vXl7iA8FuoG26L4bVKi72rH26fVtKM9cRHW7XOr+SsRgwbQknJfYMkYnvWgHjCy63XpEmNeEE2nXtR/OhoXPLFJpjW9glx5oy4T0aDl4xW0XDjNTrMJc+1lc0ouiPQRcQb/JUJHMtsOz2kNLwFV+JtytUCnorqNbirVH577nti4wFwl63qB6CewIj2vsMsQuwmGMAhBMK4KpzIxJQhEC1grbhD7dEHlApL20OZteTtSVdCFGjy589rcbQoJedrkulLS9aYk+th6Y5Fv824z43g1mkw2nbh0JMbEvBOoTHhf024z2ejpQynaOi50OQiedh7ZIZ8U2MdGkFLHhMqW5nhL40UmQNsILqOMhM8C2gSCg4x5VS3a8uYnpimz4OwNPkOwLCWVKXr2mO5/SbiWKNZkUmCYmPNniGtFkdNuMejEVbUHcDSha20v2fOxFZraV02R6tKTb5EOYimvlHthtOJW2hHktbeUqwfXaMzKApkdFMH3txWAnM3WC0pZkKg90UwlNbtjw+Xt0h/ntVjl9nZCd3NzJtQAidFWVBNy4jbfcZSG6lc/GdRr9+EPcTYNrJHQrzpbQyEYgfXCN9fREVyOBVpEQN3fvm0461IGSoJ+iE/5H0ond0ms49MTAyuG6ALuQrAkqmYsHaSPAMb9Bik7WguA1gYzyvFiQENHQVNKC17mwGWwbysZPO8avP5cOhXKoVFrzafIxTHcIZAalDcwSuGk840B0RfLBOJyVk/pO6Egv9/Fu86tiIff1i5Tg0DM+lIOsE52RT/CMTtZDMqptIuw6Ryct2asXqMyKa6ALh24kPlztrmsP+fl01yfe/qkDhRrJQzlIfCCNkhut3+S067G0p1ugh+BQWnSDoruI/QKmvDDQ+3N99rRrtFji/HbEdcHglxfBnxNMaOgIZh0q6+i3mRdjuWH235epWIj8w1JnAtJxnV84leQJDKE92klgUS4/21Yx7cWBvMZMyUNs3NwGa/TAJBUefa4EZBL1uK1Xz/rfLTrayNzdgr3SA4gHH97ietnhmzoR0EgqCXFqJu+1zvyNE6nh4RaOasGlIg+N3e/gOj2ouYOzDjX5YT7ZH75UYIdsdNIYWyaYSk5TfRtJx8RDb/AuDJySkUnciYgq6umrVDIWqmRwaOxGCQTWpwKKw0MFov+wTUx3M8W0EavHZxLikkmXwDmF9RwK2wXeptUNgUv1i0P6NvLAHAlWMlUyRAmriXAsdmQ+HXcCSUEwyt1GSIbNOqf1f0Z0H/2QUBjPtrfXVWAI8noYmdHLz9E+chhAR0bCmUIzzKdV1Iuf3F9T0f01rg/kdLnNhKOxm//77oFO2mEW7doAlXjUbhML1yC8Q1+31oNGx3Va8L1aWP+7boZ90mOXiWzsfUS3a8FZRAuSdLhGY7dyLXTrkdxvTnmwlYEwgDgcgo7QDj0xkq2RGMhlho6VjdptfLC8pt1GT8UyE3dGe9Fli86s2JLVbQ7CDuBDYMXlUwrIJzisCY4b9KSV51XoCA4bgofd0LnA+sCjOEJ97F7UUQ9FJ/exlRGeigXxLaJBPVgPRU8gzw4BDyEboUw0N2YnaDwI5b8l/ncv5dCkT7U8w6yMk4Yxl9cq7euzXZ881a8QD7ytY3nQNhFkkiSl45YoVwNAJxzVJF9cEz0osKuAbjGhenJK/ugCjfhyo9kW42NwTRXTvj8lOV79AJc/mootzjURfSExoK4KIJEwG7UT8nUhXS0z+NGPXMiDRxuicI1Zy65Z5VpU0rbH3ThUogjSjtguZYGuRCwUncmpVH5nh2ss2IDkweJ2Y/K8wTYhwasadTIhnS2pv3UAyAkjvrdEjRwkIa7s6ZdjwsON2BbHJeHhlv4q91wA2YHrzCNQUVBZmg/nxK8sqd8TBnh8b0lw2skpu3GC9Xw4hihGbW8GepqrjGcVhHe2uw8flDS/NZPWcnaBbSXvASCZbjHbjPi1NZHy7cZxAjpgNH2B3cSYc7FxxtMtfZGx+uA+6bQQS1Ml+NvZqy+ol2NSkMjgWYVrfeRtJ+3Vxf9xj2kbo+I10UFBul3LyT7uiA/XkvI43xKfLAnShsV3XyceVV5kKNCo6vmxYFc3Ofq3hSiXTgvCcSWtawvdi9wHRjVEsy3xwUZ+r92mc9YJytXP/LurKUEmSu9uOSb7/UvsC+FEtDcTlHID+dF5IWT7oaTY6KSTDeEmFy983MsGZT2ivJqDdqTTgvTRpUT9AmGlWX37dUanC/ndYCAApqeCOd6pwO+i7AuHPlSoicUVXqNy7aNuRy3qKMA8k06fHjVEb5a4lYgRXS2n8ejhlu6pWFSzL15hV1p0LL0nBnqtA9qhAkX4hqH7XiIHg8QKejevhKoHotD3P1unrbTPCy0uo02JXlfUVw+Z/O8l7qOS/mJE+GUN6wIenqH7nthKt2QXYR2kDc1iBu8dkzxcoB/EmO9LoFl8skRPAjhK6b8pmF4d94T9VlrtfhMaHJbYwscba9kouDagW+aEk0oYApME+6QXEqB2hPNCID1JI26JQKEzR9BJqBrWidWydwTTCp3XAje6NqixQ00U8WiFXWnREHldxV3Xy4b87Ovl1g+x+Eu72lQytwpes9T/+UDmbbFErY6+co15ISItGp9GNjMksyXx/SXlu/fJXjsXIU9t0XlLfz0Wtfjhhub8gPLqgNHRiuTsWk6zoUMnArdoL+dEBxsJzNlKCzaebQcWeL8e0bw7I3ljebt770IRHloH2wZXONRBiHkiG5lgXtI9H8usLDKoznck0p0lML3NGUi6oT0XHHWwAcoWppksFBo/PpDRR/vtMd0mx7QRqVXyPUdjSMbo64UE9Gjf7tsIuUxvZdyQvLak+XCOCq2ImAKL2yh/ugplpNFvaZ/PCScx4fHGR6iKlW0XMXoX1b9IZaPWhdSPTwDE/uZP+/02g/fFSqejnvjHwX24oT2fA5C8sRy6Ol2ZYtqIyR86p38/IGpiMquoLg84+Kn36F+kFB/cF3LZF0ryz21gW1J945BX/7ff9BueCONb+/GskNOVU3KifxIOVqjDL3xIeyObFLFnxgJ2KVJ6H1gVpq2gfK9klBWOK0lsTNrBzmqaeFDa2yommHVDS1l5clswL0WYuakwj0N672xJj5dEJ2uZWR8WhKcFxTfvy0ijC0FZgSF5HKspUtqbCeM/fEX24gK78a6AxLL5xivD+CU9WBOdrsUi1oW3f/9xj10rtt97yOhOrgah04UUfuSg0G9N4XqFXSvxsh9MCZorXC2b9/I/nbB5cczRl79PcChY7LDdDpY6cyEb+fpJPtx7o69c0D/ORMWfWOpvTEjfvsFc+LS+RwWuQFT4TUR/MxFBKbIRsnVMMC2Z/pGn0BrsWmBJ3a8bnJ1K5+SmZPObj8juXxGMG0HuFqFs5Dxtcfp/e4G7buSefdLSXh+TvnUhY4KPFMGDGlMdDM4N5Z+w4cNK3BelEkrjVXTrXgmNcPi1E0AToE8d/WMJF4v+YIr9jh/bBArmE7hcExx7q3ABzUeHpG/fyN+xUoTHG1Tqhvcyui/PSJ13OCuZCHct+NvXp7s+8eKvX43gWYmpEqrrOdnmalAoq6RD5x10oLOOiC12JT7wYCwzuX6Ri30mcJBpVORwBYT3tnLjrEcEWcvoaEU4Lm9bYIlsIsBbArNOwCd+PqwzEYNZHxikAp/bnWm0tpTfn6Hfu5S2eiQYUIoenUvrsF+IJzk8rtml9AVZQ38h877wtKR7IRSwnVUoyGsIFHYjLdagK6VzsMvsLr2dyC/cOuqJDje4UuGKEmyJSqwo4sFbmgz9C8Ehh1E/fDICIDLYSqI8g7z2VEDpFCjtfGiSw1yJIG0HN3FbfScK7+5mKqjdcUU430qEaD2SpL75VuahgUV7MZt7XtDfjIcHYn8h/IT0wTUpkFzOMB+p4edGDwusCQTEVKSDzdI8DiDwD+U2EjHbVmBD0WwreoAyoSrmtGXG4dsf0K3G9LXnv1ctOt7ZQKXDtOOzq9AST0qcCcjuLYbviQ42dNdTXO9g1KAnHeapOFp212J/lcpoqxUWhk46usupXKNHLf2LZABGqUAEsv16hCpFOJq9dk53NSU6WKMig9kmhMc15kY6JKPXXtD9doYe+VN9FcP5lMmXHmPWKf06Jz5ay+bK//1B6FHUiUMfwvhzT+/gSrgtW4lgTU86aGqxyE0dZC39f9oSfj5F9TXWakZfOhfLW5XAlRb3T6ZEv1KkGK9f0T7AJ8hr7JWWhT+UjkHkNQ866zwvX6y1wUGLSnu6D2SzZZqYIBF3gDOa7vuJ/3Mt8efW9E8TgqRDzwwczxk9uhD6X5UQHm8IPKWPUNLy7LMO18YSRhY4wklB89GhHIiyBjorgTuZHwHUkiGhJ3Y4kOwyTFwX0hcp8bQkbMQqSq+gaCDSBHOvRVr5TId1iOo6dFhibnZOI4uaedroUjoCemLFelmrIXMj0giHQMs4Yqcxurty8NJn/nur38usT978qVt0bgVvGXfyYMkEMKLzzs/XAmwjy02/zsUCszvB1AIqAYQ81knrSYJo1CC6CieFPCC9GtUVwbBIClTHDX5ZnTUSeONbmnIDiroW6yDSEt6xHomK3v8cZ0GlArowZSqL1SSCLJQAokDU27uAld18cVCFN9FwwsdoucFb8Rm7TnvnQEMwqokONiRnC0n064TG1S/HP/DWKnXbhnO9lha+dyTt5t+ukxP9zv5IHsrDbhc65MTju5tD7zzDd1G7E4tOevTMn4pDee+DcSOt3tgMlqv+fEzvOyimiWivZ+jQeiaBiOa6m8lA0SNRBIk4MbpNLthlqySbfJmLENBrAkwbDcApHdpbvoCnQQZpQ+AfdM6qIfVNZp96AElpz1YI0kZSEkf1AKlxHgiEVaL1CKzM0stYvP2ezwAMc2RTxUKKtLdArB11UcXicjGVUB71zA7teqWt3yDgNyZaMNVtKN5uI0FGtgtRufKpmk7mzx4i43qN0mA3qb/P9J0BnwA5NWuJ8FUaKBuorFh8tTwLSFLfGVOQRxJ9vRPJpYLidW04pGCqqL9lBYxaucdDAQnRC89+9wwRrY68TyTiqddRj867QfehZiKIM7UAwHTSwyjxFjknJ+pYoqmVv9d3c3TRuDToeSMgrjKVsSd4i6f/fBPRDOikRx2EkIsozxbx8LywH0/bVG7498C/h67TEtZjRXOkUnA3nQh8rRLt1NXHYEYAWmyyrg4HTRAhXi+hhkUf7fymgAGetK/Pbn3ig+H6a68w/epHhG9rpscfYgu52XbErs03Hg2Lu7Nig6rXY5IHV6iZJlzWHu4S4rZit4nf3GCvNN1iImS0ww34hcsZechu331EerwkPN4QTmoR9VSehlWkMieb7lqOyBy0B7dxqNgR/fQU87VreV1tiF316FMNrRlS15xVEEdQt/Tr3M/RfFxskQ5jBRUKera7mRLML1CZQ+cNRArVWX+TOdmxf+Eh6t2nIrg5OcK+s/Dt+A50i7o3Qm0raJwXSSrCSYEpU7p1TmgVzmm/+MgiFb+5oX13imsCVG9or8VSZQuNLQKCM4O+Eg+60m44bb7sCsfVcHLY5ZsnR2t5rU42T3btoSxGU10cyAmqiX2inmb61hOxPfrv2dHMcAp7JcKs1eMzwqQlm2+GFrz2LHddSN7BzguvQo+SzRqSk6UXBk4kCCm0g0jRVDHO+cteWf8+NfJgbWLxhm8Tv8FTtJfz4QFtm4hgFBIfbKjOD2kWU6Zf+QDXa1Hs+9Ns+KAUZ0kbSlfAEyijaYE+MJCEA8cABNKkk5bmci4n/Z9YYl/IJtA0EaaJSf+Xms7PkqMDiWht35sKIGm2RaWQPLoWPkYXQig2OxX1Inqt4jvz+eu8HZw8ttCo3g5IXJ21JK9eQ3jsBWwh/fuWYN4RHkorvH+R4mpL/UJO0MnDxSB+dZXDrmKCVx32mRo2+rZMUFGBM2qI6g2/BKxKXCWQJvVwSthtZXOcpthKk7y6QE1CCGPceQ1EcjCoe4LxGlerIWacSQTXnWzYcoM6SlGLXkaBTQTKCYr5zSvZ9GggjbDnGh03KA39ZoxzmvC4llHg9ZTsi1fU3ztCBYboeE13NfUJhKFQK2fFsLFxLfRXOdHbPeq8ob/JaZ6OJaegd2CAVUP4lTn2nUZAYI0hOOiEAbDpvKtKhIVmORrYFndde5//p7uUc26/BdzXvva1r339ruvRo0c8ffoUUGg9eak/29oN4Hj48CFPnjx5qT/7s1h70sO+9rWvfe1rX5+xujPi5772ta997euzXPu2/6e59if/fe1rX/va174+Y7U/+e9rX/va175efu0Ff5/q2p/897Wvfe1rX/v6jNV+8d/Xvva1r3295HI47Ev952VBfv7Df/gPhGHIv/23//Z3fO2jjz7iL/yFv8CDBw8YjUZ89atf5Zd+6Zf+iz/na1/7Gj/zMz/DwcEB8/mcP/Wn/hTf/va3X8pr/L2o/eK/r33ta1/7+kzUO++8w5/5M38GY35n7sCLFy/4o3/0j/Iv/sW/4C/9pb/E3//7f5++7/nTf/pP88/+2T/7ge/9d//u3/HTP/3TPH78mL/1t/4Wf/Nv/k2+/vWv81M/9VP89m//9u/Vr/O7qr3Pf1/72te+9vVS6tbnD2pHgHtJ5ST57L/b5/+Lv/iL/OW//Je5uZGchH/zb/4NP/MzPzN8/a/8lb/CP/kn/4Rf+ZVf4Q//4T8MQFVV/MRP/AQvXrzggw8+IM9znHP8yI/8CIvFgm9961scHh4CsrH4yle+wk//9E/zL//lv/zd/rp3XvuT/772ta997evll3Mv95/fRf2JP/En+Lmf+znu37/Pn//zf/53fN0Ywz/9p/+Un/zJnxwWfoAsy/hrf+2vcXV1xS//8i8D8PWvf51vf/vb/MW/+BeHhR/g85//PD/3cz/Hv/pX/4rnz5//rl7v70XtF/997Wtf+9rX/1/Xb//2b/N3/s7f4Td+4zf4whe+8Du+/q1vfYvtdstXv/rV3/G1n/zJnwREKwDw7//9vwf4r36vtZavf/3rL/Pl30ntrX772te+9rWvl17uU5TC9+1vf5skSf6rX9+NEV555ZXf8bVHjx4B8P777//Q3/tprv3iv6997Wtf+7qDuptgsefPnw+L7Mfr53/+5/n5n//5/+Kf+W8t/ACr1QqA8Xj8O742Go0AKIrih/7eT3PtF/997Wtf+9rX/zRlrR1EhR+v9Xr93/0z/1u6993XtNY/9Pd+mmu/+O9rX/va175eSp2dnd3pz99ut5Rlyenp6e/42nQ6/e/+uZOJJBCWZfk7vrb7b/P5/If+3k9z7Rf/fe1rX/va10upX/u1X/sf/RL+u+qNN94A+C9aCP9/Z/wf/94f+7Ef+29+76e5Pv29iX3ta1/72te+7rC++MUvMpvN+I//8T/+jq/tVP5/6A/9IeBW/f9f+16l1H/RCfBpq/3iv6997Wtf+/pMVxiG/Lk/9+f41V/9Vb72ta8N/72qKv7RP/pH3Lt3j5/92Z8F4A/+wT/I22+/zS/8wi+wWCyG733nnXf4xV/8Rf7kn/yTHB0d/Z7/Dj9s7dv++9rXvva1r898/e2//bf5pV/6JX72Z3+Wn//5n+f09JRf+IVf4Fvf+hb//J//c9I0Hb73H//jf8zP/uzP8lM/9VP81b/6V6nrmn/wD/4Bo9GIv/t3/+7/wN/ik9f+5L+vfe1rX/v6zNfZ2Rlf+9rX+ON//I/zD//hP+Sv//W/ThzH/PIv/zJ/9s/+2R/43p/5mZ/hX//rf829e/f4G3/jb/D3/t7f46tf/Sq/8iu/wttvv/0/6Df44WrP9t/Xvva1r33t6zNW+5P/vva1r33ta1+fsdov/vva1772ta99fcZqv/jva1/72te+9vUZq/3iv6997Wtf+9rXZ6z2i/++9rWvfe1rX5+x2i/++9rXvva1r319xmq/+O9rX/va17729Rmr/eK/r33ta1/72tdnrPaL/772ta997Wtfn7HaL/772te+9rWvfX3Gar/472tf+9rXvvb1Gav94r+vfe1rX/va12es9ov/vva1r33ta1+fsfr/AsCq3FplQYZYAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 600x600 with 10 Axes>"
       ]
@@ -801,9 +880,9 @@
     }
    ],
    "source": [
-    "photons_per_pixel = 150\n",
+    "photons_per_pixel = 450\n",
     "vmin = 50\n",
-    "vmax = 250\n",
+    "vmax = 100\n",
     "\n",
     "# # take vmin and vmax as 1st and 99th percentile over all channels\n",
     "# vmin = 19999\n",

From 63a5b703800937a68ef40e61beca95e0bb640a03 Mon Sep 17 00:00:00 2001
From: Henry Pinkard <7969470+henrypinkard@users.noreply.github.com>
Date: Mon, 21 Oct 2024 11:13:57 -0700
Subject: [PATCH 2/2] lots of updates for redoing supplementary figures

---
 figure_making/animations/tree_figure.ipynb    |   110 +
 figure_making/bayer_figure.ipynb              |    64 +
 .../config_files/make_config_files.ipynb      |     2 +-
 ...tic_gaussian_entropy_vs_test_set_nll.ipynb |  5337 +-
 .../MI_estimator_consistency.ipynb            | 33201 +++++-
 .../mi_of_background.ipynb                    | 14540 +--
 .../5_mi_vs_bandlimited_signal_space.ipynb    | 98732 +++++++++++-----
 .../mi_vs_sampling_density_SNRs.ipynb         | 51190 +++++++-
 simulations_1d/mi_vs_signal_bandwidth.ipynb   | 10142 +-
 simulations_1d/mi_vs_snr.ipynb                | 11914 +-
 simulations_1d/signal_utils_1D.py             |    45 +-
 .../information_estimation.py                 |     3 +-
 .../models/gaussian_process.py                |     2 +-
 .../models/model_base_class.py                |     3 +
 14 files changed, 183001 insertions(+), 42284 deletions(-)

diff --git a/figure_making/animations/tree_figure.ipynb b/figure_making/animations/tree_figure.ipynb
index 39b7d1c..a297541 100644
--- a/figure_making/animations/tree_figure.ipynb
+++ b/figure_making/animations/tree_figure.ipynb
@@ -1754,6 +1754,108 @@
     "video_output_path = '/home/hpinkard_waller/data/trees/animation.mp4'\n",
     "os.system(f'ffmpeg -y -framerate 20 -i {output_dir}/frame_%03d.png -vf \"scale=ceil(iw/2)*2:ceil(ih/2)*2\" -vcodec libx264 -crf 23 -pix_fmt yuv420p {video_output_path}')\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_216680/1253613364.py:82: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
+      "  montage_image = montage_image.resize((1920, 1080), Image.ANTIALIAS)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "from scipy.ndimage import gaussian_filter\n",
+    "import os\n",
+    "\n",
+    "# Function to add Gaussian noise to an image\n",
+    "def add_noise(image, gaussian_sigma):\n",
+    "    noise = np.random.normal(0, gaussian_sigma, image.shape)\n",
+    "    noisy_image = image + noise\n",
+    "    return noisy_image\n",
+    "\n",
+    "# Function to apply Gaussian blur to each channel of an image\n",
+    "def blur_each_channel(image, sigma):\n",
+    "    blurred_image = np.zeros_like(image)\n",
+    "    for channel in range(3):  # Assuming RGB image\n",
+    "        blurred_image[:, :, channel] = gaussian_filter(\n",
+    "            image[:, :, channel], sigma=sigma, mode='reflect'\n",
+    "        )\n",
+    "    return blurred_image\n",
+    "\n",
+    "# Parameters\n",
+    "shape = (220, 220)  # Resize shape for each tree image\n",
+    "grid_size = (8, 8)  # Grid size (rows, columns)\n",
+    "min_blur = 0        # Minimum blur level\n",
+    "max_blur = 13      # Maximum blur level\n",
+    "min_noise = 0       # Minimum noise level\n",
+    "max_noise = 120     # Maximum noise level\n",
+    "\n",
+    "# Directories (update these paths as needed)\n",
+    "tree_dir = '/home/hpinkard_waller/data/trees/high_res'\n",
+    "output_path = '/home/hpinkard_waller/data/trees/montage.png'\n",
+    "\n",
+    "# Load tree images\n",
+    "trees = []\n",
+    "for name in os.listdir(tree_dir):\n",
+    "    img_path = os.path.join(tree_dir, name)\n",
+    "    img = Image.open(img_path).resize(shape)\n",
+    "    trees.append(np.array(img))\n",
+    "\n",
+    "# Generate the montage image\n",
+    "row_images = []\n",
+    "for y in range(grid_size[0]):  # Rows\n",
+    "    col_images = []\n",
+    "    for x in range(grid_size[1]):  # Columns\n",
+    "        # Generate random blur and noise levels within limits\n",
+    "        blur_sigma = np.random.uniform(min_blur, max_blur)\n",
+    "        noise_sigma = np.random.uniform(min_noise, max_noise)\n",
+    "\n",
+    "        # Pick a random tree image\n",
+    "        img = trees[np.random.randint(len(trees))]\n",
+    "\n",
+    "        # Apply blur and noise\n",
+    "        blurred_img = blur_each_channel(img, blur_sigma)\n",
+    "        noisy_img = add_noise(blurred_img, gaussian_sigma=noise_sigma)\n",
+    "        noisy_img = np.clip(noisy_img, 0, 255).astype(np.uint8)\n",
+    "        col_images.append(noisy_img)\n",
+    "\n",
+    "    # Concatenate images horizontally\n",
+    "    row_image = np.hstack(col_images)\n",
+    "    row_images.append(row_image)\n",
+    "\n",
+    "# Concatenate images vertically to form the grid\n",
+    "montage_image = np.vstack(row_images)\n",
+    "\n",
+    "# Crop to 16:9 aspect ratio and resize to 1920x1080\n",
+    "h, w = montage_image.shape[:2]\n",
+    "target_aspect_ratio = 16 / 9\n",
+    "current_aspect_ratio = w / h\n",
+    "\n",
+    "if current_aspect_ratio > target_aspect_ratio:\n",
+    "    # Crop width\n",
+    "    new_w = int(h * target_aspect_ratio)\n",
+    "    start_w = (w - new_w) // 2\n",
+    "    montage_image = montage_image[:, start_w:start_w + new_w]\n",
+    "elif current_aspect_ratio < target_aspect_ratio:\n",
+    "    # Crop height\n",
+    "    new_h = int(w / target_aspect_ratio)\n",
+    "    start_h = (h - new_h) // 2\n",
+    "    montage_image = montage_image[start_h:start_h + new_h, :]\n",
+    "\n",
+    "montage_image = Image.fromarray(montage_image)\n",
+    "montage_image = montage_image.resize((1920, 1080), Image.ANTIALIAS)\n",
+    "\n",
+    "# Save the final montage image\n",
+    "montage_image.save(output_path)\n"
+   ]
   }
  ],
  "metadata": {
@@ -1763,7 +1865,15 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
    "version": "3.10.6"
   }
  },
diff --git a/figure_making/bayer_figure.ipynb b/figure_making/bayer_figure.ipynb
index 815bf8c..8f42cc6 100644
--- a/figure_making/bayer_figure.ipynb
+++ b/figure_making/bayer_figure.ipynb
@@ -334,6 +334,70 @@
     "for i, tile in zip(tile_indices, tiles):\n",
     "    np.save(f'/home/hpinkard_waller/figures/bayer/tile_{i}.npy', tile)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x200 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x200 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 600x200 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# open all the npy files in the subdirs of 'recon_patches'\n",
+    "import os\n",
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "recons = {}\n",
+    "for subdir in os.listdir('recon_patches'):\n",
+    "    if os.path.isdir(f'recon_patches/{subdir}'):\n",
+    "        recons[subdir] = []\n",
+    "        for file in os.listdir(f'recon_patches/{subdir}'):\n",
+    "            if file.endswith('.npy'):\n",
+    "                recons[subdir].append(np.load(f'recon_patches/{subdir}/{file}'))\n",
+    "\n",
+    "rescale_for_display = lambda x: np.clip((x / 255 * 3) ** (1 / 2.2), 0, 1)\n",
+    "\n",
+    "\n",
+    "# plot them\n",
+    "for subdir in recons.keys():\n",
+    "    fig, ax = plt.subplots(1, 3, figsize=(6, 2))\n",
+    "    for i in range(len(recons[subdir])):\n",
+    "        ax[i].imshow(rescale_for_display(\n",
+    "            recons[subdir][i]), interpolation='none')\n",
+    "        ax[i].axis('off')\n",
+    "    fig.supxlabel(subdir)\n",
+    "    fig.savefig(f'/home/hpinkard_waller/figures/bayer/recons_{subdir}.pdf', transparent=True)\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/led_array/phenotyping_experiments/config_files/make_config_files.ipynb b/led_array/phenotyping_experiments/config_files/make_config_files.ipynb
index 0f839c1..1189aa7 100644
--- a/led_array/phenotyping_experiments/config_files/make_config_files.ipynb
+++ b/led_array/phenotyping_experiments/config_files/make_config_files.ipynb
@@ -870,7 +870,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/mi_estimator_experiments/analytic_gaussian_entropy_vs_test_set_nll.ipynb b/mi_estimator_experiments/analytic_gaussian_entropy_vs_test_set_nll.ipynb
index 7f1b0d2..8ed5811 100644
--- a/mi_estimator_experiments/analytic_gaussian_entropy_vs_test_set_nll.ipynb
+++ b/mi_estimator_experiments/analytic_gaussian_entropy_vs_test_set_nll.ipynb
@@ -5,10 +5,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-13T05:01:57.821487Z",
-     "iopub.status.busy": "2024-01-13T05:01:57.820771Z",
-     "iopub.status.idle": "2024-01-13T05:02:03.466091Z",
-     "shell.execute_reply": "2024-01-13T05:02:03.464745Z"
+     "iopub.execute_input": "2024-10-15T04:47:46.031372Z",
+     "iopub.status.busy": "2024-10-15T04:47:46.030760Z",
+     "iopub.status.idle": "2024-10-15T04:47:52.616186Z",
+     "shell.execute_reply": "2024-10-15T04:47:52.615586Z"
     }
    },
    "outputs": [
@@ -16,10 +16,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-10-14 10:14:42.187566: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-14 10:14:43.246897: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-14 10:14:43.247012: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-14 10:14:43.247024: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+      "2024-10-14 21:47:47.382674: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-14 21:47:48.035838: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-14 21:47:48.035912: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-14 21:47:48.035919: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
      ]
     },
     {
@@ -40,7 +40,7 @@
     "\n",
     "import os\n",
     "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '3'\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n",
     "from encoding_information.gpu_utils import limit_gpu_memory_growth\n",
     "limit_gpu_memory_growth()\n",
     "\n",
@@ -60,7 +60,14 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-15T04:47:52.619230Z",
+     "iopub.status.busy": "2024-10-15T04:47:52.619052Z",
+     "iopub.status.idle": "2024-10-15T04:47:52.654689Z",
+     "shell.execute_reply": "2024-10-15T04:47:52.654091Z"
+    }
+   },
    "outputs": [],
    "source": [
     "def estimate_information_analytic(h_y_estimates, noise_model, full_dataset,\n",
@@ -113,7 +120,7 @@
     "        hy_given_xs = np.array(hy_given_xs)\n",
     "\n",
     "        # Combine h_y_estimates with hy_given_xs to compute mutual information estimates\n",
-    "        mutual_infos = (h_y_estimates[None, :] - hy_given_xs[:, None]) / np.log(2)\n",
+    "        mutual_infos = (np.array(h_y_estimates)[None, :] - hy_given_xs[:, None]) / np.log(2)\n",
     "        mutual_infos = mutual_infos.flatten()\n",
     "\n",
     "        # Compute confidence interval\n",
@@ -132,13 +139,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-13T05:02:03.470038Z",
-     "iopub.status.busy": "2024-01-13T05:02:03.469658Z",
-     "iopub.status.idle": "2024-01-14T13:08:03.477250Z",
-     "shell.execute_reply": "2024-01-14T13:08:03.476518Z"
+     "iopub.execute_input": "2024-10-15T04:47:52.658234Z",
+     "iopub.status.busy": "2024-10-15T04:47:52.658043Z",
+     "iopub.status.idle": "2024-10-15T10:20:35.802984Z",
+     "shell.execute_reply": "2024-10-15T10:20:35.802321Z"
     }
    },
    "outputs": [
@@ -146,20 +153,5221 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/3 [00:00<?, ?it/s]2024-10-14 10:19:12.958922: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
-      "training:  60%|█████▉    | 119/200 [03:09<02:08,  1.59s/it]\n",
-      "  0%|          | 0/3 [03:41<?, ?it/s]\n"
-     ]
-    },
-    {
-     "ename": "NameError",
-     "evalue": "name 'confidence' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [5], line 80\u001b[0m\n\u001b[1;32m     75\u001b[0m models_gaussian\u001b[38;5;241m.\u001b[39mappend(gaussian_process)\n\u001b[1;32m     78\u001b[0m noise_model \u001b[38;5;241m=\u001b[39m PoissonNoiseModel()\n\u001b[0;32m---> 80\u001b[0m estimate, lower, upper \u001b[38;5;241m=\u001b[39m estimate_information(models_pcnn, noise_model, train_patches, test_patches, confidence_interval\u001b[38;5;241m=\u001b[39m\u001b[43mconfidence\u001b[49m)\n\u001b[1;32m     81\u001b[0m mi_means_by_channel_photons_pixel_cnn[channel_name]\u001b[38;5;241m.\u001b[39mappend(mi_mean_pixel_cnn)\n\u001b[1;32m     82\u001b[0m mi_confidences_by_channel_photons_pixel_cnn[channel_name]\u001b[38;5;241m.\u001b[39mappend(mi_confidence_pixel_cnn)\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'confidence' is not defined"
+      "  0%|                                                             | 0/3 [00:00<?, ?it/s]2024-10-14 21:48:41.178312: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:11<38:54, 11.73s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:16<24:30,  7.43s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:20<19:53,  6.06s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:25<17:58,  5.50s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:29<16:33,  5.09s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:34<15:54,  4.92s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:38<15:28,  4.81s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:43<15:08,  4.73s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:47<14:50,  4.66s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:52<14:38,  4.62s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:56<14:29,  4.60s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:01<14:24,  4.60s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:05<14:12,  4.56s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:10<14:13,  4.59s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:15<14:09,  4.59s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:19<13:49,  4.51s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:23<13:27,  4.41s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:28<13:22,  4.41s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:32<13:25,  4.45s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:37<13:27,  4.49s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:41<13:28,  4.52s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:46<13:34,  4.57s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:51<13:38,  4.63s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:55<13:28,  4.60s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [02:00<13:21,  4.58s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:04<13:15,  4.57s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:09<13:10,  4.57s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:14<13:08,  4.58s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:18<12:58,  4.55s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:23<12:53,  4.55s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:27<12:47,  4.54s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:32<12:41,  4.53s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:36<12:37,  4.54s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:41<12:32,  4.54s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:28,  4.54s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:50<12:26,  4.55s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:54<12:24,  4.57s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:59<12:18,  4.56s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:04<12:12,  4.55s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:08,  4.55s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:13<12:03,  4.55s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:17<12:00,  4.56s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:26<12:35,  4.81s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:09<30:12,  9.11s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:58,  6.36s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:32,  5.65s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<17:00,  5.21s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:07,  4.96s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:32<15:41,  4.86s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:20,  4.77s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:01,  4.70s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<14:48,  4.65s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:38,  4.62s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:28,  4.60s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:22,  4.59s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:17,  4.58s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:15,  4.60s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:10,  4.60s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:02,  4.58s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<13:58,  4.58s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<13:53,  4.58s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<13:47,  4.57s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<13:46,  4.59s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:43,  4.60s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:45<13:36,  4.59s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:32,  4.59s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:54<13:29,  4.60s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:59<13:27,  4.61s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:28,  4.65s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:08<13:29,  4.68s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:13<13:20,  4.65s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:17<13:13,  4.64s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:22<13:06,  4.63s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:27<13:01,  4.62s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:31<13:10,  4.70s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:36<12:54,  4.64s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:41<12:49,  4.64s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:39,  4.60s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:50<12:33,  4.60s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:54<12:30,  4.60s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:59<12:24,  4.59s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:03<12:18,  4.59s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:13,  4.58s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:12<12:05,  4.56s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:17<12:00,  4.56s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:22<11:57,  4.57s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:31<12:29,  4.80s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:53,  8.71s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<21:20,  6.47s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:56,  5.77s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:23<17:40,  5.41s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:25,  5.05s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:32<16:02,  4.96s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:37<15:42,  4.88s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:42<15:43,  4.91s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:47<15:41,  4.93s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:52<15:35,  4.92s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:57<15:32,  4.93s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:02<15:31,  4.96s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:06<15:20,  4.92s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:11<15:11,  4.90s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:17<15:23,  4.99s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:21<15:08,  4.94s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:26<14:38,  4.80s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:31<14:35,  4.81s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:35<14:30,  4.81s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:40<14:24,  4.80s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:45<14:23,  4.82s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:50<14:23,  4.85s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:55<14:21,  4.87s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [02:00<14:17,  4.87s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [02:05<14:13,  4.88s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:10<14:09,  4.88s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:14<13:57,  4.84s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:19<13:35,  4.74s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:24<13:32,  4.75s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:28<13:23,  4.73s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:33<13:22,  4.75s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:38<13:17,  4.75s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:43<13:14,  4.76s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:48<13:18,  4.81s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:52<13:18,  4.84s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:57<13:18,  4.87s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [03:02<13:16,  4.89s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [03:07<13:14,  4.90s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:12<13:11,  4.92s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:17<13:07,  4.92s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:22<13:02,  4.92s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:32<13:20,  5.07s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:50,  8.40s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:22,  6.18s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:59,  5.48s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:43,  5.12s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:58,  4.91s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:32,  4.81s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<15:13,  4.73s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<15:00,  4.69s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<14:50,  4.66s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:44,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:38,  4.65s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:31,  4.63s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:26,  4.63s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:21,  4.63s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:12<14:12,  4.61s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:08,  4.61s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:21<13:57,  4.58s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<13:51,  4.57s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<13:51,  4.59s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:35<13:45,  4.58s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:44,  4.61s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:45<13:41,  4.62s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:37,  4.62s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:53<13:14,  4.52s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:58<13:14,  4.54s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:15,  4.57s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:08<13:25,  4.65s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:12<13:14,  4.62s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:17<13:07,  4.60s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:21<13:00,  4.59s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:26<12:56,  4.59s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:30<12:49,  4.58s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:35<12:44,  4.58s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:39<12:40,  4.58s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:44<12:34,  4.57s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:49<12:29,  4.57s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:53<12:26,  4.58s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:58<12:21,  4.58s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:02<12:17,  4.58s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:07<12:12,  4.58s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:12<12:07,  4.57s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:21<12:36,  4.79s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:28,  8.58s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<21:02,  6.38s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:19,  5.58s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:49,  5.15s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:05,  4.95s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:42,  4.86s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:24,  4.79s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:09,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<14:59,  4.71s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:45,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:38,  4.65s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:47,  4.72s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:04<14:34,  4.68s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:09<14:26,  4.66s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:15,  4.63s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:18<14:05,  4.60s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<14:01,  4.60s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:27<13:55,  4.59s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<13:50,  4.59s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<13:44,  4.58s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:37,  4.57s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:45<13:30,  4.55s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:50<13:32,  4.59s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:54<13:22,  4.56s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:59<13:19,  4.57s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:17,  4.58s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:08<13:15,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:13<13:09,  4.59s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:17<13:06,  4.60s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:22<13:01,  4.60s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:26<12:59,  4.61s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:31<12:55,  4.62s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:36<12:51,  4.62s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:40<12:50,  4.64s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:50,  4.67s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:50<12:47,  4.68s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:54<12:43,  4.68s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:59<12:38,  4.68s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:04<12:31,  4.67s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:22,  4.64s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:13<12:23,  4.68s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:18<12:14,  4.65s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:27<12:37,  4.83s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.77s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:05,  2.90it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.10it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.08it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.76it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.51s/it]\u001b[A\n",
+      "Evaluating NLL: 39it [00:01, 33.50it/s]\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 69.39it/s]\u001b[A\n",
+      "Evaluating NLL: 111it [00:01, 108.72it/s]\u001b[A\n",
+      "Evaluating NLL: 151it [00:01, 155.73it/s]\u001b[A\n",
+      "Evaluating NLL: 191it [00:02, 199.34it/s]\u001b[A\n",
+      "Evaluating NLL: 235it [00:02, 248.45it/s]\u001b[A\n",
+      "Evaluating NLL: 273it [00:02, 278.54it/s]\u001b[A\n",
+      "Evaluating NLL: 314it [00:02, 310.73it/s]\u001b[A\n",
+      "Evaluating NLL: 355it [00:02, 335.86it/s]\u001b[A\n",
+      "Evaluating NLL: 399it [00:02, 361.62it/s]\u001b[A\n",
+      "Evaluating NLL: 446it [00:02, 391.57it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 181.32it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%|     | 1/100 [00:00<00:36,  2.72it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▌   | 14/100 [00:00<00:02, 37.87it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|█   | 26/100 [00:00<00:01, 60.86it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|█▌  | 38/100 [00:00<00:00, 77.76it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|██  | 51/100 [00:00<00:00, 91.02it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|██▌ | 63/100 [00:00<00:00, 96.36it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|██▎| 76/100 [00:00<00:00, 103.87it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 83.30it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%|     | 1/100 [00:00<00:38,  2.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▍   | 11/100 [00:00<00:03, 28.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 44.31it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|█▏  | 29/100 [00:00<00:01, 56.52it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:00, 71.00it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██  | 52/100 [00:00<00:00, 83.96it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|██▌ | 63/100 [00:01<00:00, 90.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|██▉ | 74/100 [00:01<00:00, 93.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|███▍| 85/100 [00:01<00:00, 96.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 71.86it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 141.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▉  | 30/100 [00:00<00:00, 134.27it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█▎ | 45/100 [00:00<00:00, 136.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|█▊ | 59/100 [00:00<00:00, 129.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|██▏| 72/100 [00:00<00:00, 122.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 129.92it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:20,  8.24s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<20:23,  6.18s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<18:08,  5.52s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:31,  5.06s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:54,  4.90s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:21,  4.75s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:27,  4.81s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<15:21,  4.80s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:04,  4.74s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:53,  4.70s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:43,  4.67s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:33,  4.65s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:31,  4.66s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:22,  4.64s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:15,  4.62s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:10,  4.62s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<13:56,  4.57s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<13:52,  4.58s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<13:51,  4.60s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<13:49,  4.61s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:45,  4.61s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:45<13:40,  4.61s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:37,  4.62s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:54<13:31,  4.61s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:59<13:28,  4.62s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:22,  4.61s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:08<13:20,  4.63s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:13<13:14,  4.62s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:17<13:13,  4.64s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:22<13:11,  4.66s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:27<13:07,  4.66s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:31<13:02,  4.66s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:36<13:03,  4.69s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:40<12:44,  4.61s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:34,  4.57s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:50<12:41,  4.64s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:54<12:35,  4.64s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:59<12:29,  4.63s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:03<12:21,  4.61s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:19,  4.62s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:13<12:15,  4.63s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:17<12:08,  4.61s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:27<12:35,  4.81s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:32,  8.30s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:46,  6.29s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:58,  5.47s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:45,  5.13s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<16:03,  4.94s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:36,  4.83s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:12,  4.73s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<14:58,  4.68s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<14:54,  4.68s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:48,  4.68s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:40,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:35,  4.65s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:27,  4.64s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:26,  4.66s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:25,  4.68s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:23,  4.69s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<14:31,  4.76s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:27<14:18,  4.72s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:32<14:08,  4.69s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<13:59,  4.67s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:41<13:51,  4.65s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:45<13:43,  4.63s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:50<13:38,  4.62s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:55<13:29,  4.60s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:59<13:23,  4.59s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:04<13:18,  4.59s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:08<13:14,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:13<13:07,  4.58s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:18<13:05,  4.59s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:22<12:59,  4.59s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:27<12:55,  4.59s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:31<12:49,  4.58s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:36<12:42,  4.57s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:40<12:41,  4.59s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:37,  4.59s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:50<12:35,  4.61s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:54<12:34,  4.63s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:59<12:32,  4.65s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:04<12:30,  4.66s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:24,  4.65s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:13<12:19,  4.65s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:17<12:05,  4.59s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:22<12:09,  4.64s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:27<12:06,  4.66s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:32<12:07,  4.69s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:41<12:21,  4.81s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:49,  8.39s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:58,  6.36s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:23,  5.60s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<17:05,  5.23s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:18,  5.02s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:48,  4.89s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:31,  4.83s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:15,  4.77s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:03,  4.73s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:45,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:55<14:40,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:33,  4.65s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:04<14:27,  4.64s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:21,  4.63s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:18,  4.64s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:18<14:15,  4.65s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<14:10,  4.65s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:27<14:10,  4.67s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:32<14:07,  4.68s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:37<14:07,  4.71s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:41<14:01,  4.70s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:46<14:00,  4.72s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:51<13:55,  4.72s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:56<13:55,  4.75s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [02:00<13:47,  4.73s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:05<13:44,  4.74s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:10<13:45,  4.77s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:15<13:36,  4.75s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:19<13:23,  4.70s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:24<13:15,  4.68s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:29<13:11,  4.68s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:33<13:08,  4.69s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:38<12:49,  4.61s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:42<12:47,  4.62s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:47<12:40,  4.61s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:52<12:40,  4.64s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:56<12:38,  4.65s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [03:01<12:34,  4.66s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:06<12:31,  4.67s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:10<12:23,  4.65s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:15<12:17,  4.64s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:19<12:10,  4.62s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:24<12:06,  4.63s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:33<12:38,  4.86s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<29:34,  8.92s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<21:12,  6.43s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:28,  5.63s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<17:11,  5.26s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:28,  5.07s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:32<15:53,  4.92s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:31,  4.83s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:21,  4.80s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:46<15:17,  4.80s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:51<15:13,  4.81s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:55<14:59,  4.76s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:00<14:42,  4.69s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:05<14:34,  4.68s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:09<14:25,  4.65s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:14<14:17,  4.64s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:18<14:09,  4.62s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:23<14:01,  4.60s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:28<13:56,  4.60s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:32<13:55,  4.62s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:37<13:50,  4.62s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:42<13:48,  4.63s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:46<13:40,  4.61s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:51<13:38,  4.62s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:55<13:33,  4.62s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [02:00<13:29,  4.63s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:05<13:20,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:09<13:16,  4.61s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:14<13:10,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:18<13:07,  4.61s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:23<13:04,  4.62s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:28<13:01,  4.62s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:32<12:56,  4.62s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:37<12:54,  4.64s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:42<12:53,  4.66s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:46<12:50,  4.67s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:51<12:46,  4.68s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:56<12:33,  4.62s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [03:00<12:26,  4.61s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:05<12:21,  4.60s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:09<12:15,  4.60s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:14<12:08,  4.58s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:18<12:05,  4.59s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:23<12:00,  4.59s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:28<11:56,  4.59s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:32<11:50,  4.59s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:41<12:21,  4.82s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:48,  8.39s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<20:17,  6.15s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:44,  5.40s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:36,  5.08s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:56,  4.91s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:28,  4.79s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<15:18,  4.76s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<15:10,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:03,  4.73s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:45,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:29,  4.60s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<14:26,  4.61s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:27,  4.64s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:30,  4.68s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:26,  4.68s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:17,  4.66s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<14:03,  4.61s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<13:58,  4.60s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<13:54,  4.61s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<13:50,  4.61s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:45,  4.61s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:45<13:40,  4.61s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:41,  4.64s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:54<13:34,  4.63s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:59<13:28,  4.62s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:22,  4.61s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:08<13:18,  4.62s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:13<13:14,  4.62s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:17<13:08,  4.61s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:22<13:04,  4.62s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:26<12:57,  4.60s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:31<12:54,  4.61s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:35<12:41,  4.56s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:40<12:40,  4.58s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:41,  4.62s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:49<12:38,  4.62s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:54<12:35,  4.63s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:59<12:30,  4.63s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:03<12:25,  4.63s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:21,  4.63s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:13<12:29,  4.71s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:17<12:09,  4.62s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:22<12:04,  4.62s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:26<11:58,  4.61s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:36<12:24,  4.80s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.96it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.20it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 46it [00:00, 64.92it/s]\u001b[A\n",
+      "Evaluating NLL: 94it [00:01, 134.51it/s]\u001b[A\n",
+      "Evaluating NLL: 139it [00:01, 195.11it/s]\u001b[A\n",
+      "Evaluating NLL: 189it [00:01, 260.27it/s]\u001b[A\n",
+      "Evaluating NLL: 232it [00:01, 300.00it/s]\u001b[A\n",
+      "Evaluating NLL: 284it [00:01, 353.83it/s]\u001b[A\n",
+      "Evaluating NLL: 335it [00:01, 394.52it/s]\u001b[A\n",
+      "Evaluating NLL: 383it [00:01, 417.30it/s]\u001b[A\n",
+      "Evaluating NLL: 433it [00:01, 438.77it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 261.71it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 128.58it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▊  | 27/100 [00:00<00:00, 121.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|█▏ | 40/100 [00:00<00:00, 123.79it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|█▌ | 53/100 [00:00<00:00, 124.82it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|█▉ | 66/100 [00:00<00:00, 121.32it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|██▎| 79/100 [00:00<00:00, 120.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 121.38it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▎  | 11/100 [00:00<00:00, 100.77it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▉   | 22/100 [00:00<00:00, 97.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|█▎  | 32/100 [00:00<00:00, 96.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|█▎ | 43/100 [00:00<00:00, 100.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|█▌ | 54/100 [00:00<00:00, 101.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|█▉ | 65/100 [00:00<00:00, 102.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|██▎| 76/100 [00:00<00:00, 104.75it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|██▋| 88/100 [00:00<00:00, 107.43it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 103.75it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 137.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 136.34it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 130.48it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|█▋ | 56/100 [00:00<00:00, 130.06it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 130.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██▌| 84/100 [00:00<00:00, 128.87it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 128.80it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:01,  8.15s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:44,  5.98s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<17:10,  5.23s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:13,  4.97s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:35,  4.80s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<15:22,  4.76s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<15:12,  4.73s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:50,  4.64s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<15:02,  4.73s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:53,  4.70s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:53<14:41,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<14:32,  4.64s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:25,  4.63s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<14:18,  4.61s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:12<14:12,  4.61s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:16<14:09,  4.62s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:21<14:02,  4.60s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<13:58,  4.61s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:30<13:51,  4.60s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:35<13:50,  4.62s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:48,  4.63s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:44<13:42,  4.62s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:37,  4.62s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:53<13:31,  4.61s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:58<13:28,  4.62s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:23,  4.62s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:07<13:15,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:12<13:12,  4.61s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:16<13:10,  4.63s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:21<13:08,  4.64s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:26<13:02,  4.63s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:30<12:56,  4.62s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:35<12:55,  4.65s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:40<12:51,  4.65s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:58,  4.72s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:49<12:30,  4.58s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:53<12:24,  4.56s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:58<12:22,  4.58s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:02<12:08,  4.53s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:07<12:06,  4.54s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:12<12:03,  4.55s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:16<12:01,  4.57s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:21<12:00,  4.59s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:25<11:55,  4.59s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:30<11:41,  4.52s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:34<11:40,  4.55s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:44<12:09,  4.77s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:25,  8.57s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:51,  6.02s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:44,  5.41s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:39,  5.10s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<16:02,  4.94s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:37,  4.83s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<15:17,  4.76s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<15:05,  4.72s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:01,  4.72s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:57,  4.72s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:48,  4.70s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:42,  4.70s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:29,  4.65s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:18,  4.61s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:33,  4.72s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:05,  4.60s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<14:01,  4.60s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<13:55,  4.59s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<13:41,  4.54s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:35<13:35,  4.53s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:31,  4.53s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:44<13:25,  4.53s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:20,  4.52s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:53<13:18,  4.54s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:58<13:15,  4.54s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:11,  4.55s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:07<13:13,  4.59s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:12<13:09,  4.59s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:17<13:06,  4.60s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:21<13:01,  4.59s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:26<12:56,  4.60s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:30<12:51,  4.59s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:35<12:48,  4.60s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:40<12:46,  4.62s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:44<12:45,  4.64s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:49<12:40,  4.63s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:53<12:34,  4.63s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:58<12:31,  4.64s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:03<12:27,  4.64s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:25,  4.66s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:12<12:19,  4.65s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:17<12:18,  4.67s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:22<12:13,  4.67s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:26<12:11,  4.69s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:31<11:58,  4.64s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:35<11:52,  4.63s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:40<11:46,  4.62s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:49<12:07,  4.78s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:37,  8.63s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:22,  6.17s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<18:01,  5.49s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<17:17,  5.29s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:25,  5.05s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:51,  4.91s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:34,  4.84s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:10,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:00,  4.71s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:49,  4.68s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:20,  4.55s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:11,  4.53s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:11,  4.56s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:07,  4.55s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:09,  4.59s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:10,  4.62s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<14:09,  4.64s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:27<14:06,  4.65s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<14:10,  4.70s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<14:05,  4.70s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:41<14:04,  4.72s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:46<13:55,  4.70s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:50<14:00,  4.75s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:55<13:56,  4.75s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [02:00<13:46,  4.72s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:05<13:39,  4.71s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:09<13:30,  4.68s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:14<13:27,  4.69s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:18<13:19,  4.67s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:23<13:10,  4.65s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:28<13:08,  4.67s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:32<13:01,  4.65s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:37<12:56,  4.65s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:42<12:49,  4.64s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:46<12:50,  4.67s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:51<12:42,  4.65s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:56<12:34,  4.63s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [03:00<12:30,  4.63s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:05<12:27,  4.64s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:10<12:21,  4.64s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:14<12:14,  4.62s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:19<12:09,  4.62s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:23<12:02,  4.60s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:28<11:59,  4.61s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:32<11:54,  4.61s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:37<11:50,  4.61s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:42<11:50,  4.64s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:47<11:47,  4.65s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:56<12:08,  4.82s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<29:06,  8.78s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<21:25,  6.49s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:28,  5.63s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<17:04,  5.23s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:07,  4.96s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:17,  4.73s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:17,  4.75s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:20,  4.80s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:05,  4.74s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:59,  4.73s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:55<15:04,  4.78s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:00<14:48,  4.73s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:05<14:51,  4.77s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:09<14:39,  4.73s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:14<14:46,  4.79s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:19<14:42,  4.80s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:24<14:42,  4.82s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:29<14:42,  4.85s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:34<14:44,  4.89s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:38<14:27,  4.82s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:43<14:12,  4.76s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:48<14:04,  4.74s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:53<14:11,  4.81s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:58<14:09,  4.83s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [02:02<13:58,  4.79s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:07<13:52,  4.79s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:12<13:54,  4.82s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:17<14:00,  4.88s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:22<13:56,  4.89s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:27<13:51,  4.89s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:32<13:49,  4.91s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:37<13:44,  4.91s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:41<13:39,  4.91s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:46<13:26,  4.86s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:51<13:08,  4.78s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:55<12:52,  4.71s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [03:00<12:49,  4.72s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [03:05<12:35,  4.67s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:09<12:38,  4.71s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:14<12:45,  4.78s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:19<12:41,  4.79s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:24<12:44,  4.84s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:29<12:40,  4.85s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:34<12:38,  4.86s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:43<12:50,  4.97s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:09<29:51,  9.00s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<21:27,  6.50s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:16,  5.56s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<17:05,  5.23s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:21,  5.03s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:32<15:59,  4.95s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:37<15:50,  4.93s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:05,  4.72s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:46<14:52,  4.67s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:45,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:55<14:38,  4.65s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:00<14:38,  4.67s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:04<14:30,  4.65s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:09<14:23,  4.64s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:15,  4.63s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:18<14:08,  4.61s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:23<14:01,  4.60s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:27<13:57,  4.60s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:32<13:56,  4.62s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<13:50,  4.61s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:41<13:42,  4.60s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:46<13:37,  4.59s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:50<13:34,  4.60s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:55<13:31,  4.61s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:59<13:25,  4.60s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:04<13:24,  4.62s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:09<13:21,  4.63s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:13<13:17,  4.64s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:18<13:12,  4.63s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:23<13:10,  4.65s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:27<13:08,  4.67s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:32<13:06,  4.68s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:37<13:10,  4.73s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:41<12:53,  4.66s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:46<12:37,  4.59s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:50<12:32,  4.59s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:55<12:29,  4.60s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [03:00<12:25,  4.60s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:04<12:21,  4.61s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:09<12:13,  4.59s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:13<12:08,  4.58s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:23<12:43,  4.83s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.84it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.54it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.65it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 51it [00:00, 73.11it/s]\u001b[A\n",
+      "Evaluating NLL: 101it [00:01, 145.75it/s]\u001b[A\n",
+      "Evaluating NLL: 153it [00:01, 218.81it/s]\u001b[A\n",
+      "Evaluating NLL: 205it [00:01, 283.49it/s]\u001b[A\n",
+      "Evaluating NLL: 252it [00:01, 327.39it/s]\u001b[A\n",
+      "Evaluating NLL: 302it [00:01, 369.92it/s]\u001b[A\n",
+      "Evaluating NLL: 351it [00:01, 400.47it/s]\u001b[A\n",
+      "Evaluating NLL: 400it [00:01, 422.93it/s]\u001b[A\n",
+      "Evaluating NLL: 448it [00:01, 436.00it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 267.05it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 131.75it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 130.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 124.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|█▋ | 55/100 [00:00<00:00, 121.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|██ | 68/100 [00:00<00:00, 119.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  81%|██▍| 81/100 [00:00<00:00, 122.40it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 121.86it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:00, 94.07it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:00, 93.20it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|█▏  | 30/100 [00:00<00:00, 95.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|█▋  | 41/100 [00:00<00:00, 99.83it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|██  | 51/100 [00:00<00:00, 98.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|█▊ | 62/100 [00:00<00:00, 101.27it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|██▏| 74/100 [00:00<00:00, 104.97it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|██▌| 86/100 [00:00<00:00, 107.51it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 103.14it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 133.38it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▊  | 29/100 [00:00<00:00, 136.84it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█▎ | 44/100 [00:00<00:00, 138.23it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|█▋ | 58/100 [00:00<00:00, 138.44it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|██▏| 72/100 [00:00<00:00, 134.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|██▌| 86/100 [00:00<00:00, 132.77it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 132.53it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:48,  8.69s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:40,  6.27s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:51,  5.44s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:30,  5.05s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:41,  4.83s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:19,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<15:05,  4.69s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<14:53,  4.65s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<14:49,  4.66s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:44,  4.66s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:35,  4.63s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<14:29,  4.62s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:20,  4.60s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<14:12,  4.59s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:12<14:11,  4.60s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:07,  4.61s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:21<14:04,  4.61s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<13:56,  4.60s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:30<13:51,  4.59s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:35<13:46,  4.59s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:42,  4.60s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:44<13:34,  4.58s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:29,  4.57s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:53<13:23,  4.56s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:58<13:17,  4.56s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:02<13:18,  4.59s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:07<13:17,  4.61s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:12<13:21,  4.66s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:16<13:01,  4.57s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:21<12:52,  4.54s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:25<12:52,  4.57s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:30<12:47,  4.57s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:35<12:43,  4.57s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:39<12:41,  4.59s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:44<12:38,  4.60s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:48<12:31,  4.58s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:53<12:28,  4.59s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:58<12:26,  4.61s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:02<12:18,  4.59s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:07<12:11,  4.57s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:11<12:06,  4.57s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:16<12:02,  4.57s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:25<12:30,  4.78s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:14,  8.21s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:34,  6.24s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:49,  5.43s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:35,  5.08s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<16:05,  4.95s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:41,  4.85s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:22,  4.78s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<15:10,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:04,  4.74s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:55,  4.72s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:55<15:03,  4.78s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:59<14:47,  4.72s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:04<14:36,  4.69s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:08<14:28,  4.67s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:13<14:18,  4.64s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:18<14:16,  4.65s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:22<14:08,  4.64s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:27<13:54,  4.58s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<13:47,  4.57s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:36<13:41,  4.56s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:35,  4.55s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:45<13:35,  4.58s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:50<13:33,  4.60s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:54<13:27,  4.59s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:59<13:22,  4.58s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:17,  4.58s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:08<13:13,  4.59s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:13<13:11,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:17<13:05,  4.59s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:22<13:07,  4.63s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:27<13:05,  4.65s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:31<12:59,  4.64s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:36<12:57,  4.65s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:41<12:52,  4.65s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:45<12:48,  4.66s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:50<12:42,  4.65s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:55<12:40,  4.67s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:59<12:35,  4.66s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:04<12:39,  4.72s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:08<12:19,  4.62s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:13<12:13,  4.61s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:18<12:06,  4.60s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:22<12:02,  4.60s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:27<12:01,  4.62s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:31<11:53,  4.61s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:41<12:20,  4.81s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:41,  8.35s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:26,  6.20s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:58,  5.47s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:40,  5.11s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<16:01,  4.93s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:34,  4.82s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:18,  4.76s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<15:06,  4.72s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<15:00,  4.71s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:50<14:50,  4.68s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:27,  4.59s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<14:12,  4.53s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:02,  4.51s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<14:07,  4.56s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:12<14:03,  4.56s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:17<14:07,  4.61s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:21<14:08,  4.64s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:26<14:04,  4.64s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:31<14:04,  4.67s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:35<13:58,  4.66s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:40<13:51,  4.64s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:44<13:32,  4.57s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:49<13:18,  4.51s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:53<13:14,  4.52s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:58<13:16,  4.55s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:03<13:13,  4.56s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:07<13:11,  4.57s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:12<13:05,  4.57s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:16<12:57,  4.55s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:21<12:55,  4.56s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:25<12:44,  4.52s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:29<12:24,  4.43s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:34<12:17,  4.42s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:38<12:23,  4.48s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:43<12:20,  4.49s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:48<12:22,  4.53s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:52<12:12,  4.50s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:56<11:53,  4.40s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:01<11:58,  4.46s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:05<11:57,  4.48s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:10<11:57,  4.51s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:14<11:52,  4.51s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:19<11:49,  4.52s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:23<11:44,  4.52s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:28<11:51,  4.59s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:33<11:46,  4.59s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:37<11:37,  4.56s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:42<11:33,  4.56s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:46<11:28,  4.56s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:51<11:23,  4.55s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [04:00<11:41,  4.71s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:07,  8.18s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:39,  5.96s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:16,  5.26s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:06,  4.93s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:33,  4.79s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<15:16,  4.72s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:45,  4.59s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:22,  4.49s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:23,  4.52s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:48<14:27,  4.56s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:53<14:31,  4.61s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:57<14:28,  4.62s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:02<14:10,  4.55s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:06<14:11,  4.58s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:11<14:06,  4.58s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:15<14:03,  4.59s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:20<13:59,  4.59s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:25<13:51,  4.57s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:29<13:54,  4.61s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:34<13:43,  4.58s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:38<13:32,  4.54s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:43<13:14,  4.46s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:47<13:07,  4.45s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:52<13:09,  4.49s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:56<13:09,  4.51s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:01<13:08,  4.53s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:05<13:03,  4.53s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:10<12:50,  4.48s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:14<12:31,  4.39s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:18<12:39,  4.47s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:23<12:33,  4.46s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:27<12:22,  4.42s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:32<12:17,  4.42s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:36<12:19,  4.46s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:41<12:19,  4.48s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:45<12:20,  4.52s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:50<12:04,  4.45s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:54<12:07,  4.49s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:59<12:05,  4.50s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:03<11:52,  4.45s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:07<11:36,  4.38s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:12<11:39,  4.43s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:16<11:39,  4.45s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:21<11:30,  4.43s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:25<11:37,  4.50s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:30<11:34,  4.51s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:34<11:26,  4.49s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:39<11:24,  4.51s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:43<11:13,  4.46s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:47<10:58,  4.39s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:52<11:11,  4.51s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:57<11:09,  4.52s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [04:01<11:07,  4.54s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:06<10:57,  4.50s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:10<10:53,  4.51s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:15<10:49,  4.51s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:19<10:37,  4.46s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:24<10:36,  4.48s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:28<10:33,  4.49s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:33<10:34,  4.53s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:37<10:36,  4.58s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:42<10:30,  4.57s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:46<10:18,  4.51s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:51<10:06,  4.46s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:55<09:51,  4.38s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [05:00<09:57,  4.46s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [05:04<09:51,  4.45s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [05:09<09:51,  4.48s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [05:13<09:51,  4.51s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [05:18<09:47,  4.52s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [05:22<09:36,  4.47s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [05:27<09:33,  4.48s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [05:31<09:22,  4.43s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [05:35<09:15,  4.41s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [05:40<09:11,  4.41s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [05:44<09:16,  4.49s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [05:49<09:14,  4.51s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [05:53<09:13,  4.54s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [05:58<09:05,  4.51s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [06:03<09:18,  4.66s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [06:07<09:08,  4.61s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [06:12<09:04,  4.61s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [06:17<08:55,  4.58s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [06:21<08:51,  4.58s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [06:26<08:50,  4.61s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [06:30<08:43,  4.60s/it]\u001b[A\n",
+      "training:  44%|█████████████████▍                      | 87/200 [06:35<08:41,  4.62s/it]\u001b[A\n",
+      "training:  44%|█████████████████▌                      | 88/200 [06:40<08:37,  4.62s/it]\u001b[A\n",
+      "training:  44%|█████████████████▊                      | 89/200 [06:44<08:27,  4.57s/it]\u001b[A\n",
+      "training:  45%|██████████████████                      | 90/200 [06:48<08:12,  4.48s/it]\u001b[A\n",
+      "training:  46%|██████████████████▏                     | 91/200 [06:53<08:12,  4.52s/it]\u001b[A\n",
+      "training:  46%|██████████████████▍                     | 92/200 [06:58<08:09,  4.53s/it]\u001b[A\n",
+      "training:  46%|██████████████████▌                     | 93/200 [07:02<08:05,  4.54s/it]\u001b[A\n",
+      "training:  47%|██████████████████▊                     | 94/200 [07:07<08:01,  4.54s/it]\u001b[A\n",
+      "training:  48%|███████████████████                     | 95/200 [07:11<07:53,  4.51s/it]\u001b[A\n",
+      "training:  48%|███████████████████▏                    | 96/200 [07:16<07:49,  4.51s/it]\u001b[A\n",
+      "training:  48%|███████████████████▍                    | 97/200 [07:20<07:48,  4.55s/it]\u001b[A\n",
+      "training:  49%|███████████████████▌                    | 98/200 [07:25<07:44,  4.55s/it]\u001b[A\n",
+      "training:  50%|███████████████████▊                    | 99/200 [07:29<07:34,  4.50s/it]\u001b[A\n",
+      "training:  50%|███████████████████▌                   | 100/200 [07:34<07:31,  4.52s/it]\u001b[A\n",
+      "training:  50%|███████████████████▋                   | 101/200 [07:43<07:34,  4.59s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:09<30:08,  9.09s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:29,  6.21s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:33,  5.35s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:21,  5.01s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:53,  4.89s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:54,  4.92s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:38,  4.86s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:41<15:13,  4.76s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<14:47,  4.64s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:33,  4.60s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:54<14:07,  4.49s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<14:10,  4.53s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:08,  4.54s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<13:51,  4.47s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:11<13:38,  4.42s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:16<13:44,  4.48s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:21<13:40,  4.48s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:25<13:23,  4.41s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:29<13:28,  4.47s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:34<13:30,  4.50s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:38<13:19,  4.46s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:43<13:14,  4.47s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:47<13:08,  4.46s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:52<12:55,  4.40s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:56<12:48,  4.39s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:01<12:58,  4.47s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:05<12:55,  4.48s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:09<12:43,  4.44s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:14<12:32,  4.40s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:18<12:23,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:23<12:33,  4.46s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:27<12:38,  4.52s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:32<12:37,  4.54s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:36<12:23,  4.48s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:41<12:32,  4.56s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:46<12:26,  4.55s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:50<12:28,  4.59s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:55<12:20,  4.57s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:59<12:19,  4.60s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:04<12:14,  4.59s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:08<11:58,  4.52s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:13<11:52,  4.51s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:17<11:40,  4.46s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:21<11:28,  4.41s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:26<11:29,  4.45s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:35<12:00,  4.68s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.54it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.09it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.04it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.25it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 55it [00:00, 75.47it/s]\u001b[A\n",
+      "Evaluating NLL: 110it [00:01, 153.35it/s]\u001b[A\n",
+      "Evaluating NLL: 166it [00:01, 230.17it/s]\u001b[A\n",
+      "Evaluating NLL: 223it [00:01, 301.92it/s]\u001b[A\n",
+      "Evaluating NLL: 279it [00:01, 360.67it/s]\u001b[A\n",
+      "Evaluating NLL: 333it [00:01, 405.08it/s]\u001b[A\n",
+      "Evaluating NLL: 390it [00:01, 446.34it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 278.06it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 147.65it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▉  | 30/100 [00:00<00:00, 146.40it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█▎ | 45/100 [00:00<00:00, 144.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|█▊ | 60/100 [00:00<00:00, 144.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|██▎| 75/100 [00:00<00:00, 144.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 144.18it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▎  | 11/100 [00:00<00:00, 108.83it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▋  | 22/100 [00:00<00:00, 108.67it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|▉  | 33/100 [00:00<00:00, 107.74it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█▎ | 44/100 [00:00<00:00, 108.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|█▋ | 55/100 [00:00<00:00, 108.97it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|██ | 67/100 [00:00<00:00, 107.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|██▎| 78/100 [00:00<00:00, 107.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 109.93it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▍  | 16/100 [00:00<00:00, 151.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▉  | 32/100 [00:00<00:00, 148.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█▍ | 47/100 [00:00<00:00, 148.23it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|█▊ | 62/100 [00:00<00:00, 148.50it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|██▎| 77/100 [00:00<00:00, 148.53it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 146.99it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:21,  8.25s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:45,  5.99s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:58,  5.17s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:24,  5.02s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:26,  4.75s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<15:12,  4.70s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<15:03,  4.68s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:53,  4.65s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<14:25,  4.53s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:48<14:14,  4.50s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<14:12,  4.51s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:57<14:03,  4.48s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:01<13:58,  4.48s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:06<14:00,  4.52s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:10<13:51,  4.49s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:15<13:39,  4.45s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:19<13:32,  4.44s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:24<13:23,  4.42s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:28<13:27,  4.46s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:33<13:23,  4.46s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:37<13:17,  4.46s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:42<13:18,  4.49s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:46<13:19,  4.52s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:50<13:00,  4.43s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:55<12:56,  4.44s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:59<13:01,  4.49s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:04<13:00,  4.51s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:08<12:41,  4.43s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:13<12:30,  4.39s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:17<12:39,  4.47s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:22<12:42,  4.51s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:26<12:42,  4.54s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:31<12:32,  4.50s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:35<12:16,  4.44s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:40<12:19,  4.48s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:44<12:14,  4.48s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:48<11:57,  4.40s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:53<11:46,  4.36s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:57<11:48,  4.40s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:02<11:54,  4.46s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:06<11:46,  4.44s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:11<11:49,  4.49s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:15<11:44,  4.49s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:20<11:33,  4.45s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:24<11:32,  4.47s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:29<11:34,  4.51s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:33<11:32,  4.52s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:42<11:45,  4.64s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:09<30:18,  9.14s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:37,  6.25s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:57,  5.47s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:49,  5.15s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:04,  4.95s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:19,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:36<15:03,  4.68s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<14:53,  4.65s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:45<14:31,  4.56s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:21,  4.53s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:53<14:02,  4.46s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<13:47,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:02<13:51,  4.45s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<13:57,  4.50s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:11<13:50,  4.49s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:16<13:51,  4.52s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:20<13:41,  4.49s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:25<13:41,  4.52s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:29<13:29,  4.47s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:34<13:31,  4.51s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:38<13:17,  4.46s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:43<13:22,  4.51s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:47<13:15,  4.49s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:51<13:00,  4.43s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:56<12:41,  4.35s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:00<12:36,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:05<12:46,  4.43s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:09<12:40,  4.42s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:14<12:46,  4.48s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:18<12:52,  4.55s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:22<12:30,  4.44s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:27<12:18,  4.39s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:31<12:26,  4.47s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:36<12:26,  4.50s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:40<12:22,  4.50s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:45<12:12,  4.47s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:49<12:14,  4.50s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:54<12:07,  4.49s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:59<12:07,  4.52s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:03<12:07,  4.54s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:08<12:06,  4.57s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:12<11:49,  4.49s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:16<11:35,  4.43s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:21<11:34,  4.45s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:25<11:29,  4.45s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:30<11:31,  4.49s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:34<11:32,  4.53s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:39<11:21,  4.48s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:43<11:15,  4.47s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:48<11:10,  4.47s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:52<11:06,  4.47s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:57<11:05,  4.50s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [04:01<10:57,  4.48s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:06<10:46,  4.43s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:14<11:11,  4.63s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:51,  8.70s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:31,  6.22s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:58,  5.47s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:55,  5.18s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:27<16:02,  4.94s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:19,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<15:04,  4.69s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:40<14:48,  4.63s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<14:34,  4.58s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:26,  4.56s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:53<14:19,  4.55s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<14:17,  4.56s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:03<14:15,  4.58s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<14:07,  4.56s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:12<14:10,  4.60s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:16<14:00,  4.57s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:21<13:52,  4.55s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:25<13:48,  4.55s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:30<13:34,  4.50s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:34<13:40,  4.56s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:39<13:26,  4.51s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:43<13:18,  4.49s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:48<13:24,  4.54s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:53<13:19,  4.54s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:57<13:00,  4.46s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:01<13:05,  4.52s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:06<13:08,  4.56s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:11<13:02,  4.55s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:15<12:41,  4.45s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:19<12:44,  4.50s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:24<12:47,  4.54s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:29<13:03,  4.67s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:33<12:38,  4.54s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:38<12:37,  4.56s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:42<12:24,  4.51s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:47<12:23,  4.53s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:51<12:10,  4.48s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:56<12:03,  4.46s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:00<11:50,  4.41s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:04<11:47,  4.42s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:09<11:46,  4.44s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:14<12:01,  4.57s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:18<11:47,  4.51s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:23<11:39,  4.48s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:27<11:33,  4.47s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:32<11:34,  4.51s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:36<11:31,  4.52s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:41<11:24,  4.50s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:45<11:13,  4.46s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:50<11:12,  4.48s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:54<11:05,  4.46s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:58<10:57,  4.44s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [04:03<10:47,  4.41s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:07<10:47,  4.43s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:12<10:51,  4.49s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:16<10:53,  4.54s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:21<10:49,  4.54s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:25<10:33,  4.46s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:30<10:34,  4.50s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:34<10:34,  4.53s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:39<10:29,  4.53s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:43<10:21,  4.50s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:48<10:04,  4.41s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:52<10:05,  4.45s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:57<10:01,  4.45s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [05:01<09:49,  4.40s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [05:05<09:40,  4.37s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [05:10<09:36,  4.37s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [05:14<09:40,  4.43s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [05:19<09:37,  4.44s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [05:23<09:39,  4.49s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [05:28<09:35,  4.49s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [05:32<09:24,  4.45s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [05:37<09:23,  4.47s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [05:41<09:26,  4.53s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [05:46<09:17,  4.50s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [05:50<09:23,  4.58s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [05:55<09:17,  4.57s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [06:00<09:21,  4.64s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [06:05<09:22,  4.69s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [06:09<09:17,  4.68s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [06:14<09:18,  4.73s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [06:19<09:08,  4.69s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [06:23<08:56,  4.63s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [06:28<08:50,  4.61s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [06:33<08:53,  4.68s/it]\u001b[A\n",
+      "training:  44%|█████████████████▍                      | 87/200 [06:38<08:57,  4.76s/it]\u001b[A\n",
+      "training:  44%|█████████████████▌                      | 88/200 [06:42<08:46,  4.70s/it]\u001b[A\n",
+      "training:  44%|█████████████████▊                      | 89/200 [06:46<08:31,  4.61s/it]\u001b[A\n",
+      "training:  45%|██████████████████                      | 90/200 [06:51<08:21,  4.56s/it]\u001b[A\n",
+      "training:  46%|██████████████████▏                     | 91/200 [06:55<08:12,  4.52s/it]\u001b[A\n",
+      "training:  46%|██████████████████▍                     | 92/200 [07:00<08:08,  4.52s/it]\u001b[A\n",
+      "training:  46%|██████████████████▌                     | 93/200 [07:04<08:00,  4.49s/it]\u001b[A\n",
+      "training:  47%|██████████████████▊                     | 94/200 [07:09<07:59,  4.52s/it]\u001b[A\n",
+      "training:  48%|███████████████████                     | 95/200 [07:14<07:58,  4.55s/it]\u001b[A\n",
+      "training:  48%|███████████████████▏                    | 96/200 [07:18<08:04,  4.66s/it]\u001b[A\n",
+      "training:  48%|███████████████████▍                    | 97/200 [07:23<08:06,  4.72s/it]\u001b[A\n",
+      "training:  49%|███████████████████▌                    | 98/200 [07:28<08:02,  4.73s/it]\u001b[A\n",
+      "training:  50%|███████████████████▊                    | 99/200 [07:33<07:51,  4.67s/it]\u001b[A\n",
+      "training:  50%|███████████████████▌                   | 100/200 [07:37<07:37,  4.58s/it]\u001b[A\n",
+      "training:  50%|███████████████████▋                   | 101/200 [07:41<07:28,  4.53s/it]\u001b[A\n",
+      "training:  51%|███████████████████▉                   | 102/200 [07:46<07:20,  4.49s/it]\u001b[A\n",
+      "training:  52%|████████████████████                   | 103/200 [07:50<07:13,  4.47s/it]\u001b[A\n",
+      "training:  52%|████████████████████▎                  | 104/200 [07:55<07:20,  4.59s/it]\u001b[A\n",
+      "training:  52%|████████████████████▍                  | 105/200 [08:00<07:17,  4.61s/it]\u001b[A\n",
+      "training:  53%|████████████████████▋                  | 106/200 [08:04<07:14,  4.62s/it]\u001b[A\n",
+      "training:  54%|████████████████████▊                  | 107/200 [08:09<07:15,  4.68s/it]\u001b[A\n",
+      "training:  54%|█████████████████████                  | 108/200 [08:14<07:15,  4.74s/it]\u001b[A\n",
+      "training:  55%|█████████████████████▎                 | 109/200 [08:18<07:03,  4.65s/it]\u001b[A\n",
+      "training:  55%|█████████████████████▍                 | 110/200 [08:23<07:01,  4.68s/it]\u001b[A\n",
+      "training:  56%|█████████████████████▋                 | 111/200 [08:28<06:55,  4.67s/it]\u001b[A\n",
+      "training:  56%|█████████████████████▊                 | 112/200 [08:33<06:50,  4.66s/it]\u001b[A\n",
+      "training:  56%|██████████████████████                 | 113/200 [08:42<06:42,  4.62s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:50,  8.69s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:36,  6.25s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:45,  5.41s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:06,  4.93s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:09,  4.66s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<15:01,  4.65s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<14:49,  4.61s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:44,  4.60s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<14:58,  4.70s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:49<14:48,  4.68s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:53<14:17,  4.54s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:57<14:03,  4.49s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:02<14:05,  4.52s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<14:06,  4.55s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:11<13:42,  4.45s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:15<13:32,  4.42s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:20<13:23,  4.39s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:24<13:36,  4.49s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:29<13:34,  4.50s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:33<13:25,  4.47s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:38<13:24,  4.49s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:42<13:27,  4.53s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:47<13:24,  4.55s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:51<13:20,  4.55s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:56<13:16,  4.55s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:00<13:02,  4.50s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:05<12:53,  4.47s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:09<12:34,  4.39s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:13<12:23,  4.35s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:18<12:29,  4.41s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:22<12:32,  4.45s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:27<12:29,  4.46s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:31<12:13,  4.39s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:36<12:14,  4.42s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:40<12:04,  4.39s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:44<12:11,  4.46s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:49<12:10,  4.48s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:53<11:59,  4.44s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:58<11:47,  4.39s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:02<11:47,  4.42s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:07<11:51,  4.47s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:11<11:51,  4.50s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:16<11:43,  4.48s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:20<11:44,  4.51s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:25<11:40,  4.52s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:29<11:39,  4.54s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:34<11:31,  4.52s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:39<11:36,  4.58s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:43<11:32,  4.58s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:48<11:24,  4.56s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:52<11:14,  4.53s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:57<11:09,  4.52s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [04:01<11:11,  4.57s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:06<11:06,  4.56s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:15<11:12,  4.64s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:22,  8.25s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:27,  6.20s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:22,  5.29s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:19,  5.00s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:40,  4.82s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<15:20,  4.74s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<14:55,  4.64s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:50,  4.64s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<14:39,  4.60s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:48<14:14,  4.50s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:53<14:18,  4.54s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:58<14:21,  4.58s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:02<14:21,  4.61s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:07<14:15,  4.60s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:12<14:17,  4.63s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:16<13:54,  4.53s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:20<13:51,  4.54s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:25<13:51,  4.57s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:30<13:52,  4.60s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:34<13:50,  4.61s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:39<13:51,  4.65s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:43<13:28,  4.54s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:48<13:25,  4.55s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:53<13:28,  4.59s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:57<13:23,  4.59s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [02:02<13:11,  4.55s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:06<13:15,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:11<13:11,  4.60s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:16<13:04,  4.59s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:20<12:41,  4.48s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:24<12:44,  4.52s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:29<12:41,  4.54s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:33<12:35,  4.52s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:38<12:16,  4.44s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:42<12:09,  4.42s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:47<12:15,  4.48s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:51<12:17,  4.53s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:56<12:04,  4.47s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [03:00<12:10,  4.54s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [03:05<12:02,  4.51s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:09<11:58,  4.52s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:14<11:47,  4.48s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:18<11:43,  4.48s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:23<11:40,  4.49s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:27<11:33,  4.47s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:32<11:28,  4.47s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:36<11:25,  4.48s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:40<11:08,  4.40s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:45<11:01,  4.38s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:49<11:08,  4.46s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:54<11:05,  4.47s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:58<11:07,  4.51s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [04:03<11:06,  4.53s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:07<10:58,  4.51s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:16<11:17,  4.67s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.64it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.88it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.37it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 45it [00:00, 63.82it/s]\u001b[A\n",
+      "Evaluating NLL: 89it [00:01, 127.32it/s]\u001b[A\n",
+      "Evaluating NLL: 131it [00:01, 184.24it/s]\u001b[A\n",
+      "Evaluating NLL: 178it [00:01, 246.12it/s]\u001b[A\n",
+      "Evaluating NLL: 226it [00:01, 301.25it/s]\u001b[A\n",
+      "Evaluating NLL: 275it [00:01, 348.37it/s]\u001b[A\n",
+      "Evaluating NLL: 324it [00:01, 384.62it/s]\u001b[A\n",
+      "Evaluating NLL: 374it [00:01, 415.06it/s]\u001b[A\n",
+      "Evaluating NLL: 424it [00:01, 436.22it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 261.55it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 140.92it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▉  | 30/100 [00:00<00:00, 140.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█▎ | 45/100 [00:00<00:00, 139.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|█▊ | 59/100 [00:00<00:00, 137.82it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|██▏| 73/100 [00:00<00:00, 134.00it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 135.42it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▎  | 11/100 [00:00<00:00, 107.08it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▋  | 23/100 [00:00<00:00, 109.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  34%|█  | 34/100 [00:00<00:00, 108.02it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█▍ | 46/100 [00:00<00:00, 110.11it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|█▋ | 58/100 [00:00<00:00, 108.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|██ | 69/100 [00:00<00:00, 107.77it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|██▍| 80/100 [00:00<00:00, 108.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 107.00it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 139.36it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 133.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 131.84it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|█▋ | 56/100 [00:00<00:00, 131.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 128.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██▌| 84/100 [00:00<00:00, 128.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 128.26it/s]\n",
+      " 33%|███████████████▋                               | 1/3 [1:49:00<3:38:00, 6540.09s/it]\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:27,  8.28s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:02,  5.77s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:40,  5.08s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:40,  4.80s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<14:57,  4.60s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:38,  4.53s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:24,  4.48s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:19,  4.48s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:01,  4.41s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:54,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:47,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:40,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:33,  4.35s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:28,  4.35s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:23,  4.34s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:18,  4.34s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:15,  4.34s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:12,  4.35s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:08,  4.36s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<13:01,  4.34s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:50,  4.31s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:38<12:44,  4.30s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:42,  4.31s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:34,  4.28s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:51<12:33,  4.31s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:55<12:15,  4.23s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:59<12:02,  4.18s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:03<11:51,  4.14s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:08<11:49,  4.15s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:12<11:54,  4.20s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:16<11:58,  4.25s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:21<11:59,  4.28s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:25<11:47,  4.24s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:29<11:36,  4.19s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:33<11:37,  4.23s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:38<11:37,  4.26s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:42<11:41,  4.30s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:46<11:43,  4.34s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:51<11:37,  4.33s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:55<11:38,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:00<11:36,  4.38s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:04<11:30,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:08<11:24,  4.36s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:13<11:20,  4.36s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:17<11:15,  4.36s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:21<11:10,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:26<11:04,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:30<10:59,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:34<10:54,  4.34s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:39<10:50,  4.34s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:43<10:43,  4.32s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:47<10:38,  4.31s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:52<10:32,  4.30s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:56<10:29,  4.31s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:05<10:46,  4.46s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:48,  8.39s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:33,  5.93s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:43,  5.10s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:40,  4.80s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:08,  4.66s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:44,  4.56s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:26,  4.49s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<13:58,  4.37s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<13:37,  4.28s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:46<13:37,  4.30s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:33,  4.30s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:30,  4.31s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:59<13:43,  4.41s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:03<13:14,  4.27s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:07,  4.26s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:12<13:09,  4.29s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:16<13:06,  4.30s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:01,  4.29s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:25<12:57,  4.30s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:29<12:58,  4.33s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:55,  4.33s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:38<12:40,  4.27s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:42<12:38,  4.29s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:46<12:37,  4.30s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:51<12:36,  4.32s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:55<12:35,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:59<12:25,  4.31s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:04<12:22,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:08<12:20,  4.33s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:12<12:16,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:17<12:08,  4.31s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:21<12:07,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:25<12:02,  4.32s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:30<12:01,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:34<11:51,  4.31s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:38<11:40,  4.27s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:43<11:43,  4.32s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:47<11:40,  4.32s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:51<11:38,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:56<11:38,  4.36s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:00<11:34,  4.37s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:05<11:30,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:09<11:26,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:13<11:11,  4.31s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:17<11:08,  4.32s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:26<11:31,  4.49s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<26:04,  7.86s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<18:56,  5.74s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:30,  5.03s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:31,  4.75s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:57,  4.60s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:38,  4.53s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:21,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<14:05,  4.41s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<13:57,  4.39s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:46<13:47,  4.36s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:50<13:39,  4.34s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:25,  4.29s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:59<13:07,  4.21s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:03<13:09,  4.24s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:07<13:11,  4.28s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:12<13:10,  4.30s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:16<13:02,  4.28s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:20<12:57,  4.27s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:24<12:49,  4.25s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:29<12:48,  4.27s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:33<12:42,  4.26s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:37<12:44,  4.29s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:41<12:34,  4.26s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:46<12:22,  4.22s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:50<12:08,  4.16s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:54<12:12,  4.21s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:58<12:25,  4.31s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:03<12:18,  4.29s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:07<12:16,  4.31s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:11<12:11,  4.30s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:16<12:04,  4.28s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:20<12:01,  4.29s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:24<11:59,  4.31s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:29<11:57,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:33<11:52,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:37<11:49,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:42<11:44,  4.32s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:46<11:40,  4.33s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:50<11:40,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:55<11:34,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:59<11:29,  4.34s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:03<11:21,  4.32s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:08<11:20,  4.33s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:37,  4.47s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:49,  8.09s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:48,  6.00s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:40,  5.08s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:33,  4.76s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:00,  4.62s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:38,  4.53s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:21,  4.46s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:14,  4.45s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:04,  4.42s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:54,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:46,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:49,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:34,  4.36s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:23,  4.32s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:17,  4.31s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:12<13:13,  4.31s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:06,  4.30s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:02,  4.30s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:25<13:00,  4.31s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<12:57,  4.32s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:51,  4.31s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:38<12:49,  4.32s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:42,  4.31s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:43,  4.34s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:51<12:39,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:32,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:00<12:28,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:04<12:26,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:18,  4.32s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:13<12:15,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:17<12:07,  4.31s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:06,  4.32s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:26<12:05,  4.34s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:30<12:01,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:59,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:39<11:55,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:53,  4.38s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:48,  4.37s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:52<11:43,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:40,  4.38s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:34,  4.37s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:05<11:29,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:24,  4.36s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:14<11:32,  4.44s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:22,  4.40s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:23<11:16,  4.39s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:27<11:11,  4.39s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:07,  4.39s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:36<11:02,  4.38s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:57,  4.38s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:51,  4.37s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:49<10:46,  4.37s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:39,  4.35s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:36,  4.36s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:02<10:31,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:07<10:26,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:11<10:21,  4.35s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:15<10:18,  4.35s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:20<10:13,  4.35s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:24<10:07,  4.34s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:28<10:03,  4.34s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:33<10:01,  4.36s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:37<09:57,  4.36s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:41<09:52,  4.36s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:46<09:50,  4.37s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:50<09:46,  4.37s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:59<09:54,  4.47s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<29:48,  8.99s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:31,  6.22s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<18:01,  5.49s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:28,  5.04s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:37,  4.81s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:06,  4.67s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<14:40,  4.56s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:23,  4.50s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<14:10,  4.45s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:48<13:59,  4.42s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:50,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:57<13:42,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:01<13:35,  4.36s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:28,  4.35s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:10<13:26,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:21,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:16,  4.35s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:23<13:11,  4.35s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:07,  4.35s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:01,  4.34s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<12:59,  4.36s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<12:53,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:50,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:48,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:45,  4.37s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:58<12:40,  4.37s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:36,  4.38s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:32,  4.37s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:11<12:27,  4.37s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:22,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:18,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:24<12:14,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:14,  4.40s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:33<12:06,  4.38s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<12:01,  4.37s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:55,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:46<11:49,  4.35s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<11:43,  4.34s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:38,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:59<11:33,  4.33s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:29,  4.34s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:26,  4.34s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:12<11:20,  4.33s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:18,  4.35s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:13,  4.35s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:25<11:06,  4.33s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:29<11:04,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<11:00,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:38<10:48,  4.30s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:42<10:46,  4.31s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:46<10:40,  4.30s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:50<10:35,  4.30s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:55<10:32,  4.30s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:59<10:31,  4.32s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:04<10:30,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:08<10:26,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:12<10:22,  4.35s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:21<10:40,  4.51s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.46it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.05s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.40it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.07it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 54it [00:00, 76.14it/s]\u001b[A\n",
+      "Evaluating NLL: 109it [00:01, 155.71it/s]\u001b[A\n",
+      "Evaluating NLL: 164it [00:01, 232.01it/s]\u001b[A\n",
+      "Evaluating NLL: 219it [00:01, 300.13it/s]\u001b[A\n",
+      "Evaluating NLL: 274it [00:01, 358.82it/s]\u001b[A\n",
+      "Evaluating NLL: 330it [00:01, 408.49it/s]\u001b[A\n",
+      "Evaluating NLL: 387it [00:01, 449.91it/s]\u001b[A\n",
+      "Evaluating NLL: 441it [00:01, 471.99it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 280.47it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%|▎  | 12/100 [00:00<00:00, 116.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▊  | 25/100 [00:00<00:00, 122.77it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|█▏ | 38/100 [00:00<00:00, 122.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|█▌ | 51/100 [00:00<00:00, 123.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|█▉ | 65/100 [00:00<00:00, 126.51it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|██▎| 79/100 [00:00<00:00, 130.19it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 127.58it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▎  | 11/100 [00:00<00:00, 101.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▉   | 22/100 [00:00<00:00, 96.86it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|█▎  | 32/100 [00:00<00:00, 95.89it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▋  | 42/100 [00:00<00:00, 93.65it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██  | 52/100 [00:00<00:00, 94.26it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|██▌ | 63/100 [00:00<00:00, 98.11it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|██▉ | 73/100 [00:00<00:00, 97.43it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|███▎| 84/100 [00:00<00:00, 99.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 99.03it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 138.52it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▊  | 29/100 [00:00<00:00, 140.15it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█▎ | 44/100 [00:00<00:00, 133.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|█▋ | 58/100 [00:00<00:00, 133.38it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|██▏| 72/100 [00:00<00:00, 133.96it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|██▌| 86/100 [00:00<00:00, 135.37it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 135.53it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:58,  8.44s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:42,  5.97s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<17:06,  5.21s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:58,  4.89s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:16,  4.70s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:47,  4.57s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:27,  4.49s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:16,  4.46s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:08,  4.44s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<14:01,  4.43s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:52,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:44,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:37,  4.37s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:32,  4.37s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:29,  4.38s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:28,  4.39s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:22,  4.39s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:14,  4.37s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:09,  4.36s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:05,  4.36s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<13:00,  4.36s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:55,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:48,  4.34s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:46,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:39,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:35,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:33,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:28,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:25,  4.36s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:22,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:16,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:10,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:04,  4.34s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:02,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:36<11:57,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:52,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:53,  4.38s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:49<11:50,  4.39s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:42,  4.36s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:37,  4.36s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:02<11:34,  4.37s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:26,  4.34s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:11<11:26,  4.38s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:21,  4.37s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:14,  4.35s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:10,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:04,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<10:59,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<10:55,  4.34s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:51,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:46<10:46,  4.34s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:50<10:41,  4.34s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:37,  4.34s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:03<10:58,  4.51s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:13,  8.21s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<20:05,  6.09s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:10,  5.23s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:54,  4.87s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:10,  4.67s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:46,  4.57s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:29,  4.51s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:18,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:07,  4.44s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<14:00,  4.42s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:52,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<14:02,  4.48s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:52,  4.45s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:43,  4.43s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:34,  4.40s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:28,  4.39s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:21,  4.38s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:15,  4.37s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:12,  4.38s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:10,  4.39s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<13:05,  4.39s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<13:00,  4.38s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:55,  4.38s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:49,  4.37s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:42,  4.36s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:40,  4.37s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:34,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:32,  4.37s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:28,  4.38s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:22,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:17,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:14,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:09,  4.37s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:03,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<12:04,  4.39s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<12:08,  4.44s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:55,  4.39s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<11:47,  4.37s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:42,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:37,  4.36s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:32,  4.36s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:27,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:12<11:23,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:19,  4.35s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:14,  4.35s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:25<11:09,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:29<11:06,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<11:02,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:38<10:57,  4.36s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:42<10:51,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:51<11:15,  4.53s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:26,  8.57s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<20:00,  6.06s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:10,  5.23s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:55,  4.88s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:12,  4.68s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:51,  4.59s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:32,  4.52s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:19,  4.48s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:08,  4.44s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<14:01,  4.43s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:55,  4.42s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:47,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:47,  4.43s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:41,  4.42s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:33,  4.40s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:26,  4.38s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:18,  4.36s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:12,  4.35s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:05,  4.34s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:01,  4.34s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<12:56,  4.34s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<12:50,  4.33s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:45,  4.33s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:40,  4.32s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:39,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:36,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:32,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:28,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:22,  4.34s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:16,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:12,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:08,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:04,  4.34s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:01,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:36<11:58,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:54,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:49,  4.35s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:49<11:48,  4.37s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<12:02,  4.49s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:50,  4.44s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:43,  4.42s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:35,  4.40s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:11<11:29,  4.39s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:23,  4.38s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:17,  4.37s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:11,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:29<11:05,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<11:00,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<10:56,  4.35s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:42<10:49,  4.33s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:46<10:45,  4.33s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:50<10:40,  4.33s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:59<11:04,  4.52s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:47,  8.38s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:41,  5.97s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<17:09,  5.22s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:58,  4.89s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:19,  4.72s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:58,  4.63s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:37,  4.55s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:22,  4.49s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:10,  4.45s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<14:00,  4.42s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:49,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:45,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:39,  4.38s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:34,  4.38s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:33,  4.39s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:26,  4.38s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:21,  4.38s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:16,  4.38s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:09,  4.36s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:05,  4.36s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<12:57,  4.34s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<12:54,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:49,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:43,  4.34s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:42,  4.36s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:34,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:31,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:30,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:21,  4.34s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:19,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:14,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:09,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:06,  4.35s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:03,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:36<11:59,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:56,  4.37s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:54,  4.39s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:49<11:49,  4.38s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:43,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:36,  4.36s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:02<11:36,  4.38s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:44,  4.46s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:11<11:33,  4.41s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:24,  4.39s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:19,  4.38s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:15,  4.39s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:29<11:08,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<11:04,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:38<11:00,  4.38s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:42<10:55,  4.37s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:51<11:14,  4.53s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<29:26,  8.88s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<21:06,  6.40s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:18<18:19,  5.58s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:37,  5.09s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:43,  4.84s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:31<15:08,  4.69s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<14:42,  4.57s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:23,  4.50s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:44<14:13,  4.47s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:48<14:03,  4.44s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:53<13:54,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:57<13:47,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:01<13:42,  4.40s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:06<13:49,  4.46s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:10<13:42,  4.44s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:15<13:32,  4.41s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:19<13:21,  4.38s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:23<13:14,  4.37s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:28<13:10,  4.37s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:32<13:05,  4.36s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<12:57,  4.34s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:41<12:52,  4.34s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:45<12:47,  4.33s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:43,  4.34s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:54<12:38,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:58<12:35,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:31,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:07<12:25,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:11<12:22,  4.34s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:19,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:20<12:13,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:24<12:08,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:04,  4.34s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:33<11:59,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<11:55,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:51,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:46<11:47,  4.34s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<11:44,  4.35s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:55<11:43,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:59<11:37,  4.36s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:34,  4.37s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:08<11:29,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:12<11:25,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:23,  4.38s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:21<11:23,  4.41s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:25<11:16,  4.40s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:30<11:08,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:34<11:06,  4.39s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:38<11:00,  4.37s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:43<10:54,  4.36s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:51<11:17,  4.54s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.23it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.28it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.28it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.08it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.07it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 48it [00:00, 68.20it/s]\u001b[A\n",
+      "Evaluating NLL: 91it [00:01, 129.35it/s]\u001b[A\n",
+      "Evaluating NLL: 137it [00:01, 193.36it/s]\u001b[A\n",
+      "Evaluating NLL: 186it [00:01, 257.58it/s]\u001b[A\n",
+      "Evaluating NLL: 232it [00:01, 305.32it/s]\u001b[A\n",
+      "Evaluating NLL: 276it [00:01, 338.93it/s]\u001b[A\n",
+      "Evaluating NLL: 321it [00:01, 367.53it/s]\u001b[A\n",
+      "Evaluating NLL: 366it [00:01, 389.57it/s]\u001b[A\n",
+      "Evaluating NLL: 411it [00:01, 406.09it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 253.91it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▍  | 13/100 [00:00<00:00, 121.07it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▊  | 26/100 [00:00<00:00, 115.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|█▏ | 38/100 [00:00<00:00, 113.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|█▌ | 50/100 [00:00<00:00, 112.87it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|█▊ | 62/100 [00:00<00:00, 113.30it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|██▎| 75/100 [00:00<00:00, 116.15it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|██▋| 88/100 [00:00<00:00, 116.82it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 113.65it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▎  | 11/100 [00:00<00:00, 104.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▋  | 22/100 [00:00<00:00, 100.27it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|█▎  | 33/100 [00:00<00:00, 96.25it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█▊  | 44/100 [00:00<00:00, 98.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|█▋ | 56/100 [00:00<00:00, 103.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|██ | 67/100 [00:00<00:00, 103.87it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|██▎| 78/100 [00:00<00:00, 103.28it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  89%|██▋| 89/100 [00:00<00:00, 103.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 102.89it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 138.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 137.74it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 136.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|█▋ | 57/100 [00:00<00:00, 138.08it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██▏| 71/100 [00:00<00:00, 137.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|██▌| 85/100 [00:00<00:00, 137.36it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 137.51it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:52,  8.71s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:25,  6.19s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:32,  5.34s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:10,  4.95s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:21,  4.73s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:54,  4.61s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<14:53,  4.63s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:35,  4.56s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:18,  4.50s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:48<14:04,  4.45s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:54,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:45,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:01<13:38,  4.38s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:31,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:10<13:27,  4.37s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:19,  4.35s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:17,  4.36s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:23<13:15,  4.37s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:09,  4.36s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:04,  4.36s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<13:00,  4.36s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<12:56,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:53,  4.37s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:50,  4.38s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:44,  4.37s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:38,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:32,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:31,  4.37s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:11<12:27,  4.37s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:24,  4.38s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:19,  4.38s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:24<12:13,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:09,  4.37s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:03,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<11:59,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:58,  4.38s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:46<11:57,  4.40s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<12:02,  4.46s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:55<11:54,  4.44s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:59<11:45,  4.41s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:38,  4.39s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:08<11:31,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:12<11:24,  4.36s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:20,  4.36s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:21<11:17,  4.37s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:25<11:14,  4.38s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:30<11:08,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:34<11:04,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:38<10:58,  4.36s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:43<10:54,  4.36s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:47<10:51,  4.37s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:56<11:12,  4.54s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:02,  8.15s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:29,  5.91s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:52,  5.14s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:46,  4.83s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:05,  4.65s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:41,  4.55s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:27,  4.50s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:16,  4.46s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:05,  4.43s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:53,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:51,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:48,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:44,  4.41s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:39,  4.41s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:23,  4.34s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:13,  4.31s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:06,  4.30s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:07,  4.33s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:02,  4.33s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<12:59,  4.33s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:57,  4.34s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:52,  4.34s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:48,  4.34s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:27,  4.25s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:51<12:26,  4.27s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:16,  4.23s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:00<12:18,  4.27s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:04<12:12,  4.26s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:08<12:06,  4.25s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:13<12:07,  4.28s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:17<12:01,  4.27s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:21<11:58,  4.28s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:26<12:00,  4.32s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:30<11:55,  4.31s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:34<11:52,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:39<11:49,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:43<11:45,  4.33s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:47<11:42,  4.34s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:52<11:40,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:56<11:35,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:34,  4.37s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:05<11:31,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:09<11:26,  4.38s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:14<11:24,  4.38s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:18<11:18,  4.37s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:22<11:03,  4.31s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:27<10:59,  4.31s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:31<11:00,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:35<10:54,  4.33s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:40<10:43,  4.29s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:48<11:08,  4.49s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:50,  8.39s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:29,  5.91s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<17:04,  5.20s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:52,  4.86s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:17,  4.70s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:40,  4.54s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:30,  4.51s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:17,  4.46s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:09,  4.45s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:55,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:46,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:31,  4.32s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:23,  4.30s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:22,  4.32s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:14,  4.29s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:17,  4.33s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:14,  4.34s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:23,  4.42s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:16,  4.40s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<13:08,  4.38s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<13:01,  4.37s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:53,  4.34s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:50,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:48,  4.37s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:42,  4.36s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:37,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:37,  4.38s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:29,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:16,  4.31s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:15,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:12,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<11:54,  4.26s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:26<11:46,  4.23s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<11:48,  4.27s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:48,  4.30s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:39<11:44,  4.29s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:43,  4.32s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:42,  4.34s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:45,  4.38s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:39,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:34,  4.37s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:28,  4.36s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:23,  4.36s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:14<11:19,  4.36s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:16,  4.36s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:23<11:14,  4.38s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:11,  4.39s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:05,  4.38s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:36<11:03,  4.39s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:52,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:48,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:49<10:43,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:39,  4.35s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:34,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:02<10:29,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:07<10:24,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:11<10:20,  4.34s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:15<10:14,  4.33s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:20<10:12,  4.34s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:28<10:27,  4.48s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:16,  8.22s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:27,  5.90s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:59,  5.17s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:51,  4.86s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:11,  4.67s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:45,  4.56s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:27,  4.50s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:14,  4.45s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:04,  4.42s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:53,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:47,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:40,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:31,  4.34s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:30,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:25,  4.35s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:22,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:22,  4.38s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:14,  4.36s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:06,  4.35s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<13:01,  4.34s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:47,  4.29s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:45,  4.30s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:44,  4.32s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:41,  4.32s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:33,  4.31s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:31,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:00<12:26,  4.31s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:23,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:18,  4.32s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:13<12:16,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:11,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:05,  4.32s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:26<12:01,  4.32s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<12:00,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:55,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:39<11:50,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:49,  4.35s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:46,  4.36s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:43,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:39,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:37,  4.38s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:33,  4.39s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:28,  4.38s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:14<11:21,  4.37s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:19,  4.38s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:23<11:12,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:07,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:07,  4.39s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:36<11:01,  4.38s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:54,  4.36s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:48,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:49<10:43,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:38,  4.34s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:34,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:02<10:30,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:07<10:25,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:11<10:20,  4.34s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:15<10:17,  4.35s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:24<10:32,  4.48s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:20,  8.24s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<20:03,  6.08s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:17,  5.27s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:00,  4.90s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:19,  4.71s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:52,  4.60s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:30,  4.51s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:18,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:08,  4.44s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:59,  4.42s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:52,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:44,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:37,  4.37s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:37,  4.40s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:36,  4.41s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:45,  4.48s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:34,  4.45s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:23<13:27,  4.44s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:18,  4.41s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:12,  4.40s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<13:03,  4.38s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<12:57,  4.37s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:52,  4.37s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:46,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:39,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:35,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:32,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:29,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:23,  4.35s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:21,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:17,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:12,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:07,  4.35s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:03,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<11:56,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:53,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:51,  4.36s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<11:46,  4.36s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:42,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:39,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:35,  4.37s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:31,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:12<11:30,  4.40s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:28,  4.41s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:19,  4.39s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:25<11:14,  4.38s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:29<11:07,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<11:04,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:38<10:59,  4.37s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:47<11:21,  4.54s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.96it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.08it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.09it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\n",
+      "Evaluating NLL: 45it [00:00, 65.59it/s]\u001b[A\n",
+      "Evaluating NLL: 89it [00:01, 130.34it/s]\u001b[A\n",
+      "Evaluating NLL: 133it [00:01, 190.97it/s]\u001b[A\n",
+      "Evaluating NLL: 181it [00:01, 254.15it/s]\u001b[A\n",
+      "Evaluating NLL: 226it [00:01, 300.39it/s]\u001b[A\n",
+      "Evaluating NLL: 277it [00:01, 352.16it/s]\u001b[A\n",
+      "Evaluating NLL: 329it [00:01, 395.82it/s]\u001b[A\n",
+      "Evaluating NLL: 381it [00:01, 429.77it/s]\u001b[A\n",
+      "Evaluating NLL: 433it [00:01, 454.41it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 266.34it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 135.44it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▊  | 29/100 [00:00<00:00, 138.32it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|█▎ | 43/100 [00:00<00:00, 137.93it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|█▋ | 57/100 [00:00<00:00, 138.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██▏| 71/100 [00:00<00:00, 137.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|██▌| 85/100 [00:00<00:00, 136.39it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 135.93it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▎  | 11/100 [00:00<00:00, 104.81it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▉   | 22/100 [00:00<00:00, 99.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|▉  | 33/100 [00:00<00:00, 102.43it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█▎ | 44/100 [00:00<00:00, 102.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|█▋ | 55/100 [00:00<00:00, 104.20it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|█▉ | 66/100 [00:00<00:00, 103.00it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|██▎| 77/100 [00:00<00:00, 105.17it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|██▋| 88/100 [00:00<00:00, 104.84it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 104.13it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 137.72it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 137.07it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 132.34it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|█▋ | 56/100 [00:00<00:00, 135.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 135.36it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██▌| 84/100 [00:00<00:00, 136.72it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 135.87it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:33,  8.31s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<20:04,  6.08s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:49,  5.13s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:24,  4.72s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<14:59,  4.61s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:54,  4.61s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:31,  4.52s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:18,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:05,  4.43s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:59,  4.42s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:45,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:37,  4.35s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:31,  4.34s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:20,  4.30s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:18,  4.32s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:18,  4.34s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:13,  4.33s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<12:54,  4.26s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:25<12:40,  4.20s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<12:44,  4.25s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:47,  4.29s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:38<12:41,  4.28s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:42<12:37,  4.28s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:37,  4.30s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:51<12:34,  4.31s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:55<12:32,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:00<12:31,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:04<12:25,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:20,  4.33s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:13<12:04,  4.26s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:17<12:04,  4.29s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:21<12:02,  4.30s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:26<11:58,  4.30s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:30<11:45,  4.25s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:34<11:48,  4.30s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:39<11:48,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:43<11:51,  4.36s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:47<11:38,  4.31s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:51<11:32,  4.30s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:56<11:29,  4.31s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:00<11:30,  4.34s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:05<11:26,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:09<11:22,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:13<11:16,  4.33s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:17<11:08,  4.31s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:22<11:04,  4.31s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:26<11:00,  4.31s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:30<10:52,  4.29s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:35<10:50,  4.31s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:39<10:49,  4.33s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:43<10:46,  4.34s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:48<10:43,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:52<10:39,  4.35s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:56<10:33,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:01<10:28,  4.33s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:05<10:23,  4.33s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:09<10:16,  4.31s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:14<10:14,  4.33s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:18<10:10,  4.33s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:22<10:10,  4.36s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:27<10:06,  4.36s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:31<10:03,  4.37s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:36<09:55,  4.34s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:44<10:05,  4.45s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<29:08,  8.79s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<21:06,  6.40s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:54,  5.46s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:22<16:24,  5.03s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:30,  4.77s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:57,  4.63s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:35<14:34,  4.53s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:17,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:03,  4.42s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:48<13:55,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:51,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:43,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:01<13:36,  4.37s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:30,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:10<13:28,  4.37s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:24,  4.37s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:18,  4.36s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:23<13:12,  4.35s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:06,  4.35s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:04,  4.36s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<12:59,  4.35s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<12:55,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:52,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:50,  4.38s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:44,  4.37s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:58<12:45,  4.40s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:38,  4.39s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:31,  4.37s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:11<12:30,  4.39s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:21,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:19,  4.38s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:24<12:16,  4.38s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:13,  4.39s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:33<12:22,  4.47s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<12:13,  4.45s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:42<12:02,  4.40s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:46<11:53,  4.38s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<11:46,  4.36s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:55<11:41,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:59<11:34,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:31,  4.35s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:08<11:28,  4.36s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:12<11:22,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:18,  4.35s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:21<11:15,  4.35s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:25<11:09,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:29<11:05,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:34<10:59,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:38<10:55,  4.34s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:47<11:21,  4.54s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:45,  8.37s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:26,  5.89s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<17:06,  5.21s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:56,  4.88s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:14,  4.69s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:46,  4.57s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:27,  4.50s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:15,  4.46s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:00,  4.40s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:51,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:46,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:47,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:51,  4.44s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:43,  4.43s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:21,  4.33s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:20,  4.35s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:15,  4.35s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:10,  4.34s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:07,  4.35s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<12:55,  4.31s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<12:53,  4.32s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:51,  4.34s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:50,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:45,  4.35s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:38,  4.33s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:29,  4.31s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:29,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:23,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:20,  4.33s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:19,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:11,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:10,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:03,  4.33s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<11:58,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:49,  4.30s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:43,  4.29s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:42,  4.31s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:39,  4.32s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:38,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:25,  4.28s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:25,  4.31s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:25,  4.34s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:30,  4.40s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:14<11:21,  4.37s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:15,  4.36s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:23<11:10,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:27<11:05,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:01,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:36<10:56,  4.35s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:54,  4.36s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:45,  4.33s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:49<10:43,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:53<10:38,  4.34s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:02<10:56,  4.49s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:13,  8.51s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:43,  5.97s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:08,  5.22s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:56,  4.88s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:14,  4.69s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:43,  4.55s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:26,  4.49s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:28,  4.52s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:12,  4.46s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<14:01,  4.43s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:51,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:44,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:40,  4.39s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:39,  4.41s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:37,  4.42s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:51,  4.52s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:49,  4.53s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:23<13:29,  4.45s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:19,  4.41s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:12,  4.40s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<13:05,  4.39s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<12:59,  4.38s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:53,  4.37s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:47,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:44,  4.37s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:58<12:39,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:33,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:29,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:11<12:24,  4.35s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:20,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:13,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:24<12:08,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:03,  4.34s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:00,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<11:56,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:45,  4.30s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:44,  4.32s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<11:40,  4.32s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:31,  4.30s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:30,  4.32s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:02<11:26,  4.32s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:13,  4.26s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:11<11:10,  4.27s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:13,  4.32s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:16,  4.36s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<10:59,  4.28s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<10:56,  4.29s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<10:53,  4.30s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<10:50,  4.31s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:46,  4.31s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:41,  4.30s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:50<10:38,  4.31s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:36,  4.33s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:33,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:03<10:28,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:11<10:47,  4.50s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<25:47,  7.78s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:07,  5.80s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:45,  5.11s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:40,  4.80s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:06,  4.65s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:42,  4.55s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:25,  4.49s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:12,  4.44s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<13:56,  4.38s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:46<13:50,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:45,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:41,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:59<13:34,  4.35s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:45,  4.44s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:37,  4.42s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:26,  4.38s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:18,  4.36s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:13,  4.36s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:04,  4.34s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<13:00,  4.34s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:57,  4.34s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:56,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:53,  4.37s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:46,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:39,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:34,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:00<12:32,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:27,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:24,  4.35s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:19,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:12,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:11,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:07,  4.36s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<12:03,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<12:00,  4.37s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:55,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:52,  4.37s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:48,  4.37s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:43,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:38,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:30,  4.35s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:27,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:27,  4.38s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:40,  4.49s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:29,  4.45s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:20,  4.42s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:13,  4.40s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:05,  4.38s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<10:59,  4.37s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:53,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:47,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:50<10:42,  4.34s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:38,  4.34s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:35,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:03<10:32,  4.36s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:07<10:26,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:16<10:43,  4.50s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.99it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.06s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 48it [00:00, 69.51it/s]\u001b[A\n",
+      "Evaluating NLL: 97it [00:01, 141.76it/s]\u001b[A\n",
+      "Evaluating NLL: 148it [00:01, 213.78it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 280.15it/s]\u001b[A\n",
+      "Evaluating NLL: 255it [00:01, 343.13it/s]\u001b[A\n",
+      "Evaluating NLL: 311it [00:01, 397.06it/s]\u001b[A\n",
+      "Evaluating NLL: 367it [00:01, 439.40it/s]\u001b[A\n",
+      "Evaluating NLL: 427it [00:01, 481.35it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 280.75it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 141.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▉  | 30/100 [00:00<00:00, 141.26it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█▎ | 45/100 [00:00<00:00, 139.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|█▊ | 59/100 [00:00<00:00, 138.83it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|██▏| 73/100 [00:00<00:00, 139.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 136.05it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|▎  | 11/100 [00:00<00:00, 100.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▉   | 22/100 [00:00<00:00, 96.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|█▎  | 32/100 [00:00<00:00, 96.08it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▋  | 42/100 [00:00<00:00, 91.35it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██  | 52/100 [00:00<00:00, 92.58it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|██▍ | 62/100 [00:00<00:00, 89.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██▊ | 71/100 [00:00<00:00, 89.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:00<00:00, 88.87it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  89%|███▌| 89/100 [00:00<00:00, 88.67it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 91.51it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 133.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 133.48it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|█▎ | 43/100 [00:00<00:00, 137.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|█▋ | 57/100 [00:00<00:00, 133.32it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██▏| 71/100 [00:00<00:00, 134.48it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|██▌| 85/100 [00:00<00:00, 135.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 134.44it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:36,  8.02s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<18:59,  5.75s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:33,  5.04s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:27,  4.73s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:55,  4.59s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:36,  4.52s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:21,  4.46s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:12,  4.44s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<13:59,  4.40s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:46<13:52,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:44,  4.36s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:40,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:59<13:34,  4.35s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:30,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:26,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:12<13:19,  4.35s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:16<13:00,  4.27s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:02,  4.30s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:25<13:00,  4.31s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:29<12:58,  4.32s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:55,  4.34s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:38<12:54,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:50,  4.35s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:52,  4.39s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:51<12:45,  4.38s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:39,  4.37s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:00<12:33,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:04<12:28,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:24,  4.36s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:13<12:19,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:17<12:14,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:11,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:26<12:06,  4.35s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<12:02,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:59,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:39<11:53,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:49,  4.35s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:44,  4.35s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:52<11:40,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:36,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:31,  4.35s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:05<11:28,  4.36s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:22,  4.34s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:14<11:17,  4.34s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:18<11:14,  4.35s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:23<11:13,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:27<11:08,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:03,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:36<11:00,  4.37s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:40<11:02,  4.42s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:55,  4.40s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:49<10:48,  4.38s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:53<10:41,  4.37s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:02<10:56,  4.49s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:05,  8.47s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:41,  5.97s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<17:00,  5.18s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:51,  4.85s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:11,  4.68s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:39,  4.54s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:23,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:12,  4.44s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:03,  4.42s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:56,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:50,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:46,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:37,  4.37s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:32,  4.37s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:25,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:21,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:09,  4.31s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:11,  4.35s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:05,  4.34s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<13:03,  4.35s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<12:58,  4.35s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:56,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:55,  4.38s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:47,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:45,  4.38s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:39,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:33,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:26,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:18,  4.32s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:06,  4.27s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:06,  4.30s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:02,  4.30s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:00,  4.32s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<11:58,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:54,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:50,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:48,  4.34s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:38,  4.31s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:33,  4.31s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:32,  4.33s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:28,  4.33s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:24,  4.33s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:19,  4.33s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:14<11:14,  4.32s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:06,  4.30s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:23<11:06,  4.32s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:27<11:03,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<10:56,  4.32s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:36<10:54,  4.33s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:40<10:49,  4.33s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:47,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:49<10:45,  4.36s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:53<10:41,  4.37s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:37,  4.37s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:02<10:30,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:06<10:27,  4.36s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:11<10:28,  4.40s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:15<10:22,  4.39s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:20<10:16,  4.37s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:24<10:11,  4.37s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:28<10:07,  4.37s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:33<10:01,  4.36s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:37<09:56,  4.35s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:41<09:51,  4.35s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:46<09:45,  4.34s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:50<09:40,  4.33s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:54<09:36,  4.34s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [05:03<09:49,  4.46s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:07,  8.18s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:49,  6.01s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:12,  5.24s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:56,  4.88s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:12,  4.68s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:46,  4.57s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:28,  4.50s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:13,  4.44s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:03,  4.42s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:55,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:47,  4.38s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:57,  4.46s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:46,  4.42s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:35,  4.39s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:29,  4.38s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:25,  4.38s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:11,  4.32s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:06,  4.32s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:02,  4.32s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<12:59,  4.33s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<12:50,  4.31s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:46,  4.31s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:42,  4.31s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:42,  4.33s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:36,  4.32s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:32,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:29,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:25,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:18,  4.32s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:16,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:13,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:11,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:05,  4.34s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<11:52,  4.29s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:52,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:49,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:45,  4.33s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:41,  4.33s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:39,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:33,  4.33s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:30,  4.34s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:38,  4.42s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:30,  4.40s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:24,  4.39s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:19,  4.38s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:23<11:14,  4.38s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:07,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:03,  4.36s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:36<10:57,  4.36s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:52,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:49,  4.36s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:50<10:46,  4.37s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:38,  4.34s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:33,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:02<10:29,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:07<10:26,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:11<10:22,  4.35s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:16<10:18,  4.36s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:20<10:12,  4.35s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:24<10:09,  4.36s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:29<10:03,  4.34s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:33<09:58,  4.33s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:37<09:55,  4.35s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:42<09:53,  4.36s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:46<09:49,  4.37s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:50<09:43,  4.36s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:55<09:38,  4.35s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [04:59<09:34,  4.35s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [05:03<09:31,  4.37s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [05:08<09:28,  4.38s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [05:12<09:23,  4.37s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [05:17<09:18,  4.36s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [05:21<09:13,  4.36s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [05:25<09:17,  4.42s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [05:30<09:10,  4.41s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [05:34<09:04,  4.39s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [05:39<08:57,  4.37s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [05:47<09:03,  4.46s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:38,  8.33s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:13<20:41,  6.27s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:38,  5.38s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:14,  4.97s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:24,  4.74s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:54,  4.61s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:30,  4.51s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:17,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:05,  4.42s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:56,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:49,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:41,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:34,  4.35s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:30,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:26,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:22,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:17,  4.36s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:12,  4.35s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:10,  4.37s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:08,  4.38s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<13:13,  4.43s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:40<13:02,  4.40s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:56,  4.39s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<12:47,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:53<12:41,  4.35s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:39,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:34,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:27,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:22,  4.34s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:18,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:14,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:08,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:06,  4.35s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:05,  4.37s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:36<11:58,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:54,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:49,  4.35s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:49<11:44,  4.35s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:39,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:34,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:02<11:30,  4.34s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:25,  4.34s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:11<11:22,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:19,  4.36s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:15,  4.36s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:13,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:29<11:08,  4.37s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<11:05,  4.38s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<11:00,  4.37s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:42<10:57,  4.39s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:46<10:54,  4.39s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:51<10:47,  4.37s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:55<10:42,  4.37s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:04<10:59,  4.52s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:46,  8.07s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:04,  5.78s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:47,  5.11s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:43,  4.81s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:06,  4.65s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:41,  4.54s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:23,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:11,  4.44s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<13:57,  4.39s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:46<13:48,  4.36s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:43,  4.36s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:40,  4.36s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:59<13:36,  4.37s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:30,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:27,  4.37s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:21,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:16,  4.35s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:15,  4.37s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:10,  4.37s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<13:14,  4.42s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<13:04,  4.38s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:57,  4.37s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:54,  4.37s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:48,  4.36s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:43,  4.37s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:39,  4.36s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:37,  4.38s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:32,  4.38s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:26,  4.36s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:21,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:16,  4.36s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:10,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:05,  4.34s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<12:00,  4.34s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<11:57,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:53,  4.35s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:44<11:48,  4.35s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:48<11:43,  4.34s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:38,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:57<11:35,  4.34s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:01<11:29,  4.34s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:26,  4.35s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:25,  4.37s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:22,  4.37s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:19,  4.38s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:26,  4.46s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:19,  4.44s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:12,  4.42s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<11:04,  4.40s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:57,  4.38s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:48,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:50<10:43,  4.35s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:39,  4.35s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:34,  4.34s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:03<10:30,  4.35s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:07<10:24,  4.33s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:12<10:24,  4.37s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:16<10:19,  4.36s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:20<10:13,  4.35s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:25<10:08,  4.35s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:29<10:04,  4.35s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:33<10:00,  4.35s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:38<09:53,  4.34s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:42<09:51,  4.35s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:46<09:48,  4.36s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:51<09:42,  4.35s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:59<09:55,  4.48s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.58it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.39it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 44it [00:01, 59.22it/s]\u001b[A\n",
+      "Evaluating NLL: 92it [00:01, 127.17it/s]\u001b[A\n",
+      "Evaluating NLL: 138it [00:01, 189.52it/s]\u001b[A\n",
+      "Evaluating NLL: 189it [00:01, 256.73it/s]\u001b[A\n",
+      "Evaluating NLL: 234it [00:01, 300.65it/s]\u001b[A\n",
+      "Evaluating NLL: 284it [00:01, 349.87it/s]\u001b[A\n",
+      "Evaluating NLL: 333it [00:01, 384.49it/s]\u001b[A\n",
+      "Evaluating NLL: 381it [00:01, 409.67it/s]\u001b[A\n",
+      "Evaluating NLL: 428it [00:01, 422.52it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 250.61it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:00, 96.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:00, 86.07it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|█▏  | 29/100 [00:00<00:00, 82.08it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|█▌  | 38/100 [00:00<00:00, 76.20it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█▊  | 46/100 [00:00<00:00, 73.81it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|██▏ | 54/100 [00:00<00:00, 61.94it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|██▍ | 62/100 [00:00<00:00, 66.13it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|██▊ | 69/100 [00:01<00:00, 56.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:01<00:00, 67.76it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 73.57it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:00, 96.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:00, 91.49it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|█▏  | 31/100 [00:00<00:00, 97.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▋  | 42/100 [00:00<00:00, 98.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|█▌ | 53/100 [00:00<00:00, 100.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|█▉ | 64/100 [00:00<00:00, 101.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|██▎| 75/100 [00:00<00:00, 101.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|██▌| 86/100 [00:00<00:00, 102.75it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 100.57it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 139.54it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▊  | 29/100 [00:00<00:00, 135.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|█▎ | 43/100 [00:00<00:00, 135.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|█▋ | 57/100 [00:00<00:00, 136.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██▏| 71/100 [00:00<00:00, 136.49it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|██▌| 85/100 [00:00<00:00, 135.48it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 136.14it/s]\n",
+      " 67%|███████████████████████████████▎               | 2/3 [3:41:15<1:50:54, 6654.73s/it]\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:56,  8.12s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:10,  5.81s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:43,  5.09s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:20,  5.00s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:30,  4.77s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<15:02,  4.65s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:39,  4.56s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:27,  4.52s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:10,  4.45s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<14:02,  4.43s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<14:02,  4.46s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:41,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:36,  4.36s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:29,  4.35s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:24,  4.35s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:11,  4.30s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:11,  4.32s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:08,  4.33s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:04,  4.33s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<13:00,  4.34s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<12:57,  4.34s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:50,  4.33s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:46,  4.33s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:42,  4.33s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:40,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:36,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:31,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:27,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:24,  4.35s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:20,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:15,  4.35s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:14,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:09,  4.37s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<12:03,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:36<12:00,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<11:58,  4.38s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:53,  4.38s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:49<11:47,  4.37s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:53<11:43,  4.37s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:35,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:02<11:28,  4.33s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:06<11:22,  4.32s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:10<11:17,  4.31s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:14,  4.32s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:19<11:12,  4.34s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:09,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:05,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<11:00,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:41<11:22,  4.52s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<25:59,  7.83s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<18:59,  5.75s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:37,  5.06s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:41,  4.80s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:03,  4.63s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:37,  4.52s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:22,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<14:03,  4.39s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<13:59,  4.40s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:46<13:53,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:46,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:55<13:40,  4.36s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:59<13:36,  4.37s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:30,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:08<13:26,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:12<13:19,  4.34s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:13,  4.33s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:21<13:00,  4.29s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:25<12:43,  4.22s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:29<12:44,  4.25s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:34<12:47,  4.29s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:38<12:55,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:42<12:51,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:37,  4.31s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:51<12:32,  4.30s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:55<12:32,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:00<12:26,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:04<12:24,  4.33s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:08<12:15,  4.30s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:13<12:16,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:17<12:12,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:21<12:09,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:26<11:58,  4.30s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:30<11:56,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:34<11:52,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:39<11:50,  4.33s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:43<11:47,  4.34s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:47<11:42,  4.34s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:52<11:36,  4.33s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:56<11:30,  4.32s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:00<11:25,  4.31s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:09<11:52,  4.51s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:58,  8.44s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:52,  6.02s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:17<17:12,  5.24s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:51,  4.85s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:29,  4.77s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<14:57,  4.62s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:34,  4.53s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:39<14:19,  4.48s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:07,  4.44s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:58,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:52<13:49,  4.39s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:41,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:35,  4.36s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:05<13:31,  4.36s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:27,  4.37s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:22,  4.36s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:21,  4.38s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:15,  4.37s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<13:07,  4.35s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:04,  4.36s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<13:00,  4.36s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:55,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:44<12:50,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:48<12:44,  4.35s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:39,  4.34s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:57<12:35,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:33,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:06<12:28,  4.35s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:10<12:26,  4.37s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:14<12:23,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:19<12:17,  4.37s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:23<12:29,  4.46s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:18,  4.42s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:32<12:07,  4.39s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:36<12:01,  4.37s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:56,  4.37s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<11:53,  4.37s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:49<11:45,  4.36s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:41,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:36,  4.35s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:02<11:30,  4.34s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:24,  4.33s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:11<11:21,  4.34s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:15<11:17,  4.35s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<11:13,  4.35s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<11:09,  4.35s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<11:04,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:33<10:59,  4.34s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<10:56,  4.35s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:51,  4.34s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:50<11:13,  4.52s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:28,  8.28s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:28,  5.90s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<17:07,  5.22s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<16:05,  4.92s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:26<15:38,  4.81s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:30<15:03,  4.66s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:34<14:37,  4.55s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:17,  4.47s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:43<14:05,  4.43s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:57,  4.41s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:46,  4.37s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:35,  4.34s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:31,  4.34s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:25,  4.33s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:18,  4.31s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:13<13:14,  4.32s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:17<13:04,  4.28s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:00,  4.29s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:26<12:58,  4.30s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:30<12:58,  4.32s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:35<12:55,  4.33s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:39<12:55,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:43<12:51,  4.36s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:47<12:33,  4.28s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:52<12:32,  4.30s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:56<12:32,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:01<12:30,  4.34s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:05<12:21,  4.31s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:09<12:17,  4.31s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:13<12:11,  4.30s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:18<12:08,  4.31s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:22<12:01,  4.29s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:27<12:10,  4.38s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:31<12:04,  4.36s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:35<12:12,  4.44s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:40<12:29,  4.57s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:45<12:23,  4.56s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:49<12:14,  4.53s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<12:07,  4.52s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<12:03,  4.52s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:55,  4.50s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:45,  4.46s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:12<11:46,  4.50s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:38,  4.48s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:21<11:38,  4.50s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:25<11:32,  4.50s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:30<11:24,  4.47s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:34<11:21,  4.48s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:39<11:13,  4.46s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:43<10:57,  4.38s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:47<10:54,  4.39s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:52<10:59,  4.45s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:56<10:58,  4.48s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [04:01<10:57,  4.51s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:05<10:48,  4.47s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:10<10:48,  4.51s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:14<10:43,  4.50s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:19<10:34,  4.47s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:23<10:22,  4.41s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:28<10:27,  4.48s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:32<10:24,  4.49s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:37<10:23,  4.51s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:41<10:09,  4.45s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:45<09:59,  4.41s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:50<10:01,  4.46s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:55<10:02,  4.49s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:59<10:01,  4.52s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [05:03<09:47,  4.45s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [05:08<09:32,  4.37s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [05:12<09:35,  4.43s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [05:21<09:44,  4.53s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:18,  8.23s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:30,  5.91s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:49,  5.12s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:21<15:45,  4.82s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<15:08,  4.66s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:40,  4.54s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:18,  4.45s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:38<14:09,  4.42s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:42<14:01,  4.41s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:47<13:55,  4.40s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:51<13:58,  4.44s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:56<13:56,  4.45s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:00<13:55,  4.47s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:04<13:44,  4.43s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:09<13:44,  4.46s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:14<13:44,  4.48s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:18<13:23,  4.39s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:22<13:25,  4.43s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:27<13:30,  4.48s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:31<13:33,  4.52s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:36<13:27,  4.51s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:41<13:27,  4.54s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:45<13:24,  4.54s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:49<13:03,  4.45s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:54<12:48,  4.39s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:58<12:54,  4.45s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [02:02<12:43,  4.41s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:07<12:35,  4.39s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:11<12:26,  4.37s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:15<12:17,  4.34s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:20<12:11,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:24<12:07,  4.33s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:28<12:00,  4.31s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:33<11:57,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:37<11:52,  4.32s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:41<11:47,  4.31s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:46<11:39,  4.29s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:50<11:27,  4.25s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:54<11:26,  4.26s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:58<11:24,  4.28s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [03:03<11:24,  4.30s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:07<11:19,  4.30s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:11<11:16,  4.31s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:16<11:10,  4.30s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:20<10:56,  4.23s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:24<10:49,  4.22s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:28<10:50,  4.25s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:32<10:50,  4.28s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:37<10:44,  4.27s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:41<10:41,  4.28s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:45<10:25,  4.20s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:49<10:25,  4.22s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:54<10:25,  4.26s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:58<10:20,  4.25s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [04:02<10:13,  4.23s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:06<10:05,  4.21s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:10<09:53,  4.15s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:14<09:51,  4.17s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:19<09:55,  4.22s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:23<09:44,  4.17s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:27<09:40,  4.17s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:31<09:35,  4.17s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:35<09:31,  4.17s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:39<09:17,  4.10s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:44<09:18,  4.14s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:48<09:22,  4.20s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:52<09:20,  4.21s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [04:56<09:09,  4.16s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [05:00<09:09,  4.19s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [05:05<09:08,  4.22s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [05:09<09:04,  4.22s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [05:13<08:53,  4.17s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [05:17<08:52,  4.19s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [05:21<08:48,  4.19s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [05:26<08:44,  4.20s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [05:30<08:42,  4.21s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [05:34<08:29,  4.14s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [05:38<08:26,  4.15s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [05:42<08:25,  4.18s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [05:46<08:20,  4.17s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [05:51<08:14,  4.15s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [05:55<08:04,  4.11s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [05:59<07:59,  4.10s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [06:03<08:01,  4.15s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [06:07<07:56,  4.15s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [06:11<07:49,  4.12s/it]\u001b[A\n",
+      "training:  44%|█████████████████▍                      | 87/200 [06:15<07:45,  4.12s/it]\u001b[A\n",
+      "training:  44%|█████████████████▌                      | 88/200 [06:19<07:39,  4.11s/it]\u001b[A\n",
+      "training:  44%|█████████████████▊                      | 89/200 [06:23<07:34,  4.09s/it]\u001b[A\n",
+      "training:  45%|██████████████████                      | 90/200 [06:27<07:28,  4.08s/it]\u001b[A\n",
+      "training:  46%|██████████████████▏                     | 91/200 [06:31<07:23,  4.07s/it]\u001b[A\n",
+      "training:  46%|██████████████████▍                     | 92/200 [06:36<07:25,  4.12s/it]\u001b[A\n",
+      "training:  46%|██████████████████▌                     | 93/200 [06:40<07:23,  4.14s/it]\u001b[A\n",
+      "training:  47%|██████████████████▊                     | 94/200 [06:44<07:14,  4.10s/it]\u001b[A\n",
+      "training:  48%|███████████████████                     | 95/200 [06:48<07:14,  4.14s/it]\u001b[A\n",
+      "training:  48%|███████████████████▏                    | 96/200 [06:52<07:07,  4.11s/it]\u001b[A\n",
+      "training:  48%|███████████████████▍                    | 97/200 [06:56<07:00,  4.08s/it]\u001b[A\n",
+      "training:  49%|███████████████████▌                    | 98/200 [07:00<07:00,  4.13s/it]\u001b[A\n",
+      "training:  50%|███████████████████▊                    | 99/200 [07:09<07:18,  4.34s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.14s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.47it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.30it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.16it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.23it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 46it [00:01, 60.64it/s]\u001b[A\n",
+      "Evaluating NLL: 90it [00:01, 120.93it/s]\u001b[A\n",
+      "Evaluating NLL: 138it [00:01, 186.74it/s]\u001b[A\n",
+      "Evaluating NLL: 185it [00:01, 245.09it/s]\u001b[A\n",
+      "Evaluating NLL: 235it [00:01, 302.28it/s]\u001b[A\n",
+      "Evaluating NLL: 284it [00:01, 346.72it/s]\u001b[A\n",
+      "Evaluating NLL: 332it [00:01, 379.96it/s]\u001b[A\n",
+      "Evaluating NLL: 379it [00:01, 403.23it/s]\u001b[A\n",
+      "Evaluating NLL: 428it [00:01, 426.28it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 248.98it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▍  | 13/100 [00:00<00:00, 122.28it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▊  | 26/100 [00:00<00:00, 119.96it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|█▏ | 39/100 [00:00<00:00, 120.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|█▌ | 52/100 [00:00<00:00, 121.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|█▉ | 65/100 [00:00<00:00, 120.31it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|██▎| 78/100 [00:00<00:00, 122.92it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 122.89it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%|▎  | 12/100 [00:00<00:00, 113.27it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▋  | 24/100 [00:00<00:00, 112.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|█  | 36/100 [00:00<00:00, 109.26it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█▍ | 47/100 [00:00<00:00, 109.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|█▋ | 58/100 [00:00<00:00, 108.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|██ | 69/100 [00:00<00:00, 108.74it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|██▍| 80/100 [00:00<00:00, 108.75it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 108.82it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 137.35it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 136.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 135.33it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|█▋ | 56/100 [00:00<00:00, 134.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 134.85it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██▌| 84/100 [00:00<00:00, 135.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 129.43it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:11,  8.20s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:07,  5.80s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:09,  4.92s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<14:47,  4.53s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<13:59,  4.30s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:52,  4.29s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:33,  4.22s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:34,  4.24s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:23,  4.21s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:13,  4.18s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:22,  4.24s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:14,  4.23s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<13:09,  4.22s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<13:02,  4.21s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:06<12:59,  4.22s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:10<12:57,  4.22s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:50,  4.21s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:48,  4.22s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:42,  4.21s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:27<12:38,  4.21s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:31<12:34,  4.21s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:35<12:32,  4.23s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:40<12:40,  4.30s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:44<12:31,  4.27s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:48<12:23,  4.25s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:52<12:16,  4.23s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:56<12:10,  4.22s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:01<12:03,  4.21s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:05<11:58,  4.20s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:09<11:54,  4.20s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:13<11:49,  4.20s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:17<11:46,  4.21s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:22<11:39,  4.19s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:26<11:33,  4.18s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:30<11:34,  4.21s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:34<11:29,  4.20s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:38<11:22,  4.19s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:42<11:17,  4.18s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:47<11:13,  4.19s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:51<11:09,  4.18s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:55<11:06,  4.19s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:59<11:02,  4.19s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:03<11:00,  4.21s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:08<10:55,  4.20s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:12<10:49,  4.19s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:16<10:45,  4.19s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:20<10:43,  4.20s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:25<10:40,  4.21s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:33<10:57,  4.35s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<28:01,  8.45s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:36,  5.94s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:51,  5.14s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:27,  4.73s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:25<14:41,  4.52s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:29<14:18,  4.42s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<14:00,  4.35s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<13:48,  4.31s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:25,  4.22s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:17,  4.20s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:50<13:15,  4.21s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:54<13:11,  4.21s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:58<12:50,  4.12s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:02<12:44,  4.11s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:06<12:45,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:10<12:40,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:29,  4.10s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:22,  4.08s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:23<12:29,  4.14s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:27<12:23,  4.13s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:31<12:16,  4.11s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:35<12:02,  4.06s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<11:53,  4.03s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:43<12:02,  4.10s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:47<11:54,  4.08s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:51<11:43,  4.05s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:55<11:43,  4.07s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:59<11:47,  4.11s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:44,  4.12s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:39,  4.11s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:32,  4.10s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:16<11:26,  4.08s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:19,  4.07s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:15,  4.07s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:22,  4.14s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:18,  4.14s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<11:13,  4.13s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<11:10,  4.14s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:44<11:01,  4.11s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:49<10:58,  4.12s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:53<10:50,  4.09s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:56<10:36,  4.03s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:42,  4.09s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:05<10:40,  4.11s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:34,  4.09s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:13<10:26,  4.07s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:25,  4.09s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:21<10:13,  4.04s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:25<10:19,  4.10s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:29<10:13,  4.09s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:37<10:36,  4.27s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:12,  8.21s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:23,  5.88s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:34,  5.05s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:09,  4.64s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:22,  4.42s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<14:02,  4.34s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<13:52,  4.32s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<13:44,  4.29s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:26,  4.22s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:16,  4.19s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:21,  4.24s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:13,  4.22s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:58<13:07,  4.21s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:02<12:50,  4.14s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:06<12:50,  4.16s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:10<12:38,  4.12s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:41,  4.16s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:37,  4.16s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:29,  4.14s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:27<12:34,  4.19s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:31<12:27,  4.18s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:35<12:20,  4.16s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<12:15,  4.16s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:43<11:59,  4.09s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:47<11:51,  4.07s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:51<11:56,  4.12s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:56<11:59,  4.16s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:00<11:48,  4.12s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:04<11:46,  4.13s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:08<11:38,  4.11s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:12<11:25,  4.06s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:16<11:27,  4.09s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:25,  4.11s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:19,  4.10s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:16,  4.10s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:10,  4.09s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<10:59,  4.04s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<10:52,  4.03s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:44<10:58,  4.09s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:49<11:02,  4.14s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:53<10:52,  4.10s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:57<10:43,  4.07s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:47,  4.13s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:05<10:47,  4.15s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:47,  4.18s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:14<10:44,  4.18s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:18<10:38,  4.17s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:22<10:31,  4.15s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:26<10:25,  4.14s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:30<10:14,  4.10s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:34<10:16,  4.14s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:38<10:11,  4.13s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:47<10:29,  4.28s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<25:23,  7.65s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:11<18:30,  5.61s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:15<16:03,  4.89s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:04,  4.61s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:26,  4.44s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:53,  4.30s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:46,  4.28s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:31,  4.23s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:14,  4.16s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<12:56,  4.09s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<13:03,  4.15s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:07,  4.19s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:55,  4.15s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:45,  4.12s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:33,  4.07s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:42,  4.15s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:42,  4.17s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:33,  4.14s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:21<12:32,  4.16s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:25<12:17,  4.10s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:19,  4.13s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:15,  4.13s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:09,  4.12s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<11:59,  4.09s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<11:48,  4.05s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:50<11:41,  4.03s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:54<11:45,  4.08s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:58<11:42,  4.08s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:02<11:35,  4.07s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:06<11:31,  4.07s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:10<11:26,  4.06s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:14<11:26,  4.08s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:19<11:27,  4.12s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:23<11:26,  4.14s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:27<11:18,  4.12s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:31<11:10,  4.09s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:35<11:01,  4.06s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:39<11:02,  4.09s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:43<11:00,  4.10s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:47<10:55,  4.10s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:51<10:52,  4.10s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:55<10:47,  4.10s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:00<10:50,  4.14s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:04<10:46,  4.15s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:08<10:41,  4.14s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:12<10:35,  4.13s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:16<10:34,  4.15s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:20<10:30,  4.15s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:24<10:20,  4.11s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:29<10:22,  4.15s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:37<10:35,  4.26s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:13,  8.21s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:14,  5.83s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:42,  5.09s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:22,  4.71s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:37,  4.50s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<14:10,  4.38s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<13:51,  4.31s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<13:29,  4.22s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:33,  4.26s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:12,  4.17s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:06,  4.16s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:13,  4.22s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:58<13:00,  4.17s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:02<12:48,  4.13s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:06<12:47,  4.15s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:10<12:48,  4.18s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:42,  4.17s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:31,  4.13s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:23<12:39,  4.20s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:27<12:29,  4.16s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:31<12:15,  4.11s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:35<12:13,  4.12s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<12:20,  4.19s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:43<12:15,  4.18s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:47<12:04,  4.14s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:52<12:06,  4.17s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:56<11:57,  4.15s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:00<11:48,  4.12s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:04<11:41,  4.10s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:08<11:42,  4.13s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:12<11:46,  4.18s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:16<11:34,  4.13s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:22,  4.09s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:25<11:26,  4.14s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:29<11:36,  4.22s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:33<11:25,  4.18s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:37<11:11,  4.12s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:41<11:06,  4.12s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:45<11:08,  4.15s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:50<11:05,  4.16s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:54<10:56,  4.13s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:58<10:44,  4.08s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:02<10:36,  4.05s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:06<10:42,  4.12s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:10<10:39,  4.13s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:14<10:33,  4.11s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:18<10:34,  4.15s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:22<10:28,  4.13s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:26<10:19,  4.11s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:35<10:45,  4.31s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.64it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.31it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.08s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.16it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 49it [00:00, 70.00it/s]\u001b[A\n",
+      "Evaluating NLL: 95it [00:01, 136.19it/s]\u001b[A\n",
+      "Evaluating NLL: 143it [00:01, 203.22it/s]\u001b[A\n",
+      "Evaluating NLL: 192it [00:01, 264.64it/s]\u001b[A\n",
+      "Evaluating NLL: 240it [00:01, 315.54it/s]\u001b[A\n",
+      "Evaluating NLL: 290it [00:01, 360.96it/s]\u001b[A\n",
+      "Evaluating NLL: 343it [00:01, 404.04it/s]\u001b[A\n",
+      "Evaluating NLL: 395it [00:01, 433.89it/s]\u001b[A\n",
+      "Evaluating NLL: 447it [00:01, 457.02it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 268.33it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▍  | 13/100 [00:00<00:00, 122.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▊  | 27/100 [00:00<00:00, 131.50it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|█▏ | 41/100 [00:00<00:00, 134.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|█▋ | 55/100 [00:00<00:00, 134.89it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 136.76it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██▌| 84/100 [00:00<00:00, 134.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 131.89it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:00, 98.53it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:00, 95.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|█▏  | 31/100 [00:00<00:00, 99.56it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|█▋  | 41/100 [00:00<00:00, 95.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|█▌ | 52/100 [00:00<00:00, 100.33it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|█▉ | 64/100 [00:00<00:00, 105.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|██▎| 75/100 [00:00<00:00, 106.44it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|██▌| 86/100 [00:00<00:00, 106.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 104.03it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 142.79it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▉  | 30/100 [00:00<00:00, 138.56it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█▎ | 44/100 [00:00<00:00, 138.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|█▋ | 58/100 [00:00<00:00, 137.30it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|██▏| 73/100 [00:00<00:00, 138.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 136.95it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<26:13,  7.91s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:11<18:39,  5.65s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:15<15:52,  4.83s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:19<14:42,  4.50s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:23<14:06,  4.34s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:27<13:41,  4.23s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:30,  4.20s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:35,  4.25s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:28,  4.23s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:13,  4.18s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<13:18,  4.23s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:19,  4.25s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<13:05,  4.20s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:49,  4.14s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:48,  4.16s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:53,  4.20s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:54,  4.23s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:44,  4.20s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:28,  4.14s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:26<12:21,  4.12s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:18,  4.13s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:09,  4.10s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:04,  4.09s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<12:00,  4.09s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<11:54,  4.08s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:50<11:43,  4.04s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:54<11:34,  4.01s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:59<11:46,  4.11s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:38,  4.09s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:37,  4.11s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:34,  4.11s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:15<11:30,  4.11s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:19<11:25,  4.10s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:23<11:19,  4.09s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:27<11:08,  4.05s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:31<11:02,  4.04s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:35<10:56,  4.03s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:39<10:52,  4.03s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:43<10:57,  4.09s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:47<10:50,  4.07s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:52<10:57,  4.14s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:56<10:59,  4.17s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:00<10:53,  4.16s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:04<10:44,  4.13s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:08<10:37,  4.11s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:12<10:34,  4.12s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:36,  4.16s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:21<10:36,  4.19s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:25<10:31,  4.18s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:29<10:25,  4.17s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:33<10:10,  4.09s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:37<10:14,  4.15s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:45<10:26,  4.26s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<26:31,  8.00s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:06,  5.79s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:33,  5.04s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:26,  4.73s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:30,  4.47s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<14:04,  4.35s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:49,  4.30s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:29,  4.21s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:14,  4.16s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:11,  4.17s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<12:55,  4.11s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<12:49,  4.10s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:54,  4.14s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:45,  4.12s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:37,  4.09s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:38,  4.12s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:31,  4.11s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:34,  4.15s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:35,  4.17s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:26<12:31,  4.18s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:27,  4.18s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:26,  4.19s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<12:15,  4.16s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:43<12:09,  4.14s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:47<12:18,  4.22s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:51<12:11,  4.20s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:55<12:02,  4.18s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:59<11:58,  4.18s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:04<11:47,  4.14s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:08<11:39,  4.11s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:12<11:48,  4.19s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:16<11:33,  4.13s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:27,  4.12s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:28,  4.15s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:22,  4.14s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:13,  4.11s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<11:09,  4.11s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:41<11:11,  4.15s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:45<11:07,  4.15s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:49<10:54,  4.09s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:53<10:44,  4.06s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:57<10:52,  4.13s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:53,  4.17s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:06<10:49,  4.16s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:32,  4.08s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:14<10:28,  4.08s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:18<10:30,  4.12s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:22<10:28,  4.14s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:26<10:21,  4.11s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:30<10:23,  4.16s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:34<10:10,  4.10s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:38<10:12,  4.14s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:42<10:05,  4.12s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:47<09:57,  4.09s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:51<10:03,  4.16s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:55<10:03,  4.19s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [03:59<09:54,  4.16s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:03<09:55,  4.19s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:08<09:55,  4.23s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:16<09:58,  4.27s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:55,  8.12s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:26,  5.89s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:46,  5.11s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:09,  4.64s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:25,  4.44s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:55,  4.31s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:40,  4.25s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<13:41,  4.28s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:25,  4.22s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:19,  4.21s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:12,  4.19s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:01,  4.15s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<13:03,  4.19s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:02<12:54,  4.16s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:06<12:53,  4.18s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:10<12:42,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:34,  4.12s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:38,  4.17s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:27,  4.13s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:26<12:19,  4.11s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:22,  4.15s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:35<12:22,  4.17s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<12:25,  4.21s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:43<12:17,  4.19s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:47<12:14,  4.20s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:51<11:55,  4.11s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:55<11:47,  4.09s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:00<11:56,  4.17s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:04<11:55,  4.19s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:08<11:41,  4.12s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:12<11:30,  4.09s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:16<11:25,  4.08s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:22,  4.09s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:21,  4.11s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:13,  4.08s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:12,  4.10s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<11:04,  4.07s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<10:56,  4.05s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:45<10:56,  4.08s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:49<10:52,  4.08s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:53<10:48,  4.08s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:57<10:41,  4.06s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:41,  4.08s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:05<10:36,  4.08s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:28,  4.05s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:13<10:24,  4.05s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:30,  4.12s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:21<10:20,  4.08s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:30<10:47,  4.29s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:59,  8.14s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<18:51,  5.72s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:10,  4.93s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:01,  4.60s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:23,  4.43s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:47,  4.26s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:25,  4.17s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:33,  4.24s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:23,  4.21s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:06,  4.14s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<12:52,  4.09s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:52<12:49,  4.09s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:48,  4.11s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:50,  4.14s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:45,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:39,  4.13s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:33,  4.12s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:26,  4.10s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:21<12:16,  4.07s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:25<12:23,  4.13s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:29<12:14,  4.11s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:21,  4.16s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:13,  4.14s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<12:04,  4.12s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<12:09,  4.17s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:50<12:11,  4.20s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:55<12:05,  4.19s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:59<11:58,  4.18s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:44,  4.12s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:35,  4.09s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:31,  4.09s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:15<11:38,  4.16s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:19<11:38,  4.18s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:32,  4.17s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:23,  4.14s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:06,  4.06s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<11:07,  4.10s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<11:12,  4.15s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:44<11:05,  4.13s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:48<10:52,  4.08s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:52<10:55,  4.12s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:56<10:55,  4.15s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:47,  4.12s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:05<10:36,  4.08s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:30,  4.07s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:13<10:26,  4.07s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:35,  4.15s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:21<10:29,  4.14s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:25<10:21,  4.12s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:29<10:15,  4.10s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:33<10:12,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:37<10:11,  4.13s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:42<10:02,  4.10s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:46<09:54,  4.07s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:50<09:50,  4.07s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:54<09:48,  4.09s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:02<10:07,  4.25s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:45,  8.37s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<18:58,  5.75s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:06,  4.91s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<14:53,  4.56s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:22,  4.43s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:54,  4.30s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:33,  4.21s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:17,  4.15s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:15,  4.16s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:21,  4.22s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:14,  4.20s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:01,  4.16s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:47,  4.10s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:38,  4.08s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:33,  4.07s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:31,  4.09s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:37,  4.14s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:30,  4.12s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:31,  4.15s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:26<12:23,  4.13s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:13,  4.10s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:05,  4.08s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:09,  4.12s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<11:58,  4.08s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<11:58,  4.11s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:50<11:56,  4.12s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:54<11:55,  4.14s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:58<11:52,  4.14s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:46,  4.13s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:37,  4.10s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:26,  4.06s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:15<11:21,  4.05s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:19<11:24,  4.10s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:23<11:18,  4.09s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:27<11:12,  4.08s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:31<11:05,  4.06s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:35<10:58,  4.04s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:39<10:52,  4.03s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:43<10:52,  4.05s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:47<10:54,  4.09s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:51<10:46,  4.06s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:55<10:39,  4.05s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:00<10:43,  4.10s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:03<10:34,  4.07s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:08<10:36,  4.11s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:12<10:30,  4.09s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:16<10:21,  4.06s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:20<10:24,  4.11s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:24<10:15,  4.07s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:28<10:07,  4.05s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:32<10:12,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:40<10:28,  4.25s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.37s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  5.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.41it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.25it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.05it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 52it [00:00, 75.69it/s]\u001b[A\n",
+      "Evaluating NLL: 98it [00:01, 142.04it/s]\u001b[A\n",
+      "Evaluating NLL: 147it [00:01, 210.63it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 280.17it/s]\u001b[A\n",
+      "Evaluating NLL: 251it [00:01, 334.25it/s]\u001b[A\n",
+      "Evaluating NLL: 303it [00:01, 380.33it/s]\u001b[A\n",
+      "Evaluating NLL: 352it [00:01, 408.25it/s]\u001b[A\n",
+      "Evaluating NLL: 401it [00:01, 426.58it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 271.31it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 136.13it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 133.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 133.94it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|█▋ | 56/100 [00:00<00:00, 134.38it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 131.15it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|██▌| 85/100 [00:00<00:00, 134.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 134.08it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%|▎  | 12/100 [00:00<00:00, 113.34it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▋  | 24/100 [00:00<00:00, 101.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|█  | 35/100 [00:00<00:00, 102.76it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█▍ | 46/100 [00:00<00:00, 103.52it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|█▋ | 57/100 [00:00<00:00, 103.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|██ | 69/100 [00:00<00:00, 106.37it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|██▍| 80/100 [00:00<00:00, 104.64it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 105.73it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 140.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▉  | 30/100 [00:00<00:00, 141.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█▎ | 45/100 [00:00<00:00, 142.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|█▊ | 60/100 [00:00<00:00, 138.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|██▎| 75/100 [00:00<00:00, 139.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 138.71it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:40,  8.04s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:11<18:20,  5.56s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:15<15:34,  4.74s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:19<14:27,  4.43s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:23<13:49,  4.25s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:27<13:39,  4.22s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:31<13:38,  4.24s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:29,  4.21s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:27,  4.23s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:15,  4.19s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<13:10,  4.18s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:16,  4.24s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<13:08,  4.22s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:50,  4.14s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<13:02,  4.23s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<13:01,  4.25s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:54,  4.23s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:56,  4.27s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:59,  4.31s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:27<12:49,  4.28s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:31<12:40,  4.25s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:35<12:38,  4.26s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<12:46,  4.33s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:44<12:33,  4.28s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:48<12:24,  4.25s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:52<12:31,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:57<12:27,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:01<12:22,  4.32s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:05<12:10,  4.27s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:09<12:01,  4.24s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:14<12:04,  4.28s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:18<11:55,  4.26s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:22<11:47,  4.23s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:26<11:40,  4.22s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:30<11:33,  4.21s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:35<11:38,  4.26s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:39<11:34,  4.26s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:43<11:29,  4.25s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:48<11:25,  4.26s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:52<11:19,  4.25s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:56<11:21,  4.29s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:00<11:16,  4.28s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:05<11:07,  4.25s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:09<11:05,  4.27s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:13<11:00,  4.26s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:17<10:52,  4.24s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:22<10:47,  4.23s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:26<10:40,  4.21s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:30<10:44,  4.27s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:34<10:44,  4.30s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:39<10:35,  4.27s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:43<10:28,  4.25s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:47<10:21,  4.23s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:55<10:37,  4.37s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:33,  8.01s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:11<18:25,  5.58s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:15<15:50,  4.82s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<14:56,  4.57s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:13,  4.38s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:52,  4.29s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:22,  4.16s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:07,  4.10s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:15,  4.16s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:11,  4.17s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<13:14,  4.21s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:52<13:10,  4.20s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:56<12:53,  4.14s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:49,  4.14s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:51,  4.17s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:38,  4.12s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:24,  4.07s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:14,  4.04s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:21<12:20,  4.09s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:25<12:11,  4.07s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:29<12:02,  4.04s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:33<11:55,  4.02s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:37<12:07,  4.11s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:41<12:06,  4.13s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:45<11:51,  4.06s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:49<11:46,  4.06s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:53<11:44,  4.07s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:58<11:46,  4.11s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:02<11:46,  4.13s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:06<11:39,  4.12s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:10<11:32,  4.10s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:14<11:22,  4.06s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:18<11:12,  4.03s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:22<11:08,  4.03s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:26<11:06,  4.04s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:30<11:03,  4.04s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:34<11:12,  4.13s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:38<11:07,  4.12s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:43<11:02,  4.12s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:47<10:54,  4.09s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:51<10:46,  4.07s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:55<10:46,  4.09s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [02:59<10:48,  4.13s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:03<10:46,  4.15s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:07<10:35,  4.10s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:11<10:23,  4.05s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:15<10:19,  4.05s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:19<10:15,  4.05s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:23<10:10,  4.04s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:27<10:10,  4.07s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:31<10:07,  4.08s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:40<10:26,  4.23s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:54,  8.41s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:21,  5.86s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:26,  5.01s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<14:59,  4.59s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:43,  4.53s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<14:01,  4.34s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:32,  4.21s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<13:31,  4.23s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:24,  4.21s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:18,  4.20s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:31,  4.30s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:54<13:24,  4.28s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:58<13:25,  4.31s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:03<13:32,  4.37s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:07<13:20,  4.33s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:11<12:58,  4.23s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:15<12:56,  4.24s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:19<13:01,  4.30s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:24<12:59,  4.31s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:28<12:49,  4.28s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:32<12:43,  4.27s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:36<12:36,  4.25s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:41<12:30,  4.24s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:45<12:25,  4.24s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:49<12:17,  4.21s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:53<12:17,  4.24s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:58<12:11,  4.23s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [02:02<12:04,  4.21s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:06<11:57,  4.20s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:10<11:54,  4.20s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:15<12:09,  4.31s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:19<12:05,  4.32s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:23<11:52,  4.26s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:27<11:43,  4.24s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:32<11:38,  4.24s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:36<11:32,  4.22s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:40<11:25,  4.20s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:44<11:20,  4.20s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:48<11:18,  4.22s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:52<11:11,  4.20s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:57<11:08,  4.21s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [03:01<11:05,  4.21s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:05<11:02,  4.22s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:09<10:59,  4.23s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:14<10:55,  4.23s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:18<10:52,  4.23s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:22<10:45,  4.22s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:26<10:40,  4.21s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:31<10:36,  4.22s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:35<10:32,  4.22s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:39<10:27,  4.21s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:43<10:21,  4.20s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:47<10:15,  4.19s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:51<10:11,  4.19s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:56<10:04,  4.17s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [04:00<10:01,  4.18s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [04:04<09:59,  4.19s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:12<10:18,  4.36s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<25:36,  7.72s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:11<18:31,  5.61s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:15<15:57,  4.86s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:19<14:41,  4.50s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:23<14:02,  4.32s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:27<13:41,  4.23s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:31<13:29,  4.19s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:18,  4.16s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:39<13:02,  4.10s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<12:59,  4.10s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<13:06,  4.16s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:52<13:00,  4.15s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:56<12:51,  4.12s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:00<12:43,  4.10s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:04<12:31,  4.06s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:08<12:26,  4.06s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:12<12:19,  4.04s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:16<12:27,  4.11s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:20<12:20,  4.09s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:24<12:07,  4.04s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:29<12:12,  4.09s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:33<12:08,  4.09s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:37<12:07,  4.11s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:41<11:55,  4.06s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:45<11:43,  4.02s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:49<11:41,  4.03s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:53<11:42,  4.06s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:57<11:45,  4.10s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:01<11:36,  4.08s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:05<11:29,  4.06s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:09<11:23,  4.05s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:13<11:15,  4.02s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:17<11:14,  4.04s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:21<11:08,  4.03s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:25<11:08,  4.05s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:30<11:20,  4.15s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:34<11:20,  4.17s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:38<11:09,  4.13s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:42<11:06,  4.14s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:46<11:02,  4.14s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:50<10:54,  4.12s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:55<10:53,  4.14s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [02:59<10:47,  4.12s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:03<10:39,  4.10s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:07<10:41,  4.14s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:11<10:30,  4.09s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:15<10:23,  4.07s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:19<10:31,  4.16s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:23<10:27,  4.15s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:27<10:19,  4.13s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:32<10:12,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:36<10:08,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:40<10:10,  4.15s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:44<10:01,  4.12s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:48<09:55,  4.11s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:52<09:57,  4.15s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [03:56<09:53,  4.15s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:00<09:42,  4.10s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:04<09:38,  4.10s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:13<09:50,  4.22s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:46,  8.37s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:09,  5.81s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:41,  5.08s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:17,  4.68s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:35,  4.49s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<14:00,  4.33s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:33<13:46,  4.28s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:37<13:29,  4.22s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:34,  4.26s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:29,  4.26s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:07,  4.17s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<12:55,  4.13s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:53,  4.14s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:02<12:55,  4.17s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:06<12:47,  4.15s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:10<12:41,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:38,  4.15s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:28,  4.11s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:28,  4.13s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:26<12:19,  4.11s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:12,  4.09s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:07,  4.09s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<12:07,  4.11s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:43<11:59,  4.09s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:47<12:02,  4.13s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:51<11:52,  4.10s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:55<11:45,  4.08s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:59<11:44,  4.10s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:36,  4.08s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:33,  4.08s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:29,  4.08s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:15<11:33,  4.13s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:38,  4.18s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:29,  4.15s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:17,  4.11s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:09,  4.08s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<11:13,  4.13s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<11:15,  4.17s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:45<11:10,  4.17s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:49<11:04,  4.15s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:53<10:56,  4.13s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:57<10:46,  4.09s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:46,  4.12s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:05<10:39,  4.10s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:39,  4.13s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:13<10:32,  4.10s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:27,  4.10s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:21<10:17,  4.06s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:25<10:08,  4.03s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:30<10:16,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:34<10:13,  4.12s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:38<10:05,  4.09s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:42<09:57,  4.06s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:46<09:52,  4.06s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:50<09:49,  4.06s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:54<09:44,  4.06s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [03:58<09:39,  4.05s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:02<09:34,  4.04s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:06<09:29,  4.04s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:10<09:39,  4.14s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:14<09:34,  4.13s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:19<09:29,  4.13s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:23<09:18,  4.08s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:31<09:36,  4.24s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.99it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.15s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  5.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.28it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.45it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 50it [00:00, 72.16it/s]\u001b[A\n",
+      "Evaluating NLL: 98it [00:01, 141.85it/s]\u001b[A\n",
+      "Evaluating NLL: 149it [00:01, 213.86it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 278.34it/s]\u001b[A\n",
+      "Evaluating NLL: 252it [00:01, 335.73it/s]\u001b[A\n",
+      "Evaluating NLL: 301it [00:01, 373.82it/s]\u001b[A\n",
+      "Evaluating NLL: 351it [00:01, 407.31it/s]\u001b[A\n",
+      "Evaluating NLL: 400it [00:01, 429.15it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 271.09it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▍  | 14/100 [00:00<00:00, 136.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▊  | 28/100 [00:00<00:00, 137.10it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▎ | 42/100 [00:00<00:00, 133.96it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|█▋ | 56/100 [00:00<00:00, 134.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 136.00it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██▌| 84/100 [00:00<00:00, 134.04it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 132.56it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%|▎  | 12/100 [00:00<00:00, 113.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▋  | 24/100 [00:00<00:00, 109.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|█  | 35/100 [00:00<00:00, 109.43it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█▍ | 47/100 [00:00<00:00, 110.67it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|█▊ | 59/100 [00:00<00:00, 109.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██ | 70/100 [00:00<00:00, 106.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|██▍| 82/100 [00:00<00:00, 109.19it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 107.97it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▍  | 15/100 [00:00<00:00, 143.56it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▉  | 30/100 [00:00<00:00, 135.63it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█▎ | 44/100 [00:00<00:00, 135.85it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|█▋ | 58/100 [00:00<00:00, 136.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|██▏| 73/100 [00:00<00:00, 138.13it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 138.14it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<26:02,  7.85s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:11<18:36,  5.64s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:15<15:46,  4.80s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:19<14:28,  4.43s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:23<13:53,  4.28s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:27<13:40,  4.23s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:31<13:28,  4.19s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:27,  4.21s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:25,  4.22s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:16,  4.19s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<13:21,  4.24s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:17,  4.24s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<13:01,  4.18s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:46,  4.12s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:45,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:52,  4.20s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:46,  4.19s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:34,  4.15s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:21<12:23,  4.11s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:25<12:17,  4.10s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:13,  4.10s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:11,  4.11s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:03,  4.09s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<11:57,  4.08s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<12:00,  4.12s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:50<11:50,  4.08s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:54<11:41,  4.06s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:58<11:35,  4.04s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:02<11:30,  4.04s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:06<11:27,  4.04s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:10<11:16,  4.00s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:14<11:10,  3.99s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:18<11:11,  4.02s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:22<11:21,  4.11s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:26<11:13,  4.08s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:31<11:16,  4.13s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:35<11:12,  4.13s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:39<11:10,  4.14s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:43<11:05,  4.13s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:47<10:58,  4.12s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:51<10:52,  4.11s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:55<10:45,  4.08s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [02:59<10:39,  4.07s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:03<10:39,  4.10s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:07<10:30,  4.07s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:12<10:35,  4.13s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:16<10:25,  4.09s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:20<10:16,  4.06s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:24<10:21,  4.12s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:28<10:15,  4.10s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:32<10:05,  4.06s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:36<10:08,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:40<10:01,  4.09s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:44<10:00,  4.11s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:48<09:56,  4.11s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:52<09:46,  4.08s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [03:56<09:38,  4.05s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:01<09:42,  4.10s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:05<09:36,  4.09s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:09<09:38,  4.14s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:13<09:30,  4.11s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:17<09:22,  4.08s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:21<09:26,  4.13s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:25<09:15,  4.08s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:30<09:18,  4.14s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:34<09:25,  4.22s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:38<09:17,  4.19s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [04:42<09:02,  4.11s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [04:50<09:11,  4.21s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<26:03,  7.86s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:08,  5.80s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:31,  5.03s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:02,  4.61s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:15,  4.39s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:57,  4.32s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:32,  4.21s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:16,  4.15s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:01,  4.09s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:12,  4.17s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<13:06,  4.16s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:52<12:49,  4.09s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:56<12:37,  4.05s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:44,  4.11s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:49,  4.16s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:45,  4.16s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:36,  4.13s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:31,  4.13s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:21<12:27,  4.13s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:25<12:17,  4.09s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:29<12:11,  4.09s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:16,  4.14s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:13,  4.14s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<12:05,  4.12s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<11:53,  4.07s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:50<11:48,  4.07s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:54<11:52,  4.12s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:58<11:45,  4.10s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:02<11:45,  4.12s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:40,  4.12s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:47,  4.19s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:15<11:34,  4.13s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:19<11:24,  4.10s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:23<11:27,  4.14s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:27<11:16,  4.10s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:31<11:07,  4.07s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:35<11:03,  4.07s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<11:11,  4.14s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:44<11:08,  4.15s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:48<11:05,  4.16s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:52<10:52,  4.10s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:56<10:55,  4.15s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:00<10:44,  4.11s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:04<10:33,  4.06s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:08<10:38,  4.12s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:12<10:37,  4.14s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:28,  4.11s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:21<10:20,  4.08s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:25<10:27,  4.16s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:29<10:17,  4.12s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:33<10:18,  4.15s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:37<10:10,  4.13s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:41<10:06,  4.12s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:45<10:00,  4.11s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:50<09:57,  4.12s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:54<09:56,  4.14s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [03:58<09:48,  4.12s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:02<09:42,  4.11s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:06<09:36,  4.09s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:10<09:30,  4.07s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:14<09:31,  4.11s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:18<09:32,  4.15s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:22<09:22,  4.11s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:27<09:29,  4.19s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:31<09:21,  4.16s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:39<09:27,  4.23s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<26:32,  8.00s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<19:14,  5.83s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:39,  5.08s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:11,  4.65s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:26,  4.44s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<14:06,  4.36s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:42,  4.26s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:25,  4.20s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:41<13:20,  4.19s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:05,  4.13s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:07,  4.17s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<13:06,  4.18s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:52,  4.13s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:52,  4.15s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:45,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:10<12:45,  4.16s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:14<12:32,  4.11s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:18<12:26,  4.10s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:22<12:37,  4.18s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:26<12:21,  4.12s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:20,  4.13s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:35<12:29,  4.21s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:39<12:26,  4.22s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:43<12:12,  4.16s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:47<11:57,  4.10s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:51<12:04,  4.16s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:55<11:57,  4.15s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:59<11:48,  4.12s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:37,  4.08s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:08<11:45,  4.15s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:12<11:41,  4.15s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:16<11:32,  4.12s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:24,  4.10s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:17,  4.08s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:24,  4.15s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:08,  4.08s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<11:00,  4.05s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<10:53,  4.04s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:44<11:00,  4.10s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:48<10:54,  4.09s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:53<10:54,  4.12s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:57<10:58,  4.16s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:42,  4.09s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:05<10:44,  4.13s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:42,  4.14s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:13<10:34,  4.12s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:32,  4.13s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:21<10:26,  4.12s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:26<10:19,  4.10s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:30<10:15,  4.10s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:34<10:20,  4.17s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:38<10:08,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:42<10:04,  4.11s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:47<10:17,  4.23s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:55<10:19,  4.27s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:07<26:03,  7.85s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<18:48,  5.70s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:13,  4.94s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<15:10,  4.65s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:26,  4.44s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:55,  4.31s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:40,  4.25s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:16,  4.15s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:14,  4.16s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:44<13:13,  4.17s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:48<12:56,  4.11s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<12:56,  4.13s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:45,  4.09s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:39,  4.08s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:33,  4.07s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:32,  4.09s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:38,  4.15s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:35,  4.15s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:21<12:29,  4.14s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:25<12:16,  4.09s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:25,  4.16s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:23,  4.18s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:16,  4.16s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<12:06,  4.13s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<11:54,  4.08s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:50<11:50,  4.08s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:54<11:58,  4.16s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:58<11:49,  4.13s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:52,  4.17s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:47,  4.16s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:42,  4.16s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:15<11:28,  4.10s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:19<11:21,  4.08s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:23<11:24,  4.13s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:27<11:18,  4.11s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:31<11:05,  4.06s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:35<11:05,  4.08s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:39<10:57,  4.06s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:43<10:51,  4.04s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:47<10:46,  4.04s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:52<10:48,  4.08s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:56<10:38,  4.04s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:00<10:33,  4.04s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:04<10:26,  4.02s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:07<10:19,  3.99s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:12<10:30,  4.10s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:16<10:31,  4.12s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:20<10:23,  4.10s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:24<10:14,  4.07s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:28<10:07,  4.05s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:32<09:58,  4.02s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:36<10:06,  4.10s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:40<10:04,  4.12s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:44<09:58,  4.10s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:49<09:52,  4.09s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:53<09:56,  4.14s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [03:57<09:50,  4.13s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:01<09:44,  4.11s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:05<09:47,  4.16s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:09<09:38,  4.13s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:13<09:22,  4.05s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:22<09:43,  4.23s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:08<27:23,  8.26s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:12<18:49,  5.70s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:16<16:08,  4.92s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:20<14:50,  4.54s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:24<14:23,  4.43s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:28<13:57,  4.32s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:32<13:46,  4.28s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [00:36<13:28,  4.21s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [00:40<13:20,  4.19s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [00:45<13:17,  4.20s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [00:49<13:12,  4.19s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [00:53<12:58,  4.14s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [00:57<12:50,  4.12s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:01<12:43,  4.10s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:05<12:45,  4.14s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [01:09<12:31,  4.09s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [01:13<12:26,  4.08s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [01:17<12:17,  4.05s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [01:21<12:19,  4.09s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [01:26<12:27,  4.15s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [01:30<12:21,  4.14s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [01:34<12:18,  4.15s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [01:38<12:12,  4.14s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [01:42<12:13,  4.17s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [01:46<12:09,  4.17s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [01:51<12:06,  4.18s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [01:55<11:56,  4.14s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [01:59<11:45,  4.10s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [02:03<11:50,  4.16s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [02:07<11:47,  4.16s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [02:11<11:31,  4.09s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [02:15<11:27,  4.09s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [02:20<11:34,  4.16s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [02:24<11:30,  4.16s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [02:28<11:25,  4.16s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [02:32<11:25,  4.18s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [02:36<11:10,  4.12s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [02:40<11:00,  4.08s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [02:44<11:03,  4.12s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [02:49<11:07,  4.17s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [02:53<10:55,  4.12s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [02:57<10:50,  4.12s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [03:01<10:54,  4.17s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [03:05<10:42,  4.12s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [03:09<10:29,  4.06s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [03:13<10:35,  4.13s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [03:17<10:41,  4.19s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [03:22<10:35,  4.18s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [03:26<10:24,  4.14s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [03:30<10:16,  4.11s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [03:34<10:10,  4.10s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [03:38<10:16,  4.17s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [03:42<10:14,  4.18s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [03:46<10:03,  4.13s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [03:51<10:03,  4.16s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [03:55<10:00,  4.17s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [03:59<09:58,  4.19s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [04:03<09:46,  4.13s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [04:07<09:42,  4.13s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [04:11<09:39,  4.14s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [04:15<09:29,  4.10s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [04:19<09:24,  4.09s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [04:24<09:28,  4.15s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [04:28<09:17,  4.10s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [04:32<09:19,  4.14s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [04:36<09:18,  4.17s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [04:40<09:15,  4.18s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [04:44<09:06,  4.14s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [04:48<09:01,  4.13s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [04:53<09:00,  4.16s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [04:57<08:49,  4.10s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [05:01<08:40,  4.06s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [05:05<08:35,  4.06s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [05:09<08:34,  4.08s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [05:13<08:36,  4.13s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [05:17<08:27,  4.09s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [05:21<08:29,  4.14s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [05:29<08:36,  4.23s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.20it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.10it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 40it [00:01, 51.10it/s]\u001b[A\n",
+      "Evaluating NLL: 77it [00:01, 100.35it/s]\u001b[A\n",
+      "Evaluating NLL: 111it [00:01, 143.37it/s]\u001b[A\n",
+      "Evaluating NLL: 151it [00:01, 195.88it/s]\u001b[A\n",
+      "Evaluating NLL: 191it [00:01, 241.44it/s]\u001b[A\n",
+      "Evaluating NLL: 230it [00:01, 277.53it/s]\u001b[A\n",
+      "Evaluating NLL: 269it [00:01, 305.74it/s]\u001b[A\n",
+      "Evaluating NLL: 309it [00:01, 329.58it/s]\u001b[A\n",
+      "Evaluating NLL: 350it [00:01, 351.30it/s]\u001b[A\n",
+      "Evaluating NLL: 391it [00:01, 366.54it/s]\u001b[A\n",
+      "Evaluating NLL: 431it [00:02, 365.55it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 220.83it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▍  | 13/100 [00:00<00:00, 127.26it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▊  | 26/100 [00:00<00:00, 124.82it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|█▏ | 39/100 [00:00<00:00, 124.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|█▌ | 52/100 [00:00<00:00, 122.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|█▉ | 65/100 [00:00<00:00, 121.70it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|██▎| 78/100 [00:00<00:00, 119.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 121.00it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:00, 92.34it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:00, 91.70it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|█▏  | 30/100 [00:00<00:00, 94.65it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:00, 92.44it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|██  | 50/100 [00:00<00:00, 93.25it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|██▍ | 60/100 [00:00<00:00, 94.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██▊ | 70/100 [00:00<00:00, 95.33it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  81%|███▏| 81/100 [00:00<00:00, 97.59it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 92.89it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▍  | 13/100 [00:00<00:00, 121.81it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▊  | 26/100 [00:00<00:00, 111.10it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|█▏ | 39/100 [00:00<00:00, 117.14it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|█▌ | 52/100 [00:00<00:00, 120.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|█▉ | 65/100 [00:00<00:00, 123.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|██▎| 78/100 [00:00<00:00, 124.38it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 121.56it/s]\n",
+      "100%|█████████████████████████████████████████████████| 3/3 [5:32:42<00:00, 6654.27s/it]\n"
      ]
     }
    ],
@@ -174,20 +5382,20 @@
     "photons_per_pixel = [30, 60, 120, 240, 480]\n",
     "num_models_upper_bound = 5\n",
     "num_bootstrapped_analytic_models = 11\n",
-    "confidence_interval = 95\n",
+    "confidence = .95\n",
     "patch_size = 20\n",
     "test_fraction = 0.1\n",
     "\n",
     "\n",
-    "channel_names = ['Brightfield', 'DPC_Right', 'LED119' ]\n",
-    "edge_crop = 24\n",
-    "num_images = 500\n",
-    "num_patches = 500\n",
-    "photons_per_pixel = [20, 200]\n",
-    "num_models_upper_bound = 3\n",
-    "num_bootstrapped_analytic_models = 11\n",
-    "confidence_interval = 95\n",
-    "patch_size = 5\n",
+    "# channel_names = ['Brightfield', 'DPC_Right', 'LED119' ]\n",
+    "# edge_crop = 24\n",
+    "# num_images = 500\n",
+    "# num_patches = 500\n",
+    "# photons_per_pixel = [20, 200]\n",
+    "# num_models_upper_bound = 3\n",
+    "# num_bootstrapped_analytic_models = 11\n",
+    "# confidence = .95\n",
+    "# patch_size = 5\n",
     "\n",
     "if not os.path.exists('.cache'):\n",
     "    os.mkdir('.cache')\n",
@@ -241,30 +5449,29 @@
     "        models_gaussian.append(gaussian_process)\n",
     "\n",
     "      \n",
-    "        noise_model = PoissonNoiseModel()\n",
+    "      noise_model = PoissonNoiseModel()\n",
     "        \n",
-    "        estimate, lower, upper = estimate_information(models_pcnn, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
-    "        mi_means_by_channel_photons_pixel_cnn[channel_name].append(mi_mean_pixel_cnn)\n",
-    "        mi_confidences_by_channel_photons_pixel_cnn[channel_name].append(mi_confidence_pixel_cnn)\n",
-    "\n",
+    "      estimate, lower, upper = estimate_information(models_pcnn, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "      mi_means_by_channel_photons_pixel_cnn[channel_name].append(estimate)\n",
+    "      mi_confidences_by_channel_photons_pixel_cnn[channel_name].append((lower, upper))\n",
     "\n",
-    "        estimate, lower, upper = estimate_information(models_gaussian, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
-    "        mi_means_by_channel_photons[channel_name].append(estimate)\n",
-    "        mi_confidences_by_channel_photons[channel_name].append((lower, upper))\n",
+    "      estimate, lower, upper = estimate_information(models_gaussian, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "      mi_means_by_channel_photons[channel_name].append(estimate)\n",
+    "      mi_confidences_by_channel_photons[channel_name].append((lower, upper))\n",
     "\n",
     "\n",
-    "        # fit Gaussian to full, resampled\n",
-    "        analytic_entropies = []\n",
-    "        for i in range(num_bootstrapped_analytic_models):\n",
-    "          resampled_noisy_patces = noisy_patches[np.random.choice(noisy_patches.shape[0], noisy_patches.shape[0], replace=True)]\n",
-    "          gaussian_process = FullGaussianProcess(resampled_noisy_patces)\n",
-    "          analytic_entropy = gaussian_process.compute_analytic_entropy()\n",
-    "          analytic_entropies.append(analytic_entropy)\n",
+    "      # fit Gaussian to full, resampled\n",
+    "      analytic_entropies = []\n",
+    "      for i in range(num_bootstrapped_analytic_models):\n",
+    "        resampled_noisy_patces = noisy_patches[onp.random.choice(noisy_patches.shape[0], noisy_patches.shape[0], replace=True)]\n",
+    "        gaussian_process = FullGaussianProcess(resampled_noisy_patces)\n",
+    "        analytic_entropy = gaussian_process.compute_analytic_entropy()\n",
+    "        analytic_entropies.append(analytic_entropy)\n",
     "\n",
-    "        estimate, lower, upper = estimate_information_analytic(h_y_estimates, noise_model, full_dataset, confidence_interval=confidence)\n",
+    "      estimate, lower, upper = estimate_information_analytic(analytic_entropies, noise_model, noisy_patches, confidence_interval=confidence)\n",
     "\n",
-    "        mi_means_by_channel_photons_analytic[channel_name].append(estimate)\n",
-    "        mi_confidences_by_channel_photons_analytic[channel_name].append(lower, upper)\n",
+    "      mi_means_by_channel_photons_analytic[channel_name].append(estimate)\n",
+    "      mi_confidences_by_channel_photons_analytic[channel_name].append((lower, upper))\n",
     "\n",
     "\n",
     "        # # save all these results to a numpy file\n",
@@ -288,19 +5495,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-14T13:08:03.481623Z",
-     "iopub.status.busy": "2024-01-14T13:08:03.481136Z",
-     "iopub.status.idle": "2024-01-14T13:08:04.240834Z",
-     "shell.execute_reply": "2024-01-14T13:08:04.240131Z"
+     "iopub.execute_input": "2024-10-15T10:20:35.808161Z",
+     "iopub.status.busy": "2024-10-15T10:20:35.807937Z",
+     "iopub.status.idle": "2024-10-15T10:20:36.653404Z",
+     "shell.execute_reply": "2024-10-15T10:20:36.652050Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -326,7 +5533,7 @@
     "ax.set(xlabel='Photons per pixel', ylabel='Estimated information\\n(bits/pixel)')\n",
     "ax.legend()\n",
     "clear_spines(ax)\n",
-    "ax.title.set_text(f'{confidence_interval}% confidence interval')\n",
+    "ax.title.set_text(f'{confidence}% confidence interval')\n",
     "\n",
     "fig.savefig('/home/hpinkard_waller/figures/mi_estimation/analytic_vs_nll_entropy.pdf', transparent=True)"
    ]
diff --git a/mi_estimator_experiments/estimator_consistency/MI_estimator_consistency.ipynb b/mi_estimator_experiments/estimator_consistency/MI_estimator_consistency.ipynb
index 5c97a2e..52c6036 100644
--- a/mi_estimator_experiments/estimator_consistency/MI_estimator_consistency.ipynb
+++ b/mi_estimator_experiments/estimator_consistency/MI_estimator_consistency.ipynb
@@ -5,10 +5,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-13T05:00:50.175137Z",
-     "iopub.status.busy": "2024-01-13T05:00:50.174428Z",
-     "iopub.status.idle": "2024-01-13T05:00:57.205447Z",
-     "shell.execute_reply": "2024-01-13T05:00:57.204294Z"
+     "iopub.execute_input": "2024-10-21T00:05:20.698473Z",
+     "iopub.status.busy": "2024-10-21T00:05:20.697939Z",
+     "iopub.status.idle": "2024-10-21T00:05:29.657298Z",
+     "shell.execute_reply": "2024-10-21T00:05:29.656434Z"
     }
    },
    "outputs": [
@@ -16,10 +16,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-01-14 10:34:40.925688: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-01-14 10:34:41.573573: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cublas/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_cupti/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_nvcc/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_runtime/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cudnn/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cufft/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cusolver/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cusparse/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/nccl/lib\n",
-      "2024-01-14 10:34:41.573665: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cublas/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_cupti/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_nvcc/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_nvrtc/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cuda_runtime/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cudnn/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cufft/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cusolver/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/cusparse/lib:/home/hpinkard_waller/mambaforge/envs/phenotypes/lib/python3.10/site-packages/nvidia/nccl/lib\n",
-      "2024-01-14 10:34:41.573673: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+      "2024-10-20 17:05:22.408782: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-20 17:05:23.244339: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-20 17:05:23.244428: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-20 17:05:23.244437: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
      ]
     },
     {
@@ -40,10 +40,10 @@
     "\n",
     "import os\n",
     "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '3'\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
     "\n",
     "from encoding_information.gpu_utils import limit_gpu_memory_growth\n",
-    "limit_gpu_memory_growth()\n",
+    "# limit_gpu_memory_growth()\n",
     "\n",
     "from cleanplots import *\n",
     "from tqdm import tqdm\n",
@@ -52,7 +52,7 @@
     "from encoding_information.models.gaussian_process import *\n",
     "\n",
     "\n",
-    "from encoding_information.bsccm_utils import *\n",
+    "from encoding_information.datasets.bsccm_utils import *\n",
     "from bsccm import BSCCM\n",
     "from jax import jit\n",
     "import numpy as onp\n",
@@ -68,15 +68,507 @@
     "### Generate a ground truth stationary matrix by estimating from samples"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T00:05:29.663536Z",
+     "iopub.status.busy": "2024-10-21T00:05:29.663209Z",
+     "iopub.status.idle": "2024-10-21T00:05:29.696961Z",
+     "shell.execute_reply": "2024-10-21T00:05:29.696355Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# # Generate a ground truth covariance matrix for each channel, and then noiseless samples from it\n",
+    "# from encoding_information.models.gaussian_process import StationaryGaussianProcess\n",
+    "\n",
+    "# num_images = 12000\n",
+    "# num_patches = 12000\n",
+    "# edge_crop = 24\n",
+    "\n",
+    "# patch_size = 20\n",
+    "\n",
+    "# channel = 'LED119'\n",
+    "\n",
+    "\n",
+    "# images = load_bsccm_images(bsccm, channel=channel, num_images=num_images, edge_crop=edge_crop, median_filter=False, verbose=True)\n",
+    "# patches = extract_patches(images, num_patches=num_patches, patch_size=patch_size, verbose=True)\n",
+    "\n",
+    "# # from encoding_information.datasets.bsccm_utils import add_shot_noise_to_experimenal_data\n",
+    "# # patches = add_shot_noise_to_experimenal_data(patches, 0.5) # bring photon counts to approximately 500\n",
+    "\n",
+    "# cov_mat = estimate_full_cov_mat(patches)\n",
+    "# gp = StationaryGaussianProcess(patches)\n",
+    "# gp.fit(patches)\n",
+    "# true_cov_mat_px = gp.get_cov_mat()\n",
+    "# true_mean_px = np.mean(images)\n",
+    "\n",
+    "# samples = gp.generate_samples(num_samples=num_patches, sample_shape=patch_size, ensure_nonnegative=True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-13T05:00:57.209172Z",
-     "iopub.status.busy": "2024-01-13T05:00:57.208699Z",
-     "iopub.status.idle": "2024-01-13T05:04:07.644758Z",
-     "shell.execute_reply": "2024-01-13T05:04:07.644100Z"
+     "iopub.execute_input": "2024-10-21T00:05:29.702072Z",
+     "iopub.status.busy": "2024-10-21T00:05:29.701814Z",
+     "iopub.status.idle": "2024-10-21T00:05:29.735645Z",
+     "shell.execute_reply": "2024-10-21T00:05:29.735038Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# # plot some samples to ensure they look reasonable\n",
+    "# fig, axs = plt.subplots(1, 3, figsize=(6, 2))\n",
+    "# for i, ax in enumerate(axs):\n",
+    "#     ax.imshow(samples[i], cmap='inferno')\n",
+    "#     ax.axis('off')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Verify that mutual information estimates are consistent: they converge to the correct value given enough samples (for stationary gaussian, optimized stationary gaussian, pixelCNN)\n",
+    "Of course, this is on data that is sampled form a stationary gaussian process, so its only really showing that the gaussian approximation estimator can estimate gaussian entropy and MI. \n",
+    "\n",
+    "\n",
+    "This all uses additive gaussian noise, which has an easy analytic formula for h(y | x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T00:05:29.740967Z",
+     "iopub.status.busy": "2024-10-21T00:05:29.740697Z",
+     "iopub.status.idle": "2024-10-21T00:05:29.776159Z",
+     "shell.execute_reply": "2024-10-21T00:05:29.775421Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# from encoding_information.models import PixelCNN, PoissonNoiseModel, AnalyticGaussianNoiseModel, FullGaussianProcess, StationaryGaussianProcess\n",
+    "# from encoding_information import estimate_information\n",
+    "# import time\n",
+    "\n",
+    "\n",
+    "# num_samples_to_use = np.array([20, 40, 80, 160, 320, 640, 1280, 2560, 5120, 10240]).astype(int)\n",
+    "num_models_to_train = 15 # train this many models and take the best one \n",
+    "\n",
+    "\n",
+    "# gaussian_sigma = 50\n",
+    "# confidence = .95   \n",
+    "# test_set_fraction = 0.1\n",
+    "\n",
+    "# true_cov_mat_py = true_cov_mat_px + gaussian_sigma**2 * np.eye(patch_size**2)\n",
+    "# h_y_analytic = analytic_multivariate_gaussian_entropy(true_cov_mat_py) \n",
+    "# # Constant conditional entropy for Gaussian noise\n",
+    "# h_y_given_x_analytic = analytic_multivariate_gaussian_entropy(np.eye(patch_size**2) * gaussian_sigma**2) \n",
+    "# mi_analytic_per_pixel = (h_y_analytic - h_y_given_x_analytic) / np.log(2)\n",
+    "\n",
+    "# noisy_samples = add_noise(samples, gaussian_sigma=gaussian_sigma, ensure_positive=True)\n",
+    "\n",
+    "\n",
+    "# stationary_mi_estimates_fgp_mean = []\n",
+    "# stationary_mi_estimates_fgp_conf_int = []\n",
+    "# stationary_mi_estimates_sgp = []\n",
+    "# stationary_mi_estimates_sgp_conf_int = []\n",
+    "# stationary_mi_estimates_pixelcnn_optimized_mean = []\n",
+    "# stationary_mi_estimates_pixelcnn_optimized_conf_int = []\n",
+    "\n",
+    "# training_time = {'sgp': [], 'fgp': [], 'pcnn': []}\n",
+    "\n",
+    "# ev_floor = 1e-1\n",
+    "# for num_to_use in tqdm(num_samples_to_use):\n",
+    "\n",
+    "#     # num_val_samples = max(1, min(1000, 0.1 * num_to_use))\n",
+    "\n",
+    "#     data = noisy_samples[:num_to_use]\n",
+    "#     train_patches = data[:int(num_to_use * (1 - test_set_fraction))]\n",
+    "#     test_patches = data[int(num_to_use * (1 - test_set_fraction)):]\n",
+    "    \n",
+    "\n",
+    "#     pixel_cnns = []\n",
+    "#     stationary_gaussians = []\n",
+    "#     full_gaussians = []\n",
+    "#     for i in tqdm(range(num_models_to_train), desc='Training models'):\n",
+    "#         start_time = time.time()\n",
+    "#         stationary_gaussian = StationaryGaussianProcess(train_patches, eigenvalue_floor=ev_floor)\n",
+    "#         stationary_gaussian.fit(train_patches, verbose=False, eigenvalue_floor=ev_floor)\n",
+    "#         stationary_gaussians.append(stationary_gaussian)\n",
+    "#         training_time['sgp'].append(time.time() - start_time)\n",
+    "        \n",
+    "#         start_time = time.time()\n",
+    "#         full_gaussian = FullGaussianProcess(train_patches, eigenvalue_floor=ev_floor)\n",
+    "#         full_gaussians.append(full_gaussian)\n",
+    "#         training_time['fgp'].append(time.time() - start_time)\n",
+    "\n",
+    "#         start_time = time.time()\n",
+    "#         pixelcnn = PixelCNN()\n",
+    "#         pixelcnn.fit(train_patches, verbose=False)\n",
+    "#         pixel_cnns.append(pixelcnn)\n",
+    "#         training_time['pcnn'].append(time.time() - start_time)\n",
+    "    \n",
+    "#     noise_model = AnalyticGaussianNoiseModel(gaussian_sigma)\n",
+    "\n",
+    "\n",
+    "#     estimate_sgp, lower_sgp, upper_sgp = estimate_information(stationary_gaussians, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "#     estimate_full_gp, lower_full_gp, upper_full_gp = estimate_information(full_gaussians, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "#     estimate_pcnn, lower_pcnn, upper_pcnn = estimate_information(pixel_cnns, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "\n",
+    "\n",
+    "#     stationary_mi_estimates_fgp_mean.append(estimate_full_gp)\n",
+    "#     stationary_mi_estimates_fgp_conf_int.append((lower_full_gp, upper_full_gp))\n",
+    "#     stationary_mi_estimates_sgp.append(estimate_sgp)\n",
+    "#     stationary_mi_estimates_sgp_conf_int.append((lower_sgp, upper_sgp))\n",
+    "#     stationary_mi_estimates_pixelcnn_optimized_mean.append(estimate_pcnn)\n",
+    "#     stationary_mi_estimates_pixelcnn_optimized_conf_int.append((lower_pcnn, upper_pcnn))\n",
+    "\n",
+    "\n",
+    "# stationary_mi_estimates_fgp_mean = np.array(stationary_mi_estimates_fgp_mean)\n",
+    "# stationary_mi_estimates_fgp_conf_int = np.array(stationary_mi_estimates_fgp_conf_int)\n",
+    "# stationary_mi_estimates_sgp = np.array(stationary_mi_estimates_sgp)\n",
+    "# stationary_mi_estimates_sgp_conf_int = np.array(stationary_mi_estimates_sgp_conf_int)\n",
+    "# stationary_mi_estimates_pixelcnn_optimized_mean = np.array(stationary_mi_estimates_pixelcnn_optimized_mean)\n",
+    "# stationary_mi_estimates_pixelcnn_optimized_conf_int = np.array(stationary_mi_estimates_pixelcnn_optimized_conf_int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T00:05:29.779186Z",
+     "iopub.status.busy": "2024-10-21T00:05:29.778901Z",
+     "iopub.status.idle": "2024-10-21T00:05:29.813869Z",
+     "shell.execute_reply": "2024-10-21T00:05:29.813128Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# colors = get_color_cycle()\n",
+    "# fig, ax = plt.subplots(1, 2, figsize=(10, 5), sharex=True)\n",
+    "\n",
+    "# ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_sgp_conf_int[:,0],\n",
+    "#                 stationary_mi_estimates_sgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[0])\n",
+    "# ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_fgp_conf_int[:,0], \n",
+    "#                 stationary_mi_estimates_fgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[1])\n",
+    "# ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
+    "#                 stationary_mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[3])\n",
+    "\n",
+    "\n",
+    "# ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_sgp, label='Stationary Gaussian Process', color=colors[0])\n",
+    "# ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_fgp_mean, label='Full Gaussian Process', color=colors[1])\n",
+    "# ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[3])\n",
+    "\n",
+    "\n",
+    "# ax[0].axhline(mi_analytic_per_pixel, color='k', linestyle='--', label='True')\n",
+    "\n",
+    "# # set log scale\n",
+    "# ax[0].set_yscale('log')\n",
+    "\n",
+    "# ax[0].set(xlabel='Number of samples', title='stationary MI estimate\\nper pixel', ylabel='bits per pixel')\n",
+    "# clear_spines(ax[0])\n",
+    "# ax[0].legend()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# # same thing but a different ylim\n",
+    "\n",
+    "# ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_sgp_conf_int[:,0],\n",
+    "#                 stationary_mi_estimates_sgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[0])\n",
+    "# ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_fgp_conf_int[:,0],\n",
+    "#                 stationary_mi_estimates_fgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[1]) \n",
+    "# ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
+    "#                 stationary_mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[3])\n",
+    "\n",
+    "# ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_sgp, label='Stationary Gaussian Process', color=colors[0])\n",
+    "# ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_fgp_mean, label='Full Gaussian Process', color=colors[1])\n",
+    "# ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[3])\n",
+    "\n",
+    "# ax[1].axhline(mi_analytic_per_pixel, color='k', linestyle='--', label='True')\n",
+    "\n",
+    "# # set log scale\n",
+    "# ax[1].set_yscale('log')\n",
+    "\n",
+    "# ax[1].set(xlabel='Number of samples', title='stationary MI estimate\\nper pixel', ylabel='bits per pixel')\n",
+    "# clear_spines(ax[1])\n",
+    "# ax[1].legend()\n",
+    "\n",
+    "# ax[1].set_ylim(None, 1)\n",
+    "# fig.savefig(f'/home/hpinkard_waller/figures/mi_estimation/mi_consistency_stationary_zoomin.pdf', transparent=True)\n",
+    "\n",
+    "\n",
+    "# # make a bar graph of training time with an error bar for the standard deviation\n",
+    "\n",
+    "# training_time = {k: np.array(v) for k, v in training_time.items()}\n",
+    "# training_time_mean = {k: np.mean(v) for k, v in training_time.items()}\n",
+    "# training_time_std = {k: np.std(v) for k, v in training_time.items()}\n",
+    "\n",
+    "# fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n",
+    "# ax.bar(training_time_mean.keys(), training_time_mean.values(), yerr=training_time_std.values(), capsize=8)\n",
+    "# # color each bar\n",
+    "# colors = [colors[0], colors[1], colors[3]]\n",
+    "# for i, bar in enumerate(ax.patches):\n",
+    "#     bar.set_facecolor(colors[i])\n",
+    "# ax.set_ylabel('Training time (s)')\n",
+    "# clear_spines(ax)\n",
+    "# # plot on log scale\n",
+    "\n",
+    "# ax.set_yscale('log')\n",
+    "# fig.savefig(f'/home/hpinkard_waller/figures/mi_estimation/training_time_stationary.pdf', transparent=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Same MI consistency plot, but with samples from data distrbution instead of GP\n",
+    "Cant show it converging to true value, but can show that it converges to a (probably biased) estimate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T00:05:29.846002Z",
+     "iopub.status.busy": "2024-10-21T00:05:29.845527Z",
+     "iopub.status.idle": "2024-10-21T00:05:29.885208Z",
+     "shell.execute_reply": "2024-10-21T00:05:29.884468Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# from encoding_information.models import PixelCNN, PoissonNoiseModel, AnalyticGaussianNoiseModel, FullGaussianProcess, StationaryGaussianProcess\n",
+    "# from encoding_information import estimate_information\n",
+    "# import time\n",
+    "\n",
+    "\n",
+    "# mi_estimates_fgp_mean = []\n",
+    "# mi_estimates_fgp_conf_int = []\n",
+    "# mi_estimates_sgp = []\n",
+    "# mi_estimates_sgp_conf_int = []\n",
+    "# mi_estimates_pixelcnn_optimized_mean = []\n",
+    "# mi_estimates_pixelcnn_optimized_conf_int = []\n",
+    "\n",
+    "# training_time = {'sgp': [], 'fgp': [], 'pcnn': []}\n",
+    "\n",
+    "# ev_floor = 1e-1\n",
+    "# for num_to_use in tqdm(num_samples_to_use):\n",
+    "\n",
+    "#     data = patches[:num_to_use]\n",
+    "#     train_patches = data[:int(num_to_use * (1 - test_set_fraction))]\n",
+    "#     test_patches = data[int(num_to_use * (1 - test_set_fraction)):]\n",
+    "    \n",
+    "\n",
+    "#     pixel_cnns = []\n",
+    "#     stationary_gaussians = []\n",
+    "#     full_gaussians = []\n",
+    "#     for i in tqdm(range(num_models_to_train), desc='Training models'):\n",
+    "#         start_time = time.time()\n",
+    "#         stationary_gaussian = StationaryGaussianProcess(train_patches, eigenvalue_floor=ev_floor)\n",
+    "#         stationary_gaussian.fit(train_patches, verbose=False, eigenvalue_floor=ev_floor)\n",
+    "#         stationary_gaussians.append(stationary_gaussian)\n",
+    "#         training_time['sgp'].append(time.time() - start_time)\n",
+    "        \n",
+    "#         start_time = time.time()\n",
+    "#         full_gaussian = FullGaussianProcess(train_patches, eigenvalue_floor=ev_floor)\n",
+    "#         full_gaussians.append(full_gaussian)\n",
+    "#         training_time['fgp'].append(time.time() - start_time)\n",
+    "\n",
+    "#         start_time = time.time()\n",
+    "#         pixelcnn = PixelCNN()\n",
+    "#         pixelcnn.fit(train_patches, verbose=False)\n",
+    "#         pixel_cnns.append(pixelcnn)\n",
+    "#         training_time['pcnn'].append(time.time() - start_time)\n",
+    "    \n",
+    "#     noise_model = PoissonNoiseModel()\n",
+    "\n",
+    "\n",
+    "#     estimate_pcnn, lower_pcnn, upper_pcnn = estimate_information(pixel_cnns, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "#     estimate_sgp, lower_sgp, upper_sgp = estimate_information(stationary_gaussians, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "#     estimate_full_gp, lower_full_gp, upper_full_gp = estimate_information(full_gaussians, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "\n",
+    "\n",
+    "#     mi_estimates_fgp_mean.append(estimate_full_gp)\n",
+    "#     mi_estimates_fgp_conf_int.append((lower_full_gp, upper_full_gp))\n",
+    "#     mi_estimates_sgp.append(estimate_sgp)\n",
+    "#     mi_estimates_sgp_conf_int.append((lower_sgp, upper_sgp))\n",
+    "#     mi_estimates_pixelcnn_optimized_mean.append(estimate_pcnn)\n",
+    "#     mi_estimates_pixelcnn_optimized_conf_int.append((lower_pcnn, upper_pcnn))\n",
+    "\n",
+    "\n",
+    "# mi_estimates_fgp_mean = np.array(mi_estimates_fgp_mean)\n",
+    "# mi_estimates_fgp_conf_int = np.array(mi_estimates_fgp_conf_int)\n",
+    "# mi_estimates_sgp = np.array(mi_estimates_sgp)\n",
+    "# mi_estimates_sgp_conf_int = np.array(mi_estimates_sgp_conf_int)\n",
+    "# mi_estimates_pixelcnn_optimized_mean = np.array(mi_estimates_pixelcnn_optimized_mean)\n",
+    "# mi_estimates_pixelcnn_optimized_conf_int = np.array(mi_estimates_pixelcnn_optimized_conf_int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T00:05:29.890339Z",
+     "iopub.status.busy": "2024-10-21T00:05:29.890121Z",
+     "iopub.status.idle": "2024-10-21T00:05:29.925903Z",
+     "shell.execute_reply": "2024-10-21T00:05:29.925164Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# colors = get_color_cycle()\n",
+    "# fig, ax = plt.subplots(1, 2, figsize=(10, 5), sharex=True)\n",
+    "\n",
+    "# ax[0].fill_between(num_samples_to_use, mi_estimates_sgp_conf_int[:,0],\n",
+    "#                 mi_estimates_sgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[0])\n",
+    "# ax[0].fill_between(num_samples_to_use, mi_estimates_fgp_conf_int[:,0], \n",
+    "#                 mi_estimates_fgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[1])\n",
+    "# ax[0].fill_between(num_samples_to_use, mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
+    "#                 mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[3])\n",
+    "\n",
+    "\n",
+    "# ax[0].semilogx(num_samples_to_use, mi_estimates_sgp, label='Stationary Gaussian Process', color=colors[0])\n",
+    "# ax[0].semilogx(num_samples_to_use, mi_estimates_fgp_mean, label='Full Gaussian Process', color=colors[1])\n",
+    "# ax[0].semilogx(num_samples_to_use, mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[3])\n",
+    "\n",
+    "\n",
+    "# # set log scale\n",
+    "# ax[0].set_yscale('log')\n",
+    "\n",
+    "# ax[0].set(xlabel='Number of samples', title='MI estimate\\nper pixel', ylabel='bits per pixel')\n",
+    "# clear_spines(ax[0])\n",
+    "# ax[0].legend()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# # same thing but a different ylim\n",
+    "\n",
+    "# ax[1].fill_between(num_samples_to_use, mi_estimates_sgp_conf_int[:,0],\n",
+    "#                 mi_estimates_sgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[0])\n",
+    "# ax[1].fill_between(num_samples_to_use, mi_estimates_fgp_conf_int[:,0],\n",
+    "#                 mi_estimates_fgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[1]) \n",
+    "# ax[1].fill_between(num_samples_to_use, mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
+    "#                 mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[3])\n",
+    "\n",
+    "# ax[1].semilogx(num_samples_to_use, mi_estimates_sgp, label='Stationary Gaussian Process', color=colors[0])\n",
+    "# ax[1].semilogx(num_samples_to_use, mi_estimates_fgp_mean, label='Full Gaussian Process', color=colors[1])\n",
+    "# ax[1].semilogx(num_samples_to_use, mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[3])\n",
+    "\n",
+    "# # set log scale\n",
+    "# ax[1].set_yscale('log')\n",
+    "\n",
+    "# ax[1].set(xlabel='Number of samples', title='stationary MI estimate\\nper pixel', ylabel='bits per pixel')\n",
+    "# clear_spines(ax[1])\n",
+    "# ax[1].legend()\n",
+    "\n",
+    "# ax[1].set_ylim(None, 1)\n",
+    "# fig.savefig(f'/home/hpinkard_waller/figures/mi_estimation/mi_consistency_realdata_random_patch_zoomin.pdf', transparent=True)\n",
+    "\n",
+    "\n",
+    "# # make a bar graph of training time with an error bar for the standard deviation\n",
+    "\n",
+    "# training_time = {k: np.array(v) for k, v in training_time.items()}\n",
+    "# training_time_mean = {k: np.mean(v) for k, v in training_time.items()}\n",
+    "# training_time_std = {k: np.std(v) for k, v in training_time.items()}\n",
+    "\n",
+    "# fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n",
+    "# ax.bar(training_time_mean.keys(), training_time_mean.values(), yerr=training_time_std.values(), capsize=8)\n",
+    "# # color each bar\n",
+    "# colors = [colors[0], colors[1], colors[3]]\n",
+    "# for i, bar in enumerate(ax.patches):\n",
+    "#     bar.set_facecolor(colors[i])\n",
+    "# ax.set_ylabel('Training time (s)')\n",
+    "# clear_spines(ax)\n",
+    "# # plot on log scale\n",
+    "\n",
+    "# ax.set_yscale('log')\n",
+    "# fig.savefig(f'/home/hpinkard_waller/figures/mi_estimation/training_time_realdata.pdf', transparent=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# One more time, with fixed patches instead of random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T00:05:29.930017Z",
+     "iopub.status.busy": "2024-10-21T00:05:29.929742Z",
+     "iopub.status.idle": "2024-10-21T00:05:54.371872Z",
+     "shell.execute_reply": "2024-10-21T00:05:54.371194Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████| 12000/12000 [00:10<00:00, 1176.34it/s]\n",
+      "2024-10-20 17:05:43.788083: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 1073741824 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:43.788701: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 1073741824\n",
+      "2024-10-20 17:05:43.789403: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 966367744 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:43.789459: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 966367744\n",
+      "2024-10-20 17:05:43.790005: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 869731072 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:43.790061: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 869731072\n",
+      "2024-10-20 17:05:43.790587: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 782758144 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:43.790697: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 782758144\n",
+      "2024-10-20 17:05:43.791185: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 704482304 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:43.791279: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 704482304\n",
+      "2024-10-20 17:05:43.791815: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 634034176 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:43.791863: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 634034176\n",
+      "2024-10-20 17:05:43.792551: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 1073741824 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:43.792605: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 1073741824\n",
+      "2024-10-20 17:05:53.794550: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 1073741824 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:53.794588: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 1073741824\n",
+      "2024-10-20 17:05:53.795139: E external/xla/xla/stream_executor/cuda/cuda_driver.cc:1125] failed to alloc 1073741824 bytes on host: CUDA_ERROR_INVALID_VALUE: invalid argument\n",
+      "2024-10-20 17:05:53.795155: W external/xla/xla/stream_executor/device_host_allocator.h:52] could not allocate pinned host memory of size: 1073741824\n",
+      "2024-10-20 17:05:53.795173: W external/tsl/tsl/framework/bfc_allocator.cc:485] Allocator (xla_gpu_host_bfc) ran out of memory trying to allocate 585.94MiB (rounded to 614400000)requested by op \n",
+      "2024-10-20 17:05:53.795252: W external/tsl/tsl/framework/bfc_allocator.cc:497] ____________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Generate a ground truth covariance matrix for each channel, and then noiseless samples from it\n",
+    "from encoding_information.models.gaussian_process import StationaryGaussianProcess\n",
+    "\n",
+    "num_images = 12000\n",
+    "num_patches = 12000\n",
+    "# num_images = 1000\n",
+    "# num_patches = 1000\n",
+    "edge_crop = 24\n",
+    "\n",
+    "patch_size = 20\n",
+    "\n",
+    "channel = 'LED119'\n",
+    "\n",
+    "\n",
+    "images = load_bsccm_images(bsccm, channel=channel, num_images=num_images, edge_crop=edge_crop, median_filter=False, verbose=True)\n",
+    "patches = extract_patches(images, num_patches=num_patches, patch_size=patch_size, verbose=True, strategy='cropped')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T00:05:54.375390Z",
+     "iopub.status.busy": "2024-10-21T00:05:54.375116Z",
+     "iopub.status.idle": "2024-10-21T04:40:34.221038Z",
+     "shell.execute_reply": "2024-10-21T04:40:34.218723Z"
     }
    },
    "outputs": [
@@ -84,1273 +576,32915 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 10000/10000 [00:05<00:00, 1936.85it/s]\n",
-      "100%|██████████| 10000/10000 [01:16<00:00, 130.68it/s]\n",
-      "2024-01-14 10:36:58.123668: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n"
+      "  0%|                                                        | 0/11 [00:00<?, ?it/s]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:55: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 0.1\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/model_base_class.py:461: UserWarning: Number of validation samples must be at least 1. Using 1 validation sample instead.\n",
+      "  warnings.warn('Number of validation samples must be at least 1. Using 1 validation sample instead.')\n",
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:10<10:05, 10.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:10<02:34,  2.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:10<01:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:10<00:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:10<00:26,  1.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:14,  3.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:10,  4.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:07,  5.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:05,  7.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:11<00:04,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:11<00:03, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:11<00:02, 12.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:11<00:02, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:11<00:02, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:11<00:01, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:11<00:01, 16.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:12<00:01, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:12<00:01, 17.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:12<00:01, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:12<00:01, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:12<00:04,  3.42it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:815: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 0.1\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:815: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 1.0\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:09<31:28,  9.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:10<15:11,  4.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:11<10:00,  3.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:13<07:33,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:14<06:12,  1.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:15<05:23,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:16<04:51,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:17<04:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:19<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:20<04:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:21<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:22<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:23<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:25<03:47,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:26<03:45,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:27<03:43,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<03:41,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:39,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:31<03:38,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:32<03:37,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:33<03:35,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:34<03:34,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:35<03:33,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:37<03:32,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:38<03:31,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:39<03:30,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:40<03:28,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:41<03:27,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:43<03:26,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:44<03:25,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:45<03:24,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:46<03:23,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:47<03:22,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:49<03:21,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:50<03:20,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:51<03:19,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:52<03:17,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:54<03:16,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:55<03:15,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:56<03:14,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:57<03:13,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:58<03:12,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:00<03:10,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:02<03:41,  1.42s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:36<22:35, 96.81s/it]\u001b[A/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:55: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 0.1\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:55: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 1.0\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:14,  7.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:51,  1.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:53,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  5.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:06,  6.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 16.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:00, 17.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████▎        | 46/60 [00:09<00:03,  4.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:59,  5.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:44,  2.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:05,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:36,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:34,  1.36s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:45<17:23, 80.28s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:30,  6.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:01, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:08<00:03,  4.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:31,  5.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:32,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:36,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:34,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:31,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:30,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:29,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:26,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:25,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:24,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:23,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:22,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:21,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:17,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:13,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:12,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:25,  1.34s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [03:59<15:27, 77.26s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:01,  7.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:47,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:52,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04, 10.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:04,  4.37it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:44,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:13,  2.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:48,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:35,  1.36s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [05:05<13:23, 73.05s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:35,  6.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:01, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:00, 17.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:00, 17.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:09<00:00, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|██████████████████████████████▏      | 49/60 [00:09<00:00, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|███████████████████████████████▍     | 51/60 [00:09<00:00, 18.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|████████████████████████████████▋    | 53/60 [00:09<00:00, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████▉   | 55/60 [00:09<00:00,  5.67it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:02,  5.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:45,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:06,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:30,  1.36s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [06:18<12:07, 72.78s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:30,  6.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:01, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:08<00:00, 18.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:00, 18.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:09<00:00, 18.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|██████████████████████████████▏      | 49/60 [00:09<00:00, 18.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|███████████████████████████████▍     | 51/60 [00:09<00:01,  5.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:31,  4.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:07,  2.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:45,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:38,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:17,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:12,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:01<03:11,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:10,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:09,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:05<03:08,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:06<03:06,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:05,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:04,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:10<03:03,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:11<03:02,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:00,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:14<02:59,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:15<02:58,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:16<02:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:18<02:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:19<02:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:20<02:53,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:21<02:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:23<02:50,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:24<02:49,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:25<02:48,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:26<02:46,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:28<02:45,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:29<02:44,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:30<02:42,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:31<02:41,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:33<02:40,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:34<02:39,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:36<02:44,  1.31s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [08:05<12:42, 84.70s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:13,  7.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:51,  1.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:53,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  5.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:06,  6.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 18.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|█████████████████████████▉           | 42/60 [00:09<00:04,  4.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:57,  5.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<10:08,  3.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:18,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:58,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:13,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:46,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:36,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:31,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:30,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:29,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:26,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:25,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:37,  1.37s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [09:13<10:32, 79.07s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:44,  6.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:04,  4.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:40,  5.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:11,  2.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:47,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:35,  1.36s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [10:19<08:45, 75.03s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:54,  7.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:46,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:51,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 18.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:04,  4.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:30,  5.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:32,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:23,  1.35s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [11:37<07:35, 75.88s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:42,  6.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|█████████████████████████▉           | 42/60 [00:09<00:03,  4.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:45,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:13,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:37,  1.36s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [12:42<06:02, 72.56s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:47,  6.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:44,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|███████████████████████████▏         | 44/60 [00:09<00:03,  4.72it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:01,  5.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:52,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:21,  1.34s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [14:00<04:57, 74.28s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:45,  6.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:08<00:05,  4.25it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:01,  5.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:52,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:43,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:37,  1.36s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [15:05<03:34, 71.47s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:58,  7.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:47,  1.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:51,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04, 10.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 17.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:04,  4.38it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:01,  5.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:52,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [16:15<02:21, 70.78s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:37,  6.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:42,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:04,  4.38it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:36,  5.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:34,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:00,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:38,  1.37s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [17:20<01:09, 69.11s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:00,  7.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:47,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:52,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04, 10.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|█████████████████████████▉           | 42/60 [00:09<00:04,  4.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:00,  5.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:19,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:52,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:26,  1.35s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [18:34<00:00, 74.32s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.37it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.36it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.33it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.25it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.32it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.33it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.34it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.36it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.31it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.29it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.23it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:46,  2.11it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:02, 38.62it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 74.49it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 121.77it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 108.29it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:   5%|▋             | 1/20 [00:00<00:03,  5.77it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 64.57it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 148.95it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 162.08it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 156.57it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 141.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 155.64it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 138.45it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.69it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 140.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 132.75it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 130.37it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.19it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 149.51it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 151.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 156.99it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 155.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 156.00it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 134.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 153.99it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 145.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 162.60it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 141.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 131.92it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 135.58it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 157.16it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 146.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 138.83it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 133.31it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 166.90it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 146.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 152.61it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 149.73it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 194.29it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:00<00:00, 206.48it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 209.33it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 156.54it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 191.79it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 211.63it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 205.67it\n",
+      "  9%|████                                        | 1/11 [19:03<3:10:38, 1143.90s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:55: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 0.1\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:28,  7.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:54,  2.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:55,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:20,  2.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:06,  6.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:01, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:03,  4.31it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:815: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 0.1\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:23,  5.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:53,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:10,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:52,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:34,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:34,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:37,  1.37s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:09<16:08, 69.15s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:28,  6.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:04,  4.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:27,  4.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:06,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:22,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:47<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:35,  1.35s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:14<14:33, 67.16s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:43,  6.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 18.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 18.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:01, 18.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:00, 19.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:03,  4.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:06<22:02,  6.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:07<11:24,  3.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:09<07:59,  2.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:10<06:25,  1.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:11<05:31,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:12<04:57,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:36,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:15<04:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:16<04:11,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:17<04:04,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:20<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:21<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:36<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:37<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:41<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:58<03:40,  1.39s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [03:24<13:38, 68.21s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:27,  6.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 12.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:04,  4.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:53,  5.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:17,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:29,  1.35s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [04:35<12:43, 69.40s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:51,  7.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<02:00,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:57,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:21,  2.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:14,  3.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:03, 11.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:02, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:01, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:10<00:01, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:10<00:01, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|███████████████████████████▏         | 44/60 [00:10<00:03,  4.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:37,  5.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:34,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [05:57<12:19, 73.94s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:55,  7.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:46,  1.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:51,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04, 10.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 18.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|█████████████████████████▉           | 42/60 [00:09<00:03,  4.52it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:02,  5.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:52,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:36,  1.36s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [07:04<10:43, 71.47s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:30,  6.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:08<00:05,  4.37it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:29,  5.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:31,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:35,  1.36s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [08:11<09:21, 70.18s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:08<08:20,  8.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<02:07,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<01:01,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:22,  2.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:14,  3.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:09<00:10,  4.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:07,  6.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:05,  7.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:04,  9.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:03, 11.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:02, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:01, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:10<00:01, 16.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:10<00:01, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:10<00:01, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:10<00:01, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:10<00:01, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:10<00:01, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:10<00:00, 18.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:10<00:00, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:10<00:00, 18.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|██████████████████████████████▏      | 49/60 [00:11<00:02,  4.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:04,  5.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:21,  2.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:53,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:43,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:34,  1.36s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [09:21<08:10, 70.07s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:43,  7.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:58,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:57,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:21,  2.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:14,  3.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:10,  4.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:03, 11.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:02, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 13.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:01, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:10<00:01, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:10<00:01, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:10<00:00, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:10<00:03,  4.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:30,  5.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:32,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:35,  1.36s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [10:30<06:58, 69.81s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:41,  6.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:00, 19.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|███████████████████████████▏         | 44/60 [00:09<00:03,  4.82it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:44,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:13,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:31,  1.36s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [11:41<05:49, 69.99s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:49,  6.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:44,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 18.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|███████████████████████████▏         | 44/60 [00:09<00:03,  4.73it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:56,  5.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:43,  2.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:04,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:36,  1.37s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [12:49<04:37, 69.48s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:54,  7.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:46,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:51,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04, 10.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|████████████████████████▋            | 40/60 [00:09<00:04,  4.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:23,  5.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:29,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:57,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:45,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:30,  1.36s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [14:01<03:30, 70.26s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:49,  6.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:44,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 18.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 18.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 18.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:03,  4.65it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:59,  5.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:44,  2.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:05,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:34,  1.37s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [15:11<02:20, 70.08s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:01,  7.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:48,  1.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:52,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04, 10.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 18.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 18.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:00, 18.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:03,  4.69it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:27,  5.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:30,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:26<02:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:27<02:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:29<02:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:30<02:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:31<02:44,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:32<02:43,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:34<02:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:35<02:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:38<02:46,  1.32s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [17:00<01:21, 81.98s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:58,  7.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:47,  1.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:51,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04, 10.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 18.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 18.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:00, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████▎        | 46/60 [00:09<00:02,  4.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:02,  5.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:45,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:05,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:34,  1.36s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [18:10<00:00, 72.70s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.71s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.13s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.05s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.12s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.19s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.15s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:01,  1.68it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<01:08,  1.45it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:01, 40.34it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:01, 60.90it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:01<00:00, 96.75it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 82.74it/\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 109.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.53it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 151.05it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 153.49it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 133.67it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 148.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 138.97it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 155.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 143.93it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 134.00it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 145.63it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 136.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 143.17it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 136.80it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 139.67it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 145.96it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 155.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.19it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.65it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 143.32it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 139.20it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 144.38it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 133.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.47it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 137.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 134.64it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 151.65it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:47,  2.07it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:01, 63.04it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:00<00:00, 108.04it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 116.48it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:02, 38.94it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 171.28it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 215.12it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 207.27it\n",
+      " 18%|████████                                    | 2/11 [37:42<2:49:22, 1129.18s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:40,  6.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:42,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 18.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:08<00:05,  4.31it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:56,  5.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:17,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:22,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<03:59,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:31,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:35,  1.36s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:09<16:11, 69.42s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:41,  6.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:42,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:04,  4.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:26,  5.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:31,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:38,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:33,  1.37s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:20<15:12, 70.16s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:27,  6.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:27,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:08<00:06,  4.03it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:07,  5.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:23,  2.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:54,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:39,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:38,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:36,  1.37s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [03:27<13:47, 68.97s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:35,  6.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:01, 17.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:03,  4.72it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:26,  5.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:30,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:30,  1.36s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [04:39<12:51, 70.17s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:41,  7.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:58,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:57,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:32,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:20,  2.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:02, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:01, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|████████████████████████▋            | 40/60 [00:10<00:05,  3.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:56,  5.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:34,  1.36s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [05:48<11:37, 69.72s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:49,  6.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:45,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:04,  4.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:33,  5.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:33,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [06:59<10:30, 70.07s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:32,  7.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:56,  2.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:56,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:32,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:20,  2.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:09<00:05,  3.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:38,  5.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:11,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:47,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:27,  1.35s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [08:12<09:29, 71.22s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:35,  6.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:08<00:06,  3.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:33,  5.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:33,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [09:23<08:16, 70.89s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:27,  7.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:54,  2.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:55,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:20,  2.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 16.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:05,  3.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:47,  5.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:14,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:25,  1.35s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [10:37<07:12, 72.10s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:42,  6.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:08<00:05,  4.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:51,  5.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:40,  2.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:03,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:49,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [11:48<05:58, 71.62s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:27,  6.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:27,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:03, 10.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:05,  4.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:24,  4.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:05,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:38,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:32,  1.35s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [12:57<04:42, 70.71s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:30,  6.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:01, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|███████████████████████████▏         | 44/60 [00:09<00:03,  4.83it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:51,  5.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:40,  2.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:03,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:49,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:34,  1.36s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [14:06<03:30, 70.31s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:19,  7.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:52,  1.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:54,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:20,  2.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:01, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:09<00:05,  3.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:22,  4.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:43,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:28,  1.35s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [15:18<02:21, 70.83s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:27,  6.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:17,  2.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  4.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  7.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:04,  8.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:07<00:02, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:04,  4.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:46,  5.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:39,  2.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:02,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:49,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:34,  1.36s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [16:27<01:10, 70.29s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:35,  6.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04, 10.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:01, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:05,  4.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:24,  4.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:05,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:38,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:30,  1.35s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [17:37<00:00, 70.49s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.51s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.18s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.34it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:39,  2.50it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 99.07it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 149.27it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.75it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 159.18it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 149.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 143.45it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 154.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 154.48it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 137.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.65it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 149.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 154.95it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.99it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 154.70it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 137.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 132.18it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 138.66it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 140.46it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 135.94it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 146.73it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 157.55it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 131.09it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.40it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 154.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 154.40it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 142.93it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.77it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 154.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 142.76it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.69it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 141.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 132.83it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:50,  1.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:01, 56.33it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|▍| 49/100 [00:00<00:00, 96.85it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:00<00:00, 129.38it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 106.29it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   2%| | 2/100 [00:00<00:05, 19.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:00, 129.83it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:00<00:00, 170.23it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 185.83it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 181.33it\n",
+      " 27%|████████████                                | 3/11 [55:46<2:27:48, 1108.51s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:13,  7.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:51,  1.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:53,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:01, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:10<00:00, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:10<00:02,  4.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:13,  5.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:49,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:07,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:36,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:33,  1.36s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:12<17:00, 72.86s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:28,  6.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 12.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:08<00:01, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:03,  4.69it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:22,  4.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:30,  1.35s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:23<15:27, 71.32s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:17,  7.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:52,  1.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:54,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:20,  2.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:01, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:10<00:00, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:10<00:00, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|██████████████████████████████▏      | 49/60 [00:10<00:02,  4.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:37,  5.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:35,  2.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:00,  2.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:29,  1.35s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [03:36<14:26, 72.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:47,  6.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:44,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:01, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:00, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:09<00:02,  4.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:34,  5.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:09,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:47,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:32,  1.35s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [04:45<13:03, 71.19s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:10,  7.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:50,  1.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:53,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 14.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:06,  3.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:08,  5.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:47,  2.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:07,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:51,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:20,  1.35s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [06:06<12:23, 74.38s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:39,  6.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:42,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|█████████████████████████▉           | 42/60 [00:09<00:03,  4.52it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:24,  4.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:05,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:28,  1.35s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [07:17<11:00, 73.41s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:32,  7.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:55,  2.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:56,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:32,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:20,  2.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 12.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:09<00:09,  3.17it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:23,  4.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:31,  1.35s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [08:26<09:36, 72.01s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:43,  6.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:09<00:05,  4.17it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:15,  4.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:01,  2.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:42,  2.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:36,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:22,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:25,  1.34s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [09:40<08:28, 72.62s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:30,  6.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 12.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:08<00:05,  4.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:15,  4.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:01,  2.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:42,  2.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:36,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:30,  1.35s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [10:50<07:10, 71.78s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:46,  6.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:44,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:01, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:03,  4.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:05,  5.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:22,  2.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:53,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:43,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [12:00<05:55, 71.15s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:33,  6.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|███████████████████████████▏         | 44/60 [00:09<00:03,  4.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:36,  5.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:10,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:30,  1.35s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [13:10<04:43, 70.92s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:56,  7.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:47,  1.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:51,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:09<00:01, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:01, 16.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:01, 16.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:00, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:09<00:00, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|██████████████████████████████▏      | 49/60 [00:09<00:00, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|███████████████████████████████▍     | 51/60 [00:10<00:00, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|████████████████████████████████▋    | 53/60 [00:10<00:00, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████▉   | 55/60 [00:10<00:00, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████▏ | 57/60 [00:10<00:00, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|█████████████████████████████████████| 60/60 [00:10<00:00,  5.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:13,  5.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:25,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:23,  1.35s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [14:28<03:39, 73.14s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:29,  6.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:02, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:08<00:01, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|█████████████████████████▎           | 41/60 [00:09<00:01, 16.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|██████████████████████████▌          | 43/60 [00:09<00:01, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████▊         | 45/60 [00:09<00:00, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████▉        | 47/60 [00:09<00:00, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|██████████████████████████████▏      | 49/60 [00:09<00:00, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|███████████████████████████████▍     | 51/60 [00:09<00:00, 16.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████▎   | 54/60 [00:09<00:01,  5.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:26,  4.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:05,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:38,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:30,  1.35s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [15:39<02:24, 72.47s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:09,  7.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:50,  1.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:53,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 14.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:01, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████▍             | 38/60 [00:09<00:05,  3.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:57,  5.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:43,  2.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:04,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [16:51<01:12, 72.11s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:36,  6.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:08,  5.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  8.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 11.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:02, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:01, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:08<00:06,  3.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:50,  5.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:16,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [17:59<00:00, 71.99s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.58s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.14s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.33it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.26it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.25it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.26it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.70it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:39,  2.48it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 105.83it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 149.36it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 140.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 128.24it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 134.58it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 151.70it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 148.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.78it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 137.13it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.81it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.19it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 143.70it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 141.65it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 143.24it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 136.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 149.55it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 145.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.07it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 151.45it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 135.56it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.81it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 144.12it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 143.41it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 131.52it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 149.31it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 138.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.10it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 152.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.97it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 140.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.03it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:50,  1.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:01, 49.77it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 92.72it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 131.82it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 107.21it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:02, 39.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:00, 136.93it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|▍| 49/100 [00:00<00:00, 183.28it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:00<00:00, 198.93it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 189.54it\n",
+      " 36%|███████████████▎                          | 4/11 [1:14:13<2:09:14, 1107.77s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:00,  7.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:48,  1.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:52,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:02, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████▏              | 36/60 [00:09<00:06,  3.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:28,  5.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:55,  3.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:10,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:52,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:36,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:14<17:18, 74.19s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:21,  6.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:38,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 10.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:03, 11.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:08<00:07,  3.72it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:22,  4.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:43,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:01<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:05<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:06<03:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:06,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:16,  1.33s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:35<16:58, 78.37s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:54,  7.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:46,  1.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:52,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:30,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:07,  6.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:02, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:07,  3.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:06<20:35,  6.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:07<10:47,  3.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:39,  2.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<06:11,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:11<05:21,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:12<04:51,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:17<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:34,  1.38s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [03:47<15:05, 75.44s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:29,  6.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:28,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 10.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 13.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:02, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:06,  3.92it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:815: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 1.0\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:31,  4.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:08,  2.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:01<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:09,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:05<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:06<03:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:05,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:04,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:10<03:03,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:11<03:02,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:01,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:10,  1.33s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [05:14<14:38, 79.88s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:28,  7.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:55,  2.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:55,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:32,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:20,  2.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:14,  3.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:10,  4.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  5.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:03, 11.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 13.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:02, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 14.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:07,  3.52it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:19,  5.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:27,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:56,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:45,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:27,  1.35s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [06:28<12:58, 77.81s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:24,  6.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:07<00:03, 11.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:02, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:06,  3.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n",
+      "trying eigenvalue floor of 10.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:02,  5.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:21,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:53,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:43,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:29,  1.35s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [07:39<11:21, 75.71s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:16,  7.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:52,  1.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:54,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:20,  2.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 12.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:02, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:09<00:07,  3.52it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:24,  5.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:29,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:57,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:45,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:17,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:32,  1.36s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [08:49<09:50, 73.83s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:28,  6.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 10.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:02, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████▏              | 36/60 [00:08<00:05,  4.01it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:23,  4.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:28,  1.35s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [10:01<08:32, 73.15s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:32,  7.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:56,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:56,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:32,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:21,  2.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:14,  3.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:10,  4.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  5.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:03,  9.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:03, 11.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:02, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:02, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:09<00:01, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:10<00:06,  3.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:33,  5.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:33,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:29,  1.35s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [11:14<07:18, 73.15s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:44,  6.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:44,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:07,  6.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:08<00:08,  3.37it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:35,  5.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:09,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:29,  1.35s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [12:26<06:03, 72.67s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:25,  6.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 10.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:08,  3.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:32,  5.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:32,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:33,  1.36s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [13:35<04:46, 71.56s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:11,  7.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:51,  1.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:54,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:20,  2.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:13,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:02, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████▏              | 36/60 [00:09<00:06,  3.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:53,  5.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:41,  2.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:04,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:49,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:30,  1.36s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [14:48<03:36, 72.02s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:27,  6.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:06,  6.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:07<00:03, 10.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:02, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:01, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:08<00:01, 14.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████▊              | 37/60 [00:08<00:01, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████             | 39/60 [00:09<00:04,  4.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:22,  5.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:28,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:57,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:45,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:33,  1.36s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [15:58<02:22, 71.32s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:09,  7.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:50,  1.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:53,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:31,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:20,  2.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  4.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  6.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  7.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03,  9.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 10.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:02, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:09<00:02, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:09<00:02, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:09<00:01, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████▏              | 36/60 [00:09<00:06,  3.69it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:02,  5.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:45,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:06,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:30,  1.36s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [17:11<01:11, 71.84s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:36,  6.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:42,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:12,  3.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  5.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:07,  6.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:05,  7.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:07<00:04,  9.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:03, 10.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:08<00:03, 11.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:08<00:02, 12.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:08<00:02, 12.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:08<00:02, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:08<00:02, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:08<00:07,  3.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:34,  5.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:09,  2.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:27,  1.35s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [18:24<00:00, 73.61s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.07s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:00, 18.28it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:39,  2.53it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 113.13it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 157.09it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 146.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 125.51it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 131.37it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 153.32it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 138.72it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 138.70it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 146.25it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 123.03it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 160.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 154.21it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 136.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 144.38it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 155.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 153.24it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 141.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 139.53it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 138.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 163.88it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 154.37it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 162.28it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 149.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 148.24it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 161.61it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 152.07it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 138.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 131.22it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 139.51it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 145.76it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 154.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 154.33it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 153.08it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 153.84it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:48,  2.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00<00:01, 48.66it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:00<00:00, 92.30it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:00<00:00, 127.47it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 106.85it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:02, 38.36it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:00, 112.57it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:00<00:00, 144.45it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 155.62it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:00<00:00, 152.67it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 145.47it\n",
+      " 45%|███████████████████                       | 5/11 [1:33:05<1:51:39, 1116.66s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:23,  6.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:23,  1.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:20,  5.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:26,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:43,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:22,  1.34s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:18<18:17, 78.42s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:30,  6.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:23,  1.92it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:23,  4.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:25,  1.34s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:31<16:17, 75.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:26,  6.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:07,  6.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:20,  2.15it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:35,  5.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:34,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:00,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:20,  1.34s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [03:49<15:20, 76.73s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:29,  6.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:07<00:21,  2.03it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:49,  5.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:15,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:29,  1.35s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [05:00<13:37, 74.34s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:24,  6.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:48,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:06<00:28,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:18,  2.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:07<00:25,  1.83it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:33,  4.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:09,  2.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:28,  1.35s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [06:10<12:09, 72.91s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:28,  6.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:07<00:21,  2.03it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:57,  5.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:24,  1.35s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [07:25<11:02, 73.57s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:26,  6.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:40,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:07,  5.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:07<00:06,  7.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:08<00:16,  2.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:38,  5.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:11,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:47,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:25,  1.35s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [08:39<09:48, 73.56s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:12,  7.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:51,  1.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:54,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:32,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:20,  2.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:14,  3.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:10,  4.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:23,  1.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:42,  5.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:37,  2.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:01,  2.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:48,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:26,  1.35s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [09:53<08:36, 73.83s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:47,  7.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<02:00,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:58,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:22,  2.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:15,  3.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:10,  4.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:25,  1.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:18,  5.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:51,  2.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:09,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:52,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:44,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:16,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:21,  1.35s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [11:14<07:35, 75.84s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:08<08:05,  8.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<02:05,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<01:00,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:35,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:22,  2.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:15,  3.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:30,  1.50it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<19:52,  5.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:07<10:30,  3.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:30,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<06:05,  1.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:18,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:12<04:49,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:30,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:18,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:25,  1.36s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [12:33<06:24, 76.96s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:32,  6.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:23,  1.91it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:44,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:13,  2.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:48,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:22,  1.34s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [13:49<05:06, 76.59s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:08<07:56,  8.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<02:02,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:59,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:22,  2.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:15,  3.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:09<00:11,  4.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:27,  1.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:45,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:14,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:56<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:01<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:24,  1.34s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [15:05<03:49, 76.54s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:31,  6.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:06<00:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:07<00:21,  2.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:10,  5.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:54,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:43,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:26,  1.35s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [16:19<02:31, 75.85s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:35,  7.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:57,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:57,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:33,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:21,  2.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:14,  3.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:10,  4.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:08,  5.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:22,  1.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:17,  5.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:26,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:56,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:24,  1.35s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [17:36<01:15, 75.93s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:38,  6.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:43,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:50,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:29,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:19,  2.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:13,  3.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:09,  4.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:07<00:07,  5.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:06,  7.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:05,  8.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:15,  2.48it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:11,  5.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:54,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:30,  1.36s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [18:47<00:00, 75.18s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.06s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.29it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.09s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.23it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.26it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.25it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.28it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.26it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:00, 36.06it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:37,  2.67it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00<00:00, 128.31it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 163.21it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 140.03it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 163.47it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 155.91it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.41it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 129.20it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 145.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 154.96it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 142.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 142.55it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 151.87it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 140.25it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 146.15it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 142.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 144.37it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 130.56it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.50it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 156.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 133.88it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 133.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 157.92it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 163.05it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 132.51it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 153.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 140.98it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 152.08it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 148.64it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 137.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.72it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 129.29it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 139.61it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:49,  1.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:01, 52.04it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|▍| 49/100 [00:00<00:00, 98.50it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 110.04it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   3%| | 3/100 [00:00<00:03, 29.64it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 175.48it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 216.31it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 216.25it\n",
+      " 55%|██████████████████████▉                   | 6/11 [1:52:22<1:34:11, 1130.35s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:35,  6.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:43,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:30,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:56,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:26,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:14,  3.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:07<00:09,  5.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:07,  6.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:07<00:06,  6.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:07<00:06,  7.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  8.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:21,  2.00it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:59,  5.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:05<03:09,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:05,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:04,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:01,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:14<03:00,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<02:59,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:18<02:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:19<02:55,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:54,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<03:01,  1.33s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:37<22:47, 97.68s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:59,  7.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:53,  2.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:35,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:59,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:27,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:15,  3.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:09,  5.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:08,  5.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:22,  1.91it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:56,  5.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:26,  1.35s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:53<18:23, 84.86s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:39,  6.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:45,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:31,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:56,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:26,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:14,  3.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:07<00:09,  5.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:08,  6.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:07<00:07,  6.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:04,  8.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:08<00:04,  9.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:04,  9.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:15,  2.41it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:52,  5.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:17,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:24,  1.35s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [04:11<16:19, 81.64s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:57,  7.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:52,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:35,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:58,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:27,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:15,  3.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:09,  5.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:08,  5.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:04,  8.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:09<00:04,  8.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  9.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:04,  9.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:14,  2.48it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:51,  5.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:16,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:26,  1.35s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [05:26<14:31, 79.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:41,  6.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:46,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:31,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:56,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:26,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:14,  3.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:07<00:09,  5.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:08,  5.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:04,  8.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  9.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:08<00:04,  9.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:04,  9.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:04,  9.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:03,  9.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:13,  2.63it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:10,  5.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:22,  1.35s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [06:46<13:12, 79.30s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:56,  7.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:52,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:35,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:58,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:27,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:15,  3.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:09,  5.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:08,  5.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:04,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:04,  9.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:09<00:04,  9.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  9.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:15,  2.41it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:35,  5.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:10,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:47,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:25,  1.35s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [08:02<11:45, 78.39s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:54,  7.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:51,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:34,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:58,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:27,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:15,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:09,  5.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:08,  5.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:06,  7.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  7.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:04,  8.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:09<00:04,  8.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  8.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  8.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:04,  9.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:03,  9.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:03,  8.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:09<00:12,  2.62it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:38,  5.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<10:00,  3.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:14,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:55,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:12,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:45,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:20,  1.35s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [09:25<10:38, 79.83s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:14,  7.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:59,  3.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:38,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:01,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:28,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:20,  2.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:15,  3.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:12,  4.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:09,  5.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:08,  5.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:04,  8.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:04,  8.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:04,  8.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:09<00:04,  8.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  9.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:15,  2.33it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:08,  5.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:48,  2.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:08,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:52,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:22,  1.35s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [10:46<09:20, 80.00s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:05,  7.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:56,  3.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:36,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:00,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:27,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:20,  2.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:15,  3.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:12,  4.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:09,  5.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:08,  5.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:04,  8.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:04,  8.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:09<00:04,  8.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  8.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:04,  9.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:03,  9.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:13,  2.52it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:07,  5.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:47,  2.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:07,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:52,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:44,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:23,  1.35s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [12:06<08:00, 80.02s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:38,  6.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:45,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:31,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:56,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:26,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:14,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:07<00:09,  5.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:08,  5.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:07<00:07,  6.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  7.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:08<00:04,  8.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:04,  9.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:04,  9.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:03,  9.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:13,  2.63it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:40,  5.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:37,  2.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:01,  2.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:48,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:22,  1.35s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [13:25<06:39, 79.86s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:39,  6.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:45,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:31,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:56,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:26,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:14,  3.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:07<00:09,  5.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:08,  6.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:07<00:07,  6.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:04,  8.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:08<00:04,  8.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:08<00:04,  9.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:03,  9.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:03,  9.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:03,  9.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:09<00:03,  9.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:09<00:03,  9.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:09<00:11,  2.85it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:41,  5.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:12,  2.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:48,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:05<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:15,  1.34s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [14:50<05:25, 81.29s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:00,  7.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:54,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:35,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:59,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:27,  1.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:20,  2.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:15,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:12,  4.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:09,  5.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:08,  5.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:04,  8.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:09<00:04,  9.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  9.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:15,  2.39it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<19:43,  5.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:07<10:26,  3.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:28,  2.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<06:04,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:17,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:12<04:49,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:30,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:18,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:18,  1.36s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [16:15<04:07, 82.55s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:47,  6.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:48,  2.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:32,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:57,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:26,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:14,  3.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:07<00:09,  5.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:08,  6.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:05,  8.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:08<00:04,  9.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  9.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:04,  9.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:03,  9.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:13,  2.60it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:39,  5.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:11,  2.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:48,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:25,  1.35s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [17:32<02:41, 80.77s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:08<07:59,  8.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:18,  3.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<01:48,  1.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:07,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:30,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:22,  2.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:16,  3.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:12,  4.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:10,  4.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:08,  5.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:07,  6.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:09<00:06,  7.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:06,  7.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:05,  8.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:05,  8.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:25,  1.70it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:20,  5.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:52,  2.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:10,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:53,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:44,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:25,  1.36s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [18:51<01:20, 80.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:43,  6.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:47,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:32,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:57,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:26,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:19,  2.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:14,  3.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:11,  4.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:07<00:09,  5.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:07<00:08,  6.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:07,  6.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:06,  7.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:05,  7.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:05,  8.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:08<00:05,  8.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:08<00:04,  8.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:08<00:04,  8.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:08<00:04,  8.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:08<00:04,  8.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:09<00:04,  9.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:09<00:04,  9.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:09<00:03,  9.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:09<00:03,  9.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:09<00:13,  2.62it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:32,  5.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:33,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:18,  1.35s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [20:14<00:00, 80.97s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.41it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.44it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.37it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.48it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.49it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.41it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.49it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.44it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.39it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.46it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.48it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.41it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:01,  1.76it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 2it [00:00,  2.43it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 64it [00:00, 72.16it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:40,  2.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 113.61it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 156.90it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 161.80it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 162.34it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 145.57it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 163.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.91it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 159.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.44it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 142.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 153.89it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 163.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 145.77it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 149.82it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 149.52it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 148.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 148.93it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.63it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Initial validation NLL: 47465.32\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 1: 100%|██████████| 1/1 [00:09<00:00,  9.31s/it]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 152.73it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 146.73it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1: validation NLL: 24159.65\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 2: 100%|██████████| 1/1 [00:00<00:00, 17.74it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 139.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 157.08it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 2: validation NLL: 5.91\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 3: 100%|██████████| 1/1 [00:00<00:00, 18.22it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 140.40it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 135.44it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 3: validation NLL: 5.45\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 4: 100%|██████████| 1/1 [00:00<00:00, 18.56it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 144.74it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 154.58it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 4: validation NLL: 5.41\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 5: 100%|██████████| 1/1 [00:00<00:00, 18.14it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 148.97it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 150.44it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 5: validation NLL: 5.39\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 6: 100%|██████████| 1/1 [00:00<00:00, 18.42it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 142.57it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 153.56it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 6: validation NLL: 5.38\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 7: 100%|██████████| 1/1 [00:00<00:00, 17.78it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 146.49it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 148.60it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 7: validation NLL: 5.37\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 8: 100%|██████████| 1/1 [00:00<00:00, 18.58it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 161.37it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:48,  2.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:01, 62.32it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 102.73it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 114.02it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   2%| | 2/100 [00:00<00:04, 19.65it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:00, 156.90it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:00<00:00, 207.56it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 204.40it\n",
+      " 64%|██████████████████████████▋               | 7/11 [2:13:09<1:17:53, 1168.30s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:29,  6.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:43,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:31,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:57,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:28,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:21,  2.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  3.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:14,  3.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  5.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:29,  1.58it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:36,  5.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:09,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:38,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:35,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:34,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:31,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:16,  1.34s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:27<20:21, 87.28s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:31,  6.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:44,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:31,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:58,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:21,  2.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:14,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:08,  5.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:25,  1.73it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:23,  5.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:29,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:11,  1.34s/it]\n",
+      "\n",
+      "Training models:  13%|████▏                          | 2/15 [02:56<19:06, 88.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:06,  7.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:58,  3.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:39,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:02,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:42,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:30,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:22,  2.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:18,  2.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  3.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:11,  4.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:09,  4.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:09<00:09,  5.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:08,  5.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:07,  5.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:07,  5.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:06,  6.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:10<00:22,  1.84it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:28,  5.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:31,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:20<03:07,  1.34s/it]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 3/15 [04:30<18:09, 90.81s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:36,  6.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:46,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:33,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:59,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:29,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:22,  2.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:07,  5.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:07,  6.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:06,  6.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:21,  1.94it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:11,  5.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:25,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:17<03:09,  1.34s/it]\n",
+      "\n",
+      "Training models:  27%|████████▎                      | 4/15 [06:00<16:37, 90.69s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:37,  6.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:46,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:33,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:59,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:29,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:22,  2.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:07,  5.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:07,  5.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:07,  5.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:06,  6.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:09<00:19,  2.00it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:31,  4.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:08,  2.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:17,  1.34s/it]\n",
+      "\n",
+      "Training models:  33%|██████████▎                    | 5/15 [07:24<14:43, 88.32s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:14,  7.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:01,  3.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:03,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:43,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:31,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:23,  2.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:18,  2.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  3.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:11,  4.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:09,  4.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:09<00:08,  5.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:08,  5.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:07,  5.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:07,  6.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:10<00:23,  1.75it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:14,  5.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:50,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:09,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:53,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:11,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:45,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:26<02:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:28<02:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:29<02:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:30<02:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:31<02:44,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:33<02:43,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:35<02:49,  1.33s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [09:13<14:19, 95.45s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:39,  6.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:47,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:33,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:59,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:29,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:22,  2.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:29,  1.55it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:48,  5.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:15,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:14<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<03:08,  1.33s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [10:44<12:31, 93.98s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:36,  6.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:46,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:33,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:59,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:29,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:22,  2.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  5.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:07,  5.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:25,  1.71it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:33,  5.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:34,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:00,  2.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:18<02:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:25<03:01,  1.33s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [12:22<11:06, 95.16s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:55,  7.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:54,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:37,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:01,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:30,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:22,  2.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:17,  2.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:11,  4.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:09<00:09,  5.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:08,  5.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:08,  5.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:24,  1.73it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:05,  5.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:05,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:17,  1.34s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 9/15 [13:46<09:10, 91.74s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:33,  6.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:44,  2.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:32,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:58,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:28,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:22,  2.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:14,  3.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:07,  5.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:23,  1.80it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:30,  5.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:32,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:07,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:20,  1.35s/it]\n",
+      "\n",
+      "Training models:  67%|████████████████████          | 10/15 [15:08<07:22, 88.51s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:35,  6.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:45,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:32,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:58,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:28,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:21,  2.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:08,  5.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:07,  5.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:22,  1.87it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:19,  4.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:38,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:15<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:36<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:41<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:46<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:51<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:05<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:10<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:12,  1.34s/it]\n",
+      "\n",
+      "Training models:  73%|██████████████████████        | 11/15 [16:35<05:52, 88.22s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:46,  6.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:50,  2.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:35,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:00,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:41,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:29,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:22,  2.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:17,  2.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:08,  5.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:08,  5.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:07,  5.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:22,  1.83it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:47,  5.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<10:03,  3.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:16,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:57,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:13,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:46,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:29,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:17,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<03:00,  1.34s/it]\n",
+      "\n",
+      "Training models:  80%|████████████████████████      | 12/15 [18:15<04:35, 91.82s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:37,  6.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:46,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:33,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:59,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:29,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:22,  2.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  2.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:08,  5.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:08,  5.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:07,  5.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:09<00:07,  5.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:09<00:07,  5.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:09<00:21,  1.93it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:57,  5.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:19,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:52,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:14,  1.34s/it]\n",
+      "\n",
+      "Training models:  87%|██████████████████████████    | 13/15 [19:42<03:00, 90.22s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:32,  6.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:06<02:44,  2.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:06<01:32,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<00:58,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:07<00:28,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:07<00:21,  2.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:07<00:17,  3.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:07<00:14,  3.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  4.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:08<00:10,  4.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:08<00:09,  4.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:08<00:09,  5.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:08<00:08,  5.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:08<00:08,  5.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:09<00:25,  1.72it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:27,  5.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:31,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:46,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:26,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:25,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:09,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:05,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:00,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:18<02:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:55,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:54,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<03:02,  1.33s/it]\n",
+      "\n",
+      "Training models:  93%|████████████████████████████  | 14/15 [21:18<01:32, 92.03s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:21,  7.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:04,  3.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:42,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:04,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:43,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:31,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:23,  2.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:18,  2.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:14,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:08<00:12,  3.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:11,  4.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:09,  4.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:09<00:09,  5.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:09<00:08,  5.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:09<00:29,  1.51it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:48,  5.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:15,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:41,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:29,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:55<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:00<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:09<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:14<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:20<03:06,  1.33s/it]\n",
+      "\n",
+      "Training models: 100%|██████████████████████████████| 15/15 [22:52<00:00, 91.53s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:01,  3.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.16it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:01,  3.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:01,  2.90it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:01,  3.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:01,  2.97it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.79it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.80it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.52it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.94it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.80it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:00,  4.53it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 4it [00:01,  3.49it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 128it [00:01, 127.18it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:40,  2.45it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:00<00:00, 115.81it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 156.78it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 140.03it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 8: validation NLL: 5.37\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 9: 100%|██████████| 1/1 [00:00<00:00, 18.71it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.37it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 157.69it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 9: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 10: 100%|██████████| 1/1 [00:00<00:00, 18.38it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 151.29it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 121.75it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 10: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 11: 100%|██████████| 1/1 [00:00<00:00, 18.28it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 156.46it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 150.83it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 11: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 12: 100%|██████████| 1/1 [00:00<00:00, 18.38it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.80it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 166.23it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 12: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 13: 100%|██████████| 1/1 [00:00<00:00, 18.26it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.24it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 145.84it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 13: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 14: 100%|██████████| 1/1 [00:00<00:00, 18.74it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 145.22it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.92it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 14: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 15: 100%|██████████| 1/1 [00:00<00:00, 18.44it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.90it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.06it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 15: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 16: 100%|██████████| 1/1 [00:00<00:00, 18.40it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 142.42it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 137.11it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 16: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 17: 100%|██████████| 1/1 [00:00<00:00, 18.16it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 155.58it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 151.09it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 17: validation NLL: 5.36\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 18: 100%|██████████| 1/1 [00:00<00:00, 18.23it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 140.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 138.07it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 18: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 19: 100%|██████████| 1/1 [00:00<00:00, 18.33it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 158.29it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 159.06it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 19: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 20: 100%|██████████| 1/1 [00:00<00:00, 18.64it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.80it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 145.64it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 20: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 21: 100%|██████████| 1/1 [00:00<00:00, 18.62it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 141.43it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 160.91it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 21: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 22: 100%|██████████| 1/1 [00:00<00:00, 18.14it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 134.61it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 112.29it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 22: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 23: 100%|██████████| 1/1 [00:00<00:00, 18.21it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.14it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 147.04it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 23: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 24: 100%|██████████| 1/1 [00:00<00:00, 18.46it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 159.95it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:49,  1.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:01, 54.06it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 105.30it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 111.70it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   6%| | 6/100 [00:00<00:01, 59.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 174.17it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 185.51it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:00<00:00, 195.98it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 188.31it\n",
+      " 73%|██████████████████████████████▌           | 8/11 [2:36:45<1:02:21, 1247.13s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:42,  7.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:16,  3.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<01:51,  1.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:50,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:09<00:37,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:09<00:29,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:09<00:24,  2.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:10<00:18,  2.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:10<00:16,  3.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:15,  3.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:11<00:14,  3.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:11<00:13,  3.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:11<00:35,  1.26it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:45,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:13,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:33,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:18<02:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<03:05,  1.34s/it]\n",
+      "\n",
+      "Training models:   7%|██                             | 1/15 [01:38<23:00, 98.62s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:14,  7.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:05,  3.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:46,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:09,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:09<00:28,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:09<00:23,  2.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:18,  2.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:10<00:16,  3.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:15,  3.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:11<00:12,  3.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:12,  3.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:27,  1.52it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<19:39,  5.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:07<10:25,  3.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:28,  2.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<06:05,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:18,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:12<04:50,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:04,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:17<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:22<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:25<02:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<02:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:27<02:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:28<02:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:30<02:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:31<02:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:32<02:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:33<02:44,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:35<02:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:36<02:41,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:37<02:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:39<02:38,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:41<02:45,  1.34s/it]\n",
+      "\n",
+      "Training models:  13%|████                          | 2/15 [03:36<23:46, 109.75s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:51,  6.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:55,  3.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:40,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:06,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:46,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:28,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:23,  2.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:19,  2.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:15,  3.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:12,  3.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:26,  1.57it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:48,  5.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:16,  2.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:26<02:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:28<02:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:29<02:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:31<02:54,  1.33s/it]\n",
+      "\n",
+      "Training models:  20%|██████                        | 3/15 [05:23<21:43, 108.60s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:55,  7.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:57,  3.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:41,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:06,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:47,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:23,  2.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:14,  3.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:11,  3.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:11<00:11,  3.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:12<00:10,  3.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:12<00:10,  3.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:12<00:10,  3.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:13<00:21,  1.73it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:46,  5.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:39,  2.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:04,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:44,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:22,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:20,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:18,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:13,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:17<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:19<03:10,  1.35s/it]\n",
+      "\n",
+      "Training models:  27%|████████                      | 4/15 [06:59<19:00, 103.65s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:44,  6.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:52,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:39,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:05,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:46,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:27,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:22,  2.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:19,  2.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:14,  3.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:10<00:12,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:12,  3.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:25,  1.58it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:43,  5.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:14,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:50,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:43,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:20,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:18,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<03:00,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<03:06,  1.34s/it]\n",
+      "\n",
+      "Training models:  33%|██████████                    | 5/15 [08:36<16:52, 101.27s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:08<08:07,  8.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:27,  3.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<01:57,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:09<01:15,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:09<00:53,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:09<00:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:09<00:30,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:10<00:25,  2.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:10<00:21,  2.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:10<00:18,  2.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:10<00:16,  2.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:11<00:15,  3.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:11<00:14,  3.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:11<00:13,  3.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:12<00:12,  3.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:12<00:12,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:12<00:12,  3.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:12<00:11,  3.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:13<00:11,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:13<00:10,  3.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:13<00:10,  3.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:14<00:24,  1.54it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:03,  5.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:47,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:07,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:52,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:45,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:17,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:43,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:39,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:38,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:36,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:35,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:34,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:32,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:32,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:31,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:28,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:26,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:25,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:23,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:22,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:18,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:13,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:12,  1.35s/it]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 6/15 [10:12<14:55, 99.47s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:47,  6.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:54,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:05,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:46,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:27,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:23,  2.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:19,  2.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:15,  3.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:10<00:12,  3.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:11,  3.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:26,  1.58it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:12,  5.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:26,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:56,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:16,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:55,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:50,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:43,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:20,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:13,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:12,  1.35s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████▍                | 7/15 [11:44<12:56, 97.05s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:55,  7.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:57,  3.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:42,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:06,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:28,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:23,  2.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:15,  3.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:12,  3.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:26,  1.55it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:26,  4.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:07,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:46,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:43,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:20,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:13,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<03:00,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:28<02:59,  1.34s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████▌              | 8/15 [13:28<11:33, 99.12s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:02,  7.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:00,  3.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:43,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:07,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:47,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:28,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:09<00:23,  2.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:15,  3.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:11,  3.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:11,  3.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:12<00:11,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:12<00:10,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:13<00:22,  1.67it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:31,  4.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:09,  2.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:47,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:28<02:58,  1.34s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████            | 9/15 [15:12<10:04, 100.78s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:40,  6.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:51,  2.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:38,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:04,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:46,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:27,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:22,  2.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:19,  2.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:14,  3.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:10<00:12,  3.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:28,  1.49it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:21,  4.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:45,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:04<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<02:59,  1.33s/it]\n",
+      "\n",
+      "Training models:  67%|███████████████████▎         | 10/15 [16:54<08:25, 101.00s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:17,  7.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:06,  3.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:46,  1.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:09,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:09<00:28,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:09<00:23,  2.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:18,  2.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:10<00:16,  2.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:15,  3.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:11<00:12,  3.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:11,  3.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:12<00:11,  3.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:11,  3.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:12<00:10,  3.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:13<00:24,  1.60it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:06<19:58,  6.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:07<10:33,  3.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<07:33,  2.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<06:08,  1.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:20,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:12<04:51,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:33,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:20,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:11,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:17<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:22<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:20,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:15<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:17<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:20<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:22<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:25<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<02:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:27<02:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:29<02:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:30<02:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:31<02:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:32<02:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:53,  1.34s/it]\n",
+      "\n",
+      "Training models:  73%|█████████████████████▎       | 11/15 [18:45<06:57, 104.31s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:58,  7.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:58,  3.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:42,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:06,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:28,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:23,  2.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:14,  3.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:13,  3.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:29,  1.46it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:11,  5.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:25,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:56,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:24<03:03,  1.34s/it]\n",
+      "\n",
+      "Training models:  80%|███████████████████████▏     | 12/15 [20:25<05:08, 102.78s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:41,  6.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:51,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:38,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:04,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:46,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:27,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:23,  2.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:08<00:19,  2.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:14,  3.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:10<00:12,  3.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:11,  3.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:11<00:11,  3.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:12<00:24,  1.63it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:34,  5.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:10,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:48,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:27<02:58,  1.33s/it]\n",
+      "\n",
+      "Training models:  87%|█████████████████████████▏   | 13/15 [22:08<03:26, 103.04s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:46,  6.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<02:53,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:39,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:05,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:07<00:46,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:27,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:08<00:23,  2.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:19,  2.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:09<00:15,  3.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:10<00:12,  3.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:12,  3.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:11<00:11,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:12<00:11,  3.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:12<00:10,  3.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:12<00:10,  3.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:12<00:10,  3.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:13<00:09,  3.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:13<00:19,  1.83it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:37,  5.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:11,  2.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:48,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:43,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:45<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:50<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:23,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:59<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:08<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:13<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:17<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:22<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:26<02:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:27<02:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:29<02:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:30<02:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:31<02:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:32<02:43,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:34<02:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:36<02:48,  1.33s/it]\n",
+      "\n",
+      "Training models:  93%|███████████████████████████  | 14/15 [24:02<01:46, 106.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:03,  7.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:00,  3.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:43,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:07<01:07,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:47,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:08<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:08<00:28,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:09<00:23,  2.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:09<00:20,  2.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:09<00:17,  2.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:09<00:16,  3.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:10<00:14,  3.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:10<00:14,  3.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:10<00:13,  3.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:10<00:12,  3.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:11<00:12,  3.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:11<00:11,  3.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:11<00:11,  3.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:12<00:26,  1.54it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:42,  5.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:38,  2.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:02,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:49,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:08,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:46,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:18,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:13,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<03:00,  1.34s/it]\n",
+      "\n",
+      "Training models: 100%|█████████████████████████████| 15/15 [25:44<00:00, 102.99s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.71it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.71it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.72it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.06it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.42it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.33it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.40it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  9.52it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.88it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00,  8.63it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 70it [00:00, 97.36it/s]\u001b[A\n",
+      "Evaluating NLL: 140it [00:01, 197.50it/s]\u001b[A\n",
+      "Evaluating NLL: 195it [00:01, 264.58it/s]\u001b[A\n",
+      "Evaluating NLL: 256it [00:01, 193.37it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:49,  2.00it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:01, 57.11it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:00<00:00, 90.38it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:00<00:00, 103.63it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 95.13it/\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  30%|████▏         | 6/20 [00:00<00:00, 49.90it/s]\u001b[A\n",
+      "precomputing masks and variances:  55%|███████▏     | 11/20 [00:00<00:00, 47.91it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 56.11it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 24: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 25: 100%|██████████| 1/1 [00:00<00:00, 18.59it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 132.60it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.85it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 25: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 26: 100%|██████████| 1/1 [00:00<00:00, 18.64it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.89it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 149.30it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 26: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 27: 100%|██████████| 1/1 [00:00<00:00, 18.63it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 149.80it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 149.36it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 27: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 28: 100%|██████████| 1/1 [00:00<00:00, 18.63it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.96it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 157.75it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 28: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 29: 100%|██████████| 1/1 [00:00<00:00, 18.70it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 128.60it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 132.99it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 29: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 30: 100%|██████████| 1/1 [00:00<00:00, 18.46it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 146.98it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.91it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 30: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 31: 100%|██████████| 1/1 [00:00<00:00, 18.75it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.36it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 150.62it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 31: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 32: 100%|██████████| 1/1 [00:00<00:00, 18.58it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 151.88it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 134.48it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 32: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 33: 100%|██████████| 1/1 [00:00<00:00, 18.28it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 159.78it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 146.10it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 33: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 34: 100%|██████████| 1/1 [00:00<00:00, 18.24it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 138.01it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 143.79it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 34: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 35: 100%|██████████| 1/1 [00:00<00:00, 18.17it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.93it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 129.92it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 35: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 36: 100%|██████████| 1/1 [00:00<00:00, 18.35it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 144.53it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 162.91it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 36: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 37: 100%|██████████| 1/1 [00:00<00:00, 18.66it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 163.21it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 148.10it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 37: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 38: 100%|██████████| 1/1 [00:00<00:00, 18.57it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 149.41it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 161.89it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 38: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 39: 100%|██████████| 1/1 [00:00<00:00, 18.57it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 158.52it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 149.10it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 39: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 40: 100%|██████████| 1/1 [00:00<00:00, 17.86it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 149.85it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:46,  2.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:01, 57.01it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|▍| 49/100 [00:00<00:00, 101.84it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 132.15it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 111.47it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:10,  9.11it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:00, 141.68it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:00<00:00, 195.89it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 195.90it\n",
+      " 82%|████████████████████████████████████        | 9/11 [3:03:27<45:16, 1358.24s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:06<06:50,  6.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:02,  3.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:07<01:49,  1.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:57,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:09<00:45,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:09<00:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:10<00:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:10<00:30,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:11<00:27,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:11<00:26,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:12<00:24,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:12<00:23,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:13<00:22,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:13<00:22,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:14<00:21,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:14<00:20,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:15<00:20,  2.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:15<00:19,  2.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:16<00:19,  2.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:16<00:18,  2.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:17<00:18,  2.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:17<00:17,  2.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:17<00:17,  2.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:18<00:16,  2.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:18<00:16,  2.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:19<00:15,  2.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:19<00:15,  2.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:20<00:14,  2.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:21<00:21,  1.41it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:17,  4.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:03,  2.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:44,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:20<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:50,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:49,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:48,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:25<03:46,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:45,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:44,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:43,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:30<03:41,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:40,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:39,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:38,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:35<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:39<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:40<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:32,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:44<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:29,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:49<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:26,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:25,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:53<03:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:23,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:22,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:57<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:58<03:20,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:18,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:02<03:16,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:03<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:13,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:06<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:07<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:08,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:11<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:12<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:15<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:16<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<03:00,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:20<02:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:21<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:24<02:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:25<02:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:27<02:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:28<02:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:29<02:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:30<02:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:32<02:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:33<02:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:34<02:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:36<02:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:37<02:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:38<02:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:39<02:38,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:41<02:37,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:43<02:42,  1.33s/it]\n",
+      "\n",
+      "Training models:   7%|██                            | 1/15 [02:10<30:28, 130.58s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<06:55,  7.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:04,  3.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<01:51,  1.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:16,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:08<00:58,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:09<00:46,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:09<00:39,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:10<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:10<00:30,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:11<00:28,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:11<00:26,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:12<00:25,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:12<00:24,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:13<00:23,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:13<00:22,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:14<00:21,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:14<00:21,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:15<00:20,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:15<00:20,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:16<00:19,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:16<00:19,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:17<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:17<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:18<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:18<00:16,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:19<00:16,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:19<00:16,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:20<00:15,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:20<00:15,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:21<00:21,  1.39it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:31,  4.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:10,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<05:05,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:14<04:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:55,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:53,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:50,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:49,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:46,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:45,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:44,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:42,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:41,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:41,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:40,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:39,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:39,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:38,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:37,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:36,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:36,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:35,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:34,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:32,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:30,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:28,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:27,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:24,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:22,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:20,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:19,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:16,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:15,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:11,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:10,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:03,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:17<03:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<02:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:22<02:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:25<02:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<02:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:27<02:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:28<02:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:30<02:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:31<02:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:32<02:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:34<02:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:35<02:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:36<02:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:37<02:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:39<02:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:40<02:38,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:41<02:37,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:42<02:36,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:44<02:34,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:45<02:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:46<02:32,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:48<02:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:49<02:29,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:50<02:28,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:51<02:27,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:53<02:25,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:54<02:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:55<02:23,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:57<02:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:59<02:26,  1.33s/it]\n",
+      "\n",
+      "Training models:  13%|████                          | 2/15 [04:35<30:05, 138.87s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:10<10:06, 10.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:10<04:22,  4.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:11<02:32,  2.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:11<01:41,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:12<01:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<00:57,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<00:46,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:13<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:14<00:30,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:15<00:28,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:15<00:26,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:16<00:25,  1.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:16<00:23,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:17<00:22,  1.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:17<00:22,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:18<00:21,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:18<00:20,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:19<00:20,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:19<00:19,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:20<00:19,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:20<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:21<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:21<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:22<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:22<00:16,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:22<00:15,  2.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:23<00:15,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:24<00:26,  1.19it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:56,  5.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:53,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:04,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:19<03:55,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:50,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:48,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:46,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:44,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:43,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:42,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:41,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:40,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:34<03:39,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:39,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:38,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:37,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:37,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:36,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:35,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:43<03:34,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:32,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:30,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:28,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:52<03:27,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:26,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:24,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:22,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:20,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:19,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:01<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:16,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:15,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:13,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:11,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:10,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:10<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:17<03:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<03:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:19<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<02:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:22<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:25<02:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<02:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:27<02:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:28<02:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:30<02:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:31<02:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:32<02:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:34<02:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:35<02:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:37<02:50,  1.34s/it]\n",
+      "\n",
+      "Training models:  20%|██████                        | 3/15 [06:41<26:37, 133.16s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:10,  7.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:10,  3.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<01:54,  2.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:19,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:09<00:59,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:09<00:47,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:10<00:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:10<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:11<00:31,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:11<00:28,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:12<00:26,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:12<00:25,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:13<00:24,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:13<00:23,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:14<00:22,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:14<00:21,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:15<00:21,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:15<00:20,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:16<00:20,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:16<00:19,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:17<00:19,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:17<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:17<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:18<00:17,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:18<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:19<00:16,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:19<00:16,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:20<00:15,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:20<00:15,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:21<00:21,  1.37it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:57,  5.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:54,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:45,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:43,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<03:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:55,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:53,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:50,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:24<03:48,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:46,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:45,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:29<03:43,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:42,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:41,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:33<03:41,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:40,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:40,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:39,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:38<03:38,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:37,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:37,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:42<03:36,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:35,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:34,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:47<03:32,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:30,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:30,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:51<03:28,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:27,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:26,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:55<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:56<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:22,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:20,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:00<03:19,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:17,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:04<03:15,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:05<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:13,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:08<03:11,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:09<03:10,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:13<03:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:14<03:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:03,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:17<03:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:18<03:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:20<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<02:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:22<02:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:23<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:25<02:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:26<02:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:27<02:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:29<02:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:30<02:50,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:31<02:48,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:32<02:47,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:34<02:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:35<02:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:36<02:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:49,  1.34s/it]\n",
+      "\n",
+      "Training models:  27%|████████                      | 4/15 [08:46<23:48, 129.86s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:07,  7.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:07<03:09,  3.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<01:54,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:08<01:18,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:09<00:59,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:09<00:47,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:10<00:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:10<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:11<00:31,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:11<00:28,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:12<00:26,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:12<00:25,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:13<00:24,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:13<00:23,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:14<00:22,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:14<00:21,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:15<00:21,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:15<00:20,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:16<00:20,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:16<00:19,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:16<00:19,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:17<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:17<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:18<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:18<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:19<00:16,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:19<00:16,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:20<00:15,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:21<00:22,  1.36it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:05,  5.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:48,  2.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:09,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:54,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:12,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:30,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:13,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:54,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:52,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:50,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:49,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:46,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:45,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:43,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:44,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:41,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:41,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:39,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:37,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:36,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:33,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:32,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:31,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:30,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:28,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:27,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:26,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:22,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:21,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:19,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:03<03:17,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:15,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:13,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:10,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:12<03:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:15<03:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:16<03:03,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:19<03:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:20<03:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:21<02:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:23<02:57,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:24<02:56,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:25<02:54,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:53,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:28<02:52,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:29<02:50,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:30<02:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:32<02:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:33<02:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:34<02:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:36<02:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:37<02:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:38<02:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:39<02:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:41<02:38,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:45,  1.35s/it]\n",
+      "\n",
+      "Training models:  33%|██████████                    | 5/15 [10:55<21:34, 129.43s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:35,  7.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:21,  3.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<02:00,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:09<01:22,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:09<01:01,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:10<00:48,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:10<00:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:11<00:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:11<00:31,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:12<00:28,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:12<00:27,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:13<00:25,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:13<00:24,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:14<00:23,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:14<00:22,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:15<00:21,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:15<00:21,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:16<00:21,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:16<00:20,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:17<00:19,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:17<00:19,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:17<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:18<00:18,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:18<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:19<00:17,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:19<00:16,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:20<00:16,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:20<00:15,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:21<00:23,  1.32it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:18,  5.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:29,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:48,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:30,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:11,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<04:00,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:55,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:53,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:23<03:52,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:50,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:48,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:28<03:45,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:44,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:43,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:42,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:41,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:41,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:37<03:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:39,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:38,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:37,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:36,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:46<03:34,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:33,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:32,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:50<03:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:54<03:27,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:24,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:58<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:59<03:21,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:24,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:26,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:26,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:26,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:07<03:18,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:13,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:11<03:11,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:15<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:19<03:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:20<03:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<02:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:23<02:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:24<02:57,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:56,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:54,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:28<02:53,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:29<02:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:50,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:32<02:48,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:33<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:36<02:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:37<02:44,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:40<02:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:41<02:39,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:42<02:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:44<02:36,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:43,  1.35s/it]\n",
+      "\n",
+      "Training models:  40%|████████████                  | 6/15 [13:07<19:34, 130.51s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:31,  7.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:19,  3.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:08<01:59,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:09<01:21,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:09<01:03,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:10<00:50,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:10<00:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:11<00:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:11<00:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:12<00:29,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:12<00:27,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:13<00:26,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:13<00:24,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:14<00:23,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:14<00:22,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:15<00:21,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:15<00:21,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:16<00:20,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:16<00:20,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:17<00:20,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:17<00:19,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:18<00:18,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:18<00:18,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:19<00:17,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:19<00:17,  2.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:20<00:16,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:20<00:16,  2.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:21<00:15,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:21<00:15,  2.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:22<00:22,  1.33it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:08,  5.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:50,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:11,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:56,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:13,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:13,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:02,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:50,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:49,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:47,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:45,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:44,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:43,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:42,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:39,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:37,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:36,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:34,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:33,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:31,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:26,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:26,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:19,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:17,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:11,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:08,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:15<03:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:04,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:19<03:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<02:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:23<02:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:24<02:56,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:55,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:53,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:28<02:52,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:51,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:58,  1.36s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████                | 7/15 [15:07<16:56, 127.11s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:08<07:52,  8.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:27,  3.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:05,  2.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:09<01:25,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:10<01:03,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:10<00:51,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:11<00:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:11<00:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:12<00:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:12<00:29,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:13<00:27,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:13<00:25,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:14<00:24,  1.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:14<00:24,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:15<00:23,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:15<00:22,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:16<00:21,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:16<00:20,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:17<00:20,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:17<00:20,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:18<00:19,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:18<00:18,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:19<00:18,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:19<00:17,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:20<00:17,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:20<00:16,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:21<00:17,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:21<00:16,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:22<00:15,  1.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:22<00:15,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:23<00:22,  1.31it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:08<26:52,  8.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:09<13:25,  4.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<09:07,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<07:13,  2.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:13<06:08,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:14<05:26,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:57,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:17<04:42,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:18<04:28,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:18,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:22<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:23<04:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:24<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<03:57,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:27<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<03:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:31<03:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:32<03:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:33<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:34<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:36<03:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:37<03:54,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:38<03:52,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:40<03:49,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:41<03:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:42<03:47,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:44<03:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:45<03:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:46<03:45,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:48<03:41,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:49<03:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:50<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:52<03:34,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:53<03:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:54<03:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:55<03:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:57<03:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:58<03:27,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:59<03:26,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:01<03:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:02<03:27,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:03<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:05<03:28,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:06<03:24,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:07<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:09<03:24,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:10<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:11<03:22,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:13<03:18,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:14<03:20,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:15<03:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:17<03:18,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:18<03:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:20<03:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:21<03:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:22<03:07,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:23<03:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:25<03:05,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:26<03:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:27<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:29<03:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:30<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:31<02:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:33<02:58,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:34<02:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:35<02:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:37<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:38<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:39<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:41<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:42<02:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:43<02:43,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:44<02:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:47<02:55,  1.41s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████              | 8/15 [17:24<15:10, 130.10s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:11<11:15, 11.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:11<04:50,  5.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:12<02:47,  2.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:12<01:51,  1.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:13<01:20,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:13<01:00,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:14<00:48,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:14<00:40,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:15<00:35,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:29,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:27,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:17<00:25,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:18<00:24,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:18<00:23,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:19<00:22,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:19<00:22,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:20<00:21,  1.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:20<00:21,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:21<00:21,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:21<00:20,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:22<00:19,  1.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:22<00:18,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:23<00:18,  1.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:23<00:18,  1.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:24<00:17,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:24<00:17,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:25<00:16,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:26<00:28,  1.11it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:08<27:58,  8.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:09<13:53,  4.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<09:24,  2.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:12<07:17,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:13<06:11,  1.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:14<05:26,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:16<05:03,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:17<04:48,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:18<04:31,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:20<04:22,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:21<04:19,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:22<04:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:23<04:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:25<04:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:26<04:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:27<04:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:29<03:57,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:30<03:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:31<03:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:33<03:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:34<03:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:35<03:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:37<03:55,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:38<03:59,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:39<04:04,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:41<04:10,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:42<04:00,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:44<03:58,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:45<03:52,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:46<03:47,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:48<03:44,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:49<03:43,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:50<03:40,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:51<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:53<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:54<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:55<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:57<03:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:58<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:59<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [01:01<03:31,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:02<03:41,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:04<03:39,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:05<03:32,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:06<03:29,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:08<03:26,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:09<03:26,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:10<03:25,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:12<03:23,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:13<03:22,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:14<03:18,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:16<03:16,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:17<03:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:18<03:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:20<03:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:21<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:22<03:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:23<03:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:25<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:26<03:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:27<03:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:29<02:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:30<02:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:31<03:01,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:33<02:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:34<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:35<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:37<02:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:38<02:52,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:39<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:41<02:51,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:42<02:48,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:43<02:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:44<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:47<02:59,  1.44s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████            | 9/15 [20:18<14:23, 143.90s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:11<11:24, 11.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:12<04:54,  5.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:12<02:49,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:13<01:52,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:13<01:20,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:14<01:01,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:14<00:49,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:15<00:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:15<00:35,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:29,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:27,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:17<00:25,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:18<00:24,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:18<00:22,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:19<00:22,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:19<00:21,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:19<00:20,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:20<00:20,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:20<00:19,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:21<00:19,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:22<00:19,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:22<00:18,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:23<00:18,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:23<00:17,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:24<00:17,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:24<00:16,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:25<00:15,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:25<00:27,  1.12it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<26:22,  7.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:09<13:21,  4.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<09:15,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<07:11,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:13<06:03,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:14<05:22,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:55,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:36,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:18<04:23,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:21<04:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:23<03:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:24<03:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<03:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:26<03:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<03:50,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:30<03:48,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:31<03:46,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:33<03:45,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:34<03:44,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:35<03:42,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:37<03:46,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:38<03:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:39<03:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:41<03:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:42<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:43<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:44<03:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:46<03:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:47<03:36,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:48<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:50<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:51<03:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:52<03:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:53<03:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:55<03:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:56<03:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:57<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:59<03:30,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:00<03:43,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:02<03:41,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:03<03:35,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:04<03:30,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:06<03:25,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:07<03:23,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:08<03:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:10<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:11<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:12<03:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:14<03:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:15<03:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:16<03:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:17<03:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:19<03:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:20<03:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:21<03:03,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:23<03:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:24<03:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:25<02:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:26<02:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:28<02:57,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:29<02:56,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:30<02:55,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:32<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:33<02:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:34<02:52,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:36<02:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:37<02:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:38<02:47,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:41<03:00,  1.41s/it]\n",
+      "\n",
+      "Training models:  67%|███████████████████▎         | 10/15 [22:32<11:44, 140.89s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:11<11:47, 11.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:12<05:03,  5.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:13<02:59,  3.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:13<01:57,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:14<01:23,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:14<01:03,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:15<00:50,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:15<00:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:16<00:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:17<00:29,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:27,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:25,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:18<00:24,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:19<00:23,  1.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:19<00:22,  1.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:20<00:21,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:20<00:21,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:21<00:20,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:21<00:19,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:22<00:19,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:22<00:19,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:23<00:18,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:23<00:18,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:24<00:17,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:24<00:17,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:25<00:16,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:25<00:15,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:26<00:28,  1.09it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<25:32,  7.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:08<12:52,  3.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<08:50,  2.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<06:55,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:12<05:51,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:13<05:13,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:48,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:34,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:17<04:21,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:18<04:12,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:21<04:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:22<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:23<03:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<03:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:26<03:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:27<03:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:28<03:49,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:30<03:47,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:31<03:46,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:32<03:45,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:34<03:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:35<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:36<03:46,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:37<03:44,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:39<03:44,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:40<03:44,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:41<03:44,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:43<03:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:44<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:45<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:47<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:48<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:49<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:50<03:34,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:52<03:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:53<03:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:54<03:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:56<03:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:57<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:58<03:31,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:00<03:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:01<03:27,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:02<03:30,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:04<03:26,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:05<03:24,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:06<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:08<03:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:09<03:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:10<03:17,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:12<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:13<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:14<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:16<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:17<03:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:18<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:19<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:21<03:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:22<03:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:23<03:06,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:25<03:07,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:26<03:05,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:28<03:05,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:29<03:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:30<02:59,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:31<02:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:33<02:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:34<02:55,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:36<02:57,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:37<03:01,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:38<02:59,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:40<02:54,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:41<02:50,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:42<02:47,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:44<02:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:45<02:45,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:46<02:43,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:48<02:40,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:49<02:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:50<02:39,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:52<02:37,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:53<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:54<02:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:55<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:57<02:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:58<02:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:59<02:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [02:01<02:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [02:02<02:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:03<02:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:05<02:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:06<02:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:07<02:19,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:08<02:17,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:10<02:16,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:11<02:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:12<02:13,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:14<02:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:15<02:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:16<02:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:18<02:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:19<02:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:20<02:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:23<02:12,  1.38s/it]\n",
+      "\n",
+      "Training models:  73%|█████████████████████▎       | 11/15 [25:30<10:08, 152.16s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:11<11:25, 11.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:12<04:54,  5.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:12<02:50,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:13<01:52,  2.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:13<01:20,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:14<01:01,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:14<00:49,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:15<00:40,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:15<00:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:31,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:29,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:26,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:17<00:25,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:18<00:24,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:18<00:23,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:19<00:22,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:19<00:21,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:20<00:21,  1.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:20<00:20,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:21<00:19,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:21<00:19,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:22<00:18,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:22<00:18,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:23<00:17,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:23<00:17,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:24<00:16,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:24<00:16,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:25<00:29,  1.10it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<26:06,  7.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:09<13:07,  3.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<09:00,  2.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<07:03,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:12<05:56,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:14<05:16,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:50,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:33,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:17<04:21,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:11,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:21<04:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:22<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:24<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<03:55,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:26<03:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<03:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:49,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:30<03:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:31<03:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:33<03:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:34<03:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:35<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:36<03:47,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:38<03:45,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:39<03:44,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:40<03:44,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:42<03:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:43<03:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:44<03:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:46<03:43,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:47<03:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:48<03:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:50<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:51<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:52<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:54<03:36,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:55<03:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:56<03:31,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:58<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:59<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:00<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:01<03:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:03<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:04<03:23,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:05<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:07<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:08<03:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:09<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:11<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:12<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:13<03:17,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:15<03:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:16<03:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:17<03:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:19<03:09,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:20<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:21<03:09,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:23<03:07,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:24<03:05,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:25<03:06,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:27<03:03,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:28<03:01,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:29<02:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:31<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:32<02:58,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:33<02:56,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:35<02:56,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:36<02:53,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:37<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:40<03:02,  1.41s/it]\n",
+      "\n",
+      "Training models:  80%|███████████████████████▏     | 12/15 [27:44<07:19, 146.64s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:12<11:58, 12.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:12<05:07,  5.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:13<02:58,  3.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:13<01:57,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:14<01:24,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:14<01:03,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:15<00:51,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:15<00:43,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:16<00:37,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:17<00:30,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:28,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:26,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:18<00:25,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:19<00:23,  1.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:19<00:22,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:20<00:22,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:20<00:21,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:21<00:20,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:21<00:20,  1.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:22<00:19,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:22<00:19,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:23<00:18,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:23<00:18,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:24<00:17,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:24<00:16,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:25<00:18,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:26<00:17,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:27<00:28,  1.07it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<25:13,  7.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:08<12:44,  3.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<08:45,  2.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<06:53,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:12<05:50,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:13<05:12,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:47,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:17<04:20,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:18<04:12,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:21<04:03,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:22<03:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:23<03:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<03:54,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:26<03:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:27<03:49,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:28<03:48,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:30<03:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:31<03:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:32<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:33<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:35<03:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:36<03:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:37<03:46,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:39<03:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:40<03:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:41<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:43<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:44<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:45<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:47<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:48<03:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:49<03:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:50<03:37,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:52<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:53<03:37,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:54<03:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:56<03:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:57<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:58<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:00<03:29,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:01<03:28,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:02<03:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:04<03:24,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:05<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:06<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:08<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:09<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:10<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:12<03:19,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:13<03:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:14<03:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:16<03:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:17<03:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:18<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:20<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:21<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:22<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:23<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:25<03:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:26<02:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:27<03:01,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:29<02:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:30<02:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:31<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:33<02:57,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:34<02:53,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:35<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:37<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:38<02:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:39<02:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:40<02:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:42<02:43,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:43<02:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:46<02:53,  1.40s/it]\n",
+      "\n",
+      "Training models:  87%|█████████████████████████▏   | 13/15 [30:39<05:10, 155.14s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:12<12:29, 12.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:13<05:20,  5.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:13<03:03,  3.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:14<02:00,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:14<01:25,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:15<01:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:15<00:53,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:16<00:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:16<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:17<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:17<00:29,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:18<00:27,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:25,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:24,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:19<00:23,  1.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:20<00:22,  1.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:20<00:21,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:21<00:21,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:21<00:20,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:22<00:19,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:22<00:19,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:23<00:18,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:23<00:18,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:24<00:17,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:24<00:17,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:25<00:16,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:25<00:16,  2.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:26<00:16,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:27<00:29,  1.06it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:09<30:14,  9.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:10<14:47,  4.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:11<09:51,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:12<07:33,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:14<06:16,  1.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:15<05:30,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:16<04:59,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:17<04:40,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:19<04:27,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:20<04:17,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:21<04:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:22<04:09,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:24<04:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:25<04:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:26<03:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:28<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:29<03:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:30<03:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:31<03:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:33<03:46,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:34<03:45,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:35<03:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:36<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:38<03:43,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:39<03:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:40<03:41,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:42<03:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:43<03:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:44<03:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:46<03:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:47<03:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:48<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:49<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:51<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:52<03:34,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:53<03:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:55<03:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:56<03:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:57<03:29,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:59<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [01:00<03:31,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:01<03:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:03<03:30,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:04<03:28,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:05<03:25,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:07<03:24,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:08<03:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:09<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:11<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:12<03:19,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:13<03:16,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:14<03:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:16<03:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:17<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:18<03:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:20<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:21<03:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:22<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:24<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:25<03:06,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:26<03:04,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:28<03:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:29<03:03,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:30<03:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:32<02:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:33<02:56,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:34<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:36<02:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:37<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:38<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:39<02:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:41<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:42<02:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:43<02:43,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:45<02:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:46<02:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:47<02:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:49<02:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:50<02:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:51<02:38,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:53<02:39,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:54<02:36,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:55<02:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:57<02:35,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:58<02:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:59<02:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [02:00<02:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [02:02<02:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [02:03<02:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:04<02:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:06<02:21,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:07<02:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:08<02:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:10<02:19,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:11<02:18,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:12<02:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:14<02:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:15<02:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:16<02:11,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:18<02:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:19<02:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:20<02:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:21<02:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:23<02:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:24<02:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:25<02:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [02:27<02:00,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [02:28<01:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [02:29<01:57,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [02:31<01:56,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [02:32<01:55,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [02:33<01:54,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [02:34<01:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [02:36<01:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [02:37<01:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [02:38<01:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [02:40<01:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [02:41<01:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [02:42<01:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [02:44<01:45,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [02:45<01:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [02:46<01:43,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [02:48<01:41,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [02:49<01:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [02:50<01:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [02:51<01:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [02:53<01:34,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [02:54<01:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [02:55<01:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [02:57<01:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [02:59<01:34,  1.37s/it]\n",
+      "\n",
+      "Training models:  93%|███████████████████████████  | 14/15 [34:34<02:59, 179.50s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:12<12:36, 12.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:13<05:23,  5.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:13<03:05,  3.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:14<02:01,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:14<01:25,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:15<01:04,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:15<00:51,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:16<00:42,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:16<00:36,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:17<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:17<00:29,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:18<00:27,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:25,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:24,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:19<00:23,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:20<00:22,  1.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:20<00:21,  1.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:21<00:21,  1.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:21<00:20,  1.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:22<00:19,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:22<00:19,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:23<00:18,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:23<00:18,  2.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:24<00:17,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:24<00:17,  2.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:25<00:16,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:25<00:16,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:26<00:15,  2.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:27<00:29,  1.06it/s]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<25:43,  7.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:08<12:56,  3.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<08:51,  2.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<06:55,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:12<05:51,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:13<05:12,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:52,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:36,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:17<04:22,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:21<04:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:22<04:00,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:24<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:26<03:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:27<03:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:55,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:30<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:31<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:32<03:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:34<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:35<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:36<03:43,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:38<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:39<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:40<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:41<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:43<03:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:44<03:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:45<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:47<03:42,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:48<03:40,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:49<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:51<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:52<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:53<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:55<03:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:56<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:57<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:59<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:00<03:26,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:01<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:02<03:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:04<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:05<03:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:06<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:08<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:09<03:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:10<03:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:12<03:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:13<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:14<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:16<03:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:17<03:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:18<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:19<03:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:21<03:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:22<03:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:23<03:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:25<03:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:26<02:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:27<02:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:29<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:30<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:31<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:33<02:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:34<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:35<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:36<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:38<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:39<02:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:40<02:45,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:42<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:43<02:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:44<02:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:47<02:51,  1.39s/it]\n",
+      "\n",
+      "Training models: 100%|█████████████████████████████| 15/15 [36:55<00:00, 147.70s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 10.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 11.47it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 10.89it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 11.26it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 11.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.44s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 10.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 12.36it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 10.69it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 11.04it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 11.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 10.41it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 10.16it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.54it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 10.47it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.49s/it]\u001b[A\n",
+      "Evaluating NLL: 42it [00:01, 36.49it/s]\u001b[A\n",
+      "Evaluating NLL: 80it [00:01, 74.60it/s]\u001b[A\n",
+      "Evaluating NLL: 127it [00:01, 128.35it/s]\u001b[A\n",
+      "Evaluating NLL: 165it [00:01, 168.85it/s]\u001b[A\n",
+      "Evaluating NLL: 208it [00:01, 217.55it/s]\u001b[A\n",
+      "Evaluating NLL: 260it [00:02, 280.53it/s]\u001b[A\n",
+      "Evaluating NLL: 303it [00:02, 308.97it/s]\u001b[A\n",
+      "Evaluating NLL: 345it [00:02, 328.04it/s]\u001b[A\n",
+      "Evaluating NLL: 386it [00:02, 327.33it/s]\u001b[A\n",
+      "Evaluating NLL: 425it [00:02, 289.71it/s]\u001b[A\n",
+      "Evaluating NLL: 462it [00:02, 307.67it/s]\u001b[A\n",
+      "Evaluating NLL: 512it [00:02, 180.72it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:57,  1.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:02, 31.03it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 68.11it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 85.03it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:00<00:00, 118.44it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 88.67it/\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  35%|████▉         | 7/20 [00:00<00:00, 69.54it/s]\u001b[A\n",
+      "precomputing masks and variances:  70%|█████████    | 14/20 [00:00<00:00, 52.34it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 52.93it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 40: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 41: 100%|██████████| 1/1 [00:00<00:00, 18.59it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  35%|███████▎             | 7/20 [00:00<00:00, 69.28it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 76.72it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  30%|████▏         | 6/20 [00:00<00:00, 54.03it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 70.48it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 41: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 42: 100%|██████████| 1/1 [00:00<00:00, 18.44it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  40%|████████▍            | 8/20 [00:00<00:00, 77.20it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 69.40it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  30%|████▏         | 6/20 [00:00<00:00, 57.76it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 84.12it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 42: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 43: 100%|██████████| 1/1 [00:00<00:00, 18.00it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  45%|█████████▍           | 9/20 [00:00<00:00, 83.61it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 82.24it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  50%|██████▌      | 10/20 [00:00<00:00, 89.47it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 75.15it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 43: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 44: 100%|██████████| 1/1 [00:00<00:00, 18.30it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  45%|█████████▍           | 9/20 [00:00<00:00, 82.78it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 82.97it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  45%|██████▎       | 9/20 [00:00<00:00, 87.34it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 73.32it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 44: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 45: 100%|██████████| 1/1 [00:00<00:00, 18.61it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 100.31it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  40%|█████▌        | 8/20 [00:00<00:00, 73.96it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 79.84it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 45: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 46: 100%|██████████| 1/1 [00:00<00:00, 18.29it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  35%|███████▎             | 7/20 [00:00<00:00, 69.12it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 77.63it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  45%|██████▎       | 9/20 [00:00<00:00, 84.78it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 87.96it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 46: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 47: 100%|██████████| 1/1 [00:00<00:00, 18.08it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  35%|███████▎             | 7/20 [00:00<00:00, 67.45it/s]\u001b[A\n",
+      "computing log likelihoods:  70%|██████████████      | 14/20 [00:00<00:00, 57.22it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 52.90it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  30%|████▏         | 6/20 [00:00<00:00, 57.16it/s]\u001b[A\n",
+      "precomputing masks and variances:  60%|███████▊     | 12/20 [00:00<00:00, 53.68it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 56.76it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 47: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 48: 100%|██████████| 1/1 [00:00<00:00, 18.00it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  35%|███████▎             | 7/20 [00:00<00:00, 65.16it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 70.06it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  45%|██████▎       | 9/20 [00:00<00:00, 88.03it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 82.43it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 48: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 49: 100%|██████████| 1/1 [00:00<00:00, 17.23it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  40%|████████▍            | 8/20 [00:00<00:00, 79.24it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 68.54it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  40%|█████▌        | 8/20 [00:00<00:00, 73.44it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 67.10it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 49: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 50: 100%|██████████| 1/1 [00:00<00:00, 18.02it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  50%|██████████          | 10/20 [00:00<00:00, 94.78it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 82.11it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  35%|████▉         | 7/20 [00:00<00:00, 66.94it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 67.81it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 50: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 51: 100%|██████████| 1/1 [00:00<00:00, 18.41it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  40%|████████▍            | 8/20 [00:00<00:00, 77.27it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 76.64it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  35%|████▉         | 7/20 [00:00<00:00, 64.54it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 86.39it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 51: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 52: 100%|██████████| 1/1 [00:00<00:00, 18.55it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  35%|███████▎             | 7/20 [00:00<00:00, 63.91it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 77.14it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  35%|████▉         | 7/20 [00:00<00:00, 65.66it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 86.37it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 52: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 53: 100%|██████████| 1/1 [00:00<00:00, 18.47it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  35%|███████▎             | 7/20 [00:00<00:00, 66.95it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 70.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  35%|████▉         | 7/20 [00:00<00:00, 64.84it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 82.08it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 53: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 54: 100%|██████████| 1/1 [00:00<00:00, 18.02it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  40%|████████▍            | 8/20 [00:00<00:00, 70.59it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 65.16it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances:  45%|██████▎       | 9/20 [00:00<00:00, 88.12it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 75.41it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 54: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 55: 100%|██████████| 1/1 [00:00<00:00, 18.61it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 92.40it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 85.93it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 55: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 56: 100%|██████████| 1/1 [00:00<00:00, 18.34it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods:  35%|███████▎             | 7/20 [00:00<00:00, 67.29it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|████████████████████| 20/20 [00:00<00:00, 73.13it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<01:10,  1.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:02, 29.45it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:01, 50.38it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:01<00:00, 66.80it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:01<00:00, 87.92it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|▊| 76/100 [00:01<00:00, 102.24it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 71.46it/\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:12,  8.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:00, 104.30it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 109.52it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 111.85it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 117.41it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:00<00:00, 134.02it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 127.75it\n",
+      " 91%|███████████████████████████████████████    | 10/11 [3:42:58<27:50, 1670.81s/it]\n",
+      "Training models:   0%|                                       | 0/15 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:12<11:48, 12.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:12<05:18,  5.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:13<03:13,  3.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:14<02:15,  2.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:15<01:42,  1.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:16<01:23,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:17<01:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:18<01:02,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:19<00:57,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:20<00:52,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:21<00:50,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:22<00:47,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:23<00:45,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:23<00:43,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:24<00:42,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:25<00:41,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:27<00:45,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:28<00:42,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:28<00:40,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:29<00:38,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:30<00:37,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:31<00:35,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:32<00:34,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:33<00:33,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:34<00:32,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:35<00:31,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:36<00:30,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:37<00:29,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:38<00:28,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:39<00:27,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:40<00:27,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:40<00:25,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:41<00:24,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:42<00:23,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:44<00:31,  1.27s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<25:58,  7.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:09<13:09,  3.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<09:07,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<07:12,  2.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:13<06:03,  1.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:14<05:22,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:59,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:40,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:18<04:27,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:20,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:22<04:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:23<04:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:24<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<04:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:27<04:05,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<04:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:31<03:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:32<03:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:33<03:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:35<03:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:36<03:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:37<03:54,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:39<04:07,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:40<04:03,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:42<03:57,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:43<03:53,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:44<03:50,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:46<03:47,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:47<03:47,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:48<03:44,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:50<03:43,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:51<03:41,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:52<03:39,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:54<03:38,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:55<03:38,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:56<03:36,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:58<03:35,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:59<03:35,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [01:00<03:33,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:02<03:31,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:03<03:31,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:04<03:30,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:06<03:28,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:07<03:27,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:08<03:25,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:10<03:24,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:11<03:23,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:12<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:14<03:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:15<03:18,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:16<03:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:18<03:15,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:19<03:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:21<03:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:22<03:11,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:23<03:09,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:24<03:08,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:26<03:07,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:27<03:06,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:29<03:04,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:30<03:05,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:31<03:04,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:33<03:04,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:34<03:01,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:35<02:59,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:37<02:58,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:38<02:58,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:39<02:56,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:41<02:54,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:42<02:53,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:43<02:51,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:45<02:50,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:46<02:47,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:47<02:45,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:49<02:43,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:50<02:42,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:51<02:40,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:53<02:39,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:54<02:37,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:55<02:36,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:57<02:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:58<02:33,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:59<02:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [02:01<02:30,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [02:02<02:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [02:03<02:27,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [02:05<02:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:06<02:25,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:07<02:23,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:09<02:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:10<02:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:11<02:21,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:13<02:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:14<02:20,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:15<02:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:17<02:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:18<02:18,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:20<02:17,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:21<02:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:22<02:15,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:24<02:14,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:25<02:13,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:27<02:14,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:28<02:11,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [02:29<02:10,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [02:31<02:07,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [02:32<02:06,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [02:33<02:05,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [02:35<02:01,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [02:36<01:58,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [02:37<01:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [02:39<01:54,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [02:40<01:52,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [02:41<01:51,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [02:43<01:50,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [02:44<01:48,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [02:45<01:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [02:47<01:45,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [02:48<01:45,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [02:49<01:44,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [02:51<01:43,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [02:52<01:42,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [02:53<01:42,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [02:55<01:41,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [02:56<01:40,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [02:59<01:41,  1.40s/it]\n",
+      "\n",
+      "Training models:   7%|██                            | 1/15 [03:54<54:44, 234.59s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:13<13:29, 13.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:14<05:59,  6.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:15<03:37,  3.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:16<02:30,  2.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:17<01:53,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:18<01:30,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:19<01:16,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:20<01:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:21<00:59,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:22<00:55,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:23<00:51,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:24<00:49,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:25<00:47,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:26<00:45,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:27<00:44,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:27<00:42,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:28<00:41,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:29<00:40,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:30<00:38,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:31<00:37,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:32<00:37,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:33<00:36,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:34<00:35,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:35<00:34,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:36<00:33,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:37<00:31,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:38<00:30,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:39<00:29,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:40<00:29,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:41<00:28,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:42<00:27,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:43<00:26,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:43<00:25,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:44<00:24,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:45<00:23,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████▏              | 36/60 [00:47<00:31,  1.32s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:08<28:42,  8.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:10<14:23,  4.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:11<09:41,  2.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:12<07:32,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:13<06:21,  1.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:15<05:37,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:16<05:07,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:17<04:46,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:19<04:35,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:20<04:26,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:21<04:17,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:23<04:11,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:24<04:07,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:25<04:09,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:26<04:05,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:28<04:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:29<04:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:30<03:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:32<03:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:33<03:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:34<03:57,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:36<03:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:37<03:54,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:38<03:52,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:40<03:52,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:41<03:50,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:42<03:51,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:44<03:50,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:45<03:48,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:46<03:48,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:48<03:47,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:49<03:47,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:51<03:46,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:52<03:44,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:53<03:41,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:55<03:42,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:56<03:41,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:57<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:59<03:38,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [01:00<03:37,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [01:01<03:35,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:03<03:35,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:04<03:32,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:05<03:31,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:07<03:31,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:08<03:28,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:10<03:27,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:11<03:30,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:12<03:28,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:14<03:24,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:15<03:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:16<03:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:18<03:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:19<03:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:20<03:16,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:22<03:15,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:23<03:14,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:24<03:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:26<03:09,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:27<03:07,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:28<03:06,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:30<03:04,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:31<03:03,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:33<03:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:34<03:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:35<02:59,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:37<03:00,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:38<02:58,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:39<02:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:41<02:55,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:42<02:52,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:43<02:51,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:45<02:49,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:46<02:48,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:47<02:47,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:49<02:46,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:50<02:44,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:51<02:43,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:53<02:41,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:54<02:41,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:55<02:40,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:57<02:40,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:58<02:38,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:59<02:37,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [02:01<02:35,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [02:02<02:35,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [02:03<02:32,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [02:05<02:30,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [02:06<02:29,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:08<02:29,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:09<02:26,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:10<02:24,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:12<02:23,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:13<02:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:14<02:21,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:16<02:19,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:17<02:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:18<02:17,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:20<02:15,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:21<02:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:22<02:13,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:24<02:11,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:25<02:10,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:26<02:09,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:28<02:07,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:29<02:07,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [02:30<02:05,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [02:32<02:03,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [02:33<02:02,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [02:34<02:01,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [02:36<01:59,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [02:37<01:57,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [02:38<01:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [02:40<01:55,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [02:41<01:53,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [02:43<01:53,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [02:44<01:52,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [02:45<01:49,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [02:46<01:48,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [02:48<01:46,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [02:49<01:45,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [02:51<01:45,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [02:53<01:48,  1.41s/it]\n",
+      "\n",
+      "Training models:  13%|████                          | 2/15 [07:46<50:25, 232.72s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:12<12:08, 12.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:13<05:27,  5.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:14<03:20,  3.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:15<02:20,  2.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:16<01:46,  1.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:17<01:27,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:18<01:14,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:19<01:05,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:19<00:58,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:20<00:54,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:21<00:50,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:22<00:47,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:23<00:46,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:24<00:44,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:25<00:43,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:26<00:41,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:27<00:40,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:28<00:39,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:29<00:38,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:30<00:37,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:31<00:36,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:32<00:35,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:33<00:34,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:33<00:33,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:34<00:32,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:35<00:31,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:36<00:30,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:37<00:29,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:38<00:28,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:39<00:27,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:40<00:26,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:41<00:26,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:42<00:25,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:43<00:24,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:45<00:32,  1.29s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:08<27:14,  8.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:09<13:39,  4.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<09:19,  2.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:12<07:18,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:13<06:10,  1.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:14<05:28,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:59,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:17<04:44,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:18<04:31,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:24,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:21<04:16,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:22<04:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:23<04:08,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:25<04:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:26<04:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:27<04:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<03:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:30<03:55,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:31<03:55,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:32<03:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:34<03:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:35<03:50,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:36<03:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:38<03:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:39<03:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:40<03:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:42<03:48,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:43<03:48,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:44<03:49,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:46<03:47,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:47<03:48,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:48<03:48,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:50<03:44,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:51<03:44,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:52<03:44,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:54<03:44,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:55<03:43,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:57<03:42,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:58<03:38,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:59<03:36,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [01:01<03:35,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:02<03:32,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:03<03:32,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:05<03:30,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:06<03:27,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:07<03:27,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:09<03:27,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:10<03:25,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:11<03:23,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:13<03:22,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:14<03:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:15<03:20,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:17<03:17,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:18<03:17,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:19<03:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:21<03:13,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:22<03:13,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:23<03:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:25<03:09,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:26<03:08,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:28<03:06,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:29<03:04,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:30<03:03,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:32<03:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:33<03:00,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:34<02:59,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:36<02:57,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:37<02:55,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:38<02:55,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:40<02:53,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:41<02:52,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:42<02:51,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:44<02:49,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:45<02:48,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:46<02:49,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:48<02:47,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:49<02:47,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:50<02:44,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:52<02:42,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:53<02:40,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:54<02:39,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:56<02:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:57<02:37,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:58<02:36,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [02:00<02:34,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [02:01<02:32,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [02:02<02:30,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [02:04<02:28,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [02:05<02:27,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:06<02:25,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:08<02:24,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:09<02:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:10<02:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:12<02:20,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:13<02:19,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:14<02:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:16<02:20,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:17<02:20,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:18<02:17,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:20<02:15,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:21<02:14,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:23<02:12,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:24<02:10,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:25<02:12,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:27<02:13,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:30<02:13,  1.42s/it]\n",
+      "\n",
+      "Training models:  20%|██████                        | 3/15 [12:00<48:31, 242.59s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:12<11:49, 12.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:12<05:19,  5.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:13<03:13,  3.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:14<02:15,  2.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:15<01:43,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:16<01:24,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:17<01:18,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:18<01:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:19<01:00,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:20<00:54,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:21<00:50,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:22<00:48,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:23<00:45,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:24<00:44,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:25<00:42,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:26<00:41,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:27<00:40,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:28<00:39,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:28<00:38,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:29<00:37,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:30<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:31<00:35,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:32<00:34,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:33<00:33,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:34<00:32,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:35<00:31,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:36<00:30,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:37<00:29,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:38<00:29,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:39<00:27,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:40<00:26,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:40<00:25,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:41<00:24,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:42<00:24,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:43<00:23,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████▏              | 36/60 [00:46<00:30,  1.28s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<24:25,  7.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:08<12:27,  3.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:09<08:43,  2.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<06:57,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:12<05:55,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:13<05:19,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<04:57,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:40,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:17<04:30,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:23,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:16,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:21<04:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:22<04:05,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:24<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:25<03:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:26<03:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<03:56,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:54,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:30<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:31<03:54,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:33<03:56,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:34<03:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:35<03:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:37<03:51,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:38<03:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:39<03:51,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:41<03:49,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:42<03:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:43<03:46,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:45<03:46,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:46<03:44,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:47<03:43,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:49<03:42,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:50<03:43,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:52<03:43,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:53<03:40,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:54<03:38,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:56<03:38,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:57<03:36,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:58<03:35,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [01:00<03:34,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:01<03:34,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:02<03:32,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:04<03:35,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:05<03:35,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:06<03:31,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:08<03:29,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:09<03:25,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:11<03:25,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:12<03:24,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:13<03:21,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:15<03:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:16<03:20,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:17<03:17,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:19<03:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:20<03:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:21<03:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:23<03:10,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:24<03:13,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:26<03:29,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:27<03:24,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:29<03:18,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:30<03:12,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:31<03:09,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:33<03:05,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:34<03:02,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:35<03:01,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:37<02:59,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:38<02:56,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:39<02:54,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:41<02:52,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:42<02:52,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:43<02:51,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:45<02:48,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:46<02:48,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:47<02:46,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:49<02:43,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:50<02:42,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:51<02:41,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:53<02:40,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:54<02:38,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:55<02:37,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:57<02:35,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:58<02:35,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:59<02:32,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [02:01<02:31,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [02:02<02:31,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [02:03<02:29,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [02:05<02:29,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:06<02:28,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:08<02:28,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:09<02:25,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:10<02:24,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:13<02:30,  1.42s/it]\n",
+      "\n",
+      "Training models:  27%|████████                      | 4/15 [15:17<41:10, 224.64s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:11<11:38, 11.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:12<05:15,  5.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:13<03:14,  3.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:14<02:16,  2.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:15<01:44,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:16<01:24,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:17<01:11,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:18<01:03,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:19<00:57,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:20<00:53,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:21<00:50,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:22<00:47,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:23<00:45,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:24<00:44,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:24<00:43,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:25<00:41,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:26<00:40,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:27<00:39,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:28<00:38,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:29<00:37,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:30<00:36,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:31<00:35,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:32<00:34,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:33<00:33,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:34<00:32,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:35<00:31,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:36<00:30,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:37<00:29,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:37<00:28,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:39<00:31,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:40<00:29,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:41<00:27,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:42<00:35,  1.30s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:07<26:06,  7.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:09<13:14,  4.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:10<09:04,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:11<07:05,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:13<06:00,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:14<05:23,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:15<05:00,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:16<04:45,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:18<04:31,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:19<04:22,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:20<04:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:22<04:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:23<04:08,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:24<04:04,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:26<04:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:27<03:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:28<03:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:29<03:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:31<03:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:32<03:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:33<03:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:35<03:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:36<03:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:37<03:54,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:39<03:55,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:40<03:54,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:41<03:52,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:43<03:50,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:44<03:51,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:46<03:50,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:47<03:48,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:48<03:45,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:50<03:47,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:51<03:45,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:52<03:45,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:54<03:42,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:55<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:56<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:58<03:37,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:59<03:37,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [01:00<03:35,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [01:02<03:34,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [01:03<03:34,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:05<03:32,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:06<03:31,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:07<03:29,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:09<03:28,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:10<03:27,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:11<03:26,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:13<03:23,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:14<03:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:15<03:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:17<03:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:18<03:16,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:19<03:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:21<03:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:22<03:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:23<03:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:25<03:09,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:26<03:07,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:27<03:05,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:29<03:03,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:30<03:02,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:31<03:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:33<02:58,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:34<02:57,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:35<02:56,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:37<02:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:38<02:53,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:39<02:51,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:41<02:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:42<02:49,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:43<02:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:45<02:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:46<02:45,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:47<02:43,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:49<02:42,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:50<02:41,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:51<02:39,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:53<02:38,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:54<02:37,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:55<02:35,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:57<02:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:58<02:33,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [02:01<02:43,  1.42s/it]\n",
+      "\n",
+      "Training models:  33%|██████████                    | 5/15 [18:10<34:20, 206.05s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:21,  7.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:29,  3.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:15,  2.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:40,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:21,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:12<01:01,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:13<00:56,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:52,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:15<00:49,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:47,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:23<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:24<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:25<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:26<00:34,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:27<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:32,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:31,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:33<00:27,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:34<00:26,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:35<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:37<00:30,  1.14s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:08,  5.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:53,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:14,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:59,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:17,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:52,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:35,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:24,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:11,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<04:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<04:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:27<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:32<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:47,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:47,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:36<03:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:37<03:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:40<03:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:41<03:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:44<03:43,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:45<03:42,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:46<03:40,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:48<03:39,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:49<03:38,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:50<03:37,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:52<03:35,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:53<03:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:54<03:33,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:31,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:57<03:30,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:58<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:28,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:01<03:26,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:02<03:25,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:24,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:05<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:06<03:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:07<03:20,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:09<03:18,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:10<03:17,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:11<03:15,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:13<03:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:14<03:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:15<03:11,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:17<03:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:18<03:09,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:19<03:07,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:21<03:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:22<03:04,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:23<03:03,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:25<03:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:26<03:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:27<02:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:29<02:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:30<02:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:31<02:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:33<02:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:34<02:52,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:35<02:51,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:36<02:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:38<02:48,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:39<02:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:40<02:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:42<02:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:43<02:43,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:44<02:42,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:46<02:40,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:47<02:39,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:48<02:38,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:50<02:36,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:51<02:35,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:52<02:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:54<02:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:55<02:31,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:56<02:30,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:58<02:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:59<02:27,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [02:00<02:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:02<02:24,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:03<02:23,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:04<02:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:05<02:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:07<02:19,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:08<02:18,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:09<02:16,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:11<02:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:13<02:19,  1.37s/it]\n",
+      "\n",
+      "Training models:  40%|████████████                  | 6/15 [21:06<29:22, 195.79s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:33,  7.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:34,  3.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:18,  2.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:42,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:22,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<01:02,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:14<00:56,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:52,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:15<00:49,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:47,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:24<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:25<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:25<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:26<00:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:27<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:32,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:31,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:34<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:35<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:35<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:37<00:30,  1.15s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:50,  5.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:56,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:48,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:15,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<04:03,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:45,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:44,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:43,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:46<03:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:50<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:33,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:54<03:31,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:30,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:25,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:24,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:07<03:18,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:17,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:16,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:11<03:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:15<03:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:19<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:20<03:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:24<03:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:28<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:32<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:36<02:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:40<02:45,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:41<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:45<02:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:39,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:38,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:49<02:36,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:53<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:54<02:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:57<02:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:58<02:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:00<02:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:02<02:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:04<02:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:06<02:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:07<02:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:08<02:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:10<02:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:11<02:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:12<02:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:14<02:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:15<02:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:16<02:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:18<02:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:19<02:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:20<02:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:21<02:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [02:23<02:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [02:24<02:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [02:25<01:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [02:27<01:57,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [02:28<01:55,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [02:29<01:54,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [02:31<01:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [02:32<01:52,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [02:33<01:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [02:34<01:49,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [02:36<01:47,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [02:37<01:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [02:38<01:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [02:40<01:44,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [02:41<01:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [02:42<01:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [02:45<01:43,  1.34s/it]\n",
+      "\n",
+      "Training models:  47%|██████████████                | 7/15 [24:33<26:36, 199.62s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:25,  7.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:31,  3.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:16,  2.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:41,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:21,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<01:01,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:13<00:56,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:52,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:15<00:49,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:47,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:23<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:24<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:25<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:26<00:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:27<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:31,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:34<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:34<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:35<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:36<00:24,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:37<00:23,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:39<00:28,  1.13s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<18:08,  5.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:52,  2.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:14,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:59,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:17,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:51,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:13<04:35,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:24,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:18<04:09,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:22<03:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:23<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:27<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:32<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:36<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:37<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:41<03:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:45<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:46<03:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:49<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:50<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:53<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:54<03:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:57<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:58<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:02<03:23,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:06<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:07<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:10<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:11<03:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:14<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:15<03:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:18<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:19<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:20<03:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:23<03:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:24<03:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:27<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:28<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:31<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:32<02:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:35<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:36<02:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:39<02:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:40<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:41<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:44<02:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:45<02:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:48<02:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:49<02:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:52<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:53<02:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:54<02:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:56<02:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:57<02:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:58<02:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:01<02:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:02<02:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:05<02:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:06<02:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:07<02:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:09<02:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:10<02:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:11<02:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:13<02:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:14<02:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:15<02:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:16<02:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:18<02:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:20<02:09,  1.35s/it]\n",
+      "\n",
+      "Training models:  53%|████████████████              | 8/15 [27:38<22:44, 194.87s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:44,  7.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:39,  3.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:20,  2.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:43,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:23,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<01:02,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:14<00:57,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:15<00:53,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:50,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:17<00:47,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:46,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:43,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:24<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:25<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:26<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:27<00:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:28<00:33,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:34<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:35<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:36<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:37<00:24,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:37<00:23,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:39<00:28,  1.14s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:48,  5.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:19,  2.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:48,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<04:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:00,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:20<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:24<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:41<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:44<02:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:48<02:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:52<02:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:54<02:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:56<02:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:58<02:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:00<02:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:01<02:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:04<02:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:05<02:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:07<02:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:08<02:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:09<02:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:11<02:13,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:12<02:11,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:13<02:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:15<02:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:16<02:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:17<02:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:18<02:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:20<02:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:21<02:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [02:22<02:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [02:24<01:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [02:25<01:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [02:26<01:57,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [02:28<01:55,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [02:29<01:54,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [02:30<01:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [02:31<01:51,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [02:33<01:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [02:34<01:49,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [02:35<01:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [02:37<01:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [02:38<01:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [02:39<01:44,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [02:41<01:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [02:42<01:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [02:43<01:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [02:44<01:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [02:47<01:40,  1.34s/it]\n",
+      "\n",
+      "Training models:  60%|██████████████████            | 9/15 [31:10<20:00, 200.15s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:17,  7.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:28,  3.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:14,  2.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:40,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:21,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:11<01:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:12<01:01,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:13<00:56,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:52,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:15<00:49,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:47,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:22<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:23<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:24<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:25<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:26<00:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:27<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:32<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:33<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:34<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:35<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:36<00:24,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:37<00:23,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:39<00:28,  1.12s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:20,  5.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:32,  2.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<07:03,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:09<05:52,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:13,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:49,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:33,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:14<04:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:15,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<04:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:28<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:37<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:46<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:50<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:54<03:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:30,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:27,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:07<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:11<03:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:15<03:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:20<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:24<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:28<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:32<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:36<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:41<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:45<02:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:49<02:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:53<02:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:54<02:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:56<02:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:58<02:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:00<02:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:02<02:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:04<02:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:06<02:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:07<02:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:08<02:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:09<02:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:11<02:13,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:12<02:11,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:13<02:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:15<02:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:16<02:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:17<02:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:19<02:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:20<02:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:23<02:06,  1.35s/it]\n",
+      "\n",
+      "Training models:  67%|███████████████████▎         | 10/15 [34:17<16:19, 195.98s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:32,  7.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:34,  3.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:18,  2.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:42,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:22,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:09,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<01:01,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:14<00:56,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:52,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:15<00:49,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:47,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:24<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:24<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:25<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:26<00:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:27<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:34<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:35<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:35<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:37<00:30,  1.14s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:44,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:48,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<04:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:14,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:20<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:24<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:41<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:42,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:44<02:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:48<02:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:52<02:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:54<02:31,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:58<02:33,  1.36s/it]\n",
+      "\n",
+      "Training models:  73%|█████████████████████▎       | 11/15 [36:57<12:20, 185.12s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:20,  7.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:29,  3.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:15,  2.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:40,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:21,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:12<01:01,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:13<00:56,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:52,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:15<00:49,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:47,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:23<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:24<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:25<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:26<00:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:27<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:31,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:33<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:34<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:35<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:36<00:24,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:37<00:23,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:39<00:28,  1.13s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:45,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:55,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:46,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<04:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<03:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:39,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:09,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:19<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:23<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:36<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:45,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:40<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:42,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:44<02:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:48<02:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:52<02:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:53<02:29,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:56<02:27,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:57<02:25,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:00<02:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:01<02:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:20,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:04<02:19,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:05<02:18,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:07<02:16,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:08<02:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:10<02:19,  1.35s/it]\n",
+      "\n",
+      "Training models:  80%|███████████████████████▏     | 12/15 [39:52<09:05, 181.95s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:47,  7.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:40,  3.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:21,  2.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:44,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:23,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<01:02,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:14<00:57,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:15<00:52,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:50,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:17<00:47,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:24<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:25<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:26<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:27<00:34,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:28<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:34<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:35<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:36<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:37<00:24,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:38<00:23,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|█████████████████████▌               | 35/60 [00:38<00:22,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████▏              | 36/60 [00:40<00:27,  1.13s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:59,  5.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:49,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<04:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:39,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:46<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:07<03:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:20<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:24<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:45,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:44,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:40<02:43,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:44<02:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:48<02:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:52<02:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:54<02:29,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:56<02:27,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:57<02:25,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:00<02:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:01<02:21,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:20,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:04<02:19,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:05<02:17,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:07<02:16,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:08<02:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:09<02:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:10<02:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:12<02:11,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:13<02:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:14<02:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:17<02:12,  1.35s/it]\n",
+      "\n",
+      "Training models:  87%|█████████████████████████▏   | 13/15 [42:54<06:04, 182.12s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:08<08:15,  8.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:09<03:51,  4.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:10<02:27,  2.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:11<01:47,  1.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:12<01:25,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<01:03,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:14<00:57,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:15<00:53,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:16<00:50,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:17<00:47,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:18<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:19<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:20<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:21<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:22<00:40,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:24<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:25<00:36,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:26<00:35,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:27<00:34,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:28<00:33,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:29<00:32,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:30<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:31<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:34<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:35<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:36<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:37<00:24,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▉                | 34/60 [00:39<00:30,  1.16s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:33,  4.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:13,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:52,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:09,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:46,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:18<04:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:32<03:46,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:41<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:29,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:02<03:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:06<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:19<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:23<03:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:36<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:45,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:40<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:44<02:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:38,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:48<02:35,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:52<02:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:53<02:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:56<02:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:57<02:26,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:00<02:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:01<02:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:20,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:04<02:19,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:05<02:18,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:06<02:16,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:08<02:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:10<02:18,  1.35s/it]\n",
+      "\n",
+      "Training models:  93%|███████████████████████████  | 14/15 [45:49<02:59, 179.86s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                              | 0/60 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                     | 1/60 [00:07<07:24,  7.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                    | 2/60 [00:08<03:31,  3.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▉                                    | 3/60 [00:09<02:16,  2.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                   | 4/60 [00:10<01:41,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                  | 5/60 [00:11<01:21,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                  | 6/60 [00:12<01:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▍                                 | 7/60 [00:13<01:01,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████                                 | 8/60 [00:13<00:56,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▋                                | 9/60 [00:14<00:52,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▏                              | 10/60 [00:15<00:49,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▊                              | 11/60 [00:16<00:47,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                             | 12/60 [00:17<00:45,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                             | 13/60 [00:18<00:44,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▋                            | 14/60 [00:19<00:42,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████▎                           | 15/60 [00:20<00:41,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▊                           | 16/60 [00:21<00:40,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▍                          | 17/60 [00:22<00:39,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████                          | 18/60 [00:23<00:38,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                         | 19/60 [00:23<00:37,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|████████████▎                        | 20/60 [00:24<00:36,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▉                        | 21/60 [00:25<00:35,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▌                       | 22/60 [00:26<00:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████▏                      | 23/60 [00:27<00:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▊                      | 24/60 [00:28<00:32,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▍                     | 25/60 [00:29<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████                     | 26/60 [00:30<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▋                    | 27/60 [00:31<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|█████████████████▎                   | 28/60 [00:32<00:29,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▉                   | 29/60 [00:33<00:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████████▌                  | 30/60 [00:33<00:27,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████████                  | 31/60 [00:34<00:26,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|███████████████████▋                 | 32/60 [00:35<00:25,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|████████████████████▎                | 33/60 [00:37<00:30,  1.14s/it]\n",
+      "\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:00,  5.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:07<06:58,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<05:49,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:10<05:11,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:12<04:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:13<04:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:15<04:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:17<04:05,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:19<04:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:20<04:00,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:24<03:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:25<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:27<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:29<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:30<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:31<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:33<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:34<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:35<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:36<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:38<03:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:39<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:40<03:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:42<03:39,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:43<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:45<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:47<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:48<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:49<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:52<03:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:53<03:33,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:55<03:31,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:56<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:57<03:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:59<03:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [01:00<03:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [01:03<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [01:04<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [01:05<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [01:07<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [01:08<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [01:09<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [01:10<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [01:12<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [01:14<03:09,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [01:16<03:08,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [01:17<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [01:18<03:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [01:20<03:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [01:21<03:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [01:22<03:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [01:24<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [01:25<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [01:26<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [01:27<02:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:30<02:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:31<02:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:34<02:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:35<02:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:38<02:45,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:39<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:41<02:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:42<02:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:43<02:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:44<02:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:46<02:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:47<02:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:48<02:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:50<02:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:51<02:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:52<02:31,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:54<02:29,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:55<02:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:56<02:27,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:57<02:25,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:59<02:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [02:00<02:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [02:01<02:21,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [02:03<02:20,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [02:04<02:19,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [02:05<02:17,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [02:07<02:16,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [02:08<02:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [02:09<02:13,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [02:10<02:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [02:12<02:11,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [02:13<02:10,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [02:14<02:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [02:16<02:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [02:17<02:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [02:18<02:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [02:20<02:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [02:21<02:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [02:22<02:00,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [02:23<01:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [02:25<01:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [02:26<01:56,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [02:29<01:59,  1.34s/it]\n",
+      "\n",
+      "Training models: 100%|█████████████████████████████| 15/15 [49:00<00:00, 196.05s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:00, 34.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:00, 33.00it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 31.81it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:00, 33.95it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:00, 36.25it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:00, 33.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 30.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 30.26it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 31.34it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 30.85it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 31.42it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 31.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 31.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 30.66it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 30.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\n",
+      "Evaluating NLL: 60it [00:01, 74.40it/s]\u001b[A\n",
+      "Evaluating NLL: 118it [00:01, 150.71it/s]\u001b[A\n",
+      "Evaluating NLL: 173it [00:01, 221.46it/s]\u001b[A\n",
+      "Evaluating NLL: 229it [00:01, 288.97it/s]\u001b[A\n",
+      "Evaluating NLL: 286it [00:01, 350.74it/s]\u001b[A\n",
+      "Evaluating NLL: 343it [00:01, 403.10it/s]\u001b[A\n",
+      "Evaluating NLL: 398it [00:01, 431.12it/s]\u001b[A\n",
+      "Evaluating NLL: 452it [00:01, 453.46it/s]\u001b[A\n",
+      "Evaluating NLL: 505it [00:01, 469.82it/s]\u001b[A\n",
+      "Evaluating NLL: 561it [00:02, 493.25it/s]\u001b[A\n",
+      "Evaluating NLL: 618it [00:02, 513.94it/s]\u001b[A\n",
+      "Evaluating NLL: 674it [00:02, 526.34it/s]\u001b[A\n",
+      "Evaluating NLL: 731it [00:02, 538.73it/s]\u001b[A\n",
+      "Evaluating NLL: 788it [00:02, 546.63it/s]\u001b[A\n",
+      "Evaluating NLL: 846it [00:02, 554.49it/s]\u001b[A\n",
+      "Evaluating NLL: 903it [00:02, 558.71it/s]\u001b[A\n",
+      "Evaluating NLL: 961it [00:02, 563.27it/s]\u001b[A\n",
+      "Evaluating NLL: 1024it [00:02, 359.93it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:25,  3.93it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 112.68it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:00<00:00, 171.59it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 170.62it\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 144.30it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 56: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 57: 100%|██████████| 1/1 [00:00<00:00, 17.80it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 139.56it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 138.24it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 57: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 58: 100%|██████████| 1/1 [00:00<00:00, 18.66it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 142.97it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 154.03it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 58: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 59: 100%|██████████| 1/1 [00:00<00:00, 18.43it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.14it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 150.25it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 59: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 60: 100%|██████████| 1/1 [00:00<00:00, 18.22it/s]\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 147.82it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 165.54it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 60: validation NLL: 5.35\n"
+      "evaluating likelihood\n"
      ]
-    }
-   ],
-   "source": [
-    "# Generate a ground truth covariance matrix for each channel, and then noiseless samples from it\n",
-    "from encoding_information.models.gaussian_process import StationaryGaussianProcess\n",
-    "\n",
-    "num_images = 10000\n",
-    "num_patches = 10000\n",
-    "# num_images = 1000\n",
-    "# num_patches = 1000\n",
-    "edge_crop = 24\n",
-    "\n",
-    "patch_size = 20\n",
-    "\n",
-    "channel = 'LED119'\n",
-    "\n",
-    "\n",
-    "images = load_bsccm_images(bsccm, channel=channel, num_images=num_images, edge_crop=edge_crop, median_filter=False, verbose=True)\n",
-    "patches = extract_patches(images, patch_size, num_patches=num_patches, verbose=True)\n",
-    "\n",
-    "cov_mat = estimate_full_cov_mat(patches)\n",
-    "gp = StationaryGaussianProcess(patches)\n",
-    "gp.fit(patches)\n",
-    "true_cov_mat_px = gp.get_cov_mat()\n",
-    "true_mean_px = np.mean(images)\n",
-    "\n",
-    "samples = gp.generate_samples(num_samples=num_patches, sample_shape=patch_size, ensure_nonnegative=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Verify that mutual information estimates are consistent: they converge to the correct value given enough samples (for stationary gaussian, optimized stationary gaussian, pixelCNN)\n",
-    "Of course, this is on data that is sampled form a stationary gaussian process, so its only really showing that the gaussian approximation estimator can estimate gaussian entropy and MI. \n",
-    "\n",
-    "\n",
-    "This all uses additive gaussian noise, which has an easy analytic formula for h(y | x)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-13T05:04:07.650068Z",
-     "iopub.status.busy": "2024-01-13T05:04:07.649884Z",
-     "iopub.status.idle": "2024-01-13T05:04:10.058460Z",
-     "shell.execute_reply": "2024-01-13T05:04:10.057697Z"
-    }
-   },
-   "outputs": [
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 9/9 [00:00<00:00, 836.19it/s]"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 111.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|█████████████| 20/20 [00:00<00:00, 90.85it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 148.82it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 159.09it/s]\n"
      ]
-    }
-   ],
-   "source": [
-    "num_samples_to_use = np.array([10, 20, 40, 80, 160, 320, 640, 1280, 2560]).astype(int)\n",
-    "# num_bootstrap_samples = 10\n",
-    "num_bootstrap_samples = 80\n",
-    "\n",
-    "gaussian_sigma = 50\n",
-    "confidence_interval = 90\n",
-    "\n",
-    "true_cov_mat_py = true_cov_mat_px + gaussian_sigma**2 * np.eye(patch_size**2)\n",
-    "h_y_analytic = analytic_multivariate_gaussian_entropy(true_cov_mat_py) \n",
-    "# Constant conditional entropy for Gaussian noise\n",
-    "h_y_given_x_analytic = analytic_multivariate_gaussian_entropy(np.eye(patch_size**2) * gaussian_sigma**2) \n",
-    "mi_analytic_per_pixel = (h_y_analytic - h_y_given_x_analytic) / np.log(2)\n",
-    "\n",
-    "noisy_samples = add_noise(samples, gaussian_sigma=gaussian_sigma, ensure_positive=True)\n",
-    "\n",
-    "\n",
-    "stationary_mi_estimates_gp_mean = []\n",
-    "stationary_mi_estimates_gp_conf_int = []\n",
-    "stationary_mi_estimates_gp_optimized_mean = []\n",
-    "stationary_mi_estimates_gp_optimized_conf_int = []\n",
-    "stationary_mi_estimates_pixelcnn_optimized_mean = []\n",
-    "stationary_mi_estimates_pixelcnn_optimized_conf_int = []\n",
-    "\n",
-    "ev_floor = 1e-1\n",
-    "for num_to_use in tqdm(num_samples_to_use):\n",
-    "\n",
-    "\n",
-    "    if not os.path.exists('.cache'):\n",
-    "        os.mkdir('.cache')\n",
-    "    if os.path.exists('.cache/mi_estimates_{}_cache_{}_samples.npz'.format(channel, num_to_use)):\n",
-    "        print('loading cached version of MI estimates')\n",
-    "        loaded = onp.load('.cache/mi_estimates_{}_cache_{}_samples.npz'.format(channel, num_to_use))\n",
-    "        # append to the list of estimates\n",
-    "        stationary_mi_estimates_gp_mean.append(loaded['stationary_mi_estimates_gp_mean'])\n",
-    "        stationary_mi_estimates_gp_conf_int.append(loaded['stationary_mi_estimates_gp_conf_int'])\n",
-    "        stationary_mi_estimates_gp_optimized_mean.append(loaded['stationary_mi_estimates_gp_optimized_mean'])\n",
-    "        stationary_mi_estimates_gp_optimized_conf_int.append(loaded['stationary_mi_estimates_gp_optimized_conf_int'])\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_mean.append(loaded['stationary_mi_estimates_pixelcnn_optimized_mean'])\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_conf_int.append(loaded['stationary_mi_estimates_pixelcnn_optimized_conf_int'])\n",
-    "    else:\n",
-    "\n",
-    "        # estimate MI using stationary estimator\n",
-    "        num_val_samples = max(1, min(1000, 0.1 * num_to_use))\n",
-    "        \n",
-    "\n",
-    "        # stationary estimator with GP no optimization\n",
-    "        mean, conf_int = run_bootstrap(noisy_samples[:num_to_use],\n",
-    "                        num_bootstrap_samples=num_bootstrap_samples, confidence_interval=confidence_interval, estimation_fn=\n",
-    "                        lambda noisy_images: estimate_mutual_information(noisy_images, num_val_samples=num_val_samples,\n",
-    "                        eigenvalue_floor=ev_floor, gaussian_noise_sigma = gaussian_sigma, use_iterative_optimization=False ) ,\n",
-    "                        upper_bound_confidence_interval=True,\n",
-    "                        )\n",
-    "        stationary_mi_estimates_gp_mean.append(mean)\n",
-    "        stationary_mi_estimates_gp_conf_int.append(conf_int)\n",
-    "\n",
-    "        # stationary estimator with GP optimization\n",
-    "        mean, conf_int = run_bootstrap(noisy_samples[:num_to_use], \n",
-    "                        num_bootstrap_samples=num_bootstrap_samples, confidence_interval=confidence_interval, estimation_fn=\n",
-    "                        lambda noisy_images: estimate_mutual_information(noisy_images, num_val_samples=num_val_samples,\n",
-    "                        eigenvalue_floor=ev_floor, gaussian_noise_sigma = gaussian_sigma, patience=50, max_epochs=100, use_iterative_optimization=True),\n",
-    "                         upper_bound_confidence_interval=True,\n",
-    "                          )\n",
-    "        stationary_mi_estimates_gp_optimized_mean.append(mean)\n",
-    "        stationary_mi_estimates_gp_optimized_conf_int.append(conf_int)\n",
-    "\n",
-    "        # pixelcnn\n",
-    "        mean, conf_int = run_bootstrap(noisy_samples[:num_to_use],\n",
-    "                        num_bootstrap_samples=num_bootstrap_samples, confidence_interval=confidence_interval, estimation_fn=\n",
-    "                        lambda noisy_images: estimate_mutual_information(noisy_images, num_val_samples=num_val_samples,\n",
-    "                        eigenvalue_floor=ev_floor, gaussian_noise_sigma = gaussian_sigma, \n",
-    "                        entropy_model='pixel_cnn'),\n",
-    "                        upper_bound_confidence_interval=True,\n",
-    "                        )\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_mean.append(mean)\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_conf_int.append(conf_int)\n",
-    "\n",
-    "\n",
-    "            # save all these results to a numpy file\n",
-    "        onp.savez('.cache/mi_estimates_{}_cache_{}_samples'.format(channel, num_to_use),\n",
-    "                stationary_mi_estimates_gp_mean=onp.array(stationary_mi_estimates_gp_mean[-1]),\n",
-    "                stationary_mi_estimates_gp_conf_int=onp.array(stationary_mi_estimates_gp_conf_int[-1]),\n",
-    "                stationary_mi_estimates_gp_optimized_mean=onp.array(stationary_mi_estimates_gp_optimized_mean[-1]),\n",
-    "                stationary_mi_estimates_gp_optimized_conf_int=onp.array(stationary_mi_estimates_gp_optimized_conf_int[-1]),\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_mean=onp.array(stationary_mi_estimates_pixelcnn_optimized_mean[-1]),\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_conf_int=onp.array(stationary_mi_estimates_pixelcnn_optimized_conf_int[-1]),\n",
-    "                )\n",
-    "                \n",
-    "\n",
-    "\n",
-    "stationary_mi_estimates_gp_mean = np.array(stationary_mi_estimates_gp_mean)\n",
-    "stationary_mi_estimates_gp_conf_int = np.array(stationary_mi_estimates_gp_conf_int)\n",
-    "stationary_mi_estimates_gp_optimized_mean = np.array(stationary_mi_estimates_gp_optimized_mean)\n",
-    "stationary_mi_estimates_gp_optimized_conf_int = np.array(stationary_mi_estimates_gp_optimized_conf_int)\n",
-    "stationary_mi_estimates_pixelcnn_optimized_mean = np.array(stationary_mi_estimates_pixelcnn_optimized_mean)\n",
-    "stationary_mi_estimates_pixelcnn_optimized_conf_int = np.array(stationary_mi_estimates_pixelcnn_optimized_conf_int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-13T05:04:10.064364Z",
-     "iopub.status.busy": "2024-01-13T05:04:10.064146Z",
-     "iopub.status.idle": "2024-01-13T05:04:11.385178Z",
-     "shell.execute_reply": "2024-01-13T05:04:11.384560Z"
-    }
-   },
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "colors = get_color_cycle()\n",
-    "fig, ax = plt.subplots(1, 2, figsize=(10, 5), sharex=True)\n",
-    "ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_gp_conf_int[:,0], \n",
-    "                stationary_mi_estimates_gp_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[2])\n",
-    "ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_gp_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_gp_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[0])\n",
-    "ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[1])\n",
-    "ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_gp_mean, label='GP', color=colors[2])\n",
-    "ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_gp_optimized_mean, label='Optimized GP', color=colors[0])\n",
-    "ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[1])\n",
-    "\n",
-    "ax[0].axhline(mi_analytic_per_pixel, color='k', linestyle='--', label='True')\n",
-    "\n",
-    "# ax[0].set(ylim=[0, 10], yticks=[0, 200])\n",
-    "# set log scale\n",
-    "ax[0].set_yscale('log')\n",
-    "\n",
-    "ax[0].set(xlabel='Number of samples', title='stationary MI estimate\\nper pixel', ylabel='bits per pixel')\n",
-    "clear_spines(ax[0])\n",
-    "ax[0].legend()\n",
-    "\n",
-    "\n",
-    "ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_gp_conf_int[:,0], \n",
-    "                stationary_mi_estimates_gp_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[2])\n",
-    "ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_gp_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_gp_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[0])\n",
-    "ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[1])\n",
-    "ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_gp_mean, label='GP', color=colors[2])\n",
-    "ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_gp_optimized_mean, label='Optimized GP', color=colors[0])\n",
-    "ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[1])\n",
-    "\n",
-    "ax[1].axhline(mi_analytic_per_pixel, color='k', linestyle='--', label='True')\n",
-    "\n",
-    "ax[1].set(ylim=[0, 2], yticks=[0, 1, 2])\n",
-    "clear_spines(ax[1])\n",
-    "\n",
-    "fig.savefig('/home/hpinkard_waller/figures/mi_estimation/' + 'mi_estimator_consistency_gaussian_target' + '.pdf', transparent=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Same MI consistency plot, but with samples from data distrbution instead of GP\n",
-    "Cant show it converging to true value, but can show that it converges to a (probably biased) estimate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-13T05:04:11.390812Z",
-     "iopub.status.busy": "2024-01-13T05:04:11.390632Z",
-     "iopub.status.idle": "2024-01-14T07:00:10.309425Z",
-     "shell.execute_reply": "2024-01-14T07:00:10.308801Z"
-    }
-   },
-   "outputs": [
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 139.23it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 133.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 138.18it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 137.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 160.50it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 155.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 137.46it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 148.20it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.86it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 159.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 161.66it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 139.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 145.15it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 163.99it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 157.41it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 151.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 9/9 [00:00<00:00, 327.99it/s]"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 151.79it/s]\n",
+      "\n",
+      "precomputing masks and variances:   0%|                      | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "precomputing masks and variances: 100%|████████████| 20/20 [00:00<00:00, 148.30it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n",
-      "loading cached version of MI estimates\n"
+      "evaluating likelihood\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "\n",
+      "computing log likelihoods:   0%|                             | 0/20 [00:00<?, ?it/s]\u001b[A\n",
+      "computing log likelihoods: 100%|███████████████████| 20/20 [00:00<00:00, 150.47it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:52,  1.89it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:01, 52.22it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:00<00:00, 93.23it/s\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:00<00:00, 129.61it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 105.23it\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:10,  9.88it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:00, 135.65it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 164.23it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|▋| 65/100 [00:00<00:00, 182.10it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 172.34it\n",
+      "100%|███████████████████████████████████████████| 11/11 [4:34:39<00:00, 1498.14s/it]\n"
      ]
     }
    ],
    "source": [
+    "from encoding_information.models import PixelCNN, PoissonNoiseModel, AnalyticGaussianNoiseModel, FullGaussianProcess, StationaryGaussianProcess\n",
+    "from encoding_information import estimate_information\n",
+    "import time\n",
+    "\n",
+    "num_samples_to_use = np.array([20, 40, 80, 160, 320, 640, 1280, 2560, 5120, 10240]).astype(int)\n",
+    "\n",
+    "gaussian_sigma = 50\n",
+    "confidence = .95   \n",
+    "test_set_fraction = 0.1\n",
     "\n",
-    "stationary_mi_estimates_gp_mean = []\n",
-    "stationary_mi_estimates_gp_conf_int = []\n",
-    "stationary_mi_estimates_gp_optimized_mean = []\n",
-    "stationary_mi_estimates_gp_optimized_conf_int = []\n",
-    "stationary_mi_estimates_pixelcnn_optimized_mean = []\n",
-    "stationary_mi_estimates_pixelcnn_optimized_conf_int = []\n",
     "\n",
+    "mi_estimates_fgp_mean = []\n",
+    "mi_estimates_fgp_conf_int = []\n",
+    "mi_estimates_sgp = []\n",
+    "mi_estimates_sgp_conf_int = []\n",
+    "mi_estimates_pixelcnn_optimized_mean = []\n",
+    "mi_estimates_pixelcnn_optimized_conf_int = []\n",
     "\n",
-    "# images = load_bsccm_images(bsccm, channel=channel, num_images=num_images, edge_crop=edge_crop, median_filter=True)\n",
-    "# patches = extract_patches(images, patch_size, num_patches=num_patches)\n",
-    "# patches = add_noise(patches)\n",
+    "training_time = {'sgp': [], 'fgp': [], 'pcnn': []}\n",
     "\n",
-    "ev_floor = 1e-3\n",
+    "ev_floor = 1e-1\n",
     "for num_to_use in tqdm(num_samples_to_use):\n",
     "\n",
-    "    if not os.path.exists('.cache'):\n",
-    "        os.mkdir('.cache')\n",
-    "    if os.path.exists('.cache/mi_estimates_{}_no_gt_cache_{}_samples.npz'.format(channel, num_to_use)):\n",
-    "        print('loading cached version of MI estimates')\n",
-    "        loaded = onp.load('.cache/mi_estimates_{}_no_gt_cache_{}_samples.npz'.format(channel, num_to_use))\n",
-    "        # append to the list of estimates\n",
-    "        stationary_mi_estimates_gp_mean.append(loaded['stationary_mi_estimates_gp_mean'])\n",
-    "        stationary_mi_estimates_gp_conf_int.append(loaded['stationary_mi_estimates_gp_conf_int'])\n",
-    "        stationary_mi_estimates_gp_optimized_mean.append(loaded['stationary_mi_estimates_gp_optimized_mean'])\n",
-    "        stationary_mi_estimates_gp_optimized_conf_int.append(loaded['stationary_mi_estimates_gp_optimized_conf_int'])\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_mean.append(loaded['stationary_mi_estimates_pixelcnn_optimized_mean'])\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_conf_int.append(loaded['stationary_mi_estimates_pixelcnn_optimized_conf_int'])\n",
-    "    else:\n",
-    "\n",
-    "        # estimate MI using stationary estimator\n",
-    "        num_val_samples = max(1, min(1000, 0.1 * num_to_use))\n",
-    "\n",
-    "        mean, conf_int = run_bootstrap(patches[:num_to_use], \n",
-    "                        num_bootstrap_samples=num_bootstrap_samples, confidence_interval=confidence_interval, estimation_fn=\n",
-    "                        lambda noisy_images: estimate_mutual_information(noisy_images, num_val_samples=num_val_samples,\n",
-    "                        eigenvalue_floor=ev_floor, use_iterative_optimization=False, verbose=True) ,\n",
-    "                        upper_bound_confidence_interval=True,\n",
-    "                        )\n",
-    "        stationary_mi_estimates_gp_mean.append(mean)\n",
-    "        stationary_mi_estimates_gp_conf_int.append(conf_int)\n",
-    "\n",
-    "        mean, conf_int = run_bootstrap(patches[:num_to_use], \n",
-    "                        num_bootstrap_samples=num_bootstrap_samples, confidence_interval=confidence_interval, estimation_fn=\n",
-    "                        lambda noisy_images: estimate_mutual_information(noisy_images, num_val_samples=num_val_samples,\n",
-    "                        eigenvalue_floor=ev_floor, patience=50, max_epochs=100, use_iterative_optimization=True) ,\n",
-    "                        upper_bound_confidence_interval=True,                        \n",
-    "                        )\n",
-    "        stationary_mi_estimates_gp_optimized_mean.append(mean)\n",
-    "        stationary_mi_estimates_gp_optimized_conf_int.append(conf_int)\n",
-    "\n",
-    "        mean, conf_int = run_bootstrap(patches[:num_to_use],\n",
-    "                        num_bootstrap_samples=num_bootstrap_samples, confidence_interval=confidence_interval, estimation_fn=\n",
-    "                        lambda noisy_images: estimate_mutual_information(noisy_images, num_val_samples=num_val_samples,\n",
-    "                        eigenvalue_floor=ev_floor,  entropy_model='pixel_cnn') ,\n",
-    "                        upper_bound_confidence_interval=True,\n",
-    "                        )\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_mean.append(mean)\n",
-    "        stationary_mi_estimates_pixelcnn_optimized_conf_int.append(conf_int)\n",
-    "\n",
-    "\n",
-    "\n",
-    "            # save all these results to a numpy file\n",
-    "        onp.savez('.cache/mi_estimates_{}_no_gt_cache_{}_samples'.format(channel, num_to_use),\n",
-    "                stationary_mi_estimates_gp_mean=onp.array(stationary_mi_estimates_gp_mean[-1]),\n",
-    "                stationary_mi_estimates_gp_conf_int=onp.array(stationary_mi_estimates_gp_conf_int[-1]),\n",
-    "                stationary_mi_estimates_gp_optimized_mean=onp.array(stationary_mi_estimates_gp_optimized_mean[-1]),\n",
-    "                stationary_mi_estimates_gp_optimized_conf_int=onp.array(stationary_mi_estimates_gp_optimized_conf_int[-1]),\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_mean=onp.array(stationary_mi_estimates_pixelcnn_optimized_mean[-1]),\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_conf_int=onp.array(stationary_mi_estimates_pixelcnn_optimized_conf_int[-1]),\n",
-    "                )\n",
-    "                \n",
-    "\n",
-    "stationary_mi_estimates_gp_mean = np.array(stationary_mi_estimates_gp_mean)\n",
-    "stationary_mi_estimates_gp_conf_int = np.array(stationary_mi_estimates_gp_conf_int)\n",
-    "stationary_mi_estimates_gp_optimized_mean = np.array(stationary_mi_estimates_gp_optimized_mean)\n",
-    "stationary_mi_estimates_gp_optimized_conf_int = np.array(stationary_mi_estimates_gp_optimized_conf_int)\n",
-    "stationary_mi_estimates_pixelcnn_optimized_mean = np.array(stationary_mi_estimates_pixelcnn_optimized_mean)\n",
-    "stationary_mi_estimates_pixelcnn_optimized_conf_int = np.array(stationary_mi_estimates_pixelcnn_optimized_conf_int)"
+    "    data = patches[:num_to_use]\n",
+    "    train_patches = data[:int(num_to_use * (1 - test_set_fraction))]\n",
+    "    test_patches = data[int(num_to_use * (1 - test_set_fraction)):]\n",
+    "    \n",
+    "\n",
+    "    pixel_cnns = []\n",
+    "    stationary_gaussians = []\n",
+    "    full_gaussians = []\n",
+    "    for i in tqdm(range(num_models_to_train), desc='Training models'):\n",
+    "        start_time = time.time()\n",
+    "        stationary_gaussian = StationaryGaussianProcess(train_patches, eigenvalue_floor=ev_floor)\n",
+    "        stationary_gaussian.fit(train_patches, verbose=False, eigenvalue_floor=ev_floor)\n",
+    "        stationary_gaussians.append(stationary_gaussian)\n",
+    "        training_time['sgp'].append(time.time() - start_time)\n",
+    "        \n",
+    "        start_time = time.time()\n",
+    "        full_gaussian = FullGaussianProcess(train_patches, eigenvalue_floor=ev_floor)\n",
+    "        full_gaussians.append(full_gaussian)\n",
+    "        training_time['fgp'].append(time.time() - start_time)\n",
+    "\n",
+    "        start_time = time.time()\n",
+    "        pixelcnn = PixelCNN()\n",
+    "        pixelcnn.fit(train_patches, verbose=False)\n",
+    "        pixel_cnns.append(pixelcnn)\n",
+    "        training_time['pcnn'].append(time.time() - start_time)\n",
+    "    \n",
+    "    noise_model = PoissonNoiseModel()\n",
+    "\n",
+    "\n",
+    "    estimate_pcnn, lower_pcnn, upper_pcnn = estimate_information(pixel_cnns, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "    estimate_sgp, lower_sgp, upper_sgp = estimate_information(stationary_gaussians, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "    estimate_full_gp, lower_full_gp, upper_full_gp = estimate_information(full_gaussians, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "\n",
+    "\n",
+    "    mi_estimates_fgp_mean.append(estimate_full_gp)\n",
+    "    mi_estimates_fgp_conf_int.append((lower_full_gp, upper_full_gp))\n",
+    "    mi_estimates_sgp.append(estimate_sgp)\n",
+    "    mi_estimates_sgp_conf_int.append((lower_sgp, upper_sgp))\n",
+    "    mi_estimates_pixelcnn_optimized_mean.append(estimate_pcnn)\n",
+    "    mi_estimates_pixelcnn_optimized_conf_int.append((lower_pcnn, upper_pcnn))\n",
+    "\n",
+    "\n",
+    "mi_estimates_fgp_mean = np.array(mi_estimates_fgp_mean)\n",
+    "mi_estimates_fgp_conf_int = np.array(mi_estimates_fgp_conf_int)\n",
+    "mi_estimates_sgp = np.array(mi_estimates_sgp)\n",
+    "mi_estimates_sgp_conf_int = np.array(mi_estimates_sgp_conf_int)\n",
+    "mi_estimates_pixelcnn_optimized_mean = np.array(mi_estimates_pixelcnn_optimized_mean)\n",
+    "mi_estimates_pixelcnn_optimized_conf_int = np.array(mi_estimates_pixelcnn_optimized_conf_int)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-21T04:40:34.236595Z",
+     "iopub.status.busy": "2024-10-21T04:40:34.236192Z",
+     "iopub.status.idle": "2024-10-21T04:40:37.885853Z",
+     "shell.execute_reply": "2024-10-21T04:40:37.885323Z"
+    }
+   },
    "outputs": [
     {
      "data": {
+      "image/png": "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",
       "text/plain": [
-       "Array([1004958.0754122 ,  328071.83320122,  165867.97846841,\n",
-       "        178996.2762604 ,  109044.85014423,  106842.67150193,\n",
-       "         96142.91782633,   86159.90052236,   76933.15423024],      dtype=float64)"
+       "<Figure size 1000x500 with 2 Axes>"
       ]
      },
-     "execution_count": 14,
      "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "stationary_mi_estimates_gp_mean"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-01-14T07:00:10.313733Z",
-     "iopub.status.busy": "2024-01-14T07:00:10.313239Z",
-     "iopub.status.idle": "2024-01-14T07:00:10.990448Z",
-     "shell.execute_reply": "2024-01-14T07:00:10.989932Z"
-    }
-   },
-   "outputs": [
+     "output_type": "display_data"
+    },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 1000x500 with 2 Axes>"
+       "<Figure size 300x300 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1359,44 +33493,71 @@
    ],
    "source": [
     "colors = get_color_cycle()\n",
-    "\n",
     "fig, ax = plt.subplots(1, 2, figsize=(10, 5), sharex=True)\n",
-    "ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_gp_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_gp_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[0])\n",
-    "ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[1])\n",
-    "ax[0].fill_between(num_samples_to_use, stationary_mi_estimates_gp_conf_int[:,0], \n",
-    "                stationary_mi_estimates_gp_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[2])\n",
     "\n",
-    "ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_gp_optimized_mean, label='Estimated (optimized)', color=colors[0])\n",
-    "ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_mean, label='Estimated (optimized, PixelCNN)', color=colors[1])\n",
-    "ax[0].semilogx(num_samples_to_use, stationary_mi_estimates_gp_mean, label='Estimated regular GP', color=colors[2])\n",
+    "ax[0].fill_between(num_samples_to_use, mi_estimates_sgp_conf_int[:,0],\n",
+    "                mi_estimates_sgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[0])\n",
+    "ax[0].fill_between(num_samples_to_use, mi_estimates_fgp_conf_int[:,0], \n",
+    "                mi_estimates_fgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[1])\n",
+    "ax[0].fill_between(num_samples_to_use, mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
+    "                mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[3])\n",
+    "\n",
+    "\n",
+    "ax[0].semilogx(num_samples_to_use, mi_estimates_sgp, label='Stationary Gaussian Process', color=colors[0])\n",
+    "ax[0].semilogx(num_samples_to_use, mi_estimates_fgp_mean, label='Full Gaussian Process', color=colors[1])\n",
+    "ax[0].semilogx(num_samples_to_use, mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[3])\n",
     "\n",
     "\n",
-    "# ax[0].set(ylim=[0, 10], yticks=[0, 10])\n",
     "# set log scale\n",
     "ax[0].set_yscale('log')\n",
     "\n",
-    "ax[0].set(xlabel='Number of samples', title='stationary MI estimate\\nper pixel', ylabel='bits per pixel')\n",
+    "ax[0].set(xlabel='Number of samples', title='MI estimate\\nper pixel', ylabel='bits per pixel')\n",
     "clear_spines(ax[0])\n",
     "ax[0].legend()\n",
     "\n",
     "\n",
-    "ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_gp_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_gp_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[0])\n",
-    "ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
-    "                stationary_mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[1])\n",
-    "ax[1].fill_between(num_samples_to_use, stationary_mi_estimates_gp_conf_int[:,0], \n",
-    "                stationary_mi_estimates_gp_conf_int[:,1], alpha=0.5, label=f'{confidence_interval}% confidence interval', color=colors[2])\n",
-    "ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_gp_optimized_mean, label='Estimated (optimized)', color=colors[0])\n",
-    "ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_pixelcnn_optimized_mean, label='Estimated (optimized, PixelCNN)', color=colors[1])\n",
-    "ax[1].semilogx(num_samples_to_use, stationary_mi_estimates_gp_mean, label='Estimated', color=colors[2])\n",
+    "# same thing but a different ylim\n",
     "\n",
+    "ax[1].fill_between(num_samples_to_use, mi_estimates_sgp_conf_int[:,0],\n",
+    "                mi_estimates_sgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[0])\n",
+    "ax[1].fill_between(num_samples_to_use, mi_estimates_fgp_conf_int[:,0],\n",
+    "                mi_estimates_fgp_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[1]) \n",
+    "ax[1].fill_between(num_samples_to_use, mi_estimates_pixelcnn_optimized_conf_int[:,0],\n",
+    "                mi_estimates_pixelcnn_optimized_conf_int[:,1], alpha=0.5, label=f'{confidence}% confidence interval', color=colors[3])\n",
     "\n",
-    "ax[1].set(ylim=[0, 2], yticks=[0, 1, 2])\n",
+    "ax[1].semilogx(num_samples_to_use, mi_estimates_sgp, label='Stationary Gaussian Process', color=colors[0])\n",
+    "ax[1].semilogx(num_samples_to_use, mi_estimates_fgp_mean, label='Full Gaussian Process', color=colors[1])\n",
+    "ax[1].semilogx(num_samples_to_use, mi_estimates_pixelcnn_optimized_mean, label='PixelCNN', color=colors[3])\n",
+    "\n",
+    "# set log scale\n",
+    "ax[1].set_yscale('log')\n",
+    "\n",
+    "ax[1].set(xlabel='Number of samples', title='stationary MI estimate\\nper pixel', ylabel='bits per pixel')\n",
     "clear_spines(ax[1])\n",
+    "ax[1].legend()\n",
+    "\n",
+    "ax[1].set_ylim(None, 1)\n",
+    "fig.savefig(f'/home/hpinkard_waller/figures/mi_estimation/mi_consistency_realdata_cropped_patch_zoomin.pdf', transparent=True)\n",
+    "\n",
+    "\n",
+    "# make a bar graph of training time with an error bar for the standard deviation\n",
+    "\n",
+    "training_time = {k: np.array(v) for k, v in training_time.items()}\n",
+    "training_time_mean = {k: np.mean(v) for k, v in training_time.items()}\n",
+    "training_time_std = {k: np.std(v) for k, v in training_time.items()}\n",
+    "\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(3, 3))\n",
+    "ax.bar(training_time_mean.keys(), training_time_mean.values(), yerr=training_time_std.values(), capsize=8)\n",
+    "# color each bar\n",
+    "colors = [colors[0], colors[1], colors[3]]\n",
+    "for i, bar in enumerate(ax.patches):\n",
+    "    bar.set_facecolor(colors[i])\n",
+    "ax.set_ylabel('Training time (s)')\n",
+    "clear_spines(ax)\n",
+    "# plot on log scale\n",
     "\n",
-    "fig.savefig('/home/hpinkard_waller/figures/mi_estimation/' + 'mi_estimator_consistency_real_samples' + '.pdf', transparent=True)"
+    "ax.set_yscale('log')\n",
+    "fig.savefig(f'/home/hpinkard_waller/figures/mi_estimation/training_time_realdata_cropped_patches.pdf', transparent=True)"
    ]
   }
  ],
diff --git a/mi_estimator_experiments/mi_of_background.ipynb b/mi_estimator_experiments/mi_of_background.ipynb
index 699af07..a88aa91 100644
--- a/mi_estimator_experiments/mi_of_background.ipynb
+++ b/mi_estimator_experiments/mi_of_background.ipynb
@@ -2,22 +2,30 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-25T18:41:44.635640Z",
-     "iopub.status.busy": "2023-11-25T18:41:44.635051Z",
-     "iopub.status.idle": "2023-11-25T18:41:51.574759Z",
-     "shell.execute_reply": "2023-11-25T18:41:51.574180Z"
+     "iopub.execute_input": "2024-10-14T22:12:41.765019Z",
+     "iopub.status.busy": "2024-10-14T22:12:41.764695Z",
+     "iopub.status.idle": "2024-10-14T22:12:56.023742Z",
+     "shell.execute_reply": "2024-10-14T22:12:56.022863Z"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-10-14 15:12:43.454422: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-14 15:12:44.197357: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-14 15:12:44.197449: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-14 15:12:44.197457: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n",
       "Opening BSCCM\n",
       "Opened BSCCM\n"
      ]
@@ -32,7 +40,7 @@
     "config.update(\"jax_enable_x64\", True)\n",
     "\n",
     "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '3'\n",
     "from encoding_information.gpu_utils import limit_gpu_memory_growth\n",
     "limit_gpu_memory_growth()\n",
     "\n",
@@ -57,10 +65,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-25T18:41:51.579918Z",
-     "iopub.status.busy": "2023-11-25T18:41:51.579657Z",
-     "iopub.status.idle": "2023-11-26T16:43:59.132866Z",
-     "shell.execute_reply": "2023-11-26T16:43:59.131229Z"
+     "iopub.execute_input": "2024-10-14T22:12:56.027185Z",
+     "iopub.status.busy": "2024-10-14T22:12:56.026825Z",
+     "iopub.status.idle": "2024-10-15T05:55:22.034850Z",
+     "shell.execute_reply": "2024-10-15T05:55:22.034272Z"
     }
    },
    "outputs": [
@@ -68,10500 +76,3984 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                  | 0/3 [00:00<?, ?it/s]\n",
-      "Running bootstraps:   0%|                             | 0/25 [00:00<?, ?it/s]\u001b[A2023-11-25 10:42:58.870695: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
-      "\n",
-      "\n",
-      "Computing loss:   0%|                                 | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▊                        | 1/32 [00:12<06:21, 12.30s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▌                       | 2/32 [00:12<02:37,  5.25s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▎                      | 3/32 [00:12<01:26,  2.97s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▏                     | 4/32 [00:13<00:53,  1.90s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|███▉                     | 5/32 [00:13<00:35,  1.31s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|████▋                    | 6/32 [00:13<00:24,  1.05it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|█████▍                   | 7/32 [00:13<00:18,  1.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|██████▎                  | 8/32 [00:14<00:13,  1.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████                  | 9/32 [00:14<00:11,  2.08it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|███████▌                | 10/32 [00:14<00:09,  2.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|████████▎               | 11/32 [00:14<00:07,  2.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|█████████               | 12/32 [00:15<00:06,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|█████████▊              | 13/32 [00:15<00:05,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|██████████▌             | 14/32 [00:15<00:05,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|███████████▎            | 15/32 [00:16<00:04,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|████████████            | 16/32 [00:16<00:04,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|████████████▊           | 17/32 [00:16<00:04,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|█████████████▌          | 18/32 [00:16<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|██████████████▎         | 19/32 [00:17<00:03,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|███████████████         | 20/32 [00:17<00:03,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|███████████████▊        | 21/32 [00:30<00:46,  4.22s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|████████████████▌       | 22/32 [00:30<00:30,  3.02s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|█████████████████▎      | 23/32 [00:31<00:19,  2.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|██████████████████      | 24/32 [00:31<00:12,  1.58s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|██████████████████▊     | 25/32 [00:31<00:08,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|███████████████████▌    | 26/32 [00:31<00:05,  1.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|████████████████████▎   | 27/32 [00:31<00:03,  1.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|█████████████████████   | 28/32 [00:32<00:02,  1.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|█████████████████████▊  | 29/32 [00:32<00:01,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|██████████████████████▌ | 30/32 [00:32<00:00,  2.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|███████████████████████▎| 31/32 [00:32<00:00,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|████████████████████████| 32/32 [00:32<00:00,  1.03s/it]\n",
-      "\n",
-      "Running bootstraps:   4%|▋                 | 1/25 [09:58<3:59:20, 598.34s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                 | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▊                        | 1/32 [00:01<00:33,  1.09s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▌                       | 2/32 [00:01<00:18,  1.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▎                      | 3/32 [00:01<00:13,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▏                     | 4/32 [00:01<00:10,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|███▉                     | 5/32 [00:02<00:09,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|████▋                    | 6/32 [00:02<00:08,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|█████▍                   | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|██████▎                  | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████                  | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|███████▌                | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|████████▎               | 11/32 [00:03<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|█████████               | 12/32 [00:03<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|█████████▊              | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|██████████▌             | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|███████████▎            | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|████████████            | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|████████████▊           | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|█████████████▌          | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|██████████████▎         | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|███████████████         | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|███████████████▊        | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|████████████████▌       | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|█████████████████▎      | 23/32 [00:07<00:03,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|██████████████████      | 24/32 [00:07<00:02,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|██████████████████▊     | 25/32 [00:08<00:02,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|███████████████████▌    | 26/32 [00:08<00:01,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|████████████████████▎   | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|█████████████████████   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|█████████████████████▊  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|██████████████████████▌ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|███████████████████████▎| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|████████████████████████| 32/32 [00:09<00:00,  3.23it/s]\n",
-      "\n",
-      "Running bootstraps:   8%|█▍                | 2/25 [18:43<3:32:48, 555.16s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                 | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▊                        | 1/32 [00:01<00:33,  1.09s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▌                       | 2/32 [00:01<00:18,  1.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▎                      | 3/32 [00:01<00:13,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▏                     | 4/32 [00:01<00:10,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|███▉                     | 5/32 [00:02<00:09,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|████▋                    | 6/32 [00:02<00:08,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|█████▍                   | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|██████▎                  | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████                  | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|███████▌                | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|████████▎               | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|█████████               | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|█████████▊              | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|██████████▌             | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|███████████▎            | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|████████████            | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|████████████▊           | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|█████████████▌          | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|██████████████▎         | 19/32 [00:05<00:03,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|███████████████         | 20/32 [00:06<00:03,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|███████████████▊        | 21/32 [00:07<00:05,  2.02it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|████████████████▌       | 22/32 [00:07<00:04,  2.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|█████████████████▎      | 23/32 [00:07<00:03,  2.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|██████████████████      | 24/32 [00:07<00:02,  3.01it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|██████████████████▊     | 25/32 [00:08<00:02,  3.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|███████████████████▌    | 26/32 [00:08<00:01,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|████████████████████▎   | 27/32 [00:08<00:01,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|█████████████████████   | 28/32 [00:08<00:01,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|█████████████████████▊  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|██████████████████████▌ | 30/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|███████████████████████▎| 31/32 [00:09<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|████████████████████████| 32/32 [00:09<00:00,  3.24it/s]\n",
-      "\n",
-      "Running bootstraps:  12%|██▏               | 3/25 [27:30<3:18:56, 542.57s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                 | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▊                        | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▌                       | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▎                      | 3/32 [00:01<00:13,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▏                     | 4/32 [00:01<00:10,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|███▉                     | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|████▋                    | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|█████▍                   | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|██████▎                  | 8/32 [00:02<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████                  | 9/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|███████▌                | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|████████▎               | 11/32 [00:03<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|█████████               | 12/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|█████████▊              | 13/32 [00:04<00:05,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|██████████▌             | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|███████████▎            | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|████████████            | 16/32 [00:05<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|████████████▊           | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|█████████████▌          | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|██████████████▎         | 19/32 [00:05<00:03,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|███████████████         | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|███████████████▊        | 21/32 [00:07<00:06,  1.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|████████████████▌       | 22/32 [00:07<00:04,  2.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|█████████████████▎      | 23/32 [00:07<00:03,  2.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|██████████████████      | 24/32 [00:08<00:02,  2.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|██████████████████▊     | 25/32 [00:08<00:02,  3.06it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|███████████████████▌    | 26/32 [00:08<00:01,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|████████████████████▎   | 27/32 [00:08<00:01,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|█████████████████████   | 28/32 [00:09<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|█████████████████████▊  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|██████████████████████▌ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|███████████████████████▎| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  16%|██▉               | 4/25 [36:18<3:07:48, 536.62s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  2.00it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  3.00it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps:  20%|███▌              | 5/25 [45:05<2:57:40, 533.04s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:33,  1.10s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:03<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  24%|████▎             | 6/25 [53:53<2:48:15, 531.34s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  2.00it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.23it/s]\n",
-      "\n",
-      "Running bootstraps:  28%|████▍           | 7/25 [1:02:40<2:39:01, 530.08s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.11s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  2.00it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  3.00it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.25it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.23it/s]\n",
-      "\n",
-      "Running bootstraps:  32%|█████           | 8/25 [1:11:30<2:30:12, 530.15s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  36%|█████▊          | 9/25 [1:20:19<2:21:12, 529.55s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:08,  3.00it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:03<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:06,  1.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.05it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  40%|██████         | 10/25 [1:29:09<2:12:26, 529.77s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  44%|██████▌        | 11/25 [1:37:56<2:03:25, 528.98s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  48%|███████▏       | 12/25 [1:46:45<1:54:37, 529.01s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  52%|███████▊       | 13/25 [1:55:33<1:45:43, 528.63s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  56%|████████▍      | 14/25 [2:04:22<1:36:57, 528.84s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps:  60%|█████████      | 15/25 [2:13:12<1:28:11, 529.11s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps:  64%|█████████▌     | 16/25 [2:21:59<1:19:16, 528.55s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  68%|██████████▏    | 17/25 [2:26:56<1:01:10, 458.83s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps:  72%|████████████▏    | 18/25 [2:35:45<55:59, 479.87s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps:  76%|████████████▉    | 19/25 [2:44:31<49:23, 493.85s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  80%|█████████████▌   | 20/25 [2:53:20<42:01, 504.37s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:33,  1.08s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:17,  1.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:12,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:08,  3.02it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:03<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.24it/s]\n",
-      "\n",
-      "Running bootstraps:  84%|██████████████▎  | 21/25 [2:58:18<29:29, 442.34s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  88%|██████████████▉  | 22/25 [3:03:15<19:56, 398.85s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:38,  1.24s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.06it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.14it/s]\n",
-      "\n",
-      "Running bootstraps:  92%|███████████████▋ | 23/25 [3:12:04<14:35, 437.75s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:48,  1.55s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:23,  1.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:02<00:16,  1.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:12,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:10,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:03<00:07,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:04<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.09it/s]\n",
-      "\n",
-      "Running bootstraps:  96%|████████████████▎| 24/25 [3:20:56<07:46, 466.23s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps: 100%|█████████████████| 25/25 [3:29:42<00:00, 503.30s/it]\n",
-      "\n",
-      "  0%|                                                  | 0/5000 [00:00<?, ?it/s]\u001b[A\n",
-      "  0%|                                          | 2/5000 [00:00<05:49, 14.31it/s]\u001b[A\n",
-      "  0%|                                          | 5/5000 [00:00<03:46, 22.02it/s]\u001b[A\n",
-      "  0%|                                          | 9/5000 [00:00<02:49, 29.36it/s]\u001b[A\n",
-      "  0%|▏                                        | 19/5000 [00:00<01:37, 51.01it/s]\u001b[A\n",
-      "  1%|▍                                       | 61/5000 [00:00<00:28, 171.55it/s]\u001b[A\n",
-      "  2%|▊                                      | 107/5000 [00:00<00:18, 258.68it/s]\u001b[A\n",
-      "  3%|█▏                                     | 158/5000 [00:00<00:14, 334.98it/s]\u001b[A\n",
-      "  4%|█▋                                     | 209/5000 [00:00<00:12, 387.31it/s]\u001b[A\n",
-      "  5%|██                                     | 269/5000 [00:00<00:10, 449.58it/s]\u001b[A\n",
-      "  6%|██▌                                    | 321/5000 [00:01<00:09, 468.65it/s]\u001b[A\n",
-      "  7%|██▉                                    | 373/5000 [00:01<00:09, 482.16it/s]\u001b[A\n",
-      "  9%|███▎                                   | 429/5000 [00:01<00:09, 501.20it/s]\u001b[A\n",
-      " 10%|███▊                                   | 484/5000 [00:01<00:08, 514.98it/s]\u001b[A\n",
-      " 11%|████▏                                  | 542/5000 [00:01<00:08, 533.48it/s]\u001b[A\n",
-      " 12%|████▋                                  | 596/5000 [00:01<00:08, 513.91it/s]\u001b[A\n",
-      " 13%|█████                                  | 648/5000 [00:01<00:08, 506.16it/s]\u001b[A\n",
-      " 14%|█████▌                                 | 709/5000 [00:01<00:08, 533.66it/s]\u001b[A\n",
-      " 15%|██████                                 | 771/5000 [00:01<00:07, 556.66it/s]\u001b[A\n",
-      " 17%|██████▍                                | 827/5000 [00:02<00:07, 549.79it/s]\u001b[A\n",
-      " 18%|██████▉                                | 883/5000 [00:02<00:07, 526.11it/s]\u001b[A\n",
-      " 19%|███████▎                               | 936/5000 [00:02<00:07, 508.77it/s]\u001b[A\n",
-      " 20%|███████▋                               | 990/5000 [00:02<00:07, 513.91it/s]\u001b[A\n",
-      " 21%|███████▉                              | 1042/5000 [00:02<00:07, 511.51it/s]\u001b[A\n",
-      " 22%|████████▎                             | 1098/5000 [00:02<00:07, 521.68it/s]\u001b[A\n",
-      " 23%|████████▊                             | 1154/5000 [00:02<00:07, 532.60it/s]\u001b[A\n",
-      " 24%|█████████▏                            | 1210/5000 [00:02<00:07, 540.12it/s]\u001b[A\n",
-      " 25%|█████████▌                            | 1266/5000 [00:02<00:06, 544.51it/s]\u001b[A\n",
-      " 26%|██████████                            | 1322/5000 [00:02<00:06, 546.04it/s]\u001b[A\n",
-      " 28%|██████████▍                           | 1377/5000 [00:03<00:06, 545.42it/s]\u001b[A\n",
-      " 29%|██████████▉                           | 1432/5000 [00:03<00:06, 542.59it/s]\u001b[A\n",
-      " 30%|███████████▎                          | 1487/5000 [00:03<00:06, 532.70it/s]\u001b[A\n",
-      " 31%|███████████▋                          | 1541/5000 [00:03<00:06, 528.56it/s]\u001b[A\n",
-      " 32%|████████████                          | 1594/5000 [00:03<00:06, 515.50it/s]\u001b[A\n",
-      " 33%|████████████▌                         | 1650/5000 [00:03<00:06, 526.85it/s]\u001b[A\n",
-      " 34%|████████████▉                         | 1703/5000 [00:03<00:06, 522.28it/s]\u001b[A\n",
-      " 35%|█████████████▎                        | 1756/5000 [00:03<00:06, 502.20it/s]\u001b[A\n",
-      " 36%|█████████████▊                        | 1810/5000 [00:03<00:06, 511.89it/s]\u001b[A\n",
-      " 37%|██████████████▏                       | 1862/5000 [00:04<00:06, 510.52it/s]\u001b[A\n",
-      " 38%|██████████████▌                       | 1914/5000 [00:04<00:06, 499.83it/s]\u001b[A\n",
-      " 39%|██████████████▉                       | 1965/5000 [00:04<00:06, 502.69it/s]\u001b[A\n",
-      " 40%|███████████████▎                      | 2016/5000 [00:04<00:06, 489.13it/s]\u001b[A\n",
-      " 41%|███████████████▋                      | 2069/5000 [00:04<00:05, 500.20it/s]\u001b[A\n",
-      " 42%|████████████████▏                     | 2122/5000 [00:04<00:05, 505.91it/s]\u001b[A\n",
-      " 43%|████████████████▌                     | 2174/5000 [00:04<00:05, 509.69it/s]\u001b[A\n",
-      " 45%|████████████████▉                     | 2230/5000 [00:04<00:05, 519.88it/s]\u001b[A\n",
-      " 46%|█████████████████▎                    | 2284/5000 [00:04<00:05, 524.90it/s]\u001b[A\n",
-      " 47%|█████████████████▊                    | 2338/5000 [00:04<00:05, 528.65it/s]\u001b[A\n",
-      " 48%|██████████████████▏                   | 2392/5000 [00:05<00:04, 530.81it/s]\u001b[A\n",
-      " 49%|██████████████████▌                   | 2446/5000 [00:05<00:04, 528.72it/s]\u001b[A\n",
-      " 50%|███████████████████                   | 2502/5000 [00:05<00:04, 535.35it/s]\u001b[A\n",
-      " 51%|███████████████████▍                  | 2562/5000 [00:05<00:04, 553.76it/s]\u001b[A\n",
-      " 52%|███████████████████▉                  | 2618/5000 [00:05<00:04, 553.53it/s]\u001b[A\n",
-      " 53%|████████████████████▎                 | 2674/5000 [00:05<00:04, 543.39it/s]\u001b[A\n",
-      " 55%|████████████████████▋                 | 2729/5000 [00:05<00:04, 539.01it/s]\u001b[A\n",
-      " 56%|█████████████████████▏                | 2783/5000 [00:05<00:04, 538.59it/s]\u001b[A\n",
-      " 57%|█████████████████████▌                | 2837/5000 [00:05<00:04, 534.41it/s]\u001b[A\n",
-      " 58%|█████████████████████▉                | 2891/5000 [00:05<00:04, 514.35it/s]\u001b[A\n",
-      " 59%|██████████████████████▎               | 2943/5000 [00:06<00:04, 504.04it/s]\u001b[A\n",
-      " 60%|██████████████████████▊               | 2994/5000 [00:06<00:03, 501.94it/s]\u001b[A\n",
-      " 61%|███████████████████████▏              | 3048/5000 [00:06<00:03, 511.36it/s]\u001b[A\n",
-      " 62%|███████████████████████▌              | 3100/5000 [00:06<00:03, 512.84it/s]\u001b[A\n",
-      " 63%|███████████████████████▉              | 3152/5000 [00:06<00:03, 501.04it/s]\u001b[A\n",
-      " 64%|████████████████████████▎             | 3203/5000 [00:06<00:03, 493.57it/s]\u001b[A\n",
-      " 65%|████████████████████████▋             | 3254/5000 [00:06<00:03, 495.87it/s]\u001b[A\n",
-      " 66%|█████████████████████████             | 3304/5000 [00:06<00:03, 491.27it/s]\u001b[A\n",
-      " 67%|█████████████████████████▌            | 3362/5000 [00:06<00:03, 514.40it/s]\u001b[A\n",
-      " 68%|█████████████████████████▉            | 3414/5000 [00:07<00:03, 502.62it/s]\u001b[A\n",
-      " 69%|██████████████████████████▎           | 3466/5000 [00:07<00:03, 507.28it/s]\u001b[A\n",
-      " 70%|██████████████████████████▊           | 3524/5000 [00:07<00:02, 527.13it/s]\u001b[A\n",
-      " 72%|███████████████████████████▏          | 3583/5000 [00:07<00:02, 545.20it/s]\u001b[A\n",
-      " 73%|███████████████████████████▋          | 3646/5000 [00:07<00:02, 568.49it/s]\u001b[A\n",
-      " 74%|████████████████████████████▏         | 3703/5000 [00:07<00:02, 553.05it/s]\u001b[A\n",
-      " 75%|████████████████████████████▌         | 3759/5000 [00:07<00:02, 551.87it/s]\u001b[A\n",
-      " 76%|████████████████████████████▉         | 3815/5000 [00:07<00:02, 546.88it/s]\u001b[A\n",
-      " 77%|█████████████████████████████▍        | 3870/5000 [00:07<00:02, 545.73it/s]\u001b[A\n",
-      " 79%|█████████████████████████████▊        | 3926/5000 [00:07<00:01, 545.63it/s]\u001b[A\n",
-      " 80%|██████████████████████████████▎       | 3981/5000 [00:08<00:01, 528.78it/s]\u001b[A\n",
-      " 81%|██████████████████████████████▋       | 4035/5000 [00:08<00:01, 530.77it/s]\u001b[A\n",
-      " 82%|███████████████████████████████       | 4089/5000 [00:08<00:01, 525.52it/s]\u001b[A\n",
-      " 83%|███████████████████████████████▍      | 4142/5000 [00:08<00:01, 515.10it/s]\u001b[A\n",
-      " 84%|███████████████████████████████▉      | 4195/5000 [00:08<00:01, 519.37it/s]\u001b[A\n",
-      " 85%|████████████████████████████████▎     | 4250/5000 [00:08<00:01, 527.45it/s]\u001b[A\n",
-      " 86%|████████████████████████████████▋     | 4304/5000 [00:08<00:01, 530.13it/s]\u001b[A\n",
-      " 87%|█████████████████████████████████     | 4358/5000 [00:08<00:01, 516.24it/s]\u001b[A\n",
-      " 88%|█████████████████████████████████▌    | 4413/5000 [00:08<00:01, 525.63it/s]\u001b[A\n",
-      " 89%|█████████████████████████████████▉    | 4466/5000 [00:08<00:01, 521.58it/s]\u001b[A\n",
-      " 90%|██████████████████████████████████▎   | 4519/5000 [00:09<00:00, 522.97it/s]\u001b[A\n",
-      " 92%|██████████████████████████████████▊   | 4576/5000 [00:09<00:00, 535.28it/s]\u001b[A\n",
-      " 93%|███████████████████████████████████▏  | 4630/5000 [00:09<00:00, 517.14it/s]\u001b[A\n",
-      " 94%|███████████████████████████████████▌  | 4682/5000 [00:09<00:00, 491.78it/s]\u001b[A\n",
-      " 95%|████████████████████████████████████  | 4739/5000 [00:09<00:00, 512.05it/s]\u001b[A\n",
-      " 96%|████████████████████████████████████▍ | 4792/5000 [00:09<00:00, 516.73it/s]\u001b[A\n",
-      " 97%|████████████████████████████████████▊ | 4848/5000 [00:09<00:00, 525.70it/s]\u001b[A\n",
-      " 98%|█████████████████████████████████████▏| 4901/5000 [00:09<00:00, 524.69it/s]\u001b[A\n",
-      "100%|██████████████████████████████████████| 5000/5000 [00:09<00:00, 500.24it/s]\n",
-      "\n",
-      "Running bootstraps:   0%|                                | 0/25 [00:00<?, ?it/s]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps:   4%|▊                    | 1/25 [08:52<3:33:05, 532.73s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  2.00it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.23it/s]\n",
-      "\n",
-      "Running bootstraps:   8%|█▋                   | 2/25 [17:39<3:22:49, 529.10s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  12%|██▌                  | 3/25 [26:28<3:14:01, 529.14s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  16%|███▎                 | 4/25 [35:16<3:05:01, 528.66s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  20%|████▏                | 5/25 [44:04<2:56:10, 528.51s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  24%|█████                | 6/25 [52:50<2:47:07, 527.74s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  28%|█████▎             | 7/25 [1:01:34<2:37:54, 526.36s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:38,  1.25s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.05it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  32%|██████             | 8/25 [1:10:18<2:28:55, 525.60s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  36%|██████▊            | 9/25 [1:19:08<2:20:31, 526.98s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:52,  1.70s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:25,  1.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:02<00:17,  1.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:12,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:03<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:03<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:04<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:04<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:08<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:09<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:10<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.03it/s]\n",
-      "\n",
-      "Running bootstraps:  40%|███████▏          | 10/25 [1:27:57<2:11:53, 527.60s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  44%|███████▉          | 11/25 [1:36:45<2:03:06, 527.62s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  48%|████████▋         | 12/25 [1:45:34<1:54:26, 528.18s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  52%|█████████▎        | 13/25 [1:54:22<1:45:37, 528.13s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  56%|██████████        | 14/25 [2:03:13<1:37:00, 529.14s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.20s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  60%|██████████▊       | 15/25 [2:12:04<1:28:14, 529.48s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  64%|███████████▌      | 16/25 [2:20:50<1:19:16, 528.47s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.11s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  68%|████████████▏     | 17/25 [2:29:40<1:10:31, 528.99s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  72%|████████████▉     | 18/25 [2:38:32<1:01:48, 529.75s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  76%|███████████████▏    | 19/25 [2:47:17<52:50, 528.35s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  80%|████████████████    | 20/25 [2:56:07<44:05, 529.02s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  84%|████████████████▊   | 21/25 [3:04:58<35:18, 529.63s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.20s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  88%|█████████████████▌  | 22/25 [3:13:46<26:27, 529.07s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  92%|██████████████████▍ | 23/25 [3:22:35<17:38, 529.07s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  96%|███████████████████▏| 24/25 [3:31:23<08:48, 528.75s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps: 100%|████████████████████| 25/25 [3:40:14<00:00, 528.57s/it]\n",
-      " 33%|███████████▎                      | 1/3 [7:11:51<14:23:42, 25911.14s/it]\n",
-      "Running bootstraps:   0%|                                | 0/25 [00:00<?, ?it/s]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:   4%|▊                    | 1/25 [08:49<3:31:43, 529.32s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:   8%|█▋                   | 2/25 [17:36<3:22:26, 528.12s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  12%|██▌                  | 3/25 [26:30<3:14:36, 530.75s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  16%|███▎                 | 4/25 [35:23<3:06:07, 531.77s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:40,  1.30s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.01it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.08it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:06,  1.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.06it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.08it/s]\n",
-      "\n",
-      "Running bootstraps:  20%|████▏                | 5/25 [44:15<2:57:12, 531.63s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:40,  1.30s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.01it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.07it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:06,  1.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.10it/s]\n",
-      "\n",
-      "Running bootstraps:  24%|█████                | 6/25 [53:06<2:48:20, 531.59s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:39,  1.26s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.04it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.08it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.12it/s]\n",
-      "\n",
-      "Running bootstraps:  28%|█████▎             | 7/25 [1:01:59<2:39:36, 532.04s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.21s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.25it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  32%|██████             | 8/25 [1:10:48<2:30:27, 531.01s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:40,  1.30s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.01it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:06,  1.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.11it/s]\n",
-      "\n",
-      "Running bootstraps:  36%|██████▊            | 9/25 [1:19:40<2:21:41, 531.32s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  40%|███████▏          | 10/25 [1:28:32<2:12:54, 531.63s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.21s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.08it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.13it/s]\n",
-      "\n",
-      "Running bootstraps:  44%|███████▉          | 11/25 [1:37:23<2:04:00, 531.43s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:39,  1.28s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.03it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.08it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.12it/s]\n",
-      "\n",
-      "Running bootstraps:  48%|████████▋         | 12/25 [1:46:18<1:55:22, 532.50s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.21it/s]\n",
-      "\n",
-      "Running bootstraps:  52%|█████████▎        | 13/25 [1:55:07<1:46:17, 531.47s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  56%|██████████        | 14/25 [2:03:57<1:37:19, 530.88s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  60%|██████████▊       | 15/25 [2:12:45<1:28:21, 530.19s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  64%|███████████▌      | 16/25 [2:21:35<1:19:29, 529.99s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  68%|████████████▏     | 17/25 [2:30:26<1:10:42, 530.31s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  72%|████████████▉     | 18/25 [2:39:18<1:01:56, 530.89s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:12,  2.25it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:07<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  76%|███████████████▏    | 19/25 [2:48:10<53:07, 531.17s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.20it/s]\n",
-      "\n",
-      "Running bootstraps:  80%|████████████████    | 20/25 [2:57:00<44:13, 530.72s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  84%|████████████████▊   | 21/25 [3:05:51<35:23, 530.89s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  88%|█████████████████▌  | 22/25 [3:14:41<26:32, 530.69s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.11s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  92%|██████████████████▍ | 23/25 [3:23:33<17:42, 531.02s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  96%|███████████████████▏| 24/25 [3:32:24<08:50, 530.92s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.22s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.08it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.13it/s]\n",
-      "\n",
-      "Running bootstraps: 100%|████████████████████| 25/25 [3:41:16<00:00, 531.05s/it]\n",
-      "\n",
-      "  0%|                                                  | 0/5000 [00:00<?, ?it/s]\u001b[A\n",
-      "  1%|▍                                       | 50/5000 [00:00<00:09, 496.99it/s]\u001b[A\n",
-      "  2%|▊                                      | 100/5000 [00:00<00:09, 493.89it/s]\u001b[A\n",
-      "  3%|█▏                                     | 153/5000 [00:00<00:09, 510.23it/s]\u001b[A\n",
-      "  4%|█▌                                     | 205/5000 [00:00<00:09, 510.48it/s]\u001b[A\n",
-      "  5%|██                                     | 265/5000 [00:00<00:08, 540.66it/s]\u001b[A\n",
-      "  6%|██▍                                    | 320/5000 [00:00<00:09, 516.05it/s]\u001b[A\n",
-      "  7%|██▉                                    | 372/5000 [00:00<00:08, 514.65it/s]\u001b[A\n",
-      "  8%|███▎                                   | 424/5000 [00:00<00:08, 512.71it/s]\u001b[A\n",
-      " 10%|███▋                                   | 479/5000 [00:00<00:08, 521.69it/s]\u001b[A\n",
-      " 11%|████▏                                  | 532/5000 [00:01<00:08, 521.96it/s]\u001b[A\n",
-      " 12%|████▌                                  | 585/5000 [00:01<00:08, 505.37it/s]\u001b[A\n",
-      " 13%|█████                                  | 644/5000 [00:01<00:08, 528.06it/s]\u001b[A\n",
-      " 14%|█████▍                                 | 700/5000 [00:01<00:08, 536.12it/s]\u001b[A\n",
-      " 15%|█████▉                                 | 758/5000 [00:01<00:07, 547.17it/s]\u001b[A\n",
-      " 16%|██████▍                                | 818/5000 [00:01<00:07, 561.68it/s]\u001b[A\n",
-      " 18%|██████▊                                | 875/5000 [00:01<00:07, 551.96it/s]\u001b[A\n",
-      " 19%|███████▎                               | 931/5000 [00:01<00:07, 540.06it/s]\u001b[A\n",
-      " 20%|███████▋                               | 989/5000 [00:01<00:07, 550.48it/s]\u001b[A\n",
-      " 21%|███████▉                              | 1047/5000 [00:01<00:07, 557.23it/s]\u001b[A\n",
-      " 22%|████████▍                             | 1106/5000 [00:02<00:06, 564.50it/s]\u001b[A\n",
-      " 23%|████████▊                             | 1167/5000 [00:02<00:06, 577.51it/s]\u001b[A\n",
-      " 24%|█████████▎                            | 1225/5000 [00:02<00:06, 565.91it/s]\u001b[A\n",
-      " 26%|█████████▋                            | 1282/5000 [00:02<00:06, 563.31it/s]\u001b[A\n",
-      " 27%|██████████▏                           | 1339/5000 [00:02<00:06, 543.59it/s]\u001b[A\n",
-      " 28%|██████████▌                           | 1395/5000 [00:02<00:06, 547.92it/s]\u001b[A\n",
-      " 29%|███████████                           | 1450/5000 [00:02<00:06, 536.41it/s]\u001b[A\n",
-      " 30%|███████████▍                          | 1507/5000 [00:02<00:06, 545.89it/s]\u001b[A\n",
-      " 31%|███████████▊                          | 1562/5000 [00:02<00:06, 517.39it/s]\u001b[A\n",
-      " 32%|████████████▎                         | 1615/5000 [00:03<00:06, 512.42it/s]\u001b[A\n",
-      " 33%|████████████▋                         | 1667/5000 [00:03<00:06, 509.91it/s]\u001b[A\n",
-      " 34%|█████████████                         | 1722/5000 [00:03<00:06, 519.52it/s]\u001b[A\n",
-      " 36%|█████████████▌                        | 1782/5000 [00:03<00:05, 540.85it/s]\u001b[A\n",
-      " 37%|█████████████▉                        | 1837/5000 [00:03<00:06, 521.47it/s]\u001b[A\n",
-      " 38%|██████████████▎                       | 1890/5000 [00:03<00:05, 521.19it/s]\u001b[A\n",
-      " 39%|██████████████▊                       | 1943/5000 [00:03<00:05, 510.34it/s]\u001b[A\n",
-      " 40%|███████████████▏                      | 1996/5000 [00:03<00:05, 514.60it/s]\u001b[A\n",
-      " 41%|███████████████▌                      | 2055/5000 [00:03<00:05, 535.83it/s]\u001b[A\n",
-      " 42%|████████████████                      | 2109/5000 [00:03<00:05, 533.88it/s]\u001b[A\n",
-      " 43%|████████████████▍                     | 2163/5000 [00:04<00:05, 511.52it/s]\u001b[A\n",
-      " 44%|████████████████▊                     | 2218/5000 [00:04<00:05, 517.73it/s]\u001b[A\n",
-      " 45%|█████████████████▎                    | 2270/5000 [00:04<00:05, 516.47it/s]\u001b[A\n",
-      " 47%|█████████████████▋                    | 2329/5000 [00:04<00:04, 536.77it/s]\u001b[A\n",
-      " 48%|██████████████████                    | 2383/5000 [00:04<00:04, 535.29it/s]\u001b[A\n",
-      " 49%|██████████████████▌                   | 2437/5000 [00:04<00:04, 533.20it/s]\u001b[A\n",
-      " 50%|██████████████████▉                   | 2491/5000 [00:04<00:04, 504.80it/s]\u001b[A\n",
-      " 51%|███████████████████▎                  | 2549/5000 [00:04<00:04, 525.11it/s]\u001b[A\n",
-      " 52%|███████████████████▊                  | 2608/5000 [00:04<00:04, 542.86it/s]\u001b[A\n",
-      " 53%|████████████████████▏                 | 2663/5000 [00:05<00:04, 543.72it/s]\u001b[A\n",
-      " 55%|████████████████████▋                 | 2725/5000 [00:05<00:04, 562.96it/s]\u001b[A\n",
-      " 56%|█████████████████████▏                | 2784/5000 [00:05<00:03, 570.76it/s]\u001b[A\n",
-      " 57%|█████████████████████▌                | 2843/5000 [00:05<00:03, 573.50it/s]\u001b[A\n",
-      " 58%|██████████████████████                | 2901/5000 [00:05<00:03, 573.17it/s]\u001b[A\n",
-      " 59%|██████████████████████▍               | 2959/5000 [00:05<00:03, 572.34it/s]\u001b[A\n",
-      " 60%|██████████████████████▉               | 3017/5000 [00:05<00:03, 548.70it/s]\u001b[A\n",
-      " 61%|███████████████████████▎              | 3073/5000 [00:05<00:03, 536.91it/s]\u001b[A\n",
-      " 63%|███████████████████████▊              | 3127/5000 [00:05<00:03, 522.51it/s]\u001b[A\n",
-      " 64%|████████████████████████▏             | 3181/5000 [00:05<00:03, 524.83it/s]\u001b[A\n",
-      " 65%|████████████████████████▌             | 3234/5000 [00:06<00:03, 517.12it/s]\u001b[A\n",
-      " 66%|████████████████████████▉             | 3286/5000 [00:06<00:03, 482.23it/s]\u001b[A\n",
-      " 67%|█████████████████████████▍            | 3339/5000 [00:06<00:03, 494.99it/s]\u001b[A\n",
-      " 68%|█████████████████████████▊            | 3391/5000 [00:06<00:03, 501.70it/s]\u001b[A\n",
-      " 69%|██████████████████████████▏           | 3448/5000 [00:06<00:02, 519.26it/s]\u001b[A\n",
-      " 70%|██████████████████████████▋           | 3505/5000 [00:06<00:02, 531.52it/s]\u001b[A\n",
-      " 71%|███████████████████████████           | 3563/5000 [00:06<00:02, 545.43it/s]\u001b[A\n",
-      " 72%|███████████████████████████▍          | 3618/5000 [00:06<00:02, 542.20it/s]\u001b[A\n",
-      " 74%|███████████████████████████▉          | 3675/5000 [00:06<00:02, 547.17it/s]\u001b[A\n",
-      " 75%|████████████████████████████▎         | 3730/5000 [00:06<00:02, 547.45it/s]\u001b[A\n",
-      " 76%|████████████████████████████▊         | 3785/5000 [00:07<00:02, 532.87it/s]\u001b[A\n",
-      " 77%|█████████████████████████████▏        | 3839/5000 [00:07<00:02, 525.06it/s]\u001b[A\n",
-      " 78%|█████████████████████████████▌        | 3894/5000 [00:07<00:02, 531.24it/s]\u001b[A\n",
-      " 79%|██████████████████████████████        | 3950/5000 [00:07<00:01, 538.61it/s]\u001b[A\n",
-      " 80%|██████████████████████████████▍       | 4007/5000 [00:07<00:01, 545.58it/s]\u001b[A\n",
-      " 81%|██████████████████████████████▉       | 4067/5000 [00:07<00:01, 559.42it/s]\u001b[A\n",
-      " 82%|███████████████████████████████▎      | 4123/5000 [00:07<00:01, 555.01it/s]\u001b[A\n",
-      " 84%|███████████████████████████████▊      | 4179/5000 [00:07<00:01, 543.95it/s]\u001b[A\n",
-      " 85%|████████████████████████████████▏     | 4234/5000 [00:07<00:01, 534.36it/s]\u001b[A\n",
-      " 86%|████████████████████████████████▌     | 4291/5000 [00:08<00:01, 542.30it/s]\u001b[A\n",
-      " 87%|█████████████████████████████████     | 4346/5000 [00:08<00:01, 528.09it/s]\u001b[A\n",
-      " 88%|█████████████████████████████████▍    | 4399/5000 [00:08<00:01, 513.11it/s]\u001b[A\n",
-      " 89%|█████████████████████████████████▉    | 4462/5000 [00:08<00:00, 544.04it/s]\u001b[A\n",
-      " 90%|██████████████████████████████████▎   | 4518/5000 [00:08<00:00, 548.07it/s]\u001b[A\n",
-      " 91%|██████████████████████████████████▊   | 4574/5000 [00:08<00:00, 547.68it/s]\u001b[A\n",
-      " 93%|███████████████████████████████████▏  | 4629/5000 [00:08<00:00, 536.10it/s]\u001b[A\n",
-      " 94%|███████████████████████████████████▌  | 4683/5000 [00:08<00:00, 516.13it/s]\u001b[A\n",
-      " 95%|███████████████████████████████████▉  | 4736/5000 [00:08<00:00, 518.59it/s]\u001b[A\n",
-      " 96%|████████████████████████████████████▍ | 4790/5000 [00:08<00:00, 523.77it/s]\u001b[A\n",
-      " 97%|████████████████████████████████████▊ | 4843/5000 [00:09<00:00, 522.14it/s]\u001b[A\n",
-      " 98%|█████████████████████████████████████▏| 4896/5000 [00:09<00:00, 518.15it/s]\u001b[A\n",
-      "100%|██████████████████████████████████████| 5000/5000 [00:09<00:00, 532.86it/s]\n",
-      "\n",
-      "Running bootstraps:   0%|                                | 0/25 [00:00<?, ?it/s]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:   4%|▊                    | 1/25 [08:51<3:32:38, 531.59s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:   8%|█▋                   | 2/25 [17:40<3:23:10, 530.04s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:38,  1.24s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.06it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  12%|██▌                  | 3/25 [26:42<3:16:25, 535.69s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:39,  1.29s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  2.02it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.07it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:06,  1.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.07it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.09it/s]\n",
-      "\n",
-      "Running bootstraps:  16%|███▎                 | 4/25 [35:53<3:09:36, 541.73s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:39,  1.27s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:20,  1.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:07,  3.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  20%|████▏                | 5/25 [45:05<3:01:43, 545.18s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:06,  1.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.12it/s]\n",
-      "\n",
-      "Running bootstraps:  24%|█████                | 6/25 [53:59<2:51:26, 541.42s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:41,  1.33s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:21,  1.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:14,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.05it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:04<00:05,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:08<00:03,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.10it/s]\n",
-      "\n",
-      "Running bootstraps:  28%|█████▎             | 7/25 [1:02:57<2:42:07, 540.41s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  32%|██████             | 8/25 [1:11:52<2:32:35, 538.55s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  36%|██████▊            | 9/25 [1:20:44<2:23:06, 536.64s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:34,  1.12s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  40%|███████▏          | 10/25 [1:29:35<2:13:42, 534.86s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  44%|███████▉          | 11/25 [1:38:27<2:04:36, 534.02s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  48%|████████▋         | 12/25 [1:47:18<1:55:29, 533.04s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  52%|█████████▎        | 13/25 [1:56:08<1:46:25, 532.14s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  56%|██████████        | 14/25 [2:04:58<1:37:25, 531.39s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  60%|██████████▊       | 15/25 [2:13:48<1:28:31, 531.10s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  64%|███████████▌      | 16/25 [2:22:39<1:19:38, 530.91s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.14it/s]\n",
-      "\n",
-      "Running bootstraps:  68%|████████████▏     | 17/25 [2:31:29<1:10:46, 530.79s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  72%|████████████▉     | 18/25 [2:40:23<1:02:01, 531.70s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  76%|███████████████▏    | 19/25 [2:49:16<53:13, 532.24s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.21s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.07it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.08it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  80%|████████████████    | 20/25 [2:58:09<44:21, 532.36s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.21s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  84%|████████████████▊   | 21/25 [3:07:01<35:28, 532.21s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  88%|█████████████████▌  | 22/25 [3:15:52<26:35, 531.82s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  92%|██████████████████▍ | 23/25 [3:24:43<17:43, 531.61s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:08<00:09,  1.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:08<00:07,  1.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:09<00:05,  1.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:09<00:03,  2.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:09<00:02,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:09<00:02,  2.99it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:09<00:01,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:10<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:10<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:10<00:00,  3.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:10<00:00,  4.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:11<00:00,  2.88it/s]\n",
-      "\n",
-      "Running bootstraps:  96%|███████████████████▏| 24/25 [3:33:37<08:52, 532.40s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:11,  2.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps: 100%|████████████████████| 25/25 [3:42:29<00:00, 533.99s/it]\n",
-      " 67%|██████████████████████▋           | 2/3 [14:37:25<7:19:55, 26395.38s/it]\n",
-      "Running bootstraps:   0%|                                | 0/25 [00:00<?, ?it/s]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:   4%|▊                    | 1/25 [08:52<3:33:00, 532.51s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  3.03it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:08<00:01,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  4.03it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  4.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  4.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:09<00:00,  3.22it/s]\n",
-      "\n",
-      "Running bootstraps:   8%|█▋                   | 2/25 [17:43<3:23:51, 531.79s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  12%|██▌                  | 3/25 [26:34<3:14:53, 531.51s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  16%|███▎                 | 4/25 [35:26<3:05:58, 531.36s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.25it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  20%|████▏                | 5/25 [44:18<2:57:13, 531.65s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  24%|█████                | 6/25 [53:11<2:48:31, 532.19s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  28%|█████▎             | 7/25 [1:02:01<2:39:24, 531.38s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:02<00:06,  3.48it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  32%|██████             | 8/25 [1:10:52<2:30:31, 531.25s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  36%|██████▊            | 9/25 [1:19:45<2:21:51, 531.95s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  40%|███████▏          | 10/25 [1:28:35<2:12:50, 531.38s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  44%|███████▉          | 11/25 [1:37:28<2:04:05, 531.86s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  48%|████████▋         | 12/25 [1:46:21<1:55:18, 532.22s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  52%|█████████▎        | 13/25 [1:55:12<1:46:21, 531.79s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  56%|██████████        | 14/25 [2:04:02<1:37:22, 531.16s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  60%|██████████▊       | 15/25 [2:12:54<1:28:34, 531.40s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  64%|███████████▌      | 16/25 [2:21:45<1:19:43, 531.46s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  68%|████████████▏     | 17/25 [2:30:39<1:10:56, 532.07s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  72%|████████████▉     | 18/25 [2:39:29<1:02:00, 531.44s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:02<01:21,  2.62s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:02<00:37,  1.24s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:03<00:23,  1.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:03<00:16,  1.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:03<00:12,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:03<00:10,  2.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:04<00:08,  2.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:04<00:07,  3.04it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:04<00:07,  3.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:05<00:06,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:05<00:06,  3.47it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:05<00:05,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:05<00:05,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:06<00:04,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:06<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:06<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:06<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:07<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:07<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:07<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:08<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:09<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:09<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:09<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:09<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:10<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:10<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:10<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:10<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:11<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:11<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:11<00:00,  2.77it/s]\n",
-      "\n",
-      "Running bootstraps:  76%|███████████████▏    | 19/25 [2:48:23<53:14, 532.37s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.25it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  80%|████████████████    | 20/25 [2:57:15<44:20, 532.05s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  84%|████████████████▊   | 21/25 [3:06:08<35:29, 532.50s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.20s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  88%|█████████████████▌  | 22/25 [3:14:59<26:36, 532.03s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  92%|██████████████████▍ | 23/25 [3:23:50<17:43, 531.67s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  96%|███████████████████▏| 24/25 [3:32:40<08:51, 531.06s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.22it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps: 100%|████████████████████| 25/25 [3:41:30<00:00, 531.61s/it]\n",
-      "\n",
-      "  0%|                                                  | 0/5000 [00:00<?, ?it/s]\u001b[A\n",
-      "  1%|▎                                       | 35/5000 [00:00<00:14, 348.15it/s]\u001b[A\n",
-      "  2%|▋                                       | 80/5000 [00:00<00:12, 407.57it/s]\u001b[A\n",
-      "  3%|█                                      | 134/5000 [00:00<00:10, 465.14it/s]\u001b[A\n",
-      "  4%|█▍                                     | 181/5000 [00:00<00:10, 460.56it/s]\u001b[A\n",
-      "  5%|█▊                                     | 235/5000 [00:00<00:09, 488.45it/s]\u001b[A\n",
-      "  6%|██▏                                    | 284/5000 [00:00<00:09, 486.62it/s]\u001b[A\n",
-      "  7%|██▋                                    | 337/5000 [00:00<00:09, 495.12it/s]\u001b[A\n",
-      "  8%|███                                    | 390/5000 [00:00<00:09, 505.35it/s]\u001b[A\n",
-      "  9%|███▌                                   | 449/5000 [00:00<00:08, 530.62it/s]\u001b[A\n",
-      " 10%|███▉                                   | 503/5000 [00:01<00:08, 524.96it/s]\u001b[A\n",
-      " 11%|████▎                                  | 556/5000 [00:01<00:09, 487.11it/s]\u001b[A\n",
-      " 12%|████▋                                  | 606/5000 [00:01<00:09, 484.94it/s]\u001b[A\n",
-      " 13%|█████▏                                 | 661/5000 [00:01<00:08, 502.30it/s]\u001b[A\n",
-      " 14%|█████▌                                 | 720/5000 [00:01<00:08, 526.83it/s]\u001b[A\n",
-      " 16%|██████                                 | 778/5000 [00:01<00:07, 540.12it/s]\u001b[A\n",
-      " 17%|██████▍                                | 833/5000 [00:01<00:07, 540.55it/s]\u001b[A\n",
-      " 18%|██████▉                                | 888/5000 [00:01<00:07, 525.92it/s]\u001b[A\n",
-      " 19%|███████▎                               | 943/5000 [00:01<00:07, 532.70it/s]\u001b[A\n",
-      " 20%|███████▌                              | 1001/5000 [00:01<00:07, 545.70it/s]\u001b[A\n",
-      " 21%|████████                              | 1060/5000 [00:02<00:07, 556.85it/s]\u001b[A\n",
-      " 22%|████████▍                             | 1116/5000 [00:02<00:07, 542.26it/s]\u001b[A\n",
-      " 23%|████████▉                             | 1171/5000 [00:02<00:07, 536.95it/s]\u001b[A\n",
-      " 25%|█████████▎                            | 1226/5000 [00:02<00:07, 538.69it/s]\u001b[A\n",
-      " 26%|█████████▋                            | 1280/5000 [00:02<00:07, 520.29it/s]\u001b[A\n",
-      " 27%|██████████▏                           | 1335/5000 [00:02<00:06, 527.89it/s]\u001b[A\n",
-      " 28%|██████████▌                           | 1389/5000 [00:02<00:06, 530.58it/s]\u001b[A\n",
-      " 29%|██████████▉                           | 1443/5000 [00:02<00:06, 529.45it/s]\u001b[A\n",
-      " 30%|███████████▍                          | 1501/5000 [00:02<00:06, 542.68it/s]\u001b[A\n",
-      " 31%|███████████▊                          | 1556/5000 [00:03<00:06, 533.99it/s]\u001b[A\n",
-      " 32%|████████████▎                         | 1615/5000 [00:03<00:06, 548.44it/s]\u001b[A\n",
-      " 33%|████████████▋                         | 1670/5000 [00:03<00:06, 541.98it/s]\u001b[A\n",
-      " 34%|█████████████                         | 1725/5000 [00:03<00:06, 525.45it/s]\u001b[A\n",
-      " 36%|█████████████▌                        | 1780/5000 [00:03<00:06, 531.58it/s]\u001b[A\n",
-      " 37%|█████████████▉                        | 1840/5000 [00:03<00:05, 550.13it/s]\u001b[A\n",
-      " 38%|██████████████▍                       | 1899/5000 [00:03<00:05, 560.23it/s]\u001b[A\n",
-      " 39%|██████████████▊                       | 1956/5000 [00:03<00:05, 541.19it/s]\u001b[A\n",
-      " 40%|███████████████▎                      | 2016/5000 [00:03<00:05, 555.21it/s]\u001b[A\n",
-      " 42%|███████████████▊                      | 2076/5000 [00:03<00:05, 566.22it/s]\u001b[A\n",
-      " 43%|████████████████▏                     | 2133/5000 [00:04<00:05, 542.31it/s]\u001b[A\n",
-      " 44%|████████████████▋                     | 2189/5000 [00:04<00:05, 546.51it/s]\u001b[A\n",
-      " 45%|█████████████████                     | 2244/5000 [00:04<00:05, 543.77it/s]\u001b[A\n",
-      " 46%|█████████████████▍                    | 2299/5000 [00:04<00:05, 532.87it/s]\u001b[A\n",
-      " 47%|█████████████████▉                    | 2353/5000 [00:04<00:04, 530.71it/s]\u001b[A\n",
-      " 48%|██████████████████▎                   | 2408/5000 [00:04<00:04, 536.17it/s]\u001b[A\n",
-      " 49%|██████████████████▋                   | 2464/5000 [00:04<00:04, 541.40it/s]\u001b[A\n",
-      " 51%|███████████████████▏                  | 2526/5000 [00:04<00:04, 563.39it/s]\u001b[A\n",
-      " 52%|███████████████████▋                  | 2583/5000 [00:04<00:04, 564.16it/s]\u001b[A\n",
-      " 53%|████████████████████                  | 2642/5000 [00:04<00:04, 568.62it/s]\u001b[A\n",
-      " 54%|████████████████████▌                 | 2699/5000 [00:05<00:04, 553.34it/s]\u001b[A\n",
-      " 55%|████████████████████▉                 | 2755/5000 [00:05<00:04, 546.40it/s]\u001b[A\n",
-      " 56%|█████████████████████▎                | 2810/5000 [00:05<00:04, 543.37it/s]\u001b[A\n",
-      " 57%|█████████████████████▊                | 2867/5000 [00:05<00:03, 549.63it/s]\u001b[A\n",
-      " 58%|██████████████████████▏               | 2924/5000 [00:05<00:03, 553.67it/s]\u001b[A\n",
-      " 60%|██████████████████████▋               | 2980/5000 [00:05<00:03, 542.47it/s]\u001b[A\n",
-      " 61%|███████████████████████               | 3037/5000 [00:05<00:03, 548.72it/s]\u001b[A\n",
-      " 62%|███████████████████████▍              | 3092/5000 [00:05<00:03, 536.34it/s]\u001b[A\n",
-      " 63%|███████████████████████▉              | 3146/5000 [00:05<00:03, 533.72it/s]\u001b[A\n",
-      " 64%|████████████████████████▎             | 3200/5000 [00:06<00:03, 525.43it/s]\u001b[A\n",
-      " 65%|████████████████████████▋             | 3253/5000 [00:06<00:03, 516.78it/s]\u001b[A\n",
-      " 66%|█████████████████████████             | 3305/5000 [00:06<00:03, 511.91it/s]\u001b[A\n",
-      " 67%|█████████████████████████▌            | 3357/5000 [00:06<00:03, 487.96it/s]\u001b[A\n",
-      " 68%|█████████████████████████▉            | 3407/5000 [00:06<00:03, 474.48it/s]\u001b[A\n",
-      " 69%|██████████████████████████▎           | 3462/5000 [00:06<00:03, 492.17it/s]\u001b[A\n",
-      " 70%|██████████████████████████▋           | 3516/5000 [00:06<00:02, 504.28it/s]\u001b[A\n",
-      " 71%|███████████████████████████▏          | 3571/5000 [00:06<00:02, 516.54it/s]\u001b[A\n",
-      " 72%|███████████████████████████▌          | 3624/5000 [00:06<00:02, 518.80it/s]\u001b[A\n",
-      " 74%|███████████████████████████▉          | 3680/5000 [00:06<00:02, 527.25it/s]\u001b[A\n",
-      " 75%|████████████████████████████▍         | 3740/5000 [00:07<00:02, 546.05it/s]\u001b[A\n",
-      " 76%|████████████████████████████▊         | 3795/5000 [00:07<00:02, 537.61it/s]\u001b[A\n",
-      " 77%|█████████████████████████████▎        | 3849/5000 [00:07<00:02, 529.08it/s]\u001b[A\n",
-      " 78%|█████████████████████████████▋        | 3902/5000 [00:07<00:02, 522.02it/s]\u001b[A\n",
-      " 79%|██████████████████████████████        | 3958/5000 [00:07<00:01, 530.05it/s]\u001b[A\n",
-      " 80%|██████████████████████████████▌       | 4016/5000 [00:07<00:01, 543.31it/s]\u001b[A\n",
-      " 81%|██████████████████████████████▉       | 4071/5000 [00:07<00:01, 540.67it/s]\u001b[A\n",
-      " 83%|███████████████████████████████▎      | 4128/5000 [00:07<00:01, 548.58it/s]\u001b[A\n",
-      " 84%|███████████████████████████████▊      | 4185/5000 [00:07<00:01, 552.95it/s]\u001b[A\n",
-      " 85%|████████████████████████████████▏     | 4241/5000 [00:07<00:01, 553.90it/s]\u001b[A\n",
-      " 86%|████████████████████████████████▋     | 4297/5000 [00:08<00:01, 543.16it/s]\u001b[A\n",
-      " 87%|█████████████████████████████████     | 4352/5000 [00:08<00:01, 536.40it/s]\u001b[A\n",
-      " 88%|█████████████████████████████████▍    | 4406/5000 [00:08<00:01, 536.03it/s]\u001b[A\n",
-      " 89%|█████████████████████████████████▉    | 4460/5000 [00:08<00:01, 536.87it/s]\u001b[A\n",
-      " 90%|██████████████████████████████████▎   | 4517/5000 [00:08<00:00, 546.19it/s]\u001b[A\n",
-      " 92%|██████████████████████████████████▊   | 4575/5000 [00:08<00:00, 554.40it/s]\u001b[A\n",
-      " 93%|███████████████████████████████████▏  | 4631/5000 [00:08<00:00, 513.63it/s]\u001b[A\n",
-      " 94%|███████████████████████████████████▌  | 4683/5000 [00:08<00:00, 510.47it/s]\u001b[A\n",
-      " 95%|████████████████████████████████████  | 4738/5000 [00:08<00:00, 520.47it/s]\u001b[A\n",
-      " 96%|████████████████████████████████████▍ | 4793/5000 [00:09<00:00, 527.90it/s]\u001b[A\n",
-      " 97%|████████████████████████████████████▊ | 4851/5000 [00:09<00:00, 540.93it/s]\u001b[A\n",
-      " 98%|█████████████████████████████████████▎| 4906/5000 [00:09<00:00, 535.77it/s]\u001b[A\n",
-      "100%|██████████████████████████████████████| 5000/5000 [00:09<00:00, 530.19it/s]\n",
-      "\n",
-      "Running bootstraps:   0%|                                | 0/25 [00:00<?, ?it/s]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:   4%|▊                    | 1/25 [08:53<3:33:26, 533.59s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.34it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:   8%|█▋                   | 2/25 [17:22<3:18:57, 519.02s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  12%|██▌                  | 3/25 [26:13<3:12:21, 524.60s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.45it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  16%|███▎                 | 4/25 [35:06<3:04:45, 527.88s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.21it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  20%|████▏                | 5/25 [43:58<2:56:25, 529.30s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.50it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  24%|█████                | 6/25 [52:48<2:47:43, 529.68s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.37it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  28%|█████▎             | 7/25 [1:01:39<2:38:59, 529.96s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.13s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.18it/s]\n",
-      "\n",
-      "Running bootstraps:  32%|██████             | 8/25 [1:10:30<2:30:18, 530.49s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:11,  2.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  36%|██████▊            | 9/25 [1:19:23<2:21:35, 530.98s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.21s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.98it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.46it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  40%|███████▏          | 10/25 [1:28:17<2:13:00, 532.03s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.19s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:11,  2.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.10it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.14it/s]\n",
-      "\n",
-      "Running bootstraps:  44%|███████▉          | 11/25 [1:37:08<2:04:04, 531.74s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.96it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.41it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  48%|████████▋         | 12/25 [1:45:58<1:55:04, 531.10s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.24it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  52%|█████████▎        | 13/25 [1:54:53<1:46:28, 532.36s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.17s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.33it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.20it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.80it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.19it/s]\n",
-      "\n",
-      "Running bootstraps:  56%|██████████        | 14/25 [2:03:47<1:37:41, 532.88s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.19it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps:  60%|██████████▊       | 15/25 [2:12:41<1:28:53, 533.32s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.27it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.16it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  64%|███████████▌      | 16/25 [2:21:34<1:19:58, 533.20s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.14s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.15it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  68%|████████████▏     | 17/25 [2:30:24<1:10:58, 532.28s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.78it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.23it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.35it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  72%|████████████▉     | 18/25 [2:39:18<1:02:09, 532.77s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.92it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.81it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.15it/s]\n",
-      "\n",
-      "Running bootstraps:  76%|███████████████▏    | 19/25 [2:48:10<53:14, 532.37s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.20s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.84it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.14it/s]\n",
-      "\n",
-      "Running bootstraps:  80%|████████████████    | 20/25 [2:57:03<44:23, 532.61s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.44it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.77it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.28it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.91it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.36it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.49it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.79it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  84%|████████████████▊   | 21/25 [3:05:56<35:30, 532.73s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.16s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.59it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.61it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.17it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.38it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.87it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  88%|█████████████████▌  | 22/25 [3:14:45<26:35, 531.68s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:37,  1.20s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.09it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:02<00:11,  2.53it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.86it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.42it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.74it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.97it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.32it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.65it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.85it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  92%|██████████████████▍ | 23/25 [3:23:36<17:43, 531.52s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:36,  1.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:19,  1.56it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.90it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:07,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.51it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.57it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.64it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.67it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.71it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:06<00:03,  3.72it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.95it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.62it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.54it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.83it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.16it/s]\n",
-      "\n",
-      "Running bootstraps:  96%|███████████████████▏| 24/25 [3:32:29<08:51, 531.76s/it]\u001b[A\n",
-      "\n",
-      "Computing loss:   0%|                                    | 0/32 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   3%|▉                           | 1/32 [00:01<00:35,  1.15s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   6%|█▊                          | 2/32 [00:01<00:18,  1.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:   9%|██▋                         | 3/32 [00:01<00:13,  2.14it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  12%|███▌                        | 4/32 [00:01<00:10,  2.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  16%|████▍                       | 5/32 [00:02<00:09,  2.89it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  19%|█████▎                      | 6/32 [00:02<00:08,  3.12it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  22%|██████▏                     | 7/32 [00:02<00:07,  3.30it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  25%|███████                     | 8/32 [00:03<00:06,  3.43it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  28%|███████▉                    | 9/32 [00:03<00:06,  3.52it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  31%|████████▍                  | 10/32 [00:03<00:06,  3.58it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  34%|█████████▎                 | 11/32 [00:03<00:05,  3.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  38%|██████████▏                | 12/32 [00:04<00:05,  3.66it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  41%|██████████▉                | 13/32 [00:04<00:05,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  44%|███████████▊               | 14/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  47%|████████████▋              | 15/32 [00:04<00:04,  3.69it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  50%|█████████████▌             | 16/32 [00:05<00:04,  3.70it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  53%|██████████████▎            | 17/32 [00:05<00:04,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  56%|███████████████▏           | 18/32 [00:05<00:03,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  59%|████████████████           | 19/32 [00:05<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  62%|████████████████▉          | 20/32 [00:06<00:03,  3.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  66%|█████████████████▋         | 21/32 [00:07<00:05,  1.94it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  69%|██████████████████▌        | 22/32 [00:07<00:04,  2.29it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  72%|███████████████████▍       | 23/32 [00:07<00:03,  2.63it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  75%|████████████████████▎      | 24/32 [00:08<00:02,  2.93it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  78%|█████████████████████      | 25/32 [00:08<00:02,  3.18it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  81%|█████████████████████▉     | 26/32 [00:08<00:01,  3.39it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  84%|██████████████████████▊    | 27/32 [00:08<00:01,  3.55it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  88%|███████████████████████▋   | 28/32 [00:09<00:01,  3.68it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  91%|████████████████████████▍  | 29/32 [00:09<00:00,  3.75it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  94%|█████████████████████████▎ | 30/32 [00:09<00:00,  3.82it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss:  97%|██████████████████████████▏| 31/32 [00:09<00:00,  3.88it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Computing loss: 100%|███████████████████████████| 32/32 [00:10<00:00,  3.17it/s]\n",
-      "\n",
-      "Running bootstraps: 100%|████████████████████| 25/25 [3:41:21<00:00, 531.27s/it]\n",
-      "100%|████████████████████████████████████| 3/3 [22:02:07<00:00, 26442.34s/it]\n"
+      "  0%|                                                             | 0/3 [00:00<?, ?it/s]2024-10-14 15:13:27.760446: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                      | 1/200 [00:26<1:26:26, 26.06s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:33<50:05, 15.18s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:41<38:30, 11.73s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:48<33:00, 10.10s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:56<29:56,  9.21s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [01:04<28:05,  8.69s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:11<26:53,  8.36s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:19<26:17,  8.22s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:27<25:33,  8.03s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:34<25:01,  7.90s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:42<24:37,  7.82s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:50<24:21,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:57<24:07,  7.74s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:05<23:53,  7.71s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:13<23:42,  7.69s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:20<23:31,  7.67s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:28<23:23,  7.67s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:36<23:11,  7.65s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:43<23:00,  7.63s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:51<22:52,  7.63s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:58<22:42,  7.61s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:06<22:34,  7.61s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:14<22:27,  7.61s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:21<22:18,  7.61s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:29<22:20,  7.66s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:37<22:09,  7.64s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:44<21:58,  7.62s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:52<21:45,  7.59s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:59<21:37,  7.59s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:07<21:33,  7.61s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:15<21:24,  7.60s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:22<21:16,  7.60s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:30<21:09,  7.60s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:37<20:59,  7.59s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:45<20:51,  7.58s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:52<20:46,  7.60s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:00<20:39,  7.60s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:08<20:33,  7.61s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:15<20:29,  7.63s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:23<20:25,  7.66s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:31<20:17,  7.66s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:38<20:08,  7.65s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:46<20:09,  7.71s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:54<19:56,  7.67s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:01<19:44,  7.64s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:09<19:31,  7.61s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:16<19:21,  7.59s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [06:24<19:13,  7.59s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [06:32<19:05,  7.58s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [06:39<18:57,  7.58s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [06:47<18:47,  7.57s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [06:54<18:38,  7.56s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [07:02<18:30,  7.56s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [07:09<18:24,  7.56s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [07:17<18:16,  7.56s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [07:25<18:09,  7.56s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [07:32<18:02,  7.57s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [07:40<17:56,  7.58s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [07:47<17:48,  7.58s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [07:55<17:40,  7.58s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [08:03<17:43,  7.65s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [08:10<17:32,  7.63s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [08:18<17:21,  7.60s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [08:25<17:13,  7.60s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [08:41<18:02,  8.02s/it]\n",
+      "\n",
+      "training:   0%|                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|                   | 1/200 [00:12<40:05, 12.09s/it]\u001b[A\n",
+      "training:   1%|▏                  | 2/200 [00:19<31:29,  9.55s/it]\u001b[A\n",
+      "training:   2%|▎                  | 3/200 [00:27<28:28,  8.67s/it]\u001b[A\n",
+      "training:   2%|▍                  | 4/200 [00:35<26:59,  8.26s/it]\u001b[A\n",
+      "training:   2%|▍                  | 5/200 [00:42<26:15,  8.08s/it]\u001b[A\n",
+      "training:   3%|▌                  | 6/200 [00:50<25:42,  7.95s/it]\u001b[A\n",
+      "training:   4%|▋                  | 7/200 [00:58<25:21,  7.88s/it]\u001b[A\n",
+      "training:   4%|▊                  | 8/200 [01:06<25:04,  7.84s/it]\u001b[A\n",
+      "training:   4%|▊                  | 9/200 [01:13<24:56,  7.83s/it]\u001b[A\n",
+      "training:   5%|▉                 | 10/200 [01:21<24:53,  7.86s/it]\u001b[A\n",
+      "training:   6%|▉                 | 11/200 [01:29<24:33,  7.79s/it]\u001b[A\n",
+      "training:   6%|█                 | 12/200 [01:37<24:17,  7.75s/it]\u001b[A\n",
+      "training:   6%|█▏                | 13/200 [01:44<24:03,  7.72s/it]\u001b[A\n",
+      "training:   7%|█▎                | 14/200 [01:52<23:50,  7.69s/it]\u001b[A\n",
+      "training:   8%|█▎                | 15/200 [01:59<23:38,  7.67s/it]\u001b[A\n",
+      "training:   8%|█▍                | 16/200 [02:07<23:28,  7.66s/it]\u001b[A\n",
+      "training:   8%|█▌                | 17/200 [02:15<23:19,  7.65s/it]\u001b[A\n",
+      "training:   9%|█▌                | 18/200 [02:22<23:11,  7.64s/it]\u001b[A\n",
+      "training:  10%|█▋                | 19/200 [02:30<23:06,  7.66s/it]\u001b[A\n",
+      "training:  10%|█▊                | 20/200 [02:38<22:56,  7.65s/it]\u001b[A\n",
+      "training:  10%|█▉                | 21/200 [02:45<22:49,  7.65s/it]\u001b[A\n",
+      "training:  11%|█▉                | 22/200 [02:53<22:40,  7.65s/it]\u001b[A\n",
+      "training:  12%|██                | 23/200 [03:01<22:34,  7.65s/it]\u001b[A\n",
+      "training:  12%|██▏               | 24/200 [03:08<22:28,  7.66s/it]\u001b[A\n",
+      "training:  12%|██▎               | 25/200 [03:16<22:23,  7.68s/it]\u001b[A\n",
+      "training:  13%|██▎               | 26/200 [03:24<22:17,  7.69s/it]\u001b[A\n",
+      "training:  14%|██▍               | 27/200 [03:31<22:09,  7.68s/it]\u001b[A\n",
+      "training:  14%|██▌               | 28/200 [03:39<22:15,  7.76s/it]\u001b[A\n",
+      "training:  14%|██▌               | 29/200 [03:47<22:07,  7.76s/it]\u001b[A\n",
+      "training:  15%|██▋               | 30/200 [03:55<21:54,  7.73s/it]\u001b[A\n",
+      "training:  16%|██▊               | 31/200 [04:03<21:44,  7.72s/it]\u001b[A\n",
+      "training:  16%|██▉               | 32/200 [04:10<21:33,  7.70s/it]\u001b[A\n",
+      "training:  16%|██▉               | 33/200 [04:18<21:25,  7.70s/it]\u001b[A\n",
+      "training:  17%|███               | 34/200 [04:25<21:15,  7.68s/it]\u001b[A\n",
+      "training:  18%|███▏              | 35/200 [04:33<21:08,  7.69s/it]\u001b[A\n",
+      "training:  18%|███▏              | 36/200 [04:41<21:00,  7.69s/it]\u001b[A\n",
+      "training:  18%|███▎              | 37/200 [04:49<20:50,  7.67s/it]\u001b[A\n",
+      "training:  19%|███▍              | 38/200 [04:56<20:41,  7.66s/it]\u001b[A\n",
+      "training:  20%|███▌              | 39/200 [05:04<20:31,  7.65s/it]\u001b[A\n",
+      "training:  20%|███▌              | 40/200 [05:11<20:21,  7.64s/it]\u001b[A\n",
+      "training:  20%|███▋              | 41/200 [05:19<20:14,  7.64s/it]\u001b[A\n",
+      "training:  21%|███▊              | 42/200 [05:27<20:07,  7.64s/it]\u001b[A\n",
+      "training:  22%|███▊              | 43/200 [05:34<20:02,  7.66s/it]\u001b[A\n",
+      "training:  22%|███▉              | 44/200 [05:42<19:54,  7.66s/it]\u001b[A\n",
+      "training:  22%|████              | 45/200 [05:50<19:47,  7.66s/it]\u001b[A\n",
+      "training:  23%|████▏             | 46/200 [05:57<19:43,  7.69s/it]\u001b[A\n",
+      "training:  24%|████▏             | 47/200 [06:05<19:32,  7.67s/it]\u001b[A\n",
+      "training:  24%|████▎             | 48/200 [06:13<19:20,  7.63s/it]\u001b[A\n",
+      "training:  24%|████▍             | 49/200 [06:20<19:10,  7.62s/it]\u001b[A\n",
+      "training:  25%|████▌             | 50/200 [06:28<19:01,  7.61s/it]\u001b[A\n",
+      "training:  26%|████▌             | 51/200 [06:35<18:54,  7.62s/it]\u001b[A\n",
+      "training:  26%|████▋             | 52/200 [06:43<18:46,  7.61s/it]\u001b[A\n",
+      "training:  26%|████▊             | 53/200 [06:51<18:38,  7.61s/it]\u001b[A\n",
+      "training:  27%|████▊             | 54/200 [06:58<18:29,  7.60s/it]\u001b[A\n",
+      "training:  28%|████▉             | 55/200 [07:13<19:03,  7.89s/it]\n",
+      "\n",
+      "training:   0%|                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|                   | 1/200 [00:12<40:21, 12.17s/it]\u001b[A\n",
+      "training:   1%|▏                  | 2/200 [00:19<31:32,  9.56s/it]\u001b[A\n",
+      "training:   2%|▎                  | 3/200 [00:27<28:32,  8.69s/it]\u001b[A\n",
+      "training:   2%|▍                  | 4/200 [00:35<27:06,  8.30s/it]\u001b[A\n",
+      "training:   2%|▍                  | 5/200 [00:43<26:19,  8.10s/it]\u001b[A\n",
+      "training:   3%|▌                  | 6/200 [00:50<25:49,  7.99s/it]\u001b[A\n",
+      "training:   4%|▋                  | 7/200 [00:58<25:32,  7.94s/it]\u001b[A\n",
+      "training:   4%|▊                  | 8/200 [01:06<25:08,  7.85s/it]\u001b[A\n",
+      "training:   4%|▊                  | 9/200 [01:13<24:49,  7.80s/it]\u001b[A\n",
+      "training:   5%|▉                 | 10/200 [01:21<24:33,  7.76s/it]\u001b[A\n",
+      "training:   6%|▉                 | 11/200 [01:29<24:18,  7.72s/it]\u001b[A\n",
+      "training:   6%|█                 | 12/200 [01:36<24:07,  7.70s/it]\u001b[A\n",
+      "training:   6%|█▏                | 13/200 [01:44<23:52,  7.66s/it]\u001b[A\n",
+      "training:   7%|█▎                | 14/200 [01:52<23:41,  7.64s/it]\u001b[A\n",
+      "training:   8%|█▎                | 15/200 [01:59<23:28,  7.61s/it]\u001b[A\n",
+      "training:   8%|█▍                | 16/200 [02:07<23:19,  7.61s/it]\u001b[A\n",
+      "training:   8%|█▌                | 17/200 [02:14<23:11,  7.60s/it]\u001b[A\n",
+      "training:   9%|█▌                | 18/200 [02:22<23:02,  7.60s/it]\u001b[A\n",
+      "training:  10%|█▋                | 19/200 [02:30<22:56,  7.60s/it]\u001b[A\n",
+      "training:  10%|█▊                | 20/200 [02:37<22:49,  7.61s/it]\u001b[A\n",
+      "training:  10%|█▉                | 21/200 [02:45<22:45,  7.63s/it]\u001b[A\n",
+      "training:  11%|█▉                | 22/200 [02:52<22:38,  7.63s/it]\u001b[A\n",
+      "training:  12%|██                | 23/200 [03:00<22:31,  7.64s/it]\u001b[A\n",
+      "training:  12%|██▏               | 24/200 [03:08<22:28,  7.66s/it]\u001b[A\n",
+      "training:  12%|██▎               | 25/200 [03:15<22:16,  7.63s/it]\u001b[A\n",
+      "training:  13%|██▎               | 26/200 [03:23<22:06,  7.62s/it]\u001b[A\n",
+      "training:  14%|██▍               | 27/200 [03:31<21:56,  7.61s/it]\u001b[A\n",
+      "training:  14%|██▌               | 28/200 [03:38<21:47,  7.60s/it]\u001b[A\n",
+      "training:  14%|██▌               | 29/200 [03:46<21:38,  7.59s/it]\u001b[A\n",
+      "training:  15%|██▋               | 30/200 [03:53<21:32,  7.60s/it]\u001b[A\n",
+      "training:  16%|██▊               | 31/200 [04:01<21:23,  7.60s/it]\u001b[A\n",
+      "training:  16%|██▉               | 32/200 [04:09<21:16,  7.60s/it]\u001b[A\n",
+      "training:  16%|██▉               | 33/200 [04:16<21:07,  7.59s/it]\u001b[A\n",
+      "training:  17%|███               | 34/200 [04:24<21:01,  7.60s/it]\u001b[A\n",
+      "training:  18%|███▏              | 35/200 [04:31<20:53,  7.60s/it]\u001b[A\n",
+      "training:  18%|███▏              | 36/200 [04:39<20:45,  7.60s/it]\u001b[A\n",
+      "training:  18%|███▎              | 37/200 [04:47<20:40,  7.61s/it]\u001b[A\n",
+      "training:  19%|███▍              | 38/200 [04:54<20:34,  7.62s/it]\u001b[A\n",
+      "training:  20%|███▌              | 39/200 [05:02<20:29,  7.63s/it]\u001b[A\n",
+      "training:  20%|███▌              | 40/200 [05:10<20:22,  7.64s/it]\u001b[A\n",
+      "training:  20%|███▋              | 41/200 [05:17<20:21,  7.68s/it]\u001b[A\n",
+      "training:  21%|███▊              | 42/200 [05:25<20:08,  7.65s/it]\u001b[A\n",
+      "training:  22%|███▊              | 43/200 [05:32<19:58,  7.63s/it]\u001b[A\n",
+      "training:  22%|███▉              | 44/200 [05:40<19:49,  7.63s/it]\u001b[A\n",
+      "training:  22%|████              | 45/200 [05:48<19:39,  7.61s/it]\u001b[A\n",
+      "training:  23%|████▏             | 46/200 [05:55<19:30,  7.60s/it]\u001b[A\n",
+      "training:  24%|████▏             | 47/200 [06:03<19:21,  7.59s/it]\u001b[A\n",
+      "training:  24%|████▎             | 48/200 [06:10<19:14,  7.60s/it]\u001b[A\n",
+      "training:  24%|████▍             | 49/200 [06:18<19:07,  7.60s/it]\u001b[A\n",
+      "training:  25%|████▌             | 50/200 [06:26<18:59,  7.59s/it]\u001b[A\n",
+      "training:  26%|████▌             | 51/200 [06:33<18:50,  7.59s/it]\u001b[A\n",
+      "training:  26%|████▋             | 52/200 [06:41<18:42,  7.59s/it]\u001b[A\n",
+      "training:  26%|████▊             | 53/200 [06:48<18:35,  7.59s/it]\u001b[A\n",
+      "training:  27%|████▊             | 54/200 [06:56<18:28,  7.59s/it]\u001b[A\n",
+      "training:  28%|████▉             | 55/200 [07:04<18:21,  7.60s/it]\u001b[A\n",
+      "training:  28%|█████             | 56/200 [07:11<18:14,  7.60s/it]\u001b[A\n",
+      "training:  28%|█████▏            | 57/200 [07:19<18:12,  7.64s/it]\u001b[A\n",
+      "training:  29%|█████▏            | 58/200 [07:26<18:02,  7.62s/it]\u001b[A\n",
+      "training:  30%|█████▎            | 59/200 [07:34<17:52,  7.61s/it]\u001b[A\n",
+      "training:  30%|█████▍            | 60/200 [07:42<17:43,  7.60s/it]\u001b[A\n",
+      "training:  30%|█████▍            | 61/200 [07:49<17:34,  7.59s/it]\u001b[A\n",
+      "training:  31%|█████▌            | 62/200 [07:57<17:26,  7.58s/it]\u001b[A\n",
+      "training:  32%|█████▋            | 63/200 [08:04<17:18,  7.58s/it]\u001b[A\n",
+      "training:  32%|█████▊            | 64/200 [08:12<17:11,  7.59s/it]\u001b[A\n",
+      "training:  32%|█████▊            | 65/200 [08:19<17:03,  7.58s/it]\u001b[A\n",
+      "training:  33%|█████▉            | 66/200 [08:27<16:54,  7.57s/it]\u001b[A\n",
+      "training:  34%|██████            | 67/200 [08:35<16:47,  7.57s/it]\u001b[A\n",
+      "training:  34%|██████            | 68/200 [08:42<16:39,  7.57s/it]\u001b[A\n",
+      "training:  34%|██████▏           | 69/200 [08:57<17:01,  7.80s/it]\n",
+      "\n",
+      "training:   0%|                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|                   | 1/200 [00:12<41:14, 12.44s/it]\u001b[A\n",
+      "training:   1%|▏                  | 2/200 [00:20<31:40,  9.60s/it]\u001b[A\n",
+      "training:   2%|▎                  | 3/200 [00:27<28:30,  8.68s/it]\u001b[A\n",
+      "training:   2%|▍                  | 4/200 [00:35<27:08,  8.31s/it]\u001b[A\n",
+      "training:   2%|▍                  | 5/200 [00:43<26:14,  8.08s/it]\u001b[A\n",
+      "training:   3%|▌                  | 6/200 [00:50<25:40,  7.94s/it]\u001b[A\n",
+      "training:   4%|▋                  | 7/200 [00:58<25:13,  7.84s/it]\u001b[A\n",
+      "training:   4%|▊                  | 8/200 [01:06<24:55,  7.79s/it]\u001b[A\n",
+      "training:   4%|▊                  | 9/200 [01:13<24:46,  7.79s/it]\u001b[A\n",
+      "training:   5%|▉                 | 10/200 [01:21<24:33,  7.75s/it]\u001b[A\n",
+      "training:   6%|▉                 | 11/200 [01:29<24:18,  7.72s/it]\u001b[A\n",
+      "training:   6%|█                 | 12/200 [01:36<24:03,  7.68s/it]\u001b[A\n",
+      "training:   6%|█▏                | 13/200 [01:44<23:52,  7.66s/it]\u001b[A\n",
+      "training:   7%|█▎                | 14/200 [01:51<23:42,  7.65s/it]\u001b[A\n",
+      "training:   8%|█▎                | 15/200 [01:59<23:31,  7.63s/it]\u001b[A\n",
+      "training:   8%|█▍                | 16/200 [02:07<23:23,  7.63s/it]\u001b[A\n",
+      "training:   8%|█▌                | 17/200 [02:14<23:17,  7.64s/it]\u001b[A\n",
+      "training:   9%|█▌                | 18/200 [02:22<23:10,  7.64s/it]\u001b[A\n",
+      "training:  10%|█▋                | 19/200 [02:30<23:02,  7.64s/it]\u001b[A\n",
+      "training:  10%|█▊                | 20/200 [02:37<22:55,  7.64s/it]\u001b[A\n",
+      "training:  10%|█▉                | 21/200 [02:45<22:55,  7.69s/it]\u001b[A\n",
+      "training:  11%|█▉                | 22/200 [02:53<22:41,  7.65s/it]\u001b[A\n",
+      "training:  12%|██                | 23/200 [03:00<22:30,  7.63s/it]\u001b[A\n",
+      "training:  12%|██▏               | 24/200 [03:08<22:23,  7.63s/it]\u001b[A\n",
+      "training:  12%|██▎               | 25/200 [03:15<22:14,  7.63s/it]\u001b[A\n",
+      "training:  13%|██▎               | 26/200 [03:23<22:05,  7.62s/it]\u001b[A\n",
+      "training:  14%|██▍               | 27/200 [03:31<21:58,  7.62s/it]\u001b[A\n",
+      "training:  14%|██▌               | 28/200 [03:38<21:50,  7.62s/it]\u001b[A\n",
+      "training:  14%|██▌               | 29/200 [03:46<21:41,  7.61s/it]\u001b[A\n",
+      "training:  15%|██▋               | 30/200 [03:53<21:32,  7.60s/it]\u001b[A\n",
+      "training:  16%|██▊               | 31/200 [04:01<21:25,  7.61s/it]\u001b[A\n",
+      "training:  16%|██▉               | 32/200 [04:09<21:17,  7.60s/it]\u001b[A\n",
+      "training:  16%|██▉               | 33/200 [04:16<21:12,  7.62s/it]\u001b[A\n",
+      "training:  17%|███               | 34/200 [04:24<21:08,  7.64s/it]\u001b[A\n",
+      "training:  18%|███▏              | 35/200 [04:32<21:03,  7.66s/it]\u001b[A\n",
+      "training:  18%|███▏              | 36/200 [04:39<20:54,  7.65s/it]\u001b[A\n",
+      "training:  18%|███▎              | 37/200 [04:47<20:46,  7.65s/it]\u001b[A\n",
+      "training:  19%|███▍              | 38/200 [04:55<20:48,  7.71s/it]\u001b[A\n",
+      "training:  20%|███▌              | 39/200 [05:03<20:39,  7.70s/it]\u001b[A\n",
+      "training:  20%|███▌              | 40/200 [05:10<20:26,  7.67s/it]\u001b[A\n",
+      "training:  20%|███▋              | 41/200 [05:18<20:16,  7.65s/it]\u001b[A\n",
+      "training:  21%|███▊              | 42/200 [05:25<20:05,  7.63s/it]\u001b[A\n",
+      "training:  22%|███▊              | 43/200 [05:33<19:56,  7.62s/it]\u001b[A\n",
+      "training:  22%|███▉              | 44/200 [05:41<19:49,  7.62s/it]\u001b[A\n",
+      "training:  22%|████              | 45/200 [05:48<19:40,  7.61s/it]\u001b[A\n",
+      "training:  23%|████▏             | 46/200 [05:56<19:30,  7.60s/it]\u001b[A\n",
+      "training:  24%|████▏             | 47/200 [06:03<19:22,  7.59s/it]\u001b[A\n",
+      "training:  24%|████▎             | 48/200 [06:11<19:15,  7.60s/it]\u001b[A\n",
+      "training:  24%|████▍             | 49/200 [06:19<19:08,  7.61s/it]\u001b[A\n",
+      "training:  25%|████▌             | 50/200 [06:26<19:01,  7.61s/it]\u001b[A\n",
+      "training:  26%|████▌             | 51/200 [06:34<18:56,  7.63s/it]\u001b[A\n",
+      "training:  26%|████▋             | 52/200 [06:41<18:50,  7.64s/it]\u001b[A\n",
+      "training:  26%|████▊             | 53/200 [06:49<18:41,  7.63s/it]\u001b[A\n",
+      "training:  27%|████▊             | 54/200 [06:57<18:44,  7.70s/it]\u001b[A\n",
+      "training:  28%|████▉             | 55/200 [07:05<18:32,  7.68s/it]\u001b[A\n",
+      "training:  28%|█████             | 56/200 [07:12<18:22,  7.65s/it]\u001b[A\n",
+      "training:  28%|█████▏            | 57/200 [07:20<18:11,  7.63s/it]\u001b[A\n",
+      "training:  29%|█████▏            | 58/200 [07:27<18:02,  7.62s/it]\u001b[A\n",
+      "training:  30%|█████▎            | 59/200 [07:35<17:53,  7.61s/it]\u001b[A\n",
+      "training:  30%|█████▍            | 60/200 [07:43<17:44,  7.60s/it]\u001b[A\n",
+      "training:  30%|█████▍            | 61/200 [07:50<17:36,  7.60s/it]\u001b[A\n",
+      "training:  31%|█████▌            | 62/200 [08:05<18:01,  7.83s/it]\n",
+      "\n",
+      "training:   0%|                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|                   | 1/200 [00:11<39:29, 11.91s/it]\u001b[A\n",
+      "training:   1%|▏                  | 2/200 [00:19<30:56,  9.38s/it]\u001b[A\n",
+      "training:   2%|▎                  | 3/200 [00:27<28:07,  8.57s/it]\u001b[A\n",
+      "training:   2%|▍                  | 4/200 [00:34<26:46,  8.20s/it]\u001b[A\n",
+      "training:   2%|▍                  | 5/200 [00:42<26:10,  8.05s/it]\u001b[A\n",
+      "training:   3%|▌                  | 6/200 [00:50<25:35,  7.92s/it]\u001b[A\n",
+      "training:   4%|▋                  | 7/200 [00:57<25:11,  7.83s/it]\u001b[A\n",
+      "training:   4%|▊                  | 8/200 [01:05<24:51,  7.77s/it]\u001b[A\n",
+      "training:   4%|▊                  | 9/200 [01:13<24:36,  7.73s/it]\u001b[A\n",
+      "training:   5%|▉                 | 10/200 [01:20<24:23,  7.70s/it]\u001b[A\n",
+      "training:   6%|▉                 | 11/200 [01:28<24:11,  7.68s/it]\u001b[A\n",
+      "training:   6%|█                 | 12/200 [01:36<23:59,  7.66s/it]\u001b[A\n",
+      "training:   6%|█▏                | 13/200 [01:43<23:48,  7.64s/it]\u001b[A\n",
+      "training:   7%|█▎                | 14/200 [01:51<23:38,  7.63s/it]\u001b[A\n",
+      "training:   8%|█▎                | 15/200 [01:58<23:31,  7.63s/it]\u001b[A\n",
+      "training:   8%|█▍                | 16/200 [02:06<23:22,  7.62s/it]\u001b[A\n",
+      "training:   8%|█▌                | 17/200 [02:14<23:14,  7.62s/it]\u001b[A\n",
+      "training:   9%|█▌                | 18/200 [02:21<23:08,  7.63s/it]\u001b[A\n",
+      "training:  10%|█▋                | 19/200 [02:29<23:01,  7.63s/it]\u001b[A\n",
+      "training:  10%|█▊                | 20/200 [02:37<22:55,  7.64s/it]\u001b[A\n",
+      "training:  10%|█▉                | 21/200 [02:44<22:53,  7.67s/it]\u001b[A\n",
+      "training:  11%|█▉                | 22/200 [02:52<22:43,  7.66s/it]\u001b[A\n",
+      "training:  12%|██                | 23/200 [02:59<22:33,  7.65s/it]\u001b[A\n",
+      "training:  12%|██▏               | 24/200 [03:07<22:25,  7.65s/it]\u001b[A\n",
+      "training:  12%|██▎               | 25/200 [03:15<22:17,  7.64s/it]\u001b[A\n",
+      "training:  13%|██▎               | 26/200 [03:22<22:08,  7.64s/it]\u001b[A\n",
+      "training:  14%|██▍               | 27/200 [03:30<21:58,  7.62s/it]\u001b[A\n",
+      "training:  14%|██▌               | 28/200 [03:38<21:52,  7.63s/it]\u001b[A\n",
+      "training:  14%|██▌               | 29/200 [03:45<21:43,  7.62s/it]\u001b[A\n",
+      "training:  15%|██▋               | 30/200 [03:53<21:34,  7.62s/it]\u001b[A\n",
+      "training:  16%|██▊               | 31/200 [04:00<21:27,  7.62s/it]\u001b[A\n",
+      "training:  16%|██▉               | 32/200 [04:08<21:20,  7.62s/it]\u001b[A\n",
+      "training:  16%|██▉               | 33/200 [04:16<21:13,  7.63s/it]\u001b[A\n",
+      "training:  17%|███               | 34/200 [04:23<21:05,  7.62s/it]\u001b[A\n",
+      "training:  18%|███▏              | 35/200 [04:31<20:58,  7.63s/it]\u001b[A\n",
+      "training:  18%|███▏              | 36/200 [04:39<20:56,  7.66s/it]\u001b[A\n",
+      "training:  18%|███▎              | 37/200 [04:46<20:45,  7.64s/it]\u001b[A\n",
+      "training:  19%|███▍              | 38/200 [04:54<20:37,  7.64s/it]\u001b[A\n",
+      "training:  20%|███▌              | 39/200 [05:02<20:26,  7.62s/it]\u001b[A\n",
+      "training:  20%|███▌              | 40/200 [05:09<20:19,  7.62s/it]\u001b[A\n",
+      "training:  20%|███▋              | 41/200 [05:17<20:10,  7.61s/it]\u001b[A\n",
+      "training:  21%|███▊              | 42/200 [05:24<20:03,  7.61s/it]\u001b[A\n",
+      "training:  22%|███▊              | 43/200 [05:32<19:54,  7.61s/it]\u001b[A\n",
+      "training:  22%|███▉              | 44/200 [05:40<19:46,  7.60s/it]\u001b[A\n",
+      "training:  22%|████              | 45/200 [05:47<19:38,  7.60s/it]\u001b[A\n",
+      "training:  23%|████▏             | 46/200 [05:55<19:30,  7.60s/it]\u001b[A\n",
+      "training:  24%|████▏             | 47/200 [06:02<19:22,  7.60s/it]\u001b[A\n",
+      "training:  24%|████▎             | 48/200 [06:10<19:15,  7.60s/it]\u001b[A\n",
+      "training:  24%|████▍             | 49/200 [06:18<19:10,  7.62s/it]\u001b[A\n",
+      "training:  25%|████▌             | 50/200 [06:25<19:08,  7.66s/it]\u001b[A\n",
+      "training:  26%|████▌             | 51/200 [06:33<18:58,  7.64s/it]\u001b[A\n",
+      "training:  26%|████▋             | 52/200 [06:41<18:49,  7.63s/it]\u001b[A\n",
+      "training:  26%|████▊             | 53/200 [06:56<19:14,  7.85s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:11<39:20, 11.86s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:19<31:07,  9.43s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:27<28:11,  8.59s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:34<26:47,  8.20s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:42<26:02,  8.01s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:50<25:30,  7.89s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:57<25:08,  7.82s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:05<24:54,  7.78s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:13<24:43,  7.76s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:20<24:34,  7.76s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:28<24:21,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:36<24:07,  7.70s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:44<24:05,  7.73s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:51<23:50,  7.69s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:59<23:40,  7.68s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:06<23:28,  7.65s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:14<23:18,  7.64s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:22<23:07,  7.62s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:29<22:57,  7.61s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:37<22:48,  7.60s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:44<22:42,  7.61s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [02:52<22:33,  7.60s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:00<22:24,  7.60s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:07<22:18,  7.60s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:15<22:11,  7.61s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:22<22:03,  7.61s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:30<21:57,  7.62s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:38<21:53,  7.64s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:45<21:45,  7.63s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [03:53<21:34,  7.61s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:01<21:29,  7.63s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:08<21:20,  7.62s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:16<21:12,  7.62s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:23<21:04,  7.62s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:31<20:56,  7.61s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:39<20:47,  7.61s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [04:46<20:41,  7.61s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [04:54<20:32,  7.61s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:01<20:24,  7.60s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:09<20:17,  7.61s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:17<20:09,  7.61s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:24<20:05,  7.63s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:32<20:00,  7.65s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:40<19:55,  7.67s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [05:48<19:57,  7.73s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [05:55<19:44,  7.69s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:03<19:32,  7.66s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [06:10<19:23,  7.66s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [06:18<19:13,  7.64s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [06:26<19:03,  7.62s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [06:33<18:52,  7.60s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [06:41<18:45,  7.60s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [06:48<18:37,  7.60s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [06:56<18:32,  7.62s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [07:04<18:22,  7.61s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [07:19<18:49,  7.85s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:12<41:46, 12.59s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:20<32:12,  9.76s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:28<28:51,  8.79s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:35<27:12,  8.33s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:43<26:23,  8.12s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:51<25:48,  7.98s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:58<25:23,  7.90s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:06<24:59,  7.81s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:14<24:42,  7.76s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:21<24:28,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:29<24:16,  7.70s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:37<24:02,  7.67s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:44<23:50,  7.65s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:52<23:43,  7.66s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [01:59<23:35,  7.65s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:07<23:29,  7.66s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:15<23:37,  7.75s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:24<24:11,  7.97s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:33<25:39,  8.50s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:42<25:32,  8.51s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:50<25:18,  8.48s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [02:58<24:43,  8.33s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:06<23:56,  8.12s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:13<23:21,  7.96s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:21<22:55,  7.86s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:29<22:35,  7.79s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:36<22:21,  7.75s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:44<22:09,  7.73s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:52<21:59,  7.72s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [03:59<21:52,  7.72s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:07<21:47,  7.74s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:15<21:37,  7.73s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:23<21:28,  7.72s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:30<21:17,  7.70s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:38<21:14,  7.72s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:46<21:07,  7.73s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [04:53<20:59,  7.73s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:01<20:52,  7.73s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:09<20:45,  7.73s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:17<20:56,  7.86s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:25<20:43,  7.82s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:32<20:27,  7.77s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:40<20:13,  7.73s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:48<19:58,  7.68s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [05:55<19:49,  7.67s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:03<19:40,  7.67s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:11<19:30,  7.65s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [06:18<19:21,  7.64s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [06:26<19:14,  7.65s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [06:34<19:08,  7.66s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [06:41<19:00,  7.65s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [06:49<18:56,  7.68s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [06:57<18:47,  7.67s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [07:04<18:40,  7.67s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [07:12<18:37,  7.71s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [07:20<18:34,  7.74s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [07:28<18:28,  7.75s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [07:36<18:24,  7.78s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [07:51<18:47,  7.99s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:12<41:01, 12.37s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:19<31:35,  9.57s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:27<28:27,  8.67s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:35<27:05,  8.29s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:42<26:08,  8.05s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:50<25:34,  7.91s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:58<25:10,  7.83s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:05<24:59,  7.81s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:13<24:46,  7.78s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:21<24:41,  7.79s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:29<24:28,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:36<24:20,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:44<24:09,  7.75s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:52<23:59,  7.74s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:00<23:59,  7.78s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:08<23:49,  7.77s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:15<23:49,  7.81s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:23<23:32,  7.76s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:31<23:17,  7.72s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:38<23:05,  7.70s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:46<22:53,  7.67s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [02:54<22:40,  7.65s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:01<22:30,  7.63s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:09<22:22,  7.63s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:16<22:15,  7.63s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:24<22:06,  7.63s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:32<21:59,  7.63s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:39<21:56,  7.65s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:47<21:50,  7.66s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [03:55<21:48,  7.70s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:03<21:42,  7.71s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:10<21:33,  7.70s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:18<21:41,  7.79s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:26<21:36,  7.81s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:34<21:19,  7.75s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:41<21:07,  7.73s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [04:49<20:52,  7.69s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [04:57<20:43,  7.67s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:04<20:34,  7.67s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:12<20:23,  7.65s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:20<20:17,  7.65s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:27<20:07,  7.64s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:35<19:57,  7.63s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:42<19:52,  7.64s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [05:50<19:44,  7.65s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [05:58<19:37,  7.65s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:06<19:34,  7.68s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [06:13<19:27,  7.68s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [06:21<19:20,  7.69s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [06:29<19:20,  7.73s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [06:37<19:19,  7.78s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [06:45<19:35,  7.94s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [06:53<19:30,  7.96s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [07:01<19:08,  7.87s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [07:08<18:50,  7.80s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [07:16<18:36,  7.76s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [07:24<18:23,  7.72s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [07:31<18:12,  7.70s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [07:39<18:00,  7.66s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [07:46<17:52,  7.66s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [07:54<17:43,  7.65s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [08:02<17:32,  7.63s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [08:17<18:01,  7.90s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:05,  5.21s/it]\u001b[A\n",
+      "Evaluating NLL: 10it [00:05,  2.58it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:09,  1.62it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.06s/it]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 11.40it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.48it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.22s/it]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01,  9.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.04s/it]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 11.55it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.02it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.04s/it]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 10.48it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.84it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.07s/it]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 11.27it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.12s/it]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01,  9.82it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.27s/it]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01,  8.76it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.65it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.31s/it]\u001b[A\n",
+      "Evaluating NLL: 41it [00:02, 23.64it/s]\u001b[A\n",
+      "Evaluating NLL: 83it [00:02, 53.98it/s]\u001b[A\n",
+      "Evaluating NLL: 123it [00:02, 88.09it/s]\u001b[A\n",
+      "Evaluating NLL: 160it [00:02, 122.53it/s]\u001b[A\n",
+      "Evaluating NLL: 196it [00:02, 153.92it/s]\u001b[A\n",
+      "Evaluating NLL: 230it [00:03, 158.89it/s]\u001b[A\n",
+      "Evaluating NLL: 259it [00:03, 173.25it/s]\u001b[A\n",
+      "Evaluating NLL: 286it [00:03, 171.88it/s]\u001b[A\n",
+      "Evaluating NLL: 310it [00:03, 184.07it/s]\u001b[A\n",
+      "Evaluating NLL: 334it [00:03, 192.12it/s]\u001b[A\n",
+      "Evaluating NLL: 357it [00:03, 185.31it/s]\u001b[A\n",
+      "Evaluating NLL: 379it [00:03, 193.00it/s]\u001b[A\n",
+      "Evaluating NLL: 401it [00:03, 179.21it/s]\u001b[A\n",
+      "Evaluating NLL: 421it [00:04, 179.73it/s]\u001b[A\n",
+      "Evaluating NLL: 446it [00:04, 197.18it/s]\u001b[A\n",
+      "Evaluating NLL: 468it [00:04, 202.89it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:04, 113.28it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%|     | 1/100 [00:00<00:42,  2.35it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%|▍    | 9/100 [00:00<00:04, 21.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  17%|▋   | 17/100 [00:00<00:02, 36.08it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|█   | 25/100 [00:00<00:01, 46.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|█▎  | 33/100 [00:00<00:01, 54.27it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|█▋  | 41/100 [00:00<00:00, 59.52it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|█▉  | 48/100 [00:01<00:00, 59.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|██▏ | 56/100 [00:01<00:00, 63.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|██▌ | 64/100 [00:01<00:00, 68.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|██▉ | 72/100 [00:01<00:00, 71.36it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:01<00:00, 73.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  89%|███▌| 89/100 [00:01<00:00, 76.08it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 57.04it/s]\n",
+      "\n",
+      "  0%|                                                          | 0/5000 [00:00<?, ?it/s]\u001b[A\n",
+      "  0%|▏                                               | 19/5000 [00:00<00:26, 185.63it/s]\u001b[A\n",
+      "  1%|▍                                               | 49/5000 [00:00<00:19, 250.36it/s]\u001b[A\n",
+      "  2%|▊                                               | 80/5000 [00:00<00:17, 274.86it/s]\u001b[A\n",
+      "  2%|█                                              | 115/5000 [00:00<00:16, 303.73it/s]\u001b[A\n",
+      "  3%|█▌                                             | 167/5000 [00:00<00:12, 377.13it/s]\u001b[A\n",
+      "  4%|██                                             | 220/5000 [00:00<00:11, 425.09it/s]\u001b[A\n",
+      "  5%|██▍                                            | 263/5000 [00:00<00:11, 413.91it/s]\u001b[A\n",
+      "  6%|██▊                                            | 305/5000 [00:00<00:11, 409.15it/s]\u001b[A\n",
+      "  7%|███▎                                           | 346/5000 [00:00<00:12, 380.72it/s]\u001b[A\n",
+      "  8%|███▌                                           | 385/5000 [00:01<00:12, 366.97it/s]\u001b[A\n",
+      "  8%|███▉                                           | 422/5000 [00:01<00:12, 355.77it/s]\u001b[A\n",
+      "  9%|████▎                                          | 460/5000 [00:01<00:12, 359.78it/s]\u001b[A\n",
+      " 10%|████▋                                          | 497/5000 [00:01<00:12, 358.31it/s]\u001b[A\n",
+      " 11%|█████                                          | 535/5000 [00:01<00:12, 363.39it/s]\u001b[A\n",
+      " 11%|█████▍                                         | 572/5000 [00:01<00:12, 361.64it/s]\u001b[A\n",
+      " 12%|█████▋                                         | 609/5000 [00:01<00:13, 329.05it/s]\u001b[A\n",
+      " 13%|██████▏                                        | 661/5000 [00:01<00:11, 377.93it/s]\u001b[A\n",
+      " 14%|██████▌                                        | 702/5000 [00:01<00:11, 385.47it/s]\u001b[A\n",
+      " 15%|██████▉                                        | 742/5000 [00:02<00:11, 381.95it/s]\u001b[A\n",
+      " 16%|███████▎                                       | 784/5000 [00:02<00:10, 390.44it/s]\u001b[A\n",
+      " 16%|███████▋                                       | 824/5000 [00:02<00:10, 384.80it/s]\u001b[A\n",
+      " 17%|████████▏                                      | 868/5000 [00:02<00:10, 399.81it/s]\u001b[A\n",
+      " 18%|████████▋                                      | 919/5000 [00:02<00:09, 431.61it/s]\u001b[A\n",
+      " 19%|█████████                                      | 969/5000 [00:02<00:08, 451.06it/s]\u001b[A\n",
+      " 20%|█████████▎                                    | 1015/5000 [00:02<00:09, 442.42it/s]\u001b[A\n",
+      " 21%|█████████▊                                    | 1060/5000 [00:02<00:09, 423.68it/s]\u001b[A\n",
+      " 22%|██████████▏                                   | 1103/5000 [00:02<00:09, 407.61it/s]\u001b[A\n",
+      " 23%|██████████▋                                   | 1157/5000 [00:02<00:08, 442.60it/s]\u001b[A\n",
+      " 24%|███████████                                   | 1209/5000 [00:03<00:08, 463.86it/s]\u001b[A\n",
+      " 25%|███████████▌                                  | 1260/5000 [00:03<00:07, 473.14it/s]\u001b[A\n",
+      " 26%|████████████                                  | 1311/5000 [00:03<00:07, 482.49it/s]\u001b[A\n",
+      " 27%|████████████▌                                 | 1360/5000 [00:03<00:08, 440.77it/s]\u001b[A\n",
+      " 28%|████████████▉                                 | 1405/5000 [00:03<00:09, 391.81it/s]\u001b[A\n",
+      " 29%|█████████████▍                                | 1459/5000 [00:03<00:08, 428.05it/s]\u001b[A\n",
+      " 30%|█████████████▉                                | 1513/5000 [00:03<00:07, 456.02it/s]\u001b[A\n",
+      " 31%|██████████████▎                               | 1560/5000 [00:03<00:07, 458.11it/s]\u001b[A\n",
+      " 32%|██████████████▊                               | 1614/5000 [00:03<00:07, 480.45it/s]\u001b[A\n",
+      " 33%|███████████████▎                              | 1667/5000 [00:04<00:06, 493.37it/s]\u001b[A\n",
+      " 34%|███████████████▊                              | 1717/5000 [00:04<00:06, 486.91it/s]\u001b[A\n",
+      " 35%|████████████████▎                             | 1767/5000 [00:04<00:06, 484.46it/s]\u001b[A\n",
+      " 36%|████████████████▊                             | 1823/5000 [00:04<00:06, 504.98it/s]\u001b[A\n",
+      " 37%|█████████████████▏                            | 1874/5000 [00:04<00:06, 480.52it/s]\u001b[A\n",
+      " 38%|█████████████████▋                            | 1923/5000 [00:04<00:07, 434.08it/s]\u001b[A\n",
+      " 39%|██████████████████                            | 1970/5000 [00:04<00:06, 443.43it/s]\u001b[A\n",
+      " 40%|██████████████████▌                           | 2016/5000 [00:04<00:06, 446.80it/s]\u001b[A\n",
+      " 41%|███████████████████                           | 2070/5000 [00:04<00:06, 471.90it/s]\u001b[A\n",
+      " 42%|███████████████████▌                          | 2121/5000 [00:05<00:05, 481.49it/s]\u001b[A\n",
+      " 43%|███████████████████▉                          | 2171/5000 [00:05<00:05, 483.43it/s]\u001b[A\n",
+      " 45%|████████████████████▍                         | 2227/5000 [00:05<00:05, 503.32it/s]\u001b[A\n",
+      " 46%|████████████████████▉                         | 2278/5000 [00:05<00:05, 503.88it/s]\u001b[A\n",
+      " 47%|█████████████████████▍                        | 2329/5000 [00:05<00:05, 450.99it/s]\u001b[A\n",
+      " 48%|█████████████████████▊                        | 2376/5000 [00:05<00:06, 420.85it/s]\u001b[A\n",
+      " 48%|██████████████████████▎                       | 2420/5000 [00:05<00:06, 413.05it/s]\u001b[A\n",
+      " 49%|██████████████████████▋                       | 2472/5000 [00:05<00:05, 440.43it/s]\u001b[A\n",
+      " 51%|███████████████████████▎                      | 2529/5000 [00:05<00:05, 475.20it/s]\u001b[A\n",
+      " 52%|███████████████████████▋                      | 2580/5000 [00:06<00:05, 483.85it/s]\u001b[A\n",
+      " 53%|████████████████████████▎                     | 2636/5000 [00:06<00:04, 504.11it/s]\u001b[A\n",
+      " 54%|████████████████████████▋                     | 2687/5000 [00:06<00:04, 494.80it/s]\u001b[A\n",
+      " 55%|█████████████████████████▏                    | 2737/5000 [00:06<00:04, 463.25it/s]\u001b[A\n",
+      " 56%|█████████████████████████▌                    | 2784/5000 [00:06<00:04, 446.38it/s]\u001b[A\n",
+      " 57%|██████████████████████████                    | 2836/5000 [00:06<00:04, 465.02it/s]\u001b[A\n",
+      " 58%|██████████████████████████▌                   | 2887/5000 [00:06<00:04, 477.30it/s]\u001b[A\n",
+      " 59%|███████████████████████████                   | 2938/5000 [00:06<00:04, 483.82it/s]\u001b[A\n",
+      " 60%|███████████████████████████▍                  | 2987/5000 [00:06<00:04, 444.21it/s]\u001b[A\n",
+      " 61%|███████████████████████████▉                  | 3040/5000 [00:07<00:04, 466.54it/s]\u001b[A\n",
+      " 62%|████████████████████████████▍                 | 3093/5000 [00:07<00:03, 483.12it/s]\u001b[A\n",
+      " 63%|████████████████████████████▉                 | 3142/5000 [00:07<00:03, 466.86it/s]\u001b[A\n",
+      " 64%|█████████████████████████████▎                | 3190/5000 [00:07<00:03, 468.83it/s]\u001b[A\n",
+      " 65%|█████████████████████████████▊                | 3238/5000 [00:07<00:04, 415.32it/s]\u001b[A\n",
+      " 66%|██████████████████████████████▏               | 3281/5000 [00:07<00:04, 396.12it/s]\u001b[A\n",
+      " 67%|██████████████████████████████▋               | 3330/5000 [00:07<00:03, 420.39it/s]\u001b[A\n",
+      " 68%|███████████████████████████████               | 3376/5000 [00:07<00:03, 429.37it/s]\u001b[A\n",
+      " 69%|███████████████████████████████▌              | 3426/5000 [00:07<00:03, 445.25it/s]\u001b[A\n",
+      " 70%|████████████████████████████████              | 3479/5000 [00:08<00:03, 467.74it/s]\u001b[A\n",
+      " 71%|████████████████████████████████▌             | 3534/5000 [00:08<00:02, 490.45it/s]\u001b[A\n",
+      " 72%|█████████████████████████████████             | 3589/5000 [00:08<00:02, 505.87it/s]\u001b[A\n",
+      " 73%|█████████████████████████████████▍            | 3640/5000 [00:08<00:02, 495.46it/s]\u001b[A\n",
+      " 74%|█████████████████████████████████▉            | 3690/5000 [00:08<00:02, 495.69it/s]\u001b[A\n",
+      " 75%|██████████████████████████████████▍           | 3740/5000 [00:08<00:02, 460.37it/s]\u001b[A\n",
+      " 76%|██████████████████████████████████▊           | 3787/5000 [00:08<00:02, 438.33it/s]\u001b[A\n",
+      " 77%|███████████████████████████████████▎          | 3832/5000 [00:08<00:02, 417.10it/s]\u001b[A\n",
+      " 78%|███████████████████████████████████▋          | 3875/5000 [00:08<00:02, 412.43it/s]\u001b[A\n",
+      " 78%|████████████████████████████████████          | 3921/5000 [00:09<00:02, 424.85it/s]\u001b[A\n",
+      " 79%|████████████████████████████████████▌         | 3973/5000 [00:09<00:02, 450.42it/s]\u001b[A\n",
+      " 81%|█████████████████████████████████████         | 4029/5000 [00:09<00:02, 481.26it/s]\u001b[A\n",
+      " 82%|█████████████████████████████████████▌        | 4084/5000 [00:09<00:01, 499.60it/s]\u001b[A\n",
+      " 83%|██████████████████████████████████████        | 4139/5000 [00:09<00:01, 513.11it/s]\u001b[A\n",
+      " 84%|██████████████████████████████████████▌       | 4195/5000 [00:09<00:01, 524.70it/s]\u001b[A\n",
+      " 85%|███████████████████████████████████████       | 4248/5000 [00:09<00:01, 523.53it/s]\u001b[A\n",
+      " 86%|███████████████████████████████████████▌      | 4301/5000 [00:09<00:01, 512.68it/s]\u001b[A\n",
+      " 87%|████████████████████████████████████████      | 4357/5000 [00:09<00:01, 523.13it/s]\u001b[A\n",
+      " 88%|████████████████████████████████████████▌     | 4410/5000 [00:09<00:01, 498.49it/s]\u001b[A\n",
+      " 89%|█████████████████████████████████████████     | 4461/5000 [00:10<00:01, 442.50it/s]\u001b[A\n",
+      " 90%|█████████████████████████████████████████▍    | 4507/5000 [00:10<00:01, 408.78it/s]\u001b[A\n",
+      " 91%|█████████████████████████████████████████▉    | 4564/5000 [00:10<00:00, 448.89it/s]\u001b[A\n",
+      " 92%|██████████████████████████████████████████▍   | 4612/5000 [00:10<00:00, 456.92it/s]\u001b[A\n",
+      " 93%|██████████████████████████████████████████▊   | 4659/5000 [00:10<00:00, 453.15it/s]\u001b[A\n",
+      " 94%|███████████████████████████████████████████▎  | 4706/5000 [00:10<00:00, 454.56it/s]\u001b[A\n",
+      " 95%|███████████████████████████████████████████▋  | 4755/5000 [00:10<00:00, 462.62it/s]\u001b[A\n",
+      " 96%|████████████████████████████████████████████▎ | 4810/5000 [00:10<00:00, 487.00it/s]\u001b[A\n",
+      " 97%|████████████████████████████████████████████▋ | 4863/5000 [00:10<00:00, 495.51it/s]\u001b[A\n",
+      " 98%|█████████████████████████████████████████████▏| 4914/5000 [00:11<00:00, 497.39it/s]\u001b[A\n",
+      "100%|██████████████████████████████████████████████| 5000/5000 [00:11<00:00, 445.25it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:12<42:16, 12.75s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:20<32:08,  9.74s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:27<28:44,  8.75s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:35<27:10,  8.32s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:43<26:15,  8.08s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:50<25:40,  7.94s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:58<25:20,  7.88s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:06<25:08,  7.86s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:14<24:54,  7.82s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:22<24:42,  7.80s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:29<24:35,  7.81s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:37<24:26,  7.80s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:45<24:15,  7.78s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:53<24:20,  7.85s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:01<24:01,  7.79s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:08<23:56,  7.81s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:16<23:41,  7.77s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:24<23:27,  7.73s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:31<23:14,  7.70s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:39<23:02,  7.68s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:47<22:54,  7.68s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [02:54<22:45,  7.67s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:02<22:39,  7.68s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:10<22:35,  7.70s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:18<22:31,  7.72s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:25<22:25,  7.73s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:33<22:15,  7.72s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:41<22:06,  7.71s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:48<21:55,  7.69s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [03:56<21:50,  7.71s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:04<21:53,  7.77s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:12<21:49,  7.80s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:20<21:56,  7.89s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:28<21:55,  7.92s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:36<21:34,  7.84s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:43<21:16,  7.79s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [04:51<21:02,  7.75s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [04:59<20:50,  7.72s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:06<20:38,  7.69s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:14<20:32,  7.70s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:22<20:23,  7.69s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:29<20:13,  7.68s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:37<20:03,  7.67s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:44<19:55,  7.66s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [05:52<19:47,  7.66s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:00<19:41,  7.67s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:15<20:23,  8.00s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<44:23, 13.38s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:21<33:56, 10.29s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:29<29:44,  9.06s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:36<27:41,  8.48s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:44<26:33,  8.17s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:52<25:53,  8.01s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:59<25:22,  7.89s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:07<25:01,  7.82s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:15<24:48,  7.79s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:22<24:36,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:30<24:25,  7.75s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:38<24:17,  7.75s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:45<24:07,  7.74s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:53<24:09,  7.79s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:01<24:11,  7.84s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:09<23:54,  7.80s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:17<23:40,  7.76s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:24<23:27,  7.73s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:32<23:16,  7.71s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:40<23:22,  7.79s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:48<23:06,  7.75s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [02:55<22:53,  7.71s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:03<22:40,  7.69s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:11<22:29,  7.67s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:18<22:18,  7.65s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:26<22:12,  7.66s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:34<22:07,  7.67s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:41<21:59,  7.67s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:49<21:50,  7.66s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [03:56<21:42,  7.66s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:04<21:42,  7.71s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:12<21:29,  7.68s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:20<21:20,  7.67s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:27<21:13,  7.67s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:35<21:06,  7.68s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:43<20:59,  7.68s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [04:50<20:58,  7.72s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [04:58<20:50,  7.72s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:06<20:49,  7.76s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:14<20:34,  7.72s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:21<20:23,  7.69s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:29<20:14,  7.69s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:37<20:02,  7.66s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:44<19:57,  7.68s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [05:52<19:51,  7.69s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:00<19:46,  7.70s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:07<19:37,  7.70s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [06:15<19:28,  7.69s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [06:23<19:32,  7.77s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [06:38<19:56,  7.98s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:12<42:42, 12.88s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:20<32:22,  9.81s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:28<29:20,  8.94s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:36<27:32,  8.43s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:43<26:28,  8.15s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:51<25:49,  7.99s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:59<25:21,  7.88s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:06<25:02,  7.83s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:14<24:46,  7.78s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:22<24:35,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:29<24:25,  7.75s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:37<24:16,  7.75s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:45<24:07,  7.74s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:53<24:01,  7.75s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:00<23:48,  7.72s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:08<23:48,  7.76s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:16<23:37,  7.75s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:24<23:26,  7.73s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:31<23:22,  7.75s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:39<23:06,  7.70s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:47<22:54,  7.68s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [02:54<22:42,  7.66s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:02<22:37,  7.67s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:10<22:28,  7.66s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:17<22:19,  7.65s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:25<22:12,  7.66s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:32<22:04,  7.66s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:40<21:55,  7.65s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:48<21:48,  7.65s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [03:55<21:43,  7.67s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:03<21:37,  7.68s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:11<21:36,  7.72s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:19<21:28,  7.72s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:27<21:30,  7.77s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:34<21:24,  7.79s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:42<21:13,  7.76s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [04:50<21:06,  7.77s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [04:58<20:50,  7.72s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:05<20:37,  7.69s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:13<20:26,  7.66s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:20<20:20,  7.68s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:28<20:10,  7.66s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:36<20:01,  7.66s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:43<19:53,  7.65s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [05:51<19:43,  7.64s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [05:59<19:35,  7.63s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:06<19:31,  7.66s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [06:14<19:26,  7.68s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [06:22<19:19,  7.68s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [06:29<19:12,  7.68s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [06:45<19:44,  7.95s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<44:42, 13.48s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:21<33:04, 10.02s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:28<29:17,  8.92s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:36<27:26,  8.40s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:43<26:26,  8.14s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:51<25:52,  8.00s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [00:59<25:29,  7.93s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:07<25:10,  7.87s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:14<24:55,  7.83s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:22<24:43,  7.81s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:30<24:34,  7.80s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:38<24:24,  7.79s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [01:46<24:18,  7.80s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [01:53<24:14,  7.82s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:01<24:09,  7.83s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:09<23:58,  7.82s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:17<23:45,  7.79s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:25<23:37,  7.79s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:33<23:42,  7.86s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [02:40<23:25,  7.81s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [02:48<23:10,  7.77s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [02:56<23:02,  7.76s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:04<23:06,  7.83s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:12<23:15,  7.93s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:20<23:18,  7.99s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:28<23:00,  7.93s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [03:36<22:44,  7.89s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [03:43<22:35,  7.88s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [03:51<22:23,  7.86s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [03:59<22:14,  7.85s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:07<22:12,  7.89s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:15<22:05,  7.89s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [04:23<22:04,  7.93s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [04:32<22:26,  8.11s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [04:40<22:46,  8.28s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [04:49<22:42,  8.31s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [04:57<22:14,  8.19s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:05<22:07,  8.19s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:13<21:39,  8.07s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [05:20<21:18,  7.99s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [05:28<21:02,  7.94s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [05:36<20:45,  7.89s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [05:44<20:30,  7.83s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [05:51<20:17,  7.81s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [05:59<20:07,  7.79s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:07<20:03,  7.81s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [06:15<19:56,  7.82s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [06:23<19:49,  7.83s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [06:31<19:46,  7.86s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [06:46<20:20,  8.14s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<43:30, 13.12s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:21<33:22, 10.12s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:28<29:43,  9.05s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:37<28:26,  8.70s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:44<27:12,  8.37s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:52<26:31,  8.20s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:00<25:58,  8.08s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:08<25:31,  7.97s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:16<25:10,  7.91s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:23<24:52,  7.86s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:31<24:40,  7.83s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:39<24:28,  7.81s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:47<24:18,  7.80s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:54<24:07,  7.78s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:02<24:01,  7.79s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:10<24:09,  7.88s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:18<23:57,  7.85s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:26<23:42,  7.82s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:34<23:34,  7.81s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:42<23:32,  7.85s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:49<23:25,  7.85s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:58<23:32,  7.94s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:05<23:14,  7.88s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:13<22:57,  7.83s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:21<22:47,  7.82s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:28<22:33,  7.78s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:36<22:23,  7.77s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:44<22:15,  7.77s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:52<22:08,  7.77s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:00<22:00,  7.77s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:07<21:54,  7.78s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:15<21:49,  7.80s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:23<21:36,  7.76s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:31<21:24,  7.74s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:39<21:30,  7.82s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:46<21:15,  7.78s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:54<21:05,  7.76s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:02<20:55,  7.75s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:09<20:48,  7.76s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:17<20:39,  7.75s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:25<20:29,  7.73s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:33<20:28,  7.78s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:40<20:14,  7.74s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:48<20:04,  7.72s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:56<19:56,  7.72s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:03<19:49,  7.72s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:19<20:35,  8.08s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<43:51, 13.22s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:20<32:59, 10.00s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:28<29:21,  8.94s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:36<27:38,  8.46s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:44<26:54,  8.28s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:52<26:12,  8.11s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:00<25:53,  8.05s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:07<25:34,  7.99s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:15<25:21,  7.96s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:23<25:05,  7.92s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:31<24:55,  7.91s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:39<24:46,  7.91s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:47<24:48,  7.96s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:55<24:25,  7.88s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:02<24:09,  7.83s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:10<23:54,  7.80s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:18<23:42,  7.77s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:26<23:32,  7.76s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:33<23:22,  7.75s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:41<23:14,  7.75s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:49<23:04,  7.73s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:56<22:55,  7.73s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:04<22:48,  7.73s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:12<22:43,  7.75s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:20<22:40,  7.77s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:27<22:26,  7.74s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:35<22:16,  7.73s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:43<22:08,  7.72s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:51<22:10,  7.78s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:58<21:55,  7.74s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:06<21:43,  7.71s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:14<21:32,  7.69s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:21<21:19,  7.66s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:29<21:11,  7.66s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:37<21:01,  7.65s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:44<20:55,  7.66s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:52<20:49,  7.67s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:00<20:42,  7.67s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:08<20:46,  7.74s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:15<20:44,  7.78s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:23<20:34,  7.76s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:31<20:22,  7.74s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:39<20:13,  7.73s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:46<20:05,  7.73s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:54<19:56,  7.72s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:02<19:47,  7.71s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:09<19:43,  7.74s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:17<19:30,  7.70s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:25<19:21,  7.69s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:32<19:09,  7.66s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:40<19:01,  7.66s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:48<18:49,  7.63s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [06:55<18:43,  7.64s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [07:11<19:25,  7.98s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<44:50, 13.52s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:21<33:58, 10.29s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:29<29:52,  9.10s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:36<27:59,  8.57s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:44<26:54,  8.28s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:52<26:13,  8.11s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:00<25:45,  8.01s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:08<25:29,  7.96s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:16<25:12,  7.92s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:23<24:47,  7.83s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:31<24:27,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:38<24:12,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:46<23:59,  7.70s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:54<23:47,  7.67s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:01<23:39,  7.67s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:09<23:31,  7.67s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:17<23:24,  7.68s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:24<23:17,  7.68s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:32<23:14,  7.70s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:40<23:17,  7.76s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:48<23:02,  7.73s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:55<22:51,  7.70s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:03<22:42,  7.70s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:11<22:40,  7.73s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:19<22:30,  7.72s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:26<22:17,  7.69s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:34<22:07,  7.67s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:41<21:59,  7.67s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:49<21:50,  7.66s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:57<21:41,  7.66s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:04<21:33,  7.65s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:12<21:28,  7.67s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:20<21:23,  7.69s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:28<21:20,  7.71s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:35<21:11,  7.71s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:43<21:05,  7.72s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:52<21:49,  8.03s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:00<22:11,  8.22s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:08<21:37,  8.06s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:16<21:15,  7.97s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:24<20:55,  7.90s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:31<20:39,  7.84s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:39<20:33,  7.86s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:47<20:15,  7.79s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:54<20:01,  7.75s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:02<19:49,  7.72s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:10<19:38,  7.70s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:17<19:27,  7.68s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:25<19:19,  7.68s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:33<19:12,  7.69s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:40<19:04,  7.68s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:48<18:56,  7.68s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [06:56<18:49,  7.68s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [07:04<18:43,  7.70s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:19<19:18,  7.99s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<44:23, 13.38s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:21<33:25, 10.13s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:28<29:29,  8.98s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:36<27:41,  8.48s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:44<26:40,  8.21s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:52<26:05,  8.07s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:59<25:41,  7.99s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:08<25:56,  8.11s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:16<26:16,  8.26s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:24<25:44,  8.13s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:32<25:11,  8.00s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:40<24:43,  7.89s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:47<24:28,  7.85s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:55<24:11,  7.80s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:03<24:00,  7.79s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:11<23:52,  7.79s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:18<23:42,  7.77s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:26<23:38,  7.80s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:34<23:34,  7.81s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:42<23:16,  7.76s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:49<23:02,  7.72s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:57<22:51,  7.71s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:05<22:39,  7.68s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:12<22:28,  7.66s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:20<22:21,  7.66s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:27<22:12,  7.66s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:35<22:05,  7.66s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:43<21:57,  7.66s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:51<21:52,  7.68s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:58<21:44,  7.67s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:06<21:39,  7.69s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:14<21:33,  7.70s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:21<21:27,  7.71s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:29<21:22,  7.72s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:37<21:17,  7.74s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:45<21:16,  7.78s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:53<21:21,  7.86s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:00<21:02,  7.80s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:09<21:15,  7.92s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:16<20:55,  7.85s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:24<20:37,  7.78s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:32<20:50,  7.91s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:40<20:29,  7.83s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:47<20:11,  7.76s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:55<19:59,  7.74s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:03<19:48,  7.72s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:11<19:40,  7.71s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:18<19:30,  7.70s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:26<19:22,  7.70s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:34<19:17,  7.71s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:41<19:07,  7.70s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:49<19:03,  7.73s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [07:05<19:40,  8.03s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.83it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.23it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.48it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.02it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.94it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:00, 12.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:00, 11.99it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.49it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.66it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.77it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:00, 12.00it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.98it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.78it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 56it [00:01, 74.88it/s]\u001b[A\n",
+      "Evaluating NLL: 108it [00:01, 146.33it/s]\u001b[A\n",
+      "Evaluating NLL: 163it [00:01, 221.13it/s]\u001b[A\n",
+      "Evaluating NLL: 210it [00:01, 245.68it/s]\u001b[A\n",
+      "Evaluating NLL: 269it [00:01, 317.87it/s]\u001b[A\n",
+      "Evaluating NLL: 327it [00:01, 378.50it/s]\u001b[A\n",
+      "Evaluating NLL: 380it [00:01, 415.44it/s]\u001b[A\n",
+      "Evaluating NLL: 434it [00:01, 446.75it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 263.46it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|               | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%|▎      | 5/100 [00:00<00:01, 48.06it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%|▋     | 12/100 [00:00<00:01, 58.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|█▏    | 20/100 [00:00<00:01, 66.65it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|█▋    | 29/100 [00:00<00:00, 72.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|██▎   | 38/100 [00:00<00:00, 75.63it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|██▊   | 46/100 [00:00<00:00, 76.64it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|███▏  | 54/100 [00:00<00:00, 71.19it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|███▋  | 62/100 [00:00<00:00, 64.86it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|████▏ | 70/100 [00:01<00:00, 67.74it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|████▋ | 78/100 [00:01<00:00, 68.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|█████▏| 87/100 [00:01<00:00, 71.88it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█████| 100/100 [00:01<00:00, 70.77it/s]\n",
+      " 33%|███████████████▋                               | 1/3 [2:01:08<4:02:17, 7268.60s/it]\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<44:27, 13.41s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:21<32:59, 10.00s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:28<29:16,  8.92s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:36<27:31,  8.43s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:44<26:36,  8.19s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:51<26:03,  8.06s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:59<25:53,  8.05s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:07<25:32,  7.98s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:15<25:04,  7.87s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:23<24:49,  7.84s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:30<24:29,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:38<24:13,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:46<23:58,  7.69s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:53<23:46,  7.67s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:01<23:34,  7.65s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:08<23:26,  7.64s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:16<23:17,  7.64s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:24<23:10,  7.64s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:31<23:07,  7.67s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:39<23:17,  7.76s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:48<24:02,  8.06s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:57<24:43,  8.33s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:06<24:51,  8.43s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:15<25:10,  8.58s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:24<25:57,  8.90s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:33<25:27,  8.78s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:40<24:21,  8.45s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:48<23:27,  8.19s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:56<22:49,  8.01s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:03<22:20,  7.88s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:11<21:56,  7.79s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:18<21:40,  7.74s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:26<21:30,  7.73s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:34<21:18,  7.70s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:41<21:09,  7.69s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:49<21:01,  7.69s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:57<20:55,  7.70s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:05<20:49,  7.71s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:12<20:42,  7.72s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:21<21:25,  8.04s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:30<21:41,  8.19s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:38<21:57,  8.34s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:47<22:05,  8.44s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:56<22:07,  8.51s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:04<21:59,  8.52s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:14<22:35,  8.80s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:23<23:05,  9.05s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:33<23:23,  9.23s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:41<22:11,  8.82s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:48<21:10,  8.47s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:56<20:25,  8.22s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [07:04<19:55,  8.08s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [07:12<19:31,  7.97s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [07:19<19:13,  7.90s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:27<18:56,  7.84s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [07:35<18:42,  7.79s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [07:42<18:32,  7.78s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [07:50<18:17,  7.73s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [07:58<18:05,  7.70s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [08:05<17:55,  7.68s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [08:13<17:46,  7.67s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [08:21<17:36,  7.66s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [08:28<17:28,  7.65s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [08:36<17:20,  7.65s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [08:44<17:13,  7.65s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [08:51<17:05,  7.65s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [08:59<16:57,  7.65s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [09:07<16:49,  7.65s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [09:14<16:44,  7.66s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [09:22<16:38,  7.68s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [09:37<17:29,  8.14s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:12<39:51, 12.02s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<30:59,  9.39s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:27<28:05,  8.56s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:34<26:42,  8.18s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<25:54,  7.97s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:49<25:25,  7.86s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:57<25:04,  7.80s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<24:50,  7.76s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:12<24:35,  7.72s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:20<24:21,  7.69s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:28<24:10,  7.67s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:35<23:58,  7.65s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:43<23:49,  7.64s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:51<23:39,  7.63s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:58<23:28,  7.61s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:06<23:26,  7.65s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:13<23:16,  7.63s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:21<23:05,  7.61s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:29<22:55,  7.60s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:36<22:46,  7.59s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:44<22:37,  7.59s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:51<22:29,  7.58s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [02:59<22:22,  7.58s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:07<22:16,  7.59s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:14<22:10,  7.60s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:22<22:03,  7.61s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:29<21:58,  7.62s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:37<21:50,  7.62s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:45<21:45,  7.63s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:52<21:40,  7.65s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:00<21:33,  7.65s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:08<21:31,  7.69s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:16<21:40,  7.79s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:24<22:09,  8.01s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:33<22:23,  8.14s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:41<22:35,  8.27s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:50<22:42,  8.36s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [04:58<22:39,  8.39s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:08<23:14,  8.66s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:17<23:51,  8.95s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:27<24:13,  9.14s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:35<23:03,  8.76s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:42<22:02,  8.42s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:50<21:15,  8.18s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:58<21:19,  8.26s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:07<21:28,  8.37s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:16<21:28,  8.42s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:24<21:26,  8.46s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:33<21:22,  8.50s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:41<21:17,  8.51s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:51<21:56,  8.83s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [07:01<22:21,  9.06s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [07:10<22:14,  9.08s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [07:17<20:59,  8.63s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:25<20:06,  8.32s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [07:32<19:26,  8.10s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [07:40<18:58,  7.96s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [07:48<18:37,  7.87s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [07:55<18:20,  7.81s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [08:03<18:06,  7.76s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [08:11<17:52,  7.72s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [08:18<17:46,  7.73s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [08:26<17:32,  7.68s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [08:34<17:21,  7.66s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [08:41<17:10,  7.63s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [08:49<17:00,  7.62s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [08:56<16:52,  7.61s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [09:04<16:42,  7.60s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [09:11<16:35,  7.60s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [09:19<16:27,  7.60s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [09:27<16:20,  7.60s/it]\u001b[A\n",
+      "training:  36%|███████████████                           | 72/200 [09:34<16:12,  7.60s/it]\u001b[A\n",
+      "training:  36%|███████████████▎                          | 73/200 [09:49<17:06,  8.08s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<46:14, 13.94s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:22<35:12, 10.67s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:30<31:56,  9.73s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:39<30:22,  9.30s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:48<29:20,  9.03s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:57<29:13,  9.04s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:06<29:45,  9.25s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:16<30:00,  9.38s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:24<28:41,  9.01s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:32<27:09,  8.58s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:39<26:04,  8.28s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:47<25:15,  8.06s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:55<24:40,  7.92s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [02:02<24:13,  7.81s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:10<23:52,  7.75s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:17<23:40,  7.72s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:25<23:29,  7.70s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:33<23:18,  7.68s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:40<23:05,  7.65s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:48<23:01,  7.68s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:56<22:48,  7.65s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [03:03<22:36,  7.62s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:11<22:27,  7.61s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:18<22:18,  7.61s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:26<22:10,  7.60s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:34<22:02,  7.60s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:41<21:53,  7.59s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:49<21:45,  7.59s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:56<21:39,  7.60s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:04<21:32,  7.60s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:12<21:24,  7.60s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:19<21:17,  7.60s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:27<21:14,  7.63s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:34<21:09,  7.65s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:42<21:03,  7.65s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:50<20:55,  7.66s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:57<20:48,  7.66s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:05<20:46,  7.69s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:13<20:33,  7.66s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:21<20:38,  7.74s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:29<21:07,  7.97s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:38<21:22,  8.12s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:46<21:33,  8.24s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:55<21:46,  8.38s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:05<22:35,  8.75s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:14<23:10,  9.03s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:23<23:03,  9.04s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:32<22:31,  8.89s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:41<22:11,  8.82s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:49<21:55,  8.77s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:58<21:35,  8.69s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [07:07<22:09,  8.98s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [07:17<22:30,  9.19s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [07:26<22:28,  9.24s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:35<21:44,  9.00s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [07:43<21:17,  8.87s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [07:52<20:55,  8.78s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [08:00<20:32,  8.68s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [08:10<20:51,  8.88s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [08:19<21:14,  9.10s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [08:29<21:27,  9.26s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [08:37<20:46,  9.03s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [08:46<20:19,  8.90s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [08:55<19:58,  8.81s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [09:03<19:42,  8.76s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [09:12<19:50,  8.88s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [09:22<20:10,  9.10s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [09:32<20:22,  9.26s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [09:40<19:47,  9.06s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [09:49<19:18,  8.91s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [09:57<18:55,  8.80s/it]\u001b[A\n",
+      "training:  36%|███████████████                           | 72/200 [10:15<18:13,  8.55s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:14<46:56, 14.15s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:23<36:51, 11.17s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:31<32:47,  9.99s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:40<31:09,  9.54s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:49<30:08,  9.27s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:58<29:25,  9.10s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:07<28:56,  9.00s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:16<29:34,  9.24s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:26<29:53,  9.39s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:35<29:25,  9.29s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:44<28:34,  9.07s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:52<28:00,  8.94s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [02:01<27:30,  8.83s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [02:09<27:03,  8.73s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:19<27:44,  9.00s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:29<28:10,  9.19s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:38<28:06,  9.22s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:46<27:19,  9.01s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:55<26:57,  8.94s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [03:04<26:30,  8.84s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [03:12<26:03,  8.74s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [03:22<26:36,  8.97s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:31<27:06,  9.19s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:41<27:21,  9.33s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:50<26:30,  9.09s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:58<25:57,  8.95s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [04:07<25:32,  8.86s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [04:16<25:13,  8.80s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [04:24<24:47,  8.70s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:34<25:20,  8.94s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:43<25:47,  9.16s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:53<26:06,  9.32s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [05:01<25:11,  9.05s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [05:10<24:41,  8.92s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [05:19<24:36,  8.95s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [05:29<25:02,  9.16s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [05:38<25:18,  9.31s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:47<24:39,  9.13s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:56<24:05,  8.98s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [06:04<23:35,  8.85s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [06:13<23:11,  8.75s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [06:22<23:03,  8.76s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [06:31<23:35,  9.01s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [06:41<23:58,  9.22s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:50<23:38,  9.15s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:58<23:02,  8.98s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [07:07<22:34,  8.85s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [07:15<22:11,  8.76s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [07:24<21:50,  8.68s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [07:34<22:27,  8.99s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [07:43<22:47,  9.18s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [07:53<22:48,  9.25s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [08:01<22:04,  9.01s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [08:10<21:36,  8.88s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [08:18<21:15,  8.79s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [08:27<20:52,  8.70s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [08:36<21:05,  8.85s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [08:46<21:31,  9.10s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [08:55<21:48,  9.28s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [09:04<21:12,  9.09s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [09:13<20:44,  8.95s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [09:21<20:22,  8.86s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [09:30<20:04,  8.79s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [09:39<19:46,  8.72s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [09:48<19:48,  8.81s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [09:57<20:12,  9.05s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [10:07<20:27,  9.23s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [10:15<19:57,  9.07s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [10:24<19:29,  8.93s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [10:33<19:11,  8.85s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [10:41<18:54,  8.80s/it]\u001b[A\n",
+      "training:  36%|███████████████                           | 72/200 [10:50<18:44,  8.78s/it]\u001b[A\n",
+      "training:  36%|███████████████▎                          | 73/200 [11:00<19:10,  9.06s/it]\u001b[A\n",
+      "training:  37%|███████████████▌                          | 74/200 [11:10<19:23,  9.23s/it]\u001b[A\n",
+      "training:  38%|███████████████▊                          | 75/200 [11:19<19:09,  9.19s/it]\u001b[A\n",
+      "training:  38%|███████████████▉                          | 76/200 [11:36<18:56,  9.16s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<43:28, 13.11s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:22<36:27, 11.05s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:32<34:11, 10.42s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:41<31:57,  9.78s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:49<30:33,  9.40s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:58<29:41,  9.18s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:07<28:55,  8.99s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:17<29:32,  9.23s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:26<29:50,  9.37s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:36<29:40,  9.37s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:44<28:34,  9.07s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:52<27:54,  8.91s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [02:01<27:26,  8.81s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [02:09<26:56,  8.69s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:19<27:28,  8.91s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:29<27:59,  9.13s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:38<28:17,  9.28s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:47<27:21,  9.02s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:55<26:49,  8.89s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [03:04<26:22,  8.79s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [03:12<26:09,  8.77s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [03:22<26:19,  8.87s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:31<26:53,  9.12s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:41<27:13,  9.28s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:50<26:27,  9.07s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:58<25:52,  8.93s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [04:07<25:25,  8.82s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [04:15<25:01,  8.73s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [04:24<25:01,  8.78s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:34<25:35,  9.03s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:43<25:57,  9.22s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:52<25:34,  9.14s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [05:01<25:08,  9.03s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [05:10<24:43,  8.94s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [05:18<24:20,  8.85s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [05:27<24:05,  8.81s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [05:37<24:38,  9.07s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:46<24:57,  9.24s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:56<24:44,  9.22s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [06:04<24:02,  9.02s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [06:13<23:34,  8.89s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [06:21<23:13,  8.82s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [06:30<22:45,  8.70s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [06:40<23:23,  8.99s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:49<23:45,  9.20s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:59<23:53,  9.31s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [07:07<23:08,  9.07s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [07:16<22:46,  8.99s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [07:25<22:23,  8.90s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [07:33<22:05,  8.84s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [07:42<21:39,  8.72s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [07:51<22:06,  8.97s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [08:01<22:29,  9.18s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [08:11<22:39,  9.31s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [08:19<21:53,  9.06s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [08:28<21:24,  8.92s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [08:36<21:02,  8.83s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [08:45<20:42,  8.75s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [08:54<20:57,  8.92s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [09:04<21:21,  9.15s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [09:14<21:35,  9.32s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [09:22<20:59,  9.12s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [09:31<20:29,  8.97s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [09:40<20:06,  8.87s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [09:48<19:46,  8.79s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [09:57<19:45,  8.85s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [10:07<20:11,  9.11s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [10:17<20:27,  9.30s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [10:25<19:56,  9.13s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [10:34<19:27,  8.98s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [10:51<19:44,  9.18s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:15<49:47, 15.01s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:24<39:01, 11.83s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:33<34:23, 10.48s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:42<31:50,  9.75s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:50<30:29,  9.38s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:59<29:35,  9.15s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:08<28:53,  8.98s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:17<29:23,  9.19s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:27<29:42,  9.33s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:36<29:05,  9.19s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:43<27:23,  8.70s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:51<26:11,  8.36s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:59<25:21,  8.14s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [02:06<24:46,  7.99s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:14<24:14,  7.86s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:21<23:52,  7.78s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:29<23:31,  7.72s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:37<23:15,  7.67s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:44<23:02,  7.64s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:52<22:52,  7.62s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:59<22:42,  7.61s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [03:07<22:32,  7.60s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:14<22:23,  7.59s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:22<22:14,  7.58s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:30<22:09,  7.60s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:37<22:04,  7.61s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:45<21:58,  7.62s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:53<21:53,  7.64s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [04:00<21:46,  7.64s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:08<21:38,  7.64s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:16<21:33,  7.65s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:23<21:22,  7.63s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:31<21:12,  7.62s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:38<21:03,  7.61s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:46<20:55,  7.61s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:53<20:45,  7.59s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [05:01<20:36,  7.59s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:09<20:26,  7.57s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:16<20:17,  7.56s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:24<20:09,  7.56s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:31<20:01,  7.55s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:39<19:52,  7.55s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:46<19:46,  7.55s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:54<19:41,  7.57s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:02<19:35,  7.59s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:09<19:28,  7.59s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:17<19:22,  7.60s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:24<19:13,  7.59s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:32<19:09,  7.61s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:40<18:58,  7.59s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:47<18:47,  7.57s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:55<18:37,  7.55s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [07:02<18:29,  7.55s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [07:10<18:22,  7.55s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:17<18:13,  7.54s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [07:25<18:04,  7.53s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [07:32<17:56,  7.53s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [07:40<17:49,  7.53s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [07:47<17:41,  7.52s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [07:55<17:33,  7.53s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [08:02<17:27,  7.53s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [08:10<17:21,  7.55s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [08:18<17:15,  7.56s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [08:25<17:10,  7.58s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [08:33<17:01,  7.57s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [08:40<16:53,  7.56s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [08:48<16:44,  7.55s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [08:55<16:36,  7.55s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [09:03<16:28,  7.55s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [09:10<16:20,  7.55s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [09:18<16:12,  7.54s/it]\u001b[A\n",
+      "training:  36%|███████████████                           | 72/200 [09:25<16:04,  7.54s/it]\u001b[A\n",
+      "training:  36%|███████████████▎                          | 73/200 [09:33<15:58,  7.54s/it]\u001b[A\n",
+      "training:  37%|███████████████▌                          | 74/200 [09:41<15:49,  7.54s/it]\u001b[A\n",
+      "training:  38%|███████████████▊                          | 75/200 [09:56<16:33,  7.95s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:12<41:18, 12.45s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<31:32,  9.56s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:27<28:25,  8.66s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:35<26:57,  8.25s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<26:06,  8.03s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:50<25:35,  7.92s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:58<25:11,  7.83s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<24:51,  7.77s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:13<24:36,  7.73s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:21<24:22,  7.70s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:28<24:09,  7.67s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:36<23:57,  7.64s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:43<23:48,  7.64s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:51<23:41,  7.64s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:59<23:34,  7.65s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:06<23:23,  7.63s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:14<23:23,  7.67s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:22<23:11,  7.65s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:29<22:58,  7.62s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:37<22:47,  7.60s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:44<22:37,  7.58s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:52<22:30,  7.59s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:00<22:23,  7.59s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:07<22:14,  7.58s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:15<22:07,  7.59s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:22<21:59,  7.58s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:30<21:52,  7.59s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:37<21:44,  7.58s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:45<21:37,  7.59s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:53<21:31,  7.59s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:00<21:23,  7.60s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:08<21:19,  7.62s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:15<21:11,  7.61s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:23<21:01,  7.60s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:31<20:52,  7.59s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:38<20:43,  7.58s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:46<20:37,  7.59s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [04:53<20:30,  7.60s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:01<20:23,  7.60s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:09<20:15,  7.60s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:16<20:08,  7.60s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:24<19:59,  7.59s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:31<19:52,  7.60s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:39<19:47,  7.62s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:47<19:43,  7.63s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [05:54<19:37,  7.64s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:02<19:26,  7.62s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:10<19:17,  7.62s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:17<19:14,  7.65s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:25<19:04,  7.63s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:33<18:55,  7.62s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:40<18:46,  7.61s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [06:48<18:38,  7.61s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [06:55<18:29,  7.60s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:03<18:21,  7.60s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [07:10<18:14,  7.60s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [07:18<18:06,  7.60s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [07:26<17:58,  7.59s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [07:33<17:50,  7.59s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [07:41<17:44,  7.60s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [07:48<17:36,  7.60s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [07:56<17:29,  7.60s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [08:04<17:25,  7.63s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [08:11<17:15,  7.61s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [08:19<17:05,  7.60s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [08:27<16:58,  7.60s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [08:34<16:51,  7.60s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [08:42<16:42,  7.59s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [08:49<16:34,  7.59s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [08:57<16:26,  7.59s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [09:04<16:19,  7.59s/it]\u001b[A\n",
+      "training:  36%|███████████████                           | 72/200 [09:12<16:11,  7.59s/it]\u001b[A\n",
+      "training:  36%|███████████████▎                          | 73/200 [09:20<16:03,  7.59s/it]\u001b[A\n",
+      "training:  37%|███████████████▌                          | 74/200 [09:27<15:56,  7.59s/it]\u001b[A\n",
+      "training:  38%|███████████████▊                          | 75/200 [09:35<15:49,  7.60s/it]\u001b[A\n",
+      "training:  38%|███████████████▉                          | 76/200 [09:42<15:41,  7.60s/it]\u001b[A\n",
+      "training:  38%|████████████████▏                         | 77/200 [09:50<15:38,  7.63s/it]\u001b[A\n",
+      "training:  39%|████████████████▍                         | 78/200 [09:58<15:29,  7.62s/it]\u001b[A\n",
+      "training:  40%|████████████████▌                         | 79/200 [10:05<15:19,  7.60s/it]\u001b[A\n",
+      "training:  40%|████████████████▊                         | 80/200 [10:13<15:11,  7.59s/it]\u001b[A\n",
+      "training:  40%|█████████████████                         | 81/200 [10:20<15:03,  7.60s/it]\u001b[A\n",
+      "training:  41%|█████████████████▏                        | 82/200 [10:28<14:56,  7.59s/it]\u001b[A\n",
+      "training:  42%|█████████████████▍                        | 83/200 [10:36<14:47,  7.59s/it]\u001b[A\n",
+      "training:  42%|█████████████████▋                        | 84/200 [10:43<14:39,  7.58s/it]\u001b[A\n",
+      "training:  42%|█████████████████▊                        | 85/200 [10:51<14:31,  7.58s/it]\u001b[A\n",
+      "training:  43%|██████████████████                        | 86/200 [11:06<14:43,  7.75s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:11<38:45, 11.69s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<30:43,  9.31s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:26<28:04,  8.55s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:34<26:49,  8.21s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<26:07,  8.04s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:50<25:40,  7.94s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:57<25:20,  7.88s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<24:59,  7.81s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:13<24:41,  7.76s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:20<24:27,  7.72s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:28<24:13,  7.69s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:36<23:59,  7.66s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:43<23:47,  7.64s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:51<23:37,  7.62s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:58<23:27,  7.61s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:06<23:17,  7.59s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:13<23:08,  7.59s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:21<23:01,  7.59s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:29<22:54,  7.59s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:36<22:49,  7.61s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:44<22:44,  7.62s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:52<22:38,  7.63s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [02:59<22:30,  7.63s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:07<22:22,  7.63s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:15<22:18,  7.65s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:22<22:07,  7.63s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:30<21:57,  7.62s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:37<21:47,  7.60s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:45<21:40,  7.60s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:52<21:33,  7.61s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:00<21:25,  7.61s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:08<21:18,  7.61s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:15<21:09,  7.60s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:23<21:03,  7.61s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:31<20:56,  7.62s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:38<20:49,  7.62s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:46<20:40,  7.61s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [04:53<20:37,  7.64s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:01<20:32,  7.66s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:09<20:26,  7.66s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:17<20:19,  7.67s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:24<20:18,  7.71s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:32<20:08,  7.70s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:40<19:55,  7.66s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:47<19:43,  7.64s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [05:55<19:33,  7.62s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:02<19:24,  7.61s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:10<19:15,  7.60s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:18<19:07,  7.60s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:25<18:59,  7.59s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:33<18:51,  7.59s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:40<18:44,  7.60s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [06:48<18:38,  7.61s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [06:56<18:31,  7.61s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:03<18:25,  7.62s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [07:11<18:16,  7.62s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [07:18<18:11,  7.63s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [07:26<18:02,  7.62s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [07:34<17:52,  7.61s/it]\u001b[A\n",
+      "training:  30%|████████████▌                             | 60/200 [07:41<17:44,  7.61s/it]\u001b[A\n",
+      "training:  30%|████████████▊                             | 61/200 [07:49<17:36,  7.60s/it]\u001b[A\n",
+      "training:  31%|█████████████                             | 62/200 [07:56<17:28,  7.59s/it]\u001b[A\n",
+      "training:  32%|█████████████▏                            | 63/200 [08:04<17:19,  7.59s/it]\u001b[A\n",
+      "training:  32%|█████████████▍                            | 64/200 [08:12<17:11,  7.58s/it]\u001b[A\n",
+      "training:  32%|█████████████▋                            | 65/200 [08:19<17:04,  7.59s/it]\u001b[A\n",
+      "training:  33%|█████████████▊                            | 66/200 [08:27<16:57,  7.59s/it]\u001b[A\n",
+      "training:  34%|██████████████                            | 67/200 [08:34<16:51,  7.60s/it]\u001b[A\n",
+      "training:  34%|██████████████▎                           | 68/200 [08:42<16:44,  7.61s/it]\u001b[A\n",
+      "training:  34%|██████████████▍                           | 69/200 [08:50<16:37,  7.61s/it]\u001b[A\n",
+      "training:  35%|██████████████▋                           | 70/200 [08:57<16:31,  7.63s/it]\u001b[A\n",
+      "training:  36%|██████████████▉                           | 71/200 [09:05<16:26,  7.65s/it]\u001b[A\n",
+      "training:  36%|███████████████                           | 72/200 [09:13<16:19,  7.65s/it]\u001b[A\n",
+      "training:  36%|███████████████▎                          | 73/200 [09:20<16:10,  7.64s/it]\u001b[A\n",
+      "training:  37%|███████████████▌                          | 74/200 [09:28<16:08,  7.69s/it]\u001b[A\n",
+      "training:  38%|███████████████▊                          | 75/200 [09:43<16:12,  7.78s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 12.50it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 12.63it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 12.75it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 10it [00:00, 13.29it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.13s/it]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 10.74it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 13.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 12.79it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.19it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 10it [00:01, 13.05it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.44it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 53it [00:00, 74.13it/s]\u001b[A\n",
+      "Evaluating NLL: 111it [00:01, 158.49it/s]\u001b[A\n",
+      "Evaluating NLL: 173it [00:01, 246.85it/s]\u001b[A\n",
+      "Evaluating NLL: 230it [00:01, 315.39it/s]\u001b[A\n",
+      "Evaluating NLL: 287it [00:01, 374.35it/s]\u001b[A\n",
+      "Evaluating NLL: 341it [00:01, 413.52it/s]\u001b[A\n",
+      "Evaluating NLL: 397it [00:01, 451.21it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 284.24it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|               | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|▌      | 8/100 [00:00<00:01, 76.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▉     | 16/100 [00:00<00:01, 76.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|█▍    | 24/100 [00:00<00:00, 77.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|█▉    | 32/100 [00:00<00:00, 75.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|██▍   | 40/100 [00:00<00:00, 76.50it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|██▉   | 48/100 [00:00<00:00, 76.86it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|███▎  | 56/100 [00:00<00:00, 76.39it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|███▊  | 64/100 [00:00<00:00, 77.35it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|████▎ | 72/100 [00:00<00:00, 77.64it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|████▊ | 80/100 [00:01<00:00, 78.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|█████▎| 88/100 [00:01<00:00, 77.08it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█████| 100/100 [00:01<00:00, 77.01it/s]\n",
+      "\n",
+      "  0%|                                                            | 0/5000 [00:00<?, ?it/s]\u001b[A\n",
+      "  1%|▍                                                 | 44/5000 [00:00<00:11, 437.43it/s]\u001b[A\n",
+      "  2%|▉                                                 | 91/5000 [00:00<00:10, 453.04it/s]\u001b[A\n",
+      "  3%|█▎                                               | 137/5000 [00:00<00:10, 445.62it/s]\u001b[A\n",
+      "  4%|█▊                                               | 186/5000 [00:00<00:10, 461.28it/s]\u001b[A\n",
+      "  5%|██▎                                              | 240/5000 [00:00<00:09, 488.45it/s]\u001b[A\n",
+      "  6%|██▊                                              | 290/5000 [00:00<00:09, 492.21it/s]\u001b[A\n",
+      "  7%|███▎                                             | 343/5000 [00:00<00:09, 502.65it/s]\u001b[A\n",
+      "  8%|███▊                                             | 394/5000 [00:00<00:09, 472.62it/s]\u001b[A\n",
+      "  9%|████▍                                            | 448/5000 [00:00<00:09, 492.42it/s]\u001b[A\n",
+      " 10%|████▉                                            | 498/5000 [00:01<00:09, 491.47it/s]\u001b[A\n",
+      " 11%|█████▍                                           | 549/5000 [00:01<00:08, 494.96it/s]\u001b[A\n",
+      " 12%|█████▊                                           | 599/5000 [00:01<00:09, 478.98it/s]\u001b[A\n",
+      " 13%|██████▍                                          | 651/5000 [00:01<00:08, 490.86it/s]\u001b[A\n",
+      " 14%|██████▉                                          | 706/5000 [00:01<00:08, 506.87it/s]\u001b[A\n",
+      " 15%|███████▍                                         | 762/5000 [00:01<00:08, 520.11it/s]\u001b[A\n",
+      " 16%|████████                                         | 817/5000 [00:01<00:07, 528.69it/s]\u001b[A\n",
+      " 17%|████████▌                                        | 870/5000 [00:01<00:08, 499.87it/s]\u001b[A\n",
+      " 18%|█████████                                        | 924/5000 [00:01<00:07, 509.69it/s]\u001b[A\n",
+      " 20%|█████████▌                                       | 978/5000 [00:01<00:07, 516.10it/s]\u001b[A\n",
+      " 21%|█████████▉                                      | 1030/5000 [00:02<00:08, 495.71it/s]\u001b[A\n",
+      " 22%|██████████▍                                     | 1084/5000 [00:02<00:07, 507.36it/s]\u001b[A\n",
+      " 23%|██████████▉                                     | 1135/5000 [00:02<00:07, 504.36it/s]\u001b[A\n",
+      " 24%|███████████▍                                    | 1194/5000 [00:02<00:07, 527.77it/s]\u001b[A\n",
+      " 25%|████████████                                    | 1252/5000 [00:02<00:06, 542.59it/s]\u001b[A\n",
+      " 26%|████████████▌                                   | 1307/5000 [00:02<00:07, 522.50it/s]\u001b[A\n",
+      " 27%|█████████████                                   | 1360/5000 [00:02<00:07, 508.90it/s]\u001b[A\n",
+      " 28%|█████████████▌                                  | 1413/5000 [00:02<00:06, 513.23it/s]\u001b[A\n",
+      " 29%|██████████████                                  | 1469/5000 [00:02<00:06, 524.63it/s]\u001b[A\n",
+      " 31%|██████████████▋                                 | 1528/5000 [00:03<00:06, 541.88it/s]\u001b[A\n",
+      " 32%|███████████████▏                                | 1583/5000 [00:03<00:06, 543.74it/s]\u001b[A\n",
+      " 33%|███████████████▊                                | 1642/5000 [00:03<00:06, 556.14it/s]\u001b[A\n",
+      " 34%|████████████████▎                               | 1698/5000 [00:03<00:05, 552.00it/s]\u001b[A\n",
+      " 35%|████████████████▊                               | 1754/5000 [00:03<00:06, 534.22it/s]\u001b[A\n",
+      " 36%|█████████████████▍                              | 1814/5000 [00:03<00:05, 550.61it/s]\u001b[A\n",
+      " 37%|█████████████████▉                              | 1870/5000 [00:03<00:05, 538.03it/s]\u001b[A\n",
+      " 38%|██████████████████▍                             | 1924/5000 [00:03<00:05, 524.72it/s]\u001b[A\n",
+      " 40%|██████████████████▉                             | 1977/5000 [00:03<00:05, 520.87it/s]\u001b[A\n",
+      " 41%|███████████████████▍                            | 2030/5000 [00:03<00:05, 512.46it/s]\u001b[A\n",
+      " 42%|███████████████████▉                            | 2082/5000 [00:04<00:05, 505.98it/s]\u001b[A\n",
+      " 43%|████████████████████▍                           | 2133/5000 [00:04<00:05, 500.74it/s]\u001b[A\n",
+      " 44%|████████████████████▉                           | 2186/5000 [00:04<00:05, 507.85it/s]\u001b[A\n",
+      " 45%|█████████████████████▍                          | 2237/5000 [00:04<00:05, 503.23it/s]\u001b[A\n",
+      " 46%|█████████████████████▉                          | 2291/5000 [00:04<00:05, 509.95it/s]\u001b[A\n",
+      " 47%|██████████████████████▌                         | 2348/5000 [00:04<00:05, 524.79it/s]\u001b[A\n",
+      " 48%|███████████████████████                         | 2401/5000 [00:04<00:05, 511.89it/s]\u001b[A\n",
+      " 49%|███████████████████████▌                        | 2453/5000 [00:04<00:05, 480.19it/s]\u001b[A\n",
+      " 50%|████████████████████████                        | 2508/5000 [00:04<00:05, 497.78it/s]\u001b[A\n",
+      " 51%|████████████████████████▌                       | 2561/5000 [00:05<00:04, 505.84it/s]\u001b[A\n",
+      " 52%|█████████████████████████                       | 2612/5000 [00:05<00:04, 504.99it/s]\u001b[A\n",
+      " 53%|█████████████████████████▌                      | 2665/5000 [00:05<00:04, 510.69it/s]\u001b[A\n",
+      " 54%|██████████████████████████▏                     | 2722/5000 [00:05<00:04, 527.06it/s]\u001b[A\n",
+      " 56%|██████████████████████████▋                     | 2781/5000 [00:05<00:04, 545.58it/s]\u001b[A\n",
+      " 57%|███████████████████████████▏                    | 2838/5000 [00:05<00:03, 551.09it/s]\u001b[A\n",
+      " 58%|███████████████████████████▊                    | 2894/5000 [00:05<00:03, 548.73it/s]\u001b[A\n",
+      " 59%|████████████████████████████▎                   | 2950/5000 [00:05<00:03, 549.12it/s]\u001b[A\n",
+      " 60%|████████████████████████████▊                   | 3005/5000 [00:05<00:03, 547.05it/s]\u001b[A\n",
+      " 61%|█████████████████████████████▍                  | 3063/5000 [00:05<00:03, 555.81it/s]\u001b[A\n",
+      " 62%|█████████████████████████████▉                  | 3119/5000 [00:06<00:03, 545.82it/s]\u001b[A\n",
+      " 63%|██████████████████████████████▍                 | 3174/5000 [00:06<00:03, 546.43it/s]\u001b[A\n",
+      " 65%|██████████████████████████████▉                 | 3229/5000 [00:06<00:03, 539.96it/s]\u001b[A\n",
+      " 66%|███████████████████████████████▌                | 3284/5000 [00:06<00:03, 516.49it/s]\u001b[A\n",
+      " 67%|████████████████████████████████                | 3338/5000 [00:06<00:03, 521.79it/s]\u001b[A\n",
+      " 68%|████████████████████████████████▌               | 3391/5000 [00:06<00:03, 514.98it/s]\u001b[A\n",
+      " 69%|█████████████████████████████████               | 3443/5000 [00:06<00:03, 495.07it/s]\u001b[A\n",
+      " 70%|█████████████████████████████████▌              | 3493/5000 [00:06<00:03, 483.68it/s]\u001b[A\n",
+      " 71%|██████████████████████████████████              | 3549/5000 [00:06<00:02, 505.12it/s]\u001b[A\n",
+      " 72%|██████████████████████████████████▋             | 3608/5000 [00:06<00:02, 528.72it/s]\u001b[A\n",
+      " 73%|███████████████████████████████████▏            | 3663/5000 [00:07<00:02, 533.28it/s]\u001b[A\n",
+      " 74%|███████████████████████████████████▋            | 3718/5000 [00:07<00:02, 536.65it/s]\u001b[A\n",
+      " 75%|████████████████████████████████████▏           | 3774/5000 [00:07<00:02, 543.03it/s]\u001b[A\n",
+      " 77%|████████████████████████████████████▊           | 3829/5000 [00:07<00:02, 537.80it/s]\u001b[A\n",
+      " 78%|█████████████████████████████████████▎          | 3884/5000 [00:07<00:02, 539.94it/s]\u001b[A\n",
+      " 79%|█████████████████████████████████████▊          | 3939/5000 [00:07<00:02, 527.88it/s]\u001b[A\n",
+      " 80%|██████████████████████████████████████▎         | 3993/5000 [00:07<00:01, 529.45it/s]\u001b[A\n",
+      " 81%|██████████████████████████████████████▊         | 4049/5000 [00:07<00:01, 537.08it/s]\u001b[A\n",
+      " 82%|███████████████████████████████████████▍        | 4104/5000 [00:07<00:01, 539.01it/s]\u001b[A\n",
+      " 83%|███████████████████████████████████████▉        | 4159/5000 [00:08<00:01, 542.25it/s]\u001b[A\n",
+      " 84%|████████████████████████████████████████▍       | 4215/5000 [00:08<00:01, 547.25it/s]\u001b[A\n",
+      " 85%|█████████████████████████████████████████       | 4274/5000 [00:08<00:01, 559.81it/s]\u001b[A\n",
+      " 87%|█████████████████████████████████████████▌      | 4331/5000 [00:08<00:01, 544.62it/s]\u001b[A\n",
+      " 88%|██████████████████████████████████████████▏     | 4389/5000 [00:08<00:01, 552.43it/s]\u001b[A\n",
+      " 89%|██████████████████████████████████████████▋     | 4445/5000 [00:08<00:01, 522.85it/s]\u001b[A\n",
+      " 90%|███████████████████████████████████████████▏    | 4498/5000 [00:08<00:00, 514.55it/s]\u001b[A\n",
+      " 91%|███████████████████████████████████████████▋    | 4554/5000 [00:08<00:00, 526.74it/s]\u001b[A\n",
+      " 92%|████████████████████████████████████████████▏   | 4607/5000 [00:08<00:00, 514.97it/s]\u001b[A\n",
+      " 93%|████████████████████████████████████████████▋   | 4659/5000 [00:08<00:00, 509.44it/s]\u001b[A\n",
+      " 94%|█████████████████████████████████████████████▎  | 4714/5000 [00:09<00:00, 518.39it/s]\u001b[A\n",
+      " 95%|█████████████████████████████████████████████▊  | 4770/5000 [00:09<00:00, 529.07it/s]\u001b[A\n",
+      " 97%|██████████████████████████████████████████████▎ | 4828/5000 [00:09<00:00, 541.90it/s]\u001b[A\n",
+      " 98%|██████████████████████████████████████████████▉ | 4883/5000 [00:09<00:00, 537.27it/s]\u001b[A\n",
+      " 99%|███████████████████████████████████████████████▍| 4938/5000 [00:09<00:00, 540.06it/s]\u001b[A\n",
+      "100%|████████████████████████████████████████████████| 5000/5000 [00:09<00:00, 521.01it/s]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:11<39:12, 11.82s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<30:50,  9.35s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:27<28:10,  8.58s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:34<26:53,  8.23s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<26:07,  8.04s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:50<25:35,  7.92s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:57<25:13,  7.84s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<24:54,  7.78s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:13<24:44,  7.77s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:20<24:28,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:28<24:14,  7.70s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:36<24:03,  7.68s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:43<23:51,  7.66s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:51<23:42,  7.65s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:59<23:33,  7.64s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:06<23:22,  7.62s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:14<23:14,  7.62s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:21<23:07,  7.63s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:29<22:58,  7.61s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:37<22:50,  7.62s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:44<22:44,  7.62s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:52<22:39,  7.64s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:00<22:33,  7.65s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:07<22:26,  7.65s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:15<22:18,  7.65s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:23<22:15,  7.67s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:30<22:04,  7.66s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:38<21:55,  7.65s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:45<21:47,  7.65s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:53<21:37,  7.63s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:01<21:29,  7.63s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:08<21:19,  7.62s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:16<21:12,  7.62s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:24<21:06,  7.63s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:31<20:57,  7.62s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:39<20:49,  7.62s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:46<20:41,  7.62s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [04:54<20:35,  7.62s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:02<20:29,  7.63s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:09<20:20,  7.63s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:17<20:20,  7.67s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:25<20:09,  7.66s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:32<20:00,  7.65s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:40<19:51,  7.64s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:48<19:42,  7.63s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [05:55<19:33,  7.62s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:10<20:07,  7.89s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:11<39:35, 11.94s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<30:54,  9.37s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:27<28:06,  8.56s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:34<26:46,  8.20s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<26:06,  8.03s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:50<25:37,  7.93s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:58<25:21,  7.88s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<25:00,  7.81s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:13<24:46,  7.78s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:20<24:28,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:28<24:14,  7.70s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:36<24:03,  7.68s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:43<23:54,  7.67s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:51<23:45,  7.66s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:59<23:36,  7.66s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:06<23:27,  7.65s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:14<23:18,  7.64s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:22<23:10,  7.64s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:29<23:03,  7.64s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:37<22:55,  7.64s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:45<22:49,  7.65s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:52<22:48,  7.69s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:00<22:37,  7.67s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:08<22:28,  7.66s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:15<22:19,  7.66s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:23<22:11,  7.65s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:31<22:03,  7.65s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:38<21:55,  7.65s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:46<21:47,  7.64s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:53<21:40,  7.65s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:01<21:32,  7.65s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:09<21:24,  7.65s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:16<21:18,  7.66s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:24<21:10,  7.65s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:32<21:07,  7.68s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:40<21:03,  7.70s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:47<20:56,  7.71s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [04:55<20:46,  7.70s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:03<20:44,  7.73s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:10<20:31,  7.70s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:18<20:21,  7.68s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:26<20:11,  7.67s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:33<20:02,  7.66s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:41<19:53,  7.65s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:49<19:45,  7.65s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [05:56<19:38,  7.65s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:04<19:30,  7.65s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:12<19:22,  7.65s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:19<19:15,  7.65s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:27<19:06,  7.65s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:34<18:59,  7.65s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:42<18:55,  7.67s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [06:50<18:50,  7.69s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [06:58<18:43,  7.70s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:13<19:03,  7.89s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:12<40:32, 12.23s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<31:19,  9.49s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:27<28:21,  8.64s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:35<26:56,  8.25s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<26:12,  8.07s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:50<25:44,  7.96s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:58<25:20,  7.88s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<25:01,  7.82s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:13<24:49,  7.80s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:21<24:35,  7.77s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:29<24:21,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:36<24:14,  7.73s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:44<24:03,  7.72s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:52<23:58,  7.73s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:59<23:47,  7.72s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:07<23:36,  7.70s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:15<23:23,  7.67s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:22<23:15,  7.67s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:30<23:04,  7.65s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:38<22:57,  7.65s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:45<22:49,  7.65s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:53<22:42,  7.66s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:01<22:34,  7.65s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:08<22:28,  7.66s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:16<22:21,  7.66s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:24<22:15,  7.68s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:31<22:08,  7.68s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:39<22:00,  7.68s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:47<22:01,  7.73s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:55<21:49,  7.70s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:02<21:39,  7.69s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:10<21:30,  7.68s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:17<21:21,  7.68s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:25<21:13,  7.67s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:33<21:05,  7.67s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:40<20:57,  7.67s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:48<20:48,  7.66s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [04:56<20:42,  7.67s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:03<20:34,  7.67s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:11<20:26,  7.66s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:19<20:21,  7.68s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:27<20:18,  7.71s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:34<20:11,  7.72s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:42<20:01,  7.70s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:50<19:50,  7.68s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [05:57<19:40,  7.67s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:05<19:32,  7.66s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:13<19:22,  7.65s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:20<19:14,  7.65s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:35<19:47,  7.92s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:12<40:19, 12.16s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<31:16,  9.48s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:27<28:17,  8.62s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:35<26:58,  8.26s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<26:09,  8.05s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:50<25:38,  7.93s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:58<25:13,  7.84s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<24:54,  7.78s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:13<24:38,  7.74s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:21<24:25,  7.71s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:28<24:12,  7.69s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:36<23:59,  7.66s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:43<23:48,  7.64s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:51<23:38,  7.63s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:59<23:27,  7.61s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:06<23:19,  7.61s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:14<23:11,  7.60s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:21<23:04,  7.61s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:29<22:54,  7.60s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:37<22:49,  7.61s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:44<22:39,  7.59s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:52<22:31,  7.59s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [02:59<22:22,  7.58s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:07<22:16,  7.60s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:14<22:09,  7.60s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:22<22:01,  7.60s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:30<21:54,  7.60s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:37<21:45,  7.59s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:45<21:39,  7.60s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [03:52<21:31,  7.60s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:00<21:26,  7.61s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:08<21:19,  7.62s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:15<21:14,  7.63s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:23<21:09,  7.65s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:31<21:01,  7.64s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:38<20:52,  7.63s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [04:46<20:43,  7.63s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [04:54<20:34,  7.62s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:01<20:24,  7.61s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:09<20:18,  7.61s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:16<20:11,  7.62s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:24<20:02,  7.61s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [05:32<19:55,  7.62s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [05:39<19:47,  7.61s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [05:47<19:39,  7.61s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [05:54<19:33,  7.62s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:02<19:28,  7.64s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:10<19:22,  7.65s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:17<19:15,  7.65s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [06:25<19:06,  7.65s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [06:33<19:01,  7.66s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [06:40<18:54,  7.67s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [06:56<19:14,  7.85s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:12<40:08, 12.10s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:19<31:08,  9.44s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:27<28:13,  8.60s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:34<26:47,  8.20s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:42<26:03,  8.02s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:50<25:33,  7.91s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [00:57<25:11,  7.83s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:05<24:52,  7.77s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:13<24:38,  7.74s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:20<24:29,  7.74s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:28<24:16,  7.71s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:36<24:03,  7.68s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [01:43<23:50,  7.65s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [01:51<23:37,  7.62s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [01:58<23:26,  7.60s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:06<23:17,  7.59s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:14<23:09,  7.60s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:21<23:01,  7.59s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:29<22:53,  7.59s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [02:36<22:51,  7.62s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [02:45<23:33,  7.90s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [02:53<23:53,  8.05s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:02<24:14,  8.22s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:11<24:27,  8.34s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:19<24:35,  8.43s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:28<24:58,  8.61s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [03:38<25:42,  8.92s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [03:48<26:10,  9.13s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [03:56<25:37,  8.99s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:05<25:07,  8.87s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:13<24:43,  8.78s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:22<24:25,  8.72s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [04:31<24:21,  8.75s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [04:40<24:57,  9.02s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [04:50<25:18,  9.20s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [04:59<24:59,  9.14s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [05:08<24:24,  8.98s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:16<24:05,  8.92s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:25<23:44,  8.85s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [05:34<23:25,  8.78s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [05:43<23:15,  8.78s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [05:52<23:48,  9.04s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [06:02<24:06,  9.21s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [06:11<23:47,  9.15s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:19<23:10,  8.97s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:28<22:43,  8.86s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [06:37<22:21,  8.77s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [06:45<22:02,  8.70s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [06:55<22:37,  8.99s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [07:04<22:59,  9.19s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [07:14<22:58,  9.25s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [07:22<22:21,  9.06s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [07:31<21:54,  8.94s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [07:40<21:29,  8.84s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [07:48<21:04,  8.72s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [07:57<21:22,  8.91s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [08:07<21:44,  9.12s/it]\u001b[A\n",
+      "training:  29%|████████████▏                             | 58/200 [08:17<21:57,  9.28s/it]\u001b[A\n",
+      "training:  30%|████████████▍                             | 59/200 [08:34<20:29,  8.72s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:13<43:07, 13.00s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:22<36:34, 11.08s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:32<34:26, 10.49s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:41<32:47, 10.04s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:50<31:00,  9.54s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:59<30:00,  9.28s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:08<29:14,  9.09s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:16<28:25,  8.88s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:26<29:01,  9.12s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:35<29:25,  9.29s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:45<29:28,  9.35s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:53<28:28,  9.09s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [02:02<27:50,  8.93s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [02:10<27:22,  8.83s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:19<26:52,  8.72s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:28<27:15,  8.89s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:38<27:48,  9.12s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:47<28:08,  9.28s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:56<27:23,  9.08s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [03:05<26:46,  8.92s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [03:13<26:18,  8.82s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [03:22<25:55,  8.74s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:31<25:52,  8.77s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:40<26:30,  9.04s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:50<26:53,  9.22s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:59<26:29,  9.13s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [04:07<25:51,  8.97s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [04:16<25:23,  8.86s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [04:25<24:58,  8.76s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:34<25:26,  8.98s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:44<25:55,  9.20s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:53<26:07,  9.33s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [05:02<25:38,  9.21s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [05:11<25:03,  9.06s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [05:20<24:33,  8.93s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [05:28<24:10,  8.84s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [05:37<23:42,  8.73s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:46<24:07,  8.94s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:56<24:32,  9.14s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [06:05<24:43,  9.27s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [06:14<23:53,  9.02s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [06:22<23:27,  8.91s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [06:31<23:05,  8.82s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [06:40<22:47,  8.77s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:48<22:26,  8.69s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:58<22:46,  8.87s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [07:07<23:12,  9.10s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [07:17<23:26,  9.26s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [07:25<22:46,  9.05s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [07:34<22:18,  8.92s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [07:43<21:55,  8.83s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [08:01<22:49,  9.26s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:14<48:19, 14.57s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:23<36:09, 10.96s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:31<32:26,  9.88s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:40<30:51,  9.44s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:48<29:42,  9.14s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:58<29:44,  9.20s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:07<30:06,  9.36s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:17<30:15,  9.46s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:26<29:21,  9.22s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:35<28:39,  9.05s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:43<28:06,  8.92s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:52<27:42,  8.84s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [02:00<27:20,  8.77s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [02:09<27:20,  8.82s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:19<27:56,  9.06s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:29<28:17,  9.23s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:38<27:53,  9.14s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:46<27:15,  8.99s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:55<26:56,  8.93s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [03:04<26:32,  8.85s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [03:12<26:13,  8.79s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [03:21<25:58,  8.75s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:31<26:35,  9.01s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:40<26:59,  9.20s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:49<26:47,  9.18s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [03:58<26:05,  9.00s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [04:07<25:39,  8.90s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [04:15<25:19,  8.83s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [04:24<25:03,  8.79s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:33<24:45,  8.74s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:42<25:21,  9.00s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:52<25:52,  9.24s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [05:01<25:45,  9.25s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [05:10<25:04,  9.06s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [05:18<24:32,  8.93s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [05:27<24:09,  8.84s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [05:36<23:42,  8.72s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:45<24:09,  8.95s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:55<24:34,  9.16s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [06:04<24:49,  9.31s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [06:13<23:58,  9.05s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [06:21<23:28,  8.91s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [06:30<23:07,  8.84s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [06:39<22:45,  8.75s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:48<22:54,  8.87s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:57<23:22,  9.10s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [07:07<23:41,  9.29s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [07:16<23:14,  9.17s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [07:25<22:40,  9.01s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [07:33<22:18,  8.93s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [07:42<21:57,  8.84s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [07:51<21:46,  8.83s/it]\u001b[A\n",
+      "training:  26%|███████████▏                              | 53/200 [08:00<22:14,  9.08s/it]\u001b[A\n",
+      "training:  27%|███████████▎                              | 54/200 [08:10<22:30,  9.25s/it]\u001b[A\n",
+      "training:  28%|███████████▌                              | 55/200 [08:19<22:16,  9.22s/it]\u001b[A\n",
+      "training:  28%|███████████▊                              | 56/200 [08:28<21:38,  9.02s/it]\u001b[A\n",
+      "training:  28%|███████████▉                              | 57/200 [08:45<21:58,  9.22s/it]\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                          | 1/200 [00:14<47:14, 14.24s/it]\u001b[A\n",
+      "training:   1%|▍                                          | 2/200 [00:23<38:15, 11.59s/it]\u001b[A\n",
+      "training:   2%|▋                                          | 3/200 [00:32<33:49, 10.30s/it]\u001b[A\n",
+      "training:   2%|▊                                          | 4/200 [00:41<31:48,  9.74s/it]\u001b[A\n",
+      "training:   2%|█                                          | 5/200 [00:50<30:40,  9.44s/it]\u001b[A\n",
+      "training:   3%|█▎                                         | 6/200 [00:59<30:07,  9.32s/it]\u001b[A\n",
+      "training:   4%|█▌                                         | 7/200 [01:08<29:27,  9.16s/it]\u001b[A\n",
+      "training:   4%|█▋                                         | 8/200 [01:16<28:41,  8.97s/it]\u001b[A\n",
+      "training:   4%|█▉                                         | 9/200 [01:26<29:12,  9.17s/it]\u001b[A\n",
+      "training:   5%|██                                        | 10/200 [01:36<29:31,  9.33s/it]\u001b[A\n",
+      "training:   6%|██▎                                       | 11/200 [01:45<29:42,  9.43s/it]\u001b[A\n",
+      "training:   6%|██▌                                       | 12/200 [01:54<28:37,  9.13s/it]\u001b[A\n",
+      "training:   6%|██▋                                       | 13/200 [02:03<27:56,  8.97s/it]\u001b[A\n",
+      "training:   7%|██▉                                       | 14/200 [02:11<27:28,  8.86s/it]\u001b[A\n",
+      "training:   8%|███▏                                      | 15/200 [02:20<27:02,  8.77s/it]\u001b[A\n",
+      "training:   8%|███▎                                      | 16/200 [02:29<27:12,  8.87s/it]\u001b[A\n",
+      "training:   8%|███▌                                      | 17/200 [02:38<27:47,  9.11s/it]\u001b[A\n",
+      "training:   9%|███▊                                      | 18/200 [02:48<28:07,  9.27s/it]\u001b[A\n",
+      "training:  10%|███▉                                      | 19/200 [02:57<27:43,  9.19s/it]\u001b[A\n",
+      "training:  10%|████▏                                     | 20/200 [03:06<26:59,  9.00s/it]\u001b[A\n",
+      "training:  10%|████▍                                     | 21/200 [03:14<26:29,  8.88s/it]\u001b[A\n",
+      "training:  11%|████▌                                     | 22/200 [03:23<26:04,  8.79s/it]\u001b[A\n",
+      "training:  12%|████▊                                     | 23/200 [03:31<25:48,  8.75s/it]\u001b[A\n",
+      "training:  12%|█████                                     | 24/200 [03:41<26:30,  9.04s/it]\u001b[A\n",
+      "training:  12%|█████▎                                    | 25/200 [03:51<26:56,  9.24s/it]\u001b[A\n",
+      "training:  13%|█████▍                                    | 26/200 [04:00<26:51,  9.26s/it]\u001b[A\n",
+      "training:  14%|█████▋                                    | 27/200 [04:09<26:09,  9.07s/it]\u001b[A\n",
+      "training:  14%|█████▉                                    | 28/200 [04:18<25:43,  8.97s/it]\u001b[A\n",
+      "training:  14%|██████                                    | 29/200 [04:26<25:21,  8.90s/it]\u001b[A\n",
+      "training:  15%|██████▎                                   | 30/200 [04:35<24:48,  8.76s/it]\u001b[A\n",
+      "training:  16%|██████▌                                   | 31/200 [04:44<25:24,  9.02s/it]\u001b[A\n",
+      "training:  16%|██████▋                                   | 32/200 [04:54<25:51,  9.24s/it]\u001b[A\n",
+      "training:  16%|██████▉                                   | 33/200 [05:04<25:58,  9.33s/it]\u001b[A\n",
+      "training:  17%|███████▏                                  | 34/200 [05:12<25:06,  9.08s/it]\u001b[A\n",
+      "training:  18%|███████▎                                  | 35/200 [05:21<24:37,  8.95s/it]\u001b[A\n",
+      "training:  18%|███████▌                                  | 36/200 [05:29<24:09,  8.84s/it]\u001b[A\n",
+      "training:  18%|███████▊                                  | 37/200 [05:38<23:45,  8.74s/it]\u001b[A\n",
+      "training:  19%|███████▉                                  | 38/200 [05:47<23:58,  8.88s/it]\u001b[A\n",
+      "training:  20%|████████▏                                 | 39/200 [05:57<24:26,  9.11s/it]\u001b[A\n",
+      "training:  20%|████████▍                                 | 40/200 [06:06<24:42,  9.27s/it]\u001b[A\n",
+      "training:  20%|████████▌                                 | 41/200 [06:15<24:02,  9.07s/it]\u001b[A\n",
+      "training:  21%|████████▊                                 | 42/200 [06:24<23:31,  8.93s/it]\u001b[A\n",
+      "training:  22%|█████████                                 | 43/200 [06:32<23:06,  8.83s/it]\u001b[A\n",
+      "training:  22%|█████████▏                                | 44/200 [06:41<22:46,  8.76s/it]\u001b[A\n",
+      "training:  22%|█████████▍                                | 45/200 [06:50<22:41,  8.78s/it]\u001b[A\n",
+      "training:  23%|█████████▋                                | 46/200 [06:59<23:15,  9.06s/it]\u001b[A\n",
+      "training:  24%|█████████▊                                | 47/200 [07:09<23:38,  9.27s/it]\u001b[A\n",
+      "training:  24%|██████████                                | 48/200 [07:18<23:25,  9.24s/it]\u001b[A\n",
+      "training:  24%|██████████▎                               | 49/200 [07:27<22:46,  9.05s/it]\u001b[A\n",
+      "training:  25%|██████████▌                               | 50/200 [07:36<22:18,  8.92s/it]\u001b[A\n",
+      "training:  26%|██████████▋                               | 51/200 [07:44<21:53,  8.81s/it]\u001b[A\n",
+      "training:  26%|██████████▉                               | 52/200 [08:03<22:56,  9.30s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00, 10.62it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.50it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00, 10.52it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.03it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.38it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.96it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.20s/it]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01,  9.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.97it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.30it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.72it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 46it [00:00, 64.96it/s]\u001b[A\n",
+      "Evaluating NLL: 94it [00:01, 134.84it/s]\u001b[A\n",
+      "Evaluating NLL: 149it [00:01, 215.15it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 278.11it/s]\u001b[A\n",
+      "Evaluating NLL: 256it [00:01, 343.80it/s]\u001b[A\n",
+      "Evaluating NLL: 312it [00:01, 398.16it/s]\u001b[A\n",
+      "Evaluating NLL: 364it [00:01, 416.95it/s]\u001b[A\n",
+      "Evaluating NLL: 414it [00:01, 435.24it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 270.34it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%|▎    | 7/100 [00:00<00:01, 61.82it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:01, 67.56it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▉   | 23/100 [00:00<00:01, 69.52it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|█▏  | 30/100 [00:00<00:01, 69.40it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|█▌  | 38/100 [00:00<00:00, 70.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█▊  | 46/100 [00:00<00:00, 71.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|██▏ | 54/100 [00:00<00:00, 71.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|██▍ | 62/100 [00:00<00:00, 71.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██▊ | 70/100 [00:00<00:00, 70.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|███ | 78/100 [00:01<00:00, 67.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|███▍| 85/100 [00:01<00:00, 66.56it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 69.30it/s]\n",
+      " 67%|███████████████████████████████▎               | 2/3 [4:26:46<2:15:33, 8133.12s/it]\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<43:53, 13.24s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:21<34:36, 10.49s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:30<31:39,  9.64s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:38<30:01,  9.19s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:48<29:58,  9.22s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:57<30:22,  9.40s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:07<30:31,  9.49s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:16<29:43,  9.29s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:25<28:59,  9.10s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:33<28:25,  8.97s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:42<27:57,  8.88s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:51<27:39,  8.82s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:00<28:22,  9.10s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:10<28:45,  9.27s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:20<28:47,  9.34s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<27:49,  9.07s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:37<27:15,  8.93s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:45<26:48,  8.84s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:54<26:22,  8.75s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:03<26:28,  8.82s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:13<27:03,  9.07s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:22<27:23,  9.24s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:53,  9.12s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:40<26:21,  8.98s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:48<25:56,  8.90s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:57<25:35,  8.83s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:06<25:20,  8.79s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:14<25:12,  8.79s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:24<25:45,  9.04s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:34<26:06,  9.21s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:43<25:58,  9.22s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:52<25:18,  9.04s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:00<24:52,  8.94s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:09<24:33,  8.88s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:18<24:16,  8.83s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:26<23:55,  8.75s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:36<24:30,  9.02s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:46<24:53,  9.22s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<24:57,  9.30s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:04<24:09,  9.06s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:12<23:39,  8.93s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:21<23:20,  8.86s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:29<22:55,  8.76s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:39<23:15,  8.95s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:49<23:39,  9.16s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:58<23:54,  9.31s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:07<23:04,  9.05s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:15<22:35,  8.91s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:24<22:13,  8.83s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:32<21:50,  8.74s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:42<22:01,  8.87s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:51<22:29,  9.12s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:01<22:43,  9.28s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:10<22:11,  9.12s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:18<21:46,  9.01s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:27<21:19,  8.89s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:36<20:57,  8.80s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:44<20:54,  8.83s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:54<21:21,  9.09s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:04<21:35,  9.25s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:13<21:14,  9.17s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:21<20:40,  8.99s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:30<20:14,  8.86s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:39<19:55,  8.79s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:57<20:40,  9.19s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<47:22, 14.28s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:22<35:45, 10.83s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:31<32:08,  9.79s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:39<30:28,  9.33s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:48<29:23,  9.04s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:57<29:44,  9.20s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:07<30:04,  9.35s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<30:13,  9.45s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:25<29:04,  9.13s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:34<28:27,  8.99s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:43<28:00,  8.89s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:51<27:41,  8.84s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:00<27:16,  8.75s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:09<27:34,  8.89s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:19<28:17,  9.18s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<28:34,  9.32s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:37<28:00,  9.18s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:46<27:24,  9.04s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:55<26:58,  8.94s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:03<26:36,  8.87s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:12<26:16,  8.81s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:21<26:19,  8.87s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:54,  9.12s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<27:15,  9.29s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:49<26:41,  9.15s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:58<26:06,  9.00s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:07<25:48,  8.95s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:16<25:32,  8.91s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:24<25:16,  8.87s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:34<25:53,  9.14s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:44<26:12,  9.30s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:53<26:07,  9.33s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:02<25:19,  9.10s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:11<24:51,  8.99s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:19<24:29,  8.91s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:28<24:10,  8.85s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:37<23:47,  8.75s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:46<24:24,  9.04s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:56<24:48,  9.25s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:05<24:52,  9.33s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:14<24:04,  9.08s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:23<23:36,  8.97s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:31<23:15,  8.89s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:40<22:55,  8.82s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:49<23:15,  9.01s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:59<23:37,  9.21s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:09<23:51,  9.35s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:18<23:15,  9.18s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:26<22:45,  9.04s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:35<22:19,  8.93s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:44<22:02,  8.87s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:53<21:47,  8.84s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:02<22:17,  9.10s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:12<22:33,  9.27s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:21<22:25,  9.28s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:30<21:49,  9.09s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:39<21:26,  8.99s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:47<21:06,  8.92s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:56<20:42,  8.81s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:06<21:13,  9.10s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:15<21:29,  9.28s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:25<21:41,  9.43s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:34<20:54,  9.16s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:42<20:29,  9.04s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [10:00<20:47,  9.24s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<49:14, 14.85s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:24<38:52, 11.78s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:34<35:25, 10.79s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:42<32:24,  9.92s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:51<31:02,  9.55s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [01:00<30:19,  9.38s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:09<29:32,  9.18s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<28:45,  8.99s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:27<29:11,  9.17s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:37<29:34,  9.34s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:46<29:47,  9.46s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:55<28:44,  9.18s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:04<28:05,  9.01s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:12<27:35,  8.90s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:21<27:08,  8.80s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:30<27:15,  8.89s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:40<27:53,  9.14s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:49<28:16,  9.32s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:58<27:43,  9.19s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:07<27:08,  9.05s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:16<26:42,  8.95s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:24<26:19,  8.87s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:34<26:52,  9.11s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:44<27:15,  9.29s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:53<27:21,  9.38s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [04:02<26:31,  9.14s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:11<26:01,  9.03s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:20<25:38,  8.94s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:28<25:13,  8.85s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:37<25:21,  8.95s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:47<25:52,  9.19s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:57<26:10,  9.35s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:06<25:36,  9.20s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:14<25:06,  9.07s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:23<24:43,  8.99s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:32<24:19,  8.90s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:41<24:15,  8.93s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:51<24:43,  9.16s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [06:00<25:02,  9.33s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:10<24:51,  9.32s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:18<24:09,  9.12s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:27<23:41,  9.00s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:36<23:13,  8.87s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:45<23:26,  9.02s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:55<23:47,  9.21s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [07:04<24:03,  9.37s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:13<23:26,  9.19s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:22<22:56,  9.06s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:31<22:34,  8.97s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:39<22:17,  8.91s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:48<22:14,  8.95s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:58<22:40,  9.19s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:08<22:52,  9.34s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:17<22:36,  9.29s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:26<21:57,  9.09s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:34<21:29,  8.95s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:43<21:07,  8.86s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:51<20:42,  8.75s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [09:01<21:13,  9.03s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:20<21:48,  9.35s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<46:27, 14.01s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:22<35:43, 10.83s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:31<32:54, 10.02s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:41<32:18,  9.89s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:51<32:00,  9.85s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [01:00<30:50,  9.54s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:08<29:48,  9.26s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<29:04,  9.09s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:26<28:31,  8.96s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:34<28:06,  8.87s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:44<28:41,  9.11s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:54<29:04,  9.28s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:03<28:53,  9.27s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:11<28:03,  9.05s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:20<27:39,  8.97s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:29<27:07,  8.84s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:38<27:12,  8.92s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:47<27:41,  9.13s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:57<27:58,  9.27s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:06<27:25,  9.14s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:14<26:44,  8.97s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:23<26:17,  8.86s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:32<25:58,  8.80s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<25:45,  8.78s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:50<26:22,  9.04s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [04:00<26:46,  9.23s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:09<26:39,  9.25s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:18<26:07,  9.11s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:27<25:38,  8.99s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:35<25:13,  8.90s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:44<24:54,  8.84s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:53<24:35,  8.79s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:02<25:10,  9.04s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:12<25:30,  9.22s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:21<25:24,  9.24s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:30<24:38,  9.01s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:38<24:07,  8.88s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:47<23:45,  8.80s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<23:18,  8.69s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:05<23:51,  8.95s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:15<24:18,  9.18s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:24<24:29,  9.30s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:33<23:36,  9.02s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:41<23:08,  8.90s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:50<22:44,  8.80s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:58<22:32,  8.78s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:08<23:02,  9.04s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:18<23:20,  9.21s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:27<22:59,  9.14s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:35<22:24,  8.96s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:44<22:02,  8.87s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:53<21:44,  8.81s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:01<21:29,  8.77s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:10<21:08,  8.69s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:19<21:39,  8.96s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:29<21:59,  9.16s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:38<21:59,  9.23s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:47<21:15,  8.98s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:55<20:49,  8.86s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:04<20:28,  8.77s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:12<20:04,  8.67s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:22<20:16,  8.82s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:31<20:37,  9.04s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:41<20:51,  9.20s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:49<20:18,  9.03s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:58<19:52,  8.90s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:06<19:31,  8.81s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:15<19:14,  8.74s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:24<19:03,  8.73s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:33<19:29,  9.00s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:43<19:42,  9.17s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:52<19:38,  9.20s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:01<19:01,  8.99s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:18<19:15,  9.17s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<48:03, 14.49s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:24<38:20, 11.62s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:33<35:05, 10.69s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:42<32:11,  9.86s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:51<30:47,  9.47s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:59<30:00,  9.28s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:08<29:01,  9.03s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<29:06,  9.10s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:27<29:29,  9.27s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:36<29:42,  9.38s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:45<28:45,  9.13s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:54<28:05,  8.97s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:02<27:33,  8.84s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:11<27:07,  8.75s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:20<26:59,  8.76s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:29<27:40,  9.02s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:39<28:04,  9.20s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:48<27:42,  9.13s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:56<27:04,  8.98s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:05<26:38,  8.88s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:14<26:23,  8.84s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:22<26:03,  8.78s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<25:46,  8.74s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<26:21,  8.99s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:50<26:46,  9.18s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:59<26:36,  9.17s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:08<25:51,  8.97s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:16<25:23,  8.86s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:25<25:00,  8.78s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:34<24:36,  8.68s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:43<25:09,  8.93s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:53<25:35,  9.14s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:02<25:55,  9.32s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:11<25:04,  9.06s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:20<24:37,  8.95s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:28<24:20,  8.91s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:37<24:00,  8.84s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:46<23:35,  8.74s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<23:54,  8.91s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:05<24:20,  9.13s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:14<24:37,  9.29s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:23<23:52,  9.06s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:31<23:24,  8.94s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:40<23:02,  8.86s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:49<22:44,  8.81s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:57<22:23,  8.73s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:06<22:35,  8.86s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:16<23:03,  9.10s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:26<23:21,  9.28s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:35<22:46,  9.11s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:43<22:21,  9.01s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:52<21:54,  8.88s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:00<21:32,  8.79s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:09<21:19,  8.76s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:19<21:47,  9.02s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:28<22:07,  9.22s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:38<21:53,  9.18s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:46<21:16,  8.99s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:55<20:51,  8.88s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:03<20:31,  8.80s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:12<20:07,  8.69s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:21<20:39,  8.98s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:31<20:58,  9.19s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:41<21:03,  9.29s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:49<20:20,  9.04s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:58<19:57,  8.94s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:06<19:34,  8.83s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:15<19:13,  8.74s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:24<19:19,  8.85s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:34<19:42,  9.10s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:43<19:54,  9.26s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:52<19:24,  9.10s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:01<18:55,  8.94s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:09<18:33,  8.84s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:18<18:16,  8.77s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:27<18:13,  8.82s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:36<18:35,  9.07s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:46<18:50,  9.27s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [11:55<18:29,  9.17s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [12:04<17:58,  8.99s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [12:12<17:36,  8.88s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [12:21<17:16,  8.79s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [12:29<17:00,  8.73s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [12:39<17:26,  9.02s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [12:49<17:39,  9.21s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [13:06<17:23,  9.15s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<43:53, 13.23s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:22<36:55, 11.19s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:32<34:21, 10.46s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:41<32:27,  9.94s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:50<30:55,  9.52s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:59<29:48,  9.22s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:07<29:00,  9.02s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:16<28:15,  8.83s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:25<28:54,  9.08s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:35<29:17,  9.25s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:44<29:19,  9.31s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:53<28:20,  9.04s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:01<27:45,  8.90s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:10<27:18,  8.81s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:18<26:52,  8.71s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<27:12,  8.87s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:37<27:46,  9.11s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:47<28:09,  9.28s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:56<27:23,  9.08s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:04<26:57,  8.98s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:13<26:27,  8.87s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:22<26:00,  8.76s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:34,  9.01s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<26:55,  9.18s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:50<26:51,  9.21s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:59<26:05,  9.00s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:07<25:37,  8.89s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:16<25:17,  8.82s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:24<24:58,  8.77s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:33<24:36,  8.69s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:43<25:16,  8.97s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:52<25:47,  9.21s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:02<25:51,  9.29s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:10<25:01,  9.05s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:19<24:36,  8.95s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:28<24:10,  8.84s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:36<23:45,  8.75s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:45<23:50,  8.83s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<24:22,  9.08s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:04<24:40,  9.25s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:13<24:13,  9.14s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:22<23:38,  8.98s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:31<23:12,  8.87s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:39<22:52,  8.80s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:48<22:36,  8.75s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:57<22:26,  8.74s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:06<23:03,  9.04s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:16<23:21,  9.22s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:25<23:07,  9.19s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:34<22:27,  8.99s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:42<22:01,  8.87s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:51<21:39,  8.78s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [07:59<21:14,  8.67s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:09<21:45,  8.94s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:18<22:07,  9.16s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:28<22:13,  9.26s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:36<21:30,  9.02s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:45<21:04,  8.90s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:54<20:49,  8.86s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:02<20:30,  8.79s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:11<20:07,  8.69s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:20<20:27,  8.90s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:30<20:46,  9.10s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:39<20:59,  9.26s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:48<20:18,  9.03s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:57<19:53,  8.91s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:05<19:34,  8.83s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:14<19:18,  8.78s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:23<19:04,  8.74s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:31<18:51,  8.70s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:41<19:18,  8.98s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:50<19:35,  9.19s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:00<19:30,  9.21s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:08<18:52,  8.99s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:17<18:29,  8.87s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:25<18:07,  8.77s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:34<18:11,  8.87s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:44<18:31,  9.11s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [11:54<18:41,  9.27s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [12:02<18:12,  9.10s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [12:11<17:44,  8.95s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [12:20<17:24,  8.85s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [12:28<17:09,  8.80s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [12:37<17:03,  8.82s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [12:47<17:23,  9.07s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [12:57<17:34,  9.25s/it]\u001b[A\n",
+      "training:  44%|█████████████████▍                      | 87/200 [13:06<17:18,  9.19s/it]\u001b[A\n",
+      "training:  44%|█████████████████▌                      | 88/200 [13:14<16:46,  8.98s/it]\u001b[A\n",
+      "training:  44%|█████████████████▊                      | 89/200 [13:23<16:24,  8.87s/it]\u001b[A\n",
+      "training:  45%|██████████████████                      | 90/200 [13:31<16:07,  8.79s/it]\u001b[A\n",
+      "training:  46%|██████████████████▏                     | 91/200 [13:40<15:44,  8.67s/it]\u001b[A\n",
+      "training:  46%|██████████████████▍                     | 92/200 [13:59<16:25,  9.12s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<44:20, 13.37s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:21<34:53, 10.58s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:30<31:48,  9.69s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:39<30:17,  9.27s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:48<30:10,  9.28s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:58<30:27,  9.42s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:07<30:34,  9.50s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:16<29:33,  9.23s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:25<28:47,  9.05s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:33<28:15,  8.92s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:42<27:52,  8.85s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:52<28:32,  9.11s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:01<28:56,  9.28s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:11<28:39,  9.24s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:19<27:53,  9.04s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<27:28,  8.96s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:37<27:01,  8.86s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:45<26:31,  8.74s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:55<27:11,  9.01s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:04<27:37,  9.21s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:14<27:44,  9.30s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:22<26:51,  9.06s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:20,  8.93s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:40<25:55,  8.84s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:48<25:29,  8.74s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:58<25:56,  8.95s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:07<26:25,  9.17s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:17<26:41,  9.31s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:25<25:53,  9.08s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:34<25:28,  8.99s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:43<25:17,  8.98s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:52<24:53,  8.89s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:00<24:26,  8.78s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:10<24:46,  8.95s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:19<25:13,  9.17s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:29<25:29,  9.33s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:38<24:45,  9.11s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:46<24:17,  8.99s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<23:56,  8.92s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:04<23:37,  8.86s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:13<23:20,  8.81s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:21<23:05,  8.77s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:31<23:40,  9.05s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:41<24:00,  9.23s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:50<23:52,  9.24s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:58<23:09,  9.02s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:07<22:40,  8.89s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:16<22:17,  8.80s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:24<21:54,  8.71s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:34<22:21,  8.94s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:43<22:44,  9.16s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:53<22:57,  9.31s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:01<22:11,  9.06s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:10<21:42,  8.92s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:19<21:22,  8.84s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:27<21:00,  8.75s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:36<21:09,  8.88s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:46<21:33,  9.11s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:56<21:46,  9.27s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:04<21:18,  9.13s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:13<20:46,  8.97s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:22<20:22,  8.86s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:30<20:03,  8.78s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:39<19:49,  8.75s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:48<20:15,  9.00s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:58<20:33,  9.21s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:07<20:26,  9.22s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:16<19:49,  9.01s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:25<19:24,  8.89s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:33<19:06,  8.82s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:42<18:49,  8.76s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:51<19:03,  8.93s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:01<19:21,  9.14s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:10<19:29,  9.29s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:19<18:55,  9.08s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:28<18:29,  8.95s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:36<18:08,  8.85s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:45<17:48,  8.76s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [12:03<18:28,  9.16s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<45:24, 13.69s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:22<35:31, 10.77s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:31<32:13,  9.82s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:39<30:36,  9.37s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:48<29:35,  9.11s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:58<30:05,  9.31s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:07<30:19,  9.43s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<30:08,  9.42s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:25<29:04,  9.13s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:34<28:24,  8.97s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:42<27:53,  8.85s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:51<27:21,  8.73s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:00<27:55,  8.96s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:10<28:26,  9.17s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:20<28:43,  9.31s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<27:45,  9.05s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:37<27:16,  8.95s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:45<26:54,  8.87s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:54<26:39,  8.83s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:04<27:16,  9.09s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:14<27:39,  9.27s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:23<27:21,  9.22s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:37,  9.03s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:40<26:09,  8.92s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:48<25:37,  8.78s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:58<26:02,  8.98s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:07<26:27,  9.18s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:17<26:43,  9.32s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:26<25:51,  9.07s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:34<25:21,  8.95s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:43<24:58,  8.87s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:52<24:34,  8.78s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:01<24:45,  8.89s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:10<25:12,  9.11s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:20<25:29,  9.27s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:29<24:56,  9.12s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:37<24:24,  8.98s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:46<24:01,  8.90s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<23:41,  8.83s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:03<23:24,  8.78s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:12<23:01,  8.69s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:21<23:31,  8.94s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:31<23:59,  9.17s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:41<24:10,  9.30s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:49<23:22,  9.05s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:58<22:57,  8.94s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:07<22:40,  8.89s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:15<22:16,  8.79s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:25<22:32,  8.95s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:34<22:54,  9.16s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:44<23:06,  9.31s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:52<22:25,  9.09s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:01<21:57,  8.96s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:10<21:34,  8.87s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:18<21:17,  8.81s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:27<20:57,  8.73s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:36<21:10,  8.88s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:46<21:33,  9.11s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:55<21:46,  9.27s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:04<21:12,  9.09s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:13<20:46,  8.97s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:22<20:30,  8.92s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:30<20:12,  8.85s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:40<20:37,  9.10s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:50<20:49,  9.25s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:59<20:38,  9.24s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:07<19:58,  9.01s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:16<19:33,  8.89s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:25<19:12,  8.80s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:33<18:50,  8.70s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:42<19:10,  8.92s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:52<19:28,  9.13s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:02<19:40,  9.29s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:10<19:00,  9.05s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:19<18:37,  8.94s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:28<18:19,  8.87s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:36<18:05,  8.82s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:45<18:09,  8.93s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [11:55<18:25,  9.14s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [12:05<18:32,  9.27s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [12:13<18:02,  9.09s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [12:31<18:00,  9.16s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.38it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.19it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.49it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.49it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.04it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 9it [00:01, 11.43it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.01it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.09it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00, 10.81it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.48it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:00, 10.75it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.37it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.47it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.16it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 52it [00:01, 63.28it/s]\u001b[A\n",
+      "Evaluating NLL: 107it [00:01, 135.49it/s]\u001b[A\n",
+      "Evaluating NLL: 162it [00:01, 208.03it/s]\u001b[A\n",
+      "Evaluating NLL: 210it [00:01, 262.08it/s]\u001b[A\n",
+      "Evaluating NLL: 261it [00:01, 316.61it/s]\u001b[A\n",
+      "Evaluating NLL: 317it [00:01, 375.04it/s]\u001b[A\n",
+      "Evaluating NLL: 375it [00:01, 425.76it/s]\u001b[A\n",
+      "Evaluating NLL: 433it [00:01, 465.52it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 255.15it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|▍    | 8/100 [00:00<00:01, 72.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▋   | 16/100 [00:00<00:01, 72.76it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▉   | 24/100 [00:00<00:01, 72.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|█▎  | 32/100 [00:00<00:00, 73.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:00, 73.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|█▉  | 48/100 [00:00<00:00, 73.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|██▏ | 56/100 [00:00<00:00, 74.40it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|██▌ | 64/100 [00:00<00:00, 72.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|██▉ | 72/100 [00:00<00:00, 70.45it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:01<00:00, 70.49it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|███▌| 88/100 [00:01<00:00, 69.90it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 71.40it/s]\n",
+      "\n",
+      "  0%|                                                          | 0/5000 [00:00<?, ?it/s]\u001b[A\n",
+      "  1%|▎                                               | 39/5000 [00:00<00:13, 380.78it/s]\u001b[A\n",
+      "  2%|▊                                               | 82/5000 [00:00<00:12, 404.88it/s]\u001b[A\n",
+      "  2%|█▏                                             | 123/5000 [00:00<00:12, 402.57it/s]\u001b[A\n",
+      "  3%|█▌                                             | 164/5000 [00:00<00:11, 405.34it/s]\u001b[A\n",
+      "  4%|█▉                                             | 207/5000 [00:00<00:11, 412.96it/s]\u001b[A\n",
+      "  5%|██▎                                            | 250/5000 [00:00<00:11, 414.77it/s]\u001b[A\n",
+      "  6%|██▋                                            | 292/5000 [00:00<00:11, 395.63it/s]\u001b[A\n",
+      "  7%|███▏                                           | 336/5000 [00:00<00:11, 407.61it/s]\u001b[A\n",
+      "  8%|███▋                                           | 386/5000 [00:00<00:10, 431.70it/s]\u001b[A\n",
+      "  9%|████▏                                          | 440/5000 [00:01<00:09, 463.88it/s]\u001b[A\n",
+      " 10%|████▋                                          | 493/5000 [00:01<00:09, 482.24it/s]\u001b[A\n",
+      " 11%|█████▏                                         | 550/5000 [00:01<00:08, 504.78it/s]\u001b[A\n",
+      " 12%|█████▋                                         | 601/5000 [00:01<00:09, 478.88it/s]\u001b[A\n",
+      " 13%|██████▏                                        | 657/5000 [00:01<00:08, 499.76it/s]\u001b[A\n",
+      " 14%|██████▋                                        | 716/5000 [00:01<00:08, 521.69it/s]\u001b[A\n",
+      " 15%|███████▏                                       | 771/5000 [00:01<00:08, 527.53it/s]\u001b[A\n",
+      " 17%|███████▊                                       | 828/5000 [00:01<00:07, 538.93it/s]\u001b[A\n",
+      " 18%|████████▎                                      | 883/5000 [00:01<00:07, 518.42it/s]\u001b[A\n",
+      " 19%|████████▊                                      | 937/5000 [00:01<00:07, 523.01it/s]\u001b[A\n",
+      " 20%|█████████▎                                     | 991/5000 [00:02<00:07, 527.32it/s]\u001b[A\n",
+      " 21%|█████████▌                                    | 1045/5000 [00:02<00:07, 530.66it/s]\u001b[A\n",
+      " 22%|██████████                                    | 1100/5000 [00:02<00:07, 534.75it/s]\u001b[A\n",
+      " 23%|██████████▌                                   | 1154/5000 [00:02<00:07, 534.45it/s]\u001b[A\n",
+      " 24%|███████████                                   | 1208/5000 [00:02<00:07, 533.89it/s]\u001b[A\n",
+      " 25%|███████████▌                                  | 1262/5000 [00:02<00:06, 534.65it/s]\u001b[A\n",
+      " 26%|████████████                                  | 1316/5000 [00:02<00:07, 519.03it/s]\u001b[A\n",
+      " 27%|████████████▌                                 | 1369/5000 [00:02<00:07, 514.28it/s]\u001b[A\n",
+      " 28%|█████████████                                 | 1421/5000 [00:02<00:07, 506.69it/s]\u001b[A\n",
+      " 30%|█████████████▌                                | 1476/5000 [00:03<00:06, 518.59it/s]\u001b[A\n",
+      " 31%|██████████████                                | 1529/5000 [00:03<00:06, 521.64it/s]\u001b[A\n",
+      " 32%|██████████████▌                               | 1582/5000 [00:03<00:06, 504.88it/s]\u001b[A\n",
+      " 33%|███████████████                               | 1636/5000 [00:03<00:06, 512.20it/s]\u001b[A\n",
+      " 34%|███████████████▌                              | 1688/5000 [00:03<00:06, 481.77it/s]\u001b[A\n",
+      " 35%|███████████████▉                              | 1737/5000 [00:03<00:06, 479.27it/s]\u001b[A\n",
+      " 36%|████████████████▍                             | 1790/5000 [00:03<00:06, 492.13it/s]\u001b[A\n",
+      " 37%|████████████████▉                             | 1844/5000 [00:03<00:06, 505.03it/s]\u001b[A\n",
+      " 38%|█████████████████▍                            | 1898/5000 [00:03<00:06, 515.04it/s]\u001b[A\n",
+      " 39%|█████████████████▉                            | 1950/5000 [00:03<00:05, 509.80it/s]\u001b[A\n",
+      " 40%|██████████████████▍                           | 2005/5000 [00:04<00:05, 520.98it/s]\u001b[A\n",
+      " 41%|██████████████████▉                           | 2058/5000 [00:04<00:05, 519.78it/s]\u001b[A\n",
+      " 42%|███████████████████▍                          | 2111/5000 [00:04<00:05, 520.41it/s]\u001b[A\n",
+      " 43%|███████████████████▉                          | 2164/5000 [00:04<00:05, 515.33it/s]\u001b[A\n",
+      " 44%|████████████████████▍                         | 2221/5000 [00:04<00:05, 530.59it/s]\u001b[A\n",
+      " 46%|████████████████████▉                         | 2275/5000 [00:04<00:05, 525.92it/s]\u001b[A\n",
+      " 47%|█████████████████████▍                        | 2330/5000 [00:04<00:05, 531.70it/s]\u001b[A\n",
+      " 48%|█████████████████████▉                        | 2384/5000 [00:04<00:05, 522.73it/s]\u001b[A\n",
+      " 49%|██████████████████████▍                       | 2437/5000 [00:04<00:04, 524.81it/s]\u001b[A\n",
+      " 50%|██████████████████████▉                       | 2490/5000 [00:04<00:04, 520.32it/s]\u001b[A\n",
+      " 51%|███████████████████████▍                      | 2547/5000 [00:05<00:04, 532.86it/s]\u001b[A\n",
+      " 52%|███████████████████████▉                      | 2602/5000 [00:05<00:04, 533.36it/s]\u001b[A\n",
+      " 53%|████████████████████████▍                     | 2657/5000 [00:05<00:04, 536.14it/s]\u001b[A\n",
+      " 54%|████████████████████████▉                     | 2711/5000 [00:05<00:04, 519.55it/s]\u001b[A\n",
+      " 55%|█████████████████████████▍                    | 2764/5000 [00:05<00:04, 514.20it/s]\u001b[A\n",
+      " 56%|█████████████████████████▉                    | 2818/5000 [00:05<00:04, 519.33it/s]\u001b[A\n",
+      " 57%|██████████████████████████▍                   | 2871/5000 [00:05<00:04, 508.66it/s]\u001b[A\n",
+      " 58%|██████████████████████████▉                   | 2925/5000 [00:05<00:04, 516.21it/s]\u001b[A\n",
+      " 60%|███████████████████████████▍                  | 2977/5000 [00:05<00:04, 487.47it/s]\u001b[A\n",
+      " 61%|███████████████████████████▊                  | 3029/5000 [00:06<00:03, 496.25it/s]\u001b[A\n",
+      " 62%|████████████████████████████▎                 | 3082/5000 [00:06<00:03, 505.18it/s]\u001b[A\n",
+      " 63%|████████████████████████████▊                 | 3133/5000 [00:06<00:03, 497.17it/s]\u001b[A\n",
+      " 64%|█████████████████████████████▎                | 3183/5000 [00:06<00:03, 489.50it/s]\u001b[A\n",
+      " 65%|█████████████████████████████▋                | 3233/5000 [00:06<00:03, 475.66it/s]\u001b[A\n",
+      " 66%|██████████████████████████████▏               | 3282/5000 [00:06<00:03, 477.36it/s]\u001b[A\n",
+      " 67%|██████████████████████████████▋               | 3333/5000 [00:06<00:03, 483.20it/s]\u001b[A\n",
+      " 68%|███████████████████████████████               | 3382/5000 [00:06<00:03, 473.86it/s]\u001b[A\n",
+      " 69%|███████████████████████████████▌              | 3431/5000 [00:06<00:03, 477.37it/s]\u001b[A\n",
+      " 70%|████████████████████████████████              | 3485/5000 [00:06<00:03, 494.92it/s]\u001b[A\n",
+      " 71%|████████████████████████████████▌             | 3543/5000 [00:07<00:02, 519.63it/s]\u001b[A\n",
+      " 72%|█████████████████████████████████             | 3596/5000 [00:07<00:02, 520.89it/s]\u001b[A\n",
+      " 73%|█████████████████████████████████▌            | 3654/5000 [00:07<00:02, 535.83it/s]\u001b[A\n",
+      " 74%|██████████████████████████████████            | 3708/5000 [00:07<00:02, 535.63it/s]\u001b[A\n",
+      " 75%|██████████████████████████████████▋           | 3764/5000 [00:07<00:02, 541.09it/s]\u001b[A\n",
+      " 76%|███████████████████████████████████▏          | 3819/5000 [00:07<00:02, 530.38it/s]\u001b[A\n",
+      " 77%|███████████████████████████████████▋          | 3873/5000 [00:07<00:02, 524.66it/s]\u001b[A\n",
+      " 79%|████████████████████████████████████▏         | 3928/5000 [00:07<00:02, 532.03it/s]\u001b[A\n",
+      " 80%|████████████████████████████████████▋         | 3982/5000 [00:07<00:01, 531.61it/s]\u001b[A\n",
+      " 81%|█████████████████████████████████████▏        | 4039/5000 [00:07<00:01, 542.35it/s]\u001b[A\n",
+      " 82%|█████████████████████████████████████▋        | 4094/5000 [00:08<00:01, 537.33it/s]\u001b[A\n",
+      " 83%|██████████████████████████████████████▏       | 4149/5000 [00:08<00:01, 537.90it/s]\u001b[A\n",
+      " 84%|██████████████████████████████████████▋       | 4206/5000 [00:08<00:01, 545.97it/s]\u001b[A\n",
+      " 85%|███████████████████████████████████████▏      | 4261/5000 [00:08<00:01, 546.30it/s]\u001b[A\n",
+      " 86%|███████████████████████████████████████▋      | 4316/5000 [00:08<00:01, 508.48it/s]\u001b[A\n",
+      " 87%|████████████████████████████████████████▏     | 4369/5000 [00:08<00:01, 512.41it/s]\u001b[A\n",
+      " 88%|████████████████████████████████████████▋     | 4421/5000 [00:08<00:01, 510.13it/s]\u001b[A\n",
+      " 89%|█████████████████████████████████████████▏    | 4474/5000 [00:08<00:01, 515.55it/s]\u001b[A\n",
+      " 91%|█████████████████████████████████████████▋    | 4529/5000 [00:08<00:00, 524.90it/s]\u001b[A\n",
+      " 92%|██████████████████████████████████████████▏   | 4586/5000 [00:09<00:00, 537.66it/s]\u001b[A\n",
+      " 93%|██████████████████████████████████████████▋   | 4640/5000 [00:09<00:00, 503.53it/s]\u001b[A\n",
+      " 94%|███████████████████████████████████████████▏  | 4691/5000 [00:09<00:00, 498.02it/s]\u001b[A\n",
+      " 95%|███████████████████████████████████████████▋  | 4745/5000 [00:09<00:00, 509.11it/s]\u001b[A\n",
+      " 96%|████████████████████████████████████████████▏ | 4801/5000 [00:09<00:00, 522.75it/s]\u001b[A\n",
+      " 97%|████████████████████████████████████████████▋ | 4857/5000 [00:09<00:00, 530.23it/s]\u001b[A\n",
+      " 98%|█████████████████████████████████████████████▏| 4911/5000 [00:09<00:00, 520.53it/s]\u001b[A\n",
+      "100%|██████████████████████████████████████████████| 5000/5000 [00:09<00:00, 507.95it/s]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<49:20, 14.88s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:24<39:00, 11.82s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:33<34:09, 10.40s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:41<31:39,  9.69s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:50<30:17,  9.32s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:59<29:14,  9.04s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:08<29:47,  9.26s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:18<30:01,  9.38s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:27<29:50,  9.37s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:36<28:51,  9.11s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:44<28:18,  8.99s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:53<27:46,  8.86s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:02<27:50,  8.93s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:12<28:22,  9.15s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:22<28:48,  9.34s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:30<28:08,  9.17s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:39<27:26,  9.00s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:48<26:54,  8.87s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:56<26:31,  8.79s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:05<26:14,  8.75s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:14<26:52,  9.01s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:24<27:17,  9.20s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:33<27:08,  9.20s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:42<26:22,  8.99s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:50<25:55,  8.89s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:59<25:32,  8.81s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:07<25:05,  8.70s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:17<25:43,  8.98s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:27<26:12,  9.19s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:36<26:24,  9.32s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:45<25:30,  9.06s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:53<24:58,  8.92s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:02<24:33,  8.83s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:11<24:11,  8.74s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:20<24:15,  8.82s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:29<24:48,  9.08s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:39<25:06,  9.24s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:48<24:36,  9.12s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:56<24:03,  8.97s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:05<23:46,  8.92s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:14<23:22,  8.82s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:23<23:11,  8.81s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:32<23:42,  9.06s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:42<24:00,  9.23s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:51<23:41,  9.17s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:59<23:02,  8.98s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:08<22:35,  8.86s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:17<22:14,  8.78s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:25<21:53,  8.70s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:35<22:30,  9.00s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:44<22:50,  9.20s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:54<22:48,  9.25s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:02<22:11,  9.06s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:11<21:41,  8.91s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:20<21:17,  8.81s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:28<20:52,  8.70s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:38<21:22,  8.97s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:47<21:43,  9.18s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:57<21:46,  9.27s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:05<21:02,  9.02s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:14<20:35,  8.89s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:22<20:14,  8.80s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:31<19:53,  8.71s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:40<20:12,  8.91s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:50<20:32,  9.13s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:59<20:43,  9.28s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:08<20:05,  9.07s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:17<19:39,  8.93s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:25<19:18,  8.84s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:34<18:59,  8.77s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:44<19:24,  9.03s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:53<19:39,  9.22s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:02<19:27,  9.20s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:11<18:51,  8.98s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:19<18:28,  8.87s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:28<18:09,  8.79s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:37<17:49,  8.69s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:46<18:10,  8.94s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [11:56<18:28,  9.16s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [12:05<18:34,  9.29s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [12:14<17:55,  9.04s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [12:22<17:30,  8.90s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [12:31<17:10,  8.81s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [12:39<16:51,  8.72s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [12:49<16:55,  8.83s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [12:58<17:13,  9.07s/it]\u001b[A\n",
+      "training:  44%|█████████████████▍                      | 87/200 [13:08<17:25,  9.25s/it]\u001b[A\n",
+      "training:  44%|█████████████████▌                      | 88/200 [13:17<16:57,  9.08s/it]\u001b[A\n",
+      "training:  44%|█████████████████▊                      | 89/200 [13:25<16:33,  8.95s/it]\u001b[A\n",
+      "training:  45%|██████████████████                      | 90/200 [13:34<16:16,  8.87s/it]\u001b[A\n",
+      "training:  46%|██████████████████▏                     | 91/200 [13:43<16:00,  8.82s/it]\u001b[A\n",
+      "training:  46%|██████████████████▍                     | 92/200 [13:51<15:45,  8.75s/it]\u001b[A\n",
+      "training:  46%|██████████████████▌                     | 93/200 [14:00<15:40,  8.79s/it]\u001b[A\n",
+      "training:  47%|██████████████████▊                     | 94/200 [14:10<15:57,  9.04s/it]\u001b[A\n",
+      "training:  48%|███████████████████                     | 95/200 [14:19<16:07,  9.21s/it]\u001b[A\n",
+      "training:  48%|███████████████████▏                    | 96/200 [14:28<15:51,  9.15s/it]\u001b[A\n",
+      "training:  48%|███████████████████▍                    | 97/200 [14:37<15:22,  8.95s/it]\u001b[A\n",
+      "training:  49%|███████████████████▌                    | 98/200 [14:45<15:01,  8.84s/it]\u001b[A\n",
+      "training:  50%|███████████████████▊                    | 99/200 [14:54<14:44,  8.76s/it]\u001b[A\n",
+      "training:  50%|███████████████████▌                   | 100/200 [15:02<14:24,  8.65s/it]\u001b[A\n",
+      "training:  50%|███████████████████▋                   | 101/200 [15:12<14:46,  8.96s/it]\u001b[A\n",
+      "training:  51%|███████████████████▉                   | 102/200 [15:22<14:57,  9.16s/it]\u001b[A\n",
+      "training:  52%|████████████████████                   | 103/200 [15:31<14:56,  9.24s/it]\u001b[A\n",
+      "training:  52%|████████████████████▎                  | 104/200 [15:39<14:23,  9.00s/it]\u001b[A\n",
+      "training:  52%|████████████████████▍                  | 105/200 [15:48<14:04,  8.89s/it]\u001b[A\n",
+      "training:  53%|████████████████████▋                  | 106/200 [15:57<13:48,  8.81s/it]\u001b[A\n",
+      "training:  54%|████████████████████▊                  | 107/200 [16:06<13:40,  8.82s/it]\u001b[A\n",
+      "training:  54%|█████████████████████                  | 108/200 [16:15<13:53,  9.06s/it]\u001b[A\n",
+      "training:  55%|█████████████████████▎                 | 109/200 [16:25<13:59,  9.23s/it]\u001b[A\n",
+      "training:  55%|█████████████████████▍                 | 110/200 [16:34<13:40,  9.11s/it]\u001b[A\n",
+      "training:  56%|█████████████████████▋                 | 111/200 [16:42<13:16,  8.94s/it]\u001b[A\n",
+      "training:  56%|█████████████████████▊                 | 112/200 [16:51<12:57,  8.83s/it]\u001b[A\n",
+      "training:  56%|██████████████████████                 | 113/200 [16:59<12:40,  8.75s/it]\u001b[A\n",
+      "training:  57%|██████████████████████▏                | 114/200 [17:08<12:26,  8.68s/it]\u001b[A\n",
+      "training:  57%|██████████████████████▍                | 115/200 [17:17<12:40,  8.94s/it]\u001b[A\n",
+      "training:  58%|██████████████████████▌                | 116/200 [17:27<12:47,  9.14s/it]\u001b[A\n",
+      "training:  58%|██████████████████████▊                | 117/200 [17:36<12:42,  9.19s/it]\u001b[A\n",
+      "training:  59%|███████████████████████                | 118/200 [17:45<12:17,  9.00s/it]\u001b[A\n",
+      "training:  60%|███████████████████████▏               | 119/200 [17:53<11:58,  8.87s/it]\u001b[A\n",
+      "training:  60%|███████████████████████▍               | 120/200 [18:02<11:42,  8.78s/it]\u001b[A\n",
+      "training:  60%|███████████████████████▌               | 121/200 [18:10<11:25,  8.68s/it]\u001b[A\n",
+      "training:  61%|███████████████████████▊               | 122/200 [18:20<11:30,  8.86s/it]\u001b[A\n",
+      "training:  62%|███████████████████████▉               | 123/200 [18:29<11:38,  9.07s/it]\u001b[A\n",
+      "training:  62%|████████████████████████▏              | 124/200 [18:39<11:40,  9.22s/it]\u001b[A\n",
+      "training:  62%|████████████████████████▍              | 125/200 [18:56<11:21,  9.09s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<44:48, 13.51s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:23<37:18, 11.30s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:32<34:36, 10.54s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:41<32:16,  9.88s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:50<30:46,  9.47s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:59<29:50,  9.23s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:07<29:03,  9.04s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:16<28:51,  9.02s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:26<29:20,  9.22s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:36<29:35,  9.35s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:44<28:55,  9.18s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:53<28:10,  8.99s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:02<27:35,  8.85s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:10<27:14,  8.79s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:19<26:58,  8.75s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:29<27:39,  9.02s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:38<28:05,  9.21s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:47<27:53,  9.20s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:56<27:06,  8.99s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:04<26:36,  8.87s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:13<26:12,  8.79s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:21<25:44,  8.68s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:25,  8.96s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<26:55,  9.18s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:50<27:04,  9.28s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:59<26:10,  9.03s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:07<25:39,  8.90s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:16<25:16,  8.82s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:25<24:58,  8.76s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:34<24:59,  8.82s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:43<25:36,  9.09s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:53<25:59,  9.28s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:02<25:40,  9.22s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:11<24:54,  9.00s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:19<24:24,  8.88s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:28<24:04,  8.81s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:36<23:46,  8.75s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:45<23:26,  8.68s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:54<23:28,  8.75s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:03<23:59,  9.00s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:13<24:16,  9.16s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:22<23:51,  9.06s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:30<23:20,  8.92s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:39<22:59,  8.84s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:48<22:42,  8.79s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:56<22:27,  8.75s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:05<22:08,  8.68s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:14<22:26,  8.86s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:24<22:48,  9.06s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:33<23:00,  9.20s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:42<22:19,  8.99s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:50<21:53,  8.88s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [07:59<21:32,  8.79s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:08<21:15,  8.73s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:16<21:02,  8.70s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:25<20:46,  8.65s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:34<21:19,  8.95s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:44<21:37,  9.13s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:53<21:38,  9.21s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:02<20:55,  8.97s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:10<20:34,  8.88s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:19<20:15,  8.81s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:27<19:51,  8.70s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:37<20:04,  8.86s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:46<20:23,  9.07s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:56<20:34,  9.21s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:04<19:56,  9.00s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:21<20:07,  9.15s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<46:35, 14.05s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:23<37:44, 11.44s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:33<34:35, 10.54s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:41<31:53,  9.76s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:50<30:39,  9.43s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:59<29:43,  9.20s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:07<28:51,  8.97s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<29:11,  9.12s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:26<29:36,  9.30s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:36<29:48,  9.41s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:45<28:41,  9.11s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:53<28:06,  8.97s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:02<27:40,  8.88s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:11<27:19,  8.82s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:19<26:54,  8.73s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<27:22,  8.93s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:38<27:53,  9.15s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:48<28:17,  9.33s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:56<27:20,  9.07s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:05<26:53,  8.97s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:14<26:25,  8.86s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:22<25:57,  8.75s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:03,  8.83s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<26:36,  9.07s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:50<26:56,  9.24s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:59<26:20,  9.08s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:08<25:44,  8.93s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:16<25:16,  8.82s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:25<24:56,  8.75s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:33<24:39,  8.70s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:43<25:18,  8.98s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:53<25:45,  9.20s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:02<25:42,  9.24s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:11<24:57,  9.02s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:19<24:34,  8.94s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:28<24:10,  8.85s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:37<23:52,  8.79s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:45<23:24,  8.67s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<24:01,  8.95s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:04<24:30,  9.19s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:14<24:39,  9.31s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:23<23:53,  9.07s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:31<23:27,  8.96s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:40<23:05,  8.88s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:49<22:52,  8.85s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:57<22:28,  8.76s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:07<23:01,  9.03s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:17<23:23,  9.24s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:26<23:29,  9.34s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:35<22:52,  9.15s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:44<22:19,  8.99s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:52<21:54,  8.88s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:01<21:28,  8.77s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:10<21:58,  9.03s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:20<22:16,  9.22s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:30<22:23,  9.33s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:38<21:38,  9.08s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:47<21:13,  8.97s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:55<20:53,  8.89s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:04<20:36,  8.84s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:13<20:12,  8.72s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:22<20:40,  8.99s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:32<21:05,  9.24s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:42<21:12,  9.36s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:50<20:25,  9.08s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [09:59<19:57,  8.94s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:07<19:35,  8.84s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:16<19:14,  8.74s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:25<19:16,  8.83s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:34<19:36,  9.05s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:44<19:49,  9.22s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:53<19:22,  9.08s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:01<18:57,  8.95s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:10<18:38,  8.88s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:19<18:12,  8.74s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:38<18:59,  9.19s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<48:41, 14.68s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:23<36:42, 11.12s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:31<32:49, 10.00s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:40<30:44,  9.41s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:49<30:26,  9.37s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:59<30:37,  9.47s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:09<30:40,  9.54s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<29:46,  9.31s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:26<29:07,  9.15s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:35<28:29,  9.00s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:44<28:06,  8.92s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:53<27:54,  8.91s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:02<28:35,  9.17s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:12<28:58,  9.35s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:22<28:56,  9.38s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:30<28:08,  9.18s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:39<27:32,  9.03s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:48<27:04,  8.92s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:56<26:34,  8.81s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:06<27:11,  9.06s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:16<27:37,  9.26s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:25<27:48,  9.37s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:34<26:56,  9.13s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:42<26:26,  9.01s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:51<26:04,  8.94s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [04:00<25:46,  8.89s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:09<25:28,  8.84s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:18<26:03,  9.09s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:28<26:26,  9.28s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:38<26:42,  9.42s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:46<25:43,  9.13s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:55<25:08,  8.98s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:04<24:44,  8.89s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:13<25:15,  9.13s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:23<25:31,  9.28s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:32<25:26,  9.31s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:41<24:39,  9.08s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:50<24:14,  8.98s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:58<23:55,  8.91s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:07<23:33,  8.84s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:16<23:18,  8.80s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:25<23:52,  9.06s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:35<24:11,  9.25s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:44<24:08,  9.28s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:53<23:36,  9.14s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [07:02<23:05,  9.00s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:11<22:42,  8.91s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:19<22:24,  8.85s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:28<22:07,  8.79s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:38<22:36,  9.04s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:47<22:51,  9.20s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:56<22:40,  9.19s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:05<22:04,  9.01s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:14<21:39,  8.90s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:22<21:24,  8.86s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:31<21:05,  8.78s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:41<21:38,  9.08s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:50<21:55,  9.26s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [09:00<21:54,  9.32s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:09<21:16,  9.12s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:17<20:48,  8.98s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:26<20:23,  8.87s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:34<19:59,  8.75s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:44<20:12,  8.92s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:53<20:33,  9.14s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [10:03<20:47,  9.31s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:12<20:09,  9.09s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:20<19:45,  8.98s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:29<19:24,  8.89s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:38<19:10,  8.85s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:46<18:51,  8.77s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:56<19:03,  8.93s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:05<19:23,  9.16s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:15<19:34,  9.32s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:24<19:02,  9.14s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:32<18:34,  8.99s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:41<18:13,  8.89s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:49<17:48,  8.76s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [11:59<18:10,  9.01s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [12:09<18:19,  9.16s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [12:18<18:19,  9.24s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [12:26<17:40,  8.98s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [12:35<17:16,  8.86s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [12:43<16:56,  8.77s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [12:52<16:37,  8.67s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [13:11<17:28,  9.20s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:13<45:27, 13.70s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:22<35:50, 10.86s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:31<32:21,  9.86s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:39<30:35,  9.36s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:48<29:39,  9.13s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:58<30:13,  9.35s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:08<30:31,  9.49s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<30:11,  9.43s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:25<29:08,  9.15s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:34<28:27,  8.99s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:43<27:57,  8.88s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:51<27:25,  8.75s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:01<28:04,  9.01s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:10<28:35,  9.23s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:20<28:56,  9.39s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:29<28:01,  9.14s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:38<27:39,  9.07s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:46<27:07,  8.94s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:55<26:52,  8.91s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:05<27:24,  9.14s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:15<27:42,  9.29s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:24<27:24,  9.24s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:32<26:38,  9.03s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<26:05,  8.89s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:49<25:39,  8.80s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:58<25:18,  8.73s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:08<25:59,  9.01s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:17<26:23,  9.21s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:27<26:20,  9.24s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:35<25:40,  9.06s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:44<25:08,  8.93s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:52<24:44,  8.83s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:01<24:17,  8.73s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:10<24:34,  8.88s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:20<25:00,  9.09s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:29<25:16,  9.25s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:38<24:29,  9.02s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:46<23:59,  8.89s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:55<23:37,  8.80s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:04<23:22,  8.77s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:13<23:55,  9.03s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:23<24:14,  9.20s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:32<24:06,  9.22s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:41<23:24,  9.00s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:50<23:07,  8.95s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:58<22:44,  8.86s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:07<22:30,  8.83s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:15<22:08,  8.74s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:25<22:38,  9.00s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:35<23:00,  9.21s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:44<23:00,  9.27s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:53<22:16,  9.03s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:01<21:49,  8.91s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:10<21:30,  8.84s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:18<21:06,  8.74s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:28<21:18,  8.88s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:37<21:41,  9.10s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:56<21:53,  9.25s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<48:09, 14.52s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:23<36:23, 11.03s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:32<33:13, 10.12s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:41<32:32,  9.96s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:51<32:07,  9.88s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [01:00<30:49,  9.53s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:09<29:49,  9.27s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:17<29:05,  9.09s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:26<28:35,  8.98s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:35<28:05,  8.87s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:44<28:14,  8.97s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:54<28:47,  9.19s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:03<29:11,  9.37s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:12<28:33,  9.21s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:21<28:02,  9.10s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:30<27:28,  8.96s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:38<26:51,  8.80s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:48<27:27,  9.05s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:57<27:51,  9.24s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:07<27:56,  9.32s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:16<27:06,  9.09s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:24<26:33,  8.95s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:33<26:09,  8.87s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<25:41,  8.76s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:51<26:03,  8.94s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [04:00<26:34,  9.16s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:10<26:51,  9.32s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:19<26:17,  9.17s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:28<25:49,  9.06s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:36<25:19,  8.94s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:45<24:52,  8.83s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:54<25:02,  8.94s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:04<25:28,  9.15s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:13<25:44,  9.30s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:22<25:07,  9.14s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:31<24:40,  9.03s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:40<24:12,  8.91s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:48<23:51,  8.84s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:57<23:32,  8.77s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:06<23:53,  8.96s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:16<24:21,  9.19s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:26<24:37,  9.35s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:35<24:04,  9.20s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:43<23:31,  9.05s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:52<23:02,  8.92s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [07:01<22:40,  8.83s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:09<22:34,  8.85s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:19<22:59,  9.07s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:29<23:17,  9.25s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:38<23:00,  9.20s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:46<22:24,  9.02s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:55<21:59,  8.91s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:04<21:38,  8.83s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:12<21:17,  8.75s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:22<21:51,  9.05s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:32<22:13,  9.26s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:41<22:14,  9.33s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:50<21:28,  9.07s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:58<21:02,  8.96s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:07<20:39,  8.86s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:16<20:20,  8.78s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:24<19:56,  8.67s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:33<20:19,  8.90s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:43<20:40,  9.12s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:53<20:52,  9.28s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [10:01<20:09,  9.02s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:10<19:45,  8.91s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:19<19:28,  8.85s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:27<19:10,  8.79s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:36<19:20,  8.92s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:46<19:44,  9.18s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:56<19:52,  9.32s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:05<19:26,  9.18s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:13<18:54,  9.01s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:22<18:34,  8.91s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:31<18:16,  8.84s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:39<18:02,  8.80s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:48<17:55,  8.82s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [11:58<18:16,  9.06s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [12:07<18:27,  9.23s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [12:16<18:06,  9.13s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [12:25<17:37,  8.96s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [12:34<17:15,  8.85s/it]\u001b[A\n",
+      "training:  42%|████████████████▊                       | 84/200 [12:42<16:59,  8.79s/it]\u001b[A\n",
+      "training:  42%|█████████████████                       | 85/200 [12:51<16:41,  8.71s/it]\u001b[A\n",
+      "training:  43%|█████████████████▏                      | 86/200 [13:00<17:05,  8.99s/it]\u001b[A\n",
+      "training:  44%|█████████████████▍                      | 87/200 [13:10<17:16,  9.17s/it]\u001b[A\n",
+      "training:  44%|█████████████████▌                      | 88/200 [13:19<17:13,  9.23s/it]\u001b[A\n",
+      "training:  44%|█████████████████▊                      | 89/200 [13:28<16:38,  8.99s/it]\u001b[A\n",
+      "training:  45%|██████████████████                      | 90/200 [13:36<16:15,  8.86s/it]\u001b[A\n",
+      "training:  46%|██████████████████▏                     | 91/200 [13:45<15:57,  8.79s/it]\u001b[A\n",
+      "training:  46%|██████████████████▍                     | 92/200 [13:55<16:15,  9.03s/it]\u001b[A\n",
+      "training:  46%|██████████████████▌                     | 93/200 [14:04<16:24,  9.20s/it]\u001b[A\n",
+      "training:  47%|██████████████████▊                     | 94/200 [14:13<16:15,  9.21s/it]\u001b[A\n",
+      "training:  48%|███████████████████                     | 95/200 [14:22<15:45,  9.01s/it]\u001b[A\n",
+      "training:  48%|███████████████████▏                    | 96/200 [14:30<15:23,  8.88s/it]\u001b[A\n",
+      "training:  48%|███████████████████▍                    | 97/200 [14:39<15:10,  8.84s/it]\u001b[A\n",
+      "training:  49%|███████████████████▌                    | 98/200 [14:48<14:49,  8.72s/it]\u001b[A\n",
+      "training:  50%|███████████████████▊                    | 99/200 [15:07<15:25,  9.17s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:12<43:06, 13.00s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:21<34:32, 10.47s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:30<31:40,  9.65s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:39<30:19,  9.28s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:47<29:22,  9.04s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:56<29:26,  9.11s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:06<29:55,  9.30s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:16<30:10,  9.43s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:25<29:19,  9.21s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:33<28:38,  9.05s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:42<28:13,  8.96s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:51<27:46,  8.87s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:00<27:46,  8.91s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:10<28:29,  9.19s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:19<28:43,  9.32s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<28:13,  9.20s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:37<27:31,  9.02s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:45<27:06,  8.94s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:54<26:34,  8.81s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:04<27:07,  9.04s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:13<27:33,  9.24s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:23<27:48,  9.37s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<26:53,  9.12s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:40<26:20,  8.98s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:49<25:57,  8.90s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:57<25:35,  8.82s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:07<25:40,  8.91s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:16<26:12,  9.14s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:26<26:35,  9.33s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:35<26:11,  9.24s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:44<25:32,  9.07s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:52<25:05,  8.96s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:01<24:32,  8.82s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:10<24:52,  8.99s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:20<25:20,  9.21s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:30<25:31,  9.34s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:38<24:44,  9.11s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:47<24:13,  8.97s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:56<23:50,  8.89s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:04<23:31,  8.82s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:13<23:09,  8.74s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:22<23:35,  8.96s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:32<23:58,  9.16s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:42<24:15,  9.33s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:50<23:36,  9.14s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:59<23:04,  8.99s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:08<22:41,  8.90s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:16<22:22,  8.83s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:26<22:53,  9.10s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:36<23:12,  9.28s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:45<23:03,  9.29s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:54<22:25,  9.09s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:02<21:58,  8.97s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:11<21:35,  8.87s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:20<21:16,  8.80s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:29<21:46,  9.07s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:39<22:00,  9.24s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:48<22:02,  9.31s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:57<21:22,  9.10s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:06<20:58,  8.99s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:14<20:38,  8.91s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:23<20:21,  8.85s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:32<20:03,  8.79s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:42<20:31,  9.06s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:51<20:47,  9.24s/it]\u001b[A\n",
+      "training:  33%|█████████████▏                          | 66/200 [10:01<20:40,  9.26s/it]\u001b[A\n",
+      "training:  34%|█████████████▍                          | 67/200 [10:09<20:03,  9.05s/it]\u001b[A\n",
+      "training:  34%|█████████████▌                          | 68/200 [10:18<19:40,  8.94s/it]\u001b[A\n",
+      "training:  34%|█████████████▊                          | 69/200 [10:26<19:23,  8.88s/it]\u001b[A\n",
+      "training:  35%|██████████████                          | 70/200 [10:35<19:10,  8.85s/it]\u001b[A\n",
+      "training:  36%|██████████████▏                         | 71/200 [10:44<18:53,  8.79s/it]\u001b[A\n",
+      "training:  36%|██████████████▍                         | 72/200 [10:54<19:20,  9.07s/it]\u001b[A\n",
+      "training:  36%|██████████████▌                         | 73/200 [11:03<19:30,  9.21s/it]\u001b[A\n",
+      "training:  37%|██████████████▊                         | 74/200 [11:13<19:30,  9.29s/it]\u001b[A\n",
+      "training:  38%|███████████████                         | 75/200 [11:21<18:47,  9.02s/it]\u001b[A\n",
+      "training:  38%|███████████████▏                        | 76/200 [11:30<18:22,  8.89s/it]\u001b[A\n",
+      "training:  38%|███████████████▍                        | 77/200 [11:38<18:00,  8.78s/it]\u001b[A\n",
+      "training:  39%|███████████████▌                        | 78/200 [11:47<17:40,  8.69s/it]\u001b[A\n",
+      "training:  40%|███████████████▊                        | 79/200 [11:56<17:58,  8.91s/it]\u001b[A\n",
+      "training:  40%|████████████████                        | 80/200 [12:06<18:14,  9.12s/it]\u001b[A\n",
+      "training:  40%|████████████████▏                       | 81/200 [12:15<18:22,  9.26s/it]\u001b[A\n",
+      "training:  41%|████████████████▍                       | 82/200 [12:24<17:43,  9.01s/it]\u001b[A\n",
+      "training:  42%|████████████████▌                       | 83/200 [12:41<17:53,  9.18s/it]\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                        | 1/200 [00:14<48:27, 14.61s/it]\u001b[A\n",
+      "training:   1%|▍                                        | 2/200 [00:24<38:38, 11.71s/it]\u001b[A\n",
+      "training:   2%|▌                                        | 3/200 [00:33<34:43, 10.58s/it]\u001b[A\n",
+      "training:   2%|▊                                        | 4/200 [00:42<31:51,  9.75s/it]\u001b[A\n",
+      "training:   2%|█                                        | 5/200 [00:50<30:27,  9.37s/it]\u001b[A\n",
+      "training:   3%|█▏                                       | 6/200 [00:59<29:32,  9.14s/it]\u001b[A\n",
+      "training:   4%|█▍                                       | 7/200 [01:08<28:52,  8.98s/it]\u001b[A\n",
+      "training:   4%|█▋                                       | 8/200 [01:16<28:09,  8.80s/it]\u001b[A\n",
+      "training:   4%|█▊                                       | 9/200 [01:26<28:48,  9.05s/it]\u001b[A\n",
+      "training:   5%|██                                      | 10/200 [01:35<29:13,  9.23s/it]\u001b[A\n",
+      "training:   6%|██▏                                     | 11/200 [01:45<29:20,  9.32s/it]\u001b[A\n",
+      "training:   6%|██▍                                     | 12/200 [01:53<28:19,  9.04s/it]\u001b[A\n",
+      "training:   6%|██▌                                     | 13/200 [02:02<27:48,  8.92s/it]\u001b[A\n",
+      "training:   7%|██▊                                     | 14/200 [02:10<27:23,  8.84s/it]\u001b[A\n",
+      "training:   8%|███                                     | 15/200 [02:19<26:58,  8.75s/it]\u001b[A\n",
+      "training:   8%|███▏                                    | 16/200 [02:28<27:23,  8.93s/it]\u001b[A\n",
+      "training:   8%|███▍                                    | 17/200 [02:38<27:57,  9.17s/it]\u001b[A\n",
+      "training:   9%|███▌                                    | 18/200 [02:48<28:14,  9.31s/it]\u001b[A\n",
+      "training:  10%|███▊                                    | 19/200 [02:56<27:33,  9.14s/it]\u001b[A\n",
+      "training:  10%|████                                    | 20/200 [03:05<26:59,  9.00s/it]\u001b[A\n",
+      "training:  10%|████▏                                   | 21/200 [03:14<26:28,  8.87s/it]\u001b[A\n",
+      "training:  11%|████▍                                   | 22/200 [03:22<26:05,  8.79s/it]\u001b[A\n",
+      "training:  12%|████▌                                   | 23/200 [03:31<25:57,  8.80s/it]\u001b[A\n",
+      "training:  12%|████▊                                   | 24/200 [03:41<26:32,  9.05s/it]\u001b[A\n",
+      "training:  12%|█████                                   | 25/200 [03:50<26:54,  9.22s/it]\u001b[A\n",
+      "training:  13%|█████▏                                  | 26/200 [03:59<26:36,  9.17s/it]\u001b[A\n",
+      "training:  14%|█████▍                                  | 27/200 [04:08<25:54,  8.98s/it]\u001b[A\n",
+      "training:  14%|█████▌                                  | 28/200 [04:17<25:31,  8.90s/it]\u001b[A\n",
+      "training:  14%|█████▊                                  | 29/200 [04:26<25:20,  8.89s/it]\u001b[A\n",
+      "training:  15%|██████                                  | 30/200 [04:34<25:03,  8.84s/it]\u001b[A\n",
+      "training:  16%|██████▏                                 | 31/200 [04:44<25:32,  9.07s/it]\u001b[A\n",
+      "training:  16%|██████▍                                 | 32/200 [04:53<25:52,  9.24s/it]\u001b[A\n",
+      "training:  16%|██████▌                                 | 33/200 [05:03<25:42,  9.24s/it]\u001b[A\n",
+      "training:  17%|██████▊                                 | 34/200 [05:11<24:56,  9.02s/it]\u001b[A\n",
+      "training:  18%|███████                                 | 35/200 [05:20<24:29,  8.91s/it]\u001b[A\n",
+      "training:  18%|███████▏                                | 36/200 [05:28<24:06,  8.82s/it]\u001b[A\n",
+      "training:  18%|███████▍                                | 37/200 [05:37<23:41,  8.72s/it]\u001b[A\n",
+      "training:  19%|███████▌                                | 38/200 [05:46<24:11,  8.96s/it]\u001b[A\n",
+      "training:  20%|███████▊                                | 39/200 [05:56<24:38,  9.18s/it]\u001b[A\n",
+      "training:  20%|████████                                | 40/200 [06:06<24:51,  9.32s/it]\u001b[A\n",
+      "training:  20%|████████▏                               | 41/200 [06:14<24:04,  9.08s/it]\u001b[A\n",
+      "training:  21%|████████▍                               | 42/200 [06:23<23:41,  8.99s/it]\u001b[A\n",
+      "training:  22%|████████▌                               | 43/200 [06:32<23:26,  8.96s/it]\u001b[A\n",
+      "training:  22%|████████▊                               | 44/200 [06:41<23:02,  8.86s/it]\u001b[A\n",
+      "training:  22%|█████████                               | 45/200 [06:49<22:46,  8.82s/it]\u001b[A\n",
+      "training:  23%|█████████▏                              | 46/200 [06:58<22:32,  8.78s/it]\u001b[A\n",
+      "training:  24%|█████████▍                              | 47/200 [07:08<23:03,  9.04s/it]\u001b[A\n",
+      "training:  24%|█████████▌                              | 48/200 [07:17<23:20,  9.21s/it]\u001b[A\n",
+      "training:  24%|█████████▊                              | 49/200 [07:26<23:06,  9.18s/it]\u001b[A\n",
+      "training:  25%|██████████                              | 50/200 [07:35<22:29,  9.00s/it]\u001b[A\n",
+      "training:  26%|██████████▏                             | 51/200 [07:44<22:03,  8.88s/it]\u001b[A\n",
+      "training:  26%|██████████▍                             | 52/200 [07:52<21:42,  8.80s/it]\u001b[A\n",
+      "training:  26%|██████████▌                             | 53/200 [08:01<21:24,  8.74s/it]\u001b[A\n",
+      "training:  27%|██████████▊                             | 54/200 [08:10<21:54,  9.00s/it]\u001b[A\n",
+      "training:  28%|███████████                             | 55/200 [08:20<22:14,  9.20s/it]\u001b[A\n",
+      "training:  28%|███████████▏                            | 56/200 [08:30<22:14,  9.27s/it]\u001b[A\n",
+      "training:  28%|███████████▍                            | 57/200 [08:38<21:31,  9.03s/it]\u001b[A\n",
+      "training:  29%|███████████▌                            | 58/200 [08:47<21:14,  8.98s/it]\u001b[A\n",
+      "training:  30%|███████████▊                            | 59/200 [08:55<20:49,  8.86s/it]\u001b[A\n",
+      "training:  30%|████████████                            | 60/200 [09:04<20:21,  8.73s/it]\u001b[A\n",
+      "training:  30%|████████████▏                           | 61/200 [09:13<20:43,  8.95s/it]\u001b[A\n",
+      "training:  31%|████████████▍                           | 62/200 [09:23<21:04,  9.16s/it]\u001b[A\n",
+      "training:  32%|████████████▌                           | 63/200 [09:33<21:14,  9.30s/it]\u001b[A\n",
+      "training:  32%|████████████▊                           | 64/200 [09:41<20:31,  9.06s/it]\u001b[A\n",
+      "training:  32%|█████████████                           | 65/200 [09:59<20:44,  9.22s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01,  9.98it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.89it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01,  9.34it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.45it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01,  9.49it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.98it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01,  9.32it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.44it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  8.42it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.49it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  8.65it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.74it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 8it [00:01, 10.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.79it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.15s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  7.33it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.80it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 45it [00:01, 55.22it/s]\u001b[A\n",
+      "Evaluating NLL: 88it [00:01, 109.95it/s]\u001b[A\n",
+      "Evaluating NLL: 130it [00:01, 163.94it/s]\u001b[A\n",
+      "Evaluating NLL: 176it [00:01, 222.66it/s]\u001b[A\n",
+      "Evaluating NLL: 223it [00:01, 276.98it/s]\u001b[A\n",
+      "Evaluating NLL: 266it [00:01, 313.57it/s]\u001b[A\n",
+      "Evaluating NLL: 309it [00:01, 335.14it/s]\u001b[A\n",
+      "Evaluating NLL: 352it [00:01, 359.38it/s]\u001b[A\n",
+      "Evaluating NLL: 399it [00:01, 389.50it/s]\u001b[A\n",
+      "Evaluating NLL: 447it [00:02, 413.96it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 230.55it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%|▎    | 7/100 [00:00<00:01, 61.26it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▌   | 14/100 [00:00<00:01, 60.44it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|▊   | 21/100 [00:00<00:01, 59.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|█   | 28/100 [00:00<00:01, 61.23it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|█▍  | 35/100 [00:00<00:01, 60.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█▋  | 42/100 [00:00<00:00, 58.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|█▉  | 49/100 [00:00<00:00, 59.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|██▏ | 55/100 [00:00<00:00, 56.59it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|██▍ | 61/100 [00:01<00:00, 56.96it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|██▋ | 67/100 [00:01<00:00, 56.20it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|██▉ | 73/100 [00:01<00:00, 55.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|███▏| 79/100 [00:01<00:00, 54.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|███▍| 85/100 [00:01<00:00, 55.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|███▋| 91/100 [00:01<00:00, 55.89it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 56.99it/s]\n",
+      "100%|█████████████████████████████████████████████████| 3/3 [7:42:25<00:00, 9248.57s/it]\n"
      ]
     }
    ],
@@ -10601,7 +4093,7 @@
     "    noise_model = PoissonNoiseModel()\n",
     "    estimate, lower, upper = estimate_information(models, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
     "\n",
-    "    mi_means.append(mi_mean)\n",
+    "    mi_means.append(estimate)\n",
     "    mi_confidences.append((lower, upper))\n",
     "\n",
     "    # background\n",
@@ -10624,8 +4116,8 @@
     "    estimate, lower, upper = estimate_information(models, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
     "\n",
     "\n",
-    "    mi_means_background.append(mi_mean)\n",
-    "    mi_confidences_background.append(mi_confidence)\n",
+    "    mi_means_background.append(estimate)\n",
+    "    mi_confidences_background.append((lower, upper))\n",
     "\n",
     "    \n",
     "mi_means = np.array(mi_means)\n",
@@ -10639,16 +4131,16 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-26T16:43:59.149590Z",
-     "iopub.status.busy": "2023-11-26T16:43:59.149233Z",
-     "iopub.status.idle": "2023-11-26T16:44:14.731427Z",
-     "shell.execute_reply": "2023-11-26T16:44:14.730726Z"
+     "iopub.execute_input": "2024-10-15T05:55:22.037813Z",
+     "iopub.status.busy": "2024-10-15T05:55:22.037588Z",
+     "iopub.status.idle": "2024-10-15T05:55:23.995960Z",
+     "shell.execute_reply": "2024-10-15T05:55:23.995147Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -10721,10 +4213,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-26T16:44:14.735996Z",
-     "iopub.status.busy": "2023-11-26T16:44:14.735740Z",
-     "iopub.status.idle": "2023-11-26T16:44:19.471897Z",
-     "shell.execute_reply": "2023-11-26T16:44:19.471351Z"
+     "iopub.execute_input": "2024-10-15T05:55:23.999985Z",
+     "iopub.status.busy": "2024-10-15T05:55:23.999617Z",
+     "iopub.status.idle": "2024-10-15T05:55:27.675373Z",
+     "shell.execute_reply": "2024-10-15T05:55:27.674686Z"
     }
    },
    "outputs": [
@@ -10732,14 +4224,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 277.20it/s]\n",
-      "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 338.92it/s]\n",
-      "100%|████████████████████████████████████████| 100/100 [00:00<00:00, 330.55it/s]\n"
+      "100%|████████████████████████████████████████████████| 100/100 [00:00<00:00, 536.95it/s]\n",
+      "100%|████████████████████████████████████████████████| 100/100 [00:00<00:00, 497.09it/s]\n",
+      "100%|████████████████████████████████████████████████| 100/100 [00:00<00:00, 412.45it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6QAAAFBCAYAAABgqWLFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9d7gvVXU+vubT6yn33H4BKRqNMYgdlOgXNRoMFoJKRKPRgAUTVCKGWLDgV7/mIXYeE0uUmDw2jGD8WYKxISLWaLBgoiBwufX08+llfn/s9e7Ze83MOeeee5re9T7Pfc79fD5T9szsvWdmvet9VxCGYUgKhUKhUCgUCoVCoVCsMzIb3QCFQqFQKBQKhUKhUByb0BdShUKhUCgUCoVCoVBsCPSFVKFQKBQKhUKhUCgUGwJ9IVUoFAqFQqFQKBQKxYZAX0gVCoVCoVAoFAqFQrEh0BdShUKhUCgUCoVCoVBsCPSFVKFQKBQKhUKhUCgUGwJ9IVUoFAqFQqFQKBQKxYYgt9ENcDG/97lERJSbvpOIiIJ+z/s96LZS1w26HfO33fLXHQ79BadniIgoPP4Es9zkQfN5dNx83rcvvvFW3/9czS9yFERUr5u/8/Pmb7cfXyYTmL+5rPlbKvq/zzXM30Fo/mZ5+Y75HPJh9Q9Wo3VCXiYwy2RHzbnI1MU5YAwOFr3lgex2PnfbxhPXMxvlWAbOLz7Pzpqm7Nhpfq6N+KstzKVuq791t/f1sFQjIqLqvT+X3o5VRP9fq97ncNcu8x9xrF4fafC1zfO5D/lcTvBxT4rjLS+z7xBF/WfoX59Yny7wNtGnJMqV6P+zCeefiKjDx8H9LGyYfQY7Kv5yct+ZhJjWnBinI+XkfWK5MT7vXe53hYRzVK0lt6PP7e51eZs8btBM7Ls/ICKicNceu4lQtD0zM+Vvm5F7yoHk9q8B+teUvM/Dw6KNo+Y4qJhw3ksF87fZSdw2rinCkMFxW5IbMUiYr2a4L+7YZv7efTC9HU5bwgmzfFiI5rewwn28b65Z9q7b/XVz5rYUjm/11g1zpl9gbs/MTJrlec5xEU5s9T4Hjfnkdi40zd8C3wpHx7yfh5Wo32V+fYf5zzZ/29TibWTF7bRQSNxlmMlG/xfzI+5jIc5BwfSH4kO/m9z+VUb/czznoQ/0E/oCUTRWiaKxNc33poGZQzKjYq7Iivmpx8u3zceAT1forsa7H7Z4ThiabXQO+vemkL/P5MzKha0z/u+9+KMO1glypv1Bxp9nw37W+xzk/XMRDkzfz1S7TnuT5+BBo5T4fabc9T6HvWzq8gEfW5Dhv9mh145waP4O+VizJdOXslVzgoMqjjOpIYnNs8hdmDynrDaavzjb+5y/8zbvc3CA5+KJaO7CvGCXwbNfY0Fs3JyHcNact8GcOcfZEfP9sBEfr5my2dawaX7LjJjz0Lnb7D8/Yu43vbmq97k7ZeY4XLOwH53gcOD3K6wz7Pp9tDfvP4/k6w1vmxbDeJ/DNbeL8LblOMiNmXOUqQ/8DWAxOWZdjPE8Xkzu29Tx22DvISPV2KJ4/rafS+a+HTTNMecf/6v0dih+a6AMqUKhUCgUCoVCoVAoNgSbiiHNH/ql91kyooFkZ4iIBIuK6Hrs+yUQzE4vvVAKMxpOmn0Fo/x+vxgzKgH2q80RyCTGiSjGjIIBcqO6oTw9/Dls+l8HTHoh4isjwRbMJhNRxOSCwUtgJbx9TB4iIqIsIv5gSJKuISO7MMPLmohbMBykLrsmEAwjonNhxUT0wF4kIWwxoyiDhWBKwTRYZk8cW4P7a6YR3zjOfTelT9tzmkn+vtGIf9fjv3mOrnOgPshxf8Imp5ve7zEsFtJCH+1xBwSLXBLTjuz79lw5jEV/xl8nJ7YxJ84b2gVmlaPlQTsaDEE35aDSxuB6YHzU/OXBnMlwlB/nAt3GPTc9XDM+jyndIaiKaPckz3kVkZ2RwrASEdE8M+xyLpT9mT9jHiBmSokSxpG8lpJpxHo262Xgt6Udv46WSQGbiSyBlpgMwYzimuN37oMZt4/UeBv4LolJXgbczJ9gzlyD4YhhCCQzuu44OOV/LvL5AfueNAe1uMPxIhnBNFoMBAPZ9PsjmFJy1gczClYw5E6dKTJz1Um+J/emzX0qV0vPqgLSmFEwj3abc2YuzzOrNGyJcUNEfWbKCttmzBc5P1sp7ArWFWxnkft02T+/SUwpmFAwpHJb2aLpn2B+0YYk2GcGzP8b1O2A7ExyNgoy3+wYdNi3AP8HU5fG6mN5fk7LDo+c9e3tM/NzJuvPd5l88j7BjLqsaCgYze6MuUcN+2asgeHGNrH8oG3626Br+nxxzDxndmairKqAs93yyBjgcQJm1D7zCaY0dn/HZ6fvBHJaPsTz77g4dvsMzt/LjKl2/LwHxPcinofx7KU4tqAMqUKhUCgUCoVCoVAoNgSbiiFNg2VGOTIeLBEBSwQi4Kw9cJkSIoqiOS5L1hX6wJxgEgUrAG1CUAE1xOu5r/2ISA58qnM4bbZtI1jQg1JyDj8irUExiqiGLROitvoBjpKFHbNs+x7DFuRHfW2F1DRkocdzI9odsG0mgh7iEqQQnsE2EQm22qh0DWXQXEj9bV0gdbzQjMpoXde57tw3AhwW2D8IddDv8BnRXDCizDRZHZUjIbX9q8BR1ga3pyriSFiuJ9gLMOTOcAlbWJcjkWUwtvx71xzPsMNMzVwKe54ARF0zJT/cCh1OOM/75D4e1JAdIMYz9zs3amsj98zoUoHXTWONa0K3mqZz8RoqdNEbARldHmGGd47HxiCd7Yj9JrVFOf5s2Vb+O8/aKkyJTveyUfEir5vChGKbyBRA5wuY8A3+965onToPlnm+djt8HSWYx2D6sPk7J+aFvmh/IlKyKyRTCp0nWE+cdxxX3ukLWcGm5rhP8ZgOR8a8XUGjHNh5JD6/9bcf538hdc2cNbJeCBd4PivBu4DPg2ShK8544vti7E6FdWR/wynFGJY6UQdDvqdJrSUAFmkgmFLc0wa8fhguI/bO99zW/gmzLjOlmezi80F3NtIZZ/l+DKY04POX28L3ECFTjLGc/Htuq9DfERExM4pjIhyy8ICwzKgE5nj3O+7SYIMDPI80NoarsGNFeILYORnjNNG74LD/GVlAo2bM2/vnBPfdQtv7nDngPzcRxfuk1R0zq96fN/cZsJqDptlWv52sHyeKs6vy+qWxrdgH2tA6FPf4QH8Da5qv+Gwk7uZ9bieyEcDkWr0xf+8+XxI/K8SY0gW+RqWUYy7wXpM8LjA3zPD4gAdHfYOpesWGQBlShUKhUCgUCoVCoVBsCDYVQ9of3el9znRMjnyWI18ZaI9cfWga4yZZDqkX64nPMvLvAhorLAOmFJ/xWi/0nTYW6QTALOvDTBSYUKtbwV/k/HO0c8B6lWzZnANoSzLFOEMk9QFwgLOfOaKLdWXErruXtTK7EhxZU8hpRHYtm4zIF7SmiI6NTyRvgMheyw3TT8moa5qW0HWNXYpNAyNqWX6hV5plLRRHYjPTTkSTz3Uwzi6WLdHRukKDxQ6N1k0xYXT3DptrG3P6Y11Uj/Us6GcLd2/3lssWJAvruAdyX67s8iPViL7HNFcsL8yyzis7bgbHcD7OyoYchM2MYwymnHdoAu3YBKMqmLAkSHZyI7WkQBMMgWCX3FMkm4kxKqdGq0UWbqEJZIz9Dcke0LC3WC9fZiadXYAzW0W/bph9DVlOHzjOslZDyY6kw1+YYwQbNhDR+/yIfyCZWrr2C0y61XTfvd98hkbuZL7HgHUFA52q3XdODvpQxmcC4BA5ZGfIzNyM2VdqKyPk7rmD/8P6McfVl8hxft4ogNWUc4mbpQQmFO7YMmtBMKPQjvanwSKyPjTBLRQaUfyVzqNghMBuDnv+3DHsM2PViu4pQ56PBnyfbMzWvXWKpeT+NeC5Lsv3z2wuPVMrx1ki5Ql2nef5FnNdUEBmAbKozDkaTPF9fku8DWDIpObVfs4kz4lwj7VsrLN+jE3l+3eGTWwH96zvI2JmTnh5xBzleey5/c9mCInMNPyd9LPhwknck/mzYEYHc9H9XTLsYBIlizlkXedwkJxRNHSeyXpN//kG901oQwsVX3OJfbW436EvYb2ho3XOsX64wPfv7rzvkh8smGODzri/4GcS2Xby8Xpu1chQFI689h4wyn0WzrwFcS4qCW77cDkPF8t2URwr2ARPXAqFQqFQKBQKhUKhOBaxqRhSAMxopt3gvxwxWsw5V9YdTWM5UmrDJWrRwOpJ10kwDB3BWgiESQFU6DhCPxosI5VgOa0DLqJ3YJmQ+58QVQYQVY45qtltMSMh1rPubneN2e8Ku2bMd4OUGEYX+kZub5Oj5jWfFUtyMw6ZNXVrFW4I0Heky6esp+Wy6RnBCAMt7rNNwcgxWzA4bI41M2b6VqacxHRzVPYgaplxZHQ2ZdiCbZ9lppT7VOvuyOEUTn4ySkusR+mK7wdD/3oXCtDwmOOYnx6zvx2eMf/fes8OIiKq1g0Llc0zq8brVHdO+vuYNKztYK9//Uvbo75itdVNaNp4m3I44zOzNbbmI/etVGddonhN043Qksp9g9VspbBORJETKpZt8Hwlo85Cywe9IOpGAplq/LiRNTEUc4l1Ob2Hdwl3R8Eg5Jy6eOGMHzlvHjB0TK+TMjff5bP0xRFf013aEmVyoM/LzBHU4csN9/Ny/EOH17UO0OnaL8pD4AcnXO5TB8w2M9u4nVZjKvRn/L1bmzk7ZdIEbI1V6UCcdr9aI9i+wDpd2zOk7s3VkMqEFlFHOJxPZhxl3UUgVmeR0jWgYJU6Cz4T1Gn6bMzsbHTOJ+eMsHliZNb73O777SmlMKADbks+a34v56M5ZXyU+xMnBs0dNvNqddQ804C5kuMDTG9+hF3ND/uZU+4yISWzcGBAMQbs/V9kX3ljA1YH8BLATzxXZH9X1KFea8zOcIOOgCvBfCcz0/Cp7c9vqClqzwueocBUps1DFL9u0Gr2WslZXeifLpBlhN8GospBa87Pksjy9WzzPjLMhA/htuxoUPvch7vsyIv7dWz8cKZBsSa8VNBunuddhjXHmQPl400N6lA8Gwy5zrW9f+DZSDKjzQTn6+L6vYo89KEPpf3796/6dnfu3Enf+973Vn27xxI25QupQqFQKBQKhUKhUKwW9u/fT3v37t3oZigSsKleSAv7frHo74gsW8aUKMaMBm0RfZE17tIAdiGhpp1FTzAOMognJH6IwntMKZbhgDOiW/2F5EhkrEao0Jz4C8MhNe9tU7KofRHNQzQsy9G/7CDO1nX3jRERUX4Li8JEbTNoILNVsEz8+wJHL6EnKMSvh9WU2ZqlG6MhhRbsiGpgSWZ0KWSTGW04IdNipdH4Ovbn+Lou4RwJZrzjRFwlA9rhSCqirQNonTga32yZ6Gavl6zV7g7i13Nuvub9rZRZNxX4rD4Y00I1uVZgc1+kN5ZOl+Xd7MDKzLKNylrXV6kh5xPr1qGE8y4YcDiwLsairjVkhoeYj6C/GyxEbHKWNZWDOdaK5f0If1DimprDtFp53Pc4+6E7E20bjGh31tfu4fs8XztbI4+3tZjLZGPeZwD2HTYM/sEFX8sHJmrPyAwREZUL5jjHmn4N5ODA1tg+RneY/lEYNWPZskXcv/PbDJMVY9iBQkJ/51q2NDpmtsl6TztDiz4XinsP5rnMQoI2XywDxjTMJLNhawY4s7d4v2UeT2BMcUhuLUHpv9CCJpQ32RCMaS/5mKw2LkGHN+D+NBQZOh32R+h2ze8HDhiWGhlI/zNpPrcTtvnzaTO/DHnZnshaygr30wb3bXxfY+Yq52g3T2ia9mR5btvGLCzm1+y8f64CsY/KiOljtV2HYu2VzLGsKSmzrOBMLLOsXIRtvqBYFm7vGBcHZ2LrrAswltKYUjejrcz9S2aRBMn3WkBme8nssyRAwww9Z9I5JSKameK6wuhb/fh9stVJfs4p5BbJBKTofgrkHCbfZt5xnzh8yMyN7Z4/0ZWY1c/yfTXLbH+G++MIM/1u/8zy+ckU/IwnsMZZZvfRSwPOYqJJXn5c+DskHtz6ZcllMgHt2jV21NvZt2+Ghkf6HKhIxKZ6IVUoFAqFQqFQKBSKtcKuXWP0q19fddTbOfler6S9e+NSNMWRQ19IFQqFQqFQKBQKxTGCkMJwEbb2CLajWB1syhdSpN2GbKNv03Jzxnxg6GQ6ZOCVIQ2PQpH6JlN3YRZh06wS0iewDqypkXmUUpA9VmYDKUvzKaVpiKxVO0qz5GrJ6YsoZJyFJT3SHh2ziS6X7ED6XIfXKVbMSUJqBf7C2EamcZbq8ZRVm0bFVQiQLpoTovgBV/yASU+AwbqYNwcbGVjDGVz33PoaegTTbLZTFH1h6KeDhpNRXwsmUtKF0K04K2g4z9boI8JwoQU7eS7k7lwLmE5Jm/+klDaiqC+gryywUVHfSRdCSqU01ZIpuVNs9DHTTE4lr5fase3keP8LnIqENDWk9WYDpAf5aUK5hm96ACOGTD7q2/mybyzVYKObyi5zzYKA04Jx4rnZNh0cqV/y2hI5JVU2wMQoDXa+gsmR+WONrlyzk5Jpf65kxuJgmvsNUvhQXgVmV3mzsf6MOUndKTYsSuhXNt2fr2VHmmExFqZNf2mxmQz6xZ2HtseWnW6bZQYpKefzsfTwMSIiKmWFURKnquWdlDX0rVlO4x1N3EM0zlDWACWHSJbBSMK0iYZn+a+ViyDdVpqzHUHabSiMkILhajwwLR/SqIRQbgmlHsr8+6hzZlHaa56PX9z3ZIrukE1jcJ+SRjEu5veadO6iuC/C+GWeS7bcw2nfh1vmPrqfpQZtTst056mU2zcVxDw7YEunJm+j0RcpnrzNvJPW+Ku5MSIiGmEjtwHu07Nj3rowQjpuJ5ts8TbaPBcO7zTliQqOGVhlt5/GGyvZIsvBZDnNGoZ5XCYsqEXL4b40bPnHhjJbQXaD50Sk5sJIMpc+lmxJPRhH9jj9XJQOGsjyY1iN5wTXIAvGQ9Wt095nmAYBzQXT79DPupwi2+ZU8rYzpzX5u1bffDfK99IMPyu1+Pci96FS3pdtDMUYlfdyIqIGp47Pt/17K1JykcJbzSbX/JpjEzA3ZTfH/Q3PHzBEKnCa+eCQmRPwvILScZmK+Rt0hNkREQVFmGGKY5hepBaZ4rcWm/KFVKFQKBQKhUKhUChWGyERDRNLYRz5dhSrg035QgpmdFgbIyKirC37wsyJNC4iiiz2B9zBYOwBwxLJkIIxseUV+HfXVAbsqiiXEBWn54hjyf9+2GA7brZSz9Qd9kwYPbglOYiIejCs4SgUInKW/WJzB2sdPhMZgXQbCYWHKWI1sC0U9Eb5hAHb78MYwhYKd7xDRrZPeduAmUnMdMk/TMqwj3xQTVyMG87nuWuE9NjiYPe9FllpDSANrWSfSRLjt8WENsrR1TlR3Fowo0Ex2bgArCgR2eLUoWDuhhypRx/BdYfhRxOmQnMjJAFWcsCsZSbDJQKG/nXsMquaT4mQoxyMG52FOUift9WHSQ6zFQXuw0Vmk6sV7n/C8KHFnz3DD2ZEYkXruR3lgWFKs6GJ2mbyoHZ4nOcXYdsRscX1zrFhxaHJ5OXXEk2erzrcrzAFzaczA2BEwQCgrIMcm5HZGfcXHstgxZJKSKGPwcQDpQcsE9D1z+vP9+0hIqKZrmEQDqSwEUSRv1dTmIv0RTsmuZ0V7os1NjcaKcdLFiAFK+B0FTClYFMrY1xSDKzc4THzec6MfRjHZBMyVbLbU1iaGo95vl/IsxhWfBMnzzSN71cBr2uzgjYZcE/LoHmzs7FlwIwi62OwkHws3Wlzf0EfssZZPI/NcqkUoshkbfbO4806fHYx14ABOsSMEMyLcsHSj4kd7vdFzlKa5/EyweOnP5T9kv/yvheY+R0vRPeAHq8z2Sl6n8dLyYzP3fsNE1pkIxuUjenwnFl0xlehbvo7sqTSDA8xngORUSCNCM0yeCDhdWU5uvVNUloayFJqJVxf9D+w4h0/2wDzIhhSa6LFx96cMgxfzyltleW+sXDIlKaa5UyQGWHAdmDe3GubfX+fI4V0l0JkiMy2zZxqmVJZrQv9tGi2lVski0NmOlV4/9LUqF7xs+DAnPb66dl8C01zXtqc4dC6y8z1yFDJM6Nb5uOojZm+XOMybzl+3gyczKfIFIrn7TG0cz0Y0pDCVXgh1VfS1cOmfCFVKBQKhUKhUCgUirXA6ryQKlYLm+qFFPoZMKDZGS7twJ8Rr3RLuwy27vC2kamayFUgNaUp+7LxQLChrsYM7CqsqGf5syzALEgkaLTCTnpZjgEzocVtM2ZXh8a836W9vSx7kaYjJIpHOYdgnBB647991kFkEeHtpodDO1wgOdYOjnBlBeOHKFisHEwtQZMIPS+0ug0TScve82vz+cGpzVpdZAS30ReTFTPlw0Z0njIZZlVZpxcM+RiEhnRwOIUp4shk5yCXnHEL0LPmEtcN5xrXvs264w6zViin0Rd9Y+gwToMBM+wpzCeWlTqVFkdec8yo9gbxvvLreRM9hraqDCaeo8xgPEsFn4kGS5tWWsZFQayLwjC5ih9RDQKOALeMviXYxWxxkDAmMeZxvZnqSYzCbxAGjfRSSGCiAqHFg8YMv0OnDkjNKEpouNrdHrMJYELBpB+aNv21x9uYZIZqH//d32b2lfuCLKFBFFVl6HKfaw/M34yIOEMHSDyE/nvSZJWcVDcR+JFSdD+oFU0/GOFj7zPb0e8l3+rQJwvcf8oTM6bdnIXgldoYmmWy27gPWoqX9w/mlMsHBXz/AEMazDOrmE1oyyD5wWi9y77EdImMzBiuCR+zkxkSdvg3fCWGGBhQMPPIsul3/fE+NzlGRJH+jYhonplClATqiUyOhZQ5o4hsDP7rNqnFfbYj5riRwCyLvjrgv3lBWcnbxLzDVNbF+UMpmQa3syJKesiyWb2e/BwxcQu3mv8ff///9RsAzR6z+lazW0VJPH/x0J1Csbs+ssDEs0Mr/Rlm00Do6+XxWkaUx3R33s9C64osjl4nure1RPbOwRkz7023zDbmmXnE9W0nZA4R+UwpMjwkAz/PrPr22rzffqnrZiDbyfWYyHL/K7HnAp4ZZCYL5j15n89z/8QzhJulVCqabbe5nei7+CuT4HqHzDlBxl111BzXyL3222WQqRL0eOzl0ktirTpCojClFNqRbkexOthUL6QKhUKhUCgUCoVCsXYIiTRld1NhU76QoqC4ddutMbuRwHpm5mZStsEMKLMfMefDNLi6wUZKrr4MWPFnFJW2kbpFgosD1jf0EK3jSJVlRvlzjzWjiHTlSkLnmNS8rNQc+r9b7UTWZ66KHFUbJhRxxjbzzCRAwyqBIt0hR+o8vQAR0dxC9H8Uny+lXJtWXCO2Lqj6mi/LlC+wa1w9fg0GC+YYcqyfGEz5xySd/cAayMLaScx3Jm/22+Qi132O4FodHzPdssg60HV0IXmrIfU751C0o8pM03wH+k+zntSJ7m1E52pvy4/CFjLmGGvMMpWYrbj1kMlqmCgm60RwXOV81MegP60POFuC2eODB4yLK8aNVM1mx7gP3cWFvo93lpBsaeiPG8uqrica/mCFdjRTEN9342MUY494WcmqSo0o5qBOw8xBfWZ6WtPxLAYUdp9k9+UGa0Tb/D2Y0TRPzl6CEySk0WBIe9aE0f8MCf8Ct++UuvnhdtZtneBse6Zt2j7CDAb0TdCC1dr+nAJn6DIzHHAch9M4dHsuspQyBzfhgM4NbsMtmt1RJzn6P+qcX2ic+W/QMCxCOL41eR9rDTEkMtt5PmJGdAiX9XY0pwQlv2+GXd8xHP0MOmTMeX0+5w12KG2zO/fUfMQKHmyadQ6zzg7ZF9AdjzLDUsj4PQ9zjXRmJiLK8nxYxj02BDOfDGR4FJmJmuLjK2Xj822WlynxOnKbmD+h2bM6Wv6L8QVNaTHhPnv3z04hIqKdJ91FRBFDVhYZEvCpkHBZcPhc4EnQupTjGs0ku6yvGebFPQFZCB1kCZmPidln1p/D9BUcZyjckXGPxT0DjD308S6gYb6LXZwbzIgeaPnnpSOYUfTPu3luvb/DkMI9dwf/RdZRf5icDRGIvoKMInweOKw65nDoTZcLbKvDTGoGWVlOF8d+MWd2hN40Wofnb743wLG/ODNGRERb5qNnhokT7iEiovwIez8MRbvXOKNWU3Y3FzblC6lCoVAoFAqFQqFQrD5CouHi0r5lb0exKthcL6SoO2nrjvoRmMEWw6xkFmZiqwZJzrvu79gmal2yTtHWU8ssQytR5fawK2w4n1KPFBpSsFD9KIIPZzzkzqMWGxgzqR0FpN4G6DnMGxzzktwyiYgyzBbEdKBwDZwxjAPc0dzlchypzXB0DFHFXNmvfQkHQLAzuTFznkOWRQSjzjmr+051xPrAcJyVgcu5JquIcNdu/wvUFxRMrVtvFgG2bK3jfU4Drjuut9WFol6dowfBd439hi1BDVH0EehCUBMW11HqV9oJOivUh5Wsqo26ihqR+DzH+pGkGpJgupp9f/9w5uwM/bp97RQdNH4vOM6AYDy60LZw+3eMGF3eAWZK4aIKLa51S8Wu2k4EVorBAGiak7R+awy4mQLSqTTR1VrWqUU/6STPGbZOccOP8rf488x8xAyjJt5CN/m6N1JcGbcVTT+f7Jhz2EuYk+R3OIphyv29njc/wN20zcd9p+N4Oc7MQFtkeYCR2sP9G6wEGKjBgjmuWs3MV72pMbPPpIawtB119rK7BGMKt25Mn5NT/ueEWrjhiNlfKOa8YJ1r46IuIBio4UGwaTy3dBNq1bb8+o7QinbmpKrMAK7aU1N+neQ7p82833DGPdxqZ/magyGd6Zr2tZj92lk25zxn2UxmSHNwPo46VZ6Xge4PV6PHfRsa0izPNVgetUWDID3bqp7zbwBpWSsSPavH4+MUNZyJIsfUQdb8dvDXxuV0dGLa21aeWX2sCUf3oMBZS8Wl+9SwszGPhuGCqJUqisaGCZpWMKKo6Y55L+SMHXuv5XOLZylkQ9isH+6HA+e+9D8HzDMBHOMPsqvzPG+zLnX7uHdxW8Z4fsln4tl21ZL/zNpv+zWc0/pOk3XVpaLp8wuNOIstNaDj4zNERDQ9PWbak9JPs1m/ne65GA79PoGsE/RV6L0B+Ryyj2v0etvne9X2k+/yvs9PzMeWXQsoQ7q5sLleSBUKhUKhUCgUCoVizRDG9Wwr3Y5iVbCpXkgzC4btgIbUMqUcNc7OHOLfo6h85tB+bxmSmlFsC5FmuBmipiTqqVWr/nJEUX3MNCZFAmezy8tD7+A4mYE1Haa4Pi4FOGNaPYHUaCagWOX8/BRmFDpVuw/LLsQHq3TmRK1MMHvSETaGjjN4wRyUhGPrOjOjEla7jD7UXoYeI6HGG1Gk9QtE5FFqR8GcuhrSDtfe7LX9iLx0zLNNYM1MhiOXcwvsuutoU5rMfMgIKXQhVkvC7MGU0Mp0hc5lv9N3ZLleAGzGCGcODFgj2BYOf9JdtZaPzlFX9Du4VR5iNm9L1WiTJ+8xTOnO+95ujgfjDP3T3Qga3BVpO6wh3oiakJIBzVS470HvlOC4GGSQGWF+swwV1yjOidqtoWUEzL7mZs05nOZz6e5jtuOfgznR98ByC1NpGnLEfYTnp8A5rhY7ecK9FDKxCuSKPFSawvETjCryFdAzZ3rOmBmyBpTnuooYd21m4SqsLdzFDHteaA2rPGcucN1BIqIia7fB+8kshwyzdJEjrUARltvOvpiFD5rsBs1zX1haZ+1eGnAPS9AsAzh+MFNgRtEfwYIgq2Zy0tR0vHvKZH4g62KOmdG7mtF8h2uOvoLPXW7XVBc9zqx7XJV9EHikgxl16ynj/9Cow0Fc1pAsCB0qxs32Evpt/DoPbIYJdNBcv5V8fR0AZqrD91ywdU2wxAkZhdDuFYVjr7ynBKyzj+pW8t9FCNKww8was6nZwuKZZ2uFUEi34csBNjQt+4OIqIc6yyJLwtbrBtsOt3rWjkI/eeekXxueKM5Wghmt8n20hjqesXtaOnAPDkXWxjAh+4go7kLfT/D6gGsufkP9ZTCj2Bf02kAh3/XaBLgMqaxlCsBvQrr7Y/lZZn6RXZPElNKvTJ3hnb/za+/rDLPAS/vvrwAhrc4Lqb6Prhp+Azy9FQqFQqFQKBQKhULx24hNxZAOBSMR1vy6pJaxWqTGKJYZjpnoawa1TBdETnpOMH1gRqdno23ZOoQiBAIGlCQLKJbjiCVqjhLFmUOwG9A/gAmJMZFHALiwVrbMet8jyicZlTTdqmRUzTocDYfmlf9i2z2ObuerHFnlbfenTdQ8N96I2oNQLVxsT9i55LGtJWQfkdpRyvG1kxQgEQUcPESNtwxrSjPE2lLrnsy/g13p+4y52z8GKaxEfxn1OomISglOezJCb7cJ7RJHMVtiH/OoQ4r6ZbyditNHGsyEgPGCS2rbMl1+P5MawlrO17W2nX6ZY4YErOqWgvlbZPaiJ+r5tacN24c6kgWu90vzzjmp83yS4va8lC59TQGXS547oPuMMe0UrwFs6+xhG7yu1E5ZzShrMNPq3RFFDE/aMugXZW4faj2GfN2KjguqZNlHRJZHjlnChb7PhuHvSN5nSrNOP+pyn2lxv5xJucXtZv0nsQMk2LI9zHy0mqzTKkWun3CzzjLzXBxxHMPJvZmaSSBgF1arm18s0wb3H2SHLFFHe80gs3tSmCh3nupbRjT5+JD1scDumnunDEsNZnSa9cmHeV+y7qf7HXrfRNF8ngdzxt8f5oyNvGC0xrPRWM4Jt9ASs0NdRztNRNQZ8LFzd+0MMb+hPml8LA7EPJS3NZj95ZDxUWRGCtpXWZvSrbt6gNuHOpaVAmsiU54VcuVkF/NgNMGhe5a9DfgaZHJLu/mvBax+cuh/BjMKr4qMMw/arDG46SIDhPuCfJaChhlsIpjR2w/7Ne1dQIO+q9Lwvsd1bfH9O8tjfmDvj3EGrsn9fYzY7wBzJ881HR4X88ws1llrKu/d+Cxrc/vtC732k60/yl4m3IfzPP9lhFu1O9/3RXZeq+e7/eO4wNzLe8WA54eWU8P87tktXjtr+83n2s6p1GNaPWjZl82GTfVCqlAoFAqFQqFQKBRriWBVNKSK1cKmfCHNpDETHDUO3OgxHAtZKwpmNMxk/WUzPkNl2ZApZsUaCTrBrohIIRqLKGLZj4RL9KbiHo1SOyo1oIgmZzMcOS0la0STNKhw3M2IyK1lRjmqmMnhJPiOuaPbTFQKTAqYVvf/0KVkudZhwIXBwKYGqO02X/WOJwMWxzEEzO/yGQbq8zb7GzRJWH0xtMPcZ8CgtfgYEjQ4oQhSDua4X0pNSTU5ai1dl71tg+kSmpECM6Ddjn/d0WzUAXOZ0nbH37501e3yOtAwgRmV2ihE+Jsp7LpZ1vzt8Gk7KA59DGa2PMymeLxVc1g+Gn9MiNIJVbTbr706VvHZ7B7rgvKsJ0L0vEhR5DWT5YtWgbs3X3dZn3QDgDkmyImINfcPN1reF/XzMObABGB8d3hdsH/zjaq3LTh6FpyofinL9RCZyYE2D9cfTHlHsBBVoUUac+RHe5nlKOf8Wo040q5g2trckeZ6pn+DiZ8oxhm5KrPscEzNi0XAtN/FbZjnczSBMTJtIvR11kWNO+e5zlo8aCK7C4ZhlsqqDM+VuTEzv4Uz/D20pTMJDpLQ0eO+VGftam5N1FNLA2QTtL9g+hLYS0C6mALQ6MG9OeQ+BM3oLN/LcgmazHrB7/8LPI773EeKfO+N+gwz5Dbjw3y/rSLuNRS5hII92lI2c8gs6+vAWrZ5XGBbWcE6dRxWFMvAZbXK7Cs0om5t5SRgny3Wifad+bUPp/Ou7w7bFLruEjOjJT7vwQwzumTOwfBw1AZUBABwn85U19fd2QLMqMgmSPWkcFflfjQQf+1cKe6j88zYQzOKmps5hyXM8vNOpYDrmDweC/y81mUn2v4i90Wgy/0f+k0ANUSHKdlMlbJ5Ps7lkhh6s99Gs+Itk2HWEmxqGqsqHX4zzpis8n4XeNsFoYHt8b6R4YIsK/R96GtdZ3aMrQJnquTF+Khu9x2kVx36QrqpsClfSBUKhUKhUCgUCoVi9aEuu5sNm/KFVGpJJWMaNJ08fmZGrb6UAUdeW3dUsl9AjdeDo24/ijoFHKSGlnS44EfHhlznCjUohzNcq6+RzFgQRVE8MGJWLMFCFemIWRg30fQBR93bh0cpDXnWRWHbqDsaCvZi2BesHWpjcm4/ao6Gw6UdfK2DsNSbCi0CGKqBwydkuaaYjcZKHZXUcK41wFwUeFiAGZUurAkYzMRrCxIRBSKKCbfKbIWd6VgjCEbZrSsLwFUX0cMiRyoRvZSfux1f2+FCuulCF9IV7GuFo7aI6B9scvvENl0NKVjWgVhmAZeX5+02X+deGw6a/vKTPARmetF5r2S5nax9ud+I2djhNte/hXsqR5cR4c0LHRXGLBFRwL8F0tAUmub+0v1/tTFkDXZnMn2cE0WOkd66HKHGeG81/Lp2M7PJ27Qapnw8ai61b2BMwQiAyUnS07lw65fu4b4v2Xd7HGBsU0iaBl+XDOZMp//M9/xtQfZdsFOg+WKswJpkrqHZFYxGEjsBbdeeE+4moogJHLBeGdkfYBGrQn9Z6M+Y3/PxAwswfci6o+utJYVbeM/3CgCSNMzQ9bVmDeMk3V4brFWGJu7XzJTOwIGcF8dfV/9pmRr+PMoZRfPM3Bay0LSb32VfApqOQ2gloZ8T+XpNFzIrJM/3bPQRt+8jU6AiHHCRYYB9oz1g0jCOKnl/fPUHkn9328X3z8DsHxq+ew74WsjRncZHAyxjzs3SwTx5H8724ns/jXKHXOc5EPMzss8sU49MK/Q/t143O8cjEwR/4duA+uyUxZg3x3Zw1tTBlQ7u7vWEezsga2+CMc1yewpcmaA7YC8T1HjvRvf1ejHZ9Rz37x5v097feXnpgJsEMJ9SV4zv0+qOA1Jb6kJqQtHf5PczcNXl72d4ufkEV2DrVN00c0eNj7VeZzZ/v2nPSGzNVUBIFKyGhlTfR1cNm/KFVKFQKBQKhUKhUChWHyHRMkiXZW1HsSrYVC+kYEKDA/cQEVE4bliPoMFuZNCLJtWp5Mhy9pc/879PqyUqXXYZ4WzStxzBYn0FdDVgt4YJrBZRFDkmJwoFphPf5euGBYRTpmTUJCybyZHrXiceQUVcPltIjq4j2ohod9YyooH3e+hE0wYiuoVIW5bbg23I9llGGOu5GiSQAdvHEttJ5Q2uxSeZUe5KblAtbLGWDvpdcR1lXckBR4BxHlqHxlN3H4p6ZFneJrY4ENsGMwqnR0Q7247GCPUWLYOYQ0TX19sUeF+I4KMmH1n3VHPxStmob0fsqc/y5O145XEEJ+mUeXymx865YTQWWmJY/GrBtPeUull2Eo6dXHsVEd7+PlOXdPvx+0y73XNmRYviOuNzyhyxlujO1BK/j2VWJKC1YFhsMKVz80bD3hW6J+kyCiDa70a8oScFM1ARkfbRwMzZcCqd47q16E9wDU1iHKEpAgPVXMJZvMwseU5s6q52xPgUA7PMWJ51idlkxqzDzs8lZk0anMGxr+lH910N1QL013ceZ/Yxam4W5aqfwYPMlCaP7co2Q53CU8CdA7M1vufdzeOSz2OAPigZ07VGyu6sCzjPX8joICLq8H0OzBTqJS5wfzwwY3S5t7NOrC/6wha+d8yjjzs/25lDMDsjqNssNG/QoULDWeZr4bqGg63sCCdx9PVmD6w/s2HcL0s8XgoJLDFQZWZUsm5yHWgTwYyCnV3oBt5nF2BfZf3TPs+vcp2Dh7Z6n8GUugBb2vsfzo7azvev+WSvg7UG5ucYUwp2d4t5Fuw782SWl2lNjhFR5CIO9DgLYpYzROZbXI/UMtxwQuZnmkV00nCQTdOSQkMKYP7Y6jCttaJ/bntiW3neRxAMxXI5/t30U2RGyWczF3gWQCl1tAeMKcZqkh6VSLjs8n6gAcdv0N6CsW/zctCHSmbUddfPCv+VGb5/FA4ZXe+ObYdSj201oKZGmwub6oVUoVAoFAqFQqFQKNYUq8KQKlYLm+qFdMAOuTbO1+WwDjOjITsOui670I56ulIiokLB/9vkKHYbERH+W/PZzcAxxg1hhoj6iBxFCvocuZrjdgtHzB7XAcT3+UoUEcsWTWQqWzJ/JfOBCOFSrnJYPutEYjMpkdsk3Q8R0TAlsoblQyfal+WoHKJxUnsAhhfayAGi5il17IicY71zxuz3eFYK5DjC2VnnKG1N6PKWoR0NiqxngzthWehDxHVciunqteJaVLgfI4LbZTYWDKnVsoGVEsxpyXHUQ9QV28J1TKtPCkS191g/hW07y+C3KbSHg58Fjjhv59MLdqrBp2Cu5+vARlgX3l6kLib0hf87z0wpB8zvnBsjIqIqR6HBAk7v57nF0Wjntoo5Q2qYN4AhRbRfasVRS2+xCRv1aRdYs5emD84V/NpzcGHOMQPo1nTNyCg9/5YVGnFsayJvWENE02dZe+xqSG0duwzvh6+zZH5AbkYsvPkddUjRb1xgyQLTbIWUS4i+GdXI5ePrMUND8ayXNGYMGm+wDkXuY9mcueegLmxeMKlE8RqJ+VHDpIAxzexc2l10LdCf8vWgIZxLF5nP4d4MRhHM6Gzbn9PAYo6xU3tV1OJ03XYlmyqZR8zQWAd9qCz0dh2HPZxiFgauumAr0afHuO7jPLvtlkQtScydYP/dfgF2CEwUWKSe1Rz6Y7EM91POBkFbGqwxdVlPnAswe9hmNuNrW+VcPjlprkOBnz2qTnt7fD4LE4mpYesO9LMwoYoAkcOMOvfV5gFzfMgMwTm3dZd5fN4zbTIWMBfZGqfI3OFuV3T0v1gG9xFbr9tee79v4LpXeZggG6Xg9MeRuq9L7XR9zShQq5r7U08w+bllaEltezK+ZwSyUaTbPh6HUYd0CLdgp78i02aWxw+YUZn9UuL2gSHdwvMiPAPyDisanS9zzuFZ0eZlDzBTetKyjvZIoaZGmw2b6oVUoVAoFAqFQqFQKNYMIVGwGgypvo+uGvSFVKFQKBQKhUKhUBwjUFOjzYZN9UKanWHRvegk4SLFwYMFUWRcGuEMfEo+7PidJyCRYulsLuyxbTj5qTBh2zfsSSpWT0RU2mrSYNxUWqRoScF6GmBsg+XzFS4xw6llA6cIOVKDe5wuK82LYtvmtBdZ7BzruWL5YUqhZ6SPFuqLl2ixphhOqk2Oj4HKbKp0l8mBDrbxNWkm2/OvGRY4pW6EDTtgiIXPSPfuusZMXCx9hNflcxl2RK4gDAAGSAk0QKozUuEyTjoOUqp7bS77UvLPB0yMhkhR6ifnJw6ca8e+MBRwqYACp8hsHzdpwVNzfqooSmqMcN+e7pi0PKTnun0eqTsTbFLS6CO9yXzfEcXsI3MaTuEcoNA871vWHKEoJXPA+UJZ3ucs96U9/HuD0+1QzBvozEVmGIXD5rrm6yKVssZzSD695MJaAalqOZHSj3mj343PhY1ZozOQqbrtnt9+pJ2VS34qfD7vG13VHYOLjkh7s7ldDKT7YptI+UJh9tFKw2sTUZTGBhziMgFFXneCx0R/6Lc/MrYxf2s5GBNFKaFRmi8loi2mQmyxxam7KDnSCjjV0unfaA2MS1D2QBru2FIlXG4Cpbwwzy4mx4BRHpDrm3l1vW/UuGcMeBzZewXkHE4/RP9Dqi7MV1oi1RCzEFJzZYp2SZSkcpcpizIqSN2d6SYbCrb6KCnjp9eabZk+jNTdbRWTQilLesh0TAlsUx4nUZQaKftG9DvS1P3nABguIWU35/wu51zMt2hvO8S9xe9fkC9A4jFwjBCzosxcdHD8N101saaQpoj2WYbnKsiiFgPmrP/dv5uIiKa5L48V/GPOiBcKKUdIAkpktfg64fP4qHmGmZ418qNtWyeJKJJTEBHl+J6L72BSNDI2l7iv4sBvLwwkYXIoTZyIIkMxidT+OPTTnZEmnCTlgSnTQgemneYPxgP6LsaANVJKeA4d43TeKszceFk80zRlarHitxqb6oVUoVAoFAqFQqFQKNYSq5Kyq1g1bKoXUmtqNHWAiIiCQwfNDxOm/AvNcwSpGI8IpQJGJUMYE/H3shyANciIvgdb0Z8x+8uyRXp/vszbYoMYmDFUROQt6xveEBHlylzwl5nFoJhsnNOf8SNciK4johnk2YzAXYYj8mArZVHzaDk2ZxJRXGvnnbAeov1pTGln1rS3iIgujCIEO7CYKUYwytsu8DLrzZCiD8wJs5sFZn+5eYFz0kOwpcwgDNli3n4WZXmsedXQP/dgxLJOf2hNmyhrP6G0D1FkUgGmNDIkSA9rD9EuYVoABisnTBAk0QSDj0aC7b2MvRa4z4CT7HU56s0L5riZRd4JSrvk+Xu3JjvYUjCjEjCn+fmc6Yc72WZfFu0edKO+3Tk8RkREGTZXyd5LRMZz6z89ggEgHidp42328Bb7/3mOhscyNPLJ4ydWZoD3WSr7LCcRUYEZUFliCAxopRw36iEiqlaaXpumZsbsbxXBUNT48+1sSAVWbAcbPM3wNRvNsxkTt6/NfbaWj9rWRcYCmM4+GPSAt2W+31Lk8cfr9W238s/hnMMEwnwHjD/GDNh4AIXsY0Xoeb1eIzJPk0x4rrIx5TaA/hSbUAnWBZk4OCY36wbXGMzoPBtZ9cXYAzPVZLOTSgoD2XZMtbaU/Mwb3KPAkEpWU5qvtRNKCYE9RXskw9kUhkKYciTDA4ay7xh25QX7nxFmSzbpg7eJz5iX+7xP7Ms1ccL5gkGTNHjqizJKAIzI5ufqJFGdmDHtGxP9DoeUUjZprZD2zGJ/x7zoLJdW9mR63tw/YSSFPgJWfYTZuW1VP8vOzeBA/4JhWV6c2yKzlVXOBCnw885OnhelURGRU3IN22TGFNlRyITCch0xFm35N563C86c0eNsvTobJ4EpzQsjpCz3L5gbRaXi/LnNLQ0m76UYF/IYeynPH0kZEGBG0d9l9sxizzKrAn0h3VTYVC+kCoVCoVAoFAqFQrFmCMNVMjVSDelqYVO9kOZu/7n5T5+jOWDKGmyTnUmIlqA0SFGUCsGyTRH545IBsZIOiMwkBKgR2W7dYxhcGcVDKRdbUmORwsrQGMaYUcGEoPzDMIVRBPs6zC6tdwhFJFXqRsDKyeX67Sh/XzIkgI2YL3bMCfsiihjdAHKQHh/LNF/v8RqtK3AqoTMuInxtQ4G8QHSslhHFL9DFIlrd9DVYdj3Rh2wpDGZFieLsWJs1gqWqzxqAKbXaoiG0m2Yc9ftxHYbVuWVQSN4s2xXtklFRILuILsTqp+zCHGXOQM/ln4sya0qbGZQzwPfRMvbUExgw1rby6S/xNsaYRTvM+rDRsn+uso4+skwmMt5jVjXbn/EPpLvODD0RdWeTtT8Y7zMHTbYI9KJEUZH2NmumoCEbYXYpJ64RSgGgn0DTBGRdzTvKHhSQIcGsZE1kEaSBr2WxED+XBVH6CO2ELmt/w4z/otB0QYNY43bnMlEGwWTHLzkk0eaMmSHr7e7gLIzjy34WQi9BOwVGCuzbXfNGbz3KjGi1mMJuQpfFzKg79nEGckIfnuXyUZnq+vbB7KhhdgbCh0CiPRuxbfA5aHF5l3nWBOM6gbnDVdxdM+MO+sn+0N9HzZkfwCD2bfkUn0GUnB/6fj0Pdt3MfQsJOs+obIpph2RK+zz/ugyoi1ZClohkSDF+rBYW7eL5KeB7CTSlPd4ntlNymC3LzLOOvyAYp64oMYNz0RPXsOHoCzt8j4cusXKf/WabvNtgnQ1bbPYWZ5/h+Qf3z/ZMnOXFbyGfu9kZMy4PL/jL4jpv5Xmxzrp3sHJgO/O5eNZaNoHdIyLK5cw2wIyCLZSlUNxsCZnZBCATCgxpvtby9imfIaBNzznPc1nhB4ASVLjOva6/T3gAtO0mOFODOLvPea4b4ayjZsvvf4OU8YHnAPg8bOVySnOO7hvnCXMrSu7kUhjTVYcypJsKm+qFVKFQKBQKhUKhUCjWCgGtjoZ0YypF/3ZiU72QhnUT2QqmJ5MXqCYwZj2OIIMZhfCsz983ONRX5UOtCLaoK3QsOScimML+IWJVGGE9qHC+hOuu1U96rCC2xe0U0T0ZBZOay/yIz/jkHcYHTA90ivhrHVwlI8HaA+nGm+iYy8cEpgZun4jmIcJKiJZJtjiBQc2wu64VDVaY5YZ2TzombxQsYxr/KeDzP2B3zAFrgyWzkMaISp1ZmmbQRUs46OGaRKwna0y4YHomwTUwYlPN574sOM+fwb6BDQBDAWfCgTMdS+2SZU95WyVmSlvcR9qIeg/AIpjF4QTccLorGFEss6XA2ls+XWMF/xw0WDd094wphn7iFtZkOa59NtrN7RjsNceY7bOmqLT+02MaI9Wa8+e+JG0NrnMLrBAzVTWOTEumtMYR70ICexk1yHdKtO7Q0FPyeU7qY2Z5086yE8Xv2PnSX2eMmbNCu+x9v8BR/a51KPf3MVGMtg0mbarLrsRgF/j3fOB/HmMH93nWj4OJL3FHO9iJ94Fpbs9C3+wr0zTtPcjMwR+wfgxsfL4M5hTtj8Z82jyRH1vg39fX5nTY8BmULruZWu07zqcz1q27M88NYAHLfJnBeoCB6iQ4RbtoOgxKkVlwsK5w28V8tL3m6/+w7+mmP15cZ9X5XrImH0wP+noO2Ucpz6zS+XcxgJ1b4PlnuczPIMGReaxo+tc0nxMskluGOyyRf+16khU+ZM5bboKzlIob+7gN/w2MC7CIuWI0Z+EZo8HZJfBFkFpGaHDhko17HtyUpX6SKJrX8ryPHPcvfA+2E/dteG1gC5Uts177iYg6C2VvXQlbuYCfL+VzG5CrxvX7WX4eiz3rIeOBrz2csdFu3BtwX8kkZCNK92GMTes0zOOhyWMT939kB1jdtzP/dQd+xoNtL4+PkdpCrB2rBy37stmwpk9crVaLyuXy0gsqFAqFQqFQKBQKxTogGC4viKNYH6z4hfTkk0+md77znfSUpzwl8fd//dd/pUsuuYQmJ1PYzgQEe/dyqzi+VOGXWa4HGBY4ctqPoj8BagWCIW2DMRVRixavA9oFkb+M+JsQ7QALBsYTjoNgRqG1zOT9aAuYUpflzLEuIMMaUUphRtNq1SFiDjfOpKViTnVgwxCZRgQYrsENZjlEVK0wGunE5DZzHO1Oqz+K5TOinZkkV+GemBSCjSl+FspmcB8azjOzwdG0wVTkeifZcxwvNtVPqBFG5GpvzZKu86YE2KiscMprMyMzFCxLRuiKs54jJs6tYP370HKY6wZmFOxUmk7EjXaivh++q3DEdF7UJWv1k7cFt13UIXW9ArosCqyyBhzMaE4MAESAbW0+Pp6ZlomeuzU4Ux0dsePW8hmQ1UJabeDloCFqMkJDBqZ0jBnRrdvMnAzNe7aQXm+xw/2yjGh91q+F2J5P1rza9TE3OoxQMaW8K+rlSt1arQCNtPk81zVjapjA1mFcQa/c5aHQ7PvbnO0mn1/IxaN6pYHzG9fXZEYf2mckeBzmesH7Jo3XwC4ydbWL0KqVfK03EVEmxXfAegcs4kq+lrCMFNx1UXObvQTc+xPGFPpZk+eMCl+3inAcBTPfYbYQjBSYrXopzvyAZS3lofMzfRaMqHVBFXNkjtvk6lTBvsBNF3MF+k5VuEAPhSZTwq0lOi8cl3tC+yoB7b5kWxfzSZH6xKRakUSRgynO+wxrKsccVhnH3Jwy2WnQLQ7mzHFkOus/BxIRhX2/bvfCAaOdhxOte8xWQ8rf7Zs1WTG4Z022/WtS4W3g/ICRHy/4fYvIYUhTskiQVYY7WqHCfR1VCXjcgBU16/C8K+ZSCZu5llI7NPkewVkZzJ6m3ePw/Gbnb37Uw1gss852oRHPSkx7FkBGwXZ2HJYu0G0e31JnTRTVLgVGKovXtV8VhLQ6DKkSpKuGZb+QHj58mH7605/az3fccQd997vfpbGxsdiyw+GQrr/+emq3N9bCXqFQKBQKhUKhUCg8qKnRpsKyX0gLhQI94xnPoMOHTdQ3CAJ6y1veQm95y1sSlw/DkM4+++wja82O7f42WN8Dx9xhJR6tyXY73jIxZhSAHGQ2OeIcFKA9jaI6Q454hxzlBKOIv+EKaiQh8h2LbnEkLuT9y3pbaToCF4VxX08T20ZKzVPbhEWYktJWo4UAqyIjb1JHi3NjXQZL8QjjsMn1M4ldiqsoRLloM9ccQVlELPm0DufZ8c89dsFGpmlFi1vmEvfVY90L2JNuM50pRWQ1TVeWxqoNkmrxcVS4xi60iBIjGiv1IklaJglZA7CImqUi+pmD07StGcn74OaXbHMdnRpr/NJa0eZzAp1qGw6aAzgOM2PqaEgP3b2TiIh28Niyuj0OzgYbIGEGIwWtFPpPUg1BiTq7vKY5I5dsfUyO+sPJdRGnbunaiHkK16E8Zvq11KG3ZiK3aLmPHLTtMeduZsiqJsKOmpNtoXPLcv85wNH7ptO/rSMvr3KwbX6r5KBXTh4jXZG6dYhvK2NOjVPJTM1z/63wkK/z33sW/GMHxrZOmaYlaMeQRYPzBy36eqNxpxkT0JZJbRzg9jFZs3AEGlruZ2BIMT+BdYIDKOYnuL8mafhq3CdQ6xTaR7CzReEcLzX67RTdqIsajw+wsbYuN1hObhcYR3xfzMfvq3DsBasKVsi6BDMzmufzCofmbt/PUEmadzuc9YFxMGRaH3UekZkinUuBREYONXJZM1wsd+LLrANQXUBmFpXqfP0TanJDD/nLe44jovg4BZCh0+RzjPsSmFJoSHO59JcU2a/kvBd7TgP7njDHYjwgIwSsZY+fO5E1JRlS7CMp+wrtkM+NmVxy5g0Y3RYyYUo+ieTWmcZ9A5pb6RuA8YAsqyIzzV2e5+EYneTcPN0wxzLH4wZzx0R1bTWkgUyLW+F2jga33347vfa1r6Wvf/3rND09Taeeeiq94hWvoGc+85necjfeeCO95S1voW9/+9vUbDbpXve6F11wwQX0t3/7t1Qs+veLn/zkJ/Sa17yGvvWtb1Gr1aKHP/zh9KY3vYke9ahHHVVb1xrLfiEdGRmhj370o3TzzTdTGIb0pje9ic4991w69dRTY8tms1nasWMH/emf/umqNlahUCgUCoVCoVAojgobzJDedddddPrpp1O326VLLrmEduzYQR//+Mfp/PPPp1//+td02WWXERHR17/+dXrc4x5HO3bsoEsvvZS2bt1KN9xwA73xjW+km266ib70pS9ZI6qf/exndOaZZ1K5XKZLLrmE6vU6XX311XTWWWfRDTfcQI95zGM28pAXxRFpSJ/whCfQE57wBCIyJ+jiiy+mxz3ucavXmrtNDSw6bueiiyUKkbmQktX7lYWec4ajSYhYxSJZvJyT6y8ZUBnlwuc8ay1j7BhcGBPYTegeoCVF/UobRWY2M8v5+mA74aRbmDCMJblRKrCS3I6YLpXbgxpf9jhE1My69SYwqlg3z5ErrIv2daeTmZwkTax1H2bHVOuQ3OKo3Gwyq7hWCBB8TStiiOWcfgC2ctjN8W/McLB2Dow3zgucjXFuoTvusJbD1dqBNU2r9So1y0PuQx3WzOQ5ci8ddImIatxOlzEkirto2ij8IDnqnHf6Xy1vxmBBRJhHWJMF59tKFvUHk7eJmqIzXXf8gWkgXpfby597A7TbZ0olW+gyJaWBaVeXo8MxQrS4/i67YEanp8eIKNKvgUUCy+keF6LJgyCZsYFmrMzR8JhmVES685U4O9Jr+vUKh3DXhdsuJPEiU6JSX/CWJ4oi7XDohXaw25VMOvpRPvH7NrSHzrlIY0egNYZ7LtBaYqzP9aK+vMA66zHuhOUcthl6fwEwVNYRGQxMQrYIkBFzc5rr8loB2jOwM5IZRXu6To1qqw1FtgXcnVkHBjYT8xEEdzYbA8xeNa4bwzrQz6N2LrTJ8i90edh2jter5eL3YDCH88LVOVyikINkhDOujp61hhiD000zr4MVAlMqmVGJbkLf7tn60n774HgONla67XZEvdT5ZqT7rrBet9X0Z78BP0NUjz+Y2L61QprmMSuyClymVDoFSwa4InXF4vzsmThkvs+hH0beGfL+gWfCotCKrgT2GVBsIzbmUhz57bNXb+k5wlZcaCdnXlTrPhOJudk9fnhVjI6Y57L5BZOhgmwqW89VZhjwfIltJT2PnDg6Q0REv5od876Xffe3Da997Wvp0KFDdPPNN9MjHvEIIiJ60YteRA972MPoDW94A73whS+k0dFReuELX0gjIyP0/e9/n3buNO9HL3nJS+jSSy+ld7zjHfTpT3+anvGMZxAR0aWXXkqdToe+//3v08knn0xERM95znPo1FNPpYsvvphuvfVWCqRV/SbBit1jvvrVr9JjH/tY+sIXvuBpRT/zmc/Q9ddfvyqNUygUCoVCoVAoFIpVxXB49P+OAkEQ0JOe9CT7MkpkMkwf+9jHUrPZpNtuu43uuusu+sUvfkHnnnuufRkFnvvc5xKRIQiJiA4cOEBf/OIX6dxzz7Uvo0REExMTdOGFF9JPf/pT+s53vnNUbV5LrJgCmJ+fpyc/+cl044030g9/+EObuvuxj32MPv3pT9M555xDn/rUp6hQWFq7YTHB2puWiZQGXIcSWlIwo0HTiebAcZfp6sy40EvgXXkJvWfYjZ8KOKAiKpapm41JpjScNJEiyYQOKV27ma0zC4hoMUfn0I6wIzWZvgagN2P2Cd2bC+v0OmTNo8hKAJspa3wNWLsQ2ChzdJzQdgCSreuzBsCuK9nkBH2jrcmaJl0tbI7oWGaUWc/D5jy5UdwwxTFW1oKFFgsMw3AZEh3oScGa4px2WuZcF8sius7nGK6eiKxWMxHz0Gb2FNFKOArmBn5tNmAp7WjG0U9IZhS/wW1zqx2DPiPRhE4t8NfbVoq2XeotPn6L0ONI1138TSDCwA4jwtyfN+0qHs/Hkdm4KGKV2aL5eV83P8e6yYKjC4OuDtoeRNLHRub5s88aAeib5e3TRBSx+S7Q1+GmbWsag7Hq+/Mmam8Wuf223m0//VYDBlEyG3BOLTEzBC0pjqPGLNSCw5B00a/5c8Ramm3X83CTNt8WxTXOprg3ExEd7kAb7X8vTcIlDnIt3Dyfb5ehLjAjicwJwDIa68yQyv1neGxC2wwmUmZWEDn9ru5ntoDlxD0ArKWtXY19JrBNWV63IGouYgaBO2yb58QSz4n4nISu6EcjzOz2eA7MBH47bG1JwaxVEtyAJVsnHXsBOAnHxiTqKHdRrzTaZ1uMITCi1ukct9OUZ50eX0t3RgEv2hLeBQX2yWjv30JEiSW41wQD1kXaDDY8N6DGNn/f5utOFPWvHcyy3TVlHHmROVJkJhv3AGgZjxs1mm4wo4Vi/FrZ+stgKfFMiHGZTZ5bATzX5Z2siL7IIskI/al9Zkhhi6PnUm5TwriRmRbIbEOliD6Pjz4/8+E8Y/5GJkLWqRWN82THMx9zkhOv2QbPtX1/vCUuy+f33qPT3vdJbOqqIaSjfqG021khPvKRjyR+/8Mf/pAymQwdf/zxtG3bNvrFL35BpVJ8Tjtw4AARmZdYIqJbbrmFiIhOP/302LJ46b3lllu8F+DNhBUzpG9961vpm9/8Jl1++eV00kkn2e/f+9730pve9Cb6/Oc/T29/+9tXpZEKhUKhUCgUCoVCcfQIKRgOjvrfatV9mZubo+9973v0vOc9j77yla/QJZdcQrt27aJcLkf3uc996Pjjj4+tc9VVVxER0VlnnUVERHfffTcREZ1wwgmxZY87zph+3X777avS3rXAihnST33qU3TRRRfR//2//9f7fvv27fSa17yG7rjjDvroRz9Kl19++fI3OsV2pltYhwimlH8OZjl6khTVSIt0sO5Huo5Zp9SMz+i52lIbeVqiJqBdRzAMuQTtaKaaUgoHTGmp7360GtNhZ4u/fEotKG8R1OfiKJP9O/TPiQR0kK5+Kc3Z1TYfUT3RrlT3OSJ7XoPxlG64UUWLoU/kJocNEQ3NDhJWMkD0ctDxz0NGros+xceY48hl4ETEbfQSn1k3g6htj9mKArMroWARrUbLYS5LzJ6CKV1gTSW0GmCpoGGChrTRYU2R0OOUExwmAbCvYGNHhYPfCDuYItKPyH6H/046OqFRaG3hnpsSbYWT4gxnGmzjLAA4KtadNqBdDWYh69x3w1lmGkfXX0MKHSWYKDBkqPEIfU4vgXGEO63Vz/G1gRtiKUGjl4Sck3XR50wM6IekRlRmnsA5V0bDh878keHhJBnRIjMUUkvaEp8tE1c0LJPLkCLSDtdlSEbBlPbAKggGFERpTZzWdsJQn5RO7iWz8lTXHFg1ZzZSG5h2oT7fPLvIuho1yfzZ40DWygbVIQWgJcS9oNkwnNpgEJ2oPPdJywBnk1NecN/B9bU6b+4zlol05hSwqtLNVP5eEtki+Jzk6IyxA0dRsDDQn3bFfbEA5irjs0d2e07/s5pX6Op4DuzLTALpWsz9Nc/nDsyqrC1MFM2Xss5jC/pe/gvtfqSr5rrEDruNeX9L1deZd9nlebF73VoCbr+9lDreSUA/PH6LqbN8++Ft5ntR9xJjvyBqi6KP5Jx7nL2HCh+HtPMCfXiHfThw73XnQ7jp2nMcgI3k+RputKJWOZBljX+Y4hVilmGvCuHIK5/5pA8F5mT09dCpCW8z5vgrOeZQVxj31SzPuWBI8dySSzh3ob1vm23IurxrhlV8xty3b5996XNx6aWX0qWXXrrk+n/+539On/nMZ4jIMJyvfvWrF13+zW9+M335y1+mhzzkIfSUpzyFiIhmZ423TK0WZ60rFTOmGo1G7LfNghUzpHv37qUHP/jBqb8//OEP39Rv4gqFQqFQKBQKheIYxCpqSIfDIe3duzf2b25ueeacL3jBC+i6666j1772tfTjH/+YHvSgB6W+Q735zW+m173udbRjxw761Kc+ZR12Q85LDxM0SvgO6b2bESumALZv304//vGPU3//2c9+Rlu2bEn9PRFgRiX6yRHX5GX9j4i85ITWsnfYrxWHGpxePj7+jwgUs62x8PoSSGVFiSI2LkHD6gI1RrGt/pQ5V642c9DxNY4xfaPVxvgdEuwn3EazzEwVRqNzBhdZnCfpLgv3NqsTqvlsjKyvao7BRBHzFY7YbICraRLCpu80C9hIpfO9dC4eiuuYqzEDkqLFXE59WUTk0R6pK2uxQ6/t6xxhTXLnQ70+/HUdF4niUXfoliQzmoQOLztWFkyciPCOCo0rdKpzXY6G83a2OBqzWe7TQbB4RFMmM0BTON0x2x5xdEKjZWYj2GEQEefeATM35DPr6/JMFNeMgr0p5f1ofiGh3wyEFjPL4kYwpQPL5DCjJdx0wYJ2JyN9FtwtEekv8E9drp8LVgG/Wzaer3ljcizWzh7YP8EaSbarB+aa9XYLfA17A3+9msOo4Xp3U8ZbLoPlEn+mBT6tSRrSrUV/PgVTeqgNnRl+58yFTMpODkT/hXssFHySDVlvYF6XDLfVAqNvOSyorEeYBjCgJdQ2xL0CGucELVxO6EylQ3F9p6mL3jg4zu30r9HImMmqmnc0h6OFWW+Z5gLmQHNMRWYW8yL7w7pBC/LGPVdYJhDrYk4eWOfl5P6JbVW4DZmETJA5od8FM9qWejtmm6qiLS77lCf/nFu2bRmO0GsJPIvYbC5bf5a9DpxMDbD1sh/uYC3zQVEXePeIr1PMZJJZQheY1+Rv1e1TSx1KDLjGBVHrdTGNZfKGMG6c70T/T8uCa8/5932MTfgpYBz5x4uaqgNvHbQbGS7NFntfiHq9gfjsAt+tGzMKrCJDmslkaNeuXbHvR0ZGEpaO45xzziEioqc+9an08Ic/nJ7ylKfQm970Jvrwhz9sl+n3+/TSl76U3v/+99OePXvohhtu8CST9bp5L2g249lQ+G50dDT222bBit8A/viP/5je//7301Of+lR6/OMf7/32zW9+k973vvfRc57znKNuoEKhUCgUCoVCoVCsCsJwdeqQMvO4a9cuq+E8Wjz5yU+mkZER+t73vme/W1hYoGc84xn0xS9+kX7v936PPv/5z8e0ong5TWoHvkvSom4WrDhl9/Wvfz1t2bKFnvjEJ9KDH/xgevazn03Pec5z6GEPexg95jGPobGxMXrjG9+4mm1VKBQKhUKhUCgUiqNCMBwe9b+V4vDhw3Tf+96Xzj///Nhv3W6X2u02lcuGbW40GvRHf/RH9MUvfpEe+9jH0k033ZRoXPSwhz2MMpmMddt1ge8e+chHrrjNa42jStn9wQ9+QJdffjl99rOfpf/6r/8iIqJqtUrnn38+ve1tb6Pdu3cf2UbnOEUU5T5KnJrS5yhGm9NHao5FeUYULkaB9hk2NEcaB4TWIyZdKGZyBGMFN0UlxcxImsdYgyRp4V5OsHzHqrIfi1QQtAeGSUFC6RgiokGr6Pw/ucQOBO4yfWPIaS9Ij7H7hrHNfFQsGxb92QFbfnPaxpANKpLKurjHAVF90nLhAh/7AhsATCRuau1R5XO5wH0AXaeVbnovDaPQZ9Ks21FaJyvSJVGQ3jWNQXmIDNftgakR0r6QLrnQMOk3I1zmA7/LwvNE0flvC7OIfoppFQwhBiJNsmiLYEepezDcQVpfnveLjCJbNgGO/tw+GGuMFJDaXvK2Q0Q0yucTpkaDhLQqIje10P8ew+3u+Sh9ZqRkUlhQFL6eP5i80gagw+OqLwxUUHw8yTgG5xfnoA+TDk7Z6wlDIqTD9RfM8Q/7fmki85uZGyAZWLhnKxE5ZjMwTuPrAvMipKOhD7qpX7jufZG2iGVa7WQjk1rR9I97Zse976VpCVHUTxoss0B3aNq+Z/6inwzktU4o/1LhISDTfdv8ULKXM1FLrNGZ5zTDDKdYIv0dx0EUpS23pn25CsxGZMrqWmP+gD/5SoOpYkJ7YMiDPlmpGQmGW96GKJq/UFIEpi6YM20fcfpFr518TwNgBlfm/tmaNWne6HfYh1siC2nJSEtEai7MiZBeK1MS7T75d5i4dJ0UxIwYg9aYxo4DpC8Ovc+DAY9FHnty3BMRVThtv8XthOFW26bswtCLO6o4dXkY7jnnt8rbRMrk7JyZHyHpSEv5XCvgWaXA193KiAq4Rpzu79yPkCI6SLmHba/50gvMPyWWI8D4CunybvkhaxCHZfEcBCkDnq2E+Vg+6dkP25Qp7jI9fuBLdOSdTsqEvGPLsjGSMDMCIK2Kll/8Juf2fZgtFet+OiieBZtzvtwEY0GWS0riJGV/z6ySc+2S2CjjTCLaunUr5fN5uu666+jWW2+lBzzgAfa3q666irrdLp177rlERHTRRRfRTTfdRE9+8pPp2muvTS2nuWPHDnr84x9P1157Lb3+9a+3tUgnJyfpgx/8ID3wgQ+kBz3oQWt/cCvEUc02u3btomuuuYaIiKampqjX69G2bduswFahUCgUCoVCoVAoNhU28IWUiOh973sfPeEJT6CzzjqLLr74Ytq5cyd95StfoWuvvZbOPPNMesUrXkE333wzfexjH6NCoUBnn302ffKTn4xt55RTTqEzzjiDiIj+/u//ns444wy7frFYpKuvvpqmpqYS191MWJXw1y9+8Qu644476CEPeQh1Oh3KZDJULK6glPIosxcDjqy2OcoEZnTAUZMFxyp/hMXZLRMZsqYMwvQDEfyQo4hZwThmR9JLIgQFjulwtD2oMpPHQYqB75dkGdNBo8T7jKI/MFcazBlWIsPtQIQtI9oVygiliAK6rKi0xpcRNERlB2x2gHNio49cLNl+77ZjgHIm5nOWL2+aeZFtn4jIDR2jJRsJ7OGaickhk8yCrRlyPstJ3FcCUSqo30i3opcMMM55n8t2DMBy8l9EWhEllUWzicgyBmC4uglF6YkidgqRfkS53bIEYQfGSGy+QbBiN+0cK5rrOcnFrkvCzAhR261V04/d6KcsIo6oezehRInZORemZ6ZypuUbLZSdfWd5W7UU44dZMIpgB4WRQhKwv21kygTg+ga4Jv117n9E1Gj72Qo4h2Cfy6V0gzRp6pHhvjgUcwb6UZPNi2o7zfGDSR84wX2M0S4zeKUR3zK+w+wqov6BYK7BFIQe6+UboLWayRkadhvMYEnDmmGMO4jMtypZcy0nmeqc6qB8g1mniKHuV8Qg9oGyc9/WYtR/0JdK3L4Cr/zzucWj+RgP3QQzD0AyZ8BgnRmq+g7TF6bu3mn23/dNTqZnzT3aHfcwMxmrJZcTKNaZMUX5FP6bxn0msaIw5MoLk51QnNPyqH8zjpUpciBLd7Rb/ryeVhYErDH6gzQ/8vaBZbhfDrm9BV4H2QAoAyMNX/rOs0NbzKMwM+qlGCQt9H2zowm+TtlFpjWMPZSdyjLjPJa+ypoigzmJxwGM1w5MRYaZsgRWtejPkYUsStLx2Md54O/BeGM+Sio7BCYez0qlHb4xkmRI0beKE4ad7UxGmTl5NjocChMqaXDYmUo2w5HPdRmnH2eFgWYsUwvliPg48LzR5nJOsi+DGSaKxmCHM+dsiRjuu7Vxc6xlzvaanTb3l9GayV5A3z8wHV073LPAiOLa5ZdhovjbgD/4gz+gm2++mV7/+tfTu9/9bmo2m3TyySfTlVdeSZdddhkVi0W64YYbiMik8V588cWJ23ne855nX0gf8IAH0I033kivfvWr6corr6RMJkMPfehD6ZprrqHTTz993Y5tJTiqu923v/1tuuiii+inP/0pERHdcMMNNBwO6dnPfja9973vpWc84xmr0kiFQqFQKBQKhUKhOGqE4eowpAklVo4Ep512Gl1//fWpv19xxRV0xRVXHPE2P//5zx9VuzYCK34h/clPfkKPf/zjqVKp0HOe8xz6l3/5FyIyGtJ+v08XXHAB7dixgx796Ecvf6MDERWBlhQaUvw+cDrAtKAnAclUwbYbZQo4khSU/YiQy6gEJf6/CGyH3IzhNOtOhR4wzKU7d2H/NrKLYuBjTW//g7lyfGUHQ6ldJKK+iCwXx+Z5n742TEaVAUTNFiuGjW1ky8nn3WrKurC/Z3aRI4hue3vMEmet3oKXPchRs53rXC+p7x83jmGAotJ8DC7rm+XIqSx3Y4tWL9M3DNvM5ly9pz9Z9jlCKfVHiCaCCZVRbpdxQiQe+sJaxbS3UnayDigq0D7LZWFgx46i1tbq3bFpR9kL9E1EpLGOLGEgS5lMcHF2zO+2DAxFJTQkC1sUWkowpYgEQ2va4Ch63Ym8tli7akuX8LXMbFCpA6Iomt9lBhtMVJKmDMD1z4g5T5YzqNT9MQud54D7NfpwdyHSjkvG3uqSMa5RlkGUkoqt57QtX/Lny0GBmR7RfxE9R/8BE1LnKPqA+1uzG2fUCigxwsXZccsYcOcqCZqoL55LxvK+9o+IqIKxA8aTmeeqqOtWy5l1Znj+yPE2agnlUTBGkc2Q4U0NumbbLkOxHmgcNvrcgch0sTo9lINwWTnO2MD8hDkMmlHJeMuHDunjkHWYItyrBikkJBjQrNC2oqxFki5VMu3dTjJX62aWLIYkNqcvdIBW94+MA9sWtMsfu23u09Pt+HNAk+eGeZSpEudX6u/6/HGWj2esEP1+WGSljHDJLmhJKyMpz1drBDwTALiuuKc0mcmD7wBRNP5Rsgx/C2BXc36WCYAsoQ6TiiXWgy7MRMzkOJcVssD1ZG0ynq16XHotz9kAmQR9J4BjQXZbtmx6A1hMZE2Vts6Y5UQ2FT7nWTc9WIj3EeuJAn0sn4scz/Uo54MnmeVo1TEusS6eNyXTi2sGthWlkDCfF/PuMwPfP8QzKe5lKMG0ZpBmE4oNxYrFnm94wxuoVqvRrbfeSn//939vi66eccYZ9OMf/5h27dpFb3vb21atoQqFQqFQKBQKhUJx1BgOj/6fYtWwYob0a1/7Gr30pS+l7du30+TkpPfbnj176EUvehG9973vPbKNZqVeUkQece0TXqNDljLaiDbrPsM2aw+6/rYz7ABoXfBq7IK3EEUbhy2zo0xVaDE5oDqcT36fD9ghMezFWQ1Es8C24S+iXVTidgndol1f6Bc9NhNRpgyKHDPDl8CUJSGKevta1CR0Z4y2pMCaWJxfqRkNU5w0zcKscTlgovLl4w55yw73r0KNqCMBa0gDnGIhicKxZEsJUTuO6MGx1GpHG4sz3a0Zo81bzO0Okci+0MqkuQoCMupotuH3Sct4CTfIIuvBwJzKiH87Qcea4WMucnQYjKnURYH1k+6WAPSskeuu77hLRNTsFbxto/19sVx2EQ1pV0R2rfP2Eud1LWGdCYdgeIX+EyyUMzbhyFtkzZt0CU1DjhkBqWN3WcE8R+XTXLSHg+TvEU1P0kRD5y/bh/6Av+hj0PLZgvJ8fK2ez4wQEeVw/nhZaERH8tAWJzaXsnyakSQx1TXLb3dOf45ZUyZdaaZnlqmLQ0QbRvk+UOPjqXNGgNuXLRPVM+Osxiz2UtdurQBGFjzVzNQYERF1+DpivLm6NalrluyqBNYtsNbMuozabbqMpe82DMZHakMlUwqtMzTOPoHP92eez7E3MKVSU2izflLun1LDaJZl/Ty0j8gegf546D/bYC7Hcgvd+Px6sGWOpSXmrbyY4zAkreO4/T7efjjuIlsE3ODoyFxs2fXAkM+lzbzg8zd90Lg/Y05wr8UMn6senL5ZEF7Ls4cCZ5eAVbUZGHze2ilMPlE0fxVrPoPY3Oe7UWO8SvYdLrcuY2qzqoLkdQYpjD2esTLCFTib4Ogrn7uiDC4z5sDsyswsOXZzTraQrfyAZwZeBz4BGIM4Z9CSNnkMIjvAzYxq8rUrCu3qEP0yxXF91aAvlJsKK34hnZ+fpz179qT+vmXLFpqZmVnp5hUKhUKhUCgUCoVi9aEpu5sKK34hPemkk+i73/0uXXTRRYm//+d//iedeOKJR7ZRlIvpcQSlLSI/ZW5uJ67ZAKslmdIQdSFFTTRoNykntKYl5zMCv7zoYDo5chWA1Syy0yQfRthmlnPg6Fegk0GkCtpKOLgK913JTFg2o8nb8Vx52c0QmgTh/IaomYz8ptUac/ctHeDQDkTzEO1D9A+RwcVcDkOOZCI+2OYah8XtxsEukNdsrTEtHIPhugr9YkJt0RhzxOe0Mzvqf70Um5nCGhFF2qw0SLdHMKNgVF2mNM11Nt0pMrndpWI8KgvnSDB2UY29jLeOrbWHaPgSzD1RFM1mk1Sqi/2Dda2z5mRfw6+JZpdbhDGVGHbW1+GUKNLOQJcGd2HJMrtRdYw9nHdEoqvMckstcmGRGnkSPVErUmpIwXah38IhFVHyIWeJdJxId5EZtULFzIFg5eYmx7x9S81xJ4E1IiKquTojXqYksjtKHPhnUtN+Bpt0uM3aZLuGWXCm69ZP9fvDdr5XoP5jhanTihjDc0LjmnP65gS7VUsNN65pZpEslfVAt5dyz3N0itmUNsq5Buwlvs8Uku9xXl3G0K9za1khXlfW0IYbq9TEuYxRmhM6jiNrHXFZhyeuJ9qfxIxi3C5wbWPLeHb4fijGcb1krjv6+kGukzzP533BdUjnv9JVtz30Nc3FlPqpYENnnP64jfXYYG6nm6Zvjo/NmAVSHHzXCpaN5jlsYdq/j86zpnbOydBBhsSkYBZxTMi0QRYEmNLyIq7Xdtuon456pELTmrEVDbguO57ncN76CY7RsdqgYhlx3SS7iTGQ5AUiMwHtNvh5zXpUwNV5vzmPyJYZzPmaYteXBOtaB+G6n0KWEdlxffts6B+PnOuIomeC3HrOdyERhathanT0m1AYrDg37YILLqBrrrmG/u3f/s1+FwQBDYdDetvb3kaf+cxn6OlPf/qqNFKhUCgUCoVCoVAojh6hYUiP9p++ka4aVkwBvOpVr6IbbriBnvGMZ9DY2BgFQUAvfvGL6fDhwzQ9PU0PfOAD6W//9m+PbKMFESlqipp7tk6kE7Vrg0XzOwXYDelAhuhOrN4n7yrsOO/oYBBbyZoYMKOZMd53jk8nC5EQRQ5brm7Sr5Magr2wEeBkRjdy503XMQbQjLJ7G5zfwEBZ1pgWjwqBCcyVo/Mva5zaZcVnRAoHMyZSiNqmSQxhUdQwjXbmO/VuFOB83J80UeMkJtlGQkVkNw3WgZbPh9X5cLTddZOE3tOyTYL5xLq5nNBZiYjk/Fzd/h8aRUT3wVoWuD8NUmqGQlc1WOL4iOKuk0FBRHxTItOIXMMlsuD0Lakf7DBzIDWWRf79xNEZIiJq8XKtfpxlPnHcOCgi8mszBuBiyPXT1hMF1teAmYJTbkZoXYYOc5HJ+OegNjLjfUZ/WC4z6mZDFPO+nk468tqax3BFFJkU7Zk4Uy0Z/wxrLUs836CPQS/bYTYExwzmo58wp0B/jHqke9hFfX87z9+b3+e59jHYza0l8xdMaRepXF4t2mS2qJIyTYGNhwtqg4+n7mio4sw37wP3gXXWkqLeItqBOQjZLPkjYDDQdjDglk2P1Qj3M3dW0l4J6brbS9HlmfZwH7bzanINYzCnuZw/B3ad/gxmdK7tu8XiOsM1F07ih1v+csBkglYSKMT6BLc35b4us0K2OXU6kXGS53lmvGIYezuea3E2ay1h6x4LrwzMDZj/24No0LXFPUk6q4/AeJ18h3boUavM2OXy8YysQiW57jP6MjSWObFcTzD3rhYTz57QcaL/AW6tdqJojsXyQYE9GppFb3tE8ee0WGYgo3vQ+HbIsZdf5B5hnXr5WFEPFe3CM2mX65Sinmsuz8+CPE7czIKs6Mt4HknyqFgTaMrupsKKGdJisUj/+Z//Sf/v//0/OvHEE6lcLtOdd95JO3bsoNe+9rX0zW9+kyqV5MlWoVAoFAqFQqFQKDYEq8KQKlYLR0VB5fN5uuyyy+iyyy5blcYMtmwnIqLs4f3mi/Ex87cjolSutrTEhwC9pnTZROQqRacWcqQ8bHHk342SgQUo+HXSlkSJQ3IddlMrOlGgIetm8qwRYg1rf6rm7RN1ruCgJl3TkoBloSNATazunGApOFpb3DLLTRLubQk1tGzNLET3wAjKdvE5QkQQGlIwgkmsqLxmUdQu6SjXENvHzN+DM0RENOQ+gWsBuE7COHc4hjw7yyGK2G+u3CVOOpSC3YTrqDxvYDczeaHVSqh/CBYz5gooap0iUonIpdSLJTGm2B/0kNCQgklodZLPCerEdfk4Cgn1/eyyfIxgHqA9yUJPyYwoPpd4W24tukJCRJwo0nd7evJ1AhgW2TZZY9QFrpVEpLvl69D3NXHou+ijSWyMnROWqb3FNsFw9EVtUaKI4cffwtD0Z9TkXG79x1wCe1jLMtPPc8hYwa/jCfYEbEl3iHrB4jj4i7bjIgyH3pECz09inaJlZ/0xAs1fERYJTk1ZaJ+h943YxI11f2ynuIMj6ye7SE1VsFkym6Y42kj8PganrwRLnAfblwVbLp3ls4vMJdF4kPUUOSsE932RIWOzNhz9vXRCBxZYt5mxrLlpL1i8Vsp6LuSZaDEzJZ3FS7YGLzPUvM9R61YdLQt9Jb7LLVJDfT2wVN/YN2c0pa4uW56XPt8TSnDtxnngbcPlFfc0jL0Cjz23Li2Y0IG49ugzmB/BUvYTaoIS+c9JUkMqWUr5jGWPE3VKRd1S7/kE2yqK84h78KQ5fzEWVuxb/k4UOQUnPR8SxbPDbIYEj70yewZkHfZTOg3jM9x1F6u/vRpYDQmpYvWwsTmRCoVCoVAoFAqFQrFeCGl1GE4lSVcNy34hPfnkk+md73wnPeUpT7Gfl4NMJkP1ep0e+tCH0lve8hbatm3bkusMtu4kIocpBSOaWZoyQ0Q1HAoNqWAYLPPYEtF4Z7mAXRPhkivdKi3wdXfg/YUrL7SmZvtLHoIHyc5Z3YGoHegtIyJViOSW2L0WbCciXRnymVGrWXDr+zFrhAggfpPRPengmxWudF67eNnyjinzRWbofb9RCGX5WxEdTdKSSgZJHkNj1ug45XlAVBZoO9FTqf2V51bCsgSiS7gOupJ9QkSy207WbCCKDH1NH2w6XyuXIQWLB90ZZI9g/cBaFrJmW3Dba7COCtHQMuppZqIoL36T9Ujt79yOztCvpWnrqubiLsIjXGuvIFi0oLBxLMFIzWi45hZYt8yRY9R4lTUfiaJ+Uq2Y7AOwV2lsG5xwZdQ/CdYZVWimADknpjl2L4Ze179mRaELlOxXm/W1uKauDhPHPsrDqMF9DkyprVUYcl/MgCnlc8j1Sme7izDSKd/XUH9XfG+ZKrTN0fDt3nHAtENo0EojC7yzjZkLS8j0YMas32VXd5GlYZYx47k25teuhBNzadx8b/0RBKsEWP8Brkvt/4h7Mc8DIqsnDXZedrqQvSeJvlqosoZZOPX2hf4UenvMmUlZIvWiOX/zHXO/HOF5FO6wLX4+meO+j/6XtS7QS1M3Ze5vbR4fo5ijeT6A0zT+5lH/2ZlXwdjib5bn5qSsmvWA1WTy3+7dO4iIqNMy53HXiMnqOuhob3FpkZlQgON63p+rJKyTOeY2ni/dWpzdptkv7mk54QyNPmTryqew6Is908jf0u7vssa7HU/OOLL7t7+ZY0J9dABsJ3Sfth4p5t6U5wGiKIMI6M9UvX3neS6DA7ad2/jZJrdItoLMMGiul5ZUsSmw7KeHMPQHzXA4pCBIuzVH6Pf7dMcdd9CPfvQj2r9/P/37v//7kbdSoVAoFAqFQqFQKFYDmrK7qbDsF9Lbb7/d+3zHHXcc0Y4uuugi+uQnP7n4QkO43Qk2A7T60GcgXYQNRFC5bl9KLj8iRtCKZnZzfA1uvWE8kmVrrqHzSpaTfw4XxAs63GIdFja7i/V/bODZP1z3VrGRKhExt7Uwy2Az/bpX3rJwvuRAX2HcdwuFBsEuL9zSoHFw9Ry5GjsMcnSsN+vrUqWmFK6xYGfyrCuSDAtRdM1SGeh1RjDKjE2DmQ3BDnvLwqGVI6jQzCI62BMMd5oWz9Z3dDQ8g+VqllOAc19y+lIW7L3QE/a6yS6UWaEpkpFzlx3osSMpWNihcBAFM9plh8QaHzNq9NWYOXKZUbsf3lZPsBHYhzyvZREdB7O4LC3kBnbDGa5DCOB4i0K349Zy66ZcO8mM9kW9u6FYDuPPZfEtO5lSJxe18NDvpe45x+x8krYO2QFtoanCPuW1yoNx42vb4H7jao2hGUZf28r9/FDLr6+XZQZhlB0uZ6x+kGuK5uLB1m1Fn3XHraIghil0qnA3zfPfET4Xo+VIR19mnT80jrau9DKYlbUAriO0c+hncDgGu+H2v+qoub/I2p+AdW2v8zo4f4JNwhzqsp74f8QO8b0N99bQz8SRLNJiNY7TvAtizvH4DM0eHNF5nCzmPI45DXMc+l0H4wYu69zvsvwwkU/S6MKpFxUERD/j25bVjOL4bAZBgj4TGSeWSBZjrttaX4ZKMowDqzc35xjj2m2nHCFl9FFRWxvzRlXUwYT22WqJ3X4B7SWWwZgGs4g+IeY99Efblx2dP1h+OdbgtovnDTk+0Ldz1WTnXxf2Wa6f7NcAJjS3lcfu0PwdcvacHEdeO/g5KDdmsjiy3B5bm57n89oWkxnR52oPqFvadjxN8Hxk7+/iPBZT66OvEjbHI6eCsWq2MQcPHqSpqanU3x/84AfTzp07V2t3CoVCoVAoFAqFQnHkCFfhn2LVcFSmRnfeeSddccUVdP3119PcnImGbNmyhZ75zGfSlVdeSVu2bLHLvuQlL6GXvOQly9puZoG1KGma0ZLTbGZPA46cUiPFAQw1nMqiB7VFPrubhizZUjQHQo9Bcm+02r9yPLoTzvKqcyanPxAMFCJXlq1gx1zUnoL+07qjJugNrAZUOKhaR1jUjUphSoNBAgPN+0G0a7m6B7Qla1lbp7Yk9peiA8rUllc3cdWwf8b7GPbTa9elAXUawfIg8ifrj0bMpFk+zxFVV1+RFt1vp7jUSi2kZQ2cqCP202yY/letc905juCCEQWDKlmqNrvfIYrsRqrBVtq/YpkC66hqIvoPRlQypZVSFMmGhnQgti2ZhaUwGMSnvLR6uOHiEqQ1QT6b7BZuf0/Q3+C6Q3uZ5jwqgcwEMNoZ1Jp1+ovcRh66VDFmZd1AoNWMl/6y7Uxgwt32p80p0DXbfTs1ZlGXFppiOGyOFMw6h9p+lgM0pGX2C9gpDmOxAHrEfC6uOR7B9eFrWyrEO5Z17eRrkElxslxrSAa8Pj6TuBxYUaL0rAO46lrnZXb4BHCvA8OS5GKPdaP7BrOoqEfMbEsRmlsBm3niaPX70H5axlO4T3OfR1+3TBaYR+ho+feFBZ99J4rqtgKY05oimwHsJv5CB1nh4+s5WSbQho4XzW+TzJTikWWWjwfbwLqyTmc1wcQ6FyawgxsAmVGEjBzp/vv72/fb///3QfYc4XNYSfALIIr0z/YYh/78R43ltzPNBbs0MUNEcSfa0HGt7XJtdsxvgyH00HzvZYZRzrFpeum+M/f2W/4EBtd/aEpzyMio+yxxKDLtkE2TlNGGZ048w2ZHzP1T1o2Xtaq7CTWprS8GGF3uq8ho6jfX2mV3Y/u7wseKX0h/+ctf0hlnnEGHDx+m+9///nTWWWfRcDikn//85/S+972P/uM//oO+9a1vLcvESKFQKBQKhUKhUCjWHCGtTsqusqSrhhW/kL7mNa+h+fl5+uxnP0vnnHOO99snPvEJ+rM/+zN6zWteQ+9///uXvU3LjEqMsq5qP6cEF+ORf2hIhwsmwobaesWdfhqxZSRFZASazsCVdCIghd2h84qgTShS+gOh4XDRn/ajqYgKo12SMc0U/KgYWE2r2XSin2FC7SgX0JvmhD4VrKutS8hRtlw5Wg7M33Ij97YGZs5nO/IjERuVVqMzgI5qnYsShe3kaBn0vFbfktBuySjiLyK7iKoPslz3Vugr7XrZaHbLk6+/wz7A/uRSarYNhEbFBaL/laq5DpkE7SpRpEsEy45aohmhM+y7/S9cvP8hQo1j7bJbqqw1ZllQ53vouOGsinXkGCuKGqy2firv262fiPPZYQfC4giHyPkwgpWXkF01QEMahqYPjGXic4u8dhJwO+0P+Hyn1PksVOKMuo2Gcx8cCi2ojDDj2uVF/VJXD91ifXW56kfp7S5TNKRg8XPchBKZa9tz2pRZIuAt65J2+fyOcOaAZJMOtqNzMVEUczHGuGgnvt9d9Vm7MdaOFhx9M1g4O/8j8ySltux6A9dgZItxaJ+fHo0tEwo9J9bpzJp7nazZWD7+oFlxkJzBs2h7kP3DrFIOWj7us+hnGBM9dsh1+x/Ge76c7OaMPg6eDZkmuFYLfA4yCXNKW2jd82KORh9o9oVWTrB3cGTuDONzKuqP5rmvFES20jQzVXU4qWK+5W11HedS/DZR8h3PcV8oVZOzR9YKkSbd7wv9RfwUsmL8Na2bO7PN+eS63XafyLZJqHmJLA45x8q+XmCGXj6vIcNtqXsjUVQH1y4JnxPupwXRX5t7t/F60bmSLv5Z4QpMPF4kI2qfJ8UYlF4mie1mhjawdexTNNhiX0SRfjcNbpbUmkAZ0k2FFWtIb7jhBvqrv/qr2MsoEdH5559PL3rRi+j6668/qsYpFAqFQqFQKBQKxWoiHAZH/U+xeljxC2mn06ETTzwx9fcHPvCBtLCQrOtQKBQKhUKhUCgUivVHYBjSo/2XWplacaRYcVLk//k//4euvfZauvjiixN//9KXvkSPfOQjj2ibw5pJzUXqbqz8Sz79wgfsndG5fWzxnfAreAjr9IOcKolUi/kwtqw8S0FBfNHmQtu8zUzRT1dzkRtveMsOmyZlQabu2iawqDyt8LLXLlG+JakkDJGT1oHsZbFNN1U3ti5vW4rnkZaRr3BaSUpqb3c6yolesqTB4jXP1wyLlQqILYtyLynXBSk/Q87zhoHPcOCn9sKkwz0nQ2nwwKlvSNUdpKT92vQ0Xr3biZsz2RRjYTAk0znbvC7S0rDPKE0vHtOCIUGj5RvIpBVbxznpcKoV9jFwUvpk2RbsP63kiYQ0fCKKCtzD1CgW7dyArMl6zcwP07M8F/LxjtXNnJhkBIRriXNS4vGLVF1rwAFjC2GlL40rsosULgdwrnD+sQ7mluWY9SN1N80UJ62kz2KQY0Iiz+dvuyj9MMlmXRUYcPB2ZJqu+Y5NmfgzUgNRcgYlO/C5zn23zvuEkZgLWy6L55GsLQWxvpNgmtwEn2GK5qa6IdUxxL0LJcqE8QtK2nQPjhMRUWHCOPwh1S+HY12EdcD5gRQkyHS970ncP2VKNFGUfolj6rUL3mc7xoYoKeNfg2KFy3LAqM7pc72+b4Bkzdd4nsQcl+O+MSLGYmeREjIAUnQLQrw2D0kHt3uS58+tJT8dPWlaC1CWhp9/YLzVmK0nLL12wP0I57wQ+um2bU43biaUo4GJU0H0Xcg7huKeUa346cj2Xp2YumvaVWSZgSwNZPfF6atWWrXI9cS93vbdVRzraJ+dT4qLz8gDbjdSjnFPcOcf+TwZ5Pl5ExIL/lmakw35fCNFPu1ZaUOwwSZeCh/L7hl33nmn9/kVr3gFnXvuuXTuuefSFVdcQfe73/0om83SL3/5S3rXu95FN954I335y19e9QYrFAqFQqFQKBQKxYoQrpLLrpoarRqW/UJ64oknUhDIqGlI119/PX32s5+NLR+GIZ122mnU7y8/6pOdOuR/Icu+lJgN6TrRnj5HdDnIj6hrT1pMQ7Td5kOWUXd0TMdUxn7XFyVOZnn/Xf4dmyxw1CzHy/cD/zMRDec5GgsDITZYwMAA04jix4js2nIcHMlC5M0VjMcE9SuNRCFqlMBMSKMcGEFIYwGJJDY0P5qS0g0We4myFauNcIBrAAMALsHRhi27+ZyvRexKr7mywuFgD7IiQu5OkJKpQmQXDFijUUn8HcA5d40DyhVh926NLnyWIA2y7EinGz9+sFQwFGq1fTbdsptgO0TfSTNrciHNo2S7wYhWmJVCqRG3rIE0dCpum1lyv2sNWYYH19QaQfFnl21GqSB5c42MsszxwRTDlmjh/iGZLJcxtSYjbPQCFmFhIZk5QZ+FqVFS1L/Jc57NDkgpoSCZX7DxM3P+vqUplguYYBVFKYgul//p8xyzvey34UDTGPKMOsz6gNuBua6EMi5iTFQLvpFWlUt+1GvGOa/slhlKyRoAlswiWWUUt5j754DnDPQplOOwDKkDW6pGjFv0u9LWWe+zLDmB+ywyQmSWEFHEuqTd03I8J/cXyom/L3YvzHN/t0wp33YwL8SOC3OO+ExEVCzAeIbNFbndPe5n6Ie5LM/V4lCL4h7eT2D8pYkWjI/GeN3Dbf8e3cOzD/ffQsJ4k9vEceQSrvdGAHOBLfnUiOYAMKMzfI0bvGyBx1adj6XHY36kNkNEfok1F24WSozlh3EP+kRKRgaMIrtz8VInS5VzgblRlqdpmSmFvox5uuv0bbQvLwzjrOEQSrHwZ2liBBOmJFM1lHfJiKyRwVzZ31bKM0QSS9vmTDs806CUEsbU9PRY4rZWDUtk1CjWF8t+Y3nuc58beyFVKBQKhUKhUCgUCoVipVj2C+lHPvKRNWwGA2yqZEbxuZD3/xIRLZioTcCavAzn7heL02Ib5o/Mb4+xcE5E2jJl8iz1sS6/oPfENjigFVTi0e1MnTUvcxxth219iv01GApoK8CChn2hP3C/S9GO2tCvUJFYvUMPpR1wspwABNgWe344eoxNg8UY+scF/YBlgI8g4h+kMCdrhUwVJWp4/0yOLIdpBvvUERF6qbGUjF5PXHdX3wdWqm/ZKS4NkBLVi+m++Jy7pVlazbL3XUVo6WwheWhQoB3jiOpwkRQXa5GfwvrMLZho8UCcExSLB2u1lVmE7DKuv2UHRXkayYzaNjpMQGXMMFa25EbWL0uzEQCjAj1dWrkAtwwCNE/ZLLTryeetJ4qd26wLMArM3HUXIua9wJH2Lpc6Qp/Euvn8kTMoiIajv6LPd5hJl2VE0nAkWu8CNNokmXVopFk7xX0Y5TncsTPFJTHK3D/BjMr+YvWWYAiLfh+UY57IYaKQObGE5mutIO9DsowEjrVYSyjHIMZ9EeNLln1AKQwxry6WEZPEmvrtYn29aBfuhUl9RX6TSZnnbVmQZeg7I90z64nz/jlpcakraJnRl0JU+wnI++xipoPMBrNNsKc4a2BCm/xckuG+XoM2VixHRLSD50dc192j5rkJmTT1reI5ao0hy6EBMiNmpBRlGWQCo0lGOZy0QiFDqxGGlh5zrD/fDJ1+aEv7LCNrJwkoB9Obr8Z+i+lQkTHC7RpIzWbG/x0ob0u/RrHxjPJ9ooSMZE4BN+smQFkk9j9BRqDMzOnNVb1tNidNmSR4NbjPgCjZNeBsL4yfImemFBO8H1YV6pK7qXBU6uLDhw/T3r17KQxDOu6442jr1q2r1S6FQqFQKBQKhUKhWFWEdGRBzcW2o1gdHPEL6WAwoHe/+9304Q9/mH7yk594v93//venv/iLv6C/+qu/omx26WjiESPPuotePGoSgrUU7qFpjGh30i/wbXVWW+ajLxGlbjG7B1cxyYhiG4iG2raYv8OGo+0TAyDD+7CaHY7Cwq0tJ5YD+k1fl2d2JDRkgo0Mh8nMCaK5WDrJGS5t4CJah/OM84uIIM4rtKdZV5OyhH5qvTWkcHEOesyeiPO3GFMKZrTA2l+wUQGz0YOeH6mHrhPRWGhVer04uy2XQUQSLGCHnQeLzMTI6+5qMsEY9oX+FExXiQthg4HLxJxy/W27Trj4f8BRTbCRaCeccg9OTRARUZejo2BGi8w4JTGjecFGDa0TL2+Dr01BRHhxnnHcRUdbg22Vts6Y9pa5iHiJj3ERV+/1AsYmWHLLrPejvpgT7JF0Sg547IGFk2MZbOcQTLxz/mWfx/gdwmU7JRkDWOyGvxQTPViCkVpMOyq3DddQy6T1zTH3ZSF5ZKIgU8CZo7azBtQ6paawJmBjJ8YMcxGx96xxm490ZXXrTmyWqbGufsX6/6OEZDPLW4y788L+LUREVN02FVsHjreYl3CvyvO9NCimsUvmmgxm/LnI9S7Ijvp8F+6LcHmHI7xleAgu5r7mFEwVUbKujyjS3xXqhslpSx8KgR5vu+nMpWkuz+ir6FcFkYEi1VD4XMzGdY5p+5hk5pmTxWjAHDC0z9h3LYFtLnF74Dwr3dfXC0W+Th2+RmneBtJ3gIiolveztsAgN5iV3jkyY763z4Tm3pDkXC6B67QwbdzPK05/IiLKprjZdmc5K8h5DoVOEshVfddmwGaVwVGa5y6wm5LlNA31KyxgLNpMgSyfHe7bWc4owN9B0nOlBM9Vg9lkh3s8m3aF9rXNzOnc1Lj9riN8L+ScWiwmHONqQjWkmwpHdNfbv38/PfnJT6Yf/OAHlM1m6eEPfzgdd9xxlMvlaO/evfT973+f/vqv/5o+9rGP0XXXXUe7du1aq3YrFAqFQqFQKBQKxZEhDFbJZTcg5UlXB8t+IR0MBvSMZzyDfvSjH9GrXvUqetWrXkXj4+PeMvPz8/SOd7yD3vzmN9Of/umf0le/+lXKSD3o0SCBGYUDboAj4eiNZdegPWDt5WBqibpai0TtwYxCAyN1LZI5tRoZd5v4PyKjvA24A3dF+5CPXxj39W75EVOvEK67iQCbBRdDsJQZcR5TiAY3KtmcMoxyhV0Yrcsva6+iCKCJePV5X9CggVlx2Ts4DOeYlVuKMV1zFPgYUEO1zUwpnCbnK/F1oBliVgnHjeOE62ga24tzDMdcN0Io9XlDsQ2r5+IoItjN5UR8pcYyH4u2mus4ZGYJben10jVZEkOxTKPpnz+4HgLQTTWZ8a040VG5P6s9STmvCw0TAZ6YmCSi5PpxGFOo9RvTfA827iaTVucVmqayo40t8rUEU5orQiMq6pCmAGM3I+oyEkUOpIUKmCizrdoYM2czI962rAZ5wHrirN8mooiJhcN0n1kQsN5wXsSxgpVHvx4bMdetxex3qxPNgUOhl4sYKF8fDO3xCGtF0RcXq2MKh1SwM3lmr7Bumd08R+rz3r7QR9Fe99rJcwEU2PG5e2gstT3rAj5vFWQRJNSXziAjgu+xWaHjDDucPdNPvtHYGqzQlibopqXeNG0blo1ml9MkprnIrC8A9giaPXgodPj+JOdGXLM0vSNRnKGvlMw5abb92rv5NL33Iuw/tmxrnPLnvN1nisspnoUS2jla9Os8V0fZEXr7+mpIM0LXCdjzxePErSGay6D+L85p8rzdZqYUmm4wpYWCf79050vppYDa1X2RdWIzwJiRhJbZ9u2Z6Lkuw+v0uA6zfXTt+NuIZbiF/rNG0jMF2EnpI2BrhfLxWK0o5kfODsqG3NcTzqH1GOG/GGPAsJucLoP5vdkwz7L7Jyfiy/DzpPTJQFbVmkE1pJsKy34h/dSnPkU33XQTfehDH6LnP//5icvU63W64oor6JRTTqE/+7M/o0984hP0rGc9a9Uaq1AoFAqFQqFQKBRHg9XQkCpWD8t+If3whz9Mp59+eurLqItnP/vZ9MEPfpCuueaaI3shzaU0Bywr/k45Ok/QKiBEOdITtjhawx2uzznz0rkMTKPUaBJRVLt0CWdesK9D1AhF1AXRemfbAf8/jWXNCHfNUGhhCFqFBP0AlpE6IFv/jdkOq1tt+jVNcW4Q7RskRJc7zBLiHEAzmVavy7YhwV13qaj3uiPD140PZdhktpf1sGB53etvHUr5evQ4Ggh2CnokAPX8wHa2mQ1ERNA9a2kOpjL6jigtIr7Fsr9epR7pXYZ9/5xb/TAcebn91tFvCXfBI3FZBTPUaPu61KKoB4do6byjX53I+/XJoCkFYwuNVnbAxyO0utAjutqfwo4ZIiLKoAYlNKM9ZBYs+9BWDWBdECmGA+1gwBpzaJka0bmx0XDrmMmRduhuUxxKMa9hnHebzO47/avb4DrDBcm2ms+jrClsSKZUZDu49QxDaFZ5DPRFn7QuvNwnB4PF54nASZcSQzhihfgv+oscQwVmP6WmNAiiz2kPL5IZzQuN2GHWX6LPthzny/r4jGknz5dW88XzRPGEycR9rhWkBg7eBpYZhQa3ms5c2PsldJ3oCzJjB/cb4QTq3hcGuAcLJgPaUcDeH8GQtv3x4upGpe5UbrvFOsGcuI6hqOfZ7UCDGPX1VA0p93H0s2w3WX9n94VS5g5T2ua+ibqb2CvcZYEx9rLI8fGVuH2VhHlghNntMs+v1Zp5HnJrEa8ncE3aPL/hvhjTvTvPG7+79SAREf1g/27zG3wGxLYXOJMCmQ7IEpL1u917mvRv6HK3K5SStY3oI+2DJnswiaFHX8RzGZ6/LDOK72WmADK1WJea9PwkXXKBjKgnazXX2CfmyQpXgWgleFmAGRU6U/lMDfYYzxLy/Bac+z18JOBgDszztdpFM4nHs2pQDemmwrKvxk9/+lN66lOfuuwNn3POOXTrrbeuqFEKhUKhUCgUCoVCsRYIh8FR/1OsHpZNUR06dIi2b9++7A1v2bKFpqbijnyL7+Sw+Ts+Zv4yYxrmOKo9ydsrOFHsjmDiODoYZLkOJkd6LEsIhnKYHEkKStH3ti5hmSNWrRRdBwhcjhqHsg6oE82T0eM+a0RzzNTaVUR0FlHmWG1Or25qSmRNQGpmbNs4CgnGBBEuF3CLTdMpZmXdR8mUJmiQwDjmxpqx3zYEQvecEbWwvPOboldBZLUrdKfy2iAC3LPn1dGv8P+l01y56mu04PYIdg3XqFCJV2RL07KCObWMI2/T1moTkcRBih6MKOpHiLq2uL5kkxnPBkc/ocuBGyJ0U2AHKs55n5xjDTOzUWlsVZ0j/KjJCQfZYglMftRvLUtmC/SJa7kBwdOYu6Wta+i3zXU7xvnF3xLPHXU+VkT3wZRiDgKbbx2gwQA5KrMMX2Y5F6AuKWrLVVlTmhWaqsV0drYOKUfMJVM65AuQZTYWUX3UXUV2gQuwpVlxvtCvwWBBq2112NzXKvw93B/7DpvfFTpTaEjBqOA8Q8uLjAWcT+ie3dq/YI6ru829D/cB3HOCZZherioEi4k+kYf7b1qNa4q02BRzd2dGT2jOLMMj3HmTMnPI6udS2H7plsxjoJ9S3zsJgxQNHGBZJcu643o793fcQ1OYPds+1Lfkv13R9+e65sJPO328y9vsIHNAbDsnzjs0lQWMf/5+VzV61thSNv/fvc2wjNCKF0Z9F9n1Qpq7NMYWsj56znw0XjdzzwP5/Pxiyi8/OLTXxPydbNT5s1l+21aThTAzY+4xTSczB3Vjd06Y8Yn7Ovo0xi/a3XG0okQpmWH2JcbPfou+97WkdjxBIyueFXuLuEFb7Tw/F2fs8y+Pbx7PwRgc+nm9AXuDzEfnYmg1pIu7n+dK/j0aDGnSM6O8r/V5nPf5vO+bG1t0X0cLTdndXFj2I9e2bdvojjvuWPaG77zzTtqzZ89K2qRQKBQKhUKhUCgUq4+QTMD3aP8dpffh7bffTs9+9rPpuOOOo2q1SmeccQZ98pOfjC1355130nOf+1zavXs3VatVOv300+mzn/1s4jZ/8pOf0NOe9jTavn071et1etzjHkc33XTT0TV0HbBshvT000+nT3ziE/SGN7yBAlk0S2AwGNDHPvYxOv3004+sNWBGhWY0LLEzXdI6VREBbftsEhiHTJlz4/n7GIuJ793cebB7OVGPUqwLfZXdhtCeulGyNIq/J9x14Tho60kJptHWU52O1pPRRctAcMSqy7oMRBsRic5zVLQntAGeM6Zg1sCqwJETGkpojWwkW0QIXeYXxyiPyTLT680O5Djy12at7Qjrurid6EOhEyEMWb+T5jDXbfp6ySY7N6J2qHQPXKo2I1F0LeBaV0ip1YV9F6sR8wyGFtfWRnz7iEzCvTbr/YWuL6ZfdRgLqZ8CKzXkfU5xey1jxExXf4DIv/k8yUxfxgkE76gs8LJZb7+WRc4tT8u6qDYKGtIC98vVdAhfJixzAs3P0LQX5x8OtG3HWRbMojw3rYWq9xfsO9jiDp/nvpg3XA0kGAmM425Knbqs1MJDc2XHRTZ12ayojypr8UqWYWZOsBBOn7TuxNzHchmfCYVDLs4Vlsf4A6sExnRBaJuI4ro22wdFdghqYY7W5r3vS47+UZ4LMKNA99esZ4y1Yo0AfSfmAbiHwoGWv3d9ESw7Kdkg6bUgNJtwLc+KMZmtROcH9z/JjOJ+KO95YG8kM+ou1yeeX6wDeja2zGLAemXOQOk4+7JsOfSe/eRtYr5CP4WuzrJ2PN9hLiSKWP8M6ovieJZgeXAVxvk+UXbmSmScpPXdjVLYweW3MWvGOnSJaJc7bjAOq+wUPMbHhPsLmOTukJ93+MzNd8z9Mct1MaHXbTvzz0zb9NEG36/HK+amZPXgfI9FX5A1RpGl1E94PrDeD5hT4TDL/bDX8J8d4EeBygrQi7rPhllouyXzOGeOQz67IkvBVmuQY9h5XsWyGa73KplS6wmAGuspTGrB6X89kdnQlRk1vcW11r/puOuuu+j000+nbrdLl1xyCe3YsYM+/vGP0/nnn0+//vWv6bLLLiMiU3Lz0Y9+NE1NTdEll1xCe/bsoQ996EP01Kc+lf71X/+VLrjgArvNn/3sZ3TmmWdSuVymSy65hOr1Ol199dV01lln0Q033ECPecxjNupwl8Sy73MvfOEL6YlPfCJdfvnl9La3vW3RZS+99FL6n//5H/rwhz981A1UKBQKhUKhUCgUitXBamlAV76N1772tXTo0CG6+eab6RGPeAQREb3oRS+ihz3sYfSGN7yBXvjCF9Lo6Ci98Y1vpDvvvJO++c1v0iMf+UgiInr+859Pp59+Or3sZS+jpz71qVStmsDzpZdeSp1Oh77//e/TySefTEREz3nOc+jUU0+liy++mG699dYlScWNwrJfSP/wD/+Qnv3sZ9NVV11Ft912G/31X/81PeIRj6BCgXVIgwF961vfore85S30H//xH/SXf/mXdMYZZxxZa8BIDFEHknVfDaFnyC2Sw57ijJkdTXEZS3GgJHIiQnC05eBSNmeiXoNZP4KFaFKQ97c5TNBiLoUh591b99MFrlnlRI+J4syl+x2OEUxoWj3CtEHp6sYsG8ARYOwji3PCkcE0lzcLJ3KHKHt21JzPmD52vdEWut0qn5fD/mKu67Jkb3AehoJJ6nakkx/rz7DrhD4iNRhAmusoaqbJ6+le91Kd69fyMaBvSt1nmq6vx5HevGW1ov6XGfrXr9X2z0EW/VFcZjCjDd4X/no9u2l0MmMcBUeUv8pRf7cuHVEUQUfEujzus1Sm7XzMyLmRdUeH698fwVxbbQv6FxIOOIo+NjZj10GUHmxMGssOZq8xX/P2MRBRapchtbXvyJ/T0C8G7BgJRgPXrNtK1+6h71l37xQ9sqxJZ49D6LBdl10wTrZGY0pd0k5f1NNlWScyF6AXdWvpYp0WMygt/lxnTZ505MQYktkFrk4Ymvv8BNcu5SETrnH5vTTktpp2DGaYvZQ1OKEjc5gW+AJYPRrYU8w7Vnfnz0sF1AMFqw29tKujTnH7xn3byr/buF9yBggcpxPuKdZFvuuzvpLFAoOFrAp5r4Xu3mXFMC6gvR80TN8Gc4r+FYqHWLDsM8zId8EEuucCWnuMW/RtOEuLYS911EAhG92jUecR7S6CheX2F+4X9yFYD+C+WmK/BAyH4SD92Q/1RXeNmFrp+9h3YJrPKVyKR/P+ff7gvMlCaDIb13bur1inzD4m7YH5u6s+Q0REIetNca/DfGJrnR40elY3k6iA2t7oXzlfIw8GXrp128wCfhbMc61Tl9kPRNaZrB4hPSwwr1t3b5El4Dpg93mMYSwOxDNLEgtMFI2bpBrm6O85HqclSn8eXwtstIY0CAJ60pOeZF9GiYiy2Sw99rGPpR/+8Id022230UMe8hD6l3/5FzrjjDPsyygRUalUope97GX0ghe8gD73uc/R+eefTwcOHKAvfvGLdMEFF9iXUSKiiYkJuvDCC+lNb3oTfec73/H2t5lwRJlAH/rQh6hQKNCHP/xh+vd//3fK5XI0Pj5O+XyeJicnqdMxg/DlL385/d3f/d2aNFihUCgUCoVCoVAoVowNLvvykY98JPH7H/7wh5TJZOj444+nn/zkJ7SwsJAogcSL5S233ELnn38+3XLLLURESy77W/FCWigU6EMf+hBddNFF9IEPfIC++c1v0j333ENhGNK97nUvOuuss+jCCy+kBz/4wStsDTenL1g2BE0WY0Y5TBjs4BDzNGJqYI14MdRoLKPeG0dJ2QHQZUytBrTFESw4H7Jjmc3Xh+sd6pBCn9SPd3brMmvZVHaNm4trlbz1UJ/r8BgfCLaTsA/pcsjtAWsBvWdO6EZQCw/RZjhouusCeV7X6lFRf7MitsnLyVpzRES57SwS7PK5QMANh1Rc58miw+c060fNrDsp9EkLcec5IKrp6LPnYHlwvobD5L6cz6UzzBGjxex0GuMton5uZB/RfkTAey2fxZQOvi2hY0GEtZvgcAoWAMc6NmLYFmj+6iWz7Xk4ly5BQJYXOReIVBN3t23i99q4iZLjeMG6FBKYUto2Ev+OKKZHXw/0RR1b6NNlfcx2K67lRN+RejAJ9L3DM2NERLSFGQWg64xVaC6xd8u286UBM2odcNHXBBvmRubzzBj2hGu5ZOOBjpg7cikO30Rxlgi6sNwS9XTBiIZ9n1kNPYaUmXs+JxVRGxdMc9px1CdmiMjRB1OkuR/McQYMskUKnKkwtr5up/3DZqzKzKE+a9/B0rhDN5tjVh/XPoUpzXBflnW94QoCP4Ti1qg/go2xDI51iC94n+F/gOtlXVn5XEsPBtNezhKB9hqaPtRR5L9JTqlEka41cOdXkVlSYf1+s2HOn2XJ+ZibXa6TLN3XE5ibTFrmQ4rtAPTQVWhlbSJI/L4Klreyw1QyAGMfHkre9loB57TA9aIz0MyjLqnI/iLynemJiCrMTm4pm3MPxhNsJ5xcodfNSmfzBMAJvsEsH/Sn9WIrcRtTzLreOTse29YDdt1NRERl1Jrnay31pwM5jwgPELucw0yWOYNO1gpNwyCl4kI/4f7S5ooQgHS2loD7P5hRuBe3HV1omZ8ZFrrLd8NeTaxm2ZZ9+/bRcccdF/v+0ksvpUsvvXTJ9efm5ugXv/gFvec976GvfOUr9PKXv5x27dpFP/zhD4mI6IQTToitg/3dfvvtRER09913L3vZzYgVeSWcfvrpR25YpFAoFAqFQqFQKBQbiXCVUnZRKWs4pL1798Z+npubW9Zm/vzP/5w+85nPEJF5x3r1q19NRESzsyZAV6vFy/tUKmy61Wgc8bKbEetm3ndEAFPaXcQRE5hlNmB08QhL2OLIWl5Ew1LqMhJFUdmgyJHQur9siLqF/eRODR2oV8dSRGR6s8k1pKBPka6g0A10Ztmx1BlQiL7KKLHUvoDtdBlQoiiaDIY0CbWdpmZXN6XdACLScKa1f6sROxCg98kItBUGbZCmFKFkDqpb9qebPlwQZU+LpgNgEaP1/DqTkgkjSo+Mo8YYXFKhIW01zcRTFrpKIkcDxvuVEWfUTrQaV0TOOWI9nBrjfcb1IpH7qPkLXayMHsNhsiucTu3x8t80DVQSwE7lcmD/fSYY2uusU+s2U4ZWneeQAh8TtOz99dWzEEV1AJO04URRFN1lSF3XVvc3WRMRgN5JMo1gVnuOZlm6LYMBkronmW3RExkV6KtEkb43W/dvjPNTRvMla5dizLj1AV3kHDZv2PN1dWmQfW45DyYVoT2DQ6nU7OIzxmN13DyQoA+6cwTqe4JNDC05uEHaJuuEy/NDR9SotS680fXNwn0c1w33oSbrJZFplPf7jPQbyNfj85W9B/M1DnD/FPMkHEhDUSdyMSCbJRRsuKw3KuuiSg10ErAtsPtDMRabghFq8piDLhT6ZBfQ1sv6o4Ds83Ye5fm3xs8hW6pRlkiV6zaPn2yYFWiINwroS/I5qcZjaGHaMI+9BM8FWa97vGbG1oGG78qN8wfmNMPeAUPyfRSIiEopmRVg9DAHIGUAmRb75kcT1yMiupvrpO4cmSEiojo/SqFfRVlKfi3tNA8QFzYTLY3VD/xt5Eb8e4d8HnUZRDCxYDxt3+Y+PTI65/0uvQHAjLr3owy/zdX4PM5z5tX+pnm+7a21xnMVU3YzmQzt2rUr9v3ISEoGlsALXvACet7znkff+9736O1vfzs96EEPohtvvJHCEM/1CTVtQ9xrst7n5Sy7GbE5X0gVCoVCoVAoFAqFYpUR0uqk7OLVb9euXTZldiU455xziIjoqU99Kj384Q+npzzlKfSmN72J/uRP/oSIiJrNeMAO342OmgBIvV5f9rKbERur6FUoFAqFQqFQKBSKdUNAYXj0/9Yim+XJT34yjYyM0Pe+9z066aSTiIgSX3bx3fHHH09EdETLbkb8ZjCkSKFbLA8Lqbtc3H7YEu/aSDnqCfOArm9j7/4qLbOpz7GQHKeF1dnuHsEIzkCTRbyT0jix7RxSJdkoR0Zs8NmaGs2YlAqZbkQUTx0bcPP7Ir1FpvABZU4fKyBlZJF0BpjDwHQCRc6tPb8sPVLn1KukbGCYCOXE9d2olF2ARwfS6ZAuZo2piIh6ciUDpD+3hSlQT6S6oqQLvndTetNSddPKdfQXct73QYIxQVpJELlPm5YGM4cZk3aCVNJBQkopMkHmF0y6TU4Ue4fBTIPTnZCW1oO5BO+7xucg57S1wOmlsnxWVoytieP3m+OQqbqc6h4UE/pUIZ4eZxq8/im7MBUZcLojioxjXNnxPhNPA5LlX9opJSYynLaFVL4MSiyUW95fIqImX0tZegXXNlYMHangQ/QfLhe1iEEVzGPQHlkCSabqRimxaH90nYqFobdMlI7JqZKcStwTpZOQRt4RqZLueEGK87bRGd6/+SzT8K2BlijRhTm8sHXGfmdLi/H8aA3eKr75zboBKbkDPy01dg8Lo3GE+529hwpzlowwurMmcZhjev48FjpzC8q2xI2QeNu4R1uzJb4vylJBTtpth+9VuTTDI25+0j3WtA9mSAmGginzK0p1oY+PsOFO06Z+mp3OcF9vwojQmeM74p46x88ynYH5fgtLi3A7RVvyPN53j06b5bZM221sv58xOMmI8j5pKZ9rDfSlEDZqPG6Rko2E0un9W6N1bDkl83eUDe1mpw0TNM5meoOWbxzZ4nlSmue5UhHMLOgDMDea5bl1hMse5q1xFMpM+fOP2y+Qvn14wczhKANT5GkvtQwfz5Pot5FcIi5ts/eLXvIjvpXosPnRgE09IRVDG5pTEZuGZxT0+w7SzDFHzPmp0cDsgvm+lyLRcbFuqbqM1TQ1OlIcPnyYHvWoR9Fpp51Gn/jEJ7zfut0utdttKpfLdL/73Y9GR0etg64LfIdyMA972MMok8nQLbfcQhdffPGiy25GKEOqUCgUCoVCoVAojhmEYeao/60UW7dupXw+T9dddx3deuut3m9XXXUVdbtdOvfccymXy9H5559PN954I33rW9+yy7TbbXrXu95FO3bsoLPPPpuIiHbs2EGPf/zj6dprr6Vf/epXdtnJyUn64Ac/SA984APpQQ960IrbvNbYXAzppDHMoYowr8hz6GgRk6OQf4oiyhypYuOS4XxyhDVb98tc5BzTE0SpY/uaFSxLxl/XRsnK8faivExaZEZGolGMWH6PEi1JhaKlUY5lJ3gbSSVY3PWSUGFTAct4wLSII9TFol86AhFpFEsnNjMKnVMSMzXqifOdTqqsDeTh5/0vcD2zSVHHps8s5MvJle27iEjycoiOwrreZT3BcJWKyWU80iKNiMBifZjYuOugbxSY3YG5RYMZMRjl9MWxSgOQuUbc3AqMaJGXAQsA9gm/I0KN0QSzCbACVSdyDcOJAfe/Ev82XjGsPgwtUP7BLa1BRLaPBe4lxaHNcWrDCNvaBxsXpwPLZE2yEOkemsbiuiWVdkFfwl9cf/QHGGaBDbT7RNmqhHIlA8Ek9hClB/tt+0fyYM0V43OgZfhF38I8lROMVbbjz7dDlJpKmK8iU66U1AUB9KdJjsy3+RygfxWzUVvKGP8iAyYyw+Hi7pxhUmIjsNIuc1+zJccK7jzHpWOKPJZ5ag7neZwN1jmCj7mYDxHzvc3ogfFVgqkdyqAFeZEhBEMizk4IO8IcEKZHuXhGAhicrCxRxp+t6RIzarIMVxJKI2a8494q18EYg4lMj++XbTt3m+U7PBbcUl2ydIcs1QXILCWYGjV4jlxAqQxnvR5fCzCjA1vGxfw92DbnYEfJP4/HMaOPMYuSWOYgfXYRCBavQrdmyO801yZs+/1eljGp1qNySDDxkyhXzDgssxnZBB/iDJdsafO1me/52XH5hDIwyOIBczfC88vhlpk3tpZNu9tiDs0GyNhIbKLZJu+/yPcsmVlht5X3WVig75ybgszqY8SuL2/LPkfiviPGgmd8yaWfJue2EFHEkOK+PstzaLngjwF7/+GsmWwQP78wiZJP3LkjMDY8YoQUy+Rb8XZWiPe97330hCc8gc466yy6+OKLaefOnfSVr3yFrr32WjrzzDPpFa94BRERvfGNb6TPfvazdPbZZ9Oll15KO3bsoA996EP03//93/Txj3+cSqWoD/z93/89nXHGGXb9YrFIV199NU1NTdEnP/nJoz3aNcXmeiFVKBQKhUKhUCgUit9i/MEf/AHdfPPN9PrXv57e/e53U7PZpJNPPpmuvPJKuuyyy6hYNC/qO3fupG9961t0+eWX07ve9S7q9Xr0+7//+/S5z32OnvSkJ3nbfMADHkA33ngjvfrVr6Yrr7ySMpkMPfShD6Vrrrlm05fr3FwvpKjw3GTWMsdsERhSaLrcStCsGbXMKB+RLemAz/WB93k465eByW1hlsQJXIYIfCKgg8/irAVljmZLx3REX9wizqI4eMjNkoW/reZgCbImk2BLHvuOScpQFIIeimhehyNgiIo1ZyJNQBW6J2Yxs7Ykjh+RAyMqdTmDGRPBcaPnQYHtzKt8gnGs5RRN31pDMKLofyHrujLVONuT5Yg4zjmild15P2oL9gcW7mBC8RmF1F32pVrxGXcAGsGe0Ltlmc1psFYmn42zVm5RaiKieoUj0vM+44FC7pJpQjQXzGhSAfcW7+POGVMUvJrCVtk+bnU3Ptw4amQPz2Vo2Ca+VjXtr41xaQ1mTmzpBrAzBX/8E1HUz1rcviZHpkdZnzkRL2q+1shB69pkjY8owRSVdIr6SVIJBKJonIPJ6Q6Sp3v0xQXuA+41R5+SGlL0AzCgXdavFpiVsNkZecmkRvOKLYUh5qF5bkdRRNplWYcgyHptIYrY+GHHjMMx7h/zQocKZhQ65rZl6833U7z+RCliK3qiBI5kSi0zOmpuBMVtM6ad0Ezi2uWc+xcEf6LzBzz1But8hw77i99wbEbM9kiHGJS4nAu0fDXWz/GpwzGEYjqy2TPYDvSiCazFQGipreaRt5FWhiwpiwTZE3m+XihnAW0o9g9mFFkBADTLmYSL0+Wx2Ob+0+76YzMj2KG+0Ip2ZMkjV8/Ih1LKsp8Ef4HHodE8awp5nW1FcwGq/Bfjyb0343nDauzXkpFaDqqcCUIoEcTPZ1wiKsfPJr3bd9tVkF2Ea43rhWM5ftc+IiK6a59fkqM7NIwezv2Ql+851wBzaAFeGSmld+a65noXMn4nbySU76kyY5sXZbe2Hn+Pv6D08MC5YM0osmdyDqOa9DxIRNRjj5JMDv4BobcNW9pOMPk9x4cCZV7AjGKunebnDfsswJ+xD5R0sdkCzmRnMwN4Dod/BJbYUoq7xa4mVqUO6VHitNNOo+uvv37J5U466aSY1nSxbX7+858/2qatO1RDqlAoFAqFQqFQKI4ZhMPgqP8dS+h2u7Rv3z7vu+uuu47OO+88etrTnkYf+MAHaDhcuRnp5mJI4aKbE7rIBusFEAp03S/TXqlxZEX+T53/dln7OMpMJYJLKziHYEapxNueT9ZR9Weq9v+xYtoit3+Y4uxnF4ezpHQGdGDZV2jy2Fkym6DRIYp0V0OO7hY4+gzXXSKiDDN8NsI2YiJXGWhDW3xcYExtgXJmsPPJ+yZyIukbpFuxgKsvn9qwz0xyp5qyQjoWuz5ERHmO0iOiDs1mkKBfaTFbKfsEou/dBO0fURSxdN16EfGFhmmedR9wEM2B6eV+is/SlTTJqRnM6KEGO7My2zTHkVTr+shtgJthyH/nhabURUFEfreNGYZmbKtxpZVFwxFJz7Ku27rrLibmYYQ1ZvAqhnXZiElS3uiQkYDvCw57CIYU5wB9CTrTAwe2ExFRt2WOBExpgcf0YlrlGmu1wOBDhya1obi2yLJAW8CMymwMoogZtU692EfWzyIA8LvUSLvOuFafDA0ejxHpNFxmHS36VbtvrnVf9OuMIxACKy+B8wxmND+SHNUP2+jf0dyQGeHtF/j8lFhECrb+KG7uK0GaBhNMWuegyRpwGVI7YGGMuuCf62FHuNQKx1yZzTBsxT0OrINvxV+m4bitEkWMvGXopZac4i74acech15WMKSRy7Nwvnb+P4M5kPtTpDM1x475db7jayMxJ5YxD4fRuRqygQoegwp83iq8SJXP3w6+f++omrFbYdfsiV0HiSjZldUeG3wzwNyXkrMv1hyc+QbmdtDgzKNpMze7832pasZbi/0P5H0S1+u4ncaBfd/B7d7vB1kHmlZ9gCi6Z42kZPuEVs/L/gl8vctWNxnNI2BISzyHb99+yCwjfEFCHlh4ZrTVDxj2GWORZ41UvxDriJ28HjJe5mejLLm5BTNHIssKmlCgPfCfXZO0okT+nIpnBJwf/D15izknMktmtXE0pkTHGv7pn/6JXvnKV9J5551HH/jAB4iI6IMf/CC96EUvIiKiMAzp3//93+kLX/gC/du//duK9qFXQ6FQKBQKhUKhUBwjOHp21ASIf/tZ0m9961t00UUX0czMjK1nOhgM6LWvfS0RET3kIQ+hv/zLv6SRkRG6/vrr6WMf+9iK9rO5GFLprgtXXWY1E6PFYFOtYysiexz6yYh3boQXu0KPahkJh7HE7lDDFJG3XuAv0PL1n3DSRW1RF0k1SYki7Q72gaitjPDis6395jAPoWwnA4wn1hl0/cueg85WOBm6kWTsJ1s0UTrof0LB6FmGj//G3N3KzvJ8WS0zWtwk3VEYh8JdF2zwIOG6LnvTzCxBn7SYGyiuI5bpoK4kmG++JoiyS4fQJBTFb4gKQ88CPU7e1oMTrGwvXd97uOkzyYiEDsjvl3DXzUETA3dS6FkEk+pijN0M4WZZ5Pp0iPoXmLlBPwyyy2CYAsEG9Nfb3jkO6IIQBYc2OcjGa89VQl9rLMf/No7AkyFIaIYZgQXuN11mrLaw3hK6YqKIlQRQNxc1T+FkmYYkZhTAfIV9dLlvQRMqHaHB4MZqojrzlGQ5BiICDnZCAmOmHPjXfufIjP1/Ac7iGZ+JLrG+LYt5ogiNF2dYJDB+ANjSDCabYfL9Yb2A/oZay7Y+9iIO7CHfT9ByOKLmJwxjDEYU8ybubXnOwIH78LAVn1usi67Yf+uQYWqlRrnAzsYkrrur6Q+E9tNmHyBTSOwLTCj+djvJum4iooOsm0cfRf8bMKuEORpZK+jb/WUwNXm+l3bhfMzfgxmd4OM4oW709MgiqTNzjwyp4s6p2Lazo+I+VNiYe3E47Y8/eHxAu4z650nzennM/LZwaIv3fZbvn7hO9yqYB+q77zE61DrrxH85PUFERGNOJgQyh8AGzlt20L9eReHXgOs7xvpdt54w5itZZ1zWzrUsMP+Fv4BdPqHCQkwDDpYyjC9LRDGXWTCjhw+Zc9FyGPwWz/0HGn690YG470Q6aPN3js/ZSMLcC50p5t/77DA62hJfk633uie2zmpiM2hIfxNw9dVXUxiG9Jd/+Zd01VVXERHRN77xDTp48CCNjY3R1772NapUKvTMZz6THv3oR9NHP/pRetaznnXE+9kkbwAKhUKhUCgUCoVCsbYIaXVeSDfYBmxdcNNNN9H4+DhdddVVVCiYAMMXv/hFIiJ60pOeRJWKCfideeaZdK973Yt+8IMfrGg/m+uFVNb/K0h33YR1wJoisgzGdJIjpVWOukr2lXWfQcjRM17c0zFCR8HOsmGDP/c5IgStJjQxHO2Bk25hi4nY9WYiB0CpXwH6bY5Ei4iVZAmwPrScmZyjJWsWE/ch9XVljjIOuslsV766OOvhIedHem27Rf28zGjCxUOgGe62uJZduK+u81BfghgbNtiR0NGI9aBpQY0w1GlN0C4ROXVKmY0GW52kZYCrHfoAdEc9wVJKrUZPaDvc3yWDBJfRFvnbHCmYCCUi/NPcP+FAmF3EjXEoWdWESC5R3FESKDEDVXXqZW6vmT47Wjd/x3cbug+ugtmKr8ENqqIW4igfX9u5yIW8/7fo67kyczOJ7VtL5HcZdmM4w6wa2DVRh88db+hrfcHcSAfkEad2H1EU9Z/qG8YULrJT8yN2GTCgfe5zLTiP8ufZObMsWENoTEdGzHUCk5oEOFbmBWuPbWAfVm/HDHwgHgEGCa6YZcGKALF5in+XmtKRMjPvju6+CGdnPo9gRotc1zIIfFbeOjxjG3DXdYZrpo6MEvFg1E++T6w1UEsU9zTJyiRl/fTnksX//RnWE6N+K28rJzIfwIyCQXXrKsaYWTAqEzNEFDG5g5bf9wdwjxXaayKiHB9jnx1El3okRV1Ie79kAk1qnIkiV2c4W0NXDw0+mCBZrzK2T+6HE869fB+3N2OXMccOZnScMwh2bzlMRBGji6yc2u/cRUQiSwmoJ8yPmwDQtGaIs5OQKeJmXtlxZ463ts0wwGAcG5Nj3jYxr1R5jCPz6L4T5p4y6dTWRoZQV1xree+CT8KWkj9eMFdlUzLjiIjm53zG0WbB4ZlP1qDnZ4dsUp37FD10Bh4ege9kjXGCZ0fU2gUz2nJcou+aHzXLDv1sviafmzaP1bYYs3XrHxJnSnfWzP1uYsTUxq3xfWPLCUbvW5hwauauNsJkR++VbOe3HQcOHKDf//3fty+jRERf/vKXKQgCOuuss7xlt23bRj/60Y9WtJ/N9UKqUCgUCoVCoVAoFGsINTVaHkZGRqjRiKQ8hw8fpv/6r/8iIoq9kN5zzz1UqyWX4VoKm+uFNETOPIqF8vdgN+cWYqvENDcdEeFrpGj08D33R9ROs6xoAlAvczibko8vgdz/kehCDtNqBiKSa+sOsoYPulC4kUEHCr2qExGD9gURtZzQhEqNgmTx8hzxT9K5ysgbItdhl89FQUT00yJPbo3RgmBo4SwJZnSdg7VhEgNPkXbUoh8dG+qQ4txC+zfkyGKeI3+I4EP3Ac2T1cLwMSfpQhDBBcDajObNeCjm2HFy1uwDjKjUzxERTbWSGauh0HmiLhj0IWA5URcP+hfJdrjbwC890e8Q/ceRZsT30N2MONHmraMzZh1E/VlDGUWLWSddBivFK1ZFH3NZ9zne/oSJ/IKhD5o8XgfrzxYEcAOfMX+gR4R2qpfARpVYN9eZNwxinvtgc8ocF5j24dDXJGcG5nihoeolsDZ379+Z2E7JtNcFMzAnov7tTrqOsttNnhOnm/5NDbVD0U9yKdkmRPG+Dz1zIUVfjW3BSRc1gGv1+D3HakZ5/kQfzHHmRCDczG0N7CTY5lgFZvqy6wDUWh42/PkK7BN0n+Qwz9D3xZicEfYb4GyZwbyZe8DC9ud8zbm91yXpVXm+wTwKZjTLbAu0c8NuMlPqup6D6cQxWSd41IiWmQaiPdCvN5rxsdgV90npRApg/pRZDNDX9+Af4DD6+P+IYMbAjP7+cb82v3NNZtRLHfudO3mn8XbIpDQLPFdV0sftWiDAqU9pV9apuQkMRT1bEtlkVWbTJVNaq/E4Fs7zWeeedpCzRaDxPdhOdkUuJtwHiaLntr7zPATWPCdcmttg7Hm+rrL2V9YWtc650CfL43fbh2fAFMfbIY8FW4sX927+e89ClC0DlhjPBA3uq00eH70hnhXMX9TLbfHYtP3XcXMf48wnaEZjzOga18U91sq2rBT3vve96bvf/S7t3buX9uzZQ9deey2FYUj3uc996OSTT7bLfelLX6J77rmHHvWoR61oP5vrhVShUCgUCoVCoVAo1hBqarQ8POUpT6Gbb76Z/vAP/5D+6I/+iD70oQ9REAR0wQUXEBHR5OQkXXPNNfTGN76RgiCgZz7zmSvaz+Z6IYWrblE6hfFn6EOrTmS1x+sscIQehBN/bSNuYETLKYecxIxC15P3P2dG2eEWTCl0nhyRtGwhglJOhDUj6veFcNMVbGa/7Udpbc4/R8cQ8XUjv2GYHilLQmE0gXFO2Ke7H0SRM8xQBQXfGZUK/JeDeEER2+C/LYdthE6qxpFmOCLbqNjGJudblg0S1wYz0AlRO7DgNurfS45IYl1EPQfQHy8SqatwnbX5eZ8xkg6oafqk1iDqS2COZgSTYLtqilOr3QeccNG2BMZpntnV+b7PqsptAGXexu66iYqOivp5RJGWsb5jkoji4yVA/8RhgRkFC4++VXIi/sjEkE7cwAaSVZmaOb6w57ctzwHrzlx6SkyHHUVRX3gg6j5Cs4kIPTSb0Gi6rA1qfHZEn5LMOPr1UNam60JDF30PBgLaUNRFjTnk8phAv84KsmmqFa8P3B/4YxSuujnOROikuERjebRhbMt0bBnLyvMcmK8wK5/zNaPWS6CaknKRd8aW7HqyBvd6a0lx/+wnM3v2GJ0xDc8Ey0yVWE+/1MMeGFHLjMLlNto35kl7/xEsrM08ASOeVvvQ8VHAtpD1A3aoMSm0fDmfwUIfgcsu+voggdHFsmXuf5YV42eEPLuyNro+A1nlvj7H/dSdK+W8OcI1Gh980v8SEVGFay9Dyzx6ol/A3mqbneaGMHe+m5fhuTCzZYMmPzwv9JLv/ehbuVL07DKcF9p/Zuyhdx5w/V/4NrQazNTz9S2IWpflUsTCYv6TbvP7RKZRH873KX4JSZBZJfK5Ap4A1kMCGmYeL5iP3PXk+AAr3BMMaGXEf/ZbmDY3lhYzwGHC8eDZYJbHTaMP7WjyOAd7DKb0vuNG27tjLHJ5Hh0z93zJjNr7eXVt+6G+kC4PL3vZy+j666+nb3/723TbbbdRGIb0e7/3e/TKV76SiIh+9rOf2f8/6UlPope85CUr2s/meiFVKBQKhUKhUCgUCsWGo1Qq0de+9jX68Ic/TD/60Y/oPve5D1144YVUZXLwvve9L5122mn0Z3/2Z/Syl72MMmlB/iWwuV5Ic4KxgIYrVejgADUEC3DZxDY5wjPLUa8WbzONKU2KSA9Cbx/hNEfUOBplmUToR/rpzF6PI1H5caPzsFFL4YQb5H3mKUxxT0vKgU+rwSg1pGg/lsfvdv0EJtDqheAgifZXBROaBrdJaZ22AF3q4ptabaR1M0SRgyIcnaPfssKFEucuP+ZHIMGMIzrfYX1ftABfC4eWg5Nsj51WS8wYzk6PmXZxdO/AtF93DQCz5EY5wYw2RVQ/YkbN556NkJrl4Ogo9SHTTk1buOthWx0+TxNFZgVEfVFoAXcxM7pji4mcTuw+QES+xtmeV647mt9mxo89bxNgQsU4kbWL3c9Wqy6WQb8sJ7uHrimSai1TNM6goaruPLzkpvIcQZ/ev5WI0h1nC6wpHWFmetpx2QVjiL9SD1xjdgFMgtR9D4QjIxHRHLMLUj+XBrD6C9x3G8w2JZ0pqYFa4GXHUQ+QjxnL1VgzmhWML3StNZ6niaKxC2YiBy0payKtVlTUMY4hif2ByynuV3B87sQ1c2sJzHG5ojm24RwzeLiXYN5wmRgwJPiu71/XQcMci63TbWuLmmONsm94vDci9gls6aCRUpc7wUXX3abMNDLbNN+BNYWeHxkn6BtwY0WfQSaBrTHK56LvzK/QjKLfg/1Hrd8uK+cLfP+E3h91oKcG5nxjDp1zGP0RHqdwHz/tXrcTUVQPF8xobZeZGzJWV+/7ZbhAFhB8EmyGSenIsq1WHcgi4LECt11UOrD3YiLKEPs2CKYUfcJmk/H1qm/xnVtxf8U1cxn63TvMvYgO7CCiyNegzvPGfs4qwT1NsulJSKsTLteVbvryczHBI8LWoh76fbgvnh/3372LiCJ3dPTpBrvp75szzyddZ74HMypddJFICEZ0C2fNHceO0+N8zk7caWqKTvBzDRFReafJeLIZF5hnUL2huLYMpmpIl49CoUAvetGLEn/btm3biku9uNhcL6QKhUKhUCgUCoVCsVYIg9VJ2dW031XD5nohzXN4zjJn3Lyc+Oui5+f/23URtsHncWY7JAMBRrTLuhcneBaMctQm70eEgnHe9jzXAiV2+oPbmeygTrSsdMpBXpajcSKqB2YUe7TOf5SMrOseKGqx5Z16me7vubqJWCFyGrZE5Bpuc4L9I3LY0zz/rYtjRYRfMs1D8ZcoYqtJsABSR7VeSBkNUpYUOl0Orq72s3R5Tqm1WeYodueXx3nfF8qRNtI6W5Z9vWSd3fcarCndwdoMMKVgHsESFRztEfSaPdYbwxlvX8s/+HoOLrrJkV4wpT2nryOqL5krnIExZjegPzyBtST3OtG4Q+LY4Q6dLUUnurDDaPoyY6G/UTDyPH5tpgTAbpHh+LhZeuhcr66YOwCeZ/q7TzQfk5daUwQ8LYRHIKHps768wCxybFiJ+p4ZwQpKbSkRUX7gs349Zg5RVw+MOljPua5puNRSjZficwmAGrdoDS4t9J8dbkJa7UZ3hA2h8xOOkO2BYTKg0Stlk1mKnTsNI5KzGSCOhm/rjPkOczS7nSbWWE7COF/UxXSh6Mddw1YPDy9z26uMoM73gDrfIxrMpMwnMGeytivqczOjIrN7ApuZw9pgUfu059QhzYpMoSAjPBhSNHtd1lEPE37HNmUd0aUeTqEdlfVxbXZAAqAZXWoSQX8dZ+atPYivABb/d3cabWiVHaBtpgOfR2iakc2USao7yrDZP3Vo7JFpts7pSUuBPQEydtw616rPzyLI3oJrMvezQVc8vwkfhx4bXuD8utkSYOR3bTfPbegD9Y6Zz/bwvfcwZ5VIrX0SijlfYz3HdU/LfX/CxjxcZFdaMPoRg5qLtRd9E/WfF9gJGvuSNZ+RFYA+bPs0M/0u04uzWLLZUuabCWZE97AzeZWZ/PufcAcREW3Zdcgc3654Vg/mUDxXRswofy6s7d1Xy74cGX784x/TTTfdRDMzM9Tv9ykM0zMBrrjiiiPe/uZ6IVUoFAqFQqFQKBSKNUJIy5eNLLWd33b0+3167nOfS5/4xCeWvY6+kCoUCoVCoVAoFArFIlAN6fLw3ve+lz7+8Y8TEdH4+Djd5z73oXI5uab90eA384XUTbtF+hO+Q4puf4lUGRiWtDi9rZuQwgXzCaQtIv2mB5t/tAGlTji9iY1egkJ8m2GLjRBmTJoGUsKiNCaUDPDTmLKyXExCKmi27BvnwNSAxDYt2HwCKWgwiACCbBT7QfkJm6orPHkspGkUrgOa6zZ7+5j5K8yswtLqd/RloSg6C6ffhsnZfUcGpAOJazBy3IHUVXA9m3u3mc+wqecSKCjr0eVUzd1bTUrM/IJJT5xrmT5eS8i+mhMGCTAcggGRTNWtcJpOQ5SDcFN6BpweW8v5KWJ17l8wlnnwvf/HbJOt55E6VeDPSDkrbJuJNzwNMlVXpI4HkwkmQKmp4SZlMHenKadApy6/GUcN2aZ58Ttfl5ybjs/flcSiWS55M84GSLOHTEq3TNUFOh3TjypO4XKbxuhPDba8ynTPjNUeirSnpKgjLZcoStlGOq0sQVRNKV+ENEaZ2tt2otxob0Osi21iXZgzFTm9bNu4SQlHqm51Ysb8PhFpOLJ8zmPlXCDpkKWe6iKV096rEmLqkJjg+qPE2HGbJKWsy+0p8nnsROcXKbm2JAwDEpClANMj24/Hok7fa/j3ApRoydhU6qH3/YD32W366wWO7kKm5sLUBg+nbS59gVJTcr1ma+n7E5gXmBwtcHkXmBfh9wzPnzAHw+8HuaTR8fWo/1V5/qzXzPkpsalWbN5E6QzISaQxTCb6bP+XlqrbTE+1X1MgVRPNwbiQkxyRLemHEnTZnGlzb9Y8Y8GEDLfxjEgDr7DJUVL6N/pEjlN30a8Kbd4m30d3sdEUUmQBlPtB+R/3u5hhHMsK0Cdwb62IlHCUQ0PKbpKBUrcnSsagHBdStHkdfI9UXbRla9nMdVln3sYcOo37BD8TnLrtgLfNe5/yKyIiKrIBH55dbcnAcnQu3GdMIrJlA4PR9THV0rIvy8M///M/UxAEdPnll9OVV165YhfdpfCb+UKqUCgUCoVCoVAoFCuAvpAuD7fddhtt376d3vzmN1MQrN0525wvpCmlDyL204lwodB9m6P6iKSlUfGw08c+wJQ22SJ9womSZVK2gYh4Xwix+WdrFsTGL0GCqUC2z5FHWGhnk4/ZbhNRvRSTGSKi7FYYu4Bx9C9vOO2b41i6ADb9YGkRlXQKuAdwV9k6Zv42Gv62uoIZRYSzJIyqsk6bmBkd7DzeNKdgrk12zliBB/3VoCZXAMFSBXVuM0dFXSYEBkc4Z9ktzGwzEz5kIykw38Pu8occTKgQWUQkHCVjwDAW2fa/xczo6AhHimE+4DBQAx4XJ3CJjxkbuff7FUoMyGLs4zzcECUdOBN6UWxjB0dy77vVRFAnJqa8dmc54lpkRqTIxkWW0d/iMPS+U//S4HEejpoIr2VIq9VoGS6pEU4YBjpYEHTkoekj3OkqAH0PUX8OUIeylFTCPADWdMCmMFmYQ3HmQ4VLYIQoV8HRfZi7FMK4ydMQBiEc/c6yaVGbmfJp7j+yHEyjbz6XmbWpumYagomQMx+MsvLCcALmHT1E87E5Z9/7RZmPrGD+88yUPWDPXUREVGTmY3yHMd4ocbkme+4cYzdkh8TKZ2A+EOZ3qfcm19RIsqUNXrbKrN0h35hurQEzLRzTcJ4zY3iYgxkNHfYTLNKQmU7JlNptY1wjkwj9sOHTXkPHBKnPzGdOlHsBJDPanq96v6N8j5cVwEkqtkQQt2OhwcZXbM4yFOUtWm1hQLhIaQ8AZV1QXghMqW1/6C9X4nn3lJK5B1ZLUUbNVs6AqfB8CZMtnE9KyXxYljHMZjExGqubv3guwzNAg8uoLWYIBsCf6Tgzpm1fPTRGREQ5Zu56C2ZyBTPa6y7NymVwz+KuUGBmu93y+0aPWU3ML/lc1H+3FGaIiGjf4W2J+5D9qsvjoZBH2aHA20cnod0wRMLfEme9dKwpnd9Xitw+jAmUK5qoRvdEzLMwRiqX/GyviZ3mfBeqbJqJzCfMqTXx/ElkGVHL3KKrJmWRrAH0hXR5KBQKtHv37jV9GSXarC+kCoVCoVAoFAqFQrHqCGi4Ki67v/0vtaeddhp9//vfp263S4U1DF5tzhdSFAOX0TBEnIOETmQ1OGCzOBrTFlF/RF66HLFinZVdfzQqCm/1pRITE+bvIROxDBqsNYTfFjOOtkSFE9wdNoQFObOpbqFnoihSFIrIkWXkYNPectZDMeuU0jfBNo46zpkIVtjh9oHhPZLeAKYJ+5oUbBJfu3DPbrPtmanYJsLaiPc50zasa1hAtHF9i8LHdIgSONZqFJEMiPtZ4FvNB3ztcY5lOR9ogwOhtwycyCV0wmBGgSJrXgasHe3MmWsB5tEyFsO4JmYH2+b3OAK6naPD002fWUCUdlSU62hyhHWsGL82ZY6yToyY9hU5elzj8gQoX1MenyMiotJuoevEuUNGgZPlgMLotk/jWmFOyC7ReZmdcvXJQUccwxrpIlaEOh/XoYSoMpGfAYLzVuKyLSXuBwfN+IK1Pc57n6P5QZvPM/cjFE9P0pgigr69ZrZx58yW5GaJz62+KOFFTkkr7mPQJVcFswbWtcV9tG/1UOb7A22zzZKrdQ+xbfP3Xhyt38kZAffavp+P0SwomdEc6/JyYzz3O0y0ZQ/TuslS5aoWY3fwG1hW3lawo5KywtoiXDAnMORz3G8KdtDJnJDau7C/xHmQ+0op3UJEVKz5bCW0oyjn0uRsEZTjQB9Gnx8M0sc0+t9QMD9gVa3+Dgwayr3wvNrmz0OHoZfsFpZt9JLvLQUufTPB/ROsE5itkdE5u2yVGVFklEhmNMizXp61o8GomBMxv7kZaOizsgTWOjFUEsOJ7UREFPT5uWiejx9tlzptirKTQtyqUohiMKM4bzkwjYJVd/05AjEXgqnP8rbQL6HnbczWE/ctGUkiol1bzdzTE34Osl+mAaxr1ilhhb5bKvr3DczphbyfaQA9dL1m2p8vpJRCo6hsWL7kl6FxNd9e+0b955bBgrnPZKrRPjL87DnkMojUNdsMUVJxa2pzjh7hKpkaHQM2u5dccgmdd955dOWVV9KVV165ZvvZnC+kCoVCoVAoFAqFQqHYMPzxH/8xvfKVr6S3vOUtdOutt9I555xDe/bsWZQtfexjH3vE+9mcL6RI0O+zTpFZkLBqok9hJYpoZQ4f9NcBQ5oHg8LRmDRdqnXj5UjhvBPtQVSu5Os+LLDOKEezof9B0AVaTie4nLF0qWgPgmF5nwEK5lMYErStuIhjY7XGuxr6fycKXjNJMkWLQbBIwy0cwuK/mSnDeoUjY95y4ZhhVEJn/bAiookZ6H+ZXS34Ufk1h2TTgcU0OFZPzNch70fcsntYmzGL4t1w2uTl5qBjYYfhJaKi3rY5UlngbYFp6CwYVmV0q2GlC04EuMvazxazYuALRypmrMkIf63ma4WhO4TDb85htVCoHRFUaEmKHNmHtiw7wv1NHGpQtorp+O9peu40ZhR9Gv2wbtjCYOiwVDxnBE2hh95ICGYiYIIsI2xuh434jcBqHBmFPTNERNTdO0ZEcRfuHOu9C9AgTZrl3MQE6+jYB/PEGuQx07dys2adPjNSU9y/wqx/vdxLmedoPRjPgXXGNX2qxX/H2Z1xnlkv6FKbg/So9r1H2HmU94F27txm2Ig8MwSje8x9A2xJcafI8IDbo3uaxTFZASAIinm+RkU/MyWGJCY1QZ++6DbWGEGJj40zPNBnwCgEnoYZTuo8D9nsD58RDrJD73vMedi2ZGG9dYVmOiO07fZzb+lHGvRlsEVgj8CaY25rMHs0FA7S0DCDIZ3rRu3eVvFZoRy75lbz/r1lG2eRFPh7aBHz3OfhC1CsR1laSzGjmSp058KxWd7/XeCBsp3ynFGtJn+/xghz5tweDX8lNc2oOoC+lC3718TqkZdzDwbjaZl787lc87PqiiK7iYhoyP2v2GbNP/dZeEAAUt8oM1dKVbN+x9GvYv9pTC3WwX1+K489ZC/lan5GVG8+nqGRZZZYLmvbaf1OeFwxI2rdyYvejd20p4s+KjbWSnluXwWEtDoa0mOAIPVKvHz2s5+lz372s4suHwQB9VfgAbM5X0gVCoVCoVAoFAqFYg2gpkbLQxge2Wv3kS4PbO4XUo4aD4+/l/c1omdEEVt6xCwHtKPLcW2TSNOWAhWOOiIiOeoW7WS7UDAhafvniEQ46Ucug2qQvh6cbsfHF29v/uhFydDiSRZzsH2X9zm7f6/5fuceIiIKuvFIbNA0kV8wpoMRw7YO8yY6l8JPrz5aIqKDSF6MOXUYjvEx8xeuw01eFuxJgdmCcaHrZRfVLJko4wBMaUJ0VrIDYBKg0QiY7QxYj5TN+cdRG49b1MK9ssfrLszXYssQRYxSfbthmgaLOBGWuHYjgJqrlhFNwZLaPBcT3LetTlqs1IhHpImIwpLpS0E7YexK5gDbHGyC2GdVOLUyMnWnlhszImFaMgUzKqgLGZLPTOEv4vNZJ7oObR4JF8kia6crXHcvFFzGAbBL/Dmf5gDqABrSltAgtpmVS2NGT6lHc38aMzq6jRldZp5KJ5jvLWuHeXWxG6nMlOAxHLb9dgWsi1oyK4eIqJXsHrthemaeOuQxAXIuIooYKDB1S+5ixvQ0ZHQM2r5m0wWYz0yKC32Nr+uhO/b4bWLX2qwlCePbrtaSnxkOHfKFa5IZle0cONs+1DTz6Dhr78GQghGtikwU1JbGcUpmFDU0iZy64vYz13csij4svRBkbXbHJyOsmbEekMgQSPLpWAdkDtzjf7HALFxN1H51bnHSHDxcrpM9GDyuF5/njB5yat9CI1rkzAv4NZS2GG1rY/+Ety2gzPfCJH00nHpzJSxr+kZ2ku/fqHnK7UCmkf0s+kHe6SPQdo/wnI7PyJqqbTfXeSBqBJePP+h9HjbjT124f0CLCwdy1H8NW0u4FBfT+5TVAfOjgsz2WSsM9YV0WRim3ctWGZv7hVShUCgUCoVCoVAoVhHKkG4ubKoX0tw5+za6CYo1wqbqaCnIXZhCMa0Dlq6Alo7y0osofkOQO39u6YWOEEfTt5bC76/hthXrj9wL134OXIuMl9GlF1k2jl/FbSmODLnzNqD2M2Ml99HV9MBOzlFSrCX0hfTI0Ww26Wtf+xrddtttND8/T/V6ne5zn/vQYx7zGKrXk7XLy8VvwnuCQqFQKBQKhUKhUKwKNGX3yPCud72L3vjGN9LsbFwGVqlU6IorrqDLLrtsxdvXF1KFQqFQKBQKhUJxzEAZ0uXjr//6r+md73wnhWFIhUKB7nvf+9LIyAhNT0/TL37xC2o0GnT55ZfT3r176Z3vfOeK9rGJKsErFAqFQqFQKBQKxRoiDChchX90DLzUfvWrX6V3vOMdlM1m6a1vfStNTU3Rj370I7rxxhvp1ltvpcnJSXrzm99M2WyW3vOe99A3vvGNFe1HX0gVCoVCoVAoFArFMYGQTMru0f7bBF78a46rr76agiCgd73rXfQ3f/M3VKn46ul6vU6vfvWr6V3veheFYUjvf//7V7QffSFVKBQKhUKhUCgUxwxWhSE9BvCtb32Ltm7dSi9+8YsXXe7FL34xbd26lW666aYV7UdfSBUKhUKhUCgUCsUxg41+If3v//5vevrTn07btm2jQqFAJ554Ir385S+PmQZ997vfpSc96Uk0NjZGpVKJHvzgB9NHP/rRxG3+5Cc/oac97Wm0fft2qtfr9LjHPW7FL4jA5OQknXTSSRQEix9vEAR08skn0/79+1e0HzU1UigUCoVCoVAoFIp1wG233UZnnHEG5XI5eulLX0onnHAC3XzzzfSe97yHvvKVr9DNN99M1WqVvvOd79CjH/1oKpfL9PKXv5y2bt1K11xzDT33uc+le+65h/7mb/7GbvNnP/sZnXnmmVQul+mSSy6her1OV199NZ111ll0ww030GMe85gVtXV0dJTuvvvuZS179913r7j8SxCG4bGQAq1QKBQKhUKhUCiOURx33HG0d+9e2lYo0Wce/oSj3t653/kPOtRt0549e5b90kZE9MQnPpG++tWv0g9+8AN6wAMeYL9/97vfTS972cvo7/7u7+iyyy6jJz/5yfS5z32Ovv3tb9MjHvEIIiLqdDp02mmn0R133EH79++n0VFTifnss8+mr3/963TrrbfSySefTESG3Tz11FNpbGyMbr311iVZziScffbZ9B//8R907bXX0rnnnpu63Kc//Wl6xjOeQU984hPpC1/4whHvR1N2FQqFQqFQKBQKxTGDjUrZ7Xa7dOONN9If/MEfeC+jRETPfe5ziYjo61//OhER/eIXv6CtW7fal1EiomKxSE960pOo3W7Tz372MyIiOnDgAH3xi1+kc889176MEhFNTEzQhRdeSD/96U/pO9/5zorae+GFF1IYhvTnf/7n9MlPfjJxmU984hP0/Oc/n4IgoL/4i79Y0X40ZVehUCgUCoVCoVAcMxhukClRLpejn/zkJzQcDmO/HThwgIiIstksERHd7373o8997nO0f/9+2rlzp13utttuIyKi3bt3ExHRLbfcQkREp59+emybeJm95ZZbvBfb5eK8886jpz3taXTdddfRs571LHrFK15BD3rQg2h0dJRmZ2fphz/8Ie3fv5/CMKSnPe1p9PSnP/2I90GkL6QKhUKhUCgUCoXiGEFIRCEd/QspNI/79u2j4447Lvb7pZdeSpdeeqn3XSaToZNOOilxe1dddRUREZ111llERPTWt76VvvOd79B5551Hb3/722nbtm10zTXX0P/3//1/9IIXvIBOOOEEIiKbLozPLtCu22+//cgPkPHJT36SXvGKV9A//uM/0r59+2jfvn3e77lcjl74whfS29/+9hXvQ19IFQqFQqFQKBQKxTGD1SzbMhwOae/evbHv5+bmlr2Nj370o/TBD36Qjj/+eLrwwguJiOh3f/d36XWvex29/OUv99jPP/mTP6F//Md/tJ/hzFur1WLbRd3QRqOx7LZI5HI5es973kOXX345feELX6Cf//znNDc3R/V6ne53v/vR2WefnfhCfkT7OKq1FQqFQqFQKBQKheI3BsEqpeyabWQyGdq1a1fs15GRkWVt5ZprrqG/+Iu/oGq1Sp/+9Kfti+WLX/xiev/7308PechD6KUvfSnV63X6/Oc/Tx/5yEfoaU97Gv3bv/0bFQoFgj9tkk8tvkMa8NFgz5499mU5CdPT03THHXfQgx70oCPetr6QKhQKhUKhUCgUimMD4SoxpPz+t2vXriNy2XVx5ZVX0hVXXEGjo6P0uc99jh72sIcRkTE0+sAHPkAPfOAD6eabb6Z8Pk9ERE9/+tPp5JNPpte97nX0gQ98wL6oEhE1m83Y9vEd3HiPFNlsls4880xrtLQYnvCEJ9Ddd98dS+ldDtRlV6FQKBQKhUKhUBwzGIbBUf87GvR6PXr+859PV1xxBe3Zs4e+8Y1v0Jlnnml///GPf0xhGNJznvMc+zIKXHTRRURE9OUvf5mIyGpSk16K8d3xxx+/onaGYZjIvEo0Gg265557aGZmZkX7UYZUoVAoFAqFQqFQHDNYTQ3pkWIwGNCznvUs+vSnP02nnnoqff7zn6c9e/Z4yxSLRbts0vpEUTruwx72MMpkMnTLLbfQxRdf7C0LB95HPvKRS7brpz/9KZ199tmxF9Dvfve7iYZJQBiGND09Ta1Wi37nd35nyf0kQRlShUKhUCgUCoVCcUwgJKIhBUf9b2neMBmve93r6NOf/jQ9/OEPp2984xuxl1Eiokc/+tFUr9fpgx/8IM3Pz3u/vec97yEikyJLRLRjxw56/OMfT9deey396le/sstNTk7SBz/4QXrgAx+4LF3n/e9/f3rUox5Fd999t/1HRNTpdLzv5L+9e/dSs9mkIAjota997YrOSRAuh4dVKBQKhUKhUCgUit9QHHfccbR3716ayJfpow986lFv789+dD1N9lq0Z8+eZWtI77zzTjrllFNoMBjQW9/61sSX0R07dtAf/uEf0jXXXEPPf/7z6ZRTTqELL7yQ6vU63XDDDXTdddfRox/9aPryl79s03lvvfVWOuOMM6her9MrXvEKKhaLdPXVV9Ovf/1r+vKXv+ylAy+G/fv305e+9CUiMsznC17wAvqd3/kd+tu//dvUdTKZDNVqNTr11FPplFNOWdZ+JPSFVKFQKBQKhUKhUPxWYzO8kP7zP/8zPe95z1t0mcc85jH0ta99jYiIvvKVr9h6pO12m04++WS64IIL6FWvepVN6wX+67/+i1796lfTN7/5TcpkMvTQhz6U3vzmN3slY44UmUyGzjzzTPrGN76x4m0sB/pCqlAoFAqFQqFQKH6r4b6QXnPq0456e8/78XVH/EKqSIaaGikUCoVCoVAoFIpjBhtpavSbijvvvJM+97nP0W233Ubz8/NUr9fpPve5D/3RH/0R3fve9z6qbesLqUKhUCgUCoVCoThmMNzoBvwGYTAY0Ctf+Uq6+uqrPYffIDAv9UEQ0Ite9CJ6xzveQYVCYUX70BdShUKhUCgUCoVCccxAGdLl49nPfjZ96lOfojAMac+ePfTgBz+YRkZGaHp6mn74wx/Svn376B/+4R9ocnKSPv7xj69oH/pCqlAoFAqFQqFQKI4JhBTQcBVeSEP67X+p/cxnPkOf/OQnqV6v0/vf/346//zzvd/DMKSPfexj9OIXv5g+9alP0XOe8xw655xzjng/WodUoVAoFAqFQqFQHDMIKTjqf8cCPvCBD1AQBPRP//RPsZdRIpOue8EFF9CHP/xhCsOQPvShD61oP8qQKhQKhUKhUCgUimMGq8GQHgv43ve+R7t376bzzjtv0eXOO+882r17N33ve99b0X6UIVUoFAqFQqFQKBTHDIbh0f87FjA7O0t79uxZ1rLHHXccHTp0aEX7UYZUoVAoFAqFQqFQHDM4VlJujxYTExP0q1/9asnlwjCkX/3qV7Rly5YV7UcZUoVCoVAoFAqFQnFsIDQpu0f7j44BlvSMM86gyclJ+sd//MdFl/uHf/gHOnz4MJ1xxhkr2o++kCoUCoVCoVAoFAqFwsP/396Xx9tRVemuM9x7E5IQESFBDArYD/rXGBAJMgQhEF6Lz+4WH040BBFeaIIyBLAZmkEIP1AGBwZtxIdpfD+etOH5BGkkIgRkbOm2ldmn2IyCAoKQ4d5zTr0/bq1Vtb+9166qc8+554rr48fv5pxTtWtX1a5dVetb37eWLVtGSZLQscceS1/4whfotddec35/7bXX6POf/zwdd9xxVKvV6Oijj+5qO/ZCajAYDAaDwWAwGP4kkBBRkvTg/0HvyCRgv/32o2OOOYbGxsbo1FNPpU033ZR22GEH2nPPPWmHHXagTTfdlE477TRqtVp09NFH0+LFi7vajmlIDQaDwWAwGAwGw58MOqYhLY1LL72U3vGOd9B5551Hv//97+nhhx92ft9kk03o1FNPpZNOOqnrbdgLqcFgMBgMBoPBYPiTQWJlXyrhxBNPpE9/+tN055130qOPPkqvvvoqzZo1i7bffntauHAhTZ8+fULtT6kX0vUrNnG/qHeCy9XqGUmetBrj3w21xj+Pje9S0g5nI9dHxoLfJx1/YNYa4e2TMog7o01nG9wX7lu4Q+P7UlP2NenUnd/5c2fdsN8U7pt2sdXiSQbBfkNbfNzlMx9vaDt/rpzlSvQraY9vY6PPdWchXRWvnPD24PZj4Amtlva9k+5fPR07+FmbAHn84fFyluFjDmMF16k32kRE1B7TL29eh5etCh6HznclJ3c+Vt6xg/52ImNlaKP1RBTfR6L48WTItc/7lB5fXneTS39Z2EavwGNw7PXwxM59ys9Xci5j8wxlx1mbaxjtDf7cUmuOj5PG8Pgc01rvL0NE1Ej70Fo/4vTJmS+U8YtzMI4HRnPa6Pjv6fho5PYbt4vw5i0Yg7yf8nvovqCMKb6WOum8UW+O96HTGt8PPnbt0SFZh7/jfW2MhI/vZI3BF/9uO+cz7n9o3/k8hOaE4HLp306n4Wwj1Ha9Pn5M60PueVmvXB8I7lNobuLz1UzPOe5rO123kY5X/lzn+Spts567b2l1DXkbPCYYNbzn8fqBcSf9aoWvi6HpG8ZXhXkzu+4j9xblHPAx2uzrj6jr9hIvf2ZbZ7tFY4qody8VoWudnwGC81gOcp/n5dL1amXuryX2Md+2t3ponkzHLM+V0ecuyj1nNnicRu4RNfe3Tm4+IyJqpNuM9s/bfvwczv7ifxa20Q2sDml1jIyM0OLFi7tOy41hSr2QGgwGg8FgMBgMBkM/8aeg//xjwtR8IdXYwjQ6lY+4cES71oxHOpDtLIrIjC+ULoOMIn9OwtHjJBYRUtqUSKDWLwhw1YfjbEgQBcwo9w1ZAmeRguOmsR2l4B3POJPTc6Tbx6gsRibz0UbcS4xDMjPamJZGr3FspNvsUBpRzZ0jZG9IOS/IOMr3zL5HzjvuKzLx2jmQ7/P9TSP3zAIgw8z9w34Wfc6D2aciZjRjO/WxjOeCo8Nljlu/wGxaEbMb+l0bBwj13CosYRXw+ZG+KIzC+MIpe0XpmCuRkUDkn7cYi+LNQ7zPfAz4HHcx13jsFvQjafvXNJGblZAxU+72kUmbLGjztpfpEmKTcL5SwPfiWsLXqPt7PXfNMjOKY7tesI0yzAePVTzSzIg2IFOCKgwR7p/0Oz1eHZgj/U4V97tM1kd+29InYPDz/cLPZbfRa/D2k1p5v00t88O7HmF8VmFW1Xks3SZvKRsrcPzyfYE5yNuWch3xvFdmfm4IU+tmL6iZeHxvlvGZMqv5TBxmrXGeroUzdNS5JMgaw7NCN8+PXcAY0vL47W9/S5dccgndeeed9Nvf/pbWrVunLlur1eg//7M6qz01X0gNBoPBYDAYDAaDocdIqFKMKdrOGx3PP/88vec976HnnnuOkqR4j2u17l70p9YLaYFmNHYYJLKm5coL8xPWi8X7pWy5XVIPGIrCiFZE+R6/5gidsHiBSCayrxhFVvdDO2a55dtw3ADIQEu/UC+W+3eRrmGyobGF2WeOxGYRvk4L1gGWTTQlPO6U8xvTzCRwzlGPKqwmRC5D+tAiTY4XVcbIaoSZ0KL+2H+Gx7rAsekEGDNehrWCjaGwJos1PU3RNA5728RraFCsQAjeuIlo4bzjWpLZ1aLm+XGj6uiLEGMBtDkEtcUlsy2cjBlkZjW9dXoNRxlcCo+JQgYQdfQN0CgG1keN5KDGYivVg/F1VQOmj9FJsnOI2Qq1RrzvdRlTwKgkNfjd/y27rlPtbUc5f3xvabgMK1E5tjcGZFXybfNRCe1DGSQJM/cpOxtop6H4EXA/GqiDfgOxQGVZ+Bh4vuOjIlkdoWuuE75Hede4xlqW1Ifm25ZnWci/UsdU4BmWr0nfw8PN2tAYU75f5u/pNX4ExWcZRaPMnztj6XUd8TjQ1u0vaj26Nt4415eGc889l5599lkaGRmhJUuW0Pz582nmzJk9387UeiE1GAwGg8FgMBgMhj7CUnbL4fvf/z7V63W6+eabae+99+7bdqbUC2mNo8QKm8S6yXaIOVE0ZeJ4q0VBo/qjtF+oGa3FI/cchUrS3anlmUCMJtXdSCiyNtlnCv4eRbdRJt7f/P55eilFr8jHe4PrvJb1KYvI4dGbaowpI+oC2YTzVuCqp7GwITZQ0zgW9ZNQ4pGLrJZxLcxD1YWEtNCKFtTLSpC20q/bzNSVSQVx3YGL9EDoXhr6TTuug2AW1D7xPBHI7GgMs9YtdQNN2/CYdOVYJeAKG4I4oyo6Ty3CzfrQKFOA7sbwPTIF0qfI+anMoICeNTYWUWddBH8uLzHOJ6LF7wFQC4zszEQYKnHbRYfjqNY97D6LGRHasW3nzlVdSTlD19w63JMR9cjcU6TFFefllOkdSzM+pO3G+PqN3HFXx0T6uZW6Y7Nzs6ZxLqP/Fc0r3kz6DMx4QXDP89ee9D3wm7OukjnWjaa0CMyYynweGtvIThZo6KV/sayOklp4NSsmcg2im25ZRB17FdQkQ6O/PiJTJydqauM3v/kNbb311n19GSWaYi+kBoPBYDAYDAaDwdBPGENaDm9605toxowZfd/O1Hwh5SinotEM5aKLvi5lUbkmKMPLpUfxZki7x5FEZKoKXM6irqzMJIILmqYtlH5L5CqiIZUNSqww2s9uIJEr7C9G0PEchfYPHVCZeShRt6of0FxHS62rsWwcaU6jix5rOAFWWHUk5Ah/JDKp6Y+0bWRfRCZwjjRzhoDCjDKwXl4ZNogjucx4anUly8DTaWI/KrLJvYDKEqHzs8OcoDNymAGovE0i36WR2awCnSDqQPPXuqaLrStztDfXlOg3gxkXrq1aa6T3hy7mGE83BrpqrD2oscalsltEQ1i5mz1BlvHhskeha9RjTwsY5GIX6Oz3omVFa1l3GT6P3QzWIXWXaShMGe5fG66FXgC3wU7DUaae+5s+E3CGgzdOC2qDE1Fv3F16ANXHATXMgfum5sybaZPhHq24vjuIeGCMd8TNrCjbTgzCouM+chN4uy+RWYDO8UWuu3xs8tp29MsgmOPFw4JZzaT6/VOeadIMO65OYBgs9tlnH7r++uvpN7/5Dc2dO7dv25maOZIGg8FgMBgMBoPB0Ad0evD/nwL+4R/+ger1On3yk5+k1157rW/bmZoMKUeGFO1mHsJWFrglltWQOtFZ1II2lG1xlAn0oBJByrGFmk6SGV3eH40BjUVMPcfI9HMtoJ+LInK8VWjnDBHRHMq+FehI+o3GtFHnMzJ+QZaAryRF8yBjQnH0QwddolxAVHFVlLahFloMzAq2U8ZWZX0QqDsMRVjBuY+ZLU1HqNX75MhwyCXY65bXr7COlXVWeWZsaKP1RETUbrnsWaI5d04mUCvK5zji3qnVc+V5YSL1RbVtdMUS4b4how7LFW0jxJbwcUIH2NCyoW2GGA90whVo9X89RqZCvVRy2dbJgja+xlLXTXTfHV8nvWcpdZwZwvzwvS6dKzWNcBXU6+gsm9YrDvSljbVywd23VpBZ1IiwXdq83ko1o82UxQz1qyq06521pM2RUW8dRPH10F3fuoXclzgTRjnW+XGKbvMImfMLpvVY1lmhphvnNJ67Sly/GcNY0D/RykbGJ28XniMx40HfiH4/9cc2bDplUPlZaCI1beW6WTut8rqlt0G90Q2/0XSoZ555ZvD7HXfckVavXk3bbrstLV68mLbcckuaNk0/P+ecc07lbU/NF1KDwWAwGAwGg8Fg6AP+VBjOKlixYoVaRzRJEvrtb39L//t//291/SRJqFar2QupwWAwGAwGg8FgMKhIeuSs/AajSN/3vvepL6T9xtR6Ie3G4IXTBDnNk9MaIGVBUCFVRtItiowVMEWXtx0xzigqcSJpdsPufkWhpRLxMdBSJ7Q029A20UpeS1/G1ep6Kq9WxH6qIZiuwwXbx9LSGYozumqcE0FZcxqvFEf6WVLT8qV2Gpz62XY+Y3+4LEGhIU6oiL1iRlaEaBkPOBZoSqOWYJJUJf83OW5ceidtc6CF5AuugWCJCa0UC6ckFlj8Z9KE4lRlKbPB5QyU9Ppo+psyN2PR9omAjxOXzejgvI/HeQIplFVLKcXbwmPSXamFXiOWKs6Q8wbHFssQddeBcqVY+L7KfeGUT3ccurmRjWZqkFYw/rV03Pz33thlJQv0o4mGfoAkYTMxv+yLB5Q+pNd3Jn2oXrJnQueqB+Bz0YF9C6XlorTDN0Zik6Dw+fVM1qjEMcc2+NkvbaOdPtNwSa480GgNU18Lt4Wlj/JSAiztA6nEWB4Jx7xWKogoYCYqKfr4OSxtqtX5Ypg6z3mdN9jLZC9w++23D2zbpV9Iu6Ffa7UanXHGGZXXMxgMBoPBYDAYDIZ+wN5H4/j3f/93uvfee+kPf/gDzZs3jxYvXkybbbZZ37ZX+oX07LPP9r5jWjdJkMnJIiM9fSGFkin570iJynimRchMSuQtJmQndZlgm8iM5lgNz9ihwGikSBjumDBp5iW8fWauCiKksbIXhcYoKO6HNrsRuE8WQmwfUcDuPLRMmxnScsYnss0Sx0OzrdfKYXhmF7koqOyJNgZKsoNZaYcIqymMSdo0VtUBRqUmRhCRUgdQcLwshAXM7V8ZlnpQQIai01Yi30TZ8UMGB8cBZyI0FVaTo/yBsYEGSYlkBjSdPnjrBa57L3JekRHFEiRh1jj9DkxSNHD0v9DkpQSybJCwYV6pOXBALH1RyRZG/phrrAqyWUXHlufO0HWplZ1Bs5nMPI5/Lz6OnA0ibRaUwIqV72Gjpk7HNeqqd8n6558tMnPFOOOJZk3tAsPH4HYHdL9ulizz4czjJTMrkGVNhBEtka0E5a5kPuZ5UBnbjUAGiTCjMKa1jCi1DEwIYKrE8I5RIUPv3wsSMK5MwLhJ5gzIvOGLUZsXQyg0keoRrA5pGL/61a/osMMOo7vvvtv5fnh4mJYvX07nnHMONRq9N38s/VR32223OZ9feOEFOvLII2nHHXek5cuX01/8xV9Qp9Ohxx9/nC655BJ69NFH6dvf/nbPO2wwGAwGg8FgMBgM3SCh3pgaTV2KpTv84Q9/oH333Zeeeuopj2zcsGEDXXDBBfTiiy/S1772tZ5vu/QL6d577+18Puyww2i77bajW2+9lYaGMp3LdtttR//tv/032meffegrX/kKve997yvfG4luKhEUjojn2BFPO4qRFSViJJAISSDqKcwDb1/rF0bC4fd6RJMAxezlb6odzfSV3dv/C5OQtlXXysBINMyPGnnMKFvkF0V+cX/zEV8l+jUoFlWLysWidQloNuQvazUaYaYY9SoxzY53jKX4O2uF3Gi89LdEuY9Y4fgoQufdK3/hXnu8hkRfK5S0yCzvw2inLEdDYf9i0WUZb/Vi7fdkQdWxRpgrldmBo8alFRhV9IGoGZ0Qo9hxWQbZljYusAwMM+8BzTLvUx3KlGgssFY6Kd83ZvAwY0JjFT1ddjquGoH512O3u9RhTxRynAqWS0IMvcLUYdmqenqNavN88HtliOJYwBVYQxzybOhULO+k9RdLzuS/S9KHB+060a7ZWkBnx7rSGoWPNx5nLg/SzTVaqsTIJKIDDF8vdOYMzIRzflO0q6xN5jNfg2wNXo/Z0BjwWHvzhmTHuNlncgwC12K38LwZcv+WuVSei8E7BTJ1sIxNKNOs6Fmv3uyv3nSgfhFTFJdddhk9+eSTtPHGG9P5559PBx54IM2ePZt+8Ytf0MUXX0zXXHMNff3rX6fjjz+ett9++55uu+unie9973t0yCGHOC+j0mi9Th/96EfplltumVDnDAaDwWAwGAwGg6GX6PTg/4ng5z//OR100EG02Wab0fDwML3jHe+g448/nl555RVnufXr19O5555L2223HU2bNo222WYbOu644+jll1/22nzooYfoQx/6EG2++eY0a9Ys2m+//eiuu+4q3afvf//7VKvV6Hvf+x4dffTRNHfuXJo+fTrNnz+fVq5cSUcddRQREX33u9+d0L6H0DUVUKvVvIOWx7PPPksjIyPV2hxOo4rrOPRdPnqBGhLO9We2o8jVVhArep1GiKTNsgxfLoLFjJiwVhBVZ22p1yavH9F3qui4x7OzARiSNGIfdQUGHYowf6DhzRoN6zUoz8ZUjFD3G8L+eMWkOdTnMpFEFaLP7Po4PH6sUbcUYm6EYdCcTNvuOonoltL10zGW155qLI9oYBv+OuNtds+E8RhopIXai6LG6ARIlDl11sBBshHYR1yXSGGcJOIM61ZgbnsOpU+l1lF0p0XwtKYxt+NutXD5+aFoLFXQL5btU9lIuDCl6XXS3hAIuDZRP836MtD21ZklgTYiLpPsRFmbQEbMROA5sirOso5nLdw3kKnz1m3H9zE/VqQtCo/RNnsxVHCFRX0nQ2VsWSPXCt8f80wrsqXMdNYb7uduIPpUcUDnZ52wplSOv3JvdqA48fdCU10FPJZEewn3zWhGD2qZkVWt49iG7LlAJlHI1TcPuV46iottiXknu39XY+ui7CLua/qXj+PY69PHFyu6bpzsPjSBAIaen4/FTyP9HDlnCfiaePrsPjvyJt3dznqCxx57jHbffXdqNpt0zDHH0FZbbUX33HMPXXrppfSjH/2I7rnnHpoxYwa1Wi36wAc+QLfddhsddthhdOKJJ9L9999Pl156Kd133310xx130PDw+DPVI488QgsXLqTp06fTscceS7NmzaLLL7+cFi1aRKtXr/YyXUN4/PHH6e1vf7ua3XrUUUfRP/7jP9LPfvaznh4Pogm8kO655570la98hT760Y/Sdttt5/zGB+uDH/zghDtoMBgMBoPBYDAYDG8EHHvssTQ6Okr33nsv7bDDDkQ0/rK3884703HHHUdXXHEFnXzyyXTZZZfRbbfdRl/4whfo5JNPJiKipUuX0rx58+jss8+m66+/nj7+8Y8TEdHy5ctpw4YN9MADD9A222xDRESHHHIIzZ8/n5YtW0YPPvhgYY3RV199VdYNgdN0X3zxxQkfA0TXL6QrVqyghQsX0vz58+kDH/gAbbvttpQkCT388MO0evVq2mSTTei8886r1GayPo38TU81Ni3lwAUiLUUR0pqiJRFGNaSzE7cwWIejd/z9ROrQMYNY4IRXxpXXc2njYyK12yBSrzCmschbTdOfIiSSPrkR1l6gKFKZPz4YyUWmHrWk4roI+k+GxoY6beN5FlYnjUiCVivURvYFfIZIpc8SY4N519qwto6BTApqCNE1Nc9qCtMOx4sdJPk4sobUd/D1HVmRZfVdgaeOvsRz/C5yu84DjjPvlUSjKzCMvdTcYFvCLijO555GETIXiOLXTwgyRpmJmT7O4icRvWdxo6A3GwF2J4QSzpOTCY3llDGTE9zVlOvX81bgeUlzIoflQtuV+Uhq7IbX9cdKvr+uuyqyRDxvYj9EF5rUg7/HUJUZDT2vZMdNqeOrObnzM07AC8HLHCuqWT7ZAI+PJFBbW1vHy87Ce5uwmQU+I7l1/X6lP3OmDv9cZWwoWQjq8rH5ukCXj/4ByIDHGFM+jnLfVOZavSIE/yOwf3DPn6wMkU4Jh+V+YHR0lO68807aa6+95GWUsWTJEjruuONozZo1dPLJJ9NXv/pV2nbbbWn58uXOckcddRSNjo7S5ptvTkREzz//PN1888108MEHOy+Um266KR155JF0zjnn0P3330/vfe97C/vGjGsI06ZNI6LxNOJeo+sX0h133JHWrFlDJ554It1www3USVMW6vU67b///vSVr3wl+pZtMBgMBoPBYDAYDJOJhIg6PYi7dNNEs9mkhx56SN6b8nj++eeJiKjRaNAzzzxDjz/+OH3605+WMiuvv/46jYyM0Ny5cx3S77777iMiot12281rk19C77vvvsIX0rJAB95eYEJ2kjvvvDPddttt9NJLL9ETTzxBRETbbLMNbbLJJl21x86ygrrLWLCCOM/SJSm7xwyi6iDL21AiQOGoFER6PWYK3Nk8jUIX2gCJNrs1nuRn1HJGIpnIznn9Kai3FoL8gpFU/utpTpLw8vm+18MR30GhxhqxVGfJznPBunNwbFmbqTnN9QJFNThDzCjDO8bg2Fvkeuf/nn3GCCqPBXE8bXCNvrAbaZmoMjIkDNSQatdiFReCQdbM1eopyzEKZFRovdVYLu33WH+Qoc4Yf9aUp3rhNHoueuEIo9Gt1pURYkU9x2lkk9KZjNk9yViIZIt4bZSs1chzeC2QBMNt+M69gxl7zPTIviksZ4il0cZVVYfZEEtTlL0g2xJ9JbP/+rjT2CC/Lmn5Z4YOwf3Zcx5W+iP3i1Tf2gowXfBoI667NWZTw03Ltuv+HM/fdcbYzX+KMKMMRV8Z8zRAZhTdX2twr8vu2YFxJ54k6XXB5ydl8uTZADJWcPmJQHO+zvTc+vzN46cBDCm3id4RgsCxwOsYjy8+J+Hf6HMnZsvIOenvM+GgNKT1ep223nrr4G8XXXQREREtWrSIHnnkESIi2nrrrelrX/saXXTRRfTLX/6ShoeH6W/+5m/oy1/+Mm2xxRZERPT0008TEdFWW23ltfm2t72NiEje06YqelLf4M1vfjO9+c1v7kVTBoPBYDAYDAaDwdA39DJl97nnnpMXvzyWL1/updtquOaaa+iqq66iefPm0ZFHHkn/8i//QkREX//61+nZZ5+lU045hbbffnv68Y9/TF/+8pfpgQceoJ/85Ce0ySabiMnszJkzvXY32mgjIhpnV6cyJvRCumbNGrrqqqvoN7/5DbXbAYeyWo1uvfXW0u2JMy46YkKwxmEJORKqaKrw+6yup6vdrJFbTzIIZCXqLiuACEXQ1VqRdWXfFY1fDNh2xmKmTFUzjY4pjr4h1IddB1oV4jqnOPzl9StwLDJntQFHablfKVNahwJlUQYZI8yaHq6RHs+x8O8haNFWTecWqvvlN1pOLzsR9lpjSPrh4Fh0HPOMQy9r2fUNChMQ1NlV3J8yy2vnyBsPBW6U7hwYXgZrh8ryir4+nh3Ckf/02kCtIWoRNZ1ZqPZw211W+lPkzqw5kof6X5J97ReEPWqz7tN1mM/3i9kVjbVqps6envsrgLXc+THXRjdyzdkeMyZYBxqqB82nqUBDzUwpu+iig25oP/j+3VCeR1B/GqphShS+7pB9ZRR4lOSW849/xjyXZ6v7CfRkkHHYRW1oZEK961PY6vD1HGyTnbNHlXPRDGd75ccYZhlgXe6kAwx9us1G3XWt7ZTR06Yoqj2NGS5RH5Gq9012g667WnN3mXD2Yb/RS4a00+nQM888433/6quvllp/5cqVdMQRR9CMGTNo1apVNHPmTNqwYQMRjTvf3nvvvbRgwQIiIjrwwAPpHe94Bx177LF08cUX04oVKySFNpRKy99x2m8RXnnlFbrjjjsmtIzm0htD1y+k1157LR1yyCHRPOIiNyeDwWAwGAwGg8FgmEz0MtxSr9clfTaPjTfeuHDdc889l84880yaPXs23XjjjfLiOWPGDCIi2n333eU7xlFHHUUnnHACrV69mlasWEGzZs0iIqK1a9d67fN3s2fPLrUvDz74IC1atEj9vVarRZep1WrUalUzGCSawAvpF77wBdpiiy3o29/+Ni1YsCDqylQWHF30dZLlh43niAusmzCmHJHpwiFXIjxtiGSVcXgsqDMokVzN1Y3/RqJUEkHjaDZE9FmrK6wna/vWueewjpreUH8kyFg++s+Qc8P7zP2sw7maLGhsLkdSWbMTix6qOmM3CsrwHDhztpEYSWSWgs8r1vny+lXF6a8RjoyWrrVLOaaWdd2gIc3cdJU+iJ7VremW357WnzpofTRXzlB0F48nYyCaZi3boozOEyLtPNdoEW0tUu8sw1F4pc5rEZMXvIZ5H9O2m9NG3XWwP4oLZqgms1ZTFWsRamxT5uKpz32qSyxfu7VwRkWZ0VTVJXiygWOLyGdGRbOnZIfgU6DK1hARUTrnsXYPj6Kio2uPjc9B4owbYBy164LXZfD5awNzJYx+xBGU2VWVheUapnyLKVkb1ekfaEkRwjIH2kJ9/8CzRtSMovQYyxyXr4+pzD1KxkIVF33tfijHCR3aGTLcAn1oKzpjRa8t3ildsIZ8bWoeERoD3tU4gPu7bMuzws4d/wExo4xemBoxtthiC9FxlsXY2BgtXbqUvvnNb9KWW25JN910E82fP19+nzdvHhERzZ0711t3eHiY3vzmN0uqLmtSQ33g77i9IvTDsKgMun4hffTRR2nFihW055579rI/BoPBYDAYDAaDwdAXJOQbRXXbTjdot9v0iU98glatWkXz58+nm266ibbccktnmXe96100Y8YM+tnPfuat/+qrr9Jvf/tbes973kNERAsWLKB6vU733XcfLVu2zFmWHXj32GOPwn4N0vio6xfSN73pTTQyMtLLvmTMaJVaWMymUbjOqAeMyGBEOqBVEDczcJpE5lb0oCWYPV5Wry2JkTn+BzClkcuhUNMk20qjpMMu4xZcljUSqOuaAJskbcL3SQWNay/QjW6rKoumMU5VUMSi9JPZw2uh3syuAXQrxJpsmhbQY0SZYYnUI8T+qBHdqBvj1NOQevuXzms1+D2vqVIdHUEPhHOOsCLAQEbrQKIZI9TX9SPw/jhnpkm7Bjz9nOb0Gugv1p/N+s8M+vjfZspyajrr0DUkYx0yJvx1NJ0Oszw55j89BnxND3pMFmr1AtcT1lT2nWVdxlutPyu61Rj7ihknwPCkjONYqpkbYpPniPN4EbRzEmRGIVMIl+H+sj4VazAjQt/rLtnhus7t9BiGRiVea5r2dcqAGdTQ3FFQyzeUhUQUv+ayeQ3r16fbn4CLbpGHQmFmS8gVGNxzs22lOmhix+/u5xktYxErQ2geIWUqD6ADcl+QEHV6UVe7y0N5xhln0KpVq2jXXXelW265JZhOOzIyQgcffDB9/etfp2uuuYYOPfRQ+e38888nIqKPf/zjREQ0Z84cWrx4MX3nO9+hs846S8puvvjii3TVVVfRjjvuSO9+97sL+/X2t7+9ux3qAbp+4v/gBz9I1113HX3605/uZX8MBoPBYDAYDAaDoW8YVNmXJ598ki688EKq1Wr04Q9/mG644QZvmTlz5tD+++9P559/Pq1Zs4YOP/xwuvvuu2mnnXaiNWvW0LXXXkuLFy92XlIvvvhi2n333WnhwoV0wgkn0MjICF1++eX00ksv0XXXXTeZu9gVun4hPemkk+gDH/gA/fVf/zUdfPDBNHfuXKrX/WhGJaclrDXFNeGQEcpHtFBz0SjJlCIikRIvIlkU4eHIPfctF3XUHBpVppTX7YRZpnwENau1WHLfMbrMjAlaUoZQFFnyItfsXBuKKo//qYHzcRUNZC8hTBNocmrgNEkUiOQpfeYIZSN1nMzqmaWrST1C/5LkddT+CnOvsT0B/RRHOTWdlwJkKGIRTGTeCM49ryljAjSnWK+UKKAJrBjpdfRBBQ7djQHq+YpY7mDtTR5LyFQhS8PnUDRKbv3YPNPquZfyNaCw3TIuSKlvVwXAgKqMQX4O1MZDl9q4fAaApg3VoGZcRGpjTjXwmOi0XX+BcB1SqDcMGQ/i0KtolstkdhReFzLWXR176FGHl0UnX2850MBH9XXQP267AQxtDR1e+Q/XBQ1oT7XtI/OMx6jBxz/A5qHbL26jrVznkw3fHyF3b6i5WRpZDd1yely8p4WeqbT7HNY87SdkflY02+P/TvcxvQwwe4Hh1aaGMYVZC+PbTY9rwTisadl7POZrxVlLU6Umfb9w++23i+nPKaecElxm7733pv3335823XRTuueee+icc86h66+/nv7n//yf9La3vY3OPPNMOv300533rh122IHuvPNOOu200+jcc8+ler1Ou+yyC61cuZJ22223Sdm3iaDrF9I///M/J6LxfOPvf//76nKhcjAGg8FgMBgMBoPBMAgMKjS4ZMkSWrJkSenl3/zmN9OXvvQl+tKXvlS47E477UQ33XTTBHo3OHT9QnrmmWf2v6wL5qrHWBzQbqC2hFmQzgbXQa9wm5SLEjGjgrVEi7SHQYYqHAnyXPZwPU3DGeiH1x/cN464ST9ha/kIWEm2soY1MSXq2FD7XQs50eH2JwOJy2xzPTBmSZIAYcaRU4w+azoLrgeGmiatxmi+P0U12aq4URcxo6XdHiPjAqOyXhfSsdyGa3Ii9Un1YxNgdKagXgqPhaYXDtbH7LhMiTgoanU88foKufJqY6pgrEn/KhAsyHp3FJfgMg6QPpPP13JYJ4/a7mzO1/cTs12KfACi0f907umMudkO9YLsiF7Dq40ImrRQ1kC2jMu0e8spztc+U6pnRhQxO/wcMJQet5DOM0Htr1YXsotxh0AX0wSd5DvsbgvrCWOZ0xsr2/eYKorP3fl7z9j6Ee+78e0OiKHSnFpxMaeeOcz53EbbfRbsyiMCx3LJMRByAJcmeJwV+G6XZQud60Vck11tOqKd3if5epYxH90Sby89vsrSnB3T6KKGbV81owEMKmXXEEbXL6Rnn312D7thMBgMBoPBYDAYDP3HH4944k8Dk2tjajAYDAaDwWAwGAwDQkK9qUNqJGvv0PULKVsKx1Cr1eiXv/xl9cbBbIPTW5IxPfXVC3VACm+CqXxoPBQAl0HBfhWlZuHywSErhi9KTlu6bictfaKlFwZTPRO3xEFRep1asiWQtsPH0UvpLEjPiKYaTxHjBE7PQ4t3L20v/zlR0iHheDQKzLawmLz7G9ulF6zLZiKcjjNtAxERtdePeMsitOtAK7MSbkPpn5Kah8Ai7SFToyIjIl4D0wbLmCQMvCg8+ceIz6WXhltiXXU5TLnqwpBDZAGQroloh1KouVh7kWmUV7rFPT+Y3uysmqYg8tjn8jn8ma8Nz0RMSnrpfZtwmnt+HTY3A2MyTpFtvz7YmDHvQyNyPHBZHBOaHCEz4XHNq1jWQER+eQ9JSx9fpx0wXyHKperK3Jn1YYznR2V8caqnfN+FCRWmBauA55ReoIoxjMx5YjroztWx+aYvKGn26NwLcd4uKUfpZ4kluYfxF8FySa75V5HpUo2fI9P1eG4N3dPxvFU9j0GJS1njQ+UceqnVRKrkpyYlyfp7Tx78Hd+QR9d3u06n42lIW60Wvfjii7R+/XraZptt6F3veteEO2gwGAwGg8FgMBgMvUIvGFJD79D1C+mvf/3r4PetVou+/e1v09FHH02nnnpqpTaRRatBBJ3NLoJMEUdaIOKnmq4UMZQBFEYcscxFALhP+oLdXylFbGXpbeeOncqgcSQL2WREKOKWriPlfSY7GovwTI2UyF6EiSoqcJ90/GNb2K2OyyR4gIgwswrRIvdgHiFNaYZYvFoFwyHNFIT75bGcdSgXkf8pPa64T9q4xFINoeWYYZPoMfRnELYeUlwc2ZsyRj4FpiwCYKT42PBxiJk9ectyqQ/+XTvesdJD0jaY4nSQ5U7/AeVF8kDWVa7H9JrIZwtUhVwT6V8xHFLmDQ0xAzO5lrssa9RvBLMdOEPDK0ehzCEKM4qmXETZNYimU1lZC7xOOCuo/HHz28ByFnVn21lZJf1+5TG7+Dtug4d8QQmaMm1l5Z7SPylbHHouaI6Mji9TcAwmDXhfrIWf+YKlzEqWmKrEuhUch6JyL6FsH3zObaRGkPy5bKZLKKtK2N8Jzhsy/+Tm6A5kc3iZgjimlRKHeXCbWB4sEda4v8aDZmo0tdBzS6tms0l/+7d/S0uWLKHTTjut180bDAaDwWAwGAwGQ9fo9OB/Q+/QN4HKu9/9blq5cmWldYq0hBw1yS/HAQ7ReyrFtr2oWAXdVGmNo9KmU2i5YgzA005E+iIRM0WvKZ+bUNqAIVrdyP7iPg4pEawKutSpgiINGOq78uAIH0bRtbZVvWUoylukvWT2kEvKDDFDGioNFGbgsJ/daDeQnUqUQt4qi4XHOzDd89gubZ3PrCyzHJForVwfzNRWqVkyyQgVb++V3it0jBLQz2vjR85LhcwThsqMYqkP/kegD9xGA7IEtFk3xASMfw4cAynlENdbY6ZCjDnVSnXFNKyTCY99C5RPq6Wnmo991XHozbv5bSrleVQo81Yo66JIs6cBmdGgXrQdXlbW4VHMpbxKMKPa8S3KiOik12I9olcd3TBMRETDG60v7Edfoc3Pgaytyk0XsdboHUF6toPH2EsbYf1kaP7xsuX4uS2W2ZRfP6CV1TJTYplCwbZ5PsydD37GwfuiNodmbema5qw8DV8wbhud0YIyjROEpexOLfTthfTf/u3faGiov4PJYDAYDAaDwWAwGMoiod6YGtk7be/Q9QvpP/3TPwW/X7duHf3kJz+hq6++mg488MBKbSaa2xhEgkIDoMiNtpcoYpFQ8+dGjsIOvWrkCiP3qHPLffYZT1fT6ukDlUh2lB3QHHs7bsRX/T3fJkfZ0Tm1qK0+oQOudVhQO8RqZswou+iWY4Y0V1J0fgxBdWJlDV4Lx1ikTdBee8XrtXNQRtOIkfyKbq75bfM+yDnSNDI45tMxVWcWIRfRjmpsBwTUBVVxORYUjCEuhi4ujV0QwYXaKWG9U0fIlIHJ/+ZpRnEbENWvwtqLU6uiSxaGA5iA2Da8rIe0Td63oZnrgut5beaES0X7FnMS7gt4/MHXnott/truuJkjVbMrvDknwGTVOmFWkhnIBDIgPFY916ehBnhTICvTYY0oZsjA+Wcn2oDODVnTjKVsu/3twPXu7U/WN1xG6ycun3VCnzsaqR7aO26TrCX1nofg/hSad6TP3r0XMsH4nGisdeRarAmTDc90mJHXLsdAOv3GbYkG2L3nqe1UcFNmZl69RqX6RODZFfTt8vWEtLuDfZ0zhnRqoesnsk9+8pOeyy4RUZLebLfZZhv6/Oc/333PDAaDwWAwGAwGg8HwhkbXL6RXX311uMFmk9761rfSPvvsE3xhjYKjMwQ1iDhS3tIjsEWujqJHK4jsl4nuFtahE71V95qu0jWf8st1q7MARlIihXl9aIEmTCKXvMtS1wyiZwFW1ndbdV0CJwsSseTooMYghzR2ZVmcdBuNsm7L+e0qLs4eKxs7/4Wugd0zo7huxmaizoaZ57gmKobMbTPs7InuwaGal56WFZ0mJ3D9dovmtHHXS0//jZF4x4k0zF61QX+D5wGZ0mDbEBUX1rIous9tReZbjRnVgKxxpfrFcC5Fj8dty5jEFXN+BQXO7cye1JtuZkUZoM6Uz13ZjItBopX2VeqOJuOfG01gIrvJeNH0uuimmw5tHpdlNJmevpMziMB6s2g+6uTujXiddNrQDyxxzZk1rfLXAm9P9IlKVhgyqMjSuguPtzW6Ydr4Mg2XuR04IuegqI/+dRjOdAjd23z2T1m2l94Ydff5t0pdWWwDnxWK6kdz5lHw3ofPtQXH3dPSB+ayojlBnMz7BHPZnVro+oX0sMMO62U/DAaDwWAwGAwGg6HvMJfcqYUJi6ieeOIJWrVqFT3xxBM0MjJCW221FR144IH09re/vXpjEBnSfqdATSeMHXmRbs3VlKNPrF/NR3U5Is+aJ7U+HuguOLoYYGW0NlT9pwJZPsLWqW0VRbZCLGZZbSFH+uV4htmxKCaZGWVktf9cd8xSYOaozTqoTvB3+eid/8BxQcYbtRvANE4IidtvzRk0Ng4qHa8IsCaZg3Rfkf3rbjuahjqtc6k5SPcRRbXoJOpcQnsj0f2S9Wvl68j8oEbWeVzgOE/3J8RQI+R8AMMr+1zFH0CbZxSmlJn2LHOhfOi8kdZylDaFKXWXE9frrq9UuwAAVgpJREFU/PFNmBFNdeCtsIPloJCxGvpYaqYsBo6JdspS1oUh0Vl+otw4zJ0i1rxpeska6wSxjmFkLpJ65lw/lrWgaVvoZjuRc6A6+YpWtOZ8xt/D33EGj7sfuFypfkP/mkPpuezE56F+Qa55paaynKvcc5qnB1fgOd0r67nLuVl5WvYDj23tXtzNcSxb1ziErGayW3EBx4TUSeUxXyFzqygThK/JGi9XRmMOOt8q/amMpEcaUmNZe4YJvZB+6Utfor//+7+nsTGXVv/sZz9L559/Pp144okT6pzBYDAYDAaDwWAw9BL2Ljm10PUL6c0330zLly+n7bffnk4//XTaYYcdqN1u04MPPkgXXHABffazn6X58+fT/vvvX7pNrJMp6AUDxNBy6zk6H2MHmDFpQ04/a0g4mpdGGUMusTWOWI3xZ2wLGFR02eV+csQtF3nz2mAw4zOSOunxcUZNFEewQ8dA2BT/pxiidS8L671NssNfSbe4PDSmE2uWdrMvmh4yWwCincjkl4DUMBWnXkUn57GzuhOhfObjWZIdTsCtMwrl+pA2oV4pI8Ss8jLo/iqs0B8J/GwEd/xkn5lhcY9RdPzjeBir5mpepQ6k2oUyOm1lfsfsG7VeH69X5notWCZz5NSzWaQpccEcLDTNKn5fZj7zrj1xMZ/4XjLjNLZ+hIiIhodTlrqE7k7TLrc7YS01tzE2ltZ55tqJPG9E6q5WnffFXTdyLSatcvrtrE1kpnMZZsJ8D9ZdtyxC/dLuM/xZ6nK38JnPdeHNrtPiLC6/ZmnYUV62EeiX+mwATSNLHLt+qjKK4kKv/B5ifMXxHuc3b+V0Tk27FL+/TH5aXEK9YUin5pXyx4muX0gvvPBCeuc730n/+q//SjNmzJDvd955Z/rv//2/084770yXXHJJpRdSg8FgMBgMBoPBYOgnzNRoaqHrF9Kf/OQn9Pd///fOyyhjxowZdNhhh9Ell1xSqc0i7ajHDsbaaoIOUImYe9vOt61p9goYqE5aO44ZySBTOuRGJoV5TBlUqauqbSrmqIv7xtsSXQFrdZC5gr7kS1A1XS1CFolGp0IK/95FVHyy9StdudnJuqhNc50QWQulRdNjTLKnc/ZYc9RPRVhpQGfUrQWrQs5FcXS2A0xIYW02T9fiR1IlmozjUBZwHbql6YareQzWOAT9I0eiJ70GZL5POPfAfFaJZeIMiWZB9Bx0xM5PoJHCOqM4H0lkPcCgFjE5ap07YcVZm5RG93P7Je6qdfd4edcdzqvcZpksiZI1Yr2MAbiO3d/G++fpUaeoy27IiRlRBwaKKJ0fCvYpf9zqXDN0lLfrHrumwtCzk2xtSL9nt8GJV5v/eeyMNDYQEdFYOr91RHuaawPHrsK2ieMtZGGUGX9ardXsszvXJbifoT6h++pY95k9E0HR9pDVdNHd/btK7WFcR/rDSXKNlP3k+2rkGUb1SlDqM3ej9Y+5sxNlzC0yq1V8KbTMqOw5AK2l9ecS1N/3IpsiBjM1mlro+oV0bGyMZs2apf4+c+ZMWrt2bbfNGwwGg8FgMBgMBkPP0TGKdEqh6xfS7bbbjr73ve/RZz7zmeDv//f//l/6sz/7s0ptSvSfY47ofjbmd7eIOdB0eEUuqKHfEtEZlYzA9UIr03KZoa7QdtmKTLOn6IWQKc2DI4PDaX80PQueu0DNQJ3BYyZX+XkqQmOsUS/JkUiNUQpAHW+gkfGc/wJMaWkWWOsfsEP5iL7qCijrxHXa6G6Lmh9nWbjuJfLLTCqzA5qTZ/47JZo8COB1XsS6Ob8p++ExeQUMVZ5NwnmybG1WXo57EmLFvOg9ZBOoTGq6XCNQo47ZUnF0bChjTXHxjAFdOdkZl7eJtS+FIcR7TWhb6KQ9BcZiHh5bk98HJECAbdPOcxX2Q5zrifVrKftSoPfL6ubmxnS6DtdJRaZUayNYv5OIWrl7GzO2sqyw4uFrrgP34HodmKouGHKpgao4kSe5GztrSHk7eEYmP0vJ9ROQsdJw7zvuOsX+BlV+d5bVWMwE+gc6VLwnV/F1kPsgbyqiUR7flp/5gjXLtT3G41umX17moHKM9GeM8mO63xki9jo6tdD1I/+nPvUpuvXWW+mII46gZ599Vr5/9tln6VOf+hTdfvvtVqvUYDAYDAaDwWAwTBmwqdFE/7eX2t6ha4b0mGOOoR/+8Id09dVX0ze/+U3aeOONqVar0SuvvEJJktAHPvABOv7443vSSaypFISiIfPagmhTJ6ITqw+n0Xas4VRQ189zFR3LojxldbC8ba9tL7qXfa7VFIdUXjLAUoYQiiB6zqgc/eYRpDCl1Ewjhx3fzU/VOsoyA7rUK0SFi7Sj+nq8b8xYgstnri1mWvAzohPIIEAUsa1ZBzXnPI4IB64zZiG0tqBNjsp7esQUyDgR+UyWx6ahvlbcS/399vVeg2dKvXNbIUCsXaPye8loczs3jho1YCGLmESN/Q65YyrnsshVvQ5zaEjzSsD8+LUG62q/NHj6Nb7WFQdauV7lh8i24N4xKO2oqrktAY+JFyfcYSLK13h1t4XIL5fdI8LaTKmbjAxUTBfITrbAlHJbyL5mzrfjn5vg4B3T0+LY0OaWQn13F8AakzFkOm1mlAcz/rI62O5593wTYiwn1nTlZVmfCF4HiNg8mbGR8Yy16P2/YhaEZPtAv7T7ptMPmU/CelTt+IY7Emai+Th6jv0yP7r7UYahFi3pxCpTFiKx18kpha7Pdr1ep+9+97v0rW99i6677jr61a9+RUmS0J577kkHHXQQHXrooVT3rLENBoPBYDAYDAaDwWAYR9cvpP/wD/9ABxxwAB166KF06KGH9rJPBoPBYDAYDAaDwdAX9KIOqaF36PqF9Etf+hLNmjWL9txzz172h4hC5kaRItFKgfOykJSFiGmQbKMgJVJSfNFcJrderT7mLOOlObXCKZ/cPzZ1CKYkKcYYkqorJVvcIvEkhhHlj10yxkZJivEIb4uveE4ZCZWUwFRDEeRPbuokp31p/UHDhfy/xYiA02uJz6O7DU6H5FRILc0tD0zf81J5I+Y/4xvJpXV7BjeuaZEce88YJ25INBHwWOdUn8aIb1ajl5ZI09LSz5gWKOvjtUBZQjiaQkyF1N0q8IyH4HfNMAvPKY+NfJquWrrHM6rRy+pMGJzOnrjjRMq+RMa39AfnRg0llmuDWYw2TmQc4bUV6C9fA+0N4+mtEzKxmwDKpuqGZR1hMy1J7StpptWp565RvJeWvCY57ZbNhUJptZi6K/M/lE+R9UqkGmLqbdJyt1Fo2yEps4HSQJBCrJks4bpo3uSkb6b/1s57mXTfXoKvLTEEU9JEuwHeV3Cuytpu6Mso6bOFEp3c8dX2oTMGqf/83CEymfDzpbsSPPthCn5JU7ogCkwUtRToGjxvOvuP6dVcPic9vnhMeg0r+zK10PUL6cyZM6nRmJo10gwGg8FgMBgMBoMhhMTKvkwpdP1Cev7559NJJ51Es2bNor/6q7+iuXPnTlwzCiYVZaLEIoLnLzQ2IMC6ON9Hok716eNVuRM2QIIoU72pRPECbXZGoUj1cDmjh2wBNv4I/KSUa8ESGd7yXZgtS9SfzYrEeCrdZt01hBLk+wCCemRyqhRn7gXQUAH7If3MRdvRcEgFmqrI8lCKp4KZCfcLo9ieWVCuv8iIam0ifFMYHmuxcjWKaRUuB/vcSk1QnPHK40oMY8r13+t3CYblj4EZdSLuEMX3si8UEw1uA80oghF8zYijxmUO4vNYzPiFwVH8RCkAL+c8Un5LYx88hlcDsMX5bUhJBygtg6Vjiuaz/JjEfoVKOEwmvLJDYOhXhhnNmGwwA0zBDAtni8jYiJT0kDGdttVqjd+Lm41RdwVmhKDsilNORWEhxbRoKP7c0RgKP0uEwGMlK4UTPr88tjOTNpcNzfdPjiuW6ui463r9jpS50UyNOpM8F2KpJDz/ocwyyRCSvoL5T6A8FLaRhzue02ON1yWuq5XnCpwLb24B0yyVaeRrEM57fp7R5h6tD93AY3LluSm99prufaXSNuGa7Le5mzGkUwtdP/FfcskltGHDBlq2bBnNmzePhoaGqNFoOP83m/11yDIYDAaDwWAwGAyGskhonCGd8P+D3pE3ELp+Y9x0003pLW95Sy/7IpDIkRbpDyCLOsH3YI0tEV9hDVmnClrHPFj6U1C03t+RmtOH8bb06L6zKpQrEKaRtaQcFAy0o7aNUbNu9AQYDU8ZX2F6O7BcbD856kkKMzrJ+hXWEBVFGZ3SHDWXNWFIyRLRlnKZgrAGRWsn3xZqG5HJyyKQ7jhlXRpRbty3XBZVfkcNoFZCKKSBTqPB9eHxv+31I+P9x3I1wCTxZxnzUsIlsN2CiD2yaJgdEGLnPIZrgBrSwih5SKOJTI9n6Z8yjC1khIBFSrNF8pp3PM/I6Mk2RKvplmsIaYBUBhHHMzJrJfXCVeBpTKUP5ZlAacsrTVE+8wN16v3QapeBsNFjAb0hKdcPufORmn3B1xm3JZlEsFzuOAtrTuFx1x5z9byerjJ0f0xw3KX35PT6aKSMGusZ8bNsKzBXh5j1/GeP1VQgfcqNMd5XPif1ujI343wAbGtoXkO2f1BAZpTvKbFMnCI9u5QlaeLzTjxzzOmHtmnNv0FhqZ32Azrh8W1iGbz4c1Ao+4T3XdYsKAFUJpuJnyO0cjN8fGVOrSn7GWONseqZwm73CsaQTi10/UJ6++2397AbBoPBYDAYDAaDwdB/dExDOqUwpXJqwwXOqVK0mNlIr1A2/950WQF2re2wPjQUvQZWSRxvS+bhO/sljrYpwA1N081q24ppejxHVY4HdVwWKVHiRE6/i9hg5XePiQ7qvsJF6ycd6OinsZkRSKRZGGLXXRjbEjdIYA+DbcNYkbaFiYFIL+p3A995LE9RhNyLRuf1qalDImiwVLdRWS98fPNsDDKGwrYUuLqKazWwoMHtlexXP1FUqDzmVOixCDWXbajj9xzRBgYlf0yTDupKFX0QsBHZ96muKOAsi/N9WVa4AZ8d5gOOhYxP5drO9stlBlnfmHcf5X2Ose3jbbuurWVQVm/db3TDkCHTKUe0ZFtl7qMyhmVOTvXznZLXaG4bNYrP68jqIzPKkIyawHNLXWGkOuBqqzFW7cDczYjNYUT+XIeMaVAXyhkzsC/NSXZ79p5dkM3mrKrAcfMyESDLzANmkGwIn+d4h9ncAJhd6G9wndgypOsnUaveDaeNOngtmyPqslz2uZwZUcieGd8ePNPwKkqmQa9hCbdTC12/kO67777R32u1Go2MjNDcuXNpjz32oMMOO4yGhrq44A0Gg8FgMBgMBoOhR7CU3amFrl9In3zySXrhhRfotddeIyKi2bNn07Rp0+iFF16gJEmoVquJpfLKlSvpyiuvpDVr1tD06dPVNv2aoim7wdG7kFOuaD81PR05bcjvwHrGkED9Ti2a40WMyjh6FjCLlSDbD0edVGiMZMmadEQljmOIyfXcYf0I2qRCcSH1hMkRSD3S9LO2JrIH7QA7hNFqjxkFNITBSdkhjjIGzg1H4PGXBPWdiFqJCHAKZJS068HTzQW0aHy8pE126ATdqVzXcORroWPGEWbV9XXya0F6cwuwbajVjDfGLGH3t12/Dl/ZbepzkOZAWQPGCecDdHFkTWxeG1sDXV3GqITZkpoyp/M11nGWjWteZRsFTtV55gOv6UJNd5+BGQXePMXzRu7+hJrvpKUxPuMLMOMoOjeso+uw6ZDVk4KzMDobgM1MM04amqaf9Os927fgzyry97F6A9ymxRXfPSbaFSn3nMCjDvcb9y3kmjveVgLLpc9R+cwTdkTmfgKDq9U67Tf4mvbqCstckH2fzQfu/UN+T8eKZM/xfAFzA27D+Q51j9ozoNxz+WKIudAr98OG+wzhuekzKSsrlMgw4IwQnLMUh3BBgCH1s0uUbXo1z9N/5L9Hj4IOzs/9y5ZLiKjTA4Z0Ii38/Oc/p8997nO0Zs0aeuWVV+itb30rfehDH6LPfe5zNHv27OA6r7/+Ou200040NjZGv/71r73fH3roITr99NPp7rvvpnXr1tGuu+5K55xzDu25554T6OnkoOuz/a1vfYtarRYtXbqUnnnmGXr55ZfpueeeoxdffJFOO+00mj59Ov34xz+mZ555hs4//3z693//d7rgggt62XeDwWAwGAwGg8Fg+KPBY489Rrvvvjv98Ic/pKVLl9Kll15K++yzD1166aW011570euvvx5c79hjj6X/9//+X/C3Rx55hBYuXEj3338/HXvssbRixQp66qmnaNGiRbRmzZp+7k5P0DVDesopp9DixYvpa1/7mvP9m970JlqxYgU9/vjjdMYZZ9Ctt95Kn/3sZ+kXv/gFXXfddfS5z31ObVONuGBUe9h3t/PW1EKQEolx3XVZSxrsgRrxdplR7pcwUrHad7xdRVQ9kdx5jWHI2i7XThJg7TzmliO6ZTWmeUc4jIxPkm6gEFBzr1ZnfVx5TaHoO5SIpHyO7GsHzx8zn8jkatvmdvK/gYMsutCiE6aHCOvP11R7g8v2oEMramO4T1k9zIAza1EU2GO+isfSwJj4CHDMedokYCCJAqwlMo9K9NnbNmtJPd68PNTskRLgeoHIWAgDVYIJUFkHhfH36hsyI8lzXxd6dmHzWe8ozur+ufM05cBQTVXkNY7CpqXXL981sM6oXMMVWF+1livPGcDgddKxi9pSx7l3GPRzaX94Da2OJ2oxY5C5W/a5nHM3O+mGzr/GVqr+ErwfrL8NbXuSnewnilLPBnj/LqiH6V37+fOM+no4prU6PIti/wJjqao3AT5LRO+FGgOK5x7mqGxjbtuOk7mmmdeOJ7Lbget+oM98SY9Mjbps4thjj6XR0VG69957aYcddiAioqOOOop23nlnOu644+iKK66gk08+2Vnn+uuvp6uvvpqGh4dDTdLy5ctpw4YN9MADD9A222xDRESHHHIIzZ8/n5YtW0YPPvgg1WoDONYl0fVs9K//+q90wAEHqL/vu+++dM8998jnXXfdlZ588sluN2cwGAwGg8FgMBgME0bSg/+6wejoKN1555201157ycsoY8mSJUREHqP57LPP0tKlS2nZsmW0xRZbeG0+//zzdPPNN9OBBx4oL6NE4yU6jzzySHr44Yfp/vvv76q/k4WuGdLZs2fTL37xC/X3xx9/nDbaaCP5/Prrr9OsWbO63ZyDMrpPFcjUsestuO8SURY9LKmjkH5htCcfnVI0eF40iZ1KUWcZrZkVX6aXjFCmfWJtVlhHKyxrHZgeIj+a14d+VoJybrAenaN9VbSgHFnEaKb3GXQ+jqudMCql9yBt1NXS1B3XVLfWnkRQOeKLUVCINvO+d+OE3AEH4lKOhAoy3VrJgxNZjo9BZwNEHQfgeOprSMuspNTLBWaAHXPlc4tdtkHvFKsTx/0DrRS68cbOZfYbzh0Fxzs9T+3UDTPknFxGj+z2RanNyp8DWv6JuoC74x4cg6UO42AYUj6mIU27hjYcD1w3q42YupnyHMLXXYQVRkZRdf0sUdNUvlPdS6GuoucWPn49tdJ5opEyrXm22HPHFfbN1avz/aGV3ls6mg40Px/jfbFgvkRtaei+ylpg7ThHXVYHgEy/6PfLrzOaAuasbIXeP2eUuYepjuR1vNfCet7zSbEzN9/7Ncder28VsunU5zS+jyB7HIJkKPL2JpO9S3qiIe2GIm02m/TQQw9RJ5C19PzzzxMRUaOR8xpIEjrssMNos802owsvvJBuvPFGb7377ruPiIh2220377f3vve9sgz/eyqi6xfSAw44gK644grae++96W/+5m+c39asWUNf/epX6cADDyQiorVr19I//dM/0bve9a6J9dZgMBgMBoPBYDAYJoDevJCO47nnnqO3ve1t3vfLly+n5cuXO9/V63Xaeuutg+1cdNFFRES0aNEi+e6SSy6hNWvW0N13360awz799NNERLTVVlt5v3G/nnjiiRJ7Mjh0/UJ63nnn0a233kof/vCH6c///M9p++23p5GREXr00Ufppz/9Kc2dO5fOP/986nQ69Na3vpX+8Ic/BN/q86iBi24yBhF0Rj6KokZpykVaopFoiLIXQovuxGofNlxNAvarox2DEuimHp4KYB6E2VO0CVSFwRIWbqKd7C1QXxaqkyj6DnBH1HSShdsMOHCq7CWAt8k1xkLjF+uNoaY027YbfWX9Z6LoWEP964CGLOtDuWs2tI2qEdQyx4776TE1A6iHW5YtzB9DTyMFulNxpxWNbkG9z4ALIkayJfIObSQ89CIBeR7j4phcpBnV3Jmr1KdW6uzh74zmRuvVtvDa0dpQt5HrA+sZtX1UWZ8+QWovQ+3N5rB7b84zZ6Id1zSj3DZq4ZSHwdh1r81XGfM43k9hHOvu70RZdgii1nQZXF5HPqf3vObIaHD9UH8IMmDwOHYFZQ5EjWm7U/3xrso11Q/geMd7cH0ozB4Gl+322gnNvdw2MrPdMHrMIHou3dx/Hneug7yqP87NJ0VaeYEIpuPnO1QztLBtRjofaPcnomJddt5BvR/oZR3STqdDzzzzjPf9q6++WrqNa665hq666iqaN28eHXnkkURE9B//8R90+umn05lnnkm77LKLuu4rr7xCREQzZ870fuNsVc0oaaqg6xfSuXPn0gMPPEBnnXUW/fM//zM9/PDDRDSeynvEEUfQOeecQ3PnzqUXXniB9thjD/rUpz4V1ZwaDAaDwWAwGAwGQz+RUG/LvtTr9aC2c+ONNy7VzsqVK+mII46gGTNm0KpVq2jmzJm0fv16Ovjgg2nnnXemU089Nd6PJHH+hn7LpwFPRXT9Qko0Lpa97LLL6LLLLqOXX36ZRkdHafPNNxcXp06nQ5tvvjnddNNN5RrEaLymS8xFZ2sYo9fqjyounKWgRZFYnwQMJLrHBllcjrhpWp1muVpPUYA+UPoHuijU5IY0fl4/EjcaiZgQ2yns6mCjtQjP9ZYyxoi/G9poAxERja0dcdbl6LrG1CQFx9NpCyOm4hitaJ3z47ck2++1zU0ByxOKyGosqufwq7r2pdkBOU2nrKPUa/T6zZ/Tsc1R2rxGUK0/NyD93ngn4hFu2e/8GAQnSFzH20TKHCSt8PLREVJUew4ZjkA9QXSmzFwu002Adq8oSu6MQUUDrbpP8pj0xnP1uQfHvXadBt0me5nN0gNwrUutxmV+/sJ5MTuf45/RyVPPygjcTyvqJmW1tP8hbWZVrwKNmYrpK7H+KJ9frU4qQrJcQhpYdHxVfi8DvO8kcPV3St4v+gWcw1g/Hjp33nxXUDsUa2CW6g/MRZxtgtkenhbdYRq5X/jcFZ4fCp8N8qbAfG2xX4N2/sAbAvsZ3FbijuXCZ5UiTwDKjeH0UKDPQV/9RGpEnRJ9LNMOJURbbLGFpM1WxbnnnktnnnkmzZ49m2688UZasGABERGdfPLJ9Mtf/pLWrFlDL7/8sizP2tPf/e53NDQ0RLNnzxaPnrVr13rt83dabdOpgq7vfqtXr3Y+b7LJJjRnzhx5GX344YeD4lqDwWAwGAwGg8FgGBQ6qbHRRP6fCMbGxujwww+nM888k7bccku64447aOHChfL7DTfcQBs2bKDddtuNNttsM/n/qaeeoqeeeoo222wz8fBhTWropZi/mzdv3oT62290zZAeeOCB9C//8i+01157Od8nSUIXXXQRnXXWWbRhw4ZqjWI0BJnGWERGIpLK78IipToWjmihpii2DXHy01hBJTofaL8oEi7MDrOZinbTYWMVRrRrhJgHcDMscvaNQmMzpgrS48mOiEHnybK1uRTGEaOfeafJTLuk1J+DMZ0xDG6U1tGl8vZaro5PQ1HN026A9RmRNS6jT9Wg1VkNLstzAS8zaJfnwLbxOgrpPxvT3HlWY0hEWyr1RpH9juw3ZK8UujaKmzlvI3YeEljWBWvBRIsE4zzGoHo6VZ5HC5gDhutiHs5y4OtUnLWhyap1B/Ntc03fyQKOL2Q3u3Ff9cZ0q+a0Ueba1up5ylyn3PjrBUxkcB2sj11zz3vMgbhI74e/s8st/8Xj6jCWOBd0XNfypCAtKXQs2qAVlmXTY9AY0D3Zu6bBmyJ0XXptAHsZquGcXz9zt/WvV2E1wYlcm7uQaQzWHlaObdX5ItS2Ov68fQzPy4lc535fGkqNZDXzQNOv5tfFub8Cyz8RJOoLQ//RbrfpE5/4BK1atYrmz59PN910E2255ZbOMv/rf/0vWrdunbfuIYccQkRE3/rWt2iTTTYhIqIFCxZQvV6n++67j5YtW+Yszw68e+yxRz92pWfo+oV0yy23pA9+8IO0evVq2nXXXYmI6Be/+AV98pOfpHvvvZfmzZtH//iP/9izjhoMBoPBYDAYDAbDxDC4si9ERGeccQatWrWKdt11V7rllluC6bR77rlncN1p06YREdHixYvluzlz5tDixYvpO9/5Dp111llSi/TFF1+kq666inbccUd697vf3VVfJwtdv5DedttttGjRInr/+99Pt956K91xxx10+umn07p16+joo4+mCy64IOj2FIVWLwo1mlUYDNSODrnsTCWXtJIsYCLMVhpJ3xCOQkaBOrYyWg6MuCnrVHZLo9w+cUSppOaxjCZBZRkj7FZfgP0BnUWDUl1SiQgmR6O1iKWwc7LpdDzmo4+BWmv5/hRG9/gchRjHLo+tp5OLLluOcfT0tDIPZPtX1mlYQ8wB1dNQU6Bm7mShIAujnjqRt9dl+trW2mnOMg12K1f6rzGowfOF/QlE/PPraDqu8PVfMgqu6Hw4AyDYisdg4LEABgOyCphdyY8bdJpGNgtrEDMwI8DtBrdJzjKq9muSINcbPGyVmft8Jtmdh9AxtyasO2d8hEw53PEn2+LxBfdcb/28K3A67jzGUMn2QXj9DUFpQ5y8o1uIQzIdUrZYYzlxzhTX4JyuVtP547YmC2q2Q9ovzpaIZUXgXJRdy7BvqNUM6XVlTozfi7O2WAif9oG37dQC19jLsL8EjlM+f8H+Kvr10nrpjjvHhbbhZVFwZggwzdo2Q/cf1Uulj0ioNxrSbp4SnnzySbrwwgupVqvRhz/8Ybrhhhu8ZebMmUP7779/pXYvvvhi2n333WnhwoV0wgkn0MjICF1++eX00ksv0XXXXddFTycXXY+Ct771rXT77bfTokWLaNddd6VOp0PvfOc76Rvf+IaTA20wGAwGg8FgMBgMf+q4/fbbqdUaf5E/5ZRTgsvsvffelV9Id9hhB7rzzjvptNNOo3PPPZfq9TrtsssutHLlyj8KT58JhSW22GILuv3222mfffahX/7yl3TttdfSzjvv3Ku+GQwGg8FgMBgMBkNP0RmQhnTJkiW0ZMmSrtf/9a9/rf620047la9sMsVQ+oX0nHPOUX9btGgRPf744/ShD31IirkSEdVqNTrjjDPK90YxjSgFKWbvpl2qOn/VQls3IsKUTjVNTdIdukjMqUOqWck03PF1UUwOv+O6Wv/A+IZIT/FUjZy0dOFQm7gsp5u0qpuBTAZiaaOSSqTtP5hxcDpfKKWUz46UQVHKlIQMkca/j5QlSI9tAttF2388v5Kuw9dohfo+aOyE6UVljFKwxASei8JUx9zvUnh8slPDKyBLf0rTotIUo8b00WwZSVt008XwdwaWLsBthdZNyDVQKeovpv8G14O0Wm8ZLGNToki6b+QVLr/gpyeXHwNiSFNgCOb1LVZOSEmdjK7TB8g1kSipr5B+S5TNO9hnTMnllGcGXsMhY5/Sqcu8jSQ8F9ackljhVE09lZLHDEiHYvIBNHKClOJQCbF8/4P9UEoDefO+VqZGKeFDlCvvkv5taiXEJhlFJm95cDpvgs9r2drhFcGoyPlJjjWMpwYuBwZTqUwr62/kWUYpSaVB0nEDghksudStxCUGLy2Yj5FnCOenKxOR++ytyTYkLX2CnS3AoF5IDWGUfiE9++yzC5d5+umnneUqv5AaDAaDwWAwGAwGQx8xSJddg4/SL6S33XZbP/tBRETJWBplojATVAresulnjuKwnfVoGr0BdqSWN7/gyKNn4KMxouW7icgi9toCYUMld3nYdzAk8aKLaPiUcnJi3pEvF6IUfEaw6YrH7LJFfT76zfvQZJYQ2NXJniuUYtHeYgHWQjNZkGg/lGiRqHaEUZYoekkDKW+92H5ozAuyqmmknJlcj3TP9dszxNCYL6y0hIYGgb6pRlwF0WQpWxE4zn5/i8vPTDpKzH1ynNO5S0qdaExos3w5rYzdcr/H0gSdsZQ1TK9/LCeEBc/zbUtmjCybmpGk/a8PpawSlnEogWLWq/fnmM2NhDlsuJkg+euyk97zcE6ZbGYUgddAmeOEzGjWVpgZRbOnMuWePFao4PoQE6Eci1g0l3ilW3hVrExRYn5ANssrV4PGOiXvs/lltf70gx3rN2T/JVMMs34CY0Rho7MFikz1SrCY0HZmfgbraKVcQmVfcBnt/BWVcClRAgfvbTXMxAv0k0gpQ1ey1J2KHCuKx68+HL5/9AdJT0yNVObdUBmlX0j33nvv4PdJklCtlg3i5557jubMmUN1xZXMYDAYDAaDwWAwGAaBhHqTsmuvo73DhEyNrrrqKjrrrLPoxz/+MW299dZERHT66afTjTfeSFdccQUddNBBXbXLpVnU33OsZpIynVgyBplPGTQc5VF0Y060iV+0mTlruBGrIo0jb8PRTTKDyFE8jqIrefihNtJf9P4iNOvvmnussm34w0Ki2AURXM1+34ta5ruh2YNXKL7eC3isDuhCQtFPjWnx9JtpRFLVnUXalogkRuhBO6pZvOf7JstSCY1fBEUF4GP9Fht+tKZXGFanf0o2goxhrdRMuo1QUXvUYA0SXM5FSrdoyJ8vrrPO0V7tGPBnGIJJheAhai7VbajrRQAZHawJIynTUeE8sQaJdWV42SXxLALW7IauizrMfYWlULyST0nu38yiun9l1T4wuDHgtRjTqzE85kRhSvmzf/wqjD/lWsVSLDF2UPd+SD83wvOQ1nYZJtI7r/y3YOzkS2RwyTUsm1GsOdSvm+ZIyvQppUQ6ncn1cdDuBVl5Jv85qayu3V9eYSrLeBmMskbUZRyLStm5/Yp7paDngt6Ov61MSx1/lvae1yJjSTI+lOwNLKElz1Nly3tRd1kwE0FSwTvA0H90/cR//fXX09KlS2mjjTaidjs7qYsXL6bNN9+cPvaxj9GPfvSjnnTSYDAYDAaDwWAwGHqBTg/+M/QOXTOkF110ES1YsIDWrFlD06ZlhdkPPvhgOuigg2jhwoW0YsUK2nfffas3DmwnQlhRoqzwsDCebpTdc7tT9CzB35VoV6H7K0aQQhEljKBB9CnRnHq5v4HATqI59yn7LMdMYZVjTKkGcW9lJioWKUyPr6fn7caduAfgqJxWSDsWifWjlOP70lCiiVqk3+kPFm5HzZCwnK7GrpHqMDjqHXRPVdjXWiNlhiBSqrFAjjYGWFdkWVBjhkwp9i0Pjs52NL0ZX5NwvJERddgpTe8zBbSjkomAjqABNlNlhQFy/IfTY4nZJYwQM631E4rPi9Mlb5P1rIHxg1kWzOTUWDM6OhzcFjMbMZ0d6mTxczLmFn7XjqHLZqbjFpy0vfsB6t9AC5bXTfF15Z3VdJ3Wumn4S1+BLKb0LzJfMXOI+kgGsltFc19Mly7LMIulOPi2W+E+RAFOvQzeP55PvXm5BKpm+/AxzO8H9ktzss5cj9PjDJkHDgqehyYbeL7bqVst35t5PnczjcKZX/JryXEXA7eBzz/C6PJnZkzB7TvEKhY+S/H5HHKvxSj7r2Qu4f07gbbEwTnSp0Jde8H4xO9jwLm/P0h69EJpSbu9QtdP/g8//DAdfvjhzssoY3h4mJYsWUI//elPJ9I3g8FgMBgMBoPBYOgpEmpP+H9D79A1Q9poNOjll19Wf1+7di21WhXrtBVoR0MRPmHVahAF5CC2wmbq2qESuf9cL220GfzegxN10up5FkRqvBqjgViCxsYV7ZNak7XjLVMfGa9/yMc1c+GEyCFqKwLwoo3osDdFtH0x1oShRv3QKa8HzodyfJS6uAxkOYJtcR1UadtlLys5N4qTcPw6RkalTNtFrKUfqQbmbpI0KROFr29jgWh6ziPreChkTPlcp62yrjI/R5WUkKn1R5kFj7CZ7AaclIyP1pF1zWvkumR4NKfOeuCexCwwszeeU7tSO7rMnNjeMBnMQHmIEy7sQx5FGkvv3lDgM9AaG5LvmiOjzjLiGM9jtaOwgJH6nqJxK2I6C5icdqojHJm5Vr5rjQ5piwfBLGYZraY3rurwPZ4azIJphO/V+WUGnR2C84S4JKeHtTltFFcRqO7JKutXfa7w3Goxu6eEO7KqwVTu08wKa7/nt4la7w4VZApwEQDlfu9sR/4Rnt+85at4gwzIEdpSbqcWumZIFyxYQN/4xjfo9ddf935bt24dffOb36RddtllQp0zGAwGg8FgMBgMhl4hofGk3Yn/b+gVumZITzrpJPrLv/xLWrBgAR199NG0/fbbU61Wo8cee4yuvPJKeuyxx+iSSy6ZUOckGhXTbGrR6LGwa1wvoGk3tOWCzGWRO2UXgRthLYe7Y4N4/c6GQJQX9VISFVMuR9FmlajtJfVIBxutknqYFVzhirRnojcrUZ8M28E2Oy1w1UV9JG5DcbUk8hlR7/e6y8ghu8Of81qeYk2M+ztG5SU6G9CDSu1C1vkyy1pCV5NfP7+cF6meAnX7spqVBWMwX8tSdH7gNOytkzYt+nSXRZY+5F2Zle36jGIj+L38HqjDJ9+xI+QwOAun7JHnfs3HhpNjQhrpWtiRlOe2sm7geb2nNr5VnaOiNwsxHSH351jb/Uahc3AAhdcgz1uYIRG7R6N7eYLZFS5TimMj61sn+O9Q/7ntItaSx13+3InOFLJEhNGtWPcwf0xRF8toKJlZ3JfG0FjaB5ddDrY15PoPTDr42CvzSSzbq6i+LHpDNIfc+bKKzrfQxdbTU+bckqvWu06X012ec/2Ge1qnRIZKqT7E1u0wi91K/+LF57qlh+6znHVSw0ymSXYZNwwWXc86ixcvpiuvvJJOOOEEOu6446TkSJIktNFGG9FXv/pVev/739+zjhoMBoPBYDAYDAbDxJBQpyca0MEHst8omFAY7IgjjqCPfexjtHr1anriiSdobGyMtt56a9p///1pk002mXjvMDqWMn95l12vhiZo9rqtsxiDtEnsplhu+fBvrIFJv2BjxuH0QklZMWom7ue0TmESYjMZHP1XIn/IsCETXVQjbbyjrkub1hYjz8bgOWk0R4NtTRbY0bIBTE0V50FhRFOI6yi4eRay6oHto+ZJmFLNQS/EjEq9Q9DqKM7CCI/JCemRNFe9goiwHBt2680v1+FrDralsDJ8bOqNUXWbmbPg1InCNqa7GimuSxrTwDLj4bFaHbiOWsBMau3l2cyi7AVxgtTrdhIB8990r6+kVZ6ZcBsF/SBFGCjMRNDmmB5ovovqCwbHLLPXfAMYkNM4AmuEitt1hE1C9pn/cjbFWGt8nkVWk89dc6igBi/5DGQVFN1fvLlDGdONSD+RXeXj1eAkHGB4pW+eJjGfCVF3+pfV7OTPDed77nc71eSGxp14CEg2jbvvg0IVTat3fkDvrJ0/dPANdyTM0NYw+0HJIAr3N3wf9Oviji83um6EiPQa5k47msN1x72/y/Fl8hLqvwoDnNsmXjeYWaMiCT8TEfnPS15GRJ/vzYlpSKcUJpyXMXPmTDrwwAN70ReDwWAwGAwGg8Fg6Cs6ibnkTiVM6IV07dq1dMstt9Brr71GnVw0vtVq0e9//3v6wQ9+QD/4wQ/KN9iGHHOJzoOLYf6zUkNTIlRaJLemrJdn9jjyBBHHzE1UcUtjLSc6AMfQwuhY2hZH3lqgOxjzGUhhbnn72C+tLhQwJ6WY0ZKoVrtzsND0IKxrqLMjYqBGFh5bT/si+r5mdDnHMRQii76TaViLktX95LZLOEer2r84U5NfT2V/oQ2PTZPrSd+OFinPHCY5sq9ck3zMcoyDts9l3IknC6p+Mg+NbceapcCYou4zdK3KMgpDWjaC7SwHrCv/xYyNsnAYVi5/zNeRMia1fcZrJrZ/yFip7plKXctgP6YIM6ohxIx68xJcg5kLr5u50cv7DMJz8A5c97gvWrZFN1lWquNwwTyG2ud8HzVXctHVQv9aqQ60mWbWYJ1oIhK9OP82VfgiuZaA5Qw9w2g1QrO24ve/StrRopq78Dk0pqpqRpkZxbaC9zo4bjzuPTZWy1LC5fJ1cBvx51nUq6rPODl411aNn7knYx5MesSQTq3n2D9mdP1C+m//9m/0l3/5l/TSSy/Jd0mSiJaUaLweqcFgMBgMBoPBYDBMFVgd0amFrl9Izz77bHr11VfppJNOomazSRdccAFdccUV9OKLL9I3vvENeuGFF+jhhx+u1GYt1UWqzlolIpRelIyj1zUlIoTL56M57Wr56xIJSplRiRQ7zqnlIjLI8KoxmFB7BdqJIgTdjYuijOCYKcw1tym1W3PrYBQMXT8nWdtXB9e9ejoeUZdW5rhKG8BwyecJRACziGNYr1JKd1PATniaYCX6HHXARtTDzAnXLfU0zbnjrOlShfng/lXQHxfWjh0EVKfIuvN7UGvcdFlBrQ3UbGZ64kDWBbNZ3eo8K4AzEXBbuO8xYE1TtQ6tXNOgt45dl6ngCnXUwmIBoyEsCmbM/Imh8n0oxqhg3VEEn0ZkvnPXNP4mrs/pZ/QQ8PpQgjHFLJUa3ME9rR5oYvmeU2/43yHwPorzPzKlZeo9+1k2kwytj5Ktpq+K+1CEaH1g1KNCtlwh+4rVCUIAVrOIuZeMjKb7vJLvDx8fmXNaYX1xyH0+v5yWBRiCVgvc75vvUaDVMu0nEiLqxAZShXYMvUHXTxn33HMPffKTn6TPf/7zdNppp1GtVqPtt9+eTj/9dLr//vtp4403nnDZF4PBYDAYDAaDwWDoJXpRh9TQO3TNkL7yyiu0yy67EBHRjBkz6O1vfzv99Kc/pX322Yfe8pa30OGHH07/5//8n0pt1oZYS8SRfNY4uUxQPpLFjrscCVJdzjiSJbVBS8Q1kG1R6pJJlIyXL1N7EyNm2B/UNSg6BydCJ3o6V0uabcN1FZXoGEfaRt3hkI/oq1FEiCB6/ZMoXrp+XovGZCG4hNYHlEbBjm/1gnqojWkb5N8cXddcapl5kd+b7hiPoqBmXY2NpbW6k72AuCiP70eD94fr1NXzY0TRjhboOxmiLZU6pJFlSrIuUk9VXLdzDn8KeyauxgW12/oBdn4UfWWBFjIPXrYDOmWP3URGRdhmXdvnOTdLTTmF9VbWd75rKetA22r/I9cHbs+rtcrXjuYIrXwug/aGsFQlqNFVtKMxF9tBANmNPAvH7F9pNo1PW4WanMzgdBL2R0jHIdzP+RpopNdPO52n8jpBrb4ob6M9yq60rktw1obrchuq2VmaOeOsoJSp17Sn421qWV5xPSC3xUzpcMhBF56PWFOaJAPif1DL2HafBUOMmjz7wbIyz4M7sbQNdUpj2UMekyiVFpR5D5jJWFvCRirXvnb/DNZh5udh3gYwpd69FbOXAm3LMxzcF1HvW6YWNfa9B0RldSQJJb0wNRrUNfIGRNcvpLNnz6bR0Wxi22abbeihhx6Sz9tuuy09/fTTE+udwWAwGAwGg8FgMPQQHWM4pxS6fiHdeeed6frrr6djjjmGiIje+c530o9//GP5/Ve/+hWNjIxUarMUa+StlEbyOAIOjodRjShRFhHkyORQFjHR2+g4y3oMbgOjeH6USVjMMY7mQaQG6+EBgrXUJMqJ34dZDE/bVCGPHxlTrpfYnLG+dBvSH3AWZJZocpzWMjRYwwwMmaY5ykOc5TQ3UrU+GOosdN2kxigUsTzB/pSoFxlrO8jsIHsOn9GVtAhBloC1VxX5YGkr1yZGej1d0AC0pDwGJVqPTrmha6LmMgGyqKb79FhNjGzn50Ceh+LHItMDa06lGQvN/VJZJI05qzAfoC5W1zMVZCE4DEF4GRzPbXTa9BiFgJ5RYRkmu0Zukf485C7Key/atgJnWV6uXYIJ6qT3x3YHGG7tuMCYajT92o1ValwSkVdXNfs+cB8oaLvofHp1SCtoOP3jPD7umhHtMvsm4HbbzKRN8vgTJ2Y4jt69IP9ZqeeZ1MJZB3JtAUsdY0bbkgHlZvv05OjA86Xvpg9jogstOjKlqPPEOSyUoSHjq8B/wqtD3MVcVlajO1EkA6FmDRq6fuL/u7/7O7rttttowYIF9Morr9BHPvIReuihh2jJkiX0+c9/nr7yla9ISq/BYDAYDAaDwWAwGAyIrhnSAw88kL74xS/SeeedRzNmzKB9992Xli5dSldeeSUREW266aZ0/vnn96yjBoPBYDAYDAaDwTBRWNmXqYWuX0iJiI477jj6zGc+Q/U05etrX/saHXroofS73/2OFi5cSJtuumml9tDESF2umwLqipmR35Y+QPPpvA64zVY4daQ+lDNhQqMhtvpelxr6jMQt50sB04WwLIiSN4/mDaGyL7KMYmLUen2a87kxfTRtK2BqxNuVVOO07e6J+95AUn6UNL9A+qKYABSl1Sbh9Jzg5wLTj7KF5fMpg17h7i5TYprTxs9ra31m4CLlLhSLfA2YairIzwPKeCsLSbWKGFZNdnpkqT4oqd2O1X+FyjvjK4NpkJgawe9EJPNhkQGNIoUIpfBqKVzeNcLbxP6KwVK8S+PLuKm7NUzDR5mA9IXrJvipgdxPHreYoluE/LYkVa5CeYXJBM4P3N98byW9EksxKSgybAqmC+J1gYtUMStU2mYZgCYpkPsizuF5GYBcJ9VKcXEf2jDPNVHKE1qXTXm4hEzaH/4bK3+i/dZIUzvboxN6RKwMz0hOm/cjx7OBJZ74/gLXmGYoFtxcAss23LmIOixZcNNUa5AiSxSQVihpyb1IV/UMnCAVtkg+Eyu9pkHSrkscV68c16Qi6VHKrpka9QpdzTa/+93vaNNNN6VarUb1ep1eeuklWrFiBd1+++3UaDTov/7X/0rve9/7et1Xg8FgMBgMBoPBYJgQrGzL1EKlF9KVK1fSqaeeSs8//7yk5B5yyCG0zz770IMPPijLPfDAA3TttdfSvffeS5tvvnnp9tngh9IAskSpNbOeEDiKyVFWNAfioLuU4UjLWHD5GKdoL1tku5boteGUCeLooRe9ZRv84ugmF4Mv5GYKCkUTEVEb2BQwytBMZ5C1DRZxhn3E8hsNYHaZBegA8+BsJ40014fSMirpORFjl4rMQ7/QgX7lIYZMdTd6jkxpTUqasOC/xIZLlLcIQaKzgTIeCbAPGCAsioJyiQOOZIfKDqENPBoQCQMCTGqZQuZi9pOuoxWxx4h2ZjymW88XlfuZDOimWCkTzaUo0OyoSlst93evdFGZcjcwJtXSLB2/LAKyk9n3WFIqXGaAWdsQs87nEEvZYCS+kLVPj0l+fCVtZtBclrVspoL0sYSpl1wLk5wsgtcgMlZZGZVsn2sB4yAinZ3xy25w2a/xv3kjonagpEoQFcq6eSymRsL10FQP912b62KspmaYVMNnnAKErm/NwLExPMkMltxDwsenzDOBx3wWZR8wqxlg9LQSMVobGmoRMz00CSpzH6wKvwRgeI739s8xLWxHl5GMKN4Wlp4JwGOLoVu9qMqiISHqSdkX40d7h9Iz7ne/+106/PDDqdls0kc/+lHaeuutaenSpfS3f/u39Nhjj9Fll11Gv//97+mll16iSy65hJ566in6whe+0M++GwwGg8FgMBgMBkMlJElnwv8beofSDOmll15K73nPe+iOO+6g6dOnExHR8uXL6ctf/jJ9+tOfpmXLlsmyxx9/PN1///10880300UXXVS6M5mGlEs7UPo5jcykDGo+2iM5+kp5Cnb+lnWQKUF2MM8gMbuSbkPaULRxYnfNhbO5LZ0k9CDMbRrRZ+ZW1ZZGNE7yNV8zWvka3D9Zz48GttaOa0Qb0zY422RgBNPXvvlDrr1+vDwQRuM9PckkQ2M+8uynjFWM7AFTKuvg7mNE32Ho40W3uRSHpi9sj07zVtGOqcYOejo/LlAfKnkgx4LHYdr/BJmlNKIq2msl6j1B3Wi+D8HmNeY7PVf1kcm/2SDTWKbIOLLtGjPKkOyGEgxQoa42baNIDxoKRGtzg6erHoLPXkPZN1wmRJgWODbIzqqsXTomW6GxiZknCoQZiGiqJZtBYXMGrWrWGCmHOYbSabwH6rxVUHKq08lldGjlUxL3mLZGx9fhEif1iBcErou6utLlYAL70YBrD/WwrDceHhnX4LdhfLEOVJ4xcvNBw7s/crk0VzvK8DSJci/KZSsEyvgMEvp8V/5ewPtSVp8ouujANcjXZXOYs7b4vh4fK9l9krM8inW83N+OZPnFmdNQhobHvsLck30f13tnOn+121k/gPWvdaEL1bIp+oukRym7xpH2CqWv8v/4j/+gj33sY/IySkR05JFHUpIktNdee3nL77fffvSf//mfvemlwWAwGAwGg8FgMPQAxpBOLZRmSF9++WXabLPNnO/e8pa3EBHRm970Jm/5adOm0dq1ayt1ps56hZrLFiWsQ4Si4SGoBXUVpiQaiVE0lpWRi5Jm2iaIhHOxYyxuXcV1F7VJvC1mRArcDbONBtg61HspzqjMgnUirqDSPWao6uk+DjhIq7E8scLOmiuq/M47NQHHPHVMg54Q+xJlmCvqjjKHSX152T5Hj5Woe1n33RhqqFeVPkAEGx2mA5F21jsz69/vYtwxCLsJzHmZqLPXb4V97wSyFJz1A8xpp+VG7VXmuUADFoNoeXlftTbguhRWlAIMgTCh4NDruce66wnLl2MlRO+EhexBT+1lmkTGe6YVTVl5mMPLsnW9AjJ6ohlPdZ34mci/r2RzRVgPqTHzVbRzvTwuVccqsuqdXF9a68ezUpqgq83uJS6rKb8zSwaf86Oyg88G/JcZPjgPXqYUZ+3kWCH13gK/TxbwXluGGS16PlPZTMzUSZF36W1vGHbaQFbaA99/Uka1CV4bRAGDaJxPZL52+y3bTPc3eg1ozCj87j0zdoGy7trBdXvwLDARmKnR1ELpF9IkSWhoyM09bTTS1IgSBhsGg8FgMBgMBoPBMGj0wtTI0DtMbpGpArAjq7CCUN8shLKRPS16XdROsG3WujaVfP0ydT+5P2NuW6h9zToIehDcdn57GFXEGlqATsvVIniOmUQe89dJmT9hRNvu56wvxRoVYR6Qyemm3uwEgFpCv16Yf1y0uqOqpg6BUezIcdJcSL1tRiKndXCl7YyWFDgr+iQnAsv/1tx25S/0r0wNxpKOvOgWGXIaliaBUZAynOLSOICoLdQdrYtTIUfmi7NEZFneo5JusNEotafxcd2L8fqP1U2VfoIbtKdHZTYTHXJD85MCZnZrdfe4avvajbO3p3lVNPnBdcGJ0hu/fXDcLAPRg8rfYVjCn5vVuZDb6MQ1cbJ8ZP7yap7CsWfNb9IZn9dGpq1P2+w+aK7VTUXGsgyacL6R3RQGjt3rc3Mj1ij1GFFFY9iBfa8HXJFL368mCUWMWeja4msJtbTyu8KUelUJchBdZ8GziGReKOfA6Ue6HWZfuT/t9F5c2AYMx3xWCLO7be5vwXzWwWoTkf3E44r6Y/GuVljXkJOzmpU2KfNeQuVKd5Rpx9ALVJqlH330Ubrjjjvk/7vvvpuIiH72s585399xxx30yCOP9KXDBoPBYDAYDAaDwdAtBq0h/fnPf04HHXQQbbbZZjQ8PEzveMc76Pjjj6dXXnnFWe7OO++kAw44gDbZZBMaGRmh//Jf/gudffbZtGHDBq/Nhx56iD70oQ/R5ptvTrNmzaL99tuP7rrrrgn1c7JQiYI677zz6LzzzvO+P/HEE73vkiShWq07hkEYSNRNsWtnieidzkgxa4N1mdK289EdyW+HdRkTyL8XB9LUyVPTZHrMFOtsQ3pGjBpiBFerO9p2mZQgWN+FGi0+zkjYgs4rpEvDGoEYxS7rktcriJaQmdIiTR4FIu8B/W0l5FkGaBujl6hxZadc1tQJGxphB+pD8ZSVNjCowtzxeqHabRCVxbqjKiNatm5cCaCWkD/j/kw1SOaDp0PsvywiykooDB5Dy4yIQXePVvTq6bXBrtyxeqlYj7QqZH9y8y/3qkj3hIwLO3XWAtdakZP4ZGtIZXt1l8ErU49Ru90jM5ot77L+2fdJ8N9E5Zm7jmRUuEwvEVEzdbhtpQwVspQN1H+CYypjeMj3d9D2FaG6B0fGawP0p8hE1evs3xBm6/h459eTffbq9Ba7w/YDrfV8TsqnUmpZBowEsmsS0GCiw3VIt1q2pi67TwtjGtgPZkaxLUHBVO9lBwT6i+7/VbXCoeWLxiy7pCdt8ALgPhW086eGxx57jHbffXdqNpt0zDHH0FZbbUX33HMPXXrppfSjH/2I7rnnHpoxYwatWbOG9ttvP5ozZw4tX76c3vKWt9Dq1avpc5/7HN111130gx/8QGSTjzzyCC1cuJCmT59Oxx57LM2aNYsuv/xyWrRoEa1evZr23nvvAe91HKVfSA877LB+9sNgMBgMBoPBYDAY+o5Bmhode+yxNDo6Svfeey/tsMMORER01FFH0c4770zHHXccXXHFFXTyySfT0qVLaeONN6YHHniA5s6dS0RERx99NC1fvpy++MUv0qpVq+gjH/kIEY2X4tywYQM98MADtM022xAR0SGHHELz58+nZcuW0YMPPtg1UTgZKP1CevXVV/ezHw6knmczHtWJtqFpIhQdpbCGVWofcmRojCNDHFV0o/OeHpR8DZb/O/cfo2HuNvMRfNVFFyOAqCso0FURBVwoOQLYwWikq78rw1B4Ws1IdLGfwPqNUrctPfbtVqpxjvRLnD/Tz9qy6nHJHYqEA4yKbhDdPRnREcyMAbOozNowM8u/s/YOdDdeH2J1PpXxqDoV1vXj2h5DJpq/T52aFTYd+xtaroglnkz4tYBLzEfeOWH32bAWW6vZjBrg/LK4TqelsZuKjjmmCcKaoOqcEdbGh8ZgR6nnii6e7Brrrc+a2BxbJnpkWKdwjquQ2YOYsMN7RXC9xZbo2fhaLe4Hs5GoUUTmrg7XueY4S+SzgNwfZCJZ34butKE5SJvLsnnJXWcMrp+hHmbuZDrP8HVTDxyLWoFmDY8vzg/BY1LwPDJZ4PHeLmD08h4awoyCXwA6GiMzKttEpjQ35tCzAMcOnwvPaZsdmEtk5EzU4Ti/P6iBZ5aSs7+y7I1020WO+Lk+eBlkRf1SfEdCc1q2D22nf/133x3MeB8dHaU777yT9tprL3kZZSxZsoSOO+44WrNmDX384x+nxx9/nD71qU/Jy2h+uS9+8Yu0Zs0a+shHPkLPP/883XzzzXTwwQfLyygR0aabbkpHHnkknXPOOXT//ffTe9/73knZx24wpUyNDAaDwWAwGAwGg6GvGFAd0WazSQ899BB10KiSiJ5//nkiGq9issUWW9Djjz9O06ZNiy5HRHTfffcREdFuu+3mLcsvoffdd5+9kJYFM4sS/VJqJToRFtBFIruG7rWqhpTbjjr6wrJtN7KP30dZAV63oHaY1780WtsZTevBDeeitajBQ7ffFjCPHDnkcFQX0SivPik79WJbigNjCFPd4S8GzaVTcyFG5J0qa0PIaKd/tWMpLDpfR3xt5PoxypFSZkLH12mMcL9Yw6ixm762TsBRYW1M1zmKHB6fXnO5675IT4yMFi6fzQuRcchjGdjiyUQ2X5XftsrCw1hU2QbRG+lOuKVRwh1RGFxxwC12Uy+9rQJm1sucUZw3Y9dp1X56WQWBfnvO7Mq1NChoekQi8p3CxdU9rEtrK3VwNXdOb3s54PHR9GmhccxzRZH7NGo3ewHuNx5XzSE2tGzZDCK1DudUhjiyh3+O3Q9EFysZDQ3ne9WpnR+DKrj7a/2IVRcoOo+4jsrSkq9trtUKnNTl2uQdCPctobBGloho9PXpREQ0lGqx1XuEUm0i36bnml1jzfdkjNWkRym74/vw3HPP0dve9jbv1+XLl9Py5cud7+r1Om299dbB1i666CIiIlq0aBE1m036sz/7s8LliIiefvppIiLaaqutvGW5X0888UTh3gwSU+qF1GAwGAwGg8FgMBj6i94xpJ1Oh5555hnv+1dffbV0G9dccw1dddVVNG/ePDryyCPV5VasWEE//OEP6T3veQ/99V//NRGROPPOnDnTW36jjTYiIqLXX3+9dF8GgSn1QiqRZ6jziTnn+QhWrM5gEBy1Ab1UKd2iVkexSuSR2yDQFXHUTFxrQVfVYufU8voVZF81nYh85sMa0XKVrtOnsEtRBhqP/QAYqhgyJilSK1SpnYiMi6rjC0H0b2lEv4XnFXQtqIXNfx5znQgz9lJjz1i3lC4Gesu8Vi8bX+DIrGQ6eDUXcVzmt6PUvUUIQ6/oKvPHObs+8HoY3LiTuU5xa5Zzn782ijRtctxBDwpMMDsyxljRqm6NUfYT+uVpWJU5mlmJ0FztabbQKV0+u2wJAp0wxzdc4A6twIvC5xbHuYTHt/RqJNp0z8Espacl5cwJPv8BNrMqA+fpQwP3ctH/JS7LpYEZF3bKzWol5vuWZhc13XHE/RlbN+J8Fraa60WmY6MB6xHp47+o5mqMGUVobHER5N6U10WD5rEuDNmAgNcY3xsidddl0VTbLZ4PJR3VkXGMLssu8xOosOAxtMr8kfmGxPtSBXzfRCdp7FsMUms6ov0OtVkH519n3UFlxSW92269XqctttjC+37jjTcutf7KlSvpiCOOoBkzZtCqVauCL5ZE4y+jZ5xxBs2ZM4f++Z//WRx2kyRx/ubB33F671TFlHohNRgMBoPBYDAYDIZ+IqFyBk1lsMUWW0jabFWce+65dOaZZ9Ls2bPpxhtvpAULFnjLtFotOuaYY+jKK6+kLbfcklavXu2k/c6aNYuIiNauXeuty9/Nnj27q/5NFqb0C6noQgui8kQUrBEZbFOYx2IWRK+T57tROj9HGF7PtbIg0iYRTHWBnCMmRBFrqRuaRNJSZllcgMEVWPqfhvDz8a/q+i7YL3ZgzLE5zHTzd/h5suuQoquu5/oZqTGK2lleQtyIue0mRoBrzvd59lOWTQ9DAm67ReckVOcvG9OuOyCiMX1cH9JeNwzrAXJR0qw+XneutXhN5hlTZFuZ3UCXVKwRiWygy34O1lEyCI15DDGjBfBqsda1c54yPbytPIvcpV5Sjr9sKxCZFeYz7J4r0Xyp7eduoxKAGZUuaHN44DuVFS4YRziv5Y8pMmhF9V37DXEshb9Yn7QMkyKuuui6y14RCtOX/x7dfbFmqLdu2r9G5JygEzxeczze+PdGkxcb/9yC21KoL8ymcj+wnqc27iS7JOI4TgrrqmkjszrVLedzftk6uMTWKZ6J0i9o2UfIIOfvDR289+J1ysvCsx8v5Tmv59oWVlnR4aLzbBmNejY3cnYSZArAHNBuo0u6rmnGsdhO74/CakL9VM4c8LIVIr4C4j+RfsZ1tXEoGvJchqPqjl8hY+CPGWNjY7R06VL65je/SVtuuSXddNNNNH/+fG+51157jT7ykY/QzTffTH/xF39BN910k6cV5ZfT0Esxfzdv3rw+7EXvMKVfSA0Gg8FgMBgMBoNhosDyKYNqt91u0yc+8QlatWoVzZ8/n2666SbacsstveVef/11ev/730933XUX7bvvvnT99dcHmc4FCxZQvV6n++67j5YtW+b8xg68e+yxR6U+TjbshdRgMBgMBoPBYDC8ofGTn/xk0F0gIqIzzjiDVq1aRbvuuivdcsstajrt//gf/4Puuusu+qu/+iv6zne+Q8PDAX8DIpozZw4tXryYvvOd79BZZ50ltUhffPFFuuqqq2jHHXekd7/73X3bn15gSr6QaqZBbEwRLN6M6WhQDFkzHkJjlWg/+HswWUIDG06dxdTYdG0iypVt4SLDsdI25Keh1EPFiT0bc04xhTQY3uc03URS59BQpRtoZiySpuKvopoEDUjorhlTcDmVJGB6w4ZDkhadpp21148bZDQ3Wj++rlLqgreYT+n17ODHuKyPe4wlFRBL8LBZTSuUshv+LN+n/efU3QSs8DuBsjBqmo1mViN90K9BbxteMXs3tUzvA5R0IaJgGqm3zCSjKEU2UJKG08ZkkaaSHp4CU9sklWqDbgJSlD4v14w2zwZSTxOl/AfPm9poyPofmrPd76QExJBrcuOVUChRGkNLCSxKIY4ZoGD6Hm6/tFlfn8DHjc2NgmmukgboGkXxsW+1XGemIkOYTqAkG7eF607MXCadi9N5vaOMuEz6AOaKEUMiHoc86tWUyC6MabwUSaUNTAfHtOE8pGwWYZr/5M6FCdzTMolTrCQQp76W2wYbdqlzQW5st0fT+YOf6dJlC821RGrF6cK+UZeMaaUNXo7722ZjS0hbj6W3YqmlwrKCMMcFDbvqyviru5+5dExsm5y+y/NMGfOqNwKefPJJuvDCC6lWq9GHP/xhuuGGG7xl5syZQzNnzqRrr72WhoeH6YADDqDrrrvOW27bbbel3XffnYiILr74Ytp9991p4cKFdMIJJ9DIyAhdfvnl9NJLLwXXnWqYki+kBoPBYDAYDAaDwfBGwu23306tVIx+yimnBJfZe++9ad999yUiotHRUS8Nl3HYYYfJC+kOO+xAd955J5122ml07rnnUr1ep1122YVWrlxJu+22Wx/2pLeYWi+kHDkaVQpnB0qeeBEVMK/QImtqxC0QIZeoe8c129BLeUAEPRTF5X4y8wRlOMQQqSiCmu+vZpQw5Jor4TYoCUS9FYSMcsbbTiOJIxzNc5lAiRXntsEsLx8Dj7FZF05NmCzIPnYguu2YboBJCjBJzI4L+8TmTsPjzCManuSjicKOKCYcXnkXhdlL8jFYMG4S4DhruG0m/LetZxzgNaWxlrKcwpgG94GvQY1lrYUj6R6blTtmUkaExywbODXc3wcBvO6RBU9yw6yIbQkZZuWX5zEYMqPIzC8UAzRgRj0TlNG0/8Pl3QwzVsSdX3lu6YylJiY8peRKEeH5b+B4wHFSgpXX4JVbKmBZY+fJOwdgMjNZYNYlgfsoQ1hFKmYyhMlpha8j/x4CJnKR/mmfyzCmqqlSgTFiZjQ0vj9DJUz3isxZfBYTjmvgWHhGV+nhZRabz12j0Qr2IT8+8XqtWrqnX/DGPcz7+XFZdL0xPJZamFWXxcyPP1mHn88wgwGyS3j5+lA68fEtPNeXTnvY7TfMRTzXc/mhbFvptRk5n7hvXgk2Beq9I2+amTLsY1ASS4yxyC0H4427yP20LZll8PyrzB1/7FiyZAktWbKk1LJnnnlmpbZ32mknuummm7rp1sAxtQo9GgwGg8FgMBgMBoPhTwZTiiFtrwtXARd2AMqp5L+riiIGNb9M9rm76KGznqcRctlXarqR8cIIeaDsi6bF8xjmCgwVArWQ3raQKUw1DMjSTCVgxB6/DzKQrJdgNpAjkSzHlZIVoD8aTdk4jLKHSsrgd15JjTgmwrLIvjOzyAxdRx+fuqYJmFH5QdF/58YxM0jIeGlt1xX9pNNPTQfE+1igE5oMaGMydNwzrTMwPYpmtDNWPP2jBqnBNYhK2vLLOczth6Y3DWaSEAV1s/m2O2P+d9q8rkXvvb6Ffmu7zKx0rx5myrR7k6PLEmbZZQd5vpyIRrIbaIyZ7Dsfvwr3QrwfjaYMyxCw5rzvrVwpp4Yyx3WUexayscIq5ZavtcbP/ci0VNePWS6QERPStDrbyLcN5XK086cyp4oelyhjaMs8u8QQ2raMe2b0BpQdwlkQqNVsj4JOvlQ2F2hDkXVTsidiTF4D9KcMZkjLaCB5TCTw7JDpZ8PbLDNf47ir1cPXmHxOwvcG1IHml0G2VeZhOG61RnGWH/pHeLraSdYwGwaLwT9xGQwGg8FgMBgMBoPhTxJTiiFF7YYUa45EndD9Uy8s77KrqN0LQnFbRf1MFlErjo55hdnTfRT2EiOqRSxNXnOoRE5lm9w2R+LSbbJmF1mmvAump8cQVzS3TVxec/Qc/xDXXxY6jvYYqgOuOOb5kdMinSHqPDEaSswY86HuRCKCXTKdeSa1qL+ZY1583JVhIDVdp/yuXasRN+12yq5kmltw3URdbcO93kMjSjvvg0SmX67QF+4/74/aOLOW6RLCKgc0+sAEaPp56UIDWJwSLL6f0YHnkJdTovv5ZTUWHrdZxABV0JiiU67KZMByeWRsgqslHRSEuU31up6raOCwZBkk7rkf1thoz217fLkQK6o52uI9rwnPCmOjuhOpln3AjGiWHVB32mbtqIztwFhCnaq2La1PQV0guKtyi60NrH1HJ1j3Wo26scL2ijSH/YI8p/GzAF9/JZgy1QUbPQwwe4uzANJ5pxbQp2ptil43ZXa145afE+Q8wbDhax/ZYK+f+AwZODbZsqBzx2cbz9/B/exc9zwlIhNa9voOLCfHUbbnZmLUG4O/FxsmD8aQGgwGg8FgMBgMBoNhIJhSDGknzcMf2vh1IvI1RUW17sYbQbdQN1pI4FwqkSuofxXtJ0SI6qwhwn6FIkIQwfddQDE/P77P+ehYpo3gjoFbpegCmH1xt1lFjyu1DlkDoNVw476VCLg2Rkadz5qmuF8QHWgd9a9w7HPRbmE2OBrNemdkjBVG0WNQI5F01LB2w+ihboXRmLZhvE1mo6CGqZzvSOQ8O34p4wbHopNqw0JMXB4h9rW9AbYlEdSUNWAGtAHuxcNpf8d8DbPMLyXZi8mEl/mRIouuB+p6ahpohQ1MwNWxJ8yw5haaG7uZi7HLBvHWMXvF01+jzjHAYhYxs169T632cN7pt6WwhWnmRLsd/j1jBtLfS/RL6lgW3456CmRW2q3wOAyNNcn2qUGnG3D/Kbje8rpLz1VXqWHcbIZZwGid8QI9cQv2PUmzVzizSJjIgM4TUZYpxf1tRNypuS4lXj8+y6WN22IUuQT3Glkf3WNfRkvNekcvSwPGgOfEzuvX0vkod/y0GVFjIHuBqvOwM7YlsyP9k577Rt0ds5LNwXMwbFOqJYxNy7YD/fPrMWt1r4uzZbyxKXWHRwNLG96omHpPYgaDwWAwGAwGg8Fg+JNALUmSyQ2BGQwGg8FgMBgMBoPBQMaQGgwGg8FgMBgMBoNhQLAXUoPBYDAYDAaDwWAwDAT2QmowGAwGg8FgMBgMhoHAXkgNBoPBYDAYDAaDwTAQ2AupwWAwGAwGg8FgMBgGAnshNRgMBoPBYDAYDAbDQGAvpAaDwWAwGAwGg8FgGAjshdRgMBgMBoPBYDAYDAOBvZAaDAaDwWAwGAwGg2Eg+P+h5+/ddu92WQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1000x400 with 11 Axes>"
       ]
@@ -10749,7 +4241,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1000x400 with 11 Axes>"
       ]
@@ -10759,7 +4251,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1000x400 with 11 Axes>"
       ]
diff --git a/simulations_1d/5_mi_vs_bandlimited_signal_space.ipynb b/simulations_1d/5_mi_vs_bandlimited_signal_space.ipynb
index 79134a5..b036d95 100644
--- a/simulations_1d/5_mi_vs_bandlimited_signal_space.ipynb
+++ b/simulations_1d/5_mi_vs_bandlimited_signal_space.ipynb
@@ -5,10 +5,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T03:02:49.975516Z",
-     "iopub.status.busy": "2024-10-14T03:02:49.975177Z",
-     "iopub.status.idle": "2024-10-14T03:02:57.032151Z",
-     "shell.execute_reply": "2024-10-14T03:02:57.030652Z"
+     "iopub.execute_input": "2024-10-14T14:05:31.442231Z",
+     "iopub.status.busy": "2024-10-14T14:05:31.441498Z",
+     "iopub.status.idle": "2024-10-14T14:05:36.166193Z",
+     "shell.execute_reply": "2024-10-14T14:05:36.165350Z"
     }
    },
    "outputs": [
@@ -16,10 +16,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-10-13 20:02:53.139945: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-13 20:02:54.634007: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-13 20:02:54.634385: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-13 20:02:54.634402: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+      "2024-10-14 07:05:33.582716: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-14 07:05:34.569141: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-14 07:05:34.569273: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-14 07:05:34.569287: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
      ]
     }
    ],
@@ -62,10 +62,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T03:02:57.037375Z",
-     "iopub.status.busy": "2024-10-14T03:02:57.036541Z",
-     "iopub.status.idle": "2024-10-14T03:03:47.407465Z",
-     "shell.execute_reply": "2024-10-14T03:03:47.406503Z"
+     "iopub.execute_input": "2024-10-14T14:05:36.171173Z",
+     "iopub.status.busy": "2024-10-14T14:05:36.170180Z",
+     "iopub.status.idle": "2024-10-14T14:10:41.183631Z",
+     "shell.execute_reply": "2024-10-14T14:10:41.182384Z"
     }
    },
    "outputs": [
@@ -80,21 +80,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss 0.08489868730756678\n",
-      "1999: 0.0000006\r"
+      "initial loss 0.08318112279708355\n",
+      "3999: 0.0000067\r"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████| 1/1 [00:43<00:00, 43.72s/it]\n"
+      "100%|██████████████████████████████████████████| 1/1 [04:56<00:00, 296.41s/it]\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "Array(1., dtype=float64)"
+       "Array(0.99774883, dtype=float64)"
       ]
      },
      "execution_count": 2,
@@ -103,7 +103,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1000x400 with 2 Axes>"
       ]
@@ -154,10 +154,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T03:03:47.411339Z",
-     "iopub.status.busy": "2024-10-14T03:03:47.410929Z",
-     "iopub.status.idle": "2024-10-14T03:04:04.660260Z",
-     "shell.execute_reply": "2024-10-14T03:04:04.659181Z"
+     "iopub.execute_input": "2024-10-14T14:10:41.191267Z",
+     "iopub.status.busy": "2024-10-14T14:10:41.189840Z",
+     "iopub.status.idle": "2024-10-14T14:11:08.708905Z",
+     "shell.execute_reply": "2024-10-14T14:11:08.708138Z"
     }
    },
    "outputs": [
@@ -165,7 +165,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating signals: 1067it [00:15, 70.62it/s] \n"
+      "Generating signals: 1065it [00:23, 45.21it/s]\n"
      ]
     },
     {
@@ -177,7 +177,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -220,10 +220,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T03:04:04.665072Z",
-     "iopub.status.busy": "2024-10-14T03:04:04.664690Z",
-     "iopub.status.idle": "2024-10-14T03:04:04.727457Z",
-     "shell.execute_reply": "2024-10-14T03:04:04.726472Z"
+     "iopub.execute_input": "2024-10-14T14:11:08.713263Z",
+     "iopub.status.busy": "2024-10-14T14:11:08.712524Z",
+     "iopub.status.idle": "2024-10-14T14:11:08.780426Z",
+     "shell.execute_reply": "2024-10-14T14:11:08.778750Z"
     }
    },
    "outputs": [],
@@ -319,10 +319,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T03:04:04.731629Z",
-     "iopub.status.busy": "2024-10-14T03:04:04.731294Z",
-     "iopub.status.idle": "2024-10-14T07:42:18.040461Z",
-     "shell.execute_reply": "2024-10-14T07:42:18.039020Z"
+     "iopub.execute_input": "2024-10-14T14:11:08.785325Z",
+     "iopub.status.busy": "2024-10-14T14:11:08.784856Z",
+     "iopub.status.idle": "2024-10-15T00:15:17.312490Z",
+     "shell.execute_reply": "2024-10-15T00:15:17.311094Z"
     }
    },
    "outputs": [
@@ -330,7 +330,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Replicates:   0%|                                       | 0/8 [00:00<?, ?it/s]\n",
+      "Replicates:   0%|                                      | 0/15 [00:00<?, ?it/s]\n",
       "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
      ]
     },
@@ -338,356 +338,442 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -5.167615489975782\n",
-      "2999: -5.9159663\r"
+      "initial loss -5.307553272857522\n",
+      "2999: -5.9249617\r"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-10-13 20:05:33.334479: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
+      "2024-10-14 07:16:32.928232: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
       "\n",
       "\n",
       "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:12<41:23, 12.48s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                            | 1/200 [00:32<1:47:24, 32.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:13<19:31,  5.92s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:35<49:07, 14.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:15<12:21,  3.77s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:37<30:32,  9.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:16<09:02,  2.77s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:40<21:48,  6.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:17<07:12,  2.22s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:43<17:05,  5.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:18<06:01,  1.86s/it]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:45<14:12,  4.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:19<05:17,  1.64s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:48<12:03,  3.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:21<04:51,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:50<10:53,  3.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:22<04:30,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:53<09:45,  3.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:23<04:19,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:55<09:10,  2.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:24<04:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:58<08:42,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:25<03:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [01:00<08:35,  2.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:27<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [01:03<08:16,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [01:05<07:53,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<03:52,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [01:07<07:25,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<03:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [01:10<07:49,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<03:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [01:13<07:53,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [01:15<07:20,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<03:52,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [01:17<07:00,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<03:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [01:19<06:51,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [01:21<06:46,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<03:49,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [01:23<06:38,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [01:26<06:27,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:41<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [01:28<06:20,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:42<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:30<06:17,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<03:44,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:32<06:17,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<03:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:34<06:07,  2.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:36<06:00,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:38<05:57,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<03:36,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:40<06:00,  2.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<03:34,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:42<05:54,  2.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:44<05:49,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:47<05:51,  2.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:49<05:58,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:55<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:51<05:56,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:53<05:56,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:55<05:54,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:59<03:23,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:58<05:51,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:18,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [02:00<05:47,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:14,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [02:02<05:48,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [02:04<06:01,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:22,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [02:07<06:04,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [02:09<05:58,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [02:11<05:58,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:07<03:13,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [02:14<06:08,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:08<03:08,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [02:16<06:11,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:10,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [02:19<06:10,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:11<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [02:21<06:06,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:12<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [02:24<06:01,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:14<03:05,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [02:26<06:06,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:15<03:04,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [02:29<06:05,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:16<03:05,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:31<06:11,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:17<03:02,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:34<06:09,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:18<02:59,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [02:36<06:06,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:20<02:56,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [02:39<06:10,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:21<02:56,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:42<06:10,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:22<02:54,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [02:44<06:07,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:23<02:52,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [02:46<05:54,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:25<02:54,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:49<05:51,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:26<02:55,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:51<05:49,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:27<02:53,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [02:54<05:47,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:28<02:51,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [02:57<05:52,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:30<02:48,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [02:59<05:44,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:31<02:49,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [03:01<05:37,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:32<02:49,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▊                    | 65/200 [03:04<05:37,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:33<02:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  33%|█████████▉                    | 66/200 [03:07<05:37,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:35<02:45,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████                    | 67/200 [03:09<05:28,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:36<02:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▏                   | 68/200 [03:11<05:04,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:37<02:40,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▎                   | 69/200 [03:13<04:42,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:38<02:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  35%|██████████▌                   | 70/200 [03:14<04:27,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:39<02:37,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▋                   | 71/200 [03:16<04:16,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:41<02:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▊                   | 72/200 [03:18<04:06,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:42<02:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▉                   | 73/200 [03:20<04:19,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:43<02:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  37%|███████████                   | 74/200 [03:22<04:04,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:44<02:36,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▎                  | 75/200 [03:25<04:25,  2.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:46<02:35,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▍                  | 76/200 [03:27<04:41,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:47<02:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▌                  | 77/200 [03:30<04:50,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:48<02:32,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  39%|███████████▋                  | 78/200 [03:32<04:56,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:49<02:31,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  40%|███████████▊                  | 79/200 [03:35<04:51,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████                  | 80/200 [01:51<02:27,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  40%|████████████                  | 80/200 [03:37<04:51,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████▏                 | 81/200 [01:52<02:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▏                 | 81/200 [03:40<04:53,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  41%|████████████▎                 | 82/200 [01:53<02:25,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  41%|████████████▎                 | 82/200 [03:42<04:45,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▍                 | 83/200 [01:55<02:29,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  42%|████████████▍                 | 83/200 [03:45<04:46,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▌                 | 84/200 [01:56<02:34,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  42%|████████████▌                 | 84/200 [03:48<04:59,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▊                 | 85/200 [01:57<02:27,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  42%|████████████▊                 | 85/200 [03:50<04:56,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  43%|████████████▉                 | 86/200 [01:58<02:25,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  43%|████████████▉                 | 86/200 [03:53<04:54,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  44%|█████████████                 | 87/200 [02:00<02:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████                 | 87/200 [03:55<04:56,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  44%|█████████████▏                | 88/200 [02:02<02:36,  1.39s/it]\n",
+      "training:  44%|█████████████▏                | 88/200 [03:58<04:53,  2.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████▎                | 89/200 [04:01<04:46,  2.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|█████████████▌                | 90/200 [04:03<04:43,  2.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████▋                | 91/200 [04:06<04:40,  2.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████▊                | 92/200 [04:08<04:45,  2.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████▉                | 93/200 [04:11<04:43,  2.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████                | 94/200 [04:14<04:34,  2.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████▎               | 95/200 [04:16<04:34,  2.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████▍               | 96/200 [04:19<04:42,  2.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████▌               | 97/200 [04:22<04:39,  2.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|██████████████▋               | 98/200 [04:24<04:20,  2.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████▊               | 99/200 [04:27<04:16,  2.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████▌              | 100/200 [04:29<04:13,  2.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|██████████████▋              | 101/200 [04:32<04:25,  2.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|██████████████▊              | 102/200 [04:35<04:42,  2.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████▉              | 103/200 [04:38<04:25,  2.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|███████████████              | 104/200 [04:43<04:21,  2.72s/it]\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:03,  7.10it/s]\n",
+      "Evaluating NLL: 1it [00:11, 11.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:11,  2.19it/s]\n",
       "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
       "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:03,  3.15s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:09,  9.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 31it [00:03, 13.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 28it [00:10,  3.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 66it [00:03, 32.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 51it [00:10,  8.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 113it [00:03, 66.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:10, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 156it [00:03, 102.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 97it [00:10, 22.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 193it [00:03, 131.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 118it [00:10, 31.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 242it [00:03, 183.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 138it [00:10, 42.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 282it [00:03, 215.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 159it [00:10, 57.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 320it [00:04, 240.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 180it [00:10, 74.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 365it [00:04, 282.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 207it [00:10, 100.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 411it [00:04, 323.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 233it [00:10, 125.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 457it [00:04, 355.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 260it [00:11, 152.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 499it [00:04, 369.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 285it [00:11, 172.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 541it [00:04, 371.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 310it [00:11, 174.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 582it [00:04, 372.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 336it [00:11, 193.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 622it [00:04, 357.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 362it [00:11, 209.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 665it [00:04, 375.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 387it [00:11, 209.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 707it [00:04, 385.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 411it [00:11, 209.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 752it [00:05, 402.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 434it [00:11, 211.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:05, 154.32it/s]\n",
+      "Evaluating NLL: 466it [00:11, 239.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 494it [00:12, 249.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 520it [00:12, 230.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 544it [00:12, 227.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 568it [00:12, 207.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 594it [00:12, 220.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 620it [00:12, 224.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 643it [00:12, 213.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 667it [00:12, 218.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 693it [00:12, 226.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 727it [00:13, 256.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 753it [00:13, 240.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:13, 59.72it/s] \n",
       "\n",
       "\n",
       "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:52,  1.8\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<01:10,  1.3\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:16,  5.9\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   8%| | 8/100 [00:00<00:07, 11.6\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:01<00:05, 17.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:01<00:03, 24.\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:01, 43.\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:01<00:02, 28.\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 71.\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▎| 27/100 [00:01<00:02, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 99.\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:01<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:00<00:00, 124\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:01<00:01, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 93\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:01<00:01, 42.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  49%|▍| 49/100 [00:01<00:01, 35.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:02<00:01, 32.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:02<00:01, 35.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:02<00:00, 39.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:02<00:00, 46.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  76%|▊| 76/100 [00:02<00:00, 44.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:02<00:00, 47.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:02<00:00, 43.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  92%|▉| 92/100 [00:03<00:00, 36.\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:03<00:00, 31\n",
       "\n",
       "\n",
       "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 168/8000 [00:05<04:07, 31.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 161/8000 [00:06<05:14, 24.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 346/8000 [00:06<02:09, 59.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 330/8000 [00:08<02:49, 45.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 507/8000 [00:07<01:36, 77.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 510/8000 [00:09<02:00, 61.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 681/8000 [00:09<01:17, 94.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 689/8000 [00:11<01:39, 73.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 848/8000 [00:10<01:10, 100.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 862/8000 [00:13<01:26, 82.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1006/8000 [00:11<01:03, 109.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██              | 1035/8000 [00:13<00:58, 119.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1178/8000 [00:12<00:57, 119.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍             | 1210/8000 [00:15<01:03, 106.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1357/8000 [00:14<00:52, 126.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▋             | 1372/8000 [00:17<01:03, 105.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1523/8000 [00:15<00:49, 132.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███             | 1529/8000 [00:18<01:03, 101.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1709/8000 [00:16<00:46, 134.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍            | 1700/8000 [00:20<01:02, 100.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊            | 1879/8000 [00:17<00:45, 135.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███▊            | 1883/8000 [00:22<01:00, 100.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2048/8000 [00:18<00:42, 139.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████            | 2060/8000 [00:24<00:58, 101.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2229/8000 [00:20<00:42, 137.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▍           | 2225/8000 [00:25<00:57, 101.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2406/8000 [00:21<00:40, 136.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2412/8000 [00:27<00:58, 96.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2579/8000 [00:23<00:41, 130.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2593/8000 [00:29<00:55, 97.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2755/8000 [00:24<00:40, 129.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▌          | 2762/8000 [00:29<00:40, 130.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2925/8000 [00:24<00:28, 175.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▉          | 2940/8000 [00:31<00:40, 123.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3107/8000 [00:26<00:31, 157.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▎         | 3126/8000 [00:33<00:41, 117.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3289/8000 [00:26<00:21, 215.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▌         | 3309/8000 [00:33<00:29, 158.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3460/8000 [00:27<00:24, 184.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████▉         | 3488/8000 [00:33<00:21, 208.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3628/8000 [00:27<00:18, 241.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▎        | 3664/8000 [00:35<00:26, 163.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3794/8000 [00:27<00:14, 295.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████▋        | 3826/8000 [00:35<00:20, 203.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3975/8000 [00:28<00:10, 382.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████        | 4004/8000 [00:35<00:15, 262.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4142/8000 [00:28<00:08, 472.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▎       | 4159/8000 [00:37<00:21, 176.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4305/8000 [00:29<00:13, 267.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4331/8000 [00:39<00:25, 145.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4490/8000 [00:30<00:16, 210.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████       | 4515/8000 [00:40<00:26, 129.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4662/8000 [00:30<00:11, 280.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▍      | 4701/8000 [00:41<00:18, 175.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4833/8000 [00:31<00:08, 363.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▊      | 4876/8000 [00:41<00:14, 216.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5011/8000 [00:31<00:06, 451.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████      | 5054/8000 [00:41<00:11, 265.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5180/8000 [00:31<00:05, 562.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▍     | 5215/8000 [00:42<00:08, 320.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5366/8000 [00:31<00:03, 682.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▊     | 5378/8000 [00:43<00:13, 187.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5535/8000 [00:31<00:03, 784.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████     | 5561/8000 [00:44<00:10, 233.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5702/8000 [00:31<00:02, 829.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▍    | 5732/8000 [00:44<00:08, 280.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▊    | 5876/8000 [00:31<00:02, 851.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5903/8000 [00:44<00:06, 323.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6040/8000 [00:33<00:05, 327.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6064/8000 [00:45<00:05, 366.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6212/8000 [00:33<00:04, 424.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████████████▍   | 6243/8000 [00:45<00:04, 422.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6380/8000 [00:33<00:03, 520.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████▊   | 6422/8000 [00:45<00:03, 465.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6551/8000 [00:33<00:02, 596.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▏  | 6590/8000 [00:47<00:06, 223.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6729/8000 [00:33<00:01, 676.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████▌  | 6770/8000 [00:47<00:04, 281.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6906/8000 [00:34<00:01, 777.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▉  | 6938/8000 [00:47<00:02, 354.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7090/8000 [00:35<00:02, 328.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████▏ | 7111/8000 [00:48<00:02, 404.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7262/8000 [00:35<00:01, 412.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████▌ | 7275/8000 [00:49<00:03, 211.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7441/8000 [00:35<00:01, 524.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|██████████████▉ | 7444/8000 [00:50<00:02, 265.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7616/8000 [00:36<00:01, 285.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|███████████████▏| 7616/8000 [00:50<00:01, 309.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▌| 7801/8000 [00:37<00:00, 373.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▌| 7799/8000 [00:50<00:00, 364.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7965/8000 [00:37<00:00, 460.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|███████████████▉| 7976/8000 [00:50<00:00, 409.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8139it [00:37, 218.20it/s]\n"
+      "Generating signals: 8145it [00:51, 158.68it/s]\n"
      ]
     },
     {
@@ -705,986 +791,972 @@
       "\n",
       "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:08<28:11,  8.50s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   1%|▎                              | 2/200 [00:09<14:11,  4.30s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   2%|▍                              | 3/200 [00:11<09:32,  2.91s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<28:02,  8.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:26,  2.28s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:10<16:21,  4.96s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:14,  1.92s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:13<12:36,  3.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:30,  1.71s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:16<10:56,  3.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:02,  1.57s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:18<10:03,  3.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<04:43,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:21<09:08,  2.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:29,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:24<09:16,  2.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:19,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:26<08:50,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:29<08:33,  2.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:31<08:23,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:34<08:13,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:36<08:11,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<03:53,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:39<08:01,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<03:49,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:41<07:53,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<03:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:44<07:43,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<03:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:46<07:45,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<03:44,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:49<07:47,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:44,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:51<07:41,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:54<07:37,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:34<03:36,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:57<08:09,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [01:00<08:07,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:34,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [01:02<08:06,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:38<03:30,  1.20s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [01:05<08:12,  2.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:31,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [01:08<07:57,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:33,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:11<07:52,  2.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:42<03:31,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:14<08:02,  2.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:43<03:27,  1.21s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:16<07:58,  2.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:44<03:27,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:19<08:06,  2.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:45<03:26,  1.22s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:23<08:40,  3.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:47<03:27,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:26<09:01,  3.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:48<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:29<08:23,  2.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:49<03:29,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:31<07:55,  2.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:51<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:34<07:21,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:52<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:36<07:16,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:53<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:38<06:58,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:54<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:41<06:50,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:56<03:25,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:43<06:48,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:57<03:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:46<06:35,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:58<03:22,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:48<06:45,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [00:59<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:51<06:42,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:01<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:53<06:41,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:02<03:21,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:56<06:51,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:03<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:59<06:43,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:05<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [02:01<06:40,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:06<03:17,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [02:04<06:34,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:07<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [02:06<06:35,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:08<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [02:09<06:30,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:10<03:13,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [02:11<06:28,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:11<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [02:14<06:30,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:12<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [02:17<06:22,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:14<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [02:19<06:24,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:15<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:21<06:06,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:16<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:24<06:08,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:19<03:24,  1.42s/it]\n",
+      "training:  27%|████████                      | 54/200 [02:27<06:08,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  28%|████████▎                     | 55/200 [02:29<06:10,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:32<06:10,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.20it/s]\n",
-      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
-      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "training:  28%|████████▌                     | 57/200 [02:34<05:59,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  29%|████████▋                     | 58/200 [02:37<06:00,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:39<06:03,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:42<05:59,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 32.67it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [02:45<06:01,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 70it [00:01, 68.09it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [02:47<05:52,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 109it [00:01, 113.61it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [02:50<05:48,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 148it [00:01, 159.79it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [02:53<06:01,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 186it [00:01, 201.55it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▊                    | 65/200 [02:55<05:51,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 227it [00:02, 245.63it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████▉                    | 66/200 [02:57<05:39,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 266it [00:02, 278.29it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████                    | 67/200 [03:00<05:31,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 304it [00:02, 302.90it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▏                   | 68/200 [03:02<05:31,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 349it [00:02, 339.92it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▎                   | 69/200 [03:05<05:36,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 390it [00:02, 355.22it/s]\u001b[A\u001b[A\n",
+      "training:  35%|██████████▌                   | 70/200 [03:08<05:38,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 436it [00:02, 383.38it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▋                   | 71/200 [03:10<05:25,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 482it [00:02, 404.21it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▊                   | 72/200 [03:13<05:26,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 525it [00:02, 392.80it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▉                   | 73/200 [03:15<05:27,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 567it [00:02, 398.68it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████                   | 74/200 [03:18<05:27,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 609it [00:02, 404.26it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▎                  | 75/200 [03:20<05:12,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 651it [00:03, 408.22it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▍                  | 76/200 [03:23<05:16,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 693it [00:03, 396.38it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▌                  | 77/200 [03:25<05:08,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 734it [00:03, 381.38it/s]\u001b[A\u001b[A\n",
+      "training:  39%|███████████▋                  | 78/200 [03:28<05:04,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 231.39it/s]\n",
+      "training:  40%|███████████▊                  | 79/200 [03:30<05:03,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  40%|████████████                  | 80/200 [03:33<04:58,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▏                 | 81/200 [03:35<04:53,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00<00:00, 201\u001b[A\u001b[A\n",
+      "training:  41%|████████████▎                 | 82/200 [03:38<04:49,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 182\u001b[A\u001b[A\n",
+      "training:  42%|████████████▍                 | 83/200 [03:41<05:05,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00<00:00, 188\u001b[A\u001b[A\n",
+      "training:  42%|████████████▌                 | 84/200 [03:43<05:02,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "training:  42%|████████████▊                 | 85/200 [03:46<04:55,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [06:19<18:59, 379.94s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.837768013014178\n",
-      "2999: -6.2277235\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "training:  43%|████████████▉                 | 86/200 [03:48<04:58,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  44%|█████████████                 | 87/200 [03:51<04:56,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▏                | 88/200 [03:54<04:58,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:12<40:52, 12.33s/it]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▎                | 89/200 [03:56<04:51,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:13<19:15,  5.84s/it]\u001b[A\u001b[A\n",
+      "training:  45%|█████████████▌                | 90/200 [03:59<04:52,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:14<12:12,  3.72s/it]\u001b[A\u001b[A\n",
+      "training:  46%|█████████████▋                | 91/200 [04:02<04:49,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:16<09:02,  2.77s/it]\u001b[A\u001b[A\n",
+      "training:  46%|█████████████▊                | 92/200 [04:05<04:59,  2.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:17<07:09,  2.20s/it]\u001b[A\u001b[A\n",
+      "training:  46%|█████████████▉                | 93/200 [04:07<04:49,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:18<06:05,  1.88s/it]\u001b[A\u001b[A\n",
+      "training:  47%|██████████████                | 94/200 [04:10<04:43,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:19<05:27,  1.70s/it]\u001b[A\u001b[A\n",
+      "training:  48%|██████████████▎               | 95/200 [04:13<04:49,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:21<04:59,  1.56s/it]\u001b[A\u001b[A\n",
+      "training:  48%|██████████████▍               | 96/200 [04:15<04:37,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:22<04:41,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  48%|██████████████▌               | 97/200 [04:18<04:35,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:23<04:23,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  49%|██████████████▋               | 98/200 [04:21<04:30,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:24<04:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  50%|██████████████▊               | 99/200 [04:23<04:25,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:26<04:03,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  50%|██████████████▌              | 100/200 [04:26<04:35,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:27<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  50%|██████████████▋              | 101/200 [04:29<04:31,  2.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<03:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  51%|██████████████▊              | 102/200 [04:32<04:21,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  52%|██████████████▉              | 103/200 [04:34<04:21,  2.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:31<03:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  52%|███████████████              | 104/200 [04:37<04:15,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<03:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  52%|███████████████▏             | 105/200 [04:39<04:09,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<03:48,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  53%|███████████████▎             | 106/200 [04:42<04:15,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<03:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  54%|███████████████▌             | 107/200 [04:45<04:13,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<03:48,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  54%|███████████████▋             | 108/200 [04:48<04:13,  2.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<03:50,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  55%|███████████████▊             | 109/200 [04:51<04:10,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<03:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  55%|███████████████▉             | 110/200 [04:53<04:05,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<03:43,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  56%|████████████████             | 111/200 [04:56<04:02,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:41<03:41,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  56%|████████████████▏            | 112/200 [04:59<04:03,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:42<03:39,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  56%|████████████████▍            | 113/200 [05:02<03:57,  2.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  57%|████████████████▌            | 114/200 [05:04<03:52,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  57%|████████████████▋            | 115/200 [05:08<04:14,  2.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  58%|████████████████▊            | 116/200 [05:11<04:05,  2.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  58%|████████████████▉            | 117/200 [05:13<03:56,  2.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<03:33,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  59%|█████████████████            | 118/200 [05:16<03:49,  2.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:49<03:28,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  60%|█████████████████▎           | 119/200 [05:18<03:40,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<03:27,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  60%|█████████████████▍           | 120/200 [05:21<03:33,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<03:28,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  60%|█████████████████▌           | 121/200 [05:24<03:29,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  61%|█████████████████▋           | 122/200 [05:26<03:26,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:55<03:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  62%|█████████████████▊           | 123/200 [05:29<03:22,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:29,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  62%|█████████████████▉           | 124/200 [05:31<03:14,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  62%|██████████████████▏          | 125/200 [05:34<03:16,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:59<03:32,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  63%|██████████████████▎          | 126/200 [05:39<03:19,  2.69s/it]\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:33,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 12.64it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:22,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 27it [00:02, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:13,  1.26s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 43it [00:02, 26.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:11<03:13,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 64it [00:02, 44.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:12,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 89it [00:02, 70.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:14<03:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 109it [00:02, 88.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:15<03:16,  1.32s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 131it [00:02, 110.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:17<03:26,  1.40s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 155it [00:03, 136.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:18<03:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 177it [00:03, 153.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:19<03:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 199it [00:03, 164.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:21<03:07,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 220it [00:03, 165.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:22<03:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 244it [00:03, 184.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:23<03:00,  1.26s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 272it [00:03, 208.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:24<02:55,  1.23s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 295it [00:03, 211.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:26<02:53,  1.23s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 321it [00:03, 223.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:27<02:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 345it [00:03, 209.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:28<02:56,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 376it [00:03, 236.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:29<02:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 401it [00:04, 225.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:31<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 425it [00:04, 216.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:32<02:52,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 448it [00:04, 200.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:33<02:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 471it [00:04, 207.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:35<02:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 497it [00:04, 220.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:36<02:50,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 521it [00:04, 223.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:37<02:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 544it [00:04, 216.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:38<02:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 566it [00:04, 203.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:40<02:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 587it [00:05, 205.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:41<02:41,  1.25s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 610it [00:05, 211.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:42<02:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 632it [00:05, 193.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:43<02:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 652it [00:05, 189.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:45<02:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 672it [00:05, 183.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:46<02:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 699it [00:05, 205.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:47<02:40,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 720it [00:05, 200.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:49<02:37,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 741it [00:05, 195.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:50<02:36,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 770it [00:05, 217.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:51<02:33,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:06, 131.89it/s]\n",
       "\n",
-      "training:  40%|████████████                  | 80/200 [01:52<02:31,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████▏                 | 81/200 [01:55<02:49,  1.43s/it]\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Bootstrapping to compute confidence interval:   5%| | 5/100 [00:00<00:02, 44.5\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%| | 10/100 [00:00<00:02, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:04,  6.24it/s]\n",
-      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
-      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:01, 52.\u001b[A\u001b[A\n",
       "\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:01, 60.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:01, 57.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:03,  3.56s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:01, 59.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 45it [00:03, 17.22it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 75.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 84it [00:03, 36.64it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:00<00:00, 79.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 125it [00:03, 62.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|▋| 70/100 [00:01<00:00, 72.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 162it [00:03, 90.07it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:01<00:00, 61.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 198it [00:04, 120.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|▊| 85/100 [00:01<00:00, 53.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 242it [00:04, 164.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|▉| 91/100 [00:01<00:00, 49.\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 285it [00:04, 207.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 56\n",
       "\n",
-      "Evaluating NLL: 327it [00:04, 247.66it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|██████                  | 1/4 [18:52<56:36, 1132.16s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.894869165834319\n",
+      "2999: -6.2202522\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Evaluating NLL: 374it [00:04, 295.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 417it [00:04, 315.80it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 458it [00:04, 324.22it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                            | 1/200 [00:33<1:51:44, 33.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 497it [00:04, 330.77it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:36<50:27, 15.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 536it [00:04, 344.08it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:38<30:35,  9.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 584it [00:05, 379.76it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:40<21:23,  6.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 632it [00:05, 407.04it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:42<16:12,  4.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 677it [00:05, 417.47it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:45<13:17,  4.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 722it [00:05, 426.24it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:47<11:19,  3.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:05, 143.33it/s]\n",
+      "training:   4%|█▏                             | 8/200 [00:49<09:55,  3.10s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:   4%|█▍                             | 9/200 [00:52<09:09,  2.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:54<08:36,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:12,  8.2\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:56<08:12,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:00, 121\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:59<08:00,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 154\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [01:01<07:55,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  60%|▌| 60/100 [00:00<00:00, 141\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [01:04<07:42,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "training:   8%|██▎                           | 15/200 [01:06<07:36,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:   8%|██▍                           | 16/200 [01:08<07:26,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [01:11<07:14,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 58/8000 [00:04<11:05, 11.94it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [01:13<07:14,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 106/8000 [00:06<06:53, 19.09it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [01:15<07:06,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 160/8000 [00:07<04:58, 26.22it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [01:18<07:13,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 204/8000 [00:08<04:18, 30.21it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [01:20<07:20,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 250/8000 [00:09<03:55, 32.90it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [01:23<07:07,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 302/8000 [00:10<03:30, 36.64it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [01:25<07:03,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 363/8000 [00:12<03:19, 38.32it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [01:27<06:58,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 410/8000 [00:13<03:14, 38.96it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:30<06:55,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 464/8000 [00:13<02:17, 54.77it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:32<06:55,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 503/8000 [00:14<02:39, 47.06it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:35<06:49,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 559/8000 [00:15<02:39, 46.64it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:37<06:42,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 606/8000 [00:15<01:59, 61.91it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:39<06:40,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 653/8000 [00:16<01:29, 81.78it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:42<06:40,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 696/8000 [00:17<01:55, 63.00it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:44<06:36,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 735/8000 [00:17<01:31, 79.46it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:46<06:33,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 791/8000 [00:17<01:05, 110.65it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:48<06:20,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 851/8000 [00:18<01:34, 75.67it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:51<06:21,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 895/8000 [00:18<01:14, 95.33it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:53<06:20,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 947/8000 [00:18<00:56, 125.54it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:55<06:23,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 993/8000 [00:19<00:46, 149.72it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:58<06:26,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1049/8000 [00:19<00:37, 187.19it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [02:00<06:18,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1095/8000 [00:19<00:34, 202.25it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [02:03<06:33,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1149/8000 [00:19<00:28, 240.40it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [02:05<06:29,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1209/8000 [00:19<00:24, 280.98it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [02:08<06:29,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1265/8000 [00:19<00:21, 314.60it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [02:10<06:28,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋             | 1316/8000 [00:21<01:00, 111.34it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [02:13<06:17,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1368/8000 [00:21<00:47, 138.62it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [02:15<06:11,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1420/8000 [00:21<00:38, 170.02it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [02:17<06:08,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1473/8000 [00:22<01:07, 97.21it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [02:20<06:05,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1518/8000 [00:23<01:32, 69.77it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [02:22<06:05,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1578/8000 [00:23<01:06, 96.94it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [02:25<06:12,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1637/8000 [00:24<01:25, 74.73it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [02:27<06:06,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1692/8000 [00:26<01:37, 64.48it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [02:29<06:08,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1751/8000 [00:26<01:10, 88.41it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [02:32<06:02,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1793/8000 [00:27<01:36, 64.57it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:34<06:07,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1845/8000 [00:27<01:12, 85.23it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:37<05:56,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1888/8000 [00:27<00:58, 105.35it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [02:39<05:56,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1937/8000 [00:28<01:20, 75.38it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [02:41<05:45,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1983/8000 [00:28<01:04, 92.57it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:44<05:53,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2034/8000 [00:29<00:49, 121.75it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [02:47<05:49,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2095/8000 [00:29<00:36, 161.69it/s]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [02:49<06:03,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2147/8000 [00:29<00:30, 190.79it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:52<05:57,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▍           | 2192/8000 [00:29<00:27, 210.43it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:54<05:46,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2238/8000 [00:29<00:24, 233.76it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [02:57<05:40,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2296/8000 [00:29<00:20, 280.95it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [02:59<05:42,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2338/8000 [00:29<00:19, 286.80it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [03:04<06:41,  2.93s/it]\n",
       "\n",
-      "Generating signals:  30%|█████            | 2404/8000 [00:31<01:08, 82.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2451/8000 [00:31<00:54, 101.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2504/8000 [00:32<00:41, 131.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:14, 14.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2551/8000 [00:32<00:34, 160.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 9it [00:14,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2591/8000 [00:33<01:04, 83.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 17it [00:14,  1.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2639/8000 [00:33<00:50, 106.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:14,  1.70it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2688/8000 [00:33<00:39, 132.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2738/8000 [00:34<01:09, 75.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2792/8000 [00:35<00:51, 101.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:12, 12.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2828/8000 [00:36<01:12, 71.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 24it [00:12,  2.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2879/8000 [00:36<00:53, 95.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 53it [00:12,  7.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2924/8000 [00:36<00:42, 120.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 81it [00:12, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2969/8000 [00:36<00:34, 147.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 114it [00:12, 23.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3023/8000 [00:36<00:27, 183.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 143it [00:12, 34.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3067/8000 [00:36<00:23, 210.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 171it [00:12, 47.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3120/8000 [00:36<00:19, 247.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 197it [00:13, 62.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3170/8000 [00:37<00:17, 274.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 222it [00:13, 73.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3224/8000 [00:37<00:16, 290.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 244it [00:13, 89.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3271/8000 [00:37<00:16, 292.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 269it [00:13, 111.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3325/8000 [00:37<00:16, 284.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 301it [00:13, 145.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3375/8000 [00:37<00:16, 284.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 330it [00:13, 171.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3432/8000 [00:40<01:19, 57.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 357it [00:13, 190.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3482/8000 [00:40<01:01, 73.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 383it [00:13, 196.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3524/8000 [00:40<00:48, 91.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 408it [00:14, 187.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3582/8000 [00:40<00:35, 123.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 436it [00:14, 208.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3633/8000 [00:41<00:28, 150.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 463it [00:14, 222.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3677/8000 [00:41<00:24, 176.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 488it [00:14, 218.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3735/8000 [00:41<00:20, 210.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 512it [00:14, 218.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3779/8000 [00:41<00:18, 229.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 542it [00:14, 240.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3827/8000 [00:41<00:16, 254.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 568it [00:14, 245.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3872/8000 [00:41<00:15, 268.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 598it [00:14, 259.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3916/8000 [00:41<00:14, 283.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 626it [00:14, 263.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3967/8000 [00:42<00:12, 310.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 653it [00:15, 262.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4013/8000 [00:42<00:12, 320.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 680it [00:15, 239.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4061/8000 [00:42<00:12, 320.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 713it [00:15, 262.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4110/8000 [00:42<00:11, 331.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 740it [00:15, 236.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4168/8000 [00:42<00:11, 331.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 765it [00:15, 224.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4209/8000 [00:43<00:33, 112.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:15, 51.06it/s] \n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4256/8000 [00:43<00:27, 137.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4306/8000 [00:43<00:21, 169.22it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4350/8000 [00:44<00:18, 197.06it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:17,  5.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4409/8000 [00:44<00:14, 250.18it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:06, 15.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4448/8000 [00:44<00:14, 245.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%| | 9/100 [00:00<00:03, 26.6\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4512/8000 [00:45<00:32, 107.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:02, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4564/8000 [00:45<00:25, 136.21it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  19%|▏| 19/100 [00:00<00:02, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4621/8000 [00:45<00:20, 167.15it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:01, 43.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4667/8000 [00:46<00:17, 188.69it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:01, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4709/8000 [00:46<00:15, 206.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:01, 43.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4774/8000 [00:47<00:31, 103.04it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:01<00:01, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4827/8000 [00:47<00:24, 128.65it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:01<00:01, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4880/8000 [00:47<00:19, 162.00it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:01<00:01, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4924/8000 [00:47<00:16, 190.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:01<00:00, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4965/8000 [00:47<00:14, 211.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:01<00:00, 45.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5002/8000 [00:48<00:31, 96.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:01<00:00, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5048/8000 [00:49<00:23, 124.69it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:01<00:00, 50.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5096/8000 [00:49<00:18, 154.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:01<00:00, 46.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5154/8000 [00:49<00:14, 199.91it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|▊| 84/100 [00:02<00:00, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5216/8000 [00:50<00:27, 101.47it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  89%|▉| 89/100 [00:02<00:00, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5267/8000 [00:50<00:20, 130.77it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  94%|▉| 94/100 [00:02<00:00, 41.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5337/8000 [00:51<00:30, 87.04it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:02<00:00, 40\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5397/8000 [00:52<00:22, 114.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5442/8000 [00:52<00:18, 137.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5495/8000 [00:52<00:14, 168.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 53/8000 [00:05<12:31, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5539/8000 [00:52<00:12, 192.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 91/8000 [00:06<08:40, 15.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5577/8000 [00:53<00:26, 91.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 136/8000 [00:07<06:31, 20.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5624/8000 [00:53<00:20, 116.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 189/8000 [00:08<03:56, 32.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5675/8000 [00:53<00:16, 144.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 232/8000 [00:09<03:52, 33.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5718/8000 [00:53<00:13, 172.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 288/8000 [00:10<03:35, 35.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5764/8000 [00:54<00:10, 205.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 343/8000 [00:12<03:21, 38.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▌    | 5811/8000 [00:54<00:09, 237.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 392/8000 [00:13<03:25, 36.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5863/8000 [00:54<00:08, 259.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▉                 | 443/8000 [00:14<03:16, 38.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5910/8000 [00:54<00:07, 277.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 491/8000 [00:15<03:15, 38.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5957/8000 [00:54<00:07, 291.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 537/8000 [00:17<03:24, 36.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6005/8000 [00:54<00:06, 309.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 583/8000 [00:17<02:37, 47.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6059/8000 [00:54<00:06, 323.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 642/8000 [00:19<02:47, 44.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6108/8000 [00:55<00:05, 341.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 690/8000 [00:19<02:10, 55.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6159/8000 [00:55<00:05, 349.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 747/8000 [00:20<02:23, 50.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6202/8000 [00:55<00:05, 344.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 796/8000 [00:20<01:51, 64.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6249/8000 [00:55<00:04, 350.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                | 839/8000 [00:21<01:31, 78.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6306/8000 [00:55<00:04, 366.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 891/8000 [00:22<01:51, 64.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6350/8000 [00:55<00:04, 362.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 941/8000 [00:23<02:11, 53.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6447/8000 [00:56<00:11, 138.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏              | 1000/8000 [00:23<01:37, 71.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6495/8000 [00:57<00:09, 165.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1052/8000 [00:24<01:19, 87.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6542/8000 [00:57<00:07, 191.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▏             | 1104/8000 [00:24<01:04, 107.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6600/8000 [00:57<00:06, 230.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎             | 1154/8000 [00:24<00:54, 125.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6650/8000 [00:57<00:05, 260.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍             | 1203/8000 [00:24<00:46, 146.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6701/8000 [00:57<00:04, 297.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1247/8000 [00:26<01:23, 80.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6792/8000 [00:57<00:03, 322.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▌             | 1303/8000 [00:26<01:06, 101.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6874/8000 [00:59<00:08, 134.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1344/8000 [00:27<01:44, 63.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6929/8000 [00:59<00:06, 160.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1382/8000 [00:27<01:29, 74.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6975/8000 [00:59<00:05, 185.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1431/8000 [00:28<01:11, 92.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7021/8000 [00:59<00:04, 206.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██▉             | 1488/8000 [00:28<00:54, 120.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7069/8000 [00:59<00:03, 236.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███             | 1532/8000 [00:28<00:47, 135.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7121/8000 [00:59<00:03, 268.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▏            | 1578/8000 [00:28<00:45, 139.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7167/8000 [00:59<00:03, 275.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎            | 1627/8000 [00:29<00:41, 153.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7204/8000 [01:00<00:02, 274.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▎            | 1680/8000 [00:29<00:35, 178.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7254/8000 [01:00<00:02, 296.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1727/8000 [00:30<01:19, 79.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7292/8000 [01:00<00:02, 276.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1772/8000 [00:30<01:05, 95.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7336/8000 [01:00<00:02, 285.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▋            | 1823/8000 [00:31<00:52, 118.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7388/8000 [01:00<00:01, 317.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1862/8000 [00:32<01:28, 69.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7446/8000 [01:00<00:01, 369.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1913/8000 [00:32<01:09, 87.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7493/8000 [01:00<00:01, 360.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███▉            | 1960/8000 [00:32<00:56, 107.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7533/8000 [01:01<00:01, 348.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████            | 2019/8000 [00:33<00:42, 140.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7582/8000 [01:01<00:01, 352.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▏           | 2071/8000 [00:33<00:37, 157.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7637/8000 [01:01<00:01, 347.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▏           | 2123/8000 [00:33<00:36, 162.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7683/8000 [01:01<00:00, 354.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2187/8000 [00:34<01:06, 87.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7732/8000 [01:01<00:00, 353.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▍           | 2240/8000 [00:35<00:53, 107.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7789/8000 [01:01<00:00, 377.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▌           | 2287/8000 [00:35<00:46, 121.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7847/8000 [01:01<00:00, 399.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▋           | 2344/8000 [00:35<00:40, 141.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7890/8000 [01:01<00:00, 363.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|████▊           | 2403/8000 [00:35<00:33, 168.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7934/8000 [01:02<00:00, 356.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|████▉           | 2446/8000 [00:36<00:31, 175.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7977/8000 [01:02<00:00, 354.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████           | 2501/8000 [00:36<00:27, 196.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8028it [01:02, 128.73it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  32%|█████           | 2558/8000 [00:36<00:25, 214.44it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  33%|█████▌           | 2623/8000 [00:37<00:55, 96.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2677/8000 [00:39<01:17, 68.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:14,  7.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2719/8000 [00:40<01:35, 55.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:44,  3.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2770/8000 [00:40<01:13, 70.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:01,  2.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2812/8000 [00:40<01:01, 84.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:16,  2.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|█████▋          | 2861/8000 [00:41<00:49, 104.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:18,  1.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|█████▊          | 2915/8000 [00:41<00:40, 126.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:46,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▉          | 2968/8000 [00:41<00:33, 148.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:23,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████          | 3019/8000 [00:41<00:29, 168.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:02,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▏         | 3069/8000 [00:42<00:28, 174.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:50,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▏         | 3118/8000 [00:42<00:26, 183.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3176/8000 [00:43<00:56, 84.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:41,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3219/8000 [00:44<00:49, 97.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:32,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▌         | 3263/8000 [00:44<00:42, 110.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:27,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▌         | 3301/8000 [00:44<00:41, 113.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:26,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████▋         | 3346/8000 [00:44<00:34, 133.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:22,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████▊         | 3396/8000 [00:44<00:29, 155.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:19,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████▉         | 3446/8000 [00:45<00:29, 153.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:14,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████▉         | 3490/8000 [00:45<00:27, 161.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:11,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████         | 3538/8000 [00:45<00:24, 181.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3598/8000 [00:47<00:49, 88.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:11,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3643/8000 [00:47<00:43, 99.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:09,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▍        | 3694/8000 [00:47<00:35, 120.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:36<04:04,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▍        | 3739/8000 [00:47<00:32, 132.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:03,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▌        | 3788/8000 [00:48<00:28, 149.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<04:08,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████▋        | 3835/8000 [00:48<00:24, 170.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:06,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████▊        | 3877/8000 [00:48<00:22, 181.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<04:04,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████▊        | 3919/8000 [00:48<00:23, 173.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:43<03:57,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████▉        | 3970/8000 [00:48<00:23, 174.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:45<03:52,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████        | 4026/8000 [00:49<00:20, 197.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:46<03:53,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▏       | 4076/8000 [00:49<00:18, 214.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:47<03:50,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▏       | 4114/8000 [00:49<00:19, 195.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:49<03:50,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▎       | 4159/8000 [00:49<00:19, 197.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:50<03:45,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▍       | 4210/8000 [00:50<00:17, 213.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:51<03:44,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▌       | 4252/8000 [00:50<00:17, 214.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<03:42,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▌       | 4290/8000 [00:50<00:18, 199.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:54<03:42,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4339/8000 [00:50<00:18, 193.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:55<03:39,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|████████▊       | 4385/8000 [00:50<00:17, 203.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:40,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|████████▊       | 4436/8000 [00:51<00:15, 223.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:58<03:43,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|████████▉       | 4479/8000 [00:51<00:16, 217.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:41,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████       | 4525/8000 [00:51<00:16, 212.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:37,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▏      | 4568/8000 [00:51<00:16, 204.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:36,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▏      | 4617/8000 [00:51<00:15, 219.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:35,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4664/8000 [00:52<00:14, 223.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:32,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▍      | 4713/8000 [00:52<00:15, 213.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:31,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▌      | 4763/8000 [00:52<00:14, 226.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:28,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▌      | 4809/8000 [00:52<00:15, 209.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:25,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▋      | 4861/8000 [00:53<00:15, 204.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:25,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4898/8000 [00:54<00:37, 82.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:30,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4946/8000 [00:54<00:31, 97.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:28,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████▉      | 4999/8000 [00:54<00:24, 120.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:26,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████      | 5045/8000 [00:55<00:23, 125.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:16<03:23,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▏     | 5100/8000 [00:55<00:19, 149.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:17<03:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▎     | 5137/8000 [00:55<00:19, 144.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:20,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▎     | 5187/8000 [00:56<00:18, 154.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:20<03:20,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5227/8000 [00:57<00:35, 77.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:21<03:16,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5273/8000 [00:57<00:27, 97.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:23<03:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▋     | 5333/8000 [00:57<00:21, 122.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:24<03:14,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▊     | 5377/8000 [00:57<00:19, 136.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:25<03:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████▊     | 5419/8000 [00:58<00:17, 144.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:27<03:09,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████▉     | 5468/8000 [00:58<00:15, 162.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:28<03:06,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████     | 5517/8000 [00:58<00:14, 174.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:29<03:06,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5568/8000 [00:58<00:12, 198.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:31<03:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5607/8000 [00:59<00:13, 171.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:32<02:59,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▉     | 5643/8000 [01:00<00:29, 80.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:33<03:00,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▍    | 5694/8000 [01:00<00:22, 104.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:35<03:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▍    | 5736/8000 [01:00<00:18, 120.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:36<02:59,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▌    | 5775/8000 [01:01<00:17, 125.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:37<02:56,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5810/8000 [01:02<00:33, 65.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:39<02:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5861/8000 [01:02<00:24, 86.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:40<02:53,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5906/8000 [01:02<00:20, 104.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:41<02:53,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|███████████▉    | 5965/8000 [01:02<00:14, 143.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:43<02:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████    | 6010/8000 [01:03<00:13, 148.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:44<02:48,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████    | 6062/8000 [01:03<00:12, 153.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:45<02:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6120/8000 [01:03<00:10, 174.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:47<02:47,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████████████▎   | 6169/8000 [01:03<00:09, 186.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:49<03:03,  1.46s/it]\n",
+      "Generating signals:  78%|████████████▍   | 6217/8000 [01:04<00:09, 192.34it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  78%|████████████▌   | 6274/8000 [01:04<00:08, 199.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████████████▋   | 6328/8000 [01:04<00:07, 209.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████▊   | 6385/8000 [01:04<00:07, 222.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.80it/s]\n",
+      "Generating signals:  80%|████████████▊   | 6434/8000 [01:05<00:07, 223.05it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  81%|████████████▉   | 6490/8000 [01:05<00:07, 213.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████   | 6539/8000 [01:05<00:06, 220.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▏  | 6590/8000 [01:05<00:06, 222.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 42it [00:01, 39.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████▎  | 6637/8000 [01:06<00:06, 222.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 82it [00:01, 82.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▎  | 6685/8000 [01:06<00:05, 223.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 126it [00:01, 134.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▍  | 6726/8000 [01:06<00:05, 212.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 162it [00:01, 170.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████▌  | 6776/8000 [01:06<00:05, 214.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 197it [00:01, 200.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████▋  | 6818/8000 [01:06<00:05, 202.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 231it [00:01, 227.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████▋  | 6873/8000 [01:07<00:05, 206.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 267it [00:02, 255.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▊  | 6929/8000 [01:07<00:05, 211.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 302it [00:02, 277.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▉  | 6981/8000 [01:07<00:04, 221.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 336it [00:02, 288.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████  | 7024/8000 [01:07<00:04, 203.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 370it [00:02, 293.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▏ | 7080/8000 [01:08<00:04, 211.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 408it [00:02, 316.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████▎ | 7130/8000 [01:08<00:04, 210.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 442it [00:02, 321.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7161/8000 [01:09<00:09, 85.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 476it [00:02, 286.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████▍ | 7213/8000 [01:09<00:07, 112.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 508it [00:02, 295.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████▌ | 7258/8000 [01:10<00:06, 122.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 543it [00:02, 308.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████▌ | 7305/8000 [01:10<00:04, 146.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 576it [00:03, 312.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|██████████████▋ | 7351/8000 [01:10<00:03, 170.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 609it [00:03, 300.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|██████████████▊ | 7390/8000 [01:10<00:03, 174.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 644it [00:03, 306.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|██████████████▉ | 7446/8000 [01:10<00:02, 185.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 676it [00:03, 294.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████ | 7500/8000 [01:11<00:02, 196.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 706it [00:03, 289.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████ | 7557/8000 [01:11<00:02, 205.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 749it [00:03, 327.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|███████████████▏| 7609/8000 [01:11<00:01, 212.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 212.97it/s]\n",
+      "Generating signals:  96%|███████████████▎| 7669/8000 [01:11<00:01, 225.82it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  96%|███████████████▍| 7713/8000 [01:12<00:01, 212.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▌| 7763/8000 [01:12<00:01, 225.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 171\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7797/8000 [01:13<00:02, 92.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 185\u001b[A\u001b[A\n",
+      "Generating signals:  98%|███████████████▋| 7842/8000 [01:13<00:01, 104.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 174\u001b[A\u001b[A\n",
+      "Generating signals:  99%|███████████████▊| 7885/8000 [01:13<00:00, 121.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:00<00:00, 172\u001b[A\u001b[A\n",
+      "Generating signals:  99%|███████████████▊| 7927/8000 [01:14<00:00, 138.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "Generating signals: 100%|███████████████▉| 7973/8000 [01:14<00:00, 157.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [14:04<14:19, 429.95s/it]\u001b[A"
+      "Generating signals: 8024it [01:14, 107.72it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -3.9320028028332903\n",
-      "2999: -6.2006965\r"
+      "concatenating...\n"
      ]
     },
     {
@@ -1695,7112 +1767,7062 @@
       "\n",
       "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:12<42:01, 12.67s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<29:29,  8.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:14<19:51,  6.02s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:11<17:07,  5.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:15<13:03,  3.98s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:14<13:04,  3.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:16<09:35,  2.94s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:16<10:50,  3.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:18<07:53,  2.43s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:18<09:47,  3.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:19<06:48,  2.10s/it]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:21<09:00,  2.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:21<06:02,  1.88s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:23<08:42,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:22<05:31,  1.73s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:26<08:25,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:24<05:13,  1.64s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:28<08:13,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:25<04:59,  1.58s/it]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:31<07:59,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:26<04:44,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:33<07:53,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:28<04:41,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:35<07:36,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:29<04:35,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:38<07:34,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:31<04:27,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:41<08:01,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:32<04:24,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:43<07:50,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:34<04:35,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:45<07:37,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:36<04:51,  1.59s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:48<07:37,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:37<04:39,  1.54s/it]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:50<07:24,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:38<04:30,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:53<07:13,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:40<04:24,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:55<07:13,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:41<04:21,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:57<07:09,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:43<04:17,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [01:00<07:05,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:44<04:13,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [01:02<07:05,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:46<04:13,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [01:05<07:09,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:47<04:06,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:07<07:06,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:48<04:03,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:10<07:15,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:50<04:04,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:12<07:17,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:51<04:04,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:15<07:11,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:53<04:05,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:17<07:04,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:54<04:15,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:20<07:04,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:56<04:21,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:22<06:58,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:57<04:11,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:25<06:51,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:59<04:09,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:27<06:48,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [01:00<04:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:30<06:56,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:02<04:06,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:33<07:47,  2.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:03<04:11,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:36<07:28,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:05<04:02,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:38<07:06,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:06<03:59,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:41<06:55,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:07<03:48,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:43<06:44,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:09<03:49,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:45<06:31,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:10<03:44,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:48<06:22,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:12<03:41,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:50<06:20,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:13<03:38,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:53<06:27,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:14<03:36,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:55<06:29,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:16<03:38,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:57<06:16,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:17<03:35,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [02:00<06:06,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:19<03:32,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [02:02<06:10,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:20<03:48,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [02:05<06:08,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:22<03:59,  1.58s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [02:07<06:08,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:24<03:52,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [02:09<05:56,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:25<03:40,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [02:12<05:57,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:26<03:39,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:14<05:58,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:28<03:40,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:17<05:50,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:29<03:40,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [02:19<05:44,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:31<03:36,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [02:21<05:41,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:32<03:32,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:24<05:43,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:34<03:27,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [02:26<05:46,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:35<03:26,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [02:29<05:40,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:37<03:27,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:31<05:35,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:38<03:32,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:33<05:33,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:41<03:51,  1.67s/it]\n",
+      "training:  30%|█████████▏                    | 61/200 [02:36<05:34,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  31%|█████████▎                    | 62/200 [02:38<05:32,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [02:41<05:30,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:04,  4.62s/it]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [02:43<05:24,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:04,  5.22it/s]\n",
-      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
-      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "training:  32%|█████████▊                    | 65/200 [02:45<05:22,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  33%|█████████▉                    | 66/200 [02:48<05:25,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████                    | 67/200 [02:50<05:25,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:04,  4.44s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▏                   | 68/200 [02:53<05:20,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:04, 11.12it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▎                   | 69/200 [02:55<05:23,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 63it [00:04, 22.11it/s]\u001b[A\u001b[A\n",
+      "training:  35%|██████████▌                   | 70/200 [02:58<05:21,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 91it [00:04, 36.87it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▋                   | 71/200 [03:00<05:17,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 118it [00:04, 54.43it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▊                   | 72/200 [03:02<05:11,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 144it [00:04, 73.90it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▉                   | 73/200 [03:05<05:04,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 176it [00:05, 103.65it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████                   | 74/200 [03:07<05:00,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 206it [00:05, 131.31it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▎                  | 75/200 [03:10<04:57,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 234it [00:05, 150.79it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▍                  | 76/200 [03:12<05:03,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 270it [00:05, 190.45it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▌                  | 77/200 [03:15<05:00,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 299it [00:05, 211.79it/s]\u001b[A\u001b[A\n",
+      "training:  39%|███████████▋                  | 78/200 [03:17<04:58,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 330it [00:05, 233.75it/s]\u001b[A\u001b[A\n",
+      "training:  40%|███████████▊                  | 79/200 [03:22<05:09,  2.56s/it]\n",
       "\n",
-      "Evaluating NLL: 360it [00:05, 239.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 393it [00:05, 262.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 423it [00:05, 232.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 450it [00:06, 234.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 13it [00:01, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 479it [00:06, 247.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.42it/s]\n",
       "\n",
-      "Evaluating NLL: 506it [00:06, 247.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 532it [00:06, 240.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 560it [00:06, 243.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 587it [00:06, 249.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 23it [00:01, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 613it [00:06, 240.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 44it [00:01, 36.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 641it [00:06, 246.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 63it [00:02, 55.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 666it [00:06, 247.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 83it [00:02, 76.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 691it [00:07, 242.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 101it [00:02, 94.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 717it [00:07, 246.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 129it [00:02, 131.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 742it [00:07, 236.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 155it [00:02, 158.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:07, 107.41it/s]\n",
+      "Evaluating NLL: 182it [00:02, 184.48it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 206it [00:02, 179.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 229it [00:02, 191.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:13,  7.1\u001b[A\u001b[A\n",
+      "Evaluating NLL: 251it [00:02, 189.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 85.\u001b[A\u001b[A\n",
+      "Evaluating NLL: 277it [00:02, 207.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:00<00:00, 108\u001b[A\u001b[A\n",
+      "Evaluating NLL: 300it [00:03, 205.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 128\u001b[A\u001b[A\n",
+      "Evaluating NLL: 322it [00:03, 197.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:00<00:00, 127\u001b[A\u001b[A\n",
+      "Evaluating NLL: 349it [00:03, 216.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:00<00:00, 125\u001b[A\u001b[A\n",
+      "Evaluating NLL: 372it [00:03, 208.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 11\n",
+      "Evaluating NLL: 394it [00:03, 198.48it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 419it [00:03, 210.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 442it [00:03, 212.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 20/8000 [00:04<26:45,  4.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 464it [00:03, 209.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 43/8000 [00:05<13:59,  9.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 486it [00:03, 211.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 60/8000 [00:06<11:13, 11.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 511it [00:04, 221.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 78/8000 [00:06<09:22, 14.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 534it [00:04, 215.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 103/8000 [00:07<07:24, 17.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 561it [00:04, 230.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 119/8000 [00:08<07:15, 18.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 588it [00:04, 239.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 135/8000 [00:08<05:30, 23.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 615it [00:04, 247.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 162/8000 [00:09<05:04, 25.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 647it [00:04, 268.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 175/8000 [00:10<05:49, 22.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 674it [00:04, 225.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 186/8000 [00:11<06:48, 19.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 698it [00:04, 216.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 211/8000 [00:11<04:18, 30.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 721it [00:05, 211.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 223/8000 [00:12<05:14, 24.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 743it [00:05, 213.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 243/8000 [00:12<03:43, 34.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 765it [00:05, 214.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 268/8000 [00:12<02:34, 50.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 149.41it/s]\n",
       "\n",
-      "Generating signals:   4%|▋                 | 288/8000 [00:12<02:04, 62.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 321/8000 [00:14<03:53, 32.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 341/8000 [00:14<03:05, 41.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%| | 5/100 [00:00<00:02, 39.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 367/8000 [00:15<03:26, 37.00it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%| | 9/100 [00:00<00:02, 38.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 385/8000 [00:15<02:47, 45.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:01, 45.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 406/8000 [00:16<03:41, 34.29it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:01, 48.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 419/8000 [00:16<03:13, 39.26it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:01, 47.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 438/8000 [00:17<03:50, 32.78it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:01, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 446/8000 [00:18<05:13, 24.11it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 467/8000 [00:18<03:41, 34.01it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:01<00:01, 35.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 485/8000 [00:18<02:51, 43.70it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:01<00:01, 36.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 504/8000 [00:18<02:18, 53.98it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:01<00:01, 41.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 523/8000 [00:18<01:55, 64.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:01<00:00, 43.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 534/8000 [00:19<01:52, 66.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:01<00:00, 43.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 554/8000 [00:19<01:30, 82.33it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:01<00:00, 47.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 577/8000 [00:19<01:12, 101.92it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:01<00:00, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 591/8000 [00:20<02:41, 45.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:01<00:00, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 607/8000 [00:20<02:14, 54.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|▊| 85/100 [00:02<00:00, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 621/8000 [00:20<02:00, 61.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|▉| 91/100 [00:02<00:00, 46.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 638/8000 [00:20<01:41, 72.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:02<00:00, 43\n",
       "\n",
-      "Generating signals:   8%|█▍                | 659/8000 [00:20<01:22, 89.26it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|████████████            | 2/4 [34:10<33:32, 1006.12s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.005161840614497\n",
+      "2999: -6.1932582\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   8%|█▍               | 679/8000 [00:20<01:12, 100.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 694/8000 [00:21<02:33, 47.63it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 718/8000 [00:21<01:51, 65.39it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                            | 1/200 [00:33<1:51:38, 33.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 730/8000 [00:22<01:45, 68.92it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:35<49:33, 15.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 742/8000 [00:22<01:39, 72.91it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:37<30:01,  9.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 760/8000 [00:22<01:23, 86.62it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:39<20:54,  6.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 778/8000 [00:22<01:14, 96.40it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:42<15:51,  4.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 795/8000 [00:22<01:07, 106.16it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:44<12:43,  3.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 813/8000 [00:22<01:01, 116.07it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:46<10:47,  3.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 827/8000 [00:22<01:04, 112.06it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:48<09:39,  3.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 844/8000 [00:22<01:01, 117.08it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:50<08:40,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 861/8000 [00:23<00:57, 125.13it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:52<07:57,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 878/8000 [00:23<00:55, 129.19it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:55<07:38,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 917/8000 [00:23<00:55, 127.40it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:57<07:11,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 940/8000 [00:23<00:57, 123.53it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:59<06:57,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 956/8000 [00:23<01:02, 113.42it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [01:01<06:49,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 974/8000 [00:24<01:02, 112.24it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [01:03<06:43,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 999/8000 [00:24<00:54, 128.37it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [01:05<06:43,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1013/8000 [00:24<00:57, 121.27it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [01:07<06:41,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1033/8000 [00:24<00:52, 132.49it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [01:10<06:41,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1057/8000 [00:24<00:49, 139.24it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [01:12<06:41,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1074/8000 [00:24<00:50, 138.47it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [01:14<06:35,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1103/8000 [00:24<00:54, 126.53it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [01:16<06:38,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1123/8000 [00:25<00:50, 136.58it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [01:18<06:32,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1142/8000 [00:25<00:53, 128.92it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [01:21<06:32,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1160/8000 [00:25<00:53, 127.18it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [01:23<06:20,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1177/8000 [00:25<00:52, 130.25it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:25<06:16,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1194/8000 [00:25<00:53, 126.58it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:27<06:17,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1220/8000 [00:25<01:02, 108.69it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:29<06:15,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1254/8000 [00:26<00:58, 114.70it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:32<06:25,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1274/8000 [00:26<00:55, 120.48it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:34<06:33,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1288/8000 [00:26<00:55, 121.10it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:37<06:39,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1312/8000 [00:26<00:48, 137.07it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:39<06:35,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1327/8000 [00:26<00:52, 127.53it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:41<06:34,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1346/8000 [00:26<00:54, 121.13it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:43<06:27,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1362/8000 [00:27<00:53, 124.07it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:46<06:20,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1385/8000 [00:27<01:03, 104.18it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:48<06:14,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1406/8000 [00:27<00:57, 113.94it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:50<06:04,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1423/8000 [00:27<00:57, 113.66it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:52<06:05,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1440/8000 [00:27<00:54, 120.10it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:55<06:03,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1456/8000 [00:27<00:54, 120.45it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:57<06:14,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1474/8000 [00:28<00:53, 122.26it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:59<06:10,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1497/8000 [00:28<00:48, 133.05it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [02:01<05:55,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1516/8000 [00:28<00:50, 128.30it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [02:03<05:46,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1535/8000 [00:28<00:49, 129.98it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [02:06<05:49,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1558/8000 [00:28<00:46, 137.17it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [02:08<05:47,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1581/8000 [00:28<00:42, 151.29it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [02:10<05:47,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1603/8000 [00:29<02:17, 46.46it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [02:13<05:48,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1621/8000 [00:30<03:14, 32.80it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [02:15<05:39,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1639/8000 [00:31<02:32, 41.83it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [02:17<05:45,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1666/8000 [00:31<01:46, 59.43it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [02:19<05:43,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1687/8000 [00:31<01:25, 73.78it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [02:22<05:37,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1705/8000 [00:31<01:14, 84.68it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [02:24<05:40,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1727/8000 [00:31<01:04, 96.73it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:26<05:32,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1749/8000 [00:31<00:55, 111.94it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:29<05:41,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1771/8000 [00:31<00:49, 125.55it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [02:31<05:34,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▌            | 1805/8000 [00:32<00:49, 124.20it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [02:33<05:32,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1827/8000 [00:32<00:46, 133.27it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:35<05:26,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1849/8000 [00:32<00:43, 140.75it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [02:38<05:21,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1881/8000 [00:32<00:49, 122.75it/s]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [02:40<05:24,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1900/8000 [00:32<00:50, 120.62it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:42<05:17,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1917/8000 [00:33<00:51, 118.64it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:44<05:19,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1937/8000 [00:33<00:48, 126.21it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [02:47<05:16,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1959/8000 [00:33<00:44, 134.82it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [02:49<05:10,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1978/8000 [00:33<00:46, 130.38it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [02:52<05:27,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1993/8000 [00:33<00:49, 120.67it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [02:55<05:52,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2015/8000 [00:33<00:47, 125.00it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▊                    | 65/200 [02:57<05:29,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2034/8000 [00:33<00:46, 127.14it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████▉                    | 66/200 [03:01<06:08,  2.75s/it]\n",
       "\n",
-      "Generating signals:  26%|████            | 2055/8000 [00:34<00:44, 134.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2071/8000 [00:34<00:44, 133.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2092/8000 [00:34<00:41, 141.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:13, 13.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2109/8000 [00:34<00:45, 128.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 11it [00:13,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2128/8000 [00:34<00:46, 126.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:13,  1.84it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2143/8000 [00:34<00:49, 119.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2159/8000 [00:34<00:49, 118.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2179/8000 [00:35<00:46, 126.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:11, 11.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▍           | 2192/8000 [00:35<00:49, 117.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 26it [00:11,  3.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2210/8000 [00:35<00:47, 121.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 51it [00:11,  7.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2230/8000 [00:35<00:46, 124.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:11, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2251/8000 [00:35<00:44, 128.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 100it [00:11, 20.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2268/8000 [00:35<00:45, 125.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 124it [00:11, 30.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2287/8000 [00:35<00:43, 130.99it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 149it [00:11, 44.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2309/8000 [00:36<00:41, 135.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 173it [00:12, 59.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2324/8000 [00:36<00:44, 126.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 197it [00:12, 76.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2341/8000 [00:36<00:43, 130.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 226it [00:12, 103.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2368/8000 [00:36<00:37, 149.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 251it [00:12, 125.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2395/8000 [00:36<00:32, 170.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 278it [00:12, 151.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2414/8000 [00:36<00:36, 152.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 304it [00:12, 169.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2430/8000 [00:36<00:40, 138.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 329it [00:12, 186.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2447/8000 [00:37<00:42, 130.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 354it [00:12, 175.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2466/8000 [00:37<00:43, 128.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 378it [00:12, 189.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2486/8000 [00:37<00:42, 130.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 401it [00:13, 186.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2506/8000 [00:37<00:40, 136.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 431it [00:13, 214.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2542/8000 [00:37<00:38, 141.99it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 459it [00:13, 229.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2559/8000 [00:37<00:38, 141.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 484it [00:13, 222.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2574/8000 [00:37<00:41, 131.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 509it [00:13, 228.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2591/8000 [00:38<00:42, 126.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 533it [00:13, 227.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2608/8000 [00:38<00:43, 122.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 557it [00:13, 226.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2624/8000 [00:38<00:44, 121.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 581it [00:13, 218.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2645/8000 [00:38<00:53, 99.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 604it [00:13, 203.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2662/8000 [00:38<00:48, 110.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 631it [00:14, 219.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▎          | 2682/8000 [00:38<00:43, 122.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 661it [00:14, 238.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2700/8000 [00:39<00:40, 130.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 694it [00:14, 262.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2723/8000 [00:39<00:47, 110.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 721it [00:14, 255.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2740/8000 [00:39<00:45, 114.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 748it [00:14, 259.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2760/8000 [00:39<00:42, 124.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:14, 54.42it/s] \n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2779/8000 [00:39<00:40, 127.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2806/8000 [00:39<00:34, 149.44it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2826/8000 [00:39<00:34, 147.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:16,  6.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2843/8000 [00:40<00:37, 137.13it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:06, 15.1\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2867/8000 [00:40<00:35, 142.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%| | 8/100 [00:00<00:04, 22.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2892/8000 [00:40<00:31, 163.33it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:03, 24.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2915/8000 [00:40<00:31, 162.58it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:02, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2932/8000 [00:40<00:32, 158.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:02, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2958/8000 [00:40<00:39, 128.22it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:02, 34.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2977/8000 [00:41<00:38, 129.29it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2996/8000 [00:41<00:36, 136.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:01<00:01, 41.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3017/8000 [00:41<00:35, 141.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:01<00:01, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3037/8000 [00:41<00:34, 144.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:01<00:01, 39.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3060/8000 [00:41<00:32, 152.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:01<00:01, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3077/8000 [00:41<00:33, 147.63it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:01<00:01, 39.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3096/8000 [00:41<00:34, 142.53it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:01<00:01, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3115/8000 [00:42<00:35, 135.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:01<00:00, 39.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3145/8000 [00:42<00:40, 120.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:01<00:00, 41.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3161/8000 [00:42<00:40, 118.58it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:02<00:00, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3182/8000 [00:42<00:37, 129.30it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:02<00:00, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3201/8000 [00:42<00:36, 131.62it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|▊| 84/100 [00:02<00:00, 39.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3219/8000 [00:42<00:35, 133.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|▉| 88/100 [00:02<00:00, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▍         | 3242/8000 [00:43<00:35, 132.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|▉| 92/100 [00:02<00:00, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3268/8000 [00:43<00:32, 146.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:02<00:00, 35\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3286/8000 [00:43<00:33, 138.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3305/8000 [00:43<00:32, 145.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3322/8000 [00:43<00:34, 136.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 18/8000 [00:04<32:29,  4.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3341/8000 [00:43<00:33, 139.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 38/8000 [00:05<17:12,  7.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3360/8000 [00:43<00:32, 141.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 60/8000 [00:06<12:00, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3382/8000 [00:43<00:29, 156.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 75/8000 [00:08<12:01, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3401/8000 [00:44<00:31, 145.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 90/8000 [00:08<08:52, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3418/8000 [00:44<00:32, 140.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 107/8000 [00:09<08:46, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3437/8000 [00:44<00:33, 137.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 129/8000 [00:09<06:02, 21.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3462/8000 [00:44<00:28, 159.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 148/8000 [00:10<06:23, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3484/8000 [00:44<00:28, 160.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 164/8000 [00:11<06:55, 18.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3525/8000 [00:44<00:29, 152.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 182/8000 [00:11<05:11, 25.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3557/8000 [00:45<01:04, 69.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 199/8000 [00:12<04:17, 30.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3576/8000 [00:46<00:56, 78.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 222/8000 [00:13<04:56, 26.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3596/8000 [00:46<00:49, 89.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 244/8000 [00:13<03:48, 33.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3620/8000 [00:46<00:41, 105.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 269/8000 [00:14<04:20, 29.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3644/8000 [00:46<00:36, 118.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 283/8000 [00:15<05:20, 24.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3672/8000 [00:47<01:09, 62.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 299/8000 [00:15<04:29, 28.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3690/8000 [00:47<01:01, 70.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 316/8000 [00:16<03:38, 35.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3710/8000 [00:47<00:52, 81.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 338/8000 [00:16<02:46, 46.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3729/8000 [00:47<00:47, 89.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 353/8000 [00:16<02:35, 49.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3754/8000 [00:47<00:39, 108.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 370/8000 [00:16<02:27, 51.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3772/8000 [00:47<00:35, 118.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 390/8000 [00:16<02:06, 60.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3791/8000 [00:48<00:33, 125.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 405/8000 [00:17<01:59, 63.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3812/8000 [00:48<00:31, 134.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 421/8000 [00:17<01:48, 69.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3832/8000 [00:48<00:30, 136.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 434/8000 [00:18<03:31, 35.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3856/8000 [00:48<00:28, 144.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 456/8000 [00:18<02:46, 45.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3873/8000 [00:49<01:15, 54.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 477/8000 [00:19<03:49, 32.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3891/8000 [00:49<01:03, 64.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 498/8000 [00:19<03:06, 40.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3916/8000 [00:49<00:47, 86.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 522/8000 [00:20<03:43, 33.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3931/8000 [00:49<00:44, 91.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 540/8000 [00:21<03:15, 38.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3954/8000 [00:49<00:35, 114.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 561/8000 [00:21<02:38, 46.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3971/8000 [00:49<00:32, 123.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 582/8000 [00:21<02:19, 53.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3990/8000 [00:50<00:29, 135.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎                | 603/8000 [00:21<01:59, 61.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4012/8000 [00:50<00:26, 150.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 623/8000 [00:21<01:45, 69.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4043/8000 [00:50<00:26, 147.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 633/8000 [00:22<03:25, 35.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4066/8000 [00:50<00:25, 152.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 650/8000 [00:23<02:50, 43.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4086/8000 [00:50<00:26, 145.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 665/8000 [00:23<02:44, 44.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4111/8000 [00:50<00:24, 161.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 681/8000 [00:23<02:40, 45.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4131/8000 [00:50<00:23, 163.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 696/8000 [00:24<02:36, 46.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4153/8000 [00:51<00:23, 167.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 712/8000 [00:24<02:15, 53.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4172/8000 [00:51<00:23, 160.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 731/8000 [00:24<02:10, 55.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4190/8000 [00:51<00:23, 159.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 757/8000 [00:25<03:12, 37.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4208/8000 [00:51<00:24, 157.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 770/8000 [00:25<02:57, 40.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4226/8000 [00:51<00:24, 151.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 786/8000 [00:25<02:32, 47.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4245/8000 [00:51<00:23, 159.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 799/8000 [00:26<02:22, 50.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4270/8000 [00:51<00:22, 166.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 816/8000 [00:26<02:06, 56.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4305/8000 [00:52<00:24, 153.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 833/8000 [00:26<02:01, 58.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4327/8000 [00:52<00:22, 160.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 846/8000 [00:26<02:01, 58.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▋       | 4360/8000 [00:52<00:24, 150.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 862/8000 [00:27<01:54, 62.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4383/8000 [00:52<00:22, 161.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 878/8000 [00:27<01:46, 66.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4419/8000 [00:52<00:22, 157.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 897/8000 [00:27<01:37, 72.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4452/8000 [00:53<00:23, 147.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 915/8000 [00:27<01:34, 75.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4472/8000 [00:53<00:23, 149.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 934/8000 [00:27<01:30, 78.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4488/8000 [00:53<00:23, 146.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 952/8000 [00:28<01:29, 79.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4504/8000 [00:53<00:25, 137.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 978/8000 [00:28<01:26, 80.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4548/8000 [00:53<00:21, 163.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1001/8000 [00:28<01:17, 89.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4588/8000 [00:53<00:20, 165.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1018/8000 [00:28<01:20, 86.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4614/8000 [00:54<00:19, 176.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1033/8000 [00:29<01:19, 87.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4637/8000 [00:54<00:19, 173.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1052/8000 [00:29<01:20, 86.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4679/8000 [00:54<00:19, 170.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1068/8000 [00:29<01:23, 82.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4723/8000 [00:55<00:38, 85.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1082/8000 [00:29<01:37, 70.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4738/8000 [00:55<00:35, 91.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1104/8000 [00:30<01:28, 77.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4761/8000 [00:55<00:31, 104.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1119/8000 [00:30<01:30, 76.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4785/8000 [00:55<00:26, 122.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1140/8000 [00:30<01:32, 74.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4816/8000 [00:55<00:24, 127.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1160/8000 [00:30<01:29, 76.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4840/8000 [00:56<00:22, 138.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1171/8000 [00:31<03:24, 33.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4860/8000 [00:56<00:21, 147.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1192/8000 [00:32<02:32, 44.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4891/8000 [00:56<00:22, 139.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1211/8000 [00:32<02:04, 54.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4912/8000 [00:56<00:20, 150.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1229/8000 [00:32<01:57, 57.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4946/8000 [00:56<00:20, 150.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1241/8000 [00:33<03:29, 32.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4966/8000 [00:56<00:19, 156.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1260/8000 [00:33<02:40, 41.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4987/8000 [00:56<00:18, 165.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1274/8000 [00:33<02:23, 46.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5028/8000 [00:57<00:17, 173.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1295/8000 [00:34<02:01, 55.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5054/8000 [00:57<00:16, 179.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1320/8000 [00:34<01:36, 69.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5080/8000 [00:57<00:15, 185.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1336/8000 [00:34<01:35, 70.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5112/8000 [00:57<00:17, 166.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1353/8000 [00:34<01:31, 72.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5143/8000 [00:57<00:18, 156.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1362/8000 [00:35<03:15, 33.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5162/8000 [00:57<00:18, 156.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1377/8000 [00:35<02:51, 38.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5190/8000 [00:58<00:19, 142.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1396/8000 [00:36<02:12, 49.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5218/8000 [00:58<00:17, 163.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1415/8000 [00:36<01:51, 59.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▍     | 5248/8000 [00:58<00:18, 147.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1430/8000 [00:36<01:50, 59.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5267/8000 [00:58<00:18, 150.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1449/8000 [00:36<01:39, 66.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5294/8000 [00:58<00:19, 140.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1466/8000 [00:36<01:29, 72.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5312/8000 [00:59<00:19, 140.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏             | 1479/8000 [00:37<01:37, 66.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5331/8000 [00:59<00:18, 144.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1497/8000 [00:37<01:32, 70.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5350/8000 [00:59<00:18, 145.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1514/8000 [00:37<01:38, 65.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5371/8000 [00:59<00:17, 153.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1531/8000 [00:37<01:31, 70.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5400/8000 [00:59<00:18, 137.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1552/8000 [00:38<01:30, 71.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5424/8000 [00:59<00:17, 151.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1567/8000 [00:38<01:31, 70.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5442/8000 [00:59<00:16, 151.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1587/8000 [00:38<01:28, 72.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5467/8000 [01:00<00:14, 168.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1612/8000 [00:38<01:16, 83.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5500/8000 [01:00<00:16, 154.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1629/8000 [00:39<01:25, 74.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5529/8000 [01:00<00:14, 172.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍             | 1645/8000 [00:39<01:23, 76.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5563/8000 [01:00<00:14, 164.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1672/8000 [00:40<02:21, 44.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5586/8000 [01:00<00:13, 173.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1690/8000 [00:40<02:10, 48.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5606/8000 [01:00<00:13, 177.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1713/8000 [00:40<01:47, 58.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5635/8000 [01:01<00:15, 153.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1725/8000 [00:41<01:46, 59.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5655/8000 [01:01<00:14, 161.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1741/8000 [00:41<01:37, 64.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5680/8000 [01:01<00:14, 164.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1759/8000 [00:41<01:31, 67.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5724/8000 [01:01<00:12, 178.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1779/8000 [00:41<01:25, 72.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5750/8000 [01:01<00:11, 188.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1796/8000 [00:41<01:23, 74.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5772/8000 [01:01<00:12, 182.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1811/8000 [00:42<01:30, 68.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5796/8000 [01:01<00:11, 190.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1827/8000 [00:42<01:26, 71.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5833/8000 [01:02<00:12, 180.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1846/8000 [00:42<01:26, 70.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5852/8000 [01:02<00:11, 182.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1868/8000 [00:42<01:23, 73.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▊    | 5879/8000 [01:02<00:11, 182.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1885/8000 [00:43<01:23, 73.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5909/8000 [01:02<00:13, 155.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1898/8000 [00:43<01:28, 69.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5927/8000 [01:02<00:13, 158.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1912/8000 [00:43<01:32, 65.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5947/8000 [01:02<00:12, 161.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1929/8000 [00:43<01:25, 71.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5964/8000 [01:02<00:12, 157.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1940/8000 [00:44<01:31, 66.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5989/8000 [01:03<00:11, 176.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1958/8000 [00:44<01:17, 78.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6026/8000 [01:03<00:12, 161.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1972/8000 [00:44<01:21, 73.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6054/8000 [01:03<00:11, 169.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1992/8000 [00:44<01:23, 72.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6088/8000 [01:03<00:12, 157.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2007/8000 [00:44<01:22, 72.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6109/8000 [01:03<00:11, 163.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2018/8000 [00:45<01:30, 65.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6128/8000 [01:03<00:11, 164.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2029/8000 [00:45<01:37, 61.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6151/8000 [01:04<00:10, 174.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2047/8000 [00:45<01:29, 66.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6184/8000 [01:04<00:16, 112.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2061/8000 [00:45<01:24, 70.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6215/8000 [01:05<00:27, 65.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2084/8000 [00:45<01:10, 84.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6233/8000 [01:05<00:23, 75.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2101/8000 [00:46<01:14, 79.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6256/8000 [01:05<00:19, 89.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2123/8000 [00:46<01:09, 84.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6276/8000 [01:05<00:17, 101.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2140/8000 [00:46<01:08, 85.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6297/8000 [01:05<00:14, 116.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2161/8000 [00:46<01:03, 91.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6319/8000 [01:06<00:12, 129.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▎           | 2185/8000 [00:47<00:57, 101.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6336/8000 [01:06<00:12, 131.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2206/8000 [00:47<01:03, 91.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▋   | 6369/8000 [01:06<00:12, 128.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2221/8000 [00:47<01:06, 86.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6387/8000 [01:06<00:12, 130.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2243/8000 [00:47<01:10, 81.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6402/8000 [01:06<00:12, 131.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2265/8000 [00:48<01:07, 84.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6436/8000 [01:06<00:11, 139.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2286/8000 [00:48<01:05, 87.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6462/8000 [01:06<00:09, 154.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2301/8000 [00:48<01:06, 86.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6480/8000 [01:07<00:09, 155.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2316/8000 [00:48<01:06, 85.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6498/8000 [01:07<00:09, 156.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2332/8000 [00:48<01:11, 79.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6524/8000 [01:07<00:08, 177.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2347/8000 [00:49<01:17, 72.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6545/8000 [01:07<00:08, 167.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2367/8000 [00:49<01:08, 81.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6565/8000 [01:07<00:08, 164.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2388/8000 [00:49<01:01, 90.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6586/8000 [01:07<00:08, 167.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2404/8000 [00:49<01:08, 81.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6615/8000 [01:07<00:09, 147.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2424/8000 [00:49<01:05, 84.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6646/8000 [01:08<00:09, 138.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2445/8000 [00:50<01:01, 90.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6670/8000 [01:08<00:11, 120.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2461/8000 [00:50<01:03, 87.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6693/8000 [01:08<00:09, 133.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2481/8000 [00:50<01:07, 81.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6714/8000 [01:08<00:08, 143.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2498/8000 [00:50<01:11, 76.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6730/8000 [01:08<00:08, 141.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2520/8000 [00:51<01:04, 84.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▌  | 6750/8000 [01:08<00:08, 143.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2543/8000 [00:51<00:59, 91.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6770/8000 [01:09<00:08, 153.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2565/8000 [00:51<00:57, 94.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6793/8000 [01:09<00:07, 159.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2584/8000 [00:51<00:59, 91.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6831/8000 [01:09<00:07, 151.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▏          | 2610/8000 [00:51<00:52, 102.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6861/8000 [01:09<00:07, 142.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▎          | 2631/8000 [00:52<00:51, 103.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6885/8000 [01:09<00:07, 156.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2649/8000 [00:52<00:56, 94.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6924/8000 [01:10<00:06, 154.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2665/8000 [00:52<01:00, 88.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6942/8000 [01:10<00:07, 148.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2687/8000 [00:52<01:00, 87.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6963/8000 [01:10<00:06, 153.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2714/8000 [00:53<00:54, 97.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6986/8000 [01:10<00:06, 163.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2730/8000 [00:53<01:00, 87.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7003/8000 [01:10<00:06, 156.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2748/8000 [00:53<01:01, 85.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7042/8000 [01:11<00:13, 69.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2768/8000 [00:53<01:04, 81.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7069/8000 [01:11<00:10, 85.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2788/8000 [00:54<01:02, 83.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7093/8000 [01:11<00:08, 101.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2805/8000 [00:54<01:06, 78.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7112/8000 [01:11<00:07, 111.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2829/8000 [00:54<00:58, 88.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7130/8000 [01:12<00:07, 118.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2848/8000 [00:54<00:55, 92.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7151/8000 [01:12<00:06, 131.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2868/8000 [00:54<00:57, 88.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7168/8000 [01:12<00:06, 131.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2886/8000 [00:55<01:00, 85.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7202/8000 [01:12<00:05, 134.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2900/8000 [00:55<01:00, 83.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7228/8000 [01:12<00:05, 152.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2916/8000 [00:55<00:56, 90.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7261/8000 [01:12<00:05, 144.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2935/8000 [00:55<00:53, 95.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7278/8000 [01:13<00:05, 136.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2949/8000 [00:55<01:02, 80.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7299/8000 [01:13<00:05, 133.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2965/8000 [00:56<01:08, 73.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7322/8000 [01:13<00:04, 139.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2981/8000 [00:56<01:06, 74.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7340/8000 [01:13<00:04, 132.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3007/8000 [00:56<00:55, 89.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7360/8000 [01:13<00:04, 136.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3021/8000 [00:56<01:08, 72.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7380/8000 [01:13<00:04, 141.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3040/8000 [00:57<01:02, 78.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7401/8000 [01:13<00:04, 134.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3063/8000 [00:57<00:56, 87.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7430/8000 [01:14<00:04, 114.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3086/8000 [00:57<00:52, 94.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7443/8000 [01:14<00:04, 114.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3101/8000 [00:57<01:02, 78.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7460/8000 [01:14<00:04, 114.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3118/8000 [00:58<01:04, 75.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7480/8000 [01:14<00:04, 126.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3139/8000 [00:58<01:03, 76.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7501/8000 [01:14<00:03, 136.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3148/8000 [00:58<01:14, 64.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7517/8000 [01:14<00:03, 134.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3171/8000 [00:58<01:00, 79.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7534/8000 [01:15<00:03, 128.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3192/8000 [00:58<00:56, 84.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7563/8000 [01:15<00:03, 128.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3209/8000 [00:59<00:57, 83.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7580/8000 [01:15<00:03, 133.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3231/8000 [00:59<00:54, 88.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7602/8000 [01:15<00:02, 145.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3249/8000 [00:59<01:00, 78.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7623/8000 [01:15<00:02, 154.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3272/8000 [00:59<00:56, 84.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7642/8000 [01:15<00:02, 157.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3289/8000 [01:00<00:56, 83.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7660/8000 [01:15<00:02, 157.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3306/8000 [01:00<00:56, 82.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7680/8000 [01:15<00:01, 165.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3330/8000 [01:00<00:50, 92.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7698/8000 [01:16<00:01, 158.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3347/8000 [01:00<00:58, 79.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7731/8000 [01:16<00:01, 143.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3367/8000 [01:01<00:55, 83.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7751/8000 [01:16<00:01, 149.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3394/8000 [01:01<00:49, 93.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7770/8000 [01:16<00:01, 154.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3410/8000 [01:01<00:56, 80.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7796/8000 [01:16<00:01, 175.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3428/8000 [01:01<00:55, 81.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7818/8000 [01:16<00:01, 180.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3450/8000 [01:02<00:56, 80.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7850/8000 [01:17<00:00, 165.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3464/8000 [01:02<01:00, 75.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7869/8000 [01:17<00:00, 168.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▍         | 3478/8000 [01:02<01:01, 72.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7902/8000 [01:17<00:00, 160.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3497/8000 [01:02<00:58, 76.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7926/8000 [01:17<00:00, 165.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3513/8000 [01:03<01:06, 67.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7950/8000 [01:17<00:00, 172.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3526/8000 [01:03<01:13, 61.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7976/8000 [01:17<00:00, 147.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3540/8000 [01:03<01:12, 61.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8007it [01:18, 102.53it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  45%|███████▌         | 3561/8000 [01:03<01:02, 71.39it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  45%|███████▌         | 3585/8000 [01:03<00:53, 81.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3605/8000 [01:04<00:50, 86.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<26:21,  7.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3621/8000 [01:04<00:57, 75.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:22,  4.05s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3644/8000 [01:04<00:54, 79.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:31,  2.90s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3660/8000 [01:04<00:56, 76.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:34,  2.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3685/8000 [01:05<00:51, 83.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:25,  1.98s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3716/8000 [01:05<01:11, 59.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:47,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3730/8000 [01:06<01:10, 60.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:17,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3746/8000 [01:06<01:06, 64.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:00,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3761/8000 [01:06<01:09, 60.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<04:49,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3775/8000 [01:06<01:09, 60.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:38,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3793/8000 [01:07<01:06, 63.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:34,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3806/8000 [01:07<01:11, 58.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:29,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3829/8000 [01:07<01:01, 67.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:29,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3840/8000 [01:07<01:08, 60.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:25,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3857/8000 [01:08<01:08, 60.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:22,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3870/8000 [01:08<01:07, 61.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:19,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3893/8000 [01:08<00:57, 71.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:32,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3912/8000 [01:08<00:53, 75.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:50,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3930/8000 [01:09<00:50, 80.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:39,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3950/8000 [01:09<00:50, 80.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:35<04:37,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3969/8000 [01:09<00:52, 77.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:35,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3993/8000 [01:09<00:47, 83.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<04:28,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4015/8000 [01:10<00:44, 89.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<04:21,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4042/8000 [01:10<00:41, 96.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:41<04:17,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4057/8000 [01:10<00:42, 93.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:42<04:18,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4070/8000 [01:10<00:46, 83.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:44<04:16,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4086/8000 [01:10<00:51, 75.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<04:19,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4106/8000 [01:11<00:51, 75.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:47<04:15,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4128/8000 [01:11<00:47, 82.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<04:13,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4147/8000 [01:11<00:47, 80.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:50<04:12,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4165/8000 [01:11<00:45, 83.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:51<04:13,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4181/8000 [01:12<00:47, 80.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:53<04:14,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4194/8000 [01:12<00:49, 76.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:54<04:16,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4213/8000 [01:12<00:49, 76.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:56<04:22,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4233/8000 [01:12<00:43, 86.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:58<04:34,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4251/8000 [01:12<00:43, 85.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:59<04:25,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4272/8000 [01:13<00:42, 88.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:01<04:18,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4296/8000 [01:13<00:39, 92.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:03<04:23,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4311/8000 [01:13<00:42, 86.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:04<04:24,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4329/8000 [01:13<00:45, 80.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:06<04:14,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4346/8000 [01:14<00:47, 77.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:07<04:08,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4371/8000 [01:14<00:41, 87.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:09<04:01,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4383/8000 [01:14<00:46, 78.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:10<04:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4400/8000 [01:14<00:45, 78.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:12<04:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4418/8000 [01:14<00:46, 76.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:14<04:09,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4433/8000 [01:15<00:51, 69.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:15<04:08,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4448/8000 [01:15<00:50, 70.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:17<04:03,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4464/8000 [01:15<00:48, 72.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:18<04:00,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4475/8000 [01:15<00:54, 64.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:20<03:56,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4494/8000 [01:16<00:48, 72.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:22<03:59,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4514/8000 [01:16<00:50, 69.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:23<03:51,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▌       | 4528/8000 [01:16<00:50, 68.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:25<03:59,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4548/8000 [01:16<00:46, 73.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:26<03:56,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4560/8000 [01:17<00:47, 72.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:28<03:54,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4579/8000 [01:17<00:43, 79.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:30<03:58,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4604/8000 [01:17<00:34, 98.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:31<03:48,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4627/8000 [01:17<00:30, 112.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:33<03:42,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4641/8000 [01:17<00:31, 106.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:34<03:37,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4658/8000 [01:17<00:32, 102.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:36<03:31,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4677/8000 [01:18<00:30, 109.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:39<03:51,  1.65s/it]\n",
+      "Generating signals:  59%|█████████▍      | 4712/8000 [01:18<00:29, 110.70it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  59%|█████████▍      | 4729/8000 [01:18<00:30, 108.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▌      | 4751/8000 [01:18<00:27, 119.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▌      | 4764/8000 [01:18<00:29, 111.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.72it/s]\n",
+      "Generating signals:  60%|██████████▏      | 4793/8000 [01:19<00:56, 57.24it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  60%|██████████▏      | 4811/8000 [01:19<00:49, 64.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4831/8000 [01:20<00:43, 72.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4860/8000 [01:20<00:33, 93.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 30.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▊      | 4887/8000 [01:20<00:29, 104.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 72it [00:01, 66.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▊      | 4910/8000 [01:20<00:26, 118.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 103it [00:01, 99.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████▉      | 4948/8000 [01:20<00:25, 120.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 135it [00:01, 134.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████▉      | 4972/8000 [01:21<00:28, 107.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 165it [00:02, 163.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████▉      | 4986/8000 [01:21<00:30, 100.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 197it [00:02, 196.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████      | 5008/8000 [01:21<00:28, 105.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 228it [00:02, 220.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████      | 5024/8000 [01:21<00:28, 104.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 259it [00:02, 240.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████      | 5044/8000 [01:21<00:26, 110.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 290it [00:02, 247.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5069/8000 [01:22<01:01, 47.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 326it [00:02, 274.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5087/8000 [01:23<00:51, 56.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 360it [00:02, 291.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5103/8000 [01:23<00:44, 64.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 399it [00:02, 318.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5126/8000 [01:23<00:36, 79.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 433it [00:02, 322.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5149/8000 [01:23<00:29, 95.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 467it [00:02, 315.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▎     | 5169/8000 [01:23<00:26, 105.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 500it [00:03, 317.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▍     | 5190/8000 [01:23<00:25, 109.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 538it [00:03, 334.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▍     | 5207/8000 [01:24<00:27, 101.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 573it [00:03, 337.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▍     | 5230/8000 [01:24<00:27, 100.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 612it [00:03, 351.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5248/8000 [01:24<00:29, 92.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 651it [00:03, 359.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████▌     | 5270/8000 [01:24<00:26, 101.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 691it [00:03, 370.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████▌     | 5287/8000 [01:24<00:26, 104.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 729it [00:03, 364.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5306/8000 [01:25<00:28, 93.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 206.04it/s]\n",
+      "Generating signals:  67%|██████████▋     | 5326/8000 [01:25<00:26, 100.92it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  67%|███████████▎     | 5341/8000 [01:25<00:27, 96.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5355/8000 [01:25<00:30, 87.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   6%| | 6/100 [00:00<00:01, 51.6\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5376/8000 [01:25<00:30, 87.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:01, 53.\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5394/8000 [01:26<00:29, 87.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:00, 79.\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5414/8000 [01:26<00:26, 98.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 73.\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5428/8000 [01:26<00:28, 91.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 81.\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5438/8000 [01:26<00:35, 72.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:00<00:00, 90.\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5458/8000 [01:26<00:33, 75.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  70%|▋| 70/100 [00:00<00:00, 110\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▋     | 5479/8000 [01:27<00:29, 85.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:00<00:00, 110\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5496/8000 [01:27<00:28, 89.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 96\n",
+      "Generating signals:  69%|███████████     | 5523/8000 [01:27<00:24, 102.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [22:13<07:36, 456.50s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.374325974032371\n",
-      "2999: -5.8260404\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  69%|███████████▊     | 5534/8000 [01:27<00:25, 96.99it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  69%|███████████▊     | 5552/8000 [01:27<00:24, 99.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5569/8000 [01:27<00:22, 107.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:12<42:01, 12.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5594/8000 [01:28<00:21, 113.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:14<20:24,  6.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5609/8000 [01:28<00:21, 111.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:15<13:13,  4.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▎    | 5629/8000 [01:28<00:19, 119.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:17<09:52,  3.02s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▎    | 5646/8000 [01:28<00:19, 118.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:18<08:12,  2.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▎    | 5668/8000 [01:28<00:20, 115.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:20<06:58,  2.16s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▎    | 5684/8000 [01:28<00:22, 101.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:21<06:14,  1.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▍    | 5703/8000 [01:29<00:21, 104.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:23<05:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▍    | 5728/8000 [01:29<00:19, 114.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:24<05:16,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▌    | 5750/8000 [01:29<00:19, 116.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:26<05:05,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▌    | 5768/8000 [01:29<00:18, 119.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:27<04:54,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▌    | 5787/8000 [01:29<00:19, 113.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:29<04:44,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▌    | 5804/8000 [01:29<00:19, 111.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:30<04:43,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▋    | 5819/8000 [01:30<00:21, 100.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:31<04:34,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▋    | 5845/8000 [01:30<00:17, 124.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:33<04:27,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▋    | 5863/8000 [01:30<00:17, 119.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:34<04:30,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▊    | 5876/8000 [01:30<00:18, 113.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:36<04:31,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5894/8000 [01:30<00:18, 111.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:37<04:31,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5911/8000 [01:30<00:18, 112.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:39<04:28,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5931/8000 [01:31<00:17, 117.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:40<04:26,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▉    | 5955/8000 [01:31<00:16, 121.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:42<04:25,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|███████████▉    | 5976/8000 [01:31<00:15, 131.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:43<04:30,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████    | 6001/8000 [01:31<00:14, 138.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:45<04:29,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████    | 6019/8000 [01:31<00:15, 126.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:46<04:26,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████    | 6041/8000 [01:31<00:15, 125.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:48<04:17,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████    | 6060/8000 [01:32<00:15, 125.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:49<04:11,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6076/8000 [01:32<00:17, 111.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:51<04:12,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6103/8000 [01:32<00:17, 108.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:52<04:22,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6120/8000 [01:32<00:18, 103.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:54<04:19,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6141/8000 [01:32<00:19, 95.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:55<04:15,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████████████▎   | 6163/8000 [01:33<00:18, 101.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:57<04:16,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6179/8000 [01:33<00:19, 93.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:58<04:16,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6198/8000 [01:33<00:19, 94.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [01:00<04:14,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6214/8000 [01:33<00:20, 87.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [01:02<04:20,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6233/8000 [01:33<00:20, 85.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:03<04:23,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6249/8000 [01:34<00:20, 84.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:05<04:16,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6272/8000 [01:34<00:19, 88.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:06<04:10,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6298/8000 [01:34<00:17, 95.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:08<04:08,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6309/8000 [01:34<00:20, 81.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:09<04:03,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6328/8000 [01:35<00:19, 87.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:11<04:01,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6349/8000 [01:35<00:19, 86.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:12<03:59,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6373/8000 [01:35<00:17, 93.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:14<03:55,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6391/8000 [01:35<00:17, 93.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:15<03:45,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6409/8000 [01:35<00:17, 92.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:16<03:37,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6434/8000 [01:36<00:16, 94.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:18<03:33,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████▉   | 6460/8000 [01:36<00:14, 102.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:19<03:28,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6480/8000 [01:36<00:15, 99.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:20<03:25,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6497/8000 [01:36<00:16, 90.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:22<03:22,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6513/8000 [01:37<00:17, 85.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:23<03:21,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6535/8000 [01:37<00:15, 92.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:24<03:19,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████   | 6560/8000 [01:37<00:14, 100.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:26<03:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6581/8000 [01:37<00:14, 96.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:27<03:30,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6596/8000 [01:37<00:15, 88.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:29<03:26,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6622/8000 [01:38<00:13, 99.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:30<03:21,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████▎  | 6641/8000 [01:38<00:13, 100.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:31<03:16,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6660/8000 [01:38<00:14, 91.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:33<03:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6674/8000 [01:38<00:15, 83.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:34<03:25,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6697/8000 [01:39<00:15, 83.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:36<03:22,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6724/8000 [01:39<00:13, 95.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:37<03:16,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6742/8000 [01:39<00:13, 90.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:38<03:11,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6758/8000 [01:39<00:14, 83.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:40<03:08,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6775/8000 [01:39<00:14, 85.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:41<03:07,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6785/8000 [01:40<00:16, 74.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:42<03:05,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6800/8000 [01:40<00:17, 68.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:45<03:44,  1.65s/it]\n",
+      "Generating signals:  85%|██████████████▍  | 6819/8000 [01:40<00:17, 68.10it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  85%|██████████████▌  | 6839/8000 [01:40<00:15, 73.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6854/8000 [01:41<00:17, 66.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:04,  4.12s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6867/8000 [01:41<00:18, 61.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:04,  5.89it/s]\n",
-      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
-      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "Generating signals:  86%|██████████████▋  | 6886/8000 [01:41<00:17, 64.30it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  86%|██████████████▋  | 6905/8000 [01:41<00:15, 69.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6920/8000 [01:42<00:16, 66.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:03,  3.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6937/8000 [01:42<00:14, 72.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 35it [00:03, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6948/8000 [01:42<00:15, 68.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 72it [00:03, 33.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6963/8000 [01:42<00:14, 69.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 102it [00:03, 52.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6980/8000 [01:42<00:13, 76.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 142it [00:03, 84.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7001/8000 [01:43<00:11, 86.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 175it [00:03, 111.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7025/8000 [01:43<00:10, 91.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 206it [00:04, 138.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████  | 7054/8000 [01:43<00:09, 100.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 241it [00:04, 172.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7075/8000 [01:43<00:09, 97.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 275it [00:04, 203.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7090/8000 [01:44<00:10, 89.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 308it [00:04, 223.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7105/8000 [01:44<00:10, 83.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 342it [00:04, 249.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7129/8000 [01:44<00:10, 81.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 376it [00:04, 270.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7151/8000 [01:44<00:09, 87.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 413it [00:04, 295.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7168/8000 [01:44<00:09, 87.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 447it [00:04, 307.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7186/8000 [01:45<00:09, 86.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 481it [00:04, 308.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7204/8000 [01:45<00:09, 88.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 515it [00:04, 317.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7225/8000 [01:45<00:08, 88.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 549it [00:05, 314.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▍ | 7239/8000 [01:45<00:10, 73.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 582it [00:05, 313.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7251/8000 [01:46<00:10, 72.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 620it [00:05, 329.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7269/8000 [01:46<00:11, 65.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 654it [00:05, 321.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7291/8000 [01:46<00:09, 75.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 689it [00:05, 329.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7305/8000 [01:46<00:10, 66.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 723it [00:05, 325.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7326/8000 [01:47<00:09, 73.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 756it [00:05, 319.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7343/8000 [01:47<00:08, 73.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:05, 136.15it/s]\n",
+      "Generating signals:  92%|███████████████▋ | 7370/8000 [01:47<00:07, 82.11it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  92%|███████████████▋ | 7400/8000 [01:48<00:13, 45.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7417/8000 [01:49<00:11, 48.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:11,  8.4\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7437/8000 [01:49<00:09, 57.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  19%|▏| 19/100 [00:00<00:00, 99.\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7465/8000 [01:50<00:13, 40.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  37%|▎| 37/100 [00:00<00:00, 131\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7480/8000 [01:50<00:12, 43.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 132\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7498/8000 [01:50<00:10, 48.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  65%|▋| 65/100 [00:00<00:00, 126\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7518/8000 [01:51<00:08, 56.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:00<00:00, 102\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7526/8000 [01:51<00:15, 31.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 11\n",
+      "Generating signals:  94%|████████████████ | 7551/8000 [01:52<00:10, 42.76it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  95%|████████████████ | 7575/8000 [01:52<00:08, 52.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7594/8000 [01:52<00:06, 58.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████▏| 7613/8000 [01:52<00:06, 61.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 4/8000 [00:03<2:03:37,  1.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▏| 7640/8000 [01:53<00:04, 72.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 8/8000 [00:03<52:58,  2.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7660/8000 [01:53<00:04, 79.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 13/8000 [00:05<42:45,  3.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7674/8000 [01:53<00:04, 78.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 16/8000 [00:05<31:48,  4.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7695/8000 [01:53<00:03, 81.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 18/8000 [00:05<26:23,  5.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▍| 7719/8000 [01:54<00:03, 91.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 20/8000 [00:06<41:19,  3.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7740/8000 [01:54<00:02, 93.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 24/8000 [00:07<29:48,  4.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▌| 7767/8000 [01:54<00:02, 100.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 27/8000 [00:07<23:17,  5.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7783/8000 [01:54<00:02, 85.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 29/8000 [00:07<25:50,  5.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7805/8000 [01:54<00:02, 89.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 30/8000 [00:07<26:19,  5.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7823/8000 [01:55<00:02, 86.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 34/8000 [00:08<17:07,  7.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7840/8000 [01:55<00:02, 78.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 38/8000 [00:08<20:18,  6.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7860/8000 [01:55<00:01, 80.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 47/8000 [00:09<13:23,  9.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7875/8000 [01:55<00:01, 72.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 51/8000 [00:09<10:46, 12.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7893/8000 [01:56<00:01, 75.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 55/8000 [00:09<09:46, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7909/8000 [01:56<00:01, 74.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 58/8000 [00:09<09:50, 13.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7928/8000 [01:56<00:00, 77.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 60/8000 [00:10<09:26, 14.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7948/8000 [01:56<00:00, 75.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 67/8000 [00:10<09:08, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7966/8000 [01:57<00:00, 78.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 72/8000 [00:11<13:20,  9.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7979/8000 [01:57<00:00, 70.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 74/8000 [00:11<12:35, 10.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7995/8000 [01:57<00:00, 71.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8021it [01:57, 68.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   1%|▏                  | 79/8000 [00:11<09:20, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 84/8000 [00:11<07:22, 17.89it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 88/8000 [00:12<08:58, 14.69it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<28:10,  8.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 91/8000 [00:12<10:33, 12.49it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:10<15:38,  4.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 97/8000 [00:13<13:46,  9.56it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:12<11:21,  3.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 99/8000 [00:13<13:00, 10.12it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:14<09:49,  3.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 101/8000 [00:13<12:12, 10.78it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:17<08:53,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 103/8000 [00:13<11:35, 11.35it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:19<08:08,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 109/8000 [00:14<08:39, 15.18it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:21<07:56,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 113/8000 [00:14<07:09, 18.37it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:23<07:30,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 116/8000 [00:14<08:11, 16.03it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:26<07:38,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 120/8000 [00:14<07:56, 16.53it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:28<07:20,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 123/8000 [00:14<07:13, 18.17it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:30<07:07,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 128/8000 [00:15<08:04, 16.24it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:32<06:55,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 131/8000 [00:15<07:13, 18.17it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:34<06:49,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 135/8000 [00:15<07:30, 17.45it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:37<07:10,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 137/8000 [00:15<07:37, 17.18it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:40<07:33,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 140/8000 [00:15<06:55, 18.93it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:42<07:10,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 143/8000 [00:16<08:19, 15.71it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:44<06:45,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 150/8000 [00:16<05:26, 24.05it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:46<06:32,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 153/8000 [00:16<06:52, 19.00it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:48<06:26,  2.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 157/8000 [00:16<06:23, 20.47it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:50<06:23,  2.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 161/8000 [00:16<06:47, 19.24it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:52<06:22,  2.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 164/8000 [00:16<06:24, 20.38it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:54<06:21,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 167/8000 [00:17<06:15, 20.87it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:56<06:12,  2.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 172/8000 [00:17<07:09, 18.21it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:58<06:05,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 177/8000 [00:17<06:59, 18.65it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:00<06:00,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 180/8000 [00:18<09:42, 13.42it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:02<05:56,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 183/8000 [00:18<09:07, 14.28it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:04<05:55,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 185/8000 [00:18<09:01, 14.44it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:06<05:50,  2.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 190/8000 [00:18<06:48, 19.14it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:08<05:51,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 196/8000 [00:18<06:05, 21.36it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:10<05:51,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 199/8000 [00:18<06:59, 18.60it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:13<05:49,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 202/8000 [00:19<07:53, 16.46it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:14<05:42,  2.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 205/8000 [00:19<07:07, 18.23it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:16<05:38,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 208/8000 [00:19<06:27, 20.11it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:19<05:41,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 211/8000 [00:19<07:40, 16.90it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:21<05:34,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 216/8000 [00:19<06:02, 21.47it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:23<05:33,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 220/8000 [00:20<06:44, 19.23it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:25<05:24,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 223/8000 [00:20<06:29, 19.99it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:27<05:23,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 228/8000 [00:20<05:16, 24.52it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:29<05:30,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 231/8000 [00:20<05:24, 23.97it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:31<05:28,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 236/8000 [00:20<06:01, 21.50it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:33<05:25,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 241/8000 [00:20<05:19, 24.25it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:35<05:26,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 245/8000 [00:21<04:55, 26.25it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:37<05:25,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 248/8000 [00:21<05:33, 23.27it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:39<05:26,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 251/8000 [00:21<05:25, 23.82it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:41<05:20,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 255/8000 [00:21<06:50, 18.87it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:43<05:12,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 260/8000 [00:22<07:55, 16.28it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [01:47<05:50,  2.29s/it]\n",
       "\n",
-      "Generating signals:   3%|▌                 | 264/8000 [00:22<07:11, 17.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 271/8000 [00:22<07:16, 17.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 275/8000 [00:22<06:27, 19.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▋                 | 279/8000 [00:22<05:57, 21.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 10it [00:01,  7.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 287/8000 [00:23<06:37, 19.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.47it/s]\n",
       "\n",
-      "Generating signals:   4%|▋                 | 290/8000 [00:23<08:29, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 293/8000 [00:23<07:46, 16.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 297/8000 [00:23<06:56, 18.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 301/8000 [00:24<07:39, 16.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 16it [00:01, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 303/8000 [00:24<07:45, 16.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 35it [00:01, 32.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 305/8000 [00:24<07:50, 16.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 55it [00:01, 54.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 307/8000 [00:24<08:09, 15.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 73it [00:01, 74.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 311/8000 [00:24<06:33, 19.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 94it [00:02, 98.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 316/8000 [00:25<06:23, 20.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 112it [00:02, 112.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 319/8000 [00:25<06:04, 21.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 133it [00:02, 134.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 323/8000 [00:25<06:47, 18.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 153it [00:02, 148.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 326/8000 [00:25<07:56, 16.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 172it [00:02, 150.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 332/8000 [00:25<05:50, 21.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 191it [00:02, 159.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 336/8000 [00:26<06:33, 19.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 210it [00:02, 165.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 340/8000 [00:26<07:14, 17.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 230it [00:02, 173.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 344/8000 [00:26<06:12, 20.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 249it [00:02, 164.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 350/8000 [00:26<05:52, 21.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 267it [00:03, 166.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 354/8000 [00:26<05:34, 22.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 289it [00:03, 180.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 357/8000 [00:27<06:54, 18.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 310it [00:03, 181.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 363/8000 [00:27<05:21, 23.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 330it [00:03, 185.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 367/8000 [00:27<05:11, 24.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 349it [00:03, 181.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 371/8000 [00:27<04:45, 26.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 368it [00:03, 183.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 374/8000 [00:27<04:55, 25.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 389it [00:03, 189.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 377/8000 [00:27<06:28, 19.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 409it [00:03, 171.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 380/8000 [00:28<06:17, 20.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 428it [00:03, 176.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 387/8000 [00:28<05:21, 23.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 447it [00:03, 179.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 391/8000 [00:28<04:57, 25.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 466it [00:04, 178.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 394/8000 [00:28<06:16, 20.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 486it [00:04, 183.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 399/8000 [00:28<05:08, 24.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 505it [00:04, 185.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 404/8000 [00:29<05:47, 21.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 524it [00:04, 184.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 409/8000 [00:29<05:27, 23.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 543it [00:04, 180.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 414/8000 [00:29<05:33, 22.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 563it [00:04, 184.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 420/8000 [00:29<05:32, 22.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 582it [00:04, 179.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 424/8000 [00:30<06:13, 20.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 601it [00:04, 177.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 427/8000 [00:30<05:56, 21.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 623it [00:04, 188.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 432/8000 [00:30<08:49, 14.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 644it [00:05, 192.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 435/8000 [00:30<08:22, 15.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 664it [00:05, 189.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 438/8000 [00:31<08:54, 14.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 683it [00:05, 184.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 441/8000 [00:31<07:54, 15.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 704it [00:05, 187.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 443/8000 [00:31<10:19, 12.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 723it [00:05, 182.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 445/8000 [00:31<10:07, 12.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 742it [00:05, 183.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 448/8000 [00:31<08:28, 14.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 761it [00:05, 183.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 453/8000 [00:31<06:05, 20.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 135.80it/s]\n",
       "\n",
-      "Generating signals:   6%|█                 | 458/8000 [00:32<04:56, 25.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 462/8000 [00:32<05:56, 21.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 468/8000 [00:32<05:46, 21.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%| | 5/100 [00:00<00:02, 41.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 473/8000 [00:32<04:57, 25.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%| | 10/100 [00:00<00:02, 35.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 477/8000 [00:32<04:46, 26.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:02, 36.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 480/8000 [00:33<05:46, 21.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:02, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 483/8000 [00:33<06:00, 20.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00<00:02, 30.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 486/8000 [00:33<06:00, 20.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▎| 27/100 [00:00<00:02, 34.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 489/8000 [00:33<09:31, 13.15it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:01, 36.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 494/8000 [00:34<07:19, 17.08it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:01<00:01, 36.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 498/8000 [00:34<07:20, 17.03it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:01<00:01, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 501/8000 [00:34<06:51, 18.24it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:01<00:00, 57.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 504/8000 [00:34<06:47, 18.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:01<00:00, 45.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 508/8000 [00:34<08:44, 14.29it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:01<00:00, 60.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 511/8000 [00:35<07:55, 15.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|▊| 86/100 [00:01<00:00, 89.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 514/8000 [00:35<07:03, 17.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 54\n",
       "\n",
-      "Generating signals:   6%|█▏                | 518/8000 [00:35<05:57, 20.94it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|██████████████████      | 3/4 [51:36<17:04, 1024.76s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.6006077789567956\n",
+      "2999: -5.8688010\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   7%|█▏                | 521/8000 [00:35<07:09, 17.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 528/8000 [00:35<05:57, 20.89it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 533/8000 [00:36<07:14, 17.17it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                            | 1/200 [00:34<1:54:55, 34.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 538/8000 [00:36<06:06, 20.37it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:37<51:59, 15.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 545/8000 [00:36<06:06, 20.32it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:39<31:51,  9.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 550/8000 [00:37<06:08, 20.21it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:42<22:22,  6.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 553/8000 [00:37<05:55, 20.93it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:44<16:51,  5.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 556/8000 [00:37<06:53, 17.99it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:46<13:43,  4.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 558/8000 [00:37<07:02, 17.60it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:49<11:45,  3.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 564/8000 [00:37<06:19, 19.58it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:52<10:56,  3.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 570/8000 [00:38<05:58, 20.70it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:54<10:09,  3.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 573/8000 [00:38<05:47, 21.37it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:57<09:29,  3.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 578/8000 [00:38<05:57, 20.75it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:59<08:53,  2.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 581/8000 [00:38<07:13, 17.11it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [01:02<08:31,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 584/8000 [00:38<06:50, 18.06it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [01:04<08:06,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 587/8000 [00:39<10:35, 11.66it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [01:06<07:48,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 589/8000 [00:39<09:58, 12.38it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [01:09<07:32,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 594/8000 [00:39<10:12, 12.08it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [01:11<07:44,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 599/8000 [00:40<08:56, 13.79it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [01:14<07:41,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 605/8000 [00:40<06:28, 19.02it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [01:16<07:30,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 609/8000 [00:40<06:45, 18.23it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [01:19<07:22,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 614/8000 [00:40<06:34, 18.74it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [01:21<06:52,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 620/8000 [00:40<05:24, 22.76it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [01:23<06:48,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 623/8000 [00:41<05:25, 22.65it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [01:25<06:47,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 628/8000 [00:41<05:55, 20.72it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [01:27<06:40,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 631/8000 [00:41<05:46, 21.29it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [01:30<06:43,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 637/8000 [00:41<05:39, 21.67it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:32<06:40,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 640/8000 [00:41<05:45, 21.28it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:35<06:45,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 648/8000 [00:42<04:59, 24.59it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:37<06:44,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 653/8000 [00:42<04:19, 28.29it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:39<06:43,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 658/8000 [00:42<04:59, 24.54it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:42<06:39,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 661/8000 [00:42<05:06, 23.92it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:44<06:37,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 664/8000 [00:42<06:07, 19.98it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:46<06:41,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 669/8000 [00:43<05:15, 23.24it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:49<06:50,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 674/8000 [00:43<04:30, 27.06it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:51<06:48,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 677/8000 [00:43<05:48, 21.01it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:54<06:47,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 681/8000 [00:43<05:18, 23.00it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:56<06:47,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 684/8000 [00:43<06:28, 18.82it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:59<06:45,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 687/8000 [00:44<07:27, 16.35it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [02:01<06:42,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 692/8000 [00:44<07:02, 17.31it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [02:04<06:49,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 694/8000 [00:44<07:07, 17.09it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [02:06<06:44,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 697/8000 [00:44<08:10, 14.88it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [02:09<06:42,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 700/8000 [00:44<07:33, 16.09it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [02:12<06:56,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 703/8000 [00:45<06:51, 17.72it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [02:15<07:20,  2.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 705/8000 [00:45<06:55, 17.54it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [02:17<07:01,  2.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 710/8000 [00:45<06:53, 17.65it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [02:20<06:50,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 712/8000 [00:45<07:35, 16.00it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [02:22<06:40,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 718/8000 [00:45<06:33, 18.53it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [02:25<06:27,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 720/8000 [00:46<09:00, 13.47it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [02:27<06:27,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 723/8000 [00:46<08:04, 15.01it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [02:30<06:18,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 726/8000 [00:46<07:05, 17.10it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [02:32<06:09,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 730/8000 [00:46<05:58, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [02:35<06:11,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 733/8000 [00:46<05:44, 21.06it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [02:37<06:07,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 738/8000 [00:47<07:46, 15.56it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:40<06:02,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 741/8000 [00:47<07:03, 17.14it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:42<06:02,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 744/8000 [00:47<06:35, 18.34it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [02:45<06:02,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 748/8000 [00:47<05:34, 21.66it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [02:47<06:04,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 751/8000 [00:47<07:02, 17.16it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:50<06:01,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 756/8000 [00:47<05:40, 21.30it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [02:52<05:53,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 761/8000 [00:48<07:08, 16.88it/s]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [02:55<05:53,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 764/8000 [00:48<08:01, 15.02it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:57<05:42,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 770/8000 [00:48<05:57, 20.20it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:59<05:43,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 774/8000 [00:49<06:29, 18.53it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [03:02<05:46,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 779/8000 [00:49<05:23, 22.30it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [03:04<05:44,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 783/8000 [00:49<07:21, 16.33it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [03:07<05:47,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 787/8000 [00:49<06:28, 18.57it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [03:10<05:46,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 791/8000 [00:50<06:58, 17.21it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▊                    | 65/200 [03:12<05:37,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 796/8000 [00:50<05:42, 21.06it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████▉                    | 66/200 [03:14<05:31,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 800/8000 [00:50<05:13, 23.00it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████                    | 67/200 [03:17<05:25,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 805/8000 [00:50<05:50, 20.51it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▏                   | 68/200 [03:22<06:32,  2.98s/it]\n",
       "\n",
-      "Generating signals:  10%|█▊                | 810/8000 [00:50<05:59, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 814/8000 [00:50<05:35, 21.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 818/8000 [00:51<05:13, 22.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:14, 14.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 822/8000 [00:51<06:20, 18.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 9it [00:14,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 829/8000 [00:51<05:23, 22.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 17it [00:14,  1.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 833/8000 [00:52<08:18, 14.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:14,  1.69it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
       "\n",
-      "Generating signals:  10%|█▉                | 837/8000 [00:52<07:12, 16.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 844/8000 [00:52<05:18, 22.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 852/8000 [00:52<04:55, 24.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:12, 12.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 860/8000 [00:53<04:42, 25.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 18it [00:12,  1.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 863/8000 [00:53<05:40, 20.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 35it [00:12,  4.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 868/8000 [00:53<06:00, 19.81it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 57it [00:12,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 872/8000 [00:54<08:02, 14.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:13, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 874/8000 [00:54<07:59, 14.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 95it [00:13, 21.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 877/8000 [00:54<11:03, 10.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 113it [00:13, 30.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 880/8000 [00:54<09:36, 12.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 130it [00:13, 39.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 884/8000 [00:55<07:52, 15.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 147it [00:13, 51.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 891/8000 [00:55<06:09, 19.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 164it [00:13, 65.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 897/8000 [00:55<04:47, 24.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 181it [00:13, 80.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 903/8000 [00:55<04:51, 24.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 199it [00:13, 96.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 906/8000 [00:55<04:56, 23.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 216it [00:13, 109.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 910/8000 [00:55<04:33, 25.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 235it [00:14, 126.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 915/8000 [00:56<05:00, 23.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 253it [00:14, 133.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 919/8000 [00:56<05:53, 20.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 270it [00:14, 127.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 926/8000 [00:56<04:23, 26.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 292it [00:14, 148.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 931/8000 [00:57<06:05, 19.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 313it [00:14, 163.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 934/8000 [00:57<06:54, 17.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 332it [00:14, 166.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 939/8000 [00:57<06:41, 17.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 351it [00:14, 171.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 942/8000 [00:57<06:30, 18.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 370it [00:14, 174.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 947/8000 [00:57<05:22, 21.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 389it [00:14, 177.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 950/8000 [00:58<06:51, 17.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 408it [00:15, 168.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 953/8000 [00:58<06:24, 18.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 426it [00:15, 166.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 957/8000 [00:58<05:44, 20.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 443it [00:15, 164.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 961/8000 [00:58<05:07, 22.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 460it [00:15, 163.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 965/8000 [00:58<05:06, 22.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 482it [00:15, 178.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 971/8000 [00:59<07:56, 14.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 501it [00:15, 164.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 974/8000 [00:59<07:41, 15.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 521it [00:15, 173.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 976/8000 [00:59<07:49, 14.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 541it [00:15, 178.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 981/8000 [00:59<07:21, 15.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 561it [00:15, 182.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 983/8000 [01:00<07:49, 14.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 580it [00:16, 178.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 986/8000 [01:00<09:03, 12.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 598it [00:16, 155.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 992/8000 [01:00<06:09, 18.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 615it [00:16, 147.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 995/8000 [01:00<05:45, 20.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 633it [00:16, 155.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 998/8000 [01:00<06:53, 16.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 650it [00:16, 158.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1001/8000 [01:01<06:38, 17.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 673it [00:16, 178.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1005/8000 [01:01<07:24, 15.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 692it [00:16, 180.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1009/8000 [01:01<07:23, 15.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 712it [00:16, 182.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1013/8000 [01:01<07:33, 15.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 731it [00:16, 183.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1015/8000 [01:02<07:47, 14.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 750it [00:17, 184.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1017/8000 [01:02<09:43, 11.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 769it [00:17, 185.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1020/8000 [01:02<08:28, 13.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:17, 46.13it/s] \n",
       "\n",
-      "Generating signals:  13%|██▏              | 1023/8000 [01:02<07:26, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1026/8000 [01:03<09:02, 12.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1028/8000 [01:03<08:47, 13.23it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%| | 1/100 [00:00<00:17,  5.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1031/8000 [01:03<07:35, 15.30it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:05, 16.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1034/8000 [01:03<06:44, 17.20it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%| | 7/100 [00:00<00:04, 20.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1037/8000 [01:03<06:22, 18.21it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:03, 26.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1040/8000 [01:03<07:26, 15.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:03, 25.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1042/8000 [01:03<07:47, 14.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:02, 28.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1044/8000 [01:04<07:55, 14.62it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:02, 27.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1046/8000 [01:04<09:42, 11.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:02, 26.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1050/8000 [01:04<07:06, 16.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▎| 27/100 [00:01<00:02, 26.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1053/8000 [01:04<06:45, 17.14it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:01<00:02, 26.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1055/8000 [01:04<09:55, 11.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:01<00:02, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1058/8000 [01:05<11:21, 10.18it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:01<00:01, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1060/8000 [01:05<12:10,  9.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:01<00:01, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1062/8000 [01:06<15:29,  7.46it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:01<00:01, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1064/8000 [01:06<15:20,  7.54it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:01<00:01, 28.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1067/8000 [01:06<11:46,  9.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:01<00:01, 29.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1070/8000 [01:06<09:28, 12.19it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:02<00:01, 30.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1074/8000 [01:06<07:19, 15.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:02<00:01, 30.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1076/8000 [01:06<07:13, 15.97it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:02<00:00, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1078/8000 [01:06<07:20, 15.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:02<00:00, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1081/8000 [01:07<06:55, 16.64it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:02<00:00, 34.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1087/8000 [01:07<07:28, 15.41it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:02<00:00, 34.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1090/8000 [01:07<06:54, 16.69it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|▊| 83/100 [00:02<00:00, 32.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1093/8000 [01:07<07:37, 15.09it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:02<00:00, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1096/8000 [01:08<06:53, 16.68it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|▉| 91/100 [00:03<00:00, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1098/8000 [01:08<07:04, 16.24it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  95%|▉| 95/100 [00:03<00:00, 31.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1103/8000 [01:08<06:37, 17.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:03<00:00, 29\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1105/8000 [01:08<06:38, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1108/8000 [01:08<06:25, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1110/8000 [01:08<06:30, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                  | 3/8000 [00:04<3:07:08,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1112/8000 [01:08<06:38, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                  | 5/8000 [00:04<1:51:02,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1118/8000 [01:09<04:24, 25.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                    | 8/8000 [00:04<58:31,  2.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1121/8000 [01:09<06:18, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 11/8000 [00:05<38:50,  3.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1126/8000 [01:09<06:15, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 13/8000 [00:05<31:54,  4.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1129/8000 [01:09<05:52, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 15/8000 [00:05<26:11,  5.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1133/8000 [01:09<05:27, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 18/8000 [00:05<19:38,  6.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1136/8000 [01:10<05:20, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 20/8000 [00:05<18:24,  7.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1139/8000 [01:10<08:03, 14.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 23/8000 [00:06<15:58,  8.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1142/8000 [01:10<08:47, 13.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 26/8000 [00:06<13:29,  9.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1145/8000 [01:11<10:37, 10.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 30/8000 [00:07<28:38,  4.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1150/8000 [01:11<08:41, 13.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 33/8000 [00:08<22:56,  5.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1152/8000 [01:11<09:01, 12.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 35/8000 [00:08<21:05,  6.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1155/8000 [01:11<09:12, 12.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 37/8000 [00:08<18:55,  7.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1158/8000 [01:12<08:16, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 40/8000 [00:08<15:50,  8.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1160/8000 [01:12<08:25, 13.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                   | 44/8000 [00:09<24:17,  5.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1162/8000 [01:12<08:18, 13.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                   | 46/8000 [00:10<21:38,  6.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1166/8000 [01:12<06:26, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                   | 47/8000 [00:10<22:25,  5.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1168/8000 [01:12<06:41, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                   | 52/8000 [00:11<23:25,  5.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1170/8000 [01:12<07:04, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 54/8000 [00:11<21:42,  6.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1174/8000 [01:12<05:48, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 55/8000 [00:11<22:53,  5.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1179/8000 [01:13<05:48, 19.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 65/8000 [00:12<13:36,  9.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1182/8000 [01:13<08:12, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 66/8000 [00:12<14:52,  8.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1185/8000 [01:13<07:16, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 70/8000 [00:12<13:07, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1190/8000 [01:13<06:42, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 71/8000 [00:13<14:15,  9.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1195/8000 [01:14<05:34, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 73/8000 [00:13<15:08,  8.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1200/8000 [01:14<05:24, 20.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 76/8000 [00:13<13:22,  9.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1208/8000 [01:14<04:38, 24.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 78/8000 [00:13<13:15,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1211/8000 [01:14<05:35, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 82/8000 [00:13<10:35, 12.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1216/8000 [01:14<04:50, 23.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 85/8000 [00:14<11:53, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1219/8000 [01:15<04:44, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 88/8000 [00:14<13:59,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1223/8000 [01:15<05:21, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 92/8000 [00:15<12:27, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1229/8000 [01:15<05:18, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 98/8000 [00:15<12:32, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1232/8000 [01:15<07:08, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 103/8000 [00:15<10:22, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1235/8000 [01:16<08:07, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 107/8000 [00:16<09:04, 14.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1238/8000 [01:16<07:37, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 111/8000 [00:16<08:12, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1243/8000 [01:16<08:07, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                 | 114/8000 [00:16<10:54, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1246/8000 [01:16<07:26, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                 | 116/8000 [00:16<11:21, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1248/8000 [01:17<09:14, 12.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                 | 118/8000 [00:17<12:20, 10.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1250/8000 [01:17<08:37, 13.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 122/8000 [00:17<10:24, 12.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1253/8000 [01:17<08:31, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 125/8000 [00:17<09:56, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1257/8000 [01:17<06:33, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 127/8000 [00:17<13:40,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1260/8000 [01:17<06:21, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 132/8000 [00:18<10:13, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1263/8000 [01:18<06:06, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 135/8000 [00:18<12:44, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1270/8000 [01:18<04:12, 26.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 139/8000 [00:18<10:25, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1273/8000 [01:18<05:26, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 144/8000 [00:19<10:47, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1276/8000 [01:18<06:21, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 147/8000 [00:19<13:01, 10.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1279/8000 [01:18<07:12, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 150/8000 [00:20<14:42,  8.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1283/8000 [01:19<07:12, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 156/8000 [00:20<13:16,  9.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1287/8000 [01:19<06:22, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 158/8000 [00:20<13:23,  9.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1290/8000 [01:19<06:31, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 161/8000 [00:21<12:19, 10.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1292/8000 [01:19<06:47, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 164/8000 [00:21<11:28, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1294/8000 [01:19<07:29, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 166/8000 [00:21<17:51,  7.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1297/8000 [01:20<08:43, 12.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 168/8000 [00:22<16:19,  7.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1299/8000 [01:20<08:22, 13.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 170/8000 [00:22<15:50,  8.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1302/8000 [01:20<07:09, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 174/8000 [00:22<12:19, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1306/8000 [01:20<07:48, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 179/8000 [00:22<09:29, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1309/8000 [01:20<07:10, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 181/8000 [00:23<12:49, 10.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1314/8000 [01:21<07:10, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 185/8000 [00:23<11:21, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1316/8000 [01:21<07:32, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 187/8000 [00:23<11:35, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1321/8000 [01:21<05:59, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 189/8000 [00:23<12:59, 10.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1323/8000 [01:21<06:36, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 194/8000 [00:24<10:14, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1326/8000 [01:21<06:03, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 197/8000 [00:24<10:19, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1329/8000 [01:21<05:39, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 201/8000 [00:24<09:14, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1332/8000 [01:22<05:16, 21.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 204/8000 [00:24<09:04, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1335/8000 [01:22<06:50, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 207/8000 [00:25<09:17, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1337/8000 [01:22<07:03, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 210/8000 [00:25<09:28, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1339/8000 [01:22<07:11, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 212/8000 [00:25<10:33, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1341/8000 [01:22<07:29, 14.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 216/8000 [00:26<12:50, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1344/8000 [01:22<07:00, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 218/8000 [00:26<13:11,  9.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1347/8000 [01:23<08:04, 13.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 222/8000 [00:26<13:14,  9.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1350/8000 [01:23<06:52, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 224/8000 [00:27<16:10,  8.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1353/8000 [01:23<07:55, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 227/8000 [00:27<13:38,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1356/8000 [01:23<07:05, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 230/8000 [00:27<11:59, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1360/8000 [01:23<05:56, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 235/8000 [00:28<12:51, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1365/8000 [01:24<06:10, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 239/8000 [00:28<11:01, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1368/8000 [01:24<06:54, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 243/8000 [00:28<09:16, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1371/8000 [01:24<06:30, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 246/8000 [00:28<09:20, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1376/8000 [01:24<05:05, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 248/8000 [00:28<10:20, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1380/8000 [01:24<05:47, 19.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 253/8000 [00:29<10:48, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1383/8000 [01:25<07:32, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 256/8000 [00:29<12:57,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1385/8000 [01:25<07:35, 14.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 260/8000 [00:30<11:17, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1387/8000 [01:25<07:32, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 265/8000 [00:30<08:54, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1390/8000 [01:25<06:37, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 270/8000 [00:30<07:27, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1393/8000 [01:26<07:59, 13.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 275/8000 [00:30<09:43, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1396/8000 [01:26<08:54, 12.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                 | 278/8000 [00:31<11:14, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1398/8000 [01:26<08:32, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 281/8000 [00:31<10:56, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1401/8000 [01:26<07:20, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 283/8000 [00:32<14:13,  9.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1406/8000 [01:26<05:15, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 290/8000 [00:32<11:39, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1410/8000 [01:27<06:04, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 294/8000 [00:32<10:40, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1413/8000 [01:27<05:49, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 297/8000 [00:32<09:58, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1416/8000 [01:27<05:39, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 302/8000 [00:33<08:17, 15.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1419/8000 [01:27<06:43, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 305/8000 [00:33<11:11, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1424/8000 [01:27<05:10, 21.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 308/8000 [00:33<10:24, 12.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1428/8000 [01:27<04:41, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 310/8000 [00:33<10:46, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1433/8000 [01:28<04:54, 22.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 312/8000 [00:34<11:46, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1437/8000 [01:28<04:51, 22.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 315/8000 [00:34<10:48, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1441/8000 [01:28<04:30, 24.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 317/8000 [00:34<11:24, 11.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1446/8000 [01:28<04:45, 22.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 319/8000 [00:35<15:48,  8.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1451/8000 [01:28<05:15, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 323/8000 [00:35<11:46, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1455/8000 [01:29<05:50, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 327/8000 [00:35<12:08, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1458/8000 [01:29<05:33, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 329/8000 [00:35<12:28, 10.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1462/8000 [01:29<04:55, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 332/8000 [00:36<11:41, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1466/8000 [01:29<04:32, 23.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 336/8000 [00:36<10:27, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1470/8000 [01:29<04:20, 25.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 341/8000 [00:36<08:35, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1475/8000 [01:30<05:57, 18.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 343/8000 [00:37<12:26, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1482/8000 [01:30<05:13, 20.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 346/8000 [00:37<11:07, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1485/8000 [01:30<06:22, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 353/8000 [00:37<09:58, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1487/8000 [01:30<06:44, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 355/8000 [00:38<12:09, 10.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1491/8000 [01:31<06:02, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 359/8000 [00:38<10:57, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1494/8000 [01:31<06:58, 15.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 361/8000 [00:38<10:39, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1501/8000 [01:31<06:08, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 365/8000 [00:38<09:09, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1505/8000 [01:31<05:33, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 367/8000 [00:38<09:44, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1509/8000 [01:32<06:28, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 369/8000 [00:39<13:03,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1513/8000 [01:32<05:44, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 372/8000 [00:39<16:05,  7.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1519/8000 [01:32<06:18, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 373/8000 [00:40<18:12,  6.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1522/8000 [01:32<06:09, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 375/8000 [00:40<21:10,  6.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1525/8000 [01:32<05:59, 17.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 376/8000 [00:41<29:16,  4.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1530/8000 [01:33<06:00, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 377/8000 [00:41<30:08,  4.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1534/8000 [01:33<05:34, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 380/8000 [00:41<21:33,  5.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1538/8000 [01:33<07:32, 14.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 384/8000 [00:42<19:06,  6.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1540/8000 [01:34<08:31, 12.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 386/8000 [00:42<17:23,  7.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1543/8000 [01:34<07:35, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 389/8000 [00:42<15:15,  8.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1545/8000 [01:34<09:04, 11.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 394/8000 [00:42<11:36, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1550/8000 [01:34<06:28, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 396/8000 [00:43<12:49,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1553/8000 [01:34<06:04, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 398/8000 [00:43<12:35, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1557/8000 [01:35<06:58, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 402/8000 [00:43<13:33,  9.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1559/8000 [01:35<06:49, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 407/8000 [00:44<10:39, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1561/8000 [01:35<06:42, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 412/8000 [00:44<08:37, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1563/8000 [01:35<08:36, 12.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 414/8000 [00:44<10:54, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1567/8000 [01:35<07:54, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 417/8000 [00:44<10:03, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1570/8000 [01:36<06:57, 15.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 419/8000 [00:45<10:29, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1572/8000 [01:36<07:29, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 423/8000 [00:45<09:08, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1576/8000 [01:36<06:08, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 427/8000 [00:45<09:40, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1579/8000 [01:36<07:13, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 431/8000 [00:45<08:56, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1582/8000 [01:36<06:18, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 434/8000 [00:46<09:12, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1586/8000 [01:36<05:12, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 438/8000 [00:46<10:45, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1594/8000 [01:37<04:45, 22.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▉                 | 440/8000 [00:46<11:32, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1597/8000 [01:37<04:44, 22.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▉                 | 443/8000 [00:47<18:15,  6.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1602/8000 [01:37<04:09, 25.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 445/8000 [00:47<17:19,  7.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1605/8000 [01:37<05:38, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 446/8000 [00:48<19:10,  6.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1608/8000 [01:37<05:22, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 450/8000 [00:48<13:32,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1613/8000 [01:38<04:26, 23.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 452/8000 [00:48<13:35,  9.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1618/8000 [01:38<04:41, 22.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 455/8000 [00:49<17:12,  7.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1621/8000 [01:38<04:27, 23.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 458/8000 [00:49<14:35,  8.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1628/8000 [01:38<04:07, 25.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 462/8000 [00:49<12:23, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1634/8000 [01:38<04:15, 24.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 464/8000 [00:49<12:27, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1640/8000 [01:39<04:24, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 467/8000 [00:49<11:00, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1643/8000 [01:39<06:10, 17.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 470/8000 [00:50<19:26,  6.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1645/8000 [01:39<07:36, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 472/8000 [00:51<17:45,  7.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1648/8000 [01:40<08:28, 12.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 475/8000 [00:51<14:29,  8.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1651/8000 [01:40<07:33, 14.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 477/8000 [00:51<15:15,  8.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1653/8000 [01:40<07:33, 14.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 479/8000 [00:51<15:35,  8.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1655/8000 [01:40<07:16, 14.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 481/8000 [00:51<14:55,  8.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1657/8000 [01:40<07:07, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 487/8000 [00:52<15:42,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1662/8000 [01:40<05:08, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 491/8000 [00:52<12:57,  9.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1665/8000 [01:40<05:06, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 496/8000 [00:53<10:18, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1668/8000 [01:41<07:25, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 498/8000 [00:53<11:57, 10.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1671/8000 [01:41<08:01, 13.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 501/8000 [00:53<10:54, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1675/8000 [01:41<06:20, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 506/8000 [00:53<09:15, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1678/8000 [01:42<08:34, 12.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 512/8000 [00:54<07:39, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1680/8000 [01:42<08:12, 12.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 514/8000 [00:54<09:16, 13.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1684/8000 [01:42<07:43, 13.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 521/8000 [00:55<14:10,  8.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1688/8000 [01:42<07:23, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 524/8000 [00:55<13:38,  9.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1691/8000 [01:42<06:39, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 530/8000 [00:56<11:02, 11.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1694/8000 [01:43<06:05, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 533/8000 [00:56<10:46, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1700/8000 [01:43<06:00, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 535/8000 [00:56<11:41, 10.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1704/8000 [01:43<06:08, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 537/8000 [00:57<12:05, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1706/8000 [01:43<06:15, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 539/8000 [00:57<13:04,  9.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1711/8000 [01:43<05:57, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 542/8000 [00:57<11:34, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1716/8000 [01:44<05:39, 18.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 545/8000 [00:57<11:42, 10.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1723/8000 [01:44<04:14, 24.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 548/8000 [00:58<12:11, 10.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1727/8000 [01:44<03:53, 26.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 552/8000 [00:58<10:58, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1733/8000 [01:44<04:18, 24.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 555/8000 [00:58<09:55, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1738/8000 [01:45<04:43, 22.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 560/8000 [00:58<07:54, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1742/8000 [01:45<06:09, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 562/8000 [00:59<11:52, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1747/8000 [01:45<05:43, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 564/8000 [00:59<11:52, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1751/8000 [01:45<05:53, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 568/8000 [00:59<12:55,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1756/8000 [01:46<05:04, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 570/8000 [01:00<13:16,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1759/8000 [01:46<04:48, 21.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 573/8000 [01:00<11:57, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1766/8000 [01:46<04:47, 21.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 575/8000 [01:00<16:04,  7.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1772/8000 [01:46<03:57, 26.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 577/8000 [01:01<15:45,  7.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1775/8000 [01:46<04:10, 24.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 581/8000 [01:01<11:36, 10.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1779/8000 [01:47<04:55, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 584/8000 [01:01<13:20,  9.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1782/8000 [01:47<04:51, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 586/8000 [01:01<14:40,  8.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1785/8000 [01:47<05:02, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 589/8000 [01:02<12:48,  9.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1789/8000 [01:47<04:38, 22.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 592/8000 [01:02<11:14, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1795/8000 [01:47<05:46, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 596/8000 [01:02<12:28,  9.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1799/8000 [01:48<05:10, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 599/8000 [01:03<12:07, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1803/8000 [01:48<04:41, 22.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎                | 602/8000 [01:03<13:59,  8.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1807/8000 [01:48<04:27, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎                | 605/8000 [01:03<12:30,  9.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1816/8000 [01:48<05:25, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎                | 608/8000 [01:03<11:07, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1819/8000 [01:49<06:26, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 612/8000 [01:04<12:49,  9.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1822/8000 [01:49<05:57, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 616/8000 [01:04<10:50, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1826/8000 [01:49<05:14, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 619/8000 [01:04<10:41, 11.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1829/8000 [01:49<05:09, 19.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 623/8000 [01:05<12:04, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1833/8000 [01:49<04:31, 22.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 625/8000 [01:05<13:07,  9.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1837/8000 [01:50<05:33, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 629/8000 [01:06<13:09,  9.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1843/8000 [01:50<04:10, 24.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 636/8000 [01:06<11:22, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1847/8000 [01:50<05:08, 19.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 638/8000 [01:06<11:31, 10.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1851/8000 [01:50<05:28, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 643/8000 [01:07<13:01,  9.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1854/8000 [01:51<06:15, 16.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 645/8000 [01:07<12:49,  9.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1856/8000 [01:51<07:44, 13.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 650/8000 [01:07<10:13, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1860/8000 [01:51<06:35, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 653/8000 [01:08<10:31, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1865/8000 [01:51<05:11, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 655/8000 [01:08<11:12, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1868/8000 [01:51<05:11, 19.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 659/8000 [01:08<10:14, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1872/8000 [01:52<05:36, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 661/8000 [01:09<11:08, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1877/8000 [01:52<05:20, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 663/8000 [01:09<10:47, 11.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉             | 1880/8000 [01:52<05:09, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                | 668/8000 [01:09<09:18, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1887/8000 [01:52<05:00, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                | 671/8000 [01:09<11:04, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1890/8000 [01:52<04:56, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                | 676/8000 [01:10<11:29, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1894/8000 [01:53<05:33, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                | 680/8000 [01:10<10:07, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1899/8000 [01:53<04:43, 21.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 683/8000 [01:11<12:04, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1905/8000 [01:53<03:56, 25.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 686/8000 [01:11<12:46,  9.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1908/8000 [01:53<04:02, 25.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 691/8000 [01:11<12:01, 10.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1913/8000 [01:53<03:38, 27.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 694/8000 [01:12<11:09, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1916/8000 [01:54<05:46, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 697/8000 [01:12<10:26, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1923/8000 [01:54<05:25, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 700/8000 [01:12<10:04, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1927/8000 [01:54<06:52, 14.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 703/8000 [01:12<09:32, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1930/8000 [01:55<07:20, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 706/8000 [01:12<09:42, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1934/8000 [01:55<07:09, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 710/8000 [01:13<09:11, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1937/8000 [01:55<06:31, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 713/8000 [01:13<11:10, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1940/8000 [01:55<06:02, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 716/8000 [01:14<13:58,  8.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1947/8000 [01:55<05:09, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 719/8000 [01:14<12:41,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1952/8000 [01:56<04:25, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 721/8000 [01:14<12:30,  9.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1955/8000 [01:56<05:27, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 724/8000 [01:15<15:10,  7.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1959/8000 [01:56<04:47, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 728/8000 [01:15<12:01, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1963/8000 [01:56<05:03, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 730/8000 [01:15<12:05, 10.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1968/8000 [01:57<06:14, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 734/8000 [01:16<14:16,  8.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1971/8000 [01:57<06:00, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 736/8000 [01:16<14:36,  8.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1974/8000 [01:57<05:31, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 739/8000 [01:16<12:40,  9.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1979/8000 [01:57<05:42, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 742/8000 [01:16<12:36,  9.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1981/8000 [01:58<07:04, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 744/8000 [01:17<15:35,  7.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1984/8000 [01:58<06:15, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 747/8000 [01:17<14:14,  8.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1986/8000 [01:58<06:26, 15.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 748/8000 [01:17<15:52,  7.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1990/8000 [01:58<05:14, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 749/8000 [01:18<17:58,  6.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1996/8000 [01:58<03:46, 26.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 752/8000 [01:18<14:40,  8.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2000/8000 [01:58<03:46, 26.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 755/8000 [01:18<12:52,  9.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2003/8000 [01:58<03:59, 25.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 756/8000 [01:18<14:50,  8.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2008/8000 [01:59<06:26, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 759/8000 [01:19<13:05,  9.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2012/8000 [01:59<05:39, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 762/8000 [01:19<11:53, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2015/8000 [01:59<05:19, 18.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 765/8000 [01:19<15:22,  7.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2019/8000 [01:59<05:43, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 770/8000 [01:20<11:19, 10.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2022/8000 [02:00<05:23, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 773/8000 [01:20<10:39, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2025/8000 [02:00<05:09, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 779/8000 [01:20<10:30, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2030/8000 [02:00<05:05, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 781/8000 [01:21<10:46, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2033/8000 [02:00<05:11, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 784/8000 [01:21<10:10, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2039/8000 [02:00<04:54, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 788/8000 [01:21<11:07, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2042/8000 [02:01<06:05, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 790/8000 [01:21<11:35, 10.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2046/8000 [02:01<05:25, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 793/8000 [01:22<10:32, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2048/8000 [02:01<05:28, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 795/8000 [01:22<10:06, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2053/8000 [02:01<06:26, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 799/8000 [01:22<07:50, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2058/8000 [02:02<05:09, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 801/8000 [01:22<09:03, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2061/8000 [02:02<05:13, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 804/8000 [01:22<09:26, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2064/8000 [02:02<06:25, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 809/8000 [01:23<11:38, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2067/8000 [02:02<07:02, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 813/8000 [01:23<12:55,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2070/8000 [02:03<07:52, 12.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 815/8000 [01:24<13:11,  9.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2073/8000 [02:03<07:13, 13.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 818/8000 [01:24<11:52, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2076/8000 [02:03<06:32, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 823/8000 [01:24<10:57, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2078/8000 [02:03<06:43, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 828/8000 [01:25<11:37, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2081/8000 [02:03<06:10, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 830/8000 [01:25<13:47,  8.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2084/8000 [02:03<06:40, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 831/8000 [01:26<15:52,  7.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2087/8000 [02:04<05:55, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                | 836/8000 [01:26<11:34, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2091/8000 [02:04<05:22, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 844/8000 [01:27<11:59,  9.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2095/8000 [02:04<04:52, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 848/8000 [01:27<11:11, 10.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2098/8000 [02:04<04:44, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 852/8000 [01:27<10:32, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2101/8000 [02:04<04:41, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 856/8000 [01:27<09:37, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2104/8000 [02:04<04:56, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 861/8000 [01:28<11:16, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2107/8000 [02:04<04:48, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 865/8000 [01:28<10:07, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2110/8000 [02:05<04:33, 21.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 868/8000 [01:29<11:06, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2115/8000 [02:05<05:44, 17.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 870/8000 [01:29<11:48, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2118/8000 [02:05<05:07, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 873/8000 [01:29<10:42, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2121/8000 [02:05<04:53, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 877/8000 [01:29<09:19, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2126/8000 [02:05<04:56, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 879/8000 [01:30<12:13,  9.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2130/8000 [02:06<04:35, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 881/8000 [01:30<12:50,  9.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2133/8000 [02:06<05:37, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 883/8000 [01:30<12:19,  9.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2136/8000 [02:06<06:25, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 885/8000 [01:30<12:16,  9.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2138/8000 [02:06<06:33, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 887/8000 [01:31<12:22,  9.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2141/8000 [02:06<06:18, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 889/8000 [01:31<12:28,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2144/8000 [02:07<06:03, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 891/8000 [01:31<12:36,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2146/8000 [02:07<09:25, 10.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 893/8000 [01:32<16:31,  7.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2148/8000 [02:07<10:24,  9.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 896/8000 [01:32<13:24,  8.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2151/8000 [02:07<08:24, 11.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 898/8000 [01:32<14:33,  8.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2155/8000 [02:08<06:25, 15.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 900/8000 [01:32<15:25,  7.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2159/8000 [02:08<05:19, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 905/8000 [01:33<11:36, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2164/8000 [02:08<04:28, 21.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 909/8000 [01:33<10:04, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2169/8000 [02:08<05:53, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 912/8000 [01:34<13:54,  8.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2172/8000 [02:08<05:33, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 915/8000 [01:34<14:04,  8.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2177/8000 [02:09<04:27, 21.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 917/8000 [01:34<15:05,  7.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2182/8000 [02:09<04:33, 21.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 921/8000 [01:34<12:16,  9.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2188/8000 [02:09<05:13, 18.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 925/8000 [01:35<15:41,  7.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2192/8000 [02:10<06:10, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 926/8000 [01:36<17:57,  6.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2194/8000 [02:10<06:09, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 928/8000 [01:36<16:48,  7.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2197/8000 [02:10<05:52, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 929/8000 [01:36<19:29,  6.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2200/8000 [02:10<05:31, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 936/8000 [01:36<11:01, 10.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2202/8000 [02:10<05:38, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 939/8000 [01:37<11:14, 10.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2205/8000 [02:10<05:08, 18.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 945/8000 [01:37<08:07, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2207/8000 [02:11<07:16, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 948/8000 [01:37<08:10, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2211/8000 [02:11<05:59, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 950/8000 [01:38<11:20, 10.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2215/8000 [02:11<05:20, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 952/8000 [01:38<12:04,  9.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2218/8000 [02:11<06:23, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 955/8000 [01:38<13:51,  8.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2220/8000 [02:11<06:10, 15.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 958/8000 [01:38<12:10,  9.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2223/8000 [02:12<06:41, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 964/8000 [01:39<08:47, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2227/8000 [02:12<05:22, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 966/8000 [01:39<09:55, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2231/8000 [02:12<04:39, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 969/8000 [01:39<12:45,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2236/8000 [02:12<05:08, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 972/8000 [01:40<12:13,  9.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2239/8000 [02:13<08:29, 11.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 974/8000 [01:40<15:10,  7.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2242/8000 [02:13<07:27, 12.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 975/8000 [01:40<16:29,  7.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2245/8000 [02:13<07:01, 13.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 980/8000 [01:41<11:24, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2247/8000 [02:13<08:08, 11.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 986/8000 [01:41<08:30, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2249/8000 [02:13<07:52, 12.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 988/8000 [01:41<09:56, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2251/8000 [02:14<07:23, 12.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 990/8000 [01:42<13:33,  8.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2253/8000 [02:14<08:47, 10.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 991/8000 [01:42<16:24,  7.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2258/8000 [02:14<05:47, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 992/8000 [01:42<17:24,  6.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2262/8000 [02:14<06:20, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 998/8000 [01:42<09:55, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2265/8000 [02:14<05:50, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1002/8000 [01:43<09:28, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2267/8000 [02:15<05:54, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1004/8000 [01:43<10:19, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2271/8000 [02:15<04:53, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1009/8000 [01:43<10:14, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2277/8000 [02:15<05:05, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1014/8000 [01:44<10:54, 10.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2280/8000 [02:16<08:42, 10.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1016/8000 [01:44<11:19, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2285/8000 [02:16<06:50, 13.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1019/8000 [01:44<10:24, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2290/8000 [02:16<05:50, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1024/8000 [01:45<10:51, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2295/8000 [02:16<05:03, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1028/8000 [01:45<09:27, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2301/8000 [02:17<04:47, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1031/8000 [01:45<08:59, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2305/8000 [02:17<04:36, 20.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1034/8000 [01:45<08:36, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2309/8000 [02:17<05:31, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1036/8000 [01:46<09:10, 12.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2311/8000 [02:18<08:10, 11.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1040/8000 [01:46<08:15, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2313/8000 [02:18<07:47, 12.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1044/8000 [01:46<10:27, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2315/8000 [02:18<07:33, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1049/8000 [01:47<08:16, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2318/8000 [02:18<06:50, 13.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1052/8000 [01:47<08:24, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2320/8000 [02:18<06:40, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1056/8000 [01:47<07:41, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2325/8000 [02:18<05:05, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1060/8000 [01:47<06:59, 16.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2327/8000 [02:19<07:01, 13.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1062/8000 [01:48<09:58, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2331/8000 [02:19<06:48, 13.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1066/8000 [01:48<11:01, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2334/8000 [02:19<06:07, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1072/8000 [01:49<10:11, 11.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2338/8000 [02:19<05:02, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1075/8000 [01:49<09:52, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2341/8000 [02:19<04:52, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1077/8000 [01:49<10:14, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2347/8000 [02:20<04:42, 19.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1079/8000 [01:49<10:42, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2353/8000 [02:20<03:39, 25.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1083/8000 [01:49<09:44, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2357/8000 [02:20<04:24, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1087/8000 [01:50<10:32, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2361/8000 [02:20<06:36, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1089/8000 [01:50<12:00,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2366/8000 [02:21<05:54, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1091/8000 [01:50<12:06,  9.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2369/8000 [02:21<05:23, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1093/8000 [01:51<13:12,  8.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2372/8000 [02:21<07:19, 12.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1094/8000 [01:51<14:49,  7.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2376/8000 [02:22<08:36, 10.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1098/8000 [01:51<11:16, 10.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2379/8000 [02:22<08:41, 10.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1101/8000 [01:52<13:29,  8.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2382/8000 [02:22<09:34,  9.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1102/8000 [01:52<18:06,  6.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2387/8000 [02:23<06:58, 13.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1103/8000 [01:52<19:24,  5.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2390/8000 [02:23<06:28, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1104/8000 [01:52<19:59,  5.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2392/8000 [02:23<07:30, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1107/8000 [01:53<14:42,  7.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2396/8000 [02:23<05:55, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1108/8000 [01:53<16:11,  7.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2400/8000 [02:23<05:58, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1110/8000 [01:53<16:03,  7.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2404/8000 [02:23<04:49, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1113/8000 [01:53<12:28,  9.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2408/8000 [02:24<06:26, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1117/8000 [01:54<13:17,  8.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2411/8000 [02:24<05:47, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1120/8000 [01:54<11:55,  9.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2414/8000 [02:25<08:34, 10.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1122/8000 [01:54<11:59,  9.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2417/8000 [02:25<07:31, 12.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1123/8000 [01:55<14:29,  7.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2420/8000 [02:25<06:36, 14.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1124/8000 [01:55<21:00,  5.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2422/8000 [02:25<06:26, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1128/8000 [01:55<14:06,  8.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2424/8000 [02:25<07:47, 11.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1137/8000 [01:56<10:51, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2427/8000 [02:25<06:33, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1140/8000 [01:56<10:30, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2429/8000 [02:25<06:37, 14.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1143/8000 [01:57<12:20,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2431/8000 [02:26<06:34, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1146/8000 [01:57<13:48,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2434/8000 [02:26<05:40, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1150/8000 [01:57<11:16, 10.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2438/8000 [02:26<06:08, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1153/8000 [01:58<10:40, 10.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2441/8000 [02:26<05:31, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1157/8000 [01:58<09:06, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2444/8000 [02:26<06:00, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1160/8000 [01:58<08:43, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2447/8000 [02:27<07:01, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1164/8000 [01:58<07:57, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2453/8000 [02:27<04:46, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1167/8000 [01:59<10:35, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2456/8000 [02:27<05:25, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1171/8000 [01:59<09:07, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2461/8000 [02:27<05:26, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1173/8000 [01:59<09:29, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2467/8000 [02:28<04:56, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1176/8000 [02:00<11:41,  9.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2469/8000 [02:28<05:57, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1182/8000 [02:00<10:07, 11.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2475/8000 [02:28<05:59, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1184/8000 [02:00<10:31, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2477/8000 [02:28<06:12, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1190/8000 [02:01<10:06, 11.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2480/8000 [02:29<06:56, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1193/8000 [02:01<09:32, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2483/8000 [02:29<06:26, 14.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1195/8000 [02:01<10:21, 10.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2486/8000 [02:29<07:10, 12.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1197/8000 [02:01<11:43,  9.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2491/8000 [02:29<06:12, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1199/8000 [02:02<11:34,  9.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2495/8000 [02:30<07:21, 12.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1201/8000 [02:02<13:09,  8.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2498/8000 [02:30<07:26, 12.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1203/8000 [02:02<12:41,  8.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2502/8000 [02:30<06:13, 14.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1205/8000 [02:02<12:51,  8.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2507/8000 [02:30<04:47, 19.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1208/8000 [02:03<10:53, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2510/8000 [02:31<06:28, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1211/8000 [02:03<09:46, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2513/8000 [02:31<06:43, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1213/8000 [02:03<10:34, 10.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2516/8000 [02:31<07:38, 11.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1218/8000 [02:03<07:59, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2519/8000 [02:32<07:52, 11.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1221/8000 [02:04<08:16, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2524/8000 [02:32<05:30, 16.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1223/8000 [02:04<09:48, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2530/8000 [02:32<05:02, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1226/8000 [02:04<12:41,  8.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2533/8000 [02:32<05:39, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1231/8000 [02:05<09:19, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2535/8000 [02:32<05:44, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1233/8000 [02:05<09:53, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2537/8000 [02:33<05:52, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋              | 1238/8000 [02:05<07:43, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2539/8000 [02:33<05:39, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1240/8000 [02:05<08:37, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2542/8000 [02:33<05:03, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1242/8000 [02:05<08:35, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2546/8000 [02:33<04:20, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1244/8000 [02:05<09:05, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2551/8000 [02:33<03:39, 24.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1246/8000 [02:06<09:22, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2554/8000 [02:33<03:53, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1251/8000 [02:06<07:18, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2557/8000 [02:34<05:17, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1255/8000 [02:06<07:38, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2559/8000 [02:34<05:31, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1257/8000 [02:07<10:56, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2562/8000 [02:34<04:58, 18.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1260/8000 [02:07<09:52, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2566/8000 [02:34<07:03, 12.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1262/8000 [02:07<10:25, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2571/8000 [02:34<05:15, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1264/8000 [02:07<10:47, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2575/8000 [02:35<05:15, 17.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1269/8000 [02:07<08:01, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2579/8000 [02:35<05:31, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1273/8000 [02:08<09:23, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2585/8000 [02:35<04:06, 21.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1275/8000 [02:08<09:58, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2588/8000 [02:35<04:11, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1277/8000 [02:08<10:28, 10.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2593/8000 [02:35<03:31, 25.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1280/8000 [02:09<10:01, 11.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2599/8000 [02:36<03:03, 29.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1282/8000 [02:09<10:46, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2605/8000 [02:36<03:16, 27.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1285/8000 [02:09<09:53, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2610/8000 [02:36<03:51, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1287/8000 [02:09<10:34, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2614/8000 [02:37<05:20, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1290/8000 [02:10<12:43,  8.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2618/8000 [02:37<05:43, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1292/8000 [02:10<13:31,  8.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2621/8000 [02:37<05:31, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1293/8000 [02:10<15:15,  7.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2624/8000 [02:37<06:29, 13.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1298/8000 [02:10<09:55, 11.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2628/8000 [02:37<05:29, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1300/8000 [02:11<10:01, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2632/8000 [02:38<07:24, 12.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1303/8000 [02:11<09:21, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2635/8000 [02:38<06:33, 13.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1305/8000 [02:11<10:05, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2639/8000 [02:38<05:16, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1307/8000 [02:12<14:26,  7.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2642/8000 [02:38<05:58, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1310/8000 [02:12<12:24,  8.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2644/8000 [02:39<07:28, 11.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1312/8000 [02:12<12:20,  9.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2646/8000 [02:39<07:11, 12.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1318/8000 [02:12<08:44, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2649/8000 [02:39<06:08, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1322/8000 [02:13<07:51, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2653/8000 [02:39<04:47, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1324/8000 [02:13<08:31, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2657/8000 [02:39<04:22, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1330/8000 [02:13<07:04, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2660/8000 [02:39<04:22, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1334/8000 [02:13<07:01, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2663/8000 [02:40<05:44, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1337/8000 [02:14<09:21, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2668/8000 [02:40<04:20, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1339/8000 [02:14<09:49, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2674/8000 [02:40<04:01, 22.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1341/8000 [02:14<11:04, 10.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2677/8000 [02:40<04:10, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1343/8000 [02:15<13:55,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2682/8000 [02:41<04:16, 20.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1344/8000 [02:15<15:15,  7.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2689/8000 [02:41<03:14, 27.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1345/8000 [02:15<17:09,  6.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2693/8000 [02:41<03:47, 23.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1351/8000 [02:16<14:33,  7.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2696/8000 [02:41<03:49, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1354/8000 [02:16<13:15,  8.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2701/8000 [02:42<05:08, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1358/8000 [02:16<10:36, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2704/8000 [02:42<05:42, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1360/8000 [02:16<10:42, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2708/8000 [02:42<04:54, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1364/8000 [02:17<13:55,  7.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2711/8000 [02:42<04:40, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1369/8000 [02:17<10:02, 11.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2715/8000 [02:42<05:07, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1373/8000 [02:18<11:00, 10.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2717/8000 [02:43<06:01, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1376/8000 [02:19<15:08,  7.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2720/8000 [02:43<05:34, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1378/8000 [02:19<14:42,  7.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2724/8000 [02:43<04:57, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1381/8000 [02:19<13:05,  8.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2726/8000 [02:43<05:25, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1384/8000 [02:20<14:19,  7.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2730/8000 [02:43<04:22, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1387/8000 [02:20<12:32,  8.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2733/8000 [02:43<04:23, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1391/8000 [02:20<10:37, 10.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2737/8000 [02:44<05:27, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1395/8000 [02:20<11:15,  9.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2740/8000 [02:44<05:00, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1400/8000 [02:21<10:15, 10.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2743/8000 [02:44<05:02, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1402/8000 [02:21<10:40, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2746/8000 [02:44<04:51, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1404/8000 [02:21<10:58, 10.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2748/8000 [02:44<05:19, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1408/8000 [02:22<08:59, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2754/8000 [02:44<03:53, 22.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1414/8000 [02:22<07:10, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2758/8000 [02:45<04:13, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1417/8000 [02:22<09:31, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2764/8000 [02:45<04:05, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1421/8000 [02:22<08:20, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2770/8000 [02:45<04:35, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1424/8000 [02:23<08:30, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2773/8000 [02:46<06:42, 12.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1426/8000 [02:23<12:00,  9.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2775/8000 [02:46<06:27, 13.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1429/8000 [02:24<12:35,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2778/8000 [02:46<05:53, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1431/8000 [02:24<14:05,  7.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2781/8000 [02:46<05:15, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1436/8000 [02:24<09:59, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2783/8000 [02:46<05:37, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1439/8000 [02:24<09:54, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2786/8000 [02:47<05:13, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1441/8000 [02:25<10:39, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2790/8000 [02:47<04:33, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1444/8000 [02:25<10:21, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2794/8000 [02:47<05:04, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1446/8000 [02:25<13:33,  8.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2797/8000 [02:47<04:38, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1448/8000 [02:26<13:20,  8.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2801/8000 [02:47<04:12, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1451/8000 [02:26<11:42,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2806/8000 [02:47<03:28, 24.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1457/8000 [02:26<09:18, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2810/8000 [02:48<03:14, 26.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1460/8000 [02:27<09:41, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2813/8000 [02:48<03:19, 26.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1464/8000 [02:27<08:48, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2816/8000 [02:48<04:25, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1467/8000 [02:27<08:27, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2820/8000 [02:48<05:54, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏             | 1472/8000 [02:27<08:53, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2822/8000 [02:49<07:08, 12.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏             | 1477/8000 [02:28<08:50, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2824/8000 [02:49<06:40, 12.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1482/8000 [02:28<09:43, 11.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2827/8000 [02:49<05:35, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1484/8000 [02:29<09:59, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2829/8000 [02:49<06:52, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1486/8000 [02:29<12:57,  8.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2833/8000 [02:49<07:18, 11.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1491/8000 [02:30<12:23,  8.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2836/8000 [02:50<06:20, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1492/8000 [02:30<13:17,  8.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2838/8000 [02:50<06:03, 14.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1493/8000 [02:30<15:36,  6.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2840/8000 [02:50<07:23, 11.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1495/8000 [02:30<14:38,  7.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2844/8000 [02:50<05:36, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1498/8000 [02:30<11:41,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2847/8000 [02:50<05:16, 16.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1502/8000 [02:31<11:36,  9.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2850/8000 [02:51<07:16, 11.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1504/8000 [02:31<11:32,  9.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2855/8000 [02:51<05:15, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1507/8000 [02:31<10:58,  9.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2859/8000 [02:51<04:33, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1510/8000 [02:32<10:08, 10.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2863/8000 [02:51<05:07, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1514/8000 [02:32<08:22, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2866/8000 [02:51<04:51, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1516/8000 [02:32<11:40,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2869/8000 [02:52<04:32, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1520/8000 [02:33<10:15, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2872/8000 [02:52<04:25, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1523/8000 [02:33<12:16,  8.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2876/8000 [02:52<04:01, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1526/8000 [02:33<11:30,  9.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2881/8000 [02:52<04:19, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1531/8000 [02:34<08:51, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2885/8000 [02:52<04:55, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1534/8000 [02:34<09:09, 11.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2888/8000 [02:53<04:48, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1538/8000 [02:34<10:04, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2893/8000 [02:53<04:05, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1540/8000 [02:34<10:34, 10.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2897/8000 [02:53<04:37, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1542/8000 [02:35<15:02,  7.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2900/8000 [02:53<04:29, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1543/8000 [02:35<16:08,  6.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2903/8000 [02:53<04:19, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1545/8000 [02:35<14:45,  7.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2906/8000 [02:53<04:16, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1548/8000 [02:36<12:10,  8.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2909/8000 [02:54<04:16, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1550/8000 [02:36<12:35,  8.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2912/8000 [02:54<05:21, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1556/8000 [02:36<07:48, 13.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2915/8000 [02:54<04:48, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1560/8000 [02:37<09:46, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2921/8000 [02:54<04:17, 19.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1562/8000 [02:37<10:28, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2925/8000 [02:55<04:42, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1565/8000 [02:37<10:04, 10.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2932/8000 [02:55<03:28, 24.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1570/8000 [02:37<07:59, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2937/8000 [02:55<03:53, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1573/8000 [02:38<11:30,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2942/8000 [02:55<04:22, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1575/8000 [02:38<11:32,  9.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2948/8000 [02:56<05:01, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1577/8000 [02:39<14:32,  7.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2953/8000 [02:56<04:53, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1580/8000 [02:39<12:23,  8.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2955/8000 [02:56<05:05, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1582/8000 [02:39<12:41,  8.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2957/8000 [02:56<05:17, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1586/8000 [02:39<09:43, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2960/8000 [02:57<05:07, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1590/8000 [02:40<13:29,  7.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2965/8000 [02:57<04:57, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1593/8000 [02:40<11:31,  9.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2968/8000 [02:57<04:45, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1598/8000 [02:40<08:29, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2972/8000 [02:57<04:10, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1605/8000 [02:41<06:03, 17.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2975/8000 [02:57<04:15, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1609/8000 [02:41<06:46, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2980/8000 [02:58<04:21, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1612/8000 [02:41<06:51, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2982/8000 [02:58<04:29, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1614/8000 [02:41<07:29, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2989/8000 [02:58<04:48, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1618/8000 [02:42<06:59, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2992/8000 [02:58<04:35, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1621/8000 [02:42<10:01, 10.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2995/8000 [02:58<04:37, 18.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1624/8000 [02:43<11:01,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3002/8000 [02:59<03:21, 24.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1626/8000 [02:43<13:58,  7.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3005/8000 [02:59<03:34, 23.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1628/8000 [02:43<13:51,  7.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3008/8000 [02:59<06:40, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1632/8000 [02:44<10:48,  9.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3010/8000 [02:59<06:35, 12.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1635/8000 [02:44<10:10, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3013/8000 [03:00<07:27, 11.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1637/8000 [02:44<10:22, 10.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3016/8000 [03:00<07:46, 10.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1639/8000 [02:45<16:14,  6.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3020/8000 [03:01<08:21,  9.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1640/8000 [02:45<17:56,  5.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3023/8000 [03:01<07:31, 11.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍             | 1642/8000 [02:45<15:50,  6.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3030/8000 [03:01<05:33, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍             | 1645/8000 [02:45<12:58,  8.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3032/8000 [03:01<05:37, 14.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1649/8000 [02:46<09:51, 10.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3036/8000 [03:01<04:57, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1656/8000 [02:46<06:31, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3042/8000 [03:01<03:34, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1661/8000 [02:46<07:40, 13.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3045/8000 [03:02<03:52, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1667/8000 [02:46<06:20, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3048/8000 [03:02<03:50, 21.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1669/8000 [02:47<09:09, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3051/8000 [03:02<04:41, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1671/8000 [02:47<10:35,  9.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3055/8000 [03:02<04:47, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1677/8000 [02:47<07:38, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3058/8000 [03:02<04:39, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1679/8000 [02:48<08:07, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3062/8000 [03:03<03:52, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1681/8000 [02:48<08:38, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3067/8000 [03:03<04:14, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1683/8000 [02:48<12:01,  8.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3070/8000 [03:03<05:05, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1685/8000 [02:49<12:08,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3072/8000 [03:03<05:18, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1687/8000 [02:49<12:21,  8.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3075/8000 [03:04<05:41, 14.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1688/8000 [02:49<14:35,  7.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3077/8000 [03:04<08:34,  9.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1692/8000 [02:49<10:47,  9.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3081/8000 [03:04<07:52, 10.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1694/8000 [02:50<11:16,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3085/8000 [03:04<06:15, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1696/8000 [02:50<11:11,  9.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3089/8000 [03:05<05:07, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1700/8000 [02:50<11:04,  9.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3091/8000 [03:05<05:14, 15.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1701/8000 [02:50<12:53,  8.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3096/8000 [03:05<04:19, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1703/8000 [02:51<12:35,  8.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3099/8000 [03:05<04:12, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1705/8000 [02:51<13:17,  7.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3102/8000 [03:05<04:20, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1708/8000 [02:51<10:47,  9.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3104/8000 [03:05<04:41, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1710/8000 [02:51<10:54,  9.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3107/8000 [03:06<05:53, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1712/8000 [02:52<11:12,  9.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3109/8000 [03:06<06:23, 12.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1713/8000 [02:52<16:13,  6.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3112/8000 [03:06<05:38, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1716/8000 [02:52<13:55,  7.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3114/8000 [03:06<06:07, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1721/8000 [02:53<09:10, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3116/8000 [03:06<06:02, 13.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1723/8000 [02:53<09:40, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3118/8000 [03:07<06:21, 12.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1732/8000 [02:53<07:31, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3122/8000 [03:07<07:27, 10.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1735/8000 [02:53<07:23, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3124/8000 [03:07<07:17, 11.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1738/8000 [02:54<07:26, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3126/8000 [03:07<07:09, 11.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1740/8000 [02:54<08:03, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3129/8000 [03:08<05:59, 13.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1744/8000 [02:54<10:08, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3131/8000 [03:08<05:49, 13.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1746/8000 [02:55<11:26,  9.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3136/8000 [03:08<05:08, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1747/8000 [02:55<13:21,  7.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3139/8000 [03:08<04:44, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1748/8000 [02:55<15:11,  6.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3141/8000 [03:08<04:49, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1749/8000 [02:55<15:55,  6.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3145/8000 [03:08<04:04, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1751/8000 [02:56<14:03,  7.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3148/8000 [03:08<03:49, 21.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1753/8000 [02:56<14:24,  7.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3152/8000 [03:09<06:13, 12.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1757/8000 [02:56<10:36,  9.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3154/8000 [03:09<07:02, 11.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1761/8000 [02:56<08:18, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3156/8000 [03:09<06:50, 11.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1764/8000 [02:57<10:59,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3158/8000 [03:10<06:41, 12.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1768/8000 [02:57<12:05,  8.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3165/8000 [03:10<03:48, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1772/8000 [02:58<11:44,  8.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3168/8000 [03:10<03:37, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1773/8000 [02:58<13:57,  7.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3171/8000 [03:10<04:35, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1776/8000 [02:58<13:00,  7.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3175/8000 [03:10<05:33, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1781/8000 [02:59<11:26,  9.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3177/8000 [03:11<10:05,  7.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1785/8000 [02:59<09:26, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3183/8000 [03:11<07:12, 11.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1789/8000 [03:00<10:02, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3187/8000 [03:12<05:49, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1792/8000 [03:00<10:03, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3191/8000 [03:12<05:48, 13.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1794/8000 [03:00<10:26,  9.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3194/8000 [03:12<06:02, 13.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1796/8000 [03:00<10:09, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3201/8000 [03:12<04:51, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1798/8000 [03:01<10:56,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3204/8000 [03:13<04:48, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1800/8000 [03:01<11:36,  8.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3207/8000 [03:13<04:26, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1802/8000 [03:01<11:52,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3210/8000 [03:13<05:38, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1807/8000 [03:01<08:26, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3213/8000 [03:13<05:20, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1812/8000 [03:02<07:13, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3216/8000 [03:13<04:51, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1815/8000 [03:02<09:08, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3219/8000 [03:14<05:06, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1819/8000 [03:02<07:44, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3222/8000 [03:14<04:54, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1822/8000 [03:03<09:36, 10.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3224/8000 [03:15<10:38,  7.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1824/8000 [03:03<10:01, 10.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3228/8000 [03:15<07:36, 10.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1826/8000 [03:03<11:08,  9.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3233/8000 [03:15<05:26, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1830/8000 [03:03<10:44,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3239/8000 [03:15<04:02, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1833/8000 [03:04<10:11, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3245/8000 [03:15<04:00, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1837/8000 [03:04<10:18,  9.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3249/8000 [03:16<04:29, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1840/8000 [03:04<09:20, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3252/8000 [03:16<04:56, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1842/8000 [03:05<13:04,  7.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3259/8000 [03:16<03:34, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1846/8000 [03:05<10:25,  9.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3262/8000 [03:16<05:02, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1849/8000 [03:05<10:02, 10.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3268/8000 [03:17<04:29, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1856/8000 [03:06<06:27, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3272/8000 [03:17<05:32, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1862/8000 [03:06<05:39, 18.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3276/8000 [03:17<05:29, 14.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1865/8000 [03:06<05:54, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3279/8000 [03:17<05:16, 14.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1869/8000 [03:06<06:07, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3283/8000 [03:18<04:38, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1874/8000 [03:06<05:33, 18.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3285/8000 [03:18<06:05, 12.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1876/8000 [03:07<06:27, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3292/8000 [03:18<04:58, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1879/8000 [03:07<06:34, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3298/8000 [03:19<04:56, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███▉             | 1881/8000 [03:07<07:23, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3300/8000 [03:19<04:53, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1884/8000 [03:07<07:08, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3302/8000 [03:19<05:00, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1886/8000 [03:08<11:56,  8.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3305/8000 [03:19<04:32, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1891/8000 [03:08<08:21, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3307/8000 [03:19<04:45, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1894/8000 [03:08<08:04, 12.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3310/8000 [03:19<04:24, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1896/8000 [03:09<09:15, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3312/8000 [03:19<04:37, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1899/8000 [03:09<08:49, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3315/8000 [03:20<05:25, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1904/8000 [03:09<06:59, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3318/8000 [03:20<04:46, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1911/8000 [03:10<07:07, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3321/8000 [03:20<04:16, 18.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1916/8000 [03:10<06:23, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3323/8000 [03:20<04:27, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1919/8000 [03:10<06:40, 15.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3325/8000 [03:20<04:50, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1921/8000 [03:10<09:32, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3328/8000 [03:20<04:27, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1925/8000 [03:11<08:06, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3332/8000 [03:21<03:45, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1927/8000 [03:11<08:18, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3336/8000 [03:21<03:24, 22.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1932/8000 [03:11<09:12, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3341/8000 [03:21<02:53, 26.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1934/8000 [03:12<09:45, 10.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3344/8000 [03:21<02:53, 26.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1936/8000 [03:12<10:20,  9.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3349/8000 [03:21<04:06, 18.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1939/8000 [03:12<09:46, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3357/8000 [03:22<03:29, 22.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1941/8000 [03:12<09:49, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3361/8000 [03:22<03:14, 23.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1944/8000 [03:13<09:40, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3365/8000 [03:22<03:07, 24.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1947/8000 [03:13<08:52, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3368/8000 [03:22<03:44, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1951/8000 [03:13<08:24, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3373/8000 [03:22<03:59, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1953/8000 [03:13<09:19, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3376/8000 [03:23<05:34, 13.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1955/8000 [03:14<10:20,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3379/8000 [03:23<04:59, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1957/8000 [03:14<11:22,  8.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3382/8000 [03:23<04:40, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1958/8000 [03:14<13:41,  7.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3386/8000 [03:23<03:55, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1960/8000 [03:14<13:00,  7.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3391/8000 [03:24<04:36, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1963/8000 [03:15<10:59,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3394/8000 [03:24<04:16, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1965/8000 [03:15<10:53,  9.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3397/8000 [03:24<04:51, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1970/8000 [03:15<07:46, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3400/8000 [03:25<06:52, 11.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1972/8000 [03:16<11:32,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3402/8000 [03:25<07:17, 10.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1976/8000 [03:16<12:05,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3406/8000 [03:25<05:41, 13.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1983/8000 [03:16<07:56, 12.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3411/8000 [03:25<05:11, 14.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1985/8000 [03:17<10:19,  9.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3417/8000 [03:26<06:40, 11.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1987/8000 [03:17<12:38,  7.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3419/8000 [03:26<08:17,  9.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1991/8000 [03:18<10:43,  9.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3421/8000 [03:27<08:20,  9.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1996/8000 [03:18<08:32, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3423/8000 [03:27<08:04,  9.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1998/8000 [03:18<09:26, 10.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3425/8000 [03:27<09:19,  8.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2001/8000 [03:18<08:44, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3428/8000 [03:27<08:14,  9.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2004/8000 [03:18<08:27, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3430/8000 [03:28<08:46,  8.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2007/8000 [03:19<08:37, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3432/8000 [03:28<08:26,  9.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2014/8000 [03:19<05:50, 17.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3436/8000 [03:28<07:56,  9.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2018/8000 [03:19<05:48, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3439/8000 [03:28<06:38, 11.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2022/8000 [03:19<05:36, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3443/8000 [03:28<05:13, 14.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2027/8000 [03:20<05:17, 18.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3445/8000 [03:29<05:29, 13.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2029/8000 [03:20<06:10, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3449/8000 [03:29<04:20, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2031/8000 [03:20<06:47, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3453/8000 [03:29<03:49, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2034/8000 [03:20<07:11, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3456/8000 [03:29<04:28, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2037/8000 [03:21<09:40, 10.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3458/8000 [03:30<06:43, 11.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2040/8000 [03:21<10:55,  9.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3460/8000 [03:30<07:20, 10.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2042/8000 [03:21<10:07,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3464/8000 [03:30<05:21, 14.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2044/8000 [03:22<12:46,  7.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3467/8000 [03:30<04:36, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2048/8000 [03:22<09:41, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3471/8000 [03:30<03:52, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2052/8000 [03:22<08:01, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3474/8000 [03:30<03:44, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2056/8000 [03:22<07:00, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3477/8000 [03:31<05:33, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2058/8000 [03:23<07:32, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3481/8000 [03:31<06:43, 11.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2060/8000 [03:23<08:42, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3484/8000 [03:31<05:53, 12.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2063/8000 [03:23<10:59,  9.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3486/8000 [03:32<05:38, 13.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2065/8000 [03:24<11:30,  8.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3491/8000 [03:32<04:09, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2068/8000 [03:24<10:13,  9.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3494/8000 [03:32<03:52, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2070/8000 [03:24<10:40,  9.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3498/8000 [03:32<04:10, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2074/8000 [03:24<08:44, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3501/8000 [03:32<04:02, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2079/8000 [03:25<06:55, 14.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3507/8000 [03:32<03:35, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2084/8000 [03:25<06:09, 15.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3512/8000 [03:33<04:22, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2089/8000 [03:25<07:50, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3514/8000 [03:33<04:26, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2094/8000 [03:26<06:46, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3516/8000 [03:33<04:27, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2097/8000 [03:26<06:56, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3520/8000 [03:33<04:36, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2102/8000 [03:26<08:08, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3523/8000 [03:34<05:06, 14.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2107/8000 [03:27<07:03, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3529/8000 [03:34<03:32, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2111/8000 [03:27<06:44, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3532/8000 [03:34<04:59, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2117/8000 [03:27<05:16, 18.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3536/8000 [03:34<04:11, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▌            | 2120/8000 [03:27<06:53, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3541/8000 [03:35<04:19, 17.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2122/8000 [03:28<07:49, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3544/8000 [03:35<04:12, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2124/8000 [03:28<08:39, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3549/8000 [03:35<03:28, 21.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2128/8000 [03:28<07:47, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3553/8000 [03:35<03:13, 22.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2131/8000 [03:29<10:20,  9.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3556/8000 [03:35<04:07, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2135/8000 [03:29<08:30, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3559/8000 [03:35<03:53, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2137/8000 [03:30<13:42,  7.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3563/8000 [03:36<04:22, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2140/8000 [03:30<12:26,  7.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3569/8000 [03:36<03:17, 22.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2145/8000 [03:30<08:55, 10.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3573/8000 [03:36<04:34, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2148/8000 [03:30<08:25, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3579/8000 [03:37<04:03, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2153/8000 [03:31<09:12, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3582/8000 [03:37<03:53, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2155/8000 [03:31<12:47,  7.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3585/8000 [03:37<03:49, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2163/8000 [03:32<09:13, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3588/8000 [03:37<03:38, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2167/8000 [03:32<08:23, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3594/8000 [03:37<03:32, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2170/8000 [03:33<11:45,  8.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3598/8000 [03:37<03:10, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2174/8000 [03:33<09:49,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3602/8000 [03:37<03:08, 23.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2176/8000 [03:33<09:56,  9.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3605/8000 [03:38<03:14, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2179/8000 [03:34<10:51,  8.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3610/8000 [03:38<02:37, 27.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2185/8000 [03:34<09:51,  9.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3614/8000 [03:38<03:19, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2190/8000 [03:35<10:21,  9.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3618/8000 [03:38<03:03, 23.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2192/8000 [03:35<11:10,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3621/8000 [03:38<03:06, 23.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2196/8000 [03:35<09:26, 10.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3625/8000 [03:38<02:57, 24.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2198/8000 [03:36<09:50,  9.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3629/8000 [03:39<03:36, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2204/8000 [03:36<07:24, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3634/8000 [03:39<03:40, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2208/8000 [03:36<06:35, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3639/8000 [03:39<03:00, 24.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2210/8000 [03:36<07:32, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3646/8000 [03:39<03:28, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2215/8000 [03:37<07:29, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3653/8000 [03:40<03:41, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2218/8000 [03:37<07:06, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3657/8000 [03:40<03:27, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2222/8000 [03:37<06:56, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3661/8000 [03:40<03:19, 21.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2225/8000 [03:37<07:17, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3664/8000 [03:40<03:08, 22.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2227/8000 [03:38<07:49, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3669/8000 [03:41<03:18, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2231/8000 [03:38<09:04, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3673/8000 [03:41<04:29, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2233/8000 [03:38<09:52,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3675/8000 [03:41<04:40, 15.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2236/8000 [03:39<09:16, 10.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3679/8000 [03:41<04:46, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2240/8000 [03:39<10:19,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3682/8000 [03:42<05:58, 12.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2243/8000 [03:39<09:25, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3686/8000 [03:42<04:54, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2250/8000 [03:40<08:09, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3690/8000 [03:42<04:05, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2253/8000 [03:40<08:04, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3694/8000 [03:42<04:22, 16.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2257/8000 [03:41<09:18, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3697/8000 [03:43<04:49, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2260/8000 [03:41<08:46, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3700/8000 [03:43<05:25, 13.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2264/8000 [03:41<08:04, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3703/8000 [03:43<04:45, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2266/8000 [03:41<09:06, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3706/8000 [03:43<04:15, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2268/8000 [03:42<09:34,  9.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3710/8000 [03:43<04:21, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2270/8000 [03:42<09:38,  9.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3713/8000 [03:44<04:05, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2274/8000 [03:42<08:20, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3715/8000 [03:44<04:20, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2277/8000 [03:42<07:46, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3719/8000 [03:44<03:52, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2279/8000 [03:43<08:37, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3721/8000 [03:44<04:10, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2281/8000 [03:43<08:37, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3724/8000 [03:44<03:57, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2286/8000 [03:43<08:23, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3727/8000 [03:44<03:48, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2288/8000 [03:44<10:01,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3729/8000 [03:44<03:50, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2290/8000 [03:44<10:44,  8.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3732/8000 [03:45<04:30, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2294/8000 [03:44<08:44, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3734/8000 [03:45<04:42, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2298/8000 [03:44<08:20, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3738/8000 [03:45<04:38, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2300/8000 [03:45<10:23,  9.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3740/8000 [03:45<04:39, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2302/8000 [03:45<10:25,  9.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3744/8000 [03:45<04:35, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2306/8000 [03:45<08:35, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3748/8000 [03:46<04:55, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2309/8000 [03:46<09:11, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3751/8000 [03:46<04:19, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2311/8000 [03:46<10:16,  9.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3754/8000 [03:46<04:06, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2312/8000 [03:46<12:12,  7.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3759/8000 [03:46<03:24, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2315/8000 [03:46<10:07,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:46<03:49, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2317/8000 [03:47<10:07,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3765/8000 [03:47<03:54, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2318/8000 [03:47<11:42,  8.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3769/8000 [03:47<03:15, 21.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2319/8000 [03:47<13:35,  6.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3772/8000 [03:47<03:56, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2323/8000 [03:47<09:05, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3779/8000 [03:47<02:41, 26.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2327/8000 [03:47<07:19, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3784/8000 [03:47<03:02, 23.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2329/8000 [03:48<07:44, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3790/8000 [03:48<03:10, 22.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2332/8000 [03:48<09:43,  9.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3793/8000 [03:48<03:47, 18.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2335/8000 [03:48<10:53,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3796/8000 [03:48<03:34, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2337/8000 [03:49<14:14,  6.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3802/8000 [03:48<02:48, 24.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2339/8000 [03:50<17:07,  5.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3805/8000 [03:48<03:26, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2341/8000 [03:50<18:35,  5.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3811/8000 [03:49<02:45, 25.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2344/8000 [03:50<15:17,  6.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3815/8000 [03:49<03:17, 21.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2347/8000 [03:51<12:08,  7.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3818/8000 [03:49<04:38, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2350/8000 [03:51<10:16,  9.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3825/8000 [03:50<04:01, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2352/8000 [03:51<10:22,  9.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3829/8000 [03:50<04:12, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████            | 2354/8000 [03:51<10:25,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3831/8000 [03:50<05:07, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████            | 2357/8000 [03:51<08:57, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3835/8000 [03:50<04:16, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2362/8000 [03:52<07:08, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3837/8000 [03:51<05:06, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2364/8000 [03:52<10:05,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3840/8000 [03:51<04:43, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2366/8000 [03:53<12:15,  7.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3842/8000 [03:51<04:34, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2372/8000 [03:53<07:40, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3845/8000 [03:51<04:09, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2375/8000 [03:53<09:21, 10.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3847/8000 [03:51<04:11, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2378/8000 [03:53<08:33, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3851/8000 [03:51<03:41, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2380/8000 [03:54<09:57,  9.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3853/8000 [03:51<03:49, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2384/8000 [03:54<08:03, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3856/8000 [03:52<04:39, 14.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2387/8000 [03:54<07:55, 11.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3860/8000 [03:52<03:47, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2390/8000 [03:55<09:57,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3863/8000 [03:52<03:34, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2395/8000 [03:55<07:13, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3868/8000 [03:52<04:23, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2398/8000 [03:55<10:22,  8.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3871/8000 [03:52<04:04, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2400/8000 [03:56<12:32,  7.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3875/8000 [03:53<03:36, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2403/8000 [03:56<10:42,  8.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▏        | 3882/8000 [03:53<03:41, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2405/8000 [03:56<10:19,  9.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3885/8000 [03:53<03:27, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2408/8000 [03:57<10:11,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3888/8000 [03:54<05:24, 12.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2413/8000 [03:57<07:28, 12.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3891/8000 [03:54<04:50, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2419/8000 [03:57<06:48, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3896/8000 [03:54<04:31, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2421/8000 [03:58<13:17,  6.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3899/8000 [03:54<04:13, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2424/8000 [03:58<12:03,  7.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3902/8000 [03:54<03:56, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2428/8000 [03:59<12:00,  7.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3909/8000 [03:54<02:45, 24.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2431/8000 [03:59<10:43,  8.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3912/8000 [03:55<02:48, 24.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2437/8000 [03:59<07:35, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3918/8000 [03:55<03:06, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2439/8000 [04:00<08:29, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3922/8000 [03:55<02:51, 23.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2442/8000 [04:00<10:26,  8.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3925/8000 [03:55<03:36, 18.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2448/8000 [04:01<08:46, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3928/8000 [03:56<04:21, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2452/8000 [04:01<07:32, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3930/8000 [03:56<06:48,  9.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2455/8000 [04:01<07:35, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3932/8000 [03:56<06:23, 10.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2458/8000 [04:02<09:23,  9.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3934/8000 [03:56<06:01, 11.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2462/8000 [04:02<07:58, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3937/8000 [03:57<05:06, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2464/8000 [04:02<10:35,  8.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3940/8000 [03:57<04:21, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2466/8000 [04:03<12:43,  7.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3943/8000 [03:57<03:50, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2467/8000 [04:03<13:33,  6.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3947/8000 [03:57<03:30, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2471/8000 [04:03<11:42,  7.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3950/8000 [03:57<03:23, 19.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2474/8000 [04:03<09:53,  9.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3953/8000 [03:57<03:20, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2477/8000 [04:04<09:03, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3956/8000 [03:58<04:17, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2481/8000 [04:04<09:17,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3958/8000 [03:58<05:11, 13.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2484/8000 [04:05<10:47,  8.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3960/8000 [03:58<06:09, 10.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2485/8000 [04:05<12:49,  7.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3964/8000 [03:58<04:45, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2487/8000 [04:05<12:05,  7.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3970/8000 [03:58<03:14, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2490/8000 [04:05<11:03,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3975/8000 [03:59<03:25, 19.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2492/8000 [04:06<11:49,  7.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3979/8000 [03:59<03:39, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2496/8000 [04:06<08:59, 10.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3982/8000 [03:59<04:16, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2500/8000 [04:06<09:38,  9.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3988/8000 [03:59<03:12, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2502/8000 [04:07<09:39,  9.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3991/8000 [03:59<03:07, 21.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2504/8000 [04:07<10:04,  9.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3994/8000 [04:00<03:02, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2507/8000 [04:07<08:48, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3999/8000 [04:00<02:31, 26.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2511/8000 [04:07<07:51, 11.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4003/8000 [04:00<02:59, 22.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2515/8000 [04:08<07:30, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4006/8000 [04:00<03:44, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2517/8000 [04:08<08:33, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4012/8000 [04:01<03:42, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2519/8000 [04:08<09:02, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4016/8000 [04:01<04:38, 14.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2521/8000 [04:08<09:21,  9.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4019/8000 [04:01<04:19, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2524/8000 [04:09<08:09, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4024/8000 [04:01<03:27, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2527/8000 [04:09<07:29, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4028/8000 [04:02<03:47, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2529/8000 [04:09<08:54, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4032/8000 [04:02<03:58, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2533/8000 [04:09<07:24, 12.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4034/8000 [04:02<04:10, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2536/8000 [04:10<09:48,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4037/8000 [04:02<04:43, 14.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2539/8000 [04:10<08:47, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4041/8000 [04:02<03:46, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2541/8000 [04:10<09:22,  9.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4044/8000 [04:03<03:33, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2546/8000 [04:11<10:57,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4047/8000 [04:03<03:20, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2548/8000 [04:11<11:33,  7.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4051/8000 [04:03<03:38, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2552/8000 [04:12<09:10,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4054/8000 [04:03<03:25, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2556/8000 [04:12<09:50,  9.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4058/8000 [04:03<02:56, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2562/8000 [04:13<10:41,  8.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4063/8000 [04:03<02:28, 26.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2567/8000 [04:13<08:42, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4067/8000 [04:04<03:06, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2570/8000 [04:13<08:39, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4072/8000 [04:04<02:36, 25.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2574/8000 [04:14<07:28, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4075/8000 [04:04<03:24, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2578/8000 [04:14<08:53, 10.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4078/8000 [04:04<03:21, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2581/8000 [04:14<08:13, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4082/8000 [04:04<03:01, 21.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2584/8000 [04:15<12:45,  7.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4085/8000 [04:04<03:07, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2587/8000 [04:16<12:47,  7.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4088/8000 [04:05<03:50, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2591/8000 [04:16<10:04,  8.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4091/8000 [04:05<03:36, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2594/8000 [04:16<10:38,  8.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4093/8000 [04:05<04:40, 13.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2598/8000 [04:16<08:58, 10.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4095/8000 [04:05<04:35, 14.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2600/8000 [04:17<09:36,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4098/8000 [04:05<04:02, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2602/8000 [04:17<09:23,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4102/8000 [04:06<03:15, 19.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2607/8000 [04:17<07:40, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4105/8000 [04:06<04:11, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2612/8000 [04:18<08:22, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4107/8000 [04:06<04:15, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2615/8000 [04:19<11:53,  7.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4109/8000 [04:06<04:08, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2616/8000 [04:19<12:28,  7.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4112/8000 [04:06<03:45, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2618/8000 [04:19<11:33,  7.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4117/8000 [04:06<03:35, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2620/8000 [04:19<11:11,  8.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4121/8000 [04:07<03:20, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2623/8000 [04:19<09:56,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4124/8000 [04:07<03:49, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2624/8000 [04:20<11:47,  7.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4127/8000 [04:07<04:59, 12.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2628/8000 [04:20<08:32, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4132/8000 [04:07<03:50, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2633/8000 [04:20<07:11, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4134/8000 [04:08<03:50, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2637/8000 [04:21<07:51, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4137/8000 [04:08<04:23, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2640/8000 [04:21<07:17, 12.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4140/8000 [04:08<03:51, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2643/8000 [04:21<09:46,  9.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4143/8000 [04:08<05:03, 12.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2647/8000 [04:22<07:53, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4145/8000 [04:08<04:52, 13.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2650/8000 [04:22<08:16, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4153/8000 [04:09<03:19, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2653/8000 [04:22<08:36, 10.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4158/8000 [04:09<03:29, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2655/8000 [04:23<10:23,  8.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4160/8000 [04:09<04:21, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2660/8000 [04:23<08:05, 11.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4164/8000 [04:09<03:40, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2664/8000 [04:23<07:18, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4170/8000 [04:10<03:24, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2667/8000 [04:23<07:39, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4174/8000 [04:10<03:36, 17.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2670/8000 [04:24<07:03, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4176/8000 [04:10<03:36, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2673/8000 [04:24<07:08, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4179/8000 [04:10<04:03, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2675/8000 [04:24<08:26, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4184/8000 [04:10<03:13, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2678/8000 [04:24<08:18, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4188/8000 [04:11<03:32, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2682/8000 [04:25<06:55, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4190/8000 [04:11<04:23, 14.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2686/8000 [04:25<07:56, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4194/8000 [04:11<03:38, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2689/8000 [04:26<09:47,  9.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4196/8000 [04:11<03:50, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2691/8000 [04:26<11:29,  7.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4199/8000 [04:11<03:34, 17.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2694/8000 [04:26<10:10,  8.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4202/8000 [04:12<03:29, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2696/8000 [04:26<10:11,  8.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4206/8000 [04:12<02:56, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2697/8000 [04:27<11:23,  7.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4209/8000 [04:12<03:28, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2700/8000 [04:27<09:16,  9.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4213/8000 [04:12<02:59, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2703/8000 [04:27<08:58,  9.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4217/8000 [04:12<02:47, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2710/8000 [04:28<07:24, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4221/8000 [04:13<03:22, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2713/8000 [04:28<07:04, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4224/8000 [04:13<05:20, 11.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2715/8000 [04:28<09:17,  9.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4229/8000 [04:13<04:40, 13.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2717/8000 [04:29<09:59,  8.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4234/8000 [04:14<03:48, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2720/8000 [04:29<08:51,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4237/8000 [04:14<03:31, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2722/8000 [04:29<09:10,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4240/8000 [04:14<03:55, 15.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2724/8000 [04:29<12:12,  7.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4244/8000 [04:14<03:25, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2727/8000 [04:30<10:11,  8.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4247/8000 [04:14<03:12, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2728/8000 [04:30<11:13,  7.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4251/8000 [04:14<03:26, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2730/8000 [04:30<11:40,  7.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4256/8000 [04:15<03:54, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2732/8000 [04:30<10:53,  8.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4258/8000 [04:15<03:59, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2734/8000 [04:31<10:48,  8.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4260/8000 [04:15<04:03, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2736/8000 [04:31<14:17,  6.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4263/8000 [04:15<03:42, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2737/8000 [04:31<15:08,  5.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4267/8000 [04:15<03:12, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2738/8000 [04:32<15:55,  5.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4270/8000 [04:15<03:07, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2740/8000 [04:32<19:27,  4.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4273/8000 [04:16<02:55, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2742/8000 [04:32<16:18,  5.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4277/8000 [04:16<02:37, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2746/8000 [04:33<11:08,  7.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4280/8000 [04:16<03:29, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2749/8000 [04:33<10:05,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4284/8000 [04:17<05:10, 11.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2750/8000 [04:33<11:21,  7.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4286/8000 [04:17<04:59, 12.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2753/8000 [04:33<09:16,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4288/8000 [04:17<05:45, 10.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2756/8000 [04:34<08:28, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4290/8000 [04:17<06:23,  9.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2760/8000 [04:34<08:44,  9.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4293/8000 [04:17<05:05, 12.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▊           | 2764/8000 [04:34<07:47, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4299/8000 [04:18<03:14, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2767/8000 [04:35<07:44, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4304/8000 [04:18<03:16, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2771/8000 [04:35<08:03, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4310/8000 [04:18<03:02, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2776/8000 [04:35<08:15, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4313/8000 [04:18<02:59, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2779/8000 [04:36<07:35, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4319/8000 [04:18<02:57, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2782/8000 [04:36<07:52, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4324/8000 [04:19<02:31, 24.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2786/8000 [04:36<08:51,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4328/8000 [04:19<02:51, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2789/8000 [04:37<08:06, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4331/8000 [04:19<02:53, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2792/8000 [04:37<07:34, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4335/8000 [04:19<02:38, 23.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2794/8000 [04:37<09:41,  8.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4338/8000 [04:19<02:42, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2796/8000 [04:38<10:20,  8.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4346/8000 [04:19<01:54, 31.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2798/8000 [04:38<10:14,  8.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4350/8000 [04:20<01:55, 31.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2800/8000 [04:38<09:59,  8.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4357/8000 [04:20<02:31, 24.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2802/8000 [04:38<09:37,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4361/8000 [04:20<02:29, 24.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2806/8000 [04:38<08:17, 10.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4364/8000 [04:20<03:09, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2808/8000 [04:39<08:37, 10.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4369/8000 [04:21<03:07, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2810/8000 [04:39<10:34,  8.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4372/8000 [04:21<03:06, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2812/8000 [04:39<11:25,  7.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4375/8000 [04:21<03:08, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2813/8000 [04:40<12:36,  6.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4378/8000 [04:21<03:08, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2814/8000 [04:40<13:52,  6.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4380/8000 [04:21<03:15, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2816/8000 [04:40<12:01,  7.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4383/8000 [04:21<03:04, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2817/8000 [04:40<13:48,  6.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4386/8000 [04:21<02:57, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2818/8000 [04:41<15:13,  5.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4389/8000 [04:22<03:48, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2821/8000 [04:41<10:24,  8.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4394/8000 [04:22<02:49, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2822/8000 [04:41<12:09,  7.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4397/8000 [04:22<02:44, 21.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2826/8000 [04:41<09:23,  9.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4400/8000 [04:22<02:46, 21.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2828/8000 [04:41<08:57,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4403/8000 [04:22<02:48, 21.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2829/8000 [04:42<13:48,  6.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4406/8000 [04:23<03:29, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2832/8000 [04:42<11:51,  7.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4408/8000 [04:23<03:42, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2833/8000 [04:43<13:58,  6.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4410/8000 [04:23<03:46, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2834/8000 [04:43<14:40,  5.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4415/8000 [04:23<02:46, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2835/8000 [04:43<16:05,  5.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4419/8000 [04:23<03:09, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2837/8000 [04:43<14:20,  6.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4423/8000 [04:23<02:47, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2840/8000 [04:43<10:46,  7.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4426/8000 [04:24<02:43, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2844/8000 [04:44<07:53, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4430/8000 [04:24<02:29, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2846/8000 [04:44<08:28, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4433/8000 [04:24<02:26, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2849/8000 [04:44<09:47,  8.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4439/8000 [04:24<01:57, 30.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2852/8000 [04:45<09:08,  9.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4445/8000 [04:24<02:41, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2854/8000 [04:45<09:24,  9.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4449/8000 [04:24<02:31, 23.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2860/8000 [04:45<06:01, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4453/8000 [04:25<02:52, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2864/8000 [04:45<05:35, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4457/8000 [04:25<03:43, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2867/8000 [04:45<05:57, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [04:25<02:58, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2869/8000 [04:46<06:29, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4465/8000 [04:25<02:50, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2872/8000 [04:46<06:21, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4469/8000 [04:26<03:41, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2875/8000 [04:46<08:15, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4476/8000 [04:26<02:57, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2878/8000 [04:47<08:10, 10.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4479/8000 [04:26<03:15, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2881/8000 [04:47<07:28, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4486/8000 [04:26<02:20, 25.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2883/8000 [04:47<08:37,  9.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4491/8000 [04:26<02:04, 28.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2888/8000 [04:48<07:51, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4497/8000 [04:27<02:12, 26.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2895/8000 [04:48<05:21, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4503/8000 [04:27<02:16, 25.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2901/8000 [04:48<04:44, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4507/8000 [04:27<02:35, 22.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2903/8000 [04:48<06:28, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4511/8000 [04:27<02:50, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2907/8000 [04:49<05:41, 14.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4516/8000 [04:28<02:26, 23.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2909/8000 [04:49<06:32, 12.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4519/8000 [04:28<02:34, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2913/8000 [04:49<06:08, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4522/8000 [04:28<03:09, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2917/8000 [04:49<05:24, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4527/8000 [04:28<02:34, 22.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2919/8000 [04:49<05:51, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4534/8000 [04:28<02:30, 23.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2921/8000 [04:50<06:54, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4539/8000 [04:29<02:34, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2923/8000 [04:50<09:44,  8.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4542/8000 [04:29<02:31, 22.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2925/8000 [04:50<09:16,  9.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4546/8000 [04:29<02:21, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2930/8000 [04:51<06:39, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4550/8000 [04:29<02:46, 20.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2932/8000 [04:51<11:00,  7.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4553/8000 [04:29<02:37, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2934/8000 [04:51<10:10,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4559/8000 [04:29<02:04, 27.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2939/8000 [04:52<07:24, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4563/8000 [04:30<02:32, 22.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2942/8000 [04:52<07:10, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4567/8000 [04:30<02:48, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2949/8000 [04:52<05:55, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4574/8000 [04:30<02:27, 23.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2953/8000 [04:53<06:11, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4577/8000 [04:30<02:31, 22.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2958/8000 [04:53<06:53, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4583/8000 [04:31<02:32, 22.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2960/8000 [04:54<09:19,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4587/8000 [04:31<02:27, 23.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2965/8000 [04:54<07:47, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4590/8000 [04:31<02:25, 23.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2968/8000 [04:54<07:37, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4594/8000 [04:31<02:15, 25.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2970/8000 [04:54<08:11, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4597/8000 [04:31<02:22, 23.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2973/8000 [04:55<08:00, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4603/8000 [04:31<02:31, 22.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2979/8000 [04:55<05:34, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4607/8000 [04:32<02:57, 19.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2982/8000 [04:55<06:04, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4610/8000 [04:32<02:48, 20.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2986/8000 [04:55<05:41, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4616/8000 [04:32<02:10, 25.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2988/8000 [04:56<07:09, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4621/8000 [04:32<02:55, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2990/8000 [04:56<07:08, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4625/8000 [04:33<03:00, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2992/8000 [04:56<07:46, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4628/8000 [04:33<03:29, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2995/8000 [04:56<08:30,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4633/8000 [04:33<02:48, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3000/8000 [04:57<09:02,  9.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4637/8000 [04:33<02:31, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3005/8000 [04:57<06:56, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4640/8000 [04:33<02:42, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3009/8000 [04:58<06:21, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4644/8000 [04:33<02:26, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3012/8000 [04:58<06:54, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4649/8000 [04:34<02:35, 21.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3014/8000 [04:58<07:59, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4652/8000 [04:34<03:08, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3018/8000 [04:59<08:08, 10.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4655/8000 [04:34<02:53, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3022/8000 [04:59<07:10, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4658/8000 [04:34<03:32, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3026/8000 [04:59<05:59, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4660/8000 [04:35<03:37, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3028/8000 [04:59<06:57, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4662/8000 [04:35<03:40, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3030/8000 [05:00<08:00, 10.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4665/8000 [04:35<04:01, 13.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3034/8000 [05:00<07:21, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4667/8000 [04:35<03:48, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3036/8000 [05:00<08:20,  9.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4672/8000 [04:35<02:41, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3042/8000 [05:00<06:14, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4676/8000 [04:35<02:24, 22.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3045/8000 [05:01<07:41, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4680/8000 [04:35<02:17, 24.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3047/8000 [05:01<08:27,  9.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4683/8000 [04:36<02:20, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3050/8000 [05:01<09:02,  9.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4688/8000 [04:36<02:03, 26.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3054/8000 [05:02<09:05,  9.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4691/8000 [04:36<02:10, 25.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3058/8000 [05:02<08:21,  9.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4694/8000 [04:36<03:38, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3061/8000 [05:02<07:35, 10.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4697/8000 [04:36<03:18, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3070/8000 [05:03<04:35, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4704/8000 [04:37<02:12, 24.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3074/8000 [05:03<04:45, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4708/8000 [04:37<02:08, 25.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3077/8000 [05:03<06:27, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4713/8000 [04:37<01:53, 29.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3079/8000 [05:04<06:35, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4718/8000 [04:37<02:10, 25.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3082/8000 [05:04<06:23, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4721/8000 [04:37<02:16, 23.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3086/8000 [05:05<09:15,  8.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4726/8000 [04:37<02:01, 26.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3088/8000 [05:05<09:41,  8.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4731/8000 [04:38<01:53, 28.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3092/8000 [05:05<08:13,  9.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4736/8000 [04:38<01:45, 30.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3095/8000 [05:05<07:27, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4744/8000 [04:38<02:16, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3097/8000 [05:06<10:46,  7.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4747/8000 [04:38<02:40, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3101/8000 [05:06<08:39,  9.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4751/8000 [04:39<02:58, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3103/8000 [05:06<10:04,  8.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4754/8000 [04:39<02:45, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3106/8000 [05:07<09:10,  8.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4758/8000 [04:39<03:09, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3110/8000 [05:07<09:14,  8.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4762/8000 [04:39<02:44, 19.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3114/8000 [05:07<07:48, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4765/8000 [04:39<02:51, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3118/8000 [05:08<07:19, 11.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4769/8000 [04:40<03:04, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3120/8000 [05:08<07:32, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4773/8000 [04:40<02:39, 20.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3123/8000 [05:08<06:59, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4777/8000 [04:40<02:58, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3125/8000 [05:08<07:35, 10.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4782/8000 [04:40<02:56, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3127/8000 [05:09<08:54,  9.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4786/8000 [04:40<02:41, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3129/8000 [05:09<11:30,  7.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4791/8000 [04:41<02:44, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3135/8000 [05:09<07:10, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4795/8000 [04:41<02:27, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3138/8000 [05:10<06:33, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4801/8000 [04:41<02:35, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3140/8000 [05:10<08:35,  9.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4804/8000 [04:41<03:01, 17.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3143/8000 [05:10<07:42, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4806/8000 [04:42<03:06, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3146/8000 [05:11<08:18,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4812/8000 [04:42<02:52, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3148/8000 [05:11<08:33,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4815/8000 [04:42<03:23, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3151/8000 [05:11<08:31,  9.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4819/8000 [04:42<03:02, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3155/8000 [05:11<06:47, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4827/8000 [04:43<02:28, 21.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3161/8000 [05:12<05:46, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4830/8000 [04:43<02:28, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3163/8000 [05:12<06:09, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4833/8000 [04:43<02:57, 17.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3165/8000 [05:12<06:10, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4837/8000 [04:43<02:36, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3170/8000 [05:12<06:25, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4842/8000 [04:43<02:45, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3172/8000 [05:13<07:13, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4847/8000 [04:44<03:19, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3174/8000 [05:13<09:19,  8.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4849/8000 [04:44<03:26, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3179/8000 [05:13<06:36, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4855/8000 [04:44<02:53, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3182/8000 [05:14<07:47, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4857/8000 [04:44<02:57, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3185/8000 [05:14<06:58, 11.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4860/8000 [04:45<02:51, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3187/8000 [05:14<07:00, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4863/8000 [04:45<02:40, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3189/8000 [05:14<07:09, 11.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4867/8000 [04:45<02:24, 21.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3193/8000 [05:15<05:59, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4871/8000 [04:45<02:42, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3195/8000 [05:15<06:09, 12.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4878/8000 [04:45<01:51, 28.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3197/8000 [05:15<08:08,  9.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4883/8000 [04:45<02:09, 24.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3201/8000 [05:15<06:20, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4887/8000 [04:46<02:32, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3203/8000 [05:15<06:49, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4891/8000 [04:46<02:45, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3207/8000 [05:16<06:36, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4895/8000 [04:46<02:27, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3211/8000 [05:16<05:32, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4898/8000 [04:46<03:02, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3216/8000 [05:16<05:23, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4901/8000 [04:47<02:50, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3218/8000 [05:16<05:36, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4904/8000 [04:47<02:46, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3221/8000 [05:17<06:24, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4907/8000 [04:47<02:36, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3227/8000 [05:17<04:28, 17.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4910/8000 [04:47<02:32, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3231/8000 [05:17<03:58, 19.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4915/8000 [04:47<02:00, 25.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▉          | 3239/8000 [05:17<02:51, 27.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4921/8000 [04:47<01:47, 28.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3243/8000 [05:17<02:51, 27.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4926/8000 [04:48<02:17, 22.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3247/8000 [05:18<05:12, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4929/8000 [04:48<03:09, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3250/8000 [05:18<05:14, 15.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4932/8000 [04:48<04:03, 12.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3255/8000 [05:18<05:10, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4934/8000 [04:48<04:07, 12.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3260/8000 [05:19<05:09, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4940/8000 [04:49<03:20, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3268/8000 [05:19<04:17, 18.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4942/8000 [04:49<03:25, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3276/8000 [05:19<03:42, 21.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4945/8000 [04:49<03:02, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3280/8000 [05:20<04:32, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4948/8000 [04:49<02:58, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3282/8000 [05:20<05:28, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4952/8000 [04:49<02:31, 20.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3286/8000 [05:20<04:58, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4957/8000 [04:50<02:37, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3288/8000 [05:20<05:11, 15.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4960/8000 [04:50<03:01, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3291/8000 [05:21<05:03, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4963/8000 [04:50<02:46, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3298/8000 [05:21<03:37, 21.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4967/8000 [04:50<02:22, 21.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3305/8000 [05:21<03:38, 21.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4971/8000 [04:50<02:55, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3309/8000 [05:21<03:37, 21.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4975/8000 [04:51<03:12, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3313/8000 [05:21<03:32, 22.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4977/8000 [04:51<03:10, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3316/8000 [05:22<03:42, 21.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4983/8000 [04:51<02:51, 17.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3322/8000 [05:22<03:40, 21.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4986/8000 [04:51<02:46, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3325/8000 [05:22<03:42, 21.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4989/8000 [04:51<02:42, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3328/8000 [05:22<04:50, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4996/8000 [04:52<01:54, 26.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3331/8000 [05:23<05:50, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5003/8000 [04:52<02:04, 24.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3333/8000 [05:23<06:58, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5007/8000 [04:52<01:57, 25.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3336/8000 [05:23<05:55, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5011/8000 [04:52<01:58, 25.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3339/8000 [05:23<05:24, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5014/8000 [04:52<02:10, 22.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3341/8000 [05:24<06:50, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5019/8000 [04:53<02:52, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3344/8000 [05:24<06:07, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5024/8000 [04:53<02:25, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3348/8000 [05:24<05:08, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5031/8000 [04:53<02:16, 21.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3350/8000 [05:24<05:30, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5037/8000 [04:53<01:56, 25.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3352/8000 [05:24<06:06, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5041/8000 [04:54<02:28, 19.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3355/8000 [05:25<08:02,  9.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5044/8000 [04:54<03:42, 13.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3358/8000 [05:25<07:23, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5049/8000 [04:54<03:02, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3360/8000 [05:25<09:11,  8.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5056/8000 [04:55<02:42, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3361/8000 [05:26<11:31,  6.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5060/8000 [04:55<02:48, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3364/8000 [05:26<09:19,  8.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5063/8000 [04:55<03:14, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3365/8000 [05:26<10:58,  7.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5068/8000 [04:55<02:39, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3367/8000 [05:27<10:48,  7.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5074/8000 [04:56<02:32, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3373/8000 [05:27<06:39, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5078/8000 [04:56<02:44, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3378/8000 [05:27<06:48, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5080/8000 [04:56<03:19, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3381/8000 [05:28<06:38, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5083/8000 [04:56<03:00, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3386/8000 [05:28<05:50, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5085/8000 [04:57<03:03, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3388/8000 [05:28<07:33, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5088/8000 [04:57<03:20, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3390/8000 [05:28<07:52,  9.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5090/8000 [04:57<03:22, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3393/8000 [05:29<07:38, 10.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5095/8000 [04:57<03:02, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3398/8000 [05:29<05:52, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5097/8000 [04:58<05:14,  9.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3400/8000 [05:29<06:55, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5099/8000 [04:58<04:46, 10.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3402/8000 [05:30<07:34, 10.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5101/8000 [04:58<04:22, 11.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3407/8000 [05:30<05:48, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5104/8000 [04:58<03:40, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3409/8000 [05:30<06:36, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5109/8000 [04:58<02:33, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3411/8000 [05:30<07:21, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5112/8000 [04:59<03:42, 13.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3413/8000 [05:31<08:22,  9.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5114/8000 [04:59<03:35, 13.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3416/8000 [05:31<07:14, 10.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5117/8000 [04:59<03:09, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3421/8000 [05:31<05:26, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5119/8000 [04:59<03:14, 14.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3423/8000 [05:31<06:27, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5122/8000 [04:59<03:46, 12.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3425/8000 [05:32<07:03, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5124/8000 [05:00<03:38, 13.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3428/8000 [05:32<10:29,  7.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5129/8000 [05:00<03:14, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3431/8000 [05:32<09:10,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5132/8000 [05:00<02:58, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3433/8000 [05:33<09:23,  8.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5134/8000 [05:00<03:10, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3435/8000 [05:33<09:13,  8.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5136/8000 [05:00<03:14, 14.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3438/8000 [05:33<07:53,  9.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5138/8000 [05:00<03:19, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3440/8000 [05:33<08:36,  8.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5142/8000 [05:01<02:45, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3442/8000 [05:34<08:47,  8.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5144/8000 [05:01<02:54, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3443/8000 [05:34<10:52,  6.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5146/8000 [05:01<03:40, 12.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3445/8000 [05:34<13:03,  5.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5149/8000 [05:01<04:06, 11.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3450/8000 [05:35<08:12,  9.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5151/8000 [05:01<03:52, 12.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3454/8000 [05:35<06:29, 11.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5153/8000 [05:02<03:39, 12.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3456/8000 [05:36<10:14,  7.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5155/8000 [05:02<03:36, 13.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3463/8000 [05:36<06:26, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5158/8000 [05:02<03:39, 12.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3466/8000 [05:36<07:43,  9.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5160/8000 [05:02<03:29, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3469/8000 [05:36<07:10, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5163/8000 [05:02<02:59, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▍         | 3471/8000 [05:37<10:04,  7.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5167/8000 [05:02<02:21, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▍         | 3475/8000 [05:37<08:31,  8.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5170/8000 [05:03<02:53, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▍         | 3477/8000 [05:38<09:49,  7.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5172/8000 [05:03<02:54, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3480/8000 [05:38<09:07,  8.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5175/8000 [05:03<02:39, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3482/8000 [05:38<09:00,  8.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5177/8000 [05:03<02:44, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3484/8000 [05:38<08:15,  9.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5180/8000 [05:03<02:26, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3487/8000 [05:39<09:58,  7.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5185/8000 [05:03<02:22, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3492/8000 [05:39<07:02, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5188/8000 [05:04<02:19, 20.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3494/8000 [05:40<09:02,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5193/8000 [05:04<02:16, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3498/8000 [05:40<07:33,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5199/8000 [05:04<02:33, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3500/8000 [05:40<08:51,  8.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5205/8000 [05:04<01:58, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3505/8000 [05:41<06:23, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5208/8000 [05:04<01:57, 23.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3512/8000 [05:41<04:51, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5211/8000 [05:05<02:31, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3515/8000 [05:41<05:11, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5214/8000 [05:05<02:27, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3518/8000 [05:41<05:36, 13.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5218/8000 [05:05<02:36, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3520/8000 [05:42<07:30,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5220/8000 [05:05<02:37, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3523/8000 [05:42<06:54, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5223/8000 [05:05<03:00, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3525/8000 [05:42<07:13, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5227/8000 [05:06<02:34, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3527/8000 [05:43<11:33,  6.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5232/8000 [05:06<02:25, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3528/8000 [05:43<13:02,  5.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5235/8000 [05:06<02:42, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3530/8000 [05:43<12:14,  6.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5238/8000 [05:06<02:30, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3532/8000 [05:44<11:18,  6.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5240/8000 [05:06<02:37, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3537/8000 [05:44<07:11, 10.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5243/8000 [05:06<02:21, 19.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3540/8000 [05:44<07:37,  9.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5246/8000 [05:07<02:45, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3544/8000 [05:45<06:35, 11.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5253/8000 [05:07<02:10, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3546/8000 [05:45<07:29,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5257/8000 [05:07<02:25, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3548/8000 [05:45<07:41,  9.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5260/8000 [05:08<02:52, 15.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3552/8000 [05:45<06:47, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5264/8000 [05:08<02:23, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3555/8000 [05:46<06:18, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5269/8000 [05:08<02:33, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3558/8000 [05:46<06:19, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5273/8000 [05:08<02:16, 19.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3561/8000 [05:46<08:04,  9.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5276/8000 [05:08<02:41, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3566/8000 [05:47<05:52, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5280/8000 [05:08<02:18, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3570/8000 [05:47<07:10, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5283/8000 [05:09<02:20, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3575/8000 [05:47<05:37, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5289/8000 [05:09<01:47, 25.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3579/8000 [05:48<06:40, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5292/8000 [05:09<02:24, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3582/8000 [05:48<07:47,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5295/8000 [05:09<03:17, 13.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3584/8000 [05:48<08:15,  8.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5297/8000 [05:10<03:09, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3587/8000 [05:49<07:24,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5299/8000 [05:10<03:08, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3593/8000 [05:49<05:10, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5301/8000 [05:10<03:06, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3595/8000 [05:49<06:01, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5303/8000 [05:10<02:58, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3597/8000 [05:50<08:10,  8.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5306/8000 [05:10<02:39, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3599/8000 [05:50<08:04,  9.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5310/8000 [05:10<02:57, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3604/8000 [05:50<05:52, 12.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5315/8000 [05:11<02:21, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3608/8000 [05:50<05:21, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5321/8000 [05:11<01:45, 25.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3610/8000 [05:51<06:16, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5324/8000 [05:11<01:52, 23.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3613/8000 [05:51<09:16,  7.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5332/8000 [05:11<01:42, 26.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3617/8000 [05:51<07:16, 10.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5337/8000 [05:11<01:54, 23.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3619/8000 [05:52<08:05,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5343/8000 [05:12<02:11, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3626/8000 [05:52<05:06, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5346/8000 [05:12<02:51, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3630/8000 [05:52<05:11, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5349/8000 [05:12<02:38, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3632/8000 [05:52<05:37, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5352/8000 [05:13<02:54, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3635/8000 [05:53<05:59, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5355/8000 [05:13<02:40, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3637/8000 [05:53<06:48, 10.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5357/8000 [05:13<02:50, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3640/8000 [05:53<08:01,  9.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5362/8000 [05:13<02:11, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3642/8000 [05:54<07:56,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5367/8000 [05:13<01:50, 23.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3643/8000 [05:54<09:17,  7.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5374/8000 [05:13<01:46, 24.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3645/8000 [05:54<08:59,  8.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5379/8000 [05:14<01:38, 26.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3648/8000 [05:54<08:01,  9.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5384/8000 [05:14<01:50, 23.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3651/8000 [05:55<07:38,  9.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5388/8000 [05:14<02:04, 20.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3655/8000 [05:55<06:24, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5391/8000 [05:14<02:00, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3657/8000 [05:55<06:31, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5395/8000 [05:14<02:17, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3661/8000 [05:55<06:04, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5401/8000 [05:15<02:19, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3668/8000 [05:56<05:40, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5403/8000 [05:15<02:47, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3673/8000 [05:56<06:04, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5405/8000 [05:15<02:48, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3678/8000 [05:57<06:07, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5410/8000 [05:15<02:36, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3683/8000 [05:57<06:08, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5412/8000 [05:16<02:33, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3685/8000 [05:58<07:55,  9.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5414/8000 [05:16<02:39, 16.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3687/8000 [05:58<07:47,  9.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5417/8000 [05:16<02:26, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3689/8000 [05:58<09:28,  7.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5419/8000 [05:16<02:31, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3691/8000 [05:59<08:59,  7.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5423/8000 [05:16<02:14, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3696/8000 [05:59<06:36, 10.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5427/8000 [05:16<01:57, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3702/8000 [05:59<06:12, 11.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5432/8000 [05:16<01:38, 25.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3704/8000 [06:00<06:20, 11.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5435/8000 [05:17<01:43, 24.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3710/8000 [06:00<05:09, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5438/8000 [05:17<02:28, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3712/8000 [06:00<05:34, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5443/8000 [05:17<02:49, 15.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3715/8000 [06:00<05:25, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5445/8000 [05:17<02:52, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3719/8000 [06:01<04:44, 15.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5447/8000 [05:18<02:54, 14.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3721/8000 [06:01<05:35, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5449/8000 [05:18<03:32, 11.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3723/8000 [06:01<06:09, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5451/8000 [05:18<03:28, 12.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3726/8000 [06:01<05:53, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5453/8000 [05:18<03:29, 12.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3732/8000 [06:02<05:58, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5456/8000 [05:18<02:57, 14.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3734/8000 [06:02<07:39,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5459/8000 [05:18<02:41, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3736/8000 [06:02<07:23,  9.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5463/8000 [05:19<02:16, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3739/8000 [06:03<10:37,  6.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5469/8000 [05:19<01:43, 24.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3740/8000 [06:03<11:19,  6.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5475/8000 [05:19<01:51, 22.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3743/8000 [06:04<09:47,  7.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5479/8000 [05:19<02:28, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3744/8000 [06:04<10:38,  6.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5481/8000 [05:20<02:32, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3747/8000 [06:04<08:58,  7.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5486/8000 [05:20<02:48, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3751/8000 [06:04<07:08,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5488/8000 [05:20<02:46, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3754/8000 [06:05<06:22, 11.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5491/8000 [05:20<02:33, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3756/8000 [06:05<06:55, 10.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5495/8000 [05:20<02:08, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3760/8000 [06:05<05:50, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5500/8000 [05:21<01:44, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3764/8000 [06:05<05:19, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5504/8000 [05:21<02:02, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3767/8000 [06:06<05:42, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5507/8000 [05:21<01:59, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3770/8000 [06:06<05:40, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5511/8000 [05:21<01:46, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3773/8000 [06:06<07:15,  9.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5514/8000 [05:21<01:47, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3775/8000 [06:07<07:19,  9.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5519/8000 [05:21<01:56, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3780/8000 [06:07<07:40,  9.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5522/8000 [05:22<01:53, 21.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3785/8000 [06:07<06:13, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5527/8000 [05:22<01:38, 25.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3787/8000 [06:08<06:20, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5532/8000 [05:22<02:09, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3791/8000 [06:08<05:01, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5536/8000 [05:22<02:20, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3793/8000 [06:08<05:34, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5539/8000 [05:22<02:12, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3796/8000 [06:09<08:40,  8.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5542/8000 [05:23<02:32, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3798/8000 [06:09<08:33,  8.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5546/8000 [05:23<02:09, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3800/8000 [06:09<08:05,  8.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5550/8000 [05:23<01:53, 21.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3802/8000 [06:09<08:52,  7.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5554/8000 [05:23<01:45, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3804/8000 [06:10<08:24,  8.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5559/8000 [05:23<01:57, 20.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3807/8000 [06:10<07:50,  8.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5562/8000 [05:24<02:00, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3809/8000 [06:10<07:53,  8.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5566/8000 [05:24<02:21, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3811/8000 [06:10<07:45,  8.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5568/8000 [05:24<02:26, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3814/8000 [06:11<06:42, 10.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5573/8000 [05:24<01:53, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3816/8000 [06:11<06:46, 10.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5578/8000 [05:25<02:45, 14.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3818/8000 [06:11<09:40,  7.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5583/8000 [05:25<02:30, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3819/8000 [06:11<10:52,  6.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5585/8000 [05:25<02:28, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3821/8000 [06:12<09:30,  7.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5588/8000 [05:25<02:17, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3823/8000 [06:12<08:44,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5592/8000 [05:26<02:44, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3825/8000 [06:12<08:37,  8.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5595/8000 [05:26<02:53, 13.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3827/8000 [06:13<11:44,  5.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5598/8000 [05:26<03:04, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3829/8000 [06:13<10:32,  6.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5601/8000 [05:26<02:39, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3832/8000 [06:13<08:01,  8.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5605/8000 [05:27<02:45, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3834/8000 [06:13<07:40,  9.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5608/8000 [05:27<02:53, 13.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3837/8000 [06:13<06:48, 10.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5610/8000 [05:27<02:46, 14.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3841/8000 [06:14<05:36, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5614/8000 [05:27<02:13, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3845/8000 [06:14<05:16, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5618/8000 [05:27<01:57, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3847/8000 [06:14<05:41, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5622/8000 [05:27<01:44, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3849/8000 [06:14<06:05, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5628/8000 [05:27<01:30, 26.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3852/8000 [06:15<07:31,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5631/8000 [05:28<02:01, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3856/8000 [06:15<05:58, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5634/8000 [05:28<01:57, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3860/8000 [06:15<05:45, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5637/8000 [05:28<01:54, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3866/8000 [06:16<04:24, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5640/8000 [05:28<01:57, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3870/8000 [06:16<05:12, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5644/8000 [05:28<01:46, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3873/8000 [06:16<05:12, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5647/8000 [05:28<01:46, 22.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3875/8000 [06:16<05:55, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5650/8000 [05:29<01:46, 22.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3878/8000 [06:17<05:43, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5656/8000 [05:29<01:36, 24.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3884/8000 [06:17<04:05, 16.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5663/8000 [05:29<01:30, 25.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3886/8000 [06:17<04:56, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5669/8000 [05:29<01:33, 25.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3892/8000 [06:17<03:51, 17.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5674/8000 [05:29<01:27, 26.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3896/8000 [06:18<03:50, 17.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5678/8000 [05:30<01:27, 26.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3898/8000 [06:18<04:57, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5683/8000 [05:30<01:39, 23.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3905/8000 [06:18<03:44, 18.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5687/8000 [05:30<01:49, 21.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3907/8000 [06:18<04:09, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5690/8000 [05:30<02:18, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3912/8000 [06:19<03:56, 17.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5693/8000 [05:31<02:35, 14.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3917/8000 [06:19<03:31, 19.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5696/8000 [05:31<02:23, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3920/8000 [06:19<04:05, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5699/8000 [05:31<02:12, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3922/8000 [06:19<04:37, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5701/8000 [05:31<02:16, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3926/8000 [06:20<06:14, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5704/8000 [05:31<02:11, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3930/8000 [06:20<05:13, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5708/8000 [05:31<01:55, 19.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3934/8000 [06:20<04:46, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5713/8000 [05:32<01:33, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3938/8000 [06:20<04:33, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5717/8000 [05:32<01:31, 24.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3943/8000 [06:21<05:14, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5720/8000 [05:32<01:34, 24.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3946/8000 [06:22<07:37,  8.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5725/8000 [05:32<01:21, 27.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3949/8000 [06:22<07:24,  9.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5728/8000 [05:32<01:30, 24.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3953/8000 [06:22<07:50,  8.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5731/8000 [05:32<01:34, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3958/8000 [06:23<05:55, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5734/8000 [05:33<02:07, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3960/8000 [06:23<06:43, 10.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5738/8000 [05:33<01:51, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3962/8000 [06:23<07:02,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5743/8000 [05:33<01:55, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3965/8000 [06:23<06:18, 10.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5746/8000 [05:33<01:51, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3967/8000 [06:24<08:49,  7.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5751/8000 [05:33<01:57, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3971/8000 [06:24<06:54,  9.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5755/8000 [05:34<01:42, 21.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3974/8000 [06:24<06:24, 10.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5760/8000 [05:34<01:52, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3978/8000 [06:25<05:54, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5763/8000 [05:34<01:51, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3980/8000 [06:25<06:38, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5766/8000 [05:34<02:11, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3982/8000 [06:25<06:46,  9.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5769/8000 [05:34<02:02, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3985/8000 [06:25<06:04, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5771/8000 [05:35<02:09, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3988/8000 [06:26<05:55, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5776/8000 [05:35<02:07, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3992/8000 [06:26<06:25, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5779/8000 [05:35<02:00, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3996/8000 [06:27<08:22,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5784/8000 [05:35<01:36, 22.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3998/8000 [06:27<08:48,  7.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5790/8000 [05:35<01:55, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4006/8000 [06:28<06:41,  9.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5794/8000 [05:36<01:48, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4009/8000 [06:29<09:01,  7.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5801/8000 [05:36<01:21, 26.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4014/8000 [06:29<07:10,  9.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5807/8000 [05:36<01:33, 23.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4017/8000 [06:29<08:11,  8.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5810/8000 [05:36<01:57, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4020/8000 [06:30<07:34,  8.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5813/8000 [05:37<02:01, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4024/8000 [06:30<07:58,  8.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5817/8000 [05:37<01:54, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4025/8000 [06:30<08:19,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5820/8000 [05:37<01:58, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4029/8000 [06:31<06:41,  9.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5823/8000 [05:37<01:59, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4032/8000 [06:31<09:54,  6.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5827/8000 [05:37<01:48, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4034/8000 [06:32<09:41,  6.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5831/8000 [05:37<01:41, 21.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4036/8000 [06:32<09:37,  6.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5836/8000 [05:38<01:50, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4037/8000 [06:32<10:07,  6.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5840/8000 [05:38<02:11, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4040/8000 [06:32<08:00,  8.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5842/8000 [05:38<02:18, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4043/8000 [06:33<07:18,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5846/8000 [05:39<02:43, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4045/8000 [06:33<07:04,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5852/8000 [05:39<02:00, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4048/8000 [06:33<06:37,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5855/8000 [05:39<02:16, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4052/8000 [06:33<05:39, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5858/8000 [05:39<02:53, 12.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4054/8000 [06:34<08:12,  8.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5860/8000 [05:40<02:55, 12.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4057/8000 [06:34<06:58,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5866/8000 [05:40<02:25, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4063/8000 [06:35<06:34,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5868/8000 [05:40<02:23, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4067/8000 [06:35<06:56,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5871/8000 [05:40<02:09, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4071/8000 [06:35<06:10, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5876/8000 [05:41<02:26, 14.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4075/8000 [06:36<07:01,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5879/8000 [05:41<02:10, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4077/8000 [06:36<07:25,  8.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5884/8000 [05:41<01:45, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4079/8000 [06:36<07:11,  9.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5888/8000 [05:41<01:35, 22.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4080/8000 [06:37<08:22,  7.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5894/8000 [05:41<01:39, 21.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4085/8000 [06:37<05:48, 11.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5897/8000 [05:41<01:41, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4087/8000 [06:37<07:49,  8.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5902/8000 [05:42<01:46, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4088/8000 [06:38<08:59,  7.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5905/8000 [05:42<01:43, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4093/8000 [06:38<07:34,  8.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5909/8000 [05:42<01:34, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4095/8000 [06:38<07:52,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5913/8000 [05:42<01:28, 23.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4100/8000 [06:38<05:38, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5919/8000 [05:42<01:33, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4104/8000 [06:39<04:57, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5923/8000 [05:43<01:46, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4108/8000 [06:39<04:44, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5930/8000 [05:43<01:35, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4110/8000 [06:39<05:13, 12.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5933/8000 [05:43<01:37, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4114/8000 [06:40<06:11, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5938/8000 [05:43<01:41, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4116/8000 [06:40<06:29,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5941/8000 [05:44<01:40, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4120/8000 [06:40<05:40, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5944/8000 [05:44<01:41, 20.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4123/8000 [06:40<05:17, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5948/8000 [05:44<01:55, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4125/8000 [06:41<09:22,  6.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5953/8000 [05:44<01:33, 21.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4128/8000 [06:41<07:35,  8.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5956/8000 [05:44<01:31, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4132/8000 [06:42<07:08,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5959/8000 [05:45<01:51, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4135/8000 [06:42<06:33,  9.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5962/8000 [05:45<01:45, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4140/8000 [06:42<05:01, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5966/8000 [05:45<01:53, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4143/8000 [06:43<05:56, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5971/8000 [05:45<01:51, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4146/8000 [06:43<06:52,  9.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5973/8000 [05:46<02:44, 12.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4149/8000 [06:43<06:04, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5978/8000 [05:46<01:59, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4152/8000 [06:43<05:34, 11.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5981/8000 [05:46<01:54, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4155/8000 [06:44<05:45, 11.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5985/8000 [05:46<01:39, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4158/8000 [06:44<05:38, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5988/8000 [05:46<01:41, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4162/8000 [06:44<05:15, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5991/8000 [05:46<02:12, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4168/8000 [06:45<05:08, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5996/8000 [05:47<01:59, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4171/8000 [06:45<04:50, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5999/8000 [05:47<01:56, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4175/8000 [06:45<04:35, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6001/8000 [05:47<02:00, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4180/8000 [06:46<05:10, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6005/8000 [05:47<01:40, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4184/8000 [06:46<04:37, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6008/8000 [05:47<01:41, 19.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4187/8000 [06:46<04:27, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6011/8000 [05:47<01:39, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4191/8000 [06:46<04:16, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6016/8000 [05:48<01:39, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4193/8000 [06:47<04:55, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6019/8000 [05:48<02:22, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4198/8000 [06:47<04:21, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6021/8000 [05:48<02:18, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4201/8000 [06:47<05:28, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6023/8000 [05:48<02:13, 14.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4207/8000 [06:48<04:34, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6025/8000 [05:49<02:50, 11.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4211/8000 [06:48<04:13, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6028/8000 [05:49<02:25, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4213/8000 [06:48<05:11, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6031/8000 [05:49<02:06, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4216/8000 [06:48<05:08, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6033/8000 [05:49<02:08, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4218/8000 [06:49<06:04, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6035/8000 [05:49<02:09, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4223/8000 [06:49<06:27,  9.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6039/8000 [05:49<02:09, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4225/8000 [06:49<06:26,  9.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6043/8000 [05:50<01:50, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4229/8000 [06:50<05:35, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6048/8000 [05:50<01:25, 22.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4234/8000 [06:50<04:50, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6052/8000 [05:50<01:40, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4238/8000 [06:50<04:37, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6056/8000 [05:50<01:45, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4240/8000 [06:50<04:54, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6060/8000 [05:50<01:40, 19.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4242/8000 [06:51<05:06, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6063/8000 [05:51<01:37, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4246/8000 [06:51<07:04,  8.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6067/8000 [05:51<01:32, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4249/8000 [06:51<06:12, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6072/8000 [05:51<01:42, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4255/8000 [06:52<04:26, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6080/8000 [05:52<01:55, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4257/8000 [06:52<04:47, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6084/8000 [05:52<01:59, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4260/8000 [06:52<06:04, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6087/8000 [05:52<01:50, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4262/8000 [06:53<06:56,  8.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6090/8000 [05:52<02:03, 15.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4264/8000 [06:53<08:19,  7.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6093/8000 [05:52<01:53, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4266/8000 [06:54<10:24,  5.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6096/8000 [05:53<01:45, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4267/8000 [06:54<10:39,  5.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6102/8000 [05:53<01:50, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4269/8000 [06:54<12:28,  4.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6106/8000 [05:53<01:36, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4270/8000 [06:55<15:28,  4.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6113/8000 [05:53<01:29, 20.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4271/8000 [06:55<15:01,  4.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6117/8000 [05:53<01:24, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4273/8000 [06:55<12:11,  5.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6122/8000 [05:54<01:13, 25.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4275/8000 [06:56<12:34,  4.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6127/8000 [05:54<01:27, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4280/8000 [06:56<07:33,  8.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6131/8000 [05:54<01:20, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4288/8000 [06:56<04:24, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6135/8000 [05:54<01:17, 24.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4290/8000 [06:56<04:41, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6138/8000 [05:55<01:40, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4293/8000 [06:57<04:34, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6141/8000 [05:55<01:36, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4297/8000 [06:57<05:24, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6145/8000 [05:55<01:34, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4300/8000 [06:57<05:09, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6150/8000 [05:55<01:34, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4304/8000 [06:58<04:39, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6154/8000 [05:55<01:23, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4307/8000 [06:58<04:46, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6158/8000 [05:55<01:15, 24.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4311/8000 [06:58<04:50, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6161/8000 [05:56<01:35, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4313/8000 [06:58<05:06, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6164/8000 [05:56<01:31, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4316/8000 [06:59<06:49,  8.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6170/8000 [05:56<01:45, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4322/8000 [07:00<07:13,  8.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6173/8000 [05:56<01:37, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4324/8000 [07:00<07:23,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6179/8000 [05:57<01:31, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4327/8000 [07:00<08:03,  7.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6184/8000 [05:57<01:17, 23.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4331/8000 [07:01<06:36,  9.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6187/8000 [05:57<01:33, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4336/8000 [07:01<05:56, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6192/8000 [05:57<01:33, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4340/8000 [07:01<05:16, 11.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6195/8000 [05:58<02:03, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4342/8000 [07:02<07:10,  8.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6198/8000 [05:58<02:15, 13.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4345/8000 [07:02<06:32,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6202/8000 [05:58<01:47, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4347/8000 [07:02<07:05,  8.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6207/8000 [05:58<01:28, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4349/8000 [07:03<06:52,  8.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6210/8000 [05:58<01:25, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4352/8000 [07:03<06:01, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6218/8000 [05:59<01:19, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▎       | 4355/8000 [07:03<05:08, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6223/8000 [05:59<01:13, 24.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▎       | 4358/8000 [07:03<05:08, 11.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6227/8000 [05:59<01:08, 25.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4360/8000 [07:04<06:52,  8.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6233/8000 [05:59<01:27, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4367/8000 [07:04<05:11, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6238/8000 [06:00<01:34, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4370/8000 [07:05<06:26,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6241/8000 [06:00<01:42, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4375/8000 [07:05<06:04,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6243/8000 [06:00<01:45, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4379/8000 [07:05<05:28, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6245/8000 [06:00<01:50, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4381/8000 [07:05<05:47, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6249/8000 [06:00<01:30, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4384/8000 [07:06<05:43, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6252/8000 [06:01<02:02, 14.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4387/8000 [07:06<05:29, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6255/8000 [06:01<01:56, 14.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4389/8000 [07:07<07:39,  7.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6257/8000 [06:01<01:57, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4391/8000 [07:07<07:53,  7.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6260/8000 [06:01<02:06, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4393/8000 [07:07<07:48,  7.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6263/8000 [06:01<01:50, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4395/8000 [07:07<07:26,  8.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6266/8000 [06:01<01:42, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4397/8000 [07:08<07:15,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6269/8000 [06:02<01:36, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4398/8000 [07:08<08:40,  6.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6271/8000 [06:02<02:01, 14.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4399/8000 [07:08<09:39,  6.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6273/8000 [06:02<01:59, 14.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4404/8000 [07:08<06:02,  9.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6275/8000 [06:02<02:29, 11.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4411/8000 [07:09<03:53, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6278/8000 [06:02<02:14, 12.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4413/8000 [07:09<04:11, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6281/8000 [06:03<02:07, 13.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4416/8000 [07:09<04:21, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6284/8000 [06:03<01:52, 15.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4421/8000 [07:09<04:00, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6287/8000 [06:03<01:37, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4424/8000 [07:10<04:19, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6290/8000 [06:03<01:55, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4432/8000 [07:10<04:16, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6295/8000 [06:03<01:45, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4434/8000 [07:10<04:41, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6299/8000 [06:04<01:30, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4436/8000 [07:11<06:23,  9.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6303/8000 [06:04<01:41, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4438/8000 [07:11<06:21,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6306/8000 [06:04<01:48, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4439/8000 [07:11<07:54,  7.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6308/8000 [06:04<01:49, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4443/8000 [07:12<07:48,  7.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6311/8000 [06:04<02:04, 13.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4446/8000 [07:12<06:43,  8.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6313/8000 [06:05<02:04, 13.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4447/8000 [07:12<08:04,  7.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6317/8000 [06:05<01:37, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4452/8000 [07:13<06:16,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6321/8000 [06:05<01:24, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4453/8000 [07:13<07:33,  7.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6324/8000 [06:05<01:21, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4456/8000 [07:13<06:14,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6329/8000 [06:05<01:43, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4460/8000 [07:13<05:10, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6331/8000 [06:06<01:46, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4463/8000 [07:14<05:04, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6337/8000 [06:06<01:51, 14.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4467/8000 [07:14<04:21, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6339/8000 [06:06<02:33, 10.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4470/8000 [07:14<04:08, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6342/8000 [06:07<02:08, 12.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4473/8000 [07:15<05:38, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6346/8000 [06:07<02:16, 12.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4476/8000 [07:15<05:05, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6350/8000 [06:07<01:51, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4480/8000 [07:15<04:29, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6352/8000 [06:07<01:53, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4484/8000 [07:15<04:10, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6355/8000 [06:08<02:09, 12.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4486/8000 [07:15<04:23, 13.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6357/8000 [06:08<02:06, 12.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4488/8000 [07:16<05:16, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6362/8000 [06:08<01:58, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4491/8000 [07:16<05:12, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6365/8000 [06:08<01:45, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4494/8000 [07:16<04:56, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6368/8000 [06:08<01:37, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4496/8000 [07:17<07:21,  7.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6370/8000 [06:08<01:44, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4500/8000 [07:17<05:50,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6372/8000 [06:09<01:47, 15.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4503/8000 [07:17<05:13, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6376/8000 [06:09<01:27, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4507/8000 [07:17<04:50, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6378/8000 [06:09<01:34, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4514/8000 [07:18<03:34, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6380/8000 [06:09<02:08, 12.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4517/8000 [07:18<03:43, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6384/8000 [06:10<02:21, 11.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4520/8000 [07:18<03:59, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6387/8000 [06:10<01:58, 13.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▌       | 4524/8000 [07:18<04:02, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6391/8000 [06:10<01:36, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▌       | 4526/8000 [07:19<07:23,  7.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6393/8000 [06:10<01:37, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4530/8000 [07:20<06:19,  9.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6397/8000 [06:10<01:23, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4533/8000 [07:20<05:58,  9.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6402/8000 [06:10<01:05, 24.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4535/8000 [07:20<05:51,  9.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6406/8000 [06:10<01:01, 26.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4537/8000 [07:20<07:43,  7.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6411/8000 [06:11<00:53, 29.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4539/8000 [07:21<07:10,  8.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6416/8000 [06:11<01:01, 25.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4541/8000 [07:21<07:40,  7.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6419/8000 [06:11<01:03, 25.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4547/8000 [07:21<05:17, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6426/8000 [06:11<01:03, 24.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4550/8000 [07:22<05:52,  9.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6430/8000 [06:11<01:11, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4553/8000 [07:22<06:44,  8.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6436/8000 [06:12<01:08, 22.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4556/8000 [07:22<05:54,  9.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6442/8000 [06:12<01:13, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4559/8000 [07:23<06:45,  8.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6446/8000 [06:12<01:08, 22.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4560/8000 [07:23<07:51,  7.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6449/8000 [06:12<01:07, 23.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4563/8000 [07:24<08:09,  7.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6453/8000 [06:13<01:36, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4566/8000 [07:24<06:55,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6455/8000 [06:13<01:37, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4568/8000 [07:24<06:55,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6459/8000 [06:13<01:21, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4570/8000 [07:24<06:43,  8.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6462/8000 [06:13<01:35, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4572/8000 [07:24<06:13,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6464/8000 [06:13<01:35, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4573/8000 [07:25<06:51,  8.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6469/8000 [06:13<01:13, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4574/8000 [07:25<09:42,  5.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6472/8000 [06:14<01:14, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4576/8000 [07:25<08:08,  7.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6475/8000 [06:14<01:31, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4580/8000 [07:25<05:18, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6479/8000 [06:14<01:33, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4582/8000 [07:26<07:42,  7.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6483/8000 [06:14<01:18, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4585/8000 [07:26<06:32,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6486/8000 [06:15<02:11, 11.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4587/8000 [07:26<06:32,  8.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6489/8000 [06:15<02:30, 10.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4589/8000 [07:27<07:50,  7.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6491/8000 [06:16<02:43,  9.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4590/8000 [07:27<09:06,  6.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6497/8000 [06:16<01:56, 12.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4591/8000 [07:27<09:53,  5.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6500/8000 [06:16<01:59, 12.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4595/8000 [07:27<06:22,  8.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6502/8000 [06:16<02:00, 12.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4597/8000 [07:28<06:14,  9.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6504/8000 [06:16<02:00, 12.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4598/8000 [07:28<08:07,  6.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6507/8000 [06:17<01:48, 13.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4599/8000 [07:28<08:57,  6.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6511/8000 [06:17<01:29, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4602/8000 [07:28<06:34,  8.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6513/8000 [06:17<02:11, 11.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4609/8000 [07:29<03:37, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6516/8000 [06:17<01:52, 13.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4611/8000 [07:29<04:46, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6518/8000 [06:17<01:47, 13.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4613/8000 [07:29<04:59, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6521/8000 [06:18<01:56, 12.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4618/8000 [07:30<05:05, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6524/8000 [06:18<01:38, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4620/8000 [07:30<06:39,  8.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6531/8000 [06:18<01:16, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4621/8000 [07:30<07:07,  7.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6534/8000 [06:18<01:14, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4623/8000 [07:31<08:28,  6.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6539/8000 [06:18<01:12, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4624/8000 [07:31<09:08,  6.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6542/8000 [06:19<01:13, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4625/8000 [07:31<09:21,  6.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6544/8000 [06:19<01:14, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4628/8000 [07:31<07:37,  7.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6546/8000 [06:19<01:40, 14.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4631/8000 [07:32<06:27,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6549/8000 [06:19<01:32, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4632/8000 [07:32<11:38,  4.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6552/8000 [06:19<01:26, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4633/8000 [07:33<11:52,  4.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6556/8000 [06:19<01:14, 19.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4635/8000 [07:33<10:40,  5.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6559/8000 [06:20<01:12, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4636/8000 [07:33<10:58,  5.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6563/8000 [06:20<01:43, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4639/8000 [07:33<08:22,  6.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6566/8000 [06:20<01:31, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4641/8000 [07:34<08:09,  6.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6569/8000 [06:20<01:43, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4642/8000 [07:34<08:38,  6.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6573/8000 [06:21<01:24, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4644/8000 [07:34<07:31,  7.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6578/8000 [06:21<01:06, 21.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4645/8000 [07:34<08:49,  6.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6585/8000 [06:21<00:48, 29.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4649/8000 [07:35<06:28,  8.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6590/8000 [06:21<00:55, 25.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4650/8000 [07:35<07:07,  7.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6595/8000 [06:21<01:03, 22.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4652/8000 [07:35<07:59,  6.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6602/8000 [06:22<00:56, 24.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4657/8000 [07:35<05:17, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6605/8000 [06:22<01:09, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4660/8000 [07:36<04:44, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6609/8000 [06:22<01:05, 21.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4665/8000 [07:36<03:43, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6615/8000 [06:22<01:02, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4667/8000 [07:36<03:52, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6620/8000 [06:22<00:54, 25.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4670/8000 [07:36<03:37, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6623/8000 [06:22<00:54, 25.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4672/8000 [07:36<05:09, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6627/8000 [06:23<01:18, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4677/8000 [07:37<05:11, 10.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6630/8000 [06:23<01:27, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4682/8000 [07:37<04:51, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6633/8000 [06:23<01:31, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4685/8000 [07:38<05:41,  9.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6635/8000 [06:24<01:30, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4687/8000 [07:38<05:48,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6637/8000 [06:24<01:33, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4693/8000 [07:38<03:57, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6639/8000 [06:24<01:32, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4695/8000 [07:39<04:49, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6645/8000 [06:24<01:06, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4700/8000 [07:39<04:13, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6650/8000 [06:24<01:31, 14.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4702/8000 [07:39<04:26, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6654/8000 [06:25<01:19, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4704/8000 [07:39<04:45, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6657/8000 [06:25<01:44, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4706/8000 [07:39<05:10, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6661/8000 [06:25<01:46, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4711/8000 [07:40<05:15, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6663/8000 [06:26<02:50,  7.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4714/8000 [07:40<04:51, 11.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6666/8000 [06:26<02:21,  9.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4718/8000 [07:41<05:16, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6669/8000 [06:26<02:02, 10.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4721/8000 [07:41<05:14, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6672/8000 [06:27<01:49, 12.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4726/8000 [07:41<04:21, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6675/8000 [06:27<01:37, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4729/8000 [07:42<04:57, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6678/8000 [06:27<01:26, 15.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4731/8000 [07:42<05:10, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6680/8000 [06:27<01:39, 13.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4737/8000 [07:42<05:32,  9.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6685/8000 [06:27<01:10, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4740/8000 [07:43<05:16, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6688/8000 [06:27<01:09, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4744/8000 [07:43<06:30,  8.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6691/8000 [06:28<01:19, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4745/8000 [07:44<06:51,  7.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6694/8000 [06:28<01:12, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4746/8000 [07:44<07:22,  7.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6697/8000 [06:28<01:07, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4749/8000 [07:44<06:29,  8.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6702/8000 [06:28<00:52, 24.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4753/8000 [07:44<06:23,  8.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6707/8000 [06:28<01:02, 20.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4756/8000 [07:45<05:43,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6711/8000 [06:28<01:01, 21.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4758/8000 [07:45<06:01,  8.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6718/8000 [06:29<01:09, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████       | 4760/8000 [07:45<06:27,  8.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6721/8000 [06:29<01:07, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████       | 4764/8000 [07:46<05:06, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6726/8000 [06:29<01:07, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4766/8000 [07:46<06:53,  7.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6731/8000 [06:30<01:07, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4770/8000 [07:46<05:33,  9.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6737/8000 [06:30<01:03, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4772/8000 [07:46<05:26,  9.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6740/8000 [06:30<01:06, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4776/8000 [07:47<04:28, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6743/8000 [06:30<01:07, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4778/8000 [07:47<04:36, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6747/8000 [06:30<01:02, 20.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4780/8000 [07:47<04:54, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6750/8000 [06:31<01:27, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4785/8000 [07:47<03:44, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6752/8000 [06:31<01:27, 14.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4788/8000 [07:48<03:44, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6756/8000 [06:31<01:12, 17.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4790/8000 [07:48<04:33, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6758/8000 [06:31<01:13, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4794/8000 [07:48<05:37,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6760/8000 [06:31<01:16, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4796/8000 [07:49<05:53,  9.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6763/8000 [06:31<01:10, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4798/8000 [07:49<05:49,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6768/8000 [06:32<00:54, 22.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4802/8000 [07:49<04:38, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6773/8000 [06:32<00:45, 27.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4804/8000 [07:49<04:37, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6778/8000 [06:32<00:50, 24.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4806/8000 [07:50<05:34,  9.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6782/8000 [06:32<01:01, 19.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4810/8000 [07:50<04:22, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6787/8000 [06:32<00:52, 23.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4813/8000 [07:50<04:13, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6790/8000 [06:33<00:55, 21.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4816/8000 [07:50<04:32, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6794/8000 [06:33<00:49, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4819/8000 [07:50<04:17, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6798/8000 [06:33<00:57, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4823/8000 [07:51<05:37,  9.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6801/8000 [06:33<00:58, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4825/8000 [07:51<05:52,  9.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6804/8000 [06:33<00:55, 21.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4828/8000 [07:52<07:00,  7.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6810/8000 [06:33<00:43, 27.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4829/8000 [07:52<07:32,  7.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6817/8000 [06:34<00:34, 34.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4832/8000 [07:52<06:13,  8.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6825/8000 [06:34<00:39, 29.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4833/8000 [07:53<07:14,  7.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6829/8000 [06:34<00:45, 26.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4834/8000 [07:53<09:49,  5.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6833/8000 [06:34<00:55, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4837/8000 [07:53<07:15,  7.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6838/8000 [06:35<00:50, 23.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4839/8000 [07:54<08:41,  6.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6844/8000 [06:35<00:50, 22.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4843/8000 [07:54<05:53,  8.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6847/8000 [06:35<00:51, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4847/8000 [07:54<04:51, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6852/8000 [06:35<00:52, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4849/8000 [07:54<05:28,  9.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6856/8000 [06:35<00:59, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4853/8000 [07:55<04:36, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6861/8000 [06:36<01:03, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4859/8000 [07:55<04:25, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6864/8000 [06:36<01:09, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4862/8000 [07:55<04:34, 11.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6866/8000 [06:36<01:10, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4865/8000 [07:56<05:42,  9.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6868/8000 [06:36<01:10, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4867/8000 [07:56<06:09,  8.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6871/8000 [06:36<01:01, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4869/8000 [07:56<06:25,  8.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6874/8000 [06:37<01:00, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4870/8000 [07:57<07:38,  6.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6877/8000 [06:37<00:57, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4874/8000 [07:57<05:56,  8.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6881/8000 [06:37<01:04, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4876/8000 [07:57<05:47,  8.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6885/8000 [06:37<00:53, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4880/8000 [07:58<04:36, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6890/8000 [06:37<00:56, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4885/8000 [07:58<03:22, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6894/8000 [06:38<01:02, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4888/8000 [07:58<03:21, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6898/8000 [06:38<00:54, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4893/8000 [07:58<04:00, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6901/8000 [06:38<00:51, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4899/8000 [07:59<04:03, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6906/8000 [06:38<01:06, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4904/8000 [07:59<04:23, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6910/8000 [06:39<01:09, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4909/8000 [08:00<04:35, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6912/8000 [06:39<01:13, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4917/8000 [08:00<03:49, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6917/8000 [06:39<00:55, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4919/8000 [08:01<04:15, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6921/8000 [06:39<00:56, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4921/8000 [08:01<04:22, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6926/8000 [06:40<01:07, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4924/8000 [08:01<05:09,  9.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6932/8000 [06:40<00:51, 20.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4927/8000 [08:01<05:08,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6936/8000 [06:40<00:57, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4930/8000 [08:02<04:53, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6942/8000 [06:40<01:02, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4934/8000 [08:02<05:15,  9.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6945/8000 [06:41<01:22, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4938/8000 [08:03<05:27,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6952/8000 [06:41<01:24, 12.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4940/8000 [08:03<05:35,  9.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6954/8000 [06:42<01:21, 12.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4941/8000 [08:03<06:10,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6956/8000 [06:42<01:25, 12.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4944/8000 [08:03<05:42,  8.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6961/8000 [06:42<01:01, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4947/8000 [08:04<06:13,  8.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6964/8000 [06:42<01:16, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4950/8000 [08:04<05:49,  8.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6966/8000 [06:43<01:39, 10.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4953/8000 [08:04<05:16,  9.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6969/8000 [06:43<01:28, 11.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4956/8000 [08:05<05:07,  9.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6972/8000 [06:43<01:21, 12.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4960/8000 [08:05<04:03, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6974/8000 [06:43<01:53,  9.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4963/8000 [08:05<04:55, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6979/8000 [06:44<01:17, 13.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4966/8000 [08:05<04:25, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6983/8000 [06:44<01:04, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4968/8000 [08:06<06:42,  7.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6986/8000 [06:44<01:05, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4970/8000 [08:06<07:44,  6.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6990/8000 [06:44<00:56, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4973/8000 [08:07<06:09,  8.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6994/8000 [06:44<01:04, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4976/8000 [08:07<05:23,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6997/8000 [06:45<01:00, 16.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4978/8000 [08:07<05:36,  8.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6999/8000 [06:45<01:14, 13.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4980/8000 [08:07<05:46,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7002/8000 [06:45<01:07, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4982/8000 [08:08<05:49,  8.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7006/8000 [06:45<00:55, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4986/8000 [08:08<04:24, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7011/8000 [06:45<00:54, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4990/8000 [08:08<04:34, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7013/8000 [06:46<00:58, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4996/8000 [08:08<03:35, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7015/8000 [06:46<01:20, 12.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▋      | 5000/8000 [08:09<03:12, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7017/8000 [06:46<01:18, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5002/8000 [08:09<03:42, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7020/8000 [06:46<01:29, 10.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5006/8000 [08:09<04:27, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7024/8000 [06:46<01:07, 14.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5008/8000 [08:10<04:28, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7028/8000 [06:47<00:55, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5011/8000 [08:10<05:12,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7032/8000 [06:47<00:49, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5016/8000 [08:10<04:10, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7041/8000 [06:47<00:40, 23.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5019/8000 [08:10<04:00, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7045/8000 [06:47<00:44, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5021/8000 [08:11<04:06, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7048/8000 [06:47<00:45, 21.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5023/8000 [08:11<04:26, 11.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7051/8000 [06:48<01:03, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5026/8000 [08:11<04:16, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7054/8000 [06:48<01:07, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5029/8000 [08:11<04:28, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7058/8000 [06:48<00:56, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5031/8000 [08:12<04:59,  9.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7062/8000 [06:49<01:02, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5035/8000 [08:12<03:55, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7065/8000 [06:49<00:57, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5037/8000 [08:12<04:19, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7068/8000 [06:49<00:55, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5040/8000 [08:12<04:17, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7071/8000 [06:49<01:03, 14.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5042/8000 [08:13<04:29, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7073/8000 [06:49<01:02, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5045/8000 [08:13<04:16, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7075/8000 [06:50<01:14, 12.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5051/8000 [08:13<03:20, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7080/8000 [06:50<00:50, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5054/8000 [08:13<03:20, 14.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7085/8000 [06:50<00:49, 18.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5056/8000 [08:13<03:41, 13.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7089/8000 [06:50<01:05, 13.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5060/8000 [08:14<03:21, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7091/8000 [06:51<01:16, 11.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5064/8000 [08:14<02:58, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7094/8000 [06:51<01:27, 10.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5066/8000 [08:14<03:44, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7098/8000 [06:51<01:07, 13.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5070/8000 [08:15<04:12, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7100/8000 [06:51<01:09, 12.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5072/8000 [08:15<04:19, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7102/8000 [06:51<01:06, 13.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5077/8000 [08:15<04:31, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7107/8000 [06:52<00:48, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5080/8000 [08:16<04:21, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7111/8000 [06:52<01:02, 14.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5082/8000 [08:16<04:29, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7116/8000 [06:52<00:56, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5084/8000 [08:16<07:03,  6.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7118/8000 [06:52<00:55, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5087/8000 [08:17<06:00,  8.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7121/8000 [06:53<01:02, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5088/8000 [08:17<06:56,  7.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7124/8000 [06:53<01:07, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5091/8000 [08:17<05:44,  8.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7129/8000 [06:53<00:49, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5095/8000 [08:17<04:20, 11.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7132/8000 [06:53<00:45, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5097/8000 [08:18<04:22, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7135/8000 [06:54<00:58, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5103/8000 [08:18<04:14, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7138/8000 [06:54<00:54, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5110/8000 [08:18<03:21, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7140/8000 [06:54<00:58, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5113/8000 [08:19<03:44, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7147/8000 [06:54<00:46, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5115/8000 [08:19<03:52, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7153/8000 [06:54<00:35, 24.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5117/8000 [08:19<05:42,  8.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7160/8000 [06:55<00:33, 25.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5119/8000 [08:20<06:05,  7.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7163/8000 [06:55<00:48, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5121/8000 [08:20<05:49,  8.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7169/8000 [06:55<00:36, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5123/8000 [08:20<05:37,  8.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7175/8000 [06:55<00:36, 22.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5124/8000 [08:20<06:26,  7.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7178/8000 [06:55<00:35, 22.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5127/8000 [08:21<06:48,  7.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7181/8000 [06:56<00:37, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5128/8000 [08:21<07:17,  6.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7184/8000 [06:56<00:36, 22.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5129/8000 [08:21<07:59,  5.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7189/8000 [06:56<00:40, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5133/8000 [08:22<05:56,  8.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7192/8000 [06:56<00:40, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5135/8000 [08:22<06:15,  7.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7195/8000 [06:56<00:41, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5137/8000 [08:22<06:12,  7.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7198/8000 [06:56<00:39, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5138/8000 [08:22<06:57,  6.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7204/8000 [06:57<00:31, 25.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5141/8000 [08:23<06:28,  7.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7208/8000 [06:57<00:31, 24.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5147/8000 [08:23<04:24, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7215/8000 [06:57<00:30, 25.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5152/8000 [08:23<03:23, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7218/8000 [06:57<00:40, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5158/8000 [08:23<02:34, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7222/8000 [06:57<00:36, 21.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5162/8000 [08:24<02:32, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7226/8000 [06:58<00:41, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5165/8000 [08:24<02:51, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7231/8000 [06:58<00:43, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5171/8000 [08:24<03:13, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7235/8000 [06:58<00:38, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5173/8000 [08:25<03:35, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7238/8000 [06:58<00:36, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5176/8000 [08:25<03:50, 12.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7244/8000 [06:59<00:36, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5180/8000 [08:25<04:23, 10.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7248/8000 [06:59<00:34, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5186/8000 [08:26<04:05, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7254/8000 [06:59<00:39, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5188/8000 [08:26<04:16, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7259/8000 [06:59<00:40, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5190/8000 [08:26<04:30, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7264/8000 [07:00<00:40, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5192/8000 [08:27<04:37, 10.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7267/8000 [07:00<00:52, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5195/8000 [08:27<04:15, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7270/8000 [07:00<00:47, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5197/8000 [08:27<04:31, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7273/8000 [07:00<00:45, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5199/8000 [08:27<04:39, 10.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7278/8000 [07:01<00:37, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5201/8000 [08:27<04:47,  9.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7282/8000 [07:01<00:42, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5204/8000 [08:28<04:10, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7286/8000 [07:01<00:43, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5208/8000 [08:28<04:10, 11.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7288/8000 [07:01<00:44, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5213/8000 [08:28<03:17, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7290/8000 [07:02<00:53, 13.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5215/8000 [08:28<03:32, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7294/8000 [07:02<00:43, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5218/8000 [08:29<03:24, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7299/8000 [07:02<00:34, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5220/8000 [08:29<03:32, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7302/8000 [07:02<00:41, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5224/8000 [08:29<04:10, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7306/8000 [07:02<00:35, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5228/8000 [08:29<03:26, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7309/8000 [07:02<00:34, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5232/8000 [08:30<02:59, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7313/8000 [07:03<00:37, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▏     | 5236/8000 [08:30<02:51, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7317/8000 [07:03<00:39, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▏     | 5238/8000 [08:30<03:12, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7320/8000 [07:03<00:36, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5241/8000 [08:30<03:07, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7324/8000 [07:03<00:32, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5243/8000 [08:30<03:35, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7327/8000 [07:03<00:31, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5247/8000 [08:31<03:24, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7334/8000 [07:04<00:34, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5249/8000 [08:31<04:46,  9.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7338/8000 [07:04<00:44, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5251/8000 [08:31<05:09,  8.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7342/8000 [07:04<00:39, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5252/8000 [08:32<05:44,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7347/8000 [07:04<00:32, 19.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5255/8000 [08:32<05:10,  8.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7350/8000 [07:05<00:32, 20.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5256/8000 [08:32<05:40,  8.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7357/8000 [07:05<00:22, 28.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5258/8000 [08:33<06:53,  6.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7365/8000 [07:05<00:22, 27.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5260/8000 [08:33<07:32,  6.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7371/8000 [07:05<00:23, 26.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5262/8000 [08:33<06:42,  6.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7377/8000 [07:06<00:29, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5265/8000 [08:33<05:07,  8.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7382/8000 [07:06<00:25, 24.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5267/8000 [08:34<05:10,  8.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7387/8000 [07:06<00:25, 23.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5270/8000 [08:34<04:18, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7390/8000 [07:06<00:26, 23.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5272/8000 [08:34<04:55,  9.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7393/8000 [07:06<00:33, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5276/8000 [08:35<05:39,  8.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7397/8000 [07:07<00:29, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5278/8000 [08:35<05:51,  7.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7400/8000 [07:07<00:30, 19.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5280/8000 [08:35<05:45,  7.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7404/8000 [07:07<00:26, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5283/8000 [08:35<04:48,  9.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7407/8000 [07:07<00:33, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5286/8000 [08:36<04:11, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7410/8000 [07:07<00:32, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5288/8000 [08:36<04:19, 10.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7417/8000 [07:08<00:28, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5291/8000 [08:36<04:57,  9.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7422/8000 [07:08<00:24, 23.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5293/8000 [08:36<05:18,  8.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7426/8000 [07:08<00:23, 24.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5299/8000 [08:37<03:40, 12.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7429/8000 [07:08<00:24, 23.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5301/8000 [08:37<03:59, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7432/8000 [07:08<00:25, 22.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5305/8000 [08:37<03:53, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7437/8000 [07:09<00:28, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5309/8000 [08:38<03:25, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7442/8000 [07:09<00:29, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5312/8000 [08:38<03:24, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7445/8000 [07:09<00:28, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5315/8000 [08:38<04:03, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7448/8000 [07:09<00:27, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5318/8000 [08:38<03:55, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7454/8000 [07:09<00:26, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5322/8000 [08:39<04:26, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7458/8000 [07:09<00:24, 22.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5327/8000 [08:39<03:23, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7463/8000 [07:10<00:24, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5330/8000 [08:39<03:33, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7466/8000 [07:10<00:24, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5332/8000 [08:40<03:43, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7469/8000 [07:10<00:23, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5335/8000 [08:40<04:24, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7474/8000 [07:10<00:20, 25.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5337/8000 [08:41<06:49,  6.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7478/8000 [07:10<00:20, 25.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5344/8000 [08:41<03:59, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7481/8000 [07:11<00:26, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5346/8000 [08:41<04:01, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7484/8000 [07:11<00:30, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5349/8000 [08:41<03:51, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7486/8000 [07:11<00:31, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5351/8000 [08:41<03:55, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7489/8000 [07:11<00:35, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5357/8000 [08:42<04:14, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7492/8000 [07:11<00:32, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5359/8000 [08:43<05:09,  8.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7496/8000 [07:12<00:32, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5361/8000 [08:43<05:19,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7498/8000 [07:12<00:31, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5363/8000 [08:43<05:01,  8.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7501/8000 [07:12<00:29, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5365/8000 [08:43<05:18,  8.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7505/8000 [07:12<00:25, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5366/8000 [08:44<07:24,  5.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7511/8000 [07:12<00:24, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5369/8000 [08:44<05:40,  7.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7514/8000 [07:13<00:25, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5370/8000 [08:44<06:11,  7.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7518/8000 [07:13<00:21, 22.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5372/8000 [08:44<06:26,  6.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7523/8000 [07:13<00:23, 20.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5376/8000 [08:45<04:23,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7527/8000 [07:13<00:22, 21.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5378/8000 [08:45<04:48,  9.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7531/8000 [07:13<00:25, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5381/8000 [08:45<04:09, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7535/8000 [07:14<00:23, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5387/8000 [08:46<03:43, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7539/8000 [07:14<00:21, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5391/8000 [08:46<03:09, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7542/8000 [07:14<00:21, 21.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5393/8000 [08:46<04:20, 10.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7546/8000 [07:14<00:18, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5395/8000 [08:46<04:37,  9.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7551/8000 [07:14<00:20, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5397/8000 [08:47<04:37,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7554/8000 [07:14<00:24, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5399/8000 [08:47<04:39,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7561/8000 [07:15<00:23, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▍     | 5404/8000 [08:47<03:42, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7565/8000 [07:15<00:21, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▍     | 5406/8000 [08:48<04:42,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7572/8000 [07:15<00:19, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▍     | 5410/8000 [08:48<04:05, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7576/8000 [07:16<00:22, 18.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5412/8000 [08:48<05:07,  8.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7579/8000 [07:16<00:22, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5413/8000 [08:49<05:56,  7.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7584/8000 [07:16<00:18, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5417/8000 [08:49<04:23,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7588/8000 [07:16<00:19, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5419/8000 [08:49<05:30,  7.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7591/8000 [07:16<00:24, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5421/8000 [08:50<06:41,  6.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7594/8000 [07:17<00:23, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5424/8000 [08:50<05:23,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7599/8000 [07:17<00:23, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5427/8000 [08:50<04:36,  9.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7602/8000 [07:17<00:26, 14.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5430/8000 [08:50<03:59, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7605/8000 [07:17<00:25, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5434/8000 [08:50<03:17, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7607/8000 [07:18<00:30, 12.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5438/8000 [08:51<03:54, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7609/8000 [07:18<00:30, 12.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5440/8000 [08:51<03:59, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7612/8000 [07:18<00:25, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5442/8000 [08:51<04:11, 10.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7615/8000 [07:18<00:28, 13.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5444/8000 [08:52<04:11, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7617/8000 [07:18<00:27, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5446/8000 [08:52<04:07, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7620/8000 [07:18<00:23, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5448/8000 [08:52<04:04, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7623/8000 [07:19<00:21, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5450/8000 [08:52<04:23,  9.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7626/8000 [07:19<00:19, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5451/8000 [08:52<05:04,  8.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7630/8000 [07:19<00:17, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5454/8000 [08:53<04:05, 10.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7633/8000 [07:19<00:18, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5457/8000 [08:53<03:31, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7637/8000 [07:19<00:21, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5461/8000 [08:53<03:05, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7640/8000 [07:19<00:20, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5464/8000 [08:53<03:11, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7643/8000 [07:20<00:24, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5467/8000 [08:53<03:15, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7645/8000 [07:20<00:24, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5469/8000 [08:54<03:48, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7649/8000 [07:20<00:23, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▋     | 5472/8000 [08:54<04:00, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7651/8000 [07:20<00:26, 12.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▋     | 5476/8000 [08:54<03:19, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7656/8000 [07:21<00:23, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▋     | 5479/8000 [08:55<03:20, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7658/8000 [07:21<00:22, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5483/8000 [08:55<03:15, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7663/8000 [07:21<00:17, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5486/8000 [08:55<03:03, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7668/8000 [07:21<00:17, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5488/8000 [08:55<03:19, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7671/8000 [07:21<00:17, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5490/8000 [08:55<03:38, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7676/8000 [07:21<00:14, 22.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5494/8000 [08:56<05:03,  8.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7681/8000 [07:22<00:12, 25.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5495/8000 [08:56<05:45,  7.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7684/8000 [07:22<00:13, 24.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5500/8000 [08:57<05:29,  7.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7689/8000 [07:22<00:11, 26.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5504/8000 [08:57<04:25,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7693/8000 [07:22<00:11, 26.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5511/8000 [08:58<03:30, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7699/8000 [07:22<00:12, 23.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5514/8000 [08:58<04:17,  9.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7702/8000 [07:23<00:15, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5517/8000 [08:58<03:57, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7708/8000 [07:23<00:12, 22.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5519/8000 [08:59<03:57, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7712/8000 [07:23<00:12, 23.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5522/8000 [08:59<03:35, 11.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7715/8000 [07:23<00:17, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5524/8000 [08:59<03:49, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7719/8000 [07:24<00:15, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5527/8000 [08:59<03:54, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7725/8000 [07:24<00:17, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5529/8000 [09:00<03:58, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7728/8000 [07:24<00:16, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5531/8000 [09:00<04:19,  9.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7731/8000 [07:24<00:18, 14.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5534/8000 [09:00<03:51, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7733/8000 [07:25<00:18, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5538/8000 [09:00<03:11, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7739/8000 [07:25<00:13, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5540/8000 [09:00<03:26, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7745/8000 [07:25<00:12, 20.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5542/8000 [09:01<04:01, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7749/8000 [07:25<00:11, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5545/8000 [09:01<04:28,  9.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7753/8000 [07:25<00:10, 23.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5547/8000 [09:01<04:32,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7757/8000 [07:26<00:11, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5550/8000 [09:02<04:58,  8.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7762/8000 [07:26<00:09, 23.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5553/8000 [09:02<04:19,  9.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7765/8000 [07:26<00:10, 22.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5555/8000 [09:02<04:42,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7768/8000 [07:26<00:12, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5558/8000 [09:02<04:01, 10.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7771/8000 [07:26<00:11, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5563/8000 [09:03<02:55, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7775/8000 [07:27<00:13, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5565/8000 [09:03<03:19, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7779/8000 [07:27<00:14, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5567/8000 [09:03<04:55,  8.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7784/8000 [07:27<00:18, 11.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5573/8000 [09:04<03:19, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7786/8000 [07:28<00:17, 12.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5576/8000 [09:04<03:29, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7789/8000 [07:28<00:17, 12.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5581/8000 [09:04<02:46, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7791/8000 [07:28<00:17, 12.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5585/8000 [09:04<02:32, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7793/8000 [07:28<00:16, 12.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5589/8000 [09:05<02:29, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7797/8000 [07:28<00:12, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5591/8000 [09:05<02:51, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7801/8000 [07:28<00:10, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5593/8000 [09:05<04:18,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7804/8000 [07:29<00:12, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5595/8000 [09:06<05:45,  6.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7808/8000 [07:29<00:14, 12.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5596/8000 [09:06<06:05,  6.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7810/8000 [07:29<00:17, 11.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5599/8000 [09:06<04:45,  8.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7813/8000 [07:30<00:14, 12.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5601/8000 [09:07<06:53,  5.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7816/8000 [07:30<00:15, 11.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5608/8000 [09:07<03:36, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7818/8000 [07:30<00:15, 12.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5610/8000 [09:07<03:39, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7823/8000 [07:30<00:10, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5616/8000 [09:08<03:21, 11.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7826/8000 [07:30<00:10, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5620/8000 [09:08<03:39, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7829/8000 [07:30<00:09, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5623/8000 [09:09<04:23,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7833/8000 [07:31<00:08, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5627/8000 [09:09<04:36,  8.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7836/8000 [07:31<00:08, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5631/8000 [09:09<03:50, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7842/8000 [07:31<00:09, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5637/8000 [09:10<03:04, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7845/8000 [07:31<00:08, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▉     | 5641/8000 [09:10<02:59, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7851/8000 [07:32<00:07, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▉     | 5643/8000 [09:10<03:16, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7855/8000 [07:32<00:08, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▉     | 5646/8000 [09:10<03:15, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7860/8000 [07:32<00:06, 20.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5648/8000 [09:11<03:43, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7863/8000 [07:32<00:08, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5650/8000 [09:11<03:55,  9.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7866/8000 [07:32<00:07, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5653/8000 [09:11<04:39,  8.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7869/8000 [07:33<00:08, 14.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5657/8000 [09:12<03:28, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7872/8000 [07:33<00:08, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5662/8000 [09:12<02:49, 13.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7877/8000 [07:33<00:07, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5667/8000 [09:12<02:24, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7879/8000 [07:33<00:07, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5669/8000 [09:13<03:39, 10.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▋| 7882/8000 [07:33<00:06, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5673/8000 [09:13<03:22, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7885/8000 [07:34<00:06, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5676/8000 [09:13<03:12, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7889/8000 [07:34<00:05, 19.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5678/8000 [09:13<03:24, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7891/8000 [07:34<00:05, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5682/8000 [09:14<03:01, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7894/8000 [07:34<00:05, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5686/8000 [09:14<02:51, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7897/8000 [07:34<00:05, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5690/8000 [09:14<02:36, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7902/8000 [07:34<00:03, 24.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5693/8000 [09:14<02:33, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7905/8000 [07:35<00:04, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5696/8000 [09:14<02:35, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7908/8000 [07:35<00:04, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5698/8000 [09:15<03:00, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7912/8000 [07:35<00:04, 20.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5706/8000 [09:15<02:33, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7915/8000 [07:35<00:04, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5708/8000 [09:15<02:53, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7918/8000 [07:35<00:03, 20.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5713/8000 [09:16<02:32, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7923/8000 [07:36<00:04, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5716/8000 [09:16<02:43, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7925/8000 [07:36<00:05, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5720/8000 [09:16<02:27, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7927/8000 [07:36<00:05, 13.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5722/8000 [09:16<02:47, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7930/8000 [07:36<00:04, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5725/8000 [09:17<03:06, 12.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7932/8000 [07:36<00:04, 15.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5729/8000 [09:17<03:19, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7934/8000 [07:36<00:04, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5734/8000 [09:17<03:19, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7936/8000 [07:37<00:04, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5737/8000 [09:18<04:09,  9.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7938/8000 [07:37<00:05, 11.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5739/8000 [09:18<04:11,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7941/8000 [07:37<00:06,  9.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5741/8000 [09:18<04:12,  8.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7945/8000 [07:38<00:05,  9.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5745/8000 [09:19<03:35, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7948/8000 [07:38<00:04, 11.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5749/8000 [09:19<03:22, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7951/8000 [07:38<00:04, 11.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5751/8000 [09:19<03:28, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7956/8000 [07:38<00:02, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5754/8000 [09:20<03:15, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7960/8000 [07:38<00:02, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5757/8000 [09:20<03:11, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7963/8000 [07:39<00:02, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5760/8000 [09:20<03:12, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7967/8000 [07:39<00:01, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5766/8000 [09:20<02:17, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7969/8000 [07:39<00:01, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5769/8000 [09:20<02:37, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7975/8000 [07:39<00:01, 22.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5774/8000 [09:21<02:07, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7979/8000 [07:40<00:01, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5779/8000 [09:21<03:07, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7982/8000 [07:40<00:01, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5782/8000 [09:22<03:41, 10.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7987/8000 [07:40<00:00, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5785/8000 [09:22<03:32, 10.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7991/8000 [07:40<00:00, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5789/8000 [09:23<04:37,  7.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7994/8000 [07:41<00:00, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5792/8000 [09:23<04:08,  8.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7996/8000 [07:41<00:00, 14.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5795/8000 [09:23<03:57,  9.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7999/8000 [07:41<00:00, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5801/8000 [09:24<03:59,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8003it [07:41, 17.34it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  73%|████████████▎    | 5803/8000 [09:24<03:54,  9.38it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  73%|████████████▎    | 5807/8000 [09:24<03:18, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5812/8000 [09:25<02:47, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:39,  7.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5814/8000 [09:25<03:05, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:24,  4.07s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5817/8000 [09:25<03:02, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:22,  2.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5820/8000 [09:25<03:08, 11.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:29,  2.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5823/8000 [09:26<03:04, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:26,  1.98s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5826/8000 [09:26<02:53, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:58,  1.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5828/8000 [09:26<03:24, 10.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:36,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5830/8000 [09:27<04:34,  7.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:13,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5831/8000 [09:27<06:27,  5.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<04:59,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5835/8000 [09:27<04:28,  8.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:48,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5837/8000 [09:28<04:15,  8.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:39,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5839/8000 [09:28<04:12,  8.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:31,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5840/8000 [09:28<04:35,  7.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:27,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5843/8000 [09:28<03:53,  9.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:22,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5846/8000 [09:28<03:23, 10.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:17,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5848/8000 [09:29<04:25,  8.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:12,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5850/8000 [09:29<05:19,  6.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:12,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5854/8000 [09:30<04:54,  7.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:06,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5857/8000 [09:30<04:36,  7.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:09,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5859/8000 [09:30<04:36,  7.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:05,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5863/8000 [09:31<03:34,  9.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:07,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5866/8000 [09:31<03:13, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:10,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5869/8000 [09:31<03:03, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:10,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5872/8000 [09:31<03:34,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<04:11,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5875/8000 [09:32<03:12, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:08,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5878/8000 [09:32<03:03, 11.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<04:10,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5884/8000 [09:32<02:21, 14.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<04:06,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5888/8000 [09:32<02:12, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<04:06,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5891/8000 [09:33<02:18, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<04:04,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5895/8000 [09:33<02:13, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<04:03,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5899/8000 [09:33<02:15, 15.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<04:02,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5902/8000 [09:33<02:17, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<04:02,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5905/8000 [09:34<02:29, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<03:57,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5907/8000 [09:34<02:28, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:54<03:55,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5913/8000 [09:34<02:38, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:56<04:01,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5915/8000 [09:34<02:44, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:57<03:51,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5920/8000 [09:35<02:58, 11.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:58<03:56,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5922/8000 [09:35<03:16, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:00<03:52,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5927/8000 [09:35<02:34, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:01<03:54,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5931/8000 [09:36<03:05, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:03<03:50,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5936/8000 [09:36<02:35, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:04<03:49,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5938/8000 [09:36<03:27,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:06<04:02,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5942/8000 [09:37<03:39,  9.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:07<03:56,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5944/8000 [09:37<03:42,  9.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:09<03:52,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5946/8000 [09:37<03:59,  8.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:10<03:50,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5950/8000 [09:38<03:15, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:12<03:54,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5955/8000 [09:38<03:07, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:13<03:45,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5961/8000 [09:39<02:46, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:15<03:39,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5964/8000 [09:39<02:42, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:18<04:00,  1.59s/it]\n",
+      "Generating signals:  75%|████████████▋    | 5971/8000 [09:39<01:58, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  75%|████████████▋    | 5975/8000 [09:39<02:21, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5977/8000 [09:40<02:36, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5979/8000 [09:40<02:43, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.38it/s]\n",
+      "Generating signals:  75%|████████████▋    | 5981/8000 [09:40<03:07, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  75%|████████████▋    | 5983/8000 [09:40<03:12, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5986/8000 [09:41<03:07, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5989/8000 [09:41<03:50,  8.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 37it [00:01, 27.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5992/8000 [09:41<03:28,  9.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 79it [00:01, 65.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5994/8000 [09:42<03:31,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 118it [00:02, 104.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5995/8000 [09:42<04:01,  8.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 154it [00:02, 141.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5997/8000 [09:42<04:12,  7.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 193it [00:02, 184.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6001/8000 [09:42<03:16, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 231it [00:02, 222.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6006/8000 [09:42<02:22, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 268it [00:02, 241.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6010/8000 [09:43<02:06, 15.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 308it [00:02, 276.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6012/8000 [09:43<02:29, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 350it [00:02, 310.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6017/8000 [09:43<02:18, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 393it [00:02, 340.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6022/8000 [09:43<02:00, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 434it [00:02, 353.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6026/8000 [09:44<01:59, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 476it [00:03, 370.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6028/8000 [09:44<02:27, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 516it [00:03, 378.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6030/8000 [09:44<02:48, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 556it [00:03, 379.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6038/8000 [09:44<01:43, 18.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 596it [00:03, 380.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6043/8000 [09:45<02:14, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 635it [00:03, 380.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6045/8000 [09:45<02:18, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 674it [00:03, 373.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6052/8000 [09:45<01:45, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 716it [00:03, 385.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6057/8000 [09:46<02:04, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 759it [00:03, 398.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6059/8000 [09:46<02:42, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 207.07it/s]\n",
+      "Generating signals:  76%|████████████▉    | 6061/8000 [09:46<02:58, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  76%|████████████▉    | 6065/8000 [09:47<03:38,  8.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6067/8000 [09:47<03:43,  8.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 128\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6069/8000 [09:48<03:55,  8.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:00, 137\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6073/8000 [09:48<03:03, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 135\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6075/8000 [09:48<03:20,  9.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 158\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6081/8000 [09:48<02:22, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 165\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6083/8000 [09:49<03:14,  9.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 14\n",
+      "Generating signals:  76%|████████████▉    | 6086/8000 [09:49<03:09, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [37:43<00:00, 643.47s/it]\u001b[A\n",
-      "Replicates:  12%|███▍                       | 1/8 [37:43<4:24:04, 2263.51s/it]\n",
-      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.660510326444298\n",
-      "2999: -5.9073271\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  76%|████████████▉    | 6091/8000 [09:50<03:08, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  76%|████████████▉    | 6095/8000 [09:51<05:19,  5.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6098/8000 [09:51<04:41,  6.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:24,  7.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6101/8000 [09:51<04:00,  7.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:51,  3.90s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6105/8000 [09:51<03:18,  9.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:00,  2.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6109/8000 [09:52<02:48, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:14,  2.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6114/8000 [09:52<02:21, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:09,  1.89s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6116/8000 [09:52<02:31, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:26,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████    | 6118/8000 [09:53<03:25,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<04:56,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6123/8000 [09:53<02:36, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:38,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6125/8000 [09:53<02:47, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:27,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6127/8000 [09:53<02:44, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:20,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6129/8000 [09:53<02:53, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:18,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6132/8000 [09:54<02:46, 11.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:15,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6135/8000 [09:54<02:52, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:07,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6137/8000 [09:54<03:41,  8.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6143/8000 [09:55<02:30, 12.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6147/8000 [09:55<02:19, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:26<03:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6150/8000 [09:55<02:12, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<03:50,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6153/8000 [09:55<02:13, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:29<03:47,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6156/8000 [09:56<02:19, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:30<03:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6159/8000 [09:56<02:12, 13.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6161/8000 [09:56<02:22, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6165/8000 [09:56<02:52, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:34<03:47,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6168/8000 [09:57<02:34, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:35<03:43,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6170/8000 [09:57<04:29,  6.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6172/8000 [09:58<04:06,  7.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:38<03:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6177/8000 [09:58<03:27,  8.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6179/8000 [09:58<03:26,  8.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6182/8000 [09:59<03:59,  7.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:42<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6186/8000 [09:59<03:05,  9.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:43<03:48,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6188/8000 [09:59<03:08,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:45<03:52,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6190/8000 [09:59<03:10,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:46<03:56,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6194/8000 [10:00<02:45, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:48<03:52,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6196/8000 [10:00<03:35,  8.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:49<03:52,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6199/8000 [10:00<03:07,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:50<03:53,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6204/8000 [10:01<02:22, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:52<03:46,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6207/8000 [10:01<02:28, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:53<03:53,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6209/8000 [10:01<02:37, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:55<03:57,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6212/8000 [10:02<03:25,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:56<04:00,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6215/8000 [10:02<03:14,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:58<03:53,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6220/8000 [10:02<02:52, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:59<03:46,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6222/8000 [10:03<02:57, 10.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:00<03:44,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6226/8000 [10:03<02:26, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:02<03:37,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6230/8000 [10:03<02:43, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:03<03:36,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6232/8000 [10:04<03:27,  8.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:04<03:36,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6233/8000 [10:04<03:45,  7.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:06<03:30,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6237/8000 [10:04<02:58,  9.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:07<03:28,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6239/8000 [10:05<03:49,  7.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:09<03:31,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6241/8000 [10:05<03:37,  8.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:10<03:27,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6245/8000 [10:05<02:50, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:11<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6247/8000 [10:05<02:53, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:13<03:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6250/8000 [10:05<02:34, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:14<03:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6253/8000 [10:06<02:19, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:17<03:40,  1.49s/it]\n",
+      "Generating signals:  78%|█████████████▎   | 6257/8000 [10:06<02:30, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  78%|█████████████▎   | 6260/8000 [10:06<02:18, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6263/8000 [10:06<02:15, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6268/8000 [10:07<01:50, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.77it/s]\n",
+      "Generating signals:  78%|█████████████▎   | 6271/8000 [10:07<01:49, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  78%|█████████████▎   | 6274/8000 [10:07<01:57, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6277/8000 [10:07<01:57, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6280/8000 [10:07<02:00, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 37it [00:01, 32.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6284/8000 [10:08<01:46, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 71it [00:01, 66.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6286/8000 [10:08<02:07, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 113it [00:01, 114.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6288/8000 [10:08<02:29, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 147it [00:01, 150.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6290/8000 [10:09<03:20,  8.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 184it [00:02, 190.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6295/8000 [10:09<02:22, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 218it [00:02, 217.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6297/8000 [10:09<02:44, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 262it [00:02, 267.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6299/8000 [10:10<03:27,  8.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 299it [00:02, 289.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6304/8000 [10:10<02:25, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 338it [00:02, 314.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6306/8000 [10:10<02:38, 10.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 379it [00:02, 339.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6309/8000 [10:10<02:23, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 423it [00:02, 366.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6311/8000 [10:10<02:31, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 464it [00:02, 377.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6313/8000 [10:11<02:44, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 504it [00:02, 346.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6317/8000 [10:11<02:12, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 541it [00:02, 350.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6320/8000 [10:11<02:15, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 579it [00:03, 356.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6323/8000 [10:12<02:50,  9.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 620it [00:03, 369.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6329/8000 [10:12<02:23, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 658it [00:03, 370.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6334/8000 [10:13<02:54,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 703it [00:03, 391.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6340/8000 [10:13<02:55,  9.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 743it [00:03, 380.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6343/8000 [10:13<02:38, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 218.22it/s]\n",
+      "Generating signals:  79%|█████████████▍   | 6346/8000 [10:14<02:28, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  79%|█████████████▍   | 6349/8000 [10:14<02:18, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6351/8000 [10:14<02:30, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:00, 199\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▌   | 6355/8000 [10:15<02:38, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 171\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▌   | 6359/8000 [10:15<02:09, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 186\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6363/8000 [10:15<01:58, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  81%|▊| 81/100 [00:00<00:00, 174\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6369/8000 [10:15<01:40, 16.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  80%|█████████████▌   | 6371/8000 [10:15<01:53, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  80%|█████████████▌   | 6373/8000 [10:16<01:58, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6376/8000 [10:16<02:33, 10.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎               | 181/8000 [00:00<00:06, 1148.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6379/8000 [10:16<02:21, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                | 359/8000 [00:00<00:08, 885.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6381/8000 [10:17<03:16,  8.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 519/8000 [00:01<00:30, 244.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6387/8000 [10:17<02:07, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 689/8000 [00:01<00:21, 344.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6390/8000 [10:17<02:12, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 855/8000 [00:02<00:15, 453.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6393/8000 [10:18<02:36, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1041/8000 [00:02<00:12, 576.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6398/8000 [10:18<02:28, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1220/8000 [00:02<00:09, 690.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6400/8000 [10:18<03:02,  8.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1398/8000 [00:02<00:08, 789.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6402/8000 [10:19<02:57,  9.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1556/8000 [00:02<00:07, 862.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6403/8000 [10:19<03:20,  7.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1721/8000 [00:03<00:20, 309.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6406/8000 [10:19<03:26,  7.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1902/8000 [00:04<00:14, 411.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6407/8000 [10:20<03:45,  7.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2087/8000 [00:04<00:11, 507.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6408/8000 [10:20<03:55,  6.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2259/8000 [00:04<00:09, 613.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6410/8000 [10:20<03:33,  7.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▉           | 2440/8000 [00:04<00:07, 726.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6415/8000 [10:20<02:48,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2612/8000 [00:04<00:06, 816.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6418/8000 [10:21<02:30, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2788/8000 [00:04<00:05, 893.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6424/8000 [10:21<01:45, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▌         | 2973/8000 [00:05<00:04, 1009.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6428/8000 [10:21<01:47, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3123/8000 [00:06<00:14, 340.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6431/8000 [10:21<01:48, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3283/8000 [00:06<00:11, 426.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6434/8000 [10:21<01:45, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3457/8000 [00:06<00:08, 527.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6439/8000 [10:22<01:29, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3622/8000 [00:06<00:06, 630.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6441/8000 [10:22<01:44, 14.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3793/8000 [00:06<00:05, 724.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6444/8000 [10:22<01:44, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3967/8000 [00:06<00:04, 826.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6447/8000 [10:22<01:43, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4150/8000 [00:08<00:12, 311.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6452/8000 [10:22<01:25, 18.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4325/8000 [00:08<00:09, 388.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6454/8000 [10:23<01:42, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4488/8000 [00:08<00:07, 478.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6456/8000 [10:23<02:00, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4664/8000 [00:08<00:05, 592.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6458/8000 [10:23<02:18, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4846/8000 [00:09<00:04, 695.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6461/8000 [10:23<02:04, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5024/8000 [00:09<00:03, 748.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6463/8000 [10:24<03:04,  8.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5197/8000 [00:09<00:03, 833.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6465/8000 [10:24<02:54,  8.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5370/8000 [00:09<00:02, 917.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6468/8000 [10:24<02:27, 10.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5549/8000 [00:09<00:02, 996.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6470/8000 [10:24<02:31, 10.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|██████████▋    | 5725/8000 [00:09<00:02, 1026.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6473/8000 [10:25<02:12, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████    | 5904/8000 [00:09<00:01, 1135.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6475/8000 [10:25<03:46,  6.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|███████████▍   | 6076/8000 [00:10<00:01, 1163.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6477/8000 [10:26<03:32,  7.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|███████████▋   | 6257/8000 [00:10<00:01, 1130.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6481/8000 [10:26<02:32,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████   | 6438/8000 [00:10<00:01, 1130.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6487/8000 [10:26<02:30, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|████████████▍  | 6621/8000 [00:10<00:01, 1192.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6495/8000 [10:27<01:38, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|████████████▋  | 6790/8000 [00:10<00:01, 1177.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6499/8000 [10:27<01:51, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6970/8000 [00:12<00:03, 341.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6501/8000 [10:28<03:09,  7.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7150/8000 [00:12<00:01, 438.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6503/8000 [10:28<03:27,  7.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7321/8000 [00:12<00:01, 539.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6507/8000 [10:28<02:44,  9.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7501/8000 [00:12<00:00, 628.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6510/8000 [10:29<02:26, 10.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7671/8000 [00:12<00:00, 726.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6514/8000 [10:29<02:27, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7848/8000 [00:12<00:00, 781.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6516/8000 [10:29<02:30,  9.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8019it [00:13, 615.63it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  81%|█████████████▊   | 6519/8000 [10:29<02:19, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  82%|█████████████▊   | 6521/8000 [10:30<02:20, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▊   | 6525/8000 [10:30<01:51, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:51,  7.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▊   | 6528/8000 [10:30<01:51, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:22,  4.05s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6533/8000 [10:30<01:42, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:28,  2.89s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6536/8000 [10:31<01:40, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:34,  2.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6540/8000 [10:31<02:01, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:41,  2.06s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6545/8000 [10:31<01:45, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:26,  1.99s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6547/8000 [10:32<01:54, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:56,  1.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6549/8000 [10:32<02:01, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:27,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6553/8000 [10:32<01:41, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:10,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6556/8000 [10:32<01:39, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<05:01,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6559/8000 [10:32<01:45, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<04:53,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6562/8000 [10:33<01:42, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:41,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6564/8000 [10:33<03:01,  7.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:38,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6568/8000 [10:34<02:47,  8.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:32,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6570/8000 [10:34<03:17,  7.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:28,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6573/8000 [10:34<02:44,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:28,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6575/8000 [10:35<03:14,  7.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:40,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6579/8000 [10:35<02:54,  8.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:39,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6580/8000 [10:35<03:06,  7.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:38,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6585/8000 [10:35<02:08, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:39,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6587/8000 [10:36<02:10, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:34,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6589/8000 [10:36<02:15, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:30,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6592/8000 [10:36<01:59, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:25,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6596/8000 [10:36<02:10, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:26,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6600/8000 [10:37<01:50, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:44<04:24,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6602/8000 [10:37<01:58, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:37,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6606/8000 [10:37<01:49, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:47<04:32,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6608/8000 [10:37<01:58, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:48<04:30,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6610/8000 [10:38<02:32,  9.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:50<04:23,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6612/8000 [10:38<02:30,  9.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:52<04:30,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6615/8000 [10:38<02:07, 10.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:53<04:35,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6620/8000 [10:39<01:53, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:55<04:33,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6623/8000 [10:39<01:51, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:56<04:21,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6625/8000 [10:39<02:05, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:58<04:17,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6627/8000 [10:39<02:13, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:59<04:10,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6633/8000 [10:40<01:52, 12.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:01<04:04,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6638/8000 [10:40<01:52, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:02<04:07,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6642/8000 [10:41<02:01, 11.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:04<03:58,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6644/8000 [10:41<02:08, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:05<03:55,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6646/8000 [10:41<03:00,  7.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:07<03:53,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6650/8000 [10:42<02:21,  9.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:08<03:51,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6653/8000 [10:42<02:34,  8.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:10<03:59,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6654/8000 [10:42<02:46,  8.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:11<04:03,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6659/8000 [10:42<01:56, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:13<04:06,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6662/8000 [10:43<01:53, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:16<04:51,  1.88s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6664/8000 [10:43<02:09, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:18<04:54,  1.91s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6666/8000 [10:43<02:10, 10.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:19<04:28,  1.76s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6668/8000 [10:43<02:10, 10.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:20<04:10,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6673/8000 [10:44<02:01, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:22<03:56,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6676/8000 [10:44<01:54, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:23<03:49,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6680/8000 [10:44<02:02, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:24<03:39,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6683/8000 [10:45<02:17,  9.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:26<03:38,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6688/8000 [10:45<01:51, 11.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:27<03:34,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6690/8000 [10:45<02:01, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:29<03:39,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6693/8000 [10:45<01:50, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:30<03:38,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6695/8000 [10:46<02:21,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:32<03:32,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6697/8000 [10:46<02:25,  8.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:33<03:27,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6698/8000 [10:46<02:47,  7.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:35<03:30,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6700/8000 [10:47<02:35,  8.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:36<03:26,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6705/8000 [10:47<01:45, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:38<03:24,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6708/8000 [10:47<01:53, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:39<03:22,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6712/8000 [10:47<01:35, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:41<03:18,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6717/8000 [10:47<01:18, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:42<03:16,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6719/8000 [10:48<01:36, 13.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:43<03:14,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6722/8000 [10:48<01:38, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:45<03:11,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6725/8000 [10:48<02:01, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:46<03:11,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6728/8000 [10:49<01:52, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:48<03:09,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6733/8000 [10:49<01:31, 13.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:49<03:08,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6735/8000 [10:49<01:36, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:51<03:09,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6738/8000 [10:49<01:39, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:52<03:08,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6742/8000 [10:50<01:37, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:54<03:07,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6745/8000 [10:50<01:32, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:55<03:05,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6749/8000 [10:50<01:53, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:56<03:04,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6753/8000 [10:51<01:42, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:58<03:10,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6755/8000 [10:51<01:57, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:59<03:05,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6757/8000 [10:51<02:43,  7.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [02:01<03:03,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6760/8000 [10:52<02:27,  8.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [02:04<03:18,  1.61s/it]\n",
+      "Generating signals:  85%|██████████████▎  | 6763/8000 [10:52<02:05,  9.83it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  85%|██████████████▍  | 6767/8000 [10:52<02:12,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6773/8000 [10:53<01:37, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6778/8000 [10:53<01:36, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.22it/s]\n",
+      "Generating signals:  85%|██████████████▍  | 6781/8000 [10:53<01:57, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  85%|██████████████▍  | 6783/8000 [10:54<01:55, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6787/8000 [10:54<01:37, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6790/8000 [10:54<01:42, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 41it [00:01, 36.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6792/8000 [10:54<01:55, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 79it [00:01, 74.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6797/8000 [10:55<01:45, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 122it [00:01, 123.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6800/8000 [10:55<01:44, 11.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 165it [00:01, 173.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6803/8000 [10:55<01:38, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 203it [00:01, 201.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6805/8000 [10:56<01:49, 10.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 239it [00:02, 233.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6810/8000 [10:56<01:45, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 275it [00:02, 259.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6816/8000 [10:56<01:38, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 313it [00:02, 288.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6820/8000 [10:57<01:32, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 356it [00:02, 323.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▌  | 6825/8000 [10:57<01:21, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 394it [00:02, 315.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▌  | 6832/8000 [10:57<01:06, 17.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 433it [00:02, 333.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▌  | 6837/8000 [10:57<01:01, 18.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 470it [00:02, 329.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▌  | 6839/8000 [10:58<01:36, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 505it [00:02, 310.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6841/8000 [10:58<01:41, 11.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 538it [00:02, 306.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6844/8000 [10:58<01:34, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 580it [00:03, 336.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6847/8000 [10:59<02:07,  9.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 619it [00:03, 341.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6849/8000 [10:59<02:05,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 654it [00:03, 338.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6851/8000 [10:59<02:01,  9.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 699it [00:03, 368.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6853/8000 [11:00<02:07,  8.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 740it [00:03, 378.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6856/8000 [11:00<01:52, 10.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 217.05it/s]\n",
+      "Generating signals:  86%|██████████████▌  | 6860/8000 [11:00<01:26, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  86%|██████████████▌  | 6862/8000 [11:00<01:34, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6866/8000 [11:00<01:22, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 123\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6868/8000 [11:01<01:29, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 147\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6871/8000 [11:01<01:24, 13.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00<00:00, 157\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6873/8000 [11:01<01:32, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 168\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6876/8000 [11:01<01:32, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  86%|██████████████▌  | 6880/8000 [11:01<01:15, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [05:46<17:20, 346.85s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.114370331533411\n",
-      "2999: -6.1998643\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  86%|██████████████▋  | 6884/8000 [11:02<01:07, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  86%|██████████████▋  | 6887/8000 [11:02<01:10, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6889/8000 [11:02<01:22, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:54,  7.81s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6891/8000 [11:02<01:41, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:41,  4.15s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6893/8000 [11:03<01:44, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:11<10:03,  3.06s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6896/8000 [11:03<01:33, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<08:10,  2.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6898/8000 [11:03<01:41, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<06:54,  2.12s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6901/8000 [11:03<01:35, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:15,  1.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6905/8000 [11:04<01:30, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:52,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6909/8000 [11:04<01:16, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:31,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6912/8000 [11:04<01:37, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:14,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6918/8000 [11:05<01:33, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<04:55,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6923/8000 [11:05<01:18, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<04:48,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6925/8000 [11:05<01:23, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:44,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6928/8000 [11:05<01:23, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:31,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6930/8000 [11:06<01:59,  8.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:26,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6932/8000 [11:06<02:09,  8.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<05:22,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6935/8000 [11:07<02:12,  8.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:31<05:08,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6937/8000 [11:07<02:37,  6.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<04:42,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6941/8000 [11:07<02:02,  8.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:24,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6942/8000 [11:08<02:24,  7.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:35<04:17,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6945/8000 [11:08<02:08,  8.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:14,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6949/8000 [11:08<01:44, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:12,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6951/8000 [11:08<01:48,  9.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:11,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6957/8000 [11:09<01:22, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:05,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6961/8000 [11:09<01:28, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<03:57,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6964/8000 [11:09<01:23, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<03:55,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6967/8000 [11:09<01:24, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:44<03:55,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6972/8000 [11:10<01:22, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<03:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6977/8000 [11:10<01:28, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:47<03:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6980/8000 [11:11<01:25, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<03:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6986/8000 [11:11<01:04, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:49<03:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6990/8000 [11:11<01:05, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:51<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6993/8000 [11:11<01:12, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:52<03:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6997/8000 [11:12<01:07, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6999/8000 [11:12<01:15, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:55<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7004/8000 [11:12<01:09, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:56<03:39,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7006/8000 [11:12<01:15, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:57<03:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7009/8000 [11:13<01:19, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:58<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7011/8000 [11:13<01:25, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:00<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7013/8000 [11:13<01:31, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:01<03:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7015/8000 [11:13<01:37, 10.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:03<03:38,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7017/8000 [11:13<01:40,  9.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:04<03:30,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7021/8000 [11:14<01:27, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:05<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7023/8000 [11:14<01:32, 10.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:06<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7026/8000 [11:14<01:23, 11.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:08<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7029/8000 [11:14<01:18, 12.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:09<03:21,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7033/8000 [11:15<01:25, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:10<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7036/8000 [11:15<01:20, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:11<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7038/8000 [11:15<01:48,  8.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:13<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7042/8000 [11:16<01:25, 11.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:14<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7046/8000 [11:16<01:13, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:10,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7050/8000 [11:16<01:21, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:17<03:13,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7055/8000 [11:17<01:27, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:18<03:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7058/8000 [11:17<01:37,  9.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7061/8000 [11:17<01:29, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:20<03:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7065/8000 [11:18<01:22, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:22<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7070/8000 [11:18<01:27, 10.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:23<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7075/8000 [11:19<01:37,  9.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:24<02:59,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7078/8000 [11:19<01:28, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:25<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7081/8000 [11:20<01:39,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:27<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7087/8000 [11:20<01:15, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:28<02:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7090/8000 [11:20<01:17, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:29<02:54,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7093/8000 [11:20<01:12, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:30<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7095/8000 [11:21<01:41,  8.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:32<02:52,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7097/8000 [11:21<02:07,  7.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:33<02:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7103/8000 [11:21<01:21, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:34<02:48,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7105/8000 [11:22<01:25, 10.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:37<03:17,  1.47s/it]\n",
+      "Generating signals:  89%|███████████████  | 7109/8000 [11:22<01:15, 11.79it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  89%|███████████████  | 7114/8000 [11:23<01:38,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7120/8000 [11:24<01:43,  8.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7123/8000 [11:24<01:54,  7.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 16.20it/s]\n",
+      "Generating signals:  89%|███████████████▏ | 7125/8000 [11:24<01:51,  7.83it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  89%|███████████████▏ | 7127/8000 [11:25<01:51,  7.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7129/8000 [11:25<01:51,  7.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7131/8000 [11:25<01:58,  7.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 42it [00:01, 38.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7132/8000 [11:25<02:11,  6.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 82it [00:01, 79.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7133/8000 [11:26<02:16,  6.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 131it [00:01, 137.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7135/8000 [11:26<02:03,  6.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 169it [00:01, 175.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7137/8000 [11:26<02:01,  7.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 210it [00:01, 220.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7141/8000 [11:26<01:25, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 249it [00:02, 255.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7144/8000 [11:27<01:36,  8.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 292it [00:02, 295.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7149/8000 [11:27<01:17, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 335it [00:02, 327.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7152/8000 [11:27<01:18, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 376it [00:02, 343.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7160/8000 [11:28<00:52, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 416it [00:02, 358.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7162/8000 [11:28<01:00, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 456it [00:02, 366.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7164/8000 [11:28<01:14, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 496it [00:02, 371.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7167/8000 [11:28<01:09, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 536it [00:02, 361.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7172/8000 [11:29<00:58, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 574it [00:02, 361.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7174/8000 [11:29<01:23,  9.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 614it [00:02, 371.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7176/8000 [11:29<01:23,  9.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 652it [00:03, 370.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7179/8000 [11:30<01:41,  8.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 692it [00:03, 378.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7183/8000 [11:30<01:18, 10.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 733it [00:03, 386.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7185/8000 [11:30<01:21,  9.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 231.06it/s]\n",
+      "Generating signals:  90%|███████████████▎ | 7187/8000 [11:31<01:50,  7.38it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  90%|███████████████▎ | 7188/8000 [11:31<01:57,  6.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:00, 194\u001b[A\u001b[A\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 206\u001b[A\u001b[A\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00<00:00, 200\u001b[A\u001b[A\n",
-      "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 18\n",
-      "\n",
-      "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7192/8000 [11:31<01:22,  9.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 47/8000 [00:00<00:26, 299.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7195/8000 [11:31<01:13, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 101/8000 [00:00<00:22, 354.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7199/8000 [11:32<01:03, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 148/8000 [00:00<00:23, 338.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7202/8000 [11:32<01:00, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 206/8000 [00:00<00:22, 353.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7204/8000 [11:32<01:25,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 248/8000 [00:00<00:23, 329.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7207/8000 [11:32<01:19,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 306/8000 [00:00<00:21, 362.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7209/8000 [11:33<01:39,  7.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 350/8000 [00:00<00:20, 369.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7211/8000 [11:33<01:41,  7.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 393/8000 [00:01<00:20, 372.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7213/8000 [11:33<01:35,  8.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 446/8000 [00:01<00:21, 357.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7217/8000 [11:34<01:10, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 495/8000 [00:01<00:19, 376.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7220/8000 [11:34<01:15, 10.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 541/8000 [00:01<00:19, 379.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7225/8000 [11:34<01:10, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎               | 591/8000 [00:01<00:19, 387.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7229/8000 [11:35<01:21,  9.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 652/8000 [00:01<00:18, 407.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7232/8000 [11:35<01:15, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 737/8000 [00:02<00:20, 352.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▍ | 7236/8000 [11:35<01:03, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 794/8000 [00:02<00:18, 383.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▍ | 7240/8000 [11:36<00:59, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 852/8000 [00:02<00:17, 413.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7242/8000 [11:36<01:18,  9.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 898/8000 [00:02<00:17, 406.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7245/8000 [11:36<01:15,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 981/8000 [00:02<00:19, 354.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7247/8000 [11:37<01:19,  9.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1021/8000 [00:02<00:19, 354.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7250/8000 [11:37<01:17,  9.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1069/8000 [00:02<00:18, 365.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7252/8000 [11:37<01:21,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1130/8000 [00:03<00:16, 406.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7255/8000 [11:37<01:12, 10.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1181/8000 [00:03<00:16, 406.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7259/8000 [11:38<01:02, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▍             | 1243/8000 [00:03<00:15, 430.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7263/8000 [11:38<01:11, 10.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1289/8000 [00:03<00:16, 409.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7265/8000 [11:38<01:14,  9.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1338/8000 [00:03<00:17, 390.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7267/8000 [11:39<01:19,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1415/8000 [00:03<00:19, 343.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7274/8000 [11:39<00:50, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1458/8000 [00:03<00:19, 342.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7280/8000 [11:39<00:50, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1518/8000 [00:04<00:17, 370.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7283/8000 [11:40<01:05, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1570/8000 [00:04<00:16, 388.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7286/8000 [11:40<01:07, 10.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1625/8000 [00:04<00:15, 400.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7291/8000 [11:40<00:59, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1672/8000 [00:04<00:16, 392.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7293/8000 [11:41<01:35,  7.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1716/8000 [00:04<00:17, 360.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7294/8000 [11:41<01:40,  7.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1767/8000 [00:04<00:16, 368.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7296/8000 [11:42<01:57,  6.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1815/8000 [00:04<00:16, 381.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7300/8000 [11:42<01:33,  7.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1858/8000 [00:04<00:16, 375.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7303/8000 [11:42<01:22,  8.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1898/8000 [00:05<00:17, 353.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7305/8000 [11:43<01:24,  8.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1945/8000 [00:05<00:17, 344.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7307/8000 [11:43<01:28,  7.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2001/8000 [00:05<00:16, 363.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7309/8000 [11:43<01:26,  8.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2056/8000 [00:05<00:15, 372.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7315/8000 [11:43<00:51, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2102/8000 [00:05<00:16, 356.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7318/8000 [11:44<00:50, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2156/8000 [00:05<00:16, 360.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7322/8000 [11:44<00:46, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2201/8000 [00:05<00:17, 334.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7325/8000 [11:44<00:57, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2257/8000 [00:06<00:15, 360.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7328/8000 [11:44<00:55, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2307/8000 [00:06<00:15, 362.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7331/8000 [11:45<01:05, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2357/8000 [00:06<00:15, 357.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7338/8000 [11:45<00:45, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2404/8000 [00:06<00:15, 353.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7340/8000 [11:46<01:13,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2448/8000 [00:06<00:16, 338.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7343/8000 [11:46<01:18,  8.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2489/8000 [00:06<00:16, 335.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7345/8000 [11:46<01:16,  8.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2540/8000 [00:06<00:16, 336.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7350/8000 [11:47<00:55, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2584/8000 [00:07<00:16, 328.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7353/8000 [11:47<01:06,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2629/8000 [00:07<00:16, 332.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7355/8000 [11:47<01:20,  8.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2679/8000 [00:07<00:15, 333.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7356/8000 [11:48<01:34,  6.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2726/8000 [00:07<00:14, 355.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7360/8000 [11:48<01:09,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2762/8000 [00:07<00:16, 321.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7362/8000 [11:48<01:13,  8.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2820/8000 [00:07<00:14, 345.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7368/8000 [11:48<00:48, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2863/8000 [00:07<00:15, 337.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7371/8000 [11:49<00:52, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2908/8000 [00:08<00:16, 313.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7377/8000 [11:49<00:51, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2980/8000 [00:08<00:35, 142.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7381/8000 [11:50<00:47, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3026/8000 [00:09<00:29, 166.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7384/8000 [11:50<00:46, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3060/8000 [00:10<00:55, 88.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7386/8000 [11:50<00:48, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3108/8000 [00:10<00:42, 114.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7388/8000 [11:50<00:52, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3159/8000 [00:10<00:33, 143.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7392/8000 [11:51<00:58, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3210/8000 [00:10<00:27, 172.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7394/8000 [11:51<01:04,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3266/8000 [00:10<00:22, 207.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7395/8000 [11:51<01:19,  7.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3305/8000 [00:10<00:20, 229.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▋ | 7403/8000 [11:52<01:06,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3353/8000 [00:11<00:18, 244.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▋ | 7406/8000 [11:52<01:04,  9.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3395/8000 [00:11<00:17, 269.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▋ | 7409/8000 [11:53<01:09,  8.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3450/8000 [00:11<00:14, 306.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7412/8000 [11:53<01:11,  8.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3496/8000 [00:11<00:13, 323.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7415/8000 [11:54<01:13,  8.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3549/8000 [00:11<00:12, 353.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7417/8000 [11:54<01:23,  7.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3604/8000 [00:11<00:12, 362.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7420/8000 [11:54<01:13,  7.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3659/8000 [00:11<00:11, 387.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7422/8000 [11:54<01:12,  8.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3709/8000 [00:11<00:10, 391.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7423/8000 [11:55<01:23,  6.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3764/8000 [00:12<00:10, 406.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7429/8000 [11:55<00:54, 10.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3811/8000 [00:12<00:10, 400.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7433/8000 [11:56<01:00,  9.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3858/8000 [00:12<00:10, 395.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7435/8000 [11:56<01:20,  7.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3905/8000 [00:12<00:11, 368.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7438/8000 [11:56<01:16,  7.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3959/8000 [00:12<00:10, 370.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7439/8000 [11:57<01:23,  6.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4002/8000 [00:12<00:11, 357.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7442/8000 [11:57<01:13,  7.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4049/8000 [00:12<00:11, 346.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7446/8000 [11:57<00:56,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4109/8000 [00:12<00:10, 385.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7448/8000 [11:57<00:56,  9.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4157/8000 [00:13<00:09, 390.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7451/8000 [11:58<00:52, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4202/8000 [00:13<00:10, 372.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7454/8000 [11:58<00:50, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4262/8000 [00:13<00:09, 403.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7458/8000 [11:58<00:52, 10.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4311/8000 [00:13<00:09, 395.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7460/8000 [11:59<00:52, 10.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▋       | 4368/8000 [00:13<00:09, 391.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7463/8000 [11:59<01:12,  7.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4428/8000 [00:13<00:09, 391.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7467/8000 [12:00<00:58,  9.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4483/8000 [00:13<00:08, 392.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7469/8000 [12:00<01:28,  6.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4543/8000 [00:14<00:08, 395.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7470/8000 [12:00<01:30,  5.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4597/8000 [00:14<00:08, 410.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▉ | 7472/8000 [12:01<01:23,  6.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4646/8000 [00:14<00:08, 398.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▉ | 7475/8000 [12:01<01:12,  7.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4695/8000 [00:14<00:08, 391.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7482/8000 [12:01<00:40, 12.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▌      | 4750/8000 [00:14<00:07, 413.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7484/8000 [12:02<01:00,  8.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4842/8000 [00:14<00:08, 371.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7486/8000 [12:02<01:00,  8.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4888/8000 [00:15<00:08, 360.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7491/8000 [12:02<00:53,  9.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4931/8000 [00:15<00:08, 367.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7493/8000 [12:03<00:58,  8.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4986/8000 [00:15<00:08, 372.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7495/8000 [12:03<01:00,  8.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5035/8000 [00:15<00:07, 375.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7498/8000 [12:03<00:51,  9.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5078/8000 [00:15<00:08, 353.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7500/8000 [12:04<00:53,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5123/8000 [00:15<00:08, 350.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7502/8000 [12:04<00:53,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5174/8000 [00:15<00:08, 349.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7503/8000 [12:04<01:04,  7.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5223/8000 [00:15<00:07, 371.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7509/8000 [12:04<00:40, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5275/8000 [00:16<00:07, 371.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7511/8000 [12:05<00:48, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5341/8000 [00:16<00:06, 417.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7514/8000 [12:05<00:42, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5397/8000 [00:16<00:06, 418.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7519/8000 [12:05<00:32, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5452/8000 [00:16<00:06, 413.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7522/8000 [12:05<00:34, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5502/8000 [00:16<00:05, 416.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7524/8000 [12:06<00:41, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5544/8000 [00:16<00:06, 372.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7527/8000 [12:06<00:38, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5604/8000 [00:16<00:05, 399.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7530/8000 [12:06<00:37, 12.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5657/8000 [00:17<00:06, 375.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7533/8000 [12:06<00:35, 13.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5698/8000 [00:17<00:06, 340.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7538/8000 [12:06<00:31, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5755/8000 [00:17<00:06, 371.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7542/8000 [12:07<00:36, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▌    | 5808/8000 [00:17<00:05, 385.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7546/8000 [12:07<00:47,  9.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5856/8000 [00:17<00:05, 387.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7551/8000 [12:08<00:45,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5905/8000 [00:17<00:05, 373.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7554/8000 [12:08<00:43, 10.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5948/8000 [00:17<00:05, 368.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7556/8000 [12:08<00:43, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5998/8000 [00:17<00:05, 381.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7558/8000 [12:09<00:42, 10.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6048/8000 [00:18<00:05, 378.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7560/8000 [12:09<00:43, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6096/8000 [00:18<00:05, 358.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7563/8000 [12:09<00:42, 10.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6143/8000 [00:18<00:05, 357.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7568/8000 [12:10<00:40, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6201/8000 [00:18<00:04, 393.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7573/8000 [12:10<00:38, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6247/8000 [00:18<00:04, 366.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7576/8000 [12:10<00:37, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6289/8000 [00:18<00:04, 351.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7582/8000 [12:11<00:35, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6342/8000 [00:18<00:04, 364.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7584/8000 [12:11<00:37, 11.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6398/8000 [00:19<00:04, 376.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7588/8000 [12:11<00:42,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6442/8000 [00:19<00:04, 378.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7592/8000 [12:12<00:37, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6495/8000 [00:19<00:03, 383.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7598/8000 [12:12<00:28, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6534/8000 [00:19<00:04, 357.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7600/8000 [12:12<00:33, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6589/8000 [00:19<00:03, 389.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7603/8000 [12:13<00:41,  9.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6636/8000 [00:19<00:03, 388.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7605/8000 [12:13<00:49,  8.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▎  | 6687/8000 [00:19<00:03, 367.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7606/8000 [12:14<01:33,  4.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6733/8000 [00:19<00:03, 351.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7608/8000 [12:15<01:26,  4.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6779/8000 [00:20<00:03, 345.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7610/8000 [12:15<01:12,  5.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6830/8000 [00:20<00:03, 346.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7612/8000 [12:15<01:03,  6.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6873/8000 [00:20<00:03, 357.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7616/8000 [12:15<00:44,  8.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6924/8000 [00:20<00:03, 356.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7618/8000 [12:15<00:46,  8.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6968/8000 [00:20<00:02, 359.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7620/8000 [12:16<00:47,  8.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7020/8000 [00:20<00:02, 347.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7622/8000 [12:16<00:44,  8.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7064/8000 [00:20<00:02, 340.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7625/8000 [12:16<00:39,  9.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7108/8000 [00:21<00:02, 352.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7627/8000 [12:17<00:49,  7.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7150/8000 [00:21<00:02, 335.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7630/8000 [12:17<00:43,  8.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7191/8000 [00:21<00:02, 324.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7632/8000 [12:17<00:44,  8.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7229/8000 [00:21<00:02, 310.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7634/8000 [12:18<00:55,  6.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7284/8000 [00:21<00:02, 336.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7638/8000 [12:18<00:43,  8.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7335/8000 [00:21<00:01, 360.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▏| 7642/8000 [12:18<00:34, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7377/8000 [00:21<00:01, 337.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▏| 7646/8000 [12:18<00:30, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7434/8000 [00:21<00:01, 376.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7653/8000 [12:19<00:25, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7480/8000 [00:22<00:01, 360.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7656/8000 [12:19<00:27, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7522/8000 [00:22<00:01, 355.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7659/8000 [12:19<00:25, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7565/8000 [00:22<00:01, 335.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7661/8000 [12:20<00:34,  9.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7604/8000 [00:22<00:01, 306.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7663/8000 [12:20<00:42,  7.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7650/8000 [00:22<00:01, 312.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7666/8000 [12:20<00:35,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7706/8000 [00:22<00:00, 338.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7670/8000 [12:21<00:36,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7760/8000 [00:22<00:00, 359.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7675/8000 [12:21<00:28, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▌| 7805/8000 [00:23<00:00, 354.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7679/8000 [12:21<00:27, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7851/8000 [00:23<00:00, 365.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7681/8000 [12:22<00:28, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7897/8000 [00:23<00:00, 359.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7683/8000 [12:22<00:31,  9.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7940/8000 [00:23<00:00, 363.76it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Generating signals: 100%|███████████████▉| 7987/8000 [00:23<00:00, 356.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Generating signals: 8040it [00:23, 339.52it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  96%|████████████████▎| 7685/8000 [12:22<00:43,  7.28it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  96%|████████████████▎| 7690/8000 [12:23<00:31,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7692/8000 [12:23<00:31,  9.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<26:01,  7.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7694/8000 [12:23<00:33,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:27,  4.08s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7697/8000 [12:23<00:31,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:22,  2.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7701/8000 [12:24<00:26, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:26,  2.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7704/8000 [12:24<00:31,  9.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:23,  1.97s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▍| 7708/8000 [12:24<00:25, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:42,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▍| 7711/8000 [12:25<00:24, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:17,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▍| 7716/8000 [12:25<00:20, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<04:55,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7721/8000 [12:25<00:18, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<04:50,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7723/8000 [12:25<00:21, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:50,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7729/8000 [12:26<00:15, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:48,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7731/8000 [12:26<00:18, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:35,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7733/8000 [12:26<00:20, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:29,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7736/8000 [12:26<00:19, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:21,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7740/8000 [12:26<00:16, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:26,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7742/8000 [12:27<00:19, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:26,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7746/8000 [12:27<00:19, 13.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:18,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7748/8000 [12:27<00:23, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:15,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7752/8000 [12:28<00:18, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:12,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7756/8000 [12:28<00:16, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:11,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7760/8000 [12:28<00:16, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:08,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7762/8000 [12:28<00:23, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:05,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7765/8000 [12:29<00:21, 10.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:01,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7767/8000 [12:29<00:24,  9.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<03:59,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7773/8000 [12:29<00:16, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<03:59,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7779/8000 [12:29<00:12, 17.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<04:12,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7781/8000 [12:30<00:14, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<04:16,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7783/8000 [12:30<00:17, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<04:16,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7786/8000 [12:30<00:17, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<04:06,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7789/8000 [12:30<00:16, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<04:04,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7792/8000 [12:31<00:17, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<04:02,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7795/8000 [12:31<00:16, 12.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<03:57,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7798/8000 [12:31<00:15, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<03:51,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7804/8000 [12:32<00:16, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:54<03:45,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7807/8000 [12:32<00:15, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:55<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7812/8000 [12:32<00:12, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7818/8000 [12:33<00:13, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:58<03:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7820/8000 [12:33<00:14, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:59<03:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7824/8000 [12:33<00:16, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:29,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7828/8000 [12:34<00:14, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7831/8000 [12:34<00:13, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:03<03:26,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7834/8000 [12:34<00:12, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:22,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7837/8000 [12:34<00:12, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7842/8000 [12:34<00:10, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:07<03:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7846/8000 [12:35<00:11, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7850/8000 [12:35<00:10, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:09,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7853/8000 [12:35<00:11, 12.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:11,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7859/8000 [12:36<00:10, 13.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7861/8000 [12:36<00:11, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:08,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7863/8000 [12:36<00:12, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:14<03:04,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7868/8000 [12:37<00:11, 11.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:15<03:02,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7872/8000 [12:37<00:10, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:16<02:57,  1.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7877/8000 [12:37<00:08, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:18<03:13,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7880/8000 [12:38<00:10, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:20<03:55,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7883/8000 [12:38<00:11,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:22<03:49,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7886/8000 [12:38<00:10, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:23<03:27,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7888/8000 [12:39<00:13,  8.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:24<03:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7890/8000 [12:39<00:13,  8.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:26<03:32,  1.50s/it]\n",
+      "Generating signals:  99%|████████████████▊| 7891/8000 [12:39<00:14,  7.60it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  99%|████████████████▊| 7896/8000 [12:39<00:09, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7899/8000 [12:40<00:08, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.52it/s]\n",
+      "Generating signals:  99%|████████████████▊| 7901/8000 [12:40<00:09, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  99%|████████████████▊| 7903/8000 [12:40<00:09, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7906/8000 [12:41<00:10,  8.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7907/8000 [12:41<00:12,  7.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 48it [00:01, 44.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7910/8000 [12:41<00:09,  9.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 91it [00:01, 89.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7913/8000 [12:41<00:09,  9.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 136it [00:01, 140.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7916/8000 [12:42<00:07, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 175it [00:01, 181.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7919/8000 [12:42<00:06, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 214it [00:01, 215.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7923/8000 [12:42<00:05, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 253it [00:02, 250.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7928/8000 [12:42<00:04, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 291it [00:02, 272.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7930/8000 [12:42<00:05, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 327it [00:02, 282.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7934/8000 [12:43<00:04, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 373it [00:02, 326.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7938/8000 [12:43<00:04, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 415it [00:02, 348.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7942/8000 [12:43<00:04, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 456it [00:02, 365.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7946/8000 [12:44<00:04, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 496it [00:02, 373.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7950/8000 [12:44<00:03, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 536it [00:02, 366.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7955/8000 [12:44<00:02, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 575it [00:02, 369.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7958/8000 [12:44<00:03, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 618it [00:02, 385.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7960/8000 [12:45<00:04,  8.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 658it [00:03, 374.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7963/8000 [12:45<00:04,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 697it [00:03, 371.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7968/8000 [12:46<00:02, 11.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 736it [00:03, 374.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7971/8000 [12:46<00:03,  7.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 229.87it/s]\n",
+      "Generating signals: 100%|████████████████▉| 7973/8000 [12:47<00:03,  6.86it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals: 100%|████████████████▉| 7976/8000 [12:47<00:02,  8.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7978/8000 [12:47<00:02,  8.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 138\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7980/8000 [12:47<00:02,  8.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 154\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7985/8000 [12:48<00:01, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:00<00:00, 160\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7992/8000 [12:48<00:00, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  65%|▋| 65/100 [00:00<00:00, 148\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7994/8000 [12:48<00:00, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals: 100%|████████████████▉| 7997/8000 [12:48<00:00, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [12:22<12:30, 375.43s/it]\u001b[A"
+      "Generating signals: 8002it [12:49, 10.40it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -3.974320417216375\n",
-      "2999: -6.1280093\r"
+      "concatenating...\n"
      ]
     },
     {
@@ -8811,29134 +8833,29074 @@
       "\n",
       "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:40,  7.44s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:50,  3.89s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:09<30:47,  9.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:59,  2.74s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:12<18:01,  5.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:09,  2.19s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:14<13:26,  4.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:08,  1.89s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:17<11:16,  3.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:32,  1.71s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:19<10:10,  3.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:15,  1.64s/it]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:21<09:19,  2.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<04:58,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:24<08:51,  2.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:48,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:27<08:44,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:32,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:29<08:23,  2.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:23,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:32<08:11,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:16,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:34<07:50,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:13,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:36<07:42,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:09,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:39<07:40,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:06,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:41<07:37,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:03,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:44<07:41,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:46<07:36,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<03:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:49<07:34,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<03:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:51<07:28,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:54<07:42,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:34<03:56,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:56<07:38,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<03:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:59<07:33,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [01:01<07:25,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:38<03:46,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [01:04<07:21,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<03:47,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [01:06<07:14,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:40<03:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:09<07:13,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:42<03:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:11<07:06,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:13<06:59,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:44<03:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:16<07:12,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:45<03:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:19<07:07,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:21<07:20,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:48<03:36,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:24<07:21,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:49<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:27<07:12,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:51<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:29<07:06,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:52<03:32,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:32<07:02,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:53<03:31,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:34<07:00,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:54<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:37<07:01,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:56<03:28,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:39<06:52,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:57<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:42<06:48,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:58<03:26,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:44<06:40,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:00<03:27,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:47<06:40,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:01<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:49<06:40,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:02<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:52<06:38,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:04<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:54<06:33,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:05<03:20,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:57<06:27,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:06<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:59<06:29,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:07<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [02:02<06:34,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:09<03:10,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [02:04<06:30,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:10<03:11,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [02:08<07:31,  2.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:11<03:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [02:14<06:54,  2.74s/it]\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:13<03:14,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:14<03:11,  1.30s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:15<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:16<03:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 11it [00:01,  8.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:18<03:06,  1.29s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 12.36it/s]\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:19<03:12,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:21<03:14,  1.36s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:22<03:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:23<03:12,  1.37s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 29it [00:01, 24.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:25<03:10,  1.36s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 52it [00:01, 46.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:26<03:08,  1.36s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 78it [00:01, 74.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:29<03:18,  1.44s/it]\n",
+      "Evaluating NLL: 108it [00:01, 110.19it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 135it [00:02, 139.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 161it [00:02, 163.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.46s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 192it [00:02, 195.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.78it/s]\n",
+      "Evaluating NLL: 219it [00:02, 211.91it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 246it [00:02, 213.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 272it [00:02, 219.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 299it [00:02, 229.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 41it [00:01, 39.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 326it [00:02, 239.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 85it [00:01, 87.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 352it [00:02, 241.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 129it [00:01, 139.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 383it [00:03, 256.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 174it [00:01, 192.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 410it [00:03, 234.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 214it [00:01, 221.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 435it [00:03, 237.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 251it [00:01, 244.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 460it [00:03, 237.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 293it [00:02, 283.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 485it [00:03, 236.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 331it [00:02, 291.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 512it [00:03, 243.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 374it [00:02, 326.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 537it [00:03, 237.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 422it [00:02, 365.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 561it [00:03, 237.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 463it [00:02, 354.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 588it [00:03, 243.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 502it [00:02, 363.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 614it [00:03, 247.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 541it [00:02, 364.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 643it [00:04, 258.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 581it [00:02, 372.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 669it [00:04, 247.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 625it [00:02, 389.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 694it [00:04, 235.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 666it [00:03, 393.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 718it [00:04, 234.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 706it [00:03, 369.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 744it [00:04, 240.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 748it [00:03, 381.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 770it [00:04, 244.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 233.83it/s]\n",
+      "Evaluating NLL: 800it [00:04, 167.51it/s]\n",
       "\n",
       "\n",
       "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 174\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:02, 32.3\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 171\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%| | 8/100 [00:00<00:03, 30.6\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:00<00:00, 176\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:02, 30.\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00<00:00, 149\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:02, 36.\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:01, 40.\u001b[A\u001b[A\n",
       "\n",
+      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:01, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 16/8000 [00:00<01:17, 103.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:01, 36.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 36/8000 [00:00<01:03, 125.90it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:01<00:01, 34.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 55/8000 [00:00<00:58, 135.83it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:01<00:01, 35.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 70/8000 [00:00<01:06, 119.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:01<00:01, 34.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 89/8000 [00:00<01:06, 118.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:01<00:01, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 110/8000 [00:00<01:01, 128.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:01<00:01, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 131/8000 [00:01<00:57, 136.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:01<00:00, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 149/8000 [00:01<00:57, 135.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:01<00:00, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 166/8000 [00:01<00:59, 131.23it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:01<00:00, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 183/8000 [00:01<00:58, 132.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:02<00:00, 39.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 204/8000 [00:01<00:55, 139.33it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:02<00:00, 35.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 222/8000 [00:01<00:56, 136.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:02<00:00, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 239/8000 [00:01<00:57, 135.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|▉| 92/100 [00:02<00:00, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 261/8000 [00:01<00:54, 141.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  96%|▉| 96/100 [00:02<00:00, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 301/8000 [00:02<00:49, 154.45it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:02<00:00, 36\n",
       "\n",
-      "Generating signals:   4%|▋                | 328/8000 [00:02<00:45, 167.36it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples: 100%|██████████████████████| 4/4 [1:22:02<00:00, 1340.85s/it]\u001b[A\n",
+      "Replicates:   7%|█▌                     | 1/15 [1:22:02<19:08:40, 4922.90s/it]\n",
+      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.219305273621923\n",
+      "2999: -5.9029062\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   4%|▋                | 348/8000 [00:02<00:47, 161.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 373/8000 [00:02<00:44, 170.05it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 394/8000 [00:02<00:48, 155.51it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<27:32,  8.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 411/8000 [00:02<00:51, 146.93it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:10<15:27,  4.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 435/8000 [00:03<00:49, 151.96it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:12<11:16,  3.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 455/8000 [00:03<00:50, 150.18it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:14<09:36,  2.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 473/8000 [00:03<00:52, 144.49it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:16<08:36,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 491/8000 [00:03<00:52, 143.08it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:18<07:58,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 517/8000 [00:03<00:48, 153.19it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:21<07:51,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 543/8000 [00:03<00:58, 127.65it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:23<07:29,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 566/8000 [00:03<00:53, 139.10it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:25<07:21,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 588/8000 [00:04<00:51, 145.14it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:27<07:19,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 614/8000 [00:04<00:45, 161.14it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:29<07:02,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 634/8000 [00:04<00:47, 156.02it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:32<07:03,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 653/8000 [00:04<00:49, 149.74it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:34<07:01,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 675/8000 [00:04<00:47, 152.98it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:36<06:45,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 696/8000 [00:04<00:49, 148.63it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:38<06:45,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 718/8000 [00:04<00:47, 154.74it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:40<06:45,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 737/8000 [00:05<00:51, 142.34it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:43<06:44,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 759/8000 [00:05<00:48, 150.02it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:45<06:43,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 781/8000 [00:05<00:46, 155.22it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:47<06:41,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 814/8000 [00:05<00:54, 132.73it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:49<06:43,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 834/8000 [00:05<00:53, 133.57it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:52<06:42,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 859/8000 [00:05<00:48, 147.19it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:54<06:41,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 879/8000 [00:06<00:46, 152.56it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:56<06:41,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 895/8000 [00:06<00:50, 140.18it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:58<06:32,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 911/8000 [00:06<00:51, 137.50it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:01<06:29,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 931/8000 [00:06<00:49, 142.48it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:03<06:40,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 951/8000 [00:06<00:50, 138.90it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:05<06:29,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 991/8000 [00:06<00:46, 150.55it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:08<06:33,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1022/8000 [00:07<00:54, 127.17it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:10<06:31,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1040/8000 [00:07<00:56, 124.03it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:12<06:17,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1060/8000 [00:07<00:54, 126.95it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:14<06:11,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1079/8000 [00:07<00:53, 130.09it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:16<06:16,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1098/8000 [00:07<00:54, 125.91it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:19<06:12,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1118/8000 [00:07<00:53, 129.59it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:21<06:07,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1146/8000 [00:08<00:45, 150.36it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:23<06:05,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1168/8000 [00:08<00:45, 150.66it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:25<06:01,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1188/8000 [00:08<00:45, 150.76it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:27<05:57,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1221/8000 [00:08<00:50, 133.03it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:30<05:58,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1239/8000 [00:08<00:52, 129.85it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:32<05:55,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1262/8000 [00:08<00:49, 135.08it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:34<05:52,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1286/8000 [00:09<00:47, 142.34it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:36<05:43,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1307/8000 [00:09<00:47, 140.00it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:38<05:38,  2.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1342/8000 [00:09<00:50, 131.39it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:40<05:42,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1356/8000 [00:09<00:53, 123.91it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:43<05:33,  2.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1373/8000 [00:09<00:53, 123.72it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:45<05:34,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1389/8000 [00:09<00:54, 120.29it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:47<05:40,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1411/8000 [00:10<00:47, 138.34it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [01:50<05:49,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1431/8000 [00:10<00:46, 140.09it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [01:52<05:45,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1450/8000 [00:10<00:48, 133.86it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [01:54<05:42,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1473/8000 [00:10<00:44, 146.53it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [01:56<05:42,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1489/8000 [00:10<00:49, 132.12it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [01:59<05:34,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1511/8000 [00:10<00:48, 132.92it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:01<05:32,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1534/8000 [00:10<00:45, 142.06it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:03<05:38,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1565/8000 [00:11<00:49, 131.06it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [02:05<05:36,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1590/8000 [00:11<00:46, 138.09it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [02:08<05:37,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1609/8000 [00:11<00:46, 137.24it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:11<05:47,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1627/8000 [00:11<00:48, 131.59it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [02:13<05:45,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1665/8000 [00:11<00:47, 132.48it/s]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [02:15<05:40,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1685/8000 [00:12<00:46, 135.47it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:18<05:38,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1706/8000 [00:12<00:46, 135.66it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:20<05:26,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1721/8000 [00:12<00:47, 131.76it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [02:22<05:21,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1741/8000 [00:12<00:47, 131.75it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [02:24<05:21,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1758/8000 [00:12<00:48, 129.57it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [02:27<05:20,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1788/8000 [00:12<00:41, 151.22it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [02:29<05:14,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▌            | 1809/8000 [00:12<00:40, 151.21it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▊                    | 65/200 [02:31<05:08,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1826/8000 [00:13<00:44, 139.70it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████▉                    | 66/200 [02:33<04:59,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1845/8000 [00:13<00:49, 125.21it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████                    | 67/200 [02:36<04:56,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1861/8000 [00:13<00:48, 125.51it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▏                   | 68/200 [02:38<04:58,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1880/8000 [00:13<00:47, 128.05it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▎                   | 69/200 [02:40<04:55,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1903/8000 [00:13<00:44, 137.11it/s]\u001b[A\u001b[A\n",
+      "training:  35%|██████████▌                   | 70/200 [02:43<05:03,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1935/8000 [00:14<00:50, 121.24it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▋                   | 71/200 [02:45<05:03,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1954/8000 [00:14<00:48, 123.91it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▊                   | 72/200 [02:48<05:16,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1972/8000 [00:14<00:50, 118.56it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▉                   | 73/200 [02:51<05:22,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1991/8000 [00:14<00:49, 121.39it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████                   | 74/200 [02:53<05:31,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2004/8000 [00:14<00:50, 118.48it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▎                  | 75/200 [02:56<05:13,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2021/8000 [00:14<00:49, 120.57it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▍                  | 76/200 [02:58<04:59,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2041/8000 [00:14<00:47, 126.00it/s]\u001b[A\u001b[A\n",
+      "training:  38%|███████████▌                  | 77/200 [03:00<04:53,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2063/8000 [00:15<00:46, 128.91it/s]\u001b[A\u001b[A\n",
+      "training:  39%|███████████▋                  | 78/200 [03:05<04:49,  2.38s/it]\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2082/8000 [00:15<00:45, 129.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2096/8000 [00:15<00:50, 116.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2110/8000 [00:15<00:53, 109.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2129/8000 [00:15<00:48, 121.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 10it [00:01,  7.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2148/8000 [00:15<00:49, 118.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.11it/s]\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2164/8000 [00:15<00:48, 119.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▍           | 2192/8000 [00:16<00:51, 112.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2210/8000 [00:16<00:50, 114.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2244/8000 [00:16<01:14, 76.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 29it [00:01, 22.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2264/8000 [00:17<01:07, 84.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 48it [00:01, 39.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2284/8000 [00:17<01:04, 88.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 66it [00:01, 54.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2303/8000 [00:17<01:01, 93.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 82it [00:02, 68.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2319/8000 [00:17<00:58, 96.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 97it [00:02, 81.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2338/8000 [00:17<00:55, 102.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 120it [00:02, 110.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2357/8000 [00:18<00:56, 100.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 138it [00:02, 123.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2374/8000 [00:18<00:53, 104.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 156it [00:02, 125.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2400/8000 [00:18<00:50, 111.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 172it [00:02, 122.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2419/8000 [00:18<00:48, 115.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 187it [00:02, 114.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2442/8000 [00:18<00:45, 123.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 201it [00:03, 101.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2459/8000 [00:18<00:47, 117.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 213it [00:03, 101.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2485/8000 [00:18<00:39, 138.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 224it [00:03, 97.02it/s] \u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2520/8000 [00:19<00:48, 113.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 235it [00:03, 94.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2542/8000 [00:19<00:47, 116.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 249it [00:03, 103.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2557/8000 [00:19<00:48, 112.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 260it [00:03, 104.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2575/8000 [00:19<00:47, 114.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 271it [00:03, 97.97it/s] \u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2598/8000 [00:19<00:43, 124.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 282it [00:03, 93.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2625/8000 [00:20<00:38, 140.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 299it [00:03, 110.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2643/8000 [00:20<00:39, 135.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 312it [00:04, 113.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2659/8000 [00:20<00:42, 127.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 325it [00:04, 116.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▎          | 2685/8000 [00:20<00:36, 144.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 337it [00:04, 106.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2703/8000 [00:20<00:37, 139.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 348it [00:04, 103.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2718/8000 [00:20<00:38, 137.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 359it [00:04, 103.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2735/8000 [00:20<00:39, 133.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 371it [00:04, 103.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2758/8000 [00:21<00:37, 140.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 387it [00:04, 118.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2774/8000 [00:21<00:37, 139.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 400it [00:04, 113.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2800/8000 [00:21<00:34, 149.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 412it [00:05, 107.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2818/8000 [00:21<00:34, 149.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 423it [00:05, 107.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2840/8000 [00:21<00:33, 155.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 439it [00:05, 120.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2880/8000 [00:21<00:34, 146.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 460it [00:05, 145.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2900/8000 [00:22<00:34, 149.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 482it [00:05, 164.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2916/8000 [00:22<00:37, 134.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 499it [00:05, 164.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2936/8000 [00:22<00:38, 132.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 528it [00:05, 199.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2956/8000 [00:22<00:39, 126.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 550it [00:05, 203.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2988/8000 [00:22<00:33, 150.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 579it [00:05, 227.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3007/8000 [00:22<00:34, 146.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 602it [00:05, 227.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3022/8000 [00:22<00:36, 134.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 625it [00:06, 223.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3040/8000 [00:23<00:35, 138.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 655it [00:06, 244.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3078/8000 [00:23<00:35, 139.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 686it [00:06, 262.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3098/8000 [00:23<00:34, 140.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 713it [00:06, 257.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3122/8000 [00:23<00:32, 149.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 739it [00:06, 250.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3140/8000 [00:23<00:33, 144.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 768it [00:06, 260.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3163/8000 [00:23<00:32, 147.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:06, 119.18it/s]\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3184/8000 [00:24<00:31, 155.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3217/8000 [00:24<00:27, 177.05it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▍         | 3243/8000 [00:24<00:26, 177.83it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   3%| | 3/100 [00:00<00:03, 28.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3264/8000 [00:24<00:29, 162.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%| | 8/100 [00:00<00:02, 39.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3282/8000 [00:24<00:31, 148.79it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:01, 43.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3303/8000 [00:24<00:34, 137.90it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:01, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▋         | 3318/8000 [00:24<00:34, 134.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:01, 45.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3343/8000 [00:25<00:30, 154.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3365/8000 [00:25<00:28, 160.25it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:00<00:01, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3400/8000 [00:25<00:32, 142.91it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3430/8000 [00:25<00:27, 167.26it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:01<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3466/8000 [00:25<00:30, 148.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:01<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3494/8000 [00:26<00:36, 124.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:01<00:01, 41.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3510/8000 [00:26<00:37, 119.78it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:01<00:01, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3536/8000 [00:26<00:40, 110.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:01<00:00, 39.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3555/8000 [00:26<00:38, 116.05it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:01<00:00, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3572/8000 [00:26<00:37, 117.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:01<00:00, 43.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3591/8000 [00:27<00:35, 122.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:01<00:00, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3609/8000 [00:27<00:34, 127.95it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:02<00:00, 41.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3624/8000 [00:27<00:35, 122.29it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:02<00:00, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3637/8000 [00:27<00:36, 118.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  93%|▉| 93/100 [00:02<00:00, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3662/8000 [00:27<00:32, 134.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:02<00:00, 41\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3677/8000 [00:27<00:36, 119.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3698/8000 [00:28<00:44, 95.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3717/8000 [00:28<00:41, 102.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                | 169/8000 [00:00<00:12, 639.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3736/8000 [00:28<00:38, 110.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                | 337/8000 [00:00<00:08, 856.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3753/8000 [00:28<00:38, 110.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                | 499/8000 [00:00<00:10, 732.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3775/8000 [00:28<00:33, 124.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍               | 670/8000 [00:00<00:11, 636.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3797/8000 [00:28<00:32, 130.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▊               | 849/8000 [00:01<00:11, 638.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3815/8000 [00:28<00:32, 129.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██              | 1023/8000 [00:02<00:28, 243.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3829/8000 [00:29<00:35, 116.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍             | 1196/8000 [00:03<00:23, 292.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3850/8000 [00:29<00:31, 130.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊             | 1382/8000 [00:03<00:17, 370.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3867/8000 [00:29<00:33, 125.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███             | 1557/8000 [00:03<00:14, 443.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▊        | 3880/8000 [00:29<00:35, 117.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▍            | 1733/8000 [00:03<00:12, 498.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3899/8000 [00:29<00:33, 123.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███▊            | 1906/8000 [00:04<00:11, 539.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3915/8000 [00:29<00:34, 118.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▏           | 2082/8000 [00:04<00:10, 550.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3930/8000 [00:29<00:36, 112.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▍           | 2248/8000 [00:05<00:22, 257.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3949/8000 [00:30<00:34, 116.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|████▊           | 2417/8000 [00:06<00:17, 314.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3968/8000 [00:30<00:34, 115.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▏          | 2579/8000 [00:06<00:15, 353.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3986/8000 [00:30<00:35, 112.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▌          | 2751/8000 [00:06<00:12, 405.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4012/8000 [00:30<00:30, 130.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▊          | 2931/8000 [00:07<00:10, 479.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4031/8000 [00:30<00:31, 127.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▏         | 3100/8000 [00:07<00:09, 507.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4046/8000 [00:30<00:34, 115.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▌         | 3272/8000 [00:07<00:08, 544.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4069/8000 [00:31<00:30, 127.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████▉         | 3442/8000 [00:09<00:18, 240.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4085/8000 [00:31<00:36, 105.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▏        | 3624/8000 [00:10<00:25, 172.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4100/8000 [00:31<00:36, 107.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▌        | 3795/8000 [00:11<00:19, 215.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4113/8000 [00:31<00:39, 99.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████▉        | 3971/8000 [00:11<00:14, 268.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4126/8000 [00:31<00:40, 96.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▎       | 4142/8000 [00:11<00:12, 318.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4144/8000 [00:31<00:35, 109.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4314/8000 [00:12<00:10, 348.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4164/8000 [00:31<00:31, 120.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|████████▉       | 4499/8000 [00:13<00:16, 212.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4188/8000 [00:32<00:36, 104.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4677/8000 [00:14<00:12, 271.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4205/8000 [00:32<00:33, 113.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▋      | 4850/8000 [00:14<00:09, 318.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4220/8000 [00:32<00:33, 113.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████      | 5034/8000 [00:14<00:07, 376.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4238/8000 [00:32<00:31, 118.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▍     | 5212/8000 [00:14<00:06, 445.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4256/8000 [00:32<00:30, 122.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▊     | 5386/8000 [00:15<00:05, 487.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4278/8000 [00:32<00:30, 123.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████     | 5555/8000 [00:15<00:04, 501.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4294/8000 [00:33<00:30, 122.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▍    | 5749/8000 [00:17<00:09, 245.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4316/8000 [00:33<00:28, 128.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5919/8000 [00:17<00:07, 295.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4334/8000 [00:33<00:29, 125.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6098/8000 [00:17<00:05, 355.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▋       | 4361/8000 [00:33<00:24, 148.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████████████▌   | 6279/8000 [00:17<00:04, 427.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4382/8000 [00:33<00:23, 151.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████▉   | 6454/8000 [00:18<00:03, 471.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4399/8000 [00:33<00:25, 142.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████▎  | 6642/8000 [00:19<00:05, 231.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4415/8000 [00:33<00:27, 132.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████▋  | 6821/8000 [00:20<00:04, 283.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4441/8000 [00:34<00:24, 147.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▉  | 6996/8000 [00:20<00:02, 340.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4458/8000 [00:34<00:25, 140.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████▎ | 7175/8000 [00:20<00:02, 387.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4474/8000 [00:34<00:25, 137.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|██████████████▋ | 7346/8000 [00:21<00:01, 429.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4503/8000 [00:34<00:28, 123.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████ | 7525/8000 [00:21<00:00, 487.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4518/8000 [00:34<00:29, 119.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|███████████████▍| 7695/8000 [00:21<00:00, 552.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4533/8000 [00:34<00:29, 118.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|███████████████▊| 7878/8000 [00:21<00:00, 638.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4549/8000 [00:34<00:29, 117.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8062it [00:22, 366.29it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  57%|█████████▏      | 4572/8000 [00:35<00:26, 128.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4585/8000 [00:35<00:30, 111.52it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4601/8000 [00:35<00:33, 102.54it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<29:36,  8.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4621/8000 [00:35<00:29, 112.80it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:11<16:21,  4.96s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4642/8000 [00:35<00:26, 127.22it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:13<12:24,  3.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4665/8000 [00:35<00:23, 144.39it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:15<10:19,  3.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4692/8000 [00:36<00:25, 127.25it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:17<09:00,  2.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4712/8000 [00:36<00:25, 129.57it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:19<08:19,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4729/8000 [00:36<00:26, 125.24it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:22<07:57,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▌      | 4751/8000 [00:36<00:24, 132.65it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:24<07:30,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4769/8000 [00:36<00:24, 130.90it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:26<07:24,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4790/8000 [00:36<00:23, 134.21it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:28<07:21,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4815/8000 [00:37<00:22, 144.03it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:31<07:16,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4845/8000 [00:37<00:24, 130.73it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:33<07:05,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4870/8000 [00:37<00:21, 143.71it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:35<07:00,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4898/8000 [00:37<00:25, 123.23it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:37<06:57,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4917/8000 [00:37<00:24, 127.38it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:40<06:54,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4947/8000 [00:38<00:26, 116.44it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:42<06:46,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4969/8000 [00:38<00:25, 120.84it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:44<06:33,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4986/8000 [00:38<00:25, 120.08it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:45<06:10,  2.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5014/8000 [00:38<00:27, 109.09it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:47<06:01,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5027/8000 [00:38<00:27, 107.66it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:50<06:11,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5048/8000 [00:39<00:27, 107.97it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:52<06:13,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5064/8000 [00:39<00:29, 99.14it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:54<06:10,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5078/8000 [00:39<00:31, 93.65it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:56<06:05,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5094/8000 [00:39<00:31, 91.63it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:58<06:04,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5109/8000 [00:39<00:33, 85.23it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [01:00<06:06,  2.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5128/8000 [00:39<00:28, 101.44it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [01:02<06:07,  2.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5143/8000 [00:40<00:31, 91.36it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [01:05<06:17,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5158/8000 [00:40<00:30, 92.19it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:07<06:19,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5173/8000 [00:40<00:28, 100.11it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:09<06:11,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5191/8000 [00:40<00:27, 102.79it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:11<06:09,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5208/8000 [00:40<00:25, 108.41it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:13<06:17,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5227/8000 [00:40<00:24, 115.22it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:16<06:16,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▍     | 5248/8000 [00:41<00:22, 120.46it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:18<06:14,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5265/8000 [00:41<00:23, 116.81it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:20<06:13,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5279/8000 [00:41<00:25, 105.83it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:22<06:06,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5297/8000 [00:41<00:23, 114.55it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:25<06:10,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▋     | 5313/8000 [00:41<00:23, 114.56it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:27<06:16,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5332/8000 [00:41<00:21, 121.69it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:29<06:10,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5351/8000 [00:41<00:22, 120.07it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:32<06:00,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5373/8000 [00:42<00:21, 122.71it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:34<05:56,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5389/8000 [00:42<00:23, 109.29it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:36<05:58,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5406/8000 [00:42<00:22, 114.59it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:38<05:51,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5422/8000 [00:42<00:22, 112.42it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:40<05:46,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5438/8000 [00:42<00:23, 111.27it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:42<05:40,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5456/8000 [00:42<00:24, 104.99it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:45<05:35,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5474/8000 [00:43<00:22, 110.93it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:47<05:34,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5491/8000 [00:43<00:23, 108.78it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [01:49<05:39,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5514/8000 [00:43<00:20, 123.36it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [01:51<05:38,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5534/8000 [00:43<00:19, 125.03it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [01:54<05:33,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5555/8000 [00:43<00:18, 130.84it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [01:56<05:31,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5575/8000 [00:43<00:18, 130.48it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [01:58<05:38,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5605/8000 [00:44<00:20, 118.78it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [02:00<05:35,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5622/8000 [00:44<00:20, 118.20it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [02:03<05:30,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5644/8000 [00:44<00:18, 124.19it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [02:05<05:24,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5659/8000 [00:44<00:19, 117.18it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [02:07<05:19,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5677/8000 [00:44<00:19, 119.67it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [02:09<05:17,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5692/8000 [00:44<00:20, 113.74it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [02:12<05:21,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5712/8000 [00:44<00:18, 125.47it/s]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [02:14<05:23,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5735/8000 [00:45<00:17, 129.30it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [02:16<05:15,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5753/8000 [00:45<00:17, 126.58it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:18<05:06,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5772/8000 [00:45<00:17, 130.74it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [02:22<05:25,  2.34s/it]\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5790/8000 [00:45<00:17, 129.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▌    | 5808/8000 [00:45<00:16, 130.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5839/8000 [00:46<00:17, 121.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5858/8000 [00:46<00:17, 119.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.65it/s]\n",
       "\n",
-      "Generating signals:  73%|███████████▊    | 5876/8000 [00:46<00:19, 111.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5894/8000 [00:46<00:19, 108.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5911/8000 [00:46<00:18, 112.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5936/8000 [00:46<00:15, 129.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 26it [00:01, 20.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5960/8000 [00:47<00:19, 105.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 53it [00:01, 46.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5977/8000 [00:47<00:18, 106.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 79it [00:01, 73.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5989/8000 [00:47<00:19, 101.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 102it [00:02, 93.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6009/8000 [00:47<00:18, 108.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 126it [00:02, 118.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6032/8000 [00:47<00:16, 116.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 159it [00:02, 159.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6050/8000 [00:47<00:18, 107.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 190it [00:02, 192.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6066/8000 [00:48<00:18, 104.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 217it [00:02, 208.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6093/8000 [00:48<00:15, 123.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 244it [00:02, 221.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6110/8000 [00:48<00:15, 118.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 271it [00:02, 224.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6131/8000 [00:48<00:14, 125.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 297it [00:02, 216.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6154/8000 [00:48<00:13, 137.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 321it [00:02, 208.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6171/8000 [00:48<00:14, 126.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 344it [00:03, 206.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6185/8000 [00:49<00:16, 113.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 368it [00:03, 215.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6202/8000 [00:49<00:16, 111.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 394it [00:03, 226.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6217/8000 [00:49<00:15, 114.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 423it [00:03, 241.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6231/8000 [00:49<00:16, 108.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 449it [00:03, 244.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6248/8000 [00:49<00:16, 109.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 477it [00:03, 254.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6264/8000 [00:49<00:15, 109.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 505it [00:03, 261.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6292/8000 [00:49<00:12, 137.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 532it [00:03, 246.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6319/8000 [00:50<00:10, 154.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 559it [00:03, 251.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6354/8000 [00:50<00:11, 142.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 585it [00:04, 235.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▋   | 6373/8000 [00:50<00:11, 144.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 610it [00:04, 238.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6398/8000 [00:50<00:13, 122.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 635it [00:04, 231.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6417/8000 [00:50<00:12, 124.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 660it [00:04, 235.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6441/8000 [00:50<00:11, 136.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 687it [00:04, 244.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6475/8000 [00:51<00:11, 134.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 716it [00:04, 256.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6492/8000 [00:51<00:11, 127.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 742it [00:04, 252.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6518/8000 [00:51<00:09, 150.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 768it [00:04, 228.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6536/8000 [00:51<00:10, 140.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 161.83it/s]\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6557/8000 [00:51<00:10, 142.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6579/8000 [00:51<00:09, 142.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6595/8000 [00:52<00:11, 123.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<00:03, 31.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6615/8000 [00:52<00:11, 124.81it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%| | 8/100 [00:00<00:02, 35.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6641/8000 [00:52<00:09, 138.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:02, 33.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6658/8000 [00:52<00:09, 134.30it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:02, 37.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▎  | 6682/8000 [00:52<00:08, 147.36it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:01, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6702/8000 [00:52<00:08, 145.36it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▎| 29/100 [00:00<00:01, 47.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6721/8000 [00:53<00:10, 127.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00<00:01, 46.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▌  | 6751/8000 [00:53<00:08, 151.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:01, 41.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6774/8000 [00:53<00:07, 156.15it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:01<00:01, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6811/8000 [00:53<00:08, 143.77it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|▍| 49/100 [00:01<00:01, 39.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6844/8000 [00:53<00:08, 131.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:01<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6859/8000 [00:54<00:08, 130.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:01<00:01, 38.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6887/8000 [00:54<00:09, 114.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:01<00:00, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6912/8000 [00:54<00:08, 121.09it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:01<00:00, 45.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6938/8000 [00:54<00:07, 136.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:01<00:00, 43.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6960/8000 [00:54<00:07, 138.41it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:01<00:00, 44.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6975/8000 [00:54<00:08, 124.23it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|▊| 84/100 [00:02<00:00, 40.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6995/8000 [00:55<00:07, 131.04it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  90%|▉| 90/100 [00:02<00:00, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7027/8000 [00:55<00:08, 120.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  95%|▉| 95/100 [00:02<00:00, 42.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7040/8000 [00:55<00:08, 114.95it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:02<00:00, 41\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7064/8000 [00:55<00:07, 128.80it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|██████▎                  | 1/4 [10:52<32:36, 652.21s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.290316121665634\n",
+      "2999: -6.2004489\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7078/8000 [00:55<00:07, 121.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7097/8000 [00:55<00:07, 121.69it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7118/8000 [00:56<00:07, 124.27it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:07<25:16,  7.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7149/8000 [00:56<00:07, 109.95it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:09<13:03,  3.96s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7169/8000 [00:56<00:07, 116.27it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:10<09:15,  2.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7188/8000 [00:56<00:06, 119.18it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:12<07:43,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7214/8000 [00:56<00:05, 133.17it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:13<06:46,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7233/8000 [00:57<00:06, 127.53it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:15<06:03,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7261/8000 [00:57<00:06, 112.99it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:16<05:38,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7282/8000 [00:57<00:05, 120.50it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:18<05:24,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7296/8000 [00:57<00:05, 118.88it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:19<05:12,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▋ | 7313/8000 [00:57<00:05, 120.83it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:21<05:04,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7329/8000 [00:57<00:05, 114.50it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:22<04:59,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7359/8000 [00:58<00:05, 110.69it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:24<05:02,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7378/8000 [00:58<00:05, 119.68it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:26<04:59,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7400/8000 [00:58<00:04, 128.63it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:27<04:54,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7421/8000 [00:58<00:04, 126.77it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:29<04:49,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7443/8000 [00:58<00:04, 127.39it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:30<04:53,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7464/8000 [00:58<00:03, 137.22it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:32<04:49,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7479/8000 [00:59<00:04, 129.81it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:33<04:45,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7502/8000 [00:59<00:03, 130.55it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:35<04:43,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7517/8000 [00:59<00:03, 124.94it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:37<04:42,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7541/8000 [00:59<00:03, 141.31it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:38<04:35,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████ | 7562/8000 [00:59<00:03, 143.15it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:40<04:31,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7587/8000 [00:59<00:02, 145.70it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:41<04:31,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7605/8000 [00:59<00:02, 147.30it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:43<04:26,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7621/8000 [01:00<00:02, 136.72it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [00:44<04:25,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7654/8000 [01:00<00:02, 133.12it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [00:46<04:33,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7675/8000 [01:00<00:02, 132.76it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [00:47<04:40,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7695/8000 [01:00<00:02, 126.22it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [00:49<04:38,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7712/8000 [01:00<00:02, 120.58it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [00:51<04:34,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7725/8000 [01:01<00:02, 109.82it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [00:52<04:35,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7747/8000 [01:01<00:02, 120.21it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [00:54<04:37,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7764/8000 [01:01<00:02, 111.30it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [00:56<04:34,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7787/8000 [01:01<00:01, 121.66it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [00:57<04:26,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▌| 7812/8000 [01:01<00:01, 133.70it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [00:59<04:40,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7832/8000 [01:01<00:01, 134.74it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:01<04:49,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7853/8000 [01:01<00:01, 139.90it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:03<04:44,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7874/8000 [01:02<00:00, 146.34it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:04<04:43,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7904/8000 [01:02<00:00, 125.66it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:06<04:33,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7921/8000 [01:02<00:00, 128.73it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:07<04:23,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7939/8000 [01:02<00:00, 132.90it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:09<04:14,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7955/8000 [01:02<00:00, 122.66it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:10<04:09,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7973/8000 [01:02<00:00, 117.95it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:12<04:10,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7989/8000 [01:03<00:00, 111.67it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:14<04:15,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8006it [01:03, 126.60it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "training:  22%|██████▌                       | 44/200 [01:15<04:09,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  22%|██████▊                       | 45/200 [01:17<04:04,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:18<04:03,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<26:26,  7.97s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [01:20<03:59,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:43,  4.16s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [01:22<04:06,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:33,  2.91s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [01:23<04:09,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:48,  2.39s/it]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [01:25<04:09,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:37,  2.04s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [01:27<04:07,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:10,  1.91s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [01:29<04:11,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:43,  1.78s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [01:30<04:16,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:20,  1.67s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [01:32<04:11,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:06,  1.61s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [01:34<04:16,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<04:52,  1.54s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [01:36<04:04,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:50,  1.54s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [01:37<04:05,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:40,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [01:39<04:11,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<05:32,  1.78s/it]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [01:43<04:06,  1.75s/it]\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<05:52,  1.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:30<05:24,  1.75s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:31<05:13,  1.70s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:33<05:01,  1.65s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 12.99it/s]\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:35<04:59,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:36<04:46,  1.58s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:37<04:38,  1.55s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:39<04:30,  1.51s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 30it [00:01, 23.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:40<04:24,  1.48s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 62it [00:01, 54.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:42<04:24,  1.49s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 86it [00:01, 76.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:43<04:27,  1.52s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 109it [00:02, 99.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:45<04:24,  1.51s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 138it [00:02, 131.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:46<04:21,  1.50s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 168it [00:02, 164.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:48<04:14,  1.47s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 197.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:50<04:28,  1.56s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 229it [00:02, 214.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:51<04:20,  1.52s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 257it [00:02, 229.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:53<04:21,  1.54s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 290it [00:02, 253.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:54<04:14,  1.51s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 324it [00:02, 275.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:55<04:13,  1.51s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 355it [00:02, 263.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:57<04:06,  1.48s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 387it [00:03, 278.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:58<04:04,  1.47s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 422it [00:03, 297.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:00<04:03,  1.48s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 453it [00:03, 295.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:01<04:04,  1.49s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 484it [00:03, 274.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:03<04:00,  1.48s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 521it [00:03, 299.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:04<03:55,  1.45s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 560it [00:03, 322.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:06<03:55,  1.46s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 594it [00:03, 308.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:07<03:54,  1.46s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 626it [00:03, 295.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:09<03:52,  1.46s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 663it [00:03, 315.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:10<03:48,  1.45s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 696it [00:04, 313.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:12<03:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 728it [00:04, 290.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:13<03:49,  1.47s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 764it [00:04, 308.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:15<03:49,  1.48s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 183.63it/s]\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:16<03:44,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:17<03:46,  1.48s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:19<03:43,  1.47s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 137\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:20<03:41,  1.47s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:00, 115\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:22<03:43,  1.49s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00<00:00, 140\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:25<04:09,  1.67s/it]\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 124\u001b[A\u001b[A\n",
       "\n",
+      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:00<00:00, 120\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:02, 12.07it/s]\n",
+      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   1%|                  | 42/8000 [00:00<00:35, 222.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 96/8000 [00:00<00:29, 268.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                | 144/8000 [00:00<00:26, 291.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 22.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                | 190/8000 [00:00<00:28, 275.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 53it [00:01, 52.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                | 233/8000 [00:00<00:27, 284.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 75it [00:01, 76.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▌                | 293/8000 [00:01<00:25, 299.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 103it [00:01, 108.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                | 347/8000 [00:01<00:23, 320.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 134it [00:01, 146.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                | 404/8000 [00:01<00:22, 332.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 160it [00:02, 166.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▉                | 445/8000 [00:01<00:24, 305.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 189it [00:02, 194.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                | 491/8000 [00:01<00:28, 266.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 215it [00:02, 209.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏               | 536/8000 [00:01<00:28, 266.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 244it [00:02, 228.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏               | 588/8000 [00:02<00:27, 265.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 277it [00:02, 251.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎               | 636/8000 [00:02<00:27, 270.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 306it [00:02, 261.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍               | 679/8000 [00:02<00:29, 244.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 335it [00:02, 265.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌               | 723/8000 [00:02<00:29, 244.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 363it [00:02, 263.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋               | 770/8000 [00:02<00:31, 227.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 398it [00:02, 287.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋               | 823/8000 [00:03<00:31, 225.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 428it [00:02, 283.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▊               | 870/8000 [00:03<00:29, 238.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 457it [00:03, 278.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉               | 912/8000 [00:03<00:27, 256.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 486it [00:03, 278.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██               | 971/8000 [00:03<00:25, 271.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 515it [00:03, 281.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██              | 1007/8000 [00:03<00:26, 261.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 544it [00:03, 275.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██              | 1058/8000 [00:03<00:25, 274.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 572it [00:03, 272.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▏             | 1101/8000 [00:04<00:23, 289.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 600it [00:03, 250.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎             | 1155/8000 [00:04<00:22, 303.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 626it [00:03, 246.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍             | 1198/8000 [00:04<00:24, 279.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 657it [00:03, 262.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▌             | 1254/8000 [00:04<00:24, 278.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 685it [00:03, 265.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▌             | 1310/8000 [00:04<00:24, 267.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 713it [00:04, 269.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▋             | 1367/8000 [00:05<00:25, 263.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 743it [00:04, 277.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▊             | 1410/8000 [00:05<00:24, 265.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 183.53it/s]\n",
+      "Generating signals:  18%|██▉             | 1460/8000 [00:05<00:24, 266.88it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  19%|███             | 1503/8000 [00:05<00:25, 258.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███             | 1549/8000 [00:05<00:23, 271.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 148\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▏            | 1589/8000 [00:05<00:24, 260.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:00<00:00, 144\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▎            | 1645/8000 [00:06<00:21, 293.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  49%|▍| 49/100 [00:00<00:00, 145\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍            | 1691/8000 [00:06<00:21, 294.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:00<00:00, 145\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▍            | 1730/8000 [00:06<00:23, 268.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 150\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▌            | 1786/8000 [00:06<00:24, 258.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  23%|███▋            | 1841/8000 [00:06<00:22, 273.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [19:23<06:36, 396.54s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.271825199956027\n",
-      "2999: -5.8715099\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  24%|███▊            | 1887/8000 [00:07<00:23, 264.91it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  24%|███▊            | 1937/8000 [00:07<00:21, 279.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|███▉            | 1988/8000 [00:07<00:21, 274.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:25,  7.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████            | 2037/8000 [00:07<00:22, 265.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:44,  4.16s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▏           | 2094/8000 [00:07<00:20, 285.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:50,  3.00s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▎           | 2150/8000 [00:07<00:19, 292.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<08:09,  2.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▍           | 2206/8000 [00:08<00:18, 309.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<07:22,  2.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▌           | 2262/8000 [00:08<00:17, 329.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:16<06:31,  2.02s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▌           | 2306/8000 [00:08<00:17, 329.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:58,  1.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▋           | 2350/8000 [00:08<00:17, 331.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:19<05:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|████▊           | 2394/8000 [00:08<00:16, 335.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:33,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|████▉           | 2441/8000 [00:08<00:17, 318.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:22<05:24,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|████▉           | 2489/8000 [00:08<00:16, 327.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:24<05:24,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████           | 2536/8000 [00:09<00:16, 333.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:25<05:18,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▏          | 2591/8000 [00:09<00:15, 349.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:27<05:08,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▎          | 2637/8000 [00:09<00:15, 351.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:29<05:02,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▍          | 2693/8000 [00:09<00:15, 339.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:30<04:59,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▍          | 2735/8000 [00:09<00:16, 318.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:32<04:56,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▌          | 2779/8000 [00:09<00:16, 307.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:34<05:04,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▋          | 2827/8000 [00:09<00:17, 302.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:36<05:22,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|█████▋          | 2871/8000 [00:10<00:18, 284.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:37<05:12,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▊          | 2929/8000 [00:10<00:16, 313.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:39<05:13,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▉          | 2976/8000 [00:10<00:16, 300.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:41<05:02,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████          | 3027/8000 [00:10<00:17, 287.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:43<05:27,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▏         | 3072/8000 [00:10<00:17, 281.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:45<05:23,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▏         | 3119/8000 [00:10<00:16, 289.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:46<05:06,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▎         | 3171/8000 [00:11<00:16, 288.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:48<04:59,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▍         | 3220/8000 [00:11<00:15, 311.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:49<04:59,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▌         | 3272/8000 [00:11<00:16, 289.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:51<04:52,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████▋         | 3330/8000 [00:11<00:15, 296.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:53<04:51,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████▊         | 3382/8000 [00:11<00:15, 306.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:54<04:47,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████▊         | 3435/8000 [00:12<00:18, 248.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:56<04:42,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████▉         | 3482/8000 [00:12<00:19, 236.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:58<04:42,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████         | 3530/8000 [00:12<00:19, 226.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:59<04:41,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▏        | 3573/8000 [00:12<00:21, 209.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [01:01<04:39,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▎        | 3630/8000 [00:13<00:18, 236.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [01:03<04:43,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▎        | 3679/8000 [00:13<00:18, 229.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:05<04:48,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▍        | 3726/8000 [00:13<00:20, 213.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:06<04:40,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▌        | 3774/8000 [00:13<00:20, 204.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:08<04:36,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████▋        | 3816/8000 [00:13<00:20, 203.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:10<04:30,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████▋        | 3859/8000 [00:14<00:19, 212.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:11<04:31,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████▊        | 3899/8000 [00:14<00:20, 201.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:13<04:37,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████▉        | 3948/8000 [00:14<00:17, 230.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:15<04:29,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████▉        | 3994/8000 [00:14<00:16, 238.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:17<04:38,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████        | 4045/8000 [00:14<00:17, 228.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:18<04:38,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▏       | 4095/8000 [00:15<00:17, 223.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:20<04:25,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▎       | 4149/8000 [00:15<00:16, 238.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:22<04:27,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▍       | 4209/8000 [00:15<00:16, 228.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:23<04:18,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▌       | 4267/8000 [00:15<00:17, 214.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:25<04:16,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4318/8000 [00:16<00:16, 219.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:27<04:15,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4359/8000 [00:16<00:15, 228.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:28<04:12,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|████████▊       | 4402/8000 [00:16<00:15, 228.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:30<04:10,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|████████▉       | 4440/8000 [00:16<00:15, 228.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:33<04:50,  1.95s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|████████▉       | 4484/8000 [00:16<00:14, 235.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:35<04:59,  2.02s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████       | 4540/8000 [00:17<00:12, 267.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:37<04:41,  1.91s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▏      | 4577/8000 [00:17<00:13, 253.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:38<04:33,  1.88s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▏      | 4615/8000 [00:17<00:13, 259.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:40<04:20,  1.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4661/8000 [00:17<00:12, 274.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:43<04:26,  1.85s/it]\n",
+      "Generating signals:  59%|█████████▍      | 4710/8000 [00:17<00:11, 277.52it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  59%|█████████▌      | 4754/8000 [00:17<00:11, 287.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▌      | 4800/8000 [00:17<00:10, 295.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▋      | 4839/8000 [00:18<00:10, 293.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.55it/s]\n",
+      "Generating signals:  61%|█████████▊      | 4904/8000 [00:18<00:09, 329.02it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  62%|█████████▉      | 4952/8000 [00:18<00:09, 320.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████▉      | 4996/8000 [00:18<00:09, 321.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████      | 5041/8000 [00:18<00:09, 299.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 32.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▏     | 5082/8000 [00:18<00:10, 288.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 71it [00:01, 69.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▎     | 5146/8000 [00:19<00:08, 333.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 110it [00:01, 114.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▍     | 5192/8000 [00:19<00:08, 316.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 149it [00:01, 160.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████▍     | 5240/8000 [00:19<00:08, 309.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 189it [00:01, 206.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████▌     | 5290/8000 [00:19<00:08, 306.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 226it [00:02, 242.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▋     | 5336/8000 [00:19<00:09, 294.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 263it [00:02, 272.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▊     | 5389/8000 [00:19<00:08, 303.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 300it [00:02, 295.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████▉     | 5448/8000 [00:19<00:07, 319.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 337it [00:02, 306.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████     | 5506/8000 [00:20<00:07, 335.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 373it [00:02, 296.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████     | 5553/8000 [00:20<00:07, 314.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 413it [00:02, 322.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5605/8000 [00:20<00:07, 316.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 453it [00:02, 342.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▎    | 5657/8000 [00:20<00:07, 309.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 490it [00:02, 330.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▍    | 5711/8000 [00:20<00:07, 309.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 525it [00:02, 329.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▌    | 5750/8000 [00:20<00:07, 298.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 565it [00:02, 348.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▌    | 5802/8000 [00:21<00:07, 300.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 601it [00:03, 351.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▋    | 5850/8000 [00:21<00:07, 293.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 643it [00:03, 362.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5899/8000 [00:21<00:06, 305.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 680it [00:03, 343.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▉    | 5954/8000 [00:21<00:06, 321.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 727it [00:03, 377.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████    | 6002/8000 [00:21<00:06, 300.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 220.25it/s]\n",
+      "Generating signals:  76%|████████████    | 6042/8000 [00:21<00:06, 282.47it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  76%|████████████▏   | 6090/8000 [00:22<00:06, 293.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████████████▎   | 6140/8000 [00:22<00:06, 303.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 102\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████████████▍   | 6192/8000 [00:22<00:05, 326.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00<00:00, 102\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████████████▍   | 6239/8000 [00:22<00:05, 305.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:00<00:00, 100\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████████████▌   | 6290/8000 [00:22<00:05, 317.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 111\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████████████▋   | 6351/8000 [00:24<00:15, 107.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  60%|▌| 60/100 [00:00<00:00, 119\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████▊   | 6401/8000 [00:24<00:12, 129.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:00<00:00, 127\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████▉   | 6456/8000 [00:24<00:09, 157.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 11\n",
+      "Generating signals:  81%|█████████████▊   | 6519/8000 [00:25<00:16, 87.41it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  82%|█████████████▏  | 6569/8000 [00:25<00:12, 111.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████▏  | 6617/8000 [00:26<00:10, 135.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 3/8000 [00:00<06:02, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████▎  | 6662/8000 [00:26<00:08, 157.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 6/8000 [00:00<06:32, 20.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▍  | 6709/8000 [00:26<00:07, 179.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 10/8000 [00:00<09:34, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▌  | 6752/8000 [00:26<00:06, 195.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 14/8000 [00:00<07:16, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████▌  | 6798/8000 [00:26<00:05, 220.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 17/8000 [00:00<06:58, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████▋  | 6845/8000 [00:26<00:04, 232.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 22/8000 [00:01<06:58, 19.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████▊  | 6895/8000 [00:26<00:04, 254.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 25/8000 [00:01<08:23, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▉  | 6941/8000 [00:27<00:04, 257.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 27/8000 [00:01<08:25, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▉  | 6998/8000 [00:27<00:03, 303.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 30/8000 [00:01<07:23, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████  | 7062/8000 [00:27<00:02, 338.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 32/8000 [00:01<07:54, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████▏ | 7106/8000 [00:27<00:02, 343.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 34/8000 [00:01<07:55, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████▎ | 7156/8000 [00:27<00:02, 340.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 36/8000 [00:02<08:12, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████▍ | 7206/8000 [00:27<00:02, 333.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 38/8000 [00:02<08:23, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████▌ | 7252/8000 [00:27<00:02, 333.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 42/8000 [00:02<06:51, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████▌ | 7294/8000 [00:28<00:02, 300.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 44/8000 [00:02<07:38, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|██████████████▋ | 7336/8000 [00:28<00:02, 294.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 51/8000 [00:03<09:06, 14.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|██████████████▊ | 7379/8000 [00:28<00:02, 301.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 53/8000 [00:03<09:31, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|██████████████▊ | 7422/8000 [00:28<00:01, 291.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 57/8000 [00:03<09:24, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|██████████████▉ | 7476/8000 [00:28<00:01, 303.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 59/8000 [00:03<09:25, 14.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████ | 7514/8000 [00:28<00:01, 294.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 61/8000 [00:03<10:54, 12.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|███████████████▏| 7582/8000 [00:30<00:04, 101.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 63/8000 [00:04<12:31, 10.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|███████████████▎| 7647/8000 [00:30<00:02, 136.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 66/8000 [00:04<12:47, 10.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|███████████████▍| 7689/8000 [00:30<00:02, 155.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 68/8000 [00:04<11:43, 11.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▍| 7748/8000 [00:30<00:01, 191.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 72/8000 [00:04<08:46, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|███████████████▌| 7801/8000 [00:30<00:00, 212.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 75/8000 [00:04<08:15, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|███████████████▋| 7843/8000 [00:31<00:00, 226.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 78/8000 [00:05<07:38, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|███████████████▊| 7898/8000 [00:31<00:00, 239.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 84/8000 [00:05<05:55, 22.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|███████████████▉| 7941/8000 [00:31<00:00, 240.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 89/8000 [00:05<06:37, 19.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|███████████████▉| 7989/8000 [00:31<00:00, 247.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 93/8000 [00:05<05:48, 22.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8030it [00:31, 252.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   1%|▏                  | 96/8000 [00:05<06:47, 19.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 99/8000 [00:06<06:43, 19.60it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 102/8000 [00:06<06:12, 21.19it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<27:06,  8.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 109/8000 [00:06<05:52, 22.40it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:09<14:03,  4.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 112/8000 [00:06<05:56, 22.12it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:11<10:03,  3.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 115/8000 [00:06<05:57, 22.07it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:12<08:07,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 119/8000 [00:07<08:06, 16.20it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:14<07:02,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 121/8000 [00:07<08:25, 15.60it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:16<06:19,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 125/8000 [00:07<07:05, 18.52it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:17<05:50,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 128/8000 [00:07<06:56, 18.92it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:19<05:39,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 132/8000 [00:07<06:00, 21.84it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:20<05:27,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 137/8000 [00:07<06:22, 20.57it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:22<05:14,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 141/8000 [00:08<06:59, 18.73it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:24<05:10,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 145/8000 [00:08<07:56, 16.48it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:25<04:59,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 148/8000 [00:08<07:22, 17.76it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:27<05:01,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 152/8000 [00:08<06:17, 20.81it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:28<04:59,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 156/8000 [00:09<07:32, 17.32it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:30<04:53,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 159/8000 [00:09<07:03, 18.51it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:31<04:46,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 166/8000 [00:09<05:05, 25.61it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:33<04:40,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 169/8000 [00:09<06:26, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:34<04:42,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 172/8000 [00:09<06:15, 20.83it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:36<04:43,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 175/8000 [00:10<07:32, 17.30it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:38<04:43,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 179/8000 [00:10<06:21, 20.51it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:39<04:37,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 182/8000 [00:10<08:36, 15.13it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:41<04:33,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 184/8000 [00:10<08:57, 14.53it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:42<04:26,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 186/8000 [00:10<08:52, 14.67it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:44<04:29,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 189/8000 [00:10<07:41, 16.92it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [00:45<04:30,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 194/8000 [00:11<05:57, 21.83it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [00:47<04:27,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 199/8000 [00:11<06:06, 21.26it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [00:48<04:28,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 202/8000 [00:11<06:20, 20.48it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [00:50<04:25,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 206/8000 [00:11<06:57, 18.65it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [00:51<04:22,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 208/8000 [00:11<07:30, 17.30it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [00:53<04:24,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 211/8000 [00:11<07:08, 18.19it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [00:54<04:20,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 214/8000 [00:12<08:12, 15.82it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [00:56<04:18,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 218/8000 [00:12<06:44, 19.24it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [00:57<04:16,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 224/8000 [00:12<05:14, 24.74it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [00:59<04:30,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 228/8000 [00:12<04:58, 26.07it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:01<04:22,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 231/8000 [00:12<05:11, 24.94it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:02<04:19,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 236/8000 [00:13<06:05, 21.26it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:04<04:16,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 241/8000 [00:13<06:15, 20.65it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:05<04:12,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 245/8000 [00:13<05:30, 23.43it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:07<04:15,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 250/8000 [00:13<05:03, 25.54it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:09<04:12,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 253/8000 [00:13<05:22, 24.04it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:10<04:11,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 258/8000 [00:14<06:01, 21.44it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:12<04:11,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 261/8000 [00:14<06:04, 21.22it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:13<04:06,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 266/8000 [00:14<05:11, 24.80it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:15<03:59,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 271/8000 [00:14<05:41, 22.64it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:16<03:59,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 275/8000 [00:14<06:34, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:19<04:27,  1.74s/it]\n",
       "\n",
-      "Generating signals:   4%|▋                 | 280/8000 [00:15<09:22, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 283/8000 [00:15<08:39, 14.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 285/8000 [00:15<08:47, 14.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 288/8000 [00:15<07:58, 16.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.84it/s]\n",
       "\n",
-      "Generating signals:   4%|▋                 | 291/8000 [00:16<07:25, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 295/8000 [00:16<07:47, 16.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 298/8000 [00:16<07:20, 17.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 307/8000 [00:16<05:21, 23.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 33it [00:01, 27.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 313/8000 [00:16<04:38, 27.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 72it [00:01, 66.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 318/8000 [00:16<04:15, 30.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 106it [00:01, 102.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 322/8000 [00:17<04:29, 28.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 136it [00:01, 132.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 325/8000 [00:17<06:15, 20.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 166it [00:02, 161.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 329/8000 [00:17<05:53, 21.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 197.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 332/8000 [00:17<05:48, 21.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 235it [00:02, 231.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 337/8000 [00:17<05:13, 24.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 271it [00:02, 262.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 341/8000 [00:18<06:03, 21.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 307it [00:02, 283.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 344/8000 [00:18<06:04, 20.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 341it [00:02, 291.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 349/8000 [00:18<06:20, 20.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 377it [00:02, 308.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 355/8000 [00:18<07:00, 18.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 411it [00:02, 296.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 358/8000 [00:19<07:53, 16.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 443it [00:02, 277.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 362/8000 [00:19<06:44, 18.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 480it [00:03, 300.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 370/8000 [00:19<05:40, 22.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 512it [00:03, 284.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 374/8000 [00:19<05:18, 23.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 544it [00:03, 287.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 381/8000 [00:20<06:06, 20.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 575it [00:03, 292.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 385/8000 [00:20<06:42, 18.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 606it [00:03, 294.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 388/8000 [00:20<07:33, 16.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 636it [00:03, 279.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 392/8000 [00:20<07:39, 16.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 670it [00:03, 294.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 394/8000 [00:21<08:01, 15.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 702it [00:03, 299.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 396/8000 [00:21<11:39, 10.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 733it [00:03, 279.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 401/8000 [00:21<08:13, 15.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 762it [00:04, 275.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 405/8000 [00:21<06:48, 18.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 189.83it/s]\n",
       "\n",
-      "Generating signals:   5%|▉                 | 409/8000 [00:22<07:42, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 412/8000 [00:22<09:15, 13.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 414/8000 [00:22<09:29, 13.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:00, 220\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 417/8000 [00:22<08:23, 15.07it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 128\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 423/8000 [00:22<06:10, 20.45it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00<00:00, 141\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 427/8000 [00:23<06:43, 18.77it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:00<00:00, 142\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 432/8000 [00:23<05:23, 23.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
+      "\n",
+      "Nyquist Samples:  50%|████████████▌            | 2/4 [18:39<18:07, 543.66s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.9999058232425075\n",
+      "2999: -6.1495356\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   5%|▉                 | 437/8000 [00:23<05:53, 21.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 440/8000 [00:23<05:48, 21.71it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                              | 1/200 [00:07<26:28,  7.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 445/8000 [00:23<06:19, 19.92it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:09<13:46,  4.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▍                              | 3/200 [00:11<09:56,  3.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 449/8000 [00:24<05:52, 21.44it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:12<07:50,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 454/8000 [00:24<05:02, 24.92it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:14<06:39,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 458/8000 [00:24<06:03, 20.74it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:15<06:12,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 463/8000 [00:24<06:33, 19.16it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:17<05:44,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 467/8000 [00:25<07:05, 17.72it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:18<05:28,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 470/8000 [00:25<06:50, 18.36it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:20<05:22,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 473/8000 [00:25<08:23, 14.95it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:21<05:08,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 478/8000 [00:25<06:32, 19.18it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:23<05:00,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 484/8000 [00:25<06:10, 20.28it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:24<04:54,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 488/8000 [00:26<06:43, 18.63it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:26<04:58,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 491/8000 [00:26<07:46, 16.08it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:28<04:52,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 493/8000 [00:26<07:56, 15.76it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:29<04:45,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 495/8000 [00:27<11:38, 10.75it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:31<04:56,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 498/8000 [00:27<11:49, 10.57it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:33<04:59,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 502/8000 [00:27<11:05, 11.26it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:34<04:59,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 504/8000 [00:27<10:51, 11.51it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:36<05:04,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 507/8000 [00:28<11:03, 11.29it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:38<05:15,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 511/8000 [00:28<08:54, 14.01it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:39<05:03,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 513/8000 [00:28<08:47, 14.19it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:41<05:00,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 515/8000 [00:28<11:29, 10.86it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:43<04:48,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 517/8000 [00:28<10:47, 11.56it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:44<04:49,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 519/8000 [00:29<12:44,  9.78it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [00:46<04:49,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 522/8000 [00:29<12:04, 10.32it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [00:48<04:48,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 524/8000 [00:29<11:07, 11.21it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [00:49<04:42,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 527/8000 [00:29<09:49, 12.69it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [00:51<04:42,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 530/8000 [00:29<08:30, 14.64it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [00:53<04:46,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 534/8000 [00:29<07:07, 17.48it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [00:54<04:42,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 537/8000 [00:30<08:08, 15.26it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [00:56<04:48,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 542/8000 [00:30<06:01, 20.62it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [00:58<04:47,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 545/8000 [00:30<05:58, 20.82it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:00<05:00,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 548/8000 [00:30<05:58, 20.81it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:01<04:48,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 555/8000 [00:31<07:30, 16.53it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:03<04:44,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 558/8000 [00:31<07:08, 17.38it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:05<04:43,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 564/8000 [00:31<05:33, 22.32it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:07<04:55,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 569/8000 [00:31<05:48, 21.34it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:09<05:02,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 572/8000 [00:31<06:02, 20.48it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:11<05:07,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 575/8000 [00:32<07:14, 17.10it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:13<04:56,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 578/8000 [00:32<06:45, 18.32it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:14<04:51,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 581/8000 [00:32<06:46, 18.25it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:16<04:46,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 583/8000 [00:32<08:59, 13.74it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:18<04:38,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 587/8000 [00:32<07:20, 16.82it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:20<04:38,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 592/8000 [00:33<05:54, 20.90it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:21<04:31,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 596/8000 [00:33<05:24, 22.85it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:23<04:30,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 603/8000 [00:33<04:00, 30.76it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [01:25<04:52,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 607/8000 [00:33<05:16, 23.35it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [01:27<04:51,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 614/8000 [00:33<05:01, 24.53it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [01:29<04:44,  1.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 617/8000 [00:34<05:23, 22.83it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [01:31<04:42,  1.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 621/8000 [00:34<07:22, 16.67it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [01:33<04:31,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 628/8000 [00:34<06:23, 19.23it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [01:34<04:26,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 633/8000 [00:35<07:49, 15.70it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [01:36<04:33,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 638/8000 [00:35<07:40, 16.00it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [01:38<04:34,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 641/8000 [00:35<07:38, 16.06it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [01:40<04:41,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 643/8000 [00:35<09:26, 12.98it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [01:43<04:50,  2.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 646/8000 [00:36<08:25, 14.54it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [01:44<04:35,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 650/8000 [00:36<08:12, 14.93it/s]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [01:46<04:33,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 653/8000 [00:36<07:48, 15.68it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [01:48<04:21,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 657/8000 [00:36<08:25, 14.53it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [01:50<04:12,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 662/8000 [00:37<07:53, 15.49it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [01:51<04:08,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 665/8000 [00:37<07:09, 17.06it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [01:54<04:40,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 667/8000 [00:37<07:18, 16.71it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [01:57<05:09,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 672/8000 [00:37<06:46, 18.03it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [01:58<04:41,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 675/8000 [00:37<07:51, 15.54it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▊                    | 65/200 [02:00<04:30,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 677/8000 [00:38<08:03, 15.16it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████▉                    | 66/200 [02:02<04:19,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 681/8000 [00:38<06:57, 17.53it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████                    | 67/200 [02:04<04:14,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 683/8000 [00:38<09:17, 13.13it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▏                   | 68/200 [02:06<04:05,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 686/8000 [00:38<08:10, 14.92it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▎                   | 69/200 [02:07<03:59,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 688/8000 [00:38<08:25, 14.46it/s]\u001b[A\u001b[A\n",
+      "training:  35%|██████████▌                   | 70/200 [02:09<03:51,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 690/8000 [00:38<09:09, 13.29it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▋                   | 71/200 [02:11<03:50,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 694/8000 [00:39<06:58, 17.44it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▊                   | 72/200 [02:12<03:45,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 697/8000 [00:39<07:10, 16.98it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████████▉                   | 73/200 [02:14<03:43,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 699/8000 [00:39<10:53, 11.18it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████                   | 74/200 [02:18<03:55,  1.87s/it]\n",
       "\n",
-      "Generating signals:   9%|█▌                | 702/8000 [00:39<10:45, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 705/8000 [00:40<11:34, 10.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 707/8000 [00:40<11:23, 10.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 711/8000 [00:40<08:18, 14.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.46it/s]\n",
       "\n",
-      "Generating signals:   9%|█▌                | 714/8000 [00:40<07:36, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 720/8000 [00:41<06:59, 17.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 722/8000 [00:41<07:17, 16.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 726/8000 [00:41<07:33, 16.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 37it [00:01, 29.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 729/8000 [00:41<06:55, 17.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:01, 66.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 731/8000 [00:41<08:41, 13.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 105it [00:01, 94.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 734/8000 [00:42<09:24, 12.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 134it [00:02, 119.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 740/8000 [00:42<06:24, 18.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 167it [00:02, 156.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 745/8000 [00:42<06:33, 18.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 196it [00:02, 172.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 748/8000 [00:42<06:17, 19.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 223it [00:02, 179.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 751/8000 [00:42<06:09, 19.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 256it [00:02, 212.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 754/8000 [00:42<05:50, 20.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 297it [00:02, 258.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 757/8000 [00:43<05:43, 21.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 332it [00:02, 280.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 760/8000 [00:43<05:34, 21.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 365it [00:02, 258.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 767/8000 [00:43<06:07, 19.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 397it [00:02, 273.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 773/8000 [00:43<05:51, 20.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 428it [00:03, 282.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 776/8000 [00:43<05:40, 21.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 464it [00:03, 301.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 781/8000 [00:44<04:58, 24.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 496it [00:03, 298.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 784/8000 [00:44<06:01, 19.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 531it [00:03, 312.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 787/8000 [00:44<07:05, 16.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 565it [00:03, 319.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 791/8000 [00:44<06:08, 19.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 598it [00:03, 321.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 794/8000 [00:45<07:21, 16.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 631it [00:03, 308.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 796/8000 [00:45<09:23, 12.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 663it [00:03, 309.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 799/8000 [00:45<09:25, 12.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 695it [00:03, 284.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 802/8000 [00:45<08:02, 14.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 725it [00:04, 280.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 805/8000 [00:45<07:22, 16.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 759it [00:04, 296.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 807/8000 [00:45<07:30, 15.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 184.45it/s]\n",
       "\n",
-      "Generating signals:  10%|█▊                | 811/8000 [00:46<07:27, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 813/8000 [00:46<08:14, 14.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 815/8000 [00:46<10:15, 11.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%| | 7/100 [00:00<00:01, 61.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 817/8000 [00:46<11:00, 10.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:00, 98.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 821/8000 [00:47<08:08, 14.70it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 92.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 824/8000 [00:47<07:18, 16.37it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 97.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 829/8000 [00:47<05:40, 21.08it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 110\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 832/8000 [00:47<05:28, 21.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:00<00:00, 99.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 835/8000 [00:47<05:15, 22.70it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 94.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 839/8000 [00:47<06:21, 18.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 99\n",
       "\n",
-      "Generating signals:  11%|█▉                | 844/8000 [00:48<06:25, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 848/8000 [00:48<05:46, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 852/8000 [00:48<07:58, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                  | 23/8000 [00:00<00:58, 136.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 855/8000 [00:48<07:26, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                  | 42/8000 [00:00<01:07, 118.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 857/8000 [00:48<07:33, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 54/8000 [00:00<01:22, 96.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 862/8000 [00:49<07:05, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 77/8000 [00:00<01:10, 112.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 864/8000 [00:49<07:15, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 97/8000 [00:00<01:08, 115.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 868/8000 [00:49<07:20, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                | 117/8000 [00:01<01:07, 116.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 870/8000 [00:49<09:38, 12.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                | 132/8000 [00:01<01:14, 105.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 873/8000 [00:50<09:49, 12.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                | 155/8000 [00:01<01:11, 110.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 879/8000 [00:50<06:24, 18.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 169/8000 [00:01<01:21, 95.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 884/8000 [00:50<07:29, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 184/8000 [00:01<01:28, 88.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 888/8000 [00:51<07:33, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                | 212/8000 [00:01<01:12, 107.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 892/8000 [00:51<06:29, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                | 234/8000 [00:02<01:08, 113.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 895/8000 [00:51<07:43, 15.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                | 247/8000 [00:02<01:12, 106.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 898/8000 [00:51<08:13, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                | 272/8000 [00:02<01:08, 113.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 900/8000 [00:51<08:19, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▌                | 287/8000 [00:02<01:13, 105.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 904/8000 [00:51<06:52, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 301/8000 [00:02<01:21, 94.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 906/8000 [00:52<07:00, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                | 328/8000 [00:03<01:08, 112.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 909/8000 [00:52<06:25, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                | 349/8000 [00:03<01:07, 114.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 911/8000 [00:52<08:57, 13.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                | 378/8000 [00:03<00:56, 134.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 917/8000 [00:52<05:58, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                | 392/8000 [00:03<01:06, 115.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 923/8000 [00:52<05:43, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                | 404/8000 [00:03<01:12, 104.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 927/8000 [00:53<05:59, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                | 423/8000 [00:03<01:08, 111.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 930/8000 [00:53<07:05, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▉                | 445/8000 [00:04<01:04, 116.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 934/8000 [00:53<06:33, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▉                | 463/8000 [00:04<01:04, 116.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 936/8000 [00:53<08:20, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                | 483/8000 [00:04<01:04, 117.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 941/8000 [00:54<07:31, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                | 515/8000 [00:04<01:09, 107.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 944/8000 [00:54<06:55, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏               | 539/8000 [00:04<01:03, 117.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 946/8000 [00:54<07:14, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏               | 556/8000 [00:05<01:07, 110.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 949/8000 [00:54<06:57, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏               | 575/8000 [00:05<01:09, 106.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 952/8000 [00:54<06:34, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 588/8000 [00:05<01:14, 99.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 958/8000 [00:54<04:58, 23.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎               | 609/8000 [00:05<01:11, 103.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 961/8000 [00:55<04:59, 23.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎               | 630/8000 [00:05<01:06, 111.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 964/8000 [00:55<05:15, 22.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍               | 649/8000 [00:05<01:05, 111.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 967/8000 [00:55<05:26, 21.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍               | 673/8000 [00:06<01:04, 113.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 972/8000 [00:55<06:05, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▍               | 693/8000 [00:06<01:07, 108.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 977/8000 [00:55<05:05, 22.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌               | 718/8000 [00:06<01:03, 114.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 980/8000 [00:56<06:22, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 732/8000 [00:06<01:13, 98.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 983/8000 [00:56<05:58, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 752/8000 [00:06<01:12, 99.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 988/8000 [00:56<04:59, 23.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 784/8000 [00:08<02:26, 49.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 991/8000 [00:56<05:01, 23.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 801/8000 [00:08<02:06, 56.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 994/8000 [00:56<05:03, 23.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 817/8000 [00:08<01:55, 62.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 997/8000 [00:56<04:59, 23.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 827/8000 [00:08<01:58, 60.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1004/8000 [00:57<05:04, 22.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 846/8000 [00:08<01:39, 71.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1008/8000 [00:57<05:01, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 869/8000 [00:08<01:24, 84.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1012/8000 [00:57<05:56, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 884/8000 [00:09<01:22, 85.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1018/8000 [00:57<04:38, 25.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 894/8000 [00:09<01:29, 79.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1024/8000 [00:57<04:54, 23.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 911/8000 [00:09<01:31, 77.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1028/8000 [00:58<05:37, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 927/8000 [00:09<01:26, 82.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1034/8000 [00:58<05:35, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 943/8000 [00:09<01:24, 83.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1037/8000 [00:58<07:37, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 961/8000 [00:10<01:26, 81.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1040/8000 [00:59<08:16, 14.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 979/8000 [00:10<01:21, 86.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1043/8000 [00:59<07:32, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 999/8000 [00:10<01:13, 94.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1046/8000 [00:59<06:59, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██              | 1027/8000 [00:10<01:00, 114.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1050/8000 [00:59<06:14, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██              | 1040/8000 [00:10<01:04, 107.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1055/8000 [00:59<06:06, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██              | 1061/8000 [00:10<01:02, 110.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1060/8000 [01:00<05:56, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▏             | 1089/8000 [00:11<00:52, 130.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1063/8000 [01:00<07:22, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▏             | 1105/8000 [00:11<00:59, 116.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1067/8000 [01:00<06:14, 18.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▏             | 1124/8000 [00:11<00:56, 121.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1070/8000 [01:00<06:06, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎             | 1160/8000 [00:11<01:03, 107.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1073/8000 [01:00<05:49, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▎             | 1173/8000 [00:11<01:04, 106.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1076/8000 [01:01<08:08, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍             | 1195/8000 [00:12<00:58, 115.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1080/8000 [01:01<06:41, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍             | 1214/8000 [00:12<01:00, 112.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1083/8000 [01:01<06:45, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍             | 1232/8000 [00:12<01:04, 105.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1086/8000 [01:01<06:24, 17.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▍             | 1249/8000 [00:12<01:04, 105.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1092/8000 [01:01<04:47, 24.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▌             | 1265/8000 [00:12<01:05, 102.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1100/8000 [01:02<04:17, 26.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▌             | 1285/8000 [00:12<01:03, 105.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1105/8000 [01:02<04:02, 28.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▌             | 1308/8000 [00:13<00:56, 118.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1108/8000 [01:02<04:14, 27.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▋             | 1330/8000 [00:13<00:57, 115.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1113/8000 [01:02<03:55, 29.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▋             | 1352/8000 [00:13<00:59, 110.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1119/8000 [01:02<03:27, 33.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▋             | 1374/8000 [00:13<00:58, 112.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1124/8000 [01:02<03:12, 35.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊             | 1395/8000 [00:13<00:53, 122.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1129/8000 [01:03<05:06, 22.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▊             | 1419/8000 [00:14<00:58, 111.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1132/8000 [01:03<05:28, 20.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▊             | 1436/8000 [00:14<01:03, 103.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1138/8000 [01:03<06:39, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉             | 1459/8000 [00:14<01:00, 107.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1141/8000 [01:03<06:30, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██▉             | 1483/8000 [00:14<00:54, 118.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1144/8000 [01:04<07:48, 14.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███             | 1503/8000 [00:14<00:54, 118.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1147/8000 [01:04<07:21, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1534/8000 [00:15<01:09, 93.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1149/8000 [01:04<07:35, 15.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1554/8000 [00:15<01:09, 92.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1151/8000 [01:04<07:56, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▏            | 1574/8000 [00:15<01:03, 100.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1154/8000 [01:04<07:05, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1587/8000 [00:15<01:08, 93.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1157/8000 [01:04<06:37, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1604/8000 [00:15<01:09, 92.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1161/8000 [01:05<07:17, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1617/8000 [00:16<01:12, 87.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1164/8000 [01:05<06:32, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1631/8000 [00:16<01:17, 81.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1168/8000 [01:05<06:59, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1651/8000 [00:16<01:08, 92.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1173/8000 [01:06<07:18, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▎            | 1671/8000 [00:16<01:00, 104.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1179/8000 [01:06<05:34, 20.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▎            | 1687/8000 [00:16<01:02, 101.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1182/8000 [01:06<08:48, 12.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍            | 1704/8000 [00:16<00:59, 105.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1185/8000 [01:06<07:52, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▍            | 1725/8000 [00:17<00:53, 117.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1187/8000 [01:06<07:55, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▍            | 1743/8000 [00:17<01:01, 101.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1189/8000 [01:07<11:05, 10.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▌            | 1763/8000 [00:17<00:57, 107.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1191/8000 [01:07<10:37, 10.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▌            | 1781/8000 [00:17<00:59, 104.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1194/8000 [01:07<10:35, 10.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1794/8000 [00:17<01:07, 92.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1196/8000 [01:07<09:40, 11.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1813/8000 [00:18<01:04, 95.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1199/8000 [01:08<07:42, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1825/8000 [00:18<01:12, 84.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1204/8000 [01:08<06:46, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1846/8000 [00:18<01:04, 95.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1207/8000 [01:08<06:09, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1858/8000 [00:18<01:12, 85.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1210/8000 [01:08<10:02, 11.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1878/8000 [00:18<01:05, 92.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1213/8000 [01:09<09:55, 11.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1897/8000 [00:19<01:03, 96.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1215/8000 [01:09<09:41, 11.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1913/8000 [00:19<01:02, 96.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1219/8000 [01:09<07:38, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███▊            | 1934/8000 [00:19<00:58, 104.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1223/8000 [01:09<07:30, 15.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1971/8000 [00:20<01:21, 74.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1225/8000 [01:10<08:48, 12.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1987/8000 [00:20<01:15, 79.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1230/8000 [01:10<07:25, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2010/8000 [00:20<01:03, 94.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1236/8000 [01:10<05:19, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████            | 2033/8000 [00:20<00:57, 104.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1240/8000 [01:10<05:57, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████            | 2055/8000 [00:20<00:50, 117.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1247/8000 [01:11<05:59, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▏           | 2080/8000 [00:20<00:48, 122.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1250/8000 [01:11<05:49, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▏           | 2102/8000 [00:20<00:49, 118.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1258/8000 [01:11<04:58, 22.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▎           | 2133/8000 [00:21<00:46, 126.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1265/8000 [01:11<04:38, 24.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▎           | 2157/8000 [00:21<00:47, 122.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1268/8000 [01:12<06:46, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▎           | 2176/8000 [00:21<00:49, 117.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1270/8000 [01:12<06:37, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▍           | 2195/8000 [00:21<00:55, 105.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1273/8000 [01:12<06:37, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▍           | 2212/8000 [00:22<00:55, 103.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1275/8000 [01:12<06:49, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▍           | 2232/8000 [00:22<00:55, 103.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1280/8000 [01:12<06:21, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2248/8000 [00:22<01:00, 94.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1283/8000 [01:13<08:29, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▌           | 2273/8000 [00:22<00:54, 105.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1288/8000 [01:13<06:18, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2291/8000 [00:22<00:58, 97.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1291/8000 [01:13<07:11, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2304/8000 [00:23<01:03, 90.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1294/8000 [01:13<08:04, 13.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2324/8000 [00:23<01:00, 94.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1296/8000 [01:14<07:48, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2334/8000 [00:23<01:03, 89.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1299/8000 [01:14<07:15, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2349/8000 [00:23<01:05, 85.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1301/8000 [01:14<08:51, 12.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2368/8000 [00:23<01:03, 89.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1304/8000 [01:14<09:03, 12.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2386/8000 [00:23<01:00, 93.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1307/8000 [01:14<07:40, 14.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|████▊           | 2412/8000 [00:24<00:54, 102.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1311/8000 [01:14<06:23, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|████▊           | 2434/8000 [00:24<00:50, 109.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1315/8000 [01:15<05:36, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|████▉           | 2451/8000 [00:24<00:53, 102.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1321/8000 [01:15<05:22, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|████▉           | 2469/8000 [00:24<00:53, 102.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1325/8000 [01:15<05:54, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|████▉           | 2486/8000 [00:24<00:54, 101.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1327/8000 [01:15<06:20, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████           | 2503/8000 [00:24<00:54, 100.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1332/8000 [01:16<06:13, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████           | 2519/8000 [00:25<00:54, 100.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1335/8000 [01:16<06:03, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████           | 2542/8000 [00:25<00:48, 112.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1340/8000 [01:16<04:51, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2554/8000 [00:25<00:57, 94.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1343/8000 [01:16<04:55, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▏          | 2574/8000 [00:25<00:52, 103.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1346/8000 [01:16<06:00, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▏          | 2592/8000 [00:25<00:52, 103.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1349/8000 [01:16<06:54, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▏          | 2608/8000 [00:25<00:49, 108.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1353/8000 [01:17<05:54, 18.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▎          | 2626/8000 [00:26<00:51, 103.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1358/8000 [01:17<04:40, 23.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▎          | 2651/8000 [00:26<00:46, 114.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1361/8000 [01:17<04:50, 22.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▎          | 2668/8000 [00:26<00:47, 111.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1364/8000 [01:17<06:00, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▍          | 2690/8000 [00:26<00:43, 121.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1370/8000 [01:18<06:23, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▍          | 2704/8000 [00:26<00:50, 105.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1375/8000 [01:18<05:12, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▍          | 2718/8000 [00:27<00:52, 100.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1378/8000 [01:18<07:37, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2735/8000 [00:27<00:53, 99.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1384/8000 [01:18<05:36, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▌          | 2763/8000 [00:27<00:45, 116.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1388/8000 [01:18<05:21, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▌          | 2784/8000 [00:27<00:47, 110.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1393/8000 [01:19<05:38, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▌          | 2799/8000 [00:27<00:50, 103.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1398/8000 [01:19<04:44, 23.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▋          | 2824/8000 [00:27<00:49, 105.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1401/8000 [01:19<04:43, 23.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|█████▋          | 2841/8000 [00:28<00:49, 104.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1404/8000 [01:19<04:46, 23.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|█████▋          | 2865/8000 [00:28<00:45, 112.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1408/8000 [01:19<06:40, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|█████▊          | 2887/8000 [00:28<00:43, 118.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1415/8000 [01:20<05:39, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|█████▊          | 2906/8000 [00:28<00:42, 120.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1419/8000 [01:20<05:19, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▊          | 2934/8000 [00:28<00:38, 132.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1422/8000 [01:20<05:15, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▉          | 2953/8000 [00:29<00:41, 120.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1425/8000 [01:20<06:24, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▉          | 2970/8000 [00:29<00:46, 108.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1429/8000 [01:20<05:39, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████▉          | 2998/8000 [00:29<00:42, 118.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1433/8000 [01:21<04:59, 21.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████          | 3018/8000 [00:29<00:44, 112.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1439/8000 [01:21<05:56, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████          | 3031/8000 [00:29<00:46, 106.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1442/8000 [01:21<05:42, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████          | 3048/8000 [00:29<00:47, 103.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1447/8000 [01:21<05:25, 20.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▏         | 3069/8000 [00:30<00:46, 107.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1451/8000 [01:22<05:08, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▏         | 3085/8000 [00:30<00:48, 102.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1454/8000 [01:22<05:01, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▏         | 3101/8000 [00:30<00:47, 103.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1459/8000 [01:22<04:13, 25.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▎         | 3126/8000 [00:30<00:43, 113.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1462/8000 [01:22<04:15, 25.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▎         | 3147/8000 [00:30<00:42, 113.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1466/8000 [01:22<04:03, 26.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▎         | 3168/8000 [00:31<00:43, 110.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1470/8000 [01:22<05:37, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▍         | 3189/8000 [00:31<00:42, 114.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1476/8000 [01:23<05:14, 20.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▍         | 3205/8000 [00:31<00:45, 104.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1480/8000 [01:23<04:44, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▍         | 3224/8000 [00:31<00:44, 107.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1483/8000 [01:23<04:55, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▍         | 3243/8000 [00:31<00:45, 105.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1486/8000 [01:23<04:54, 22.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▌         | 3260/8000 [00:31<00:43, 107.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1489/8000 [01:23<06:39, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3275/8000 [00:32<00:50, 93.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1492/8000 [01:23<06:14, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3295/8000 [00:32<00:49, 95.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1495/8000 [01:24<07:13, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3314/8000 [00:32<00:49, 94.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1497/8000 [01:24<07:02, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████▋         | 3336/8000 [00:32<00:43, 106.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1503/8000 [01:24<06:02, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3347/8000 [00:32<00:49, 94.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1509/8000 [01:24<05:20, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3363/8000 [00:33<00:53, 87.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1513/8000 [01:25<04:49, 22.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3382/8000 [00:33<00:51, 89.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1517/8000 [01:25<04:41, 23.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3399/8000 [00:33<00:51, 88.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1521/8000 [01:25<05:15, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3412/8000 [00:33<00:54, 83.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1524/8000 [01:25<05:15, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3427/8000 [00:33<00:57, 80.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1527/8000 [01:25<06:11, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3437/8000 [00:34<01:02, 73.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1529/8000 [01:25<06:36, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3449/8000 [00:34<01:03, 71.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1534/8000 [01:26<05:03, 21.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▍         | 3473/8000 [00:34<00:46, 96.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1538/8000 [01:26<06:22, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████         | 3500/8000 [00:34<00:39, 112.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1542/8000 [01:26<05:33, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████         | 3519/8000 [00:34<00:39, 112.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1546/8000 [01:26<04:56, 21.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████         | 3534/8000 [00:34<00:43, 101.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1553/8000 [01:26<03:49, 28.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████         | 3557/8000 [00:35<00:40, 108.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1559/8000 [01:27<04:14, 25.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▏        | 3583/8000 [00:35<00:36, 121.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1562/8000 [01:27<05:37, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▏        | 3610/8000 [00:35<00:33, 129.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1568/8000 [01:27<05:12, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▎        | 3627/8000 [00:35<00:36, 120.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1573/8000 [01:27<05:12, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▎        | 3645/8000 [00:35<00:39, 110.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1576/8000 [01:28<05:55, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▎        | 3665/8000 [00:35<00:38, 112.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1578/8000 [01:28<07:15, 14.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3678/8000 [00:36<00:47, 90.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1580/8000 [01:28<07:20, 14.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3692/8000 [00:36<00:50, 86.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1583/8000 [01:28<06:32, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3707/8000 [00:36<00:51, 83.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1586/8000 [01:29<07:52, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3726/8000 [00:36<00:46, 92.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1591/8000 [01:29<06:57, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3750/8000 [00:36<00:42, 99.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1595/8000 [01:29<07:04, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▌        | 3768/8000 [00:37<00:40, 104.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1599/8000 [01:29<06:02, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3784/8000 [00:37<00:43, 96.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1603/8000 [01:29<05:12, 20.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████▌        | 3804/8000 [00:37<00:40, 103.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1606/8000 [01:30<06:22, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3820/8000 [00:37<00:43, 96.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1608/8000 [01:30<06:37, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3834/8000 [00:37<00:46, 88.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1611/8000 [01:30<05:57, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3851/8000 [00:38<00:42, 97.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1613/8000 [01:30<08:21, 12.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████▋        | 3874/8000 [00:38<00:38, 107.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1616/8000 [01:30<07:21, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████▊        | 3897/8000 [00:38<00:35, 114.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1620/8000 [01:31<06:05, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████▊        | 3909/8000 [00:38<00:39, 102.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1622/8000 [01:31<06:34, 16.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████▊        | 3931/8000 [00:38<00:35, 113.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1627/8000 [01:31<06:10, 17.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████▉        | 3952/8000 [00:38<00:33, 121.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1629/8000 [01:31<06:22, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████▉        | 3970/8000 [00:39<00:34, 115.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1633/8000 [01:31<05:16, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████▉        | 3985/8000 [00:39<00:38, 105.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1638/8000 [01:32<05:33, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4002/8000 [00:39<00:42, 95.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1642/8000 [01:32<07:13, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4017/8000 [00:39<00:46, 85.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1645/8000 [01:32<07:46, 13.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████        | 4046/8000 [00:39<00:36, 109.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1647/8000 [01:32<07:40, 13.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▏       | 4066/8000 [00:40<00:38, 102.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1652/8000 [01:32<05:46, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4082/8000 [00:40<00:40, 96.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1655/8000 [01:33<05:52, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▏       | 4103/8000 [00:40<00:37, 102.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1658/8000 [01:33<05:31, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▏       | 4124/8000 [00:40<00:36, 107.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1662/8000 [01:33<05:50, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4135/8000 [00:40<00:42, 90.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1667/8000 [01:33<05:31, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4151/8000 [00:40<00:39, 97.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1669/8000 [01:33<05:39, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4169/8000 [00:41<00:39, 96.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1673/8000 [01:34<04:55, 21.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▍       | 4193/8000 [00:41<00:34, 109.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1676/8000 [01:34<06:08, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▍       | 4215/8000 [00:41<00:34, 109.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1678/8000 [01:34<06:14, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▍       | 4232/8000 [00:41<00:35, 106.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1682/8000 [01:34<05:13, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4246/8000 [00:41<00:38, 96.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1686/8000 [01:34<05:44, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4263/8000 [00:41<00:38, 96.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1689/8000 [01:34<05:23, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4282/8000 [00:42<00:37, 99.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1692/8000 [01:35<05:06, 20.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4296/8000 [00:42<00:39, 94.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1695/8000 [01:35<05:29, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4315/8000 [00:42<00:35, 105.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1701/8000 [01:35<06:33, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4331/8000 [00:42<00:34, 105.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1704/8000 [01:35<07:21, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|████████▋       | 4349/8000 [00:42<00:35, 104.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1707/8000 [01:36<06:54, 15.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|████████▋       | 4370/8000 [00:43<00:34, 104.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1709/8000 [01:36<07:10, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|████████▊       | 4390/8000 [00:43<00:31, 113.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1712/8000 [01:36<06:36, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4404/8000 [00:43<00:37, 96.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1714/8000 [01:36<08:19, 12.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4423/8000 [00:43<00:36, 98.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1716/8000 [01:36<07:55, 13.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|████████▉       | 4443/8000 [00:43<00:34, 102.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1720/8000 [01:36<06:07, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|████████▉       | 4463/8000 [00:43<00:34, 103.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1722/8000 [01:37<06:20, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4482/8000 [00:44<00:36, 97.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1724/8000 [01:37<06:24, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████       | 4504/8000 [00:44<00:32, 107.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1726/8000 [01:37<06:32, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████       | 4522/8000 [00:44<00:31, 108.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1729/8000 [01:37<07:44, 13.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████       | 4539/8000 [00:44<00:32, 105.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1735/8000 [01:37<05:02, 20.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████       | 4557/8000 [00:44<00:33, 103.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1739/8000 [01:38<05:51, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▏      | 4577/8000 [00:44<00:31, 107.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1744/8000 [01:38<06:54, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▏      | 4597/8000 [00:45<00:33, 102.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1747/8000 [01:38<07:22, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▏      | 4620/8000 [00:45<00:30, 110.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1750/8000 [01:38<06:46, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4637/8000 [00:45<00:31, 105.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1754/8000 [01:39<05:48, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4649/8000 [00:45<00:36, 91.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1757/8000 [01:39<05:26, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▎      | 4671/8000 [00:45<00:31, 104.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1761/8000 [01:39<05:47, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▍      | 4692/8000 [00:46<00:31, 103.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1764/8000 [01:39<06:38, 15.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▍      | 4711/8000 [00:46<00:30, 106.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1767/8000 [01:39<06:11, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▍      | 4732/8000 [00:46<00:29, 111.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1771/8000 [01:39<05:06, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▍      | 4749/8000 [00:46<00:31, 102.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1774/8000 [01:40<06:29, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▌      | 4765/8000 [00:46<00:31, 101.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1780/8000 [01:40<05:58, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▌      | 4783/8000 [00:47<00:31, 101.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1785/8000 [01:40<05:00, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████▌      | 4809/8000 [00:47<00:27, 114.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1789/8000 [01:40<05:33, 18.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4826/8000 [00:47<00:32, 98.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1792/8000 [01:41<07:44, 13.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4840/8000 [00:47<00:32, 98.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1794/8000 [01:41<07:42, 13.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▋      | 4868/8000 [00:47<00:26, 120.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1796/8000 [01:41<08:56, 11.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▊      | 4896/8000 [00:48<00:29, 106.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1801/8000 [01:42<07:20, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████▊      | 4911/8000 [00:48<00:30, 100.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1804/8000 [01:42<06:31, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4931/8000 [00:48<00:31, 97.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1806/8000 [01:42<06:36, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4955/8000 [00:48<00:30, 98.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1808/8000 [01:42<10:11, 10.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4971/8000 [00:48<00:31, 97.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1810/8000 [01:42<09:39, 10.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4988/8000 [00:49<00:30, 98.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1812/8000 [01:43<08:55, 11.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5006/8000 [00:49<00:33, 88.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1815/8000 [01:43<09:19, 11.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5032/8000 [00:49<00:35, 83.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1817/8000 [01:43<08:28, 12.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5060/8000 [00:49<00:30, 96.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1821/8000 [01:43<08:00, 12.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5077/8000 [00:50<00:32, 91.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1825/8000 [01:44<07:45, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5093/8000 [00:50<00:32, 90.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1829/8000 [01:44<06:12, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5112/8000 [00:50<00:35, 80.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1832/8000 [01:44<08:18, 12.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5129/8000 [00:50<00:34, 83.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1836/8000 [01:44<06:42, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5146/8000 [00:50<00:35, 80.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1838/8000 [01:44<06:46, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5164/8000 [00:51<00:34, 83.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1840/8000 [01:44<06:43, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5179/8000 [00:51<00:35, 79.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1846/8000 [01:45<05:29, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5202/8000 [00:51<00:31, 89.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1849/8000 [01:45<05:07, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▍     | 5226/8000 [00:51<00:27, 101.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1853/8000 [01:45<04:32, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████▍     | 5247/8000 [00:51<00:26, 105.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1859/8000 [01:45<04:49, 21.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5260/8000 [00:52<00:27, 99.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1862/8000 [01:46<05:43, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5274/8000 [00:52<00:29, 91.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1868/8000 [01:46<04:25, 23.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████▌     | 5299/8000 [00:52<00:25, 104.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1874/8000 [01:46<03:40, 27.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▋     | 5324/8000 [00:52<00:25, 104.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1878/8000 [01:46<04:30, 22.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5339/8000 [00:52<00:26, 99.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉             | 1881/8000 [01:46<04:55, 20.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████▋     | 5361/8000 [00:53<00:25, 105.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1885/8000 [01:46<04:37, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5372/8000 [00:53<00:29, 90.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1889/8000 [01:47<04:38, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5394/8000 [00:53<00:27, 94.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1897/8000 [01:47<04:11, 24.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████▊     | 5412/8000 [00:53<00:25, 100.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1901/8000 [01:47<06:00, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5431/8000 [00:53<00:26, 97.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1905/8000 [01:48<06:10, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████▉     | 5460/8000 [00:53<00:20, 121.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1911/8000 [01:48<04:56, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████▉     | 5478/8000 [00:54<00:20, 122.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1915/8000 [01:48<05:01, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|██████████▉     | 5494/8000 [00:54<00:23, 108.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1919/8000 [01:48<04:56, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████     | 5513/8000 [00:54<00:22, 112.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1922/8000 [01:48<04:59, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████     | 5537/8000 [00:54<00:21, 113.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1926/8000 [01:49<05:32, 18.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████     | 5556/8000 [00:54<00:21, 111.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1928/8000 [01:49<06:55, 14.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5581/8000 [00:54<00:19, 123.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1930/8000 [01:49<08:11, 12.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5603/8000 [00:55<00:19, 123.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1932/8000 [01:49<09:32, 10.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▏    | 5620/8000 [00:55<00:20, 113.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1937/8000 [01:50<06:51, 14.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▎    | 5657/8000 [00:55<00:21, 108.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1940/8000 [01:50<06:02, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5671/8000 [00:55<00:23, 98.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1942/8000 [01:50<07:44, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5686/8000 [00:56<00:24, 93.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1944/8000 [01:50<07:53, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▍    | 5709/8000 [00:56<00:21, 105.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1946/8000 [01:50<07:32, 13.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5725/8000 [00:56<00:22, 98.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1949/8000 [01:51<07:47, 12.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▍    | 5745/8000 [00:56<00:21, 104.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1951/8000 [01:51<09:41, 10.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▌    | 5771/8000 [00:56<00:18, 118.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1953/8000 [01:51<09:03, 11.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|███████████▌    | 5788/8000 [00:56<00:20, 107.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1956/8000 [01:51<09:16, 10.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▌    | 5808/8000 [00:57<00:20, 107.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1958/8000 [01:52<10:15,  9.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5822/8000 [00:57<00:22, 98.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1960/8000 [01:52<09:18, 10.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5838/8000 [00:57<00:23, 93.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1965/8000 [01:52<07:50, 12.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|███████████▋    | 5863/8000 [00:57<00:20, 102.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1970/8000 [01:52<06:52, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5876/8000 [00:57<00:22, 96.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1972/8000 [01:53<08:29, 11.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5890/8000 [00:58<00:23, 90.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1974/8000 [01:53<08:10, 12.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5910/8000 [00:58<00:22, 93.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1979/8000 [01:53<06:35, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▊    | 5934/8000 [00:58<00:19, 107.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1982/8000 [01:53<06:02, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|███████████▉    | 5948/8000 [00:58<00:19, 104.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1984/8000 [01:53<06:19, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5963/8000 [00:58<00:20, 99.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1986/8000 [01:53<06:40, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|███████████▉    | 5988/8000 [00:59<00:19, 105.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1991/8000 [01:53<04:57, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5999/8000 [00:59<00:21, 94.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1997/8000 [01:54<04:38, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████    | 6019/8000 [00:59<00:19, 101.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2001/8000 [01:54<04:30, 22.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████    | 6039/8000 [00:59<00:18, 104.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2011/8000 [01:54<04:36, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████    | 6062/8000 [00:59<00:18, 107.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2014/8000 [01:55<04:30, 22.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6078/8000 [00:59<00:18, 101.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2017/8000 [01:55<04:32, 21.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6096/8000 [01:00<00:20, 92.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2022/8000 [01:55<03:53, 25.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▏   | 6120/8000 [01:00<00:18, 101.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2025/8000 [01:55<05:11, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████████████▎   | 6139/8000 [01:00<00:17, 105.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2029/8000 [01:55<05:39, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6153/8000 [01:00<00:20, 91.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2033/8000 [01:56<06:54, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6169/8000 [01:00<00:20, 91.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2039/8000 [01:56<06:44, 14.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████████████▍   | 6194/8000 [01:01<00:17, 104.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2041/8000 [01:56<06:43, 14.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████████████▍   | 6214/8000 [01:01<00:17, 102.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2043/8000 [01:57<07:57, 12.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████████████▍   | 6236/8000 [01:01<00:16, 105.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2045/8000 [01:57<08:01, 12.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6253/8000 [01:01<00:18, 96.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2048/8000 [01:57<06:37, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6270/8000 [01:01<00:18, 95.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2050/8000 [01:57<06:53, 14.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6289/8000 [01:02<00:17, 96.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2053/8000 [01:57<06:00, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6300/8000 [01:02<00:18, 91.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2056/8000 [01:57<05:28, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████████████▋   | 6322/8000 [01:02<00:15, 107.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2058/8000 [01:57<05:55, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6337/8000 [01:02<00:17, 95.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2060/8000 [01:58<06:01, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████████████▋   | 6357/8000 [01:02<00:16, 101.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2062/8000 [01:58<07:45, 12.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6375/8000 [01:02<00:16, 99.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2065/8000 [01:58<06:39, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6396/8000 [01:03<00:16, 99.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2067/8000 [01:58<06:44, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████▊   | 6419/8000 [01:03<00:14, 110.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2071/8000 [01:58<05:08, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6432/8000 [01:03<00:15, 98.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2076/8000 [01:58<05:14, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6443/8000 [01:03<00:17, 87.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2081/8000 [01:59<04:14, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6458/8000 [01:03<00:19, 79.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2084/8000 [01:59<04:16, 23.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6480/8000 [01:04<00:16, 90.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2088/8000 [01:59<04:05, 24.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6501/8000 [01:04<00:16, 91.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2091/8000 [01:59<05:29, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████   | 6521/8000 [01:04<00:14, 100.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2094/8000 [01:59<06:04, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6534/8000 [01:04<00:16, 91.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2098/8000 [02:00<06:08, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6550/8000 [01:04<00:15, 93.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2103/8000 [02:00<04:39, 21.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▏  | 6575/8000 [01:04<00:13, 107.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2111/8000 [02:00<04:01, 24.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6590/8000 [01:05<00:14, 98.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2114/8000 [02:00<04:57, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████▏  | 6611/8000 [01:05<00:13, 104.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2119/8000 [02:01<06:38, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6626/8000 [01:05<00:14, 95.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2121/8000 [02:01<06:36, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6642/8000 [01:05<00:15, 90.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2124/8000 [02:01<05:59, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6661/8000 [01:05<00:14, 92.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2128/8000 [02:01<05:01, 19.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▎  | 6682/8000 [01:06<00:13, 100.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2132/8000 [02:01<05:29, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▍  | 6705/8000 [01:06<00:12, 106.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2135/8000 [02:02<08:44, 11.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▍  | 6723/8000 [01:06<00:12, 102.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2138/8000 [02:02<10:00,  9.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████▍  | 6742/8000 [01:06<00:12, 100.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2140/8000 [02:03<09:37, 10.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6759/8000 [01:06<00:12, 98.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2145/8000 [02:03<08:04, 12.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6772/8000 [01:07<00:14, 87.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2147/8000 [02:03<07:52, 12.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6794/8000 [01:07<00:13, 91.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2149/8000 [02:03<07:46, 12.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████▋  | 6816/8000 [01:07<00:11, 101.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2151/8000 [02:03<07:39, 12.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████▋  | 6839/8000 [01:07<00:10, 109.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2155/8000 [02:03<05:55, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████▋  | 6860/8000 [01:07<00:10, 110.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2157/8000 [02:04<06:04, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████▊  | 6878/8000 [01:08<00:10, 108.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2161/8000 [02:04<06:14, 15.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████▊  | 6898/8000 [01:08<00:09, 112.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2164/8000 [02:04<05:37, 17.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▊  | 6922/8000 [01:08<00:09, 113.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2166/8000 [02:04<06:04, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▉  | 6947/8000 [01:08<00:08, 122.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2169/8000 [02:04<05:36, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████▉  | 6965/8000 [01:08<00:08, 118.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2173/8000 [02:04<04:36, 21.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6977/8000 [01:08<00:10, 97.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2176/8000 [02:05<04:46, 20.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6992/8000 [01:09<00:10, 99.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2180/8000 [02:05<04:21, 22.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████  | 7015/8000 [01:09<00:09, 108.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2184/8000 [02:05<05:06, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████  | 7035/8000 [01:09<00:08, 114.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2187/8000 [02:05<06:11, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████  | 7053/8000 [01:09<00:07, 119.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2190/8000 [02:05<05:41, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▏ | 7069/8000 [01:09<00:08, 105.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2192/8000 [02:06<08:36, 11.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████▏ | 7095/8000 [01:09<00:07, 118.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2194/8000 [02:06<09:34, 10.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7122/8000 [01:10<00:08, 99.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2199/8000 [02:06<06:27, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7139/8000 [01:10<00:08, 96.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2201/8000 [02:06<06:39, 14.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████▎ | 7158/8000 [01:10<00:08, 101.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2206/8000 [02:07<06:00, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7172/8000 [01:10<00:08, 93.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2209/8000 [02:07<05:41, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7189/8000 [01:11<00:09, 86.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2212/8000 [02:07<07:41, 12.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7203/8000 [01:11<00:09, 81.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2218/8000 [02:07<05:40, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7220/8000 [01:11<00:08, 89.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2222/8000 [02:08<06:35, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▍ | 7239/8000 [01:11<00:08, 92.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2224/8000 [02:08<07:10, 13.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7254/8000 [01:11<00:08, 88.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2228/8000 [02:08<06:28, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7273/8000 [01:11<00:07, 93.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2233/8000 [02:08<05:43, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7287/8000 [01:12<00:08, 87.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2236/8000 [02:09<05:36, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7304/8000 [01:12<00:08, 85.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2240/8000 [02:09<05:44, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7315/8000 [01:12<00:08, 81.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2244/8000 [02:09<04:56, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7331/8000 [01:12<00:07, 85.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2249/8000 [02:09<05:03, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7348/8000 [01:12<00:07, 91.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2252/8000 [02:10<06:01, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7365/8000 [01:12<00:06, 95.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2254/8000 [02:10<07:23, 12.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7387/8000 [01:13<00:06, 98.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2258/8000 [02:10<06:20, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|██████████████▊ | 7409/8000 [01:13<00:05, 102.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2260/8000 [02:10<08:29, 11.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|██████████████▊ | 7430/8000 [01:13<00:05, 112.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2263/8000 [02:11<07:13, 13.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7455/8000 [01:13<00:06, 82.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2267/8000 [02:11<06:56, 13.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7470/8000 [01:14<00:06, 80.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2269/8000 [02:11<06:53, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7488/8000 [01:14<00:05, 88.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2271/8000 [02:11<06:34, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7505/8000 [01:14<00:05, 92.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2275/8000 [02:11<06:31, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7524/8000 [01:14<00:04, 95.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2277/8000 [02:11<06:33, 14.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████ | 7546/8000 [01:14<00:04, 100.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2279/8000 [02:12<08:00, 11.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7562/8000 [01:15<00:04, 99.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2282/8000 [02:12<07:04, 13.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7577/8000 [01:15<00:04, 91.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2289/8000 [02:12<04:14, 22.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7595/8000 [01:15<00:04, 89.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2292/8000 [02:12<05:15, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7617/8000 [01:15<00:03, 97.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2298/8000 [02:13<04:57, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|███████████████▎| 7636/8000 [01:15<00:03, 102.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2301/8000 [02:13<05:35, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|███████████████▎| 7657/8000 [01:16<00:03, 106.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2304/8000 [02:13<06:09, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7672/8000 [01:16<00:03, 95.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2308/8000 [02:13<05:06, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|███████████████▍| 7692/8000 [01:16<00:03, 102.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2311/8000 [02:13<04:49, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|███████████████▍| 7713/8000 [01:16<00:02, 106.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2315/8000 [02:14<05:16, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▍| 7728/8000 [01:16<00:02, 101.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2318/8000 [02:14<05:02, 18.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▌| 7750/8000 [01:16<00:02, 112.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2322/8000 [02:14<05:34, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▌| 7768/8000 [01:17<00:01, 117.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2325/8000 [02:14<05:07, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|███████████████▌| 7787/8000 [01:17<00:01, 113.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2329/8000 [02:14<04:43, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|███████████████▌| 7804/8000 [01:17<00:01, 111.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2332/8000 [02:15<05:41, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7817/8000 [01:17<00:01, 95.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2335/8000 [02:15<05:13, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7827/8000 [01:17<00:02, 83.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2339/8000 [02:15<04:19, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7851/8000 [01:17<00:01, 93.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2342/8000 [02:15<05:40, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7862/8000 [01:18<00:01, 85.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2346/8000 [02:15<04:49, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7875/8000 [01:18<00:01, 79.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2351/8000 [02:16<05:35, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7894/8000 [01:18<00:01, 85.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2355/8000 [02:16<05:26, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7908/8000 [01:18<00:01, 83.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2357/8000 [02:16<05:54, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7920/8000 [01:18<00:00, 82.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2360/8000 [02:16<05:31, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7943/8000 [01:19<00:00, 96.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2362/8000 [02:16<06:04, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|███████████████▉| 7968/8000 [01:19<00:00, 116.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2364/8000 [02:16<06:09, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|███████████████▉| 7986/8000 [01:19<00:00, 102.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2366/8000 [02:17<08:13, 11.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8003it [01:19, 100.56it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  30%|█████            | 2369/8000 [02:17<08:54, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2372/8000 [02:17<07:51, 11.94it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2377/8000 [02:17<05:32, 16.89it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<28:52,  8.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2380/8000 [02:18<07:13, 12.96it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:10<15:26,  4.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2384/8000 [02:18<06:46, 13.82it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:12<11:25,  3.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2386/8000 [02:18<06:40, 14.03it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:14<09:26,  2.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2389/8000 [02:18<05:54, 15.83it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:16<08:10,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2393/8000 [02:18<04:52, 19.14it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:18<07:15,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2396/8000 [02:19<05:54, 15.81it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:19<06:38,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2400/8000 [02:19<05:02, 18.53it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:21<06:18,  1.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2404/8000 [02:19<04:28, 20.87it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:23<06:02,  1.90s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2407/8000 [02:19<05:24, 17.25it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:25<05:48,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2409/8000 [02:19<05:38, 16.50it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:26<05:40,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2413/8000 [02:19<04:40, 19.90it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:28<05:47,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2418/8000 [02:20<05:01, 18.49it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:30<06:08,  1.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2421/8000 [02:20<05:44, 16.19it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:32<06:07,  1.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2425/8000 [02:20<04:52, 19.03it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:34<05:56,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2429/8000 [02:21<08:21, 11.11it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:36<06:04,  1.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2431/8000 [02:21<07:54, 11.74it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:38<06:00,  1.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2433/8000 [02:21<09:01, 10.29it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:40<05:41,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2435/8000 [02:21<08:16, 11.20it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:42<05:40,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2437/8000 [02:22<07:42, 12.02it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:44<05:28,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2439/8000 [02:22<10:31,  8.81it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:45<05:30,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2442/8000 [02:22<08:08, 11.37it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:47<05:35,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2449/8000 [02:22<05:33, 16.65it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:49<05:27,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2454/8000 [02:22<04:31, 20.43it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:51<05:22,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2458/8000 [02:23<04:14, 21.75it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [00:53<05:24,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2463/8000 [02:23<04:40, 19.71it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [00:55<05:25,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2468/8000 [02:23<06:11, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [00:57<05:49,  2.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2473/8000 [02:24<06:07, 15.03it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [00:59<05:34,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2478/8000 [02:24<05:06, 18.01it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:01<05:22,  1.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2481/8000 [02:24<06:10, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:02<05:14,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2484/8000 [02:24<05:46, 15.90it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:05<05:22,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2486/8000 [02:24<05:47, 15.87it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:07<05:24,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2489/8000 [02:25<05:33, 16.53it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:09<05:26,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2492/8000 [02:25<05:23, 17.01it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:11<05:38,  2.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2497/8000 [02:25<04:19, 21.17it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:13<05:38,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2501/8000 [02:25<04:10, 21.97it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:15<05:32,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2504/8000 [02:25<04:17, 21.35it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:17<05:34,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2508/8000 [02:25<03:46, 24.28it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:19<05:20,  1.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2511/8000 [02:26<05:01, 18.19it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:21<05:29,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2514/8000 [02:26<07:18, 12.50it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:23<05:25,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2516/8000 [02:26<08:41, 10.51it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:26<05:50,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2518/8000 [02:27<08:12, 11.13it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:29<06:32,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2520/8000 [02:27<07:52, 11.61it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:31<06:31,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2523/8000 [02:27<09:05, 10.03it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:33<06:02,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2526/8000 [02:27<07:37, 11.95it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:35<05:37,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2528/8000 [02:27<07:33, 12.07it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:37<05:23,  2.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2534/8000 [02:28<05:48, 15.69it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [01:39<05:04,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2537/8000 [02:28<05:24, 16.84it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [01:40<04:55,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2539/8000 [02:28<07:07, 12.77it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [01:42<04:45,  1.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2542/8000 [02:28<06:00, 15.15it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [01:44<04:33,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2544/8000 [02:29<07:36, 11.96it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [01:46<04:27,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2546/8000 [02:29<07:15, 12.52it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [01:47<04:25,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2548/8000 [02:29<07:02, 12.90it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [01:49<04:24,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2551/8000 [02:29<06:06, 14.86it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [01:51<04:24,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2556/8000 [02:29<06:31, 13.91it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [01:53<04:28,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2559/8000 [02:30<06:13, 14.58it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [01:55<04:31,  1.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2562/8000 [02:30<05:54, 15.35it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [01:58<04:58,  2.09s/it]\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2565/8000 [02:30<05:31, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2567/8000 [02:30<05:47, 15.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2571/8000 [02:30<04:58, 18.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2578/8000 [02:30<03:42, 24.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 11.06it/s]\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2581/8000 [02:31<05:44, 15.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2583/8000 [02:31<05:54, 15.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2586/8000 [02:31<05:33, 16.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2590/8000 [02:31<05:39, 15.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 19.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2592/8000 [02:31<05:55, 15.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 45it [00:01, 37.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2595/8000 [02:32<05:40, 15.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 74it [00:02, 68.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2598/8000 [02:32<05:14, 17.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 99it [00:02, 95.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2601/8000 [02:32<04:58, 18.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 122it [00:02, 115.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2604/8000 [02:32<04:38, 19.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 144it [00:02, 133.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2607/8000 [02:32<04:44, 18.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 169it [00:02, 158.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2611/8000 [02:33<05:22, 16.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 198it [00:02, 188.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2613/8000 [02:33<06:43, 13.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 228it [00:02, 214.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2615/8000 [02:33<06:30, 13.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 260it [00:02, 241.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2618/8000 [02:33<06:51, 13.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 293it [00:02, 263.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2620/8000 [02:33<06:57, 12.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 323it [00:02, 272.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2623/8000 [02:34<07:16, 12.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 353it [00:03, 270.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2625/8000 [02:34<06:56, 12.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 382it [00:03, 257.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2628/8000 [02:34<05:52, 15.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 418it [00:03, 284.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2630/8000 [02:34<06:02, 14.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 448it [00:03, 260.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2633/8000 [02:34<05:39, 15.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 476it [00:03, 264.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2637/8000 [02:34<04:36, 19.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 504it [00:03, 251.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2644/8000 [02:34<03:19, 26.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 530it [00:03, 246.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2650/8000 [02:35<04:20, 20.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 556it [00:03, 217.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2653/8000 [02:35<05:12, 17.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 580it [00:04, 220.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2656/8000 [02:36<06:56, 12.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 605it [00:04, 226.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2659/8000 [02:36<06:21, 14.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 636it [00:04, 248.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2661/8000 [02:36<07:27, 11.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 662it [00:04, 237.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2666/8000 [02:36<06:23, 13.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 687it [00:04, 229.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2673/8000 [02:37<06:01, 14.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 719it [00:04, 248.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2676/8000 [02:37<05:37, 15.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 746it [00:04, 253.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2678/8000 [02:37<05:42, 15.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 164.21it/s]\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2682/8000 [02:37<05:06, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2684/8000 [02:37<06:42, 13.19it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2688/8000 [02:38<05:30, 16.07it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%| | 10/100 [00:00<00:00, 98.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2693/8000 [02:38<04:19, 20.47it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:00, 112\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2698/8000 [02:38<04:27, 19.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 112\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2703/8000 [02:38<03:50, 23.00it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00<00:00, 106\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2706/8000 [02:39<05:57, 14.79it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 92.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2709/8000 [02:39<05:30, 16.01it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 102\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2713/8000 [02:39<05:50, 15.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:00<00:00, 99.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2717/8000 [02:39<05:06, 17.22it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 10\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2721/8000 [02:40<05:42, 15.39it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [27:56<09:09, 549.66s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -2.9814433827421984\n",
+      "2999: -5.8028305\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  34%|█████▊           | 2726/8000 [02:40<04:47, 18.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2732/8000 [02:40<04:44, 18.52it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2736/8000 [02:40<05:32, 15.81it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                              | 1/200 [00:08<28:13,  8.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2742/8000 [02:41<06:13, 14.07it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                              | 2/200 [00:10<15:13,  4.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2744/8000 [02:41<06:09, 14.23it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                              | 3/200 [00:12<11:00,  3.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2747/8000 [02:41<06:39, 13.16it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                              | 4/200 [00:14<09:26,  2.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2751/8000 [02:42<06:36, 13.25it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                              | 5/200 [00:16<08:11,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2754/8000 [02:42<06:04, 14.41it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                              | 6/200 [00:18<07:17,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2759/8000 [02:42<04:49, 18.13it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█                              | 7/200 [00:20<07:11,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2762/8000 [02:42<04:34, 19.08it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                             | 8/200 [00:22<06:42,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2765/8000 [02:42<05:21, 16.30it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                             | 9/200 [00:23<06:22,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2768/8000 [02:42<05:00, 17.40it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                            | 10/200 [00:25<06:09,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2777/8000 [02:43<03:03, 28.51it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▋                            | 11/200 [00:27<05:52,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2781/8000 [02:43<04:37, 18.79it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                            | 12/200 [00:29<06:01,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2784/8000 [02:43<04:43, 18.41it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                            | 13/200 [00:31<05:51,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2788/8000 [02:43<04:25, 19.66it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██                            | 14/200 [00:32<05:44,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2794/8000 [02:44<04:26, 19.53it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▎                           | 15/200 [00:34<05:43,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2798/8000 [02:44<05:02, 17.22it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                           | 16/200 [00:36<05:32,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2803/8000 [02:44<04:26, 19.52it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                           | 17/200 [00:38<05:36,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2806/8000 [02:45<06:35, 13.12it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▋                           | 18/200 [00:40<05:41,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2809/8000 [02:45<06:20, 13.63it/s]\u001b[A\u001b[A\n",
+      "training:  10%|██▊                           | 19/200 [00:42<05:39,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2811/8000 [02:45<06:45, 12.80it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                           | 20/200 [00:44<05:39,  1.89s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2815/8000 [02:45<06:15, 13.80it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                          | 21/200 [00:46<05:41,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2818/8000 [02:45<05:40, 15.20it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▎                          | 22/200 [00:48<05:47,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2820/8000 [02:46<05:54, 14.63it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▍                          | 23/200 [00:50<05:47,  1.96s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2822/8000 [02:46<05:58, 14.44it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▌                          | 24/200 [00:51<05:36,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2824/8000 [02:46<05:53, 14.63it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                          | 25/200 [00:53<05:38,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2827/8000 [02:46<05:32, 15.54it/s]\u001b[A\u001b[A\n",
+      "training:  13%|███▉                          | 26/200 [00:56<06:00,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2829/8000 [02:46<05:46, 14.91it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████                          | 27/200 [00:58<05:50,  2.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2831/8000 [02:47<07:33, 11.39it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▏                         | 28/200 [01:00<05:52,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2833/8000 [02:47<07:11, 11.97it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                         | 29/200 [01:02<05:58,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2840/8000 [02:47<04:05, 21.02it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▌                         | 30/200 [01:04<05:40,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2844/8000 [02:47<03:45, 22.88it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▋                         | 31/200 [01:06<05:50,  2.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2847/8000 [02:47<03:51, 22.29it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▊                         | 32/200 [01:08<05:40,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2853/8000 [02:47<03:04, 27.85it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                         | 33/200 [01:10<05:40,  2.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2859/8000 [02:48<03:32, 24.15it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████                         | 34/200 [01:13<06:21,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2862/8000 [02:48<03:41, 23.25it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▎                        | 35/200 [01:15<06:12,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2865/8000 [02:48<04:35, 18.66it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▍                        | 36/200 [01:17<05:45,  2.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2869/8000 [02:48<05:45, 14.83it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                        | 37/200 [01:19<05:24,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2872/8000 [02:49<05:23, 15.85it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████▋                        | 38/200 [01:21<05:25,  2.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2875/8000 [02:49<05:01, 17.02it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████▊                        | 39/200 [01:23<05:17,  1.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2879/8000 [02:49<04:37, 18.44it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████                        | 40/200 [01:24<05:12,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2882/8000 [02:49<06:38, 12.83it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                       | 41/200 [01:26<05:04,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2885/8000 [02:49<05:48, 14.67it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▎                       | 42/200 [01:28<04:52,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2889/8000 [02:50<04:55, 17.28it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▍                       | 43/200 [01:30<04:44,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2892/8000 [02:50<04:52, 17.45it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▌                       | 44/200 [01:32<04:45,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2894/8000 [02:50<05:05, 16.72it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▊                       | 45/200 [01:34<04:49,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2896/8000 [02:50<05:16, 16.13it/s]\u001b[A\u001b[A\n",
+      "training:  23%|██████▉                       | 46/200 [01:36<04:59,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2898/8000 [02:50<05:44, 14.81it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████                       | 47/200 [01:38<04:54,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2902/8000 [02:50<05:55, 14.32it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▏                      | 48/200 [01:39<04:43,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2904/8000 [02:51<07:33, 11.23it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▎                      | 49/200 [01:41<04:35,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2907/8000 [02:51<06:20, 13.37it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████▌                      | 50/200 [01:43<04:36,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2909/8000 [02:51<06:35, 12.87it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▋                      | 51/200 [01:45<04:31,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2914/8000 [02:51<04:33, 18.61it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▊                      | 52/200 [01:46<04:29,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2921/8000 [02:51<03:58, 21.34it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████▉                      | 53/200 [01:48<04:23,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2925/8000 [02:52<04:26, 19.02it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████                      | 54/200 [01:50<04:21,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2928/8000 [02:52<05:10, 16.32it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▎                     | 55/200 [01:52<04:14,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2931/8000 [02:52<05:48, 14.53it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▍                     | 56/200 [01:53<04:14,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2933/8000 [02:52<05:32, 15.22it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▌                     | 57/200 [01:55<04:08,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2936/8000 [02:53<04:58, 16.94it/s]\u001b[A\u001b[A\n",
+      "training:  29%|████████▋                     | 58/200 [01:57<04:11,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2942/8000 [02:53<04:27, 18.90it/s]\u001b[A\u001b[A\n",
+      "training:  30%|████████▊                     | 59/200 [01:59<04:06,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2945/8000 [02:53<04:17, 19.60it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████                     | 60/200 [02:01<04:10,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2954/8000 [02:53<03:27, 24.27it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▏                    | 61/200 [02:02<04:06,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2960/8000 [02:53<03:28, 24.20it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▎                    | 62/200 [02:04<03:57,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2964/8000 [02:54<04:28, 18.73it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▍                    | 63/200 [02:06<03:51,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2967/8000 [02:54<05:02, 16.62it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▌                    | 64/200 [02:07<03:48,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2969/8000 [02:54<06:09, 13.60it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████▊                    | 65/200 [02:09<03:52,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2971/8000 [02:55<07:16, 11.53it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████▉                    | 66/200 [02:13<04:30,  2.02s/it]\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2974/8000 [02:55<06:18, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2976/8000 [02:55<06:25, 13.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2980/8000 [02:55<06:11, 13.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2985/8000 [02:56<06:19, 13.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.90it/s]\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2987/8000 [02:56<07:09, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2989/8000 [02:56<06:42, 12.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2991/8000 [02:56<06:25, 13.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2996/8000 [02:56<04:30, 18.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 34it [00:01, 27.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3004/8000 [02:57<03:38, 22.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 69it [00:01, 61.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3010/8000 [02:57<02:56, 28.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 104it [00:01, 98.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3014/8000 [02:57<03:38, 22.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 139it [00:02, 137.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3017/8000 [02:57<03:53, 21.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 175it [00:02, 177.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3022/8000 [02:57<03:19, 24.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 211it [00:02, 215.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3025/8000 [02:57<03:33, 23.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 245it [00:02, 232.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3029/8000 [02:58<04:47, 17.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 277it [00:02, 251.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3033/8000 [02:58<04:08, 19.99it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 309it [00:02, 254.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3036/8000 [02:58<04:13, 19.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 347it [00:02, 284.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3042/8000 [02:59<04:59, 16.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 381it [00:02, 298.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3044/8000 [02:59<06:13, 13.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 422it [00:02, 327.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3046/8000 [02:59<06:02, 13.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 461it [00:02, 342.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3049/8000 [02:59<07:32, 10.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 498it [00:03, 348.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3055/8000 [02:59<04:55, 16.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 535it [00:03, 352.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3058/8000 [03:00<06:36, 12.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 573it [00:03, 357.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3062/8000 [03:00<05:20, 15.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 610it [00:03, 354.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3069/8000 [03:00<04:26, 18.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 646it [00:03, 354.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3072/8000 [03:00<04:12, 19.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 682it [00:03, 339.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3077/8000 [03:01<03:28, 23.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 717it [00:03, 298.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3082/8000 [03:01<03:48, 21.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 748it [00:03, 284.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3085/8000 [03:01<04:30, 18.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 197.80it/s]\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3089/8000 [03:01<05:25, 15.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3094/8000 [03:02<04:59, 16.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3097/8000 [03:02<04:45, 17.18it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%| | 10/100 [00:00<00:00, 95.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3100/8000 [03:02<04:41, 17.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:00, 93.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3102/8000 [03:02<04:49, 16.91it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 91.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3105/8000 [03:02<04:29, 18.15it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 103\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3108/8000 [03:02<04:10, 19.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:00<00:00, 97.\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3113/8000 [03:03<05:15, 15.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 104\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3116/8000 [03:03<04:49, 16.89it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 114\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3121/8000 [03:03<04:37, 17.61it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 10\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3123/8000 [03:03<05:01, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3127/8000 [03:04<04:17, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3134/8000 [03:04<02:57, 27.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                    | 1/8000 [00:00<21:04,  6.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3138/8000 [03:04<03:41, 21.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                    | 2/8000 [00:00<20:32,  6.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3142/8000 [03:04<04:42, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                    | 4/8000 [00:00<17:02,  7.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3145/8000 [03:04<04:22, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                    | 9/8000 [00:00<07:55, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3148/8000 [03:05<05:27, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 12/8000 [00:01<12:39, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3151/8000 [03:05<04:59, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 15/8000 [00:01<10:20, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3154/8000 [03:05<04:40, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 17/8000 [00:01<10:28, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3157/8000 [03:05<05:28, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 21/8000 [00:01<13:22,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3161/8000 [03:05<04:30, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 25/8000 [00:02<10:43, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3165/8000 [03:06<04:02, 19.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 29/8000 [00:02<08:53, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3168/8000 [03:06<03:47, 21.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 32/8000 [00:02<08:59, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3171/8000 [03:06<03:39, 21.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 34/8000 [00:02<09:48, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3175/8000 [03:06<03:13, 24.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                   | 40/8000 [00:02<08:01, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3181/8000 [03:06<03:20, 24.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                   | 45/8000 [00:03<08:27, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3185/8000 [03:06<03:14, 24.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                   | 49/8000 [00:03<08:51, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3189/8000 [03:07<03:56, 20.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                   | 51/8000 [00:03<09:28, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3193/8000 [03:07<03:50, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 54/8000 [00:04<09:32, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3198/8000 [03:07<03:38, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 59/8000 [00:04<09:51, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3203/8000 [03:07<03:55, 20.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 61/8000 [00:04<12:16, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3206/8000 [03:08<04:10, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 66/8000 [00:05<10:59, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3208/8000 [03:08<05:14, 15.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 68/8000 [00:05<10:56, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3210/8000 [03:08<05:29, 14.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 71/8000 [00:05<10:00, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3213/8000 [03:08<07:10, 11.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 74/8000 [00:05<11:27, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3218/8000 [03:09<05:39, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 80/8000 [00:06<09:28, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3224/8000 [03:09<04:22, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 84/8000 [00:06<08:14, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3228/8000 [03:09<05:17, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 89/8000 [00:06<07:13, 18.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3230/8000 [03:09<05:53, 13.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 91/8000 [00:06<07:38, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3237/8000 [03:10<04:30, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 96/8000 [00:06<07:46, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3239/8000 [03:10<05:07, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                  | 99/8000 [00:07<09:26, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3241/8000 [03:10<06:13, 12.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 103/8000 [00:07<08:13, 15.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3244/8000 [03:10<06:41, 11.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 107/8000 [00:07<09:12, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3248/8000 [03:11<05:31, 14.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                 | 109/8000 [00:07<09:37, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3252/8000 [03:11<04:46, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                 | 112/8000 [00:08<09:18, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3260/8000 [03:11<03:45, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                 | 114/8000 [00:08<09:36, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3263/8000 [03:11<03:51, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                 | 116/8000 [00:08<10:15, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3269/8000 [03:11<03:42, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 120/8000 [00:08<10:17, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3274/8000 [03:12<04:00, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 124/8000 [00:09<08:40, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3278/8000 [03:12<03:35, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 126/8000 [00:09<09:08, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3282/8000 [03:12<04:47, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 128/8000 [00:09<10:09, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3284/8000 [03:12<04:56, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 130/8000 [00:09<12:42, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3286/8000 [03:13<04:54, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 134/8000 [00:10<12:10, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3288/8000 [03:13<05:03, 15.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 138/8000 [00:10<09:42, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3293/8000 [03:13<03:44, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 141/8000 [00:10<08:53, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3297/8000 [03:13<05:52, 13.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 145/8000 [00:10<07:30, 17.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3299/8000 [03:14<06:18, 12.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 149/8000 [00:10<06:45, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3301/8000 [03:14<06:06, 12.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 152/8000 [00:11<08:40, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3305/8000 [03:14<05:39, 13.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 156/8000 [00:11<07:08, 18.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3307/8000 [03:14<05:38, 13.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 161/8000 [00:11<05:51, 22.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3310/8000 [03:14<06:05, 12.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                 | 164/8000 [00:11<06:35, 19.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3314/8000 [03:15<05:29, 14.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 168/8000 [00:11<08:15, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3317/8000 [03:15<04:53, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 172/8000 [00:12<08:41, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3321/8000 [03:15<04:06, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 176/8000 [00:12<10:45, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3324/8000 [03:15<04:03, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 179/8000 [00:12<09:56, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3327/8000 [03:15<03:53, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 181/8000 [00:13<11:54, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3331/8000 [03:15<03:36, 21.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 184/8000 [00:13<10:25, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3338/8000 [03:15<02:39, 29.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 187/8000 [00:13<10:00, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3344/8000 [03:16<03:27, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 191/8000 [00:13<10:13, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3349/8000 [03:16<02:59, 25.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 193/8000 [00:14<12:51, 10.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3355/8000 [03:16<02:30, 30.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 195/8000 [00:14<14:40,  8.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3359/8000 [03:16<03:12, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 196/8000 [00:14<15:32,  8.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3362/8000 [03:16<03:17, 23.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                 | 199/8000 [00:14<13:17,  9.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3365/8000 [03:17<04:07, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 201/8000 [00:15<12:30, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3371/8000 [03:17<04:28, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 205/8000 [00:15<10:15, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3375/8000 [03:17<03:51, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 208/8000 [00:15<09:39, 13.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3383/8000 [03:18<03:47, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 212/8000 [00:15<08:08, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3388/8000 [03:18<03:19, 23.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 214/8000 [00:15<11:05, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3393/8000 [03:18<03:35, 21.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 217/8000 [00:16<09:43, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3396/8000 [03:18<04:10, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 219/8000 [00:16<09:48, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3399/8000 [03:19<04:17, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                 | 221/8000 [00:16<10:48, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3402/8000 [03:19<04:01, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 223/8000 [00:16<10:48, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3407/8000 [03:19<04:01, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 230/8000 [00:16<06:18, 20.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3411/8000 [03:19<03:27, 22.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 233/8000 [00:16<06:56, 18.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3416/8000 [03:19<03:34, 21.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 237/8000 [00:17<07:43, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3419/8000 [03:20<04:31, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 240/8000 [00:17<07:34, 17.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3422/8000 [03:20<05:02, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 244/8000 [00:17<08:46, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3425/8000 [03:20<04:45, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 247/8000 [00:18<09:27, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3429/8000 [03:20<04:01, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 252/8000 [00:18<08:04, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3432/8000 [03:20<04:44, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 255/8000 [00:18<08:17, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3436/8000 [03:21<03:58, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 258/8000 [00:18<08:23, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3440/8000 [03:21<03:23, 22.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 260/8000 [00:18<09:31, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3444/8000 [03:21<03:00, 25.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 264/8000 [00:19<09:44, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3448/8000 [03:21<04:20, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 266/8000 [00:19<09:59, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3451/8000 [03:21<05:14, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 268/8000 [00:19<09:54, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3453/8000 [03:22<05:16, 14.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 271/8000 [00:19<10:23, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3457/8000 [03:22<04:17, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                 | 275/8000 [00:20<08:45, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3461/8000 [03:22<04:28, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                 | 278/8000 [00:20<10:12, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3464/8000 [03:22<04:06, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 284/8000 [00:20<06:56, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3468/8000 [03:22<04:24, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 289/8000 [00:20<06:01, 21.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3474/8000 [03:22<03:14, 23.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 293/8000 [00:20<07:19, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3477/8000 [03:23<03:20, 22.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 298/8000 [00:21<07:56, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3481/8000 [03:23<04:45, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 303/8000 [00:21<08:41, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3484/8000 [03:23<04:27, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 305/8000 [00:22<12:29, 10.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3490/8000 [03:23<03:16, 22.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 309/8000 [00:22<10:16, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3493/8000 [03:23<03:15, 23.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 314/8000 [00:22<09:08, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3496/8000 [03:24<03:21, 22.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 316/8000 [00:22<11:04, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3499/8000 [03:24<03:28, 21.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 318/8000 [00:23<13:27,  9.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3503/8000 [03:24<03:05, 24.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 321/8000 [00:23<11:26, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3510/8000 [03:24<03:03, 24.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 325/8000 [00:23<09:15, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3514/8000 [03:25<04:20, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 329/8000 [00:23<08:21, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3522/8000 [03:25<03:45, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                 | 332/8000 [00:24<10:06, 12.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3525/8000 [03:25<03:40, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 334/8000 [00:24<10:04, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3528/8000 [03:25<03:50, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 338/8000 [00:24<07:57, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3531/8000 [03:25<03:43, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 342/8000 [00:24<07:00, 18.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3535/8000 [03:26<04:03, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 345/8000 [00:24<07:01, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3539/8000 [03:26<03:25, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 347/8000 [00:25<07:54, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3543/8000 [03:26<03:06, 23.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 351/8000 [00:25<06:46, 18.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3549/8000 [03:26<02:28, 29.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 357/8000 [00:25<06:29, 19.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3553/8000 [03:26<02:27, 30.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 362/8000 [00:25<05:30, 23.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3557/8000 [03:26<02:34, 28.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 366/8000 [00:25<06:02, 21.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3562/8000 [03:26<02:17, 32.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 369/8000 [00:26<08:01, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3569/8000 [03:27<02:26, 30.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 372/8000 [00:26<07:20, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3575/8000 [03:27<02:51, 25.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 375/8000 [00:26<08:51, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3581/8000 [03:27<03:30, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 380/8000 [00:26<06:54, 18.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3584/8000 [03:28<03:34, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                 | 384/8000 [00:26<06:28, 19.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3589/8000 [03:28<03:53, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 389/8000 [00:27<05:42, 22.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3592/8000 [03:28<04:33, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 394/8000 [00:27<06:36, 19.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3595/8000 [03:28<04:14, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 402/8000 [00:27<06:10, 20.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3597/8000 [03:28<04:19, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 406/8000 [00:28<06:05, 20.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3599/8000 [03:29<04:31, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 410/8000 [00:28<06:45, 18.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3605/8000 [03:29<03:53, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 412/8000 [00:28<07:16, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3609/8000 [03:29<03:24, 21.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 416/8000 [00:28<06:20, 19.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3614/8000 [03:29<04:17, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 420/8000 [00:28<05:52, 21.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3616/8000 [03:30<05:09, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 423/8000 [00:29<07:50, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3622/8000 [03:30<03:48, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 427/8000 [00:29<08:17, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3628/8000 [03:30<04:39, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 430/8000 [00:29<07:52, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3631/8000 [03:30<05:08, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 433/8000 [00:29<07:37, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3634/8000 [03:31<05:28, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 436/8000 [00:29<07:03, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3639/8000 [03:31<04:07, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                 | 438/8000 [00:29<07:55, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3643/8000 [03:31<04:29, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▉                 | 443/8000 [00:30<06:23, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3647/8000 [03:31<04:43, 15.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 447/8000 [00:30<07:48, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3651/8000 [03:32<04:04, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 450/8000 [00:30<11:12, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3657/8000 [03:32<03:49, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 454/8000 [00:31<08:55, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3660/8000 [03:32<03:41, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 460/8000 [00:31<08:04, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3664/8000 [03:32<03:17, 22.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 462/8000 [00:31<08:29, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3667/8000 [03:32<04:08, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 465/8000 [00:32<10:44, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3671/8000 [03:33<04:16, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 469/8000 [00:32<10:09, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3675/8000 [03:33<03:49, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 472/8000 [00:32<09:00, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3678/8000 [03:33<05:04, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 474/8000 [00:32<09:39, 12.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3680/8000 [03:33<05:06, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 477/8000 [00:32<08:59, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3684/8000 [03:34<04:12, 17.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 480/8000 [00:33<08:32, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3686/8000 [03:34<06:29, 11.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 485/8000 [00:33<08:32, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3689/8000 [03:34<05:28, 13.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 490/8000 [00:33<07:03, 17.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3693/8000 [03:34<04:16, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 492/8000 [00:33<08:15, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3696/8000 [03:34<04:03, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 497/8000 [00:33<07:03, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3700/8000 [03:34<03:27, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                 | 499/8000 [00:34<07:42, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3703/8000 [03:35<06:55, 10.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 502/8000 [00:34<07:24, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3706/8000 [03:35<06:34, 10.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 506/8000 [00:34<06:27, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3712/8000 [03:36<04:18, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 511/8000 [00:34<05:37, 22.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3715/8000 [03:36<03:55, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 514/8000 [00:34<06:01, 20.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3719/8000 [03:36<04:08, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                | 518/8000 [00:34<05:48, 21.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3722/8000 [03:36<03:54, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 521/8000 [00:35<06:07, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3725/8000 [03:36<04:28, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 524/8000 [00:35<11:04, 11.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3727/8000 [03:37<05:31, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 529/8000 [00:36<09:50, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3730/8000 [03:37<04:38, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 535/8000 [00:36<08:21, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3733/8000 [03:37<04:06, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 537/8000 [00:36<11:47, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3737/8000 [03:37<03:28, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 539/8000 [00:36<11:28, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3742/8000 [03:37<03:38, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 542/8000 [00:37<10:23, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3747/8000 [03:38<04:24, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 549/8000 [00:37<06:39, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3749/8000 [03:38<04:19, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                | 554/8000 [00:37<06:25, 19.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3752/8000 [03:38<04:43, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 558/8000 [00:37<07:41, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3754/8000 [03:38<04:34, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 561/8000 [00:38<07:34, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3756/8000 [03:38<05:36, 12.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 563/8000 [00:38<09:22, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3758/8000 [03:38<05:19, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 566/8000 [00:38<08:13, 15.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3761/8000 [03:39<04:37, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 569/8000 [00:38<10:22, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:39<04:38, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 572/8000 [00:39<09:50, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3767/8000 [03:39<03:36, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 578/8000 [00:39<07:13, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3770/8000 [03:39<05:13, 13.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 580/8000 [00:39<11:24, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3775/8000 [03:39<04:29, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 583/8000 [00:40<12:14, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3778/8000 [03:40<04:05, 17.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 587/8000 [00:40<09:54, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3782/8000 [03:40<03:35, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 591/8000 [00:40<08:43, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3785/8000 [03:40<04:19, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                | 595/8000 [00:40<07:34, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3788/8000 [03:40<03:57, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎                | 601/8000 [00:40<05:54, 20.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3791/8000 [03:40<04:26, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎                | 607/8000 [00:41<06:37, 18.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3793/8000 [03:41<06:20, 11.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▎                | 611/8000 [00:41<06:24, 19.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3797/8000 [03:41<05:45, 12.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 614/8000 [00:41<09:40, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3801/8000 [03:41<04:29, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 616/8000 [00:42<10:17, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3803/8000 [03:41<04:29, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 622/8000 [00:42<07:10, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3808/8000 [03:42<04:14, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 627/8000 [00:42<06:10, 19.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3810/8000 [03:42<04:13, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 634/8000 [00:42<06:04, 20.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3816/8000 [03:42<03:46, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 637/8000 [00:43<07:57, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3818/8000 [03:42<03:53, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 642/8000 [00:43<06:48, 18.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3821/8000 [03:42<03:43, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 645/8000 [00:43<07:56, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3824/8000 [03:42<03:41, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 647/8000 [00:43<08:16, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3827/8000 [03:43<03:49, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 652/8000 [00:44<06:41, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3829/8000 [03:43<05:00, 13.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 656/8000 [00:44<08:40, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3832/8000 [03:43<04:29, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 664/8000 [00:44<07:07, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3836/8000 [03:43<04:22, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                | 666/8000 [00:44<07:44, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3840/8000 [03:43<03:41, 18.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                | 671/8000 [00:45<08:28, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3846/8000 [03:44<03:33, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                | 676/8000 [00:45<07:04, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3849/8000 [03:44<03:25, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                | 678/8000 [00:45<07:36, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3852/8000 [03:44<03:20, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 681/8000 [00:45<07:39, 15.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3856/8000 [03:44<03:03, 22.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 683/8000 [00:46<08:07, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3859/8000 [03:44<03:48, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 687/8000 [00:46<08:40, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3862/8000 [03:45<03:39, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 690/8000 [00:46<07:47, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3865/8000 [03:45<03:39, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 693/8000 [00:46<07:34, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3868/8000 [03:45<03:23, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 696/8000 [00:46<07:05, 17.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3871/8000 [03:45<03:16, 21.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 700/8000 [00:47<10:06, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3876/8000 [03:45<03:27, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 703/8000 [00:47<09:17, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▏        | 3882/8000 [03:45<03:23, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 710/8000 [00:47<06:06, 19.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3886/8000 [03:46<03:04, 22.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 714/8000 [00:47<05:35, 21.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3890/8000 [03:46<02:49, 24.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 719/8000 [00:48<05:02, 24.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3894/8000 [03:46<02:47, 24.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 724/8000 [00:48<07:08, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3899/8000 [03:46<03:54, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 728/8000 [00:48<06:17, 19.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3902/8000 [03:47<04:24, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 732/8000 [00:48<07:11, 16.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3906/8000 [03:47<03:47, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 736/8000 [00:49<06:35, 18.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3909/8000 [03:47<04:22, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 740/8000 [00:49<06:05, 19.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3912/8000 [03:47<04:05, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 743/8000 [00:49<07:35, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3914/8000 [03:47<04:12, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 746/8000 [00:49<07:23, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3917/8000 [03:47<03:54, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 752/8000 [00:49<05:40, 21.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3922/8000 [03:48<03:45, 18.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 757/8000 [00:50<08:45, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3924/8000 [03:48<03:53, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                | 759/8000 [00:50<08:39, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3929/8000 [03:48<04:02, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 761/8000 [00:50<08:26, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3933/8000 [03:48<04:06, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 763/8000 [00:50<08:51, 13.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3935/8000 [03:49<04:16, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 767/8000 [00:51<08:01, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3937/8000 [03:49<04:26, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 771/8000 [00:51<07:30, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3941/8000 [03:49<03:32, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 774/8000 [00:51<09:06, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3945/8000 [03:49<03:09, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▋                | 777/8000 [00:52<10:57, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3950/8000 [03:49<02:43, 24.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 781/8000 [00:52<09:18, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3953/8000 [03:49<03:22, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 784/8000 [00:52<08:30, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3959/8000 [03:50<03:24, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 789/8000 [00:52<08:28, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3962/8000 [03:50<04:01, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 791/8000 [00:53<08:52, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3968/8000 [03:50<03:07, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 793/8000 [00:53<09:24, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3971/8000 [03:50<03:04, 21.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 797/8000 [00:53<10:00, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3974/8000 [03:50<03:50, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 800/8000 [00:53<08:56, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3976/8000 [03:51<04:01, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 807/8000 [00:54<07:06, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3981/8000 [03:51<03:53, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 809/8000 [00:54<08:00, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3983/8000 [03:51<04:01, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 817/8000 [00:54<06:26, 18.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3988/8000 [03:51<03:24, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 821/8000 [00:54<05:50, 20.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3992/8000 [03:52<03:45, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 825/8000 [00:54<06:37, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3995/8000 [03:52<03:32, 18.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                | 831/8000 [00:55<05:55, 20.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3998/8000 [03:52<03:27, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                | 834/8000 [00:55<07:06, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4004/8000 [03:52<03:23, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                | 837/8000 [00:55<07:09, 16.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4007/8000 [03:52<03:28, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                | 839/8000 [00:55<07:37, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4011/8000 [03:52<03:08, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 846/8000 [00:56<06:46, 17.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4015/8000 [03:53<02:55, 22.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 848/8000 [00:56<07:14, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4021/8000 [03:53<02:54, 22.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 850/8000 [00:56<07:22, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4027/8000 [03:53<02:27, 27.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 852/8000 [00:56<08:16, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4031/8000 [03:53<02:26, 27.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 854/8000 [00:56<08:42, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4034/8000 [03:53<03:06, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 857/8000 [00:57<08:04, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4037/8000 [03:53<03:02, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 862/8000 [00:57<08:15, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4043/8000 [03:54<02:25, 27.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 865/8000 [00:57<10:04, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4049/8000 [03:54<02:43, 24.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 871/8000 [00:58<10:29, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4052/8000 [03:54<03:15, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 873/8000 [00:58<10:06, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4056/8000 [03:54<02:55, 22.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 875/8000 [00:58<10:03, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4059/8000 [03:54<02:57, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 878/8000 [00:58<08:42, 13.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4062/8000 [03:55<03:44, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 880/8000 [00:58<08:54, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4069/8000 [03:55<03:51, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 882/8000 [00:59<11:13, 10.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4071/8000 [03:55<03:56, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 884/8000 [00:59<10:30, 11.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4073/8000 [03:55<04:03, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|█▉                | 888/8000 [00:59<07:55, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4075/8000 [03:56<04:12, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 893/8000 [00:59<06:03, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4077/8000 [03:56<05:18, 12.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 899/8000 [00:59<04:37, 25.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4081/8000 [03:56<04:54, 13.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 902/8000 [00:59<05:00, 23.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4084/8000 [03:56<04:28, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 905/8000 [01:00<05:38, 20.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4090/8000 [03:57<03:42, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 908/8000 [01:00<05:51, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4092/8000 [03:57<03:52, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 911/8000 [01:00<05:51, 20.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4099/8000 [03:57<02:37, 24.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 914/8000 [01:01<10:51, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4107/8000 [03:57<02:23, 27.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                | 916/8000 [01:01<11:06, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4114/8000 [03:57<02:34, 25.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 921/8000 [01:01<09:43, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4122/8000 [03:58<02:25, 26.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 926/8000 [01:01<07:34, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4126/8000 [03:58<02:23, 26.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 928/8000 [01:01<07:57, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4130/8000 [03:58<02:53, 22.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 930/8000 [01:02<10:48, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4134/8000 [03:58<02:38, 24.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 932/8000 [01:02<12:30,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4141/8000 [03:58<02:37, 24.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 934/8000 [01:02<11:53,  9.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4145/8000 [03:59<03:12, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 936/8000 [01:03<11:39, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4148/8000 [03:59<03:21, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 941/8000 [01:03<10:02, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4153/8000 [03:59<03:53, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██                | 944/8000 [01:03<08:54, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4155/8000 [03:59<03:58, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 948/8000 [01:03<09:33, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4158/8000 [04:00<05:26, 11.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 951/8000 [01:04<10:43, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4160/8000 [04:00<05:07, 12.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 954/8000 [01:04<09:10, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4162/8000 [04:00<05:01, 12.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 957/8000 [01:04<08:24, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4164/8000 [04:00<05:46, 11.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 961/8000 [01:04<06:57, 16.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4166/8000 [04:01<05:28, 11.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 964/8000 [01:05<09:03, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4172/8000 [04:01<03:20, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 967/8000 [01:05<08:47, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4177/8000 [04:01<03:35, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 969/8000 [01:05<09:27, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4181/8000 [04:01<04:00, 15.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 972/8000 [01:05<10:52, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4184/8000 [04:01<03:37, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 976/8000 [01:06<08:41, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4189/8000 [04:02<03:36, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 978/8000 [01:06<11:44,  9.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4193/8000 [04:02<03:09, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 982/8000 [01:06<09:30, 12.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4198/8000 [04:02<03:08, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 985/8000 [01:06<08:36, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4201/8000 [04:02<03:51, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 987/8000 [01:06<08:57, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4203/8000 [04:03<05:44, 11.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 990/8000 [01:07<08:17, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4205/8000 [04:03<05:26, 11.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 994/8000 [01:07<07:24, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4207/8000 [04:03<06:02, 10.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏               | 997/8000 [01:07<11:33, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4210/8000 [04:03<05:12, 12.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1001/8000 [01:08<11:41,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4214/8000 [04:04<04:10, 15.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1003/8000 [01:08<11:22, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4216/8000 [04:04<04:19, 14.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1007/8000 [01:08<09:05, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4221/8000 [04:04<04:01, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1009/8000 [01:08<09:14, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4227/8000 [04:04<02:57, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1014/8000 [01:08<06:52, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4230/8000 [04:04<02:55, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1017/8000 [01:09<07:01, 16.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4233/8000 [04:05<03:57, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1019/8000 [01:09<09:17, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4238/8000 [04:05<03:03, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1021/8000 [01:09<11:18, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4241/8000 [04:05<02:56, 21.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1024/8000 [01:09<09:27, 12.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4245/8000 [04:05<02:59, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1029/8000 [01:10<06:51, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4248/8000 [04:05<03:14, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1034/8000 [01:10<05:28, 21.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4251/8000 [04:05<03:08, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1037/8000 [01:10<06:03, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4255/8000 [04:06<02:51, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1040/8000 [01:10<07:15, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4258/8000 [04:06<02:52, 21.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1047/8000 [01:10<05:08, 22.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4262/8000 [04:06<04:11, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1052/8000 [01:11<04:49, 23.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4264/8000 [04:06<04:06, 15.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1060/8000 [01:11<05:05, 22.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4267/8000 [04:07<04:41, 13.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1065/8000 [01:11<05:08, 22.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4271/8000 [04:07<04:27, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1073/8000 [01:11<04:49, 23.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4276/8000 [04:07<04:01, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎              | 1077/8000 [01:12<04:56, 23.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4278/8000 [04:07<04:00, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1081/8000 [01:12<04:52, 23.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4282/8000 [04:08<03:58, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1085/8000 [01:12<06:15, 18.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4288/8000 [04:08<02:50, 21.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1089/8000 [01:12<06:53, 16.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4294/8000 [04:08<02:46, 22.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1093/8000 [01:13<07:25, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4299/8000 [04:08<03:23, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1097/8000 [01:13<06:50, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4304/8000 [04:08<02:48, 21.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1100/8000 [01:13<06:46, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4308/8000 [04:09<03:09, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1102/8000 [01:13<07:21, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4312/8000 [04:09<02:57, 20.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1104/8000 [01:13<07:54, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4315/8000 [04:09<02:56, 20.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1106/8000 [01:14<13:13,  8.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4320/8000 [04:09<03:42, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1110/8000 [01:14<10:01, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4326/8000 [04:10<03:16, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1113/8000 [01:15<11:23, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4330/8000 [04:10<04:02, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▎              | 1116/8000 [01:15<09:54, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4334/8000 [04:10<03:31, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1121/8000 [01:15<09:48, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4338/8000 [04:10<03:09, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1124/8000 [01:15<09:17, 12.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4345/8000 [04:11<02:52, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1126/8000 [01:16<09:29, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4348/8000 [04:11<02:55, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1130/8000 [01:16<09:41, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4355/8000 [04:11<02:44, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1136/8000 [01:16<06:50, 16.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4358/8000 [04:11<02:41, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1138/8000 [01:16<07:20, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4364/8000 [04:11<02:46, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1143/8000 [01:16<05:54, 19.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4368/8000 [04:12<03:10, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1148/8000 [01:17<07:56, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4370/8000 [04:12<03:20, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1150/8000 [01:18<12:13,  9.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4373/8000 [04:12<03:16, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1156/8000 [01:18<08:23, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4375/8000 [04:12<03:26, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍              | 1160/8000 [01:18<07:27, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4378/8000 [04:12<03:10, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1164/8000 [01:18<08:23, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4381/8000 [04:12<03:10, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1168/8000 [01:19<08:51, 12.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4383/8000 [04:13<03:21, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▍              | 1172/8000 [01:19<09:25, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4386/8000 [04:13<03:13, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1177/8000 [01:19<07:37, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4391/8000 [04:13<03:10, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1180/8000 [01:20<10:28, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4396/8000 [04:13<02:34, 23.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1182/8000 [01:20<10:21, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4399/8000 [04:13<02:34, 23.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1189/8000 [01:20<06:49, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4406/8000 [04:14<02:30, 23.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1194/8000 [01:20<06:08, 18.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4410/8000 [04:14<02:22, 25.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1197/8000 [01:21<07:23, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4413/8000 [04:14<02:25, 24.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1199/8000 [01:21<09:34, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4417/8000 [04:14<02:52, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1201/8000 [01:21<09:50, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4422/8000 [04:14<02:23, 24.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1203/8000 [01:21<12:07,  9.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4425/8000 [04:14<02:37, 22.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1205/8000 [01:22<11:22,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4429/8000 [04:15<03:11, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1209/8000 [01:22<10:22, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4432/8000 [04:15<03:09, 18.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1211/8000 [01:22<10:11, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4437/8000 [04:15<03:13, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1214/8000 [01:22<11:12, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4442/8000 [04:15<02:48, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1217/8000 [01:23<09:30, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4445/8000 [04:15<02:44, 21.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1220/8000 [01:23<08:44, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4448/8000 [04:16<02:44, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1222/8000 [01:23<08:42, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4452/8000 [04:16<02:36, 22.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1224/8000 [01:23<08:56, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4455/8000 [04:16<02:36, 22.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1226/8000 [01:23<09:14, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4458/8000 [04:16<02:31, 23.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1230/8000 [01:24<10:11, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [04:16<03:12, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▌              | 1233/8000 [01:24<09:11, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4465/8000 [04:17<04:00, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋              | 1236/8000 [01:24<10:21, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4468/8000 [04:17<04:26, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋              | 1238/8000 [01:25<13:52,  8.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4471/8000 [04:17<04:43, 12.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1240/8000 [01:25<12:50,  8.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4473/8000 [04:17<05:31, 10.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1242/8000 [01:25<11:56,  9.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4477/8000 [04:18<04:07, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1245/8000 [01:25<12:08,  9.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4479/8000 [04:18<04:07, 14.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1247/8000 [01:26<12:29,  9.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4481/8000 [04:18<04:04, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1249/8000 [01:26<11:54,  9.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4486/8000 [04:18<03:43, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1251/8000 [01:26<10:55, 10.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4491/8000 [04:18<02:51, 20.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1257/8000 [01:26<09:36, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4498/8000 [04:19<03:16, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1263/8000 [01:27<06:49, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4504/8000 [04:19<03:02, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1265/8000 [01:27<07:15, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4507/8000 [04:19<03:27, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1267/8000 [01:27<08:05, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4511/8000 [04:19<03:08, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1270/8000 [01:27<08:05, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4514/8000 [04:20<03:01, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1273/8000 [01:27<07:04, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4517/8000 [04:20<03:06, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1275/8000 [01:27<07:23, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4520/8000 [04:20<02:59, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1279/8000 [01:28<07:36, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4523/8000 [04:20<03:49, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1283/8000 [01:28<06:38, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4525/8000 [04:20<03:51, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1285/8000 [01:28<06:58, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4527/8000 [04:20<04:01, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1287/8000 [01:28<07:26, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4531/8000 [04:21<03:21, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▋              | 1291/8000 [01:28<06:08, 18.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4536/8000 [04:21<03:16, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1296/8000 [01:29<07:34, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4540/8000 [04:21<03:32, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1301/8000 [01:29<06:02, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4545/8000 [04:21<02:46, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1308/8000 [01:29<05:49, 19.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4548/8000 [04:21<02:44, 20.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1313/8000 [01:30<06:26, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4551/8000 [04:22<03:31, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1317/8000 [01:30<07:16, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4556/8000 [04:22<02:52, 19.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊              | 1319/8000 [01:30<07:18, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4560/8000 [04:22<03:13, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1323/8000 [01:30<06:51, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4570/8000 [04:23<02:26, 23.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1325/8000 [01:31<08:36, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4574/8000 [04:23<02:46, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1327/8000 [01:31<09:07, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4580/8000 [04:23<03:06, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1330/8000 [01:31<08:34, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4585/8000 [04:23<03:08, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1333/8000 [01:31<07:39, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4589/8000 [04:24<02:57, 19.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1340/8000 [01:31<06:10, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4593/8000 [04:24<03:16, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1344/8000 [01:32<05:27, 20.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4597/8000 [04:24<03:00, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1347/8000 [01:32<07:07, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4600/8000 [04:24<03:30, 16.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1349/8000 [01:32<09:10, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4603/8000 [04:25<03:13, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▊              | 1351/8000 [01:32<09:02, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4607/8000 [04:25<03:28, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1356/8000 [01:33<07:08, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4609/8000 [04:25<03:37, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1359/8000 [01:33<07:08, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4613/8000 [04:25<03:38, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1362/8000 [01:33<08:07, 13.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4616/8000 [04:25<03:17, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1364/8000 [01:33<08:58, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4618/8000 [04:26<05:00, 11.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1366/8000 [01:34<10:07, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4620/8000 [04:26<04:42, 11.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1371/8000 [01:34<11:08,  9.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4623/8000 [04:26<04:58, 11.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1376/8000 [01:34<09:08, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4625/8000 [04:26<04:44, 11.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1378/8000 [01:35<08:53, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4628/8000 [04:26<04:08, 13.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1384/8000 [01:35<08:01, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4633/8000 [04:27<02:55, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1387/8000 [01:35<07:42, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4636/8000 [04:27<02:54, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1390/8000 [01:35<07:23, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4641/8000 [04:27<04:01, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1393/8000 [01:35<07:04, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4644/8000 [04:28<04:26, 12.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉              | 1398/8000 [01:36<05:51, 18.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4646/8000 [04:28<04:19, 12.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1403/8000 [01:36<05:45, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4650/8000 [04:28<04:07, 13.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1406/8000 [01:36<05:52, 18.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4654/8000 [04:28<03:26, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1411/8000 [01:36<06:31, 16.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4658/8000 [04:28<03:01, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1413/8000 [01:37<07:06, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4663/8000 [04:28<02:32, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1415/8000 [01:37<08:11, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4667/8000 [04:29<02:58, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1419/8000 [01:37<07:18, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4671/8000 [04:29<02:36, 21.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1421/8000 [01:37<07:39, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4674/8000 [04:29<03:10, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1424/8000 [01:37<07:05, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4679/8000 [04:29<02:36, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1428/8000 [01:38<06:04, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4682/8000 [04:30<03:17, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1430/8000 [01:38<06:53, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4686/8000 [04:30<03:55, 14.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1432/8000 [01:38<10:22, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4688/8000 [04:30<03:53, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1437/8000 [01:38<07:29, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4693/8000 [04:30<03:07, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1443/8000 [01:38<05:23, 20.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4697/8000 [04:31<03:26, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1447/8000 [01:39<07:23, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4701/8000 [04:31<02:58, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1451/8000 [01:39<07:45, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4708/8000 [04:31<02:15, 24.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1455/8000 [01:39<06:45, 16.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4713/8000 [04:31<02:31, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1458/8000 [01:40<06:24, 17.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4716/8000 [04:31<02:59, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1463/8000 [01:40<07:22, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4719/8000 [04:32<02:54, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1466/8000 [01:40<07:17, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4722/8000 [04:32<02:50, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███              | 1470/8000 [01:40<06:39, 16.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4727/8000 [04:32<02:22, 22.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏             | 1474/8000 [01:40<06:05, 17.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4732/8000 [04:32<02:45, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏             | 1479/8000 [01:41<05:19, 20.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4739/8000 [04:32<02:34, 21.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1483/8000 [01:41<05:17, 20.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4742/8000 [04:33<03:05, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1487/8000 [01:41<05:08, 21.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4744/8000 [04:33<03:15, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1492/8000 [01:41<04:48, 22.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4747/8000 [04:33<03:47, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1496/8000 [01:42<06:24, 16.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4750/8000 [04:33<03:32, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1498/8000 [01:42<06:41, 16.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4754/8000 [04:34<03:04, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1504/8000 [01:42<05:10, 20.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4760/8000 [04:34<02:25, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1507/8000 [01:42<06:36, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4766/8000 [04:34<02:28, 21.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1511/8000 [01:42<06:06, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4770/8000 [04:34<02:54, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1515/8000 [01:43<05:53, 18.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4772/8000 [04:34<03:00, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1518/8000 [01:43<06:05, 17.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4774/8000 [04:35<03:31, 15.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1520/8000 [01:43<06:53, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4776/8000 [04:35<03:36, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1523/8000 [01:43<06:35, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4780/8000 [04:35<02:56, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1525/8000 [01:44<09:19, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4785/8000 [04:35<02:14, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▏             | 1527/8000 [01:44<09:06, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4788/8000 [04:35<02:23, 22.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1530/8000 [01:44<10:26, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4792/8000 [04:35<02:09, 24.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1534/8000 [01:44<07:48, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4796/8000 [04:35<02:02, 26.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1537/8000 [01:44<07:15, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4799/8000 [04:36<02:05, 25.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1539/8000 [01:45<09:18, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4802/8000 [04:36<03:05, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1542/8000 [01:45<08:44, 12.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4806/8000 [04:36<04:07, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1545/8000 [01:45<08:21, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4809/8000 [04:37<04:17, 12.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1548/8000 [01:45<07:49, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4812/8000 [04:37<05:14, 10.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1551/8000 [01:45<06:49, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4817/8000 [04:37<03:45, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎             | 1556/8000 [01:46<05:31, 19.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4819/8000 [04:38<04:28, 11.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1561/8000 [01:46<06:35, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4821/8000 [04:38<04:24, 12.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1565/8000 [01:46<05:57, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4825/8000 [04:38<04:10, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1568/8000 [01:46<05:41, 18.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4828/8000 [04:38<04:30, 11.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1571/8000 [01:46<05:49, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4830/8000 [04:38<04:16, 12.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1576/8000 [01:47<06:16, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4834/8000 [04:39<03:16, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1579/8000 [01:47<06:14, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4839/8000 [04:39<02:40, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1582/8000 [01:47<07:38, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4844/8000 [04:39<02:47, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1586/8000 [01:47<06:28, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4848/8000 [04:39<02:29, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▎             | 1588/8000 [01:48<06:54, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4852/8000 [04:39<02:57, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1590/8000 [01:48<07:06, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4855/8000 [04:40<02:44, 19.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1594/8000 [01:48<06:42, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4859/8000 [04:40<03:16, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1596/8000 [01:48<07:22, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4862/8000 [04:40<03:11, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1598/8000 [01:49<12:35,  8.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4866/8000 [04:40<02:45, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1601/8000 [01:49<14:45,  7.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4870/8000 [04:40<03:04, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1606/8000 [01:49<09:47, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4872/8000 [04:41<04:31, 11.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1609/8000 [01:50<09:18, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4874/8000 [04:41<04:22, 11.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1614/8000 [01:50<07:13, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4876/8000 [04:41<04:52, 10.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1616/8000 [01:50<07:32, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4881/8000 [04:41<03:23, 15.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1620/8000 [01:50<06:26, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4885/8000 [04:42<02:44, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1622/8000 [01:50<06:45, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4890/8000 [04:42<02:44, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1627/8000 [01:51<05:48, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4893/8000 [04:42<02:37, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1630/8000 [01:51<06:53, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4896/8000 [04:42<02:39, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1633/8000 [01:51<08:41, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4902/8000 [04:42<02:30, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▍             | 1639/8000 [01:52<07:46, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4907/8000 [04:43<02:08, 24.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍             | 1641/8000 [01:52<08:15, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4911/8000 [04:43<02:40, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▍             | 1645/8000 [01:52<08:27, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4914/8000 [04:43<02:35, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1649/8000 [01:52<08:21, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4917/8000 [04:43<02:31, 20.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1655/8000 [01:53<06:08, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4921/8000 [04:43<02:15, 22.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1657/8000 [01:53<06:47, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4924/8000 [04:43<02:13, 23.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1661/8000 [01:53<07:29, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4927/8000 [04:44<02:15, 22.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1663/8000 [01:53<07:46, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4933/8000 [04:44<02:24, 21.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1667/8000 [01:53<07:05, 14.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4942/8000 [04:44<01:59, 25.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1671/8000 [01:54<06:08, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4945/8000 [04:44<02:08, 23.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1675/8000 [01:54<05:49, 18.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4948/8000 [04:45<02:45, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1679/8000 [01:54<05:17, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4951/8000 [04:45<02:39, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1685/8000 [01:54<05:12, 20.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4955/8000 [04:45<02:54, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1691/8000 [01:55<04:38, 22.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4958/8000 [04:45<03:23, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1696/8000 [01:55<04:29, 23.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4960/8000 [04:45<03:25, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▌             | 1701/8000 [01:55<04:20, 24.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4964/8000 [04:46<02:53, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1706/8000 [01:55<04:52, 21.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4969/8000 [04:46<02:27, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1711/8000 [01:56<07:04, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4973/8000 [04:46<02:08, 23.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1713/8000 [01:56<07:17, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4979/8000 [04:46<02:10, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋             | 1717/8000 [01:56<06:28, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4982/8000 [04:47<03:13, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1720/8000 [01:56<06:13, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4990/8000 [04:47<02:29, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1722/8000 [01:56<06:35, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4994/8000 [04:47<02:45, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1728/8000 [01:57<04:57, 21.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4997/8000 [04:47<02:40, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1731/8000 [01:57<05:06, 20.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▋      | 5000/8000 [04:47<02:34, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1734/8000 [01:57<05:18, 19.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5004/8000 [04:48<02:45, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1739/8000 [01:57<05:57, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5006/8000 [04:48<03:27, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1742/8000 [01:57<05:46, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5010/8000 [04:48<02:48, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1745/8000 [01:58<05:37, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5013/8000 [04:48<03:07, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1748/8000 [01:58<05:47, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5016/8000 [04:48<02:57, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1751/8000 [01:58<06:08, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5018/8000 [04:48<02:58, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1755/8000 [01:58<07:02, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5020/8000 [04:49<03:01, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1759/8000 [01:58<06:15, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5023/8000 [04:49<02:51, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1761/8000 [01:59<08:38, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5026/8000 [04:49<03:35, 13.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1764/8000 [01:59<07:45, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5031/8000 [04:49<02:32, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1768/8000 [01:59<06:24, 16.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5034/8000 [04:49<02:25, 20.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1772/8000 [01:59<06:01, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5037/8000 [04:50<03:01, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1774/8000 [01:59<06:30, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5039/8000 [04:50<03:02, 16.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1778/8000 [02:00<07:47, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5041/8000 [04:50<03:03, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1782/8000 [02:00<07:29, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5046/8000 [04:50<02:19, 21.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1784/8000 [02:00<07:28, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5049/8000 [04:50<02:17, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1786/8000 [02:00<07:26, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5052/8000 [04:50<02:15, 21.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1789/8000 [02:01<06:55, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5056/8000 [04:51<02:26, 20.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊             | 1792/8000 [02:01<08:37, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5063/8000 [04:51<02:06, 23.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1801/8000 [02:01<04:50, 21.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5066/8000 [04:51<03:17, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1805/8000 [02:01<05:43, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5068/8000 [04:51<03:22, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1809/8000 [02:02<05:35, 18.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5071/8000 [04:52<03:07, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1813/8000 [02:02<06:44, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5077/8000 [04:52<02:35, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1815/8000 [02:02<07:10, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5082/8000 [04:52<02:07, 22.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1817/8000 [02:02<08:03, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5087/8000 [04:52<02:47, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1819/8000 [02:03<09:53, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5092/8000 [04:52<02:21, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▊             | 1821/8000 [02:03<09:23, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5095/8000 [04:53<02:46, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1824/8000 [02:03<12:03,  8.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5101/8000 [04:53<02:37, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1827/8000 [02:04<09:41, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5104/8000 [04:53<02:27, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1832/8000 [02:04<06:57, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5108/8000 [04:53<02:38, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1834/8000 [02:04<07:17, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5111/8000 [04:54<02:33, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1836/8000 [02:04<09:04, 11.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5115/8000 [04:54<02:46, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1838/8000 [02:04<08:55, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5121/8000 [04:54<02:39, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1840/8000 [02:05<08:52, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5128/8000 [04:54<01:59, 23.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1842/8000 [02:05<11:13,  9.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5133/8000 [04:54<01:46, 26.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1848/8000 [02:05<06:49, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5140/8000 [04:55<01:46, 26.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1852/8000 [02:05<07:28, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5143/8000 [04:55<01:51, 25.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1861/8000 [02:06<06:55, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5147/8000 [04:55<02:16, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1868/8000 [02:06<05:17, 19.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5150/8000 [04:56<03:09, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1872/8000 [02:06<05:58, 17.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5153/8000 [04:56<02:52, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1875/8000 [02:07<06:35, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5158/8000 [04:56<02:19, 20.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███▉             | 1877/8000 [02:07<07:06, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5161/8000 [04:56<02:15, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███▉             | 1882/8000 [02:07<05:39, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5166/8000 [04:56<03:21, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1887/8000 [02:08<07:35, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5168/8000 [04:57<03:24, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1893/8000 [02:08<06:07, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5173/8000 [04:57<02:33, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1899/8000 [02:08<05:43, 17.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5176/8000 [04:57<02:57, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1903/8000 [02:08<06:16, 16.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5181/8000 [04:57<02:46, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1909/8000 [02:09<06:00, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5185/8000 [04:57<02:22, 19.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1911/8000 [02:09<06:20, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5190/8000 [04:58<02:23, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1914/8000 [02:09<07:50, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5195/8000 [04:58<02:24, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1917/8000 [02:09<07:19, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5198/8000 [04:58<02:22, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1923/8000 [02:10<06:51, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5202/8000 [04:58<02:32, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1926/8000 [02:10<07:21, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5204/8000 [04:58<02:39, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1928/8000 [02:10<08:52, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5207/8000 [04:59<02:29, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1933/8000 [02:11<06:48, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5212/8000 [04:59<02:00, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1935/8000 [02:11<08:49, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5217/8000 [04:59<02:15, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████             | 1939/8000 [02:11<07:12, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5224/8000 [04:59<01:43, 26.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1943/8000 [02:11<06:05, 16.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5228/8000 [04:59<01:41, 27.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1946/8000 [02:11<06:03, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5231/8000 [04:59<01:55, 23.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1950/8000 [02:12<08:20, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5236/8000 [05:00<01:44, 26.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1953/8000 [02:12<08:57, 11.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5240/8000 [05:00<02:15, 20.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1955/8000 [02:12<09:09, 11.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5244/8000 [05:00<02:01, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▏            | 1957/8000 [02:13<09:00, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5247/8000 [05:00<02:00, 22.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1961/8000 [02:13<07:15, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5251/8000 [05:00<02:15, 20.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1965/8000 [02:13<07:46, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5256/8000 [05:01<02:20, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1967/8000 [02:13<07:53, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5261/8000 [05:01<02:03, 22.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1970/8000 [02:14<07:14, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5266/8000 [05:01<02:17, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1972/8000 [02:14<07:48, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5269/8000 [05:01<02:13, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1975/8000 [02:14<09:37, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5274/8000 [05:02<02:13, 20.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1980/8000 [02:14<06:58, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5279/8000 [05:02<02:27, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1985/8000 [02:15<07:05, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5283/8000 [05:02<02:30, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1987/8000 [02:15<09:09, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5290/8000 [05:02<02:08, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1990/8000 [02:15<09:53, 10.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5295/8000 [05:03<02:20, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1993/8000 [02:16<08:35, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5299/8000 [05:03<02:08, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1995/8000 [02:16<11:39,  8.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5304/8000 [05:03<02:20, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▏            | 1997/8000 [02:16<11:07,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5308/8000 [05:03<02:06, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2001/8000 [02:17<10:45,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5312/8000 [05:03<01:56, 23.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2005/8000 [02:17<08:32, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5315/8000 [05:04<02:00, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2007/8000 [02:17<10:10,  9.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5318/8000 [05:04<02:27, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2011/8000 [02:18<12:30,  7.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5322/8000 [05:04<02:11, 20.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2013/8000 [02:18<12:27,  8.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5327/8000 [05:04<02:13, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2016/8000 [02:18<11:16,  8.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5330/8000 [05:04<02:11, 20.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2021/8000 [02:19<08:20, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5333/8000 [05:05<03:09, 14.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2024/8000 [02:19<11:00,  9.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5338/8000 [05:05<02:30, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2027/8000 [02:19<10:36,  9.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5342/8000 [05:05<02:09, 20.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2029/8000 [02:20<11:09,  8.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5348/8000 [05:05<02:24, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2034/8000 [02:20<08:09, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5351/8000 [05:06<03:02, 14.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▎            | 2036/8000 [02:20<10:19,  9.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5356/8000 [05:06<02:52, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2040/8000 [02:21<09:39, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5358/8000 [05:06<03:20, 13.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2043/8000 [02:21<09:16, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5362/8000 [05:06<02:43, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2045/8000 [02:21<09:21, 10.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5366/8000 [05:07<02:22, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2049/8000 [02:21<07:31, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5370/8000 [05:07<02:07, 20.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2052/8000 [02:22<11:09,  8.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5374/8000 [05:07<02:04, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2054/8000 [02:22<10:31,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5377/8000 [05:07<02:01, 21.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▎            | 2057/8000 [02:22<08:59, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5382/8000 [05:07<02:17, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2062/8000 [02:22<06:57, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5385/8000 [05:08<02:08, 20.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2066/8000 [02:23<06:12, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5391/8000 [05:08<02:00, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2069/8000 [02:23<07:24, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5395/8000 [05:08<01:53, 22.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2071/8000 [02:23<07:57, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5398/8000 [05:08<02:25, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2075/8000 [02:23<08:33, 11.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5400/8000 [05:08<02:55, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2077/8000 [02:24<09:01, 10.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5403/8000 [05:09<02:43, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2079/8000 [02:24<08:37, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5405/8000 [05:09<02:40, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2082/8000 [02:24<09:10, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5411/8000 [05:09<02:57, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2085/8000 [02:24<08:09, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5414/8000 [05:09<03:09, 13.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2088/8000 [02:25<08:12, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5417/8000 [05:10<03:30, 12.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2092/8000 [02:25<06:32, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5419/8000 [05:10<03:17, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2096/8000 [02:25<08:21, 11.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5424/8000 [05:10<02:24, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2098/8000 [02:25<08:58, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5427/8000 [05:10<02:16, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2103/8000 [02:26<08:17, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5431/8000 [05:10<02:34, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2108/8000 [02:26<07:58, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5433/8000 [05:11<02:41, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2110/8000 [02:27<09:19, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5438/8000 [05:11<02:05, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2112/8000 [02:27<09:03, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5443/8000 [05:11<02:10, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2117/8000 [02:27<07:50, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5448/8000 [05:11<01:48, 23.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▌            | 2120/8000 [02:27<09:48, 10.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5452/8000 [05:11<01:40, 25.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2124/8000 [02:28<07:49, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5456/8000 [05:12<02:06, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2127/8000 [02:28<07:18, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5459/8000 [05:12<02:27, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2133/8000 [02:28<05:23, 18.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5464/8000 [05:12<02:24, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2140/8000 [02:29<06:16, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5467/8000 [05:12<02:49, 14.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2142/8000 [02:29<07:50, 12.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5469/8000 [05:13<03:29, 12.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2145/8000 [02:29<07:16, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5472/8000 [05:13<03:31, 11.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2151/8000 [02:29<05:27, 17.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5475/8000 [05:13<03:05, 13.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2155/8000 [02:29<04:54, 19.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5477/8000 [05:13<03:10, 13.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2158/8000 [02:29<04:53, 19.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5481/8000 [05:14<03:11, 13.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2161/8000 [02:30<04:51, 20.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5485/8000 [05:14<03:05, 13.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2165/8000 [02:30<05:46, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5488/8000 [05:14<02:42, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2167/8000 [02:30<08:02, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5490/8000 [05:14<02:44, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2171/8000 [02:30<06:33, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5492/8000 [05:14<02:45, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2173/8000 [02:31<06:27, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5494/8000 [05:14<02:59, 13.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▌            | 2176/8000 [02:31<05:59, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5496/8000 [05:15<02:55, 14.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2180/8000 [02:31<05:06, 19.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5500/8000 [05:15<02:21, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2185/8000 [02:31<05:49, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5507/8000 [05:15<01:36, 25.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2190/8000 [02:31<04:46, 20.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5511/8000 [05:15<01:42, 24.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2194/8000 [02:32<04:27, 21.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5516/8000 [05:15<01:36, 25.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▋            | 2198/8000 [02:32<05:28, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5519/8000 [05:15<01:39, 25.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2203/8000 [02:32<05:33, 17.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5525/8000 [05:16<01:44, 23.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2205/8000 [02:33<07:11, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5528/8000 [05:16<01:50, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2209/8000 [02:33<06:05, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5532/8000 [05:16<02:56, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2214/8000 [02:33<05:13, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5536/8000 [05:17<02:48, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2217/8000 [02:33<06:59, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5538/8000 [05:17<04:17,  9.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2219/8000 [02:33<07:31, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5540/8000 [05:17<04:28,  9.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2222/8000 [02:34<07:50, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5544/8000 [05:18<03:49, 10.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2227/8000 [02:34<05:57, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5547/8000 [05:18<03:20, 12.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2230/8000 [02:34<07:17, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5549/8000 [05:18<04:21,  9.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2232/8000 [02:34<07:17, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5554/8000 [05:18<02:54, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▋            | 2234/8000 [02:35<07:30, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5558/8000 [05:18<02:30, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2238/8000 [02:35<09:30, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5561/8000 [05:19<02:19, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2240/8000 [02:35<09:39,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5564/8000 [05:19<03:07, 12.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2244/8000 [02:35<07:37, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5568/8000 [05:19<02:36, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2247/8000 [02:36<06:48, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5573/8000 [05:19<02:36, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2249/8000 [02:36<08:46, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5577/8000 [05:20<02:13, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2252/8000 [02:36<07:24, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5582/8000 [05:20<01:51, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2255/8000 [02:36<08:38, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5585/8000 [05:20<02:48, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2260/8000 [02:37<06:22, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5587/8000 [05:20<02:50, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2265/8000 [02:37<05:30, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5591/8000 [05:21<02:51, 14.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2267/8000 [02:37<09:14, 10.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5596/8000 [05:21<03:05, 12.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2269/8000 [02:38<13:08,  7.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5600/8000 [05:21<02:32, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2271/8000 [02:38<11:44,  8.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5603/8000 [05:21<02:44, 14.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▊            | 2277/8000 [02:39<09:06, 10.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5606/8000 [05:22<02:28, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2281/8000 [02:39<07:39, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5609/8000 [05:22<02:19, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2283/8000 [02:39<07:53, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5613/8000 [05:22<01:59, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2285/8000 [02:39<07:59, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5621/8000 [05:22<01:21, 29.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2287/8000 [02:39<07:54, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5627/8000 [05:22<01:10, 33.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2290/8000 [02:40<09:24, 10.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5631/8000 [05:23<01:51, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▊            | 2293/8000 [02:40<10:31,  9.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5635/8000 [05:23<02:02, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2296/8000 [02:40<09:07, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5638/8000 [05:23<02:00, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2300/8000 [02:41<08:19, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5641/8000 [05:23<02:01, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2303/8000 [02:41<07:29, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5644/8000 [05:23<02:21, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2306/8000 [02:41<06:57, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5648/8000 [05:24<02:23, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2308/8000 [02:41<07:18, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5650/8000 [05:24<02:30, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2312/8000 [02:41<06:17, 15.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5652/8000 [05:24<02:28, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2314/8000 [02:41<06:35, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5655/8000 [05:24<02:18, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2316/8000 [02:42<06:45, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5659/8000 [05:24<01:56, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2320/8000 [02:42<07:37, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5662/8000 [05:25<02:51, 13.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2323/8000 [02:42<07:05, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5665/8000 [05:25<02:39, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2327/8000 [02:43<07:38, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5668/8000 [05:25<02:24, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2330/8000 [02:43<08:42, 10.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5671/8000 [05:25<02:15, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2332/8000 [02:43<08:20, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5673/8000 [05:25<03:28, 11.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2339/8000 [02:43<05:22, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5676/8000 [05:26<04:09,  9.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2344/8000 [02:43<04:33, 20.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5678/8000 [05:26<03:43, 10.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|████▉            | 2350/8000 [02:44<03:56, 23.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5682/8000 [05:26<03:09, 12.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████            | 2353/8000 [02:44<05:25, 17.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5684/8000 [05:27<03:42, 10.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████            | 2358/8000 [02:44<06:02, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5686/8000 [05:27<03:51, 10.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2362/8000 [02:45<05:39, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5688/8000 [05:27<03:27, 11.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2365/8000 [02:45<05:59, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5691/8000 [05:27<04:16,  9.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2369/8000 [02:45<05:40, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5695/8000 [05:28<03:33, 10.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2371/8000 [02:45<07:38, 12.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5698/8000 [05:28<03:05, 12.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2373/8000 [02:45<07:52, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5700/8000 [05:28<03:36, 10.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2375/8000 [02:46<08:06, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5703/8000 [05:28<03:01, 12.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2378/8000 [02:46<07:49, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5705/8000 [05:28<02:55, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2380/8000 [02:46<07:43, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5707/8000 [05:29<03:28, 11.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2382/8000 [02:46<10:09,  9.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5710/8000 [05:29<03:32, 10.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2385/8000 [02:47<08:05, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5715/8000 [05:29<02:50, 13.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2387/8000 [02:47<07:41, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5717/8000 [05:29<02:47, 13.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2391/8000 [02:47<05:51, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5722/8000 [05:29<02:26, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2393/8000 [02:47<06:52, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5724/8000 [05:30<02:31, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2397/8000 [02:47<06:59, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5728/8000 [05:30<02:04, 18.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2399/8000 [02:48<07:14, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5733/8000 [05:30<01:40, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2402/8000 [02:48<06:37, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5737/8000 [05:30<01:58, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2405/8000 [02:48<06:12, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5742/8000 [05:30<01:37, 23.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2409/8000 [02:48<05:31, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5746/8000 [05:31<01:55, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████            | 2411/8000 [02:48<07:38, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5749/8000 [05:31<01:49, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2416/8000 [02:49<05:48, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5757/8000 [05:31<01:35, 23.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2419/8000 [02:49<05:43, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5763/8000 [05:31<01:53, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2423/8000 [02:49<08:01, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5766/8000 [05:32<02:10, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2425/8000 [02:50<09:12, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5771/8000 [05:32<02:03, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2428/8000 [02:50<08:04, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5773/8000 [05:32<02:30, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2431/8000 [02:50<07:15, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5778/8000 [05:32<02:21, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2434/8000 [02:50<07:08, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5782/8000 [05:33<02:02, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▏           | 2439/8000 [02:50<05:34, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5785/8000 [05:33<02:19, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2441/8000 [02:51<08:54, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5788/8000 [05:33<02:10, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2443/8000 [02:51<10:15,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5791/8000 [05:33<02:01, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2445/8000 [02:52<11:47,  7.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5793/8000 [05:33<02:46, 13.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2447/8000 [02:52<10:51,  8.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5795/8000 [05:34<03:18, 11.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2450/8000 [02:52<10:39,  8.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5798/8000 [05:34<02:50, 12.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2452/8000 [02:52<10:09,  9.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5800/8000 [05:34<02:47, 13.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2456/8000 [02:52<07:37, 12.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5805/8000 [05:34<02:00, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2460/8000 [02:53<06:09, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5810/8000 [05:35<02:20, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2462/8000 [02:53<08:20, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5815/8000 [05:35<02:08, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2466/8000 [02:53<06:45, 13.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5819/8000 [05:35<02:24, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2468/8000 [02:53<07:11, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5822/8000 [05:35<02:34, 14.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2470/8000 [02:54<07:30, 12.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5824/8000 [05:36<02:38, 13.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2475/8000 [02:54<05:32, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5827/8000 [05:36<02:57, 12.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2480/8000 [02:54<04:31, 20.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5829/8000 [05:36<02:47, 13.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2484/8000 [02:54<05:52, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5832/8000 [05:36<02:25, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2486/8000 [02:54<06:12, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5835/8000 [05:36<02:13, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2489/8000 [02:55<07:24, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5837/8000 [05:36<02:16, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2492/8000 [02:55<08:24, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5840/8000 [05:37<02:38, 13.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2497/8000 [02:55<06:07, 14.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5845/8000 [05:37<02:09, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2500/8000 [02:55<05:52, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5848/8000 [05:37<02:03, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2504/8000 [02:56<05:08, 17.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5850/8000 [05:37<02:30, 14.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2508/8000 [02:56<06:18, 14.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5853/8000 [05:38<02:21, 15.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2514/8000 [02:56<04:52, 18.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5855/8000 [05:38<02:26, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▎           | 2518/8000 [02:57<05:48, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5857/8000 [05:38<02:24, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2521/8000 [02:57<06:46, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5860/8000 [05:38<02:37, 13.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2523/8000 [02:57<06:56, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5863/8000 [05:38<02:23, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2525/8000 [02:57<07:35, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5869/8000 [05:39<02:09, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▎           | 2528/8000 [02:58<10:39,  8.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5871/8000 [05:39<02:44, 12.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2531/8000 [02:58<09:10,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5874/8000 [05:39<02:20, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2533/8000 [02:58<10:33,  8.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5877/8000 [05:39<02:37, 13.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2535/8000 [02:58<09:38,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▍    | 5881/8000 [05:39<02:03, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2539/8000 [02:59<07:07, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5885/8000 [05:39<01:46, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2542/8000 [02:59<06:27, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5888/8000 [05:40<01:43, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2545/8000 [02:59<07:46, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5892/8000 [05:40<02:12, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2548/8000 [02:59<08:29, 10.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5898/8000 [05:40<01:40, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2550/8000 [03:00<08:08, 11.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5903/8000 [05:40<01:46, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2553/8000 [03:00<07:03, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5909/8000 [05:41<01:43, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2556/8000 [03:00<07:24, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5912/8000 [05:41<01:46, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2559/8000 [03:00<08:36, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5918/8000 [05:41<01:49, 19.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2562/8000 [03:01<07:41, 11.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5921/8000 [05:41<01:47, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2567/8000 [03:01<06:12, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5924/8000 [05:41<01:42, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2569/8000 [03:01<06:22, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5927/8000 [05:42<02:01, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2574/8000 [03:01<05:19, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5929/8000 [05:42<02:06, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2577/8000 [03:01<05:24, 16.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5935/8000 [05:42<01:27, 23.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2579/8000 [03:02<06:13, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5938/8000 [05:42<01:49, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2582/8000 [03:02<05:52, 15.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5941/8000 [05:43<02:07, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▍           | 2584/8000 [03:02<05:58, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5944/8000 [05:43<02:02, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2591/8000 [03:02<03:57, 22.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5946/8000 [05:43<02:06, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2594/8000 [03:02<04:03, 22.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5950/8000 [05:43<01:45, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▌           | 2599/8000 [03:03<04:35, 19.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5953/8000 [05:43<02:08, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2605/8000 [03:03<04:00, 22.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5956/8000 [05:43<01:55, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2609/8000 [03:03<05:42, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5960/8000 [05:44<01:59, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2611/8000 [03:03<06:19, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5962/8000 [05:44<02:11, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2613/8000 [03:04<06:17, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5966/8000 [05:44<01:51, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2615/8000 [03:04<06:31, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5968/8000 [05:44<02:35, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2617/8000 [03:04<06:30, 13.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5970/8000 [05:44<02:33, 13.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2619/8000 [03:04<06:34, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5972/8000 [05:45<02:42, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2622/8000 [03:04<06:20, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5974/8000 [05:45<02:45, 12.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2625/8000 [03:04<06:20, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5979/8000 [05:45<01:48, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2627/8000 [03:05<08:19, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5986/8000 [05:45<01:37, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2633/8000 [03:05<05:11, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5989/8000 [05:45<01:37, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2637/8000 [03:05<04:45, 18.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5992/8000 [05:46<02:01, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2642/8000 [03:05<05:10, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5997/8000 [05:46<01:44, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2644/8000 [03:06<05:43, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6002/8000 [05:46<01:40, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2646/8000 [03:06<05:55, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6005/8000 [05:46<01:42, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2651/8000 [03:06<06:15, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6008/8000 [05:46<01:36, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2653/8000 [03:06<06:45, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6013/8000 [05:47<01:37, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2655/8000 [03:07<07:11, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6016/8000 [05:47<01:32, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2657/8000 [03:07<07:27, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6019/8000 [05:47<01:30, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2660/8000 [03:07<08:25, 10.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6022/8000 [05:47<01:58, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2664/8000 [03:07<06:29, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6028/8000 [05:47<01:25, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2669/8000 [03:07<05:08, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6037/8000 [05:47<00:59, 33.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2672/8000 [03:08<04:58, 17.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6042/8000 [05:48<01:11, 27.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2676/8000 [03:08<04:43, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6049/8000 [05:48<01:14, 26.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▋           | 2679/8000 [03:08<06:02, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6054/8000 [05:48<01:21, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2682/8000 [03:08<05:51, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6058/8000 [05:48<01:34, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2685/8000 [03:09<07:10, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6061/8000 [05:49<01:31, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2687/8000 [03:09<09:09,  9.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6065/8000 [05:49<01:39, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2691/8000 [03:09<07:21, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6068/8000 [05:49<02:00, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2694/8000 [03:09<06:59, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6071/8000 [05:49<01:54, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2699/8000 [03:10<07:02, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6073/8000 [05:49<02:00, 15.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2701/8000 [03:10<07:06, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6076/8000 [05:50<01:50, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▋           | 2705/8000 [03:10<05:59, 14.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6079/8000 [05:50<01:44, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2708/8000 [03:10<05:35, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6084/8000 [05:50<01:43, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2710/8000 [03:11<07:37, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6087/8000 [05:50<02:01, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2714/8000 [03:11<07:54, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6090/8000 [05:50<01:52, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2717/8000 [03:11<07:22, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6095/8000 [05:51<01:27, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2720/8000 [03:11<06:47, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6100/8000 [05:51<01:32, 20.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2725/8000 [03:12<05:19, 16.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6103/8000 [05:51<01:29, 21.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2727/8000 [03:12<05:35, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6106/8000 [05:51<01:51, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2729/8000 [03:12<07:14, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6110/8000 [05:51<01:35, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2731/8000 [03:12<07:29, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6114/8000 [05:52<01:44, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2735/8000 [03:12<05:54, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|█████████████    | 6118/8000 [05:52<02:21, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2738/8000 [03:13<05:27, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|█████████████    | 6120/8000 [05:52<02:23, 13.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2741/8000 [03:13<08:48,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6124/8000 [05:53<02:14, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2743/8000 [03:13<08:39, 10.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6126/8000 [05:53<02:13, 14.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2748/8000 [03:14<07:23, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6128/8000 [05:53<02:09, 14.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2750/8000 [03:14<07:34, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6130/8000 [05:53<02:41, 11.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2753/8000 [03:14<07:02, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6134/8000 [05:53<02:09, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████▊           | 2757/8000 [03:14<05:42, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6138/8000 [05:53<01:44, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▊           | 2761/8000 [03:15<06:19, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6143/8000 [05:54<01:26, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2766/8000 [03:15<05:00, 17.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6146/8000 [05:54<01:54, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2769/8000 [03:15<06:18, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6149/8000 [05:54<02:12, 13.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2772/8000 [03:15<05:59, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6152/8000 [05:54<01:58, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2774/8000 [03:15<06:15, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6155/8000 [05:54<01:45, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2777/8000 [03:16<05:51, 14.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6161/8000 [05:55<01:55, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2781/8000 [03:16<05:30, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6163/8000 [05:55<02:23, 12.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2785/8000 [03:16<05:01, 17.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6167/8000 [05:55<02:19, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2787/8000 [03:16<06:06, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6176/8000 [05:56<01:42, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2791/8000 [03:16<05:30, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6179/8000 [05:56<02:26, 12.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2793/8000 [03:17<05:47, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6183/8000 [05:56<02:06, 14.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2798/8000 [03:17<06:06, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6186/8000 [05:57<01:57, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2804/8000 [03:17<04:50, 17.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6192/8000 [05:57<01:39, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2811/8000 [03:17<03:51, 22.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6196/8000 [05:57<01:45, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2816/8000 [03:18<04:32, 19.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6199/8000 [05:57<01:40, 17.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2823/8000 [03:18<03:38, 23.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6201/8000 [05:57<01:46, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2827/8000 [03:18<03:34, 24.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6203/8000 [05:58<01:52, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2832/8000 [03:18<04:18, 20.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6207/8000 [05:58<01:32, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████           | 2837/8000 [03:19<03:55, 21.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6211/8000 [05:58<01:19, 22.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2841/8000 [03:19<04:05, 21.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6215/8000 [05:58<02:00, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2846/8000 [03:19<03:45, 22.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6218/8000 [05:58<01:52, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2853/8000 [03:19<03:02, 28.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6221/8000 [05:59<01:46, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2859/8000 [03:19<03:36, 23.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6227/8000 [05:59<01:18, 22.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2863/8000 [03:20<04:18, 19.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6231/8000 [05:59<01:13, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2869/8000 [03:20<05:18, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6236/8000 [05:59<01:21, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2871/8000 [03:20<05:37, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6242/8000 [05:59<01:22, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2874/8000 [03:21<05:24, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6245/8000 [06:00<01:21, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2877/8000 [03:21<06:19, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6249/8000 [06:00<01:14, 23.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2879/8000 [03:21<06:44, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6255/8000 [06:00<01:15, 23.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████           | 2882/8000 [03:22<07:37, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6259/8000 [06:00<01:30, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2884/8000 [03:22<09:17,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6266/8000 [06:01<01:46, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2887/8000 [03:22<11:04,  7.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6268/8000 [06:01<01:50, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2890/8000 [03:23<09:05,  9.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6272/8000 [06:01<01:33, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2896/8000 [03:23<05:47, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6281/8000 [06:01<01:12, 23.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2903/8000 [03:23<05:19, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6285/8000 [06:01<01:09, 24.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2905/8000 [03:23<05:28, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6289/8000 [06:02<01:07, 25.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2907/8000 [03:24<07:25, 11.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6293/8000 [06:02<01:09, 24.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2909/8000 [03:24<07:21, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6296/8000 [06:02<01:30, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2912/8000 [03:24<08:22, 10.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6304/8000 [06:02<01:19, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▏          | 2919/8000 [03:24<05:17, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6309/8000 [06:03<01:24, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2922/8000 [03:25<05:10, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6316/8000 [06:03<01:16, 22.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2925/8000 [03:25<05:14, 16.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6320/8000 [06:03<01:26, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2927/8000 [03:25<05:38, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6324/8000 [06:03<01:19, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2929/8000 [03:25<05:57, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6330/8000 [06:04<01:16, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2932/8000 [03:25<05:26, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6335/8000 [06:04<01:07, 24.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▏          | 2938/8000 [03:25<03:58, 21.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6339/8000 [06:04<01:06, 24.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2943/8000 [03:26<04:41, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6342/8000 [06:04<01:07, 24.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2945/8000 [03:26<05:07, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6345/8000 [06:04<01:10, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2949/8000 [03:26<04:41, 17.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6350/8000 [06:05<01:56, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2956/8000 [03:26<03:29, 24.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6353/8000 [06:05<01:49, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2959/8000 [03:26<03:57, 21.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6360/8000 [06:05<01:13, 22.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2963/8000 [03:27<04:28, 18.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6364/8000 [06:05<01:10, 23.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2966/8000 [03:27<04:40, 17.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6367/8000 [06:05<01:12, 22.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2971/8000 [03:27<04:01, 20.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6373/8000 [06:06<01:34, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2975/8000 [03:27<03:59, 20.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6377/8000 [06:06<01:47, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2979/8000 [03:28<04:51, 17.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6382/8000 [06:06<01:40, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2982/8000 [03:28<04:36, 18.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6388/8000 [06:07<01:16, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2985/8000 [03:28<04:42, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6392/8000 [06:07<01:09, 23.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2989/8000 [03:28<04:14, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6398/8000 [06:07<01:10, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2994/8000 [03:28<04:44, 17.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6401/8000 [06:07<01:10, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2996/8000 [03:29<05:00, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6406/8000 [06:07<01:14, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2999/8000 [03:29<04:48, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6409/8000 [06:08<01:26, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3001/8000 [03:29<05:06, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6414/8000 [06:08<01:26, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3004/8000 [03:29<05:05, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6416/8000 [06:08<01:27, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3008/8000 [03:29<04:16, 19.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6422/8000 [06:08<01:06, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3011/8000 [03:29<04:21, 19.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6426/8000 [06:08<01:18, 20.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3014/8000 [03:30<04:27, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6429/8000 [06:09<01:41, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3016/8000 [03:30<06:17, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6437/8000 [06:09<01:07, 23.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3019/8000 [03:30<05:41, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6441/8000 [06:09<01:04, 24.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3021/8000 [03:30<07:43, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6444/8000 [06:09<01:06, 23.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3026/8000 [03:31<05:32, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6447/8000 [06:09<01:21, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3028/8000 [03:31<05:55, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6452/8000 [06:10<01:11, 21.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3032/8000 [03:31<04:43, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6455/8000 [06:10<01:31, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3035/8000 [03:31<04:34, 18.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6459/8000 [06:10<01:42, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3039/8000 [03:31<04:01, 20.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6461/8000 [06:10<01:43, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3043/8000 [03:31<03:54, 21.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6465/8000 [06:11<01:58, 12.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3048/8000 [03:31<03:14, 25.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6467/8000 [06:11<01:51, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3053/8000 [03:32<04:03, 20.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6469/8000 [06:11<01:50, 13.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▍          | 3058/8000 [03:32<03:29, 23.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6472/8000 [06:11<02:17, 11.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3062/8000 [03:32<03:23, 24.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6475/8000 [06:12<02:13, 11.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3067/8000 [03:32<03:08, 26.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6481/8000 [06:12<01:43, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3070/8000 [03:33<05:16, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6484/8000 [06:12<01:33, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3074/8000 [03:33<04:35, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6487/8000 [06:12<01:28, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3077/8000 [03:33<05:43, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6493/8000 [06:12<01:06, 22.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▌          | 3079/8000 [03:33<06:04, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6498/8000 [06:13<01:00, 24.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3084/8000 [03:34<04:43, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6501/8000 [06:13<01:06, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3089/8000 [03:34<04:02, 20.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6504/8000 [06:13<01:08, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3094/8000 [03:34<04:35, 17.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6507/8000 [06:13<01:07, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3099/8000 [03:35<06:32, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6510/8000 [06:14<01:57, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3101/8000 [03:35<06:53, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6512/8000 [06:14<02:24, 10.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3103/8000 [03:35<08:21,  9.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6515/8000 [06:14<02:07, 11.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3106/8000 [03:35<07:16, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6519/8000 [06:14<01:45, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3108/8000 [03:36<08:57,  9.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6521/8000 [06:15<02:30,  9.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3111/8000 [03:36<07:42, 10.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6523/8000 [06:15<02:26, 10.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3115/8000 [03:36<06:10, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6527/8000 [06:15<02:17, 10.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3118/8000 [03:36<05:38, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6533/8000 [06:15<01:28, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3120/8000 [03:37<06:08, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6537/8000 [06:15<01:17, 18.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3124/8000 [03:37<06:51, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6544/8000 [06:16<01:10, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3127/8000 [03:37<07:51, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6555/8000 [06:16<01:01, 23.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3129/8000 [03:38<07:31, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6558/8000 [06:16<01:09, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3134/8000 [03:38<05:19, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6561/8000 [06:17<01:07, 21.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3136/8000 [03:38<07:13, 11.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6565/8000 [06:17<01:02, 22.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3139/8000 [03:38<08:08,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6568/8000 [06:17<01:01, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3141/8000 [03:39<09:59,  8.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6574/8000 [06:17<00:49, 28.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3143/8000 [03:39<12:20,  6.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6578/8000 [06:17<01:01, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3145/8000 [03:39<11:05,  7.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6581/8000 [06:17<01:01, 23.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3146/8000 [03:40<11:20,  7.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6586/8000 [06:17<00:52, 27.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3151/8000 [03:40<06:36, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6592/8000 [06:18<00:42, 33.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3153/8000 [03:40<06:21, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6596/8000 [06:18<00:57, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▋          | 3158/8000 [03:41<08:19,  9.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6605/8000 [06:18<00:48, 28.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3160/8000 [03:41<07:35, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6611/8000 [06:18<00:41, 33.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3164/8000 [03:41<07:19, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6616/8000 [06:18<00:41, 33.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3166/8000 [03:41<07:20, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6622/8000 [06:19<01:00, 22.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3168/8000 [03:41<07:18, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6629/8000 [06:19<00:58, 23.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3171/8000 [03:42<06:30, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6634/8000 [06:19<00:52, 26.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3173/8000 [03:42<06:58, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6638/8000 [06:19<00:59, 22.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3175/8000 [03:42<07:03, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6643/8000 [06:20<01:05, 20.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3177/8000 [03:42<07:13, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6646/8000 [06:20<01:15, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3179/8000 [03:43<09:21,  8.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6649/8000 [06:20<01:22, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3183/8000 [03:43<06:42, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6653/8000 [06:20<01:21, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3185/8000 [03:43<08:33,  9.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6658/8000 [06:21<01:04, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3187/8000 [03:43<08:15,  9.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6662/8000 [06:21<00:58, 22.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3189/8000 [03:43<07:45, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6665/8000 [06:21<01:22, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3191/8000 [03:44<07:58, 10.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6668/8000 [06:21<01:29, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3193/8000 [03:44<09:40,  8.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6671/8000 [06:21<01:21, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3197/8000 [03:44<06:37, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6675/8000 [06:22<01:08, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3200/8000 [03:44<06:01, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6679/8000 [06:22<01:16, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3203/8000 [03:45<07:01, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6685/8000 [06:23<01:38, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3206/8000 [03:45<06:13, 12.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6688/8000 [06:23<01:32, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3211/8000 [03:45<05:38, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6692/8000 [06:23<01:16, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3214/8000 [03:45<05:18, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6697/8000 [06:23<01:14, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3219/8000 [03:46<05:27, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6700/8000 [06:23<01:13, 17.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3222/8000 [03:46<05:17, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6704/8000 [06:23<01:16, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3225/8000 [03:46<05:08, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6708/8000 [06:24<01:05, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3228/8000 [03:46<04:59, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6711/8000 [06:24<01:15, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3232/8000 [03:46<05:13, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6714/8000 [06:24<01:23, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▊          | 3235/8000 [03:47<06:59, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6719/8000 [06:24<01:03, 20.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▉          | 3237/8000 [03:47<06:56, 11.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6722/8000 [06:25<01:18, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▉          | 3240/8000 [03:47<06:42, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6725/8000 [06:25<01:10, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3242/8000 [03:47<06:38, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6729/8000 [06:25<01:13, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3245/8000 [03:48<05:55, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6735/8000 [06:25<01:03, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3248/8000 [03:48<06:51, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6739/8000 [06:25<01:10, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3251/8000 [03:48<06:19, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6742/8000 [06:26<01:09, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3253/8000 [03:48<06:17, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6747/8000 [06:26<01:34, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3256/8000 [03:49<05:52, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6750/8000 [06:26<01:38, 12.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3261/8000 [03:49<05:55, 13.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6752/8000 [06:27<01:36, 12.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3267/8000 [03:49<05:20, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6755/8000 [06:27<01:39, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3269/8000 [03:49<05:35, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6758/8000 [06:27<01:24, 14.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3272/8000 [03:50<05:19, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6760/8000 [06:27<01:24, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3274/8000 [03:50<06:50, 11.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6762/8000 [06:27<01:23, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3278/8000 [03:50<05:31, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6764/8000 [06:27<01:23, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3282/8000 [03:50<04:49, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6767/8000 [06:28<01:32, 13.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3284/8000 [03:50<05:06, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6772/8000 [06:28<01:20, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3287/8000 [03:51<05:01, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6776/8000 [06:28<01:08, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3290/8000 [03:51<04:48, 16.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6780/8000 [06:28<00:59, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3294/8000 [03:51<04:14, 18.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6785/8000 [06:28<00:59, 20.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3297/8000 [03:51<04:25, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6788/8000 [06:29<01:09, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3299/8000 [03:51<05:01, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6792/8000 [06:29<01:11, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3303/8000 [03:52<05:31, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6794/8000 [06:29<01:32, 13.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3305/8000 [03:52<07:23, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6797/8000 [06:29<01:40, 11.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3308/8000 [03:52<06:31, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6804/8000 [06:30<01:16, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3312/8000 [03:53<06:35, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6807/8000 [06:30<01:10, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3315/8000 [03:53<05:59, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6809/8000 [06:30<01:11, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████          | 3319/8000 [03:53<05:06, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6812/8000 [06:30<01:06, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3321/8000 [03:53<05:28, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6817/8000 [06:30<00:54, 21.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3323/8000 [03:53<05:51, 13.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6821/8000 [06:30<00:51, 22.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3325/8000 [03:53<06:01, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6826/8000 [06:31<00:55, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3328/8000 [03:54<05:30, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6832/8000 [06:31<00:54, 21.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3330/8000 [03:54<06:51, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6837/8000 [06:31<00:47, 24.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3332/8000 [03:54<08:20,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6840/8000 [06:31<00:49, 23.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3338/8000 [03:54<04:58, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6843/8000 [06:31<00:48, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3345/8000 [03:55<03:33, 21.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6852/8000 [06:32<00:42, 27.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3348/8000 [03:55<06:14, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6856/8000 [06:32<00:42, 27.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3352/8000 [03:55<06:16, 12.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6860/8000 [06:32<00:51, 22.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3354/8000 [03:56<07:10, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6865/8000 [06:33<00:59, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3356/8000 [03:56<07:03, 10.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6869/8000 [06:33<01:13, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3360/8000 [03:56<05:50, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6872/8000 [06:33<01:26, 13.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3364/8000 [03:56<06:07, 12.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6874/8000 [06:33<01:31, 12.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3367/8000 [03:57<06:46, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6878/8000 [06:34<01:35, 11.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3369/8000 [03:57<08:03,  9.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6881/8000 [06:34<01:50, 10.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3375/8000 [03:57<06:14, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6883/8000 [06:34<01:50, 10.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3380/8000 [03:58<04:58, 15.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6887/8000 [06:35<01:22, 13.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3384/8000 [03:58<04:36, 16.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6891/8000 [06:35<01:08, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3389/8000 [03:58<04:06, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6893/8000 [06:35<01:12, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3393/8000 [03:58<04:43, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6895/8000 [06:35<01:16, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3396/8000 [03:59<04:30, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6900/8000 [06:35<00:59, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▏         | 3400/8000 [03:59<06:16, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6903/8000 [06:35<01:10, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3402/8000 [03:59<07:34, 10.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6906/8000 [06:36<01:12, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3406/8000 [04:00<06:10, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6911/8000 [06:36<01:21, 13.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▏         | 3409/8000 [04:00<05:53, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6913/8000 [06:36<01:19, 13.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3412/8000 [04:00<05:16, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6916/8000 [06:37<01:22, 13.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3414/8000 [04:00<05:41, 13.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6919/8000 [06:37<01:12, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3416/8000 [04:00<07:09, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6922/8000 [06:37<01:08, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3419/8000 [04:01<06:13, 12.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6924/8000 [06:37<01:06, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3421/8000 [04:01<06:46, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6928/8000 [06:37<00:54, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3425/8000 [04:01<05:29, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6933/8000 [06:37<00:44, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3427/8000 [04:01<05:31, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6938/8000 [06:38<01:01, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3430/8000 [04:01<05:33, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6941/8000 [06:38<01:08, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3434/8000 [04:02<07:35, 10.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6945/8000 [06:38<00:57, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3438/8000 [04:02<07:59,  9.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6949/8000 [06:38<00:51, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3441/8000 [04:03<07:14, 10.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6953/8000 [06:39<01:06, 15.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3443/8000 [04:03<07:13, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6958/8000 [06:39<01:12, 14.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3447/8000 [04:03<05:44, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6962/8000 [06:39<01:00, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3449/8000 [04:03<06:07, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6969/8000 [06:39<00:51, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3455/8000 [04:04<05:34, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6973/8000 [06:40<00:48, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3457/8000 [04:04<05:34, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6976/8000 [06:40<00:48, 21.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3462/8000 [04:04<04:16, 17.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6979/8000 [06:40<00:49, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3465/8000 [04:04<04:27, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6983/8000 [06:40<00:44, 22.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3467/8000 [04:04<04:46, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6986/8000 [06:40<00:53, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3469/8000 [04:04<05:03, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6990/8000 [06:41<01:17, 12.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▍         | 3476/8000 [04:05<03:09, 23.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6993/8000 [06:41<01:08, 14.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▍         | 3479/8000 [04:05<03:22, 22.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6996/8000 [06:41<01:11, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3482/8000 [04:05<04:49, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7000/8000 [06:41<00:58, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3486/8000 [04:05<05:12, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7003/8000 [06:41<00:55, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3489/8000 [04:06<04:55, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7009/8000 [06:42<00:49, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3493/8000 [04:06<04:09, 18.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7012/8000 [06:42<00:48, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3496/8000 [04:06<04:08, 18.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7017/8000 [06:42<00:39, 24.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3499/8000 [04:06<04:08, 18.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7023/8000 [06:42<00:32, 29.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3501/8000 [04:06<04:42, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7027/8000 [06:42<00:32, 30.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3505/8000 [04:07<05:43, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7031/8000 [06:42<00:40, 23.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3508/8000 [04:07<05:26, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7034/8000 [06:42<00:40, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3512/8000 [04:07<04:42, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7037/8000 [06:43<00:40, 24.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3518/8000 [04:07<04:21, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7040/8000 [06:43<00:49, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3522/8000 [04:07<04:10, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7043/8000 [06:43<01:00, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3525/8000 [04:08<05:17, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7046/8000 [06:43<00:55, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3527/8000 [04:08<05:43, 13.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7049/8000 [06:43<00:52, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3531/8000 [04:08<05:04, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7053/8000 [06:44<00:43, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3534/8000 [04:08<04:52, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7057/8000 [06:44<00:39, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3537/8000 [04:09<06:13, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7061/8000 [06:44<00:36, 25.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3540/8000 [04:09<05:45, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7065/8000 [06:44<00:43, 21.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3546/8000 [04:09<05:34, 13.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7070/8000 [06:44<00:45, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3549/8000 [04:10<05:18, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7074/8000 [06:45<00:50, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3552/8000 [04:10<04:56, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7077/8000 [06:45<01:06, 13.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▌         | 3559/8000 [04:10<04:42, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7080/8000 [06:45<01:13, 12.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3562/8000 [04:10<04:43, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7084/8000 [06:46<01:09, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3564/8000 [04:11<05:13, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7086/8000 [06:46<01:06, 13.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3567/8000 [04:11<05:03, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7090/8000 [06:46<01:03, 14.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3572/8000 [04:11<04:21, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7095/8000 [06:46<00:48, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3574/8000 [04:11<05:01, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7098/8000 [06:46<00:55, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▌         | 3580/8000 [04:12<04:56, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7103/8000 [06:47<01:06, 13.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3589/8000 [04:12<03:06, 23.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7108/8000 [06:47<00:50, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3592/8000 [04:12<05:07, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7111/8000 [06:47<00:56, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3595/8000 [04:13<05:04, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7115/8000 [06:47<00:46, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3598/8000 [04:13<04:52, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7119/8000 [06:48<00:50, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3601/8000 [04:13<05:27, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7124/8000 [06:48<00:39, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3605/8000 [04:13<05:31, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7131/8000 [06:48<00:36, 24.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3608/8000 [04:13<05:00, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7134/8000 [06:48<00:48, 17.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3610/8000 [04:14<05:18, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7137/8000 [06:48<00:46, 18.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3613/8000 [04:14<06:06, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7140/8000 [06:49<00:43, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3618/8000 [04:14<06:31, 11.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7144/8000 [06:49<00:47, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3620/8000 [04:15<06:34, 11.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7147/8000 [06:49<00:45, 18.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3622/8000 [04:15<07:03, 10.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7152/8000 [06:49<00:43, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3624/8000 [04:15<06:50, 10.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7155/8000 [06:49<00:41, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3627/8000 [04:15<06:37, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7161/8000 [06:49<00:32, 25.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3629/8000 [04:15<06:16, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7165/8000 [06:50<00:37, 22.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3632/8000 [04:16<07:05, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7168/8000 [06:50<00:36, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3638/8000 [04:16<04:59, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7173/8000 [06:50<00:38, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3643/8000 [04:16<04:12, 17.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7178/8000 [06:50<00:46, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▋         | 3646/8000 [04:16<04:31, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7183/8000 [06:51<00:43, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3650/8000 [04:17<05:11, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7188/8000 [06:51<00:41, 19.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3656/8000 [04:17<03:55, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7192/8000 [06:51<00:52, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3659/8000 [04:17<04:45, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7195/8000 [06:51<00:48, 16.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3661/8000 [04:17<04:57, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7198/8000 [06:52<00:44, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3663/8000 [04:18<05:18, 13.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7203/8000 [06:52<00:43, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3665/8000 [04:18<06:03, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7206/8000 [06:52<00:42, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3667/8000 [04:18<06:17, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7209/8000 [06:52<00:50, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3672/8000 [04:18<04:35, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7212/8000 [06:53<00:54, 14.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3674/8000 [04:18<05:14, 13.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7214/8000 [06:53<00:53, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3677/8000 [04:19<04:55, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7217/8000 [06:53<00:58, 13.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3680/8000 [04:19<06:49, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7219/8000 [06:53<00:57, 13.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3682/8000 [04:19<07:59,  9.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7222/8000 [06:53<01:01, 12.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3685/8000 [04:20<07:54,  9.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7226/8000 [06:53<00:46, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3687/8000 [04:20<07:29,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7228/8000 [06:54<01:09, 11.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3692/8000 [04:20<06:06, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7231/8000 [06:54<01:06, 11.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3694/8000 [04:20<06:21, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7233/8000 [06:54<01:03, 12.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3698/8000 [04:21<06:09, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7236/8000 [06:54<00:53, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3701/8000 [04:21<06:33, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7239/8000 [06:55<00:56, 13.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3704/8000 [04:21<07:05, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7242/8000 [06:55<00:49, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3706/8000 [04:22<06:39, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7245/8000 [06:55<00:55, 13.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3710/8000 [04:22<05:26, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7249/8000 [06:55<00:43, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3713/8000 [04:22<05:04, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7255/8000 [06:55<00:30, 24.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3717/8000 [04:22<04:19, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7258/8000 [06:56<00:38, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▉         | 3719/8000 [04:22<04:53, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7265/8000 [06:56<00:32, 22.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3723/8000 [04:22<04:02, 17.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7275/8000 [06:56<00:25, 28.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3726/8000 [04:23<06:10, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7280/8000 [06:56<00:33, 21.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3729/8000 [04:23<06:33, 10.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7283/8000 [06:57<00:40, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3731/8000 [04:23<06:19, 11.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7291/8000 [06:57<00:33, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3737/8000 [04:24<05:10, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7296/8000 [06:57<00:28, 24.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3740/8000 [04:24<05:37, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7299/8000 [06:57<00:28, 24.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3744/8000 [04:24<05:03, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7304/8000 [06:58<00:30, 22.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3750/8000 [04:24<03:39, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7310/8000 [06:58<00:25, 27.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3754/8000 [04:25<03:36, 19.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7315/8000 [06:58<00:28, 23.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3758/8000 [04:25<04:20, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7321/8000 [06:58<00:28, 24.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3760/8000 [04:25<04:37, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7327/8000 [06:58<00:28, 23.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████▉         | 3762/8000 [04:25<05:28, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7332/8000 [06:59<00:31, 21.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3766/8000 [04:26<05:29, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7336/8000 [06:59<00:32, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3768/8000 [04:26<06:06, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7342/8000 [06:59<00:30, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3771/8000 [04:26<06:38, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7347/8000 [06:59<00:27, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3773/8000 [04:26<06:26, 10.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7350/8000 [06:59<00:27, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3775/8000 [04:27<07:49,  9.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7355/8000 [07:00<00:24, 25.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3777/8000 [04:27<07:23,  9.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7358/8000 [07:00<00:25, 24.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3779/8000 [04:27<06:49, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7362/8000 [07:00<00:30, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3782/8000 [04:27<06:50, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7366/8000 [07:00<00:27, 23.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3787/8000 [04:28<05:05, 13.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7370/8000 [07:00<00:25, 24.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3793/8000 [04:28<03:46, 18.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7373/8000 [07:00<00:25, 24.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3796/8000 [04:28<04:53, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7379/8000 [07:01<00:20, 29.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████         | 3799/8000 [04:28<04:40, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7390/8000 [07:01<00:23, 25.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3801/8000 [04:29<04:49, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7395/8000 [07:01<00:25, 24.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3804/8000 [04:29<05:51, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7398/8000 [07:01<00:25, 23.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3806/8000 [04:29<05:45, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7401/8000 [07:02<00:30, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3810/8000 [04:29<04:48, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7404/8000 [07:02<00:35, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3813/8000 [04:29<04:23, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7408/8000 [07:02<00:30, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3816/8000 [04:30<04:18, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7412/8000 [07:02<00:27, 21.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3822/8000 [04:30<03:31, 19.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7415/8000 [07:02<00:26, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3824/8000 [04:30<03:49, 18.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7420/8000 [07:03<00:28, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3828/8000 [04:30<03:29, 19.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7424/8000 [07:03<00:43, 13.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3834/8000 [04:31<04:39, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7426/8000 [07:03<00:42, 13.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3836/8000 [04:31<04:55, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7429/8000 [07:03<00:38, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3841/8000 [04:31<03:58, 17.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7433/8000 [07:04<00:38, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3845/8000 [04:31<03:49, 18.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7436/8000 [07:04<00:34, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3847/8000 [04:31<04:10, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7441/8000 [07:04<00:32, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3850/8000 [04:32<04:16, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7445/8000 [07:04<00:28, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3853/8000 [04:32<05:22, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7451/8000 [07:05<00:32, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3859/8000 [04:32<03:55, 17.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7454/8000 [07:05<00:30, 18.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3864/8000 [04:32<04:08, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7458/8000 [07:05<00:26, 20.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3868/8000 [04:33<04:52, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7464/8000 [07:05<00:25, 20.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3871/8000 [04:33<05:29, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7468/8000 [07:06<00:31, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3874/8000 [04:33<05:27, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7473/8000 [07:06<00:26, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3877/8000 [04:34<05:04, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7478/8000 [07:06<00:22, 23.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▏        | 3879/8000 [04:34<05:24, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7481/8000 [07:06<00:21, 23.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▏        | 3881/8000 [04:34<05:31, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7488/8000 [07:06<00:20, 24.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3885/8000 [04:34<04:41, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7492/8000 [07:07<00:23, 21.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3888/8000 [04:34<04:52, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7498/8000 [07:07<00:26, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3892/8000 [04:35<05:37, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7503/8000 [07:07<00:27, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3894/8000 [04:35<06:38, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7505/8000 [07:07<00:29, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3897/8000 [04:35<05:51, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7509/8000 [07:08<00:25, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3900/8000 [04:36<06:24, 10.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7513/8000 [07:08<00:25, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3903/8000 [04:36<05:49, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7518/8000 [07:08<00:25, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3908/8000 [04:36<04:15, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7522/8000 [07:08<00:23, 20.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3912/8000 [04:36<03:41, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7525/8000 [07:08<00:27, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3915/8000 [04:36<03:37, 18.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7529/8000 [07:09<00:23, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3919/8000 [04:37<04:11, 16.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7533/8000 [07:09<00:26, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3921/8000 [04:37<04:43, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7536/8000 [07:09<00:24, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3924/8000 [04:37<04:30, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7541/8000 [07:09<00:20, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3927/8000 [04:37<04:18, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7545/8000 [07:09<00:24, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3932/8000 [04:37<03:18, 20.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7549/8000 [07:10<00:26, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3935/8000 [04:37<03:27, 19.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7551/8000 [07:10<00:25, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▎        | 3938/8000 [04:38<03:23, 19.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7555/8000 [07:10<00:22, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3946/8000 [04:38<03:10, 21.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7558/8000 [07:10<00:21, 20.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3951/8000 [04:38<02:57, 22.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7562/8000 [07:10<00:23, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3955/8000 [04:38<03:44, 18.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7565/8000 [07:10<00:21, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▍        | 3957/8000 [04:39<04:04, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7568/8000 [07:11<00:20, 21.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3962/8000 [04:39<04:16, 15.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7576/8000 [07:11<00:16, 25.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3966/8000 [04:39<03:51, 17.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7579/8000 [07:11<00:17, 24.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3969/8000 [04:39<03:36, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7582/8000 [07:11<00:22, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3973/8000 [04:40<04:21, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7587/8000 [07:12<00:21, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3976/8000 [04:40<04:09, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7591/8000 [07:12<00:19, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3980/8000 [04:40<05:02, 13.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7597/8000 [07:12<00:14, 27.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3983/8000 [04:41<05:34, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7603/8000 [07:12<00:25, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3988/8000 [04:41<05:21, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7607/8000 [07:13<00:28, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3991/8000 [04:41<05:06, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7611/8000 [07:13<00:27, 14.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3998/8000 [04:41<03:47, 17.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7613/8000 [07:13<00:27, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4001/8000 [04:41<03:50, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7618/8000 [07:14<00:25, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4005/8000 [04:42<05:30, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7622/8000 [07:14<00:21, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4008/8000 [04:42<04:59, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7625/8000 [07:14<00:19, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4010/8000 [04:42<05:19, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7628/8000 [07:14<00:20, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4012/8000 [04:43<05:20, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7632/8000 [07:14<00:17, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4015/8000 [04:43<06:10, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7635/8000 [07:14<00:16, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4018/8000 [04:43<06:29, 10.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7638/8000 [07:14<00:16, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4020/8000 [04:43<06:15, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7643/8000 [07:15<00:17, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4023/8000 [04:44<05:25, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7647/8000 [07:15<00:18, 18.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4027/8000 [04:44<04:27, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7652/8000 [07:15<00:21, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4030/8000 [04:44<05:06, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7654/8000 [07:15<00:21, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4032/8000 [04:44<05:19, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7656/8000 [07:16<00:22, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4035/8000 [04:44<04:47, 13.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7659/8000 [07:16<00:24, 14.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4037/8000 [04:45<06:53,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7663/8000 [07:16<00:19, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▌        | 4039/8000 [04:45<06:29, 10.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7665/8000 [07:16<00:19, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4041/8000 [04:45<06:45,  9.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7668/8000 [07:16<00:19, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4043/8000 [04:45<06:40,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7670/8000 [07:16<00:19, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4046/8000 [04:46<05:44, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7674/8000 [07:16<00:15, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4050/8000 [04:46<07:08,  9.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7680/8000 [07:17<00:13, 23.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▌        | 4055/8000 [04:46<04:55, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7683/8000 [07:17<00:13, 23.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4060/8000 [04:46<03:49, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7689/8000 [07:17<00:13, 23.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4065/8000 [04:47<03:12, 20.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7693/8000 [07:17<00:11, 25.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4068/8000 [04:47<04:53, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7696/8000 [07:17<00:15, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4075/8000 [04:47<03:23, 19.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7700/8000 [07:18<00:13, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4079/8000 [04:47<03:17, 19.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7704/8000 [07:18<00:14, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4082/8000 [04:48<04:08, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7708/8000 [07:18<00:16, 18.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4085/8000 [04:48<03:52, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7712/8000 [07:18<00:13, 20.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4092/8000 [04:48<02:46, 23.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7718/8000 [07:18<00:10, 27.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4099/8000 [04:48<02:54, 22.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7724/8000 [07:19<00:11, 24.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4102/8000 [04:49<04:48, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7727/8000 [07:19<00:11, 23.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4105/8000 [04:49<04:34, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7730/8000 [07:19<00:11, 23.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4108/8000 [04:49<04:14, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7733/8000 [07:19<00:11, 22.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4110/8000 [04:50<04:30, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7736/8000 [07:19<00:13, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4114/8000 [04:50<04:01, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7739/8000 [07:19<00:13, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▋        | 4116/8000 [04:50<04:32, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7742/8000 [07:20<00:13, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████▊        | 4118/8000 [04:50<04:43, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7745/8000 [07:20<00:12, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4123/8000 [04:50<04:38, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7748/8000 [07:20<00:11, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4129/8000 [04:51<03:29, 18.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7752/8000 [07:20<00:10, 24.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4135/8000 [04:51<03:00, 21.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7756/8000 [07:20<00:09, 25.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4139/8000 [04:51<03:43, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7759/8000 [07:20<00:09, 24.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4141/8000 [04:51<04:06, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7763/8000 [07:20<00:09, 26.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4143/8000 [04:52<04:35, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7766/8000 [07:21<00:09, 24.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4145/8000 [04:52<04:48, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7772/8000 [07:21<00:09, 24.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4148/8000 [04:52<04:25, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7775/8000 [07:21<00:09, 23.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4150/8000 [04:52<04:43, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7780/8000 [07:21<00:09, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4153/8000 [04:52<04:25, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7783/8000 [07:21<00:12, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4155/8000 [04:52<04:37, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7787/8000 [07:22<00:10, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4160/8000 [04:53<03:34, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7791/8000 [07:22<00:10, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4163/8000 [04:53<03:44, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7794/8000 [07:22<00:10, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4166/8000 [04:53<03:46, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7802/8000 [07:22<00:08, 24.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4168/8000 [04:53<04:32, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7807/8000 [07:23<00:09, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4172/8000 [04:53<04:00, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7811/8000 [07:23<00:08, 22.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4175/8000 [04:54<04:13, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7816/8000 [07:23<00:07, 25.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4177/8000 [04:54<04:42, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7820/8000 [07:23<00:06, 26.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4181/8000 [04:54<04:53, 13.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7825/8000 [07:23<00:07, 23.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4183/8000 [04:55<06:10, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7830/8000 [07:23<00:07, 22.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4188/8000 [04:55<05:19, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7834/8000 [07:24<00:08, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4190/8000 [04:55<05:18, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7838/8000 [07:24<00:07, 21.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4193/8000 [04:55<05:51, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7843/8000 [07:24<00:07, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4197/8000 [04:56<04:36, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7846/8000 [07:24<00:07, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▉        | 4199/8000 [04:56<04:55, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7850/8000 [07:24<00:07, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4201/8000 [04:56<05:27, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7854/8000 [07:25<00:08, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4204/8000 [04:56<05:05, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7857/8000 [07:25<00:07, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4207/8000 [04:56<05:42, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7860/8000 [07:25<00:06, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4212/8000 [04:57<04:14, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7865/8000 [07:25<00:06, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4214/8000 [04:57<05:04, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7869/8000 [07:26<00:07, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4217/8000 [04:57<05:44, 11.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7873/8000 [07:26<00:06, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4219/8000 [04:58<06:11, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7877/8000 [07:26<00:05, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4222/8000 [04:58<05:24, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7880/8000 [07:26<00:05, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4228/8000 [04:58<03:28, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7886/8000 [07:26<00:05, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|████████▉        | 4234/8000 [04:58<02:41, 23.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7890/8000 [07:26<00:05, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4238/8000 [04:58<02:39, 23.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7894/8000 [07:27<00:05, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4241/8000 [04:58<03:32, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7896/8000 [07:27<00:05, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4247/8000 [04:59<04:17, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7899/8000 [07:27<00:06, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4252/8000 [04:59<03:48, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7901/8000 [07:27<00:06, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4254/8000 [04:59<04:00, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7906/8000 [07:27<00:05, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4256/8000 [05:00<04:03, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7910/8000 [07:28<00:04, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4259/8000 [05:00<03:57, 15.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7916/8000 [07:28<00:03, 24.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4265/8000 [05:00<02:59, 20.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7920/8000 [07:28<00:03, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4269/8000 [05:00<03:40, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7924/8000 [07:28<00:04, 15.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4272/8000 [05:00<03:37, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7926/8000 [07:29<00:04, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████        | 4275/8000 [05:01<04:19, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7928/8000 [07:29<00:04, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4280/8000 [05:01<03:34, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7932/8000 [07:29<00:03, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4287/8000 [05:01<03:21, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7935/8000 [07:29<00:03, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4289/8000 [05:01<03:47, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7939/8000 [07:29<00:03, 17.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4291/8000 [05:02<05:16, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7943/8000 [07:29<00:02, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████        | 4294/8000 [05:02<04:45, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7947/8000 [07:30<00:02, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4298/8000 [05:02<05:38, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7951/8000 [07:30<00:03, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4300/8000 [05:03<06:46,  9.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7953/8000 [07:30<00:02, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4304/8000 [05:03<05:29, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7959/8000 [07:30<00:01, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4306/8000 [05:03<05:26, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7963/8000 [07:30<00:01, 24.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4310/8000 [05:03<04:38, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7966/8000 [07:30<00:01, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4314/8000 [05:04<05:25, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7969/8000 [07:31<00:01, 23.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4318/8000 [05:04<05:56, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7976/8000 [07:31<00:00, 30.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4322/8000 [05:05<06:02, 10.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7982/8000 [07:31<00:00, 26.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4324/8000 [05:05<06:08,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7985/8000 [07:31<00:00, 26.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4326/8000 [05:06<08:56,  6.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7988/8000 [07:31<00:00, 26.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4330/8000 [05:06<06:46,  9.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7992/8000 [07:32<00:00, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4333/8000 [05:06<05:48, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7995/8000 [07:32<00:00, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4335/8000 [05:06<05:30, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7998/8000 [07:32<00:00, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4337/8000 [05:06<05:35, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8002it [07:32, 17.67it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  54%|█████████▏       | 4340/8000 [05:06<04:58, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  54%|█████████▏       | 4342/8000 [05:07<04:57, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4344/8000 [05:07<05:11, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:14,  7.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4350/8000 [05:07<03:29, 17.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:15,  4.02s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4352/8000 [05:07<03:53, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:30,  2.89s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▎       | 4355/8000 [05:07<03:46, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:50,  2.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▎       | 4357/8000 [05:08<03:50, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:43,  2.07s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▎       | 4359/8000 [05:08<04:06, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:59,  1.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4361/8000 [05:08<04:38, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:46,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4363/8000 [05:08<04:45, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:28,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4365/8000 [05:08<04:47, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:13,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4370/8000 [05:08<03:25, 17.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<05:06,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4372/8000 [05:09<03:43, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<05:10,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4375/8000 [05:09<03:46, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:58,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4378/8000 [05:09<03:39, 16.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:56,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4381/8000 [05:09<03:36, 16.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:49,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4384/8000 [05:09<04:40, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<04:47,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4387/8000 [05:10<05:20, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:48,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4389/8000 [05:10<05:31, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<04:43,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4394/8000 [05:10<05:11, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:40,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4397/8000 [05:11<05:02, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:35<04:42,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4400/8000 [05:11<06:12,  9.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:37<04:44,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4403/8000 [05:11<05:20, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:35,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4405/8000 [05:12<05:58, 10.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:40<04:39,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▎       | 4409/8000 [05:12<04:38, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:41<04:34,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4414/8000 [05:12<05:09, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:43<04:32,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4417/8000 [05:12<04:54, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:44<04:26,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4420/8000 [05:13<04:31, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:46<04:27,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4423/8000 [05:13<04:12, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:47<04:24,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4427/8000 [05:13<04:47, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:49<04:19,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4432/8000 [05:13<04:29, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:50<04:18,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4434/8000 [05:14<04:38, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:52<04:16,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▍       | 4439/8000 [05:14<05:13, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:54<04:32,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4441/8000 [05:14<05:15, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:55<04:25,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4445/8000 [05:14<04:12, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:57<04:19,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4449/8000 [05:15<03:30, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:58<04:15,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4451/8000 [05:15<03:55, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:00<04:13,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4454/8000 [05:15<03:49, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:01<04:11,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4458/8000 [05:15<03:55, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:03<04:11,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4465/8000 [05:16<03:19, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:04<04:06,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4467/8000 [05:16<03:45, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:06<04:13,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▍       | 4469/8000 [05:16<04:07, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:08<04:12,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4472/8000 [05:16<03:49, 15.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:09<04:09,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4474/8000 [05:16<05:04, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:11<04:09,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4477/8000 [05:17<04:30, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:12<04:09,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4480/8000 [05:17<04:16, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:14<04:21,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4484/8000 [05:17<03:45, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:16<04:27,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4486/8000 [05:17<03:51, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:18<04:16,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4488/8000 [05:18<05:13, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:19<04:05,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4490/8000 [05:18<05:15, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:21<04:05,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4492/8000 [05:18<05:22, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:22<03:57,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4494/8000 [05:18<05:01, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:23<03:45,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4496/8000 [05:18<05:07, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:25<03:41,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4498/8000 [05:18<05:04, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:26<03:36,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4503/8000 [05:19<03:30, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:28<03:35,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4508/8000 [05:19<02:41, 21.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:29<03:35,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4514/8000 [05:19<03:31, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:31<03:36,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4517/8000 [05:19<03:33, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:34<04:02,  1.68s/it]\n",
+      "Generating signals:  56%|█████████▌       | 4519/8000 [05:20<03:37, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  57%|█████████▌       | 4525/8000 [05:20<03:20, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▌       | 4527/8000 [05:20<03:31, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4531/8000 [05:20<03:24, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.44it/s]\n",
+      "Generating signals:  57%|█████████▋       | 4537/8000 [05:20<02:47, 20.68it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  57%|█████████▋       | 4542/8000 [05:21<02:32, 22.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4546/8000 [05:21<04:17, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.95s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4548/8000 [05:21<04:09, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 34it [00:02, 22.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4556/8000 [05:22<03:28, 16.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 65it [00:02, 48.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4560/8000 [05:22<03:22, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 95it [00:02, 76.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4562/8000 [05:22<03:50, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 125it [00:02, 107.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4565/8000 [05:22<03:47, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 155it [00:02, 138.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4567/8000 [05:23<05:07, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 186it [00:02, 171.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4570/8000 [05:23<05:25, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 216it [00:02, 197.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4572/8000 [05:23<05:24, 10.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 246it [00:02, 219.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4578/8000 [05:23<03:27, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 276it [00:02, 228.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4581/8000 [05:24<03:30, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 309it [00:02, 252.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4583/8000 [05:24<03:52, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 339it [00:03, 263.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4586/8000 [05:24<03:40, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 369it [00:03, 266.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▋       | 4588/8000 [05:24<04:15, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 401it [00:03, 279.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4590/8000 [05:24<04:31, 12.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 431it [00:03, 281.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4593/8000 [05:25<05:10, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 464it [00:03, 292.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4596/8000 [05:25<04:37, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 495it [00:03, 296.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|█████████▊       | 4599/8000 [05:25<04:59, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 526it [00:03, 285.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4602/8000 [05:26<06:38,  8.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 556it [00:03, 287.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4604/8000 [05:26<06:43,  8.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 587it [00:03, 292.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4605/8000 [05:26<07:37,  7.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 617it [00:04, 289.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4608/8000 [05:26<06:02,  9.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 647it [00:04, 291.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4614/8000 [05:27<04:31, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 677it [00:04, 277.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4618/8000 [05:27<04:45, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 709it [00:04, 287.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4622/8000 [05:27<04:03, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 742it [00:04, 298.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4624/8000 [05:27<04:12, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 170.80it/s]\n",
+      "Generating signals:  58%|█████████▊       | 4627/8000 [05:28<04:01, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  58%|█████████▊       | 4634/8000 [05:28<03:29, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▊       | 4641/8000 [05:28<02:31, 22.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 152\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4648/8000 [05:28<02:12, 25.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 132\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4651/8000 [05:29<02:30, 22.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 130\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4655/8000 [05:29<02:50, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  60%|▌| 60/100 [00:00<00:00, 123\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4660/8000 [05:29<03:46, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:00<00:00, 131\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4663/8000 [05:29<03:31, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
+      "Generating signals:  58%|█████████▉       | 4667/8000 [05:30<03:04, 18.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [34:52<00:00, 606.71s/it]\u001b[A\n",
-      "Replicates:  25%|██████▎                  | 2/8 [1:12:36<3:36:19, 2163.27s/it]\n",
-      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.217542919050387\n",
-      "2999: -5.9192592\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  58%|█████████▉       | 4670/8000 [05:30<04:34, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  58%|█████████▉       | 4673/8000 [05:30<04:21, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4675/8000 [05:31<05:15, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:41,  7.14s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4677/8000 [05:31<05:25, 10.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:21,  3.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4680/8000 [05:31<04:34, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:33,  2.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4683/8000 [05:31<04:58, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:46,  2.08s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4685/8000 [05:32<04:56, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:46,  1.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4691/8000 [05:32<03:13, 17.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:17,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4695/8000 [05:32<02:54, 18.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:14<04:53,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4698/8000 [05:32<03:43, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:35,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4702/8000 [05:32<03:56, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:25,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████▉       | 4705/8000 [05:33<04:17, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:18<04:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4707/8000 [05:33<04:35, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:14,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4710/8000 [05:33<04:45, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4713/8000 [05:33<04:14, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:22<04:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4716/8000 [05:34<03:43, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:23<04:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4719/8000 [05:34<04:22, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<03:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4721/8000 [05:34<04:35, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:26<03:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4726/8000 [05:35<04:46, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:27<03:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4730/8000 [05:35<04:39, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:28<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4732/8000 [05:35<04:42, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:29<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4736/8000 [05:35<03:47, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:31<03:45,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4738/8000 [05:35<04:30, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:32<03:43,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4741/8000 [05:36<04:11, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:33<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4743/8000 [05:36<04:05, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:35<03:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4746/8000 [05:36<04:37, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:36<03:47,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4748/8000 [05:36<04:43, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:37<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4751/8000 [05:37<04:16, 12.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4754/8000 [05:37<04:39, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:40<03:38,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4756/8000 [05:37<04:42, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:41<03:39,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████       | 4758/8000 [05:37<04:52, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:42<03:39,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████       | 4763/8000 [05:37<03:29, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:44<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4765/8000 [05:38<04:41, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:45<03:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4769/8000 [05:38<04:43, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:46<03:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4775/8000 [05:38<03:10, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:48<03:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4780/8000 [05:39<03:27, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:49<03:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4782/8000 [05:39<03:44, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:50<03:32,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4786/8000 [05:39<03:52, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:51<03:30,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4790/8000 [05:39<03:18, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:53<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4793/8000 [05:39<03:13, 16.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:54<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4798/8000 [05:40<02:45, 19.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:55<03:21,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4803/8000 [05:40<02:20, 22.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:56<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4808/8000 [05:40<03:50, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:58<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4810/8000 [05:41<04:00, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [00:59<03:22,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4813/8000 [05:41<03:42, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:00<03:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4816/8000 [05:41<04:30, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:02<03:21,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▏      | 4822/8000 [05:42<04:01, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:03<03:18,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4825/8000 [05:42<04:06, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:04<03:16,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4828/8000 [05:42<04:10, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:06<03:20,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4832/8000 [05:42<04:43, 11.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:07<03:17,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4834/8000 [05:43<04:35, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:08<03:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4836/8000 [05:43<04:39, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:09<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▎      | 4839/8000 [05:43<04:24, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:11<03:15,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4841/8000 [05:43<05:13, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:12<03:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4844/8000 [05:44<04:39, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:14<03:18,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4847/8000 [05:44<04:46, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:15<03:18,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4850/8000 [05:44<05:18,  9.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:16<03:19,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4853/8000 [05:45<06:19,  8.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:18<03:25,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4854/8000 [05:45<06:41,  7.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:20<03:40,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4859/8000 [05:45<05:33,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:21<03:33,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4864/8000 [05:45<04:05, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:23<03:33,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4869/8000 [05:46<03:19, 15.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:24<03:24,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4871/8000 [05:46<04:31, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:25<03:20,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4876/8000 [05:46<03:44, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:28<03:17,  1.43s/it]\n",
+      "Generating signals:  61%|██████████▎      | 4881/8000 [05:46<02:58, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  61%|██████████▍      | 4886/8000 [05:47<03:44, 13.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4888/8000 [05:47<04:55, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.90it/s]\n",
+      "Generating signals:  61%|██████████▍      | 4890/8000 [05:48<05:26,  9.51it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  61%|██████████▍      | 4896/8000 [05:48<03:55, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4901/8000 [05:48<04:01, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4903/8000 [05:48<04:08, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 38it [00:01, 33.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4907/8000 [05:49<04:46, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 70it [00:01, 65.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4912/8000 [05:49<04:23, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 104it [00:01, 102.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▍      | 4918/8000 [05:50<03:40, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 146it [00:01, 154.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4920/8000 [05:50<04:36, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 182it [00:01, 192.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4922/8000 [05:50<04:20, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 216it [00:02, 213.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4926/8000 [05:50<03:55, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 257it [00:02, 256.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4928/8000 [05:51<04:08, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 297it [00:02, 290.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4930/8000 [05:51<04:13, 12.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 335it [00:02, 312.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4932/8000 [05:51<05:14,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 377it [00:02, 341.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4934/8000 [05:51<05:06, 10.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 416it [00:02, 348.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▍      | 4938/8000 [05:51<03:37, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 458it [00:02, 365.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4944/8000 [05:52<02:28, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 497it [00:02, 365.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4948/8000 [05:52<02:49, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 540it [00:02, 381.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4953/8000 [05:52<03:35, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 580it [00:03, 380.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4958/8000 [05:53<03:15, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 619it [00:03, 378.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4961/8000 [05:53<03:04, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 660it [00:03, 386.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4966/8000 [05:53<03:07, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 703it [00:03, 397.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4970/8000 [05:53<02:46, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 744it [00:03, 383.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4975/8000 [05:53<02:40, 18.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 221.09it/s]\n",
+      "Generating signals:  62%|██████████▌      | 4980/8000 [05:54<02:54, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  62%|██████████▌      | 4982/8000 [05:54<03:13, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4986/8000 [05:54<02:59, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 175\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4989/8000 [05:54<02:54, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 171\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4992/8000 [05:54<02:47, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:00<00:00, 152\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4994/8000 [05:55<03:00, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:00<00:00, 161\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4996/8000 [05:55<03:13, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  62%|██████████▌      | 4999/8000 [05:55<03:14, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  63%|██████████▋      | 5001/8000 [05:55<04:33, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5005/8000 [05:56<03:38, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎               | 166/8000 [00:00<00:06, 1257.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5007/8000 [05:56<03:45, 13.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋               | 335/8000 [00:00<00:06, 1098.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5011/8000 [05:56<03:00, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█               | 514/8000 [00:00<00:06, 1189.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5015/8000 [05:56<03:46, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▎              | 687/8000 [00:00<00:06, 1184.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5018/8000 [05:56<03:30, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▋              | 854/8000 [00:00<00:06, 1093.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5022/8000 [05:57<03:07, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|█▉             | 1019/8000 [00:00<00:06, 1075.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5027/8000 [05:57<02:40, 18.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▏            | 1195/8000 [00:01<00:06, 1089.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5030/8000 [05:57<02:34, 19.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1382/8000 [00:02<00:25, 255.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5036/8000 [05:57<02:04, 23.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1555/8000 [00:03<00:19, 329.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5039/8000 [05:58<03:41, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1732/8000 [00:03<00:14, 418.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5042/8000 [05:58<03:30, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1905/8000 [00:03<00:11, 510.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5045/8000 [05:58<03:21, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2086/8000 [00:03<00:09, 612.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5051/8000 [05:59<03:39, 13.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2262/8000 [00:03<00:08, 695.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5057/8000 [05:59<02:53, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2433/8000 [00:03<00:07, 760.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5060/8000 [05:59<04:01, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2614/8000 [00:04<00:06, 865.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5063/8000 [06:00<04:24, 11.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2792/8000 [00:04<00:05, 954.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5069/8000 [06:00<03:40, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▌         | 2965/8000 [00:04<00:04, 1015.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5074/8000 [06:00<03:05, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|█████▊         | 3132/8000 [00:04<00:04, 1040.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▊      | 5078/8000 [06:00<03:21, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▏        | 3298/8000 [00:04<00:04, 1059.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5082/8000 [06:01<03:29, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▌        | 3477/8000 [00:04<00:04, 1037.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5084/8000 [06:01<03:38, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|██████▊        | 3644/8000 [00:05<00:04, 1011.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5089/8000 [06:01<02:53, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▏       | 3816/8000 [00:05<00:03, 1068.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5093/8000 [06:01<02:40, 18.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▍       | 3991/8000 [00:05<00:03, 1118.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5095/8000 [06:02<03:36, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|███████▊       | 4168/8000 [00:05<00:03, 1108.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5098/8000 [06:02<03:13, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▏      | 4340/8000 [00:05<00:03, 1168.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5101/8000 [06:02<03:40, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▍      | 4507/8000 [00:05<00:02, 1195.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5106/8000 [06:02<03:32, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|████████▊      | 4685/8000 [00:05<00:02, 1196.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5111/8000 [06:03<02:46, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████      | 4862/8000 [00:06<00:02, 1253.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▊      | 5116/8000 [06:03<03:33, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|█████████▍     | 5046/8000 [00:06<00:02, 1293.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5119/8000 [06:03<03:18, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|█████████▊     | 5214/8000 [00:06<00:02, 1273.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5124/8000 [06:03<02:45, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████     | 5387/8000 [00:06<00:02, 1221.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5128/8000 [06:04<02:32, 18.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|██████████▍    | 5568/8000 [00:06<00:02, 1159.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5131/8000 [06:04<02:31, 18.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|██████████▊    | 5744/8000 [00:06<00:02, 1115.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5136/8000 [06:04<02:47, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████    | 5907/8000 [00:06<00:01, 1142.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5138/8000 [06:04<03:01, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|███████████▍   | 6079/8000 [00:07<00:01, 1054.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5142/8000 [06:05<03:20, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|███████████▋   | 6250/8000 [00:07<00:01, 1100.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5145/8000 [06:05<03:07, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████   | 6421/8000 [00:07<00:01, 1028.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5147/8000 [06:05<04:23, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6610/8000 [00:08<00:04, 321.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5151/8000 [06:05<03:39, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6781/8000 [00:09<00:02, 412.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5154/8000 [06:06<03:22, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6954/8000 [00:09<00:02, 496.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5160/8000 [06:06<02:29, 18.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7133/8000 [00:09<00:01, 617.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5167/8000 [06:06<01:57, 24.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7302/8000 [00:09<00:01, 626.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████▉      | 5175/8000 [06:06<01:59, 23.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7479/8000 [00:09<00:00, 657.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5178/8000 [06:06<02:00, 23.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7662/8000 [00:09<00:00, 743.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5182/8000 [06:07<02:17, 20.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7837/8000 [00:10<00:00, 764.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5185/8000 [06:07<02:49, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8003it [00:10, 768.58it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  65%|███████████      | 5188/8000 [06:07<02:44, 17.09it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  65%|███████████      | 5190/8000 [06:07<02:55, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5192/8000 [06:07<03:06, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<26:20,  7.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5196/8000 [06:08<02:46, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<14:03,  4.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5200/8000 [06:08<02:30, 18.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:11<09:49,  2.99s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5203/8000 [06:08<02:36, 17.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:50,  2.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5206/8000 [06:08<02:34, 18.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<06:43,  2.07s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5210/8000 [06:08<02:17, 20.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:07,  1.89s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5213/8000 [06:09<03:01, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:46,  1.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5220/8000 [06:09<02:40, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:33,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5222/8000 [06:09<02:50, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:17,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5225/8000 [06:09<02:50, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:22<05:23,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5228/8000 [06:10<03:20, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<05:16,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5231/8000 [06:10<03:54, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:25<05:01,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5233/8000 [06:10<03:47, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:48,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▏     | 5238/8000 [06:10<03:22, 13.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<04:57,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5240/8000 [06:11<03:21, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:30<05:13,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5243/8000 [06:11<03:07, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:32<05:25,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5247/8000 [06:11<03:11, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:33<05:17,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5251/8000 [06:11<02:42, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:35<05:21,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5254/8000 [06:11<02:42, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:37<05:04,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5257/8000 [06:11<02:34, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:38<04:48,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5259/8000 [06:12<02:54, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:40<04:39,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5263/8000 [06:12<02:27, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:41<04:29,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5267/8000 [06:12<02:31, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:43<05:07,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5272/8000 [06:12<02:05, 21.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:46<05:47,  1.98s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5277/8000 [06:13<02:22, 19.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:47<05:19,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5280/8000 [06:13<02:30, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:49<05:05,  1.76s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5282/8000 [06:13<03:28, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:50<04:45,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5285/8000 [06:13<04:21, 10.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:52<04:29,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▏     | 5290/8000 [06:14<03:11, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:53<04:16,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5295/8000 [06:14<03:04, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:54<04:11,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5300/8000 [06:14<03:21, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:56<04:05,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5304/8000 [06:15<03:00, 14.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:57<04:01,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5308/8000 [06:15<03:32, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:59<03:59,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5313/8000 [06:15<03:19, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [01:00<03:58,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5319/8000 [06:16<02:52, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:01<03:55,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5322/8000 [06:16<02:42, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:03<04:01,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5328/8000 [06:16<02:57, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:04<03:56,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5331/8000 [06:16<02:48, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:06<03:51,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5337/8000 [06:17<02:13, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:07<03:46,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5340/8000 [06:17<02:37, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:09<03:45,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5343/8000 [06:17<02:33, 17.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:10<03:46,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5347/8000 [06:17<02:49, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:11<03:41,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▎     | 5351/8000 [06:18<03:02, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:13<03:41,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5357/8000 [06:18<02:49, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:14<03:37,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5363/8000 [06:18<02:10, 20.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:16<03:37,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5366/8000 [06:18<02:39, 16.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:17<03:34,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5369/8000 [06:19<02:54, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:18<03:34,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5371/8000 [06:19<02:59, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:20<03:42,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5374/8000 [06:19<02:55, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:21<03:38,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5381/8000 [06:19<02:25, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:23<03:34,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5385/8000 [06:19<02:16, 19.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:26<04:11,  1.69s/it]\n",
+      "Generating signals:  67%|███████████▍     | 5387/8000 [06:20<02:34, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  67%|███████████▍     | 5389/8000 [06:20<02:45, 15.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5396/8000 [06:20<02:20, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.60it/s]\n",
+      "Generating signals:  68%|███████████▍     | 5400/8000 [06:20<02:10, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  68%|███████████▍     | 5402/8000 [06:21<02:55, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▍     | 5404/8000 [06:21<03:00, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.91s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▍     | 5408/8000 [06:21<03:01, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 40it [00:02, 27.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▍     | 5411/8000 [06:21<02:49, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 80it [00:02, 60.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5413/8000 [06:22<03:35, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 119it [00:02, 98.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5419/8000 [06:22<02:20, 18.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 153it [00:02, 132.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5423/8000 [06:22<02:07, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 195it [00:02, 179.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5427/8000 [06:22<01:58, 21.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 235it [00:02, 221.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5431/8000 [06:22<02:59, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 278it [00:02, 265.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5433/8000 [06:23<02:59, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 325it [00:02, 313.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5436/8000 [06:23<02:47, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 367it [00:02, 336.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5438/8000 [06:23<02:52, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 409it [00:02, 335.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5442/8000 [06:23<02:50, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 448it [00:03, 336.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5444/8000 [06:23<03:03, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 486it [00:03, 337.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5447/8000 [06:24<03:21, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 523it [00:03, 337.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5450/8000 [06:24<03:37, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 563it [00:03, 353.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5455/8000 [06:24<02:36, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 608it [00:03, 377.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5464/8000 [06:24<01:59, 21.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 647it [00:03, 373.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5467/8000 [06:25<02:00, 20.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 690it [00:03, 387.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▌     | 5470/8000 [06:25<02:03, 20.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 730it [00:03, 379.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▋     | 5473/8000 [06:25<02:36, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 199.97it/s]\n",
+      "Generating signals:  68%|███████████▋     | 5477/8000 [06:25<02:17, 18.30it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  68%|███████████▋     | 5479/8000 [06:25<02:29, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5482/8000 [06:25<02:22, 17.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 174\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5485/8000 [06:26<02:18, 18.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 165\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5490/8000 [06:26<01:49, 22.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 182\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5496/8000 [06:26<02:00, 20.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  76%|▊| 76/100 [00:00<00:00, 168\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5500/8000 [06:27<02:41, 15.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "Generating signals:  69%|███████████▋     | 5503/8000 [06:27<02:30, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [05:49<17:28, 349.57s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.721185662807849\n",
-      "2999: -6.2085909\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  69%|███████████▋     | 5507/8000 [06:27<02:10, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  69%|███████████▋     | 5511/8000 [06:27<02:00, 20.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5514/8000 [06:27<02:21, 17.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:24,  7.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5519/8000 [06:28<02:24, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<13:04,  3.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5521/8000 [06:28<02:33, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:07,  2.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5524/8000 [06:28<02:22, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:12,  2.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▋     | 5527/8000 [06:28<02:16, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:10,  1.90s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5530/8000 [06:28<02:11, 18.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:38,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5532/8000 [06:28<02:57, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:14,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5538/8000 [06:29<01:58, 20.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<04:57,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5542/8000 [06:29<01:50, 22.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:44,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5548/8000 [06:29<01:29, 27.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:39,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5553/8000 [06:29<01:22, 29.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:36,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5560/8000 [06:29<02:01, 20.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:35,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5565/8000 [06:30<02:28, 16.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:30,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5572/8000 [06:30<02:13, 18.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:24,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5576/8000 [06:31<02:24, 16.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:24,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5578/8000 [06:31<03:19, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:29,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5580/8000 [06:31<03:52, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:27,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5584/8000 [06:31<03:07, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:25,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▊     | 5587/8000 [06:32<02:51, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:19,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5589/8000 [06:32<02:58, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:11,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5592/8000 [06:32<03:14, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:10,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5594/8000 [06:32<03:46, 10.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:09,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5598/8000 [06:33<02:56, 13.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:05,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5600/8000 [06:33<02:55, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<04:04,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5603/8000 [06:33<02:34, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<03:59,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5606/8000 [06:33<02:28, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<03:59,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5609/8000 [06:33<02:54, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:43<04:00,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5611/8000 [06:33<02:55, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:45<03:57,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5614/8000 [06:34<02:48, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:46<03:56,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5620/8000 [06:34<01:55, 20.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<03:54,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5623/8000 [06:34<01:55, 20.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:49<03:51,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5628/8000 [06:34<02:07, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:50<03:53,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5632/8000 [06:35<02:43, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<03:53,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5634/8000 [06:35<03:14, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<03:58,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5636/8000 [06:35<03:09, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:55<03:53,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████▉     | 5638/8000 [06:35<03:07, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:52,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▉     | 5642/8000 [06:35<02:26, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:49,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▉     | 5646/8000 [06:36<02:29, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:59<03:51,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5649/8000 [06:36<02:22, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:50,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5652/8000 [06:36<02:14, 17.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:02<03:48,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5655/8000 [06:36<02:11, 17.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:03<03:46,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5657/8000 [06:36<02:18, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:05<03:46,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5661/8000 [06:37<02:35, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:06<03:44,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5664/8000 [06:37<02:23, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:08<03:50,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5667/8000 [06:37<02:16, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:09<03:41,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5670/8000 [06:37<02:09, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:10<03:37,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5673/8000 [06:37<02:35, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:12<03:36,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5675/8000 [06:37<02:36, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:13<03:36,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5678/8000 [06:38<03:04, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:15<03:33,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5682/8000 [06:38<03:06, 12.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:16<03:30,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5686/8000 [06:38<02:53, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:17<03:30,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5688/8000 [06:38<02:49, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:19<03:33,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5691/8000 [06:39<02:26, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:21<03:39,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5694/8000 [06:39<02:11, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:22<03:37,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5696/8000 [06:39<02:21, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:23<03:32,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5700/8000 [06:39<02:35, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:25<03:28,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████     | 5702/8000 [06:39<02:40, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:26<03:30,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5707/8000 [06:39<01:56, 19.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:28<03:30,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5711/8000 [06:40<02:12, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:29<03:24,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5714/8000 [06:40<02:09, 17.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:31<03:20,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▏    | 5716/8000 [06:40<02:15, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:32<03:16,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5721/8000 [06:40<02:26, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:34<03:17,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5724/8000 [06:41<02:18, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:35<03:16,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5726/8000 [06:41<02:23, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:36<03:17,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5728/8000 [06:41<03:08, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:38<03:15,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5733/8000 [06:41<02:15, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:39<03:12,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5736/8000 [06:42<03:13, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:41<03:11,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5738/8000 [06:42<03:06, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:42<03:11,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5741/8000 [06:42<02:39, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:44<03:07,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5746/8000 [06:42<02:00, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:45<03:06,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5753/8000 [06:42<01:47, 20.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:46<03:04,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5758/8000 [06:43<01:56, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:48<03:01,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5761/8000 [06:43<02:16, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:49<02:59,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5764/8000 [06:43<02:07, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:52<03:11,  1.52s/it]\n",
+      "Generating signals:  72%|████████████▎    | 5767/8000 [06:43<02:04, 17.89it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  72%|████████████▎    | 5769/8000 [06:43<02:14, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5771/8000 [06:44<03:27, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.62it/s]\n",
+      "Generating signals:  72%|████████████▎    | 5774/8000 [06:44<02:54, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  72%|████████████▎    | 5778/8000 [06:44<02:51, 12.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5781/8000 [06:44<02:33, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5784/8000 [06:44<02:17, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 45it [00:01, 39.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5789/8000 [06:45<01:49, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 75it [00:01, 69.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5795/8000 [06:45<01:53, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 115it [00:01, 114.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▎    | 5800/8000 [06:45<02:25, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 148it [00:01, 146.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5803/8000 [06:46<02:42, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 185it [00:01, 187.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5806/8000 [06:46<02:29, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 225it [00:02, 231.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5811/8000 [06:46<02:21, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 265it [00:02, 270.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5816/8000 [06:46<01:56, 18.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 302it [00:02, 288.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5819/8000 [06:46<01:51, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 348it [00:02, 325.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▎    | 5822/8000 [06:47<01:47, 20.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 387it [00:02, 340.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5825/8000 [06:47<01:49, 19.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 425it [00:02, 338.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5829/8000 [06:47<02:03, 17.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 464it [00:02, 352.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5833/8000 [06:47<01:47, 20.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 502it [00:02, 345.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5837/8000 [06:47<01:39, 21.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 542it [00:02, 359.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5841/8000 [06:48<02:24, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 580it [00:03, 365.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5844/8000 [06:48<02:31, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 618it [00:03, 356.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5846/8000 [06:48<02:32, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 655it [00:03, 348.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5850/8000 [06:48<02:02, 17.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 700it [00:03, 376.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5857/8000 [06:49<02:05, 17.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 739it [00:03, 376.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5860/8000 [06:49<02:23, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 219.39it/s]\n",
+      "Generating signals:  73%|████████████▍    | 5863/8000 [06:49<02:13, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  73%|████████████▍    | 5866/8000 [06:49<02:06, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5872/8000 [06:50<01:53, 18.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 127\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5875/8000 [06:50<01:50, 19.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:00, 124\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▍    | 5880/8000 [06:50<02:10, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 146\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5886/8000 [06:50<01:41, 20.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:00<00:00, 140\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5890/8000 [06:50<01:34, 22.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:00<00:00, 152\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5894/8000 [06:51<02:12, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  74%|████████████▌    | 5901/8000 [06:51<01:36, 21.72it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  74%|████████████▌    | 5911/8000 [06:51<01:16, 27.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5917/8000 [06:52<01:20, 25.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 50/8000 [00:00<00:24, 326.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5923/8000 [06:52<01:11, 29.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 95/8000 [00:00<00:25, 308.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5931/8000 [06:52<00:59, 34.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 146/8000 [00:00<00:25, 304.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5936/8000 [06:52<01:25, 24.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 197/8000 [00:00<00:22, 342.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5941/8000 [06:53<01:31, 22.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 247/8000 [00:00<00:23, 332.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5944/8000 [06:53<01:46, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 298/8000 [00:00<00:24, 319.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5950/8000 [06:53<01:45, 19.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 343/8000 [00:01<00:24, 312.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5953/8000 [06:53<01:42, 19.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 395/8000 [00:01<00:23, 329.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5956/8000 [06:53<02:01, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 447/8000 [00:01<00:22, 341.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▋    | 5959/8000 [06:54<01:56, 17.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 497/8000 [00:01<00:21, 345.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5961/8000 [06:54<02:04, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 554/8000 [00:01<00:20, 355.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5963/8000 [06:54<02:10, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 600/8000 [00:01<00:21, 345.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5965/8000 [06:54<02:16, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 650/8000 [00:01<00:20, 361.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5968/8000 [06:54<02:32, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 694/8000 [00:02<00:22, 331.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5971/8000 [06:54<02:09, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 733/8000 [00:02<00:24, 301.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5975/8000 [06:55<02:10, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 781/8000 [00:02<00:24, 294.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5977/8000 [06:55<02:10, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 824/8000 [00:02<00:25, 283.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5984/8000 [06:55<01:26, 23.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 874/8000 [00:02<00:23, 307.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5988/8000 [06:55<01:19, 25.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 925/8000 [00:02<00:25, 279.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5991/8000 [06:55<01:29, 22.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 962/8000 [00:03<00:26, 264.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5998/8000 [06:56<01:26, 23.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1019/8000 [00:03<00:23, 297.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6001/8000 [06:56<01:46, 18.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1070/8000 [00:03<00:21, 315.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6008/8000 [06:56<01:32, 21.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1120/8000 [00:03<00:21, 314.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6016/8000 [06:56<01:23, 23.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1173/8000 [00:03<00:22, 304.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6020/8000 [06:57<01:19, 24.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1223/8000 [00:03<00:21, 317.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6025/8000 [06:57<01:14, 26.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1264/8000 [00:04<00:23, 290.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6028/8000 [06:57<01:35, 20.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1333/8000 [00:05<01:09, 95.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6031/8000 [06:57<01:36, 20.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1393/8000 [00:05<00:52, 126.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6034/8000 [06:57<01:36, 20.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1446/8000 [00:05<00:41, 158.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6037/8000 [06:58<01:57, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1491/8000 [00:05<00:35, 184.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▊    | 6039/8000 [06:58<02:21, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1539/8000 [00:06<00:31, 207.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6043/8000 [06:58<01:52, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1588/8000 [00:06<00:25, 246.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6046/8000 [06:58<01:46, 18.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1631/8000 [00:06<00:24, 264.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6050/8000 [06:58<01:32, 21.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1680/8000 [00:06<00:22, 286.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6053/8000 [06:59<02:21, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1731/8000 [00:06<00:20, 311.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▊    | 6055/8000 [06:59<02:19, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1786/8000 [00:06<00:17, 346.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6061/8000 [06:59<01:39, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1833/8000 [00:06<00:17, 353.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6064/8000 [06:59<01:57, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1872/8000 [00:07<00:18, 336.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6066/8000 [06:59<02:01, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1924/8000 [00:07<00:16, 364.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6069/8000 [07:00<02:46, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1979/8000 [00:07<00:16, 368.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6071/8000 [07:00<02:42, 11.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2030/8000 [00:07<00:16, 371.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6077/8000 [07:00<01:48, 17.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2077/8000 [00:07<00:15, 377.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6083/8000 [07:00<01:41, 18.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2133/8000 [00:07<00:15, 380.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6087/8000 [07:01<01:32, 20.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2209/8000 [00:07<00:16, 349.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6090/8000 [07:01<01:30, 21.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2251/8000 [00:08<00:17, 333.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6095/8000 [07:01<01:57, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2293/8000 [00:08<00:16, 337.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6097/8000 [07:01<01:59, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2338/8000 [00:08<00:17, 316.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6101/8000 [07:02<02:02, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2388/8000 [00:08<00:17, 312.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6103/8000 [07:02<02:02, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2434/8000 [00:08<00:17, 324.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6107/8000 [07:02<02:05, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2471/8000 [00:08<00:17, 309.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6109/8000 [07:02<02:09, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2522/8000 [00:08<00:17, 318.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6111/8000 [07:02<02:08, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2573/8000 [00:09<00:16, 337.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6113/8000 [07:02<02:06, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2616/8000 [00:09<00:16, 329.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6117/8000 [07:03<02:12, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2660/8000 [00:09<00:16, 328.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████    | 6119/8000 [07:03<02:14, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2711/8000 [00:09<00:15, 332.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6123/8000 [07:03<01:48, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2763/8000 [00:09<00:14, 364.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6127/8000 [07:03<01:36, 19.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2810/8000 [00:09<00:13, 377.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6130/8000 [07:03<01:36, 19.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2853/8000 [00:09<00:14, 346.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6134/8000 [07:04<01:55, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2902/8000 [00:09<00:14, 350.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6137/8000 [07:04<01:57, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2957/8000 [00:10<00:13, 370.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6139/8000 [07:04<02:01, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3015/8000 [00:10<00:13, 378.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6144/8000 [07:04<01:34, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3068/8000 [00:10<00:12, 381.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6148/8000 [07:04<01:48, 17.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3156/8000 [00:10<00:13, 360.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6151/8000 [07:05<02:38, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3196/8000 [00:10<00:13, 352.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6153/8000 [07:05<02:31, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▍         | 3241/8000 [00:10<00:13, 358.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6155/8000 [07:05<02:30, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3335/8000 [00:11<00:12, 368.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6158/8000 [07:06<02:48, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3399/8000 [00:11<00:12, 377.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6161/8000 [07:06<02:23, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3447/8000 [00:11<00:12, 361.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6163/8000 [07:06<02:21, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3494/8000 [00:11<00:12, 350.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6166/8000 [07:06<03:10,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3542/8000 [00:11<00:14, 311.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6168/8000 [07:06<03:05,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3586/8000 [00:11<00:14, 312.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6170/8000 [07:07<02:58, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3633/8000 [00:12<00:13, 325.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6177/8000 [07:07<01:59, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3685/8000 [00:12<00:12, 346.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6182/8000 [07:07<01:34, 19.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3729/8000 [00:12<00:12, 349.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6185/8000 [07:07<01:35, 19.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3791/8000 [00:12<00:10, 386.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6188/8000 [07:07<01:31, 19.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3841/8000 [00:12<00:10, 380.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6192/8000 [07:08<01:22, 21.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3884/8000 [00:12<00:11, 347.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6196/8000 [07:08<01:25, 21.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3942/8000 [00:12<00:11, 363.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▏   | 6199/8000 [07:08<01:46, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3990/8000 [00:13<00:11, 345.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6203/8000 [07:08<01:32, 19.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4036/8000 [00:13<00:11, 330.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6210/8000 [07:08<01:24, 21.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4079/8000 [00:13<00:11, 332.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6214/8000 [07:09<01:59, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4128/8000 [00:13<00:11, 348.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6217/8000 [07:09<01:50, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4191/8000 [00:14<00:34, 109.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6222/8000 [07:09<01:32, 19.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4240/8000 [00:14<00:27, 136.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6225/8000 [07:10<01:56, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4296/8000 [00:14<00:21, 175.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▏   | 6235/8000 [07:10<01:07, 26.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4354/8000 [00:15<00:16, 218.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6240/8000 [07:10<01:34, 18.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4397/8000 [00:15<00:15, 227.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6243/8000 [07:11<02:05, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4434/8000 [00:15<00:15, 232.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6247/8000 [07:11<01:48, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4480/8000 [00:15<00:13, 257.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6252/8000 [07:11<01:49, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4524/8000 [00:15<00:12, 286.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6256/8000 [07:11<01:39, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4575/8000 [00:15<00:11, 294.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6266/8000 [07:11<01:14, 23.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4618/8000 [00:15<00:10, 307.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6269/8000 [07:12<01:16, 22.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4679/8000 [00:16<00:09, 342.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6273/8000 [07:12<01:13, 23.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4726/8000 [00:16<00:10, 326.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6276/8000 [07:12<01:36, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4765/8000 [00:16<00:09, 323.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6280/8000 [07:12<01:45, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4803/8000 [00:16<00:10, 319.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6284/8000 [07:13<01:31, 18.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4857/8000 [00:16<00:08, 352.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6291/8000 [07:13<01:14, 22.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4919/8000 [00:16<00:08, 373.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▎   | 6294/8000 [07:13<01:18, 21.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4969/8000 [00:16<00:08, 372.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6298/8000 [07:13<01:52, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5020/8000 [00:17<00:08, 365.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6300/8000 [07:14<01:55, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5074/8000 [00:17<00:07, 383.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6303/8000 [07:14<01:44, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5133/8000 [00:17<00:07, 390.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6306/8000 [07:14<02:06, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5178/8000 [00:17<00:07, 360.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6309/8000 [07:14<01:54, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5229/8000 [00:17<00:07, 369.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6311/8000 [07:15<03:00,  9.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5283/8000 [00:17<00:06, 393.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6313/8000 [07:15<03:21,  8.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5332/8000 [00:17<00:06, 390.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6315/8000 [07:15<03:00,  9.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5433/8000 [00:18<00:07, 344.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6318/8000 [07:15<02:25, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5480/8000 [00:18<00:07, 332.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6320/8000 [07:16<02:55,  9.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5520/8000 [00:18<00:07, 325.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6322/8000 [07:16<02:42, 10.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5566/8000 [00:18<00:07, 320.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6324/8000 [07:16<02:30, 11.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5611/8000 [00:18<00:07, 328.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6327/8000 [07:16<02:10, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5657/8000 [00:18<00:06, 341.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6330/8000 [07:16<01:50, 15.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5715/8000 [00:19<00:06, 368.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6334/8000 [07:17<01:57, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5763/8000 [00:19<00:06, 367.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6339/8000 [07:17<01:57, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5816/8000 [00:19<00:05, 373.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6341/8000 [07:17<02:36, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5857/8000 [00:19<00:05, 361.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6345/8000 [07:17<02:05, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5912/8000 [00:19<00:05, 365.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6347/8000 [07:18<02:28, 11.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5951/8000 [00:19<00:05, 344.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6350/8000 [07:18<02:36, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5998/8000 [00:19<00:05, 356.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▍   | 6352/8000 [07:18<02:32, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6040/8000 [00:19<00:05, 352.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▌   | 6354/8000 [07:18<02:26, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6099/8000 [00:20<00:04, 392.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▌   | 6358/8000 [07:19<01:54, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6149/8000 [00:20<00:05, 358.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6360/8000 [07:19<02:28, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6206/8000 [00:20<00:04, 387.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6362/8000 [07:19<02:26, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6295/8000 [00:20<00:04, 367.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6367/8000 [07:19<01:48, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6351/8000 [00:20<00:04, 382.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6369/8000 [07:20<02:43,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6398/8000 [00:20<00:04, 373.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6374/8000 [07:20<01:51, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6445/8000 [00:21<00:04, 349.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6377/8000 [07:20<01:44, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6500/8000 [00:21<00:04, 360.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6381/8000 [07:20<01:50, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6553/8000 [00:21<00:04, 361.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6383/8000 [07:20<01:55, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6608/8000 [00:21<00:03, 390.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6385/8000 [07:21<01:56, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6649/8000 [00:21<00:03, 372.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6388/8000 [07:21<01:53, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6693/8000 [00:21<00:03, 357.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6390/8000 [07:21<02:00, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▌  | 6753/8000 [00:21<00:03, 372.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6393/8000 [07:21<01:45, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6803/8000 [00:21<00:03, 366.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6395/8000 [07:21<01:48, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6854/8000 [00:22<00:03, 367.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6398/8000 [07:21<01:36, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6903/8000 [00:22<00:02, 377.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6401/8000 [07:22<01:36, 16.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6947/8000 [00:22<00:02, 353.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6403/8000 [07:22<01:45, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7007/8000 [00:22<00:02, 359.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▌   | 6408/8000 [07:22<01:18, 20.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7061/8000 [00:22<00:02, 398.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6413/8000 [07:22<01:44, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7109/8000 [00:22<00:02, 357.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6419/8000 [07:23<01:21, 19.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7166/8000 [00:22<00:02, 378.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6423/8000 [07:23<01:17, 20.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7223/8000 [00:23<00:02, 385.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6426/8000 [07:23<01:15, 20.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7271/8000 [00:23<00:02, 358.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6429/8000 [07:23<01:15, 20.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▋ | 7316/8000 [00:23<00:01, 366.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6436/8000 [07:23<00:58, 26.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7370/8000 [00:23<00:01, 385.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6442/8000 [07:23<00:51, 29.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7416/8000 [00:23<00:01, 344.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6447/8000 [07:24<01:07, 23.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7471/8000 [00:23<00:01, 373.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6450/8000 [07:24<01:24, 18.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7511/8000 [00:23<00:01, 327.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6453/8000 [07:24<01:39, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7565/8000 [00:24<00:01, 351.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6456/8000 [07:24<01:34, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7616/8000 [00:24<00:01, 297.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6461/8000 [07:25<01:15, 20.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7673/8000 [00:24<00:01, 298.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▋   | 6467/8000 [07:25<01:15, 20.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7719/8000 [00:24<00:01, 279.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6471/8000 [07:25<01:10, 21.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7769/8000 [00:24<00:00, 273.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6477/8000 [07:25<01:12, 21.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7822/8000 [00:25<00:00, 279.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6484/8000 [07:25<00:56, 26.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7874/8000 [00:25<00:00, 287.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6491/8000 [07:26<01:25, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7919/8000 [00:25<00:00, 295.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6495/8000 [07:26<01:22, 18.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7960/8000 [00:25<00:00, 297.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6498/8000 [07:27<01:46, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8009it [00:25, 312.00it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  81%|█████████████▊   | 6500/8000 [07:27<02:28, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  81%|█████████████▊   | 6502/8000 [07:27<02:22, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6506/8000 [07:27<01:56, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:47,  7.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6512/8000 [07:28<01:24, 17.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:10,  3.99s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6515/8000 [07:28<01:41, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:22,  2.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|█████████████▊   | 6518/8000 [07:28<01:54, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:40,  2.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▊   | 6521/8000 [07:28<01:45, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:34,  2.02s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▊   | 6523/8000 [07:29<01:45, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:02,  1.87s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▊   | 6525/8000 [07:29<01:50, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:35,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6530/8000 [07:29<01:44, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:19,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6532/8000 [07:29<01:45, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:10,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6537/8000 [07:29<01:25, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<05:12,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6542/8000 [07:30<01:30, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<05:15,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6544/8000 [07:30<01:53, 12.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<05:00,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6548/8000 [07:30<01:34, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:47,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6552/8000 [07:31<02:18, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:40,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6555/8000 [07:31<02:00, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<04:58,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6557/8000 [07:31<01:59, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:42,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6561/8000 [07:32<01:54, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:35,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6567/8000 [07:32<01:24, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:36,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6574/8000 [07:32<01:16, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:35<04:36,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6578/8000 [07:32<01:23, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:30,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6580/8000 [07:33<01:34, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:29,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6583/8000 [07:33<01:39, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:27,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6586/8000 [07:33<01:31, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:41<04:25,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▉   | 6588/8000 [07:33<02:03, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:16,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6591/8000 [07:33<01:55, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:17,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6595/8000 [07:34<01:54, 12.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:12,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████   | 6599/8000 [07:34<01:53, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:47<04:30,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6602/8000 [07:34<01:48, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:49<05:18,  1.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6604/8000 [07:34<01:44, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:51<04:56,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6609/8000 [07:35<01:31, 15.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:52<04:36,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6611/8000 [07:35<01:53, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:53<04:24,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6613/8000 [07:35<01:49, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:55<04:14,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6615/8000 [07:35<02:16, 10.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:56<04:07,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6618/8000 [07:36<01:52, 12.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:58<04:03,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6621/8000 [07:36<01:39, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:59<03:58,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6628/8000 [07:36<01:05, 20.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:00<03:51,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6632/8000 [07:36<01:04, 21.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:02<03:49,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6635/8000 [07:37<01:43, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:03<03:45,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6641/8000 [07:37<01:16, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:05<03:50,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6644/8000 [07:37<01:12, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:06<03:52,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6647/8000 [07:37<01:14, 18.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:08<03:49,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6651/8000 [07:37<01:25, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:09<03:44,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6655/8000 [07:38<01:22, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:10<03:49,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6662/8000 [07:38<01:05, 20.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:12<03:54,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6667/8000 [07:38<01:01, 21.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:14<03:52,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6670/8000 [07:39<01:44, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:15<03:44,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6672/8000 [07:39<02:07, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:16<03:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6674/8000 [07:39<02:33,  8.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:18<03:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▏  | 6678/8000 [07:40<02:13,  9.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:19<03:40,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6683/8000 [07:40<02:06, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:21<03:33,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6685/8000 [07:40<02:03, 10.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:22<03:35,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6689/8000 [07:41<01:39, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:24<03:31,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6691/8000 [07:41<02:04, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:25<03:28,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6693/8000 [07:41<02:25,  8.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:26<03:27,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6696/8000 [07:41<02:04, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:28<03:26,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6701/8000 [07:42<01:34, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:29<03:22,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6703/8000 [07:42<02:01, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:31<03:22,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6708/8000 [07:42<01:47, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:32<03:23,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6712/8000 [07:43<01:48, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:33<03:21,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6717/8000 [07:43<01:23, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:35<03:18,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6719/8000 [07:43<01:28, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:36<03:15,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6723/8000 [07:43<01:19, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:39<03:41,  1.61s/it]\n",
+      "Generating signals:  84%|██████████████▎  | 6725/8000 [07:44<02:19,  9.13it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  84%|██████████████▎  | 6729/8000 [07:44<01:51, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6731/8000 [07:44<01:52, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6736/8000 [07:44<01:26, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.74it/s]\n",
+      "Generating signals:  84%|██████████████▎  | 6739/8000 [07:45<01:26, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  84%|██████████████▎  | 6741/8000 [07:45<01:56, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6743/8000 [07:45<01:53, 11.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6746/8000 [07:45<01:40, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 39it [00:01, 34.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6749/8000 [07:45<01:29, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 78it [00:01, 73.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6751/8000 [07:46<01:34, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 114it [00:01, 113.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6753/8000 [07:46<01:46, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 160it [00:01, 169.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6755/8000 [07:46<01:46, 11.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 197it [00:01, 205.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▎  | 6758/8000 [07:47<02:23,  8.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 233it [00:02, 233.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▎  | 6761/8000 [07:47<01:57, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 273it [00:02, 271.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▎  | 6763/8000 [07:47<01:55, 10.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 313it [00:02, 299.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6766/8000 [07:47<01:40, 12.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 352it [00:02, 321.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6769/8000 [07:47<01:33, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 395it [00:02, 346.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6771/8000 [07:47<01:31, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 434it [00:02, 342.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6776/8000 [07:48<01:07, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 471it [00:02, 326.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6779/8000 [07:48<01:10, 17.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 506it [00:02, 324.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6783/8000 [07:48<01:06, 18.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 550it [00:02, 354.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6785/8000 [07:48<01:33, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 589it [00:03, 362.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6790/8000 [07:49<01:18, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 631it [00:03, 376.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6793/8000 [07:49<01:17, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 670it [00:03, 366.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6796/8000 [07:49<01:16, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 708it [00:03, 365.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6798/8000 [07:49<01:19, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 745it [00:03, 350.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6801/8000 [07:49<01:14, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 220.83it/s]\n",
+      "Generating signals:  85%|██████████████▍  | 6803/8000 [07:50<01:49, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  85%|██████████████▍  | 6807/8000 [07:50<01:24, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6810/8000 [07:50<01:36, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00<00:00, 208\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6814/8000 [07:50<01:21, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 154\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6816/8000 [07:50<01:26, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:00<00:00, 162\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6818/8000 [07:51<01:50, 10.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:00<00:00, 164\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6823/8000 [07:51<01:16, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  85%|██████████████▌  | 6826/8000 [07:51<01:14, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [11:50<11:52, 356.28s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.450832561585959\n",
-      "2999: -6.1574654\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  85%|██████████████▌  | 6830/8000 [07:51<01:07, 17.24it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  85%|██████████████▌  | 6833/8000 [07:52<01:28, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▌  | 6836/8000 [07:52<01:42, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:44,  7.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6840/8000 [07:52<01:22, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:14,  4.01s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6843/8000 [07:52<01:30, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:37,  2.93s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6845/8000 [07:53<01:32, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:45,  2.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6847/8000 [07:53<01:29, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:42,  2.07s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6849/8000 [07:53<01:50, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:03,  1.87s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6851/8000 [07:53<01:50, 10.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:34,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6857/8000 [07:54<01:13, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:19,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6859/8000 [07:54<01:19, 14.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:04,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6864/8000 [07:54<01:04, 17.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<04:59,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6867/8000 [07:54<01:06, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:49,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6870/8000 [07:55<01:32, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:43,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6876/8000 [07:55<01:07, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:42,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6878/8000 [07:55<01:14, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:43,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6880/8000 [07:55<01:23, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:40,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6883/8000 [07:55<01:23, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:37,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6887/8000 [07:56<01:11, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:35,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6891/8000 [07:56<01:05, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:32,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6894/8000 [07:56<01:08, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:39,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6897/8000 [07:56<01:08, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:32,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6899/8000 [07:56<01:18, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<04:28,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6901/8000 [07:56<01:19, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:23,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6904/8000 [07:57<01:13, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:25,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6908/8000 [07:57<01:04, 17.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:22,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6910/8000 [07:57<01:10, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:20,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6913/8000 [07:57<01:26, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:20,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6915/8000 [07:58<01:30, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:46<04:27,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▋  | 6920/8000 [07:58<01:13, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:48<04:20,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6922/8000 [07:58<01:18, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:49<04:13,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6925/8000 [07:58<01:16, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:51<04:13,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6928/8000 [07:58<01:08, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:52<04:23,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6930/8000 [07:59<01:31, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:54<04:21,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6932/8000 [07:59<01:36, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:55<04:16,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6935/8000 [07:59<01:25, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:57<04:13,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▋  | 6938/8000 [07:59<01:15, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:58<04:09,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6944/8000 [07:59<00:54, 19.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:00<04:07,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6948/8000 [08:00<00:53, 19.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:01<04:01,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6955/8000 [08:00<00:40, 26.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:03<03:54,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6959/8000 [08:00<00:45, 22.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:04<03:56,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6962/8000 [08:00<01:16, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:06<03:54,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6965/8000 [08:01<01:19, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:07<03:54,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6969/8000 [08:01<01:06, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:09<03:50,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6973/8000 [08:01<01:13, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:10<03:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6977/8000 [08:01<01:06, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:12<03:53,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6979/8000 [08:02<01:06, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:13<03:57,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6981/8000 [08:02<01:19, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:15<03:52,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6984/8000 [08:02<01:13, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:16<03:47,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6991/8000 [08:02<00:50, 19.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:18<03:45,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6997/8000 [08:03<00:50, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:19<03:48,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7001/8000 [08:03<00:49, 20.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:21<03:45,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7006/8000 [08:03<00:56, 17.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:22<03:40,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7010/8000 [08:03<00:51, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:24<03:42,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7014/8000 [08:04<00:57, 17.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:25<03:41,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7016/8000 [08:04<01:04, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:27<03:35,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7020/8000 [08:04<00:57, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:28<03:33,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7023/8000 [08:04<00:55, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:29<03:30,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7026/8000 [08:04<00:55, 17.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:31<03:30,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7028/8000 [08:05<01:14, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:32<03:30,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7030/8000 [08:05<01:15, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:34<03:25,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7033/8000 [08:05<01:10, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:36<03:35,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7035/8000 [08:05<01:12, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:37<03:39,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7038/8000 [08:05<01:03, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:39<03:38,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7041/8000 [08:05<01:14, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:40<03:34,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7045/8000 [08:06<00:59, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:42<03:26,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7051/8000 [08:06<00:44, 21.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:43<03:21,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7055/8000 [08:06<00:51, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:45<03:21,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7059/8000 [08:06<00:46, 20.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:46<03:19,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7065/8000 [08:07<00:49, 18.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:48<03:19,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7069/8000 [08:07<00:46, 20.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:49<03:17,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7072/8000 [08:07<00:49, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:51<03:15,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7074/8000 [08:07<01:02, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:52<03:14,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████  | 7076/8000 [08:08<01:18, 11.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:54<03:12,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7081/8000 [08:08<00:58, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:55<03:07,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7086/8000 [08:08<00:48, 18.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:57<03:04,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7094/8000 [08:08<00:42, 21.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:58<03:06,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7099/8000 [08:09<00:53, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [02:00<03:08,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7101/8000 [08:09<00:58, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [02:01<03:12,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7104/8000 [08:09<00:57, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [02:04<03:15,  1.60s/it]\n",
+      "Generating signals:  89%|███████████████  | 7106/8000 [08:09<01:00, 14.87it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  89%|███████████████  | 7108/8000 [08:09<01:13, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7110/8000 [08:10<01:11, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████  | 7114/8000 [08:10<01:17, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 12.77it/s]\n",
+      "Generating signals:  89%|███████████████  | 7116/8000 [08:10<01:15, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  89%|███████████████▏ | 7118/8000 [08:10<01:13, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7122/8000 [08:11<01:25, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7127/8000 [08:11<01:00, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 44it [00:01, 40.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7131/8000 [08:11<00:55, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 79it [00:01, 75.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7133/8000 [08:11<00:56, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 120it [00:01, 122.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7139/8000 [08:12<00:52, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 154it [00:01, 159.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7141/8000 [08:12<00:57, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 188it [00:01, 193.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7143/8000 [08:12<00:58, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 223it [00:02, 226.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7150/8000 [08:12<00:39, 21.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 260it [00:02, 259.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7154/8000 [08:12<00:38, 21.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 296it [00:02, 283.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7160/8000 [08:12<00:32, 25.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 335it [00:02, 311.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7164/8000 [08:13<00:44, 18.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 372it [00:02, 319.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7170/8000 [08:13<00:42, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 408it [00:02, 318.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7174/8000 [08:13<00:48, 17.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 444it [00:02, 328.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▏ | 7176/8000 [08:14<01:00, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 484it [00:02, 348.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7178/8000 [08:14<01:04, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 521it [00:02, 343.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7184/8000 [08:14<00:43, 18.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 567it [00:02, 375.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7189/8000 [08:14<00:40, 20.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 621it [00:03, 422.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7195/8000 [08:15<00:43, 18.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 665it [00:03, 420.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7202/8000 [08:15<00:34, 23.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 708it [00:03, 384.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7207/8000 [08:15<00:31, 24.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 748it [00:03, 376.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7211/8000 [08:15<00:31, 25.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 225.55it/s]\n",
+      "Generating signals:  90%|███████████████▎ | 7215/8000 [08:15<00:31, 24.56it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  90%|███████████████▎ | 7219/8000 [08:16<00:33, 23.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7223/8000 [08:16<01:07, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 155\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7225/8000 [08:17<01:06, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 142\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7227/8000 [08:17<01:05, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 158\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7232/8000 [08:17<00:48, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:00<00:00, 166\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▍ | 7237/8000 [08:18<01:09, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  90%|███████████████▍ | 7239/8000 [08:18<01:20,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  91%|███████████████▍ | 7242/8000 [08:18<01:11, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7245/8000 [08:18<01:01, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 20/8000 [00:00<01:01, 129.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7247/8000 [08:19<01:14, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 37/8000 [00:00<01:12, 110.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7249/8000 [08:19<01:12, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 54/8000 [00:00<01:13, 108.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7253/8000 [08:19<01:04, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 72/8000 [00:00<01:10, 112.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7260/8000 [08:19<00:49, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 97/8000 [00:00<01:00, 131.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7262/8000 [08:20<00:51, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 115/8000 [00:00<01:05, 120.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7264/8000 [08:20<00:52, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 132/8000 [00:01<01:04, 121.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7272/8000 [08:20<00:39, 18.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 145/8000 [00:01<01:16, 102.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7280/8000 [08:20<00:29, 24.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 161/8000 [00:01<01:17, 101.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7285/8000 [08:21<00:34, 20.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 183/8000 [00:01<01:09, 111.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▍ | 7291/8000 [08:21<00:30, 23.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 207/8000 [00:01<01:02, 124.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7297/8000 [08:21<00:26, 26.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 224/8000 [00:01<01:00, 127.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7302/8000 [08:21<00:33, 20.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 244/8000 [00:02<00:59, 129.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7305/8000 [08:21<00:35, 19.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 263/8000 [00:02<00:59, 130.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7308/8000 [08:22<00:44, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▌                | 284/8000 [00:02<00:58, 131.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7312/8000 [08:22<00:48, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 306/8000 [00:02<01:15, 102.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7315/8000 [08:22<00:45, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 321/8000 [00:02<01:12, 105.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████▌ | 7318/8000 [08:22<00:43, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 342/8000 [00:02<01:05, 116.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7322/8000 [08:23<00:40, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 368/8000 [00:03<00:55, 137.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7325/8000 [08:23<00:40, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 385/8000 [00:03<01:00, 124.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7327/8000 [08:23<00:44, 15.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 399/8000 [00:03<01:05, 115.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7330/8000 [08:23<00:52, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 418/8000 [00:03<01:02, 121.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7335/8000 [08:24<00:40, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 437/8000 [00:03<01:04, 117.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7338/8000 [08:24<00:40, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 463/8000 [00:03<00:58, 129.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7341/8000 [08:24<00:51, 12.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 482/8000 [00:04<00:58, 127.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7344/8000 [08:24<01:01, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 516/8000 [00:04<01:00, 124.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7346/8000 [08:25<01:01, 10.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 533/8000 [00:04<00:59, 124.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7349/8000 [08:25<00:53, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 549/8000 [00:04<01:00, 123.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7352/8000 [08:25<01:12,  8.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 576/8000 [00:04<00:52, 140.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7356/8000 [08:26<01:02, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎               | 592/8000 [00:04<00:55, 132.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7360/8000 [08:26<00:59, 10.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 609/8000 [00:05<00:58, 126.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7363/8000 [08:26<01:12,  8.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 626/8000 [00:05<01:00, 121.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7365/8000 [08:27<01:08,  9.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 642/8000 [00:05<01:02, 117.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7367/8000 [08:27<01:04,  9.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 658/8000 [00:05<01:04, 113.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7372/8000 [08:27<00:46, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 679/8000 [00:05<01:00, 120.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7374/8000 [08:27<00:56, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 705/8000 [00:05<01:10, 103.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7378/8000 [08:27<00:44, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 718/8000 [00:06<01:13, 99.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7381/8000 [08:28<00:57, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 730/8000 [00:06<01:17, 93.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7386/8000 [08:28<00:59, 10.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 748/8000 [00:06<01:09, 105.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7388/8000 [08:29<00:56, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 763/8000 [00:06<01:13, 98.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7390/8000 [08:29<00:56, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 785/8000 [00:06<01:03, 113.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7393/8000 [08:29<00:51, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 802/8000 [00:06<01:04, 112.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7396/8000 [08:29<00:46, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 814/8000 [00:06<01:09, 102.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7398/8000 [08:29<00:45, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 830/8000 [00:07<01:06, 107.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▋ | 7401/8000 [08:29<00:40, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 843/8000 [00:07<01:10, 101.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▋ | 7404/8000 [08:30<00:37, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 866/8000 [00:07<01:00, 118.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▋ | 7409/8000 [08:30<00:37, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 883/8000 [00:07<01:00, 117.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7415/8000 [08:30<00:34, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 899/8000 [00:07<01:01, 115.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7420/8000 [08:30<00:29, 19.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 912/8000 [00:07<01:07, 104.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7425/8000 [08:31<00:31, 18.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 923/8000 [00:08<01:12, 96.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7428/8000 [08:31<00:37, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 943/8000 [00:08<01:04, 109.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7432/8000 [08:31<00:39, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 960/8000 [00:08<01:01, 113.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7435/8000 [08:32<00:43, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 979/8000 [00:08<00:57, 121.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7439/8000 [08:32<00:37, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1009/8000 [00:08<01:01, 113.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7444/8000 [08:32<00:32, 16.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1024/8000 [00:08<01:03, 109.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7448/8000 [08:32<00:31, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1039/8000 [00:08<01:03, 108.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7451/8000 [08:32<00:32, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1051/8000 [00:09<01:10, 98.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7457/8000 [08:33<00:27, 19.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1070/8000 [00:09<01:05, 105.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7459/8000 [08:33<00:31, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1086/8000 [00:09<01:05, 105.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7463/8000 [08:33<00:29, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1107/8000 [00:09<01:01, 112.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7466/8000 [08:33<00:35, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1123/8000 [00:09<01:02, 110.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▊ | 7468/8000 [08:34<00:37, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1140/8000 [00:09<01:03, 107.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▉ | 7472/8000 [08:34<00:45, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1161/8000 [00:10<01:02, 110.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▉ | 7476/8000 [08:34<00:38, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1175/8000 [00:10<01:06, 103.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████▉ | 7479/8000 [08:34<00:37, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1198/8000 [00:10<00:59, 114.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7481/8000 [08:35<00:38, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1213/8000 [00:10<01:02, 109.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7483/8000 [08:35<00:51,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1226/8000 [00:10<01:06, 101.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7487/8000 [08:35<00:38, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▍             | 1241/8000 [00:10<01:07, 100.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7491/8000 [08:35<00:31, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1258/8000 [00:11<01:02, 107.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7495/8000 [08:35<00:26, 19.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1278/8000 [00:11<01:00, 111.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7498/8000 [08:36<00:27, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1299/8000 [00:11<00:55, 120.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7501/8000 [08:36<00:40, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋             | 1316/8000 [00:11<00:56, 119.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7505/8000 [08:36<00:32, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1334/8000 [00:11<00:51, 128.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7508/8000 [08:37<00:45, 10.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1352/8000 [00:11<00:53, 123.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7510/8000 [08:37<00:44, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1371/8000 [00:11<00:51, 127.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7513/8000 [08:37<00:36, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1385/8000 [00:12<00:57, 115.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7519/8000 [08:37<00:26, 18.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1399/8000 [00:12<01:05, 101.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7523/8000 [08:38<00:34, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1424/8000 [00:12<00:57, 114.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7525/8000 [08:38<00:34, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1443/8000 [00:12<00:58, 112.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7527/8000 [08:38<00:35, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1466/8000 [00:12<00:52, 123.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7530/8000 [08:38<00:31, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1483/8000 [00:12<00:55, 116.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7533/8000 [08:38<00:28, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1503/8000 [00:13<00:53, 121.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7535/8000 [08:39<00:39, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1519/8000 [00:13<00:51, 125.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7539/8000 [08:39<00:30, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1537/8000 [00:13<00:52, 122.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7541/8000 [08:39<00:31, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███             | 1561/8000 [00:13<00:47, 135.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7543/8000 [08:39<00:35, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1581/8000 [00:13<00:47, 136.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7547/8000 [08:39<00:28, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1601/8000 [00:13<00:50, 127.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7549/8000 [08:39<00:29, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1631/8000 [00:13<00:43, 145.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7552/8000 [08:40<00:28, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1649/8000 [00:14<00:45, 138.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7554/8000 [08:40<00:29, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1664/8000 [00:14<00:48, 130.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████ | 7557/8000 [08:40<00:27, 16.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1677/8000 [00:14<00:50, 124.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7562/8000 [08:40<00:20, 20.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1690/8000 [00:14<00:57, 110.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7572/8000 [08:40<00:17, 23.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1711/8000 [00:14<00:53, 117.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7577/8000 [08:41<00:19, 21.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1728/8000 [00:14<00:53, 116.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7580/8000 [08:41<00:24, 17.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1754/8000 [00:15<00:47, 131.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7583/8000 [08:41<00:34, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1777/8000 [00:15<00:45, 136.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7590/8000 [08:42<00:26, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1796/8000 [00:15<00:47, 129.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7592/8000 [08:42<00:27, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1822/8000 [00:15<00:48, 126.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7596/8000 [08:42<00:25, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1837/8000 [00:15<00:51, 119.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7598/8000 [08:42<00:27, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1857/8000 [00:15<00:52, 116.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7600/8000 [08:42<00:27, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1874/8000 [00:16<00:57, 106.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7604/8000 [08:43<00:30, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1892/8000 [00:16<01:00, 100.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7607/8000 [08:43<00:30, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1904/8000 [00:16<01:04, 95.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7609/8000 [08:43<00:30, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1926/8000 [00:16<00:55, 109.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7615/8000 [08:43<00:20, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1939/8000 [00:16<00:57, 104.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7620/8000 [08:44<00:21, 17.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1956/8000 [00:16<01:02, 97.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7623/8000 [08:44<00:25, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1981/8000 [00:17<01:00, 99.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7627/8000 [08:44<00:21, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2002/8000 [00:17<00:56, 107.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7632/8000 [08:44<00:18, 19.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2024/8000 [00:17<00:48, 123.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████▏| 7637/8000 [08:45<00:20, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2047/8000 [00:17<00:42, 140.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▏| 7640/8000 [08:45<00:20, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2074/8000 [00:17<00:38, 152.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▏| 7643/8000 [08:45<00:20, 17.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2094/8000 [00:17<00:38, 152.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▏| 7646/8000 [08:45<00:19, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2110/8000 [00:17<00:41, 140.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7648/8000 [08:45<00:25, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2126/8000 [00:18<00:44, 131.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7650/8000 [08:46<00:24, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2147/8000 [00:18<00:44, 130.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7655/8000 [08:46<00:17, 19.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2161/8000 [00:18<00:47, 122.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7660/8000 [08:46<00:22, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2182/8000 [00:18<00:52, 110.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7663/8000 [08:46<00:23, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2202/8000 [00:18<00:47, 123.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7666/8000 [08:47<00:21, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2218/8000 [00:18<00:47, 121.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7670/8000 [08:47<00:21, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2237/8000 [00:19<00:45, 125.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7677/8000 [08:47<00:15, 20.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2251/8000 [00:19<00:51, 111.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7682/8000 [08:47<00:13, 24.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2281/8000 [00:19<00:39, 146.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7685/8000 [08:47<00:16, 18.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2298/8000 [00:19<00:41, 137.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7690/8000 [08:48<00:17, 17.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2316/8000 [00:19<00:43, 129.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7693/8000 [08:48<00:18, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2333/8000 [00:19<00:44, 127.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7699/8000 [08:48<00:17, 17.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2359/8000 [00:19<00:38, 145.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7703/8000 [08:49<00:17, 17.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2386/8000 [00:20<00:33, 167.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7705/8000 [08:49<00:19, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2416/8000 [00:20<00:47, 118.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▍| 7709/8000 [08:49<00:17, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2441/8000 [00:20<00:54, 101.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▍| 7716/8000 [08:49<00:12, 23.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2457/8000 [00:20<00:53, 103.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▍| 7719/8000 [08:49<00:13, 21.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2480/8000 [00:21<00:46, 119.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7722/8000 [08:49<00:13, 20.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2499/8000 [00:21<00:43, 125.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7725/8000 [08:50<00:14, 19.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2515/8000 [00:21<00:48, 114.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7731/8000 [08:50<00:14, 18.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2534/8000 [00:21<00:44, 123.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7735/8000 [08:50<00:13, 19.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2553/8000 [00:21<00:45, 119.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7737/8000 [08:50<00:16, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2571/8000 [00:21<00:44, 121.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7741/8000 [08:51<00:14, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2591/8000 [00:21<00:42, 127.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7743/8000 [08:51<00:15, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2606/8000 [00:22<00:46, 115.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7745/8000 [08:51<00:17, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2627/8000 [00:22<00:44, 121.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7748/8000 [08:51<00:15, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2643/8000 [00:22<00:45, 118.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7752/8000 [08:51<00:12, 19.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2657/8000 [00:22<00:46, 115.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7756/8000 [08:52<00:18, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2678/8000 [00:22<00:44, 120.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7760/8000 [08:52<00:15, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2693/8000 [00:22<00:46, 115.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7764/8000 [08:52<00:20, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2711/8000 [00:22<00:45, 117.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7767/8000 [08:53<00:19, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2730/8000 [00:23<00:43, 122.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7769/8000 [08:53<00:19, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2743/8000 [00:23<00:48, 109.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7773/8000 [08:53<00:20, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2763/8000 [00:23<00:43, 119.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7776/8000 [08:54<00:26,  8.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2780/8000 [00:23<00:45, 114.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7778/8000 [08:54<00:29,  7.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2794/8000 [00:23<00:45, 113.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7780/8000 [08:54<00:26,  8.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2809/8000 [00:23<00:46, 110.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7783/8000 [08:54<00:21, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2830/8000 [00:24<00:46, 112.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7787/8000 [08:55<00:15, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2847/8000 [00:24<00:46, 110.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7790/8000 [08:55<00:14, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2866/8000 [00:24<00:42, 119.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7792/8000 [08:55<00:15, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2890/8000 [00:24<00:38, 132.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▌| 7798/8000 [08:55<00:10, 19.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2909/8000 [00:24<00:40, 125.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7805/8000 [08:55<00:09, 20.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2923/8000 [00:24<00:44, 114.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7808/8000 [08:56<00:09, 20.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2945/8000 [00:24<00:39, 127.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7812/8000 [08:56<00:08, 22.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2964/8000 [00:25<00:38, 129.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7815/8000 [08:56<00:09, 19.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2988/8000 [00:25<00:34, 145.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7818/8000 [08:56<00:09, 18.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3004/8000 [00:25<00:36, 135.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▌| 7820/8000 [08:56<00:10, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3028/8000 [00:25<00:43, 113.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7824/8000 [08:56<00:08, 20.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3044/8000 [00:25<00:44, 112.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7829/8000 [08:57<00:07, 23.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3064/8000 [00:25<00:40, 120.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7833/8000 [08:57<00:10, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3084/8000 [00:26<00:41, 119.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7836/8000 [08:57<00:11, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3109/8000 [00:26<00:36, 132.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7839/8000 [08:57<00:11, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3137/8000 [00:26<00:33, 146.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7843/8000 [08:58<00:10, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3166/8000 [00:26<00:37, 128.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7846/8000 [08:58<00:10, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3183/8000 [00:26<00:36, 132.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7849/8000 [08:58<00:10, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3204/8000 [00:26<00:34, 138.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7852/8000 [08:59<00:16,  8.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3224/8000 [00:27<00:33, 143.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7855/8000 [08:59<00:17,  8.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3250/8000 [00:27<00:30, 154.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7858/8000 [08:59<00:15,  9.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3270/8000 [00:27<00:32, 147.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7862/8000 [09:00<00:11, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3301/8000 [00:27<00:36, 129.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7865/8000 [09:00<00:13, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3321/8000 [00:27<00:37, 126.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7869/8000 [09:00<00:10, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3343/8000 [00:27<00:36, 126.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7872/8000 [09:00<00:08, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3364/8000 [00:28<00:36, 127.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7876/8000 [09:01<00:07, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3380/8000 [00:28<00:36, 127.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7878/8000 [09:01<00:07, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3407/8000 [00:28<00:41, 110.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7880/8000 [09:01<00:07, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3435/8000 [00:28<00:34, 131.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7885/8000 [09:01<00:05, 19.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3452/8000 [00:28<00:36, 124.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7889/8000 [09:01<00:05, 20.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3469/8000 [00:28<00:36, 122.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7892/8000 [09:01<00:06, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3484/8000 [00:29<00:37, 119.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7895/8000 [09:02<00:05, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3502/8000 [00:29<00:36, 121.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7897/8000 [09:02<00:06, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3524/8000 [00:29<00:32, 137.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7899/8000 [09:02<00:06, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3553/8000 [00:29<00:40, 109.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7902/8000 [09:02<00:07, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3571/8000 [00:29<00:39, 112.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7906/8000 [09:03<00:07, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3588/8000 [00:30<00:40, 110.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7908/8000 [09:03<00:08, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3607/8000 [00:30<00:35, 122.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7911/8000 [09:03<00:07, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3628/8000 [00:30<00:33, 130.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7915/8000 [09:03<00:05, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3646/8000 [00:30<00:34, 125.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7918/8000 [09:03<00:05, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3674/8000 [00:30<00:38, 113.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7924/8000 [09:03<00:03, 22.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3694/8000 [00:30<00:42, 101.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7928/8000 [09:04<00:03, 22.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3715/8000 [00:31<00:39, 107.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7932/8000 [09:04<00:03, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3748/8000 [00:31<00:31, 133.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7937/8000 [09:04<00:03, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3772/8000 [00:31<00:29, 145.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▊| 7940/8000 [09:04<00:03, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3790/8000 [00:31<00:30, 137.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7945/8000 [09:05<00:02, 22.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3807/8000 [00:31<00:32, 130.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7948/8000 [09:05<00:02, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3830/8000 [00:31<00:31, 132.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7951/8000 [09:05<00:03, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3850/8000 [00:32<00:30, 137.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7954/8000 [09:05<00:03, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3883/8000 [00:32<00:32, 127.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|████████████████▉| 7957/8000 [09:06<00:03, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3897/8000 [00:32<00:33, 123.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7961/8000 [09:06<00:02, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3917/8000 [00:32<00:30, 132.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7965/8000 [09:06<00:02, 15.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3935/8000 [00:32<00:30, 134.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7968/8000 [09:07<00:02, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3954/8000 [00:32<00:29, 138.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7973/8000 [09:07<00:01, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3972/8000 [00:32<00:28, 139.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7975/8000 [09:07<00:01, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3987/8000 [00:33<00:32, 125.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7978/8000 [09:07<00:02, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4007/8000 [00:33<00:31, 127.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7980/8000 [09:08<00:01, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4026/8000 [00:33<00:31, 124.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7985/8000 [09:08<00:01, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4048/8000 [00:33<00:29, 133.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7989/8000 [09:08<00:00, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4067/8000 [00:33<00:29, 134.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7992/8000 [09:09<00:00, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4084/8000 [00:33<00:29, 131.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7995/8000 [09:09<00:00, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4114/8000 [00:34<00:25, 152.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|████████████████▉| 7997/8000 [09:09<00:00, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4140/8000 [00:34<00:33, 113.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████| 8000/8000 [09:09<00:00, 14.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  52%|████████▎       | 4155/8000 [00:34<00:33, 113.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4174/8000 [00:34<00:32, 119.35it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                                                      | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4195/8000 [00:34<00:32, 118.89it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▍                                                                             | 1/200 [00:08<28:23,  8.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4214/8000 [00:34<00:30, 125.18it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▊                                                                             | 2/200 [00:10<15:24,  4.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4239/8000 [00:35<00:28, 132.78it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                                                            | 3/200 [00:12<11:05,  3.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4261/8000 [00:35<00:27, 137.59it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▌                                                                            | 4/200 [00:14<09:03,  2.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4283/8000 [00:35<00:25, 147.48it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▉                                                                            | 5/200 [00:16<08:03,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4306/8000 [00:35<00:23, 156.22it/s]\u001b[A\u001b[A\n",
+      "training:   3%|██▎                                                                           | 6/200 [00:18<07:25,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4326/8000 [00:35<00:24, 151.56it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▋                                                                           | 7/200 [00:20<07:00,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4349/8000 [00:35<00:24, 150.23it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███                                                                           | 8/200 [00:21<06:38,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▋       | 4369/8000 [00:35<00:23, 156.10it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███▌                                                                          | 9/200 [00:24<06:38,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4388/8000 [00:36<00:25, 142.66it/s]\u001b[A\u001b[A\n",
+      "training:   5%|███▊                                                                         | 10/200 [00:26<06:45,  2.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4421/8000 [00:36<00:29, 123.22it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▏                                                                        | 11/200 [00:28<07:02,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4454/8000 [00:36<00:30, 117.74it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▌                                                                        | 12/200 [00:30<06:45,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4473/8000 [00:36<00:28, 122.37it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█████                                                                        | 13/200 [00:33<07:10,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4494/8000 [00:36<00:26, 131.99it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█████▍                                                                       | 14/200 [00:35<07:03,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4516/8000 [00:37<00:25, 136.73it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█████▊                                                                       | 15/200 [00:37<07:08,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4550/8000 [00:37<00:25, 134.92it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▏                                                                      | 16/200 [00:40<06:57,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4568/8000 [00:37<00:24, 140.50it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▌                                                                      | 17/200 [00:42<06:41,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4588/8000 [00:37<00:24, 140.20it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██████▉                                                                      | 18/200 [00:44<06:29,  2.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4619/8000 [00:37<00:27, 120.86it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▎                                                                     | 19/200 [00:46<06:17,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4651/8000 [00:38<00:28, 117.89it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▋                                                                     | 20/200 [00:47<06:01,  2.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4672/8000 [00:38<00:26, 127.29it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████████                                                                     | 21/200 [00:49<05:57,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4706/8000 [00:38<00:25, 130.29it/s]\u001b[A\u001b[A\n",
+      "training:  11%|████████▍                                                                    | 22/200 [00:52<06:01,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4728/8000 [00:38<00:24, 135.48it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████████▊                                                                    | 23/200 [00:54<06:20,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▌      | 4751/8000 [00:38<00:22, 145.82it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▏                                                                   | 24/200 [00:56<06:37,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4771/8000 [00:38<00:22, 146.46it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▋                                                                   | 25/200 [00:59<06:29,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4788/8000 [00:39<00:23, 137.31it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████████                                                                   | 26/200 [01:01<06:25,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4819/8000 [00:39<00:26, 120.54it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▍                                                                  | 27/200 [01:03<06:24,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4841/8000 [00:39<00:24, 130.22it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▊                                                                  | 28/200 [01:06<06:37,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4857/8000 [00:39<00:25, 123.00it/s]\u001b[A\u001b[A\n",
+      "training:  14%|███████████▏                                                                 | 29/200 [01:08<06:40,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4886/8000 [00:40<00:27, 114.36it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████████▌                                                                 | 30/200 [01:10<06:42,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4917/8000 [00:40<00:23, 131.10it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████████▉                                                                 | 31/200 [01:13<06:44,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4931/8000 [00:40<00:24, 122.95it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▎                                                                | 32/200 [01:15<06:37,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4946/8000 [00:40<00:26, 114.21it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▋                                                                | 33/200 [01:17<06:26,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4962/8000 [00:40<00:26, 115.99it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████████████                                                                | 34/200 [01:19<06:05,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4982/8000 [00:40<00:24, 124.76it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▍                                                               | 35/200 [01:22<06:10,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5011/8000 [00:41<00:27, 107.54it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▊                                                               | 36/200 [01:24<06:19,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5030/8000 [00:41<00:27, 108.34it/s]\u001b[A\u001b[A\n",
+      "training:  18%|██████████████▏                                                              | 37/200 [01:27<06:28,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5061/8000 [00:41<00:24, 122.05it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████████████▋                                                              | 38/200 [01:29<06:33,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5078/8000 [00:41<00:25, 115.16it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████                                                              | 39/200 [01:32<06:35,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5100/8000 [00:41<00:26, 107.71it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▍                                                             | 40/200 [01:34<06:36,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5118/8000 [00:42<00:29, 96.76it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▊                                                             | 41/200 [01:37<06:48,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5134/8000 [00:42<00:31, 90.90it/s]\u001b[A\u001b[A\n",
+      "training:  21%|████████████████▏                                                            | 42/200 [01:39<06:40,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5144/8000 [00:42<00:37, 75.18it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▌                                                            | 43/200 [01:42<06:47,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5162/8000 [00:42<00:33, 84.81it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▉                                                            | 44/200 [01:45<06:48,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5174/8000 [00:42<00:34, 81.04it/s]\u001b[A\u001b[A\n",
+      "training:  22%|█████████████████▎                                                           | 45/200 [01:47<06:44,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5189/8000 [00:43<00:32, 87.52it/s]\u001b[A\u001b[A\n",
+      "training:  23%|█████████████████▋                                                           | 46/200 [01:50<06:42,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5213/8000 [00:43<00:26, 103.35it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████                                                           | 47/200 [01:53<06:40,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5230/8000 [00:43<00:31, 88.21it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▍                                                          | 48/200 [01:55<06:40,  2.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5248/8000 [00:43<00:35, 78.11it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▊                                                          | 49/200 [01:58<06:35,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5271/8000 [00:43<00:32, 84.60it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████████████████▎                                                         | 50/200 [02:01<06:34,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5292/8000 [00:44<00:30, 89.77it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████████████████▋                                                         | 51/200 [02:03<06:32,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5314/8000 [00:44<00:28, 92.91it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████                                                         | 52/200 [02:06<06:34,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5337/8000 [00:44<00:24, 107.70it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████▍                                                        | 53/200 [02:09<06:26,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5356/8000 [00:44<00:23, 113.57it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████████████▊                                                        | 54/200 [02:11<06:21,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5377/8000 [00:44<00:21, 119.48it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▏                                                       | 55/200 [02:14<06:19,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5399/8000 [00:44<00:19, 131.37it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▌                                                       | 56/200 [02:16<06:18,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5415/8000 [00:45<00:19, 131.03it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▉                                                       | 57/200 [02:19<06:19,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5434/8000 [00:45<00:19, 128.35it/s]\u001b[A\u001b[A\n",
+      "training:  29%|██████████████████████▎                                                      | 58/200 [02:22<06:15,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5455/8000 [00:45<00:19, 128.58it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████████████▋                                                      | 59/200 [02:24<06:14,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5488/8000 [00:45<00:19, 127.68it/s]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████                                                      | 60/200 [02:27<06:08,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5508/8000 [00:45<00:19, 124.93it/s]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████▍                                                     | 61/200 [02:30<06:04,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5534/8000 [00:45<00:17, 145.02it/s]\u001b[A\u001b[A\n",
+      "training:  31%|███████████████████████▊                                                     | 62/200 [02:33<06:20,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5558/8000 [00:46<00:15, 155.48it/s]\u001b[A\u001b[A\n",
+      "training:  32%|████████████████████████▎                                                    | 63/200 [02:38<05:44,  2.52s/it]\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5579/8000 [00:46<00:15, 156.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5597/8000 [00:46<00:17, 141.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5617/8000 [00:46<00:16, 144.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5634/8000 [00:46<00:17, 135.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 10it [00:02,  5.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5651/8000 [00:46<00:19, 122.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 10.04it/s]\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5666/8000 [00:46<00:19, 118.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5680/8000 [00:47<00:19, 116.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5693/8000 [00:47<00:22, 104.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5711/8000 [00:47<00:20, 109.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 29it [00:01, 24.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5738/8000 [00:47<00:17, 131.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 59it [00:01, 55.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5757/8000 [00:47<00:17, 130.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 83it [00:01, 79.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5781/8000 [00:47<00:15, 143.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 106it [00:01, 101.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▌    | 5802/8000 [00:47<00:14, 149.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 128it [00:02, 115.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5821/8000 [00:48<00:15, 142.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 151it [00:02, 137.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5846/8000 [00:48<00:14, 150.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 174it [00:02, 156.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5869/8000 [00:48<00:14, 151.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 205it [00:02, 191.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5890/8000 [00:48<00:13, 156.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 229it [00:02, 197.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5921/8000 [00:48<00:16, 127.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 253it [00:02, 205.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5945/8000 [00:49<00:15, 136.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 279it [00:02, 218.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5963/8000 [00:49<00:15, 134.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 303it [00:02, 224.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5980/8000 [00:49<00:14, 135.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 327it [00:02, 217.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6003/8000 [00:49<00:13, 150.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 350it [00:03, 218.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6026/8000 [00:49<00:12, 152.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 373it [00:03, 215.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6044/8000 [00:49<00:13, 146.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 396it [00:03, 218.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6086/8000 [00:49<00:12, 148.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 424it [00:03, 235.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6105/8000 [00:50<00:13, 145.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 456it [00:03, 259.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6140/8000 [00:50<00:14, 132.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 483it [00:03, 235.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6158/8000 [00:50<00:14, 126.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 508it [00:03, 232.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6174/8000 [00:50<00:14, 122.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 534it [00:03, 238.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▍   | 6197/8000 [00:50<00:13, 136.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 559it [00:03, 224.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6214/8000 [00:50<00:13, 132.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 582it [00:04, 214.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6228/8000 [00:51<00:14, 125.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 606it [00:04, 220.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6255/8000 [00:51<00:12, 144.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 631it [00:04, 227.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6270/8000 [00:51<00:13, 125.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 655it [00:04, 219.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6286/8000 [00:51<00:14, 117.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 678it [00:04, 217.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6307/8000 [00:51<00:13, 123.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 704it [00:04, 227.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6324/8000 [00:51<00:13, 128.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 732it [00:04, 240.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6352/8000 [00:52<00:11, 143.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 757it [00:04, 226.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▋   | 6368/8000 [00:52<00:11, 136.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 160.79it/s]\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6396/8000 [00:52<00:13, 122.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6422/8000 [00:52<00:14, 107.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                                                  | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▉   | 6440/8000 [00:52<00:14, 104.83it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|█▋                                        | 4/100 [00:00<00:02, 33.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6459/8000 [00:53<00:14, 109.99it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|███▎                                      | 8/100 [00:00<00:02, 31.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6481/8000 [00:53<00:12, 123.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%|████▉                                    | 12/100 [00:00<00:02, 29.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6504/8000 [00:53<00:11, 133.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|██████▌                                  | 16/100 [00:00<00:03, 27.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6522/8000 [00:53<00:11, 133.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|████████▏                                | 20/100 [00:00<00:02, 29.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6547/8000 [00:53<00:10, 144.15it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|█████████▊                               | 24/100 [00:00<00:02, 30.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6581/8000 [00:53<00:10, 136.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|███████████▉                             | 29/100 [00:00<00:02, 34.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6598/8000 [00:54<00:11, 126.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|█████████████▌                           | 33/100 [00:01<00:01, 34.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6622/8000 [00:54<00:09, 139.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|███████████████▏                         | 37/100 [00:01<00:01, 34.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6639/8000 [00:54<00:10, 135.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████████████████▊                        | 41/100 [00:01<00:01, 33.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6659/8000 [00:54<00:09, 137.19it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|██████████████████▍                      | 45/100 [00:01<00:01, 29.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6679/8000 [00:54<00:09, 140.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|████████████████████                     | 49/100 [00:01<00:01, 27.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6700/8000 [00:54<00:09, 136.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|█████████████████████▋                   | 53/100 [00:01<00:01, 29.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6726/8000 [00:54<00:08, 149.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|███████████████████████▎                 | 57/100 [00:01<00:01, 31.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6747/8000 [00:55<00:08, 146.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|█████████████████████████                | 61/100 [00:01<00:01, 31.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6765/8000 [00:55<00:08, 142.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|██████████████████████████▋              | 65/100 [00:02<00:01, 31.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6785/8000 [00:55<00:08, 135.90it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|████████████████████████████▎            | 69/100 [00:02<00:00, 32.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6802/8000 [00:55<00:09, 128.05it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|█████████████████████████████▉           | 73/100 [00:02<00:00, 32.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6826/8000 [00:55<00:08, 140.70it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|███████████████████████████████▌         | 77/100 [00:02<00:00, 30.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6845/8000 [00:55<00:08, 135.45it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  81%|█████████████████████████████████▏       | 81/100 [00:02<00:00, 30.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6869/8000 [00:55<00:07, 145.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|███████████████████████████████████▎     | 86/100 [00:02<00:00, 34.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6892/8000 [00:56<00:07, 153.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  95%|██████████████████████████████████████▉  | 95/100 [00:02<00:00, 46.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6908/8000 [00:56<00:07, 141.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|████████████████████████████████████████| 100/100 [00:02<00:00, 33.88it/s]\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6930/8000 [00:56<00:07, 142.38it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples: 100%|█████████████████████████| 4/4 [47:30<00:00, 796.00s/it]\u001b[A\n",
+      "Replicates:  13%|███                    | 2/15 [2:09:33<13:22:30, 3703.89s/it]\n",
+      "Nyquist Samples:   0%|                                                                                 | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.156090146542046\n",
+      "2999: -5.9254599\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6950/8000 [00:56<00:07, 139.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6965/8000 [00:56<00:07, 132.68it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                                                      | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6987/8000 [00:56<00:07, 141.53it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▍                                                                             | 1/200 [00:09<29:55,  9.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7008/8000 [00:56<00:06, 146.06it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▊                                                                             | 2/200 [00:11<17:16,  5.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7031/8000 [00:57<00:06, 152.17it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                                                            | 3/200 [00:13<12:32,  3.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7057/8000 [00:57<00:07, 125.97it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▌                                                                            | 4/200 [00:16<10:29,  3.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7084/8000 [00:57<00:06, 134.72it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▉                                                                            | 5/200 [00:18<09:19,  2.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7106/8000 [00:57<00:06, 142.09it/s]\u001b[A\u001b[A\n",
+      "training:   3%|██▎                                                                           | 6/200 [00:20<08:47,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7151/8000 [00:57<00:05, 150.04it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▋                                                                           | 7/200 [00:22<08:13,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7169/8000 [00:58<00:05, 143.48it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███                                                                           | 8/200 [00:25<07:55,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7187/8000 [00:58<00:05, 143.16it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███▌                                                                          | 9/200 [00:27<07:42,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7215/8000 [00:58<00:05, 153.83it/s]\u001b[A\u001b[A\n",
+      "training:   5%|███▊                                                                         | 10/200 [00:29<07:19,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7233/8000 [00:58<00:05, 145.69it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▏                                                                        | 11/200 [00:31<07:02,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7259/8000 [00:58<00:04, 148.99it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▌                                                                        | 12/200 [00:33<07:00,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7278/8000 [00:58<00:05, 142.94it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█████                                                                        | 13/200 [00:36<07:18,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7311/8000 [00:58<00:04, 167.38it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█████▍                                                                       | 14/200 [00:39<07:25,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7329/8000 [00:59<00:04, 157.85it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█████▊                                                                       | 15/200 [00:41<07:14,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7345/8000 [00:59<00:04, 145.18it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▏                                                                      | 16/200 [00:43<07:18,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7362/8000 [00:59<00:04, 142.03it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▌                                                                      | 17/200 [00:45<07:10,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7383/8000 [00:59<00:04, 132.11it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██████▉                                                                      | 18/200 [00:48<07:07,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7410/8000 [00:59<00:04, 141.27it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▎                                                                     | 19/200 [00:50<07:16,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7431/8000 [00:59<00:04, 136.69it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▋                                                                     | 20/200 [00:53<07:14,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7450/8000 [00:59<00:03, 138.95it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████████                                                                     | 21/200 [00:55<07:11,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7474/8000 [01:00<00:03, 146.78it/s]\u001b[A\u001b[A\n",
+      "training:  11%|████████▍                                                                    | 22/200 [00:58<07:04,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7495/8000 [01:00<00:03, 146.16it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████████▊                                                                    | 23/200 [01:00<07:10,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7513/8000 [01:00<00:03, 142.32it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▏                                                                   | 24/200 [01:03<07:16,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7529/8000 [01:00<00:03, 129.75it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▋                                                                   | 25/200 [01:05<07:11,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7555/8000 [01:00<00:02, 148.70it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████████                                                                   | 26/200 [01:08<07:07,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7578/8000 [01:00<00:02, 158.02it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▍                                                                  | 27/200 [01:10<06:57,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7608/8000 [01:01<00:02, 136.54it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▊                                                                  | 28/200 [01:13<07:11,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7626/8000 [01:01<00:02, 132.06it/s]\u001b[A\u001b[A\n",
+      "training:  14%|███████████▏                                                                 | 29/200 [01:15<06:56,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7653/8000 [01:01<00:02, 141.81it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████████▌                                                                 | 30/200 [01:17<06:50,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7671/8000 [01:01<00:02, 133.62it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████████▉                                                                 | 31/200 [01:20<06:53,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7690/8000 [01:01<00:02, 139.04it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▎                                                                | 32/200 [01:22<06:47,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7714/8000 [01:01<00:01, 151.38it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▋                                                                | 33/200 [01:24<06:42,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7734/8000 [01:01<00:01, 146.78it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████████████                                                                | 34/200 [01:27<06:35,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7752/8000 [01:02<00:01, 133.41it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▍                                                               | 35/200 [01:29<06:29,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7782/8000 [01:02<00:01, 122.30it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▊                                                               | 36/200 [01:32<06:29,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7795/8000 [01:02<00:01, 116.26it/s]\u001b[A\u001b[A\n",
+      "training:  18%|██████████████▏                                                              | 37/200 [01:34<06:36,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7818/8000 [01:02<00:01, 125.71it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████████████▋                                                              | 38/200 [01:36<06:20,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7843/8000 [01:02<00:01, 135.77it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████                                                              | 39/200 [01:39<06:35,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7863/8000 [01:02<00:00, 138.10it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▍                                                             | 40/200 [01:41<06:35,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7884/8000 [01:03<00:00, 144.28it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▊                                                             | 41/200 [01:44<06:36,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7909/8000 [01:03<00:00, 151.03it/s]\u001b[A\u001b[A\n",
+      "training:  21%|████████████████▏                                                            | 42/200 [01:47<06:41,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7932/8000 [01:03<00:00, 156.15it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▌                                                            | 43/200 [01:49<06:30,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7952/8000 [01:03<00:00, 157.66it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▉                                                            | 44/200 [01:52<06:35,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7974/8000 [01:03<00:00, 157.18it/s]\u001b[A\u001b[A\n",
+      "training:  22%|█████████████████▎                                                           | 45/200 [01:55<06:54,  2.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7998/8000 [01:03<00:00, 159.07it/s]\u001b[A\u001b[A\n",
+      "training:  23%|█████████████████▋                                                           | 46/200 [01:58<07:34,  2.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8021it [01:03, 125.35it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "training:  24%|██████████████████                                                           | 47/200 [02:00<06:53,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  24%|██████████████████▍                                                          | 48/200 [02:03<06:40,  2.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▊                                                          | 49/200 [02:05<06:25,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:03,  7.56s/it]\u001b[A\u001b[A\n",
+      "training:  25%|███████████████████▎                                                         | 50/200 [02:08<06:17,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:20,  4.04s/it]\u001b[A\u001b[A\n",
+      "training:  26%|███████████████████▋                                                         | 51/200 [02:10<06:06,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:27,  2.88s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████                                                         | 52/200 [02:12<05:49,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:33,  2.31s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████▍                                                        | 53/200 [02:15<05:55,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:34,  2.02s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████████████████▊                                                        | 54/200 [02:17<05:55,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:56,  1.84s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▏                                                       | 55/200 [02:20<05:53,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:34,  1.73s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▌                                                       | 56/200 [02:22<05:52,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:15,  1.64s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▉                                                       | 57/200 [02:24<05:42,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<04:58,  1.56s/it]\u001b[A\u001b[A\n",
+      "training:  29%|██████████████████████▎                                                      | 58/200 [02:27<05:38,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:46,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████████████▋                                                      | 59/200 [02:29<05:33,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:46,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████                                                      | 60/200 [02:32<05:37,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:40,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████▍                                                     | 61/200 [02:34<05:38,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:42,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:  31%|███████████████████████▊                                                     | 62/200 [02:36<05:31,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:36,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  32%|████████████████████████▎                                                    | 63/200 [02:39<05:26,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:33,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  32%|████████████████████████▋                                                    | 64/200 [02:41<05:24,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:34,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  32%|█████████████████████████                                                    | 65/200 [02:44<05:26,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:26,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  33%|█████████████████████████▍                                                   | 66/200 [02:46<05:19,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:27,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  34%|█████████████████████████▊                                                   | 67/200 [02:48<05:22,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:18,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████████████████████▏                                                  | 68/200 [02:51<05:17,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:35<04:14,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████████████████████▌                                                  | 69/200 [02:53<05:22,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:15,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  35%|██████████████████████████▉                                                  | 70/200 [02:56<05:17,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<04:11,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  36%|███████████████████████████▎                                                 | 71/200 [02:58<05:05,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<04:14,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  36%|███████████████████████████▋                                                 | 72/200 [03:00<05:07,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:41<04:16,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  36%|████████████████████████████                                                 | 73/200 [03:03<04:58,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:42<04:16,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  37%|████████████████████████████▍                                                | 74/200 [03:05<04:58,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:44<04:10,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  38%|████████████████████████████▉                                                | 75/200 [03:07<04:56,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<04:11,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  38%|█████████████████████████████▎                                               | 76/200 [03:09<04:40,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<04:08,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  38%|█████████████████████████████▋                                               | 77/200 [03:12<04:48,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<04:14,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  39%|██████████████████████████████                                               | 78/200 [03:15<04:52,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:49<04:09,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  40%|██████████████████████████████▍                                              | 79/200 [03:17<04:43,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:51<04:16,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  40%|██████████████████████████████▊                                              | 80/200 [03:19<04:37,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:53<04:09,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  40%|███████████████████████████████▏                                             | 81/200 [03:21<04:33,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:54<04:02,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  41%|███████████████████████████████▌                                             | 82/200 [03:24<04:43,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:55<03:59,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  42%|███████████████████████████████▉                                             | 83/200 [03:26<04:42,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:57<03:55,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  42%|████████████████████████████████▎                                            | 84/200 [03:29<04:37,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:58<03:51,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  42%|████████████████████████████████▋                                            | 85/200 [03:31<04:33,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:59<03:48,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  43%|█████████████████████████████████                                            | 86/200 [03:33<04:33,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:01<03:53,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████████████████████████▍                                           | 87/200 [03:36<04:36,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<03:49,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████████████████████████▉                                           | 88/200 [03:38<04:31,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:04<03:50,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████████████████████████▎                                          | 89/200 [03:41<04:32,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:05<03:45,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  45%|██████████████████████████████████▋                                          | 90/200 [03:43<04:23,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:07<03:43,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  46%|███████████████████████████████████                                          | 91/200 [03:46<04:23,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:08<03:38,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  46%|███████████████████████████████████▍                                         | 92/200 [03:48<04:25,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:09<03:37,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  46%|███████████████████████████████████▊                                         | 93/200 [03:51<04:25,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:11<03:32,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  47%|████████████████████████████████████▏                                        | 94/200 [03:53<04:21,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:12<03:32,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  48%|████████████████████████████████████▌                                        | 95/200 [03:55<04:14,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:13<03:26,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  48%|████████████████████████████████████▉                                        | 96/200 [03:58<04:13,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:15<03:28,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  48%|█████████████████████████████████████▎                                       | 97/200 [04:01<04:16,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:16<03:26,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  49%|█████████████████████████████████████▋                                       | 98/200 [04:03<04:12,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:18<03:27,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  50%|██████████████████████████████████████                                       | 99/200 [04:06<04:12,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:19<03:20,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  50%|██████████████████████████████████████                                      | 100/200 [04:08<04:08,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:21<03:53,  1.58s/it]\n",
+      "training:  50%|██████████████████████████████████████▍                                     | 101/200 [04:10<04:03,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  51%|██████████████████████████████████████▊                                     | 102/200 [04:13<04:05,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  52%|███████████████████████████████████████▏                                    | 103/200 [04:15<03:57,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  52%|███████████████████████████████████████▌                                    | 104/200 [04:18<03:51,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 16.05it/s]\n",
+      "training:  52%|███████████████████████████████████████▉                                    | 105/200 [04:20<03:46,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  53%|████████████████████████████████████████▎                                   | 106/200 [04:22<03:42,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  54%|████████████████████████████████████████▋                                   | 107/200 [04:25<03:44,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  54%|█████████████████████████████████████████                                   | 108/200 [04:27<03:38,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 46it [00:01, 44.67it/s]\u001b[A\u001b[A\n",
+      "training:  55%|█████████████████████████████████████████▍                                  | 109/200 [04:30<03:41,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 82it [00:01, 83.24it/s]\u001b[A\u001b[A\n",
+      "training:  55%|█████████████████████████████████████████▊                                  | 110/200 [04:32<03:37,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 126it [00:01, 136.03it/s]\u001b[A\u001b[A\n",
+      "training:  56%|██████████████████████████████████████████▏                                 | 111/200 [04:35<03:45,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 170it [00:01, 189.03it/s]\u001b[A\u001b[A\n",
+      "training:  56%|██████████████████████████████████████████▌                                 | 112/200 [04:38<03:46,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 213it [00:01, 236.73it/s]\u001b[A\u001b[A\n",
+      "training:  56%|██████████████████████████████████████████▉                                 | 113/200 [04:40<03:42,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 253it [00:01, 260.47it/s]\u001b[A\u001b[A\n",
+      "training:  57%|███████████████████████████████████████████▎                                | 114/200 [04:43<03:37,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 295it [00:02, 297.16it/s]\u001b[A\u001b[A\n",
+      "training:  57%|███████████████████████████████████████████▋                                | 115/200 [04:45<03:43,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 335it [00:02, 322.64it/s]\u001b[A\u001b[A\n",
+      "training:  58%|████████████████████████████████████████████                                | 116/200 [04:51<03:30,  2.51s/it]\n",
       "\n",
-      "Evaluating NLL: 379it [00:02, 350.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 430it [00:02, 391.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 474it [00:02, 392.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 517it [00:02, 391.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 10.57it/s]\n",
       "\n",
-      "Evaluating NLL: 559it [00:02, 377.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 607it [00:02, 400.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 652it [00:02, 412.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 699it [00:02, 428.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 29it [00:01, 21.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 748it [00:03, 445.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 57it [00:01, 46.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 247.93it/s]\n",
+      "Evaluating NLL: 83it [00:02, 72.30it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 109it [00:02, 99.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 134it [00:02, 122.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 104\u001b[A\u001b[A\n",
+      "Evaluating NLL: 161it [00:02, 150.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:00<00:00, 154\u001b[A\u001b[A\n",
+      "Evaluating NLL: 186it [00:02, 166.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 167\u001b[A\u001b[A\n",
+      "Evaluating NLL: 210it [00:02, 168.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  70%|▋| 70/100 [00:00<00:00, 172\u001b[A\u001b[A\n",
+      "Evaluating NLL: 232it [00:02, 174.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Evaluating NLL: 253it [00:02, 181.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [19:27<06:42, 402.39s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.7096301678360897\n",
-      "2999: -5.8209399\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 274it [00:02, 182.27it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 300it [00:03, 200.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 323it [00:03, 207.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:06,  7.57s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 345it [00:03, 207.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:25,  4.07s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 370it [00:03, 217.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:36,  2.93s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 393it [00:03, 194.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:13<08:53,  2.72s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 417it [00:03, 205.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:15<07:59,  2.46s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 439it [00:03, 202.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:16<06:53,  2.13s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 461it [00:03, 206.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:18<06:12,  1.93s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 483it [00:03, 195.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:19<05:42,  1.78s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 510it [00:04, 215.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:21<05:22,  1.69s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 535it [00:04, 225.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:22<05:09,  1.63s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 560it [00:04, 230.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:24<05:02,  1.60s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 584it [00:04, 219.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:25<04:55,  1.57s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 607it [00:04, 214.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:27<04:54,  1.57s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 629it [00:04, 210.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<04:47,  1.55s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 651it [00:04, 196.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:30<04:42,  1.53s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 680it [00:04, 220.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:31<04:42,  1.53s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 707it [00:04, 231.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:33<04:40,  1.53s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 733it [00:05, 238.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:34<04:39,  1.53s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 763it [00:05, 255.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:36<04:41,  1.55s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 149.85it/s]\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:37<04:39,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:39<04:33,  1.53s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                                                  | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:40<04:30,  1.52s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|█▋                                        | 4/100 [00:00<00:02, 35.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:42<04:28,  1.52s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%|███▊                                      | 9/100 [00:00<00:02, 41.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:43<04:25,  1.51s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|█████▋                                   | 14/100 [00:00<00:01, 43.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:45<04:26,  1.52s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  19%|███████▊                                 | 19/100 [00:00<00:01, 42.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:46<04:22,  1.51s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|██████████▎                              | 25/100 [00:00<00:01, 43.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:48<04:26,  1.54s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|████████████▎                            | 30/100 [00:00<00:01, 45.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:50<04:24,  1.54s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|██████████████▎                          | 35/100 [00:00<00:01, 43.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:51<04:25,  1.55s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|████████████████▍                        | 40/100 [00:00<00:01, 43.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:53<04:28,  1.58s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|██████████████████▍                      | 45/100 [00:01<00:01, 43.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:54<04:19,  1.54s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|█████████████████████▎                   | 52/100 [00:01<00:01, 47.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:56<04:15,  1.52s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|████████████████████████▏                | 59/100 [00:01<00:00, 51.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:57<04:14,  1.53s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|██████████████████████████▋              | 65/100 [00:01<00:00, 43.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:59<04:09,  1.50s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|████████████████████████████▋            | 70/100 [00:01<00:00, 35.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:00<04:05,  1.49s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|██████████████████████████████▎          | 74/100 [00:01<00:00, 36.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:02<04:05,  1.49s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|████████████████████████████████▊        | 80/100 [00:01<00:00, 41.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:03<04:02,  1.49s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|███████████████████████████████████▎     | 86/100 [00:01<00:00, 45.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:05<04:05,  1.51s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|█████████████████████████████████████▎   | 91/100 [00:02<00:00, 45.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:06<04:05,  1.52s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|████████████████████████████████████████| 100/100 [00:02<00:00, 44.28it/s]\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:08<04:12,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:10<04:18,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                                                           | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:11<04:19,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|█▍                                                              | 173/8000 [00:00<00:11, 679.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:13<04:17,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|██▊                                                             | 350/8000 [00:00<00:12, 611.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:15<04:17,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|████▎                                                           | 536/8000 [00:00<00:11, 678.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:16<04:17,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█████▋                                                          | 707/8000 [00:01<00:10, 666.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:18<04:11,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██████▉                                                         | 866/8000 [00:03<00:39, 182.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:20<04:17,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████████▎                                                      | 1055/8000 [00:05<00:55, 126.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:21<04:07,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████████▋                                                     | 1233/8000 [00:05<00:39, 169.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:23<04:04,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███████████▏                                                   | 1421/8000 [00:06<00:29, 226.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:24<03:59,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|████████████▌                                                  | 1591/8000 [00:06<00:24, 264.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:26<04:12,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|██████████████                                                 | 1778/8000 [00:06<00:18, 329.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:28<04:07,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███████████████▏                                               | 1925/8000 [00:08<00:32, 187.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:29<03:58,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████████████▌                                              | 2104/8000 [00:08<00:24, 242.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:31<04:02,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████████████▉                                             | 2277/8000 [00:08<00:19, 300.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:33<04:09,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|███████████████████▎                                           | 2448/8000 [00:09<00:16, 346.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:35<04:01,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|████████████████████▋                                          | 2624/8000 [00:09<00:13, 404.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:36<03:54,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████████████████████                                         | 2794/8000 [00:09<00:11, 440.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:38<03:49,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████████████████████▍                                       | 2971/8000 [00:10<00:10, 470.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:39<03:43,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|████████████████████████▊                                      | 3148/8000 [00:10<00:10, 480.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:41<03:39,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████████████████▏                                    | 3323/8000 [00:10<00:09, 518.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:42<03:35,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████████████████▍                                   | 3486/8000 [00:11<00:07, 572.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:44<03:34,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████████████████▊                                  | 3658/8000 [00:11<00:07, 592.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:45<03:27,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██████████████████████████████▏                                | 3835/8000 [00:11<00:06, 637.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:47<03:29,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████████████████████████████▌                               | 4001/8000 [00:11<00:07, 555.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:48<03:26,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████████████████████████████▊                              | 4165/8000 [00:12<00:05, 646.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:50<03:24,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████████████████████▏                            | 4340/8000 [00:12<00:06, 588.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:51<03:21,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████████████████████▌                           | 4521/8000 [00:12<00:05, 638.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:53<03:17,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████████████████████████████████                          | 4706/8000 [00:12<00:05, 637.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:55<03:23,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████████████████████████████████▍                        | 4878/8000 [00:13<00:05, 622.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:56<03:27,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████████████████████████████████▊                       | 5048/8000 [00:13<00:04, 595.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:58<03:24,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|█████████████████████████████████████████                      | 5220/8000 [00:13<00:04, 582.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:59<03:19,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████████████████████████▌                    | 5398/8000 [00:14<00:04, 604.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [02:01<03:16,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████████████████████████                   | 5588/8000 [00:16<00:12, 199.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [02:02<03:11,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████████████████████████▎                 | 5759/8000 [00:16<00:08, 257.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [02:06<03:30,  1.68s/it]\n",
+      "Generating signals:  74%|██████████████████████████████████████████████▊                | 5948/8000 [00:16<00:06, 323.75it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  77%|████████████████████████████████████████████████▏              | 6124/8000 [00:17<00:04, 395.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████████████████████████████████████████▌             | 6292/8000 [00:17<00:03, 446.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████████████████████████████████████████▉            | 6471/8000 [00:17<00:02, 518.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.38it/s]\n",
+      "Generating signals:  83%|████████████████████████████████████████████████████▍          | 6659/8000 [00:17<00:02, 610.94it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  85%|█████████████████████████████████████████████████████▊         | 6834/8000 [00:17<00:01, 658.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████████████████████████████████████████████▏       | 7003/8000 [00:18<00:01, 688.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████████████████████████████████████████████▌      | 7177/8000 [00:18<00:01, 748.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 31it [00:01, 26.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████████████████████████████████████████████▊     | 7342/8000 [00:18<00:00, 750.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 55it [00:01, 49.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████████████████████████████████    | 7504/8000 [00:18<00:00, 703.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 84it [00:01, 81.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████████████████████████████████▍  | 7673/8000 [00:19<00:00, 693.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 116it [00:01, 119.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████████████████████████████████▊ | 7845/8000 [00:19<00:00, 690.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 145it [00:02, 151.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8019it [00:19, 409.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Evaluating NLL: 177it [00:02, 186.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 208it [00:02, 213.40it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                                                      | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 242it [00:02, 243.93it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▍                                                                             | 1/200 [00:09<32:23,  9.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 278it [00:02, 272.80it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▊                                                                             | 2/200 [00:12<17:53,  5.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 311it [00:02, 263.48it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                                                            | 3/200 [00:14<13:29,  4.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 349it [00:02, 293.24it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▌                                                                            | 4/200 [00:17<11:10,  3.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 386it [00:02, 313.58it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▉                                                                            | 5/200 [00:19<09:39,  2.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 420it [00:02, 314.52it/s]\u001b[A\u001b[A\n",
+      "training:   3%|██▎                                                                           | 6/200 [00:21<08:55,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 453it [00:02, 312.12it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▋                                                                           | 7/200 [00:24<08:32,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 487it [00:03, 318.37it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███                                                                           | 8/200 [00:26<08:14,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 520it [00:03, 314.61it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███▌                                                                          | 9/200 [00:28<07:52,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 556it [00:03, 325.77it/s]\u001b[A\u001b[A\n",
+      "training:   5%|███▊                                                                         | 10/200 [00:31<07:51,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 592it [00:03, 333.42it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▏                                                                        | 11/200 [00:33<07:54,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 626it [00:03, 334.88it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▌                                                                        | 12/200 [00:36<07:46,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 660it [00:03, 327.28it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█████                                                                        | 13/200 [00:38<07:37,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 693it [00:03, 321.34it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█████▍                                                                       | 14/200 [00:40<07:36,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 728it [00:03, 327.91it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█████▊                                                                       | 15/200 [00:43<07:36,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 761it [00:03, 316.01it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▏                                                                      | 16/200 [00:45<07:31,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 197.58it/s]\n",
+      "training:   8%|██████▌                                                                      | 17/200 [00:48<07:24,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:   9%|██████▉                                                                      | 18/200 [00:50<07:20,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▎                                                                     | 19/200 [00:53<07:25,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 129\u001b[A\u001b[A\n",
+      "training:  10%|███████▋                                                                     | 20/200 [00:55<07:21,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:00, 98.\u001b[A\u001b[A\n",
+      "training:  10%|████████                                                                     | 21/200 [00:57<07:11,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 116\u001b[A\u001b[A\n",
+      "training:  11%|████████▍                                                                    | 22/200 [01:00<07:06,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:00<00:00, 109\u001b[A\u001b[A\n",
+      "training:  12%|████████▊                                                                    | 23/200 [01:02<07:01,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 110\u001b[A\u001b[A\n",
+      "training:  12%|█████████▏                                                                   | 24/200 [01:05<07:04,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:00<00:00, 113\u001b[A\u001b[A\n",
+      "training:  12%|█████████▋                                                                   | 25/200 [01:07<07:09,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 11\n",
+      "training:  13%|██████████                                                                   | 26/200 [01:10<07:12,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  14%|██████████▍                                                                  | 27/200 [01:12<07:09,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▊                                                                  | 28/200 [01:15<07:00,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 1/8000 [00:00<22:59,  5.80it/s]\u001b[A\u001b[A\n",
+      "training:  14%|███████████▏                                                                 | 29/200 [01:17<07:12,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 3/8000 [00:00<12:13, 10.90it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████████▌                                                                 | 30/200 [01:20<07:09,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 6/8000 [00:00<13:39,  9.75it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████████▉                                                                 | 31/200 [01:22<06:56,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 9/8000 [00:00<10:48, 12.32it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▎                                                                | 32/200 [01:25<06:51,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 11/8000 [00:00<10:36, 12.56it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▋                                                                | 33/200 [01:27<06:39,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 14/8000 [00:01<09:00, 14.77it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████████████                                                                | 34/200 [01:29<06:34,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 17/8000 [00:01<10:13, 13.02it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▍                                                               | 35/200 [01:32<06:50,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 19/8000 [00:01<10:02, 13.24it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▊                                                               | 36/200 [01:34<06:47,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 25/8000 [00:01<06:17, 21.12it/s]\u001b[A\u001b[A\n",
+      "training:  18%|██████████████▏                                                              | 37/200 [01:37<06:57,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 29/8000 [00:01<05:43, 23.21it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████████████▋                                                              | 38/200 [01:40<06:47,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 33/8000 [00:01<05:13, 25.42it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████                                                              | 39/200 [01:42<06:49,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 37/8000 [00:02<05:08, 25.80it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▍                                                             | 40/200 [01:45<06:42,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 42/8000 [00:02<05:52, 22.59it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▊                                                             | 41/200 [01:47<06:34,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 47/8000 [00:02<05:05, 26.05it/s]\u001b[A\u001b[A\n",
+      "training:  21%|████████████████▏                                                            | 42/200 [01:49<06:30,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 53/8000 [00:02<05:33, 23.84it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▌                                                            | 43/200 [01:52<06:19,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 57/8000 [00:03<06:30, 20.32it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▉                                                            | 44/200 [01:54<06:16,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 64/8000 [00:03<04:59, 26.47it/s]\u001b[A\u001b[A\n",
+      "training:  22%|█████████████████▎                                                           | 45/200 [01:57<06:20,  2.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 68/8000 [00:03<05:05, 25.94it/s]\u001b[A\u001b[A\n",
+      "training:  23%|█████████████████▋                                                           | 46/200 [01:59<06:24,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 72/8000 [00:03<05:17, 24.94it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████                                                           | 47/200 [02:02<06:12,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 80/8000 [00:03<05:31, 23.90it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▍                                                          | 48/200 [02:04<06:20,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 83/8000 [00:04<05:36, 23.53it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▊                                                          | 49/200 [02:07<06:41,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 86/8000 [00:04<07:07, 18.52it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████████████████▎                                                         | 50/200 [02:10<06:32,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 91/8000 [00:04<08:28, 15.56it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████████████████▋                                                         | 51/200 [02:12<06:27,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 93/8000 [00:05<10:30, 12.55it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████                                                         | 52/200 [02:15<06:22,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 97/8000 [00:05<08:49, 14.94it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████▍                                                        | 53/200 [02:18<06:22,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 99/8000 [00:05<11:28, 11.48it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████████████▊                                                        | 54/200 [02:20<06:15,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 103/8000 [00:05<09:16, 14.19it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▏                                                       | 55/200 [02:23<06:09,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 105/8000 [00:05<11:02, 11.91it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▌                                                       | 56/200 [02:25<06:14,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 110/8000 [00:06<08:28, 15.52it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▉                                                       | 57/200 [02:28<06:18,  2.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 114/8000 [00:06<07:22, 17.83it/s]\u001b[A\u001b[A\n",
+      "training:  29%|██████████████████████▎                                                      | 58/200 [02:31<06:26,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 117/8000 [00:06<08:31, 15.43it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████████████▋                                                      | 59/200 [02:33<06:13,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 119/8000 [00:06<09:08, 14.38it/s]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████                                                      | 60/200 [02:36<06:10,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 125/8000 [00:06<06:27, 20.33it/s]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████▍                                                     | 61/200 [02:38<05:58,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 128/8000 [00:07<06:15, 20.95it/s]\u001b[A\u001b[A\n",
+      "training:  31%|███████████████████████▊                                                     | 62/200 [02:41<05:50,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 132/8000 [00:07<08:56, 14.67it/s]\u001b[A\u001b[A\n",
+      "training:  32%|████████████████████████▎                                                    | 63/200 [02:43<05:47,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 134/8000 [00:07<09:06, 14.39it/s]\u001b[A\u001b[A\n",
+      "training:  32%|████████████████████████▋                                                    | 64/200 [02:46<05:48,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 139/8000 [00:08<09:42, 13.50it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████████████████████                                                    | 65/200 [02:49<05:43,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 141/8000 [00:08<09:30, 13.76it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████████████████████▍                                                   | 66/200 [02:51<05:44,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 145/8000 [00:08<08:14, 15.87it/s]\u001b[A\u001b[A\n",
+      "training:  34%|█████████████████████████▊                                                   | 67/200 [02:57<05:51,  2.64s/it]\n",
       "\n",
-      "Generating signals:   2%|▎                 | 147/8000 [00:08<08:31, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 150/8000 [00:08<08:10, 16.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 158/8000 [00:08<06:15, 20.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 164/8000 [00:09<06:19, 20.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 10it [00:01,  6.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 167/8000 [00:09<06:26, 20.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 11.99it/s]\n",
       "\n",
-      "Generating signals:   2%|▍                 | 171/8000 [00:09<05:47, 22.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 175/8000 [00:09<06:59, 18.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 180/8000 [00:10<06:03, 21.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 187/8000 [00:10<06:01, 21.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 21it [00:02, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 190/8000 [00:10<08:34, 15.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 38it [00:02, 26.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 194/8000 [00:10<07:34, 17.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 59it [00:02, 46.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 198/8000 [00:11<06:53, 18.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:02, 59.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 208/8000 [00:11<05:15, 24.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 94it [00:02, 77.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 212/8000 [00:11<05:06, 25.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 113it [00:02, 97.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 217/8000 [00:11<05:45, 22.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 138it [00:02, 127.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 222/8000 [00:12<07:22, 17.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 160it [00:02, 147.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 225/8000 [00:12<07:30, 17.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 183it [00:03, 166.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 229/8000 [00:12<06:59, 18.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 207it [00:03, 182.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 232/8000 [00:13<09:45, 13.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 229it [00:03, 191.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 234/8000 [00:13<10:40, 12.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 256it [00:03, 211.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 236/8000 [00:13<10:48, 11.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 279it [00:03, 215.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 240/8000 [00:13<08:26, 15.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 302it [00:03, 193.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 243/8000 [00:13<07:45, 16.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 323it [00:03, 193.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 246/8000 [00:14<09:26, 13.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 344it [00:03, 190.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 248/8000 [00:14<08:57, 14.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 367it [00:03, 200.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 250/8000 [00:14<12:18, 10.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 392it [00:03, 212.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 254/8000 [00:14<11:08, 11.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 421it [00:04, 232.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 260/8000 [00:14<07:31, 17.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 450it [00:04, 248.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 264/8000 [00:15<06:25, 20.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 477it [00:04, 254.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 267/8000 [00:15<08:01, 16.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 503it [00:04, 215.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 270/8000 [00:15<09:25, 13.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 526it [00:04, 175.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 273/8000 [00:15<09:04, 14.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 546it [00:04, 174.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▋                 | 278/8000 [00:16<07:19, 17.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 568it [00:04, 183.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 285/8000 [00:16<06:31, 19.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 588it [00:04, 186.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 289/8000 [00:16<08:36, 14.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 608it [00:05, 177.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 291/8000 [00:16<08:41, 14.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 627it [00:05, 180.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 296/8000 [00:17<09:51, 13.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 646it [00:05, 175.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 298/8000 [00:17<09:51, 13.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 664it [00:05, 172.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 304/8000 [00:17<07:26, 17.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 685it [00:05, 180.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 308/8000 [00:17<06:51, 18.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 707it [00:05, 190.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 311/8000 [00:18<09:58, 12.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 730it [00:05, 192.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 313/8000 [00:18<09:45, 13.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 750it [00:05, 194.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 320/8000 [00:18<06:36, 19.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 770it [00:05, 195.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 324/8000 [00:18<06:20, 20.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:06, 130.60it/s]\n",
       "\n",
-      "Generating signals:   4%|▋                 | 328/8000 [00:19<08:55, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 331/8000 [00:19<10:44, 11.90it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                                                  | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 334/8000 [00:19<10:00, 12.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|█▋                                        | 4/100 [00:00<00:03, 29.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 336/8000 [00:20<10:00, 12.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|███▎                                      | 8/100 [00:00<00:02, 33.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 338/8000 [00:20<09:43, 13.14it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|█████▎                                   | 13/100 [00:00<00:02, 35.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 342/8000 [00:20<08:07, 15.71it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  17%|██████▉                                  | 17/100 [00:00<00:02, 33.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 344/8000 [00:20<08:45, 14.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|█████████                                | 22/100 [00:00<00:02, 33.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 348/8000 [00:20<07:31, 16.95it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|███████████                              | 27/100 [00:00<00:02, 35.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 352/8000 [00:20<06:55, 18.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|█████████████                            | 32/100 [00:00<00:01, 38.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 355/8000 [00:21<09:00, 14.14it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|███████████████▏                         | 37/100 [00:00<00:01, 40.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 358/8000 [00:21<08:26, 15.08it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|█████████████████▋                       | 43/100 [00:01<00:01, 46.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 361/8000 [00:21<08:03, 15.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|███████████████████▋                     | 48/100 [00:01<00:01, 46.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 365/8000 [00:21<07:00, 18.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|██████████████████████▏                  | 54/100 [00:01<00:00, 46.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 369/8000 [00:21<06:01, 21.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|████████████████████████▏                | 59/100 [00:01<00:00, 45.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 373/8000 [00:22<07:33, 16.81it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|██████████████████████████▏              | 64/100 [00:01<00:00, 38.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 378/8000 [00:22<06:13, 20.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|████████████████████████████▋            | 70/100 [00:01<00:00, 39.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 382/8000 [00:22<07:25, 17.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|██████████████████████████████▊          | 75/100 [00:01<00:00, 38.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 386/8000 [00:22<06:34, 19.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|████████████████████████████████▍        | 79/100 [00:02<00:00, 37.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 390/8000 [00:23<06:04, 20.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██████████████████████████████████▍      | 84/100 [00:02<00:00, 39.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 396/8000 [00:23<06:35, 19.25it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  89%|████████████████████████████████████▍    | 89/100 [00:02<00:00, 40.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 399/8000 [00:23<08:17, 15.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  94%|██████████████████████████████████████▌  | 94/100 [00:02<00:00, 41.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 402/8000 [00:23<07:56, 15.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|████████████████████████████████████████| 100/100 [00:02<00:00, 39.97it/s]\n",
       "\n",
-      "Generating signals:   5%|▉                 | 407/8000 [00:24<06:22, 19.87it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|██████████████████                                                      | 1/4 [14:12<42:38, 852.79s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.159907630690807\n",
+      "2999: -6.2146827\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   5%|▉                 | 411/8000 [00:24<08:49, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 417/8000 [00:24<06:46, 18.65it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                                                      | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 420/8000 [00:24<08:16, 15.27it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▍                                                                             | 1/200 [00:09<31:55,  9.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 424/8000 [00:25<07:12, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▊                                                                             | 2/200 [00:12<18:45,  5.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 429/8000 [00:25<07:23, 17.06it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                                                            | 3/200 [00:15<14:02,  4.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 432/8000 [00:25<08:36, 14.65it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▌                                                                            | 4/200 [00:17<11:37,  3.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 435/8000 [00:25<07:50, 16.07it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▉                                                                            | 5/200 [00:20<10:18,  3.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 439/8000 [00:26<06:46, 18.60it/s]\u001b[A\u001b[A\n",
+      "training:   3%|██▎                                                                           | 6/200 [00:22<09:36,  2.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 443/8000 [00:26<07:07, 17.69it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▋                                                                           | 7/200 [00:25<09:22,  2.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 445/8000 [00:26<07:19, 17.20it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███                                                                           | 8/200 [00:28<08:55,  2.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 450/8000 [00:26<06:21, 19.79it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███▌                                                                          | 9/200 [00:30<08:44,  2.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 453/8000 [00:26<06:03, 20.74it/s]\u001b[A\u001b[A\n",
+      "training:   5%|███▊                                                                         | 10/200 [00:33<08:44,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 459/8000 [00:27<06:05, 20.63it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▏                                                                        | 11/200 [00:36<08:50,  2.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 463/8000 [00:27<08:52, 14.14it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▌                                                                        | 12/200 [00:39<09:09,  2.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 465/8000 [00:27<09:02, 13.90it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█████                                                                        | 13/200 [00:42<08:46,  2.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 472/8000 [00:27<06:27, 19.42it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█████▍                                                                       | 14/200 [00:44<08:19,  2.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 477/8000 [00:28<05:39, 22.15it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█████▊                                                                       | 15/200 [00:48<09:28,  3.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 481/8000 [00:28<08:23, 14.92it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▏                                                                      | 16/200 [00:50<08:44,  2.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 483/8000 [00:28<08:22, 14.97it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▌                                                                      | 17/200 [00:53<08:16,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 486/8000 [00:28<07:56, 15.77it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██████▉                                                                      | 18/200 [00:55<07:51,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 491/8000 [00:29<07:59, 15.68it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▎                                                                     | 19/200 [00:57<07:31,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 495/8000 [00:29<08:32, 14.65it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▋                                                                     | 20/200 [01:00<07:24,  2.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 499/8000 [00:29<08:56, 13.97it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████████                                                                     | 21/200 [01:02<07:19,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 501/8000 [00:29<09:04, 13.77it/s]\u001b[A\u001b[A\n",
+      "training:  11%|████████▍                                                                    | 22/200 [01:05<07:26,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 503/8000 [00:30<08:53, 14.05it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████████▊                                                                    | 23/200 [01:07<07:22,  2.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 507/8000 [00:30<07:25, 16.80it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▏                                                                   | 24/200 [01:10<07:23,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 509/8000 [00:30<08:01, 15.55it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▋                                                                   | 25/200 [01:12<07:25,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 514/8000 [00:30<08:23, 14.88it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████████                                                                   | 26/200 [01:15<07:24,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 517/8000 [00:30<08:28, 14.72it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▍                                                                  | 27/200 [01:18<07:25,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 522/8000 [00:31<06:35, 18.92it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▊                                                                  | 28/200 [01:20<07:07,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 525/8000 [00:31<09:37, 12.94it/s]\u001b[A\u001b[A\n",
+      "training:  14%|███████████▏                                                                 | 29/200 [01:22<07:05,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 531/8000 [00:31<07:03, 17.64it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████████▌                                                                 | 30/200 [01:25<07:11,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 535/8000 [00:31<06:23, 19.44it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████████▉                                                                 | 31/200 [01:28<07:12,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 539/8000 [00:32<05:51, 21.26it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▎                                                                | 32/200 [01:30<07:08,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 544/8000 [00:32<05:05, 24.40it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▋                                                                | 33/200 [01:33<07:10,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 548/8000 [00:32<09:01, 13.76it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████████████                                                                | 34/200 [01:35<07:12,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 552/8000 [00:33<09:21, 13.26it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▍                                                               | 35/200 [01:38<07:16,  2.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 555/8000 [00:33<10:00, 12.40it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▊                                                               | 36/200 [01:41<07:06,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 557/8000 [00:33<10:15, 12.08it/s]\u001b[A\u001b[A\n",
+      "training:  18%|██████████████▏                                                              | 37/200 [01:43<06:54,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 559/8000 [00:33<10:19, 12.01it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████████████▋                                                              | 38/200 [01:46<06:54,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 561/8000 [00:33<10:12, 12.15it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████                                                              | 39/200 [01:48<06:54,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 563/8000 [00:34<10:16, 12.06it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▍                                                             | 40/200 [01:51<06:49,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 568/8000 [00:34<07:04, 17.52it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▊                                                             | 41/200 [01:53<06:39,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 572/8000 [00:34<07:46, 15.92it/s]\u001b[A\u001b[A\n",
+      "training:  21%|████████████████▏                                                            | 42/200 [01:56<06:37,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 574/8000 [00:34<08:14, 15.02it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▌                                                            | 43/200 [01:58<06:41,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 577/8000 [00:34<07:52, 15.71it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▉                                                            | 44/200 [02:01<06:41,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 579/8000 [00:35<10:40, 11.58it/s]\u001b[A\u001b[A\n",
+      "training:  22%|█████████████████▎                                                           | 45/200 [02:04<06:35,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 583/8000 [00:35<10:04, 12.26it/s]\u001b[A\u001b[A\n",
+      "training:  23%|█████████████████▋                                                           | 46/200 [02:06<06:30,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 587/8000 [00:35<08:10, 15.10it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████                                                           | 47/200 [02:09<06:30,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 589/8000 [00:35<08:34, 14.40it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▍                                                          | 48/200 [02:11<06:29,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 592/8000 [00:35<08:01, 15.38it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▊                                                          | 49/200 [02:14<06:27,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 595/8000 [00:36<07:48, 15.81it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████████████████▎                                                         | 50/200 [02:16<06:23,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 599/8000 [00:36<07:17, 16.92it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████████████████▋                                                         | 51/200 [02:19<06:30,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 601/8000 [00:36<08:03, 15.29it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████                                                         | 52/200 [02:22<06:28,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 605/8000 [00:36<06:38, 18.57it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████▍                                                        | 53/200 [02:24<06:23,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 610/8000 [00:37<08:12, 15.02it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████████████▊                                                        | 54/200 [02:27<06:23,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 615/8000 [00:37<07:59, 15.41it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▏                                                       | 55/200 [02:29<06:03,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 617/8000 [00:37<08:39, 14.22it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▌                                                       | 56/200 [02:32<06:06,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 619/8000 [00:37<08:38, 14.24it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▉                                                       | 57/200 [02:34<06:03,  2.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 623/8000 [00:37<07:10, 17.14it/s]\u001b[A\u001b[A\n",
+      "training:  29%|██████████████████████▎                                                      | 58/200 [02:37<06:05,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 626/8000 [00:38<06:46, 18.12it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████████████▋                                                      | 59/200 [02:40<06:05,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 629/8000 [00:38<06:51, 17.89it/s]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████                                                      | 60/200 [02:42<06:01,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 631/8000 [00:38<07:23, 16.60it/s]\u001b[A\u001b[A\n",
+      "training:  30%|███████████████████████▍                                                     | 61/200 [02:45<06:02,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 634/8000 [00:38<09:46, 12.57it/s]\u001b[A\u001b[A\n",
+      "training:  31%|███████████████████████▊                                                     | 62/200 [02:47<05:55,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 636/8000 [00:39<12:38,  9.70it/s]\u001b[A\u001b[A\n",
+      "training:  32%|████████████████████████▎                                                    | 63/200 [02:50<06:00,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 638/8000 [00:39<11:44, 10.45it/s]\u001b[A\u001b[A\n",
+      "training:  32%|████████████████████████▋                                                    | 64/200 [02:52<05:44,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 640/8000 [00:39<11:12, 10.95it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████████████████████████                                                    | 65/200 [02:54<05:16,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 646/8000 [00:39<08:31, 14.37it/s]\u001b[A\u001b[A\n",
+      "training:  33%|█████████████████████████▍                                                   | 66/200 [02:56<04:59,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 648/8000 [00:39<08:54, 13.76it/s]\u001b[A\u001b[A\n",
+      "training:  34%|█████████████████████████▊                                                   | 67/200 [02:58<04:42,  2.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 651/8000 [00:40<08:11, 14.96it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████████████████████▏                                                  | 68/200 [03:00<04:32,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 654/8000 [00:40<09:44, 12.57it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████████████████████▌                                                  | 69/200 [03:02<04:21,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 659/8000 [00:40<08:49, 13.85it/s]\u001b[A\u001b[A\n",
+      "training:  35%|██████████████████████████▉                                                  | 70/200 [03:04<04:27,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 661/8000 [00:40<09:16, 13.19it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████████████████████▎                                                 | 71/200 [03:09<05:44,  2.67s/it]\n",
       "\n",
-      "Generating signals:   8%|█▍                | 665/8000 [00:40<07:46, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 669/8000 [00:41<08:17, 14.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 674/8000 [00:41<06:38, 18.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 679/8000 [00:41<05:54, 20.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 10it [00:01,  7.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 684/8000 [00:41<06:52, 17.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 11.93it/s]\n",
       "\n",
-      "Generating signals:   9%|█▌                | 686/8000 [00:42<07:24, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 689/8000 [00:42<07:19, 16.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 692/8000 [00:42<07:32, 16.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 696/8000 [00:42<07:33, 16.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 23it [00:01, 18.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 699/8000 [00:43<07:52, 15.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 48it [00:01, 43.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 702/8000 [00:43<07:24, 16.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 70it [00:01, 66.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 706/8000 [00:43<06:23, 19.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 90it [00:01, 85.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 708/8000 [00:43<06:54, 17.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 112it [00:02, 109.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 710/8000 [00:43<07:16, 16.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 133it [00:02, 128.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 712/8000 [00:43<07:36, 15.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 162it [00:02, 164.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 714/8000 [00:43<08:04, 15.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 192it [00:02, 195.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 718/8000 [00:44<07:16, 16.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 217it [00:02, 201.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 722/8000 [00:44<07:47, 15.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 241it [00:02, 193.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 726/8000 [00:44<06:34, 18.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 266it [00:02, 204.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 728/8000 [00:44<07:00, 17.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 289it [00:02, 209.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 730/8000 [00:44<08:08, 14.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 312it [00:02, 198.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 732/8000 [00:45<08:29, 14.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 336it [00:03, 209.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 735/8000 [00:45<07:51, 15.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 361it [00:03, 220.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 737/8000 [00:45<11:22, 10.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 389it [00:03, 236.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 739/8000 [00:45<13:55,  8.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 414it [00:03, 238.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 743/8000 [00:46<12:33,  9.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 439it [00:03, 236.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 747/8000 [00:46<09:23, 12.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 463it [00:03, 214.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 751/8000 [00:46<07:54, 15.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 487it [00:03, 220.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 755/8000 [00:46<07:14, 16.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 510it [00:03, 185.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 757/8000 [00:46<08:13, 14.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 530it [00:04, 185.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 759/8000 [00:47<10:39, 11.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 551it [00:04, 191.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 762/8000 [00:47<09:14, 13.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 576it [00:04, 206.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 766/8000 [00:47<07:20, 16.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 605it [00:04, 229.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 768/8000 [00:47<07:21, 16.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 634it [00:04, 234.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 770/8000 [00:47<07:37, 15.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 658it [00:04, 234.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 772/8000 [00:48<08:36, 14.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 682it [00:04, 228.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 774/8000 [00:48<08:58, 13.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 706it [00:04, 192.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 779/8000 [00:48<06:21, 18.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 727it [00:04, 186.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 783/8000 [00:48<09:40, 12.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 753it [00:05, 205.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 787/8000 [00:49<09:40, 12.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 152.08it/s]\n",
       "\n",
-      "Generating signals:  10%|█▊                | 789/8000 [00:49<09:35, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 792/8000 [00:49<09:47, 12.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                                                  | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 794/8000 [00:49<10:14, 11.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|█▋                                        | 4/100 [00:00<00:02, 37.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 796/8000 [00:49<10:17, 11.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|███▎                                      | 8/100 [00:00<00:02, 37.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 799/8000 [00:50<08:58, 13.37it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|█████▎                                   | 13/100 [00:00<00:02, 38.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 803/8000 [00:50<08:59, 13.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|████████▏                                | 20/100 [00:00<00:01, 45.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 805/8000 [00:50<09:07, 13.14it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|██████████▎                              | 25/100 [00:00<00:01, 44.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 807/8000 [00:50<08:58, 13.36it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|████████████▎                            | 30/100 [00:00<00:01, 44.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 809/8000 [00:50<08:49, 13.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|██████████████▊                          | 36/100 [00:00<00:01, 45.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 811/8000 [00:51<11:14, 10.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████████████████▊                        | 41/100 [00:00<00:01, 45.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 815/8000 [00:51<08:31, 14.05it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|██████████████████▊                      | 46/100 [00:01<00:01, 43.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 818/8000 [00:51<09:37, 12.44it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|████████████████████▉                    | 51/100 [00:01<00:01, 42.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 820/8000 [00:51<11:24, 10.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|██████████████████████▉                  | 56/100 [00:01<00:01, 40.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 822/8000 [00:52<11:06, 10.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|█████████████████████████                | 61/100 [00:01<00:00, 42.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 824/8000 [00:52<10:20, 11.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|███████████████████████████▍             | 67/100 [00:01<00:00, 45.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 828/8000 [00:52<07:17, 16.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|█████████████████████████████▌           | 72/100 [00:01<00:00, 45.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 832/8000 [00:52<09:57, 12.00it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|███████████████████████████████▉         | 78/100 [00:01<00:00, 46.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 836/8000 [00:53<09:24, 12.69it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|██████████████████████████████████▊      | 85/100 [00:01<00:00, 50.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 840/8000 [00:53<07:40, 15.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|█████████████████████████████████████▎   | 91/100 [00:02<00:00, 48.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 844/8000 [00:53<06:20, 18.79it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|████████████████████████████████████████| 100/100 [00:02<00:00, 44.53it/s]\n",
       "\n",
-      "Generating signals:  11%|█▉                | 850/8000 [00:53<05:16, 22.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 854/8000 [00:53<05:49, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                                                           | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 858/8000 [00:53<05:25, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                                                | 47/8000 [00:00<00:39, 203.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 864/8000 [00:54<05:39, 21.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▊                                                               | 100/8000 [00:00<00:37, 212.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 871/8000 [00:54<07:01, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|█▏                                                              | 150/8000 [00:00<00:39, 198.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 875/8000 [00:54<06:32, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▌                                                              | 201/8000 [00:00<00:36, 216.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 878/8000 [00:55<06:21, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|██                                                              | 250/8000 [00:01<00:36, 211.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 881/8000 [00:55<06:12, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|██▍                                                             | 301/8000 [00:01<00:34, 222.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 884/8000 [00:55<06:03, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|██▊                                                             | 351/8000 [00:01<00:32, 233.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 888/8000 [00:55<06:45, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|███▎                                                             | 413/8000 [00:03<01:38, 76.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 891/8000 [00:55<08:05, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|███▋                                                             | 457/8000 [00:03<01:21, 93.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 895/8000 [00:56<06:45, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|████                                                            | 510/8000 [00:03<01:02, 120.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 898/8000 [00:56<06:13, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|████▌                                                           | 568/8000 [00:03<00:49, 149.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 903/8000 [00:56<08:07, 14.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|████▉                                                           | 618/8000 [00:04<00:43, 169.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 906/8000 [00:57<09:09, 12.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█████▏                                                          | 656/8000 [00:04<00:47, 155.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 908/8000 [00:57<10:48, 10.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█████▌                                                          | 702/8000 [00:04<00:42, 170.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 911/8000 [00:57<09:52, 11.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█████▉                                                          | 747/8000 [00:04<00:40, 181.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 915/8000 [00:57<08:38, 13.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██████▎                                                         | 795/8000 [00:05<00:36, 195.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 917/8000 [00:57<08:28, 13.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██████▋                                                         | 835/8000 [00:05<00:40, 177.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 921/8000 [00:58<07:04, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███████                                                         | 881/8000 [00:05<00:37, 188.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 924/8000 [00:58<06:34, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|███████▍                                                        | 935/8000 [00:05<00:33, 208.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 926/8000 [00:58<07:07, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|███████▊                                                        | 984/8000 [00:05<00:31, 219.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 928/8000 [00:58<07:32, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████████▏                                                      | 1040/8000 [00:06<00:30, 228.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 931/8000 [00:58<06:51, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████████▌                                                      | 1095/8000 [00:06<00:28, 239.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 939/8000 [00:58<04:06, 28.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████████                                                      | 1147/8000 [00:06<00:26, 257.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 944/8000 [00:59<06:04, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████████▎                                                     | 1190/8000 [00:06<00:28, 235.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 947/8000 [00:59<10:05, 11.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████████▊                                                     | 1242/8000 [00:06<00:28, 236.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 951/8000 [00:59<08:29, 13.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██████████▏                                                    | 1296/8000 [00:07<00:26, 248.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 954/8000 [01:00<07:49, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████████▌                                                    | 1346/8000 [00:07<00:29, 222.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 957/8000 [01:00<08:30, 13.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████████▉                                                    | 1389/8000 [00:07<00:30, 214.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 959/8000 [01:00<08:21, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███████████▏                                                   | 1428/8000 [00:07<00:30, 214.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 964/8000 [01:00<06:25, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███████████▋                                                   | 1478/8000 [00:08<00:29, 220.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 968/8000 [01:00<07:05, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|████████████                                                   | 1524/8000 [00:08<00:29, 222.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 970/8000 [01:01<07:28, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|████████████▍                                                  | 1578/8000 [00:08<00:26, 237.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 972/8000 [01:01<10:02, 11.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|████████████▋                                                  | 1615/8000 [00:08<00:28, 222.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 975/8000 [01:01<08:58, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█████████████▏                                                 | 1675/8000 [00:08<00:25, 248.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 978/8000 [01:01<07:52, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█████████████▋                                                 | 1736/8000 [00:09<00:24, 251.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 980/8000 [01:01<07:56, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|██████████████                                                 | 1780/8000 [00:09<00:29, 213.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 985/8000 [01:02<06:07, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|██████████████▎                                                | 1822/8000 [00:09<00:34, 181.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 989/8000 [01:02<05:30, 21.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|██████████████▋                                                | 1872/8000 [00:09<00:32, 189.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 993/8000 [01:02<05:18, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███████████████                                                | 1917/8000 [00:10<00:33, 181.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1002/8000 [01:02<04:24, 26.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|███████████████▍                                               | 1961/8000 [00:10<00:32, 187.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1006/8000 [01:02<05:33, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|███████████████▊                                               | 2009/8000 [00:10<00:30, 199.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1010/8000 [01:03<06:24, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████████████▎                                              | 2068/8000 [00:10<00:28, 208.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1012/8000 [01:03<06:38, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████████████▌                                              | 2109/8000 [00:11<00:29, 199.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1016/8000 [01:03<05:54, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████████████████▉                                              | 2158/8000 [00:11<00:30, 190.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1022/8000 [01:03<05:58, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████████████▍                                             | 2211/8000 [00:11<00:29, 196.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1025/8000 [01:04<05:53, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████████████▊                                             | 2268/8000 [00:11<00:25, 224.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1029/8000 [01:04<05:24, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████████████▏                                            | 2309/8000 [00:12<00:26, 212.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1032/8000 [01:04<06:55, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████████████▋                                            | 2367/8000 [00:12<00:26, 215.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1036/8000 [01:04<06:17, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████████████▉                                            | 2411/8000 [00:12<00:26, 207.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1039/8000 [01:04<06:13, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|███████████████████▎                                           | 2451/8000 [00:12<00:30, 179.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1042/8000 [01:05<08:09, 14.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|███████████████████▋                                           | 2497/8000 [00:13<00:29, 187.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1047/8000 [01:05<07:52, 14.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|████████████████████                                           | 2544/8000 [00:13<00:27, 201.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1054/8000 [01:05<05:45, 20.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|████████████████████▍                                          | 2596/8000 [00:13<00:26, 203.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1057/8000 [01:06<08:07, 14.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|████████████████████▉                                          | 2653/8000 [00:13<00:24, 220.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1061/8000 [01:06<07:03, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████████████████████▏                                         | 2695/8000 [00:13<00:25, 210.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1066/8000 [01:06<06:56, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|█████████████████████▌                                         | 2743/8000 [00:14<00:22, 234.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1068/8000 [01:06<08:35, 13.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████████████████████                                         | 2797/8000 [00:14<00:22, 229.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1071/8000 [01:06<08:10, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████████████████████▎                                        | 2834/8000 [00:14<00:24, 207.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1074/8000 [01:07<07:35, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████████████████████▋                                        | 2886/8000 [00:14<00:22, 224.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1076/8000 [01:07<07:54, 14.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████████████████████                                        | 2935/8000 [00:15<00:22, 227.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1081/8000 [01:07<07:28, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████████████████████▍                                       | 2980/8000 [00:15<00:22, 220.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1084/8000 [01:07<07:01, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████████████████████▊                                       | 3030/8000 [00:15<00:22, 219.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1087/8000 [01:07<06:34, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████████████████▏                                      | 3071/8000 [00:15<00:22, 215.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1090/8000 [01:08<08:09, 14.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|████████████████████████▌                                      | 3120/8000 [00:15<00:23, 205.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1092/8000 [01:08<08:05, 14.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|████████████████████████▉                                      | 3171/8000 [00:16<00:24, 198.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1096/8000 [01:08<09:56, 11.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████████████████▍                                     | 3227/8000 [00:16<00:23, 206.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1100/8000 [01:08<07:48, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████████████████▉                                     | 3292/8000 [00:16<00:21, 220.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1104/8000 [01:09<08:41, 13.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████████████████▎                                    | 3346/8000 [00:16<00:20, 221.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1107/8000 [01:09<08:50, 12.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████████████████▊                                    | 3408/8000 [00:17<00:21, 211.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1111/8000 [01:09<07:29, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████████████████▏                                   | 3458/8000 [00:17<00:20, 221.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1118/8000 [01:09<05:34, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████████████████▋                                   | 3520/8000 [00:17<00:18, 240.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1122/8000 [01:10<07:26, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████████████████████████                                   | 3570/8000 [00:17<00:18, 245.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1124/8000 [01:10<08:42, 13.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████████████████████████▍                                  | 3613/8000 [00:18<00:19, 221.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1126/8000 [01:11<14:18,  8.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████████████████▊                                  | 3657/8000 [00:18<00:19, 222.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1131/8000 [01:11<11:02, 10.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|█████████████████████████████                                  | 3697/8000 [00:18<00:21, 198.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1135/8000 [01:11<09:13, 12.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██████████████████████████████                                  | 3763/8000 [00:19<00:46, 91.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1137/8000 [01:11<09:40, 11.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████████████████████████▉                                 | 3804/8000 [00:20<00:39, 105.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1141/8000 [01:12<08:33, 13.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██████████████████████████████▎                                | 3855/8000 [00:20<00:33, 123.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1144/8000 [01:12<10:58, 10.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██████████████████████████████▋                                | 3897/8000 [00:20<00:31, 130.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1149/8000 [01:12<08:55, 12.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|███████████████████████████████                                | 3946/8000 [00:20<00:28, 142.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1154/8000 [01:13<09:07, 12.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████████████████████████████▍                               | 3991/8000 [00:21<00:24, 161.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1156/8000 [01:13<08:50, 12.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███████████████████████████████▊                               | 4038/8000 [00:21<00:24, 158.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1159/8000 [01:13<07:42, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|████████████████████████████████▏                              | 4089/8000 [00:21<00:21, 178.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1161/8000 [01:13<07:45, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████████████████████████████▌                              | 4142/8000 [00:21<00:19, 199.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1166/8000 [01:14<08:12, 13.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████████████████████                              | 4194/8000 [00:22<00:17, 216.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1168/8000 [01:14<08:03, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████████████████████▎                             | 4234/8000 [00:22<00:17, 216.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1173/8000 [01:14<07:13, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████████████████████▋                             | 4275/8000 [00:22<00:18, 202.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1175/8000 [01:14<09:10, 12.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████████████████████████████▉                             | 4315/8000 [00:22<00:18, 197.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1177/8000 [01:14<09:00, 12.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████████████████████▍                            | 4367/8000 [00:22<00:17, 206.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1181/8000 [01:15<08:49, 12.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████████████████████▊                            | 4425/8000 [00:23<00:16, 219.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1185/8000 [01:15<07:14, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████████████████████▎                           | 4479/8000 [00:23<00:15, 221.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1191/8000 [01:15<05:20, 21.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████████████████████▋                           | 4535/8000 [00:23<00:14, 234.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1195/8000 [01:15<06:21, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████████████████████▏                          | 4592/8000 [00:23<00:14, 240.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1199/8000 [01:16<07:08, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████████████████████▌                          | 4637/8000 [00:24<00:15, 218.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1202/8000 [01:16<06:39, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████████████████████▉                          | 4690/8000 [00:24<00:15, 218.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1204/8000 [01:16<06:55, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|█████████████████████████████████████▎                         | 4745/8000 [00:24<00:13, 239.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1206/8000 [01:16<09:47, 11.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████████████████████▊                         | 4801/8000 [00:24<00:12, 246.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1209/8000 [01:17<09:55, 11.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████████████████████████████████▏                        | 4842/8000 [00:24<00:13, 241.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1213/8000 [01:17<07:27, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████████████████████████████████▍                        | 4887/8000 [00:25<00:13, 237.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1216/8000 [01:17<06:54, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████████████████████████████████▉                        | 4942/8000 [00:25<00:13, 219.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1219/8000 [01:17<06:10, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████████████████████████████████▏                       | 4983/8000 [00:25<00:14, 202.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1224/8000 [01:17<04:48, 23.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████████████████████████████████▋                       | 5039/8000 [00:25<00:13, 216.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1227/8000 [01:17<04:59, 22.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████████████████████████████████                       | 5080/8000 [00:26<00:13, 210.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1231/8000 [01:17<04:55, 22.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████████████████████████████████▍                      | 5137/8000 [00:26<00:12, 231.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1235/8000 [01:18<05:55, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████████████████████████████████▊                      | 5190/8000 [00:26<00:12, 233.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1238/8000 [01:18<07:07, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|█████████████████████████████████████████▎                     | 5245/8000 [00:26<00:12, 213.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1241/8000 [01:18<06:38, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|█████████████████████████████████████████▊                     | 5304/8000 [00:27<00:11, 227.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1244/8000 [01:18<07:40, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████████████████████████▏                    | 5361/8000 [00:27<00:10, 240.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1247/8000 [01:19<07:03, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████████████████████████████████████▋                    | 5418/8000 [00:27<00:10, 254.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1251/8000 [01:19<07:28, 15.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████████████████████████                    | 5468/8000 [00:27<00:10, 247.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1256/8000 [01:19<05:44, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████████████████████████▍                   | 5510/8000 [00:27<00:10, 235.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1262/8000 [01:19<05:36, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████████████████████████▊                   | 5569/8000 [00:28<00:09, 251.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1265/8000 [01:19<05:44, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████████████████████████▏                  | 5619/8000 [00:28<00:09, 256.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1268/8000 [01:20<06:45, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████████████████████████▌                  | 5665/8000 [00:28<00:09, 241.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1271/8000 [01:20<06:14, 17.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████████████████████████                  | 5720/8000 [00:28<00:09, 246.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1273/8000 [01:20<06:34, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████████████████████████▎                 | 5759/8000 [00:28<00:10, 222.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1275/8000 [01:20<07:16, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████████████████████████▊                 | 5811/8000 [00:29<00:10, 209.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1278/8000 [01:20<07:03, 15.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|██████████████████████████████████████████████                 | 5849/8000 [00:29<00:10, 198.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1280/8000 [01:21<09:17, 12.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████████████████████████▍                | 5890/8000 [00:29<00:10, 197.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1283/8000 [01:21<08:32, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████████████████████████▊                | 5937/8000 [00:29<00:10, 194.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1287/8000 [01:21<06:53, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|███████████████████████████████████████████████▏               | 5991/8000 [00:30<00:10, 193.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1289/8000 [01:21<07:38, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|███████████████████████████████████████████████▋               | 6055/8000 [00:30<00:08, 223.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1291/8000 [01:21<09:53, 11.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████████████████████████████████████████               | 6097/8000 [00:30<00:08, 216.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1294/8000 [01:22<10:53, 10.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████████████████████████████████████████████████▍              | 6149/8000 [00:30<00:09, 203.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1298/8000 [01:22<08:27, 13.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████████████████████████████████████████████████▊              | 6203/8000 [00:31<00:08, 220.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1302/8000 [01:22<06:59, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████████████████████████████████████████▏             | 6248/8000 [00:31<00:08, 197.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1307/8000 [01:22<05:32, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████████████████████████████████████████▌             | 6299/8000 [00:31<00:09, 178.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1313/8000 [01:23<05:52, 18.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████████████████████████████████████████▉             | 6346/8000 [00:31<00:09, 176.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1316/8000 [01:23<06:14, 17.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████████████████████████████████████████▎            | 6393/8000 [00:32<00:08, 192.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1319/8000 [01:23<07:10, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████████████████████████████████████████▊            | 6445/8000 [00:32<00:07, 208.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1324/8000 [01:23<06:48, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████████████████████████████▏           | 6504/8000 [00:32<00:06, 223.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1328/8000 [01:24<07:17, 15.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████████████████████████████▌           | 6548/8000 [00:32<00:06, 216.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1330/8000 [01:24<09:05, 12.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████████████████████████████▋           | 6581/8000 [00:34<00:19, 73.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1332/8000 [01:24<08:45, 12.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████████████████████████████           | 6639/8000 [00:34<00:13, 97.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1334/8000 [01:24<08:24, 13.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████████████████████████████▋          | 6691/8000 [00:34<00:10, 120.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1337/8000 [01:24<07:20, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████████████████████████████          | 6739/8000 [00:34<00:09, 139.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1339/8000 [01:25<09:20, 11.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████████████████████████████▍         | 6782/8000 [00:35<00:08, 149.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1341/8000 [01:25<09:14, 12.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████████████████████████████▊         | 6830/8000 [00:35<00:06, 170.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1347/8000 [01:25<05:47, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████████████████████████████▎        | 6895/8000 [00:35<00:05, 215.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1350/8000 [01:25<05:37, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████████████████████████████▋        | 6949/8000 [00:35<00:04, 210.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1353/8000 [01:25<05:38, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████████████████████████████████████████████▏       | 7005/8000 [00:36<00:04, 204.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1357/8000 [01:25<05:24, 20.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████████████████████████████████████████████▌       | 7054/8000 [00:36<00:04, 202.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1362/8000 [01:26<06:05, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████████████████████████████▉       | 7099/8000 [00:36<00:04, 207.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1365/8000 [01:26<07:41, 14.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████████████████████████████████████████████▎      | 7155/8000 [00:36<00:03, 221.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1369/8000 [01:26<06:34, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████████████████████████████████████████████▋      | 7203/8000 [00:36<00:03, 230.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1374/8000 [01:26<05:10, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████████████████████████████████████████████      | 7245/8000 [00:37<00:03, 213.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1378/8000 [01:27<04:54, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████████████████████████████████████████████▍     | 7297/8000 [00:37<00:03, 206.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1381/8000 [01:27<04:48, 22.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████████████████████████████████████████████▉     | 7357/8000 [00:37<00:02, 214.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1386/8000 [01:27<07:55, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|██████████████████████████████████████████████████████████▎    | 7404/8000 [00:37<00:02, 217.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1390/8000 [01:28<07:47, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████████████████████████████████▊    | 7474/8000 [00:39<00:06, 79.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1398/8000 [01:28<06:06, 17.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████████████████████████████████▏   | 7528/8000 [00:39<00:04, 97.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1402/8000 [01:28<06:37, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|███████████████████████████████████████████████████████████▋   | 7574/8000 [00:40<00:03, 108.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1404/8000 [01:28<07:48, 14.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|███████████████████████████████████████████████████████████▉   | 7610/8000 [00:40<00:03, 114.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1406/8000 [01:29<09:24, 11.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████████████████████████████████▎  | 7656/8000 [00:40<00:02, 127.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1410/8000 [01:29<08:56, 12.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████████████████████████████████▋  | 7707/8000 [00:41<00:01, 151.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1414/8000 [01:29<08:44, 12.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████████████████████████████████  | 7761/8000 [00:41<00:01, 167.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1420/8000 [01:29<06:16, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████████████████████████████████▌ | 7817/8000 [00:41<00:00, 188.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1425/8000 [01:30<06:37, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████████████████████████████████▉ | 7868/8000 [00:41<00:00, 192.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1428/8000 [01:30<06:33, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████████████████████████████████▎| 7917/8000 [00:41<00:00, 190.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1431/8000 [01:30<06:06, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████████████████████████████████▋| 7964/8000 [00:42<00:00, 201.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1435/8000 [01:30<06:31, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8011it [00:42, 189.01it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  18%|███              | 1437/8000 [01:31<08:02, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1440/8000 [01:31<07:25, 14.73it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                                                      | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1445/8000 [01:31<05:53, 18.52it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▍                                                                             | 1/200 [00:09<31:38,  9.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1450/8000 [01:31<05:57, 18.33it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▊                                                                             | 2/200 [00:12<18:06,  5.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1453/8000 [01:31<05:55, 18.42it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                                                            | 3/200 [00:14<13:40,  4.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1456/8000 [01:32<05:51, 18.62it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▌                                                                            | 4/200 [00:17<12:00,  3.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1458/8000 [01:32<06:14, 17.45it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▉                                                                            | 5/200 [00:20<10:53,  3.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1461/8000 [01:32<06:03, 18.00it/s]\u001b[A\u001b[A\n",
+      "training:   3%|██▎                                                                           | 6/200 [00:23<10:02,  3.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1464/8000 [01:32<06:04, 17.95it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▋                                                                           | 7/200 [00:25<09:22,  2.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1468/8000 [01:32<05:03, 21.50it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███                                                                           | 8/200 [00:28<09:12,  2.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1471/8000 [01:32<05:16, 20.65it/s]\u001b[A\u001b[A\n",
+      "training:   4%|███▌                                                                          | 9/200 [00:31<08:58,  2.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1474/8000 [01:32<05:13, 20.79it/s]\u001b[A\u001b[A\n",
+      "training:   5%|███▊                                                                         | 10/200 [00:33<08:45,  2.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1477/8000 [01:33<05:08, 21.12it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▏                                                                        | 11/200 [00:36<08:20,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1481/8000 [01:33<05:57, 18.24it/s]\u001b[A\u001b[A\n",
+      "training:   6%|████▌                                                                        | 12/200 [00:39<08:31,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1483/8000 [01:33<06:22, 17.06it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█████                                                                        | 13/200 [00:41<08:29,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1485/8000 [01:33<07:07, 15.24it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█████▍                                                                       | 14/200 [00:44<08:26,  2.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1489/8000 [01:33<05:41, 19.07it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█████▊                                                                       | 15/200 [00:47<08:27,  2.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1492/8000 [01:33<05:28, 19.80it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▏                                                                      | 16/200 [00:49<08:21,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1495/8000 [01:34<08:30, 12.74it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██████▌                                                                      | 17/200 [00:52<08:10,  2.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1497/8000 [01:34<08:25, 12.86it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██████▉                                                                      | 18/200 [00:55<08:06,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1503/8000 [01:34<05:38, 19.21it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▎                                                                     | 19/200 [00:57<08:01,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1508/8000 [01:34<04:44, 22.82it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███████▋                                                                     | 20/200 [01:00<07:44,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1514/8000 [01:35<04:37, 23.33it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████████                                                                     | 21/200 [01:02<07:45,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1518/8000 [01:35<05:41, 18.98it/s]\u001b[A\u001b[A\n",
+      "training:  11%|████████▍                                                                    | 22/200 [01:05<07:53,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1521/8000 [01:35<05:33, 19.42it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████████▊                                                                    | 23/200 [01:08<07:52,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1524/8000 [01:35<06:56, 15.55it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▏                                                                   | 24/200 [01:10<07:33,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1528/8000 [01:35<05:55, 18.19it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████████▋                                                                   | 25/200 [01:13<07:41,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1532/8000 [01:36<06:24, 16.82it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████████                                                                   | 26/200 [01:16<07:36,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1535/8000 [01:36<05:56, 18.12it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▍                                                                  | 27/200 [01:18<07:31,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1539/8000 [01:36<05:20, 20.16it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████████▊                                                                  | 28/200 [01:21<07:24,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1542/8000 [01:36<06:28, 16.63it/s]\u001b[A\u001b[A\n",
+      "training:  14%|███████████▏                                                                 | 29/200 [01:23<07:11,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1547/8000 [01:36<05:03, 21.25it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████████▌                                                                 | 30/200 [01:26<07:07,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1552/8000 [01:37<05:31, 19.44it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████████▉                                                                 | 31/200 [01:28<07:20,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1556/8000 [01:37<06:28, 16.57it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▎                                                                | 32/200 [01:31<07:22,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1558/8000 [01:37<06:30, 16.48it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████████████▋                                                                | 33/200 [01:34<07:24,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1561/8000 [01:37<06:10, 17.39it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████████████                                                                | 34/200 [01:36<07:09,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1563/8000 [01:38<09:22, 11.45it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▍                                                               | 35/200 [01:39<07:04,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1566/8000 [01:38<08:06, 13.23it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████████████▊                                                               | 36/200 [01:41<07:01,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1569/8000 [01:38<07:18, 14.68it/s]\u001b[A\u001b[A\n",
+      "training:  18%|██████████████▏                                                              | 37/200 [01:44<06:59,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1575/8000 [01:38<05:23, 19.88it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████████████▋                                                              | 38/200 [01:47<06:58,  2.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1579/8000 [01:38<04:52, 21.99it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████                                                              | 39/200 [01:49<07:01,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1583/8000 [01:39<06:36, 16.17it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▍                                                             | 40/200 [01:52<07:01,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1589/8000 [01:39<07:56, 13.46it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███████████████▊                                                             | 41/200 [01:55<07:02,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1591/8000 [01:39<07:43, 13.83it/s]\u001b[A\u001b[A\n",
+      "training:  21%|████████████████▏                                                            | 42/200 [01:57<07:07,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1594/8000 [01:40<06:54, 15.44it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▌                                                            | 43/200 [02:00<06:59,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1596/8000 [01:40<07:07, 14.98it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████████████████▉                                                            | 44/200 [02:03<06:56,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1598/8000 [01:40<08:31, 12.53it/s]\u001b[A\u001b[A\n",
+      "training:  22%|█████████████████▎                                                           | 45/200 [02:05<06:54,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1600/8000 [01:40<08:24, 12.70it/s]\u001b[A\u001b[A\n",
+      "training:  23%|█████████████████▋                                                           | 46/200 [02:08<06:49,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1603/8000 [01:40<07:10, 14.87it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████                                                           | 47/200 [02:11<06:48,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1606/8000 [01:40<06:30, 16.39it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▍                                                          | 48/200 [02:13<06:46,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1608/8000 [01:41<08:45, 12.17it/s]\u001b[A\u001b[A\n",
+      "training:  24%|██████████████████▊                                                          | 49/200 [02:16<06:52,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1610/8000 [01:41<08:48, 12.09it/s]\u001b[A\u001b[A\n",
+      "training:  25%|███████████████████▎                                                         | 50/200 [02:19<06:46,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1612/8000 [01:41<10:26, 10.20it/s]\u001b[A\u001b[A\n",
+      "training:  26%|███████████████████▋                                                         | 51/200 [02:21<06:36,  2.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1614/8000 [01:41<09:36, 11.07it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████                                                         | 52/200 [02:24<06:32,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1616/8000 [01:41<09:08, 11.65it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████████████▍                                                        | 53/200 [02:27<06:29,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1619/8000 [01:42<08:05, 13.15it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████████████▊                                                        | 54/200 [02:29<06:25,  2.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1621/8000 [01:42<07:48, 13.62it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▏                                                       | 55/200 [02:32<06:24,  2.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1625/8000 [01:42<06:12, 17.11it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▌                                                       | 56/200 [02:35<06:24,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1629/8000 [01:42<05:10, 20.53it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████████████▉                                                       | 57/200 [02:37<06:13,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1632/8000 [01:42<05:09, 20.58it/s]\u001b[A\u001b[A\n",
+      "training:  29%|██████████████████████▎                                                      | 58/200 [02:40<06:37,  2.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1636/8000 [01:43<06:33, 16.17it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████████████▋                                                      | 59/200 [02:46<06:37,  2.82s/it]\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1638/8000 [01:43<06:31, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1642/8000 [01:43<05:34, 19.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1645/8000 [01:43<05:33, 19.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1649/8000 [01:43<06:12, 17.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 10it [00:01,  7.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1651/8000 [01:44<08:08, 12.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 12.71it/s]\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1654/8000 [01:44<07:27, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1658/8000 [01:44<06:05, 17.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1662/8000 [01:44<05:21, 19.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1666/8000 [01:44<04:52, 21.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 29it [00:02, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1670/8000 [01:44<05:37, 18.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 45it [00:02, 30.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1674/8000 [01:45<06:08, 17.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 63it [00:02, 46.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1678/8000 [01:45<05:22, 19.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 87it [00:02, 72.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1681/8000 [01:45<07:36, 13.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 106it [00:02, 91.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1684/8000 [01:45<06:48, 15.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 125it [00:02, 106.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1688/8000 [01:46<05:51, 17.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 150it [00:02, 133.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1694/8000 [01:46<05:32, 18.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 176it [00:02, 160.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1697/8000 [01:46<05:33, 18.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 198it [00:02, 161.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1703/8000 [01:46<06:15, 16.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 220it [00:03, 174.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1708/8000 [01:47<05:04, 20.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 251it [00:03, 206.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1712/8000 [01:47<05:41, 18.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 277it [00:03, 220.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1718/8000 [01:47<04:26, 23.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 301it [00:03, 214.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1724/8000 [01:47<03:50, 27.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 324it [00:03, 204.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1728/8000 [01:48<05:39, 18.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 346it [00:03, 208.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1732/8000 [01:48<05:10, 20.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 370it [00:03, 216.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1740/8000 [01:48<03:48, 27.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 396it [00:03, 227.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1744/8000 [01:48<06:25, 16.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 420it [00:03, 212.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1749/8000 [01:49<07:05, 14.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 444it [00:04, 218.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1752/8000 [01:49<08:39, 12.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 467it [00:04, 195.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1756/8000 [01:50<08:53, 11.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 488it [00:04, 197.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1762/8000 [01:50<07:22, 14.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 509it [00:04, 190.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1764/8000 [01:50<07:22, 14.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 530it [00:04, 195.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1768/8000 [01:50<06:22, 16.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 559it [00:04, 220.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1771/8000 [01:50<07:06, 14.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 582it [00:04, 199.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1776/8000 [01:51<05:25, 19.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 605it [00:04, 206.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1779/8000 [01:51<05:10, 20.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 627it [00:05, 196.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1782/8000 [01:51<06:22, 16.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 650it [00:05, 204.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1785/8000 [01:51<05:50, 17.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 671it [00:05, 199.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1791/8000 [01:51<05:25, 19.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 696it [00:05, 211.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1794/8000 [01:52<05:16, 19.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 718it [00:05, 209.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1798/8000 [01:52<04:48, 21.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 745it [00:05, 225.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1802/8000 [01:52<04:17, 24.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 768it [00:05, 214.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1806/8000 [01:52<04:08, 24.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 136.55it/s]\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1810/8000 [01:52<05:09, 19.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1813/8000 [01:53<06:11, 16.63it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%| | 0/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1815/8000 [01:53<06:12, 16.61it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1819/8000 [01:53<05:25, 18.99it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1824/8000 [01:53<05:33, 18.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▎| 29/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1826/8000 [01:53<05:44, 17.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1829/8000 [01:53<05:27, 18.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1835/8000 [01:53<04:01, 25.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1840/8000 [01:54<04:33, 22.53it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1844/8000 [01:54<04:27, 23.03it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1850/8000 [01:54<04:39, 22.04it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1853/8000 [01:54<04:37, 22.18it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1856/8000 [01:55<06:52, 14.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1859/8000 [01:55<06:17, 16.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1862/8000 [01:55<07:03, 14.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|▊| 83/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1864/8000 [01:55<07:10, 14.24it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1867/8000 [01:56<07:40, 13.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|▉| 92/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1869/8000 [01:56<07:24, 13.79it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:0\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1872/8000 [01:56<06:36, 15.46it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|████████████████████████████████████                                    | 2/4 [27:36<27:27, 823.63s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.161827379579567\n",
+      "2999: -6.1390261\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  23%|███▉             | 1875/8000 [01:56<06:00, 17.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1879/8000 [01:56<05:10, 19.69it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                            | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1885/8000 [01:56<05:04, 20.10it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                    | 1/200 [00:08<27:04,  8.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1888/8000 [01:57<06:02, 16.85it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▏                   | 2/200 [00:10<15:45,  4.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1894/8000 [01:57<04:27, 22.79it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▎                   | 3/200 [00:12<11:54,  3.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1897/8000 [01:57<04:25, 22.98it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                   | 4/200 [00:15<10:00,  3.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1901/8000 [01:57<05:14, 19.36it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                   | 5/200 [00:17<08:52,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1905/8000 [01:57<05:49, 17.46it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▌                   | 6/200 [00:19<08:19,  2.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1908/8000 [01:58<05:23, 18.81it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▋                   | 7/200 [00:21<07:59,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1912/8000 [01:58<05:56, 17.07it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▊                   | 8/200 [00:24<07:49,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1916/8000 [01:58<05:29, 18.48it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▉                   | 9/200 [00:26<07:41,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1919/8000 [01:58<05:11, 19.55it/s]\u001b[A\u001b[A\n",
+      "training:   5%|▉                  | 10/200 [00:28<07:31,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1922/8000 [01:58<06:25, 15.75it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█                  | 11/200 [00:31<07:36,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1927/8000 [01:59<06:08, 16.49it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 12/200 [00:33<07:21,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1930/8000 [01:59<05:37, 17.99it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 13/200 [00:35<07:17,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1935/8000 [01:59<04:46, 21.20it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█▎                 | 14/200 [00:38<07:17,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1938/8000 [01:59<04:38, 21.77it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▍                 | 15/200 [00:40<07:07,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1942/8000 [01:59<05:14, 19.28it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 16/200 [00:42<06:59,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1945/8000 [02:00<06:07, 16.49it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 17/200 [00:44<06:59,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1947/8000 [02:00<06:20, 15.91it/s]\u001b[A\u001b[A\n",
+      "training:   9%|█▋                 | 18/200 [00:47<07:02,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1951/8000 [02:00<06:23, 15.77it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▊                 | 19/200 [00:49<07:05,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1955/8000 [02:00<05:30, 18.28it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 20/200 [00:51<06:57,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1958/8000 [02:00<05:01, 20.02it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 21/200 [00:54<06:57,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1961/8000 [02:01<06:13, 16.16it/s]\u001b[A\u001b[A\n",
+      "training:  11%|██                 | 22/200 [00:56<06:57,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1963/8000 [02:01<06:12, 16.20it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▏                | 23/200 [00:58<06:52,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1965/8000 [02:01<07:46, 12.92it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▎                | 24/200 [01:01<06:45,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1968/8000 [02:01<06:52, 14.64it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▍                | 25/200 [01:03<06:37,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1971/8000 [02:01<06:05, 16.50it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██▍                | 26/200 [01:05<06:37,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1973/8000 [02:01<06:26, 15.59it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▌                | 27/200 [01:08<06:35,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1976/8000 [02:02<07:39, 13.12it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▋                | 28/200 [01:10<06:31,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1978/8000 [02:02<07:37, 13.17it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▊                | 29/200 [01:12<06:28,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1981/8000 [02:02<06:36, 15.16it/s]\u001b[A\u001b[A\n",
+      "training:  15%|██▊                | 30/200 [01:14<06:30,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1985/8000 [02:02<05:14, 19.12it/s]\u001b[A\u001b[A\n",
+      "training:  16%|██▉                | 31/200 [01:17<06:29,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1988/8000 [02:02<04:48, 20.87it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███                | 32/200 [01:19<06:25,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1994/8000 [02:03<04:50, 20.65it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███▏               | 33/200 [01:21<06:22,  2.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1998/8000 [02:03<04:22, 22.90it/s]\u001b[A\u001b[A\n",
+      "training:  17%|███▏               | 34/200 [01:24<06:23,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2003/8000 [02:03<04:44, 21.10it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▎               | 35/200 [01:26<06:15,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2009/8000 [02:03<04:37, 21.57it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▍               | 36/200 [01:28<06:02,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2014/8000 [02:03<03:54, 25.49it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▌               | 37/200 [01:30<06:00,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2018/8000 [02:04<04:41, 21.28it/s]\u001b[A\u001b[A\n",
+      "training:  19%|███▌               | 38/200 [01:32<05:49,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2024/8000 [02:04<03:47, 26.29it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▋               | 39/200 [01:34<05:53,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2028/8000 [02:04<03:41, 27.00it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▊               | 40/200 [01:37<05:49,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2032/8000 [02:04<05:07, 19.40it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▉               | 41/200 [01:39<05:55,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2035/8000 [02:04<04:59, 19.91it/s]\u001b[A\u001b[A\n",
+      "training:  21%|███▉               | 42/200 [01:41<05:54,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2038/8000 [02:05<05:55, 16.78it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████               | 43/200 [01:43<05:51,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2042/8000 [02:05<05:00, 19.84it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▏              | 44/200 [01:46<05:44,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2045/8000 [02:05<04:58, 19.98it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▎              | 45/200 [01:48<05:47,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2048/8000 [02:05<05:56, 16.70it/s]\u001b[A\u001b[A\n",
+      "training:  23%|████▎              | 46/200 [01:50<05:43,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2051/8000 [02:05<05:18, 18.67it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▍              | 47/200 [01:52<05:38,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2054/8000 [02:05<05:04, 19.50it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▌              | 48/200 [01:54<05:37,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2057/8000 [02:06<06:13, 15.89it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▋              | 49/200 [01:57<05:35,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2059/8000 [02:06<06:15, 15.82it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████▊              | 50/200 [01:59<05:39,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2065/8000 [02:06<04:38, 21.32it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████▊              | 51/200 [02:01<05:40,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2068/8000 [02:06<06:53, 14.36it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████▉              | 52/200 [02:04<05:38,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2070/8000 [02:07<07:13, 13.67it/s]\u001b[A\u001b[A\n",
+      "training:  26%|█████              | 53/200 [02:06<05:54,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2074/8000 [02:07<05:47, 17.03it/s]\u001b[A\u001b[A\n",
+      "training:  27%|█████▏             | 54/200 [02:09<05:53,  2.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2079/8000 [02:07<06:49, 14.47it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▏             | 55/200 [02:11<05:44,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2082/8000 [02:07<06:13, 15.83it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▎             | 56/200 [02:13<05:38,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2087/8000 [02:08<07:08, 13.79it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▍             | 57/200 [02:16<05:48,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2091/8000 [02:08<06:09, 15.99it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████▌             | 58/200 [02:18<05:42,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2094/8000 [02:08<06:53, 14.30it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████▌             | 59/200 [02:21<05:40,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2098/8000 [02:08<05:45, 17.07it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████▋             | 60/200 [02:24<06:02,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2103/8000 [02:08<04:40, 21.04it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████▊             | 61/200 [02:26<06:03,  2.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2107/8000 [02:09<04:21, 22.50it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████▉             | 62/200 [02:32<05:39,  2.46s/it]\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2110/8000 [02:09<05:42, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2115/8000 [02:09<05:36, 17.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2118/8000 [02:10<09:29, 10.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2122/8000 [02:10<07:51, 12.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 11it [00:02,  5.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2124/8000 [02:10<07:36, 12.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 17it [00:02,  8.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2127/8000 [02:10<07:54, 12.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:03,  8.07it/s]\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2131/8000 [02:11<06:10, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2135/8000 [02:11<06:19, 15.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2137/8000 [02:11<06:23, 15.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2145/8000 [02:11<04:04, 23.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 27it [00:01, 20.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2152/8000 [02:11<03:59, 24.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 53it [00:01, 44.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2155/8000 [02:12<04:58, 19.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:01, 67.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2158/8000 [02:12<05:03, 19.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 97it [00:02, 85.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2161/8000 [02:12<05:03, 19.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 118it [00:02, 105.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2164/8000 [02:12<04:56, 19.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 140it [00:02, 127.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2167/8000 [02:12<04:51, 20.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 163it [00:02, 149.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2171/8000 [02:13<05:37, 17.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 189it [00:02, 175.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2175/8000 [02:13<04:49, 20.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 212it [00:02, 180.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2180/8000 [02:13<04:05, 23.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 234it [00:02, 187.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2184/8000 [02:13<03:53, 24.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 256it [00:02, 190.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2187/8000 [02:13<03:54, 24.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 278it [00:02, 195.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2190/8000 [02:13<05:10, 18.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 300it [00:03, 200.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2194/8000 [02:14<04:35, 21.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 321it [00:03, 198.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2197/8000 [02:14<04:39, 20.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 342it [00:03, 172.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2202/8000 [02:14<04:02, 23.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 361it [00:03, 175.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2206/8000 [02:14<03:50, 25.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 380it [00:03, 154.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2210/8000 [02:14<04:32, 21.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 405it [00:03, 177.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2216/8000 [02:14<03:33, 27.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 430it [00:03, 194.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2220/8000 [02:15<03:35, 26.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 452it [00:03, 199.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2223/8000 [02:15<03:38, 26.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 475it [00:04, 203.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2226/8000 [02:15<03:46, 25.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 496it [00:04, 184.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2229/8000 [02:15<03:51, 24.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 518it [00:04, 192.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2234/8000 [02:15<05:20, 17.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 539it [00:04, 196.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2237/8000 [02:16<06:06, 15.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 561it [00:04, 202.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2243/8000 [02:16<04:25, 21.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 582it [00:04, 180.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2246/8000 [02:16<04:21, 22.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 601it [00:04, 176.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2252/8000 [02:16<04:33, 21.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 623it [00:04, 187.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2257/8000 [02:16<04:51, 19.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 643it [00:04, 185.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2260/8000 [02:17<04:48, 19.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 662it [00:05, 179.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2266/8000 [02:17<03:41, 25.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 681it [00:05, 173.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2274/8000 [02:17<03:27, 27.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 703it [00:05, 181.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2278/8000 [02:17<04:09, 22.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 727it [00:05, 197.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2281/8000 [02:17<04:19, 22.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 755it [00:05, 221.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2284/8000 [02:18<06:44, 14.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 140.59it/s]\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2287/8000 [02:18<08:29, 11.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2291/8000 [02:19<07:41, 12.37it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%| | 0/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2298/8000 [02:19<05:09, 18.45it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2301/8000 [02:19<05:01, 18.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%| | 8/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2305/8000 [02:19<04:32, 20.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2309/8000 [02:19<05:13, 18.18it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2314/8000 [02:19<04:25, 21.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  19%|▏| 19/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2317/8000 [02:20<05:23, 17.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2321/8000 [02:20<04:48, 19.71it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2325/8000 [02:20<04:22, 21.61it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2330/8000 [02:20<05:33, 16.99it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2334/8000 [02:21<05:57, 15.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2336/8000 [02:21<06:21, 14.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2339/8000 [02:21<06:01, 15.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2341/8000 [02:21<06:16, 15.03it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2343/8000 [02:21<06:34, 14.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2348/8000 [02:22<05:52, 16.02it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2350/8000 [02:22<05:58, 15.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2353/8000 [02:22<06:33, 14.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2357/8000 [02:22<05:35, 16.83it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2362/8000 [02:22<05:26, 17.29it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|▊| 83/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2364/8000 [02:23<06:52, 13.68it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2366/8000 [02:23<07:05, 13.26it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|▉| 91/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2373/8000 [02:23<04:21, 21.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  95%|▉| 95/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2376/8000 [02:23<04:23, 21.37it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:0\n",
       "\n",
-      "Generating signals:  30%|█████            | 2380/8000 [02:23<04:55, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2385/8000 [02:24<05:16, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                 | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2391/8000 [02:24<05:48, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 11/8000 [00:00<03:05, 43.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2395/8000 [02:24<05:09, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 33/8000 [00:00<01:49, 72.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2398/8000 [02:24<04:52, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 50/8000 [00:00<01:59, 66.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2405/8000 [02:25<04:30, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 66/8000 [00:01<02:18, 57.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2408/8000 [02:25<04:31, 20.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 83/8000 [00:01<02:06, 62.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2415/8000 [02:25<03:29, 26.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 95/8000 [00:01<02:10, 60.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2419/8000 [02:25<04:10, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 110/8000 [00:01<02:09, 60.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2422/8000 [02:25<04:22, 21.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 128/8000 [00:02<01:56, 67.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2425/8000 [02:26<05:31, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 145/8000 [00:02<01:50, 70.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2427/8000 [02:26<05:35, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 161/8000 [00:02<01:51, 70.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2432/8000 [02:26<04:36, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 177/8000 [00:02<01:54, 68.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2437/8000 [02:26<04:39, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 192/8000 [00:02<01:52, 69.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2441/8000 [02:27<05:20, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 203/8000 [00:03<02:02, 63.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2444/8000 [02:27<05:05, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 217/8000 [00:03<01:57, 66.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2448/8000 [02:27<05:29, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 231/8000 [00:03<02:09, 60.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2452/8000 [02:27<04:56, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 247/8000 [00:03<02:06, 61.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2457/8000 [02:28<06:08, 15.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 269/8000 [00:04<01:50, 70.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2460/8000 [02:28<05:55, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▏      | 282/8000 [00:04<01:51, 69.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2465/8000 [02:28<05:40, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 298/8000 [00:04<01:57, 65.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2468/8000 [02:28<05:20, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 321/8000 [00:04<01:50, 69.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2472/8000 [02:29<06:02, 15.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 337/8000 [00:05<01:48, 70.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2477/8000 [02:29<05:57, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 363/8000 [00:05<01:28, 85.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2481/8000 [02:29<05:05, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 389/8000 [00:05<01:22, 92.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2484/8000 [02:29<04:53, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎     | 419/8000 [00:05<01:13, 102.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2487/8000 [02:29<04:38, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▍      | 435/8000 [00:06<01:25, 88.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2492/8000 [02:30<04:48, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 453/8000 [00:06<01:24, 89.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2495/8000 [02:30<06:11, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 468/8000 [00:06<01:34, 80.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2498/8000 [02:30<05:37, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 487/8000 [00:06<01:32, 81.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2500/8000 [02:30<05:48, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 497/8000 [00:06<01:47, 69.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2502/8000 [02:30<05:55, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 513/8000 [00:07<01:53, 66.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2504/8000 [02:30<06:01, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 539/8000 [00:07<01:35, 78.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2507/8000 [02:31<06:40, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 559/8000 [00:07<01:32, 80.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2514/8000 [02:31<04:09, 22.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 577/8000 [00:07<01:28, 84.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2518/8000 [02:31<05:16, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 593/8000 [00:08<01:44, 70.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2521/8000 [02:31<05:07, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 615/8000 [00:08<01:31, 80.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2524/8000 [02:32<06:02, 15.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 630/8000 [00:08<01:42, 71.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2526/8000 [02:32<06:11, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 650/8000 [00:08<01:42, 71.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2528/8000 [02:32<06:29, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 664/8000 [00:09<01:44, 70.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2533/8000 [02:32<05:52, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 684/8000 [00:09<01:43, 70.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2536/8000 [02:32<05:32, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 705/8000 [00:09<01:39, 73.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2540/8000 [02:33<04:44, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 723/8000 [00:10<01:49, 66.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2545/8000 [02:33<04:51, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 741/8000 [00:10<01:51, 65.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2547/8000 [02:33<05:18, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 760/8000 [00:10<01:44, 69.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2554/8000 [02:33<04:24, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 774/8000 [00:10<01:59, 60.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2557/8000 [02:33<04:15, 21.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 788/8000 [00:11<02:09, 55.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2560/8000 [02:34<04:15, 21.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 805/8000 [00:11<01:58, 60.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2563/8000 [02:34<05:28, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 819/8000 [00:11<01:55, 62.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2567/8000 [02:34<06:37, 13.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 837/8000 [00:11<01:45, 67.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2570/8000 [02:35<08:25, 10.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▋      | 855/8000 [00:12<01:40, 71.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2572/8000 [02:35<08:09, 11.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 876/8000 [00:12<01:29, 79.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2574/8000 [02:35<07:44, 11.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 897/8000 [00:12<01:23, 84.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2577/8000 [02:35<08:13, 10.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 921/8000 [00:12<01:31, 77.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2579/8000 [02:35<07:42, 11.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 942/8000 [00:13<01:36, 73.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2585/8000 [02:36<05:01, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 963/8000 [00:13<01:37, 72.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2588/8000 [02:36<06:16, 14.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 981/8000 [00:13<01:38, 71.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2590/8000 [02:36<06:40, 13.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 996/8000 [00:13<01:36, 72.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2595/8000 [02:36<06:34, 13.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1019/8000 [00:14<01:39, 70.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2601/8000 [02:37<05:40, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1038/8000 [00:14<01:47, 65.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2605/8000 [02:37<06:36, 13.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1056/8000 [00:14<01:39, 69.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2608/8000 [02:37<06:13, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1077/8000 [00:15<01:37, 70.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2611/8000 [02:37<05:41, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1093/8000 [00:15<01:54, 60.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2618/8000 [02:38<04:10, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1114/8000 [00:15<01:40, 68.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2622/8000 [02:38<03:42, 24.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1126/8000 [00:15<01:50, 62.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2625/8000 [02:38<03:49, 23.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1149/8000 [00:16<01:41, 67.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2629/8000 [02:38<03:34, 25.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▊     | 1166/8000 [00:16<01:50, 61.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2633/8000 [02:38<04:34, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1187/8000 [00:16<01:42, 66.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2638/8000 [02:39<07:56, 11.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1203/8000 [00:17<01:49, 61.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2641/8000 [02:39<07:03, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1223/8000 [00:17<01:40, 67.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2643/8000 [02:39<07:10, 12.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1239/8000 [00:17<01:35, 70.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2645/8000 [02:40<08:09, 10.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1259/8000 [00:17<01:39, 67.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2648/8000 [02:40<06:58, 12.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1283/8000 [00:18<01:25, 78.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2652/8000 [02:40<05:41, 15.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1306/8000 [00:18<01:47, 62.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2654/8000 [02:40<05:45, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|▉     | 1327/8000 [00:18<01:39, 66.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2656/8000 [02:40<07:23, 12.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1345/8000 [00:19<01:37, 68.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2662/8000 [02:41<04:44, 18.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1365/8000 [00:19<01:32, 71.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2669/8000 [02:41<04:05, 21.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1383/8000 [00:19<01:32, 71.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2675/8000 [02:41<03:26, 25.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1404/8000 [00:20<01:37, 67.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2680/8000 [02:41<03:53, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1425/8000 [00:20<01:41, 65.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2683/8000 [02:41<04:12, 21.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1448/8000 [00:20<01:35, 68.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2686/8000 [02:42<04:58, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1464/8000 [00:20<01:41, 64.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2688/8000 [02:42<06:26, 13.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█     | 1485/8000 [00:21<01:51, 58.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2692/8000 [02:42<05:10, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1501/8000 [00:21<02:01, 53.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2696/8000 [02:42<04:30, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1519/8000 [00:22<01:54, 56.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2700/8000 [02:43<05:17, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1542/8000 [00:22<01:42, 63.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2705/8000 [02:43<05:09, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1563/8000 [00:22<01:38, 65.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2710/8000 [02:43<04:11, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1592/8000 [00:22<01:14, 85.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2713/8000 [02:43<04:18, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1606/8000 [00:23<01:25, 74.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2720/8000 [02:43<03:06, 28.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1615/8000 [00:23<01:44, 61.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2724/8000 [02:43<03:15, 26.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1631/8000 [00:23<01:57, 54.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2732/8000 [02:44<03:04, 28.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1645/8000 [00:23<01:52, 56.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2736/8000 [02:44<03:10, 27.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1668/8000 [00:24<01:36, 65.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2739/8000 [02:44<03:26, 25.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1683/8000 [00:24<01:40, 62.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2745/8000 [02:44<03:41, 23.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1697/8000 [00:24<01:44, 60.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2750/8000 [02:45<04:01, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1712/8000 [00:24<01:34, 66.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2753/8000 [02:45<04:46, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1730/8000 [00:25<01:31, 68.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2756/8000 [02:45<05:26, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1746/8000 [00:25<01:34, 66.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2758/8000 [02:45<05:46, 15.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1770/8000 [00:25<01:24, 74.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2761/8000 [02:45<05:48, 15.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1793/8000 [00:25<01:15, 82.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2764/8000 [02:46<06:44, 12.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1819/8000 [00:26<01:08, 90.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2767/8000 [02:46<07:43, 11.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1845/8000 [00:26<01:25, 72.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2770/8000 [02:46<06:37, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1863/8000 [00:26<01:24, 72.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2775/8000 [02:46<04:46, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1883/8000 [00:27<01:28, 69.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2783/8000 [02:47<04:03, 21.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1895/8000 [00:27<01:32, 66.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2786/8000 [02:47<06:04, 14.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1915/8000 [00:27<01:23, 72.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2788/8000 [02:47<06:08, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1929/8000 [00:27<01:25, 71.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2793/8000 [02:47<04:42, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1945/8000 [00:28<01:23, 72.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2796/8000 [02:48<04:28, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1968/8000 [00:28<01:23, 72.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2800/8000 [02:48<05:03, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1992/8000 [00:28<01:12, 82.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2803/8000 [02:48<04:40, 18.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2009/8000 [00:28<01:13, 81.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2808/8000 [02:48<04:30, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2030/8000 [00:29<01:09, 86.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2811/8000 [02:49<05:38, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2053/8000 [00:29<01:08, 87.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2814/8000 [02:49<06:18, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2078/8000 [00:29<01:06, 89.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2817/8000 [02:49<05:47, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2095/8000 [00:29<01:13, 80.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2819/8000 [02:49<06:07, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2114/8000 [00:30<01:13, 79.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2821/8000 [02:49<06:02, 14.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2138/8000 [00:30<01:14, 78.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2825/8000 [02:50<05:50, 14.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2159/8000 [00:30<01:08, 85.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2828/8000 [02:50<05:07, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2174/8000 [00:30<01:14, 78.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2831/8000 [02:50<05:38, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2191/8000 [00:31<01:11, 81.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2835/8000 [02:50<04:45, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2216/8000 [00:31<01:04, 89.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2839/8000 [02:50<05:18, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▍   | 2246/8000 [00:31<00:56, 101.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2845/8000 [02:50<03:48, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2265/8000 [00:31<00:59, 96.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2850/8000 [02:51<03:22, 25.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2283/8000 [00:31<01:02, 90.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2854/8000 [02:51<05:00, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2303/8000 [00:32<01:04, 88.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2857/8000 [02:51<04:40, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2322/8000 [00:32<01:06, 85.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2860/8000 [02:51<04:26, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▊    | 2342/8000 [00:32<01:08, 82.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2863/8000 [02:51<04:31, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2363/8000 [00:32<01:06, 85.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2867/8000 [02:52<03:57, 21.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2381/8000 [00:33<01:07, 83.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2872/8000 [02:52<04:26, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2403/8000 [00:33<01:03, 88.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2877/8000 [02:52<04:27, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2423/8000 [00:33<01:01, 90.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2882/8000 [02:52<03:44, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2446/8000 [00:33<00:57, 96.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2889/8000 [02:52<02:51, 29.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2460/8000 [00:33<01:03, 87.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2894/8000 [02:53<03:20, 25.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2484/8000 [00:34<00:59, 93.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2897/8000 [02:53<05:13, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2507/8000 [00:34<00:56, 98.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2901/8000 [02:53<04:41, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2522/8000 [00:34<01:09, 79.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2905/8000 [02:53<04:10, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2533/8000 [00:35<01:21, 67.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2908/8000 [02:54<04:05, 20.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2548/8000 [00:35<01:17, 70.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2913/8000 [02:54<04:16, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2568/8000 [00:35<01:19, 68.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2917/8000 [02:54<03:55, 21.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2586/8000 [00:35<01:12, 74.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2922/8000 [02:54<04:15, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2605/8000 [00:35<01:12, 74.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2928/8000 [02:55<04:09, 20.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2624/8000 [00:36<01:07, 79.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2931/8000 [02:55<04:14, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2648/8000 [00:36<01:00, 88.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2937/8000 [02:55<04:10, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2664/8000 [00:36<01:03, 84.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2941/8000 [02:55<04:43, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2681/8000 [00:36<01:05, 80.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2943/8000 [02:55<04:51, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2717/8000 [00:37<01:27, 60.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2946/8000 [02:56<04:37, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2738/8000 [00:37<01:22, 64.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2948/8000 [02:56<06:08, 13.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2761/8000 [00:38<01:11, 73.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2950/8000 [02:56<07:07, 11.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2780/8000 [00:38<01:15, 68.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2953/8000 [02:56<06:12, 13.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2805/8000 [00:38<01:14, 70.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2956/8000 [02:57<06:42, 12.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2826/8000 [00:38<01:07, 76.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2958/8000 [02:57<06:38, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2845/8000 [00:39<01:08, 75.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2965/8000 [02:57<04:08, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2859/8000 [00:39<01:16, 67.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2972/8000 [02:57<03:45, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2877/8000 [00:39<01:20, 63.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2978/8000 [02:57<03:06, 26.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2898/8000 [00:40<01:13, 69.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2981/8000 [02:57<03:14, 25.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2924/8000 [00:40<01:04, 78.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2986/8000 [02:58<03:30, 23.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2948/8000 [00:40<00:58, 86.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2993/8000 [02:58<02:46, 30.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2964/8000 [00:40<01:01, 81.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2997/8000 [02:58<04:22, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2981/8000 [00:41<01:04, 77.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3001/8000 [02:59<04:40, 17.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3011/8000 [00:41<00:57, 87.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3005/8000 [02:59<05:01, 16.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3024/8000 [00:41<01:04, 77.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3008/8000 [02:59<04:49, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3045/8000 [00:41<00:58, 85.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3013/8000 [02:59<04:51, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3058/8000 [00:41<01:02, 78.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3015/8000 [02:59<05:01, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3074/8000 [00:42<01:06, 74.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3020/8000 [03:00<04:51, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3093/8000 [00:42<01:04, 76.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3022/8000 [03:00<05:03, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3108/8000 [00:42<01:10, 69.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3025/8000 [03:00<04:32, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3130/8000 [00:42<01:04, 75.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3027/8000 [03:00<04:53, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3151/8000 [00:43<01:06, 72.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3029/8000 [03:00<04:59, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▎   | 3166/8000 [00:43<01:05, 73.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3034/8000 [03:00<03:49, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3184/8000 [00:43<01:04, 74.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3037/8000 [03:01<05:01, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3206/8000 [00:43<01:04, 74.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3041/8000 [03:01<04:21, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3222/8000 [00:44<01:06, 71.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3044/8000 [03:01<04:05, 20.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3243/8000 [00:44<00:56, 84.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3047/8000 [03:01<06:33, 12.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3261/8000 [00:44<00:58, 81.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3050/8000 [03:02<05:45, 14.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3281/8000 [00:44<00:54, 86.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3053/8000 [03:02<05:17, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3297/8000 [00:45<01:01, 76.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3059/8000 [03:02<04:38, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3314/8000 [00:45<01:00, 77.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3061/8000 [03:02<04:41, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3334/8000 [00:45<01:00, 76.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3065/8000 [03:03<06:12, 13.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3359/8000 [00:45<00:55, 83.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3072/8000 [03:03<05:48, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3377/8000 [00:46<00:57, 80.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3076/8000 [03:03<05:03, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3394/8000 [00:46<00:56, 82.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3079/8000 [03:03<04:49, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3408/8000 [00:46<01:06, 69.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3083/8000 [03:04<05:04, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3433/8000 [00:46<00:56, 80.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3085/8000 [03:04<05:12, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3452/8000 [00:47<01:02, 72.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3090/8000 [03:04<04:08, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3474/8000 [00:47<00:57, 78.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3096/8000 [03:04<03:14, 25.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▌   | 3491/8000 [00:47<01:01, 73.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3103/8000 [03:04<03:44, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3514/8000 [00:47<00:57, 77.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3107/8000 [03:05<03:39, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3530/8000 [00:48<01:03, 70.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3112/8000 [03:05<04:05, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3549/8000 [00:48<00:58, 76.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3115/8000 [03:05<05:47, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3573/8000 [00:48<01:00, 73.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3117/8000 [03:05<05:45, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3600/8000 [00:48<00:52, 84.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3120/8000 [03:06<05:15, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3621/8000 [00:49<00:48, 90.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3124/8000 [03:06<04:38, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3639/8000 [00:49<00:54, 79.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3128/8000 [03:06<04:01, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3659/8000 [00:49<00:54, 79.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3133/8000 [03:06<03:18, 24.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3679/8000 [00:49<00:53, 81.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3137/8000 [03:06<03:54, 20.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3694/8000 [00:50<00:56, 76.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3140/8000 [03:06<04:06, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3707/8000 [00:50<01:03, 67.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3144/8000 [03:07<04:27, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3727/8000 [00:50<01:05, 64.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3148/8000 [03:07<03:55, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3746/8000 [00:50<00:58, 72.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3151/8000 [03:07<03:56, 20.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3768/8000 [00:51<00:51, 82.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3156/8000 [03:07<04:19, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3791/8000 [00:51<00:50, 83.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3160/8000 [03:07<03:57, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3803/8000 [00:51<00:57, 72.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3164/8000 [03:08<03:43, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3817/8000 [00:51<01:00, 68.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3169/8000 [03:08<03:48, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3837/8000 [00:52<00:55, 74.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3172/8000 [03:08<03:46, 21.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3859/8000 [00:52<00:53, 77.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3175/8000 [03:08<04:35, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3879/8000 [00:52<00:50, 80.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3180/8000 [03:08<03:43, 21.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3894/8000 [00:52<00:51, 80.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3183/8000 [03:09<03:56, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3907/8000 [00:53<00:53, 76.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3189/8000 [03:09<04:02, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3929/8000 [00:53<00:46, 87.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3196/8000 [03:09<03:46, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3944/8000 [00:53<00:52, 77.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3200/8000 [03:09<04:12, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3965/8000 [00:53<00:48, 83.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3208/8000 [03:10<03:32, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3981/8000 [00:53<00:52, 76.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3215/8000 [03:10<03:31, 22.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3996/8000 [00:54<00:58, 68.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3218/8000 [03:10<04:09, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4020/8000 [00:54<00:49, 79.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3222/8000 [03:11<05:11, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4038/8000 [00:54<00:48, 82.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3225/8000 [03:11<05:38, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4062/8000 [00:54<00:44, 88.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3229/8000 [03:11<04:53, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4083/8000 [00:55<00:48, 80.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3234/8000 [03:11<03:55, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4103/8000 [00:55<00:46, 82.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3240/8000 [03:12<03:56, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4120/8000 [00:55<00:48, 79.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3244/8000 [03:12<04:30, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4144/8000 [00:55<00:46, 82.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3246/8000 [03:12<04:37, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4153/8000 [00:56<00:55, 69.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3248/8000 [03:12<05:44, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4178/8000 [00:56<00:47, 80.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3250/8000 [03:12<05:35, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4199/8000 [00:56<00:46, 80.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3252/8000 [03:13<05:39, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4217/8000 [00:56<00:47, 79.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3257/8000 [03:13<04:10, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4237/8000 [00:57<00:50, 73.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3261/8000 [03:13<03:36, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4256/8000 [00:57<00:48, 77.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3264/8000 [03:13<03:43, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4267/8000 [00:57<00:51, 72.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3271/8000 [03:13<03:37, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4287/8000 [00:57<00:46, 80.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3274/8000 [03:14<03:43, 21.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4308/8000 [00:58<00:45, 80.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3278/8000 [03:14<03:20, 23.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4323/8000 [00:58<00:53, 68.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3283/8000 [03:14<03:01, 25.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4337/8000 [00:58<00:51, 70.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3289/8000 [03:14<03:20, 23.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4354/8000 [00:58<00:50, 72.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3292/8000 [03:14<03:18, 23.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4369/8000 [00:59<00:51, 70.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3297/8000 [03:14<03:31, 22.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4386/8000 [00:59<00:52, 69.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3307/8000 [03:15<03:16, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4403/8000 [00:59<00:52, 68.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3311/8000 [03:15<03:40, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4421/8000 [00:59<00:49, 72.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3314/8000 [03:15<03:35, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4438/8000 [00:59<00:49, 71.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3317/8000 [03:15<03:25, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4453/8000 [01:00<00:52, 67.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3320/8000 [03:16<04:16, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4470/8000 [01:00<00:46, 75.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3323/8000 [03:16<04:04, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4487/8000 [01:00<00:46, 74.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3327/8000 [03:16<03:29, 22.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▍  | 4510/8000 [01:00<00:42, 82.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3332/8000 [03:16<03:58, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4527/8000 [01:01<00:47, 72.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3335/8000 [03:16<04:44, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4541/8000 [01:01<00:48, 71.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3338/8000 [03:17<04:22, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4562/8000 [01:01<00:42, 80.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3341/8000 [03:17<04:14, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4582/8000 [01:01<00:42, 80.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3344/8000 [03:17<05:12, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4597/8000 [01:02<00:45, 74.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3347/8000 [03:17<04:42, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4608/8000 [01:02<00:54, 62.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3349/8000 [03:17<04:42, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4627/8000 [01:02<00:48, 69.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3352/8000 [03:17<04:17, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4641/8000 [01:02<00:51, 64.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3354/8000 [03:18<04:29, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4658/8000 [01:03<00:50, 66.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3357/8000 [03:18<04:07, 18.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▌  | 4680/8000 [01:03<00:47, 69.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3362/8000 [03:18<02:59, 25.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4699/8000 [01:03<00:43, 76.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3365/8000 [03:18<03:57, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4717/8000 [01:03<00:44, 73.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3368/8000 [03:18<04:35, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4727/8000 [01:04<00:53, 61.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3371/8000 [03:18<04:12, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4739/8000 [01:04<00:56, 57.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3378/8000 [03:19<03:29, 22.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4759/8000 [01:04<00:51, 62.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3381/8000 [03:19<03:27, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4772/8000 [01:04<00:56, 57.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3385/8000 [03:19<03:21, 22.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4792/8000 [01:05<00:50, 62.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3388/8000 [03:19<03:14, 23.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4810/8000 [01:05<00:50, 62.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3393/8000 [03:19<03:24, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4823/8000 [01:05<00:55, 57.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3396/8000 [03:19<03:32, 21.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4841/8000 [01:05<00:51, 61.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3400/8000 [03:20<04:09, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4860/8000 [01:06<00:50, 62.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3405/8000 [03:20<03:22, 22.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4878/8000 [01:06<00:45, 68.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3408/8000 [03:20<05:06, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4894/8000 [01:06<00:45, 68.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3411/8000 [03:20<04:42, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4910/8000 [01:07<00:49, 62.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3414/8000 [03:21<04:22, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4932/8000 [01:07<00:43, 70.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3417/8000 [03:21<04:08, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4949/8000 [01:07<00:42, 71.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3421/8000 [03:21<03:25, 22.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4964/8000 [01:07<00:49, 61.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3424/8000 [03:21<04:16, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4975/8000 [01:08<00:52, 57.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3428/8000 [03:21<04:46, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4989/8000 [01:08<00:55, 53.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3431/8000 [03:22<04:36, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5012/8000 [01:08<00:44, 66.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3435/8000 [03:22<04:32, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5035/8000 [01:08<00:38, 76.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3437/8000 [03:22<04:34, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5046/8000 [01:09<00:44, 65.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3439/8000 [03:22<04:44, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5063/8000 [01:09<00:45, 64.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3441/8000 [03:22<04:52, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5084/8000 [01:09<00:39, 73.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3445/8000 [03:22<04:46, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5103/8000 [01:09<00:40, 71.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3448/8000 [03:23<05:21, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5122/8000 [01:10<00:40, 71.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3453/8000 [03:23<04:51, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5144/8000 [01:10<00:38, 73.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3455/8000 [03:23<04:49, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▊  | 5161/8000 [01:10<00:39, 72.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3458/8000 [03:23<04:21, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5177/8000 [01:10<00:43, 64.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3461/8000 [03:23<04:01, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5204/8000 [01:11<00:34, 81.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3466/8000 [03:24<04:09, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5226/8000 [01:11<00:32, 86.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3468/8000 [03:24<04:23, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5252/8000 [01:11<00:29, 92.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3472/8000 [03:24<03:50, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5268/8000 [01:11<00:31, 87.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3476/8000 [03:24<03:21, 22.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5288/8000 [01:12<00:31, 86.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3481/8000 [03:24<03:35, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5307/8000 [01:12<00:33, 80.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3484/8000 [03:24<03:41, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5320/8000 [01:12<00:39, 68.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3487/8000 [03:25<03:41, 20.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5351/8000 [01:12<00:31, 84.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3492/8000 [03:25<03:39, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5365/8000 [01:13<00:33, 78.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3497/8000 [03:25<04:00, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5395/8000 [01:13<00:30, 86.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3501/8000 [03:25<03:31, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5415/8000 [01:13<00:30, 83.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3504/8000 [03:26<04:23, 17.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5427/8000 [01:13<00:33, 76.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3506/8000 [03:26<04:34, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5443/8000 [01:14<00:34, 74.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3508/8000 [03:26<04:44, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5457/8000 [01:14<00:38, 66.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3511/8000 [03:26<04:13, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5475/8000 [01:14<00:38, 65.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3515/8000 [03:26<04:23, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████  | 5490/8000 [01:14<00:35, 69.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3519/8000 [03:26<03:44, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5511/8000 [01:15<00:32, 76.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3524/8000 [03:27<04:01, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5531/8000 [01:15<00:30, 80.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3529/8000 [03:27<04:04, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5555/8000 [01:15<00:27, 90.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3531/8000 [03:27<04:12, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5574/8000 [01:15<00:25, 96.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3536/8000 [03:27<04:15, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5590/8000 [01:15<00:24, 97.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3538/8000 [03:28<04:38, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5609/8000 [01:16<00:27, 86.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3541/8000 [03:28<04:10, 17.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5619/8000 [01:16<00:33, 70.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3544/8000 [03:28<03:54, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5636/8000 [01:16<00:34, 68.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3549/8000 [03:28<03:56, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▏ | 5660/8000 [01:16<00:30, 75.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3554/8000 [03:28<03:11, 23.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5674/8000 [01:17<00:31, 74.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3557/8000 [03:28<03:20, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5698/8000 [01:17<00:26, 86.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3563/8000 [03:29<03:19, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5715/8000 [01:17<00:26, 84.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3566/8000 [03:29<03:59, 18.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5731/8000 [01:17<00:30, 75.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3569/8000 [03:29<04:47, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5755/8000 [01:17<00:25, 89.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3571/8000 [03:29<05:51, 12.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5777/8000 [01:18<00:23, 94.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3578/8000 [03:30<03:44, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5794/8000 [01:18<00:24, 88.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3583/8000 [03:30<03:54, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5811/8000 [01:18<00:26, 82.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3587/8000 [03:30<04:08, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5830/8000 [01:18<00:29, 74.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3592/8000 [03:30<03:32, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5850/8000 [01:19<00:27, 77.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3595/8000 [03:30<03:30, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5871/8000 [01:19<00:26, 78.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3599/8000 [03:31<03:56, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5883/8000 [01:19<00:32, 66.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3605/8000 [03:31<03:01, 24.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5907/8000 [01:19<00:25, 80.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3609/8000 [03:32<05:25, 13.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5926/8000 [01:20<00:24, 85.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3613/8000 [03:32<04:38, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5948/8000 [01:20<00:24, 83.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3617/8000 [03:32<04:14, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5963/8000 [01:20<00:27, 72.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3621/8000 [03:32<04:33, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5981/8000 [01:20<00:29, 69.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3629/8000 [03:32<03:31, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5998/8000 [01:21<00:27, 73.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3634/8000 [03:33<03:41, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6016/8000 [01:21<00:27, 72.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3637/8000 [03:33<03:39, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6036/8000 [01:21<00:26, 75.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3643/8000 [03:33<03:57, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6056/8000 [01:21<00:25, 75.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3647/8000 [03:33<03:39, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6076/8000 [01:22<00:24, 78.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3650/8000 [03:33<03:27, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6096/8000 [01:22<00:24, 78.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3653/8000 [03:34<03:29, 20.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6118/8000 [01:22<00:23, 80.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3656/8000 [03:34<04:04, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6127/8000 [01:22<00:27, 67.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3659/8000 [03:34<03:42, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6151/8000 [01:23<00:23, 80.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3663/8000 [03:34<05:06, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6173/8000 [01:23<00:23, 76.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3665/8000 [03:35<05:29, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6187/8000 [01:23<00:23, 76.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3669/8000 [03:35<04:25, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6210/8000 [01:23<00:21, 83.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3671/8000 [03:35<04:39, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6226/8000 [01:24<00:22, 77.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3676/8000 [03:35<04:46, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6249/8000 [01:24<00:23, 73.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3682/8000 [03:35<03:25, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6271/8000 [01:24<00:22, 78.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3686/8000 [03:36<03:20, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6286/8000 [01:24<00:21, 78.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3689/8000 [03:36<03:19, 21.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6305/8000 [01:25<00:21, 79.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3692/8000 [03:36<04:16, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6328/8000 [01:25<00:20, 83.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3695/8000 [03:36<04:14, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▊ | 6346/8000 [01:25<00:21, 77.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3699/8000 [03:36<04:05, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6374/8000 [01:25<00:17, 94.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3701/8000 [03:37<04:56, 14.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6394/8000 [01:26<00:17, 91.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3705/8000 [03:37<03:52, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6411/8000 [01:26<00:18, 85.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3709/8000 [03:37<03:18, 21.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6428/8000 [01:26<00:20, 76.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3712/8000 [03:37<03:59, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6450/8000 [01:26<00:17, 87.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3716/8000 [03:37<04:12, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6466/8000 [01:26<00:19, 80.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3718/8000 [03:37<04:21, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6483/8000 [01:27<00:19, 76.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3720/8000 [03:38<04:24, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▉ | 6501/8000 [01:27<00:18, 81.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3723/8000 [03:38<03:56, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▉ | 6514/8000 [01:27<00:19, 76.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3728/8000 [03:38<02:52, 24.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6526/8000 [01:27<00:22, 65.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3733/8000 [03:38<02:25, 29.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6542/8000 [01:28<00:21, 67.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3741/8000 [03:38<02:42, 26.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6566/8000 [01:28<00:17, 82.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3744/8000 [03:38<02:45, 25.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6584/8000 [01:28<00:19, 72.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3747/8000 [03:39<02:49, 25.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6608/8000 [01:28<00:17, 80.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3753/8000 [03:39<02:56, 24.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6628/8000 [01:29<00:16, 84.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3756/8000 [03:39<02:51, 24.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6656/8000 [01:29<00:13, 99.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3759/8000 [03:39<02:55, 24.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▏| 6678/8000 [01:29<00:13, 100.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:39<03:24, 20.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6693/8000 [01:29<00:14, 89.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3766/8000 [03:39<03:23, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6707/8000 [01:29<00:16, 79.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3769/8000 [03:40<03:59, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6725/8000 [01:30<00:15, 83.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3772/8000 [03:40<03:36, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6742/8000 [01:30<00:14, 85.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3775/8000 [03:40<03:29, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6774/8000 [01:30<00:12, 96.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3778/8000 [03:40<04:13, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6796/8000 [01:30<00:12, 94.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3780/8000 [03:40<04:09, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6807/8000 [01:31<00:13, 85.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3783/8000 [03:40<03:47, 18.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6823/8000 [01:31<00:15, 77.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3787/8000 [03:41<03:13, 21.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████▏| 6839/8000 [01:31<00:16, 72.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3790/8000 [03:41<03:08, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6860/8000 [01:31<00:14, 76.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3794/8000 [03:41<04:58, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6881/8000 [01:32<00:13, 82.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3798/8000 [03:41<04:52, 14.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6904/8000 [01:32<00:12, 88.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3801/8000 [03:42<04:18, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6926/8000 [01:32<00:13, 78.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3806/8000 [03:42<03:17, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6944/8000 [01:32<00:13, 79.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3809/8000 [03:42<03:05, 22.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6962/8000 [01:33<00:12, 82.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3813/8000 [03:42<02:46, 25.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6979/8000 [01:33<00:12, 81.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3817/8000 [03:42<03:22, 20.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7003/8000 [01:33<00:12, 80.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3822/8000 [03:42<03:27, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7016/8000 [01:33<00:13, 72.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3825/8000 [03:43<03:20, 20.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7036/8000 [01:34<00:12, 76.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3829/8000 [03:43<03:33, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7048/8000 [01:34<00:14, 65.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3832/8000 [03:43<03:20, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7065/8000 [01:34<00:14, 66.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3836/8000 [03:43<03:00, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7081/8000 [01:34<00:13, 67.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3840/8000 [03:43<03:23, 20.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7096/8000 [01:35<00:13, 64.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3845/8000 [03:43<02:42, 25.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7110/8000 [01:35<00:14, 61.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3848/8000 [03:44<03:58, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7135/8000 [01:35<00:11, 74.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3851/8000 [03:44<03:41, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7150/8000 [01:35<00:11, 73.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3854/8000 [03:44<04:52, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▎| 7166/8000 [01:35<00:11, 74.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3858/8000 [03:44<04:53, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7180/8000 [01:36<00:12, 65.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3864/8000 [03:45<04:05, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7210/8000 [01:36<00:09, 79.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3867/8000 [03:45<03:43, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7229/8000 [01:36<00:09, 80.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3871/8000 [03:45<04:02, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7258/8000 [01:36<00:08, 90.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3874/8000 [03:45<03:40, 18.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7276/8000 [01:37<00:08, 82.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3880/8000 [03:45<03:16, 20.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7293/8000 [01:37<00:08, 81.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3884/8000 [03:46<03:29, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7313/8000 [01:37<00:08, 79.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3887/8000 [03:46<03:19, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▍| 7325/8000 [01:37<00:08, 77.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3891/8000 [03:46<03:32, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7342/8000 [01:38<00:08, 79.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3894/8000 [03:46<04:32, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7363/8000 [01:38<00:07, 86.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3896/8000 [03:47<04:27, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7376/8000 [01:38<00:08, 73.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3898/8000 [03:47<04:23, 15.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7391/8000 [01:38<00:09, 64.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3901/8000 [03:47<04:00, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7408/8000 [01:39<00:09, 65.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3906/8000 [03:47<04:24, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7425/8000 [01:39<00:08, 66.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3911/8000 [03:47<03:19, 20.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7438/8000 [01:39<00:08, 65.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3915/8000 [03:47<02:58, 22.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7461/8000 [01:39<00:07, 69.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3919/8000 [03:48<02:46, 24.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7480/8000 [01:40<00:07, 71.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3924/8000 [03:48<04:30, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7496/8000 [01:40<00:07, 66.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3928/8000 [03:48<03:47, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7511/8000 [01:40<00:07, 64.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3931/8000 [03:48<04:06, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7531/8000 [01:40<00:06, 70.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3934/8000 [03:49<03:46, 17.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7546/8000 [01:41<00:06, 70.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3937/8000 [03:49<04:50, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7566/8000 [01:41<00:05, 74.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3939/8000 [03:49<04:38, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7586/8000 [01:41<00:06, 66.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3942/8000 [03:49<04:48, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7604/8000 [01:42<00:05, 66.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3946/8000 [03:49<03:44, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7622/8000 [01:42<00:05, 67.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3951/8000 [03:50<02:55, 23.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7638/8000 [01:42<00:05, 68.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3956/8000 [03:50<02:29, 26.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▋| 7656/8000 [01:42<00:05, 66.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3961/8000 [03:50<02:45, 24.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7678/8000 [01:42<00:04, 77.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3964/8000 [03:50<03:51, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7697/8000 [01:43<00:03, 78.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3967/8000 [03:50<03:30, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7720/8000 [01:43<00:03, 79.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3971/8000 [03:50<03:02, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7739/8000 [01:43<00:03, 79.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3974/8000 [03:51<02:53, 23.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7758/8000 [01:43<00:02, 82.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3978/8000 [03:51<03:37, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7772/8000 [01:44<00:03, 68.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3981/8000 [03:51<03:24, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7782/8000 [01:44<00:04, 54.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3985/8000 [03:51<02:55, 22.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7804/8000 [01:44<00:03, 60.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3989/8000 [03:51<02:36, 25.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7825/8000 [01:45<00:02, 66.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3992/8000 [03:51<03:14, 20.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7838/8000 [01:45<00:02, 63.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3998/8000 [03:52<02:57, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7855/8000 [01:45<00:02, 63.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4003/8000 [03:52<02:58, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7865/8000 [01:45<00:02, 59.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4006/8000 [03:52<03:27, 19.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7896/8000 [01:46<00:01, 74.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4009/8000 [03:52<03:11, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7919/8000 [01:46<00:01, 79.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4012/8000 [03:52<03:34, 18.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7941/8000 [01:46<00:00, 80.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4015/8000 [03:53<03:15, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7962/8000 [01:46<00:00, 81.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4020/8000 [03:53<03:16, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7982/8000 [01:47<00:00, 83.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4024/8000 [03:53<03:26, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7999/8000 [01:47<00:00, 68.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4027/8000 [03:53<03:58, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8022it [01:47, 74.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  50%|████████▌        | 4030/8000 [03:54<05:10, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4034/8000 [03:54<04:05, 16.13it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                            | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4038/8000 [03:54<05:08, 12.85it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                    | 1/200 [00:08<28:52,  8.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4040/8000 [03:55<05:42, 11.55it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▏                   | 2/200 [00:10<16:14,  4.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4045/8000 [03:55<04:37, 14.23it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▎                   | 3/200 [00:13<12:19,  3.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4049/8000 [03:55<03:49, 17.22it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                   | 4/200 [00:15<10:05,  3.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4055/8000 [03:55<03:18, 19.85it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                   | 5/200 [00:17<08:56,  2.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4058/8000 [03:55<03:09, 20.76it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▌                   | 6/200 [00:19<08:21,  2.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4063/8000 [03:56<03:42, 17.68it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▋                   | 7/200 [00:22<07:58,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4067/8000 [03:56<03:07, 20.98it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▊                   | 8/200 [00:24<07:41,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4070/8000 [03:56<02:57, 22.12it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▉                   | 9/200 [00:26<07:21,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4073/8000 [03:56<02:45, 23.67it/s]\u001b[A\u001b[A\n",
+      "training:   5%|▉                  | 10/200 [00:29<07:35,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4079/8000 [03:56<03:12, 20.37it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█                  | 11/200 [00:31<07:38,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4082/8000 [03:57<04:15, 15.36it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 12/200 [00:33<07:32,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4086/8000 [03:57<03:33, 18.35it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 13/200 [00:36<07:17,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4093/8000 [03:57<03:30, 18.53it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█▎                 | 14/200 [00:38<07:09,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4097/8000 [03:57<03:31, 18.48it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▍                 | 15/200 [00:40<07:11,  2.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4102/8000 [03:58<03:18, 19.59it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 16/200 [00:42<06:56,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4105/8000 [03:58<03:44, 17.38it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 17/200 [00:45<06:51,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4108/8000 [03:58<04:06, 15.78it/s]\u001b[A\u001b[A\n",
+      "training:   9%|█▋                 | 18/200 [00:47<06:42,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4111/8000 [03:58<03:42, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▊                 | 19/200 [00:49<06:36,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4113/8000 [03:58<03:41, 17.56it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 20/200 [00:51<06:44,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4115/8000 [03:58<03:40, 17.63it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 21/200 [00:54<06:51,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4117/8000 [03:58<03:48, 17.02it/s]\u001b[A\u001b[A\n",
+      "training:  11%|██                 | 22/200 [00:56<06:51,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4123/8000 [03:59<03:09, 20.46it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▏                | 23/200 [00:58<06:46,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4128/8000 [03:59<03:01, 21.32it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▎                | 24/200 [01:01<06:58,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4133/8000 [03:59<02:56, 21.86it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▍                | 25/200 [01:03<06:54,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4139/8000 [03:59<02:48, 22.96it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██▍                | 26/200 [01:05<06:43,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4144/8000 [04:00<02:20, 27.41it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▌                | 27/200 [01:08<06:37,  2.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4148/8000 [04:00<03:09, 20.29it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▋                | 28/200 [01:10<06:39,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4152/8000 [04:00<02:51, 22.42it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▊                | 29/200 [01:13<06:54,  2.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4155/8000 [04:00<02:42, 23.63it/s]\u001b[A\u001b[A\n",
+      "training:  15%|██▊                | 30/200 [01:15<06:42,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4159/8000 [04:00<03:36, 17.72it/s]\u001b[A\u001b[A\n",
+      "training:  16%|██▉                | 31/200 [01:17<06:45,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4163/8000 [04:01<04:10, 15.31it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███                | 32/200 [01:20<06:41,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4165/8000 [04:01<04:42, 13.58it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███▏               | 33/200 [01:22<06:33,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4167/8000 [04:01<04:30, 14.18it/s]\u001b[A\u001b[A\n",
+      "training:  17%|███▏               | 34/200 [01:25<06:53,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4171/8000 [04:01<03:33, 17.94it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▎               | 35/200 [01:27<06:35,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4175/8000 [04:01<03:42, 17.20it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▍               | 36/200 [01:29<06:35,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4179/8000 [04:02<03:06, 20.48it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▌               | 37/200 [01:32<06:28,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4185/8000 [04:02<02:46, 22.94it/s]\u001b[A\u001b[A\n",
+      "training:  19%|███▌               | 38/200 [01:34<06:34,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4189/8000 [04:02<02:56, 21.64it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▋               | 39/200 [01:36<06:22,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4192/8000 [04:02<03:58, 16.00it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▊               | 40/200 [01:39<06:23,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4195/8000 [04:03<04:13, 14.99it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▉               | 41/200 [01:41<06:20,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4197/8000 [04:03<04:48, 13.20it/s]\u001b[A\u001b[A\n",
+      "training:  21%|███▉               | 42/200 [01:44<06:37,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4200/8000 [04:03<04:58, 12.71it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████               | 43/200 [01:47<06:30,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4205/8000 [04:03<03:28, 18.21it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▏              | 44/200 [01:49<06:21,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4208/8000 [04:03<03:13, 19.61it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▎              | 45/200 [01:51<06:20,  2.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4213/8000 [04:04<03:03, 20.66it/s]\u001b[A\u001b[A\n",
+      "training:  23%|████▎              | 46/200 [01:54<06:11,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4217/8000 [04:04<02:40, 23.58it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▍              | 47/200 [01:56<06:07,  2.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4221/8000 [04:04<02:23, 26.37it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▌              | 48/200 [01:58<05:58,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4226/8000 [04:04<02:00, 31.34it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▋              | 49/200 [02:01<06:15,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4231/8000 [04:04<02:00, 31.40it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████▊              | 50/200 [02:05<07:08,  2.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4235/8000 [04:04<02:51, 21.97it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████▊              | 51/200 [02:07<06:51,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4238/8000 [04:05<03:33, 17.60it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████▉              | 52/200 [02:10<06:27,  2.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4241/8000 [04:05<03:28, 18.01it/s]\u001b[A\u001b[A\n",
+      "training:  26%|█████              | 53/200 [02:12<06:10,  2.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4246/8000 [04:05<03:26, 18.15it/s]\u001b[A\u001b[A\n",
+      "training:  27%|█████▏             | 54/200 [02:14<05:51,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4249/8000 [04:05<04:36, 13.55it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▏             | 55/200 [02:16<05:42,  2.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4255/8000 [04:06<03:18, 18.91it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▎             | 56/200 [02:21<06:04,  2.53s/it]\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4260/8000 [04:06<03:06, 20.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4267/8000 [04:06<02:47, 22.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4270/8000 [04:06<03:13, 19.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4275/8000 [04:07<03:27, 17.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 8it [00:01,  6.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4279/8000 [04:07<03:46, 16.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.39it/s]\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4283/8000 [04:07<03:13, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4289/8000 [04:07<02:58, 20.81it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4292/8000 [04:08<03:17, 18.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4298/8000 [04:08<02:43, 22.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 24it [00:01, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4301/8000 [04:08<03:11, 19.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 52it [00:01, 47.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4307/8000 [04:08<02:51, 21.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:01, 72.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4310/8000 [04:08<03:15, 18.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 103it [00:01, 103.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4313/8000 [04:09<03:01, 20.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 132it [00:02, 136.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4317/8000 [04:09<03:08, 19.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 158it [00:02, 161.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4322/8000 [04:09<03:06, 19.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 184it [00:02, 183.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4328/8000 [04:09<02:24, 25.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 210it [00:02, 166.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4333/8000 [04:09<02:04, 29.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 232it [00:02, 163.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4338/8000 [04:09<02:16, 26.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 255it [00:02, 177.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4343/8000 [04:10<02:46, 21.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 280it [00:02, 195.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4346/8000 [04:10<03:11, 19.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 309it [00:02, 218.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4352/8000 [04:10<02:56, 20.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 334it [00:03, 225.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4357/8000 [04:10<02:54, 20.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 359it [00:03, 182.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4361/8000 [04:11<03:05, 19.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 380it [00:03, 174.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4366/8000 [04:11<02:30, 24.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 400it [00:03, 178.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4370/8000 [04:11<03:40, 16.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 420it [00:03, 182.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4375/8000 [04:12<03:24, 17.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 445it [00:03, 198.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4379/8000 [04:12<03:05, 19.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 468it [00:03, 201.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4383/8000 [04:12<03:09, 19.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 494it [00:03, 216.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4386/8000 [04:12<03:02, 19.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 517it [00:03, 219.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4390/8000 [04:12<02:40, 22.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 540it [00:04, 212.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4394/8000 [04:12<02:53, 20.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 562it [00:04, 199.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4397/8000 [04:13<03:15, 18.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 583it [00:04, 187.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4401/8000 [04:13<02:52, 20.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 603it [00:04, 187.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4404/8000 [04:13<02:49, 21.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 628it [00:04, 200.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4409/8000 [04:13<03:11, 18.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 656it [00:04, 221.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4412/8000 [04:13<03:51, 15.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 686it [00:04, 241.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4417/8000 [04:14<03:03, 19.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 713it [00:04, 246.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4421/8000 [04:14<02:41, 22.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 738it [00:04, 236.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4426/8000 [04:14<02:50, 20.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 762it [00:05, 230.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4430/8000 [04:14<02:47, 21.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 152.18it/s]\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4435/8000 [04:14<02:48, 21.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4439/8000 [04:15<03:53, 15.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%| | 0/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4442/8000 [04:15<03:31, 16.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%| | 4/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4446/8000 [04:15<03:03, 19.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%| | 9/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4449/8000 [04:15<04:04, 14.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4454/8000 [04:16<03:07, 18.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4459/8000 [04:16<02:56, 20.01it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [04:16<03:52, 15.21it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4467/8000 [04:16<02:56, 19.99it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4471/8000 [04:17<03:00, 19.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4477/8000 [04:17<02:49, 20.81it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4480/8000 [04:17<03:08, 18.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4486/8000 [04:17<02:49, 20.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4490/8000 [04:17<02:30, 23.37it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4497/8000 [04:18<02:20, 24.97it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4500/8000 [04:18<02:43, 21.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4503/8000 [04:18<02:39, 21.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4508/8000 [04:18<02:39, 21.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:01\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4511/8000 [04:18<02:35, 22.41it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4515/8000 [04:19<02:53, 20.07it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4518/8000 [04:19<02:43, 21.26it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4521/8000 [04:19<02:40, 21.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|▊| 86/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4524/8000 [04:19<02:33, 22.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  90%|▉| 90/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4527/8000 [04:19<02:30, 23.02it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  94%|▉| 94/100 [00:02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4532/8000 [04:19<02:36, 22.12it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:0\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4535/8000 [04:19<02:32, 22.75it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|██████████████████████████████████████████████████████                  | 3/4 [42:31<14:16, 856.62s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.8767375237444974\n",
+      "2999: -5.8194172\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  57%|█████████▋       | 4542/8000 [04:20<02:47, 20.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4548/8000 [04:20<02:40, 21.45it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                            | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4551/8000 [04:20<02:35, 22.20it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                    | 1/200 [00:09<31:10,  9.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4554/8000 [04:20<02:28, 23.17it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▏                   | 2/200 [00:12<18:22,  5.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4559/8000 [04:20<02:06, 27.21it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▎                   | 3/200 [00:15<14:06,  4.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4563/8000 [04:21<01:59, 28.68it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                   | 4/200 [00:17<11:59,  3.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4568/8000 [04:21<02:40, 21.42it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                   | 5/200 [00:20<11:04,  3.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4571/8000 [04:21<02:33, 22.29it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▌                   | 6/200 [00:23<10:03,  3.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4578/8000 [04:21<01:58, 28.91it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▋                   | 7/200 [00:26<10:08,  3.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4582/8000 [04:21<02:22, 24.04it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▊                   | 8/200 [00:29<09:53,  3.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4585/8000 [04:22<02:53, 19.68it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▉                   | 9/200 [00:32<09:41,  3.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4588/8000 [04:22<02:44, 20.69it/s]\u001b[A\u001b[A\n",
+      "training:   5%|▉                  | 10/200 [00:35<09:26,  2.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4593/8000 [04:22<02:42, 20.98it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█                  | 11/200 [00:38<09:14,  2.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4596/8000 [04:22<02:39, 21.31it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 12/200 [00:42<10:35,  3.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4601/8000 [04:22<02:14, 25.30it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 13/200 [00:45<10:18,  3.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4604/8000 [04:22<02:45, 20.48it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█▎                 | 14/200 [00:48<09:48,  3.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4608/8000 [04:23<03:01, 18.64it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▍                 | 15/200 [00:51<09:25,  3.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4613/8000 [04:23<02:36, 21.66it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 16/200 [00:54<09:07,  2.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4618/8000 [04:23<02:19, 24.25it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 17/200 [00:56<08:51,  2.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4622/8000 [04:23<02:13, 25.37it/s]\u001b[A\u001b[A\n",
+      "training:   9%|█▋                 | 18/200 [00:59<08:54,  2.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4626/8000 [04:23<02:04, 27.00it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▊                 | 19/200 [01:02<08:13,  2.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4634/8000 [04:23<01:34, 35.57it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 20/200 [01:03<07:18,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4638/8000 [04:24<02:36, 21.50it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 21/200 [01:06<07:08,  2.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4641/8000 [04:24<03:01, 18.47it/s]\u001b[A\u001b[A\n",
+      "training:  11%|██                 | 22/200 [01:08<06:45,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4644/8000 [04:24<03:20, 16.70it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▏                | 23/200 [01:09<06:21,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4648/8000 [04:24<02:48, 19.84it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▎                | 24/200 [01:11<06:09,  2.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4653/8000 [04:25<02:26, 22.80it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▍                | 25/200 [01:13<06:00,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4658/8000 [04:25<02:33, 21.81it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██▍                | 26/200 [01:15<05:47,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4661/8000 [04:25<02:28, 22.48it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▌                | 27/200 [01:17<05:39,  1.96s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4665/8000 [04:25<02:50, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▋                | 28/200 [01:19<05:21,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4669/8000 [04:26<02:59, 18.53it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▊                | 29/200 [01:21<05:29,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4672/8000 [04:26<03:27, 16.03it/s]\u001b[A\u001b[A\n",
+      "training:  15%|██▊                | 30/200 [01:23<05:28,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4675/8000 [04:26<03:09, 17.54it/s]\u001b[A\u001b[A\n",
+      "training:  16%|██▉                | 31/200 [01:24<05:12,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4681/8000 [04:26<02:17, 24.05it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███                | 32/200 [01:26<05:00,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4685/8000 [04:26<02:36, 21.16it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███▏               | 33/200 [01:28<04:52,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4688/8000 [04:27<03:04, 17.94it/s]\u001b[A\u001b[A\n",
+      "training:  17%|███▏               | 34/200 [01:29<04:49,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4694/8000 [04:27<02:42, 20.36it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▎               | 35/200 [01:31<04:46,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4697/8000 [04:27<03:05, 17.84it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▍               | 36/200 [01:33<04:49,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4701/8000 [04:27<02:40, 20.53it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▌               | 37/200 [01:35<04:48,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4705/8000 [04:27<02:59, 18.35it/s]\u001b[A\u001b[A\n",
+      "training:  19%|███▌               | 38/200 [01:37<04:46,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4712/8000 [04:28<02:10, 25.22it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▋               | 39/200 [01:38<04:38,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4719/8000 [04:28<02:07, 25.76it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▊               | 40/200 [01:40<04:39,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4722/8000 [04:28<02:36, 20.97it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▉               | 41/200 [01:42<04:30,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4726/8000 [04:29<03:38, 14.97it/s]\u001b[A\u001b[A\n",
+      "training:  21%|███▉               | 42/200 [01:43<04:26,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4728/8000 [04:29<04:52, 11.19it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████               | 43/200 [01:45<04:22,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4733/8000 [04:29<03:39, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▏              | 44/200 [01:46<04:16,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4738/8000 [04:29<03:05, 17.63it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▎              | 45/200 [01:48<04:15,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4743/8000 [04:29<02:40, 20.29it/s]\u001b[A\u001b[A\n",
+      "training:  23%|████▎              | 46/200 [01:50<04:09,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4748/8000 [04:30<02:41, 20.11it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▍              | 47/200 [01:51<04:11,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4754/8000 [04:30<02:36, 20.77it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▌              | 48/200 [01:53<04:09,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4760/8000 [04:30<02:35, 20.78it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▋              | 49/200 [01:55<04:08,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4763/8000 [04:31<03:06, 17.34it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████▊              | 50/200 [01:56<04:08,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4766/8000 [04:31<02:58, 18.13it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████▊              | 51/200 [01:58<04:02,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4769/8000 [04:31<02:49, 19.08it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████▉              | 52/200 [01:59<03:57,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4773/8000 [04:31<02:34, 20.92it/s]\u001b[A\u001b[A\n",
+      "training:  26%|█████              | 53/200 [02:01<03:54,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4778/8000 [04:31<02:36, 20.54it/s]\u001b[A\u001b[A\n",
+      "training:  27%|█████▏             | 54/200 [02:03<03:50,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4781/8000 [04:31<02:43, 19.68it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▏             | 55/200 [02:04<03:48,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4785/8000 [04:32<02:25, 22.14it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▎             | 56/200 [02:06<03:47,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4789/8000 [04:32<02:20, 22.93it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████▍             | 57/200 [02:07<03:51,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4792/8000 [04:32<02:57, 18.03it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████▌             | 58/200 [02:09<04:00,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4795/8000 [04:32<02:51, 18.67it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████▌             | 59/200 [02:11<03:55,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4798/8000 [04:32<03:35, 14.84it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████▋             | 60/200 [02:13<03:54,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4801/8000 [04:33<03:51, 13.83it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████▊             | 61/200 [02:14<03:45,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4803/8000 [04:33<03:46, 14.14it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████▉             | 62/200 [02:16<03:40,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4806/8000 [04:33<03:21, 15.83it/s]\u001b[A\u001b[A\n",
+      "training:  32%|█████▉             | 63/200 [02:17<03:36,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4808/8000 [04:33<03:43, 14.28it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████             | 64/200 [02:19<03:32,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4813/8000 [04:33<03:24, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████▏            | 65/200 [02:20<03:29,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4816/8000 [04:34<03:11, 16.60it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████▎            | 66/200 [02:22<03:29,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4818/8000 [04:34<03:16, 16.16it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████▎            | 67/200 [02:23<03:28,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4821/8000 [04:34<02:57, 17.89it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████▍            | 68/200 [02:25<03:34,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4825/8000 [04:34<02:46, 19.09it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████▌            | 69/200 [02:27<03:36,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4827/8000 [04:34<02:59, 17.68it/s]\u001b[A\u001b[A\n",
+      "training:  35%|██████▋            | 70/200 [02:29<03:33,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4831/8000 [04:34<03:13, 16.38it/s]\u001b[A\u001b[A\n",
+      "training:  36%|██████▋            | 71/200 [02:32<04:36,  2.14s/it]\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4835/8000 [04:35<02:46, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4838/8000 [04:35<02:37, 20.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4844/8000 [04:35<02:40, 19.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4847/8000 [04:35<03:31, 14.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.89it/s]\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4850/8000 [04:36<03:17, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4855/8000 [04:36<03:12, 16.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4859/8000 [04:36<02:52, 18.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4861/8000 [04:36<03:10, 16.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 39it [00:01, 32.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4866/8000 [04:36<02:33, 20.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 70it [00:01, 61.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4870/8000 [04:36<02:16, 22.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 106it [00:01, 100.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4873/8000 [04:37<02:52, 18.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 149it [00:01, 151.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4877/8000 [04:37<02:38, 19.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 184it [00:02, 187.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4882/8000 [04:37<03:02, 17.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 225it [00:02, 232.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4885/8000 [04:38<03:58, 13.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 263it [00:02, 264.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4889/8000 [04:38<03:17, 15.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 300it [00:02, 261.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4894/8000 [04:38<02:37, 19.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 334it [00:02, 280.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4899/8000 [04:38<02:12, 23.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 370it [00:02, 299.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4903/8000 [04:38<02:12, 23.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 407it [00:02, 317.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4906/8000 [04:39<03:16, 15.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 442it [00:02, 315.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4909/8000 [04:39<02:59, 17.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 482it [00:02, 338.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4914/8000 [04:39<02:51, 18.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 518it [00:03, 342.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4921/8000 [04:39<02:47, 18.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 554it [00:03, 337.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4924/8000 [04:40<03:07, 16.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 589it [00:03, 330.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4926/8000 [04:40<03:43, 13.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 623it [00:03, 312.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4930/8000 [04:40<03:21, 15.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 658it [00:03, 321.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4932/8000 [04:41<04:41, 10.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 696it [00:03, 335.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4937/8000 [04:41<03:56, 12.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 736it [00:03, 352.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4942/8000 [04:41<03:00, 16.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 206.65it/s]\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4945/8000 [04:41<03:31, 14.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4948/8000 [04:41<03:17, 15.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%| | 0/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4953/8000 [04:42<03:10, 16.03it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4956/8000 [04:42<03:08, 16.18it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4960/8000 [04:42<02:47, 18.13it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4967/8000 [04:42<02:52, 17.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4969/8000 [04:43<02:59, 16.91it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4975/8000 [04:43<02:40, 18.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4978/8000 [04:43<02:38, 19.08it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|▊| 86/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4982/8000 [04:43<02:51, 17.64it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:0\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4984/8000 [04:43<02:57, 17.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4987/8000 [04:44<02:47, 17.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                 | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4989/8000 [04:44<02:50, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|         | 2/8000 [00:00<12:16, 10.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4992/8000 [04:44<03:36, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|         | 7/8000 [00:00<09:27, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4994/8000 [04:44<03:29, 14.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 11/8000 [00:00<08:29, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4999/8000 [04:44<03:03, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 13/8000 [00:00<08:46, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5003/8000 [04:45<02:45, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 16/8000 [00:01<08:20, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5009/8000 [04:45<02:42, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 19/8000 [00:01<11:21, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5011/8000 [04:45<02:48, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 21/8000 [00:01<10:56, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5013/8000 [04:45<03:05, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 26/8000 [00:01<09:26, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5016/8000 [04:45<03:06, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 28/8000 [00:02<10:08, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5020/8000 [04:46<03:18, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 31/8000 [00:02<09:09, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5022/8000 [04:46<03:14, 15.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 34/8000 [00:02<10:55, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5028/8000 [04:46<02:35, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 36/8000 [00:02<11:21, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5031/8000 [04:46<02:32, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 38/8000 [00:02<10:57, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5037/8000 [04:46<01:54, 25.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|        | 40/8000 [00:03<10:58, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5042/8000 [04:46<02:02, 24.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 42/8000 [00:03<12:01, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5045/8000 [04:47<02:00, 24.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 44/8000 [00:03<11:37, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5049/8000 [04:47<02:32, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 51/8000 [00:03<08:39, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5052/8000 [04:47<03:04, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 53/8000 [00:03<09:25, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5055/8000 [04:47<02:45, 17.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 55/8000 [00:04<09:16, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5058/8000 [04:47<02:35, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 61/8000 [00:04<07:57, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5061/8000 [04:48<02:57, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 63/8000 [00:04<08:29, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5063/8000 [04:48<02:57, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 65/8000 [00:04<08:43, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5065/8000 [04:48<03:47, 12.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 68/8000 [00:04<07:55, 16.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5068/8000 [04:48<03:09, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 70/8000 [00:05<12:47, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5070/8000 [04:48<03:13, 15.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 76/8000 [00:05<10:10, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5072/8000 [04:48<03:11, 15.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 82/8000 [00:05<07:22, 17.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5076/8000 [04:49<02:31, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 87/8000 [00:06<07:26, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5080/8000 [04:49<02:19, 20.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 90/8000 [00:06<08:34, 15.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5085/8000 [04:49<02:40, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 93/8000 [00:06<08:04, 16.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5089/8000 [04:49<02:24, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 96/8000 [00:06<09:44, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5094/8000 [04:50<03:26, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|        | 99/8000 [00:07<11:41, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5099/8000 [04:50<02:50, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 101/8000 [00:07<12:21, 10.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5104/8000 [04:50<02:32, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 103/8000 [00:07<11:37, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5110/8000 [04:50<02:21, 20.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 106/8000 [00:07<10:12, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5113/8000 [04:51<03:06, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 111/8000 [00:07<07:34, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5115/8000 [04:51<03:46, 12.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 113/8000 [00:08<07:51, 16.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5118/8000 [04:51<03:55, 12.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 115/8000 [00:08<08:32, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5122/8000 [04:51<03:11, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|       | 117/8000 [00:08<09:11, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5126/8000 [04:52<02:38, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 123/8000 [00:08<06:23, 20.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5130/8000 [04:52<02:19, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 127/8000 [00:08<07:59, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5136/8000 [04:52<02:15, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 130/8000 [00:09<07:37, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5141/8000 [04:52<01:59, 23.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 132/8000 [00:09<07:46, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5144/8000 [04:52<02:01, 23.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 134/8000 [00:09<08:33, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5147/8000 [04:52<02:03, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 138/8000 [00:09<07:28, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5150/8000 [04:53<02:05, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|       | 142/8000 [00:09<08:30, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5153/8000 [04:53<02:07, 22.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 147/8000 [00:10<08:29, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5157/8000 [04:53<02:26, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 149/8000 [00:10<08:35, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5160/8000 [04:53<03:25, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 151/8000 [00:10<08:57, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5164/8000 [04:54<02:45, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 154/8000 [00:10<08:08, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5169/8000 [04:54<03:06, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 156/8000 [00:10<08:14, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5171/8000 [04:54<03:11, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 159/8000 [00:10<08:10, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5173/8000 [04:54<03:12, 14.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 161/8000 [00:11<08:58, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5176/8000 [04:54<02:59, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 164/8000 [00:11<07:48, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5179/8000 [04:55<04:07, 11.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 169/8000 [00:11<06:02, 21.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5182/8000 [04:55<03:31, 13.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 174/8000 [00:11<05:05, 25.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5185/8000 [04:55<04:19, 10.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 178/8000 [00:11<05:05, 25.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5188/8000 [04:55<03:34, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 183/8000 [00:12<07:34, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5191/8000 [04:56<04:02, 11.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 187/8000 [00:12<10:03, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5193/8000 [04:56<04:51,  9.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 191/8000 [00:12<08:30, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5197/8000 [04:56<04:24, 10.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▏      | 199/8000 [00:13<06:57, 18.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5203/8000 [04:57<02:57, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 203/8000 [00:13<06:35, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5209/8000 [04:57<02:52, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 208/8000 [00:13<05:44, 22.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5211/8000 [04:57<03:03, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 213/8000 [00:13<05:23, 24.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5215/8000 [04:57<02:49, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 216/8000 [00:14<08:48, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5217/8000 [04:58<03:31, 13.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 220/8000 [00:14<07:54, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5220/8000 [04:58<03:10, 14.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 224/8000 [00:14<07:01, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5228/8000 [04:58<01:59, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 231/8000 [00:14<06:19, 20.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5233/8000 [04:58<02:06, 21.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 235/8000 [00:14<05:47, 22.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5237/8000 [04:58<02:00, 22.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 238/8000 [00:15<07:15, 17.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5240/8000 [04:59<03:17, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 243/8000 [00:15<06:20, 20.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5244/8000 [04:59<02:44, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 246/8000 [00:15<06:27, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5249/8000 [04:59<02:38, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 253/8000 [00:15<05:09, 25.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5254/8000 [04:59<02:37, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 257/8000 [00:15<05:21, 24.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5258/8000 [05:00<02:18, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 263/8000 [00:16<06:47, 18.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5262/8000 [05:00<02:09, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 267/8000 [00:16<07:30, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5269/8000 [05:00<02:06, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 270/8000 [00:16<08:38, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5273/8000 [05:00<02:42, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 273/8000 [00:17<07:55, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5275/8000 [05:01<02:51, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 276/8000 [00:17<10:11, 12.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5277/8000 [05:01<04:12, 10.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▏      | 279/8000 [00:17<09:41, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5280/8000 [05:01<04:04, 11.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▏      | 281/8000 [00:17<10:06, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5283/8000 [05:02<03:55, 11.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▏      | 283/8000 [00:17<09:53, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5285/8000 [05:02<05:24,  8.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 286/8000 [00:18<08:33, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5287/8000 [05:02<05:29,  8.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 288/8000 [00:18<12:20, 10.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5290/8000 [05:03<04:55,  9.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 291/8000 [00:18<11:00, 11.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5292/8000 [05:03<04:44,  9.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 296/8000 [00:19<11:17, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5300/8000 [05:03<02:26, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 299/8000 [00:19<10:17, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5303/8000 [05:03<02:33, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 304/8000 [00:19<09:33, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5307/8000 [05:03<02:24, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 307/8000 [00:19<08:52, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5311/8000 [05:04<02:47, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 310/8000 [00:20<10:02, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5316/8000 [05:04<02:49, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 314/8000 [00:20<08:26, 15.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5318/8000 [05:04<02:52, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 319/8000 [00:20<06:50, 18.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5320/8000 [05:04<02:53, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 322/8000 [00:20<06:55, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5322/8000 [05:04<02:54, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 326/8000 [00:20<07:50, 16.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5326/8000 [05:05<03:02, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 329/8000 [00:21<07:12, 17.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5328/8000 [05:05<03:12, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 331/8000 [00:21<07:48, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5330/8000 [05:05<03:08, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 333/8000 [00:21<08:04, 15.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5332/8000 [05:05<03:02, 14.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 336/8000 [00:21<09:38, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5336/8000 [05:05<02:24, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 340/8000 [00:21<07:45, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5338/8000 [05:05<02:28, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 342/8000 [00:21<07:56, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5345/8000 [05:05<01:36, 27.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 345/8000 [00:22<09:27, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5349/8000 [05:06<02:06, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 348/8000 [00:22<10:55, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5357/8000 [05:06<01:48, 24.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 352/8000 [00:22<08:30, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5361/8000 [05:06<01:41, 26.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 355/8000 [00:22<07:50, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5364/8000 [05:06<02:09, 20.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 358/8000 [00:23<07:48, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5367/8000 [05:07<03:06, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▎      | 360/8000 [00:23<08:52, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5370/8000 [05:07<03:30, 12.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 364/8000 [00:23<09:05, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5374/8000 [05:07<02:52, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 367/8000 [00:23<07:59, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5379/8000 [05:07<02:10, 20.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 369/8000 [00:23<08:16, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5384/8000 [05:08<02:58, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 371/8000 [00:23<08:39, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5387/8000 [05:08<02:44, 15.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 374/8000 [00:24<07:48, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5390/8000 [05:08<02:33, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 376/8000 [00:24<11:02, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5393/8000 [05:08<02:22, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 379/8000 [00:24<09:43, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5397/8000 [05:08<02:11, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 383/8000 [00:24<07:51, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5401/8000 [05:09<01:56, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 387/8000 [00:24<06:53, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5406/8000 [05:09<02:14, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 390/8000 [00:25<06:33, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5409/8000 [05:09<02:38, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 394/8000 [00:25<05:55, 21.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5412/8000 [05:09<02:52, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 399/8000 [00:25<05:15, 24.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5415/8000 [05:10<03:12, 13.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 402/8000 [00:25<05:37, 22.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5418/8000 [05:10<03:23, 12.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 405/8000 [00:25<05:34, 22.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5421/8000 [05:10<03:36, 11.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 409/8000 [00:25<06:49, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5424/8000 [05:11<03:41, 11.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 412/8000 [00:26<06:38, 19.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5426/8000 [05:11<03:39, 11.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 415/8000 [00:26<09:08, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5430/8000 [05:11<02:51, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 417/8000 [00:26<09:17, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5433/8000 [05:11<02:31, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 421/8000 [00:26<07:32, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5435/8000 [05:11<02:34, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▎      | 427/8000 [00:27<07:02, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5437/8000 [05:11<02:37, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▍      | 431/8000 [00:27<07:56, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5441/8000 [05:11<02:11, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▍      | 433/8000 [00:27<08:10, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5447/8000 [05:12<02:03, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▍      | 437/8000 [00:27<07:07, 17.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5450/8000 [05:12<02:06, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 440/8000 [00:27<06:37, 19.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5455/8000 [05:12<01:47, 23.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 443/8000 [00:27<06:23, 19.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5459/8000 [05:12<02:10, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 446/8000 [00:28<06:32, 19.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5462/8000 [05:13<04:58,  8.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 449/8000 [00:28<06:36, 19.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5465/8000 [05:13<04:13,  9.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 455/8000 [00:28<07:50, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5469/8000 [05:14<03:18, 12.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 459/8000 [00:28<06:41, 18.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5472/8000 [05:14<02:52, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 465/8000 [00:29<06:41, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5480/8000 [05:14<02:11, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 470/8000 [00:29<07:07, 17.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5485/8000 [05:14<02:19, 17.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 472/8000 [00:29<07:24, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5489/8000 [05:14<02:09, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 479/8000 [00:29<05:10, 24.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5492/8000 [05:15<02:03, 20.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 483/8000 [00:30<06:24, 19.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5497/8000 [05:15<02:53, 14.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 491/8000 [00:30<05:45, 21.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5500/8000 [05:15<03:27, 12.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 494/8000 [00:30<07:53, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5504/8000 [05:16<03:27, 12.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 496/8000 [00:30<08:04, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5507/8000 [05:16<03:04, 13.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 498/8000 [00:31<07:55, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5509/8000 [05:16<03:00, 13.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 501/8000 [00:31<08:31, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5511/8000 [05:16<02:59, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 505/8000 [00:31<07:15, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5513/8000 [05:16<02:54, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 507/8000 [00:31<07:39, 16.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5516/8000 [05:17<02:37, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 511/8000 [00:31<06:31, 19.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5520/8000 [05:17<02:08, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 515/8000 [00:32<08:08, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5524/8000 [05:17<03:05, 13.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 517/8000 [00:32<08:26, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5527/8000 [05:17<02:39, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|▍      | 519/8000 [00:32<08:43, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5532/8000 [05:18<02:31, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 522/8000 [00:32<08:09, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5535/8000 [05:18<02:58, 13.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 524/8000 [00:32<11:04, 11.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5539/8000 [05:18<02:51, 14.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 529/8000 [00:33<08:15, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5541/8000 [05:18<02:51, 14.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 532/8000 [00:33<10:36, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5543/8000 [05:18<02:49, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 538/8000 [00:33<07:48, 15.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5545/8000 [05:18<02:46, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 542/8000 [00:33<07:33, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5548/8000 [05:19<02:28, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 545/8000 [00:34<09:44, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5552/8000 [05:19<02:06, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 552/8000 [00:34<07:56, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5558/8000 [05:19<02:01, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 554/8000 [00:35<10:19, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5562/8000 [05:19<02:15, 18.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 557/8000 [00:35<09:15, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5567/8000 [05:19<01:56, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 561/8000 [00:35<08:48, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5573/8000 [05:20<01:33, 25.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 564/8000 [00:35<09:23, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5577/8000 [05:20<01:53, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 567/8000 [00:35<09:43, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5580/8000 [05:20<02:15, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▍      | 570/8000 [00:36<09:21, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5583/8000 [05:20<02:08, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 574/8000 [00:36<08:08, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5586/8000 [05:21<02:32, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 577/8000 [00:36<07:38, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5588/8000 [05:21<03:04, 13.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 580/8000 [00:36<07:36, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5590/8000 [05:21<03:00, 13.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 582/8000 [00:36<08:04, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5594/8000 [05:21<02:28, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 584/8000 [00:37<13:23,  9.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5597/8000 [05:21<02:24, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 587/8000 [00:37<12:49,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5599/8000 [05:21<02:24, 16.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 589/8000 [00:37<11:57, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5602/8000 [05:22<02:51, 14.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 592/8000 [00:37<09:48, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5606/8000 [05:22<02:18, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 594/8000 [00:38<09:30, 12.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5609/8000 [05:22<02:36, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|▌      | 599/8000 [00:38<06:45, 18.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5612/8000 [05:22<02:18, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 602/8000 [00:38<06:27, 19.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5618/8000 [05:23<02:03, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 605/8000 [00:38<06:05, 20.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5621/8000 [05:23<02:03, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 608/8000 [00:38<07:37, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5625/8000 [05:23<02:17, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 612/8000 [00:39<10:04, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5628/8000 [05:23<02:13, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 616/8000 [00:39<11:42, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5630/8000 [05:23<02:15, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 619/8000 [00:39<10:10, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5635/8000 [05:23<01:50, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 623/8000 [00:40<08:05, 15.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5639/8000 [05:24<02:28, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 627/8000 [00:40<08:05, 15.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5645/8000 [05:24<01:45, 22.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 629/8000 [00:40<10:03, 12.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5651/8000 [05:24<02:02, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 631/8000 [00:40<09:42, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5654/8000 [05:24<02:15, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 633/8000 [00:40<09:29, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5658/8000 [05:25<02:04, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 636/8000 [00:41<08:39, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5661/8000 [05:25<02:22, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 638/8000 [00:41<08:32, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5666/8000 [05:25<02:15, 17.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 644/8000 [00:41<07:20, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5669/8000 [05:25<02:09, 17.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 646/8000 [00:41<07:32, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5675/8000 [05:26<01:58, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 650/8000 [00:42<10:04, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5679/8000 [05:26<02:13, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 655/8000 [00:42<07:37, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5682/8000 [05:26<02:05, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 657/8000 [00:42<08:15, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5688/8000 [05:26<01:55, 19.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 661/8000 [00:42<06:56, 17.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5695/8000 [05:26<01:29, 25.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 668/8000 [00:42<05:02, 24.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5698/8000 [05:27<02:31, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 671/8000 [00:42<05:17, 23.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5702/8000 [05:27<02:34, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 675/8000 [00:43<06:17, 19.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5705/8000 [05:27<02:22, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|▌      | 680/8000 [00:43<05:21, 22.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5711/8000 [05:28<01:46, 21.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 684/8000 [00:43<06:24, 19.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5714/8000 [05:28<01:49, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 687/8000 [00:43<06:23, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5718/8000 [05:28<01:42, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 690/8000 [00:43<06:36, 18.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5722/8000 [05:28<01:35, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 692/8000 [00:44<07:21, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5726/8000 [05:28<01:30, 25.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 695/8000 [00:44<09:05, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5732/8000 [05:29<01:57, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 698/8000 [00:44<08:18, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5735/8000 [05:29<01:55, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 703/8000 [00:44<06:21, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5738/8000 [05:29<02:23, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 706/8000 [00:44<06:41, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5743/8000 [05:29<01:59, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 710/8000 [00:45<07:16, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5746/8000 [05:29<01:54, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▌      | 713/8000 [00:45<06:47, 17.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5749/8000 [05:30<02:20, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 717/8000 [00:45<08:09, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5752/8000 [05:30<02:43, 13.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 719/8000 [00:45<08:13, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5755/8000 [05:30<02:55, 12.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 723/8000 [00:45<06:44, 17.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5762/8000 [05:30<01:51, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 729/8000 [00:46<06:44, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5766/8000 [05:31<02:02, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 733/8000 [00:46<07:45, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5769/8000 [05:31<02:21, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 735/8000 [00:46<07:54, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5772/8000 [05:31<02:16, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 737/8000 [00:46<08:28, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5776/8000 [05:31<02:03, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 739/8000 [00:47<08:29, 14.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5783/8000 [05:31<01:26, 25.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 743/8000 [00:47<09:00, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5787/8000 [05:32<01:41, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 746/8000 [00:47<08:18, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5790/8000 [05:32<02:04, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 748/8000 [00:47<08:28, 14.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5794/8000 [05:32<02:14, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|▋      | 754/8000 [00:47<06:02, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5799/8000 [05:32<01:51, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 760/8000 [00:48<04:57, 24.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5806/8000 [05:33<01:38, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 763/8000 [00:48<06:53, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5810/8000 [05:33<01:53, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 768/8000 [00:48<05:45, 20.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5813/8000 [05:33<02:55, 12.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 774/8000 [00:48<06:05, 19.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5815/8000 [05:34<02:49, 12.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 778/8000 [00:49<06:57, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5818/8000 [05:34<03:25, 10.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 780/8000 [00:49<07:15, 16.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5821/8000 [05:34<02:54, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 783/8000 [00:49<07:10, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5828/8000 [05:34<01:51, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 787/8000 [00:49<06:32, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5831/8000 [05:34<01:47, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 792/8000 [00:50<06:48, 17.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5835/8000 [05:34<01:34, 22.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 794/8000 [00:50<07:18, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5841/8000 [05:35<01:39, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 799/8000 [00:50<07:19, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5847/8000 [05:35<01:37, 21.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 804/8000 [00:50<07:02, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5852/8000 [05:35<01:29, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 808/8000 [00:51<07:17, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5855/8000 [05:35<01:30, 23.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 810/8000 [00:51<07:41, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5858/8000 [05:35<01:36, 22.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 812/8000 [00:51<07:59, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5861/8000 [05:36<01:36, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 817/8000 [00:51<06:09, 19.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5864/8000 [05:36<01:34, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 821/8000 [00:51<05:48, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5868/8000 [05:36<01:50, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 825/8000 [00:51<06:36, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5872/8000 [05:36<01:38, 21.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 827/8000 [00:52<07:06, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5876/8000 [05:36<01:31, 23.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 830/8000 [00:52<06:43, 17.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5879/8000 [05:36<01:31, 23.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 833/8000 [00:52<06:18, 18.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5886/8000 [05:37<01:29, 23.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 835/8000 [00:52<08:53, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5890/8000 [05:37<01:40, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|▋      | 840/8000 [00:53<08:20, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5893/8000 [05:37<01:37, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▋      | 842/8000 [00:53<08:36, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5896/8000 [05:37<01:37, 21.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▋      | 846/8000 [00:53<07:07, 16.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5901/8000 [05:37<01:23, 25.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▋      | 850/8000 [00:53<06:10, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5905/8000 [05:38<01:35, 21.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 858/8000 [00:53<06:29, 18.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5910/8000 [05:38<01:41, 20.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 862/8000 [00:54<06:11, 19.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5913/8000 [05:38<02:12, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 865/8000 [00:54<05:51, 20.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5917/8000 [05:38<01:53, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 868/8000 [00:54<05:57, 19.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5920/8000 [05:38<01:49, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 874/8000 [00:54<06:22, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5923/8000 [05:39<01:44, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 876/8000 [00:54<06:55, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5927/8000 [05:39<01:58, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 880/8000 [00:55<06:00, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5930/8000 [05:39<01:53, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 884/8000 [00:55<07:02, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5937/8000 [05:39<01:19, 25.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 887/8000 [00:55<06:47, 17.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5941/8000 [05:39<01:18, 26.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 893/8000 [00:55<05:03, 23.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5944/8000 [05:40<02:33, 13.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 899/8000 [00:55<05:15, 22.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5950/8000 [05:40<02:09, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 902/8000 [00:56<07:52, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5953/8000 [05:40<02:19, 14.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 906/8000 [00:56<08:22, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5956/8000 [05:41<02:04, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 908/8000 [00:56<08:36, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5959/8000 [05:41<01:51, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 911/8000 [00:57<07:52, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5964/8000 [05:41<01:52, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 916/8000 [00:57<07:47, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5968/8000 [05:41<01:52, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|▊      | 919/8000 [00:57<07:30, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5972/8000 [05:41<01:38, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 921/8000 [00:57<07:54, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5977/8000 [05:42<01:41, 19.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 926/8000 [00:57<06:07, 19.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5980/8000 [05:42<01:35, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 931/8000 [00:58<08:38, 13.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5984/8000 [05:42<01:24, 23.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 935/8000 [00:58<07:37, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5989/8000 [05:42<01:26, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 937/8000 [00:58<08:08, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5995/8000 [05:42<01:10, 28.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 940/8000 [00:59<09:20, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6001/8000 [05:42<01:14, 26.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 947/8000 [00:59<08:46, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6006/8000 [05:43<01:19, 24.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 950/8000 [00:59<08:20, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6011/8000 [05:43<01:41, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 954/8000 [01:00<08:44, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6016/8000 [05:43<01:24, 23.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 959/8000 [01:00<08:12, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6021/8000 [05:43<01:28, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 965/8000 [01:00<07:25, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6025/8000 [05:44<01:22, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 968/8000 [01:00<07:06, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6030/8000 [05:44<01:26, 22.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 971/8000 [01:00<07:06, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6034/8000 [05:44<01:18, 24.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 976/8000 [01:01<05:53, 19.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6037/8000 [05:44<01:19, 24.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 980/8000 [01:01<06:38, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6043/8000 [05:44<01:21, 23.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 986/8000 [01:01<05:42, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6048/8000 [05:45<01:29, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 990/8000 [01:01<06:22, 18.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6053/8000 [05:45<01:59, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 992/8000 [01:02<06:36, 17.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6057/8000 [05:45<01:46, 18.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊      | 994/8000 [01:02<06:59, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6061/8000 [05:45<01:32, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|▊     | 1000/8000 [01:02<05:16, 22.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6064/8000 [05:45<01:30, 21.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1003/8000 [01:02<05:40, 20.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6067/8000 [05:46<01:27, 22.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1007/8000 [01:02<05:12, 22.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6073/8000 [05:46<01:40, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1011/8000 [01:03<07:06, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6079/8000 [05:46<01:19, 24.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1013/8000 [01:03<07:25, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6083/8000 [05:46<01:27, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1015/8000 [01:03<07:53, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6091/8000 [05:47<01:39, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1018/8000 [01:03<07:04, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6095/8000 [05:47<01:43, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1021/8000 [01:03<07:20, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6102/8000 [05:47<01:19, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1024/8000 [01:03<06:47, 17.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6106/8000 [05:47<01:14, 25.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1027/8000 [01:04<06:29, 17.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6111/8000 [05:47<01:06, 28.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1031/8000 [01:04<07:11, 16.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6115/8000 [05:48<01:18, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1034/8000 [01:04<06:53, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|█████████████    | 6118/8000 [05:48<01:17, 24.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1039/8000 [01:04<05:30, 21.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6122/8000 [05:48<01:26, 21.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1044/8000 [01:05<07:08, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6125/8000 [05:48<01:22, 22.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1046/8000 [01:05<07:37, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6128/8000 [05:48<01:21, 23.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1049/8000 [01:05<06:58, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6135/8000 [05:49<01:28, 20.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1053/8000 [01:05<05:59, 19.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6140/8000 [05:49<01:19, 23.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1057/8000 [01:05<05:36, 20.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6143/8000 [05:49<01:35, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1060/8000 [01:05<05:38, 20.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6149/8000 [05:49<01:28, 20.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1065/8000 [01:06<05:05, 22.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6152/8000 [05:50<01:39, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1070/8000 [01:06<05:41, 20.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6158/8000 [05:50<01:30, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|▊     | 1075/8000 [01:06<04:56, 23.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6163/8000 [05:50<01:16, 23.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1083/8000 [01:06<05:39, 20.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6171/8000 [05:50<01:24, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1088/8000 [01:07<05:04, 22.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6175/8000 [05:51<01:57, 15.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1092/8000 [01:07<05:48, 19.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6179/8000 [05:51<01:43, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1095/8000 [01:07<05:37, 20.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6185/8000 [05:51<01:21, 22.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1098/8000 [01:07<05:57, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6188/8000 [05:51<01:21, 22.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1102/8000 [01:07<05:29, 20.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6194/8000 [05:52<01:34, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1105/8000 [01:08<07:11, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6199/8000 [05:52<01:36, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1109/8000 [01:08<07:37, 15.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6202/8000 [05:52<01:31, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1111/8000 [01:08<09:50, 11.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6205/8000 [05:52<01:30, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1114/8000 [01:08<08:34, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6209/8000 [05:52<01:20, 22.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1117/8000 [01:09<08:02, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6212/8000 [05:53<01:33, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1120/8000 [01:09<07:53, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6215/8000 [05:53<01:49, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1125/8000 [01:10<11:43,  9.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6218/8000 [05:53<01:39, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1128/8000 [01:10<10:13, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6224/8000 [05:53<01:42, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1131/8000 [01:10<11:07, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6227/8000 [05:54<01:52, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1135/8000 [01:10<08:43, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6229/8000 [05:54<01:48, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1139/8000 [01:11<08:42, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6232/8000 [05:54<01:59, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1142/8000 [01:11<08:24, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6236/8000 [05:54<02:00, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1146/8000 [01:11<07:45, 14.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6239/8000 [05:54<02:04, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1154/8000 [01:11<06:14, 18.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6241/8000 [05:55<02:21, 12.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|▊     | 1157/8000 [01:11<06:28, 17.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6244/8000 [05:55<02:22, 12.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▊     | 1163/8000 [01:12<06:36, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6247/8000 [05:55<02:22, 12.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▊     | 1165/8000 [01:12<06:56, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6251/8000 [05:55<01:51, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1169/8000 [01:12<06:15, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6253/8000 [05:55<01:50, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1171/8000 [01:12<08:17, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6256/8000 [05:55<01:38, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1173/8000 [01:13<08:22, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6259/8000 [05:56<01:29, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1176/8000 [01:13<10:50, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6263/8000 [05:56<01:14, 23.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1180/8000 [01:13<09:55, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6266/8000 [05:56<01:33, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1186/8000 [01:14<07:39, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6269/8000 [05:56<01:49, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1189/8000 [01:14<07:09, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6273/8000 [05:56<01:31, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1191/8000 [01:14<07:18, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6278/8000 [05:57<01:24, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1196/8000 [01:14<08:48, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6283/8000 [05:57<01:23, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1201/8000 [01:15<08:00, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6291/8000 [05:57<01:09, 24.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1203/8000 [01:15<09:44, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6294/8000 [05:57<01:24, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1207/8000 [01:15<08:03, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6300/8000 [05:58<01:29, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1212/8000 [01:15<06:20, 17.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6304/8000 [05:58<01:31, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1215/8000 [01:15<06:01, 18.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6307/8000 [05:58<01:26, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1219/8000 [01:16<08:18, 13.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6311/8000 [05:58<01:17, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1222/8000 [01:16<07:51, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6314/8000 [05:58<01:15, 22.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1224/8000 [01:16<08:12, 13.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6320/8000 [05:59<01:11, 23.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1228/8000 [01:16<07:02, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6323/8000 [05:59<01:38, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1232/8000 [01:17<07:36, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6325/8000 [05:59<01:54, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1235/8000 [01:17<07:10, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6327/8000 [05:59<01:51, 14.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|▉     | 1239/8000 [01:17<06:32, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6330/8000 [05:59<02:02, 13.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1241/8000 [01:17<07:11, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6332/8000 [06:00<01:56, 14.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1244/8000 [01:18<08:32, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6334/8000 [06:00<01:52, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1247/8000 [01:18<07:45, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6340/8000 [06:00<01:16, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1249/8000 [01:18<07:58, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6343/8000 [06:00<01:29, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1251/8000 [01:18<10:24, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6347/8000 [06:00<01:15, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1253/8000 [01:18<10:16, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6355/8000 [06:00<01:03, 25.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1255/8000 [01:19<09:50, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6359/8000 [06:01<00:57, 28.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1260/8000 [01:19<06:44, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6363/8000 [06:01<01:21, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1262/8000 [01:19<08:45, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6370/8000 [06:01<01:22, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1267/8000 [01:19<06:55, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6373/8000 [06:01<01:19, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1273/8000 [01:20<06:48, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6376/8000 [06:02<01:32, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1276/8000 [01:20<08:42, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6379/8000 [06:02<01:26, 18.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1282/8000 [01:20<06:46, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6387/8000 [06:02<01:09, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1286/8000 [01:20<06:19, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6391/8000 [06:02<01:06, 24.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1288/8000 [01:21<07:30, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6394/8000 [06:02<01:23, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1291/8000 [01:21<08:46, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6398/8000 [06:03<01:12, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1295/8000 [01:21<07:40, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6402/8000 [06:03<02:04, 12.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1297/8000 [01:21<07:48, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6405/8000 [06:03<02:02, 13.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1299/8000 [01:21<07:59, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6407/8000 [06:04<01:58, 13.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1303/8000 [01:22<06:26, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6410/8000 [06:04<01:40, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1305/8000 [01:22<06:42, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6413/8000 [06:04<01:30, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1311/8000 [01:22<06:25, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6416/8000 [06:04<01:40, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1314/8000 [01:22<06:39, 16.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6418/8000 [06:04<01:40, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|▉     | 1317/8000 [01:23<08:47, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6420/8000 [06:04<01:46, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|▉     | 1321/8000 [01:23<06:58, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6426/8000 [06:04<01:11, 22.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|▉     | 1325/8000 [01:23<05:57, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6430/8000 [06:05<01:19, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|▉     | 1331/8000 [01:23<06:00, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6433/8000 [06:05<01:15, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1334/8000 [01:24<08:45, 12.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6439/8000 [06:05<00:57, 27.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1336/8000 [01:24<10:54, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6446/8000 [06:05<01:08, 22.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1339/8000 [01:24<09:14, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6450/8000 [06:06<01:14, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1342/8000 [01:24<08:17, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6455/8000 [06:06<01:01, 25.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1344/8000 [01:25<08:29, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6459/8000 [06:06<01:08, 22.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1346/8000 [01:25<08:49, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6463/8000 [06:06<01:18, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1350/8000 [01:25<07:02, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6469/8000 [06:06<01:01, 24.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1353/8000 [01:25<07:32, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6472/8000 [06:07<01:11, 21.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1355/8000 [01:25<10:43, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6476/8000 [06:07<01:04, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1357/8000 [01:26<10:21, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6481/8000 [06:07<01:07, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1360/8000 [01:26<08:57, 12.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6484/8000 [06:07<01:36, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1364/8000 [01:26<07:36, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6490/8000 [06:08<01:22, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1368/8000 [01:26<08:43, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6493/8000 [06:08<01:29, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1371/8000 [01:27<07:51, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6496/8000 [06:08<01:23, 18.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1374/8000 [01:27<07:17, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6501/8000 [06:08<01:17, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1377/8000 [01:27<06:51, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6505/8000 [06:08<01:20, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1381/8000 [01:27<08:00, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6509/8000 [06:08<01:09, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1384/8000 [01:28<09:42, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6513/8000 [06:09<01:24, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1387/8000 [01:28<10:44, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6516/8000 [06:09<01:32, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1390/8000 [01:28<09:47, 11.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6519/8000 [06:09<01:22, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█     | 1396/8000 [01:29<08:08, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6523/8000 [06:09<01:25, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1400/8000 [01:29<07:00, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6525/8000 [06:09<01:26, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1403/8000 [01:29<06:36, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6530/8000 [06:10<01:19, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1406/8000 [01:29<08:11, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6532/8000 [06:10<01:21, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1411/8000 [01:30<07:46, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6535/8000 [06:10<01:33, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1415/8000 [01:30<06:57, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6538/8000 [06:10<01:24, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1419/8000 [01:30<06:48, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6542/8000 [06:10<01:12, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1422/8000 [01:30<08:41, 12.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6545/8000 [06:11<01:22, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1426/8000 [01:30<07:25, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6547/8000 [06:11<01:23, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1428/8000 [01:31<07:37, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6552/8000 [06:11<01:04, 22.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1432/8000 [01:31<06:32, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6556/8000 [06:11<00:57, 25.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1435/8000 [01:31<07:53, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6560/8000 [06:11<00:55, 25.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1437/8000 [01:31<07:44, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6564/8000 [06:11<01:06, 21.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1440/8000 [01:31<07:44, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6569/8000 [06:11<00:54, 26.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1443/8000 [01:32<08:54, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6577/8000 [06:12<00:49, 28.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1445/8000 [01:32<08:37, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6581/8000 [06:12<00:53, 26.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1451/8000 [01:32<07:42, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6587/8000 [06:12<00:54, 26.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1453/8000 [01:32<07:54, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6591/8000 [06:12<00:59, 23.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1456/8000 [01:33<07:17, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6596/8000 [06:12<00:51, 27.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1460/8000 [01:33<06:34, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6603/8000 [06:13<00:50, 27.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1466/8000 [01:33<05:01, 21.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6607/8000 [06:13<00:48, 28.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1469/8000 [01:33<05:06, 21.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6613/8000 [06:13<01:01, 22.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1474/8000 [01:34<08:40, 12.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6619/8000 [06:13<00:51, 26.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█     | 1477/8000 [01:34<09:13, 11.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6625/8000 [06:14<00:51, 26.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█     | 1481/8000 [01:34<07:38, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6628/8000 [06:14<01:12, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█     | 1484/8000 [01:35<11:09,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6632/8000 [06:14<01:04, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█     | 1486/8000 [01:35<10:33, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6639/8000 [06:14<00:58, 23.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█     | 1492/8000 [01:36<09:53, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6644/8000 [06:15<01:00, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█     | 1494/8000 [01:36<11:51,  9.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6647/8000 [06:15<01:09, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█     | 1496/8000 [01:36<11:19,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6652/8000 [06:15<01:00, 22.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1503/8000 [01:36<07:06, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6656/8000 [06:15<00:54, 24.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1507/8000 [01:37<07:36, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6659/8000 [06:16<01:27, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1509/8000 [01:37<07:48, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6663/8000 [06:16<01:26, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1511/8000 [01:37<09:59, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6668/8000 [06:16<01:06, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1515/8000 [01:38<11:07,  9.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6675/8000 [06:16<01:09, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1518/8000 [01:38<09:23, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6678/8000 [06:17<01:17, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1521/8000 [01:38<09:33, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6682/8000 [06:17<01:18, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1523/8000 [01:38<09:26, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6684/8000 [06:17<01:17, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1529/8000 [01:39<07:57, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6691/8000 [06:17<00:54, 24.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1532/8000 [01:39<07:09, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6695/8000 [06:18<01:22, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1537/8000 [01:39<05:48, 18.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6699/8000 [06:18<01:20, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1546/8000 [01:39<06:05, 17.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6703/8000 [06:18<01:08, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1548/8000 [01:40<06:37, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6708/8000 [06:18<01:06, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1552/8000 [01:40<07:23, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6712/8000 [06:18<00:57, 22.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1555/8000 [01:40<08:10, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6715/8000 [06:18<01:07, 18.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|█▏    | 1558/8000 [01:40<07:27, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6719/8000 [06:19<01:00, 21.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1560/8000 [01:41<09:29, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6725/8000 [06:19<00:57, 22.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1563/8000 [01:41<08:09, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6732/8000 [06:19<00:50, 24.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1565/8000 [01:41<08:15, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6736/8000 [06:19<00:55, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1568/8000 [01:41<07:24, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6739/8000 [06:19<00:53, 23.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1571/8000 [01:41<07:18, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6742/8000 [06:20<01:05, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1573/8000 [01:42<09:39, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6745/8000 [06:20<01:17, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1576/8000 [01:42<08:24, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6749/8000 [06:20<01:05, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1581/8000 [01:42<07:58, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6752/8000 [06:20<01:13, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1584/8000 [01:43<09:20, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6754/8000 [06:20<01:15, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1586/8000 [01:43<09:06, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6757/8000 [06:21<01:07, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1588/8000 [01:43<09:18, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6760/8000 [06:21<01:32, 13.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1591/8000 [01:43<07:41, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6764/8000 [06:21<01:12, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1595/8000 [01:43<06:34, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6770/8000 [06:21<01:03, 19.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1599/8000 [01:44<08:55, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6774/8000 [06:22<01:16, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1601/8000 [01:44<09:00, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6778/8000 [06:22<01:17, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1603/8000 [01:44<09:00, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6782/8000 [06:22<01:05, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1606/8000 [01:44<08:24, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6785/8000 [06:22<01:13, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1610/8000 [01:44<07:18, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6788/8000 [06:22<01:09, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1613/8000 [01:45<08:53, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6791/8000 [06:23<01:02, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1616/8000 [01:45<08:02, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6799/8000 [06:23<00:51, 23.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1619/8000 [01:45<07:36, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6802/8000 [06:23<00:52, 22.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1621/8000 [01:45<07:43, 13.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6812/8000 [06:23<00:40, 29.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1627/8000 [01:46<06:46, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6815/8000 [06:23<00:41, 28.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1633/8000 [01:46<05:27, 19.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6820/8000 [06:23<00:36, 32.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1635/8000 [01:46<06:02, 17.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6825/8000 [06:24<00:33, 34.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|█▏    | 1637/8000 [01:46<06:55, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6832/8000 [06:24<00:44, 26.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1641/8000 [01:47<08:07, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6836/8000 [06:24<00:51, 22.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1648/8000 [01:47<07:42, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6840/8000 [06:24<00:57, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1650/8000 [01:47<07:44, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6845/8000 [06:25<00:57, 19.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1653/8000 [01:47<07:31, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6849/8000 [06:25<01:01, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1655/8000 [01:48<07:43, 13.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6854/8000 [06:25<01:12, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1658/8000 [01:48<07:15, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6856/8000 [06:26<01:12, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1662/8000 [01:48<06:20, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6864/8000 [06:26<01:05, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▏    | 1666/8000 [01:48<05:39, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6866/8000 [06:26<01:05, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1668/8000 [01:48<08:05, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6868/8000 [06:26<01:06, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1670/8000 [01:49<08:26, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6871/8000 [06:26<01:00, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1674/8000 [01:49<08:29, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6874/8000 [06:26<00:56, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1679/8000 [01:49<06:46, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6877/8000 [06:27<00:53, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1684/8000 [01:50<07:01, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6881/8000 [06:27<00:46, 24.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1686/8000 [01:50<07:16, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6884/8000 [06:27<00:46, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1689/8000 [01:50<06:59, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6887/8000 [06:27<00:58, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1692/8000 [01:50<06:26, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6891/8000 [06:27<01:14, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1695/8000 [01:50<07:48, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6895/8000 [06:28<01:00, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1699/8000 [01:50<06:29, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6903/8000 [06:28<00:47, 23.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1702/8000 [01:51<06:18, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6907/8000 [06:28<00:43, 25.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1704/8000 [01:51<08:41, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6910/8000 [06:28<00:44, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1706/8000 [01:51<08:15, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6913/8000 [06:28<00:43, 25.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1710/8000 [01:51<06:52, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6918/8000 [06:29<00:59, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1712/8000 [01:51<07:21, 14.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6923/8000 [06:29<00:50, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1714/8000 [01:52<09:40, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6927/8000 [06:29<00:48, 21.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|█▎    | 1718/8000 [01:52<07:11, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6930/8000 [06:29<01:04, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1720/8000 [01:52<09:51, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6935/8000 [06:30<01:07, 15.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1725/8000 [01:52<07:12, 14.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6939/8000 [06:30<01:00, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1730/8000 [01:53<08:23, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6942/8000 [06:30<00:56, 18.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1733/8000 [01:53<07:54, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6945/8000 [06:30<01:02, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1735/8000 [01:53<08:27, 12.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6948/8000 [06:30<00:59, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1739/8000 [01:54<07:16, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6950/8000 [06:30<01:02, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1743/8000 [01:54<07:20, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6955/8000 [06:30<00:47, 22.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1746/8000 [01:54<06:59, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6962/8000 [06:31<00:42, 24.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1748/8000 [01:54<07:22, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6968/8000 [06:31<00:34, 30.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1753/8000 [01:55<07:34, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6973/8000 [06:31<00:38, 26.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1758/8000 [01:55<06:18, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6982/8000 [06:31<00:33, 30.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1760/8000 [01:55<06:46, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6988/8000 [06:32<00:47, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1764/8000 [01:55<05:59, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6993/8000 [06:32<00:41, 24.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1770/8000 [01:55<04:54, 21.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6997/8000 [06:32<00:48, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1775/8000 [01:55<04:23, 23.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7000/8000 [06:33<00:56, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1778/8000 [01:56<04:37, 22.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7004/8000 [06:33<00:49, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1782/8000 [01:56<04:36, 22.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7008/8000 [06:33<00:44, 22.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1785/8000 [01:56<04:56, 20.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7012/8000 [06:33<00:40, 24.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1790/8000 [01:56<05:46, 17.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7016/8000 [06:33<00:37, 25.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1792/8000 [01:57<06:45, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7020/8000 [06:33<00:35, 27.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|█▎    | 1794/8000 [01:57<07:21, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7024/8000 [06:33<00:33, 29.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1801/8000 [01:57<04:58, 20.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7029/8000 [06:33<00:31, 30.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1804/8000 [01:57<07:01, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7034/8000 [06:34<00:37, 25.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1807/8000 [01:58<07:09, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7037/8000 [06:34<00:37, 25.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1809/8000 [01:58<07:26, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7043/8000 [06:34<00:37, 25.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1811/8000 [01:58<07:31, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7048/8000 [06:34<00:32, 29.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1814/8000 [01:58<06:44, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7052/8000 [06:34<00:32, 29.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1818/8000 [01:58<06:18, 16.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7058/8000 [06:35<00:34, 27.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1822/8000 [01:58<05:39, 18.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7062/8000 [06:35<00:33, 28.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1828/8000 [01:59<06:35, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7065/8000 [06:35<00:33, 27.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▎    | 1832/8000 [01:59<05:52, 17.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7071/8000 [06:35<00:35, 25.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1837/8000 [01:59<05:12, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7076/8000 [06:35<00:38, 23.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1840/8000 [02:00<06:55, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7079/8000 [06:35<00:37, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1842/8000 [02:00<07:01, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7087/8000 [06:36<00:35, 25.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1848/8000 [02:00<05:12, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7091/8000 [06:36<00:42, 21.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1853/8000 [02:00<05:47, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7095/8000 [06:36<00:38, 23.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1857/8000 [02:01<07:06, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7100/8000 [06:36<00:42, 21.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1861/8000 [02:01<06:19, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7106/8000 [06:37<00:36, 24.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1863/8000 [02:01<06:28, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7109/8000 [06:37<00:47, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1866/8000 [02:01<06:09, 16.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7114/8000 [06:37<00:39, 22.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1870/8000 [02:01<07:01, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7117/8000 [06:37<00:39, 22.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1872/8000 [02:02<09:17, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7120/8000 [06:37<00:38, 22.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1876/8000 [02:02<07:23, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7125/8000 [06:38<00:42, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|█▍    | 1878/8000 [02:02<07:23, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7129/8000 [06:38<00:51, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1887/8000 [02:02<04:28, 22.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7132/8000 [06:38<01:06, 12.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1890/8000 [02:03<04:48, 21.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7136/8000 [06:38<00:56, 15.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1896/8000 [02:03<03:53, 26.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7139/8000 [06:39<00:52, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1901/8000 [02:03<04:46, 21.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7141/8000 [06:39<00:52, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1906/8000 [02:03<05:45, 17.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7143/8000 [06:39<00:52, 16.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1911/8000 [02:04<06:32, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7148/8000 [06:39<00:38, 22.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1913/8000 [02:04<06:52, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7152/8000 [06:39<00:44, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1917/8000 [02:04<07:41, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7158/8000 [06:39<00:33, 25.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1921/8000 [02:05<06:51, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7163/8000 [06:40<00:37, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1924/8000 [02:05<06:44, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7166/8000 [06:40<00:46, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1929/8000 [02:05<05:42, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7172/8000 [06:40<00:41, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1932/8000 [02:05<05:47, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7175/8000 [06:40<00:47, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1935/8000 [02:05<07:02, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7178/8000 [06:41<00:43, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1940/8000 [02:06<07:03, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7183/8000 [06:41<00:43, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1943/8000 [02:06<08:27, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7187/8000 [06:41<00:41, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1949/8000 [02:06<06:21, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7190/8000 [06:41<00:48, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1952/8000 [02:07<06:17, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7192/8000 [06:41<00:48, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|█▍    | 1955/8000 [02:07<06:12, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7198/8000 [06:41<00:34, 23.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1961/8000 [02:07<04:37, 21.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7201/8000 [06:42<00:35, 22.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1964/8000 [02:07<06:38, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7208/8000 [06:42<00:32, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1966/8000 [02:07<07:13, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7212/8000 [06:42<00:36, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1969/8000 [02:08<07:04, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7217/8000 [06:42<00:38, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1971/8000 [02:08<07:23, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7221/8000 [06:43<00:41, 18.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1973/8000 [02:08<10:38,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7225/8000 [06:43<00:38, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1976/8000 [02:08<09:03, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7228/8000 [06:43<00:36, 21.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1980/8000 [02:09<07:12, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7233/8000 [06:43<00:39, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1982/8000 [02:09<09:45, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7236/8000 [06:44<00:46, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1987/8000 [02:09<08:32, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7240/8000 [06:44<00:47, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1991/8000 [02:10<07:18, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7243/8000 [06:44<00:43, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▍    | 1995/8000 [02:10<06:25, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7250/8000 [06:44<00:31, 24.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2000/8000 [02:10<05:15, 19.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7253/8000 [06:44<00:47, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2004/8000 [02:10<06:44, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7257/8000 [06:45<00:49, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2008/8000 [02:10<06:15, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7260/8000 [06:45<00:53, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2011/8000 [02:11<06:02, 16.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7263/8000 [06:45<00:49, 14.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2015/8000 [02:11<05:28, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7269/8000 [06:45<00:43, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2021/8000 [02:11<07:44, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7271/8000 [06:46<00:45, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2024/8000 [02:12<07:47, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7275/8000 [06:46<00:38, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2028/8000 [02:12<06:27, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7280/8000 [06:46<00:36, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2033/8000 [02:12<06:53, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7285/8000 [06:46<00:37, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2035/8000 [02:12<07:29, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7291/8000 [06:46<00:29, 23.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|█▌    | 2038/8000 [02:13<08:48, 11.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7297/8000 [06:47<00:36, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2040/8000 [02:13<08:43, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7300/8000 [06:47<00:34, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2045/8000 [02:13<06:16, 15.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7308/8000 [06:47<00:25, 27.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2049/8000 [02:14<07:15, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7312/8000 [06:48<00:34, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2054/8000 [02:14<06:06, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7315/8000 [06:48<00:33, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2059/8000 [02:14<06:34, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7318/8000 [06:48<00:32, 20.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2062/8000 [02:14<06:13, 15.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7321/8000 [06:48<00:32, 20.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2064/8000 [02:14<07:12, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7326/8000 [06:48<00:34, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2068/8000 [02:15<05:56, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7331/8000 [06:48<00:34, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2071/8000 [02:15<05:48, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7334/8000 [06:49<00:41, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2076/8000 [02:15<04:47, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7337/8000 [06:49<00:40, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2081/8000 [02:15<05:46, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7341/8000 [06:49<00:43, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2087/8000 [02:16<04:55, 19.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7344/8000 [06:50<00:48, 13.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2090/8000 [02:16<05:05, 19.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7348/8000 [06:50<00:41, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2093/8000 [02:16<06:38, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7351/8000 [06:50<00:37, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2095/8000 [02:16<06:58, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7355/8000 [06:50<00:30, 21.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2097/8000 [02:17<10:22,  9.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7360/8000 [06:50<00:31, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2099/8000 [02:17<09:53,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7364/8000 [06:50<00:34, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2102/8000 [02:17<08:11, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7368/8000 [06:51<00:36, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2109/8000 [02:17<06:23, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7370/8000 [06:51<00:37, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2113/8000 [02:18<05:48, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7375/8000 [06:51<00:35, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2116/8000 [02:18<05:54, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7379/8000 [06:51<00:31, 19.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2118/8000 [02:18<06:26, 15.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7382/8000 [06:51<00:30, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█▌    | 2120/8000 [02:18<10:21,  9.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7389/8000 [06:52<00:21, 28.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2122/8000 [02:19<11:55,  8.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7394/8000 [06:52<00:26, 23.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2125/8000 [02:19<09:44, 10.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7397/8000 [06:52<00:25, 23.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2128/8000 [02:19<11:41,  8.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7402/8000 [06:52<00:27, 21.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2131/8000 [02:20<10:07,  9.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7406/8000 [06:53<00:32, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2137/8000 [02:20<06:28, 15.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7409/8000 [06:53<00:30, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2144/8000 [02:20<05:41, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7413/8000 [06:53<00:28, 20.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2148/8000 [02:20<05:45, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7417/8000 [06:53<00:32, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2152/8000 [02:21<07:55, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7422/8000 [06:53<00:31, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2154/8000 [02:21<07:49, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7425/8000 [06:53<00:29, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2159/8000 [02:21<06:04, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7430/8000 [06:54<00:28, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2163/8000 [02:21<05:42, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7433/8000 [06:54<00:27, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▌    | 2165/8000 [02:22<08:02, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7437/8000 [06:54<00:30, 18.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2168/8000 [02:22<08:43, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7441/8000 [06:54<00:25, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2171/8000 [02:22<08:27, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7446/8000 [06:54<00:21, 25.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2174/8000 [02:23<07:50, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7451/8000 [06:55<00:19, 28.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2176/8000 [02:23<08:00, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7455/8000 [06:55<00:18, 29.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2178/8000 [02:23<08:04, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7462/8000 [06:55<00:24, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2180/8000 [02:23<08:04, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7468/8000 [06:55<00:25, 21.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2186/8000 [02:24<07:13, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7473/8000 [06:55<00:21, 24.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2189/8000 [02:24<07:13, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7477/8000 [06:56<00:25, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2192/8000 [02:24<07:52, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7481/8000 [06:56<00:28, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2195/8000 [02:24<07:02, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7484/8000 [06:56<00:28, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█▋    | 2199/8000 [02:24<06:14, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7489/8000 [06:56<00:23, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2203/8000 [02:25<05:44, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7492/8000 [06:57<00:34, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2205/8000 [02:25<08:00, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7494/8000 [06:57<00:33, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2209/8000 [02:25<09:23, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7499/8000 [06:57<00:25, 19.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2213/8000 [02:26<07:55, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7503/8000 [06:57<00:27, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2217/8000 [02:26<06:44, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7506/8000 [06:57<00:26, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2220/8000 [02:26<06:13, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7511/8000 [06:58<00:31, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2222/8000 [02:26<06:46, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7513/8000 [06:58<00:30, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2224/8000 [02:26<06:58, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7516/8000 [06:58<00:30, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2226/8000 [02:26<07:18, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7519/8000 [06:58<00:27, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2228/8000 [02:27<07:46, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7523/8000 [06:58<00:23, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2231/8000 [02:27<07:08, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7527/8000 [06:59<00:26, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2234/8000 [02:27<06:14, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7533/8000 [06:59<00:19, 23.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2239/8000 [02:27<04:54, 19.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7537/8000 [06:59<00:18, 25.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2243/8000 [02:28<07:31, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7540/8000 [06:59<00:23, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2248/8000 [02:28<07:15, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7544/8000 [06:59<00:21, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2253/8000 [02:28<05:45, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7548/8000 [07:00<00:18, 23.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2256/8000 [02:28<06:03, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7552/8000 [07:00<00:18, 24.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2258/8000 [02:29<06:42, 14.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7555/8000 [07:00<00:32, 13.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2260/8000 [02:29<07:22, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7559/8000 [07:00<00:30, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2264/8000 [02:29<07:20, 13.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7561/8000 [07:01<00:35, 12.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2267/8000 [02:29<06:41, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7565/8000 [07:01<00:32, 13.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2273/8000 [02:30<06:18, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7570/8000 [07:01<00:34, 12.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█▋    | 2277/8000 [02:30<05:30, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7572/8000 [07:02<00:32, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2281/8000 [02:30<05:15, 18.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7574/8000 [07:02<00:32, 13.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2285/8000 [02:30<06:00, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7576/8000 [07:02<00:32, 13.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2289/8000 [02:31<07:48, 12.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7578/8000 [07:02<00:32, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2291/8000 [02:31<07:53, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7580/8000 [07:02<00:38, 10.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2293/8000 [02:31<09:27, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7585/8000 [07:03<00:30, 13.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2295/8000 [02:32<11:28,  8.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7587/8000 [07:03<00:29, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2298/8000 [02:32<09:34,  9.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7592/8000 [07:03<00:32, 12.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2304/8000 [02:32<06:11, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7594/8000 [07:03<00:30, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2308/8000 [02:33<09:03, 10.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7599/8000 [07:03<00:25, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2310/8000 [02:33<08:48, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7603/8000 [07:04<00:21, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2314/8000 [02:33<07:18, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7608/8000 [07:04<00:21, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2316/8000 [02:33<07:34, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7611/8000 [07:04<00:20, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2320/8000 [02:34<09:29,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7614/8000 [07:04<00:24, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2328/8000 [02:34<05:33, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7617/8000 [07:04<00:23, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▋    | 2332/8000 [02:35<07:51, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7619/8000 [07:05<00:22, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▊    | 2338/8000 [02:35<06:04, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7622/8000 [07:05<00:21, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▊    | 2343/8000 [02:35<05:18, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7627/8000 [07:05<00:16, 23.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▊    | 2347/8000 [02:35<06:56, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7631/8000 [07:05<00:14, 25.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▊    | 2352/8000 [02:36<05:53, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7634/8000 [07:05<00:20, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▊    | 2355/8000 [02:36<05:58, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7638/8000 [07:06<00:22, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█▊    | 2358/8000 [02:36<06:56, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7640/8000 [07:06<00:24, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2361/8000 [02:36<06:17, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7644/8000 [07:06<00:31, 11.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2364/8000 [02:37<06:27, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7647/8000 [07:06<00:26, 13.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2367/8000 [02:37<05:54, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7650/8000 [07:06<00:22, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2370/8000 [02:37<07:18, 12.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7652/8000 [07:07<00:32, 10.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2373/8000 [02:37<06:55, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7654/8000 [07:07<00:31, 11.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2377/8000 [02:38<07:36, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7656/8000 [07:07<00:34,  9.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2380/8000 [02:38<09:04, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7658/8000 [07:08<00:39,  8.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2382/8000 [02:38<08:39, 10.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7660/8000 [07:08<00:33, 10.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2384/8000 [02:39<10:29,  8.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7664/8000 [07:08<00:24, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2386/8000 [02:39<09:59,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7667/8000 [07:08<00:21, 15.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2390/8000 [02:39<07:21, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7670/8000 [07:09<00:32, 10.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2393/8000 [02:39<06:31, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7675/8000 [07:09<00:21, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2398/8000 [02:39<05:16, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7682/8000 [07:09<00:17, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2402/8000 [02:39<05:29, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7687/8000 [07:09<00:17, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2409/8000 [02:40<05:34, 16.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7690/8000 [07:09<00:16, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2411/8000 [02:40<07:41, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7693/8000 [07:10<00:18, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2414/8000 [02:40<06:59, 13.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7695/8000 [07:10<00:19, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2416/8000 [02:41<07:19, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7698/8000 [07:10<00:18, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2418/8000 [02:41<07:25, 12.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7700/8000 [07:10<00:18, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2422/8000 [02:41<07:35, 12.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7704/8000 [07:10<00:19, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2425/8000 [02:41<07:15, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7707/8000 [07:11<00:17, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2431/8000 [02:42<06:38, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7710/8000 [07:11<00:16, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█▊    | 2435/8000 [02:42<06:00, 15.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7712/8000 [07:11<00:17, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2441/8000 [02:42<05:29, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7714/8000 [07:11<00:18, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2444/8000 [02:42<05:37, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7719/8000 [07:11<00:16, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2446/8000 [02:43<05:49, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7721/8000 [07:11<00:20, 13.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2451/8000 [02:43<05:07, 18.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7723/8000 [07:12<00:19, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2453/8000 [02:43<07:13, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7728/8000 [07:12<00:13, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2455/8000 [02:43<07:08, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7732/8000 [07:12<00:14, 17.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2457/8000 [02:44<07:53, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7736/8000 [07:12<00:13, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2460/8000 [02:44<09:08, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7739/8000 [07:12<00:16, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2464/8000 [02:44<07:02, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7741/8000 [07:13<00:16, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2468/8000 [02:44<06:04, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7745/8000 [07:13<00:13, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2471/8000 [02:44<05:44, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7749/8000 [07:13<00:14, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2473/8000 [02:45<06:36, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7754/8000 [07:13<00:11, 21.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2475/8000 [02:45<07:15, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7757/8000 [07:13<00:13, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2479/8000 [02:46<11:23,  8.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7760/8000 [07:14<00:13, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2481/8000 [02:46<12:10,  7.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7763/8000 [07:14<00:13, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2484/8000 [02:46<09:40,  9.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7766/8000 [07:14<00:16, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2488/8000 [02:46<07:30, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7769/8000 [07:14<00:15, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2492/8000 [02:47<08:06, 11.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7771/8000 [07:14<00:15, 14.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2495/8000 [02:47<09:05, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7775/8000 [07:15<00:15, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▊    | 2497/8000 [02:47<10:04,  9.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7777/8000 [07:15<00:15, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2500/8000 [02:47<08:26, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7780/8000 [07:15<00:13, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2502/8000 [02:48<08:30, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7786/8000 [07:15<00:09, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2507/8000 [02:48<06:13, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7791/8000 [07:15<00:12, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2509/8000 [02:48<08:00, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7795/8000 [07:16<00:10, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2511/8000 [02:48<07:46, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7800/8000 [07:16<00:10, 18.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2514/8000 [02:49<06:53, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7804/8000 [07:16<00:14, 13.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2516/8000 [02:49<07:11, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7807/8000 [07:17<00:13, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█▉    | 2519/8000 [02:49<06:35, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7810/8000 [07:17<00:13, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2523/8000 [02:49<05:26, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7813/8000 [07:17<00:14, 12.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2527/8000 [02:49<04:41, 19.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7815/8000 [07:17<00:14, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2532/8000 [02:49<03:47, 24.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7817/8000 [07:18<00:16, 11.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2536/8000 [02:50<04:47, 19.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7822/8000 [07:18<00:10, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2539/8000 [02:50<04:55, 18.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7825/8000 [07:18<00:10, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2542/8000 [02:50<06:05, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7828/8000 [07:18<00:08, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2544/8000 [02:50<06:26, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7833/8000 [07:18<00:06, 24.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2547/8000 [02:50<05:49, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7836/8000 [07:18<00:07, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2551/8000 [02:51<05:04, 17.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7841/8000 [07:18<00:07, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2555/8000 [02:51<05:00, 18.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7845/8000 [07:19<00:08, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2557/8000 [02:51<05:23, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7850/8000 [07:19<00:06, 22.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2562/8000 [02:51<04:33, 19.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7856/8000 [07:19<00:06, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2566/8000 [02:52<07:17, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7861/8000 [07:19<00:05, 25.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2569/8000 [02:52<07:46, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7868/8000 [07:19<00:04, 31.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2571/8000 [02:52<09:06,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7874/8000 [07:20<00:03, 35.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2574/8000 [02:52<07:38, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7879/8000 [07:20<00:03, 32.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2577/8000 [02:53<07:06, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7883/8000 [07:20<00:04, 24.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2580/8000 [02:53<06:41, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7888/8000 [07:20<00:05, 21.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2584/8000 [02:53<07:24, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7893/8000 [07:20<00:04, 24.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2586/8000 [02:53<07:23, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7897/8000 [07:21<00:04, 24.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2589/8000 [02:54<08:05, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7902/8000 [07:21<00:04, 22.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2591/8000 [02:54<07:42, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7909/8000 [07:21<00:03, 23.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2593/8000 [02:54<07:34, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7912/8000 [07:21<00:04, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2595/8000 [02:54<07:37, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7919/8000 [07:22<00:04, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█▉    | 2599/8000 [02:54<06:05, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7924/8000 [07:22<00:04, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2603/8000 [02:55<05:14, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7929/8000 [07:22<00:03, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2607/8000 [02:55<05:08, 17.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7932/8000 [07:23<00:03, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2609/8000 [02:55<05:34, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7936/8000 [07:23<00:03, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2614/8000 [02:55<04:37, 19.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7939/8000 [07:23<00:03, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2617/8000 [02:56<07:52, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7943/8000 [07:23<00:03, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2622/8000 [02:56<06:13, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7945/8000 [07:23<00:03, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2625/8000 [02:56<05:57, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7948/8000 [07:23<00:02, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2629/8000 [02:56<05:20, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7951/8000 [07:24<00:03, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2631/8000 [02:56<05:37, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7957/8000 [07:24<00:02, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2635/8000 [02:57<05:12, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7962/8000 [07:24<00:01, 23.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2640/8000 [02:57<04:26, 20.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7967/8000 [07:25<00:02, 12.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2646/8000 [02:57<04:32, 19.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7970/8000 [07:25<00:02, 13.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2651/8000 [02:57<05:06, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7975/8000 [07:25<00:01, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2653/8000 [02:58<05:29, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7979/8000 [07:25<00:01, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2658/8000 [02:58<04:37, 19.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7982/8000 [07:25<00:00, 18.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2663/8000 [02:58<06:06, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7987/8000 [07:26<00:00, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█▉    | 2665/8000 [02:58<06:18, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7990/8000 [07:26<00:00, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██    | 2668/8000 [02:59<07:41, 11.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7992/8000 [07:26<00:00, 14.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██    | 2671/8000 [02:59<08:43, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7996/8000 [07:26<00:00, 17.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██    | 2675/8000 [02:59<06:52, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7999/8000 [07:26<00:00, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██    | 2679/8000 [03:00<05:37, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8003it [07:27, 17.90it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  34%|██    | 2682/8000 [03:00<05:39, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  34%|██    | 2688/8000 [03:00<05:29, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2691/8000 [03:00<05:07, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:08<26:47,  8.08s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2694/8000 [03:00<04:58, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<14:11,  4.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2702/8000 [03:01<03:36, 24.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:11<10:03,  3.06s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2707/8000 [03:01<03:33, 24.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<08:05,  2.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2713/8000 [03:01<03:58, 22.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<07:03,  2.17s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2717/8000 [03:01<04:33, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:16<06:35,  2.04s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2720/8000 [03:02<05:37, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<06:04,  1.89s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2722/8000 [03:02<06:17, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:19<05:49,  1.82s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2724/8000 [03:02<06:31, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:21<05:31,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2728/8000 [03:02<05:37, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:22<05:30,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2731/8000 [03:02<05:08, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:24<05:35,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2736/8000 [03:03<04:18, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:26<05:23,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2741/8000 [03:03<04:49, 18.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:27<05:17,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2744/8000 [03:03<04:51, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:29<05:17,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2747/8000 [03:03<04:46, 18.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:31<05:32,  1.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2749/8000 [03:03<05:05, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:33<05:29,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2753/8000 [03:04<04:31, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:35<05:40,  1.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2756/8000 [03:04<06:05, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:37<05:23,  1.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██    | 2758/8000 [03:04<06:34, 13.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:38<05:20,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2762/8000 [03:05<07:29, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:40<05:21,  1.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2765/8000 [03:05<08:15, 10.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:42<05:09,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2771/8000 [03:05<05:46, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:44<05:20,  1.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2774/8000 [03:05<05:34, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:46<05:45,  1.95s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2776/8000 [03:06<07:37, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:48<05:24,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2778/8000 [03:06<07:23, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:49<05:05,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2782/8000 [03:06<07:12, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:51<04:54,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2784/8000 [03:06<07:31, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:52<04:51,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2786/8000 [03:06<07:51, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:54<04:42,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2789/8000 [03:07<06:49, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:55<04:32,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2791/8000 [03:07<09:50,  8.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:57<04:29,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2799/8000 [03:07<06:34, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:59<04:26,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2801/8000 [03:08<08:57,  9.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [01:00<04:20,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2804/8000 [03:08<08:13, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [01:02<04:19,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2806/8000 [03:08<08:00, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [01:03<04:14,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2809/8000 [03:09<07:32, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:05<04:13,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2812/8000 [03:09<08:58,  9.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:06<04:09,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2814/8000 [03:09<08:22, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:08<04:05,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2818/8000 [03:10<08:33, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:09<04:04,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2823/8000 [03:10<06:20, 13.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:11<04:01,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2827/8000 [03:10<06:28, 13.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:12<03:58,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2830/8000 [03:10<05:56, 14.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:13<03:56,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██    | 2833/8000 [03:10<05:50, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:15<03:54,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██▏   | 2837/8000 [03:11<06:57, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:16<03:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2840/8000 [03:11<06:35, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:18<03:52,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2843/8000 [03:11<07:10, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:19<03:49,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2846/8000 [03:12<07:54, 10.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:21<03:49,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2849/8000 [03:12<09:08,  9.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:22<03:48,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2851/8000 [03:12<10:35,  8.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:25<04:32,  1.79s/it]\n",
+      "Generating signals:  36%|██▏   | 2853/8000 [03:13<09:50,  8.71it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  36%|██▏   | 2855/8000 [03:13<08:57,  9.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2860/8000 [03:13<07:48, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2862/8000 [03:13<07:37, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.92it/s]\n",
+      "Generating signals:  36%|██▏   | 2864/8000 [03:14<09:02,  9.47it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  36%|██▏   | 2871/8000 [03:14<06:30, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2874/8000 [03:14<06:03, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2880/8000 [03:14<04:34, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 40it [00:01, 34.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2883/8000 [03:15<08:30, 10.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 78it [00:01, 73.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2885/8000 [03:15<09:29,  8.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 120it [00:01, 120.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2890/8000 [03:16<08:16, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 159it [00:01, 164.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2893/8000 [03:16<09:03,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 195it [00:01, 201.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2895/8000 [03:16<08:42,  9.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 235it [00:02, 242.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2899/8000 [03:17<07:44, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 274it [00:02, 276.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2903/8000 [03:17<06:16, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 314it [00:02, 305.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2908/8000 [03:17<05:33, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 353it [00:02, 325.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2910/8000 [03:17<05:42, 14.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 392it [00:02, 335.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2912/8000 [03:17<05:59, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 430it [00:02, 338.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2914/8000 [03:18<06:20, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 467it [00:02, 346.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██▏   | 2917/8000 [03:18<05:52, 14.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 504it [00:02, 343.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2922/8000 [03:18<05:44, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 540it [00:02, 328.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2926/8000 [03:18<04:58, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 577it [00:03, 339.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2929/8000 [03:18<04:42, 17.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 612it [00:03, 340.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2933/8000 [03:19<04:06, 20.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 647it [00:03, 339.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2939/8000 [03:19<05:47, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 682it [00:03, 342.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2942/8000 [03:19<05:34, 15.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 721it [00:03, 355.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2947/8000 [03:19<04:30, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 758it [00:03, 358.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2950/8000 [03:20<07:01, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 218.51it/s]\n",
+      "Generating signals:  37%|██▏   | 2952/8000 [03:20<07:01, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  37%|██▏   | 2955/8000 [03:20<06:04, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2957/8000 [03:20<06:22, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 143\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2959/8000 [03:21<06:19, 13.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 153\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2966/8000 [03:21<06:04, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:00<00:00, 115\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2971/8000 [03:21<04:59, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 122\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2973/8000 [03:21<05:16, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:00<00:00, 128\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2976/8000 [03:22<05:02, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 12\n",
+      "Generating signals:  37%|██▏   | 2980/8000 [03:22<06:01, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [35:05<00:00, 613.68s/it]\u001b[A\n",
-      "Replicates:  38%|█████████▍               | 3/8 [1:47:42<2:58:04, 2136.96s/it]\n",
-      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.890302808628405\n",
-      "2117: -5.9012635\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  37%|██▏   | 2984/8000 [03:22<05:34, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  37%|██▏   | 2987/8000 [03:23<07:45, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2993/8000 [03:23<05:25, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:22,  7.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2996/8000 [03:23<05:08, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:07,  3.97s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██▏   | 2999/8000 [03:23<05:02, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:16,  2.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3003/8000 [03:23<04:29, 18.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:21,  2.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3007/8000 [03:24<04:12, 19.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:22,  1.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3011/8000 [03:24<05:07, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:44,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3013/8000 [03:24<08:18, 10.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:20,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3015/8000 [03:25<10:12,  8.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:12,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3018/8000 [03:25<10:40,  7.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:15,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3021/8000 [03:26<09:38,  8.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:59,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3027/8000 [03:26<06:31, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:48,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3029/8000 [03:26<08:04, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:40,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3031/8000 [03:26<09:34,  8.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:32,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3034/8000 [03:27<10:02,  8.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:36,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3038/8000 [03:27<07:46, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:31,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3041/8000 [03:27<07:59, 10.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:31,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3045/8000 [03:28<06:23, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:31,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3047/8000 [03:28<06:22, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:38,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3050/8000 [03:28<05:52, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:38,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3052/8000 [03:28<05:51, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:35<04:29,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3055/8000 [03:28<06:52, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<04:28,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3058/8000 [03:28<05:54, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<04:36,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3061/8000 [03:29<05:24, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:44,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3063/8000 [03:29<07:40, 10.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:46,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3067/8000 [03:29<06:12, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:37,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3070/8000 [03:29<05:48, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:32,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3074/8000 [03:30<06:33, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:46<04:36,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3077/8000 [03:30<07:06, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:48<04:31,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██▎   | 3080/8000 [03:30<07:43, 10.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:49<04:29,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3082/8000 [03:31<07:38, 10.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:51<04:22,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3084/8000 [03:31<09:37,  8.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:52<04:17,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3085/8000 [03:31<10:50,  7.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:54<04:08,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3091/8000 [03:31<06:16, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:55<04:08,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3094/8000 [03:32<05:51, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:57<04:14,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3096/8000 [03:32<06:05, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:58<04:10,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3099/8000 [03:32<05:53, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:00<04:23,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3103/8000 [03:32<04:53, 16.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:02<04:27,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3107/8000 [03:32<04:31, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:03<04:16,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3110/8000 [03:32<04:44, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:05<04:08,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3115/8000 [03:33<04:04, 20.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:06<04:04,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3118/8000 [03:33<05:47, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:08<04:24,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3120/8000 [03:33<06:16, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:10<04:27,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3122/8000 [03:33<06:48, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:12<04:19,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3124/8000 [03:34<07:07, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:13<04:10,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3128/8000 [03:34<05:58, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:15<04:04,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3131/8000 [03:34<07:01, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:16<04:00,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3135/8000 [03:34<05:48, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:18<03:55,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3137/8000 [03:35<06:12, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:19<03:52,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3139/8000 [03:35<06:49, 11.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:21<03:47,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3143/8000 [03:35<05:34, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:23<04:19,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3148/8000 [03:35<04:41, 17.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:25<04:48,  1.93s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3154/8000 [03:36<04:31, 17.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:27<04:21,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3156/8000 [03:36<04:49, 16.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:28<04:06,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██▎   | 3159/8000 [03:36<06:12, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:30<04:00,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▎   | 3163/8000 [03:37<07:06, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:31<03:49,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▎   | 3166/8000 [03:37<06:12, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:33<03:45,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3169/8000 [03:37<05:36, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:34<03:43,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3171/8000 [03:37<06:22, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:37<03:59,  1.69s/it]\n",
+      "Generating signals:  40%|██▍   | 3175/8000 [03:37<05:27, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  40%|██▍   | 3177/8000 [03:37<06:01, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3181/8000 [03:38<06:37, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.36it/s]\n",
+      "Generating signals:  40%|██▍   | 3184/8000 [03:38<07:43, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  40%|██▍   | 3188/8000 [03:39<07:49, 10.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3190/8000 [03:39<08:00, 10.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3193/8000 [03:39<08:37,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 39it [00:01, 34.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3198/8000 [03:39<06:11, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 74it [00:01, 69.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3204/8000 [03:40<04:43, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 110it [00:01, 109.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3209/8000 [03:40<04:00, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 151it [00:01, 158.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3214/8000 [03:40<03:55, 20.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 187it [00:01, 196.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3218/8000 [03:40<05:12, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 229it [00:02, 243.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3221/8000 [03:41<05:06, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 271it [00:02, 283.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3225/8000 [03:41<04:35, 17.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 310it [00:02, 307.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3228/8000 [03:41<05:47, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 349it [00:02, 324.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3233/8000 [03:41<04:52, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 387it [00:02, 328.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3235/8000 [03:42<05:20, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 429it [00:02, 351.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██▍   | 3240/8000 [03:42<04:24, 18.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 468it [00:02, 361.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3246/8000 [03:42<04:50, 16.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 515it [00:02, 390.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3248/8000 [03:42<04:57, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 556it [00:02, 386.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3251/8000 [03:42<04:38, 17.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 598it [00:03, 393.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3254/8000 [03:43<05:18, 14.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 639it [00:03, 397.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3257/8000 [03:43<06:23, 12.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 680it [00:03, 383.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3261/8000 [03:43<06:44, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 722it [00:03, 391.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3265/8000 [03:44<06:45, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 225.64it/s]\n",
+      "Generating signals:  41%|██▍   | 3270/8000 [03:44<05:29, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  41%|██▍   | 3273/8000 [03:44<06:54, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3278/8000 [03:45<05:15, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:00, 169\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3281/8000 [03:45<05:03, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00<00:00, 166\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3283/8000 [03:45<05:36, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 196\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3286/8000 [03:45<06:36, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:00<00:00, 178\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3292/8000 [03:45<04:36, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 18\n",
+      "Generating signals:  41%|██▍   | 3295/8000 [03:46<04:36, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  41%|██▍   | 3298/8000 [03:46<05:54, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3301/8000 [03:46<05:31, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎               | 165/8000 [00:00<00:06, 1248.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3303/8000 [03:46<06:07, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋               | 332/8000 [00:00<00:06, 1155.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3305/8000 [03:47<05:50, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█               | 513/8000 [00:00<00:06, 1178.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3308/8000 [03:47<06:45, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍              | 696/8000 [00:00<00:06, 1158.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3310/8000 [03:47<06:49, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▋              | 854/8000 [00:00<00:06, 1144.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3313/8000 [03:47<06:09, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|█▉             | 1028/8000 [00:00<00:05, 1191.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3315/8000 [03:47<06:11, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎            | 1210/8000 [00:01<00:05, 1152.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██▍   | 3319/8000 [03:48<05:01, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▌            | 1383/8000 [00:01<00:05, 1157.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▍   | 3321/8000 [03:48<04:55, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉            | 1551/8000 [00:01<00:05, 1245.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▍   | 3323/8000 [03:48<05:23, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▏           | 1718/8000 [00:01<00:05, 1174.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▍   | 3325/8000 [03:48<05:45, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▌           | 1892/8000 [00:01<00:05, 1171.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▍   | 3328/8000 [03:48<06:59, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|███▉           | 2072/8000 [00:01<00:05, 1150.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▍   | 3332/8000 [03:49<05:34, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▏          | 2253/8000 [00:01<00:04, 1167.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3334/8000 [03:49<07:36, 10.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▌          | 2426/8000 [00:02<00:04, 1168.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3338/8000 [03:49<06:39, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|████▊          | 2598/8000 [00:02<00:04, 1182.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3340/8000 [03:49<07:41, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▏         | 2768/8000 [00:02<00:04, 1217.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3345/8000 [03:50<05:20, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▌         | 2948/8000 [00:02<00:03, 1264.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3348/8000 [03:50<07:37, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|█████▊         | 3130/8000 [00:02<00:04, 1191.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3353/8000 [03:51<06:45, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▏        | 3308/8000 [00:02<00:03, 1195.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3357/8000 [03:51<06:04, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▌        | 3484/8000 [00:02<00:03, 1132.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3360/8000 [03:51<05:52, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|██████▊        | 3645/8000 [00:03<00:03, 1131.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3368/8000 [03:51<04:27, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▏       | 3827/8000 [00:03<00:03, 1120.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3373/8000 [03:52<04:15, 18.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▍       | 3998/8000 [00:03<00:03, 1139.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3377/8000 [03:52<04:51, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|███████▊       | 4159/8000 [00:03<00:03, 1200.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3380/8000 [03:52<04:49, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4349/8000 [00:05<00:11, 317.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3384/8000 [03:52<04:41, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4529/8000 [00:05<00:08, 407.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3388/8000 [03:52<04:17, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4693/8000 [00:05<00:06, 495.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3391/8000 [03:53<04:21, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4873/8000 [00:05<00:05, 614.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3393/8000 [03:53<04:32, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5044/8000 [00:05<00:04, 698.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3396/8000 [03:53<05:51, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5231/8000 [00:05<00:03, 792.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██▌   | 3399/8000 [03:53<06:50, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5398/8000 [00:05<00:03, 855.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3401/8000 [03:54<06:44, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5558/8000 [00:06<00:02, 894.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3405/8000 [03:54<06:42, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5728/8000 [00:06<00:02, 985.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3409/8000 [03:54<07:44,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████    | 5904/8000 [00:06<00:02, 1046.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3411/8000 [03:55<07:16, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|███████████▍   | 6074/8000 [00:06<00:01, 1107.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3416/8000 [03:55<05:22, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|███████████▋   | 6249/8000 [00:06<00:01, 1150.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3418/8000 [03:55<06:59, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████   | 6414/8000 [00:06<00:01, 1161.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3423/8000 [03:55<05:17, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|████████████▎  | 6592/8000 [00:07<00:01, 1066.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3428/8000 [03:56<05:13, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|████████████▋  | 6773/8000 [00:07<00:01, 1152.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3430/8000 [03:56<05:09, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████  | 6948/8000 [00:07<00:00, 1196.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3434/8000 [03:56<04:23, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|█████████████▎ | 7122/8000 [00:07<00:00, 1162.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3436/8000 [03:56<04:41, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|█████████████▋ | 7303/8000 [00:07<00:00, 1198.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3439/8000 [03:56<04:25, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████ | 7486/8000 [00:07<00:00, 1203.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3442/8000 [03:57<05:48, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|██████████████▎| 7651/8000 [00:07<00:00, 1253.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3444/8000 [03:57<06:25, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|██████████████▋| 7834/8000 [00:08<00:00, 1228.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3448/8000 [03:57<05:24, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8015it [00:08, 983.88it/s] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  43%|██▌   | 3452/8000 [03:57<06:21, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  43%|██▌   | 3456/8000 [03:58<05:13, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3459/8000 [03:58<04:53, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:51,  7.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3464/8000 [03:58<05:14, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<13:05,  3.97s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3468/8000 [03:59<05:49, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:16,  2.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3472/8000 [03:59<05:16, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:20,  2.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3474/8000 [03:59<06:06, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:17,  1.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██▌   | 3479/8000 [03:59<04:49, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:38,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▌   | 3484/8000 [03:59<04:21, 17.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:20,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▌   | 3492/8000 [04:00<03:13, 23.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:04,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▌   | 3495/8000 [04:00<06:30, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:53,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▌   | 3498/8000 [04:01<06:35, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3501/8000 [04:01<06:28, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:32,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3505/8000 [04:02<08:29,  8.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:28,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3508/8000 [04:02<09:18,  8.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:21,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3510/8000 [04:02<09:09,  8.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3512/8000 [04:03<08:51,  8.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:20,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3515/8000 [04:03<07:47,  9.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:15,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3520/8000 [04:03<06:44, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:18,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3524/8000 [04:04<06:39, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:14,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3528/8000 [04:04<08:20,  8.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:12,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3530/8000 [04:04<07:56,  9.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:07,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3532/8000 [04:04<07:30,  9.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:06,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3537/8000 [04:05<05:18, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:36<04:06,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3541/8000 [04:05<04:39, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:03,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3543/8000 [04:05<05:04, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<04:14,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3545/8000 [04:05<05:13, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:11,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3547/8000 [04:05<05:19, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<04:08,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3550/8000 [04:05<04:54, 15.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:43<04:02,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3553/8000 [04:06<04:51, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:45<03:58,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3555/8000 [04:06<04:59, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:46<03:54,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██▋   | 3559/8000 [04:06<04:14, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<03:52,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3562/8000 [04:06<05:42, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:49<03:49,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3565/8000 [04:07<06:27, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:50<03:47,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3567/8000 [04:07<06:20, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<03:46,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3570/8000 [04:07<05:43, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<03:46,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3573/8000 [04:07<05:15, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:54<03:44,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3575/8000 [04:07<05:43, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:40,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3577/8000 [04:08<05:42, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3580/8000 [04:08<05:27, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:58<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3583/8000 [04:08<05:14, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:37,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3587/8000 [04:08<05:30, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:35,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3590/8000 [04:08<05:17, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:34,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3599/8000 [04:09<03:12, 22.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:34,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3605/8000 [04:09<03:45, 19.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:31,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3609/8000 [04:09<03:28, 21.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:29,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3612/8000 [04:10<04:27, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:27,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3614/8000 [04:10<04:42, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:25,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3616/8000 [04:10<04:56, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:28,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3623/8000 [04:10<04:14, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:31,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3626/8000 [04:10<04:13, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3630/8000 [04:11<03:48, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3633/8000 [04:11<04:12, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:16<03:29,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3636/8000 [04:11<05:08, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:18<03:29,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|██▋   | 3639/8000 [04:11<05:53, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:32,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3641/8000 [04:12<05:42, 12.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:21<03:30,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3646/8000 [04:12<05:13, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:22<03:25,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3649/8000 [04:12<04:53, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:23<03:23,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3651/8000 [04:12<05:41, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:25<03:35,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3655/8000 [04:12<04:40, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:27<03:44,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3659/8000 [04:13<04:52, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:28<03:36,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3661/8000 [04:13<05:06, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:30<03:29,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▋   | 3664/8000 [04:13<05:25, 13.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:31<03:21,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3669/8000 [04:13<04:11, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:32<03:18,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3671/8000 [04:13<04:17, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:34<03:15,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3675/8000 [04:14<03:47, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:35<03:10,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3680/8000 [04:14<03:20, 21.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:36<03:06,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3685/8000 [04:14<03:13, 22.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:38<03:04,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3692/8000 [04:14<03:18, 21.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:39<03:05,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3695/8000 [04:14<03:18, 21.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:41<03:04,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3698/8000 [04:15<05:50, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:42<03:07,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3700/8000 [04:15<06:07, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:44<03:05,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3703/8000 [04:15<05:21, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:45<03:02,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3705/8000 [04:16<05:35, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:46<02:59,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3707/8000 [04:16<05:37, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:48<03:13,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3709/8000 [04:16<05:42, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:50<03:08,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3711/8000 [04:16<09:04,  7.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:51<03:06,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3714/8000 [04:17<11:39,  6.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:52<02:59,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3717/8000 [04:17<09:14,  7.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:54<02:56,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|██▊   | 3719/8000 [04:18<10:07,  7.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:55<03:02,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3721/8000 [04:18<12:16,  5.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:57<02:59,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3723/8000 [04:19<11:50,  6.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████                  | 80/200 [01:58<02:56,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3726/8000 [04:19<09:23,  7.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████▏                 | 81/200 [02:00<02:53,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3728/8000 [04:19<09:09,  7.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  41%|████████████▎                 | 82/200 [02:01<02:49,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3730/8000 [04:19<08:12,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▍                 | 83/200 [02:03<02:52,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3733/8000 [04:19<06:34, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▌                 | 84/200 [02:04<02:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3738/8000 [04:20<05:31, 12.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▊                 | 85/200 [02:06<02:50,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3741/8000 [04:20<06:16, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  43%|████████████▉                 | 86/200 [02:09<02:51,  1.50s/it]\n",
+      "Generating signals:  47%|██▊   | 3744/8000 [04:20<05:47, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  47%|██▊   | 3747/8000 [04:20<05:17, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3754/8000 [04:20<03:29, 20.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3757/8000 [04:21<04:34, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.69it/s]\n",
+      "Generating signals:  47%|██▊   | 3759/8000 [04:21<04:46, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  47%|██▊   | 3763/8000 [04:21<04:13, 16.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3766/8000 [04:21<04:06, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3769/8000 [04:21<04:00, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 29.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3774/8000 [04:22<03:07, 22.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 68it [00:01, 59.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3778/8000 [04:22<03:08, 22.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 102it [00:01, 95.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3784/8000 [04:22<04:12, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 137it [00:02, 135.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3793/8000 [04:23<03:59, 17.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 178it [00:02, 184.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|██▊   | 3796/8000 [04:23<03:59, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 214it [00:02, 219.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3802/8000 [04:23<04:01, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 251it [00:02, 252.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3804/8000 [04:23<04:16, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 286it [00:02, 226.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3807/8000 [04:24<04:07, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 316it [00:02, 235.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3812/8000 [04:24<03:38, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 345it [00:02, 222.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3814/8000 [04:24<04:03, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 382it [00:02, 253.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3817/8000 [04:24<03:58, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 411it [00:03, 237.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3821/8000 [04:24<04:35, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 438it [00:03, 218.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3823/8000 [04:25<04:51, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 462it [00:03, 208.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3825/8000 [04:25<04:55, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 487it [00:03, 218.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3828/8000 [04:25<04:41, 14.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 518it [00:03, 240.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▊   | 3830/8000 [04:25<06:42, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 552it [00:03, 259.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3834/8000 [04:26<05:17, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 579it [00:03, 244.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3836/8000 [04:26<05:23, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 605it [00:03, 246.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3840/8000 [04:26<04:35, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 631it [00:03, 234.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3842/8000 [04:26<04:48, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 655it [00:04, 217.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3844/8000 [04:26<04:57, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 683it [00:04, 226.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3846/8000 [04:26<05:08, 13.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 712it [00:04, 243.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3849/8000 [04:27<04:43, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 738it [00:04, 246.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3851/8000 [04:27<04:38, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 765it [00:04, 252.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3854/8000 [04:27<04:26, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 169.72it/s]\n",
+      "Generating signals:  48%|██▉   | 3859/8000 [04:27<03:26, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  48%|██▉   | 3865/8000 [04:28<04:42, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3869/8000 [04:28<04:22, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 177\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3875/8000 [04:28<03:25, 20.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 137\u001b[A\u001b[A\n",
+      "Generating signals:  48%|██▉   | 3878/8000 [04:28<04:26, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 135\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3881/8000 [04:28<04:19, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  65%|▋| 65/100 [00:00<00:00, 131\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3884/8000 [04:29<06:03, 11.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  83%|▊| 83/100 [00:00<00:00, 145\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3886/8000 [04:29<06:11, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
+      "Generating signals:  49%|██▉   | 3890/8000 [04:29<05:13, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [05:40<17:00, 340.20s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.763499165001471\n",
-      "2999: -6.2345229\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  49%|██▉   | 3893/8000 [04:30<05:52, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  49%|██▉   | 3895/8000 [04:30<06:06, 11.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3901/8000 [04:30<04:09, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:23,  7.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3903/8000 [04:30<04:22, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:40,  3.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3906/8000 [04:30<04:06, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:45,  2.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3910/8000 [04:31<04:53, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:55,  2.12s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3912/8000 [04:31<04:58, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:53,  1.81s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3914/8000 [04:31<06:33, 10.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:21,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3918/8000 [04:31<05:01, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:02,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3921/8000 [04:32<04:43, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:50,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3929/8000 [04:32<02:57, 22.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:39,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3933/8000 [04:32<03:00, 22.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:34,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3937/8000 [04:32<03:59, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:25,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3941/8000 [04:33<04:59, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:28,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3943/8000 [04:33<05:22, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:34,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3945/8000 [04:33<05:34, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:42,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3948/8000 [04:33<05:10, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:38,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3951/8000 [04:34<04:30, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:33,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3953/8000 [04:34<04:49, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:27,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3955/8000 [04:34<04:57, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:20,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|██▉   | 3957/8000 [04:34<05:44, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:12,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3960/8000 [04:34<05:25, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:33<04:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3962/8000 [04:34<05:24, 12.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:13,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3966/8000 [04:35<04:05, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:36<04:10,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3969/8000 [04:35<04:54, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:05,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3971/8000 [04:35<06:28, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<04:05,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3974/8000 [04:35<05:34, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:40<04:00,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3980/8000 [04:36<04:43, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<04:00,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3982/8000 [04:36<05:02, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:43<03:59,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3985/8000 [04:36<04:52, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:44<03:58,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3987/8000 [04:36<05:13, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:46<03:55,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3992/8000 [04:37<03:56, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:47<04:02,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3995/8000 [04:37<04:41, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:49<03:59,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|██▉   | 3998/8000 [04:37<04:50, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:50<04:02,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4001/8000 [04:37<05:51, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<04:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4003/8000 [04:38<06:52,  9.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<04:01,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4006/8000 [04:38<06:52,  9.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:54<03:51,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4008/8000 [04:39<08:55,  7.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:42,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4012/8000 [04:39<06:20, 10.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:54,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4014/8000 [04:39<06:11, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:59<03:47,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4017/8000 [04:39<05:42, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:40,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4019/8000 [04:39<05:55, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:37,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4022/8000 [04:40<06:19, 10.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:03<03:34,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4025/8000 [04:40<05:21, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:39,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4027/8000 [04:40<05:17, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:37,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4031/8000 [04:40<05:59, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:07<03:39,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4034/8000 [04:40<05:11, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:40,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4036/8000 [04:41<05:10, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:10<03:39,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|███   | 4038/8000 [04:41<05:05, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:11<03:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4042/8000 [04:41<04:49, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:13<03:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4045/8000 [04:41<04:29, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:14<03:39,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4047/8000 [04:41<04:33, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:16<03:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4050/8000 [04:42<04:12, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:17<03:40,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4054/8000 [04:42<05:30, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:19<03:39,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4057/8000 [04:42<06:02, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:20<03:42,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4060/8000 [04:43<05:37, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:22<03:38,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4062/8000 [04:43<06:31, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:24<03:48,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4065/8000 [04:43<05:30, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:25<03:45,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4067/8000 [04:44<08:39,  7.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:26<03:38,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4071/8000 [04:44<06:32, 10.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:28<03:29,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4074/8000 [04:44<05:32, 11.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:29<03:28,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4077/8000 [04:44<05:08, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:31<03:23,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4079/8000 [04:44<05:23, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:34<03:35,  1.55s/it]\n",
+      "Generating signals:  51%|███   | 4082/8000 [04:44<04:46, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  51%|███   | 4084/8000 [04:45<05:43, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4088/8000 [04:45<04:32, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4090/8000 [04:45<04:46, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.43it/s]\n",
+      "Generating signals:  51%|███   | 4093/8000 [04:45<05:52, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  51%|███   | 4097/8000 [04:46<04:35, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4099/8000 [04:46<04:42, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4102/8000 [04:46<04:25, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 54it [00:01, 50.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4104/8000 [04:46<04:36, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 92it [00:01, 89.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4106/8000 [04:46<04:48, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 127it [00:01, 126.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4109/8000 [04:46<04:23, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 182it [00:01, 198.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4112/8000 [04:47<04:03, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 225it [00:01, 242.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4114/8000 [04:47<05:54, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 267it [00:02, 262.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4117/8000 [04:47<05:02, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 309it [00:02, 297.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|███   | 4119/8000 [04:47<05:39, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 349it [00:02, 317.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4123/8000 [04:48<04:22, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 388it [00:02, 323.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4126/8000 [04:48<03:55, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 438it [00:02, 369.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4130/8000 [04:48<03:21, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 480it [00:02, 350.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4136/8000 [04:48<03:45, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 519it [00:02, 359.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4138/8000 [04:48<04:07, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 562it [00:02, 378.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4141/8000 [04:49<03:49, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 602it [00:02, 379.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4144/8000 [04:49<03:37, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 642it [00:02, 374.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4146/8000 [04:49<04:13, 15.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 681it [00:03, 346.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4151/8000 [04:49<03:15, 19.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 717it [00:03, 342.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4156/8000 [04:49<03:08, 20.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 752it [00:03, 317.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███   | 4162/8000 [04:49<02:45, 23.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 227.65it/s]\n",
+      "Generating signals:  52%|███   | 4166/8000 [04:50<04:46, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  52%|███▏  | 4171/8000 [04:50<04:09, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4175/8000 [04:51<04:25, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:00, 112\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4180/8000 [04:51<03:33, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:00, 128\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4183/8000 [04:51<04:35, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 160\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4185/8000 [04:51<04:55, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:00<00:00, 164\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4187/8000 [04:52<06:06, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  84%|▊| 84/100 [00:00<00:00, 155\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4190/8000 [04:52<05:31, 11.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  52%|███▏  | 4192/8000 [04:52<06:28,  9.81it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  52%|███▏  | 4195/8000 [04:53<06:54,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|███▏  | 4197/8000 [04:53<06:18, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 49/8000 [00:00<00:26, 297.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4202/8000 [04:53<04:21, 14.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 109/8000 [00:00<00:23, 337.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4204/8000 [04:53<04:18, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 150/8000 [00:00<00:25, 308.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4206/8000 [04:53<04:44, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 199/8000 [00:00<00:24, 321.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4208/8000 [04:53<05:12, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 251/8000 [00:00<00:23, 330.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4210/8000 [04:54<06:12, 10.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 295/8000 [00:00<00:27, 283.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4217/8000 [04:54<03:28, 18.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 346/8000 [00:01<00:25, 302.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4220/8000 [04:54<03:29, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 395/8000 [00:01<00:23, 317.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4223/8000 [04:54<03:27, 18.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 449/8000 [00:01<00:22, 335.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4226/8000 [04:55<04:52, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 496/8000 [00:01<00:24, 306.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4228/8000 [04:55<05:24, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 537/8000 [00:01<00:24, 305.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4233/8000 [04:55<03:54, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 579/8000 [00:01<00:23, 311.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4235/8000 [04:55<04:19, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 622/8000 [00:02<00:26, 274.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4239/8000 [04:55<03:41, 17.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 667/8000 [00:02<00:29, 248.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4244/8000 [04:56<03:09, 19.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 705/8000 [00:02<00:34, 212.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4247/8000 [04:56<03:20, 18.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▌               | 763/8000 [00:02<00:34, 210.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4250/8000 [04:56<03:50, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 808/8000 [00:02<00:32, 222.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4254/8000 [04:56<03:19, 18.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 851/8000 [00:03<00:32, 219.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4258/8000 [04:56<03:04, 20.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 889/8000 [00:03<00:36, 194.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4263/8000 [04:56<02:43, 22.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 940/8000 [00:03<00:34, 203.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4269/8000 [04:57<02:53, 21.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 990/8000 [00:03<00:31, 221.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4272/8000 [04:57<02:52, 21.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1035/8000 [00:04<00:31, 222.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|███▏  | 4278/8000 [04:57<02:51, 21.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1089/8000 [00:04<00:33, 206.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4282/8000 [04:58<04:08, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1138/8000 [00:04<00:34, 196.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4285/8000 [04:58<04:52, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1186/8000 [00:04<00:34, 197.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4287/8000 [04:58<05:01, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1238/8000 [00:05<00:32, 210.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4289/8000 [04:59<06:02, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1274/8000 [00:05<00:35, 190.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4291/8000 [04:59<06:49,  9.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1326/8000 [00:05<00:34, 192.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4295/8000 [04:59<05:03, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1367/8000 [00:05<00:33, 195.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4297/8000 [04:59<06:28,  9.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1420/8000 [00:05<00:30, 215.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4302/8000 [05:00<04:40, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1459/8000 [00:06<00:32, 203.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4306/8000 [05:00<03:58, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1502/8000 [00:06<00:28, 224.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4311/8000 [05:00<03:59, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1559/8000 [00:06<00:25, 255.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4313/8000 [05:00<04:15, 14.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1619/8000 [00:06<00:20, 307.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4315/8000 [05:00<04:31, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1660/8000 [00:06<00:22, 284.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4318/8000 [05:01<04:16, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1701/8000 [00:06<00:22, 279.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4320/8000 [05:01<04:08, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1756/8000 [00:07<00:21, 295.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4323/8000 [05:01<04:46, 12.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1813/8000 [00:07<00:18, 331.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4326/8000 [05:01<04:20, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1858/8000 [00:07<00:18, 339.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▏  | 4329/8000 [05:01<03:58, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1915/8000 [00:07<00:16, 366.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4336/8000 [05:02<02:34, 23.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1964/8000 [00:07<00:16, 364.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4339/8000 [05:02<04:16, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2017/8000 [00:07<00:15, 389.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4346/8000 [05:02<04:19, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2062/8000 [00:07<00:15, 392.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4348/8000 [05:03<04:17, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2115/8000 [00:08<00:15, 376.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4350/8000 [05:03<04:22, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2160/8000 [00:08<00:16, 357.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4355/8000 [05:03<04:11, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2202/8000 [00:08<00:16, 351.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███▎  | 4358/8000 [05:03<04:00, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2259/8000 [00:08<00:15, 380.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4360/8000 [05:04<05:14, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2317/8000 [00:08<00:14, 393.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4364/8000 [05:04<04:17, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2370/8000 [00:08<00:15, 368.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4367/8000 [05:04<04:07, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2419/8000 [00:08<00:14, 377.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4376/8000 [05:04<02:23, 25.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2482/8000 [00:08<00:13, 406.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4380/8000 [05:04<03:12, 18.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2534/8000 [00:09<00:13, 399.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4383/8000 [05:05<03:09, 19.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2588/8000 [00:09<00:13, 412.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4386/8000 [05:05<04:11, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2637/8000 [00:09<00:13, 383.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4388/8000 [05:05<04:15, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2697/8000 [00:09<00:13, 395.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4391/8000 [05:05<03:59, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2758/8000 [00:09<00:12, 419.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4395/8000 [05:06<03:35, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2812/8000 [00:09<00:13, 384.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4398/8000 [05:06<03:39, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2855/8000 [00:09<00:14, 358.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4401/8000 [05:06<03:31, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2905/8000 [00:10<00:13, 373.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4404/8000 [05:06<03:33, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2961/8000 [00:10<00:12, 388.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4407/8000 [05:06<04:41, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3004/8000 [00:10<00:13, 361.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4410/8000 [05:07<04:09, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3043/8000 [00:10<00:14, 332.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4412/8000 [05:07<04:15, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3094/8000 [00:10<00:13, 351.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4418/8000 [05:07<04:49, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3139/8000 [00:10<00:14, 346.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4421/8000 [05:08<05:12, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3185/8000 [00:10<00:15, 313.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4423/8000 [05:08<05:04, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3236/8000 [00:11<00:14, 328.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4425/8000 [05:08<05:16, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3274/8000 [00:11<00:15, 315.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4427/8000 [05:08<05:13, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3322/8000 [00:11<00:14, 317.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4429/8000 [05:08<05:10, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3373/8000 [00:11<00:13, 335.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4431/8000 [05:09<06:53,  8.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3412/8000 [00:11<00:15, 299.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4434/8000 [05:09<05:19, 11.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3459/8000 [00:11<00:13, 329.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███▎  | 4439/8000 [05:09<03:36, 16.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3508/8000 [00:11<00:12, 346.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4442/8000 [05:09<03:41, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3572/8000 [00:12<00:11, 385.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4448/8000 [05:09<02:57, 20.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3616/8000 [00:12<00:11, 368.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4454/8000 [05:10<03:14, 18.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3666/8000 [00:12<00:12, 338.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4457/8000 [05:10<04:03, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3717/8000 [00:12<00:12, 343.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4460/8000 [05:10<03:55, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3772/8000 [00:12<00:11, 361.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4462/8000 [05:11<05:08, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3829/8000 [00:12<00:11, 357.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4464/8000 [05:11<05:09, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3870/8000 [00:12<00:12, 337.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4467/8000 [05:11<07:35,  7.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3933/8000 [00:13<00:10, 377.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4470/8000 [05:12<06:21,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4030/8000 [00:13<00:11, 354.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4473/8000 [05:12<05:20, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4082/8000 [00:13<00:11, 346.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4477/8000 [05:12<05:09, 11.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4131/8000 [00:13<00:10, 360.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4479/8000 [05:12<04:55, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4189/8000 [00:13<00:09, 381.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4481/8000 [05:12<05:11, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4237/8000 [00:13<00:10, 367.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4486/8000 [05:13<03:37, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4279/8000 [00:14<00:11, 337.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4489/8000 [05:13<04:34, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4333/8000 [00:14<00:10, 354.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4492/8000 [05:13<04:12, 13.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▋       | 4373/8000 [00:14<00:10, 347.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▎  | 4498/8000 [05:13<03:06, 18.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4432/8000 [00:14<00:10, 355.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▍  | 4503/8000 [05:13<02:42, 21.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4487/8000 [00:14<00:10, 349.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▍  | 4507/8000 [05:14<02:39, 21.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4537/8000 [00:14<00:09, 346.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▍  | 4511/8000 [05:14<03:02, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4591/8000 [00:14<00:09, 374.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▍  | 4514/8000 [05:14<02:54, 19.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4644/8000 [00:15<00:08, 374.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▍  | 4518/8000 [05:14<03:34, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4688/8000 [00:15<00:09, 342.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███▍  | 4520/8000 [05:15<03:57, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4735/8000 [00:15<00:09, 333.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4522/8000 [05:15<04:06, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4779/8000 [00:15<00:09, 331.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4525/8000 [05:15<03:56, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4828/8000 [00:15<00:08, 354.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4527/8000 [05:15<04:16, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4878/8000 [00:15<00:08, 348.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4530/8000 [05:15<05:04, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4927/8000 [00:15<00:08, 358.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4532/8000 [05:16<05:23, 10.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4975/8000 [00:16<00:08, 358.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4534/8000 [05:16<06:09,  9.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5031/8000 [00:16<00:08, 358.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4539/8000 [05:16<04:06, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5073/8000 [00:16<00:08, 335.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4541/8000 [05:16<04:21, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5126/8000 [00:16<00:07, 361.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4546/8000 [05:17<03:23, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5178/8000 [00:16<00:07, 364.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4552/8000 [05:17<02:47, 20.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5217/8000 [00:16<00:08, 333.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4558/8000 [05:17<02:25, 23.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5265/8000 [00:16<00:08, 341.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4561/8000 [05:17<02:29, 23.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▋     | 5313/8000 [00:17<00:07, 337.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4565/8000 [05:17<03:12, 17.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5366/8000 [00:17<00:07, 346.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4573/8000 [05:18<02:54, 19.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5408/8000 [00:17<00:07, 334.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4577/8000 [05:18<03:12, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5460/8000 [00:17<00:07, 320.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4582/8000 [05:18<03:29, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5510/8000 [00:17<00:07, 351.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4584/8000 [05:19<03:39, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5554/8000 [00:17<00:07, 329.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4587/8000 [05:19<03:24, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5604/8000 [00:17<00:07, 336.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4590/8000 [05:19<03:32, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5652/8000 [00:18<00:07, 334.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4592/8000 [05:19<03:43, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5689/8000 [00:18<00:07, 297.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4594/8000 [05:19<03:49, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5737/8000 [00:18<00:07, 312.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███▍  | 4599/8000 [05:20<04:50, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5775/8000 [00:18<00:07, 300.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4604/8000 [05:20<03:39, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5826/8000 [00:18<00:06, 332.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4608/8000 [05:20<03:53, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▊    | 5875/8000 [00:18<00:06, 313.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4610/8000 [05:21<04:29, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5926/8000 [00:18<00:06, 322.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4615/8000 [05:21<03:29, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5974/8000 [00:19<00:06, 329.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4618/8000 [05:21<03:27, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6017/8000 [00:19<00:06, 320.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4621/8000 [05:21<03:28, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6069/8000 [00:19<00:05, 338.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4623/8000 [05:21<03:43, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6128/8000 [00:19<00:05, 336.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4625/8000 [05:21<04:06, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6172/8000 [00:19<00:05, 333.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4629/8000 [05:22<03:24, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6225/8000 [00:19<00:05, 347.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4632/8000 [05:22<03:20, 16.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6274/8000 [00:19<00:04, 355.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4634/8000 [05:22<03:36, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6323/8000 [00:20<00:04, 369.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4636/8000 [05:22<03:42, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▋   | 6369/8000 [00:20<00:04, 355.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4638/8000 [05:22<03:57, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6413/8000 [00:20<00:04, 354.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4640/8000 [05:22<04:13, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6474/8000 [00:20<00:04, 371.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4645/8000 [05:23<03:10, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6528/8000 [00:20<00:04, 364.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4648/8000 [05:23<03:04, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6589/8000 [00:20<00:03, 389.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4651/8000 [05:23<03:48, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6646/8000 [00:20<00:03, 382.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4657/8000 [05:23<02:57, 18.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6695/8000 [00:21<00:03, 367.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4661/8000 [05:23<02:52, 19.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6741/8000 [00:21<00:03, 379.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▍  | 4664/8000 [05:24<02:51, 19.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6790/8000 [00:21<00:03, 367.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▌  | 4667/8000 [05:24<02:52, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6846/8000 [00:21<00:03, 376.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▌  | 4669/8000 [05:24<03:51, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6891/8000 [00:21<00:03, 336.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▌  | 4671/8000 [05:24<04:16, 12.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6937/8000 [00:21<00:03, 316.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███▌  | 4677/8000 [05:25<03:53, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6999/8000 [00:21<00:02, 364.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4681/8000 [05:25<03:16, 16.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7051/8000 [00:22<00:02, 363.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4684/8000 [05:25<03:13, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7103/8000 [00:22<00:02, 364.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4688/8000 [05:25<02:52, 19.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7164/8000 [00:22<00:02, 386.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4695/8000 [05:26<03:40, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7222/8000 [00:22<00:01, 389.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4697/8000 [05:26<03:42, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7275/8000 [00:22<00:01, 391.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4699/8000 [05:26<03:55, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7322/8000 [00:22<00:01, 382.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4701/8000 [05:26<03:59, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7376/8000 [00:22<00:01, 374.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4703/8000 [05:26<04:04, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7417/8000 [00:23<00:01, 348.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4706/8000 [05:26<03:36, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7461/8000 [00:23<00:01, 335.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4711/8000 [05:27<02:53, 18.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7504/8000 [00:23<00:01, 310.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4713/8000 [05:27<03:09, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7542/8000 [00:23<00:01, 312.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4716/8000 [05:27<03:04, 17.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7591/8000 [00:23<00:01, 329.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4718/8000 [05:27<03:27, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7644/8000 [00:23<00:01, 343.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4724/8000 [05:27<02:37, 20.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7705/8000 [00:23<00:00, 364.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4727/8000 [05:28<02:51, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7745/8000 [00:24<00:00, 339.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4732/8000 [05:28<02:38, 20.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7791/8000 [00:24<00:00, 335.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4735/8000 [05:28<04:30, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7836/8000 [00:24<00:00, 332.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4737/8000 [05:29<05:32,  9.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7892/8000 [00:24<00:00, 352.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4739/8000 [05:29<05:12, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7948/8000 [00:24<00:00, 376.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4742/8000 [05:29<04:34, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7993/8000 [00:24<00:00, 365.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4745/8000 [05:29<05:05, 10.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8044it [00:24, 323.78it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  59%|███▌  | 4747/8000 [05:30<05:22, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  59%|███▌  | 4749/8000 [05:30<05:02, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4752/8000 [05:30<04:20, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:15,  7.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███▌  | 4757/8000 [05:30<03:22, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:41,  3.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4760/8000 [05:30<03:10, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:03,  2.76s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4763/8000 [05:30<03:11, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:19,  2.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4767/8000 [05:31<03:05, 17.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:14,  1.92s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4769/8000 [05:31<04:02, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:38,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4772/8000 [05:31<03:43, 14.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:19,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4775/8000 [05:31<03:27, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:01,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4778/8000 [05:31<03:25, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:50,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4781/8000 [05:32<05:10, 10.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:50,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4784/8000 [05:32<04:28, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:43,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4786/8000 [05:32<04:18, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:39,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4788/8000 [05:32<04:17, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:39,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4790/8000 [05:33<04:27, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:37,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4792/8000 [05:33<04:17, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:33,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4795/8000 [05:33<05:14, 10.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:30,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4799/8000 [05:33<03:53, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:28,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4801/8000 [05:34<05:00, 10.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:24,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4803/8000 [05:34<04:45, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:17,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4805/8000 [05:34<05:57,  8.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:15,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4809/8000 [05:34<04:23, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:14,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4811/8000 [05:34<04:39, 11.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:11,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4815/8000 [05:35<04:29, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:09,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4817/8000 [05:35<04:23, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<04:11,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4821/8000 [05:35<03:27, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:17,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4824/8000 [05:35<03:21, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<04:12,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▌  | 4829/8000 [05:35<02:42, 19.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<04:08,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▋  | 4836/8000 [05:36<02:42, 19.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<04:06,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███▋  | 4840/8000 [05:36<02:26, 21.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<04:15,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4844/8000 [05:36<02:24, 21.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:49<04:16,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4850/8000 [05:36<02:31, 20.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<04:20,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4853/8000 [05:37<02:42, 19.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:52<04:10,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4856/8000 [05:37<02:42, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<04:07,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4859/8000 [05:37<02:43, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:55<04:06,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4862/8000 [05:37<02:41, 19.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:56<03:58,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4866/8000 [05:37<03:19, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:58<03:54,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4872/8000 [05:38<02:27, 21.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:59<03:58,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4877/8000 [05:38<02:10, 23.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:00<03:55,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4882/8000 [05:38<03:06, 16.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<03:55,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4886/8000 [05:39<04:05, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:03<03:51,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4891/8000 [05:39<03:23, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:05<03:47,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4894/8000 [05:39<03:12, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:06<03:48,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4897/8000 [05:39<03:51, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:08<03:47,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4902/8000 [05:40<03:06, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:09<03:43,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4905/8000 [05:40<04:19, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:10<03:38,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4909/8000 [05:40<03:34, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:12<03:40,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4911/8000 [05:40<03:38, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:13<03:38,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███▋  | 4915/8000 [05:41<03:46, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:15<03:35,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4920/8000 [05:41<03:30, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:16<03:34,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4923/8000 [05:41<03:17, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:18<03:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4929/8000 [05:42<03:04, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:19<03:36,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4933/8000 [05:42<02:43, 18.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:21<03:34,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4936/8000 [05:42<02:39, 19.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:22<03:30,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4940/8000 [05:42<03:06, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:23<03:25,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4946/8000 [05:42<02:17, 22.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:26<03:48,  1.58s/it]\n",
+      "Generating signals:  62%|███▋  | 4950/8000 [05:43<02:55, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  62%|███▋  | 4957/8000 [05:43<02:07, 23.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4961/8000 [05:43<02:35, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.47it/s]\n",
+      "Generating signals:  62%|███▋  | 4965/8000 [05:43<02:54, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  62%|███▋  | 4970/8000 [05:44<02:31, 19.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4976/8000 [05:44<03:03, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4979/8000 [05:44<03:19, 15.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 43it [00:01, 38.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4984/8000 [05:44<02:47, 17.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 84it [00:01, 81.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4988/8000 [05:45<02:34, 19.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 118it [00:01, 117.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███▋  | 4995/8000 [05:45<02:21, 21.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 157it [00:01, 162.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5001/8000 [05:45<02:42, 18.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 192it [00:01, 195.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5004/8000 [05:45<02:43, 18.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 226it [00:02, 224.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5007/8000 [05:46<02:45, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 260it [00:02, 250.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5012/8000 [05:46<02:15, 22.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 298it [00:02, 281.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5018/8000 [05:46<02:29, 19.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 335it [00:02, 304.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5021/8000 [05:46<02:23, 20.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 378it [00:02, 336.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5025/8000 [05:47<02:49, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 423it [00:02, 368.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5028/8000 [05:47<02:43, 18.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 463it [00:02, 375.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5036/8000 [05:47<02:16, 21.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 505it [00:02, 384.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5043/8000 [05:47<02:08, 23.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 545it [00:02, 376.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5046/8000 [05:47<02:12, 22.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 588it [00:02, 389.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5049/8000 [05:48<02:18, 21.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 628it [00:03, 377.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5054/8000 [05:48<02:03, 23.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 667it [00:03, 361.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5062/8000 [05:48<01:58, 24.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 709it [00:03, 375.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5065/8000 [05:48<02:24, 20.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 748it [00:03, 356.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███▊  | 5069/8000 [05:48<02:17, 21.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 224.90it/s]\n",
+      "Generating signals:  63%|███▊  | 5073/8000 [05:49<02:09, 22.62it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  63%|███▊  | 5078/8000 [05:49<01:54, 25.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5081/8000 [05:49<03:00, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  19%|▏| 19/100 [00:00<00:00, 185\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5085/8000 [05:50<03:50, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 155\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5089/8000 [05:50<04:17, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:00<00:00, 165\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5093/8000 [05:50<03:56, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00<00:00, 162\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5098/8000 [05:51<03:06, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  64%|███▊  | 5102/8000 [05:51<03:13, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [11:17<11:16, 338.44s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.8661539583049107\n",
-      "2999: -6.1326485\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  64%|███▊  | 5104/8000 [05:51<03:51, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  64%|███▊  | 5107/8000 [05:51<03:25, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5109/8000 [05:51<03:21, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:08<27:01,  8.15s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5113/8000 [05:52<02:53, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:47,  4.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5115/8000 [05:52<03:08, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:34,  2.92s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5117/8000 [05:52<03:16, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:38,  2.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5121/8000 [05:52<03:31, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:28,  1.99s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5123/8000 [05:53<04:21, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:47,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5126/8000 [05:53<04:37, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:20,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5128/8000 [05:53<04:27, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:05,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5131/8000 [05:53<03:36, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<04:46,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5136/8000 [05:53<02:33, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:40,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5140/8000 [05:54<02:57, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:54,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5145/8000 [05:54<02:24, 19.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:40,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5150/8000 [05:54<02:00, 23.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:37,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5154/8000 [05:54<02:24, 19.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:36,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███▊  | 5158/8000 [05:54<02:17, 20.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:37,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▊  | 5162/8000 [05:54<02:14, 21.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:33,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5170/8000 [05:55<02:05, 22.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:25,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5173/8000 [05:55<02:56, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:27,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5175/8000 [05:55<03:00, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:28,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5177/8000 [05:56<03:09, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:35<04:26,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5180/8000 [05:56<03:11, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<04:24,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5182/8000 [05:56<03:57, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<04:27,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5185/8000 [05:56<03:34, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:26,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5187/8000 [05:56<03:37, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:41<04:22,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5189/8000 [05:57<03:33, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:17,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5195/8000 [05:57<02:16, 20.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:44<04:14,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5200/8000 [05:57<01:56, 24.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:46<04:13,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5207/8000 [05:57<01:54, 24.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:47<04:15,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5212/8000 [05:57<02:06, 21.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<04:11,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5215/8000 [05:58<02:12, 20.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:50<04:07,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5221/8000 [05:58<02:16, 20.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:51<04:04,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5225/8000 [05:58<02:39, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:53<04:03,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5227/8000 [05:58<02:45, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:54<03:56,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5230/8000 [05:59<02:47, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:55<03:51,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5235/8000 [05:59<02:17, 20.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:57<03:46,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███▉  | 5238/8000 [05:59<03:30, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:58<03:40,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5241/8000 [06:00<04:33, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:59<03:37,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5243/8000 [06:00<04:55,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:01<03:35,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5246/8000 [06:00<04:02, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<03:33,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5248/8000 [06:00<04:04, 11.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:03<03:38,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5250/8000 [06:00<03:51, 11.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:05<03:43,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5253/8000 [06:01<03:23, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:06<03:36,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5255/8000 [06:01<03:27, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:07<03:28,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5258/8000 [06:01<03:01, 15.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:09<03:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5260/8000 [06:01<03:49, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:10<03:21,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5262/8000 [06:01<03:45, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:11<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5269/8000 [06:01<02:09, 21.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:13<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5272/8000 [06:02<02:17, 19.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:14<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5282/8000 [06:02<01:45, 25.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:15<03:12,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5286/8000 [06:02<02:28, 18.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:18<03:54,  1.56s/it]\n",
+      "Generating signals:  66%|███▉  | 5291/8000 [06:03<02:59, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  66%|███▉  | 5295/8000 [06:03<02:34, 17.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5299/8000 [06:03<02:24, 18.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.70it/s]\n",
+      "Generating signals:  66%|███▉  | 5303/8000 [06:03<02:48, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  66%|███▉  | 5305/8000 [06:04<03:04, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5310/8000 [06:04<02:29, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5314/8000 [06:04<02:17, 19.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 33.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███▉  | 5318/8000 [06:04<02:08, 20.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 77it [00:01, 77.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███▉  | 5322/8000 [06:04<02:27, 18.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 127it [00:01, 137.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███▉  | 5325/8000 [06:05<03:08, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 184it [00:01, 210.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███▉  | 5328/8000 [06:05<02:53, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 238it [00:01, 273.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███▉  | 5330/8000 [06:05<02:58, 14.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 289it [00:01, 323.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5335/8000 [06:05<02:10, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 341it [00:02, 368.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5338/8000 [06:05<02:09, 20.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 392it [00:02, 403.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5342/8000 [06:06<02:31, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 442it [00:02, 421.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5345/8000 [06:06<02:18, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 491it [00:02, 438.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5348/8000 [06:06<02:42, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 540it [00:02, 448.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5352/8000 [06:06<02:20, 18.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 589it [00:02, 444.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5355/8000 [06:07<03:45, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 636it [00:02, 435.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5358/8000 [06:07<03:55, 11.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 682it [00:02, 429.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5363/8000 [06:07<03:22, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 727it [00:02, 430.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5369/8000 [06:08<02:53, 15.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 255.65it/s]\n",
+      "Generating signals:  67%|████  | 5372/8000 [06:08<03:15, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  67%|████  | 5377/8000 [06:08<02:57, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5381/8000 [06:08<03:01, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  19%|▏| 19/100 [00:00<00:00, 186\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5383/8000 [06:09<03:02, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:00<00:00, 194\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5385/8000 [06:09<04:14, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  60%|▌| 60/100 [00:00<00:00, 191\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5387/8000 [06:09<04:02, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 19\n",
+      "Generating signals:  67%|████  | 5390/8000 [06:09<04:03, 10.70it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  67%|████  | 5392/8000 [06:10<05:19,  8.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5396/8000 [06:10<04:26,  9.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 14/8000 [00:00<01:12, 109.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████  | 5399/8000 [06:10<03:39, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 37/8000 [00:00<00:57, 137.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5401/8000 [06:10<03:37, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 52/8000 [00:00<00:59, 133.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5404/8000 [06:11<03:55, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 69/8000 [00:00<01:01, 129.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5409/8000 [06:11<03:17, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 87/8000 [00:00<00:57, 137.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5412/8000 [06:11<03:27, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 106/8000 [00:00<00:52, 151.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5416/8000 [06:12<03:13, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 137/8000 [00:01<00:58, 135.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5421/8000 [06:12<02:30, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 157/8000 [00:01<01:00, 129.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5423/8000 [06:12<02:38, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 180/8000 [00:01<00:57, 136.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5427/8000 [06:12<02:46, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 195/8000 [00:01<01:01, 127.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5430/8000 [06:12<02:32, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 215/8000 [00:01<01:00, 129.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5435/8000 [06:12<02:05, 20.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 234/8000 [00:01<01:00, 129.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5438/8000 [06:13<02:07, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 257/8000 [00:01<00:57, 133.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5443/8000 [06:13<01:53, 22.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 279/8000 [00:02<00:52, 147.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5450/8000 [06:13<01:58, 21.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 303/8000 [00:02<00:49, 156.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5456/8000 [06:13<01:40, 25.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 325/8000 [00:02<00:52, 145.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5460/8000 [06:13<01:39, 25.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 342/8000 [00:02<00:54, 139.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5465/8000 [06:14<02:00, 20.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 371/8000 [00:02<00:57, 133.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5470/8000 [06:14<02:02, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 386/8000 [00:02<00:57, 132.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5475/8000 [06:14<02:05, 20.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 405/8000 [00:02<00:52, 143.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████  | 5478/8000 [06:15<02:24, 17.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 427/8000 [00:03<00:47, 159.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████  | 5484/8000 [06:15<01:52, 22.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 447/8000 [00:03<00:45, 165.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████  | 5487/8000 [06:15<01:53, 22.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 465/8000 [00:03<00:46, 160.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████  | 5493/8000 [06:15<01:31, 27.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 500/8000 [00:03<00:47, 158.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████  | 5498/8000 [06:15<02:05, 19.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 520/8000 [00:03<00:46, 160.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5504/8000 [06:16<01:58, 21.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 542/8000 [00:03<00:44, 168.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5510/8000 [06:16<01:53, 21.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 561/8000 [00:03<00:44, 168.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5514/8000 [06:16<02:26, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 584/8000 [00:03<00:42, 174.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5518/8000 [06:17<02:31, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 622/8000 [00:04<00:41, 178.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5523/8000 [06:17<02:07, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 654/8000 [00:04<00:42, 171.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5526/8000 [06:17<02:05, 19.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 680/8000 [00:04<00:39, 183.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5532/8000 [06:17<02:06, 19.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 700/8000 [00:04<00:40, 182.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5536/8000 [06:17<01:56, 21.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 733/8000 [00:04<00:42, 169.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5544/8000 [06:17<01:23, 29.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▌               | 763/8000 [00:05<00:45, 158.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5549/8000 [06:18<01:58, 20.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 796/8000 [00:05<00:47, 151.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5553/8000 [06:18<01:50, 22.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 826/8000 [00:05<00:50, 142.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████▏ | 5559/8000 [06:18<01:33, 25.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 851/8000 [00:05<00:45, 158.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5563/8000 [06:18<01:50, 22.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 870/8000 [00:05<00:44, 161.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5574/8000 [06:19<01:57, 20.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 891/8000 [00:05<00:43, 165.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5577/8000 [06:19<02:14, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 909/8000 [00:05<00:43, 164.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5580/8000 [06:19<02:12, 18.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 944/8000 [00:06<00:43, 160.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5583/8000 [06:20<02:30, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 963/8000 [00:06<00:42, 164.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5586/8000 [06:20<02:23, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 987/8000 [00:06<00:41, 170.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5591/8000 [06:20<01:58, 20.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1026/8000 [00:06<00:43, 161.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5594/8000 [06:20<01:56, 20.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1050/8000 [00:06<00:40, 171.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5597/8000 [06:20<01:58, 20.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1075/8000 [00:06<00:39, 173.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5602/8000 [06:21<02:04, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1103/8000 [00:07<00:47, 144.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5605/8000 [06:21<02:23, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1123/8000 [00:07<00:45, 151.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5611/8000 [06:21<02:08, 18.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1140/8000 [00:07<00:46, 147.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5615/8000 [06:21<02:22, 16.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1163/8000 [00:07<00:44, 152.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5617/8000 [06:22<02:27, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1185/8000 [00:07<00:52, 129.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5620/8000 [06:22<02:19, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1203/8000 [00:07<00:49, 136.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5622/8000 [06:22<02:53, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1237/8000 [00:08<00:51, 130.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5625/8000 [06:22<02:41, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1254/8000 [00:08<00:51, 130.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5627/8000 [06:22<02:41, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1271/8000 [00:08<00:50, 134.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5630/8000 [06:23<03:32, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1295/8000 [00:08<00:43, 155.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5634/8000 [06:23<02:41, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋             | 1315/8000 [00:08<00:43, 153.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████▏ | 5638/8000 [06:23<02:46, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1351/8000 [00:08<00:42, 158.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▏ | 5641/8000 [06:23<03:06, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1368/8000 [00:09<00:41, 158.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▏ | 5645/8000 [06:24<02:33, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1386/8000 [00:09<00:40, 162.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▏ | 5647/8000 [06:24<02:37, 14.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1413/8000 [00:09<00:35, 187.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▏ | 5652/8000 [06:24<01:59, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1445/8000 [00:09<00:41, 156.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▏ | 5657/8000 [06:24<02:07, 18.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1480/8000 [00:09<00:42, 153.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▏ | 5661/8000 [06:24<01:52, 20.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1513/8000 [00:10<00:47, 137.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5667/8000 [06:25<02:16, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1532/8000 [00:10<00:45, 143.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5670/8000 [06:25<02:09, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1548/8000 [00:10<00:45, 141.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5674/8000 [06:25<01:58, 19.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1568/8000 [00:10<00:43, 147.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5680/8000 [06:25<01:55, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1590/8000 [00:10<00:41, 154.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5685/8000 [06:25<01:37, 23.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1616/8000 [00:10<00:50, 127.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5688/8000 [06:26<01:38, 23.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1647/8000 [00:11<00:50, 126.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5691/8000 [06:26<02:04, 18.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1669/8000 [00:11<00:46, 135.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5697/8000 [06:26<02:00, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1687/8000 [00:11<00:45, 138.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5700/8000 [06:26<01:55, 19.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1705/8000 [00:11<00:44, 143.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5703/8000 [06:26<01:53, 20.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1724/8000 [00:11<00:44, 139.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5707/8000 [06:27<01:41, 22.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1745/8000 [00:11<00:41, 150.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5710/8000 [06:27<02:09, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1775/8000 [00:11<00:46, 135.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5713/8000 [06:27<02:32, 14.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▌            | 1802/8000 [00:12<00:39, 158.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5715/8000 [06:27<03:34, 10.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1820/8000 [00:12<00:39, 156.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████▎ | 5717/8000 [06:28<03:17, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1853/8000 [00:12<00:42, 144.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5721/8000 [06:28<02:30, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1873/8000 [00:12<00:39, 153.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5723/8000 [06:28<02:33, 14.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1891/8000 [00:12<00:41, 146.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5725/8000 [06:28<02:36, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1914/8000 [00:12<00:38, 157.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5730/8000 [06:28<01:55, 19.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1931/8000 [00:12<00:39, 153.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5733/8000 [06:28<01:52, 20.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1952/8000 [00:12<00:37, 162.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5736/8000 [06:28<01:55, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1974/8000 [00:13<00:37, 159.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5740/8000 [06:29<01:42, 22.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2012/8000 [00:13<00:39, 153.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5746/8000 [06:29<01:41, 22.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2040/8000 [00:13<00:42, 141.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5750/8000 [06:29<02:22, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2056/8000 [00:13<00:42, 140.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5757/8000 [06:30<02:00, 18.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2086/8000 [00:13<00:44, 132.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5761/8000 [06:30<02:16, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2108/8000 [00:14<00:41, 141.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5765/8000 [06:30<02:01, 18.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2129/8000 [00:14<00:39, 150.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5768/8000 [06:30<02:22, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2153/8000 [00:14<00:36, 158.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5771/8000 [06:31<02:15, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2179/8000 [00:14<00:42, 138.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5778/8000 [06:31<01:54, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▍           | 2195/8000 [00:14<00:40, 142.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5782/8000 [06:31<02:47, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2215/8000 [00:14<00:39, 145.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5785/8000 [06:32<02:35, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2232/8000 [00:14<00:38, 148.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5787/8000 [06:32<02:32, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2256/8000 [00:15<00:36, 158.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5789/8000 [06:32<03:07, 11.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2274/8000 [00:15<00:37, 151.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5793/8000 [06:32<02:28, 14.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2308/8000 [00:15<00:38, 147.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████▎ | 5795/8000 [06:32<02:26, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2324/8000 [00:15<00:40, 139.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5801/8000 [06:33<02:06, 17.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2346/8000 [00:15<00:39, 142.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5808/8000 [06:33<01:33, 23.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2373/8000 [00:15<00:36, 154.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5811/8000 [06:33<01:34, 23.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2401/8000 [00:16<00:33, 167.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5814/8000 [06:33<02:06, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2433/8000 [00:16<00:37, 148.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5820/8000 [06:33<01:57, 18.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2449/8000 [00:16<00:38, 143.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5823/8000 [06:34<01:52, 19.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2467/8000 [00:16<00:41, 134.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5827/8000 [06:34<01:46, 20.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2489/8000 [00:16<00:39, 139.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▎ | 5830/8000 [06:34<01:50, 19.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2517/8000 [00:16<00:45, 121.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5834/8000 [06:34<01:42, 21.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2538/8000 [00:17<00:42, 129.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5839/8000 [06:35<02:28, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2572/8000 [00:17<00:44, 122.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5843/8000 [06:35<02:21, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2591/8000 [00:17<00:42, 128.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5846/8000 [06:35<02:49, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2608/8000 [00:17<00:43, 125.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5848/8000 [06:36<03:31, 10.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2628/8000 [00:17<00:39, 134.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5855/8000 [06:36<03:31, 10.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2644/8000 [00:17<00:43, 123.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5862/8000 [06:36<02:21, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2672/8000 [00:18<00:36, 146.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5866/8000 [06:37<02:21, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2690/8000 [00:18<00:38, 138.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5869/8000 [06:37<02:19, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2709/8000 [00:18<00:38, 136.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5871/8000 [06:37<02:19, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2725/8000 [00:18<00:40, 131.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5874/8000 [06:37<02:19, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2739/8000 [00:18<00:41, 127.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████▍ | 5877/8000 [06:37<02:32, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2754/8000 [00:18<00:46, 113.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5880/8000 [06:38<02:46, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2773/8000 [00:18<00:41, 125.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5883/8000 [06:38<02:28, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2793/8000 [00:19<00:38, 136.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5887/8000 [06:38<02:01, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2815/8000 [00:19<00:35, 145.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5890/8000 [06:38<01:58, 17.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2833/8000 [00:19<00:37, 138.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5892/8000 [06:38<02:01, 17.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2848/8000 [00:19<00:39, 131.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5894/8000 [06:38<02:08, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2865/8000 [00:19<00:40, 127.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5899/8000 [06:39<01:34, 22.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2883/8000 [00:19<00:37, 136.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5903/8000 [06:39<01:28, 23.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2897/8000 [00:19<00:41, 123.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5907/8000 [06:39<01:25, 24.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2920/8000 [00:20<00:38, 131.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5912/8000 [06:39<01:16, 27.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2955/8000 [00:20<00:37, 135.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5917/8000 [06:39<01:52, 18.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2978/8000 [00:20<00:36, 137.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5923/8000 [06:40<01:24, 24.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3006/8000 [00:20<00:47, 105.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5927/8000 [06:40<01:26, 24.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3025/8000 [00:21<00:47, 105.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5932/8000 [06:40<01:33, 22.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3039/8000 [00:21<00:48, 102.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5938/8000 [06:40<01:35, 21.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3050/8000 [00:21<00:55, 89.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5941/8000 [06:41<01:53, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3068/8000 [00:21<00:52, 93.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5944/8000 [06:41<01:52, 18.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3083/8000 [00:21<00:51, 95.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5947/8000 [06:41<02:55, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3104/8000 [00:21<00:43, 112.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5949/8000 [06:41<02:56, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3124/8000 [00:21<00:40, 120.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5953/8000 [06:42<02:25, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3143/8000 [00:22<00:37, 129.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5957/8000 [06:42<02:06, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3166/8000 [00:22<00:34, 139.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████▍ | 5959/8000 [06:42<02:16, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3190/8000 [00:22<00:32, 146.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5962/8000 [06:42<02:40, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3217/8000 [00:22<00:40, 117.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5965/8000 [06:42<02:28, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3236/8000 [00:22<00:38, 124.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5970/8000 [06:43<02:43, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3258/8000 [00:22<00:40, 117.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5972/8000 [06:43<02:39, 12.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3284/8000 [00:23<00:37, 127.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5979/8000 [06:43<02:22, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3322/8000 [00:23<00:39, 118.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5981/8000 [06:44<02:25, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3342/8000 [00:23<00:40, 116.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5986/8000 [06:44<02:17, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3360/8000 [00:23<00:39, 117.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5988/8000 [06:44<02:17, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3377/8000 [00:24<00:41, 112.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5993/8000 [06:44<02:05, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3390/8000 [00:24<00:42, 107.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5997/8000 [06:45<02:10, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3405/8000 [00:24<00:53, 85.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▍ | 5999/8000 [06:45<02:15, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3433/8000 [00:24<00:47, 95.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6002/8000 [06:45<02:52, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3455/8000 [00:24<00:42, 108.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6004/8000 [06:45<02:50, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3475/8000 [00:25<00:43, 103.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6006/8000 [06:45<02:39, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3493/8000 [00:25<00:43, 103.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6011/8000 [06:46<01:51, 17.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3507/8000 [00:25<00:46, 95.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6015/8000 [06:46<02:26, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3522/8000 [00:25<00:51, 86.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6018/8000 [06:46<02:12, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3546/8000 [00:25<00:48, 91.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6020/8000 [06:46<02:16, 14.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3562/8000 [00:26<00:46, 95.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6022/8000 [06:47<02:46, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3579/8000 [00:26<00:45, 97.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6026/8000 [06:47<02:08, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3602/8000 [00:26<00:45, 97.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6029/8000 [06:47<02:30, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3612/8000 [00:26<00:55, 79.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6034/8000 [06:48<03:00, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3631/8000 [00:26<00:48, 89.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████▌ | 6038/8000 [06:48<02:29, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3656/8000 [00:26<00:42, 101.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6040/8000 [06:48<02:32, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3673/8000 [00:27<00:43, 98.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6042/8000 [06:48<02:29, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3695/8000 [00:27<00:37, 116.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6044/8000 [06:48<02:27, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3714/8000 [00:27<00:38, 110.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6048/8000 [06:48<01:56, 16.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3733/8000 [00:27<00:40, 104.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6053/8000 [06:49<01:32, 21.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3753/8000 [00:27<00:45, 94.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6056/8000 [06:49<01:35, 20.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3770/8000 [00:28<00:42, 99.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6060/8000 [06:49<01:52, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3789/8000 [00:28<00:37, 111.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6063/8000 [06:49<01:46, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3809/8000 [00:28<00:36, 114.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6066/8000 [06:49<01:42, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3833/8000 [00:28<00:35, 118.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6071/8000 [06:49<01:25, 22.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3850/8000 [00:28<00:35, 116.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6074/8000 [06:50<02:17, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3866/8000 [00:28<00:38, 108.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6076/8000 [06:50<02:22, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3886/8000 [00:29<00:40, 102.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6081/8000 [06:50<01:44, 18.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3902/8000 [00:29<00:39, 102.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6085/8000 [06:50<01:49, 17.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3920/8000 [00:29<00:36, 112.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6088/8000 [06:51<01:45, 18.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3938/8000 [00:29<00:35, 114.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6093/8000 [06:51<01:43, 18.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3954/8000 [00:29<00:37, 106.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6098/8000 [06:51<01:27, 21.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3977/8000 [00:29<00:36, 109.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6103/8000 [06:51<01:35, 19.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3999/8000 [00:30<00:32, 124.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6107/8000 [06:52<01:48, 17.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4012/8000 [00:30<00:33, 120.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6109/8000 [06:52<01:51, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4040/8000 [00:30<00:33, 116.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6111/8000 [06:52<01:56, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4064/8000 [00:30<00:30, 129.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6113/8000 [06:52<02:04, 15.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4085/8000 [00:30<00:32, 120.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████▌ | 6118/8000 [06:52<01:33, 20.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4106/8000 [00:30<00:34, 111.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6122/8000 [06:53<02:10, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4129/8000 [00:31<00:35, 107.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6124/8000 [06:53<02:11, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4154/8000 [00:31<00:36, 105.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6126/8000 [06:53<02:14, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4175/8000 [00:31<00:35, 107.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6129/8000 [06:53<02:30, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4205/8000 [00:31<00:35, 106.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6131/8000 [06:54<02:58, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4221/8000 [00:32<00:34, 108.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6135/8000 [06:54<02:44, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4241/8000 [00:32<00:34, 109.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6137/8000 [06:54<02:37, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4255/8000 [00:32<00:37, 98.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6140/8000 [06:54<02:17, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4268/8000 [00:32<00:38, 96.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6142/8000 [06:54<02:17, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4289/8000 [00:32<00:41, 88.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6147/8000 [06:54<01:40, 18.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4309/8000 [00:33<00:38, 95.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6151/8000 [06:55<01:30, 20.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4326/8000 [00:33<00:37, 98.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6156/8000 [06:55<01:18, 23.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4343/8000 [00:33<00:38, 93.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6160/8000 [06:55<01:13, 24.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4358/8000 [00:33<00:40, 88.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▌ | 6164/8000 [06:55<01:13, 25.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4373/8000 [00:33<00:42, 85.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6168/8000 [06:55<01:27, 20.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4396/8000 [00:33<00:34, 105.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6174/8000 [06:56<01:11, 25.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4413/8000 [00:34<00:33, 105.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6177/8000 [06:56<01:36, 18.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4430/8000 [00:34<00:35, 100.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6181/8000 [06:56<01:49, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4450/8000 [00:34<00:35, 100.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6187/8000 [06:56<01:23, 21.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4475/8000 [00:34<00:33, 105.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6190/8000 [06:56<01:24, 21.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4500/8000 [00:34<00:32, 108.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|████▋ | 6195/8000 [06:57<01:15, 23.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4529/8000 [00:35<00:30, 112.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6202/8000 [06:57<01:02, 28.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4544/8000 [00:35<00:34, 100.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6206/8000 [06:57<01:33, 19.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4557/8000 [00:35<00:32, 104.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6212/8000 [06:58<01:49, 16.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4575/8000 [00:35<00:37, 91.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6218/8000 [06:58<01:30, 19.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4593/8000 [00:35<00:35, 95.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6223/8000 [06:58<01:34, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4610/8000 [00:36<00:36, 92.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6226/8000 [06:58<01:32, 19.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4625/8000 [00:36<00:37, 90.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6229/8000 [06:59<01:52, 15.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4647/8000 [00:36<00:38, 86.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6233/8000 [06:59<01:54, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4663/8000 [00:36<00:38, 86.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6235/8000 [06:59<02:18, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4678/8000 [00:36<00:36, 90.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6238/8000 [06:59<02:05, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4695/8000 [00:37<00:35, 92.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6240/8000 [06:59<02:06, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4716/8000 [00:37<00:32, 102.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6242/8000 [07:00<02:01, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4743/8000 [00:37<00:26, 124.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6246/8000 [07:00<02:03, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4763/8000 [00:37<00:24, 130.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6248/8000 [07:00<02:05, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4781/8000 [00:37<00:25, 127.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6254/8000 [07:00<01:26, 20.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4796/8000 [00:37<00:26, 120.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6257/8000 [07:00<01:27, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4816/8000 [00:37<00:26, 118.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6260/8000 [07:00<01:29, 19.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4833/8000 [00:38<00:27, 115.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6263/8000 [07:01<01:29, 19.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4845/8000 [00:38<00:29, 107.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6268/8000 [07:01<01:58, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4859/8000 [00:38<00:29, 105.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6272/8000 [07:01<02:13, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4876/8000 [00:38<00:28, 108.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6275/8000 [07:02<02:32, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4900/8000 [00:38<00:24, 126.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|████▋ | 6278/8000 [07:02<02:17, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4914/8000 [00:38<00:26, 114.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6287/8000 [07:02<01:19, 21.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4934/8000 [00:38<00:24, 124.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6292/8000 [07:02<01:11, 24.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4950/8000 [00:39<00:27, 111.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6295/8000 [07:02<01:17, 22.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4966/8000 [00:39<00:27, 109.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6298/8000 [07:03<01:39, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4982/8000 [00:39<00:26, 114.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6302/8000 [07:03<01:29, 18.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████      | 5000/8000 [00:39<00:25, 118.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6307/8000 [07:03<01:32, 18.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5021/8000 [00:39<00:23, 126.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6310/8000 [07:04<01:56, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5042/8000 [00:39<00:22, 133.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6312/8000 [07:04<02:02, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5056/8000 [00:39<00:23, 126.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6320/8000 [07:04<01:18, 21.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5079/8000 [00:40<00:20, 139.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6324/8000 [07:04<01:20, 20.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5094/8000 [00:40<00:22, 128.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▋ | 6328/8000 [07:05<01:46, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5112/8000 [00:40<00:20, 138.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▊ | 6334/8000 [07:05<01:25, 19.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5136/8000 [00:40<00:18, 154.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▊ | 6339/8000 [07:05<01:40, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5157/8000 [00:40<00:19, 147.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▊ | 6343/8000 [07:05<01:48, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5194/8000 [00:40<00:19, 140.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▊ | 6349/8000 [07:06<01:31, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5224/8000 [00:41<00:21, 131.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▊ | 6352/8000 [07:06<01:53, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▍     | 5243/8000 [00:41<00:20, 131.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|████▊ | 6358/8000 [07:06<01:25, 19.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5259/8000 [00:41<00:22, 123.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6363/8000 [07:07<01:31, 17.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5272/8000 [00:41<00:24, 110.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6366/8000 [07:07<01:50, 14.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5300/8000 [00:41<00:20, 131.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6368/8000 [07:07<01:52, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▋     | 5316/8000 [00:41<00:20, 128.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6371/8000 [07:07<02:04, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5337/8000 [00:42<00:19, 137.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6376/8000 [07:07<01:34, 17.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5368/8000 [00:42<00:20, 131.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6380/8000 [07:08<01:26, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5384/8000 [00:42<00:20, 130.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6383/8000 [07:08<01:30, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5400/8000 [00:42<00:20, 126.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6388/8000 [07:08<01:14, 21.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5414/8000 [00:42<00:20, 124.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6392/8000 [07:08<01:31, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5430/8000 [00:42<00:20, 124.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6396/8000 [07:08<01:21, 19.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5456/8000 [00:42<00:17, 143.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6400/8000 [07:09<01:29, 17.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5477/8000 [00:43<00:16, 150.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6403/8000 [07:09<01:29, 17.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5493/8000 [00:43<00:18, 133.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6406/8000 [07:09<01:50, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5515/8000 [00:43<00:17, 141.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6408/8000 [07:09<01:54, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5536/8000 [00:43<00:17, 139.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6411/8000 [07:10<01:47, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████     | 5561/8000 [00:43<00:15, 152.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6415/8000 [07:10<01:47, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5580/8000 [00:43<00:18, 134.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6419/8000 [07:10<01:30, 17.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5598/8000 [00:43<00:17, 137.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6422/8000 [07:10<01:26, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5613/8000 [00:44<00:19, 123.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6426/8000 [07:10<01:36, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5632/8000 [00:44<00:18, 125.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6430/8000 [07:11<01:21, 19.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5661/8000 [00:44<00:14, 158.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6434/8000 [07:11<01:16, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5697/8000 [00:44<00:15, 153.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████▊ | 6440/8000 [07:11<01:15, 20.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5730/8000 [00:44<00:12, 177.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6444/8000 [07:11<01:09, 22.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5762/8000 [00:45<00:14, 153.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6451/8000 [07:12<01:24, 18.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5786/8000 [00:45<00:14, 151.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6457/8000 [07:12<01:21, 18.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5816/8000 [00:45<00:17, 121.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6463/8000 [07:12<01:33, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5837/8000 [00:45<00:16, 130.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6467/8000 [07:13<01:38, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5860/8000 [00:45<00:15, 141.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6472/8000 [07:13<01:21, 18.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▊    | 5879/8000 [00:45<00:14, 146.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6476/8000 [07:13<01:19, 19.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5900/8000 [00:46<00:14, 149.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6479/8000 [07:13<01:35, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5922/8000 [00:46<00:21, 97.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6482/8000 [07:14<02:07, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5938/8000 [00:46<00:20, 99.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6486/8000 [07:14<02:16, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5957/8000 [00:46<00:20, 100.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6488/8000 [07:14<02:19, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5973/8000 [00:46<00:21, 95.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6490/8000 [07:15<02:29, 10.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5987/8000 [00:47<00:20, 98.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6493/8000 [07:15<02:31,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6006/8000 [00:47<00:20, 98.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6496/8000 [07:15<02:26, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6031/8000 [00:47<00:18, 104.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▊ | 6499/8000 [07:15<02:04, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6042/8000 [00:47<00:21, 89.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▉ | 6501/8000 [07:16<02:02, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6060/8000 [00:47<00:22, 85.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▉ | 6505/8000 [07:16<01:34, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6082/8000 [00:48<00:20, 91.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▉ | 6512/8000 [07:16<01:19, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6099/8000 [00:48<00:20, 91.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▉ | 6515/8000 [07:16<01:33, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6116/8000 [00:48<00:22, 84.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████▉ | 6518/8000 [07:17<01:47, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6132/8000 [00:48<00:20, 88.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6523/8000 [07:17<01:26, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6148/8000 [00:49<00:24, 75.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6526/8000 [07:17<01:23, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6167/8000 [00:49<00:24, 74.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6531/8000 [07:17<01:11, 20.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6184/8000 [00:49<00:22, 79.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6534/8000 [07:17<01:29, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6210/8000 [00:49<00:17, 101.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6539/8000 [07:18<01:34, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6224/8000 [00:49<00:19, 92.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6541/8000 [07:18<01:39, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6238/8000 [00:50<00:22, 77.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6543/8000 [07:18<01:42, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6258/8000 [00:50<00:22, 77.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6547/8000 [07:18<01:27, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6282/8000 [00:50<00:18, 92.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6553/8000 [07:19<01:21, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6301/8000 [00:50<00:16, 100.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6556/8000 [07:19<01:33, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6315/8000 [00:50<00:16, 100.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6562/8000 [07:19<01:11, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6336/8000 [00:50<00:14, 112.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6565/8000 [07:19<01:08, 20.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6357/8000 [00:51<00:13, 119.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6569/8000 [07:19<01:02, 22.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6377/8000 [00:51<00:12, 129.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6573/8000 [07:19<01:02, 22.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6393/8000 [00:51<00:12, 124.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6577/8000 [07:20<01:02, 22.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6414/8000 [00:51<00:11, 134.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6581/8000 [07:20<01:00, 23.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6428/8000 [00:51<00:12, 127.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6586/8000 [07:20<01:12, 19.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6449/8000 [00:51<00:11, 140.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6590/8000 [07:20<01:05, 21.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6467/8000 [00:51<00:10, 145.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6595/8000 [07:21<01:11, 19.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6493/8000 [00:52<00:11, 127.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████▉ | 6598/8000 [07:21<01:12, 19.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6524/8000 [00:52<00:11, 123.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6602/8000 [07:21<01:06, 20.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6554/8000 [00:52<00:12, 118.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6605/8000 [07:21<01:08, 20.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6579/8000 [00:52<00:10, 133.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6608/8000 [07:21<01:09, 19.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6601/8000 [00:52<00:10, 138.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6612/8000 [07:21<01:00, 22.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6626/8000 [00:53<00:09, 147.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6617/8000 [07:22<01:06, 20.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6647/8000 [00:53<00:09, 147.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6624/8000 [07:22<00:48, 28.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6665/8000 [00:53<00:09, 137.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6628/8000 [07:22<00:49, 27.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▎  | 6681/8000 [00:53<00:10, 129.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6633/8000 [07:22<01:10, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6696/8000 [00:53<00:10, 118.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6638/8000 [07:23<01:38, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6712/8000 [00:53<00:10, 122.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6641/8000 [07:23<01:46, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6734/8000 [00:53<00:09, 130.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6644/8000 [07:23<01:38, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6748/8000 [00:54<00:09, 132.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6650/8000 [07:24<01:28, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6776/8000 [00:54<00:07, 153.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6652/8000 [07:24<01:33, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6803/8000 [00:54<00:06, 173.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6657/8000 [07:24<01:36, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6821/8000 [00:54<00:07, 163.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6659/8000 [07:24<01:44, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6849/8000 [00:54<00:09, 125.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████▉ | 6664/8000 [07:25<01:35, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6867/8000 [00:54<00:09, 124.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████ | 6667/8000 [07:25<01:31, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6885/8000 [00:55<00:08, 124.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████ | 6670/8000 [07:25<01:31, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6902/8000 [00:55<00:08, 123.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████ | 6673/8000 [07:25<01:28, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6921/8000 [00:55<00:08, 120.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████ | 6676/8000 [07:26<01:36, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6936/8000 [00:55<00:09, 114.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████ | 6678/8000 [07:26<02:02, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6964/8000 [00:55<00:07, 131.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6680/8000 [07:26<02:10, 10.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6981/8000 [00:55<00:07, 127.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6684/8000 [07:26<01:50, 11.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6997/8000 [00:55<00:08, 118.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6687/8000 [07:27<01:37, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7011/8000 [00:56<00:08, 112.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6689/8000 [07:27<02:18,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7036/8000 [00:56<00:07, 129.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6693/8000 [07:27<01:53, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7060/8000 [00:56<00:06, 141.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6695/8000 [07:27<02:01, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7080/8000 [00:56<00:06, 138.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6700/8000 [07:28<01:47, 12.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7102/8000 [00:56<00:06, 141.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6705/8000 [07:28<01:27, 14.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7117/8000 [00:56<00:06, 131.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6707/8000 [07:28<01:35, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7137/8000 [00:57<00:06, 127.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6713/8000 [07:29<01:50, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7167/8000 [00:57<00:05, 153.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6715/8000 [07:29<01:51, 11.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7189/8000 [00:57<00:05, 159.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6719/8000 [07:29<01:38, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7223/8000 [00:57<00:05, 146.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6726/8000 [07:29<01:15, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▍ | 7245/8000 [00:57<00:05, 143.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6728/8000 [07:30<01:19, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7266/8000 [00:57<00:04, 151.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6733/8000 [07:30<01:18, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7284/8000 [00:57<00:05, 137.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6735/8000 [07:30<01:20, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7312/8000 [00:58<00:05, 119.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6738/8000 [07:30<01:17, 16.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7331/8000 [00:58<00:05, 124.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6742/8000 [07:30<01:07, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7346/8000 [00:58<00:05, 120.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6744/8000 [07:31<01:09, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7364/8000 [00:58<00:05, 124.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6746/8000 [07:31<01:13, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7384/8000 [00:58<00:05, 121.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6750/8000 [07:31<01:04, 19.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7397/8000 [00:58<00:05, 115.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6755/8000 [07:31<01:24, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7413/8000 [00:59<00:05, 106.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6758/8000 [07:31<01:19, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7438/8000 [00:59<00:04, 126.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████ | 6760/8000 [07:32<01:21, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7455/8000 [00:59<00:04, 125.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6762/8000 [07:32<01:29, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7470/8000 [00:59<00:04, 123.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6765/8000 [07:32<01:19, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7489/8000 [00:59<00:04, 126.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6767/8000 [07:32<01:20, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7513/8000 [00:59<00:03, 137.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6772/8000 [07:32<01:01, 20.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7534/8000 [00:59<00:03, 142.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6775/8000 [07:32<00:58, 20.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7553/8000 [01:00<00:03, 140.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6778/8000 [07:33<01:02, 19.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7579/8000 [01:00<00:03, 115.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6781/8000 [07:33<01:02, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7599/8000 [01:00<00:03, 128.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6784/8000 [07:33<01:03, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7620/8000 [01:00<00:02, 129.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6786/8000 [07:33<01:09, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7637/8000 [01:00<00:02, 125.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6788/8000 [07:33<01:36, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7664/8000 [01:00<00:02, 143.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6791/8000 [07:33<01:25, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7683/8000 [01:01<00:02, 141.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6794/8000 [07:34<01:34, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7705/8000 [01:01<00:02, 144.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6799/8000 [07:34<01:24, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7722/8000 [01:01<00:02, 132.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6803/8000 [07:34<01:11, 16.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7741/8000 [01:01<00:02, 124.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6805/8000 [07:34<01:16, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7760/8000 [01:01<00:01, 130.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6807/8000 [07:35<01:35, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7774/8000 [01:01<00:01, 120.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6810/8000 [07:35<01:25, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7796/8000 [01:02<00:01, 131.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6813/8000 [07:35<01:17, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7816/8000 [01:02<00:01, 129.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6817/8000 [07:35<01:04, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7833/8000 [01:02<00:01, 129.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6819/8000 [07:35<01:09, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7857/8000 [01:02<00:01, 135.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6822/8000 [07:36<01:23, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▊| 7878/8000 [01:02<00:00, 132.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6827/8000 [07:36<01:32, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7894/8000 [01:02<00:00, 128.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████ | 6832/8000 [07:36<01:28, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7913/8000 [01:02<00:00, 130.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████▏| 6836/8000 [07:36<01:13, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7934/8000 [01:03<00:00, 130.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████▏| 6838/8000 [07:37<01:16, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7956/8000 [01:03<00:00, 138.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6841/8000 [07:37<01:11, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7970/8000 [01:03<00:00, 122.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6843/8000 [07:37<01:29, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7994/8000 [01:03<00:00, 138.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6845/8000 [07:37<01:28, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8015it [01:03, 125.84it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  86%|█████▏| 6848/8000 [07:37<01:15, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  86%|█████▏| 6850/8000 [07:37<01:17, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6852/8000 [07:38<01:37, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:14,  7.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6855/8000 [07:38<01:21, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<13:05,  3.97s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6860/8000 [07:38<01:17, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:20,  2.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6866/8000 [07:38<01:07, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:38,  2.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6870/8000 [07:39<01:01, 18.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:45,  2.08s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6873/8000 [07:39<00:59, 18.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:33,  2.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6876/8000 [07:39<00:59, 18.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<06:31,  2.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6881/8000 [07:39<00:53, 21.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:19<06:33,  2.05s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6889/8000 [07:39<00:49, 22.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:21<05:51,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6893/8000 [07:40<00:56, 19.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:22<05:27,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6895/8000 [07:40<01:00, 18.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:24<05:19,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6898/8000 [07:40<01:17, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:25<05:01,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6900/8000 [07:41<01:40, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:49,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6902/8000 [07:41<01:36, 11.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<04:41,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6907/8000 [07:41<01:26, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<04:42,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6910/8000 [07:41<01:17, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:31<04:45,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6912/8000 [07:41<01:19, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:33<04:47,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████▏| 6918/8000 [07:42<00:57, 18.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:34<04:44,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6922/8000 [07:42<01:02, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:36<04:41,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6925/8000 [07:42<01:01, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:37<04:39,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6927/8000 [07:42<01:03, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:39<04:33,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6930/8000 [07:42<01:00, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:40<04:28,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6933/8000 [07:42<00:57, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:42<04:30,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6935/8000 [07:43<01:03, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:43<04:27,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6937/8000 [07:43<01:07, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:45<04:25,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6940/8000 [07:43<01:02, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:46<04:31,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6944/8000 [07:43<01:10, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:48<04:45,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6946/8000 [07:43<01:13, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:50<04:40,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6949/8000 [07:44<01:05, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:51<04:33,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6951/8000 [07:44<01:05, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:53<04:30,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6953/8000 [07:44<01:05, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:55<04:38,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6958/8000 [07:44<00:49, 20.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:57<05:05,  1.82s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6961/8000 [07:44<01:03, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:59<05:23,  1.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6966/8000 [07:45<01:03, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [01:01<05:04,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6968/8000 [07:45<01:06, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:02<04:51,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6974/8000 [07:45<00:49, 20.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:04<04:50,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6980/8000 [07:45<00:40, 25.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:06<04:34,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6987/8000 [07:45<00:49, 20.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:07<04:29,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6992/8000 [07:46<00:54, 18.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:09<04:22,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6996/8000 [07:46<00:49, 20.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:11<04:21,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████▏| 6999/8000 [07:46<00:49, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:12<04:16,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7002/8000 [07:46<00:50, 19.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:14<04:09,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7005/8000 [07:47<01:26, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:15<04:12,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7009/8000 [07:47<01:11, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:17<04:16,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7014/8000 [07:47<01:06, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:19<04:12,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7019/8000 [07:47<00:54, 18.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:21<04:27,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7023/8000 [07:48<00:49, 19.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:22<04:14,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7026/8000 [07:48<00:59, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:24<04:07,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7029/8000 [07:48<00:56, 17.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:25<04:07,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7034/8000 [07:48<00:45, 21.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:27<03:55,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7040/8000 [07:49<00:45, 21.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:28<03:50,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7045/8000 [07:49<00:48, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:30<03:48,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7049/8000 [07:49<00:53, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:31<03:55,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7051/8000 [07:49<00:56, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:33<03:49,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7054/8000 [07:50<01:07, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:35<03:47,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7057/8000 [07:50<01:10, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:36<03:54,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7061/8000 [07:50<00:58, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:38<03:50,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7064/8000 [07:50<00:58, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:39<03:45,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7067/8000 [07:50<00:55, 16.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:42<04:05,  1.74s/it]\n",
+      "Generating signals:  88%|█████▎| 7070/8000 [07:50<00:51, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  88%|█████▎| 7075/8000 [07:51<00:41, 22.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████▎| 7080/8000 [07:51<00:37, 24.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7083/8000 [07:51<00:58, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.29it/s]\n",
+      "Generating signals:  89%|█████▎| 7086/8000 [07:51<01:07, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  89%|█████▎| 7089/8000 [07:52<01:02, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7091/8000 [07:52<01:01, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7096/8000 [07:52<00:59, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 30it [00:01, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7102/8000 [07:52<00:44, 20.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 51it [00:01, 40.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7105/8000 [07:52<00:44, 19.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 78it [00:02, 68.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7109/8000 [07:53<00:50, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 106it [00:02, 100.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7111/8000 [07:53<00:55, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 130it [00:02, 124.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7115/8000 [07:53<00:50, 17.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 154it [00:02, 146.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7118/8000 [07:53<00:48, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 188it [00:02, 188.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7121/8000 [07:53<00:45, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 218it [00:02, 214.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7124/8000 [07:53<00:44, 19.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 246it [00:02, 222.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7131/8000 [07:54<00:40, 21.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 273it [00:02, 197.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7137/8000 [07:54<01:00, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 302it [00:02, 218.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7142/8000 [07:55<00:48, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 327it [00:03, 220.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7147/8000 [07:55<00:54, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 352it [00:03, 223.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7151/8000 [07:55<00:57, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 377it [00:03, 228.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7155/8000 [07:55<00:52, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 402it [00:03, 226.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████▎| 7157/8000 [07:56<01:00, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 426it [00:03, 199.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▎| 7161/8000 [07:56<00:50, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 448it [00:03, 202.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7168/8000 [07:56<00:44, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 470it [00:03, 199.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7172/8000 [07:56<00:49, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 493it [00:03, 204.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7177/8000 [07:57<00:49, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 514it [00:03, 186.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7179/8000 [07:57<00:50, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 534it [00:04, 182.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7181/8000 [07:57<00:53, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 570it [00:04, 228.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7185/8000 [07:57<00:46, 17.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 595it [00:04, 234.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7187/8000 [07:57<00:50, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 620it [00:04, 225.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7190/8000 [07:58<00:45, 17.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 646it [00:04, 233.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7193/8000 [07:58<00:45, 17.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 675it [00:04, 248.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7196/8000 [07:58<00:43, 18.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 704it [00:04, 258.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7198/8000 [07:58<00:52, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 735it [00:04, 271.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7203/8000 [07:58<00:41, 19.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 763it [00:04, 270.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7207/8000 [07:58<00:37, 21.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:05, 158.36it/s]\n",
+      "Generating signals:  90%|█████▍| 7210/8000 [07:59<00:47, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  90%|█████▍| 7214/8000 [07:59<00:41, 18.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7217/8000 [07:59<01:06, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 129\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7222/8000 [08:00<00:49, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 150\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7225/8000 [08:00<00:48, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 140\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7228/8000 [08:00<00:45, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  65%|▋| 65/100 [00:00<00:00, 157\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7230/8000 [08:00<00:46, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  81%|▊| 81/100 [00:00<00:00, 157\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████▍| 7235/8000 [08:00<00:37, 20.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  90%|█████▍| 7238/8000 [08:00<00:37, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [18:13<06:14, 374.04s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.492791622577864\n",
-      "2999: -5.6777011\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  91%|█████▍| 7243/8000 [08:00<00:30, 24.65it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  91%|█████▍| 7246/8000 [08:01<00:32, 22.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7252/8000 [08:01<00:26, 27.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:51,  7.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7256/8000 [08:01<00:35, 20.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:38,  4.13s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7262/8000 [08:01<00:37, 19.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:40,  2.95s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7266/8000 [08:02<00:35, 20.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:50,  2.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7270/8000 [08:02<00:33, 22.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:47,  2.09s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7273/8000 [08:02<00:32, 22.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:03,  1.88s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7276/8000 [08:02<00:34, 20.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:44,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7281/8000 [08:02<00:30, 23.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:25,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7285/8000 [08:02<00:29, 23.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:24,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7288/8000 [08:02<00:31, 22.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<05:19,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7291/8000 [08:03<00:32, 21.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<05:05,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7296/8000 [08:03<00:36, 19.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:56,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7302/8000 [08:03<00:29, 23.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:49,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7305/8000 [08:03<00:31, 21.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:47,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7309/8000 [08:03<00:29, 23.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<04:47,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7312/8000 [08:04<00:38, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:31<04:45,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████▍| 7315/8000 [08:04<00:36, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<04:38,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▍| 7320/8000 [08:04<00:30, 22.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:36,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▍| 7324/8000 [08:04<00:43, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:35<04:31,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▍| 7328/8000 [08:05<00:42, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:30,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▍| 7333/8000 [08:05<00:34, 19.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:28,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7336/8000 [08:05<00:33, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:28,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7341/8000 [08:05<00:28, 23.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:41<04:23,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7346/8000 [08:05<00:25, 25.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:26,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7349/8000 [08:06<00:32, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:44<04:23,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7352/8000 [08:06<00:31, 20.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:46<04:25,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7355/8000 [08:06<00:32, 19.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:48<04:56,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7358/8000 [08:06<00:32, 19.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:49<04:59,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7363/8000 [08:06<00:33, 19.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:51<04:51,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7368/8000 [08:07<00:31, 19.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:53<04:40,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7371/8000 [08:07<00:31, 20.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:54<04:37,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7374/8000 [08:07<00:30, 20.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:56<04:38,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7378/8000 [08:07<00:41, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:58<04:31,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7381/8000 [08:07<00:45, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:59<04:26,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7384/8000 [08:08<00:40, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:01<04:21,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7387/8000 [08:08<00:37, 16.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:02<04:16,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7391/8000 [08:08<00:40, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:04<04:18,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████▌| 7394/8000 [08:08<00:44, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:05<04:21,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7403/8000 [08:09<00:30, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:07<04:23,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7406/8000 [08:09<00:34, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:09<04:20,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7409/8000 [08:09<00:32, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:10<04:18,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7415/8000 [08:09<00:24, 23.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:12<04:17,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7418/8000 [08:09<00:25, 22.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:14<04:23,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7423/8000 [08:09<00:22, 25.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:16<04:23,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7429/8000 [08:10<00:23, 24.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:17<04:17,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7432/8000 [08:10<00:23, 24.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:19<04:11,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7435/8000 [08:10<00:31, 17.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:21<04:24,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7439/8000 [08:10<00:30, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:22<04:12,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7443/8000 [08:10<00:26, 21.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:24<04:06,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7450/8000 [08:11<00:23, 23.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:25<04:05,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7455/8000 [08:11<00:21, 25.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:27<04:08,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7459/8000 [08:11<00:25, 21.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:29<04:12,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7465/8000 [08:11<00:25, 21.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:31<04:16,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7470/8000 [08:12<00:22, 23.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:32<04:12,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7473/8000 [08:12<00:23, 22.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:34<04:03,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████▌| 7476/8000 [08:12<00:23, 22.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:36<03:59,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7480/8000 [08:12<00:21, 23.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:37<03:51,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7483/8000 [08:12<00:33, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:39<03:46,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7487/8000 [08:13<00:28, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:40<03:53,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7490/8000 [08:13<00:34, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:43<04:20,  1.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7492/8000 [08:13<00:34, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:45<04:36,  1.99s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▌| 7494/8000 [08:13<00:35, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:47<04:17,  1.87s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7501/8000 [08:13<00:22, 22.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:48<04:07,  1.81s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7507/8000 [08:14<00:17, 27.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:51<03:57,  1.75s/it]\n",
+      "Generating signals:  94%|█████▋| 7511/8000 [08:14<00:18, 26.96it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  94%|█████▋| 7516/8000 [08:14<00:16, 28.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7521/8000 [08:14<00:20, 23.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7524/8000 [08:14<00:20, 22.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.77it/s]\n",
+      "Generating signals:  94%|█████▋| 7527/8000 [08:14<00:21, 22.12it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  94%|█████▋| 7530/8000 [08:15<00:21, 22.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7535/8000 [08:15<00:23, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7542/8000 [08:15<00:21, 20.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 41it [00:01, 36.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7547/8000 [08:15<00:23, 18.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 77it [00:01, 72.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7552/8000 [08:16<00:25, 17.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 113it [00:01, 112.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7556/8000 [08:16<00:23, 18.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 146it [00:01, 148.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████▋| 7559/8000 [08:16<00:23, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 183it [00:01, 190.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7563/8000 [08:16<00:26, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 217it [00:02, 220.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7567/8000 [08:17<00:23, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 253it [00:02, 252.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7572/8000 [08:17<00:19, 22.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 288it [00:02, 274.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7575/8000 [08:17<00:25, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 324it [00:02, 296.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7579/8000 [08:17<00:22, 18.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 359it [00:02, 307.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7582/8000 [08:18<00:28, 14.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 395it [00:02, 320.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7586/8000 [08:18<00:23, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 430it [00:02, 325.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7589/8000 [08:18<00:24, 16.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 469it [00:02, 341.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7593/8000 [08:18<00:21, 18.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 505it [00:02, 342.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7597/8000 [08:18<00:22, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 543it [00:02, 351.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7600/8000 [08:18<00:20, 19.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 579it [00:03, 349.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7605/8000 [08:19<00:22, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 615it [00:03, 350.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7607/8000 [08:19<00:24, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 655it [00:03, 363.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7611/8000 [08:19<00:20, 18.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 692it [00:03, 362.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7613/8000 [08:19<00:29, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 731it [00:03, 369.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7615/8000 [08:20<00:29, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 216.14it/s]\n",
+      "Generating signals:  95%|█████▋| 7618/8000 [08:20<00:25, 14.88it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  95%|█████▋| 7624/8000 [08:20<00:17, 21.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7628/8000 [08:20<00:16, 22.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:00, 114\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7632/8000 [08:20<00:24, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:00, 121\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████▋| 7635/8000 [08:21<00:27, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:00, 128\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▋| 7641/8000 [08:21<00:19, 18.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:00<00:00, 143\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▋| 7646/8000 [08:21<00:16, 21.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 128\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▋| 7649/8000 [08:21<00:16, 21.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
+      "Generating signals:  96%|█████▋| 7652/8000 [08:21<00:16, 20.48it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  96%|█████▋| 7656/8000 [08:22<00:21, 16.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▋| 7661/8000 [08:22<00:17, 19.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 1/8000 [00:00<18:16,  7.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7667/8000 [08:22<00:13, 24.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 3/8000 [00:00<11:23, 11.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7672/8000 [08:22<00:15, 20.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 6/8000 [00:00<08:11, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7676/8000 [08:23<00:20, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 8/8000 [00:00<08:36, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7678/8000 [08:23<00:29, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 12/8000 [00:00<08:10, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7680/8000 [08:23<00:28, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 15/8000 [00:01<09:07, 14.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7683/8000 [08:24<00:25, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 17/8000 [00:01<09:20, 14.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7687/8000 [08:24<00:20, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 19/8000 [00:01<09:15, 14.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7689/8000 [08:24<00:20, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 22/8000 [00:01<08:02, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7691/8000 [08:24<00:22, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 25/8000 [00:01<07:27, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7693/8000 [08:24<00:30, 10.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 29/8000 [00:01<05:59, 22.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7698/8000 [08:25<00:25, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 33/8000 [00:02<08:31, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7700/8000 [08:25<00:24, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 35/8000 [00:02<08:38, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7702/8000 [08:25<00:29, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 39/8000 [00:02<07:02, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7704/8000 [08:25<00:29, 10.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 42/8000 [00:02<06:41, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7710/8000 [08:26<00:17, 16.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 45/8000 [00:02<08:06, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7713/8000 [08:26<00:17, 16.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 48/8000 [00:02<07:37, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7715/8000 [08:26<00:18, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 50/8000 [00:03<10:02, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7718/8000 [08:26<00:21, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 52/8000 [00:03<12:11, 10.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████▊| 7720/8000 [08:27<00:25, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 54/8000 [00:03<11:06, 11.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7723/8000 [08:27<00:22, 12.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 58/8000 [00:03<08:08, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7729/8000 [08:27<00:14, 18.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 61/8000 [00:03<09:11, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7734/8000 [08:27<00:17, 14.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 66/8000 [00:04<08:29, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7737/8000 [08:27<00:16, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 68/8000 [00:04<10:13, 12.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7740/8000 [08:28<00:15, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 71/8000 [00:04<09:04, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7743/8000 [08:28<00:15, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 76/8000 [00:05<09:23, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7745/8000 [08:28<00:15, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 79/8000 [00:05<08:28, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7747/8000 [08:28<00:16, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 82/8000 [00:05<07:53, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7750/8000 [08:28<00:15, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 85/8000 [00:05<07:15, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7752/8000 [08:28<00:16, 15.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 87/8000 [00:05<09:15, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7756/8000 [08:29<00:13, 18.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 90/8000 [00:05<08:05, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7758/8000 [08:29<00:13, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 99/8000 [00:06<05:39, 23.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7762/8000 [08:29<00:12, 19.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 103/8000 [00:06<05:13, 25.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7769/8000 [08:29<00:10, 21.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 107/8000 [00:06<05:10, 25.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7772/8000 [08:29<00:14, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 116/8000 [00:06<05:51, 22.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7774/8000 [08:30<00:14, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 122/8000 [00:07<05:41, 23.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7777/8000 [08:30<00:14, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 126/8000 [00:07<08:26, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7779/8000 [08:30<00:14, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 129/8000 [00:07<08:06, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7781/8000 [08:30<00:15, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 131/8000 [00:07<08:21, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7783/8000 [08:30<00:15, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 133/8000 [00:08<08:33, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7787/8000 [08:31<00:17, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 135/8000 [00:08<08:57, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7790/8000 [08:31<00:15, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 139/8000 [00:08<07:22, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7792/8000 [08:31<00:15, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 142/8000 [00:08<07:10, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7795/8000 [08:31<00:17, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 146/8000 [00:08<06:26, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████▊| 7799/8000 [08:32<00:17, 11.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 149/8000 [00:08<06:05, 21.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7801/8000 [08:32<00:19, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 153/8000 [00:08<05:24, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7804/8000 [08:32<00:16, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 157/8000 [00:09<06:45, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7808/8000 [08:32<00:12, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 160/8000 [00:09<06:34, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7812/8000 [08:33<00:13, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 163/8000 [00:09<06:12, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7814/8000 [08:33<00:13, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 169/8000 [00:09<08:20, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7819/8000 [08:33<00:10, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 172/8000 [00:10<07:47, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7825/8000 [08:33<00:08, 20.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 177/8000 [00:10<07:55, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▊| 7830/8000 [08:33<00:07, 22.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 182/8000 [00:10<06:42, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7834/8000 [08:34<00:07, 23.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 185/8000 [00:10<06:35, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7839/8000 [08:34<00:06, 26.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 190/8000 [00:11<07:04, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7842/8000 [08:34<00:06, 23.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 193/8000 [00:11<08:04, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7846/8000 [08:34<00:07, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 197/8000 [00:11<07:02, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7849/8000 [08:34<00:07, 20.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 202/8000 [00:11<06:54, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7853/8000 [08:34<00:06, 21.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 206/8000 [00:11<07:28, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7858/8000 [08:35<00:05, 24.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 209/8000 [00:12<07:08, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7863/8000 [08:35<00:06, 20.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 212/8000 [00:12<08:10, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7866/8000 [08:35<00:06, 19.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 215/8000 [00:12<07:30, 17.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7869/8000 [08:35<00:06, 19.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 219/8000 [00:12<06:40, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7876/8000 [08:36<00:06, 20.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 223/8000 [00:12<07:07, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████▉| 7879/8000 [08:36<00:05, 20.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 227/8000 [00:13<07:55, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7885/8000 [08:36<00:06, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 231/8000 [00:13<06:46, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7887/8000 [08:36<00:08, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 234/8000 [00:13<08:33, 15.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7891/8000 [08:37<00:09, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 237/8000 [00:13<07:39, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7894/8000 [08:37<00:08, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 241/8000 [00:13<06:34, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7897/8000 [08:37<00:07, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 249/8000 [00:14<04:20, 29.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7900/8000 [08:38<00:08, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 255/8000 [00:14<04:49, 26.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7904/8000 [08:38<00:07, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 259/8000 [00:14<04:47, 26.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7906/8000 [08:38<00:08, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 266/8000 [00:14<04:47, 26.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7910/8000 [08:38<00:06, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 271/8000 [00:14<05:07, 25.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7916/8000 [08:39<00:05, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▋                 | 278/8000 [00:15<05:04, 25.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7919/8000 [08:39<00:05, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 281/8000 [00:15<05:14, 24.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7921/8000 [08:39<00:05, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 284/8000 [00:15<05:09, 24.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7923/8000 [08:39<00:05, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 288/8000 [00:15<06:13, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7926/8000 [08:39<00:04, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 293/8000 [00:15<06:05, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7933/8000 [08:40<00:03, 19.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 298/8000 [00:16<07:20, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7938/8000 [08:40<00:02, 22.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 300/8000 [00:16<07:27, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7941/8000 [08:40<00:02, 21.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 302/8000 [00:16<07:37, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7944/8000 [08:40<00:02, 20.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 304/8000 [00:16<07:28, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7948/8000 [08:40<00:02, 21.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 306/8000 [00:16<07:27, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████▉| 7957/8000 [08:41<00:02, 20.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 308/8000 [00:16<07:26, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7960/8000 [08:41<00:02, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 314/8000 [00:17<04:57, 25.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7964/8000 [08:41<00:02, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 318/8000 [00:17<04:35, 27.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7967/8000 [08:42<00:02, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 323/8000 [00:17<04:06, 31.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7970/8000 [08:42<00:01, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 327/8000 [00:17<06:17, 20.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7973/8000 [08:42<00:02, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 331/8000 [00:17<06:40, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7978/8000 [08:42<00:01, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 335/8000 [00:18<09:35, 13.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7984/8000 [08:43<00:00, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 337/8000 [00:18<09:34, 13.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7986/8000 [08:43<00:00, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 343/8000 [00:18<07:52, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7989/8000 [08:43<00:00, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 345/8000 [00:18<07:41, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7991/8000 [08:43<00:00, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 348/8000 [00:19<08:47, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████▉| 7993/8000 [08:43<00:00, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 350/8000 [00:19<13:19,  9.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████| 8000/8000 [08:43<00:00, 15.27it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   4%|▊                 | 353/8000 [00:19<10:54, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 355/8000 [00:20<10:31, 12.11it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                            | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 361/8000 [00:20<09:07, 13.95it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                    | 1/200 [00:08<26:38,  8.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 364/8000 [00:20<08:24, 15.15it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▏                   | 2/200 [00:09<14:29,  4.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 366/8000 [00:20<08:29, 14.99it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▎                   | 3/200 [00:11<10:24,  3.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 371/8000 [00:20<06:34, 19.33it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                   | 4/200 [00:13<08:28,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 374/8000 [00:21<07:37, 16.68it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▌                   | 5/200 [00:15<07:23,  2.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 376/8000 [00:21<07:35, 16.72it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▌                   | 6/200 [00:16<06:51,  2.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 378/8000 [00:21<07:56, 16.00it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▋                   | 7/200 [00:18<06:27,  2.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 380/8000 [00:21<08:05, 15.70it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▊                   | 8/200 [00:20<06:14,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 384/8000 [00:21<06:51, 18.51it/s]\u001b[A\u001b[A\n",
+      "training:   4%|▉                   | 9/200 [00:22<06:09,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 388/8000 [00:21<06:10, 20.54it/s]\u001b[A\u001b[A\n",
+      "training:   5%|▉                  | 10/200 [00:24<05:54,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 391/8000 [00:21<06:15, 20.24it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█                  | 11/200 [00:25<05:43,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 396/8000 [00:22<04:57, 25.53it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 12/200 [00:27<05:38,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 400/8000 [00:22<06:39, 19.01it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▏                 | 13/200 [00:29<05:28,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 405/8000 [00:22<08:01, 15.76it/s]\u001b[A\u001b[A\n",
+      "training:   7%|█▎                 | 14/200 [00:30<05:27,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 408/8000 [00:23<08:39, 14.61it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▍                 | 15/200 [00:32<05:27,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 411/8000 [00:23<10:19, 12.25it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 16/200 [00:34<05:21,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 414/8000 [00:23<08:51, 14.26it/s]\u001b[A\u001b[A\n",
+      "training:   8%|█▌                 | 17/200 [00:36<05:20,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 417/8000 [00:23<08:08, 15.53it/s]\u001b[A\u001b[A\n",
+      "training:   9%|█▋                 | 18/200 [00:37<05:16,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 420/8000 [00:23<07:25, 17.03it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▊                 | 19/200 [00:39<05:13,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 422/8000 [00:24<11:18, 11.18it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 20/200 [00:41<05:10,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 424/8000 [00:24<10:57, 11.53it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█▉                 | 21/200 [00:43<05:17,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 427/8000 [00:24<11:04, 11.40it/s]\u001b[A\u001b[A\n",
+      "training:  11%|██                 | 22/200 [00:45<05:32,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 431/8000 [00:24<08:35, 14.70it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▏                | 23/200 [00:47<05:33,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 434/8000 [00:25<09:09, 13.76it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▎                | 24/200 [00:48<05:21,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 437/8000 [00:25<08:05, 15.58it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██▍                | 25/200 [00:50<05:13,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 442/8000 [00:25<06:06, 20.62it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██▍                | 26/200 [00:52<05:08,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 445/8000 [00:25<06:18, 19.97it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▌                | 27/200 [00:54<05:14,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 450/8000 [00:25<05:12, 24.16it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▋                | 28/200 [00:56<05:13,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 453/8000 [00:25<06:35, 19.09it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██▊                | 29/200 [00:57<05:14,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 456/8000 [00:26<06:32, 19.22it/s]\u001b[A\u001b[A\n",
+      "training:  15%|██▊                | 30/200 [00:59<05:05,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 459/8000 [00:26<06:27, 19.44it/s]\u001b[A\u001b[A\n",
+      "training:  16%|██▉                | 31/200 [01:01<05:10,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 463/8000 [00:26<05:38, 22.26it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███                | 32/200 [01:03<05:14,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 466/8000 [00:26<05:34, 22.52it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███▏               | 33/200 [01:05<05:07,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 471/8000 [00:26<06:27, 19.41it/s]\u001b[A\u001b[A\n",
+      "training:  17%|███▏               | 34/200 [01:07<05:21,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 475/8000 [00:26<05:37, 22.30it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▎               | 35/200 [01:09<05:06,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 478/8000 [00:27<06:27, 19.43it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▍               | 36/200 [01:10<05:01,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 482/8000 [00:27<07:06, 17.62it/s]\u001b[A\u001b[A\n",
+      "training:  18%|███▌               | 37/200 [01:12<04:58,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 484/8000 [00:27<09:17, 13.49it/s]\u001b[A\u001b[A\n",
+      "training:  19%|███▌               | 38/200 [01:14<05:11,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 491/8000 [00:27<06:06, 20.49it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▋               | 39/200 [01:16<05:05,  1.90s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 494/8000 [00:27<06:03, 20.62it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▊               | 40/200 [01:18<05:05,  1.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 500/8000 [00:28<04:44, 26.38it/s]\u001b[A\u001b[A\n",
+      "training:  20%|███▉               | 41/200 [01:20<04:54,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 508/8000 [00:28<04:33, 27.40it/s]\u001b[A\u001b[A\n",
+      "training:  21%|███▉               | 42/200 [01:22<04:46,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 514/8000 [00:28<04:55, 25.37it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████               | 43/200 [01:23<04:45,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 519/8000 [00:28<04:34, 27.22it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▏              | 44/200 [01:25<04:46,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 522/8000 [00:28<04:51, 25.68it/s]\u001b[A\u001b[A\n",
+      "training:  22%|████▎              | 45/200 [01:27<04:41,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 527/8000 [00:29<06:43, 18.54it/s]\u001b[A\u001b[A\n",
+      "training:  23%|████▎              | 46/200 [01:29<04:45,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 531/8000 [00:29<08:18, 14.98it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▍              | 47/200 [01:31<04:40,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 535/8000 [00:30<08:11, 15.19it/s]\u001b[A\u001b[A\n",
+      "training:  24%|████▌              | 48/200 [01:35<05:00,  1.98s/it]\n",
       "\n",
-      "Generating signals:   7%|█▏                | 540/8000 [00:30<07:42, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 542/8000 [00:30<09:28, 13.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 550/8000 [00:30<06:00, 20.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 554/8000 [00:30<06:27, 19.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.60it/s]\n",
       "\n",
-      "Generating signals:   7%|█▎                | 558/8000 [00:31<05:39, 21.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 562/8000 [00:31<05:17, 23.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 567/8000 [00:31<05:43, 21.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 572/8000 [00:31<07:41, 16.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 29it [00:01, 24.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 575/8000 [00:32<07:15, 17.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 66it [00:01, 61.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 578/8000 [00:32<07:02, 17.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 98it [00:01, 95.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 581/8000 [00:32<09:04, 13.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 134it [00:01, 137.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 583/8000 [00:32<08:57, 13.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 165it [00:02, 168.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 585/8000 [00:32<08:49, 14.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 196it [00:02, 196.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 590/8000 [00:33<07:38, 16.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 229it [00:02, 226.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 593/8000 [00:33<07:00, 17.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 265it [00:02, 258.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 598/8000 [00:33<06:42, 18.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 305it [00:02, 294.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 600/8000 [00:33<06:51, 17.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 341it [00:02, 310.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 602/8000 [00:34<10:52, 11.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 376it [00:02, 305.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 604/8000 [00:34<10:47, 11.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 412it [00:02, 318.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 607/8000 [00:34<09:00, 13.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 446it [00:02, 297.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 610/8000 [00:34<09:15, 13.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 480it [00:03, 305.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 613/8000 [00:34<08:07, 15.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 512it [00:03, 276.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 617/8000 [00:34<06:44, 18.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 551it [00:03, 305.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 623/8000 [00:35<04:57, 24.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 583it [00:03, 307.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 626/8000 [00:35<08:03, 15.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 615it [00:03, 304.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 629/8000 [00:35<07:30, 16.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 647it [00:03, 291.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 634/8000 [00:35<05:51, 20.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 684it [00:03, 312.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 637/8000 [00:36<06:59, 17.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 721it [00:03, 327.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 641/8000 [00:36<07:19, 16.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 755it [00:03, 321.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 647/8000 [00:36<05:31, 22.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 196.39it/s]\n",
       "\n",
-      "Generating signals:   8%|█▍                | 652/8000 [00:36<07:39, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 657/8000 [00:37<07:27, 16.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%| | 0/100 [00:00<\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 660/8000 [00:37<08:23, 14.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 662/8000 [00:37<08:40, 14.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 666/8000 [00:38<09:32, 12.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 668/8000 [00:38<09:19, 13.11it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 671/8000 [00:38<10:10, 12.01it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 674/8000 [00:38<09:29, 12.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 677/8000 [00:38<08:31, 14.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|▉| 88/100 [00:00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 680/8000 [00:38<07:31, 16.21it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:0\n",
       "\n",
-      "Generating signals:   9%|█▌                | 684/8000 [00:39<06:12, 19.66it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples: 100%|█████████████████████████████████████████████████████████████████████| 4/4 [1:07:43<00:00, 1115.39s/it]\u001b[A\n",
+      "Replicates:  20%|████▌                  | 3/15 [3:17:17<12:53:41, 3868.43s/it]\n",
+      "Nyquist Samples:   0%|                       | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.2847788383043754\n",
+      "2999: -5.9228057\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   9%|█▌                | 691/8000 [00:39<06:40, 18.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 694/8000 [00:39<06:28, 18.79it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 700/8000 [00:39<06:06, 19.93it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<25:20,  7.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 703/8000 [00:40<05:50, 20.83it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<13:28,  4.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 707/8000 [00:40<05:37, 21.58it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:10<09:47,  2.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 710/8000 [00:40<07:06, 17.10it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<08:05,  2.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 712/8000 [00:40<07:19, 16.58it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<07:02,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 715/8000 [00:40<06:41, 18.15it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:15<06:20,  1.96s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 719/8000 [00:40<05:38, 21.50it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<05:48,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 722/8000 [00:41<07:20, 16.53it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:18<05:25,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 724/8000 [00:41<07:16, 16.67it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:20<05:18,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 726/8000 [00:41<07:46, 15.58it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:21<05:16,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 731/8000 [00:41<06:05, 19.91it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:23<05:10,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 734/8000 [00:41<05:52, 20.60it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:25<05:05,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 739/8000 [00:41<04:55, 24.54it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:26<05:06,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 746/8000 [00:42<04:46, 25.35it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:28<05:04,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 751/8000 [00:42<05:13, 23.11it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:30<05:05,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 754/8000 [00:42<05:16, 22.91it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:31<05:02,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 757/8000 [00:42<06:36, 18.25it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:33<05:02,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 759/8000 [00:42<06:51, 17.58it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:35<05:11,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 762/8000 [00:43<06:19, 19.07it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:37<05:13,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 771/8000 [00:43<03:50, 31.41it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:38<05:00,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 778/8000 [00:43<04:10, 28.89it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:40<04:47,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 787/8000 [00:43<04:10, 28.82it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:41<04:42,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 794/8000 [00:44<04:15, 28.26it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:43<04:39,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 799/8000 [00:44<03:52, 30.97it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:44<04:33,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 803/8000 [00:44<03:54, 30.72it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:46<04:44,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 811/8000 [00:44<04:12, 28.46it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:47<04:33,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 818/8000 [00:45<06:05, 19.67it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:49<04:31,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 824/8000 [00:45<05:10, 23.09it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:51<04:33,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 827/8000 [00:45<05:10, 23.12it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:52<04:37,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 830/8000 [00:45<07:27, 16.03it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:54<04:34,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 838/8000 [00:46<07:15, 16.45it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:55<04:28,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 841/8000 [00:46<06:57, 17.15it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:57<04:33,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 844/8000 [00:46<06:33, 18.21it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:59<04:34,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 847/8000 [00:46<06:25, 18.56it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [01:00<04:22,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 850/8000 [00:46<06:12, 19.18it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:02<04:42,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 855/8000 [00:47<06:29, 18.36it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:05<05:43,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 858/8000 [00:47<06:03, 19.67it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:07<05:15,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 861/8000 [00:47<05:53, 20.18it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:08<04:55,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 865/8000 [00:47<08:08, 14.62it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:10<04:40,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 867/8000 [00:48<08:03, 14.76it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:11<04:23,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 871/8000 [00:48<07:02, 16.88it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:13<04:15,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 875/8000 [00:48<06:04, 19.56it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:14<04:06,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 879/8000 [00:48<05:28, 21.67it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:16<04:01,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 884/8000 [00:48<05:57, 19.92it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:17<03:57,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 888/8000 [00:49<06:30, 18.23it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:19<04:01,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 891/8000 [00:49<07:35, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:21<04:04,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 894/8000 [00:49<07:05, 16.70it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:22<04:02,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 897/8000 [00:49<06:45, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:24<03:57,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 899/8000 [00:49<06:59, 16.93it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:25<03:53,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 901/8000 [00:49<07:17, 16.22it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:27<03:51,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 909/8000 [00:50<05:24, 21.83it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:28<03:46,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 913/8000 [00:50<06:15, 18.87it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:30<03:41,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 916/8000 [00:50<05:57, 19.82it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:31<03:40,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 919/8000 [00:50<05:56, 19.89it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:33<03:40,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 922/8000 [00:50<07:09, 16.48it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:34<03:39,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 925/8000 [00:51<06:58, 16.92it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:36<03:36,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 928/8000 [00:51<06:28, 18.22it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [01:37<03:37,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 930/8000 [00:51<06:47, 17.34it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [01:39<03:35,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 933/8000 [00:51<06:20, 18.59it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:40<03:31,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 936/8000 [00:51<07:41, 15.30it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:42<03:29,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 939/8000 [00:51<06:53, 17.08it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:43<03:31,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 941/8000 [00:52<07:13, 16.28it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [01:45<03:30,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 943/8000 [00:52<07:46, 15.13it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [01:46<03:24,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 946/8000 [00:52<07:01, 16.73it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [01:48<03:25,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 948/8000 [00:52<09:51, 11.92it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [01:49<03:28,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 951/8000 [00:52<08:27, 13.88it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [01:51<03:26,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 954/8000 [00:53<09:00, 13.05it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [01:53<03:26,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 956/8000 [00:53<08:44, 13.43it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [01:54<03:25,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 958/8000 [00:53<08:17, 14.14it/s]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [01:56<03:24,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 962/8000 [00:53<06:23, 18.36it/s]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [01:57<03:21,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 965/8000 [00:53<08:20, 14.05it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [01:59<03:13,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 969/8000 [00:53<06:30, 18.01it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▌                    | 72/200 [02:00<03:08,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 975/8000 [00:54<05:56, 19.73it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▋                    | 73/200 [02:01<03:02,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 981/8000 [00:54<05:42, 20.49it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [02:03<02:58,  1.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 984/8000 [00:54<05:34, 20.96it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [02:06<03:30,  1.68s/it]\n",
       "\n",
-      "Generating signals:  12%|██▏               | 988/8000 [00:54<05:08, 22.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 992/8000 [00:55<06:06, 19.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 996/8000 [00:55<06:45, 17.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 16.07it/s]\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1001/8000 [00:55<05:45, 20.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1005/8000 [00:55<05:19, 21.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1008/8000 [00:55<05:23, 21.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1012/8000 [00:56<07:24, 15.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 43it [00:01, 40.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1016/8000 [00:56<06:21, 18.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 83it [00:01, 82.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1019/8000 [00:56<07:13, 16.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 121it [00:01, 125.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1024/8000 [00:56<05:46, 20.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 161it [00:01, 172.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1031/8000 [00:57<06:15, 18.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 199it [00:01, 212.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1034/8000 [00:57<08:15, 14.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 241it [00:01, 256.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1036/8000 [00:57<08:08, 14.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 279it [00:02, 262.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1038/8000 [00:57<08:18, 13.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 314it [00:02, 272.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1040/8000 [00:58<09:59, 11.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 348it [00:02, 281.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1042/8000 [00:58<09:25, 12.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 385it [00:02, 302.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1046/8000 [00:58<07:21, 15.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 426it [00:02, 329.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1049/8000 [00:58<06:50, 16.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 462it [00:02, 332.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1051/8000 [00:58<06:56, 16.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 498it [00:02, 333.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1055/8000 [00:58<05:46, 20.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 537it [00:02, 347.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1060/8000 [00:59<05:30, 21.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 575it [00:02, 351.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1063/8000 [00:59<05:24, 21.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 611it [00:03, 350.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1067/8000 [00:59<07:23, 15.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 649it [00:03, 358.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1069/8000 [00:59<07:22, 15.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 686it [00:03, 360.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1071/8000 [00:59<07:52, 14.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 723it [00:03, 349.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1073/8000 [01:00<09:42, 11.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 759it [00:03, 342.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1077/8000 [01:00<07:32, 15.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 220.66it/s]\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1080/8000 [01:00<08:51, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1083/8000 [01:00<07:46, 14.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1088/8000 [01:00<07:08, 16.12it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 172.3\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1090/8000 [01:01<07:25, 15.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 135.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1094/8000 [01:01<07:35, 15.15it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 131.6\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1096/8000 [01:01<07:30, 15.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|▋| 65/100 [00:00<00:00, 130.6\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1099/8000 [01:01<06:46, 16.99it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:00<00:00, 132.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1104/8000 [01:01<05:16, 21.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 139.\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1107/8000 [01:01<05:10, 22.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1111/8000 [01:02<07:18, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1114/8000 [01:02<06:44, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                  | 169/8000 [00:00<00:09, 857.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1119/8000 [01:02<06:46, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                  | 349/8000 [00:00<00:08, 934.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1121/8000 [01:03<08:05, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 516/8000 [00:01<00:31, 235.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1124/8000 [01:03<07:25, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 686/8000 [00:01<00:21, 339.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1129/8000 [01:03<05:37, 20.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 856/8000 [00:02<00:16, 431.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1132/8000 [01:03<05:26, 21.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1022/8000 [00:02<00:13, 535.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1136/8000 [01:03<07:43, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1189/8000 [00:02<00:10, 624.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1141/8000 [01:04<07:07, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1373/8000 [00:02<00:09, 731.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1144/8000 [01:04<06:32, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▌              | 1563/8000 [00:02<00:07, 827.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1148/8000 [01:04<05:54, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▉              | 1737/8000 [00:02<00:07, 888.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1151/8000 [01:04<07:21, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1923/8000 [00:03<00:06, 940.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1153/8000 [01:04<08:47, 12.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2093/8000 [00:03<00:05, 1012.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1155/8000 [01:05<08:33, 13.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2268/8000 [00:03<00:05, 991.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1159/8000 [01:05<06:44, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2428/8000 [00:04<00:15, 359.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1162/8000 [01:05<06:41, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▊            | 2608/8000 [00:04<00:11, 460.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1164/8000 [01:05<06:53, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2780/8000 [00:04<00:09, 536.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1166/8000 [01:05<07:13, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▋           | 2953/8000 [00:05<00:08, 628.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1170/8000 [01:05<06:00, 18.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3114/8000 [00:05<00:07, 679.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1173/8000 [01:06<07:05, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▍          | 3291/8000 [00:05<00:06, 761.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1178/8000 [01:06<05:29, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▊          | 3477/8000 [00:05<00:05, 806.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1181/8000 [01:06<07:01, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▏         | 3644/8000 [00:05<00:04, 910.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1184/8000 [01:06<06:44, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▌         | 3810/8000 [00:05<00:04, 922.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1187/8000 [01:06<07:51, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▍        | 3992/8000 [00:06<00:03, 1008.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1190/8000 [01:07<06:59, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|████████▊        | 4152/8000 [00:06<00:03, 1000.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1193/8000 [01:07<06:19, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▏       | 4333/8000 [00:06<00:03, 1029.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1196/8000 [01:07<07:27, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|█████████▌       | 4507/8000 [00:06<00:03, 1053.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1199/8000 [01:07<07:03, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|█████████▉       | 4675/8000 [00:06<00:03, 1033.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1204/8000 [01:07<05:28, 20.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▎      | 4842/8000 [00:06<00:03, 1002.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1209/8000 [01:08<05:42, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████▋      | 5016/8000 [00:07<00:02, 1035.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1218/8000 [01:08<03:53, 29.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████      | 5195/8000 [00:07<00:02, 1070.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1226/8000 [01:08<04:00, 28.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▍     | 5369/8000 [00:07<00:02, 1087.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1232/8000 [01:08<04:54, 22.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████▊     | 5556/8000 [00:07<00:02, 1100.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1236/8000 [01:09<04:36, 24.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▏    | 5728/8000 [00:07<00:01, 1137.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1239/8000 [01:09<05:45, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████▌    | 5906/8000 [00:07<00:01, 1149.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1244/8000 [01:09<05:58, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|████████████▉    | 6086/8000 [00:08<00:01, 1178.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1247/8000 [01:09<06:45, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▎   | 6254/8000 [00:08<00:01, 1144.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1249/8000 [01:10<08:00, 14.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|█████████████▋   | 6437/8000 [00:08<00:01, 1159.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1252/8000 [01:10<09:46, 11.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████   | 6620/8000 [00:08<00:01, 1175.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1257/8000 [01:11<11:03, 10.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|██████████████▍  | 6787/8000 [00:08<00:01, 1130.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1264/8000 [01:11<07:15, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████▊  | 6969/8000 [00:08<00:00, 1109.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1268/8000 [01:11<07:15, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▏ | 7149/8000 [00:08<00:00, 1147.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1273/8000 [01:11<08:01, 13.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▌ | 7322/8000 [00:09<00:00, 1085.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1277/8000 [01:12<09:03, 12.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████▉ | 7495/8000 [00:09<00:00, 1042.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1280/8000 [01:12<08:13, 13.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████▎| 7668/8000 [00:09<00:00, 1082.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1284/8000 [01:12<06:43, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|████████████████▋| 7825/8000 [00:09<00:00, 1043.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1287/8000 [01:12<06:26, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8010it [00:09, 818.98it/s] \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  16%|██▋              | 1291/8000 [01:13<07:06, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1293/8000 [01:13<07:16, 15.37it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1295/8000 [01:13<07:42, 14.48it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<26:15,  7.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1297/8000 [01:13<13:14,  8.44it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<13:39,  4.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1299/8000 [01:14<11:47,  9.48it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:10<09:32,  2.91s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1302/8000 [01:14<11:29,  9.71it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<07:41,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1305/8000 [01:14<10:43, 10.40it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:13<06:29,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1310/8000 [01:15<09:49, 11.34it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:15<05:55,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1312/8000 [01:15<09:31, 11.70it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:16<05:31,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1314/8000 [01:15<10:31, 10.59it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:18<05:22,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1317/8000 [01:15<08:53, 12.53it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:19<05:11,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1321/8000 [01:15<07:17, 15.25it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:21<04:56,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1323/8000 [01:15<07:45, 14.33it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:22<04:51,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1328/8000 [01:16<05:40, 19.60it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:24<04:50,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1332/8000 [01:16<07:33, 14.70it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:25<04:48,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1336/8000 [01:16<07:47, 14.26it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:27<04:43,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1340/8000 [01:16<06:33, 16.93it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:28<04:41,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1345/8000 [01:17<05:12, 21.26it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:30<04:32,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1349/8000 [01:17<04:37, 23.98it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:31<04:28,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1355/8000 [01:17<04:52, 22.72it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:33<04:25,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1360/8000 [01:17<05:20, 20.71it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:34<04:33,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1364/8000 [01:17<05:42, 19.40it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:36<04:47,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1367/8000 [01:18<07:27, 14.83it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:38<04:46,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1369/8000 [01:18<08:53, 12.43it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:39<04:49,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1375/8000 [01:18<06:19, 17.45it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:41<04:42,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1379/8000 [01:19<07:34, 14.56it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:42<04:35,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1382/8000 [01:19<06:49, 16.17it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:44<04:30,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1387/8000 [01:19<06:49, 16.15it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:45<04:24,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1390/8000 [01:19<06:52, 16.02it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:47<04:22,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1394/8000 [01:19<06:14, 17.63it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:48<04:22,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1396/8000 [01:20<06:24, 17.19it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:50<04:20,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1399/8000 [01:20<06:16, 17.55it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:51<04:19,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1403/8000 [01:20<05:26, 20.19it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:53<04:22,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1409/8000 [01:20<05:27, 20.12it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:54<04:16,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1412/8000 [01:20<05:33, 19.77it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:56<04:21,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1415/8000 [01:20<05:39, 19.37it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:58<04:13,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1420/8000 [01:21<05:06, 21.46it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [00:59<04:20,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1425/8000 [01:21<07:00, 15.65it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:01<04:33,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1431/8000 [01:21<05:19, 20.56it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:03<04:24,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1434/8000 [01:21<05:21, 20.45it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:04<04:11,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1439/8000 [01:22<05:32, 19.75it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:05<04:02,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1443/8000 [01:22<05:06, 21.38it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:07<04:11,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1451/8000 [01:22<04:25, 24.70it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:09<04:04,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1459/8000 [01:22<04:11, 26.04it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:10<03:54,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1463/8000 [01:23<04:37, 23.57it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:11<03:49,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1470/8000 [01:23<05:51, 18.60it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:13<03:44,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1478/8000 [01:24<05:31, 19.65it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:14<03:43,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1485/8000 [01:24<05:01, 21.62it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:16<03:43,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1490/8000 [01:24<05:16, 20.54it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:17<03:50,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1493/8000 [01:24<05:23, 20.13it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:19<03:47,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1497/8000 [01:25<06:44, 16.08it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:20<03:48,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1500/8000 [01:25<06:36, 16.38it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:22<03:52,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1504/8000 [01:25<05:50, 18.51it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:24<03:47,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1507/8000 [01:25<05:40, 19.07it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:25<03:42,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1510/8000 [01:25<05:22, 20.14it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:26<03:37,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1514/8000 [01:26<08:33, 12.64it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:28<03:30,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1517/8000 [01:26<07:42, 14.02it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:29<03:34,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1519/8000 [01:26<07:52, 13.73it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:31<03:32,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1521/8000 [01:26<07:41, 14.05it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [01:33<03:44,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1524/8000 [01:26<06:52, 15.69it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [01:34<03:42,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1527/8000 [01:27<09:24, 11.46it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:36<03:34,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1530/8000 [01:27<08:04, 13.36it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:37<03:34,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1533/8000 [01:27<08:32, 12.62it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:38<03:26,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1538/8000 [01:27<05:58, 18.03it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [01:40<03:22,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1544/8000 [01:28<05:25, 19.86it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [01:41<03:18,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1548/8000 [01:28<06:00, 17.91it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [01:43<03:13,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1551/8000 [01:28<06:49, 15.74it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [01:44<03:15,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1553/8000 [01:28<07:15, 14.80it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [01:46<03:15,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1557/8000 [01:28<05:53, 18.24it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [01:47<03:14,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1560/8000 [01:29<06:59, 15.35it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [01:49<03:39,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1565/8000 [01:29<06:39, 16.09it/s]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [01:52<04:25,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1568/8000 [01:29<07:36, 14.11it/s]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [01:54<04:07,  1.90s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1571/8000 [01:29<07:13, 14.84it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [01:55<03:48,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1574/8000 [01:30<07:42, 13.90it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▌                    | 72/200 [01:57<03:46,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1576/8000 [01:30<08:59, 11.91it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▋                    | 73/200 [01:58<03:33,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1582/8000 [01:30<05:58, 17.89it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [02:00<03:27,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1588/8000 [01:30<05:42, 18.74it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [02:02<03:28,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1594/8000 [01:31<07:02, 15.17it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▏                   | 76/200 [02:03<03:20,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1597/8000 [01:31<06:32, 16.33it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▎                   | 77/200 [02:05<03:13,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1603/8000 [01:31<04:59, 21.35it/s]\u001b[A\u001b[A\n",
+      "training:  39%|████████████▍                   | 78/200 [02:06<03:09,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1606/8000 [01:31<05:49, 18.31it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▋                   | 79/200 [02:08<03:07,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1611/8000 [01:32<04:43, 22.52it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▊                   | 80/200 [02:09<03:03,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1614/8000 [01:32<04:43, 22.51it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▉                   | 81/200 [02:11<02:59,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1619/8000 [01:32<04:09, 25.55it/s]\u001b[A\u001b[A\n",
+      "training:  41%|█████████████                   | 82/200 [02:12<02:58,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1622/8000 [01:32<04:24, 24.07it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▎                  | 83/200 [02:14<03:01,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1625/8000 [01:32<06:51, 15.50it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▍                  | 84/200 [02:15<03:00,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1628/8000 [01:33<07:28, 14.19it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▌                  | 85/200 [02:17<02:56,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1633/8000 [01:33<06:40, 15.91it/s]\u001b[A\u001b[A\n",
+      "training:  43%|█████████████▊                  | 86/200 [02:19<02:59,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1636/8000 [01:33<06:15, 16.97it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▉                  | 87/200 [02:20<02:52,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1639/8000 [01:33<06:00, 17.65it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████                  | 88/200 [02:21<02:48,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1642/8000 [01:33<05:34, 18.99it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████▏                 | 89/200 [02:23<02:47,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1648/8000 [01:33<04:18, 24.61it/s]\u001b[A\u001b[A\n",
+      "training:  45%|██████████████▍                 | 90/200 [02:25<02:47,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1653/8000 [01:34<03:43, 28.41it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▌                 | 91/200 [02:26<02:43,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1657/8000 [01:34<04:33, 23.20it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▋                 | 92/200 [02:28<02:48,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1660/8000 [01:34<04:33, 23.21it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▉                 | 93/200 [02:29<02:47,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1666/8000 [01:34<05:35, 18.87it/s]\u001b[A\u001b[A\n",
+      "training:  47%|███████████████                 | 94/200 [02:31<02:44,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1671/8000 [01:35<04:43, 22.31it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▏                | 95/200 [02:32<02:40,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1675/8000 [01:35<04:29, 23.46it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▎                | 96/200 [02:34<02:39,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1679/8000 [01:35<04:29, 23.49it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▌                | 97/200 [02:35<02:42,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1682/8000 [01:35<04:34, 22.98it/s]\u001b[A\u001b[A\n",
+      "training:  49%|███████████████▋                | 98/200 [02:37<02:37,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1686/8000 [01:35<04:13, 24.88it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▊                | 99/200 [02:38<02:34,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1690/8000 [01:36<06:23, 16.46it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▌               | 100/200 [02:40<02:34,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1694/8000 [01:36<06:44, 15.60it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▋               | 101/200 [02:41<02:30,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1699/8000 [01:36<05:15, 19.94it/s]\u001b[A\u001b[A\n",
+      "training:  51%|███████████████▊               | 102/200 [02:43<02:31,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1703/8000 [01:36<05:49, 18.04it/s]\u001b[A\u001b[A\n",
+      "training:  52%|███████████████▉               | 103/200 [02:45<02:28,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1708/8000 [01:37<06:02, 17.33it/s]\u001b[A\u001b[A\n",
+      "training:  52%|████████████████               | 104/200 [02:46<02:28,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1711/8000 [01:37<06:45, 15.49it/s]\u001b[A\u001b[A\n",
+      "training:  52%|████████████████▎              | 105/200 [02:48<02:25,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1714/8000 [01:37<06:19, 16.55it/s]\u001b[A\u001b[A\n",
+      "training:  53%|████████████████▍              | 106/200 [02:51<02:31,  1.61s/it]\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1716/8000 [01:37<06:23, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1720/8000 [01:37<05:10, 20.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1723/8000 [01:37<05:02, 20.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1727/8000 [01:38<05:54, 17.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 14.01it/s]\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1732/8000 [01:38<04:37, 22.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1735/8000 [01:38<04:28, 23.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1738/8000 [01:38<05:16, 19.81it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1746/8000 [01:38<03:36, 28.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 35it [00:01, 28.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1753/8000 [01:38<03:57, 26.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 60it [00:01, 51.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1758/8000 [01:39<03:34, 29.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 97it [00:01, 92.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1763/8000 [01:39<04:41, 22.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 133it [00:02, 134.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1767/8000 [01:39<04:37, 22.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 164it [00:02, 166.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1771/8000 [01:40<06:38, 15.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 195it [00:02, 195.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1775/8000 [01:40<07:10, 14.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 231it [00:02, 232.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1777/8000 [01:40<07:20, 14.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 268it [00:02, 266.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1780/8000 [01:40<06:53, 15.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 303it [00:02, 286.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1782/8000 [01:40<06:54, 15.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 337it [00:02, 288.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1785/8000 [01:41<06:14, 16.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 371it [00:02, 300.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1790/8000 [01:41<05:15, 19.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 408it [00:02, 318.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1793/8000 [01:41<05:46, 17.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 446it [00:02, 332.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1798/8000 [01:41<04:41, 22.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 481it [00:03, 306.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1803/8000 [01:41<04:48, 21.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 514it [00:03, 300.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1810/8000 [01:42<04:20, 23.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 550it [00:03, 316.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1815/8000 [01:42<04:39, 22.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 583it [00:03, 299.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1820/8000 [01:42<04:42, 21.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 621it [00:03, 318.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1827/8000 [01:42<04:28, 23.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 660it [00:03, 335.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1830/8000 [01:43<06:09, 16.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 697it [00:03, 342.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1833/8000 [01:43<05:48, 17.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 733it [00:03, 344.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1835/8000 [01:43<05:51, 17.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 196.40it/s]\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1837/8000 [01:43<06:16, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1839/8000 [01:43<06:37, 15.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1843/8000 [01:43<05:27, 18.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 129.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1845/8000 [01:44<06:57, 14.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▎| 27/100 [00:00<00:00, 121.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1850/8000 [01:44<04:59, 20.53it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:00<00:00, 118.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1853/8000 [01:44<06:52, 14.91it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:00<00:00, 123.6\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1855/8000 [01:44<06:58, 14.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:00<00:00, 105.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1858/8000 [01:44<06:30, 15.74it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|▊| 84/100 [00:00<00:00, 122.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1860/8000 [01:45<06:31, 15.70it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 120.\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1862/8000 [01:45<08:38, 11.84it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|███▌          | 1/4 [07:21<22:03, 441.13s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.548391682426977\n",
+      "2999: -6.2113204\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  23%|███▉             | 1864/8000 [01:45<08:15, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1869/8000 [01:45<05:47, 17.65it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1872/8000 [01:45<05:51, 17.43it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:08<26:58,  8.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1874/8000 [01:46<07:21, 13.88it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:10<14:54,  4.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1877/8000 [01:46<08:55, 11.44it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:11<10:23,  3.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉             | 1881/8000 [01:46<06:54, 14.76it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:13<08:20,  2.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1885/8000 [01:46<06:07, 16.64it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<07:12,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1887/8000 [01:47<07:51, 12.97it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:16<06:26,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1893/8000 [01:47<06:36, 15.41it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<05:50,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1895/8000 [01:47<06:56, 14.67it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:19<05:43,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1899/8000 [01:47<05:59, 16.98it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:21<05:35,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1902/8000 [01:47<05:31, 18.39it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:22<05:20,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1907/8000 [01:48<05:23, 18.82it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:24<05:20,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1910/8000 [01:48<05:05, 19.94it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:26<05:06,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1913/8000 [01:48<04:53, 20.73it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:27<05:03,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1916/8000 [01:48<04:38, 21.87it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:29<04:57,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1919/8000 [01:48<07:18, 13.88it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:30<04:53,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1922/8000 [01:49<06:33, 15.44it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:32<04:53,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1928/8000 [01:49<04:35, 22.04it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:34<04:54,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1934/8000 [01:49<04:36, 21.91it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:35<04:53,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1939/8000 [01:49<04:10, 24.19it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:37<04:47,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1942/8000 [01:49<05:07, 19.68it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:38<04:40,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1946/8000 [01:50<04:39, 21.67it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:40<04:35,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1951/8000 [01:50<03:56, 25.53it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:41<04:30,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1955/8000 [01:50<04:33, 22.07it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:43<04:27,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1961/8000 [01:50<04:29, 22.38it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:44<04:24,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1965/8000 [01:50<04:07, 24.41it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:46<04:30,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1969/8000 [01:50<04:37, 21.76it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:47<04:26,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1973/8000 [01:51<04:20, 23.17it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:49<04:25,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1978/8000 [01:51<04:42, 21.35it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:50<04:24,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1984/8000 [01:51<04:41, 21.39it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:52<04:20,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1988/8000 [01:51<05:08, 19.48it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:53<04:14,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1992/8000 [01:52<04:43, 21.19it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:55<04:21,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1996/8000 [01:52<05:34, 17.97it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:56<04:18,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1998/8000 [01:52<05:40, 17.63it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:58<04:25,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2001/8000 [01:52<06:46, 14.74it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [01:00<04:22,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2005/8000 [01:53<07:11, 13.89it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:01<04:15,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2008/8000 [01:53<06:33, 15.23it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:03<04:09,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2011/8000 [01:53<06:01, 16.54it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:04<04:06,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2013/8000 [01:53<06:32, 15.26it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:06<04:04,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2017/8000 [01:53<05:25, 18.38it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:07<04:01,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2019/8000 [01:53<05:44, 17.38it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:09<04:04,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2021/8000 [01:54<09:16, 10.74it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:10<04:03,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2024/8000 [01:54<09:29, 10.49it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:12<03:58,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2027/8000 [01:54<10:36,  9.39it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:13<04:02,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2029/8000 [01:55<09:30, 10.47it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:15<04:05,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2036/8000 [01:55<05:25, 18.35it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:17<04:04,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2039/8000 [01:55<06:36, 15.03it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:18<04:11,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2043/8000 [01:55<06:43, 14.75it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:20<04:12,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2045/8000 [01:55<06:47, 14.63it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:22<04:06,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2047/8000 [01:56<06:50, 14.51it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:23<04:00,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2054/8000 [01:56<05:25, 18.26it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:25<03:56,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2058/8000 [01:56<04:40, 21.18it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:26<04:01,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2065/8000 [01:56<03:34, 27.61it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:28<03:52,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2071/8000 [01:56<03:50, 25.77it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:29<03:48,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2076/8000 [01:57<05:07, 19.27it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:31<03:44,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2079/8000 [01:57<06:49, 14.47it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:32<03:41,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2082/8000 [01:57<06:21, 15.52it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:34<03:41,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2084/8000 [01:58<07:14, 13.62it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [01:35<03:38,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2086/8000 [01:58<06:59, 14.11it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [01:37<03:38,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2090/8000 [01:58<05:44, 17.13it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:38<03:35,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2095/8000 [01:58<04:33, 21.61it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:40<03:41,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2099/8000 [01:58<04:23, 22.36it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:42<03:43,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2102/8000 [01:59<05:42, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [01:43<03:37,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2107/8000 [01:59<04:33, 21.58it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [01:45<03:39,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2112/8000 [01:59<04:57, 19.76it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [01:47<03:38,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2115/8000 [01:59<06:07, 15.99it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [01:48<03:32,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2117/8000 [02:00<07:36, 12.90it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [01:50<03:37,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2119/8000 [02:00<07:28, 13.10it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [01:51<03:31,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2121/8000 [02:00<07:21, 13.33it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [01:53<03:28,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2124/8000 [02:00<08:12, 11.93it/s]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [01:55<03:29,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2126/8000 [02:00<07:53, 12.40it/s]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [01:56<03:31,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2130/8000 [02:01<07:22, 13.27it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [01:58<03:32,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2132/8000 [02:01<07:02, 13.90it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▌                    | 72/200 [02:00<03:28,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2134/8000 [02:01<07:04, 13.82it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▋                    | 73/200 [02:01<03:31,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2139/8000 [02:01<06:01, 16.21it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [02:03<03:34,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2144/8000 [02:01<04:43, 20.67it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [02:05<03:27,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2147/8000 [02:01<04:36, 21.16it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▏                   | 76/200 [02:06<03:20,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2152/8000 [02:02<05:02, 19.33it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▎                   | 77/200 [02:08<03:15,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2157/8000 [02:02<05:06, 19.05it/s]\u001b[A\u001b[A\n",
+      "training:  39%|████████████▍                   | 78/200 [02:09<03:15,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2159/8000 [02:02<05:16, 18.48it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▋                   | 79/200 [02:11<03:11,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2161/8000 [02:02<05:49, 16.70it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▊                   | 80/200 [02:12<03:06,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2166/8000 [02:02<04:24, 22.04it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▉                   | 81/200 [02:14<03:06,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2172/8000 [02:02<03:20, 29.00it/s]\u001b[A\u001b[A\n",
+      "training:  41%|█████████████                   | 82/200 [02:16<03:06,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2179/8000 [02:03<03:30, 27.60it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▎                  | 83/200 [02:17<03:05,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2185/8000 [02:03<03:08, 30.91it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▍                  | 84/200 [02:19<03:08,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2189/8000 [02:03<03:51, 25.10it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▌                  | 85/200 [02:21<03:07,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2194/8000 [02:03<04:20, 22.28it/s]\u001b[A\u001b[A\n",
+      "training:  43%|█████████████▊                  | 86/200 [02:22<03:06,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2200/8000 [02:04<04:10, 23.15it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▉                  | 87/200 [02:24<03:19,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2204/8000 [02:04<04:55, 19.60it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████                  | 88/200 [02:26<03:13,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2209/8000 [02:04<05:12, 18.54it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████▏                 | 89/200 [02:27<03:08,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2216/8000 [02:05<04:57, 19.41it/s]\u001b[A\u001b[A\n",
+      "training:  45%|██████████████▍                 | 90/200 [02:29<03:02,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2220/8000 [02:05<06:00, 16.04it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▌                 | 91/200 [02:31<03:00,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2222/8000 [02:05<06:08, 15.69it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▋                 | 92/200 [02:32<03:00,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2228/8000 [02:05<04:30, 21.35it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▉                 | 93/200 [02:36<02:59,  1.68s/it]\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2236/8000 [02:05<03:18, 29.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2240/8000 [02:06<03:59, 24.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2243/8000 [02:06<04:05, 23.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2246/8000 [02:06<05:10, 18.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 14.68it/s]\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2251/8000 [02:06<05:08, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2255/8000 [02:07<05:47, 16.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2257/8000 [02:07<05:55, 16.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2261/8000 [02:07<06:09, 15.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 31it [00:01, 25.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2264/8000 [02:07<07:55, 12.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 61it [00:01, 55.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2266/8000 [02:08<07:26, 12.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 92it [00:01, 89.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2272/8000 [02:08<05:04, 18.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 119it [00:01, 115.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2277/8000 [02:08<04:06, 23.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 145it [00:02, 138.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2280/8000 [02:08<04:05, 23.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 174it [00:02, 168.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2285/8000 [02:08<05:26, 17.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 187.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2289/8000 [02:09<05:52, 16.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 229it [00:02, 211.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2294/8000 [02:09<04:46, 19.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 260it [00:02, 234.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2299/8000 [02:09<04:47, 19.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 293it [00:02, 258.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2307/8000 [02:09<03:26, 27.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 323it [00:02, 269.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2311/8000 [02:09<04:13, 22.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 353it [00:02, 270.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2314/8000 [02:10<05:11, 18.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 382it [00:02, 274.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2317/8000 [02:10<05:12, 18.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 411it [00:03, 261.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2321/8000 [02:10<04:35, 20.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 439it [00:03, 260.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2324/8000 [02:10<05:40, 16.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 466it [00:03, 248.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2327/8000 [02:11<06:41, 14.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 492it [00:03, 249.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2330/8000 [02:11<06:19, 14.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 518it [00:03, 248.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2333/8000 [02:11<05:37, 16.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 544it [00:03, 244.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2336/8000 [02:11<05:08, 18.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 570it [00:03, 247.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2342/8000 [02:11<04:24, 21.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 597it [00:03, 252.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2348/8000 [02:12<04:19, 21.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 623it [00:03, 251.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2351/8000 [02:12<06:18, 14.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 649it [00:03, 250.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2353/8000 [02:12<06:12, 15.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 680it [00:04, 266.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2355/8000 [02:12<06:20, 14.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 711it [00:04, 278.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2358/8000 [02:13<07:20, 12.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 740it [00:04, 279.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2360/8000 [02:13<08:19, 11.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 769it [00:04, 267.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2365/8000 [02:13<06:39, 14.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 176.36it/s]\n",
       "\n",
-      "Generating signals:  30%|█████            | 2368/8000 [02:13<05:54, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2374/8000 [02:13<04:11, 22.37it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2377/8000 [02:13<04:10, 22.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:00, 108.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2381/8000 [02:14<05:00, 18.68it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|▎| 25/100 [00:00<00:00, 120.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2387/8000 [02:14<05:26, 17.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 110.1\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2393/8000 [02:15<05:45, 16.21it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 112.6\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2396/8000 [02:15<05:20, 17.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 114.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2399/8000 [02:15<06:48, 13.70it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:00<00:00, 105.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2401/8000 [02:15<06:38, 14.04it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:00<00:00, 110.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2403/8000 [02:15<06:21, 14.69it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 110.\n",
       "\n",
-      "Generating signals:  30%|█████            | 2405/8000 [02:16<07:52, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2408/8000 [02:16<06:48, 13.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2411/8000 [02:16<05:55, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                    | 44/8000 [00:00<00:32, 241.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2417/8000 [02:16<04:02, 23.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                   | 88/8000 [00:00<00:41, 192.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2421/8000 [02:16<03:45, 24.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                  | 137/8000 [00:00<00:31, 245.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2425/8000 [02:16<03:32, 26.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                  | 179/8000 [00:00<00:31, 247.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2428/8000 [02:16<04:32, 20.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 227/8000 [00:00<00:30, 251.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2431/8000 [02:17<05:24, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                  | 268/8000 [00:01<00:34, 222.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2435/8000 [02:17<04:37, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                  | 320/8000 [00:01<00:31, 245.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2440/8000 [02:17<04:48, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                  | 363/8000 [00:01<00:29, 254.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2444/8000 [02:17<04:19, 21.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                  | 413/8000 [00:01<00:28, 263.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2449/8000 [02:17<03:49, 24.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                  | 459/8000 [00:01<00:31, 241.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2455/8000 [02:18<03:46, 24.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 511/8000 [00:02<00:29, 254.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2459/8000 [02:18<04:17, 21.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 557/8000 [00:02<00:29, 248.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2464/8000 [02:18<03:44, 24.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                 | 599/8000 [00:02<00:29, 249.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2469/8000 [02:18<04:09, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                 | 653/8000 [00:02<00:27, 269.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2476/8000 [02:19<03:51, 23.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 695/8000 [00:02<00:27, 264.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2480/8000 [02:19<03:34, 25.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                 | 746/8000 [00:02<00:26, 269.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2484/8000 [02:19<04:15, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                 | 787/8000 [00:03<00:28, 254.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2489/8000 [02:19<03:48, 24.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                 | 827/8000 [00:03<00:30, 234.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2493/8000 [02:19<03:28, 26.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 882/8000 [00:03<00:26, 266.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2497/8000 [02:19<03:29, 26.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏                | 933/8000 [00:03<00:25, 277.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2500/8000 [02:19<03:24, 26.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                | 979/8000 [00:03<00:25, 273.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2505/8000 [02:20<03:04, 29.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1028/8000 [00:04<00:24, 281.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2511/8000 [02:20<04:58, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍               | 1083/8000 [00:04<00:22, 305.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2519/8000 [02:20<04:12, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1136/8000 [00:04<00:22, 300.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2524/8000 [02:21<04:29, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1182/8000 [00:04<00:22, 298.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2528/8000 [02:21<04:56, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊               | 1225/8000 [00:04<00:24, 277.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2533/8000 [02:21<04:17, 21.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉               | 1279/8000 [00:04<00:22, 296.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2539/8000 [02:21<04:02, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉               | 1326/8000 [00:05<00:23, 288.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2542/8000 [02:22<04:44, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1361/8000 [00:05<00:25, 262.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2547/8000 [02:22<04:03, 22.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1429/8000 [00:05<00:21, 310.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2553/8000 [02:22<05:27, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎              | 1481/8000 [00:05<00:21, 308.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2558/8000 [02:23<05:03, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1529/8000 [00:05<00:22, 293.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2562/8000 [02:23<05:15, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▌              | 1572/8000 [00:05<00:22, 283.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2565/8000 [02:23<04:55, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋              | 1618/8000 [00:06<00:23, 276.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2571/8000 [02:23<04:32, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▊              | 1667/8000 [00:06<00:23, 268.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2574/8000 [02:23<04:37, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▊              | 1719/8000 [00:06<00:22, 285.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2580/8000 [02:24<04:38, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▉              | 1769/8000 [00:06<00:22, 281.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2585/8000 [02:24<03:59, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████              | 1814/8000 [00:06<00:21, 286.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2590/8000 [02:24<03:43, 24.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▏             | 1855/8000 [00:06<00:21, 285.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2594/8000 [02:24<03:43, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1901/8000 [00:07<00:23, 262.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2598/8000 [02:25<05:01, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍             | 1946/8000 [00:07<00:23, 252.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2601/8000 [02:25<04:44, 18.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▌             | 2008/8000 [00:07<00:21, 276.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2606/8000 [02:25<04:33, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2060/8000 [00:07<00:19, 310.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2610/8000 [02:25<04:37, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2106/8000 [00:07<00:19, 303.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2614/8000 [02:25<04:14, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▊             | 2147/8000 [00:07<00:21, 278.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2617/8000 [02:26<06:17, 14.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▉             | 2197/8000 [00:08<00:19, 292.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2620/8000 [02:26<05:40, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2253/8000 [00:08<00:19, 297.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2624/8000 [02:26<04:52, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▏            | 2296/8000 [00:08<00:19, 293.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2627/8000 [02:26<04:33, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▎            | 2338/8000 [00:08<00:18, 300.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2632/8000 [02:26<03:46, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▎            | 2377/8000 [00:08<00:19, 295.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2635/8000 [02:26<04:07, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2425/8000 [00:08<00:18, 308.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2638/8000 [02:27<06:19, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▌            | 2479/8000 [00:09<00:18, 304.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2643/8000 [02:27<04:48, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▋            | 2538/8000 [00:09<00:16, 327.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2647/8000 [02:27<05:18, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▊            | 2588/8000 [00:09<00:18, 299.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2651/8000 [02:27<05:01, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2640/8000 [00:09<00:17, 312.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2658/8000 [02:28<04:49, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████            | 2697/8000 [00:09<00:15, 332.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2665/8000 [02:28<03:43, 23.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▏           | 2748/8000 [00:09<00:17, 307.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2668/8000 [02:28<03:40, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2801/8000 [00:10<00:17, 291.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2677/8000 [02:28<03:13, 27.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▍           | 2849/8000 [00:10<00:17, 293.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2682/8000 [02:29<03:42, 23.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▌           | 2897/8000 [00:10<00:17, 292.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2686/8000 [02:29<04:00, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▌           | 2941/8000 [00:10<00:17, 287.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2690/8000 [02:29<05:17, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▋           | 2993/8000 [00:10<00:16, 296.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2693/8000 [02:30<05:48, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▊           | 3052/8000 [00:10<00:15, 316.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2695/8000 [02:30<05:54, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▉           | 3111/8000 [00:11<00:15, 307.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2697/8000 [02:30<06:43, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3159/8000 [00:11<00:15, 316.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2700/8000 [02:30<05:55, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3203/8000 [00:11<00:17, 274.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2704/8000 [02:30<04:58, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▎          | 3245/8000 [00:11<00:16, 280.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2706/8000 [02:30<05:03, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▍          | 3293/8000 [00:11<00:17, 263.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2710/8000 [02:30<04:14, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▌          | 3348/8000 [00:11<00:16, 288.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2716/8000 [02:31<04:01, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▋          | 3399/8000 [00:12<00:16, 275.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2719/8000 [02:31<04:42, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▊          | 3445/8000 [00:12<00:16, 281.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2721/8000 [02:31<05:58, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▊          | 3493/8000 [00:12<00:16, 273.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2726/8000 [02:32<06:04, 14.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▉          | 3539/8000 [00:12<00:15, 292.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2729/8000 [02:32<05:32, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████          | 3584/8000 [00:12<00:14, 296.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2732/8000 [02:32<05:05, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▏         | 3647/8000 [00:12<00:13, 317.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2735/8000 [02:32<04:48, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▎         | 3692/8000 [00:13<00:13, 312.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2739/8000 [02:32<04:01, 21.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3738/8000 [00:13<00:14, 293.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2742/8000 [02:32<06:18, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▌         | 3788/8000 [00:13<00:14, 285.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2747/8000 [02:33<04:56, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▌         | 3830/8000 [00:13<00:15, 270.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2751/8000 [02:33<05:13, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▋         | 3885/8000 [00:13<00:13, 297.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2755/8000 [02:33<04:43, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▊         | 3926/8000 [00:13<00:14, 277.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2759/8000 [02:33<04:18, 20.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▉         | 3976/8000 [00:14<00:13, 303.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2763/8000 [02:34<04:47, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████         | 4038/8000 [00:14<00:11, 335.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2769/8000 [02:34<05:04, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4078/8000 [00:14<00:12, 307.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2771/8000 [02:34<05:32, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▎        | 4128/8000 [00:14<00:12, 307.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2773/8000 [02:34<05:56, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▍        | 4170/8000 [00:14<00:12, 299.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2776/8000 [02:35<09:09,  9.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▍        | 4216/8000 [00:14<00:12, 291.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2778/8000 [02:35<09:07,  9.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4273/8000 [00:15<00:11, 314.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2783/8000 [02:35<08:18, 10.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4316/8000 [00:15<00:12, 306.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2788/8000 [02:36<09:19,  9.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▊        | 4359/8000 [00:15<00:11, 306.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2790/8000 [02:36<09:30,  9.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4408/8000 [00:15<00:11, 316.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2792/8000 [02:37<09:26,  9.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████        | 4449/8000 [00:15<00:11, 310.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2793/8000 [02:37<10:50,  8.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████        | 4492/8000 [00:15<00:11, 296.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2798/8000 [02:37<07:44, 11.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▏       | 4538/8000 [00:15<00:12, 286.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2801/8000 [02:37<07:28, 11.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▎       | 4594/8000 [00:16<00:10, 312.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2804/8000 [02:37<06:12, 13.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4646/8000 [00:16<00:10, 305.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2806/8000 [02:38<06:32, 13.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▌       | 4709/8000 [00:16<00:10, 323.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2810/8000 [02:38<06:03, 14.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▋       | 4752/8000 [00:16<00:11, 294.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2812/8000 [02:38<08:17, 10.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▊       | 4805/8000 [00:16<00:10, 297.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2814/8000 [02:38<08:02, 10.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4864/8000 [00:16<00:09, 328.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2817/8000 [02:39<06:45, 12.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████       | 4908/8000 [00:17<00:10, 302.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2820/8000 [02:39<08:20, 10.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4952/8000 [00:17<00:10, 293.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2823/8000 [02:39<06:55, 12.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5001/8000 [00:17<00:09, 304.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2828/8000 [02:39<05:49, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▍      | 5065/8000 [00:17<00:08, 355.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2835/8000 [02:40<04:30, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▌      | 5113/8000 [00:17<00:08, 337.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2840/8000 [02:40<03:41, 23.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5167/8000 [00:17<00:08, 351.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2845/8000 [02:40<03:19, 25.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5216/8000 [00:18<00:08, 314.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2848/8000 [02:40<03:19, 25.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▊      | 5267/8000 [00:18<00:08, 322.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2853/8000 [02:40<03:42, 23.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▉      | 5318/8000 [00:18<00:07, 336.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2858/8000 [02:40<03:19, 25.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5379/8000 [00:18<00:07, 349.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2862/8000 [02:41<04:00, 21.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▏     | 5433/8000 [00:18<00:07, 364.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2866/8000 [02:41<04:34, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▎     | 5490/8000 [00:18<00:07, 353.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2869/8000 [02:41<04:31, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▍     | 5536/8000 [00:19<00:08, 303.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2873/8000 [02:41<04:55, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▌     | 5581/8000 [00:19<00:08, 297.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2877/8000 [02:41<04:18, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▋     | 5623/8000 [00:19<00:08, 290.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2884/8000 [02:42<03:55, 21.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▋     | 5664/8000 [00:19<00:08, 272.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2887/8000 [02:42<04:15, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▊     | 5709/8000 [00:19<00:08, 274.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2890/8000 [02:42<05:14, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▉     | 5752/8000 [00:19<00:07, 282.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2892/8000 [02:42<05:29, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████     | 5800/8000 [00:19<00:07, 282.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2894/8000 [02:43<06:42, 12.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▏    | 5845/8000 [00:20<00:07, 286.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2899/8000 [02:43<05:07, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▎    | 5900/8000 [00:20<00:06, 324.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2901/8000 [02:43<05:09, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▍    | 5948/8000 [00:20<00:06, 329.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2904/8000 [02:43<04:50, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▍    | 5992/8000 [00:20<00:06, 303.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2908/8000 [02:43<04:09, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████▌    | 6049/8000 [00:20<00:06, 319.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2914/8000 [02:44<04:02, 20.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████▋    | 6097/8000 [00:20<00:06, 314.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2919/8000 [02:44<03:31, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▊    | 6144/8000 [00:21<00:06, 289.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2923/8000 [02:44<04:55, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▉    | 6191/8000 [00:21<00:06, 274.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2930/8000 [02:44<04:16, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6237/8000 [00:21<00:06, 261.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2934/8000 [02:44<03:55, 21.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6289/8000 [00:21<00:05, 288.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2938/8000 [02:45<03:46, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▎   | 6339/8000 [00:21<00:05, 288.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2942/8000 [02:45<03:24, 24.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▎   | 6387/8000 [00:21<00:05, 284.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2946/8000 [02:45<03:53, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▍   | 6433/8000 [00:22<00:05, 277.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2951/8000 [02:45<04:22, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6491/8000 [00:22<00:04, 316.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2957/8000 [02:46<04:11, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▋   | 6536/8000 [00:22<00:05, 291.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2960/8000 [02:46<05:03, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▊   | 6574/8000 [00:22<00:05, 271.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2964/8000 [02:46<05:16, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▉   | 6617/8000 [00:22<00:05, 265.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2967/8000 [02:46<05:56, 14.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████   | 6672/8000 [00:22<00:04, 270.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2973/8000 [02:47<04:13, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████   | 6719/8000 [00:23<00:04, 280.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2976/8000 [02:47<05:11, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▏  | 6760/8000 [00:23<00:04, 262.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2979/8000 [02:47<05:00, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▎  | 6810/8000 [00:23<00:04, 280.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2982/8000 [02:47<04:45, 17.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▍  | 6866/8000 [00:23<00:03, 322.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2987/8000 [02:47<04:30, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▌  | 6918/8000 [00:23<00:03, 313.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2991/8000 [02:48<04:04, 20.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▋  | 6966/8000 [00:23<00:03, 313.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2995/8000 [02:48<04:39, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7016/8000 [00:24<00:03, 302.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2997/8000 [02:48<04:56, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▉  | 7063/8000 [00:24<00:03, 277.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2999/8000 [02:48<04:59, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▉  | 7109/8000 [00:24<00:03, 278.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3003/8000 [02:48<04:12, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████  | 7158/8000 [00:24<00:02, 303.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3006/8000 [02:48<04:18, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7217/8000 [00:24<00:02, 322.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3009/8000 [02:49<04:11, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▎ | 7261/8000 [00:24<00:02, 308.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3012/8000 [02:49<04:01, 20.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▍ | 7313/8000 [00:25<00:02, 333.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3015/8000 [02:49<03:52, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▌ | 7357/8000 [00:25<00:02, 307.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3022/8000 [02:49<04:26, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▋ | 7418/8000 [00:25<00:01, 337.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3027/8000 [02:50<04:30, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▊ | 7465/8000 [00:25<00:01, 318.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3031/8000 [02:50<03:56, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7508/8000 [00:25<00:01, 261.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3036/8000 [02:50<03:29, 23.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████ | 7568/8000 [00:25<00:01, 277.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3039/8000 [02:50<04:21, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▏| 7613/8000 [00:26<00:01, 274.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3044/8000 [02:50<04:31, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▏| 7655/8000 [00:26<00:01, 259.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3047/8000 [02:51<05:06, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7704/8000 [00:26<00:01, 220.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3049/8000 [02:51<05:13, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▍| 7755/8000 [00:26<00:01, 224.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3054/8000 [02:51<04:55, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▌| 7796/8000 [00:27<00:00, 218.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3058/8000 [02:51<05:12, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▋| 7841/8000 [00:27<00:00, 234.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3063/8000 [02:51<04:08, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▋| 7887/8000 [00:27<00:00, 238.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3067/8000 [02:52<06:39, 12.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▉| 7949/8000 [00:27<00:00, 280.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3070/8000 [02:52<06:08, 13.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████▉| 7995/8000 [00:27<00:00, 278.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3073/8000 [02:52<05:24, 15.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8041it [00:27, 288.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  38%|██████▌          | 3078/8000 [02:53<05:19, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3080/8000 [02:53<05:36, 14.62it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3082/8000 [02:53<05:45, 14.23it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<25:47,  7.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3085/8000 [02:53<05:30, 14.89it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<13:38,  4.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3089/8000 [02:53<04:33, 17.96it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:10<09:45,  2.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3092/8000 [02:53<04:08, 19.76it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<07:45,  2.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3096/8000 [02:54<03:36, 22.63it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:13<06:46,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3100/8000 [02:54<03:24, 23.99it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:15<06:03,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3103/8000 [02:54<03:35, 22.70it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:16<05:41,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3111/8000 [02:54<04:03, 20.06it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:18<05:22,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3116/8000 [02:55<04:19, 18.80it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:19<05:03,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3119/8000 [02:55<04:49, 16.86it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:21<05:01,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3121/8000 [02:55<05:00, 16.26it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:22<04:56,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3123/8000 [02:55<05:26, 14.92it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:24<04:47,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3125/8000 [02:55<05:42, 14.22it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:25<04:45,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3128/8000 [02:56<06:14, 13.01it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:27<04:49,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3133/8000 [02:56<04:32, 17.89it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:29<04:46,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3136/8000 [02:56<04:17, 18.89it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:30<04:45,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3141/8000 [02:56<04:18, 18.78it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:32<04:37,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3145/8000 [02:57<04:52, 16.60it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:33<04:32,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3149/8000 [02:57<05:02, 16.05it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:35<04:37,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3151/8000 [02:57<08:16,  9.77it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:36<04:34,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3157/8000 [02:57<05:20, 15.13it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:38<04:24,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3163/8000 [02:58<03:55, 20.55it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:39<04:22,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3168/8000 [02:58<04:13, 19.05it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:41<04:22,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3173/8000 [02:58<04:09, 19.37it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:42<04:19,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3176/8000 [02:58<04:15, 18.91it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:43<04:16,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3182/8000 [02:59<04:08, 19.40it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:45<04:12,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3186/8000 [02:59<04:29, 17.85it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:46<04:11,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3188/8000 [02:59<04:55, 16.27it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:48<04:18,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3190/8000 [02:59<05:12, 15.40it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:49<04:18,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3192/8000 [03:00<06:41, 11.97it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:51<04:14,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3194/8000 [03:00<06:26, 12.42it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:52<04:10,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3197/8000 [03:00<05:32, 14.44it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:54<04:19,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3201/8000 [03:00<04:25, 18.06it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:55<04:13,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3204/8000 [03:00<05:20, 14.95it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:57<04:09,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3207/8000 [03:00<04:59, 16.01it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [00:58<04:07,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3209/8000 [03:01<05:13, 15.29it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:00<04:00,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3212/8000 [03:01<04:54, 16.27it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:01<04:02,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3217/8000 [03:01<04:54, 16.23it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:03<03:59,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3220/8000 [03:01<05:19, 14.97it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:04<03:57,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3223/8000 [03:02<05:55, 13.43it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:06<03:55,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3226/8000 [03:02<05:22, 14.80it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:07<03:52,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3228/8000 [03:02<05:17, 15.02it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:09<03:52,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3230/8000 [03:02<05:24, 14.71it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:10<03:52,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3232/8000 [03:02<05:18, 14.99it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:12<03:47,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3237/8000 [03:02<04:44, 16.74it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:13<03:48,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3240/8000 [03:03<04:39, 17.02it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:15<03:44,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3243/8000 [03:03<04:15, 18.61it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:16<03:45,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3247/8000 [03:03<03:42, 21.36it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:18<03:44,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3254/8000 [03:03<03:27, 22.85it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:19<03:46,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3258/8000 [03:03<04:03, 19.48it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:22<04:08,  1.66s/it]\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3261/8000 [03:04<04:45, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3263/8000 [03:04<04:54, 16.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3265/8000 [03:04<05:19, 14.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3269/8000 [03:04<05:35, 14.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 14.99it/s]\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3274/8000 [03:04<04:26, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3277/8000 [03:05<04:16, 18.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3280/8000 [03:05<05:15, 14.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3284/8000 [03:05<04:19, 18.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 32it [00:01, 27.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3287/8000 [03:05<04:12, 18.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 64it [00:01, 58.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3291/8000 [03:05<03:32, 22.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 92it [00:01, 88.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3294/8000 [03:05<03:52, 20.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 119it [00:01, 116.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3297/8000 [03:06<04:42, 16.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 146it [00:02, 144.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3299/8000 [03:06<04:40, 16.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 173it [00:02, 170.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3302/8000 [03:06<04:28, 17.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 206it [00:02, 207.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3304/8000 [03:06<04:52, 16.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 242it [00:02, 243.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3307/8000 [03:06<04:21, 17.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 276it [00:02, 267.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3309/8000 [03:06<04:41, 16.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 308it [00:02, 261.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3311/8000 [03:07<04:43, 16.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 339it [00:02, 273.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3315/8000 [03:07<04:41, 16.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 369it [00:02, 276.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3319/8000 [03:07<04:02, 19.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 401it [00:02, 287.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3322/8000 [03:07<04:48, 16.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 432it [00:03, 277.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3325/8000 [03:07<04:30, 17.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 461it [00:03, 273.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3329/8000 [03:08<04:31, 17.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 499it [00:03, 302.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3332/8000 [03:08<05:13, 14.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 531it [00:03, 274.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3335/8000 [03:08<04:41, 16.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 560it [00:03, 276.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3338/8000 [03:08<05:13, 14.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 593it [00:03, 290.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3343/8000 [03:08<03:50, 20.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 629it [00:03, 307.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3346/8000 [03:08<03:48, 20.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 663it [00:03, 315.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3350/8000 [03:09<04:22, 17.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 697it [00:03, 321.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3354/8000 [03:09<04:27, 17.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 730it [00:03, 323.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3357/8000 [03:09<05:03, 15.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 190.62it/s]\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3362/8000 [03:09<03:58, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3367/8000 [03:10<03:22, 22.90it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3370/8000 [03:10<04:30, 17.09it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 105.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3373/8000 [03:10<04:24, 17.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▎| 27/100 [00:00<00:00, 134.3\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3382/8000 [03:11<05:07, 15.04it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 134.6\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3385/8000 [03:11<04:51, 15.83it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 145.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3388/8000 [03:11<04:39, 16.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00<00:00, 128.1\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3390/8000 [03:11<04:41, 16.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 132.\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3392/8000 [03:11<06:04, 12.65it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|███████       | 2/4 [14:40<14:39, 439.88s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.625758958731393\n",
+      "2999: -6.1971016\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  42%|███████▏         | 3397/8000 [03:12<04:31, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3399/8000 [03:12<04:49, 15.88it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3402/8000 [03:12<04:33, 16.78it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<25:43,  7.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3404/8000 [03:12<04:41, 16.35it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<14:03,  4.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3407/8000 [03:12<06:47, 11.27it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:11<09:48,  2.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3409/8000 [03:13<06:25, 11.91it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<08:07,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3415/8000 [03:13<04:09, 18.38it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<06:57,  2.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3421/8000 [03:13<03:58, 19.20it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:15<06:12,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3427/8000 [03:13<03:53, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<05:40,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3431/8000 [03:14<05:00, 15.19it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:18<05:20,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3438/8000 [03:14<03:32, 21.46it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:20<05:08,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3442/8000 [03:14<03:13, 23.50it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:21<05:00,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3450/8000 [03:14<02:20, 32.33it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:23<04:49,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3456/8000 [03:14<02:07, 35.60it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:24<04:46,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3465/8000 [03:15<03:03, 24.72it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:26<04:58,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3472/8000 [03:15<03:08, 24.04it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:27<04:48,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3477/8000 [03:15<02:58, 25.39it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:29<04:50,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3482/8000 [03:16<03:49, 19.72it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:30<04:44,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3485/8000 [03:16<04:17, 17.57it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:32<04:36,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3489/8000 [03:16<05:09, 14.58it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:33<04:36,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3493/8000 [03:17<05:53, 12.76it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:35<04:33,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3497/8000 [03:17<05:04, 14.79it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:36<04:31,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3499/8000 [03:17<05:21, 14.01it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:38<04:29,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3502/8000 [03:17<05:32, 13.52it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:39<04:24,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3505/8000 [03:17<04:52, 15.38it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:41<04:23,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3509/8000 [03:18<04:57, 15.07it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:42<04:19,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3513/8000 [03:18<05:10, 14.46it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:44<04:24,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3515/8000 [03:18<05:17, 14.14it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:45<04:22,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3517/8000 [03:18<06:24, 11.67it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:47<04:20,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3520/8000 [03:19<05:33, 13.44it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:48<04:21,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3524/8000 [03:19<05:05, 14.67it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:50<04:15,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3531/8000 [03:19<03:57, 18.82it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:52<04:27,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3535/8000 [03:19<03:34, 20.78it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:53<04:41,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3538/8000 [03:19<03:28, 21.42it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:55<04:33,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3541/8000 [03:20<04:17, 17.32it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:56<04:23,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3546/8000 [03:20<04:24, 16.85it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:58<04:17,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3553/8000 [03:20<03:06, 23.87it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:00<04:17,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3557/8000 [03:20<03:49, 19.33it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:01<04:12,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3563/8000 [03:21<03:34, 20.72it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:03<04:06,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3567/8000 [03:21<03:19, 22.17it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:04<04:04,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3570/8000 [03:21<04:02, 18.28it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:05<03:58,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3574/8000 [03:21<04:29, 16.44it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:07<03:59,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3579/8000 [03:22<04:19, 17.03it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:09<04:04,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3583/8000 [03:22<03:51, 19.10it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:10<04:11,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3587/8000 [03:22<04:04, 18.06it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:12<04:07,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3591/8000 [03:22<04:28, 16.44it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:13<04:01,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3594/8000 [03:23<04:19, 16.99it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:15<04:08,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3599/8000 [03:23<04:10, 17.58it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:17<04:11,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3601/8000 [03:23<05:01, 14.58it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:18<04:07,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3605/8000 [03:23<04:15, 17.23it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:20<03:58,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3607/8000 [03:23<04:23, 16.68it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:21<03:59,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3609/8000 [03:23<04:30, 16.23it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:23<04:05,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3611/8000 [03:24<04:25, 16.52it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:25<03:59,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3617/8000 [03:24<03:07, 23.41it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:26<03:54,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3620/8000 [03:24<03:10, 23.05it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:28<03:57,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3626/8000 [03:24<03:12, 22.67it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:30<03:56,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3630/8000 [03:24<03:46, 19.33it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:31<03:54,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3634/8000 [03:25<04:09, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:34<04:03,  1.69s/it]\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3639/8000 [03:25<03:26, 21.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3643/8000 [03:25<03:52, 18.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3649/8000 [03:25<03:11, 22.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3654/8000 [03:26<03:27, 20.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.63it/s]\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3658/8000 [03:26<03:07, 23.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3661/8000 [03:26<03:14, 22.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3664/8000 [03:26<03:10, 22.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3673/8000 [03:26<02:06, 34.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 27it [00:01, 21.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3678/8000 [03:27<03:23, 21.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 50it [00:01, 42.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3681/8000 [03:27<03:54, 18.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 72it [00:01, 64.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3685/8000 [03:27<03:29, 20.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 92it [00:02, 85.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3689/8000 [03:27<03:13, 22.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 113it [00:02, 107.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3693/8000 [03:27<04:24, 16.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 136it [00:02, 130.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3696/8000 [03:28<04:16, 16.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 157it [00:02, 141.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3699/8000 [03:28<04:15, 16.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 182it [00:02, 165.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3704/8000 [03:28<04:16, 16.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 206it [00:02, 183.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3708/8000 [03:28<04:23, 16.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 232it [00:02, 202.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3712/8000 [03:29<03:47, 18.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 256it [00:02, 207.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3715/8000 [03:29<03:34, 19.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 279it [00:02, 206.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3719/8000 [03:29<03:48, 18.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 309it [00:03, 230.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3723/8000 [03:29<04:05, 17.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 334it [00:03, 222.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3725/8000 [03:29<04:14, 16.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 358it [00:03, 224.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3727/8000 [03:29<04:11, 16.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 383it [00:03, 230.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3732/8000 [03:30<04:19, 16.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 407it [00:03, 208.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3735/8000 [03:30<05:42, 12.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 429it [00:03, 207.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3738/8000 [03:30<05:06, 13.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 459it [00:03, 231.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3741/8000 [03:30<04:32, 15.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 489it [00:03, 247.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3747/8000 [03:31<03:11, 22.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 515it [00:03, 247.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3750/8000 [03:31<03:51, 18.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 541it [00:04, 246.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3754/8000 [03:31<03:27, 20.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 566it [00:04, 241.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3759/8000 [03:31<02:54, 24.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 591it [00:04, 202.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:31<02:43, 25.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 613it [00:04, 175.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3767/8000 [03:31<02:39, 26.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 634it [00:04, 176.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3774/8000 [03:32<03:44, 18.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 655it [00:04, 183.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3778/8000 [03:32<03:28, 20.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 675it [00:04, 169.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3782/8000 [03:32<03:07, 22.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 694it [00:04, 173.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3786/8000 [03:32<03:38, 19.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 713it [00:05, 177.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3790/8000 [03:33<04:03, 17.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 735it [00:05, 186.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3795/8000 [03:33<03:59, 17.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 754it [00:05, 180.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3802/8000 [03:33<02:58, 23.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 776it [00:05, 189.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3805/8000 [03:33<02:58, 23.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 144.85it/s]\n",
       "\n",
-      "Generating signals:  48%|████████         | 3808/8000 [03:33<03:06, 22.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3814/8000 [03:34<03:18, 21.07it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3818/8000 [03:34<03:48, 18.30it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 97.89\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3821/8000 [03:34<03:49, 18.20it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:00, 82.00\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3825/8000 [03:34<03:24, 20.37it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 87.19\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3828/8000 [03:34<03:18, 21.01it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 91.93\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3832/8000 [03:35<03:04, 22.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:00<00:00, 97.17\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3839/8000 [03:35<02:54, 23.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00<00:00, 95.37\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3843/8000 [03:35<02:55, 23.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00<00:00, 92.72\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3847/8000 [03:35<03:24, 20.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|▊| 83/100 [00:00<00:00, 89.12\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3850/8000 [03:35<03:29, 19.78it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 91.6\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3856/8000 [03:36<02:41, 25.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3860/8000 [03:36<03:13, 21.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3863/8000 [03:36<03:16, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 21/8000 [00:00<01:28, 89.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3867/8000 [03:36<02:57, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                    | 42/8000 [00:00<01:12, 110.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3871/8000 [03:36<02:47, 24.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 56/8000 [00:00<01:33, 85.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3874/8000 [03:36<02:49, 24.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 73/8000 [00:00<01:32, 85.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3878/8000 [03:37<03:38, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 95/8000 [00:01<01:25, 92.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▏        | 3882/8000 [03:37<03:12, 21.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 109/8000 [00:01<01:39, 79.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3887/8000 [03:37<02:42, 25.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 132/8000 [00:01<01:21, 96.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3890/8000 [03:37<02:51, 23.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 148/8000 [00:01<01:25, 91.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3895/8000 [03:37<02:25, 28.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                  | 172/8000 [00:01<01:14, 105.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3902/8000 [03:38<02:34, 26.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                  | 193/8000 [00:02<01:16, 102.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3905/8000 [03:38<02:44, 24.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 213/8000 [00:02<01:10, 109.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3911/8000 [03:38<02:51, 23.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 230/8000 [00:02<01:08, 112.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3917/8000 [03:38<02:28, 27.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 245/8000 [00:02<01:15, 102.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3920/8000 [03:38<03:12, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 258/8000 [00:02<01:24, 92.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3924/8000 [03:39<03:33, 19.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 272/8000 [00:02<01:21, 95.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3930/8000 [03:39<03:54, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 288/8000 [00:02<01:21, 94.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3935/8000 [03:39<03:19, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 304/8000 [00:03<01:32, 82.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3938/8000 [03:39<03:22, 20.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 320/8000 [00:03<01:32, 83.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3941/8000 [03:40<03:27, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 337/8000 [00:03<01:38, 78.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3944/8000 [03:40<04:39, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▉                   | 356/8000 [00:03<01:24, 90.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3948/8000 [03:40<04:37, 14.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 368/8000 [00:03<01:26, 88.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3950/8000 [03:40<04:51, 13.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 388/8000 [00:04<01:17, 98.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3953/8000 [03:41<04:27, 15.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                  | 408/8000 [00:04<01:11, 106.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3956/8000 [03:41<04:04, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                  | 426/8000 [00:04<01:09, 108.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3958/8000 [03:41<04:06, 16.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                  | 452/8000 [00:04<01:01, 122.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3960/8000 [03:41<04:16, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                  | 468/8000 [00:04<01:01, 123.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3963/8000 [03:41<04:03, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 482/8000 [00:04<01:04, 116.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3968/8000 [03:41<03:11, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 499/8000 [00:05<01:08, 109.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3974/8000 [03:42<03:05, 21.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▏                 | 522/8000 [00:05<00:59, 125.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3977/8000 [03:42<03:02, 22.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 540/8000 [00:05<01:00, 122.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3981/8000 [03:42<03:27, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 565/8000 [00:05<00:54, 135.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3984/8000 [03:42<04:03, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                 | 582/8000 [00:05<00:58, 127.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3989/8000 [03:42<03:14, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                 | 603/8000 [00:05<00:59, 123.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3994/8000 [03:43<03:20, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                 | 620/8000 [00:05<01:00, 122.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3998/8000 [03:43<03:38, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                 | 648/8000 [00:06<01:08, 107.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4000/8000 [03:43<04:00, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                 | 665/8000 [00:06<01:09, 105.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4002/8000 [03:43<04:03, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 693/8000 [00:06<01:00, 120.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4004/8000 [03:43<05:12, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 713/8000 [00:06<00:56, 127.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4006/8000 [03:44<06:10, 10.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 736/8000 [00:06<00:55, 129.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4008/8000 [03:44<05:55, 11.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                 | 753/8000 [00:07<01:02, 115.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4010/8000 [03:44<05:25, 12.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                 | 769/8000 [00:07<01:06, 108.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4014/8000 [03:44<05:03, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                 | 785/8000 [00:07<01:04, 111.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4017/8000 [03:44<04:36, 14.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                 | 806/8000 [00:07<00:58, 122.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4021/8000 [03:45<03:49, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                 | 833/8000 [00:07<00:52, 135.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4023/8000 [03:45<04:51, 13.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 856/8000 [00:07<00:52, 135.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4027/8000 [03:45<04:07, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 876/8000 [00:08<00:54, 129.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4032/8000 [03:45<03:14, 20.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                | 900/8000 [00:08<00:52, 136.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4036/8000 [03:45<03:01, 21.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                | 919/8000 [00:08<00:56, 126.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4041/8000 [03:45<02:35, 25.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏                | 942/8000 [00:08<00:58, 119.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4045/8000 [03:46<03:22, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                | 960/8000 [00:08<00:58, 120.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4049/8000 [03:46<03:07, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                | 975/8000 [00:08<01:01, 114.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4053/8000 [03:46<02:56, 22.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                | 994/8000 [00:09<00:59, 118.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4056/8000 [03:46<02:52, 22.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1016/8000 [00:09<00:55, 125.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4061/8000 [03:46<02:30, 26.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1033/8000 [00:09<00:55, 125.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4069/8000 [03:47<02:16, 28.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1053/8000 [00:09<00:56, 123.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4074/8000 [03:47<03:20, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍               | 1083/8000 [00:09<01:04, 106.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4078/8000 [03:47<03:04, 21.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍               | 1102/8000 [00:10<01:02, 109.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4082/8000 [03:47<03:25, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1115/8000 [00:10<01:05, 105.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4089/8000 [03:48<03:08, 20.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1136/8000 [00:10<01:02, 109.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4092/8000 [03:48<04:07, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1152/8000 [00:10<01:05, 104.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4095/8000 [03:48<03:46, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1171/8000 [00:10<01:01, 110.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4102/8000 [03:49<03:26, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1185/8000 [00:10<01:05, 104.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4105/8000 [03:49<03:18, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1201/8000 [00:11<01:08, 99.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4108/8000 [03:49<03:44, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1218/8000 [00:11<01:04, 104.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4111/8000 [03:49<05:02, 12.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1229/8000 [00:11<01:11, 94.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4113/8000 [03:50<05:38, 11.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊               | 1254/8000 [00:11<01:00, 112.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4116/8000 [03:50<05:27, 11.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊               | 1276/8000 [00:11<00:58, 115.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4121/8000 [03:50<03:57, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉               | 1298/8000 [00:11<00:54, 122.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4125/8000 [03:50<04:13, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉               | 1311/8000 [00:11<00:59, 112.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4127/8000 [03:51<05:48, 11.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██▉               | 1332/8000 [00:12<00:58, 114.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4130/8000 [03:51<05:06, 12.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1358/8000 [00:12<00:52, 127.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4133/8000 [03:51<04:34, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1377/8000 [00:12<00:54, 122.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4135/8000 [03:51<05:20, 12.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏              | 1392/8000 [00:12<00:59, 111.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4138/8000 [03:52<05:23, 11.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1410/8000 [00:12<01:01, 107.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4144/8000 [03:52<03:35, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1431/8000 [00:12<00:55, 117.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4147/8000 [03:52<04:09, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1444/8000 [00:13<01:01, 106.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4149/8000 [03:52<04:03, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎              | 1465/8000 [00:13<00:56, 114.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4151/8000 [03:52<05:03, 12.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎              | 1488/8000 [00:13<00:52, 123.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4153/8000 [03:52<04:54, 13.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1511/8000 [00:13<00:49, 131.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4157/8000 [03:53<04:39, 13.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1530/8000 [00:13<00:50, 127.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4160/8000 [03:53<04:02, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1544/8000 [00:13<01:01, 105.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4162/8000 [03:53<04:05, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋               | 1560/8000 [00:14<01:04, 99.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4165/8000 [03:53<03:38, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋               | 1573/8000 [00:14<01:08, 93.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4170/8000 [03:53<03:35, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▌              | 1596/8000 [00:14<01:00, 105.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4172/8000 [03:54<03:42, 17.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋              | 1614/8000 [00:14<01:00, 105.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4175/8000 [03:54<03:19, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▉               | 1632/8000 [00:14<01:04, 98.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4178/8000 [03:54<03:33, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋              | 1650/8000 [00:15<01:02, 101.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4180/8000 [03:54<03:43, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1669/8000 [00:15<01:07, 94.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4185/8000 [03:54<03:33, 17.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1688/8000 [00:15<01:11, 88.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4188/8000 [03:54<04:12, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1713/8000 [00:15<01:07, 93.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4190/8000 [03:55<04:09, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1729/8000 [00:15<01:11, 88.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4193/8000 [03:55<04:02, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1739/8000 [00:16<01:29, 69.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4196/8000 [03:55<04:34, 13.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1752/8000 [00:16<01:28, 70.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4199/8000 [03:55<04:00, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1768/8000 [00:16<01:29, 70.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4205/8000 [03:55<02:56, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1787/8000 [00:16<01:22, 75.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4208/8000 [03:55<02:51, 22.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1804/8000 [00:17<01:22, 75.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4211/8000 [03:56<02:53, 21.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1822/8000 [00:17<01:25, 72.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4215/8000 [03:56<04:59, 12.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1842/8000 [00:17<01:19, 77.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4217/8000 [03:57<06:23,  9.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1866/8000 [00:17<01:25, 71.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4220/8000 [03:57<06:05, 10.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍              | 1885/8000 [00:18<01:21, 74.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4223/8000 [03:57<05:05, 12.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1904/8000 [00:18<01:15, 80.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4225/8000 [03:57<04:57, 12.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1926/8000 [00:18<01:15, 80.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4227/8000 [03:57<04:45, 13.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1940/8000 [00:18<01:14, 81.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4231/8000 [03:58<05:20, 11.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1961/8000 [00:19<01:08, 88.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4233/8000 [03:58<05:09, 12.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1977/8000 [00:19<01:09, 86.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4235/8000 [03:58<04:48, 13.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1995/8000 [00:19<01:04, 92.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4237/8000 [03:58<04:39, 13.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2010/8000 [00:19<01:04, 93.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4241/8000 [03:58<05:02, 12.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2028/8000 [00:19<01:01, 96.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4248/8000 [03:59<03:06, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊              | 2044/8000 [00:19<01:01, 97.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4251/8000 [03:59<03:08, 19.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2070/8000 [00:20<00:55, 107.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4254/8000 [03:59<03:10, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2085/8000 [00:20<00:57, 103.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4257/8000 [03:59<03:12, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2098/8000 [00:20<01:01, 96.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4262/8000 [03:59<03:30, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊             | 2118/8000 [00:20<00:58, 100.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4264/8000 [04:00<04:54, 12.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▊             | 2139/8000 [00:20<00:55, 105.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4267/8000 [04:00<04:27, 13.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▊             | 2158/8000 [00:20<00:54, 107.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4272/8000 [04:00<04:08, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▉             | 2177/8000 [00:21<00:53, 108.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4276/8000 [04:00<03:29, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▉             | 2190/8000 [00:21<00:55, 103.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4281/8000 [04:00<02:51, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▉             | 2209/8000 [00:21<00:54, 105.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4284/8000 [04:01<02:53, 21.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2229/8000 [00:21<00:52, 109.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4289/8000 [04:01<02:29, 24.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2244/8000 [00:21<00:57, 100.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4292/8000 [04:01<02:37, 23.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2259/8000 [00:21<01:00, 95.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4295/8000 [04:01<03:54, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▍             | 2278/8000 [00:22<00:57, 99.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4301/8000 [04:01<02:49, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▏            | 2296/8000 [00:22<00:53, 107.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4304/8000 [04:02<02:56, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▏            | 2318/8000 [00:22<00:48, 118.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4307/8000 [04:02<02:50, 21.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▎            | 2334/8000 [00:22<00:49, 113.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4312/8000 [04:02<02:57, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▎            | 2352/8000 [00:22<00:49, 114.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4316/8000 [04:02<03:33, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▎            | 2371/8000 [00:22<00:49, 114.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4320/8000 [04:03<04:24, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2395/8000 [00:23<00:45, 122.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4324/8000 [04:03<03:37, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2419/8000 [00:23<00:42, 131.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4327/8000 [04:03<03:53, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2439/8000 [00:23<00:45, 122.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4332/8000 [04:03<03:03, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▌            | 2464/8000 [00:23<00:42, 129.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4335/8000 [04:03<03:00, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▌            | 2483/8000 [00:23<00:45, 121.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4340/8000 [04:04<03:02, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▋            | 2502/8000 [00:23<00:46, 117.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4343/8000 [04:04<02:59, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▋            | 2516/8000 [00:24<00:49, 111.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4347/8000 [04:04<03:22, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▋            | 2536/8000 [00:24<00:51, 105.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4350/8000 [04:04<03:11, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▊            | 2556/8000 [00:24<00:51, 105.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4354/8000 [04:04<02:56, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▊            | 2572/8000 [00:24<00:52, 102.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4358/8000 [04:05<04:50, 12.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████▏            | 2589/8000 [00:24<00:54, 99.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4360/8000 [04:05<04:47, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2604/8000 [00:25<00:55, 96.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4362/8000 [04:05<04:52, 12.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2624/8000 [00:25<00:52, 101.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4366/8000 [04:05<04:36, 13.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2644/8000 [00:25<00:48, 110.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4368/8000 [04:06<04:33, 13.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2663/8000 [00:25<00:46, 114.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4370/8000 [04:06<04:25, 13.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████            | 2680/8000 [00:25<00:47, 112.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4373/8000 [04:06<03:47, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████            | 2698/8000 [00:25<00:48, 109.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4375/8000 [04:06<03:49, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████            | 2717/8000 [00:26<00:49, 106.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4378/8000 [04:06<04:19, 13.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▏           | 2744/8000 [00:26<00:43, 120.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4380/8000 [04:06<04:14, 14.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▏           | 2759/8000 [00:26<00:45, 114.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4382/8000 [04:07<04:13, 14.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2783/8000 [00:26<00:41, 126.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4386/8000 [04:07<03:16, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2800/8000 [00:26<00:42, 121.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4391/8000 [04:07<03:15, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2815/8000 [00:26<00:47, 109.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4394/8000 [04:07<03:07, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2831/8000 [00:26<00:48, 106.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4398/8000 [04:07<02:44, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▍           | 2850/8000 [00:27<00:47, 108.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4402/8000 [04:07<02:30, 23.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▍           | 2867/8000 [00:27<00:47, 107.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4406/8000 [04:07<02:17, 26.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▍           | 2880/8000 [00:27<00:49, 102.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4409/8000 [04:08<03:14, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▌           | 2907/8000 [00:27<00:40, 124.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4413/8000 [04:08<03:25, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▌           | 2928/8000 [00:27<00:41, 122.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4417/8000 [04:08<03:15, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2953/8000 [00:28<00:51, 98.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4420/8000 [04:08<03:13, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▋           | 2971/8000 [00:28<00:49, 101.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4422/8000 [04:08<03:21, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▋           | 2995/8000 [00:28<00:46, 107.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4426/8000 [04:09<03:26, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▊           | 3013/8000 [00:28<00:47, 105.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4428/8000 [04:09<03:30, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▊           | 3038/8000 [00:28<00:41, 118.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4432/8000 [04:09<03:41, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▉           | 3060/8000 [00:28<00:39, 124.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4436/8000 [04:09<03:10, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▉           | 3075/8000 [00:29<00:42, 116.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4439/8000 [04:10<03:32, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▉           | 3096/8000 [00:29<00:39, 123.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4442/8000 [04:10<03:21, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3116/8000 [00:29<00:39, 122.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4444/8000 [04:10<03:25, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3138/8000 [00:29<00:39, 123.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4446/8000 [04:10<03:28, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3154/8000 [00:29<00:41, 118.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4450/8000 [04:10<03:42, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3176/8000 [00:29<00:37, 128.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4453/8000 [04:10<03:23, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3195/8000 [00:30<00:37, 127.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4455/8000 [04:10<03:32, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3212/8000 [00:30<00:38, 124.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4457/8000 [04:11<03:32, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▎          | 3227/8000 [00:30<00:40, 119.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [04:11<03:14, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▎          | 3248/8000 [00:30<00:36, 129.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4467/8000 [04:11<03:18, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▎          | 3264/8000 [00:30<00:40, 115.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4473/8000 [04:11<02:59, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3289/8000 [00:31<00:49, 96.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4480/8000 [04:12<02:17, 25.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3305/8000 [00:31<00:50, 93.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4485/8000 [04:12<02:23, 24.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▍          | 3332/8000 [00:31<00:43, 106.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4488/8000 [04:12<02:21, 24.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▌          | 3353/8000 [00:31<00:41, 112.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4492/8000 [04:12<02:13, 26.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▌          | 3367/8000 [00:31<00:44, 103.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4497/8000 [04:12<02:57, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▋          | 3391/8000 [00:31<00:39, 116.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4501/8000 [04:12<02:42, 21.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▋          | 3409/8000 [00:32<00:41, 111.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4504/8000 [04:13<02:38, 22.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▋          | 3428/8000 [00:32<00:40, 113.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4509/8000 [04:13<02:17, 25.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▊          | 3446/8000 [00:32<00:39, 114.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4513/8000 [04:13<02:47, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▊          | 3470/8000 [00:32<00:35, 126.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4519/8000 [04:13<02:16, 25.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▊          | 3499/8000 [00:32<00:32, 138.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4526/8000 [04:13<01:46, 32.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▉          | 3521/8000 [00:32<00:31, 140.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4531/8000 [04:14<02:15, 25.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▉          | 3539/8000 [00:33<00:33, 134.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4537/8000 [04:14<03:02, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████          | 3556/8000 [00:33<00:36, 123.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4540/8000 [04:15<04:02, 14.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████          | 3570/8000 [00:33<00:41, 107.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4543/8000 [04:15<03:44, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████          | 3598/8000 [00:33<00:35, 122.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4546/8000 [04:15<03:31, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▏         | 3623/8000 [00:33<00:34, 127.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4550/8000 [04:15<03:02, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▏         | 3640/8000 [00:33<00:37, 117.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4553/8000 [04:15<03:27, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▏         | 3662/8000 [00:34<00:36, 118.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4557/8000 [04:15<03:41, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▎         | 3680/8000 [00:34<00:38, 113.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4561/8000 [04:16<03:11, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▎         | 3696/8000 [00:34<00:38, 111.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4565/8000 [04:16<03:29, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▎         | 3722/8000 [00:34<00:33, 128.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4567/8000 [04:16<03:30, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3756/8000 [00:34<00:35, 117.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4571/8000 [04:16<02:59, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3771/8000 [00:35<00:37, 111.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4574/8000 [04:16<02:52, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▌         | 3799/8000 [00:35<00:40, 102.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4577/8000 [04:17<04:25, 12.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▌         | 3814/8000 [00:35<00:40, 104.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4579/8000 [04:17<04:28, 12.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▌         | 3833/8000 [00:35<00:39, 106.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4584/8000 [04:17<03:24, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3846/8000 [00:35<00:43, 96.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4586/8000 [04:17<03:34, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3858/8000 [00:36<00:44, 92.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4588/8000 [04:17<03:39, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3877/8000 [00:36<00:43, 94.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4592/8000 [04:18<03:46, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▊         | 3896/8000 [00:36<00:39, 103.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4595/8000 [04:18<04:07, 13.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3912/8000 [00:36<00:45, 90.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4598/8000 [04:18<03:35, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3931/8000 [00:36<00:44, 92.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4601/8000 [04:18<03:21, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3946/8000 [00:36<00:43, 94.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4604/8000 [04:18<03:10, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▉         | 3974/8000 [00:37<00:34, 116.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4607/8000 [04:19<03:00, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▉         | 3993/8000 [00:37<00:34, 116.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4609/8000 [04:19<03:06, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████         | 4008/8000 [00:37<00:36, 110.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4611/8000 [04:19<03:19, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████         | 4023/8000 [00:37<00:36, 110.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4616/8000 [04:19<02:31, 22.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████         | 4047/8000 [00:37<00:31, 124.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4619/8000 [04:19<03:14, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4060/8000 [00:37<00:33, 116.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4622/8000 [04:19<03:03, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4084/8000 [00:37<00:29, 132.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4625/8000 [04:20<04:17, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▎        | 4118/8000 [00:38<00:31, 121.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4630/8000 [04:20<03:05, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▎        | 4144/8000 [00:38<00:30, 128.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4633/8000 [04:20<02:57, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▎        | 4163/8000 [00:38<00:30, 126.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4636/8000 [04:20<02:45, 20.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▍        | 4180/8000 [00:38<00:32, 117.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4639/8000 [04:20<03:32, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▍        | 4194/8000 [00:38<00:35, 106.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4641/8000 [04:21<03:36, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▍        | 4214/8000 [00:39<00:32, 117.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4643/8000 [04:21<03:35, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4247/8000 [00:39<00:33, 113.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4645/8000 [04:21<03:45, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4271/8000 [00:39<00:30, 123.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4647/8000 [04:21<04:02, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4290/8000 [00:39<00:33, 112.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4650/8000 [04:21<03:27, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4310/8000 [00:39<00:32, 114.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4652/8000 [04:21<04:30, 12.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4333/8000 [00:40<00:29, 124.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4655/8000 [04:22<05:03, 11.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▊        | 4347/8000 [00:40<00:33, 109.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4657/8000 [04:22<05:56,  9.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▎        | 4360/8000 [00:40<00:37, 97.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4659/8000 [04:22<05:28, 10.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▊        | 4379/8000 [00:40<00:35, 102.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4663/8000 [04:22<03:56, 14.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4393/8000 [00:40<00:38, 92.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4666/8000 [04:22<03:23, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4415/8000 [00:40<00:32, 108.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4668/8000 [04:23<03:33, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4432/8000 [00:41<00:34, 102.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4670/8000 [04:23<04:27, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4446/8000 [00:41<00:35, 99.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4674/8000 [04:23<03:30, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████        | 4469/8000 [00:41<00:29, 120.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4676/8000 [04:23<03:43, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████        | 4486/8000 [00:41<00:31, 110.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4678/8000 [04:23<03:52, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▏       | 4510/8000 [00:41<00:27, 127.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4684/8000 [04:23<02:40, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▏       | 4527/8000 [00:41<00:28, 121.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4688/8000 [04:24<03:14, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▏       | 4551/8000 [00:42<00:25, 136.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4692/8000 [04:24<02:50, 19.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▎       | 4569/8000 [00:42<00:27, 124.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4698/8000 [04:24<02:49, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▎       | 4590/8000 [00:42<00:27, 125.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4702/8000 [04:25<03:07, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4613/8000 [00:42<00:25, 133.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4706/8000 [04:25<03:18, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4631/8000 [00:42<00:28, 118.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4710/8000 [04:25<02:53, 18.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4646/8000 [00:42<00:31, 106.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4713/8000 [04:25<02:47, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4659/8000 [00:43<00:33, 100.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4716/8000 [04:25<03:25, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▌       | 4678/8000 [00:43<00:31, 106.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4719/8000 [04:25<03:08, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▌       | 4693/8000 [00:43<00:32, 100.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4722/8000 [04:26<02:58, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▌       | 4714/8000 [00:43<00:30, 108.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4727/8000 [04:26<02:25, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▋       | 4733/8000 [00:43<00:29, 111.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4730/8000 [04:26<02:22, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▋       | 4749/8000 [00:43<00:30, 106.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4735/8000 [04:26<02:15, 24.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▋       | 4767/8000 [00:44<00:28, 112.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4739/8000 [04:26<02:15, 24.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▊       | 4788/8000 [00:44<00:27, 117.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4744/8000 [04:27<02:37, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▊       | 4810/8000 [00:44<00:25, 125.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4747/8000 [04:27<03:17, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4841/8000 [00:44<00:27, 115.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4749/8000 [04:27<03:28, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4865/8000 [00:44<00:25, 124.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4753/8000 [04:27<02:59, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4881/8000 [00:44<00:26, 118.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4755/8000 [04:27<03:57, 13.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████       | 4896/8000 [00:45<00:28, 109.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4758/8000 [04:28<03:25, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████       | 4912/8000 [00:45<00:30, 102.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4761/8000 [04:28<03:11, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████       | 4932/8000 [00:45<00:28, 106.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4763/8000 [04:28<03:22, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4950/8000 [00:45<00:28, 105.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4765/8000 [04:28<03:37, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4968/8000 [00:45<00:28, 106.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4768/8000 [04:28<03:15, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4987/8000 [00:45<00:27, 110.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4772/8000 [04:28<02:46, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5006/8000 [00:46<00:26, 114.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4776/8000 [04:28<02:21, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5022/8000 [00:46<00:26, 110.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4780/8000 [04:29<02:07, 25.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5039/8000 [00:46<00:27, 107.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4785/8000 [04:29<02:28, 21.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▍      | 5059/8000 [00:46<00:26, 110.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4790/8000 [04:29<02:31, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5071/8000 [00:46<00:30, 97.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4794/8000 [04:29<02:58, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▍      | 5101/8000 [00:47<00:24, 117.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4796/8000 [04:30<03:01, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▌      | 5115/8000 [00:47<00:27, 105.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4801/8000 [04:30<02:27, 21.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5130/8000 [00:47<00:29, 98.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4805/8000 [04:30<02:18, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5145/8000 [00:47<00:30, 94.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4809/8000 [04:30<02:18, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5161/8000 [00:47<00:29, 95.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4812/8000 [04:30<03:37, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5181/8000 [00:47<00:28, 98.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4816/8000 [04:31<03:04, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5199/8000 [00:48<00:27, 102.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4820/8000 [04:31<02:49, 18.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5220/8000 [00:48<00:26, 105.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4823/8000 [04:31<03:20, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▊      | 5245/8000 [00:48<00:23, 118.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4827/8000 [04:31<03:33, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▊      | 5266/8000 [00:48<00:23, 117.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4829/8000 [04:32<04:10, 12.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▉      | 5284/8000 [00:48<00:22, 120.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4833/8000 [04:32<03:24, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▉      | 5305/8000 [00:48<00:23, 114.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4836/8000 [04:32<03:44, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▉      | 5320/8000 [00:49<00:24, 108.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4839/8000 [04:32<03:20, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5343/8000 [00:49<00:21, 122.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4842/8000 [04:32<03:01, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5366/8000 [00:49<00:23, 112.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4845/8000 [04:32<02:42, 19.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5386/8000 [00:49<00:22, 118.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4850/8000 [04:33<03:28, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▏     | 5400/8000 [00:49<00:24, 107.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4853/8000 [04:33<03:57, 13.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▏     | 5431/8000 [00:49<00:20, 122.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4857/8000 [04:33<03:43, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▎     | 5450/8000 [00:50<00:20, 122.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4859/8000 [04:34<03:41, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▎     | 5479/8000 [00:50<00:18, 133.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4862/8000 [04:34<03:14, 16.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▎     | 5494/8000 [00:50<00:21, 119.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4865/8000 [04:34<03:44, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▍     | 5511/8000 [00:50<00:21, 115.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4868/8000 [04:34<04:12, 12.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5544/8000 [00:51<00:29, 82.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4871/8000 [04:34<03:41, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5557/8000 [00:51<00:29, 81.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4873/8000 [04:35<03:39, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5580/8000 [00:51<00:26, 91.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4875/8000 [04:35<04:34, 11.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▌     | 5601/8000 [00:51<00:23, 100.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4878/8000 [04:35<04:46, 10.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5616/8000 [00:51<00:24, 97.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4880/8000 [04:35<04:26, 11.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▋     | 5638/8000 [00:52<00:21, 111.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4885/8000 [04:36<03:44, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5662/8000 [00:52<00:25, 91.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4887/8000 [04:36<04:22, 11.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5675/8000 [00:52<00:25, 90.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4891/8000 [04:36<03:37, 14.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5692/8000 [00:52<00:24, 94.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4895/8000 [04:36<03:04, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▊     | 5713/8000 [00:52<00:22, 102.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4897/8000 [04:36<03:04, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5729/8000 [00:53<00:22, 99.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4901/8000 [04:37<04:34, 11.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5744/8000 [00:53<00:24, 93.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4903/8000 [04:37<04:27, 11.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▉     | 5769/8000 [00:53<00:20, 111.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4906/8000 [04:37<04:03, 12.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████     | 5790/8000 [00:53<00:19, 114.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4909/8000 [04:37<03:36, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████     | 5808/8000 [00:53<00:19, 111.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4915/8000 [04:37<02:28, 20.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████     | 5832/8000 [00:53<00:17, 122.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4918/8000 [04:38<02:57, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▏    | 5847/8000 [00:54<00:18, 113.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4922/8000 [04:38<02:31, 20.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▏    | 5863/8000 [00:54<00:19, 109.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4925/8000 [04:38<03:06, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▏    | 5880/8000 [00:54<00:19, 108.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4930/8000 [04:38<02:25, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▎    | 5897/8000 [00:54<00:19, 109.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4935/8000 [04:39<03:00, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▎    | 5914/8000 [00:54<00:18, 110.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4938/8000 [04:39<03:19, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▎    | 5926/8000 [00:54<00:19, 104.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4940/8000 [04:39<03:20, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5940/8000 [00:55<00:22, 92.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4942/8000 [04:39<03:26, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▍    | 5962/8000 [00:55<00:20, 100.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4945/8000 [04:39<04:02, 12.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5977/8000 [00:55<00:21, 96.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4947/8000 [04:40<03:56, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▍    | 5995/8000 [00:55<00:19, 101.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4950/8000 [04:40<03:30, 14.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▌    | 6013/8000 [00:55<00:19, 102.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4955/8000 [04:40<02:40, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6030/8000 [00:55<00:20, 94.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4958/8000 [04:40<03:35, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▎    | 6046/8000 [00:56<00:20, 96.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4960/8000 [04:41<04:18, 11.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████▋    | 6068/8000 [00:56<00:17, 109.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4966/8000 [04:41<02:53, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████▋    | 6088/8000 [00:56<00:15, 120.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4970/8000 [04:41<03:33, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████▋    | 6110/8000 [00:56<00:15, 124.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4972/8000 [04:41<03:34, 14.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▊    | 6131/8000 [00:56<00:14, 132.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4977/8000 [04:41<02:50, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▊    | 6152/8000 [00:56<00:14, 131.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4980/8000 [04:42<02:47, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▉    | 6178/8000 [00:57<00:13, 139.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4984/8000 [04:42<03:02, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▉    | 6204/8000 [00:57<00:15, 114.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4988/8000 [04:42<02:43, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6227/8000 [00:57<00:15, 112.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4994/8000 [04:42<02:39, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6246/8000 [00:57<00:14, 119.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4998/8000 [04:43<02:23, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6275/8000 [00:57<00:16, 106.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5001/8000 [04:43<03:05, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6291/8000 [00:58<00:15, 106.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5004/8000 [04:43<02:53, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6310/8000 [00:58<00:15, 110.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5006/8000 [04:43<02:53, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6322/8000 [00:58<00:16, 102.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5010/8000 [04:43<02:54, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▎   | 6339/8000 [00:58<00:15, 109.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5015/8000 [04:44<02:59, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▎   | 6362/8000 [00:58<00:13, 122.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5018/8000 [04:44<04:01, 12.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▎   | 6382/8000 [00:58<00:12, 125.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5021/8000 [04:44<03:46, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▍   | 6419/8000 [00:59<00:13, 115.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5027/8000 [04:44<02:47, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▍   | 6435/8000 [00:59<00:13, 114.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5032/8000 [04:45<02:23, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6458/8000 [00:59<00:12, 119.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5037/8000 [04:45<02:13, 22.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6471/8000 [00:59<00:14, 105.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5041/8000 [04:45<02:20, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6486/8000 [00:59<00:14, 104.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5044/8000 [04:45<02:31, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6498/8000 [01:00<00:17, 88.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5047/8000 [04:46<03:16, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6511/8000 [01:00<00:18, 81.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5050/8000 [04:46<03:04, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▋   | 6542/8000 [01:00<00:13, 109.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5053/8000 [04:46<02:53, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▊   | 6561/8000 [01:00<00:12, 114.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5055/8000 [04:46<03:44, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▊   | 6583/8000 [01:00<00:11, 121.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5057/8000 [04:46<03:36, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▊   | 6597/8000 [01:00<00:12, 110.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5060/8000 [04:47<03:59, 12.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▉   | 6614/8000 [01:01<00:12, 115.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5062/8000 [04:47<04:27, 10.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▉   | 6634/8000 [01:01<00:12, 113.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5068/8000 [04:47<03:28, 14.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▉   | 6656/8000 [01:01<00:11, 120.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5072/8000 [04:47<02:55, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████   | 6679/8000 [01:01<00:10, 127.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5078/8000 [04:47<02:13, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████   | 6698/8000 [01:01<00:10, 126.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5083/8000 [04:48<02:24, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████   | 6722/8000 [01:01<00:09, 134.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5086/8000 [04:48<02:23, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▏  | 6738/8000 [01:02<00:10, 122.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5089/8000 [04:48<02:57, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▏  | 6753/8000 [01:02<00:11, 104.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5091/8000 [04:48<03:41, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▏  | 6777/8000 [01:02<00:10, 118.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5096/8000 [04:49<02:43, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▎  | 6800/8000 [01:02<00:10, 119.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5099/8000 [04:49<03:10, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▎  | 6819/8000 [01:02<00:09, 127.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5105/8000 [04:49<02:56, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▍  | 6837/8000 [01:02<00:10, 114.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5109/8000 [04:49<02:36, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▍  | 6855/8000 [01:03<00:09, 115.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5114/8000 [04:50<02:42, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▍  | 6876/8000 [01:03<00:08, 125.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5118/8000 [04:50<02:24, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▌  | 6898/8000 [01:03<00:08, 131.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5121/8000 [04:50<02:20, 20.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▌  | 6923/8000 [01:03<00:07, 142.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5124/8000 [04:50<03:26, 13.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▌  | 6943/8000 [01:03<00:08, 131.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5126/8000 [04:50<03:21, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▋  | 6959/8000 [01:03<00:08, 117.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5130/8000 [04:51<02:54, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▋  | 6978/8000 [01:04<00:08, 118.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5133/8000 [04:51<02:38, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7003/8000 [01:04<00:07, 128.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5136/8000 [04:51<03:47, 12.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7024/8000 [01:04<00:07, 130.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5139/8000 [04:51<03:21, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7038/8000 [01:04<00:07, 122.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5142/8000 [04:51<03:11, 14.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7053/8000 [01:04<00:08, 110.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5144/8000 [04:52<03:06, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7065/8000 [01:04<00:09, 98.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5147/8000 [04:52<02:45, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▉  | 7084/8000 [01:04<00:08, 107.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5149/8000 [04:52<03:01, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▉  | 7106/8000 [01:05<00:07, 117.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5151/8000 [04:52<03:08, 15.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████  | 7123/8000 [01:05<00:07, 116.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5153/8000 [04:52<03:10, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████  | 7140/8000 [01:05<00:07, 109.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5157/8000 [04:52<02:27, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████  | 7160/8000 [01:05<00:07, 115.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5161/8000 [04:53<02:51, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7186/8000 [01:05<00:06, 135.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5163/8000 [04:53<03:00, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7205/8000 [01:05<00:05, 137.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5166/8000 [04:53<02:52, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7222/8000 [01:06<00:06, 129.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5171/8000 [04:53<02:11, 21.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▎ | 7242/8000 [01:06<00:06, 124.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5176/8000 [04:53<02:44, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▎ | 7257/8000 [01:06<00:06, 117.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5179/8000 [04:54<02:37, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▍ | 7281/8000 [01:06<00:05, 126.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5183/8000 [04:54<02:50, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▍ | 7298/8000 [01:06<00:05, 121.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5185/8000 [04:54<03:39, 12.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▍ | 7317/8000 [01:06<00:05, 127.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5190/8000 [04:54<03:14, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▌ | 7340/8000 [01:06<00:04, 132.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5192/8000 [04:55<03:45, 12.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▌ | 7358/8000 [01:07<00:04, 129.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5196/8000 [04:55<04:13, 11.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▌ | 7378/8000 [01:07<00:04, 126.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5200/8000 [04:55<04:01, 11.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▋ | 7400/8000 [01:07<00:04, 124.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5202/8000 [04:56<03:53, 11.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▋ | 7421/8000 [01:07<00:04, 125.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5207/8000 [04:56<03:24, 13.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▊ | 7447/8000 [01:07<00:04, 127.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5210/8000 [04:56<03:04, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▊ | 7471/8000 [01:07<00:04, 131.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5214/8000 [04:56<02:36, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▊ | 7491/8000 [01:08<00:04, 123.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5218/8000 [04:56<02:15, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7507/8000 [01:08<00:04, 114.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5221/8000 [04:57<03:31, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7533/8000 [01:08<00:03, 122.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5225/8000 [04:57<03:57, 11.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7551/8000 [01:08<00:03, 114.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5230/8000 [04:57<02:59, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████ | 7575/8000 [01:08<00:03, 119.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5233/8000 [04:57<02:44, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████ | 7609/8000 [01:09<00:03, 113.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5238/8000 [04:58<02:47, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▏| 7628/8000 [01:09<00:03, 111.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5241/8000 [04:58<03:07, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▏| 7650/8000 [01:09<00:02, 119.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5244/8000 [04:58<02:54, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7667/8000 [01:09<00:02, 121.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5249/8000 [04:58<02:18, 19.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7689/8000 [01:09<00:02, 124.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5254/8000 [04:58<01:56, 23.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7704/8000 [01:10<00:02, 114.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5257/8000 [04:59<02:00, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7720/8000 [01:10<00:02, 110.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5260/8000 [04:59<02:37, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▍| 7735/8000 [01:10<00:02, 107.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5263/8000 [04:59<02:30, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▍| 7756/8000 [01:10<00:02, 116.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5266/8000 [04:59<03:00, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▌| 7784/8000 [01:10<00:02, 102.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5268/8000 [05:00<03:06, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▌| 7797/8000 [01:10<00:02, 93.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5270/8000 [05:00<03:05, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7815/8000 [01:11<00:01, 97.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5274/8000 [05:00<02:28, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▋| 7838/8000 [01:11<00:01, 110.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5277/8000 [05:00<02:59, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▋| 7858/8000 [01:11<00:01, 118.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5279/8000 [05:00<04:28, 10.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▋| 7878/8000 [01:11<00:00, 124.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5285/8000 [05:01<02:49, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7893/8000 [01:11<00:00, 119.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5288/8000 [05:01<02:37, 17.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7914/8000 [01:11<00:00, 122.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5294/8000 [05:01<02:24, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7927/8000 [01:12<00:00, 103.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5298/8000 [05:01<02:45, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▉| 7951/8000 [01:12<00:00, 115.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5303/8000 [05:02<02:58, 15.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7985/8000 [01:12<00:00, 87.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5307/8000 [05:02<02:37, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8006it [01:12, 109.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  66%|███████████▎     | 5310/8000 [05:02<02:36, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5314/8000 [05:02<02:17, 19.58it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5319/8000 [05:02<01:53, 23.67it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<24:28,  7.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5323/8000 [05:03<02:12, 20.18it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:08<13:01,  3.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5326/8000 [05:03<02:08, 20.87it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:10<09:08,  2.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5331/8000 [05:03<02:35, 17.20it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:11<07:22,  2.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5334/8000 [05:03<02:41, 16.55it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:13<06:28,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5341/8000 [05:03<01:57, 22.62it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:14<05:59,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5344/8000 [05:04<02:34, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:16<05:30,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5347/8000 [05:04<03:00, 14.71it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:17<05:07,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5352/8000 [05:04<02:24, 18.34it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:19<04:55,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5355/8000 [05:04<02:22, 18.55it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:20<04:55,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5359/8000 [05:05<02:37, 16.75it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:22<04:45,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5363/8000 [05:05<02:39, 16.50it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:23<04:36,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5368/8000 [05:05<02:12, 19.92it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:24<04:31,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5372/8000 [05:05<02:25, 18.00it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:26<04:24,  1.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5376/8000 [05:06<02:10, 20.16it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:27<04:20,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5380/8000 [05:06<02:30, 17.43it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:29<04:23,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5384/8000 [05:06<02:11, 19.92it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:30<04:24,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5387/8000 [05:06<02:17, 19.02it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:32<04:23,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5390/8000 [05:06<02:14, 19.45it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:33<04:19,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5397/8000 [05:07<02:02, 21.21it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:34<04:20,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5401/8000 [05:07<02:20, 18.46it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:36<04:26,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5403/8000 [05:07<02:29, 17.41it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:38<04:32,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5405/8000 [05:07<02:34, 16.80it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:39<04:30,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5408/8000 [05:07<02:27, 17.62it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:41<04:34,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5411/8000 [05:07<02:16, 18.94it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:42<04:31,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5417/8000 [05:08<02:31, 17.02it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:44<04:26,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5422/8000 [05:08<02:26, 17.62it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:45<04:25,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5428/8000 [05:08<01:54, 22.47it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:47<04:23,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5431/8000 [05:08<01:57, 21.84it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:48<04:17,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5434/8000 [05:09<02:03, 20.79it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:50<04:17,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5438/8000 [05:09<02:20, 18.25it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:51<04:23,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5440/8000 [05:09<03:29, 12.22it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:53<04:22,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5443/8000 [05:09<03:08, 13.59it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:55<04:16,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5445/8000 [05:10<03:09, 13.52it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:56<04:13,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5447/8000 [05:10<03:05, 13.80it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [00:58<04:22,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5450/8000 [05:10<03:17, 12.92it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:00<04:36,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5453/8000 [05:10<02:55, 14.51it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:01<04:24,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5457/8000 [05:10<02:24, 17.55it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:03<04:14,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5460/8000 [05:10<02:19, 18.24it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:04<04:10,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5464/8000 [05:11<01:56, 21.72it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:06<04:03,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5468/8000 [05:11<01:49, 23.16it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:07<04:02,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5473/8000 [05:11<02:18, 18.23it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:09<04:06,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5478/8000 [05:11<02:39, 15.76it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:10<04:03,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5482/8000 [05:12<03:56, 10.65it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:12<04:16,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5487/8000 [05:12<03:03, 13.71it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:14<04:08,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5492/8000 [05:13<02:54, 14.35it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:15<04:03,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5495/8000 [05:13<02:41, 15.52it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:17<04:04,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5500/8000 [05:13<02:12, 18.93it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:20<04:15,  1.68s/it]\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5504/8000 [05:13<02:01, 20.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5509/8000 [05:13<02:03, 20.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5513/8000 [05:13<01:49, 22.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5517/8000 [05:14<02:09, 19.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.76it/s]\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5520/8000 [05:14<02:06, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5527/8000 [05:14<01:50, 22.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5533/8000 [05:14<01:37, 25.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5538/8000 [05:15<01:49, 22.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 27it [00:01, 22.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5542/8000 [05:15<02:02, 20.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 48it [00:01, 41.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5545/8000 [05:15<02:47, 14.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 67it [00:01, 60.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5552/8000 [05:16<02:47, 14.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 89it [00:01, 85.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5556/8000 [05:16<03:09, 12.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 109it [00:02, 104.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5560/8000 [05:16<03:02, 13.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 128it [00:02, 114.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5564/8000 [05:17<03:00, 13.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 154it [00:02, 145.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5566/8000 [05:17<03:01, 13.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 178it [00:02, 166.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5570/8000 [05:17<02:59, 13.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 201it [00:02, 179.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5572/8000 [05:17<02:57, 13.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 229it [00:02, 204.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5575/8000 [05:17<02:41, 15.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 253it [00:02, 208.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5580/8000 [05:18<02:34, 15.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 279it [00:02, 217.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5584/8000 [05:18<02:15, 17.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 303it [00:02, 213.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5587/8000 [05:18<02:11, 18.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 326it [00:03, 213.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5590/8000 [05:18<02:39, 15.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 348it [00:03, 189.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5592/8000 [05:19<02:44, 14.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 368it [00:03, 188.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5595/8000 [05:19<02:27, 16.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 390it [00:03, 195.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5598/8000 [05:19<02:50, 14.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 411it [00:03, 183.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5602/8000 [05:19<02:54, 13.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 430it [00:03, 184.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5606/8000 [05:19<02:25, 16.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 449it [00:03, 183.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5610/8000 [05:20<02:57, 13.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 468it [00:03, 178.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5614/8000 [05:20<02:26, 16.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 487it [00:04, 173.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5618/8000 [05:20<03:09, 12.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 505it [00:04, 167.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5620/8000 [05:21<03:05, 12.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 528it [00:04, 180.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5622/8000 [05:21<03:07, 12.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 547it [00:04, 169.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5625/8000 [05:21<03:22, 11.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 565it [00:04, 167.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5629/8000 [05:21<03:20, 11.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 585it [00:04, 175.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5632/8000 [05:22<03:01, 13.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 603it [00:04, 172.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5634/8000 [05:22<02:57, 13.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 623it [00:04, 177.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5636/8000 [05:22<02:56, 13.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 643it [00:04, 182.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5638/8000 [05:22<03:05, 12.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 662it [00:05, 181.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5641/8000 [05:22<02:41, 14.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 681it [00:05, 164.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5645/8000 [05:22<02:48, 14.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 698it [00:05, 164.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5648/8000 [05:23<02:27, 15.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 717it [00:05, 164.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5650/8000 [05:23<02:25, 16.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 734it [00:05, 155.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5654/8000 [05:23<02:00, 19.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 752it [00:05, 160.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5657/8000 [05:23<02:35, 15.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 771it [00:05, 167.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5660/8000 [05:23<02:21, 16.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 136.65it/s]\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5665/8000 [05:23<01:54, 20.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5671/8000 [05:24<01:31, 25.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5678/8000 [05:24<02:09, 17.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 136.1\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5681/8000 [05:24<02:04, 18.63it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|▎| 28/100 [00:00<00:00, 111.3\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5685/8000 [05:25<02:20, 16.53it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:00<00:00, 102.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5687/8000 [05:25<02:55, 13.20it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 97.46\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5690/8000 [05:25<02:40, 14.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 101.1\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5692/8000 [05:25<03:22, 11.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00<00:00, 92.09\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5694/8000 [05:26<03:14, 11.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|▊| 84/100 [00:00<00:00, 96.16\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5697/8000 [05:26<02:52, 13.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 89.4\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5702/8000 [05:26<02:16, 16.81it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|██████████▌   | 3/4 [22:33<07:35, 455.14s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.4717763781326894\n",
+      "2999: -5.7717684\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  71%|████████████     | 5705/8000 [05:26<02:12, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5707/8000 [05:26<02:20, 16.28it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5710/8000 [05:26<02:10, 17.53it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:08<27:57,  8.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5714/8000 [05:26<01:53, 20.21it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:10<15:04,  4.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5719/8000 [05:27<02:27, 15.42it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:12<10:56,  3.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5722/8000 [05:27<02:17, 16.52it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:13<08:50,  2.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5725/8000 [05:27<02:08, 17.67it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:15<07:56,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5727/8000 [05:27<02:14, 16.86it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:17<07:08,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5731/8000 [05:27<01:59, 18.97it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:19<06:35,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5734/8000 [05:28<02:32, 14.89it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:20<06:08,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5738/8000 [05:28<02:11, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:22<05:57,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5741/8000 [05:28<02:31, 14.92it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:24<05:56,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5744/8000 [05:29<02:49, 13.34it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:26<05:51,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5746/8000 [05:29<02:46, 13.53it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:28<05:43,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5749/8000 [05:29<02:30, 14.99it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:29<05:32,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5752/8000 [05:29<02:18, 16.20it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:31<05:24,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5754/8000 [05:29<03:12, 11.67it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:33<05:21,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5760/8000 [05:29<02:06, 17.77it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:35<05:19,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5766/8000 [05:30<01:36, 23.21it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:36<05:24,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5769/8000 [05:30<02:26, 15.19it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:38<05:26,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5772/8000 [05:30<02:18, 16.04it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:40<05:24,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5776/8000 [05:30<02:21, 15.73it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:42<05:23,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5779/8000 [05:31<02:12, 16.71it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:43<05:15,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5781/8000 [05:31<02:11, 16.94it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:45<05:14,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5783/8000 [05:31<02:14, 16.44it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:47<05:08,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5787/8000 [05:31<02:26, 15.08it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:49<05:07,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5792/8000 [05:31<01:55, 19.07it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:50<05:07,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5798/8000 [05:32<02:10, 16.93it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:52<05:04,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5800/8000 [05:32<02:21, 15.53it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:54<05:01,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5803/8000 [05:32<02:33, 14.33it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:56<04:59,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5807/8000 [05:32<02:10, 16.76it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:57<04:54,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5809/8000 [05:32<02:22, 15.37it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:59<05:08,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5811/8000 [05:33<02:23, 15.21it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [01:01<05:03,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5814/8000 [05:33<02:12, 16.56it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [01:03<04:56,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5819/8000 [05:33<01:57, 18.61it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [01:04<04:48,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5823/8000 [05:33<01:46, 20.48it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [01:06<04:39,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5826/8000 [05:33<01:45, 20.58it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:08<04:43,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5829/8000 [05:33<01:44, 20.83it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:10<04:41,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5833/8000 [05:34<02:02, 17.76it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:11<04:35,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5835/8000 [05:34<02:11, 16.47it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:13<04:34,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5840/8000 [05:34<01:46, 20.36it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:15<04:36,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5843/8000 [05:34<02:17, 15.66it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:16<04:27,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5846/8000 [05:35<02:08, 16.71it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:18<04:22,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5848/8000 [05:35<02:22, 15.08it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:20<04:24,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5851/8000 [05:35<02:12, 16.18it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:21<04:34,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5853/8000 [05:35<02:22, 15.10it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:23<04:32,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5859/8000 [05:35<02:13, 16.01it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:25<04:24,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5865/8000 [05:36<02:13, 15.97it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:27<04:32,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5867/8000 [05:36<02:17, 15.52it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:29<04:38,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5870/8000 [05:36<02:35, 13.66it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:30<04:34,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5872/8000 [05:36<03:06, 11.42it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:32<04:24,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5876/8000 [05:37<02:24, 14.72it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:34<04:27,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5878/8000 [05:37<02:24, 14.64it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:37<04:45,  1.92s/it]\n",
       "\n",
-      "Generating signals:  74%|████████████▍    | 5882/8000 [05:37<01:59, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5886/8000 [05:37<01:46, 19.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5892/8000 [05:37<01:41, 20.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5895/8000 [05:37<01:40, 21.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.78it/s]\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5899/8000 [05:38<01:34, 22.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5903/8000 [05:38<01:53, 18.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5906/8000 [05:38<02:46, 12.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5908/8000 [05:39<02:51, 12.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 22it [00:01, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5911/8000 [05:39<03:08, 11.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 48it [00:01, 41.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5914/8000 [05:39<03:17, 10.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 68it [00:01, 60.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5917/8000 [05:39<02:52, 12.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 87it [00:02, 79.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5919/8000 [05:40<03:01, 11.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 109it [00:02, 103.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5922/8000 [05:40<02:35, 13.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 132it [00:02, 128.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5925/8000 [05:40<02:21, 14.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 153it [00:02, 144.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5928/8000 [05:40<02:14, 15.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 177it [00:02, 164.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5931/8000 [05:40<02:38, 13.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 198it [00:02, 165.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5933/8000 [05:41<02:40, 12.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 222it [00:02, 183.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5937/8000 [05:41<02:10, 15.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 243it [00:02, 187.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5939/8000 [05:41<02:14, 15.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 264it [00:02, 180.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5941/8000 [05:41<02:26, 14.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 287it [00:03, 187.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5943/8000 [05:41<02:40, 12.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 309it [00:03, 194.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5946/8000 [05:41<02:45, 12.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 330it [00:03, 194.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5948/8000 [05:42<03:45,  9.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 355it [00:03, 208.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5951/8000 [05:42<02:59, 11.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 377it [00:03, 211.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5954/8000 [05:42<02:45, 12.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 401it [00:03, 217.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5956/8000 [05:42<02:46, 12.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 423it [00:03, 207.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5958/8000 [05:43<02:40, 12.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 444it [00:03, 199.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5961/8000 [05:43<02:12, 15.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 467it [00:03, 206.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5964/8000 [05:43<01:57, 17.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 489it [00:04, 209.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5966/8000 [05:43<02:04, 16.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 511it [00:04, 210.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5970/8000 [05:43<01:41, 20.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 533it [00:04, 199.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5974/8000 [05:43<02:05, 16.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 558it [00:04, 212.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5977/8000 [05:44<01:55, 17.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 581it [00:04, 217.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5980/8000 [05:44<01:47, 18.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 605it [00:04, 220.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5984/8000 [05:44<01:56, 17.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 631it [00:04, 227.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5989/8000 [05:44<01:30, 22.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 654it [00:04, 197.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5994/8000 [05:45<02:16, 14.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 675it [00:04, 178.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5998/8000 [05:45<01:59, 16.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 694it [00:05, 174.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6001/8000 [05:45<01:57, 17.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 712it [00:05, 173.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6007/8000 [05:45<01:32, 21.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 737it [00:05, 194.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6010/8000 [05:45<02:13, 14.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 757it [00:05, 193.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6012/8000 [05:46<02:12, 15.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 777it [00:05, 188.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6015/8000 [05:46<01:56, 17.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:05, 141.99it/s]\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6020/8000 [05:46<01:41, 19.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6023/8000 [05:46<01:37, 20.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6026/8000 [05:46<01:40, 19.61it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:00, 111.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6030/8000 [05:47<02:11, 14.97it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▎| 27/100 [00:00<00:00, 131.1\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6033/8000 [05:47<02:41, 12.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 120.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6036/8000 [05:47<02:38, 12.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:00<00:00, 130.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6041/8000 [05:47<02:03, 15.81it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|▋| 70/100 [00:00<00:00, 119.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6043/8000 [05:48<02:17, 14.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|▊| 83/100 [00:00<00:00, 115.6\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6046/8000 [05:48<02:11, 14.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 119.\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6048/8000 [05:48<02:21, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6051/8000 [05:48<02:28, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6055/8000 [05:48<01:57, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                      | 2/8000 [00:00<09:46, 13.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6057/8000 [05:49<02:34, 12.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                      | 5/8000 [00:00<07:50, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6060/8000 [05:49<02:49, 11.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 10/8000 [00:00<05:27, 24.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6065/8000 [05:49<02:08, 15.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 18/8000 [00:00<06:54, 19.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6068/8000 [05:49<02:27, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 21/8000 [00:01<06:31, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6070/8000 [05:50<02:28, 12.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 24/8000 [00:01<08:46, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6075/8000 [05:50<02:18, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 28/8000 [00:01<07:47, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6077/8000 [05:50<02:22, 13.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 32/8000 [00:01<09:11, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6079/8000 [05:50<02:37, 12.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 37/8000 [00:02<07:58, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6084/8000 [05:51<01:55, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                     | 42/8000 [00:02<07:05, 18.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6087/8000 [05:51<01:53, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                     | 46/8000 [00:02<07:56, 16.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6090/8000 [05:51<01:49, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 48/8000 [00:02<08:49, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6094/8000 [05:51<01:30, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 50/8000 [00:03<09:46, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6100/8000 [05:51<01:13, 25.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 54/8000 [00:03<07:43, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6103/8000 [05:51<01:15, 25.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 61/8000 [00:03<05:18, 24.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6108/8000 [05:52<01:24, 22.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 64/8000 [00:03<06:44, 19.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6113/8000 [05:52<01:45, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 67/8000 [00:03<06:29, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|█████████████    | 6118/8000 [05:52<01:44, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 71/8000 [00:04<07:56, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|█████████████    | 6120/8000 [05:52<01:45, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 74/8000 [00:04<07:48, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6125/8000 [05:52<01:26, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 76/8000 [00:04<12:10, 10.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6129/8000 [05:53<01:41, 18.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 80/8000 [00:05<11:49, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6135/8000 [05:53<01:38, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 84/8000 [00:05<10:50, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6138/8000 [05:53<01:53, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 86/8000 [00:05<12:21, 10.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6140/8000 [05:53<01:54, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 92/8000 [00:05<08:08, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6142/8000 [05:54<01:53, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 95/8000 [00:06<09:38, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6145/8000 [05:54<01:46, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                    | 99/8000 [00:06<08:14, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6148/8000 [05:54<01:39, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 102/8000 [00:06<08:41, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6152/8000 [05:54<01:40, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 104/8000 [00:06<09:22, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6154/8000 [05:54<01:43, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 111/8000 [00:06<07:34, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6158/8000 [05:54<01:50, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 113/8000 [00:07<07:47, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6161/8000 [05:55<01:40, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 115/8000 [00:07<08:15, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6164/8000 [05:55<01:35, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 118/8000 [00:07<09:40, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6168/8000 [05:55<01:46, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 122/8000 [00:07<07:53, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6171/8000 [05:55<01:42, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 124/8000 [00:07<08:21, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6173/8000 [05:55<01:53, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 127/8000 [00:08<09:41, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6175/8000 [05:55<01:54, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 129/8000 [00:08<09:41, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6178/8000 [05:56<01:45, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 132/8000 [00:08<08:27, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6181/8000 [05:56<01:39, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 135/8000 [00:08<09:17, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6184/8000 [05:56<01:30, 20.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 137/8000 [00:08<09:16, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6187/8000 [05:56<01:26, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 141/8000 [00:09<09:26, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6190/8000 [05:56<01:23, 21.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 144/8000 [00:09<12:11, 10.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6193/8000 [05:56<01:21, 22.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 149/8000 [00:09<10:16, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6199/8000 [05:57<01:41, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 153/8000 [00:09<08:26, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6203/8000 [05:57<01:28, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 155/8000 [00:10<08:26, 15.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6207/8000 [05:57<01:43, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 157/8000 [00:10<08:41, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6210/8000 [05:57<01:38, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 163/8000 [00:10<06:13, 21.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6215/8000 [05:57<01:23, 21.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 168/8000 [00:10<06:10, 21.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6218/8000 [05:58<01:48, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 174/8000 [00:10<05:01, 25.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6221/8000 [05:58<01:42, 17.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 177/8000 [00:10<05:39, 23.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6224/8000 [05:58<01:40, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 182/8000 [00:11<05:05, 25.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6229/8000 [05:58<01:17, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 186/8000 [00:11<04:59, 26.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6233/8000 [05:58<01:11, 24.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 189/8000 [00:11<06:42, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6237/8000 [05:59<01:30, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 195/8000 [00:11<05:24, 24.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6244/8000 [05:59<01:39, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 199/8000 [00:11<05:41, 22.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6248/8000 [05:59<01:30, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 202/8000 [00:12<07:14, 17.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6256/8000 [06:00<01:32, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 205/8000 [00:12<06:54, 18.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6261/8000 [06:00<01:53, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 209/8000 [00:12<06:16, 20.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6264/8000 [06:00<01:54, 15.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 213/8000 [00:12<07:13, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6266/8000 [06:00<02:03, 14.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 218/8000 [00:12<05:55, 21.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6269/8000 [06:01<02:02, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 222/8000 [00:13<08:25, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6272/8000 [06:01<01:53, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 224/8000 [00:13<08:40, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6275/8000 [06:01<01:47, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 230/8000 [00:13<06:21, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6279/8000 [06:01<01:41, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 233/8000 [00:13<06:35, 19.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6281/8000 [06:01<01:57, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 236/8000 [00:14<08:39, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6284/8000 [06:02<02:29, 11.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 239/8000 [00:14<09:40, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6287/8000 [06:02<02:23, 11.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 242/8000 [00:14<10:51, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6291/8000 [06:03<03:03,  9.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 247/8000 [00:14<08:00, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6295/8000 [06:03<03:11,  8.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 250/8000 [00:15<09:25, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6298/8000 [06:04<03:32,  8.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 252/8000 [00:15<09:53, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6300/8000 [06:04<03:55,  7.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 257/8000 [00:15<09:05, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6302/8000 [06:04<03:58,  7.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 260/8000 [00:15<08:11, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6303/8000 [06:05<05:26,  5.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 262/8000 [00:16<08:45, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6304/8000 [06:05<05:22,  5.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 264/8000 [00:16<08:50, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6308/8000 [06:05<03:43,  7.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 269/8000 [00:16<06:42, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6311/8000 [06:06<03:22,  8.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 272/8000 [00:16<09:08, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6318/8000 [06:06<01:58, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 274/8000 [00:16<09:14, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6323/8000 [06:06<01:31, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 277/8000 [00:16<08:18, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6328/8000 [06:06<01:16, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 281/8000 [00:17<08:39, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6331/8000 [06:06<01:42, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 283/8000 [00:17<08:36, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6335/8000 [06:07<02:25, 11.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 285/8000 [00:17<11:09, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6337/8000 [06:07<02:21, 11.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 287/8000 [00:18<14:21,  8.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6340/8000 [06:07<02:05, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 292/8000 [00:18<09:44, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6345/8000 [06:07<01:36, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 295/8000 [00:18<09:09, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6348/8000 [06:08<01:31, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 298/8000 [00:18<08:11, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6354/8000 [06:08<01:54, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 301/8000 [00:18<07:41, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6358/8000 [06:08<01:43, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 304/8000 [00:18<07:09, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6362/8000 [06:08<01:28, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 306/8000 [00:19<07:44, 16.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6367/8000 [06:09<01:14, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 308/8000 [00:19<08:29, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6373/8000 [06:09<01:11, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 311/8000 [00:19<08:23, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6377/8000 [06:09<01:09, 23.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 314/8000 [00:19<08:04, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6384/8000 [06:09<00:57, 28.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 320/8000 [00:19<05:29, 23.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6389/8000 [06:09<00:55, 29.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 324/8000 [00:19<05:07, 24.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6394/8000 [06:10<01:07, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 329/8000 [00:19<04:33, 28.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6400/8000 [06:10<00:55, 28.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 332/8000 [00:20<05:06, 25.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6404/8000 [06:10<01:07, 23.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 336/8000 [00:20<04:53, 26.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6409/8000 [06:10<01:12, 21.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 339/8000 [00:20<06:53, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6413/8000 [06:11<01:27, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 343/8000 [00:20<06:22, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6416/8000 [06:11<01:38, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 346/8000 [00:20<06:29, 19.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6419/8000 [06:11<01:33, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▉                   | 350/8000 [00:21<05:54, 21.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6426/8000 [06:11<01:22, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▉                   | 357/8000 [00:21<05:41, 22.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6428/8000 [06:11<01:28, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▉                   | 360/8000 [00:21<08:37, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6432/8000 [06:12<01:16, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 362/8000 [00:21<08:43, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6435/8000 [06:12<01:14, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 364/8000 [00:22<11:17, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6438/8000 [06:12<01:30, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 370/8000 [00:22<09:09, 13.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6440/8000 [06:12<01:38, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 373/8000 [00:22<08:34, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6442/8000 [06:12<01:41, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 376/8000 [00:22<08:16, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6447/8000 [06:13<01:37, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 380/8000 [00:23<08:16, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6450/8000 [06:13<01:51, 13.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 384/8000 [00:23<10:12, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6453/8000 [06:13<01:58, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 386/8000 [00:23<09:55, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6456/8000 [06:13<01:59, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 390/8000 [00:23<08:05, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6459/8000 [06:14<01:46, 14.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 392/8000 [00:24<08:11, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6463/8000 [06:14<02:05, 12.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 397/8000 [00:24<07:37, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6466/8000 [06:14<02:07, 12.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 404/8000 [00:24<05:27, 23.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6468/8000 [06:14<01:58, 12.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 407/8000 [00:24<06:05, 20.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6470/8000 [06:14<01:57, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 411/8000 [00:24<05:43, 22.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6472/8000 [06:15<01:53, 13.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 415/8000 [00:24<05:30, 22.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6475/8000 [06:15<01:38, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 418/8000 [00:25<07:27, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6479/8000 [06:15<01:19, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 421/8000 [00:25<08:38, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6484/8000 [06:15<01:03, 23.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 423/8000 [00:25<08:52, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6487/8000 [06:15<01:19, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 429/8000 [00:25<06:08, 20.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6491/8000 [06:15<01:15, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 432/8000 [00:26<06:06, 20.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6494/8000 [06:16<01:16, 19.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 435/8000 [00:26<06:04, 20.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6497/8000 [06:16<01:35, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 439/8000 [00:26<05:29, 22.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6501/8000 [06:16<01:19, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                   | 442/8000 [00:26<05:24, 23.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6507/8000 [06:16<00:59, 25.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                   | 446/8000 [00:26<07:12, 17.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6510/8000 [06:16<01:01, 24.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                   | 449/8000 [00:27<08:49, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6514/8000 [06:16<01:00, 24.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 452/8000 [00:27<09:28, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6519/8000 [06:17<00:53, 27.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 455/8000 [00:27<10:34, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6525/8000 [06:17<01:16, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 458/8000 [00:27<11:08, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6529/8000 [06:17<01:09, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 460/8000 [00:28<10:57, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6535/8000 [06:17<01:09, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 464/8000 [00:28<08:28, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6542/8000 [06:18<01:07, 21.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 467/8000 [00:28<08:21, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6547/8000 [06:18<01:10, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 470/8000 [00:28<08:15, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6550/8000 [06:18<01:33, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 473/8000 [00:28<08:08, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6556/8000 [06:19<01:24, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 476/8000 [00:29<07:32, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6558/8000 [06:19<01:26, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 480/8000 [00:29<07:36, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6560/8000 [06:19<01:30, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 483/8000 [00:29<08:32, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6562/8000 [06:19<01:29, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 486/8000 [00:29<07:54, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6565/8000 [06:19<01:22, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 488/8000 [00:29<09:17, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6567/8000 [06:19<01:35, 14.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 491/8000 [00:30<09:19, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6569/8000 [06:20<01:36, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 493/8000 [00:30<11:04, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6571/8000 [06:20<02:00, 11.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 497/8000 [00:30<12:46,  9.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6573/8000 [06:20<01:55, 12.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 500/8000 [00:31<11:48, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6576/8000 [06:21<02:50,  8.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 503/8000 [00:31<11:00, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6581/8000 [06:21<01:48, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 508/8000 [00:31<09:12, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6584/8000 [06:21<01:55, 12.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 510/8000 [00:31<11:00, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6589/8000 [06:21<01:23, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 512/8000 [00:32<11:00, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6592/8000 [06:21<01:35, 14.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 516/8000 [00:32<08:39, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6594/8000 [06:22<01:36, 14.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 519/8000 [00:32<08:44, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6597/8000 [06:22<01:30, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 523/8000 [00:32<09:07, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6600/8000 [06:22<01:19, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 525/8000 [00:33<10:14, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6604/8000 [06:22<01:32, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 528/8000 [00:33<09:49, 12.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6612/8000 [06:22<00:57, 24.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 530/8000 [00:33<13:04,  9.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6616/8000 [06:22<00:57, 23.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 534/8000 [00:33<11:17, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6620/8000 [06:23<00:53, 25.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 536/8000 [00:34<10:46, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6623/8000 [06:23<00:57, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 539/8000 [00:34<09:49, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6627/8000 [06:23<01:08, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 541/8000 [00:34<12:21, 10.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6631/8000 [06:23<01:00, 22.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 543/8000 [00:34<11:24, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6634/8000 [06:24<01:36, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 548/8000 [00:34<09:18, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6640/8000 [06:24<01:31, 14.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 551/8000 [00:35<08:38, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6645/8000 [06:24<01:13, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 556/8000 [00:35<06:30, 19.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6651/8000 [06:24<00:59, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 559/8000 [00:35<08:10, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6654/8000 [06:25<01:13, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 562/8000 [00:35<07:54, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6657/8000 [06:25<01:11, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 566/8000 [00:35<06:52, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6662/8000 [06:25<01:17, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 570/8000 [00:36<06:25, 19.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6667/8000 [06:25<01:03, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 575/8000 [00:36<08:17, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6671/8000 [06:25<01:12, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 580/8000 [00:36<08:23, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6675/8000 [06:26<01:36, 13.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 585/8000 [00:37<07:08, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6680/8000 [06:26<01:28, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 589/8000 [00:37<06:25, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6682/8000 [06:27<01:44, 12.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 593/8000 [00:37<08:23, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6684/8000 [06:27<01:41, 12.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 596/8000 [00:37<07:40, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6689/8000 [06:27<01:14, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 598/8000 [00:37<08:22, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6696/8000 [06:27<00:54, 23.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 600/8000 [00:38<08:33, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6699/8000 [06:27<01:13, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 603/8000 [00:38<09:23, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6702/8000 [06:27<01:10, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 605/8000 [00:38<11:04, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6705/8000 [06:28<01:05, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 607/8000 [00:38<11:03, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6708/8000 [06:28<01:18, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 612/8000 [00:39<09:31, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6712/8000 [06:28<01:08, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 616/8000 [00:39<08:01, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6716/8000 [06:28<01:28, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 619/8000 [00:39<09:04, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6719/8000 [06:29<01:22, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 622/8000 [00:39<08:06, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6722/8000 [06:29<01:33, 13.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 624/8000 [00:39<08:15, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6724/8000 [06:29<01:33, 13.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 626/8000 [00:40<08:48, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6729/8000 [06:29<01:25, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 629/8000 [00:40<11:41, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6733/8000 [06:29<01:11, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 632/8000 [00:40<12:07, 10.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6735/8000 [06:30<01:29, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 634/8000 [00:41<11:46, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6737/8000 [06:30<01:30, 14.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 637/8000 [00:41<10:11, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6740/8000 [06:30<01:56, 10.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 640/8000 [00:41<08:32, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6742/8000 [06:30<01:47, 11.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 644/8000 [00:41<08:48, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6744/8000 [06:31<01:44, 11.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 648/8000 [00:41<07:17, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6752/8000 [06:31<00:55, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 651/8000 [00:42<09:52, 12.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6760/8000 [06:31<01:12, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 655/8000 [00:42<08:08, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6763/8000 [06:31<01:09, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 658/8000 [00:42<07:26, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6769/8000 [06:32<01:17, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 662/8000 [00:42<06:08, 19.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6772/8000 [06:32<01:12, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 665/8000 [00:42<06:01, 20.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6775/8000 [06:32<01:06, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 669/8000 [00:43<07:06, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6781/8000 [06:32<01:04, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 676/8000 [00:43<07:28, 16.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6784/8000 [06:33<01:19, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 681/8000 [00:43<07:40, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6786/8000 [06:33<01:20, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 683/8000 [00:43<08:08, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6789/8000 [06:33<01:30, 13.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 687/8000 [00:44<08:09, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6793/8000 [06:33<01:11, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 692/8000 [00:44<07:45, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6797/8000 [06:34<01:15, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 694/8000 [00:44<07:47, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6799/8000 [06:34<01:18, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 699/8000 [00:44<07:23, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6803/8000 [06:34<01:05, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 701/8000 [00:45<09:17, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6809/8000 [06:34<00:59, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 705/8000 [00:45<07:58, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6815/8000 [06:34<01:01, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 708/8000 [00:45<07:27, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6819/8000 [06:35<01:07, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 710/8000 [00:45<09:34, 12.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6821/8000 [06:35<01:11, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 712/8000 [00:46<09:26, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6823/8000 [06:35<01:16, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 715/8000 [00:46<08:27, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6826/8000 [06:35<01:11, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 717/8000 [00:46<10:20, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6833/8000 [06:35<00:48, 24.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 721/8000 [00:46<08:04, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6837/8000 [06:36<00:48, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 725/8000 [00:46<06:35, 18.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6843/8000 [06:36<00:50, 22.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 733/8000 [00:47<05:17, 22.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6847/8000 [06:36<01:10, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 737/8000 [00:47<05:06, 23.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6851/8000 [06:36<01:02, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 740/8000 [00:47<05:44, 21.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6854/8000 [06:37<00:59, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 744/8000 [00:47<06:54, 17.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6857/8000 [06:37<00:55, 20.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▉                  | 751/8000 [00:47<06:05, 19.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6861/8000 [06:37<00:51, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▉                  | 755/8000 [00:48<06:54, 17.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6864/8000 [06:37<00:52, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▉                  | 758/8000 [00:48<06:37, 18.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6869/8000 [06:37<00:45, 25.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 763/8000 [00:48<05:48, 20.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6874/8000 [06:37<00:51, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 766/8000 [00:48<07:46, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6879/8000 [06:38<00:45, 24.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 768/8000 [00:49<07:59, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6884/8000 [06:38<00:49, 22.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 770/8000 [00:49<08:03, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6889/8000 [06:38<00:42, 26.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 772/8000 [00:49<08:26, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6892/8000 [06:38<00:43, 25.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 776/8000 [00:49<07:04, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6895/8000 [06:38<00:55, 19.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 778/8000 [00:49<07:28, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6898/8000 [06:38<00:55, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 783/8000 [00:49<05:36, 21.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6902/8000 [06:39<00:53, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 786/8000 [00:49<05:32, 21.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6907/8000 [06:39<00:47, 23.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 789/8000 [00:50<06:04, 19.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6911/8000 [06:39<01:00, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 792/8000 [00:50<06:19, 18.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6916/8000 [06:40<01:32, 11.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 797/8000 [00:50<05:20, 22.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6919/8000 [06:40<01:22, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 803/8000 [00:50<05:22, 22.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6922/8000 [06:40<01:14, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 809/8000 [00:51<05:17, 22.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6924/8000 [06:40<01:17, 13.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 812/8000 [00:51<07:02, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6926/8000 [06:40<01:18, 13.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 815/8000 [00:51<06:42, 17.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6928/8000 [06:41<01:17, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 823/8000 [00:51<05:42, 20.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6935/8000 [06:41<00:44, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 826/8000 [00:52<07:05, 16.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6939/8000 [06:41<00:44, 23.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 828/8000 [00:52<10:31, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6942/8000 [06:41<00:58, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 830/8000 [00:52<10:10, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6947/8000 [06:41<00:47, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 832/8000 [00:53<11:42, 10.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6950/8000 [06:41<00:49, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 835/8000 [00:53<09:44, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6955/8000 [06:42<00:40, 25.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 838/8000 [00:53<08:29, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6958/8000 [06:42<00:54, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                  | 842/8000 [00:53<07:06, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6962/8000 [06:42<01:03, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                  | 845/8000 [00:53<06:55, 17.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6966/8000 [06:42<00:53, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 850/8000 [00:53<07:02, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6969/8000 [06:42<00:52, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 852/8000 [00:54<07:18, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6972/8000 [06:43<00:50, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 854/8000 [00:54<07:43, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6977/8000 [06:43<01:00, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 859/8000 [00:54<06:00, 19.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6980/8000 [06:43<01:21, 12.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 863/8000 [00:54<05:31, 21.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6983/8000 [06:44<01:23, 12.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 867/8000 [00:54<05:16, 22.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6985/8000 [06:44<01:21, 12.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 871/8000 [00:54<05:09, 23.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6989/8000 [06:44<01:15, 13.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 874/8000 [00:55<06:29, 18.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6992/8000 [06:44<01:05, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 877/8000 [00:55<06:04, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6995/8000 [06:44<00:58, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 883/8000 [00:55<07:17, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6998/8000 [06:44<00:55, 18.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 886/8000 [00:55<06:59, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7002/8000 [06:45<00:46, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 889/8000 [00:56<06:34, 18.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7008/8000 [06:45<00:46, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 891/8000 [00:56<06:53, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7014/8000 [06:45<00:39, 25.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 893/8000 [00:56<07:26, 15.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7020/8000 [06:45<00:51, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 895/8000 [00:56<08:03, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7023/8000 [06:46<00:49, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 898/8000 [00:56<09:01, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7027/8000 [06:46<01:12, 13.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 904/8000 [00:56<05:46, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7030/8000 [06:46<01:06, 14.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 908/8000 [00:57<07:52, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7035/8000 [06:46<00:53, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 910/8000 [00:57<08:15, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7038/8000 [06:47<00:59, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 916/8000 [00:57<05:54, 19.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7041/8000 [06:47<00:54, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 919/8000 [00:57<07:14, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7045/8000 [06:47<00:46, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 922/8000 [00:58<06:58, 16.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7050/8000 [06:47<00:40, 23.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 926/8000 [00:58<09:01, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7053/8000 [06:47<00:49, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 928/8000 [00:58<10:55, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7057/8000 [06:48<00:51, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 933/8000 [00:59<09:36, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7061/8000 [06:48<00:57, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 936/8000 [00:59<08:28, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7063/8000 [06:48<00:57, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 941/8000 [00:59<06:37, 17.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7065/8000 [06:48<01:15, 12.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 948/8000 [00:59<05:50, 20.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7070/8000 [06:49<01:05, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 953/8000 [00:59<05:06, 23.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7073/8000 [06:49<00:56, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 957/8000 [01:00<04:44, 24.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7076/8000 [06:49<00:52, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 960/8000 [01:00<04:53, 24.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7083/8000 [06:49<00:44, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 964/8000 [01:00<04:54, 23.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7087/8000 [06:49<00:42, 21.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 969/8000 [01:00<05:38, 20.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7091/8000 [06:50<00:46, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 975/8000 [01:01<06:42, 17.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7095/8000 [06:50<00:44, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 978/8000 [01:01<06:29, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7099/8000 [06:50<00:40, 22.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 982/8000 [01:01<07:04, 16.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7105/8000 [06:50<00:56, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 985/8000 [01:01<06:45, 17.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7108/8000 [06:51<00:52, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 989/8000 [01:01<06:05, 19.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7111/8000 [06:51<00:50, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 992/8000 [01:01<06:01, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7115/8000 [06:51<00:52, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 996/8000 [01:02<06:15, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7117/8000 [06:51<00:55, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 998/8000 [01:02<06:39, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7119/8000 [06:51<00:58, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1003/8000 [01:02<05:22, 21.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7122/8000 [06:51<00:55, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1006/8000 [01:02<05:20, 21.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7124/8000 [06:52<00:55, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1009/8000 [01:02<05:27, 21.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7127/8000 [06:52<00:49, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1012/8000 [01:02<05:24, 21.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7133/8000 [06:52<00:46, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1016/8000 [01:03<04:54, 23.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7135/8000 [06:52<00:49, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1020/8000 [01:03<04:38, 25.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7139/8000 [06:52<00:42, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1024/8000 [01:03<04:33, 25.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7142/8000 [06:52<00:42, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1029/8000 [01:03<04:15, 27.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7145/8000 [06:53<00:53, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1032/8000 [01:03<04:25, 26.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7150/8000 [06:53<00:52, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1035/8000 [01:03<04:40, 24.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7153/8000 [06:54<01:13, 11.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1039/8000 [01:04<05:55, 19.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7159/8000 [06:54<01:00, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1047/8000 [01:04<05:16, 21.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7165/8000 [06:54<00:44, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1052/8000 [01:04<05:45, 20.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7168/8000 [06:54<00:43, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1055/8000 [01:04<05:47, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7171/8000 [06:54<00:40, 20.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1058/8000 [01:05<07:01, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7175/8000 [06:54<00:44, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1062/8000 [01:05<07:33, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7178/8000 [06:55<01:00, 13.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1064/8000 [01:05<07:45, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7181/8000 [06:55<00:54, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1066/8000 [01:05<08:01, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7184/8000 [06:55<00:49, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1069/8000 [01:06<08:56, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7190/8000 [06:55<00:36, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1074/8000 [01:06<06:42, 17.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7194/8000 [06:56<00:42, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1076/8000 [01:06<08:29, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7197/8000 [06:56<00:51, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1079/8000 [01:06<09:15, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7200/8000 [06:56<00:48, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1086/8000 [01:06<05:52, 19.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7206/8000 [06:56<00:44, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1089/8000 [01:07<05:45, 19.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7209/8000 [06:57<00:52, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1092/8000 [01:07<07:30, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7213/8000 [06:57<00:44, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1094/8000 [01:07<07:40, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7215/8000 [06:57<00:49, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1097/8000 [01:07<07:09, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7217/8000 [06:57<00:49, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1103/8000 [01:07<05:18, 21.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7220/8000 [06:57<00:45, 17.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1107/8000 [01:07<04:51, 23.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7223/8000 [06:57<00:44, 17.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1111/8000 [01:08<07:22, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7229/8000 [06:58<00:40, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1114/8000 [01:08<08:16, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7231/8000 [06:58<00:41, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1117/8000 [01:08<07:39, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7234/8000 [06:58<00:38, 19.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1122/8000 [01:09<07:16, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7239/8000 [06:58<00:38, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1125/8000 [01:09<08:20, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7242/8000 [06:58<00:38, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1129/8000 [01:09<06:54, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7246/8000 [06:59<00:36, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1131/8000 [01:09<07:12, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7251/8000 [06:59<00:40, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1134/8000 [01:09<06:49, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7254/8000 [06:59<00:56, 13.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1138/8000 [01:10<05:49, 19.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7256/8000 [07:00<01:07, 10.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1141/8000 [01:10<05:35, 20.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7261/8000 [07:00<00:49, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1144/8000 [01:10<07:08, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7266/8000 [07:00<00:40, 18.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1147/8000 [01:10<06:41, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7269/8000 [07:00<00:45, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1149/8000 [01:10<08:46, 13.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7271/8000 [07:00<00:48, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1154/8000 [01:11<09:12, 12.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7274/8000 [07:00<00:45, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▊                | 1158/8000 [01:11<07:34, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7277/8000 [07:01<00:42, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1165/8000 [01:11<07:28, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7281/8000 [07:01<00:45, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1167/8000 [01:12<07:22, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7285/8000 [07:01<01:02, 11.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1169/8000 [01:12<07:40, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7288/8000 [07:02<01:05, 10.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1171/8000 [01:12<07:38, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7290/8000 [07:02<01:11,  9.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1174/8000 [01:12<08:33, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7293/8000 [07:02<00:59, 11.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1176/8000 [01:12<08:18, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7295/8000 [07:03<01:21,  8.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1178/8000 [01:12<08:17, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7299/8000 [07:03<01:20,  8.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1181/8000 [01:13<07:18, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7301/8000 [07:03<01:13,  9.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1183/8000 [01:13<07:39, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7304/8000 [07:03<01:09,  9.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1186/8000 [01:13<10:18, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7308/8000 [07:04<00:55, 12.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1191/8000 [01:13<06:54, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7315/8000 [07:04<00:42, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1194/8000 [01:13<06:41, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7318/8000 [07:04<00:42, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1200/8000 [01:14<06:02, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7321/8000 [07:04<00:47, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1205/8000 [01:14<06:26, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7325/8000 [07:05<00:41, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1209/8000 [01:14<07:03, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7330/8000 [07:05<00:32, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1211/8000 [01:15<08:19, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7333/8000 [07:05<00:32, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1213/8000 [01:15<08:25, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7338/8000 [07:05<00:33, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1216/8000 [01:15<07:38, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7341/8000 [07:05<00:40, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1220/8000 [01:15<06:20, 17.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7343/8000 [07:06<00:40, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1223/8000 [01:15<07:23, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7345/8000 [07:06<00:51, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1225/8000 [01:15<07:24, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7349/8000 [07:06<00:41, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1228/8000 [01:16<07:03, 15.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7351/8000 [07:06<00:42, 15.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1233/8000 [01:16<05:16, 21.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7353/8000 [07:07<01:03, 10.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1236/8000 [01:16<05:20, 21.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7358/8000 [07:07<00:49, 12.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1240/8000 [01:16<04:59, 22.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7361/8000 [07:07<00:54, 11.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1243/8000 [01:16<05:05, 22.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7363/8000 [07:07<00:52, 12.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1247/8000 [01:17<07:27, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7365/8000 [07:07<00:51, 12.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1249/8000 [01:17<10:47, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7367/8000 [07:08<00:51, 12.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1253/8000 [01:17<08:24, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7373/8000 [07:08<00:32, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1255/8000 [01:17<09:48, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7380/8000 [07:08<00:29, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1258/8000 [01:18<08:24, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7384/8000 [07:08<00:38, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1261/8000 [01:18<07:23, 15.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7387/8000 [07:09<00:41, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1266/8000 [01:18<07:19, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7394/8000 [07:09<00:28, 21.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1270/8000 [01:18<06:27, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7397/8000 [07:09<00:27, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1272/8000 [01:18<06:47, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7402/8000 [07:09<00:29, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1274/8000 [01:18<06:55, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7406/8000 [07:10<00:33, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1279/8000 [01:19<05:27, 20.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7410/8000 [07:10<00:35, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1283/8000 [01:19<06:05, 18.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7412/8000 [07:10<00:37, 15.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1286/8000 [01:19<07:20, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7414/8000 [07:10<00:55, 10.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1288/8000 [01:19<08:48, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7417/8000 [07:11<00:46, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1291/8000 [01:20<07:47, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7421/8000 [07:11<00:36, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1294/8000 [01:20<07:07, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7424/8000 [07:11<00:34, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1297/8000 [01:20<06:34, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7427/8000 [07:11<00:31, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1300/8000 [01:20<06:12, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7431/8000 [07:11<00:34, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1302/8000 [01:20<06:32, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7433/8000 [07:11<00:34, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1305/8000 [01:20<08:14, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7437/8000 [07:12<00:29, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1309/8000 [01:21<08:12, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7440/8000 [07:12<00:33, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1313/8000 [01:21<06:56, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7442/8000 [07:12<00:45, 12.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███▏               | 1318/8000 [01:21<05:25, 20.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7446/8000 [07:12<00:35, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1321/8000 [01:21<05:20, 20.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7449/8000 [07:13<00:39, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1324/8000 [01:21<06:35, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7452/8000 [07:13<00:42, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1328/8000 [01:22<07:00, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7455/8000 [07:13<00:37, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1330/8000 [01:22<07:18, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7457/8000 [07:13<00:45, 12.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1334/8000 [01:22<06:28, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7462/8000 [07:13<00:32, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1336/8000 [01:22<06:51, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7466/8000 [07:14<00:28, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1339/8000 [01:23<07:57, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7471/8000 [07:14<00:29, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1343/8000 [01:23<06:25, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7474/8000 [07:14<00:34, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1345/8000 [01:23<06:51, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7477/8000 [07:14<00:31, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1348/8000 [01:23<07:49, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7481/8000 [07:14<00:26, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1351/8000 [01:23<07:04, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7485/8000 [07:15<00:34, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1358/8000 [01:23<04:55, 22.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7489/8000 [07:15<00:30, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1361/8000 [01:24<05:01, 22.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7493/8000 [07:15<00:31, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1364/8000 [01:24<06:30, 17.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7498/8000 [07:15<00:25, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1367/8000 [01:24<07:33, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7503/8000 [07:16<00:26, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1369/8000 [01:24<07:37, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7507/8000 [07:16<00:27, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1373/8000 [01:24<06:11, 17.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7509/8000 [07:16<00:28, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1375/8000 [01:25<07:44, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7511/8000 [07:16<00:36, 13.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1377/8000 [01:25<07:32, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7513/8000 [07:16<00:35, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1380/8000 [01:25<07:04, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7517/8000 [07:17<00:29, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1383/8000 [01:25<06:42, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7520/8000 [07:17<00:27, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1385/8000 [01:25<06:56, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7524/8000 [07:17<00:23, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1389/8000 [01:25<05:46, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7527/8000 [07:17<00:29, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1394/8000 [01:26<04:39, 23.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7531/8000 [07:17<00:24, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1397/8000 [01:26<05:58, 18.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7534/8000 [07:18<00:28, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1400/8000 [01:26<07:03, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7536/8000 [07:18<00:28, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1404/8000 [01:26<07:22, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7538/8000 [07:18<00:28, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1409/8000 [01:26<05:41, 19.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7540/8000 [07:18<00:31, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1413/8000 [01:27<06:27, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7542/8000 [07:18<00:31, 14.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1416/8000 [01:27<06:06, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7546/8000 [07:18<00:31, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1421/8000 [01:27<04:55, 22.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7548/8000 [07:19<00:30, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1425/8000 [01:27<05:43, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7550/8000 [07:19<00:41, 10.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1428/8000 [01:28<07:03, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7555/8000 [07:19<00:28, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1431/8000 [01:28<06:39, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7558/8000 [07:19<00:26, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1435/8000 [01:28<07:23, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7564/8000 [07:19<00:19, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1438/8000 [01:28<06:41, 16.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7567/8000 [07:20<00:25, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1441/8000 [01:28<06:30, 16.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7570/8000 [07:20<00:28, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1444/8000 [01:29<06:03, 18.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7574/8000 [07:20<00:24, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1446/8000 [01:29<06:30, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7577/8000 [07:20<00:26, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1452/8000 [01:29<05:51, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7580/8000 [07:20<00:24, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1456/8000 [01:29<06:34, 16.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7583/8000 [07:21<00:29, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1458/8000 [01:29<06:37, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7588/8000 [07:21<00:21, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1462/8000 [01:30<05:37, 19.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7592/8000 [07:21<00:23, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1465/8000 [01:30<06:36, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7596/8000 [07:22<00:29, 13.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1472/8000 [01:30<04:29, 24.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7598/8000 [07:22<00:34, 11.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▌               | 1475/8000 [01:30<05:40, 19.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7601/8000 [07:22<00:35, 11.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▌               | 1478/8000 [01:30<05:31, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7606/8000 [07:22<00:25, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1481/8000 [01:31<05:35, 19.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7608/8000 [07:22<00:26, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1484/8000 [01:31<05:25, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7610/8000 [07:23<00:26, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1488/8000 [01:31<05:07, 21.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7613/8000 [07:23<00:29, 12.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1491/8000 [01:31<04:56, 21.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7619/8000 [07:23<00:19, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1496/8000 [01:31<05:26, 19.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7624/8000 [07:23<00:19, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1500/8000 [01:31<04:52, 22.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7629/8000 [07:24<00:20, 18.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1505/8000 [01:32<06:11, 17.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7633/8000 [07:24<00:24, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1509/8000 [01:32<07:28, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7636/8000 [07:24<00:29, 12.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1512/8000 [01:32<06:54, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7640/8000 [07:25<00:24, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1518/8000 [01:32<05:05, 21.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7643/8000 [07:25<00:27, 13.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1523/8000 [01:33<05:28, 19.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7648/8000 [07:25<00:20, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1526/8000 [01:33<05:12, 20.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7653/8000 [07:25<00:23, 14.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1531/8000 [01:34<08:37, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7657/8000 [07:26<00:24, 14.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1533/8000 [01:34<08:28, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7663/8000 [07:26<00:17, 18.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1535/8000 [01:34<09:30, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7666/8000 [07:26<00:18, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1538/8000 [01:34<08:25, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7672/8000 [07:26<00:14, 23.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1540/8000 [01:34<08:38, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7679/8000 [07:27<00:13, 22.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1544/8000 [01:35<08:27, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7685/8000 [07:27<00:14, 22.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1548/8000 [01:35<06:57, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7688/8000 [07:27<00:14, 20.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1550/8000 [01:35<07:06, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7691/8000 [07:27<00:18, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1552/8000 [01:35<07:02, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7693/8000 [07:28<00:23, 12.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1555/8000 [01:35<09:44, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7695/8000 [07:28<00:23, 13.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1559/8000 [01:36<07:27, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7697/8000 [07:28<00:23, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋               | 1565/8000 [01:36<06:19, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7701/8000 [07:28<00:18, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋               | 1570/8000 [01:36<06:10, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7704/8000 [07:28<00:20, 14.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋               | 1572/8000 [01:36<06:17, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7709/8000 [07:29<00:15, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1579/8000 [01:36<04:20, 24.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7714/8000 [07:29<00:12, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1582/8000 [01:37<04:28, 23.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7720/8000 [07:29<00:15, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1585/8000 [01:37<04:36, 23.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7725/8000 [07:29<00:15, 17.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1589/8000 [01:37<05:36, 19.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7730/8000 [07:30<00:13, 20.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1595/8000 [01:37<05:29, 19.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7734/8000 [07:30<00:14, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1598/8000 [01:37<05:28, 19.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7738/8000 [07:30<00:12, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1601/8000 [01:38<05:28, 19.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7742/8000 [07:30<00:11, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1607/8000 [01:38<05:17, 20.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7746/8000 [07:30<00:13, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1611/8000 [01:38<06:02, 17.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7749/8000 [07:31<00:12, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1616/8000 [01:38<05:19, 19.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7752/8000 [07:31<00:14, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1620/8000 [01:39<06:25, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7754/8000 [07:31<00:18, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1624/8000 [01:39<06:41, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7759/8000 [07:31<00:14, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1626/8000 [01:39<06:50, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7762/8000 [07:31<00:13, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1631/8000 [01:39<05:36, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7765/8000 [07:32<00:20, 11.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▉               | 1634/8000 [01:39<05:33, 19.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7769/8000 [07:32<00:17, 13.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▉               | 1637/8000 [01:40<05:23, 19.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7772/8000 [07:32<00:15, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1642/8000 [01:40<06:22, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7775/8000 [07:32<00:13, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1644/8000 [01:40<06:14, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7777/8000 [07:33<00:14, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1650/8000 [01:40<04:40, 22.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7781/8000 [07:33<00:18, 12.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1653/8000 [01:40<04:51, 21.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7783/8000 [07:33<00:16, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1656/8000 [01:40<04:44, 22.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7786/8000 [07:33<00:14, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1659/8000 [01:41<06:02, 17.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7788/8000 [07:34<00:18, 11.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1663/8000 [01:41<05:19, 19.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7791/8000 [07:34<00:15, 13.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1667/8000 [01:41<04:35, 23.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7795/8000 [07:34<00:15, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1672/8000 [01:41<04:01, 26.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7797/8000 [07:34<00:15, 13.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1676/8000 [01:41<04:01, 26.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7800/8000 [07:34<00:12, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1680/8000 [01:42<05:02, 20.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7802/8000 [07:34<00:13, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1683/8000 [01:42<05:01, 20.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7804/8000 [07:35<00:12, 15.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1690/8000 [01:42<04:27, 23.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7806/8000 [07:35<00:14, 13.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1694/8000 [01:42<05:12, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7811/8000 [07:35<00:09, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1697/8000 [01:42<05:09, 20.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7815/8000 [07:35<00:10, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1703/8000 [01:43<05:14, 20.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7819/8000 [07:35<00:09, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1708/8000 [01:43<04:29, 23.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7822/8000 [07:36<00:13, 12.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1712/8000 [01:43<07:20, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7825/8000 [07:36<00:11, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1714/8000 [01:44<07:40, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7830/8000 [07:36<00:11, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1717/8000 [01:44<07:02, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7833/8000 [07:36<00:10, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1720/8000 [01:44<06:33, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7836/8000 [07:37<00:09, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1723/8000 [01:44<07:10, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7839/8000 [07:37<00:11, 14.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1729/8000 [01:44<06:13, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7841/8000 [07:37<00:10, 14.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1732/8000 [01:45<06:03, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7844/8000 [07:37<00:12, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1739/8000 [01:45<05:06, 20.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7847/8000 [07:37<00:10, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1743/8000 [01:45<04:45, 21.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7849/8000 [07:38<00:10, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1747/8000 [01:45<04:45, 21.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7852/8000 [07:38<00:09, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1750/8000 [01:45<04:36, 22.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7854/8000 [07:38<00:09, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1756/8000 [01:45<03:48, 27.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7856/8000 [07:38<00:11, 12.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1761/8000 [01:46<04:26, 23.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7859/8000 [07:38<00:10, 14.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1767/8000 [01:46<04:25, 23.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7866/8000 [07:38<00:05, 23.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1773/8000 [01:46<04:47, 21.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7869/8000 [07:39<00:07, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1777/8000 [01:47<05:25, 19.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7872/8000 [07:39<00:06, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1783/8000 [01:47<05:35, 18.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7876/8000 [07:39<00:07, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1788/8000 [01:47<05:39, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7880/8000 [07:39<00:07, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▎              | 1793/8000 [01:48<05:41, 18.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▋| 7882/8000 [07:40<00:07, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▎              | 1796/8000 [01:48<05:41, 18.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7884/8000 [07:40<00:09, 12.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▎              | 1800/8000 [01:48<05:16, 19.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7886/8000 [07:40<00:09, 12.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1803/8000 [01:48<05:17, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7888/8000 [07:40<00:08, 12.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1807/8000 [01:48<04:42, 21.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7891/8000 [07:40<00:07, 14.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1812/8000 [01:48<04:01, 25.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7896/8000 [07:41<00:06, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1815/8000 [01:49<05:26, 18.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7898/8000 [07:41<00:06, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1819/8000 [01:49<04:48, 21.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7900/8000 [07:41<00:06, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1822/8000 [01:49<06:24, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7906/8000 [07:41<00:05, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1828/8000 [01:49<05:42, 18.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7912/8000 [07:41<00:03, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1832/8000 [01:49<05:19, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7917/8000 [07:42<00:04, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1837/8000 [01:50<05:20, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7922/8000 [07:42<00:04, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1840/8000 [01:50<05:20, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7927/8000 [07:42<00:03, 21.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1843/8000 [01:50<06:38, 15.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7932/8000 [07:42<00:03, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1846/8000 [01:50<06:14, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7936/8000 [07:43<00:03, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1848/8000 [01:51<06:39, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7942/8000 [07:43<00:02, 21.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1852/8000 [01:51<05:29, 18.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7947/8000 [07:43<00:02, 24.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1855/8000 [01:51<05:16, 19.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7950/8000 [07:43<00:02, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1858/8000 [01:51<05:07, 19.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7953/8000 [07:43<00:02, 20.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1863/8000 [01:51<04:14, 24.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7956/8000 [07:44<00:02, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1866/8000 [01:51<05:38, 18.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7963/8000 [07:44<00:02, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1869/8000 [01:51<05:20, 19.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7966/8000 [07:44<00:01, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1874/8000 [01:52<05:39, 18.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7971/8000 [07:44<00:01, 20.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1877/8000 [01:52<05:23, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7976/8000 [07:45<00:01, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍              | 1881/8000 [01:52<04:51, 21.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7980/8000 [07:45<00:01, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍              | 1885/8000 [01:52<04:29, 22.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7986/8000 [07:45<00:00, 22.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍              | 1888/8000 [01:52<05:40, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7989/8000 [07:45<00:00, 21.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍              | 1892/8000 [01:53<04:55, 20.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7993/8000 [07:45<00:00, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1899/8000 [01:53<04:37, 21.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7996/8000 [07:46<00:00, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1902/8000 [01:53<04:40, 21.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7998/8000 [07:46<00:00, 13.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1905/8000 [01:53<04:50, 20.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|█████████████████| 8000/8000 [07:46<00:00, 17.14it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  24%|████▌              | 1908/8000 [01:53<05:39, 17.94it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  24%|████▌              | 1914/8000 [01:54<05:12, 19.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1918/8000 [01:54<04:46, 21.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:23,  7.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1922/8000 [01:54<04:35, 22.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:27,  4.08s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1927/8000 [01:55<06:17, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:36,  2.93s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1929/8000 [01:55<07:46, 13.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:40,  2.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1933/8000 [01:55<07:20, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<07:04,  2.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1938/8000 [01:55<05:40, 17.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:22,  1.97s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1942/8000 [01:56<06:19, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:56,  1.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1945/8000 [01:56<05:52, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:34,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▋              | 1948/8000 [01:56<05:56, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:24,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▋              | 1953/8000 [01:56<04:42, 21.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:22<05:17,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▋              | 1958/8000 [01:56<04:13, 23.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<05:05,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1961/8000 [01:56<05:14, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:25<05:02,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1967/8000 [01:57<05:13, 19.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<05:03,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1970/8000 [01:57<05:04, 19.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<05:05,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1976/8000 [01:57<04:08, 24.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:30<05:01,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1980/8000 [01:57<04:01, 24.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:31<05:02,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1983/8000 [01:57<05:12, 19.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:33<04:58,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1987/8000 [01:58<05:50, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:35<05:03,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1991/8000 [01:58<05:09, 19.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:37<05:19,  1.76s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1996/8000 [01:58<05:38, 17.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:38<05:18,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2003/8000 [01:58<04:59, 20.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:40<05:13,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2010/8000 [01:59<04:38, 21.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:42<05:27,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2013/8000 [01:59<04:39, 21.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:44<05:14,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2017/8000 [01:59<05:19, 18.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:45<05:01,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2023/8000 [01:59<05:00, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:47<05:01,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2027/8000 [02:00<05:33, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:49<04:58,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2031/8000 [02:00<05:00, 19.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:50<04:52,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2036/8000 [02:00<05:02, 19.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:52<04:47,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2039/8000 [02:01<07:07, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:54<04:48,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊              | 2041/8000 [02:01<07:09, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:55<04:48,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊              | 2047/8000 [02:01<06:51, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:57<04:51,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊              | 2049/8000 [02:01<06:55, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:59<04:54,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊              | 2051/8000 [02:01<06:53, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [01:01<04:50,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2057/8000 [02:02<05:01, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [01:03<04:50,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2062/8000 [02:02<04:20, 22.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:04<04:45,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2065/8000 [02:02<05:19, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:06<04:49,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2071/8000 [02:02<05:13, 18.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:08<04:53,  1.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2075/8000 [02:03<05:41, 17.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:10<05:01,  1.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2077/8000 [02:03<05:56, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:12<05:22,  2.00s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2081/8000 [02:03<05:00, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:14<05:30,  2.06s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2084/8000 [02:03<06:06, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:17<05:32,  2.09s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2086/8000 [02:03<06:12, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:19<05:27,  2.07s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2088/8000 [02:03<06:26, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:20<05:01,  1.92s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2091/8000 [02:04<08:44, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:22<05:14,  2.02s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2096/8000 [02:04<07:28, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:25<05:50,  2.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2098/8000 [02:04<07:22, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:27<05:38,  2.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2103/8000 [02:05<06:47, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:29<05:10,  2.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2105/8000 [02:05<06:52, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:31<04:57,  1.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████              | 2107/8000 [02:05<06:58, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:32<04:36,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████              | 2110/8000 [02:05<07:39, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:36<04:48,  1.93s/it]\n",
+      "Generating signals:  26%|█████              | 2112/8000 [02:05<09:12, 10.65it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  26%|█████              | 2114/8000 [02:06<08:48, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████              | 2116/8000 [02:06<08:05, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████              | 2120/8000 [02:06<06:23, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.90it/s]\n",
+      "Generating signals:  27%|█████              | 2128/8000 [02:06<04:56, 19.81it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  27%|█████              | 2131/8000 [02:07<06:03, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2133/8000 [02:07<06:13, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2135/8000 [02:07<06:27, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2137/8000 [02:07<09:17, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 40it [00:01, 34.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2139/8000 [02:07<08:33, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 68it [00:01, 66.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2141/8000 [02:07<08:07, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 90it [00:01, 90.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2143/8000 [02:08<07:40, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 111it [00:02, 112.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2148/8000 [02:08<05:13, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 139it [00:02, 146.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2151/8000 [02:08<07:45, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 165it [00:02, 172.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2155/8000 [02:08<06:11, 15.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 189it [00:02, 181.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2158/8000 [02:08<05:47, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 218it [00:02, 208.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2161/8000 [02:09<08:22, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 243it [00:02, 208.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2164/8000 [02:09<07:20, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 267it [00:02, 212.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2170/8000 [02:09<06:10, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 291it [00:02, 209.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2173/8000 [02:09<05:53, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 314it [00:02, 210.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2177/8000 [02:10<06:12, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 342it [00:03, 228.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2181/8000 [02:10<05:30, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 369it [00:03, 238.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2185/8000 [02:10<05:04, 19.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 398it [00:03, 250.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2189/8000 [02:10<05:48, 16.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 424it [00:03, 243.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2191/8000 [02:11<06:03, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 449it [00:03, 240.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2193/8000 [02:11<07:52, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 475it [00:03, 246.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2197/8000 [02:11<07:51, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 500it [00:03, 246.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2199/8000 [02:11<07:41, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 525it [00:03, 235.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▏             | 2202/8000 [02:12<06:58, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 549it [00:03, 222.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▏             | 2208/8000 [02:12<04:53, 19.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 578it [00:04, 239.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2212/8000 [02:12<05:20, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 604it [00:04, 243.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2214/8000 [02:12<05:31, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 632it [00:04, 251.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2217/8000 [02:12<05:13, 18.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 658it [00:04, 253.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2220/8000 [02:12<05:12, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 684it [00:04, 243.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2223/8000 [02:13<06:11, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 716it [00:04, 263.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2225/8000 [02:13<07:48, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 743it [00:04, 253.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2229/8000 [02:13<06:01, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 769it [00:04, 238.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2231/8000 [02:13<07:55, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 163.88it/s]\n",
+      "Generating signals:  28%|█████▎             | 2233/8000 [02:14<07:43, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  28%|█████▎             | 2236/8000 [02:14<06:50, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2239/8000 [02:14<06:15, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 108\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2243/8000 [02:14<05:12, 18.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00<00:00, 106\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2248/8000 [02:14<04:12, 22.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  37%|▎| 37/100 [00:00<00:00, 123\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2252/8000 [02:14<04:11, 22.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 125\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2259/8000 [02:15<05:15, 18.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 132\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2263/8000 [02:15<04:54, 19.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 119\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▍             | 2266/8000 [02:15<05:00, 19.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 12\n",
+      "Generating signals:  28%|█████▍             | 2269/8000 [02:15<06:14, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [34:32<00:00, 612.87s/it]\u001b[A\n",
-      "Replicates:  50%|████████████▌            | 4/8 [2:22:15<2:20:46, 2111.75s/it]\n",
-      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.206484286328587\n",
-      "2275: -5.9272686\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  28%|█████▍             | 2274/8000 [02:16<06:17, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  28%|█████▍             | 2276/8000 [02:16<07:48, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▍             | 2278/8000 [02:16<09:18, 10.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:46,  7.17s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2281/8000 [02:17<08:07, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:31,  3.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2283/8000 [02:17<08:13, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:55,  2.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2287/8000 [02:17<06:32, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:14,  2.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2290/8000 [02:17<06:18, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:21,  1.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2293/8000 [02:17<06:01, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:50,  1.81s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2296/8000 [02:18<06:57, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:25,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2298/8000 [02:18<06:57, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:13,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2301/8000 [02:18<06:34, 14.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:56,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2305/8000 [02:18<08:10, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:42,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2307/8000 [02:18<08:00, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:32,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2311/8000 [02:19<07:44, 12.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:24,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2314/8000 [02:19<08:11, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:22,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2318/8000 [02:19<06:20, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:19,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2322/8000 [02:20<06:35, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:16,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2324/8000 [02:20<08:19, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:09,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2326/8000 [02:20<08:09, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:10,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2331/8000 [02:20<07:15, 13.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:22,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2334/8000 [02:21<07:55, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:22,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2336/8000 [02:21<09:18, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:15,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2341/8000 [02:21<06:31, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:10,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2343/8000 [02:21<06:36, 14.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:36<04:06,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2345/8000 [02:21<06:52, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:03,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2347/8000 [02:22<07:02, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<04:04,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2349/8000 [02:22<07:03, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:40<04:05,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2352/8000 [02:22<06:01, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<04:02,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2354/8000 [02:22<06:35, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:43<04:00,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2358/8000 [02:22<05:18, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:45<03:53,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▌             | 2363/8000 [02:22<04:19, 21.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:46<03:51,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▌             | 2367/8000 [02:23<06:13, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:47<03:52,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2369/8000 [02:23<06:36, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:49<03:53,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2371/8000 [02:23<06:49, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:50<04:08,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2378/8000 [02:23<05:45, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<04:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2384/8000 [02:24<05:11, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<03:55,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2386/8000 [02:24<05:40, 16.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:54<03:47,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2390/8000 [02:24<05:12, 17.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:40,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2394/8000 [02:24<04:44, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2398/8000 [02:24<04:29, 20.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:58<03:42,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2402/8000 [02:25<05:29, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:00<03:38,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2404/8000 [02:25<05:51, 15.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:34,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2410/8000 [02:25<04:12, 22.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:32,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2415/8000 [02:25<03:39, 25.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2418/8000 [02:25<04:50, 19.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2424/8000 [02:26<05:33, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2427/8000 [02:26<05:21, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:25,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2431/8000 [02:26<05:41, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:23,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2435/8000 [02:26<04:59, 18.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:29,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2438/8000 [02:27<06:19, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:30,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2440/8000 [02:27<06:20, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2445/8000 [02:27<05:56, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:34,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2449/8000 [02:27<05:07, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:16<03:26,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2453/8000 [02:28<05:28, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:17<03:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2455/8000 [02:28<05:42, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:24,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2457/8000 [02:28<05:55, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:20<03:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2460/8000 [02:28<05:45, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:23<03:39,  1.51s/it]\n",
+      "Generating signals:  31%|█████▊             | 2465/8000 [02:29<07:00, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  31%|█████▊             | 2468/8000 [02:29<06:33, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2471/8000 [02:29<05:52, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2473/8000 [02:29<07:42, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.53it/s]\n",
+      "Generating signals:  31%|█████▉             | 2475/8000 [02:29<07:25, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  31%|█████▉             | 2478/8000 [02:30<06:24, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2481/8000 [02:30<05:52, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:02,  2.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2485/8000 [02:30<04:58, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 33it [00:02, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2487/8000 [02:30<05:16, 17.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 51it [00:02, 29.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2490/8000 [02:30<05:00, 18.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 80it [00:02, 53.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2494/8000 [02:30<04:19, 21.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 103it [00:02, 70.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2499/8000 [02:31<05:44, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 128it [00:03, 94.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2502/8000 [02:31<05:37, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 164it [00:03, 137.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2504/8000 [02:31<05:45, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 202it [00:03, 182.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2506/8000 [02:31<05:56, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 234it [00:03, 211.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2510/8000 [02:31<06:05, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 271it [00:03, 247.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2512/8000 [02:32<06:05, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 309it [00:03, 278.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2515/8000 [02:32<05:21, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 354it [00:03, 322.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▉             | 2522/8000 [02:32<04:28, 20.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 396it [00:03, 347.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▉             | 2525/8000 [02:32<04:24, 20.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 438it [00:03, 366.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2528/8000 [02:33<06:48, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 478it [00:03, 376.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2530/8000 [02:33<06:49, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 518it [00:04, 367.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2535/8000 [02:33<05:06, 17.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 561it [00:04, 384.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2541/8000 [02:33<03:49, 23.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 603it [00:04, 392.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2545/8000 [02:33<04:44, 19.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 644it [00:04, 394.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2551/8000 [02:33<03:47, 24.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 690it [00:04, 412.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2554/8000 [02:34<03:53, 23.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 732it [00:04, 408.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2560/8000 [02:34<04:02, 22.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 167.99it/s]\n",
+      "Generating signals:  32%|██████             | 2565/8000 [02:34<03:40, 24.66it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  32%|██████             | 2568/8000 [02:34<04:46, 18.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2572/8000 [02:34<04:12, 21.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:00, 169\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2575/8000 [02:35<04:23, 20.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00<00:00, 166\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████▏            | 2579/8000 [02:35<03:52, 23.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 171\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████▏            | 2584/8000 [02:35<04:30, 20.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  70%|▋| 70/100 [00:00<00:00, 158\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████▏            | 2590/8000 [02:35<04:26, 20.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "Generating signals:  32%|██████▏            | 2593/8000 [02:36<04:35, 19.63it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  32%|██████▏            | 2596/8000 [02:36<04:37, 19.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████▏            | 2600/8000 [02:36<05:20, 16.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎               | 183/8000 [00:00<00:06, 1298.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2602/8000 [02:36<05:34, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋               | 352/8000 [00:00<00:06, 1257.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2604/8000 [02:36<05:41, 15.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█               | 527/8000 [00:00<00:06, 1203.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2607/8000 [02:37<06:29, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 686/8000 [00:01<00:23, 310.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2609/8000 [02:37<06:33, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 864/8000 [00:01<00:16, 433.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2613/8000 [02:37<06:33, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1024/8000 [00:01<00:12, 544.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2615/8000 [02:37<08:08, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1207/8000 [00:01<00:09, 684.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2617/8000 [02:37<08:11, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1384/8000 [00:02<00:08, 813.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2619/8000 [02:38<09:58,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1556/8000 [00:02<00:07, 907.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2623/8000 [02:38<07:19, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1729/8000 [00:02<00:06, 974.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2626/8000 [02:38<06:35, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1922/8000 [00:03<00:18, 327.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2630/8000 [02:38<05:20, 16.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2099/8000 [00:03<00:14, 418.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2633/8000 [02:38<05:13, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2267/8000 [00:04<00:11, 516.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2635/8000 [02:39<05:51, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2452/8000 [00:04<00:08, 638.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2638/8000 [02:39<05:59, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2628/8000 [00:04<00:07, 752.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2640/8000 [02:39<06:31, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2803/8000 [00:04<00:06, 863.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2642/8000 [02:39<06:39, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2974/8000 [00:04<00:05, 926.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2646/8000 [02:39<05:14, 17.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|█████▉         | 3148/8000 [00:04<00:04, 1015.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2648/8000 [02:40<05:46, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▏        | 3321/8000 [00:04<00:04, 1143.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2650/8000 [02:40<05:54, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▌        | 3510/8000 [00:05<00:03, 1209.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2654/8000 [02:40<06:08, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|██████▉        | 3687/8000 [00:05<00:03, 1247.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2657/8000 [02:40<05:33, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▏       | 3850/8000 [00:05<00:03, 1266.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2661/8000 [02:40<04:33, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▌       | 4011/8000 [00:05<00:03, 1259.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2667/8000 [02:41<05:36, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|███████▉       | 4200/8000 [00:05<00:02, 1324.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2671/8000 [02:41<04:58, 17.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▏      | 4369/8000 [00:05<00:02, 1325.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2674/8000 [02:41<05:49, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|████████▌      | 4554/8000 [00:05<00:02, 1375.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▎            | 2682/8000 [02:41<03:57, 22.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|████████▉      | 4740/8000 [00:05<00:02, 1386.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2685/8000 [02:42<04:53, 18.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▏     | 4917/8000 [00:06<00:02, 1354.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2691/8000 [02:42<03:50, 23.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|█████████▌     | 5085/8000 [00:06<00:02, 1312.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2695/8000 [02:42<04:28, 19.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|█████████▉     | 5267/8000 [00:06<00:02, 1321.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2699/8000 [02:42<04:57, 17.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▏    | 5444/8000 [00:06<00:01, 1307.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2704/8000 [02:43<05:02, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|██████████▌    | 5624/8000 [00:06<00:01, 1302.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2707/8000 [02:43<05:44, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|██████████▉    | 5800/8000 [00:06<00:01, 1236.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2710/8000 [02:43<06:31, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▏   | 5963/8000 [00:06<00:01, 1181.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2713/8000 [02:43<05:58, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6125/8000 [00:08<00:04, 376.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2716/8000 [02:43<05:37, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6306/8000 [00:08<00:03, 482.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2718/8000 [02:44<05:49, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6479/8000 [00:08<00:02, 600.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2722/8000 [02:44<04:50, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6664/8000 [00:08<00:01, 697.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2725/8000 [02:44<04:44, 18.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6849/8000 [00:08<00:01, 803.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2729/8000 [02:44<04:17, 20.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7018/8000 [00:08<00:01, 882.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2733/8000 [02:44<04:57, 17.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7187/8000 [00:08<00:00, 963.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2736/8000 [02:45<04:46, 18.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|█████████████▊ | 7373/8000 [00:09<00:00, 1055.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2738/8000 [02:45<04:50, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▏| 7549/8000 [00:09<00:00, 1132.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2742/8000 [02:45<05:20, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|██████████████▌| 7739/8000 [00:09<00:00, 1183.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2746/8000 [02:45<04:29, 19.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|██████████████▊| 7909/8000 [00:09<00:00, 1193.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2750/8000 [02:45<05:02, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8075it [00:09, 837.77it/s] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  34%|██████▌            | 2752/8000 [02:45<05:16, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  34%|██████▌            | 2754/8000 [02:46<05:46, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2757/8000 [02:46<06:50, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:00,  7.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2761/8000 [02:46<05:26, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:25,  3.76s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2763/8000 [02:46<05:39, 15.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:40,  2.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2769/8000 [02:46<03:59, 21.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:54,  2.12s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2776/8000 [02:47<05:18, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:58,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2780/8000 [02:47<05:27, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:27,  1.69s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2784/8000 [02:47<05:03, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:00,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2790/8000 [02:48<05:02, 17.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:44,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2792/8000 [02:48<06:14, 13.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:30,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2795/8000 [02:48<05:40, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:22,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2798/8000 [02:48<05:21, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2801/8000 [02:49<05:03, 17.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2803/8000 [02:49<05:37, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:22<04:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2807/8000 [02:49<04:44, 18.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2811/8000 [02:49<05:22, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2813/8000 [02:49<05:38, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:26<03:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2816/8000 [02:49<05:20, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<03:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2820/8000 [02:50<04:30, 19.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:29<03:56,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2826/8000 [02:50<04:16, 20.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:30<04:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2829/8000 [02:50<05:40, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2835/8000 [02:51<05:11, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2838/8000 [02:51<06:50, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:34<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▋            | 2841/8000 [02:51<06:13, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:35<03:48,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2843/8000 [02:51<07:22, 11.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:45,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2846/8000 [02:52<06:36, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:38<03:44,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2848/8000 [02:52<06:35, 13.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2852/8000 [02:52<05:23, 15.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:41,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2857/8000 [02:52<04:20, 19.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:42<03:38,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2861/8000 [02:52<05:15, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:43<03:34,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2863/8000 [02:53<05:27, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:44<03:39,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2867/8000 [02:53<04:53, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:46<03:34,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2869/8000 [02:53<05:00, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:47<03:33,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2873/8000 [02:53<04:20, 19.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:48<03:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2877/8000 [02:53<04:59, 17.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:49<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2880/8000 [02:53<04:48, 17.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:51<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2882/8000 [02:54<05:13, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:52<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2885/8000 [02:54<04:59, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:53<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2887/8000 [02:54<05:25, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:54<03:28,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2889/8000 [02:54<05:42, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:56<03:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2891/8000 [02:54<07:51, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:57<03:26,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2894/8000 [02:55<08:19, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:58<03:24,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2897/8000 [02:55<07:16, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:00<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2899/8000 [02:55<07:23, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:01<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2902/8000 [02:55<06:58, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:02<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2904/8000 [02:56<09:58,  8.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:03<03:13,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2909/8000 [02:56<07:53, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:04<03:08,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2912/8000 [02:56<07:04, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:06<03:09,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2914/8000 [02:56<07:04, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:07<03:07,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2917/8000 [02:57<06:03, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:08<03:06,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2921/8000 [02:57<04:49, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:09<03:06,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2924/8000 [02:57<04:42, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:11<03:03,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2927/8000 [02:57<04:30, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:12<03:01,  1.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2932/8000 [02:57<03:47, 22.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:13<03:00,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2938/8000 [02:57<03:51, 21.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:14<02:59,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2942/8000 [02:58<04:27, 18.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:16<02:57,  1.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2946/8000 [02:58<04:04, 20.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:17<02:53,  1.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2949/8000 [02:58<04:23, 19.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:18<02:48,  1.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2954/8000 [02:58<04:53, 17.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:19<02:44,  1.16s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2962/8000 [02:59<04:01, 20.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:20<02:40,  1.14s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2969/8000 [02:59<04:42, 17.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:21<02:37,  1.13s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2972/8000 [02:59<04:43, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:22<02:36,  1.12s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2974/8000 [03:00<05:10, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:23<02:39,  1.16s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2977/8000 [03:00<05:57, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:25<02:41,  1.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2979/8000 [03:00<06:12, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:26<02:41,  1.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2981/8000 [03:00<06:27, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:27<02:41,  1.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2983/8000 [03:00<06:29, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:28<02:40,  1.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2985/8000 [03:01<06:27, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:30<02:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2989/8000 [03:01<05:08, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:31<02:40,  1.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2991/8000 [03:01<05:49, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:32<02:37,  1.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2993/8000 [03:01<05:50, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:33<02:36,  1.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2998/8000 [03:01<04:08, 20.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:34<02:34,  1.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3003/8000 [03:01<03:35, 23.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:36<02:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3008/8000 [03:02<04:29, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:37<02:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3012/8000 [03:02<04:36, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:38<02:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3014/8000 [03:02<04:54, 16.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:39<02:33,  1.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3016/8000 [03:02<06:26, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:41<02:29,  1.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3020/8000 [03:03<06:26, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:42<02:29,  1.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3023/8000 [03:03<05:45, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:43<02:25,  1.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3027/8000 [03:03<05:54, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:44<02:23,  1.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3029/8000 [03:03<06:04, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████                  | 80/200 [01:47<02:40,  1.34s/it]\n",
+      "Generating signals:  38%|███████▏           | 3032/8000 [03:03<05:46, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  38%|███████▏           | 3035/8000 [03:04<05:22, 15.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3037/8000 [03:04<05:31, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3042/8000 [03:04<04:18, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.48it/s]\n",
+      "Generating signals:  38%|███████▏           | 3046/8000 [03:04<04:54, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  38%|███████▏           | 3050/8000 [03:04<04:12, 19.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3057/8000 [03:05<04:26, 18.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3060/8000 [03:05<04:28, 18.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 39it [00:01, 33.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3063/8000 [03:05<04:24, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 71it [00:01, 64.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3065/8000 [03:06<06:57, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 100it [00:01, 94.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3069/8000 [03:06<05:38, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 135it [00:01, 135.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3072/8000 [03:06<06:56, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 165it [00:02, 165.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3075/8000 [03:06<06:24, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 200it [00:02, 204.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3077/8000 [03:06<06:37, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 232it [00:02, 221.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3080/8000 [03:07<05:57, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 265it [00:02, 246.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3086/8000 [03:07<05:43, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 300it [00:02, 272.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3088/8000 [03:07<05:43, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 333it [00:02, 287.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3091/8000 [03:07<05:24, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 369it [00:02, 306.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3096/8000 [03:07<04:13, 19.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 403it [00:02, 307.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3100/8000 [03:08<04:04, 20.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 436it [00:02, 312.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3103/8000 [03:08<04:21, 18.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 469it [00:02, 316.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3107/8000 [03:08<03:50, 21.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 502it [00:03, 319.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3110/8000 [03:08<03:50, 21.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 535it [00:03, 320.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3115/8000 [03:08<04:16, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 568it [00:03, 319.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3119/8000 [03:09<03:52, 21.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 601it [00:03, 322.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3123/8000 [03:09<03:47, 21.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 634it [00:03, 304.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3126/8000 [03:09<03:58, 20.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 665it [00:03, 289.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3131/8000 [03:09<04:15, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 700it [00:03, 304.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3134/8000 [03:09<04:06, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 731it [00:03, 304.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3138/8000 [03:10<04:57, 16.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 764it [00:03, 310.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3140/8000 [03:10<06:17, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 197.00it/s]\n",
+      "Generating signals:  39%|███████▍           | 3144/8000 [03:10<06:07, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  39%|███████▍           | 3147/8000 [03:10<05:33, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3149/8000 [03:11<05:33, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:00, 205\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3151/8000 [03:11<05:44, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 184\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▌           | 3158/8000 [03:11<03:25, 23.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:00<00:00, 177\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3163/8000 [03:11<03:03, 26.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:00<00:00, 177\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3167/8000 [03:11<03:05, 26.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "Generating signals:  40%|███████▌           | 3171/8000 [03:11<03:44, 21.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [05:29<16:29, 329.71s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.107080906436135\n",
-      "2999: -6.1749676\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  40%|███████▌           | 3174/8000 [03:12<05:02, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  40%|███████▌           | 3177/8000 [03:12<06:13, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3180/8000 [03:12<05:27, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:48,  7.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3185/8000 [03:13<06:19, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:13,  3.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3192/8000 [03:13<05:09, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:39,  2.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3197/8000 [03:13<04:12, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:49,  2.09s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3200/8000 [03:13<04:17, 18.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:45,  1.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3203/8000 [03:14<05:23, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:05,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3205/8000 [03:14<06:34, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:14<04:40,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3210/8000 [03:14<05:00, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:15<04:24,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3212/8000 [03:14<05:33, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3217/8000 [03:15<04:31, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:18<04:04,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3220/8000 [03:15<06:21, 12.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:19<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3226/8000 [03:15<04:36, 17.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:20<03:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3231/8000 [03:16<07:26, 10.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:22<03:57,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3235/8000 [03:16<06:17, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:23<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3239/8000 [03:16<06:12, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:24<03:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3245/8000 [03:17<04:43, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:25<03:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3250/8000 [03:17<04:34, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:27<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3256/8000 [03:17<04:23, 18.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:28<03:54,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3259/8000 [03:18<05:47, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:29<03:52,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3263/8000 [03:18<05:01, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:31<03:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3268/8000 [03:18<04:50, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:32<03:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3270/8000 [03:18<05:01, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:33<03:51,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3274/8000 [03:19<05:31, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:34<03:48,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3277/8000 [03:19<06:16, 12.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:36<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3283/8000 [03:19<04:41, 16.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:37<03:33,  1.22s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3288/8000 [03:19<03:57, 19.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:38<03:30,  1.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3293/8000 [03:20<04:13, 18.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:39<03:26,  1.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3298/8000 [03:20<03:46, 20.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:40<03:24,  1.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3304/8000 [03:20<03:49, 20.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:42<03:24,  1.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3307/8000 [03:20<03:51, 20.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:43<03:21,  1.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3310/8000 [03:21<04:53, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:44<03:24,  1.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3314/8000 [03:21<04:23, 17.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:45<03:22,  1.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▉           | 3317/8000 [03:21<04:29, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:46<03:21,  1.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▉           | 3319/8000 [03:21<04:56, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:48<03:19,  1.20s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3322/8000 [03:21<06:14, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:49<03:19,  1.21s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3325/8000 [03:22<05:40, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:50<03:30,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3327/8000 [03:22<05:51, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:51<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3331/8000 [03:22<04:53, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:53<03:21,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3334/8000 [03:22<04:30, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:54<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3336/8000 [03:22<06:35, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:55<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3340/8000 [03:23<06:28, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:56<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3342/8000 [03:23<06:30, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [00:58<03:18,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3347/8000 [03:23<04:50, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [00:59<03:15,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3353/8000 [03:23<04:34, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:00<03:16,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3357/8000 [03:24<05:08, 15.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:01<03:12,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3360/8000 [03:24<04:51, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:03<03:09,  1.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3364/8000 [03:24<04:24, 17.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:04<03:09,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3366/8000 [03:24<04:30, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:05<03:08,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3369/8000 [03:25<05:47, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:06<03:08,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3371/8000 [03:25<07:23, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:08<03:08,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3374/8000 [03:25<06:47, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:09<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3378/8000 [03:26<07:01, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:11<03:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3381/8000 [03:26<06:05, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:12<03:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3384/8000 [03:26<05:42, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:13<03:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3388/8000 [03:26<04:44, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:14<03:08,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3393/8000 [03:26<03:54, 19.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:16<03:06,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3396/8000 [03:26<03:55, 19.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:17<03:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3401/8000 [03:27<06:47, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:18<03:09,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3404/8000 [03:27<06:05, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:20<03:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3407/8000 [03:27<05:38, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:21<02:59,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3413/8000 [03:28<06:02, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:22<02:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3416/8000 [03:29<08:14,  9.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:23<02:55,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3418/8000 [03:29<07:57,  9.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:25<02:57,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3420/8000 [03:29<08:03,  9.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:26<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3423/8000 [03:29<06:50, 11.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:28<03:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3426/8000 [03:29<06:12, 12.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:30<03:04,  1.38s/it]\n",
+      "Generating signals:  43%|████████▏          | 3431/8000 [03:30<05:49, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  43%|████████▏          | 3433/8000 [03:30<05:54, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3435/8000 [03:30<05:58, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3437/8000 [03:30<07:09, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.73it/s]\n",
+      "Generating signals:  43%|████████▏          | 3441/8000 [03:30<05:23, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  43%|████████▏          | 3444/8000 [03:31<05:11, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3447/8000 [03:31<04:50, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3452/8000 [03:31<03:36, 21.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 46it [00:01, 43.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3456/8000 [03:31<04:23, 17.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 85it [00:01, 84.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3461/8000 [03:32<05:33, 13.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 132it [00:01, 139.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3463/8000 [03:32<05:27, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 178it [00:01, 193.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3465/8000 [03:32<06:36, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 220it [00:01, 237.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3469/8000 [03:32<06:24, 11.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 261it [00:01, 269.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▎          | 3474/8000 [03:33<05:32, 13.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 301it [00:02, 275.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3481/8000 [03:33<04:55, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 344it [00:02, 310.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3483/8000 [03:33<05:20, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 386it [00:02, 336.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3485/8000 [03:33<05:25, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 426it [00:02, 340.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3489/8000 [03:34<04:38, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 466it [00:02, 355.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3492/8000 [03:34<04:33, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 505it [00:02, 361.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3496/8000 [03:34<04:09, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 544it [00:02, 325.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3498/8000 [03:34<04:50, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 585it [00:02, 346.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3500/8000 [03:34<04:50, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 623it [00:03, 354.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3502/8000 [03:35<06:12, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 660it [00:03, 331.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3504/8000 [03:35<05:59, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 695it [00:03, 329.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3507/8000 [03:35<05:12, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 734it [00:03, 345.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3511/8000 [03:35<04:05, 18.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 228.19it/s]\n",
+      "Generating signals:  44%|████████▎          | 3513/8000 [03:35<05:50, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  44%|████████▎          | 3516/8000 [03:36<06:22, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3519/8000 [03:36<05:32, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:00, 223\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3524/8000 [03:36<05:05, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 191\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3527/8000 [03:36<04:43, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 185\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3530/8000 [03:37<05:45, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "Generating signals:  44%|████████▍          | 3532/8000 [03:37<07:26, 10.01it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  44%|████████▍          | 3534/8000 [03:37<07:17, 10.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3536/8000 [03:37<06:53, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 50/8000 [00:00<00:23, 334.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3539/8000 [03:38<07:15, 10.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 101/8000 [00:00<00:22, 358.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3543/8000 [03:38<05:30, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 149/8000 [00:00<00:22, 351.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3545/8000 [03:38<05:32, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 199/8000 [00:00<00:21, 364.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3547/8000 [03:38<07:07, 10.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 248/8000 [00:00<00:21, 356.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3549/8000 [03:38<06:34, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▌                | 294/8000 [00:00<00:22, 348.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3551/8000 [03:39<06:19, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 340/8000 [00:00<00:22, 345.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3553/8000 [03:39<06:15, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 393/8000 [00:01<00:21, 361.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3560/8000 [03:39<03:28, 21.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 443/8000 [00:01<00:20, 366.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▍          | 3563/8000 [03:39<03:29, 21.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 489/8000 [00:01<00:20, 363.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▍          | 3567/8000 [03:39<04:06, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 535/8000 [00:01<00:21, 343.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▍          | 3570/8000 [03:39<03:52, 19.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎               | 591/8000 [00:01<00:19, 373.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▍          | 3573/8000 [03:40<03:55, 18.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 641/8000 [00:01<00:18, 390.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▍          | 3578/8000 [03:40<03:20, 22.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 697/8000 [00:01<00:17, 406.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3581/8000 [03:40<04:26, 16.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 740/8000 [00:01<00:18, 395.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3584/8000 [03:40<04:13, 17.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 795/8000 [00:02<00:18, 388.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3589/8000 [03:41<04:44, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 838/8000 [00:02<00:18, 384.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3592/8000 [03:41<04:53, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 894/8000 [00:02<00:18, 379.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3594/8000 [03:41<05:15, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 949/8000 [00:02<00:17, 394.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3598/8000 [03:41<04:32, 16.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 998/8000 [00:02<00:17, 405.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3600/8000 [03:41<04:43, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1044/8000 [00:02<00:17, 406.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3602/8000 [03:42<05:31, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1090/8000 [00:02<00:18, 374.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3605/8000 [03:42<05:22, 13.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1144/8000 [00:03<00:17, 400.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3607/8000 [03:42<05:33, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1200/8000 [00:03<00:16, 412.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3610/8000 [03:42<06:10, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1252/8000 [00:03<00:15, 427.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3614/8000 [03:42<04:45, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1345/8000 [00:03<00:17, 387.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3619/8000 [03:43<04:49, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1391/8000 [00:03<00:17, 386.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3624/8000 [03:43<03:48, 19.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1439/8000 [00:03<00:16, 389.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3628/8000 [03:43<04:10, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1487/8000 [00:03<00:16, 389.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▋          | 3632/8000 [03:43<04:35, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1531/8000 [00:04<00:17, 378.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▋          | 3634/8000 [03:44<04:47, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1577/8000 [00:04<00:16, 382.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▋          | 3636/8000 [03:44<04:55, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1631/8000 [00:04<00:17, 374.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▋          | 3639/8000 [03:44<04:25, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1682/8000 [00:04<00:16, 377.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3647/8000 [03:44<03:27, 20.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1735/8000 [00:04<00:17, 364.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3652/8000 [03:44<03:02, 23.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1784/8000 [00:04<00:16, 366.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3655/8000 [03:44<03:15, 22.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1841/8000 [00:04<00:16, 381.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3660/8000 [03:45<03:40, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1896/8000 [00:04<00:15, 401.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3665/8000 [03:45<03:50, 18.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1941/8000 [00:05<00:15, 380.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3669/8000 [03:45<04:16, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1993/8000 [00:05<00:15, 379.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3671/8000 [03:46<04:27, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2032/8000 [00:05<00:16, 361.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3674/8000 [03:46<04:21, 16.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2078/8000 [00:05<00:16, 361.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3677/8000 [03:46<06:08, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2125/8000 [00:05<00:16, 362.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3681/8000 [03:46<05:03, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2170/8000 [00:05<00:15, 373.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3683/8000 [03:46<05:03, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2214/8000 [00:05<00:15, 371.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3687/8000 [03:47<04:13, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2264/8000 [00:05<00:14, 397.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3689/8000 [03:47<04:28, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2313/8000 [00:06<00:14, 389.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3692/8000 [03:47<05:13, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2356/8000 [00:06<00:15, 370.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3694/8000 [03:47<05:14, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2420/8000 [00:06<00:14, 397.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3700/8000 [03:47<03:33, 20.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2467/8000 [00:06<00:14, 378.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3705/8000 [03:48<04:11, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2515/8000 [00:06<00:15, 352.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3708/8000 [03:48<03:59, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2569/8000 [00:06<00:14, 377.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3713/8000 [03:48<03:24, 21.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2614/8000 [00:06<00:15, 348.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3717/8000 [03:48<04:18, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2659/8000 [00:07<00:14, 366.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3719/8000 [03:49<04:49, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2707/8000 [00:07<00:14, 366.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▊          | 3721/8000 [03:49<05:10, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2755/8000 [00:07<00:14, 361.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▊          | 3726/8000 [03:49<04:02, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2802/8000 [00:07<00:13, 371.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▊          | 3730/8000 [03:49<04:47, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2852/8000 [00:07<00:13, 369.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▊          | 3733/8000 [03:50<07:39,  9.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2909/8000 [00:07<00:13, 390.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▊          | 3735/8000 [03:50<09:14,  7.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3008/8000 [00:07<00:13, 382.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3737/8000 [03:51<09:28,  7.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3058/8000 [00:08<00:12, 384.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3738/8000 [03:51<10:48,  6.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3113/8000 [00:08<00:12, 386.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3741/8000 [03:51<08:45,  8.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3174/8000 [00:08<00:12, 401.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3745/8000 [03:51<07:16,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3215/8000 [00:08<00:13, 352.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3747/8000 [03:52<09:26,  7.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3260/8000 [00:08<00:13, 353.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3750/8000 [03:52<08:03,  8.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3306/8000 [00:08<00:12, 373.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3753/8000 [03:52<06:33, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3346/8000 [00:08<00:12, 368.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3755/8000 [03:53<09:02,  7.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3394/8000 [00:09<00:12, 365.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3759/8000 [03:53<08:52,  7.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3439/8000 [00:09<00:12, 365.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3760/8000 [03:53<08:59,  7.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3489/8000 [00:09<00:11, 384.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3762/8000 [03:54<08:09,  8.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3537/8000 [00:09<00:11, 374.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3764/8000 [03:54<07:09,  9.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3586/8000 [00:09<00:11, 384.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3772/8000 [03:54<04:21, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3660/8000 [00:10<00:23, 181.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3775/8000 [03:54<04:14, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3716/8000 [00:10<00:19, 221.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3778/8000 [03:54<04:06, 17.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3771/8000 [00:10<00:16, 261.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3781/8000 [03:55<04:06, 17.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3818/8000 [00:10<00:14, 289.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3785/8000 [03:55<03:38, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3867/8000 [00:10<00:13, 305.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3787/8000 [03:55<04:41, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3924/8000 [00:10<00:11, 351.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|█████████          | 3791/8000 [03:55<03:50, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3977/8000 [00:10<00:11, 356.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|█████████          | 3796/8000 [03:55<02:58, 23.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4033/8000 [00:11<00:10, 386.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|█████████          | 3799/8000 [03:55<03:10, 22.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4077/8000 [00:11<00:10, 376.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3802/8000 [03:56<05:00, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▏       | 4123/8000 [00:11<00:10, 353.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3806/8000 [03:56<04:14, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4161/8000 [00:11<00:11, 338.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3809/8000 [03:56<04:51, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4219/8000 [00:11<00:09, 389.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3812/8000 [03:56<04:29, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4264/8000 [00:11<00:09, 386.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3816/8000 [03:57<04:58, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4312/8000 [00:11<00:09, 395.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3818/8000 [03:57<05:54, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4355/8000 [00:11<00:09, 371.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3820/8000 [03:57<05:52, 11.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4405/8000 [00:12<00:09, 375.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3824/8000 [03:57<05:36, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4462/8000 [00:12<00:08, 396.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3827/8000 [03:58<04:56, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4510/8000 [00:12<00:08, 409.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3829/8000 [03:58<06:03, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4568/8000 [00:12<00:08, 424.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3833/8000 [03:58<05:46, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4614/8000 [00:12<00:08, 416.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3837/8000 [03:58<04:44, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4669/8000 [00:12<00:07, 435.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3841/8000 [03:59<04:01, 17.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4725/8000 [00:12<00:07, 435.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3843/8000 [03:59<04:24, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4778/8000 [00:12<00:07, 453.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3846/8000 [03:59<04:09, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4862/8000 [00:13<00:08, 385.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3849/8000 [03:59<04:53, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4908/8000 [00:13<00:08, 381.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3854/8000 [03:59<03:43, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4965/8000 [00:13<00:07, 409.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3858/8000 [04:00<04:12, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5018/8000 [00:13<00:07, 416.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3860/8000 [04:00<04:37, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5063/8000 [00:13<00:07, 412.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3862/8000 [04:00<04:42, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5109/8000 [00:13<00:07, 412.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3867/8000 [04:00<04:31, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5195/8000 [00:14<00:07, 375.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3870/8000 [04:00<04:13, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5239/8000 [00:14<00:07, 350.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3877/8000 [04:01<03:34, 19.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5294/8000 [00:14<00:07, 375.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3879/8000 [04:01<03:48, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5345/8000 [00:14<00:06, 388.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▏         | 3883/8000 [04:01<04:53, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5403/8000 [00:14<00:06, 413.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▏         | 3887/8000 [04:01<04:07, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5454/8000 [00:14<00:06, 410.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▏         | 3890/8000 [04:02<04:35, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5499/8000 [00:14<00:06, 405.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▏         | 3892/8000 [04:02<04:49, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5550/8000 [00:14<00:06, 394.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3895/8000 [04:02<04:30, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5594/8000 [00:15<00:06, 382.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3897/8000 [04:02<04:33, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5640/8000 [00:15<00:06, 390.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3903/8000 [04:02<04:12, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5694/8000 [00:15<00:05, 407.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3907/8000 [04:03<04:24, 15.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5743/8000 [00:15<00:05, 399.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3909/8000 [04:03<04:34, 14.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5796/8000 [00:15<00:05, 396.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3911/8000 [04:03<04:37, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5842/8000 [00:15<00:05, 375.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3914/8000 [04:04<07:24,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5885/8000 [00:15<00:05, 374.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3917/8000 [04:04<06:08, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5934/8000 [00:15<00:05, 371.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3919/8000 [04:04<06:08, 11.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5988/8000 [00:16<00:05, 389.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3921/8000 [04:04<05:58, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6031/8000 [00:16<00:05, 369.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3924/8000 [04:04<04:58, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6075/8000 [00:16<00:05, 378.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3928/8000 [04:05<04:46, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6127/8000 [00:16<00:04, 379.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3930/8000 [04:05<05:50, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6184/8000 [00:16<00:04, 405.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3934/8000 [04:05<04:28, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6262/8000 [00:17<00:08, 210.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3938/8000 [04:05<03:39, 18.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6308/8000 [00:17<00:07, 227.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3942/8000 [04:05<04:10, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6353/8000 [00:17<00:06, 247.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3946/8000 [04:06<03:31, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6402/8000 [00:17<00:05, 277.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▍         | 3950/8000 [04:06<04:51, 13.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6449/8000 [00:17<00:05, 291.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▍         | 3952/8000 [04:06<05:41, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6496/8000 [00:17<00:05, 298.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▍         | 3955/8000 [04:06<05:06, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6544/8000 [00:18<00:04, 321.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▍         | 3958/8000 [04:07<04:32, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6590/8000 [00:18<00:04, 338.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3962/8000 [04:07<04:57, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6645/8000 [00:18<00:03, 363.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3964/8000 [04:07<05:07, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▎  | 6684/8000 [00:18<00:03, 353.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3969/8000 [04:07<03:42, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6726/8000 [00:18<00:03, 353.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3972/8000 [04:07<04:23, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6767/8000 [00:18<00:03, 360.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3974/8000 [04:08<04:27, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6818/8000 [00:18<00:03, 380.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3976/8000 [04:08<04:41, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6869/8000 [00:18<00:02, 378.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3979/8000 [04:08<04:15, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6928/8000 [00:18<00:02, 426.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3981/8000 [04:08<04:23, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6974/8000 [00:19<00:02, 391.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3984/8000 [04:08<03:56, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7061/8000 [00:19<00:02, 356.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3987/8000 [04:08<04:41, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7115/8000 [00:19<00:02, 382.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3990/8000 [04:09<04:23, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7165/8000 [00:19<00:02, 381.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3994/8000 [04:09<03:50, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7217/8000 [00:19<00:02, 388.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3996/8000 [04:09<06:22, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7270/8000 [00:19<00:01, 367.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3998/8000 [04:09<05:52, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7310/8000 [00:20<00:01, 357.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4002/8000 [04:10<04:24, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7350/8000 [00:20<00:01, 349.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4006/8000 [04:10<03:36, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7402/8000 [00:20<00:01, 371.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4015/8000 [04:10<02:10, 30.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7445/8000 [00:20<00:01, 355.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4021/8000 [04:10<02:19, 28.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7500/8000 [00:20<00:01, 390.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4025/8000 [04:10<02:50, 23.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7547/8000 [00:20<00:01, 385.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4030/8000 [04:10<02:34, 25.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7594/8000 [00:20<00:01, 383.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4033/8000 [04:11<03:16, 20.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7639/8000 [00:20<00:00, 386.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4037/8000 [04:11<02:59, 22.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7687/8000 [00:21<00:00, 371.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4040/8000 [04:11<02:56, 22.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7742/8000 [00:21<00:00, 396.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▌         | 4043/8000 [04:11<03:04, 21.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7787/8000 [00:21<00:00, 384.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▌         | 4046/8000 [04:11<03:01, 21.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7843/8000 [00:21<00:00, 401.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▌         | 4049/8000 [04:11<03:13, 20.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7896/8000 [00:21<00:00, 388.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▌         | 4052/8000 [04:12<03:21, 19.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7996/8000 [00:21<00:00, 402.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4058/8000 [04:12<03:14, 20.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8038it [00:21, 365.85it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  51%|█████████▋         | 4061/8000 [04:12<03:55, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  51%|█████████▋         | 4064/8000 [04:12<03:44, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4068/8000 [04:13<04:02, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:36,  7.12s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4072/8000 [04:13<03:32, 18.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:18,  3.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4076/8000 [04:13<03:14, 20.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:35,  2.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4079/8000 [04:13<04:20, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:55,  2.12s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4084/8000 [04:13<03:27, 18.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:56,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4090/8000 [04:14<03:15, 19.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:20,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4093/8000 [04:14<03:12, 20.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<04:59,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4096/8000 [04:14<03:51, 16.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:42,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4100/8000 [04:14<03:18, 19.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:33,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4105/8000 [04:15<03:23, 19.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:38,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▊         | 4108/8000 [04:15<03:19, 19.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:31,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▊         | 4112/8000 [04:15<04:29, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:23,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▊         | 4117/8000 [04:15<04:14, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:18,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▊         | 4119/8000 [04:16<04:18, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4121/8000 [04:16<04:34, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:07,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4123/8000 [04:16<04:32, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:04,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4125/8000 [04:16<05:36, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:03,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4129/8000 [04:16<05:10, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:29<03:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4134/8000 [04:17<03:52, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<03:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4138/8000 [04:17<04:16, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4142/8000 [04:17<03:38, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:59,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4146/8000 [04:17<03:59, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<03:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4148/8000 [04:18<04:13, 15.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:55,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4153/8000 [04:18<03:11, 20.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:55,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4157/8000 [04:18<03:41, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<03:52,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4162/8000 [04:19<04:57, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:40<03:50,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4165/8000 [04:19<05:46, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:51,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4167/8000 [04:19<05:26, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:48,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4169/8000 [04:19<05:14, 12.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:44<03:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4173/8000 [04:19<04:12, 15.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:45<03:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4175/8000 [04:19<04:21, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:46<03:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4180/8000 [04:20<03:20, 19.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:48<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4187/8000 [04:20<02:50, 22.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:49<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4190/8000 [04:20<02:53, 22.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:50<03:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4193/8000 [04:20<03:33, 17.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:52<03:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4195/8000 [04:20<03:50, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:53<03:32,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4199/8000 [04:21<04:48, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:54<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▉         | 4203/8000 [04:21<05:30, 11.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:56<03:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▉         | 4210/8000 [04:21<03:38, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:57<03:29,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4215/8000 [04:22<03:07, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:58<03:26,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4219/8000 [04:22<04:11, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:59<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4224/8000 [04:22<04:03, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:01<03:30,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4229/8000 [04:23<03:23, 18.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:02<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4233/8000 [04:23<03:16, 19.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:03<03:24,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4237/8000 [04:23<03:40, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:05<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4239/8000 [04:23<03:48, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:06<03:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4242/8000 [04:23<03:46, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:07<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4245/8000 [04:24<04:39, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:09<03:19,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4247/8000 [04:24<04:48, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:10<03:16,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4249/8000 [04:24<04:54, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:11<03:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4254/8000 [04:24<04:26, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:13<03:16,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4256/8000 [04:24<04:23, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:14<03:11,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4258/8000 [04:25<04:22, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:15<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4260/8000 [04:25<04:21, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:16<03:05,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4262/8000 [04:25<04:40, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:18<03:05,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4265/8000 [04:25<04:21, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:19<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4267/8000 [04:25<04:38, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:20<03:07,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4270/8000 [04:25<04:03, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:22<03:13,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4272/8000 [04:26<04:17, 14.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:23<03:11,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4275/8000 [04:26<05:03, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:24<03:05,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4278/8000 [04:26<04:34, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:27<03:19,  1.43s/it]\n",
+      "Generating signals:  54%|██████████▏        | 4280/8000 [04:26<04:28, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  54%|██████████▏        | 4286/8000 [04:26<03:04, 20.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4291/8000 [04:27<02:42, 22.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.88it/s]\n",
+      "Generating signals:  54%|██████████▏        | 4295/8000 [04:27<03:14, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  54%|██████████▏        | 4298/8000 [04:27<04:02, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4300/8000 [04:27<04:13, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4307/8000 [04:28<02:55, 21.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 47it [00:01, 45.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4310/8000 [04:28<03:02, 20.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 100it [00:01, 103.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4315/8000 [04:28<03:17, 18.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 147it [00:01, 157.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4317/8000 [04:28<03:34, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 194it [00:01, 211.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4321/8000 [04:28<03:08, 19.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 238it [00:01, 252.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4324/8000 [04:28<03:15, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 283it [00:01, 295.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4328/8000 [04:29<03:40, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 329it [00:02, 334.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4334/8000 [04:29<02:58, 20.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 374it [00:02, 355.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4339/8000 [04:29<02:38, 23.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 418it [00:02, 375.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4342/8000 [04:29<02:41, 22.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 462it [00:02, 383.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4349/8000 [04:30<02:33, 23.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 505it [00:02, 390.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4352/8000 [04:30<02:35, 23.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 552it [00:02, 412.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4356/8000 [04:30<02:28, 24.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 603it [00:02, 439.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4359/8000 [04:30<04:14, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 649it [00:02, 440.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▎        | 4363/8000 [04:30<03:35, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 700it [00:02, 459.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▎        | 4366/8000 [04:31<04:14, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 750it [00:02, 469.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4369/8000 [04:31<03:51, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 257.40it/s]\n",
+      "Generating signals:  55%|██████████▍        | 4371/8000 [04:31<04:46, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  55%|██████████▍        | 4375/8000 [04:31<03:45, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4381/8000 [04:31<02:45, 21.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:00, 197\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4385/8000 [04:32<03:16, 18.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:00<00:00, 192\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4389/8000 [04:32<02:57, 20.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  60%|▌| 60/100 [00:00<00:00, 173\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4393/8000 [04:32<03:23, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 18\n",
+      "Generating signals:  55%|██████████▍        | 4399/8000 [04:33<03:13, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [12:30<12:46, 383.39s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.157806554272215\n",
-      "2999: -6.1943679\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  55%|██████████▍        | 4402/8000 [04:33<03:46, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  55%|██████████▍        | 4406/8000 [04:33<03:34, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4408/8000 [04:33<03:37, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:59,  7.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4411/8000 [04:33<03:30, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:41,  4.15s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4413/8000 [04:33<03:42, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:11<10:09,  3.09s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4415/8000 [04:34<04:47, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:13<09:03,  2.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4418/8000 [04:34<04:13, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:15<08:02,  2.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▌        | 4423/8000 [04:34<03:22, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:16<06:47,  2.10s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▌        | 4425/8000 [04:34<04:35, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:18<06:01,  1.87s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▌        | 4432/8000 [04:35<02:55, 20.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:19<05:29,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▌        | 4435/8000 [04:35<02:57, 20.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:03,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4440/8000 [04:35<02:39, 22.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:22<04:49,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4443/8000 [04:35<03:33, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<04:43,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4446/8000 [04:35<03:25, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:25<04:34,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4449/8000 [04:36<03:17, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:34,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4451/8000 [04:36<03:28, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:28<04:29,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4453/8000 [04:36<04:00, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<04:25,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4459/8000 [04:36<02:53, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:23,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4467/8000 [04:36<02:35, 22.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<04:24,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4470/8000 [04:37<03:18, 17.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:17,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4476/8000 [04:37<02:36, 22.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:09,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4482/8000 [04:37<02:10, 26.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:04,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4489/8000 [04:38<03:21, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<04:03,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4494/8000 [04:38<03:20, 17.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<04:01,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4499/8000 [04:38<02:54, 20.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:01,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4502/8000 [04:38<02:56, 19.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:41<03:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4505/8000 [04:38<03:01, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<03:56,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4510/8000 [04:39<03:43, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:44<03:55,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4516/8000 [04:39<02:54, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<03:53,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▋        | 4521/8000 [04:40<04:05, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:47<03:51,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▋        | 4524/8000 [04:40<03:47, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<03:47,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4528/8000 [04:40<05:16, 10.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:49<03:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4533/8000 [04:41<05:47,  9.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<03:39,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4539/8000 [04:41<04:13, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:52<03:36,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4542/8000 [04:41<03:58, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<03:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4545/8000 [04:41<03:52, 14.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:54<03:34,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4547/8000 [04:42<04:09, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:56<03:37,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4550/8000 [04:42<03:46, 15.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:57<03:36,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4554/8000 [04:42<03:51, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:58<03:43,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4558/8000 [04:42<03:22, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:00<03:42,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4561/8000 [04:42<03:14, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:01<03:34,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4565/8000 [04:42<02:54, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:02<03:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4568/8000 [04:43<02:53, 19.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:04<03:30,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4571/8000 [04:43<02:48, 20.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:05<03:30,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4575/8000 [04:43<02:31, 22.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:06<03:33,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4578/8000 [04:43<03:19, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:08<03:31,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4580/8000 [04:44<06:19,  9.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:09<03:26,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4584/8000 [04:44<04:47, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:10<03:25,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4587/8000 [04:44<04:11, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:12<03:23,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4589/8000 [04:44<05:07, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:13<03:21,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4593/8000 [04:45<04:06, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:14<03:20,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4596/8000 [04:45<03:38, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:16<03:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4598/8000 [04:45<03:41, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:17<03:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4602/8000 [04:45<03:13, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:18<03:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4605/8000 [04:45<03:09, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:20<03:18,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4607/8000 [04:45<04:13, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:21<03:17,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4610/8000 [04:46<04:49, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:23<03:24,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4614/8000 [04:46<03:42, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:24<03:25,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4617/8000 [04:46<03:22, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:26<03:19,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4620/8000 [04:46<04:07, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:27<03:17,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4622/8000 [04:47<04:18, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:28<03:16,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4624/8000 [04:47<04:09, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:30<03:15,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4627/8000 [04:47<04:52, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:31<03:14,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4629/8000 [04:47<05:48,  9.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:32<03:11,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4631/8000 [04:47<05:23, 10.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:34<03:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4638/8000 [04:48<03:00, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:35<03:09,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4641/8000 [04:48<02:53, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:38<03:24,  1.52s/it]\n",
+      "Generating signals:  58%|███████████        | 4644/8000 [04:48<04:23, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  58%|███████████        | 4646/8000 [04:49<05:16, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4650/8000 [04:49<06:35,  8.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 16.10it/s]\n",
+      "Generating signals:  58%|███████████        | 4653/8000 [04:49<05:33, 10.02it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  58%|███████████        | 4655/8000 [04:49<05:17, 10.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4660/8000 [04:50<03:58, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4664/8000 [04:50<04:02, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 47it [00:01, 42.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4667/8000 [04:50<03:45, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 89it [00:01, 86.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4672/8000 [04:50<03:32, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 126it [00:01, 126.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4674/8000 [04:51<03:44, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 169it [00:01, 177.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4678/8000 [04:51<03:54, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 212it [00:01, 225.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4680/8000 [04:51<03:52, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 252it [00:02, 256.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4685/8000 [04:51<02:55, 18.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 298it [00:02, 303.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4689/8000 [04:51<02:39, 20.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 341it [00:02, 333.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4694/8000 [04:52<02:47, 19.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 383it [00:02, 350.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4697/8000 [04:52<02:42, 20.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 426it [00:02, 371.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4704/8000 [04:52<03:07, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 470it [00:02, 387.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4706/8000 [04:53<04:39, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 512it [00:02, 391.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4710/8000 [04:53<04:39, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 554it [00:02, 390.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4713/8000 [04:53<04:08, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 595it [00:02, 384.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4716/8000 [04:53<04:29, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 637it [00:02, 391.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4722/8000 [04:54<03:17, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 689it [00:03, 427.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4725/8000 [04:54<03:04, 17.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 737it [00:03, 441.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4728/8000 [04:54<03:03, 17.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 239.53it/s]\n",
+      "Generating signals:  59%|███████████▏       | 4734/8000 [04:54<03:01, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  59%|███████████▎       | 4737/8000 [04:54<02:57, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4742/8000 [04:55<02:59, 18.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 139\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4744/8000 [04:55<03:18, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 149\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4746/8000 [04:55<03:36, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 167\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4749/8000 [04:55<03:23, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:00<00:00, 161\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4751/8000 [04:55<03:34, 15.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  59%|███████████▎       | 4753/8000 [04:56<04:41, 11.53it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  59%|███████████▎       | 4756/8000 [04:56<04:53, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4759/8000 [04:56<05:15, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 15/8000 [00:00<01:04, 123.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4761/8000 [04:57<05:01, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 29/8000 [00:00<01:23, 95.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4764/8000 [04:57<05:05, 10.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 42/8000 [00:00<01:23, 95.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4766/8000 [04:57<04:53, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 59/8000 [00:00<01:13, 108.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4771/8000 [04:57<03:26, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 77/8000 [00:00<01:08, 114.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4773/8000 [04:58<04:55, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 97/8000 [00:00<01:04, 121.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4776/8000 [04:58<04:19, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                | 118/8000 [00:00<01:00, 131.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4779/8000 [04:58<06:09,  8.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 136/8000 [00:01<00:58, 134.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4781/8000 [04:58<05:40,  9.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 153/8000 [00:01<00:55, 140.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4783/8000 [04:59<05:12, 10.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 170/8000 [00:01<00:59, 131.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4786/8000 [04:59<04:15, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 201/8000 [00:01<01:02, 124.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4792/8000 [04:59<02:39, 20.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 221/8000 [00:01<00:57, 134.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4796/8000 [04:59<03:01, 17.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 238/8000 [00:01<00:59, 130.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4801/8000 [04:59<02:33, 20.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 261/8000 [00:02<00:52, 146.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4806/8000 [05:00<02:42, 19.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 278/8000 [00:02<00:55, 140.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4810/8000 [05:00<03:07, 17.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 309/8000 [00:02<00:59, 128.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4812/8000 [05:00<03:23, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 324/8000 [00:02<01:03, 121.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4814/8000 [05:00<05:06, 10.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 338/8000 [00:02<01:03, 120.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4818/8000 [05:01<04:44, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 361/8000 [00:02<00:57, 133.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4822/8000 [05:01<03:52, 13.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 386/8000 [00:02<00:52, 144.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4826/8000 [05:01<03:16, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 406/8000 [00:03<00:53, 142.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4830/8000 [05:02<04:15, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 422/8000 [00:03<00:59, 128.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4835/8000 [05:02<03:57, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 439/8000 [00:03<00:58, 128.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4838/8000 [05:02<03:46, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 471/8000 [00:03<01:01, 122.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▍       | 4842/8000 [05:03<04:30, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 489/8000 [00:03<01:00, 124.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4845/8000 [05:03<04:08, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 509/8000 [00:03<00:57, 129.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4848/8000 [05:03<03:50, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█                | 528/8000 [00:04<00:55, 134.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4853/8000 [05:03<02:56, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 548/8000 [00:04<00:53, 138.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4856/8000 [05:04<04:20, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 564/8000 [00:04<00:54, 136.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4859/8000 [05:04<05:09, 10.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 585/8000 [00:04<00:54, 135.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4862/8000 [05:04<04:34, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 601/8000 [00:04<00:59, 124.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4864/8000 [05:04<04:36, 11.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 620/8000 [00:04<00:57, 128.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4867/8000 [05:04<04:03, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 637/8000 [00:04<00:58, 125.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4872/8000 [05:05<03:16, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 651/8000 [00:05<01:00, 120.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4874/8000 [05:05<03:26, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 674/8000 [00:05<00:54, 134.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4878/8000 [05:05<03:54, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 691/8000 [00:05<00:57, 127.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4882/8000 [05:06<05:01, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 712/8000 [00:05<00:53, 136.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4885/8000 [05:06<04:35, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 731/8000 [00:05<00:50, 142.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4889/8000 [05:06<03:47, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 747/8000 [00:05<00:55, 130.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4893/8000 [05:06<03:52, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▌               | 762/8000 [00:05<00:59, 121.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4897/8000 [05:07<04:30, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 781/8000 [00:06<00:56, 126.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4905/8000 [05:07<03:18, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 810/8000 [00:06<01:04, 111.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4907/8000 [05:08<04:45, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 832/8000 [00:06<00:58, 123.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4912/8000 [05:08<03:48, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 849/8000 [00:06<00:57, 123.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4917/8000 [05:08<03:12, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 881/8000 [00:06<00:45, 157.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4919/8000 [05:08<03:23, 15.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 916/8000 [00:07<00:47, 148.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4924/8000 [05:08<02:38, 19.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 933/8000 [00:07<00:48, 145.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4927/8000 [05:09<03:11, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 955/8000 [00:07<00:47, 147.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4931/8000 [05:09<02:47, 18.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 971/8000 [00:07<00:48, 143.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4935/8000 [05:09<03:09, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 995/8000 [00:07<00:43, 160.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4940/8000 [05:09<02:33, 19.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1014/8000 [00:07<00:45, 154.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4943/8000 [05:10<03:19, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1036/8000 [00:07<00:43, 159.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4945/8000 [05:10<03:27, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1069/8000 [00:08<00:49, 139.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4947/8000 [05:10<03:28, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1086/8000 [00:08<00:51, 135.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4949/8000 [05:10<03:40, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1104/8000 [00:08<00:49, 138.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4951/8000 [05:10<04:36, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1124/8000 [00:08<00:51, 134.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4954/8000 [05:11<04:48, 10.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1139/8000 [00:08<00:54, 125.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4958/8000 [05:11<03:52, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1156/8000 [00:08<00:53, 127.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4963/8000 [05:11<02:54, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1171/8000 [00:08<00:54, 124.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4967/8000 [05:11<02:40, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1184/8000 [00:09<00:58, 115.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4971/8000 [05:11<02:25, 20.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1196/8000 [00:09<01:03, 107.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4975/8000 [05:12<02:13, 22.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1208/8000 [00:09<01:07, 101.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4978/8000 [05:12<02:18, 21.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1225/8000 [00:09<01:00, 111.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4982/8000 [05:12<02:06, 23.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▍             | 1246/8000 [00:09<00:53, 125.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4988/8000 [05:12<02:29, 20.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1269/8000 [00:09<00:50, 132.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4991/8000 [05:13<04:20, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1286/8000 [00:09<00:51, 129.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4994/8000 [05:13<04:02, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1303/8000 [00:09<00:53, 124.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4997/8000 [05:13<03:40, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋             | 1317/8000 [00:10<00:55, 120.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5004/8000 [05:13<02:24, 20.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1346/8000 [00:10<00:44, 151.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5011/8000 [05:13<01:50, 27.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1376/8000 [00:10<00:50, 132.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5015/8000 [05:14<02:21, 21.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1393/8000 [00:10<00:49, 133.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5018/8000 [05:14<02:20, 21.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1411/8000 [00:10<00:48, 135.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5023/8000 [05:14<02:02, 24.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1425/8000 [00:10<00:50, 129.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5027/8000 [05:14<02:26, 20.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1444/8000 [00:11<00:49, 132.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5030/8000 [05:15<03:34, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1466/8000 [00:11<00:45, 143.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5034/8000 [05:15<03:05, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1482/8000 [00:11<00:48, 134.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5038/8000 [05:15<03:18, 14.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1500/8000 [00:11<00:46, 139.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5042/8000 [05:15<02:50, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1522/8000 [00:11<00:43, 149.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5046/8000 [05:16<02:36, 18.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1544/8000 [00:11<00:41, 155.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5049/8000 [05:16<03:12, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███             | 1560/8000 [00:11<00:45, 142.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5053/8000 [05:16<02:42, 18.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1579/8000 [00:11<00:45, 142.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5056/8000 [05:16<02:34, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1610/8000 [00:12<00:47, 134.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5062/8000 [05:16<02:32, 19.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1629/8000 [00:12<00:46, 138.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5067/8000 [05:17<02:11, 22.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1659/8000 [00:12<00:48, 129.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5071/8000 [05:17<02:09, 22.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1676/8000 [00:12<00:50, 124.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5075/8000 [05:17<02:30, 19.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1700/8000 [00:12<00:45, 138.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5078/8000 [05:17<02:38, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1715/8000 [00:12<00:47, 132.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5080/8000 [05:17<02:51, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1732/8000 [00:13<00:48, 129.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5086/8000 [05:18<02:09, 22.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1746/8000 [00:13<00:49, 126.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5090/8000 [05:18<02:01, 24.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1767/8000 [00:13<00:46, 135.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5093/8000 [05:18<02:41, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1785/8000 [00:13<00:46, 134.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5096/8000 [05:18<02:42, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1800/8000 [00:13<00:48, 129.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5098/8000 [05:18<02:56, 16.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1821/8000 [00:13<00:45, 136.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5100/8000 [05:19<03:05, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1845/8000 [00:13<00:40, 150.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5106/8000 [05:19<02:49, 17.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1865/8000 [00:14<00:42, 145.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5108/8000 [05:19<02:59, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1884/8000 [00:14<00:40, 150.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5113/8000 [05:19<02:22, 20.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1907/8000 [00:14<00:39, 156.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5119/8000 [05:19<02:19, 20.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1928/8000 [00:14<00:37, 161.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5122/8000 [05:20<02:18, 20.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1959/8000 [00:14<00:44, 136.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5126/8000 [05:20<02:34, 18.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1979/8000 [00:14<00:44, 135.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5130/8000 [05:20<02:22, 20.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2007/8000 [00:15<00:48, 123.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5135/8000 [05:20<03:08, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2024/8000 [00:15<00:49, 119.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5137/8000 [05:21<03:13, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2040/8000 [00:15<00:49, 119.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5142/8000 [05:21<02:35, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2064/8000 [00:15<00:44, 133.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5153/8000 [05:21<02:11, 21.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2083/8000 [00:15<00:45, 131.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5156/8000 [05:22<02:38, 17.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2116/8000 [00:15<00:45, 128.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▎      | 5158/8000 [05:22<02:46, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2143/8000 [00:16<00:40, 144.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▎      | 5160/8000 [05:22<03:31, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2178/8000 [00:16<00:41, 138.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5163/8000 [05:22<03:58, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▍           | 2199/8000 [00:16<00:40, 143.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5167/8000 [05:22<03:16, 14.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2221/8000 [00:16<00:39, 148.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5170/8000 [05:23<03:08, 15.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2238/8000 [00:16<00:41, 140.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5176/8000 [05:23<02:23, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2261/8000 [00:16<00:38, 149.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5179/8000 [05:23<02:24, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2277/8000 [00:17<00:40, 140.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5182/8000 [05:23<02:31, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2295/8000 [00:17<00:41, 138.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5186/8000 [05:23<02:21, 19.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2309/8000 [00:17<00:44, 127.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5191/8000 [05:24<03:13, 14.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2324/8000 [00:17<00:45, 125.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5193/8000 [05:24<03:50, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2343/8000 [00:17<00:43, 129.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5199/8000 [05:25<03:42, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2366/8000 [00:17<00:40, 140.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5202/8000 [05:25<03:22, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2386/8000 [00:17<00:39, 143.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5206/8000 [05:25<02:55, 15.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2413/8000 [00:18<00:34, 163.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5208/8000 [05:25<02:59, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2442/8000 [00:18<00:30, 184.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5212/8000 [05:25<02:37, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2468/8000 [00:18<00:29, 189.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5214/8000 [05:25<02:50, 16.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2506/8000 [00:18<00:31, 176.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5216/8000 [05:26<02:59, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2526/8000 [00:18<00:32, 168.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5218/8000 [05:26<03:19, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2556/8000 [00:18<00:35, 151.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5222/8000 [05:26<02:39, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2577/8000 [00:18<00:34, 156.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5227/8000 [05:26<02:53, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2601/8000 [00:19<00:34, 158.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5229/8000 [05:26<03:09, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2632/8000 [00:19<00:38, 139.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5231/8000 [05:27<03:12, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2650/8000 [00:19<00:38, 138.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5234/8000 [05:27<03:07, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2670/8000 [00:19<00:37, 140.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5236/8000 [05:27<03:15, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2703/8000 [00:19<00:40, 131.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5239/8000 [05:27<04:29, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2721/8000 [00:20<00:39, 132.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5241/8000 [05:28<04:16, 10.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2741/8000 [00:20<00:38, 137.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5246/8000 [05:28<03:32, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2760/8000 [00:20<00:38, 134.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5249/8000 [05:28<03:55, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2782/8000 [00:20<00:37, 140.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5251/8000 [05:28<03:43, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2806/8000 [00:20<00:32, 160.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5255/8000 [05:29<03:29, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2826/8000 [00:20<00:33, 152.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5259/8000 [05:29<02:58, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2854/8000 [00:21<00:40, 127.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5261/8000 [05:29<03:08, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2875/8000 [00:21<00:36, 139.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5267/8000 [05:29<02:17, 19.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2891/8000 [00:21<00:39, 128.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5271/8000 [05:29<02:05, 21.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2919/8000 [00:21<00:41, 123.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5278/8000 [05:30<02:09, 21.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2935/8000 [00:21<00:40, 126.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5284/8000 [05:30<02:15, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2958/8000 [00:21<00:36, 138.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5291/8000 [05:30<02:20, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2989/8000 [00:22<00:36, 135.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5297/8000 [05:31<02:20, 19.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3005/8000 [00:22<00:37, 133.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5301/8000 [05:31<02:37, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3025/8000 [00:22<00:34, 142.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5303/8000 [05:31<03:07, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3052/8000 [00:22<00:31, 158.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5306/8000 [05:31<03:26, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3069/8000 [00:22<00:33, 145.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5309/8000 [05:32<03:10, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3090/8000 [00:22<00:33, 146.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5314/8000 [05:32<03:11, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3115/8000 [00:22<00:30, 161.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▋      | 5317/8000 [05:32<03:02, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3149/8000 [00:23<00:32, 148.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▋      | 5319/8000 [05:32<03:07, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3176/8000 [00:23<00:35, 136.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5321/8000 [05:32<03:22, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3205/8000 [00:23<00:38, 124.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5326/8000 [05:33<03:09, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3240/8000 [00:23<00:36, 129.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5328/8000 [05:33<03:55, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3258/8000 [00:23<00:35, 131.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5332/8000 [05:33<03:09, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3273/8000 [00:24<00:40, 115.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5334/8000 [05:33<03:14, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3288/8000 [00:24<00:40, 116.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5338/8000 [05:34<02:43, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3304/8000 [00:24<00:39, 120.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5340/8000 [05:34<03:00, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3323/8000 [00:24<00:36, 126.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5343/8000 [05:34<03:27, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3342/8000 [00:24<00:35, 132.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5345/8000 [05:34<03:25, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3364/8000 [00:24<00:34, 135.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5347/8000 [05:34<03:30, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3387/8000 [00:24<00:32, 141.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5350/8000 [05:35<03:09, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3405/8000 [00:25<00:31, 144.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5352/8000 [05:35<03:07, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3428/8000 [00:25<00:29, 156.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5355/8000 [05:35<03:32, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3453/8000 [00:25<00:26, 173.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5360/8000 [05:36<03:57, 11.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3477/8000 [00:25<00:25, 174.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5364/8000 [05:36<03:11, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3495/8000 [00:25<00:29, 154.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5367/8000 [05:36<03:03, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3514/8000 [00:25<00:28, 156.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5369/8000 [05:36<03:19, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3532/8000 [00:25<00:29, 153.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5371/8000 [05:36<03:28, 12.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3557/8000 [00:25<00:26, 168.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5373/8000 [05:37<04:21, 10.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3575/8000 [00:26<00:28, 157.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5375/8000 [05:37<04:11, 10.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3596/8000 [00:26<00:27, 162.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5379/8000 [05:37<03:12, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3617/8000 [00:26<00:28, 151.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5381/8000 [05:37<03:14, 13.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3638/8000 [00:26<00:28, 151.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5384/8000 [05:37<03:45, 11.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3656/8000 [00:26<00:30, 144.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5386/8000 [05:38<04:25,  9.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3671/8000 [00:26<00:31, 136.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5388/8000 [05:38<04:17, 10.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3697/8000 [00:26<00:27, 159.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5390/8000 [05:38<04:00, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3717/8000 [00:27<00:27, 157.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5392/8000 [05:38<04:48,  9.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3747/8000 [00:27<00:30, 139.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5395/8000 [05:38<03:42, 11.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3762/8000 [00:27<00:32, 131.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5400/8000 [05:39<03:18, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3783/8000 [00:27<00:31, 134.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5405/8000 [05:39<02:36, 16.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3807/8000 [00:27<00:28, 148.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5407/8000 [05:39<02:48, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3823/8000 [00:27<00:31, 134.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5410/8000 [05:39<02:42, 15.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3841/8000 [00:28<00:32, 126.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5413/8000 [05:40<03:06, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3861/8000 [00:28<00:31, 132.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5418/8000 [05:40<02:24, 17.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3881/8000 [00:28<00:30, 134.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5420/8000 [05:40<03:11, 13.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3896/8000 [00:28<00:30, 136.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5422/8000 [05:40<03:45, 11.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3917/8000 [00:28<00:28, 143.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5425/8000 [05:41<03:56, 10.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3933/8000 [00:28<00:29, 140.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5427/8000 [05:41<05:46,  7.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3961/8000 [00:28<00:31, 126.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5428/8000 [05:41<05:49,  7.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3977/8000 [00:29<00:32, 125.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5430/8000 [05:42<05:06,  8.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4001/8000 [00:29<00:28, 141.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5437/8000 [05:42<03:13, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4024/8000 [00:29<00:26, 152.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5442/8000 [05:42<02:29, 17.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4046/8000 [00:29<00:24, 162.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5444/8000 [05:42<02:38, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4063/8000 [00:29<00:26, 151.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5447/8000 [05:42<02:30, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4079/8000 [00:29<00:28, 136.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5452/8000 [05:43<02:28, 17.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4113/8000 [00:29<00:28, 134.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5456/8000 [05:43<02:15, 18.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4129/8000 [00:30<00:30, 127.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5460/8000 [05:43<02:05, 20.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4147/8000 [00:30<00:28, 136.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5463/8000 [05:43<02:07, 19.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4163/8000 [00:30<00:29, 132.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5466/8000 [05:43<02:02, 20.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4180/8000 [00:30<00:28, 133.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5473/8000 [05:44<01:59, 21.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4194/8000 [00:30<00:29, 127.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|█████████████      | 5476/8000 [05:44<02:06, 19.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4215/8000 [00:30<00:26, 140.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|█████████████      | 5478/8000 [05:44<02:26, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4232/8000 [00:30<00:27, 135.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5481/8000 [05:44<02:24, 17.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4249/8000 [00:31<00:28, 131.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5483/8000 [05:44<02:39, 15.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4263/8000 [00:31<00:29, 126.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5485/8000 [05:45<03:36, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4280/8000 [00:31<00:29, 124.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5488/8000 [05:45<03:05, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4294/8000 [00:31<00:32, 114.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5491/8000 [05:45<04:17,  9.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4312/8000 [00:31<00:31, 118.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5494/8000 [05:45<03:34, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4335/8000 [00:31<00:27, 132.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5496/8000 [05:46<05:05,  8.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4354/8000 [00:31<00:26, 139.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5500/8000 [05:46<05:08,  8.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4379/8000 [00:32<00:32, 111.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5502/8000 [05:47<04:48,  8.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4398/8000 [00:32<00:30, 119.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5507/8000 [05:47<03:09, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4413/8000 [00:32<00:29, 123.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5510/8000 [05:47<04:25,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4436/8000 [00:32<00:26, 135.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5512/8000 [05:47<04:30,  9.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4461/8000 [00:32<00:23, 152.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5514/8000 [05:48<04:28,  9.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4484/8000 [00:32<00:22, 154.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5518/8000 [05:48<03:23, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4505/8000 [00:32<00:22, 152.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5520/8000 [05:48<03:21, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4524/8000 [00:33<00:22, 156.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5522/8000 [05:48<03:24, 12.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4557/8000 [00:33<00:23, 148.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5524/8000 [05:48<03:24, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4577/8000 [00:33<00:23, 146.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5531/8000 [05:49<02:04, 19.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4596/8000 [00:33<00:23, 144.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5534/8000 [05:49<02:05, 19.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4611/8000 [00:33<00:24, 135.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5537/8000 [05:49<03:22, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4625/8000 [00:33<00:26, 129.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5540/8000 [05:49<03:01, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4643/8000 [00:33<00:25, 130.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5545/8000 [05:50<03:01, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4663/8000 [00:34<00:23, 140.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5549/8000 [05:50<03:12, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▎      | 4682/8000 [00:34<00:23, 140.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5552/8000 [05:50<03:29, 11.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4702/8000 [00:34<00:22, 148.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5558/8000 [05:51<03:05, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4723/8000 [00:34<00:22, 146.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5564/8000 [05:51<02:23, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4742/8000 [00:34<00:22, 143.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5567/8000 [05:51<02:26, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▌      | 4757/8000 [00:34<00:23, 135.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5569/8000 [05:51<02:39, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4780/8000 [00:34<00:22, 140.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5571/8000 [05:51<02:48, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4795/8000 [00:34<00:23, 136.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5574/8000 [05:52<02:36, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4811/8000 [00:35<00:23, 138.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5576/8000 [05:52<02:45, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4828/8000 [00:35<00:23, 135.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5579/8000 [05:52<03:11, 12.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4863/8000 [00:35<00:24, 127.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5581/8000 [05:52<03:08, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4882/8000 [00:35<00:23, 131.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5583/8000 [05:52<03:19, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4899/8000 [00:35<00:23, 130.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5587/8000 [05:53<02:32, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4913/8000 [00:35<00:23, 128.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5589/8000 [05:53<02:43, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4937/8000 [00:36<00:20, 148.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5591/8000 [05:53<02:57, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4971/8000 [00:36<00:21, 143.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5594/8000 [05:53<02:36, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4987/8000 [00:36<00:22, 134.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5596/8000 [05:53<03:00, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5009/8000 [00:36<00:22, 135.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5598/8000 [05:53<03:03, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5030/8000 [00:36<00:20, 144.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5600/8000 [05:54<03:47, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5045/8000 [00:36<00:22, 133.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5604/8000 [05:54<02:54, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5078/8000 [00:37<00:22, 132.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5608/8000 [05:54<02:21, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5100/8000 [00:37<00:20, 142.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5611/8000 [05:54<02:53, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5118/8000 [00:37<00:22, 130.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5617/8000 [05:55<03:35, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5132/8000 [00:37<00:22, 125.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5620/8000 [05:55<03:10, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5162/8000 [00:37<00:18, 153.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5625/8000 [05:55<02:28, 15.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5180/8000 [00:37<00:18, 150.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5628/8000 [05:56<02:27, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5197/8000 [00:37<00:18, 147.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▍     | 5632/8000 [05:56<02:10, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5212/8000 [00:38<00:21, 127.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▍     | 5636/8000 [05:56<02:38, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5236/8000 [00:38<00:19, 138.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5643/8000 [05:56<02:02, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5252/8000 [00:38<00:19, 138.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5649/8000 [05:57<02:04, 18.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5270/8000 [00:38<00:20, 135.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5654/8000 [05:57<02:21, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5290/8000 [00:38<00:19, 139.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5660/8000 [05:57<01:54, 20.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▋     | 5317/8000 [00:38<00:20, 127.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5665/8000 [05:58<02:05, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5345/8000 [00:38<00:17, 151.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5668/8000 [05:58<02:36, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5383/8000 [00:39<00:17, 148.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5673/8000 [05:58<02:09, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5401/8000 [00:39<00:18, 139.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5676/8000 [05:58<02:38, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5420/8000 [00:39<00:18, 141.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5679/8000 [05:59<02:33, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5440/8000 [00:39<00:17, 144.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5683/8000 [05:59<02:17, 16.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5463/8000 [00:39<00:16, 152.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5686/8000 [05:59<02:17, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5486/8000 [00:39<00:16, 156.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5690/8000 [05:59<02:04, 18.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5502/8000 [00:40<00:16, 152.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5693/8000 [05:59<02:34, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5519/8000 [00:40<00:17, 145.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5695/8000 [06:00<02:38, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5536/8000 [00:40<00:17, 143.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5702/8000 [06:00<02:13, 17.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5553/8000 [00:40<00:17, 142.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5707/8000 [06:00<01:53, 20.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5568/8000 [00:40<00:18, 129.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5711/8000 [06:00<02:13, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5590/8000 [00:40<00:16, 141.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5714/8000 [06:01<02:10, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5626/8000 [00:40<00:17, 138.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5717/8000 [06:01<02:30, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5643/8000 [00:41<00:17, 135.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5719/8000 [06:01<02:34, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5660/8000 [00:41<00:17, 136.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5721/8000 [06:01<03:19, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5680/8000 [00:41<00:20, 115.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5723/8000 [06:02<03:53,  9.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5701/8000 [00:41<00:17, 129.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5725/8000 [06:02<03:40, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5717/8000 [00:41<00:17, 128.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5730/8000 [06:02<02:29, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5739/8000 [00:41<00:20, 107.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5734/8000 [06:02<02:09, 17.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5753/8000 [00:42<00:20, 108.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5737/8000 [06:02<02:08, 17.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5772/8000 [00:42<00:19, 116.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5741/8000 [06:02<01:54, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▌    | 5801/8000 [00:42<00:14, 150.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5745/8000 [06:03<02:51, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5830/8000 [00:42<00:16, 131.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5747/8000 [06:03<02:53, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5845/8000 [00:42<00:16, 129.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5750/8000 [06:03<02:40, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5861/8000 [00:42<00:16, 128.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5753/8000 [06:04<03:01, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▊    | 5876/8000 [00:42<00:17, 123.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5755/8000 [06:04<03:03, 12.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5892/8000 [00:43<00:17, 121.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5758/8000 [06:04<03:13, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5920/8000 [00:43<00:18, 113.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5762/8000 [06:04<02:41, 13.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5939/8000 [00:43<00:17, 120.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5764/8000 [06:04<02:37, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5961/8000 [00:43<00:15, 132.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5766/8000 [06:05<03:16, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5983/8000 [00:43<00:14, 140.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5771/8000 [06:05<02:16, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6005/8000 [00:43<00:13, 147.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5773/8000 [06:05<02:20, 15.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6026/8000 [00:44<00:12, 151.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5776/8000 [06:05<02:15, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6052/8000 [00:44<00:11, 171.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5780/8000 [06:05<02:31, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6072/8000 [00:44<00:12, 158.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5783/8000 [06:06<03:04, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6093/8000 [00:44<00:11, 164.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5786/8000 [06:06<02:54, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6118/8000 [00:44<00:11, 169.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5788/8000 [06:06<03:38, 10.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6151/8000 [00:44<00:12, 150.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▊     | 5794/8000 [06:06<02:22, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6175/8000 [00:44<00:11, 157.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▊     | 5800/8000 [06:07<02:27, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6210/8000 [00:45<00:12, 147.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5803/8000 [06:07<02:19, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6227/8000 [00:45<00:12, 144.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5805/8000 [06:07<02:27, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6254/8000 [00:45<00:13, 128.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5808/8000 [06:07<02:14, 16.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6277/8000 [00:45<00:12, 136.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5813/8000 [06:08<01:50, 19.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6294/8000 [00:45<00:12, 132.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5817/8000 [06:08<02:17, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6313/8000 [00:46<00:12, 134.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5819/8000 [06:08<02:26, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6331/8000 [00:46<00:12, 132.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5823/8000 [06:08<02:46, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6356/8000 [00:46<00:11, 145.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5825/8000 [06:09<02:48, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6377/8000 [00:46<00:10, 149.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5828/8000 [06:09<02:32, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6395/8000 [00:46<00:10, 147.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5831/8000 [06:09<03:00, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6419/8000 [00:46<00:10, 157.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5835/8000 [06:09<03:01, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6437/8000 [00:46<00:10, 146.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5838/8000 [06:10<03:51,  9.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6453/8000 [00:46<00:10, 144.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5841/8000 [06:10<03:15, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6471/8000 [00:47<00:10, 139.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5845/8000 [06:10<02:45, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6501/8000 [00:47<00:11, 129.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5848/8000 [06:10<02:31, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6517/8000 [00:47<00:11, 128.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5853/8000 [06:11<01:56, 18.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6536/8000 [00:47<00:11, 129.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5857/8000 [06:11<01:50, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6556/8000 [00:47<00:11, 129.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5860/8000 [06:11<02:18, 15.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6576/8000 [00:47<00:10, 133.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5862/8000 [06:11<02:17, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6594/8000 [00:48<00:10, 138.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5870/8000 [06:11<01:28, 24.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6615/8000 [00:48<00:10, 136.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5875/8000 [06:12<01:42, 20.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6640/8000 [00:48<00:09, 148.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5878/8000 [06:12<02:48, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6658/8000 [00:48<00:09, 147.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▉     | 5880/8000 [06:12<02:44, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6678/8000 [00:48<00:08, 148.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▉     | 5884/8000 [06:13<02:49, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6695/8000 [00:48<00:09, 142.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▉     | 5886/8000 [06:13<03:14, 10.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6715/8000 [00:49<00:11, 109.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▉     | 5891/8000 [06:13<02:20, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6732/8000 [00:49<00:11, 114.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▉     | 5893/8000 [06:13<02:30, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▌  | 6755/8000 [00:49<00:09, 131.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5895/8000 [06:14<02:33, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6776/8000 [00:49<00:08, 138.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5898/8000 [06:14<02:28, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6796/8000 [00:49<00:08, 141.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5900/8000 [06:14<02:28, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6821/8000 [00:49<00:07, 159.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5902/8000 [06:14<04:06,  8.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6838/8000 [00:49<00:07, 150.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5907/8000 [06:15<03:27, 10.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6857/8000 [00:49<00:07, 149.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5911/8000 [06:15<02:45, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6875/8000 [00:50<00:08, 127.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5915/8000 [06:16<03:27, 10.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6893/8000 [00:50<00:11, 95.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5917/8000 [06:16<03:57,  8.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6928/8000 [00:50<00:11, 94.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5919/8000 [06:16<03:49,  9.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6949/8000 [00:50<00:09, 107.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5923/8000 [06:16<03:05, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6964/8000 [00:51<00:09, 108.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5927/8000 [06:17<03:12, 10.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6977/8000 [00:51<00:10, 96.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5932/8000 [06:17<02:57, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6993/8000 [00:51<00:09, 104.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5934/8000 [06:17<03:28,  9.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7015/8000 [00:51<00:08, 120.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5936/8000 [06:18<03:35,  9.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7034/8000 [00:51<00:07, 131.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5937/8000 [06:18<03:43,  9.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7052/8000 [00:51<00:07, 129.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5938/8000 [06:18<03:53,  8.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7070/8000 [00:51<00:06, 140.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5941/8000 [06:18<03:03, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7086/8000 [00:51<00:06, 134.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5947/8000 [06:18<01:51, 18.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7104/8000 [00:52<00:06, 136.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████▏    | 5951/8000 [06:19<02:08, 15.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7120/8000 [00:52<00:06, 134.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████▏    | 5954/8000 [06:19<02:10, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7146/8000 [00:52<00:05, 155.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████▏    | 5956/8000 [06:19<02:21, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7168/8000 [00:52<00:05, 161.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5962/8000 [06:19<02:09, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7200/8000 [00:52<00:06, 123.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5965/8000 [06:20<02:29, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7215/8000 [00:53<00:07, 109.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5970/8000 [06:20<02:56, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7235/8000 [00:53<00:06, 114.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5973/8000 [06:20<03:02, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7256/8000 [00:53<00:05, 126.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5977/8000 [06:21<02:49, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7272/8000 [00:53<00:06, 119.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5979/8000 [06:21<02:51, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7294/8000 [00:53<00:05, 131.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5981/8000 [06:21<02:56, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▋ | 7314/8000 [00:53<00:05, 134.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5984/8000 [06:21<02:30, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7329/8000 [00:53<00:05, 129.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5987/8000 [06:22<02:54, 11.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7346/8000 [00:54<00:05, 124.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5989/8000 [06:22<02:46, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7368/8000 [00:54<00:04, 135.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5992/8000 [06:22<02:30, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7400/8000 [00:54<00:04, 133.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5994/8000 [06:22<02:33, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7414/8000 [00:54<00:04, 126.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5996/8000 [06:22<02:28, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7431/8000 [00:54<00:04, 122.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5998/8000 [06:22<02:26, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7452/8000 [00:54<00:04, 134.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6001/8000 [06:22<02:13, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7471/8000 [00:54<00:03, 133.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6004/8000 [06:23<02:01, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7492/8000 [00:55<00:03, 142.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6007/8000 [06:23<01:58, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7507/8000 [00:55<00:03, 134.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6011/8000 [06:23<01:45, 18.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7532/8000 [00:55<00:03, 152.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6013/8000 [06:23<01:57, 16.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7549/8000 [00:55<00:03, 145.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6018/8000 [06:23<01:32, 21.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7565/8000 [00:55<00:03, 137.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6021/8000 [06:23<01:36, 20.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7588/8000 [00:55<00:02, 148.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6025/8000 [06:24<02:19, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7609/8000 [00:55<00:02, 147.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6027/8000 [06:24<02:48, 11.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7632/8000 [00:56<00:02, 157.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6030/8000 [06:24<02:30, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7655/8000 [00:56<00:02, 159.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6034/8000 [06:25<02:08, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7692/8000 [00:56<00:01, 156.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6036/8000 [06:25<02:14, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7709/8000 [00:56<00:01, 149.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6039/8000 [06:25<02:10, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7725/8000 [00:56<00:01, 147.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▎    | 6046/8000 [06:25<01:29, 21.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7742/8000 [00:56<00:01, 141.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▎    | 6050/8000 [06:25<01:51, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7769/8000 [00:57<00:01, 118.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6055/8000 [06:26<02:12, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7789/8000 [00:57<00:01, 119.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6057/8000 [06:26<02:43, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7814/8000 [00:57<00:01, 128.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6061/8000 [06:26<02:40, 12.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7835/8000 [00:57<00:01, 127.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6064/8000 [06:27<02:24, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7865/8000 [00:57<00:01, 120.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6068/8000 [06:27<02:31, 12.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7881/8000 [00:57<00:01, 114.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6070/8000 [06:27<02:56, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7910/8000 [00:58<00:00, 96.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6077/8000 [06:28<02:17, 13.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7927/8000 [00:58<00:00, 98.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6081/8000 [06:28<01:59, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7950/8000 [00:58<00:00, 108.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6084/8000 [06:28<02:00, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7965/8000 [00:58<00:00, 111.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6088/8000 [06:28<02:09, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7991/8000 [00:58<00:00, 130.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6090/8000 [06:28<02:12, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8014it [00:59, 135.62it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  76%|██████████████▍    | 6093/8000 [06:29<02:02, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  76%|██████████████▍    | 6096/8000 [06:29<02:23, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6099/8000 [06:29<02:14, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:08<27:22,  8.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6101/8000 [06:29<02:22, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<14:16,  4.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6103/8000 [06:29<02:32, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:11<09:56,  3.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6107/8000 [06:30<02:29, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:56,  2.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6111/8000 [06:30<02:04, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<06:36,  2.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6114/8000 [06:30<02:25, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:56,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6119/8000 [06:31<02:21, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:36,  1.75s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6122/8000 [06:31<02:19, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:18,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6126/8000 [06:31<02:01, 15.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:10,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6129/8000 [06:31<01:54, 16.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<04:55,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6132/8000 [06:32<03:49,  8.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:38,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6136/8000 [06:32<03:00, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:32,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6140/8000 [06:32<02:28, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:27,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6145/8000 [06:33<01:56, 15.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:32,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6150/8000 [06:33<02:38, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:43,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6153/8000 [06:33<02:28, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:39,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6156/8000 [06:34<02:13, 13.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:31,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6158/8000 [06:34<02:19, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:24,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6163/8000 [06:34<01:45, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:35<05:07,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6167/8000 [06:34<02:22, 12.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:37<05:10,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6169/8000 [06:35<02:22, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:45,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6171/8000 [06:35<02:27, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:31,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6173/8000 [06:35<02:39, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:41<04:21,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6175/8000 [06:35<03:17,  9.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:14,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6182/8000 [06:35<01:50, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:09,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6186/8000 [06:36<02:02, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:07,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6190/8000 [06:36<01:45, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:46<04:04,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6193/8000 [06:36<01:40, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:48<04:01,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6197/8000 [06:36<01:33, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:49<04:03,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▋    | 6200/8000 [06:36<01:36, 18.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:51<04:03,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▋    | 6204/8000 [06:37<01:53, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:52<03:59,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▋    | 6207/8000 [06:37<01:56, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:53<03:59,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▋    | 6210/8000 [06:37<01:50, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:55<03:54,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6212/8000 [06:37<02:30, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:56<03:51,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6217/8000 [06:38<01:53, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:58<03:48,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6222/8000 [06:38<02:19, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:59<03:44,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6224/8000 [06:38<02:23, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:00<03:41,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6226/8000 [06:38<02:23, 12.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:02<03:40,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6229/8000 [06:39<02:40, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:03<03:37,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6233/8000 [06:39<02:15, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:04<03:39,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6238/8000 [06:39<02:07, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:06<03:38,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6241/8000 [06:40<02:00, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:07<03:40,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6243/8000 [06:40<02:31, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:09<03:38,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6246/8000 [06:40<02:12, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:10<03:38,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6253/8000 [06:40<01:29, 19.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:11<03:36,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6256/8000 [06:40<01:30, 19.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:13<03:34,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6261/8000 [06:41<01:39, 17.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:14<03:31,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6266/8000 [06:41<01:21, 21.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:15<03:29,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6271/8000 [06:41<01:39, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:17<03:27,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6275/8000 [06:42<01:47, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:18<03:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6278/8000 [06:42<01:43, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:20<03:25,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6280/8000 [06:42<02:23, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:21<03:25,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6283/8000 [06:42<02:10, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:22<03:24,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6285/8000 [06:42<02:17, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:24<03:20,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6289/8000 [06:43<03:06,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:25<03:21,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6291/8000 [06:43<03:01,  9.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:27<03:21,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6294/8000 [06:43<02:34, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:28<03:21,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6298/8000 [06:44<02:06, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:31<03:43,  1.57s/it]\n",
+      "Generating signals:  79%|██████████████▉    | 6301/8000 [06:44<01:55, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  79%|██████████████▉    | 6305/8000 [06:44<01:36, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6309/8000 [06:44<01:29, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6312/8000 [06:44<01:28, 19.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.99it/s]\n",
+      "Generating signals:  79%|██████████████▉    | 6315/8000 [06:44<01:28, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  79%|███████████████    | 6322/8000 [06:45<01:22, 20.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6327/8000 [06:45<01:32, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.40s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 43it [00:01, 39.40it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 85it [00:01, 83.73it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 126it [00:01, 130.26it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 165it [00:01, 174.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 202it [00:01, 212.13it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 239it [00:02, 243.11it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 277it [00:02, 274.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6331/8000 [06:45<01:26, 19.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 314it [00:02, 295.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6336/8000 [06:46<01:35, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 355it [00:02, 325.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6339/8000 [06:46<02:03, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 395it [00:02, 344.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6343/8000 [06:46<01:48, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 434it [00:02, 356.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6345/8000 [06:46<01:48, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 477it [00:02, 374.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6349/8000 [06:46<01:35, 17.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 517it [00:02, 352.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6351/8000 [06:47<01:44, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 560it [00:02, 372.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6355/8000 [06:47<01:58, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 600it [00:02, 378.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████    | 6360/8000 [06:47<01:36, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 639it [00:03, 367.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████    | 6364/8000 [06:47<01:49, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 681it [00:03, 381.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████    | 6367/8000 [06:48<01:45, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 723it [00:03, 391.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6371/8000 [06:48<01:55, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 231.05it/s]\n",
+      "Generating signals:  80%|███████████████▏   | 6373/8000 [06:48<02:14, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  80%|███████████████▏   | 6375/8000 [06:48<02:18, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 122\u001b[A\u001b[A\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:  29%|▎| 29/100 [00:00<00:00, 132\u001b[A\u001b[A\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:00<00:00, 140\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6380/8000 [06:49<01:42, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:00<00:00, 135\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6382/8000 [06:49<01:50, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:00<00:00, 143\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6387/8000 [06:49<01:26, 18.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 14\n",
+      "Generating signals:  80%|███████████████▏   | 6389/8000 [06:49<01:38, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [19:22<06:36, 396.29s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.769687280897596\n",
-      "2999: -5.8238825\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  80%|███████████████▏   | 6393/8000 [06:49<01:26, 18.62it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  80%|███████████████▏   | 6397/8000 [06:49<01:16, 20.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6400/8000 [06:50<01:15, 21.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:39,  7.13s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6403/8000 [06:50<01:18, 20.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:35,  3.82s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6410/8000 [06:50<01:37, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:54,  2.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6413/8000 [06:50<01:32, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:06,  2.18s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6415/8000 [06:51<01:43, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:07,  1.88s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6419/8000 [06:51<01:55, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:33,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▎   | 6423/8000 [06:52<02:32, 10.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:28,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▎   | 6426/8000 [06:52<03:05,  8.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:08,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▎   | 6429/8000 [06:52<02:38,  9.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:53,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▎   | 6435/8000 [06:53<02:13, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▎   | 6438/8000 [06:53<02:12, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:32,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6442/8000 [06:53<01:47, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:30,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6444/8000 [06:53<01:56, 13.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:27,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6447/8000 [06:54<02:59,  8.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:23,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6449/8000 [06:54<02:48,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:22,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6454/8000 [06:54<02:04, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:20,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6458/8000 [06:54<01:47, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:18,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6463/8000 [06:55<01:25, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:18,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6468/8000 [06:55<01:40, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:12,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6470/8000 [06:55<01:44, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:33<04:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6474/8000 [06:56<01:50, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:23,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6481/8000 [06:56<01:43, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:30,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6484/8000 [06:56<01:36, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:22,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6486/8000 [06:56<01:39, 15.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<04:18,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6490/8000 [06:56<01:31, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:29,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6493/8000 [06:57<01:31, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<04:26,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6495/8000 [06:57<01:36, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<04:18,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6497/8000 [06:57<01:41, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<04:12,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6500/8000 [06:57<01:35, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<04:07,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6503/8000 [06:57<01:24, 17.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:49<04:15,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6506/8000 [06:57<01:22, 18.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<04:14,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6512/8000 [06:58<01:05, 22.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<04:04,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6515/8000 [06:58<01:30, 16.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<04:09,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6517/8000 [06:58<01:37, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:55<04:08,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6519/8000 [06:58<01:41, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:56<04:09,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▍   | 6523/8000 [06:58<01:22, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:58<04:06,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▍   | 6525/8000 [06:59<01:26, 17.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:59<04:01,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6529/8000 [06:59<01:18, 18.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:00<03:58,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6532/8000 [06:59<01:39, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<04:01,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6534/8000 [06:59<01:47, 13.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:04<04:01,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6536/8000 [06:59<01:52, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:05<04:00,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6539/8000 [07:00<01:42, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:07<04:32,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6544/8000 [07:00<01:48, 13.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:10<05:00,  1.91s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6550/8000 [07:00<01:40, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:11<04:39,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6554/8000 [07:01<01:28, 16.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:12<04:14,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6558/8000 [07:01<01:38, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:14<04:04,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6564/8000 [07:01<01:31, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:15<03:50,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6567/8000 [07:01<01:25, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:17<03:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6570/8000 [07:01<01:22, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:18<03:32,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6574/8000 [07:02<01:18, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:19<03:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6577/8000 [07:02<01:16, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:21<03:26,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6579/8000 [07:02<01:22, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:22<03:26,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6582/8000 [07:02<01:20, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:23<03:24,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6584/8000 [07:02<01:34, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:26<03:54,  1.60s/it]\n",
+      "Generating signals:  82%|███████████████▋   | 6586/8000 [07:02<01:33, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  82%|███████████████▋   | 6589/8000 [07:03<01:58, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6591/8000 [07:03<01:55, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6593/8000 [07:03<02:05, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.66it/s]\n",
+      "Generating signals:  82%|███████████████▋   | 6596/8000 [07:03<01:49, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  82%|███████████████▋   | 6598/8000 [07:04<01:56, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6601/8000 [07:04<01:43, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6604/8000 [07:04<01:40, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 43it [00:01, 39.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6607/8000 [07:04<02:00, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 75it [00:01, 72.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6610/8000 [07:04<01:44, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 110it [00:01, 112.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6612/8000 [07:05<01:54, 12.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 145it [00:01, 152.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6614/8000 [07:05<02:01, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 180it [00:01, 190.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6619/8000 [07:05<01:23, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 216it [00:01, 226.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6621/8000 [07:05<01:32, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 259it [00:02, 273.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6623/8000 [07:05<01:37, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 296it [00:02, 294.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6631/8000 [07:06<01:27, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 334it [00:02, 315.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6636/8000 [07:06<01:27, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 371it [00:02, 329.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6641/8000 [07:06<01:14, 18.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 411it [00:02, 347.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6646/8000 [07:07<01:05, 20.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 450it [00:02, 356.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6652/8000 [07:07<01:24, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 491it [00:02, 370.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6657/8000 [07:07<01:12, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 531it [00:02, 378.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6661/8000 [07:08<01:20, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 571it [00:02, 384.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6668/8000 [07:08<01:04, 20.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 611it [00:03, 387.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6674/8000 [07:08<01:07, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 651it [00:03, 381.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6679/8000 [07:08<01:12, 18.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 692it [00:03, 388.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▊   | 6684/8000 [07:09<01:12, 18.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 732it [00:03, 374.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6691/8000 [07:09<00:59, 22.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 226.95it/s]\n",
+      "Generating signals:  84%|███████████████▉   | 6695/8000 [07:09<01:01, 21.21it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  84%|███████████████▉   | 6700/8000 [07:10<01:20, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6704/8000 [07:10<01:27, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 169\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6710/8000 [07:10<01:20, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 152\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6713/8000 [07:10<01:26, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 158\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6715/8000 [07:11<01:31, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:00<00:00, 158\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6717/8000 [07:11<02:12,  9.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  84%|▊| 84/100 [00:00<00:00, 138\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6719/8000 [07:11<02:17,  9.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
+      "Generating signals:  84%|███████████████▉   | 6722/8000 [07:12<02:21,  9.00it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  84%|███████████████▉   | 6725/8000 [07:12<01:59, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6727/8000 [07:12<02:22,  8.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 4/8000 [00:00<04:20, 30.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6730/8000 [07:12<01:56, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 10/8000 [00:00<06:26, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6732/8000 [07:13<02:21,  8.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 13/8000 [00:00<06:43, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6737/8000 [07:13<01:37, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 16/8000 [00:00<08:46, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6741/8000 [07:13<01:41, 12.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 19/8000 [00:01<09:51, 13.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6744/8000 [07:13<01:31, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 21/8000 [00:01<09:31, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6747/8000 [07:14<01:26, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 24/8000 [00:01<08:20, 15.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6750/8000 [07:14<01:19, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 26/8000 [00:01<08:17, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6753/8000 [07:14<01:42, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 30/8000 [00:01<06:36, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6756/8000 [07:14<01:34, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 33/8000 [00:01<06:19, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6761/8000 [07:15<01:13, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 36/8000 [00:02<08:05, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6766/8000 [07:15<01:06, 18.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 38/8000 [00:02<10:00, 13.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6768/8000 [07:15<01:12, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 42/8000 [00:02<07:49, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6770/8000 [07:15<01:18, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 44/8000 [00:02<07:49, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6775/8000 [07:15<01:02, 19.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 46/8000 [00:02<08:15, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6780/8000 [07:16<01:25, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 48/8000 [00:02<08:17, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6783/8000 [07:16<01:21, 14.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 51/8000 [00:03<07:26, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6785/8000 [07:16<01:26, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 53/8000 [00:03<07:28, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6788/8000 [07:16<01:20, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 56/8000 [00:03<06:46, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6791/8000 [07:16<01:13, 16.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 63/8000 [00:03<05:43, 23.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6793/8000 [07:17<01:17, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 66/8000 [00:03<05:40, 23.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6795/8000 [07:17<01:17, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 69/8000 [00:03<05:57, 22.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6799/8000 [07:17<01:10, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 72/8000 [00:04<08:59, 14.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6807/8000 [07:17<00:46, 25.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 75/8000 [00:04<09:45, 13.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6810/8000 [07:17<00:52, 22.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 77/8000 [00:04<09:49, 13.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6815/8000 [07:17<00:46, 25.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 79/8000 [00:04<09:29, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6820/8000 [07:18<00:59, 19.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 82/8000 [00:04<08:08, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6825/8000 [07:18<00:53, 21.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 85/8000 [00:04<07:17, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6828/8000 [07:18<01:08, 17.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 89/8000 [00:05<06:12, 21.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6831/8000 [07:18<01:05, 17.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 92/8000 [00:05<06:08, 21.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6834/8000 [07:19<01:41, 11.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 95/8000 [00:05<05:58, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6837/8000 [07:19<01:56,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 99/8000 [00:05<06:50, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▏  | 6841/8000 [07:20<01:30, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 103/8000 [00:06<08:38, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6843/8000 [07:20<01:47, 10.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 106/8000 [00:06<07:46, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6846/8000 [07:20<01:34, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 109/8000 [00:06<08:51, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6850/8000 [07:20<01:29, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 114/8000 [00:06<06:58, 18.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6855/8000 [07:20<01:05, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 117/8000 [00:06<06:40, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6859/8000 [07:21<01:08, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 121/8000 [00:06<05:58, 21.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6862/8000 [07:21<01:06, 17.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 125/8000 [00:06<05:31, 23.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6864/8000 [07:21<01:10, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 129/8000 [00:07<05:00, 26.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6868/8000 [07:21<01:16, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 135/8000 [00:07<06:27, 20.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6872/8000 [07:21<01:03, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 140/8000 [00:07<05:32, 23.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6876/8000 [07:22<01:08, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 146/8000 [00:07<04:47, 27.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6879/8000 [07:22<01:07, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 151/8000 [00:07<04:19, 30.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6882/8000 [07:22<01:05, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 155/8000 [00:08<04:14, 30.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6884/8000 [07:22<01:27, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 162/8000 [00:08<04:25, 29.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6886/8000 [07:23<01:29, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 166/8000 [00:08<04:21, 30.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6892/8000 [07:23<01:00, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 172/8000 [00:08<05:51, 22.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6897/8000 [07:23<00:51, 21.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 175/8000 [00:08<06:00, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6900/8000 [07:23<00:51, 21.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 183/8000 [00:09<05:15, 24.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6903/8000 [07:23<01:07, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 186/8000 [00:09<06:16, 20.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6906/8000 [07:23<01:06, 16.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 191/8000 [00:09<06:25, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6910/8000 [07:24<01:00, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 194/8000 [00:09<07:27, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6912/8000 [07:24<01:03, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 196/8000 [00:10<07:27, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6918/8000 [07:24<00:48, 22.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 199/8000 [00:10<07:02, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6924/8000 [07:25<01:06, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 201/8000 [00:10<07:14, 17.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6927/8000 [07:25<01:01, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 203/8000 [00:10<07:39, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6931/8000 [07:25<01:07, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 205/8000 [00:10<09:38, 13.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6933/8000 [07:25<01:12, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 209/8000 [00:10<07:33, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6935/8000 [07:25<01:16, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 212/8000 [00:11<06:54, 18.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6937/8000 [07:25<01:18, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 215/8000 [00:11<07:53, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6943/8000 [07:26<00:53, 19.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 218/8000 [00:11<08:53, 14.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6948/8000 [07:26<00:48, 21.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 220/8000 [00:11<08:49, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6955/8000 [07:26<00:48, 21.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 223/8000 [00:11<07:47, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6959/8000 [07:26<00:46, 22.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 226/8000 [00:11<06:58, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6963/8000 [07:26<00:44, 23.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 230/8000 [00:12<05:57, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6967/8000 [07:27<00:53, 19.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 233/8000 [00:12<05:56, 21.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6970/8000 [07:27<00:52, 19.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 236/8000 [00:12<06:18, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6977/8000 [07:27<00:49, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 240/8000 [00:12<07:01, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6983/8000 [07:27<00:43, 23.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 242/8000 [00:12<07:20, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6989/8000 [07:28<00:44, 22.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 246/8000 [00:12<06:07, 21.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6994/8000 [07:28<00:40, 24.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 255/8000 [00:13<06:00, 21.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6997/8000 [07:28<00:43, 23.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 260/8000 [00:13<07:07, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7000/8000 [07:28<00:45, 22.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 262/8000 [00:13<07:26, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7006/8000 [07:28<00:46, 21.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 265/8000 [00:13<06:58, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7009/8000 [07:29<00:47, 20.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 268/8000 [00:14<06:42, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7012/8000 [07:29<00:49, 19.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 272/8000 [00:14<07:09, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7015/8000 [07:29<00:50, 19.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 276/8000 [00:14<06:21, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7018/8000 [07:29<01:03, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▋                 | 279/8000 [00:14<06:15, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7020/8000 [07:29<01:07, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 283/8000 [00:14<05:36, 22.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7022/8000 [07:30<01:06, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 286/8000 [00:14<05:34, 23.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7025/8000 [07:30<01:00, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 294/8000 [00:15<05:00, 25.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7029/8000 [07:30<00:51, 18.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 301/8000 [00:15<04:40, 27.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7032/8000 [07:30<00:50, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 305/8000 [00:15<05:31, 23.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7035/8000 [07:30<00:51, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 308/8000 [00:15<05:27, 23.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7038/8000 [07:30<00:50, 18.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 311/8000 [00:15<05:28, 23.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7040/8000 [07:30<00:54, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 317/8000 [00:16<05:27, 23.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7044/8000 [07:31<00:49, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 323/8000 [00:16<06:27, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7046/8000 [07:31<00:55, 17.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 326/8000 [00:16<06:11, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7050/8000 [07:31<01:06, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 329/8000 [00:16<05:56, 21.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7052/8000 [07:31<01:10, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 336/8000 [00:16<05:12, 24.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7057/8000 [07:31<00:51, 18.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 344/8000 [00:17<03:54, 32.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7061/8000 [07:32<01:00, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 348/8000 [00:17<04:55, 25.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7065/8000 [07:32<00:52, 17.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 353/8000 [00:17<04:27, 28.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7070/8000 [07:32<01:00, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 357/8000 [00:17<05:15, 24.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7073/8000 [07:33<01:00, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 360/8000 [00:17<05:32, 22.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7075/8000 [07:33<01:02, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 364/8000 [00:18<05:07, 24.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7078/8000 [07:33<01:16, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 369/8000 [00:18<04:30, 28.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7080/8000 [07:33<01:32,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 375/8000 [00:18<04:52, 26.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7083/8000 [07:34<01:36,  9.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 379/8000 [00:18<05:51, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7085/8000 [07:34<01:33,  9.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 386/8000 [00:18<05:13, 24.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7091/8000 [07:34<01:09, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 393/8000 [00:19<05:13, 24.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7094/8000 [07:34<01:06, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 397/8000 [00:19<05:58, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7099/8000 [07:35<00:55, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 400/8000 [00:19<05:49, 21.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7102/8000 [07:35<01:06, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 403/8000 [00:19<05:40, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7107/8000 [07:35<01:04, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 410/8000 [00:19<04:14, 29.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7109/8000 [07:36<01:08, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 414/8000 [00:19<04:08, 30.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7111/8000 [07:36<01:14, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 418/8000 [00:20<06:24, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7114/8000 [07:36<01:19, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 423/8000 [00:20<06:34, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7117/8000 [07:36<01:23, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 427/8000 [00:20<07:09, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7119/8000 [07:37<01:37,  9.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 432/8000 [00:21<07:11, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7121/8000 [07:37<01:28,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 435/8000 [00:21<08:10, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7124/8000 [07:37<01:13, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 438/8000 [00:21<07:33, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7127/8000 [07:37<01:20, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 440/8000 [00:21<07:28, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7131/8000 [07:38<01:02, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 442/8000 [00:21<07:24, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7134/8000 [07:38<00:57, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 446/8000 [00:21<06:09, 20.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7136/8000 [07:38<01:15, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 449/8000 [00:22<05:56, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7139/8000 [07:38<01:06, 12.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 455/8000 [00:22<05:55, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7143/8000 [07:38<00:52, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 458/8000 [00:22<05:43, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7145/8000 [07:39<01:00, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 464/8000 [00:22<06:35, 19.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7150/8000 [07:39<01:04, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 467/8000 [00:23<07:37, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7152/8000 [07:39<01:03, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 470/8000 [00:23<06:56, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7155/8000 [07:39<01:00, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 473/8000 [00:23<06:28, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|█████████████████  | 7159/8000 [07:40<01:01, 13.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 476/8000 [00:23<07:36, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7161/8000 [07:40<01:04, 13.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 479/8000 [00:23<06:56, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7166/8000 [07:40<00:58, 14.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 484/8000 [00:23<05:29, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7170/8000 [07:40<00:58, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 489/8000 [00:24<05:57, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7173/8000 [07:41<00:57, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 494/8000 [00:24<06:19, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7176/8000 [07:41<00:52, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 498/8000 [00:24<05:48, 21.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7179/8000 [07:41<00:51, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 503/8000 [00:24<06:06, 20.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7182/8000 [07:41<01:12, 11.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 509/8000 [00:24<04:43, 26.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7185/8000 [07:41<01:03, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 514/8000 [00:25<05:23, 23.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7196/8000 [07:42<00:55, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 518/8000 [00:25<05:02, 24.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7200/8000 [07:42<00:49, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 521/8000 [00:25<05:14, 23.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7204/8000 [07:43<00:52, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 525/8000 [00:25<04:55, 25.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7207/8000 [07:43<00:52, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 530/8000 [00:25<05:27, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7211/8000 [07:43<00:46, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 535/8000 [00:26<04:52, 25.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7213/8000 [07:43<00:51, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 538/8000 [00:26<04:58, 24.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7215/8000 [07:43<00:52, 15.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 543/8000 [00:26<06:34, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7218/8000 [07:44<00:49, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 549/8000 [00:26<06:01, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7222/8000 [07:44<00:42, 18.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 553/8000 [00:26<05:41, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7226/8000 [07:44<00:39, 19.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 556/8000 [00:27<05:35, 22.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7230/8000 [07:44<00:46, 16.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 560/8000 [00:27<07:29, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7233/8000 [07:44<00:45, 16.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 563/8000 [00:27<06:57, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7236/8000 [07:45<00:44, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 567/8000 [00:27<05:51, 21.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7238/8000 [07:45<00:50, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 573/8000 [00:27<04:28, 27.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7242/8000 [07:45<00:42, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 581/8000 [00:28<04:14, 29.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7245/8000 [07:45<00:42, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 587/8000 [00:28<06:03, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7248/8000 [07:45<00:40, 18.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 591/8000 [00:28<06:39, 18.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7251/8000 [07:45<00:49, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 594/8000 [00:28<06:20, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7256/8000 [07:46<00:38, 19.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 598/8000 [00:29<06:49, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7260/8000 [07:46<00:34, 21.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 602/8000 [00:29<06:07, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7264/8000 [07:46<00:39, 18.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 605/8000 [00:29<05:47, 21.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7269/8000 [07:46<00:42, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 609/8000 [00:29<05:12, 23.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7274/8000 [07:47<00:35, 20.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 615/8000 [00:30<06:09, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7277/8000 [07:47<00:36, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 619/8000 [00:30<05:33, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7287/8000 [07:47<00:36, 19.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 624/8000 [00:30<05:57, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7293/8000 [07:48<00:40, 17.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 629/8000 [00:30<06:09, 19.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7296/8000 [07:48<00:40, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 633/8000 [00:30<05:44, 21.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7302/8000 [07:48<00:31, 21.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 638/8000 [00:30<04:55, 24.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7306/8000 [07:48<00:30, 22.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 641/8000 [00:31<04:56, 24.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7310/8000 [07:49<00:41, 16.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 646/8000 [00:31<05:37, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7313/8000 [07:49<00:41, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 651/8000 [00:31<04:43, 25.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▍ | 7316/8000 [07:49<00:39, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 655/8000 [00:31<04:33, 26.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▍ | 7319/8000 [07:49<00:48, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 659/8000 [00:31<05:45, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7321/8000 [07:49<00:55, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 663/8000 [00:32<07:25, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7325/8000 [07:50<00:45, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 668/8000 [00:32<07:04, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7327/8000 [07:50<00:47, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 673/8000 [00:32<07:06, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7329/8000 [07:50<00:48, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 675/8000 [00:32<07:20, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7333/8000 [07:50<00:57, 11.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 680/8000 [00:33<05:44, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7337/8000 [07:51<00:57, 11.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 686/8000 [00:33<04:44, 25.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7342/8000 [07:51<00:42, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 693/8000 [00:33<04:31, 26.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7347/8000 [07:51<00:34, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 696/8000 [00:33<04:38, 26.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7351/8000 [07:51<00:31, 20.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 699/8000 [00:33<04:42, 25.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7357/8000 [07:51<00:26, 24.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 705/8000 [00:34<04:50, 25.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7361/8000 [07:52<00:32, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 708/8000 [00:34<05:58, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7364/8000 [07:52<00:32, 19.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 712/8000 [00:34<05:29, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7369/8000 [07:52<00:28, 22.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 718/8000 [00:34<04:30, 26.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7373/8000 [07:52<00:42, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 722/8000 [00:34<05:32, 21.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7380/8000 [07:53<00:30, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 727/8000 [00:34<04:47, 25.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7385/8000 [07:53<00:33, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 730/8000 [00:35<04:54, 24.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7390/8000 [07:53<00:28, 21.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 733/8000 [00:35<04:52, 24.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7393/8000 [07:53<00:30, 19.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 736/8000 [00:35<06:19, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7396/8000 [07:53<00:30, 19.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 739/8000 [00:35<05:58, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7399/8000 [07:54<00:29, 20.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 742/8000 [00:35<07:04, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7402/8000 [07:54<00:29, 19.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 747/8000 [00:35<05:50, 20.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7406/8000 [07:54<00:27, 21.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 752/8000 [00:36<05:51, 20.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7410/8000 [07:54<00:26, 22.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 761/8000 [00:36<04:40, 25.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7414/8000 [07:54<00:24, 24.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 766/8000 [00:36<05:08, 23.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7418/8000 [07:54<00:24, 23.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 771/8000 [00:36<04:38, 25.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7421/8000 [07:55<00:33, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 775/8000 [00:37<04:34, 26.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7424/8000 [07:55<00:34, 16.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 779/8000 [00:37<04:33, 26.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7426/8000 [07:55<00:37, 15.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 787/8000 [00:37<04:15, 28.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7428/8000 [07:55<00:50, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 793/8000 [00:37<04:36, 26.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7430/8000 [07:56<01:02,  9.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 796/8000 [00:37<05:43, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7436/8000 [07:56<00:38, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 800/8000 [00:38<08:14, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7439/8000 [07:56<00:45, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 803/8000 [00:38<08:43, 13.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7442/8000 [07:57<00:50, 11.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 805/8000 [00:39<09:57, 12.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7444/8000 [07:57<00:50, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 809/8000 [00:39<08:00, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7446/8000 [07:57<01:02,  8.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 811/8000 [00:39<09:20, 12.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7448/8000 [07:57<00:57,  9.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 815/8000 [00:39<07:18, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7450/8000 [07:58<01:03,  8.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 818/8000 [00:39<06:48, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7451/8000 [07:58<01:10,  7.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 822/8000 [00:40<09:41, 12.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7455/8000 [07:58<00:45, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 825/8000 [00:40<09:55, 12.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7457/8000 [07:58<00:45, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 827/8000 [00:40<11:03, 10.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7459/8000 [07:58<00:43, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 832/8000 [00:40<07:51, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7464/8000 [07:59<00:39, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 834/8000 [00:40<07:53, 15.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7466/8000 [07:59<00:41, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 836/8000 [00:41<07:52, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7471/8000 [07:59<00:30, 17.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 839/8000 [00:41<07:18, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7473/8000 [07:59<00:32, 16.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 841/8000 [00:41<07:44, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▊ | 7475/8000 [07:59<00:34, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 847/8000 [00:41<05:09, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▊ | 7477/8000 [08:00<00:45, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 852/8000 [00:41<05:45, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▊ | 7479/8000 [08:00<00:45, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 856/8000 [00:42<07:55, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7483/8000 [08:00<00:34, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 859/8000 [00:42<07:42, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7487/8000 [08:00<00:45, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 861/8000 [00:42<08:05, 14.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7491/8000 [08:01<00:44, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 866/8000 [00:42<06:12, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7497/8000 [08:01<00:32, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 870/8000 [00:43<07:27, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7500/8000 [08:01<00:32, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 874/8000 [00:43<08:07, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7502/8000 [08:01<00:33, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 876/8000 [00:43<08:24, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7507/8000 [08:01<00:26, 18.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 880/8000 [00:43<06:46, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7511/8000 [08:02<00:23, 20.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 885/8000 [00:43<06:31, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7518/8000 [08:02<00:18, 26.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 892/8000 [00:44<04:40, 25.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7521/8000 [08:02<00:29, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 897/8000 [00:44<06:02, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7525/8000 [08:03<00:34, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 901/8000 [00:44<07:30, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7527/8000 [08:03<00:41, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 904/8000 [00:45<07:10, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7530/8000 [08:03<00:39, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 908/8000 [00:45<06:08, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7533/8000 [08:03<00:34, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 911/8000 [00:45<08:30, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7537/8000 [08:04<00:30, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 914/8000 [00:45<07:50, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7539/8000 [08:04<00:31, 14.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 918/8000 [00:45<07:52, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7542/8000 [08:04<00:29, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 921/8000 [00:46<07:10, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7547/8000 [08:04<00:34, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 924/8000 [00:46<06:54, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7549/8000 [08:04<00:34, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 929/8000 [00:46<06:36, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7551/8000 [08:05<00:35, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 932/8000 [00:46<06:07, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7555/8000 [08:05<00:35, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 935/8000 [00:46<07:23, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7559/8000 [08:05<00:29, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 940/8000 [00:47<08:11, 14.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▉ | 7561/8000 [08:05<00:30, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 944/8000 [00:47<08:16, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▉ | 7566/8000 [08:05<00:22, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 946/8000 [00:47<08:14, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▉ | 7572/8000 [08:06<00:26, 15.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 948/8000 [00:47<08:19, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▉ | 7577/8000 [08:06<00:32, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 950/8000 [00:48<08:05, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7580/8000 [08:07<00:30, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 953/8000 [00:48<07:28, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7583/8000 [08:07<00:28, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 955/8000 [00:48<07:42, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7587/8000 [08:07<00:25, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 961/8000 [00:48<05:10, 22.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7589/8000 [08:07<00:26, 15.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 968/8000 [00:48<03:46, 31.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7592/8000 [08:08<00:43,  9.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 973/8000 [00:48<04:45, 24.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7594/8000 [08:08<00:42,  9.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 978/8000 [00:49<04:25, 26.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7597/8000 [08:08<00:43,  9.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 981/8000 [00:49<04:31, 25.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7600/8000 [08:08<00:38, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 984/8000 [00:49<05:39, 20.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7603/8000 [08:09<00:33, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 990/8000 [00:49<05:25, 21.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7609/8000 [08:09<00:22, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 993/8000 [00:49<06:34, 17.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7612/8000 [08:09<00:22, 17.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 996/8000 [00:50<06:11, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7616/8000 [08:09<00:24, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 999/8000 [00:50<05:59, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7618/8000 [08:09<00:24, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1004/8000 [00:50<07:15, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7621/8000 [08:10<00:28, 13.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1010/8000 [00:50<06:26, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7623/8000 [08:10<00:29, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1014/8000 [00:51<05:42, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7627/8000 [08:10<00:25, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1022/8000 [00:51<05:01, 23.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7629/8000 [08:10<00:31, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1025/8000 [00:51<05:10, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████▏| 7632/8000 [08:11<00:27, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1033/8000 [00:51<04:23, 26.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████▏| 7636/8000 [08:11<00:22, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1037/8000 [00:51<05:04, 22.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████▏| 7639/8000 [08:11<00:28, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1040/8000 [00:52<06:51, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7643/8000 [08:12<00:32, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1043/8000 [00:52<06:20, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7645/8000 [08:12<00:38,  9.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1047/8000 [00:52<05:26, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7648/8000 [08:12<00:36,  9.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1051/8000 [00:52<04:53, 23.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7650/8000 [08:12<00:33, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1057/8000 [00:52<04:02, 28.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7652/8000 [08:12<00:31, 11.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1064/8000 [00:53<04:51, 23.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7655/8000 [08:13<00:25, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1068/8000 [00:53<04:32, 25.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7660/8000 [08:13<00:18, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1071/8000 [00:53<04:34, 25.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7663/8000 [08:13<00:23, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1074/8000 [00:53<04:44, 24.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7665/8000 [08:13<00:24, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1079/8000 [00:53<04:14, 27.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7668/8000 [08:13<00:22, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1086/8000 [00:53<04:20, 26.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7670/8000 [08:14<00:24, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1092/8000 [00:54<03:45, 30.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7673/8000 [08:14<00:22, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1096/8000 [00:54<04:29, 25.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7677/8000 [08:14<00:20, 15.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1099/8000 [00:54<04:29, 25.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7679/8000 [08:14<00:22, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1103/8000 [00:54<04:27, 25.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7683/8000 [08:14<00:18, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1106/8000 [00:54<05:33, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7686/8000 [08:14<00:17, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1112/8000 [00:55<06:20, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7689/8000 [08:15<00:17, 17.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1115/8000 [00:55<07:13, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7691/8000 [08:15<00:18, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1119/8000 [00:55<07:23, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7694/8000 [08:15<00:17, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1121/8000 [00:56<10:11, 11.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7697/8000 [08:15<00:17, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1124/8000 [00:56<11:40,  9.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7699/8000 [08:15<00:18, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1127/8000 [00:56<09:49, 11.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7702/8000 [08:15<00:16, 17.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1129/8000 [00:57<10:51, 10.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7704/8000 [08:16<00:17, 16.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1132/8000 [00:57<08:54, 12.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7708/8000 [08:16<00:15, 18.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1136/8000 [00:57<08:07, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7712/8000 [08:16<00:18, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1138/8000 [00:57<08:00, 14.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7715/8000 [08:16<00:21, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1143/8000 [00:57<05:54, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7717/8000 [08:17<00:21, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1147/8000 [00:57<05:18, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7719/8000 [08:17<00:25, 10.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1151/8000 [00:58<06:56, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7723/8000 [08:17<00:18, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1156/8000 [00:58<05:29, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7725/8000 [08:17<00:24, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1159/8000 [00:58<05:22, 21.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7727/8000 [08:18<00:26, 10.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1164/8000 [00:58<05:18, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7730/8000 [08:18<00:22, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1168/8000 [00:58<05:53, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7732/8000 [08:18<00:22, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1172/8000 [00:59<07:18, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7736/8000 [08:18<00:17, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1175/8000 [00:59<06:42, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7741/8000 [08:18<00:14, 18.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1178/8000 [00:59<06:02, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7744/8000 [08:18<00:14, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1181/8000 [00:59<08:17, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7747/8000 [08:19<00:13, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1185/8000 [01:00<06:43, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7753/8000 [08:19<00:09, 24.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1190/8000 [01:00<07:17, 15.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7756/8000 [08:19<00:10, 22.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1192/8000 [01:00<07:14, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7759/8000 [08:19<00:11, 21.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1195/8000 [01:00<06:30, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7766/8000 [08:19<00:07, 29.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1200/8000 [01:00<06:05, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7773/8000 [08:19<00:08, 26.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1206/8000 [01:00<04:27, 25.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7777/8000 [08:20<00:10, 20.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1211/8000 [01:01<04:41, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7782/8000 [08:20<00:09, 22.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1216/8000 [01:01<04:54, 23.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7786/8000 [08:20<00:11, 18.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1220/8000 [01:01<06:21, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7789/8000 [08:20<00:11, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1224/8000 [01:02<06:37, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▌| 7794/8000 [08:21<00:15, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1226/8000 [01:02<06:43, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7801/8000 [08:21<00:10, 18.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1228/8000 [01:02<07:08, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7805/8000 [08:22<00:13, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1233/8000 [01:02<05:40, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7809/8000 [08:22<00:13, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1238/8000 [01:02<06:07, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7811/8000 [08:22<00:13, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1243/8000 [01:03<06:22, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7813/8000 [08:22<00:13, 13.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1245/8000 [01:03<06:40, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7816/8000 [08:22<00:12, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1252/8000 [01:03<06:03, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7819/8000 [08:23<00:11, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1256/8000 [01:03<05:27, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7822/8000 [08:23<00:10, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1259/8000 [01:03<06:21, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7825/8000 [08:23<00:10, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1263/8000 [01:04<05:32, 20.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7829/8000 [08:23<00:09, 18.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1269/8000 [01:04<06:14, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7832/8000 [08:23<00:08, 18.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1275/8000 [01:04<04:43, 23.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7837/8000 [08:23<00:08, 20.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1280/8000 [01:04<05:27, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7841/8000 [08:24<00:12, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1284/8000 [01:05<05:59, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7845/8000 [08:24<00:10, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1289/8000 [01:05<06:49, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7848/8000 [08:24<00:09, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1295/8000 [01:05<06:10, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7851/8000 [08:24<00:08, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1300/8000 [01:06<05:58, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7853/8000 [08:25<00:09, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1303/8000 [01:06<06:40, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7857/8000 [08:25<00:09, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1307/8000 [01:06<06:50, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7859/8000 [08:25<00:09, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1309/8000 [01:06<07:02, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7861/8000 [08:25<00:10, 13.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1311/8000 [01:06<07:02, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7864/8000 [08:25<00:09, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1313/8000 [01:07<07:20, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7868/8000 [08:26<00:07, 17.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1315/8000 [01:07<07:11, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7875/8000 [08:26<00:06, 19.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1318/8000 [01:07<06:20, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7877/8000 [08:26<00:07, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1321/8000 [01:07<07:34, 14.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7880/8000 [08:26<00:08, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1324/8000 [01:07<06:32, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▋| 7886/8000 [08:27<00:06, 18.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1327/8000 [01:07<07:18, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▋| 7888/8000 [08:27<00:08, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1332/8000 [01:08<06:32, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▋| 7890/8000 [08:27<00:08, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1334/8000 [01:08<07:03, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▋| 7894/8000 [08:27<00:08, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1336/8000 [01:08<07:16, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7897/8000 [08:28<00:07, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1340/8000 [01:08<05:47, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7899/8000 [08:28<00:10,  9.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1344/8000 [01:08<05:11, 21.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7901/8000 [08:28<00:09, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1347/8000 [01:08<05:57, 18.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7906/8000 [08:28<00:06, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1350/8000 [01:09<05:37, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7910/8000 [08:29<00:05, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1355/8000 [01:09<06:25, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7913/8000 [08:29<00:05, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1360/8000 [01:09<05:25, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7919/8000 [08:29<00:03, 22.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1367/8000 [01:09<05:11, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7925/8000 [08:30<00:05, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1371/8000 [01:10<05:39, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7927/8000 [08:30<00:04, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1377/8000 [01:10<05:31, 19.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7930/8000 [08:30<00:05, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1381/8000 [01:10<05:46, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7932/8000 [08:30<00:05, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1385/8000 [01:10<06:04, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7937/8000 [08:30<00:04, 13.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1390/8000 [01:11<05:01, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7941/8000 [08:31<00:04, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1395/8000 [01:11<04:23, 25.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7944/8000 [08:31<00:03, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1399/8000 [01:11<05:10, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▉| 7948/8000 [08:31<00:03, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1403/8000 [01:11<05:46, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▉| 7950/8000 [08:31<00:03, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1406/8000 [01:12<06:43, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▉| 7952/8000 [08:32<00:03, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1412/8000 [01:12<04:59, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▉| 7955/8000 [08:32<00:04,  9.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1415/8000 [01:12<05:02, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7961/8000 [08:32<00:02, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1420/8000 [01:12<04:14, 25.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7964/8000 [08:33<00:02, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1426/8000 [01:12<04:18, 25.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7967/8000 [08:33<00:02, 14.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1431/8000 [01:13<05:33, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7969/8000 [08:33<00:02, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1434/8000 [01:13<05:14, 20.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7972/8000 [08:33<00:02, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1437/8000 [01:13<05:12, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7974/8000 [08:33<00:01, 13.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1440/8000 [01:13<07:18, 14.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7978/8000 [08:33<00:01, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1445/8000 [01:13<05:43, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7980/8000 [08:34<00:01, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1449/8000 [01:13<05:06, 21.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7984/8000 [08:34<00:00, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1454/8000 [01:14<04:24, 24.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7987/8000 [08:34<00:00, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1458/8000 [01:14<04:12, 25.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7990/8000 [08:34<00:00, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1463/8000 [01:14<04:37, 23.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7997/8000 [08:34<00:00, 20.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1468/8000 [01:14<05:06, 21.31it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Generating signals:  18%|███▏             | 1472/8000 [01:14<04:55, 22.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8001it [08:35, 15.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  18%|███▏             | 1475/8000 [01:15<05:46, 18.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1479/8000 [01:15<05:06, 21.29it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1483/8000 [01:15<04:41, 23.14it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:08<27:17,  8.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1489/8000 [01:15<03:40, 29.54it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<14:29,  4.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1493/8000 [01:15<04:56, 21.91it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:11<10:22,  3.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1496/8000 [01:16<06:15, 17.32it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:13<08:29,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1500/8000 [01:16<05:28, 19.81it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:15<07:38,  2.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1504/8000 [01:16<06:08, 17.65it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:17<07:07,  2.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1507/8000 [01:16<07:09, 15.12it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:18<06:35,  2.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1509/8000 [01:16<07:14, 14.95it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:20<06:11,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1514/8000 [01:17<05:49, 18.55it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:22<05:58,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1517/8000 [01:17<05:43, 18.88it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:24<05:45,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1523/8000 [01:17<04:11, 25.75it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:25<05:34,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1526/8000 [01:17<04:18, 25.04it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:27<05:31,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1529/8000 [01:17<04:20, 24.88it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:29<05:52,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1534/8000 [01:17<03:39, 29.45it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:31<05:37,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1538/8000 [01:18<05:37, 19.12it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:32<05:29,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1541/8000 [01:18<06:20, 16.97it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:34<05:25,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1547/8000 [01:18<06:38, 16.18it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:36<05:19,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1549/8000 [01:18<06:43, 16.00it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:38<05:17,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1554/8000 [01:19<05:15, 20.43it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:40<05:27,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1558/8000 [01:19<04:33, 23.52it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:41<05:25,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1561/8000 [01:19<04:30, 23.78it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:43<05:16,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1564/8000 [01:19<05:25, 19.80it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:45<05:08,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1568/8000 [01:19<04:42, 22.78it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:46<05:03,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1572/8000 [01:19<04:13, 25.33it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:48<05:03,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1577/8000 [01:19<04:05, 26.20it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:50<05:13,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1580/8000 [01:20<05:41, 18.82it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:52<05:11,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1583/8000 [01:20<05:15, 20.32it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:54<05:07,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1586/8000 [01:20<05:18, 20.14it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:55<05:00,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1590/8000 [01:20<05:48, 18.38it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:57<05:19,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1594/8000 [01:21<06:04, 17.56it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:59<05:10,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1598/8000 [01:21<06:50, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [01:01<05:10,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1604/8000 [01:21<05:05, 20.95it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [01:03<05:04,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1607/8000 [01:21<04:54, 21.74it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [01:05<05:00,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1610/8000 [01:22<07:59, 13.33it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [01:06<04:59,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1612/8000 [01:22<07:44, 13.75it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:08<05:02,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1614/8000 [01:22<08:55, 11.92it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:10<05:04,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1616/8000 [01:22<08:30, 12.50it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:12<05:00,  1.84s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1619/8000 [01:22<07:16, 14.63it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:14<05:00,  1.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1625/8000 [01:22<05:52, 18.07it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:16<05:01,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1630/8000 [01:23<07:13, 14.69it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:18<05:00,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1633/8000 [01:23<06:56, 15.28it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:20<05:04,  1.92s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1638/8000 [01:24<08:13, 12.88it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:23<05:15,  2.00s/it]\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1644/8000 [01:24<06:13, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1648/8000 [01:24<06:42, 15.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1651/8000 [01:24<06:13, 17.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1654/8000 [01:24<07:05, 14.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 10.46it/s]\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1658/8000 [01:25<06:00, 17.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1661/8000 [01:25<06:39, 15.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1664/8000 [01:25<06:06, 17.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1666/8000 [01:25<07:20, 14.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 35it [00:01, 28.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1671/8000 [01:25<06:22, 16.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 60it [00:01, 52.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1674/8000 [01:26<06:09, 17.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 90it [00:01, 84.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1677/8000 [01:26<05:40, 18.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 127it [00:01, 129.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1681/8000 [01:26<06:06, 17.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 159it [00:02, 163.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1688/8000 [01:26<05:07, 20.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 189it [00:02, 187.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1697/8000 [01:26<03:33, 29.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 225it [00:02, 225.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1701/8000 [01:27<04:08, 25.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 266it [00:02, 268.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1704/8000 [01:27<04:58, 21.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 300it [00:02, 285.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1708/8000 [01:27<04:35, 22.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 334it [00:02, 279.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1712/8000 [01:27<04:09, 25.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 370it [00:02, 297.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1715/8000 [01:27<04:11, 25.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 406it [00:02, 310.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1718/8000 [01:27<04:09, 25.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 440it [00:02, 313.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1721/8000 [01:28<05:40, 18.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 473it [00:03, 306.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1727/8000 [01:28<05:27, 19.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 505it [00:03, 296.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1732/8000 [01:28<05:34, 18.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 536it [00:03, 283.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1738/8000 [01:29<05:11, 20.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 569it [00:03, 295.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1742/8000 [01:29<05:23, 19.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 600it [00:03, 277.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1745/8000 [01:29<05:24, 19.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 629it [00:03, 274.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1747/8000 [01:29<05:42, 18.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 657it [00:03, 262.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1751/8000 [01:29<04:51, 21.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 684it [00:03, 251.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1754/8000 [01:29<06:03, 17.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 710it [00:04, 223.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1756/8000 [01:30<05:55, 17.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 733it [00:04, 225.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1759/8000 [01:30<05:15, 19.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 756it [00:04, 181.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1762/8000 [01:30<04:50, 21.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 778it [00:04, 190.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1766/8000 [01:30<05:27, 19.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 176.02it/s]\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1769/8000 [01:30<05:08, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1776/8000 [01:30<04:48, 21.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1781/8000 [01:31<05:04, 20.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   6%| | 6/100 [00:00<00:02, 45.68i\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1784/8000 [01:31<05:07, 20.25it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:01, 71.09\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1791/8000 [01:31<05:17, 19.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|▎| 27/100 [00:00<00:00, 83.64\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1795/8000 [01:31<04:43, 21.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 71.43\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1798/8000 [01:32<05:58, 17.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 78.78\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1801/8000 [01:32<05:42, 18.09it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 89.62\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1806/8000 [01:32<05:53, 17.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:00<00:00, 81.80\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1810/8000 [01:32<06:06, 16.89it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:00<00:00, 83.43\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1813/8000 [01:33<05:53, 17.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:01<00:00, 81.80\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1815/8000 [01:33<06:04, 16.98it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 76.5\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1820/8000 [01:33<04:53, 21.04it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples: 100%|██████████████| 4/4 [39:20<00:00, 673.20s/it]\u001b[A\n",
+      "Replicates:  27%|██████▏                | 4/15 [3:56:39<10:00:08, 3273.48s/it]\n",
+      "Nyquist Samples:   0%|                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.8901033107254825\n",
+      "2999: -5.9111598\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  23%|███▊             | 1823/8000 [01:33<05:50, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1826/8000 [01:33<05:24, 19.00it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1830/8000 [01:33<05:56, 17.29it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<26:06,  7.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1836/8000 [01:34<04:32, 22.64it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<13:46,  4.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1840/8000 [01:34<04:09, 24.73it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:10<09:44,  2.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1844/8000 [01:34<04:48, 21.37it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<07:52,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1847/8000 [01:34<04:48, 21.31it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<06:56,  2.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1850/8000 [01:34<06:03, 16.91it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:15<06:26,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1853/8000 [01:35<05:44, 17.86it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<05:49,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1857/8000 [01:35<05:44, 17.81it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:18<05:35,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1859/8000 [01:35<06:01, 17.00it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:20<05:21,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1863/8000 [01:35<06:20, 16.14it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:22<05:17,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1869/8000 [01:35<05:45, 17.77it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:23<05:08,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1872/8000 [01:36<06:19, 16.16it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:25<04:54,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1874/8000 [01:36<06:31, 15.65it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:26<04:55,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1876/8000 [01:36<06:39, 15.33it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:28<04:49,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1878/8000 [01:36<06:43, 15.16it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:29<04:48,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉             | 1881/8000 [01:36<07:37, 13.37it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:31<04:43,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1883/8000 [01:37<07:42, 13.22it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:32<04:40,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1888/8000 [01:37<06:27, 15.76it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:34<04:34,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1891/8000 [01:37<05:48, 17.53it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:36<04:52,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1898/8000 [01:37<03:51, 26.35it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:37<04:52,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1902/8000 [01:37<03:43, 27.29it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:39<04:47,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1905/8000 [01:37<04:00, 25.38it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:40<04:41,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1908/8000 [01:38<06:23, 15.87it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:42<04:35,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1911/8000 [01:38<05:58, 16.97it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:44<04:44,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1915/8000 [01:38<06:03, 16.75it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:45<04:47,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1917/8000 [01:38<06:14, 16.26it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:47<04:40,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1921/8000 [01:38<05:12, 19.44it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:48<04:32,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1924/8000 [01:39<06:10, 16.39it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:50<04:28,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1927/8000 [01:39<05:36, 18.06it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:52<04:37,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1930/8000 [01:39<06:42, 15.08it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:53<04:31,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1937/8000 [01:39<04:18, 23.45it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:55<04:28,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1940/8000 [01:39<04:38, 21.75it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:56<04:25,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1943/8000 [01:39<04:26, 22.70it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:58<04:32,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1948/8000 [01:40<04:34, 22.07it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [01:00<04:31,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1953/8000 [01:40<03:48, 26.45it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:01<04:29,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1956/8000 [01:40<03:55, 25.69it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:03<04:23,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1960/8000 [01:40<05:43, 17.61it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:05<04:30,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1965/8000 [01:41<07:36, 13.22it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:06<04:24,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1967/8000 [01:41<07:25, 13.55it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:08<04:42,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1969/8000 [01:41<07:14, 13.87it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:10<04:35,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1974/8000 [01:41<05:14, 19.17it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:12<04:51,  1.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1977/8000 [01:41<04:51, 20.63it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:14<04:48,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1982/8000 [01:42<04:01, 24.87it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:15<04:30,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1986/8000 [01:42<05:58, 16.77it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:17<04:23,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1990/8000 [01:42<05:11, 19.30it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:18<04:15,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1993/8000 [01:42<05:56, 16.84it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:20<04:14,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1998/8000 [01:43<05:37, 17.80it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:22<04:05,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2001/8000 [01:43<05:25, 18.41it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:23<03:57,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2004/8000 [01:43<06:20, 15.77it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:25<04:00,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2009/8000 [01:43<07:26, 13.40it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:26<03:56,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2013/8000 [01:44<06:34, 15.17it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:28<04:11,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2016/8000 [01:44<06:16, 15.90it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:31<04:52,  1.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2019/8000 [01:44<07:55, 12.57it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:32<04:33,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2022/8000 [01:44<07:22, 13.52it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:34<04:22,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2026/8000 [01:45<08:26, 11.80it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:36<04:06,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2029/8000 [01:45<08:20, 11.93it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:37<03:57,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2034/8000 [01:45<08:20, 11.91it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [01:39<03:48,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2036/8000 [01:46<08:04, 12.32it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [01:40<03:48,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2042/8000 [01:46<05:46, 17.17it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:42<03:44,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2044/8000 [01:46<06:19, 15.70it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:43<03:39,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2046/8000 [01:46<06:43, 14.75it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:45<03:39,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2050/8000 [01:46<05:36, 17.66it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [01:46<03:34,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2053/8000 [01:46<05:48, 17.09it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [01:48<03:32,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2055/8000 [01:47<06:50, 14.49it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [01:50<03:36,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2058/8000 [01:47<06:38, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [01:51<03:33,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2061/8000 [01:47<06:38, 14.91it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [01:53<03:33,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2064/8000 [01:47<05:49, 17.01it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [01:54<03:29,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2066/8000 [01:47<06:04, 16.29it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [01:56<03:29,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2070/8000 [01:48<06:21, 15.55it/s]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [01:57<03:24,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2073/8000 [01:48<05:39, 17.48it/s]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [01:59<03:22,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2075/8000 [01:48<05:45, 17.14it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [02:01<03:23,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2081/8000 [01:48<04:08, 23.80it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▌                    | 72/200 [02:02<03:20,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2085/8000 [01:48<05:09, 19.13it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▋                    | 73/200 [02:04<03:15,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2090/8000 [01:49<05:07, 19.19it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [02:05<03:18,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2097/8000 [01:49<03:52, 25.36it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [02:08<03:34,  1.72s/it]\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2104/8000 [01:49<04:38, 21.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2107/8000 [01:50<06:12, 15.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2109/8000 [01:50<06:10, 15.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:02, 11.44it/s]\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2111/8000 [01:50<06:08, 15.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2116/8000 [01:50<04:46, 20.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2120/8000 [01:50<05:23, 18.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2124/8000 [01:50<05:34, 17.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 39it [00:01, 31.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2127/8000 [01:51<05:09, 18.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 70it [00:01, 60.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2130/8000 [01:51<05:47, 16.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 103it [00:01, 95.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2133/8000 [01:51<05:23, 18.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 137it [00:02, 133.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2141/8000 [01:51<03:31, 27.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 168it [00:02, 162.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2145/8000 [01:51<04:26, 21.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 201it [00:02, 195.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2148/8000 [01:51<04:36, 21.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 236it [00:02, 229.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2153/8000 [01:52<04:02, 24.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 280it [00:02, 280.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2156/8000 [01:52<05:00, 19.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 316it [00:02, 284.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2162/8000 [01:52<04:38, 20.99it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 350it [00:02, 291.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2165/8000 [01:52<04:31, 21.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 388it [00:02, 315.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2171/8000 [01:53<04:16, 22.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 426it [00:02, 330.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2175/8000 [01:53<06:34, 14.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 462it [00:02, 315.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2178/8000 [01:53<05:58, 16.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 496it [00:03, 300.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2182/8000 [01:53<06:16, 15.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 534it [00:03, 320.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2189/8000 [01:54<05:50, 16.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 568it [00:03, 317.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2193/8000 [01:54<05:19, 18.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 601it [00:03, 304.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2198/8000 [01:54<04:32, 21.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 640it [00:03, 326.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2202/8000 [01:54<04:57, 19.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 674it [00:03, 325.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2206/8000 [01:55<05:21, 18.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 707it [00:03, 307.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2208/8000 [01:55<05:25, 17.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 750it [00:03, 340.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2210/8000 [01:55<05:39, 17.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 199.93it/s]\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2212/8000 [01:55<06:49, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2215/8000 [01:55<06:07, 15.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2217/8000 [01:55<06:22, 15.12it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 139.1\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2220/8000 [01:56<05:53, 16.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▎| 29/100 [00:00<00:00, 145.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2222/8000 [01:56<06:05, 15.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 145.4\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2225/8000 [01:56<05:22, 17.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:00<00:00, 127.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2227/8000 [01:56<06:59, 13.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:00<00:00, 147.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2233/8000 [01:56<05:16, 18.24it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 137.\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2235/8000 [01:57<06:49, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2238/8000 [01:57<07:18, 13.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2242/8000 [01:57<05:44, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                  | 184/8000 [00:00<00:08, 910.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2244/8000 [01:57<06:55, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                  | 371/8000 [00:00<00:07, 961.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2247/8000 [01:57<06:02, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 566/8000 [00:01<00:31, 235.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2250/8000 [01:58<05:37, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 733/8000 [00:02<00:22, 323.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2254/8000 [01:58<05:59, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                | 907/8000 [00:02<00:16, 423.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2256/8000 [01:58<06:04, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍               | 1075/8000 [00:02<00:13, 527.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2258/8000 [01:58<06:03, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊               | 1243/8000 [00:02<00:11, 597.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2260/8000 [01:58<06:11, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1409/8000 [00:02<00:09, 683.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2263/8000 [01:58<05:40, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▌              | 1568/8000 [00:02<00:08, 725.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2266/8000 [01:58<05:21, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▉              | 1747/8000 [00:03<00:08, 775.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2268/8000 [01:59<08:23, 11.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1916/8000 [00:03<00:07, 829.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2271/8000 [01:59<06:57, 13.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2080/8000 [00:03<00:06, 890.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2274/8000 [01:59<06:01, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2258/8000 [00:03<00:06, 897.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2276/8000 [01:59<06:10, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2426/8000 [00:03<00:05, 974.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2278/8000 [01:59<06:11, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▊            | 2598/8000 [00:04<00:05, 991.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2283/8000 [02:00<04:25, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2770/8000 [00:04<00:05, 1005.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2286/8000 [02:00<05:47, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2949/8000 [00:04<00:04, 1060.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2288/8000 [02:00<05:45, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▌          | 3116/8000 [00:04<00:04, 1031.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2292/8000 [02:00<04:42, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████▉          | 3285/8000 [00:04<00:04, 1055.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2295/8000 [02:00<04:52, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▎         | 3460/8000 [00:04<00:04, 1038.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2299/8000 [02:00<04:11, 22.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████▋         | 3637/8000 [00:04<00:04, 1041.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2302/8000 [02:01<06:58, 13.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████         | 3817/8000 [00:05<00:04, 1033.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2304/8000 [02:01<07:03, 13.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▉         | 3973/8000 [00:06<00:11, 339.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2306/8000 [02:01<07:01, 13.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▎        | 4148/8000 [00:06<00:09, 427.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2309/8000 [02:01<06:05, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4328/8000 [00:06<00:07, 502.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2313/8000 [02:02<07:20, 12.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▏       | 4516/8000 [00:07<00:05, 585.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2316/8000 [02:02<06:24, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▌       | 4688/8000 [00:07<00:05, 636.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2320/8000 [02:02<06:42, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4860/8000 [00:07<00:04, 691.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2325/8000 [02:02<04:54, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5027/8000 [00:07<00:03, 778.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2330/8000 [02:02<04:08, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5201/8000 [00:07<00:03, 841.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2337/8000 [02:03<04:07, 22.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5371/8000 [00:07<00:02, 883.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2340/8000 [02:03<04:46, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▍     | 5538/8000 [00:08<00:02, 824.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2343/8000 [02:03<04:36, 20.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▊     | 5700/8000 [00:08<00:02, 829.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2347/8000 [02:03<04:12, 22.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▏    | 5873/8000 [00:08<00:02, 879.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2350/8000 [02:03<04:14, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▌    | 6031/8000 [00:09<00:06, 318.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2355/8000 [02:03<03:33, 26.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▉    | 6199/8000 [00:09<00:04, 401.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2359/8000 [02:04<05:18, 17.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▎   | 6372/8000 [00:10<00:03, 474.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2364/8000 [02:04<05:12, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▋   | 6551/8000 [00:10<00:02, 562.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2369/8000 [02:04<05:04, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▏  | 6725/8000 [00:10<00:01, 659.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2374/8000 [02:05<05:02, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▌  | 6902/8000 [00:10<00:01, 743.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2381/8000 [02:05<04:18, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▉  | 7077/8000 [00:10<00:01, 827.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2384/8000 [02:05<05:54, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▎ | 7241/8000 [00:11<00:00, 884.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2386/8000 [02:05<05:58, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▋ | 7411/8000 [00:11<00:00, 855.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2388/8000 [02:06<06:03, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████ | 7577/8000 [00:11<00:00, 901.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2390/8000 [02:06<05:58, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▍| 7740/8000 [00:11<00:00, 904.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2392/8000 [02:06<06:06, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7919/8000 [00:11<00:00, 938.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2395/8000 [02:06<05:33, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8097it [00:11, 678.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  30%|█████            | 2397/8000 [02:06<08:36, 10.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2399/8000 [02:06<08:03, 11.58it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2403/8000 [02:07<07:20, 12.71it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<24:49,  7.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2405/8000 [02:07<08:47, 10.61it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:08<13:01,  3.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2410/8000 [02:07<06:03, 15.39it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:10<09:13,  2.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2413/8000 [02:08<07:54, 11.78it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:11<07:34,  2.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2416/8000 [02:08<06:43, 13.82it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:13<06:50,  2.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2418/8000 [02:08<07:51, 11.83it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:15<06:08,  1.90s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2421/8000 [02:08<07:59, 11.64it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:16<05:42,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2426/8000 [02:08<05:47, 16.06it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:18<05:28,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2429/8000 [02:08<05:16, 17.59it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:19<05:14,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2433/8000 [02:09<04:28, 20.74it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:21<05:07,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2439/8000 [02:09<03:23, 27.27it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:22<05:02,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2443/8000 [02:09<03:22, 27.43it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:24<04:55,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2448/8000 [02:09<04:08, 22.37it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:25<04:46,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2451/8000 [02:10<05:47, 15.99it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:27<04:53,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2455/8000 [02:10<05:06, 18.07it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:29<04:52,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2460/8000 [02:10<04:57, 18.60it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:30<04:59,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2463/8000 [02:10<05:37, 16.41it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:32<04:54,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2465/8000 [02:10<05:37, 16.38it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:33<04:47,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2468/8000 [02:10<05:18, 17.34it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:35<04:56,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2471/8000 [02:11<06:04, 15.17it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:37<04:45,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2476/8000 [02:11<04:24, 20.88it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:38<04:44,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2481/8000 [02:11<04:27, 20.60it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:40<04:40,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2484/8000 [02:12<06:17, 14.63it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:41<04:42,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2486/8000 [02:12<06:17, 14.63it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:43<05:01,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2488/8000 [02:12<06:19, 14.53it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:45<05:10,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2493/8000 [02:12<04:37, 19.82it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:47<04:56,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2499/8000 [02:12<05:12, 17.63it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:48<04:45,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2503/8000 [02:12<04:35, 19.95it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:50<04:43,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2508/8000 [02:13<03:48, 23.99it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:52<04:35,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2512/8000 [02:13<05:55, 15.45it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:53<04:25,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2517/8000 [02:13<05:28, 16.70it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:55<04:23,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2521/8000 [02:13<04:55, 18.57it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:56<04:24,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2526/8000 [02:14<04:05, 22.30it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:58<04:19,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2531/8000 [02:14<04:23, 20.76it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:59<04:21,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2535/8000 [02:14<04:05, 22.23it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:01<04:37,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2538/8000 [02:14<04:06, 22.19it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:03<04:40,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2543/8000 [02:14<04:06, 22.10it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:05<04:48,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2548/8000 [02:15<04:26, 20.44it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:07<04:47,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2551/8000 [02:15<04:15, 21.31it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:08<04:37,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2557/8000 [02:15<03:35, 25.27it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:10<04:29,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2561/8000 [02:15<03:24, 26.56it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:12<04:32,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2564/8000 [02:15<04:18, 21.03it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:13<04:26,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2569/8000 [02:16<05:20, 16.95it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:15<04:17,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2577/8000 [02:16<04:11, 21.53it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:17<04:20,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2580/8000 [02:16<04:05, 22.11it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:18<04:15,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2583/8000 [02:16<04:50, 18.63it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:20<04:14,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2586/8000 [02:17<05:41, 15.86it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:22<04:11,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2592/8000 [02:17<04:09, 21.68it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:23<04:07,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2599/8000 [02:17<04:26, 20.29it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:25<04:03,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2605/8000 [02:17<03:44, 24.00it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:27<04:14,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2608/8000 [02:18<04:35, 19.58it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:28<04:14,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2611/8000 [02:18<05:33, 16.16it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:30<04:08,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2616/8000 [02:18<04:31, 19.84it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:32<04:08,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2620/8000 [02:18<04:45, 18.83it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:33<04:07,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2626/8000 [02:18<03:42, 24.13it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:35<04:07,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2629/8000 [02:19<03:58, 22.52it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:37<04:20,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2634/8000 [02:19<03:28, 25.71it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [01:39<04:08,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2637/8000 [02:19<04:33, 19.63it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [01:40<04:09,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2640/8000 [02:19<05:16, 16.94it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:42<03:57,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2642/8000 [02:19<06:24, 13.93it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:44<03:51,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2645/8000 [02:20<05:43, 15.60it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:45<03:54,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2647/8000 [02:20<05:55, 15.05it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [01:47<03:50,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2654/8000 [02:20<04:31, 19.73it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [01:49<03:53,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2657/8000 [02:20<04:16, 20.83it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [01:50<03:45,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2662/8000 [02:20<04:17, 20.69it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [01:52<03:45,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2668/8000 [02:20<03:22, 26.27it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [01:54<03:38,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2673/8000 [02:21<03:00, 29.50it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [01:55<03:32,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2679/8000 [02:21<04:02, 21.97it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [01:57<03:28,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2683/8000 [02:21<03:49, 23.13it/s]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [01:58<03:32,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2689/8000 [02:21<03:45, 23.58it/s]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [02:01<04:12,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2692/8000 [02:22<04:31, 19.52it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [02:03<04:10,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2695/8000 [02:22<04:22, 20.19it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▌                    | 72/200 [02:05<03:59,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2700/8000 [02:22<04:18, 20.51it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▋                    | 73/200 [02:06<03:51,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2705/8000 [02:22<04:30, 19.54it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [02:08<03:38,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2710/8000 [02:22<03:49, 23.06it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [02:09<03:28,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2714/8000 [02:23<03:30, 25.13it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▏                   | 76/200 [02:11<03:23,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2717/8000 [02:23<03:33, 24.75it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▎                   | 77/200 [02:12<03:14,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2720/8000 [02:23<04:45, 18.47it/s]\u001b[A\u001b[A\n",
+      "training:  39%|████████████▍                   | 78/200 [02:14<03:16,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2723/8000 [02:23<05:29, 16.01it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▋                   | 79/200 [02:16<03:24,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2727/8000 [02:23<04:37, 19.00it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▊                   | 80/200 [02:18<03:16,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2730/8000 [02:24<04:34, 19.22it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▉                   | 81/200 [02:19<03:14,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2735/8000 [02:24<03:49, 22.98it/s]\u001b[A\u001b[A\n",
+      "training:  41%|█████████████                   | 82/200 [02:21<03:08,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2739/8000 [02:24<03:37, 24.20it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▎                  | 83/200 [02:22<03:02,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2743/8000 [02:24<04:21, 20.09it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▍                  | 84/200 [02:24<03:05,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2746/8000 [02:24<04:19, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▌                  | 85/200 [02:26<03:08,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2751/8000 [02:24<03:36, 24.22it/s]\u001b[A\u001b[A\n",
+      "training:  43%|█████████████▊                  | 86/200 [02:27<03:02,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2758/8000 [02:25<03:24, 25.66it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▉                  | 87/200 [02:29<02:56,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2762/8000 [02:25<04:40, 18.66it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████                  | 88/200 [02:30<02:53,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2765/8000 [02:25<04:28, 19.53it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████▏                 | 89/200 [02:32<02:50,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2771/8000 [02:25<04:08, 21.06it/s]\u001b[A\u001b[A\n",
+      "training:  45%|██████████████▍                 | 90/200 [02:33<02:49,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2774/8000 [02:26<04:05, 21.30it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▌                 | 91/200 [02:35<02:52,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2777/8000 [02:26<04:43, 18.39it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▋                 | 92/200 [02:36<02:50,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2779/8000 [02:26<05:08, 16.93it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▉                 | 93/200 [02:38<02:45,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2781/8000 [02:26<05:14, 16.61it/s]\u001b[A\u001b[A\n",
+      "training:  47%|███████████████                 | 94/200 [02:39<02:42,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2783/8000 [02:26<05:11, 16.72it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▏                | 95/200 [02:41<02:42,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2785/8000 [02:26<05:32, 15.67it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▎                | 96/200 [02:43<02:43,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2789/8000 [02:26<04:17, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▌                | 97/200 [02:44<02:38,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2792/8000 [02:27<05:18, 16.34it/s]\u001b[A\u001b[A\n",
+      "training:  49%|███████████████▋                | 98/200 [02:46<02:37,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2794/8000 [02:27<07:31, 11.52it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▊                | 99/200 [02:47<02:38,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2798/8000 [02:27<06:50, 12.66it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▌               | 100/200 [02:49<02:34,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2800/8000 [02:28<07:54, 10.97it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▋               | 101/200 [02:50<02:33,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2805/8000 [02:28<06:26, 13.44it/s]\u001b[A\u001b[A\n",
+      "training:  51%|███████████████▊               | 102/200 [02:52<02:30,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2809/8000 [02:28<05:06, 16.91it/s]\u001b[A\u001b[A\n",
+      "training:  52%|███████████████▉               | 103/200 [02:53<02:31,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2813/8000 [02:28<05:10, 16.69it/s]\u001b[A\u001b[A\n",
+      "training:  52%|████████████████               | 104/200 [02:55<02:27,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2815/8000 [02:28<05:23, 16.01it/s]\u001b[A\u001b[A\n",
+      "training:  52%|████████████████▎              | 105/200 [02:56<02:23,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2818/8000 [02:29<05:01, 17.20it/s]\u001b[A\u001b[A\n",
+      "training:  53%|████████████████▍              | 106/200 [02:58<02:22,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2821/8000 [02:29<04:41, 18.39it/s]\u001b[A\u001b[A\n",
+      "training:  54%|████████████████▌              | 107/200 [02:59<02:21,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2823/8000 [02:29<04:52, 17.69it/s]\u001b[A\u001b[A\n",
+      "training:  54%|████████████████▋              | 108/200 [03:01<02:17,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2826/8000 [02:29<05:35, 15.43it/s]\u001b[A\u001b[A\n",
+      "training:  55%|████████████████▉              | 109/200 [03:02<02:15,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2828/8000 [02:29<05:29, 15.72it/s]\u001b[A\u001b[A\n",
+      "training:  55%|█████████████████              | 110/200 [03:04<02:20,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2833/8000 [02:29<04:00, 21.50it/s]\u001b[A\u001b[A\n",
+      "training:  56%|█████████████████▏             | 111/200 [03:05<02:16,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2836/8000 [02:29<04:01, 21.41it/s]\u001b[A\u001b[A\n",
+      "training:  56%|█████████████████▎             | 112/200 [03:07<02:14,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2841/8000 [02:30<04:23, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:  56%|█████████████████▌             | 113/200 [03:09<02:13,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2847/8000 [02:30<03:26, 24.90it/s]\u001b[A\u001b[A\n",
+      "training:  57%|█████████████████▋             | 114/200 [03:10<02:13,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2851/8000 [02:30<03:51, 22.21it/s]\u001b[A\u001b[A\n",
+      "training:  57%|█████████████████▊             | 115/200 [03:12<02:13,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2854/8000 [02:30<04:04, 21.09it/s]\u001b[A\u001b[A\n",
+      "training:  58%|█████████████████▉             | 116/200 [03:13<02:13,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2857/8000 [02:30<04:05, 20.98it/s]\u001b[A\u001b[A\n",
+      "training:  58%|██████████████████▏            | 117/200 [03:15<02:09,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2862/8000 [02:31<04:05, 20.96it/s]\u001b[A\u001b[A\n",
+      "training:  59%|██████████████████▎            | 118/200 [03:16<02:09,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2867/8000 [02:31<04:12, 20.36it/s]\u001b[A\u001b[A\n",
+      "training:  60%|██████████████████▍            | 119/200 [03:18<02:08,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2871/8000 [02:31<03:51, 22.15it/s]\u001b[A\u001b[A\n",
+      "training:  60%|██████████████████▌            | 120/200 [03:20<02:07,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2874/8000 [02:31<05:51, 14.59it/s]\u001b[A\u001b[A\n",
+      "training:  60%|██████████████████▊            | 121/200 [03:22<02:15,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2876/8000 [02:32<05:55, 14.43it/s]\u001b[A\u001b[A\n",
+      "training:  61%|██████████████████▉            | 122/200 [03:23<02:09,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2878/8000 [02:32<05:45, 14.83it/s]\u001b[A\u001b[A\n",
+      "training:  62%|███████████████████            | 123/200 [03:25<02:06,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2880/8000 [02:32<05:43, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:  62%|███████████████████▏           | 124/200 [03:26<02:00,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2882/8000 [02:32<05:44, 14.86it/s]\u001b[A\u001b[A\n",
+      "training:  62%|███████████████████▍           | 125/200 [03:28<02:00,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2886/8000 [02:32<04:27, 19.13it/s]\u001b[A\u001b[A\n",
+      "training:  63%|███████████████████▌           | 126/200 [03:29<01:56,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2891/8000 [02:32<04:21, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:  64%|███████████████████▋           | 127/200 [03:31<01:54,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2896/8000 [02:33<04:26, 19.14it/s]\u001b[A\u001b[A\n",
+      "training:  64%|███████████████████▊           | 128/200 [03:32<01:50,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2899/8000 [02:33<04:33, 18.66it/s]\u001b[A\u001b[A\n",
+      "training:  64%|███████████████████▉           | 129/200 [03:34<01:51,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2901/8000 [02:33<06:05, 13.96it/s]\u001b[A\u001b[A\n",
+      "training:  65%|████████████████████▏          | 130/200 [03:36<01:48,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2903/8000 [02:33<07:04, 12.02it/s]\u001b[A\u001b[A\n",
+      "training:  66%|████████████████████▎          | 131/200 [03:37<01:45,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2906/8000 [02:34<07:19, 11.58it/s]\u001b[A\u001b[A\n",
+      "training:  66%|████████████████████▍          | 132/200 [03:39<01:43,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2909/8000 [02:34<06:03, 13.99it/s]\u001b[A\u001b[A\n",
+      "training:  66%|████████████████████▌          | 133/200 [03:40<01:43,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2911/8000 [02:34<05:50, 14.51it/s]\u001b[A\u001b[A\n",
+      "training:  67%|████████████████████▊          | 134/200 [03:42<01:43,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2914/8000 [02:34<05:11, 16.31it/s]\u001b[A\u001b[A\n",
+      "training:  68%|████████████████████▉          | 135/200 [03:43<01:41,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2919/8000 [02:34<03:54, 21.70it/s]\u001b[A\u001b[A\n",
+      "training:  68%|█████████████████████          | 136/200 [03:45<01:40,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2925/8000 [02:35<04:29, 18.81it/s]\u001b[A\u001b[A\n",
+      "training:  68%|█████████████████████▏         | 137/200 [03:47<01:41,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2931/8000 [02:35<04:48, 17.59it/s]\u001b[A\u001b[A\n",
+      "training:  69%|█████████████████████▍         | 138/200 [03:48<01:39,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2933/8000 [02:35<05:08, 16.45it/s]\u001b[A\u001b[A\n",
+      "training:  70%|█████████████████████▌         | 139/200 [03:51<01:41,  1.67s/it]\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2936/8000 [02:35<04:40, 18.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2938/8000 [02:35<05:02, 16.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2940/8000 [02:35<04:59, 16.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2942/8000 [02:36<05:04, 16.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.72it/s]\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2944/8000 [02:36<05:11, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2949/8000 [02:36<03:39, 22.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2953/8000 [02:36<05:21, 15.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2958/8000 [02:36<04:05, 20.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 35it [00:01, 29.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2962/8000 [02:37<04:29, 18.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 76it [00:01, 71.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2972/8000 [02:37<04:03, 20.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 120it [00:01, 121.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2975/8000 [02:37<04:01, 20.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 155it [00:01, 154.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2981/8000 [02:37<03:53, 21.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 189it [00:02, 170.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2986/8000 [02:38<03:23, 24.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 224it [00:02, 205.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2992/8000 [02:38<03:25, 24.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 256it [00:02, 214.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2995/8000 [02:38<04:13, 19.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 285it [00:02, 218.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3002/8000 [02:38<03:48, 21.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 324it [00:02, 257.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3006/8000 [02:39<04:14, 19.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 355it [00:02, 269.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3009/8000 [02:39<04:08, 20.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 392it [00:02, 294.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3013/8000 [02:39<04:38, 17.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 425it [00:02, 288.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3018/8000 [02:39<03:44, 22.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 464it [00:02, 311.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3022/8000 [02:39<04:13, 19.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 500it [00:03, 324.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3026/8000 [02:40<03:47, 21.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 534it [00:03, 311.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3031/8000 [02:40<04:07, 20.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 567it [00:03, 315.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3035/8000 [02:40<03:42, 22.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 606it [00:03, 335.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3039/8000 [02:40<04:19, 19.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 641it [00:03, 328.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3046/8000 [02:40<03:11, 25.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 675it [00:03, 298.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3050/8000 [02:41<03:48, 21.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 709it [00:03, 308.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3054/8000 [02:41<04:13, 19.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 741it [00:03, 308.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3057/8000 [02:41<04:02, 20.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 196.81it/s]\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3061/8000 [02:41<03:39, 22.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3064/8000 [02:41<04:26, 18.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3071/8000 [02:42<03:59, 20.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 129.3\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3075/8000 [02:42<04:59, 16.46it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 157.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3080/8000 [02:42<04:09, 19.74it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00<00:00, 144.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3084/8000 [02:43<05:51, 14.00it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 121.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3087/8000 [02:43<06:06, 13.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 137.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3093/8000 [02:43<04:23, 18.64it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 133.\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3097/8000 [02:43<03:57, 20.68it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|██████▊                    | 1/4 [08:55<26:47, 535.90s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.631140115754981\n",
+      "2999: -6.2022745\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  39%|██████▌          | 3100/8000 [02:43<03:51, 21.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3105/8000 [02:44<04:01, 20.26it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3109/8000 [02:44<04:26, 18.38it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<25:34,  7.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3116/8000 [02:44<03:53, 20.93it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<13:34,  4.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3119/8000 [02:44<03:47, 21.44it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:11<09:52,  3.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3122/8000 [02:44<03:41, 21.97it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<07:59,  2.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3125/8000 [02:45<04:30, 18.02it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<06:58,  2.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3127/8000 [02:45<05:33, 14.62it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:16<06:33,  2.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3132/8000 [02:45<05:06, 15.87it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<06:00,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3135/8000 [02:45<04:48, 16.87it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:19<05:41,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3137/8000 [02:46<04:52, 16.63it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:20<05:36,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3139/8000 [02:46<04:58, 16.30it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:22<05:24,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3143/8000 [02:46<04:05, 19.80it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:24<05:23,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3146/8000 [02:46<04:02, 20.04it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:25<05:19,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3149/8000 [02:46<03:46, 21.40it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:27<05:38,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3153/8000 [02:46<05:05, 15.88it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:29<05:28,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3155/8000 [02:47<05:10, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:31<05:13,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3158/8000 [02:47<04:33, 17.71it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:32<05:08,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3161/8000 [02:47<04:10, 19.31it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:34<05:14,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3165/8000 [02:47<05:26, 14.81it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:36<05:07,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3169/8000 [02:47<04:27, 18.09it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:37<04:57,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3175/8000 [02:47<03:21, 23.90it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:39<04:51,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3178/8000 [02:48<03:21, 23.95it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:40<04:48,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3182/8000 [02:48<03:03, 26.32it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:42<04:44,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3185/8000 [02:48<05:26, 14.76it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:43<04:38,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3188/8000 [02:48<04:56, 16.21it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:45<04:36,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3191/8000 [02:48<04:39, 17.23it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:46<04:28,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3196/8000 [02:49<03:34, 22.39it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:48<04:29,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3200/8000 [02:49<03:55, 20.39it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:50<04:30,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3205/8000 [02:49<03:25, 23.37it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:51<04:33,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3208/8000 [02:49<04:13, 18.90it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:53<04:30,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3211/8000 [02:49<04:06, 19.40it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:54<04:27,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3216/8000 [02:50<03:32, 22.50it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:56<04:33,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3219/8000 [02:50<04:18, 18.52it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:58<04:35,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3222/8000 [02:50<04:05, 19.45it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:59<04:27,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3225/8000 [02:50<05:02, 15.76it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [01:01<04:33,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3228/8000 [02:50<04:25, 17.96it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:03<04:27,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3233/8000 [02:51<04:12, 18.87it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:04<04:25,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3239/8000 [02:51<03:58, 19.99it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:06<04:23,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3242/8000 [02:51<04:43, 16.76it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:07<04:19,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3244/8000 [02:51<04:51, 16.32it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:09<04:28,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3247/8000 [02:51<04:19, 18.34it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:11<04:37,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3251/8000 [02:52<04:37, 17.10it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:13<04:29,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3253/8000 [02:52<04:41, 16.87it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:14<04:25,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3255/8000 [02:52<04:49, 16.41it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:16<04:22,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3259/8000 [02:52<04:52, 16.23it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:18<04:15,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3262/8000 [02:53<06:23, 12.34it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:19<04:11,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3266/8000 [02:53<05:11, 15.21it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:21<04:06,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3269/8000 [02:53<04:39, 16.96it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:22<04:02,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3273/8000 [02:53<05:51, 13.44it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:24<04:00,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3275/8000 [02:53<05:50, 13.47it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:25<03:58,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3282/8000 [02:54<03:43, 21.11it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:27<03:52,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3285/8000 [02:54<03:45, 20.88it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:28<03:48,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3289/8000 [02:54<03:23, 23.19it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:30<03:45,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3292/8000 [02:54<05:16, 14.86it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:32<04:00,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3295/8000 [02:54<04:42, 16.67it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:33<03:57,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3298/8000 [02:54<04:31, 17.29it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:35<03:54,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3301/8000 [02:55<04:15, 18.38it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:37<03:47,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3304/8000 [02:55<04:02, 19.38it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [01:38<04:00,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3309/8000 [02:55<03:05, 25.23it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [01:40<03:55,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3312/8000 [02:55<03:12, 24.29it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:42<03:46,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3317/8000 [02:55<03:27, 22.53it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:43<03:39,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3321/8000 [02:55<03:08, 24.82it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:45<03:36,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3326/8000 [02:56<02:52, 27.16it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [01:46<03:45,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3330/8000 [02:56<02:57, 26.36it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [01:48<03:41,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3335/8000 [02:56<03:19, 23.43it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [01:49<03:35,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3341/8000 [02:56<03:19, 23.38it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [01:51<03:40,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3345/8000 [02:56<03:04, 25.27it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [01:53<03:40,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3348/8000 [02:56<03:05, 25.10it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [01:54<03:34,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3351/8000 [02:57<03:52, 20.00it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [01:56<03:34,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3354/8000 [02:57<03:48, 20.31it/s]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [01:58<03:34,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3357/8000 [02:57<04:33, 16.99it/s]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [01:59<03:27,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3359/8000 [02:57<04:36, 16.78it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [02:01<03:47,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3361/8000 [02:57<04:39, 16.61it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▌                    | 72/200 [02:03<03:34,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3363/8000 [02:57<04:43, 16.38it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▋                    | 73/200 [02:04<03:23,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3367/8000 [02:58<03:54, 19.76it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [02:06<03:25,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3372/8000 [02:58<03:58, 19.38it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [02:08<03:35,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3374/8000 [02:58<04:11, 18.38it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▏                   | 76/200 [02:09<03:24,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3377/8000 [02:58<03:52, 19.85it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▎                   | 77/200 [02:11<03:16,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3380/8000 [02:58<04:56, 15.60it/s]\u001b[A\u001b[A\n",
+      "training:  39%|████████████▍                   | 78/200 [02:12<03:11,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3383/8000 [02:59<04:28, 17.20it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▋                   | 79/200 [02:14<03:15,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3386/8000 [02:59<05:19, 14.44it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▊                   | 80/200 [02:17<04:06,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3388/8000 [02:59<05:15, 14.61it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▉                   | 81/200 [02:19<03:50,  1.93s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3390/8000 [02:59<05:18, 14.49it/s]\u001b[A\u001b[A\n",
+      "training:  41%|█████████████                   | 82/200 [02:20<03:30,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3392/8000 [02:59<05:19, 14.43it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▎                  | 83/200 [02:22<03:19,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3394/8000 [02:59<05:13, 14.69it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▍                  | 84/200 [02:23<03:10,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3396/8000 [03:00<05:16, 14.52it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▌                  | 85/200 [02:25<03:02,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3398/8000 [03:00<05:16, 14.54it/s]\u001b[A\u001b[A\n",
+      "training:  43%|█████████████▊                  | 86/200 [02:26<02:56,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3401/8000 [03:00<04:43, 16.20it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▉                  | 87/200 [02:28<02:50,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3405/8000 [03:00<03:43, 20.53it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████                  | 88/200 [02:29<02:46,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3412/8000 [03:00<03:20, 22.93it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████▏                 | 89/200 [02:31<02:45,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3415/8000 [03:00<03:17, 23.20it/s]\u001b[A\u001b[A\n",
+      "training:  45%|██████████████▍                 | 90/200 [02:32<02:39,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3419/8000 [03:00<03:01, 25.21it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▌                 | 91/200 [02:33<02:36,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3422/8000 [03:01<03:04, 24.76it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▋                 | 92/200 [02:35<02:35,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3426/8000 [03:01<02:50, 26.82it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████▉                 | 93/200 [02:36<02:34,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3432/8000 [03:01<02:25, 31.45it/s]\u001b[A\u001b[A\n",
+      "training:  47%|███████████████                 | 94/200 [02:38<02:32,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3436/8000 [03:01<02:26, 31.10it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▏                | 95/200 [02:39<02:34,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3441/8000 [03:01<02:18, 33.00it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▎                | 96/200 [02:41<02:29,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3447/8000 [03:01<02:41, 28.12it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████▌                | 97/200 [02:42<02:27,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3451/8000 [03:02<03:12, 23.66it/s]\u001b[A\u001b[A\n",
+      "training:  49%|███████████████▋                | 98/200 [02:43<02:27,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3455/8000 [03:02<03:46, 20.10it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▊                | 99/200 [02:45<02:25,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3458/8000 [03:02<03:38, 20.77it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▌               | 100/200 [02:46<02:22,  1.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3463/8000 [03:02<04:23, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████▋               | 101/200 [02:48<02:22,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3467/8000 [03:03<05:13, 14.48it/s]\u001b[A\u001b[A\n",
+      "training:  51%|███████████████▊               | 102/200 [02:49<02:19,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3470/8000 [03:03<04:47, 15.75it/s]\u001b[A\u001b[A\n",
+      "training:  52%|███████████████▉               | 103/200 [02:51<02:18,  1.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3472/8000 [03:03<04:46, 15.83it/s]\u001b[A\u001b[A\n",
+      "training:  52%|████████████████               | 104/200 [02:52<02:20,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3475/8000 [03:03<04:29, 16.81it/s]\u001b[A\u001b[A\n",
+      "training:  52%|████████████████▎              | 105/200 [02:54<02:19,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3481/8000 [03:04<04:07, 18.24it/s]\u001b[A\u001b[A\n",
+      "training:  53%|████████████████▍              | 106/200 [02:55<02:18,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3484/8000 [03:04<04:45, 15.81it/s]\u001b[A\u001b[A\n",
+      "training:  54%|████████████████▌              | 107/200 [02:57<02:23,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3488/8000 [03:04<04:06, 18.29it/s]\u001b[A\u001b[A\n",
+      "training:  54%|████████████████▋              | 108/200 [02:58<02:21,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3492/8000 [03:04<04:31, 16.62it/s]\u001b[A\u001b[A\n",
+      "training:  55%|████████████████▉              | 109/200 [03:00<02:16,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3497/8000 [03:04<03:36, 20.81it/s]\u001b[A\u001b[A\n",
+      "training:  55%|█████████████████              | 110/200 [03:01<02:14,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3501/8000 [03:04<03:20, 22.49it/s]\u001b[A\u001b[A\n",
+      "training:  56%|█████████████████▏             | 111/200 [03:03<02:10,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3505/8000 [03:05<03:09, 23.76it/s]\u001b[A\u001b[A\n",
+      "training:  56%|█████████████████▎             | 112/200 [03:04<02:09,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3508/8000 [03:05<03:28, 21.52it/s]\u001b[A\u001b[A\n",
+      "training:  56%|█████████████████▌             | 113/200 [03:06<02:07,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3512/8000 [03:05<04:11, 17.83it/s]\u001b[A\u001b[A\n",
+      "training:  57%|█████████████████▋             | 114/200 [03:08<02:22,  1.66s/it]\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3516/8000 [03:05<03:35, 20.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3520/8000 [03:06<04:42, 15.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3523/8000 [03:06<04:20, 17.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 14.37it/s]\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3529/8000 [03:06<03:10, 23.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3532/8000 [03:06<03:56, 18.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3535/8000 [03:06<03:42, 20.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3538/8000 [03:07<04:24, 16.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 33it [00:01, 28.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3541/8000 [03:07<04:06, 18.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 66it [00:01, 61.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3546/8000 [03:07<04:37, 16.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 101it [00:01, 100.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3549/8000 [03:07<04:18, 17.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 135it [00:01, 138.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3551/8000 [03:07<04:23, 16.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 172it [00:02, 181.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3557/8000 [03:08<03:49, 19.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 205it [00:02, 201.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3560/8000 [03:08<03:36, 20.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 237it [00:02, 227.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3563/8000 [03:08<04:23, 16.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 272it [00:02, 256.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3565/8000 [03:08<04:27, 16.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 305it [00:02, 269.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3570/8000 [03:08<04:04, 18.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 337it [00:02, 278.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3577/8000 [03:08<02:49, 26.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 369it [00:02, 282.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3581/8000 [03:09<03:15, 22.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 406it [00:02, 305.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3586/8000 [03:09<03:13, 22.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 441it [00:02, 315.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3589/8000 [03:09<04:00, 18.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 476it [00:02, 324.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3594/8000 [03:09<03:48, 19.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 510it [00:03, 323.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3597/8000 [03:10<03:34, 20.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 545it [00:03, 330.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3601/8000 [03:10<04:05, 17.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 580it [00:03, 334.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3603/8000 [03:10<04:08, 17.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 615it [00:03, 336.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3605/8000 [03:10<04:07, 17.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 649it [00:03, 293.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3610/8000 [03:10<04:02, 18.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 680it [00:03, 269.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3616/8000 [03:11<04:11, 17.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 709it [00:03, 273.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3623/8000 [03:11<03:29, 20.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 741it [00:03, 284.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3629/8000 [03:11<02:48, 25.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 771it [00:03, 275.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3633/8000 [03:11<03:14, 22.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 195.40it/s]\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3637/8000 [03:11<03:03, 23.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3642/8000 [03:12<03:51, 18.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3646/8000 [03:12<04:07, 17.62it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 133.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3653/8000 [03:12<03:30, 20.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|▎| 31/100 [00:00<00:00, 139.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3656/8000 [03:12<03:28, 20.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 131.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3660/8000 [03:13<03:16, 22.11it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|▌| 60/100 [00:00<00:00, 131.0\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3665/8000 [03:13<02:50, 25.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|▊| 76/100 [00:00<00:00, 140.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3670/8000 [03:13<02:32, 28.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 132.\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3677/8000 [03:13<02:34, 27.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3680/8000 [03:13<03:13, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3684/8000 [03:14<03:40, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                   | 55/8000 [00:00<00:26, 297.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3687/8000 [03:14<04:11, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                  | 107/8000 [00:00<00:27, 287.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3693/8000 [03:14<03:43, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                  | 156/8000 [00:00<00:24, 317.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3697/8000 [03:14<03:19, 21.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▍                  | 209/8000 [00:00<00:23, 326.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3704/8000 [03:15<03:03, 23.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 255/8000 [00:00<00:24, 311.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3708/8000 [03:15<03:57, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                  | 309/8000 [00:01<00:25, 304.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3711/8000 [03:15<04:23, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                  | 346/8000 [00:01<00:27, 280.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3714/8000 [03:15<04:05, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                  | 397/8000 [00:01<00:24, 310.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3719/8000 [03:15<03:22, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                  | 446/8000 [00:01<00:23, 321.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3722/8000 [03:16<03:19, 21.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 497/8000 [00:01<00:21, 344.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3727/8000 [03:16<02:54, 24.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 536/8000 [00:01<00:23, 318.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3731/8000 [03:16<03:22, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                 | 584/8000 [00:01<00:22, 322.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3734/8000 [03:16<04:41, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                 | 624/8000 [00:01<00:23, 315.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3736/8000 [03:16<04:44, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                 | 670/8000 [00:02<00:23, 313.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3739/8000 [03:17<04:15, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 724/8000 [00:02<00:22, 323.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3743/8000 [03:17<04:30, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                 | 780/8000 [00:02<00:20, 353.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3746/8000 [03:17<04:11, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                 | 825/8000 [00:02<00:21, 334.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3750/8000 [03:17<03:36, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 882/8000 [00:02<00:19, 365.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3753/8000 [03:18<05:13, 13.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏                | 938/8000 [00:02<00:19, 353.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3759/8000 [03:18<03:38, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                | 989/8000 [00:03<00:20, 348.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:18<03:15, 21.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1042/8000 [00:03<00:20, 334.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3767/8000 [03:18<03:43, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍               | 1089/8000 [00:03<00:21, 322.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3771/8000 [03:18<03:13, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1132/8000 [00:03<00:22, 304.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3775/8000 [03:19<03:40, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1179/8000 [00:03<00:21, 315.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3778/8000 [03:19<04:18, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1220/8000 [00:03<00:23, 283.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3781/8000 [03:19<04:03, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊               | 1260/8000 [00:03<00:23, 289.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3783/8000 [03:19<04:16, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉               | 1308/8000 [00:04<00:22, 301.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3785/8000 [03:19<04:14, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1355/8000 [00:04<00:22, 301.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3789/8000 [03:19<03:35, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1409/8000 [00:04<00:20, 321.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3795/8000 [03:20<04:02, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎              | 1458/8000 [00:04<00:20, 313.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3799/8000 [03:20<03:32, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1502/8000 [00:04<00:20, 314.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3805/8000 [03:20<03:19, 21.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1549/8000 [00:04<00:20, 309.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3808/8000 [03:20<03:13, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▌              | 1596/8000 [00:05<00:39, 160.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3811/8000 [03:20<03:11, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋              | 1648/8000 [00:05<00:33, 189.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3816/8000 [03:21<02:41, 25.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▊              | 1692/8000 [00:05<00:29, 212.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3819/8000 [03:21<02:46, 25.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▉              | 1740/8000 [00:05<00:26, 234.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3822/8000 [03:21<03:42, 18.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████              | 1796/8000 [00:06<00:23, 268.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3829/8000 [03:21<03:13, 21.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▏             | 1845/8000 [00:06<00:22, 276.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3833/8000 [03:22<04:03, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1892/8000 [00:06<00:21, 281.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3838/8000 [03:22<03:53, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1935/8000 [00:06<00:22, 272.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3841/8000 [03:22<03:40, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▍             | 1975/8000 [00:06<00:23, 260.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3844/8000 [03:22<03:24, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▌             | 2023/8000 [00:06<00:22, 271.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3849/8000 [03:22<03:33, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2084/8000 [00:07<00:19, 301.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3854/8000 [03:23<03:42, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▊             | 2144/8000 [00:07<00:17, 339.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3856/8000 [03:23<03:55, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▉             | 2195/8000 [00:07<00:16, 346.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3859/8000 [03:23<04:20, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2245/8000 [00:07<00:17, 331.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3867/8000 [03:23<02:43, 25.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▏            | 2297/8000 [00:07<00:16, 344.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3875/8000 [03:23<02:31, 27.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▎            | 2344/8000 [00:07<00:15, 355.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3880/8000 [03:24<02:58, 23.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2401/8000 [00:07<00:15, 360.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3884/8000 [03:24<03:23, 20.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▌            | 2455/8000 [00:08<00:14, 370.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3888/8000 [03:24<03:29, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▋            | 2504/8000 [00:08<00:14, 366.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3892/8000 [03:24<03:47, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▊            | 2558/8000 [00:08<00:15, 353.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3894/8000 [03:25<03:59, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▊            | 2603/8000 [00:08<00:16, 328.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3897/8000 [03:25<04:34, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2652/8000 [00:08<00:15, 338.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3903/8000 [03:25<03:46, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████            | 2696/8000 [00:08<00:16, 317.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3905/8000 [03:25<03:49, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▏           | 2748/8000 [00:09<00:16, 316.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3910/8000 [03:25<03:04, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2788/8000 [00:09<00:16, 308.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3915/8000 [03:26<02:39, 25.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▍           | 2843/8000 [00:09<00:15, 329.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3920/8000 [03:26<02:56, 23.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▍           | 2884/8000 [00:09<00:16, 304.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3925/8000 [03:26<02:36, 26.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▌           | 2938/8000 [00:09<00:16, 313.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3929/8000 [03:26<03:01, 22.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▋           | 2985/8000 [00:09<00:16, 312.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3934/8000 [03:26<03:13, 20.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▊           | 3033/8000 [00:09<00:15, 325.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3939/8000 [03:27<03:52, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▉           | 3067/8000 [00:10<00:17, 281.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3942/8000 [03:27<04:13, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▉           | 3103/8000 [00:10<00:17, 274.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3944/8000 [03:27<04:15, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3148/8000 [00:10<00:17, 272.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3946/8000 [03:27<04:12, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3194/8000 [00:10<00:16, 293.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3948/8000 [03:27<04:22, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▎          | 3241/8000 [00:10<00:15, 307.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3952/8000 [03:28<03:31, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▍          | 3284/8000 [00:10<00:15, 309.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3957/8000 [03:28<02:42, 24.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▍          | 3329/8000 [00:11<00:16, 281.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3963/8000 [03:28<02:51, 23.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▌          | 3378/8000 [00:11<00:16, 284.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3966/8000 [03:28<02:52, 23.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▋          | 3427/8000 [00:11<00:15, 288.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3970/8000 [03:28<03:21, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▊          | 3482/8000 [00:11<00:14, 311.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3976/8000 [03:29<03:05, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▉          | 3543/8000 [00:11<00:12, 356.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3980/8000 [03:29<03:20, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████          | 3586/8000 [00:11<00:12, 345.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3986/8000 [03:29<03:15, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▏         | 3636/8000 [00:11<00:13, 313.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3996/8000 [03:30<02:55, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▎         | 3686/8000 [00:12<00:14, 307.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4004/8000 [03:30<02:37, 25.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3727/8000 [00:12<00:14, 304.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4008/8000 [03:30<02:28, 26.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3773/8000 [00:12<00:14, 291.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4012/8000 [03:30<03:20, 19.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▌         | 3818/8000 [00:12<00:14, 297.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4017/8000 [03:31<03:21, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▋         | 3867/8000 [00:12<00:13, 299.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4020/8000 [03:31<03:15, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▊         | 3915/8000 [00:12<00:13, 304.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4023/8000 [03:31<03:13, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▉         | 3964/8000 [00:13<00:13, 304.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4026/8000 [03:31<04:31, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████         | 4014/8000 [00:13<00:13, 300.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4031/8000 [03:31<03:25, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4060/8000 [00:13<00:12, 309.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4035/8000 [03:32<03:47, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4108/8000 [00:13<00:12, 317.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4040/8000 [03:32<03:05, 21.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▍        | 4168/8000 [00:13<00:10, 356.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4045/8000 [03:32<03:09, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▍        | 4214/8000 [00:13<00:11, 343.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4048/8000 [03:32<03:17, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4266/8000 [00:13<00:11, 325.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4053/8000 [03:33<03:45, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4318/8000 [00:14<00:11, 326.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4062/8000 [03:33<02:24, 27.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▊        | 4367/8000 [00:14<00:11, 306.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4066/8000 [03:33<02:45, 23.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4421/8000 [00:14<00:11, 318.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4070/8000 [03:33<03:52, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████        | 4481/8000 [00:14<00:10, 332.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4078/8000 [03:33<02:43, 23.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▏       | 4522/8000 [00:14<00:11, 302.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4083/8000 [03:34<03:19, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▎       | 4568/8000 [00:14<00:11, 295.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4087/8000 [03:34<03:05, 21.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4621/8000 [00:15<00:10, 315.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4091/8000 [03:34<03:23, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▌       | 4672/8000 [00:15<00:10, 302.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4094/8000 [03:34<03:13, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▌       | 4715/8000 [00:15<00:10, 309.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4098/8000 [03:35<03:31, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▋       | 4763/8000 [00:15<00:10, 301.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4103/8000 [03:35<03:38, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▊       | 4811/8000 [00:15<00:10, 305.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4107/8000 [03:35<03:49, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4855/8000 [00:15<00:10, 291.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4112/8000 [03:35<03:43, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████       | 4910/8000 [00:16<00:09, 315.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4116/8000 [03:36<03:52, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4956/8000 [00:16<00:09, 319.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▊        | 4119/8000 [03:36<03:39, 17.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5004/8000 [00:16<00:09, 315.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4121/8000 [03:36<03:49, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5052/8000 [00:16<00:09, 310.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4126/8000 [03:36<02:54, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▍      | 5105/8000 [00:16<00:09, 314.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4129/8000 [03:36<02:46, 23.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▌      | 5150/8000 [00:16<00:09, 302.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4133/8000 [03:36<03:13, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5200/8000 [00:16<00:08, 317.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4136/8000 [03:37<03:46, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▊      | 5245/8000 [00:17<00:09, 299.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4139/8000 [03:37<04:07, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▉      | 5299/8000 [00:17<00:08, 318.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4144/8000 [03:37<03:10, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5362/8000 [00:17<00:07, 332.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4147/8000 [03:37<03:10, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▏     | 5409/8000 [00:17<00:07, 337.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4151/8000 [03:38<03:27, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▎     | 5454/8000 [00:17<00:08, 311.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4156/8000 [03:38<03:29, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▎     | 5499/8000 [00:17<00:07, 322.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4159/8000 [03:38<03:22, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▌     | 5557/8000 [00:18<00:07, 346.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4163/8000 [03:38<03:38, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▌     | 5608/8000 [00:18<00:07, 339.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4167/8000 [03:38<03:47, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▋     | 5663/8000 [00:18<00:06, 343.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4170/8000 [03:39<03:32, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▊     | 5718/8000 [00:18<00:06, 348.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4174/8000 [03:39<03:03, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▉     | 5772/8000 [00:18<00:06, 343.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4177/8000 [03:39<03:38, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████     | 5823/8000 [00:18<00:06, 314.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4179/8000 [03:39<03:39, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▏    | 5864/8000 [00:18<00:06, 315.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4183/8000 [03:39<04:30, 14.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████▎    | 5916/8000 [00:19<00:06, 313.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4185/8000 [03:40<04:37, 13.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▍    | 5961/8000 [00:19<00:06, 305.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4189/8000 [03:40<03:41, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▌    | 6015/8000 [00:19<00:06, 312.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4191/8000 [03:40<03:46, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████▋    | 6070/8000 [00:19<00:05, 328.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4193/8000 [03:40<03:50, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████▋    | 6104/8000 [00:19<00:06, 297.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4197/8000 [03:40<04:01, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▊    | 6152/8000 [00:19<00:06, 306.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4200/8000 [03:40<03:42, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▉    | 6202/8000 [00:20<00:05, 312.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4203/8000 [03:41<03:29, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6248/8000 [00:20<00:05, 307.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4207/8000 [03:41<02:54, 21.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6291/8000 [00:20<00:05, 298.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4210/8000 [03:41<03:34, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▎   | 6337/8000 [00:20<00:05, 280.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4213/8000 [03:41<03:14, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▍   | 6402/8000 [00:20<00:05, 317.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4217/8000 [03:41<03:24, 18.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6447/8000 [00:20<00:05, 296.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4222/8000 [03:42<03:19, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6489/8000 [00:21<00:05, 284.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4225/8000 [03:42<03:09, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▋   | 6531/8000 [00:21<00:05, 257.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4232/8000 [03:42<02:14, 28.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▊   | 6586/8000 [00:21<00:05, 274.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4237/8000 [03:42<02:35, 24.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▉   | 6649/8000 [00:21<00:04, 316.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4241/8000 [03:42<02:58, 21.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████   | 6701/8000 [00:21<00:04, 320.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4245/8000 [03:42<02:44, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▏  | 6760/8000 [00:21<00:03, 336.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4251/8000 [03:43<02:40, 23.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▎  | 6813/8000 [00:22<00:03, 323.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4255/8000 [03:43<02:59, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▍  | 6860/8000 [00:22<00:04, 274.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4258/8000 [03:43<03:42, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▌  | 6903/8000 [00:22<00:04, 263.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4261/8000 [03:43<03:27, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▋  | 6961/8000 [00:22<00:03, 291.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4264/8000 [03:44<03:52, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7017/8000 [00:22<00:03, 311.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4267/8000 [03:44<03:33, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▉  | 7076/8000 [00:22<00:02, 347.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4269/8000 [03:44<03:37, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████  | 7121/8000 [00:23<00:02, 323.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4272/8000 [03:44<03:22, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7169/8000 [00:23<00:02, 280.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4277/8000 [03:44<03:20, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7222/8000 [00:23<00:02, 291.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4280/8000 [03:44<03:25, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▎ | 7272/8000 [00:23<00:02, 293.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4284/8000 [03:45<03:05, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▍ | 7320/8000 [00:23<00:02, 299.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4291/8000 [03:45<02:15, 27.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▌ | 7360/8000 [00:23<00:02, 292.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4295/8000 [03:45<03:21, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▋ | 7401/8000 [00:24<00:02, 277.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4302/8000 [03:45<02:59, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▊ | 7454/8000 [00:24<00:01, 284.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4308/8000 [03:46<03:02, 20.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7501/8000 [00:24<00:01, 274.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4312/8000 [03:46<02:52, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7548/8000 [00:24<00:01, 297.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4317/8000 [03:46<03:08, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████ | 7604/8000 [00:24<00:01, 308.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4321/8000 [03:46<02:58, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▏| 7657/8000 [00:24<00:01, 323.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4327/8000 [03:47<03:06, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7710/8000 [00:25<00:00, 299.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4331/8000 [03:47<02:49, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▍| 7759/8000 [00:25<00:00, 299.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4334/8000 [03:47<02:53, 21.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▌| 7800/8000 [00:25<00:00, 284.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4337/8000 [03:47<02:48, 21.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▋| 7855/8000 [00:25<00:00, 304.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4344/8000 [03:47<02:02, 29.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7905/8000 [00:25<00:00, 311.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4348/8000 [03:48<02:34, 23.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████▉| 7960/8000 [00:25<00:00, 322.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4351/8000 [03:48<02:36, 23.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8009it [00:26, 306.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  54%|█████████▎       | 4354/8000 [03:48<02:41, 22.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4359/8000 [03:48<02:45, 21.97it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4363/8000 [03:48<03:34, 16.98it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<25:50,  7.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4365/8000 [03:49<04:14, 14.26it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<13:38,  4.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4367/8000 [03:49<05:05, 11.90it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:11<10:03,  3.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4372/8000 [03:49<03:35, 16.86it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<08:11,  2.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4377/8000 [03:49<02:48, 21.49it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<07:00,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4380/8000 [03:49<02:39, 22.64it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:16<06:27,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4383/8000 [03:50<03:56, 15.27it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<06:03,  1.88s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4386/8000 [03:50<05:30, 10.93it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:19<05:45,  1.80s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4390/8000 [03:50<04:16, 14.10it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:20<05:29,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4395/8000 [03:51<03:58, 15.13it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:22<05:20,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4402/8000 [03:51<04:06, 14.62it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:24<05:16,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4406/8000 [03:51<03:32, 16.94it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:25<05:08,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4409/8000 [03:51<03:28, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:27<05:02,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4416/8000 [03:52<03:23, 17.65it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:29<05:13,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4419/8000 [03:52<03:47, 15.74it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:30<05:14,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4421/8000 [03:52<03:53, 15.36it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:32<05:07,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4424/8000 [03:52<03:38, 16.33it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:34<05:10,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4429/8000 [03:53<03:23, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:35<05:09,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4432/8000 [03:53<03:06, 19.09it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:37<05:02,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4436/8000 [03:53<02:45, 21.54it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:39<04:59,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4439/8000 [03:53<02:47, 21.29it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:40<04:54,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4442/8000 [03:53<02:45, 21.56it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:42<04:55,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4446/8000 [03:53<03:08, 18.86it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:44<04:52,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4450/8000 [03:54<03:52, 15.26it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:45<04:50,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4453/8000 [03:54<03:31, 16.74it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:47<04:47,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4459/8000 [03:54<03:02, 19.44it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:49<04:57,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [03:54<04:12, 14.03it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:50<04:57,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4464/8000 [03:55<04:07, 14.31it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:52<04:57,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4467/8000 [03:55<04:36, 12.80it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:54<04:48,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4472/8000 [03:55<04:01, 14.62it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:56<04:57,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4476/8000 [03:55<03:18, 17.78it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:58<05:05,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4482/8000 [03:55<02:29, 23.57it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:59<04:54,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4487/8000 [03:56<02:40, 21.89it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [01:01<04:42,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4491/8000 [03:56<02:25, 24.19it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [01:02<04:37,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4496/8000 [03:56<02:32, 22.91it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:04<04:40,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4502/8000 [03:56<02:27, 23.68it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:06<04:48,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4507/8000 [03:57<02:42, 21.44it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:08<04:38,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4510/8000 [03:57<03:04, 18.92it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:09<04:39,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4514/8000 [03:57<02:44, 21.19it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:11<04:38,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4518/8000 [03:57<03:06, 18.70it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:13<04:32,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4521/8000 [03:58<03:39, 15.84it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:15<04:36,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4523/8000 [03:58<03:34, 16.21it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:16<04:26,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4526/8000 [03:58<03:52, 14.96it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:18<04:32,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4528/8000 [03:58<03:50, 15.08it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:20<04:25,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4535/8000 [03:58<02:23, 24.16it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:22<04:31,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4539/8000 [03:58<02:47, 20.61it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:23<04:22,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4543/8000 [03:59<03:32, 16.23it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:25<04:13,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4547/8000 [03:59<03:29, 16.45it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:26<04:07,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4549/8000 [03:59<04:05, 14.07it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:28<04:03,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4553/8000 [03:59<03:20, 17.18it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:29<03:56,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4557/8000 [03:59<02:47, 20.54it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:31<03:57,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4560/8000 [04:00<03:21, 17.08it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:32<03:53,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4565/8000 [04:00<02:37, 21.85it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:34<04:05,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4568/8000 [04:00<02:32, 22.51it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:36<04:07,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4572/8000 [04:00<03:23, 16.85it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:38<04:11,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4576/8000 [04:01<03:37, 15.73it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:40<04:02,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4580/8000 [04:01<03:03, 18.61it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [01:41<03:56,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4583/8000 [04:01<03:34, 15.92it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [01:43<04:04,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4587/8000 [04:01<04:19, 13.15it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:46<04:41,  1.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4591/8000 [04:02<03:31, 16.10it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:48<04:51,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4594/8000 [04:02<03:13, 17.57it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:51<04:14,  1.83s/it]\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4597/8000 [04:02<02:58, 19.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4601/8000 [04:02<03:12, 17.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4605/8000 [04:02<02:43, 20.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4610/8000 [04:02<02:47, 20.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 14.31it/s]\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4616/8000 [04:03<02:11, 25.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4619/8000 [04:03<02:46, 20.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4624/8000 [04:03<02:16, 24.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4629/8000 [04:03<02:30, 22.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 39it [00:01, 33.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4633/8000 [04:03<02:45, 20.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 74it [00:01, 66.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4637/8000 [04:04<02:26, 22.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 101it [00:01, 93.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4641/8000 [04:04<02:50, 19.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 135it [00:01, 131.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4644/8000 [04:04<02:46, 20.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 167it [00:02, 165.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4647/8000 [04:04<03:58, 14.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 198.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4650/8000 [04:05<03:44, 14.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 235it [00:02, 231.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4652/8000 [04:05<03:46, 14.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 271it [00:02, 262.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4655/8000 [04:05<03:21, 16.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 304it [00:02, 279.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4657/8000 [04:05<03:41, 15.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 337it [00:02, 286.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4661/8000 [04:05<03:48, 14.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 370it [00:02, 289.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4665/8000 [04:05<03:18, 16.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 405it [00:02, 305.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4667/8000 [04:06<03:25, 16.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 438it [00:02, 252.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4670/8000 [04:06<03:01, 18.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 469it [00:03, 266.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4673/8000 [04:06<02:50, 19.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 505it [00:03, 288.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4676/8000 [04:06<02:42, 20.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 536it [00:03, 292.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4680/8000 [04:06<02:27, 22.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 567it [00:03, 277.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4684/8000 [04:06<02:18, 23.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 605it [00:03, 305.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4689/8000 [04:07<02:27, 22.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 643it [00:03, 324.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4693/8000 [04:07<02:51, 19.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 681it [00:03, 336.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4698/8000 [04:07<02:26, 22.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 716it [00:03, 326.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4701/8000 [04:07<02:48, 19.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 750it [00:03, 317.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4705/8000 [04:07<03:04, 17.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 194.81it/s]\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4707/8000 [04:08<03:07, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4709/8000 [04:08<03:12, 17.06it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4713/8000 [04:08<02:39, 20.62it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 109.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4717/8000 [04:08<02:53, 18.96it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:00, 119.5\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4720/8000 [04:08<02:42, 20.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 146.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4723/8000 [04:08<02:33, 21.35it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 126.3\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4728/8000 [04:09<02:39, 20.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 118.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4731/8000 [04:09<02:32, 21.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 125.\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4736/8000 [04:09<02:05, 25.96it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|█████████████▌             | 2/4 [17:10<17:03, 511.59s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.5588870058374145\n",
+      "2999: -6.1598376\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  59%|██████████       | 4739/8000 [04:09<02:43, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4746/8000 [04:09<02:51, 19.02it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4751/8000 [04:10<02:22, 22.82it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:07<25:25,  7.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4754/8000 [04:10<02:21, 22.88it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<13:25,  4.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4758/8000 [04:10<02:08, 25.31it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:10<09:48,  2.99s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4763/8000 [04:10<02:21, 22.82it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:12<07:53,  2.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4767/8000 [04:10<02:09, 24.88it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<06:56,  2.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4774/8000 [04:10<01:40, 32.12it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:15<06:16,  1.94s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4780/8000 [04:11<02:17, 23.50it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<05:50,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4785/8000 [04:11<02:21, 22.66it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:18<05:27,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4792/8000 [04:11<01:51, 28.88it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:20<05:07,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4798/8000 [04:11<01:55, 27.73it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:21<04:59,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4803/8000 [04:12<02:15, 23.57it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:23<05:01,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4807/8000 [04:12<02:53, 18.36it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:24<04:53,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4811/8000 [04:12<02:59, 17.75it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:26<04:46,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4814/8000 [04:13<03:18, 16.03it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:27<04:40,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4816/8000 [04:13<03:17, 16.15it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:29<04:45,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4822/8000 [04:13<02:24, 21.98it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:30<04:35,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4827/8000 [04:13<02:04, 25.58it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:32<04:29,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4831/8000 [04:13<02:04, 25.55it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:33<04:29,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4836/8000 [04:13<02:18, 22.81it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:34<04:25,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4841/8000 [04:13<01:59, 26.44it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:36<04:24,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4847/8000 [04:14<01:39, 31.58it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:38<04:35,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4851/8000 [04:14<02:04, 25.27it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:39<04:32,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4855/8000 [04:14<01:57, 26.81it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:41<04:28,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4859/8000 [04:14<01:52, 28.01it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:42<04:24,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4864/8000 [04:14<02:06, 24.82it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:44<04:28,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4869/8000 [04:15<02:20, 22.23it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:45<04:24,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4872/8000 [04:15<02:19, 22.36it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:47<04:32,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4875/8000 [04:15<02:17, 22.74it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:48<04:29,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4878/8000 [04:15<02:49, 18.44it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:50<04:27,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4883/8000 [04:15<02:20, 22.19it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:52<04:27,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4887/8000 [04:16<02:37, 19.77it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:53<04:26,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4890/8000 [04:16<02:32, 20.35it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:55<04:29,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4894/8000 [04:16<02:15, 22.94it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:56<04:25,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4897/8000 [04:16<02:12, 23.50it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:58<04:28,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4900/8000 [04:16<02:09, 23.95it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:00<04:21,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4903/8000 [04:16<02:44, 18.78it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:01<04:13,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4906/8000 [04:17<03:20, 15.41it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:03<04:13,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4909/8000 [04:17<02:58, 17.36it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:04<04:11,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4912/8000 [04:17<02:52, 17.94it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:06<04:05,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4915/8000 [04:17<03:46, 13.63it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:07<04:03,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4918/8000 [04:17<03:21, 15.32it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:09<03:59,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4921/8000 [04:18<03:38, 14.07it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:10<04:00,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4923/8000 [04:18<04:12, 12.18it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:12<03:59,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4925/8000 [04:18<03:54, 13.09it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:13<03:54,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4928/8000 [04:18<03:21, 15.23it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:15<04:00,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4934/8000 [04:18<03:26, 14.83it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:16<03:56,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4939/8000 [04:19<02:40, 19.02it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:18<04:03,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4943/8000 [04:19<03:30, 14.54it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:20<03:57,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4948/8000 [04:19<02:48, 18.12it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:21<03:51,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4951/8000 [04:19<03:09, 16.07it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:24<04:13,  1.69s/it]\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4954/8000 [04:20<02:54, 17.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4961/8000 [04:20<02:27, 20.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4966/8000 [04:20<02:35, 19.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4970/8000 [04:20<02:53, 17.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 14.24it/s]\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4974/8000 [04:21<02:34, 19.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4979/8000 [04:21<02:13, 22.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4986/8000 [04:21<02:06, 23.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4989/8000 [04:21<02:29, 20.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 31it [00:01, 25.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4992/8000 [04:21<02:54, 17.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 64it [00:01, 57.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4996/8000 [04:22<02:34, 19.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 99it [00:01, 95.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5001/8000 [04:22<02:32, 19.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 135it [00:01, 136.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5005/8000 [04:22<02:43, 18.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 167it [00:02, 169.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5007/8000 [04:22<02:51, 17.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 199it [00:02, 199.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5010/8000 [04:22<02:41, 18.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 231it [00:02, 217.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5013/8000 [04:23<02:35, 19.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 262it [00:02, 239.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5015/8000 [04:23<02:43, 18.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 293it [00:02, 256.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5022/8000 [04:23<01:46, 28.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 325it [00:02, 272.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5030/8000 [04:23<01:41, 29.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 357it [00:02, 282.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5037/8000 [04:24<02:25, 20.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 390it [00:02, 294.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5040/8000 [04:24<02:21, 20.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 422it [00:02, 290.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5043/8000 [04:24<02:17, 21.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 453it [00:03, 281.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5046/8000 [04:24<02:46, 17.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 490it [00:03, 304.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5051/8000 [04:24<02:37, 18.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 522it [00:03, 291.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5054/8000 [04:24<02:34, 19.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 554it [00:03, 297.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5060/8000 [04:25<02:26, 20.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 590it [00:03, 314.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5064/8000 [04:25<02:13, 22.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 622it [00:03, 297.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5070/8000 [04:25<02:03, 23.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 660it [00:03, 319.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5077/8000 [04:25<01:38, 29.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 698it [00:03, 334.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5081/8000 [04:26<02:02, 23.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 732it [00:03, 327.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5084/8000 [04:26<02:06, 23.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 766it [00:04, 325.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5088/8000 [04:26<02:25, 20.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 193.70it/s]\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5093/8000 [04:26<02:23, 20.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5096/8000 [04:26<02:16, 21.26it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5103/8000 [04:27<02:28, 19.46it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 147.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5106/8000 [04:27<02:43, 17.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 123.9\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5108/8000 [04:27<02:51, 16.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:00<00:00, 144.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5110/8000 [04:27<02:55, 16.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00<00:00, 130.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5115/8000 [04:27<02:46, 17.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:00<00:00, 133.3\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5118/8000 [04:28<02:43, 17.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 129.\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5121/8000 [04:28<02:38, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5128/8000 [04:28<01:49, 26.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5132/8000 [04:28<02:06, 22.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                    | 16/8000 [00:00<01:16, 104.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5140/8000 [04:28<01:54, 24.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                    | 37/8000 [00:00<01:05, 121.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5144/8000 [04:29<01:47, 26.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 51/8000 [00:00<01:20, 99.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5147/8000 [04:29<01:49, 25.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                   | 69/8000 [00:00<01:18, 101.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5150/8000 [04:29<01:53, 25.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 89/8000 [00:00<01:19, 99.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5154/8000 [04:29<01:47, 26.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                  | 113/8000 [00:01<01:11, 110.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5158/8000 [04:29<02:42, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                  | 130/8000 [00:01<01:10, 111.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5162/8000 [04:30<02:48, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 143/8000 [00:01<01:23, 94.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5164/8000 [04:30<02:52, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                  | 164/8000 [00:01<01:16, 102.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5166/8000 [04:30<02:56, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                  | 179/8000 [00:01<01:17, 101.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5169/8000 [04:30<02:44, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 193/8000 [00:01<01:24, 92.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5171/8000 [04:30<02:52, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 210/8000 [00:02<01:18, 99.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5174/8000 [04:30<02:39, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 229/8000 [00:02<01:16, 101.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5176/8000 [04:30<02:48, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 248/8000 [00:02<01:11, 107.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5179/8000 [04:31<02:40, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                  | 263/8000 [00:02<01:12, 106.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5185/8000 [04:31<01:53, 24.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                  | 285/8000 [00:02<01:08, 112.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5189/8000 [04:31<01:50, 25.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 310/8000 [00:03<01:20, 95.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5193/8000 [04:31<01:44, 26.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 325/8000 [00:03<01:25, 90.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5197/8000 [04:31<01:44, 26.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 343/8000 [00:03<01:19, 96.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5201/8000 [04:31<02:06, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▊                  | 363/8000 [00:03<01:12, 105.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5204/8000 [04:32<02:35, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                  | 383/8000 [00:03<01:05, 116.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5210/8000 [04:32<02:21, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                  | 401/8000 [00:03<01:05, 115.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5218/8000 [04:32<01:39, 27.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                  | 424/8000 [00:03<00:58, 130.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5224/8000 [04:32<01:28, 31.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                  | 443/8000 [00:04<00:57, 130.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5228/8000 [04:32<01:29, 30.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 479/8000 [00:04<01:00, 124.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5232/8000 [04:33<01:31, 30.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 497/8000 [00:04<01:03, 118.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5241/8000 [04:33<01:42, 26.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                 | 519/8000 [00:04<01:00, 123.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5247/8000 [04:33<01:45, 26.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 541/8000 [00:04<00:57, 128.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5251/8000 [04:33<01:42, 26.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 559/8000 [00:05<01:00, 122.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5254/8000 [04:34<02:09, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                 | 572/8000 [00:05<01:10, 105.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5257/8000 [04:34<02:07, 21.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                 | 592/8000 [00:05<01:05, 112.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5261/8000 [04:34<02:02, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                 | 611/8000 [00:05<01:04, 113.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5266/8000 [04:34<01:42, 26.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▍                 | 631/8000 [00:05<01:04, 114.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5272/8000 [04:34<01:23, 32.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                 | 656/8000 [00:05<00:55, 131.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5276/8000 [04:34<02:04, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                 | 674/8000 [00:06<00:56, 130.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5280/8000 [04:35<02:21, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 689/8000 [00:06<00:57, 127.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5283/8000 [04:35<02:23, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 718/8000 [00:06<01:03, 114.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5290/8000 [04:35<02:04, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                 | 732/8000 [00:06<01:09, 104.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5297/8000 [04:35<01:51, 24.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                 | 757/8000 [00:06<00:59, 120.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5300/8000 [04:35<01:53, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▊                 | 783/8000 [00:06<00:56, 128.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5303/8000 [04:36<02:37, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                 | 802/8000 [00:07<00:56, 128.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5307/8000 [04:36<02:15, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                 | 820/8000 [00:07<00:54, 130.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5316/8000 [04:36<01:49, 24.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                 | 836/8000 [00:07<01:01, 116.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5321/8000 [04:36<01:54, 23.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 854/8000 [00:07<01:03, 112.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5325/8000 [04:37<01:52, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 872/8000 [00:07<01:01, 115.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5328/8000 [04:37<02:12, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                 | 892/8000 [00:07<01:00, 116.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5332/8000 [04:37<01:56, 22.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                | 913/8000 [00:08<00:57, 123.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5336/8000 [04:37<02:35, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏                | 928/8000 [00:08<01:01, 115.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5340/8000 [04:38<02:37, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▏                | 947/8000 [00:08<01:02, 111.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5344/8000 [04:38<02:42, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                | 963/8000 [00:08<01:08, 103.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5349/8000 [04:38<02:11, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                | 985/8000 [00:08<01:01, 113.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5355/8000 [04:38<02:01, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1009/8000 [00:08<00:56, 124.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5360/8000 [04:38<01:44, 25.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1031/8000 [00:09<00:54, 127.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5363/8000 [04:39<02:10, 20.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▎               | 1052/8000 [00:09<00:52, 133.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5367/8000 [04:39<01:58, 22.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍               | 1066/8000 [00:09<00:58, 118.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5371/8000 [04:39<01:47, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍               | 1081/8000 [00:09<01:07, 102.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5375/8000 [04:39<01:46, 24.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▍               | 1102/8000 [00:09<01:04, 107.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5378/8000 [04:39<02:11, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1119/8000 [00:09<01:03, 108.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5381/8000 [04:40<02:45, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1137/8000 [00:10<01:04, 106.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5384/8000 [04:40<02:29, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌               | 1157/8000 [00:10<01:00, 113.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5387/8000 [04:40<02:18, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1169/8000 [00:10<01:04, 106.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5391/8000 [04:40<02:21, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1188/8000 [00:10<01:03, 107.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5394/8000 [04:40<02:19, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▋               | 1217/8000 [00:10<00:52, 128.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5397/8000 [04:41<02:44, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊               | 1230/8000 [00:10<01:02, 109.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5399/8000 [04:41<03:11, 13.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊               | 1251/8000 [00:11<00:56, 119.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5405/8000 [04:41<02:06, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▊               | 1268/8000 [00:11<00:57, 117.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5408/8000 [04:41<02:00, 21.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉               | 1292/8000 [00:11<00:50, 132.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5411/8000 [04:41<02:23, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉               | 1310/8000 [00:11<00:52, 126.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5414/8000 [04:41<02:42, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1342/8000 [00:11<00:56, 117.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5418/8000 [04:42<02:12, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1356/8000 [00:11<00:59, 111.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5423/8000 [04:42<02:08, 20.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███               | 1379/8000 [00:12<00:53, 123.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5427/8000 [04:43<03:57, 10.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏              | 1394/8000 [00:12<00:54, 120.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5430/8000 [04:43<03:26, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1414/8000 [00:12<00:55, 119.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5433/8000 [04:43<03:40, 11.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▏              | 1435/8000 [00:12<00:52, 124.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5435/8000 [04:43<03:36, 11.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎              | 1452/8000 [00:12<00:54, 119.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5438/8000 [04:43<03:13, 13.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎              | 1476/8000 [00:12<00:50, 128.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5441/8000 [04:43<02:48, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▎              | 1498/8000 [00:13<00:50, 129.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5443/8000 [04:44<02:50, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1516/8000 [00:13<00:54, 119.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5447/8000 [04:44<02:42, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▍              | 1541/8000 [00:13<00:50, 127.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5450/8000 [04:44<02:26, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌              | 1559/8000 [00:13<00:51, 124.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5452/8000 [04:44<02:31, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▌              | 1580/8000 [00:13<00:51, 124.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5455/8000 [04:44<02:21, 17.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▌              | 1604/8000 [00:13<00:48, 131.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5458/8000 [04:44<02:12, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋              | 1627/8000 [00:14<00:47, 133.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5461/8000 [04:44<02:02, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋              | 1642/8000 [00:14<00:54, 115.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5464/8000 [04:45<02:27, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▋              | 1655/8000 [00:14<01:00, 105.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5466/8000 [04:45<02:29, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▊              | 1673/8000 [00:14<00:58, 107.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5470/8000 [04:45<02:33, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▊              | 1689/8000 [00:14<00:57, 109.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5472/8000 [04:45<02:29, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▊              | 1706/8000 [00:14<01:00, 104.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5475/8000 [04:45<02:14, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▊              | 1720/8000 [00:15<01:02, 100.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5477/8000 [04:45<02:21, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1733/8000 [00:15<01:10, 89.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5479/8000 [04:46<02:21, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1750/8000 [00:15<01:08, 91.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5482/8000 [04:46<02:06, 19.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1768/8000 [00:15<01:05, 95.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5487/8000 [04:46<01:34, 26.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1787/8000 [00:15<01:03, 98.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5490/8000 [04:46<01:36, 25.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████              | 1809/8000 [00:15<00:58, 105.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5495/8000 [04:46<01:20, 31.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████              | 1828/8000 [00:16<00:56, 108.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5502/8000 [04:46<01:42, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▏             | 1846/8000 [00:16<00:54, 111.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5506/8000 [04:47<01:52, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▏             | 1866/8000 [00:16<00:54, 111.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5510/8000 [04:47<01:42, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1893/8000 [00:16<01:00, 101.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5516/8000 [04:47<01:37, 25.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1915/8000 [00:16<00:53, 114.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5520/8000 [04:47<01:49, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▎             | 1941/8000 [00:17<00:47, 128.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5523/8000 [04:47<01:47, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍             | 1955/8000 [00:17<00:50, 119.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5527/8000 [04:48<02:08, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▍             | 1983/8000 [00:17<00:56, 106.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5531/8000 [04:48<01:55, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▌             | 2010/8000 [00:17<00:48, 122.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5535/8000 [04:48<02:33, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▌             | 2031/8000 [00:17<00:47, 126.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5538/8000 [04:48<02:24, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▌             | 2053/8000 [00:17<00:45, 131.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5543/8000 [04:48<01:56, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2069/8000 [00:18<00:49, 120.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5546/8000 [04:49<02:00, 20.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▋             | 2092/8000 [00:18<00:46, 127.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5553/8000 [04:49<01:51, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊             | 2113/8000 [00:18<00:50, 117.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5561/8000 [04:49<01:57, 20.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▊             | 2132/8000 [00:18<00:47, 123.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5566/8000 [04:50<02:24, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▊             | 2154/8000 [00:18<00:47, 123.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5568/8000 [04:50<02:25, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|████▉             | 2182/8000 [00:18<00:40, 142.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5571/8000 [04:50<02:13, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|████▉             | 2205/8000 [00:19<00:40, 141.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5577/8000 [04:50<01:59, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2225/8000 [00:19<00:42, 137.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5582/8000 [04:50<01:57, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2253/8000 [00:19<00:51, 111.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5588/8000 [04:51<02:08, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████             | 2271/8000 [00:19<00:50, 114.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5591/8000 [04:51<02:39, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▏            | 2288/8000 [00:19<00:47, 119.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5593/8000 [04:51<02:37, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▏            | 2311/8000 [00:19<00:44, 127.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5595/8000 [04:51<02:36, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▎            | 2334/8000 [00:20<00:43, 131.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5597/8000 [04:52<02:39, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▎            | 2360/8000 [00:20<00:53, 106.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5602/8000 [04:52<01:56, 20.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▎            | 2378/8000 [00:20<00:53, 104.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5607/8000 [04:52<01:31, 26.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2400/8000 [00:20<00:49, 113.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5610/8000 [04:52<01:52, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▍            | 2421/8000 [00:20<00:46, 120.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5614/8000 [04:52<01:43, 23.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▍            | 2443/8000 [00:21<00:48, 113.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5617/8000 [04:52<01:41, 23.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▌            | 2470/8000 [00:21<00:42, 129.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5622/8000 [04:53<01:59, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▌            | 2484/8000 [00:21<00:46, 118.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5626/8000 [04:53<01:44, 22.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▋            | 2509/8000 [00:21<00:43, 127.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5629/8000 [04:53<02:04, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▋            | 2526/8000 [00:21<00:43, 126.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5633/8000 [04:53<02:08, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▋            | 2544/8000 [00:21<00:44, 123.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5636/8000 [04:53<02:01, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▊            | 2565/8000 [00:22<00:43, 123.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5639/8000 [04:53<01:56, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▊            | 2582/8000 [00:22<00:47, 114.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5642/8000 [04:54<01:50, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▊            | 2603/8000 [00:22<00:44, 120.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5649/8000 [04:54<01:16, 30.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2619/8000 [00:22<00:46, 115.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5656/8000 [04:54<01:33, 25.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2636/8000 [00:22<00:50, 107.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5659/8000 [04:54<01:31, 25.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▉            | 2665/8000 [00:22<00:41, 128.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5662/8000 [04:55<02:15, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████            | 2689/8000 [00:23<00:40, 132.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5669/8000 [04:55<01:50, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████            | 2703/8000 [00:23<00:44, 119.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5673/8000 [04:55<01:57, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▏           | 2724/8000 [00:23<00:42, 123.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5681/8000 [04:55<01:54, 20.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▏           | 2744/8000 [00:23<00:40, 131.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5686/8000 [04:56<01:54, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▏           | 2764/8000 [00:23<00:38, 135.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5690/8000 [04:56<02:13, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2785/8000 [00:23<00:37, 139.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5693/8000 [04:56<02:28, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2801/8000 [00:24<00:39, 131.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5696/8000 [04:56<02:22, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▎           | 2829/8000 [00:24<00:33, 153.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5701/8000 [04:57<02:17, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▍           | 2867/8000 [00:24<00:37, 136.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5705/8000 [04:57<02:03, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▌           | 2897/8000 [00:24<00:42, 120.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5708/8000 [04:57<02:38, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▌           | 2921/8000 [00:24<00:39, 130.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5712/8000 [04:57<02:26, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▌           | 2943/8000 [00:25<00:37, 135.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5714/8000 [04:58<03:55,  9.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▋           | 2961/8000 [00:25<00:40, 123.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5717/8000 [04:58<03:17, 11.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▋           | 2985/8000 [00:25<00:40, 124.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5720/8000 [04:58<02:48, 13.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▊           | 3007/8000 [00:25<00:40, 124.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5722/8000 [04:58<02:39, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▊           | 3029/8000 [00:25<00:36, 135.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5724/8000 [04:58<02:31, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|██████▉           | 3066/8000 [00:26<00:38, 126.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5728/8000 [04:59<02:24, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|██████▉           | 3095/8000 [00:26<00:46, 105.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5733/8000 [04:59<01:46, 21.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3121/8000 [00:26<00:42, 113.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5737/8000 [04:59<01:35, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████           | 3142/8000 [00:26<00:41, 115.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5740/8000 [04:59<01:32, 24.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3169/8000 [00:26<00:38, 126.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5743/8000 [04:59<01:31, 24.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3195/8000 [00:27<00:33, 141.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5747/8000 [05:00<02:36, 14.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▏          | 3212/8000 [00:27<00:36, 132.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5750/8000 [05:00<02:17, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▎          | 3234/8000 [00:27<00:36, 131.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5755/8000 [05:00<02:25, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▎          | 3248/8000 [00:27<00:40, 118.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5759/8000 [05:00<01:59, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▎          | 3275/8000 [00:27<00:34, 136.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5762/8000 [05:00<01:51, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▍          | 3308/8000 [00:28<00:40, 116.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5766/8000 [05:00<01:36, 23.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▍          | 3328/8000 [00:28<00:41, 112.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5770/8000 [05:01<01:50, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▌          | 3348/8000 [00:28<00:40, 116.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5773/8000 [05:01<02:31, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▌          | 3374/8000 [00:28<00:43, 106.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5775/8000 [05:01<02:30, 14.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▋          | 3391/8000 [00:28<00:44, 102.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5780/8000 [05:01<01:48, 20.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▋          | 3413/8000 [00:29<00:41, 111.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5785/8000 [05:01<01:29, 24.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3428/8000 [00:29<00:46, 98.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5789/8000 [05:02<02:21, 15.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▊          | 3446/8000 [00:29<00:43, 104.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5793/8000 [05:02<02:01, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████▊          | 3462/8000 [00:29<00:43, 104.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5796/8000 [05:02<01:53, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▊          | 3480/8000 [00:29<00:43, 104.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5799/8000 [05:02<01:46, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▉          | 3506/8000 [00:29<00:37, 121.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5804/8000 [05:03<01:42, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▉          | 3519/8000 [00:30<00:39, 113.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5811/8000 [05:03<01:32, 23.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▉          | 3535/8000 [00:30<00:40, 109.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5814/8000 [05:03<01:31, 23.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████          | 3556/8000 [00:30<00:38, 115.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5817/8000 [05:03<01:34, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████          | 3573/8000 [00:30<00:39, 111.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5821/8000 [05:03<01:24, 25.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████          | 3596/8000 [00:30<00:35, 122.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5825/8000 [05:04<01:52, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▏         | 3616/8000 [00:30<00:35, 123.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5830/8000 [05:04<01:30, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▏         | 3635/8000 [00:31<00:36, 120.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5834/8000 [05:04<02:02, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▏         | 3659/8000 [00:31<00:33, 128.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5838/8000 [05:05<03:33, 10.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▎         | 3677/8000 [00:31<00:34, 126.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5840/8000 [05:05<03:35, 10.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▎         | 3703/8000 [00:31<00:40, 106.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5842/8000 [05:05<04:25,  8.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▎         | 3721/8000 [00:31<00:38, 111.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5844/8000 [05:06<04:28,  8.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3737/8000 [00:31<00:39, 107.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5847/8000 [05:06<03:40,  9.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3760/8000 [00:32<00:40, 104.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5853/8000 [05:06<02:55, 12.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▍         | 3776/8000 [00:32<00:41, 102.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5857/8000 [05:07<03:05, 11.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▌         | 3796/8000 [00:32<00:38, 110.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5859/8000 [05:07<03:13, 11.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▌         | 3818/8000 [00:32<00:33, 123.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5861/8000 [05:07<03:15, 10.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▋         | 3835/8000 [00:32<00:33, 124.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5864/8000 [05:07<03:00, 11.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▋         | 3849/8000 [00:32<00:35, 118.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5867/8000 [05:07<02:54, 12.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████▋         | 3875/8000 [00:33<00:31, 130.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5873/8000 [05:08<02:07, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▊         | 3902/8000 [00:33<00:38, 105.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5876/8000 [05:08<02:23, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▊         | 3922/8000 [00:33<00:38, 105.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5878/8000 [05:08<02:28, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▊         | 3940/8000 [00:33<00:36, 111.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▍    | 5880/8000 [05:08<03:05, 11.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▉         | 3958/8000 [00:33<00:35, 114.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▍    | 5882/8000 [05:09<03:24, 10.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████▉         | 3980/8000 [00:34<00:32, 122.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5886/8000 [05:09<02:51, 12.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████         | 4002/8000 [00:34<00:31, 126.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5889/8000 [05:09<02:28, 14.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████         | 4019/8000 [00:34<00:32, 124.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5894/8000 [05:09<02:06, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████         | 4035/8000 [00:34<00:32, 123.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5898/8000 [05:10<02:54, 12.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4058/8000 [00:34<00:30, 131.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5900/8000 [05:10<02:44, 12.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4076/8000 [00:34<00:30, 127.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5903/8000 [05:10<02:20, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4096/8000 [00:34<00:30, 128.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5906/8000 [05:10<02:05, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▎        | 4112/8000 [00:35<00:32, 120.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5915/8000 [05:10<01:27, 23.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▎        | 4128/8000 [00:35<00:33, 116.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5921/8000 [05:11<01:41, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▎        | 4141/8000 [00:35<00:35, 109.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5924/8000 [05:11<01:54, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▎        | 4160/8000 [00:35<00:32, 118.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5926/8000 [05:11<01:57, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▍        | 4175/8000 [00:35<00:35, 107.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5929/8000 [05:11<01:51, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4188/8000 [00:35<00:40, 94.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5933/8000 [05:11<01:36, 21.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▍        | 4208/8000 [00:36<00:35, 108.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5937/8000 [05:11<01:23, 24.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▍        | 4220/8000 [00:36<00:37, 100.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5942/8000 [05:12<01:27, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4240/8000 [00:36<00:33, 113.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5946/8000 [05:12<01:38, 20.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4255/8000 [00:36<00:36, 102.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5950/8000 [05:12<01:48, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4273/8000 [00:36<00:33, 110.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5954/8000 [05:12<01:35, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4291/8000 [00:36<00:32, 115.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5957/8000 [05:12<01:32, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4312/8000 [00:36<00:30, 119.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5963/8000 [05:13<01:08, 29.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▋        | 4328/8000 [00:37<00:31, 116.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5969/8000 [05:13<01:12, 27.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|█████████▊        | 4350/8000 [00:37<00:28, 127.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5973/8000 [05:13<01:11, 28.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▊        | 4372/8000 [00:37<00:27, 132.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5979/8000 [05:13<01:18, 25.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4390/8000 [00:37<00:27, 129.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5983/8000 [05:13<01:27, 23.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4405/8000 [00:37<00:31, 114.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5986/8000 [05:14<01:24, 23.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4418/8000 [00:37<00:33, 106.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5990/8000 [05:14<01:52, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4435/8000 [00:37<00:33, 106.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5995/8000 [05:14<01:45, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████        | 4462/8000 [00:38<00:28, 126.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6000/8000 [05:14<01:41, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████        | 4487/8000 [00:38<00:25, 138.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6003/8000 [05:14<01:34, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▏       | 4507/8000 [00:38<00:25, 135.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6008/8000 [05:15<01:32, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▏       | 4528/8000 [00:38<00:24, 142.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6013/8000 [05:15<01:33, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▎       | 4557/8000 [00:38<00:21, 159.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6017/8000 [05:15<01:24, 23.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▎       | 4586/8000 [00:39<00:25, 132.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6023/8000 [05:15<01:18, 25.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▎       | 4604/8000 [00:39<00:26, 127.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6026/8000 [05:16<01:35, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4621/8000 [00:39<00:27, 122.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6030/8000 [05:16<01:44, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4639/8000 [00:39<00:28, 119.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6033/8000 [05:16<01:54, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▍       | 4655/8000 [00:39<00:27, 121.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6038/8000 [05:16<01:31, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▌       | 4671/8000 [00:39<00:27, 119.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6044/8000 [05:16<01:26, 22.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▌       | 4688/8000 [00:39<00:28, 115.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6047/8000 [05:17<01:56, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▌       | 4712/8000 [00:40<00:25, 130.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6050/8000 [05:17<01:45, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|██████████▋       | 4738/8000 [00:40<00:30, 108.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6056/8000 [05:17<01:34, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▋       | 4770/8000 [00:40<00:29, 107.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6061/8000 [05:17<01:47, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▊       | 4785/8000 [00:40<00:31, 101.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6067/8000 [05:18<01:36, 19.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▊       | 4806/8000 [00:41<00:29, 107.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6070/8000 [05:18<01:47, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|██████████▊       | 4828/8000 [00:41<00:27, 114.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6072/8000 [05:18<01:51, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4845/8000 [00:41<00:27, 114.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6077/8000 [05:18<01:27, 21.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4868/8000 [00:41<00:27, 114.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6080/8000 [05:18<01:25, 22.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|██████████▉       | 4888/8000 [00:41<00:26, 119.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6083/8000 [05:18<01:24, 22.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████       | 4910/8000 [00:41<00:24, 124.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6087/8000 [05:19<01:17, 24.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████       | 4928/8000 [00:42<00:25, 118.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6090/8000 [05:19<01:16, 25.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4952/8000 [00:42<00:23, 129.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6098/8000 [05:19<01:08, 27.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4975/8000 [00:42<00:21, 139.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6101/8000 [05:19<01:10, 27.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▏      | 4991/8000 [00:42<00:25, 119.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6104/8000 [05:19<01:12, 26.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5006/8000 [00:42<00:29, 102.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6107/8000 [05:19<01:14, 25.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5031/8000 [00:42<00:25, 118.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6110/8000 [05:19<01:21, 23.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▎      | 5050/8000 [00:43<00:25, 114.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6113/8000 [05:20<01:21, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▍      | 5064/8000 [00:43<00:27, 105.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|█████████████    | 6119/8000 [05:20<01:02, 30.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▍      | 5088/8000 [00:43<00:24, 117.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6124/8000 [05:20<00:57, 32.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▍      | 5101/8000 [00:43<00:26, 109.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6129/8000 [05:20<01:06, 28.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▌      | 5118/8000 [00:43<00:26, 107.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6133/8000 [05:20<01:16, 24.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▌      | 5135/8000 [00:43<00:28, 100.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6136/8000 [05:20<01:19, 23.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|███████████▌      | 5153/8000 [00:44<00:26, 108.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6140/8000 [05:21<01:28, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5172/8000 [00:44<00:24, 113.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6145/8000 [05:21<01:12, 25.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5190/8000 [00:44<00:24, 112.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6149/8000 [05:21<01:07, 27.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5206/8000 [00:44<00:26, 104.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6156/8000 [05:21<01:06, 27.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▋      | 5220/8000 [00:44<00:27, 101.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6162/8000 [05:22<01:22, 22.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|███████████▊      | 5237/8000 [00:44<00:26, 103.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6168/8000 [05:22<01:19, 23.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▊      | 5255/8000 [00:44<00:25, 107.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6173/8000 [05:22<01:08, 26.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▊      | 5269/8000 [00:45<00:25, 107.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6178/8000 [05:22<01:28, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▉      | 5282/8000 [00:45<00:26, 104.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6181/8000 [05:22<01:24, 21.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▉      | 5303/8000 [00:45<00:22, 117.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6184/8000 [05:23<01:35, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████▉      | 5324/8000 [00:45<00:21, 122.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6189/8000 [05:23<01:33, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5346/8000 [00:45<00:22, 116.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6192/8000 [05:23<01:46, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████      | 5369/8000 [00:45<00:20, 126.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6195/8000 [05:23<01:52, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▏     | 5392/8000 [00:46<00:20, 126.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6199/8000 [05:23<01:34, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▏     | 5412/8000 [00:46<00:20, 126.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6204/8000 [05:24<01:16, 23.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▏     | 5429/8000 [00:46<00:21, 118.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6210/8000 [05:24<01:14, 24.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▏     | 5444/8000 [00:46<00:22, 112.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6215/8000 [05:24<01:03, 28.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▎     | 5466/8000 [00:46<00:21, 117.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6220/8000 [05:24<01:09, 25.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▎     | 5485/8000 [00:46<00:21, 118.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6223/8000 [05:24<01:09, 25.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▍     | 5506/8000 [00:47<00:20, 122.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6227/8000 [05:25<01:22, 21.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▍     | 5524/8000 [00:47<00:20, 123.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6231/8000 [05:25<01:13, 24.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████▍     | 5539/8000 [00:47<00:21, 112.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6235/8000 [05:25<01:20, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▌     | 5563/8000 [00:47<00:19, 123.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6239/8000 [05:25<01:15, 23.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▌     | 5577/8000 [00:47<00:21, 113.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6244/8000 [05:25<01:18, 22.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▌     | 5595/8000 [00:47<00:21, 111.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6249/8000 [05:25<01:19, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▌     | 5610/8000 [00:48<00:23, 103.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6252/8000 [05:26<01:16, 22.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████▋     | 5633/8000 [00:48<00:20, 117.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6256/8000 [05:26<01:10, 24.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▋     | 5648/8000 [00:48<00:22, 103.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6259/8000 [05:26<01:26, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▊     | 5668/8000 [00:48<00:20, 114.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6263/8000 [05:26<01:15, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▊     | 5695/8000 [00:48<00:18, 124.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6266/8000 [05:26<01:13, 23.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████▊     | 5713/8000 [00:48<00:19, 118.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6277/8000 [05:26<00:55, 31.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▉     | 5727/8000 [00:49<00:20, 109.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6282/8000 [05:27<01:03, 27.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▉     | 5751/8000 [00:49<00:18, 120.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6286/8000 [05:27<01:00, 28.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████▉     | 5769/8000 [00:49<00:18, 118.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6289/8000 [05:27<01:12, 23.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████     | 5790/8000 [00:49<00:18, 122.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6292/8000 [05:27<01:26, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████     | 5810/8000 [00:49<00:20, 105.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6295/8000 [05:28<01:53, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5826/8000 [00:49<00:22, 98.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6300/8000 [05:28<01:42, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▏    | 5843/8000 [00:50<00:20, 102.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6303/8000 [05:28<01:37, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5858/8000 [00:50<00:24, 86.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6305/8000 [05:28<01:41, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5877/8000 [00:50<00:22, 94.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6312/8000 [05:29<01:37, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5895/8000 [00:50<00:22, 92.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6316/8000 [05:29<01:24, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5906/8000 [00:50<00:24, 85.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6321/8000 [05:29<01:10, 23.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5918/8000 [00:51<00:26, 80.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6325/8000 [05:29<01:04, 25.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5930/8000 [00:51<00:26, 78.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6329/8000 [05:29<01:00, 27.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5947/8000 [00:51<00:22, 89.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6334/8000 [05:29<01:19, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5964/8000 [00:51<00:21, 94.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6337/8000 [05:30<01:47, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████▍    | 5981/8000 [00:51<00:19, 103.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6341/8000 [05:30<02:00, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6010/8000 [00:51<00:21, 92.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6343/8000 [05:30<01:59, 13.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6022/8000 [00:52<00:23, 84.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6347/8000 [05:30<01:36, 17.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6036/8000 [00:52<00:23, 82.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6350/8000 [05:31<01:48, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6056/8000 [00:52<00:21, 90.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6352/8000 [05:31<01:45, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6070/8000 [00:52<00:21, 89.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6357/8000 [05:31<01:17, 21.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6085/8000 [00:52<00:20, 93.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6361/8000 [05:31<01:26, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6099/8000 [00:53<00:23, 79.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6368/8000 [05:31<01:01, 26.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6117/8000 [00:53<00:22, 85.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6373/8000 [05:32<01:06, 24.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▊    | 6137/8000 [00:53<00:18, 100.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6377/8000 [05:32<01:23, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▊    | 6156/8000 [00:53<00:17, 107.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6380/8000 [05:32<01:31, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6168/8000 [00:53<00:18, 97.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6383/8000 [05:32<01:39, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████▉    | 6189/8000 [00:53<00:16, 106.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6385/8000 [05:32<01:39, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|█████████████▉    | 6212/8000 [00:54<00:15, 117.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6387/8000 [05:33<01:37, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6238/8000 [00:54<00:13, 132.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6390/8000 [05:33<01:30, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6259/8000 [00:54<00:13, 130.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6394/8000 [05:33<01:13, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████    | 6277/8000 [00:54<00:13, 126.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6398/8000 [05:33<01:26, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6291/8000 [00:54<00:14, 119.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6405/8000 [05:33<00:58, 27.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6311/8000 [00:54<00:13, 122.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6410/8000 [05:33<01:07, 23.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▏   | 6332/8000 [00:54<00:12, 129.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6413/8000 [05:34<01:22, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▎   | 6357/8000 [00:55<00:11, 144.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6418/8000 [05:34<01:20, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▎   | 6382/8000 [00:55<00:10, 156.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6423/8000 [05:34<01:32, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▍   | 6399/8000 [00:55<00:11, 145.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6425/8000 [05:34<01:34, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▍   | 6419/8000 [00:55<00:10, 143.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6434/8000 [05:35<01:20, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|██████████████▍   | 6438/8000 [00:55<00:11, 139.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6438/8000 [05:35<01:10, 22.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6460/8000 [00:55<00:10, 147.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6441/8000 [05:35<01:35, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6477/8000 [00:55<00:10, 138.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6447/8000 [05:36<01:19, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▌   | 6494/8000 [00:56<00:11, 130.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6452/8000 [05:36<01:19, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████▋   | 6512/8000 [00:56<00:12, 123.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6458/8000 [05:36<01:02, 24.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▋   | 6534/8000 [00:56<00:11, 124.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6462/8000 [05:36<01:09, 21.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▊   | 6563/8000 [00:56<00:13, 109.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6466/8000 [05:36<01:14, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|██████████████▊   | 6585/8000 [00:56<00:11, 120.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6469/8000 [05:37<01:26, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▊   | 6604/8000 [00:57<00:11, 118.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6471/8000 [05:37<01:28, 17.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▉   | 6621/8000 [00:57<00:12, 109.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6473/8000 [05:37<01:32, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|██████████████▉   | 6643/8000 [00:57<00:12, 105.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6476/8000 [05:37<01:25, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6658/8000 [00:57<00:13, 97.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6480/8000 [05:37<01:09, 21.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6671/8000 [00:57<00:14, 94.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6483/8000 [05:37<01:22, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████   | 6692/8000 [00:57<00:12, 103.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6486/8000 [05:38<01:17, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6709/8000 [00:58<00:12, 99.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6492/8000 [05:38<01:10, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▏  | 6726/8000 [00:58<00:12, 102.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6496/8000 [05:38<01:03, 23.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▏  | 6744/8000 [00:58<00:11, 106.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6499/8000 [05:38<01:02, 23.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▏  | 6763/8000 [00:58<00:11, 107.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6502/8000 [05:38<01:03, 23.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▎  | 6784/8000 [00:58<00:10, 120.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6506/8000 [05:38<00:57, 25.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▎  | 6806/8000 [00:58<00:09, 131.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6510/8000 [05:38<00:54, 27.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|███████████████▎  | 6828/8000 [00:59<00:08, 139.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6514/8000 [05:39<01:05, 22.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▍  | 6844/8000 [00:59<00:09, 125.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6518/8000 [05:39<01:00, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▍  | 6857/8000 [00:59<00:10, 107.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6522/8000 [05:39<00:56, 26.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▍  | 6880/8000 [00:59<00:09, 116.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6525/8000 [05:39<01:10, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|███████████████▌  | 6895/8000 [00:59<00:10, 109.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6530/8000 [05:39<00:59, 24.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▌  | 6923/8000 [00:59<00:08, 134.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6533/8000 [05:40<01:14, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▋  | 6950/8000 [01:00<00:07, 148.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6537/8000 [05:40<01:05, 22.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▋  | 6975/8000 [01:00<00:06, 152.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6543/8000 [05:40<00:50, 28.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|███████████████▋  | 6997/8000 [01:00<00:06, 150.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6547/8000 [05:40<00:49, 29.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7028/8000 [01:00<00:07, 129.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6551/8000 [05:40<01:04, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▊  | 7045/8000 [01:00<00:07, 125.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6555/8000 [05:41<01:25, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|███████████████▉  | 7064/8000 [01:00<00:07, 121.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6558/8000 [05:41<01:33, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▉  | 7083/8000 [01:01<00:08, 109.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6560/8000 [05:41<01:32, 15.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████▉  | 7101/8000 [01:01<00:08, 109.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6564/8000 [05:41<01:31, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████  | 7118/8000 [01:01<00:08, 107.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6568/8000 [05:42<01:41, 14.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████  | 7139/8000 [01:01<00:07, 107.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6571/8000 [05:42<01:28, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████  | 7156/8000 [01:01<00:07, 107.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6574/8000 [05:42<01:19, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7171/8000 [01:01<00:08, 101.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6577/8000 [05:42<01:27, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7197/8000 [01:02<00:06, 124.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6579/8000 [05:42<01:27, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▏ | 7216/8000 [01:02<00:06, 123.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6585/8000 [05:42<01:15, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|████████████████▎ | 7235/8000 [01:02<00:06, 126.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6588/8000 [05:43<01:09, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▎ | 7248/8000 [01:02<00:07, 106.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6594/8000 [05:43<00:52, 26.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▎ | 7263/8000 [01:02<00:07, 103.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6598/8000 [05:43<00:59, 23.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7280/8000 [01:02<00:07, 99.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6602/8000 [05:43<01:06, 21.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████▍ | 7303/8000 [01:03<00:06, 110.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6605/8000 [05:43<01:15, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▍ | 7329/8000 [01:03<00:05, 120.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6609/8000 [05:43<01:04, 21.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▌ | 7352/8000 [01:03<00:05, 128.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6614/8000 [05:44<01:05, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████▌ | 7383/8000 [01:03<00:05, 103.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6618/8000 [05:44<00:59, 23.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7411/8000 [01:04<00:06, 97.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6624/8000 [05:44<00:46, 29.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▋ | 7432/8000 [01:04<00:05, 101.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6628/8000 [05:44<01:09, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████▊ | 7449/8000 [01:04<00:05, 104.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6632/8000 [05:45<01:15, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7465/8000 [01:04<00:05, 99.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6635/8000 [05:45<01:27, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▊ | 7485/8000 [01:04<00:04, 104.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6639/8000 [05:45<01:13, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7506/8000 [01:05<00:04, 114.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6644/8000 [05:45<01:00, 22.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7522/8000 [01:05<00:04, 106.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6647/8000 [05:45<00:59, 22.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████▉ | 7535/8000 [01:05<00:04, 100.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6650/8000 [05:46<01:28, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7550/8000 [01:05<00:04, 98.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6656/8000 [05:46<01:12, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▉ | 7564/8000 [01:05<00:04, 92.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6659/8000 [05:46<01:08, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7579/8000 [01:05<00:04, 91.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6663/8000 [05:46<01:28, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████ | 7599/8000 [01:06<00:03, 101.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6666/8000 [05:47<01:36, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▏| 7621/8000 [01:06<00:03, 109.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6669/8000 [05:47<01:25, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▏| 7638/8000 [01:06<00:03, 108.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6675/8000 [05:47<01:14, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▏| 7664/8000 [01:06<00:02, 123.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6677/8000 [05:47<01:14, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7682/8000 [01:06<00:02, 124.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6680/8000 [05:47<01:10, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████▎| 7698/8000 [01:06<00:02, 117.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6683/8000 [05:47<01:05, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▎| 7722/8000 [01:06<00:02, 127.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6688/8000 [05:48<01:09, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▍| 7749/8000 [01:07<00:01, 145.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6692/8000 [05:48<01:01, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▍| 7768/8000 [01:07<00:01, 149.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6695/8000 [05:48<01:27, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████▌| 7789/8000 [01:07<00:01, 143.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6697/8000 [05:48<01:43, 12.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▌| 7815/8000 [01:07<00:01, 138.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6700/8000 [05:49<01:30, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▋| 7834/8000 [01:07<00:01, 127.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6705/8000 [05:49<01:06, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████▋| 7861/8000 [01:07<00:01, 136.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6709/8000 [05:49<01:13, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▋| 7881/8000 [01:08<00:00, 126.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6713/8000 [05:49<01:28, 14.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7898/8000 [01:08<00:00, 118.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6716/8000 [05:50<01:43, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7918/8000 [01:08<00:00, 112.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6721/8000 [05:50<01:15, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▊| 7932/8000 [01:08<00:00, 104.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6725/8000 [05:50<01:15, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████▉| 7946/8000 [01:08<00:00, 101.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6728/8000 [05:50<01:09, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████▉| 7961/8000 [01:08<00:00, 104.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6731/8000 [05:51<01:23, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████▉| 7984/8000 [01:09<00:00, 111.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6734/8000 [05:51<01:13, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8003it [01:09, 115.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6738/8000 [05:51<01:14, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6741/8000 [05:51<01:09, 18.19it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6747/8000 [05:51<00:49, 25.43it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:08<27:12,  8.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6753/8000 [05:51<00:51, 24.33it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:09<14:13,  4.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6757/8000 [05:52<00:48, 25.50it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:11<10:01,  3.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6763/8000 [05:52<00:39, 31.21it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:13<08:16,  2.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6768/8000 [05:52<00:47, 26.16it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:14<07:19,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6774/8000 [05:52<00:47, 25.68it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:16<06:24,  1.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6780/8000 [05:52<00:50, 24.22it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:17<05:45,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6786/8000 [05:53<00:49, 24.59it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:19<05:20,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6789/8000 [05:53<00:59, 20.21it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:20<05:01,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6793/8000 [05:53<01:06, 18.02it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:22<05:05,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6797/8000 [05:53<00:57, 20.99it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:24<05:43,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6801/8000 [05:54<01:02, 19.28it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:26<05:57,  1.90s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6806/8000 [05:54<01:00, 19.83it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:27<05:29,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6811/8000 [05:54<01:08, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:29<05:09,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6815/8000 [05:55<01:17, 15.23it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:30<04:52,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6822/8000 [05:55<00:55, 21.35it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:32<04:45,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6825/8000 [05:55<00:53, 21.85it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:33<04:34,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6828/8000 [05:55<01:03, 18.53it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:35<04:36,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6831/8000 [05:55<01:09, 16.87it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:36<04:25,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6833/8000 [05:55<01:10, 16.45it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:38<04:31,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6837/8000 [05:56<01:12, 16.12it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:39<04:25,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6839/8000 [05:56<01:14, 15.51it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:41<04:25,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6843/8000 [05:56<01:12, 15.93it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:42<04:20,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6848/8000 [05:56<00:55, 20.70it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:44<04:23,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6853/8000 [05:56<00:55, 20.76it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:45<04:24,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6858/8000 [05:57<00:45, 25.30it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:47<04:22,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6862/8000 [05:57<00:52, 21.68it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:48<04:24,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6865/8000 [05:57<01:00, 18.74it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:50<04:19,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6869/8000 [05:57<01:05, 17.22it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:51<04:21,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6873/8000 [05:58<01:18, 14.33it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:53<04:15,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6876/8000 [05:58<01:11, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:54<04:16,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6879/8000 [05:58<01:05, 17.05it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:56<04:09,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6881/8000 [05:58<01:06, 16.78it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:57<04:04,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6885/8000 [05:58<00:55, 20.08it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:59<04:01,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6889/8000 [05:58<00:59, 18.58it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [01:00<04:00,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6897/8000 [05:59<00:45, 24.05it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [01:01<03:56,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6902/8000 [05:59<00:39, 27.71it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [01:03<03:49,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6907/8000 [05:59<00:42, 25.64it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [01:04<03:48,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6910/8000 [05:59<00:52, 20.94it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [01:05<03:44,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6914/8000 [06:00<01:24, 12.89it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [01:07<03:38,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6917/8000 [06:00<01:15, 14.42it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [01:08<03:37,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6921/8000 [06:00<01:01, 17.49it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:10<03:39,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6927/8000 [06:00<00:56, 19.03it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:11<03:42,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6932/8000 [06:01<00:55, 19.08it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:13<03:45,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6936/8000 [06:01<01:06, 15.90it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:14<03:41,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6939/8000 [06:01<01:01, 17.21it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:15<03:39,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6943/8000 [06:01<00:52, 19.99it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:17<03:47,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6946/8000 [06:01<00:50, 20.73it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [01:19<04:06,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6950/8000 [06:02<00:45, 22.90it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [01:22<04:14,  1.69s/it]\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6954/8000 [06:02<00:40, 25.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6957/8000 [06:02<00:41, 25.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6961/8000 [06:02<01:00, 17.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6966/8000 [06:02<00:49, 20.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 13.84it/s]\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6972/8000 [06:02<00:38, 26.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6977/8000 [06:03<01:07, 15.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6980/8000 [06:03<01:18, 13.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6983/8000 [06:04<01:10, 14.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 31it [00:01, 26.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6986/8000 [06:04<01:13, 13.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 62it [00:01, 58.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6990/8000 [06:04<01:17, 12.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 95it [00:01, 95.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6992/8000 [06:04<01:15, 13.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 123it [00:01, 123.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6994/8000 [06:04<01:10, 14.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 152it [00:02, 153.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6998/8000 [06:05<01:06, 15.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 180it [00:02, 172.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7001/8000 [06:05<01:12, 13.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 219it [00:02, 220.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7005/8000 [06:05<00:57, 17.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 249it [00:02, 238.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7009/8000 [06:05<00:49, 20.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 279it [00:02, 253.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7012/8000 [06:05<00:58, 16.81it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 309it [00:02, 231.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7014/8000 [06:06<00:58, 16.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 336it [00:02, 218.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7017/8000 [06:06<00:52, 18.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 361it [00:02, 214.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7021/8000 [06:06<00:46, 20.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 385it [00:02, 205.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7024/8000 [06:06<00:44, 21.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 411it [00:03, 216.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7027/8000 [06:06<00:58, 16.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 436it [00:03, 215.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7030/8000 [06:06<00:54, 17.81it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 459it [00:03, 213.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7037/8000 [06:06<00:36, 26.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 483it [00:03, 219.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7041/8000 [06:07<00:52, 18.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 514it [00:03, 242.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7047/8000 [06:07<00:40, 23.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 542it [00:03, 251.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7051/8000 [06:07<00:47, 20.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 568it [00:03, 219.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7055/8000 [06:08<00:50, 18.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 592it [00:03, 223.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7061/8000 [06:08<00:46, 20.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 618it [00:03, 231.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7064/8000 [06:08<00:52, 17.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 642it [00:04, 221.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7068/8000 [06:08<00:45, 20.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 667it [00:04, 227.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7071/8000 [06:08<00:55, 16.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 691it [00:04, 227.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7074/8000 [06:09<00:50, 18.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 719it [00:04, 240.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7078/8000 [06:09<00:42, 21.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 744it [00:04, 229.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7084/8000 [06:09<00:49, 18.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 768it [00:04, 226.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7089/8000 [06:09<00:49, 18.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:04, 165.63it/s]\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7094/8000 [06:10<00:46, 19.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7097/8000 [06:10<00:44, 20.44it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7101/8000 [06:10<00:47, 18.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 146.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7105/8000 [06:10<00:49, 18.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 126.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7107/8000 [06:10<00:59, 14.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 95.92\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7110/8000 [06:11<00:54, 16.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:00<00:00, 102.8\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7115/8000 [06:11<00:58, 15.14it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 101.2\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7117/8000 [06:11<00:57, 15.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|▊| 77/100 [00:00<00:00, 89.50\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7123/8000 [06:11<00:40, 21.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 103.\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7126/8000 [06:11<00:40, 21.42it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|████████████████████▎      | 3/4 [25:45<08:33, 513.23s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.4242395230195184\n",
+      "2999: -5.7753568\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7129/8000 [06:11<00:40, 21.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7135/8000 [06:12<00:35, 24.31it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7138/8000 [06:12<00:35, 24.28it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:04<14:16,  4.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7143/8000 [06:12<00:35, 24.04it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:05<07:19,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7149/8000 [06:12<00:33, 25.04it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:05<05:03,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7153/8000 [06:12<00:30, 27.48it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:06<04:02,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7157/8000 [06:13<00:35, 23.48it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7160/8000 [06:13<00:41, 20.30it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:08<03:06,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7163/8000 [06:13<00:38, 21.53it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:08<02:52,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7166/8000 [06:13<00:44, 18.68it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:09<02:42,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7170/8000 [06:13<00:45, 18.09it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:10<02:35,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7174/8000 [06:14<00:45, 17.98it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:11<02:29,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7179/8000 [06:14<00:35, 22.93it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7184/8000 [06:14<00:29, 27.58it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:12<02:28,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7189/8000 [06:14<00:30, 26.50it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:13<02:26,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7193/8000 [06:14<00:27, 29.01it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:14<02:25,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7198/8000 [06:14<00:36, 21.92it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:14<02:25,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7202/8000 [06:15<00:32, 24.57it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:15<02:22,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7205/8000 [06:15<00:31, 25.08it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:16<02:21,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7210/8000 [06:15<00:33, 23.58it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:17<02:20,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7215/8000 [06:15<00:27, 28.12it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:18<02:17,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7222/8000 [06:15<00:26, 29.23it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:18<02:16,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7228/8000 [06:15<00:27, 28.24it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:19<02:13,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7233/8000 [06:16<00:29, 26.10it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:20<02:13,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7237/8000 [06:16<00:26, 28.44it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:21<02:14,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7243/8000 [06:16<00:26, 28.62it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:21<02:14,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7247/8000 [06:16<00:30, 24.93it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:22<02:13,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7251/8000 [06:17<00:37, 19.93it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:23<02:12,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7255/8000 [06:17<00:39, 18.86it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:24<02:12,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7261/8000 [06:17<00:34, 21.20it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:24<02:11,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7267/8000 [06:17<00:27, 26.76it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:25<02:11,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7272/8000 [06:18<00:35, 20.65it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:26<02:10,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7277/8000 [06:18<00:34, 21.17it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:27<02:10,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7280/8000 [06:18<00:33, 21.75it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:27<02:07,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7286/8000 [06:18<00:25, 27.50it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:28<02:07,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7293/8000 [06:18<00:34, 20.34it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:29<02:05,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7298/8000 [06:19<00:33, 20.78it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [00:30<02:04,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7304/8000 [06:19<00:32, 21.57it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [00:30<02:04,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7308/8000 [06:19<00:44, 15.40it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [00:31<02:02,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7310/8000 [06:20<00:43, 15.82it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [00:32<02:04,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7313/8000 [06:20<00:39, 17.56it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [00:33<02:02,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7316/8000 [06:20<00:36, 18.71it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [00:34<02:04,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7319/8000 [06:20<00:33, 20.35it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [00:34<02:03,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7324/8000 [06:20<00:32, 20.82it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [00:35<02:01,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7329/8000 [06:20<00:27, 24.83it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [00:36<02:00,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7333/8000 [06:21<00:31, 20.98it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [00:37<01:58,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7338/8000 [06:21<00:26, 25.07it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [00:37<01:56,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7343/8000 [06:21<00:33, 19.72it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [00:38<01:56,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7350/8000 [06:21<00:29, 21.82it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [00:39<01:54,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7354/8000 [06:21<00:27, 23.33it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [00:40<01:56,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7358/8000 [06:22<00:34, 18.34it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [00:40<01:57,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7362/8000 [06:22<00:42, 15.09it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [00:41<01:56,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7364/8000 [06:22<00:41, 15.29it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [00:42<01:53,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7367/8000 [06:23<00:43, 14.68it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [00:43<01:54,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7369/8000 [06:23<00:42, 15.01it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [00:43<01:51,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7372/8000 [06:23<00:42, 14.62it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [00:44<01:49,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7376/8000 [06:23<00:42, 14.54it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [00:45<01:47,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7378/8000 [06:23<00:53, 11.57it/s]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [00:46<01:45,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7382/8000 [06:24<00:41, 14.78it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [00:46<01:44,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7389/8000 [06:24<00:27, 22.48it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [00:47<01:43,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7394/8000 [06:24<00:23, 25.54it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [00:48<01:43,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7402/8000 [06:24<00:23, 25.95it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [00:49<01:42,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7406/8000 [06:24<00:22, 26.77it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [00:50<01:55,  1.21it/s]\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7409/8000 [06:25<00:29, 20.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7416/8000 [06:25<00:25, 23.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7422/8000 [06:25<00:24, 23.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 25.44it/s]\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7426/8000 [06:25<00:27, 21.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7432/8000 [06:26<00:25, 22.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7435/8000 [06:26<00:24, 22.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7438/8000 [06:26<00:29, 19.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 80it [00:00, 119.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7443/8000 [06:26<00:23, 24.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 167it [00:01, 251.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7446/8000 [06:26<00:33, 16.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 255it [00:01, 377.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7450/8000 [06:27<00:33, 16.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 332it [00:01, 464.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7457/8000 [06:27<00:27, 19.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 415it [00:01, 551.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7460/8000 [06:27<00:35, 15.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 497it [00:01, 618.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7462/8000 [06:27<00:34, 15.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 576it [00:01, 651.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7465/8000 [06:28<00:31, 17.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 659it [00:01, 699.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7468/8000 [06:28<00:28, 18.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 448.47it/s]\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7472/8000 [06:28<00:44, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7479/8000 [06:28<00:27, 18.64it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7483/8000 [06:28<00:24, 20.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 343.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7487/8000 [06:29<00:29, 17.41it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 349.\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7493/8000 [06:29<00:25, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7496/8000 [06:29<00:24, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7499/8000 [06:29<00:28, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                      | 5/8000 [00:00<03:03, 43.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7502/8000 [06:30<00:26, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 14/8000 [00:00<02:33, 51.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7505/8000 [06:30<00:24, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 23/8000 [00:00<02:00, 66.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7508/8000 [06:30<00:28, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 31/8000 [00:00<01:52, 70.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7511/8000 [06:30<00:30, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                     | 39/8000 [00:00<02:07, 62.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7516/8000 [06:30<00:27, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|                     | 46/8000 [00:00<02:07, 62.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7518/8000 [06:30<00:28, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 56/8000 [00:00<02:12, 60.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7523/8000 [06:31<00:20, 22.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 63/8000 [00:01<02:16, 58.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7526/8000 [06:31<00:24, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 69/8000 [00:01<02:33, 51.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7537/8000 [06:31<00:15, 30.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 82/8000 [00:01<01:55, 68.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7541/8000 [06:31<00:18, 24.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                    | 92/8000 [00:01<02:30, 52.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7544/8000 [06:32<00:22, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 100/8000 [00:01<02:36, 50.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7547/8000 [06:32<00:20, 21.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 107/8000 [00:01<02:43, 48.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7551/8000 [06:32<00:21, 20.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                   | 114/8000 [00:02<02:34, 51.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7556/8000 [06:32<00:20, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 120/8000 [00:02<03:06, 42.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7559/8000 [06:32<00:19, 22.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 127/8000 [00:02<03:07, 42.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7562/8000 [06:32<00:18, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 134/8000 [00:02<03:40, 35.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7567/8000 [06:33<00:22, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 138/8000 [00:02<03:38, 36.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7570/8000 [06:33<00:21, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▎                   | 142/8000 [00:02<03:36, 36.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7574/8000 [06:33<00:21, 20.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 150/8000 [00:03<02:53, 45.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7578/8000 [06:33<00:18, 22.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 155/8000 [00:03<03:43, 35.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7581/8000 [06:33<00:17, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 162/8000 [00:03<03:08, 41.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7585/8000 [06:33<00:20, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 169/8000 [00:03<02:45, 47.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7590/8000 [06:34<00:19, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 176/8000 [00:03<02:29, 52.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7593/8000 [06:34<00:18, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 182/8000 [00:03<02:33, 50.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7596/8000 [06:34<00:18, 22.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 190/8000 [00:03<02:22, 54.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7601/8000 [06:34<00:14, 27.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▍                   | 196/8000 [00:03<02:28, 52.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7607/8000 [06:34<00:14, 26.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 204/8000 [00:04<02:14, 57.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7613/8000 [06:35<00:14, 26.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 210/8000 [00:04<02:14, 57.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7617/8000 [06:35<00:16, 23.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 218/8000 [00:04<02:25, 53.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7623/8000 [06:35<00:15, 23.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 229/8000 [00:04<02:12, 58.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7628/8000 [06:35<00:20, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 236/8000 [00:04<02:44, 47.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7633/8000 [06:36<00:17, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▌                   | 246/8000 [00:04<02:15, 57.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7639/8000 [06:36<00:20, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 255/8000 [00:05<02:57, 43.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7642/8000 [06:36<00:21, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 261/8000 [00:05<03:01, 42.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7648/8000 [06:36<00:18, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▋                   | 271/8000 [00:05<02:26, 52.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7653/8000 [06:37<00:19, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 281/8000 [00:05<02:34, 49.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7658/8000 [06:37<00:18, 18.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 287/8000 [00:05<02:46, 46.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7662/8000 [06:37<00:16, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▋                   | 293/8000 [00:05<02:41, 47.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7665/8000 [06:37<00:17, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 302/8000 [00:06<02:32, 50.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7670/8000 [06:37<00:14, 23.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 312/8000 [00:06<02:09, 59.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7673/8000 [06:38<00:13, 24.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 322/8000 [00:06<02:08, 59.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7678/8000 [06:38<00:13, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 329/8000 [00:06<02:19, 54.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7681/8000 [06:38<00:16, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 335/8000 [00:06<03:46, 33.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7689/8000 [06:38<00:12, 24.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 340/8000 [00:07<03:35, 35.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7693/8000 [06:39<00:16, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                   | 346/8000 [00:07<03:31, 36.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7699/8000 [06:39<00:14, 20.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▉                   | 351/8000 [00:07<03:19, 38.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7702/8000 [06:39<00:14, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▉                   | 356/8000 [00:07<03:52, 32.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7709/8000 [06:39<00:11, 24.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▉                   | 360/8000 [00:07<03:43, 34.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7712/8000 [06:39<00:13, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 365/8000 [00:07<03:30, 36.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7716/8000 [06:40<00:11, 23.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 369/8000 [00:07<03:54, 32.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7721/8000 [06:40<00:16, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 375/8000 [00:07<03:20, 37.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7726/8000 [06:40<00:14, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 381/8000 [00:08<02:56, 43.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7731/8000 [06:41<00:18, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 388/8000 [00:08<02:59, 42.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7736/8000 [06:41<00:14, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|▉                   | 395/8000 [00:08<02:55, 43.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7745/8000 [06:41<00:10, 24.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 400/8000 [00:08<02:50, 44.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7749/8000 [06:42<00:14, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 405/8000 [00:08<02:53, 43.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7753/8000 [06:42<00:14, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 410/8000 [00:08<02:59, 42.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7756/8000 [06:42<00:14, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 416/8000 [00:08<02:45, 45.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7759/8000 [06:42<00:13, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 421/8000 [00:08<02:49, 44.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7763/8000 [06:42<00:11, 21.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 427/8000 [00:09<02:40, 47.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7767/8000 [06:43<00:13, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█                   | 435/8000 [00:09<02:18, 54.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7771/8000 [06:43<00:13, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█                   | 442/8000 [00:09<02:14, 56.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7774/8000 [06:43<00:11, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 450/8000 [00:09<02:38, 47.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7777/8000 [06:43<00:13, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 458/8000 [00:09<02:56, 42.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7780/8000 [06:43<00:14, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 463/8000 [00:09<03:11, 39.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7783/8000 [06:44<00:15, 14.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 469/8000 [00:10<03:14, 38.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7786/8000 [06:44<00:15, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 477/8000 [00:10<02:41, 46.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7789/8000 [06:44<00:13, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 486/8000 [00:10<03:04, 40.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7795/8000 [06:44<00:10, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▏                  | 496/8000 [00:10<02:25, 51.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7801/8000 [06:44<00:09, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 503/8000 [00:10<03:03, 40.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7805/8000 [06:45<00:09, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 509/8000 [00:11<03:08, 39.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7810/8000 [06:45<00:07, 24.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▎                  | 517/8000 [00:11<02:54, 42.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7815/8000 [06:45<00:06, 28.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 523/8000 [00:11<02:43, 45.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7823/8000 [06:45<00:07, 22.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 530/8000 [00:11<03:21, 37.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7828/8000 [06:46<00:07, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 538/8000 [00:11<02:48, 44.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7832/8000 [06:46<00:08, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▎                  | 545/8000 [00:11<02:50, 43.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7836/8000 [06:46<00:09, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 554/8000 [00:12<02:46, 44.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7841/8000 [06:46<00:07, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 559/8000 [00:12<02:48, 44.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7845/8000 [06:47<00:09, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 567/8000 [00:12<02:50, 43.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7852/8000 [06:47<00:08, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 573/8000 [00:12<03:25, 36.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7856/8000 [06:47<00:09, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 579/8000 [00:12<03:26, 35.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7859/8000 [06:48<00:08, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 586/8000 [00:12<02:59, 41.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7864/8000 [06:48<00:08, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|█▍                  | 597/8000 [00:12<02:16, 54.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7868/8000 [06:48<00:08, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 609/8000 [00:13<02:07, 57.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7871/8000 [06:48<00:08, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 618/8000 [00:13<02:17, 53.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7874/8000 [06:48<00:07, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 627/8000 [00:13<02:12, 55.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7877/8000 [06:49<00:07, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 634/8000 [00:13<02:25, 50.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▋| 7881/8000 [06:49<00:06, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 640/8000 [00:13<02:21, 51.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7885/8000 [06:49<00:07, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▌                  | 646/8000 [00:13<02:19, 52.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7889/8000 [06:49<00:05, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 652/8000 [00:13<02:16, 53.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7894/8000 [06:49<00:04, 23.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 658/8000 [00:14<02:17, 53.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7900/8000 [06:50<00:04, 23.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 667/8000 [00:14<02:16, 53.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7905/8000 [06:50<00:04, 22.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 673/8000 [00:14<02:31, 48.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7910/8000 [06:50<00:04, 21.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|█▋                  | 680/8000 [00:14<03:00, 40.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7913/8000 [06:50<00:04, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 686/8000 [00:14<02:44, 44.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7919/8000 [06:51<00:04, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▋                  | 693/8000 [00:14<02:26, 49.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7921/8000 [06:51<00:04, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 701/8000 [00:14<02:13, 54.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7928/8000 [06:51<00:03, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 708/8000 [00:15<02:23, 50.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7933/8000 [06:51<00:03, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 717/8000 [00:15<02:07, 57.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7937/8000 [06:52<00:03, 19.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 726/8000 [00:15<02:11, 55.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7941/8000 [06:52<00:03, 19.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 732/8000 [00:15<02:10, 55.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7944/8000 [06:52<00:02, 20.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 739/8000 [00:15<02:22, 51.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7949/8000 [06:52<00:02, 24.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▊                  | 746/8000 [00:15<02:48, 43.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7953/8000 [06:52<00:02, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▉                  | 752/8000 [00:16<02:38, 45.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7956/8000 [06:52<00:02, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▉                  | 758/8000 [00:16<02:51, 42.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7963/8000 [06:53<00:02, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 766/8000 [00:16<02:44, 43.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7970/8000 [06:53<00:01, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 772/8000 [00:16<02:34, 46.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7972/8000 [06:54<00:01, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 777/8000 [00:16<02:54, 41.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7975/8000 [06:54<00:01, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 784/8000 [00:16<02:52, 41.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7978/8000 [06:54<00:01, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 790/8000 [00:16<02:53, 41.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7981/8000 [06:54<00:01, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|█▉                  | 795/8000 [00:17<03:06, 38.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7984/8000 [06:54<00:00, 19.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 803/8000 [00:17<02:48, 42.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7988/8000 [06:54<00:00, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 809/8000 [00:17<02:37, 45.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7994/8000 [06:55<00:00, 22.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 816/8000 [00:17<02:36, 45.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8002it [06:55, 19.27it/s]                                 \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  10%|██                  | 822/8000 [00:17<03:05, 38.61it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  10%|██                  | 829/8000 [00:17<02:43, 43.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|██                  | 834/8000 [00:18<02:59, 39.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:08<26:32,  8.00s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                  | 841/8000 [00:18<02:36, 45.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<14:00,  4.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██                  | 848/8000 [00:18<02:20, 50.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:11<09:42,  2.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 855/8000 [00:18<02:12, 54.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:45,  2.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 861/8000 [00:18<02:09, 55.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:39,  2.05s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 869/8000 [00:18<02:00, 59.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:58,  1.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 876/8000 [00:18<02:13, 53.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:32,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 885/8000 [00:18<02:08, 55.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:21,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 892/8000 [00:19<02:15, 52.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:04,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▏                 | 899/8000 [00:19<02:09, 54.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<04:57,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 906/8000 [00:19<02:32, 46.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<05:05,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 911/8000 [00:19<03:02, 38.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<05:08,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|██▎                 | 916/8000 [00:19<02:55, 40.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:54,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 921/8000 [00:19<03:04, 38.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:45,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 928/8000 [00:19<02:56, 40.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<04:53,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 935/8000 [00:20<02:33, 46.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:48,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 941/8000 [00:20<02:40, 44.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<04:43,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▎                 | 946/8000 [00:20<02:36, 45.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:37,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 952/8000 [00:20<02:26, 48.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:35<04:45,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 957/8000 [00:20<02:48, 41.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:37<04:44,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 964/8000 [00:20<02:27, 47.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:36,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 970/8000 [00:20<02:19, 50.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:40<04:33,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 977/8000 [00:20<02:22, 49.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:41<04:31,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 984/8000 [00:21<02:12, 52.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:43<04:33,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 990/8000 [00:21<02:23, 48.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:44<04:30,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|██▍                 | 998/8000 [00:21<02:32, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:46<04:29,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1003/8000 [00:21<02:47, 41.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:47<04:27,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1010/8000 [00:21<03:04, 37.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:49<04:28,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1014/8000 [00:21<03:31, 33.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:51<04:24,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1020/8000 [00:22<03:25, 34.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:52<04:20,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1025/8000 [00:22<03:15, 35.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:54<04:16,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1033/8000 [00:22<02:34, 45.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:55<04:19,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1038/8000 [00:22<02:34, 45.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:57<04:22,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1045/8000 [00:22<02:16, 51.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:58<04:25,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▍                | 1051/8000 [00:22<02:44, 42.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [01:00<04:20,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1057/8000 [00:22<02:37, 44.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:02<04:18,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1064/8000 [00:23<02:35, 44.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:03<04:18,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1071/8000 [00:23<02:18, 49.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:05<04:11,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▌                | 1079/8000 [00:23<02:22, 48.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:06<04:08,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1086/8000 [00:23<02:28, 46.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:08<04:02,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1092/8000 [00:23<02:37, 43.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:09<03:57,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1097/8000 [00:23<02:52, 40.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:11<03:56,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▌                | 1104/8000 [00:23<02:28, 46.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:12<03:54,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1110/8000 [00:24<02:23, 48.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:13<03:53,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1116/8000 [00:24<02:44, 41.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:15<03:46,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1121/8000 [00:24<03:03, 37.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:16<03:45,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1132/8000 [00:24<02:13, 51.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:18<03:47,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1139/8000 [00:24<02:27, 46.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:19<03:44,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1147/8000 [00:24<02:27, 46.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:21<03:48,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|██▋                | 1156/8000 [00:24<02:17, 49.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:22<03:46,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1163/8000 [00:25<02:21, 48.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:24<03:44,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1171/8000 [00:25<02:18, 49.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:25<03:41,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1177/8000 [00:25<02:12, 51.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:28<04:11,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1183/8000 [00:25<02:25, 46.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:30<04:44,  1.95s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▊                | 1193/8000 [00:25<02:09, 52.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:33<04:07,  1.71s/it]\n",
+      "Generating signals:  15%|██▊                | 1200/8000 [00:25<02:02, 55.46it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  15%|██▊                | 1206/8000 [00:25<02:05, 54.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1216/8000 [00:26<01:46, 63.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|██▉                | 1227/8000 [00:26<01:55, 58.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.75it/s]\n",
+      "Generating signals:  15%|██▉                | 1238/8000 [00:26<01:49, 61.84it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  16%|██▉                | 1247/8000 [00:26<02:04, 54.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1253/8000 [00:26<02:30, 44.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|██▉                | 1259/8000 [00:26<02:22, 47.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 42it [00:01, 37.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1266/8000 [00:27<02:11, 51.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 79it [00:01, 74.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1272/8000 [00:27<02:13, 50.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 121it [00:01, 122.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1278/8000 [00:27<02:08, 52.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 155it [00:01, 154.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1286/8000 [00:27<02:25, 46.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 196it [00:01, 202.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1291/8000 [00:27<02:44, 40.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 234it [00:02, 239.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1298/8000 [00:27<02:24, 46.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 280it [00:02, 288.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1305/8000 [00:27<02:14, 49.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 320it [00:02, 314.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|███                | 1313/8000 [00:28<01:58, 56.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 359it [00:02, 331.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1321/8000 [00:28<02:37, 42.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 398it [00:02, 335.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1328/8000 [00:28<03:03, 36.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 436it [00:02, 346.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1333/8000 [00:28<02:55, 37.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 478it [00:02, 365.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1339/8000 [00:28<02:41, 41.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 517it [00:02, 366.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1344/8000 [00:28<03:02, 36.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 556it [00:02, 366.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1349/8000 [00:29<02:51, 38.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 596it [00:03, 373.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1354/8000 [00:29<02:45, 40.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 635it [00:03, 375.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▏               | 1364/8000 [00:29<02:05, 52.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 676it [00:03, 382.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1372/8000 [00:29<02:29, 44.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 715it [00:03, 380.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1377/8000 [00:29<03:04, 35.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 756it [00:03, 387.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|███▎               | 1387/8000 [00:29<02:33, 43.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 225.06it/s]\n",
+      "Generating signals:  17%|███▎               | 1392/8000 [00:30<03:13, 34.10it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  17%|███▎               | 1399/8000 [00:30<02:57, 37.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1406/8000 [00:30<02:46, 39.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 105\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▎               | 1414/8000 [00:30<02:34, 42.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:00, 127\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1428/8000 [00:30<01:47, 61.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:00, 121\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1437/8000 [00:30<02:01, 53.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:00<00:00, 130\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1446/8000 [00:31<01:57, 55.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 136\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1453/8000 [00:31<02:11, 49.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  85%|▊| 85/100 [00:00<00:00, 136\u001b[A\u001b[A\n",
+      "Generating signals:  18%|███▍               | 1459/8000 [00:31<02:09, 50.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
+      "Generating signals:  18%|███▍               | 1466/8000 [00:31<02:00, 54.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [34:32<00:00, 599.03s/it]\u001b[A\n",
-      "Replicates:  62%|███████████████▋         | 5/8 [2:56:47<1:44:52, 2097.54s/it]\n",
-      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.106934598990834\n",
-      "2999: -5.9254448\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  18%|███▍               | 1473/8000 [00:31<02:22, 45.83it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  19%|███▌               | 1481/8000 [00:31<02:05, 52.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1488/8000 [00:31<01:58, 55.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:06<23:04,  6.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1496/8000 [00:32<02:28, 43.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:05,  3.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1503/8000 [00:32<02:19, 46.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:30,  2.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1511/8000 [00:32<02:18, 46.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:10<06:51,  2.10s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1517/8000 [00:32<02:29, 43.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:54,  1.82s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▌               | 1522/8000 [00:32<02:29, 43.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:20,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1528/8000 [00:32<02:34, 41.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:14<04:57,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1534/8000 [00:33<02:22, 45.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:39,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1543/8000 [00:33<02:27, 43.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:30,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1548/8000 [00:33<02:28, 43.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:18<04:24,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|███▋               | 1557/8000 [00:33<02:07, 50.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:20,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋               | 1566/8000 [00:33<01:51, 57.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:15,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▋               | 1573/8000 [00:33<02:15, 47.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:22<04:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1580/8000 [00:34<02:32, 42.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:19,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1591/8000 [00:34<01:57, 54.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:14,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1601/8000 [00:34<01:54, 55.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:13,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1609/8000 [00:34<02:00, 53.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:11,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1615/8000 [00:34<02:10, 48.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:29<04:08,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1621/8000 [00:34<02:05, 50.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<04:07,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▊               | 1628/8000 [00:34<02:15, 47.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<04:11,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▉               | 1633/8000 [00:35<02:18, 45.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:34<04:14,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███▉               | 1638/8000 [00:35<02:18, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<04:09,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1644/8000 [00:35<02:12, 47.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<04:07,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1651/8000 [00:35<02:01, 52.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:38<04:03,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1659/8000 [00:35<02:03, 51.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<04:00,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1665/8000 [00:35<02:01, 51.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:40<04:00,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1672/8000 [00:35<02:39, 39.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:42<03:58,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1678/8000 [00:36<02:27, 42.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:50,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███▉               | 1683/8000 [00:36<02:22, 44.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:45<03:53,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1692/8000 [00:36<02:11, 48.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:46<03:57,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1699/8000 [00:36<02:01, 51.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:57,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1708/8000 [00:36<02:36, 40.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:49<03:59,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1713/8000 [00:36<02:30, 41.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:50<03:58,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|████               | 1718/8000 [00:37<02:56, 35.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:52<03:56,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1724/8000 [00:37<02:55, 35.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:53<04:03,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1731/8000 [00:37<02:30, 41.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:55<03:59,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████               | 1736/8000 [00:37<02:25, 43.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:56<03:53,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1742/8000 [00:37<02:34, 40.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:57<03:46,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1748/8000 [00:37<02:40, 38.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:59<03:42,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1760/8000 [00:37<01:53, 54.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:00<03:54,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1767/8000 [00:38<02:15, 45.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:52,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1773/8000 [00:38<02:12, 47.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:03<03:45,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1779/8000 [00:38<02:07, 48.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:35,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▏              | 1786/8000 [00:38<02:10, 47.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:32,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▎              | 1792/8000 [00:38<02:41, 38.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:07<03:35,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|████▎              | 1799/8000 [00:38<02:27, 41.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:36,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1804/8000 [00:39<02:45, 37.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:37,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1809/8000 [00:39<02:38, 39.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:31,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1814/8000 [00:39<02:51, 36.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:31,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1822/8000 [00:39<02:50, 36.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:14<03:32,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1826/8000 [00:39<03:51, 26.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:16<03:30,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1830/8000 [00:39<03:38, 28.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:17<03:27,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1834/8000 [00:40<03:35, 28.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:26,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▎              | 1839/8000 [00:40<03:17, 31.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:20<03:21,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1844/8000 [00:40<03:04, 33.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:21<03:21,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1849/8000 [00:40<02:56, 34.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:24<03:58,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1854/8000 [00:40<03:08, 32.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:26<04:07,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1860/8000 [00:40<02:44, 37.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:27<03:47,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1866/8000 [00:40<02:31, 40.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:28<03:35,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████▍              | 1873/8000 [00:40<02:13, 46.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:29<03:25,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍              | 1881/8000 [00:41<02:15, 45.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:31<03:15,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▍              | 1892/8000 [00:41<01:43, 59.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:32<03:12,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1901/8000 [00:41<01:36, 63.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:34<03:12,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1909/8000 [00:41<01:46, 56.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:35<03:08,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1917/8000 [00:41<01:39, 61.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:36<03:07,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1925/8000 [00:41<02:10, 46.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:38<03:05,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1935/8000 [00:42<02:04, 48.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:39<03:05,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▌              | 1942/8000 [00:42<02:11, 45.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:40<03:03,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▋              | 1949/8000 [00:42<02:29, 40.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:42<03:02,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▋              | 1955/8000 [00:42<02:51, 35.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:43<02:58,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████▋              | 1959/8000 [00:42<02:49, 35.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:45<02:56,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1966/8000 [00:42<02:27, 41.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:46<02:57,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1972/8000 [00:43<02:36, 38.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:47<02:51,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1977/8000 [00:43<03:09, 31.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:49<02:50,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1981/8000 [00:43<03:03, 32.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:50<02:48,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▋              | 1992/8000 [00:43<02:05, 48.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:51<02:46,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2002/8000 [00:43<01:54, 52.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:53<02:42,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2010/8000 [00:43<01:55, 51.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:54<02:41,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2016/8000 [00:44<02:18, 43.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:57<02:59,  1.48s/it]\n",
+      "Generating signals:  25%|████▊              | 2022/8000 [00:44<02:17, 43.62it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  25%|████▊              | 2029/8000 [00:44<02:21, 42.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████▊              | 2035/8000 [00:44<02:18, 42.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▊              | 2040/8000 [00:44<02:52, 34.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.27it/s]\n",
+      "Generating signals:  26%|████▊              | 2045/8000 [00:44<02:40, 37.21it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  26%|████▉              | 2054/8000 [00:45<02:33, 38.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2059/8000 [00:45<03:38, 27.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2067/8000 [00:45<03:12, 30.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 50it [00:01, 45.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2072/8000 [00:45<03:38, 27.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 88it [00:01, 83.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2079/8000 [00:46<03:09, 31.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 125it [00:01, 123.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2084/8000 [00:46<02:59, 32.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 170it [00:01, 178.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2090/8000 [00:46<02:36, 37.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 208it [00:01, 214.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2096/8000 [00:46<02:23, 41.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 246it [00:02, 239.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2101/8000 [00:46<02:50, 34.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 292it [00:02, 288.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▉              | 2105/8000 [00:46<02:45, 35.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 331it [00:02, 312.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████              | 2111/8000 [00:46<02:25, 40.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 376it [00:02, 347.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████              | 2116/8000 [00:47<02:54, 33.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 417it [00:02, 347.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2124/8000 [00:47<02:28, 39.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 456it [00:02, 343.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2130/8000 [00:47<02:27, 39.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 498it [00:02, 363.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2137/8000 [00:47<02:09, 45.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 541it [00:02, 381.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2144/8000 [00:47<02:07, 46.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 585it [00:02, 396.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████              | 2152/8000 [00:47<01:50, 52.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 627it [00:02, 403.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2158/8000 [00:47<02:03, 47.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 669it [00:03, 400.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2164/8000 [00:48<02:09, 45.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 713it [00:03, 411.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2169/8000 [00:48<02:06, 45.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 757it [00:03, 417.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2174/8000 [00:48<02:21, 41.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 232.28it/s]\n",
+      "Generating signals:  27%|█████▏             | 2180/8000 [00:48<02:10, 44.63it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  27%|█████▏             | 2186/8000 [00:48<02:01, 47.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████▏             | 2192/8000 [00:48<02:11, 44.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  19%|▏| 19/100 [00:00<00:00, 181\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▏             | 2204/8000 [00:48<01:45, 55.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 183\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2213/8000 [00:48<01:34, 61.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 175\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2221/8000 [00:49<01:27, 65.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:00<00:00, 170\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2230/8000 [00:49<01:55, 50.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "Generating signals:  28%|█████▎             | 2242/8000 [00:49<01:39, 57.93it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  28%|█████▎             | 2251/8000 [00:49<01:29, 64.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▎             | 2262/8000 [00:49<01:26, 66.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎               | 181/8000 [00:00<00:07, 1066.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▍             | 2270/8000 [00:49<01:33, 61.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋               | 343/8000 [00:00<00:06, 1145.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████▍             | 2279/8000 [00:50<01:27, 65.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█               | 521/8000 [00:00<00:06, 1182.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2289/8000 [00:50<01:19, 71.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍              | 697/8000 [00:00<00:05, 1224.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2297/8000 [00:50<01:41, 56.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊              | 877/8000 [00:00<00:05, 1203.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2305/8000 [00:50<02:17, 41.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|█▉             | 1057/8000 [00:00<00:05, 1272.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▍             | 2311/8000 [00:50<02:22, 39.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎            | 1231/8000 [00:01<00:05, 1269.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2319/8000 [00:50<02:02, 46.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▋            | 1402/8000 [00:01<00:05, 1230.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2325/8000 [00:51<02:11, 43.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|██▉            | 1580/8000 [00:01<00:05, 1244.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2332/8000 [00:51<01:59, 47.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▎           | 1750/8000 [00:01<00:05, 1209.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2339/8000 [00:51<02:04, 45.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▌           | 1922/8000 [00:01<00:04, 1245.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2350/8000 [00:51<01:37, 58.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|███▉           | 2100/8000 [00:01<00:04, 1206.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████▌             | 2358/8000 [00:51<01:39, 56.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▎          | 2276/8000 [00:01<00:04, 1191.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▌             | 2366/8000 [00:51<01:40, 56.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▌          | 2438/8000 [00:02<00:04, 1131.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2375/8000 [00:51<01:38, 57.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|████▉          | 2606/8000 [00:02<00:04, 1106.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2381/8000 [00:52<01:51, 50.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▏         | 2779/8000 [00:02<00:04, 1105.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2392/8000 [00:52<01:29, 62.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▌         | 2959/8000 [00:02<00:04, 1195.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2399/8000 [00:52<01:52, 49.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|█████▉         | 3137/8000 [00:02<00:04, 1172.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2405/8000 [00:52<01:47, 51.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▏        | 3327/8000 [00:02<00:03, 1247.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2412/8000 [00:52<01:42, 54.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▌        | 3500/8000 [00:02<00:03, 1256.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▋             | 2419/8000 [00:52<02:06, 44.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|██████▉        | 3674/8000 [00:03<00:03, 1305.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2426/8000 [00:53<02:10, 42.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▏       | 3851/8000 [00:03<00:03, 1252.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2431/8000 [00:53<02:39, 34.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▌       | 4029/8000 [00:03<00:03, 1270.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2435/8000 [00:53<02:39, 34.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|███████▉       | 4204/8000 [00:03<00:02, 1295.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████▊             | 2439/8000 [00:53<02:40, 34.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▏      | 4386/8000 [00:03<00:02, 1224.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2444/8000 [00:53<02:27, 37.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|████████▌      | 4564/8000 [00:03<00:02, 1246.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2449/8000 [00:53<02:53, 32.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|████████▉      | 4746/8000 [00:03<00:02, 1264.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2453/8000 [00:54<03:04, 30.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▏     | 4913/8000 [00:04<00:02, 1257.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2460/8000 [00:54<02:36, 35.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|█████████▌     | 5078/8000 [00:04<00:02, 1186.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2464/8000 [00:54<03:09, 29.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|█████████▊     | 5246/8000 [00:04<00:02, 1191.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2469/8000 [00:54<03:08, 29.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▏    | 5404/8000 [00:04<00:02, 1133.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▊             | 2473/8000 [00:54<03:21, 27.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|██████████▍    | 5580/8000 [00:04<00:02, 1158.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2478/8000 [00:54<02:56, 31.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|██████████▊    | 5750/8000 [00:04<00:01, 1175.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2482/8000 [00:54<02:48, 32.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████    | 5924/8000 [00:04<00:01, 1187.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2487/8000 [00:55<02:48, 32.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|███████████▍   | 6095/8000 [00:05<00:01, 1185.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2497/8000 [00:55<02:08, 42.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|███████████▊   | 6271/8000 [00:05<00:01, 1244.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2503/8000 [00:55<01:59, 46.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████   | 6441/8000 [00:05<00:01, 1249.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2510/8000 [00:55<02:13, 40.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|████████████▍  | 6623/8000 [00:05<00:01, 1265.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▉             | 2516/8000 [00:55<02:06, 43.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|████████████▊  | 6804/8000 [00:05<00:00, 1203.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|█████▉             | 2524/8000 [00:55<02:01, 45.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████  | 6976/8000 [00:05<00:00, 1213.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2531/8000 [00:55<01:50, 49.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|█████████████▍ | 7160/8000 [00:05<00:00, 1250.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2537/8000 [00:56<02:14, 40.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|█████████████▋ | 7323/8000 [00:06<00:00, 1215.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2542/8000 [00:56<02:08, 42.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████ | 7499/8000 [00:06<00:00, 1263.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2548/8000 [00:56<02:14, 40.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7690/8000 [00:07<00:00, 366.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2558/8000 [00:56<01:41, 53.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7863/8000 [00:07<00:00, 456.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████             | 2567/8000 [00:56<01:28, 61.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8029it [00:07, 1032.75it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  32%|██████             | 2574/8000 [00:56<01:26, 62.65it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  32%|██████▏            | 2583/8000 [00:56<01:37, 55.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████▏            | 2591/8000 [00:57<01:39, 54.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:25,  7.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2601/8000 [00:57<01:27, 61.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:59,  3.94s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2612/8000 [00:57<01:23, 64.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:57,  2.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2619/8000 [00:57<01:42, 52.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:04,  2.17s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▏            | 2628/8000 [00:57<01:50, 48.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:03,  1.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2634/8000 [00:58<02:08, 41.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:26,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2639/8000 [00:58<02:21, 37.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:00,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2643/8000 [00:58<02:34, 34.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:45,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2650/8000 [00:58<02:13, 40.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:28,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2656/8000 [00:58<02:27, 36.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:22,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2668/8000 [00:58<01:44, 51.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|██████▎            | 2674/8000 [00:58<01:55, 46.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:16,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▎            | 2680/8000 [00:59<01:51, 47.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2688/8000 [00:59<01:47, 49.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:14,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2694/8000 [00:59<02:00, 44.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:10,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2699/8000 [00:59<02:27, 35.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:07,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2703/8000 [00:59<02:24, 36.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:00,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2710/8000 [00:59<02:01, 43.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<03:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2718/8000 [00:59<01:42, 51.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<03:55,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2726/8000 [01:00<01:44, 50.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:53,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▍            | 2735/8000 [01:00<01:39, 52.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2742/8000 [01:00<01:57, 44.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<03:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2748/8000 [01:00<01:52, 46.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|██████▌            | 2757/8000 [01:00<01:44, 50.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2763/8000 [01:00<01:57, 44.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<03:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2769/8000 [01:01<02:05, 41.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:40<03:49,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2778/8000 [01:01<01:54, 45.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▌            | 2784/8000 [01:01<01:52, 46.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2790/8000 [01:01<02:02, 42.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:44<03:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2795/8000 [01:01<01:58, 43.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:45<03:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2802/8000 [01:01<02:14, 38.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:42,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2811/8000 [01:02<02:00, 43.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:48<03:42,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2818/8000 [01:02<01:46, 48.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:49<03:43,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2825/8000 [01:02<02:00, 42.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:51<03:40,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2830/8000 [01:02<01:59, 43.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:52<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|██████▋            | 2836/8000 [01:02<02:08, 40.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:53<03:30,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2845/8000 [01:02<01:56, 44.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:54<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2852/8000 [01:02<01:57, 43.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:56<03:26,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2861/8000 [01:03<01:39, 51.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:57<03:25,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2868/8000 [01:03<01:59, 43.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:58<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2873/8000 [01:03<01:55, 44.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:59<03:20,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2878/8000 [01:03<02:08, 39.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:01<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2883/8000 [01:03<02:05, 40.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:02<03:19,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2890/8000 [01:03<02:17, 37.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:03<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▊            | 2894/8000 [01:04<02:17, 37.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:05<03:29,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2900/8000 [01:04<02:00, 42.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:06<03:23,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2907/8000 [01:04<01:44, 48.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:07<03:18,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|██████▉            | 2915/8000 [01:04<01:33, 54.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:09<03:17,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2921/8000 [01:04<01:45, 48.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:10<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2928/8000 [01:04<01:49, 46.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:11<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2933/8000 [01:04<01:50, 46.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:12<03:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2938/8000 [01:04<01:53, 44.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:14<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▉            | 2944/8000 [01:05<02:00, 41.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:15<03:03,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2952/8000 [01:05<01:57, 42.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:16<03:04,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2957/8000 [01:05<01:54, 44.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:17<03:02,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2967/8000 [01:05<01:38, 50.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:19<03:00,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2973/8000 [01:05<02:11, 38.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:21<03:25,  1.43s/it]\n",
+      "Generating signals:  37%|███████            | 2981/8000 [01:05<01:51, 45.00it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  37%|███████            | 2990/8000 [01:06<01:43, 48.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|███████            | 2996/8000 [01:06<01:39, 50.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.75it/s]\n",
+      "Generating signals:  38%|███████▏           | 3002/8000 [01:06<01:59, 41.91it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  38%|███████▏           | 3008/8000 [01:06<02:30, 33.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3016/8000 [01:06<02:02, 40.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3023/8000 [01:06<01:47, 46.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 41it [00:01, 35.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3029/8000 [01:07<01:53, 43.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 87it [00:01, 81.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3039/8000 [01:07<01:38, 50.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 128it [00:01, 125.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▏           | 3047/8000 [01:07<02:02, 40.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 172it [00:01, 176.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3053/8000 [01:07<01:54, 43.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 213it [00:02, 220.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3059/8000 [01:07<02:19, 35.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 253it [00:02, 248.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3065/8000 [01:08<02:36, 31.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 291it [00:02, 267.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3069/8000 [01:08<02:35, 31.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 328it [00:02, 276.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|███████▎           | 3077/8000 [01:08<02:09, 37.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 363it [00:02, 287.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3082/8000 [01:08<02:04, 39.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 397it [00:02, 281.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3089/8000 [01:08<01:57, 41.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 436it [00:02, 307.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3094/8000 [01:08<02:09, 37.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 474it [00:02, 326.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3099/8000 [01:08<02:05, 39.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 510it [00:02, 334.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▎           | 3105/8000 [01:08<01:54, 42.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 551it [00:03, 354.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3112/8000 [01:09<01:58, 41.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 591it [00:03, 364.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3117/8000 [01:09<02:10, 37.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 634it [00:03, 382.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3121/8000 [01:09<02:09, 37.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 675it [00:03, 389.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3129/8000 [01:09<01:43, 47.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 715it [00:03, 384.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3138/8000 [01:09<01:35, 51.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 757it [00:03, 392.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|███████▍           | 3144/8000 [01:09<01:33, 52.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 220.00it/s]\n",
+      "Generating signals:  39%|███████▍           | 3150/8000 [01:09<01:44, 46.33it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  39%|███████▍           | 3156/8000 [01:10<01:39, 48.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3162/8000 [01:10<01:45, 46.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:00, 194\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3167/8000 [01:10<02:00, 40.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 200\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3173/8000 [01:10<02:15, 35.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  62%|▌| 62/100 [00:00<00:00, 194\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3180/8000 [01:10<01:53, 42.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 19\n",
+      "Generating signals:  40%|███████▌           | 3189/8000 [01:10<01:40, 48.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [05:16<15:49, 316.42s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.340581843256334\n",
-      "2999: -6.2318021\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:52,  7.20s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:49,  3.89s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:56,  2.73s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:07,  2.18s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:00,  1.85s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:21,  1.66s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   4%|█                              | 7/200 [00:15<04:55,  1.53s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:36,  1.44s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:25,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3195/8000 [01:11<02:11, 36.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:16,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3201/8000 [01:11<02:12, 36.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:14,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▌           | 3207/8000 [01:11<02:02, 38.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:09,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3214/8000 [01:11<01:48, 44.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:22<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3220/8000 [01:11<01:41, 46.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3227/8000 [01:11<01:43, 46.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:00,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3232/8000 [01:11<01:43, 46.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:26<03:56,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|███████▋           | 3239/8000 [01:12<01:46, 44.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<03:57,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3246/8000 [01:12<01:35, 49.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:29<03:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3253/8000 [01:12<01:37, 48.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:30<03:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▋           | 3259/8000 [01:12<01:45, 44.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:54,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3266/8000 [01:12<01:47, 44.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3271/8000 [01:12<01:59, 39.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:34<03:46,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3278/8000 [01:12<01:55, 40.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:35<03:42,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3284/8000 [01:13<01:56, 40.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:43,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3291/8000 [01:13<02:16, 34.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:38<03:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3296/8000 [01:13<02:21, 33.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3301/8000 [01:13<02:10, 35.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:40<03:39,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3308/8000 [01:13<02:12, 35.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:42<03:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▊           | 3312/8000 [01:13<02:09, 36.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:43<03:36,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|███████▉           | 3316/8000 [01:14<02:11, 35.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:44<03:35,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3323/8000 [01:14<01:48, 43.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:45<03:35,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3329/8000 [01:14<01:40, 46.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:47<03:33,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3336/8000 [01:14<01:48, 42.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:48<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3341/8000 [01:14<02:12, 35.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:49<03:37,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3350/8000 [01:14<01:42, 45.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:51<03:43,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3356/8000 [01:15<01:58, 39.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:52<03:36,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3362/8000 [01:15<01:50, 41.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:53<03:30,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████▉           | 3368/8000 [01:15<01:56, 39.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:55<03:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3376/8000 [01:15<02:02, 37.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:56<03:30,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3383/8000 [01:15<01:54, 40.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:57<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3389/8000 [01:15<01:44, 44.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:58<03:23,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|████████           | 3394/8000 [01:15<01:44, 44.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:00<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3405/8000 [01:16<01:20, 57.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:01<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3412/8000 [01:16<01:56, 39.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:02<03:14,  1.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████           | 3420/8000 [01:16<01:39, 45.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:03<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3429/8000 [01:16<01:50, 41.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:05<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3434/8000 [01:16<01:59, 38.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:06<03:14,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3441/8000 [01:17<01:45, 43.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:07<03:16,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3448/8000 [01:17<01:37, 46.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:09<03:15,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3456/8000 [01:17<01:34, 48.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:10<03:23,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3462/8000 [01:17<01:31, 49.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:11<03:22,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▏          | 3472/8000 [01:17<01:16, 59.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:13<03:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|████████▎          | 3479/8000 [01:17<01:24, 53.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:14<03:20,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3488/8000 [01:17<01:26, 52.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:16<03:18,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3495/8000 [01:18<01:58, 38.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:17<03:27,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3500/8000 [01:18<02:03, 36.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:19<03:26,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3506/8000 [01:18<02:05, 35.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:20<03:20,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3512/8000 [01:18<01:53, 39.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:21<03:16,  1.39s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:  30%|████████▊                     | 59/200 [01:23<03:13,  1.37s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:  30%|█████████                     | 60/200 [01:24<03:11,  1.37s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:25<03:12,  1.39s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:27<03:09,  1.38s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:30<03:15,  1.43s/it]\n",
-      "\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 25it [00:01, 14.74it/s]\n",
-      "\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "Evaluating NLL: 1it [00:01,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3517/8000 [01:18<01:50, 40.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 31.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▎          | 3522/8000 [01:18<01:46, 42.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 66it [00:01, 60.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3529/8000 [01:19<01:56, 38.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 97it [00:01, 94.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3535/8000 [01:19<01:56, 38.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 131it [00:01, 133.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3542/8000 [01:19<01:39, 44.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 161it [00:02, 161.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3548/8000 [01:19<01:32, 48.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 198it [00:02, 203.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|████████▍          | 3554/8000 [01:19<01:28, 50.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 232it [00:02, 233.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▍          | 3564/8000 [01:19<01:12, 61.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 267it [00:02, 259.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▍          | 3572/8000 [01:19<01:16, 57.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 300it [00:02, 270.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3579/8000 [01:19<01:22, 53.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 332it [00:02, 283.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3585/8000 [01:20<01:40, 43.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 365it [00:02, 294.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3591/8000 [01:20<01:33, 47.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 397it [00:02, 301.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3599/8000 [01:20<01:42, 42.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 432it [00:02, 314.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3607/8000 [01:20<01:28, 49.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 465it [00:02, 308.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3618/8000 [01:20<01:10, 61.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 497it [00:03, 307.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▌          | 3625/8000 [01:20<01:18, 55.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 529it [00:03, 296.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|████████▋          | 3635/8000 [01:21<01:32, 47.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 564it [00:03, 310.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3648/8000 [01:21<01:17, 56.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 596it [00:03, 289.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3656/8000 [01:21<01:12, 60.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 626it [00:03, 289.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3665/8000 [01:21<01:14, 58.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 659it [00:03, 300.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3675/8000 [01:21<01:13, 58.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 690it [00:03, 302.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▋          | 3682/8000 [01:21<01:21, 53.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 721it [00:03, 296.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3690/8000 [01:22<01:21, 52.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 751it [00:03, 282.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3698/8000 [01:22<01:22, 52.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 194.66it/s]\n",
+      "Generating signals:  46%|████████▊          | 3704/8000 [01:22<01:20, 53.62it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  46%|████████▊          | 3710/8000 [01:22<01:18, 54.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████▊          | 3717/8000 [01:22<01:13, 58.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 136\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▊          | 3725/8000 [01:22<01:18, 54.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 175\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▊          | 3733/8000 [01:22<01:18, 54.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:00<00:00, 169\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3739/8000 [01:22<01:18, 54.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  70%|▋| 70/100 [00:00<00:00, 161\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3748/8000 [01:23<01:27, 48.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  47%|████████▉          | 3755/8000 [01:23<01:38, 43.27it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  47%|████████▉          | 3763/8000 [01:23<01:25, 49.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3771/8000 [01:23<01:33, 45.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 55/8000 [00:00<00:19, 403.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3779/8000 [01:23<01:22, 51.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 111/8000 [00:00<00:20, 385.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████▉          | 3785/8000 [01:24<01:43, 40.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 177/8000 [00:00<00:16, 461.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|█████████          | 3793/8000 [01:24<01:28, 47.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 228/8000 [00:00<00:18, 424.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3800/8000 [01:24<01:33, 45.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▌                | 280/8000 [00:00<00:19, 401.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3806/8000 [01:24<01:29, 46.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 334/8000 [00:00<00:18, 409.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3813/8000 [01:24<01:21, 51.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 383/8000 [00:00<00:19, 398.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3819/8000 [01:24<01:39, 42.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 430/8000 [00:01<00:19, 394.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3825/8000 [01:24<01:43, 40.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 480/8000 [00:01<00:19, 383.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3831/8000 [01:25<01:33, 44.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 520/8000 [00:01<00:22, 328.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████          | 3838/8000 [01:25<01:35, 43.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 567/8000 [00:01<00:21, 345.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3845/8000 [01:25<01:26, 47.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 620/8000 [00:01<00:19, 378.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3851/8000 [01:25<01:23, 49.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 662/8000 [00:01<00:20, 357.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3859/8000 [01:25<01:24, 49.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 713/8000 [00:01<00:19, 364.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3865/8000 [01:25<01:32, 44.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 769/8000 [00:02<00:19, 376.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3870/8000 [01:25<01:33, 44.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 835/8000 [00:02<00:17, 407.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|█████████▏         | 3876/8000 [01:26<01:39, 41.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 888/8000 [00:02<00:18, 391.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▏         | 3882/8000 [01:26<01:30, 45.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 931/8000 [00:02<00:18, 374.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▏         | 3887/8000 [01:26<01:32, 44.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 981/8000 [00:02<00:19, 359.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▏         | 3894/8000 [01:26<01:43, 39.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1035/8000 [00:02<00:19, 359.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3903/8000 [01:26<01:22, 49.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1085/8000 [00:02<00:19, 360.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3910/8000 [01:26<01:25, 47.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1136/8000 [00:03<00:19, 359.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3918/8000 [01:26<01:15, 54.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1176/8000 [00:03<00:20, 340.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3927/8000 [01:26<01:08, 59.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1215/8000 [00:03<00:21, 311.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3936/8000 [01:27<01:10, 57.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1268/8000 [00:03<00:20, 331.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▎         | 3944/8000 [01:27<01:13, 55.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋             | 1320/8000 [00:03<00:18, 364.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▍         | 3950/8000 [01:27<01:14, 54.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1373/8000 [00:03<00:17, 373.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████▍         | 3956/8000 [01:27<01:12, 55.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1429/8000 [00:03<00:16, 389.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3963/8000 [01:27<01:28, 45.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1478/8000 [00:03<00:16, 386.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3970/8000 [01:27<01:29, 44.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1526/8000 [00:04<00:17, 361.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3975/8000 [01:28<01:40, 39.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1566/8000 [00:04<00:18, 352.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3981/8000 [01:28<01:32, 43.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1622/8000 [00:04<00:17, 368.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3988/8000 [01:28<01:43, 38.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1669/8000 [00:04<00:17, 353.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▍         | 3994/8000 [01:28<01:34, 42.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1726/8000 [00:04<00:17, 354.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4007/8000 [01:28<01:04, 61.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1772/8000 [00:04<00:18, 336.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4015/8000 [01:28<01:21, 49.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1816/8000 [00:04<00:18, 331.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4022/8000 [01:29<01:25, 46.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1872/8000 [00:05<00:16, 364.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4028/8000 [01:29<01:24, 47.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1923/8000 [00:05<00:16, 368.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4034/8000 [01:29<01:30, 43.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1971/8000 [00:05<00:16, 360.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████▌         | 4040/8000 [01:29<01:32, 42.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2025/8000 [00:05<00:16, 369.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▌         | 4048/8000 [01:29<01:43, 38.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2074/8000 [00:05<00:15, 371.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4053/8000 [01:29<01:48, 36.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▏           | 2123/8000 [00:05<00:16, 348.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4057/8000 [01:30<01:57, 33.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2177/8000 [00:05<00:15, 373.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4061/8000 [01:30<01:54, 34.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2231/8000 [00:06<00:15, 365.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4070/8000 [01:30<01:24, 46.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2297/8000 [00:06<00:14, 392.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4080/8000 [01:30<01:25, 45.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2346/8000 [00:06<00:15, 372.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4087/8000 [01:30<01:25, 45.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2409/8000 [00:06<00:13, 410.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4093/8000 [01:30<01:22, 47.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2464/8000 [00:06<00:13, 413.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▋         | 4099/8000 [01:30<01:29, 43.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2509/8000 [00:06<00:14, 385.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▊         | 4106/8000 [01:31<01:30, 43.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2562/8000 [00:06<00:13, 388.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▊         | 4113/8000 [01:31<01:20, 48.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2608/8000 [00:07<00:15, 348.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▊         | 4119/8000 [01:31<01:27, 44.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2651/8000 [00:07<00:15, 348.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4124/8000 [01:31<01:28, 43.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2699/8000 [00:07<00:15, 342.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4131/8000 [01:31<01:18, 49.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2757/8000 [00:07<00:14, 367.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4137/8000 [01:31<01:24, 45.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2811/8000 [00:07<00:13, 388.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4143/8000 [01:31<01:50, 34.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2852/8000 [00:07<00:15, 337.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4150/8000 [01:32<01:32, 41.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2900/8000 [00:07<00:14, 357.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▊         | 4157/8000 [01:32<01:22, 46.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2942/8000 [00:07<00:14, 353.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4164/8000 [01:32<01:26, 44.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2993/8000 [00:08<00:13, 382.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4172/8000 [01:32<01:16, 50.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3040/8000 [00:08<00:13, 374.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4179/8000 [01:32<01:11, 53.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3080/8000 [00:08<00:13, 359.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4185/8000 [01:32<01:36, 39.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3131/8000 [00:08<00:13, 367.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4190/8000 [01:32<01:32, 41.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3176/8000 [00:08<00:13, 365.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████▉         | 4197/8000 [01:33<01:32, 41.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3227/8000 [00:08<00:13, 353.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▉         | 4203/8000 [01:33<01:34, 40.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3273/8000 [00:08<00:12, 366.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▉         | 4208/8000 [01:33<01:32, 41.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▋         | 3314/8000 [00:09<00:13, 348.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4214/8000 [01:33<01:26, 43.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3366/8000 [00:09<00:13, 343.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4221/8000 [01:33<01:25, 44.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3410/8000 [00:09<00:13, 348.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4229/8000 [01:33<01:32, 40.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3458/8000 [00:09<00:12, 355.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4235/8000 [01:34<01:37, 38.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3507/8000 [00:09<00:12, 373.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4241/8000 [01:34<01:29, 41.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3552/8000 [00:09<00:12, 368.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4250/8000 [01:34<01:20, 46.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3597/8000 [00:09<00:12, 366.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████         | 4257/8000 [01:34<01:19, 46.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3644/8000 [00:09<00:12, 346.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4265/8000 [01:34<01:24, 44.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3681/8000 [00:10<00:13, 316.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4271/8000 [01:34<01:47, 34.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3719/8000 [00:10<00:13, 317.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████▏        | 4275/8000 [01:35<01:46, 34.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3764/8000 [00:10<00:14, 284.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4284/8000 [01:35<01:22, 44.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3804/8000 [00:10<00:16, 252.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4293/8000 [01:35<01:14, 49.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3851/8000 [00:10<00:15, 273.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4300/8000 [01:35<01:10, 52.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3907/8000 [00:10<00:13, 294.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4307/8000 [01:35<01:25, 43.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3963/8000 [00:11<00:13, 307.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▏        | 4314/8000 [01:35<01:35, 38.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4015/8000 [00:11<00:12, 315.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4321/8000 [01:36<01:24, 43.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4072/8000 [00:11<00:11, 330.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4326/8000 [01:36<01:21, 44.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4116/8000 [00:11<00:11, 324.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4331/8000 [01:36<01:20, 45.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4178/8000 [00:11<00:10, 351.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4338/8000 [01:36<01:12, 50.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4225/8000 [00:11<00:10, 359.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4346/8000 [01:36<01:11, 50.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4275/8000 [00:11<00:09, 373.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4352/8000 [01:36<01:16, 47.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4320/8000 [00:12<00:10, 363.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████▎        | 4357/8000 [01:36<01:29, 40.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4380/8000 [00:12<00:09, 395.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▎        | 4364/8000 [01:36<01:28, 41.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4424/8000 [00:12<00:09, 362.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4369/8000 [01:37<01:25, 42.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4476/8000 [00:12<00:10, 337.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4378/8000 [01:37<01:17, 46.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4522/8000 [00:12<00:11, 301.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4384/8000 [01:37<01:22, 43.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4574/8000 [00:12<00:10, 331.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4392/8000 [01:37<01:11, 50.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4626/8000 [00:12<00:10, 329.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4398/8000 [01:37<01:16, 47.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4674/8000 [00:13<00:10, 327.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4404/8000 [01:37<01:12, 49.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4721/8000 [00:13<00:10, 323.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4411/8000 [01:37<01:08, 52.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4767/8000 [00:13<00:10, 313.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▍        | 4418/8000 [01:38<01:11, 50.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4819/8000 [00:13<00:10, 310.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▌        | 4425/8000 [01:38<01:14, 47.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4863/8000 [00:13<00:09, 323.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████▌        | 4430/8000 [01:38<01:15, 47.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4908/8000 [00:13<00:09, 330.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4440/8000 [01:38<01:07, 53.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4965/8000 [00:13<00:07, 381.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4446/8000 [01:38<01:06, 53.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5011/8000 [00:14<00:08, 359.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4452/8000 [01:38<01:12, 48.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5055/8000 [00:14<00:08, 361.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4459/8000 [01:38<01:13, 48.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5114/8000 [00:14<00:07, 387.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4466/8000 [01:38<01:06, 53.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5159/8000 [00:14<00:07, 390.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▌        | 4472/8000 [01:39<01:05, 53.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5209/8000 [00:14<00:07, 385.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4481/8000 [01:39<00:58, 60.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5255/8000 [00:14<00:06, 394.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4489/8000 [01:39<01:04, 54.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5302/8000 [00:14<00:07, 384.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4496/8000 [01:39<01:01, 56.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5342/8000 [00:14<00:07, 369.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4504/8000 [01:39<00:58, 60.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5397/8000 [00:15<00:06, 377.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4512/8000 [01:39<01:02, 55.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5444/8000 [00:15<00:06, 377.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████▋        | 4519/8000 [01:40<01:31, 38.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5491/8000 [00:15<00:06, 367.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▋        | 4525/8000 [01:40<01:29, 38.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5549/8000 [00:15<00:06, 389.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4531/8000 [01:40<01:24, 40.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5594/8000 [00:15<00:06, 386.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4536/8000 [01:40<01:24, 41.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5645/8000 [00:15<00:05, 393.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4542/8000 [01:40<01:29, 38.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5702/8000 [00:15<00:05, 431.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4548/8000 [01:40<01:23, 41.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5749/8000 [00:15<00:05, 421.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4554/8000 [01:40<01:27, 39.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▌    | 5808/8000 [00:16<00:05, 429.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4561/8000 [01:41<01:17, 44.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5856/8000 [00:16<00:05, 400.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4566/8000 [01:41<01:24, 40.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5912/8000 [00:16<00:05, 385.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4572/8000 [01:41<01:26, 39.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5957/8000 [00:16<00:05, 377.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▊        | 4578/8000 [01:41<01:18, 43.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6046/8000 [00:16<00:05, 365.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4585/8000 [01:41<01:11, 47.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6089/8000 [00:16<00:05, 356.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4591/8000 [01:41<01:20, 42.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6144/8000 [00:17<00:05, 354.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████▉        | 4598/8000 [01:42<01:32, 36.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▍   | 6192/8000 [00:17<00:05, 315.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4603/8000 [01:42<01:28, 38.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6238/8000 [00:17<00:05, 329.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4608/8000 [01:42<01:28, 38.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6304/8000 [00:17<00:04, 346.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4616/8000 [01:42<01:24, 40.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6356/8000 [00:17<00:04, 331.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4621/8000 [01:42<01:21, 41.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6399/8000 [00:17<00:05, 278.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▉        | 4628/8000 [01:42<01:14, 45.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▉   | 6439/8000 [00:18<00:05, 273.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4640/8000 [01:42<00:55, 60.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6476/8000 [00:18<00:05, 266.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4654/8000 [01:43<00:54, 61.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6524/8000 [00:18<00:04, 299.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4661/8000 [01:43<01:02, 53.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6575/8000 [00:18<00:05, 278.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4668/8000 [01:43<01:02, 53.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6622/8000 [00:18<00:04, 283.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████        | 4676/8000 [01:43<01:04, 51.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6661/8000 [00:18<00:04, 287.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████        | 4682/8000 [01:43<01:17, 42.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6699/8000 [00:18<00:04, 299.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4687/8000 [01:43<01:23, 39.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▌  | 6751/8000 [00:19<00:03, 325.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4693/8000 [01:44<01:25, 38.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6790/8000 [00:19<00:03, 309.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4697/8000 [01:44<01:28, 37.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6839/8000 [00:19<00:03, 327.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4704/8000 [01:44<01:15, 43.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6886/8000 [00:19<00:03, 345.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4712/8000 [01:44<01:05, 50.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6940/8000 [00:19<00:02, 367.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4718/8000 [01:44<01:02, 52.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6990/8000 [00:19<00:02, 389.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4724/8000 [01:44<01:15, 43.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7042/8000 [00:19<00:02, 410.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▏       | 4732/8000 [01:44<01:14, 43.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7096/8000 [00:19<00:02, 418.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4740/8000 [01:45<01:12, 45.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7142/8000 [00:20<00:02, 413.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4747/8000 [01:45<01:06, 48.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7200/8000 [00:20<00:01, 427.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4754/8000 [01:45<01:07, 48.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7258/8000 [00:20<00:01, 441.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████▎       | 4759/8000 [01:45<01:27, 36.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7309/8000 [00:20<00:01, 443.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4773/8000 [01:45<00:56, 56.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7364/8000 [00:20<00:01, 415.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4780/8000 [01:45<00:54, 58.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7415/8000 [00:20<00:01, 428.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▎       | 4789/8000 [01:45<00:55, 58.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7466/8000 [00:20<00:01, 432.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4798/8000 [01:46<00:55, 57.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████ | 7561/8000 [00:21<00:01, 418.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4811/8000 [01:46<00:53, 59.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7607/8000 [00:21<00:00, 424.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4818/8000 [01:46<01:03, 49.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7697/8000 [00:21<00:00, 401.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4825/8000 [01:46<01:12, 43.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7785/8000 [00:21<00:00, 371.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4831/8000 [01:46<01:10, 44.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7865/8000 [00:22<00:00, 328.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|███████████▍       | 4836/8000 [01:46<01:11, 44.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7920/8000 [00:22<00:00, 353.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▍       | 4841/8000 [01:47<01:12, 43.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7966/8000 [00:22<00:00, 351.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4849/8000 [01:47<01:08, 46.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8008it [00:22, 358.12it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  61%|███████████▌       | 4855/8000 [01:47<01:05, 48.25it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  61%|███████████▌       | 4860/8000 [01:47<01:19, 39.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4865/8000 [01:47<01:16, 40.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:06<23:08,  6.98s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4872/8000 [01:47<01:14, 42.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<11:56,  3.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4877/8000 [01:48<01:20, 38.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:21,  2.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4883/8000 [01:48<01:13, 42.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:10<06:44,  2.06s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▌       | 4891/8000 [01:48<01:01, 50.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:48,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4899/8000 [01:48<01:11, 43.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:12,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4905/8000 [01:48<01:24, 36.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:14<04:47,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4912/8000 [01:48<01:13, 42.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:15<04:35,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|███████████▋       | 4917/8000 [01:49<01:28, 34.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4923/8000 [01:49<01:19, 38.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:18<04:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4929/8000 [01:49<01:11, 42.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:19<04:09,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4935/8000 [01:49<01:08, 44.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▋       | 4944/8000 [01:49<01:05, 46.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:22<04:04,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4949/8000 [01:49<01:18, 38.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:23<04:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4954/8000 [01:49<01:27, 34.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:24<03:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4961/8000 [01:50<01:33, 32.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:26<04:01,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4968/8000 [01:50<01:19, 38.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:27<03:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4973/8000 [01:50<01:36, 31.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:28<03:53,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4977/8000 [01:50<01:35, 31.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:30<03:53,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4981/8000 [01:50<01:33, 32.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:31<03:52,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4987/8000 [01:50<01:22, 36.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:32<03:51,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4992/8000 [01:51<01:19, 37.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:33<03:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|███████████▊       | 4997/8000 [01:51<01:18, 38.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:35<03:49,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5002/8000 [01:51<01:15, 39.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:36<03:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5008/8000 [01:51<01:22, 36.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:37<03:44,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5012/8000 [01:51<01:22, 36.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5019/8000 [01:51<01:11, 41.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:40<03:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5024/8000 [01:51<01:11, 41.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:41<03:39,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5031/8000 [01:51<01:12, 40.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:42<03:39,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5038/8000 [01:52<01:29, 33.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:44<03:37,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5042/8000 [01:52<01:27, 33.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:45<03:35,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5046/8000 [01:52<01:35, 30.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:46<03:34,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|███████████▉       | 5050/8000 [01:52<01:31, 32.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:48<03:34,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5056/8000 [01:52<01:18, 37.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:49<03:32,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5063/8000 [01:53<01:31, 32.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:50<03:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|████████████       | 5069/8000 [01:53<01:18, 37.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:51<03:30,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5082/8000 [01:53<00:52, 55.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:53<03:29,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5089/8000 [01:53<01:16, 38.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:54<03:28,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5095/8000 [01:53<01:10, 41.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:55<03:27,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████       | 5105/8000 [01:53<00:56, 51.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:57<03:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5113/8000 [01:54<01:04, 44.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:58<03:23,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5121/8000 [01:54<01:12, 39.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [00:59<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5126/8000 [01:54<01:10, 40.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:00<03:17,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5131/8000 [01:54<01:07, 42.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:02<03:15,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5136/8000 [01:54<01:06, 43.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:03<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5144/8000 [01:54<01:05, 43.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:04<03:14,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5150/8000 [01:54<01:01, 46.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:05<03:12,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|████████████▏      | 5156/8000 [01:55<01:06, 42.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:07<03:11,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5163/8000 [01:55<01:06, 42.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:08<03:09,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5170/8000 [01:55<01:06, 42.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:09<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5176/8000 [01:55<01:03, 44.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:10<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5182/8000 [01:55<01:14, 37.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:12<03:06,  1.26s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5193/8000 [01:55<00:54, 51.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:13<03:07,  1.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5201/8000 [01:56<01:04, 43.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:14<03:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▎      | 5209/8000 [01:56<01:08, 40.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:16<03:12,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5215/8000 [01:56<01:10, 39.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:18<03:22,  1.41s/it]\n",
+      "Generating signals:  65%|████████████▍      | 5221/8000 [01:56<01:13, 37.75it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  65%|████████████▍      | 5226/8000 [01:56<01:13, 37.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|████████████▍      | 5231/8000 [01:56<01:19, 34.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.67it/s]\n",
+      "Generating signals:  65%|████████████▍      | 5238/8000 [01:57<01:16, 36.21it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  66%|████████████▍      | 5243/8000 [01:57<01:12, 38.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5250/8000 [01:57<01:03, 43.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▍      | 5255/8000 [01:57<01:02, 43.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 34it [00:01, 30.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5265/8000 [01:57<00:49, 55.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 75it [00:01, 74.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5275/8000 [01:57<00:51, 52.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 116it [00:01, 121.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5281/8000 [01:57<00:56, 48.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 156it [00:01, 168.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5287/8000 [01:58<01:07, 40.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 202it [00:01, 224.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5292/8000 [01:58<01:12, 37.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 242it [00:02, 255.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5296/8000 [01:58<01:13, 36.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 285it [00:02, 294.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5302/8000 [01:58<01:15, 35.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 328it [00:02, 328.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5307/8000 [01:58<01:11, 37.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 369it [00:02, 343.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▌      | 5313/8000 [01:58<01:11, 37.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 409it [00:02, 350.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|████████████▋      | 5317/8000 [01:59<01:20, 33.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 458it [00:02, 386.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5322/8000 [01:59<01:14, 35.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 500it [00:02, 379.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5326/8000 [01:59<01:15, 35.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 541it [00:02, 369.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5334/8000 [01:59<01:02, 42.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 580it [00:02, 363.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5340/8000 [01:59<01:08, 38.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 619it [00:03, 370.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5346/8000 [01:59<01:04, 41.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 659it [00:03, 378.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5354/8000 [01:59<00:55, 47.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 698it [00:03, 370.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5360/8000 [02:00<01:03, 41.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 736it [00:03, 353.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▋      | 5365/8000 [02:00<01:11, 36.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 230.98it/s]\n",
+      "Generating signals:  67%|████████████▊      | 5369/8000 [02:00<01:13, 35.79it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  67%|████████████▊      | 5375/8000 [02:00<01:06, 39.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5382/8000 [02:00<01:05, 39.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:00, 168\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5387/8000 [02:00<01:20, 32.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00<00:00, 158\u001b[A\u001b[A\n",
+      "Generating signals:  67%|████████████▊      | 5391/8000 [02:01<01:18, 33.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 162\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5402/8000 [02:01<00:55, 47.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:00<00:00, 156\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▊      | 5410/8000 [02:01<00:53, 48.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  68%|████████████▊      | 5418/8000 [02:01<00:48, 53.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [10:27<10:26, 313.25s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.7862902059043932\n",
-      "2999: -6.1698457\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  68%|████████████▉      | 5424/8000 [02:01<00:52, 49.06it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  68%|████████████▉      | 5431/8000 [02:01<01:10, 36.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5438/8000 [02:01<01:00, 42.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:45,  7.16s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5446/8000 [02:02<00:58, 43.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:32,  3.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5451/8000 [02:02<00:57, 44.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:48,  2.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5458/8000 [02:02<01:05, 38.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:58,  2.13s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5464/8000 [02:02<01:06, 38.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:59,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|████████████▉      | 5469/8000 [02:02<01:11, 35.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:25,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|█████████████      | 5474/8000 [02:02<01:07, 37.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:15,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|█████████████      | 5480/8000 [02:03<01:06, 37.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:53,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5490/8000 [02:03<00:49, 50.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:39,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5496/8000 [02:03<00:58, 42.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:30,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5503/8000 [02:03<00:52, 47.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:25,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5510/8000 [02:03<00:48, 50.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:22,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5517/8000 [02:03<00:56, 44.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:16,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████      | 5522/8000 [02:03<00:55, 44.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:17,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5530/8000 [02:04<00:47, 52.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:17,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5536/8000 [02:04<00:52, 46.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:10,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5544/8000 [02:04<00:57, 42.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:09,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|█████████████▏     | 5552/8000 [02:04<00:49, 49.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<04:06,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5560/8000 [02:04<00:53, 45.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<04:02,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5567/8000 [02:04<00:54, 44.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<04:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▏     | 5575/8000 [02:05<00:52, 46.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:34<03:58,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5580/8000 [02:05<00:52, 46.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<03:57,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5588/8000 [02:05<00:52, 45.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:37<04:00,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5593/8000 [02:05<00:52, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:38<04:02,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5599/8000 [02:05<00:50, 47.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<03:59,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5605/8000 [02:05<00:47, 50.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:41<03:55,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5616/8000 [02:05<00:41, 57.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:42<03:52,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5622/8000 [02:06<00:57, 41.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:53,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▎     | 5627/8000 [02:06<00:55, 42.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:45<03:53,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|█████████████▍     | 5637/8000 [02:06<00:48, 48.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:46<03:47,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5645/8000 [02:06<00:47, 49.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:43,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5654/8000 [02:06<00:46, 50.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:49<03:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5661/8000 [02:06<00:47, 49.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:50<03:39,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5667/8000 [02:06<00:45, 51.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:51<03:41,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5673/8000 [02:07<00:44, 52.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:53<03:40,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▍     | 5684/8000 [02:07<00:45, 51.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:54<03:37,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5690/8000 [02:07<00:52, 44.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:55<03:40,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5698/8000 [02:07<00:45, 50.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:57<03:54,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5704/8000 [02:07<00:54, 41.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:58<03:53,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5709/8000 [02:08<01:03, 35.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:00<03:45,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████▌     | 5715/8000 [02:08<01:07, 33.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:01<03:43,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5720/8000 [02:08<01:14, 30.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:03<03:39,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5726/8000 [02:08<01:07, 33.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:04<03:36,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▌     | 5731/8000 [02:08<01:03, 35.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:05<03:34,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5740/8000 [02:08<00:52, 42.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:07<03:32,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5745/8000 [02:08<00:50, 44.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:08<03:30,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5752/8000 [02:09<00:45, 49.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:09<03:29,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5759/8000 [02:09<00:41, 54.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:11<03:26,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5766/8000 [02:09<00:50, 44.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:12<03:24,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5772/8000 [02:09<00:58, 38.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:13<03:24,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5778/8000 [02:09<00:58, 37.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:15<03:22,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▋     | 5786/8000 [02:09<00:48, 45.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:16<03:34,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▊     | 5794/8000 [02:10<00:48, 45.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:18<03:33,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████▊     | 5799/8000 [02:10<01:00, 36.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:19<03:29,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5804/8000 [02:10<00:58, 37.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:21<03:22,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5811/8000 [02:10<00:51, 42.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:22<03:29,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5818/8000 [02:10<00:45, 47.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:24<03:35,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5826/8000 [02:10<00:39, 54.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:25<03:25,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5834/8000 [02:10<00:40, 53.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:26<03:21,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▊     | 5841/8000 [02:11<00:52, 40.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:28<03:15,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5847/8000 [02:11<00:50, 43.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:31<03:27,  1.49s/it]\n",
+      "Generating signals:  73%|█████████████▉     | 5857/8000 [02:11<00:40, 52.72it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  73%|█████████████▉     | 5866/8000 [02:11<00:40, 52.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5872/8000 [02:11<00:45, 46.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████▉     | 5878/8000 [02:11<00:51, 41.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.87it/s]\n",
+      "Generating signals:  74%|█████████████▉     | 5884/8000 [02:12<01:04, 33.03it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  74%|█████████████▉     | 5891/8000 [02:12<00:57, 36.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5896/8000 [02:12<01:11, 29.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5902/8000 [02:12<01:06, 31.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 33it [00:01, 28.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5906/8000 [02:12<01:04, 32.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 73it [00:01, 70.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5912/8000 [02:12<00:57, 36.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 101it [00:01, 97.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5916/8000 [02:13<01:10, 29.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 134it [00:01, 134.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5921/8000 [02:13<01:10, 29.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 167it [00:01, 170.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5926/8000 [02:13<01:06, 31.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 200it [00:02, 202.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5933/8000 [02:13<00:57, 35.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 236it [00:02, 236.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5938/8000 [02:13<01:02, 32.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 272it [00:02, 266.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████     | 5943/8000 [02:13<00:59, 34.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 309it [00:02, 292.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████▏    | 5948/8000 [02:14<00:56, 36.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 344it [00:02, 298.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████▏    | 5952/8000 [02:14<00:57, 35.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 378it [00:02, 304.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████▏    | 5957/8000 [02:14<00:53, 38.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 412it [00:02, 312.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5962/8000 [02:14<00:50, 40.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 446it [00:02, 305.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5968/8000 [02:14<00:52, 38.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 482it [00:02, 320.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5972/8000 [02:14<01:04, 31.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 516it [00:03, 311.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5977/8000 [02:14<00:58, 34.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 548it [00:03, 305.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5982/8000 [02:15<01:03, 31.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 587it [00:03, 328.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5986/8000 [02:15<01:03, 31.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 621it [00:03, 323.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5990/8000 [02:15<01:06, 30.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 654it [00:03, 318.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▏    | 5997/8000 [02:15<00:51, 38.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 691it [00:03, 328.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6002/8000 [02:15<00:48, 41.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 725it [00:03, 323.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6009/8000 [02:15<00:42, 46.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 758it [00:03, 321.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6014/8000 [02:15<00:42, 46.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 202.63it/s]\n",
+      "Generating signals:  75%|██████████████▎    | 6022/8000 [02:16<00:45, 43.56it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  75%|██████████████▎    | 6028/8000 [02:16<00:46, 42.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████▎    | 6036/8000 [02:16<00:41, 47.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 137\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▎    | 6042/8000 [02:16<00:42, 46.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00<00:00, 167\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▎    | 6050/8000 [02:16<00:47, 40.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  51%|▌| 51/100 [00:00<00:00, 150\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6055/8000 [02:16<00:51, 37.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:00<00:00, 153\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6061/8000 [02:17<00:52, 36.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  76%|██████████████▍    | 6071/8000 [02:17<00:39, 48.32it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  76%|██████████████▍    | 6078/8000 [02:17<00:52, 36.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6083/8000 [02:17<00:53, 35.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 24/8000 [00:00<00:45, 176.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6088/8000 [02:17<00:54, 34.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 44/8000 [00:00<00:57, 138.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6092/8000 [02:17<00:54, 35.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 61/8000 [00:00<01:04, 123.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6096/8000 [02:17<00:53, 35.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 80/8000 [00:00<01:04, 121.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▍    | 6101/8000 [02:18<00:50, 37.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 95/8000 [00:00<01:06, 119.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6106/8000 [02:18<01:05, 28.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 115/8000 [00:00<01:00, 130.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6110/8000 [02:18<01:08, 27.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 130/8000 [00:01<01:04, 121.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6114/8000 [02:18<01:03, 29.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 154/8000 [00:01<00:58, 134.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████▌    | 6118/8000 [02:18<01:01, 30.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 174/8000 [00:01<00:55, 141.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6127/8000 [02:18<00:46, 39.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 198/8000 [00:01<00:51, 151.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6133/8000 [02:19<00:48, 38.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 215/8000 [00:01<00:53, 144.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6138/8000 [02:19<00:53, 34.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 238/8000 [00:01<00:52, 148.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6143/8000 [02:19<00:50, 37.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 263/8000 [00:01<01:02, 124.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6147/8000 [02:19<00:56, 32.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▌                | 280/8000 [00:02<01:02, 122.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▌    | 6152/8000 [02:19<01:03, 29.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 304/8000 [00:02<00:56, 136.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6161/8000 [02:19<00:45, 40.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 323/8000 [00:02<00:56, 135.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6166/8000 [02:19<00:43, 41.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 341/8000 [00:02<00:54, 141.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6173/8000 [02:20<00:48, 37.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 367/8000 [00:02<00:48, 155.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6182/8000 [02:20<00:42, 42.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 390/8000 [00:02<00:50, 150.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6187/8000 [02:20<00:46, 38.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 407/8000 [00:02<00:52, 143.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████▋    | 6192/8000 [02:20<00:46, 38.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 427/8000 [00:03<00:55, 136.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▋    | 6200/8000 [02:20<00:43, 41.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 441/8000 [00:03<01:02, 121.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▋    | 6207/8000 [02:21<00:48, 36.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 455/8000 [00:03<01:04, 117.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6213/8000 [02:21<00:45, 39.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 468/8000 [00:03<01:10, 107.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6219/8000 [02:21<00:50, 34.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 489/8000 [00:03<01:05, 114.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6223/8000 [02:21<00:51, 34.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 505/8000 [00:03<01:04, 115.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6228/8000 [02:21<00:52, 33.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█                | 525/8000 [00:03<01:00, 124.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6233/8000 [02:21<00:54, 32.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 544/8000 [00:04<00:57, 128.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6239/8000 [02:21<00:46, 37.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 565/8000 [00:04<00:54, 136.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6244/8000 [02:22<00:51, 33.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎               | 590/8000 [00:04<00:52, 141.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6254/8000 [02:22<00:37, 46.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 608/8000 [00:04<00:52, 141.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▊    | 6260/8000 [02:22<00:41, 41.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 629/8000 [00:04<00:51, 143.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6267/8000 [02:22<00:36, 47.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 649/8000 [00:04<00:50, 145.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6273/8000 [02:22<00:35, 48.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 681/8000 [00:05<00:56, 129.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████▉    | 6280/8000 [02:22<00:33, 50.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 700/8000 [00:05<00:57, 126.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6286/8000 [02:22<00:36, 46.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 724/8000 [00:05<00:52, 138.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6292/8000 [02:23<00:34, 49.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 746/8000 [00:05<00:51, 141.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6301/8000 [02:23<00:35, 47.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 776/8000 [00:05<00:55, 129.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6306/8000 [02:23<00:35, 47.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 800/8000 [00:05<00:51, 140.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████▉    | 6313/8000 [02:23<00:36, 46.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 815/8000 [00:06<00:53, 134.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6319/8000 [02:23<00:38, 43.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 833/8000 [00:06<00:53, 135.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6325/8000 [02:23<00:40, 40.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 867/8000 [00:06<00:57, 124.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6333/8000 [02:23<00:39, 42.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 888/8000 [00:06<00:53, 131.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6338/8000 [02:24<00:38, 42.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 905/8000 [00:06<00:53, 131.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6343/8000 [02:24<00:43, 37.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 940/8000 [00:07<00:52, 134.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6349/8000 [02:24<00:40, 40.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 961/8000 [00:07<00:51, 136.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████    | 6357/8000 [02:24<00:37, 43.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 979/8000 [00:07<00:50, 138.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████    | 6364/8000 [02:24<00:33, 48.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 998/8000 [00:07<00:52, 132.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6372/8000 [02:24<00:30, 53.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1015/8000 [00:07<00:52, 133.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6383/8000 [02:24<00:28, 57.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1036/8000 [00:07<00:50, 137.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6391/8000 [02:25<00:26, 61.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1050/8000 [00:07<00:53, 130.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6399/8000 [02:25<00:25, 63.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1073/8000 [00:07<00:47, 145.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6406/8000 [02:25<00:32, 49.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1099/8000 [00:08<00:55, 123.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▏   | 6417/8000 [02:25<00:32, 48.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1117/8000 [00:08<00:53, 129.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▎   | 6423/8000 [02:25<00:31, 49.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1136/8000 [00:08<00:52, 130.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████▎   | 6433/8000 [02:25<00:32, 48.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1155/8000 [00:08<00:53, 127.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6442/8000 [02:26<00:30, 51.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1178/8000 [00:08<00:50, 135.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6448/8000 [02:26<00:29, 53.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1200/8000 [00:08<00:48, 141.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6454/8000 [02:26<00:32, 47.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1220/8000 [00:09<00:48, 140.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6459/8000 [02:26<00:34, 45.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1236/8000 [00:09<00:49, 136.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6465/8000 [02:26<00:32, 46.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1254/8000 [00:09<00:49, 137.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▎   | 6473/8000 [02:26<00:29, 50.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1271/8000 [00:09<00:49, 135.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6480/8000 [02:27<00:37, 40.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1285/8000 [00:09<00:52, 128.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6487/8000 [02:27<00:33, 44.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1303/8000 [00:09<00:51, 129.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6494/8000 [02:27<00:31, 48.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1323/8000 [00:09<00:48, 137.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6503/8000 [02:27<00:29, 50.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1342/8000 [00:10<00:48, 136.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6511/8000 [02:27<00:27, 54.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1372/8000 [00:10<00:54, 122.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████▍   | 6518/8000 [02:27<00:29, 50.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1402/8000 [00:10<00:44, 147.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▍   | 6524/8000 [02:27<00:32, 45.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1421/8000 [00:10<00:48, 136.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6530/8000 [02:27<00:31, 47.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1444/8000 [00:10<00:47, 138.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6535/8000 [02:28<00:36, 39.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1467/8000 [00:10<00:50, 129.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6542/8000 [02:28<00:39, 36.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1483/8000 [00:11<00:50, 128.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6547/8000 [02:28<00:41, 34.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1505/8000 [00:11<00:45, 142.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6554/8000 [02:28<00:36, 40.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1520/8000 [00:11<00:49, 131.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6562/8000 [02:28<00:33, 42.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1543/8000 [00:11<00:44, 144.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6570/8000 [02:28<00:30, 46.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1568/8000 [00:11<00:40, 159.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▌   | 6575/8000 [02:29<00:31, 44.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1594/8000 [00:11<00:47, 134.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6582/8000 [02:29<00:28, 48.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1619/8000 [00:11<00:41, 153.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6590/8000 [02:29<00:32, 43.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1636/8000 [00:12<00:42, 149.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████▋   | 6595/8000 [02:29<00:34, 40.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1672/8000 [00:12<00:43, 145.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6604/8000 [02:29<00:27, 51.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1691/8000 [00:12<00:42, 150.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6611/8000 [02:29<00:31, 43.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1709/8000 [00:12<00:43, 145.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6617/8000 [02:30<00:29, 46.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1740/8000 [00:12<00:43, 142.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6623/8000 [02:30<00:28, 47.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1756/8000 [00:13<00:48, 129.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▋   | 6630/8000 [02:30<00:27, 49.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1776/8000 [00:13<00:47, 132.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6636/8000 [02:30<00:29, 46.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1797/8000 [00:13<00:45, 136.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6644/8000 [02:30<00:25, 53.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1820/8000 [00:13<00:43, 142.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6651/8000 [02:30<00:30, 43.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1842/8000 [00:13<00:41, 146.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6657/8000 [02:30<00:28, 46.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1870/8000 [00:13<00:37, 163.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6663/8000 [02:30<00:27, 48.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1887/8000 [00:13<00:37, 161.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6669/8000 [02:31<00:26, 50.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1919/8000 [00:14<00:45, 133.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████▊   | 6677/8000 [02:31<00:24, 55.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1935/8000 [00:14<00:45, 134.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▊   | 6684/8000 [02:31<00:26, 50.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1966/8000 [00:14<00:48, 123.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6690/8000 [02:31<00:32, 40.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1990/8000 [00:14<00:42, 141.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6696/8000 [02:31<00:29, 43.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2011/8000 [00:14<00:39, 153.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6701/8000 [02:31<00:29, 43.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2042/8000 [00:14<00:39, 151.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6709/8000 [02:32<00:34, 37.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2063/8000 [00:15<00:36, 160.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6715/8000 [02:32<00:31, 40.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2094/8000 [00:15<00:38, 154.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6721/8000 [02:32<00:29, 43.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2114/8000 [00:15<00:37, 158.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6728/8000 [02:32<00:26, 47.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2133/8000 [00:15<00:36, 160.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|███████████████▉   | 6734/8000 [02:32<00:33, 37.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2155/8000 [00:15<00:34, 170.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6739/8000 [02:32<00:32, 38.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2179/8000 [00:15<00:32, 181.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6745/8000 [02:32<00:29, 41.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2215/8000 [00:15<00:33, 172.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████   | 6756/8000 [02:33<00:32, 38.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2234/8000 [00:16<00:34, 169.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6761/8000 [02:33<00:32, 38.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2254/8000 [00:16<00:34, 166.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6767/8000 [02:33<00:35, 35.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2288/8000 [00:16<00:35, 162.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6775/8000 [02:33<00:29, 41.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2323/8000 [00:16<00:34, 162.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████   | 6785/8000 [02:33<00:29, 41.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2353/8000 [00:16<00:36, 152.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6795/8000 [02:34<00:35, 34.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2371/8000 [00:16<00:37, 148.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6803/8000 [02:34<00:31, 37.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2387/8000 [00:17<00:39, 142.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6812/8000 [02:34<00:27, 43.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2407/8000 [00:17<00:37, 149.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6818/8000 [02:34<00:25, 46.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2427/8000 [00:17<00:36, 151.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6828/8000 [02:34<00:24, 47.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2454/8000 [00:17<00:42, 131.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████▏  | 6836/8000 [02:35<00:24, 48.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2478/8000 [00:17<00:37, 146.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▏  | 6842/8000 [02:35<00:25, 44.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2497/8000 [00:17<00:36, 149.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6848/8000 [02:35<00:24, 47.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2533/8000 [00:18<00:36, 150.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6853/8000 [02:35<00:27, 41.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2555/8000 [00:18<00:34, 159.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6860/8000 [02:35<00:26, 43.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2576/8000 [00:18<00:33, 163.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6865/8000 [02:35<00:26, 43.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2599/8000 [00:18<00:32, 163.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6871/8000 [02:35<00:24, 46.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2621/8000 [00:18<00:31, 169.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6877/8000 [02:35<00:23, 47.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2642/8000 [00:18<00:35, 150.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▎  | 6886/8000 [02:36<00:19, 57.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2660/8000 [00:18<00:37, 141.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6895/8000 [02:36<00:16, 65.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▎          | 2684/8000 [00:19<00:43, 123.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6902/8000 [02:36<00:19, 57.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2703/8000 [00:19<00:39, 134.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6913/8000 [02:36<00:21, 50.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2730/8000 [00:19<00:40, 131.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|████████████████▍  | 6920/8000 [02:36<00:22, 47.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2759/8000 [00:19<00:40, 130.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6926/8000 [02:36<00:22, 47.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2782/8000 [00:19<00:36, 142.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6933/8000 [02:37<00:25, 41.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2805/8000 [00:19<00:34, 152.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6939/8000 [02:37<00:24, 43.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2826/8000 [00:20<00:32, 157.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▍  | 6945/8000 [02:37<00:30, 34.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2846/8000 [00:20<00:32, 158.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6957/8000 [02:37<00:23, 44.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2878/8000 [00:20<00:28, 181.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6964/8000 [02:37<00:21, 48.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2899/8000 [00:20<00:28, 177.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6972/8000 [02:37<00:21, 48.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2940/8000 [00:20<00:28, 180.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6978/8000 [02:38<00:20, 49.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2966/8000 [00:20<00:34, 146.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6985/8000 [02:38<00:21, 46.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2994/8000 [00:21<00:37, 131.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████▌  | 6992/8000 [02:38<00:19, 50.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3012/8000 [00:21<00:37, 132.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7001/8000 [02:38<00:18, 53.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3032/8000 [00:21<00:35, 140.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7008/8000 [02:38<00:18, 54.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3050/8000 [00:21<00:35, 140.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7014/8000 [02:38<00:20, 48.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▏         | 3069/8000 [00:21<00:34, 143.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7021/8000 [02:38<00:19, 51.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3088/8000 [00:21<00:33, 145.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7027/8000 [02:39<00:21, 45.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3121/8000 [00:22<00:33, 144.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7032/8000 [02:39<00:24, 40.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3140/8000 [00:22<00:32, 148.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7038/8000 [02:39<00:26, 36.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3159/8000 [00:22<00:34, 141.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7043/8000 [02:39<00:27, 34.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3179/8000 [00:22<00:35, 135.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▋  | 7050/8000 [02:39<00:23, 40.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3220/8000 [00:22<00:32, 148.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7056/8000 [02:39<00:24, 38.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3237/8000 [00:22<00:33, 142.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7064/8000 [02:40<00:26, 35.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3258/8000 [00:23<00:31, 151.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7068/8000 [02:40<00:32, 29.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3285/8000 [00:23<00:36, 130.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7072/8000 [02:40<00:31, 29.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3305/8000 [00:23<00:34, 136.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████▊  | 7078/8000 [02:40<00:27, 33.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3328/8000 [00:23<00:31, 148.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7082/8000 [02:40<00:26, 34.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3346/8000 [00:23<00:31, 145.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7087/8000 [02:40<00:25, 35.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3365/8000 [00:23<00:32, 144.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7093/8000 [02:41<00:25, 35.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▊         | 3400/8000 [00:24<00:31, 145.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▊  | 7102/8000 [02:41<00:20, 44.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3432/8000 [00:24<00:32, 140.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7108/8000 [02:41<00:19, 45.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3448/8000 [00:24<00:32, 138.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7113/8000 [02:41<00:23, 38.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3463/8000 [00:24<00:33, 135.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7119/8000 [02:41<00:22, 40.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3483/8000 [00:24<00:31, 141.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7124/8000 [02:41<00:25, 35.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3508/8000 [00:24<00:28, 158.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7133/8000 [02:42<00:22, 38.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3536/8000 [00:24<00:30, 144.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7140/8000 [02:42<00:19, 43.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3555/8000 [00:25<00:29, 150.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7147/8000 [02:42<00:19, 42.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3588/8000 [00:25<00:30, 146.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7152/8000 [02:42<00:19, 42.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3608/8000 [00:25<00:28, 151.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|████████████████▉  | 7157/8000 [02:42<00:20, 41.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3629/8000 [00:25<00:26, 162.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7165/8000 [02:42<00:17, 47.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3650/8000 [00:25<00:26, 161.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7173/8000 [02:42<00:15, 54.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3670/8000 [00:25<00:26, 162.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7179/8000 [02:42<00:15, 53.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3691/8000 [00:25<00:25, 165.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7185/8000 [02:43<00:15, 54.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3719/8000 [00:26<00:26, 161.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7192/8000 [02:43<00:16, 50.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3738/8000 [00:26<00:28, 150.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7200/8000 [02:43<00:16, 49.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3762/8000 [00:26<00:25, 163.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████  | 7206/8000 [02:43<00:16, 48.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3781/8000 [00:26<00:26, 156.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7214/8000 [02:43<00:14, 52.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3800/8000 [00:26<00:28, 148.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7220/8000 [02:43<00:16, 47.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3830/8000 [00:26<00:29, 140.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7225/8000 [02:43<00:16, 48.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3853/8000 [00:27<00:26, 153.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7233/8000 [02:43<00:14, 54.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3873/8000 [00:27<00:26, 157.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|█████████████████▏ | 7240/8000 [02:44<00:15, 49.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3909/8000 [00:27<00:28, 141.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7248/8000 [02:44<00:13, 54.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3930/8000 [00:27<00:27, 147.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7254/8000 [02:44<00:15, 47.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3955/8000 [00:27<00:25, 161.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▏ | 7260/8000 [02:44<00:14, 49.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3985/8000 [00:27<00:27, 147.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7269/8000 [02:44<00:12, 58.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4001/8000 [00:28<00:27, 145.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7276/8000 [02:44<00:12, 59.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4023/8000 [00:28<00:25, 153.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7285/8000 [02:45<00:15, 46.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4048/8000 [00:28<00:29, 135.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7294/8000 [02:45<00:14, 50.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4067/8000 [00:28<00:27, 141.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7301/8000 [02:45<00:14, 49.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4087/8000 [00:28<00:27, 143.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▎ | 7308/8000 [02:45<00:16, 43.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4116/8000 [00:28<00:28, 137.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|█████████████████▍ | 7317/8000 [02:45<00:16, 41.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4150/8000 [00:29<00:29, 131.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7325/8000 [02:46<00:17, 39.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4165/8000 [00:29<00:30, 126.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7332/8000 [02:46<00:16, 39.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4185/8000 [00:29<00:29, 129.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7339/8000 [02:46<00:16, 40.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4207/8000 [00:29<00:27, 136.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7345/8000 [02:46<00:15, 42.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4233/8000 [00:29<00:23, 158.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7350/8000 [02:46<00:17, 37.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4252/8000 [00:29<00:24, 154.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7356/8000 [02:46<00:15, 40.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4273/8000 [00:29<00:25, 147.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▍ | 7365/8000 [02:47<00:18, 35.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4298/8000 [00:30<00:22, 162.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7370/8000 [02:47<00:18, 34.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4328/8000 [00:30<00:24, 147.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7379/8000 [02:47<00:14, 42.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4348/8000 [00:30<00:23, 154.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7384/8000 [02:47<00:19, 31.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▋       | 4366/8000 [00:30<00:24, 151.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7391/8000 [02:47<00:16, 36.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4390/8000 [00:30<00:21, 165.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|█████████████████▌ | 7399/8000 [02:48<00:17, 33.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4423/8000 [00:30<00:23, 149.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7405/8000 [02:48<00:16, 36.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4445/8000 [00:31<00:22, 156.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7413/8000 [02:48<00:13, 43.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4475/8000 [00:31<00:24, 141.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▌ | 7420/8000 [02:48<00:15, 36.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4498/8000 [00:31<00:23, 147.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7425/8000 [02:48<00:16, 34.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4530/8000 [00:31<00:24, 139.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7429/8000 [02:48<00:16, 35.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4551/8000 [00:31<00:23, 145.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7434/8000 [02:49<00:16, 33.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4574/8000 [00:31<00:21, 159.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7440/8000 [02:49<00:14, 37.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4593/8000 [00:32<00:21, 158.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7450/8000 [02:49<00:10, 50.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4611/8000 [00:32<00:21, 155.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7459/8000 [02:49<00:11, 45.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4630/8000 [00:32<00:21, 158.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7465/8000 [02:49<00:11, 46.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4648/8000 [00:32<00:21, 156.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|█████████████████▋ | 7472/8000 [02:49<00:10, 49.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4664/8000 [00:32<00:22, 150.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7480/8000 [02:49<00:09, 53.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▎      | 4687/8000 [00:32<00:20, 158.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7486/8000 [02:49<00:09, 52.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4713/8000 [00:32<00:18, 173.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7493/8000 [02:50<00:09, 54.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▌      | 4750/8000 [00:32<00:19, 169.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7500/8000 [02:50<00:09, 53.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4782/8000 [00:33<00:23, 137.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7506/8000 [02:50<00:09, 53.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4813/8000 [00:33<00:24, 127.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7513/8000 [02:50<00:08, 54.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4833/8000 [00:33<00:24, 130.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▊ | 7522/8000 [02:50<00:10, 44.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4853/8000 [00:33<00:23, 135.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7530/8000 [02:50<00:10, 44.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4878/8000 [00:33<00:20, 151.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7539/8000 [02:51<00:11, 40.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4895/8000 [00:34<00:22, 140.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7544/8000 [02:51<00:12, 37.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4916/8000 [00:34<00:23, 130.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7553/8000 [02:51<00:09, 46.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4945/8000 [00:34<00:26, 113.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████▉ | 7559/8000 [02:51<00:10, 42.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4969/8000 [00:34<00:23, 129.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▉ | 7570/8000 [02:51<00:08, 49.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4989/8000 [00:34<00:21, 137.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████▉ | 7576/8000 [02:51<00:09, 46.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5018/8000 [00:35<00:22, 132.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7583/8000 [02:52<00:10, 38.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5039/8000 [00:35<00:21, 140.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7588/8000 [02:52<00:10, 40.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5068/8000 [00:35<00:22, 131.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7593/8000 [02:52<00:11, 36.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5095/8000 [00:35<00:19, 150.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7600/8000 [02:52<00:09, 42.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5116/8000 [00:35<00:18, 159.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7608/8000 [02:52<00:09, 39.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5157/8000 [00:35<00:17, 166.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7613/8000 [02:52<00:10, 37.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5184/8000 [00:36<00:15, 176.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7617/8000 [02:53<00:10, 36.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5211/8000 [00:36<00:14, 188.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7623/8000 [02:53<00:09, 40.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5253/8000 [00:36<00:14, 187.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████ | 7628/8000 [02:53<00:09, 41.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5276/8000 [00:36<00:14, 185.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|██████████████████▏| 7637/8000 [02:53<00:07, 50.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5295/8000 [00:36<00:15, 169.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7643/8000 [02:53<00:08, 41.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▋     | 5316/8000 [00:36<00:16, 166.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7648/8000 [02:53<00:08, 41.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5341/8000 [00:37<00:17, 155.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7655/8000 [02:53<00:07, 46.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5369/8000 [00:37<00:20, 127.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7662/8000 [02:54<00:07, 45.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5383/8000 [00:37<00:22, 118.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7672/8000 [02:54<00:07, 44.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5403/8000 [00:37<00:23, 109.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▏| 7678/8000 [02:54<00:07, 40.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5418/8000 [00:37<00:23, 109.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7686/8000 [02:54<00:07, 43.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5435/8000 [00:38<00:24, 104.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7692/8000 [02:54<00:07, 41.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5450/8000 [00:38<00:26, 94.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7697/8000 [02:54<00:08, 35.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5470/8000 [00:38<00:26, 94.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7702/8000 [02:55<00:08, 37.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5486/8000 [00:38<00:27, 92.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7706/8000 [02:55<00:09, 31.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5504/8000 [00:38<00:26, 94.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|██████████████████▎| 7711/8000 [02:55<00:09, 30.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5523/8000 [00:39<00:29, 82.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7722/8000 [02:55<00:06, 41.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5544/8000 [00:39<00:26, 93.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7728/8000 [02:55<00:06, 40.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5565/8000 [00:39<00:21, 111.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▎| 7736/8000 [02:55<00:05, 45.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5580/8000 [00:39<00:21, 111.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7741/8000 [02:56<00:05, 44.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5610/8000 [00:39<00:26, 91.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7746/8000 [02:56<00:05, 44.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5630/8000 [00:40<00:23, 100.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7751/8000 [02:56<00:06, 39.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5653/8000 [00:40<00:21, 108.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7757/8000 [02:56<00:05, 40.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5677/8000 [00:40<00:20, 116.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7763/8000 [02:56<00:05, 42.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5697/8000 [00:40<00:19, 121.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7769/8000 [02:56<00:07, 31.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5722/8000 [00:40<00:16, 139.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7773/8000 [02:57<00:07, 29.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5748/8000 [00:40<00:13, 162.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7777/8000 [02:57<00:08, 27.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5768/8000 [00:40<00:13, 165.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▍| 7786/8000 [02:57<00:05, 36.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5795/8000 [00:41<00:12, 173.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▌| 7792/8000 [02:57<00:05, 39.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5826/8000 [00:41<00:13, 157.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████▌| 7798/8000 [02:57<00:05, 39.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5846/8000 [00:41<00:13, 156.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7804/8000 [02:57<00:05, 38.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▊    | 5876/8000 [00:41<00:15, 139.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7808/8000 [02:57<00:04, 38.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5900/8000 [00:41<00:13, 155.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7813/8000 [02:58<00:04, 39.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5921/8000 [00:41<00:13, 158.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7818/8000 [02:58<00:04, 36.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5941/8000 [00:42<00:13, 149.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7825/8000 [02:58<00:04, 43.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5968/8000 [00:42<00:15, 134.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7832/8000 [02:58<00:03, 43.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5984/8000 [00:42<00:15, 131.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▌| 7837/8000 [02:58<00:03, 44.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6003/8000 [00:42<00:14, 137.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7845/8000 [02:58<00:03, 46.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6035/8000 [00:42<00:13, 140.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7851/8000 [02:58<00:03, 47.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6057/8000 [00:42<00:13, 146.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7857/8000 [02:58<00:02, 49.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6078/8000 [00:43<00:12, 152.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7865/8000 [02:59<00:02, 50.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6094/8000 [00:43<00:13, 138.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7872/8000 [02:59<00:02, 47.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6114/8000 [00:43<00:13, 140.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████▋| 7877/8000 [02:59<00:02, 41.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6141/8000 [00:43<00:14, 131.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▋| 7887/8000 [02:59<00:02, 48.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6156/8000 [00:43<00:14, 125.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▋| 7893/8000 [02:59<00:02, 45.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6173/8000 [00:43<00:14, 127.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7898/8000 [02:59<00:02, 44.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6202/8000 [00:44<00:15, 118.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7907/8000 [03:00<00:01, 46.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6217/8000 [00:44<00:15, 118.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7912/8000 [03:00<00:02, 42.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6233/8000 [00:44<00:14, 124.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7920/8000 [03:00<00:01, 49.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6248/8000 [00:44<00:14, 121.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7932/8000 [03:00<00:01, 64.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6266/8000 [00:44<00:13, 129.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▊| 7942/8000 [03:00<00:01, 52.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6280/8000 [00:44<00:13, 126.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▉| 7949/8000 [03:00<00:01, 45.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6301/8000 [00:44<00:12, 139.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████▉| 7956/8000 [03:01<00:00, 45.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6321/8000 [00:44<00:11, 144.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7963/8000 [03:01<00:00, 44.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6337/8000 [00:45<00:12, 129.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7971/8000 [03:01<00:00, 51.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6355/8000 [00:45<00:12, 129.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7978/8000 [03:01<00:00, 50.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6381/8000 [00:45<00:13, 118.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7984/8000 [03:01<00:00, 45.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6401/8000 [00:45<00:12, 128.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7990/8000 [03:01<00:00, 46.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6422/8000 [00:45<00:11, 136.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████▉| 7995/8000 [03:01<00:00, 42.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6437/8000 [00:45<00:11, 132.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8002it [03:02, 43.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  81%|████████████▉   | 6451/8000 [00:45<00:11, 130.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6473/8000 [00:46<00:10, 144.21it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6498/8000 [00:46<00:09, 156.36it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:04<15:29,  4.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6534/8000 [00:46<00:10, 145.56it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:05<07:49,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6562/8000 [00:46<00:10, 134.29it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:06<05:21,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6583/8000 [00:46<00:10, 137.09it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:06<04:11,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6602/8000 [00:46<00:09, 141.58it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:07<03:33,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6634/8000 [00:47<00:10, 136.39it/s]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:08<03:10,  1.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6658/8000 [00:47<00:08, 154.61it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:09<02:56,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6691/8000 [00:47<00:08, 149.05it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:10<02:48,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6709/8000 [00:47<00:08, 147.00it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:10<02:43,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6729/8000 [00:47<00:08, 146.73it/s]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:11<02:41,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▌  | 6751/8000 [00:47<00:08, 151.00it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:12<02:42,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6786/8000 [00:48<00:08, 144.57it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:13<02:36,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6803/8000 [00:48<00:08, 145.32it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:14<02:33,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6823/8000 [00:48<00:07, 154.18it/s]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:14<02:31,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6854/8000 [00:48<00:07, 144.68it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:15<02:28,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6885/8000 [00:48<00:08, 137.39it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:16<02:26,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6914/8000 [00:49<00:08, 131.77it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:17<02:28,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6934/8000 [00:49<00:07, 138.03it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:18<02:29,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6957/8000 [00:49<00:07, 144.68it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:18<02:25,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6977/8000 [00:49<00:07, 144.44it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:19<02:22,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7002/8000 [00:49<00:07, 129.68it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:20<02:20,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7020/8000 [00:49<00:07, 132.94it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:21<02:18,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7038/8000 [00:50<00:06, 140.05it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:21<02:16,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7063/8000 [00:50<00:06, 155.56it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:22<02:16,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7094/8000 [00:50<00:06, 142.44it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:23<02:14,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7127/8000 [00:50<00:06, 133.23it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:24<02:13,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7165/8000 [00:50<00:06, 138.47it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:25<02:12,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7181/8000 [00:51<00:05, 137.72it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:25<02:11,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7203/8000 [00:51<00:05, 150.04it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:26<02:12,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7226/8000 [00:51<00:05, 154.59it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:27<02:12,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7269/8000 [00:51<00:04, 164.72it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:28<02:12,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7296/8000 [00:51<00:04, 175.79it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:29<02:13,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▋ | 7316/8000 [00:51<00:04, 166.47it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:29<02:12,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7348/8000 [00:52<00:04, 150.99it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:30<02:11,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7371/8000 [00:52<00:04, 152.30it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [00:31<02:09,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7393/8000 [00:52<00:03, 154.63it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [00:32<02:08,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7410/8000 [00:52<00:03, 154.86it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [00:32<02:08,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7433/8000 [00:52<00:03, 168.80it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [00:33<02:08,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7463/8000 [00:52<00:02, 188.79it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [00:34<02:07,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7493/8000 [00:52<00:03, 156.84it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [00:35<02:07,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7514/8000 [00:53<00:03, 156.44it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [00:36<02:06,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7542/8000 [00:53<00:02, 164.84it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [00:36<02:03,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████ | 7562/8000 [00:53<00:02, 164.06it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [00:37<02:01,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7585/8000 [00:53<00:02, 169.08it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [00:38<02:00,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7620/8000 [00:53<00:02, 155.60it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [00:39<02:17,  1.13it/s]\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7638/8000 [00:53<00:02, 146.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7655/8000 [00:54<00:02, 140.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7672/8000 [00:54<00:02, 130.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 24.93it/s]\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7688/8000 [00:54<00:02, 125.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7705/8000 [00:54<00:02, 130.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7726/8000 [00:54<00:01, 141.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7746/8000 [00:54<00:01, 142.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 79it [00:00, 113.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7765/8000 [00:54<00:01, 148.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 168it [00:01, 245.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7796/8000 [00:55<00:01, 138.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 242it [00:01, 342.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▌| 7812/8000 [00:55<00:01, 135.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 331it [00:01, 461.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7827/8000 [00:55<00:01, 130.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 414it [00:01, 547.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7846/8000 [00:55<00:01, 137.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 493it [00:01, 607.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7866/8000 [00:55<00:00, 143.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 579it [00:01, 672.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7885/8000 [00:55<00:00, 145.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 660it [00:01, 698.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7905/8000 [00:55<00:00, 150.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 437.41it/s]\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7923/8000 [00:55<00:00, 144.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7957/8000 [00:56<00:00, 140.41it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7975/8000 [00:56<00:00, 139.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:00, 381.7\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7996/8000 [00:56<00:00, 148.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 369.\n",
       "\n",
-      "Generating signals: 8012it [00:56, 141.56it/s]\n"
+      "Nyquist Samples: 100%|███████████████████████████| 4/4 [34:05<00:00, 507.99s/it]\u001b[A\n",
+      "Replicates:  33%|████████                | 5/15 [4:30:44<7:51:47, 2830.75s/it]\n",
+      "Nyquist Samples:   0%|                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "concatenating...\n"
+      "initial loss -4.978479231898175\n",
+      "2999: -5.9055865\r"
      ]
     },
     {
@@ -37947,7083 +37909,7135 @@
      "text": [
       "\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   0%|▏                              | 1/200 [00:06<23:11,  6.99s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:13,  3.70s/it]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:33,  2.61s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:04<14:54,  4.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:50,  2.09s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:05<07:31,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:52,  1.81s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:05<05:11,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:14,  1.62s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:06<04:04,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:14<04:54,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:07<03:32,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:37,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:08<03:16,  1.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:28,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:09<03:04,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:18<04:24,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:10<02:56,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:19,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▍                               | 9/200 [00:10<02:45,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:   5%|█▌                              | 10/200 [00:11<02:40,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:22<04:10,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:12<02:36,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:07,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:13<02:29,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:04,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██                              | 13/200 [00:13<02:29,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:26<04:03,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:   7%|██▏                             | 14/200 [00:14<02:37,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:15<02:34,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:29<04:03,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:16<02:28,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:30<03:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:17<02:41,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:31<03:56,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:19<03:18,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:55,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:20<03:16,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:34<03:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:20<02:59,  1.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:35<03:51,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:21<02:43,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:50,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:22<02:35,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:38<03:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:23<02:26,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:46,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:23<02:17,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:24<02:09,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:42<03:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:25<02:07,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:43<03:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:25<02:04,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:45<03:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:26<02:02,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:46<03:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:27<02:00,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:47<03:41,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:27<01:59,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:49<03:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:28<01:57,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:50<03:38,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:29<01:57,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:51<03:43,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:30<01:58,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:53<03:38,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:31<02:10,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:54<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [00:31<02:07,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:55<03:35,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [00:32<02:03,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:57<03:36,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [00:33<01:58,  1.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:58<03:32,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [00:33<01:54,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:59<03:31,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [00:34<01:52,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:01<03:35,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [00:35<01:53,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:02<03:29,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [00:35<01:53,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:03<03:29,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [00:36<01:50,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:05<03:27,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [00:37<01:47,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:06<03:23,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [00:37<01:47,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:07<03:23,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [00:38<01:51,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:09<03:24,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [00:39<01:48,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:10<03:24,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [00:40<01:47,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:11<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▋                        | 48/200 [00:41<01:56,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:13<03:15,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████▊                        | 49/200 [00:41<02:00,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:14<03:15,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [00:42<01:55,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:15<03:14,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [00:43<01:52,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:17<03:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [00:44<01:52,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:18<03:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [00:44<01:50,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:19<03:16,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [00:45<01:46,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:21<03:16,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [00:46<01:44,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:22<03:14,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [00:46<01:41,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:24<03:14,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████                       | 57/200 [00:47<01:37,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:25<03:11,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  29%|█████████▎                      | 58/200 [00:48<01:37,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:26<03:07,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [00:48<01:36,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:28<03:06,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [00:49<01:36,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:29<03:03,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [00:50<01:36,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:30<03:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [00:50<01:33,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:32<03:09,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [00:51<01:30,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:33<03:15,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [00:52<01:29,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:35<03:07,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [00:52<01:29,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:36<03:02,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [00:53<01:27,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:37<02:59,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [00:54<01:28,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:39<02:54,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [00:54<01:28,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:40<02:51,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [00:55<01:28,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:41<02:50,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [00:56<01:26,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:43<02:50,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [00:56<01:25,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:45<03:00,  1.43s/it]\n",
+      "training:  36%|███████████▌                    | 72/200 [00:57<01:24,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  36%|███████████▋                    | 73/200 [00:58<01:29,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [00:59<01:42,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.62s/it]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [01:00<01:48,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.04it/s]\n",
+      "training:  38%|████████████▏                   | 76/200 [01:01<01:53,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  38%|████████████▎                   | 77/200 [01:02<01:58,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  39%|████████████▍                   | 78/200 [01:03<01:50,  1.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▋                   | 79/200 [01:04<01:56,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 43it [00:01, 39.39it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▊                   | 80/200 [01:05<01:56,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 82it [00:01, 80.15it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▉                   | 81/200 [01:06<01:55,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 119it [00:01, 121.30it/s]\u001b[A\u001b[A\n",
+      "training:  41%|█████████████                   | 82/200 [01:07<01:56,  1.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 162it [00:01, 173.43it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▎                  | 83/200 [01:08<01:48,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 200it [00:01, 212.60it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▍                  | 84/200 [01:08<01:41,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 237it [00:02, 242.57it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▌                  | 85/200 [01:09<01:36,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 282it [00:02, 289.51it/s]\u001b[A\u001b[A\n",
+      "training:  43%|█████████████▊                  | 86/200 [01:10<01:31,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 321it [00:02, 310.82it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▉                  | 87/200 [01:11<01:29,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 360it [00:02, 330.29it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████                  | 88/200 [01:11<01:24,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 401it [00:02, 350.39it/s]\u001b[A\u001b[A\n",
+      "training:  44%|██████████████▏                 | 89/200 [01:12<01:19,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 440it [00:02, 346.95it/s]\u001b[A\u001b[A\n",
+      "training:  45%|██████████████▍                 | 90/200 [01:13<01:30,  1.22it/s]\n",
       "\n",
-      "Evaluating NLL: 480it [00:02, 361.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 519it [00:02, 360.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 557it [00:02, 353.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 594it [00:02, 339.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 21.83it/s]\n",
       "\n",
-      "Evaluating NLL: 629it [00:03, 342.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 666it [00:03, 347.81it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 708it [00:03, 367.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 746it [00:03, 306.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 36it [00:01, 41.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 220.67it/s]\n",
+      "Evaluating NLL: 73it [00:01, 89.12it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 110it [00:01, 136.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 155it [00:01, 197.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:00, 195\u001b[A\u001b[A\n",
+      "Evaluating NLL: 192it [00:01, 234.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 167\u001b[A\u001b[A\n",
+      "Evaluating NLL: 235it [00:01, 279.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  60%|▌| 60/100 [00:00<00:00, 175\u001b[A\u001b[A\n",
+      "Evaluating NLL: 275it [00:01, 309.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  78%|▊| 78/100 [00:00<00:00, 166\u001b[A\u001b[A\n",
+      "Evaluating NLL: 345it [00:01, 412.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Evaluating NLL: 394it [00:01, 433.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [18:14<06:23, 383.49s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -3.2054544380119956\n",
-      "2999: -5.8354823\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 461it [00:02, 499.31it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 526it [00:02, 542.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 595it [00:02, 583.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:18,  7.63s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 656it [00:02, 581.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:25,  4.07s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 716it [00:02, 585.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:28,  2.89s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:02, 304.12it/s]\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:37,  2.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:44,  2.07s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:08,  1.90s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|▏| 20/100 [00:00<00:00, 198.0\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:45,  1.79s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 204.9\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:25,  1.69s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00<00:00, 194.4\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:23,  1.69s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 174.\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<05:19,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<05:04,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:54,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|▊                 | 345/8000 [00:00<00:03, 2407.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:45,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|█▌                | 693/8000 [00:00<00:03, 2211.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:36,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|██▏              | 1043/8000 [00:00<00:03, 2213.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:30,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██▉              | 1402/8000 [00:00<00:03, 2074.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:31,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███▋             | 1753/8000 [00:00<00:03, 1961.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:26,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████▍            | 2104/8000 [00:01<00:03, 1738.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:24,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|█████▏           | 2461/8000 [00:01<00:03, 1517.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:23,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|█████▌           | 2630/8000 [00:01<00:03, 1417.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:21,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|█████▉           | 2808/8000 [00:01<00:03, 1415.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<04:19,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|██████▎          | 2986/8000 [00:01<00:03, 1390.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:29,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████▋          | 3168/8000 [00:01<00:03, 1364.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:29,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|███████          | 3340/8000 [00:02<00:03, 1259.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:23,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████▍         | 3521/8000 [00:02<00:03, 1283.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:19,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████▊         | 3700/8000 [00:02<00:03, 1187.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:16,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████▊         | 3893/8000 [00:03<00:08, 464.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:46<04:16,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████▏        | 4072/8000 [00:03<00:07, 559.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:48<04:13,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████▌        | 4240/8000 [00:03<00:05, 639.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:49<04:13,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|█████████▉        | 4420/8000 [00:03<00:04, 736.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:51<04:12,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|██████████▎       | 4601/8000 [00:04<00:04, 848.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:52<04:08,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|██████████▌      | 4958/8000 [00:04<00:02, 1128.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:53<04:03,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████▉      | 5143/8000 [00:04<00:02, 1185.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:55<04:00,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████▎     | 5310/8000 [00:04<00:02, 1243.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:56<03:57,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████▋     | 5475/8000 [00:04<00:01, 1325.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:58<03:53,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████▉     | 5647/8000 [00:04<00:01, 1334.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:59<03:52,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████▍    | 5828/8000 [00:04<00:01, 1385.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:01<03:51,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|████████████▋    | 5997/8000 [00:04<00:01, 1336.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:02<03:49,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|█████████████    | 6171/8000 [00:05<00:01, 1328.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:03<03:49,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████▌   | 6356/8000 [00:05<00:01, 1343.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:05<03:47,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|█████████████▊   | 6523/8000 [00:05<00:01, 1298.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:06<03:49,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|██████████████▏  | 6699/8000 [00:05<00:00, 1358.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:08<03:45,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████▌  | 6865/8000 [00:05<00:00, 1367.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:09<03:43,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████▉  | 7039/8000 [00:05<00:00, 1368.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:11<03:44,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████▎ | 7220/8000 [00:05<00:00, 1428.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:12<03:43,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████▋ | 7387/8000 [00:05<00:00, 1424.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:13<03:42,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████ | 7565/8000 [00:06<00:00, 1379.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:15<03:39,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████▍| 7724/8000 [00:06<00:00, 1256.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:16<03:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals: 8068it [00:06, 1248.81it/s]                                 \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:18<03:38,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:19<03:40,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:   0%|                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:21<03:50,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                | 1/200 [00:05<16:58,  5.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:23<03:49,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▎                                | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:24<03:43,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▍                                | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:25<03:36,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                | 4/200 [00:08<05:45,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:27<03:30,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▊                                | 5/200 [00:10<05:14,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:28<03:28,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   3%|▉                                | 6/200 [00:11<04:55,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:30<03:24,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▏                               | 7/200 [00:12<04:39,  1.45s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:31<03:24,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▎                               | 8/200 [00:14<04:25,  1.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:34<03:45,  1.60s/it]\n",
+      "training:   4%|█▍                               | 9/200 [00:15<04:20,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:   5%|█▌                              | 10/200 [00:16<04:16,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:   6%|█▊                              | 11/200 [00:18<04:16,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.60s/it]\u001b[A\u001b[A\n",
+      "training:   6%|█▉                              | 12/200 [00:19<04:12,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.61it/s]\n",
+      "training:   6%|██                              | 13/200 [00:20<04:08,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:   7%|██▏                             | 14/200 [00:21<04:04,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▍                             | 15/200 [00:23<03:58,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   8%|██▌                             | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 32it [00:01, 28.57it/s]\u001b[A\u001b[A\n",
+      "training:   8%|██▋                             | 17/200 [00:25<03:50,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 57it [00:01, 53.50it/s]\u001b[A\u001b[A\n",
+      "training:   9%|██▉                             | 18/200 [00:26<03:46,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 80it [00:01, 77.93it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███                             | 19/200 [00:28<03:53,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 106it [00:01, 107.74it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▏                            | 20/200 [00:29<03:58,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 134it [00:01, 139.65it/s]\u001b[A\u001b[A\n",
+      "training:  10%|███▎                            | 21/200 [00:31<04:06,  1.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 159it [00:02, 158.86it/s]\u001b[A\u001b[A\n",
+      "training:  11%|███▌                            | 22/200 [00:32<04:11,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 183it [00:02, 168.51it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▋                            | 23/200 [00:34<04:05,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 206it [00:02, 154.64it/s]\u001b[A\u001b[A\n",
+      "training:  12%|███▊                            | 24/200 [00:35<03:59,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 230it [00:02, 171.90it/s]\u001b[A\u001b[A\n",
+      "training:  12%|████                            | 25/200 [00:36<03:56,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 251it [00:02, 177.86it/s]\u001b[A\u001b[A\n",
+      "training:  13%|████▏                           | 26/200 [00:38<03:57,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 272it [00:02, 184.49it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▎                           | 27/200 [00:39<03:51,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 294it [00:02, 192.38it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▍                           | 28/200 [00:40<03:49,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 315it [00:02, 193.96it/s]\u001b[A\u001b[A\n",
+      "training:  14%|████▋                           | 29/200 [00:41<03:49,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 336it [00:03, 183.26it/s]\u001b[A\u001b[A\n",
+      "training:  15%|████▊                           | 30/200 [00:43<03:48,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 356it [00:03, 178.94it/s]\u001b[A\u001b[A\n",
+      "training:  16%|████▉                           | 31/200 [00:44<03:47,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 375it [00:03, 165.39it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████                           | 32/200 [00:46<03:47,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 393it [00:03, 166.61it/s]\u001b[A\u001b[A\n",
+      "training:  16%|█████▎                          | 33/200 [00:47<03:51,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 418it [00:03, 186.08it/s]\u001b[A\u001b[A\n",
+      "training:  17%|█████▍                          | 34/200 [00:48<03:50,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 441it [00:03, 194.04it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▌                          | 35/200 [00:50<03:51,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 461it [00:03, 151.32it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▊                          | 36/200 [00:51<03:55,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 485it [00:03, 171.33it/s]\u001b[A\u001b[A\n",
+      "training:  18%|█████▉                          | 37/200 [00:53<03:48,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 512it [00:04, 195.54it/s]\u001b[A\u001b[A\n",
+      "training:  19%|██████                          | 38/200 [00:54<03:44,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 543it [00:04, 224.78it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▏                         | 39/200 [00:55<03:39,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 568it [00:04, 223.69it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▍                         | 40/200 [00:57<03:59,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 596it [00:04, 237.49it/s]\u001b[A\u001b[A\n",
+      "training:  20%|██████▌                         | 41/200 [00:58<03:45,  1.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 624it [00:04, 248.69it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████▋                         | 42/200 [01:00<03:39,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 652it [00:04, 256.09it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████▉                         | 43/200 [01:01<03:32,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 679it [00:04, 258.70it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████                         | 44/200 [01:02<03:24,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 708it [00:04, 267.62it/s]\u001b[A\u001b[A\n",
+      "training:  22%|███████▏                        | 45/200 [01:03<03:19,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 736it [00:04, 259.17it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████▎                        | 46/200 [01:05<03:20,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 763it [00:04, 258.58it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████▌                        | 47/200 [01:06<03:17,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:05, 156.94it/s]\n",
+      "training:  24%|███████▋                        | 48/200 [01:07<03:15,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  24%|███████▊                        | 49/200 [01:09<03:12,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████                        | 50/200 [01:10<03:08,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 123\u001b[A\u001b[A\n",
+      "training:  26%|████████▏                       | 51/200 [01:11<03:07,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  29%|▎| 29/100 [00:00<00:00, 133\u001b[A\u001b[A\n",
+      "training:  26%|████████▎                       | 52/200 [01:12<03:07,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 131\u001b[A\u001b[A\n",
+      "training:  26%|████████▍                       | 53/200 [01:14<03:04,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 129\u001b[A\u001b[A\n",
+      "training:  27%|████████▋                       | 54/200 [01:15<03:05,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00<00:00, 135\u001b[A\u001b[A\n",
+      "training:  28%|████████▊                       | 55/200 [01:16<03:05,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  87%|▊| 87/100 [00:00<00:00, 120\u001b[A\u001b[A\n",
+      "training:  28%|████████▉                       | 56/200 [01:17<03:05,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 11\n",
+      "training:  28%|█████████                       | 57/200 [01:19<03:05,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:  29%|█████████▎                      | 58/200 [01:20<03:01,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▍                      | 59/200 [01:21<03:01,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 2/8000 [00:00<11:34, 11.51it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▌                      | 60/200 [01:23<02:59,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 4/8000 [00:00<11:56, 11.16it/s]\u001b[A\u001b[A\n",
+      "training:  30%|█████████▊                      | 61/200 [01:24<02:55,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 6/8000 [00:00<11:58, 11.12it/s]\u001b[A\u001b[A\n",
+      "training:  31%|█████████▉                      | 62/200 [01:25<02:53,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 10/8000 [00:00<08:22, 15.89it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████                      | 63/200 [01:26<02:50,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 13/8000 [00:00<08:01, 16.59it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▏                     | 64/200 [01:28<02:50,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 17/8000 [00:01<07:05, 18.75it/s]\u001b[A\u001b[A\n",
+      "training:  32%|██████████▍                     | 65/200 [01:29<02:51,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 20/8000 [00:01<06:57, 19.12it/s]\u001b[A\u001b[A\n",
+      "training:  33%|██████████▌                     | 66/200 [01:30<02:46,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 24/8000 [00:01<05:58, 22.28it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▋                     | 67/200 [01:31<02:46,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 29/8000 [00:01<05:13, 25.45it/s]\u001b[A\u001b[A\n",
+      "training:  34%|██████████▉                     | 68/200 [01:33<02:45,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 32/8000 [00:01<06:52, 19.32it/s]\u001b[A\u001b[A\n",
+      "training:  34%|███████████                     | 69/200 [01:34<02:45,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 36/8000 [00:01<05:56, 22.34it/s]\u001b[A\u001b[A\n",
+      "training:  35%|███████████▏                    | 70/200 [01:35<02:50,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 39/8000 [00:02<10:04, 13.18it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▎                    | 71/200 [01:37<02:47,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 41/8000 [00:02<09:36, 13.81it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▌                    | 72/200 [01:38<02:45,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 46/8000 [00:02<06:56, 19.08it/s]\u001b[A\u001b[A\n",
+      "training:  36%|███████████▋                    | 73/200 [01:39<02:42,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 50/8000 [00:02<06:00, 22.05it/s]\u001b[A\u001b[A\n",
+      "training:  37%|███████████▊                    | 74/200 [01:40<02:43,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 55/8000 [00:02<06:17, 21.07it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████                    | 75/200 [01:42<02:45,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 59/8000 [00:03<08:46, 15.09it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▏                   | 76/200 [01:43<02:45,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 66/8000 [00:03<06:11, 21.37it/s]\u001b[A\u001b[A\n",
+      "training:  38%|████████████▎                   | 77/200 [01:44<02:43,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 71/8000 [00:04<08:29, 15.56it/s]\u001b[A\u001b[A\n",
+      "training:  39%|████████████▍                   | 78/200 [01:46<02:42,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 74/8000 [00:04<08:20, 15.85it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▋                   | 79/200 [01:47<02:44,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 78/8000 [00:04<09:35, 13.76it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▊                   | 80/200 [01:49<02:40,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 81/8000 [00:04<09:30, 13.88it/s]\u001b[A\u001b[A\n",
+      "training:  40%|████████████▉                   | 81/200 [01:50<02:38,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 86/8000 [00:05<07:48, 16.90it/s]\u001b[A\u001b[A\n",
+      "training:  41%|█████████████                   | 82/200 [01:51<02:38,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 89/8000 [00:05<08:16, 15.94it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▎                  | 83/200 [01:52<02:35,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 93/8000 [00:05<10:08, 13.00it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▍                  | 84/200 [01:54<02:33,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 96/8000 [00:05<10:25, 12.63it/s]\u001b[A\u001b[A\n",
+      "training:  42%|█████████████▌                  | 85/200 [01:55<02:31,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 98/8000 [00:06<10:33, 12.47it/s]\u001b[A\u001b[A\n",
+      "training:  43%|█████████████▊                  | 86/200 [01:56<02:27,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 102/8000 [00:06<09:26, 13.93it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████▉                  | 87/200 [01:59<02:35,  1.37s/it]\n",
       "\n",
-      "Generating signals:   1%|▏                 | 104/8000 [00:06<10:18, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 106/8000 [00:06<11:26, 11.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 109/8000 [00:06<10:22, 12.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 112/8000 [00:07<09:59, 13.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 23.36it/s]\n",
       "\n",
-      "Generating signals:   1%|▎                 | 116/8000 [00:07<10:39, 12.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 118/8000 [00:07<14:27,  9.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 123/8000 [00:08<12:25, 10.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 126/8000 [00:08<11:01, 11.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 40it [00:00, 56.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 131/8000 [00:08<08:13, 15.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 79it [00:01, 112.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 137/8000 [00:08<06:19, 20.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 118it [00:01, 165.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 141/8000 [00:09<06:47, 19.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 157it [00:01, 213.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 148/8000 [00:09<06:14, 20.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 196it [00:01, 254.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 152/8000 [00:09<07:00, 18.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 233it [00:01, 280.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 155/8000 [00:09<06:36, 19.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 274it [00:01, 312.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 160/8000 [00:10<06:47, 19.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 314it [00:01, 335.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 164/8000 [00:10<07:31, 17.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 353it [00:01, 348.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 168/8000 [00:10<08:57, 14.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 392it [00:01, 358.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 175/8000 [00:10<06:20, 20.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 431it [00:01, 364.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 180/8000 [00:11<06:28, 20.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 471it [00:02, 372.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 185/8000 [00:11<06:30, 19.99it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 510it [00:02, 374.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 189/8000 [00:11<06:56, 18.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 550it [00:02, 382.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 193/8000 [00:12<09:41, 13.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 590it [00:02, 384.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 196/8000 [00:12<09:42, 13.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 631it [00:02, 391.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 199/8000 [00:12<10:08, 12.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 671it [00:02, 391.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 202/8000 [00:12<09:12, 14.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 711it [00:02, 389.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 204/8000 [00:13<10:24, 12.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 751it [00:02, 389.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 209/8000 [00:13<07:26, 17.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:02, 273.35it/s]\n",
       "\n",
-      "Generating signals:   3%|▍                 | 212/8000 [00:13<06:58, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 216/8000 [00:13<10:30, 12.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 220/8000 [00:13<08:43, 14.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%| | 11/100 [00:00<00:00, 94.65\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 224/8000 [00:14<08:32, 15.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|▏| 21/100 [00:00<00:00, 79.86\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 228/8000 [00:14<07:07, 18.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 70.69\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 231/8000 [00:14<06:51, 18.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|▍| 41/100 [00:00<00:00, 79.03\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 238/8000 [00:14<06:04, 21.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 75.49\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 242/8000 [00:15<06:42, 19.29it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|▌| 58/100 [00:00<00:00, 71.18\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 245/8000 [00:15<06:38, 19.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 68.02\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 248/8000 [00:15<06:16, 20.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:01<00:00, 66.34\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 254/8000 [00:15<04:51, 26.54it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:01<00:00, 66.26\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 258/8000 [00:15<04:40, 27.64it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|▉| 88/100 [00:01<00:00, 66.57\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 261/8000 [00:15<06:21, 20.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 70.3\n",
       "\n",
-      "Generating signals:   3%|▌                 | 269/8000 [00:16<05:18, 24.31it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|██████▊                    | 1/4 [04:13<12:41, 253.96s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.88126012751482\n",
+      "2999: -6.2216643\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   3%|▌                 | 272/8000 [00:16<05:21, 24.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 276/8000 [00:16<04:57, 25.97it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 280/8000 [00:16<06:01, 21.34it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:05<18:50,  5.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 284/8000 [00:16<06:31, 19.72it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:06<10:03,  3.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 287/8000 [00:17<08:50, 14.55it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<07:04,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 289/8000 [00:17<08:38, 14.88it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:21,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 291/8000 [00:17<08:31, 15.08it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<04:16,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 293/8000 [00:17<08:24, 15.28it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:10<03:38,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 298/8000 [00:17<06:13, 20.65it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:11<03:14,  1.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 304/8000 [00:18<05:50, 21.93it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:12<03:21,  1.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 308/8000 [00:18<05:24, 23.69it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:13<03:12,  1.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 312/8000 [00:18<06:17, 20.36it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:13<03:01,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 316/8000 [00:18<09:17, 13.79it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:14<02:45,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 318/8000 [00:19<09:20, 13.71it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:15<02:36,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 320/8000 [00:19<09:14, 13.84it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:16<02:27,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 322/8000 [00:19<09:23, 13.63it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:16<02:22,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 324/8000 [00:19<11:23, 11.23it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:17<02:24,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 328/8000 [00:19<08:23, 15.23it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:18<02:34,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 333/8000 [00:19<06:20, 20.16it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:19<02:40,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 337/8000 [00:20<07:05, 18.02it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:20<02:47,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 343/8000 [00:20<05:12, 24.53it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:21<02:44,  1.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 347/8000 [00:20<05:49, 21.89it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:22<02:36,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 352/8000 [00:20<05:02, 25.32it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:23<02:31,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 355/8000 [00:20<06:28, 19.69it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:23<02:34,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 358/8000 [00:21<06:12, 20.50it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:24<02:34,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 361/8000 [00:21<05:59, 21.27it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:25<02:36,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 366/8000 [00:21<05:02, 25.27it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:26<02:48,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 370/8000 [00:21<05:59, 21.20it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:28<02:58,  1.03s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 373/8000 [00:21<05:49, 21.80it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:28<02:46,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 378/8000 [00:21<06:03, 20.94it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:29<02:34,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 381/8000 [00:22<06:02, 21.04it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:30<02:22,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 385/8000 [00:22<07:00, 18.11it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:31<02:14,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 389/8000 [00:22<07:20, 17.29it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:31<02:07,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 392/8000 [00:22<06:53, 18.39it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:32<02:03,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 395/8000 [00:22<06:45, 18.74it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:33<01:59,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 398/8000 [00:23<06:20, 19.99it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:33<01:59,  1.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 401/8000 [00:23<05:49, 21.74it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:34<02:00,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 405/8000 [00:23<05:14, 24.14it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:35<02:00,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 412/8000 [00:23<03:46, 33.54it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:36<01:59,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 418/8000 [00:23<04:16, 29.52it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:36<01:58,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 424/8000 [00:23<04:35, 27.52it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:37<01:58,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 429/8000 [00:24<04:21, 28.97it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:38<01:56,  1.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 439/8000 [00:24<03:39, 34.51it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:38<01:56,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 446/8000 [00:24<03:57, 31.85it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:39<01:53,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 451/8000 [00:25<05:57, 21.10it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:40<02:02,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 455/8000 [00:25<05:24, 23.25it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:41<01:58,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 458/8000 [00:25<05:32, 22.68it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:42<02:00,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 461/8000 [00:25<06:45, 18.61it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:42<01:58,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 464/8000 [00:25<08:55, 14.07it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:43<01:57,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 468/8000 [00:26<07:27, 16.83it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:44<01:55,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 473/8000 [00:26<05:57, 21.04it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:45<02:02,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 478/8000 [00:26<05:57, 21.06it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:46<02:03,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 482/8000 [00:26<06:21, 19.73it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:46<01:57,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 485/8000 [00:26<07:33, 16.55it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:47<01:52,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 492/8000 [00:27<05:14, 23.87it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:48<01:49,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 496/8000 [00:27<04:59, 25.03it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:48<01:48,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 499/8000 [00:27<06:06, 20.48it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:49<01:46,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 505/8000 [00:27<04:52, 25.61it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:50<01:49,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 511/8000 [00:27<04:58, 25.05it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:51<01:53,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 515/8000 [00:28<05:42, 21.87it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:52<01:55,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 520/8000 [00:28<05:57, 20.94it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:53<01:54,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 525/8000 [00:28<06:06, 20.40it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:53<01:54,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 530/8000 [00:28<06:08, 20.26it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:54<01:54,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 535/8000 [00:29<07:29, 16.60it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:55<01:56,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 539/8000 [00:29<06:49, 18.22it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:57<02:04,  1.10it/s]\n",
       "\n",
-      "Generating signals:   7%|█▏                | 544/8000 [00:29<05:57, 20.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 548/8000 [00:29<05:22, 23.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 555/8000 [00:29<05:03, 24.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 21.43it/s]\n",
       "\n",
-      "Generating signals:   7%|█▎                | 559/8000 [00:30<06:41, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 562/8000 [00:30<06:22, 19.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 566/8000 [00:30<05:43, 21.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 569/8000 [00:30<05:40, 21.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 54it [00:01, 66.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 573/8000 [00:31<06:22, 19.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 110it [00:01, 139.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 577/8000 [00:31<05:40, 21.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 161it [00:01, 204.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 581/8000 [00:31<06:32, 18.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 207it [00:01, 252.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 584/8000 [00:31<06:08, 20.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 264it [00:01, 321.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 588/8000 [00:31<06:37, 18.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 316it [00:01, 368.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 592/8000 [00:31<05:51, 21.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 375it [00:01, 423.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 597/8000 [00:32<08:31, 14.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 428it [00:01, 445.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 599/8000 [00:32<08:23, 14.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 484it [00:01, 474.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 603/8000 [00:32<06:45, 18.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 537it [00:02, 489.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 606/8000 [00:32<06:12, 19.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 593it [00:02, 507.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 609/8000 [00:32<06:06, 20.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 647it [00:02, 495.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 614/8000 [00:33<06:04, 20.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 699it [00:02, 490.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 617/8000 [00:33<06:56, 17.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:02, 313.68it/s]\n",
       "\n",
-      "Generating signals:   8%|█▍                | 620/8000 [00:33<06:37, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 623/8000 [00:33<06:13, 19.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 630/8000 [00:33<05:17, 23.24it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|███▋       | 33/100 [00:00<00:00, 320.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 634/8000 [00:34<04:59, 24.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|███████▎   | 66/100 [00:00<00:00, 316.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 637/8000 [00:34<04:58, 24.67it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 306.13it/s]\n",
       "\n",
-      "Generating signals:   8%|█▍                | 640/8000 [00:34<06:15, 19.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 644/8000 [00:34<08:54, 13.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 647/8000 [00:35<07:50, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 97/8000 [00:00<00:11, 715.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 649/8000 [00:35<08:07, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 196/8000 [00:00<00:11, 699.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 652/8000 [00:35<08:36, 14.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                 | 286/8000 [00:00<00:12, 641.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 655/8000 [00:35<09:17, 13.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                 | 376/8000 [00:00<00:12, 634.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 658/8000 [00:35<09:28, 12.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                 | 477/8000 [00:00<00:11, 650.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 666/8000 [00:36<05:40, 21.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                | 583/8000 [00:00<00:10, 679.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 669/8000 [00:36<06:44, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                | 679/8000 [00:01<00:10, 686.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 674/8000 [00:36<05:28, 22.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                               | 775/8000 [00:01<00:10, 696.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 680/8000 [00:36<04:24, 27.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                               | 865/8000 [00:01<00:10, 680.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 684/8000 [00:36<04:13, 28.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 954/8000 [00:01<00:10, 662.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 689/8000 [00:36<04:43, 25.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                             | 1049/8000 [00:01<00:10, 677.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 692/8000 [00:37<05:56, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                             | 1152/8000 [00:01<00:10, 672.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 699/8000 [00:37<05:18, 22.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1261/8000 [00:01<00:09, 714.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 702/8000 [00:37<05:19, 22.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1358/8000 [00:01<00:09, 695.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 706/8000 [00:37<06:01, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                           | 1452/8000 [00:02<00:09, 694.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 710/8000 [00:38<06:44, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                           | 1542/8000 [00:02<00:09, 679.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 717/8000 [00:38<04:59, 24.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                           | 1640/8000 [00:02<00:09, 686.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 723/8000 [00:38<04:05, 29.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▎                          | 1729/8000 [00:02<00:09, 678.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 727/8000 [00:38<03:55, 30.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▋                          | 1813/8000 [00:02<00:09, 641.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 732/8000 [00:38<03:45, 32.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1915/8000 [00:02<00:08, 678.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 738/8000 [00:38<04:13, 28.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                         | 2013/8000 [00:02<00:08, 681.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 745/8000 [00:39<04:52, 24.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                         | 2105/8000 [00:03<00:08, 678.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 749/8000 [00:39<04:33, 26.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                        | 2198/8000 [00:03<00:08, 675.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 753/8000 [00:39<05:07, 23.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▊                        | 2313/8000 [00:03<00:07, 719.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 757/8000 [00:39<05:41, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                       | 2416/8000 [00:03<00:07, 735.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 763/8000 [00:40<05:18, 22.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                       | 2507/8000 [00:03<00:07, 720.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 767/8000 [00:40<04:58, 24.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████                       | 2606/8000 [00:03<00:07, 704.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 771/8000 [00:40<05:32, 21.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▍                      | 2700/8000 [00:03<00:07, 693.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 775/8000 [00:40<06:06, 19.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▊                      | 2786/8000 [00:04<00:07, 681.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 779/8000 [00:41<07:33, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▎                     | 2888/8000 [00:04<00:07, 703.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 781/8000 [00:41<08:46, 13.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▋                     | 2996/8000 [00:04<00:06, 747.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 786/8000 [00:41<07:50, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████                     | 3086/8000 [00:04<00:06, 729.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 794/8000 [00:41<05:14, 22.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▌                    | 3190/8000 [00:04<00:06, 744.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 800/8000 [00:42<06:03, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▉                    | 3272/8000 [00:04<00:06, 709.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 807/8000 [00:42<05:32, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▎                   | 3355/8000 [00:04<00:06, 665.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 810/8000 [00:42<05:33, 21.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▋                   | 3463/8000 [00:05<00:06, 695.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 814/8000 [00:42<06:48, 17.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████                   | 3552/8000 [00:05<00:06, 684.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 818/8000 [00:43<07:05, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▍                  | 3643/8000 [00:05<00:06, 668.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 820/8000 [00:43<07:05, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▊                  | 3735/8000 [00:05<00:06, 662.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 822/8000 [00:43<07:14, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▎                 | 3826/8000 [00:05<00:06, 662.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 828/8000 [00:43<07:18, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3927/8000 [00:05<00:06, 660.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 831/8000 [00:43<06:53, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████                 | 4024/8000 [00:05<00:05, 667.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 835/8000 [00:44<05:51, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▌                | 4133/8000 [00:06<00:05, 693.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 842/8000 [00:44<06:03, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████                | 4236/8000 [00:06<00:05, 707.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 846/8000 [00:44<05:17, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4331/8000 [00:06<00:05, 699.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 851/8000 [00:44<05:26, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▊               | 4428/8000 [00:06<00:04, 715.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 854/8000 [00:44<05:18, 22.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▏              | 4526/8000 [00:06<00:05, 686.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 859/8000 [00:44<04:29, 26.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▌              | 4606/8000 [00:06<00:05, 626.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 862/8000 [00:45<05:38, 21.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4693/8000 [00:06<00:05, 627.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 866/8000 [00:45<04:54, 24.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▎             | 4779/8000 [00:07<00:08, 375.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 870/8000 [00:45<04:44, 25.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▋             | 4869/8000 [00:07<00:06, 450.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 874/8000 [00:45<04:28, 26.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▏            | 4976/8000 [00:07<00:05, 540.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 877/8000 [00:45<05:39, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5106/8000 [00:07<00:04, 636.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 880/8000 [00:46<06:34, 18.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▏           | 5208/8000 [00:07<00:03, 707.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 885/8000 [00:46<06:18, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5314/8000 [00:07<00:03, 776.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 888/8000 [00:46<05:59, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████           | 5437/8000 [00:08<00:03, 791.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 893/8000 [00:46<05:01, 23.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▋          | 5563/8000 [00:08<00:03, 778.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 898/8000 [00:46<05:14, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5664/8000 [00:08<00:02, 781.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 902/8000 [00:46<04:47, 24.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▍         | 5747/8000 [00:08<00:02, 754.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 908/8000 [00:47<06:38, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▊         | 5833/8000 [00:08<00:02, 741.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 912/8000 [00:47<06:55, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5955/8000 [00:08<00:02, 806.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 915/8000 [00:47<06:25, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6045/8000 [00:08<00:02, 690.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 918/8000 [00:47<05:57, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████        | 6146/8000 [00:09<00:02, 673.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 921/8000 [00:48<05:41, 20.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6240/8000 [00:09<00:02, 709.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 924/8000 [00:48<06:53, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6339/8000 [00:09<00:02, 733.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 926/8000 [00:48<06:57, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6435/8000 [00:09<00:02, 737.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 928/8000 [00:48<07:10, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▋      | 6523/8000 [00:09<00:01, 754.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 930/8000 [00:48<07:17, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████      | 6606/8000 [00:09<00:01, 764.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 933/8000 [00:48<07:48, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▍     | 6702/8000 [00:09<00:01, 811.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 935/8000 [00:49<07:46, 15.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6789/8000 [00:09<00:01, 812.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 939/8000 [00:49<09:02, 13.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6888/8000 [00:09<00:01, 828.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 944/8000 [00:49<06:23, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6989/8000 [00:10<00:01, 825.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 949/8000 [00:49<05:21, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7140/8000 [00:10<00:00, 900.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 952/8000 [00:49<05:09, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▊   | 7241/8000 [00:10<00:00, 922.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 957/8000 [00:49<04:13, 27.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7371/8000 [00:10<00:00, 875.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 961/8000 [00:50<04:06, 28.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▊  | 7473/8000 [00:10<00:00, 896.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 965/8000 [00:50<05:08, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7576/8000 [00:10<00:00, 921.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 968/8000 [00:50<04:57, 23.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7674/8000 [00:10<00:00, 914.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 971/8000 [00:50<04:54, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7783/8000 [00:10<00:00, 948.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 976/8000 [00:50<06:21, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7900/8000 [00:11<00:00, 985.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 980/8000 [00:51<05:25, 21.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8012it [00:11, 717.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  12%|██▏               | 987/8000 [00:51<04:00, 29.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 992/8000 [00:51<04:38, 25.16it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 996/8000 [00:51<05:28, 21.30it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:30,  4.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏              | 1000/8000 [00:51<05:05, 22.93it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:37,  2.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1003/8000 [00:51<05:02, 23.12it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:13,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1009/8000 [00:52<05:02, 23.14it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:04,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1014/8000 [00:52<04:29, 25.96it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:26,  1.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1021/8000 [00:52<05:06, 22.80it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:06,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1025/8000 [00:52<04:40, 24.84it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:50,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1032/8000 [00:53<04:30, 25.73it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:58,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1036/8000 [00:53<04:17, 27.01it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<03:25,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1042/8000 [00:53<04:20, 26.73it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:12<03:22,  1.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1045/8000 [00:53<05:35, 20.73it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:13<02:58,  1.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1048/8000 [00:53<05:39, 20.45it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:43,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1051/8000 [00:54<06:36, 17.53it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:32,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1055/8000 [00:54<05:54, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:15<02:23,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1058/8000 [00:54<05:49, 19.84it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:15<02:16,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1066/8000 [00:54<03:57, 29.24it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:16<02:12,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1072/8000 [00:54<04:18, 26.83it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:09,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1075/8000 [00:55<05:44, 20.08it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:17<02:07,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1080/8000 [00:55<05:53, 19.58it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:18<02:05,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1084/8000 [00:55<06:21, 18.14it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:19<02:04,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1087/8000 [00:55<06:00, 19.18it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:19<02:03,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1090/8000 [00:55<05:53, 19.53it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:20<02:02,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1093/8000 [00:56<07:11, 16.01it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:21<02:02,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1096/8000 [00:56<06:30, 17.66it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:21<02:04,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1101/8000 [00:56<05:17, 21.71it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:22<02:02,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1104/8000 [00:56<07:29, 15.35it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:23<02:00,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1108/8000 [00:57<06:26, 17.85it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:24<01:58,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1114/8000 [00:57<05:58, 19.21it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:24<01:57,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1119/8000 [00:57<05:57, 19.25it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:25<01:55,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1123/8000 [00:57<06:26, 17.78it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:26<01:55,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1126/8000 [00:57<06:18, 18.14it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:26<01:56,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1130/8000 [00:58<05:40, 20.19it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:27<01:55,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1135/8000 [00:58<04:39, 24.60it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:28<01:53,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1140/8000 [00:58<07:24, 15.44it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:28<01:52,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1144/8000 [00:59<07:18, 15.64it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:29<01:51,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1149/8000 [00:59<06:43, 16.97it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:30<01:50,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1155/8000 [00:59<06:00, 18.99it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:30<01:49,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1158/8000 [00:59<05:47, 19.70it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:31<01:51,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1162/8000 [01:00<07:30, 15.16it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:32<01:50,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1164/8000 [01:00<07:36, 14.99it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:32<01:48,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1166/8000 [01:00<07:32, 15.10it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:33<01:48,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1168/8000 [01:00<07:23, 15.40it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:34<01:47,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1173/8000 [01:00<06:52, 16.56it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:34<01:47,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1177/8000 [01:00<05:47, 19.63it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:35<01:46,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1180/8000 [01:01<06:46, 16.78it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:36<01:46,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1187/8000 [01:01<04:47, 23.73it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:36<01:45,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1193/8000 [01:01<04:54, 23.10it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:38<02:04,  1.23it/s]\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1198/8000 [01:01<05:17, 21.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1203/8000 [01:02<05:21, 21.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1206/8000 [01:02<07:28, 15.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 25.77it/s]\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1209/8000 [01:02<06:48, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1212/8000 [01:02<06:30, 17.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1216/8000 [01:03<06:47, 16.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1218/8000 [01:03<06:51, 16.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 85it [00:00, 122.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1221/8000 [01:03<06:21, 17.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 164it [00:01, 236.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1224/8000 [01:03<06:01, 18.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 249it [00:01, 355.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1228/8000 [01:03<05:13, 21.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 334it [00:01, 462.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1231/8000 [01:03<06:41, 16.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 417it [00:01, 549.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1233/8000 [01:04<08:30, 13.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 496it [00:01, 602.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1238/8000 [01:04<06:24, 17.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 579it [00:01, 660.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1243/8000 [01:04<05:07, 21.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 660it [00:01, 700.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1246/8000 [01:04<05:14, 21.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 438.51it/s]\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1252/8000 [01:04<04:18, 26.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1256/8000 [01:04<04:08, 27.15it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1262/8000 [01:05<05:24, 20.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 504.21it/s]\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1265/8000 [01:05<06:24, 17.53it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|█████████████▌             | 2/4 [10:22<10:42, 321.49s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.8517572768148884\n",
+      "2999: -6.1751397\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  16%|██▋              | 1271/8000 [01:05<04:54, 22.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1274/8000 [01:05<04:56, 22.70it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1278/8000 [01:06<05:36, 19.99it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:19,  5.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1283/8000 [01:06<06:24, 17.45it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<08:34,  2.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1286/8000 [01:06<07:10, 15.58it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:06<05:45,  1.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1289/8000 [01:07<09:08, 12.23it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:07<04:25,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1293/8000 [01:07<08:44, 12.79it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:08<03:41,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1296/8000 [01:07<09:21, 11.94it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:14,  1.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1298/8000 [01:07<09:01, 12.38it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<02:59,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1303/8000 [01:08<07:50, 14.23it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<02:46,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1306/8000 [01:08<07:08, 15.63it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:32,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1308/8000 [01:08<08:39, 12.88it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:24,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1313/8000 [01:08<07:21, 15.16it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:19,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1315/8000 [01:08<07:13, 15.42it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:14,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1318/8000 [01:09<06:39, 16.73it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:13<02:10,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1323/8000 [01:09<06:27, 17.22it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:14<02:09,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1328/8000 [01:09<05:15, 21.15it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:15<02:07,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1333/8000 [01:09<05:27, 20.39it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:15<02:12,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1337/8000 [01:09<05:57, 18.64it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:16<02:17,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1339/8000 [01:10<06:09, 18.02it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:17<02:21,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1345/8000 [01:10<05:37, 19.74it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:18<02:18,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1349/8000 [01:10<05:03, 21.93it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:19<02:11,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1354/8000 [01:10<04:20, 25.47it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:19<02:10,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1357/8000 [01:10<05:48, 19.07it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:20<02:15,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1364/8000 [01:11<05:15, 21.03it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:21<02:21,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1369/8000 [01:11<04:33, 24.27it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:22<02:23,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1372/8000 [01:11<04:29, 24.60it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:23<02:16,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1375/8000 [01:11<04:40, 23.60it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:23<02:12,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1378/8000 [01:11<04:51, 22.70it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:24<02:29,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1385/8000 [01:11<03:36, 30.54it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:25<02:43,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1389/8000 [01:11<03:27, 31.80it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:27<02:53,  1.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1395/8000 [01:12<03:05, 35.59it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:28<03:09,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1399/8000 [01:12<04:07, 26.67it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:29<03:17,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1404/8000 [01:12<04:48, 22.83it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:30<03:17,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1410/8000 [01:13<05:44, 19.14it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:32<03:12,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1418/8000 [01:13<05:01, 21.80it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:33<03:07,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1421/8000 [01:13<05:01, 21.79it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:34<03:06,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1426/8000 [01:13<04:20, 25.25it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:35<03:04,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1431/8000 [01:13<04:37, 23.68it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:36<03:02,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1438/8000 [01:14<04:19, 25.32it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:37<03:02,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1444/8000 [01:14<04:30, 24.20it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:38<03:00,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1451/8000 [01:14<04:16, 25.56it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:39<02:56,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1455/8000 [01:14<05:36, 19.46it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:40<02:57,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1458/8000 [01:15<05:25, 20.07it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:42<02:54,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1461/8000 [01:15<07:18, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:43<02:52,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1464/8000 [01:15<06:42, 16.22it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:44<02:50,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1466/8000 [01:15<08:08, 13.37it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:45<02:52,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1469/8000 [01:16<08:40, 12.56it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:46<02:51,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1473/8000 [01:16<08:10, 13.31it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:47<02:51,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1476/8000 [01:16<07:20, 14.81it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:48<02:49,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1478/8000 [01:16<07:29, 14.51it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:49<02:47,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1481/8000 [01:17<08:30, 12.77it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:50<02:47,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1483/8000 [01:17<08:28, 12.81it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:52<02:45,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1486/8000 [01:17<07:20, 14.80it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:53<02:42,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1491/8000 [01:17<05:14, 20.71it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:54<02:50,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1498/8000 [01:17<03:49, 28.31it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:55<02:49,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1504/8000 [01:17<04:16, 25.29it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:56<02:46,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1508/8000 [01:18<06:55, 15.62it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:57<02:42,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1512/8000 [01:18<07:38, 14.14it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:58<02:38,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1516/8000 [01:19<07:28, 14.47it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:59<02:36,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1522/8000 [01:19<05:33, 19.42it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:00<02:33,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1525/8000 [01:19<06:28, 16.68it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:02<02:32,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1530/8000 [01:19<06:16, 17.18it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:03<02:32,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1533/8000 [01:19<06:15, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:04<02:32,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1535/8000 [01:20<06:26, 16.71it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [01:06<02:24,  1.06s/it]\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1537/8000 [01:20<06:28, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1541/8000 [01:20<06:50, 15.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1545/8000 [01:20<05:43, 18.78it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1549/8000 [01:20<05:10, 20.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 23.20it/s]\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1555/8000 [01:20<04:54, 21.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1558/8000 [01:21<06:53, 15.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1561/8000 [01:21<06:19, 16.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1563/8000 [01:21<07:41, 13.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 38it [00:00, 55.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1570/8000 [01:21<05:54, 18.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 110.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1572/8000 [01:22<07:23, 14.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 111it [00:01, 159.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1574/8000 [01:22<07:26, 14.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 148it [00:01, 205.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1576/8000 [01:22<07:34, 14.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 185it [00:01, 243.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1578/8000 [01:22<07:28, 14.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 222it [00:01, 274.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1581/8000 [01:22<06:34, 16.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 259it [00:01, 297.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1588/8000 [01:23<05:19, 20.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 296it [00:01, 315.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1591/8000 [01:23<05:12, 20.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 333it [00:01, 328.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1594/8000 [01:23<06:26, 16.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 370it [00:01, 338.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1598/8000 [01:23<05:21, 19.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 411it [00:01, 358.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1601/8000 [01:23<05:09, 20.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 449it [00:02, 359.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1608/8000 [01:24<04:37, 23.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 487it [00:02, 360.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1613/8000 [01:24<04:10, 25.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 524it [00:02, 360.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1618/8000 [01:24<03:45, 28.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 561it [00:02, 361.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1621/8000 [01:24<05:43, 18.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 598it [00:02, 361.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1627/8000 [01:24<04:21, 24.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 637it [00:02, 368.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1632/8000 [01:25<04:45, 22.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 675it [00:02, 363.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1637/8000 [01:25<05:08, 20.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 712it [00:02, 363.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1641/8000 [01:25<05:31, 19.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 749it [00:02, 363.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1645/8000 [01:25<05:04, 20.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 265.52it/s]\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1651/8000 [01:25<04:07, 25.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1654/8000 [01:26<04:17, 24.61it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1657/8000 [01:26<06:44, 15.68it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%|█▏           | 9/100 [00:00<00:01, 83.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1661/8000 [01:26<06:53, 15.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|██▏         | 18/100 [00:00<00:01, 53.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1663/8000 [01:27<08:26, 12.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|██▉         | 24/100 [00:00<00:01, 50.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1667/8000 [01:27<07:48, 13.53it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|███▌        | 30/100 [00:00<00:01, 47.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1671/8000 [01:27<06:27, 16.32it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|████▌       | 38/100 [00:00<00:01, 56.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1675/8000 [01:27<05:28, 19.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|█████▎      | 44/100 [00:00<00:01, 51.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1678/8000 [01:27<05:12, 20.22it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|██████      | 50/100 [00:00<00:01, 49.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1683/8000 [01:27<05:17, 19.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|██████▋     | 56/100 [00:01<00:00, 47.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1686/8000 [01:28<05:24, 19.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|███████▎    | 61/100 [00:01<00:00, 47.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1691/8000 [01:28<05:56, 17.71it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|████████▏   | 68/100 [00:01<00:00, 51.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1696/8000 [01:28<04:50, 21.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|████████▉   | 74/100 [00:01<00:00, 48.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1699/8000 [01:28<06:20, 16.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|█████████▍  | 79/100 [00:01<00:00, 47.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1706/8000 [01:29<04:37, 22.71it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██████████  | 84/100 [00:01<00:00, 45.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1712/8000 [01:29<06:36, 15.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:01<00:00, 51.58it/s]\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1718/8000 [01:29<05:07, 20.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1725/8000 [01:30<06:09, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1729/8000 [01:30<06:34, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 14/8000 [00:00<01:33, 84.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1733/8000 [01:30<05:44, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 30/8000 [00:00<01:26, 91.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1739/8000 [01:31<06:14, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                   | 60/8000 [00:00<00:59, 133.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1744/8000 [01:31<05:18, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                   | 75/8000 [00:00<01:03, 124.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1748/8000 [01:31<04:59, 20.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 94/8000 [00:00<01:05, 120.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1752/8000 [01:31<04:34, 22.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                  | 113/8000 [00:00<01:05, 119.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1755/8000 [01:31<05:45, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                  | 134/8000 [00:01<01:04, 122.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1759/8000 [01:32<07:18, 14.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                  | 152/8000 [00:01<01:05, 119.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1764/8000 [01:32<07:02, 14.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 175/8000 [00:01<01:03, 122.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1766/8000 [01:32<07:00, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 191/8000 [00:01<01:07, 115.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1769/8000 [01:32<06:21, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                  | 217/8000 [00:01<00:55, 139.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1772/8000 [01:32<05:52, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                  | 249/8000 [00:02<01:04, 119.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1776/8000 [01:33<04:56, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▏                                 | 280/8000 [00:02<01:02, 122.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1780/8000 [01:33<05:30, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                 | 298/8000 [00:02<01:04, 119.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1784/8000 [01:33<04:49, 21.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                 | 333/8000 [00:02<01:07, 114.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1787/8000 [01:33<04:47, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                 | 350/8000 [00:02<01:09, 110.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1790/8000 [01:33<05:03, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▌                                 | 364/8000 [00:03<01:13, 103.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1794/8000 [01:34<06:42, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                 | 385/8000 [00:03<01:08, 110.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1799/8000 [01:34<05:26, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                 | 403/8000 [00:03<01:04, 118.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1802/8000 [01:34<05:17, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                 | 422/8000 [00:03<01:04, 117.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1805/8000 [01:34<06:12, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▉                                 | 441/8000 [00:03<01:04, 116.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1807/8000 [01:34<06:06, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                 | 472/8000 [00:03<00:53, 139.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1811/8000 [01:34<05:07, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                | 492/8000 [00:04<00:55, 134.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1814/8000 [01:35<05:09, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                | 513/8000 [00:04<00:58, 128.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1817/8000 [01:35<06:22, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                | 555/8000 [00:04<00:54, 136.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1819/8000 [01:35<06:36, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                | 576/8000 [00:04<00:55, 134.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1822/8000 [01:35<07:36, 13.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                | 602/8000 [00:04<00:53, 139.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1826/8000 [01:36<06:15, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                | 633/8000 [00:05<01:03, 115.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1829/8000 [01:36<07:18, 14.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                | 655/8000 [00:05<01:00, 120.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1832/8000 [01:36<07:48, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                | 674/8000 [00:05<00:59, 123.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1834/8000 [01:36<07:17, 14.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                | 693/8000 [00:05<00:55, 130.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1840/8000 [01:36<06:04, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                               | 726/8000 [00:05<01:00, 119.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1842/8000 [01:37<06:24, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                               | 743/8000 [00:06<00:59, 122.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1846/8000 [01:37<05:24, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▎                               | 762/8000 [00:06<00:56, 127.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1854/8000 [01:37<03:36, 28.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                               | 779/8000 [00:06<00:58, 123.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1859/8000 [01:37<04:16, 23.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                               | 804/8000 [00:06<01:10, 101.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1864/8000 [01:37<03:41, 27.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                               | 823/8000 [00:06<01:08, 105.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1873/8000 [01:38<03:21, 30.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                               | 839/8000 [00:07<01:10, 101.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1877/8000 [01:38<04:04, 25.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 854/8000 [00:07<01:12, 98.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉             | 1881/8000 [01:38<04:00, 25.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                               | 874/8000 [00:07<01:02, 113.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1886/8000 [01:38<04:24, 23.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                               | 895/8000 [00:07<01:00, 117.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1891/8000 [01:38<04:40, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                               | 917/8000 [00:07<00:58, 121.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1894/8000 [01:39<04:42, 21.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████                               | 936/8000 [00:07<00:58, 120.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1899/8000 [01:39<04:51, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 955/8000 [00:08<01:01, 114.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1902/8000 [01:39<04:52, 20.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 968/8000 [00:08<01:08, 102.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1907/8000 [01:39<04:22, 23.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                              | 984/8000 [00:08<01:07, 103.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1913/8000 [01:39<04:39, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                             | 1000/8000 [00:08<01:03, 109.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1916/8000 [01:40<04:49, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▎                             | 1016/8000 [00:08<01:08, 102.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1919/8000 [01:40<04:48, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1040/8000 [00:08<01:16, 90.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1924/8000 [01:40<06:12, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1054/8000 [00:09<01:19, 87.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1929/8000 [01:40<04:55, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1070/8000 [00:09<01:17, 89.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1934/8000 [01:40<04:12, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                             | 1096/8000 [00:09<01:02, 110.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1939/8000 [01:41<05:33, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                             | 1117/8000 [00:09<00:55, 124.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1943/8000 [01:41<05:47, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                             | 1131/8000 [00:09<01:00, 112.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1946/8000 [01:41<06:27, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                             | 1149/8000 [00:09<01:01, 112.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1948/8000 [01:42<08:07, 12.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|████▉                             | 1170/8000 [00:10<00:58, 116.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1953/8000 [01:42<06:04, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                             | 1193/8000 [00:10<00:56, 121.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1957/8000 [01:42<06:21, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                            | 1213/8000 [00:10<00:56, 120.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1963/8000 [01:42<05:38, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                            | 1231/8000 [00:10<00:57, 117.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1969/8000 [01:43<05:28, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1246/8000 [00:10<00:57, 117.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1973/8000 [01:43<05:10, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1262/8000 [00:10<01:01, 110.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1977/8000 [01:43<05:40, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                            | 1289/8000 [00:11<00:52, 126.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1980/8000 [01:43<05:21, 18.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                            | 1308/8000 [00:11<00:49, 134.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1985/8000 [01:43<04:22, 22.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▌                            | 1322/8000 [00:11<00:55, 119.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1989/8000 [01:44<06:57, 14.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▋                            | 1343/8000 [00:11<00:53, 123.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1993/8000 [01:44<06:03, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1369/8000 [00:11<00:48, 136.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1997/8000 [01:44<06:08, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                            | 1388/8000 [00:11<00:50, 130.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2000/8000 [01:45<07:57, 12.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████                            | 1412/8000 [00:11<00:48, 135.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2003/8000 [01:45<06:56, 14.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████                            | 1437/8000 [00:12<00:44, 146.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2005/8000 [01:45<07:01, 14.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                           | 1459/8000 [00:12<00:45, 144.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2009/8000 [01:45<05:35, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                           | 1477/8000 [00:12<00:44, 145.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2012/8000 [01:45<05:25, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▎                           | 1492/8000 [00:12<00:51, 126.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2019/8000 [01:46<04:46, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                           | 1511/8000 [00:12<00:53, 122.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2024/8000 [01:46<05:05, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                           | 1529/8000 [00:12<00:54, 119.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2033/8000 [01:46<04:47, 20.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                           | 1549/8000 [00:13<00:54, 117.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2036/8000 [01:46<04:42, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▋                           | 1565/8000 [00:13<00:57, 112.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2040/8000 [01:47<05:08, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▋                           | 1583/8000 [00:13<00:57, 112.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2044/8000 [01:47<05:40, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                           | 1601/8000 [00:13<00:53, 119.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2049/8000 [01:47<04:40, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                           | 1627/8000 [00:13<00:47, 134.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2052/8000 [01:47<05:31, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|██████▉                           | 1644/8000 [00:13<00:52, 122.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2056/8000 [01:48<05:48, 17.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████                           | 1660/8000 [00:13<00:54, 116.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2060/8000 [01:48<05:14, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                          | 1682/8000 [00:14<00:51, 123.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2063/8000 [01:48<05:02, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                          | 1705/8000 [00:14<00:51, 123.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2068/8000 [01:48<04:06, 24.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▎                          | 1725/8000 [00:14<00:50, 124.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2071/8000 [01:48<04:05, 24.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▍                          | 1746/8000 [00:14<00:46, 133.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2077/8000 [01:49<05:12, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1769/8000 [00:14<00:45, 136.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2080/8000 [01:49<05:03, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1789/8000 [00:14<00:47, 130.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2084/8000 [01:49<05:41, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▋                          | 1813/8000 [00:15<00:44, 139.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2087/8000 [01:49<05:20, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▊                          | 1830/8000 [00:15<00:48, 127.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2090/8000 [01:49<05:04, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                          | 1855/8000 [00:15<00:45, 134.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2096/8000 [01:50<04:54, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                          | 1872/8000 [00:15<00:49, 123.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2099/8000 [01:50<04:49, 20.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████                          | 1901/8000 [00:15<00:40, 151.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2105/8000 [01:50<04:54, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1921/8000 [00:15<00:43, 138.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2108/8000 [01:50<05:46, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                         | 1943/8000 [00:16<00:44, 136.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2112/8000 [01:51<06:07, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▎                         | 1965/8000 [00:16<00:43, 138.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2114/8000 [01:51<06:11, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▍                         | 1992/8000 [00:16<00:53, 111.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2116/8000 [01:51<07:34, 12.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                         | 2011/8000 [00:16<00:53, 111.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2118/8000 [01:51<09:08, 10.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                         | 2029/8000 [00:16<00:49, 119.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2122/8000 [01:51<06:59, 14.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▋                         | 2051/8000 [00:16<00:47, 124.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2126/8000 [01:52<06:53, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▊                         | 2065/8000 [00:17<00:52, 113.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2131/8000 [01:52<05:06, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▊                         | 2086/8000 [00:17<00:48, 120.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2137/8000 [01:52<05:46, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                         | 2102/8000 [00:17<00:52, 113.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2140/8000 [01:53<07:41, 12.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████                         | 2124/8000 [00:17<00:48, 120.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2143/8000 [01:53<08:03, 12.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████                         | 2141/8000 [00:17<00:53, 109.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2145/8000 [01:53<07:51, 12.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▏                        | 2160/8000 [00:17<00:48, 119.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2149/8000 [01:53<07:34, 12.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▏                        | 2175/8000 [00:18<00:52, 110.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2151/8000 [01:54<08:37, 11.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                        | 2193/8000 [00:18<00:51, 112.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2155/8000 [01:54<06:44, 14.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▍                        | 2207/8000 [00:18<00:52, 111.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2158/8000 [01:54<06:02, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▍                        | 2225/8000 [00:18<00:52, 109.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2162/8000 [01:54<05:06, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▌                        | 2251/8000 [00:18<00:45, 125.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2165/8000 [01:54<04:48, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                        | 2271/8000 [00:18<00:46, 124.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2170/8000 [01:54<04:06, 23.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▋                        | 2294/8000 [00:19<00:44, 129.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2174/8000 [01:54<03:44, 25.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▊                        | 2308/8000 [00:19<00:50, 113.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2178/8000 [01:55<03:29, 27.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                        | 2326/8000 [00:19<00:52, 108.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2182/8000 [01:55<05:23, 17.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                        | 2344/8000 [00:19<00:50, 111.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2189/8000 [01:56<06:23, 15.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████                        | 2360/8000 [00:19<00:54, 103.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2194/8000 [01:56<06:55, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████                        | 2380/8000 [00:19<00:51, 108.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2198/8000 [01:56<05:58, 16.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▏                       | 2403/8000 [00:20<00:46, 121.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2203/8000 [01:56<06:31, 14.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                       | 2420/8000 [00:20<00:44, 125.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2207/8000 [01:57<05:36, 17.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                       | 2440/8000 [00:20<00:45, 122.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2210/8000 [01:57<07:08, 13.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▍                       | 2454/8000 [00:20<00:51, 108.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2214/8000 [01:57<07:03, 13.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▌                       | 2471/8000 [00:20<00:50, 110.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2220/8000 [01:57<05:09, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2483/8000 [00:20<00:57, 95.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2224/8000 [01:58<04:36, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                       | 2504/8000 [00:21<00:52, 104.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2227/8000 [01:58<04:37, 20.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▋                       | 2524/8000 [00:21<00:48, 112.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2233/8000 [01:58<04:26, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▊                       | 2543/8000 [00:21<00:44, 122.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2238/8000 [01:58<04:49, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▉                       | 2563/8000 [00:21<00:43, 123.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2242/8000 [01:58<04:21, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                       | 2592/8000 [00:21<00:38, 139.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2245/8000 [01:59<04:29, 21.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████                       | 2608/8000 [00:21<00:42, 126.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2249/8000 [01:59<05:14, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▏                      | 2627/8000 [00:21<00:44, 119.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2254/8000 [01:59<06:58, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2658/8000 [00:22<00:48, 109.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2257/8000 [02:00<06:26, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2672/8000 [00:22<00:49, 108.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2260/8000 [02:00<06:02, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▍                      | 2688/8000 [00:22<00:51, 103.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2264/8000 [02:00<05:04, 18.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▍                      | 2703/8000 [00:22<00:52, 100.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2269/8000 [02:00<05:46, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2717/8000 [00:22<00:55, 95.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2271/8000 [02:00<05:59, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2731/8000 [00:23<00:57, 92.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2273/8000 [02:00<06:18, 15.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2744/8000 [00:23<00:58, 89.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2275/8000 [02:01<06:36, 14.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2761/8000 [00:23<00:54, 95.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2277/8000 [02:01<06:29, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▊                      | 2781/8000 [00:23<00:47, 110.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2281/8000 [02:01<05:18, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▉                      | 2802/8000 [00:23<00:44, 118.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2286/8000 [02:01<05:04, 18.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                      | 2824/8000 [00:23<00:42, 122.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2288/8000 [02:01<05:20, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████                      | 2844/8000 [00:24<00:41, 123.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2292/8000 [02:01<04:36, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▏                     | 2860/8000 [00:24<00:45, 112.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2295/8000 [02:02<04:39, 20.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▏                     | 2875/8000 [00:24<00:48, 106.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2302/8000 [02:02<03:59, 23.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▎                     | 2893/8000 [00:24<00:47, 108.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2307/8000 [02:02<05:05, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                     | 2914/8000 [00:24<00:41, 121.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2310/8000 [02:02<05:00, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▍                     | 2933/8000 [00:24<00:43, 116.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2313/8000 [02:03<06:06, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▌                     | 2949/8000 [00:25<00:46, 108.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2318/8000 [02:03<04:57, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▌                     | 2964/8000 [00:25<00:48, 102.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2324/8000 [02:03<04:33, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2980/8000 [00:25<00:50, 99.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2329/8000 [02:03<04:41, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3003/8000 [00:25<00:44, 112.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2337/8000 [02:04<03:32, 26.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3020/8000 [00:25<00:42, 116.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2341/8000 [02:04<04:26, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▉                     | 3042/8000 [00:25<00:37, 130.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2346/8000 [02:04<03:59, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████                     | 3068/8000 [00:25<00:35, 140.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2350/8000 [02:04<03:42, 25.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████                     | 3088/8000 [00:26<00:37, 131.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2353/8000 [02:04<03:53, 24.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▏                    | 3104/8000 [00:26<00:41, 119.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2359/8000 [02:05<04:09, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▎                    | 3125/8000 [00:26<00:40, 120.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2365/8000 [02:05<04:11, 22.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▍                    | 3152/8000 [00:26<00:35, 135.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2370/8000 [02:05<03:45, 24.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▍                    | 3172/8000 [00:26<00:35, 134.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2373/8000 [02:05<05:35, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▌                    | 3192/8000 [00:26<00:33, 143.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2376/8000 [02:06<06:17, 14.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3208/8000 [00:27<00:36, 129.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2380/8000 [02:06<05:27, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3234/8000 [00:27<00:42, 113.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2385/8000 [02:06<05:21, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▊                    | 3259/8000 [00:27<00:35, 132.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2387/8000 [02:06<05:30, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▉                    | 3276/8000 [00:27<00:38, 123.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2390/8000 [02:06<06:11, 15.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████                    | 3310/8000 [00:27<00:40, 115.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2394/8000 [02:07<05:06, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████                    | 3323/8000 [00:28<00:43, 106.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2397/8000 [02:07<06:04, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▏                   | 3338/8000 [00:28<00:46, 101.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2401/8000 [02:07<05:09, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▎                   | 3357/8000 [00:28<00:44, 104.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2405/8000 [02:07<05:32, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▎                   | 3378/8000 [00:28<00:38, 120.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2410/8000 [02:07<04:22, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▍                   | 3396/8000 [00:28<00:39, 115.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2418/8000 [02:08<03:53, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▍                   | 3411/8000 [00:28<00:42, 107.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2421/8000 [02:08<05:00, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▌                   | 3431/8000 [00:29<00:40, 113.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2426/8000 [02:08<04:51, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▋                   | 3450/8000 [00:29<00:40, 112.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2429/8000 [02:08<04:47, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▊                   | 3479/8000 [00:29<00:44, 100.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2436/8000 [02:09<04:15, 21.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3501/8000 [00:29<00:49, 90.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2439/8000 [02:09<04:14, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3522/8000 [00:30<00:45, 97.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2442/8000 [02:09<04:20, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3537/8000 [00:30<00:45, 98.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2446/8000 [02:09<05:00, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3556/8000 [00:30<00:44, 99.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2448/8000 [02:09<05:18, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3579/8000 [00:30<00:49, 89.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2453/8000 [02:10<04:12, 22.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3597/8000 [00:30<00:44, 98.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2456/8000 [02:10<04:12, 21.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3612/8000 [00:30<00:46, 95.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2461/8000 [02:10<04:37, 19.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3626/8000 [00:31<00:48, 90.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2465/8000 [02:10<04:05, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3641/8000 [00:31<00:49, 88.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2469/8000 [02:10<04:38, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3660/8000 [00:31<00:45, 95.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2472/8000 [02:11<05:26, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3678/8000 [00:31<00:43, 99.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2477/8000 [02:11<04:18, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▋                  | 3703/8000 [00:31<00:35, 121.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2480/8000 [02:11<04:11, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▊                  | 3720/8000 [00:31<00:38, 109.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2484/8000 [02:11<05:31, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▉                  | 3739/8000 [00:32<00:38, 109.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2488/8000 [02:11<04:47, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▉                  | 3759/8000 [00:32<00:38, 111.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2493/8000 [02:11<03:55, 23.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████                  | 3777/8000 [00:32<00:35, 118.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2497/8000 [02:12<03:38, 25.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▏                 | 3796/8000 [00:32<00:36, 114.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2501/8000 [02:12<05:15, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▏                 | 3817/8000 [00:32<00:35, 116.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2504/8000 [02:12<05:04, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▎                 | 3844/8000 [00:33<00:41, 100.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2509/8000 [02:12<04:23, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3859/8000 [00:33<00:42, 97.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2514/8000 [02:13<05:55, 15.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▌                 | 3884/8000 [00:33<00:35, 115.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2517/8000 [02:13<05:29, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3917/8000 [00:33<00:27, 146.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2520/8000 [02:13<05:07, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▊                 | 3950/8000 [00:33<00:32, 123.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2524/8000 [02:14<06:55, 13.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████████████▉                 | 3971/8000 [00:34<00:32, 124.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2527/8000 [02:14<06:16, 14.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████████████▉                 | 3990/8000 [00:34<00:32, 122.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2529/8000 [02:14<06:20, 14.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████                 | 4008/8000 [00:34<00:34, 117.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2531/8000 [02:14<06:17, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████                 | 4023/8000 [00:34<00:35, 112.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2534/8000 [02:14<05:36, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▋                 | 4035/8000 [00:34<00:40, 99.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2538/8000 [02:14<04:55, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▏                | 4054/8000 [00:34<00:38, 102.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2540/8000 [02:15<06:31, 13.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4068/8000 [00:35<00:41, 95.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2547/8000 [02:15<04:00, 22.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▍                | 4089/8000 [00:35<00:35, 110.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2551/8000 [02:15<05:52, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▍                | 4106/8000 [00:35<00:36, 106.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2556/8000 [02:16<07:09, 12.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4120/8000 [00:35<00:39, 98.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2560/8000 [02:16<07:40, 11.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4136/8000 [00:35<00:39, 97.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2563/8000 [02:16<06:51, 13.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4153/8000 [00:35<00:38, 99.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2566/8000 [02:16<06:11, 14.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▊                | 4177/8000 [00:36<00:34, 109.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2571/8000 [02:16<04:57, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▊                | 4196/8000 [00:36<00:32, 115.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2575/8000 [02:17<05:27, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████▉                | 4222/8000 [00:36<00:27, 137.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2578/8000 [02:17<05:09, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████                | 4240/8000 [00:36<00:29, 127.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2584/8000 [02:17<04:34, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████                | 4256/8000 [00:36<00:31, 117.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2590/8000 [02:17<03:40, 24.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▏               | 4275/8000 [00:36<00:32, 113.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2595/8000 [02:18<04:51, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▎               | 4296/8000 [00:37<00:32, 114.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2602/8000 [02:18<04:10, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▎               | 4311/8000 [00:37<00:33, 110.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2609/8000 [02:18<03:52, 23.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4332/8000 [00:37<00:30, 121.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2615/8000 [02:19<03:56, 22.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4350/8000 [00:37<00:28, 127.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2619/8000 [02:19<04:22, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▌               | 4367/8000 [00:37<00:30, 120.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2624/8000 [02:19<04:31, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▋               | 4392/8000 [00:37<00:28, 125.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2627/8000 [02:19<05:14, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▊               | 4412/8000 [00:38<00:29, 121.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2629/8000 [02:19<05:21, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▊               | 4433/8000 [00:38<00:29, 121.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2633/8000 [02:20<04:38, 19.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████████████▉               | 4453/8000 [00:38<00:29, 122.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2636/8000 [02:20<04:28, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████████████▉               | 4468/8000 [00:38<00:29, 119.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2639/8000 [02:20<04:21, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████               | 4483/8000 [00:38<00:29, 120.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2642/8000 [02:20<04:28, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▏              | 4500/8000 [00:38<00:31, 110.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2645/8000 [02:20<05:31, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▏              | 4513/8000 [00:38<00:34, 101.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2647/8000 [02:20<05:41, 15.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4529/8000 [00:39<00:35, 98.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2650/8000 [02:21<05:07, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▎              | 4550/8000 [00:39<00:32, 105.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2653/8000 [02:21<04:47, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▍              | 4570/8000 [00:39<00:31, 110.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2657/8000 [02:21<04:15, 20.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▌              | 4593/8000 [00:39<00:27, 124.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2660/8000 [02:21<04:09, 21.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▋              | 4624/8000 [00:39<00:28, 116.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2663/8000 [02:21<04:00, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▋              | 4642/8000 [00:40<00:29, 112.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2667/8000 [02:21<03:29, 25.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▊              | 4661/8000 [00:40<00:30, 111.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2672/8000 [02:21<02:59, 29.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▉              | 4677/8000 [00:40<00:31, 105.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2676/8000 [02:22<03:50, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4700/8000 [00:40<00:28, 116.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2679/8000 [02:22<04:53, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████              | 4718/8000 [00:40<00:28, 117.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2683/8000 [02:22<04:16, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▏             | 4738/8000 [00:40<00:28, 114.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2687/8000 [02:22<04:58, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▏             | 4756/8000 [00:41<00:28, 113.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2690/8000 [02:23<06:47, 13.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▎             | 4787/8000 [00:41<00:24, 133.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2692/8000 [02:23<06:56, 12.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▍             | 4814/8000 [00:41<00:27, 113.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2695/8000 [02:23<07:05, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▌             | 4833/8000 [00:41<00:27, 113.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2698/8000 [02:23<06:15, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▋             | 4865/8000 [00:41<00:23, 133.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2704/8000 [02:24<05:11, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▊             | 4884/8000 [00:42<00:24, 129.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2708/8000 [02:24<04:31, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▊             | 4905/8000 [00:42<00:24, 128.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2711/8000 [02:24<04:18, 20.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|████████████████████▉             | 4921/8000 [00:42<00:25, 120.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2714/8000 [02:24<04:10, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|████████████████████▉             | 4939/8000 [00:42<00:24, 124.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2720/8000 [02:24<04:17, 20.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████             | 4954/8000 [00:42<00:27, 111.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2726/8000 [02:25<04:17, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▏            | 4977/8000 [00:42<00:25, 118.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2730/8000 [02:25<04:00, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▏            | 4993/8000 [00:43<00:26, 112.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2734/8000 [02:25<04:27, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5012/8000 [00:43<00:27, 109.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2739/8000 [02:25<04:17, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5025/8000 [00:43<00:28, 103.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2743/8000 [02:25<04:40, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▍            | 5054/8000 [00:43<00:22, 130.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2748/8000 [02:26<04:00, 21.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▌            | 5077/8000 [00:43<00:20, 142.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2754/8000 [02:26<04:09, 21.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5101/8000 [00:43<00:20, 144.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2757/8000 [02:26<04:54, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▊            | 5132/8000 [00:44<00:25, 114.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2760/8000 [02:26<04:39, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5157/8000 [00:44<00:28, 99.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2763/8000 [02:27<05:12, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████            | 5183/8000 [00:44<00:25, 109.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2765/8000 [02:27<05:12, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▏           | 5207/8000 [00:44<00:23, 116.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2768/8000 [02:27<04:37, 18.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▏           | 5225/8000 [00:45<00:24, 115.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2772/8000 [02:27<05:04, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5242/8000 [00:45<00:24, 112.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2777/8000 [02:27<04:44, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5264/8000 [00:45<00:21, 126.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2783/8000 [02:27<03:42, 23.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▍           | 5284/8000 [00:45<00:21, 125.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2787/8000 [02:28<03:28, 25.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5300/8000 [00:45<00:22, 117.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2790/8000 [02:28<03:27, 25.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5320/8000 [00:45<00:22, 117.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2796/8000 [02:28<03:48, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▋           | 5333/8000 [00:45<00:26, 101.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2800/8000 [02:28<04:17, 20.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▊           | 5355/8000 [00:46<00:24, 109.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2805/8000 [02:28<04:18, 20.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▊           | 5371/8000 [00:46<00:24, 105.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2809/8000 [02:29<05:25, 15.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5388/8000 [00:46<00:26, 99.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2811/8000 [02:29<05:30, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████████████████▉           | 5407/8000 [00:46<00:25, 102.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2814/8000 [02:29<05:06, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████           | 5424/8000 [00:46<00:24, 103.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2816/8000 [02:29<05:07, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▏          | 5444/8000 [00:46<00:22, 115.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2818/8000 [02:29<05:17, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▏          | 5466/8000 [00:47<00:21, 120.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2820/8000 [02:30<05:34, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▎          | 5481/8000 [00:47<00:22, 110.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2822/8000 [02:30<05:35, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▍          | 5500/8000 [00:47<00:23, 108.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2826/8000 [02:30<04:18, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▍          | 5522/8000 [00:47<00:21, 114.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2831/8000 [02:30<05:30, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▌          | 5538/8000 [00:47<00:21, 112.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2833/8000 [02:30<05:42, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▌          | 5555/8000 [00:47<00:21, 113.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2835/8000 [02:31<05:50, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▋          | 5579/8000 [00:48<00:18, 129.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2838/8000 [02:31<05:18, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▊          | 5595/8000 [00:48<00:20, 118.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2841/8000 [02:31<05:59, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▊          | 5613/8000 [00:48<00:20, 115.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2844/8000 [02:31<05:22, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▉          | 5637/8000 [00:48<00:19, 121.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2846/8000 [02:31<05:40, 15.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5655/8000 [00:48<00:20, 114.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2848/8000 [02:31<05:39, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5676/8000 [00:48<00:19, 120.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2850/8000 [02:32<06:01, 14.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▏         | 5700/8000 [00:49<00:17, 130.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2853/8000 [02:32<05:28, 15.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▎         | 5717/8000 [00:49<00:18, 122.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2856/8000 [02:32<06:03, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▎         | 5733/8000 [00:49<00:20, 112.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2860/8000 [02:32<06:23, 13.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▍         | 5752/8000 [00:49<00:20, 111.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2865/8000 [02:33<06:19, 13.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▌         | 5775/8000 [00:49<00:17, 124.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2868/8000 [02:33<05:54, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▋         | 5796/8000 [00:49<00:17, 123.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2870/8000 [02:33<06:37, 12.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▋         | 5814/8000 [00:50<00:18, 116.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2872/8000 [02:33<06:58, 12.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▊         | 5826/8000 [00:50<00:21, 102.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2874/8000 [02:33<07:04, 12.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▊         | 5851/8000 [00:50<00:19, 112.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2876/8000 [02:34<06:41, 12.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▉         | 5869/8000 [00:50<00:19, 107.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2879/8000 [02:34<05:33, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████         | 5889/8000 [00:50<00:18, 112.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2884/8000 [02:34<05:59, 14.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████         | 5911/8000 [00:50<00:17, 117.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2886/8000 [02:34<06:03, 14.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5929/8000 [00:51<00:17, 115.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2889/8000 [02:34<06:33, 13.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5946/8000 [00:51<00:18, 113.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2894/8000 [02:35<06:06, 13.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5959/8000 [00:51<00:18, 109.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2897/8000 [02:35<05:41, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▍        | 5978/8000 [00:51<00:18, 109.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2901/8000 [02:35<05:08, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▍        | 5999/8000 [00:51<00:17, 114.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2904/8000 [02:35<04:43, 17.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6018/8000 [00:51<00:17, 113.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2911/8000 [02:35<03:18, 25.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▋        | 6033/8000 [00:52<00:18, 104.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2915/8000 [02:36<03:10, 26.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6056/8000 [00:52<00:16, 118.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2918/8000 [02:36<04:23, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6071/8000 [00:52<00:16, 114.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2921/8000 [02:36<04:23, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6098/8000 [00:52<00:13, 137.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2924/8000 [02:36<04:19, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6113/8000 [00:52<00:15, 120.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2927/8000 [02:36<05:15, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6151/8000 [00:53<00:16, 111.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2932/8000 [02:37<04:55, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▎       | 6181/8000 [00:53<00:17, 101.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2936/8000 [02:37<04:16, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6195/8000 [00:53<00:18, 96.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2939/8000 [02:37<05:00, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6214/8000 [00:53<00:18, 97.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2941/8000 [02:37<05:07, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6233/8000 [00:53<00:17, 102.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2943/8000 [02:37<06:47, 12.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6245/8000 [00:54<00:18, 94.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2947/8000 [02:38<05:17, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▋       | 6269/8000 [00:54<00:16, 108.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2953/8000 [02:38<04:35, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▋       | 6290/8000 [00:54<00:15, 111.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2956/8000 [02:38<04:21, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6310/8000 [00:54<00:14, 114.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2960/8000 [02:38<04:55, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6329/8000 [00:54<00:13, 122.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2963/8000 [02:38<04:41, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6349/8000 [00:54<00:13, 122.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2971/8000 [02:39<02:55, 28.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████       | 6372/8000 [00:55<00:12, 128.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2975/8000 [02:39<04:33, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6401/8000 [00:55<00:11, 143.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2978/8000 [02:39<04:20, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6420/8000 [00:55<00:11, 132.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2984/8000 [02:40<04:49, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6437/8000 [00:55<00:12, 121.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2988/8000 [02:40<04:22, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▍      | 6463/8000 [00:55<00:11, 135.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2996/8000 [02:40<03:39, 22.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6481/8000 [00:55<00:11, 137.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3002/8000 [02:40<03:40, 22.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▋      | 6512/8000 [00:56<00:13, 113.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3006/8000 [02:40<03:27, 24.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▊      | 6531/8000 [00:56<00:12, 113.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3009/8000 [02:40<03:36, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▊      | 6552/8000 [00:56<00:12, 116.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3013/8000 [02:41<04:11, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6571/8000 [00:56<00:12, 118.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3017/8000 [02:41<03:47, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6588/8000 [00:56<00:12, 117.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3022/8000 [02:41<04:06, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████      | 6614/8000 [00:57<00:10, 137.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3027/8000 [02:41<04:16, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6633/8000 [00:57<00:10, 131.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3030/8000 [02:42<04:06, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6653/8000 [00:57<00:10, 126.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3034/8000 [02:42<06:54, 11.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6674/8000 [00:57<00:10, 131.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3036/8000 [02:42<07:08, 11.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▍     | 6688/8000 [00:57<00:11, 115.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3038/8000 [02:43<06:59, 11.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▍     | 6703/8000 [00:57<00:12, 106.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3044/8000 [02:43<05:33, 14.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6720/8000 [00:57<00:11, 106.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3046/8000 [02:43<05:47, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6733/8000 [00:58<00:11, 106.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3048/8000 [02:43<05:47, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▋     | 6753/8000 [00:58<00:11, 107.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3050/8000 [02:43<05:40, 14.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6773/8000 [00:58<00:10, 112.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3052/8000 [02:44<06:55, 11.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6788/8000 [00:58<00:11, 103.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3054/8000 [02:44<06:30, 12.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6802/8000 [00:58<00:12, 96.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3056/8000 [02:44<06:26, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6821/8000 [00:58<00:11, 104.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3059/8000 [02:44<05:32, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6833/8000 [00:59<00:11, 98.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3061/8000 [02:44<05:32, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6857/8000 [00:59<00:09, 122.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3065/8000 [02:44<04:15, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6873/8000 [00:59<00:09, 114.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3069/8000 [02:45<04:40, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6894/8000 [00:59<00:09, 119.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3071/8000 [02:45<05:05, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▍    | 6918/8000 [00:59<00:08, 129.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3075/8000 [02:45<04:15, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▍    | 6937/8000 [00:59<00:08, 124.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3080/8000 [02:45<03:21, 24.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6961/8000 [01:00<00:08, 129.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3083/8000 [02:45<03:26, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6978/8000 [01:00<00:08, 120.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3088/8000 [02:45<04:05, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7002/8000 [01:00<00:07, 137.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3091/8000 [02:46<04:05, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7017/8000 [01:00<00:08, 121.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3099/8000 [02:46<03:26, 23.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7040/8000 [01:00<00:07, 127.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3108/8000 [02:46<02:57, 27.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████    | 7064/8000 [01:00<00:07, 131.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3111/8000 [02:46<03:09, 25.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████    | 7083/8000 [01:01<00:07, 127.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3116/8000 [02:46<03:01, 26.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7099/8000 [01:01<00:07, 114.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3120/8000 [02:47<03:02, 26.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7113/8000 [01:01<00:08, 110.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3124/8000 [02:47<04:26, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7136/8000 [01:01<00:06, 128.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3127/8000 [02:47<04:12, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▍   | 7154/8000 [01:01<00:07, 120.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3132/8000 [02:47<04:50, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▍   | 7172/8000 [01:01<00:07, 115.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3136/8000 [02:48<04:10, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7191/8000 [01:01<00:06, 119.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3140/8000 [02:48<03:47, 21.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7207/8000 [01:02<00:07, 111.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3145/8000 [02:48<03:21, 24.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7222/8000 [01:02<00:07, 102.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3149/8000 [02:48<03:57, 20.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7235/8000 [01:02<00:07, 97.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3153/8000 [02:48<03:58, 20.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▊   | 7250/8000 [01:02<00:07, 103.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3158/8000 [02:49<03:27, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7270/8000 [01:02<00:06, 111.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3162/8000 [02:49<03:19, 24.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7287/8000 [01:02<00:06, 108.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3165/8000 [02:49<03:52, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████   | 7312/8000 [01:03<00:05, 121.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3168/8000 [02:49<05:17, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7330/8000 [01:03<00:05, 116.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3172/8000 [02:50<05:34, 14.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7351/8000 [01:03<00:05, 119.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3175/8000 [02:50<06:34, 12.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7377/8000 [01:03<00:04, 130.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3180/8000 [02:50<06:35, 12.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▍  | 7398/8000 [01:03<00:04, 139.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3182/8000 [02:50<06:18, 12.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7416/8000 [01:03<00:04, 130.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3185/8000 [02:51<05:30, 14.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7447/8000 [01:04<00:03, 142.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3189/8000 [02:51<04:37, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▊  | 7472/8000 [01:04<00:03, 139.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3194/8000 [02:51<03:41, 21.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████▊  | 7487/8000 [01:04<00:04, 124.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3197/8000 [02:51<03:35, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7510/8000 [01:04<00:03, 132.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3202/8000 [02:51<05:17, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████  | 7530/8000 [01:04<00:03, 135.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3207/8000 [02:52<04:14, 18.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████  | 7546/8000 [01:04<00:03, 133.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3214/8000 [02:52<03:55, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7565/8000 [01:04<00:03, 127.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3218/8000 [02:52<04:19, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7581/8000 [01:05<00:03, 117.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3221/8000 [02:52<04:10, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7602/8000 [01:05<00:03, 118.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3225/8000 [02:53<04:33, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▍ | 7621/8000 [01:05<00:03, 115.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3227/8000 [02:53<04:51, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▍ | 7639/8000 [01:05<00:03, 114.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3229/8000 [02:53<06:23, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7660/8000 [01:05<00:02, 122.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3232/8000 [02:53<05:51, 13.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7678/8000 [01:05<00:02, 128.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3236/8000 [02:53<05:00, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7695/8000 [01:06<00:02, 117.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3240/8000 [02:54<04:16, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▊ | 7712/8000 [01:06<00:02, 113.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3246/8000 [02:54<04:19, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████████████████████▊ | 7731/8000 [01:06<00:02, 116.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3250/8000 [02:54<04:50, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████████████████████▉ | 7755/8000 [01:06<00:01, 125.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3253/8000 [02:54<04:35, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7768/8000 [01:06<00:02, 111.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3258/8000 [02:55<05:22, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7794/8000 [01:06<00:01, 123.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3263/8000 [02:55<04:19, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▏| 7821/8000 [01:07<00:01, 145.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3266/8000 [02:55<05:01, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7842/8000 [01:07<00:01, 138.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3268/8000 [02:55<05:04, 15.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7859/8000 [01:07<00:01, 118.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3272/8000 [02:56<05:02, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▍| 7882/8000 [01:07<00:01, 110.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3276/8000 [02:56<04:16, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7906/8000 [01:07<00:00, 104.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3281/8000 [02:56<03:26, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7927/8000 [01:08<00:00, 108.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3285/8000 [02:56<03:49, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7947/8000 [01:08<00:00, 94.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3290/8000 [02:56<03:17, 23.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7965/8000 [01:08<00:00, 91.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3299/8000 [02:57<03:26, 22.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7980/8000 [01:08<00:00, 84.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3303/8000 [02:57<03:50, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8004it [01:09, 115.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  41%|███████          | 3307/8000 [02:57<04:55, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3309/8000 [02:58<04:56, 15.80it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3311/8000 [02:58<06:03, 12.90it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:05<16:49,  5.07s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3314/8000 [02:58<05:23, 14.47it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:06,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3318/8000 [02:58<04:23, 17.74it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:42,  2.04s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3321/8000 [02:58<04:17, 18.16it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:32,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3325/8000 [02:58<03:38, 21.35it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<04:54,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3331/8000 [02:59<03:57, 19.65it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:10<04:30,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3335/8000 [02:59<03:51, 20.18it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:12<04:16,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3339/8000 [02:59<03:28, 22.40it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:13<04:05,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3342/8000 [02:59<03:22, 22.95it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:14<03:55,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3345/8000 [02:59<03:21, 23.10it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:15<03:47,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3349/8000 [03:00<04:10, 18.57it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:16<03:44,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3354/8000 [03:00<04:09, 18.58it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:17<03:42,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3357/8000 [03:00<04:02, 19.12it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:19<03:39,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3360/8000 [03:00<04:43, 16.39it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:20<03:38,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3364/8000 [03:00<04:04, 18.94it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:21<03:34,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3370/8000 [03:01<03:51, 19.96it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:22<03:33,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3374/8000 [03:01<04:12, 18.33it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:23<03:34,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3379/8000 [03:01<03:35, 21.40it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:24<03:32,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3382/8000 [03:01<04:17, 17.92it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:26<03:31,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3386/8000 [03:02<04:36, 16.71it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:27<03:28,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3389/8000 [03:02<05:17, 14.54it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:28<03:31,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3392/8000 [03:02<05:36, 13.70it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:29<03:29,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3396/8000 [03:02<04:41, 16.33it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:30<03:37,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3398/8000 [03:02<05:09, 14.88it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:32<03:38,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3403/8000 [03:03<03:55, 19.52it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:33<03:31,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3408/8000 [03:03<03:17, 23.29it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:34<03:29,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3411/8000 [03:03<04:27, 17.14it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:35<03:25,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3415/8000 [03:03<03:56, 19.42it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:36<03:20,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3419/8000 [03:03<04:15, 17.91it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:37<03:18,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3423/8000 [03:04<05:31, 13.80it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:39<03:18,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3425/8000 [03:04<05:22, 14.20it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:40<03:17,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3429/8000 [03:04<04:28, 17.05it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:41<03:14,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3433/8000 [03:04<04:42, 16.17it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:42<03:10,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3435/8000 [03:05<04:40, 16.29it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:43<03:10,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3437/8000 [03:05<04:50, 15.71it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:44<03:09,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3441/8000 [03:05<05:09, 14.74it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:45<03:10,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3444/8000 [03:05<05:48, 13.08it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:47<03:11,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3446/8000 [03:06<06:55, 10.97it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:48<03:10,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3449/8000 [03:06<05:51, 12.93it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:49<03:06,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3453/8000 [03:06<04:41, 16.15it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:50<03:06,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3456/8000 [03:06<04:18, 17.58it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:51<03:05,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3459/8000 [03:06<04:20, 17.42it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:53<03:03,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3462/8000 [03:06<05:11, 14.58it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:54<03:03,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3464/8000 [03:07<05:13, 14.46it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:55<03:03,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3469/8000 [03:07<03:46, 20.04it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:56<03:00,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3475/8000 [03:07<02:45, 27.28it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:57<02:58,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3479/8000 [03:07<03:23, 22.25it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:59<03:10,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3483/8000 [03:07<03:02, 24.80it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:00<03:04,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3488/8000 [03:08<03:31, 21.29it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:01<02:59,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3492/8000 [03:08<05:17, 14.18it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [01:02<02:56,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3497/8000 [03:08<04:48, 15.63it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:03<02:53,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3502/8000 [03:09<04:28, 16.78it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:04<02:51,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3506/8000 [03:09<03:57, 18.91it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:05<02:51,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3511/8000 [03:09<04:07, 18.17it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:07<02:45,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3514/8000 [03:09<04:39, 16.03it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:08<02:46,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3518/8000 [03:09<03:54, 19.10it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:09<02:44,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3523/8000 [03:10<04:04, 18.34it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:10<02:42,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3527/8000 [03:10<04:19, 17.25it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:11<02:51,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3533/8000 [03:10<03:32, 21.04it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:13<02:53,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3539/8000 [03:10<03:31, 21.10it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:14<02:47,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3543/8000 [03:11<03:18, 22.42it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:15<02:45,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3547/8000 [03:11<03:38, 20.34it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:16<02:52,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3550/8000 [03:11<04:29, 16.52it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [01:17<02:47,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3555/8000 [03:11<03:34, 20.76it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [01:19<02:43,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3558/8000 [03:12<04:14, 17.47it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [01:20<02:42,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3564/8000 [03:12<03:58, 18.58it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [01:21<02:46,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3567/8000 [03:12<03:54, 18.87it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [01:22<02:43,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3570/8000 [03:12<04:32, 16.25it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [01:25<02:45,  1.26s/it]\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3576/8000 [03:13<04:42, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3578/8000 [03:13<04:44, 15.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3582/8000 [03:13<04:03, 18.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3587/8000 [03:13<03:55, 18.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 20.89it/s]\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3589/8000 [03:13<04:18, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3592/8000 [03:13<04:00, 18.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3595/8000 [03:14<03:52, 18.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3598/8000 [03:14<04:14, 17.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 38it [00:01, 48.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3602/8000 [03:14<05:06, 14.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 82it [00:01, 109.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3604/8000 [03:14<05:25, 13.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 118it [00:01, 154.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3607/8000 [03:14<04:52, 15.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 155it [00:01, 197.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3610/8000 [03:15<04:32, 16.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 191it [00:01, 234.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3614/8000 [03:15<04:07, 17.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 228it [00:01, 265.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3616/8000 [03:15<04:32, 16.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 265it [00:01, 290.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3618/8000 [03:15<04:45, 15.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 301it [00:01, 306.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3621/8000 [03:15<05:18, 13.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 340it [00:01, 328.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3626/8000 [03:16<04:44, 15.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 377it [00:01, 338.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3628/8000 [03:16<04:47, 15.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 414it [00:02, 340.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3630/8000 [03:16<04:52, 14.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 451it [00:02, 346.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3632/8000 [03:16<04:51, 14.97it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 488it [00:02, 350.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3634/8000 [03:16<04:49, 15.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 525it [00:02, 353.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3636/8000 [03:16<04:52, 14.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 567it [00:02, 371.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3640/8000 [03:17<03:50, 18.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 605it [00:02, 368.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3645/8000 [03:17<03:06, 23.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 643it [00:02, 366.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3648/8000 [03:17<03:10, 22.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 680it [00:02, 365.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3652/8000 [03:17<02:53, 25.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 717it [00:02, 347.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3656/8000 [03:17<03:36, 20.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 753it [00:03, 300.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3659/8000 [03:18<04:46, 15.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 248.16it/s]\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3664/8000 [03:18<04:17, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3669/8000 [03:18<03:43, 19.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3677/8000 [03:18<02:49, 25.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 43.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3680/8000 [03:19<05:31, 13.02it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 42.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3684/8000 [03:19<05:01, 14.33it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 42.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3687/8000 [03:19<04:56, 14.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|██▊         | 23/100 [00:00<00:01, 55.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3690/8000 [03:20<05:09, 13.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|███▍        | 29/100 [00:00<00:01, 50.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3693/8000 [03:20<06:28, 11.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|████▏       | 35/100 [00:00<00:01, 47.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3697/8000 [03:20<06:13, 11.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|████▊       | 40/100 [00:00<00:01, 46.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3703/8000 [03:21<04:51, 14.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█████▍      | 45/100 [00:00<00:01, 45.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3707/8000 [03:21<05:37, 12.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|██████▎     | 53/100 [00:01<00:00, 52.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3711/8000 [03:21<05:07, 13.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|███████     | 59/100 [00:01<00:00, 49.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3714/8000 [03:21<04:54, 14.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|███████▊    | 65/100 [00:01<00:00, 47.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3720/8000 [03:21<03:39, 19.47it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|████████▍   | 70/100 [00:01<00:00, 45.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3724/8000 [03:22<03:19, 21.45it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|█████████▌  | 80/100 [00:01<00:00, 57.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3728/8000 [03:22<04:27, 15.95it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|██████████▎ | 86/100 [00:01<00:00, 52.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3735/8000 [03:22<03:44, 19.03it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|███████████ | 92/100 [00:01<00:00, 50.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3738/8000 [03:23<04:15, 16.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 48.73it/s]\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3740/8000 [03:23<04:18, 16.45it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|████████████████████▎      | 3/4 [16:12<05:34, 334.37s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.237120346691686\n",
+      "2999: -5.7251811\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  47%|███████▉         | 3742/8000 [03:23<04:18, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3745/8000 [03:23<03:54, 18.14it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3750/8000 [03:23<03:56, 17.96it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:40,  5.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3753/8000 [03:23<04:26, 15.96it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:06<10:20,  3.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3755/8000 [03:24<06:50, 10.33it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:08<08:11,  2.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3758/8000 [03:24<05:54, 11.95it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:10<06:47,  2.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3760/8000 [03:24<06:44, 10.47it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:11<06:02,  1.86s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:25<05:34, 12.66it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:13<05:45,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3767/8000 [03:25<04:25, 15.95it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:14<05:31,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3772/8000 [03:25<03:24, 20.67it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:16<05:14,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3775/8000 [03:25<04:09, 16.95it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:17<05:01,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3780/8000 [03:25<04:02, 17.42it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:19<04:52,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3783/8000 [03:26<04:43, 14.88it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:20<04:58,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3789/8000 [03:26<03:35, 19.51it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:22<05:00,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3794/8000 [03:26<03:03, 22.96it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:23<04:52,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3798/8000 [03:26<03:33, 19.66it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:25<04:57,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3802/8000 [03:26<03:16, 21.32it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:27<04:53,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3805/8000 [03:26<03:13, 21.63it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:28<04:46,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3808/8000 [03:27<04:09, 16.79it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:30<04:47,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3810/8000 [03:27<04:25, 15.78it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:32<04:56,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3813/8000 [03:27<04:04, 17.16it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:33<05:01,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3815/8000 [03:27<05:20, 13.06it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:35<05:05,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3819/8000 [03:28<04:11, 16.60it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:37<05:07,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3823/8000 [03:28<03:38, 19.09it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:39<05:07,  1.73s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3826/8000 [03:28<04:27, 15.58it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:40<05:08,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3829/8000 [03:28<05:07, 13.57it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:42<05:08,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3833/8000 [03:28<04:01, 17.25it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:44<05:07,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3836/8000 [03:28<03:47, 18.30it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:46<05:06,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3839/8000 [03:29<03:41, 18.81it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:47<05:04,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3843/8000 [03:29<04:09, 16.68it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:49<05:02,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3848/8000 [03:29<03:19, 20.80it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:51<04:56,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3853/8000 [03:29<02:51, 24.11it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:53<04:50,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3857/8000 [03:29<02:42, 25.46it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:54<04:50,  1.72s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3860/8000 [03:30<03:32, 19.48it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:56<04:53,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3866/8000 [03:30<03:21, 20.49it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:58<04:53,  1.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3869/8000 [03:30<03:58, 17.35it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [01:00<04:49,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3873/8000 [03:30<03:31, 19.52it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [01:01<04:46,  1.74s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3876/8000 [03:31<04:03, 16.94it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [01:03<04:39,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3879/8000 [03:31<04:43, 14.54it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [01:05<04:48,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▏        | 3881/8000 [03:31<04:36, 14.91it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [01:06<04:31,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3883/8000 [03:31<04:31, 15.14it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [01:08<04:23,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3886/8000 [03:31<04:02, 16.94it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [01:09<04:14,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3889/8000 [03:31<03:50, 17.86it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [01:11<04:11,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3892/8000 [03:32<03:47, 18.05it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [01:12<04:06,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3894/8000 [03:32<04:05, 16.74it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [01:14<04:02,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3897/8000 [03:32<03:39, 18.72it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:15<03:57,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3901/8000 [03:32<03:08, 21.73it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:17<03:54,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3906/8000 [03:32<02:34, 26.43it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [01:18<03:52,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3910/8000 [03:32<02:28, 27.46it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:20<03:49,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3914/8000 [03:32<03:09, 21.60it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:21<03:46,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3918/8000 [03:33<03:43, 18.27it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:23<03:47,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3924/8000 [03:33<02:45, 24.69it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [01:24<03:43,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3929/8000 [03:33<03:37, 18.73it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:26<03:45,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3933/8000 [03:33<03:17, 20.56it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:27<03:42,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3938/8000 [03:34<03:56, 17.17it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:29<03:41,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3942/8000 [03:34<03:32, 19.06it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:30<03:39,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3945/8000 [03:34<04:16, 15.82it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:32<03:36,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3947/8000 [03:34<04:28, 15.08it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:33<03:31,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3949/8000 [03:35<04:30, 14.95it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:35<03:32,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3951/8000 [03:35<04:29, 15.03it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:36<03:31,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3957/8000 [03:35<03:04, 21.92it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:38<03:31,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3960/8000 [03:35<03:07, 21.52it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:39<03:29,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3963/8000 [03:35<03:09, 21.26it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:41<03:27,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3969/8000 [03:36<03:48, 17.63it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:42<03:23,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3975/8000 [03:36<02:51, 23.43it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [01:44<03:22,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3980/8000 [03:36<02:32, 26.45it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [01:45<03:21,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3986/8000 [03:36<03:14, 20.65it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [01:47<03:20,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3992/8000 [03:36<02:41, 24.86it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [01:48<03:20,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3998/8000 [03:37<02:52, 23.14it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [01:50<03:20,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4005/8000 [03:37<02:21, 28.16it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [01:51<03:19,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4009/8000 [03:37<02:19, 28.70it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [01:53<03:18,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4013/8000 [03:37<03:29, 19.06it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [01:54<03:23,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4020/8000 [03:38<02:42, 24.49it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [01:56<03:21,  1.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4025/8000 [03:38<02:55, 22.63it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [01:58<03:17,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4029/8000 [03:38<02:46, 23.84it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▌                              | 73/200 [01:59<03:15,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4032/8000 [03:38<03:20, 19.79it/s]\u001b[A\u001b[A\n",
+      "training:  37%|█████████████████▊                              | 74/200 [02:01<03:13,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4036/8000 [03:38<03:01, 21.81it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████                              | 75/200 [02:02<03:09,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4039/8000 [03:38<02:56, 22.39it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▏                             | 76/200 [02:03<03:05,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4044/8000 [03:39<03:21, 19.65it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▍                             | 77/200 [02:05<03:07,  1.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4048/8000 [03:39<03:47, 17.39it/s]\u001b[A\u001b[A\n",
+      "training:  39%|██████████████████▋                             | 78/200 [02:08<03:21,  1.65s/it]\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4051/8000 [03:39<03:45, 17.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4054/8000 [03:39<03:34, 18.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4058/8000 [03:39<03:03, 21.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4061/8000 [03:40<04:36, 14.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 21.99it/s]\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4066/8000 [03:40<03:38, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4069/8000 [03:40<03:27, 18.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4074/8000 [03:40<03:37, 18.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4077/8000 [03:41<03:30, 18.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 34it [00:00, 47.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4082/8000 [03:41<02:52, 22.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 68it [00:01, 96.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4085/8000 [03:41<03:01, 21.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 104it [00:01, 147.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4088/8000 [03:41<02:58, 21.88it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 141it [00:01, 194.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4091/8000 [03:41<02:57, 22.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 176it [00:01, 229.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4094/8000 [03:41<03:03, 21.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 213it [00:01, 263.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4099/8000 [03:41<02:36, 24.86it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 247it [00:01, 282.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4104/8000 [03:42<02:32, 25.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 283it [00:01, 301.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4111/8000 [03:42<02:34, 25.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 320it [00:01, 319.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4116/8000 [03:42<02:45, 23.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 356it [00:01, 329.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▊        | 4119/8000 [03:42<02:53, 22.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 392it [00:01, 336.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4122/8000 [03:43<03:33, 18.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 429it [00:02, 345.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4125/8000 [03:43<03:20, 19.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 465it [00:02, 347.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4130/8000 [03:43<03:31, 18.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 501it [00:02, 349.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4133/8000 [03:43<03:21, 19.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 537it [00:02, 351.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4136/8000 [03:43<04:00, 16.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 573it [00:02, 345.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4138/8000 [03:44<04:05, 15.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 608it [00:02, 340.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4141/8000 [03:44<03:53, 16.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 644it [00:02, 345.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4144/8000 [03:44<06:11, 10.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 681it [00:02, 349.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4148/8000 [03:45<05:31, 11.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 717it [00:02, 351.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4150/8000 [03:45<05:25, 11.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 753it [00:03, 349.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4152/8000 [03:45<05:17, 12.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 254.11it/s]\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4155/8000 [03:45<06:32,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4159/8000 [03:45<04:54, 13.06it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4165/8000 [03:46<03:20, 19.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 42.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4174/8000 [03:46<02:40, 23.83it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|█▎          | 11/100 [00:00<00:01, 47.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4177/8000 [03:46<02:45, 23.05it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|█▉          | 16/100 [00:00<00:01, 45.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4181/8000 [03:46<02:40, 23.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|██▌         | 21/100 [00:00<00:01, 45.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4186/8000 [03:46<02:51, 22.22it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|███         | 26/100 [00:00<00:01, 44.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4189/8000 [03:47<03:59, 15.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|███▋        | 31/100 [00:00<00:01, 43.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4191/8000 [03:47<04:05, 15.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|████▍       | 37/100 [00:00<00:01, 47.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4193/8000 [03:47<04:06, 15.45it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|█████       | 42/100 [00:00<00:01, 45.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4197/8000 [03:47<03:21, 18.89it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▋      | 47/100 [00:01<00:01, 45.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4200/8000 [03:47<03:13, 19.68it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██████▏     | 52/100 [00:01<00:01, 44.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4204/8000 [03:48<03:31, 17.98it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  57%|██████▊     | 57/100 [00:01<00:00, 44.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4207/8000 [03:48<03:23, 18.64it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|███████▋    | 64/100 [00:01<00:00, 48.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4213/8000 [03:48<03:17, 19.14it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|████████▎   | 69/100 [00:01<00:00, 46.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4216/8000 [03:48<03:16, 19.25it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|████████▉   | 74/100 [00:01<00:00, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4218/8000 [03:49<05:04, 12.41it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|█████████▍  | 79/100 [00:01<00:00, 44.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4222/8000 [03:49<04:12, 14.96it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██████████  | 84/100 [00:01<00:00, 44.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4225/8000 [03:49<04:31, 13.91it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  90%|██████████▊ | 90/100 [00:01<00:00, 47.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4227/8000 [03:49<05:17, 11.88it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  95%|███████████▍| 95/100 [00:02<00:00, 46.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4231/8000 [03:49<04:07, 15.26it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 45.60it/s]\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4235/8000 [03:50<03:34, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4240/8000 [03:50<03:02, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4244/8000 [03:50<02:49, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                      | 2/8000 [00:00<11:14, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4247/8000 [03:50<02:44, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                      | 7/8000 [00:00<05:27, 24.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4250/8000 [03:50<02:46, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 10/8000 [00:00<05:53, 22.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4253/8000 [03:50<03:29, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 13/8000 [00:00<05:40, 23.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4257/8000 [03:51<03:01, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 16/8000 [00:00<06:21, 20.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4261/8000 [03:51<04:02, 15.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 21/8000 [00:01<06:56, 19.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4265/8000 [03:51<03:20, 18.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 26/8000 [00:01<10:12, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4268/8000 [03:51<03:22, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 30/8000 [00:01<09:54, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4273/8000 [03:52<03:21, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 33/8000 [00:02<09:10, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4277/8000 [03:52<03:38, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 37/8000 [00:02<07:44, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4284/8000 [03:52<02:37, 23.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 40/8000 [00:02<08:51, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4289/8000 [03:52<02:47, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 42/8000 [00:02<09:13, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4293/8000 [03:52<03:10, 19.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 46/8000 [00:02<07:35, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4297/8000 [03:53<03:21, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 49/8000 [00:02<07:02, 18.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4300/8000 [03:53<04:02, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 54/8000 [00:03<05:56, 22.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4302/8000 [03:53<04:51, 12.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 59/8000 [00:03<06:50, 19.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4304/8000 [03:53<04:42, 13.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 64/8000 [00:03<05:53, 22.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4308/8000 [03:54<04:30, 13.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 68/8000 [00:03<07:20, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4311/8000 [03:54<04:11, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 71/8000 [00:04<08:53, 14.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4315/8000 [03:54<04:11, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 73/8000 [00:04<09:05, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4318/8000 [03:54<03:50, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 76/8000 [00:04<08:35, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4320/8000 [03:54<04:02, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 78/8000 [00:04<08:25, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4324/8000 [03:55<04:03, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 80/8000 [00:04<09:05, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4332/8000 [03:55<03:31, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 82/8000 [00:04<09:30, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4334/8000 [03:56<04:49, 12.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 84/8000 [00:05<09:12, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4339/8000 [03:56<03:38, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 87/8000 [00:05<07:52, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4342/8000 [03:56<03:22, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 89/8000 [00:05<13:22,  9.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4345/8000 [03:56<03:57, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 91/8000 [00:05<11:54, 11.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4352/8000 [03:56<02:43, 22.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 94/8000 [00:06<12:48, 10.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4355/8000 [03:56<02:43, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 98/8000 [00:06<09:24, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4358/8000 [03:56<02:41, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 102/8000 [00:06<07:27, 17.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4362/8000 [03:57<03:08, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 105/8000 [00:06<07:27, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4366/8000 [03:57<02:51, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 108/8000 [00:06<07:20, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4372/8000 [03:57<02:44, 22.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▌                                   | 115/8000 [00:06<05:54, 22.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4376/8000 [03:57<03:08, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▌                                   | 119/8000 [00:07<05:41, 23.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4381/8000 [03:58<03:05, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 125/8000 [00:07<08:24, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4384/8000 [03:58<03:28, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 128/8000 [00:07<08:09, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4386/8000 [03:58<03:28, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 132/8000 [00:07<07:11, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4389/8000 [03:58<03:19, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 135/8000 [00:08<10:32, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4394/8000 [03:58<02:39, 22.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 139/8000 [00:08<08:54, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4397/8000 [03:59<03:54, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 143/8000 [00:08<09:22, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4399/8000 [03:59<04:00, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 150/8000 [00:09<07:37, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4401/8000 [03:59<04:04, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 156/8000 [00:09<08:18, 15.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4409/8000 [03:59<02:21, 25.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 161/8000 [00:10<08:16, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4413/8000 [03:59<02:17, 26.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 164/8000 [00:10<07:53, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4420/8000 [03:59<01:47, 33.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 169/8000 [00:10<06:34, 19.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4425/8000 [04:00<02:14, 26.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 173/8000 [00:10<05:49, 22.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4433/8000 [04:00<02:37, 22.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 177/8000 [00:10<07:08, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4436/8000 [04:00<02:36, 22.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 181/8000 [00:10<07:27, 17.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4441/8000 [04:00<02:14, 26.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 186/8000 [00:11<06:26, 20.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4447/8000 [04:01<02:25, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 194/8000 [00:11<05:42, 22.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4452/8000 [04:01<02:39, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▉                                   | 197/8000 [00:11<05:37, 23.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4456/8000 [04:01<02:29, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▉                                   | 200/8000 [00:12<08:32, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [04:01<02:30, 23.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 202/8000 [00:12<08:30, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4466/8000 [04:02<03:47, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 205/8000 [00:12<08:13, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4471/8000 [04:02<03:59, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 209/8000 [00:12<09:49, 13.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4473/8000 [04:02<03:56, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 212/8000 [00:12<09:13, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4475/8000 [04:02<03:54, 15.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 215/8000 [00:13<08:25, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4478/8000 [04:03<04:16, 13.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 217/8000 [00:13<08:40, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4483/8000 [04:03<03:47, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 220/8000 [00:13<07:34, 17.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4486/8000 [04:03<03:24, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 222/8000 [00:13<08:17, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4490/8000 [04:03<03:37, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 224/8000 [00:13<10:55, 11.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4493/8000 [04:04<03:26, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 227/8000 [00:13<08:52, 14.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4495/8000 [04:04<03:30, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 229/8000 [00:14<11:52, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4498/8000 [04:04<03:07, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 231/8000 [00:14<11:10, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4505/8000 [04:04<02:13, 26.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 234/8000 [00:14<11:17, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4509/8000 [04:04<02:09, 26.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 236/8000 [00:14<10:51, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4514/8000 [04:04<03:00, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 241/8000 [00:14<07:36, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4518/8000 [04:05<02:47, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 243/8000 [00:15<07:37, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4523/8000 [04:05<02:22, 24.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 247/8000 [00:15<08:57, 14.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4528/8000 [04:05<02:34, 22.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 249/8000 [00:15<08:54, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4531/8000 [04:05<03:06, 18.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 254/8000 [00:15<06:49, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4537/8000 [04:05<02:23, 24.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 257/8000 [00:16<10:21, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4543/8000 [04:06<02:29, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 260/8000 [00:16<09:29, 13.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4548/8000 [04:06<02:36, 22.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 266/8000 [00:16<06:46, 19.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4551/8000 [04:06<02:31, 22.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 270/8000 [00:16<06:19, 20.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4557/8000 [04:06<03:02, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 276/8000 [00:16<06:44, 19.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4560/8000 [04:07<03:00, 19.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 281/8000 [00:17<07:14, 17.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4565/8000 [04:07<02:30, 22.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 284/8000 [00:17<07:08, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4568/8000 [04:07<02:29, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 287/8000 [00:17<07:01, 18.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4573/8000 [04:07<02:39, 21.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 289/8000 [00:17<07:37, 16.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4576/8000 [04:07<02:39, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 291/8000 [00:18<10:19, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4582/8000 [04:07<02:04, 27.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 293/8000 [00:18<12:18, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4588/8000 [04:08<02:12, 25.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 296/8000 [00:18<12:32, 10.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4591/8000 [04:08<02:13, 25.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 299/8000 [00:18<10:40, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4597/8000 [04:08<02:16, 24.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 302/8000 [00:18<09:05, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4603/8000 [04:08<01:55, 29.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 306/8000 [00:19<07:06, 18.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4609/8000 [04:08<02:07, 26.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 309/8000 [00:19<07:00, 18.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4612/8000 [04:09<02:10, 25.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 312/8000 [00:19<10:00, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4615/8000 [04:09<02:11, 25.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 318/8000 [00:19<07:04, 18.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4619/8000 [04:09<02:38, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 322/8000 [00:20<07:52, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4622/8000 [04:09<03:15, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 324/8000 [00:20<07:47, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4624/8000 [04:09<03:23, 16.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 326/8000 [00:20<08:17, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4628/8000 [04:10<02:56, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 328/8000 [00:20<10:47, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4632/8000 [04:10<03:17, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 332/8000 [00:20<08:05, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4636/8000 [04:10<02:53, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 335/8000 [00:21<07:45, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4639/8000 [04:10<02:45, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 338/8000 [00:21<09:13, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4644/8000 [04:10<02:49, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 340/8000 [00:21<08:54, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4647/8000 [04:11<03:15, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 346/8000 [00:21<06:09, 20.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4649/8000 [04:11<03:18, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 350/8000 [00:21<05:46, 22.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4651/8000 [04:11<03:20, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 354/8000 [00:21<06:33, 19.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4654/8000 [04:11<03:01, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 358/8000 [00:22<07:31, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4658/8000 [04:11<02:34, 21.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▌                                  | 361/8000 [00:22<07:17, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4662/8000 [04:11<03:02, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 363/8000 [00:22<09:26, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4669/8000 [04:12<02:37, 21.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 367/8000 [00:22<07:56, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4672/8000 [04:12<02:34, 21.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 371/8000 [00:23<07:52, 16.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4675/8000 [04:12<02:31, 22.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 376/8000 [00:23<06:37, 19.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4678/8000 [04:12<03:04, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 380/8000 [00:23<07:19, 17.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4681/8000 [04:12<02:52, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 383/8000 [00:23<06:42, 18.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4687/8000 [04:13<02:45, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 387/8000 [00:23<06:23, 19.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4691/8000 [04:13<02:24, 22.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 391/8000 [00:24<07:43, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4696/8000 [04:13<03:12, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 395/8000 [00:24<06:53, 18.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4700/8000 [04:13<02:51, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 399/8000 [00:24<08:00, 15.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4705/8000 [04:14<02:53, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 403/8000 [00:25<08:37, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4709/8000 [04:14<03:10, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 407/8000 [00:25<07:31, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4712/8000 [04:14<03:29, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 411/8000 [00:25<07:47, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4717/8000 [04:14<03:15, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 414/8000 [00:25<07:26, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4721/8000 [04:14<02:51, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 419/8000 [00:25<06:03, 20.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4725/8000 [04:15<03:01, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 422/8000 [00:25<06:11, 20.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4727/8000 [04:15<03:04, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 426/8000 [00:26<08:31, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4731/8000 [04:15<02:40, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 430/8000 [00:26<08:26, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4737/8000 [04:15<01:59, 27.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 433/8000 [00:26<07:59, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4741/8000 [04:15<01:54, 28.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 435/8000 [00:26<08:16, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4745/8000 [04:15<01:49, 29.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 437/8000 [00:27<08:36, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4751/8000 [04:16<02:12, 24.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 439/8000 [00:27<08:20, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4756/8000 [04:16<02:22, 22.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▉                                  | 443/8000 [00:27<06:59, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4760/8000 [04:16<02:41, 20.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 445/8000 [00:27<07:27, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4766/8000 [04:16<02:07, 25.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 450/8000 [00:27<08:49, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4770/8000 [04:17<02:26, 22.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 453/8000 [00:28<10:00, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4774/8000 [04:17<02:16, 23.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 457/8000 [00:28<07:56, 15.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4778/8000 [04:17<02:04, 25.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 461/8000 [00:28<09:57, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4781/8000 [04:17<02:39, 20.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 466/8000 [00:28<07:24, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4785/8000 [04:17<02:52, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 469/8000 [00:29<08:56, 14.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4789/8000 [04:17<02:28, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 475/8000 [00:29<06:30, 19.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4797/8000 [04:18<02:02, 26.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 481/8000 [00:29<06:15, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4800/8000 [04:18<02:32, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 485/8000 [00:29<06:57, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4804/8000 [04:18<02:18, 23.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 489/8000 [00:30<07:37, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4808/8000 [04:18<02:33, 20.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 492/8000 [00:30<07:31, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4814/8000 [04:19<02:31, 21.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 494/8000 [00:30<07:32, 16.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4820/8000 [04:19<02:00, 26.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 498/8000 [00:30<06:45, 18.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4825/8000 [04:19<02:13, 23.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 500/8000 [00:31<09:16, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4829/8000 [04:19<02:04, 25.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 504/8000 [00:31<07:51, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4833/8000 [04:19<02:29, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 506/8000 [00:31<08:14, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4836/8000 [04:20<03:29, 15.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 511/8000 [00:31<06:27, 19.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4838/8000 [04:20<03:31, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 514/8000 [00:31<06:26, 19.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4847/8000 [04:20<02:04, 25.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 520/8000 [00:32<06:20, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4851/8000 [04:20<01:59, 26.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                 | 523/8000 [00:32<06:24, 19.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4859/8000 [04:20<01:55, 27.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                 | 527/8000 [00:32<05:52, 21.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4864/8000 [04:21<01:43, 30.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 532/8000 [00:32<04:54, 25.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4868/8000 [04:21<02:09, 24.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 535/8000 [00:32<05:29, 22.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4871/8000 [04:21<02:12, 23.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 540/8000 [00:32<05:03, 24.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4874/8000 [04:21<02:43, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 543/8000 [00:32<05:24, 23.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4878/8000 [04:22<03:04, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 549/8000 [00:33<04:45, 26.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4880/8000 [04:22<03:10, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 552/8000 [00:33<05:15, 23.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4884/8000 [04:22<02:42, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 560/8000 [00:33<04:52, 25.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4887/8000 [04:22<02:35, 20.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 564/8000 [00:34<07:08, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4894/8000 [04:22<02:15, 22.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 569/8000 [00:34<05:50, 21.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4900/8000 [04:23<03:07, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 574/8000 [00:34<05:20, 23.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4904/8000 [04:23<03:14, 15.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 579/8000 [00:34<04:49, 25.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4906/8000 [04:23<03:16, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 582/8000 [00:34<05:03, 24.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4909/8000 [04:23<03:31, 14.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 586/8000 [00:34<04:42, 26.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4912/8000 [04:24<03:15, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 589/8000 [00:35<06:42, 18.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4914/8000 [04:24<03:20, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 592/8000 [00:35<09:12, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4916/8000 [04:24<03:19, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 596/8000 [00:35<10:23, 11.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4921/8000 [04:24<02:25, 21.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 602/8000 [00:36<07:38, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4924/8000 [04:24<02:27, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 606/8000 [00:36<08:25, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4927/8000 [04:24<02:23, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 609/8000 [00:36<08:01, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4930/8000 [04:24<02:19, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 612/8000 [00:37<10:36, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4936/8000 [04:24<01:48, 28.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 614/8000 [00:37<10:29, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4939/8000 [04:25<01:53, 27.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 619/8000 [00:37<07:48, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4944/8000 [04:25<01:44, 29.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 621/8000 [00:37<08:10, 15.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4948/8000 [04:25<01:43, 29.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 626/8000 [00:37<06:10, 19.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4951/8000 [04:25<01:46, 28.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 629/8000 [00:37<06:15, 19.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4954/8000 [04:25<01:49, 27.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 635/8000 [00:38<06:05, 20.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4957/8000 [04:25<02:37, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 639/8000 [00:38<05:19, 23.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4961/8000 [04:26<02:18, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 642/8000 [00:38<08:20, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4965/8000 [04:26<02:34, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 647/8000 [00:38<06:32, 18.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4972/8000 [04:26<02:40, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 650/8000 [00:39<09:54, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4975/8000 [04:26<02:34, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 654/8000 [00:39<10:05, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4979/8000 [04:27<02:45, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 656/8000 [00:39<10:07, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4982/8000 [04:27<02:39, 18.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 658/8000 [00:39<09:56, 12.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4986/8000 [04:27<02:53, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 661/8000 [00:40<08:49, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4989/8000 [04:27<02:36, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 666/8000 [00:40<06:49, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4992/8000 [04:27<03:04, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 669/8000 [00:40<08:06, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4998/8000 [04:28<02:46, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 672/8000 [00:40<09:41, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5006/8000 [04:28<02:39, 18.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 674/8000 [00:41<09:32, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5009/8000 [04:28<02:31, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 677/8000 [00:41<10:30, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5012/8000 [04:28<02:27, 20.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 680/8000 [00:41<10:55, 11.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5015/8000 [04:28<02:19, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                 | 684/8000 [00:41<08:44, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5020/8000 [04:29<02:23, 20.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                 | 686/8000 [00:41<08:48, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5025/8000 [04:29<02:28, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                 | 689/8000 [00:42<07:53, 15.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5028/8000 [04:29<02:25, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                 | 692/8000 [00:42<06:56, 17.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5033/8000 [04:29<02:30, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                 | 694/8000 [00:42<07:41, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5039/8000 [04:30<02:47, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 696/8000 [00:42<08:17, 14.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5043/8000 [04:30<02:26, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 698/8000 [00:42<08:46, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5046/8000 [04:30<02:23, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 700/8000 [00:42<09:10, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5049/8000 [04:30<02:24, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 702/8000 [00:43<09:24, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5053/8000 [04:30<02:37, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 704/8000 [00:43<09:31, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5059/8000 [04:31<02:56, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 707/8000 [00:43<12:46,  9.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5061/8000 [04:31<03:00, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 711/8000 [00:44<13:33,  8.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5065/8000 [04:31<02:36, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 714/8000 [00:44<12:17,  9.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5068/8000 [04:31<03:30, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 719/8000 [00:45<18:57,  6.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5070/8000 [04:32<03:23, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 723/8000 [00:45<14:04,  8.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5072/8000 [04:32<03:23, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 725/8000 [00:45<13:12,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5074/8000 [04:32<03:13, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 728/8000 [00:45<11:16, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5080/8000 [04:32<02:39, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 730/8000 [00:46<11:07, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5085/8000 [04:32<02:30, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 736/8000 [00:46<07:16, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5087/8000 [04:33<03:10, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 739/8000 [00:46<07:08, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5090/8000 [04:33<02:56, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 743/8000 [00:46<07:56, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5096/8000 [04:33<02:06, 23.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 745/8000 [00:46<08:18, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5100/8000 [04:33<02:57, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 748/8000 [00:47<07:46, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5104/8000 [04:33<02:32, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▍                                | 750/8000 [00:47<08:14, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5107/8000 [04:34<03:00, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▍                                | 752/8000 [00:47<08:29, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5115/8000 [04:34<02:40, 18.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▍                                | 755/8000 [00:47<09:54, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5119/8000 [04:34<02:51, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▍                                | 759/8000 [00:47<08:10, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5121/8000 [04:34<02:52, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                                | 764/8000 [00:48<07:45, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5124/8000 [04:35<02:41, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                                | 769/8000 [00:48<06:19, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5126/8000 [04:35<03:19, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                                | 773/8000 [00:48<05:32, 21.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5129/8000 [04:35<03:00, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 778/8000 [00:48<04:58, 24.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5132/8000 [04:35<02:46, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 783/8000 [00:48<04:38, 25.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5138/8000 [04:35<02:04, 23.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 787/8000 [00:49<05:22, 22.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5143/8000 [04:36<02:13, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 791/8000 [00:49<05:19, 22.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5146/8000 [04:36<02:11, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 794/8000 [00:49<07:13, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5151/8000 [04:36<01:49, 26.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 796/8000 [00:49<07:54, 15.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5155/8000 [04:36<02:13, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 798/8000 [00:49<08:20, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5159/8000 [04:36<01:57, 24.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 800/8000 [00:50<08:35, 13.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5164/8000 [04:36<02:08, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 802/8000 [00:50<08:08, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5169/8000 [04:37<02:43, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 807/8000 [00:50<06:14, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5173/8000 [04:37<02:26, 19.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 809/8000 [00:50<06:43, 17.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5176/8000 [04:38<04:05, 11.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 812/8000 [00:50<07:54, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5178/8000 [04:38<03:52, 12.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 817/8000 [00:51<07:39, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5182/8000 [04:38<03:45, 12.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 819/8000 [00:51<07:58, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5187/8000 [04:38<02:50, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 821/8000 [00:51<07:42, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5190/8000 [04:39<03:24, 13.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 823/8000 [00:51<08:06, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5193/8000 [04:39<02:59, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 827/8000 [00:51<06:49, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5199/8000 [04:39<02:10, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 829/8000 [00:51<07:10, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5202/8000 [04:39<02:32, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 832/8000 [00:51<06:25, 18.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5205/8000 [04:39<02:49, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▊                                | 836/8000 [00:52<05:48, 20.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5209/8000 [04:39<02:26, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▊                                | 839/8000 [00:52<07:26, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5212/8000 [04:40<02:52, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 842/8000 [00:52<06:35, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5215/8000 [04:40<03:07, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 845/8000 [00:52<06:40, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5218/8000 [04:40<02:46, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 849/8000 [00:53<07:49, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5220/8000 [04:40<03:23, 13.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 852/8000 [00:53<07:31, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5223/8000 [04:41<03:40, 12.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 856/8000 [00:53<06:37, 17.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5226/8000 [04:41<03:43, 12.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 861/8000 [00:53<05:37, 21.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5230/8000 [04:41<02:53, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 864/8000 [00:53<06:50, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5232/8000 [04:41<02:52, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 868/8000 [00:53<06:07, 19.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5237/8000 [04:41<02:43, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 872/8000 [00:54<06:49, 17.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5239/8000 [04:41<02:44, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 877/8000 [00:54<05:45, 20.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5242/8000 [04:42<03:11, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 881/8000 [00:54<06:37, 17.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5244/8000 [04:42<03:05, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 885/8000 [00:54<07:02, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5247/8000 [04:42<03:22, 13.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 888/8000 [00:55<06:47, 17.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5249/8000 [04:42<03:18, 13.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 890/8000 [00:55<07:06, 16.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5254/8000 [04:42<02:18, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 894/8000 [00:55<05:53, 20.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5257/8000 [04:42<02:11, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 899/8000 [00:55<05:03, 23.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5264/8000 [04:43<01:57, 23.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 904/8000 [00:55<05:43, 20.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5271/8000 [04:43<01:31, 29.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 907/8000 [00:55<05:31, 21.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5275/8000 [04:43<01:54, 23.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 914/8000 [00:56<05:23, 21.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5280/8000 [04:43<01:40, 27.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████▏                               | 917/8000 [00:56<05:25, 21.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5285/8000 [04:44<01:54, 23.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 922/8000 [00:56<06:00, 19.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5288/8000 [04:44<01:53, 23.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 926/8000 [00:57<07:10, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5291/8000 [04:44<02:22, 19.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 928/8000 [00:57<10:41, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5295/8000 [04:44<02:06, 21.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 931/8000 [00:57<09:03, 13.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5299/8000 [04:44<02:19, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 933/8000 [00:57<09:06, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5304/8000 [04:44<01:53, 23.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 939/8000 [00:58<08:09, 14.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5307/8000 [04:45<01:53, 23.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 942/8000 [00:58<08:05, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5312/8000 [04:45<01:36, 27.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 945/8000 [00:58<07:42, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5317/8000 [04:45<02:08, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 947/8000 [00:58<07:58, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5320/8000 [04:45<02:29, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 949/8000 [00:58<07:47, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5323/8000 [04:46<03:19, 13.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 953/8000 [00:59<07:12, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5326/8000 [04:46<02:57, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 955/8000 [00:59<09:52, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5329/8000 [04:46<03:41, 12.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 959/8000 [00:59<07:50, 14.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5333/8000 [04:46<02:52, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 961/8000 [00:59<11:43, 10.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5336/8000 [04:47<03:09, 14.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 966/8000 [01:00<10:15, 11.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5339/8000 [04:47<03:17, 13.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 971/8000 [01:00<07:49, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5341/8000 [04:47<03:10, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 975/8000 [01:00<07:01, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5344/8000 [04:47<02:51, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 978/8000 [01:00<06:52, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5346/8000 [04:47<02:54, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 983/8000 [01:00<05:41, 20.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5348/8000 [04:47<02:53, 15.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 988/8000 [01:01<05:14, 22.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5352/8000 [04:48<02:50, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 991/8000 [01:01<07:13, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5354/8000 [04:48<02:46, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 993/8000 [01:01<07:23, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5357/8000 [04:48<03:07, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 995/8000 [01:01<07:52, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5359/8000 [04:48<03:09, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 997/8000 [01:02<08:22, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5363/8000 [04:48<02:30, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1002/8000 [01:02<08:04, 14.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5367/8000 [04:48<02:04, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1005/8000 [01:02<07:39, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5374/8000 [04:49<01:54, 22.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1007/8000 [01:02<08:08, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5377/8000 [04:49<02:01, 21.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1011/8000 [01:02<06:34, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5384/8000 [04:49<01:26, 30.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1014/8000 [01:03<06:38, 17.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5388/8000 [04:49<02:11, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1021/8000 [01:03<04:57, 23.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5393/8000 [04:50<02:10, 19.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1026/8000 [01:03<05:05, 22.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5399/8000 [04:50<02:58, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1029/8000 [01:03<05:26, 21.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5402/8000 [04:50<02:48, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1032/8000 [01:04<10:33, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5405/8000 [04:51<02:56, 14.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1036/8000 [01:04<08:48, 13.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5409/8000 [04:51<02:28, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1038/8000 [01:04<09:03, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5413/8000 [04:51<02:03, 20.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1040/8000 [01:04<10:48, 10.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5416/8000 [04:51<02:24, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1043/8000 [01:05<09:19, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5420/8000 [04:51<02:50, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1049/8000 [01:05<06:30, 17.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5423/8000 [04:52<02:36, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1054/8000 [01:05<05:36, 20.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5428/8000 [04:52<02:55, 14.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1060/8000 [01:05<05:09, 22.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5432/8000 [04:52<02:51, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1067/8000 [01:06<05:19, 21.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5438/8000 [04:53<02:25, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1071/8000 [01:06<05:39, 20.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5441/8000 [04:53<02:18, 18.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1075/8000 [01:06<06:30, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5448/8000 [04:53<02:18, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                              | 1080/8000 [01:06<06:19, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5452/8000 [04:53<02:02, 20.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                              | 1083/8000 [01:06<06:24, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5457/8000 [04:53<02:03, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1086/8000 [01:07<06:33, 17.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5460/8000 [04:54<02:03, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1089/8000 [01:07<06:30, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5467/8000 [04:54<01:30, 28.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1093/8000 [01:07<07:26, 15.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5471/8000 [04:54<01:30, 27.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1098/8000 [01:08<08:36, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5475/8000 [04:54<01:45, 23.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1102/8000 [01:08<07:40, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5478/8000 [04:54<01:49, 23.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1104/8000 [01:08<09:53, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5481/8000 [04:54<01:47, 23.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1108/8000 [01:08<08:26, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5485/8000 [04:55<02:04, 20.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1110/8000 [01:09<08:58, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5489/8000 [04:55<02:17, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1113/8000 [01:09<08:14, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5492/8000 [04:55<02:06, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1115/8000 [01:09<11:48,  9.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5495/8000 [04:55<02:31, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1122/8000 [01:09<07:10, 15.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5499/8000 [04:55<02:08, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1128/8000 [01:09<05:32, 20.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5506/8000 [04:56<01:48, 22.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1134/8000 [01:10<06:00, 19.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5510/8000 [04:56<02:00, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1139/8000 [01:10<06:28, 17.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5514/8000 [04:56<02:11, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1144/8000 [01:10<06:31, 17.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5517/8000 [04:56<02:06, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1146/8000 [01:11<07:00, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5522/8000 [04:57<02:46, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1148/8000 [01:11<07:27, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5525/8000 [04:57<02:53, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1151/8000 [01:11<07:16, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5527/8000 [04:57<02:51, 14.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1155/8000 [01:11<08:02, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5530/8000 [04:57<02:37, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1157/8000 [01:11<07:56, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5532/8000 [04:57<02:39, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1159/8000 [01:12<08:20, 13.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5534/8000 [04:57<02:41, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                              | 1162/8000 [01:12<07:56, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5537/8000 [04:58<02:24, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                              | 1165/8000 [01:12<09:31, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5540/8000 [04:58<02:11, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                              | 1167/8000 [01:12<09:33, 11.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5544/8000 [04:58<02:18, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                              | 1170/8000 [01:13<09:49, 11.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5548/8000 [04:58<01:58, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1174/8000 [01:13<07:54, 14.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5552/8000 [04:58<02:14, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1179/8000 [01:13<06:22, 17.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5556/8000 [04:59<01:56, 21.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1188/8000 [01:13<04:17, 26.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5560/8000 [04:59<02:54, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1191/8000 [01:13<05:51, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5563/8000 [04:59<02:36, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1194/8000 [01:14<05:51, 19.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5568/8000 [04:59<02:24, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1197/8000 [01:14<07:04, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5570/8000 [05:00<02:23, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1199/8000 [01:14<10:57, 10.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5572/8000 [05:00<02:25, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1202/8000 [01:15<11:15, 10.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5577/8000 [05:00<01:50, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1204/8000 [01:15<10:46, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5582/8000 [05:00<01:55, 20.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1208/8000 [01:15<08:38, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5588/8000 [05:00<01:51, 21.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1210/8000 [01:16<13:00,  8.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5593/8000 [05:01<02:14, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1214/8000 [01:16<12:04,  9.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5595/8000 [05:01<02:15, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1217/8000 [01:16<10:34, 10.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5597/8000 [05:01<03:11, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1220/8000 [01:16<11:08, 10.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5599/8000 [05:01<03:12, 12.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1223/8000 [01:17<09:32, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5601/8000 [05:02<03:35, 11.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▍                             | 1229/8000 [01:17<10:44, 10.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5603/8000 [05:02<03:19, 11.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▍                             | 1232/8000 [01:17<09:37, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5606/8000 [05:02<03:23, 11.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▍                             | 1234/8000 [01:18<09:41, 11.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5608/8000 [05:02<03:08, 12.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▍                             | 1238/8000 [01:18<07:32, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5615/8000 [05:02<01:47, 22.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1241/8000 [01:18<07:31, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5621/8000 [05:02<01:24, 28.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1247/8000 [01:18<06:56, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5626/8000 [05:03<01:36, 24.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1250/8000 [01:19<08:19, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5630/8000 [05:03<01:54, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1254/8000 [01:19<06:59, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5639/8000 [05:03<01:31, 25.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1258/8000 [01:19<09:00, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5643/8000 [05:04<02:04, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1262/8000 [01:19<07:44, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5649/8000 [05:04<02:28, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1265/8000 [01:20<07:32, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5652/8000 [05:04<02:20, 16.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1268/8000 [01:20<07:12, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5655/8000 [05:04<02:28, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1271/8000 [01:20<07:09, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5658/8000 [05:05<02:37, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1274/8000 [01:20<06:54, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5662/8000 [05:05<02:12, 17.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1277/8000 [01:20<06:50, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5669/8000 [05:05<01:34, 24.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1279/8000 [01:20<07:23, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5673/8000 [05:05<01:27, 26.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1283/8000 [01:21<07:49, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5677/8000 [05:05<01:44, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1285/8000 [01:21<08:33, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5680/8000 [05:05<01:45, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1290/8000 [01:21<08:03, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5684/8000 [05:06<01:58, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1294/8000 [01:21<06:55, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5689/8000 [05:06<01:35, 24.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1296/8000 [01:22<07:47, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5693/8000 [05:06<01:56, 19.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1301/8000 [01:22<06:05, 18.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5698/8000 [05:06<01:54, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1304/8000 [01:22<06:29, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5702/8000 [05:07<01:59, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1307/8000 [01:22<07:18, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5705/8000 [05:07<01:53, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1312/8000 [01:23<07:37, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5710/8000 [05:07<02:15, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1314/8000 [01:23<10:01, 11.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5713/8000 [05:07<02:08, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▊                             | 1316/8000 [01:23<10:04, 11.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5717/8000 [05:07<02:16, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▊                             | 1318/8000 [01:23<10:05, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5719/8000 [05:08<02:15, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1321/8000 [01:24<10:56, 10.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5724/8000 [05:08<01:44, 21.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1323/8000 [01:24<10:17, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5728/8000 [05:08<01:32, 24.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1331/8000 [01:24<05:19, 20.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5733/8000 [05:08<01:57, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1335/8000 [01:25<07:53, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5738/8000 [05:08<01:36, 23.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1339/8000 [01:25<06:59, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5742/8000 [05:09<01:49, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1342/8000 [01:25<06:53, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5746/8000 [05:09<01:59, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1348/8000 [01:25<06:20, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5752/8000 [05:09<01:32, 24.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1351/8000 [01:26<07:54, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5761/8000 [05:09<01:19, 28.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1354/8000 [01:26<07:30, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5766/8000 [05:09<01:28, 25.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1358/8000 [01:26<06:42, 16.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5769/8000 [05:10<02:01, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1360/8000 [01:26<06:55, 15.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5772/8000 [05:10<01:57, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1363/8000 [01:26<06:30, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5778/8000 [05:10<01:47, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1366/8000 [01:26<07:43, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5781/8000 [05:10<01:46, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1370/8000 [01:27<07:52, 14.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5785/8000 [05:10<01:33, 23.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1373/8000 [01:27<07:41, 14.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5788/8000 [05:11<01:33, 23.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1378/8000 [01:27<07:42, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5792/8000 [05:11<01:46, 20.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1380/8000 [01:27<07:32, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5795/8000 [05:11<02:06, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1382/8000 [01:28<08:03, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5800/8000 [05:11<02:02, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1384/8000 [01:28<08:24, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5803/8000 [05:12<02:15, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1388/8000 [01:28<07:07, 15.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5806/8000 [05:12<02:06, 17.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1393/8000 [01:28<05:39, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5808/8000 [05:12<02:07, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1399/8000 [01:29<06:02, 18.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5810/8000 [05:12<02:13, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1401/8000 [01:29<06:45, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5814/8000 [05:12<01:47, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1403/8000 [01:29<07:14, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5820/8000 [05:13<02:03, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1406/8000 [01:29<09:45, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5822/8000 [05:13<02:33, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1408/8000 [01:29<09:41, 11.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5825/8000 [05:13<02:21, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1411/8000 [01:30<08:04, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5828/8000 [05:13<02:08, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1413/8000 [01:30<07:48, 14.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5830/8000 [05:13<02:09, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1418/8000 [01:30<06:00, 18.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5833/8000 [05:14<02:34, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1422/8000 [01:30<07:00, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5836/8000 [05:14<02:44, 13.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1425/8000 [01:30<06:50, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5839/8000 [05:14<02:23, 15.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1430/8000 [01:31<05:31, 19.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5843/8000 [05:14<01:54, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1433/8000 [01:31<05:47, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5846/8000 [05:14<02:12, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1436/8000 [01:31<05:37, 19.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5849/8000 [05:14<02:01, 17.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1442/8000 [01:31<04:32, 24.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5852/8000 [05:15<02:21, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1446/8000 [01:31<04:31, 24.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5854/8000 [05:15<02:18, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1451/8000 [01:32<05:39, 19.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5856/8000 [05:15<03:24, 10.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1456/8000 [01:32<06:18, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5860/8000 [05:15<02:30, 14.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1458/8000 [01:32<06:57, 15.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5865/8000 [05:15<01:46, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1461/8000 [01:32<06:43, 16.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5868/8000 [05:16<02:30, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1463/8000 [01:33<08:42, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5871/8000 [05:16<02:16, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1465/8000 [01:33<09:02, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5876/8000 [05:16<01:46, 19.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1468/8000 [01:33<11:51,  9.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5879/8000 [05:16<02:06, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1472/8000 [01:34<10:33, 10.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▍    | 5882/8000 [05:17<02:23, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1474/8000 [01:34<09:48, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5888/8000 [05:17<01:40, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1476/8000 [01:34<09:52, 11.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5891/8000 [05:17<01:36, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1478/8000 [01:34<09:50, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5896/8000 [05:17<01:44, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                            | 1483/8000 [01:34<08:42, 12.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5900/8000 [05:17<01:36, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1487/8000 [01:35<07:18, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5903/8000 [05:18<01:55, 18.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1490/8000 [01:35<06:36, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5906/8000 [05:18<02:09, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1498/8000 [01:35<05:15, 20.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5911/8000 [05:18<01:38, 21.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1502/8000 [01:35<05:06, 21.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5916/8000 [05:18<01:23, 25.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1505/8000 [01:36<06:37, 16.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5921/8000 [05:18<01:55, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1508/8000 [01:36<06:41, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5924/8000 [05:19<02:16, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1513/8000 [01:36<05:26, 19.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5927/8000 [05:19<02:07, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1517/8000 [01:36<04:56, 21.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5932/8000 [05:19<01:38, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1521/8000 [01:36<06:17, 17.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5935/8000 [05:19<01:39, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1524/8000 [01:37<07:55, 13.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5938/8000 [05:19<01:34, 21.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1526/8000 [01:37<09:26, 11.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5941/8000 [05:19<01:36, 21.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1530/8000 [01:37<07:21, 14.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5944/8000 [05:20<02:08, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1533/8000 [01:37<08:36, 12.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5946/8000 [05:20<02:14, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1535/8000 [01:38<08:44, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5949/8000 [05:20<01:58, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1539/8000 [01:38<07:11, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5952/8000 [05:20<01:48, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1542/8000 [01:38<08:34, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5955/8000 [05:21<02:24, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1545/8000 [01:38<09:12, 11.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5958/8000 [05:21<02:11, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1547/8000 [01:39<11:30,  9.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5961/8000 [05:21<02:22, 14.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1551/8000 [01:39<08:59, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5966/8000 [05:21<02:04, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1553/8000 [01:39<08:50, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5970/8000 [05:21<01:46, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1556/8000 [01:40<11:57,  8.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5973/8000 [05:22<02:04, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                            | 1560/8000 [01:40<09:13, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5975/8000 [05:22<02:03, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                            | 1566/8000 [01:40<06:10, 17.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5979/8000 [05:22<01:44, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                            | 1569/8000 [01:40<06:14, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5983/8000 [05:22<01:28, 22.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1573/8000 [01:41<07:19, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5988/8000 [05:22<01:13, 27.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1578/8000 [01:41<06:04, 17.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5995/8000 [05:22<01:10, 28.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1582/8000 [01:41<05:36, 19.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5999/8000 [05:22<01:06, 30.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1587/8000 [01:41<05:40, 18.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6003/8000 [05:23<01:42, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1591/8000 [01:41<06:32, 16.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6006/8000 [05:23<01:57, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1595/8000 [01:42<08:28, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6010/8000 [05:23<01:42, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1597/8000 [01:42<08:43, 12.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6014/8000 [05:23<01:29, 22.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1600/8000 [01:42<09:19, 11.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6019/8000 [05:23<01:18, 25.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1603/8000 [01:43<08:38, 12.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6025/8000 [05:24<01:39, 19.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1606/8000 [01:43<08:23, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6030/8000 [05:24<01:22, 23.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1609/8000 [01:43<09:51, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6033/8000 [05:24<01:58, 16.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1611/8000 [01:43<09:28, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6036/8000 [05:25<02:10, 15.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1614/8000 [01:44<08:25, 12.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6039/8000 [05:25<02:16, 14.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1616/8000 [01:44<08:40, 12.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6041/8000 [05:25<02:16, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1619/8000 [01:44<07:43, 13.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6043/8000 [05:25<02:13, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1622/8000 [01:44<07:21, 14.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6045/8000 [05:25<02:13, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1626/8000 [01:44<07:46, 13.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6048/8000 [05:25<01:58, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████▏                           | 1629/8000 [01:45<06:50, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6050/8000 [05:26<01:58, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████▏                           | 1633/8000 [01:45<07:50, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6052/8000 [05:26<02:01, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████▏                           | 1636/8000 [01:45<09:14, 11.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6054/8000 [05:26<02:04, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████▏                           | 1638/8000 [01:46<09:25, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6057/8000 [05:26<01:49, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1648/8000 [01:46<05:53, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6060/8000 [05:26<01:45, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1650/8000 [01:46<06:29, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6063/8000 [05:26<01:38, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1655/8000 [01:46<06:07, 17.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6068/8000 [05:26<01:17, 24.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1659/8000 [01:47<06:54, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6072/8000 [05:26<01:10, 27.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1663/8000 [01:47<07:38, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6077/8000 [05:27<01:04, 29.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1666/8000 [01:47<07:16, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6085/8000 [05:27<01:15, 25.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1669/8000 [01:47<06:55, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6090/8000 [05:27<01:49, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1671/8000 [01:47<06:59, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6095/8000 [05:28<02:25, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1675/8000 [01:48<06:05, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6101/8000 [05:28<01:49, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1678/8000 [01:48<06:56, 15.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6106/8000 [05:28<01:31, 20.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1681/8000 [01:48<06:19, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6110/8000 [05:28<01:23, 22.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1685/8000 [01:49<08:47, 11.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6115/8000 [05:29<01:40, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1690/8000 [01:49<08:18, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|█████████████    | 6120/8000 [05:29<01:39, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1692/8000 [01:49<09:20, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6125/8000 [05:29<01:41, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1694/8000 [01:49<09:30, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6131/8000 [05:30<01:31, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1696/8000 [01:50<09:40, 10.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6138/8000 [05:30<01:09, 26.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1698/8000 [01:50<09:18, 11.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6142/8000 [05:30<01:21, 22.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1701/8000 [01:50<08:00, 13.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6148/8000 [05:30<01:07, 27.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1704/8000 [01:50<07:01, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6157/8000 [05:30<01:10, 25.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1706/8000 [01:50<07:22, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6161/8000 [05:31<01:20, 22.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1709/8000 [01:50<06:18, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6166/8000 [05:31<01:11, 25.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1711/8000 [01:50<06:57, 15.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6170/8000 [05:31<01:11, 25.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▌                           | 1715/8000 [01:51<08:59, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6173/8000 [05:31<01:12, 25.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▌                           | 1718/8000 [01:51<08:01, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6177/8000 [05:31<01:28, 20.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1720/8000 [01:51<08:09, 12.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6184/8000 [05:32<01:04, 28.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1722/8000 [01:52<10:01, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6189/8000 [05:32<01:13, 24.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1725/8000 [01:52<08:31, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6193/8000 [05:32<01:09, 25.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1729/8000 [01:52<06:55, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6196/8000 [05:32<01:45, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1731/8000 [01:52<06:43, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6202/8000 [05:32<01:20, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1735/8000 [01:52<05:48, 17.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6207/8000 [05:33<01:11, 25.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1737/8000 [01:52<06:15, 16.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6211/8000 [05:33<01:37, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1740/8000 [01:52<05:50, 17.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6216/8000 [05:33<01:34, 18.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1747/8000 [01:53<06:14, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6225/8000 [05:34<01:13, 24.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1750/8000 [01:53<05:59, 17.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6229/8000 [05:34<01:22, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1754/8000 [01:54<07:54, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6233/8000 [05:34<01:29, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1757/8000 [01:54<07:21, 14.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6240/8000 [05:34<01:20, 21.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1759/8000 [01:54<09:11, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6243/8000 [05:34<01:17, 22.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1764/8000 [01:54<06:50, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6246/8000 [05:35<01:51, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1769/8000 [01:54<05:12, 19.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6249/8000 [05:35<01:42, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1772/8000 [01:54<05:22, 19.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6258/8000 [05:35<01:05, 26.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1776/8000 [01:55<04:57, 20.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6263/8000 [05:35<01:24, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1783/8000 [01:55<04:22, 23.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6266/8000 [05:36<01:33, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1787/8000 [01:55<05:20, 19.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6271/8000 [05:36<01:18, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1792/8000 [01:55<05:23, 19.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6274/8000 [05:36<01:50, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1799/8000 [01:56<05:09, 20.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6278/8000 [05:36<01:49, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1802/8000 [01:56<05:04, 20.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6281/8000 [05:37<01:41, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1806/8000 [01:56<04:31, 22.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6287/8000 [05:37<01:31, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1809/8000 [01:56<04:42, 21.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6294/8000 [05:37<01:31, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1814/8000 [01:56<05:13, 19.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6297/8000 [05:38<01:42, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1819/8000 [01:57<06:14, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6302/8000 [05:38<01:52, 15.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1821/8000 [01:57<06:37, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6306/8000 [05:38<01:39, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1828/8000 [01:57<05:39, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6308/8000 [05:38<01:38, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1830/8000 [01:58<06:10, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6312/8000 [05:39<02:02, 13.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1832/8000 [01:58<06:41, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6316/8000 [05:39<01:42, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1835/8000 [01:58<06:15, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6321/8000 [05:39<01:39, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1839/8000 [01:58<07:04, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6323/8000 [05:39<01:42, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1841/8000 [01:58<06:50, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6329/8000 [05:39<01:21, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1843/8000 [01:59<08:50, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6332/8000 [05:40<01:25, 19.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1845/8000 [01:59<09:59, 10.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6339/8000 [05:40<01:06, 25.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1849/8000 [01:59<07:43, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6345/8000 [05:40<00:59, 27.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1854/8000 [01:59<05:44, 17.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6352/8000 [05:40<01:03, 26.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1858/8000 [01:59<06:05, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6355/8000 [05:40<01:04, 25.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1862/8000 [02:00<06:45, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6360/8000 [05:40<00:57, 28.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1864/8000 [02:00<06:46, 15.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6364/8000 [05:41<01:07, 24.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1866/8000 [02:00<06:40, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6369/8000 [05:41<01:14, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1868/8000 [02:00<07:04, 14.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6374/8000 [05:41<01:20, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1871/8000 [02:00<07:55, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6377/8000 [05:41<01:24, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1874/8000 [02:01<06:37, 15.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6380/8000 [05:42<01:28, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                          | 1880/8000 [02:01<04:48, 21.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6383/8000 [05:42<01:23, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                          | 1884/8000 [02:01<05:36, 18.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6386/8000 [05:42<01:36, 16.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1887/8000 [02:01<06:34, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6394/8000 [05:42<01:14, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1891/8000 [02:01<05:40, 17.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6398/8000 [05:43<01:37, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1895/8000 [02:02<05:54, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6400/8000 [05:43<01:38, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1901/8000 [02:02<05:53, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6407/8000 [05:43<01:09, 22.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1905/8000 [02:02<06:32, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6410/8000 [05:43<01:25, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1910/8000 [02:03<05:26, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6417/8000 [05:43<01:13, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1913/8000 [02:03<08:57, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6421/8000 [05:44<01:22, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1918/8000 [02:03<06:50, 14.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6425/8000 [05:44<01:26, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1921/8000 [02:04<08:37, 11.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6432/8000 [05:44<01:03, 24.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1923/8000 [02:04<08:25, 12.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6435/8000 [05:44<01:29, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1926/8000 [02:04<07:09, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6440/8000 [05:45<01:26, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1929/8000 [02:04<06:44, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6444/8000 [05:45<01:18, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1931/8000 [02:04<07:03, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6448/8000 [05:45<01:09, 22.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1935/8000 [02:05<05:53, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6452/8000 [05:45<01:20, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1937/8000 [02:05<07:56, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6456/8000 [05:46<01:27, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1940/8000 [02:05<07:20, 13.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6460/8000 [05:46<01:17, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1945/8000 [02:05<05:38, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6464/8000 [05:46<01:09, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1948/8000 [02:05<05:34, 18.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6471/8000 [05:46<01:07, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1952/8000 [02:06<06:13, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6474/8000 [05:46<01:22, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1954/8000 [02:06<06:11, 16.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6479/8000 [05:47<01:10, 21.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1956/8000 [02:06<06:41, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6483/8000 [05:47<01:28, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1958/8000 [02:06<07:05, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6488/8000 [05:47<01:32, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1960/8000 [02:06<07:04, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6491/8000 [05:48<01:44, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                          | 1963/8000 [02:07<08:31, 11.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6494/8000 [05:48<01:36, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                          | 1966/8000 [02:07<07:33, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6496/8000 [05:48<01:38, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                          | 1971/8000 [02:07<07:02, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6498/8000 [05:48<02:18, 10.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1974/8000 [02:07<09:00, 11.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6501/8000 [05:48<01:55, 12.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1977/8000 [02:08<08:01, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6505/8000 [05:48<01:29, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1979/8000 [02:08<07:57, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6510/8000 [05:49<01:10, 21.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1981/8000 [02:08<07:46, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6513/8000 [05:49<01:07, 21.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1983/8000 [02:08<09:43, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6516/8000 [05:49<01:23, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1985/8000 [02:08<09:22, 10.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6521/8000 [05:49<01:25, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1987/8000 [02:09<08:49, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6525/8000 [05:50<01:26, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1991/8000 [02:09<06:49, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6528/8000 [05:50<01:22, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1993/8000 [02:09<07:15, 13.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6530/8000 [05:50<01:25, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1996/8000 [02:09<06:38, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6533/8000 [05:50<01:34, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1999/8000 [02:09<07:32, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6537/8000 [05:50<01:22, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2004/8000 [02:10<05:35, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6539/8000 [05:50<01:44, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2008/8000 [02:10<05:01, 19.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6541/8000 [05:51<01:41, 14.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2011/8000 [02:10<07:18, 13.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6545/8000 [05:51<01:42, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2013/8000 [02:10<07:28, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6547/8000 [05:51<02:02, 11.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2019/8000 [02:10<04:57, 20.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6550/8000 [05:51<01:44, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2022/8000 [02:11<05:06, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6552/8000 [05:51<01:39, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2025/8000 [02:11<07:19, 13.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6555/8000 [05:52<01:29, 16.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2028/8000 [02:11<06:50, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6560/8000 [05:52<01:09, 20.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▉                          | 2031/8000 [02:11<06:31, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6563/8000 [05:52<02:06, 11.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▉                          | 2033/8000 [02:11<06:57, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6566/8000 [05:53<02:06, 11.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▉                          | 2037/8000 [02:12<05:54, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6569/8000 [05:53<01:49, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▉                          | 2039/8000 [02:12<06:22, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6574/8000 [05:53<01:38, 14.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2042/8000 [02:12<07:25, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6578/8000 [05:53<01:24, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2045/8000 [02:12<06:55, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6582/8000 [05:53<01:31, 15.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2049/8000 [02:13<07:24, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6586/8000 [05:54<01:25, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2052/8000 [02:13<06:52, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6588/8000 [05:54<01:26, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2056/8000 [02:13<05:47, 17.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6590/8000 [05:54<02:01, 11.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2059/8000 [02:13<05:41, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6592/8000 [05:54<01:57, 11.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2062/8000 [02:13<05:27, 18.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6594/8000 [05:54<01:55, 12.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2065/8000 [02:13<05:28, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6597/8000 [05:55<01:58, 11.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2069/8000 [02:14<04:48, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6602/8000 [05:55<01:38, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2073/8000 [02:14<04:23, 22.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6605/8000 [05:55<01:46, 13.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2076/8000 [02:14<04:12, 23.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6610/8000 [05:55<01:18, 17.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2081/8000 [02:14<03:50, 25.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6615/8000 [05:56<01:03, 21.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2085/8000 [02:14<05:17, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6618/8000 [05:56<01:02, 22.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2090/8000 [02:15<05:40, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6622/8000 [05:56<01:10, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2093/8000 [02:15<06:42, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6627/8000 [05:56<00:58, 23.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2097/8000 [02:15<06:00, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6632/8000 [05:56<01:15, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2101/8000 [02:15<05:30, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6636/8000 [05:57<01:30, 15.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2103/8000 [02:15<05:57, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6638/8000 [05:57<01:30, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2106/8000 [02:16<05:46, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6641/8000 [05:57<01:45, 12.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2110/8000 [02:16<05:10, 18.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6643/8000 [05:58<02:07, 10.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▎                         | 2115/8000 [02:16<04:22, 22.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6648/8000 [05:58<01:30, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▎                         | 2118/8000 [02:16<05:54, 16.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6652/8000 [05:58<01:17, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▎                         | 2120/8000 [02:17<07:52, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6656/8000 [05:58<01:25, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2126/8000 [02:17<05:29, 17.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6659/8000 [05:59<01:45, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2129/8000 [02:17<05:23, 18.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6663/8000 [05:59<01:27, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2135/8000 [02:17<04:14, 23.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6668/8000 [05:59<01:13, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2140/8000 [02:17<03:53, 25.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6672/8000 [05:59<01:16, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2144/8000 [02:18<05:54, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6679/8000 [05:59<00:55, 23.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2150/8000 [02:18<06:20, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6682/8000 [06:00<01:07, 19.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2154/8000 [02:18<05:34, 17.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6685/8000 [06:00<01:16, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2157/8000 [02:18<05:14, 18.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6687/8000 [06:00<01:32, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2162/8000 [02:19<05:38, 17.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6690/8000 [06:00<01:25, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2168/8000 [02:19<04:24, 22.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6693/8000 [06:00<01:17, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2172/8000 [02:19<04:19, 22.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6697/8000 [06:01<01:07, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2175/8000 [02:19<05:36, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6702/8000 [06:01<01:01, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2179/8000 [02:20<06:11, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6708/8000 [06:01<01:07, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2182/8000 [02:20<05:52, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6712/8000 [06:01<01:08, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2186/8000 [02:20<05:21, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6715/8000 [06:02<01:21, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2190/8000 [02:20<05:38, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6718/8000 [06:02<01:19, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2194/8000 [02:21<06:13, 15.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6720/8000 [06:02<01:42, 12.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2198/8000 [02:21<05:12, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6723/8000 [06:02<01:29, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2202/8000 [02:21<05:53, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6728/8000 [06:02<01:12, 17.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2205/8000 [02:21<05:36, 17.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6731/8000 [06:03<01:27, 14.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2207/8000 [02:21<05:43, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6734/8000 [06:03<01:21, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2209/8000 [02:21<06:09, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6740/8000 [06:03<00:57, 21.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2214/8000 [02:22<04:46, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6743/8000 [06:03<00:56, 22.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2218/8000 [02:22<05:12, 18.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6747/8000 [06:03<01:03, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2220/8000 [02:22<05:43, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6753/8000 [06:04<00:47, 26.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2224/8000 [02:22<05:09, 18.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6758/8000 [06:04<00:54, 22.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2226/8000 [02:22<05:20, 18.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6762/8000 [06:04<00:52, 23.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2229/8000 [02:22<05:16, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6766/8000 [06:04<01:11, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2231/8000 [02:23<05:55, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6769/8000 [06:04<01:08, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2234/8000 [02:23<05:47, 16.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6775/8000 [06:05<01:00, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2236/8000 [02:23<06:22, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6778/8000 [06:05<01:09, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2238/8000 [02:23<06:47, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6781/8000 [06:05<01:05, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2241/8000 [02:23<06:14, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6785/8000 [06:05<01:08, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2244/8000 [02:24<07:11, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6790/8000 [06:05<00:54, 22.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2249/8000 [02:24<05:25, 17.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6793/8000 [06:06<01:08, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2251/8000 [02:24<07:18, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6798/8000 [06:06<01:12, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2254/8000 [02:24<06:41, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6801/8000 [06:06<01:20, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2257/8000 [02:24<06:17, 15.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6803/8000 [06:07<01:19, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2260/8000 [02:25<06:02, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6805/8000 [06:07<01:21, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2262/8000 [02:25<06:08, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6807/8000 [06:07<01:19, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2267/8000 [02:25<04:51, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6810/8000 [06:07<01:09, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2270/8000 [02:25<04:56, 19.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6812/8000 [06:07<01:12, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2273/8000 [02:25<05:07, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6814/8000 [06:07<01:14, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                         | 2281/8000 [02:25<03:27, 27.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6817/8000 [06:07<01:03, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2287/8000 [02:26<03:10, 30.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6819/8000 [06:07<01:05, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2291/8000 [02:26<03:23, 28.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6823/8000 [06:08<00:57, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2298/8000 [02:26<03:44, 25.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6829/8000 [06:08<00:42, 27.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2301/8000 [02:26<03:56, 24.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6832/8000 [06:08<00:43, 26.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2305/8000 [02:26<04:33, 20.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6835/8000 [06:08<01:01, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2308/8000 [02:27<05:49, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6838/8000 [06:08<00:57, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2311/8000 [02:27<05:28, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6842/8000 [06:08<00:49, 23.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2316/8000 [02:27<04:34, 20.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6848/8000 [06:09<00:49, 23.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2319/8000 [02:27<04:37, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6853/8000 [06:09<00:52, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2328/8000 [02:28<03:56, 23.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6857/8000 [06:09<01:06, 17.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2333/8000 [02:28<04:31, 20.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6860/8000 [06:09<01:07, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2337/8000 [02:28<05:13, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6867/8000 [06:10<00:57, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2339/8000 [02:28<06:38, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6870/8000 [06:10<00:55, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2342/8000 [02:29<06:11, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6877/8000 [06:10<00:40, 27.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2345/8000 [02:29<05:34, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6882/8000 [06:10<00:45, 24.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2351/8000 [02:29<04:21, 21.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6889/8000 [06:10<00:43, 25.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2355/8000 [02:29<04:15, 22.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6892/8000 [06:11<00:55, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2358/8000 [02:29<05:29, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6896/8000 [06:11<01:01, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                        | 2360/8000 [02:30<05:57, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6899/8000 [06:11<01:08, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                        | 2364/8000 [02:30<05:17, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6904/8000 [06:11<00:52, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                        | 2368/8000 [02:30<05:57, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6907/8000 [06:12<01:02, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                        | 2371/8000 [02:30<05:46, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6910/8000 [06:12<01:23, 13.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2377/8000 [02:30<04:24, 21.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6914/8000 [06:12<01:20, 13.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2384/8000 [02:31<05:14, 17.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6917/8000 [06:12<01:13, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2387/8000 [02:31<05:11, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6919/8000 [06:13<01:11, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2392/8000 [02:31<04:32, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6922/8000 [06:13<01:05, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2395/8000 [02:31<04:37, 20.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6924/8000 [06:13<01:09, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2400/8000 [02:32<04:41, 19.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6928/8000 [06:13<01:08, 15.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2403/8000 [02:32<05:52, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6933/8000 [06:13<00:54, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2409/8000 [02:32<04:26, 20.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6936/8000 [06:14<01:08, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2412/8000 [02:32<04:18, 21.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6938/8000 [06:14<01:12, 14.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2415/8000 [02:32<04:28, 20.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6943/8000 [06:14<00:57, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2419/8000 [02:33<05:11, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6945/8000 [06:14<01:02, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2423/8000 [02:33<05:54, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6952/8000 [06:14<00:41, 25.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▋                        | 2430/8000 [02:33<04:19, 21.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6955/8000 [06:14<00:43, 24.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▋                        | 2434/8000 [02:33<04:13, 21.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6958/8000 [06:15<00:55, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▋                        | 2437/8000 [02:34<05:27, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6964/8000 [06:15<00:52, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▋                        | 2439/8000 [02:34<06:40, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6968/8000 [06:15<00:55, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2443/8000 [02:34<05:41, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6971/8000 [06:15<00:54, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2449/8000 [02:34<04:24, 20.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6977/8000 [06:16<00:50, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2452/8000 [02:34<04:31, 20.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6981/8000 [06:16<00:47, 21.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2455/8000 [02:35<04:46, 19.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6985/8000 [06:16<00:52, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2458/8000 [02:35<04:57, 18.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6988/8000 [06:16<00:50, 20.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2460/8000 [02:35<05:26, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6992/8000 [06:16<00:44, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2462/8000 [02:35<05:39, 16.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6995/8000 [06:16<00:46, 21.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2468/8000 [02:35<04:12, 21.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6998/8000 [06:17<01:03, 15.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2471/8000 [02:35<04:25, 20.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7002/8000 [06:17<00:55, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2476/8000 [02:36<04:30, 20.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7006/8000 [06:17<00:50, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2486/8000 [02:36<03:43, 24.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7010/8000 [06:17<01:04, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2489/8000 [02:36<03:46, 24.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7019/8000 [06:18<00:54, 17.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2492/8000 [02:37<05:46, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7021/8000 [06:18<01:03, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2494/8000 [02:37<06:48, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7025/8000 [06:19<01:28, 11.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2497/8000 [02:37<07:39, 11.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7027/8000 [06:19<01:34, 10.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2500/8000 [02:37<06:59, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7029/8000 [06:19<01:35, 10.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2504/8000 [02:37<05:52, 15.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7031/8000 [06:19<01:41,  9.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2506/8000 [02:38<06:14, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7033/8000 [06:20<01:36, 10.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2509/8000 [02:38<05:58, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7035/8000 [06:20<01:40,  9.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2512/8000 [02:38<05:25, 16.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7038/8000 [06:20<01:46,  9.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2514/8000 [02:38<07:50, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7042/8000 [06:20<01:19, 12.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|███████████                        | 2516/8000 [02:39<09:49,  9.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7044/8000 [06:21<01:16, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|███████████                        | 2519/8000 [02:39<08:06, 11.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7046/8000 [06:21<01:13, 12.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2525/8000 [02:39<05:14, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7050/8000 [06:21<01:07, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2528/8000 [02:39<07:11, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7052/8000 [06:21<01:06, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2532/8000 [02:40<09:10,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7055/8000 [06:21<01:12, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2534/8000 [02:40<09:41,  9.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7057/8000 [06:21<01:09, 13.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2537/8000 [02:40<08:24, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7059/8000 [06:22<01:07, 14.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2539/8000 [02:41<08:21, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7063/8000 [06:22<01:03, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2543/8000 [02:41<06:33, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7068/8000 [06:22<01:07, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2546/8000 [02:41<06:05, 14.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7072/8000 [06:23<01:09, 13.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2548/8000 [02:41<07:54, 11.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7076/8000 [06:23<01:06, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2554/8000 [02:41<05:12, 17.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7079/8000 [06:23<01:07, 13.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2557/8000 [02:42<05:08, 17.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7083/8000 [06:23<01:04, 14.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2562/8000 [02:42<06:10, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7085/8000 [06:23<01:03, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2565/8000 [02:42<07:02, 12.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7088/8000 [06:24<00:55, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2569/8000 [02:42<05:41, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7090/8000 [06:24<01:08, 13.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2574/8000 [02:43<04:27, 20.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7096/8000 [06:24<00:46, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2578/8000 [02:43<05:19, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7099/8000 [06:24<00:46, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2583/8000 [02:43<06:27, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7104/8000 [06:25<01:03, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2587/8000 [02:44<05:41, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7106/8000 [06:25<01:04, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2591/8000 [02:44<06:07, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7110/8000 [06:25<00:51, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2596/8000 [02:44<04:58, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7117/8000 [06:25<00:50, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2599/8000 [02:44<05:42, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7120/8000 [06:26<00:59, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2602/8000 [02:44<05:32, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7122/8000 [06:26<00:58, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2607/8000 [02:45<05:41, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7124/8000 [06:26<00:57, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2609/8000 [02:45<07:15, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7128/8000 [06:26<00:58, 14.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2614/8000 [02:45<05:40, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7130/8000 [06:26<00:58, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2619/8000 [02:45<04:36, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7136/8000 [06:27<00:48, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2622/8000 [02:46<04:47, 18.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7139/8000 [06:27<00:47, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2626/8000 [02:46<05:26, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7142/8000 [06:27<00:43, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2630/8000 [02:46<04:59, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7146/8000 [06:27<00:50, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2634/8000 [02:46<04:37, 19.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7148/8000 [06:28<01:12, 11.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2639/8000 [02:47<04:52, 18.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7152/8000 [06:28<00:57, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2641/8000 [02:47<05:18, 16.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7154/8000 [06:28<00:57, 14.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2643/8000 [02:47<06:53, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7159/8000 [06:28<00:42, 19.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2655/8000 [02:47<04:34, 19.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7162/8000 [06:28<00:50, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2657/8000 [02:48<04:48, 18.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7164/8000 [06:29<01:13, 11.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2660/8000 [02:48<04:32, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7166/8000 [06:29<01:11, 11.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2665/8000 [02:48<03:59, 22.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7168/8000 [06:29<01:07, 12.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2670/8000 [02:48<05:28, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7170/8000 [06:29<01:03, 13.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2674/8000 [02:49<04:59, 17.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7173/8000 [06:29<00:53, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2678/8000 [02:49<04:33, 19.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7176/8000 [06:29<00:47, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                       | 2681/8000 [02:49<04:37, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7178/8000 [06:30<01:05, 12.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                       | 2684/8000 [02:49<04:46, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7180/8000 [06:30<01:14, 11.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2686/8000 [02:49<05:14, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7182/8000 [06:30<01:23,  9.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2689/8000 [02:49<05:12, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7185/8000 [06:30<01:05, 12.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2692/8000 [02:50<05:06, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7187/8000 [06:30<01:05, 12.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2696/8000 [02:50<04:31, 19.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7189/8000 [06:31<01:19, 10.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2698/8000 [02:50<06:34, 13.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7192/8000 [06:31<01:03, 12.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2702/8000 [02:50<06:44, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7194/8000 [06:31<01:13, 11.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2704/8000 [02:50<06:32, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7196/8000 [06:31<01:21,  9.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2707/8000 [02:51<05:37, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7199/8000 [06:31<01:06, 12.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2714/8000 [02:51<04:51, 18.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7203/8000 [06:32<00:51, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2718/8000 [02:51<05:05, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7205/8000 [06:32<00:50, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2720/8000 [02:51<05:29, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7211/8000 [06:32<00:41, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2723/8000 [02:52<06:33, 13.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7213/8000 [06:32<00:43, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2726/8000 [02:52<06:07, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7217/8000 [06:32<00:38, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2728/8000 [02:52<06:21, 13.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7221/8000 [06:32<00:34, 22.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2732/8000 [02:52<06:24, 13.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7224/8000 [06:33<00:34, 22.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2736/8000 [02:53<06:32, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7227/8000 [06:33<00:43, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2739/8000 [02:53<05:57, 14.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7230/8000 [06:33<00:51, 14.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2741/8000 [02:53<05:52, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7233/8000 [06:33<00:55, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2744/8000 [02:53<05:43, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7238/8000 [06:33<00:40, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2747/8000 [02:53<05:39, 15.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7241/8000 [06:34<00:38, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2751/8000 [02:54<05:35, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7244/8000 [06:34<00:38, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2758/8000 [02:54<03:59, 21.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7247/8000 [06:34<00:38, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2761/8000 [02:54<04:09, 20.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7252/8000 [06:34<00:40, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2764/8000 [02:54<05:28, 15.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7254/8000 [06:34<00:42, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2767/8000 [02:54<05:21, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7256/8000 [06:34<00:45, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2770/8000 [02:54<04:53, 17.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7260/8000 [06:35<00:37, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2773/8000 [02:55<04:49, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7266/8000 [06:35<00:28, 26.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2778/8000 [02:55<04:05, 21.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7274/8000 [06:35<00:30, 24.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2783/8000 [02:55<03:35, 24.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7278/8000 [06:35<00:33, 21.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2786/8000 [02:55<05:01, 17.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7281/8000 [06:35<00:32, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2791/8000 [02:56<05:12, 16.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7284/8000 [06:36<00:38, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2795/8000 [02:56<04:29, 19.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7286/8000 [06:36<00:48, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2802/8000 [02:56<03:24, 25.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7290/8000 [06:36<00:40, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2807/8000 [02:56<04:09, 20.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7294/8000 [06:36<00:35, 20.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2811/8000 [02:57<05:00, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7298/8000 [06:37<00:38, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2815/8000 [02:57<05:21, 16.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7302/8000 [06:37<00:47, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2817/8000 [02:57<05:48, 14.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7304/8000 [06:37<00:46, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2821/8000 [02:57<05:01, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7307/8000 [06:37<00:41, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2823/8000 [02:57<05:04, 16.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7309/8000 [06:37<00:42, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2825/8000 [02:58<05:23, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7312/8000 [06:37<00:39, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▍                      | 2830/8000 [02:58<05:28, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7317/8000 [06:38<00:37, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▍                      | 2834/8000 [02:58<04:40, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7320/8000 [06:38<00:35, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▍                      | 2837/8000 [02:58<04:47, 17.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7325/8000 [06:38<00:34, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2841/8000 [02:58<05:27, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7328/8000 [06:38<00:33, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2844/8000 [02:59<06:12, 13.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7333/8000 [06:38<00:27, 24.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2846/8000 [02:59<06:24, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7336/8000 [06:39<00:34, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2849/8000 [02:59<05:49, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7341/8000 [06:39<00:27, 23.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2852/8000 [02:59<05:06, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7344/8000 [06:39<00:33, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2856/8000 [03:00<05:43, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7347/8000 [06:39<00:31, 20.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2859/8000 [03:00<05:12, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7350/8000 [06:39<00:41, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2864/8000 [03:00<05:01, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7353/8000 [06:40<00:37, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2870/8000 [03:00<03:49, 22.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7356/8000 [06:40<00:34, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2873/8000 [03:00<04:43, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7359/8000 [06:40<00:32, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2877/8000 [03:01<05:30, 15.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7364/8000 [06:40<00:26, 23.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2880/8000 [03:01<06:13, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7367/8000 [06:40<00:26, 23.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2883/8000 [03:01<05:53, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7375/8000 [06:40<00:24, 25.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2885/8000 [03:01<06:01, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7379/8000 [06:41<00:22, 27.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2887/8000 [03:02<06:15, 13.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7383/8000 [06:41<00:22, 27.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2889/8000 [03:02<07:52, 10.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7388/8000 [06:41<00:31, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2891/8000 [03:02<08:46,  9.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7392/8000 [06:41<00:33, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2896/8000 [03:02<05:52, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7397/8000 [06:41<00:27, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2898/8000 [03:03<07:19, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7400/8000 [06:42<00:27, 21.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2903/8000 [03:03<04:59, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7404/8000 [06:42<00:29, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2906/8000 [03:03<04:58, 17.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7407/8000 [06:42<00:28, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2909/8000 [03:03<06:16, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7411/8000 [06:42<00:25, 23.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▊                      | 2917/8000 [03:03<03:59, 21.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7415/8000 [06:42<00:28, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▊                      | 2920/8000 [03:04<05:14, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7420/8000 [06:43<00:40, 14.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2924/8000 [03:04<04:26, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7425/8000 [06:43<00:36, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2927/8000 [03:04<06:27, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7428/8000 [06:43<00:33, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2929/8000 [03:04<06:10, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7432/8000 [06:44<00:34, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2931/8000 [03:05<06:19, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7435/8000 [06:44<00:38, 14.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2933/8000 [03:05<07:45, 10.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7438/8000 [06:44<00:34, 16.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2939/8000 [03:05<04:43, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7441/8000 [06:44<00:30, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2942/8000 [03:05<04:45, 17.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7445/8000 [06:44<00:25, 21.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2945/8000 [03:05<05:37, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7449/8000 [06:44<00:23, 23.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2947/8000 [03:06<06:46, 12.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7452/8000 [06:45<00:28, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2953/8000 [03:06<04:33, 18.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7457/8000 [06:45<00:22, 24.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2957/8000 [03:06<05:02, 16.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7462/8000 [06:45<00:24, 21.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2961/8000 [03:07<06:56, 12.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7467/8000 [06:45<00:21, 25.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2964/8000 [03:07<06:40, 12.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7471/8000 [06:45<00:24, 22.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2967/8000 [03:07<06:34, 12.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7475/8000 [06:46<00:26, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2969/8000 [03:07<07:11, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7478/8000 [06:46<00:30, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2972/8000 [03:08<07:09, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7483/8000 [06:46<00:23, 22.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2978/8000 [03:08<06:27, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7486/8000 [06:46<00:23, 21.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2981/8000 [03:08<06:10, 13.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7489/8000 [06:47<00:38, 13.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2983/8000 [03:08<06:55, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7495/8000 [06:47<00:30, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2989/8000 [03:09<04:52, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7498/8000 [06:47<00:28, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2991/8000 [03:09<05:11, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7501/8000 [06:47<00:26, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2996/8000 [03:09<04:46, 17.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7507/8000 [06:47<00:19, 25.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2998/8000 [03:09<06:11, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7513/8000 [06:47<00:19, 25.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3001/8000 [03:10<06:35, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7516/8000 [06:47<00:18, 25.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3003/8000 [03:10<06:34, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7519/8000 [06:48<00:23, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3005/8000 [03:10<07:59, 10.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7523/8000 [06:48<00:20, 22.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3011/8000 [03:10<05:52, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7527/8000 [06:48<00:23, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3015/8000 [03:10<05:02, 16.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7530/8000 [06:48<00:21, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3020/8000 [03:11<04:04, 20.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7533/8000 [06:48<00:26, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3025/8000 [03:11<03:19, 24.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7537/8000 [06:49<00:22, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3029/8000 [03:11<03:18, 25.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7540/8000 [06:49<00:21, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3033/8000 [03:11<04:10, 19.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7543/8000 [06:49<00:27, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3036/8000 [03:11<03:58, 20.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7546/8000 [06:49<00:25, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3039/8000 [03:11<04:07, 20.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7552/8000 [06:50<00:26, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3045/8000 [03:12<04:44, 17.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7554/8000 [06:50<00:27, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3047/8000 [03:12<05:57, 13.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7557/8000 [06:50<00:25, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3051/8000 [03:12<05:06, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7559/8000 [06:50<00:25, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3056/8000 [03:12<04:00, 20.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7561/8000 [06:50<00:24, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3059/8000 [03:13<04:06, 20.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7564/8000 [06:50<00:22, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3062/8000 [03:13<04:12, 19.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7569/8000 [06:50<00:19, 22.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3065/8000 [03:13<04:16, 19.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7572/8000 [06:51<00:28, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3069/8000 [03:13<03:59, 20.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7575/8000 [06:51<00:35, 12.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3074/8000 [03:13<04:27, 18.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7578/8000 [06:51<00:29, 14.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3077/8000 [03:14<04:26, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7582/8000 [06:51<00:23, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▍                     | 3081/8000 [03:14<06:00, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7588/8000 [06:52<00:21, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▍                     | 3083/8000 [03:14<05:49, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7593/8000 [06:52<00:20, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3087/8000 [03:14<06:03, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7596/8000 [06:52<00:19, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3090/8000 [03:15<05:24, 15.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7600/8000 [06:52<00:17, 23.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3092/8000 [03:15<05:16, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7605/8000 [06:52<00:21, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3096/8000 [03:15<04:33, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7608/8000 [06:53<00:24, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3099/8000 [03:15<04:24, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7610/8000 [06:53<00:28, 13.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3103/8000 [03:15<04:41, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7612/8000 [06:53<00:27, 13.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3105/8000 [03:15<05:06, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7616/8000 [06:53<00:21, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3108/8000 [03:16<04:48, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7619/8000 [06:54<00:25, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3111/8000 [03:16<04:34, 17.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7623/8000 [06:54<00:20, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3114/8000 [03:16<04:07, 19.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7632/8000 [06:54<00:14, 25.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3117/8000 [03:16<04:17, 18.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7635/8000 [06:54<00:14, 25.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3122/8000 [03:16<03:33, 22.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7639/8000 [06:54<00:13, 27.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3126/8000 [03:16<04:01, 20.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7643/8000 [06:55<00:19, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3131/8000 [03:17<04:27, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7647/8000 [06:55<00:20, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3136/8000 [03:17<04:20, 18.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7650/8000 [06:55<00:19, 17.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3139/8000 [03:17<04:18, 18.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7655/8000 [06:55<00:19, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3143/8000 [03:17<03:54, 20.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7659/8000 [06:55<00:16, 20.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3146/8000 [03:18<04:02, 20.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7665/8000 [06:56<00:15, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3149/8000 [03:18<03:49, 21.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7669/8000 [06:56<00:16, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3155/8000 [03:18<03:55, 20.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7673/8000 [06:56<00:15, 21.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3159/8000 [03:18<04:22, 18.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7676/8000 [06:56<00:17, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▊                     | 3162/8000 [03:18<04:25, 18.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7680/8000 [06:56<00:15, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▊                     | 3166/8000 [03:19<04:01, 20.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7686/8000 [06:57<00:17, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▊                     | 3170/8000 [03:19<04:48, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7689/8000 [06:57<00:16, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3172/8000 [03:19<05:04, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7694/8000 [06:57<00:13, 23.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3174/8000 [03:20<08:05,  9.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7699/8000 [06:57<00:16, 18.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3180/8000 [03:20<05:22, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7704/8000 [06:58<00:15, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3185/8000 [03:20<04:54, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7707/8000 [06:58<00:17, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3190/8000 [03:20<04:09, 19.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7710/8000 [06:58<00:15, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3194/8000 [03:21<05:17, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7716/8000 [06:58<00:15, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3196/8000 [03:21<05:34, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7718/8000 [06:58<00:16, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3202/8000 [03:21<04:54, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7720/8000 [06:59<00:16, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3206/8000 [03:21<04:27, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7723/8000 [06:59<00:15, 18.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3209/8000 [03:21<04:26, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7725/8000 [06:59<00:15, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3211/8000 [03:22<05:49, 13.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7728/8000 [06:59<00:14, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3213/8000 [03:22<08:38,  9.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7734/8000 [06:59<00:12, 21.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3218/8000 [03:22<06:57, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7738/8000 [06:59<00:13, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3222/8000 [03:23<05:44, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7741/8000 [07:00<00:15, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3225/8000 [03:23<05:59, 13.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7743/8000 [07:00<00:15, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3227/8000 [03:23<06:09, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7747/8000 [07:00<00:12, 20.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████▏                    | 3231/8000 [03:23<05:05, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7750/8000 [07:00<00:14, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████▏                    | 3238/8000 [03:23<03:58, 19.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7752/8000 [07:00<00:15, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3242/8000 [03:24<04:42, 16.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7757/8000 [07:00<00:11, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3245/8000 [03:24<04:38, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7760/8000 [07:01<00:10, 21.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3247/8000 [03:24<06:05, 13.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7766/8000 [07:01<00:10, 22.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3249/8000 [03:25<07:13, 10.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7771/8000 [07:01<00:10, 21.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3257/8000 [03:25<04:16, 18.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7774/8000 [07:01<00:12, 18.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3262/8000 [03:25<05:59, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7776/8000 [07:02<00:15, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3264/8000 [03:25<06:06, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7780/8000 [07:02<00:14, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3267/8000 [03:26<05:40, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7782/8000 [07:02<00:14, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3269/8000 [03:26<07:08, 11.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7784/8000 [07:02<00:14, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3271/8000 [03:26<06:58, 11.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7786/8000 [07:02<00:14, 14.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3277/8000 [03:27<06:22, 12.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7789/8000 [07:02<00:12, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3280/8000 [03:27<05:49, 13.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7791/8000 [07:03<00:12, 16.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3282/8000 [03:27<07:54,  9.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7795/8000 [07:03<00:10, 19.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3286/8000 [03:27<06:11, 12.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7799/8000 [07:03<00:09, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3288/8000 [03:27<05:52, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7802/8000 [07:03<00:08, 22.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3290/8000 [03:28<05:59, 13.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7808/8000 [07:03<00:06, 30.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3292/8000 [03:28<06:03, 12.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7816/8000 [07:03<00:07, 25.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3296/8000 [03:28<05:47, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7819/8000 [07:04<00:07, 24.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3299/8000 [03:28<05:17, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7823/8000 [07:04<00:06, 26.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3302/8000 [03:28<06:05, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7826/8000 [07:04<00:08, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3307/8000 [03:29<05:41, 13.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7831/8000 [07:04<00:08, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3309/8000 [03:29<05:54, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7835/8000 [07:05<00:10, 16.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3312/8000 [03:29<06:32, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7837/8000 [07:05<00:11, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▌                    | 3316/8000 [03:29<05:22, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7841/8000 [07:05<00:09, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3320/8000 [03:30<05:23, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7846/8000 [07:05<00:08, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3324/8000 [03:30<04:42, 16.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7849/8000 [07:05<00:07, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3327/8000 [03:30<04:37, 16.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7853/8000 [07:05<00:06, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3330/8000 [03:30<05:38, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7858/8000 [07:06<00:07, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3332/8000 [03:31<05:50, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7864/8000 [07:06<00:05, 23.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3334/8000 [03:31<06:01, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7868/8000 [07:06<00:06, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3336/8000 [03:31<05:52, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7871/8000 [07:06<00:06, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3339/8000 [03:31<06:50, 11.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7878/8000 [07:07<00:05, 24.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3341/8000 [03:31<06:34, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▋| 7881/8000 [07:07<00:06, 19.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3344/8000 [03:32<08:29,  9.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7886/8000 [07:07<00:04, 23.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3348/8000 [03:32<06:18, 12.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7890/8000 [07:07<00:05, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3351/8000 [03:32<05:39, 13.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7893/8000 [07:08<00:06, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3357/8000 [03:32<04:03, 19.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7896/8000 [07:08<00:05, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3360/8000 [03:32<04:04, 18.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7899/8000 [07:08<00:07, 13.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3365/8000 [03:33<04:17, 18.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7901/8000 [07:08<00:07, 13.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3367/8000 [03:33<04:37, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7903/8000 [07:08<00:06, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3369/8000 [03:33<04:49, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7906/8000 [07:09<00:06, 13.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3372/8000 [03:33<04:19, 17.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7910/8000 [07:09<00:05, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3374/8000 [03:33<04:49, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7913/8000 [07:09<00:04, 18.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3378/8000 [03:34<04:11, 18.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7917/8000 [07:09<00:05, 15.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3380/8000 [03:34<04:37, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7919/8000 [07:09<00:06, 13.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3385/8000 [03:34<03:46, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7921/8000 [07:10<00:05, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3389/8000 [03:34<04:32, 16.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7924/8000 [07:10<00:05, 12.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3391/8000 [03:34<04:43, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7927/8000 [07:10<00:05, 14.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3393/8000 [03:35<05:08, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7930/8000 [07:10<00:05, 13.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3395/8000 [03:35<05:27, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7932/8000 [07:10<00:05, 11.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3399/8000 [03:35<05:36, 13.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7934/8000 [07:11<00:05, 12.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3401/8000 [03:35<05:44, 13.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7937/8000 [07:11<00:06, 10.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3405/8000 [03:35<04:45, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7939/8000 [07:11<00:05, 10.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3408/8000 [03:36<04:40, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7942/8000 [07:11<00:04, 13.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3411/8000 [03:36<05:40, 13.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7945/8000 [07:11<00:03, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3414/8000 [03:36<05:05, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7949/8000 [07:12<00:04, 12.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3417/8000 [03:36<04:33, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7953/8000 [07:12<00:02, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3419/8000 [03:36<04:53, 15.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7956/8000 [07:12<00:02, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3422/8000 [03:36<04:44, 16.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7963/8000 [07:12<00:01, 26.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3427/8000 [03:37<04:12, 18.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7968/8000 [07:13<00:01, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3430/8000 [03:37<05:21, 14.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7972/8000 [07:13<00:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3434/8000 [03:37<04:22, 17.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7976/8000 [07:13<00:01, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3439/8000 [03:38<06:03, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7979/8000 [07:13<00:01, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3443/8000 [03:38<04:52, 15.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7982/8000 [07:13<00:01, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3449/8000 [03:38<05:15, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7987/8000 [07:14<00:00, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3451/8000 [03:38<05:08, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7993/8000 [07:14<00:00, 14.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3459/8000 [03:39<03:28, 21.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|█████████████████| 8000/8000 [07:14<00:00, 18.39it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  43%|███████████████▏                   | 3463/8000 [03:39<04:11, 18.01it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  43%|███████████████▏                   | 3468/8000 [03:39<03:39, 20.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3475/8000 [03:39<03:37, 20.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:35,  7.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▏                   | 3480/8000 [03:40<03:56, 19.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:56,  3.92s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3486/8000 [03:40<03:50, 19.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:04,  2.77s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3489/8000 [03:40<03:39, 20.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:19,  2.24s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3492/8000 [03:40<03:45, 20.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:35,  2.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3495/8000 [03:40<03:46, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:59,  1.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3500/8000 [03:41<03:10, 23.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:28,  1.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3506/8000 [03:41<02:31, 29.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:11,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3510/8000 [03:41<03:29, 21.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:00,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3513/8000 [03:41<03:36, 20.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:50,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3521/8000 [03:41<02:40, 27.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:44,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3527/8000 [03:42<03:02, 24.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:38,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3530/8000 [03:42<04:43, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:33,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3535/8000 [03:42<04:01, 18.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:33,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3540/8000 [03:43<04:04, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:27,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3543/8000 [03:43<04:00, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:25,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3548/8000 [03:43<03:25, 21.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:21,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3553/8000 [03:43<03:43, 19.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:22,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3556/8000 [03:43<03:46, 19.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:21,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▌                   | 3563/8000 [03:44<03:14, 22.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:35<04:21,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▌                   | 3567/8000 [03:44<03:09, 23.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:20,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▌                   | 3571/8000 [03:44<03:02, 24.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:18,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3574/8000 [03:44<03:51, 19.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<04:13,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3577/8000 [03:45<04:58, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<04:16,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3580/8000 [03:45<05:19, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:42<04:13,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3586/8000 [03:45<03:57, 18.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<04:10,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3589/8000 [03:45<04:50, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<04:10,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3596/8000 [03:46<04:18, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<04:10,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3600/8000 [03:46<03:56, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<04:06,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3603/8000 [03:46<03:48, 19.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:49<04:02,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3607/8000 [03:46<05:09, 14.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<04:02,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3610/8000 [03:46<04:37, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:52<04:02,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3613/8000 [03:47<05:25, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:54<04:21,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3615/8000 [03:47<06:16, 11.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:55<04:18,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3618/8000 [03:47<05:39, 12.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:57<04:07,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3624/8000 [03:48<05:13, 13.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:58<04:01,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3627/8000 [03:48<04:56, 14.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:59<03:56,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▉                   | 3630/8000 [03:48<04:42, 15.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:01<03:57,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▉                   | 3635/8000 [03:48<03:51, 18.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<03:52,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▉                   | 3639/8000 [03:49<05:17, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:04<03:53,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3641/8000 [03:49<07:31,  9.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:05<03:47,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3645/8000 [03:49<06:35, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:07<03:44,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3647/8000 [03:50<06:28, 11.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:08<03:49,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3649/8000 [03:50<06:27, 11.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:10<03:45,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3652/8000 [03:50<07:59,  9.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:11<03:43,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3656/8000 [03:50<06:04, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:12<03:39,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3658/8000 [03:51<06:01, 12.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:14<03:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3661/8000 [03:51<05:21, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:16<03:51,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3663/8000 [03:51<05:21, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:19<04:03,  1.62s/it]\n",
+      "Generating signals:  46%|████████████████                   | 3668/8000 [03:51<04:00, 18.03it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  46%|████████████████                   | 3670/8000 [03:51<04:02, 17.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3673/8000 [03:51<04:02, 17.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3675/8000 [03:51<04:18, 16.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.80it/s]\n",
+      "Generating signals:  46%|████████████████                   | 3679/8000 [03:52<03:41, 19.49it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  46%|████████████████                   | 3682/8000 [03:52<04:41, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3685/8000 [03:52<05:20, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3687/8000 [03:53<07:24,  9.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 29.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3692/8000 [03:53<05:09, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 74it [00:01, 66.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3695/8000 [03:53<05:31, 12.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 108it [00:01, 100.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3699/8000 [03:53<04:33, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 147it [00:01, 145.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3702/8000 [03:53<05:07, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 189it [00:02, 195.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3706/8000 [03:54<04:21, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 227it [00:02, 233.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3708/8000 [03:54<04:35, 15.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 267it [00:02, 270.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3713/8000 [03:54<03:30, 20.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 305it [00:02, 288.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▎                  | 3716/8000 [03:54<03:18, 21.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 342it [00:02, 308.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3721/8000 [03:54<02:52, 24.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 379it [00:02, 323.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3724/8000 [03:54<03:10, 22.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 417it [00:02, 337.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3728/8000 [03:55<04:27, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 454it [00:02, 324.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3730/8000 [03:55<04:40, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 497it [00:02, 351.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3732/8000 [03:55<04:45, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 534it [00:03, 345.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3735/8000 [03:55<04:07, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 575it [00:03, 362.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3739/8000 [03:56<05:35, 12.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 619it [00:03, 382.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3741/8000 [03:56<06:26, 11.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 659it [00:03, 386.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3744/8000 [03:56<05:32, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 699it [00:03, 380.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3746/8000 [03:56<05:35, 12.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 738it [00:03, 379.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3748/8000 [03:56<06:43, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 213.05it/s]\n",
+      "Generating signals:  47%|████████████████▍                  | 3750/8000 [03:57<06:28, 10.95it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  47%|████████████████▍                  | 3752/8000 [03:57<05:59, 11.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3756/8000 [03:57<05:31, 12.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 159\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3762/8000 [03:57<03:43, 18.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 143\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3765/8000 [03:57<03:38, 19.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:00<00:00, 148\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3770/8000 [03:57<02:54, 24.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  63%|▋| 63/100 [00:00<00:00, 141\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3773/8000 [03:58<03:06, 22.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 146\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3776/8000 [03:58<04:07, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 13\n",
+      "Generating signals:  47%|████████████████▌                  | 3779/8000 [03:58<04:35, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [33:45<00:00, 599.72s/it]\u001b[A\n",
-      "Replicates:  75%|██████████████████▊      | 6/8 [3:30:33<1:09:06, 2073.21s/it]\n",
-      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.6547933214335355\n",
-      "2999: -5.9214834\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  47%|████████████████▌                  | 3782/8000 [03:58<04:15, 16.48it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  47%|████████████████▌                  | 3785/8000 [03:58<04:07, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3790/8000 [03:59<03:10, 22.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:37,  7.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3793/8000 [03:59<04:18, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:37,  3.82s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3797/8000 [03:59<04:22, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:46,  2.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3799/8000 [03:59<04:36, 15.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:57,  2.13s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3801/8000 [04:00<05:54, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:02,  1.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3803/8000 [04:00<07:51,  8.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:25,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3809/8000 [04:00<05:33, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:04,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3812/8000 [04:01<06:07, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:43,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3816/8000 [04:01<05:30, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:33,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3820/8000 [04:01<04:41, 14.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:28,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3825/8000 [04:01<03:44, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:16,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3829/8000 [04:01<03:21, 20.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3833/8000 [04:02<03:48, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:12,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3841/8000 [04:02<02:37, 26.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:10,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3845/8000 [04:02<02:38, 26.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:08,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3850/8000 [04:02<02:20, 29.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:12,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3854/8000 [04:02<03:09, 21.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:07,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3857/8000 [04:03<03:10, 21.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<04:06,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3860/8000 [04:03<03:04, 22.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<04:12,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3864/8000 [04:03<03:02, 22.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<04:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3867/8000 [04:03<03:57, 17.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:34<04:04,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3871/8000 [04:03<04:16, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<04:01,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3874/8000 [04:04<04:04, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:37<04:04,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3877/8000 [04:04<03:49, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:38<04:00,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3880/8000 [04:04<04:20, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<04:00,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▉                  | 3883/8000 [04:04<04:08, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:41<03:59,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▉                  | 3885/8000 [04:04<04:28, 15.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:42<04:00,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3890/8000 [04:04<03:13, 21.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:59,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3895/8000 [04:05<02:55, 23.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:45<03:56,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3901/8000 [04:05<03:05, 22.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:46<03:51,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3904/8000 [04:05<03:14, 21.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:49,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3907/8000 [04:05<03:21, 20.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:49<03:48,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3912/8000 [04:06<05:23, 12.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:50<03:44,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3914/8000 [04:06<05:21, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:52<03:47,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3916/8000 [04:06<05:05, 13.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:53<03:44,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3918/8000 [04:06<05:13, 13.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:54<03:44,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3920/8000 [04:06<05:14, 12.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:56<03:44,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3923/8000 [04:07<04:31, 15.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:57<03:45,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3925/8000 [04:07<05:50, 11.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:58<03:42,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3927/8000 [04:07<05:46, 11.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:00<03:40,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3930/8000 [04:07<06:26, 10.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:01<03:38,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3932/8000 [04:08<06:14, 10.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:03<03:37,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3937/8000 [04:08<05:08, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:04<03:34,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3939/8000 [04:08<05:20, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:05<03:34,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3942/8000 [04:08<05:50, 11.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:07<03:33,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3946/8000 [04:09<05:40, 11.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:08<03:29,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3949/8000 [04:09<06:16, 10.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:09<03:29,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3953/8000 [04:09<05:56, 11.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:11<03:25,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3956/8000 [04:09<05:16, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:12<03:23,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▎                 | 3960/8000 [04:10<04:09, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:13<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▎                 | 3965/8000 [04:10<03:25, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:15<03:18,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▎                 | 3969/8000 [04:10<05:09, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:16<03:20,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3972/8000 [04:10<04:41, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:17<03:18,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3975/8000 [04:11<04:25, 15.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:19<03:13,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3980/8000 [04:11<04:49, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:20<03:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3983/8000 [04:11<04:31, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:21<03:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3986/8000 [04:11<05:02, 13.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:23<03:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3990/8000 [04:12<04:14, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:24<03:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3995/8000 [04:12<03:22, 19.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:25<03:05,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3998/8000 [04:12<03:12, 20.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:27<03:05,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4003/8000 [04:12<03:35, 18.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:28<03:04,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4007/8000 [04:12<03:47, 17.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:29<03:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4009/8000 [04:13<04:06, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:31<03:00,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4012/8000 [04:13<04:03, 16.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:32<02:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4015/8000 [04:13<03:45, 17.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:33<02:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4019/8000 [04:13<04:14, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:35<02:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4021/8000 [04:13<04:29, 14.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:36<02:58,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4025/8000 [04:14<03:50, 17.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:38<03:10,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▋                 | 4029/8000 [04:14<03:27, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:39<03:08,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▋                 | 4032/8000 [04:14<04:22, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:40<03:05,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▋                 | 4036/8000 [04:14<03:37, 18.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:42<03:03,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4041/8000 [04:14<03:01, 21.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:43<02:59,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4045/8000 [04:15<03:39, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:45<02:57,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4049/8000 [04:15<04:39, 14.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:46<02:55,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4051/8000 [04:15<04:44, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:47<02:54,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4053/8000 [04:16<05:34, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:49<02:54,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4056/8000 [04:16<04:51, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:50<02:53,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4063/8000 [04:16<03:41, 17.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:52<02:52,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4065/8000 [04:16<03:56, 16.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:53<02:56,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4071/8000 [04:16<02:58, 22.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████                  | 80/200 [01:55<02:55,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4076/8000 [04:16<02:37, 24.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████▏                 | 81/200 [01:56<02:52,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4079/8000 [04:17<03:28, 18.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  41%|████████████▎                 | 82/200 [01:58<02:52,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4082/8000 [04:17<03:30, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▍                 | 83/200 [01:59<02:52,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4085/8000 [04:17<03:23, 19.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▌                 | 84/200 [02:01<02:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4090/8000 [04:17<03:43, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▊                 | 85/200 [02:02<02:50,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4095/8000 [04:18<03:13, 20.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  43%|████████████▉                 | 86/200 [02:04<02:48,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4102/8000 [04:18<03:03, 21.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  44%|█████████████                 | 87/200 [02:05<02:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4105/8000 [04:18<03:09, 20.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  44%|█████████████▏                | 88/200 [02:07<02:46,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4110/8000 [04:19<04:31, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  44%|█████████████▎                | 89/200 [02:10<02:42,  1.46s/it]\n",
+      "Generating signals:  51%|██████████████████                 | 4115/8000 [04:19<03:44, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  51%|██████████████████                 | 4119/8000 [04:19<04:37, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4122/8000 [04:19<04:20, 14.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4125/8000 [04:19<03:53, 16.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.15it/s]\n",
+      "Generating signals:  52%|██████████████████                 | 4129/8000 [04:20<04:59, 12.93it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  52%|██████████████████                 | 4133/8000 [04:20<04:04, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4137/8000 [04:20<04:21, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4140/8000 [04:21<04:39, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 27it [00:01, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4144/8000 [04:21<04:51, 13.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 63it [00:01, 54.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4148/8000 [04:21<04:53, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 99it [00:01, 92.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4150/8000 [04:22<05:55, 10.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 135it [00:02, 133.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4153/8000 [04:22<05:10, 12.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 172it [00:02, 174.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4157/8000 [04:22<04:19, 14.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 207it [00:02, 209.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4159/8000 [04:22<04:27, 14.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 241it [00:02, 225.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4162/8000 [04:22<04:08, 15.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 273it [00:02, 227.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4165/8000 [04:22<04:42, 13.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 302it [00:02, 240.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4169/8000 [04:23<03:53, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 331it [00:02, 217.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4172/8000 [04:23<03:38, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 357it [00:02, 216.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4176/8000 [04:23<03:00, 21.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 387it [00:03, 235.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4179/8000 [04:23<05:18, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 413it [00:03, 241.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4181/8000 [04:24<06:23,  9.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 441it [00:03, 249.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4185/8000 [04:24<04:59, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 470it [00:03, 259.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4188/8000 [04:24<04:22, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 497it [00:03, 255.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4193/8000 [04:24<03:24, 18.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 524it [00:03, 254.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4199/8000 [04:24<02:43, 23.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 550it [00:03, 248.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4202/8000 [04:25<02:56, 21.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 578it [00:03, 256.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4205/8000 [04:25<03:02, 20.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 604it [00:03, 246.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4211/8000 [04:25<02:42, 23.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 629it [00:04, 211.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4215/8000 [04:25<03:11, 19.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 663it [00:04, 242.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4221/8000 [04:26<03:11, 19.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 689it [00:04, 216.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4224/8000 [04:26<03:44, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 713it [00:04, 221.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4228/8000 [04:26<03:26, 18.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 738it [00:04, 227.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4230/8000 [04:26<03:43, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 762it [00:04, 223.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4234/8000 [04:26<03:09, 19.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 167.45it/s]\n",
+      "Generating signals:  53%|██████████████████▌                | 4237/8000 [04:26<02:57, 21.15it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  53%|██████████████████▌                | 4241/8000 [04:27<02:51, 21.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4245/8000 [04:27<02:43, 22.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 147\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4249/8000 [04:27<03:19, 18.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 159\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4252/8000 [04:27<03:24, 18.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  48%|▍| 48/100 [00:00<00:00, 149\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4255/8000 [04:27<03:23, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  68%|▋| 68/100 [00:00<00:00, 165\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4262/8000 [04:28<03:09, 19.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  53%|██████████████████▋                | 4265/8000 [04:28<03:13, 19.34it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  53%|██████████████████▋                | 4268/8000 [04:28<04:41, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4272/8000 [04:28<04:08, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎               | 172/8000 [00:00<00:06, 1179.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4275/8000 [04:29<03:54, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋               | 345/8000 [00:00<00:06, 1215.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4277/8000 [04:29<04:09, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█               | 522/8000 [00:00<00:06, 1230.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4279/8000 [04:29<04:29, 13.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍              | 691/8000 [00:00<00:06, 1134.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▋                | 4281/8000 [04:29<04:40, 13.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▋              | 874/8000 [00:00<00:05, 1202.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▋                | 4285/8000 [04:29<03:35, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|█▉             | 1043/8000 [00:00<00:05, 1174.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4290/8000 [04:29<02:53, 21.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎            | 1224/8000 [00:01<00:05, 1204.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4294/8000 [04:30<03:38, 16.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▌            | 1392/8000 [00:01<00:05, 1131.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4296/8000 [04:30<04:50, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|██▉            | 1561/8000 [00:01<00:05, 1159.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4298/8000 [04:30<05:52, 10.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▏           | 1732/8000 [00:01<00:05, 1174.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4301/8000 [04:31<05:05, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▌           | 1908/8000 [00:01<00:05, 1204.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4305/8000 [04:31<04:17, 14.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|███▉           | 2077/8000 [00:01<00:04, 1237.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4309/8000 [04:31<03:39, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▏          | 2244/8000 [00:01<00:04, 1193.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4311/8000 [04:31<04:55, 12.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▌          | 2431/8000 [00:02<00:04, 1204.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4313/8000 [04:32<06:04, 10.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|████▉          | 2606/8000 [00:02<00:04, 1118.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4317/8000 [04:32<04:40, 13.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2765/8000 [00:02<00:05, 998.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4319/8000 [04:32<04:36, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▌         | 2944/8000 [00:02<00:04, 1040.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4322/8000 [04:32<04:05, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|█████▊         | 3118/8000 [00:02<00:04, 1110.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4326/8000 [04:32<03:18, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▏        | 3303/8000 [00:02<00:04, 1111.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4330/8000 [04:32<03:04, 19.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▌        | 3479/8000 [00:03<00:03, 1169.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4333/8000 [04:32<03:11, 19.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|██████▊        | 3650/8000 [00:03<00:03, 1162.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4336/8000 [04:33<04:59, 12.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▏       | 3828/8000 [00:03<00:03, 1200.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4340/8000 [04:33<04:12, 14.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▌       | 4010/8000 [00:03<00:03, 1169.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4346/8000 [04:33<03:01, 20.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|███████▊       | 4180/8000 [00:03<00:03, 1190.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4350/8000 [04:34<03:34, 17.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▏      | 4348/8000 [00:03<00:02, 1225.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4354/8000 [04:34<03:18, 18.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▍      | 4519/8000 [00:03<00:02, 1200.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4358/8000 [04:34<03:48, 15.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|████████▊      | 4697/8000 [00:04<00:02, 1151.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4360/8000 [04:34<04:04, 14.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▏     | 4872/8000 [00:04<00:02, 1158.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4363/8000 [04:34<03:42, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|█████████▍     | 5045/8000 [00:04<00:02, 1141.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4365/8000 [04:35<03:58, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|█████████▊     | 5223/8000 [00:04<00:02, 1143.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4370/8000 [04:35<03:03, 19.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████     | 5389/8000 [00:04<00:02, 1144.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4373/8000 [04:35<03:40, 16.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▍    | 5549/8000 [00:04<00:02, 1091.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4375/8000 [04:35<03:57, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|██████████▋    | 5708/8000 [00:04<00:02, 1096.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4377/8000 [04:35<04:10, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████    | 5884/8000 [00:05<00:01, 1126.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4379/8000 [04:35<04:06, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|███████████▎   | 6059/8000 [00:05<00:01, 1135.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4382/8000 [04:36<03:33, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|███████████▋   | 6235/8000 [00:05<00:01, 1145.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4384/8000 [04:36<03:51, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████   | 6407/8000 [00:05<00:01, 1183.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4387/8000 [04:36<03:41, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|████████████▎  | 6574/8000 [00:05<00:01, 1156.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4394/8000 [04:36<02:28, 24.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|████████████▋  | 6746/8000 [00:05<00:01, 1145.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4397/8000 [04:36<03:30, 17.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|████████████▉  | 6911/8000 [00:06<00:01, 1056.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4399/8000 [04:37<03:44, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|█████████████▎ | 7100/8000 [00:06<00:00, 1099.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4403/8000 [04:37<04:01, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|█████████████▋ | 7273/8000 [00:06<00:00, 1123.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4408/8000 [04:37<03:52, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|█████████████▉ | 7455/8000 [00:06<00:00, 1165.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4410/8000 [04:37<03:53, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|██████████████▎| 7638/8000 [00:06<00:00, 1196.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4415/8000 [04:38<03:57, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|██████████████▋| 7805/8000 [00:06<00:00, 1186.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4418/8000 [04:38<03:48, 15.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|██████████████▉| 7976/8000 [00:06<00:00, 1202.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4420/8000 [04:38<05:02, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8142it [00:07, 1153.65it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  55%|███████████████████▎               | 4423/8000 [04:38<04:28, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  55%|███████████████████▎               | 4425/8000 [04:38<04:14, 14.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4427/8000 [04:39<04:33, 13.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:38,  7.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▍               | 4431/8000 [04:39<03:39, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:54,  3.91s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▍               | 4434/8000 [04:39<03:32, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:08,  2.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▍               | 4437/8000 [04:39<04:23, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:21,  2.25s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4440/8000 [04:40<04:54, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:22,  1.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4442/8000 [04:40<05:52, 10.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:45,  1.78s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4445/8000 [04:40<04:54, 12.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:22,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4448/8000 [04:40<04:05, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:07,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4451/8000 [04:40<03:52, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:53,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4456/8000 [04:41<03:05, 19.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:47,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4463/8000 [04:41<02:17, 25.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:42,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4466/8000 [04:41<03:52, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:42,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4471/8000 [04:41<03:52, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:40,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4475/8000 [04:42<03:29, 16.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:35,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4478/8000 [04:42<03:22, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:30,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4483/8000 [04:42<02:56, 19.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:38,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4489/8000 [04:42<03:15, 17.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:30,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4494/8000 [04:43<03:25, 17.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:21,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4496/8000 [04:43<03:42, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:19,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4499/8000 [04:43<03:28, 16.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:17,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4505/8000 [04:43<02:40, 21.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:15,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4510/8000 [04:43<02:53, 20.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:12,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4514/8000 [04:44<03:17, 17.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<04:05,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▊               | 4517/8000 [04:44<03:14, 17.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<04:05,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▊               | 4520/8000 [04:44<03:11, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:04,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4525/8000 [04:44<02:43, 21.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<04:03,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4530/8000 [04:45<03:38, 15.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<04:01,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4532/8000 [04:45<05:11, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<03:57,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4534/8000 [04:45<04:49, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<03:58,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4536/8000 [04:45<04:46, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<03:58,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4538/8000 [04:46<04:40, 12.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<03:54,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4540/8000 [04:46<04:34, 12.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<03:54,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4542/8000 [04:46<04:15, 13.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<03:50,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4545/8000 [04:46<03:49, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:54<03:49,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4547/8000 [04:46<03:54, 14.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:55<03:44,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4551/8000 [04:46<03:53, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:56<03:41,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4553/8000 [04:47<05:02, 11.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:58<03:39,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4555/8000 [04:47<04:50, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:59<03:36,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4562/8000 [04:47<03:33, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:01<03:36,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4564/8000 [04:47<03:49, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:02<03:33,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4569/8000 [04:48<03:46, 15.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:03<03:31,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4571/8000 [04:48<03:55, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:26,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4576/8000 [04:48<03:08, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:06<03:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4584/8000 [04:48<02:36, 21.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:07<03:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4589/8000 [04:48<02:21, 24.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4593/8000 [04:49<02:41, 21.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:10<03:21,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4596/8000 [04:49<03:18, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:11<03:19,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4598/8000 [04:49<03:29, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████▏              | 4600/8000 [04:49<03:25, 16.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:14<03:21,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4604/8000 [04:49<03:01, 18.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:20,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4609/8000 [04:50<02:28, 22.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:16<03:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4614/8000 [04:50<02:12, 25.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:18<03:20,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4621/8000 [04:50<02:14, 25.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:16,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4627/8000 [04:51<03:21, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:20<03:14,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4630/8000 [04:51<03:20, 16.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:22<03:11,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4632/8000 [04:51<03:28, 16.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:23<03:09,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4635/8000 [04:51<04:49, 11.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:24<03:09,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4637/8000 [04:52<04:43, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:26<03:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4641/8000 [04:52<03:51, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:27<03:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4648/8000 [04:52<02:35, 21.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:28<03:05,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4653/8000 [04:52<02:15, 24.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:30<03:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4656/8000 [04:52<02:16, 24.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:31<03:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4660/8000 [04:53<03:38, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:32<03:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4666/8000 [04:53<03:04, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:33<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4669/8000 [04:53<04:38, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:35<02:57,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4674/8000 [04:54<03:43, 14.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:36<02:54,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4677/8000 [04:54<04:40, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:37<02:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▍              | 4681/8000 [04:54<04:37, 11.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:40<03:15,  1.48s/it]\n",
+      "Generating signals:  59%|████████████████████▍              | 4683/8000 [04:55<04:36, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  59%|████████████████████▌              | 4687/8000 [04:55<03:51, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4690/8000 [04:55<03:32, 15.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.54it/s]\n",
+      "Generating signals:  59%|████████████████████▌              | 4694/8000 [04:55<02:52, 19.15it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  59%|████████████████████▌              | 4697/8000 [04:55<03:38, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4700/8000 [04:55<03:21, 16.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4702/8000 [04:56<04:09, 13.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 34it [00:01, 29.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4706/8000 [04:56<04:03, 13.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 70it [00:01, 66.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4710/8000 [04:56<04:06, 13.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 109it [00:01, 111.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4713/8000 [04:56<03:50, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 144it [00:01, 150.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4716/8000 [04:57<03:27, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 180it [00:01, 189.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4719/8000 [04:57<03:21, 16.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 219it [00:02, 231.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4722/8000 [04:57<03:16, 16.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 264it [00:02, 281.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4725/8000 [04:57<03:11, 17.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 303it [00:02, 305.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4729/8000 [04:57<03:35, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 343it [00:02, 329.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4732/8000 [04:58<04:01, 13.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 382it [00:02, 336.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4734/8000 [04:58<04:48, 11.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 420it [00:02, 344.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4737/8000 [04:58<04:59, 10.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 458it [00:02, 352.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4739/8000 [04:59<06:53,  7.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 496it [00:02, 357.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4745/8000 [04:59<04:35, 11.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 535it [00:02, 362.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4748/8000 [04:59<04:51, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 574it [00:03, 369.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4753/8000 [05:00<03:54, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 612it [00:03, 365.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4755/8000 [05:00<04:28, 12.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 652it [00:03, 374.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4757/8000 [05:00<04:34, 11.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 692it [00:03, 381.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4759/8000 [05:00<04:30, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 731it [00:03, 363.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▊              | 4763/8000 [05:00<03:52, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 221.41it/s]\n",
+      "Generating signals:  60%|████████████████████▊              | 4765/8000 [05:01<04:05, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  60%|████████████████████▊              | 4768/8000 [05:01<04:28, 12.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▊              | 4770/8000 [05:01<04:24, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:00, 168\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4772/8000 [05:01<04:13, 12.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00<00:00, 157\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4775/8000 [05:01<03:35, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 155\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4780/8000 [05:01<02:43, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 149\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4785/8000 [05:02<03:02, 17.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  83%|▊| 83/100 [00:00<00:00, 155\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4788/8000 [05:02<03:00, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  60%|████████████████████▉              | 4790/8000 [05:02<04:03, 13.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [05:39<16:58, 339.55s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.605601227335788\n",
-      "2999: -6.2272309\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  60%|████████████████████▉              | 4792/8000 [05:02<04:06, 13.02it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  60%|████████████████████▉              | 4796/8000 [05:03<03:21, 15.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4801/8000 [05:03<02:43, 19.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:05,  7.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4806/8000 [05:03<02:13, 23.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:41,  3.85s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4811/8000 [05:03<01:54, 27.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:46,  2.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4815/8000 [05:03<02:38, 20.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:00,  2.14s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4819/8000 [05:04<03:45, 14.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:57,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4822/8000 [05:04<03:31, 15.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:19,  1.65s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4826/8000 [05:04<03:01, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<04:57,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4831/8000 [05:04<02:35, 20.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:43,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4835/8000 [05:04<02:27, 21.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:36,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4838/8000 [05:05<03:41, 14.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:29,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4840/8000 [05:05<05:25,  9.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:20,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4843/8000 [05:06<04:42, 11.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:17,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4849/8000 [05:06<04:52, 10.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:12,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4852/8000 [05:06<04:52, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:14,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4857/8000 [05:07<03:42, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:14,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4859/8000 [05:07<04:23, 11.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:11,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4861/8000 [05:07<04:17, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:10,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4867/8000 [05:07<03:35, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<04:06,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4870/8000 [05:08<03:22, 15.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<04:06,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4874/8000 [05:08<03:43, 14.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<04:07,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4883/8000 [05:08<02:20, 22.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:34<04:02,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4886/8000 [05:08<02:20, 22.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<04:02,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4889/8000 [05:09<03:09, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:57,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4892/8000 [05:09<03:01, 17.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:38<03:59,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4894/8000 [05:09<03:16, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<03:59,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4899/8000 [05:09<02:39, 19.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:41<04:09,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4902/8000 [05:09<02:38, 19.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:42<04:05,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4907/8000 [05:10<04:27, 11.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:44<04:01,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4909/8000 [05:10<04:23, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:45<03:58,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4913/8000 [05:10<03:37, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:46<03:58,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▌             | 4915/8000 [05:10<03:49, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:48<03:54,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4920/8000 [05:11<02:57, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:49<03:57,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4926/8000 [05:11<02:16, 22.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:51<03:55,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4931/8000 [05:11<02:35, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:52<03:50,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4934/8000 [05:11<03:11, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:53<03:48,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4938/8000 [05:12<03:14, 15.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:55<03:47,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4940/8000 [05:12<03:27, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:56<03:44,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4942/8000 [05:12<03:34, 14.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:57<03:43,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4944/8000 [05:12<04:08, 12.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:59<03:53,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4946/8000 [05:12<04:15, 11.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:00<03:47,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4949/8000 [05:13<03:53, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:44,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4951/8000 [05:13<03:51, 13.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:03<03:43,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4954/8000 [05:13<03:30, 14.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:42,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4959/8000 [05:13<03:23, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:40,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4961/8000 [05:13<03:30, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:07<03:35,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4966/8000 [05:14<03:14, 15.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:32,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4968/8000 [05:14<03:22, 15.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:10<03:32,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4970/8000 [05:14<03:25, 14.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:32,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4972/8000 [05:14<04:25, 11.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:33,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4975/8000 [05:15<04:22, 11.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:35,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4980/8000 [05:15<03:11, 15.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:16<03:33,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4985/8000 [05:15<03:04, 16.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:17<03:31,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4988/8000 [05:15<02:48, 17.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:31,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4990/8000 [05:15<03:05, 16.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:20<03:28,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4992/8000 [05:15<03:19, 15.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:22<03:26,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4996/8000 [05:16<02:51, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:23<03:22,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4999/8000 [05:16<02:48, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:24<03:23,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5005/8000 [05:16<03:21, 14.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:26<03:25,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5011/8000 [05:16<02:35, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:27<03:21,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5015/8000 [05:17<02:24, 20.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:29<03:16,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5019/8000 [05:17<03:14, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:30<03:13,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5021/8000 [05:17<03:19, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:31<03:10,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5023/8000 [05:17<03:51, 12.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:33<03:12,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5025/8000 [05:18<03:57, 12.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:34<03:15,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5029/8000 [05:18<03:12, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:36<03:12,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5031/8000 [05:18<03:14, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:37<03:08,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5034/8000 [05:18<03:09, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:39<03:06,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5036/8000 [05:18<03:21, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:40<03:01,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5039/8000 [05:18<03:05, 15.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:41<03:03,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5043/8000 [05:19<03:26, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:43<03:10,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5047/8000 [05:19<02:53, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:45<03:14,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5054/8000 [05:19<02:28, 19.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:46<03:11,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5059/8000 [05:19<02:13, 22.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:47<03:04,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5062/8000 [05:19<02:18, 21.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:49<03:00,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5065/8000 [05:20<02:26, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:50<03:03,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5069/8000 [05:20<02:17, 21.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:52<03:03,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5074/8000 [05:20<02:03, 23.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:53<02:57,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5078/8000 [05:20<02:03, 23.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:55<02:53,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▏            | 5081/8000 [05:20<02:06, 23.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:56<02:48,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▏            | 5084/8000 [05:21<02:58, 16.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████                  | 80/200 [01:57<02:50,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5086/8000 [05:21<03:13, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████▏                 | 81/200 [01:59<02:51,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5088/8000 [05:21<04:13, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  41%|████████████▎                 | 82/200 [02:00<02:52,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5091/8000 [05:21<03:46, 12.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▍                 | 83/200 [02:02<02:53,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5093/8000 [05:22<04:42, 10.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▌                 | 84/200 [02:03<02:52,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5097/8000 [05:22<03:36, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▊                 | 85/200 [02:06<02:51,  1.49s/it]\n",
+      "Generating signals:  64%|██████████████████████▎            | 5101/8000 [05:22<03:43, 12.99it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5103/8000 [05:22<03:48, 12.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5110/8000 [05:22<02:26, 19.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5115/8000 [05:23<02:36, 18.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.48it/s]\n",
+      "Generating signals:  64%|██████████████████████▍            | 5118/8000 [05:23<03:33, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5121/8000 [05:23<03:19, 14.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5124/8000 [05:24<03:08, 15.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5128/8000 [05:24<02:40, 17.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 31it [00:01, 26.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5133/8000 [05:24<02:42, 17.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 65it [00:01, 60.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5136/8000 [05:24<02:37, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 97it [00:01, 95.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5138/8000 [05:24<02:46, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 125it [00:01, 123.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5141/8000 [05:25<03:15, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 156it [00:02, 157.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5144/8000 [05:25<03:41, 12.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 189it [00:02, 193.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5147/8000 [05:25<03:13, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 219it [00:02, 217.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5150/8000 [05:25<03:42, 12.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 255it [00:02, 251.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5153/8000 [05:25<03:15, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 287it [00:02, 259.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5155/8000 [05:26<03:09, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 318it [00:02, 268.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5159/8000 [05:26<02:40, 17.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 351it [00:02, 282.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▌            | 5163/8000 [05:26<02:58, 15.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 382it [00:02, 288.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▌            | 5167/8000 [05:26<02:29, 19.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 413it [00:02, 289.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▌            | 5171/8000 [05:26<02:18, 20.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 444it [00:02, 280.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5177/8000 [05:26<01:54, 24.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 478it [00:03, 296.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5181/8000 [05:27<01:55, 24.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 509it [00:03, 291.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5184/8000 [05:27<03:12, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 540it [00:03, 296.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5186/8000 [05:27<03:15, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 570it [00:03, 284.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5188/8000 [05:27<03:20, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 602it [00:03, 292.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5191/8000 [05:28<03:05, 15.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 634it [00:03, 298.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5194/8000 [05:28<02:52, 16.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 665it [00:03, 291.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5196/8000 [05:28<02:54, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 695it [00:03, 291.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5199/8000 [05:28<02:46, 16.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 725it [00:03, 286.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5204/8000 [05:28<02:44, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 755it [00:04, 289.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5208/8000 [05:28<02:21, 19.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 191.84it/s]\n",
+      "Generating signals:  65%|██████████████████████▊            | 5211/8000 [05:29<02:20, 19.78it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5216/8000 [05:29<02:02, 22.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5219/8000 [05:29<02:00, 23.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 140\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5224/8000 [05:29<02:14, 20.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 135\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5227/8000 [05:29<02:17, 20.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 161\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▉            | 5230/8000 [05:30<02:41, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:00<00:00, 164\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▉            | 5233/8000 [05:30<02:37, 17.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  65%|██████████████████████▉            | 5235/8000 [05:30<02:49, 16.35it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5242/8000 [05:30<01:53, 24.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5247/8000 [05:30<02:08, 21.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 59/8000 [00:00<00:23, 338.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5251/8000 [05:31<02:29, 18.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 100/8000 [00:00<00:29, 271.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5254/8000 [05:31<03:04, 14.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 155/8000 [00:00<00:25, 302.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5256/8000 [05:31<03:52, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 206/8000 [00:00<00:26, 298.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5259/8000 [05:31<03:28, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 251/8000 [00:00<00:24, 321.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5261/8000 [05:32<03:30, 13.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 304/8000 [00:00<00:22, 349.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5264/8000 [05:32<03:12, 14.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 351/8000 [00:01<00:21, 351.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5266/8000 [05:32<03:06, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 405/8000 [00:01<00:19, 379.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5268/8000 [05:32<03:16, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 451/8000 [00:01<00:20, 364.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5272/8000 [05:32<02:37, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 506/8000 [00:01<00:19, 393.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5274/8000 [05:32<02:45, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 561/8000 [00:01<00:18, 400.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5276/8000 [05:32<02:46, 16.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 606/8000 [00:01<00:19, 384.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5280/8000 [05:33<02:22, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 656/8000 [00:01<00:18, 393.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5282/8000 [05:33<02:36, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 705/8000 [00:01<00:19, 380.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5285/8000 [05:33<02:28, 18.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 755/8000 [00:02<00:18, 389.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5288/8000 [05:33<02:13, 20.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 795/8000 [00:02<00:18, 385.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5293/8000 [05:33<02:31, 17.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 845/8000 [00:02<00:19, 371.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5296/8000 [05:34<02:51, 15.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 895/8000 [00:02<00:20, 353.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5298/8000 [05:34<03:43, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 947/8000 [00:02<00:21, 331.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5302/8000 [05:34<03:27, 13.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1008/8000 [00:02<00:20, 344.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5305/8000 [05:35<03:49, 11.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1064/8000 [00:03<00:20, 340.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5307/8000 [05:35<03:41, 12.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1113/8000 [00:03<00:20, 335.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5310/8000 [05:35<03:08, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1157/8000 [00:03<00:23, 294.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5314/8000 [05:35<02:42, 16.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1208/8000 [00:03<00:22, 297.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▎           | 5316/8000 [05:35<02:53, 15.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1258/8000 [00:03<00:22, 306.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▎           | 5318/8000 [05:35<03:24, 13.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1301/8000 [00:03<00:21, 314.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▎           | 5320/8000 [05:36<04:19, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1360/8000 [00:03<00:19, 333.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5322/8000 [05:36<04:02, 11.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1432/8000 [00:04<00:44, 146.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5324/8000 [05:36<03:40, 12.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1489/8000 [00:05<00:35, 182.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5326/8000 [05:36<03:42, 12.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1542/8000 [00:05<00:30, 213.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5328/8000 [05:37<05:09,  8.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1590/8000 [00:05<00:26, 242.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5331/8000 [05:37<04:12, 10.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1641/8000 [00:05<00:22, 279.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5334/8000 [05:37<04:14, 10.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1699/8000 [00:05<00:20, 313.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5338/8000 [05:37<03:02, 14.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1746/8000 [00:05<00:18, 341.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5340/8000 [05:37<03:13, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▌            | 1805/8000 [00:05<00:16, 382.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5343/8000 [05:37<02:51, 15.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1851/8000 [00:05<00:16, 377.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5348/8000 [05:38<02:37, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1896/8000 [00:06<00:16, 363.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5350/8000 [05:38<03:30, 12.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1954/8000 [00:06<00:15, 393.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5352/8000 [05:38<03:58, 11.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2000/8000 [00:06<00:15, 382.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5354/8000 [05:38<03:53, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2041/8000 [00:06<00:16, 366.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5358/8000 [05:39<03:00, 14.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2098/8000 [00:06<00:14, 397.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5364/8000 [05:39<02:06, 20.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2145/8000 [00:06<00:15, 385.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5367/8000 [05:39<02:01, 21.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▍           | 2199/8000 [00:06<00:14, 395.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5375/8000 [05:39<02:15, 19.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2248/8000 [00:06<00:14, 387.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5380/8000 [05:40<02:42, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2306/8000 [00:07<00:14, 405.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5384/8000 [05:40<02:25, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2360/8000 [00:07<00:13, 410.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5387/8000 [05:40<02:15, 19.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2413/8000 [00:07<00:13, 407.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5391/8000 [05:40<02:37, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2459/8000 [00:07<00:13, 415.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5394/8000 [05:40<02:31, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2511/8000 [00:07<00:13, 411.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5397/8000 [05:41<02:57, 14.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2596/8000 [00:07<00:14, 362.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5399/8000 [05:41<03:03, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2653/8000 [00:07<00:13, 397.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5403/8000 [05:41<02:35, 16.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2709/8000 [00:08<00:12, 407.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5406/8000 [05:41<02:32, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2752/8000 [00:08<00:13, 382.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5409/8000 [05:41<02:27, 17.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2800/8000 [00:08<00:14, 367.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5414/8000 [05:42<02:02, 21.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2842/8000 [00:08<00:15, 343.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5418/8000 [05:42<01:53, 22.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2894/8000 [00:08<00:17, 299.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5422/8000 [05:42<02:20, 18.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2948/8000 [00:08<00:16, 313.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5426/8000 [05:42<02:07, 20.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3002/8000 [00:09<00:15, 325.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5430/8000 [05:42<01:52, 22.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3050/8000 [00:09<00:15, 323.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5434/8000 [05:42<01:50, 23.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3103/8000 [00:09<00:15, 324.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5438/8000 [05:43<01:47, 23.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3161/8000 [00:09<00:14, 331.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5444/8000 [05:43<01:47, 23.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3211/8000 [00:09<00:14, 328.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5448/8000 [05:43<01:48, 23.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3269/8000 [00:09<00:14, 320.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5453/8000 [05:43<02:07, 19.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3327/8000 [00:09<00:13, 354.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5461/8000 [05:44<01:34, 26.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3365/8000 [00:10<00:13, 331.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5465/8000 [05:44<01:36, 26.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3415/8000 [00:10<00:13, 351.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5470/8000 [05:44<01:57, 21.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3455/8000 [00:10<00:13, 342.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5473/8000 [05:44<02:27, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3508/8000 [00:10<00:12, 365.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5475/8000 [05:44<02:33, 16.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3553/8000 [00:10<00:12, 370.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5479/8000 [05:45<02:50, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3624/8000 [00:11<00:37, 116.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▉           | 5482/8000 [05:45<02:42, 15.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3669/8000 [00:11<00:30, 141.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▉           | 5484/8000 [05:45<02:53, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3717/8000 [00:12<00:24, 172.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5487/8000 [05:45<02:43, 15.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3759/8000 [00:12<00:21, 194.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5490/8000 [05:45<02:32, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3809/8000 [00:12<00:18, 229.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5494/8000 [05:46<02:43, 15.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3861/8000 [00:12<00:15, 263.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5498/8000 [05:46<02:22, 17.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3911/8000 [00:12<00:13, 295.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5504/8000 [05:46<01:49, 22.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4007/8000 [00:12<00:11, 339.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5510/8000 [05:47<02:53, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4053/8000 [00:13<00:12, 326.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5516/8000 [05:47<02:37, 15.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4106/8000 [00:13<00:12, 301.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5520/8000 [05:47<02:47, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4162/8000 [00:13<00:11, 334.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5523/8000 [05:48<02:41, 15.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4214/8000 [00:13<00:10, 352.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5526/8000 [05:48<02:35, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4265/8000 [00:13<00:10, 359.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5528/8000 [05:48<03:21, 12.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4322/8000 [00:13<00:09, 369.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5531/8000 [05:48<03:06, 13.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4381/8000 [00:13<00:09, 367.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5533/8000 [05:48<03:12, 12.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4424/8000 [00:14<00:09, 362.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5537/8000 [05:49<02:37, 15.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4471/8000 [00:14<00:09, 360.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5541/8000 [05:49<03:23, 12.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4529/8000 [00:14<00:09, 374.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5543/8000 [05:49<03:21, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4576/8000 [00:14<00:08, 382.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5545/8000 [05:49<03:24, 12.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4672/8000 [00:14<00:08, 376.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5549/8000 [05:50<02:41, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4715/8000 [00:14<00:08, 376.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5551/8000 [05:50<03:17, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▌      | 4757/8000 [00:14<00:08, 367.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5554/8000 [05:50<02:56, 13.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4808/8000 [00:15<00:08, 375.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5557/8000 [05:50<02:41, 15.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4861/8000 [00:15<00:08, 391.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5559/8000 [05:50<02:43, 14.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4920/8000 [00:15<00:08, 384.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5561/8000 [05:50<02:39, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4980/8000 [00:15<00:07, 405.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5564/8000 [05:51<02:34, 15.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5038/8000 [00:15<00:07, 415.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5567/8000 [05:51<02:25, 16.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5080/8000 [00:15<00:07, 402.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5572/8000 [05:51<02:31, 16.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5136/8000 [00:15<00:06, 412.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5574/8000 [05:52<04:24,  9.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5185/8000 [00:15<00:06, 419.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5577/8000 [05:52<03:41, 10.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5233/8000 [00:16<00:06, 408.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5580/8000 [05:52<03:51, 10.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5285/8000 [00:16<00:06, 422.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5585/8000 [05:52<02:47, 14.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5332/8000 [00:16<00:06, 420.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5591/8000 [05:52<02:02, 19.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5397/8000 [00:16<00:05, 452.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5594/8000 [05:53<02:04, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5447/8000 [00:16<00:06, 412.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5598/8000 [05:53<02:19, 17.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5502/8000 [00:16<00:06, 373.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5602/8000 [05:53<02:07, 18.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5543/8000 [00:16<00:07, 345.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5609/8000 [05:53<01:33, 25.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5605/8000 [00:17<00:06, 392.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5614/8000 [05:54<02:13, 17.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5660/8000 [00:17<00:05, 419.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5617/8000 [05:54<02:37, 15.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5714/8000 [00:17<00:05, 417.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5620/8000 [05:54<02:30, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5792/8000 [00:17<00:05, 370.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5622/8000 [05:54<03:08, 12.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5836/8000 [00:17<00:05, 372.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5624/8000 [05:55<03:11, 12.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5888/8000 [00:17<00:05, 402.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5627/8000 [05:55<03:51, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5972/8000 [00:17<00:05, 373.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▋          | 5630/8000 [05:55<03:20, 11.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6021/8000 [00:18<00:05, 376.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▋          | 5634/8000 [05:55<03:11, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6078/8000 [00:18<00:04, 388.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▋          | 5636/8000 [05:56<03:42, 10.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6125/8000 [00:18<00:05, 365.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5641/8000 [05:56<02:37, 14.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6174/8000 [00:18<00:04, 377.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5645/8000 [05:56<02:12, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6224/8000 [00:18<00:04, 394.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5648/8000 [05:56<02:37, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6280/8000 [00:18<00:04, 419.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5650/8000 [05:57<02:44, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6334/8000 [00:18<00:04, 414.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5653/8000 [05:57<02:27, 15.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6387/8000 [00:19<00:03, 415.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5656/8000 [05:57<03:33, 10.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6434/8000 [00:19<00:03, 413.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5660/8000 [05:57<02:47, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6480/8000 [00:19<00:03, 415.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5662/8000 [05:57<02:53, 13.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6530/8000 [00:19<00:03, 412.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5664/8000 [05:58<02:58, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6581/8000 [00:19<00:03, 414.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5668/8000 [05:58<02:25, 15.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6665/8000 [00:19<00:03, 372.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5674/8000 [05:58<01:42, 22.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6714/8000 [00:19<00:03, 373.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5678/8000 [05:58<01:40, 23.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6763/8000 [00:20<00:03, 365.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5682/8000 [05:58<02:22, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6813/8000 [00:20<00:03, 368.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5685/8000 [05:59<02:19, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6896/8000 [00:20<00:03, 345.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5688/8000 [05:59<02:16, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6948/8000 [00:20<00:02, 359.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5692/8000 [05:59<02:19, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6999/8000 [00:20<00:02, 355.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5697/8000 [05:59<01:57, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7077/8000 [00:21<00:03, 306.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5703/8000 [06:00<02:00, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7130/8000 [00:21<00:02, 331.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5706/8000 [06:00<02:22, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7181/8000 [00:21<00:02, 335.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5712/8000 [06:00<01:52, 20.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7240/8000 [00:21<00:02, 370.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████████████████          | 5718/8000 [06:00<01:57, 19.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7291/8000 [00:21<00:01, 371.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5722/8000 [06:01<01:48, 20.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7339/8000 [00:21<00:01, 384.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5728/8000 [06:01<01:47, 21.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7381/8000 [00:21<00:01, 362.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5732/8000 [06:01<01:43, 21.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7421/8000 [00:21<00:01, 323.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5735/8000 [06:01<02:09, 17.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7468/8000 [00:22<00:01, 322.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5737/8000 [06:01<02:12, 17.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7523/8000 [00:22<00:01, 340.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5739/8000 [06:02<02:21, 15.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7573/8000 [00:22<00:01, 343.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5741/8000 [06:02<03:01, 12.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7633/8000 [00:22<00:01, 339.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5743/8000 [06:02<02:49, 13.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7681/8000 [00:22<00:00, 324.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5747/8000 [06:02<02:19, 16.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7723/8000 [00:22<00:01, 259.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5751/8000 [06:02<02:29, 15.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7778/8000 [00:23<00:00, 270.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5755/8000 [06:03<02:01, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7823/8000 [00:23<00:00, 247.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5758/8000 [06:03<02:33, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7865/8000 [00:23<00:00, 236.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5760/8000 [06:03<03:12, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7898/8000 [00:23<00:00, 218.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5764/8000 [06:03<02:35, 14.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7941/8000 [00:23<00:00, 236.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5769/8000 [06:04<02:02, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7988/8000 [00:24<00:00, 242.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5772/8000 [06:04<02:02, 18.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8036it [00:24, 330.93it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  72%|█████████████████████████▎         | 5776/8000 [06:04<01:53, 19.62it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5781/8000 [06:04<02:03, 17.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5783/8000 [06:04<02:03, 17.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:15,  7.01s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5785/8000 [06:05<02:49, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:12,  3.70s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5787/8000 [06:05<02:47, 13.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:36,  2.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5790/8000 [06:05<02:23, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:53,  2.11s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5793/8000 [06:05<02:15, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:58,  1.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5796/8000 [06:05<02:13, 16.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:24,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5798/8000 [06:05<02:31, 14.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:02,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5804/8000 [06:06<02:13, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:57,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5807/8000 [06:06<02:07, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:43,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5809/8000 [06:06<02:17, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:32,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5812/8000 [06:06<02:11, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:24,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5815/8000 [06:07<02:46, 13.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:18,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5818/8000 [06:07<02:33, 14.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5821/8000 [06:07<02:23, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:13,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5823/8000 [06:07<02:29, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:12,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5827/8000 [06:07<02:01, 17.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:09,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5829/8000 [06:07<02:15, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:07,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5831/8000 [06:07<02:22, 15.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:29<04:02,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5835/8000 [06:08<01:55, 18.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<04:03,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5837/8000 [06:08<02:29, 14.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<04:02,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5841/8000 [06:08<02:39, 13.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:34<04:03,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5843/8000 [06:08<02:32, 14.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<04:01,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5847/8000 [06:09<03:08, 11.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5852/8000 [06:09<02:14, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:38<03:56,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5858/8000 [06:09<02:03, 17.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<03:58,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5861/8000 [06:10<02:31, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:40<03:51,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5865/8000 [06:10<03:03, 11.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:42<03:52,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5867/8000 [06:10<03:00, 11.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:54,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5873/8000 [06:10<02:06, 16.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:44<03:51,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5877/8000 [06:11<02:12, 16.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:46<03:50,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▋         | 5884/8000 [06:11<01:37, 21.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:48,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5891/8000 [06:11<01:33, 22.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:48<03:47,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5896/8000 [06:11<01:24, 25.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:50<03:44,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5901/8000 [06:12<01:37, 21.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:51<03:42,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5904/8000 [06:12<01:50, 19.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:52<03:42,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5907/8000 [06:12<01:51, 18.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:54<03:42,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5911/8000 [06:12<02:03, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:55<03:40,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5915/8000 [06:13<02:11, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:56<03:38,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5917/8000 [06:13<02:18, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:58<03:38,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5919/8000 [06:13<02:16, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:59<03:34,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5922/8000 [06:13<02:01, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:01<03:33,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5925/8000 [06:13<01:58, 17.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:02<03:34,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5929/8000 [06:13<01:45, 19.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:03<03:32,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5932/8000 [06:13<01:41, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:05<03:28,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5935/8000 [06:14<01:35, 21.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:06<03:26,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5939/8000 [06:14<01:59, 17.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:07<03:24,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5942/8000 [06:14<01:52, 18.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:09<03:23,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5947/8000 [06:14<01:26, 23.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:10<03:21,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5950/8000 [06:14<01:32, 22.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:11<03:25,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5953/8000 [06:15<02:24, 14.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:13<03:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5956/8000 [06:15<02:15, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:14<03:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5959/8000 [06:15<02:04, 16.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:15<03:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████         | 5962/8000 [06:15<01:58, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:16<03:10,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████         | 5966/8000 [06:15<01:36, 21.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:18<03:08,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████         | 5970/8000 [06:15<01:32, 22.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:20<03:32,  1.46s/it]\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5974/8000 [06:16<01:25, 23.61it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5977/8000 [06:16<01:30, 22.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5982/8000 [06:16<01:39, 20.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5985/8000 [06:17<02:48, 11.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.20it/s]\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5987/8000 [06:17<02:47, 12.04it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5989/8000 [06:17<02:43, 12.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5991/8000 [06:17<03:10, 10.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5994/8000 [06:17<02:42, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 27it [00:01, 24.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5997/8000 [06:18<02:45, 12.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 63it [00:01, 63.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6000/8000 [06:18<02:28, 13.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 96it [00:01, 101.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6003/8000 [06:18<02:19, 14.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 130it [00:01, 141.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6007/8000 [06:18<01:57, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 161it [00:01, 174.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6009/8000 [06:18<02:06, 15.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 192it [00:02, 191.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6016/8000 [06:18<01:23, 23.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 221it [00:02, 206.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6019/8000 [06:19<01:55, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 249it [00:02, 224.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6022/8000 [06:19<02:18, 14.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 277it [00:02, 227.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6028/8000 [06:19<01:40, 19.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 308it [00:02, 247.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6032/8000 [06:19<01:29, 22.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 336it [00:02, 251.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6037/8000 [06:20<01:41, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 367it [00:02, 267.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6040/8000 [06:20<01:38, 19.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 396it [00:02, 271.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6044/8000 [06:20<01:47, 18.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 430it [00:02, 290.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6047/8000 [06:20<01:45, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 460it [00:02, 284.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6051/8000 [06:20<01:55, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 491it [00:03, 290.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6053/8000 [06:21<02:02, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 524it [00:03, 299.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6056/8000 [06:21<01:57, 16.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 555it [00:03, 295.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6060/8000 [06:21<01:58, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 585it [00:03, 284.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6063/8000 [06:21<01:54, 16.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 615it [00:03, 287.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6066/8000 [06:21<01:49, 17.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 646it [00:03, 293.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6069/8000 [06:22<01:46, 18.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 676it [00:03, 286.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6072/8000 [06:22<02:07, 15.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 707it [00:03, 293.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6074/8000 [06:22<02:43, 11.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 737it [00:03, 294.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6078/8000 [06:22<02:04, 15.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 192.96it/s]\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6080/8000 [06:23<02:46, 11.51it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6082/8000 [06:23<02:43, 11.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6085/8000 [06:23<02:22, 13.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  23%|▏| 23/100 [00:00<00:00, 228\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6087/8000 [06:23<02:27, 12.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 206\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6091/8000 [06:23<02:01, 15.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:00<00:00, 202\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6095/8000 [06:23<01:41, 18.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 19\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6098/8000 [06:24<01:44, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [11:42<11:46, 353.30s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.151004261420278\n",
-      "2999: -6.1953398\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  76%|██████████████████████████▋        | 6102/8000 [06:24<01:36, 19.64it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6108/8000 [06:24<01:39, 19.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6111/8000 [06:24<01:39, 19.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:48,  7.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6115/8000 [06:24<01:31, 20.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<13:02,  3.95s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6120/8000 [06:25<01:41, 18.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:19,  2.84s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6123/8000 [06:25<02:02, 15.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:18,  2.23s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6130/8000 [06:25<01:23, 22.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:11,  1.91s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6135/8000 [06:25<01:16, 24.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:34,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6140/8000 [06:26<01:31, 20.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:10,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6143/8000 [06:26<01:54, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<04:54,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6149/8000 [06:26<01:30, 20.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:46,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6152/8000 [06:26<01:51, 16.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:37,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6155/8000 [06:27<02:08, 14.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:31,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6158/8000 [06:27<01:54, 16.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:25,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6160/8000 [06:27<02:03, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:25,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6164/8000 [06:27<01:45, 17.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:17,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6169/8000 [06:27<01:38, 18.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:14,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6171/8000 [06:28<01:48, 16.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:17,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6174/8000 [06:28<01:43, 17.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:18,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6179/8000 [06:28<01:23, 21.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:15,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6184/8000 [06:28<01:30, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:12,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6190/8000 [06:28<01:30, 20.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:33<04:07,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6196/8000 [06:29<01:27, 20.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:03,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6200/8000 [06:29<01:40, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:36<03:59,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6204/8000 [06:29<01:28, 20.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:37<03:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6208/8000 [06:29<01:23, 21.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<03:54,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6211/8000 [06:30<01:26, 20.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:40<03:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6214/8000 [06:30<02:02, 14.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:41<03:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6218/8000 [06:30<01:44, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:43<03:49,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6222/8000 [06:30<01:33, 18.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:44<03:47,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6227/8000 [06:31<01:52, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:45<03:43,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6229/8000 [06:31<01:58, 14.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:47<03:45,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6231/8000 [06:31<02:23, 12.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:48<03:46,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6234/8000 [06:31<02:42, 10.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:49<03:56,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6236/8000 [06:32<02:38, 11.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:51<04:03,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6240/8000 [06:32<02:05, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:53<04:02,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6243/8000 [06:32<01:57, 14.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:54<03:56,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6247/8000 [06:32<02:04, 14.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:55<03:48,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6251/8000 [06:33<02:43, 10.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:57<03:47,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6254/8000 [06:33<02:22, 12.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:58<03:43,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6258/8000 [06:33<01:59, 14.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:59<03:38,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6260/8000 [06:33<01:57, 14.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:33,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6264/8000 [06:34<02:06, 13.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6267/8000 [06:34<01:58, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:03<03:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6269/8000 [06:34<02:27, 11.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:32,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6271/8000 [06:34<02:58,  9.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:06<03:34,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6273/8000 [06:35<02:47, 10.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:07<03:31,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6276/8000 [06:35<02:21, 12.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:31,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6281/8000 [06:35<02:00, 14.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:11<03:50,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6283/8000 [06:35<02:04, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:42,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6287/8000 [06:35<01:42, 16.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:13<03:39,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6290/8000 [06:35<01:37, 17.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:36,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6293/8000 [06:36<01:56, 14.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:16<03:40,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6296/8000 [06:36<01:50, 15.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:18<03:42,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6304/8000 [06:36<01:27, 19.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:19<03:36,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6309/8000 [06:37<01:35, 17.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:21<03:33,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6312/8000 [06:37<01:34, 17.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:22<03:31,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6317/8000 [06:37<01:22, 20.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:24<03:32,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6320/8000 [06:37<01:21, 20.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:25<03:30,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6324/8000 [06:37<01:15, 22.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:27<03:26,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6327/8000 [06:37<01:12, 22.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:28<03:24,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6332/8000 [06:38<01:26, 19.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:31<03:33,  1.52s/it]\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6336/8000 [06:38<01:31, 18.13it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6340/8000 [06:38<01:42, 16.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6342/8000 [06:38<01:45, 15.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6346/8000 [06:38<01:32, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.88it/s]\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6348/8000 [06:39<01:40, 16.39it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6353/8000 [06:39<01:20, 20.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6358/8000 [06:39<01:32, 17.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6360/8000 [06:40<02:18, 11.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 34it [00:01, 29.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6364/8000 [06:40<01:53, 14.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 64it [00:01, 58.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6366/8000 [06:40<01:56, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 97it [00:01, 95.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6370/8000 [06:40<01:36, 16.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 126it [00:01, 126.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6374/8000 [06:40<01:21, 19.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 154it [00:02, 155.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6378/8000 [06:40<01:16, 21.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 188it [00:02, 192.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6381/8000 [06:41<01:20, 20.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 218it [00:02, 215.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6385/8000 [06:41<01:39, 16.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 248it [00:02, 233.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6387/8000 [06:41<01:48, 14.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 285it [00:02, 266.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6393/8000 [06:41<01:18, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 319it [00:02, 284.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6397/8000 [06:41<01:30, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 351it [00:02, 292.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6399/8000 [06:42<01:37, 16.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 388it [00:02, 312.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6402/8000 [06:42<01:29, 17.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 422it [00:02, 289.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6406/8000 [06:42<01:40, 15.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 456it [00:02, 300.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6408/8000 [06:42<01:46, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 488it [00:03, 302.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6411/8000 [06:42<01:40, 15.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 527it [00:03, 326.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6413/8000 [06:43<01:46, 14.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 561it [00:03, 318.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6415/8000 [06:43<01:52, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 594it [00:03, 318.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6423/8000 [06:43<01:07, 23.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 627it [00:03, 321.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6432/8000 [06:43<01:00, 25.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 660it [00:03, 305.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6440/8000 [06:43<00:51, 30.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 691it [00:03, 294.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6443/8000 [06:44<00:58, 26.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 723it [00:03, 301.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6446/8000 [06:44<01:21, 19.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 754it [00:03, 298.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6449/8000 [06:44<01:21, 19.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 195.71it/s]\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6453/8000 [06:44<01:26, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6455/8000 [06:45<01:33, 16.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6458/8000 [06:45<01:30, 17.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 156\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6463/8000 [06:45<01:29, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:00<00:00, 160\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6470/8000 [06:45<01:05, 23.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  50%|▌| 50/100 [00:00<00:00, 162\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6474/8000 [06:45<01:01, 24.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  67%|▋| 67/100 [00:00<00:00, 155\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6478/8000 [06:46<01:14, 20.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6483/8000 [06:46<01:19, 19.13it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6487/8000 [06:46<01:27, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6490/8000 [06:46<01:26, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6492/8000 [06:47<01:42, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6495/8000 [06:47<01:37, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6497/8000 [06:47<01:41, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6503/8000 [06:47<01:23, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6507/8000 [06:47<01:36, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6509/8000 [06:48<02:01, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6511/8000 [06:48<02:00, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6513/8000 [06:48<01:59, 12.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 26/8000 [00:00<00:48, 164.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▌      | 6515/8000 [06:48<01:58, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 53/8000 [00:00<01:07, 117.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6521/8000 [06:48<01:19, 18.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 71/8000 [00:00<01:04, 123.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6526/8000 [06:49<01:06, 22.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 87/8000 [00:00<01:07, 116.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6529/8000 [06:49<01:09, 21.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 104/8000 [00:00<01:06, 118.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6532/8000 [06:49<01:07, 21.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                | 118/8000 [00:01<01:11, 110.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6535/8000 [06:49<01:26, 16.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 142/8000 [00:01<01:02, 125.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6540/8000 [06:49<01:11, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 165/8000 [00:01<00:56, 138.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6545/8000 [06:50<01:16, 18.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 181/8000 [00:01<00:59, 130.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6551/8000 [06:50<01:31, 15.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 214/8000 [00:01<01:01, 126.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6554/8000 [06:50<01:28, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 234/8000 [00:01<01:01, 126.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6558/8000 [06:50<01:19, 18.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 249/8000 [00:02<01:05, 118.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6563/8000 [06:51<01:09, 20.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 264/8000 [00:02<01:05, 117.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6566/8000 [06:51<01:29, 16.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▌                | 285/8000 [00:02<01:03, 120.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6572/8000 [06:51<01:07, 21.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 299/8000 [00:02<01:07, 114.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6578/8000 [06:51<01:07, 20.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 316/8000 [00:02<01:07, 113.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6582/8000 [06:52<01:17, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 337/8000 [00:02<01:04, 119.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6585/8000 [06:52<01:13, 19.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 352/8000 [00:02<01:06, 115.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6591/8000 [06:52<00:56, 25.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 368/8000 [00:03<01:08, 111.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▉      | 6600/8000 [06:52<00:40, 34.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 382/8000 [00:03<01:15, 101.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6609/8000 [06:52<00:33, 41.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 395/8000 [00:03<01:26, 87.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6619/8000 [06:52<00:28, 47.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 413/8000 [00:03<01:19, 95.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6627/8000 [06:53<00:31, 43.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 435/8000 [00:03<01:07, 112.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6632/8000 [06:53<00:37, 36.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 455/8000 [00:03<01:02, 120.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6640/8000 [06:53<00:32, 41.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 474/8000 [00:04<01:02, 120.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6646/8000 [06:53<00:39, 34.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 494/8000 [00:04<00:57, 129.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6652/8000 [06:54<00:55, 24.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█                | 524/8000 [00:04<01:02, 120.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6656/8000 [06:54<01:04, 20.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 542/8000 [00:04<01:04, 116.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6659/8000 [06:54<01:20, 16.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 560/8000 [00:04<01:00, 122.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6663/8000 [06:54<01:09, 19.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 579/8000 [00:04<00:56, 131.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6668/8000 [06:55<01:15, 17.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎               | 594/8000 [00:04<00:57, 128.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6671/8000 [06:55<01:46, 12.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 609/8000 [00:05<01:00, 122.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6674/8000 [06:56<01:51, 11.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 629/8000 [00:05<00:57, 128.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6678/8000 [06:56<01:30, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 651/8000 [00:05<00:53, 138.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6680/8000 [06:56<01:34, 13.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 671/8000 [00:05<00:50, 144.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6682/8000 [06:56<01:38, 13.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 697/8000 [00:05<00:49, 148.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6685/8000 [06:56<01:31, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 716/8000 [00:05<00:48, 150.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6687/8000 [06:56<01:40, 13.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 739/8000 [00:05<00:48, 148.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6690/8000 [06:57<01:37, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▌               | 761/8000 [00:06<00:50, 142.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6693/8000 [06:57<01:41, 12.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 781/8000 [00:06<00:54, 132.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6695/8000 [06:57<02:26,  8.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 799/8000 [00:06<00:53, 134.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6697/8000 [06:57<02:22,  9.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 816/8000 [00:06<00:56, 127.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6701/8000 [06:58<01:52, 11.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 829/8000 [00:06<01:05, 110.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6706/8000 [06:58<01:46, 12.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 859/8000 [00:06<00:54, 130.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6708/8000 [06:58<01:48, 11.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 879/8000 [00:07<00:54, 130.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6710/8000 [06:59<02:04, 10.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 901/8000 [00:07<00:51, 138.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6712/8000 [06:59<01:59, 10.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 922/8000 [00:07<00:49, 141.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6714/8000 [06:59<02:18,  9.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 945/8000 [00:07<00:50, 139.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6718/8000 [06:59<01:35, 13.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 960/8000 [00:07<00:52, 133.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6721/8000 [06:59<01:26, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 992/8000 [00:07<00:57, 122.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6724/8000 [07:00<01:39, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1011/8000 [00:08<00:55, 125.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6727/8000 [07:00<01:25, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1041/8000 [00:08<00:55, 124.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6730/8000 [07:00<01:40, 12.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1056/8000 [00:08<00:59, 117.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6732/8000 [07:00<01:37, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1075/8000 [00:08<00:55, 125.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6735/8000 [07:00<01:40, 12.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1090/8000 [00:08<00:55, 123.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6737/8000 [07:01<01:38, 12.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1116/8000 [00:08<00:49, 138.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6742/8000 [07:01<01:12, 17.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1141/8000 [00:09<00:44, 154.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6746/8000 [07:01<01:00, 20.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1159/8000 [00:09<00:43, 157.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6749/8000 [07:01<01:02, 20.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1182/8000 [00:09<00:42, 161.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6752/8000 [07:01<01:02, 20.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1205/8000 [00:09<00:52, 129.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6755/8000 [07:01<01:14, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1220/8000 [00:09<00:51, 132.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6758/8000 [07:02<02:02, 10.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1239/8000 [00:09<00:48, 138.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6760/8000 [07:02<02:18,  8.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1260/8000 [00:09<00:44, 151.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6762/8000 [07:02<02:06,  9.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1277/8000 [00:09<00:46, 144.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6764/8000 [07:03<01:52, 11.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1308/8000 [00:10<00:52, 127.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6767/8000 [07:03<01:36, 12.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1348/8000 [00:10<00:49, 133.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6772/8000 [07:03<01:10, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1367/8000 [00:10<00:48, 136.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6775/8000 [07:03<01:06, 18.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1391/8000 [00:10<00:54, 121.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6778/8000 [07:03<01:01, 19.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1408/8000 [00:11<00:52, 125.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6781/8000 [07:03<01:01, 19.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1433/8000 [00:11<00:47, 137.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6784/8000 [07:04<01:52, 10.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1463/8000 [00:11<00:50, 129.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6787/8000 [07:04<01:38, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|██▉             | 1485/8000 [00:11<00:46, 140.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6790/8000 [07:04<01:25, 14.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1506/8000 [00:11<01:01, 105.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6793/8000 [07:05<01:34, 12.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1525/8000 [00:12<00:58, 110.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6796/8000 [07:05<01:26, 13.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1542/8000 [00:12<00:57, 112.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6798/8000 [07:05<01:29, 13.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1559/8000 [00:12<00:59, 108.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6800/8000 [07:05<01:33, 12.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1578/8000 [00:12<00:55, 115.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6807/8000 [07:05<01:10, 16.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1593/8000 [00:12<00:54, 117.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6809/8000 [07:05<01:14, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1607/8000 [00:12<00:55, 114.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6811/8000 [07:06<01:54, 10.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1622/8000 [00:12<00:55, 114.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6814/8000 [07:06<01:34, 12.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1644/8000 [00:13<00:48, 129.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6816/8000 [07:06<01:57, 10.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1669/8000 [00:13<00:55, 114.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6819/8000 [07:07<01:52, 10.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1685/8000 [00:13<00:55, 113.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6821/8000 [07:07<02:05,  9.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1702/8000 [00:13<00:53, 116.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6824/8000 [07:07<01:44, 11.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1720/8000 [00:13<00:51, 122.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6826/8000 [07:07<01:56, 10.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1733/8000 [00:13<00:55, 112.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6830/8000 [07:08<01:27, 13.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1754/8000 [00:13<00:49, 125.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6832/8000 [07:08<01:28, 13.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1774/8000 [00:14<00:46, 134.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6836/8000 [07:08<01:06, 17.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1792/8000 [00:14<00:46, 134.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6841/8000 [07:08<00:55, 20.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▌            | 1812/8000 [00:14<00:43, 143.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6844/8000 [07:08<01:10, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1837/8000 [00:14<00:39, 155.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6849/8000 [07:08<00:53, 21.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1853/8000 [00:14<00:42, 146.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6854/8000 [07:09<00:47, 24.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1871/8000 [00:14<00:42, 144.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6859/8000 [07:09<00:54, 20.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1899/8000 [00:15<00:57, 106.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6864/8000 [07:09<01:06, 17.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1913/8000 [00:15<01:00, 101.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6869/8000 [07:10<01:06, 17.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1929/8000 [00:15<00:59, 101.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6873/8000 [07:10<01:00, 18.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1949/8000 [00:15<00:55, 108.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6879/8000 [07:10<00:59, 18.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1967/8000 [00:15<00:59, 101.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6883/8000 [07:10<00:52, 21.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1979/8000 [00:16<01:08, 88.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6887/8000 [07:10<00:51, 21.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2002/8000 [00:16<01:02, 96.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6893/8000 [07:11<01:11, 15.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2027/8000 [00:16<00:53, 111.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6899/8000 [07:11<01:07, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2055/8000 [00:16<00:45, 129.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6903/8000 [07:11<01:01, 17.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2072/8000 [00:16<00:46, 127.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6906/8000 [07:12<01:00, 17.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2090/8000 [00:16<00:46, 127.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6912/8000 [07:12<00:49, 21.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2115/8000 [00:16<00:42, 138.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6915/8000 [07:12<00:50, 21.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2144/8000 [00:17<00:49, 119.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6921/8000 [07:12<00:42, 25.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2170/8000 [00:17<00:51, 113.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6924/8000 [07:12<00:55, 19.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2183/8000 [00:17<00:52, 110.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6928/8000 [07:12<00:48, 22.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▍           | 2198/8000 [00:17<00:55, 105.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6931/8000 [07:13<01:03, 16.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2212/8000 [00:17<00:54, 106.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6936/8000 [07:13<01:00, 17.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2226/8000 [00:18<00:56, 102.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6939/8000 [07:13<01:15, 14.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2242/8000 [00:18<00:54, 106.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6941/8000 [07:14<01:17, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2256/8000 [00:18<01:00, 94.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6944/8000 [07:14<01:12, 14.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2279/8000 [00:18<00:52, 109.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6948/8000 [07:14<01:01, 17.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2305/8000 [00:18<00:43, 130.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6954/8000 [07:14<00:58, 17.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2319/8000 [00:18<00:49, 114.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6957/8000 [07:14<00:57, 18.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2339/8000 [00:19<00:45, 125.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6961/8000 [07:14<00:51, 20.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2354/8000 [00:19<00:47, 117.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6966/8000 [07:15<00:53, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▋           | 2368/8000 [00:19<00:48, 116.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6970/8000 [07:15<01:02, 16.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2385/8000 [00:19<00:44, 125.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6973/8000 [07:15<01:08, 15.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2401/8000 [00:19<00:46, 121.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6977/8000 [07:16<01:00, 16.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2426/8000 [00:19<00:42, 129.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6982/8000 [07:16<01:02, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2443/8000 [00:19<00:44, 126.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6984/8000 [07:16<01:06, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2464/8000 [00:19<00:40, 137.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6986/8000 [07:16<01:10, 14.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2499/8000 [00:20<00:38, 143.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6988/8000 [07:16<01:14, 13.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2516/8000 [00:20<00:39, 140.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6990/8000 [07:16<01:12, 14.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2532/8000 [00:20<00:40, 135.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6993/8000 [07:17<01:06, 15.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2546/8000 [00:20<00:43, 125.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6995/8000 [07:17<01:09, 14.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2576/8000 [00:20<00:44, 122.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7000/8000 [07:17<00:49, 20.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2593/8000 [00:20<00:41, 129.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7003/8000 [07:17<00:45, 21.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2623/8000 [00:21<00:34, 154.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7010/8000 [07:17<00:35, 28.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2649/8000 [00:21<00:33, 158.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7013/8000 [07:18<01:01, 16.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2674/8000 [00:21<00:39, 133.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7017/8000 [07:18<01:07, 14.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2695/8000 [00:21<00:37, 141.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7024/8000 [07:18<01:05, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2716/8000 [00:21<00:37, 140.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7027/8000 [07:19<01:01, 15.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2737/8000 [00:21<00:36, 145.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7030/8000 [07:19<00:59, 16.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2770/8000 [00:22<00:43, 119.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7034/8000 [07:19<00:54, 17.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2791/8000 [00:22<00:45, 115.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7037/8000 [07:19<01:03, 15.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2809/8000 [00:22<00:45, 114.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7040/8000 [07:19<00:56, 16.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2832/8000 [00:22<00:44, 115.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7043/8000 [07:20<01:09, 13.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2849/8000 [00:22<00:43, 117.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7045/8000 [07:20<01:08, 13.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2870/8000 [00:23<00:44, 115.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7048/8000 [07:20<01:03, 15.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2900/8000 [00:23<00:40, 126.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7051/8000 [07:20<00:59, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2917/8000 [00:23<00:38, 130.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7056/8000 [07:20<00:47, 19.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2950/8000 [00:23<00:40, 125.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7061/8000 [07:21<00:41, 22.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2973/8000 [00:23<00:37, 134.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7066/8000 [07:21<00:50, 18.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2996/8000 [00:24<00:34, 145.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7069/8000 [07:21<00:57, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3018/8000 [00:24<00:32, 152.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7072/8000 [07:21<00:56, 16.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3042/8000 [00:24<00:30, 161.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7075/8000 [07:22<01:07, 13.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3059/8000 [00:24<00:32, 151.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7077/8000 [07:22<01:27, 10.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3081/8000 [00:24<00:30, 159.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7079/8000 [07:22<01:24, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3108/8000 [00:24<00:35, 136.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▉    | 7083/8000 [07:22<01:15, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3125/8000 [00:24<00:36, 133.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7086/8000 [07:23<01:20, 11.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3144/8000 [00:25<00:37, 128.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7092/8000 [07:23<00:52, 17.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3177/8000 [00:25<00:39, 122.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7097/8000 [07:23<00:44, 20.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3191/8000 [00:25<00:40, 119.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7100/8000 [07:23<00:57, 15.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3205/8000 [00:25<00:42, 111.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7103/8000 [07:24<01:07, 13.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3220/8000 [00:25<00:47, 101.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7110/8000 [07:24<00:54, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3236/8000 [00:26<00:50, 94.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7112/8000 [07:24<00:56, 15.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3255/8000 [00:26<00:50, 94.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7115/8000 [07:24<00:53, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3279/8000 [00:26<00:47, 99.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7119/8000 [07:25<00:54, 16.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3298/8000 [00:26<00:46, 100.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7123/8000 [07:25<00:49, 17.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3313/8000 [00:26<00:53, 88.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7127/8000 [07:25<00:46, 18.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3326/8000 [00:27<00:53, 87.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7130/8000 [07:25<00:45, 19.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3338/8000 [00:27<00:55, 83.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7135/8000 [07:25<00:51, 16.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3354/8000 [00:27<00:49, 93.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7139/8000 [07:26<00:55, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3370/8000 [00:27<00:55, 84.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7142/8000 [07:26<01:04, 13.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3392/8000 [00:27<00:49, 92.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7147/8000 [07:26<00:56, 15.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3409/8000 [00:28<01:00, 76.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7149/8000 [07:26<00:59, 14.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3428/8000 [00:28<01:00, 75.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7151/8000 [07:27<00:59, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3447/8000 [00:28<00:53, 85.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7154/8000 [07:27<00:54, 15.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3469/8000 [00:28<00:44, 102.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7157/8000 [07:27<00:48, 17.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3491/8000 [00:28<00:44, 100.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7160/8000 [07:27<01:00, 13.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3505/8000 [00:29<00:48, 91.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7165/8000 [07:27<00:45, 18.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3521/8000 [00:29<00:49, 90.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7169/8000 [07:27<00:39, 21.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3548/8000 [00:29<00:43, 102.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7172/8000 [07:28<00:50, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3569/8000 [00:29<00:40, 109.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7175/8000 [07:28<00:47, 17.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3586/8000 [00:29<00:46, 95.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7179/8000 [07:28<00:50, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3604/8000 [00:30<00:44, 97.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7182/8000 [07:28<00:49, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▎        | 3631/8000 [00:30<00:38, 112.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7186/8000 [07:29<00:41, 19.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3653/8000 [00:30<00:42, 102.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7191/8000 [07:29<00:41, 19.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3666/8000 [00:30<00:49, 87.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7194/8000 [07:29<01:10, 11.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3686/8000 [00:30<00:51, 83.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7196/8000 [07:30<01:09, 11.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3705/8000 [00:31<00:45, 93.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7200/8000 [07:30<01:13, 10.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3724/8000 [00:31<00:41, 104.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7202/8000 [07:30<01:11, 11.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▍        | 3749/8000 [00:31<00:34, 124.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7206/8000 [07:30<01:04, 12.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3763/8000 [00:31<00:37, 113.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7209/8000 [07:31<01:09, 11.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3790/8000 [00:31<00:31, 134.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7212/8000 [07:31<01:00, 13.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3817/8000 [00:31<00:27, 154.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7215/8000 [07:31<00:51, 15.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3852/8000 [00:32<00:27, 149.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7219/8000 [07:31<00:44, 17.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3886/8000 [00:32<00:28, 144.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7222/8000 [07:31<00:44, 17.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3927/8000 [00:32<00:27, 150.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7227/8000 [07:32<00:41, 18.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3947/8000 [00:32<00:27, 147.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7230/8000 [07:32<00:41, 18.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3962/8000 [00:32<00:29, 137.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7235/8000 [07:32<00:34, 22.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3985/8000 [00:32<00:28, 141.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7238/8000 [07:32<00:40, 18.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4001/8000 [00:33<00:29, 135.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7243/8000 [07:32<00:44, 17.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4019/8000 [00:33<00:27, 143.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7247/8000 [07:33<00:44, 16.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4040/8000 [00:33<00:26, 148.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7250/8000 [07:33<01:01, 12.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4059/8000 [00:33<00:27, 142.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7253/8000 [07:33<01:04, 11.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4076/8000 [00:33<00:28, 138.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7257/8000 [07:34<00:52, 14.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4095/8000 [00:33<00:29, 133.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7260/8000 [07:34<00:47, 15.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▏       | 4124/8000 [00:34<00:32, 119.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7262/8000 [07:34<01:00, 12.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4139/8000 [00:34<00:33, 115.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7264/8000 [07:34<01:00, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4159/8000 [00:34<00:30, 127.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7266/8000 [07:34<00:59, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4183/8000 [00:34<00:27, 140.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7269/8000 [07:35<00:51, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4198/8000 [00:34<00:27, 137.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7274/8000 [07:35<00:39, 18.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4215/8000 [00:34<00:28, 132.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7278/8000 [07:35<00:35, 20.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4229/8000 [00:34<00:29, 128.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7282/8000 [07:35<00:42, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4242/8000 [00:34<00:31, 120.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7287/8000 [07:35<00:41, 17.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4259/8000 [00:35<00:31, 118.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7289/8000 [07:36<01:09, 10.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4275/8000 [00:35<00:31, 118.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7292/8000 [07:36<01:01, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4291/8000 [00:35<00:31, 116.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7294/8000 [07:36<01:00, 11.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4310/8000 [00:35<00:31, 118.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7296/8000 [07:36<00:58, 12.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4326/8000 [00:35<00:31, 117.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7301/8000 [07:37<00:41, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4346/8000 [00:35<00:30, 121.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7304/8000 [07:37<00:36, 18.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▋       | 4369/8000 [00:35<00:26, 136.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7307/8000 [07:37<00:47, 14.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4395/8000 [00:36<00:23, 153.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7309/8000 [07:37<00:47, 14.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4414/8000 [00:36<00:31, 115.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7312/8000 [07:37<00:41, 16.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▉       | 4438/8000 [00:36<00:27, 131.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████████████████████   | 7316/8000 [07:38<00:46, 14.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4454/8000 [00:36<00:27, 127.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████████████████████   | 7319/8000 [07:38<00:43, 15.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4474/8000 [00:36<00:26, 133.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7322/8000 [07:38<00:41, 16.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4489/8000 [00:36<00:27, 129.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7325/8000 [07:38<00:39, 17.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4514/8000 [00:37<00:24, 144.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7329/8000 [07:38<00:35, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4530/8000 [00:37<00:25, 137.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7334/8000 [07:39<00:39, 17.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4560/8000 [00:37<00:27, 125.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7337/8000 [07:39<00:37, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4591/8000 [00:37<00:27, 123.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7339/8000 [07:39<00:39, 16.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4614/8000 [00:37<00:26, 129.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7342/8000 [07:39<00:35, 18.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4635/8000 [00:37<00:24, 140.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7344/8000 [07:39<00:39, 16.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4667/8000 [00:38<00:25, 130.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7346/8000 [07:40<00:52, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▎      | 4686/8000 [00:38<00:24, 136.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7349/8000 [07:40<00:54, 11.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4703/8000 [00:38<00:24, 132.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7352/8000 [07:40<00:58, 11.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4738/8000 [00:38<00:25, 129.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7354/8000 [07:40<00:53, 12.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4766/8000 [00:38<00:21, 149.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7360/8000 [07:41<00:42, 14.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4792/8000 [00:39<00:25, 127.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7364/8000 [07:41<00:36, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4807/8000 [00:39<00:25, 127.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7366/8000 [07:41<00:36, 17.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4828/8000 [00:39<00:22, 140.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7369/8000 [07:41<00:36, 17.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4849/8000 [00:39<00:22, 138.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7372/8000 [07:41<00:34, 18.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4870/8000 [00:39<00:22, 141.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7375/8000 [07:41<00:33, 18.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4890/8000 [00:39<00:22, 141.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7377/8000 [07:41<00:33, 18.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4911/8000 [00:39<00:20, 150.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7381/8000 [07:42<00:30, 20.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4937/8000 [00:40<00:18, 162.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7384/8000 [07:42<00:30, 20.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4954/8000 [00:40<00:19, 156.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7387/8000 [07:42<00:30, 20.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4977/8000 [00:40<00:18, 166.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7391/8000 [07:42<00:26, 23.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4997/8000 [00:40<00:18, 165.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7394/8000 [07:42<00:28, 21.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5035/8000 [00:40<00:20, 145.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7399/8000 [07:43<00:36, 16.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5055/8000 [00:40<00:20, 144.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7401/8000 [07:43<00:38, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5072/8000 [00:41<00:21, 135.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7408/8000 [07:43<00:25, 22.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5088/8000 [00:41<00:22, 129.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7413/8000 [07:43<00:23, 25.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5103/8000 [00:41<00:23, 125.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7416/8000 [07:43<00:23, 25.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5117/8000 [00:41<00:23, 122.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7422/8000 [07:43<00:20, 28.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5130/8000 [00:41<00:25, 113.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7426/8000 [07:44<00:25, 22.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5153/8000 [00:41<00:22, 129.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7429/8000 [07:44<00:24, 22.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5172/8000 [00:41<00:21, 132.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7432/8000 [07:44<00:26, 21.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5190/8000 [00:42<00:22, 125.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7438/8000 [07:44<00:26, 21.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5221/8000 [00:42<00:23, 116.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7441/8000 [07:44<00:25, 22.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▍     | 5240/8000 [00:42<00:22, 122.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7445/8000 [07:45<00:36, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5264/8000 [00:42<00:20, 136.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7447/8000 [07:45<00:36, 15.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5279/8000 [00:42<00:21, 129.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7450/8000 [07:45<00:34, 15.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5297/8000 [00:42<00:21, 126.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7453/8000 [07:45<00:32, 16.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5312/8000 [00:43<00:22, 120.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7455/8000 [07:46<00:48, 11.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5327/8000 [00:43<00:23, 112.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7459/8000 [07:46<00:38, 14.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5344/8000 [00:43<00:23, 113.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7461/8000 [07:46<00:39, 13.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5362/8000 [00:43<00:21, 119.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7466/8000 [07:46<00:27, 19.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5376/8000 [00:43<00:22, 114.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7469/8000 [07:46<00:27, 19.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5392/8000 [00:43<00:22, 118.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7473/8000 [07:46<00:31, 16.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5409/8000 [00:43<00:20, 124.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7476/8000 [07:47<00:36, 14.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5433/8000 [00:43<00:18, 140.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7478/8000 [07:47<00:37, 14.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5451/8000 [00:44<00:19, 131.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7482/8000 [07:47<00:29, 17.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5471/8000 [00:44<00:18, 137.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7485/8000 [07:47<00:27, 19.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5488/8000 [00:44<00:19, 131.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7488/8000 [07:47<00:34, 14.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5507/8000 [00:44<00:19, 129.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7493/8000 [07:48<00:31, 16.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5526/8000 [00:44<00:18, 132.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7499/8000 [07:48<00:23, 20.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5540/8000 [00:44<00:19, 126.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7504/8000 [07:48<00:25, 19.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5557/8000 [00:44<00:18, 131.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7507/8000 [07:49<00:34, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5574/8000 [00:45<00:18, 129.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7511/8000 [07:49<00:30, 16.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5598/8000 [00:45<00:16, 144.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7518/8000 [07:49<00:20, 23.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5618/8000 [00:45<00:17, 137.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7525/8000 [07:49<00:21, 22.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5636/8000 [00:45<00:17, 134.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7528/8000 [07:49<00:22, 21.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5652/8000 [00:45<00:18, 127.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7532/8000 [07:50<00:26, 17.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5673/8000 [00:45<00:16, 138.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7542/8000 [07:50<00:20, 22.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5696/8000 [00:45<00:15, 148.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7545/8000 [07:50<00:24, 18.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5719/8000 [00:46<00:15, 151.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7548/8000 [07:51<00:24, 18.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5753/8000 [00:46<00:16, 137.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7550/8000 [07:51<00:24, 18.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5779/8000 [00:46<00:14, 150.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7557/8000 [07:51<00:18, 23.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5799/8000 [00:46<00:14, 149.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7561/8000 [07:51<00:18, 23.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5823/8000 [00:46<00:14, 150.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7566/8000 [07:51<00:17, 25.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5843/8000 [00:46<00:14, 145.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7573/8000 [07:51<00:17, 23.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5864/8000 [00:47<00:14, 150.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7578/8000 [07:52<00:23, 17.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5886/8000 [00:47<00:14, 149.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7581/8000 [07:52<00:27, 15.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5909/8000 [00:47<00:13, 151.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7584/8000 [07:53<00:30, 13.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5930/8000 [00:47<00:13, 156.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7586/8000 [07:53<00:30, 13.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5948/8000 [00:47<00:13, 153.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7588/8000 [07:53<00:29, 13.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5964/8000 [00:47<00:14, 144.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7593/8000 [07:53<00:27, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5985/8000 [00:47<00:13, 151.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7595/8000 [07:53<00:28, 14.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6002/8000 [00:47<00:13, 149.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7597/8000 [07:53<00:29, 13.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6018/8000 [00:48<00:14, 138.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7600/8000 [07:54<00:32, 12.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6037/8000 [00:48<00:13, 145.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7606/8000 [07:54<00:21, 17.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6054/8000 [00:48<00:13, 140.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7615/8000 [07:54<00:17, 21.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6071/8000 [00:48<00:14, 133.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7619/8000 [07:54<00:16, 22.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6097/8000 [00:48<00:11, 160.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7626/8000 [07:55<00:18, 20.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6133/8000 [00:48<00:13, 138.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7631/8000 [07:55<00:19, 18.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6153/8000 [00:49<00:12, 142.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7633/8000 [07:55<00:23, 15.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6175/8000 [00:49<00:13, 140.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7635/8000 [07:56<00:24, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▍   | 6193/8000 [00:49<00:12, 142.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7641/8000 [07:56<00:20, 17.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6212/8000 [00:49<00:12, 139.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7643/8000 [07:56<00:22, 15.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6252/8000 [00:49<00:11, 145.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7645/8000 [07:56<00:27, 12.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6274/8000 [00:49<00:11, 147.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7647/8000 [07:56<00:26, 13.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6290/8000 [00:50<00:12, 136.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7652/8000 [07:57<00:24, 14.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6312/8000 [00:50<00:13, 128.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7656/8000 [07:57<00:20, 17.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6333/8000 [00:50<00:13, 123.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7658/8000 [07:57<00:24, 13.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▋   | 6360/8000 [00:50<00:14, 110.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7661/8000 [07:58<00:31, 10.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6394/8000 [00:50<00:12, 123.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7664/8000 [07:58<00:27, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6413/8000 [00:51<00:12, 130.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7667/8000 [07:58<00:29, 11.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6435/8000 [00:51<00:11, 132.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7669/8000 [07:58<00:26, 12.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6454/8000 [00:51<00:11, 135.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7672/8000 [07:58<00:23, 14.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6484/8000 [00:51<00:09, 154.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7677/8000 [07:58<00:17, 18.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6509/8000 [00:51<00:09, 162.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7680/8000 [07:59<00:17, 18.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6540/8000 [00:51<00:10, 145.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7685/8000 [07:59<00:17, 18.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6556/8000 [00:51<00:09, 144.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7688/8000 [07:59<00:20, 15.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6571/8000 [00:52<00:10, 134.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7691/8000 [07:59<00:18, 16.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6588/8000 [00:52<00:10, 131.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7695/8000 [07:59<00:15, 19.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6604/8000 [00:52<00:11, 124.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7699/8000 [08:00<00:14, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6624/8000 [00:52<00:10, 127.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7704/8000 [08:00<00:12, 24.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6648/8000 [00:52<00:09, 142.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7708/8000 [08:00<00:15, 18.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6670/8000 [00:52<00:09, 143.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7713/8000 [08:00<00:16, 17.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6696/8000 [00:52<00:08, 158.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7715/8000 [08:01<00:17, 16.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6720/8000 [00:53<00:07, 166.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7717/8000 [08:01<00:18, 15.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6739/8000 [00:53<00:07, 161.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7719/8000 [08:01<00:22, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6766/8000 [00:53<00:09, 131.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7721/8000 [08:01<00:22, 12.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6787/8000 [00:53<00:08, 143.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7726/8000 [08:02<00:19, 14.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6804/8000 [00:53<00:08, 140.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7730/8000 [08:02<00:15, 17.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6819/8000 [00:53<00:08, 132.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7732/8000 [08:02<00:16, 16.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6837/8000 [00:54<00:08, 133.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7734/8000 [08:02<00:21, 12.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6851/8000 [00:54<00:08, 131.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7736/8000 [08:02<00:20, 12.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6877/8000 [00:54<00:07, 154.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7738/8000 [08:02<00:20, 12.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6911/8000 [00:54<00:08, 133.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7743/8000 [08:03<00:14, 17.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6929/8000 [00:54<00:08, 130.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7746/8000 [08:03<00:13, 18.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6943/8000 [00:54<00:08, 126.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7748/8000 [08:03<00:15, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6965/8000 [00:54<00:07, 140.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7750/8000 [08:03<00:21, 11.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6983/8000 [00:55<00:07, 136.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7754/8000 [08:03<00:16, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7017/8000 [00:55<00:07, 134.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7759/8000 [08:04<00:12, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7036/8000 [00:55<00:07, 132.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7763/8000 [08:04<00:11, 20.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7050/8000 [00:55<00:07, 129.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7766/8000 [08:04<00:16, 13.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▏ | 7066/8000 [00:55<00:07, 129.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7769/8000 [08:04<00:15, 15.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7091/8000 [00:55<00:06, 146.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7773/8000 [08:04<00:12, 17.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7107/8000 [00:55<00:06, 140.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7776/8000 [08:05<00:15, 14.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7125/8000 [00:56<00:06, 137.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7779/8000 [08:05<00:14, 15.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▎ | 7151/8000 [00:56<00:05, 155.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7781/8000 [08:05<00:14, 14.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▎ | 7171/8000 [00:56<00:05, 155.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7784/8000 [08:05<00:14, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7203/8000 [00:56<00:05, 134.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7787/8000 [08:05<00:13, 16.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7235/8000 [00:56<00:04, 163.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7793/8000 [08:06<00:11, 17.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7270/8000 [00:57<00:04, 153.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7795/8000 [08:06<00:12, 15.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7287/8000 [00:57<00:05, 142.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7798/8000 [08:06<00:12, 16.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7310/8000 [00:57<00:04, 151.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7800/8000 [08:06<00:16, 11.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7330/8000 [00:57<00:04, 149.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7804/8000 [08:07<00:13, 14.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7346/8000 [00:57<00:04, 139.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7806/8000 [08:07<00:16, 11.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7363/8000 [00:57<00:04, 140.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7809/8000 [08:07<00:14, 12.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7382/8000 [00:57<00:04, 143.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7813/8000 [08:07<00:14, 12.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7407/8000 [00:58<00:05, 118.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7816/8000 [08:08<00:13, 13.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7432/8000 [00:58<00:04, 137.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7821/8000 [08:08<00:10, 17.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7451/8000 [00:58<00:04, 135.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7825/8000 [08:08<00:09, 18.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7478/8000 [00:58<00:03, 153.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7828/8000 [08:08<00:10, 15.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7515/8000 [00:58<00:03, 142.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7830/8000 [08:08<00:11, 15.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7547/8000 [00:59<00:03, 133.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7832/8000 [08:08<00:11, 14.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7566/8000 [00:59<00:03, 125.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7837/8000 [08:09<00:07, 20.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7591/8000 [00:59<00:02, 137.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7841/8000 [08:09<00:09, 17.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7614/8000 [00:59<00:02, 150.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7845/8000 [08:09<00:09, 16.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7643/8000 [00:59<00:02, 133.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7849/8000 [08:09<00:08, 18.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7665/8000 [00:59<00:02, 143.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7852/8000 [08:09<00:07, 18.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7682/8000 [01:00<00:02, 128.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7859/8000 [08:10<00:05, 25.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7717/8000 [01:00<00:02, 128.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7862/8000 [08:10<00:07, 19.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▍| 7736/8000 [01:00<00:02, 130.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7866/8000 [08:10<00:07, 17.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7764/8000 [01:00<00:01, 149.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7870/8000 [08:11<00:08, 16.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7784/8000 [01:00<00:01, 149.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7873/8000 [08:11<00:10, 12.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▌| 7803/8000 [01:00<00:01, 145.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7875/8000 [08:11<00:11, 10.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7824/8000 [01:01<00:01, 144.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7878/8000 [08:11<00:09, 12.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7841/8000 [01:01<00:01, 134.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▍| 7881/8000 [08:11<00:08, 14.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7863/8000 [01:01<00:01, 132.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▍| 7885/8000 [08:12<00:08, 13.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7885/8000 [01:01<00:00, 143.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7889/8000 [08:12<00:06, 16.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7909/8000 [01:01<00:00, 119.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7893/8000 [08:12<00:05, 19.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7924/8000 [01:01<00:00, 118.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7897/8000 [08:12<00:05, 17.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7944/8000 [01:02<00:00, 121.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7900/8000 [08:13<00:06, 14.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7962/8000 [01:02<00:00, 126.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7902/8000 [08:13<00:06, 14.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7982/8000 [01:02<00:00, 133.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7905/8000 [08:13<00:06, 15.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8008it [01:02, 127.94it/s]\n"
+      "Generating signals:  99%|██████████████████████████████████▌| 7907/8000 [08:13<00:06, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7910/8000 [08:13<00:06, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7912/8000 [08:14<00:06, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7915/8000 [08:14<00:05, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7918/8000 [08:14<00:05, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7921/8000 [08:14<00:06, 12.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7925/8000 [08:15<00:05, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7929/8000 [08:15<00:04, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7932/8000 [08:15<00:04, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7935/8000 [08:15<00:04, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7939/8000 [08:15<00:04, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7941/8000 [08:16<00:04, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7944/8000 [08:16<00:03, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7946/8000 [08:16<00:03, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7948/8000 [08:16<00:03, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7952/8000 [08:16<00:02, 18.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7958/8000 [08:16<00:02, 18.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7961/8000 [08:17<00:02, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7966/8000 [08:17<00:01, 19.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7969/8000 [08:17<00:01, 19.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7971/8000 [08:17<00:01, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7974/8000 [08:17<00:01, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7980/8000 [08:18<00:00, 22.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7985/8000 [08:18<00:00, 19.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7990/8000 [08:18<00:00, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7995/8000 [08:19<00:00, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|███████████████████████████████████| 8000/8000 [08:19<00:00, 16.02it/s]\n"
      ]
     },
     {
@@ -45039,186 +45053,214 @@
      "text": [
       "\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:05,  7.26s/it]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:38,  3.83s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:56,  5.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:50,  2.69s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:07<10:42,  3.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:05,  2.17s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:08<08:23,  2.56s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:07,  1.89s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:10<07:19,  2.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:43,  1.77s/it]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:12<06:41,  2.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:16,  1.64s/it]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:14<06:17,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:52,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:15<06:01,  1.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:40,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:17<05:49,  1.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:33,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:19<05:41,  1.79s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:26,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:20<05:35,  1.77s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:23,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:22<05:35,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:17,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:24<05:15,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:18,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:25<05:04,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:12,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:27<05:00,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:23,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:28<04:56,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:26,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:30<04:53,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:23,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:32<04:52,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:21,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:33<04:49,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:33<04:17,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:35<04:46,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:14,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:36<04:43,  1.57s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:36<04:12,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:38<04:46,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:16,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:40<04:45,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<04:15,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:41<04:44,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:15,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:43<04:44,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<04:12,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:44<04:43,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<04:16,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:46<04:43,  1.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:45<04:15,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:48<04:39,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<04:19,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:49<04:35,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<04:22,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:51<04:37,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<04:15,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:53<04:41,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<04:15,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:54<04:42,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<04:16,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:56<04:39,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:54<04:17,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:58<04:38,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:56<04:15,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:59<04:37,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:57<04:09,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [01:01<04:36,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:59<04:13,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [01:03<04:33,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:01<04:19,  1.60s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [01:04<04:33,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<04:17,  1.60s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [01:06<04:35,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:04<04:16,  1.60s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [01:08<04:33,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:06<04:17,  1.62s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [01:10<04:30,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:07<04:23,  1.67s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [01:11<04:31,  1.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:09<04:14,  1.62s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [01:13<04:29,  1.70s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:11<04:28,  1.72s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [01:15<04:24,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:13<04:29,  1.74s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:16<04:20,  1.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:14<04:22,  1.70s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:18<04:14,  1.64s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:16<04:28,  1.75s/it]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [01:19<04:09,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:18<04:19,  1.71s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:21<04:08,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:19<04:17,  1.70s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:23<04:05,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:21<04:05,  1.64s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:24<04:02,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:22<03:51,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [01:26<04:01,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:25<04:04,  1.65s/it]\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:29<04:21,  1.75s/it]\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.69s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.45it/s]\n",
+      "Evaluating NLL: 25it [00:01, 22.34it/s]\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 45it [00:01, 37.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 38it [00:00, 53.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 79it [00:01, 69.59it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 106.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 117it [00:01, 110.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 112it [00:01, 157.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 159it [00:01, 159.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 149it [00:01, 202.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 197it [00:02, 198.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 194it [00:01, 259.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 235it [00:02, 235.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 231it [00:01, 285.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 275it [00:02, 273.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 268it [00:01, 305.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 314it [00:02, 300.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 305it [00:01, 323.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 352it [00:02, 319.73it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 342it [00:01, 334.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 391it [00:02, 337.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 379it [00:01, 342.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 429it [00:02, 345.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 416it [00:01, 347.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 470it [00:02, 362.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 461it [00:02, 374.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 509it [00:02, 357.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 370.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 552it [00:02, 377.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 538it [00:02, 368.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 592it [00:03, 382.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 576it [00:02, 367.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 633it [00:03, 384.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 614it [00:02, 365.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 674it [00:03, 390.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 651it [00:02, 359.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 714it [00:03, 378.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 688it [00:02, 360.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 753it [00:03, 363.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 733it [00:02, 384.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 217.73it/s]\n",
+      "Evaluating NLL: 800it [00:03, 266.22it/s]\n",
       "\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 43.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 173\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 42.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 162\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|█▉          | 16/100 [00:00<00:01, 47.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:00<00:00, 164\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|██▌         | 21/100 [00:00<00:01, 45.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 166\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|███         | 26/100 [00:00<00:01, 45.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Bootstrapping to compute confidence interval:  31%|███▋        | 31/100 [00:00<00:01, 44.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [18:31<06:18, 378.77s/it]\u001b[A"
+      "Bootstrapping to compute confidence interval:  36%|████▎       | 36/100 [00:00<00:01, 43.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  42%|█████       | 42/100 [00:00<00:01, 45.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▋      | 47/100 [00:01<00:01, 44.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  52%|██████▏     | 52/100 [00:01<00:01, 44.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  57%|██████▊     | 57/100 [00:01<00:00, 44.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  62%|███████▍    | 62/100 [00:01<00:00, 43.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  68%|████████▏   | 68/100 [00:01<00:00, 46.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  73%|████████▊   | 73/100 [00:01<00:00, 44.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  78%|█████████▎  | 78/100 [00:01<00:00, 44.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|█████████▉  | 83/100 [00:01<00:00, 43.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  88%|██████████▌ | 88/100 [00:01<00:00, 43.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  94%|███████████▎| 94/100 [00:02<00:00, 46.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 44.87it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████| 4/4 [38:05<00:00, 720.57s/it]\u001b[A\n",
+      "Replicates:  40%|█████████▌              | 6/15 [5:08:50<6:36:47, 2645.25s/it]\n",
+      "Nyquist Samples:   0%|                                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -3.423826736029084\n",
-      "2999: -5.8266992\r"
+      "initial loss -5.062724722022335\n",
+      "2999: -5.9087285\r"
      ]
     },
     {
@@ -45227,5132 +45269,5204 @@
      "text": [
       "\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:55,  7.22s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:33,  5.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:39,  3.83s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:45,  2.96s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<08:58,  2.73s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<07:08,  2.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:07,  2.18s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:09<05:54,  1.81s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:15,  1.92s/it]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:10<05:11,  1.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:35,  1.73s/it]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:11<04:48,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:14,  1.63s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:12<04:34,  1.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<04:59,  1.56s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:14<04:24,  1.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:52,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:15<04:40,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:43,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:17<04:50,  1.53s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:39,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:18<04:29,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:30,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:19<04:15,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:30,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:21<04:04,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:24,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:22<03:58,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:26,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:23<03:53,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:25,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:24<04:01,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:28,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:26<03:59,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:29,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:27<03:54,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:33,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:28<03:51,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:35,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:30<03:51,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:33,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:31<03:54,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:32,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:32<03:55,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<04:33,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:34<03:51,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<04:34,  1.56s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:35<03:45,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:42<04:26,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:36<03:38,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:43<04:17,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:37<03:31,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<04:16,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:38<03:26,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:46<04:12,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:39<03:24,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<04:11,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:41<03:22,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:49<04:07,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:42<03:17,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:51<04:07,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:43<03:17,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:52<04:05,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:44<03:16,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:53<04:02,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:45<03:12,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:55<04:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:46<03:09,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:56<04:00,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:47<03:05,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:58<03:56,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:48<03:03,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:59<03:58,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:50<03:03,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:01<03:55,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:51<03:01,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<03:55,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:52<03:00,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:04<03:50,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:53<03:01,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:05<03:51,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:54<03:01,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:07<03:51,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:55<02:58,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:08<03:58,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:56<02:58,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:10<03:52,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:58<02:56,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:11<03:52,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:59<02:55,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:13<03:49,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [01:00<02:52,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:14<03:49,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:01<02:51,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:16<03:47,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:02<02:53,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:17<03:41,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:03<02:51,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:19<03:40,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [01:04<02:49,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:20<03:41,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:05<02:49,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:22<03:39,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:07<02:49,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:23<03:36,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:08<02:56,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:24<03:35,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:09<02:53,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:26<03:34,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:10<02:50,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:27<03:29,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:11<02:49,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:29<03:28,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:13<02:47,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:30<03:26,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:14<02:43,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:32<03:26,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:16<03:02,  1.29s/it]\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:33<03:25,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:35<03:25,  1.48s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:36<03:24,  1.48s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:39<03:36,  1.58s/it]\n",
+      "Evaluating NLL: 25it [00:01, 22.89it/s]\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.72s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 13.27it/s]\n",
+      "Evaluating NLL: 38it [00:00, 54.10it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 73it [00:01, 104.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 116it [00:01, 167.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 153it [00:01, 210.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 34it [00:01, 28.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 188it [00:01, 243.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 74it [00:01, 67.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 223it [00:01, 269.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 114it [00:01, 111.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 260it [00:01, 294.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 152it [00:01, 153.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 296it [00:01, 308.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 190it [00:02, 194.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 333it [00:01, 323.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 230it [00:02, 236.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 377it [00:01, 355.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 267it [00:02, 262.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 415it [00:01, 357.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 306it [00:02, 292.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 453it [00:02, 354.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 347it [00:02, 321.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 490it [00:02, 356.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 385it [00:02, 333.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 527it [00:02, 357.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 426it [00:02, 353.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 564it [00:02, 359.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 465it [00:02, 356.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 601it [00:02, 358.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 506it [00:02, 370.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 641it [00:02, 367.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 545it [00:03, 366.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 678it [00:02, 366.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 583it [00:03, 356.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 715it [00:02, 364.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 620it [00:03, 359.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 752it [00:02, 361.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 659it [00:03, 367.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 263.27it/s]\n",
       "\n",
-      "Evaluating NLL: 700it [00:03, 378.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 739it [00:03, 370.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 216.36it/s]\n",
+      "Bootstrapping to compute confidence interval:   9%|█▏           | 9/100 [00:00<00:01, 85.25it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Bootstrapping to compute confidence interval:  18%|██▏         | 18/100 [00:00<00:01, 54.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|███         | 25/100 [00:00<00:01, 50.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 177\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|███▋        | 31/100 [00:00<00:01, 47.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 153\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|████▋       | 39/100 [00:00<00:01, 55.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 151\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█████▍      | 45/100 [00:00<00:01, 51.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  71%|▋| 71/100 [00:00<00:00, 162\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|██████      | 51/100 [00:00<00:01, 49.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 15\n",
+      "Bootstrapping to compute confidence interval:  57%|██████▊     | 57/100 [00:01<00:00, 47.04it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Bootstrapping to compute confidence interval:  62%|███████▍    | 62/100 [00:01<00:00, 46.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|████████▏   | 68/100 [00:01<00:00, 47.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 1/8000 [00:00<20:01,  6.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|████████▊   | 73/100 [00:01<00:00, 46.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 5/8000 [00:00<07:49, 17.02it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|█████████▎  | 78/100 [00:01<00:00, 46.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 8/8000 [00:00<09:46, 13.62it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|█████████▉  | 83/100 [00:01<00:00, 45.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 10/8000 [00:00<09:18, 14.30it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|██████████▌ | 88/100 [00:01<00:00, 44.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 12/8000 [00:00<09:10, 14.50it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 49.17it/s]\n",
       "\n",
-      "Generating signals:   0%|                   | 16/8000 [00:00<07:13, 18.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 18/8000 [00:01<08:07, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 20/8000 [00:01<08:46, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 178/8000 [00:00<00:08, 946.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 23/8000 [00:01<08:08, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▌                                 | 362/8000 [00:00<00:07, 984.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 26/8000 [00:01<09:08, 14.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                               | 544/8000 [00:00<00:07, 1031.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 28/8000 [00:01<09:09, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                               | 713/8000 [00:00<00:06, 1082.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 30/8000 [00:02<11:39, 11.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                               | 904/8000 [00:01<00:18, 377.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 32/8000 [00:02<12:40, 10.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                             | 1076/8000 [00:01<00:14, 468.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 36/8000 [00:02<09:17, 14.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1262/8000 [00:02<00:11, 570.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 39/8000 [00:02<08:01, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████                            | 1439/8000 [00:02<00:10, 655.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 43/8000 [00:02<07:45, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                           | 1617/8000 [00:02<00:08, 724.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 45/8000 [00:03<08:01, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1782/8000 [00:02<00:08, 773.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 47/8000 [00:03<08:09, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▍                         | 1971/8000 [00:02<00:06, 896.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 50/8000 [00:03<07:12, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████                         | 2144/8000 [00:02<00:06, 894.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 55/8000 [00:03<07:02, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▊                        | 2317/8000 [00:03<00:06, 922.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 59/8000 [00:03<06:06, 21.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▌                       | 2487/8000 [00:03<00:06, 914.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 63/8000 [00:03<05:34, 23.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2667/8000 [00:03<00:05, 974.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 68/8000 [00:04<07:07, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████                      | 2847/8000 [00:03<00:05, 965.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 73/8000 [00:04<06:58, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3023/8000 [00:03<00:05, 946.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 76/8000 [00:04<07:45, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3207/8000 [00:04<00:05, 946.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 81/8000 [00:04<06:16, 21.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▎                   | 3381/8000 [00:04<00:04, 948.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 84/8000 [00:05<07:13, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████                   | 3555/8000 [00:04<00:04, 935.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 87/8000 [00:05<06:53, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▊                  | 3734/8000 [00:04<00:04, 931.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 90/8000 [00:05<10:53, 12.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3917/8000 [00:04<00:04, 951.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 93/8000 [00:05<09:36, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▍                | 4092/8000 [00:05<00:04, 933.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 95/8000 [00:05<09:26, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▏               | 4266/8000 [00:05<00:03, 938.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 98/8000 [00:06<08:25, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▊               | 4434/8000 [00:05<00:03, 911.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 100/8000 [00:06<08:46, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▌              | 4605/8000 [00:05<00:03, 901.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 103/8000 [00:06<07:40, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▎             | 4768/8000 [00:05<00:03, 894.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 105/8000 [00:06<08:00, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|████████████████████▉             | 4936/8000 [00:05<00:03, 902.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 109/8000 [00:06<07:49, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5098/8000 [00:06<00:03, 881.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 112/8000 [00:07<10:38, 12.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5263/8000 [00:06<00:03, 901.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 115/8000 [00:07<09:03, 14.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████           | 5425/8000 [00:06<00:02, 886.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 118/8000 [00:07<08:07, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▊          | 5604/8000 [00:06<00:02, 934.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 122/8000 [00:07<06:47, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▌         | 5774/8000 [00:06<00:02, 997.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 126/8000 [00:07<07:17, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5944/8000 [00:07<00:02, 954.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 129/8000 [00:07<06:51, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6106/8000 [00:07<00:01, 947.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 136/8000 [00:08<06:04, 21.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▋       | 6288/8000 [00:07<00:01, 950.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 140/8000 [00:08<06:54, 18.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|██████████████████████████▋      | 6464/8000 [00:07<00:01, 1002.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 142/8000 [00:08<07:13, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6642/8000 [00:07<00:01, 990.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 144/8000 [00:08<07:47, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6810/8000 [00:07<00:01, 960.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 148/8000 [00:08<06:25, 20.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6990/8000 [00:08<00:01, 925.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 151/8000 [00:08<06:21, 20.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▍   | 7157/8000 [00:08<00:00, 907.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 155/8000 [00:09<07:20, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7333/8000 [00:08<00:00, 913.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 158/8000 [00:09<06:44, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7505/8000 [00:08<00:00, 927.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 162/8000 [00:09<07:14, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7673/8000 [00:08<00:00, 919.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 165/8000 [00:09<06:58, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7853/8000 [00:09<00:00, 917.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 168/8000 [00:10<09:24, 13.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8017it [00:09, 862.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   2%|▍                 | 171/8000 [00:10<08:30, 15.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 173/8000 [00:10<08:17, 15.73it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 175/8000 [00:10<08:29, 15.36it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<16:25,  4.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 178/8000 [00:10<07:30, 17.38it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:17,  2.82s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 181/8000 [00:10<08:45, 14.87it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:57,  2.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 185/8000 [00:11<08:33, 15.22it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:49,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 189/8000 [00:11<06:46, 19.20it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:10<05:13,  1.61s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 193/8000 [00:11<06:00, 21.68it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:11<04:45,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 198/8000 [00:11<05:00, 25.93it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 202/8000 [00:11<05:54, 22.01it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:13<04:16,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 205/8000 [00:11<05:57, 21.81it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:14<04:07,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 208/8000 [00:12<05:47, 22.41it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 212/8000 [00:12<06:55, 18.75it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:17<04:00,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 217/8000 [00:12<05:34, 23.28it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:18<03:59,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 221/8000 [00:12<06:28, 20.04it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:20<03:56,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 224/8000 [00:12<06:17, 20.61it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:21<03:56,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 230/8000 [00:13<06:03, 21.40it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 234/8000 [00:13<05:43, 22.59it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:23<03:54,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 237/8000 [00:13<08:18, 15.57it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:25<03:50,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 242/8000 [00:13<07:36, 16.99it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:26<04:20,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 244/8000 [00:14<07:54, 16.36it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:27<03:59,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 248/8000 [00:14<06:47, 19.00it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:29<03:45,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 252/8000 [00:14<06:08, 21.03it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:30<03:37,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 256/8000 [00:14<08:51, 14.58it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:31<03:30,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 259/8000 [00:15<09:40, 13.34it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:32<03:26,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 262/8000 [00:15<09:43, 13.26it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:33<03:23,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 264/8000 [00:15<09:19, 13.82it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:34<03:20,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 266/8000 [00:15<09:14, 13.95it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:35<03:19,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 269/8000 [00:15<08:43, 14.76it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:36<03:15,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 272/8000 [00:15<07:42, 16.70it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:38<03:11,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 274/8000 [00:16<07:54, 16.27it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:39<03:08,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 277/8000 [00:16<08:56, 14.41it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:40<03:03,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 280/8000 [00:16<07:58, 16.12it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:41<03:05,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 283/8000 [00:16<07:07, 18.07it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:42<03:04,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 289/8000 [00:16<05:06, 25.18it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:43<03:05,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 294/8000 [00:16<04:43, 27.21it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:44<03:04,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 298/8000 [00:16<04:32, 28.23it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:45<03:10,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 302/8000 [00:17<06:13, 20.61it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:47<03:10,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 305/8000 [00:17<07:04, 18.13it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:48<03:11,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 309/8000 [00:17<07:53, 16.23it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:49<03:10,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 312/8000 [00:17<07:36, 16.83it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:50<03:12,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 317/8000 [00:18<06:28, 19.77it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:51<03:08,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 320/8000 [00:18<06:07, 20.88it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:52<03:06,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 324/8000 [00:18<08:21, 15.29it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:54<03:03,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 328/8000 [00:18<08:34, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:55<03:00,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 333/8000 [00:19<07:02, 18.14it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:56<02:59,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 336/8000 [00:19<07:46, 16.41it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:57<02:58,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 339/8000 [00:19<07:15, 17.60it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:58<02:59,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 344/8000 [00:19<05:43, 22.29it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:59<03:01,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 347/8000 [00:19<05:45, 22.16it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:01<03:01,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 351/8000 [00:19<05:10, 24.62it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:02<03:01,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 355/8000 [00:20<06:01, 21.17it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [01:03<03:01,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 358/8000 [00:20<08:21, 15.23it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:04<02:58,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 361/8000 [00:20<07:56, 16.05it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:05<02:54,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 365/8000 [00:20<08:03, 15.79it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:07<02:54,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 368/8000 [00:21<07:38, 16.66it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:08<02:51,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 370/8000 [00:21<07:47, 16.33it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:09<02:48,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 372/8000 [00:21<11:44, 10.83it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:10<02:49,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 376/8000 [00:21<09:01, 14.09it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:11<02:54,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 379/8000 [00:22<09:41, 13.10it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:13<02:55,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 383/8000 [00:22<07:51, 16.16it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:14<02:55,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 387/8000 [00:22<06:40, 19.02it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:15<02:54,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 392/8000 [00:22<05:24, 23.41it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:16<02:52,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 396/8000 [00:22<05:00, 25.32it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:18<02:50,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 400/8000 [00:22<06:09, 20.57it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [01:20<02:55,  1.28s/it]\n",
       "\n",
-      "Generating signals:   5%|▉                 | 404/8000 [00:23<07:01, 18.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 407/8000 [00:23<07:55, 15.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 409/8000 [00:23<09:51, 12.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 411/8000 [00:23<09:29, 13.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 19.54it/s]\n",
       "\n",
-      "Generating signals:   5%|▉                 | 413/8000 [00:23<09:41, 13.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 417/8000 [00:24<07:15, 17.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 420/8000 [00:24<06:53, 18.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 427/8000 [00:24<05:56, 21.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 37it [00:00, 52.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 432/8000 [00:24<06:26, 19.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 79it [00:01, 114.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 435/8000 [00:24<06:31, 19.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 117it [00:01, 165.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 438/8000 [00:25<07:34, 16.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 152it [00:01, 204.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 444/8000 [00:25<05:30, 22.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 187it [00:01, 238.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 448/8000 [00:25<06:34, 19.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 225it [00:01, 273.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 453/8000 [00:25<07:03, 17.83it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 262it [00:01, 297.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 456/8000 [00:26<07:08, 17.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 299it [00:01, 315.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 458/8000 [00:26<09:38, 13.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 344it [00:01, 350.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 461/8000 [00:26<08:22, 15.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 383it [00:01, 354.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 464/8000 [00:26<07:37, 16.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 421it [00:01, 356.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 468/8000 [00:26<07:45, 16.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 459it [00:02, 358.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 472/8000 [00:27<08:06, 15.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 496it [00:02, 355.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 474/8000 [00:27<09:48, 12.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 533it [00:02, 357.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 478/8000 [00:27<07:42, 16.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 570it [00:02, 358.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 480/8000 [00:27<07:32, 16.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 613it [00:02, 379.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 483/8000 [00:27<06:43, 18.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 652it [00:02, 374.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 486/8000 [00:28<06:07, 20.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 690it [00:02, 370.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 490/8000 [00:28<06:42, 18.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 728it [00:02, 368.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 495/8000 [00:28<06:08, 20.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 264.80it/s]\n",
       "\n",
-      "Generating signals:   6%|█                 | 499/8000 [00:28<06:31, 19.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 504/8000 [00:28<05:19, 23.43it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 511/8000 [00:29<05:40, 22.03it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   6%|▊            | 6/100 [00:00<00:01, 49.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 514/8000 [00:29<05:32, 22.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  11%|█▎          | 11/100 [00:00<00:01, 45.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 518/8000 [00:29<06:19, 19.74it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|█▉          | 16/100 [00:00<00:01, 45.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 522/8000 [00:29<05:36, 22.21it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|██▌         | 21/100 [00:00<00:01, 44.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 529/8000 [00:29<04:07, 30.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|███         | 26/100 [00:00<00:01, 43.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 536/8000 [00:30<04:14, 29.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|████▏       | 35/100 [00:00<00:01, 57.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 540/8000 [00:30<06:38, 18.70it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████▉       | 41/100 [00:00<00:01, 51.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 544/8000 [00:30<07:59, 15.54it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▋      | 47/100 [00:00<00:01, 49.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 547/8000 [00:31<08:42, 14.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|██████▎     | 53/100 [00:01<00:00, 47.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 549/8000 [00:31<08:57, 13.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|███████▌    | 63/100 [00:01<00:00, 60.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 551/8000 [00:31<14:48,  8.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|████████▍   | 70/100 [00:01<00:00, 53.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 556/8000 [00:32<11:06, 11.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|█████████   | 76/100 [00:01<00:00, 50.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 559/8000 [00:32<09:40, 12.82it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|█████████▊  | 82/100 [00:01<00:00, 48.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 564/8000 [00:32<08:17, 14.93it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|██████████▍ | 87/100 [00:01<00:00, 46.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 567/8000 [00:32<08:54, 13.91it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:01<00:00, 50.16it/s]\n",
       "\n",
-      "Generating signals:   7%|█▎                | 569/8000 [00:33<10:15, 12.08it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|██████████▊                                | 1/4 [08:18<24:54, 498.32s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.1689160805756975\n",
+      "2999: -6.2008588\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   7%|█▎                | 571/8000 [00:33<09:50, 12.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 573/8000 [00:33<09:16, 13.34it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 575/8000 [00:33<08:57, 13.81it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<16:09,  4.87s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 578/8000 [00:33<07:39, 16.15it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:07,  2.76s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 580/8000 [00:33<07:32, 16.39it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:37,  2.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 585/8000 [00:33<06:40, 18.53it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:29,  1.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 589/8000 [00:34<07:00, 17.62it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<04:50,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 591/8000 [00:34<08:48, 14.01it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:10<04:28,  1.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 598/8000 [00:34<05:23, 22.87it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:11<04:12,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 607/8000 [00:34<04:22, 28.17it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:13<04:00,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 611/8000 [00:35<05:11, 23.70it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:14<03:56,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 617/8000 [00:35<04:31, 27.15it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:15<03:51,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 622/8000 [00:35<04:50, 25.37it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:16<03:47,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 629/8000 [00:35<04:40, 26.30it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:17<03:43,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 634/8000 [00:36<05:18, 23.14it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:19<03:46,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 638/8000 [00:36<06:13, 19.71it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:20<03:47,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 641/8000 [00:36<06:08, 19.95it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:21<03:40,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 645/8000 [00:36<06:37, 18.52it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:22<03:37,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 648/8000 [00:36<06:27, 18.96it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:23<03:35,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 651/8000 [00:37<08:36, 14.23it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:24<03:35,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 656/8000 [00:37<06:33, 18.65it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:26<03:37,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 659/8000 [00:37<06:12, 19.69it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:27<03:34,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 662/8000 [00:37<06:58, 17.53it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:28<03:34,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 665/8000 [00:37<06:33, 18.63it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:29<03:32,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 668/8000 [00:37<06:06, 19.99it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:30<03:29,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 671/8000 [00:38<05:37, 21.70it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:32<03:27,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 678/8000 [00:38<04:58, 24.57it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:33<03:32,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 682/8000 [00:38<04:42, 25.94it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:34<03:27,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 688/8000 [00:38<04:02, 30.19it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:35<03:22,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 692/8000 [00:38<03:59, 30.53it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:36<03:21,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 697/8000 [00:38<04:42, 25.87it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:37<03:18,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 700/8000 [00:39<05:00, 24.28it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:39<03:14,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 703/8000 [00:39<04:49, 25.21it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:40<03:13,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 706/8000 [00:39<04:56, 24.64it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:41<03:12,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 712/8000 [00:39<07:16, 16.70it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:42<03:12,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 716/8000 [00:40<07:25, 16.34it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:43<03:12,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 722/8000 [00:40<05:33, 21.81it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:44<03:09,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 728/8000 [00:40<04:31, 26.75it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:46<03:11,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 734/8000 [00:40<05:29, 22.06it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:47<03:11,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 743/8000 [00:41<04:39, 25.98it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:48<03:11,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 747/8000 [00:41<05:18, 22.79it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:49<03:10,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 751/8000 [00:41<05:53, 20.50it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:50<03:09,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 755/8000 [00:41<06:33, 18.41it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:51<03:05,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 758/8000 [00:41<06:19, 19.07it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:53<03:04,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 767/8000 [00:42<05:35, 21.56it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:54<03:01,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 770/8000 [00:42<05:22, 22.39it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:55<03:01,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 773/8000 [00:42<05:18, 22.72it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:56<02:59,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 781/8000 [00:42<04:34, 26.31it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:57<02:56,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 784/8000 [00:42<04:34, 26.30it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:58<03:01,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 791/8000 [00:43<04:37, 25.96it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:00<03:09,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 796/8000 [00:43<05:03, 23.75it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:01<03:03,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 800/8000 [00:43<05:20, 22.48it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [01:02<03:00,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 803/8000 [00:43<05:16, 22.76it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:03<02:55,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 809/8000 [00:43<04:13, 28.40it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:04<02:53,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 814/8000 [00:44<05:50, 20.53it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:06<02:51,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 817/8000 [00:44<05:43, 20.89it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:07<02:49,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 820/8000 [00:44<07:54, 15.12it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:08<02:47,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 823/8000 [00:45<08:43, 13.70it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:09<02:44,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 826/8000 [00:45<10:05, 11.85it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:10<02:43,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 830/8000 [00:45<08:00, 14.93it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:11<02:45,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 832/8000 [00:45<07:46, 15.37it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:13<02:48,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 836/8000 [00:45<06:34, 18.14it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:14<02:50,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 839/8000 [00:46<07:26, 16.04it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:15<02:46,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 842/8000 [00:46<06:55, 17.22it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:16<02:52,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 844/8000 [00:46<08:35, 13.87it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [01:18<02:48,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 846/8000 [00:46<08:24, 14.19it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [01:19<02:44,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 848/8000 [00:46<08:07, 14.66it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [01:20<02:43,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 852/8000 [00:46<06:33, 18.15it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [01:21<02:50,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 855/8000 [00:47<06:13, 19.15it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [01:23<02:55,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 859/8000 [00:47<05:27, 21.83it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [01:24<02:57,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 862/8000 [00:47<06:00, 19.82it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [01:26<02:59,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 865/8000 [00:47<07:30, 15.84it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [01:27<02:59,  1.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 868/8000 [00:47<08:57, 13.28it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [01:28<03:00,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 871/8000 [00:48<07:41, 15.43it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [01:31<02:43,  1.27s/it]\n",
       "\n",
-      "Generating signals:  11%|█▉                | 875/8000 [00:48<07:43, 15.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 878/8000 [00:48<07:16, 16.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 886/8000 [00:48<04:53, 24.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 889/8000 [00:48<06:36, 17.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 15.92it/s]\n",
       "\n",
-      "Generating signals:  11%|██                | 892/8000 [00:49<06:41, 17.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 894/8000 [00:49<07:18, 16.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 898/8000 [00:49<05:49, 20.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 903/8000 [00:49<06:14, 18.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 38it [00:01, 50.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 906/8000 [00:49<05:45, 20.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 101.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 909/8000 [00:49<05:46, 20.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 112it [00:01, 150.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 913/8000 [00:50<06:32, 18.05it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 148it [00:01, 193.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 919/8000 [00:50<05:56, 19.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 185it [00:01, 233.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 924/8000 [00:50<05:07, 22.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 222it [00:01, 265.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 930/8000 [00:50<05:00, 23.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 259it [00:01, 290.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 933/8000 [00:51<06:05, 19.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 296it [00:01, 309.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 937/8000 [00:51<06:31, 18.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 332it [00:01, 322.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 941/8000 [00:51<06:43, 17.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 369it [00:01, 333.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 946/8000 [00:51<06:22, 18.43it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 408it [00:02, 347.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 949/8000 [00:52<06:05, 19.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 445it [00:02, 346.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 953/8000 [00:52<06:29, 18.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 482it [00:02, 351.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 955/8000 [00:52<07:03, 16.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 519it [00:02, 354.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 959/8000 [00:52<07:32, 15.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 555it [00:02, 353.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 963/8000 [00:53<08:41, 13.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 592it [00:02, 355.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 969/8000 [00:53<07:52, 14.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 632it [00:02, 366.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 975/8000 [00:53<08:12, 14.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 669it [00:02, 364.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 978/8000 [00:54<09:18, 12.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 706it [00:02, 361.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 980/8000 [00:54<09:36, 12.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 743it [00:02, 359.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 982/8000 [00:54<10:43, 10.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 254.83it/s]\n",
       "\n",
-      "Generating signals:  12%|██▏               | 984/8000 [00:54<10:58, 10.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 989/8000 [00:55<10:46, 10.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 992/8000 [00:55<09:57, 11.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|█            | 8/100 [00:00<00:01, 68.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 994/8000 [00:55<09:56, 11.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 51.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏              | 1000/8000 [00:55<06:38, 17.56it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|██▌         | 21/100 [00:00<00:01, 48.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1005/8000 [00:56<06:31, 17.86it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|███         | 26/100 [00:00<00:01, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1008/8000 [00:56<07:03, 16.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|███▋        | 31/100 [00:00<00:01, 44.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1013/8000 [00:56<07:22, 15.78it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|████▋       | 39/100 [00:00<00:01, 53.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1015/8000 [00:56<07:21, 15.84it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█████▍      | 45/100 [00:00<00:01, 49.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1023/8000 [00:57<05:18, 21.89it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|██████      | 51/100 [00:01<00:01, 47.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1026/8000 [00:57<06:01, 19.31it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|██████▋     | 56/100 [00:01<00:00, 46.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1028/8000 [00:57<06:11, 18.78it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|███████▎    | 61/100 [00:01<00:00, 46.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1031/8000 [00:57<05:40, 20.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  67%|████████    | 67/100 [00:01<00:00, 47.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1039/8000 [00:57<04:32, 25.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|████████▋   | 72/100 [00:01<00:00, 46.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1044/8000 [00:57<03:59, 29.06it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|█████████▏  | 77/100 [00:01<00:00, 46.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1050/8000 [00:58<03:29, 33.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|█████████▊  | 82/100 [00:01<00:00, 45.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1057/8000 [00:58<03:37, 31.92it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|██████████▍ | 87/100 [00:01<00:00, 44.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1064/8000 [00:58<02:59, 38.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 48.51it/s]\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1071/8000 [00:58<04:38, 24.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1077/8000 [00:58<03:55, 29.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1081/8000 [00:59<05:16, 21.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                   | 51/8000 [00:00<00:25, 313.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1085/8000 [00:59<05:38, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 97/8000 [00:00<00:26, 293.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1090/8000 [00:59<05:24, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                  | 149/8000 [00:00<00:25, 306.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1094/8000 [00:59<04:54, 23.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                  | 205/8000 [00:00<00:24, 324.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1100/8000 [01:00<05:51, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                  | 250/8000 [00:00<00:25, 304.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1104/8000 [01:00<06:02, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                 | 309/8000 [00:00<00:22, 336.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1107/8000 [01:00<05:42, 20.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                 | 358/8000 [00:01<00:22, 335.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1112/8000 [01:00<05:25, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                 | 412/8000 [00:01<00:20, 371.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1116/8000 [01:00<04:51, 23.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                 | 459/8000 [00:01<00:21, 344.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1119/8000 [01:01<04:42, 24.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                | 516/8000 [00:01<00:21, 356.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1125/8000 [01:01<05:28, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                | 562/8000 [00:01<00:21, 338.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1128/8000 [01:01<06:07, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                | 609/8000 [00:01<00:20, 357.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1131/8000 [01:01<05:54, 19.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                | 654/8000 [00:01<00:22, 329.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1134/8000 [01:01<05:38, 20.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                | 703/8000 [00:02<00:22, 325.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1137/8000 [01:02<05:21, 21.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▎                               | 760/8000 [00:02<00:21, 342.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1140/8000 [01:02<05:03, 22.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                               | 800/8000 [00:02<00:20, 346.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1145/8000 [01:02<04:07, 27.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▋                               | 853/8000 [00:02<00:21, 338.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1150/8000 [01:02<03:38, 31.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                               | 908/8000 [00:02<00:20, 348.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1155/8000 [01:02<04:07, 27.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 971/8000 [00:02<00:19, 367.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1158/8000 [01:02<06:01, 18.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▎                             | 1027/8000 [00:02<00:17, 395.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1162/8000 [01:03<06:15, 18.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                             | 1074/8000 [00:03<00:18, 368.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1166/8000 [01:03<06:30, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                             | 1136/8000 [00:03<00:17, 382.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1168/8000 [01:03<07:38, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                             | 1194/8000 [00:03<00:18, 377.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1173/8000 [01:03<05:42, 19.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1246/8000 [00:03<00:17, 388.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1177/8000 [01:03<05:50, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▋                            | 1342/8000 [00:03<00:19, 346.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1180/8000 [01:04<05:26, 20.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                            | 1394/8000 [00:03<00:18, 352.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1184/8000 [01:04<05:36, 20.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                           | 1450/8000 [00:04<00:17, 375.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1187/8000 [01:04<06:16, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                           | 1500/8000 [00:04<00:18, 354.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1190/8000 [01:04<05:42, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                           | 1542/8000 [00:04<00:19, 333.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1197/8000 [01:04<04:05, 27.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▋                           | 1588/8000 [00:04<00:19, 331.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1201/8000 [01:05<06:38, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                           | 1637/8000 [00:04<00:17, 354.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1205/8000 [01:05<06:45, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                          | 1696/8000 [00:04<00:18, 345.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1208/8000 [01:05<07:23, 15.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▍                          | 1742/8000 [00:05<00:19, 324.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1211/8000 [01:05<06:46, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1791/8000 [00:05<00:18, 342.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1214/8000 [01:05<06:17, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▊                          | 1840/8000 [00:05<00:18, 334.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1218/8000 [01:06<05:09, 21.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████                          | 1893/8000 [00:05<00:18, 335.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1223/8000 [01:06<04:12, 26.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1937/8000 [00:05<00:19, 317.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1228/8000 [01:06<04:49, 23.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▍                         | 1989/8000 [00:05<00:18, 326.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1233/8000 [01:06<04:11, 26.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                         | 2034/8000 [00:05<00:18, 317.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1237/8000 [01:06<05:00, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▊                         | 2073/8000 [00:06<00:19, 304.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1243/8000 [01:07<04:47, 23.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████                         | 2121/8000 [00:06<00:19, 306.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1248/8000 [01:07<04:56, 22.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▏                        | 2172/8000 [00:06<00:18, 311.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1252/8000 [01:07<05:25, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▍                        | 2218/8000 [00:06<00:18, 313.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1255/8000 [01:07<06:05, 18.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▋                        | 2281/8000 [00:06<00:17, 334.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1257/8000 [01:08<07:32, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                        | 2337/8000 [00:06<00:16, 340.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1261/8000 [01:08<06:04, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▏                       | 2388/8000 [00:07<00:16, 338.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1267/8000 [01:08<05:27, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                       | 2438/8000 [00:07<00:16, 330.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1270/8000 [01:08<07:16, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                       | 2505/8000 [00:08<00:39, 140.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1275/8000 [01:09<06:34, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▊                       | 2548/8000 [00:08<00:34, 158.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1279/8000 [01:09<05:49, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                       | 2593/8000 [00:08<00:30, 177.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1283/8000 [01:09<06:32, 17.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▏                      | 2640/8000 [00:08<00:26, 202.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1287/8000 [01:09<06:44, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▍                      | 2693/8000 [00:08<00:23, 229.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1289/8000 [01:09<08:05, 13.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                      | 2741/8000 [00:08<00:21, 248.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1291/8000 [01:10<07:50, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▊                      | 2794/8000 [00:09<00:19, 271.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1295/8000 [01:10<06:17, 17.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▏                     | 2856/8000 [00:09<00:16, 317.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1299/8000 [01:10<07:48, 14.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                     | 2919/8000 [00:09<00:14, 342.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1302/8000 [01:10<06:51, 16.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▋                     | 2974/8000 [00:09<00:14, 348.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1305/8000 [01:10<06:18, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3013/8000 [00:09<00:15, 319.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1309/8000 [01:11<05:37, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████                     | 3067/8000 [00:09<00:14, 339.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1312/8000 [01:11<05:33, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▏                    | 3107/8000 [00:09<00:15, 317.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1317/8000 [01:11<05:29, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▎                    | 3143/8000 [00:10<00:16, 291.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1321/8000 [01:11<04:49, 23.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▌                    | 3189/8000 [00:10<00:16, 298.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1325/8000 [01:11<04:25, 25.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▊                    | 3242/8000 [00:10<00:13, 341.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1329/8000 [01:11<04:05, 27.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▉                    | 3290/8000 [00:10<00:14, 325.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1332/8000 [01:11<04:18, 25.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▏                   | 3338/8000 [00:10<00:14, 328.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1337/8000 [01:11<03:36, 30.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▍                   | 3386/8000 [00:10<00:14, 318.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1344/8000 [01:12<03:41, 30.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▌                   | 3435/8000 [00:10<00:13, 348.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1348/8000 [01:12<05:41, 19.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▊                   | 3493/8000 [00:11<00:12, 349.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1351/8000 [01:12<05:27, 20.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████                   | 3546/8000 [00:11<00:12, 353.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1354/8000 [01:13<06:17, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▎                  | 3593/8000 [00:11<00:13, 337.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1359/8000 [01:13<06:08, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▍                  | 3641/8000 [00:11<00:12, 356.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1364/8000 [01:13<04:55, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▋                  | 3695/8000 [00:11<00:12, 340.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1369/8000 [01:13<04:11, 26.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▉                  | 3736/8000 [00:11<00:13, 316.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1373/8000 [01:13<05:55, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████                  | 3776/8000 [00:12<00:13, 311.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1376/8000 [01:14<05:43, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▏                 | 3823/8000 [00:12<00:12, 331.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1379/8000 [01:14<06:46, 16.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▍                 | 3862/8000 [00:12<00:13, 309.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1382/8000 [01:14<08:25, 13.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3919/8000 [00:12<00:12, 335.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1387/8000 [01:15<08:52, 12.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████████████▊                 | 3968/8000 [00:12<00:12, 327.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1393/8000 [01:15<07:07, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▏                | 4033/8000 [00:12<00:10, 382.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1398/8000 [01:15<05:40, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▍                | 4116/8000 [00:13<00:16, 235.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1403/8000 [01:15<06:30, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▋                | 4170/8000 [00:13<00:14, 257.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1408/8000 [01:16<06:21, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████▉                | 4214/8000 [00:13<00:14, 268.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1411/8000 [01:16<06:49, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▏               | 4268/8000 [00:13<00:12, 287.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1415/8000 [01:16<05:42, 19.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4324/8000 [00:13<00:11, 328.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1420/8000 [01:16<04:37, 23.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▌               | 4373/8000 [00:14<00:11, 318.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1426/8000 [01:16<03:45, 29.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▊               | 4433/8000 [00:14<00:10, 338.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1431/8000 [01:16<04:30, 24.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████               | 4482/8000 [00:14<00:10, 340.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1436/8000 [01:17<04:58, 21.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▎              | 4543/8000 [00:14<00:09, 383.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1439/8000 [01:17<05:55, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▌              | 4594/8000 [00:14<00:09, 367.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1442/8000 [01:17<06:49, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▋              | 4640/8000 [00:14<00:09, 353.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1447/8000 [01:17<05:34, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4686/8000 [00:14<00:09, 337.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1451/8000 [01:18<06:11, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████              | 4732/8000 [00:14<00:09, 350.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1456/8000 [01:18<05:06, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▎             | 4779/8000 [00:15<00:09, 331.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1460/8000 [01:18<05:59, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▌             | 4832/8000 [00:15<00:09, 330.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1463/8000 [01:18<05:55, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▋             | 4875/8000 [00:15<00:09, 319.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1468/8000 [01:19<05:38, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|████████████████████▉             | 4923/8000 [00:15<00:09, 334.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1472/8000 [01:19<06:09, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▏            | 4979/8000 [00:15<00:09, 326.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1477/8000 [01:19<04:58, 21.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5026/8000 [00:15<00:10, 282.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1482/8000 [01:19<05:07, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▌            | 5073/8000 [00:16<00:12, 237.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1487/8000 [01:19<04:21, 24.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5116/8000 [00:16<00:12, 223.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1490/8000 [01:19<04:21, 24.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|█████████████████████▉            | 5169/8000 [00:16<00:12, 230.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1493/8000 [01:20<05:41, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▏           | 5217/8000 [00:16<00:13, 205.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1498/8000 [01:20<04:31, 23.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5262/8000 [00:17<00:14, 191.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1502/8000 [01:20<05:10, 20.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5308/8000 [00:17<00:12, 212.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1505/8000 [01:20<05:05, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▊           | 5355/8000 [00:17<00:11, 236.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1509/8000 [01:20<04:40, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████████████████▉           | 5402/8000 [00:17<00:10, 255.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1513/8000 [01:21<04:19, 25.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▏          | 5459/8000 [00:17<00:08, 300.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1519/8000 [01:21<03:26, 31.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▍          | 5505/8000 [00:18<00:08, 300.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1523/8000 [01:21<04:29, 24.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▋          | 5569/8000 [00:18<00:07, 337.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1528/8000 [01:21<05:52, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▉          | 5625/8000 [00:18<00:06, 347.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1536/8000 [01:22<05:44, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5671/8000 [00:18<00:06, 350.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1539/8000 [01:22<05:31, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▎         | 5722/8000 [00:18<00:06, 332.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1542/8000 [01:22<06:23, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▌         | 5766/8000 [00:18<00:06, 325.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1545/8000 [01:22<06:02, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▋         | 5802/8000 [00:18<00:07, 296.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1548/8000 [01:23<06:54, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▉         | 5855/8000 [00:19<00:06, 332.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1551/8000 [01:23<06:13, 17.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████         | 5899/8000 [00:19<00:06, 314.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1554/8000 [01:23<07:04, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5946/8000 [00:19<00:06, 319.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1559/8000 [01:23<06:40, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6000/8000 [00:19<00:06, 328.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1562/8000 [01:23<06:03, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6046/8000 [00:19<00:05, 345.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1565/8000 [01:24<07:14, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6098/8000 [00:19<00:05, 338.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1567/8000 [01:24<07:06, 15.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6153/8000 [00:19<00:05, 351.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1569/8000 [01:24<06:53, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▎       | 6194/8000 [00:20<00:05, 318.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1571/8000 [01:24<06:50, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6241/8000 [00:20<00:05, 342.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1573/8000 [01:24<06:56, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▋       | 6284/8000 [00:20<00:05, 319.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1576/8000 [01:24<07:57, 13.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6325/8000 [00:20<00:05, 310.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1578/8000 [01:25<07:44, 13.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████       | 6371/8000 [00:20<00:05, 304.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1580/8000 [01:25<07:42, 13.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6419/8000 [00:20<00:04, 327.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1582/8000 [01:25<07:34, 14.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6472/8000 [00:20<00:04, 328.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1584/8000 [01:25<07:39, 13.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▋      | 6519/8000 [00:21<00:04, 329.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1586/8000 [01:25<07:30, 14.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6572/8000 [00:21<00:04, 334.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1589/8000 [01:25<06:22, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6626/8000 [00:21<00:03, 366.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1594/8000 [01:25<04:45, 22.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6672/8000 [00:21<00:03, 341.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1598/8000 [01:25<04:30, 23.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6718/8000 [00:21<00:03, 329.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1603/8000 [01:26<04:52, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6768/8000 [00:21<00:03, 328.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1606/8000 [01:26<04:52, 21.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6813/8000 [00:21<00:03, 336.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1612/8000 [01:26<04:42, 22.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6863/8000 [00:22<00:03, 325.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1616/8000 [01:26<04:22, 24.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▍    | 6917/8000 [00:22<00:03, 337.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1619/8000 [01:26<04:24, 24.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6966/8000 [00:22<00:03, 332.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1623/8000 [01:27<05:18, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7009/8000 [00:22<00:02, 342.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1626/8000 [01:27<06:40, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7054/8000 [00:22<00:03, 310.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1629/8000 [01:27<06:12, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7100/8000 [00:22<00:02, 304.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1634/8000 [01:27<05:05, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▍   | 7148/8000 [00:22<00:02, 322.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1637/8000 [01:27<04:50, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7195/8000 [00:23<00:02, 333.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1641/8000 [01:28<05:42, 18.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▊   | 7245/8000 [00:23<00:02, 329.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1644/8000 [01:28<05:36, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████   | 7297/8000 [00:23<00:02, 329.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1652/8000 [01:28<05:26, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7345/8000 [00:23<00:01, 340.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1656/8000 [01:29<06:05, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▍  | 7387/8000 [00:23<00:01, 345.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1659/8000 [01:29<05:45, 18.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7445/8000 [00:23<00:01, 349.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1661/8000 [01:29<06:12, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████▊  | 7492/8000 [00:23<00:01, 344.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1665/8000 [01:29<06:30, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████  | 7541/8000 [00:24<00:01, 336.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1668/8000 [01:29<07:16, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7580/8000 [00:24<00:01, 339.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1670/8000 [01:29<07:13, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▍ | 7620/8000 [00:24<00:01, 315.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1673/8000 [01:30<06:33, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7673/8000 [00:24<00:01, 318.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1676/8000 [01:30<06:03, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▊ | 7720/8000 [00:24<00:00, 310.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1680/8000 [01:30<05:11, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7773/8000 [00:24<00:00, 346.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1684/8000 [01:30<04:36, 22.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7830/8000 [00:24<00:00, 343.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1687/8000 [01:30<04:46, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7879/8000 [00:25<00:00, 342.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1690/8000 [01:30<05:52, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7924/8000 [00:25<00:00, 330.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1694/8000 [01:31<04:57, 21.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8025it [00:25, 314.16it/s]                                                  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  21%|███▌             | 1697/8000 [01:31<06:12, 16.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1699/8000 [01:31<06:21, 16.53it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1701/8000 [01:31<06:31, 16.07it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:44,  4.75s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1704/8000 [01:31<05:49, 18.03it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:06<08:56,  2.71s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1709/8000 [01:31<04:21, 24.04it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:30,  1.98s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1713/8000 [01:31<04:08, 25.33it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:23,  1.65s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1716/8000 [01:32<04:24, 23.75it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<04:47,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1721/8000 [01:32<04:47, 21.84it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:10<04:22,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1725/8000 [01:32<04:26, 23.58it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:11<04:11,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1732/8000 [01:32<04:18, 24.28it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:12<04:01,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1736/8000 [01:33<05:42, 18.29it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:14<03:58,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1741/8000 [01:33<05:28, 19.03it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:15<03:50,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1744/8000 [01:33<05:12, 20.04it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:16<03:47,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1748/8000 [01:33<04:40, 22.30it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:17<03:45,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1751/8000 [01:33<04:35, 22.70it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:18<03:51,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1758/8000 [01:33<03:17, 31.65it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:20<03:47,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1762/8000 [01:34<03:50, 27.06it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:21<03:42,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1766/8000 [01:34<04:25, 23.48it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:22<03:39,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1770/8000 [01:34<04:52, 21.30it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:23<03:38,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1776/8000 [01:34<04:37, 22.40it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:24<03:33,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1780/8000 [01:35<05:09, 20.10it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:25<03:30,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1784/8000 [01:35<05:27, 18.98it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:27<03:29,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1788/8000 [01:35<06:26, 16.08it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:28<03:27,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1793/8000 [01:35<05:09, 20.03it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:29<03:27,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1796/8000 [01:35<04:58, 20.80it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:30<03:27,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1802/8000 [01:36<04:36, 22.44it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:31<03:23,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1805/8000 [01:36<04:28, 23.08it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:32<03:22,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1813/8000 [01:36<03:56, 26.21it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:34<03:21,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1817/8000 [01:36<04:36, 22.37it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:35<03:20,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1820/8000 [01:36<04:31, 22.74it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:36<03:19,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1823/8000 [01:37<04:31, 22.78it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:37<03:16,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1832/8000 [01:37<05:08, 19.97it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:38<03:16,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1837/8000 [01:37<04:58, 20.63it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:39<03:14,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1840/8000 [01:37<04:46, 21.51it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:40<03:11,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1845/8000 [01:38<04:47, 21.44it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:42<03:12,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1848/8000 [01:38<04:42, 21.77it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:43<03:11,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1855/8000 [01:38<05:04, 20.15it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:44<03:12,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1859/8000 [01:38<04:32, 22.57it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:45<03:12,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1862/8000 [01:38<05:15, 19.44it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:47<03:23,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1866/8000 [01:39<05:33, 18.40it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:48<03:18,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1869/8000 [01:39<06:09, 16.59it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:49<03:15,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1873/8000 [01:39<05:09, 19.77it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:50<03:13,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1877/8000 [01:39<06:34, 15.54it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:51<03:09,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉             | 1882/8000 [01:40<05:09, 19.79it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:52<03:07,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1886/8000 [01:40<05:20, 19.09it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:54<03:08,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1891/8000 [01:40<05:51, 17.37it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:55<03:04,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1894/8000 [01:40<05:32, 18.36it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:56<02:59,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1897/8000 [01:40<05:16, 19.27it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:57<02:58,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1900/8000 [01:41<04:56, 20.60it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:58<02:59,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1903/8000 [01:41<07:33, 13.43it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:00<03:06,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1905/8000 [01:41<08:35, 11.83it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:01<03:08,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1907/8000 [01:41<08:01, 12.66it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [01:02<03:02,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1909/8000 [01:41<07:31, 13.48it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:03<03:00,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1913/8000 [01:42<05:42, 17.75it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:05<03:03,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1916/8000 [01:42<06:10, 16.42it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:06<02:57,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1918/8000 [01:42<06:22, 15.90it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:07<02:53,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1920/8000 [01:42<06:23, 15.86it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:08<02:57,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1924/8000 [01:42<05:00, 20.25it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:11<03:03,  1.27s/it]\n",
       "\n",
-      "Generating signals:  24%|████             | 1929/8000 [01:43<06:22, 15.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1931/8000 [01:43<06:19, 15.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1933/8000 [01:43<07:23, 13.69it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1935/8000 [01:43<06:51, 14.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 22.01it/s]\n",
       "\n",
-      "Generating signals:  24%|████             | 1939/8000 [01:43<05:26, 18.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1943/8000 [01:43<05:33, 18.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1945/8000 [01:43<05:33, 18.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1950/8000 [01:44<04:09, 24.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 38it [00:01, 44.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1954/8000 [01:44<03:40, 27.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 90.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1959/8000 [01:44<03:55, 25.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 117it [00:01, 145.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1963/8000 [01:44<04:27, 22.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 154it [00:01, 188.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1967/8000 [01:44<03:55, 25.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 191it [00:01, 226.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1972/8000 [01:44<04:16, 23.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 228it [00:01, 257.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1975/8000 [01:45<05:08, 19.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 264it [00:01, 245.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1978/8000 [01:45<04:54, 20.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 301it [00:01, 273.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1981/8000 [01:45<04:33, 22.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 342it [00:02, 306.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1985/8000 [01:45<04:54, 20.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 378it [00:02, 319.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1988/8000 [01:45<04:42, 21.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 414it [00:02, 326.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1992/8000 [01:46<05:05, 19.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 451it [00:02, 336.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1995/8000 [01:46<05:48, 17.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 488it [00:02, 343.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1999/8000 [01:46<04:50, 20.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 525it [00:02, 348.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2005/8000 [01:46<04:28, 22.30it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 561it [00:02, 349.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2008/8000 [01:46<04:16, 23.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 603it [00:02, 369.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2013/8000 [01:46<04:23, 22.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 641it [00:02, 362.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2016/8000 [01:47<04:13, 23.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 678it [00:02, 359.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2021/8000 [01:47<05:14, 19.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 715it [00:03, 359.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2027/8000 [01:47<03:59, 24.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 752it [00:03, 357.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2034/8000 [01:47<03:38, 27.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 240.25it/s]\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2039/8000 [01:48<03:56, 25.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2043/8000 [01:48<04:16, 23.24it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2046/8000 [01:48<05:01, 19.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 42.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2050/8000 [01:48<04:24, 22.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 42.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2056/8000 [01:48<04:10, 23.77it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 43.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2059/8000 [01:49<04:57, 19.98it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 43.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2064/8000 [01:49<04:06, 24.10it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|███         | 26/100 [00:00<00:01, 48.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2067/8000 [01:49<04:02, 24.47it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|███▋        | 31/100 [00:00<00:01, 47.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2073/8000 [01:49<03:52, 25.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|████▎       | 36/100 [00:00<00:01, 46.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2076/8000 [01:49<03:55, 25.19it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████▉       | 41/100 [00:00<00:01, 45.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2081/8000 [01:50<05:07, 19.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█████▌      | 46/100 [00:01<00:01, 44.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2086/8000 [01:50<04:50, 20.33it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|██████      | 51/100 [00:01<00:01, 43.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2089/8000 [01:50<04:48, 20.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|███████▏    | 60/100 [00:01<00:00, 53.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2092/8000 [01:50<05:19, 18.47it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|███████▉    | 66/100 [00:01<00:00, 50.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2095/8000 [01:50<05:02, 19.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|████████▋   | 72/100 [00:01<00:00, 48.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2098/8000 [01:50<04:46, 20.63it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|█████████▏  | 77/100 [00:01<00:00, 47.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2103/8000 [01:51<05:28, 17.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|██████████  | 84/100 [00:01<00:00, 52.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2108/8000 [01:51<05:01, 19.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  90%|██████████▊ | 90/100 [00:01<00:00, 49.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2114/8000 [01:51<04:32, 21.61it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 47.27it/s]\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2118/8000 [01:51<04:52, 20.09it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|█████████████████████▌                     | 2/4 [17:28<17:37, 528.81s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.007629061221412\n",
+      "2999: -6.2049668\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  27%|████▌            | 2121/8000 [01:52<05:28, 17.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2126/8000 [01:52<04:18, 22.74it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2132/8000 [01:52<04:08, 23.62it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:30,  4.68s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2138/8000 [01:52<03:17, 29.63it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<08:39,  2.63s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2144/8000 [01:52<03:22, 28.96it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:24,  1.95s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2151/8000 [01:53<03:23, 28.71it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:17,  1.62s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2156/8000 [01:53<04:12, 23.19it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<04:40,  1.44s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2160/8000 [01:53<03:49, 25.49it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:10<04:15,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2166/8000 [01:53<03:48, 25.53it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:11<04:02,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2171/8000 [01:53<03:57, 24.54it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:12<03:51,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2175/8000 [01:54<04:21, 22.29it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:13<03:42,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2178/8000 [01:54<05:14, 18.50it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:14<03:36,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2182/8000 [01:54<05:22, 18.03it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:15<03:31,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2186/8000 [01:54<05:31, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:16<03:28,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2189/8000 [01:54<05:02, 19.19it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:18<03:28,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2193/8000 [01:55<04:20, 22.27it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:19<03:29,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2196/8000 [01:55<04:09, 23.27it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:20<03:29,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2201/8000 [01:55<04:11, 23.04it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:21<03:26,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2204/8000 [01:55<04:56, 19.55it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:22<03:23,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2207/8000 [01:55<04:43, 20.41it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:23<03:23,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2210/8000 [01:55<05:17, 18.24it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:24<03:21,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2214/8000 [01:56<05:34, 17.32it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:25<03:21,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2218/8000 [01:56<04:40, 20.58it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:27<03:28,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2221/8000 [01:56<04:23, 21.92it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:28<03:23,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2226/8000 [01:56<05:13, 18.44it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:29<03:22,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2231/8000 [01:57<05:10, 18.56it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:30<03:19,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2234/8000 [01:57<04:50, 19.84it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:31<03:15,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2241/8000 [01:57<04:54, 19.56it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:32<03:13,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2244/8000 [01:58<07:46, 12.33it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:33<03:11,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2246/8000 [01:58<08:24, 11.40it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:34<03:10,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2249/8000 [01:58<07:12, 13.31it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:35<03:06,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2254/8000 [01:58<05:12, 18.41it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:37<03:06,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2257/8000 [01:58<04:45, 20.10it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:38<03:05,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2260/8000 [01:58<04:41, 20.36it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:39<03:06,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2265/8000 [01:59<04:27, 21.41it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:40<03:03,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2268/8000 [01:59<05:15, 18.18it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:41<03:01,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2275/8000 [01:59<04:17, 22.24it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:42<03:00,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2279/8000 [01:59<04:39, 20.45it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:43<03:01,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2282/8000 [01:59<04:26, 21.43it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:44<03:01,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2285/8000 [02:00<04:24, 21.63it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:45<02:58,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2290/8000 [02:00<04:29, 21.16it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:46<02:58,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2293/8000 [02:00<05:09, 18.42it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:48<02:56,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2295/8000 [02:00<06:18, 15.08it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:49<02:55,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2299/8000 [02:00<05:04, 18.70it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:50<02:54,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2304/8000 [02:01<04:44, 20.05it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:51<02:52,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2308/8000 [02:01<04:07, 22.97it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:52<02:49,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2312/8000 [02:01<04:40, 20.29it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:53<03:02,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2318/8000 [02:01<04:53, 19.33it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:54<02:58,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2326/8000 [02:02<03:57, 23.88it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:56<02:59,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2330/8000 [02:02<04:23, 21.51it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:57<02:55,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2334/8000 [02:02<04:42, 20.05it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:58<02:51,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2339/8000 [02:02<03:56, 23.95it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:59<02:47,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2343/8000 [02:02<04:09, 22.65it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:00<02:45,  1.11s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2349/8000 [02:02<03:15, 28.91it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:01<02:42,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2354/8000 [02:03<03:39, 25.73it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:02<02:40,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2359/8000 [02:03<03:46, 24.92it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:03<02:38,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2367/8000 [02:03<03:22, 27.77it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:04<02:36,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2370/8000 [02:03<04:12, 22.30it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:06<02:44,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2376/8000 [02:04<05:08, 18.23it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:07<02:45,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2383/8000 [02:04<05:08, 18.18it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:08<02:44,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2386/8000 [02:04<04:55, 19.00it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:09<02:39,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2391/8000 [02:04<04:01, 23.20it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:10<02:44,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2394/8000 [02:05<04:44, 19.68it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:11<02:44,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2397/8000 [02:05<04:33, 20.47it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:13<02:39,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2401/8000 [02:05<04:55, 18.95it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [01:14<02:35,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2408/8000 [02:05<04:05, 22.76it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [01:15<02:35,  1.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2412/8000 [02:05<03:42, 25.06it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [01:16<02:44,  1.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2419/8000 [02:06<03:34, 26.03it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [01:18<02:50,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2422/8000 [02:06<04:27, 20.86it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [01:19<02:53,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2427/8000 [02:06<04:20, 21.40it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [01:20<02:56,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2430/8000 [02:07<06:28, 14.33it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [01:22<02:59,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2434/8000 [02:07<05:18, 17.50it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [01:23<03:00,  1.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2438/8000 [02:07<04:34, 20.30it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [01:25<02:59,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2441/8000 [02:07<05:11, 17.86it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [01:26<02:58,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2444/8000 [02:07<04:43, 19.59it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▌                              | 73/200 [01:28<02:58,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2448/8000 [02:08<05:48, 15.94it/s]\u001b[A\u001b[A\n",
+      "training:  37%|█████████████████▊                              | 74/200 [01:29<02:57,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2451/8000 [02:08<05:21, 17.29it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████                              | 75/200 [01:30<02:55,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2456/8000 [02:08<04:09, 22.18it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▏                             | 76/200 [01:32<02:54,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2459/8000 [02:08<04:00, 23.03it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▍                             | 77/200 [01:33<02:51,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2464/8000 [02:08<04:12, 21.92it/s]\u001b[A\u001b[A\n",
+      "training:  39%|██████████████████▋                             | 78/200 [01:34<02:46,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2470/8000 [02:08<03:18, 27.81it/s]\u001b[A\u001b[A\n",
+      "training:  40%|██████████████████▉                             | 79/200 [01:36<02:40,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2475/8000 [02:08<02:54, 31.60it/s]\u001b[A\u001b[A\n",
+      "training:  40%|███████████████████▏                            | 80/200 [01:38<02:27,  1.23s/it]\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2482/8000 [02:09<03:32, 25.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2486/8000 [02:09<04:46, 19.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2490/8000 [02:09<04:19, 21.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2493/8000 [02:09<04:55, 18.62it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 18.48it/s]\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2499/8000 [02:10<03:40, 24.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2503/8000 [02:10<04:17, 21.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2508/8000 [02:10<04:14, 21.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2513/8000 [02:10<04:18, 21.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 38it [00:01, 42.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2517/8000 [02:10<03:59, 22.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 87.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2521/8000 [02:11<03:42, 24.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 110it [00:01, 130.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2529/8000 [02:11<02:43, 33.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 146it [00:01, 173.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2533/8000 [02:11<02:55, 31.13it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 182it [00:01, 212.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2537/8000 [02:11<04:04, 22.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 216it [00:01, 207.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2541/8000 [02:11<03:57, 23.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 252it [00:01, 240.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2544/8000 [02:11<03:53, 23.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 287it [00:02, 265.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2549/8000 [02:12<03:25, 26.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 331it [00:02, 309.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2554/8000 [02:12<03:42, 24.42it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 367it [00:02, 321.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2558/8000 [02:12<04:06, 22.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 403it [00:02, 331.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2563/8000 [02:12<04:07, 22.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 440it [00:02, 339.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2567/8000 [02:13<04:31, 20.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 476it [00:02, 341.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2572/8000 [02:13<03:41, 24.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 513it [00:02, 346.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2577/8000 [02:13<03:20, 27.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 549it [00:02, 346.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2582/8000 [02:13<02:53, 31.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 593it [00:02, 371.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2588/8000 [02:13<03:12, 28.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 631it [00:02, 367.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2592/8000 [02:13<03:48, 23.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 669it [00:03, 365.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2595/8000 [02:14<03:47, 23.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 706it [00:03, 364.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2598/8000 [02:14<05:26, 16.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 743it [00:03, 360.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2601/8000 [02:14<05:56, 15.15it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 234.68it/s]\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2604/8000 [02:14<05:23, 16.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2606/8000 [02:14<05:23, 16.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2610/8000 [02:15<04:29, 20.02it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 43.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2615/8000 [02:15<05:31, 16.25it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 43.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2619/8000 [02:15<05:42, 15.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 43.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2625/8000 [02:15<04:19, 20.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 43.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2629/8000 [02:16<04:47, 18.71it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|███▍        | 29/100 [00:00<00:01, 57.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2632/8000 [02:16<05:30, 16.23it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|████▏       | 35/100 [00:00<00:01, 52.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2635/8000 [02:16<05:09, 17.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████▉       | 41/100 [00:00<00:01, 48.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2637/8000 [02:16<06:11, 14.44it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▋      | 47/100 [00:00<00:01, 46.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2640/8000 [02:16<05:24, 16.52it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██████▏     | 52/100 [00:01<00:01, 45.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2642/8000 [02:17<05:47, 15.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|███████▏    | 60/100 [00:01<00:00, 51.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2644/8000 [02:17<07:09, 12.46it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|███████▉    | 66/100 [00:01<00:00, 48.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2649/8000 [02:17<05:01, 17.74it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|████████▌   | 71/100 [00:01<00:00, 46.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2652/8000 [02:17<04:45, 18.76it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|█████████   | 76/100 [00:01<00:00, 45.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2655/8000 [02:17<04:33, 19.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  81%|█████████▋  | 81/100 [00:01<00:00, 44.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2659/8000 [02:17<05:00, 17.79it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|██████████▌ | 88/100 [00:01<00:00, 49.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2661/8000 [02:18<05:15, 16.94it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  94%|███████████▎| 94/100 [00:01<00:00, 45.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2663/8000 [02:18<05:23, 16.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 47.11it/s]\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2665/8000 [02:18<05:22, 16.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2668/8000 [02:18<04:38, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2675/8000 [02:18<03:57, 22.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 16/8000 [00:00<01:25, 93.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2680/8000 [02:19<04:12, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                   | 35/8000 [00:00<01:16, 104.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2684/8000 [02:19<03:53, 22.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                   | 55/8000 [00:00<01:08, 116.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2687/8000 [02:19<04:39, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 82/8000 [00:00<01:21, 97.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2692/8000 [02:19<05:15, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                  | 103/8000 [00:00<01:10, 112.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2694/8000 [02:19<05:29, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                  | 121/8000 [00:01<01:04, 122.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2697/8000 [02:20<05:02, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                  | 135/8000 [00:01<01:12, 108.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2700/8000 [02:20<05:35, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                  | 151/8000 [00:01<01:17, 101.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2703/8000 [02:20<05:17, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 166/8000 [00:01<01:18, 99.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2707/8000 [02:20<04:23, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 190/8000 [00:01<01:10, 110.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2711/8000 [02:20<03:51, 22.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 206/8000 [00:01<01:20, 97.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2716/8000 [02:20<03:31, 24.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 221/8000 [00:02<01:25, 90.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2719/8000 [02:20<03:36, 24.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 238/8000 [00:02<01:29, 86.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2723/8000 [02:21<04:11, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 258/8000 [00:02<01:32, 84.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2726/8000 [02:21<05:09, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 277/8000 [00:02<01:32, 83.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2729/8000 [02:21<04:56, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 290/8000 [00:03<01:44, 73.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2731/8000 [02:21<05:04, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 310/8000 [00:03<01:33, 82.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2734/8000 [02:22<05:43, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 334/8000 [00:03<01:17, 98.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2737/8000 [02:22<05:04, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                 | 357/8000 [00:03<01:09, 110.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2742/8000 [02:22<03:57, 22.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                 | 374/8000 [00:03<01:09, 109.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2747/8000 [02:22<04:51, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 386/8000 [00:03<01:16, 99.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2750/8000 [02:22<04:28, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                 | 408/8000 [00:04<01:10, 107.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2753/8000 [02:22<04:17, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                 | 430/8000 [00:04<01:04, 117.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2759/8000 [02:23<04:09, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▉                                 | 444/8000 [00:04<01:07, 111.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2762/8000 [02:23<04:48, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                 | 463/8000 [00:04<01:02, 120.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2766/8000 [02:23<04:09, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                 | 483/8000 [00:04<01:01, 122.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2769/8000 [02:23<04:03, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                | 505/8000 [00:04<00:58, 128.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2777/8000 [02:23<03:29, 24.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                | 521/8000 [00:05<01:00, 123.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2782/8000 [02:24<03:08, 27.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                | 547/8000 [00:05<00:55, 134.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2786/8000 [02:24<02:57, 29.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                | 567/8000 [00:05<00:57, 129.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2794/8000 [02:24<03:33, 24.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                | 585/8000 [00:05<01:01, 120.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2798/8000 [02:24<03:58, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                | 603/8000 [00:05<01:02, 118.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2802/8000 [02:24<03:42, 23.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                | 624/8000 [00:05<01:00, 121.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2809/8000 [02:25<03:30, 24.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                | 646/8000 [00:05<00:57, 128.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2812/8000 [02:25<03:42, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                | 661/8000 [00:06<01:02, 117.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2817/8000 [02:25<04:35, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|██▉                                | 683/8000 [00:06<00:55, 133.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2820/8000 [02:26<06:05, 14.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                | 699/8000 [00:06<01:00, 120.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2823/8000 [02:26<07:59, 10.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                               | 717/8000 [00:06<01:03, 115.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2825/8000 [02:26<07:27, 11.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                               | 729/8000 [00:06<01:11, 102.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2827/8000 [02:27<09:19,  9.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                               | 749/8000 [00:06<01:05, 110.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2830/8000 [02:27<07:31, 11.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                               | 772/8000 [00:07<01:00, 118.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2832/8000 [02:27<07:08, 12.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                               | 785/8000 [00:07<01:08, 105.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2836/8000 [02:27<05:32, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                               | 804/8000 [00:07<01:04, 111.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2839/8000 [02:27<04:59, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                               | 822/8000 [00:07<01:04, 110.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2843/8000 [02:27<04:17, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▋                               | 841/8000 [00:07<01:02, 115.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2847/8000 [02:27<03:57, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                               | 863/8000 [00:07<00:56, 125.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2850/8000 [02:28<04:59, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                               | 883/8000 [00:08<00:52, 135.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2852/8000 [02:28<05:01, 17.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                               | 900/8000 [00:08<00:56, 126.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2854/8000 [02:28<04:56, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                               | 916/8000 [00:08<00:58, 120.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2858/8000 [02:28<04:03, 21.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████                               | 936/8000 [00:08<00:56, 125.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2863/8000 [02:28<04:11, 20.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 951/8000 [00:08<00:55, 127.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2866/8000 [02:28<04:02, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 966/8000 [00:08<01:00, 115.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2869/8000 [02:29<03:45, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                              | 981/8000 [00:08<01:03, 110.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2872/8000 [02:29<04:45, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                             | 1000/8000 [00:09<01:00, 115.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2875/8000 [02:29<05:18, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▎                             | 1027/8000 [00:09<00:50, 138.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2880/8000 [02:29<04:09, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                             | 1042/8000 [00:09<00:56, 123.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2884/8000 [02:29<04:21, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                             | 1062/8000 [00:09<00:57, 120.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2887/8000 [02:30<04:12, 20.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▌                             | 1088/8000 [00:09<00:51, 134.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2890/8000 [02:30<04:00, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                             | 1113/8000 [00:09<00:48, 141.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2893/8000 [02:30<03:48, 22.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                             | 1130/8000 [00:09<00:53, 129.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2896/8000 [02:30<04:35, 18.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                             | 1146/8000 [00:10<00:56, 120.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2900/8000 [02:30<04:03, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|████▉                             | 1164/8000 [00:10<00:57, 118.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2903/8000 [02:30<04:01, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                             | 1186/8000 [00:10<00:54, 125.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2907/8000 [02:31<05:24, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                            | 1207/8000 [00:10<00:52, 128.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2912/8000 [02:31<05:15, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                            | 1226/8000 [00:10<00:52, 128.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2915/8000 [02:31<05:36, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1251/8000 [00:10<00:45, 147.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2918/8000 [02:31<04:58, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                            | 1284/8000 [00:11<00:55, 121.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2920/8000 [02:31<04:58, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                            | 1299/8000 [00:11<00:54, 122.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2922/8000 [02:32<04:58, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▋                            | 1328/8000 [00:11<00:48, 137.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2925/8000 [02:32<04:50, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▋                            | 1348/8000 [00:11<00:48, 136.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2929/8000 [02:32<05:06, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1372/8000 [00:11<00:47, 140.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2933/8000 [02:32<05:06, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                            | 1396/8000 [00:11<00:47, 138.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2937/8000 [02:32<04:14, 19.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████                            | 1416/8000 [00:12<00:49, 133.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2941/8000 [02:33<04:27, 18.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████                            | 1441/8000 [00:12<00:47, 136.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2944/8000 [02:33<04:14, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                           | 1466/8000 [00:12<00:45, 142.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2947/8000 [02:33<04:05, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▎                           | 1487/8000 [00:12<00:43, 151.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2951/8000 [02:33<03:35, 23.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                           | 1509/8000 [00:12<00:45, 144.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2956/8000 [02:33<03:52, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                           | 1541/8000 [00:13<00:50, 126.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2959/8000 [02:33<03:47, 22.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▋                           | 1563/8000 [00:13<00:50, 128.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2963/8000 [02:34<03:19, 25.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▋                           | 1584/8000 [00:13<00:50, 128.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2966/8000 [02:34<04:19, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                           | 1606/8000 [00:13<00:48, 130.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2971/8000 [02:34<03:39, 22.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                           | 1623/8000 [00:13<00:52, 121.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2975/8000 [02:34<04:11, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                           | 1638/8000 [00:13<00:55, 114.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2980/8000 [02:34<04:12, 19.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████                           | 1653/8000 [00:14<00:59, 106.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2985/8000 [02:35<03:30, 23.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████                           | 1669/8000 [00:14<01:00, 104.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2988/8000 [02:35<04:31, 18.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                          | 1687/8000 [00:14<00:55, 113.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2993/8000 [02:35<04:23, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                          | 1705/8000 [00:14<00:56, 110.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2998/8000 [02:35<05:04, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▎                          | 1730/8000 [00:14<00:51, 121.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3002/8000 [02:36<04:20, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▍                          | 1751/8000 [00:14<00:49, 125.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3006/8000 [02:36<05:17, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1773/8000 [00:15<00:49, 126.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3011/8000 [02:36<04:11, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1789/8000 [00:15<00:52, 117.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3016/8000 [02:36<04:06, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▋                          | 1810/8000 [00:15<00:51, 119.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3019/8000 [02:36<04:00, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▊                          | 1827/8000 [00:15<00:51, 119.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3022/8000 [02:37<04:37, 17.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▊                          | 1849/8000 [00:15<00:48, 125.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3025/8000 [02:37<05:01, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                          | 1868/8000 [00:15<00:48, 125.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3029/8000 [02:37<04:13, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████                          | 1885/8000 [00:15<00:50, 121.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3032/8000 [02:37<04:01, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████                          | 1900/8000 [00:16<00:50, 120.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3036/8000 [02:37<04:18, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1914/8000 [00:16<00:55, 108.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3040/8000 [02:38<04:39, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1937/8000 [00:16<00:50, 119.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3042/8000 [02:38<04:51, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                         | 1957/8000 [00:16<00:46, 129.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3048/8000 [02:38<03:29, 23.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▍                         | 1977/8000 [00:16<00:43, 138.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3051/8000 [02:38<03:31, 23.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▍                         | 1996/8000 [00:16<00:46, 130.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3055/8000 [02:38<03:55, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                         | 2018/8000 [00:16<00:45, 132.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3058/8000 [02:39<04:39, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                         | 2034/8000 [00:17<00:45, 129.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3061/8000 [02:39<04:26, 18.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▋                         | 2048/8000 [00:17<00:47, 125.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3066/8000 [02:39<04:21, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▊                         | 2074/8000 [00:17<00:58, 101.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3068/8000 [02:39<05:16, 15.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                         | 2095/8000 [00:17<00:54, 108.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3070/8000 [02:39<06:28, 12.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                         | 2116/8000 [00:17<00:51, 113.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3077/8000 [02:40<04:42, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████                         | 2139/8000 [00:18<00:49, 118.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3080/8000 [02:40<05:10, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▏                        | 2160/8000 [00:18<00:48, 119.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3084/8000 [02:40<04:18, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                        | 2180/8000 [00:18<00:45, 127.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3087/8000 [02:40<04:05, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                        | 2196/8000 [00:18<00:49, 117.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3091/8000 [02:40<04:21, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▍                        | 2229/8000 [00:18<00:50, 115.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3094/8000 [02:41<04:49, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▌                        | 2247/8000 [00:18<00:47, 121.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3098/8000 [02:41<04:58, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                        | 2271/8000 [00:19<00:44, 129.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3101/8000 [02:41<05:20, 15.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▋                        | 2290/8000 [00:19<00:45, 124.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3109/8000 [02:42<05:57, 13.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▊                        | 2307/8000 [00:19<00:45, 125.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3114/8000 [02:42<04:44, 17.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                        | 2332/8000 [00:19<00:40, 141.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3117/8000 [02:42<04:26, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                        | 2348/8000 [00:19<00:46, 120.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3124/8000 [02:42<03:48, 21.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████                        | 2368/8000 [00:19<00:48, 116.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3130/8000 [02:42<03:09, 25.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▏                       | 2388/8000 [00:20<00:48, 114.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3134/8000 [02:43<03:30, 23.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▏                       | 2409/8000 [00:20<00:47, 117.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3137/8000 [02:43<03:30, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                       | 2426/8000 [00:20<00:48, 113.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3141/8000 [02:43<03:06, 26.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▍                       | 2451/8000 [00:20<00:41, 133.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3146/8000 [02:43<03:25, 23.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▌                       | 2474/8000 [00:20<00:37, 147.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3149/8000 [02:43<03:26, 23.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▌                       | 2492/8000 [00:20<00:40, 136.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3155/8000 [02:44<03:18, 24.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                       | 2510/8000 [00:21<00:41, 131.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3160/8000 [02:44<03:34, 22.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▊                       | 2531/8000 [00:21<00:40, 135.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3163/8000 [02:44<03:32, 22.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▊                       | 2552/8000 [00:21<00:37, 144.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3167/8000 [02:44<03:59, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▉                       | 2572/8000 [00:21<00:39, 137.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3170/8000 [02:44<04:38, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                       | 2595/8000 [00:21<00:39, 137.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3172/8000 [02:45<05:36, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████                       | 2612/8000 [00:21<00:42, 127.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3177/8000 [02:45<04:17, 18.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▏                      | 2637/8000 [00:21<00:39, 134.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3180/8000 [02:45<04:50, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2652/8000 [00:22<00:43, 122.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3184/8000 [02:45<04:11, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2665/8000 [00:22<00:48, 109.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3187/8000 [02:45<03:53, 20.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▍                      | 2687/8000 [00:22<00:45, 116.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3192/8000 [02:45<03:17, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▍                      | 2704/8000 [00:22<00:47, 111.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3196/8000 [02:46<04:31, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▌                      | 2729/8000 [00:22<00:42, 123.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3199/8000 [02:46<04:59, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                      | 2749/8000 [00:22<00:43, 121.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3202/8000 [02:46<05:26, 14.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▋                      | 2763/8000 [00:23<00:47, 110.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3207/8000 [02:47<04:56, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▊                      | 2785/8000 [00:23<00:44, 117.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3213/8000 [02:47<04:57, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▉                      | 2801/8000 [00:23<00:46, 112.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3215/8000 [02:47<05:02, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▉                      | 2822/8000 [00:23<00:43, 117.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3217/8000 [02:47<05:05, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████                      | 2848/8000 [00:23<00:40, 128.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3223/8000 [02:48<04:51, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▏                     | 2866/8000 [00:23<00:42, 120.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3225/8000 [02:48<04:54, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▎                     | 2887/8000 [00:24<00:41, 121.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3228/8000 [02:48<04:30, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▎                     | 2901/8000 [00:24<00:45, 111.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3231/8000 [02:48<04:15, 18.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▍                     | 2924/8000 [00:24<00:42, 120.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3235/8000 [02:48<03:56, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▌                     | 2948/8000 [00:24<00:40, 125.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3238/8000 [02:48<03:55, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▌                     | 2970/8000 [00:24<00:38, 132.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3245/8000 [02:49<03:24, 23.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▋                     | 2986/8000 [00:24<00:42, 118.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3248/8000 [02:49<03:25, 23.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3005/8000 [00:25<00:42, 117.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3254/8000 [02:49<03:19, 23.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3024/8000 [00:25<00:42, 116.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3259/8000 [02:49<04:40, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▉                     | 3050/8000 [00:25<00:38, 127.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3264/8000 [02:50<05:07, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████                     | 3063/8000 [00:25<00:43, 113.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3266/8000 [02:50<05:11, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████                     | 3082/8000 [00:25<00:42, 114.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3268/8000 [02:50<05:15, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▏                    | 3101/8000 [00:25<00:42, 116.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3272/8000 [02:50<04:13, 18.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▎                    | 3122/8000 [00:26<00:40, 120.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3276/8000 [02:50<04:29, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▍                    | 3155/8000 [00:26<00:34, 142.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3280/8000 [02:51<03:55, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▍                    | 3175/8000 [00:26<00:35, 136.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3283/8000 [02:51<03:40, 21.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▌                    | 3190/8000 [00:26<00:38, 124.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3286/8000 [02:51<03:37, 21.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3213/8000 [00:26<00:37, 127.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3292/8000 [02:51<02:47, 28.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3229/8000 [00:26<00:40, 117.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3296/8000 [02:51<03:30, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▊                    | 3249/8000 [00:27<00:38, 123.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3302/8000 [02:51<03:15, 24.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▉                    | 3281/8000 [00:27<00:42, 110.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3306/8000 [02:52<03:05, 25.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████                    | 3296/8000 [00:27<00:42, 110.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3309/8000 [02:52<04:12, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████                    | 3316/8000 [00:27<00:40, 114.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3315/8000 [02:52<03:59, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▏                   | 3332/8000 [00:27<00:43, 108.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3319/8000 [02:52<03:32, 22.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▏                   | 3350/8000 [00:28<00:43, 107.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3323/8000 [02:52<03:18, 23.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▎                   | 3374/8000 [00:28<00:38, 118.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3329/8000 [02:53<03:18, 23.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▍                   | 3396/8000 [00:28<00:37, 124.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3333/8000 [02:53<03:25, 22.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▌                   | 3417/8000 [00:28<00:36, 125.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3337/8000 [02:53<03:22, 22.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▌                   | 3432/8000 [00:28<00:39, 116.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3340/8000 [02:53<04:09, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▋                   | 3446/8000 [00:28<00:39, 116.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3343/8000 [02:54<05:39, 13.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▋                   | 3470/8000 [00:28<00:36, 124.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3345/8000 [02:54<06:17, 12.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▉                   | 3502/8000 [00:29<00:40, 110.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3347/8000 [02:54<05:56, 13.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▉                   | 3514/8000 [00:29<00:43, 102.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3352/8000 [02:54<05:16, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████                   | 3532/8000 [00:29<00:42, 104.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3357/8000 [02:55<04:39, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████                   | 3547/8000 [00:29<00:43, 102.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3360/8000 [02:55<04:18, 17.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▌                   | 3562/8000 [00:29<00:44, 99.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3363/8000 [02:55<04:00, 19.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3576/8000 [00:30<00:46, 96.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3366/8000 [02:55<03:52, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3592/8000 [00:30<00:44, 98.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3369/8000 [02:55<04:33, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▎                  | 3610/8000 [00:30<00:42, 103.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3371/8000 [02:55<04:36, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▍                  | 3631/8000 [00:30<00:36, 120.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3378/8000 [02:55<02:55, 26.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▌                  | 3656/8000 [00:30<00:32, 131.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3382/8000 [02:56<03:29, 22.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▌                  | 3676/8000 [00:30<00:33, 129.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3386/8000 [02:56<03:54, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▋                  | 3697/8000 [00:30<00:31, 135.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3390/8000 [02:56<04:21, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▊                  | 3716/8000 [00:31<00:31, 137.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3393/8000 [02:56<04:50, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▉                  | 3739/8000 [00:31<00:30, 138.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3395/8000 [02:57<04:43, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▉                  | 3756/8000 [00:31<00:33, 126.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3398/8000 [02:57<04:18, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████                  | 3771/8000 [00:31<00:36, 116.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3400/8000 [02:57<04:28, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████                  | 3789/8000 [00:31<00:37, 113.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3403/8000 [02:57<04:11, 18.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▏                 | 3807/8000 [00:31<00:36, 113.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3405/8000 [02:57<04:27, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▎                 | 3827/8000 [00:32<00:36, 114.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3411/8000 [02:57<03:53, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▎                 | 3850/8000 [00:32<00:32, 126.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3414/8000 [02:58<04:28, 17.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▍                 | 3870/8000 [00:32<00:33, 125.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3417/8000 [02:58<04:18, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▌                 | 3887/8000 [00:32<00:34, 120.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3420/8000 [02:58<04:51, 15.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▌                 | 3902/8000 [00:32<00:35, 116.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3423/8000 [02:58<05:14, 14.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3924/8000 [00:32<00:30, 132.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3425/8000 [02:58<05:12, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▊                 | 3944/8000 [00:33<00:32, 125.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3430/8000 [02:59<03:54, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████████████▊                 | 3965/8000 [00:33<00:31, 126.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3434/8000 [02:59<04:11, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████████████▉                 | 3988/8000 [00:33<00:30, 131.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3439/8000 [02:59<03:25, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████                 | 4010/8000 [00:33<00:30, 132.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3442/8000 [02:59<03:25, 22.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████                 | 4026/8000 [00:33<00:32, 122.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3446/8000 [02:59<03:08, 24.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▏                | 4047/8000 [00:33<00:33, 118.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3451/8000 [02:59<02:51, 26.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▎                | 4064/8000 [00:33<00:32, 122.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3455/8000 [02:59<02:44, 27.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▎                | 4081/8000 [00:34<00:33, 115.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3460/8000 [03:00<02:26, 30.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▍                | 4103/8000 [00:34<00:33, 116.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3465/8000 [03:00<03:37, 20.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▍                | 4115/8000 [00:34<00:35, 109.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3468/8000 [03:00<03:30, 21.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▌                | 4130/8000 [00:34<00:37, 104.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3472/8000 [03:00<03:16, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▋                | 4150/8000 [00:34<00:34, 110.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3475/8000 [03:00<03:13, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▋                | 4170/8000 [00:34<00:34, 112.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3481/8000 [03:01<02:30, 30.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▊                | 4184/8000 [00:35<00:36, 105.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3485/8000 [03:01<02:23, 31.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████▊                | 4201/8000 [00:35<00:37, 101.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3489/8000 [03:01<02:23, 31.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████▉                | 4219/8000 [00:35<00:35, 105.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3496/8000 [03:01<02:37, 28.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████▉                | 4233/8000 [00:35<00:36, 104.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3502/8000 [03:01<03:16, 22.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████                | 4250/8000 [00:35<00:33, 112.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3505/8000 [03:02<03:19, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▏               | 4266/8000 [00:35<00:34, 107.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3508/8000 [03:02<04:09, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▏               | 4283/8000 [00:36<00:34, 107.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3512/8000 [03:02<04:59, 15.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▎               | 4299/8000 [00:36<00:33, 110.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3518/8000 [03:02<04:10, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▎               | 4318/8000 [00:36<00:33, 110.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3525/8000 [03:03<03:35, 20.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4334/8000 [00:36<00:34, 107.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3529/8000 [03:03<03:20, 22.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4350/8000 [00:36<00:35, 103.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3534/8000 [03:03<02:51, 25.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▌               | 4366/8000 [00:36<00:35, 103.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3538/8000 [03:03<03:17, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▋               | 4387/8000 [00:36<00:32, 110.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3544/8000 [03:03<03:16, 22.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▋               | 4410/8000 [00:37<00:29, 120.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3547/8000 [03:04<03:52, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████████████▉               | 4442/8000 [00:37<00:30, 118.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3552/8000 [03:04<03:44, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████████████▉               | 4457/8000 [00:37<00:32, 110.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3558/8000 [03:04<04:06, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████               | 4476/8000 [00:37<00:30, 114.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3561/8000 [03:04<03:59, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████               | 4499/8000 [00:37<00:27, 126.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3566/8000 [03:05<03:52, 19.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▏              | 4515/8000 [00:38<00:27, 128.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3571/8000 [03:05<03:12, 22.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▎              | 4534/8000 [00:38<00:28, 123.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3575/8000 [03:05<03:33, 20.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▎              | 4547/8000 [00:38<00:30, 112.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3578/8000 [03:05<04:04, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▍              | 4566/8000 [00:38<00:28, 118.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3580/8000 [03:05<04:06, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▍              | 4584/8000 [00:38<00:26, 127.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3585/8000 [03:06<03:15, 22.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▌              | 4603/8000 [00:38<00:27, 122.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3589/8000 [03:06<03:32, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▋              | 4620/8000 [00:38<00:29, 115.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3594/8000 [03:06<03:41, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▋              | 4643/8000 [00:39<00:25, 129.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3599/8000 [03:06<03:41, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▊              | 4665/8000 [00:39<00:26, 127.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3602/8000 [03:07<04:10, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4683/8000 [00:39<00:26, 123.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3605/8000 [03:07<03:55, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4701/8000 [00:39<00:27, 120.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3609/8000 [03:07<05:31, 13.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████              | 4715/8000 [00:39<00:29, 111.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3615/8000 [03:07<03:52, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████              | 4729/8000 [00:39<00:31, 104.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3619/8000 [03:08<04:41, 15.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4755/8000 [00:40<00:34, 93.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3622/8000 [03:08<04:59, 14.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▎             | 4775/8000 [00:40<00:29, 108.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3625/8000 [03:08<05:11, 14.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▍             | 4801/8000 [00:40<00:26, 120.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3627/8000 [03:08<05:02, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▍             | 4814/8000 [00:40<00:29, 107.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3632/8000 [03:09<04:29, 16.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▌             | 4833/8000 [00:40<00:28, 110.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3635/8000 [03:09<04:01, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▌             | 4848/8000 [00:40<00:29, 106.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3639/8000 [03:09<03:31, 20.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4861/8000 [00:41<00:33, 92.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3642/8000 [03:09<03:22, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4880/8000 [00:41<00:31, 99.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3647/8000 [03:09<03:32, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▊             | 4893/8000 [00:41<00:30, 101.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3650/8000 [03:09<03:25, 21.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▊             | 4910/8000 [00:41<00:30, 101.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3653/8000 [03:10<04:02, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|████████████████████▉             | 4932/8000 [00:41<00:26, 115.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3656/8000 [03:10<03:44, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████             | 4952/8000 [00:41<00:25, 121.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3659/8000 [03:10<04:15, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▏            | 4971/8000 [00:42<00:22, 132.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3662/8000 [03:10<04:40, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▏            | 4989/8000 [00:42<00:24, 121.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3666/8000 [03:10<03:48, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5012/8000 [00:42<00:23, 129.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3669/8000 [03:11<05:23, 13.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5026/8000 [00:42<00:24, 122.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3672/8000 [03:11<04:53, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▍            | 5047/8000 [00:42<00:21, 137.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3675/8000 [03:11<04:14, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▌            | 5065/8000 [00:42<00:22, 128.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3679/8000 [03:11<03:36, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5096/8000 [00:42<00:19, 152.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3683/8000 [03:11<03:57, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5116/8000 [00:43<00:19, 147.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3689/8000 [03:12<03:40, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▊            | 5139/8000 [00:43<00:18, 157.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3692/8000 [03:12<04:09, 17.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▉            | 5158/8000 [00:43<00:19, 144.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3694/8000 [03:12<04:59, 14.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████            | 5187/8000 [00:43<00:22, 124.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3696/8000 [03:13<07:43,  9.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████            | 5203/8000 [00:43<00:22, 126.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3699/8000 [03:13<06:31, 10.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▏           | 5229/8000 [00:43<00:21, 129.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3705/8000 [03:13<04:15, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5246/8000 [00:44<00:22, 123.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3708/8000 [03:13<05:10, 13.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5262/8000 [00:44<00:24, 112.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3711/8000 [03:13<04:38, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▍           | 5280/8000 [00:44<00:24, 111.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3714/8000 [03:13<04:04, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5301/8000 [00:44<00:23, 115.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3717/8000 [03:14<03:46, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5319/8000 [00:44<00:23, 115.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3721/8000 [03:14<03:15, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▋           | 5331/8000 [00:44<00:23, 112.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3725/8000 [03:14<02:54, 24.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▋           | 5348/8000 [00:45<00:23, 111.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3729/8000 [03:14<02:37, 27.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▊           | 5364/8000 [00:45<00:24, 108.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3734/8000 [03:14<02:15, 31.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▊           | 5381/8000 [00:45<00:23, 111.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3739/8000 [03:14<02:39, 26.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████           | 5416/8000 [00:45<00:21, 118.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3744/8000 [03:15<03:30, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████           | 5435/8000 [00:45<00:21, 121.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3747/8000 [03:15<04:14, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▏          | 5454/8000 [00:45<00:20, 122.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3751/8000 [03:15<03:44, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▏          | 5467/8000 [00:46<00:21, 116.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3759/8000 [03:15<02:35, 27.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▎          | 5487/8000 [00:46<00:21, 119.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:15<03:05, 22.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▍          | 5510/8000 [00:46<00:20, 124.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3769/8000 [03:16<03:01, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▍          | 5526/8000 [00:46<00:21, 116.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3772/8000 [03:16<03:32, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▌          | 5542/8000 [00:46<00:22, 110.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3775/8000 [03:16<03:23, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5555/8000 [00:46<00:25, 97.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3778/8000 [03:16<03:22, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5571/8000 [00:47<00:25, 96.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3783/8000 [03:17<03:30, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▊          | 5592/8000 [00:47<00:20, 115.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3786/8000 [03:17<03:58, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▊          | 5607/8000 [00:47<00:22, 105.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3791/8000 [03:17<03:06, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▉          | 5627/8000 [00:47<00:21, 109.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3795/8000 [03:17<04:08, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|███████████████████████▉          | 5645/8000 [00:47<00:21, 108.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3798/8000 [03:17<04:29, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5659/8000 [00:47<00:22, 102.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3801/8000 [03:18<04:02, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5674/8000 [00:48<00:23, 98.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3804/8000 [03:18<03:40, 19.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▏         | 5697/8000 [00:48<00:20, 110.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3808/8000 [03:18<03:59, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▎         | 5716/8000 [00:48<00:19, 116.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3813/8000 [03:18<03:46, 18.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▎         | 5732/8000 [00:48<00:20, 109.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3816/8000 [03:18<03:34, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▍         | 5751/8000 [00:48<00:20, 110.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3820/8000 [03:18<03:08, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▌         | 5771/8000 [00:48<00:19, 112.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3823/8000 [03:19<03:10, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▌         | 5792/8000 [00:49<00:19, 115.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3827/8000 [03:19<02:58, 23.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▋         | 5810/8000 [00:49<00:19, 113.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3830/8000 [03:19<05:02, 13.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▊         | 5831/8000 [00:49<00:18, 117.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3833/8000 [03:20<05:48, 11.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▉         | 5853/8000 [00:49<00:16, 127.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3836/8000 [03:20<06:13, 11.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▉         | 5871/8000 [00:49<00:17, 121.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3839/8000 [03:20<06:27, 10.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████         | 5898/8000 [00:49<00:15, 134.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3841/8000 [03:20<05:57, 11.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5915/8000 [00:49<00:16, 125.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3843/8000 [03:20<05:29, 12.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5941/8000 [00:50<00:15, 136.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3845/8000 [03:21<06:07, 11.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▎        | 5961/8000 [00:50<00:15, 130.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3847/8000 [03:21<05:37, 12.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▍        | 5979/8000 [00:50<00:16, 122.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3851/8000 [03:21<04:09, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6003/8000 [00:50<00:15, 127.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3857/8000 [03:21<02:43, 25.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6021/8000 [00:50<00:15, 124.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3863/8000 [03:21<03:48, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6042/8000 [00:50<00:15, 126.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3867/8000 [03:22<03:58, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6063/8000 [00:51<00:14, 138.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3872/8000 [03:22<04:55, 13.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6087/8000 [00:51<00:12, 152.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3875/8000 [03:22<04:25, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6109/8000 [00:51<00:13, 145.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3880/8000 [03:23<03:59, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████        | 6131/8000 [00:51<00:13, 139.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3886/8000 [03:23<03:36, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6152/8000 [00:51<00:13, 139.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3889/8000 [03:23<03:28, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6171/8000 [00:51<00:14, 128.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3892/8000 [03:23<04:04, 16.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▎       | 6202/8000 [00:52<00:15, 113.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3894/8000 [03:23<04:15, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6228/8000 [00:52<00:14, 126.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3898/8000 [03:24<04:25, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6258/8000 [00:52<00:12, 140.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3902/8000 [03:24<03:34, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▋       | 6279/8000 [00:52<00:12, 135.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3905/8000 [03:24<04:15, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6296/8000 [00:52<00:13, 127.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3911/8000 [03:24<03:07, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6311/8000 [00:53<00:14, 114.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3917/8000 [03:25<03:37, 18.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6325/8000 [00:53<00:15, 105.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3922/8000 [03:25<03:05, 21.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6345/8000 [00:53<00:15, 110.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3925/8000 [03:25<03:06, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████       | 6361/8000 [00:53<00:14, 113.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3932/8000 [03:25<02:57, 22.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6386/8000 [00:53<00:12, 124.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3936/8000 [03:25<02:58, 22.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6406/8000 [00:53<00:12, 124.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3939/8000 [03:25<03:00, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6424/8000 [00:53<00:12, 125.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3944/8000 [03:26<02:37, 25.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▍      | 6448/8000 [00:54<00:11, 132.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3948/8000 [03:26<02:37, 25.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▍      | 6466/8000 [00:54<00:12, 125.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3951/8000 [03:26<02:43, 24.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6481/8000 [00:54<00:13, 115.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3954/8000 [03:26<03:28, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▋      | 6502/8000 [00:54<00:12, 117.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3958/8000 [03:26<03:02, 22.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▋      | 6523/8000 [00:54<00:12, 120.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3964/8000 [03:27<03:46, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▊      | 6542/8000 [00:54<00:12, 120.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3968/8000 [03:27<03:58, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6560/8000 [00:55<00:12, 119.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3970/8000 [03:27<04:07, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6581/8000 [00:55<00:10, 130.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3972/8000 [03:27<04:13, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████      | 6596/8000 [00:55<00:11, 120.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3975/8000 [03:28<04:34, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6618/8000 [00:55<00:10, 128.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3980/8000 [03:28<03:30, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6633/8000 [00:55<00:11, 119.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3986/8000 [03:28<03:15, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6648/8000 [00:55<00:11, 119.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3989/8000 [03:28<03:49, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6672/8000 [00:55<00:10, 127.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3991/8000 [03:28<04:00, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▍     | 6689/8000 [00:56<00:10, 119.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3993/8000 [03:29<05:06, 13.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6711/8000 [00:56<00:09, 132.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3995/8000 [03:29<05:00, 13.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6728/8000 [00:56<00:09, 136.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3999/8000 [03:29<03:57, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▋     | 6759/8000 [00:56<00:08, 152.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4001/8000 [03:29<04:07, 16.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6792/8000 [00:56<00:09, 126.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4004/8000 [03:29<03:46, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6811/8000 [00:57<00:09, 125.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4006/8000 [03:29<03:59, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████     | 6835/8000 [00:57<00:08, 129.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4008/8000 [03:29<04:07, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6857/8000 [00:57<00:08, 131.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4012/8000 [03:30<04:10, 15.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6876/8000 [00:57<00:08, 131.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4018/8000 [03:30<02:53, 22.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6898/8000 [00:57<00:08, 133.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4026/8000 [03:30<02:33, 25.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▍    | 6914/8000 [00:57<00:08, 123.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4031/8000 [03:30<02:21, 28.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▍    | 6932/8000 [00:57<00:08, 124.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4037/8000 [03:31<03:02, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6954/8000 [00:58<00:07, 139.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4040/8000 [03:31<03:03, 21.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6989/8000 [00:58<00:08, 120.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4044/8000 [03:31<02:53, 22.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7009/8000 [00:58<00:08, 121.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4047/8000 [03:31<04:22, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7029/8000 [00:58<00:08, 119.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4050/8000 [03:32<04:38, 14.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7049/8000 [00:58<00:08, 118.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4053/8000 [03:32<04:14, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████    | 7065/8000 [00:59<00:08, 113.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4055/8000 [03:32<05:06, 12.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████    | 7079/8000 [00:59<00:08, 112.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4057/8000 [03:32<04:59, 13.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7096/8000 [00:59<00:08, 106.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4060/8000 [03:32<04:20, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7121/8000 [00:59<00:07, 120.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4062/8000 [03:32<04:22, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7141/8000 [00:59<00:06, 127.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4069/8000 [03:33<03:27, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▍   | 7163/8000 [00:59<00:06, 137.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4071/8000 [03:33<03:37, 18.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7191/8000 [01:00<00:07, 112.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4073/8000 [03:33<05:33, 11.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7217/8000 [01:00<00:07, 102.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4076/8000 [03:34<05:40, 11.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▊   | 7238/8000 [01:00<00:07, 107.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4080/8000 [03:34<05:21, 12.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▊   | 7261/8000 [01:00<00:06, 116.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4084/8000 [03:34<04:22, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7284/8000 [01:00<00:05, 122.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4090/8000 [03:34<03:11, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████   | 7314/8000 [01:01<00:06, 109.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4094/8000 [03:34<02:56, 22.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7331/8000 [01:01<00:05, 113.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4099/8000 [03:34<02:29, 26.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7345/8000 [01:01<00:05, 112.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4103/8000 [03:35<02:30, 25.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7371/8000 [01:01<00:05, 124.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4106/8000 [03:35<03:19, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▍  | 7388/8000 [01:01<00:05, 119.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4114/8000 [03:35<02:47, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7419/8000 [01:02<00:05, 108.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▊        | 4118/8000 [03:35<02:37, 24.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7432/8000 [01:02<00:05, 99.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4121/8000 [03:35<03:16, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7451/8000 [01:02<00:05, 103.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4126/8000 [03:36<02:43, 23.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7466/8000 [01:02<00:05, 102.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4129/8000 [03:36<02:49, 22.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7481/8000 [01:02<00:05, 98.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4133/8000 [03:36<02:39, 24.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7494/8000 [01:03<00:05, 90.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4136/8000 [03:36<02:49, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7509/8000 [01:03<00:05, 97.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4140/8000 [03:36<02:42, 23.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████  | 7532/8000 [01:03<00:04, 110.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4143/8000 [03:36<02:46, 23.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████  | 7547/8000 [01:03<00:04, 104.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4146/8000 [03:37<04:20, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7568/8000 [01:03<00:03, 113.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4149/8000 [03:37<03:53, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7588/8000 [01:03<00:03, 115.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4153/8000 [03:37<04:03, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7605/8000 [01:04<00:03, 110.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4157/8000 [03:37<03:21, 19.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▍ | 7623/8000 [01:04<00:03, 111.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4162/8000 [03:38<03:23, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▍ | 7642/8000 [01:04<00:03, 114.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4165/8000 [03:38<03:56, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7660/8000 [01:04<00:02, 121.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4168/8000 [03:38<03:40, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7691/8000 [01:04<00:02, 108.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4170/8000 [03:38<03:49, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▊ | 7707/8000 [01:04<00:02, 106.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4172/8000 [03:38<04:00, 15.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████████████████████▊ | 7735/8000 [01:05<00:02, 121.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4175/8000 [03:38<03:36, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████████████████████▉ | 7752/8000 [01:05<00:02, 115.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4177/8000 [03:39<03:47, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7765/8000 [01:05<00:02, 103.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4179/8000 [03:39<03:53, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7783/8000 [01:05<00:01, 113.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4181/8000 [03:39<04:08, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▏| 7803/8000 [01:05<00:01, 114.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4186/8000 [03:39<03:55, 16.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7828/8000 [01:05<00:01, 127.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4191/8000 [03:39<03:05, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7846/8000 [01:06<00:01, 129.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4198/8000 [03:40<03:22, 18.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7861/8000 [01:06<00:01, 126.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4201/8000 [03:40<03:11, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7877/8000 [01:06<00:01, 117.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4204/8000 [03:40<03:39, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7895/8000 [01:06<00:00, 115.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4212/8000 [03:40<02:27, 25.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7914/8000 [01:06<00:00, 112.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4216/8000 [03:40<02:21, 26.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7935/8000 [01:06<00:00, 115.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4222/8000 [03:41<02:31, 24.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▊| 7949/8000 [01:07<00:00, 103.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4225/8000 [03:41<03:37, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████████████████████▊| 7970/8000 [01:07<00:00, 112.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4228/8000 [03:41<03:25, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████████████████████▉| 7984/8000 [01:07<00:00, 114.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4233/8000 [03:41<03:17, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8004it [01:07, 118.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  53%|█████████        | 4237/8000 [03:42<03:42, 16.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4244/8000 [03:42<03:12, 19.55it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4247/8000 [03:42<03:05, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<16:00,  4.83s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4250/8000 [03:42<03:58, 15.73it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<08:49,  2.67s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4253/8000 [03:43<03:44, 16.67it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4255/8000 [03:43<03:45, 16.63it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:48,  1.78s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4257/8000 [03:43<05:27, 11.44it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<05:08,  1.58s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4260/8000 [03:43<05:27, 11.42it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4263/8000 [03:43<04:36, 13.52it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:12<04:32,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4269/8000 [03:44<04:14, 14.67it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:13<04:15,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4272/8000 [03:44<03:46, 16.47it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:14<04:04,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4275/8000 [03:44<03:28, 17.89it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4279/8000 [03:44<03:40, 16.85it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:17<04:07,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4281/8000 [03:44<03:46, 16.43it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:18<04:12,  1.34s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4286/8000 [03:45<02:49, 21.93it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:20<04:13,  1.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4290/8000 [03:45<03:23, 18.23it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:21<04:15,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4294/8000 [03:45<03:44, 16.48it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:23<04:17,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4296/8000 [03:45<04:43, 13.05it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:24<04:18,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4299/8000 [03:46<04:08, 14.90it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:25<04:18,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4302/8000 [03:46<03:41, 16.71it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:27<04:16,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4305/8000 [03:46<03:37, 16.96it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:28<04:14,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4310/8000 [03:46<04:06, 14.95it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:30<04:12,  1.41s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4314/8000 [03:46<03:29, 17.59it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:31<04:09,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4319/8000 [03:47<02:56, 20.88it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:32<04:07,  1.39s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4322/8000 [03:47<03:08, 19.51it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:34<03:53,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4325/8000 [03:47<03:41, 16.60it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:35<03:47,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4331/8000 [03:47<03:16, 18.67it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:36<03:47,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4334/8000 [03:48<03:47, 16.13it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:37<03:43,  1.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4338/8000 [03:48<04:15, 14.32it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:39<03:44,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4340/8000 [03:48<04:08, 14.71it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:40<03:51,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4342/8000 [03:48<04:26, 13.71it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:41<03:51,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4345/8000 [03:48<04:02, 15.06it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:43<03:45,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4347/8000 [03:49<04:55, 12.35it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:44<03:40,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4350/8000 [03:49<05:10, 11.77it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:45<03:36,  1.29s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4353/8000 [03:49<04:40, 13.00it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:47<04:07,  1.48s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4355/8000 [03:49<04:30, 13.50it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:48<03:47,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4358/8000 [03:49<04:12, 14.45it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:49<03:36,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4361/8000 [03:49<03:44, 16.20it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:51<03:29,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4364/8000 [03:50<03:40, 16.46it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:52<03:25,  1.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4371/8000 [03:50<02:27, 24.66it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:53<03:25,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4376/8000 [03:50<03:07, 19.37it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:54<03:21,  1.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4381/8000 [03:51<03:30, 17.22it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:56<03:14,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4383/8000 [03:51<04:37, 13.01it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:57<03:10,  1.20s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4385/8000 [03:51<04:45, 12.66it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:58<03:06,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4389/8000 [03:51<03:41, 16.28it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:59<03:04,  1.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4393/8000 [03:52<04:04, 14.72it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:00<03:05,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4398/8000 [03:52<03:19, 18.09it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:01<03:03,  1.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4402/8000 [03:52<03:32, 16.91it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [01:03<03:01,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4406/8000 [03:52<03:02, 19.67it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:04<02:57,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4409/8000 [03:52<04:11, 14.26it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:06<03:30,  1.38s/it]\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4414/8000 [03:53<03:54, 15.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4419/8000 [03:53<03:37, 16.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4422/8000 [03:53<03:21, 17.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4427/8000 [03:53<03:19, 17.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 22.47it/s]\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4430/8000 [03:54<03:12, 18.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4434/8000 [03:54<03:25, 17.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4439/8000 [03:54<02:43, 21.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4442/8000 [03:54<02:39, 22.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 45it [00:00, 62.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4445/8000 [03:54<03:49, 15.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 81it [00:01, 112.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4450/8000 [03:55<02:57, 20.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 117it [00:01, 159.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4453/8000 [03:55<03:29, 16.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 153it [00:01, 202.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4456/8000 [03:56<06:38,  8.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 190it [00:01, 241.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4460/8000 [03:56<05:42, 10.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 227it [00:01, 271.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [03:56<05:16, 11.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 263it [00:01, 293.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4465/8000 [03:56<06:23,  9.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 306it [00:01, 330.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4468/8000 [03:57<05:15, 11.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 344it [00:01, 339.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4472/8000 [03:57<04:07, 14.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 382it [00:01, 345.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4478/8000 [03:57<03:32, 16.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 419it [00:01, 347.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4482/8000 [03:57<02:59, 19.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 456it [00:02, 351.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4485/8000 [03:57<02:52, 20.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 493it [00:02, 349.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4488/8000 [03:57<02:46, 21.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 529it [00:02, 344.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4491/8000 [03:58<02:43, 21.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 572it [00:02, 368.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4496/8000 [03:58<02:10, 26.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 610it [00:02, 366.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4501/8000 [03:58<01:54, 30.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 647it [00:02, 364.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4506/8000 [03:58<02:10, 26.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 684it [00:02, 363.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4509/8000 [03:58<02:14, 25.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 721it [00:02, 360.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4513/8000 [03:58<02:42, 21.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 758it [00:02, 360.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4516/8000 [03:59<02:40, 21.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:03, 262.53it/s]\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4521/8000 [03:59<02:44, 21.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4525/8000 [03:59<02:58, 19.49it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4528/8000 [03:59<02:57, 19.51it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 43.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4533/8000 [03:59<02:30, 22.97it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 44.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4536/8000 [03:59<02:31, 22.89it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 43.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4540/8000 [04:00<02:53, 19.96it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 43.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4543/8000 [04:00<03:23, 16.95it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|███▍        | 29/100 [00:00<00:01, 55.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4547/8000 [04:00<02:51, 20.14it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|████▏       | 35/100 [00:00<00:01, 50.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4550/8000 [04:00<02:47, 20.54it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████▉       | 41/100 [00:00<00:01, 49.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4553/8000 [04:00<02:38, 21.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█████▌      | 46/100 [00:00<00:01, 47.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4559/8000 [04:01<02:36, 22.00it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  51%|██████      | 51/100 [00:01<00:01, 45.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4562/8000 [04:01<02:46, 20.65it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|██████▉     | 58/100 [00:01<00:00, 51.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4566/8000 [04:01<02:39, 21.55it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|███████▋    | 64/100 [00:01<00:00, 48.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4571/8000 [04:01<03:41, 15.48it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|████████▎   | 69/100 [00:01<00:00, 47.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4574/8000 [04:02<03:47, 15.06it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|████████▉   | 74/100 [00:01<00:00, 46.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4576/8000 [04:02<04:15, 13.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|█████████▍  | 79/100 [00:01<00:00, 45.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4578/8000 [04:02<04:49, 11.81it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|██████████▎ | 86/100 [00:01<00:00, 50.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4583/8000 [04:02<04:31, 12.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|███████████ | 92/100 [00:01<00:00, 47.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4585/8000 [04:03<04:47, 11.87it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 47.50it/s]\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4587/8000 [04:03<05:19, 10.67it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|████████████████████████████████▎          | 3/4 [28:39<09:53, 593.54s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.226965913266592\n",
+      "2999: -5.8393394\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  57%|█████████▊       | 4589/8000 [04:03<05:33, 10.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4591/8000 [04:03<05:31, 10.29it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4596/8000 [04:04<05:22, 10.57it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:15,  4.60s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4598/8000 [04:04<05:14, 10.81it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:48,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4603/8000 [04:04<03:50, 14.74it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:06<05:32,  1.69s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4608/8000 [04:04<03:02, 18.56it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:07<04:24,  1.35s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4612/8000 [04:04<02:44, 20.60it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:50,  1.18s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4615/8000 [04:05<02:40, 21.12it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:25,  1.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4620/8000 [04:05<02:46, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:10,  1.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4623/8000 [04:05<02:37, 21.48it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<03:03,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4627/8000 [04:05<02:55, 19.17it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:56,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4631/8000 [04:06<03:43, 15.09it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:12<02:48,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4633/8000 [04:06<04:21, 12.88it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:42,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4636/8000 [04:06<04:34, 12.24it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:37,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4641/8000 [04:06<03:24, 16.41it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:36,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4645/8000 [04:06<02:52, 19.43it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:15<02:36,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4648/8000 [04:07<02:45, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:16<02:33,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4651/8000 [04:07<02:35, 21.49it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:17<02:31,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4655/8000 [04:07<02:59, 18.65it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:29,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4659/8000 [04:07<03:01, 18.43it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:18<02:27,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4662/8000 [04:07<02:55, 19.05it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:19<02:26,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4667/8000 [04:08<02:59, 18.57it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:20<02:25,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4671/8000 [04:08<03:14, 17.14it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:21<02:34,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4674/8000 [04:08<03:02, 18.25it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:22<02:29,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4679/8000 [04:08<02:24, 23.04it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:22<02:31,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4683/8000 [04:08<02:14, 24.61it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:23<02:28,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4687/8000 [04:08<02:06, 26.11it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:24<02:25,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4690/8000 [04:09<02:11, 25.17it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:25<02:30,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4693/8000 [04:09<02:17, 23.99it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:26<02:29,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4700/8000 [04:09<02:14, 24.47it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:27<02:26,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4704/8000 [04:09<02:28, 22.25it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:28<02:24,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4707/8000 [04:09<02:27, 22.28it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:28<02:23,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4711/8000 [04:09<02:16, 24.13it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:29<02:22,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4715/8000 [04:10<02:11, 24.92it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:30<02:24,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4720/8000 [04:10<02:28, 22.07it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:31<02:27,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4723/8000 [04:10<03:04, 17.75it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:32<02:28,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4726/8000 [04:10<02:56, 18.54it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:33<02:28,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4730/8000 [04:11<03:47, 14.34it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:34<02:31,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4732/8000 [04:11<04:35, 11.87it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:35<02:31,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4735/8000 [04:11<04:34, 11.90it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:36<02:29,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4737/8000 [04:11<05:07, 10.62it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:37<02:29,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4740/8000 [04:12<04:20, 12.52it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:38<02:28,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4742/8000 [04:12<04:04, 13.33it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:39<02:27,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4746/8000 [04:12<03:07, 17.39it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:39<02:27,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4750/8000 [04:12<03:28, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:40<02:27,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4752/8000 [04:12<03:34, 15.14it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:41<02:25,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4758/8000 [04:13<03:07, 17.33it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:42<02:24,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4762/8000 [04:13<03:12, 16.82it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:43<02:22,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4765/8000 [04:13<03:00, 17.96it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:44<02:21,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4769/8000 [04:13<03:13, 16.66it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:45<02:20,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4772/8000 [04:13<03:00, 17.92it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:46<02:19,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4776/8000 [04:14<02:33, 21.04it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:47<02:20,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4782/8000 [04:14<02:01, 26.46it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:48<02:18,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4787/8000 [04:14<01:51, 28.93it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:49<02:15,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4791/8000 [04:14<02:47, 19.16it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:50<02:10,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4794/8000 [04:14<03:09, 16.90it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:51<02:13,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4798/8000 [04:15<03:47, 14.05it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:51<02:08,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4800/8000 [04:15<04:24, 12.11it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:52<02:11,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4804/8000 [04:15<04:04, 13.06it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:53<02:13,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4807/8000 [04:16<04:13, 12.60it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:54<02:11,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4809/8000 [04:16<04:01, 13.19it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:55<02:05,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4814/8000 [04:16<04:05, 12.95it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:56<02:04,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4816/8000 [04:16<04:01, 13.21it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:57<02:10,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4820/8000 [04:17<05:00, 10.58it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:59<02:11,  1.05it/s]\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4822/8000 [04:17<04:36, 11.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4828/8000 [04:17<02:56, 17.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4831/8000 [04:17<03:15, 16.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 22.48it/s]\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4834/8000 [04:18<03:34, 14.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4836/8000 [04:18<03:28, 15.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4840/8000 [04:18<03:31, 14.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4843/8000 [04:18<03:13, 16.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 90it [00:00, 124.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4846/8000 [04:18<02:57, 17.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 173it [00:01, 240.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4849/8000 [04:18<02:52, 18.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 255it [00:01, 350.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4851/8000 [04:18<02:52, 18.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 347it [00:01, 471.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4853/8000 [04:19<04:00, 13.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 437it [00:01, 571.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4855/8000 [04:19<03:59, 13.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 520it [00:01, 634.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4859/8000 [04:19<03:01, 17.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 603it [00:01, 671.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4861/8000 [04:19<03:10, 16.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 688it [00:01, 717.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4865/8000 [04:19<02:42, 19.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 429.41it/s]\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4868/8000 [04:20<03:14, 16.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4870/8000 [04:20<03:20, 15.59it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4876/8000 [04:20<02:19, 22.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|███▊       | 35/100 [00:00<00:00, 341.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4879/8000 [04:20<02:55, 17.81it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 339.74it/s]\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4882/8000 [04:21<04:40, 11.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4884/8000 [04:21<04:26, 11.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4887/8000 [04:21<03:51, 13.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                      | 1/8000 [00:00<16:27,  8.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4890/8000 [04:21<04:00, 12.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                      | 5/8000 [00:00<05:41, 23.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4892/8000 [04:22<04:41, 11.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 11/8000 [00:00<03:35, 37.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4895/8000 [04:22<03:52, 13.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 18/8000 [00:00<02:42, 48.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4898/8000 [04:22<04:50, 10.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 26/8000 [00:00<02:54, 45.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4901/8000 [04:22<03:59, 12.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 32/8000 [00:00<02:50, 46.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4904/8000 [04:22<03:25, 15.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 38/8000 [00:00<02:43, 48.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4906/8000 [04:22<03:19, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 47/8000 [00:01<02:27, 54.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4909/8000 [04:23<02:54, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 55/8000 [00:01<03:11, 41.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4913/8000 [04:23<03:14, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 64/8000 [00:01<04:30, 29.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4915/8000 [04:23<03:16, 15.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 69/8000 [00:01<04:24, 29.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4919/8000 [04:23<03:16, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 75/8000 [00:02<04:58, 26.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4923/8000 [04:23<02:44, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 80/8000 [00:02<06:26, 20.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4929/8000 [04:24<02:36, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 85/8000 [00:02<05:42, 23.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4932/8000 [04:24<03:02, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 91/8000 [00:03<05:55, 22.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4934/8000 [04:24<03:07, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 95/8000 [00:03<06:05, 21.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4936/8000 [04:24<03:58, 12.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 104/8000 [00:03<04:17, 30.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4940/8000 [04:25<03:44, 13.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 108/8000 [00:03<04:06, 31.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4947/8000 [04:25<02:29, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▌                                   | 114/8000 [00:03<03:37, 36.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4950/8000 [04:25<02:23, 21.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 120/8000 [00:03<03:37, 36.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4953/8000 [04:25<02:33, 19.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 128/8000 [00:03<03:00, 43.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4957/8000 [04:25<02:47, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 133/8000 [00:04<02:58, 44.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4962/8000 [04:25<02:20, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 143/8000 [00:04<02:23, 54.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4965/8000 [04:26<02:49, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 149/8000 [00:04<02:26, 53.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4973/8000 [04:26<02:37, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 156/8000 [00:04<02:17, 56.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4976/8000 [04:26<02:34, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 162/8000 [00:04<02:41, 48.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4983/8000 [04:27<02:44, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 169/8000 [00:04<02:51, 45.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4987/8000 [04:27<02:28, 20.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 177/8000 [00:04<02:33, 51.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4990/8000 [04:27<02:24, 20.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 183/8000 [00:05<03:05, 42.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4995/8000 [04:27<02:05, 23.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 190/8000 [00:05<03:04, 42.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4999/8000 [04:27<02:21, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▉                                   | 197/8000 [00:05<02:46, 46.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5004/8000 [04:27<01:57, 25.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 206/8000 [00:05<02:37, 49.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5007/8000 [04:28<01:57, 25.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 213/8000 [00:05<02:47, 46.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5010/8000 [04:28<01:58, 25.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 218/8000 [00:05<02:49, 45.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5013/8000 [04:28<02:03, 24.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 224/8000 [00:05<02:58, 43.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5018/8000 [04:28<02:41, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 229/8000 [00:06<03:15, 39.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5021/8000 [04:28<02:33, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 235/8000 [00:06<02:58, 43.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5026/8000 [04:29<03:10, 15.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 241/8000 [00:06<02:48, 46.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5029/8000 [04:29<02:55, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 248/8000 [00:06<02:53, 44.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5032/8000 [04:29<02:42, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 256/8000 [00:06<02:41, 47.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5037/8000 [04:29<02:08, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 261/8000 [00:06<02:51, 45.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5044/8000 [04:29<01:54, 25.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 268/8000 [00:06<03:05, 41.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5047/8000 [04:29<02:02, 24.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 276/8000 [00:07<02:58, 43.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5051/8000 [04:30<01:56, 25.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 281/8000 [00:07<03:02, 42.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5056/8000 [04:30<02:07, 23.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 287/8000 [00:07<03:47, 33.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5061/8000 [04:30<02:39, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 291/8000 [00:07<04:39, 27.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5066/8000 [04:31<02:36, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 298/8000 [00:07<03:54, 32.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5070/8000 [04:31<02:39, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 303/8000 [00:07<03:43, 34.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5073/8000 [04:31<02:32, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 307/8000 [00:08<04:12, 30.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5078/8000 [04:31<02:34, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 311/8000 [00:08<03:59, 32.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5080/8000 [04:31<02:36, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 317/8000 [00:08<03:51, 33.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5084/8000 [04:31<02:24, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 323/8000 [00:08<03:21, 38.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5090/8000 [04:32<02:19, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 328/8000 [00:08<04:11, 30.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5094/8000 [04:32<02:30, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 332/8000 [00:08<04:07, 30.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5097/8000 [04:32<02:24, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 339/8000 [00:09<03:19, 38.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5100/8000 [04:32<02:21, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 347/8000 [00:09<03:43, 34.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5104/8000 [04:32<02:35, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 352/8000 [00:09<03:31, 36.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5113/8000 [04:33<01:40, 28.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 360/8000 [00:09<03:31, 36.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5117/8000 [04:33<01:38, 29.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 370/8000 [00:09<02:43, 46.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5122/8000 [04:33<01:54, 25.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 381/8000 [00:09<02:37, 48.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5125/8000 [04:33<02:18, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 392/8000 [00:10<02:09, 58.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5128/8000 [04:33<02:16, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 399/8000 [00:10<02:21, 53.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5134/8000 [04:34<01:51, 25.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 408/8000 [00:10<02:21, 53.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5140/8000 [04:34<01:58, 24.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 414/8000 [00:10<02:24, 52.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5143/8000 [04:34<02:28, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 421/8000 [00:10<02:55, 43.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5146/8000 [04:34<02:53, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 430/8000 [00:10<02:31, 49.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5149/8000 [04:35<03:16, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 436/8000 [00:11<02:36, 48.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5153/8000 [04:35<02:39, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 446/8000 [00:11<02:10, 57.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5156/8000 [04:35<02:58, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 455/8000 [00:11<02:42, 46.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5158/8000 [04:35<03:27, 13.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 461/8000 [00:11<02:53, 43.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5161/8000 [04:35<03:03, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 468/8000 [00:11<02:43, 45.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5167/8000 [04:36<02:07, 22.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 476/8000 [00:11<02:49, 44.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5170/8000 [04:36<02:04, 22.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 482/8000 [00:12<03:03, 41.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5174/8000 [04:36<01:50, 25.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 488/8000 [00:12<02:50, 43.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5179/8000 [04:36<01:35, 29.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 497/8000 [00:12<02:23, 52.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5187/8000 [04:36<01:34, 29.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 504/8000 [00:12<02:21, 53.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5193/8000 [04:36<01:47, 26.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 510/8000 [00:12<02:37, 47.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5198/8000 [04:37<01:55, 24.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 519/8000 [00:12<02:28, 50.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5201/8000 [04:37<01:58, 23.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                 | 526/8000 [00:13<02:57, 42.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5204/8000 [04:37<02:02, 22.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 532/8000 [00:13<02:52, 43.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5207/8000 [04:37<02:02, 22.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 538/8000 [00:13<02:49, 44.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5211/8000 [04:37<02:27, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 543/8000 [00:13<03:17, 37.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5214/8000 [04:38<02:19, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 547/8000 [00:13<03:48, 32.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5220/8000 [04:38<02:44, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 553/8000 [00:13<03:53, 31.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5222/8000 [04:38<02:48, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 557/8000 [00:13<03:56, 31.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5225/8000 [04:38<02:40, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 563/8000 [00:14<03:47, 32.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5230/8000 [04:38<02:03, 22.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 571/8000 [00:14<03:02, 40.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5235/8000 [04:39<02:12, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 577/8000 [00:14<02:58, 41.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5239/8000 [04:39<02:25, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 583/8000 [00:14<02:49, 43.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5245/8000 [04:39<02:12, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 590/8000 [00:14<02:57, 41.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5249/8000 [04:39<02:26, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 596/8000 [00:14<03:32, 34.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5255/8000 [04:40<01:52, 24.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 602/8000 [00:15<03:10, 38.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5261/8000 [04:40<01:35, 28.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 609/8000 [00:15<03:05, 39.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5267/8000 [04:40<01:40, 27.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 616/8000 [00:15<02:59, 41.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5272/8000 [04:40<02:12, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 622/8000 [00:15<02:56, 41.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5275/8000 [04:40<02:10, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 627/8000 [00:15<02:58, 41.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5278/8000 [04:41<03:01, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 632/8000 [00:15<03:26, 35.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5281/8000 [04:41<03:18, 13.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 636/8000 [00:16<03:50, 31.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5285/8000 [04:41<02:45, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 643/8000 [00:16<03:32, 34.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5288/8000 [04:41<02:33, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 648/8000 [00:16<03:41, 33.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5291/8000 [04:42<02:24, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 653/8000 [00:16<03:50, 31.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5296/8000 [04:42<01:55, 23.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 660/8000 [00:16<03:17, 37.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5299/8000 [04:42<01:56, 23.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 665/8000 [00:16<03:17, 37.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5304/8000 [04:42<02:35, 17.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 669/8000 [00:16<03:19, 36.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5309/8000 [04:43<02:56, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 674/8000 [00:17<03:07, 39.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5313/8000 [04:43<02:37, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 678/8000 [00:17<03:11, 38.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5322/8000 [04:43<02:02, 21.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                 | 685/8000 [00:17<03:11, 38.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5325/8000 [04:43<02:06, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 697/8000 [00:17<02:12, 54.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5330/8000 [04:43<02:09, 20.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 704/8000 [00:17<02:33, 47.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5335/8000 [04:44<02:34, 17.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 711/8000 [00:17<02:56, 41.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5338/8000 [04:44<02:28, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 717/8000 [00:18<03:21, 36.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5341/8000 [04:44<02:18, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 723/8000 [00:18<03:04, 39.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5346/8000 [04:44<01:51, 23.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 728/8000 [00:18<03:00, 40.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5352/8000 [04:45<02:37, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 733/8000 [00:18<03:29, 34.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5355/8000 [04:45<02:56, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 739/8000 [00:18<03:08, 38.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5359/8000 [04:45<02:53, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 744/8000 [00:18<03:32, 34.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5362/8000 [04:45<02:34, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                                | 749/8000 [00:18<03:18, 36.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5365/8000 [04:46<02:28, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▍                                | 754/8000 [00:19<03:08, 38.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5368/8000 [04:46<03:21, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▍                                | 759/8000 [00:19<03:21, 35.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5371/8000 [04:46<03:29, 12.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                                | 764/8000 [00:19<03:12, 37.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5374/8000 [04:46<03:05, 14.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                                | 768/8000 [00:19<03:13, 37.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5380/8000 [04:47<02:41, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                                | 773/8000 [00:19<03:25, 35.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5382/8000 [04:47<03:09, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 782/8000 [00:19<02:33, 47.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5384/8000 [04:47<03:42, 11.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 791/8000 [00:19<02:45, 43.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5386/8000 [04:47<03:33, 12.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                                | 803/8000 [00:20<02:01, 59.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5388/8000 [04:47<03:22, 12.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 811/8000 [00:20<02:12, 54.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5390/8000 [04:48<03:09, 13.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 819/8000 [00:20<02:39, 45.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5392/8000 [04:48<03:05, 14.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 826/8000 [00:20<02:57, 40.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5394/8000 [04:48<02:59, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                                | 831/8000 [00:20<03:09, 37.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5396/8000 [04:48<03:01, 14.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▊                                | 836/8000 [00:21<03:27, 34.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5401/8000 [04:48<02:02, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 841/8000 [00:21<03:43, 32.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5405/8000 [04:48<01:50, 23.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 846/8000 [00:21<03:30, 34.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5408/8000 [04:48<02:25, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 851/8000 [00:21<03:18, 35.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5411/8000 [04:49<02:14, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                                | 857/8000 [00:21<02:54, 40.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5414/8000 [04:49<02:39, 16.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 862/8000 [00:21<02:55, 40.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5417/8000 [04:49<02:28, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 869/8000 [00:21<02:58, 40.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5422/8000 [04:49<01:54, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 877/8000 [00:22<02:49, 41.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5425/8000 [04:50<02:48, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 882/8000 [00:22<03:13, 36.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5427/8000 [04:50<02:50, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▉                                | 887/8000 [00:22<03:14, 36.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5430/8000 [04:50<02:43, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 891/8000 [00:22<03:17, 35.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5435/8000 [04:50<02:29, 17.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 895/8000 [00:22<03:23, 34.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5437/8000 [04:50<03:02, 14.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 901/8000 [00:22<03:23, 34.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5440/8000 [04:50<02:38, 16.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 907/8000 [00:22<03:01, 39.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5442/8000 [04:51<02:40, 15.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                                | 914/8000 [00:23<03:02, 38.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5447/8000 [04:51<02:27, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 920/8000 [00:23<02:51, 41.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5451/8000 [04:51<02:07, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 926/8000 [00:23<03:04, 38.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5455/8000 [04:51<01:50, 23.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 932/8000 [00:23<03:08, 37.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5459/8000 [04:51<02:35, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                               | 938/8000 [00:23<02:55, 40.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5464/8000 [04:52<02:33, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 945/8000 [00:23<03:11, 36.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5467/8000 [04:52<02:51, 14.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 954/8000 [00:24<02:51, 41.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5470/8000 [04:52<03:05, 13.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 960/8000 [00:24<02:41, 43.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5473/8000 [04:52<02:43, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                               | 966/8000 [00:24<02:36, 44.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5478/8000 [04:53<02:01, 20.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 976/8000 [00:24<02:09, 54.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5481/8000 [04:53<02:48, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 984/8000 [00:24<02:43, 43.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5484/8000 [04:53<02:35, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 991/8000 [00:24<02:58, 39.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5490/8000 [04:53<02:14, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▍                               | 996/8000 [00:25<02:54, 40.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5494/8000 [04:53<01:56, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1003/8000 [00:25<02:32, 45.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5501/8000 [04:54<01:49, 22.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1012/8000 [00:25<02:40, 43.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5504/8000 [04:54<02:11, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1017/8000 [00:25<02:39, 43.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5507/8000 [04:54<02:06, 19.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                              | 1024/8000 [00:25<02:39, 43.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5513/8000 [04:54<02:01, 20.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1032/8000 [00:25<02:33, 45.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5517/8000 [04:55<02:35, 15.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1041/8000 [00:25<02:25, 47.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5521/8000 [04:55<02:35, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1047/8000 [00:26<02:29, 46.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5525/8000 [04:55<02:14, 18.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1052/8000 [00:26<02:31, 45.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5531/8000 [04:55<01:43, 23.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1057/8000 [00:26<02:48, 41.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5535/8000 [04:55<01:36, 25.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1063/8000 [00:26<02:54, 39.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5541/8000 [04:56<01:45, 23.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1068/8000 [00:26<03:12, 36.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5545/8000 [04:56<02:20, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1073/8000 [00:26<03:05, 37.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5550/8000 [04:56<02:19, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                              | 1080/8000 [00:26<02:43, 42.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5554/8000 [04:57<02:28, 16.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                              | 1085/8000 [00:27<03:06, 37.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5561/8000 [04:57<01:49, 22.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1090/8000 [00:27<03:23, 34.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5564/8000 [04:57<01:50, 22.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1100/8000 [00:27<02:30, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5569/8000 [04:57<01:53, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1110/8000 [00:27<02:04, 55.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5572/8000 [04:57<01:52, 21.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1118/8000 [00:27<01:58, 58.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5575/8000 [04:58<01:52, 21.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1130/8000 [00:27<01:49, 62.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5579/8000 [04:58<01:46, 22.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1139/8000 [00:28<02:08, 53.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5582/8000 [04:58<01:45, 22.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1147/8000 [00:28<02:29, 45.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5586/8000 [04:58<01:33, 25.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1153/8000 [00:28<02:36, 43.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5589/8000 [04:58<02:04, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1159/8000 [00:28<03:00, 37.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5592/8000 [04:58<02:22, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                              | 1166/8000 [00:28<02:50, 40.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5596/8000 [04:59<02:32, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1173/8000 [00:29<03:04, 36.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5598/8000 [04:59<03:35, 11.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1179/8000 [00:29<03:31, 32.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5600/8000 [04:59<03:20, 11.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1186/8000 [00:29<03:02, 37.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5603/8000 [04:59<03:26, 11.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1192/8000 [00:29<02:49, 40.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5606/8000 [05:00<02:59, 13.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1201/8000 [00:29<02:30, 45.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5609/8000 [05:00<02:32, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1212/8000 [00:29<01:57, 57.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5612/8000 [05:00<02:16, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1219/8000 [00:30<02:55, 38.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5615/8000 [05:00<02:10, 18.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1226/8000 [00:30<03:04, 36.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5619/8000 [05:00<01:54, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▍                             | 1233/8000 [00:30<03:00, 37.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5626/8000 [05:00<01:45, 22.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1240/8000 [00:30<02:42, 41.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5630/8000 [05:01<02:01, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1245/8000 [00:30<02:42, 41.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5635/8000 [05:01<02:00, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1250/8000 [00:30<02:37, 42.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5641/8000 [05:01<02:13, 17.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1258/8000 [00:31<02:36, 43.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5644/8000 [05:02<02:07, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1263/8000 [00:31<02:35, 43.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5647/8000 [05:02<01:59, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1271/8000 [00:31<02:27, 45.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5650/8000 [05:02<01:58, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1278/8000 [00:31<02:21, 47.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5654/8000 [05:02<02:03, 18.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1283/8000 [00:31<02:25, 46.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5659/8000 [05:02<02:07, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1290/8000 [00:31<02:22, 46.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5661/8000 [05:02<02:12, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1297/8000 [00:31<02:26, 45.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5666/8000 [05:03<01:46, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1302/8000 [00:32<02:28, 45.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5669/8000 [05:03<01:46, 21.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1314/8000 [00:32<02:20, 47.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5675/8000 [05:03<01:41, 22.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▊                             | 1320/8000 [00:32<02:16, 49.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5679/8000 [05:03<01:35, 24.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1331/8000 [00:32<01:55, 57.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5684/8000 [05:03<01:45, 21.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1337/8000 [00:32<02:27, 45.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5690/8000 [05:04<01:41, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1343/8000 [00:32<02:23, 46.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5695/8000 [05:04<01:28, 26.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1350/8000 [00:33<02:45, 40.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5698/8000 [05:04<01:48, 21.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1358/8000 [00:33<02:20, 47.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5703/8000 [05:04<02:16, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1365/8000 [00:33<02:14, 49.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5706/8000 [05:05<02:48, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1371/8000 [00:33<03:03, 36.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5708/8000 [05:05<02:46, 13.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1378/8000 [00:33<02:39, 41.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5714/8000 [05:05<01:56, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1384/8000 [00:33<02:44, 40.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5717/8000 [05:05<02:13, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1395/8000 [00:34<02:07, 51.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5721/8000 [05:05<01:52, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1406/8000 [00:34<02:08, 51.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5727/8000 [05:06<01:44, 21.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1415/8000 [00:34<02:25, 45.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5730/8000 [05:06<01:44, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1421/8000 [00:34<02:21, 46.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5734/8000 [05:06<01:55, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1430/8000 [00:34<02:03, 53.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5738/8000 [05:06<02:02, 18.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1439/8000 [00:34<02:02, 53.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5740/8000 [05:06<02:05, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1447/8000 [00:35<01:56, 56.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5743/8000 [05:07<02:04, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1453/8000 [00:35<02:41, 40.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5745/8000 [05:07<02:41, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1461/8000 [00:35<02:32, 42.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5747/8000 [05:07<02:39, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1467/8000 [00:35<02:23, 45.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5749/8000 [05:07<02:34, 14.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1473/8000 [00:35<02:32, 42.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5751/8000 [05:07<03:08, 11.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1478/8000 [00:35<02:52, 37.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5753/8000 [05:08<02:58, 12.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                            | 1484/8000 [00:36<02:37, 41.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5757/8000 [05:08<02:11, 17.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1491/8000 [00:36<02:36, 41.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5759/8000 [05:08<02:19, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1499/8000 [00:36<02:15, 47.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5763/8000 [05:08<01:54, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1508/8000 [00:36<02:01, 53.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5767/8000 [05:08<02:33, 14.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1514/8000 [00:36<02:15, 47.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5769/8000 [05:08<02:26, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1520/8000 [00:36<02:27, 43.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5771/8000 [05:09<03:07, 11.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1529/8000 [00:36<02:18, 46.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5775/8000 [05:09<02:20, 15.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1534/8000 [00:37<02:18, 46.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5779/8000 [05:09<02:24, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1539/8000 [00:37<03:04, 34.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5782/8000 [05:09<02:16, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1545/8000 [00:37<02:45, 39.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5784/8000 [05:09<02:16, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1552/8000 [00:37<02:28, 43.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5786/8000 [05:10<02:20, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1559/8000 [00:37<02:19, 46.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5788/8000 [05:10<02:22, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                            | 1565/8000 [00:37<02:45, 38.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5794/8000 [05:10<01:34, 23.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                            | 1570/8000 [00:38<02:39, 40.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5798/8000 [05:10<01:52, 19.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1579/8000 [00:38<02:38, 40.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5801/8000 [05:11<02:48, 13.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1584/8000 [00:38<02:34, 41.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5804/8000 [05:11<02:27, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1589/8000 [00:38<02:33, 41.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5808/8000 [05:11<02:03, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1598/8000 [00:38<02:24, 44.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5811/8000 [05:11<01:56, 18.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1605/8000 [00:38<02:25, 43.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5815/8000 [05:11<02:31, 14.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1610/8000 [00:38<02:23, 44.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5817/8000 [05:12<02:59, 12.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1616/8000 [00:39<02:32, 41.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5819/8000 [05:12<02:53, 12.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1622/8000 [00:39<02:22, 44.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5823/8000 [05:12<02:12, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1627/8000 [00:39<02:22, 44.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5826/8000 [05:12<02:01, 17.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████▏                           | 1633/8000 [00:39<02:15, 47.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5830/8000 [05:12<02:30, 14.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████▏                           | 1639/8000 [00:39<02:14, 47.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5832/8000 [05:13<02:31, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1646/8000 [00:39<02:17, 46.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5836/8000 [05:13<02:00, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1653/8000 [00:39<02:03, 51.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5839/8000 [05:13<01:54, 18.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1660/8000 [00:39<01:55, 54.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5844/8000 [05:13<01:53, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1667/8000 [00:40<02:17, 45.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5848/8000 [05:13<01:41, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1673/8000 [00:40<02:23, 43.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5851/8000 [05:13<01:40, 21.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1679/8000 [00:40<02:31, 41.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5854/8000 [05:14<02:21, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1685/8000 [00:40<02:39, 39.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5856/8000 [05:14<02:22, 15.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1690/8000 [00:40<03:08, 33.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5858/8000 [05:14<04:03,  8.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1695/8000 [00:41<03:19, 31.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5862/8000 [05:15<03:20, 10.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1699/8000 [00:41<03:40, 28.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5865/8000 [05:15<02:50, 12.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1705/8000 [00:41<03:02, 34.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5869/8000 [05:15<02:11, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1710/8000 [00:41<02:52, 36.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5872/8000 [05:15<02:00, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1714/8000 [00:41<02:57, 35.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5875/8000 [05:15<02:15, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▌                           | 1718/8000 [00:41<03:23, 30.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5879/8000 [05:15<01:51, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1724/8000 [00:41<03:14, 32.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5883/8000 [05:16<01:57, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1728/8000 [00:42<03:08, 33.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5886/8000 [05:16<02:17, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1733/8000 [00:42<02:49, 36.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5888/8000 [05:16<02:20, 14.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1740/8000 [00:42<02:39, 39.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5891/8000 [05:16<02:59, 11.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1747/8000 [00:42<02:40, 38.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5893/8000 [05:17<02:52, 12.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1753/8000 [00:42<02:30, 41.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5895/8000 [05:17<02:38, 13.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1758/8000 [00:42<02:45, 37.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5897/8000 [05:17<03:11, 11.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1764/8000 [00:42<02:49, 36.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5900/8000 [05:17<02:38, 13.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1770/8000 [00:43<03:26, 30.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5904/8000 [05:17<02:05, 16.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1774/8000 [00:43<03:34, 29.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5907/8000 [05:17<01:55, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1780/8000 [00:43<03:01, 34.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5910/8000 [05:18<02:07, 16.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1784/8000 [00:43<03:03, 33.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5912/8000 [05:18<02:09, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1791/8000 [00:43<02:35, 39.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5916/8000 [05:18<01:43, 20.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1797/8000 [00:43<02:22, 43.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5921/8000 [05:18<01:21, 25.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1803/8000 [00:44<02:35, 39.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5924/8000 [05:18<01:21, 25.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1811/8000 [00:44<02:14, 46.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5927/8000 [05:18<01:21, 25.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1817/8000 [00:44<02:22, 43.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5931/8000 [05:18<01:16, 27.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1822/8000 [00:44<02:25, 42.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5934/8000 [05:18<01:22, 24.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1829/8000 [00:44<02:10, 47.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5939/8000 [05:19<01:10, 29.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1838/8000 [00:44<02:06, 48.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5946/8000 [05:19<01:30, 22.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1848/8000 [00:44<01:58, 51.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5949/8000 [05:19<02:12, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1854/8000 [00:45<01:59, 51.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5952/8000 [05:20<02:26, 14.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1860/8000 [00:45<01:57, 52.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5956/8000 [05:20<01:58, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1867/8000 [00:45<02:08, 47.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5961/8000 [05:20<01:52, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1872/8000 [00:45<02:11, 46.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5964/8000 [05:20<01:49, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                          | 1880/8000 [00:45<02:32, 40.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5969/8000 [05:20<01:49, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1886/8000 [00:45<02:40, 38.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5972/8000 [05:21<01:44, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1890/8000 [00:45<02:43, 37.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5979/8000 [05:21<01:34, 21.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1897/8000 [00:46<02:25, 42.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5983/8000 [05:21<01:27, 23.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1902/8000 [00:46<02:20, 43.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5987/8000 [05:21<01:24, 23.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1910/8000 [00:46<02:35, 39.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5991/8000 [05:22<01:56, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1915/8000 [00:46<02:43, 37.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5994/8000 [05:22<02:27, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1921/8000 [00:46<02:33, 39.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5997/8000 [05:22<02:33, 13.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1928/8000 [00:46<02:19, 43.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6001/8000 [05:22<02:04, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1934/8000 [00:47<02:36, 38.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6005/8000 [05:22<01:44, 19.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1943/8000 [00:47<02:06, 47.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6011/8000 [05:23<01:32, 21.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1951/8000 [00:47<02:09, 46.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6015/8000 [05:23<01:45, 18.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1959/8000 [00:47<01:58, 51.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6020/8000 [05:23<01:43, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                          | 1965/8000 [00:47<01:56, 51.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6025/8000 [05:23<01:26, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1973/8000 [00:47<02:17, 43.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6029/8000 [05:24<01:36, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1979/8000 [00:47<02:24, 41.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6034/8000 [05:24<01:56, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1984/8000 [00:48<02:25, 41.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6040/8000 [05:24<01:27, 22.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1989/8000 [00:48<02:25, 41.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6044/8000 [05:24<01:37, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1994/8000 [00:48<02:43, 36.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6048/8000 [05:25<01:28, 22.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2000/8000 [00:48<02:29, 40.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6051/8000 [05:25<01:27, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2006/8000 [00:48<02:22, 42.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6055/8000 [05:25<01:58, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2014/8000 [00:48<02:02, 48.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6060/8000 [05:25<02:12, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2021/8000 [00:48<02:28, 40.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6064/8000 [05:26<01:52, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2026/8000 [00:49<02:24, 41.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6068/8000 [05:26<01:35, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▉                          | 2034/8000 [00:49<02:03, 48.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6071/8000 [05:26<01:53, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2040/8000 [00:49<02:29, 39.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6074/8000 [05:26<02:28, 13.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2046/8000 [00:49<02:44, 36.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6076/8000 [05:27<02:50, 11.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2052/8000 [00:49<02:30, 39.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6078/8000 [05:27<02:38, 12.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2057/8000 [00:49<02:48, 35.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6086/8000 [05:27<01:30, 21.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2061/8000 [00:50<02:46, 35.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6093/8000 [05:27<01:24, 22.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2065/8000 [00:50<02:53, 34.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6096/8000 [05:27<01:25, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2073/8000 [00:50<02:22, 41.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6101/8000 [05:27<01:14, 25.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2080/8000 [00:50<02:21, 41.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6105/8000 [05:28<01:08, 27.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2085/8000 [00:50<02:40, 36.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6111/8000 [05:28<01:25, 22.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2090/8000 [00:50<02:35, 38.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6117/8000 [05:28<01:08, 27.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2098/8000 [00:50<02:26, 40.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6123/8000 [05:28<01:12, 25.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2103/8000 [00:51<02:41, 36.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6128/8000 [05:29<01:21, 22.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2107/8000 [00:51<02:45, 35.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6132/8000 [05:29<01:31, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2114/8000 [00:51<02:16, 43.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6135/8000 [05:29<01:28, 21.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▎                         | 2119/8000 [00:51<02:18, 42.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6138/8000 [05:29<01:26, 21.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2124/8000 [00:51<02:16, 43.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6141/8000 [05:29<01:25, 21.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2129/8000 [00:51<02:23, 41.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6144/8000 [05:29<01:25, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2136/8000 [00:51<02:25, 40.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6148/8000 [05:30<01:15, 24.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2141/8000 [00:52<02:23, 40.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6152/8000 [05:30<01:50, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2147/8000 [00:52<02:50, 34.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6157/8000 [05:30<01:47, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2151/8000 [00:52<03:08, 31.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6161/8000 [05:30<01:30, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2156/8000 [00:52<02:54, 33.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6165/8000 [05:31<01:40, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2161/8000 [00:52<02:43, 35.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6168/8000 [05:31<02:11, 13.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2169/8000 [00:52<02:10, 44.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6171/8000 [05:31<02:16, 13.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2175/8000 [00:53<02:43, 35.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6174/8000 [05:31<02:06, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2180/8000 [00:53<02:33, 38.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6178/8000 [05:31<01:42, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2187/8000 [00:53<03:05, 31.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6182/8000 [05:32<01:45, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2193/8000 [00:53<02:42, 35.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6185/8000 [05:32<01:53, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2198/8000 [00:53<02:54, 33.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6188/8000 [05:32<02:02, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2203/8000 [00:53<03:00, 32.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6192/8000 [05:32<01:38, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2215/8000 [00:54<02:00, 48.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6197/8000 [05:33<01:43, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2222/8000 [00:54<01:52, 51.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6201/8000 [05:33<01:44, 17.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2229/8000 [00:54<01:49, 52.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6205/8000 [05:33<01:28, 20.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2235/8000 [00:54<02:35, 37.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6208/8000 [05:33<01:42, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2240/8000 [00:54<02:28, 38.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6214/8000 [05:33<01:31, 19.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2245/8000 [00:54<02:47, 34.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6217/8000 [05:34<02:03, 14.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2249/8000 [00:54<02:46, 34.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6219/8000 [05:34<02:00, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2253/8000 [00:55<02:48, 34.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6222/8000 [05:34<01:48, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2257/8000 [00:55<02:55, 32.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6226/8000 [05:34<01:32, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2261/8000 [00:55<03:13, 29.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6231/8000 [05:34<01:16, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2269/8000 [00:55<02:30, 38.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6236/8000 [05:35<01:21, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2274/8000 [00:55<02:23, 39.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6239/8000 [05:35<01:39, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                         | 2282/8000 [00:55<01:59, 47.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6244/8000 [05:35<01:19, 22.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2287/8000 [00:55<02:14, 42.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6247/8000 [05:35<01:34, 18.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2294/8000 [00:56<02:18, 41.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6250/8000 [05:36<01:46, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2301/8000 [00:56<02:13, 42.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6254/8000 [05:36<01:46, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2306/8000 [00:56<02:14, 42.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6258/8000 [05:36<01:49, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2311/8000 [00:56<02:13, 42.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6260/8000 [05:36<01:49, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2318/8000 [00:56<02:16, 41.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6264/8000 [05:36<01:48, 16.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2325/8000 [00:56<02:01, 46.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6266/8000 [05:37<01:49, 15.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2330/8000 [00:56<02:21, 40.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6269/8000 [05:37<01:40, 17.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2335/8000 [00:57<02:32, 37.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6272/8000 [05:37<01:29, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2344/8000 [00:57<02:02, 45.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6278/8000 [05:37<01:28, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2351/8000 [00:57<02:06, 44.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6281/8000 [05:37<01:43, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2358/8000 [00:57<01:57, 48.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6284/8000 [05:38<01:55, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                        | 2366/8000 [00:57<02:12, 42.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6286/8000 [05:38<01:54, 14.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2374/8000 [00:57<01:56, 48.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6289/8000 [05:38<02:03, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2381/8000 [00:58<02:01, 46.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6291/8000 [05:38<02:37, 10.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2388/8000 [00:58<01:49, 51.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6295/8000 [05:38<01:56, 14.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2395/8000 [00:58<01:56, 48.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6299/8000 [05:39<01:33, 18.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2404/8000 [00:58<01:50, 50.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6305/8000 [05:39<01:41, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2411/8000 [00:58<02:37, 35.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6309/8000 [05:39<01:27, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2417/8000 [00:58<02:34, 36.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6315/8000 [05:40<01:33, 17.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2422/8000 [00:59<02:30, 36.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6318/8000 [05:40<01:28, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2428/8000 [00:59<02:31, 36.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6322/8000 [05:40<01:16, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▋                        | 2432/8000 [00:59<02:31, 36.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6326/8000 [05:40<01:09, 24.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2441/8000 [00:59<01:57, 47.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6329/8000 [05:40<01:25, 19.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2448/8000 [00:59<01:49, 50.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6332/8000 [05:40<01:21, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2458/8000 [00:59<01:44, 53.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6335/8000 [05:40<01:37, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2469/8000 [00:59<01:45, 52.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6337/8000 [05:41<01:40, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2475/8000 [01:00<01:43, 53.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6340/8000 [05:41<01:29, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2483/8000 [01:00<01:45, 52.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6346/8000 [05:41<01:22, 19.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2490/8000 [01:00<01:53, 48.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6349/8000 [05:41<01:21, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2501/8000 [01:00<01:41, 54.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6353/8000 [05:41<01:12, 22.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2508/8000 [01:00<01:47, 51.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6356/8000 [05:42<01:29, 18.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|███████████                        | 2516/8000 [01:00<01:39, 54.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6359/8000 [05:42<01:46, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2522/8000 [01:00<01:42, 53.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6363/8000 [05:42<01:44, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2528/8000 [01:01<01:56, 46.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6365/8000 [05:42<01:44, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2533/8000 [01:01<02:27, 37.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6367/8000 [05:42<01:46, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2538/8000 [01:01<02:49, 32.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6371/8000 [05:43<01:44, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2542/8000 [01:01<02:43, 33.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6373/8000 [05:43<01:45, 15.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2552/8000 [01:01<01:57, 46.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6380/8000 [05:43<01:19, 20.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2560/8000 [01:01<01:56, 46.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6382/8000 [05:43<01:23, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2566/8000 [01:02<02:28, 36.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6386/8000 [05:43<01:14, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2571/8000 [01:02<02:24, 37.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6389/8000 [05:44<01:35, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2580/8000 [01:02<01:55, 47.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6393/8000 [05:44<01:19, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2587/8000 [01:02<01:59, 45.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6396/8000 [05:44<01:49, 14.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2594/8000 [01:02<02:07, 42.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6401/8000 [05:44<01:24, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2599/8000 [01:03<02:17, 39.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6409/8000 [05:44<01:07, 23.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2605/8000 [01:03<02:30, 35.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6413/8000 [05:45<01:16, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2610/8000 [01:03<02:21, 38.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6416/8000 [05:45<01:17, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2619/8000 [01:03<01:54, 46.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6419/8000 [05:45<01:48, 14.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2627/8000 [01:03<01:56, 46.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6425/8000 [05:45<01:30, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2632/8000 [01:03<01:56, 46.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6427/8000 [05:46<01:33, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2637/8000 [01:03<02:11, 40.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6434/8000 [05:46<01:05, 24.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2642/8000 [01:04<02:24, 36.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6440/8000 [05:46<01:04, 24.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2649/8000 [01:04<02:04, 42.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6444/8000 [05:46<01:13, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2654/8000 [01:04<02:07, 41.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6447/8000 [05:46<01:10, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2659/8000 [01:04<02:02, 43.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6452/8000 [05:46<01:00, 25.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2666/8000 [01:04<01:54, 46.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6457/8000 [05:47<01:18, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2674/8000 [01:04<01:41, 52.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6460/8000 [05:47<01:16, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                       | 2684/8000 [01:04<01:44, 50.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6464/8000 [05:47<01:23, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2690/8000 [01:05<01:54, 46.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6470/8000 [05:48<01:30, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2697/8000 [01:05<01:46, 49.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6474/8000 [05:48<01:20, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2706/8000 [01:05<02:11, 40.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6478/8000 [05:48<01:23, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2711/8000 [01:05<02:14, 39.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6483/8000 [05:48<01:09, 21.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2719/8000 [01:05<02:06, 41.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6486/8000 [05:48<01:23, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2728/8000 [01:05<01:47, 49.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6492/8000 [05:49<01:11, 21.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2737/8000 [01:06<01:53, 46.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6495/8000 [05:49<01:25, 17.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2744/8000 [01:06<01:53, 46.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6500/8000 [05:49<01:21, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2752/8000 [01:06<01:39, 52.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6504/8000 [05:49<01:14, 20.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2761/8000 [01:06<01:28, 59.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6507/8000 [05:50<01:27, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2768/8000 [01:06<01:39, 52.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6512/8000 [05:50<01:28, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2777/8000 [01:06<01:26, 60.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6515/8000 [05:50<01:22, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2786/8000 [01:06<01:40, 52.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6518/8000 [05:50<01:18, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2793/8000 [01:07<01:37, 53.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6521/8000 [05:50<01:12, 20.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2799/8000 [01:07<01:49, 47.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6524/8000 [05:51<01:27, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2807/8000 [01:07<02:02, 42.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6526/8000 [05:51<01:30, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2814/8000 [01:07<01:49, 47.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6529/8000 [05:51<01:24, 17.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2823/8000 [01:07<01:43, 49.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6533/8000 [05:51<01:29, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▍                      | 2829/8000 [01:07<01:42, 50.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6535/8000 [05:51<01:30, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▍                      | 2836/8000 [01:07<01:35, 54.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6538/8000 [05:51<01:24, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2842/8000 [01:08<01:45, 48.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6541/8000 [05:52<01:20, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2852/8000 [01:08<01:24, 60.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6543/8000 [05:52<01:29, 16.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2860/8000 [01:08<01:22, 62.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6549/8000 [05:52<01:01, 23.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2869/8000 [01:08<01:48, 47.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6553/8000 [05:52<01:01, 23.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2875/8000 [01:08<01:43, 49.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6558/8000 [05:52<01:06, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2881/8000 [01:08<01:50, 46.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6562/8000 [05:53<01:14, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2891/8000 [01:09<01:30, 56.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6565/8000 [05:53<01:14, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2898/8000 [01:09<01:48, 46.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6568/8000 [05:53<01:25, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2905/8000 [01:09<02:05, 40.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6570/8000 [05:53<01:29, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2910/8000 [01:09<02:15, 37.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6574/8000 [05:53<01:14, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▊                      | 2915/8000 [01:09<02:10, 38.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6579/8000 [05:53<01:13, 19.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▊                      | 2920/8000 [01:09<02:38, 32.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6582/8000 [05:54<01:14, 19.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2924/8000 [01:10<02:50, 29.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6584/8000 [05:54<01:17, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2928/8000 [01:10<03:01, 27.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6587/8000 [05:54<01:18, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2937/8000 [01:10<02:08, 39.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6590/8000 [05:54<01:13, 19.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2942/8000 [01:10<02:16, 36.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6594/8000 [05:54<01:04, 21.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2948/8000 [01:10<02:31, 33.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6601/8000 [05:54<00:47, 29.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2955/8000 [01:10<02:07, 39.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6611/8000 [05:55<00:51, 26.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2960/8000 [01:11<02:07, 39.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6615/8000 [05:55<01:01, 22.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2965/8000 [01:11<02:01, 41.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6618/8000 [05:55<01:24, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2972/8000 [01:11<01:57, 42.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6620/8000 [05:56<01:24, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2978/8000 [01:11<02:15, 37.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6622/8000 [05:56<01:42, 13.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2987/8000 [01:11<01:44, 48.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6627/8000 [05:56<01:16, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2996/8000 [01:11<01:29, 56.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6630/8000 [05:56<01:14, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3005/8000 [01:11<01:40, 49.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6635/8000 [05:56<00:59, 22.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3011/8000 [01:12<01:37, 51.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6640/8000 [05:56<01:04, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3019/8000 [01:12<01:55, 43.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6643/8000 [05:57<01:02, 21.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3029/8000 [01:12<01:53, 43.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6649/8000 [05:57<00:50, 26.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3036/8000 [01:12<01:56, 42.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6653/8000 [05:57<01:17, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3041/8000 [01:12<01:56, 42.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6658/8000 [05:57<01:03, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3048/8000 [01:12<01:55, 42.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6661/8000 [05:58<01:18, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3054/8000 [01:13<02:28, 33.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6664/8000 [05:58<01:39, 13.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3059/8000 [01:13<02:17, 35.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6666/8000 [05:58<01:39, 13.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3065/8000 [01:13<02:17, 35.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6669/8000 [05:58<01:27, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3070/8000 [01:13<02:37, 31.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6673/8000 [05:58<01:12, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3078/8000 [01:13<02:06, 38.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6676/8000 [05:59<01:39, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▍                     | 3084/8000 [01:14<02:16, 36.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6681/8000 [05:59<01:12, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3088/8000 [01:14<02:32, 32.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6686/8000 [05:59<00:59, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3096/8000 [01:14<02:13, 36.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6689/8000 [05:59<01:12, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3103/8000 [01:14<02:10, 37.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6693/8000 [06:00<01:13, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3108/8000 [01:14<02:18, 35.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6696/8000 [06:00<01:07, 19.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3114/8000 [01:14<02:03, 39.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6700/8000 [06:00<01:44, 12.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3119/8000 [01:14<02:00, 40.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6702/8000 [06:01<01:58, 10.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3126/8000 [01:15<01:57, 41.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6705/8000 [06:01<01:42, 12.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3132/8000 [01:15<01:48, 44.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6709/8000 [06:01<01:22, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3138/8000 [01:15<01:43, 46.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6712/8000 [06:01<01:16, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3143/8000 [01:15<01:47, 45.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6715/8000 [06:01<01:26, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3154/8000 [01:15<01:33, 51.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6720/8000 [06:02<01:23, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▊                     | 3160/8000 [01:15<02:07, 38.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6722/8000 [06:02<01:25, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▊                     | 3169/8000 [01:16<01:48, 44.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6724/8000 [06:02<01:26, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3179/8000 [01:16<01:35, 50.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6727/8000 [06:02<01:38, 12.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3185/8000 [01:16<01:44, 45.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6729/8000 [06:03<02:23,  8.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3191/8000 [01:16<01:42, 47.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6734/8000 [06:03<01:34, 13.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3196/8000 [01:16<01:43, 46.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6738/8000 [06:03<01:23, 15.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3205/8000 [01:16<01:28, 53.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6740/8000 [06:03<01:26, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3211/8000 [01:16<01:37, 49.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6743/8000 [06:03<01:21, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3221/8000 [01:17<01:20, 59.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6747/8000 [06:04<01:25, 14.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3228/8000 [01:17<01:49, 43.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6750/8000 [06:04<01:15, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████▏                    | 3234/8000 [01:17<01:54, 41.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6753/8000 [06:04<01:26, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3243/8000 [01:17<01:34, 50.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6758/8000 [06:04<01:04, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3250/8000 [01:17<01:40, 47.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6761/8000 [06:04<01:02, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3258/8000 [01:17<01:49, 43.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6764/8000 [06:04<01:11, 17.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3269/8000 [01:18<01:25, 55.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6767/8000 [06:05<01:05, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3280/8000 [01:18<01:35, 49.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6771/8000 [06:05<00:58, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3287/8000 [01:18<01:31, 51.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6774/8000 [06:05<01:13, 16.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3294/8000 [01:18<01:36, 48.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6777/8000 [06:05<01:23, 14.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3301/8000 [01:18<01:49, 42.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6780/8000 [06:06<01:31, 13.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3309/8000 [01:18<01:35, 49.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6783/8000 [06:06<01:19, 15.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▌                    | 3316/8000 [01:19<01:50, 42.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6786/8000 [06:06<01:12, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3322/8000 [01:19<01:46, 44.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6789/8000 [06:06<01:08, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3329/8000 [01:19<02:02, 38.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6792/8000 [06:06<01:35, 12.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3336/8000 [01:19<01:47, 43.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6795/8000 [06:07<01:52, 10.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3341/8000 [01:19<01:47, 43.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6803/8000 [06:07<01:14, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3347/8000 [01:19<01:39, 46.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6805/8000 [06:07<01:25, 13.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3353/8000 [01:19<01:34, 49.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6808/8000 [06:08<01:28, 13.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3360/8000 [01:20<01:27, 52.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6814/8000 [06:08<01:04, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3366/8000 [01:20<01:29, 51.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6818/8000 [06:08<01:17, 15.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3373/8000 [01:20<01:38, 46.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6820/8000 [06:08<01:18, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3378/8000 [01:20<01:40, 45.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6822/8000 [06:08<01:17, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3384/8000 [01:20<01:36, 47.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6824/8000 [06:08<01:17, 15.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3391/8000 [01:20<02:12, 34.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6826/8000 [06:09<01:36, 12.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3402/8000 [01:21<01:46, 43.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6828/8000 [06:09<01:30, 12.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3408/8000 [01:21<01:43, 44.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6831/8000 [06:09<02:08,  9.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3415/8000 [01:21<01:58, 38.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6833/8000 [06:09<01:58,  9.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3420/8000 [01:21<02:08, 35.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6835/8000 [06:10<01:45, 11.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3424/8000 [01:21<02:16, 33.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6838/8000 [06:10<01:22, 14.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3432/8000 [01:21<01:49, 41.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6841/8000 [06:10<01:27, 13.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3437/8000 [01:21<01:48, 42.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6844/8000 [06:10<01:33, 12.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3445/8000 [01:22<01:31, 49.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6847/8000 [06:10<01:18, 14.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3454/8000 [01:22<01:45, 43.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6849/8000 [06:11<01:19, 14.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3462/8000 [01:22<01:33, 48.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6851/8000 [06:11<01:20, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3471/8000 [01:22<01:31, 49.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6855/8000 [06:11<01:03, 18.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3477/8000 [01:22<01:53, 39.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6858/8000 [06:11<01:43, 11.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▏                   | 3484/8000 [01:23<01:55, 39.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6860/8000 [06:12<01:50, 10.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3493/8000 [01:23<01:49, 41.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6863/8000 [06:12<01:32, 12.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3500/8000 [01:23<01:39, 45.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6866/8000 [06:12<01:31, 12.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3505/8000 [01:23<01:42, 43.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6868/8000 [06:12<01:52, 10.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3513/8000 [01:23<01:28, 50.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6872/8000 [06:12<01:22, 13.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3523/8000 [01:23<01:14, 59.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6879/8000 [06:13<00:51, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3530/8000 [01:23<01:22, 54.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6885/8000 [06:13<00:59, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3540/8000 [01:24<01:27, 50.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6892/8000 [06:13<00:53, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3546/8000 [01:24<01:36, 46.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6896/8000 [06:13<00:49, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3552/8000 [01:24<01:31, 48.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6901/8000 [06:13<00:43, 25.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3560/8000 [01:24<01:42, 43.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6905/8000 [06:14<00:51, 21.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▌                   | 3571/8000 [01:24<01:21, 54.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6914/8000 [06:14<00:35, 30.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3578/8000 [01:24<01:28, 50.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6918/8000 [06:14<00:42, 25.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3584/8000 [01:25<01:47, 40.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6922/8000 [06:14<00:50, 21.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3589/8000 [01:25<01:47, 41.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6926/8000 [06:15<00:46, 23.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3597/8000 [01:25<01:30, 48.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6930/8000 [06:15<00:44, 24.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3603/8000 [01:25<01:26, 51.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6935/8000 [06:15<00:39, 26.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3609/8000 [01:25<01:34, 46.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6938/8000 [06:15<01:03, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3617/8000 [01:25<01:32, 47.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6942/8000 [06:15<00:56, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3628/8000 [01:26<01:43, 42.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6945/8000 [06:16<01:18, 13.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▉                   | 3633/8000 [01:26<01:49, 39.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6949/8000 [06:16<01:04, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▉                   | 3638/8000 [01:26<01:48, 40.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6953/8000 [06:16<01:03, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3644/8000 [01:26<01:41, 42.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6958/8000 [06:16<00:51, 20.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3649/8000 [01:26<01:41, 42.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6964/8000 [06:16<00:39, 26.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3657/8000 [01:26<01:26, 49.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6973/8000 [06:17<00:41, 24.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3663/8000 [01:26<01:50, 39.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6976/8000 [06:17<00:41, 24.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3668/8000 [01:27<02:12, 32.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6979/8000 [06:17<00:42, 24.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3673/8000 [01:27<02:13, 32.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6982/8000 [06:17<00:51, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3679/8000 [01:27<01:53, 37.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6988/8000 [06:17<00:39, 25.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3688/8000 [01:27<01:29, 48.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6992/8000 [06:18<00:45, 22.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3695/8000 [01:27<01:32, 46.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6996/8000 [06:18<00:42, 23.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3701/8000 [01:27<01:28, 48.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6999/8000 [06:18<01:04, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3708/8000 [01:28<01:55, 37.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7002/8000 [06:18<01:00, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3714/8000 [01:28<01:53, 37.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7006/8000 [06:19<00:52, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▎                  | 3719/8000 [01:28<01:51, 38.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7010/8000 [06:19<00:55, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3725/8000 [01:28<01:41, 41.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7015/8000 [06:19<00:54, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3731/8000 [01:28<01:34, 45.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7018/8000 [06:19<00:51, 19.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3737/8000 [01:28<01:52, 37.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7023/8000 [06:19<00:41, 23.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3742/8000 [01:29<02:12, 32.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7028/8000 [06:20<00:43, 22.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3746/8000 [01:29<02:23, 29.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7035/8000 [06:20<00:32, 29.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3750/8000 [01:29<02:17, 30.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7039/8000 [06:20<00:32, 29.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3755/8000 [01:29<02:08, 33.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7046/8000 [06:20<00:40, 23.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3762/8000 [01:29<02:06, 33.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7054/8000 [06:20<00:31, 30.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3768/8000 [01:29<02:04, 33.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7059/8000 [06:21<00:50, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3778/8000 [01:29<01:29, 47.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7062/8000 [06:21<01:09, 13.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3784/8000 [01:30<01:25, 49.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7068/8000 [06:22<00:59, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3792/8000 [01:30<01:27, 48.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7076/8000 [06:22<00:48, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3800/8000 [01:30<01:26, 48.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7080/8000 [06:22<00:59, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3810/8000 [01:30<01:12, 58.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7083/8000 [06:23<01:05, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3819/8000 [01:30<01:11, 58.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7086/8000 [06:23<01:03, 14.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3828/8000 [01:30<01:12, 57.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7090/8000 [06:23<01:08, 13.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3834/8000 [01:30<01:21, 51.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7094/8000 [06:23<00:57, 15.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3840/8000 [01:31<01:18, 52.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7096/8000 [06:24<00:59, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3846/8000 [01:31<01:41, 40.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7103/8000 [06:24<00:39, 22.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3851/8000 [01:31<01:40, 41.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7106/8000 [06:24<00:48, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3856/8000 [01:31<01:38, 42.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7109/8000 [06:24<00:45, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3862/8000 [01:31<01:30, 45.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7114/8000 [06:24<00:42, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3870/8000 [01:31<01:24, 48.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7118/8000 [06:24<00:38, 22.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3878/8000 [01:31<01:23, 49.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7122/8000 [06:25<00:38, 22.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3886/8000 [01:32<01:23, 49.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7125/8000 [06:25<00:52, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3893/8000 [01:32<01:27, 47.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7127/8000 [06:25<01:05, 13.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3901/8000 [01:32<01:18, 52.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7130/8000 [06:25<00:57, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3911/8000 [01:32<01:06, 61.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7132/8000 [06:26<01:09, 12.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3918/8000 [01:32<01:13, 55.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7136/8000 [06:26<00:54, 15.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3925/8000 [01:32<01:20, 50.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7139/8000 [06:26<01:12, 11.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3931/8000 [01:32<01:28, 46.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7144/8000 [06:26<00:51, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3937/8000 [01:33<01:23, 48.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7153/8000 [06:27<00:37, 22.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3943/8000 [01:33<01:43, 39.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7156/8000 [06:27<00:36, 23.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3950/8000 [01:33<01:31, 44.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7160/8000 [06:27<00:42, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3955/8000 [01:33<01:30, 44.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7165/8000 [06:27<00:35, 23.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▎                 | 3962/8000 [01:33<01:20, 49.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7169/8000 [06:27<00:42, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▎                 | 3968/8000 [01:33<01:22, 49.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7173/8000 [06:28<00:45, 18.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3977/8000 [01:33<01:19, 50.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7176/8000 [06:28<00:51, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3983/8000 [01:34<01:25, 47.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7179/8000 [06:28<00:46, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3988/8000 [01:34<01:25, 46.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7181/8000 [06:28<00:49, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3993/8000 [01:34<01:36, 41.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7183/8000 [06:28<00:50, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3998/8000 [01:34<01:47, 37.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7189/8000 [06:29<00:42, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4002/8000 [01:34<01:49, 36.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7193/8000 [06:29<00:36, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4007/8000 [01:34<02:11, 30.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7198/8000 [06:29<00:37, 21.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4013/8000 [01:35<01:51, 35.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7207/8000 [06:29<00:30, 25.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4018/8000 [01:35<01:45, 37.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7211/8000 [06:29<00:34, 22.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4026/8000 [01:35<01:26, 45.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7215/8000 [06:30<00:39, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▋                 | 4031/8000 [01:35<01:25, 46.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7221/8000 [06:30<00:37, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▋                 | 4037/8000 [01:35<01:33, 42.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7224/8000 [06:30<00:43, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4045/8000 [01:35<01:20, 49.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7230/8000 [06:30<00:34, 22.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4051/8000 [01:35<01:18, 50.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7235/8000 [06:31<00:29, 25.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4057/8000 [01:35<01:17, 50.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7239/8000 [06:31<00:27, 27.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4066/8000 [01:36<01:19, 49.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7246/8000 [06:31<00:28, 26.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4075/8000 [01:36<01:20, 48.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7250/8000 [06:31<00:32, 22.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4082/8000 [01:36<01:27, 44.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7254/8000 [06:31<00:30, 24.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4089/8000 [01:36<01:21, 47.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7258/8000 [06:32<00:41, 17.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4095/8000 [01:36<01:21, 47.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7263/8000 [06:32<00:42, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4103/8000 [01:36<01:11, 54.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7267/8000 [06:32<00:37, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4110/8000 [01:36<01:14, 52.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7271/8000 [06:32<00:34, 21.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|██████████████████                 | 4116/8000 [01:37<01:22, 47.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7279/8000 [06:33<00:35, 20.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4121/8000 [01:37<01:21, 47.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7283/8000 [06:33<00:43, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4130/8000 [01:37<01:14, 52.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7285/8000 [06:33<00:49, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4137/8000 [01:37<01:11, 54.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7287/8000 [06:33<00:49, 14.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4145/8000 [01:37<01:20, 48.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7291/8000 [06:34<00:40, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4153/8000 [01:37<01:11, 53.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7295/8000 [06:34<00:42, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4159/8000 [01:38<01:32, 41.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7297/8000 [06:34<00:43, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4164/8000 [01:38<01:40, 38.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7299/8000 [06:34<00:45, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4170/8000 [01:38<01:34, 40.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7304/8000 [06:34<00:33, 20.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4175/8000 [01:38<01:31, 41.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7312/8000 [06:35<00:34, 19.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4180/8000 [01:38<01:49, 34.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7316/8000 [06:35<00:36, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4185/8000 [01:38<01:42, 37.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7319/8000 [06:35<00:35, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4192/8000 [01:38<01:28, 43.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7325/8000 [06:36<00:38, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4201/8000 [01:38<01:12, 52.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7332/8000 [06:36<00:28, 23.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4212/8000 [01:39<01:05, 57.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7335/8000 [06:36<00:34, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4220/8000 [01:39<01:10, 53.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7341/8000 [06:36<00:33, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4229/8000 [01:39<01:12, 52.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7345/8000 [06:36<00:30, 21.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4236/8000 [01:39<01:18, 48.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7351/8000 [06:37<00:29, 21.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4243/8000 [01:39<01:13, 51.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7355/8000 [06:37<00:27, 23.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4250/8000 [01:39<01:07, 55.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7359/8000 [06:37<00:31, 20.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4257/8000 [01:39<01:03, 58.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7363/8000 [06:37<00:28, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4265/8000 [01:40<01:16, 48.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7367/8000 [06:37<00:25, 24.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4271/8000 [01:40<01:15, 49.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7370/8000 [06:37<00:26, 23.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4277/8000 [01:40<01:25, 43.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7373/8000 [06:38<00:27, 23.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▋                | 4282/8000 [01:40<01:27, 42.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7376/8000 [06:38<00:35, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4288/8000 [01:40<01:35, 38.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7380/8000 [06:38<00:31, 19.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4295/8000 [01:40<01:36, 38.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7385/8000 [06:38<00:37, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4299/8000 [01:41<01:41, 36.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7387/8000 [06:39<00:38, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4305/8000 [01:41<01:35, 38.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7389/8000 [06:39<00:39, 15.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4311/8000 [01:41<01:29, 41.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7393/8000 [06:39<00:31, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4317/8000 [01:41<01:35, 38.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7397/8000 [06:39<00:35, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4323/8000 [01:41<02:01, 30.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7399/8000 [06:39<00:43, 13.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4330/8000 [01:42<01:48, 33.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7402/8000 [06:40<00:47, 12.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4334/8000 [01:42<01:44, 35.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7404/8000 [06:40<00:45, 13.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4339/8000 [01:42<01:39, 36.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7407/8000 [06:40<00:39, 14.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4345/8000 [01:42<01:31, 39.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7411/8000 [06:40<00:31, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4350/8000 [01:42<01:41, 36.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7414/8000 [06:40<00:38, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4354/8000 [01:42<01:42, 35.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7418/8000 [06:40<00:31, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4361/8000 [01:42<01:25, 42.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7421/8000 [06:41<00:54, 10.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4366/8000 [01:42<01:34, 38.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7424/8000 [06:41<00:56, 10.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4372/8000 [01:43<01:24, 42.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7426/8000 [06:42<00:52, 10.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4379/8000 [01:43<01:33, 38.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7430/8000 [06:42<00:41, 13.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4384/8000 [01:43<01:49, 33.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7432/8000 [06:42<00:40, 13.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4388/8000 [01:43<02:11, 27.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7437/8000 [06:42<00:30, 18.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4396/8000 [01:43<01:38, 36.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7441/8000 [06:42<00:33, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4401/8000 [01:43<01:33, 38.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7446/8000 [06:42<00:25, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4407/8000 [01:44<01:54, 31.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7449/8000 [06:43<00:30, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4414/8000 [01:44<01:34, 37.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7452/8000 [06:43<00:29, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4419/8000 [01:44<01:39, 35.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7455/8000 [06:43<00:34, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4425/8000 [01:44<01:41, 35.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7459/8000 [06:43<00:35, 15.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▍               | 4429/8000 [01:44<01:41, 35.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7462/8000 [06:43<00:33, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▍               | 4436/8000 [01:44<01:23, 42.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7468/8000 [06:44<00:24, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4441/8000 [01:44<01:20, 44.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7472/8000 [06:44<00:27, 19.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4447/8000 [01:45<01:17, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7477/8000 [06:44<00:23, 22.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4452/8000 [01:45<01:28, 40.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7481/8000 [06:44<00:21, 24.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4460/8000 [01:45<01:14, 47.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7484/8000 [06:44<00:21, 23.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4466/8000 [01:45<01:23, 42.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7489/8000 [06:44<00:17, 28.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4478/8000 [01:45<01:04, 54.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7493/8000 [06:45<00:26, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4484/8000 [01:45<01:18, 44.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7498/8000 [06:45<00:27, 18.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4490/8000 [01:46<01:30, 38.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7503/8000 [06:45<00:22, 22.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4497/8000 [01:46<01:26, 40.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7506/8000 [06:45<00:26, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4502/8000 [01:46<01:42, 34.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7512/8000 [06:46<00:28, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4507/8000 [01:46<01:35, 36.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7516/8000 [06:46<00:24, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4511/8000 [01:46<01:37, 35.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7520/8000 [06:46<00:25, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▊               | 4515/8000 [01:46<01:36, 36.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7525/8000 [06:46<00:24, 19.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4521/8000 [01:46<01:25, 40.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7529/8000 [06:47<00:21, 21.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4528/8000 [01:47<01:29, 38.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7532/8000 [06:47<00:20, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4535/8000 [01:47<01:16, 45.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7536/8000 [06:47<00:24, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4541/8000 [01:47<01:21, 42.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7539/8000 [06:47<00:31, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4547/8000 [01:47<01:14, 46.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7541/8000 [06:47<00:31, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4554/8000 [01:47<01:14, 46.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7545/8000 [06:48<00:25, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4560/8000 [01:47<01:10, 48.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7548/8000 [06:48<00:24, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4566/8000 [01:47<01:17, 44.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7553/8000 [06:48<00:19, 22.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4574/8000 [01:47<01:05, 52.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7556/8000 [06:48<00:19, 22.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4581/8000 [01:48<01:10, 48.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7560/8000 [06:48<00:17, 24.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4589/8000 [01:48<01:08, 49.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7563/8000 [06:48<00:22, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4595/8000 [01:48<01:10, 48.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7567/8000 [06:49<00:26, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4601/8000 [01:48<01:20, 42.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7571/8000 [06:49<00:27, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4606/8000 [01:48<01:22, 41.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7573/8000 [06:49<00:28, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4613/8000 [01:48<01:19, 42.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7576/8000 [06:50<00:35, 11.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4621/8000 [01:49<01:16, 44.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7581/8000 [06:50<00:27, 15.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4627/8000 [01:49<01:29, 37.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7588/8000 [06:50<00:19, 20.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4632/8000 [01:49<01:27, 38.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7592/8000 [06:50<00:18, 22.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4640/8000 [01:49<01:15, 44.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7595/8000 [06:50<00:18, 21.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4645/8000 [01:49<01:16, 43.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7602/8000 [06:51<00:17, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4650/8000 [01:49<01:23, 40.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7605/8000 [06:51<00:23, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4657/8000 [01:50<01:21, 40.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7607/8000 [06:51<00:23, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4663/8000 [01:50<01:32, 36.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7613/8000 [06:51<00:20, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4668/8000 [01:50<01:33, 35.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7617/8000 [06:51<00:18, 21.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4675/8000 [01:50<01:18, 42.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7620/8000 [06:52<00:22, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4680/8000 [01:50<01:16, 43.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7623/8000 [06:52<00:20, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4686/8000 [01:50<01:11, 46.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7630/8000 [06:52<00:20, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4693/8000 [01:50<01:14, 44.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7632/8000 [06:52<00:21, 17.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4699/8000 [01:50<01:10, 46.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7635/8000 [06:52<00:19, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4704/8000 [01:51<01:12, 45.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7638/8000 [06:53<00:18, 19.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4709/8000 [01:51<01:13, 44.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7641/8000 [06:53<00:21, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4718/8000 [01:51<00:59, 55.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7643/8000 [06:53<00:21, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4726/8000 [01:51<00:53, 61.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7648/8000 [06:53<00:20, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4733/8000 [01:51<01:00, 53.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7650/8000 [06:53<00:19, 17.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4740/8000 [01:51<01:11, 45.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7655/8000 [06:54<00:19, 17.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4748/8000 [01:51<01:10, 46.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7659/8000 [06:54<00:23, 14.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4753/8000 [01:52<01:13, 44.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7662/8000 [06:54<00:22, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▊              | 4760/8000 [01:52<01:06, 48.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7667/8000 [06:54<00:17, 18.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▊              | 4766/8000 [01:52<01:16, 42.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7670/8000 [06:55<00:17, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4773/8000 [01:52<01:12, 44.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7673/8000 [06:55<00:18, 18.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4779/8000 [01:52<01:20, 39.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7675/8000 [06:55<00:20, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4787/8000 [01:52<01:09, 46.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7678/8000 [06:55<00:18, 16.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4794/8000 [01:53<01:13, 43.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7682/8000 [06:55<00:16, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4800/8000 [01:53<01:10, 45.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7685/8000 [06:55<00:19, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4806/8000 [01:53<01:16, 41.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7687/8000 [06:56<00:19, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4813/8000 [01:53<01:48, 29.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7692/8000 [06:56<00:18, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4817/8000 [01:53<01:45, 30.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7696/8000 [06:56<00:15, 19.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4823/8000 [01:53<01:29, 35.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7700/8000 [06:56<00:13, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4829/8000 [01:54<01:28, 35.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7703/8000 [06:56<00:14, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4834/8000 [01:54<01:31, 34.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7708/8000 [06:56<00:11, 26.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4839/8000 [01:54<01:31, 34.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7713/8000 [06:57<00:15, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4843/8000 [01:54<01:34, 33.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7719/8000 [06:57<00:16, 16.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4848/8000 [01:54<01:26, 36.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7723/8000 [06:58<00:18, 15.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4852/8000 [01:54<01:50, 28.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7727/8000 [06:58<00:16, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4858/8000 [01:54<01:33, 33.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7730/8000 [06:58<00:16, 16.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4865/8000 [01:55<01:16, 41.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7732/8000 [06:58<00:18, 14.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4870/8000 [01:55<01:23, 37.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7736/8000 [06:59<00:22, 11.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4876/8000 [01:55<01:14, 42.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7740/8000 [06:59<00:19, 13.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4882/8000 [01:55<01:16, 40.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7744/8000 [06:59<00:17, 14.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4889/8000 [01:55<01:15, 40.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7746/8000 [06:59<00:19, 12.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4894/8000 [01:55<01:15, 40.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7749/8000 [07:00<00:24, 10.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4899/8000 [01:55<01:17, 39.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7751/8000 [07:00<00:24, 10.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4904/8000 [01:56<01:15, 40.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7756/8000 [07:00<00:21, 11.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4909/8000 [01:56<01:12, 42.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7760/8000 [07:01<00:19, 12.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▌             | 4916/8000 [01:56<01:05, 47.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7762/8000 [07:01<00:19, 12.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4923/8000 [01:56<01:01, 50.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7767/8000 [07:01<00:14, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4929/8000 [01:56<01:09, 44.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7769/8000 [07:01<00:14, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4936/8000 [01:56<01:02, 48.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7771/8000 [07:01<00:14, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4942/8000 [01:56<01:15, 40.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7773/8000 [07:01<00:14, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4948/8000 [01:57<01:10, 43.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7776/8000 [07:01<00:13, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4954/8000 [01:57<01:14, 41.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7779/8000 [07:02<00:12, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4959/8000 [01:57<01:13, 41.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7781/8000 [07:02<00:11, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4966/8000 [01:57<01:05, 46.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7784/8000 [07:02<00:11, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4972/8000 [01:57<01:01, 49.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7792/8000 [07:02<00:08, 23.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4978/8000 [01:57<01:11, 42.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7795/8000 [07:02<00:08, 23.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4988/8000 [01:57<00:55, 54.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7799/8000 [07:03<00:11, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4994/8000 [01:57<01:02, 48.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7801/8000 [07:03<00:11, 16.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5005/8000 [01:58<00:58, 50.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7806/8000 [07:03<00:09, 21.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5011/8000 [01:58<01:05, 45.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7809/8000 [07:03<00:11, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5023/8000 [01:58<00:49, 60.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7813/8000 [07:03<00:09, 20.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5032/8000 [01:58<01:00, 49.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7816/8000 [07:03<00:08, 21.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5038/8000 [01:58<01:03, 46.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7820/8000 [07:04<00:09, 19.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5046/8000 [01:59<00:59, 50.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7824/8000 [07:04<00:08, 21.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5054/8000 [01:59<01:07, 43.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7828/8000 [07:04<00:07, 24.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5059/8000 [01:59<01:26, 33.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7832/8000 [07:04<00:08, 19.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5065/8000 [01:59<01:16, 38.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7835/8000 [07:04<00:09, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5073/8000 [01:59<01:04, 45.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7838/8000 [07:05<00:10, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▏            | 5081/8000 [01:59<01:05, 44.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7843/8000 [07:05<00:09, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5090/8000 [02:00<01:02, 46.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7846/8000 [07:05<00:08, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5096/8000 [02:00<01:08, 42.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7848/8000 [07:05<00:09, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5101/8000 [02:00<01:08, 42.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7850/8000 [07:05<00:09, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5106/8000 [02:00<01:07, 42.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7855/8000 [07:06<00:08, 17.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5113/8000 [02:00<01:01, 46.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7857/8000 [07:06<00:08, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5119/8000 [02:00<01:07, 42.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7864/8000 [07:06<00:06, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5126/8000 [02:01<01:36, 29.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7869/8000 [07:06<00:06, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5135/8000 [02:01<01:13, 38.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7875/8000 [07:07<00:06, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5143/8000 [02:01<01:08, 41.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7877/8000 [07:07<00:07, 15.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5148/8000 [02:01<01:10, 40.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7880/8000 [07:07<00:08, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5155/8000 [02:01<01:18, 36.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7884/8000 [07:07<00:07, 14.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▌            | 5162/8000 [02:01<01:14, 38.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7886/8000 [07:07<00:07, 15.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▌            | 5168/8000 [02:02<01:11, 39.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7888/8000 [07:08<00:08, 12.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5173/8000 [02:02<01:47, 26.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7892/8000 [07:08<00:06, 15.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5177/8000 [02:02<01:55, 24.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7894/8000 [07:08<00:06, 15.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5180/8000 [02:02<02:02, 22.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7896/8000 [07:08<00:06, 15.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5184/8000 [02:02<01:50, 25.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7899/8000 [07:08<00:05, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5187/8000 [02:03<01:54, 24.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7901/8000 [07:09<00:07, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5190/8000 [02:03<02:02, 22.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7907/8000 [07:09<00:04, 21.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5194/8000 [02:03<01:52, 24.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7911/8000 [07:09<00:04, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5198/8000 [02:03<01:44, 26.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7915/8000 [07:09<00:03, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5203/8000 [02:03<01:46, 26.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7919/8000 [07:09<00:04, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5207/8000 [02:03<01:39, 27.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7922/8000 [07:09<00:04, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5214/8000 [02:03<01:17, 35.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7927/8000 [07:10<00:03, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5221/8000 [02:04<01:05, 42.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7931/8000 [07:10<00:02, 23.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▉            | 5230/8000 [02:04<00:52, 53.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7935/8000 [07:10<00:02, 25.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▉            | 5236/8000 [02:04<01:00, 45.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7938/8000 [07:10<00:02, 23.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5242/8000 [02:04<01:03, 43.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7941/8000 [07:10<00:03, 18.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5247/8000 [02:04<01:02, 43.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7946/8000 [07:10<00:02, 22.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5255/8000 [02:04<00:55, 49.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7949/8000 [07:11<00:02, 18.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5261/8000 [02:05<01:17, 35.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7958/8000 [07:11<00:01, 24.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5268/8000 [02:05<01:13, 37.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7961/8000 [07:11<00:01, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5275/8000 [02:05<01:15, 35.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7966/8000 [07:11<00:01, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5280/8000 [02:05<01:13, 36.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7969/8000 [07:12<00:01, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5284/8000 [02:05<01:14, 36.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7977/8000 [07:12<00:00, 23.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5290/8000 [02:05<01:08, 39.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7986/8000 [07:12<00:00, 23.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5296/8000 [02:05<01:08, 39.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7991/8000 [07:12<00:00, 25.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5303/8000 [02:06<00:59, 44.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7994/8000 [07:13<00:00, 24.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5310/8000 [02:06<01:02, 42.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7997/8000 [07:13<00:00, 24.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▎           | 5319/8000 [02:06<00:51, 52.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8002it [07:13, 18.46it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  67%|███████████████████████▎           | 5328/8000 [02:06<00:44, 59.61it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5335/8000 [02:06<00:43, 60.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5343/8000 [02:06<00:53, 49.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:25,  7.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5350/8000 [02:06<00:58, 45.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:25,  4.07s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5358/8000 [02:07<01:04, 41.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:30,  2.90s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5366/8000 [02:07<00:56, 47.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:38,  2.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5372/8000 [02:07<01:06, 39.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:34,  2.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5377/8000 [02:07<01:31, 28.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:04,  1.88s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5383/8000 [02:08<01:31, 28.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:36,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5388/8000 [02:08<01:40, 26.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:21,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5393/8000 [02:08<01:29, 29.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:12,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5401/8000 [02:08<01:09, 37.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<05:00,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5406/8000 [02:08<01:25, 30.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:54,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5413/8000 [02:08<01:09, 37.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:48,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5418/8000 [02:09<01:16, 33.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:46,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5424/8000 [02:09<01:08, 37.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:42,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5429/8000 [02:09<01:06, 38.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:39,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5434/8000 [02:09<01:13, 34.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:35,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5440/8000 [02:09<01:06, 38.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:34,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5447/8000 [02:09<01:06, 38.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:36,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5454/8000 [02:10<01:10, 36.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:32,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5460/8000 [02:10<01:03, 39.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:30,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5465/8000 [02:10<01:12, 34.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<04:27,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5470/8000 [02:10<01:09, 36.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:39<04:24,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5475/8000 [02:10<01:05, 38.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:19,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▉           | 5481/8000 [02:10<01:05, 38.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:19,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5487/8000 [02:10<01:00, 41.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:17,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5492/8000 [02:10<00:59, 42.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:17,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5498/8000 [02:11<00:55, 45.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:46<04:17,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5504/8000 [02:11<00:57, 43.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:48<04:15,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5509/8000 [02:11<01:02, 39.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:49<04:13,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5516/8000 [02:11<01:01, 40.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:51<04:13,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5521/8000 [02:11<01:07, 36.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:52<04:10,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5526/8000 [02:11<01:03, 39.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:54<04:13,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5533/8000 [02:12<01:01, 39.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:55<04:14,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5538/8000 [02:12<01:00, 40.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:57<04:11,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5543/8000 [02:12<01:11, 34.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:58<04:04,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5549/8000 [02:12<01:02, 39.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:00<04:06,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5556/8000 [02:12<00:53, 45.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:01<04:03,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5565/8000 [02:12<00:44, 54.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:03<04:02,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5571/8000 [02:12<00:56, 42.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:04<03:57,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5577/8000 [02:13<01:00, 40.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:05<03:56,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5583/8000 [02:13<00:55, 43.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:07<03:55,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5589/8000 [02:13<01:19, 30.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:08<03:51,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5596/8000 [02:13<01:11, 33.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:10<03:52,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5601/8000 [02:13<01:26, 27.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:11<03:51,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5608/8000 [02:14<01:15, 31.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:14<04:17,  1.66s/it]\n",
+      "Generating signals:  70%|████████████████████████▌          | 5613/8000 [02:14<01:13, 32.35it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5623/8000 [02:14<00:54, 43.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▋          | 5629/8000 [02:14<00:55, 42.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▋          | 5635/8000 [02:14<00:51, 46.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.06it/s]\n",
+      "Generating signals:  71%|████████████████████████▋          | 5642/8000 [02:14<00:47, 49.49it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5648/8000 [02:14<00:46, 50.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5657/8000 [02:14<00:40, 57.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5665/8000 [02:15<00:41, 56.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 21.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5675/8000 [02:15<00:40, 56.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 53it [00:01, 49.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5682/8000 [02:15<00:45, 51.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 82it [00:01, 82.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5690/8000 [02:15<00:45, 51.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 109it [00:01, 111.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5696/8000 [02:15<00:59, 38.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 134it [00:02, 136.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5702/8000 [02:16<01:02, 36.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 159it [00:02, 157.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5709/8000 [02:16<00:54, 42.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 183it [00:02, 175.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████████████████          | 5715/8000 [02:16<00:56, 40.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 207it [00:02, 184.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5722/8000 [02:16<00:55, 41.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 234it [00:02, 205.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5728/8000 [02:16<00:56, 40.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 259it [00:02, 215.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5734/8000 [02:16<00:57, 39.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 288it [00:02, 234.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5740/8000 [02:17<00:59, 38.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 314it [00:02, 241.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5746/8000 [02:17<00:53, 42.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 340it [00:02, 236.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5753/8000 [02:17<00:47, 47.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 366it [00:02, 241.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5762/8000 [02:17<00:43, 51.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 392it [00:03, 246.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5768/8000 [02:17<00:42, 52.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 420it [00:03, 254.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5775/8000 [02:17<00:44, 50.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 447it [00:03, 247.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5781/8000 [02:17<00:48, 45.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 473it [00:03, 250.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5789/8000 [02:17<00:47, 46.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 499it [00:03, 245.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5795/8000 [02:18<00:50, 44.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 528it [00:03, 253.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5803/8000 [02:18<00:42, 51.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 554it [00:03, 251.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5809/8000 [02:18<00:47, 46.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 580it [00:03, 246.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5816/8000 [02:18<00:43, 50.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 611it [00:03, 264.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5822/8000 [02:18<00:42, 50.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 638it [00:04, 265.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5829/8000 [02:18<00:44, 48.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 665it [00:04, 260.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5836/8000 [02:19<00:54, 39.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 692it [00:04, 253.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5842/8000 [02:19<01:00, 35.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 718it [00:04, 238.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5847/8000 [02:19<00:57, 37.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 744it [00:04, 242.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5854/8000 [02:19<00:49, 43.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 769it [00:04, 218.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5863/8000 [02:19<00:46, 46.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 169.77it/s]\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5868/8000 [02:19<01:01, 34.66it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5875/8000 [02:20<00:56, 37.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▋         | 5880/8000 [02:20<00:58, 36.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  13%|▏| 13/100 [00:00<00:00, 127\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▋         | 5885/8000 [02:20<00:55, 38.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:00, 112\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5892/8000 [02:20<00:52, 39.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 103\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5901/8000 [02:20<00:46, 45.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 115\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5908/8000 [02:20<00:42, 49.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 122\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5915/8000 [02:20<00:44, 47.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:00<00:00, 131\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5920/8000 [02:21<00:44, 47.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 12\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5928/8000 [02:21<00:38, 54.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [33:50<00:00, 591.97s/it]\u001b[A\n",
-      "Replicates:  88%|███████████████████████▋   | 7/8 [4:04:24<34:19, 2059.27s/it]\n",
-      "Nyquist Samples:   0%|                                  | 0/4 [00:00<?, ?it/s]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.194289751686101\n",
-      "2999: -5.9196615\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  74%|█████████████████████████▉         | 5935/8000 [02:21<00:41, 49.61it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  74%|██████████████████████████         | 5943/8000 [02:21<00:51, 40.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5948/8000 [02:21<00:53, 38.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:50,  7.19s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5954/8000 [02:21<00:48, 42.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:34,  3.81s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5959/8000 [02:21<00:47, 43.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:43,  2.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████         | 5966/8000 [02:22<00:41, 48.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:02,  2.16s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5974/8000 [02:22<00:41, 48.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<06:03,  1.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5980/8000 [02:22<00:45, 44.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:23,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5987/8000 [02:22<00:45, 44.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:01,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5994/8000 [02:22<00:40, 49.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:49,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6003/8000 [02:22<00:42, 46.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:38,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6011/8000 [02:22<00:37, 52.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:31,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6019/8000 [02:23<00:42, 47.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:24,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6026/8000 [02:23<00:42, 45.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:18,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6031/8000 [02:23<00:47, 41.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6038/8000 [02:23<00:45, 42.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6044/8000 [02:23<00:49, 39.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:13,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6050/8000 [02:24<00:54, 36.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:10,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6060/8000 [02:24<00:40, 47.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:06,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6072/8000 [02:24<00:30, 63.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<04:00,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6080/8000 [02:24<00:35, 53.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<03:59,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6087/8000 [02:24<00:37, 50.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6095/8000 [02:24<00:38, 49.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:54,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6101/8000 [02:24<00:44, 42.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<03:52,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6107/8000 [02:25<00:41, 45.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:50,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6122/8000 [02:25<00:30, 61.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6130/8000 [02:25<00:34, 53.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:39<03:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6136/8000 [02:25<00:40, 45.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:40<03:45,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6144/8000 [02:25<00:39, 46.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:48,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6152/8000 [02:25<00:35, 52.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:43<03:44,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6158/8000 [02:26<00:38, 48.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:44<03:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6166/8000 [02:26<00:33, 54.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:45<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6172/8000 [02:26<00:41, 44.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:43,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6177/8000 [02:26<00:45, 39.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:48<03:39,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6183/8000 [02:26<00:41, 43.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:49<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6193/8000 [02:26<00:39, 45.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:50<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6201/8000 [02:26<00:35, 50.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:52<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6211/8000 [02:27<00:35, 49.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:53<03:33,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6217/8000 [02:27<00:38, 46.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:54<03:36,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6222/8000 [02:27<00:38, 45.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:56<03:30,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6228/8000 [02:27<00:37, 47.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:57<03:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6234/8000 [02:27<00:35, 49.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:58<03:25,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6244/8000 [02:27<00:31, 55.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:00<03:25,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6254/8000 [02:27<00:30, 58.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:01<03:24,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6263/8000 [02:28<00:26, 64.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:02<03:21,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6270/8000 [02:28<00:26, 65.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:03<03:23,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6278/8000 [02:28<00:28, 60.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:05<03:20,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6285/8000 [02:28<00:38, 44.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:06<03:19,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6293/8000 [02:28<00:34, 50.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:07<03:17,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6304/8000 [02:28<00:27, 62.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:09<03:18,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6313/8000 [02:29<00:28, 58.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:10<03:16,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6321/8000 [02:29<00:29, 56.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:11<03:13,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6328/8000 [02:29<00:28, 57.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:13<03:26,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6337/8000 [02:29<00:28, 57.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:14<03:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6346/8000 [02:29<00:25, 64.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:16<03:23,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6356/8000 [02:29<00:23, 70.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:17<03:21,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6366/8000 [02:29<00:23, 69.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:18<03:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6377/8000 [02:29<00:22, 70.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:19<03:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6386/8000 [02:30<00:22, 72.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:21<03:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6396/8000 [02:30<00:27, 57.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:22<03:12,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6405/8000 [02:30<00:30, 52.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:24<03:10,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6411/8000 [02:30<00:29, 53.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:25<03:12,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6417/8000 [02:30<00:32, 49.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:26<03:09,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6423/8000 [02:30<00:34, 46.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:29<03:19,  1.44s/it]\n",
+      "Generating signals:  80%|████████████████████████████       | 6428/8000 [02:31<00:37, 42.03it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6433/8000 [02:31<00:36, 43.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6440/8000 [02:31<00:32, 48.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.92it/s]\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6450/8000 [02:31<00:25, 59.65it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6457/8000 [02:31<00:32, 47.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6463/8000 [02:31<00:35, 43.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6470/8000 [02:31<00:32, 47.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 43it [00:01, 40.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6476/8000 [02:32<00:30, 49.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 86it [00:01, 86.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6482/8000 [02:32<00:33, 45.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 128it [00:01, 135.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6488/8000 [02:32<00:31, 47.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 165it [00:01, 173.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6496/8000 [02:32<00:30, 49.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 203it [00:01, 213.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6504/8000 [02:32<00:31, 48.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 245it [00:01, 258.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6511/8000 [02:32<00:28, 52.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 284it [00:02, 283.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▌      | 6518/8000 [02:32<00:27, 54.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 322it [00:02, 304.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6529/8000 [02:33<00:24, 59.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 362it [00:02, 328.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6538/8000 [02:33<00:24, 59.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 400it [00:02, 318.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6551/8000 [02:33<00:21, 67.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 444it [00:02, 349.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6558/8000 [02:33<00:25, 56.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 485it [00:02, 364.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6564/8000 [02:33<00:28, 50.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 524it [00:02, 371.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6570/8000 [02:33<00:30, 46.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 570it [00:02, 396.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6578/8000 [02:33<00:27, 51.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 611it [00:02, 373.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6584/8000 [02:34<00:30, 46.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 650it [00:03, 359.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6592/8000 [02:34<00:29, 48.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 692it [00:03, 375.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6598/8000 [02:34<00:28, 49.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 733it [00:03, 383.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6608/8000 [02:34<00:29, 47.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 232.12it/s]\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6615/8000 [02:34<00:27, 50.37it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6621/8000 [02:34<00:26, 52.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6628/8000 [02:34<00:24, 55.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 159\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6634/8000 [02:35<00:24, 55.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 156\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6640/8000 [02:35<00:27, 49.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:00<00:00, 185\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6646/8000 [02:35<00:29, 45.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  74%|▋| 74/100 [00:00<00:00, 159\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6653/8000 [02:35<00:26, 51.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6660/8000 [02:35<00:31, 42.60it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6666/8000 [02:35<00:32, 40.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6673/8000 [02:35<00:29, 45.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎               | 171/8000 [00:00<00:06, 1138.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6680/8000 [02:36<00:32, 40.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋               | 348/8000 [00:00<00:07, 1060.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6687/8000 [02:36<00:28, 45.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█               | 531/8000 [00:00<00:06, 1156.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6693/8000 [02:36<00:30, 43.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍              | 696/8000 [00:00<00:06, 1115.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6701/8000 [02:36<00:32, 40.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊              | 879/8000 [00:00<00:05, 1210.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6707/8000 [02:36<00:29, 43.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|█▉             | 1053/8000 [00:00<00:05, 1269.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6714/8000 [02:36<00:33, 38.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎            | 1217/8000 [00:01<00:05, 1283.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6722/8000 [02:37<00:27, 45.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▌            | 1392/8000 [00:01<00:05, 1255.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6729/8000 [02:37<00:28, 45.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|██▉            | 1571/8000 [00:01<00:05, 1139.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6735/8000 [02:37<00:30, 42.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▎           | 1734/8000 [00:01<00:05, 1135.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6745/8000 [02:37<00:23, 53.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▌           | 1906/8000 [00:01<00:05, 1151.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6753/8000 [02:37<00:23, 52.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|███▉           | 2080/8000 [00:01<00:05, 1160.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6760/8000 [02:37<00:24, 50.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2272/8000 [00:03<00:16, 344.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6766/8000 [02:38<00:28, 42.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2450/8000 [00:03<00:12, 437.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6773/8000 [02:38<00:28, 43.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2623/8000 [00:03<00:09, 539.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6778/8000 [02:38<00:30, 40.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2792/8000 [00:03<00:08, 649.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6785/8000 [02:38<00:26, 46.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2967/8000 [00:03<00:06, 770.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6792/8000 [02:38<00:23, 51.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3144/8000 [00:03<00:05, 854.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6799/8000 [02:38<00:24, 48.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3322/8000 [00:03<00:04, 967.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6805/8000 [02:38<00:23, 49.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▌        | 3505/8000 [00:04<00:04, 1042.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6812/8000 [02:38<00:24, 49.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|██████▉        | 3675/8000 [00:04<00:03, 1095.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6823/8000 [02:39<00:20, 57.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▏       | 3858/8000 [00:04<00:03, 1164.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6830/8000 [02:39<00:22, 52.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▌       | 4035/8000 [00:04<00:03, 1173.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6836/8000 [02:39<00:27, 42.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|███████▊       | 4197/8000 [00:04<00:03, 1167.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6842/8000 [02:39<00:25, 45.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▏      | 4370/8000 [00:04<00:03, 1150.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6848/8000 [02:39<00:26, 43.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|████████▌      | 4546/8000 [00:04<00:02, 1214.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6854/8000 [02:39<00:27, 41.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|████████▊      | 4718/8000 [00:05<00:02, 1198.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6860/8000 [02:40<00:24, 45.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▏     | 4904/8000 [00:05<00:02, 1259.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6865/8000 [02:40<00:24, 46.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|█████████▌     | 5091/8000 [00:05<00:02, 1277.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6870/8000 [02:40<00:24, 45.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|█████████▊     | 5263/8000 [00:05<00:02, 1269.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6879/8000 [02:40<00:19, 57.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5418/8000 [00:06<00:07, 368.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6885/8000 [02:40<00:20, 54.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5595/8000 [00:06<00:05, 468.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6891/8000 [02:40<00:26, 41.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5765/8000 [00:07<00:03, 566.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6896/8000 [02:40<00:26, 41.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5929/8000 [00:07<00:03, 677.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6904/8000 [02:40<00:21, 50.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6112/8000 [00:07<00:02, 799.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6912/8000 [02:41<00:26, 40.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6300/8000 [00:08<00:05, 323.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6918/8000 [02:41<00:24, 43.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6491/8000 [00:08<00:03, 433.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6926/8000 [02:41<00:23, 45.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6664/8000 [00:08<00:02, 524.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6932/8000 [02:41<00:24, 43.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6849/8000 [00:09<00:01, 646.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6940/8000 [02:41<00:21, 49.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7036/8000 [00:09<00:01, 768.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6948/8000 [02:41<00:21, 50.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7211/8000 [00:09<00:00, 892.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6954/8000 [02:42<00:26, 40.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7383/8000 [00:09<00:00, 962.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6960/8000 [02:42<00:24, 42.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▏| 7547/8000 [00:09<00:00, 1047.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6967/8000 [02:42<00:21, 47.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|██████████████▍| 7714/8000 [00:09<00:00, 1097.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6974/8000 [02:42<00:19, 51.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7871/8000 [00:10<00:00, 375.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6981/8000 [02:42<00:20, 49.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8044it [00:10, 732.21it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  87%|██████████████████████████████▌    | 6990/8000 [02:42<00:19, 51.20it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6998/8000 [02:43<00:21, 45.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7004/8000 [02:43<00:21, 46.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:43,  7.76s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7009/8000 [02:43<00:23, 42.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:25,  4.07s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7015/8000 [02:43<00:27, 35.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:23,  2.86s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7019/8000 [02:43<00:26, 36.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:28,  2.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7023/8000 [02:43<00:26, 36.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:22,  1.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7028/8000 [02:43<00:24, 39.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<05:37,  1.74s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7037/8000 [02:43<00:19, 49.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:16,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7046/8000 [02:44<00:16, 57.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<04:57,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7053/8000 [02:44<00:15, 60.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:18<04:43,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7060/8000 [02:44<00:22, 41.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:50,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7068/8000 [02:44<00:21, 43.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<04:40,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7076/8000 [02:44<00:24, 37.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:38,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▉    | 7083/8000 [02:45<00:23, 39.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:29,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7089/8000 [02:45<00:21, 42.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:24,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7094/8000 [02:45<00:25, 34.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:32,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7098/8000 [02:45<00:28, 31.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:20,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7107/8000 [02:45<00:21, 40.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:16,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7113/8000 [02:45<00:20, 42.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:08,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7118/8000 [02:45<00:19, 44.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:06,  1.36s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7125/8000 [02:46<00:18, 48.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<04:08,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7134/8000 [02:46<00:16, 51.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<04:09,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7140/8000 [02:46<00:20, 42.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<04:11,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7146/8000 [02:46<00:19, 44.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<04:10,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7153/8000 [02:46<00:19, 43.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:40<04:07,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7159/8000 [02:46<00:20, 41.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<04:01,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7166/8000 [02:46<00:18, 46.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<04:05,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7171/8000 [02:47<00:18, 45.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:44<04:08,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7178/8000 [02:47<00:16, 49.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:45<04:05,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7184/8000 [02:47<00:17, 46.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:47<04:03,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7191/8000 [02:47<00:15, 52.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:48<04:02,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7197/8000 [02:47<00:14, 53.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:50<03:58,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7206/8000 [02:47<00:16, 49.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:51<03:59,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7212/8000 [02:47<00:17, 45.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:52<03:58,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7218/8000 [02:48<00:16, 47.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:54<03:59,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7223/8000 [02:48<00:16, 47.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:55<03:55,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7229/8000 [02:48<00:15, 50.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:57<03:57,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7235/8000 [02:48<00:14, 52.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:58<03:58,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7243/8000 [02:48<00:20, 36.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:00<03:50,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7248/8000 [02:48<00:20, 36.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:01<03:46,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7257/8000 [02:49<00:19, 38.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:03<03:51,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7264/8000 [02:49<00:16, 43.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:04<04:13,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7269/8000 [02:49<00:18, 40.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:06<04:04,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7275/8000 [02:49<00:16, 44.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:07<03:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7281/8000 [02:49<00:15, 46.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:09<03:56,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7287/8000 [02:49<00:14, 49.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:10<03:54,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7295/8000 [02:49<00:12, 56.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:12<03:50,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7303/8000 [02:49<00:12, 55.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:14<04:16,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7310/8000 [02:49<00:11, 58.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:16<04:49,  1.90s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████████████████████   | 7317/8000 [02:50<00:11, 58.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:18<04:21,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7324/8000 [02:50<00:11, 59.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:19<03:57,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7331/8000 [02:50<00:14, 47.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:20<03:41,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7338/8000 [02:50<00:15, 42.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:22<03:36,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7347/8000 [02:50<00:14, 46.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:23<03:28,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7354/8000 [02:50<00:14, 45.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:24<03:22,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7359/8000 [02:51<00:13, 46.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:25<03:16,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7365/8000 [02:51<00:16, 39.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:27<03:13,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7373/8000 [02:51<00:13, 47.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:28<03:11,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7380/8000 [02:51<00:13, 44.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:29<03:07,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7391/8000 [02:51<00:11, 53.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:31<03:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▍  | 7400/8000 [02:51<00:12, 48.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:32<03:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7407/8000 [02:52<00:11, 52.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:33<03:03,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7414/8000 [02:52<00:10, 55.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:35<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7421/8000 [02:52<00:12, 46.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:36<03:01,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7430/8000 [02:52<00:12, 44.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:37<03:02,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7436/8000 [02:52<00:13, 42.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:39<03:04,  1.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7445/8000 [02:52<00:10, 51.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:40<03:00,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7452/8000 [02:52<00:09, 55.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:41<02:57,  1.34s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7459/8000 [02:53<00:10, 51.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:43<02:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7467/8000 [02:53<00:10, 51.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:44<02:52,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7475/8000 [02:53<00:09, 56.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:45<02:51,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7484/8000 [02:53<00:09, 52.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:47<02:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7496/8000 [02:53<00:08, 61.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:48<02:48,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7503/8000 [02:53<00:08, 55.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:49<02:48,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7511/8000 [02:53<00:08, 54.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:51<02:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7519/8000 [02:54<00:08, 53.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:52<02:41,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7526/8000 [02:54<00:09, 50.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▍                  | 76/200 [01:53<02:41,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7535/8000 [02:54<00:09, 51.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▌                  | 77/200 [01:54<02:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7541/8000 [02:54<00:09, 47.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  39%|███████████▋                  | 78/200 [01:56<02:35,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7548/8000 [02:54<00:10, 42.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|███████████▊                  | 79/200 [01:57<02:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7554/8000 [02:55<00:11, 39.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████                  | 80/200 [01:58<02:35,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7560/8000 [02:55<00:10, 43.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  40%|████████████▏                 | 81/200 [02:00<02:34,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7569/8000 [02:55<00:08, 52.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  41%|████████████▎                 | 82/200 [02:01<02:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7575/8000 [02:55<00:09, 44.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▍                 | 83/200 [02:02<02:31,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7581/8000 [02:55<00:09, 45.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▌                 | 84/200 [02:03<02:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7587/8000 [02:55<00:08, 48.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  42%|████████████▊                 | 85/200 [02:05<02:31,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7596/8000 [02:55<00:07, 52.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  43%|████████████▉                 | 86/200 [02:06<02:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7604/8000 [02:55<00:07, 52.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  44%|█████████████                 | 87/200 [02:07<02:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7611/8000 [02:56<00:07, 51.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  44%|█████████████▏                | 88/200 [02:10<02:46,  1.48s/it]\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7618/8000 [02:56<00:08, 44.21it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7626/8000 [02:56<00:07, 50.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7635/8000 [02:56<00:08, 42.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 15.25it/s]\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7644/8000 [02:56<00:07, 47.47it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7654/8000 [02:56<00:06, 53.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7662/8000 [02:57<00:05, 58.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.44s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7670/8000 [02:57<00:07, 46.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 34it [00:01, 30.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7677/8000 [02:57<00:08, 37.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 78it [00:01, 76.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7682/8000 [02:57<00:10, 31.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 125it [00:01, 131.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7687/8000 [02:58<00:09, 33.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 172it [00:01, 187.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7692/8000 [02:58<00:08, 34.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 213it [00:01, 228.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7698/8000 [02:58<00:07, 39.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 254it [00:02, 262.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7705/8000 [02:58<00:06, 45.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 301it [00:02, 310.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7711/8000 [02:58<00:06, 41.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 343it [00:02, 323.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7718/8000 [02:58<00:06, 41.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 383it [00:02, 340.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7724/8000 [02:58<00:06, 44.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 423it [00:02, 348.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7730/8000 [02:58<00:06, 41.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 473it [00:02, 388.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7737/8000 [02:59<00:05, 48.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 520it [00:02, 410.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7744/8000 [02:59<00:05, 44.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 564it [00:02, 406.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7753/8000 [02:59<00:05, 43.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 607it [00:02, 396.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7760/8000 [02:59<00:05, 44.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 650it [00:03, 404.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7767/8000 [02:59<00:04, 48.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 693it [00:03, 409.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7773/8000 [02:59<00:05, 39.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 735it [00:03, 411.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7783/8000 [03:00<00:05, 42.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 236.50it/s]\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7790/8000 [03:00<00:04, 45.78it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7800/8000 [03:00<00:03, 50.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7806/8000 [03:00<00:03, 51.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7814/8000 [03:00<00:04, 43.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7819/8000 [03:01<00:04, 36.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7824/8000 [03:01<00:04, 39.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7833/8000 [03:01<00:03, 44.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7841/8000 [03:01<00:03, 46.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7848/8000 [03:01<00:03, 45.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7853/8000 [03:01<00:03, 46.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7860/8000 [03:01<00:03, 41.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7866/8000 [03:02<00:03, 39.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7874/8000 [03:02<00:02, 43.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7879/8000 [03:02<00:03, 39.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7886/8000 [03:02<00:02, 45.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7891/8000 [03:02<00:02, 40.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7900/8000 [03:02<00:02, 48.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:00, 169\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7908/8000 [03:02<00:01, 47.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 165\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7914/8000 [03:03<00:01, 49.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  56%|▌| 56/100 [00:00<00:00, 183\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7920/8000 [03:03<00:01, 51.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  76%|▊| 76/100 [00:00<00:00, 184\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7926/8000 [03:03<00:01, 42.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 18\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7931/8000 [03:03<00:01, 38.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  25%|██████▎                  | 1/4 [05:49<17:29, 349.95s/it]\u001b[A"
+      "Generating signals:  99%|██████████████████████████████████▋| 7937/8000 [03:03<00:02, 28.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7945/8000 [03:04<00:01, 33.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7949/8000 [03:04<00:01, 33.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7954/8000 [03:04<00:01, 36.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7960/8000 [03:04<00:00, 40.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7968/8000 [03:04<00:00, 49.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7975/8000 [03:04<00:00, 41.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7982/8000 [03:04<00:00, 46.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7988/8000 [03:05<00:00, 36.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7993/8000 [03:05<00:00, 38.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8002it [03:05, 43.17it/s]                                                   \n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -5.028871991909894\n",
-      "2999: -6.2196324\r"
+      "concatenating...\n"
      ]
     },
     {
@@ -50361,516 +50475,556 @@
      "text": [
       "\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:20,  4.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:47,  2.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:06<05:21,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:15,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:06<22:56,  6.92s/it]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:40,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<11:38,  3.53s/it]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:17,  1.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:15,  2.52s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:02,  1.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:10<06:33,  2.01s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<02:50,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:11<05:43,  1.76s/it]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:44,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:35,  1.73s/it]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:37,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<05:19,  1.65s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:32,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<05:03,  1.58s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:31,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:48,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:32,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<05:17,  1.67s/it]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:14<02:34,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:21<05:15,  1.67s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:15<02:32,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:22<04:52,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:16<02:30,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:24<04:30,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:28,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:25<04:20,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:18<02:26,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:26<04:16,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:18<02:25,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:28<04:18,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:19<02:23,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:29<04:11,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:20<02:24,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<04:10,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:21<02:28,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:32<04:04,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:22<02:26,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:33<04:05,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:23<02:26,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:34<04:04,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:23<02:25,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:36<03:59,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:24<02:23,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:37<03:57,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:25<02:24,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:38<03:53,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:26<02:25,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:40<03:47,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:27<02:22,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:41<03:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:28<02:22,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:42<03:51,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:28<02:21,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:44<03:48,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:29<02:20,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:45<03:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:30<02:22,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:46<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:31<02:24,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:47<03:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:32<02:26,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:49<03:48,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:33<02:28,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:50<03:45,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:34<02:29,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:52<03:46,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:35<02:31,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:53<03:41,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:36<02:38,  1.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:54<03:40,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:37<02:35,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:56<03:37,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:38<02:35,  1.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:57<03:35,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:39<02:36,  1.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:58<03:32,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:40<02:38,  1.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:59<03:28,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:41<02:37,  1.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:01<03:28,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:42<02:33,  1.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:02<03:27,  1.31s/it]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:43<02:29,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:03<03:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:44<02:27,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:05<03:22,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:45<02:26,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:06<03:20,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:46<02:25,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:07<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:48<02:24,  1.04it/s]\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:09<03:20,  1.31s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:10<03:20,  1.32s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:11<03:18,  1.31s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 22.81it/s]\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:13<03:17,  1.32s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:14<03:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:15<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:16<03:05,  1.26s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 69it [00:01, 91.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:17<03:00,  1.24s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 138it [00:01, 186.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:19<02:59,  1.24s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 209it [00:01, 282.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:20<02:58,  1.24s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 285it [00:01, 381.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:21<02:55,  1.23s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 360it [00:01, 466.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:22<02:53,  1.22s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 434it [00:01, 532.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:24<02:53,  1.23s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 512it [00:01, 595.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:25<02:50,  1.22s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 585it [00:01, 613.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:26<02:49,  1.22s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 656it [00:01, 638.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:27<02:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 727it [00:01, 650.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:28<02:46,  1.21s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:02, 388.73it/s]\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:30<02:44,  1.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:31<02:44,  1.22s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:32<02:45,  1.23s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|███        | 28/100 [00:00<00:00, 272.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████                    | 67/200 [01:33<02:42,  1.22s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|██████▊    | 62/100 [00:00<00:00, 310.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:35<02:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 314.78it/s]\n",
       "\n",
-      "training:  34%|██████████▎                   | 69/200 [01:36<02:44,  1.25s/it]\u001b[A\u001b[A\n",
+      "Nyquist Samples: 100%|███████████████████████████████████████████| 4/4 [36:50<00:00, 553.42s/it]\u001b[A\n",
+      "Replicates:  47%|███████████▏            | 7/15 [5:45:41<5:33:46, 2503.33s/it]\n",
+      "Nyquist Samples:   0%|                                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.464144729043513\n",
+      "2999: -5.9295479\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "training:  35%|██████████▌                   | 70/200 [01:37<02:49,  1.30s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▋                   | 71/200 [01:39<02:42,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▊                   | 72/200 [01:40<02:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:03<13:10,  3.97s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  36%|██████████▉                   | 73/200 [01:41<02:41,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:36,  2.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  37%|███████████                   | 74/200 [01:42<02:40,  1.28s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:31,  1.38s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  38%|███████████▎                  | 75/200 [01:45<02:55,  1.40s/it]\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:32,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<03:01,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:41,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 16.02it/s]\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:24,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:14,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:10,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:05,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 40it [00:01, 35.75it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:00,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 94it [00:01, 93.14it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<01:58,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 138it [00:01, 141.55it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:56,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 179it [00:01, 186.16it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:11<01:53,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 226it [00:01, 240.05it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:52,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 269it [00:02, 276.11it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:51,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 311it [00:02, 297.13it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:49,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 360it [00:02, 343.27it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:48,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 403it [00:02, 354.40it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:14<01:46,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 451it [00:02, 386.53it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:44,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 499it [00:02, 410.93it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:44,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 544it [00:02, 409.95it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:16<01:45,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 588it [00:02, 411.22it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:44,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 631it [00:02, 390.58it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:17<01:44,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 672it [00:03, 380.67it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:46,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 720it [00:03, 406.62it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:46,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 237.84it/s]\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:45,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:46,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:20<01:45,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 158\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:46,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 171\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:45,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  54%|▌| 54/100 [00:00<00:00, 177\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:22<01:42,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:00<00:00, 177\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:42,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 17\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:42,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:42,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:41,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:25<01:39,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:37,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 37/8000 [00:00<00:26, 302.85it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:35,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 88/8000 [00:00<00:20, 386.11it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:27<01:33,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 140/8000 [00:00<00:18, 421.45it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:32,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 196/8000 [00:00<00:17, 437.62it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:28<01:36,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 246/8000 [00:00<00:17, 444.00it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:39,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 344/8000 [00:00<00:17, 433.96it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:37,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 392/8000 [00:00<00:17, 430.99it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:30<01:36,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 437/8000 [00:01<00:17, 427.87it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:31<01:35,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 485/8000 [00:01<00:17, 432.52it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:33,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█                | 529/8000 [00:01<00:17, 426.48it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:32<01:32,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 630/8000 [00:01<00:17, 418.30it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:33<01:30,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 692/8000 [00:01<00:15, 461.15it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:33<01:29,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 753/8000 [00:01<00:16, 447.65it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:34<01:27,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 807/8000 [00:01<00:15, 458.84it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:34<01:26,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 904/8000 [00:02<00:16, 433.71it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:35<01:25,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 964/8000 [00:02<00:15, 448.97it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:36<01:26,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1057/8000 [00:02<00:15, 434.31it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:36<01:25,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1111/8000 [00:02<00:15, 447.12it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:37<01:25,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1163/8000 [00:02<00:15, 446.93it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:37<01:24,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1212/8000 [00:02<00:15, 426.59it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:38<01:24,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1280/8000 [00:03<00:48, 139.51it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:39<01:24,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1333/8000 [00:04<00:39, 168.75it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:39<01:24,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1381/8000 [00:04<00:33, 198.95it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:40<01:24,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1432/8000 [00:04<00:28, 230.59it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:40<01:24,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1475/8000 [00:04<00:25, 257.51it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:41<01:23,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1524/8000 [00:04<00:22, 291.12it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:42<01:21,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1585/8000 [00:04<00:18, 341.31it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:42<01:21,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:43<01:29,  1.50it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1632/8000 [00:04<00:17, 365.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 27.55it/s]\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1720/8000 [00:05<00:17, 358.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1767/8000 [00:05<00:17, 359.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▌            | 1811/8000 [00:05<00:16, 368.49it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1866/8000 [00:05<00:16, 373.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 78it [00:00, 119.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1906/8000 [00:05<00:19, 319.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 162it [00:00, 248.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1951/8000 [00:05<00:18, 326.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 248it [00:01, 372.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1999/8000 [00:05<00:18, 324.89it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 335it [00:01, 484.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2051/8000 [00:05<00:16, 355.82it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 413it [00:01, 556.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2110/8000 [00:06<00:15, 388.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 497it [00:01, 629.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2155/8000 [00:06<00:15, 385.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 585it [00:01, 696.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2210/8000 [00:06<00:14, 395.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 669it [00:01, 735.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2260/8000 [00:06<00:15, 374.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 458.87it/s]\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2309/8000 [00:06<00:15, 379.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2353/8000 [00:06<00:16, 344.01it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2395/8000 [00:06<00:17, 324.74it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▏     | 47/100 [00:00<00:00, 468.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2454/8000 [00:07<00:15, 355.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 471.96it/s]\n",
       "\n",
-      "Generating signals:  32%|█████           | 2546/8000 [00:07<00:17, 317.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2602/8000 [00:07<00:16, 326.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2642/8000 [00:07<00:15, 337.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                | 340/8000 [00:00<00:02, 2837.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2702/8000 [00:07<00:13, 385.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                               | 673/8000 [00:00<00:02, 2799.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▌          | 2753/8000 [00:07<00:13, 387.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▏                            | 1029/8000 [00:00<00:02, 2722.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2811/8000 [00:08<00:12, 409.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1358/8000 [00:01<00:07, 840.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2877/8000 [00:08<00:12, 415.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████                          | 1703/8000 [00:01<00:05, 1140.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2928/8000 [00:08<00:12, 409.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▍                        | 2052/8000 [00:01<00:04, 1421.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3014/8000 [00:08<00:13, 361.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|█████████▉                       | 2403/8000 [00:01<00:03, 1709.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3117/8000 [00:08<00:12, 405.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▍                     | 2771/8000 [00:01<00:02, 2030.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3162/8000 [00:08<00:12, 395.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|████████████▊                    | 3118/8000 [00:01<00:02, 2283.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3210/8000 [00:09<00:12, 386.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▎                  | 3474/8000 [00:01<00:01, 2402.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3263/8000 [00:09<00:11, 398.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████████████▊                 | 3830/8000 [00:02<00:01, 2576.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▋         | 3319/8000 [00:09<00:11, 424.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▎               | 4187/8000 [00:02<00:01, 2633.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▊         | 3418/8000 [00:09<00:10, 446.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████████████▋              | 4541/8000 [00:02<00:01, 2644.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3469/8000 [00:09<00:10, 445.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▏            | 4895/8000 [00:02<00:01, 2675.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3517/8000 [00:09<00:10, 435.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|█████████████████████▋           | 5261/8000 [00:02<00:00, 2852.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3567/8000 [00:09<00:10, 409.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████          | 5605/8000 [00:02<00:00, 2880.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3654/8000 [00:10<00:11, 362.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████████████████▌        | 5944/8000 [00:02<00:00, 2956.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3713/8000 [00:10<00:11, 368.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████████████████▉       | 6291/8000 [00:02<00:00, 2835.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3757/8000 [00:10<00:11, 363.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████████████████▎     | 6635/8000 [00:03<00:00, 2915.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3809/8000 [00:10<00:12, 331.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|████████████████████████████▋    | 6969/8000 [00:03<00:00, 2893.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▋        | 3856/8000 [00:10<00:13, 302.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████   | 7296/8000 [00:03<00:00, 2868.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3909/8000 [00:10<00:12, 337.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|███████████████████████████████▍ | 7634/8000 [00:03<00:00, 2956.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3962/8000 [00:11<00:11, 344.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8148it [00:03, 2321.18it/s]                                                 \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  50%|████████        | 4006/8000 [00:11<00:11, 345.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4052/8000 [00:11<00:11, 357.61it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4103/8000 [00:11<00:10, 380.60it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:59,  4.52s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4152/8000 [00:11<00:10, 357.27it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:18,  2.21s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4215/8000 [00:11<00:09, 382.18it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:49,  1.47s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4270/8000 [00:11<00:09, 408.77it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:42,  1.13s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4323/8000 [00:11<00:08, 422.15it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<03:05,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4416/8000 [00:12<00:09, 388.42it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:40,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4468/8000 [00:12<00:09, 389.69it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:25,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4515/8000 [00:12<00:08, 390.96it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:17,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4561/8000 [00:12<00:08, 390.73it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:11,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▏      | 4616/8000 [00:12<00:08, 413.98it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:08,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4706/8000 [00:12<00:08, 396.06it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:02,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▌      | 4759/8000 [00:13<00:08, 393.88it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<01:58,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4815/8000 [00:13<00:07, 409.68it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:55,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4910/8000 [00:13<00:07, 403.70it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<02:00,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4954/8000 [00:13<00:07, 392.57it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<02:01,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5034/8000 [00:13<00:08, 346.68it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:58,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▏     | 5075/8000 [00:13<00:08, 348.95it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:14<02:00,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5124/8000 [00:14<00:08, 354.31it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:15<01:55,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5172/8000 [00:14<00:07, 359.06it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:52,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5229/8000 [00:14<00:07, 387.07it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:16<01:49,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5279/8000 [00:14<00:06, 395.58it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:17<01:58,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5323/8000 [00:14<00:07, 373.85it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:17<01:57,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5375/8000 [00:14<00:06, 401.13it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:18<01:53,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5419/8000 [00:14<00:06, 392.97it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:50,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5460/8000 [00:15<00:07, 347.28it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:19<01:47,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5506/8000 [00:15<00:07, 348.18it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:20<01:46,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5556/8000 [00:15<00:06, 357.48it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:20<01:48,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5597/8000 [00:15<00:06, 350.93it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:21<01:53,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5645/8000 [00:15<00:06, 351.69it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:22<01:57,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5694/8000 [00:15<00:06, 372.75it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:23<02:01,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5732/8000 [00:15<00:06, 342.02it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:23<02:04,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5790/8000 [00:15<00:06, 367.33it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:24<02:04,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5844/8000 [00:16<00:05, 372.43it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:25<02:05,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5888/8000 [00:16<00:06, 320.45it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:26<02:04,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5940/8000 [00:16<00:06, 329.38it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:26<02:03,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5992/8000 [00:16<00:06, 316.04it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:27<02:04,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████    | 6042/8000 [00:16<00:05, 329.20it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:28<02:03,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6098/8000 [00:16<00:05, 355.71it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:29<02:04,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6146/8000 [00:16<00:05, 366.28it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:29<02:03,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▍   | 6192/8000 [00:17<00:05, 327.23it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:30<02:04,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6250/8000 [00:17<00:05, 317.31it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:31<02:03,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6304/8000 [00:17<00:04, 349.44it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:32<02:03,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6347/8000 [00:17<00:04, 350.54it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:33<02:03,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6396/8000 [00:17<00:04, 324.04it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:33<02:03,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6462/8000 [00:17<00:04, 355.51it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:34<02:01,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6500/8000 [00:18<00:04, 314.57it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:35<02:01,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6547/8000 [00:18<00:04, 342.90it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:36<02:00,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6598/8000 [00:18<00:03, 365.56it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:37<02:01,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6645/8000 [00:18<00:03, 373.40it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:37<01:59,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6704/8000 [00:18<00:03, 383.56it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:38<01:58,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6746/8000 [00:18<00:03, 364.40it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:39<01:58,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6794/8000 [00:18<00:03, 366.82it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:40<01:57,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6836/8000 [00:18<00:03, 359.22it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:40<01:56,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6894/8000 [00:19<00:02, 384.35it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:41<01:54,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6949/8000 [00:19<00:02, 401.48it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:43<01:54,  1.27it/s]\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7007/8000 [00:19<00:02, 439.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████  | 7054/8000 [00:19<00:02, 437.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|██████████████▏ | 7105/8000 [00:19<00:02, 409.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 24.56it/s]\n",
       "\n",
-      "Generating signals:  90%|██████████████▍ | 7201/8000 [00:19<00:01, 438.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▍ | 7245/8000 [00:19<00:01, 418.54it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7295/8000 [00:20<00:01, 427.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7381/8000 [00:20<00:01, 391.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 78it [00:01, 106.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7424/8000 [00:20<00:01, 366.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 161it [00:01, 224.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7480/8000 [00:20<00:01, 367.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 248it [00:01, 346.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7524/8000 [00:20<00:01, 353.21it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 336it [00:01, 459.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7576/8000 [00:20<00:01, 328.61it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 414it [00:01, 533.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▎| 7632/8000 [00:20<00:01, 364.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 493it [00:01, 595.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7677/8000 [00:21<00:00, 364.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 571it [00:01, 614.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7720/8000 [00:21<00:00, 343.44it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 646it [00:01, 620.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7779/8000 [00:21<00:00, 372.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 414.57it/s]\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7833/8000 [00:21<00:00, 385.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7887/8000 [00:21<00:00, 391.79it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7937/8000 [00:21<00:00, 396.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|████▉      | 45/100 [00:00<00:00, 448.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7977/8000 [00:21<00:00, 365.77it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 438.17it/s]\n",
       "\n",
-      "Generating signals: 8022it [00:22, 363.86it/s]\n"
+      "Nyquist Samples:  25%|██████████▊                                | 1/4 [02:18<06:56, 138.85s/it]\u001b[A"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "concatenating...\n"
+      "initial loss -4.669975085025768\n",
+      "2999: -6.2016572\r"
      ]
     },
     {
@@ -50879,1162 +51033,1270 @@
      "text": [
       "\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:24,  7.36s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:43,  3.86s/it]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:10,  2.79s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:48,  4.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:30,  2.30s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:59,  2.12s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:29,  2.00s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:48,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<06:07,  1.90s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:44,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:48,  1.81s/it]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:23,  1.69s/it]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:45,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<05:05,  1.60s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:48,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:24,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:58,  1.58s/it]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:24,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:42,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:19,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:31,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:13,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:28,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:21,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:12<02:07,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:29,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<02:04,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:36,  1.51s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<02:04,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:40,  1.54s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:31,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:14<02:02,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:35<04:24,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:15<02:00,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:36<04:20,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:16<01:59,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<04:13,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:16<01:59,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:39<04:13,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:17<01:57,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:41<04:09,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:18<01:56,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:42<04:06,  1.41s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:18<01:55,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:44<04:11,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:19<01:54,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:45<04:10,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:20<01:55,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:47<04:11,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:20<01:55,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:48<04:22,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:21<01:54,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:50<04:26,  1.57s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:22<01:55,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:51<04:23,  1.56s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:22<01:53,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:53<04:20,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:23<01:52,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:54<04:08,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:24<01:53,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:56<04:00,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:24<01:52,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:57<03:51,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:25<01:50,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:58<03:50,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:26<01:50,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:00<03:47,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:26<01:49,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:01<03:43,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:27<01:48,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:02<03:42,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:28<01:48,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:04<03:37,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:28<01:46,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:05<03:42,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:29<01:44,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:07<03:41,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:30<01:44,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:08<03:39,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:30<01:44,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:09<03:36,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:31<01:44,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:11<03:33,  1.38s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:32<01:43,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:13<03:55,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:32<01:44,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:14<03:57,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:33<01:43,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:16<03:58,  1.57s/it]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:34<01:42,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:18<04:28,  1.78s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:34<01:42,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:20<04:24,  1.76s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:35<01:44,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:21<04:01,  1.62s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:36<01:48,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:22<03:45,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:36<01:46,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:24<03:35,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:37<01:44,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:25<03:26,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:38<01:42,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:26<03:19,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:38<01:41,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:28<03:15,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:39<01:40,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:29<03:14,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:40<01:38,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:30<03:10,  1.34s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:40<01:37,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:32<03:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:41<01:36,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:33<02:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:42<01:35,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:34<02:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:42<01:34,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:35<02:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:43<01:32,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:37<02:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:44<01:42,  1.36it/s]\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:38<02:52,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:39<02:52,  1.28s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:  33%|█████████▉                    | 66/200 [01:41<03:00,  1.35s/it]\u001b[A\u001b[A\n",
-      "\n",
-      "training:  34%|██████████                    | 67/200 [01:42<02:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  34%|██████████▏                   | 68/200 [01:44<03:23,  1.54s/it]\n",
+      "Evaluating NLL: 25it [00:00, 26.69it/s]\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:02, 12.09it/s]\n",
+      "Evaluating NLL: 1it [00:00,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Evaluating NLL: 85it [00:00, 129.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 168it [00:00, 254.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.44s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 253it [00:01, 375.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 36it [00:01, 32.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 326it [00:01, 437.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 63it [00:01, 58.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 395it [00:01, 494.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 95it [00:01, 94.81it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 464it [00:01, 538.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 133it [00:01, 141.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 536it [00:01, 586.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 170it [00:01, 184.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 607it [00:01, 618.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 204it [00:02, 216.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 682it [00:01, 654.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 245it [00:02, 261.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 428.42it/s]\n",
       "\n",
-      "Evaluating NLL: 281it [00:02, 261.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 316it [00:02, 280.09it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 359it [00:02, 318.30it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|█████▍     | 49/100 [00:00<00:00, 482.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 395it [00:02, 320.66it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 439.17it/s]\n",
       "\n",
-      "Evaluating NLL: 438it [00:02, 348.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 481it [00:02, 370.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 521it [00:02, 376.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                  | 139/8000 [00:00<00:08, 907.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 561it [00:03, 380.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▏                                | 294/8000 [00:00<00:07, 1022.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 600it [00:03, 368.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                | 398/8000 [00:00<00:07, 1026.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 638it [00:03, 370.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                | 539/8000 [00:00<00:07, 994.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 676it [00:03, 350.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                               | 652/8000 [00:00<00:07, 1022.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 714it [00:03, 358.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                               | 797/8000 [00:00<00:07, 947.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 751it [00:03, 361.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████                               | 938/8000 [00:00<00:07, 907.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 216.95it/s]\n",
+      "Generating signals:  13%|████▍                             | 1036/8000 [00:01<00:07, 907.17it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  14%|████▊                             | 1138/8000 [00:01<00:07, 924.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1243/8000 [00:01<00:07, 930.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  17%|▏| 17/100 [00:00<00:00, 154\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1358/8000 [00:01<00:06, 975.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  33%|▎| 33/100 [00:00<00:00, 143\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                           | 1501/8000 [00:01<00:06, 950.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 161\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                           | 1627/8000 [00:01<00:07, 893.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:00<00:00, 157\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▍                          | 1746/8000 [00:01<00:06, 932.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 16\n",
+      "Generating signals:  23%|███████▊                          | 1844/8000 [00:01<00:06, 923.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  50%|████████████▌            | 2/4 [11:42<11:43, 351.51s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.033857512297315\n",
-      "2999: -6.1799252\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:  24%|████████▎                         | 1955/8000 [00:02<00:07, 854.20it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:  26%|████████▋                         | 2047/8000 [00:02<00:06, 855.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                        | 2197/8000 [00:02<00:06, 913.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:08<28:37,  8.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                        | 2341/8000 [00:02<00:06, 926.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:10<14:28,  4.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▌                       | 2479/8000 [00:02<00:05, 926.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:11<09:56,  3.03s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▏                      | 2638/8000 [00:02<00:05, 972.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<07:44,  2.37s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▊                      | 2785/8000 [00:02<00:05, 975.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<06:34,  2.02s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▍                     | 2932/8000 [00:03<00:05, 994.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<05:47,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|████████████▋                    | 3090/8000 [00:03<00:04, 1018.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:22,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3223/8000 [00:03<00:04, 978.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:03,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▎                   | 3357/8000 [00:03<00:04, 935.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<04:50,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▉                   | 3517/8000 [00:03<00:04, 978.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<04:38,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▍                  | 3630/8000 [00:03<00:04, 961.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:33,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████                  | 3783/8000 [00:03<00:04, 975.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:29,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3925/8000 [00:04<00:04, 962.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:25,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▎                | 4070/8000 [00:04<00:04, 948.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:17,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▋                | 4169/8000 [00:04<00:04, 940.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:27<04:16,  1.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▎               | 4323/8000 [00:04<00:03, 971.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:09,  1.35s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▊               | 4429/8000 [00:04<00:03, 973.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:30<04:04,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▍              | 4567/8000 [00:04<00:03, 955.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:31<04:00,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4703/8000 [00:04<00:03, 935.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:33<04:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▋             | 4857/8000 [00:05<00:03, 974.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:34<03:58,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████             | 4956/8000 [00:05<00:03, 961.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:35<03:57,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5095/8000 [00:05<00:02, 976.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:37<03:54,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▎           | 5239/8000 [00:05<00:02, 952.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:38<03:55,  1.33s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▉           | 5387/8000 [00:05<00:02, 985.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:39<03:52,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▍          | 5522/8000 [00:05<00:02, 980.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:41<03:51,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5662/8000 [00:05<00:02, 965.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:42<03:49,  1.32s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████         | 5822/8000 [00:06<00:02, 1001.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:43<03:46,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5926/8000 [00:06<00:02, 963.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:44<03:42,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▋        | 6038/8000 [00:06<00:01, 981.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:46<03:42,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6162/8000 [00:06<00:01, 922.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:47<03:39,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6261/8000 [00:06<00:01, 932.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:48<03:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6397/8000 [00:06<00:01, 885.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:50<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▋      | 6507/8000 [00:06<00:01, 923.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:51<03:37,  1.30s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████      | 6613/8000 [00:06<00:01, 942.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:52<03:33,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▋     | 6763/8000 [00:07<00:01, 971.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:54<03:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6874/8000 [00:07<00:01, 979.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:55<03:31,  1.29s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7015/8000 [00:07<00:01, 926.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:56<03:47,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7112/8000 [00:07<00:00, 934.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:58<03:45,  1.39s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7211/8000 [00:07<00:00, 938.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:59<03:46,  1.41s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████   | 7316/8000 [00:07<00:00, 937.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:01<03:48,  1.43s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7418/8000 [00:07<00:00, 941.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:02<03:45,  1.42s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7521/8000 [00:07<00:00, 923.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:04<03:41,  1.40s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▍ | 7620/8000 [00:08<00:00, 907.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:05<03:48,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████████████████████▊ | 7723/8000 [00:08<00:00, 924.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:07<03:50,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7827/8000 [00:08<00:00, 927.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:08<03:44,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals: 8035it [00:08, 947.86it/s]                                                  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:09<03:37,  1.42s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:11<03:37,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:12<03:32,  1.40s/it]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:58,  4.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:14<03:35,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:49,  2.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:15<03:45,  1.50s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:06<05:27,  1.66s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:17<03:37,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:07<04:21,  1.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:18<03:38,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:44,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:20<03:36,  1.47s/it]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:23,  1.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:21<03:35,  1.48s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:08,  1.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:23<03:35,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<02:57,  1.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:24<03:41,  1.54s/it]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:51,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:26<03:40,  1.54s/it]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:12<02:45,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:27<03:36,  1.53s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:42,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:29<03:29,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:39,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:30<03:24,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:36,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:32<03:22,  1.46s/it]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:15<02:35,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:33<03:20,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:16<02:34,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:34<03:16,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:16<02:32,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:36<03:15,  1.44s/it]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:30,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▊                    | 65/200 [01:39<03:26,  1.53s/it]\n",
+      "training:   9%|████▎                                           | 18/200 [00:18<02:28,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  10%|████▌                                           | 19/200 [00:19<02:26,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:20<02:24,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.42s/it]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:20<02:23,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 16.28it/s]\n",
+      "training:  11%|█████▎                                          | 22/200 [00:21<02:15,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:22<02:12,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:23<02:16,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.52s/it]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:23<02:10,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 54it [00:01, 46.16it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:24<02:05,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 102it [00:01, 93.59it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:25<02:02,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 146it [00:01, 139.69it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:25<02:04,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 187it [00:01, 181.95it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:26<02:01,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 234it [00:02, 235.51it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:27<02:01,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 277it [00:02, 270.21it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:28<02:01,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 319it [00:02, 282.70it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:28<02:01,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 358it [00:02, 295.37it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:29<02:00,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 395it [00:02, 279.07it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:30<02:02,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 433it [00:02, 301.99it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:31<02:01,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 473it [00:02, 325.37it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:31<02:01,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 519it [00:02, 359.34it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:32<02:00,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 560it [00:02, 372.12it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:33<01:59,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 602it [00:03, 383.68it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:34<01:58,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 647it [00:03, 399.95it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:34<01:57,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 691it [00:03, 410.53it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:35<01:56,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 733it [00:03, 409.97it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:36<01:57,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 228.66it/s]\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:36<01:56,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:37<01:54,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:38<01:55,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 176\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:39<01:52,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 188\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:40<02:06,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  57%|▌| 57/100 [00:00<00:00, 183\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:41<02:15,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  76%|▊| 76/100 [00:00<00:00, 185\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:41<02:03,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 19\n",
+      "training:  25%|████████████                                    | 50/200 [00:42<01:56,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:43<01:49,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:43<01:46,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 16/8000 [00:00<01:07, 119.15it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:44<01:43,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                  | 39/8000 [00:00<00:54, 145.71it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:45<01:40,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                  | 54/8000 [00:00<01:01, 129.09it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:45<01:38,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 72/8000 [00:00<00:57, 136.76it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:46<01:36,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 92/8000 [00:00<00:58, 136.11it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:47<01:34,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                | 111/8000 [00:00<00:56, 139.03it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:47<01:34,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                | 132/8000 [00:00<00:58, 134.07it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:49<01:57,  1.20it/s]\n",
       "\n",
-      "Generating signals:   2%|▎                | 154/8000 [00:01<00:54, 142.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                | 185/8000 [00:01<01:02, 124.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 202/8000 [00:01<01:01, 127.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 19.49it/s]\n",
       "\n",
-      "Generating signals:   3%|▍                | 216/8000 [00:01<01:01, 126.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                | 234/8000 [00:01<00:59, 130.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 254/8000 [00:01<00:56, 138.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                | 269/8000 [00:02<01:00, 127.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 83it [00:00, 123.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▌                | 289/8000 [00:02<01:14, 103.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 164it [00:01, 244.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 318/8000 [00:02<00:59, 129.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 244it [00:01, 356.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                | 336/8000 [00:02<01:04, 118.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 328it [00:01, 463.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 361/8000 [00:02<00:58, 131.39it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 414it [00:01, 558.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 386/8000 [00:02<00:51, 148.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 493it [00:01, 606.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                | 404/8000 [00:03<00:50, 149.58it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 570it [00:01, 647.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                | 420/8000 [00:03<00:58, 128.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 651it [00:01, 690.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                | 441/8000 [00:03<00:54, 137.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 438.71it/s]\n",
       "\n",
-      "Generating signals:   6%|▉                | 456/8000 [00:03<00:56, 132.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 477/8000 [00:03<00:54, 138.28it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 494/8000 [00:03<00:55, 134.16it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████▌      | 41/100 [00:00<00:00, 404.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                | 517/8000 [00:03<00:51, 146.38it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 420.06it/s]\n",
       "\n",
-      "Generating signals:   7%|█▏               | 539/8000 [00:03<00:47, 158.37it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|█████████████████████▌                     | 2/4 [05:00<05:04, 152.39s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.821092518559693\n",
+      "2999: -6.1309312\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:   7%|█▏               | 560/8000 [00:04<00:46, 159.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏               | 586/8000 [00:04<00:41, 176.64it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 617/8000 [00:04<00:48, 153.09it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:01,  4.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎               | 636/8000 [00:04<00:48, 152.71it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<07:13,  2.19s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍               | 666/8000 [00:04<00:52, 138.83it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:57,  1.51s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 685/8000 [00:04<00:51, 141.30it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:45,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▍               | 701/8000 [00:05<00:51, 140.39it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:35,  1.10s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 731/8000 [00:05<00:55, 131.09it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:30,  1.08s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌               | 753/8000 [00:05<00:53, 136.69it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:56,  1.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 777/8000 [00:05<00:50, 144.13it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:34,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 796/8000 [00:05<00:49, 146.36it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:21,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋               | 811/8000 [00:05<00:53, 134.82it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:11,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊               | 826/8000 [00:06<00:56, 127.32it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:11<02:07,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 844/8000 [00:06<00:55, 129.86it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<02:01,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▊               | 865/8000 [00:06<00:53, 134.40it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:12<01:58,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉               | 897/8000 [00:06<00:55, 128.99it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:13<01:55,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|█▉               | 930/8000 [00:06<00:54, 130.72it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<01:56,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 955/8000 [00:06<00:49, 142.58it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:14<01:51,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 971/8000 [00:07<00:52, 133.92it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:14<01:52,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██               | 988/8000 [00:07<00:53, 131.25it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:15<01:53,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1018/8000 [00:07<00:57, 121.02it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:16<01:53,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1036/8000 [00:07<00:55, 124.88it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:16<01:51,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██              | 1052/8000 [00:07<00:54, 126.78it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:17<01:50,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏             | 1078/8000 [00:07<00:52, 132.41it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:18<01:51,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1101/8000 [00:08<00:46, 147.98it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:18<01:48,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▏             | 1118/8000 [00:08<00:50, 136.69it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:19<01:43,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1133/8000 [00:08<00:54, 125.93it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:19<01:41,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎             | 1150/8000 [00:08<00:54, 125.02it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:20<01:39,  1.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1164/8000 [00:08<00:56, 120.62it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:20<01:38,  1.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▎             | 1178/8000 [00:08<01:00, 113.27it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:21<01:40,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1196/8000 [00:08<01:00, 113.10it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:22<01:40,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍             | 1221/8000 [00:09<00:50, 135.02it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:22<01:39,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1250/8000 [00:09<00:57, 117.54it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:23<01:39,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1280/8000 [00:09<01:02, 108.13it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:23<01:38,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▌             | 1307/8000 [00:09<00:52, 128.35it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:24<01:37,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1326/8000 [00:09<00:49, 135.28it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:36,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1342/8000 [00:10<00:49, 134.47it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:25<01:37,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▋             | 1368/8000 [00:10<00:42, 157.40it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:26<01:35,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊             | 1389/8000 [00:10<00:40, 163.90it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:26<01:35,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▊             | 1427/8000 [00:10<00:43, 152.31it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:27<01:34,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1445/8000 [00:10<00:43, 151.96it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:36,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉             | 1467/8000 [00:10<00:41, 155.76it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:28<01:37,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1502/8000 [00:11<00:45, 142.93it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:29<01:34,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1519/8000 [00:11<00:46, 138.73it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:29<01:33,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1534/8000 [00:11<00:46, 139.24it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:30<01:31,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███             | 1549/8000 [00:11<00:47, 135.43it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:30,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1567/8000 [00:11<00:49, 128.80it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:31<01:29,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1581/8000 [00:11<00:53, 120.11it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:32<01:29,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▏            | 1603/8000 [00:11<00:47, 135.75it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:28,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎            | 1625/8000 [00:12<00:44, 144.67it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:33<01:27,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1650/8000 [00:12<00:40, 157.04it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:33<01:27,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▎            | 1668/8000 [00:12<00:41, 151.36it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:34<01:24,  1.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1689/8000 [00:12<00:42, 149.25it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:34<01:24,  1.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍            | 1707/8000 [00:12<00:41, 152.39it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:35<01:23,  1.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▍            | 1737/8000 [00:12<00:48, 128.97it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:36<01:26,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1752/8000 [00:12<00:47, 130.36it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:36<01:26,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1767/8000 [00:13<00:50, 124.14it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:37<01:24,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▌            | 1784/8000 [00:13<00:48, 128.60it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:37<01:23,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1817/8000 [00:13<00:50, 122.74it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:38<01:24,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1838/8000 [00:13<00:45, 134.51it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:39<01:24,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▋            | 1854/8000 [00:13<00:49, 123.76it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:39<01:24,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1880/8000 [00:13<00:42, 145.16it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:40<01:24,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1901/8000 [00:13<00:40, 152.00it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:40<01:21,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▊            | 1921/8000 [00:14<00:41, 144.92it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:41<01:22,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1940/8000 [00:14<00:41, 145.66it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:42<01:20,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|███▉            | 1956/8000 [00:14<00:44, 134.98it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:42<01:20,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|███▉            | 1975/8000 [00:14<00:46, 129.53it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:43<01:20,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2003/8000 [00:14<00:41, 145.12it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:43<01:18,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2018/8000 [00:14<00:43, 137.64it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [00:44<01:17,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████            | 2039/8000 [00:14<00:41, 145.14it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [00:44<01:15,  1.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████            | 2059/8000 [00:15<00:39, 150.51it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [00:45<01:15,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2083/8000 [00:15<00:37, 159.37it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [00:46<01:14,  1.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▏           | 2104/8000 [00:15<00:35, 165.76it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [00:46<01:14,  1.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▏           | 2123/8000 [00:15<00:39, 149.60it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [00:47<01:14,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2162/8000 [00:15<00:39, 148.95it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▌                              | 73/200 [00:47<01:13,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▎           | 2182/8000 [00:15<00:39, 148.51it/s]\u001b[A\u001b[A\n",
+      "training:  37%|█████████████████▊                              | 74/200 [00:48<01:14,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2211/8000 [00:16<00:43, 133.04it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████                              | 75/200 [00:48<01:12,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▍           | 2228/8000 [00:16<00:44, 128.92it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▏                             | 76/200 [00:49<01:12,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2250/8000 [00:16<00:40, 141.00it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▍                             | 77/200 [00:50<01:12,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▌           | 2271/8000 [00:16<00:40, 142.89it/s]\u001b[A\u001b[A\n",
+      "training:  39%|██████████████████▋                             | 78/200 [00:50<01:11,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2293/8000 [00:16<00:38, 148.69it/s]\u001b[A\u001b[A\n",
+      "training:  40%|██████████████████▉                             | 79/200 [00:51<01:19,  1.52it/s]\n",
       "\n",
-      "Generating signals:  29%|████▌           | 2310/8000 [00:16<00:39, 143.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2331/8000 [00:16<00:38, 148.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▋           | 2357/8000 [00:17<00:33, 168.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 27.82it/s]\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2388/8000 [00:17<00:39, 142.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2407/8000 [00:17<00:39, 140.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|████▊           | 2423/8000 [00:17<00:42, 130.51it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2448/8000 [00:17<00:36, 150.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 82it [00:00, 120.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2466/8000 [00:17<00:37, 147.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 166it [00:01, 246.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|████▉           | 2488/8000 [00:18<00:35, 155.47it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 252it [00:01, 368.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████           | 2506/8000 [00:18<00:37, 147.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 334it [00:01, 467.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████           | 2537/8000 [00:18<00:41, 131.87it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 417it [00:01, 553.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2570/8000 [00:18<00:42, 126.96it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 623.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▏          | 2586/8000 [00:18<00:42, 126.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 580it [00:01, 656.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▏          | 2619/8000 [00:19<00:43, 123.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 658it [00:01, 684.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2651/8000 [00:19<00:43, 123.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 442.81it/s]\n",
       "\n",
-      "Generating signals:  33%|█████▎          | 2677/8000 [00:19<00:38, 137.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2694/8000 [00:19<00:39, 132.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2714/8000 [00:19<00:38, 138.22it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|████▌      | 42/100 [00:00<00:00, 414.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▍          | 2743/8000 [00:20<00:39, 131.78it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 399.87it/s]\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2767/8000 [00:20<00:38, 136.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2782/8000 [00:20<00:40, 128.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▌          | 2799/8000 [00:20<00:39, 132.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                   | 62/8000 [00:00<00:49, 161.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▋          | 2822/8000 [00:20<00:35, 146.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 93/8000 [00:00<00:39, 198.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2844/8000 [00:20<00:34, 147.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                  | 132/8000 [00:00<00:31, 249.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▋          | 2860/8000 [00:20<00:35, 144.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 179/8000 [00:00<00:28, 271.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2877/8000 [00:20<00:35, 143.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                  | 214/8000 [00:00<00:27, 281.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|█████▊          | 2901/8000 [00:21<00:33, 150.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                 | 262/8000 [00:00<00:23, 329.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▊          | 2925/8000 [00:21<00:30, 164.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                 | 305/8000 [00:01<00:23, 333.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2952/8000 [00:21<00:30, 163.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                 | 352/8000 [00:01<00:24, 316.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|█████▉          | 2985/8000 [00:21<00:34, 144.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                 | 387/8000 [00:01<00:23, 323.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3022/8000 [00:21<00:35, 138.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                 | 434/8000 [00:01<00:24, 313.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3037/8000 [00:22<00:37, 132.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                 | 476/8000 [00:01<00:22, 336.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████          | 3059/8000 [00:22<00:34, 141.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                | 529/8000 [00:01<00:22, 330.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3091/8000 [00:22<00:37, 130.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                | 582/8000 [00:01<00:22, 333.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▏         | 3119/8000 [00:22<00:34, 140.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                | 618/8000 [00:02<00:23, 319.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3141/8000 [00:22<00:35, 138.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                | 659/8000 [00:02<00:21, 336.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▎         | 3159/8000 [00:22<00:36, 131.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                | 710/8000 [00:02<00:23, 315.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▎         | 3177/8000 [00:23<00:37, 129.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▎                               | 750/8000 [00:02<00:22, 319.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3198/8000 [00:23<00:36, 129.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                               | 797/8000 [00:02<00:24, 298.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3221/8000 [00:23<00:34, 136.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▋                               | 840/8000 [00:02<00:25, 281.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▍         | 3240/8000 [00:23<00:35, 132.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                               | 881/8000 [00:03<00:26, 268.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3261/8000 [00:23<00:33, 141.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|████                               | 918/8000 [00:03<00:25, 278.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▌         | 3289/8000 [00:24<00:37, 124.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 947/8000 [00:03<00:25, 272.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|██████▋         | 3324/8000 [00:24<00:35, 130.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                              | 986/8000 [00:03<00:24, 289.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3371/8000 [00:25<00:55, 83.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▎                             | 1020/8000 [00:03<00:24, 283.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3386/8000 [00:25<00:54, 85.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▍                             | 1058/8000 [00:03<00:23, 291.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3401/8000 [00:25<00:53, 85.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                             | 1102/8000 [00:03<00:21, 323.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3420/8000 [00:25<00:48, 94.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                             | 1139/8000 [00:03<00:21, 322.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3440/8000 [00:25<00:43, 104.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|████▉                             | 1174/8000 [00:03<00:21, 320.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|██████▉         | 3459/8000 [00:25<00:41, 109.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                            | 1217/8000 [00:04<00:24, 282.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|██████▉         | 3481/8000 [00:26<00:38, 116.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▎                            | 1254/8000 [00:04<00:22, 295.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3500/8000 [00:26<00:36, 122.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                            | 1295/8000 [00:04<00:24, 275.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3520/8000 [00:26<00:36, 123.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▋                            | 1330/8000 [00:04<00:23, 287.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████         | 3541/8000 [00:26<00:32, 136.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1376/8000 [00:04<00:20, 322.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████         | 3562/8000 [00:26<00:31, 141.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████                            | 1415/8000 [00:04<00:20, 327.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3580/8000 [00:26<00:32, 136.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                           | 1459/8000 [00:04<00:19, 334.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3601/8000 [00:26<00:31, 139.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▎                           | 1499/8000 [00:04<00:19, 337.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▏        | 3623/8000 [00:26<00:29, 148.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                           | 1538/8000 [00:05<00:18, 340.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3644/8000 [00:27<00:29, 146.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▋                           | 1577/8000 [00:05<00:18, 350.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▎        | 3663/8000 [00:27<00:30, 142.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                           | 1613/8000 [00:05<00:19, 325.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3691/8000 [00:27<00:25, 167.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████                           | 1652/8000 [00:05<00:19, 333.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▍        | 3714/8000 [00:27<00:25, 167.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                          | 1692/8000 [00:05<00:18, 341.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3750/8000 [00:27<00:28, 147.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▎                          | 1729/8000 [00:05<00:18, 340.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▌        | 3776/8000 [00:28<00:33, 125.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1765/8000 [00:05<00:18, 338.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|███████▌        | 3809/8000 [00:28<00:38, 107.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▋                          | 1805/8000 [00:05<00:18, 341.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3824/8000 [00:28<00:41, 99.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▊                          | 1849/8000 [00:06<00:19, 316.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3841/8000 [00:28<00:42, 97.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████                          | 1898/8000 [00:06<00:19, 312.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3855/8000 [00:29<00:43, 96.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1935/8000 [00:06<00:19, 315.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3871/8000 [00:29<00:42, 97.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▍                         | 1975/8000 [00:06<00:19, 312.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3888/8000 [00:29<00:41, 99.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                         | 2007/8000 [00:06<00:19, 300.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3908/8000 [00:29<00:37, 107.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▋                         | 2045/8000 [00:06<00:18, 313.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▊        | 3926/8000 [00:29<00:39, 102.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                         | 2090/8000 [00:06<00:17, 334.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|███████▉        | 3949/8000 [00:29<00:36, 110.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████                         | 2133/8000 [00:06<00:16, 349.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3964/8000 [00:29<00:35, 113.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                        | 2178/8000 [00:07<00:15, 364.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3981/8000 [00:30<00:34, 117.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▍                        | 2215/8000 [00:07<00:16, 359.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|███████▉        | 3993/8000 [00:30<00:35, 113.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▌                        | 2261/8000 [00:07<00:15, 376.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4011/8000 [00:30<00:32, 124.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▊                        | 2314/8000 [00:07<00:16, 345.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████        | 4027/8000 [00:30<00:31, 124.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                        | 2355/8000 [00:07<00:18, 311.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████        | 4051/8000 [00:30<00:28, 138.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▏                       | 2394/8000 [00:07<00:17, 321.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4077/8000 [00:30<00:26, 149.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                       | 2432/8000 [00:07<00:16, 328.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▏       | 4094/8000 [00:30<00:27, 143.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▌                       | 2478/8000 [00:07<00:17, 317.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▏       | 4121/8000 [00:31<00:31, 121.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▊                       | 2534/8000 [00:08<00:17, 321.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4143/8000 [00:31<00:28, 133.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▉                       | 2574/8000 [00:08<00:16, 324.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▎       | 4170/8000 [00:31<00:24, 157.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████                       | 2615/8000 [00:08<00:15, 339.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▍       | 4196/8000 [00:31<00:23, 164.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2659/8000 [00:08<00:14, 358.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▍       | 4223/8000 [00:31<00:21, 177.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▌                      | 2713/8000 [00:08<00:16, 328.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▌       | 4257/8000 [00:31<00:22, 164.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                      | 2755/8000 [00:08<00:15, 348.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▌       | 4292/8000 [00:32<00:23, 158.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▉                      | 2808/8000 [00:08<00:14, 346.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4313/8000 [00:32<00:23, 158.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████                      | 2846/8000 [00:09<00:14, 345.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4331/8000 [00:32<00:23, 155.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▎                     | 2897/8000 [00:09<00:14, 341.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|████████▋       | 4356/8000 [00:32<00:22, 159.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▍                     | 2934/8000 [00:09<00:14, 346.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4387/8000 [00:32<00:25, 140.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▋                     | 2973/8000 [00:09<00:14, 346.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4407/8000 [00:32<00:25, 140.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3024/8000 [00:09<00:15, 327.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|████████▊       | 4427/8000 [00:33<00:25, 141.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████                     | 3059/8000 [00:09<00:15, 323.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4443/8000 [00:33<00:25, 139.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▏                    | 3099/8000 [00:09<00:14, 330.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|████████▉       | 4470/8000 [00:33<00:23, 153.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▎                    | 3136/8000 [00:09<00:14, 332.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4501/8000 [00:33<00:26, 131.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▍                    | 3174/8000 [00:10<00:14, 325.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████       | 4516/8000 [00:33<00:27, 127.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3213/8000 [00:10<00:14, 337.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4538/8000 [00:33<00:24, 139.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▊                    | 3255/8000 [00:10<00:15, 303.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████       | 4554/8000 [00:34<00:30, 111.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▉                    | 3293/8000 [00:10<00:15, 305.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4575/8000 [00:34<00:28, 120.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▏                   | 3337/8000 [00:10<00:13, 333.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▏      | 4600/8000 [00:34<00:24, 139.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▍                   | 3386/8000 [00:10<00:14, 321.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4629/8000 [00:34<00:20, 160.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▌                   | 3433/8000 [00:10<00:13, 350.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4647/8000 [00:34<00:22, 146.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▋                   | 3470/8000 [00:10<00:12, 353.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▎      | 4665/8000 [00:34<00:22, 147.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▉                   | 3528/8000 [00:11<00:12, 352.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▎      | 4687/8000 [00:34<00:21, 154.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▏                  | 3568/8000 [00:11<00:12, 346.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4703/8000 [00:35<00:22, 145.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▎                  | 3614/8000 [00:11<00:12, 359.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4723/8000 [00:35<00:22, 148.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▌                  | 3663/8000 [00:11<00:12, 351.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▍      | 4744/8000 [00:35<00:20, 155.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▋                  | 3699/8000 [00:11<00:12, 346.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4766/8000 [00:35<00:19, 165.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▉                  | 3736/8000 [00:11<00:12, 349.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▌      | 4797/8000 [00:35<00:22, 141.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████                  | 3790/8000 [00:11<00:12, 334.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|█████████▋      | 4832/8000 [00:36<00:23, 132.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▎                 | 3840/8000 [00:11<00:11, 372.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▋      | 4852/8000 [00:36<00:22, 138.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▌                 | 3888/8000 [00:12<00:12, 335.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4883/8000 [00:36<00:23, 133.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3927/8000 [00:12<00:11, 342.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|█████████▊      | 4905/8000 [00:36<00:21, 143.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████████████▊                 | 3966/8000 [00:12<00:12, 330.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▊      | 4936/8000 [00:36<00:18, 166.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████                 | 4001/8000 [00:12<00:12, 324.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4962/8000 [00:36<00:22, 136.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▏                | 4038/8000 [00:12<00:12, 324.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4982/8000 [00:37<00:22, 133.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▎                | 4087/8000 [00:12<00:10, 357.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|█████████▉      | 4997/8000 [00:37<00:26, 112.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▌                | 4139/8000 [00:12<00:11, 330.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████      | 5012/8000 [00:37<00:28, 103.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▊                | 4187/8000 [00:12<00:10, 363.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5026/8000 [00:37<00:29, 99.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████                | 4239/8000 [00:13<00:10, 342.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5040/8000 [00:37<00:36, 81.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▎               | 4301/8000 [00:13<00:10, 354.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5065/8000 [00:38<00:29, 97.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4350/8000 [00:13<00:10, 337.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5087/8000 [00:38<00:27, 106.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▋               | 4401/8000 [00:13<00:09, 374.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▏     | 5110/8000 [00:38<00:25, 113.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████████████▉               | 4443/8000 [00:13<00:09, 366.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5127/8000 [00:38<00:28, 102.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████               | 4497/8000 [00:13<00:10, 342.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▎     | 5148/8000 [00:38<00:26, 107.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▎              | 4545/8000 [00:14<00:10, 323.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5166/8000 [00:38<00:24, 116.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▍              | 4582/8000 [00:14<00:10, 324.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▎     | 5182/8000 [00:39<00:23, 118.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▌              | 4617/8000 [00:14<00:10, 327.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5200/8000 [00:39<00:22, 125.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▊              | 4659/8000 [00:14<00:09, 334.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▍     | 5219/8000 [00:39<00:21, 130.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4700/8000 [00:14<00:09, 343.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▍     | 5242/8000 [00:39<00:19, 140.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▏             | 4748/8000 [00:14<00:08, 371.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5264/8000 [00:39<00:19, 143.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▎             | 4787/8000 [00:14<00:08, 358.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5280/8000 [00:39<00:20, 134.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▌             | 4843/8000 [00:14<00:08, 350.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▌     | 5301/8000 [00:39<00:18, 144.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▊             | 4887/8000 [00:14<00:08, 364.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|██████████▋     | 5320/8000 [00:39<00:18, 142.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████             | 4944/8000 [00:15<00:08, 368.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5344/8000 [00:40<00:16, 156.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▏            | 4987/8000 [00:15<00:08, 337.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▋     | 5366/8000 [00:40<00:15, 167.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5026/8000 [00:15<00:09, 328.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|██████████▊     | 5385/8000 [00:40<00:16, 162.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▌            | 5075/8000 [00:15<00:09, 308.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5405/8000 [00:40<00:15, 162.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▊            | 5123/8000 [00:15<00:08, 329.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▊     | 5422/8000 [00:40<00:16, 154.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▉            | 5160/8000 [00:15<00:08, 328.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5441/8000 [00:40<00:16, 152.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████            | 5201/8000 [00:15<00:08, 331.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5461/8000 [00:40<00:16, 153.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5241/8000 [00:16<00:08, 327.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|██████████▉     | 5477/8000 [00:40<00:17, 147.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▍           | 5281/8000 [00:16<00:08, 330.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|██████████▉     | 5493/8000 [00:41<00:18, 138.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▌           | 5323/8000 [00:16<00:08, 334.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5508/8000 [00:41<00:19, 127.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▊           | 5380/8000 [00:16<00:07, 329.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5522/8000 [00:41<00:19, 124.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████           | 5417/8000 [00:16<00:07, 337.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████     | 5545/8000 [00:41<00:18, 133.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▏          | 5453/8000 [00:16<00:07, 328.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5565/8000 [00:41<00:18, 129.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▍          | 5501/8000 [00:16<00:07, 352.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5588/8000 [00:41<00:16, 145.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▌          | 5542/8000 [00:16<00:06, 364.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▏    | 5612/8000 [00:41<00:15, 157.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▋          | 5586/8000 [00:17<00:07, 316.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▎    | 5637/8000 [00:42<00:13, 169.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▉          | 5635/8000 [00:17<00:07, 302.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5657/8000 [00:42<00:13, 167.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5670/8000 [00:17<00:07, 307.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▎    | 5675/8000 [00:42<00:14, 164.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▏         | 5705/8000 [00:17<00:07, 304.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5701/8000 [00:42<00:17, 133.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▍         | 5744/8000 [00:17<00:07, 321.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▍    | 5719/8000 [00:42<00:16, 134.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▌         | 5789/8000 [00:17<00:06, 337.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▍    | 5743/8000 [00:42<00:15, 147.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▊         | 5831/8000 [00:17<00:06, 356.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5763/8000 [00:42<00:15, 147.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████████████████▉         | 5882/8000 [00:17<00:06, 338.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|███████████▌    | 5784/8000 [00:43<00:14, 156.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5925/8000 [00:18<00:05, 348.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▌    | 5805/8000 [00:43<00:13, 162.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▎        | 5967/8000 [00:18<00:05, 357.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5826/8000 [00:43<00:12, 168.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6009/8000 [00:18<00:05, 347.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|███████████▋    | 5854/8000 [00:43<00:14, 147.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6045/8000 [00:18<00:05, 337.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5885/8000 [00:43<00:15, 139.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6082/8000 [00:18<00:05, 340.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5903/8000 [00:43<00:14, 140.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████        | 6132/8000 [00:18<00:05, 330.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▊    | 5920/8000 [00:44<00:15, 138.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6172/8000 [00:18<00:05, 340.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5940/8000 [00:44<00:14, 142.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6223/8000 [00:19<00:05, 324.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|███████████▉    | 5957/8000 [00:44<00:14, 144.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6260/8000 [00:19<00:05, 329.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5973/8000 [00:44<00:14, 141.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6310/8000 [00:19<00:05, 316.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|███████████▉    | 5990/8000 [00:44<00:16, 123.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6347/8000 [00:19<00:05, 317.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6009/8000 [00:44<00:17, 116.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6394/8000 [00:19<00:05, 307.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████    | 6039/8000 [00:44<00:16, 120.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6439/8000 [00:19<00:05, 297.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6063/8000 [00:45<00:14, 137.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6483/8000 [00:19<00:04, 317.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6086/8000 [00:45<00:13, 144.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▋      | 6527/8000 [00:20<00:04, 304.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▏   | 6106/8000 [00:45<00:12, 147.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6569/8000 [00:20<00:04, 309.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6129/8000 [00:45<00:11, 159.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████      | 6605/8000 [00:20<00:04, 306.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6148/8000 [00:45<00:12, 153.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6636/8000 [00:20<00:04, 294.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|████████████▎   | 6176/8000 [00:45<00:12, 144.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6675/8000 [00:20<00:04, 310.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6210/8000 [00:46<00:12, 141.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6710/8000 [00:20<00:04, 309.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▍   | 6232/8000 [00:46<00:11, 148.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▋     | 6761/8000 [00:20<00:03, 349.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|████████████▌   | 6257/8000 [00:46<00:10, 160.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6801/8000 [00:20<00:03, 355.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6291/8000 [00:46<00:12, 135.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████     | 6844/8000 [00:20<00:03, 375.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▌   | 6311/8000 [00:46<00:13, 127.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6888/8000 [00:21<00:02, 391.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6329/8000 [00:47<00:14, 119.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6951/8000 [00:21<00:02, 387.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|████████████▋   | 6354/8000 [00:47<00:11, 138.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6992/8000 [00:21<00:02, 383.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6385/8000 [00:47<00:13, 121.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7044/8000 [00:21<00:02, 353.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6403/8000 [00:47<00:13, 116.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7098/8000 [00:21<00:02, 341.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|████████████▊   | 6423/8000 [00:47<00:13, 120.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7136/8000 [00:21<00:02, 343.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6444/8000 [00:47<00:13, 119.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7191/8000 [00:21<00:02, 341.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6457/8000 [00:48<00:14, 109.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7231/8000 [00:22<00:02, 346.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|████████████▉   | 6482/8000 [00:48<00:12, 120.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7268/8000 [00:22<00:02, 333.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████   | 6505/8000 [00:48<00:11, 124.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████   | 7315/8000 [00:22<00:01, 360.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6524/8000 [00:48<00:11, 125.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7357/8000 [00:22<00:01, 355.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████   | 6546/8000 [00:48<00:10, 134.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▍  | 7396/8000 [00:22<00:01, 360.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6565/8000 [00:48<00:11, 130.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7433/8000 [00:22<00:01, 342.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▏  | 6591/8000 [00:49<00:12, 114.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▊  | 7476/8000 [00:22<00:01, 361.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▏  | 6614/8000 [00:49<00:10, 128.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7514/8000 [00:22<00:01, 355.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6634/8000 [00:49<00:10, 131.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7561/8000 [00:22<00:01, 339.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6650/8000 [00:49<00:11, 114.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7600/8000 [00:23<00:01, 349.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|█████████████▎  | 6669/8000 [00:49<00:11, 114.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▍ | 7642/8000 [00:23<00:01, 355.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6688/8000 [00:49<00:10, 122.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7696/8000 [00:23<00:00, 349.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6702/8000 [00:50<00:11, 116.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████████████████████▉ | 7742/8000 [00:23<00:00, 326.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6716/8000 [00:50<00:10, 119.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7784/8000 [00:23<00:00, 341.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|█████████████▍  | 6738/8000 [00:50<00:09, 134.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▏| 7823/8000 [00:23<00:00, 352.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6766/8000 [00:50<00:10, 114.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7867/8000 [00:23<00:00, 362.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6783/8000 [00:50<00:10, 120.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7908/8000 [00:24<00:00, 311.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▌  | 6802/8000 [00:50<00:09, 124.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7941/8000 [00:24<00:00, 312.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|█████████████▋  | 6826/8000 [00:51<00:08, 134.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8001it [00:24, 329.34it/s]                                                  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:27,  4.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:37,  2.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:27,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:27,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:53,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:35,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:04,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:09<01:57,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<01:54,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:54,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:11<01:51,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:50,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:54,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:14<01:53,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:51,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:49,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:16<01:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:55,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▋  | 6853/8000 [00:51<00:08, 137.99it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:54,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6881/8000 [00:51<00:07, 150.25it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:51,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|█████████████▊  | 6913/8000 [00:51<00:08, 124.23it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:51,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▊  | 6934/8000 [00:51<00:08, 119.90it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:20<01:49,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6951/8000 [00:52<00:09, 113.82it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:46,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|█████████████▉  | 6968/8000 [00:52<00:10, 102.89it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:44,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6998/8000 [00:52<00:10, 91.50it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:50,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7016/8000 [00:52<00:10, 95.75it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:53,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7032/8000 [00:53<00:11, 87.69it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:23<01:59,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7049/8000 [00:53<00:11, 83.45it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:24<02:04,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7066/8000 [00:53<00:11, 84.21it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:25<02:03,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7087/8000 [00:53<00:09, 97.84it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:25<02:04,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7102/8000 [00:53<00:09, 94.35it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:26<02:05,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7128/8000 [00:54<00:09, 94.07it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:27<02:06,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7145/8000 [00:54<00:09, 90.24it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:28<02:05,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7167/8000 [00:54<00:09, 88.83it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:28<02:04,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7191/8000 [00:54<00:08, 92.06it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:29<02:03,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7206/8000 [00:55<00:08, 89.69it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:30<02:03,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7223/8000 [00:55<00:08, 92.01it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:31<02:03,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7241/8000 [00:55<00:07, 97.00it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:32<02:03,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7260/8000 [00:55<00:08, 88.93it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:32<02:02,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7283/8000 [00:55<00:07, 100.72it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:33<02:00,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|██████████████▌ | 7302/8000 [00:55<00:06, 102.35it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:34<02:00,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7323/8000 [00:56<00:06, 105.54it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:35<02:00,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7343/8000 [00:56<00:05, 111.07it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:36<01:59,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▋ | 7364/8000 [00:56<00:05, 125.06it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:36<01:58,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|██████████████▊ | 7388/8000 [00:56<00:04, 143.11it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:37<01:57,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▊ | 7420/8000 [00:56<00:04, 126.91it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:38<01:57,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7445/8000 [00:56<00:03, 142.21it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:39<01:56,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|██████████████▉ | 7466/8000 [00:57<00:03, 149.19it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:39<01:54,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|██████████████▉ | 7488/8000 [00:57<00:03, 155.18it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:40<01:54,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████ | 7526/8000 [00:57<00:03, 157.30it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:41<01:52,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████ | 7562/8000 [00:57<00:02, 156.15it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:42<01:49,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7581/8000 [00:57<00:02, 155.61it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:42<01:49,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7601/8000 [00:57<00:02, 158.90it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:43<01:48,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|███████████████▏| 7620/8000 [00:58<00:02, 153.25it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:44<01:49,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7642/8000 [00:58<00:02, 161.76it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:45<01:49,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7670/8000 [00:58<00:02, 141.34it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:46<01:48,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▎| 7686/8000 [00:58<00:02, 132.58it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:46<01:48,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|███████████████▍| 7718/8000 [00:58<00:01, 160.78it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:47<01:40,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7754/8000 [00:58<00:01, 152.89it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:48<01:36,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7775/8000 [00:59<00:01, 155.70it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:48<01:38,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|███████████████▌| 7795/8000 [00:59<00:01, 160.24it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:49<01:34,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7816/8000 [00:59<00:01, 168.50it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [00:50<01:32,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7842/8000 [00:59<00:01, 146.04it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [00:50<01:30,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▋| 7858/8000 [00:59<00:01, 136.63it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [00:51<01:32,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|███████████████▊| 7877/8000 [00:59<00:00, 142.94it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [00:52<01:32,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7900/8000 [00:59<00:00, 160.66it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [00:53<01:29,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▊| 7925/8000 [01:00<00:00, 173.85it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [00:53<01:26,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|███████████████▉| 7959/8000 [01:00<00:00, 143.48it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▌                              | 73/200 [00:54<01:26,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|███████████████▉| 7980/8000 [01:00<00:00, 145.91it/s]\u001b[A\u001b[A\n",
+      "training:  37%|█████████████████▊                              | 74/200 [00:55<01:25,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████| 8000/8000 [01:00<00:00, 132.05it/s]\n"
+      "training:  38%|██████████████████                              | 75/200 [00:55<01:24,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▏                             | 76/200 [00:56<01:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▍                             | 77/200 [00:57<01:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████████▋                             | 78/200 [00:57<01:22,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████████▉                             | 79/200 [00:59<01:30,  1.33it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 28.22it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 81it [00:00, 113.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 162it [00:01, 229.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 244it [00:01, 342.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 323it [00:01, 438.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 405it [00:01, 527.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 489it [00:01, 605.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 572it [00:01, 663.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 656it [00:01, 709.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 428.23it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|████▍      | 40/100 [00:00<00:00, 397.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 350.20it/s]\n",
+      "\n",
+      "Nyquist Samples:  75%|████████████████████████████████▎          | 3/4 [08:53<03:08, 188.92s/it]\u001b[A"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "concatenating...\n"
+      "initial loss -3.6398558299894943\n",
+      "2999: -5.8294069\r"
      ]
     },
     {
@@ -52043,4686 +52305,46224 @@
      "text": [
       "\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:59,  4.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:24,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:08,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:05,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:30,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:07,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:44,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:34,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<23:46,  7.17s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:11<02:29,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:27,  3.78s/it]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:12<02:28,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:09<08:44,  2.66s/it]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:13<02:26,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<06:58,  2.13s/it]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:14<02:24,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:12<05:59,  1.84s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:15<02:23,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:13<05:27,  1.69s/it]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:15<02:21,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:15<04:58,  1.55s/it]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:16<02:19,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:16<04:46,  1.49s/it]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:17<02:19,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:17<04:37,  1.45s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:18<02:18,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:19<04:31,  1.43s/it]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:18<02:17,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:20<04:22,  1.39s/it]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:19<02:16,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:21<04:17,  1.37s/it]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:20<02:15,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:23<04:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:21<02:15,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:24<04:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:21<02:14,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:25<04:12,  1.36s/it]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:22<02:14,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:27<04:08,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:23<02:13,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:28<04:07,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:24<02:17,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:30<04:05,  1.35s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:25<02:20,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:31<04:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:25<02:15,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:32<03:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:26<02:13,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:33<03:50,  1.29s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:27<02:11,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:35<03:45,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:28<02:09,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:36<03:42,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:28<02:09,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:37<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:29<02:07,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:38<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:30<02:06,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:39<03:37,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:31<02:05,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:41<03:34,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:32<02:04,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:42<03:34,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:32<02:03,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:43<03:35,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:33<02:02,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:45<03:34,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:34<02:01,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:46<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:35<02:00,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:47<03:31,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:35<01:59,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:48<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:36<02:04,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:50<03:27,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:37<02:03,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:51<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:38<02:05,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [00:52<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:39<02:01,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [00:53<03:24,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:39<01:59,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [00:55<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:40<02:00,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [00:56<03:24,  1.27s/it]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:41<02:04,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [00:57<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:42<02:00,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [00:58<03:17,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:43<01:58,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:00<03:19,  1.26s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:43<01:56,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:01<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:44<01:54,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:02<03:13,  1.24s/it]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:45<02:01,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:03<03:10,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:46<02:02,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:04<03:06,  1.21s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:47<02:04,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:06<03:07,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:48<02:05,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:07<03:10,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:49<02:04,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:08<03:08,  1.25s/it]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:49<02:03,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:09<03:05,  1.23s/it]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:50<02:03,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:12<03:31,  1.42s/it]\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:51<02:03,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:52<02:02,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:54<01:58,  1.16it/s]\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 16.80it/s]\n",
+      "Evaluating NLL: 25it [00:01, 22.87it/s]\n",
       "\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.34s/it]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 46it [00:01, 44.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 64it [00:01, 86.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 101it [00:01, 105.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 117it [00:01, 157.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 151it [00:01, 163.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 179it [00:01, 244.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 201it [00:01, 221.18it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 253it [00:01, 348.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 250it [00:01, 273.75it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 326it [00:01, 436.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 297it [00:01, 313.37it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 398it [00:01, 505.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 343it [00:02, 346.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 468it [00:01, 555.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 391it [00:02, 379.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 545it [00:01, 613.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 441it [00:02, 407.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 619it [00:01, 647.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 488it [00:02, 410.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 691it [00:01, 621.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 540it [00:02, 438.94it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:02, 381.97it/s]\n",
       "\n",
-      "Evaluating NLL: 588it [00:02, 440.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 637it [00:02, 453.42it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 685it [00:02, 440.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|███▍       | 31/100 [00:00<00:00, 309.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 731it [00:02, 441.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  62%|██████▊    | 62/100 [00:00<00:00, 299.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 259.91it/s]\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 262.29it/s]\n",
       "\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  22%|▏| 22/100 [00:00<00:00, 214\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                      | 3/8000 [00:00<04:30, 29.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 210\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                      | 8/8000 [00:00<04:15, 31.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:00<00:00, 226\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 12/8000 [00:00<06:10, 21.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 22\n",
+      "Generating signals:   0%|                                     | 20/8000 [00:00<04:07, 32.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples:  75%|██████████████████▊      | 3/4 [19:09<06:35, 395.24s/it]\u001b[A"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.061796736926216\n",
-      "2999: -5.8457216\r"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Generating signals:   0%|                                     | 25/8000 [00:00<04:24, 30.12it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   0%|▏                                    | 29/8000 [00:01<04:51, 27.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 33/8000 [00:01<04:36, 28.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<24:29,  7.38s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 39/8000 [00:01<03:55, 33.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:08<12:54,  3.91s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 49/8000 [00:01<02:54, 45.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:15,  2.82s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 56/8000 [00:01<02:45, 47.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:11<07:24,  2.27s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 61/8000 [00:01<02:58, 44.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:13<06:28,  1.99s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 67/8000 [00:01<02:55, 45.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:14<06:06,  1.89s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 75/8000 [00:01<02:32, 51.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:16<05:33,  1.73s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 82/8000 [00:02<02:51, 46.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:17<05:13,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 89/8000 [00:02<03:02, 43.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:19<04:57,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 95/8000 [00:02<03:03, 43.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:20<04:51,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 102/8000 [00:02<03:14, 40.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:22<04:49,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 109/8000 [00:02<02:58, 44.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:23<04:44,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▌                                   | 114/8000 [00:02<03:02, 43.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:25<04:41,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▌                                   | 119/8000 [00:03<03:33, 36.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:26<04:37,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 126/8000 [00:03<03:06, 42.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:28<04:39,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 133/8000 [00:03<02:58, 44.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:29<04:37,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 140/8000 [00:03<02:43, 48.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:31<04:43,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 147/8000 [00:03<02:42, 48.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:32<04:41,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 153/8000 [00:03<02:47, 46.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:34<04:35,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 162/8000 [00:03<02:24, 54.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:35<04:32,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 169/8000 [00:04<02:49, 46.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:37<04:31,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 175/8000 [00:04<02:50, 45.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:38<04:33,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 182/8000 [00:04<02:35, 50.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:40<04:30,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 189/8000 [00:04<03:15, 39.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:42<04:30,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 194/8000 [00:04<03:46, 34.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:43<04:38,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▉                                   | 198/8000 [00:05<04:44, 27.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:45<04:37,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 204/8000 [00:05<04:09, 31.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:46<04:29,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 209/8000 [00:05<04:16, 30.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:48<04:23,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 218/8000 [00:05<03:19, 39.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:50<04:45,  1.67s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 223/8000 [00:05<03:46, 34.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:51<04:35,  1.62s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 227/8000 [00:05<04:50, 26.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:53<04:23,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 233/8000 [00:06<04:07, 31.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:55<04:35,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 238/8000 [00:06<03:48, 33.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:56<04:45,  1.71s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 242/8000 [00:06<03:51, 33.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:58<04:35,  1.66s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 248/8000 [00:06<03:25, 37.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:59<04:25,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 256/8000 [00:06<02:50, 45.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:01<04:17,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 263/8000 [00:06<02:36, 49.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:02<04:10,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 270/8000 [00:06<02:50, 45.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:05<04:51,  1.80s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 277/8000 [00:07<02:59, 43.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:07<05:15,  1.96s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 282/8000 [00:07<03:04, 41.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:09<04:52,  1.83s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 287/8000 [00:07<03:31, 36.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:10<04:34,  1.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 293/8000 [00:07<03:16, 39.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:12<04:17,  1.63s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 304/8000 [00:07<02:25, 52.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:13<04:06,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 312/8000 [00:07<02:35, 49.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:14<04:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 321/8000 [00:07<02:20, 54.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:16<03:56,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 328/8000 [00:08<02:30, 50.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:17<03:54,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 334/8000 [00:08<02:51, 44.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:19<03:49,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 341/8000 [00:08<02:44, 46.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:20<03:48,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 346/8000 [00:08<02:54, 43.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▎                      | 49/200 [01:22<03:46,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 353/8000 [00:08<02:58, 42.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  25%|███████▌                      | 50/200 [01:23<03:43,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 364/8000 [00:08<02:20, 54.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▋                      | 51/200 [01:25<03:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 370/8000 [00:08<02:23, 53.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▊                      | 52/200 [01:26<03:36,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 376/8000 [00:09<02:23, 52.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  26%|███████▉                      | 53/200 [01:28<03:37,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 383/8000 [00:09<03:00, 42.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  27%|████████                      | 54/200 [01:29<03:33,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 389/8000 [00:09<02:55, 43.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▎                     | 55/200 [01:31<03:31,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 396/8000 [00:09<03:46, 33.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▍                     | 56/200 [01:32<03:31,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 401/8000 [00:09<03:31, 35.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  28%|████████▌                     | 57/200 [01:34<03:28,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 408/8000 [00:10<03:24, 37.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  29%|████████▋                     | 58/200 [01:35<03:27,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 413/8000 [00:10<03:24, 37.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|████████▊                     | 59/200 [01:36<03:25,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 420/8000 [00:10<03:02, 41.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████                     | 60/200 [01:38<03:25,  1.47s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 425/8000 [00:10<03:15, 38.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  30%|█████████▏                    | 61/200 [01:39<03:21,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 431/8000 [00:10<03:29, 36.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  31%|█████████▎                    | 62/200 [01:41<03:21,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▉                                  | 437/8000 [00:10<03:15, 38.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▍                    | 63/200 [01:42<03:19,  1.46s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|█▉                                  | 442/8000 [00:10<03:32, 35.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  32%|█████████▌                    | 64/200 [01:45<03:44,  1.65s/it]\n",
+      "Generating signals:   6%|██                                  | 447/8000 [00:11<03:30, 35.92it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   6%|██                                  | 451/8000 [00:11<03:55, 32.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 457/8000 [00:11<04:06, 30.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.57s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                  | 462/8000 [00:11<03:49, 32.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.69it/s]\n",
+      "Generating signals:   6%|██                                  | 468/8000 [00:11<03:30, 35.77it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   6%|██▏                                 | 473/8000 [00:11<03:53, 32.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 478/8000 [00:12<03:48, 32.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.45s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 488/8000 [00:12<03:11, 39.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 35it [00:01, 31.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                 | 494/8000 [00:12<03:20, 37.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 68it [00:01, 64.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 503/8000 [00:12<03:08, 39.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 100it [00:01, 100.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 508/8000 [00:12<03:10, 39.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 132it [00:01, 136.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▎                                 | 518/8000 [00:12<02:33, 48.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 169it [00:01, 179.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                 | 526/8000 [00:13<02:44, 45.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 201it [00:02, 205.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 531/8000 [00:13<02:53, 42.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 242it [00:02, 251.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 538/8000 [00:13<02:41, 46.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 276it [00:02, 269.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 544/8000 [00:13<02:57, 41.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 312it [00:02, 292.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 551/8000 [00:13<02:43, 45.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 351it [00:02, 316.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 559/8000 [00:13<02:47, 44.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 389it [00:02, 332.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 565/8000 [00:13<02:39, 46.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 428it [00:02, 344.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 571/8000 [00:14<03:10, 39.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 465it [00:02, 351.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 578/8000 [00:14<03:37, 34.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 508it [00:02, 373.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 584/8000 [00:14<03:32, 34.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 547it [00:03, 365.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 588/8000 [00:14<04:36, 26.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 586it [00:03, 371.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 594/8000 [00:15<04:08, 29.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 624it [00:03, 368.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 600/8000 [00:15<03:45, 32.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 663it [00:03, 373.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 607/8000 [00:15<03:51, 32.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 706it [00:03, 389.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 613/8000 [00:15<03:58, 30.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 746it [00:03, 366.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 618/8000 [00:15<03:44, 32.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:03, 217.68it/s]\n",
+      "Generating signals:   8%|██▊                                 | 622/8000 [00:15<03:41, 33.37it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   8%|██▊                                 | 627/8000 [00:15<03:31, 34.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 632/8000 [00:16<03:21, 36.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  12%| | 12/100 [00:00<00:00, 119\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 637/8000 [00:16<03:34, 34.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  24%|▏| 24/100 [00:00<00:00, 117\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 642/8000 [00:16<03:25, 35.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 118\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 647/8000 [00:16<03:41, 33.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 131\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 652/8000 [00:16<03:29, 35.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  66%|▋| 66/100 [00:00<00:00, 123\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 658/8000 [00:16<03:12, 38.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  82%|▊| 82/100 [00:00<00:00, 134\u001b[A\u001b[A\n",
+      "Generating signals:   8%|███                                 | 667/8000 [00:16<02:35, 47.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 12\n",
+      "Generating signals:   8%|███                                 | 675/8000 [00:17<02:14, 54.37it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   9%|███                                 | 682/8000 [00:17<02:11, 55.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                                 | 689/8000 [00:17<02:45, 44.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                            | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                                | 696/8000 [00:17<02:47, 43.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 702/8000 [00:17<02:53, 42.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 707/8000 [00:17<02:51, 42.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 713/8000 [00:17<02:55, 41.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 720/8000 [00:18<03:09, 38.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 727/8000 [00:18<03:04, 39.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 734/8000 [00:18<03:02, 39.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 741/8000 [00:18<03:01, 40.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 751/8000 [00:18<02:20, 51.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 759/8000 [00:19<02:34, 46.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 765/8000 [00:19<02:46, 43.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 771/8000 [00:19<03:02, 39.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 776/8000 [00:19<03:07, 38.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 783/8000 [00:19<02:47, 43.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 793/8000 [00:19<03:06, 38.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 799/8000 [00:20<02:54, 41.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 804/8000 [00:20<02:59, 40.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 814/8000 [00:20<03:02, 39.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 819/8000 [00:20<03:30, 34.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 823/8000 [00:20<03:29, 34.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 827/8000 [00:20<03:47, 31.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 833/8000 [00:21<03:13, 37.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▊                                | 839/8000 [00:21<03:10, 37.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 847/8000 [00:21<02:42, 44.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 853/8000 [00:21<02:34, 46.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 865/8000 [00:21<01:57, 60.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 873/8000 [00:21<01:54, 62.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 881/8000 [00:21<02:24, 49.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 889/8000 [00:22<02:11, 54.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 895/8000 [00:22<02:20, 50.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 902/8000 [00:22<04:15, 27.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 907/8000 [00:22<04:23, 26.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 911/8000 [00:23<04:52, 24.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 915/8000 [00:23<04:29, 26.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 920/8000 [00:23<04:59, 23.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 928/8000 [00:23<03:42, 31.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 932/8000 [00:23<03:35, 32.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 937/8000 [00:23<03:52, 30.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 941/8000 [00:24<04:08, 28.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 946/8000 [00:24<04:24, 26.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 950/8000 [00:24<04:10, 28.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 956/8000 [00:24<03:31, 33.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 961/8000 [00:24<03:12, 36.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 970/8000 [00:24<02:25, 48.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 981/8000 [00:24<02:02, 57.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 988/8000 [00:25<02:13, 52.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 995/8000 [00:25<02:24, 48.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1002/8000 [00:25<02:19, 50.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1008/8000 [00:25<02:20, 49.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1015/8000 [00:25<02:13, 52.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1021/8000 [00:25<02:14, 51.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1027/8000 [00:25<02:31, 46.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 2/8000 [00:00<11:07, 11.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1040/8000 [00:26<01:58, 58.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                    | 8/8000 [00:00<04:28, 29.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1049/8000 [00:26<02:00, 57.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 16/8000 [00:00<04:28, 29.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                              | 1056/8000 [00:26<02:19, 49.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 21/8000 [00:01<08:00, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1062/8000 [00:26<02:45, 41.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 25/8000 [00:01<08:19, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1067/8000 [00:26<03:00, 38.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 27/8000 [00:01<08:28, 15.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1073/8000 [00:26<02:44, 42.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 29/8000 [00:01<10:20, 12.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▋                              | 1079/8000 [00:27<02:35, 44.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 32/8000 [00:02<09:10, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1086/8000 [00:27<02:32, 45.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 34/8000 [00:02<14:24,  9.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1093/8000 [00:27<02:51, 40.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   0%|                   | 38/8000 [00:02<10:40, 12.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1099/8000 [00:27<02:39, 43.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 42/8000 [00:02<08:25, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1105/8000 [00:27<02:41, 42.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 47/8000 [00:03<06:37, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                              | 1112/8000 [00:27<03:10, 36.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|                   | 50/8000 [00:03<06:48, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1116/8000 [00:28<03:25, 33.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 54/8000 [00:03<06:00, 22.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1121/8000 [00:28<03:12, 35.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 58/8000 [00:03<05:29, 24.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1125/8000 [00:28<03:57, 28.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 63/8000 [00:03<04:49, 27.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1132/8000 [00:28<03:09, 36.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 66/8000 [00:03<07:30, 17.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▉                              | 1138/8000 [00:28<03:13, 35.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 70/8000 [00:04<07:48, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1143/8000 [00:28<03:25, 33.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 75/8000 [00:04<07:39, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1150/8000 [00:29<03:08, 36.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 79/8000 [00:04<06:44, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1154/8000 [00:29<03:09, 36.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 84/8000 [00:04<06:50, 19.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|█████                              | 1158/8000 [00:29<03:32, 32.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 88/8000 [00:05<05:58, 22.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                              | 1165/8000 [00:29<03:06, 36.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 93/8000 [00:05<06:08, 21.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1172/8000 [00:29<02:40, 42.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                  | 98/8000 [00:05<05:13, 25.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1177/8000 [00:29<02:41, 42.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 102/8000 [00:05<05:07, 25.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1183/8000 [00:29<02:30, 45.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 106/8000 [00:05<05:09, 25.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1192/8000 [00:29<02:08, 52.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▏                 | 110/8000 [00:06<07:16, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                             | 1198/8000 [00:30<02:08, 53.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 113/8000 [00:06<08:15, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1207/8000 [00:30<02:10, 52.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 115/8000 [00:06<08:16, 15.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1213/8000 [00:30<02:11, 51.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 117/8000 [00:06<09:40, 13.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1219/8000 [00:30<02:11, 51.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   1%|▎                 | 119/8000 [00:06<09:21, 14.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▎                             | 1227/8000 [00:30<01:57, 57.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 123/8000 [00:06<07:22, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▍                             | 1234/8000 [00:30<02:53, 38.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 125/8000 [00:07<07:34, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1240/8000 [00:31<02:43, 41.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 130/8000 [00:07<07:33, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1249/8000 [00:31<02:17, 49.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 135/8000 [00:07<05:54, 22.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                             | 1255/8000 [00:31<02:51, 39.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 139/8000 [00:07<06:48, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1260/8000 [00:31<02:53, 38.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 143/8000 [00:08<07:14, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1265/8000 [00:31<02:56, 38.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 147/8000 [00:08<06:08, 21.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1276/8000 [00:31<02:17, 49.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 151/8000 [00:08<06:52, 19.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                             | 1283/8000 [00:31<02:25, 46.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 154/8000 [00:08<06:24, 20.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1288/8000 [00:32<02:28, 45.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 157/8000 [00:08<09:38, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1293/8000 [00:32<02:31, 44.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 160/8000 [00:09<10:01, 13.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1298/8000 [00:32<02:54, 38.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▎                 | 165/8000 [00:09<07:43, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▋                             | 1309/8000 [00:32<02:03, 53.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 171/8000 [00:09<07:02, 18.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▊                             | 1319/8000 [00:32<01:46, 63.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 175/8000 [00:09<06:13, 20.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1328/8000 [00:32<01:54, 58.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 181/8000 [00:09<05:57, 21.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1335/8000 [00:32<02:01, 54.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 184/8000 [00:10<07:03, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                             | 1341/8000 [00:33<02:04, 53.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 189/8000 [00:10<07:07, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1347/8000 [00:33<02:20, 47.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 191/8000 [00:10<07:23, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1354/8000 [00:33<02:07, 52.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 196/8000 [00:10<06:05, 21.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1361/8000 [00:33<02:15, 49.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   2%|▍                 | 199/8000 [00:10<06:06, 21.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▉                             | 1367/8000 [00:33<02:53, 38.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 202/8000 [00:11<07:25, 17.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1373/8000 [00:33<02:39, 41.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 205/8000 [00:11<08:44, 14.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1379/8000 [00:33<02:30, 44.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 211/8000 [00:11<06:06, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1388/8000 [00:34<02:17, 48.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 215/8000 [00:11<05:24, 24.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|██████                             | 1394/8000 [00:34<02:11, 50.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 218/8000 [00:12<07:02, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1406/8000 [00:34<01:38, 66.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▍                 | 221/8000 [00:12<06:45, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1414/8000 [00:34<02:01, 54.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 226/8000 [00:12<05:30, 23.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                            | 1424/8000 [00:34<01:45, 62.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 229/8000 [00:12<05:27, 23.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1433/8000 [00:34<02:02, 53.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 233/8000 [00:12<05:16, 24.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1441/8000 [00:34<01:53, 57.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 237/8000 [00:12<07:08, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                            | 1451/8000 [00:35<01:50, 59.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 241/8000 [00:13<06:23, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1461/8000 [00:35<02:00, 54.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 246/8000 [00:13<06:31, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1467/8000 [00:35<02:02, 53.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 249/8000 [00:13<07:54, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▍                            | 1474/8000 [00:35<02:00, 53.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 253/8000 [00:14<10:35, 12.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                            | 1481/8000 [00:35<02:13, 48.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 257/8000 [00:14<08:45, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1490/8000 [00:35<01:53, 57.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 260/8000 [00:14<09:26, 13.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1498/8000 [00:36<01:58, 54.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 264/8000 [00:14<09:14, 13.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▌                            | 1508/8000 [00:36<01:42, 63.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 267/8000 [00:14<08:22, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1516/8000 [00:36<01:55, 55.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 271/8000 [00:15<06:55, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1526/8000 [00:36<02:03, 52.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▌                 | 275/8000 [00:15<07:18, 17.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1532/8000 [00:36<02:24, 44.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   3%|▋                 | 278/8000 [00:15<06:59, 18.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▋                            | 1539/8000 [00:36<02:25, 44.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 281/8000 [00:15<08:19, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1546/8000 [00:37<02:24, 44.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 285/8000 [00:15<06:53, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1552/8000 [00:37<02:19, 46.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 290/8000 [00:16<07:06, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▊                            | 1558/8000 [00:37<02:12, 48.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 293/8000 [00:16<06:58, 18.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                            | 1564/8000 [00:37<02:42, 39.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 296/8000 [00:16<06:49, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                            | 1569/8000 [00:37<02:36, 41.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 300/8000 [00:16<05:54, 21.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1577/8000 [00:37<02:41, 39.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 304/8000 [00:16<05:35, 22.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1586/8000 [00:37<02:13, 48.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 308/8000 [00:16<05:16, 24.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                            | 1594/8000 [00:38<02:13, 47.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 315/8000 [00:17<05:02, 25.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1601/8000 [00:38<02:03, 51.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 319/8000 [00:17<05:53, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1609/8000 [00:38<01:53, 56.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 324/8000 [00:17<05:06, 25.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1615/8000 [00:38<02:09, 49.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 327/8000 [00:17<07:42, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1621/8000 [00:38<02:06, 50.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 330/8000 [00:18<08:36, 14.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████                            | 1627/8000 [00:38<02:15, 46.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▋                 | 333/8000 [00:18<07:44, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|███████▏                           | 1633/8000 [00:38<02:26, 43.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 336/8000 [00:18<08:32, 14.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1642/8000 [00:39<02:12, 48.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 338/8000 [00:18<08:51, 14.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1647/8000 [00:39<02:28, 42.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 340/8000 [00:18<08:44, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                           | 1654/8000 [00:39<02:10, 48.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 343/8000 [00:19<07:43, 16.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1664/8000 [00:39<01:56, 54.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 345/8000 [00:19<07:41, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1671/8000 [00:39<01:50, 57.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 347/8000 [00:19<07:58, 15.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1677/8000 [00:39<01:52, 56.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 349/8000 [00:19<07:50, 16.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▎                           | 1684/8000 [00:39<02:13, 47.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 352/8000 [00:19<07:07, 17.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1691/8000 [00:40<02:15, 46.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 354/8000 [00:19<07:29, 17.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1701/8000 [00:40<01:51, 56.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   4%|▊                 | 357/8000 [00:19<07:15, 17.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▍                           | 1711/8000 [00:40<01:48, 57.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 365/8000 [00:19<04:08, 30.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1720/8000 [00:40<02:02, 51.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 371/8000 [00:20<03:36, 35.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1726/8000 [00:40<02:01, 51.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 376/8000 [00:20<05:34, 22.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1732/8000 [00:40<02:12, 47.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 382/8000 [00:20<04:31, 28.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                           | 1738/8000 [00:41<02:42, 38.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▊                 | 386/8000 [00:20<06:29, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1744/8000 [00:41<02:29, 41.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 390/8000 [00:21<06:51, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1752/8000 [00:41<02:07, 49.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 394/8000 [00:21<06:56, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▋                           | 1764/8000 [00:41<01:35, 65.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 397/8000 [00:21<06:38, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1772/8000 [00:41<01:47, 57.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 401/8000 [00:21<05:53, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1779/8000 [00:41<01:59, 51.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 405/8000 [00:21<05:22, 23.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1786/8000 [00:42<02:42, 38.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 408/8000 [00:21<05:21, 23.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1793/8000 [00:42<02:24, 43.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 411/8000 [00:22<05:42, 22.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▊                           | 1799/8000 [00:42<02:44, 37.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 414/8000 [00:22<06:02, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1804/8000 [00:42<02:54, 35.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 418/8000 [00:22<05:19, 23.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1810/8000 [00:42<02:56, 35.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 422/8000 [00:22<05:03, 24.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1819/8000 [00:42<02:16, 45.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 426/8000 [00:22<07:26, 16.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▉                           | 1828/8000 [00:43<02:36, 39.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 432/8000 [00:23<06:40, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1835/8000 [00:43<02:46, 36.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   5%|▉                 | 437/8000 [00:23<06:40, 18.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1843/8000 [00:43<02:33, 39.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 440/8000 [00:23<07:40, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████                           | 1851/8000 [00:43<02:52, 35.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|▉                 | 442/8000 [00:23<07:49, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1861/8000 [00:43<02:22, 42.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 446/8000 [00:24<08:07, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1866/8000 [00:44<02:48, 36.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 449/8000 [00:24<08:37, 14.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1871/8000 [00:44<02:57, 34.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 451/8000 [00:24<08:47, 14.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|████████▏                          | 1875/8000 [00:44<02:53, 35.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 454/8000 [00:24<08:13, 15.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                          | 1881/8000 [00:44<02:34, 39.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 457/8000 [00:25<10:41, 11.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1886/8000 [00:44<02:49, 36.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 459/8000 [00:25<10:27, 12.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1893/8000 [00:44<02:22, 42.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 461/8000 [00:25<10:10, 12.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1898/8000 [00:44<02:20, 43.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 463/8000 [00:25<11:25, 11.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1903/8000 [00:45<02:19, 43.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 467/8000 [00:25<08:21, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▎                          | 1909/8000 [00:45<02:13, 45.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 471/8000 [00:25<07:03, 17.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1915/8000 [00:45<02:03, 49.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 473/8000 [00:26<07:32, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1921/8000 [00:45<01:59, 50.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 476/8000 [00:26<06:57, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1927/8000 [00:45<01:55, 52.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 478/8000 [00:26<07:27, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1933/8000 [00:45<01:50, 54.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 482/8000 [00:26<06:02, 20.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▍                          | 1941/8000 [00:45<02:11, 46.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 486/8000 [00:26<06:45, 18.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1946/8000 [00:45<02:26, 41.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 490/8000 [00:27<07:07, 17.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1953/8000 [00:46<02:22, 42.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 495/8000 [00:27<05:54, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▌                          | 1959/8000 [00:46<02:30, 40.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█                 | 499/8000 [00:27<06:43, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▌                          | 1969/8000 [00:46<02:04, 48.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 503/8000 [00:27<07:09, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1974/8000 [00:46<02:33, 39.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 507/8000 [00:27<07:35, 16.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1983/8000 [00:46<02:04, 48.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 509/8000 [00:28<07:49, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1989/8000 [00:46<02:16, 44.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 512/8000 [00:28<07:12, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                          | 1995/8000 [00:47<02:24, 41.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 515/8000 [00:28<06:37, 18.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2001/8000 [00:47<02:15, 44.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   6%|█▏                | 518/8000 [00:28<06:19, 19.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2007/8000 [00:47<02:24, 41.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 523/8000 [00:28<05:10, 24.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2016/8000 [00:47<02:01, 49.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 527/8000 [00:28<06:06, 20.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2022/8000 [00:47<02:30, 39.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 531/8000 [00:29<05:33, 22.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▊                          | 2028/8000 [00:47<02:19, 42.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 538/8000 [00:29<04:04, 30.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▉                          | 2035/8000 [00:48<03:06, 32.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 542/8000 [00:29<04:12, 29.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2041/8000 [00:48<02:43, 36.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 549/8000 [00:29<05:12, 23.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2046/8000 [00:48<02:35, 38.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▏                | 554/8000 [00:29<04:31, 27.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▉                          | 2052/8000 [00:48<02:23, 41.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 560/8000 [00:30<06:29, 19.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2059/8000 [00:48<02:20, 42.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 563/8000 [00:30<07:35, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2066/8000 [00:48<02:21, 42.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 568/8000 [00:31<10:09, 12.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2073/8000 [00:48<02:19, 42.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 570/8000 [00:31<13:50,  8.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                          | 2081/8000 [00:49<02:15, 43.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 572/8000 [00:31<12:48,  9.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2087/8000 [00:49<02:07, 46.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 575/8000 [00:32<10:55, 11.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2093/8000 [00:49<02:18, 42.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 579/8000 [00:32<09:59, 12.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2100/8000 [00:49<02:04, 47.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 582/8000 [00:32<08:41, 14.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2107/8000 [00:49<02:11, 44.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 584/8000 [00:32<08:48, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▏                         | 2113/8000 [00:49<02:03, 47.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 587/8000 [00:32<07:32, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████▎                         | 2120/8000 [00:49<02:06, 46.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 589/8000 [00:33<13:05,  9.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2125/8000 [00:50<02:21, 41.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 594/8000 [00:33<08:58, 13.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2130/8000 [00:50<02:49, 34.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   7%|█▎                | 597/8000 [00:33<07:56, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2135/8000 [00:50<02:59, 32.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 603/8000 [00:33<06:51, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▎                         | 2139/8000 [00:50<03:15, 29.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 606/8000 [00:34<07:55, 15.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2143/8000 [00:50<03:07, 31.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▎                | 611/8000 [00:34<06:13, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2149/8000 [00:50<02:39, 36.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 614/8000 [00:34<07:04, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2156/8000 [00:51<02:29, 39.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 618/8000 [00:34<07:19, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2162/8000 [00:51<02:17, 42.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 620/8000 [00:34<08:49, 13.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▍                         | 2167/8000 [00:51<02:18, 42.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 623/8000 [00:35<07:49, 15.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2172/8000 [00:51<03:04, 31.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 625/8000 [00:35<09:30, 12.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2176/8000 [00:51<03:03, 31.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 628/8000 [00:35<08:34, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2181/8000 [00:51<02:46, 34.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 630/8000 [00:35<08:21, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2189/8000 [00:52<02:32, 38.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 633/8000 [00:35<07:25, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▌                         | 2196/8000 [00:52<02:12, 43.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 639/8000 [00:35<05:07, 23.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2202/8000 [00:52<02:07, 45.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 642/8000 [00:36<05:16, 23.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2207/8000 [00:52<02:05, 46.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 645/8000 [00:36<05:26, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2212/8000 [00:52<02:05, 46.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 648/8000 [00:36<07:10, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2217/8000 [00:52<02:07, 45.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 650/8000 [00:36<09:18, 13.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2222/8000 [00:52<02:08, 44.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 655/8000 [00:36<06:45, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▋                         | 2227/8000 [00:52<02:27, 39.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 658/8000 [00:37<06:24, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2235/8000 [00:53<02:16, 42.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 661/8000 [00:37<06:14, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2243/8000 [00:53<02:25, 39.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▍                | 664/8000 [00:37<05:58, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2250/8000 [00:53<02:37, 36.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 667/8000 [00:37<05:49, 21.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▊                         | 2255/8000 [00:53<02:42, 35.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 670/8000 [00:37<07:18, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2259/8000 [00:53<02:44, 34.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 672/8000 [00:37<07:33, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2264/8000 [00:53<03:08, 30.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 674/8000 [00:38<08:11, 14.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▉                         | 2271/8000 [00:54<02:35, 36.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   8%|█▌                | 677/8000 [00:38<07:26, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                         | 2283/8000 [00:54<01:46, 53.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 682/8000 [00:38<07:18, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2291/8000 [00:54<02:16, 41.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 685/8000 [00:38<06:50, 17.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2298/8000 [00:54<02:02, 46.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 689/8000 [00:38<06:13, 19.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2307/8000 [00:54<01:55, 49.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 693/8000 [00:39<06:43, 18.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                         | 2313/8000 [00:54<02:01, 46.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 698/8000 [00:39<05:23, 22.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2319/8000 [00:55<02:09, 44.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 702/8000 [00:39<05:11, 23.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2327/8000 [00:55<02:01, 46.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 707/8000 [00:39<04:27, 27.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▏                        | 2333/8000 [00:55<02:15, 41.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 711/8000 [00:39<05:38, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2343/8000 [00:55<01:48, 52.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 715/8000 [00:39<05:16, 23.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2350/8000 [00:55<01:59, 47.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 718/8000 [00:40<06:30, 18.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████▎                        | 2356/8000 [00:55<01:55, 48.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▌                | 722/8000 [00:40<05:38, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                        | 2363/8000 [00:55<01:57, 47.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 725/8000 [00:40<06:41, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                        | 2369/8000 [00:56<02:03, 45.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 728/8000 [00:40<07:40, 15.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2376/8000 [00:56<01:53, 49.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 730/8000 [00:40<07:44, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2382/8000 [00:56<01:52, 49.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 732/8000 [00:41<07:42, 15.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▍                        | 2391/8000 [00:56<01:49, 51.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 737/8000 [00:41<05:32, 21.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2400/8000 [00:56<01:36, 58.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 744/8000 [00:41<03:52, 31.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2407/8000 [00:56<01:45, 52.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 748/8000 [00:41<03:56, 30.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2417/8000 [00:56<01:49, 51.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 752/8000 [00:41<04:56, 24.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▌                        | 2425/8000 [00:57<01:58, 47.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 755/8000 [00:41<06:14, 19.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▋                        | 2430/8000 [00:57<02:08, 43.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:   9%|█▋                | 758/8000 [00:42<06:00, 20.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▋                        | 2436/8000 [00:57<02:21, 39.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 764/8000 [00:42<05:43, 21.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2443/8000 [00:57<02:13, 41.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 769/8000 [00:42<06:01, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2449/8000 [00:57<02:24, 38.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 774/8000 [00:42<05:10, 23.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                        | 2455/8000 [00:58<02:23, 38.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▋                | 777/8000 [00:43<07:50, 15.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2459/8000 [00:58<02:23, 38.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 783/8000 [00:43<06:52, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2466/8000 [00:58<02:19, 39.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 786/8000 [00:43<06:32, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2470/8000 [00:58<02:21, 39.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 790/8000 [00:43<05:39, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▊                        | 2479/8000 [00:58<01:50, 49.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 795/8000 [00:43<05:49, 20.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2486/8000 [00:58<02:04, 44.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 798/8000 [00:44<05:43, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2491/8000 [00:58<02:03, 44.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 801/8000 [00:44<07:00, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2496/8000 [00:58<02:13, 41.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 804/8000 [00:44<06:33, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2501/8000 [00:59<02:09, 42.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 808/8000 [00:44<08:41, 13.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2506/8000 [00:59<02:19, 39.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 812/8000 [00:45<07:16, 16.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▉                        | 2511/8000 [00:59<02:13, 41.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 816/8000 [00:45<07:37, 15.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|███████████                        | 2517/8000 [00:59<01:59, 45.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 820/8000 [00:45<06:23, 18.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2523/8000 [00:59<01:53, 48.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 823/8000 [00:45<08:54, 13.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2528/8000 [00:59<01:54, 47.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 826/8000 [00:45<07:57, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2533/8000 [00:59<02:07, 42.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 828/8000 [00:46<08:08, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                        | 2538/8000 [00:59<02:06, 43.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 831/8000 [00:46<08:52, 13.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2544/8000 [01:00<02:09, 42.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▊                | 833/8000 [00:46<11:55, 10.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2550/8000 [01:00<01:59, 45.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 835/8000 [00:46<11:05, 10.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2555/8000 [01:00<02:19, 38.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 837/8000 [00:47<10:08, 11.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2561/8000 [01:00<02:40, 33.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  10%|█▉                | 840/8000 [00:47<10:17, 11.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2565/8000 [01:00<02:53, 31.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 843/8000 [00:47<10:21, 11.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▏                       | 2571/8000 [01:00<02:28, 36.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 845/8000 [00:47<09:38, 12.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2578/8000 [01:00<02:07, 42.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 849/8000 [00:48<10:01, 11.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2583/8000 [01:01<02:30, 35.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 852/8000 [00:48<10:15, 11.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2590/8000 [01:01<02:06, 42.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 854/8000 [00:48<09:54, 12.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████▎                       | 2597/8000 [01:01<01:54, 47.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 857/8000 [00:48<08:18, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2606/8000 [01:01<01:49, 49.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 859/8000 [00:48<10:01, 11.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2613/8000 [01:01<02:25, 36.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 862/8000 [00:48<08:27, 14.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▍                       | 2621/8000 [01:01<02:01, 44.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 864/8000 [00:49<10:30, 11.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2629/8000 [01:02<02:15, 39.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 868/8000 [00:49<08:05, 14.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2638/8000 [01:02<02:03, 43.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 870/8000 [00:49<11:23, 10.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2645/8000 [01:02<01:52, 47.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 872/8000 [00:50<12:37,  9.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▌                       | 2652/8000 [01:02<01:44, 51.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 875/8000 [00:50<10:08, 11.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2659/8000 [01:02<02:10, 41.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 878/8000 [00:50<08:39, 13.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2666/8000 [01:03<02:37, 33.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 883/8000 [00:50<08:00, 14.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▋                       | 2676/8000 [01:03<01:59, 44.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|█▉                | 886/8000 [00:51<09:44, 12.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                       | 2684/8000 [01:03<01:47, 49.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 891/8000 [00:51<07:07, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2694/8000 [01:03<01:29, 59.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 894/8000 [00:51<08:04, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▊                       | 2705/8000 [01:03<01:51, 47.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 897/8000 [00:51<08:20, 14.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2715/8000 [01:03<01:34, 55.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 900/8000 [00:51<08:53, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2722/8000 [01:04<01:39, 53.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 902/8000 [00:52<09:02, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2730/8000 [01:04<01:40, 52.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 905/8000 [00:52<07:45, 15.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2736/8000 [01:04<01:37, 54.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 913/8000 [00:52<04:38, 25.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▉                       | 2742/8000 [01:04<01:57, 44.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  11%|██                | 917/8000 [00:52<04:21, 27.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2749/8000 [01:04<02:09, 40.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 921/8000 [00:52<05:18, 22.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2755/8000 [01:04<02:02, 42.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 927/8000 [00:52<04:19, 27.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|████████████                       | 2760/8000 [01:04<01:58, 44.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 933/8000 [00:53<07:20, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████                       | 2768/8000 [01:05<01:53, 45.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 938/8000 [00:53<06:10, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2773/8000 [01:05<01:54, 45.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██                | 942/8000 [00:53<05:32, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2779/8000 [01:05<02:01, 42.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 948/8000 [00:54<05:19, 22.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2784/8000 [01:05<02:30, 34.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 953/8000 [00:54<04:35, 25.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2790/8000 [01:05<02:15, 38.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 957/8000 [00:54<04:21, 26.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▏                      | 2796/8000 [01:05<02:45, 31.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 961/8000 [00:54<04:17, 27.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2802/8000 [01:06<02:49, 30.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 967/8000 [00:54<03:37, 32.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2807/8000 [01:06<02:34, 33.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 971/8000 [00:54<04:43, 24.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2816/8000 [01:06<01:55, 44.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 977/8000 [00:55<04:52, 23.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▎                      | 2822/8000 [01:06<02:05, 41.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 981/8000 [00:55<04:38, 25.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▍                      | 2830/8000 [01:06<01:46, 48.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 984/8000 [00:55<06:05, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|████████████▍                      | 2836/8000 [01:06<02:18, 37.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 987/8000 [00:55<05:48, 20.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2846/8000 [01:07<01:45, 49.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 993/8000 [00:55<04:37, 25.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▍                      | 2853/8000 [01:07<01:46, 48.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 996/8000 [00:56<06:09, 18.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2862/8000 [01:07<01:30, 57.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  12%|██▏               | 999/8000 [00:56<08:27, 13.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2872/8000 [01:07<01:49, 46.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1002/8000 [00:56<07:38, 15.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▌                      | 2878/8000 [01:07<01:54, 44.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1006/8000 [00:56<07:34, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2887/8000 [01:07<01:44, 49.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1008/8000 [00:57<09:16, 12.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2895/8000 [01:08<01:42, 49.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1011/8000 [00:57<11:29, 10.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2902/8000 [01:08<01:57, 43.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1013/8000 [00:57<10:42, 10.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2907/8000 [01:08<02:04, 40.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1018/8000 [00:57<07:18, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▋                      | 2914/8000 [01:08<02:13, 37.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1021/8000 [00:58<06:44, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2921/8000 [01:08<02:11, 38.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1024/8000 [00:58<06:28, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2927/8000 [01:08<02:00, 42.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1028/8000 [00:58<06:45, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2932/8000 [01:09<01:56, 43.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1032/8000 [00:58<05:35, 20.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2937/8000 [01:09<02:24, 34.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1036/8000 [00:58<06:20, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▊                      | 2941/8000 [01:09<02:39, 31.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1039/8000 [00:58<06:08, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2945/8000 [01:09<02:51, 29.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1043/8000 [00:59<08:11, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2949/8000 [01:09<02:46, 30.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1047/8000 [00:59<07:58, 14.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2954/8000 [01:09<02:40, 31.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1051/8000 [00:59<06:44, 17.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2959/8000 [01:09<02:34, 32.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1055/8000 [01:00<07:12, 16.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2965/8000 [01:10<02:25, 34.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▏              | 1057/8000 [01:00<07:16, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▉                      | 2969/8000 [01:10<02:39, 31.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1059/8000 [01:00<07:40, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2973/8000 [01:10<02:51, 29.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1062/8000 [01:00<06:55, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2978/8000 [01:10<02:31, 33.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1065/8000 [01:00<06:19, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2984/8000 [01:10<02:09, 38.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1070/8000 [01:00<04:55, 23.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2991/8000 [01:10<02:07, 39.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1075/8000 [01:00<04:20, 26.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|█████████████                      | 2996/8000 [01:10<02:03, 40.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  13%|██▎              | 1079/8000 [01:01<05:20, 21.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3004/8000 [01:11<01:44, 48.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1082/8000 [01:01<07:49, 14.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3009/8000 [01:11<01:43, 48.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1086/8000 [01:01<07:51, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3018/8000 [01:11<01:31, 54.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1091/8000 [01:02<07:07, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▏                     | 3025/8000 [01:11<02:01, 40.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1095/8000 [01:02<08:37, 13.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3031/8000 [01:11<02:10, 38.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1098/8000 [01:02<07:52, 14.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3038/8000 [01:11<02:08, 38.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1102/8000 [01:02<06:45, 17.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3044/8000 [01:12<02:12, 37.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1105/8000 [01:02<06:17, 18.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3049/8000 [01:12<02:07, 38.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1109/8000 [01:03<05:17, 21.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▎                     | 3054/8000 [01:12<02:06, 39.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1112/8000 [01:03<07:07, 16.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3062/8000 [01:12<02:00, 40.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1115/8000 [01:03<08:10, 14.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3071/8000 [01:12<01:49, 44.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▎              | 1117/8000 [01:03<08:02, 14.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████▍                     | 3080/8000 [01:13<02:20, 35.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1119/8000 [01:04<09:26, 12.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3087/8000 [01:13<02:02, 40.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1123/8000 [01:04<07:16, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3093/8000 [01:13<01:55, 42.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1127/8000 [01:04<05:56, 19.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3098/8000 [01:13<01:51, 43.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1130/8000 [01:04<05:56, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▌                     | 3107/8000 [01:13<01:31, 53.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1134/8000 [01:04<05:23, 21.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3115/8000 [01:13<02:06, 38.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1137/8000 [01:04<05:08, 22.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3122/8000 [01:13<01:51, 43.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1140/8000 [01:04<04:57, 23.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3131/8000 [01:14<01:53, 42.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1143/8000 [01:05<06:30, 17.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▋                     | 3140/8000 [01:14<01:43, 46.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1149/8000 [01:05<07:04, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3146/8000 [01:14<01:38, 49.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1153/8000 [01:05<06:00, 18.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▊                     | 3153/8000 [01:14<01:41, 47.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  14%|██▍              | 1158/8000 [01:05<06:07, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▊                     | 3160/8000 [01:14<01:45, 45.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1161/8000 [01:06<06:04, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▊                     | 3166/8000 [01:14<01:41, 47.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1165/8000 [01:06<07:37, 14.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3173/8000 [01:15<02:07, 37.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1167/8000 [01:06<07:32, 15.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3179/8000 [01:15<01:56, 41.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▍              | 1171/8000 [01:06<06:15, 18.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3186/8000 [01:15<01:54, 41.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1178/8000 [01:07<05:32, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▉                     | 3193/8000 [01:15<01:42, 46.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1183/8000 [01:07<06:42, 16.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3201/8000 [01:15<01:31, 52.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1186/8000 [01:07<06:32, 17.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3208/8000 [01:15<01:37, 49.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1190/8000 [01:07<05:54, 19.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3216/8000 [01:15<01:25, 55.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1193/8000 [01:08<09:12, 12.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████                     | 3223/8000 [01:15<01:25, 56.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1198/8000 [01:08<08:15, 13.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|██████████████▏                    | 3233/8000 [01:16<01:20, 59.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1200/8000 [01:08<09:08, 12.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3243/8000 [01:16<01:20, 59.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1206/8000 [01:08<06:20, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▏                    | 3253/8000 [01:16<01:16, 61.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1211/8000 [01:09<06:03, 18.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3262/8000 [01:16<01:26, 54.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1217/8000 [01:09<06:43, 16.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3268/8000 [01:16<01:26, 54.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1225/8000 [01:09<05:25, 20.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3275/8000 [01:17<01:43, 45.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1230/8000 [01:10<05:34, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▎                    | 3281/8000 [01:17<02:07, 36.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▌              | 1233/8000 [01:10<05:34, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3286/8000 [01:17<02:01, 38.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1236/8000 [01:10<05:23, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3291/8000 [01:17<02:08, 36.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  15%|██▋              | 1239/8000 [01:10<06:18, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3296/8000 [01:17<02:12, 35.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1242/8000 [01:10<06:03, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3302/8000 [01:17<02:00, 38.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1244/8000 [01:10<06:27, 17.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3309/8000 [01:17<01:45, 44.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1247/8000 [01:11<06:00, 18.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▍                    | 3314/8000 [01:18<02:27, 31.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1250/8000 [01:11<05:47, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████▌                    | 3318/8000 [01:18<02:23, 32.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1254/8000 [01:11<05:04, 22.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3324/8000 [01:18<02:09, 36.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1261/8000 [01:11<04:39, 24.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3331/8000 [01:18<01:51, 41.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1269/8000 [01:11<04:14, 26.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▌                    | 3337/8000 [01:18<02:07, 36.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1274/8000 [01:11<03:55, 28.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3345/8000 [01:18<01:56, 39.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1279/8000 [01:12<05:24, 20.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3351/8000 [01:19<01:48, 43.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1283/8000 [01:12<05:59, 18.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3356/8000 [01:19<01:49, 42.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1288/8000 [01:12<05:27, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3361/8000 [01:19<01:53, 40.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▋              | 1292/8000 [01:13<06:36, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▋                    | 3367/8000 [01:19<01:44, 44.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1297/8000 [01:13<06:39, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3374/8000 [01:19<02:22, 32.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1302/8000 [01:13<05:37, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3380/8000 [01:19<02:04, 37.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1305/8000 [01:13<05:33, 20.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3386/8000 [01:19<01:54, 40.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1308/8000 [01:14<06:54, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3391/8000 [01:20<02:00, 38.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1311/8000 [01:14<06:49, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▊                    | 3396/8000 [01:20<01:57, 39.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  16%|██▊              | 1316/8000 [01:14<05:39, 19.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3403/8000 [01:20<01:39, 46.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1321/8000 [01:14<05:13, 21.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3410/8000 [01:20<01:28, 51.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1326/8000 [01:14<05:48, 19.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3419/8000 [01:20<01:15, 60.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1332/8000 [01:15<05:22, 20.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▉                    | 3427/8000 [01:20<01:32, 49.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1336/8000 [01:15<04:49, 23.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3436/8000 [01:20<01:28, 51.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1343/8000 [01:15<05:31, 20.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3445/8000 [01:21<01:28, 51.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1346/8000 [01:16<06:18, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3452/8000 [01:21<01:35, 47.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▊              | 1349/8000 [01:16<05:58, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████                    | 3457/8000 [01:21<01:37, 46.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1355/8000 [01:16<05:41, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3464/8000 [01:21<01:29, 50.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1361/8000 [01:16<04:31, 24.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3471/8000 [01:21<01:26, 52.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1364/8000 [01:16<04:39, 23.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|███████████████▏                   | 3479/8000 [01:21<01:38, 45.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1368/8000 [01:16<05:40, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3487/8000 [01:21<01:28, 50.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1372/8000 [01:17<05:06, 21.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3493/8000 [01:22<01:29, 50.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1376/8000 [01:17<05:34, 19.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3500/8000 [01:22<01:27, 51.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1379/8000 [01:17<05:25, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▎                   | 3509/8000 [01:22<01:24, 53.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1385/8000 [01:17<05:15, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3516/8000 [01:22<01:31, 48.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1388/8000 [01:17<05:08, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3524/8000 [01:22<01:33, 47.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1391/8000 [01:18<05:02, 21.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3533/8000 [01:22<01:22, 54.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1394/8000 [01:18<06:19, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▍                   | 3542/8000 [01:22<01:15, 58.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  17%|██▉              | 1399/8000 [01:18<06:09, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3553/8000 [01:23<01:40, 44.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1406/8000 [01:18<05:18, 20.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████▌                   | 3560/8000 [01:23<01:41, 43.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1409/8000 [01:19<05:59, 18.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▌                   | 3565/8000 [01:23<02:03, 35.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|██▉              | 1411/8000 [01:19<07:29, 14.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3575/8000 [01:23<01:34, 46.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1413/8000 [01:19<07:32, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3582/8000 [01:24<01:37, 45.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1416/8000 [01:19<07:00, 15.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3592/8000 [01:24<01:27, 50.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1418/8000 [01:19<07:14, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▋                   | 3598/8000 [01:24<01:25, 51.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1420/8000 [01:20<08:44, 12.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3605/8000 [01:24<01:37, 45.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1425/8000 [01:20<06:05, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3610/8000 [01:24<01:36, 45.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1428/8000 [01:20<05:55, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3619/8000 [01:24<01:28, 49.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1432/8000 [01:20<05:14, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▊                   | 3625/8000 [01:24<01:38, 44.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1435/8000 [01:20<05:16, 20.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▉                   | 3634/8000 [01:25<01:21, 53.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1438/8000 [01:21<08:11, 13.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3641/8000 [01:25<01:25, 50.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1442/8000 [01:21<06:32, 16.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3647/8000 [01:25<01:23, 52.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1445/8000 [01:21<06:01, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▉                   | 3653/8000 [01:25<01:21, 53.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1451/8000 [01:21<06:32, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3661/8000 [01:25<01:25, 50.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1458/8000 [01:21<05:29, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3668/8000 [01:25<01:23, 52.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1465/8000 [01:22<04:52, 22.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3674/8000 [01:25<01:48, 39.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███              | 1468/8000 [01:22<04:50, 22.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████                   | 3681/8000 [01:26<01:52, 38.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1472/8000 [01:22<04:29, 24.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3686/8000 [01:26<01:50, 39.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1475/8000 [01:22<04:34, 23.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3692/8000 [01:26<02:05, 34.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  18%|███▏             | 1478/8000 [01:22<04:38, 23.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3700/8000 [01:26<01:42, 41.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1485/8000 [01:22<03:29, 31.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3705/8000 [01:26<01:48, 39.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1489/8000 [01:23<04:33, 23.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▏                  | 3713/8000 [01:26<01:39, 43.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1494/8000 [01:23<05:00, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|████████████████▎                  | 3720/8000 [01:27<01:28, 48.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1498/8000 [01:23<05:39, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3726/8000 [01:27<01:35, 44.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1503/8000 [01:23<04:52, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▎                  | 3736/8000 [01:27<01:17, 55.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1506/8000 [01:23<04:51, 22.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3743/8000 [01:27<01:15, 56.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1510/8000 [01:24<05:29, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3752/8000 [01:27<01:24, 50.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1513/8000 [01:24<05:18, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3760/8000 [01:27<01:14, 56.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1517/8000 [01:24<04:45, 22.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▍                  | 3770/8000 [01:27<01:12, 58.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1521/8000 [01:24<04:17, 25.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3782/8000 [01:28<01:14, 56.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1526/8000 [01:25<06:55, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3790/8000 [01:28<01:15, 55.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▏             | 1529/8000 [01:25<06:31, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|████████████████▌                  | 3798/8000 [01:28<01:11, 59.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1533/8000 [01:25<06:35, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3805/8000 [01:28<01:25, 49.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1535/8000 [01:25<06:31, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3813/8000 [01:28<01:38, 42.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1539/8000 [01:25<06:54, 15.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3820/8000 [01:28<01:30, 45.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1542/8000 [01:26<06:27, 16.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▋                  | 3826/8000 [01:29<01:30, 46.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1546/8000 [01:26<06:36, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3833/8000 [01:29<01:23, 49.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1549/8000 [01:26<06:10, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3840/8000 [01:29<01:36, 42.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1551/8000 [01:26<07:43, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3848/8000 [01:29<01:42, 40.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  19%|███▎             | 1555/8000 [01:26<06:04, 17.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▊                  | 3854/8000 [01:29<01:42, 40.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1560/8000 [01:27<05:46, 18.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3862/8000 [01:29<01:46, 38.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1565/8000 [01:27<05:44, 18.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▉                  | 3873/8000 [01:30<01:19, 52.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1568/8000 [01:27<07:54, 13.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▉                  | 3882/8000 [01:30<01:27, 46.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1573/8000 [01:28<07:08, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3890/8000 [01:30<01:32, 44.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1575/8000 [01:28<07:08, 14.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3895/8000 [01:30<01:30, 45.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1578/8000 [01:28<06:26, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3901/8000 [01:30<01:26, 47.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1581/8000 [01:28<05:57, 17.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████                  | 3907/8000 [01:30<01:24, 48.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▎             | 1584/8000 [01:28<05:29, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3915/8000 [01:31<01:26, 47.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1589/8000 [01:28<05:55, 18.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3923/8000 [01:31<01:22, 49.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1594/8000 [01:29<06:07, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▏                 | 3935/8000 [01:31<01:11, 56.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1597/8000 [01:29<05:51, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3944/8000 [01:31<01:04, 62.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1600/8000 [01:29<05:33, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|█████████████████▎                 | 3953/8000 [01:31<00:59, 67.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1605/8000 [01:29<04:32, 23.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▎                 | 3963/8000 [01:31<01:27, 46.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1609/8000 [01:30<06:17, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▎                 | 3970/8000 [01:32<01:33, 43.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1612/8000 [01:30<06:57, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3977/8000 [01:32<01:31, 43.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1615/8000 [01:30<07:36, 13.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3987/8000 [01:32<01:21, 49.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1619/8000 [01:30<06:25, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▍                 | 3993/8000 [01:32<01:38, 40.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1621/8000 [01:30<07:43, 13.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4001/8000 [01:32<01:27, 45.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1624/8000 [01:31<07:23, 14.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4007/8000 [01:32<01:30, 44.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1626/8000 [01:31<10:40,  9.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4015/8000 [01:33<01:27, 45.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1629/8000 [01:31<08:53, 11.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▌                 | 4023/8000 [01:33<01:33, 42.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1634/8000 [01:31<06:23, 16.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████▋                 | 4032/8000 [01:33<01:18, 50.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  20%|███▍             | 1639/8000 [01:31<05:03, 20.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4042/8000 [01:33<01:05, 60.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▍             | 1645/8000 [01:32<05:39, 18.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▋                 | 4050/8000 [01:33<01:02, 63.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1649/8000 [01:32<05:56, 17.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4058/8000 [01:33<01:05, 60.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1652/8000 [01:32<06:46, 15.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4066/8000 [01:33<01:00, 64.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1657/8000 [01:33<06:20, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4076/8000 [01:34<00:56, 70.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1659/8000 [01:33<07:36, 13.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▊                 | 4085/8000 [01:34<00:58, 67.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1662/8000 [01:33<06:51, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4092/8000 [01:34<01:12, 53.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1666/8000 [01:33<05:43, 18.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4101/8000 [01:34<01:17, 50.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1669/8000 [01:33<05:29, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4108/8000 [01:34<01:13, 52.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1673/8000 [01:34<07:43, 13.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▉                 | 4114/8000 [01:34<01:14, 52.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1675/8000 [01:34<07:58, 13.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4120/8000 [01:34<01:21, 47.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1678/8000 [01:34<06:59, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4125/8000 [01:35<01:23, 46.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1680/8000 [01:34<07:35, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4131/8000 [01:35<01:19, 48.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1682/8000 [01:35<09:09, 11.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████                 | 4138/8000 [01:35<01:13, 52.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1686/8000 [01:35<07:13, 14.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4148/8000 [01:35<01:24, 45.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1694/8000 [01:35<05:16, 19.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4155/8000 [01:35<01:25, 44.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1697/8000 [01:35<05:14, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4161/8000 [01:35<01:29, 42.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▌             | 1702/8000 [01:35<05:13, 20.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▏                | 4168/8000 [01:36<01:28, 43.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1706/8000 [01:35<04:46, 22.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4177/8000 [01:36<01:28, 43.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1709/8000 [01:36<04:49, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4183/8000 [01:36<01:49, 34.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1713/8000 [01:36<04:41, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4190/8000 [01:36<01:33, 40.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  21%|███▋             | 1717/8000 [01:36<05:54, 17.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|██████████████████▎                | 4197/8000 [01:36<01:22, 46.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1720/8000 [01:36<06:52, 15.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4205/8000 [01:36<01:11, 52.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1725/8000 [01:37<05:21, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4213/8000 [01:37<01:31, 41.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1728/8000 [01:37<06:07, 17.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4219/8000 [01:37<01:27, 43.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1730/8000 [01:37<06:17, 16.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▍                | 4225/8000 [01:37<01:33, 40.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1732/8000 [01:37<06:33, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4232/8000 [01:37<01:32, 40.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1734/8000 [01:37<06:43, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4238/8000 [01:37<01:36, 39.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1736/8000 [01:37<06:44, 15.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4243/8000 [01:37<01:43, 36.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1739/8000 [01:38<07:40, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4247/8000 [01:38<01:45, 35.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1748/8000 [01:38<04:01, 25.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▌                | 4252/8000 [01:38<01:40, 37.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1753/8000 [01:38<03:40, 28.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4261/8000 [01:38<01:19, 46.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1760/8000 [01:38<03:37, 28.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4266/8000 [01:38<01:24, 44.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▋             | 1764/8000 [01:38<04:36, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▋                | 4275/8000 [01:38<01:16, 48.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1768/8000 [01:39<04:18, 24.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▋                | 4281/8000 [01:38<01:15, 49.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1776/8000 [01:39<04:10, 24.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4287/8000 [01:38<01:13, 50.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1779/8000 [01:39<05:10, 20.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4295/8000 [01:38<01:06, 55.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1783/8000 [01:39<04:45, 21.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4301/8000 [01:39<01:17, 47.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1788/8000 [01:40<05:09, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4307/8000 [01:39<01:13, 50.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1792/8000 [01:40<04:37, 22.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▊                | 4313/8000 [01:39<01:32, 39.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  22%|███▊             | 1796/8000 [01:40<05:20, 19.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4319/8000 [01:39<01:36, 38.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1802/8000 [01:40<05:24, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4326/8000 [01:39<01:34, 38.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1805/8000 [01:40<05:16, 19.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4332/8000 [01:39<01:35, 38.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1808/8000 [01:41<05:16, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4337/8000 [01:40<01:42, 35.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1812/8000 [01:41<06:51, 15.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▉                | 4341/8000 [01:40<01:40, 36.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1817/8000 [01:41<05:45, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4346/8000 [01:40<01:46, 34.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▊             | 1820/8000 [01:42<08:43, 11.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4351/8000 [01:40<01:42, 35.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1827/8000 [01:42<05:46, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|███████████████████                | 4357/8000 [01:40<01:30, 40.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1833/8000 [01:42<05:23, 19.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4362/8000 [01:40<01:44, 34.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1836/8000 [01:42<06:05, 16.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████                | 4368/8000 [01:40<01:34, 38.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1840/8000 [01:43<06:23, 16.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4373/8000 [01:41<01:39, 36.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1842/8000 [01:43<06:31, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4378/8000 [01:41<01:35, 37.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1844/8000 [01:43<07:58, 12.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4382/8000 [01:41<01:49, 33.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1848/8000 [01:43<06:30, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4387/8000 [01:41<01:49, 33.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1850/8000 [01:43<06:38, 15.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4393/8000 [01:41<01:42, 35.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1858/8000 [01:44<04:07, 24.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▏               | 4397/8000 [01:41<01:42, 35.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1861/8000 [01:44<05:09, 19.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4401/8000 [01:41<01:52, 32.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1864/8000 [01:44<05:09, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4407/8000 [01:42<01:35, 37.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1868/8000 [01:44<04:42, 21.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4413/8000 [01:42<01:35, 37.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1873/8000 [01:44<05:24, 18.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4417/8000 [01:42<01:34, 38.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1876/8000 [01:45<06:24, 15.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4422/8000 [01:42<01:43, 34.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  23%|███▉             | 1879/8000 [01:45<06:02, 16.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▎               | 4427/8000 [01:42<01:35, 37.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1883/8000 [01:45<05:20, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▍               | 4433/8000 [01:42<01:47, 33.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1887/8000 [01:45<04:51, 20.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|███████████████████▍               | 4437/8000 [01:42<01:45, 33.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1893/8000 [01:46<05:30, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4441/8000 [01:43<01:46, 33.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1897/8000 [01:46<05:52, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▍               | 4448/8000 [01:43<01:25, 41.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1900/8000 [01:46<05:36, 18.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4458/8000 [01:43<01:05, 54.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1903/8000 [01:46<05:19, 19.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4465/8000 [01:43<01:10, 49.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1906/8000 [01:46<06:18, 16.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4471/8000 [01:43<01:16, 46.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1908/8000 [01:46<06:13, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▌               | 4481/8000 [01:43<01:07, 52.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1910/8000 [01:47<06:17, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4490/8000 [01:43<01:18, 44.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1916/8000 [01:47<04:16, 23.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4495/8000 [01:44<01:19, 43.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1922/8000 [01:47<04:41, 21.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4503/8000 [01:44<01:10, 49.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1926/8000 [01:47<05:17, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▋               | 4510/8000 [01:44<01:13, 47.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1930/8000 [01:47<04:55, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▊               | 4520/8000 [01:44<01:06, 52.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1935/8000 [01:48<05:21, 18.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4527/8000 [01:44<01:11, 48.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████             | 1939/8000 [01:48<04:53, 20.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4533/8000 [01:44<01:17, 44.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1944/8000 [01:48<05:18, 19.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▊               | 4540/8000 [01:45<01:11, 48.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1948/8000 [01:48<04:59, 20.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4547/8000 [01:45<01:13, 47.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1951/8000 [01:49<04:55, 20.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4553/8000 [01:45<01:10, 48.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  24%|████▏            | 1956/8000 [01:49<05:10, 19.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4560/8000 [01:45<01:10, 48.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1961/8000 [01:49<04:13, 23.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4566/8000 [01:45<01:16, 44.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1965/8000 [01:49<05:03, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▉               | 4571/8000 [01:45<01:31, 37.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1969/8000 [01:49<05:34, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4578/8000 [01:45<01:24, 40.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1973/8000 [01:50<06:03, 16.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4585/8000 [01:46<01:16, 44.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1975/8000 [01:50<06:28, 15.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4592/8000 [01:46<01:11, 47.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1978/8000 [01:50<05:53, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|████████████████████               | 4598/8000 [01:46<01:16, 44.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1980/8000 [01:50<06:08, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4603/8000 [01:46<01:32, 36.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1983/8000 [01:50<05:45, 17.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4608/8000 [01:46<01:27, 38.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1986/8000 [01:51<06:46, 14.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4617/8000 [01:46<01:15, 44.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1989/8000 [01:51<06:12, 16.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▏              | 4626/8000 [01:46<01:10, 47.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1993/8000 [01:51<06:31, 15.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4634/8000 [01:47<01:02, 53.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▏            | 1997/8000 [01:51<05:24, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4640/8000 [01:47<01:19, 42.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2002/8000 [01:51<05:26, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▎              | 4649/8000 [01:47<01:14, 45.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2005/8000 [01:52<05:24, 18.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4658/8000 [01:47<01:23, 39.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2009/8000 [01:52<04:48, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4664/8000 [01:47<01:26, 38.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2012/8000 [01:52<04:46, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4671/8000 [01:48<01:17, 43.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2015/8000 [01:52<05:56, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|████████████████████▍              | 4678/8000 [01:48<01:09, 47.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2019/8000 [01:52<05:05, 19.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▍              | 4684/8000 [01:48<01:07, 49.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2022/8000 [01:52<04:55, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4692/8000 [01:48<00:59, 55.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2026/8000 [01:53<05:34, 17.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4699/8000 [01:48<01:04, 51.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2029/8000 [01:53<05:18, 18.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4706/8000 [01:48<01:07, 48.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2032/8000 [01:53<05:06, 19.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▌              | 4714/8000 [01:48<01:13, 44.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  25%|████▎            | 2037/8000 [01:53<04:01, 24.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4720/8000 [01:49<01:27, 37.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2041/8000 [01:53<03:58, 24.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4727/8000 [01:49<01:20, 40.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2045/8000 [01:54<04:59, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4734/8000 [01:49<01:13, 44.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2049/8000 [01:54<04:36, 21.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▋              | 4741/8000 [01:49<01:24, 38.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2053/8000 [01:54<04:11, 23.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████▊              | 4754/8000 [01:49<01:13, 44.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▎            | 2058/8000 [01:54<03:43, 26.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▊              | 4761/8000 [01:49<01:06, 48.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2063/8000 [01:54<03:23, 29.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▊              | 4767/8000 [01:50<01:04, 49.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2068/8000 [01:54<03:53, 25.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4774/8000 [01:50<01:06, 48.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2071/8000 [01:55<04:51, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4780/8000 [01:50<01:20, 39.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2076/8000 [01:55<05:11, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4785/8000 [01:50<01:19, 40.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2079/8000 [01:55<04:59, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▉              | 4795/8000 [01:50<01:08, 46.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2084/8000 [01:55<04:57, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4805/8000 [01:50<00:58, 54.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2087/8000 [01:56<06:51, 14.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4813/8000 [01:51<01:01, 51.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2089/8000 [01:56<07:03, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4820/8000 [01:51<00:59, 53.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2091/8000 [01:56<07:14, 13.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████              | 4827/8000 [01:51<01:19, 39.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2094/8000 [01:56<07:33, 13.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|█████████████████████▏             | 4835/8000 [01:51<01:08, 46.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2098/8000 [01:56<06:05, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4841/8000 [01:51<01:05, 47.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2101/8000 [01:57<05:47, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4847/8000 [01:51<01:13, 43.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2104/8000 [01:57<06:53, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4852/8000 [01:52<01:27, 35.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2108/8000 [01:57<05:51, 16.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▏             | 4857/8000 [01:52<01:31, 34.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2111/8000 [01:57<06:45, 14.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4868/8000 [01:52<01:04, 48.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▍            | 2115/8000 [01:57<05:41, 17.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4877/8000 [01:52<00:59, 52.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  26%|████▌            | 2118/8000 [01:58<05:23, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▎             | 4883/8000 [01:52<01:05, 47.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2121/8000 [01:58<05:07, 19.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4890/8000 [01:52<01:07, 45.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2126/8000 [01:58<06:05, 16.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4899/8000 [01:52<01:02, 49.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2128/8000 [01:58<06:08, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▍             | 4908/8000 [01:53<00:55, 55.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2131/8000 [01:58<05:32, 17.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|█████████████████████▌             | 4918/8000 [01:53<00:52, 58.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2133/8000 [01:59<05:43, 17.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4925/8000 [01:53<00:51, 59.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2135/8000 [01:59<05:50, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4935/8000 [01:53<01:01, 50.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2137/8000 [01:59<06:07, 15.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▌             | 4942/8000 [01:53<01:11, 42.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2140/8000 [01:59<05:36, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4947/8000 [01:53<01:12, 42.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2146/8000 [01:59<04:54, 19.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4953/8000 [01:54<01:15, 40.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2150/8000 [01:59<05:21, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4959/8000 [01:54<01:17, 39.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2153/8000 [02:00<05:16, 18.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▋             | 4967/8000 [01:54<01:10, 42.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2156/8000 [02:00<04:55, 19.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4975/8000 [01:54<01:09, 43.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2159/8000 [02:00<04:44, 20.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4982/8000 [01:54<01:20, 37.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2162/8000 [02:00<04:27, 21.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4992/8000 [01:55<01:06, 45.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2165/8000 [02:00<04:24, 22.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████▊             | 4997/8000 [01:55<01:17, 38.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2168/8000 [02:00<04:17, 22.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5004/8000 [01:55<01:15, 39.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2171/8000 [02:01<07:19, 13.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5010/8000 [01:55<01:09, 42.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▌            | 2175/8000 [02:01<05:48, 16.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5017/8000 [01:55<01:02, 48.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2178/8000 [02:01<06:33, 14.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▉             | 5023/8000 [01:55<01:00, 49.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2182/8000 [02:01<05:23, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5031/8000 [01:55<00:54, 54.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2189/8000 [02:01<04:33, 21.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5039/8000 [01:56<00:54, 54.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2192/8000 [02:02<05:06, 18.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5048/8000 [01:56<00:46, 62.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2196/8000 [02:02<04:42, 20.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████             | 5057/8000 [01:56<00:53, 54.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  27%|████▋            | 2199/8000 [02:02<05:51, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5064/8000 [01:56<00:51, 57.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2203/8000 [02:02<05:06, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|██████████████████████▏            | 5074/8000 [01:56<00:54, 53.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2208/8000 [02:03<05:01, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▏            | 5081/8000 [01:56<00:51, 56.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2211/8000 [02:03<06:44, 14.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5089/8000 [01:56<01:00, 48.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2214/8000 [02:03<06:03, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5099/8000 [01:57<00:55, 52.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2216/8000 [02:03<06:16, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5105/8000 [01:57<01:01, 47.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2220/8000 [02:03<06:21, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▎            | 5110/8000 [01:57<01:16, 37.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2223/8000 [02:04<06:54, 13.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5118/8000 [01:57<01:04, 44.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2227/8000 [02:04<05:38, 17.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5124/8000 [01:57<01:09, 41.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▋            | 2231/8000 [02:04<04:56, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5129/8000 [01:57<01:14, 38.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2236/8000 [02:04<05:01, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5134/8000 [01:58<01:21, 35.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2241/8000 [02:05<05:01, 19.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▍            | 5140/8000 [01:58<01:12, 39.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2244/8000 [02:05<04:51, 19.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5146/8000 [01:58<01:21, 34.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2247/8000 [02:05<05:41, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5153/8000 [01:58<01:20, 35.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2249/8000 [02:05<06:43, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|██████████████████████▌            | 5158/8000 [01:58<01:16, 37.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2253/8000 [02:05<06:54, 13.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▌            | 5166/8000 [01:58<01:04, 43.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2256/8000 [02:06<06:13, 15.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▌            | 5171/8000 [01:59<01:20, 35.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2259/8000 [02:06<05:38, 16.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5177/8000 [01:59<01:14, 37.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2262/8000 [02:06<06:14, 15.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5183/8000 [01:59<01:07, 41.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2264/8000 [02:06<09:03, 10.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▋            | 5190/8000 [01:59<00:59, 47.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2267/8000 [02:07<07:54, 12.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5200/8000 [01:59<00:56, 49.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2270/8000 [02:07<06:53, 13.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5208/8000 [01:59<00:51, 54.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2272/8000 [02:07<06:52, 13.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5215/8000 [01:59<00:49, 56.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2276/8000 [02:07<05:27, 17.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▊            | 5223/8000 [02:00<00:51, 53.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  28%|████▊            | 2278/8000 [02:07<07:10, 13.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▉            | 5229/8000 [02:00<00:51, 53.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2281/8000 [02:07<06:25, 14.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▉            | 5235/8000 [02:00<01:01, 45.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2285/8000 [02:08<05:01, 18.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5240/8000 [02:00<01:02, 43.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2290/8000 [02:08<03:57, 24.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5246/8000 [02:00<00:59, 46.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▊            | 2293/8000 [02:08<04:51, 19.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▉            | 5253/8000 [02:00<01:03, 43.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2297/8000 [02:08<05:16, 18.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5259/8000 [02:00<00:59, 46.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2300/8000 [02:08<04:57, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5265/8000 [02:01<01:04, 42.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2304/8000 [02:09<05:25, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5274/8000 [02:01<00:52, 52.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2306/8000 [02:09<06:51, 13.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5280/8000 [02:01<00:57, 47.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2308/8000 [02:09<06:53, 13.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████            | 5285/8000 [02:01<00:58, 46.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2312/8000 [02:09<05:21, 17.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5293/8000 [02:01<01:03, 42.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2318/8000 [02:09<05:09, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5299/8000 [02:01<00:59, 45.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2323/8000 [02:10<04:14, 22.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5304/8000 [02:02<01:16, 35.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2329/8000 [02:10<03:28, 27.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▏           | 5310/8000 [02:02<01:15, 35.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2333/8000 [02:10<04:40, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|███████████████████████▎           | 5316/8000 [02:02<01:09, 38.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2339/8000 [02:10<03:38, 25.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5322/8000 [02:02<01:05, 40.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2344/8000 [02:10<03:18, 28.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5328/8000 [02:02<01:01, 43.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|████▉            | 2349/8000 [02:10<03:00, 31.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5334/8000 [02:02<01:06, 39.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2353/8000 [02:11<03:51, 24.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▎           | 5340/8000 [02:02<01:17, 34.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  29%|█████            | 2358/8000 [02:11<03:17, 28.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5346/8000 [02:03<01:18, 33.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2364/8000 [02:11<03:35, 26.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5352/8000 [02:03<01:09, 38.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2368/8000 [02:11<03:25, 27.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5359/8000 [02:03<01:00, 43.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2373/8000 [02:11<03:46, 24.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▍           | 5368/8000 [02:03<00:50, 52.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2376/8000 [02:12<05:28, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5376/8000 [02:03<00:45, 57.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2384/8000 [02:12<04:21, 21.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5383/8000 [02:03<00:56, 46.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2387/8000 [02:12<04:16, 21.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5390/8000 [02:04<01:04, 40.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2390/8000 [02:12<04:18, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|███████████████████████▌           | 5395/8000 [02:04<01:10, 37.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2393/8000 [02:13<05:08, 18.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5400/8000 [02:04<01:07, 38.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2398/8000 [02:13<04:04, 22.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5407/8000 [02:04<01:11, 36.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2402/8000 [02:13<03:45, 24.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5413/8000 [02:04<01:05, 39.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2405/8000 [02:13<04:56, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▋           | 5422/8000 [02:04<01:05, 39.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2408/8000 [02:13<06:08, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5430/8000 [02:04<00:54, 46.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████            | 2410/8000 [02:14<06:12, 15.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5438/8000 [02:05<00:48, 52.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2413/8000 [02:14<05:44, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5444/8000 [02:05<00:48, 52.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2418/8000 [02:14<05:10, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▊           | 5453/8000 [02:05<00:58, 43.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2420/8000 [02:14<05:21, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5458/8000 [02:05<01:07, 37.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2424/8000 [02:14<05:38, 16.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5465/8000 [02:05<01:17, 32.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2426/8000 [02:14<05:48, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5469/8000 [02:06<01:17, 32.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2430/8000 [02:15<04:42, 19.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5473/8000 [02:06<01:16, 33.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2433/8000 [02:15<05:30, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▉           | 5478/8000 [02:06<01:18, 31.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  30%|█████▏           | 2436/8000 [02:15<05:01, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▉           | 5485/8000 [02:06<01:05, 38.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2441/8000 [02:15<05:35, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5492/8000 [02:06<00:58, 43.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2444/8000 [02:15<05:05, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5497/8000 [02:06<00:57, 43.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2448/8000 [02:16<04:20, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5502/8000 [02:06<01:06, 37.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2453/8000 [02:16<03:35, 25.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5508/8000 [02:07<01:00, 41.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2458/8000 [02:16<03:55, 23.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████           | 5514/8000 [02:07<00:55, 45.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2462/8000 [02:16<04:28, 20.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5519/8000 [02:07<00:55, 44.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2465/8000 [02:16<04:32, 20.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5526/8000 [02:07<00:50, 49.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▏           | 2469/8000 [02:16<04:04, 22.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▏          | 5533/8000 [02:07<00:52, 46.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2475/8000 [02:17<04:33, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5543/8000 [02:07<00:51, 47.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2479/8000 [02:17<05:40, 16.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5548/8000 [02:07<01:04, 37.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2483/8000 [02:17<05:17, 17.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|████████████████████████▎          | 5555/8000 [02:08<00:56, 43.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2488/8000 [02:17<04:14, 21.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5562/8000 [02:08<00:57, 42.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2492/8000 [02:18<04:40, 19.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▎          | 5569/8000 [02:08<01:02, 38.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2498/8000 [02:18<04:20, 21.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5575/8000 [02:08<01:09, 34.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2503/8000 [02:18<04:23, 20.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5579/8000 [02:08<01:17, 31.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2508/8000 [02:19<05:08, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5585/8000 [02:08<01:07, 35.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2513/8000 [02:19<04:12, 21.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5589/8000 [02:09<01:08, 35.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  31%|█████▎           | 2518/8000 [02:19<05:39, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▍          | 5594/8000 [02:09<01:04, 37.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2521/8000 [02:20<06:51, 13.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5600/8000 [02:09<00:57, 41.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2523/8000 [02:20<06:34, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5605/8000 [02:09<01:04, 37.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▎           | 2527/8000 [02:20<08:06, 11.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5610/8000 [02:09<01:01, 38.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2534/8000 [02:20<05:15, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5616/8000 [02:09<00:56, 42.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2537/8000 [02:21<05:41, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5621/8000 [02:09<00:55, 42.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2542/8000 [02:21<05:01, 18.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▌          | 5626/8000 [02:10<01:12, 32.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2549/8000 [02:21<05:21, 16.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▋          | 5630/8000 [02:10<01:11, 33.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2554/8000 [02:22<05:28, 16.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|████████████████████████▋          | 5640/8000 [02:10<00:50, 46.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2556/8000 [02:22<05:48, 15.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5648/8000 [02:10<00:55, 42.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2559/8000 [02:22<07:02, 12.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▋          | 5653/8000 [02:10<01:02, 37.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2561/8000 [02:22<06:59, 12.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5660/8000 [02:10<00:54, 42.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2565/8000 [02:22<05:44, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5666/8000 [02:10<00:50, 45.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2570/8000 [02:23<04:31, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5672/8000 [02:11<01:13, 31.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2575/8000 [02:23<03:40, 24.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5679/8000 [02:11<01:13, 31.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2578/8000 [02:23<03:39, 24.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▊          | 5683/8000 [02:11<01:10, 32.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2582/8000 [02:23<03:24, 26.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5687/8000 [02:11<01:08, 33.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▍           | 2587/8000 [02:23<03:04, 29.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5693/8000 [02:11<01:06, 34.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2594/8000 [02:23<02:34, 35.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5698/8000 [02:11<01:01, 37.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  32%|█████▌           | 2598/8000 [02:23<02:36, 34.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5704/8000 [02:12<00:56, 40.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2602/8000 [02:23<02:38, 34.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▉          | 5712/8000 [02:12<00:52, 43.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2606/8000 [02:24<03:26, 26.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|█████████████████████████          | 5719/8000 [02:12<01:01, 37.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2611/8000 [02:24<03:41, 24.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5724/8000 [02:12<01:05, 35.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2617/8000 [02:24<02:59, 29.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5729/8000 [02:12<01:01, 36.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2626/8000 [02:24<02:44, 32.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5734/8000 [02:12<01:06, 34.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2630/8000 [02:25<03:26, 25.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████          | 5741/8000 [02:13<00:56, 40.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2634/8000 [02:25<03:17, 27.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5750/8000 [02:13<00:45, 49.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2639/8000 [02:25<04:13, 21.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5756/8000 [02:13<00:45, 49.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2643/8000 [02:25<05:26, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5765/8000 [02:13<00:43, 51.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▌           | 2647/8000 [02:26<05:59, 14.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5771/8000 [02:13<00:44, 50.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2651/8000 [02:26<05:51, 15.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5778/8000 [02:13<00:52, 42.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2653/8000 [02:26<05:52, 15.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5783/8000 [02:13<00:51, 43.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2656/8000 [02:26<05:09, 17.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5790/8000 [02:14<00:58, 37.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2660/8000 [02:26<04:19, 20.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5795/8000 [02:14<00:57, 38.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2665/8000 [02:27<04:27, 19.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5803/8000 [02:14<00:47, 46.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2669/8000 [02:27<04:43, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5809/8000 [02:14<01:02, 35.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2672/8000 [02:27<04:24, 20.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5815/8000 [02:14<00:55, 39.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2675/8000 [02:27<04:05, 21.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5822/8000 [02:15<01:01, 35.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  33%|█████▋           | 2678/8000 [02:27<03:49, 23.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5830/8000 [02:15<00:49, 43.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2682/8000 [02:27<03:37, 24.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5840/8000 [02:15<00:40, 53.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2685/8000 [02:27<03:43, 23.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5847/8000 [02:15<00:57, 37.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2691/8000 [02:28<03:04, 28.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5853/8000 [02:15<00:55, 38.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2695/8000 [02:28<03:43, 23.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5866/8000 [02:15<00:38, 54.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2698/8000 [02:28<03:53, 22.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5873/8000 [02:15<00:37, 57.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▋           | 2705/8000 [02:28<03:39, 24.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▋         | 5880/8000 [02:16<01:00, 34.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2709/8000 [02:29<04:28, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5889/8000 [02:16<00:54, 38.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2713/8000 [02:29<04:26, 19.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5899/8000 [02:16<00:45, 46.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2717/8000 [02:29<04:20, 20.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5905/8000 [02:16<00:48, 42.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2722/8000 [02:29<03:54, 22.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5912/8000 [02:16<00:44, 47.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2728/8000 [02:29<03:34, 24.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5919/8000 [02:17<00:41, 50.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2732/8000 [02:29<03:35, 24.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5927/8000 [02:17<00:57, 36.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2736/8000 [02:30<03:32, 24.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5933/8000 [02:17<00:52, 39.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2740/8000 [02:30<03:20, 26.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5939/8000 [02:17<00:48, 42.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2745/8000 [02:30<03:01, 28.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5946/8000 [02:17<00:43, 47.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2749/8000 [02:30<02:56, 29.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|██████████████████████████         | 5956/8000 [02:17<00:36, 56.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2755/8000 [02:30<03:50, 22.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████         | 5967/8000 [02:18<00:38, 53.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  34%|█████▊           | 2758/8000 [02:31<04:38, 18.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5977/8000 [02:18<00:37, 54.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▊           | 2763/8000 [02:31<04:37, 18.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5983/8000 [02:18<00:37, 53.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2766/8000 [02:31<04:23, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5996/8000 [02:18<00:32, 60.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2770/8000 [02:31<04:37, 18.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6003/8000 [02:18<00:44, 44.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2773/8000 [02:32<05:26, 16.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6009/8000 [02:18<00:42, 46.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2778/8000 [02:32<04:18, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6019/8000 [02:19<00:49, 39.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2782/8000 [02:32<04:47, 18.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6024/8000 [02:19<00:48, 40.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2787/8000 [02:32<05:03, 17.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6029/8000 [02:19<00:47, 41.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2792/8000 [02:33<06:53, 12.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6038/8000 [02:19<00:44, 43.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2794/8000 [02:33<06:52, 12.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6044/8000 [02:19<00:42, 46.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2798/8000 [02:33<05:49, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6049/8000 [02:19<00:42, 45.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2805/8000 [02:33<04:14, 20.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6055/8000 [02:20<00:51, 37.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2809/8000 [02:34<07:09, 12.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6061/8000 [02:20<00:48, 40.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2812/8000 [02:34<06:45, 12.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6066/8000 [02:20<00:52, 36.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2817/8000 [02:35<05:35, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6074/8000 [02:20<00:42, 45.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|█████▉           | 2820/8000 [02:35<05:17, 16.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6081/8000 [02:20<00:38, 49.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2824/8000 [02:35<06:32, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6089/8000 [02:20<00:38, 50.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2830/8000 [02:36<07:00, 12.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6098/8000 [02:20<00:32, 59.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2832/8000 [02:36<06:51, 12.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6106/8000 [02:21<00:33, 55.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  35%|██████           | 2836/8000 [02:36<05:34, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6113/8000 [02:21<00:37, 50.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2841/8000 [02:36<05:09, 16.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6120/8000 [02:21<00:45, 41.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2843/8000 [02:36<06:06, 14.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6127/8000 [02:21<00:47, 39.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2847/8000 [02:37<04:57, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6134/8000 [02:21<00:49, 37.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2852/8000 [02:37<04:49, 17.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6140/8000 [02:22<00:52, 35.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2855/8000 [02:37<04:35, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6145/8000 [02:22<00:50, 36.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2858/8000 [02:37<06:14, 13.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6154/8000 [02:22<00:39, 46.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2860/8000 [02:37<06:08, 13.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6161/8000 [02:22<00:37, 49.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2865/8000 [02:38<04:39, 18.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6167/8000 [02:22<00:38, 47.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2868/8000 [02:38<05:23, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6173/8000 [02:22<00:37, 48.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2875/8000 [02:38<03:31, 24.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6180/8000 [02:22<00:35, 52.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████           | 2880/8000 [02:38<04:18, 19.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6186/8000 [02:23<00:48, 37.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2883/8000 [02:38<04:10, 20.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6193/8000 [02:23<00:57, 31.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2886/8000 [02:39<05:08, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|███████████████████████████        | 6198/8000 [02:23<00:54, 33.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2892/8000 [02:39<03:52, 21.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6205/8000 [02:23<00:51, 34.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2895/8000 [02:39<05:12, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6211/8000 [02:23<00:46, 38.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2898/8000 [02:39<05:39, 15.05it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6219/8000 [02:23<00:43, 40.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2901/8000 [02:40<06:07, 13.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6224/8000 [02:24<00:42, 42.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2903/8000 [02:40<07:12, 11.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6229/8000 [02:24<00:41, 42.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2907/8000 [02:40<05:37, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6234/8000 [02:24<00:46, 37.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2909/8000 [02:40<06:36, 12.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6238/8000 [02:24<00:52, 33.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2912/8000 [02:41<05:40, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6246/8000 [02:24<00:41, 41.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2914/8000 [02:41<05:24, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6251/8000 [02:24<00:48, 35.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  36%|██████▏          | 2918/8000 [02:41<04:21, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6255/8000 [02:24<00:48, 36.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2921/8000 [02:41<05:04, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6260/8000 [02:25<00:56, 30.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2926/8000 [02:41<04:46, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6266/8000 [02:25<00:50, 34.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2928/8000 [02:41<04:56, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6271/8000 [02:25<00:47, 36.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2932/8000 [02:42<04:19, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6278/8000 [02:25<00:40, 42.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2935/8000 [02:42<05:57, 14.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6284/8000 [02:25<00:43, 39.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▏          | 2938/8000 [02:42<06:20, 13.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6290/8000 [02:25<00:39, 43.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2946/8000 [02:42<03:47, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6297/8000 [02:25<00:36, 47.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2950/8000 [02:42<03:36, 23.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6304/8000 [02:26<00:36, 46.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2953/8000 [02:43<03:33, 23.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6310/8000 [02:26<00:41, 41.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2959/8000 [02:43<04:59, 16.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6317/8000 [02:26<00:58, 28.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2962/8000 [02:43<04:39, 18.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6326/8000 [02:26<00:44, 38.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2966/8000 [02:44<05:01, 16.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6331/8000 [02:26<00:43, 38.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2969/8000 [02:44<04:38, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6337/8000 [02:27<00:43, 37.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2974/8000 [02:44<05:06, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6343/8000 [02:27<00:40, 40.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2977/8000 [02:44<06:33, 12.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6348/8000 [02:27<00:40, 40.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2981/8000 [02:45<06:00, 13.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6356/8000 [02:27<00:36, 44.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2983/8000 [02:45<07:43, 10.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6363/8000 [02:27<00:32, 50.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2985/8000 [02:45<07:20, 11.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6369/8000 [02:27<00:43, 37.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2988/8000 [02:45<06:06, 13.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6375/8000 [02:27<00:40, 40.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2990/8000 [02:45<05:51, 14.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6380/8000 [02:28<00:48, 33.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2993/8000 [02:45<05:09, 16.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6386/8000 [02:28<00:41, 38.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2996/8000 [02:46<05:42, 14.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6392/8000 [02:28<00:38, 42.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  37%|██████▎          | 2998/8000 [02:46<08:00, 10.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6398/8000 [02:28<00:35, 45.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3002/8000 [02:46<05:58, 13.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6404/8000 [02:28<00:33, 47.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3004/8000 [02:47<07:08, 11.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6411/8000 [02:28<00:34, 46.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3008/8000 [02:47<05:27, 15.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6420/8000 [02:28<00:28, 54.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3011/8000 [02:47<04:55, 16.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████       | 6428/8000 [02:29<00:26, 58.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3015/8000 [02:47<04:15, 19.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6435/8000 [02:29<00:33, 47.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3018/8000 [02:47<04:07, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6443/8000 [02:29<00:29, 52.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3023/8000 [02:47<04:13, 19.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6449/8000 [02:29<00:35, 43.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3028/8000 [02:48<04:08, 20.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6458/8000 [02:29<00:35, 43.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3035/8000 [02:48<03:42, 22.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6464/8000 [02:29<00:33, 46.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3039/8000 [02:48<03:31, 23.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6472/8000 [02:29<00:28, 52.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3042/8000 [02:48<03:29, 23.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6478/8000 [02:30<00:31, 48.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3045/8000 [02:48<03:31, 23.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6484/8000 [02:30<00:37, 40.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3051/8000 [02:49<04:17, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6490/8000 [02:30<00:38, 39.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▍          | 3056/8000 [02:49<04:17, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6495/8000 [02:30<00:36, 40.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3060/8000 [02:49<04:33, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6501/8000 [02:30<00:41, 36.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3068/8000 [02:49<03:43, 22.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6505/8000 [02:30<00:41, 36.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3071/8000 [02:50<03:50, 21.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6512/8000 [02:31<00:34, 43.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  38%|██████▌          | 3075/8000 [02:50<04:17, 19.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6521/8000 [02:31<00:32, 45.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3082/8000 [02:50<03:08, 26.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6528/8000 [02:31<00:29, 50.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3086/8000 [02:50<03:34, 22.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6535/8000 [02:31<00:30, 47.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3089/8000 [02:50<04:15, 19.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6540/8000 [02:31<00:40, 36.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3093/8000 [02:51<04:39, 17.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6548/8000 [02:31<00:40, 36.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3095/8000 [02:51<04:46, 17.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6553/8000 [02:32<00:38, 37.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3097/8000 [02:51<04:58, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6558/8000 [02:32<00:37, 38.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3099/8000 [02:51<05:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6564/8000 [02:32<00:33, 42.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3101/8000 [02:51<05:13, 15.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6574/8000 [02:32<00:26, 54.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3105/8000 [02:51<04:12, 19.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6581/8000 [02:32<00:27, 50.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3108/8000 [02:52<03:57, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6591/8000 [02:32<00:25, 54.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3114/8000 [02:52<02:54, 27.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6598/8000 [02:32<00:27, 51.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▌          | 3117/8000 [02:52<03:02, 26.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6604/8000 [02:33<00:32, 42.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3121/8000 [02:52<04:45, 17.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6610/8000 [02:33<00:31, 44.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3124/8000 [02:52<04:33, 17.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6615/8000 [02:33<00:30, 44.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3129/8000 [02:52<03:31, 23.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6622/8000 [02:33<00:28, 48.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3132/8000 [02:53<04:19, 18.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6628/8000 [02:33<00:34, 39.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3137/8000 [02:53<04:13, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6634/8000 [02:33<00:38, 35.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3142/8000 [02:53<03:29, 23.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6638/8000 [02:34<00:41, 32.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3148/8000 [02:53<02:48, 28.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6645/8000 [02:34<00:38, 35.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3152/8000 [02:54<04:39, 17.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6649/8000 [02:34<00:44, 30.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  39%|██████▋          | 3158/8000 [02:54<03:35, 22.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████      | 6654/8000 [02:34<00:40, 33.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3164/8000 [02:54<03:34, 22.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6661/8000 [02:34<00:36, 36.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3168/8000 [02:54<03:52, 20.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6666/8000 [02:34<00:34, 38.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3171/8000 [02:55<04:25, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6674/8000 [02:34<00:34, 38.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▋          | 3174/8000 [02:55<04:11, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6680/8000 [02:35<00:30, 42.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3177/8000 [02:55<04:04, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6685/8000 [02:35<00:33, 38.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3184/8000 [02:55<03:32, 22.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6691/8000 [02:35<00:34, 38.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3189/8000 [02:55<03:01, 26.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6695/8000 [02:35<00:34, 38.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3193/8000 [02:55<02:50, 28.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6700/8000 [02:35<00:36, 35.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3199/8000 [02:56<03:27, 23.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6705/8000 [02:35<00:39, 32.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3204/8000 [02:56<03:39, 21.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6709/8000 [02:35<00:38, 33.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3208/8000 [02:56<03:57, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6715/8000 [02:36<00:33, 38.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3213/8000 [02:56<03:59, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6720/8000 [02:36<00:31, 40.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3216/8000 [02:57<03:45, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6727/8000 [02:36<00:31, 40.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3219/8000 [02:57<04:24, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6732/8000 [02:36<00:39, 32.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3224/8000 [02:57<03:39, 21.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6739/8000 [02:36<00:35, 35.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3228/8000 [02:57<03:23, 23.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6743/8000 [02:36<00:35, 35.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▊          | 3232/8000 [02:57<03:59, 19.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6747/8000 [02:37<00:39, 31.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3236/8000 [02:58<04:21, 18.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6755/8000 [02:37<00:29, 42.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  40%|██████▉          | 3239/8000 [02:58<04:08, 19.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6763/8000 [02:37<00:31, 39.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3243/8000 [02:58<04:28, 17.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6770/8000 [02:37<00:28, 43.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3246/8000 [02:58<05:06, 15.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6776/8000 [02:37<00:27, 45.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3250/8000 [02:58<04:17, 18.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6784/8000 [02:37<00:25, 48.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3257/8000 [02:59<03:05, 25.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6790/8000 [02:38<00:45, 26.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3260/8000 [02:59<03:54, 20.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6796/8000 [02:38<00:54, 22.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3266/8000 [02:59<03:46, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6800/8000 [02:38<00:55, 21.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3269/8000 [02:59<03:44, 21.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6804/8000 [02:38<00:49, 23.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3277/8000 [03:00<04:04, 19.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6808/8000 [02:39<00:50, 23.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3282/8000 [03:00<03:35, 21.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6813/8000 [02:39<00:43, 27.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3288/8000 [03:00<03:32, 22.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6819/8000 [02:39<00:35, 33.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|██████▉          | 3293/8000 [03:00<03:12, 24.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6825/8000 [02:39<00:33, 34.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3299/8000 [03:00<02:44, 28.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6833/8000 [02:39<00:28, 40.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3305/8000 [03:01<02:27, 31.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6848/8000 [02:39<00:19, 57.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3310/8000 [03:01<02:49, 27.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6858/8000 [02:39<00:19, 59.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  41%|███████          | 3317/8000 [03:01<02:48, 27.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6865/8000 [02:40<00:18, 60.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3321/8000 [03:01<03:23, 22.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6872/8000 [02:40<00:24, 45.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3324/8000 [03:02<04:36, 16.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████     | 6881/8000 [02:40<00:21, 53.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3331/8000 [03:02<03:47, 20.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6889/8000 [02:40<00:19, 57.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3334/8000 [03:02<04:23, 17.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6896/8000 [02:40<00:23, 47.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3339/8000 [03:03<04:41, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6903/8000 [02:40<00:23, 46.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3341/8000 [03:03<05:21, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6909/8000 [02:41<00:22, 48.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3346/8000 [03:03<04:02, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6917/8000 [02:41<00:22, 49.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3349/8000 [03:03<03:46, 20.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6925/8000 [02:41<00:24, 44.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████          | 3352/8000 [03:03<03:38, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6931/8000 [02:41<00:26, 40.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3356/8000 [03:03<04:04, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6942/8000 [02:41<00:25, 41.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3361/8000 [03:04<03:58, 19.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6947/8000 [02:42<00:27, 38.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3366/8000 [03:04<03:16, 23.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6952/8000 [02:42<00:28, 36.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3370/8000 [03:04<03:06, 24.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6958/8000 [02:42<00:26, 39.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3374/8000 [03:04<03:38, 21.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6967/8000 [02:42<00:20, 50.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3377/8000 [03:04<04:13, 18.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6974/8000 [02:42<00:19, 51.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3380/8000 [03:04<04:00, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6981/8000 [02:42<00:18, 54.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3383/8000 [03:05<03:54, 19.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6990/8000 [02:42<00:16, 62.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3386/8000 [03:05<03:36, 21.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6998/8000 [02:42<00:17, 58.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3391/8000 [03:05<03:00, 25.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7006/8000 [02:43<00:20, 48.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3395/8000 [03:05<02:44, 27.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7013/8000 [02:43<00:23, 42.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  42%|███████▏         | 3399/8000 [03:05<02:59, 25.65it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7019/8000 [02:43<00:23, 41.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3402/8000 [03:05<03:02, 25.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7026/8000 [02:43<00:20, 47.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3406/8000 [03:05<02:49, 27.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7033/8000 [02:43<00:26, 37.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▏         | 3410/8000 [03:06<02:45, 27.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7043/8000 [02:44<00:20, 47.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3413/8000 [03:06<03:43, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7049/8000 [02:44<00:21, 45.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3417/8000 [03:06<04:59, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7057/8000 [02:44<00:22, 41.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3419/8000 [03:06<04:55, 15.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7062/8000 [02:44<00:23, 39.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3422/8000 [03:06<04:22, 17.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7069/8000 [02:44<00:21, 43.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3426/8000 [03:07<03:40, 20.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7075/8000 [02:44<00:22, 41.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3429/8000 [03:07<04:24, 17.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▉    | 7082/8000 [02:44<00:19, 46.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3435/8000 [03:07<03:50, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7087/8000 [02:45<00:22, 41.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3443/8000 [03:07<03:03, 24.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7092/8000 [02:45<00:24, 37.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3448/8000 [03:08<04:37, 16.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7100/8000 [02:45<00:19, 45.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3452/8000 [03:08<04:37, 16.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7107/8000 [02:45<00:19, 44.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3458/8000 [03:08<03:36, 20.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████    | 7113/8000 [02:45<00:19, 46.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3465/8000 [03:09<03:22, 22.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7118/8000 [02:45<00:18, 46.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▎         | 3468/8000 [03:09<03:55, 19.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7129/8000 [02:45<00:15, 54.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3472/8000 [03:09<03:28, 21.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7136/8000 [02:46<00:17, 49.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  43%|███████▍         | 3476/8000 [03:09<03:11, 23.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7145/8000 [02:46<00:16, 51.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3481/8000 [03:09<03:28, 21.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7154/8000 [02:46<00:14, 58.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3485/8000 [03:09<03:08, 23.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7163/8000 [02:46<00:13, 64.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3488/8000 [03:10<03:09, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7171/8000 [02:46<00:15, 52.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3496/8000 [03:10<02:38, 28.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7177/8000 [02:46<00:15, 53.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3499/8000 [03:10<03:15, 23.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7187/8000 [02:46<00:14, 57.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3502/8000 [03:10<03:14, 23.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7193/8000 [02:47<00:14, 56.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3506/8000 [03:10<03:37, 20.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7199/8000 [02:47<00:16, 49.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3511/8000 [03:11<04:21, 17.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7208/8000 [02:47<00:15, 49.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3513/8000 [03:11<04:25, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7214/8000 [02:47<00:17, 45.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3516/8000 [03:11<04:50, 15.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7221/8000 [02:47<00:21, 36.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3518/8000 [03:11<04:43, 15.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7226/8000 [02:48<00:21, 35.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3520/8000 [03:11<04:40, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7230/8000 [02:48<00:21, 35.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3523/8000 [03:11<04:07, 18.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7234/8000 [02:48<00:26, 28.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▍         | 3526/8000 [03:12<03:49, 19.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7238/8000 [02:48<00:24, 30.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3531/8000 [03:12<03:45, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7243/8000 [02:48<00:24, 30.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3537/8000 [03:12<03:23, 21.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7248/8000 [02:48<00:21, 34.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3544/8000 [03:12<03:04, 24.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7254/8000 [02:48<00:18, 39.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3547/8000 [03:13<03:45, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7260/8000 [02:49<00:20, 36.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3553/8000 [03:13<03:32, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7264/8000 [02:49<00:20, 35.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  44%|███████▌         | 3558/8000 [03:13<03:27, 21.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7268/8000 [02:49<00:20, 36.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3561/8000 [03:13<03:52, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7273/8000 [02:49<00:21, 34.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3564/8000 [03:14<04:16, 17.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7278/8000 [02:49<00:21, 33.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3568/8000 [03:14<04:21, 16.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7284/8000 [02:49<00:20, 35.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3570/8000 [03:14<04:23, 16.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7293/8000 [02:49<00:15, 47.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3574/8000 [03:14<04:24, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7301/8000 [02:49<00:13, 53.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3576/8000 [03:14<04:30, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7307/8000 [02:50<00:14, 47.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3578/8000 [03:14<04:36, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7313/8000 [02:50<00:15, 43.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3581/8000 [03:15<05:12, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|████████████████████████████████   | 7319/8000 [02:50<00:14, 45.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▌         | 3583/8000 [03:15<06:01, 12.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7325/8000 [02:50<00:14, 47.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3589/8000 [03:15<03:44, 19.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7332/8000 [02:50<00:16, 39.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3594/8000 [03:15<04:22, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████   | 7340/8000 [02:50<00:14, 47.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3597/8000 [03:16<04:43, 15.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7348/8000 [02:51<00:14, 46.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3599/8000 [03:16<04:38, 15.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7353/8000 [02:51<00:13, 46.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3604/8000 [03:16<03:35, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7362/8000 [02:51<00:13, 48.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3607/8000 [03:16<04:03, 18.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7369/8000 [02:51<00:13, 45.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3613/8000 [03:16<03:37, 20.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7374/8000 [02:51<00:15, 41.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3616/8000 [03:17<04:06, 17.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7382/8000 [02:51<00:12, 48.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3620/8000 [03:17<04:20, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7388/8000 [02:51<00:13, 45.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3623/8000 [03:17<04:02, 18.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7397/8000 [02:52<00:12, 48.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3627/8000 [03:17<03:29, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7406/8000 [02:52<00:12, 49.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3631/8000 [03:17<03:47, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7413/8000 [02:52<00:11, 52.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3634/8000 [03:17<03:36, 20.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7426/8000 [02:52<00:09, 62.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  45%|███████▋         | 3637/8000 [03:18<03:28, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7434/8000 [02:52<00:11, 50.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▋         | 3641/8000 [03:18<03:01, 24.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7441/8000 [02:52<00:11, 48.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3651/8000 [03:18<02:21, 30.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7447/8000 [02:53<00:12, 44.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3657/8000 [03:18<03:36, 20.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7453/8000 [02:53<00:11, 46.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3661/8000 [03:19<03:48, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7460/8000 [02:53<00:10, 50.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3664/8000 [03:19<04:07, 17.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7469/8000 [02:53<00:10, 52.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3668/8000 [03:19<04:17, 16.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7478/8000 [02:53<00:10, 48.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3672/8000 [03:19<04:25, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7485/8000 [02:53<00:11, 43.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3674/8000 [03:20<04:35, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7496/8000 [02:54<00:09, 51.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3677/8000 [03:20<04:13, 17.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7502/8000 [02:54<00:10, 47.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3679/8000 [03:20<04:20, 16.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7508/8000 [02:54<00:09, 49.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3682/8000 [03:20<03:54, 18.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7514/8000 [02:54<00:10, 44.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3687/8000 [03:20<02:58, 24.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7519/8000 [02:54<00:13, 36.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3690/8000 [03:20<02:56, 24.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7523/8000 [02:54<00:14, 33.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3694/8000 [03:21<05:00, 14.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7532/8000 [02:55<00:11, 40.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3698/8000 [03:21<04:50, 14.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7537/8000 [02:55<00:12, 36.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3701/8000 [03:21<05:14, 13.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7544/8000 [02:55<00:10, 42.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▊         | 3704/8000 [03:21<04:31, 15.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7551/8000 [02:55<00:11, 38.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3707/8000 [03:22<04:14, 16.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7558/8000 [02:55<00:10, 43.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  46%|███████▉         | 3714/8000 [03:22<02:51, 25.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7564/8000 [02:55<00:09, 45.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3722/8000 [03:22<02:33, 27.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7569/8000 [02:55<00:09, 46.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3728/8000 [03:22<02:11, 32.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7576/8000 [02:55<00:08, 52.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3732/8000 [03:22<02:12, 32.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7585/8000 [02:56<00:08, 48.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3736/8000 [03:22<02:46, 25.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7593/8000 [02:56<00:09, 44.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3739/8000 [03:23<03:23, 20.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7599/8000 [02:56<00:08, 47.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3745/8000 [03:23<03:47, 18.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7606/8000 [02:56<00:08, 44.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3752/8000 [03:23<02:53, 24.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7615/8000 [02:56<00:07, 53.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3756/8000 [03:23<03:14, 21.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7622/8000 [02:56<00:07, 48.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3760/8000 [03:24<03:36, 19.58it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7628/8000 [02:57<00:07, 50.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|███████▉         | 3763/8000 [03:24<03:21, 20.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7634/8000 [02:57<00:08, 40.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3766/8000 [03:24<03:15, 21.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7643/8000 [02:57<00:08, 41.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3769/8000 [03:24<03:11, 22.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7648/8000 [02:57<00:09, 35.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3772/8000 [03:24<03:01, 23.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7652/8000 [02:57<00:10, 32.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3776/8000 [03:24<02:41, 26.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7657/8000 [02:58<00:10, 31.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3779/8000 [03:24<02:49, 24.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7662/8000 [02:58<00:10, 33.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3784/8000 [03:25<03:43, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7670/8000 [02:58<00:07, 42.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3788/8000 [03:25<04:10, 16.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7675/8000 [02:58<00:08, 38.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3793/8000 [03:25<03:46, 18.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7680/8000 [02:58<00:07, 40.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  47%|████████         | 3797/8000 [03:25<03:18, 21.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7690/8000 [02:58<00:05, 53.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3800/8000 [03:26<03:55, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7697/8000 [02:58<00:05, 55.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3803/8000 [03:26<05:07, 13.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7707/8000 [02:58<00:04, 65.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3809/8000 [03:26<04:14, 16.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7716/8000 [02:59<00:04, 57.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3812/8000 [03:26<03:53, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7723/8000 [02:59<00:05, 53.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3817/8000 [03:27<03:46, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7731/8000 [02:59<00:04, 58.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████         | 3821/8000 [03:27<03:17, 21.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7739/8000 [02:59<00:05, 49.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3824/8000 [03:27<03:09, 21.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7748/8000 [02:59<00:04, 53.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3830/8000 [03:27<03:04, 22.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7754/8000 [02:59<00:04, 53.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3833/8000 [03:27<03:44, 18.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7765/8000 [02:59<00:03, 63.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3839/8000 [03:28<03:26, 20.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7772/8000 [03:00<00:03, 57.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3846/8000 [03:28<03:36, 19.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7781/8000 [03:00<00:03, 64.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3850/8000 [03:28<03:51, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7788/8000 [03:00<00:03, 65.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3853/8000 [03:29<03:48, 18.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7795/8000 [03:00<00:03, 57.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3857/8000 [03:29<03:18, 20.92it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7802/8000 [03:00<00:04, 46.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3862/8000 [03:29<03:22, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7812/8000 [03:00<00:04, 40.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3867/8000 [03:29<02:53, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7820/8000 [03:01<00:04, 42.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3870/8000 [03:29<03:30, 19.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7827/8000 [03:01<00:03, 47.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3873/8000 [03:29<03:15, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7833/8000 [03:01<00:03, 48.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3876/8000 [03:30<04:20, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7844/8000 [03:01<00:02, 54.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  48%|████████▏        | 3880/8000 [03:30<03:40, 18.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7851/8000 [03:01<00:02, 50.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3884/8000 [03:30<03:58, 17.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7862/8000 [03:01<00:02, 57.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3888/8000 [03:30<03:27, 19.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7869/8000 [03:01<00:02, 53.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3891/8000 [03:30<03:25, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7876/8000 [03:02<00:02, 51.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3895/8000 [03:31<03:01, 22.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7886/8000 [03:02<00:01, 60.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3900/8000 [03:31<02:34, 26.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7893/8000 [03:02<00:01, 53.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3903/8000 [03:31<02:31, 27.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7899/8000 [03:02<00:01, 54.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3907/8000 [03:31<02:27, 27.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7907/8000 [03:02<00:01, 51.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3910/8000 [03:31<02:30, 27.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7913/8000 [03:02<00:01, 46.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3914/8000 [03:31<03:01, 22.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7920/8000 [03:02<00:01, 51.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3921/8000 [03:31<02:24, 28.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7928/8000 [03:03<00:01, 50.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3924/8000 [03:32<02:24, 28.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7936/8000 [03:03<00:01, 48.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3929/8000 [03:32<02:07, 31.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7944/8000 [03:03<00:01, 49.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3933/8000 [03:32<02:44, 24.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7950/8000 [03:03<00:01, 45.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3936/8000 [03:32<02:47, 24.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7958/8000 [03:03<00:00, 52.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▎        | 3941/8000 [03:32<02:25, 27.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7964/8000 [03:03<00:00, 47.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3945/8000 [03:32<02:22, 28.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7970/8000 [03:04<00:00, 45.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3950/8000 [03:33<05:04, 13.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7976/8000 [03:04<00:00, 48.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3953/8000 [03:33<04:31, 14.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7983/8000 [03:04<00:00, 46.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  49%|████████▍        | 3956/8000 [03:33<04:49, 13.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7989/8000 [03:04<00:00, 48.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3960/8000 [03:34<04:34, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8001it [03:04, 43.34it/s]                                                   \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  50%|████████▍        | 3962/8000 [03:34<04:30, 14.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3965/8000 [03:34<04:00, 16.77it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3969/8000 [03:34<04:12, 15.96it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:50,  4.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3971/8000 [03:34<04:14, 15.85it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<07:07,  2.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3974/8000 [03:35<04:44, 14.16it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:04,  1.54s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3977/8000 [03:35<04:05, 16.42it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:03,  1.24s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3979/8000 [03:35<05:15, 12.74it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:27,  1.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3983/8000 [03:35<04:56, 13.53it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:08,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3985/8000 [03:35<04:55, 13.57it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:53,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3989/8000 [03:36<03:52, 17.23it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:43,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▍        | 3994/8000 [03:36<03:01, 22.12it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:36,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4000/8000 [03:36<03:29, 19.06it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:32,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4004/8000 [03:36<03:35, 18.51it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:11<02:29,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4008/8000 [03:36<03:05, 21.53it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:12<02:26,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4012/8000 [03:37<02:44, 24.19it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:13<02:24,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4017/8000 [03:37<02:59, 22.19it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:14<02:22,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4022/8000 [03:37<03:10, 20.90it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:14<02:20,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4026/8000 [03:37<02:55, 22.66it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:15<02:20,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4030/8000 [03:37<03:10, 20.87it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:16<02:19,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4033/8000 [03:38<03:00, 22.00it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:17<02:19,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  50%|████████▌        | 4037/8000 [03:38<02:46, 23.77it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:18<02:24,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4043/8000 [03:38<02:50, 23.22it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:18<02:20,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4046/8000 [03:38<03:56, 16.72it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:19<02:19,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4048/8000 [03:38<03:54, 16.86it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:20<02:17,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4051/8000 [03:39<04:58, 13.23it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:21<02:15,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▌        | 4058/8000 [03:39<04:18, 15.28it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:21<02:14,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4062/8000 [03:39<03:39, 17.92it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:22<02:12,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4071/8000 [03:39<02:44, 23.83it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:23<02:12,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4074/8000 [03:40<02:44, 23.80it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:24<02:10,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4077/8000 [03:40<02:46, 23.50it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:24<02:09,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4081/8000 [03:40<03:08, 20.75it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:25<02:08,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4084/8000 [03:40<03:48, 17.17it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:26<02:09,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4088/8000 [03:41<03:49, 17.08it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:27<02:08,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4091/8000 [03:41<03:32, 18.44it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4093/8000 [03:41<04:18, 15.12it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:28<02:07,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4096/8000 [03:41<04:29, 14.48it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:29<02:07,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4098/8000 [03:41<05:20, 12.19it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:30<02:06,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4103/8000 [03:41<03:47, 17.14it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:30<02:05,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4108/8000 [03:42<03:31, 18.37it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:31<02:05,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▋        | 4114/8000 [03:42<02:37, 24.65it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:32<02:05,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  51%|████████▊        | 4119/8000 [03:42<02:18, 28.04it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:33<02:04,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4123/8000 [03:42<03:16, 19.76it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:34<02:02,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4126/8000 [03:42<03:10, 20.30it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:34<02:02,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4130/8000 [03:43<03:29, 18.48it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:35<02:00,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4133/8000 [03:43<04:44, 13.60it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:36<02:00,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4138/8000 [03:43<04:05, 15.75it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:37<01:59,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4141/8000 [03:43<03:42, 17.35it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:37<01:57,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4147/8000 [03:44<03:16, 19.63it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:38<01:57,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4152/8000 [03:44<02:44, 23.46it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:39<01:55,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4156/8000 [03:44<03:00, 21.28it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:40<01:55,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4160/8000 [03:44<02:40, 23.95it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:40<01:54,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4163/8000 [03:44<03:14, 19.69it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:41<01:54,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4169/8000 [03:45<03:27, 18.44it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:42<01:54,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▊        | 4174/8000 [03:45<03:15, 19.54it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:43<01:53,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4179/8000 [03:45<03:15, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:43<01:52,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4183/8000 [03:46<03:20, 19.03it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:44<01:54,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4185/8000 [03:46<03:26, 18.52it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:45<01:54,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4190/8000 [03:46<03:11, 19.87it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:46<01:51,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4193/8000 [03:46<03:49, 16.61it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:47<02:00,  1.19it/s]\n",
       "\n",
-      "Generating signals:  52%|████████▉        | 4197/8000 [03:46<03:15, 19.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4204/8000 [03:47<02:48, 22.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4207/8000 [03:47<03:19, 19.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 26.36it/s]\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4210/8000 [03:47<03:44, 16.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4213/8000 [03:47<03:24, 18.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4216/8000 [03:47<03:10, 19.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4222/8000 [03:47<02:17, 27.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 77it [00:00, 117.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4227/8000 [03:48<03:03, 20.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 161it [00:00, 247.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4230/8000 [03:48<03:34, 17.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 237it [00:01, 351.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|████████▉        | 4233/8000 [03:48<03:25, 18.36it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 328it [00:01, 476.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4236/8000 [03:48<03:51, 16.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 418it [00:01, 579.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4238/8000 [03:49<04:42, 13.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 504it [00:01, 650.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4242/8000 [03:49<03:48, 16.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 592it [00:01, 710.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4244/8000 [03:49<03:50, 16.28it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 681it [00:01, 759.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4249/8000 [03:49<02:56, 21.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 460.21it/s]\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4254/8000 [03:49<02:58, 21.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4257/8000 [03:49<03:24, 18.34it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4263/8000 [03:50<02:31, 24.72it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|████▎      | 39/100 [00:00<00:00, 381.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4267/8000 [03:50<02:22, 26.27it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 354.96it/s]\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4270/8000 [03:50<02:59, 20.77it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples: 100%|███████████████████████████████████████████| 4/4 [16:28<00:00, 294.18s/it]\u001b[A\n",
+      "Replicates:  53%|████████████▊           | 8/15 [6:02:09<3:55:48, 2021.17s/it]\n",
+      "Nyquist Samples:   0%|                                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.033655787672672\n",
+      "2999: -5.9176184\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  53%|█████████        | 4274/8000 [03:50<02:38, 23.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  53%|█████████        | 4278/8000 [03:50<02:33, 24.27it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4281/8000 [03:50<03:07, 19.79it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:26,  4.36s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4285/8000 [03:51<03:24, 18.17it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:18,  2.22s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4288/8000 [03:51<03:29, 17.75it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:55,  1.50s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████        | 4291/8000 [03:51<03:22, 18.28it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:44,  1.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4296/8000 [03:51<02:47, 22.11it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<03:05,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4299/8000 [03:51<02:48, 21.96it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:45,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4302/8000 [03:52<04:11, 14.73it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:30,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4305/8000 [03:52<03:45, 16.38it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:18,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4308/8000 [03:52<03:25, 17.95it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:11,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4311/8000 [03:52<03:56, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:08,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4313/8000 [03:52<03:51, 15.91it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:07,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4317/8000 [03:53<03:03, 20.02it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<02:03,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4321/8000 [03:53<03:16, 18.69it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:11<02:00,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4325/8000 [03:53<02:52, 21.28it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<02:00,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4329/8000 [03:53<02:28, 24.79it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<01:57,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4332/8000 [03:53<02:26, 25.06it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:58,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4335/8000 [03:53<02:21, 25.81it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:14<01:56,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4338/8000 [03:53<03:05, 19.73it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:15<01:54,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4341/8000 [03:54<02:51, 21.33it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:51,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4345/8000 [03:54<03:01, 20.09it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:16<01:50,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4348/8000 [03:54<03:03, 19.88it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:49,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▏       | 4351/8000 [03:54<02:50, 21.46it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:17<01:47,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4356/8000 [03:54<02:13, 27.23it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:18<01:48,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  54%|█████████▎       | 4359/8000 [03:54<02:14, 27.09it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:47,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4365/8000 [03:55<02:22, 25.59it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:19<01:46,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4369/8000 [03:55<02:12, 27.35it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:44,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4374/8000 [03:55<03:01, 19.93it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:20<01:44,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4377/8000 [03:55<03:27, 17.45it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:21<01:45,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4380/8000 [03:56<04:23, 13.73it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:44,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4383/8000 [03:56<04:22, 13.80it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:22<01:46,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4386/8000 [03:56<03:51, 15.58it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:23<01:47,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4388/8000 [03:56<03:50, 15.70it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:23<01:49,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4391/8000 [03:56<03:25, 17.59it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:24<01:59,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4393/8000 [03:56<03:34, 16.83it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:25<02:04,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4396/8000 [03:56<03:07, 19.19it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:26<02:06,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4401/8000 [03:57<02:22, 25.20it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:27<02:10,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4404/8000 [03:57<02:23, 25.09it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:27<02:08,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4407/8000 [03:57<02:22, 25.22it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:28<02:03,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▎       | 4410/8000 [03:57<02:19, 25.67it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:29<01:56,  1.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4414/8000 [03:57<02:13, 26.87it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:29<01:49,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4419/8000 [03:57<01:58, 30.12it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:30<01:44,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4425/8000 [03:58<02:32, 23.44it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:31<01:42,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4428/8000 [03:58<02:29, 23.89it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:31<01:40,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4431/8000 [03:58<02:29, 23.95it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:32<01:38,  1.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  55%|█████████▍       | 4435/8000 [03:58<02:15, 26.33it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:32<01:35,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4440/8000 [03:58<02:29, 23.78it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:33<01:35,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4444/8000 [03:58<02:19, 25.55it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:34<01:36,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4447/8000 [03:58<02:18, 25.65it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:34<01:38,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4450/8000 [03:59<03:05, 19.09it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:35<01:36,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4456/8000 [03:59<02:17, 25.80it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:36<01:34,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4462/8000 [03:59<02:15, 26.16it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:36<01:34,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4466/8000 [03:59<02:06, 28.04it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:37<01:32,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▍       | 4470/8000 [03:59<02:30, 23.48it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:37<01:31,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4473/8000 [04:00<03:34, 16.42it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:38<01:32,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4476/8000 [04:00<03:48, 15.42it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:39<01:31,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4480/8000 [04:00<03:12, 18.32it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:39<01:33,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4484/8000 [04:00<03:20, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:40<01:33,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4490/8000 [04:01<02:55, 20.01it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:41<01:33,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4496/8000 [04:01<02:38, 22.18it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:41<01:33,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4500/8000 [04:01<02:51, 20.40it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:42<01:32,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4505/8000 [04:01<02:26, 23.90it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:43<01:32,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4510/8000 [04:01<02:05, 27.78it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:43<01:30,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4515/8000 [04:01<01:52, 31.04it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:44<01:30,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  56%|█████████▌       | 4519/8000 [04:02<02:10, 26.68it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:45<01:28,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▌       | 4526/8000 [04:02<02:27, 23.59it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:45<01:28,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4530/8000 [04:02<02:43, 21.28it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:46<01:26,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4533/8000 [04:02<02:34, 22.37it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [00:47<01:27,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4539/8000 [04:03<03:20, 17.28it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [00:47<01:28,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4545/8000 [04:03<02:35, 22.18it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [00:48<01:26,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4548/8000 [04:03<02:33, 22.47it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [00:49<01:25,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4552/8000 [04:03<03:23, 16.93it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [00:49<01:23,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4557/8000 [04:04<02:47, 20.50it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [00:50<01:23,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4561/8000 [04:04<03:26, 16.65it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▌                              | 73/200 [00:51<01:21,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4564/8000 [04:04<03:40, 15.61it/s]\u001b[A\u001b[A\n",
+      "training:  37%|█████████████████▊                              | 74/200 [00:51<01:21,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4569/8000 [04:04<02:52, 19.87it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████                              | 75/200 [00:52<01:20,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4572/8000 [04:05<03:41, 15.50it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▏                             | 76/200 [00:53<01:19,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4575/8000 [04:05<03:21, 16.99it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▍                             | 77/200 [00:53<01:19,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4578/8000 [04:05<03:03, 18.61it/s]\u001b[A\u001b[A\n",
+      "training:  39%|██████████████████▋                             | 78/200 [00:54<01:18,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▋       | 4584/8000 [04:05<02:14, 25.37it/s]\u001b[A\u001b[A\n",
+      "training:  40%|██████████████████▉                             | 79/200 [00:54<01:17,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4589/8000 [04:05<02:21, 24.11it/s]\u001b[A\u001b[A\n",
+      "training:  40%|███████████████████▏                            | 80/200 [00:55<01:18,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4594/8000 [04:05<02:27, 23.02it/s]\u001b[A\u001b[A\n",
+      "training:  40%|███████████████████▍                            | 81/200 [00:56<01:22,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4597/8000 [04:06<03:56, 14.37it/s]\u001b[A\u001b[A\n",
+      "training:  41%|███████████████████▋                            | 82/200 [00:57<01:23,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  57%|█████████▊       | 4600/8000 [04:06<04:10, 13.58it/s]\u001b[A\u001b[A\n",
+      "training:  42%|███████████████████▉                            | 83/200 [00:57<01:20,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4602/8000 [04:06<04:02, 14.04it/s]\u001b[A\u001b[A\n",
+      "training:  42%|████████████████████▏                           | 84/200 [00:58<01:19,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4607/8000 [04:06<03:02, 18.60it/s]\u001b[A\u001b[A\n",
+      "training:  42%|████████████████████▍                           | 85/200 [00:59<01:18,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4616/8000 [04:07<01:53, 29.86it/s]\u001b[A\u001b[A\n",
+      "training:  43%|████████████████████▋                           | 86/200 [00:59<01:17,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4621/8000 [04:07<02:04, 27.18it/s]\u001b[A\u001b[A\n",
+      "training:  44%|████████████████████▉                           | 87/200 [01:00<01:15,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4628/8000 [04:07<02:03, 27.35it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████████████                           | 88/200 [01:01<01:12,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4635/8000 [04:07<02:18, 24.22it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████████████▎                          | 89/200 [01:01<01:11,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4641/8000 [04:08<02:14, 24.92it/s]\u001b[A\u001b[A\n",
+      "training:  45%|█████████████████████▌                          | 90/200 [01:02<01:09,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▊       | 4646/8000 [04:08<02:15, 24.84it/s]\u001b[A\u001b[A\n",
+      "training:  46%|█████████████████████▊                          | 91/200 [01:02<01:09,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4652/8000 [04:08<02:15, 24.75it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████████████                          | 92/200 [01:04<01:15,  1.43it/s]\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4655/8000 [04:08<02:14, 24.81it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4661/8000 [04:08<02:17, 24.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4666/8000 [04:09<02:29, 22.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 26.69it/s]\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4669/8000 [04:09<02:26, 22.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4672/8000 [04:09<02:57, 18.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  58%|█████████▉       | 4675/8000 [04:09<02:44, 20.17it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4682/8000 [04:10<02:50, 19.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 86it [00:00, 126.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4685/8000 [04:10<03:41, 14.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 167it [00:01, 245.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4690/8000 [04:10<02:54, 19.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 253it [00:01, 367.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4695/8000 [04:10<02:49, 19.52it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 342it [00:01, 481.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|█████████▉       | 4701/8000 [04:11<03:00, 18.31it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 423it [00:01, 559.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4707/8000 [04:11<03:02, 18.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 504it [00:01, 621.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4710/8000 [04:11<03:02, 18.04it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 588it [00:01, 678.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4712/8000 [04:11<03:13, 17.03it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 674it [00:01, 727.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4722/8000 [04:12<02:07, 25.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 451.29it/s]\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4725/8000 [04:12<02:30, 21.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4729/8000 [04:12<02:19, 23.46it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4732/8000 [04:12<02:17, 23.85it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▏     | 47/100 [00:00<00:00, 464.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4741/8000 [04:12<01:53, 28.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 459.77it/s]\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4745/8000 [04:12<01:49, 29.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4750/8000 [04:13<02:00, 27.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4755/8000 [04:13<01:47, 30.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▌                                | 367/8000 [00:00<00:02, 3127.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  59%|██████████       | 4759/8000 [04:13<02:57, 18.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███                               | 709/8000 [00:00<00:02, 2891.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████       | 4763/8000 [04:13<02:57, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▎                            | 1056/8000 [00:00<00:02, 2782.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4767/8000 [04:14<03:00, 17.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█████▊                           | 1401/8000 [00:00<00:02, 2873.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4771/8000 [04:14<03:31, 15.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▏                         | 1734/8000 [00:00<00:02, 2768.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4773/8000 [04:14<03:28, 15.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▌                        | 2090/8000 [00:00<00:02, 2714.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4775/8000 [04:14<03:32, 15.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████                       | 2450/8000 [00:00<00:02, 2752.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4777/8000 [04:14<03:33, 15.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▌                     | 2805/8000 [00:00<00:01, 2878.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4779/8000 [04:15<03:28, 15.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████                    | 3159/8000 [00:01<00:01, 2898.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4783/8000 [04:15<03:23, 15.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▍                  | 3505/8000 [00:01<00:01, 2984.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4787/8000 [04:15<02:45, 19.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|███████████████▉                 | 3862/8000 [00:01<00:01, 2960.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4791/8000 [04:15<02:48, 19.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████▎               | 4206/8000 [00:01<00:01, 2955.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4796/8000 [04:15<02:44, 19.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|██████████████████▊              | 4562/8000 [00:01<00:01, 2946.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4804/8000 [04:16<02:13, 23.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▏            | 4893/8000 [00:01<00:01, 2831.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4811/8000 [04:16<02:04, 25.67it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|█████████████████████▋           | 5252/8000 [00:01<00:00, 2931.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4814/8000 [04:16<02:27, 21.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████          | 5604/8000 [00:01<00:00, 2997.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4818/8000 [04:16<02:41, 19.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|████████████████████████▍        | 5934/8000 [00:02<00:00, 2912.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4820/8000 [04:17<03:15, 16.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|█████████████████████████▉       | 6291/8000 [00:02<00:00, 2941.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▏      | 4822/8000 [04:17<03:14, 16.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|███████████████████████████▍     | 6652/8000 [00:02<00:00, 3015.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4826/8000 [04:17<02:37, 20.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|████████████████████████████▉    | 7000/8000 [00:02<00:00, 2885.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4829/8000 [04:17<02:54, 18.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|██████████████████████████████▎  | 7354/8000 [00:02<00:00, 2885.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4832/8000 [04:17<02:42, 19.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|███████████████████████████████▊ | 7707/8000 [00:02<00:00, 2866.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  60%|██████████▎      | 4836/8000 [04:17<02:16, 23.12it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8085it [00:02, 2890.67it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  60%|██████████▎      | 4839/8000 [04:18<02:55, 18.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4842/8000 [04:18<03:11, 16.47it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4846/8000 [04:18<02:40, 19.69it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:43,  4.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4852/8000 [04:18<02:02, 25.65it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<07:03,  2.14s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4861/8000 [04:18<01:44, 30.00it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:42,  1.43s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4867/8000 [04:19<02:13, 23.50it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:47,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4874/8000 [04:19<02:06, 24.77it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<03:08,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4878/8000 [04:19<02:30, 20.74it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:46,  1.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▎      | 4881/8000 [04:19<02:29, 20.86it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:32,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4884/8000 [04:20<03:29, 14.88it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:22,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4888/8000 [04:20<02:58, 17.39it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:14,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4892/8000 [04:20<03:06, 16.66it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:08,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4898/8000 [04:20<02:45, 18.77it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4901/8000 [04:21<02:45, 18.71it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<02:11,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4905/8000 [04:21<02:28, 20.86it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:12<02:05,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4909/8000 [04:21<02:12, 23.35it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<01:59,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4913/8000 [04:21<02:06, 24.47it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<01:58,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4916/8000 [04:21<02:14, 22.98it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:56,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  61%|██████████▍      | 4919/8000 [04:21<02:47, 18.43it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:14<01:54,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4922/8000 [04:22<03:11, 16.05it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:15<01:57,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4925/8000 [04:22<02:52, 17.85it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<02:00,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4929/8000 [04:22<02:30, 20.36it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:16<02:03,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4933/8000 [04:22<02:55, 17.51it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:17<01:57,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▍      | 4939/8000 [04:22<02:10, 23.49it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:17<01:55,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4943/8000 [04:23<02:02, 25.05it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:18<02:02,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4948/8000 [04:23<02:17, 22.26it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:19<02:04,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4951/8000 [04:23<02:39, 19.08it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:20<02:07,  1.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4960/8000 [04:23<02:02, 24.91it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:20<02:06,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4965/8000 [04:24<02:07, 23.78it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:21<02:07,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4970/8000 [04:24<02:17, 21.96it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:22<02:10,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4974/8000 [04:24<02:32, 19.84it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:23<02:17,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4978/8000 [04:24<02:14, 22.39it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:24<02:14,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4981/8000 [04:24<02:41, 18.65it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:24<02:13,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4986/8000 [04:25<02:43, 18.45it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:25<02:10,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4991/8000 [04:25<02:18, 21.74it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:26<02:07,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4994/8000 [04:25<02:46, 18.04it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:27<02:07,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  62%|██████████▌      | 4999/8000 [04:25<02:15, 22.17it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:27<02:07,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5005/8000 [04:26<02:46, 17.98it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:28<02:01,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5010/8000 [04:26<02:17, 21.77it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:29<02:00,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5015/8000 [04:26<03:01, 16.45it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:30<02:01,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5019/8000 [04:26<02:42, 18.33it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:30<01:55,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5023/8000 [04:27<02:43, 18.24it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:31<01:50,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5026/8000 [04:27<03:01, 16.40it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:32<02:08,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5028/8000 [04:27<03:37, 13.64it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:33<02:14,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5031/8000 [04:27<03:12, 15.46it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:34<02:00,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5036/8000 [04:27<02:27, 20.06it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:34<01:50,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5040/8000 [04:28<02:40, 18.45it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:35<01:45,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5043/8000 [04:28<02:59, 16.49it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:35<01:43,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5047/8000 [04:28<02:32, 19.35it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:36<01:40,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5050/8000 [04:28<02:29, 19.73it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:37<01:37,  1.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5053/8000 [04:28<02:53, 16.95it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:37<01:36,  1.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▋      | 5056/8000 [04:29<02:36, 18.76it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:38<01:33,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5060/8000 [04:29<02:16, 21.60it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:39<01:55,  1.29it/s]\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5064/8000 [04:29<03:02, 16.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5068/8000 [04:29<02:37, 18.64it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5072/8000 [04:29<02:17, 21.26it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 28.01it/s]\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5075/8000 [04:30<03:17, 14.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  63%|██████████▊      | 5079/8000 [04:30<02:44, 17.72it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5082/8000 [04:30<03:08, 15.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5084/8000 [04:30<03:06, 15.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 89it [00:00, 132.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5090/8000 [04:30<02:05, 23.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 176it [00:01, 262.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5095/8000 [04:31<02:14, 21.65it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 257it [00:01, 371.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5099/8000 [04:31<02:09, 22.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 344it [00:01, 481.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5102/8000 [04:31<02:07, 22.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 428it [00:01, 567.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5106/8000 [04:31<01:53, 25.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 513it [00:01, 637.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5111/8000 [04:31<02:51, 16.84it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 599it [00:01, 694.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▊      | 5115/8000 [04:32<03:27, 13.92it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 690it [00:01, 753.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5118/8000 [04:32<03:17, 14.63it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 456.51it/s]\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5121/8000 [04:32<03:35, 13.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5123/8000 [04:32<03:32, 13.57it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5125/8000 [04:33<03:28, 13.80it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█████      | 46/100 [00:00<00:00, 454.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5130/8000 [04:33<02:37, 18.26it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 432.05it/s]\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5133/8000 [04:33<02:23, 19.97it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  25%|██████████▊                                | 1/4 [02:34<07:42, 154.12s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.854542426594208\n",
+      "2999: -6.2227325\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  64%|██████████▉      | 5140/8000 [04:33<01:42, 27.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5145/8000 [04:33<02:04, 23.00it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5152/8000 [04:34<02:28, 19.17it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:05,  4.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  64%|██████████▉      | 5157/8000 [04:34<02:29, 19.02it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<07:05,  2.15s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5161/8000 [04:34<02:36, 18.10it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5163/8000 [04:34<02:43, 17.36it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5165/8000 [04:35<02:48, 16.79it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<03:12,  1.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5167/8000 [04:35<03:25, 13.77it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:49,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5171/8000 [04:35<02:44, 17.24it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:36,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|██████████▉      | 5174/8000 [04:35<03:03, 15.38it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:27,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5177/8000 [04:35<03:18, 14.25it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:21,  1.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5181/8000 [04:36<02:44, 17.18it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:15,  1.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5186/8000 [04:36<02:03, 22.72it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:12,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5191/8000 [04:36<02:09, 21.76it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<02:10,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5196/8000 [04:36<02:13, 21.00it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:12<02:06,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5199/8000 [04:36<02:37, 17.84it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<02:04,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5203/8000 [04:37<02:41, 17.30it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<02:02,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5209/8000 [04:37<02:01, 23.01it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:14<02:00,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5212/8000 [04:37<02:38, 17.54it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:14<02:01,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5215/8000 [04:37<02:26, 18.99it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:15<02:00,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5219/8000 [04:38<02:32, 18.18it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:16<02:04,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5223/8000 [04:38<03:22, 13.70it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:17<02:07,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5225/8000 [04:38<03:16, 14.13it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:17<02:04,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5227/8000 [04:38<03:11, 14.50it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:18<02:02,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5229/8000 [04:38<03:08, 14.70it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:19<02:01,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████      | 5233/8000 [04:38<02:34, 17.95it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:19<01:59,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  65%|███████████▏     | 5236/8000 [04:39<02:24, 19.10it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:20<01:56,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5242/8000 [04:39<02:07, 21.59it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:21<01:55,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5247/8000 [04:39<02:09, 21.26it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:21<01:54,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5250/8000 [04:39<02:03, 22.20it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:22<01:54,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5254/8000 [04:40<02:30, 18.20it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:23<01:55,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5258/8000 [04:40<02:11, 20.78it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:23<01:53,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5263/8000 [04:40<01:53, 24.14it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:24<01:51,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5266/8000 [04:40<01:55, 23.67it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:25<01:50,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5271/8000 [04:40<02:34, 17.71it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:25<01:49,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5276/8000 [04:41<02:33, 17.70it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:26<01:48,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5283/8000 [04:41<02:11, 20.59it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:26<01:48,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5287/8000 [04:41<02:25, 18.62it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:27<01:48,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5289/8000 [04:41<02:54, 15.56it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:28<01:47,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▏     | 5292/8000 [04:42<02:40, 16.86it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:29<01:52,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5296/8000 [04:42<02:15, 20.03it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:29<01:50,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5299/8000 [04:42<02:09, 20.94it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:30<01:51,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5304/8000 [04:42<02:07, 21.16it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:31<01:48,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5308/8000 [04:42<01:57, 22.83it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:31<01:46,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5312/8000 [04:42<02:12, 20.27it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5315/8000 [04:43<02:40, 16.76it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:33<01:48,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  66%|███████████▎     | 5319/8000 [04:43<02:17, 19.46it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:33<01:49,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5324/8000 [04:43<02:17, 19.53it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:34<01:46,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5328/8000 [04:43<02:01, 22.03it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:35<01:43,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5331/8000 [04:43<02:24, 18.51it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5336/8000 [04:44<02:18, 19.24it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:36<01:41,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5341/8000 [04:44<02:40, 16.61it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:37<01:42,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5344/8000 [04:44<02:28, 17.91it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:38<01:48,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▎     | 5347/8000 [04:44<02:20, 18.92it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:38<01:52,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5354/8000 [04:45<01:57, 22.43it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:39<01:52,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5359/8000 [04:45<02:08, 20.62it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:40<01:53,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5363/8000 [04:45<02:20, 18.82it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:41<01:52,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5366/8000 [04:45<02:14, 19.57it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:42<01:52,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5370/8000 [04:45<01:58, 22.20it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:42<01:53,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5375/8000 [04:45<01:39, 26.39it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:43<01:54,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5380/8000 [04:46<01:54, 22.97it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:44<01:54,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5383/8000 [04:46<02:45, 15.77it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:45<01:54,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5386/8000 [04:46<02:31, 17.29it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:46<01:52,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5389/8000 [04:46<02:21, 18.40it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:46<01:51,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5392/8000 [04:47<02:38, 16.48it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:48<01:45,  1.29it/s]\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5395/8000 [04:47<02:22, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  67%|███████████▍     | 5399/8000 [04:47<02:26, 17.80it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5403/8000 [04:47<02:06, 20.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:01, 24.46it/s]\n",
       "\n",
-      "Generating signals:  68%|███████████▍     | 5409/8000 [04:47<02:02, 21.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5412/8000 [04:48<01:56, 22.23it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5415/8000 [04:48<02:20, 18.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.40s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5419/8000 [04:48<02:06, 20.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 61it [00:01, 55.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5423/8000 [04:48<01:50, 23.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 121it [00:01, 119.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5431/8000 [04:48<01:34, 27.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 178it [00:01, 183.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5434/8000 [04:49<01:56, 22.00it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 239it [00:01, 254.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5438/8000 [04:49<01:46, 24.14it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 302it [00:01, 326.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5442/8000 [04:49<02:06, 20.27it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 372it [00:02, 406.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5445/8000 [04:49<02:03, 20.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 433it [00:02, 452.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5448/8000 [04:49<01:56, 21.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 495it [00:02, 493.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5451/8000 [04:49<01:54, 22.29it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 556it [00:02, 493.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5455/8000 [04:50<02:08, 19.74it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 614it [00:02, 508.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5458/8000 [04:50<02:30, 16.93it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 671it [00:02, 513.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5461/8000 [04:50<02:17, 18.41it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:02, 290.15it/s]\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5464/8000 [04:50<03:01, 13.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▌     | 5467/8000 [04:51<03:07, 13.54it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5471/8000 [04:51<03:30, 12.02it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|████▌      | 41/100 [00:00<00:00, 405.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  68%|███████████▋     | 5475/8000 [04:51<02:47, 15.03it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 367.36it/s]\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5481/8000 [04:51<01:57, 21.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5484/8000 [04:51<01:58, 21.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5487/8000 [04:51<01:54, 21.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 92/8000 [00:00<00:11, 712.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5491/8000 [04:52<02:11, 19.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 188/8000 [00:00<00:10, 742.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5495/8000 [04:52<01:53, 22.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                 | 287/8000 [00:00<00:09, 792.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5500/8000 [04:52<01:34, 26.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                 | 392/8000 [00:00<00:09, 804.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5508/8000 [04:52<01:32, 27.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                | 492/8000 [00:00<00:09, 827.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5512/8000 [04:52<01:27, 28.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                | 589/8000 [00:00<00:09, 796.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5516/8000 [04:53<01:51, 22.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|██▉                                | 683/8000 [00:00<00:08, 813.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5519/8000 [04:53<01:58, 20.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▍                               | 780/8000 [00:00<00:08, 802.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5522/8000 [04:53<02:56, 14.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                               | 879/8000 [00:01<00:08, 837.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▋     | 5527/8000 [04:54<02:43, 15.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▏                              | 970/8000 [00:01<00:08, 821.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5530/8000 [04:54<02:27, 16.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                             | 1065/8000 [00:01<00:08, 837.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5535/8000 [04:54<02:14, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████                             | 1179/8000 [00:01<00:07, 887.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5541/8000 [04:54<02:00, 20.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                            | 1270/8000 [00:01<00:07, 856.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5545/8000 [04:54<01:48, 22.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1371/8000 [00:01<00:07, 888.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5551/8000 [04:55<02:03, 19.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                           | 1512/8000 [00:01<00:07, 878.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  69%|███████████▊     | 5556/8000 [04:55<02:06, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▉                           | 1623/8000 [00:01<00:06, 914.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5560/8000 [04:55<02:20, 17.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▎                          | 1727/8000 [00:02<00:06, 924.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5562/8000 [04:55<02:22, 17.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▊                          | 1826/8000 [00:02<00:06, 918.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5564/8000 [04:55<02:22, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1932/8000 [00:02<00:06, 944.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5567/8000 [04:56<02:09, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▊                         | 2075/8000 [00:02<00:06, 906.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5574/8000 [04:56<01:52, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▏                        | 2173/8000 [00:02<00:06, 890.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5578/8000 [04:56<02:06, 19.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▌                        | 2263/8000 [00:02<00:06, 855.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5580/8000 [04:56<03:03, 13.18it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|██████████                        | 2357/8000 [00:02<00:06, 839.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5583/8000 [04:57<03:15, 12.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▍                       | 2446/8000 [00:02<00:06, 837.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5585/8000 [04:57<03:04, 13.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▉                       | 2560/8000 [00:02<00:06, 887.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▊     | 5588/8000 [04:57<02:40, 15.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2661/8000 [00:03<00:05, 907.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5590/8000 [04:57<02:36, 15.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                      | 2758/8000 [00:03<00:05, 913.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5595/8000 [04:57<01:54, 20.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▏                     | 2868/8000 [00:03<00:05, 906.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5598/8000 [04:58<02:25, 16.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3008/8000 [00:03<00:05, 882.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5601/8000 [04:58<02:13, 17.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▏                    | 3102/8000 [00:03<00:05, 885.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5604/8000 [04:58<02:36, 15.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▌                    | 3205/8000 [00:03<00:05, 911.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5606/8000 [04:58<02:34, 15.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▏                   | 3341/8000 [00:03<00:05, 889.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5608/8000 [04:58<02:34, 15.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▌                   | 3431/8000 [00:03<00:05, 881.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5610/8000 [04:58<02:40, 14.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▉                   | 3528/8000 [00:04<00:05, 872.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5615/8000 [04:59<02:21, 16.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▍                  | 3634/8000 [00:04<00:04, 900.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5618/8000 [04:59<02:13, 17.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▊                  | 3729/8000 [00:04<00:04, 880.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5623/8000 [04:59<02:06, 18.83it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▎                 | 3825/8000 [00:04<00:04, 896.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5626/8000 [04:59<01:57, 20.20it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▊                 | 3950/8000 [00:04<00:04, 850.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5629/8000 [05:00<03:24, 11.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▏                | 4056/8000 [00:04<00:04, 883.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5632/8000 [05:00<03:00, 13.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▋                | 4148/8000 [00:04<00:04, 879.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  70%|███████████▉     | 5639/8000 [05:00<02:45, 14.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████                | 4251/8000 [00:04<00:04, 836.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5641/8000 [05:01<03:43, 10.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4351/8000 [00:05<00:04, 863.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5644/8000 [05:01<03:12, 12.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|██████████████████▉               | 4447/8000 [00:05<00:04, 886.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|███████████▉     | 5646/8000 [05:01<03:54, 10.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▎              | 4541/8000 [00:05<00:04, 846.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5648/8000 [05:01<03:44, 10.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▋              | 4628/8000 [00:05<00:04, 823.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5651/8000 [05:01<03:02, 12.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████              | 4715/8000 [00:05<00:04, 790.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5655/8000 [05:02<02:21, 16.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▍             | 4813/8000 [00:05<00:03, 822.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5658/8000 [05:02<02:08, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▉             | 4912/8000 [00:05<00:03, 811.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5662/8000 [05:02<01:56, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5005/8000 [00:05<00:03, 807.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5668/8000 [05:02<01:28, 26.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5107/8000 [00:05<00:03, 827.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5673/8000 [05:02<01:39, 23.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▏           | 5208/8000 [00:06<00:03, 855.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5676/8000 [05:03<02:00, 19.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5312/8000 [00:06<00:03, 878.85it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5679/8000 [05:03<01:54, 20.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|██████████████████████▉           | 5401/8000 [00:06<00:03, 854.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5682/8000 [05:03<02:17, 16.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▎          | 5495/8000 [00:06<00:03, 827.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5685/8000 [05:03<02:09, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▊          | 5592/8000 [00:06<00:02, 828.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5687/8000 [05:03<02:14, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▏         | 5682/8000 [00:06<00:02, 831.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5690/8000 [05:03<02:38, 14.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▌         | 5792/8000 [00:06<00:02, 900.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5693/8000 [05:04<02:18, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████         | 5893/8000 [00:06<00:02, 908.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5695/8000 [05:04<02:19, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6041/8000 [00:06<00:02, 916.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5697/8000 [05:04<02:21, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████        | 6140/8000 [00:07<00:02, 873.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5699/8000 [05:04<02:25, 15.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▋       | 6279/8000 [00:07<00:01, 865.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████     | 5702/8000 [05:04<02:11, 17.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████       | 6381/8000 [00:07<00:01, 866.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5707/8000 [05:04<01:36, 23.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6482/8000 [00:07<00:01, 882.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5714/8000 [05:04<01:26, 26.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6579/8000 [00:07<00:01, 902.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  71%|████████████▏    | 5718/8000 [05:05<01:47, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6676/8000 [00:07<00:01, 907.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5724/8000 [05:05<01:27, 26.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6774/8000 [00:07<00:01, 905.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5727/8000 [05:05<01:32, 24.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6876/8000 [00:07<00:01, 919.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5731/8000 [05:05<01:46, 21.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6986/8000 [00:08<00:01, 966.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5734/8000 [05:06<02:17, 16.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7125/8000 [00:08<00:00, 950.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5737/8000 [05:06<02:06, 17.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7233/8000 [00:08<00:00, 948.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5742/8000 [05:06<02:04, 18.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7347/8000 [00:08<00:00, 954.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5745/8000 [05:06<01:57, 19.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7449/8000 [00:08<00:00, 954.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5748/8000 [05:06<01:52, 20.01it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|████████████████████████████████  | 7555/8000 [00:08<00:00, 942.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5752/8000 [05:06<01:37, 22.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7695/8000 [00:08<00:00, 868.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5755/8000 [05:07<02:55, 12.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7789/8000 [00:08<00:00, 832.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5757/8000 [05:07<03:19, 11.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7898/8000 [00:09<00:00, 847.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▏    | 5759/8000 [05:07<03:09, 11.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8046it [00:09, 869.67it/s]                                                  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  72%|████████████▏    | 5763/8000 [05:07<02:23, 15.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5766/8000 [05:08<02:06, 17.71it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5772/8000 [05:08<01:48, 20.55it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:28,  4.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5775/8000 [05:08<02:08, 17.29it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:25,  2.25s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5779/8000 [05:08<01:48, 20.42it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:04,  1.55s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5782/8000 [05:08<01:44, 21.31it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:01,  1.23s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5789/8000 [05:08<01:18, 28.15it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:24,  1.05s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5795/8000 [05:09<01:57, 18.73it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:01,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  72%|████████████▎    | 5798/8000 [05:09<02:10, 16.83it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:51,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5802/8000 [05:09<01:51, 19.63it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:37,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5805/8000 [05:09<01:48, 20.17it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:31,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5809/8000 [05:10<01:39, 22.01it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<03:00,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5813/8000 [05:10<01:56, 18.79it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:46,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5817/8000 [05:10<03:04, 11.86it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:12<02:34,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▎    | 5821/8000 [05:11<02:32, 14.32it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:13<02:25,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5824/8000 [05:11<02:19, 15.63it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:14<02:17,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5830/8000 [05:11<02:00, 18.01it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:14<02:14,  1.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5833/8000 [05:11<01:54, 18.95it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:15<02:10,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5838/8000 [05:11<01:35, 22.69it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:16<02:09,  1.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5845/8000 [05:12<01:29, 24.10it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:17<02:06,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5848/8000 [05:12<01:29, 24.16it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:17<02:06,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5855/8000 [05:12<01:08, 31.34it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:18<02:04,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5862/8000 [05:12<01:25, 24.94it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:19<02:02,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5868/8000 [05:12<01:30, 23.64it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:19<02:00,  1.47it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5873/8000 [05:13<01:23, 25.33it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:20<02:00,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  73%|████████████▍    | 5878/8000 [05:13<01:34, 22.56it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:21<02:00,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▍    | 5881/8000 [05:13<01:39, 21.23it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:21<02:01,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5884/8000 [05:13<02:06, 16.75it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:22<01:59,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5889/8000 [05:14<01:48, 19.42it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:23<01:59,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5894/8000 [05:14<01:36, 21.80it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:23<01:59,  1.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5898/8000 [05:14<01:49, 19.26it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:24<01:59,  1.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5902/8000 [05:14<02:20, 14.97it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:25<02:07,  1.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5904/8000 [05:15<02:28, 14.09it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:26<02:10,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5908/8000 [05:15<01:59, 17.49it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:27<02:12,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5911/8000 [05:15<02:21, 14.80it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:27<02:09,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5913/8000 [05:15<02:48, 12.38it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:28<02:08,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5916/8000 [05:15<02:25, 14.37it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:29<02:03,  1.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5920/8000 [05:16<01:57, 17.78it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:30<01:59,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5923/8000 [05:16<01:49, 19.05it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:30<01:56,  1.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5928/8000 [05:16<02:08, 16.16it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:31<01:53,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5932/8000 [05:17<02:51, 12.05it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:32<01:51,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5934/8000 [05:17<02:52, 11.98it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:32<01:47,  1.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5938/8000 [05:17<02:20, 14.67it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:33<01:46,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▌    | 5941/8000 [05:17<02:11, 15.71it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:33<01:44,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5945/8000 [05:17<01:50, 18.61it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:34<01:45,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5950/8000 [05:18<01:53, 18.13it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:35<01:43,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5954/8000 [05:18<01:36, 21.16it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:35<01:43,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  74%|████████████▋    | 5957/8000 [05:18<01:36, 21.13it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:36<01:42,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5962/8000 [05:18<01:39, 20.49it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:37<01:41,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5966/8000 [05:18<01:49, 18.59it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:37<01:40,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5970/8000 [05:19<01:54, 17.80it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:38<01:38,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5973/8000 [05:19<01:47, 18.79it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:39<01:38,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5977/8000 [05:19<01:36, 20.91it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:39<01:38,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5984/8000 [05:19<01:28, 22.70it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:40<01:38,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5992/8000 [05:19<01:16, 26.13it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:41<01:37,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▋    | 5995/8000 [05:19<01:18, 25.43it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:41<01:36,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6000/8000 [05:20<01:09, 28.63it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:42<01:35,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6003/8000 [05:20<01:15, 26.62it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:43<01:34,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6008/8000 [05:20<01:22, 24.14it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:43<01:33,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6011/8000 [05:20<01:21, 24.29it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:44<01:33,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6014/8000 [05:20<01:24, 23.64it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:45<01:33,  1.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6017/8000 [05:20<01:20, 24.54it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:45<01:32,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6023/8000 [05:20<01:06, 29.84it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:46<01:32,  1.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6027/8000 [05:21<01:40, 19.58it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:47<01:32,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6030/8000 [05:21<01:39, 19.79it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:47<01:30,  1.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  75%|████████████▊    | 6036/8000 [05:21<01:16, 25.75it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:48<01:28,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6041/8000 [05:21<01:24, 23.22it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:49<01:43,  1.31it/s]\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6047/8000 [05:22<01:09, 28.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6051/8000 [05:22<01:37, 19.90it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▊    | 6054/8000 [05:22<01:54, 16.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 27.74it/s]\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6059/8000 [05:22<01:51, 17.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6065/8000 [05:23<01:27, 22.10it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6070/8000 [05:23<01:59, 16.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6074/8000 [05:23<02:00, 15.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 79it [00:00, 116.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6076/8000 [05:24<02:25, 13.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 161it [00:01, 237.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6078/8000 [05:24<02:43, 11.76it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 238it [00:01, 342.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6085/8000 [05:24<01:45, 18.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 316it [00:01, 438.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6091/8000 [05:24<01:38, 19.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 388it [00:01, 504.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6094/8000 [05:24<01:34, 20.11it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 467it [00:01, 576.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6097/8000 [05:25<01:34, 20.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 547it [00:01, 636.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6100/8000 [05:25<01:33, 20.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 627it [00:01, 680.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6103/8000 [05:25<01:29, 21.22it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 708it [00:01, 717.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6106/8000 [05:25<01:32, 20.55it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 434.00it/s]\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6110/8000 [05:25<01:19, 23.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6113/8000 [05:25<01:40, 18.81it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  76%|████████████▉    | 6116/8000 [05:26<01:35, 19.75it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|████▉      | 45/100 [00:00<00:00, 446.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6122/8000 [05:26<01:14, 25.30it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 462.07it/s]\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6126/8000 [05:26<01:08, 27.44it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  50%|█████████████████████▌                     | 2/4 [05:20<05:22, 161.47s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.623595039033347\n",
+      "2999: -6.2154486\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  77%|█████████████    | 6129/8000 [05:26<01:20, 23.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6134/8000 [05:26<01:29, 20.84it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6139/8000 [05:27<01:42, 18.13it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:16,  4.00s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6147/8000 [05:27<01:10, 26.19it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:40,  2.02s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6151/8000 [05:27<01:34, 19.49it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:29,  1.37s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6154/8000 [05:27<01:44, 17.61it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:27,  1.06s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6158/8000 [05:28<01:37, 18.82it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:53,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6162/8000 [05:28<02:54, 10.52it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:35,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6164/8000 [05:29<03:03, 10.01it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:21,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6167/8000 [05:29<03:56,  7.77it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:11,  1.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6169/8000 [05:29<03:47,  8.03it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:05,  1.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6173/8000 [05:30<03:03,  9.94it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:00,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████    | 6176/8000 [05:30<02:41, 11.33it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:09<01:57,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6179/8000 [05:30<02:17, 13.28it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<01:54,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6181/8000 [05:30<02:16, 13.37it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:54,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6184/8000 [05:30<02:20, 12.95it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:11<01:52,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6186/8000 [05:30<02:12, 13.66it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:49,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6188/8000 [05:31<02:41, 11.21it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:12<01:50,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6191/8000 [05:31<02:43, 11.07it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:48,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6193/8000 [05:31<02:30, 12.01it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:50,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  77%|█████████████▏   | 6197/8000 [05:31<01:50, 16.38it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:14<01:50,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6204/8000 [05:31<01:11, 25.10it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:47,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6207/8000 [05:32<01:10, 25.26it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:15<01:47,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6210/8000 [05:32<01:14, 24.00it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:16<01:46,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6217/8000 [05:32<00:53, 33.24it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:46,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6223/8000 [05:32<00:50, 35.35it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:17<01:44,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6227/8000 [05:32<00:52, 33.96it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:43,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▏   | 6235/8000 [05:32<00:56, 31.32it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:18<01:42,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6243/8000 [05:33<00:53, 32.62it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:42,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6250/8000 [05:33<00:56, 30.84it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:39,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6258/8000 [05:33<00:55, 31.18it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:20<01:38,  1.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6263/8000 [05:33<01:14, 23.19it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:38,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6268/8000 [05:34<01:23, 20.71it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:21<01:39,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6273/8000 [05:34<01:35, 18.08it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:22<01:40,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  78%|█████████████▎   | 6277/8000 [05:35<01:51, 15.47it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:39,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6282/8000 [05:35<01:45, 16.34it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:23<01:37,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6286/8000 [05:35<01:47, 16.01it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:38,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6288/8000 [05:35<01:49, 15.57it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:24<01:38,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6290/8000 [05:35<01:53, 15.12it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:25<01:38,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▎   | 6292/8000 [05:36<01:52, 15.25it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:36,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6295/8000 [05:36<02:06, 13.48it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:26<01:38,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6300/8000 [05:36<01:36, 17.53it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:27<01:36,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6304/8000 [05:36<01:25, 19.78it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:27<01:36,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6311/8000 [05:36<01:19, 21.31it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:28<01:35,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6314/8000 [05:37<01:31, 18.41it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:35,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6317/8000 [05:37<01:45, 15.90it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:29<01:33,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6322/8000 [05:37<01:25, 19.59it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:30<01:31,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6326/8000 [05:37<01:16, 21.93it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:30<01:33,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6331/8000 [05:37<01:06, 24.92it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:31<01:31,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6334/8000 [05:38<01:28, 18.77it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:32<01:31,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6338/8000 [05:38<01:49, 15.21it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:32<01:30,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6341/8000 [05:38<02:16, 12.18it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:33<01:37,  1.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6344/8000 [05:39<01:58, 14.02it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:33<01:33,  1.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▍   | 6347/8000 [05:39<01:46, 15.58it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:34<01:30,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6355/8000 [05:39<01:19, 20.80it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:35<01:30,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  79%|█████████████▌   | 6359/8000 [05:39<01:14, 22.00it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:35<01:27,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6363/8000 [05:39<01:28, 18.56it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:36<01:26,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6367/8000 [05:40<01:20, 20.19it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:36<01:25,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6370/8000 [05:40<01:19, 20.58it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:37<01:23,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6374/8000 [05:40<01:10, 23.03it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:38<01:22,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6377/8000 [05:40<01:30, 17.85it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:38<01:20,  1.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6381/8000 [05:40<01:31, 17.65it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:39<01:20,  1.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6387/8000 [05:40<01:08, 23.45it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:39<01:21,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6390/8000 [05:41<01:44, 15.43it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:40<01:20,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6394/8000 [05:41<02:01, 13.26it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:40<01:21,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6397/8000 [05:42<02:22, 11.26it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:41<01:19,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6399/8000 [05:42<02:32, 10.50it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:42<01:18,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6405/8000 [05:42<01:40, 15.90it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:42<01:19,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▌   | 6408/8000 [05:42<01:32, 17.14it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [00:43<01:18,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6412/8000 [05:42<01:34, 16.72it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [00:43<01:18,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6415/8000 [05:43<01:28, 17.93it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [00:44<01:17,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6420/8000 [05:43<01:10, 22.34it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [00:45<01:17,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6423/8000 [05:43<01:12, 21.74it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [00:45<01:16,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6426/8000 [05:43<01:28, 17.76it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [00:46<01:16,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6433/8000 [05:43<01:27, 17.83it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▌                              | 73/200 [00:47<01:22,  1.54it/s]\n",
       "\n",
-      "Generating signals:  80%|█████████████▋   | 6437/8000 [05:44<01:18, 19.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6442/8000 [05:44<01:18, 19.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6446/8000 [05:44<01:10, 22.06it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 26.77it/s]\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6450/8000 [05:44<01:20, 19.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6453/8000 [05:44<01:18, 19.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6458/8000 [05:45<01:20, 19.25it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6463/8000 [05:45<01:20, 19.07it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 84it [00:00, 124.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▋   | 6470/8000 [05:45<01:10, 21.71it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 163it [00:01, 241.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6476/8000 [05:45<01:07, 22.66it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 240it [00:01, 346.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6481/8000 [05:46<01:13, 20.60it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 319it [00:01, 444.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6488/8000 [05:46<01:14, 20.32it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 402it [00:01, 536.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6491/8000 [05:46<01:27, 17.16it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 478it [00:01, 583.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6493/8000 [05:47<01:29, 16.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 552it [00:01, 617.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6496/8000 [05:47<01:25, 17.50it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 635it [00:01, 674.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6498/8000 [05:47<01:29, 16.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 437.69it/s]\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6500/8000 [05:47<01:30, 16.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6503/8000 [05:47<01:21, 18.39it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6507/8000 [05:47<01:22, 18.18it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|█████      | 46/100 [00:00<00:00, 458.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6509/8000 [05:47<01:25, 17.44it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 454.35it/s]\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6514/8000 [05:48<01:03, 23.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  81%|█████████████▊   | 6517/8000 [05:48<01:05, 22.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6522/8000 [05:48<00:58, 25.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                   | 33/8000 [00:00<00:29, 268.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▊   | 6527/8000 [05:48<00:50, 28.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                   | 66/8000 [00:00<00:28, 275.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6536/8000 [05:48<00:47, 31.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                  | 108/8000 [00:00<00:24, 323.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6543/8000 [05:49<00:48, 30.27it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                  | 145/8000 [00:00<00:24, 319.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6550/8000 [05:49<00:59, 24.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                  | 192/8000 [00:00<00:21, 357.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6554/8000 [05:49<01:05, 22.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                  | 247/8000 [00:00<00:21, 356.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6558/8000 [05:49<01:10, 20.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▏                                 | 284/8000 [00:00<00:22, 346.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6562/8000 [05:50<01:04, 22.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                 | 328/8000 [00:00<00:21, 351.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6566/8000 [05:50<01:22, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                 | 379/8000 [00:01<00:23, 329.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6570/8000 [05:50<01:11, 20.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                 | 423/8000 [00:01<00:23, 317.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6575/8000 [05:50<00:59, 23.95it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██                                 | 458/8000 [00:01<00:23, 323.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6580/8000 [05:50<00:53, 26.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   6%|██▏                                | 492/8000 [00:01<00:23, 313.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6584/8000 [05:51<01:07, 21.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▎                                | 539/8000 [00:01<00:21, 345.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|█████████████▉   | 6588/8000 [05:51<01:14, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                | 579/8000 [00:01<00:20, 354.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6591/8000 [05:51<01:13, 19.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                | 623/8000 [00:01<00:20, 364.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6594/8000 [05:51<01:12, 19.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                | 671/8000 [00:01<00:20, 350.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  82%|██████████████   | 6598/8000 [05:51<01:05, 21.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   9%|███▏                               | 719/8000 [00:02<00:21, 337.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6601/8000 [05:51<01:06, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▎                               | 770/8000 [00:02<00:21, 330.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6608/8000 [05:52<01:10, 19.80it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  10%|███▌                               | 807/8000 [00:02<00:22, 325.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6611/8000 [05:52<01:20, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▋                               | 841/8000 [00:02<00:23, 306.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6613/8000 [05:52<01:35, 14.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  11%|███▊                               | 884/8000 [00:02<00:21, 329.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6617/8000 [05:52<01:18, 17.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████                               | 940/8000 [00:02<00:21, 334.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6621/8000 [05:53<01:07, 20.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  12%|████▎                              | 974/8000 [00:02<00:21, 324.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6628/8000 [05:53<01:09, 19.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▎                             | 1012/8000 [00:03<00:21, 324.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6631/8000 [05:53<01:07, 20.21it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  13%|████▌                             | 1060/8000 [00:03<00:22, 314.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6634/8000 [05:53<01:21, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▋                             | 1099/8000 [00:03<00:21, 321.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6636/8000 [05:54<01:23, 16.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  14%|████▊                             | 1133/8000 [00:03<00:21, 321.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6638/8000 [05:54<01:25, 16.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|████▉                             | 1170/8000 [00:03<00:20, 327.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████   | 6644/8000 [05:54<00:59, 22.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  15%|█████▏                            | 1226/8000 [00:03<00:20, 333.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6648/8000 [05:54<01:07, 19.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▍                            | 1274/8000 [00:03<00:18, 354.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6654/8000 [05:54<01:04, 20.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  16%|█████▌                            | 1314/8000 [00:03<00:18, 356.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6661/8000 [05:54<00:49, 27.25it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  17%|█████▊                            | 1359/8000 [00:04<00:20, 329.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6665/8000 [05:55<01:11, 18.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|█████▉                            | 1406/8000 [00:04<00:19, 337.76it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6669/8000 [05:55<01:29, 14.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▏                           | 1444/8000 [00:04<00:19, 333.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6672/8000 [05:56<01:33, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  18%|██████▎                           | 1480/8000 [00:04<00:19, 336.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6675/8000 [05:56<01:38, 13.46it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  19%|██████▍                           | 1515/8000 [00:04<00:20, 317.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  83%|██████████████▏  | 6678/8000 [05:56<01:43, 12.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▋                           | 1560/8000 [00:04<00:19, 332.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6682/8000 [05:56<01:39, 13.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  20%|██████▊                           | 1599/8000 [00:04<00:19, 330.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6684/8000 [05:57<01:57, 11.17it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████                           | 1651/8000 [00:04<00:19, 327.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6689/8000 [05:57<01:40, 13.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  21%|███████▏                          | 1688/8000 [00:05<00:19, 323.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6691/8000 [05:57<01:36, 13.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▎                          | 1734/8000 [00:05<00:18, 332.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6695/8000 [05:57<01:33, 13.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  22%|███████▌                          | 1770/8000 [00:05<00:18, 331.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6698/8000 [05:58<01:40, 12.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▋                          | 1806/8000 [00:05<00:18, 334.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▏  | 6703/8000 [05:58<01:14, 17.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  23%|███████▊                          | 1844/8000 [00:05<00:18, 335.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6707/8000 [05:58<01:17, 16.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|███████▉                          | 1881/8000 [00:05<00:18, 334.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6711/8000 [05:58<01:35, 13.51it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  24%|████████▏                         | 1917/8000 [00:05<00:18, 336.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6713/8000 [05:59<01:32, 13.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▎                         | 1962/8000 [00:05<00:19, 313.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6717/8000 [05:59<01:13, 17.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▍                         | 1994/8000 [00:06<00:19, 304.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6720/8000 [05:59<01:10, 18.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  25%|████████▋                         | 2038/8000 [00:06<00:19, 301.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6723/8000 [05:59<01:07, 18.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|████████▊                         | 2085/8000 [00:06<00:19, 301.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6726/8000 [05:59<01:17, 16.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  26%|█████████                         | 2119/8000 [00:06<00:19, 305.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6728/8000 [05:59<01:18, 16.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  27%|█████████▏                        | 2158/8000 [00:06<00:17, 325.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6730/8000 [05:59<01:19, 16.04it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▍                        | 2208/8000 [00:06<00:15, 370.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6732/8000 [06:00<01:38, 12.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  28%|█████████▌                        | 2251/8000 [00:06<00:17, 327.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6734/8000 [06:00<01:57, 10.79it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▋                        | 2292/8000 [00:06<00:16, 340.83it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6737/8000 [06:00<01:33, 13.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  29%|█████████▉                        | 2335/8000 [00:07<00:16, 343.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6740/8000 [06:00<01:40, 12.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▏                       | 2383/8000 [00:07<00:17, 314.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6744/8000 [06:00<01:19, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  30%|██████████▎                       | 2427/8000 [00:07<00:16, 327.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6746/8000 [06:01<01:18, 15.93it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▌                       | 2472/8000 [00:07<00:17, 308.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6748/8000 [06:01<01:19, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  31%|██████████▋                       | 2508/8000 [00:07<00:17, 308.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6750/8000 [06:01<01:19, 15.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|██████████▊                       | 2545/8000 [00:07<00:17, 315.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6753/8000 [06:01<01:26, 14.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  32%|███████████                       | 2591/8000 [00:07<00:18, 295.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6756/8000 [06:01<01:15, 16.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▏                      | 2627/8000 [00:08<00:18, 297.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  84%|██████████████▎  | 6758/8000 [06:01<01:16, 16.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  33%|███████████▎                      | 2662/8000 [00:08<00:17, 300.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▎  | 6761/8000 [06:02<01:10, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▍                      | 2697/8000 [00:08<00:18, 293.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6767/8000 [06:02<00:48, 25.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  34%|███████████▋                      | 2744/8000 [00:08<00:16, 311.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6770/8000 [06:02<00:50, 24.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▊                      | 2785/8000 [00:08<00:16, 311.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6775/8000 [06:02<00:43, 28.47it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  35%|███████████▉                      | 2821/8000 [00:08<00:16, 316.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6778/8000 [06:02<00:57, 21.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▏                     | 2861/8000 [00:08<00:15, 335.40it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6783/8000 [06:02<00:59, 20.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  36%|████████████▎                     | 2909/8000 [00:08<00:16, 311.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6786/8000 [06:03<00:58, 20.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▌                     | 2947/8000 [00:09<00:15, 316.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6790/8000 [06:03<00:53, 22.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  37%|████████████▋                     | 2988/8000 [00:09<00:15, 332.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6794/8000 [06:03<00:52, 23.09it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|████████████▊                     | 3023/8000 [00:09<00:16, 307.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6802/8000 [06:03<00:44, 26.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  38%|█████████████                     | 3072/8000 [00:09<00:16, 302.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6805/8000 [06:03<00:58, 20.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▏                    | 3104/8000 [00:09<00:16, 289.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6808/8000 [06:04<00:55, 21.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  39%|█████████████▍                    | 3149/8000 [00:09<00:15, 310.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6813/8000 [06:04<00:45, 25.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▌                    | 3184/8000 [00:09<00:15, 306.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6817/8000 [06:04<01:06, 17.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  40%|█████████████▋                    | 3225/8000 [00:09<00:14, 326.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6820/8000 [06:04<01:03, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|█████████████▉                    | 3265/8000 [00:10<00:14, 328.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▍  | 6823/8000 [06:04<01:15, 15.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  41%|██████████████                    | 3310/8000 [00:10<00:13, 347.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6829/8000 [06:05<00:55, 21.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  42%|██████████████▏                   | 3349/8000 [00:10<00:13, 351.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6833/8000 [06:05<00:50, 23.11it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▍                   | 3401/8000 [00:10<00:13, 331.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  85%|██████████████▌  | 6837/8000 [06:05<01:11, 16.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  43%|██████████████▌                   | 3441/8000 [00:10<00:13, 341.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6841/8000 [06:05<01:02, 18.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|██████████████▊                   | 3496/8000 [00:10<00:13, 345.98it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6844/8000 [06:06<01:11, 16.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  44%|███████████████                   | 3538/8000 [00:10<00:12, 354.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6848/8000 [06:06<01:11, 16.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▏                  | 3582/8000 [00:10<00:13, 322.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6851/8000 [06:06<01:05, 17.42it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  45%|███████████████▍                  | 3626/8000 [00:11<00:14, 292.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6854/8000 [06:06<01:14, 15.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▌                  | 3675/8000 [00:11<00:13, 314.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6857/8000 [06:06<01:08, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  46%|███████████████▊                  | 3718/8000 [00:11<00:13, 326.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6859/8000 [06:06<01:11, 15.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  47%|███████████████▉                  | 3760/8000 [00:11<00:12, 332.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6865/8000 [06:07<00:50, 22.59it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▏                 | 3815/8000 [00:11<00:12, 325.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6869/8000 [06:07<00:46, 24.54it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  48%|████████████████▍                 | 3867/8000 [00:11<00:12, 320.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6872/8000 [06:07<00:47, 23.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▌                 | 3900/8000 [00:12<00:13, 313.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6875/8000 [06:07<01:02, 17.87it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  49%|████████████████▋                 | 3937/8000 [00:12<00:12, 315.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6880/8000 [06:08<01:14, 14.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|████████████████▉                 | 3985/8000 [00:12<00:13, 305.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▌  | 6882/8000 [06:08<01:16, 14.69it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  50%|█████████████████                 | 4018/8000 [00:12<00:13, 300.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6887/8000 [06:08<00:57, 19.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▎                | 4061/8000 [00:12<00:12, 323.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6892/8000 [06:08<00:57, 19.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  51%|█████████████████▍                | 4107/8000 [00:12<00:11, 338.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6896/8000 [06:08<01:04, 16.99it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▌                | 4145/8000 [00:12<00:11, 346.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6899/8000 [06:09<01:00, 18.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  52%|█████████████████▊                | 4181/8000 [00:12<00:10, 348.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6904/8000 [06:09<00:50, 21.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|█████████████████▉                | 4225/8000 [00:12<00:10, 358.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6907/8000 [06:09<00:50, 21.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  53%|██████████████████▏               | 4275/8000 [00:13<00:11, 331.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6914/8000 [06:09<00:45, 23.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  54%|██████████████████▍               | 4334/8000 [00:13<00:10, 351.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  86%|██████████████▋  | 6917/8000 [06:09<00:58, 18.48it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▋               | 4387/8000 [00:13<00:10, 346.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6921/8000 [06:10<00:52, 20.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  55%|██████████████████▊               | 4429/8000 [00:13<00:10, 352.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6926/8000 [06:10<00:43, 24.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████               | 4476/8000 [00:13<00:11, 312.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6929/8000 [06:10<00:53, 19.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  56%|███████████████████▏              | 4511/8000 [00:13<00:11, 310.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6932/8000 [06:10<00:52, 20.26it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▎              | 4547/8000 [00:13<00:11, 311.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6938/8000 [06:10<00:51, 20.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  57%|███████████████████▍              | 4587/8000 [00:14<00:10, 316.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▋  | 6941/8000 [06:11<00:50, 20.77it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  58%|███████████████████▋              | 4647/8000 [00:14<00:10, 330.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6944/8000 [06:11<00:50, 20.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|███████████████████▉              | 4684/8000 [00:14<00:10, 326.43it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6948/8000 [06:11<01:10, 14.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  59%|████████████████████              | 4727/8000 [00:14<00:09, 347.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6952/8000 [06:11<00:58, 18.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▏             | 4764/8000 [00:14<00:09, 344.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6957/8000 [06:11<00:49, 21.24it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  60%|████████████████████▍             | 4808/8000 [00:14<00:08, 366.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6961/8000 [06:11<00:44, 23.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▋             | 4866/8000 [00:14<00:08, 349.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6964/8000 [06:12<00:55, 18.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  61%|████████████████████▊             | 4904/8000 [00:14<00:08, 350.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6967/8000 [06:12<01:21, 12.71it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  62%|█████████████████████             | 4963/8000 [00:15<00:08, 345.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6970/8000 [06:12<01:12, 14.14it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▎            | 5014/8000 [00:15<00:09, 316.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6974/8000 [06:13<01:01, 16.74it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  63%|█████████████████████▍            | 5052/8000 [00:15<00:09, 310.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6977/8000 [06:13<00:59, 17.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▋            | 5095/8000 [00:15<00:08, 325.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6981/8000 [06:13<00:53, 19.13it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  64%|█████████████████████▊            | 5139/8000 [00:15<00:08, 329.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6984/8000 [06:13<00:51, 19.88it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|█████████████████████▉            | 5174/8000 [00:15<00:08, 314.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6987/8000 [06:13<01:11, 14.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  65%|██████████████████████▏           | 5215/8000 [00:15<00:08, 315.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6989/8000 [06:13<01:09, 14.52it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▎           | 5263/8000 [00:16<00:08, 338.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6992/8000 [06:14<01:01, 16.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  66%|██████████████████████▌           | 5313/8000 [00:16<00:08, 310.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6996/8000 [06:14<00:51, 19.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▋           | 5351/8000 [00:16<00:08, 316.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  87%|██████████████▊  | 6999/8000 [06:14<00:50, 20.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  67%|██████████████████████▉           | 5384/8000 [00:16<00:08, 296.38it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7004/8000 [06:14<00:52, 19.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████           | 5418/8000 [00:16<00:08, 298.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7007/8000 [06:14<01:01, 16.10it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  68%|███████████████████████▏          | 5453/8000 [00:16<00:08, 306.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7009/8000 [06:15<01:04, 15.39it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▎          | 5496/8000 [00:16<00:07, 328.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7011/8000 [06:15<01:21, 12.15it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  69%|███████████████████████▌          | 5533/8000 [00:16<00:07, 334.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7017/8000 [06:15<01:03, 15.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▋          | 5571/8000 [00:17<00:07, 331.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7020/8000 [06:15<00:57, 17.16it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  70%|███████████████████████▊          | 5608/8000 [00:17<00:07, 330.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7022/8000 [06:15<00:59, 16.55it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████          | 5655/8000 [00:17<00:06, 342.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7024/8000 [06:16<00:59, 16.50it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  71%|████████████████████████▎         | 5706/8000 [00:17<00:07, 325.99it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7028/8000 [06:16<01:24, 11.56it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▍         | 5743/8000 [00:17<00:06, 329.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7032/8000 [06:16<01:14, 13.06it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  72%|████████████████████████▋         | 5795/8000 [00:17<00:06, 327.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7037/8000 [06:16<00:55, 17.35it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  73%|████████████████████████▊         | 5836/8000 [00:17<00:06, 333.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7041/8000 [06:17<01:00, 15.97it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████         | 5884/8000 [00:18<00:06, 348.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7044/8000 [06:17<01:06, 14.44it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5922/8000 [00:18<00:06, 341.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7046/8000 [06:17<01:04, 14.73it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5958/8000 [00:18<00:06, 323.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7050/8000 [06:17<00:51, 18.29it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6008/8000 [00:18<00:06, 311.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7054/8000 [06:17<00:53, 17.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6049/8000 [00:18<00:06, 292.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|██████████████▉  | 7056/8000 [06:18<00:55, 17.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6093/8000 [00:18<00:06, 314.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7059/8000 [06:18<00:50, 18.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████        | 6133/8000 [00:18<00:05, 321.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7062/8000 [06:18<00:47, 19.78it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6175/8000 [00:18<00:05, 343.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7067/8000 [06:18<00:37, 24.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6217/8000 [00:19<00:05, 326.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7072/8000 [06:18<00:50, 18.30it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  78%|██████████████████████████▋       | 6271/8000 [00:19<00:05, 317.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7077/8000 [06:18<00:40, 23.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6304/8000 [00:19<00:05, 303.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  88%|███████████████  | 7080/8000 [06:19<00:39, 23.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6344/8000 [00:19<00:05, 310.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7084/8000 [06:19<00:35, 25.61it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6386/8000 [00:19<00:05, 313.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7089/8000 [06:19<00:40, 22.33it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6422/8000 [00:19<00:05, 304.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7094/8000 [06:19<00:45, 19.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6478/8000 [00:19<00:04, 327.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7098/8000 [06:19<00:41, 21.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▋      | 6526/8000 [00:20<00:04, 362.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7102/8000 [06:20<00:45, 19.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6587/8000 [00:20<00:03, 374.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7106/8000 [06:20<00:51, 17.32it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6637/8000 [00:20<00:04, 335.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7112/8000 [06:20<00:47, 18.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6673/8000 [00:20<00:03, 338.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████  | 7115/8000 [06:20<00:45, 19.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6719/8000 [00:20<00:04, 316.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7120/8000 [06:21<00:46, 18.89it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  84%|████████████████████████████▋     | 6760/8000 [00:20<00:03, 327.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7125/8000 [06:21<00:53, 16.41it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6813/8000 [00:20<00:03, 321.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7130/8000 [06:21<00:43, 20.03it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6866/8000 [00:21<00:03, 323.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7133/8000 [06:21<00:41, 20.86it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  86%|█████████████████████████████▍    | 6919/8000 [00:21<00:03, 323.78it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7137/8000 [06:22<00:45, 18.85it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6954/8000 [00:21<00:03, 319.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7143/8000 [06:22<00:42, 20.34it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6993/8000 [00:21<00:03, 329.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7146/8000 [06:22<00:55, 15.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7027/8000 [00:21<00:02, 327.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7149/8000 [06:22<00:51, 16.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  88%|██████████████████████████████    | 7067/8000 [00:21<00:02, 333.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  89%|███████████████▏ | 7154/8000 [06:22<00:41, 20.57it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7111/8000 [00:21<00:02, 350.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7161/8000 [06:23<00:30, 27.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  89%|██████████████████████████████▍   | 7149/8000 [00:21<00:02, 350.06it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7167/8000 [06:23<00:31, 26.37it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7200/8000 [00:22<00:02, 326.59it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▏ | 7173/8000 [06:23<00:32, 25.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  90%|██████████████████████████████▊   | 7236/8000 [00:22<00:02, 326.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7177/8000 [06:23<00:31, 26.23it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7290/8000 [00:22<00:02, 325.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7183/8000 [06:23<00:27, 30.07it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7341/8000 [00:22<00:02, 327.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7187/8000 [06:24<00:27, 29.43it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  92%|███████████████████████████████▍  | 7383/8000 [00:22<00:01, 342.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7191/8000 [06:24<00:27, 29.70it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7425/8000 [00:22<00:01, 341.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7198/8000 [06:24<00:39, 20.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7465/8000 [00:22<00:01, 345.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7201/8000 [06:24<00:38, 20.90it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7512/8000 [00:22<00:01, 363.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7204/8000 [06:25<00:43, 18.31it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7566/8000 [00:23<00:01, 344.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7208/8000 [06:25<00:47, 16.66it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7612/8000 [00:23<00:01, 340.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7210/8000 [06:25<00:47, 16.68it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7654/8000 [00:23<00:01, 331.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7212/8000 [06:25<00:49, 15.76it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7693/8000 [00:23<00:00, 319.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7214/8000 [06:25<00:51, 15.38it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|████████████████████████████████▊ | 7727/8000 [00:23<00:00, 302.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7216/8000 [06:25<00:51, 15.28it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7771/8000 [00:23<00:00, 316.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7219/8000 [06:26<00:43, 18.00it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▏| 7817/8000 [00:23<00:00, 286.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7225/8000 [06:26<00:49, 15.75it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7854/8000 [00:24<00:00, 291.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7227/8000 [06:26<00:49, 15.60it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7892/8000 [00:24<00:00, 309.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7229/8000 [06:26<00:51, 15.02it/s]\u001b[A\u001b[A\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7938/8000 [00:24<00:00, 307.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▎ | 7233/8000 [06:26<00:41, 18.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 100%|█████████████████████████████████▉| 7981/8000 [00:24<00:00, 320.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  90%|███████████████▍ | 7236/8000 [06:26<00:37, 20.40it/s]\u001b[A\u001b[A\n",
+      "Generating signals: 8017it [00:24, 326.05it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7242/8000 [06:27<00:34, 21.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7251/8000 [06:27<00:33, 22.55it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7254/8000 [06:27<00:39, 19.06it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:21,  4.33s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7257/8000 [06:28<00:38, 19.47it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:04<07:09,  2.17s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7259/8000 [06:28<00:39, 18.67it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:52,  1.49s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7264/8000 [06:28<00:32, 22.92it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7268/8000 [06:28<00:35, 20.76it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:12,  1.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7273/8000 [06:28<00:31, 23.12it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:50,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7284/8000 [06:28<00:24, 29.31it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:34,  1.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7288/8000 [06:29<00:28, 24.87it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:26,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▍ | 7293/8000 [06:29<00:31, 22.77it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:26,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7296/8000 [06:29<00:37, 18.57it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:19,  1.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7298/8000 [06:29<00:39, 17.84it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:11<02:13,  1.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7301/8000 [06:30<00:40, 17.31it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:12<02:28,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7303/8000 [06:30<00:45, 15.27it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:13<02:42,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7307/8000 [06:30<00:37, 18.29it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:13<02:26,  1.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7310/8000 [06:30<00:36, 18.94it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:14<02:14,  1.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7313/8000 [06:30<00:34, 19.88it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:14<02:07,  1.45it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  91%|███████████████▌ | 7316/8000 [06:31<00:50, 13.52it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:15<02:02,  1.49it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7320/8000 [06:31<00:47, 14.35it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:16<01:57,  1.55it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7324/8000 [06:31<00:37, 17.90it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:16<01:54,  1.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7329/8000 [06:31<00:30, 22.22it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:17<01:51,  1.61it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7332/8000 [06:31<00:28, 23.12it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:17<01:50,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7336/8000 [06:32<00:39, 16.97it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:18<01:49,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7339/8000 [06:32<00:36, 18.12it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:19<01:46,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7342/8000 [06:32<00:34, 19.05it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:19<01:46,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7345/8000 [06:32<00:32, 19.97it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:20<01:45,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7348/8000 [06:32<00:40, 16.18it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:20<01:43,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▌ | 7350/8000 [06:32<00:41, 15.67it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:21<01:42,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7353/8000 [06:33<00:37, 17.34it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:22<01:40,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7356/8000 [06:33<00:35, 18.20it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:22<01:41,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7361/8000 [06:33<00:27, 23.36it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:23<01:39,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7365/8000 [06:33<00:25, 24.44it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:23<01:38,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7368/8000 [06:33<00:26, 24.04it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:24<01:38,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7371/8000 [06:33<00:33, 18.78it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:24<01:36,  1.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7375/8000 [06:34<00:44, 13.96it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:25<01:38,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7379/8000 [06:34<00:45, 13.63it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:26<01:41,  1.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7381/8000 [06:34<00:44, 13.81it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:26<01:41,  1.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7385/8000 [06:35<00:45, 13.64it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:27<01:39,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7389/8000 [06:35<00:44, 13.82it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:28<01:38,  1.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7391/8000 [06:35<00:44, 13.74it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:28<01:36,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7393/8000 [06:35<00:43, 13.83it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:29<01:34,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7397/8000 [06:35<00:42, 14.23it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:29<01:34,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  92%|███████████████▋ | 7399/8000 [06:35<00:41, 14.48it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:30<01:34,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7402/8000 [06:36<00:37, 15.76it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:30<01:33,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7406/8000 [06:36<00:30, 19.45it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:31<01:32,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▋ | 7410/8000 [06:36<00:33, 17.63it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:32<01:31,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7412/8000 [06:36<00:34, 17.08it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:32<01:31,  1.69it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7415/8000 [06:36<00:32, 18.17it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:33<01:29,  1.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7419/8000 [06:37<00:34, 16.80it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:33<01:30,  1.68it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7421/8000 [06:37<00:41, 13.81it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:34<01:32,  1.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7423/8000 [06:37<00:51, 11.23it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:35<01:30,  1.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7426/8000 [06:37<00:43, 13.19it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:35<01:29,  1.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7431/8000 [06:38<00:37, 15.20it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:36<01:27,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7433/8000 [06:38<00:37, 15.16it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:36<01:25,  1.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7435/8000 [06:38<00:37, 14.89it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:37<01:25,  1.70it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7439/8000 [06:38<00:30, 18.57it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:38<01:41,  1.42it/s]\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7442/8000 [06:38<00:33, 16.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7447/8000 [06:38<00:32, 17.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7452/8000 [06:39<00:36, 14.98it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 27.94it/s]\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7459/8000 [06:39<00:25, 21.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7462/8000 [06:39<00:24, 21.85it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7465/8000 [06:39<00:29, 18.09it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▊ | 7468/8000 [06:40<00:28, 18.95it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 70it [00:00, 104.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7471/8000 [06:40<00:44, 11.91it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 138it [00:01, 206.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7473/8000 [06:40<00:41, 12.57it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 289.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  93%|███████████████▉ | 7477/8000 [06:40<00:32, 15.99it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 267it [00:01, 373.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7480/8000 [06:40<00:29, 17.70it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 328it [00:01, 430.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7484/8000 [06:41<00:31, 16.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 393it [00:01, 487.02it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7488/8000 [06:41<00:32, 15.68it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 458it [00:01, 530.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7492/8000 [06:41<00:28, 17.77it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 528it [00:01, 575.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7496/8000 [06:41<00:30, 16.33it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 593it [00:01, 575.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7498/8000 [06:42<00:31, 16.08it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 659it [00:01, 596.88it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7503/8000 [06:42<00:34, 14.24it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 725it [00:01, 614.52it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7508/8000 [06:42<00:27, 18.12it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:02, 390.58it/s]\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7511/8000 [06:42<00:25, 19.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7515/8000 [06:42<00:22, 21.17it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7519/8000 [06:43<00:27, 17.60it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|████▋      | 43/100 [00:00<00:00, 423.08it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7523/8000 [06:43<00:23, 20.40it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 403.51it/s]\n",
       "\n",
-      "Generating signals:  94%|███████████████▉ | 7527/8000 [06:43<00:25, 18.86it/s]\u001b[A\u001b[A\n",
+      "Nyquist Samples:  75%|████████████████████████████████▎          | 3/4 [08:49<03:02, 182.94s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.9633276054405924\n",
+      "2999: -5.7880134\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "Generating signals:  94%|████████████████ | 7534/8000 [06:43<00:21, 21.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7539/8000 [06:44<00:30, 15.19it/s]\u001b[A\u001b[A\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7547/8000 [06:44<00:20, 22.12it/s]\u001b[A\u001b[A\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:08,  4.26s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7551/8000 [06:44<00:22, 20.19it/s]\u001b[A\u001b[A\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:31,  2.28s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7555/8000 [06:44<00:20, 21.82it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:13,  1.59s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  94%|████████████████ | 7560/8000 [06:45<00:17, 24.87it/s]\u001b[A\u001b[A\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:08,  1.27s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7569/8000 [06:45<00:17, 24.36it/s]\u001b[A\u001b[A\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:32,  1.09s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7572/8000 [06:45<00:17, 24.22it/s]\u001b[A\u001b[A\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:15,  1.01s/it]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7575/8000 [06:45<00:17, 24.01it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:06,  1.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7581/8000 [06:45<00:17, 24.19it/s]\u001b[A\u001b[A\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<03:03,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7585/8000 [06:46<00:18, 23.03it/s]\u001b[A\u001b[A\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:58,  1.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████ | 7588/8000 [06:46<00:18, 22.23it/s]\u001b[A\u001b[A\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:54,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7591/8000 [06:46<00:23, 17.15it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:50,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7597/8000 [06:46<00:22, 17.56it/s]\u001b[A\u001b[A\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:49,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7600/8000 [06:47<00:22, 17.87it/s]\u001b[A\u001b[A\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:47,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7602/8000 [06:47<00:24, 15.96it/s]\u001b[A\u001b[A\n",
+      "training:   7%|███▎                                            | 14/200 [00:15<02:45,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7608/8000 [06:47<00:18, 21.20it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▌                                            | 15/200 [00:16<02:43,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7612/8000 [06:47<00:19, 19.85it/s]\u001b[A\u001b[A\n",
+      "training:   8%|███▊                                            | 16/200 [00:17<02:44,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7615/8000 [06:47<00:18, 20.60it/s]\u001b[A\u001b[A\n",
+      "training:   8%|████                                            | 17/200 [00:18<02:41,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7622/8000 [06:48<00:25, 14.93it/s]\u001b[A\u001b[A\n",
+      "training:   9%|████▎                                           | 18/200 [00:18<02:40,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7626/8000 [06:48<00:21, 17.39it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▌                                           | 19/200 [00:19<02:39,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7629/8000 [06:48<00:23, 15.57it/s]\u001b[A\u001b[A\n",
+      "training:  10%|████▊                                           | 20/200 [00:20<02:39,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7633/8000 [06:48<00:20, 18.17it/s]\u001b[A\u001b[A\n",
+      "training:  10%|█████                                           | 21/200 [00:21<02:37,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  95%|████████████████▏| 7639/8000 [06:49<00:18, 19.45it/s]\u001b[A\u001b[A\n",
+      "training:  11%|█████▎                                          | 22/200 [00:22<02:35,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▏| 7642/8000 [06:49<00:24, 14.50it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▌                                          | 23/200 [00:23<02:36,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7648/8000 [06:49<00:17, 19.56it/s]\u001b[A\u001b[A\n",
+      "training:  12%|█████▊                                          | 24/200 [00:24<02:36,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7652/8000 [06:49<00:19, 18.28it/s]\u001b[A\u001b[A\n",
+      "training:  12%|██████                                          | 25/200 [00:25<02:34,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7655/8000 [06:50<00:21, 16.12it/s]\u001b[A\u001b[A\n",
+      "training:  13%|██████▏                                         | 26/200 [00:26<02:35,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7657/8000 [06:50<00:21, 15.88it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▍                                         | 27/200 [00:26<02:35,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7661/8000 [06:50<00:17, 18.91it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▋                                         | 28/200 [00:27<02:33,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7665/8000 [06:50<00:19, 17.30it/s]\u001b[A\u001b[A\n",
+      "training:  14%|██████▉                                         | 29/200 [00:28<02:33,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7667/8000 [06:50<00:19, 16.97it/s]\u001b[A\u001b[A\n",
+      "training:  15%|███████▏                                        | 30/200 [00:29<02:32,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7672/8000 [06:51<00:15, 21.79it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▍                                        | 31/200 [00:30<02:31,  1.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7675/8000 [06:51<00:14, 22.19it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▋                                        | 32/200 [00:31<02:30,  1.12it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7678/8000 [06:51<00:18, 17.89it/s]\u001b[A\u001b[A\n",
+      "training:  16%|███████▉                                        | 33/200 [00:32<02:27,  1.13it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7681/8000 [06:51<00:16, 19.26it/s]\u001b[A\u001b[A\n",
+      "training:  17%|████████▏                                       | 34/200 [00:33<02:25,  1.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7685/8000 [06:51<00:14, 22.48it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▍                                       | 35/200 [00:33<02:22,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7688/8000 [06:51<00:13, 23.19it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▋                                       | 36/200 [00:34<02:16,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7691/8000 [06:52<00:17, 17.94it/s]\u001b[A\u001b[A\n",
+      "training:  18%|████████▉                                       | 37/200 [00:35<02:17,  1.18it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7694/8000 [06:52<00:16, 18.27it/s]\u001b[A\u001b[A\n",
+      "training:  19%|█████████                                       | 38/200 [00:36<02:14,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7698/8000 [06:52<00:14, 20.50it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:37<02:11,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▎| 7704/8000 [06:52<00:10, 27.11it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:38<02:12,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7707/8000 [06:52<00:11, 25.39it/s]\u001b[A\u001b[A\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:38<02:12,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7711/8000 [06:52<00:13, 21.48it/s]\u001b[A\u001b[A\n",
+      "training:  21%|██████████                                      | 42/200 [00:39<02:10,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7715/8000 [06:53<00:12, 23.69it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:40<02:10,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  96%|████████████████▍| 7718/8000 [06:53<00:12, 22.85it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:41<02:09,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7723/8000 [06:53<00:15, 17.70it/s]\u001b[A\u001b[A\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:42<02:08,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7727/8000 [06:53<00:17, 15.20it/s]\u001b[A\u001b[A\n",
+      "training:  23%|███████████                                     | 46/200 [00:43<02:07,  1.21it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7729/8000 [06:54<00:17, 15.25it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:43<02:07,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7732/8000 [06:54<00:16, 16.37it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:44<02:10,  1.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7734/8000 [06:54<00:17, 15.62it/s]\u001b[A\u001b[A\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:45<02:06,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7736/8000 [06:54<00:17, 15.39it/s]\u001b[A\u001b[A\n",
+      "training:  25%|████████████                                    | 50/200 [00:46<02:05,  1.20it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7739/8000 [06:54<00:18, 13.94it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:47<02:21,  1.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7741/8000 [06:54<00:17, 14.41it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:48<02:16,  1.09it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7745/8000 [06:54<00:13, 18.25it/s]\u001b[A\u001b[A\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:49<02:08,  1.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7747/8000 [06:55<00:14, 17.02it/s]\u001b[A\u001b[A\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:50<02:02,  1.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7750/8000 [06:55<00:13, 18.44it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:50<01:58,  1.22it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7754/8000 [06:55<00:14, 17.18it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:51<01:56,  1.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7756/8000 [06:55<00:14, 16.45it/s]\u001b[A\u001b[A\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:52<01:53,  1.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7759/8000 [06:55<00:17, 14.04it/s]\u001b[A\u001b[A\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:53<01:51,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▍| 7764/8000 [06:56<00:15, 15.51it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:53<01:50,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7768/8000 [06:56<00:12, 18.36it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:54<01:48,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7772/8000 [06:56<00:10, 21.14it/s]\u001b[A\u001b[A\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:55<01:47,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7777/8000 [06:56<00:11, 18.96it/s]\u001b[A\u001b[A\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:56<01:46,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7780/8000 [06:56<00:11, 19.80it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████                                 | 63/200 [00:56<01:45,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7784/8000 [06:57<00:12, 17.98it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:57<01:43,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7789/8000 [06:57<00:11, 17.87it/s]\u001b[A\u001b[A\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:58<01:43,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7795/8000 [06:57<00:10, 19.54it/s]\u001b[A\u001b[A\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:59<01:42,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  97%|████████████████▌| 7798/8000 [06:57<00:11, 17.29it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████                                | 67/200 [00:59<01:41,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7802/8000 [06:58<00:10, 19.69it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▎                               | 68/200 [01:00<01:41,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7805/8000 [06:58<00:09, 19.83it/s]\u001b[A\u001b[A\n",
+      "training:  34%|████████████████▌                               | 69/200 [01:01<01:40,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7809/8000 [06:58<00:10, 17.71it/s]\u001b[A\u001b[A\n",
+      "training:  35%|████████████████▊                               | 70/200 [01:02<01:40,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7811/8000 [06:58<00:11, 16.91it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████                               | 71/200 [01:03<01:39,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7817/8000 [06:58<00:07, 23.60it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▎                              | 72/200 [01:03<01:38,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▌| 7822/8000 [06:59<00:08, 21.77it/s]\u001b[A\u001b[A\n",
+      "training:  36%|█████████████████▌                              | 73/200 [01:04<01:37,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7825/8000 [06:59<00:08, 21.78it/s]\u001b[A\u001b[A\n",
+      "training:  37%|█████████████████▊                              | 74/200 [01:05<01:36,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7828/8000 [06:59<00:09, 17.71it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████                              | 75/200 [01:06<01:35,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7832/8000 [06:59<00:09, 16.83it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▏                             | 76/200 [01:06<01:34,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7836/8000 [06:59<00:10, 16.31it/s]\u001b[A\u001b[A\n",
+      "training:  38%|██████████████████▍                             | 77/200 [01:07<01:33,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7842/8000 [07:00<00:07, 22.30it/s]\u001b[A\u001b[A\n",
+      "training:  39%|██████████████████▋                             | 78/200 [01:08<01:33,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7845/8000 [07:00<00:07, 21.89it/s]\u001b[A\u001b[A\n",
+      "training:  40%|██████████████████▉                             | 79/200 [01:09<01:33,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7849/8000 [07:00<00:07, 18.94it/s]\u001b[A\u001b[A\n",
+      "training:  40%|███████████████████▏                            | 80/200 [01:09<01:32,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7854/8000 [07:00<00:07, 19.13it/s]\u001b[A\u001b[A\n",
+      "training:  40%|███████████████████▍                            | 81/200 [01:10<01:31,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7859/8000 [07:01<00:07, 19.56it/s]\u001b[A\u001b[A\n",
+      "training:  41%|███████████████████▋                            | 82/200 [01:11<01:30,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7863/8000 [07:01<00:08, 15.98it/s]\u001b[A\u001b[A\n",
+      "training:  42%|███████████████████▉                            | 83/200 [01:12<01:29,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7868/8000 [07:01<00:07, 17.05it/s]\u001b[A\u001b[A\n",
+      "training:  42%|████████████████████▏                           | 84/200 [01:13<01:29,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7871/8000 [07:01<00:07, 17.54it/s]\u001b[A\u001b[A\n",
+      "training:  42%|████████████████████▍                           | 85/200 [01:13<01:27,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7873/8000 [07:01<00:07, 17.12it/s]\u001b[A\u001b[A\n",
+      "training:  43%|████████████████████▋                           | 86/200 [01:14<01:27,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7875/8000 [07:02<00:07, 16.41it/s]\u001b[A\u001b[A\n",
+      "training:  44%|████████████████████▉                           | 87/200 [01:15<01:26,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  98%|████████████████▋| 7878/8000 [07:02<00:07, 17.13it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████████████                           | 88/200 [01:16<01:25,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▋| 7882/8000 [07:02<00:08, 14.04it/s]\u001b[A\u001b[A\n",
+      "training:  44%|█████████████████████▎                          | 89/200 [01:16<01:24,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7884/8000 [07:02<00:08, 14.38it/s]\u001b[A\u001b[A\n",
+      "training:  45%|█████████████████████▌                          | 90/200 [01:17<01:23,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7890/8000 [07:02<00:05, 20.26it/s]\u001b[A\u001b[A\n",
+      "training:  46%|█████████████████████▊                          | 91/200 [01:18<01:24,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7894/8000 [07:03<00:04, 22.22it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████████████                          | 92/200 [01:19<01:24,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7897/8000 [07:03<00:05, 17.98it/s]\u001b[A\u001b[A\n",
+      "training:  46%|██████████████████████▎                         | 93/200 [01:19<01:23,  1.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7901/8000 [07:03<00:06, 14.51it/s]\u001b[A\u001b[A\n",
+      "training:  47%|██████████████████████▌                         | 94/200 [01:20<01:21,  1.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7905/8000 [07:03<00:05, 17.16it/s]\u001b[A\u001b[A\n",
+      "training:  48%|██████████████████████▊                         | 95/200 [01:21<01:20,  1.30it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7911/8000 [07:04<00:04, 18.92it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████████████                         | 96/200 [01:22<01:19,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7916/8000 [07:04<00:03, 22.55it/s]\u001b[A\u001b[A\n",
+      "training:  48%|███████████████████████▎                        | 97/200 [01:22<01:18,  1.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7920/8000 [07:04<00:03, 20.35it/s]\u001b[A\u001b[A\n",
+      "training:  49%|███████████████████████▌                        | 98/200 [01:23<01:17,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7925/8000 [07:04<00:03, 20.23it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████████████▊                        | 99/200 [01:24<01:16,  1.31it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7929/8000 [07:04<00:03, 22.37it/s]\u001b[A\u001b[A\n",
+      "training:  50%|███████████████████████▌                       | 100/200 [01:26<01:26,  1.16it/s]\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7932/8000 [07:05<00:03, 18.60it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7935/8000 [07:05<00:04, 16.19it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7937/8000 [07:05<00:03, 15.79it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 25it [00:00, 25.44it/s]\n",
       "\n",
-      "Generating signals:  99%|████████████████▊| 7940/8000 [07:05<00:04, 14.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7945/8000 [07:05<00:02, 19.46it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7949/8000 [07:06<00:02, 21.67it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7953/8000 [07:06<00:01, 24.35it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 62it [00:00, 92.04it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals:  99%|████████████████▉| 7957/8000 [07:06<00:02, 21.40it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 126it [00:01, 188.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7962/8000 [07:06<00:01, 25.20it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 297.96it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7966/8000 [07:06<00:01, 26.38it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 270it [00:01, 386.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7970/8000 [07:06<00:01, 27.56it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 333it [00:01, 434.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7974/8000 [07:06<00:00, 28.53it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 401it [00:01, 493.94it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7978/8000 [07:07<00:00, 27.48it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 475it [00:01, 557.34it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7981/8000 [07:07<00:00, 24.45it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 546it [00:01, 598.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7984/8000 [07:07<00:01, 15.01it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 620it [00:01, 636.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7986/8000 [07:07<00:00, 15.02it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 690it [00:01, 640.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7989/8000 [07:08<00:00, 13.34it/s]\u001b[A\u001b[A\n",
+      "Evaluating NLL: 800it [00:01, 401.39it/s]\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7992/8000 [07:08<00:00, 15.25it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7996/8000 [07:08<00:00, 14.90it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 100%|████████████████▉| 7999/8000 [07:08<00:00, 13.73it/s]\u001b[A\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|████▍      | 40/100 [00:00<00:00, 391.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Generating signals: 8002it [07:08, 18.66it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "concatenating...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 365.59it/s]\n",
       "\n",
       "\n",
-      "training:   0%|                                       | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   0%|▏                              | 1/200 [00:07<25:36,  7.72s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                      | 7/8000 [00:00<02:47, 47.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   1%|▎                              | 2/200 [00:09<13:37,  4.13s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 14/8000 [00:00<02:23, 55.48it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▍                              | 3/200 [00:10<09:34,  2.92s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|                                     | 21/8000 [00:00<03:04, 43.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▌                              | 4/200 [00:12<08:12,  2.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 29/8000 [00:00<02:48, 47.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   2%|▊                              | 5/200 [00:14<06:58,  2.15s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   0%|▏                                    | 37/8000 [00:00<02:51, 46.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   3%|▉                              | 6/200 [00:15<06:14,  1.93s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 43/8000 [00:00<02:43, 48.80it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█                              | 7/200 [00:17<05:44,  1.79s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▏                                    | 52/8000 [00:01<02:31, 52.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▏                             | 8/200 [00:18<05:23,  1.68s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 58/8000 [00:01<03:03, 43.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   4%|█▍                             | 9/200 [00:20<05:13,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 65/8000 [00:01<03:25, 38.64it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   5%|█▌                            | 10/200 [00:21<04:59,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 71/8000 [00:01<03:36, 36.58it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▋                            | 11/200 [00:23<04:50,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 75/8000 [00:01<03:40, 35.89it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▊                            | 12/200 [00:24<04:43,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▎                                    | 79/8000 [00:01<03:42, 35.67it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   6%|█▉                            | 13/200 [00:26<04:42,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 88/8000 [00:02<02:46, 47.44it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   7%|██                            | 14/200 [00:27<04:41,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                    | 96/8000 [00:02<02:26, 54.05it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▎                           | 15/200 [00:29<04:37,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 104/8000 [00:02<02:14, 58.82it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▍                           | 16/200 [00:30<04:35,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   1%|▍                                   | 111/8000 [00:02<02:09, 60.71it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   8%|██▌                           | 17/200 [00:32<04:39,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 120/8000 [00:02<01:56, 67.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:   9%|██▋                           | 18/200 [00:33<04:43,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 127/8000 [00:02<02:14, 58.37it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|██▊                           | 19/200 [00:35<04:42,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▌                                   | 135/8000 [00:02<02:40, 49.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███                           | 20/200 [00:36<04:35,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 142/8000 [00:03<03:06, 42.17it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  10%|███▏                          | 21/200 [00:38<04:31,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 148/8000 [00:03<03:10, 41.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  11%|███▎                          | 22/200 [00:40<04:42,  1.59s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 155/8000 [00:03<03:14, 40.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▍                          | 23/200 [00:41<04:40,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▋                                   | 163/8000 [00:03<03:01, 43.19it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▌                          | 24/200 [00:43<04:33,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 170/8000 [00:03<03:01, 43.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  12%|███▊                          | 25/200 [00:44<04:29,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 177/8000 [00:03<03:00, 43.26it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  13%|███▉                          | 26/200 [00:46<04:30,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 183/8000 [00:03<02:49, 46.14it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████                          | 27/200 [00:47<04:29,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 188/8000 [00:04<02:47, 46.75it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▏                         | 28/200 [00:49<04:34,  1.60s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   2%|▊                                   | 193/8000 [00:04<03:27, 37.66it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  14%|████▎                         | 29/200 [00:50<04:30,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 205/8000 [00:04<02:34, 50.36it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  15%|████▌                         | 30/200 [00:52<04:28,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|▉                                   | 214/8000 [00:04<02:25, 53.57it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▋                         | 31/200 [00:54<04:19,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 223/8000 [00:04<02:07, 60.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▊                         | 32/200 [00:55<04:15,  1.52s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 233/8000 [00:04<02:07, 60.97it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  16%|████▉                         | 33/200 [00:56<04:12,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 241/8000 [00:04<02:03, 62.77it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  17%|█████                         | 34/200 [00:58<04:10,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█                                   | 249/8000 [00:05<02:14, 57.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▎                        | 35/200 [00:59<04:06,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 255/8000 [00:05<02:29, 51.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▍                        | 36/200 [01:01<04:04,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 264/8000 [00:05<02:22, 54.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  18%|█████▌                        | 37/200 [01:02<04:06,  1.51s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▏                                  | 272/8000 [00:05<02:28, 52.10it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  19%|█████▋                        | 38/200 [01:04<04:00,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   3%|█▎                                  | 278/8000 [00:05<02:40, 48.01it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|█████▊                        | 39/200 [01:05<03:59,  1.49s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 286/8000 [00:05<02:57, 43.46it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████                        | 40/200 [01:07<04:04,  1.53s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 293/8000 [00:06<03:14, 39.62it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  20%|██████▏                       | 41/200 [01:09<04:20,  1.64s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▎                                  | 301/8000 [00:06<02:59, 42.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  21%|██████▎                       | 42/200 [01:10<04:10,  1.58s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 306/8000 [00:06<03:21, 38.15it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▍                       | 43/200 [01:12<04:03,  1.55s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 311/8000 [00:06<03:12, 39.93it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▌                       | 44/200 [01:13<04:00,  1.54s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 322/8000 [00:06<02:25, 52.91it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  22%|██████▊                       | 45/200 [01:15<03:52,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▍                                  | 330/8000 [00:06<02:45, 46.28it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  23%|██████▉                       | 46/200 [01:16<03:50,  1.50s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 337/8000 [00:07<03:12, 39.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████                       | 47/200 [01:18<03:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   4%|█▌                                  | 342/8000 [00:07<03:09, 40.42it/s]\u001b[A\u001b[A\n",
       "\n",
-      "training:  24%|███████▏                      | 48/200 [01:21<04:16,  1.69s/it]\n",
+      "Generating signals:   4%|█▌                                  | 348/8000 [00:07<03:18, 38.50it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   4%|█▌                                  | 356/8000 [00:07<03:03, 41.72it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 366/8000 [00:07<02:25, 52.41it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.56s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▋                                  | 372/8000 [00:07<02:22, 53.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 25it [00:01, 14.69it/s]\n",
+      "Generating signals:   5%|█▋                                  | 385/8000 [00:08<02:03, 61.69it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   5%|█▊                                  | 397/8000 [00:08<01:51, 68.16it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 406/8000 [00:08<01:55, 65.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 1it [00:01,  1.61s/it]\u001b[A\u001b[A\n",
+      "Generating signals:   5%|█▊                                  | 414/8000 [00:08<02:14, 56.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 421/8000 [00:08<02:22, 53.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 427/8000 [00:08<02:42, 46.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 433/8000 [00:08<02:35, 48.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                                  | 440/8000 [00:09<02:22, 52.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 448/8000 [00:09<02:10, 57.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 457/8000 [00:09<02:24, 52.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 464/8000 [00:09<02:36, 48.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 472/8000 [00:09<02:17, 54.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 479/8000 [00:09<02:12, 56.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 487/8000 [00:10<02:32, 49.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 493/8000 [00:10<02:26, 51.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 500/8000 [00:10<02:33, 48.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 510/8000 [00:10<02:25, 51.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 516/8000 [00:10<02:38, 47.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                 | 521/8000 [00:10<02:54, 42.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 528/8000 [00:10<02:35, 48.03it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 31it [00:01, 24.98it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 535/8000 [00:11<02:37, 47.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 65it [00:01, 57.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 540/8000 [00:11<02:35, 47.84it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 97it [00:01, 91.53it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 546/8000 [00:11<03:44, 33.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 127it [00:02, 123.63it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 551/8000 [00:11<03:48, 32.65it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 159it [00:02, 159.19it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▍                                 | 555/8000 [00:11<03:58, 31.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 189it [00:02, 184.62it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 559/8000 [00:11<03:53, 31.92it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 225it [00:02, 223.81it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 565/8000 [00:11<03:18, 37.51it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 261it [00:02, 256.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 571/8000 [00:12<03:16, 37.73it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 298it [00:02, 285.08it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▌                                 | 577/8000 [00:12<02:56, 42.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 335it [00:02, 306.64it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 584/8000 [00:12<02:52, 43.07it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 370it [00:02, 301.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 589/8000 [00:12<02:50, 43.39it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 403it [00:02, 307.22it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 594/8000 [00:12<02:51, 43.11it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 436it [00:02, 309.91it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   7%|██▋                                 | 599/8000 [00:12<03:32, 34.90it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 477it [00:03, 336.49it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 605/8000 [00:12<03:08, 39.33it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 516it [00:03, 350.36it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▋                                 | 611/8000 [00:13<02:49, 43.63it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 552it [00:03, 348.94it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 616/8000 [00:13<02:46, 44.27it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 588it [00:03, 348.72it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 621/8000 [00:13<02:42, 45.35it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 629it [00:03, 363.84it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 626/8000 [00:13<02:57, 41.50it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 666it [00:03, 335.96it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 631/8000 [00:13<02:51, 42.95it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 701it [00:03, 328.45it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▊                                 | 637/8000 [00:13<02:37, 46.87it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 735it [00:03, 318.82it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 643/8000 [00:13<02:53, 42.53it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Evaluating NLL: 800it [00:04, 198.21it/s]\n",
+      "Generating signals:   8%|██▉                                 | 649/8000 [00:13<02:59, 40.94it/s]\u001b[A\u001b[A\n",
       "\n",
+      "Generating signals:   8%|██▉                                 | 654/8000 [00:14<02:56, 41.54it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   0%|   | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "Generating signals:   8%|██▉                                 | 661/8000 [00:14<02:51, 42.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 667/8000 [00:14<03:19, 36.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 674/8000 [00:14<02:50, 42.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 680/8000 [00:14<02:56, 41.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 685/8000 [00:14<03:12, 38.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 691/8000 [00:15<03:10, 38.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 698/8000 [00:15<02:43, 44.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 704/8000 [00:15<02:56, 41.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 710/8000 [00:15<03:43, 32.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 716/8000 [00:15<03:13, 37.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 721/8000 [00:15<03:39, 33.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 727/8000 [00:15<03:10, 38.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 733/8000 [00:16<02:52, 42.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 738/8000 [00:16<02:48, 43.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 743/8000 [00:16<02:42, 44.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 750/8000 [00:16<02:40, 45.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 756/8000 [00:16<03:15, 37.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 761/8000 [00:16<03:08, 38.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 766/8000 [00:17<03:59, 30.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 777/8000 [00:17<02:53, 41.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 782/8000 [00:17<03:31, 34.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 786/8000 [00:17<03:48, 31.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 794/8000 [00:17<03:19, 36.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 799/8000 [00:17<03:23, 35.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 805/8000 [00:18<03:00, 39.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 813/8000 [00:18<02:59, 40.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 820/8000 [00:18<02:52, 41.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 825/8000 [00:18<02:48, 42.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 833/8000 [00:18<02:21, 50.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 841/8000 [00:18<02:36, 45.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 847/8000 [00:18<02:45, 43.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 852/8000 [00:19<02:41, 44.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 857/8000 [00:19<03:16, 36.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 862/8000 [00:19<03:10, 37.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 870/8000 [00:19<02:38, 45.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 877/8000 [00:19<02:20, 50.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 883/8000 [00:19<02:49, 41.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 891/8000 [00:19<02:23, 49.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 897/8000 [00:20<02:58, 39.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 903/8000 [00:20<02:49, 41.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 909/8000 [00:20<02:36, 45.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 916/8000 [00:20<02:57, 40.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 924/8000 [00:20<02:33, 46.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 930/8000 [00:20<03:07, 37.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 936/8000 [00:21<02:53, 40.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 945/8000 [00:21<02:19, 50.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 952/8000 [00:21<02:22, 49.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 958/8000 [00:21<02:55, 40.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 964/8000 [00:21<02:55, 40.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 969/8000 [00:21<03:07, 37.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 975/8000 [00:22<03:06, 37.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 979/8000 [00:22<03:23, 34.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 983/8000 [00:22<03:20, 34.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 989/8000 [00:22<03:13, 36.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 994/8000 [00:22<02:57, 39.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                              | 1000/8000 [00:22<02:43, 42.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1006/8000 [00:22<02:52, 40.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1012/8000 [00:22<02:40, 43.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1017/8000 [00:23<03:18, 35.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1021/8000 [00:23<03:19, 35.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1027/8000 [00:23<03:17, 35.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1031/8000 [00:23<03:13, 36.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1035/8000 [00:23<03:12, 36.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1047/8000 [00:23<02:15, 51.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1053/8000 [00:23<02:45, 42.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1058/8000 [00:24<02:41, 42.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1063/8000 [00:24<02:59, 38.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1070/8000 [00:24<02:51, 40.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1076/8000 [00:24<02:41, 42.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                              | 1082/8000 [00:24<02:29, 46.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1088/8000 [00:24<02:22, 48.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1094/8000 [00:24<02:16, 50.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1101/8000 [00:24<02:05, 54.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1110/8000 [00:25<02:22, 48.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1116/8000 [00:25<02:16, 50.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1125/8000 [00:25<01:55, 59.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1132/8000 [00:25<02:09, 53.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1141/8000 [00:25<01:52, 61.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1152/8000 [00:25<02:00, 57.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                              | 1163/8000 [00:26<01:50, 61.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                              | 1170/8000 [00:26<02:09, 52.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1178/8000 [00:26<02:11, 51.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1185/8000 [00:26<02:14, 50.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1192/8000 [00:26<02:30, 45.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1197/8000 [00:26<02:30, 45.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1202/8000 [00:27<02:44, 41.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1207/8000 [00:27<02:54, 38.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1211/8000 [00:27<02:59, 37.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1216/8000 [00:27<02:48, 40.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1221/8000 [00:27<02:46, 40.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1227/8000 [00:27<02:57, 38.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1232/8000 [00:27<03:08, 35.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1236/8000 [00:27<03:08, 35.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1246/8000 [00:28<02:17, 49.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1253/8000 [00:28<02:23, 46.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1258/8000 [00:28<02:23, 46.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1263/8000 [00:28<02:40, 41.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1274/8000 [00:28<01:58, 56.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1284/8000 [00:28<02:09, 51.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1292/8000 [00:29<02:22, 46.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1298/8000 [00:29<02:16, 49.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1304/8000 [00:29<02:27, 45.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1309/8000 [00:29<02:29, 44.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▊                             | 1317/8000 [00:29<02:08, 52.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1324/8000 [00:29<02:00, 55.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1332/8000 [00:29<01:50, 60.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1340/8000 [00:29<01:43, 64.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1347/8000 [00:29<01:42, 65.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1354/8000 [00:30<01:56, 56.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1360/8000 [00:30<02:11, 50.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1367/8000 [00:30<02:00, 55.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1377/8000 [00:30<01:53, 58.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1384/8000 [00:30<02:18, 47.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1392/8000 [00:30<02:18, 47.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1399/8000 [00:31<02:38, 41.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1406/8000 [00:31<02:20, 46.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1417/8000 [00:31<02:03, 53.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1425/8000 [00:31<02:14, 48.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1431/8000 [00:31<02:09, 50.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1437/8000 [00:31<02:18, 47.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1446/8000 [00:31<01:57, 55.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1456/8000 [00:32<02:08, 50.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1462/8000 [00:32<02:29, 43.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1469/8000 [00:32<02:27, 44.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1474/8000 [00:32<02:25, 44.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                            | 1482/8000 [00:32<02:20, 46.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1489/8000 [00:32<02:11, 49.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1496/8000 [00:33<02:19, 46.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1503/8000 [00:33<02:07, 50.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1516/8000 [00:33<01:45, 61.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1525/8000 [00:33<01:49, 59.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1533/8000 [00:33<01:55, 56.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1541/8000 [00:33<01:59, 54.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1551/8000 [00:33<01:49, 58.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1558/8000 [00:34<01:56, 55.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                            | 1567/8000 [00:34<01:58, 54.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1573/8000 [00:34<02:08, 49.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1586/8000 [00:34<01:38, 65.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1596/8000 [00:34<01:50, 57.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1605/8000 [00:34<01:51, 57.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1615/8000 [00:35<01:57, 54.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1621/8000 [00:35<01:55, 55.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1627/8000 [00:35<02:11, 48.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1636/8000 [00:35<02:05, 50.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1647/8000 [00:35<02:05, 50.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1653/8000 [00:35<02:14, 47.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1658/8000 [00:36<02:12, 47.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1665/8000 [00:36<02:31, 41.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1676/8000 [00:36<02:05, 50.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1684/8000 [00:36<02:19, 45.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1691/8000 [00:36<02:18, 45.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1697/8000 [00:36<02:23, 43.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1702/8000 [00:37<02:34, 40.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1707/8000 [00:37<03:07, 33.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▌                           | 1715/8000 [00:37<02:32, 41.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1722/8000 [00:37<02:41, 38.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1728/8000 [00:37<02:44, 38.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1735/8000 [00:37<02:24, 43.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1740/8000 [00:38<03:06, 33.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1746/8000 [00:38<03:08, 33.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1753/8000 [00:38<02:36, 39.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1758/8000 [00:38<02:50, 36.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1764/8000 [00:38<02:46, 37.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1770/8000 [00:38<02:44, 37.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1776/8000 [00:39<02:25, 42.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1781/8000 [00:39<02:41, 38.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1786/8000 [00:39<02:48, 36.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1795/8000 [00:39<02:20, 44.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▉                           | 1800/8000 [00:39<02:18, 44.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1806/8000 [00:39<02:28, 41.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1814/8000 [00:39<02:20, 44.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1819/8000 [00:40<02:18, 44.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1826/8000 [00:40<02:16, 45.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1832/8000 [00:40<02:07, 48.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1837/8000 [00:40<02:06, 48.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1844/8000 [00:40<02:41, 38.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1851/8000 [00:40<02:37, 38.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1858/8000 [00:41<02:34, 39.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1865/8000 [00:41<02:29, 41.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1870/8000 [00:41<02:43, 37.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1876/8000 [00:41<02:30, 40.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▏                          | 1882/8000 [00:41<02:37, 38.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1887/8000 [00:41<02:43, 37.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1894/8000 [00:41<02:32, 40.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1899/8000 [00:42<02:42, 37.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1906/8000 [00:42<02:18, 44.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1912/8000 [00:42<02:21, 43.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1917/8000 [00:42<02:31, 40.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1928/8000 [00:42<01:49, 55.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1935/8000 [00:42<01:58, 51.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1941/8000 [00:42<02:10, 46.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1946/8000 [00:43<02:22, 42.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1952/8000 [00:43<02:39, 37.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1958/8000 [00:43<02:25, 41.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1963/8000 [00:43<02:25, 41.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1968/8000 [00:43<02:22, 42.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1973/8000 [00:43<02:34, 39.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1980/8000 [00:43<02:10, 46.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1985/8000 [00:44<02:23, 41.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1991/8000 [00:44<02:46, 36.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1998/8000 [00:44<02:19, 43.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2003/8000 [00:44<02:43, 36.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2009/8000 [00:44<02:30, 39.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2016/8000 [00:44<02:13, 44.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2021/8000 [00:44<02:13, 44.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2027/8000 [00:44<02:05, 47.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2032/8000 [00:45<02:08, 46.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2039/8000 [00:45<01:55, 51.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2045/8000 [00:45<02:03, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2050/8000 [00:45<02:03, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2056/8000 [00:45<02:00, 49.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2063/8000 [00:45<02:00, 49.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2070/8000 [00:45<01:49, 54.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2079/8000 [00:45<01:43, 57.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2087/8000 [00:46<02:02, 48.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2098/8000 [00:46<02:01, 48.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2104/8000 [00:46<02:36, 37.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2109/8000 [00:46<02:34, 38.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2114/8000 [00:46<02:33, 38.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2123/8000 [00:47<02:04, 47.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2129/8000 [00:47<02:00, 48.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2135/8000 [00:47<01:54, 51.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2142/8000 [00:47<02:03, 47.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2149/8000 [00:47<01:53, 51.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2155/8000 [00:47<02:03, 47.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2162/8000 [00:47<02:06, 46.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2167/8000 [00:48<02:40, 36.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2176/8000 [00:48<02:05, 46.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2182/8000 [00:48<02:00, 48.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2189/8000 [00:48<02:08, 45.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2194/8000 [00:48<02:45, 35.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2199/8000 [00:48<02:56, 32.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2204/8000 [00:49<02:41, 35.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2210/8000 [00:49<02:26, 39.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2216/8000 [00:49<02:14, 43.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2221/8000 [00:49<02:14, 43.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2231/8000 [00:49<01:52, 51.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2238/8000 [00:49<02:09, 44.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2243/8000 [00:49<02:20, 41.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2248/8000 [00:49<02:15, 42.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2255/8000 [00:50<01:58, 48.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2264/8000 [00:50<01:50, 52.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2272/8000 [00:50<01:56, 49.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2280/8000 [00:50<01:44, 54.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2290/8000 [00:50<01:40, 56.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2296/8000 [00:50<01:43, 55.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2305/8000 [00:50<01:31, 62.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2312/8000 [00:51<01:53, 50.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2318/8000 [00:51<01:49, 51.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2326/8000 [00:51<01:48, 52.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2338/8000 [00:51<01:26, 65.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2345/8000 [00:51<01:30, 62.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2352/8000 [00:51<01:41, 55.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2360/8000 [00:51<01:46, 53.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2366/8000 [00:52<01:59, 47.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2372/8000 [00:52<02:22, 39.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2377/8000 [00:52<02:35, 36.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2384/8000 [00:52<02:27, 37.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2389/8000 [00:52<02:22, 39.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2395/8000 [00:52<02:11, 42.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2405/8000 [00:53<01:45, 53.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2411/8000 [00:53<02:00, 46.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2416/8000 [00:53<02:04, 44.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2422/8000 [00:53<01:58, 46.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2429/8000 [00:53<02:19, 39.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2434/8000 [00:53<02:21, 39.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2439/8000 [00:53<02:37, 35.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2445/8000 [00:54<02:54, 31.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2449/8000 [00:54<03:07, 29.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2456/8000 [00:54<02:49, 32.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2465/8000 [00:54<02:10, 42.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2470/8000 [00:54<02:08, 43.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2478/8000 [00:54<02:05, 44.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2484/8000 [00:55<02:13, 41.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2492/8000 [00:55<02:07, 43.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2501/8000 [00:55<01:45, 52.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2507/8000 [00:55<01:58, 46.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2514/8000 [00:55<01:50, 49.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2522/8000 [00:55<01:53, 48.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2528/8000 [00:56<02:02, 44.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2533/8000 [00:56<02:18, 39.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2540/8000 [00:56<02:17, 39.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2546/8000 [00:56<02:50, 32.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2552/8000 [00:56<02:32, 35.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2557/8000 [00:56<02:24, 37.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2562/8000 [00:57<02:17, 39.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2567/8000 [00:57<02:14, 40.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2572/8000 [00:57<03:00, 30.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2579/8000 [00:57<02:26, 36.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2584/8000 [00:57<02:19, 38.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2591/8000 [00:57<02:01, 44.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2596/8000 [00:57<02:18, 38.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2603/8000 [00:58<02:03, 43.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2611/8000 [00:58<01:45, 51.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2618/8000 [00:58<01:53, 47.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2625/8000 [00:58<02:12, 40.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2631/8000 [00:58<02:20, 38.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2636/8000 [00:58<02:49, 31.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2644/8000 [00:59<02:15, 39.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2650/8000 [00:59<02:50, 31.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2657/8000 [00:59<02:40, 33.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2663/8000 [00:59<02:25, 36.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2668/8000 [00:59<02:38, 33.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2675/8000 [01:00<02:20, 37.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                       | 2681/8000 [01:00<02:09, 41.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2686/8000 [01:00<02:08, 41.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2691/8000 [01:00<02:24, 36.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2695/8000 [01:00<02:26, 36.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2706/8000 [01:00<01:42, 51.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2712/8000 [01:00<02:21, 37.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2719/8000 [01:01<02:04, 42.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2728/8000 [01:01<01:45, 50.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2734/8000 [01:01<01:57, 44.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2739/8000 [01:01<02:13, 39.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2752/8000 [01:01<01:32, 56.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2760/8000 [01:01<01:39, 52.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                       | 2766/8000 [01:01<01:49, 47.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2778/8000 [01:02<01:36, 53.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2785/8000 [01:02<01:34, 55.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2791/8000 [01:02<01:46, 48.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2798/8000 [01:02<01:38, 52.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2806/8000 [01:02<01:56, 44.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2814/8000 [01:02<01:41, 51.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2820/8000 [01:02<01:38, 52.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2830/8000 [01:03<01:54, 45.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2838/8000 [01:03<01:42, 50.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2845/8000 [01:03<01:50, 46.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2852/8000 [01:03<01:42, 50.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2858/8000 [01:03<01:44, 49.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2865/8000 [01:03<01:36, 53.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2872/8000 [01:04<02:25, 35.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2877/8000 [01:04<02:30, 34.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2882/8000 [01:04<02:23, 35.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2889/8000 [01:04<02:19, 36.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2896/8000 [01:04<02:12, 38.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2902/8000 [01:04<02:00, 42.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2909/8000 [01:05<01:59, 42.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▊                      | 2916/8000 [01:05<01:46, 47.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2927/8000 [01:05<01:36, 52.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2933/8000 [01:05<01:35, 53.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2939/8000 [01:05<01:48, 46.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2944/8000 [01:05<01:48, 46.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2953/8000 [01:06<01:48, 46.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2959/8000 [01:06<01:43, 48.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2968/8000 [01:06<02:06, 39.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2975/8000 [01:06<02:14, 37.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2980/8000 [01:06<02:10, 38.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2985/8000 [01:06<02:05, 39.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2992/8000 [01:06<01:50, 45.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2997/8000 [01:07<01:51, 44.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3002/8000 [01:07<01:54, 43.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3008/8000 [01:07<02:18, 36.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3014/8000 [01:07<02:10, 38.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3019/8000 [01:07<02:05, 39.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3024/8000 [01:07<02:15, 36.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3033/8000 [01:08<01:56, 42.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3041/8000 [01:08<01:42, 48.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3049/8000 [01:08<01:47, 46.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3054/8000 [01:08<01:49, 45.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3061/8000 [01:08<02:07, 38.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3067/8000 [01:08<02:26, 33.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3071/8000 [01:09<02:26, 33.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3076/8000 [01:09<02:17, 35.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3080/8000 [01:09<02:17, 35.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3086/8000 [01:09<02:19, 35.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3093/8000 [01:09<01:56, 42.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3098/8000 [01:09<02:10, 37.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3104/8000 [01:09<01:58, 41.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3113/8000 [01:09<01:35, 51.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3120/8000 [01:10<01:30, 53.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3128/8000 [01:10<01:34, 51.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3139/8000 [01:10<01:26, 55.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3147/8000 [01:10<01:32, 52.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3155/8000 [01:10<01:25, 56.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3161/8000 [01:10<01:28, 54.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3171/8000 [01:10<01:18, 61.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3180/8000 [01:11<01:43, 46.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3186/8000 [01:11<01:52, 42.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3191/8000 [01:11<01:52, 42.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3196/8000 [01:11<02:05, 38.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3204/8000 [01:11<01:46, 45.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3212/8000 [01:11<01:32, 51.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3220/8000 [01:12<01:35, 49.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3227/8000 [01:12<02:04, 38.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3233/8000 [01:12<02:07, 37.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3238/8000 [01:12<02:22, 33.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3246/8000 [01:12<01:56, 40.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3253/8000 [01:13<01:45, 45.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3263/8000 [01:13<01:38, 47.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3269/8000 [01:13<01:48, 43.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3276/8000 [01:13<01:39, 47.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3282/8000 [01:13<01:47, 43.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3287/8000 [01:13<01:48, 43.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3294/8000 [01:13<01:50, 42.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3299/8000 [01:14<01:48, 43.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3305/8000 [01:14<02:06, 37.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3313/8000 [01:14<01:45, 44.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▌                    | 3319/8000 [01:14<01:39, 46.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3326/8000 [01:14<01:43, 45.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3335/8000 [01:14<01:35, 48.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3341/8000 [01:15<01:44, 44.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3348/8000 [01:15<01:35, 48.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3357/8000 [01:15<01:42, 45.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3363/8000 [01:15<01:41, 45.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3369/8000 [01:15<01:46, 43.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3377/8000 [01:15<01:59, 38.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3383/8000 [01:16<01:59, 38.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3390/8000 [01:16<01:55, 40.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3398/8000 [01:16<01:46, 43.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3407/8000 [01:16<01:28, 52.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3414/8000 [01:16<01:59, 38.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3423/8000 [01:16<01:36, 47.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3429/8000 [01:16<01:33, 48.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3440/8000 [01:17<01:17, 59.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3448/8000 [01:17<01:45, 43.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3456/8000 [01:17<01:39, 45.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3464/8000 [01:17<01:47, 42.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3471/8000 [01:17<01:36, 46.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3477/8000 [01:17<01:31, 49.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▏                   | 3484/8000 [01:18<01:25, 52.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3490/8000 [01:18<01:32, 48.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3496/8000 [01:18<01:41, 44.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3501/8000 [01:18<01:42, 43.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3509/8000 [01:18<01:52, 40.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3518/8000 [01:18<01:29, 50.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3524/8000 [01:18<01:27, 50.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3530/8000 [01:19<01:25, 52.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3537/8000 [01:19<01:19, 56.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3544/8000 [01:19<01:25, 52.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3550/8000 [01:19<01:23, 53.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3556/8000 [01:19<01:25, 51.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3565/8000 [01:19<01:24, 52.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3571/8000 [01:19<01:36, 45.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3578/8000 [01:20<01:28, 50.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3588/8000 [01:20<01:22, 53.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3594/8000 [01:20<01:33, 47.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3603/8000 [01:20<01:21, 53.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3609/8000 [01:20<01:24, 52.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3616/8000 [01:20<01:18, 55.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3623/8000 [01:20<01:14, 58.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3630/8000 [01:21<01:33, 46.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3636/8000 [01:21<01:38, 44.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3643/8000 [01:21<01:39, 43.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3650/8000 [01:21<01:49, 39.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3659/8000 [01:21<01:39, 43.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3665/8000 [01:21<01:52, 38.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3673/8000 [01:22<01:41, 42.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3678/8000 [01:22<01:38, 43.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3683/8000 [01:22<01:47, 40.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3689/8000 [01:22<01:38, 43.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3694/8000 [01:22<01:38, 43.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3700/8000 [01:22<01:37, 44.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3706/8000 [01:22<01:31, 47.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3711/8000 [01:23<01:58, 36.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▎                  | 3717/8000 [01:23<01:56, 36.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3723/8000 [01:23<02:21, 30.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3729/8000 [01:23<02:03, 34.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3733/8000 [01:23<02:15, 31.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3737/8000 [01:23<02:10, 32.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3743/8000 [01:23<01:52, 37.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3751/8000 [01:24<01:50, 38.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3756/8000 [01:24<02:17, 30.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3760/8000 [01:24<02:13, 31.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3764/8000 [01:24<02:07, 33.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3768/8000 [01:24<02:21, 29.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3775/8000 [01:24<01:52, 37.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3781/8000 [01:25<01:39, 42.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3789/8000 [01:25<01:36, 43.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3794/8000 [01:25<01:33, 44.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3800/8000 [01:25<01:39, 42.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3807/8000 [01:25<01:26, 48.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3814/8000 [01:25<01:20, 51.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3821/8000 [01:25<01:26, 48.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3830/8000 [01:25<01:11, 58.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3839/8000 [01:26<01:20, 51.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3847/8000 [01:26<01:22, 50.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3853/8000 [01:26<01:31, 45.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3859/8000 [01:26<01:48, 38.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3865/8000 [01:26<02:01, 34.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3871/8000 [01:27<01:48, 38.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3880/8000 [01:27<01:25, 48.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3887/8000 [01:27<01:18, 52.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3896/8000 [01:27<01:25, 48.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3904/8000 [01:27<01:33, 44.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3910/8000 [01:27<01:45, 38.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3919/8000 [01:28<01:24, 48.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3926/8000 [01:28<01:26, 47.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3933/8000 [01:28<01:29, 45.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3941/8000 [01:28<01:17, 52.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3948/8000 [01:28<01:19, 50.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3954/8000 [01:28<01:29, 45.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3959/8000 [01:28<01:31, 44.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3964/8000 [01:29<01:33, 43.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3969/8000 [01:29<01:47, 37.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3975/8000 [01:29<01:36, 41.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3984/8000 [01:29<01:24, 47.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3991/8000 [01:29<01:18, 51.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3998/8000 [01:29<01:13, 54.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4004/8000 [01:29<01:22, 48.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4010/8000 [01:29<01:20, 49.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4020/8000 [01:30<01:25, 46.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4027/8000 [01:30<01:21, 48.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4036/8000 [01:30<01:14, 53.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4043/8000 [01:30<01:23, 47.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4049/8000 [01:30<01:43, 38.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4059/8000 [01:31<01:43, 37.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4068/8000 [01:31<01:39, 39.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4075/8000 [01:31<01:53, 34.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4079/8000 [01:31<02:06, 31.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4085/8000 [01:32<02:04, 31.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4090/8000 [01:32<01:55, 33.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4096/8000 [01:32<01:42, 37.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4103/8000 [01:32<01:28, 44.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4109/8000 [01:32<01:21, 47.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████████                 | 4116/8000 [01:32<01:24, 46.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4123/8000 [01:32<01:15, 51.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4132/8000 [01:32<01:04, 59.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4141/8000 [01:33<01:07, 56.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4150/8000 [01:33<01:01, 62.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4158/8000 [01:33<01:13, 52.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4165/8000 [01:33<01:37, 39.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4173/8000 [01:33<01:46, 35.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4179/8000 [01:34<01:45, 36.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4186/8000 [01:34<01:33, 40.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4195/8000 [01:34<01:15, 50.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4201/8000 [01:34<01:13, 51.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4207/8000 [01:34<01:12, 52.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4213/8000 [01:34<01:19, 47.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4220/8000 [01:34<01:14, 50.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4226/8000 [01:34<01:22, 45.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4232/8000 [01:35<01:20, 46.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4239/8000 [01:35<01:23, 44.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4245/8000 [01:35<01:21, 46.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4250/8000 [01:35<01:51, 33.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4254/8000 [01:35<01:59, 31.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4259/8000 [01:35<01:56, 32.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4266/8000 [01:36<01:42, 36.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4270/8000 [01:36<01:43, 36.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4276/8000 [01:36<01:29, 41.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▋                | 4281/8000 [01:36<01:27, 42.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4292/8000 [01:36<01:09, 53.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4301/8000 [01:36<01:09, 53.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4307/8000 [01:36<01:26, 42.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4315/8000 [01:37<01:22, 44.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4320/8000 [01:37<01:30, 40.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4327/8000 [01:37<01:18, 46.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4335/8000 [01:37<01:09, 52.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4342/8000 [01:37<01:05, 55.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4351/8000 [01:37<00:58, 62.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4358/8000 [01:37<01:14, 48.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4365/8000 [01:38<01:10, 51.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4371/8000 [01:38<01:09, 52.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4382/8000 [01:38<01:02, 57.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4388/8000 [01:38<01:11, 50.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4402/8000 [01:38<00:53, 67.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4410/8000 [01:38<01:06, 53.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4419/8000 [01:38<01:04, 55.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4426/8000 [01:39<01:04, 55.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▍               | 4433/8000 [01:39<01:11, 50.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▍               | 4439/8000 [01:39<01:09, 51.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4447/8000 [01:39<01:22, 42.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4453/8000 [01:39<01:17, 45.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4459/8000 [01:39<01:21, 43.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4464/8000 [01:40<01:28, 39.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4469/8000 [01:40<01:36, 36.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4474/8000 [01:40<01:41, 34.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4483/8000 [01:40<01:17, 45.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4489/8000 [01:40<01:12, 48.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4496/8000 [01:40<01:16, 45.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4503/8000 [01:40<01:15, 46.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4508/8000 [01:41<01:15, 46.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4514/8000 [01:41<01:20, 43.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4522/8000 [01:41<01:23, 41.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4529/8000 [01:41<01:27, 39.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4536/8000 [01:41<01:24, 40.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4542/8000 [01:41<01:26, 40.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4548/8000 [01:42<01:18, 43.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4553/8000 [01:42<01:16, 45.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4564/8000 [01:42<01:02, 54.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4573/8000 [01:42<01:02, 55.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4581/8000 [01:42<01:09, 48.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4588/8000 [01:42<01:12, 47.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4596/8000 [01:42<01:14, 45.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4603/8000 [01:43<01:22, 40.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4611/8000 [01:43<01:26, 39.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4618/8000 [01:43<01:23, 40.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4625/8000 [01:43<01:14, 45.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4632/8000 [01:43<01:10, 47.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4637/8000 [01:43<01:20, 41.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4644/8000 [01:44<01:12, 46.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4653/8000 [01:44<01:07, 49.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4660/8000 [01:44<01:02, 53.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4667/8000 [01:44<01:05, 51.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4676/8000 [01:44<01:01, 53.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▍              | 4683/8000 [01:44<01:05, 50.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4690/8000 [01:44<01:07, 48.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4696/8000 [01:45<01:07, 48.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4703/8000 [01:45<01:08, 47.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4711/8000 [01:45<01:13, 44.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4718/8000 [01:45<01:08, 47.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4724/8000 [01:45<01:12, 45.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4729/8000 [01:45<01:12, 45.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4740/8000 [01:45<01:00, 53.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4747/8000 [01:46<01:04, 50.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4753/8000 [01:46<01:02, 51.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4759/8000 [01:46<01:01, 52.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▊              | 4767/8000 [01:46<01:02, 51.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4774/8000 [01:46<01:07, 48.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4780/8000 [01:46<01:10, 45.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4786/8000 [01:47<01:18, 41.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4797/8000 [01:47<01:05, 49.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4803/8000 [01:47<01:16, 41.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4811/8000 [01:47<01:12, 44.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4817/8000 [01:47<01:09, 46.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4822/8000 [01:47<01:12, 43.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4829/8000 [01:47<01:11, 44.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4834/8000 [01:48<01:11, 44.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4840/8000 [01:48<01:06, 47.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4847/8000 [01:48<01:01, 50.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4854/8000 [01:48<01:05, 47.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4860/8000 [01:48<01:04, 48.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4870/8000 [01:48<00:52, 59.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4878/8000 [01:48<01:11, 43.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4885/8000 [01:49<01:11, 43.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4893/8000 [01:49<01:07, 45.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4898/8000 [01:49<01:09, 44.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4903/8000 [01:49<01:08, 45.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4910/8000 [01:49<01:08, 44.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▌             | 4915/8000 [01:49<01:07, 45.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4920/8000 [01:49<01:15, 40.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4926/8000 [01:50<01:10, 43.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4934/8000 [01:50<00:59, 51.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4944/8000 [01:50<00:51, 59.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4952/8000 [01:50<00:53, 56.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4962/8000 [01:50<00:51, 58.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4970/8000 [01:50<00:53, 56.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4976/8000 [01:50<00:59, 50.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4982/8000 [01:51<00:57, 52.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4995/8000 [01:51<00:47, 62.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5004/8000 [01:51<00:54, 55.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5010/8000 [01:51<00:54, 54.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5017/8000 [01:51<00:58, 51.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5027/8000 [01:51<00:53, 55.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5033/8000 [01:52<01:13, 40.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5040/8000 [01:52<01:10, 42.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5045/8000 [01:52<01:08, 42.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5055/8000 [01:52<01:01, 48.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5062/8000 [01:52<00:58, 50.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5070/8000 [01:52<01:01, 47.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5076/8000 [01:52<01:00, 48.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▏            | 5084/8000 [01:53<00:53, 54.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5092/8000 [01:53<01:00, 48.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5098/8000 [01:53<01:05, 44.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5104/8000 [01:53<01:10, 41.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5110/8000 [01:53<01:06, 43.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5115/8000 [01:53<01:06, 43.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5120/8000 [01:54<01:13, 39.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5126/8000 [01:54<01:06, 43.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5133/8000 [01:54<01:05, 43.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5139/8000 [01:54<01:08, 41.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5144/8000 [01:54<01:12, 39.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5152/8000 [01:54<01:14, 38.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5157/8000 [01:54<01:11, 39.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▌            | 5165/8000 [01:55<01:00, 46.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▌            | 5171/8000 [01:55<00:59, 47.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5178/8000 [01:55<01:01, 45.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5183/8000 [01:55<01:10, 40.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5189/8000 [01:55<01:05, 42.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5195/8000 [01:55<01:00, 46.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5200/8000 [01:55<01:00, 46.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5210/8000 [01:55<00:53, 52.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5217/8000 [01:56<00:55, 50.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5225/8000 [01:56<01:00, 45.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5232/8000 [01:56<00:56, 49.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5238/8000 [01:56<00:55, 49.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5247/8000 [01:56<01:12, 38.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5253/8000 [01:57<01:10, 38.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5258/8000 [01:57<01:07, 40.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5265/8000 [01:57<01:11, 38.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5272/8000 [01:57<01:21, 33.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5276/8000 [01:57<01:25, 31.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5283/8000 [01:57<01:16, 35.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5287/8000 [01:58<01:17, 34.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5291/8000 [01:58<01:24, 32.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5296/8000 [01:58<01:16, 35.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5300/8000 [01:58<01:33, 28.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5306/8000 [01:58<01:26, 31.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5314/8000 [01:58<01:05, 40.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▎           | 5319/8000 [01:58<01:03, 41.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5324/8000 [01:59<01:01, 43.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5331/8000 [01:59<01:03, 42.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5337/8000 [01:59<01:07, 39.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5347/8000 [01:59<00:50, 52.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5354/8000 [01:59<00:47, 55.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5361/8000 [01:59<01:02, 42.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5367/8000 [01:59<00:57, 45.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5376/8000 [02:00<01:00, 43.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5381/8000 [02:00<01:05, 39.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5389/8000 [02:00<00:54, 47.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5399/8000 [02:00<00:48, 53.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5408/8000 [02:00<00:47, 54.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5415/8000 [02:00<01:00, 42.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5420/8000 [02:01<00:58, 43.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5427/8000 [02:01<00:52, 48.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5433/8000 [02:01<01:05, 39.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5438/8000 [02:01<01:03, 40.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5447/8000 [02:01<00:50, 51.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5453/8000 [02:01<00:54, 46.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5463/8000 [02:01<00:50, 49.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5470/8000 [02:02<00:52, 48.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5476/8000 [02:02<01:05, 38.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▉           | 5481/8000 [02:02<01:01, 40.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5486/8000 [02:02<01:14, 33.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5490/8000 [02:02<01:28, 28.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5495/8000 [02:03<01:24, 29.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5499/8000 [02:03<01:27, 28.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5503/8000 [02:03<01:31, 27.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5510/8000 [02:03<01:12, 34.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5516/8000 [02:03<01:11, 34.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5524/8000 [02:03<01:10, 35.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5528/8000 [02:04<01:08, 35.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5532/8000 [02:04<01:24, 29.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5536/8000 [02:04<01:20, 30.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5541/8000 [02:04<01:12, 34.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5546/8000 [02:04<01:06, 37.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5552/8000 [02:04<00:58, 42.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5559/8000 [02:04<00:50, 48.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5565/8000 [02:05<01:02, 38.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5570/8000 [02:05<01:07, 36.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5576/8000 [02:05<01:06, 36.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5582/8000 [02:05<00:58, 41.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5591/8000 [02:05<00:47, 50.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5598/8000 [02:05<01:00, 39.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5604/8000 [02:05<01:01, 39.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5612/8000 [02:06<01:01, 38.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5617/8000 [02:06<00:58, 41.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5623/8000 [02:06<00:53, 44.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5630/8000 [02:06<00:47, 49.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5636/8000 [02:06<00:46, 50.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5642/8000 [02:06<00:44, 52.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5648/8000 [02:06<00:58, 39.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5653/8000 [02:07<00:57, 40.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5660/8000 [02:07<00:56, 41.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5666/8000 [02:07<00:53, 43.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5672/8000 [02:07<00:55, 42.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5677/8000 [02:07<00:54, 42.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5684/8000 [02:07<00:47, 48.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5691/8000 [02:07<00:43, 53.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5698/8000 [02:07<00:40, 57.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5704/8000 [02:08<00:52, 43.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5711/8000 [02:08<00:46, 49.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5720/8000 [02:08<00:43, 52.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5728/8000 [02:08<00:44, 51.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5736/8000 [02:08<00:50, 44.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5742/8000 [02:08<00:48, 46.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5748/8000 [02:09<00:57, 39.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5753/8000 [02:09<00:54, 40.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5761/8000 [02:09<00:47, 47.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5769/8000 [02:09<00:47, 47.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5777/8000 [02:09<00:41, 53.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5784/8000 [02:09<00:38, 57.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5792/8000 [02:09<00:44, 49.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5799/8000 [02:10<00:56, 38.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5805/8000 [02:10<01:02, 35.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5809/8000 [02:10<01:02, 34.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5813/8000 [02:10<01:07, 32.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5819/8000 [02:10<00:58, 37.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5826/8000 [02:11<01:00, 36.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5833/8000 [02:11<00:50, 42.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5846/8000 [02:11<00:34, 61.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5856/8000 [02:11<00:34, 62.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5864/8000 [02:11<00:36, 58.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5871/8000 [02:11<00:35, 59.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5879/8000 [02:11<00:41, 50.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5886/8000 [02:12<00:40, 51.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5897/8000 [02:12<00:36, 57.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5904/8000 [02:12<00:43, 48.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5912/8000 [02:12<00:42, 49.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5919/8000 [02:12<00:47, 43.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5925/8000 [02:12<00:48, 42.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5931/8000 [02:12<00:46, 44.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5939/8000 [02:13<00:40, 51.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5945/8000 [02:13<00:38, 52.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5953/8000 [02:13<00:39, 52.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5959/8000 [02:13<00:42, 48.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5966/8000 [02:13<00:38, 53.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5975/8000 [02:13<00:36, 55.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5982/8000 [02:13<00:38, 51.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5988/8000 [02:14<00:46, 43.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5995/8000 [02:14<00:44, 44.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6000/8000 [02:14<00:49, 40.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6006/8000 [02:14<00:46, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6011/8000 [02:14<00:45, 44.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6016/8000 [02:14<00:44, 44.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6023/8000 [02:15<00:55, 35.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6029/8000 [02:15<00:49, 39.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6035/8000 [02:15<00:54, 35.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6040/8000 [02:15<00:51, 38.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6046/8000 [02:15<00:51, 37.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6051/8000 [02:15<00:49, 39.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6058/8000 [02:15<00:53, 36.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6065/8000 [02:16<00:50, 38.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6071/8000 [02:16<00:45, 42.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6078/8000 [02:16<00:46, 41.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6083/8000 [02:16<00:50, 38.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6087/8000 [02:16<00:50, 38.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6093/8000 [02:16<00:54, 34.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6100/8000 [02:17<00:54, 35.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6105/8000 [02:17<00:50, 37.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6109/8000 [02:17<00:51, 36.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6116/8000 [02:17<00:47, 39.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6122/8000 [02:17<00:49, 38.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6127/8000 [02:17<00:46, 39.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6132/8000 [02:17<00:59, 31.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6136/8000 [02:18<01:10, 26.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6141/8000 [02:18<01:02, 29.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6145/8000 [02:18<01:06, 27.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6149/8000 [02:18<01:02, 29.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6154/8000 [02:18<01:02, 29.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6161/8000 [02:18<00:49, 37.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6171/8000 [02:19<00:39, 46.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6177/8000 [02:19<00:41, 44.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6183/8000 [02:19<00:48, 37.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6188/8000 [02:19<00:46, 38.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6197/8000 [02:19<00:40, 44.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6202/8000 [02:19<00:40, 44.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6207/8000 [02:19<00:40, 44.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6212/8000 [02:20<00:43, 40.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6218/8000 [02:20<00:45, 39.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6226/8000 [02:20<00:41, 42.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6231/8000 [02:20<00:40, 43.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6238/8000 [02:20<00:39, 44.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6247/8000 [02:20<00:36, 47.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6253/8000 [02:20<00:36, 47.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6260/8000 [02:21<00:37, 46.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6269/8000 [02:21<00:33, 51.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6276/8000 [02:21<00:38, 44.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6286/8000 [02:21<00:30, 55.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6295/8000 [02:21<00:33, 51.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6303/8000 [02:21<00:39, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6308/8000 [02:22<00:46, 36.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6315/8000 [02:22<00:39, 42.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6326/8000 [02:22<00:32, 50.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6334/8000 [02:22<00:29, 55.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6342/8000 [02:22<00:31, 52.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6349/8000 [02:22<00:29, 55.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6356/8000 [02:23<00:31, 51.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6362/8000 [02:23<00:35, 45.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6369/8000 [02:23<00:32, 49.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6378/8000 [02:23<00:31, 52.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6386/8000 [02:23<00:34, 46.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6391/8000 [02:23<00:40, 39.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6399/8000 [02:23<00:34, 46.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6409/8000 [02:24<00:34, 46.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6418/8000 [02:24<00:32, 49.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6427/8000 [02:24<00:35, 44.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6433/8000 [02:24<00:33, 46.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6439/8000 [02:24<00:35, 44.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6444/8000 [02:24<00:34, 45.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6449/8000 [02:25<00:33, 46.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6456/8000 [02:25<00:34, 45.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6464/8000 [02:25<00:33, 46.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6470/8000 [02:25<00:31, 48.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6476/8000 [02:25<00:31, 49.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6482/8000 [02:25<00:29, 50.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6490/8000 [02:25<00:27, 54.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6497/8000 [02:25<00:26, 57.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6504/8000 [02:26<00:40, 37.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6511/8000 [02:26<00:35, 42.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▌      | 6517/8000 [02:26<00:32, 45.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6524/8000 [02:26<00:29, 49.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6530/8000 [02:26<00:29, 49.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6537/8000 [02:26<00:27, 53.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6545/8000 [02:26<00:25, 58.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6555/8000 [02:27<00:21, 66.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6563/8000 [02:27<00:23, 60.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6571/8000 [02:27<00:25, 56.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6577/8000 [02:27<00:31, 44.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6583/8000 [02:27<00:38, 36.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6588/8000 [02:27<00:36, 38.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6594/8000 [02:28<00:32, 43.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6603/8000 [02:28<00:28, 48.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6609/8000 [02:28<00:31, 44.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6620/8000 [02:28<00:24, 57.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6629/8000 [02:28<00:23, 57.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6637/8000 [02:28<00:31, 43.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6644/8000 [02:29<00:30, 44.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6649/8000 [02:29<00:32, 41.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6657/8000 [02:29<00:28, 47.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6664/8000 [02:29<00:32, 41.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6669/8000 [02:29<00:34, 38.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6674/8000 [02:29<00:36, 36.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6681/8000 [02:30<00:31, 42.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6688/8000 [02:30<00:27, 47.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6698/8000 [02:30<00:24, 52.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6705/8000 [02:30<00:26, 48.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6714/8000 [02:30<00:28, 44.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6719/8000 [02:30<00:28, 45.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6727/8000 [02:30<00:27, 45.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6737/8000 [02:31<00:22, 56.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6744/8000 [02:31<00:26, 47.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6751/8000 [02:31<00:27, 45.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6756/8000 [02:31<00:30, 40.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6766/8000 [02:31<00:23, 52.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6774/8000 [02:31<00:23, 51.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6780/8000 [02:32<00:28, 42.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6789/8000 [02:32<00:25, 47.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6797/8000 [02:32<00:24, 49.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6805/8000 [02:32<00:26, 44.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6810/8000 [02:32<00:26, 45.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6818/8000 [02:33<00:32, 36.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6826/8000 [02:33<00:26, 43.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6832/8000 [02:33<00:28, 41.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6842/8000 [02:33<00:27, 42.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6849/8000 [02:33<00:26, 43.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6858/8000 [02:33<00:26, 43.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6866/8000 [02:34<00:23, 49.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6872/8000 [02:34<00:26, 42.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6880/8000 [02:34<00:22, 48.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6887/8000 [02:34<00:26, 41.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6893/8000 [02:34<00:29, 37.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6900/8000 [02:34<00:27, 39.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6906/8000 [02:35<00:28, 38.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6914/8000 [02:35<00:28, 37.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6920/8000 [02:35<00:28, 37.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6924/8000 [02:35<00:28, 37.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6930/8000 [02:35<00:25, 42.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6938/8000 [02:35<00:25, 40.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6943/8000 [02:36<00:31, 33.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6950/8000 [02:36<00:28, 36.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6957/8000 [02:36<00:27, 38.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6964/8000 [02:36<00:25, 40.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6970/8000 [02:36<00:24, 41.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6975/8000 [02:36<00:26, 39.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6980/8000 [02:37<00:27, 37.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6985/8000 [02:37<00:28, 35.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6989/8000 [02:37<00:27, 36.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6993/8000 [02:37<00:27, 36.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7000/8000 [02:37<00:22, 44.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7006/8000 [02:37<00:23, 41.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7013/8000 [02:37<00:22, 43.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7023/8000 [02:37<00:19, 50.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7036/8000 [02:38<00:15, 62.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7046/8000 [02:38<00:19, 49.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7052/8000 [02:38<00:18, 50.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7058/8000 [02:38<00:22, 42.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7068/8000 [02:38<00:17, 52.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7074/8000 [02:38<00:19, 47.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▉    | 7082/8000 [02:39<00:17, 53.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7088/8000 [02:39<00:19, 47.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7095/8000 [02:39<00:20, 44.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7103/8000 [02:39<00:17, 50.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7110/8000 [02:39<00:18, 48.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7116/8000 [02:39<00:17, 49.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7122/8000 [02:39<00:19, 44.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7128/8000 [02:40<00:20, 42.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7137/8000 [02:40<00:16, 51.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7144/8000 [02:40<00:15, 54.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7150/8000 [02:40<00:15, 54.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7159/8000 [02:40<00:15, 55.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7165/8000 [02:40<00:19, 43.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7171/8000 [02:41<00:19, 41.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7178/8000 [02:41<00:17, 47.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7186/8000 [02:41<00:15, 53.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7194/8000 [02:41<00:13, 59.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7205/8000 [02:41<00:16, 49.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7212/8000 [02:41<00:16, 47.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7218/8000 [02:41<00:17, 44.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7224/8000 [02:42<00:17, 45.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7232/8000 [02:42<00:16, 45.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7237/8000 [02:42<00:16, 46.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7242/8000 [02:42<00:16, 45.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7247/8000 [02:42<00:18, 40.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7252/8000 [02:42<00:17, 42.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7258/8000 [02:42<00:16, 45.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7263/8000 [02:42<00:16, 44.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7268/8000 [02:43<00:21, 34.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7272/8000 [02:43<00:21, 34.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7280/8000 [02:43<00:16, 43.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7290/8000 [02:43<00:13, 53.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7297/8000 [02:43<00:13, 50.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7303/8000 [02:43<00:14, 49.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7312/8000 [02:43<00:13, 52.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████████████   | 7318/8000 [02:44<00:15, 43.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7324/8000 [02:44<00:17, 37.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7329/8000 [02:44<00:20, 32.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7334/8000 [02:44<00:19, 34.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7340/8000 [02:44<00:16, 40.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7348/8000 [02:44<00:14, 44.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7353/8000 [02:45<00:17, 35.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7360/8000 [02:45<00:16, 38.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7365/8000 [02:45<00:15, 40.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7370/8000 [02:45<00:15, 41.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7378/8000 [02:45<00:12, 48.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7389/8000 [02:45<00:12, 48.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7395/8000 [02:46<00:12, 49.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7402/8000 [02:46<00:11, 53.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7410/8000 [02:46<00:11, 52.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7417/8000 [02:46<00:11, 49.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7423/8000 [02:46<00:12, 44.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7431/8000 [02:46<00:11, 47.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7436/8000 [02:46<00:12, 45.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7441/8000 [02:47<00:13, 40.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7448/8000 [02:47<00:11, 46.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7453/8000 [02:47<00:12, 45.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7458/8000 [02:47<00:16, 32.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7463/8000 [02:47<00:17, 31.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7469/8000 [02:47<00:15, 34.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7474/8000 [02:48<00:15, 33.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7481/8000 [02:48<00:12, 41.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7488/8000 [02:48<00:11, 42.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7494/8000 [02:48<00:12, 41.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7501/8000 [02:48<00:12, 41.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7506/8000 [02:48<00:13, 37.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7511/8000 [02:48<00:13, 35.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7515/8000 [02:49<00:13, 36.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7520/8000 [02:49<00:12, 38.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7527/8000 [02:49<00:12, 39.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7531/8000 [02:49<00:12, 37.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7538/8000 [02:49<00:10, 44.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7547/8000 [02:49<00:09, 47.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7553/8000 [02:49<00:09, 47.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7558/8000 [02:49<00:09, 45.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7564/8000 [02:50<00:09, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7570/8000 [02:50<00:09, 44.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7575/8000 [02:50<00:10, 39.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7580/8000 [02:50<00:10, 41.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7587/8000 [02:50<00:12, 32.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7593/8000 [02:50<00:10, 37.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7598/8000 [02:51<00:13, 29.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7604/8000 [02:51<00:11, 34.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7610/8000 [02:51<00:10, 37.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7617/8000 [02:51<00:08, 42.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7622/8000 [02:51<00:09, 39.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7630/8000 [02:51<00:09, 39.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7636/8000 [02:52<00:08, 42.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7643/8000 [02:52<00:09, 38.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7653/8000 [02:52<00:07, 46.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7659/8000 [02:52<00:07, 43.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7664/8000 [02:52<00:07, 43.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7669/8000 [02:52<00:09, 35.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7678/8000 [02:53<00:07, 45.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7685/8000 [02:53<00:07, 41.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7693/8000 [02:53<00:06, 48.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7703/8000 [02:53<00:06, 49.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7709/8000 [02:53<00:05, 50.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7718/8000 [02:53<00:04, 59.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7726/8000 [02:53<00:04, 55.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7732/8000 [02:54<00:05, 50.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7740/8000 [02:54<00:05, 45.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7747/8000 [02:54<00:05, 43.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7752/8000 [02:54<00:06, 36.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7756/8000 [02:54<00:07, 33.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7763/8000 [02:55<00:07, 32.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7769/8000 [02:55<00:06, 36.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7775/8000 [02:55<00:06, 36.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7780/8000 [02:55<00:06, 33.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7788/8000 [02:55<00:05, 41.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7796/8000 [02:55<00:04, 49.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7806/8000 [02:55<00:03, 53.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7813/8000 [02:56<00:03, 52.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7822/8000 [02:56<00:03, 54.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7828/8000 [02:56<00:03, 55.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7834/8000 [02:56<00:02, 56.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7841/8000 [02:56<00:03, 51.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7853/8000 [02:56<00:02, 61.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7860/8000 [02:56<00:02, 51.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7868/8000 [02:57<00:02, 45.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7877/8000 [02:57<00:02, 48.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▍| 7883/8000 [02:57<00:02, 41.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7890/8000 [02:57<00:02, 46.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7899/8000 [02:57<00:02, 49.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7905/8000 [02:57<00:01, 49.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7913/8000 [02:57<00:01, 54.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7922/8000 [02:58<00:01, 63.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7931/8000 [02:58<00:01, 55.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7938/8000 [02:58<00:01, 39.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7943/8000 [02:58<00:01, 36.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7949/8000 [02:58<00:01, 40.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7956/8000 [02:58<00:00, 45.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7962/8000 [02:59<00:00, 42.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7967/8000 [02:59<00:00, 43.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7972/8000 [02:59<00:00, 44.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7980/8000 [02:59<00:00, 51.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7987/8000 [02:59<00:00, 49.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7993/8000 [02:59<00:00, 51.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|███████████████████████████████████| 8000/8000 [02:59<00:00, 44.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:25,  4.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<08:06,  2.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:06<05:41,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:07<04:29,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:08<03:54,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:25,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:08,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<02:59,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:52,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:12<02:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:13<02:44,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:38,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:35,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:15<02:34,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:16<02:30,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:17<02:30,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:30,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:18<02:31,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:19<02:32,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:20<02:27,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:21<02:40,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:22<02:50,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:23<02:38,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:24<02:29,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:24<02:23,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:25<02:20,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:26<02:17,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:27<02:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:27<02:14,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:28<02:13,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:29<02:12,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:30<02:11,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:31<02:10,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:31<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:32<02:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:33<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:34<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:34<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:35<02:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:36<02:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:37<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:37<02:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:38<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:39<02:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:40<01:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:41<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:42<02:18,  1.10it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 25.06it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 94it [00:00, 137.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 182it [00:01, 264.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 268it [00:01, 381.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 355it [00:01, 488.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 446it [00:01, 587.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 538it [00:01, 670.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 628it [00:01, 729.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 458.39it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|████▏      | 38/100 [00:00<00:00, 378.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 336.65it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████████████████████| 4/4 [16:44<00:00, 298.47s/it]\u001b[A\n",
+      "Replicates:  60%|██████████████▍         | 9/15 [6:18:54<2:50:20, 1703.48s/it]\n",
+      "Nyquist Samples:   0%|                                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.262695405727932\n",
+      "2999: -5.9120714\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:19,  4.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:43,  2.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:36,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:30,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:55,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<01:59,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<01:56,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<01:53,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:53,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:11<01:52,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:58,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:55,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:53,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:52,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:50,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:16<01:48,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:47,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:45,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:46,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:47,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:48,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:45,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:43,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:42,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:22<01:41,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:40,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:41,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:40,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:26<01:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:28<01:40,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:37,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:29<01:35,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:31<01:37,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:31<01:35,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:33,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:32<01:33,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:33<01:33,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:34<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:35<01:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:35<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:36<01:45,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:37<01:45,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:38<01:45,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:38<01:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:39<01:48,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:40<01:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:41<01:49,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:42<01:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:42<01:49,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:43<01:48,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:44<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:45<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:46<01:47,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:46<01:44,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████                                | 67/200 [00:47<01:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▎                               | 68/200 [00:48<01:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▌                               | 69/200 [00:49<01:40,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████████▊                               | 70/200 [00:49<01:40,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████                               | 71/200 [00:50<01:39,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▎                              | 72/200 [00:51<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▌                              | 73/200 [00:52<01:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████████▊                              | 74/200 [00:52<01:34,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████                              | 75/200 [00:53<01:35,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▏                             | 76/200 [00:54<01:35,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▍                             | 77/200 [00:55<01:35,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████████▋                             | 78/200 [00:55<01:33,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████████▉                             | 79/200 [00:56<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|███████████████████▏                            | 80/200 [00:57<01:29,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|███████████████████▍                            | 81/200 [00:58<01:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|███████████████████▋                            | 82/200 [00:58<01:21,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████▉                            | 83/200 [00:59<01:23,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████████▏                           | 84/200 [01:00<01:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████████▍                           | 85/200 [01:00<01:19,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████████▋                           | 86/200 [01:01<01:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████▉                           | 87/200 [01:02<01:21,  1.39it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 24.35it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 80it [00:00, 112.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 167it [00:01, 238.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 252it [00:01, 356.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 337it [00:01, 462.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 424it [00:01, 556.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 513it [00:01, 638.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 601it [00:01, 701.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 690it [00:01, 751.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 443.41it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|████▉      | 45/100 [00:00<00:00, 436.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 377.95it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                | 340/8000 [00:00<00:02, 2659.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▉                               | 687/8000 [00:00<00:02, 2863.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▎                            | 1051/8000 [00:00<00:02, 2788.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▋                           | 1389/8000 [00:00<00:02, 2778.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▏                         | 1756/8000 [00:00<00:02, 2910.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▋                        | 2113/8000 [00:00<00:02, 2876.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████                       | 2448/8000 [00:00<00:01, 2875.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▍                     | 2786/8000 [00:01<00:01, 2705.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|████████████▉                    | 3133/8000 [00:01<00:01, 2694.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▎                  | 3479/8000 [00:01<00:01, 2823.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|███████████████▊                 | 3840/8000 [00:01<00:01, 2759.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▎               | 4189/8000 [00:01<00:01, 2706.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████████▋              | 4537/8000 [00:01<00:01, 2625.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▏            | 4882/8000 [00:01<00:01, 2659.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████████▍           | 5206/8000 [00:01<00:01, 2715.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████████▉          | 5547/8000 [00:02<00:00, 2768.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████████▎        | 5902/8000 [00:02<00:00, 2859.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████████▋       | 6239/8000 [00:02<00:00, 2834.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████████      | 6562/8000 [00:02<00:00, 2784.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████████▍    | 6900/8000 [00:02<00:00, 2826.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|█████████████████████████████▉   | 7249/8000 [00:02<00:00, 2898.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████████▎ | 7581/8000 [00:02<00:00, 2910.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8121it [00:02, 2818.47it/s]                                                 \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:33,  4.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:45,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:33,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:32,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:57,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:00,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<02:00,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:58,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<01:54,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:54,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:52,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:51,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:50,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:49,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:48,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:50,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:16<01:48,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:47,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:46,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:44,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:43,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:44,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:43,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:43,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:42,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:22<01:40,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:39,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:38,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:37,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:37,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:25<01:38,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:36,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:36,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:27<01:35,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:36,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:28<01:36,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:34,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:32,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:30<01:32,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:31<01:31,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:31<01:30,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:32<01:28,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:32<01:27,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:33<01:27,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:34<01:26,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:34<01:28,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:35<01:27,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:35<01:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:36<01:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:37<01:26,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:38<01:36,  1.49it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 26.58it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 82it [00:00, 119.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 162it [00:01, 236.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 246it [00:01, 355.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 326it [00:01, 451.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 407it [00:01, 535.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 495it [00:01, 622.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 579it [00:01, 679.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 670it [00:01, 740.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 448.02it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|████▋      | 43/100 [00:00<00:00, 428.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 434.29it/s]\n",
+      "\n",
+      "Nyquist Samples:  25%|██████████▊                                | 1/4 [02:31<07:34, 151.34s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.293406299878343\n",
+      "2999: -6.2133287\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:29,  4.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:14,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:55,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:06,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:25,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<03:00,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:11<02:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:12<02:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:12<02:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:13<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:14<02:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:15<02:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:15<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:16<02:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:17<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:18<02:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:19<02:38,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:20<02:28,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:20<02:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:21<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:22<02:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:22<02:01,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:23<02:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:24<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:24<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:25<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:26<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:26<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:27<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:28<01:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:28<01:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:29<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:30<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:30<01:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:31<01:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:32<01:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:32<01:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:33<01:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:34<01:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:34<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:35<01:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:36<01:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:36<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:37<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:38<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:38<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:39<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:40<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:40<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:41<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:42<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:42<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:43<01:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:44<01:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:44<01:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:45<01:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:46<01:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:47<01:45,  1.30it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 27.61it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 72it [00:00, 107.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 144it [00:01, 216.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 210it [00:01, 304.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 283it [00:01, 399.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 347it [00:01, 451.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 411it [00:01, 499.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 475it [00:01, 528.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 538it [00:01, 549.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 602it [00:01, 571.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 669it [00:01, 598.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:02, 396.88it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|████▉      | 45/100 [00:00<00:00, 442.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 438.83it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 89/8000 [00:00<00:09, 836.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                  | 195/8000 [00:00<00:08, 920.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                 | 303/8000 [00:00<00:07, 965.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                                | 452/8000 [00:00<00:07, 1009.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                               | 560/8000 [00:00<00:07, 1030.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                | 701/8000 [00:00<00:07, 968.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▋                               | 853/8000 [00:00<00:07, 953.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                              | 957/8000 [00:00<00:07, 962.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                             | 1097/8000 [00:01<00:07, 892.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                            | 1237/8000 [00:01<00:07, 897.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                            | 1389/8000 [00:01<00:07, 932.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                           | 1502/8000 [00:01<00:06, 957.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                           | 1616/8000 [00:01<00:06, 991.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▍                          | 1755/8000 [00:01<00:06, 923.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▊                          | 1851/8000 [00:01<00:06, 892.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                         | 1949/8000 [00:02<00:06, 865.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▊                         | 2060/8000 [00:02<00:06, 868.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▏                        | 2164/8000 [00:02<00:06, 892.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                        | 2267/8000 [00:02<00:06, 890.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████                        | 2373/8000 [00:02<00:06, 881.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▌                       | 2487/8000 [00:02<00:06, 912.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                       | 2593/8000 [00:02<00:05, 907.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▌                      | 2717/8000 [00:02<00:06, 843.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▉                      | 2814/8000 [00:03<00:06, 839.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▎                     | 2905/8000 [00:03<00:05, 856.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████                     | 3062/8000 [00:03<00:05, 939.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▍                    | 3159/8000 [00:03<00:05, 945.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▉                    | 3281/8000 [00:03<00:05, 870.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▌                   | 3417/8000 [00:03<00:05, 855.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████                   | 3544/8000 [00:03<00:05, 841.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▍                  | 3637/8000 [00:04<00:05, 855.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▏                 | 3795/8000 [00:04<00:04, 929.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▋                 | 3941/8000 [00:04<00:04, 916.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▏                | 4045/8000 [00:04<00:04, 931.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▊                | 4191/8000 [00:04<00:03, 957.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▍               | 4326/8000 [00:04<00:04, 906.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████               | 4485/8000 [00:04<00:03, 978.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▌              | 4613/8000 [00:05<00:03, 889.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████              | 4714/8000 [00:05<00:03, 912.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▌             | 4836/8000 [00:06<00:09, 349.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████████▉             | 4929/8000 [00:06<00:07, 408.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▎            | 5021/8000 [00:06<00:06, 475.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▋            | 5111/8000 [00:06<00:05, 535.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▏           | 5213/8000 [00:06<00:04, 621.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▌           | 5303/8000 [00:06<00:04, 663.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▉           | 5392/8000 [00:06<00:03, 708.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▌          | 5550/8000 [00:06<00:02, 818.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████          | 5653/8000 [00:06<00:02, 860.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▌         | 5783/8000 [00:07<00:02, 846.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████         | 5892/8000 [00:07<00:02, 878.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6022/8000 [00:07<00:02, 838.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6112/8000 [00:07<00:02, 842.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6213/8000 [00:07<00:02, 857.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6316/8000 [00:07<00:01, 896.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6413/8000 [00:07<00:01, 888.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▋      | 6511/8000 [00:07<00:01, 894.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6667/8000 [00:08<00:01, 944.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6802/8000 [00:08<00:01, 903.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6905/8000 [00:08<00:01, 926.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7049/8000 [00:08<00:01, 914.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7186/8000 [00:08<00:00, 920.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7336/8000 [00:08<00:00, 950.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7441/8000 [00:08<00:00, 934.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7587/8000 [00:09<00:00, 925.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7682/8000 [00:09<00:00, 903.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7782/8000 [00:09<00:00, 886.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7875/8000 [00:09<00:00, 872.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8022it [00:09, 838.03it/s]                                                  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:11,  4.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:25,  2.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:18,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:00,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:11<02:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:12<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:12<02:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:13<02:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<02:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:14<02:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:15<02:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:16<02:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:16<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:17<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:18<01:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:18<01:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:19<01:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:19<01:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:20<01:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:21<01:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:21<01:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:22<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:23<01:52,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:23<01:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:24<01:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:25<01:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:25<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:26<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:27<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:27<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:28<01:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:29<01:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:29<01:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:30<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:31<01:52,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:32<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:32<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:33<01:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:34<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:34<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:36<01:56,  1.31it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 26.90it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 76it [00:00, 110.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 153it [00:01, 224.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 233it [00:01, 338.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 313it [00:01, 439.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 396it [00:01, 531.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 472it [00:01, 576.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 553it [00:01, 637.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 634it [00:01, 683.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 713it [00:01, 712.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 431.05it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 483.52it/s]\n",
+      "\n",
+      "Nyquist Samples:  50%|█████████████████████▌                     | 2/4 [05:03<05:03, 151.83s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.316105063273558\n",
+      "2999: -6.2039575\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:38,  4.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:13,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:39,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:59,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:12,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:05,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<01:59,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<01:56,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<01:56,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:54,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<01:55,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:53,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:51,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:50,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:53,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:49,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:17<01:48,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:45,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:43,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:41,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:20<01:41,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:43,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:41,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:40,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:23<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:38,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:38,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:37,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:37,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:26<01:37,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:38,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:40,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:28<01:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:29<01:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:41,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:38,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:31<01:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:31<01:35,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:33<01:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:33<01:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:34<01:34,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:34<01:32,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:35<01:29,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:36<01:29,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:36<01:27,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:37<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:38<01:39,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:39<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:39<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:40<01:45,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:41<01:45,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:42<01:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:43<01:47,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:43<01:46,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:44<01:46,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:46<01:35,  1.41it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 24.19it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 46it [00:01, 62.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 92it [00:01, 126.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 143it [00:01, 198.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 191it [00:01, 257.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 238it [00:01, 306.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 288it [00:01, 353.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 339it [00:01, 394.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 391it [00:01, 427.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 443it [00:01, 452.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 495it [00:01, 470.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 547it [00:02, 482.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 599it [00:02, 491.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 651it [00:02, 498.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 703it [00:02, 489.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:02, 316.16it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  31%|███▍       | 31/100 [00:00<00:00, 308.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 334.00it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                   | 50/8000 [00:00<00:22, 353.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 91/8000 [00:00<00:21, 361.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                  | 129/8000 [00:00<00:23, 336.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                  | 166/8000 [00:00<00:24, 316.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                  | 220/8000 [00:00<00:25, 303.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                 | 260/8000 [00:00<00:24, 309.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                 | 295/8000 [00:00<00:24, 308.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                 | 331/8000 [00:01<00:28, 270.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                                 | 368/8000 [00:01<00:26, 283.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                 | 411/8000 [00:01<00:28, 269.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                                 | 448/8000 [00:01<00:27, 271.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                 | 476/8000 [00:01<00:28, 260.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                | 512/8000 [00:01<00:27, 271.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                | 550/8000 [00:01<00:26, 277.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                | 591/8000 [00:02<00:24, 298.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                | 638/8000 [00:02<00:22, 324.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                | 676/8000 [00:02<00:22, 327.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                | 709/8000 [00:02<00:23, 307.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                               | 741/8000 [00:02<00:24, 297.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                               | 777/8000 [00:02<00:24, 298.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                               | 814/8000 [00:02<00:23, 305.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                               | 862/8000 [00:02<00:21, 331.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                               | 903/8000 [00:02<00:20, 343.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                              | 958/8000 [00:03<00:21, 333.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                              | 996/8000 [00:03<00:21, 331.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                             | 1041/8000 [00:03<00:20, 343.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                             | 1078/8000 [00:03<00:20, 331.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                             | 1120/8000 [00:03<00:20, 328.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▉                             | 1174/8000 [00:03<00:19, 352.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                            | 1217/8000 [00:03<00:19, 355.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▎                            | 1256/8000 [00:03<00:19, 349.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                            | 1313/8000 [00:04<00:19, 344.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                            | 1356/8000 [00:04<00:18, 353.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▉                            | 1405/8000 [00:04<00:20, 321.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                           | 1445/8000 [00:04<00:20, 323.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▎                           | 1491/8000 [00:04<00:21, 304.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                           | 1524/8000 [00:04<00:21, 303.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▋                           | 1571/8000 [00:04<00:19, 335.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                           | 1613/8000 [00:05<00:18, 344.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████                           | 1650/8000 [00:05<00:19, 328.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                          | 1686/8000 [00:05<00:19, 326.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▎                          | 1730/8000 [00:05<00:21, 290.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                          | 1766/8000 [00:05<00:20, 298.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▋                          | 1804/8000 [00:05<00:20, 301.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▊                          | 1841/8000 [00:05<00:20, 304.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                          | 1873/8000 [00:05<00:21, 288.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████                          | 1910/8000 [00:06<00:21, 288.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                         | 1945/8000 [00:06<00:20, 290.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▍                         | 1994/8000 [00:06<00:18, 319.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                         | 2033/8000 [00:06<00:18, 322.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▊                         | 2068/8000 [00:06<00:18, 321.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                         | 2108/8000 [00:06<00:18, 324.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▏                        | 2149/8000 [00:06<00:20, 283.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                        | 2186/8000 [00:07<00:20, 287.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▍                        | 2226/8000 [00:07<00:19, 295.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▌                        | 2263/8000 [00:07<00:20, 285.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▊                        | 2301/8000 [00:07<00:19, 292.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▉                        | 2349/8000 [00:07<00:20, 281.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▏                       | 2384/8000 [00:07<00:19, 282.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                       | 2413/8000 [00:07<00:20, 276.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▍                       | 2460/8000 [00:07<00:17, 311.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                       | 2509/8000 [00:08<00:17, 306.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▊                       | 2546/8000 [00:08<00:17, 312.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▉                       | 2586/8000 [00:08<00:17, 304.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▏                      | 2621/8000 [00:08<00:17, 299.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▎                      | 2661/8000 [00:08<00:17, 298.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▍                      | 2694/8000 [00:08<00:18, 293.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                      | 2746/8000 [00:08<00:18, 289.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▊                      | 2789/8000 [00:09<00:16, 308.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▉                      | 2821/8000 [00:09<00:17, 297.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▏                     | 2864/8000 [00:09<00:16, 318.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▎                     | 2900/8000 [00:09<00:16, 314.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▍                     | 2937/8000 [00:09<00:16, 311.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▋                     | 2974/8000 [00:09<00:15, 316.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|████████████▊                     | 3013/8000 [00:09<00:15, 322.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|████████████▉                     | 3053/8000 [00:09<00:15, 328.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▏                    | 3099/8000 [00:09<00:14, 335.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▎                    | 3140/8000 [00:10<00:14, 345.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▌                    | 3186/8000 [00:10<00:13, 359.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                    | 3236/8000 [00:10<00:14, 336.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▉                    | 3293/8000 [00:10<00:14, 334.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▏                   | 3335/8000 [00:10<00:13, 349.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▍                   | 3385/8000 [00:10<00:14, 327.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▌                   | 3431/8000 [00:10<00:13, 342.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▋                   | 3468/8000 [00:11<00:13, 339.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▉                   | 3505/8000 [00:11<00:13, 334.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▏                  | 3559/8000 [00:11<00:13, 327.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▎                  | 3604/8000 [00:11<00:13, 332.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▍                  | 3647/8000 [00:11<00:13, 328.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▋                  | 3688/8000 [00:11<00:13, 315.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|███████████████▊                  | 3729/8000 [00:11<00:13, 320.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████                  | 3770/8000 [00:11<00:12, 330.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▏                 | 3809/8000 [00:12<00:12, 334.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▎                 | 3848/8000 [00:12<00:12, 330.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▌                 | 3883/8000 [00:12<00:12, 331.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▋                 | 3920/8000 [00:12<00:12, 337.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████████▊                 | 3967/8000 [00:12<00:10, 370.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████                 | 4028/8000 [00:12<00:11, 356.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▎                | 4078/8000 [00:12<00:11, 331.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▍                | 4113/8000 [00:13<00:12, 323.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▋                | 4149/8000 [00:13<00:11, 326.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████████▊                | 4202/8000 [00:13<00:11, 325.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████                | 4247/8000 [00:13<00:10, 347.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▏               | 4285/8000 [00:13<00:10, 345.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▎               | 4320/8000 [00:13<00:10, 341.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▌               | 4360/8000 [00:13<00:11, 330.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▋               | 4400/8000 [00:13<00:10, 332.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████████▉               | 4455/8000 [00:14<00:10, 337.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████               | 4491/8000 [00:14<00:10, 331.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▎              | 4535/8000 [00:14<00:10, 331.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▍              | 4577/8000 [00:14<00:10, 325.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▌              | 4612/8000 [00:14<00:10, 328.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▊              | 4649/8000 [00:14<00:09, 336.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████████▉              | 4700/8000 [00:14<00:10, 329.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▏             | 4736/8000 [00:14<00:10, 320.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▎             | 4769/8000 [00:14<00:10, 319.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▍             | 4809/8000 [00:15<00:09, 326.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▋             | 4856/8000 [00:15<00:08, 352.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▊             | 4908/8000 [00:15<00:09, 336.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████             | 4948/8000 [00:15<00:10, 301.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▏            | 4988/8000 [00:15<00:09, 320.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▎            | 5026/8000 [00:15<00:09, 323.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▌            | 5062/8000 [00:15<00:08, 326.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▋            | 5102/8000 [00:15<00:08, 337.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▊            | 5139/8000 [00:16<00:08, 341.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████            | 5179/8000 [00:16<00:08, 344.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▏           | 5226/8000 [00:16<00:08, 339.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▎           | 5263/8000 [00:16<00:08, 326.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▌           | 5311/8000 [00:16<00:08, 335.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▋           | 5350/8000 [00:16<00:07, 336.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▉           | 5388/8000 [00:16<00:07, 335.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████           | 5434/8000 [00:16<00:07, 342.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▎          | 5474/8000 [00:17<00:07, 343.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▍          | 5529/8000 [00:17<00:07, 323.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▋          | 5567/8000 [00:17<00:08, 294.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▊          | 5615/8000 [00:17<00:08, 284.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████          | 5668/8000 [00:17<00:06, 333.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▎         | 5707/8000 [00:17<00:06, 343.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▌         | 5771/8000 [00:17<00:06, 362.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▊         | 5824/8000 [00:18<00:06, 354.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▉         | 5877/8000 [00:18<00:06, 335.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5920/8000 [00:18<00:06, 345.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▍        | 5974/8000 [00:18<00:05, 340.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6012/8000 [00:18<00:05, 337.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6050/8000 [00:18<00:05, 341.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6093/8000 [00:18<00:05, 359.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████        | 6138/8000 [00:19<00:04, 374.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▎       | 6187/8000 [00:19<00:05, 324.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6230/8000 [00:19<00:05, 327.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▋       | 6271/8000 [00:19<00:05, 339.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6313/8000 [00:19<00:04, 340.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████       | 6368/8000 [00:19<00:04, 337.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6409/8000 [00:19<00:04, 336.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▍      | 6445/8000 [00:19<00:04, 333.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6496/8000 [00:20<00:04, 364.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████████▊      | 6545/8000 [00:20<00:04, 344.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████      | 6593/8000 [00:20<00:04, 326.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6643/8000 [00:20<00:04, 317.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████████▍     | 6685/8000 [00:20<00:04, 302.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6722/8000 [00:20<00:04, 309.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6771/8000 [00:21<00:04, 303.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6812/8000 [00:21<00:03, 319.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████     | 6849/8000 [00:21<00:03, 318.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6888/8000 [00:21<00:03, 311.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6942/8000 [00:21<00:03, 326.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6989/8000 [00:21<00:03, 294.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7031/8000 [00:21<00:03, 266.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████    | 7081/8000 [00:22<00:03, 298.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7120/8000 [00:22<00:02, 302.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7182/8000 [00:22<00:02, 319.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7234/8000 [00:22<00:02, 355.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7286/8000 [00:22<00:01, 380.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7333/8000 [00:22<00:01, 342.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7374/8000 [00:22<00:01, 348.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7420/8000 [00:22<00:01, 365.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7461/8000 [00:23<00:01, 362.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7519/8000 [00:23<00:01, 361.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7562/8000 [00:23<00:01, 365.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7611/8000 [00:23<00:01, 383.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7652/8000 [00:23<00:00, 376.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▊ | 7707/8000 [00:23<00:00, 350.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████████▉ | 7751/8000 [00:23<00:00, 318.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7785/8000 [00:24<00:00, 311.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▏| 7822/8000 [00:24<00:00, 313.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7861/8000 [00:24<00:00, 327.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7904/8000 [00:24<00:00, 301.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7941/8000 [00:24<00:00, 307.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8008it [00:24, 322.47it/s]                                                  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:03<13:12,  3.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:33,  1.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:24,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:25,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:50,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:06<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:09<01:58,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<01:55,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:55,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:11<01:53,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:51,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:12<01:50,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:49,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:49,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:14<01:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:48,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:15<01:47,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:16<01:48,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:47,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:17<01:46,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:44,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:46,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:46,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:45,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:20<01:43,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:43,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:42,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:22<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:38,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:23<01:37,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:36,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:24<01:35,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:25<01:36,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:35,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:26<01:36,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:27<01:37,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:37,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:28<01:37,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:48,  1.44it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 27.52it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 82it [00:00, 117.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 154it [00:01, 219.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 232it [00:01, 328.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 311it [00:01, 428.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 392it [00:01, 518.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 466it [00:01, 571.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 540it [00:01, 610.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 622it [00:01, 665.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 704it [00:01, 707.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 425.44it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  46%|█████      | 46/100 [00:00<00:00, 454.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 433.89it/s]\n",
+      "\n",
+      "Nyquist Samples:  75%|████████████████████████████████▎          | 3/4 [08:22<02:53, 173.42s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.4926506242562683\n",
+      "2999: -5.7875788\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:06,  4.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:37,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:15,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:45,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:21,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:12,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<02:59,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:51,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:48,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:45,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:38,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:52,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:15<02:57,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:16<02:46,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:17<02:37,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:31,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:18<02:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:19<02:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:20<02:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:21<02:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:21<02:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:22<02:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:23<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:24<02:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:24<02:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:25<02:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:26<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:27<02:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:27<02:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:28<02:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:29<02:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:30<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:30<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:31<02:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:32<02:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:33<02:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:34<02:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:34<02:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:35<02:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:36<02:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:37<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:37<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:38<01:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:39<01:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:40<01:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:41<01:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:41<01:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:42<01:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:43<01:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:44<01:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:44<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:45<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:46<01:54,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:47<01:55,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:48<01:53,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:48<01:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:50<02:03,  1.15it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 25.68it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 96it [00:00, 132.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 173it [00:01, 237.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 366.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 353it [00:01, 477.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 448it [00:01, 587.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 545it [00:01, 682.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 638it [00:01, 746.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 442.70it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  39%|████▎      | 39/100 [00:00<00:00, 383.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 380.07it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                      | 5/8000 [00:00<02:48, 47.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 10/8000 [00:00<02:58, 44.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 16/8000 [00:00<02:59, 44.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 24/8000 [00:00<03:27, 38.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 32/8000 [00:00<02:52, 46.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 39/8000 [00:00<02:43, 48.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 46/8000 [00:01<03:32, 37.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 52/8000 [00:01<03:46, 35.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 57/8000 [00:01<03:37, 36.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 61/8000 [00:01<03:43, 35.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 67/8000 [00:01<03:23, 38.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 73/8000 [00:01<03:30, 37.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 78/8000 [00:01<03:25, 38.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 83/8000 [00:02<03:51, 34.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 90/8000 [00:02<03:59, 32.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 99/8000 [00:02<03:06, 42.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 106/8000 [00:02<03:08, 41.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▌                                   | 113/8000 [00:02<03:32, 37.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 121/8000 [00:03<03:00, 43.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 127/8000 [00:03<03:14, 40.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 133/8000 [00:03<03:10, 41.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 138/8000 [00:03<04:17, 30.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 146/8000 [00:03<03:32, 37.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 154/8000 [00:03<03:02, 42.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 163/8000 [00:04<03:03, 42.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 169/8000 [00:04<02:56, 44.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 175/8000 [00:04<03:07, 41.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 182/8000 [00:04<03:10, 41.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 189/8000 [00:04<03:05, 42.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▉                                   | 198/8000 [00:04<02:34, 50.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 204/8000 [00:04<02:30, 51.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 210/8000 [00:05<03:12, 40.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 219/8000 [00:05<03:00, 43.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 224/8000 [00:05<02:56, 44.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 229/8000 [00:05<03:14, 39.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 235/8000 [00:05<03:13, 40.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 242/8000 [00:05<02:46, 46.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 249/8000 [00:06<02:52, 45.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 254/8000 [00:06<02:52, 44.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 259/8000 [00:06<03:00, 42.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 266/8000 [00:06<02:40, 48.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 271/8000 [00:06<03:00, 42.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 277/8000 [00:06<02:48, 45.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 285/8000 [00:06<02:38, 48.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 292/8000 [00:06<02:27, 52.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 298/8000 [00:07<02:42, 47.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 305/8000 [00:07<03:09, 40.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 313/8000 [00:07<02:40, 47.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 320/8000 [00:07<02:50, 44.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 327/8000 [00:07<02:56, 43.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 335/8000 [00:07<02:32, 50.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 343/8000 [00:08<02:28, 51.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 349/8000 [00:08<03:01, 42.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 356/8000 [00:08<02:57, 43.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 363/8000 [00:08<02:40, 47.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 373/8000 [00:08<02:26, 52.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 379/8000 [00:08<02:25, 52.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 385/8000 [00:09<03:06, 40.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 395/8000 [00:09<02:29, 50.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 402/8000 [00:09<02:50, 44.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 409/8000 [00:09<02:40, 47.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 418/8000 [00:09<02:48, 44.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 423/8000 [00:09<03:22, 37.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 432/8000 [00:10<02:48, 44.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 437/8000 [00:10<02:51, 44.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                                  | 444/8000 [00:10<02:37, 48.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 452/8000 [00:10<03:09, 39.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 457/8000 [00:10<03:03, 41.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 463/8000 [00:10<03:24, 36.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 470/8000 [00:11<02:58, 42.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 476/8000 [00:11<02:53, 43.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 482/8000 [00:11<02:45, 45.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 493/8000 [00:11<02:25, 51.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 500/8000 [00:11<02:48, 44.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 507/8000 [00:11<02:33, 48.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 513/8000 [00:11<02:47, 44.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                 | 521/8000 [00:12<02:46, 44.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                 | 526/8000 [00:12<03:04, 40.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 532/8000 [00:12<03:08, 39.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 538/8000 [00:12<02:56, 42.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 545/8000 [00:12<02:57, 41.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 550/8000 [00:12<03:15, 38.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 557/8000 [00:12<02:52, 43.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 562/8000 [00:13<03:14, 38.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 569/8000 [00:13<03:25, 36.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 577/8000 [00:13<02:56, 42.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 582/8000 [00:13<02:56, 42.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▋                                 | 592/8000 [00:13<02:54, 42.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▋                                 | 597/8000 [00:13<02:52, 43.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                 | 602/8000 [00:14<03:17, 37.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                 | 609/8000 [00:14<02:52, 42.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 615/8000 [00:14<03:00, 40.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 620/8000 [00:14<02:56, 41.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 625/8000 [00:14<03:13, 38.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 631/8000 [00:14<02:53, 42.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 637/8000 [00:14<02:42, 45.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 642/8000 [00:15<03:02, 40.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 647/8000 [00:15<03:23, 36.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 654/8000 [00:15<02:55, 41.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 660/8000 [00:15<02:44, 44.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 671/8000 [00:15<02:52, 42.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 682/8000 [00:15<02:12, 55.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 690/8000 [00:16<02:32, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 697/8000 [00:16<02:40, 45.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 704/8000 [00:16<02:42, 45.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 711/8000 [00:16<02:26, 49.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 720/8000 [00:16<02:04, 58.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 727/8000 [00:16<02:03, 58.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 735/8000 [00:16<02:12, 54.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 744/8000 [00:17<01:56, 62.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 751/8000 [00:17<02:37, 45.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 758/8000 [00:17<02:37, 45.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 764/8000 [00:17<03:09, 38.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 770/8000 [00:17<03:11, 37.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 779/8000 [00:17<02:35, 46.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 785/8000 [00:18<02:47, 43.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 799/8000 [00:18<02:04, 57.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 808/8000 [00:18<02:12, 54.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 814/8000 [00:18<02:39, 44.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 821/8000 [00:18<03:17, 36.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 828/8000 [00:19<03:32, 33.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▊                                | 836/8000 [00:19<03:29, 34.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 841/8000 [00:19<03:24, 35.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 846/8000 [00:19<03:17, 36.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 850/8000 [00:19<03:16, 36.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 854/8000 [00:19<03:48, 31.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 861/8000 [00:20<03:15, 36.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 868/8000 [00:20<02:53, 41.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 874/8000 [00:20<03:11, 37.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 884/8000 [00:20<02:28, 47.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 890/8000 [00:20<02:30, 47.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 898/8000 [00:20<02:37, 45.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 904/8000 [00:21<02:38, 44.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 912/8000 [00:21<02:40, 44.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████▏                               | 917/8000 [00:21<03:07, 37.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 921/8000 [00:21<03:33, 33.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 929/8000 [00:21<02:49, 41.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 934/8000 [00:21<02:47, 42.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 940/8000 [00:21<02:55, 40.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 947/8000 [00:22<02:39, 44.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 953/8000 [00:22<02:36, 45.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 959/8000 [00:22<02:55, 40.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 966/8000 [00:22<02:40, 43.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 976/8000 [00:22<02:11, 53.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 984/8000 [00:22<02:28, 47.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 991/8000 [00:23<02:36, 44.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 999/8000 [00:23<02:35, 45.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1004/8000 [00:23<02:35, 45.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1012/8000 [00:23<02:19, 49.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1019/8000 [00:23<03:01, 38.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1027/8000 [00:23<02:36, 44.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1033/8000 [00:24<03:12, 36.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1039/8000 [00:24<03:23, 34.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1044/8000 [00:24<03:29, 33.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1049/8000 [00:24<03:16, 35.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1053/8000 [00:24<03:18, 35.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1057/8000 [00:24<03:19, 34.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1061/8000 [00:24<03:24, 33.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1069/8000 [00:25<02:44, 42.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1075/8000 [00:25<02:38, 43.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                              | 1083/8000 [00:25<02:52, 40.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1088/8000 [00:25<03:27, 33.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1094/8000 [00:25<03:29, 33.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1102/8000 [00:26<03:10, 36.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1107/8000 [00:26<03:09, 36.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1113/8000 [00:26<02:54, 39.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1121/8000 [00:26<02:45, 41.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1127/8000 [00:26<02:57, 38.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1134/8000 [00:26<02:33, 44.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1139/8000 [00:26<02:50, 40.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1144/8000 [00:27<02:58, 38.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1148/8000 [00:27<03:00, 38.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1153/8000 [00:27<02:51, 39.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1158/8000 [00:27<02:43, 41.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                              | 1164/8000 [00:27<02:28, 46.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                              | 1170/8000 [00:27<02:17, 49.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1176/8000 [00:27<02:10, 52.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1184/8000 [00:27<02:15, 50.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1191/8000 [00:27<02:04, 54.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1199/8000 [00:28<02:27, 46.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1206/8000 [00:28<03:19, 34.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1211/8000 [00:28<03:07, 36.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1220/8000 [00:28<02:30, 44.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1227/8000 [00:28<02:17, 49.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1233/8000 [00:29<02:26, 46.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1242/8000 [00:29<02:53, 38.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1249/8000 [00:29<02:46, 40.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1256/8000 [00:29<02:26, 46.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1262/8000 [00:29<02:35, 43.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1267/8000 [00:29<02:33, 43.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1275/8000 [00:29<02:10, 51.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1281/8000 [00:30<02:26, 45.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1287/8000 [00:30<02:18, 48.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1295/8000 [00:30<02:16, 49.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1301/8000 [00:30<02:26, 45.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1308/8000 [00:30<02:29, 44.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1313/8000 [00:30<02:28, 44.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▊                             | 1318/8000 [00:30<02:45, 40.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1323/8000 [00:31<02:58, 37.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1330/8000 [00:31<02:32, 43.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1336/8000 [00:31<02:54, 38.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1342/8000 [00:31<02:38, 41.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1347/8000 [00:31<02:35, 42.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1354/8000 [00:31<02:49, 39.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1360/8000 [00:32<02:51, 38.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1367/8000 [00:32<03:04, 35.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1373/8000 [00:32<03:03, 36.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1378/8000 [00:32<02:52, 38.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1384/8000 [00:32<02:38, 41.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1389/8000 [00:32<03:12, 34.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1394/8000 [00:32<03:01, 36.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1400/8000 [00:33<02:43, 40.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1405/8000 [00:33<03:00, 36.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1413/8000 [00:33<02:43, 40.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1420/8000 [00:33<02:24, 45.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1427/8000 [00:33<02:11, 49.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1433/8000 [00:33<02:53, 37.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1438/8000 [00:34<03:09, 34.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1446/8000 [00:34<02:37, 41.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1451/8000 [00:34<02:36, 41.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1458/8000 [00:34<02:20, 46.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1465/8000 [00:34<02:43, 40.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1471/8000 [00:34<02:47, 38.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1478/8000 [00:34<02:26, 44.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1487/8000 [00:35<02:26, 44.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1494/8000 [00:35<02:27, 44.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1501/8000 [00:35<02:45, 39.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1510/8000 [00:35<02:30, 43.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1518/8000 [00:35<02:26, 44.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1524/8000 [00:36<02:19, 46.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1530/8000 [00:36<02:25, 44.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1536/8000 [00:36<02:37, 41.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1547/8000 [00:36<02:13, 48.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1552/8000 [00:36<02:27, 43.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1558/8000 [00:36<02:33, 42.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                            | 1563/8000 [00:36<02:47, 38.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                            | 1569/8000 [00:37<02:32, 42.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1574/8000 [00:37<03:04, 34.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1578/8000 [00:37<03:02, 35.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1585/8000 [00:37<02:46, 38.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1591/8000 [00:37<02:29, 42.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1598/8000 [00:37<02:29, 42.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1605/8000 [00:38<02:31, 42.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1614/8000 [00:38<02:38, 40.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1621/8000 [00:38<02:21, 44.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1627/8000 [00:38<02:35, 41.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1632/8000 [00:38<02:34, 41.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1637/8000 [00:38<02:51, 37.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1643/8000 [00:38<02:37, 40.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1648/8000 [00:39<02:35, 40.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1653/8000 [00:39<03:16, 32.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1657/8000 [00:39<03:12, 32.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1662/8000 [00:39<02:56, 35.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1666/8000 [00:39<03:01, 34.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1672/8000 [00:39<03:31, 29.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1676/8000 [00:40<03:20, 31.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1681/8000 [00:40<03:01, 34.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1685/8000 [00:40<03:00, 35.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1689/8000 [00:40<03:20, 31.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1694/8000 [00:40<03:02, 34.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1701/8000 [00:40<02:32, 41.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1708/8000 [00:40<02:11, 47.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1714/8000 [00:40<02:17, 45.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▌                           | 1719/8000 [00:41<02:21, 44.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1724/8000 [00:41<02:17, 45.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1730/8000 [00:41<02:12, 47.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1738/8000 [00:41<02:28, 42.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1744/8000 [00:41<02:33, 40.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1749/8000 [00:41<02:45, 37.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1753/8000 [00:41<02:49, 36.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1759/8000 [00:41<02:28, 42.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1764/8000 [00:42<02:22, 43.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1770/8000 [00:42<02:28, 41.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1776/8000 [00:42<02:17, 45.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1783/8000 [00:42<02:21, 44.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1788/8000 [00:42<02:19, 44.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1794/8000 [00:42<02:43, 37.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1802/8000 [00:42<02:13, 46.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1808/8000 [00:43<02:43, 37.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1813/8000 [00:43<02:35, 39.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1818/8000 [00:43<02:28, 41.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1827/8000 [00:43<02:12, 46.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1834/8000 [00:43<02:02, 50.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1840/8000 [00:43<02:15, 45.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1849/8000 [00:44<02:08, 47.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1855/8000 [00:44<02:04, 49.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1861/8000 [00:44<02:16, 45.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1868/8000 [00:44<02:23, 42.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1873/8000 [00:44<02:23, 42.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▏                          | 1882/8000 [00:44<02:13, 45.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1891/8000 [00:44<02:02, 49.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1896/8000 [00:45<02:08, 47.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1901/8000 [00:45<02:31, 40.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1909/8000 [00:45<02:35, 39.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1916/8000 [00:45<02:16, 44.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1922/8000 [00:45<02:09, 47.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1930/8000 [00:45<02:04, 48.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1936/8000 [00:45<01:59, 50.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1942/8000 [00:46<01:57, 51.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1952/8000 [00:46<01:34, 63.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1959/8000 [00:46<01:50, 54.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1969/8000 [00:46<02:10, 46.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1978/8000 [00:46<01:50, 54.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1988/8000 [00:46<01:43, 58.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1996/8000 [00:47<01:59, 50.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2004/8000 [00:47<01:57, 50.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2011/8000 [00:47<02:05, 47.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2017/8000 [00:47<02:01, 49.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2024/8000 [00:47<02:02, 48.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2030/8000 [00:47<02:14, 44.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2037/8000 [00:47<02:01, 49.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2043/8000 [00:47<01:57, 50.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2049/8000 [00:48<02:08, 46.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2054/8000 [00:48<02:08, 46.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2062/8000 [00:48<02:35, 38.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2067/8000 [00:48<02:27, 40.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2073/8000 [00:48<02:59, 32.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2077/8000 [00:49<03:33, 27.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2084/8000 [00:49<02:50, 34.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2090/8000 [00:49<02:32, 38.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2095/8000 [00:49<02:40, 36.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2100/8000 [00:49<02:30, 39.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2108/8000 [00:49<02:01, 48.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2114/8000 [00:49<01:55, 51.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2122/8000 [00:49<01:51, 52.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2132/8000 [00:50<01:51, 52.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2138/8000 [00:50<01:54, 51.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2148/8000 [00:50<01:39, 59.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2158/8000 [00:50<01:52, 51.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2167/8000 [00:50<01:39, 58.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2174/8000 [00:50<01:57, 49.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2180/8000 [00:51<02:04, 46.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2190/8000 [00:51<01:52, 51.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2200/8000 [00:51<01:41, 57.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2206/8000 [00:51<02:00, 48.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2213/8000 [00:51<02:01, 47.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2219/8000 [00:51<01:57, 49.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2226/8000 [00:51<01:51, 51.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2236/8000 [00:52<01:44, 55.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2242/8000 [00:52<01:55, 49.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2250/8000 [00:52<02:09, 44.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2255/8000 [00:52<02:07, 45.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2266/8000 [00:52<01:37, 58.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2274/8000 [00:52<01:39, 57.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▉                         | 2281/8000 [00:52<01:36, 59.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2288/8000 [00:53<01:44, 54.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2294/8000 [00:53<01:55, 49.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2301/8000 [00:53<01:56, 49.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2308/8000 [00:53<01:45, 53.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2317/8000 [00:53<01:41, 55.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2325/8000 [00:53<01:43, 54.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2334/8000 [00:53<01:40, 56.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2341/8000 [00:54<01:59, 47.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2349/8000 [00:54<02:41, 35.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2354/8000 [00:54<02:48, 33.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2361/8000 [00:54<02:24, 38.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2366/8000 [00:54<02:21, 39.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2372/8000 [00:55<02:12, 42.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2377/8000 [00:55<02:12, 42.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2385/8000 [00:55<02:08, 43.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2392/8000 [00:55<02:19, 40.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2398/8000 [00:55<02:08, 43.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2407/8000 [00:55<01:56, 47.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2413/8000 [00:55<01:53, 49.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2420/8000 [00:56<01:55, 48.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2429/8000 [00:56<01:50, 50.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2436/8000 [00:56<01:57, 47.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2441/8000 [00:56<02:11, 42.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2447/8000 [00:56<02:02, 45.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2453/8000 [00:56<01:55, 48.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2461/8000 [00:57<02:08, 43.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2467/8000 [00:57<02:14, 41.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2474/8000 [00:57<02:13, 41.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2479/8000 [00:57<02:12, 41.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2486/8000 [00:57<02:13, 41.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2492/8000 [00:57<02:46, 33.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2498/8000 [00:58<02:45, 33.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2503/8000 [00:58<02:50, 32.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2510/8000 [00:58<02:24, 37.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|███████████                        | 2515/8000 [00:58<02:43, 33.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2523/8000 [00:58<02:13, 40.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2531/8000 [00:59<02:39, 34.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2536/8000 [00:59<03:04, 29.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2543/8000 [00:59<03:04, 29.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2548/8000 [00:59<02:55, 30.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2554/8000 [00:59<02:41, 33.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2559/8000 [00:59<02:53, 31.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2565/8000 [01:00<02:35, 35.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2572/8000 [01:00<02:10, 41.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2580/8000 [01:00<02:05, 43.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2586/8000 [01:00<02:02, 44.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2592/8000 [01:00<02:18, 39.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2598/8000 [01:00<02:24, 37.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2603/8000 [01:01<02:23, 37.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2609/8000 [01:01<02:09, 41.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2615/8000 [01:01<02:08, 41.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2621/8000 [01:01<02:20, 38.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2626/8000 [01:01<02:39, 33.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2634/8000 [01:01<02:26, 36.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2638/8000 [01:01<02:42, 33.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2644/8000 [01:02<02:26, 36.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2650/8000 [01:02<02:12, 40.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2655/8000 [01:02<02:11, 40.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2664/8000 [01:02<01:42, 52.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2670/8000 [01:02<02:10, 40.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2677/8000 [01:02<02:04, 42.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                       | 2683/8000 [01:02<02:02, 43.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2688/8000 [01:03<02:05, 42.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2694/8000 [01:03<02:01, 43.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2702/8000 [01:03<02:15, 39.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2709/8000 [01:03<02:19, 38.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2716/8000 [01:03<02:00, 43.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2721/8000 [01:03<01:57, 45.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2726/8000 [01:04<02:07, 41.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2732/8000 [01:04<01:55, 45.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2738/8000 [01:04<01:48, 48.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2745/8000 [01:04<01:37, 53.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2752/8000 [01:04<02:01, 43.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                       | 2765/8000 [01:04<01:46, 49.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2774/8000 [01:04<01:41, 51.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2783/8000 [01:05<01:38, 52.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2789/8000 [01:05<02:00, 43.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2796/8000 [01:05<02:03, 42.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2801/8000 [01:05<02:04, 41.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2809/8000 [01:05<01:55, 45.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2817/8000 [01:05<01:50, 47.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2824/8000 [01:06<01:54, 45.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2831/8000 [01:06<01:47, 48.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2838/8000 [01:06<01:52, 45.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2843/8000 [01:06<01:53, 45.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2848/8000 [01:06<01:53, 45.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2854/8000 [01:06<01:58, 43.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2861/8000 [01:06<01:46, 48.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2869/8000 [01:06<01:34, 54.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2878/8000 [01:07<01:46, 47.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2884/8000 [01:07<01:42, 50.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2890/8000 [01:07<01:40, 50.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2898/8000 [01:07<01:52, 45.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2904/8000 [01:07<02:21, 36.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2910/8000 [01:08<02:33, 33.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2914/8000 [01:08<02:36, 32.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▊                      | 2919/8000 [01:08<02:40, 31.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2924/8000 [01:08<02:29, 33.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2932/8000 [01:08<02:00, 42.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2937/8000 [01:08<02:01, 41.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2942/8000 [01:08<01:56, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2947/8000 [01:08<01:52, 44.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2952/8000 [01:09<02:50, 29.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2957/8000 [01:09<02:33, 32.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2962/8000 [01:09<02:20, 35.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2973/8000 [01:09<02:23, 35.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2978/8000 [01:09<02:26, 34.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2989/8000 [01:10<01:46, 46.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2996/8000 [01:10<01:42, 49.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3002/8000 [01:10<01:41, 49.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3008/8000 [01:10<01:55, 43.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3015/8000 [01:10<01:57, 42.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3022/8000 [01:10<02:19, 35.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3029/8000 [01:11<02:02, 40.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3035/8000 [01:11<01:55, 43.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3042/8000 [01:11<01:45, 47.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3052/8000 [01:11<01:26, 57.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3059/8000 [01:11<01:54, 43.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3066/8000 [01:11<01:42, 48.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3073/8000 [01:11<01:35, 51.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3079/8000 [01:12<01:45, 46.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3087/8000 [01:12<01:42, 47.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3095/8000 [01:12<01:43, 47.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3103/8000 [01:12<01:44, 46.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3108/8000 [01:12<01:55, 42.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3115/8000 [01:12<01:42, 47.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3123/8000 [01:12<01:29, 54.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3129/8000 [01:13<01:49, 44.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3137/8000 [01:13<01:34, 51.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3144/8000 [01:13<01:31, 53.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3152/8000 [01:13<01:40, 48.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3159/8000 [01:13<01:32, 52.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3168/8000 [01:13<01:30, 53.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3175/8000 [01:14<02:02, 39.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3184/8000 [01:14<01:40, 47.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3191/8000 [01:14<01:39, 48.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3199/8000 [01:14<01:49, 44.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3205/8000 [01:14<02:07, 37.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3210/8000 [01:15<02:31, 31.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3214/8000 [01:15<02:51, 27.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3218/8000 [01:15<04:49, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3221/8000 [01:16<05:32, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3224/8000 [01:16<05:22, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3227/8000 [01:16<04:59, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3232/8000 [01:16<04:06, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3235/8000 [01:16<03:59, 19.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3242/8000 [01:17<03:06, 25.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3248/8000 [01:17<02:34, 30.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3254/8000 [01:17<02:09, 36.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3261/8000 [01:17<02:02, 38.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3271/8000 [01:17<01:32, 51.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3279/8000 [01:17<01:22, 57.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3286/8000 [01:17<01:35, 49.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3295/8000 [01:17<01:32, 51.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3301/8000 [01:18<01:29, 52.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3309/8000 [01:18<01:19, 59.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▌                    | 3316/8000 [01:18<01:45, 44.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3324/8000 [01:18<01:32, 50.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3333/8000 [01:18<01:20, 58.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3341/8000 [01:18<01:31, 51.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3348/8000 [01:18<01:25, 54.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3355/8000 [01:19<01:40, 46.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3362/8000 [01:19<01:45, 43.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3372/8000 [01:19<01:33, 49.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3379/8000 [01:19<02:04, 37.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3384/8000 [01:19<01:57, 39.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3395/8000 [01:20<01:28, 51.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3402/8000 [01:20<01:33, 49.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3408/8000 [01:20<01:29, 51.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3415/8000 [01:20<01:24, 53.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3422/8000 [01:20<01:47, 42.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3427/8000 [01:20<01:52, 40.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3434/8000 [01:20<01:39, 46.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3441/8000 [01:21<01:31, 49.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3448/8000 [01:21<01:33, 48.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3456/8000 [01:21<01:23, 54.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3464/8000 [01:21<01:16, 59.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3472/8000 [01:21<01:21, 55.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▏                   | 3480/8000 [01:21<01:23, 53.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3487/8000 [01:21<01:30, 50.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3495/8000 [01:21<01:22, 54.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3501/8000 [01:22<01:21, 55.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3507/8000 [01:22<01:43, 43.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3515/8000 [01:22<01:29, 50.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3523/8000 [01:22<01:19, 56.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3533/8000 [01:22<01:26, 51.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3541/8000 [01:22<01:36, 46.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3548/8000 [01:23<01:40, 44.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3555/8000 [01:23<01:32, 48.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3561/8000 [01:23<01:35, 46.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3566/8000 [01:23<01:46, 41.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3573/8000 [01:23<01:54, 38.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3580/8000 [01:23<01:38, 44.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3585/8000 [01:23<01:38, 44.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3590/8000 [01:24<01:51, 39.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3597/8000 [01:24<01:40, 43.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3602/8000 [01:24<01:38, 44.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3612/8000 [01:24<01:18, 56.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3620/8000 [01:24<02:03, 35.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3627/8000 [01:25<01:56, 37.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3635/8000 [01:25<01:40, 43.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3641/8000 [01:25<01:45, 41.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3647/8000 [01:25<01:40, 43.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3657/8000 [01:25<01:19, 54.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3667/8000 [01:25<01:07, 64.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3677/8000 [01:25<00:59, 73.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3687/8000 [01:25<01:06, 64.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3697/8000 [01:26<01:08, 62.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3704/8000 [01:26<01:25, 50.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3713/8000 [01:26<01:13, 57.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3721/8000 [01:26<01:23, 51.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3727/8000 [01:26<01:33, 45.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3735/8000 [01:27<01:29, 47.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3741/8000 [01:27<01:26, 49.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3748/8000 [01:27<01:21, 52.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3755/8000 [01:27<01:26, 49.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3764/8000 [01:27<01:47, 39.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3770/8000 [01:27<01:45, 39.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3775/8000 [01:27<01:44, 40.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3784/8000 [01:28<01:40, 41.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3791/8000 [01:28<01:38, 42.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3796/8000 [01:28<01:56, 36.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3803/8000 [01:28<01:41, 41.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3812/8000 [01:28<01:21, 51.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3820/8000 [01:29<01:45, 39.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3829/8000 [01:29<01:28, 47.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3836/8000 [01:29<01:23, 50.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3843/8000 [01:29<01:30, 45.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3849/8000 [01:29<01:44, 39.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3854/8000 [01:29<01:49, 37.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3859/8000 [01:30<02:01, 33.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3865/8000 [01:30<01:47, 38.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3875/8000 [01:30<01:22, 50.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▉                  | 3881/8000 [01:30<01:37, 42.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3888/8000 [01:30<01:46, 38.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3894/8000 [01:30<01:48, 37.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3900/8000 [01:31<02:01, 33.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3906/8000 [01:31<02:02, 33.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3910/8000 [01:31<02:08, 31.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3921/8000 [01:31<01:35, 42.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3928/8000 [01:31<01:27, 46.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3938/8000 [01:31<01:18, 51.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3945/8000 [01:31<01:21, 49.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3951/8000 [01:32<01:29, 45.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3956/8000 [01:32<01:27, 46.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3963/8000 [01:32<01:40, 40.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3968/8000 [01:32<01:36, 41.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3975/8000 [01:32<01:35, 42.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3980/8000 [01:32<01:34, 42.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3985/8000 [01:32<01:32, 43.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3993/8000 [01:33<01:16, 52.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4000/8000 [01:33<01:30, 44.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4006/8000 [01:33<01:25, 46.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4012/8000 [01:33<01:20, 49.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4018/8000 [01:33<01:25, 46.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4029/8000 [01:33<01:11, 55.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4036/8000 [01:34<01:32, 42.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4042/8000 [01:34<01:36, 41.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4048/8000 [01:34<01:40, 39.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4056/8000 [01:34<01:23, 47.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4065/8000 [01:34<01:28, 44.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4073/8000 [01:34<01:33, 41.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4081/8000 [01:35<01:39, 39.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4086/8000 [01:35<02:05, 31.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4095/8000 [01:35<01:45, 36.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4102/8000 [01:35<01:33, 41.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4109/8000 [01:35<01:31, 42.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████████                 | 4116/8000 [01:35<01:22, 47.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4125/8000 [01:36<01:16, 50.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4132/8000 [01:36<01:30, 42.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4139/8000 [01:36<01:22, 46.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4148/8000 [01:36<01:10, 54.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4154/8000 [01:36<01:13, 52.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4161/8000 [01:36<01:09, 54.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4168/8000 [01:36<01:06, 57.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4175/8000 [01:37<01:12, 52.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4182/8000 [01:37<01:16, 49.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4189/8000 [01:37<01:26, 44.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4199/8000 [01:37<01:15, 50.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4206/8000 [01:37<01:33, 40.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4211/8000 [01:37<01:30, 42.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4217/8000 [01:38<01:25, 44.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4225/8000 [01:38<01:14, 50.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4231/8000 [01:38<01:21, 46.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4239/8000 [01:38<01:27, 43.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4244/8000 [01:38<01:33, 40.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4249/8000 [01:38<01:53, 33.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4254/8000 [01:39<01:43, 36.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4264/8000 [01:39<01:16, 49.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4270/8000 [01:39<01:34, 39.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4277/8000 [01:39<01:23, 44.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▋                | 4284/8000 [01:39<01:33, 39.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4291/8000 [01:39<01:22, 45.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4298/8000 [01:39<01:14, 49.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4306/8000 [01:40<01:35, 38.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4312/8000 [01:40<01:27, 42.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4317/8000 [01:40<01:44, 35.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4323/8000 [01:40<01:42, 35.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4330/8000 [01:40<01:26, 42.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4336/8000 [01:40<01:19, 46.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4345/8000 [01:41<01:13, 49.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4351/8000 [01:41<01:19, 46.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4359/8000 [01:41<01:17, 47.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4370/8000 [01:41<00:59, 60.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4377/8000 [01:41<01:15, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4384/8000 [01:41<01:18, 46.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4392/8000 [01:42<01:11, 50.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4399/8000 [01:42<01:07, 53.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4407/8000 [01:42<01:09, 52.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4415/8000 [01:42<01:15, 47.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4423/8000 [01:42<01:21, 43.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▍               | 4434/8000 [01:42<01:06, 54.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4441/8000 [01:43<01:17, 45.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4447/8000 [01:43<01:14, 47.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4458/8000 [01:43<00:58, 60.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4466/8000 [01:43<01:17, 45.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4473/8000 [01:43<01:12, 48.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4480/8000 [01:43<01:22, 42.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4486/8000 [01:44<01:18, 44.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4492/8000 [01:44<01:24, 41.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4501/8000 [01:44<01:08, 51.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4509/8000 [01:44<01:14, 46.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▊               | 4516/8000 [01:44<01:22, 42.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4522/8000 [01:44<01:16, 45.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4529/8000 [01:44<01:10, 49.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4537/8000 [01:45<01:03, 54.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4544/8000 [01:45<01:07, 50.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4554/8000 [01:45<01:12, 47.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4563/8000 [01:45<01:03, 54.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4572/8000 [01:45<01:10, 48.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4578/8000 [01:45<01:19, 43.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4583/8000 [01:46<01:19, 43.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4589/8000 [01:46<01:13, 46.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4597/8000 [01:46<01:04, 52.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4604/8000 [01:46<01:19, 42.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4611/8000 [01:46<01:19, 42.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4622/8000 [01:46<01:01, 54.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4631/8000 [01:46<01:01, 54.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4637/8000 [01:47<01:07, 50.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4647/8000 [01:47<01:04, 51.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4656/8000 [01:47<01:04, 52.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4663/8000 [01:47<01:00, 55.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4669/8000 [01:47<01:08, 48.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4675/8000 [01:47<01:07, 49.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▍              | 4682/8000 [01:47<01:03, 51.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4690/8000 [01:48<01:07, 49.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4698/8000 [01:48<01:06, 49.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4705/8000 [01:48<01:03, 52.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4712/8000 [01:48<01:17, 42.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4718/8000 [01:48<01:13, 44.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4724/8000 [01:48<01:09, 47.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4730/8000 [01:49<01:05, 49.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4737/8000 [01:49<01:16, 42.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4743/8000 [01:49<01:19, 40.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4748/8000 [01:49<01:16, 42.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4757/8000 [01:49<01:07, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▊              | 4764/8000 [01:49<01:01, 52.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4774/8000 [01:49<01:03, 50.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4781/8000 [01:50<01:06, 48.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4786/8000 [01:50<01:07, 47.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4793/8000 [01:50<01:01, 52.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4801/8000 [01:50<01:01, 51.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4808/8000 [01:50<01:18, 40.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4815/8000 [01:50<01:14, 42.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4820/8000 [01:51<01:13, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4828/8000 [01:51<01:04, 48.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4837/8000 [01:51<01:02, 50.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4847/8000 [01:51<00:51, 61.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4854/8000 [01:51<00:57, 55.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4860/8000 [01:51<01:22, 38.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4865/8000 [01:51<01:18, 40.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4871/8000 [01:52<01:12, 42.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4876/8000 [01:52<01:12, 43.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4881/8000 [01:52<01:38, 31.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4885/8000 [01:52<01:46, 29.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4891/8000 [01:52<01:36, 32.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4897/8000 [01:53<01:41, 30.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4902/8000 [01:53<01:32, 33.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4906/8000 [01:53<01:31, 33.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4913/8000 [01:53<01:14, 41.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▌             | 4919/8000 [01:53<01:16, 40.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4924/8000 [01:53<01:24, 36.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4929/8000 [01:53<01:19, 38.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4937/8000 [01:53<01:04, 47.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4946/8000 [01:54<01:12, 42.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4954/8000 [01:54<01:02, 48.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4961/8000 [01:54<00:57, 52.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4967/8000 [01:54<00:55, 54.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4974/8000 [01:54<01:00, 50.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4982/8000 [01:54<01:10, 43.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4989/8000 [01:55<01:16, 39.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4994/8000 [01:55<01:24, 35.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5001/8000 [01:55<01:12, 41.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5008/8000 [01:55<01:12, 41.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5013/8000 [01:55<01:25, 35.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5017/8000 [01:55<01:34, 31.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5021/8000 [01:56<01:40, 29.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5026/8000 [01:56<01:30, 32.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5031/8000 [01:56<01:21, 36.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5037/8000 [01:56<01:31, 32.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5041/8000 [01:56<01:30, 32.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5049/8000 [01:56<01:11, 41.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5055/8000 [01:56<01:12, 40.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5061/8000 [01:57<01:17, 38.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5069/8000 [01:57<01:20, 36.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5074/8000 [01:57<01:16, 38.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▏            | 5080/8000 [01:57<01:08, 42.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5087/8000 [01:57<01:01, 47.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5095/8000 [01:57<01:01, 47.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5102/8000 [01:57<00:56, 51.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5108/8000 [01:58<01:02, 46.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5114/8000 [01:58<01:07, 42.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5120/8000 [01:58<01:10, 41.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5125/8000 [01:58<01:07, 42.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5132/8000 [01:58<00:58, 48.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5142/8000 [01:58<00:47, 60.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5149/8000 [01:58<00:45, 62.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5157/8000 [01:59<00:49, 57.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▌            | 5165/8000 [01:59<00:50, 55.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▌            | 5171/8000 [01:59<00:53, 52.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5181/8000 [01:59<00:48, 57.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5188/8000 [01:59<00:53, 52.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5195/8000 [01:59<00:51, 54.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5205/8000 [01:59<00:54, 51.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5212/8000 [02:00<01:08, 40.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5218/8000 [02:00<01:10, 39.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5230/8000 [02:00<00:57, 48.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5236/8000 [02:00<00:55, 50.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5243/8000 [02:00<00:50, 54.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5249/8000 [02:00<00:49, 55.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5258/8000 [02:01<01:02, 44.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5264/8000 [02:01<01:05, 41.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5270/8000 [02:01<01:02, 43.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5277/8000 [02:01<01:03, 42.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5283/8000 [02:01<01:06, 40.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5288/8000 [02:01<01:04, 41.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5296/8000 [02:01<00:55, 48.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5302/8000 [02:02<01:09, 38.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5311/8000 [02:02<01:01, 43.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▎           | 5316/8000 [02:02<01:08, 39.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5325/8000 [02:02<01:00, 44.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5331/8000 [02:02<00:57, 46.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5337/8000 [02:03<01:08, 38.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5346/8000 [02:03<00:55, 47.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5353/8000 [02:03<01:00, 43.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5359/8000 [02:03<00:56, 46.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5367/8000 [02:03<00:55, 47.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5374/8000 [02:03<00:50, 51.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5384/8000 [02:03<00:46, 56.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5391/8000 [02:04<00:50, 51.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5398/8000 [02:04<00:58, 44.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5407/8000 [02:04<00:54, 47.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5413/8000 [02:04<00:53, 48.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5419/8000 [02:04<00:58, 44.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5424/8000 [02:04<00:58, 43.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5432/8000 [02:04<00:50, 50.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5438/8000 [02:05<01:14, 34.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5443/8000 [02:05<01:23, 30.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5447/8000 [02:05<01:20, 31.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5452/8000 [02:05<01:12, 35.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5456/8000 [02:05<01:12, 34.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5462/8000 [02:05<01:02, 40.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5467/8000 [02:06<01:10, 36.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5472/8000 [02:06<01:04, 38.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5478/8000 [02:06<01:04, 38.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▉           | 5483/8000 [02:06<01:19, 31.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5487/8000 [02:06<01:16, 32.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5491/8000 [02:06<01:13, 33.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5498/8000 [02:06<00:59, 41.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5505/8000 [02:07<00:55, 45.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5513/8000 [02:07<00:55, 44.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5520/8000 [02:07<00:49, 50.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5526/8000 [02:07<00:48, 51.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5534/8000 [02:07<00:48, 51.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5544/8000 [02:07<00:44, 55.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5552/8000 [02:07<00:52, 46.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5558/8000 [02:08<00:58, 41.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5563/8000 [02:08<00:56, 42.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5569/8000 [02:08<01:22, 29.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5579/8000 [02:08<01:00, 40.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5587/8000 [02:08<00:53, 45.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5593/8000 [02:08<00:51, 46.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5601/8000 [02:09<00:47, 50.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5609/8000 [02:09<00:42, 56.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5618/8000 [02:09<00:47, 49.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5624/8000 [02:09<00:48, 48.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5631/8000 [02:09<00:58, 40.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5638/8000 [02:10<00:59, 39.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5643/8000 [02:10<01:05, 36.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5648/8000 [02:10<01:01, 38.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5653/8000 [02:10<00:59, 39.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5659/8000 [02:10<01:01, 37.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5664/8000 [02:10<00:58, 39.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5671/8000 [02:10<00:50, 46.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5677/8000 [02:10<00:48, 48.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5686/8000 [02:11<00:40, 57.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5693/8000 [02:11<00:39, 57.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5701/8000 [02:11<00:41, 54.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5709/8000 [02:11<00:52, 43.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5714/8000 [02:11<00:52, 43.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|█████████████████████████          | 5719/8000 [02:11<00:52, 43.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5727/8000 [02:11<00:48, 47.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5736/8000 [02:12<00:46, 48.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5744/8000 [02:12<00:42, 53.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5751/8000 [02:12<00:41, 54.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5757/8000 [02:12<00:41, 54.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5763/8000 [02:12<00:41, 53.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5769/8000 [02:12<00:50, 44.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5775/8000 [02:12<00:49, 45.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5780/8000 [02:12<00:47, 46.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5786/8000 [02:13<00:52, 42.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5791/8000 [02:13<01:03, 34.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5795/8000 [02:13<01:01, 35.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5799/8000 [02:13<01:00, 36.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5805/8000 [02:13<00:54, 40.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5814/8000 [02:13<00:47, 46.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5820/8000 [02:13<00:44, 48.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5826/8000 [02:14<00:50, 42.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5832/8000 [02:14<00:48, 45.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5837/8000 [02:14<00:48, 44.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5844/8000 [02:14<00:43, 49.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5852/8000 [02:14<00:45, 47.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5862/8000 [02:14<00:37, 56.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5868/8000 [02:14<00:38, 54.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5874/8000 [02:15<00:45, 46.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5879/8000 [02:15<00:48, 44.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5886/8000 [02:15<00:56, 37.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5891/8000 [02:15<01:09, 30.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5895/8000 [02:15<01:17, 27.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5905/8000 [02:16<00:54, 38.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5912/8000 [02:16<00:58, 35.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5919/8000 [02:16<00:58, 35.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5926/8000 [02:16<00:50, 40.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5931/8000 [02:16<00:50, 40.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5936/8000 [02:16<00:50, 41.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5942/8000 [02:17<00:57, 35.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5946/8000 [02:17<01:11, 28.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5952/8000 [02:17<01:01, 33.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5957/8000 [02:17<00:59, 34.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5961/8000 [02:17<01:05, 31.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5966/8000 [02:17<01:00, 33.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5971/8000 [02:18<01:07, 30.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5975/8000 [02:18<01:05, 30.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5981/8000 [02:18<01:09, 29.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5989/8000 [02:18<00:52, 38.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5996/8000 [02:18<00:45, 44.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6001/8000 [02:18<00:51, 38.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6006/8000 [02:18<00:48, 40.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6014/8000 [02:19<00:46, 42.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6021/8000 [02:19<00:47, 41.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6028/8000 [02:19<00:46, 42.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6033/8000 [02:19<00:45, 43.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6038/8000 [02:19<00:45, 43.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6046/8000 [02:19<00:38, 50.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6052/8000 [02:19<00:42, 45.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6057/8000 [02:20<00:47, 40.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6063/8000 [02:20<00:43, 44.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6069/8000 [02:20<00:46, 41.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6075/8000 [02:20<00:47, 40.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6082/8000 [02:20<00:52, 36.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6087/8000 [02:20<00:50, 37.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6092/8000 [02:21<00:56, 33.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6097/8000 [02:21<00:52, 36.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6101/8000 [02:21<00:51, 36.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6109/8000 [02:21<00:45, 41.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6114/8000 [02:21<00:55, 33.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6118/8000 [02:21<00:54, 34.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6125/8000 [02:21<00:46, 40.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6130/8000 [02:22<00:55, 33.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6140/8000 [02:22<00:43, 43.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6146/8000 [02:22<00:40, 45.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6151/8000 [02:22<00:45, 40.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6156/8000 [02:22<00:44, 41.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6165/8000 [02:22<00:36, 50.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6174/8000 [02:22<00:35, 50.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6181/8000 [02:23<00:33, 54.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6188/8000 [02:23<00:40, 44.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6195/8000 [02:23<00:41, 43.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6205/8000 [02:23<00:33, 53.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6211/8000 [02:23<00:36, 48.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6217/8000 [02:23<00:38, 45.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6224/8000 [02:23<00:39, 45.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6230/8000 [02:24<00:49, 36.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6234/8000 [02:24<00:48, 36.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6239/8000 [02:24<00:46, 37.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6246/8000 [02:24<00:39, 44.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6251/8000 [02:24<00:45, 38.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6257/8000 [02:24<00:40, 42.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6263/8000 [02:24<00:38, 44.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6268/8000 [02:25<00:39, 44.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6274/8000 [02:25<00:38, 44.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6283/8000 [02:25<00:35, 48.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6290/8000 [02:25<00:36, 46.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6295/8000 [02:25<00:37, 45.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6301/8000 [02:25<00:49, 34.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6305/8000 [02:26<00:50, 33.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6309/8000 [02:26<00:50, 33.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6314/8000 [02:26<00:47, 35.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6318/8000 [02:26<00:54, 30.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6324/8000 [02:26<00:46, 36.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6330/8000 [02:26<00:46, 35.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6335/8000 [02:26<00:45, 36.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6340/8000 [02:27<00:43, 38.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6345/8000 [02:27<00:51, 31.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6351/8000 [02:27<00:49, 33.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6355/8000 [02:27<00:50, 32.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6359/8000 [02:27<00:48, 33.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6367/8000 [02:27<00:38, 42.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6376/8000 [02:27<00:31, 52.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6384/8000 [02:28<00:42, 38.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6389/8000 [02:28<00:46, 34.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6396/8000 [02:28<00:40, 39.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6402/8000 [02:28<00:45, 34.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6408/8000 [02:28<00:44, 35.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6413/8000 [02:29<00:43, 36.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6418/8000 [02:29<00:41, 38.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6425/8000 [02:29<00:35, 43.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6431/8000 [02:29<00:34, 46.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6437/8000 [02:29<00:38, 41.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6443/8000 [02:29<00:40, 38.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6448/8000 [02:29<00:42, 36.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6460/8000 [02:30<00:29, 52.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6467/8000 [02:30<00:27, 55.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6477/8000 [02:30<00:26, 56.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6485/8000 [02:30<00:27, 54.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6491/8000 [02:30<00:30, 49.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6499/8000 [02:30<00:33, 45.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6504/8000 [02:30<00:33, 44.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6514/8000 [02:31<00:26, 55.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6521/8000 [02:31<00:26, 55.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6529/8000 [02:31<00:35, 41.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6534/8000 [02:31<00:35, 41.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6539/8000 [02:31<00:39, 37.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6546/8000 [02:31<00:35, 41.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6553/8000 [02:32<00:43, 33.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6557/8000 [02:32<00:43, 33.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6561/8000 [02:32<00:47, 30.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6570/8000 [02:32<00:34, 41.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6577/8000 [02:32<00:30, 47.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6586/8000 [02:32<00:28, 50.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6592/8000 [02:33<00:30, 45.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6597/8000 [02:33<00:31, 45.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6602/8000 [02:33<00:31, 44.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6608/8000 [02:33<00:30, 45.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6613/8000 [02:33<00:30, 46.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6618/8000 [02:33<00:30, 45.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6627/8000 [02:33<00:32, 42.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6632/8000 [02:33<00:31, 43.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6637/8000 [02:34<00:35, 38.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6642/8000 [02:34<00:37, 35.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6650/8000 [02:34<00:30, 44.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6656/8000 [02:34<00:28, 46.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6662/8000 [02:34<00:27, 49.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6672/8000 [02:34<00:21, 60.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6680/8000 [02:34<00:23, 56.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6689/8000 [02:35<00:22, 57.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6695/8000 [02:35<00:26, 49.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6701/8000 [02:35<00:26, 49.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6707/8000 [02:35<00:28, 44.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6712/8000 [02:35<00:28, 44.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6724/8000 [02:35<00:20, 61.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6732/8000 [02:35<00:22, 56.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6739/8000 [02:36<00:27, 46.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6746/8000 [02:36<00:25, 49.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6753/8000 [02:36<00:28, 44.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6758/8000 [02:36<00:28, 42.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6764/8000 [02:36<00:26, 45.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6769/8000 [02:36<00:26, 46.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6775/8000 [02:36<00:25, 48.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6783/8000 [02:36<00:22, 54.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6790/8000 [02:37<00:25, 47.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6797/8000 [02:37<00:26, 46.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6806/8000 [02:37<00:24, 49.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6813/8000 [02:37<00:30, 39.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6821/8000 [02:37<00:25, 46.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6827/8000 [02:37<00:24, 48.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6833/8000 [02:38<00:23, 49.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6843/8000 [02:38<00:22, 52.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6849/8000 [02:38<00:23, 48.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6858/8000 [02:38<00:21, 51.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6864/8000 [02:38<00:23, 47.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6872/8000 [02:38<00:23, 48.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6877/8000 [02:38<00:23, 47.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6884/8000 [02:39<00:22, 50.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6891/8000 [02:39<00:22, 48.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6899/8000 [02:39<00:22, 49.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6905/8000 [02:39<00:26, 41.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6912/8000 [02:39<00:26, 40.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6918/8000 [02:39<00:24, 44.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6927/8000 [02:40<00:20, 53.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6936/8000 [02:40<00:19, 53.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6942/8000 [02:40<00:25, 41.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6947/8000 [02:40<00:26, 39.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6953/8000 [02:40<00:24, 42.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6958/8000 [02:40<00:24, 43.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6963/8000 [02:40<00:26, 39.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6968/8000 [02:41<00:30, 33.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6972/8000 [02:41<00:30, 33.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6979/8000 [02:41<00:24, 40.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6985/8000 [02:41<00:25, 39.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6990/8000 [02:41<00:24, 41.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6995/8000 [02:41<00:24, 41.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7002/8000 [02:41<00:20, 47.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7010/8000 [02:42<00:20, 47.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7016/8000 [02:42<00:20, 47.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7021/8000 [02:42<00:20, 46.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7027/8000 [02:42<00:20, 47.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7034/8000 [02:42<00:21, 44.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7040/8000 [02:42<00:20, 45.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7045/8000 [02:42<00:24, 38.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7050/8000 [02:43<00:23, 40.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7055/8000 [02:43<00:22, 42.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7063/8000 [02:43<00:18, 50.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7069/8000 [02:43<00:18, 51.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7075/8000 [02:43<00:26, 35.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▉    | 7082/8000 [02:43<00:22, 40.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7088/8000 [02:43<00:24, 37.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7096/8000 [02:44<00:20, 44.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7105/8000 [02:44<00:19, 46.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7111/8000 [02:44<00:20, 42.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7116/8000 [02:44<00:23, 37.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7122/8000 [02:44<00:23, 37.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7129/8000 [02:44<00:21, 41.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7141/8000 [02:45<00:17, 49.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7151/8000 [02:45<00:15, 56.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7160/8000 [02:45<00:15, 55.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7166/8000 [02:45<00:15, 52.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7172/8000 [02:45<00:18, 44.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7177/8000 [02:45<00:20, 40.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7183/8000 [02:46<00:20, 39.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7191/8000 [02:46<00:17, 46.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7199/8000 [02:46<00:17, 46.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7205/8000 [02:46<00:18, 43.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7212/8000 [02:46<00:16, 47.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7219/8000 [02:46<00:15, 50.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7225/8000 [02:46<00:17, 43.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7232/8000 [02:47<00:19, 39.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7238/8000 [02:47<00:20, 37.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7245/8000 [02:47<00:18, 41.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7252/8000 [02:47<00:16, 45.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7257/8000 [02:47<00:15, 46.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7262/8000 [02:47<00:16, 46.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7271/8000 [02:47<00:16, 43.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7282/8000 [02:48<00:13, 51.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7291/8000 [02:48<00:13, 52.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7298/8000 [02:48<00:14, 48.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7303/8000 [02:48<00:18, 38.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7312/8000 [02:48<00:17, 39.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████████████   | 7318/8000 [02:49<00:16, 42.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7325/8000 [02:49<00:14, 46.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7331/8000 [02:49<00:15, 41.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7339/8000 [02:49<00:13, 47.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7346/8000 [02:49<00:14, 46.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7354/8000 [02:49<00:13, 46.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7360/8000 [02:50<00:15, 40.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7366/8000 [02:50<00:14, 43.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7372/8000 [02:50<00:14, 41.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7379/8000 [02:50<00:14, 42.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7385/8000 [02:50<00:14, 41.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7391/8000 [02:50<00:15, 39.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7396/8000 [02:50<00:14, 40.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7401/8000 [02:50<00:14, 41.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7408/8000 [02:51<00:14, 41.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7418/8000 [02:51<00:12, 46.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7423/8000 [02:51<00:12, 45.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7429/8000 [02:51<00:13, 42.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7436/8000 [02:51<00:11, 47.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7441/8000 [02:51<00:14, 37.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7448/8000 [02:52<00:13, 41.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7453/8000 [02:52<00:14, 38.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7458/8000 [02:52<00:15, 35.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7462/8000 [02:52<00:15, 34.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7469/8000 [02:52<00:13, 39.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7475/8000 [02:52<00:13, 37.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7479/8000 [02:52<00:14, 36.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7484/8000 [02:53<00:16, 31.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7493/8000 [02:53<00:12, 41.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7501/8000 [02:53<00:10, 47.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7511/8000 [02:53<00:12, 39.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7517/8000 [02:53<00:12, 40.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7524/8000 [02:54<00:12, 39.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7530/8000 [02:54<00:13, 34.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7535/8000 [02:54<00:14, 31.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7540/8000 [02:54<00:13, 33.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7545/8000 [02:54<00:13, 34.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7552/8000 [02:54<00:11, 39.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7557/8000 [02:55<00:10, 40.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7563/8000 [02:55<00:10, 43.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7575/8000 [02:55<00:07, 58.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7584/8000 [02:55<00:06, 63.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7593/8000 [02:55<00:07, 58.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7601/8000 [02:55<00:09, 42.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7608/8000 [02:56<00:10, 37.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7615/8000 [02:56<00:10, 37.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7620/8000 [02:56<00:10, 37.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7630/8000 [02:56<00:07, 46.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7636/8000 [02:56<00:07, 46.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7645/8000 [02:56<00:07, 46.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7652/8000 [02:57<00:07, 43.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7659/8000 [02:57<00:08, 38.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7664/8000 [02:57<00:09, 34.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7670/8000 [02:57<00:08, 37.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7674/8000 [02:57<00:10, 32.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7680/8000 [02:57<00:08, 36.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7686/8000 [02:58<00:08, 36.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7690/8000 [02:58<00:08, 35.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7697/8000 [02:58<00:07, 42.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7706/8000 [02:58<00:05, 51.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7714/8000 [02:58<00:06, 47.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7721/8000 [02:58<00:05, 50.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7728/8000 [02:58<00:05, 52.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7734/8000 [02:59<00:05, 45.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7741/8000 [02:59<00:05, 44.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7748/8000 [02:59<00:05, 45.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7755/8000 [02:59<00:05, 42.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7762/8000 [02:59<00:05, 45.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7769/8000 [02:59<00:04, 49.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7775/8000 [02:59<00:04, 50.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7782/8000 [03:00<00:04, 44.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7788/8000 [03:00<00:05, 41.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7793/8000 [03:00<00:05, 37.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7799/8000 [03:00<00:05, 35.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7806/8000 [03:00<00:04, 41.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7816/8000 [03:00<00:03, 46.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7821/8000 [03:01<00:03, 44.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7830/8000 [03:01<00:03, 46.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7837/8000 [03:01<00:04, 38.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7842/8000 [03:01<00:05, 29.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7849/8000 [03:01<00:04, 35.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7855/8000 [03:02<00:04, 34.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7860/8000 [03:02<00:04, 32.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7865/8000 [03:02<00:03, 34.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7871/8000 [03:02<00:03, 38.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7877/8000 [03:02<00:03, 40.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▍| 7884/8000 [03:02<00:02, 45.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7891/8000 [03:03<00:02, 38.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7896/8000 [03:03<00:02, 35.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7903/8000 [03:03<00:02, 40.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7911/8000 [03:03<00:02, 43.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7916/8000 [03:03<00:01, 43.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7921/8000 [03:03<00:01, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7926/8000 [03:03<00:01, 43.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7934/8000 [03:03<00:01, 50.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7941/8000 [03:04<00:01, 40.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7946/8000 [03:04<00:01, 37.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7953/8000 [03:04<00:01, 34.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7957/8000 [03:04<00:01, 34.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7961/8000 [03:04<00:01, 33.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7967/8000 [03:04<00:00, 37.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7973/8000 [03:05<00:00, 41.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7978/8000 [03:05<00:00, 40.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7984/8000 [03:05<00:00, 37.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7988/8000 [03:05<00:00, 35.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8001it [03:05, 43.09it/s]                                                   \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:22,  4.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<08:09,  2.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:06<05:37,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:07<04:24,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:08<03:49,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:06,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<02:54,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:49,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:12<02:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:13<02:47,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:40,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:37,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:15<02:35,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:16<02:33,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:17<02:30,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:29,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:18<02:27,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:19<02:47,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:20<02:47,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:21<02:36,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:22<02:29,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:23<02:23,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:23<02:20,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:24<02:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:25<02:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:26<02:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:26<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:27<02:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:28<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:29<02:12,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:30<02:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:30<02:09,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:31<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:32<02:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:33<02:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:33<02:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:34<02:06,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:35<02:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:36<02:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:37<02:05,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:37<02:03,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:38<02:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:39<02:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:40<01:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:40<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:41<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:42<01:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:43<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:43<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:44<01:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:46<02:11,  1.12it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 25.26it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 85it [00:00, 123.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 176it [00:01, 259.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 266it [00:01, 385.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 350it [00:01, 484.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 442it [00:01, 588.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 527it [00:01, 653.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 611it [00:01, 699.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 695it [00:01, 728.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 452.59it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|████▍      | 40/100 [00:00<00:00, 393.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 380.02it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████████████████████| 4/4 [15:52<00:00, 282.58s/it]\u001b[A\n",
+      "Replicates:  67%|███████████████▎       | 10/15 [6:34:47<2:02:38, 1471.69s/it]\n",
+      "Nyquist Samples:   0%|                                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.853745954347637\n",
+      "2999: -5.9223294\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:03<13:12,  3.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:40,  2.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:32,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:32,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:58,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:22,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<02:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<02:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<01:57,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:54,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:52,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:14<01:51,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:50,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:50,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:49,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:46,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:17<01:45,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:44,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:44,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:43,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:42,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:41,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:43,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:23<01:46,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:43,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:41,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:25<01:42,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:26<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:27<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:28<01:39,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:37,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:29<01:35,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:30<01:37,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:31<01:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:32<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:33<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:34<01:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:34<01:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:35<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:36<01:36,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:36<01:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:37<01:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:38<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:38<01:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:39<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:40<01:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:41<01:37,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:41<01:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:42<01:56,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:43<01:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:44<01:41,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:44<01:35,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:45<01:29,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:46<01:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████                                | 67/200 [00:46<01:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▎                               | 68/200 [00:47<01:22,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▌                               | 69/200 [00:47<01:21,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████████▊                               | 70/200 [00:48<01:20,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████                               | 71/200 [00:49<01:17,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▎                              | 72/200 [00:49<01:17,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▌                              | 73/200 [00:50<01:16,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████████▊                              | 74/200 [00:50<01:16,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████                              | 75/200 [00:51<01:16,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▏                             | 76/200 [00:52<01:15,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▍                             | 77/200 [00:52<01:15,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████████▋                             | 78/200 [00:53<01:14,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████████▉                             | 79/200 [00:53<01:12,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|███████████████████▏                            | 80/200 [00:54<01:12,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|███████████████████▍                            | 81/200 [00:55<01:11,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|███████████████████▋                            | 82/200 [00:55<01:10,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████▉                            | 83/200 [00:56<01:10,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████████▏                           | 84/200 [00:56<01:10,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████████▍                           | 85/200 [00:57<01:08,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|████████████████████▋                           | 86/200 [00:58<01:07,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████▉                           | 87/200 [00:58<01:07,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████████                           | 88/200 [00:59<01:06,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████████▎                          | 89/200 [00:59<01:05,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|█████████████████████▌                          | 90/200 [01:00<01:04,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████▊                          | 91/200 [01:01<01:04,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████████                          | 92/200 [01:01<01:04,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████████▎                         | 93/200 [01:02<01:04,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████▌                         | 94/200 [01:03<01:11,  1.48it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 25.57it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 80it [00:00, 114.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 164it [00:01, 237.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 249it [00:01, 357.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 339it [00:01, 475.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 430it [00:01, 577.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 513it [00:01, 631.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 599it [00:01, 689.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 682it [00:01, 727.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 443.98it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  46%|█████      | 46/100 [00:00<00:00, 450.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 434.05it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                | 344/8000 [00:00<00:02, 2581.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▉                               | 698/8000 [00:00<00:02, 2810.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▎                            | 1052/8000 [00:00<00:02, 2828.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▋                           | 1380/8000 [00:00<00:02, 2777.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▏                         | 1733/8000 [00:00<00:02, 2747.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▌                        | 2090/8000 [00:00<00:02, 2864.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████                       | 2449/8000 [00:00<00:01, 2946.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▌                     | 2789/8000 [00:00<00:01, 2972.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|████████████▉                    | 3131/8000 [00:01<00:01, 3004.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▍                  | 3485/8000 [00:01<00:01, 2986.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|███████████████▊                 | 3844/8000 [00:01<00:01, 3058.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▎               | 4182/8000 [00:01<00:01, 3120.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████████▋              | 4540/8000 [00:01<00:01, 3104.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▏            | 4885/8000 [00:01<00:01, 3050.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████████▍           | 5207/8000 [00:01<00:00, 2985.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████████▉          | 5557/8000 [00:01<00:00, 2945.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████████▎        | 5901/8000 [00:02<00:00, 2938.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████████▊       | 6247/8000 [00:02<00:00, 3029.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████████▏     | 6589/8000 [00:02<00:00, 3051.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|████████████████████████████▌    | 6931/8000 [00:02<00:00, 3054.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|█████████████████████████████▉   | 7269/8000 [00:02<00:00, 3000.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████████▍ | 7614/8000 [00:02<00:00, 2991.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8148it [00:02, 2955.57it/s]                                                 \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:27,  4.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:12,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:46,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:37,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:59,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:39,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:16,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<01:59,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<01:58,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:56,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<01:58,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:56,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:14<01:54,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:52,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:52,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:50,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:50,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:17<01:49,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:48,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:46,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:19<01:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:20<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:21<01:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:46,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:22<01:44,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:42,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:23<01:42,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:24<01:40,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:40,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:25<01:38,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:37,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:26<01:38,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:27<01:38,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:36,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:28<01:34,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:33,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:29<01:33,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:30<01:35,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:34,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:31<01:34,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:31<01:33,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:34,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:33<01:33,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:33<01:33,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:34<01:31,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:34<01:30,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:35<01:29,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:36<01:29,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:36<01:27,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:37<01:25,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:37<01:25,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:38<01:24,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:39<01:24,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:39<01:24,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:40<01:24,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:40<01:24,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:41<01:22,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:42<01:22,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:42<01:22,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:43<01:22,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:44<01:30,  1.48it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 25.96it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 86it [00:00, 124.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 176it [00:01, 258.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 263it [00:01, 378.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 350it [00:01, 486.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 438it [00:01, 579.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 525it [00:01, 653.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 611it [00:01, 707.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 702it [00:01, 763.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 453.30it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|████▋      | 43/100 [00:00<00:00, 427.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 438.28it/s]\n",
+      "\n",
+      "Nyquist Samples:  25%|██████████▊                                | 1/4 [02:37<07:51, 157.05s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.369822243339579\n",
+      "2999: -6.2155876\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:11,  4.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:30,  2.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:14,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<04:04,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:29,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:03,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:42,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:11<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:12<02:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:13<02:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:13<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:14<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:15<02:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:16<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:16<02:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:17<02:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:18<02:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:18<02:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:19<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:20<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:20<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:21<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:22<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:22<01:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:23<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:24<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:24<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:25<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:26<01:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:26<01:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:27<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:28<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:28<01:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:29<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:30<01:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:31<01:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:31<01:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:32<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:33<01:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:33<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:34<01:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:34<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:35<01:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:36<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:37<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:37<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:38<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:39<01:41,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:39<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:40<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:41<01:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:41<01:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:42<01:36,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:43<01:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:43<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:44<01:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:45<01:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:45<01:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:46<01:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:47<01:43,  1.32it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 28.33it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 81it [00:00, 118.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 159it [00:01, 232.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 242it [00:01, 351.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 327it [00:01, 460.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 406it [00:01, 537.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 486it [00:01, 603.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 567it [00:01, 656.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 646it [00:01, 692.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 435.06it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  44%|████▊      | 44/100 [00:00<00:00, 428.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 432.61it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 94/8000 [00:00<00:09, 819.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                  | 199/8000 [00:00<00:08, 889.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                 | 298/8000 [00:00<00:08, 908.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                 | 406/8000 [00:00<00:08, 929.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                | 515/8000 [00:00<00:07, 962.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                | 613/8000 [00:00<00:07, 961.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                               | 748/8000 [00:00<00:08, 879.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▋                               | 847/8000 [00:00<00:08, 890.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                              | 965/8000 [00:01<00:07, 958.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                             | 1107/8000 [00:01<00:07, 964.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                            | 1210/8000 [00:01<00:07, 952.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▋                           | 1366/8000 [00:01<00:06, 1003.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                           | 1504/8000 [00:01<00:06, 961.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                           | 1617/8000 [00:01<00:06, 966.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▍                          | 1762/8000 [00:01<00:06, 968.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                          | 1873/8000 [00:01<00:06, 993.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▍                         | 1981/8000 [00:02<00:06, 995.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████                         | 2128/8000 [00:02<00:06, 961.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                        | 2267/8000 [00:02<00:05, 960.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                       | 2419/8000 [00:02<00:05, 972.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                       | 2519/8000 [00:02<00:05, 943.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▎                      | 2657/8000 [00:02<00:05, 933.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                      | 2758/8000 [00:02<00:05, 935.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▏                     | 2856/8000 [00:03<00:05, 897.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▌                     | 2965/8000 [00:03<00:05, 912.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▏                    | 3110/8000 [00:03<00:05, 943.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▌                   | 3276/8000 [00:03<00:04, 1011.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|█████████████▉                   | 3389/8000 [00:03<00:04, 1015.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▌                  | 3543/8000 [00:03<00:04, 1035.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▎                 | 3699/8000 [00:03<00:04, 1032.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|███████████████▉                 | 3849/8000 [00:03<00:04, 1025.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▎                | 3954/8000 [00:04<00:04, 1005.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▏                | 4058/8000 [00:04<00:04, 966.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▊                | 4189/8000 [00:04<00:04, 893.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▏               | 4294/8000 [00:04<00:04, 891.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▋               | 4399/8000 [00:04<00:03, 906.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████               | 4493/8000 [00:04<00:03, 904.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▋              | 4640/8000 [00:04<00:03, 943.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▏             | 4744/8000 [00:04<00:03, 931.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▊             | 4884/8000 [00:05<00:03, 926.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▏            | 4994/8000 [00:05<00:03, 917.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▋            | 5098/8000 [00:05<00:03, 904.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▏           | 5208/8000 [00:05<00:03, 922.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▊           | 5363/8000 [00:05<00:02, 895.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▏          | 5468/8000 [00:05<00:02, 893.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▋          | 5584/8000 [00:05<00:02, 912.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▏         | 5696/8000 [00:06<00:02, 927.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▋         | 5795/8000 [00:06<00:02, 900.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5932/8000 [00:06<00:02, 929.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▋        | 6031/8000 [00:06<00:02, 940.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6156/8000 [00:06<00:02, 842.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6257/8000 [00:06<00:02, 867.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████       | 6357/8000 [00:06<00:01, 882.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6496/8000 [00:06<00:01, 904.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████      | 6603/8000 [00:07<00:01, 917.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████████▍     | 6700/8000 [00:07<00:01, 918.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6797/8000 [00:07<00:01, 910.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6942/8000 [00:07<00:01, 887.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7051/8000 [00:07<00:01, 927.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7182/8000 [00:07<00:00, 868.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7275/8000 [00:07<00:00, 864.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7369/8000 [00:07<00:00, 882.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7511/8000 [00:08<00:00, 901.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7663/8000 [00:08<00:00, 929.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7774/8000 [00:08<00:00, 963.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7914/8000 [00:08<00:00, 945.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8010it [00:08, 933.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:46,  4.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:55,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<03:47,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:51,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:08<02:39,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:09<02:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:10<02:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:12<02:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<02:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:13<02:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:14<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:14<02:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:15<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:16<02:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:16<02:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:17<02:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:18<02:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:19<02:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:20<02:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:20<02:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:21<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:22<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:22<02:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:23<02:00,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:24<01:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:24<01:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:25<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:26<01:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:26<01:53,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:27<01:55,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:28<01:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:29<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:29<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:30<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:30<01:45,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:31<01:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:32<01:51,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:33<01:49,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:33<01:51,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:34<01:47,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:35<01:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:35<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:36<01:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:37<01:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:38<01:44,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:38<01:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:39<01:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:39<01:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:40<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:41<01:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:42<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:43<01:52,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:44<01:52,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:44<01:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:45<01:53,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:46<01:53,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:47<01:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:48<01:53,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:49<01:45,  1.28it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 24.46it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 71it [00:01, 66.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 142it [00:01, 142.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 202it [00:01, 208.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 269it [00:01, 286.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 334it [00:01, 357.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 396it [00:01, 414.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 463it [00:02, 475.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 529it [00:02, 521.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 594it [00:02, 553.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 660it [00:02, 581.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 725it [00:02, 600.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:02, 307.11it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  39%|████▎      | 39/100 [00:00<00:00, 381.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 387.44it/s]\n",
+      "\n",
+      "Nyquist Samples:  50%|█████████████████████▌                     | 2/4 [05:23<05:25, 162.85s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.379529386731727\n",
+      "2999: -6.1763592\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<13:20,  4.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:37,  2.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:30,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:28,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:52,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:06<02:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:00,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<01:56,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:12<02:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:54,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:52,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:48,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:49,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:17<01:49,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:47,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:45,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:45,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:44,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:43,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:41,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:38,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:38,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:38,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:22<01:37,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:39,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:44,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:26<01:41,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:39,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:37,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:27<01:39,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:29<01:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:38,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:31<01:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:31<01:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:33<01:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:34<01:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:34<01:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:35<01:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:36<01:57,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:37<01:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:38<01:55,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:39<01:54,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:39<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:40<01:52,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:41<01:52,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:42<01:51,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:42<01:51,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:43<01:50,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [00:44<01:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [00:45<01:51,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [00:46<01:50,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [00:47<01:48,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████████▊                                | 66/200 [00:47<01:47,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████                                | 67/200 [00:48<01:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▎                               | 68/200 [00:49<01:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▌                               | 69/200 [00:50<01:44,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████████▊                               | 70/200 [00:50<01:44,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████                               | 71/200 [00:51<01:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▎                              | 72/200 [00:52<01:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▌                              | 73/200 [00:53<01:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████████▊                              | 74/200 [00:54<01:41,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████                              | 75/200 [00:55<01:40,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▏                             | 76/200 [00:55<01:38,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▍                             | 77/200 [00:56<01:36,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████████▋                             | 78/200 [00:57<01:34,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████████▉                             | 79/200 [00:58<01:29,  1.34it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 23.32it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 80it [00:01, 109.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 163it [00:01, 226.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 240it [00:01, 328.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 317it [00:01, 421.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 392it [00:01, 497.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 465it [00:01, 502.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 531it [00:01, 530.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 611it [00:01, 598.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 693it [00:01, 656.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 401.60it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|████▋      | 43/100 [00:00<00:00, 427.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 388.44it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                   | 42/8000 [00:00<00:19, 401.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 94/8000 [00:00<00:22, 351.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                  | 136/8000 [00:00<00:21, 371.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                  | 183/8000 [00:00<00:19, 396.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                  | 230/8000 [00:00<00:23, 337.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                 | 276/8000 [00:00<00:21, 364.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                 | 322/8000 [00:00<00:22, 337.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                                 | 366/8000 [00:01<00:21, 353.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                 | 405/8000 [00:01<00:22, 342.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                                 | 451/8000 [00:01<00:20, 360.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                | 491/8000 [00:01<00:21, 357.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                | 541/8000 [00:01<00:22, 336.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                | 586/8000 [00:01<00:24, 297.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                | 618/8000 [00:01<00:25, 294.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                | 665/8000 [00:01<00:22, 327.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                | 699/8000 [00:02<00:22, 321.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                               | 754/8000 [00:02<00:22, 323.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                               | 808/8000 [00:02<00:22, 319.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                               | 863/8000 [00:02<00:19, 358.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████                               | 922/8000 [00:02<00:20, 352.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                              | 970/8000 [00:02<00:20, 335.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▎                             | 1011/8000 [00:02<00:20, 335.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                             | 1061/8000 [00:03<00:21, 322.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                             | 1109/8000 [00:03<00:22, 303.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                             | 1155/8000 [00:03<00:20, 330.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                             | 1190/8000 [00:03<00:20, 324.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                            | 1236/8000 [00:03<00:19, 350.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                            | 1297/8000 [00:03<00:19, 338.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▋                            | 1349/8000 [00:04<00:19, 335.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                            | 1397/8000 [00:04<00:18, 363.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                           | 1442/8000 [00:04<00:17, 372.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▎                           | 1482/8000 [00:04<00:17, 374.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                           | 1533/8000 [00:04<00:18, 351.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▋                           | 1575/8000 [00:04<00:18, 352.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                           | 1620/8000 [00:04<00:17, 364.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████                           | 1662/8000 [00:04<00:17, 365.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                          | 1701/8000 [00:04<00:17, 361.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▍                          | 1740/8000 [00:05<00:18, 339.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                          | 1781/8000 [00:05<00:17, 353.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▊                          | 1832/8000 [00:05<00:18, 339.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                          | 1868/8000 [00:05<00:18, 334.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▏                         | 1913/8000 [00:05<00:19, 311.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▎                         | 1962/8000 [00:05<00:20, 297.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                         | 2021/8000 [00:06<00:20, 290.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▋                         | 2051/8000 [00:06<00:20, 285.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                         | 2092/8000 [00:06<00:19, 302.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████                         | 2124/8000 [00:06<00:20, 288.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▏                        | 2170/8000 [00:06<00:18, 319.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▍                        | 2218/8000 [00:06<00:18, 304.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▌                        | 2252/8000 [00:06<00:19, 294.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▋                        | 2286/8000 [00:06<00:19, 297.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▉                        | 2330/8000 [00:07<00:17, 328.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████                        | 2376/8000 [00:07<00:15, 358.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                       | 2424/8000 [00:07<00:17, 324.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▍                       | 2469/8000 [00:07<00:16, 331.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                       | 2508/8000 [00:07<00:16, 326.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▊                       | 2547/8000 [00:07<00:17, 310.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                       | 2593/8000 [00:07<00:18, 285.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▏                      | 2637/8000 [00:08<00:25, 211.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▍                      | 2682/8000 [00:08<00:25, 211.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▌                      | 2717/8000 [00:08<00:26, 197.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                      | 2760/8000 [00:08<00:28, 186.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▊                      | 2787/8000 [00:09<00:29, 179.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▉                      | 2819/8000 [00:09<00:26, 198.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▏                     | 2859/8000 [00:09<00:21, 234.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▎                     | 2897/8000 [00:09<00:19, 256.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▌                     | 2949/8000 [00:09<00:16, 301.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▋                     | 2992/8000 [00:09<00:17, 287.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|████████████▉                     | 3030/8000 [00:09<00:16, 297.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████                     | 3077/8000 [00:09<00:16, 291.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▏                    | 3108/8000 [00:10<00:17, 280.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▍                    | 3150/8000 [00:10<00:15, 312.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▌                    | 3183/8000 [00:10<00:15, 310.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▋                    | 3222/8000 [00:10<00:15, 317.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▊                    | 3257/8000 [00:10<00:15, 310.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████                    | 3295/8000 [00:10<00:14, 318.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▏                   | 3349/8000 [00:10<00:14, 329.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▍                   | 3385/8000 [00:10<00:14, 323.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▌                   | 3432/8000 [00:11<00:14, 315.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▊                   | 3474/8000 [00:11<00:13, 333.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▉                   | 3517/8000 [00:11<00:13, 340.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▏                  | 3559/8000 [00:11<00:12, 347.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▎                  | 3605/8000 [00:11<00:13, 319.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▍                  | 3641/8000 [00:11<00:13, 323.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▋                  | 3685/8000 [00:11<00:14, 301.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|███████████████▊                  | 3733/8000 [00:11<00:12, 333.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████                  | 3774/8000 [00:12<00:12, 337.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▏                 | 3823/8000 [00:12<00:13, 315.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▍                 | 3856/8000 [00:12<00:13, 314.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▌                 | 3893/8000 [00:12<00:12, 316.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▋                 | 3928/8000 [00:12<00:12, 323.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████████▊                 | 3961/8000 [00:12<00:12, 315.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████████▉                 | 3996/8000 [00:12<00:13, 305.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▏                | 4038/8000 [00:12<00:12, 329.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▎                | 4074/8000 [00:13<00:11, 330.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▍                | 4113/8000 [00:13<00:11, 329.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▋                | 4159/8000 [00:13<00:10, 352.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████████▉                | 4207/8000 [00:13<00:11, 325.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████                | 4244/8000 [00:13<00:11, 332.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▏               | 4284/8000 [00:13<00:10, 338.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▎               | 4323/8000 [00:13<00:12, 300.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▌               | 4362/8000 [00:13<00:12, 301.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▋               | 4400/8000 [00:14<00:11, 315.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████████▉               | 4446/8000 [00:14<00:10, 347.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▏              | 4500/8000 [00:14<00:10, 342.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▎              | 4548/8000 [00:14<00:09, 369.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▌              | 4596/8000 [00:14<00:10, 326.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▊              | 4653/8000 [00:14<00:10, 309.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████████▉              | 4688/8000 [00:14<00:11, 280.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████              | 4727/8000 [00:15<00:11, 290.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▎             | 4770/8000 [00:15<00:10, 312.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▍             | 4818/8000 [00:15<00:10, 293.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▌             | 4852/8000 [00:15<00:10, 294.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▊             | 4894/8000 [00:15<00:09, 312.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████████▉             | 4932/8000 [00:15<00:10, 306.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▏            | 4975/8000 [00:15<00:09, 318.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▎            | 5017/8000 [00:16<00:09, 324.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▍            | 5054/8000 [00:16<00:08, 328.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▋            | 5093/8000 [00:16<00:08, 337.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▊            | 5130/8000 [00:16<00:08, 325.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████████▉            | 5175/8000 [00:16<00:08, 350.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▏           | 5212/8000 [00:16<00:08, 338.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▎           | 5256/8000 [00:16<00:07, 360.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▌           | 5299/8000 [00:16<00:07, 349.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▋           | 5344/8000 [00:16<00:07, 354.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▉           | 5387/8000 [00:17<00:07, 355.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████           | 5436/8000 [00:17<00:07, 323.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▎          | 5471/8000 [00:17<00:08, 296.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▍          | 5511/8000 [00:17<00:09, 265.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▌          | 5540/8000 [00:17<00:09, 253.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▋          | 5571/8000 [00:17<00:09, 253.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▊          | 5608/8000 [00:17<00:08, 271.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████████▉          | 5645/8000 [00:18<00:08, 280.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▏         | 5682/8000 [00:18<00:07, 292.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▎         | 5727/8000 [00:18<00:06, 329.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▌         | 5782/8000 [00:18<00:06, 323.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▊         | 5831/8000 [00:18<00:06, 312.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▉         | 5864/8000 [00:18<00:07, 299.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████         | 5909/8000 [00:18<00:07, 289.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5949/8000 [00:19<00:06, 299.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▍        | 5986/8000 [00:19<00:06, 313.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6026/8000 [00:19<00:06, 315.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6067/8000 [00:19<00:05, 327.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6106/8000 [00:19<00:05, 333.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6148/8000 [00:19<00:06, 305.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▎       | 6193/8000 [00:19<00:05, 330.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6247/8000 [00:19<00:04, 374.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6297/8000 [00:20<00:04, 356.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6342/8000 [00:20<00:05, 331.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████       | 6380/8000 [00:20<00:04, 325.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6420/8000 [00:20<00:04, 332.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▍      | 6466/8000 [00:20<00:04, 309.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▋      | 6505/8000 [00:20<00:04, 325.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████████▊      | 6549/8000 [00:20<00:04, 303.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████      | 6593/8000 [00:21<00:04, 298.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6636/8000 [00:21<00:04, 320.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████████▍     | 6683/8000 [00:21<00:04, 311.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6720/8000 [00:21<00:03, 320.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6768/8000 [00:21<00:03, 358.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6806/8000 [00:21<00:03, 359.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████     | 6846/8000 [00:21<00:03, 354.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6904/8000 [00:21<00:03, 306.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▍    | 6941/8000 [00:22<00:03, 315.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6979/8000 [00:22<00:03, 315.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7023/8000 [00:22<00:02, 327.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████    | 7074/8000 [00:22<00:02, 315.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7120/8000 [00:22<00:02, 335.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▍   | 7176/8000 [00:22<00:02, 342.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7217/8000 [00:22<00:02, 346.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7265/8000 [00:23<00:02, 328.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7324/8000 [00:23<00:02, 328.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7372/8000 [00:23<00:01, 349.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▍  | 7411/8000 [00:23<00:01, 343.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7454/8000 [00:23<00:01, 352.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████████▊  | 7497/8000 [00:23<00:01, 316.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████  | 7537/8000 [00:23<00:01, 330.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7576/8000 [00:23<00:01, 326.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7615/8000 [00:24<00:01, 294.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7656/8000 [00:24<00:01, 312.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7705/8000 [00:24<00:00, 311.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████████▉ | 7737/8000 [00:24<00:00, 309.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7785/8000 [00:24<00:00, 341.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7841/8000 [00:24<00:00, 351.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7880/8000 [00:24<00:00, 355.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7941/8000 [00:25<00:00, 365.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8008it [00:25, 317.79it/s]                                                  \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:03<13:11,  3.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:04<06:37,  2.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<04:33,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:05<03:29,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:06<02:56,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:07<02:34,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:07<02:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:08<02:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:08<02:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:09<02:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:10<02:00,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:10<02:02,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:11<01:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:11<01:57,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:12<01:56,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:13<01:54,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:13<01:54,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:14<01:53,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:15<01:52,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:15<01:51,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:16<01:48,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:16<01:46,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:17<01:45,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:18<01:46,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:18<01:44,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:19<01:44,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:19<01:44,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:20<01:43,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:21<01:44,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:21<01:45,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:22<01:47,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:22<01:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:23<01:43,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:24<01:41,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:24<01:40,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:25<01:41,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:26<01:40,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:26<01:40,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:27<01:38,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:27<01:37,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:28<01:40,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:29<01:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:29<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:30<01:36,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:31<01:34,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:31<01:35,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:32<01:36,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:32<01:35,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:33<01:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:34<01:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:34<01:31,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:35<01:30,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:35<01:28,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:36<01:26,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:37<01:27,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [00:37<01:26,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [00:38<01:27,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [00:39<01:27,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [00:39<01:25,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [00:40<01:25,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [00:41<01:34,  1.47it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 27.53it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 73it [00:00, 110.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 157it [00:00, 240.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 241it [00:01, 361.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 331it [00:01, 482.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 419it [00:01, 577.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 504it [00:01, 647.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 586it [00:01, 692.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 668it [00:01, 724.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 447.39it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▏     | 47/100 [00:00<00:00, 465.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 425.22it/s]\n",
+      "\n",
+      "Nyquist Samples:  75%|████████████████████████████████▎          | 3/4 [09:06<03:10, 190.27s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.4572634257450305\n",
+      "2999: -5.7867221\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:21,  4.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<08:00,  2.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:06<05:29,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:07<04:17,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:38,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:17,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<03:01,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:10<02:51,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:11<02:43,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:37,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:14<02:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:14<02:26,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:15<02:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:16<02:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:17<02:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:17<02:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:18<02:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:19<02:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:20<02:19,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:21<02:18,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:21<02:18,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:22<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:23<02:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:24<02:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:25<02:14,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:25<02:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:26<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:27<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:28<02:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:28<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:29<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:30<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:31<02:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:31<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:32<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:33<02:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:34<02:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:35<02:05,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:35<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:36<02:03,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:37<02:01,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:38<02:01,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:38<01:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:39<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:40<02:01,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:41<02:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:42<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [00:42<01:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [00:43<01:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [00:44<01:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [00:45<01:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [00:45<01:51,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [00:47<02:04,  1.16it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:00, 27.08it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:00, 109.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 160it [00:01, 237.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 243it [00:01, 354.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 331it [00:01, 469.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 420it [00:01, 569.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 509it [00:01, 650.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 596it [00:01, 708.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 684it [00:01, 753.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 452.03it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  36%|███▉       | 36/100 [00:00<00:00, 351.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 351.94it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                      | 2/8000 [00:00<06:48, 19.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                      | 6/8000 [00:00<04:41, 28.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 11/8000 [00:00<03:44, 35.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 15/8000 [00:00<03:41, 36.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 20/8000 [00:00<03:17, 40.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 27/8000 [00:00<03:12, 41.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 33/8000 [00:00<02:54, 45.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 42/8000 [00:00<02:21, 56.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 48/8000 [00:01<02:25, 54.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 54/8000 [00:01<02:24, 54.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 63/8000 [00:01<02:07, 62.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 70/8000 [00:01<02:25, 54.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 76/8000 [00:01<02:40, 49.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 86/8000 [00:01<02:34, 51.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 93/8000 [00:01<02:39, 49.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 101/8000 [00:02<03:03, 43.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 107/8000 [00:02<02:57, 44.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▌                                   | 113/8000 [00:02<02:49, 46.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▌                                   | 118/8000 [00:02<02:49, 46.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 129/8000 [00:02<02:22, 55.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 139/8000 [00:02<02:02, 64.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 148/8000 [00:02<02:09, 60.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 155/8000 [00:03<02:26, 53.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 162/8000 [00:03<02:52, 45.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 168/8000 [00:03<03:35, 36.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 177/8000 [00:03<02:53, 45.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 185/8000 [00:03<02:47, 46.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 191/8000 [00:04<02:54, 44.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 201/8000 [00:04<02:39, 49.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 209/8000 [00:04<02:40, 48.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 215/8000 [00:04<02:50, 45.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 221/8000 [00:04<02:46, 46.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 232/8000 [00:04<02:08, 60.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 239/8000 [00:04<02:24, 53.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 245/8000 [00:04<02:20, 55.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 253/8000 [00:05<02:59, 43.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 259/8000 [00:05<03:05, 41.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 266/8000 [00:05<03:01, 42.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 273/8000 [00:05<02:58, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 280/8000 [00:05<02:58, 43.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 285/8000 [00:06<02:57, 43.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 290/8000 [00:06<02:57, 43.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 295/8000 [00:06<03:14, 39.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 302/8000 [00:06<02:50, 45.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 307/8000 [00:06<02:53, 44.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 315/8000 [00:06<02:47, 46.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 320/8000 [00:06<02:44, 46.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 325/8000 [00:06<03:03, 41.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 330/8000 [00:07<03:24, 37.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 335/8000 [00:07<03:20, 38.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 339/8000 [00:07<03:23, 37.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 345/8000 [00:07<03:02, 42.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 350/8000 [00:07<03:16, 38.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 355/8000 [00:07<03:13, 39.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 360/8000 [00:07<03:02, 41.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 365/8000 [00:07<02:54, 43.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 373/8000 [00:08<02:40, 47.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 380/8000 [00:08<02:35, 48.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 386/8000 [00:08<02:52, 44.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 395/8000 [00:08<02:50, 44.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 400/8000 [00:08<02:49, 44.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 406/8000 [00:08<03:16, 38.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 412/8000 [00:09<03:38, 34.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 417/8000 [00:09<03:27, 36.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 422/8000 [00:09<03:31, 35.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 433/8000 [00:09<02:40, 47.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 439/8000 [00:09<02:32, 49.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 447/8000 [00:09<03:06, 40.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 455/8000 [00:10<02:53, 43.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 461/8000 [00:10<02:48, 44.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 468/8000 [00:10<02:34, 48.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 476/8000 [00:10<02:32, 49.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 487/8000 [00:10<02:14, 56.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 494/8000 [00:10<02:52, 43.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 501/8000 [00:10<02:35, 48.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 509/8000 [00:11<02:17, 54.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 516/8000 [00:11<03:05, 40.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                 | 521/8000 [00:11<03:01, 41.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                 | 526/8000 [00:11<03:17, 37.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 535/8000 [00:11<02:38, 47.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 541/8000 [00:11<02:33, 48.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 548/8000 [00:12<02:45, 45.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 555/8000 [00:12<02:29, 49.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 564/8000 [00:12<02:20, 52.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 573/8000 [00:12<02:19, 53.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 580/8000 [00:12<02:24, 51.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▋                                 | 588/8000 [00:12<02:26, 50.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▋                                 | 596/8000 [00:13<02:58, 41.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                 | 604/8000 [00:13<02:49, 43.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                 | 609/8000 [00:13<02:47, 44.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 618/8000 [00:13<02:20, 52.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 624/8000 [00:13<03:58, 30.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 629/8000 [00:13<03:37, 33.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 637/8000 [00:14<02:59, 40.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 645/8000 [00:14<03:04, 39.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 652/8000 [00:14<03:17, 37.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 657/8000 [00:14<03:27, 35.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 661/8000 [00:14<03:32, 34.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 666/8000 [00:14<03:19, 36.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 671/8000 [00:15<03:26, 35.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 677/8000 [00:15<03:02, 40.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 682/8000 [00:15<03:45, 32.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 689/8000 [00:15<03:09, 38.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 698/8000 [00:15<02:51, 42.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 703/8000 [00:15<02:53, 42.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 708/8000 [00:16<03:32, 34.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 712/8000 [00:16<03:52, 31.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 717/8000 [00:16<03:49, 31.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 725/8000 [00:16<03:03, 39.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 730/8000 [00:16<03:17, 36.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 737/8000 [00:16<03:02, 39.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 745/8000 [00:16<02:30, 48.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 753/8000 [00:17<02:14, 54.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 760/8000 [00:17<02:04, 57.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 769/8000 [00:17<02:01, 59.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 780/8000 [00:17<02:37, 45.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 786/8000 [00:17<02:43, 44.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 793/8000 [00:17<02:29, 48.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 800/8000 [00:17<02:20, 51.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 807/8000 [00:18<02:55, 41.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 815/8000 [00:18<02:30, 47.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 825/8000 [00:18<02:05, 57.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▊                                | 836/8000 [00:18<01:58, 60.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 846/8000 [00:18<01:58, 60.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 853/8000 [00:18<02:23, 49.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 860/8000 [00:19<02:14, 53.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 869/8000 [00:19<01:56, 61.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 876/8000 [00:19<02:29, 47.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 882/8000 [00:19<02:43, 43.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 891/8000 [00:19<02:30, 47.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 898/8000 [00:19<02:48, 42.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 904/8000 [00:20<02:49, 41.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 912/8000 [00:20<02:27, 48.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 920/8000 [00:20<02:11, 53.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 926/8000 [00:20<02:11, 53.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 932/8000 [00:20<02:08, 55.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 939/8000 [00:20<02:20, 50.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 945/8000 [00:20<02:15, 52.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 952/8000 [00:21<02:23, 49.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 959/8000 [00:21<02:29, 46.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 972/8000 [00:21<01:59, 58.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 978/8000 [00:21<02:32, 45.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 986/8000 [00:21<02:49, 41.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 992/8000 [00:21<02:53, 40.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 997/8000 [00:22<02:51, 40.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1005/8000 [00:22<02:41, 43.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1014/8000 [00:22<02:14, 51.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1022/8000 [00:22<02:16, 50.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1032/8000 [00:22<01:53, 61.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1040/8000 [00:22<01:50, 62.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1048/8000 [00:22<02:04, 55.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1055/8000 [00:23<02:18, 49.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1061/8000 [00:23<02:16, 50.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1071/8000 [00:23<01:55, 60.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1078/8000 [00:23<02:08, 54.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                              | 1084/8000 [00:23<02:23, 48.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1090/8000 [00:23<02:50, 40.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1098/8000 [00:24<02:39, 43.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1104/8000 [00:24<03:29, 32.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1110/8000 [00:24<03:05, 37.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1116/8000 [00:24<03:21, 34.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1120/8000 [00:24<03:20, 34.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1125/8000 [00:24<03:09, 36.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1129/8000 [00:25<03:29, 32.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1137/8000 [00:25<02:43, 41.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1143/8000 [00:25<02:49, 40.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1149/8000 [00:25<03:10, 36.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1154/8000 [00:25<02:58, 38.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                              | 1162/8000 [00:25<02:44, 41.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                              | 1168/8000 [00:25<02:46, 40.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1174/8000 [00:26<02:50, 40.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1179/8000 [00:26<02:47, 40.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1185/8000 [00:26<02:59, 38.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1189/8000 [00:26<03:22, 33.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1193/8000 [00:26<03:17, 34.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1199/8000 [00:26<02:52, 39.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1205/8000 [00:26<02:38, 42.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1210/8000 [00:27<03:17, 34.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1216/8000 [00:27<02:59, 37.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1222/8000 [00:27<03:09, 35.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1226/8000 [00:27<03:26, 32.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1233/8000 [00:27<03:04, 36.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1243/8000 [00:27<02:18, 48.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1251/8000 [00:27<02:11, 51.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1257/8000 [00:28<02:08, 52.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1264/8000 [00:28<02:05, 53.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1272/8000 [00:28<02:42, 41.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1278/8000 [00:28<02:33, 43.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1283/8000 [00:28<02:37, 42.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1290/8000 [00:28<02:55, 38.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1298/8000 [00:29<02:27, 45.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1305/8000 [00:29<02:37, 42.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1311/8000 [00:29<02:57, 37.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▊                             | 1317/8000 [00:29<02:42, 41.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1323/8000 [00:29<02:46, 40.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1328/8000 [00:29<02:57, 37.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1338/8000 [00:30<02:14, 49.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1344/8000 [00:30<02:12, 50.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1353/8000 [00:30<02:06, 52.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1360/8000 [00:30<02:31, 43.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1368/8000 [00:30<02:36, 42.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1375/8000 [00:30<02:19, 47.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1381/8000 [00:30<02:13, 49.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1387/8000 [00:31<02:51, 38.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1397/8000 [00:31<02:16, 48.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1403/8000 [00:31<02:17, 48.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1411/8000 [00:31<02:38, 41.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1419/8000 [00:31<02:20, 46.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1427/8000 [00:31<02:07, 51.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1434/8000 [00:32<02:16, 48.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1443/8000 [00:32<01:57, 55.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1451/8000 [00:32<02:02, 53.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1458/8000 [00:32<02:13, 49.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1465/8000 [00:32<02:06, 51.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1472/8000 [00:32<02:11, 49.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1480/8000 [00:33<02:26, 44.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1488/8000 [00:33<02:11, 49.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1498/8000 [00:33<02:16, 47.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1504/8000 [00:33<02:22, 45.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1510/8000 [00:33<02:15, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1521/8000 [00:33<01:59, 54.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1527/8000 [00:33<01:57, 54.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1534/8000 [00:34<02:07, 50.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1540/8000 [00:34<02:18, 46.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1548/8000 [00:34<02:44, 39.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1553/8000 [00:34<02:40, 40.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1559/8000 [00:34<02:47, 38.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                            | 1568/8000 [00:34<02:30, 42.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1573/8000 [00:35<02:27, 43.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1578/8000 [00:35<02:28, 43.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1585/8000 [00:35<02:50, 37.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1589/8000 [00:35<02:54, 36.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1597/8000 [00:35<02:24, 44.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1605/8000 [00:35<02:24, 44.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1611/8000 [00:35<02:16, 46.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1617/8000 [00:36<02:31, 42.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1623/8000 [00:36<02:37, 40.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1628/8000 [00:36<02:34, 41.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1634/8000 [00:36<02:31, 41.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1640/8000 [00:36<02:26, 43.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1646/8000 [00:36<02:40, 39.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1651/8000 [00:37<02:55, 36.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1655/8000 [00:37<02:59, 35.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1665/8000 [00:37<02:10, 48.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1672/8000 [00:37<02:01, 51.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1678/8000 [00:37<02:19, 45.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1683/8000 [00:37<02:40, 39.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1692/8000 [00:37<02:13, 47.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1698/8000 [00:38<02:25, 43.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1703/8000 [00:38<02:52, 36.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1709/8000 [00:38<02:38, 39.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▌                           | 1715/8000 [00:38<02:59, 34.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1723/8000 [00:38<02:28, 42.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1732/8000 [00:38<02:20, 44.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1740/8000 [00:38<02:03, 50.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1747/8000 [00:39<02:28, 42.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1756/8000 [00:39<02:19, 44.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1762/8000 [00:39<02:13, 46.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1770/8000 [00:39<02:44, 37.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1778/8000 [00:39<02:23, 43.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1785/8000 [00:40<02:24, 43.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1792/8000 [00:40<02:43, 38.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1798/8000 [00:40<03:02, 33.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1804/8000 [00:40<02:45, 37.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1812/8000 [00:40<02:17, 44.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1819/8000 [00:40<02:11, 47.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1827/8000 [00:41<02:10, 47.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1833/8000 [00:41<02:09, 47.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1840/8000 [00:41<02:19, 44.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1845/8000 [00:41<02:27, 41.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1851/8000 [00:41<03:10, 32.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1858/8000 [00:41<02:42, 37.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1865/8000 [00:42<02:39, 38.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1872/8000 [00:42<02:34, 39.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1877/8000 [00:42<02:47, 36.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▏                          | 1884/8000 [00:42<02:27, 41.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1891/8000 [00:42<02:11, 46.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1897/8000 [00:42<02:41, 37.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1904/8000 [00:43<02:19, 43.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1912/8000 [00:43<02:18, 43.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1918/8000 [00:43<02:46, 36.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1924/8000 [00:43<02:31, 40.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1932/8000 [00:43<02:10, 46.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1940/8000 [00:43<02:29, 40.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1946/8000 [00:44<02:23, 42.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1954/8000 [00:44<02:19, 43.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1963/8000 [00:44<01:59, 50.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1973/8000 [00:44<01:46, 56.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1979/8000 [00:44<02:03, 48.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1986/8000 [00:44<01:57, 51.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1995/8000 [00:44<01:57, 51.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2003/8000 [00:45<01:59, 50.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2009/8000 [00:45<02:01, 49.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2015/8000 [00:45<02:58, 33.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2021/8000 [00:45<03:02, 32.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2025/8000 [00:45<02:59, 33.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2031/8000 [00:46<02:43, 36.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2036/8000 [00:46<03:17, 30.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2041/8000 [00:46<03:05, 32.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2045/8000 [00:46<03:04, 32.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2050/8000 [00:46<03:11, 31.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2057/8000 [00:46<02:36, 37.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2062/8000 [00:46<02:29, 39.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2071/8000 [00:47<02:02, 48.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2079/8000 [00:47<02:20, 42.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2084/8000 [00:47<02:20, 42.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2089/8000 [00:47<02:36, 37.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2098/8000 [00:47<02:05, 46.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2106/8000 [00:48<02:43, 35.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2114/8000 [00:48<02:51, 34.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▎                         | 2118/8000 [00:48<02:52, 34.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2123/8000 [00:48<02:46, 35.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2129/8000 [00:48<02:49, 34.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2136/8000 [00:48<02:25, 40.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2143/8000 [00:48<02:13, 43.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2149/8000 [00:49<02:20, 41.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2155/8000 [00:49<02:15, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2160/8000 [00:49<02:17, 42.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2166/8000 [00:49<02:47, 34.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2173/8000 [00:49<02:59, 32.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2178/8000 [00:50<03:45, 25.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2184/8000 [00:50<03:12, 30.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2188/8000 [00:50<03:14, 29.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2194/8000 [00:50<02:46, 34.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2202/8000 [00:50<02:20, 41.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2211/8000 [00:50<02:11, 44.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2217/8000 [00:50<02:09, 44.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2224/8000 [00:51<01:58, 48.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2231/8000 [00:51<02:13, 43.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2238/8000 [00:51<02:19, 41.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2243/8000 [00:51<02:40, 35.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2247/8000 [00:51<02:45, 34.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2252/8000 [00:51<02:37, 36.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2257/8000 [00:52<03:10, 30.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2263/8000 [00:52<02:43, 35.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2268/8000 [00:52<02:57, 32.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2276/8000 [00:52<02:22, 40.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▉                         | 2281/8000 [00:52<02:20, 40.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2290/8000 [00:52<01:57, 48.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2297/8000 [00:53<02:20, 40.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2302/8000 [00:53<02:35, 36.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2309/8000 [00:53<02:32, 37.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2318/8000 [00:53<02:05, 45.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2323/8000 [00:53<02:08, 44.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2329/8000 [00:53<02:03, 45.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2338/8000 [00:53<01:42, 55.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2346/8000 [00:54<01:34, 59.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2356/8000 [00:54<01:40, 56.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2365/8000 [00:54<01:57, 47.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2372/8000 [00:54<02:16, 41.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2378/8000 [00:54<02:09, 43.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2386/8000 [00:55<02:10, 43.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2392/8000 [00:55<02:16, 41.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2397/8000 [00:55<02:31, 37.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2402/8000 [00:55<02:46, 33.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2408/8000 [00:55<02:32, 36.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2412/8000 [00:55<02:36, 35.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2417/8000 [00:55<02:35, 35.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2422/8000 [00:56<02:32, 36.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2427/8000 [00:56<02:29, 37.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2436/8000 [00:56<01:56, 47.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2443/8000 [00:56<01:50, 50.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2449/8000 [00:56<01:50, 50.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2455/8000 [00:56<02:08, 43.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2461/8000 [00:56<02:21, 39.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2469/8000 [00:57<02:15, 40.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2476/8000 [00:57<02:02, 45.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2483/8000 [00:57<02:19, 39.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2492/8000 [00:57<01:56, 47.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2498/8000 [00:57<01:51, 49.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2505/8000 [00:57<01:59, 45.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2510/8000 [00:57<02:00, 45.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|███████████                        | 2515/8000 [00:58<01:58, 46.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2522/8000 [00:58<01:46, 51.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2530/8000 [00:58<01:35, 57.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2536/8000 [00:58<01:52, 48.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2543/8000 [00:58<02:19, 39.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2549/8000 [00:58<02:08, 42.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2558/8000 [00:59<02:29, 36.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2563/8000 [00:59<02:23, 37.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2569/8000 [00:59<02:26, 37.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2577/8000 [00:59<02:03, 43.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2583/8000 [00:59<02:11, 41.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2589/8000 [00:59<02:17, 39.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2595/8000 [01:00<02:09, 41.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2605/8000 [01:00<01:41, 53.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2611/8000 [01:00<01:55, 46.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2618/8000 [01:00<01:56, 46.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2624/8000 [01:00<02:32, 35.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2629/8000 [01:00<02:40, 33.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2638/8000 [01:01<02:03, 43.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2646/8000 [01:01<01:49, 48.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2656/8000 [01:01<01:42, 52.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2664/8000 [01:01<01:33, 56.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2671/8000 [01:01<01:43, 51.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2678/8000 [01:01<01:56, 45.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                       | 2684/8000 [01:01<01:54, 46.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2689/8000 [01:02<01:53, 46.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2696/8000 [01:02<02:12, 40.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2705/8000 [01:02<01:45, 50.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2711/8000 [01:02<02:11, 40.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2716/8000 [01:02<02:07, 41.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2725/8000 [01:02<01:50, 47.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2732/8000 [01:02<01:44, 50.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2739/8000 [01:03<01:54, 45.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2745/8000 [01:03<01:51, 46.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2750/8000 [01:03<01:53, 46.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2756/8000 [01:03<02:03, 42.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                       | 2762/8000 [01:03<02:10, 40.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                       | 2768/8000 [01:03<02:03, 42.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2774/8000 [01:03<01:58, 44.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2780/8000 [01:04<02:13, 39.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2786/8000 [01:04<02:03, 42.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2793/8000 [01:04<02:05, 41.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2803/8000 [01:04<01:37, 53.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2809/8000 [01:04<01:38, 52.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2819/8000 [01:04<01:34, 54.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2825/8000 [01:04<01:34, 54.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2833/8000 [01:05<01:47, 47.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2839/8000 [01:05<01:56, 44.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2847/8000 [01:05<01:52, 45.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2852/8000 [01:05<01:51, 46.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2857/8000 [01:05<01:51, 46.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2863/8000 [01:05<02:31, 33.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2871/8000 [01:06<02:05, 41.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2877/8000 [01:06<01:58, 43.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2886/8000 [01:06<02:25, 35.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2892/8000 [01:06<02:18, 36.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2902/8000 [01:06<01:50, 46.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2910/8000 [01:06<01:39, 51.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▊                      | 2918/8000 [01:07<01:42, 49.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2924/8000 [01:07<02:00, 42.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2929/8000 [01:07<01:58, 42.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2934/8000 [01:07<02:24, 35.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2940/8000 [01:07<02:08, 39.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2946/8000 [01:07<02:17, 36.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2951/8000 [01:08<02:29, 33.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2955/8000 [01:08<02:32, 33.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2962/8000 [01:08<02:22, 35.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2968/8000 [01:08<02:30, 33.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2972/8000 [01:08<02:34, 32.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2977/8000 [01:08<02:19, 36.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2985/8000 [01:09<01:54, 43.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2992/8000 [01:09<01:44, 47.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2999/8000 [01:09<01:36, 51.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3006/8000 [01:09<01:56, 42.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3011/8000 [01:09<02:22, 35.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3015/8000 [01:09<02:20, 35.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3019/8000 [01:09<02:22, 34.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3024/8000 [01:10<02:47, 29.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3028/8000 [01:10<02:41, 30.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3032/8000 [01:10<02:53, 28.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3039/8000 [01:10<02:48, 29.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3046/8000 [01:10<02:18, 35.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3053/8000 [01:11<02:34, 32.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3057/8000 [01:11<03:08, 26.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3062/8000 [01:11<02:50, 28.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3068/8000 [01:11<02:24, 34.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3075/8000 [01:11<02:03, 39.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▍                     | 3082/8000 [01:11<01:46, 46.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3089/8000 [01:11<01:54, 42.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3095/8000 [01:12<01:52, 43.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3106/8000 [01:12<01:28, 55.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3115/8000 [01:12<01:33, 52.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3124/8000 [01:12<01:51, 43.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3129/8000 [01:12<02:00, 40.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3134/8000 [01:13<02:15, 35.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3138/8000 [01:13<02:19, 34.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3142/8000 [01:13<02:24, 33.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3146/8000 [01:13<02:28, 32.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3151/8000 [01:13<02:29, 32.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3156/8000 [01:13<03:24, 23.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3161/8000 [01:14<03:53, 20.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3164/8000 [01:14<04:44, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3168/8000 [01:14<04:04, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3173/8000 [01:14<03:17, 24.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3177/8000 [01:14<02:56, 27.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3181/8000 [01:14<02:55, 27.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3186/8000 [01:15<02:30, 32.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3194/8000 [01:15<01:50, 43.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3200/8000 [01:15<01:55, 41.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3206/8000 [01:15<01:59, 40.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3215/8000 [01:15<01:35, 49.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3223/8000 [01:15<01:37, 49.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3229/8000 [01:15<01:47, 44.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3236/8000 [01:16<01:48, 43.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3244/8000 [01:16<01:33, 51.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3250/8000 [01:16<01:54, 41.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3259/8000 [01:16<01:43, 45.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3265/8000 [01:16<01:49, 43.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3272/8000 [01:16<01:40, 46.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3280/8000 [01:17<01:29, 52.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3287/8000 [01:17<01:28, 53.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3295/8000 [01:17<01:31, 51.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3304/8000 [01:17<01:20, 58.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3313/8000 [01:17<01:32, 50.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3322/8000 [01:17<01:50, 42.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3328/8000 [01:18<01:56, 40.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3337/8000 [01:18<01:37, 47.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3343/8000 [01:18<01:44, 44.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3349/8000 [01:18<01:53, 40.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3355/8000 [01:18<01:44, 44.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3361/8000 [01:18<01:37, 47.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3370/8000 [01:18<01:31, 50.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3376/8000 [01:19<01:31, 50.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3385/8000 [01:19<01:31, 50.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3397/8000 [01:19<01:17, 59.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3404/8000 [01:19<01:15, 60.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3411/8000 [01:19<01:15, 60.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3419/8000 [01:19<01:31, 50.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3425/8000 [01:19<01:28, 51.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3433/8000 [01:20<01:39, 45.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3439/8000 [01:20<01:35, 47.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3447/8000 [01:20<01:42, 44.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3455/8000 [01:20<01:40, 45.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3461/8000 [01:20<01:38, 46.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3467/8000 [01:20<01:34, 48.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3472/8000 [01:20<01:35, 47.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3477/8000 [01:21<01:51, 40.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3486/8000 [01:21<01:40, 45.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3493/8000 [01:21<02:11, 34.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3500/8000 [01:21<01:51, 40.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3508/8000 [01:21<01:45, 42.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3516/8000 [01:22<01:30, 49.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3525/8000 [01:22<01:53, 39.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3531/8000 [01:22<01:47, 41.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3538/8000 [01:22<01:45, 42.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3545/8000 [01:22<01:46, 41.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3551/8000 [01:22<01:40, 44.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3557/8000 [01:23<01:35, 46.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3563/8000 [01:23<01:39, 44.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3569/8000 [01:23<01:44, 42.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3577/8000 [01:23<01:37, 45.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3582/8000 [01:23<01:50, 40.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3590/8000 [01:23<01:41, 43.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3598/8000 [01:23<01:40, 43.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3607/8000 [01:24<01:29, 49.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3613/8000 [01:24<01:47, 40.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3619/8000 [01:24<01:52, 38.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3625/8000 [01:24<01:53, 38.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3629/8000 [01:24<02:10, 33.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3634/8000 [01:24<02:03, 35.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3642/8000 [01:25<01:39, 43.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3650/8000 [01:25<01:39, 43.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3655/8000 [01:25<01:49, 39.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3660/8000 [01:25<01:45, 41.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3666/8000 [01:25<01:38, 43.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3673/8000 [01:25<01:40, 42.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3678/8000 [01:25<01:38, 43.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3685/8000 [01:26<01:29, 48.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3691/8000 [01:26<01:25, 50.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3699/8000 [01:26<01:24, 50.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3706/8000 [01:26<01:19, 54.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3712/8000 [01:26<01:28, 48.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▎                  | 3717/8000 [01:26<01:39, 42.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3722/8000 [01:26<01:39, 43.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3729/8000 [01:27<01:58, 36.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3735/8000 [01:27<02:25, 29.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3740/8000 [01:27<02:20, 30.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3746/8000 [01:27<02:00, 35.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3753/8000 [01:27<01:56, 36.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3762/8000 [01:27<01:30, 46.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3773/8000 [01:28<01:37, 43.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3781/8000 [01:28<01:40, 41.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3791/8000 [01:28<01:27, 48.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3798/8000 [01:28<01:28, 47.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3805/8000 [01:28<01:39, 42.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3812/8000 [01:29<01:32, 45.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3818/8000 [01:29<01:47, 39.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3824/8000 [01:29<01:39, 42.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3829/8000 [01:29<01:36, 43.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3834/8000 [01:29<01:35, 43.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3840/8000 [01:29<01:51, 37.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3849/8000 [01:29<01:37, 42.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3856/8000 [01:30<01:38, 42.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3862/8000 [01:30<01:31, 45.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3870/8000 [01:30<01:46, 38.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3876/8000 [01:30<01:59, 34.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▉                  | 3885/8000 [01:30<01:43, 39.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3890/8000 [01:31<01:45, 39.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3900/8000 [01:31<01:25, 48.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3906/8000 [01:31<01:32, 44.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3911/8000 [01:31<01:45, 38.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3919/8000 [01:31<01:28, 46.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3925/8000 [01:31<01:33, 43.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3934/8000 [01:31<01:15, 53.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3940/8000 [01:32<01:34, 42.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3946/8000 [01:32<01:39, 40.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3954/8000 [01:32<01:34, 42.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3960/8000 [01:32<01:37, 41.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3969/8000 [01:32<01:21, 49.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3978/8000 [01:32<01:10, 56.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3986/8000 [01:33<01:16, 52.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3994/8000 [01:33<01:17, 51.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4003/8000 [01:33<01:16, 52.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4013/8000 [01:33<01:34, 42.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4019/8000 [01:33<01:44, 37.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4028/8000 [01:34<01:26, 46.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4035/8000 [01:34<01:29, 44.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4040/8000 [01:34<01:27, 45.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4046/8000 [01:34<01:33, 42.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4051/8000 [01:34<01:43, 38.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4057/8000 [01:34<01:32, 42.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4064/8000 [01:34<01:41, 38.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4069/8000 [01:35<01:48, 36.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4074/8000 [01:35<01:53, 34.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4082/8000 [01:35<01:39, 39.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4087/8000 [01:35<01:43, 37.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4093/8000 [01:35<01:54, 34.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4101/8000 [01:35<01:30, 42.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4108/8000 [01:36<01:22, 47.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████████                 | 4116/8000 [01:36<01:20, 48.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4123/8000 [01:36<01:32, 41.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4130/8000 [01:36<01:22, 47.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4136/8000 [01:36<01:38, 39.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4144/8000 [01:36<01:23, 46.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4151/8000 [01:36<01:16, 50.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4157/8000 [01:37<01:25, 45.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4162/8000 [01:37<01:46, 36.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4169/8000 [01:37<01:31, 41.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4175/8000 [01:37<01:28, 43.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4181/8000 [01:37<01:22, 46.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4188/8000 [01:37<01:15, 50.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4195/8000 [01:37<01:19, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4203/8000 [01:38<01:09, 54.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4210/8000 [01:38<01:15, 50.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4217/8000 [01:38<01:18, 47.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4229/8000 [01:38<01:00, 62.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4239/8000 [01:38<01:03, 59.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4249/8000 [01:38<01:03, 58.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4257/8000 [01:39<01:19, 47.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4263/8000 [01:39<01:25, 43.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4269/8000 [01:39<01:37, 38.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4274/8000 [01:39<01:35, 39.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4279/8000 [01:39<01:40, 37.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▋                | 4283/8000 [01:40<02:03, 30.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4290/8000 [01:40<01:41, 36.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4298/8000 [01:40<01:29, 41.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4304/8000 [01:40<01:30, 41.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4310/8000 [01:40<01:21, 45.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4319/8000 [01:40<01:14, 49.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4325/8000 [01:40<01:14, 49.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4332/8000 [01:40<01:09, 52.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4341/8000 [01:41<01:06, 54.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4349/8000 [01:41<01:04, 56.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4356/8000 [01:41<01:03, 57.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4363/8000 [01:41<01:01, 59.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4369/8000 [01:41<01:04, 56.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4375/8000 [01:41<01:12, 50.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4382/8000 [01:41<01:07, 53.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4388/8000 [01:42<01:29, 40.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4394/8000 [01:42<01:32, 38.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4399/8000 [01:42<01:39, 36.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4403/8000 [01:42<01:40, 35.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4411/8000 [01:42<01:20, 44.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4417/8000 [01:42<01:27, 40.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4424/8000 [01:42<01:28, 40.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▍               | 4431/8000 [01:43<01:17, 45.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4441/8000 [01:43<01:01, 57.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4448/8000 [01:43<01:01, 58.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4457/8000 [01:43<00:57, 62.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4464/8000 [01:43<00:55, 63.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4471/8000 [01:43<00:56, 62.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4482/8000 [01:43<00:54, 64.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4489/8000 [01:43<01:03, 55.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4495/8000 [01:44<01:02, 56.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4503/8000 [01:44<01:11, 48.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4510/8000 [01:44<01:07, 51.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▊               | 4517/8000 [01:44<01:20, 43.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4523/8000 [01:44<01:16, 45.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4529/8000 [01:44<01:12, 48.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4537/8000 [01:45<01:23, 41.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4543/8000 [01:45<01:18, 44.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4553/8000 [01:45<01:02, 54.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4563/8000 [01:45<01:05, 52.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4571/8000 [01:45<01:07, 50.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4577/8000 [01:45<01:09, 49.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4583/8000 [01:45<01:14, 45.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4589/8000 [01:46<01:19, 42.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████▏              | 4600/8000 [01:46<01:08, 49.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4606/8000 [01:46<01:23, 40.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4612/8000 [01:46<01:24, 40.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4618/8000 [01:46<01:35, 35.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4630/8000 [01:47<01:06, 50.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4639/8000 [01:47<01:06, 50.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4646/8000 [01:47<01:10, 47.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4652/8000 [01:47<01:16, 43.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4657/8000 [01:47<01:15, 44.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4663/8000 [01:47<01:10, 47.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4670/8000 [01:47<01:12, 45.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4675/8000 [01:48<01:11, 46.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▍              | 4682/8000 [01:48<01:14, 44.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4688/8000 [01:48<01:17, 42.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4695/8000 [01:48<01:27, 37.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4702/8000 [01:48<01:15, 43.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4707/8000 [01:48<01:14, 44.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4712/8000 [01:48<01:15, 43.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4719/8000 [01:49<01:08, 47.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4726/8000 [01:49<01:12, 45.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4733/8000 [01:49<01:05, 50.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4740/8000 [01:49<01:10, 46.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4745/8000 [01:49<01:20, 40.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4752/8000 [01:49<01:10, 45.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4757/8000 [01:50<01:42, 31.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▊              | 4761/8000 [01:50<01:40, 32.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▊              | 4765/8000 [01:50<01:37, 33.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4772/8000 [01:50<01:22, 38.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4779/8000 [01:50<01:14, 43.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4786/8000 [01:50<01:14, 43.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4795/8000 [01:50<01:02, 51.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4801/8000 [01:51<01:09, 45.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4807/8000 [01:51<01:07, 47.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4813/8000 [01:51<01:03, 49.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4821/8000 [01:51<01:03, 49.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4829/8000 [01:51<01:00, 52.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4838/8000 [01:51<00:59, 53.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4845/8000 [01:51<01:11, 43.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4850/8000 [01:52<01:33, 33.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4854/8000 [01:52<01:54, 27.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4862/8000 [01:52<01:27, 35.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4867/8000 [01:52<01:24, 36.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4873/8000 [01:52<01:18, 39.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4881/8000 [01:52<01:13, 42.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4886/8000 [01:53<01:22, 37.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4891/8000 [01:53<01:17, 40.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4897/8000 [01:53<01:11, 43.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4902/8000 [01:53<01:26, 35.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4907/8000 [01:53<01:22, 37.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4912/8000 [01:53<01:19, 38.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▌             | 4918/8000 [01:53<01:17, 39.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4924/8000 [01:54<01:12, 42.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4929/8000 [01:54<01:24, 36.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4934/8000 [01:54<01:31, 33.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4941/8000 [01:54<01:18, 38.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4946/8000 [01:54<01:17, 39.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4952/8000 [01:54<01:10, 42.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4958/8000 [01:54<01:05, 46.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4964/8000 [01:55<01:03, 48.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4970/8000 [01:55<01:01, 48.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4976/8000 [01:55<01:16, 39.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4981/8000 [01:55<01:12, 41.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4986/8000 [01:55<01:18, 38.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4993/8000 [01:55<01:06, 45.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4999/8000 [01:55<01:22, 36.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5008/8000 [01:56<01:10, 42.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5014/8000 [01:56<01:07, 44.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5019/8000 [01:56<01:08, 43.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5025/8000 [01:56<01:19, 37.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5031/8000 [01:56<01:21, 36.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5038/8000 [01:56<01:17, 38.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5042/8000 [01:57<01:24, 35.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5047/8000 [01:57<01:17, 37.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5051/8000 [01:57<01:19, 37.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5055/8000 [01:57<01:20, 36.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5059/8000 [01:57<01:20, 36.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5067/8000 [01:57<01:09, 42.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5073/8000 [01:57<01:05, 44.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▏            | 5081/8000 [01:58<01:11, 40.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5088/8000 [01:58<01:09, 41.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5095/8000 [01:58<01:04, 45.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5100/8000 [01:58<01:06, 43.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5107/8000 [01:58<00:58, 49.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5113/8000 [01:58<00:57, 50.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5120/8000 [01:58<00:54, 52.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5126/8000 [01:58<00:54, 52.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5136/8000 [01:58<00:46, 61.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5143/8000 [01:59<00:46, 60.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5151/8000 [01:59<00:44, 64.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5160/8000 [01:59<00:53, 52.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5173/8000 [01:59<00:44, 62.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5180/8000 [01:59<00:55, 51.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5188/8000 [01:59<00:55, 50.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5195/8000 [02:00<00:52, 53.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5204/8000 [02:00<00:56, 49.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5211/8000 [02:00<01:01, 45.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5219/8000 [02:00<00:54, 51.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5225/8000 [02:00<00:53, 51.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5232/8000 [02:00<00:52, 52.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5239/8000 [02:01<01:01, 45.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5250/8000 [02:01<00:52, 52.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5258/8000 [02:01<00:53, 50.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5265/8000 [02:01<01:03, 43.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5270/8000 [02:01<01:21, 33.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5274/8000 [02:01<01:22, 33.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5280/8000 [02:02<01:14, 36.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5285/8000 [02:02<01:12, 37.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5294/8000 [02:02<00:56, 47.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5300/8000 [02:02<00:59, 45.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5307/8000 [02:02<00:55, 48.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▎           | 5316/8000 [02:02<00:55, 48.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5323/8000 [02:02<00:51, 51.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5329/8000 [02:03<00:52, 51.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5336/8000 [02:03<00:49, 53.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5343/8000 [02:03<00:48, 55.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5349/8000 [02:03<00:55, 47.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5356/8000 [02:03<00:58, 45.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5361/8000 [02:03<01:06, 39.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5368/8000 [02:03<00:59, 44.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5373/8000 [02:04<00:57, 45.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5378/8000 [02:04<01:06, 39.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5385/8000 [02:04<00:58, 44.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5393/8000 [02:04<01:02, 41.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5398/8000 [02:04<01:00, 42.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5407/8000 [02:04<00:49, 52.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5413/8000 [02:04<01:03, 40.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5419/8000 [02:05<00:58, 43.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5424/8000 [02:05<00:58, 43.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5429/8000 [02:05<00:58, 44.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5436/8000 [02:05<00:50, 50.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5442/8000 [02:05<00:51, 49.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5449/8000 [02:05<00:53, 48.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5457/8000 [02:05<00:52, 48.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5463/8000 [02:06<00:58, 43.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5470/8000 [02:06<01:10, 35.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5475/8000 [02:06<01:05, 38.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▉           | 5482/8000 [02:06<00:57, 43.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5487/8000 [02:06<01:05, 38.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5498/8000 [02:06<00:47, 52.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5504/8000 [02:06<00:47, 52.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5513/8000 [02:07<00:46, 53.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5520/8000 [02:07<00:49, 50.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5528/8000 [02:07<00:47, 51.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5535/8000 [02:07<00:55, 44.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5541/8000 [02:07<00:57, 43.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5547/8000 [02:07<00:56, 43.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5552/8000 [02:07<00:54, 44.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5558/8000 [02:08<00:51, 47.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5565/8000 [02:08<00:56, 42.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5570/8000 [02:08<01:02, 38.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5577/8000 [02:08<00:53, 45.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5584/8000 [02:08<00:48, 49.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5590/8000 [02:08<01:00, 39.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5596/8000 [02:09<00:55, 43.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5603/8000 [02:09<00:55, 43.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5608/8000 [02:09<00:55, 42.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5613/8000 [02:09<01:02, 38.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5619/8000 [02:09<00:57, 41.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5624/8000 [02:09<01:03, 37.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5630/8000 [02:09<01:13, 32.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5635/8000 [02:10<01:07, 34.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5643/8000 [02:10<00:54, 43.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5648/8000 [02:10<00:52, 44.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5653/8000 [02:10<01:00, 38.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5661/8000 [02:10<00:49, 47.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5668/8000 [02:10<00:46, 50.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5675/8000 [02:10<00:52, 44.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5680/8000 [02:11<00:57, 40.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5688/8000 [02:11<00:54, 42.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5695/8000 [02:11<00:49, 46.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5702/8000 [02:11<00:50, 45.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5708/8000 [02:11<00:47, 48.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|█████████████████████████          | 5715/8000 [02:11<00:53, 42.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5720/8000 [02:11<00:52, 43.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5733/8000 [02:12<00:39, 56.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5741/8000 [02:12<00:41, 54.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5750/8000 [02:12<00:44, 50.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5757/8000 [02:12<00:42, 53.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5764/8000 [02:12<00:45, 49.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5772/8000 [02:12<00:44, 49.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5778/8000 [02:13<00:53, 41.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5785/8000 [02:13<00:47, 46.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5793/8000 [02:13<00:51, 42.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5799/8000 [02:13<00:54, 40.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5808/8000 [02:13<00:48, 45.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5819/8000 [02:13<00:38, 56.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5826/8000 [02:14<00:42, 51.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5832/8000 [02:14<00:41, 51.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5838/8000 [02:14<00:46, 46.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5845/8000 [02:14<00:44, 48.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5852/8000 [02:14<00:41, 51.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5862/8000 [02:14<00:42, 50.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5870/8000 [02:15<00:50, 41.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5875/8000 [02:15<00:49, 42.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▋         | 5881/8000 [02:15<00:52, 40.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5889/8000 [02:15<00:53, 39.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5895/8000 [02:15<01:00, 34.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5900/8000 [02:15<00:58, 35.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5905/8000 [02:16<00:56, 37.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5910/8000 [02:16<00:53, 39.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5920/8000 [02:16<00:40, 51.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5927/8000 [02:16<00:43, 47.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5933/8000 [02:16<00:42, 48.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5940/8000 [02:16<00:40, 51.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5950/8000 [02:16<00:42, 48.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5956/8000 [02:17<00:46, 44.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5964/8000 [02:17<00:40, 50.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5970/8000 [02:17<00:39, 50.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5978/8000 [02:17<00:39, 50.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5986/8000 [02:17<00:45, 43.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5991/8000 [02:17<00:50, 39.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5996/8000 [02:18<00:54, 36.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6001/8000 [02:18<01:08, 29.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6006/8000 [02:18<01:01, 32.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6012/8000 [02:18<00:55, 35.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6018/8000 [02:18<00:50, 38.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6025/8000 [02:18<00:48, 40.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6030/8000 [02:18<00:52, 37.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6037/8000 [02:19<00:44, 44.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6046/8000 [02:19<00:45, 42.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6056/8000 [02:19<00:40, 48.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6062/8000 [02:19<00:38, 50.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6069/8000 [02:19<00:41, 46.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6075/8000 [02:19<00:45, 42.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6080/8000 [02:20<00:49, 38.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6087/8000 [02:20<00:42, 44.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6092/8000 [02:20<00:48, 39.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6097/8000 [02:20<00:52, 36.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6102/8000 [02:20<00:54, 34.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6111/8000 [02:20<00:45, 41.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6117/8000 [02:20<00:42, 44.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6124/8000 [02:21<00:42, 44.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6132/8000 [02:21<00:36, 51.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6140/8000 [02:21<00:33, 56.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6147/8000 [02:21<00:41, 44.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6155/8000 [02:21<00:36, 50.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6161/8000 [02:21<00:39, 45.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6169/8000 [02:21<00:34, 52.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6176/8000 [02:22<00:36, 50.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6182/8000 [02:22<00:40, 44.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6188/8000 [02:22<00:47, 38.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6194/8000 [02:22<00:49, 36.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6198/8000 [02:22<00:50, 35.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6204/8000 [02:22<00:45, 39.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6211/8000 [02:23<00:44, 40.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6216/8000 [02:23<00:48, 36.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6220/8000 [02:23<01:04, 27.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6224/8000 [02:23<01:00, 29.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6228/8000 [02:23<01:16, 23.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6232/8000 [02:24<01:09, 25.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6237/8000 [02:24<01:04, 27.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6241/8000 [02:24<01:00, 28.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6246/8000 [02:24<00:54, 32.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6251/8000 [02:24<00:48, 35.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6262/8000 [02:24<00:36, 48.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6268/8000 [02:24<00:43, 40.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6275/8000 [02:24<00:37, 46.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6281/8000 [02:25<00:35, 48.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6287/8000 [02:25<00:43, 39.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6293/8000 [02:25<00:48, 35.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6297/8000 [02:25<00:52, 32.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6304/8000 [02:25<00:56, 29.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6309/8000 [02:26<00:51, 33.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6313/8000 [02:26<00:50, 33.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6319/8000 [02:26<00:45, 36.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6325/8000 [02:26<00:40, 41.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6334/8000 [02:26<00:32, 51.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6341/8000 [02:26<00:30, 54.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6348/8000 [02:26<00:40, 40.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6356/8000 [02:27<00:33, 48.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6370/8000 [02:27<00:26, 61.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6377/8000 [02:27<00:29, 54.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6386/8000 [02:27<00:34, 46.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6393/8000 [02:27<00:36, 44.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6400/8000 [02:28<00:40, 39.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6407/8000 [02:28<00:40, 38.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6412/8000 [02:28<00:40, 38.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6421/8000 [02:28<00:41, 37.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6425/8000 [02:28<01:00, 26.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6433/8000 [02:29<00:46, 33.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6439/8000 [02:29<00:41, 37.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6445/8000 [02:29<00:42, 36.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6454/8000 [02:29<00:32, 47.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6461/8000 [02:29<00:29, 51.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6473/8000 [02:29<00:25, 60.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6481/8000 [02:29<00:30, 49.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6489/8000 [02:30<00:27, 55.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6496/8000 [02:30<00:39, 38.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6502/8000 [02:30<00:39, 37.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6507/8000 [02:30<00:42, 34.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▌      | 6515/8000 [02:30<00:35, 42.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6522/8000 [02:31<00:38, 37.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6532/8000 [02:31<00:32, 44.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6538/8000 [02:31<00:31, 46.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6545/8000 [02:31<00:28, 50.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6556/8000 [02:31<00:28, 50.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6568/8000 [02:31<00:27, 52.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6577/8000 [02:32<00:26, 53.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6586/8000 [02:32<00:23, 59.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6594/8000 [02:32<00:25, 55.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6602/8000 [02:32<00:25, 54.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6609/8000 [02:32<00:26, 52.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6616/8000 [02:32<00:28, 49.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6624/8000 [02:32<00:30, 44.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6631/8000 [02:33<00:28, 48.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6641/8000 [02:33<00:25, 53.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6649/8000 [02:33<00:23, 56.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6656/8000 [02:33<00:23, 56.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6662/8000 [02:33<00:31, 42.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6669/8000 [02:33<00:27, 47.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6675/8000 [02:34<00:29, 44.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6682/8000 [02:34<00:27, 48.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6689/8000 [02:34<00:28, 45.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6694/8000 [02:34<00:28, 45.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6703/8000 [02:34<00:23, 55.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6710/8000 [02:34<00:30, 41.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6715/8000 [02:34<00:34, 37.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6724/8000 [02:35<00:30, 41.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6729/8000 [02:35<00:30, 41.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6736/8000 [02:35<00:32, 39.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6742/8000 [02:35<00:32, 39.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6747/8000 [02:35<00:30, 40.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6753/8000 [02:35<00:27, 44.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6758/8000 [02:35<00:28, 43.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6763/8000 [02:36<00:28, 43.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6772/8000 [02:36<00:23, 52.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6778/8000 [02:36<00:22, 54.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6787/8000 [02:36<00:22, 54.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6793/8000 [02:36<00:22, 53.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6801/8000 [02:36<00:20, 58.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6807/8000 [02:36<00:21, 55.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6813/8000 [02:36<00:21, 54.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6821/8000 [02:37<00:21, 53.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6830/8000 [02:37<00:24, 48.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6836/8000 [02:37<00:23, 48.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6843/8000 [02:37<00:22, 51.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6851/8000 [02:37<00:19, 57.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6858/8000 [02:37<00:25, 45.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6864/8000 [02:37<00:23, 47.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6870/8000 [02:38<00:22, 49.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6876/8000 [02:38<00:22, 50.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6886/8000 [02:38<00:18, 60.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6893/8000 [02:38<00:23, 48.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6902/8000 [02:38<00:22, 49.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6911/8000 [02:38<00:21, 51.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6917/8000 [02:38<00:20, 52.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6924/8000 [02:39<00:19, 56.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6934/8000 [02:39<00:17, 59.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6943/8000 [02:39<00:16, 63.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6953/8000 [02:39<00:17, 58.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6962/8000 [02:39<00:24, 42.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6968/8000 [02:40<00:24, 41.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6974/8000 [02:40<00:23, 44.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6983/8000 [02:40<00:23, 42.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6990/8000 [02:40<00:21, 47.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7001/8000 [02:40<00:18, 54.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7007/8000 [02:40<00:18, 54.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7017/8000 [02:40<00:15, 63.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7025/8000 [02:41<00:16, 59.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7032/8000 [02:41<00:19, 49.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7039/8000 [02:41<00:22, 42.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7044/8000 [02:41<00:22, 43.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7052/8000 [02:41<00:20, 46.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7058/8000 [02:41<00:25, 36.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7064/8000 [02:42<00:27, 34.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7070/8000 [02:42<00:29, 31.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7075/8000 [02:42<00:28, 32.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▉    | 7081/8000 [02:42<00:25, 36.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7086/8000 [02:42<00:26, 34.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7092/8000 [02:42<00:22, 39.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7097/8000 [02:43<00:21, 41.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7103/8000 [02:43<00:22, 40.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7108/8000 [02:43<00:22, 40.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7114/8000 [02:43<00:23, 38.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7121/8000 [02:43<00:20, 43.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7129/8000 [02:43<00:20, 43.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7136/8000 [02:43<00:18, 45.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7141/8000 [02:44<00:24, 34.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7145/8000 [02:44<00:27, 31.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7150/8000 [02:44<00:24, 34.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7156/8000 [02:44<00:21, 38.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7161/8000 [02:44<00:20, 40.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7168/8000 [02:44<00:19, 42.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7173/8000 [02:44<00:19, 43.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7179/8000 [02:45<00:20, 40.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7190/8000 [02:45<00:16, 49.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7200/8000 [02:45<00:13, 59.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7207/8000 [02:45<00:14, 53.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7215/8000 [02:45<00:15, 51.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7221/8000 [02:45<00:14, 51.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7227/8000 [02:46<00:18, 42.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7233/8000 [02:46<00:18, 41.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7238/8000 [02:46<00:17, 42.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7245/8000 [02:46<00:15, 47.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7251/8000 [02:46<00:16, 44.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7256/8000 [02:46<00:16, 44.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7265/8000 [02:46<00:15, 48.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7274/8000 [02:46<00:12, 56.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7280/8000 [02:47<00:14, 48.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7288/8000 [02:47<00:14, 48.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7296/8000 [02:47<00:14, 48.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7303/8000 [02:47<00:14, 48.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████████████   | 7316/8000 [02:47<00:10, 62.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7327/8000 [02:47<00:10, 65.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7334/8000 [02:48<00:11, 58.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7344/8000 [02:48<00:10, 60.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7355/8000 [02:48<00:09, 69.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7364/8000 [02:48<00:11, 57.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7371/8000 [02:48<00:11, 53.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7377/8000 [02:49<00:17, 36.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7387/8000 [02:49<00:13, 46.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7393/8000 [02:49<00:14, 43.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7402/8000 [02:49<00:15, 39.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7412/8000 [02:49<00:16, 35.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7417/8000 [02:50<00:16, 35.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7426/8000 [02:50<00:13, 43.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7434/8000 [02:50<00:13, 42.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7441/8000 [02:50<00:13, 42.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7447/8000 [02:50<00:12, 45.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7455/8000 [02:50<00:11, 46.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7460/8000 [02:50<00:11, 46.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7467/8000 [02:51<00:12, 43.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7472/8000 [02:51<00:12, 43.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7480/8000 [02:51<00:13, 38.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7485/8000 [02:51<00:14, 35.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7489/8000 [02:51<00:14, 35.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7497/8000 [02:51<00:11, 42.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7505/8000 [02:52<00:10, 45.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7510/8000 [02:52<00:11, 44.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7516/8000 [02:52<00:10, 46.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7522/8000 [02:52<00:10, 47.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7529/8000 [02:52<00:09, 49.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7534/8000 [02:52<00:09, 47.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7541/8000 [02:52<00:08, 51.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7548/8000 [02:52<00:09, 49.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7554/8000 [02:53<00:10, 44.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7559/8000 [02:53<00:11, 39.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7564/8000 [02:53<00:13, 32.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7568/8000 [02:53<00:14, 30.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7578/8000 [02:53<00:09, 44.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7584/8000 [02:53<00:09, 42.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7590/8000 [02:54<00:09, 44.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7595/8000 [02:54<00:11, 36.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7601/8000 [02:54<00:10, 39.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7606/8000 [02:54<00:11, 35.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7615/8000 [02:54<00:08, 46.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7621/8000 [02:54<00:09, 40.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7626/8000 [02:54<00:09, 39.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7632/8000 [02:55<00:09, 40.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7641/8000 [02:55<00:07, 49.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7649/8000 [02:55<00:07, 46.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7656/8000 [02:55<00:08, 40.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7661/8000 [02:55<00:08, 38.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7668/8000 [02:55<00:07, 44.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7674/8000 [02:56<00:06, 47.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7680/8000 [02:56<00:08, 39.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7686/8000 [02:56<00:07, 41.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7691/8000 [02:56<00:07, 41.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7697/8000 [02:56<00:08, 35.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7702/8000 [02:56<00:08, 33.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7707/8000 [02:56<00:08, 35.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7711/8000 [02:57<00:08, 34.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7717/8000 [02:57<00:09, 30.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7723/8000 [02:57<00:08, 33.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7728/8000 [02:57<00:07, 35.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7733/8000 [02:57<00:06, 38.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7739/8000 [02:57<00:07, 33.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7743/8000 [02:58<00:07, 34.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7748/8000 [02:58<00:07, 32.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7754/8000 [02:58<00:06, 37.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7760/8000 [02:58<00:05, 40.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7770/8000 [02:58<00:04, 46.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7779/8000 [02:58<00:04, 49.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7787/8000 [02:58<00:03, 55.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7797/8000 [02:59<00:03, 58.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7803/8000 [02:59<00:03, 50.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7809/8000 [02:59<00:03, 51.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7816/8000 [02:59<00:03, 53.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7823/8000 [02:59<00:03, 47.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7829/8000 [02:59<00:04, 41.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7837/8000 [03:00<00:03, 41.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7842/8000 [03:00<00:03, 40.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7850/8000 [03:00<00:04, 35.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7854/8000 [03:00<00:04, 31.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7863/8000 [03:00<00:03, 40.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7869/8000 [03:00<00:03, 38.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7875/8000 [03:01<00:04, 29.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7880/8000 [03:01<00:03, 30.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▍| 7884/8000 [03:01<00:03, 30.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7891/8000 [03:01<00:02, 37.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7899/8000 [03:01<00:02, 43.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7904/8000 [03:01<00:02, 37.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7909/8000 [03:02<00:02, 37.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7913/8000 [03:02<00:02, 29.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7917/8000 [03:02<00:02, 27.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7924/8000 [03:02<00:02, 34.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7928/8000 [03:02<00:02, 34.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7933/8000 [03:02<00:02, 32.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7937/8000 [03:03<00:02, 29.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7944/8000 [03:03<00:01, 35.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7948/8000 [03:03<00:01, 36.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7953/8000 [03:03<00:01, 38.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7962/8000 [03:03<00:00, 42.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7968/8000 [03:03<00:00, 37.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7973/8000 [03:03<00:00, 36.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7978/8000 [03:04<00:00, 35.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7985/8000 [03:04<00:00, 34.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7991/8000 [03:04<00:00, 35.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8002it [03:04, 43.27it/s]                                                   \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<14:07,  4.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:05<07:29,  2.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:05<05:19,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:06<04:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:07<03:34,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:08<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:09<02:54,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:09<02:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:10<02:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:11<02:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:12<02:36,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:13<02:33,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:13<02:32,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:14<02:27,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:15<02:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:16<02:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:16<02:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:17<02:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:18<02:19,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:19<02:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:19<02:19,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:20<02:18,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:21<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:22<02:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:23<02:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:23<02:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:24<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:25<02:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:26<02:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:26<02:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:28<02:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:29<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:29<02:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:30<02:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:31<02:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:32<02:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:32<02:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:33<02:07,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:34<02:08,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:35<02:05,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:36<02:05,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:37<02:05,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:38<02:16,  1.14it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 24.14it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 66it [00:00, 96.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 156it [00:01, 235.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 231it [00:01, 337.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 312it [00:01, 441.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 396it [00:01, 536.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 481it [00:01, 616.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 572it [00:01, 693.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 665it [00:01, 757.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:01, 437.45it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|████▍      | 40/100 [00:00<00:00, 391.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████| 100/100 [00:00<00:00, 396.35it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████████████████████| 4/4 [16:25<00:00, 288.21s/it]\u001b[A\n",
+      "Replicates:  73%|████████████████▊      | 11/15 [6:51:12<1:28:11, 1322.84s/it]\n",
+      "Nyquist Samples:   0%|                                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.878056444725255\n",
+      "2999: -5.9148411\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:43,  5.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:06<08:58,  2.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:04,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:08<04:56,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<04:24,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:09<03:51,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:10<03:23,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:11<03:16,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:13<03:36,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:14<03:47,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:15<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:17<04:04,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:18<04:08,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:20<04:11,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:21<04:14,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:22<04:19,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:24<04:19,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:25<04:16,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:27<04:15,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:28<04:14,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:30<04:26,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:32<04:52,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:34<05:27,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:37<06:19,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:39<06:04,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:41<05:59,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:44<06:39,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:46<06:38,  2.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:48<06:23,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:51<06:26,  2.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:53<06:13,  2.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:54<05:46,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:57<06:17,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [01:00<06:24,  2.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [01:02<06:30,  2.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [01:04<05:49,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [01:06<05:43,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [01:08<06:15,  2.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [01:11<06:41,  2.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [01:14<06:33,  2.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [01:17<07:00,  2.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [01:19<06:51,  2.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [01:22<06:35,  2.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:23<05:45,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:25<05:49,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [01:29<06:54,  2.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:32<06:50,  2.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:35<06:47,  2.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:36<06:07,  2.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [01:38<05:44,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:40<05:19,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:42<05:20,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:44<04:50,  1.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:45<04:27,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:47<04:11,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:48<03:51,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:50<03:36,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:51<03:27,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:52<03:20,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:53<03:12,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:55<03:09,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:56<03:06,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [01:57<03:04,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [01:59<03:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [02:00<02:59,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████████▊                                | 66/200 [02:01<02:59,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████                                | 67/200 [02:03<02:59,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▎                               | 68/200 [02:04<03:02,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████████▌                               | 69/200 [02:06<03:16,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████████▊                               | 70/200 [02:08<03:18,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████                               | 71/200 [02:09<03:15,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▎                              | 72/200 [02:11<03:27,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████████▌                              | 73/200 [02:13<03:28,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████████▊                              | 74/200 [02:15<03:54,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████                              | 75/200 [02:17<04:04,  1.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|██████████████████▏                             | 76/200 [02:24<03:55,  1.90s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 13.40it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 16it [00:01, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 30it [00:01, 26.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 65it [00:01, 68.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 86it [00:02, 89.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 110it [00:02, 116.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 134it [00:02, 141.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 165it [00:02, 179.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 201it [00:02, 222.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 235it [00:02, 251.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 265it [00:02, 258.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 296it [00:02, 272.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 330it [00:02, 291.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 365it [00:02, 305.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 397it [00:03, 301.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 433it [00:03, 317.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 468it [00:03, 324.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 505it [00:03, 335.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 539it [00:03, 334.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 573it [00:03, 333.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 607it [00:03, 331.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 641it [00:03, 333.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 676it [00:03, 336.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 710it [00:03, 331.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 744it [00:04, 304.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:04, 187.87it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   7%|▉            | 7/100 [00:00<00:01, 67.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  14%|█▋          | 14/100 [00:00<00:01, 49.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 46.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|███         | 25/100 [00:00<00:01, 44.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|████        | 34/100 [00:00<00:01, 57.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  41%|████▉       | 41/100 [00:00<00:01, 53.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▋      | 47/100 [00:00<00:01, 50.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  53%|██████▎     | 53/100 [00:01<00:00, 47.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  58%|██████▉     | 58/100 [00:01<00:00, 45.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  66%|███████▉    | 66/100 [00:01<00:00, 53.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  72%|████████▋   | 72/100 [00:01<00:00, 48.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  78%|█████████▎  | 78/100 [00:01<00:00, 46.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|█████████▉  | 83/100 [00:01<00:00, 45.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  90%|██████████▊ | 90/100 [00:01<00:00, 50.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 48.60it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                  | 167/8000 [00:00<00:11, 687.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                 | 338/8000 [00:00<00:09, 839.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                | 513/8000 [00:00<00:08, 871.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▉                                | 683/8000 [00:00<00:09, 779.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                               | 861/8000 [00:01<00:09, 764.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▎                             | 1025/8000 [00:01<00:10, 685.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                             | 1194/8000 [00:01<00:10, 666.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                            | 1375/8000 [00:01<00:10, 656.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                           | 1547/8000 [00:02<00:10, 619.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                          | 1714/8000 [00:02<00:10, 611.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████▉                          | 1880/8000 [00:02<00:09, 618.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▊                         | 2062/8000 [00:03<00:09, 633.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▍                        | 2230/8000 [00:03<00:08, 659.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▏                       | 2405/8000 [00:03<00:08, 667.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▉                       | 2577/8000 [00:03<00:08, 646.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                      | 2756/8000 [00:04<00:07, 663.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▍                     | 2933/8000 [00:04<00:07, 682.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▏                    | 3113/8000 [00:04<00:06, 710.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▉                    | 3282/8000 [00:04<00:06, 725.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▋                   | 3448/8000 [00:05<00:06, 742.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▍                  | 3620/8000 [00:05<00:06, 715.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████                  | 3794/8000 [00:05<00:05, 739.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████████▊                 | 3966/8000 [00:05<00:05, 763.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▌                | 4147/8000 [00:05<00:04, 791.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▍               | 4330/8000 [00:06<00:04, 837.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▏              | 4505/8000 [00:06<00:04, 824.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████████▉              | 4681/8000 [00:06<00:04, 802.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▋             | 4861/8000 [00:06<00:03, 804.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▍            | 5042/8000 [00:06<00:03, 803.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▏           | 5220/8000 [00:07<00:03, 839.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▉           | 5390/8000 [00:07<00:03, 805.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▋          | 5572/8000 [00:07<00:02, 813.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▎         | 5726/8000 [00:07<00:02, 776.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████         | 5897/8000 [00:08<00:02, 754.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6070/8000 [00:08<00:02, 789.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6252/8000 [00:08<00:02, 787.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▍      | 6446/8000 [00:08<00:01, 817.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████      | 6610/8000 [00:08<00:01, 782.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6777/8000 [00:09<00:01, 782.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▍    | 6941/8000 [00:09<00:01, 799.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7115/8000 [00:09<00:01, 771.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7278/8000 [00:09<00:00, 748.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7436/8000 [00:10<00:00, 749.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7612/8000 [00:10<00:00, 768.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7790/8000 [00:10<00:00, 817.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▊| 7949/8000 [00:10<00:00, 781.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8123it [00:10, 743.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<16:26,  4.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:21,  2.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:53,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:10<05:09,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:11<04:46,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:12<04:32,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:14<04:30,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:15<04:20,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:16<04:14,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:17<04:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:19<04:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:20<04:16,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:21<04:07,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:23<04:03,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:24<03:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:25<04:02,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:27<04:07,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:28<04:08,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:30<04:08,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:31<04:07,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:32<04:04,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:34<04:03,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:35<04:03,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:36<04:01,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:38<04:00,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:39<04:11,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:41<03:58,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:42<03:52,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:43<03:45,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:45<03:55,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:46<03:56,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:48<03:50,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:49<03:48,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:50<03:48,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:52<03:48,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:53<03:46,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:55<03:46,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:56<03:45,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:57<03:47,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:59<03:44,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [01:00<03:43,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [01:02<03:42,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:03<03:39,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:05<03:45,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [01:06<03:38,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:07<03:36,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:09<03:32,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:10<03:31,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [01:12<03:31,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:13<03:27,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:14<03:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:16<03:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:17<03:19,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:18<03:20,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:20<03:24,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:21<03:25,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:23<03:19,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:24<03:12,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:25<03:07,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:26<03:04,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:28<03:00,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [01:29<03:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [01:31<03:11,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [01:32<02:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████████▊                                | 66/200 [01:34<03:12,  1.43s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 23.13it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:00, 53.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 106.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 113it [00:01, 159.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 150it [00:01, 204.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 187it [00:01, 242.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 224it [00:01, 273.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 260it [00:01, 291.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 295it [00:01, 304.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 336it [00:01, 333.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 373it [00:01, 342.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 410it [00:01, 348.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 448it [00:02, 355.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 485it [00:02, 357.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 522it [00:02, 359.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 559it [00:02, 360.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 600it [00:02, 372.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 638it [00:02, 369.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 676it [00:02, 368.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 714it [00:02, 369.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 752it [00:02, 363.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 262.23it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 43.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:01, 45.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 44.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 43.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  28%|███▎        | 28/100 [00:00<00:01, 51.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|████        | 34/100 [00:00<00:01, 48.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  39%|████▋       | 39/100 [00:00<00:01, 46.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  44%|█████▎      | 44/100 [00:00<00:01, 45.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|██████      | 50/100 [00:01<00:01, 47.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██████▌     | 55/100 [00:01<00:00, 46.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  60%|███████▏    | 60/100 [00:01<00:00, 45.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  65%|███████▊    | 65/100 [00:01<00:00, 45.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  70%|████████▍   | 70/100 [00:01<00:00, 44.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  76%|█████████   | 76/100 [00:01<00:00, 49.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  81%|█████████▋  | 81/100 [00:01<00:00, 47.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  86%|██████████▎ | 86/100 [00:01<00:00, 46.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  91%|██████████▉ | 91/100 [00:01<00:00, 46.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 46.09it/s]\n",
+      "\n",
+      "Nyquist Samples:  25%|██████████▊                                | 1/4 [05:36<16:48, 336.30s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.841290159577519\n",
+      "2999: -6.2232476\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:04<15:56,  4.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:03,  2.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:43,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:55,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:10<05:13,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:11<04:44,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:12<04:21,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:13<04:22,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:14<04:12,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:18<04:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:20<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:21<03:58,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:22<03:58,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:23<03:57,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:25<03:58,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:26<03:58,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:27<04:03,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:29<04:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:30<04:01,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:32<04:26,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:33<04:07,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:35<03:58,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:36<03:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:37<03:42,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:38<03:37,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:39<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:41<03:30,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:42<03:27,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:43<03:24,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:44<03:18,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:45<03:18,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:46<03:17,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:48<03:17,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:49<03:16,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:50<03:17,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:51<03:16,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:53<03:14,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:54<03:14,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:55<03:13,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:56<03:13,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:57<03:12,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:59<03:07,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:00<03:06,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [01:01<03:04,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:02<03:02,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:03<03:04,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:05<03:04,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [01:06<03:02,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:07<02:58,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:08<02:56,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:09<02:55,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:11<02:55,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:12<02:53,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:13<02:54,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:14<02:54,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:16<02:55,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▏                                 | 59/200 [01:17<02:50,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▍                                 | 60/200 [01:18<02:51,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████▋                                 | 61/200 [01:19<02:49,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|██████████████▉                                 | 62/200 [01:20<02:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████                                 | 63/200 [01:22<02:46,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▎                                | 64/200 [01:23<02:44,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████████▌                                | 65/200 [01:25<02:58,  1.32s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 23.66it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 35it [00:00, 51.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 72it [00:01, 106.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 109it [00:01, 158.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 146it [00:01, 203.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 183it [00:01, 243.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 220it [00:01, 273.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 257it [00:01, 297.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 299it [00:01, 329.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 337it [00:01, 336.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 374it [00:01, 343.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 411it [00:01, 348.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 448it [00:02, 347.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 485it [00:02, 351.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 522it [00:02, 354.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 564it [00:02, 371.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 602it [00:02, 368.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 640it [00:02, 366.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 677it [00:02, 367.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 714it [00:02, 365.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 751it [00:02, 364.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 266.12it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 43.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 44.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 43.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 43.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  27%|███▏        | 27/100 [00:00<00:01, 50.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|███▉        | 33/100 [00:00<00:01, 47.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|████▌       | 38/100 [00:00<00:01, 47.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█████▏      | 43/100 [00:00<00:01, 45.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  48%|█████▊      | 48/100 [00:01<00:01, 44.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██████▌     | 55/100 [00:01<00:00, 51.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  61%|███████▎    | 61/100 [00:01<00:00, 48.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  66%|███████▉    | 66/100 [00:01<00:00, 47.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  71%|████████▌   | 71/100 [00:01<00:00, 46.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  76%|█████████   | 76/100 [00:01<00:00, 45.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|█████████▉  | 83/100 [00:01<00:00, 51.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  89%|██████████▋ | 89/100 [00:01<00:00, 48.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  94%|███████████▎| 94/100 [00:01<00:00, 47.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 46.85it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                   | 46/8000 [00:00<00:28, 276.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 97/8000 [00:00<00:25, 307.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                  | 150/8000 [00:00<00:24, 320.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                  | 189/8000 [00:00<00:26, 294.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                  | 242/8000 [00:00<00:25, 308.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                 | 295/8000 [00:00<00:23, 321.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                 | 348/8000 [00:01<00:23, 327.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                 | 389/8000 [00:01<00:24, 306.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                 | 439/8000 [00:01<00:24, 312.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                 | 478/8000 [00:01<00:23, 313.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                | 524/8000 [00:01<00:24, 306.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                | 570/8000 [00:01<00:24, 306.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                | 616/8000 [00:01<00:24, 302.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                | 668/8000 [00:02<00:21, 341.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                | 711/8000 [00:02<00:22, 318.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                               | 759/8000 [00:02<00:23, 314.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                               | 807/8000 [00:02<00:22, 313.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▋                               | 845/8000 [00:02<00:22, 311.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                               | 899/8000 [00:02<00:22, 315.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████                               | 942/8000 [00:03<00:23, 303.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                              | 979/8000 [00:03<00:23, 299.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                             | 1031/8000 [00:03<00:20, 339.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                             | 1076/8000 [00:03<00:21, 325.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                             | 1123/8000 [00:03<00:21, 322.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                             | 1182/8000 [00:03<00:20, 337.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                            | 1229/8000 [00:03<00:19, 354.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                            | 1276/8000 [00:04<00:19, 337.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▋                            | 1327/8000 [00:04<00:19, 338.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                            | 1384/8000 [00:04<00:19, 346.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████                            | 1429/8000 [00:04<00:18, 356.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                           | 1475/8000 [00:04<00:19, 336.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                           | 1523/8000 [00:04<00:19, 337.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▋                           | 1569/8000 [00:04<00:20, 320.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                           | 1619/8000 [00:04<00:18, 353.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████                           | 1660/8000 [00:05<00:19, 325.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▎                          | 1720/8000 [00:05<00:17, 355.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                          | 1768/8000 [00:05<00:18, 329.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▋                          | 1819/8000 [00:05<00:17, 360.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                          | 1860/8000 [00:05<00:19, 321.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████                          | 1906/8000 [00:05<00:19, 318.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                         | 1954/8000 [00:06<00:19, 317.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▍                         | 1989/8000 [00:06<00:19, 312.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▋                         | 2045/8000 [00:06<00:18, 321.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                         | 2096/8000 [00:06<00:17, 329.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████                         | 2144/8000 [00:06<00:18, 324.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▎                        | 2200/8000 [00:06<00:15, 363.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▌                        | 2244/8000 [00:06<00:17, 330.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▋                        | 2291/8000 [00:07<00:17, 330.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▉                        | 2344/8000 [00:07<00:16, 337.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▏                       | 2405/8000 [00:07<00:14, 381.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▌                       | 2498/8000 [00:07<00:16, 341.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▊                       | 2540/8000 [00:07<00:16, 326.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████                       | 2603/8000 [00:07<00:14, 377.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▍                      | 2681/8000 [00:08<00:16, 322.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▌                      | 2732/8000 [00:08<00:16, 322.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▊                      | 2773/8000 [00:08<00:15, 327.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▉                      | 2810/8000 [00:08<00:17, 294.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▏                     | 2860/8000 [00:08<00:16, 312.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▎                     | 2910/8000 [00:08<00:16, 316.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▌                     | 2955/8000 [00:09<00:15, 332.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|████████████▊                     | 3004/8000 [00:09<00:15, 321.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████                     | 3062/8000 [00:09<00:14, 348.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▏                    | 3101/8000 [00:09<00:15, 317.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▍                    | 3153/8000 [00:09<00:13, 347.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▋                    | 3209/8000 [00:09<00:13, 351.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▉                    | 3265/8000 [00:09<00:13, 356.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████                    | 3302/8000 [00:10<00:14, 323.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▎                   | 3362/8000 [00:10<00:12, 375.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▍                   | 3409/8000 [00:10<00:13, 347.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▋                   | 3461/8000 [00:10<00:12, 351.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▉                   | 3512/8000 [00:10<00:13, 343.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▏                  | 3564/8000 [00:10<00:12, 357.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▎                  | 3614/8000 [00:10<00:12, 345.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▌                  | 3652/8000 [00:11<00:14, 302.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▋                  | 3705/8000 [00:11<00:12, 337.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████                  | 3767/8000 [00:11<00:11, 356.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▏                 | 3823/8000 [00:11<00:11, 357.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▍                 | 3872/8000 [00:11<00:11, 345.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▌                 | 3911/8000 [00:11<00:12, 317.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████████▊                 | 3966/8000 [00:12<00:12, 326.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████                 | 4019/8000 [00:12<00:12, 325.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▎                | 4078/8000 [00:12<00:11, 342.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▌                | 4126/8000 [00:12<00:11, 327.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▋                | 4176/8000 [00:12<00:11, 323.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████████▉                | 4230/8000 [00:12<00:11, 339.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▏               | 4280/8000 [00:12<00:11, 332.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▍               | 4336/8000 [00:13<00:10, 361.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▌               | 4381/8000 [00:13<00:10, 338.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▊               | 4439/8000 [00:13<00:10, 352.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████               | 4486/8000 [00:13<00:10, 343.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▎              | 4532/8000 [00:13<00:09, 358.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▍              | 4584/8000 [00:13<00:09, 348.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▋              | 4637/8000 [00:13<00:09, 355.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████████▉              | 4691/8000 [00:14<00:09, 354.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▏             | 4737/8000 [00:14<00:08, 363.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▎             | 4784/8000 [00:14<00:09, 344.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▌             | 4828/8000 [00:14<00:09, 328.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▋             | 4874/8000 [00:14<00:10, 312.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▉             | 4918/8000 [00:14<00:09, 323.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████             | 4961/8000 [00:14<00:10, 300.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▏            | 4998/8000 [00:15<00:10, 286.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▍            | 5041/8000 [00:15<00:10, 290.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▌            | 5088/8000 [00:15<00:09, 320.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▊            | 5147/8000 [00:15<00:08, 340.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████            | 5201/8000 [00:15<00:08, 340.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▎           | 5258/8000 [00:15<00:07, 353.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▌           | 5317/8000 [00:15<00:06, 385.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▊           | 5362/8000 [00:16<00:07, 354.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████           | 5420/8000 [00:16<00:07, 360.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▏          | 5469/8000 [00:16<00:07, 355.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▍          | 5516/8000 [00:16<00:06, 363.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▋          | 5563/8000 [00:16<00:07, 334.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▊          | 5611/8000 [00:16<00:07, 317.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████          | 5671/8000 [00:17<00:06, 333.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▎         | 5718/8000 [00:17<00:07, 318.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▍         | 5762/8000 [00:17<00:07, 305.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▋         | 5807/8000 [00:17<00:07, 301.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▉         | 5855/8000 [00:17<00:07, 304.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████         | 5900/8000 [00:17<00:07, 294.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5950/8000 [00:17<00:06, 300.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6000/8000 [00:18<00:06, 314.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6043/8000 [00:18<00:06, 323.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6082/8000 [00:18<00:06, 297.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████        | 6124/8000 [00:18<00:06, 294.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6174/8000 [00:18<00:05, 308.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6223/8000 [00:18<00:05, 342.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▋       | 6270/8000 [00:19<00:05, 315.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6320/8000 [00:19<00:05, 317.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████       | 6365/8000 [00:19<00:05, 318.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6426/8000 [00:19<00:04, 369.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████████▌      | 6482/8000 [00:19<00:04, 365.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████████▊      | 6538/8000 [00:19<00:03, 371.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████████▉      | 6585/8000 [00:19<00:04, 349.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6627/8000 [00:19<00:03, 357.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▍     | 6679/8000 [00:20<00:03, 341.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████████▋     | 6738/8000 [00:20<00:03, 364.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6790/8000 [00:20<00:03, 350.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████     | 6834/8000 [00:20<00:03, 351.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6886/8000 [00:20<00:03, 345.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▍    | 6934/8000 [00:20<00:03, 345.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6977/8000 [00:21<00:03, 320.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7030/8000 [00:21<00:02, 328.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████    | 7071/8000 [00:21<00:02, 312.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7111/8000 [00:21<00:03, 268.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▍   | 7160/8000 [00:21<00:02, 286.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7234/8000 [00:22<00:05, 135.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7285/8000 [00:22<00:04, 165.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7333/8000 [00:22<00:03, 189.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7374/8000 [00:23<00:03, 207.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7427/8000 [00:23<00:02, 234.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▊  | 7471/8000 [00:23<00:02, 260.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7513/8000 [00:23<00:01, 263.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7583/8000 [00:23<00:01, 323.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▍ | 7620/8000 [00:23<00:01, 302.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7677/8000 [00:23<00:00, 333.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████████▊ | 7729/8000 [00:24<00:00, 334.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7791/8000 [00:24<00:00, 362.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7847/8000 [00:24<00:00, 361.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7896/8000 [00:24<00:00, 368.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7937/8000 [00:24<00:00, 332.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████████████▉| 7980/8000 [00:24<00:00, 325.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8041it [00:24, 321.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:27,  5.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:59,  3.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:08<07:35,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:09<06:25,  1.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:10<05:41,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:12<05:12,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:13<04:54,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:14<04:36,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:16<04:26,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:17<04:19,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:18<04:12,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:20<04:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:21<04:07,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:22<04:06,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:24<04:03,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:25<04:10,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:26<04:04,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:28<04:05,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:29<04:14,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:31<04:27,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:32<04:09,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:33<03:58,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:34<03:49,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:36<03:43,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:37<03:39,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:38<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:39<03:35,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:41<03:31,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:42<03:28,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:43<03:25,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:44<03:26,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:45<03:24,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:47<03:20,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:48<03:18,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:49<03:20,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:50<03:19,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:52<03:20,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:53<03:21,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:54<03:19,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:55<03:17,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:56<03:14,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:58<03:11,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:59<03:09,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:00<03:07,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:01<03:08,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [01:02<03:07,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:04<03:05,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:05<03:05,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:06<03:01,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [01:07<03:00,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:08<02:57,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:11<03:23,  1.37s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 23.12it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 33it [00:00, 45.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 66it [00:01, 93.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 101it [00:01, 142.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 136it [00:01, 186.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 172it [00:01, 225.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 208it [00:01, 258.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 244it [00:01, 283.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 281it [00:01, 304.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 316it [00:01, 314.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 351it [00:01, 320.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 388it [00:01, 332.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 424it [00:02, 338.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 460it [00:02, 342.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 496it [00:02, 345.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 532it [00:02, 341.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 570it [00:02, 350.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 606it [00:02, 351.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 643it [00:02, 354.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 679it [00:02, 349.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 715it [00:02, 350.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 751it [00:03, 344.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 251.54it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 43.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:01, 45.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 44.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 43.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  28%|███▎        | 28/100 [00:00<00:01, 54.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|████        | 34/100 [00:00<00:01, 50.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|████▊       | 40/100 [00:00<00:01, 48.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|█████▍      | 45/100 [00:00<00:01, 46.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|██████      | 50/100 [00:01<00:01, 45.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  58%|██████▉     | 58/100 [00:01<00:00, 53.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  64%|███████▋    | 64/100 [00:01<00:00, 49.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  70%|████████▍   | 70/100 [00:01<00:00, 48.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  75%|█████████   | 75/100 [00:01<00:00, 46.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  80%|█████████▌  | 80/100 [00:01<00:00, 47.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  86%|██████████▎ | 86/100 [00:01<00:00, 48.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  91%|██████████▉ | 91/100 [00:01<00:00, 46.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 47.48it/s]\n",
+      "\n",
+      "Nyquist Samples:  50%|█████████████████████▌                     | 2/4 [14:48<15:26, 463.31s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.083662378278396\n",
+      "2999: -6.1720680\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:14,  5.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:39,  2.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:07<07:19,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:09<05:59,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:10<05:27,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:12<05:09,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:13<04:37,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:14<05:02,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:16<04:36,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:17<04:12,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:18<03:57,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:19<03:47,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:20<03:45,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:21<03:41,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:22<03:37,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:23<03:31,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:25<03:26,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:26<03:24,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:27<03:20,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:28<03:19,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:29<03:20,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:30<03:18,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:31<03:16,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:32<03:19,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:33<03:10,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:34<02:48,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:35<02:50,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:36<02:57,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:37<03:02,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:39<03:04,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:40<03:05,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:41<03:03,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:42<03:05,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:43<03:03,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:44<03:03,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:45<03:04,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:46<03:01,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:47<03:00,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:49<02:59,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:50<02:56,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:51<02:53,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:52<02:53,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:53<02:55,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:54<02:52,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:55<02:52,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:56<02:51,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:57<02:52,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:59<02:50,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:00<02:48,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [01:01<02:48,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:02<02:47,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:03<02:45,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:04<02:43,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:05<02:51,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:07<02:48,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:08<02:44,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:09<02:42,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:11<02:55,  1.23s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 23.66it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:00, 54.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 108.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 116it [00:01, 167.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 153it [00:01, 210.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 190it [00:01, 246.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 227it [00:01, 276.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 300.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 301it [00:01, 316.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 338it [00:01, 329.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 379it [00:01, 351.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 417it [00:01, 354.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 454it [00:02, 353.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 491it [00:02, 357.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 528it [00:02, 358.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 565it [00:02, 359.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 602it [00:02, 354.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 640it [00:02, 361.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 677it [00:02, 360.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 714it [00:02, 360.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 751it [00:02, 363.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 264.44it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   7%|▉            | 7/100 [00:00<00:01, 66.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  14%|█▋          | 14/100 [00:00<00:01, 50.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 47.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|███         | 25/100 [00:00<00:01, 45.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|███▌        | 30/100 [00:00<00:01, 45.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  37%|████▍       | 37/100 [00:00<00:01, 52.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█████▏      | 43/100 [00:00<00:01, 48.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  48%|█████▊      | 48/100 [00:00<00:01, 47.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  53%|██████▎     | 53/100 [00:01<00:01, 46.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  58%|██████▉     | 58/100 [00:01<00:00, 45.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  64%|███████▋    | 64/100 [00:01<00:00, 46.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  69%|████████▎   | 69/100 [00:01<00:00, 45.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  74%|████████▉   | 74/100 [00:01<00:00, 45.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  79%|█████████▍  | 79/100 [00:01<00:00, 44.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  84%|██████████  | 84/100 [00:01<00:00, 44.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  91%|██████████▉ | 91/100 [00:01<00:00, 49.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 47.17it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 16/8000 [00:00<01:20, 98.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 31/8000 [00:00<01:29, 89.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 47/8000 [00:00<01:22, 96.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                   | 66/8000 [00:00<01:16, 103.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                   | 81/8000 [00:00<01:18, 100.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                  | 100/8000 [00:00<01:14, 106.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                  | 123/8000 [00:01<01:05, 120.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                  | 146/8000 [00:01<01:01, 127.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                  | 168/8000 [00:01<00:58, 134.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                  | 191/8000 [00:01<00:56, 137.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                  | 206/8000 [00:01<00:58, 134.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                  | 235/8000 [00:01<00:52, 147.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                  | 252/8000 [00:02<00:58, 133.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                 | 267/8000 [00:02<00:59, 130.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                                 | 283/8000 [00:02<01:01, 125.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                 | 304/8000 [00:02<01:00, 128.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                 | 326/8000 [00:02<00:59, 129.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                 | 342/8000 [00:02<01:03, 120.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                                 | 366/8000 [00:02<01:01, 124.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                 | 383/8000 [00:03<01:05, 117.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                 | 405/8000 [00:03<01:01, 123.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                 | 426/8000 [00:03<00:57, 130.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                                 | 442/8000 [00:03<01:03, 119.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                 | 461/8000 [00:03<01:03, 117.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                 | 483/8000 [00:03<00:58, 129.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                | 499/8000 [00:04<01:02, 119.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                | 514/8000 [00:04<01:06, 112.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                | 532/8000 [00:04<01:06, 112.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                | 559/8000 [00:04<00:57, 128.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                | 579/8000 [00:04<01:00, 123.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                | 595/8000 [00:04<01:02, 119.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                | 608/8000 [00:05<01:08, 108.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                | 628/8000 [00:05<01:00, 122.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                | 641/8000 [00:05<01:07, 109.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                | 661/8000 [00:05<01:02, 117.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                | 673/8000 [00:05<01:09, 105.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                | 693/8000 [00:05<01:00, 121.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                | 709/8000 [00:05<01:04, 112.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                               | 729/8000 [00:06<01:02, 116.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                               | 748/8000 [00:06<00:57, 125.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                               | 775/8000 [00:06<00:50, 143.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                               | 794/8000 [00:06<00:53, 134.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                               | 815/8000 [00:06<00:54, 131.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                               | 833/8000 [00:06<00:56, 127.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▋                               | 849/8000 [00:06<01:00, 118.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                               | 864/8000 [00:07<01:04, 110.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                               | 878/8000 [00:07<01:09, 102.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                               | 893/8000 [00:07<01:10, 100.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████                               | 926/8000 [00:07<01:07, 104.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                              | 943/8000 [00:07<01:05, 107.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                              | 955/8000 [00:08<01:07, 104.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                              | 979/8000 [00:08<00:59, 117.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                              | 994/8000 [00:08<01:03, 109.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▎                             | 1009/8000 [00:08<01:02, 111.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                             | 1039/8000 [00:08<01:05, 106.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                             | 1057/8000 [00:08<01:04, 107.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                             | 1076/8000 [00:09<01:03, 109.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                             | 1096/8000 [00:09<01:00, 114.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                             | 1120/8000 [00:09<00:55, 124.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                             | 1134/8000 [00:09<01:01, 111.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                             | 1149/8000 [00:09<01:03, 107.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▉                             | 1166/8000 [00:09<01:05, 105.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                             | 1184/8000 [00:10<01:05, 104.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                             | 1198/8000 [00:10<01:07, 100.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                            | 1221/8000 [00:10<01:00, 111.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▎                            | 1245/8000 [00:10<00:56, 120.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                            | 1265/8000 [00:10<00:56, 120.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                            | 1287/8000 [00:10<00:51, 131.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                            | 1305/8000 [00:11<00:54, 123.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                            | 1320/8000 [00:11<00:58, 114.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▋                            | 1343/8000 [00:11<00:54, 121.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                            | 1358/8000 [00:11<00:59, 112.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                            | 1379/8000 [00:11<00:57, 116.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                            | 1396/8000 [00:11<00:59, 110.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████                            | 1425/8000 [00:12<01:03, 104.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                           | 1444/8000 [00:12<01:00, 108.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                           | 1465/8000 [00:12<00:58, 112.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▎                           | 1492/8000 [00:12<00:52, 124.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                           | 1507/8000 [00:12<00:58, 111.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                           | 1527/8000 [00:13<00:56, 115.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                           | 1551/8000 [00:13<00:50, 128.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▋                           | 1566/8000 [00:13<00:55, 115.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▋                           | 1583/8000 [00:13<00:57, 112.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                           | 1602/8000 [00:13<00:52, 121.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                           | 1617/8000 [00:13<00:58, 109.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                           | 1634/8000 [00:14<00:59, 106.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████                           | 1650/8000 [00:14<01:01, 102.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1664/8000 [00:14<01:05, 97.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1681/8000 [00:14<01:04, 98.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                          | 1700/8000 [00:14<01:01, 103.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▎                          | 1720/8000 [00:14<00:54, 115.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▍                          | 1736/8000 [00:14<00:57, 109.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▍                          | 1755/8000 [00:15<00:56, 109.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                          | 1771/8000 [00:15<00:59, 105.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                          | 1788/8000 [00:15<00:58, 105.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▋                          | 1804/8000 [00:15<00:59, 103.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▊                          | 1825/8000 [00:15<00:55, 111.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▊                          | 1846/8000 [00:15<00:52, 117.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                          | 1866/8000 [00:16<00:47, 128.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████                          | 1884/8000 [00:16<00:49, 122.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████                          | 1898/8000 [00:16<00:54, 111.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▏                         | 1925/8000 [00:16<00:44, 137.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                         | 1944/8000 [00:16<00:44, 134.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▎                         | 1963/8000 [00:16<00:47, 127.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▍                         | 1981/8000 [00:17<00:49, 120.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▍                         | 1999/8000 [00:17<00:52, 115.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                         | 2012/8000 [00:17<00:58, 103.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                         | 2031/8000 [00:17<00:54, 108.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▋                         | 2044/8000 [00:17<00:59, 100.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▊                         | 2061/8000 [00:17<00:53, 110.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▊                         | 2080/8000 [00:17<00:52, 112.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                         | 2095/8000 [00:18<00:54, 108.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                         | 2116/8000 [00:18<00:49, 119.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████                         | 2131/8000 [00:18<00:49, 118.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▏                        | 2154/8000 [00:18<00:46, 126.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▏                        | 2176/8000 [00:18<00:45, 126.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                        | 2190/8000 [00:18<00:51, 113.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▎                        | 2205/8000 [00:19<00:54, 105.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2217/8000 [00:19<01:00, 95.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2234/8000 [00:19<00:59, 97.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▌                        | 2249/8000 [00:19<00:56, 102.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2263/8000 [00:19<00:59, 95.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▋                        | 2282/8000 [00:19<00:54, 104.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▊                        | 2305/8000 [00:19<00:47, 120.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▉                        | 2326/8000 [00:20<00:41, 135.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                        | 2358/8000 [00:20<00:47, 119.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████                        | 2378/8000 [00:20<00:45, 122.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▏                       | 2397/8000 [00:20<00:45, 122.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                       | 2417/8000 [00:20<00:47, 118.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                       | 2433/8000 [00:21<00:49, 111.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▍                       | 2453/8000 [00:21<00:48, 115.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▍                       | 2469/8000 [00:21<00:51, 107.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▌                       | 2492/8000 [00:21<00:46, 119.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                       | 2509/8000 [00:21<00:46, 116.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▊                       | 2536/8000 [00:21<00:38, 140.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▊                       | 2553/8000 [00:22<00:42, 129.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▉                       | 2570/8000 [00:22<00:44, 122.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|██████████▉                       | 2587/8000 [00:22<00:43, 125.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████                       | 2606/8000 [00:22<00:41, 130.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▏                      | 2623/8000 [00:22<00:43, 122.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▏                      | 2639/8000 [00:22<00:46, 114.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▎                      | 2656/8000 [00:22<00:43, 121.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▎                      | 2671/8000 [00:22<00:43, 123.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▍                      | 2684/8000 [00:23<00:50, 106.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▌                      | 2711/8000 [00:23<00:41, 126.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▌                      | 2725/8000 [00:23<00:43, 121.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                      | 2739/8000 [00:23<00:46, 112.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▊                      | 2765/8000 [00:23<00:40, 128.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▊                      | 2788/8000 [00:23<00:40, 129.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|███████████▉                      | 2814/8000 [00:24<00:37, 138.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                      | 2834/8000 [00:24<00:39, 131.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████                      | 2851/8000 [00:24<00:41, 123.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▏                     | 2877/8000 [00:24<00:38, 134.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▎                     | 2899/8000 [00:24<00:34, 145.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▍                     | 2935/8000 [00:25<00:39, 129.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▌                     | 2949/8000 [00:25<00:40, 123.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▌                     | 2967/8000 [00:25<00:42, 118.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▋                     | 2981/8000 [00:25<00:47, 105.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2994/8000 [00:25<00:51, 97.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3007/8000 [00:25<00:54, 90.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3019/8000 [00:26<00:57, 86.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3038/8000 [00:26<00:53, 92.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3053/8000 [00:26<00:54, 91.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████                     | 3074/8000 [00:26<00:48, 101.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▏                    | 3096/8000 [00:26<00:44, 111.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▏                    | 3117/8000 [00:26<00:41, 117.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▎                    | 3136/8000 [00:27<00:40, 120.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▍                    | 3164/8000 [00:27<00:46, 103.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▌                    | 3182/8000 [00:27<00:44, 109.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▌                    | 3197/8000 [00:27<00:45, 104.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▋                    | 3214/8000 [00:27<00:46, 103.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▋                    | 3231/8000 [00:27<00:46, 103.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3245/8000 [00:28<00:49, 95.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▉                    | 3270/8000 [00:28<00:43, 108.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|█████████████▉                    | 3291/8000 [00:28<00:40, 114.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████                    | 3310/8000 [00:28<00:38, 122.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▏                   | 3325/8000 [00:28<00:38, 122.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▏                   | 3347/8000 [00:28<00:36, 127.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▎                   | 3367/8000 [00:29<00:35, 128.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▍                   | 3386/8000 [00:29<00:35, 130.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▍                   | 3403/8000 [00:29<00:34, 132.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▌                   | 3417/8000 [00:29<00:38, 118.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▋                   | 3443/8000 [00:29<00:33, 134.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▋                   | 3462/8000 [00:29<00:33, 134.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▊                   | 3485/8000 [00:29<00:30, 147.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▉                   | 3507/8000 [00:30<00:31, 144.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|██████████████▉                   | 3526/8000 [00:30<00:33, 135.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████                   | 3548/8000 [00:30<00:33, 132.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▏                  | 3563/8000 [00:30<00:37, 118.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▏                  | 3588/8000 [00:30<00:34, 126.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▎                  | 3612/8000 [00:31<00:41, 105.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▍                  | 3636/8000 [00:31<00:37, 115.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▌                  | 3655/8000 [00:31<00:37, 115.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▌                  | 3674/8000 [00:31<00:34, 123.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▋                  | 3698/8000 [00:31<00:30, 141.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▊                  | 3714/8000 [00:31<00:33, 126.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|███████████████▊                  | 3734/8000 [00:31<00:34, 122.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|███████████████▉                  | 3755/8000 [00:32<00:33, 125.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████                  | 3769/8000 [00:32<00:37, 113.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████                  | 3793/8000 [00:32<00:34, 122.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▏                 | 3806/8000 [00:32<00:38, 110.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▎                 | 3827/8000 [00:32<00:35, 117.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▎                 | 3844/8000 [00:32<00:36, 113.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▍                 | 3864/8000 [00:33<00:35, 116.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▍                 | 3882/8000 [00:33<00:33, 121.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▌                 | 3895/8000 [00:33<00:34, 118.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▋                 | 3912/8000 [00:33<00:35, 114.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▋                 | 3936/8000 [00:33<00:32, 125.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▊                 | 3956/8000 [00:33<00:30, 132.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████████▉                 | 3976/8000 [00:33<00:29, 135.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████████▉                 | 3999/8000 [00:34<00:28, 138.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████                 | 4022/8000 [00:34<00:28, 138.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▏                | 4052/8000 [00:34<00:34, 116.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▎                | 4066/8000 [00:34<00:36, 108.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4079/8000 [00:34<00:39, 99.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4091/8000 [00:35<00:40, 96.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▍                | 4115/8000 [00:35<00:34, 111.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▌                | 4134/8000 [00:35<00:33, 116.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▋                | 4153/8000 [00:35<00:32, 118.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▋                | 4169/8000 [00:35<00:31, 122.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████████▊                | 4185/8000 [00:35<00:33, 113.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████████▉                | 4207/8000 [00:35<00:31, 120.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████████▉                | 4231/8000 [00:36<00:28, 130.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████                | 4253/8000 [00:36<00:28, 132.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▏               | 4268/8000 [00:36<00:30, 121.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▏               | 4285/8000 [00:36<00:32, 116.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▎               | 4304/8000 [00:36<00:33, 110.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▍               | 4325/8000 [00:36<00:31, 116.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▌               | 4353/8000 [00:37<00:26, 135.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▌               | 4368/8000 [00:37<00:28, 128.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▋               | 4389/8000 [00:37<00:25, 139.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▊               | 4413/8000 [00:37<00:25, 141.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████████▊               | 4431/8000 [00:37<00:26, 133.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████████▉               | 4460/8000 [00:38<00:31, 112.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████               | 4476/8000 [00:38<00:32, 107.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████               | 4489/8000 [00:38<00:34, 100.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▏              | 4511/8000 [00:38<00:31, 110.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▎              | 4534/8000 [00:38<00:30, 115.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▎              | 4556/8000 [00:38<00:28, 119.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▍              | 4577/8000 [00:39<00:28, 120.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▌              | 4601/8000 [00:39<00:26, 127.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▌              | 4614/8000 [00:39<00:30, 112.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▋              | 4635/8000 [00:39<00:28, 116.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▊              | 4649/8000 [00:39<00:29, 112.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████████▊              | 4669/8000 [00:39<00:28, 116.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████████▉              | 4695/8000 [00:40<00:31, 103.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████              | 4720/8000 [00:40<00:26, 122.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▏             | 4749/8000 [00:40<00:23, 139.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▎             | 4770/8000 [00:40<00:23, 136.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▎             | 4791/8000 [00:40<00:22, 142.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▍             | 4822/8000 [00:41<00:26, 118.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▌             | 4839/8000 [00:41<00:27, 114.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▋             | 4854/8000 [00:41<00:29, 107.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▊             | 4885/8000 [00:41<00:29, 105.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████████▊             | 4906/8000 [00:41<00:28, 110.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████████▉             | 4926/8000 [00:41<00:25, 122.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████             | 4944/8000 [00:42<00:26, 117.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████             | 4967/8000 [00:42<00:24, 125.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▏            | 4992/8000 [00:42<00:21, 141.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▎            | 5016/8000 [00:42<00:19, 152.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▍            | 5033/8000 [00:42<00:21, 137.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▍            | 5056/8000 [00:42<00:21, 139.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▌            | 5074/8000 [00:43<00:21, 135.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▋            | 5092/8000 [00:43<00:22, 128.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▋            | 5116/8000 [00:43<00:21, 133.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▊            | 5132/8000 [00:43<00:23, 123.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████████▉            | 5151/8000 [00:43<00:23, 121.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████████▉            | 5166/8000 [00:43<00:25, 111.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████            | 5191/8000 [00:44<00:22, 122.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▏           | 5206/8000 [00:44<00:23, 117.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▏           | 5233/8000 [00:44<00:19, 142.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▎           | 5250/8000 [00:44<00:20, 131.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▍           | 5273/8000 [00:44<00:20, 134.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▌           | 5295/8000 [00:44<00:20, 130.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▌           | 5323/8000 [00:45<00:24, 110.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5335/8000 [00:45<00:26, 98.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▊           | 5353/8000 [00:45<00:25, 104.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████████▊           | 5374/8000 [00:45<00:23, 111.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████████▉           | 5411/8000 [00:45<00:22, 117.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████           | 5431/8000 [00:46<00:20, 125.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▏          | 5454/8000 [00:46<00:19, 130.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▎          | 5472/8000 [00:46<00:20, 125.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▎          | 5486/8000 [00:46<00:22, 111.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▍          | 5506/8000 [00:46<00:22, 112.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▍          | 5527/8000 [00:46<00:21, 115.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▌          | 5554/8000 [00:47<00:19, 128.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▋          | 5571/8000 [00:47<00:19, 127.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▊          | 5596/8000 [00:47<00:17, 134.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▊          | 5614/8000 [00:47<00:18, 126.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████████▉          | 5630/8000 [00:47<00:20, 115.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████          | 5653/8000 [00:47<00:18, 124.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████          | 5675/8000 [00:47<00:18, 127.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▏         | 5703/8000 [00:48<00:16, 140.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▎         | 5719/8000 [00:48<00:17, 130.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▍         | 5743/8000 [00:48<00:17, 132.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▌         | 5770/8000 [00:48<00:15, 141.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████████▌         | 5791/8000 [00:48<00:16, 134.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▋         | 5806/8000 [00:48<00:17, 122.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▊         | 5827/8000 [00:49<00:17, 125.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▊         | 5851/8000 [00:49<00:16, 132.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████████▉         | 5869/8000 [00:49<00:16, 128.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████         | 5889/8000 [00:49<00:16, 128.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████         | 5902/8000 [00:49<00:18, 115.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▏        | 5922/8000 [00:49<00:17, 119.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▎        | 5943/8000 [00:50<00:15, 130.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▎        | 5962/8000 [00:50<00:16, 124.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▍        | 5979/8000 [00:50<00:17, 116.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6002/8000 [00:50<00:15, 124.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████████▌        | 6028/8000 [00:50<00:15, 129.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▋        | 6048/8000 [00:50<00:15, 125.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6069/8000 [00:51<00:15, 128.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▊        | 6087/8000 [00:51<00:14, 131.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████████▉        | 6108/8000 [00:51<00:14, 131.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████        | 6125/8000 [00:51<00:15, 120.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████        | 6144/8000 [00:51<00:15, 119.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▏       | 6167/8000 [00:51<00:14, 124.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▎       | 6184/8000 [00:52<00:15, 119.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▎       | 6201/8000 [00:52<00:15, 114.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▍       | 6230/8000 [00:52<00:12, 141.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▌       | 6252/8000 [00:52<00:12, 139.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████████▋       | 6269/8000 [00:52<00:13, 131.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████████▊       | 6306/8000 [00:52<00:12, 133.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████████▉       | 6327/8000 [00:53<00:12, 132.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████       | 6354/8000 [00:53<00:11, 142.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▏      | 6384/8000 [00:53<00:10, 152.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6415/8000 [00:53<00:13, 113.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▎      | 6434/8000 [00:54<00:15, 103.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6446/8000 [00:54<00:17, 88.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6467/8000 [00:54<00:19, 79.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6489/8000 [00:54<00:17, 87.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6500/8000 [00:55<00:19, 77.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6522/8000 [00:55<00:16, 88.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6538/8000 [00:55<00:16, 90.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6557/8000 [00:55<00:14, 98.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6573/8000 [00:55<00:14, 97.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████      | 6591/8000 [00:55<00:13, 102.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████      | 6611/8000 [00:56<00:12, 108.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6627/8000 [00:56<00:13, 104.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▏     | 6646/8000 [00:56<00:11, 117.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▎     | 6660/8000 [00:56<00:12, 106.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▍     | 6678/8000 [00:56<00:12, 107.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6691/8000 [00:56<00:13, 98.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████████▌     | 6714/8000 [00:56<00:11, 110.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6737/8000 [00:57<00:13, 92.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▋     | 6763/8000 [00:57<00:11, 111.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▊     | 6781/8000 [00:57<00:11, 108.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6798/8000 [00:57<00:11, 105.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████████▉     | 6815/8000 [00:57<00:11, 106.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████     | 6836/8000 [00:58<00:10, 112.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████     | 6849/8000 [00:58<00:11, 102.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▏    | 6872/8000 [00:58<00:09, 113.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▎    | 6893/8000 [00:58<00:08, 123.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▍    | 6918/8000 [00:58<00:10, 102.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▍    | 6941/8000 [00:59<00:09, 116.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▌    | 6961/8000 [00:59<00:08, 125.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6978/8000 [00:59<00:08, 120.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████████▋    | 6992/8000 [00:59<00:09, 110.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7012/8000 [00:59<00:08, 122.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▊    | 7029/8000 [00:59<00:07, 128.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████████▉    | 7049/8000 [00:59<00:07, 127.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████    | 7073/8000 [01:00<00:06, 137.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7093/8000 [01:00<00:06, 137.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▏   | 7110/8000 [01:00<00:06, 141.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▎   | 7125/8000 [01:00<00:07, 121.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▍   | 7152/8000 [01:00<00:06, 135.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7178/8000 [01:00<00:05, 141.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▌   | 7194/8000 [01:00<00:06, 124.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7208/8000 [01:01<00:07, 110.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████████▋   | 7231/8000 [01:01<00:06, 117.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████████▊   | 7261/8000 [01:01<00:06, 112.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████████▉   | 7285/8000 [01:01<00:05, 121.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████   | 7305/8000 [01:01<00:05, 129.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████   | 7322/8000 [01:02<00:05, 132.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▏  | 7342/8000 [01:02<00:05, 130.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7366/8000 [01:02<00:04, 133.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▎  | 7381/8000 [01:02<00:05, 121.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████████████▍  | 7397/8000 [01:02<00:05, 115.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7414/8000 [01:02<00:05, 111.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▌  | 7430/8000 [01:02<00:05, 107.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7446/8000 [01:03<00:05, 106.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████████▋  | 7464/8000 [01:03<00:04, 108.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████████▊  | 7492/8000 [01:03<00:04, 126.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████████▉  | 7512/8000 [01:03<00:03, 124.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████  | 7530/8000 [01:03<00:03, 118.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████  | 7556/8000 [01:03<00:03, 132.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▏ | 7578/8000 [01:04<00:03, 131.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▎ | 7596/8000 [01:04<00:03, 129.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████████████▍ | 7620/8000 [01:04<00:02, 134.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▍ | 7641/8000 [01:04<00:02, 129.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▌ | 7667/8000 [01:04<00:02, 139.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▋ | 7691/8000 [01:04<00:02, 141.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████████████▊ | 7710/8000 [01:05<00:02, 135.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████████▊ | 7725/8000 [01:05<00:02, 118.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████████▉ | 7741/8000 [01:05<00:02, 116.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████████▉ | 7762/8000 [01:05<00:01, 119.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████ | 7780/8000 [01:05<00:01, 116.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▏| 7805/8000 [01:05<00:01, 126.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▏| 7823/8000 [01:06<00:01, 119.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▎| 7843/8000 [01:06<00:01, 120.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████████████▍| 7859/8000 [01:06<00:01, 114.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7884/8000 [01:06<00:00, 129.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▌| 7906/8000 [01:06<00:00, 132.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7921/8000 [01:06<00:00, 124.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▋| 7936/8000 [01:07<00:00, 117.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████████████▊| 7958/8000 [01:07<00:00, 131.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████████████▉| 7986/8000 [01:07<00:00, 109.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8012it [01:07, 118.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:05<17:01,  5.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:14,  2.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:08<05:32,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:09<04:50,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:10<04:24,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:11<04:07,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:13<03:56,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:14<03:46,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:15<03:54,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:16<03:46,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:17<03:42,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:18<03:35,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:19<03:31,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:21<03:26,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:22<03:25,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:23<03:25,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:24<03:23,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:25<03:22,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:26<03:19,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:27<03:17,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:28<03:16,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:29<03:16,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:31<03:17,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:32<03:16,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:33<03:16,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:34<03:14,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:35<03:11,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:36<03:09,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:37<03:09,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:38<03:08,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:40<03:08,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:41<03:07,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:42<03:19,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:43<03:17,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:44<03:12,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [00:45<03:09,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [00:47<03:06,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [00:48<03:03,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [00:49<03:03,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [00:50<03:00,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [00:51<02:56,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [00:52<02:54,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [00:53<02:53,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [00:55<03:01,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [00:56<02:59,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [00:57<03:00,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [00:58<02:56,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [00:59<03:07,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [01:01<03:00,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:02<02:55,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:03<02:51,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:04<02:59,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:05<02:55,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:07<02:56,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:08<02:57,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:09<02:55,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:12<02:56,  1.24s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.11it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 43it [00:00, 59.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 78it [00:01, 107.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 115it [00:01, 156.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 151it [00:01, 198.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 184it [00:01, 203.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 220it [00:01, 238.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 261it [00:01, 280.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 298it [00:01, 301.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 335it [00:01, 317.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 372it [00:01, 329.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 409it [00:02, 340.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 446it [00:02, 346.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 483it [00:02, 350.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 521it [00:02, 358.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 558it [00:02, 359.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 595it [00:02, 359.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 632it [00:02, 362.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 669it [00:02, 361.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 706it [00:02, 354.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 743it [00:02, 356.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 255.55it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:01, 47.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 44.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 43.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  22%|██▋         | 22/100 [00:00<00:01, 52.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  28%|███▎        | 28/100 [00:00<00:01, 48.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|███▉        | 33/100 [00:00<00:01, 47.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|████▌       | 38/100 [00:00<00:01, 46.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█████▏      | 43/100 [00:00<00:01, 45.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  51%|██████      | 51/100 [00:01<00:00, 53.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  57%|██████▊     | 57/100 [00:01<00:00, 49.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  63%|███████▌    | 63/100 [00:01<00:00, 48.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  68%|████████▏   | 68/100 [00:01<00:00, 46.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  73%|████████▊   | 73/100 [00:01<00:00, 45.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  81%|█████████▋  | 81/100 [00:01<00:00, 51.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  87%|██████████▍ | 87/100 [00:01<00:00, 48.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  92%|███████████ | 92/100 [00:01<00:00, 47.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 47.38it/s]\n",
+      "\n",
+      "Nyquist Samples:  75%|████████████████████████████████▎          | 3/4 [25:58<09:17, 557.72s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.704391222870281\n",
+      "2999: -5.8111500\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:05<16:42,  5.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:06<09:59,  3.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:08<07:35,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:09<06:44,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:11<06:09,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:12<05:42,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:14<05:39,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:16<05:27,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:17<05:14,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:19<05:14,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:20<05:06,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:22<05:10,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:24<05:14,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:26<05:18,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:27<05:19,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:29<05:19,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:31<05:19,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:33<05:20,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:35<05:19,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:36<05:16,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:38<05:22,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:40<05:09,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:41<04:54,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:43<04:47,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:44<04:43,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:46<04:40,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:48<04:36,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:49<04:31,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:51<04:25,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:52<04:25,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:54<04:24,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:55<04:21,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:57<04:20,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:58<04:19,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [01:00<04:21,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [01:02<04:20,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [01:03<04:16,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [01:05<04:12,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [01:06<04:09,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [01:08<04:06,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [01:09<04:09,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [01:11<04:08,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [01:13<04:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:14<04:01,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▊                                     | 45/200 [01:16<03:58,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|███████████                                     | 46/200 [01:17<03:56,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▎                                    | 47/200 [01:19<03:56,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▌                                    | 48/200 [01:20<03:55,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████▊                                    | 49/200 [01:22<03:57,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████                                    | 50/200 [01:23<03:54,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▏                                   | 51/200 [01:25<03:53,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▍                                   | 52/200 [01:27<03:52,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████▋                                   | 53/200 [01:28<03:50,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|████████████▉                                   | 54/200 [01:30<03:51,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▏                                  | 55/200 [01:31<03:49,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▍                                  | 56/200 [01:33<03:46,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████▋                                  | 57/200 [01:34<03:43,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|█████████████▉                                  | 58/200 [01:38<03:59,  1.69s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.23it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 37it [00:00, 51.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 73it [00:01, 102.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 107it [00:01, 148.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 144it [00:01, 195.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 180it [00:01, 233.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 223it [00:01, 282.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 260it [00:01, 303.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 297it [00:01, 317.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 334it [00:01, 330.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 372it [00:01, 341.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 409it [00:01, 347.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 446it [00:02, 351.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 490it [00:02, 376.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 529it [00:02, 374.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 568it [00:02, 370.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 606it [00:02, 370.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 644it [00:02, 367.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 681it [00:02, 366.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 718it [00:02, 358.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 759it [00:02, 373.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 262.96it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▋            | 5/100 [00:00<00:02, 44.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|█▏          | 10/100 [00:00<00:02, 43.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|█▊          | 15/100 [00:00<00:01, 44.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|██▍         | 20/100 [00:00<00:01, 43.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|███         | 25/100 [00:00<00:01, 43.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|███▌        | 30/100 [00:00<00:01, 44.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  35%|████▏       | 35/100 [00:00<00:01, 43.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|████▊       | 40/100 [00:00<00:01, 43.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  47%|█████▋      | 47/100 [00:01<00:01, 49.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  52%|██████▏     | 52/100 [00:01<00:01, 47.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  57%|██████▊     | 57/100 [00:01<00:00, 46.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  62%|███████▍    | 62/100 [00:01<00:00, 45.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  67%|████████    | 67/100 [00:01<00:00, 44.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  73%|████████▊   | 73/100 [00:01<00:00, 47.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  78%|█████████▎  | 78/100 [00:01<00:00, 46.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|█████████▉  | 83/100 [00:01<00:00, 45.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  88%|██████████▌ | 88/100 [00:01<00:00, 44.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  93%|███████████▏| 93/100 [00:02<00:00, 44.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 45.24it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                              | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                      | 3/8000 [00:00<07:37, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                      | 5/8000 [00:00<14:22,  9.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 10/8000 [00:00<07:55, 16.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 14/8000 [00:00<06:58, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 17/8000 [00:01<08:53, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 19/8000 [00:01<08:36, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 21/8000 [00:01<11:36, 11.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                     | 23/8000 [00:01<15:58,  8.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 28/8000 [00:02<10:12, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 31/8000 [00:02<12:56, 10.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 33/8000 [00:02<15:17,  8.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 35/8000 [00:03<14:09,  9.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                                    | 39/8000 [00:03<10:39, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 41/8000 [00:03<12:42, 10.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 47/8000 [00:03<10:14, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 51/8000 [00:04<08:43, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                                    | 54/8000 [00:04<08:29, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 59/8000 [00:04<07:03, 18.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 62/8000 [00:04<10:12, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 67/8000 [00:05<09:21, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 69/8000 [00:05<09:20, 14.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 71/8000 [00:05<09:33, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 74/8000 [00:05<08:50, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 77/8000 [00:05<08:20, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                                    | 79/8000 [00:05<09:06, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 84/8000 [00:06<07:02, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 86/8000 [00:06<07:48, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 88/8000 [00:06<08:00, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 92/8000 [00:06<07:03, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                    | 96/8000 [00:06<06:37, 19.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 102/8000 [00:07<06:45, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 105/8000 [00:07<08:31, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 107/8000 [00:07<08:49, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 109/8000 [00:07<09:39, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                                   | 111/8000 [00:07<09:55, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▌                                   | 115/8000 [00:08<08:06, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▌                                   | 119/8000 [00:08<07:14, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 122/8000 [00:08<07:15, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 125/8000 [00:08<07:06, 18.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 128/8000 [00:08<06:54, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 130/8000 [00:08<07:37, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 132/8000 [00:09<08:13, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 134/8000 [00:09<10:20, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                                   | 138/8000 [00:09<08:27, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 144/8000 [00:09<05:55, 22.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 147/8000 [00:09<07:30, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 150/8000 [00:10<09:03, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 152/8000 [00:10<11:28, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 155/8000 [00:10<13:53,  9.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 159/8000 [00:11<12:30, 10.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 161/8000 [00:11<12:00, 10.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                                   | 163/8000 [00:11<12:00, 10.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 168/8000 [00:11<08:41, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 172/8000 [00:11<07:35, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 174/8000 [00:12<10:11, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 176/8000 [00:12<10:16, 12.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 179/8000 [00:12<09:11, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 182/8000 [00:12<08:37, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 186/8000 [00:12<07:24, 17.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 188/8000 [00:13<07:55, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▊                                   | 191/8000 [00:13<07:24, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▉                                   | 195/8000 [00:13<06:08, 21.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▉                                   | 198/8000 [00:13<08:30, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 201/8000 [00:13<09:32, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 203/8000 [00:14<09:35, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 206/8000 [00:14<08:45, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 209/8000 [00:14<08:12, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 211/8000 [00:14<08:40, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 215/8000 [00:14<09:18, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 219/8000 [00:15<07:52, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                                   | 222/8000 [00:15<09:17, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 225/8000 [00:15<08:37, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 230/8000 [00:16<09:56, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 232/8000 [00:16<12:05, 10.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 238/8000 [00:16<08:19, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 243/8000 [00:16<09:39, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 245/8000 [00:17<11:12, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                                   | 247/8000 [00:17<10:34, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 253/8000 [00:17<08:42, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 258/8000 [00:17<07:04, 18.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 262/8000 [00:17<06:08, 20.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 271/8000 [00:18<06:22, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█▏                                  | 277/8000 [00:18<07:28, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 280/8000 [00:19<07:23, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 282/8000 [00:19<07:53, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 285/8000 [00:19<09:13, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 288/8000 [00:19<08:37, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 292/8000 [00:20<09:06, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 296/8000 [00:20<07:41, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 299/8000 [00:20<08:32, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 301/8000 [00:20<08:43, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                                  | 304/8000 [00:20<08:15, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 306/8000 [00:20<08:05, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 309/8000 [00:21<10:05, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 311/8000 [00:21<09:43, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 315/8000 [00:21<07:36, 16.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 317/8000 [00:21<08:07, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 319/8000 [00:21<08:34, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 322/8000 [00:21<07:28, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 325/8000 [00:22<06:39, 19.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 329/8000 [00:22<05:58, 21.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▍                                  | 332/8000 [00:22<05:59, 21.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 336/8000 [00:22<07:03, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 340/8000 [00:22<06:26, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 343/8000 [00:22<06:24, 19.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 346/8000 [00:23<07:42, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 352/8000 [00:23<05:57, 21.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 355/8000 [00:23<07:27, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▌                                  | 359/8000 [00:23<06:38, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 363/8000 [00:24<07:50, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 367/8000 [00:24<06:58, 18.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 371/8000 [00:24<06:37, 19.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 375/8000 [00:24<07:43, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 377/8000 [00:24<07:35, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 381/8000 [00:25<08:14, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 384/8000 [00:25<07:56, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                                  | 386/8000 [00:25<07:58, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 389/8000 [00:25<09:40, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 397/8000 [00:25<05:39, 22.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 400/8000 [00:26<05:58, 21.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 403/8000 [00:26<07:36, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 406/8000 [00:26<07:22, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▊                                  | 412/8000 [00:26<07:16, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 417/8000 [00:27<06:15, 20.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 420/8000 [00:27<06:10, 20.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 426/8000 [00:27<06:18, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 429/8000 [00:27<06:16, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 432/8000 [00:27<06:26, 19.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▉                                  | 436/8000 [00:28<05:53, 21.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                                  | 441/8000 [00:28<05:18, 23.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 445/8000 [00:28<05:19, 23.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 450/8000 [00:28<06:19, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 454/8000 [00:28<07:11, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 459/8000 [00:29<06:08, 20.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 462/8000 [00:29<09:19, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 465/8000 [00:29<08:40, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 468/8000 [00:29<08:06, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██                                  | 471/8000 [00:30<07:48, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 474/8000 [00:30<07:31, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 477/8000 [00:30<07:07, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 479/8000 [00:30<07:41, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 481/8000 [00:30<09:54, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 485/8000 [00:31<07:55, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 487/8000 [00:31<08:13, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 493/8000 [00:31<05:32, 22.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▏                                 | 496/8000 [00:31<08:52, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 503/8000 [00:31<05:47, 21.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 509/8000 [00:32<07:05, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 515/8000 [00:32<06:39, 18.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|██▎                                 | 520/8000 [00:32<06:51, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                 | 524/8000 [00:33<06:10, 20.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▎                                 | 527/8000 [00:33<06:15, 19.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 530/8000 [00:33<07:56, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 533/8000 [00:33<09:10, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 537/8000 [00:34<09:13, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 541/8000 [00:34<08:06, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 544/8000 [00:34<07:39, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 550/8000 [00:34<05:47, 21.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▍                                 | 555/8000 [00:34<06:26, 19.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 561/8000 [00:35<06:36, 18.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 564/8000 [00:35<06:41, 18.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 570/8000 [00:35<05:30, 22.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 575/8000 [00:35<05:55, 20.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 580/8000 [00:36<06:27, 19.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▌                                 | 583/8000 [00:36<06:19, 19.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▋                                 | 588/8000 [00:36<05:17, 23.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▋                                 | 592/8000 [00:36<06:36, 18.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▋                                 | 596/8000 [00:36<06:12, 19.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                 | 602/8000 [00:37<05:09, 23.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                 | 605/8000 [00:37<05:32, 22.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▋                                 | 608/8000 [00:37<05:47, 21.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 616/8000 [00:37<05:11, 23.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 620/8000 [00:38<06:10, 19.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 624/8000 [00:38<05:36, 21.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 629/8000 [00:38<05:05, 24.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 634/8000 [00:38<04:51, 25.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▊                                 | 638/8000 [00:38<06:10, 19.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 642/8000 [00:39<07:13, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 647/8000 [00:39<07:15, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 649/8000 [00:39<07:37, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 651/8000 [00:39<07:46, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 654/8000 [00:39<07:27, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 658/8000 [00:40<06:42, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 663/8000 [00:40<07:10, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▉                                 | 666/8000 [00:40<08:42, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 668/8000 [00:40<08:51, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 673/8000 [00:41<06:52, 17.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 676/8000 [00:41<06:52, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|███                                 | 679/8000 [00:41<06:48, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 681/8000 [00:41<07:39, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 684/8000 [00:41<09:23, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 686/8000 [00:42<09:08, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 690/8000 [00:42<07:05, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███                                 | 692/8000 [00:42<09:57, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 695/8000 [00:42<08:51, 13.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 701/8000 [00:42<06:17, 19.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 704/8000 [00:43<08:07, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 708/8000 [00:43<06:46, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 715/8000 [00:43<06:10, 19.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▏                                | 720/8000 [00:43<05:18, 22.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 723/8000 [00:43<05:38, 21.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 728/8000 [00:44<07:40, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 730/8000 [00:44<08:08, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 734/8000 [00:44<07:07, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 736/8000 [00:44<07:28, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 738/8000 [00:45<07:54, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 740/8000 [00:45<08:35, 14.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 744/8000 [00:45<07:21, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▎                                | 749/8000 [00:45<05:54, 20.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 752/8000 [00:45<08:08, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 754/8000 [00:46<10:23, 11.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 756/8000 [00:46<10:17, 11.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|███▍                                | 758/8000 [00:46<10:26, 11.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 761/8000 [00:47<12:54,  9.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 766/8000 [00:47<09:49, 12.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 769/8000 [00:47<10:33, 11.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▍                                | 776/8000 [00:47<06:38, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 781/8000 [00:48<06:44, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 784/8000 [00:48<08:08, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 787/8000 [00:48<07:48, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 792/8000 [00:48<06:26, 18.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 797/8000 [00:48<06:42, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 800/8000 [00:49<06:35, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 803/8000 [00:49<06:24, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▌                                | 805/8000 [00:49<06:52, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 808/8000 [00:49<09:46, 12.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 811/8000 [00:50<10:02, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 813/8000 [00:50<15:13,  7.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 816/8000 [00:50<12:29,  9.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 820/8000 [00:51<09:40, 12.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 823/8000 [00:51<10:38, 11.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 826/8000 [00:51<09:33, 12.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 831/8000 [00:51<08:31, 14.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▋                                | 833/8000 [00:51<08:47, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▊                                | 835/8000 [00:52<11:12, 10.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▊                                | 840/8000 [00:52<08:03, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 845/8000 [00:52<08:01, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 849/8000 [00:52<06:50, 17.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 854/8000 [00:53<07:09, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▊                                | 859/8000 [00:53<07:17, 16.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 863/8000 [00:53<06:32, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 867/8000 [00:54<07:28, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 871/8000 [00:54<06:48, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 876/8000 [00:54<06:52, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 878/8000 [00:54<06:53, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 885/8000 [00:54<05:02, 23.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▉                                | 888/8000 [00:55<05:21, 22.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 891/8000 [00:55<07:01, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 893/8000 [00:55<07:32, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 896/8000 [00:55<07:10, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 899/8000 [00:55<06:55, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 904/8000 [00:56<07:21, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 907/8000 [00:56<08:54, 13.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 909/8000 [00:56<09:09, 12.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████                                | 915/8000 [00:56<06:32, 18.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|████▏                               | 917/8000 [00:56<06:44, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 922/8000 [00:57<07:17, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 925/8000 [00:57<07:15, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 928/8000 [00:57<07:04, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 930/8000 [00:57<09:24, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 933/8000 [00:58<08:08, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 936/8000 [00:58<09:34, 12.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 939/8000 [00:58<10:37, 11.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 942/8000 [00:58<09:24, 12.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▏                               | 944/8000 [00:59<11:11, 10.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 947/8000 [00:59<09:43, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 949/8000 [00:59<09:42, 12.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 956/8000 [00:59<07:30, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 958/8000 [01:00<09:47, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 961/8000 [01:00<08:36, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 964/8000 [01:00<08:09, 14.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▎                               | 967/8000 [01:00<09:46, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 974/8000 [01:01<06:26, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 979/8000 [01:01<05:33, 21.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 985/8000 [01:01<06:01, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 989/8000 [01:01<05:42, 20.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|████▍                               | 996/8000 [01:02<05:21, 21.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1001/8000 [01:02<04:53, 23.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1004/8000 [01:02<05:16, 22.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1008/8000 [01:02<05:10, 22.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1012/8000 [01:02<05:10, 22.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1015/8000 [01:03<07:02, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1020/8000 [01:03<06:42, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1024/8000 [01:03<06:02, 19.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▍                              | 1027/8000 [01:03<06:03, 19.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1032/8000 [01:03<04:59, 23.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1036/8000 [01:03<04:54, 23.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1039/8000 [01:04<05:17, 21.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1043/8000 [01:04<05:04, 22.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1046/8000 [01:04<05:27, 21.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1050/8000 [01:04<06:53, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▌                              | 1056/8000 [01:05<06:22, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1059/8000 [01:05<06:27, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1063/8000 [01:05<06:12, 18.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1067/8000 [01:05<07:08, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1073/8000 [01:05<05:31, 20.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|████▋                              | 1077/8000 [01:06<06:25, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                              | 1080/8000 [01:06<06:35, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▋                              | 1084/8000 [01:06<06:09, 18.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1089/8000 [01:06<05:17, 21.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1092/8000 [01:06<05:27, 21.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1095/8000 [01:07<05:29, 20.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1098/8000 [01:07<06:47, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1100/8000 [01:07<07:19, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1103/8000 [01:07<07:05, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1106/8000 [01:07<07:02, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1109/8000 [01:08<07:13, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1112/8000 [01:08<10:39, 10.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▊                              | 1114/8000 [01:08<12:07,  9.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1116/8000 [01:09<13:22,  8.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1119/8000 [01:09<13:14,  8.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1121/8000 [01:09<13:10,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1123/8000 [01:09<12:34,  9.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1126/8000 [01:10<09:57, 11.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1130/8000 [01:10<07:44, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1134/8000 [01:10<08:19, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1136/8000 [01:10<08:36, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▉                              | 1138/8000 [01:10<08:56, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1143/8000 [01:11<07:53, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1148/8000 [01:11<07:47, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1151/8000 [01:11<07:27, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1154/8000 [01:11<07:04, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1156/8000 [01:12<09:28, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|█████                              | 1160/8000 [01:12<07:33, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████                              | 1165/8000 [01:12<06:11, 18.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1172/8000 [01:13<07:07, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1176/8000 [01:13<06:28, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1181/8000 [01:13<06:33, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1184/8000 [01:13<06:26, 17.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1187/8000 [01:13<06:31, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1192/8000 [01:13<05:27, 20.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▏                             | 1196/8000 [01:14<05:21, 21.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1200/8000 [01:14<05:12, 21.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1203/8000 [01:14<05:21, 21.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1207/8000 [01:14<04:51, 23.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1210/8000 [01:14<07:08, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1213/8000 [01:15<06:59, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1215/8000 [01:15<07:30, 15.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1220/8000 [01:15<05:56, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▎                             | 1224/8000 [01:15<07:15, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1229/8000 [01:16<06:02, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1232/8000 [01:16<07:13, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1234/8000 [01:16<07:40, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|█████▍                             | 1237/8000 [01:16<07:24, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1240/8000 [01:16<06:56, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1243/8000 [01:17<08:36, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1249/8000 [01:17<07:11, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1252/8000 [01:17<06:55, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1254/8000 [01:17<07:14, 15.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▍                             | 1256/8000 [01:17<07:56, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1258/8000 [01:18<08:19, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1260/8000 [01:18<11:11, 10.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1263/8000 [01:18<12:46,  8.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1264/8000 [01:19<13:25,  8.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1266/8000 [01:19<11:36,  9.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1268/8000 [01:19<10:48, 10.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1270/8000 [01:19<10:32, 10.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1274/8000 [01:19<10:10, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1276/8000 [01:20<09:58, 11.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1278/8000 [01:20<09:45, 11.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1281/8000 [01:20<07:58, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▌                             | 1284/8000 [01:20<11:27,  9.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1287/8000 [01:20<09:53, 11.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1290/8000 [01:21<08:49, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1293/8000 [01:21<08:01, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1297/8000 [01:21<06:52, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1300/8000 [01:21<06:37, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1306/8000 [01:21<05:05, 21.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▋                             | 1311/8000 [01:22<05:59, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▊                             | 1315/8000 [01:22<05:30, 20.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|█████▊                             | 1320/8000 [01:22<06:48, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1324/8000 [01:23<07:08, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1326/8000 [01:23<07:12, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1330/8000 [01:23<07:46, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1332/8000 [01:23<07:59, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1338/8000 [01:24<07:18, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▊                             | 1341/8000 [01:24<08:03, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1344/8000 [01:24<09:02, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1346/8000 [01:24<08:45, 12.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1350/8000 [01:24<06:52, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1353/8000 [01:25<06:38, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1356/8000 [01:25<06:31, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1360/8000 [01:25<08:53, 12.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1364/8000 [01:26<10:24, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1366/8000 [01:26<12:01,  9.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████▉                             | 1371/8000 [01:26<10:13, 10.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1373/8000 [01:27<11:25,  9.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1376/8000 [01:27<09:53, 11.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1381/8000 [01:27<08:50, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1383/8000 [01:27<08:56, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1385/8000 [01:27<08:55, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1387/8000 [01:28<09:07, 12.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1390/8000 [01:28<07:39, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1392/8000 [01:28<07:57, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|██████                             | 1398/8000 [01:28<07:06, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1402/8000 [01:28<06:28, 16.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1404/8000 [01:29<08:37, 12.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1410/8000 [01:29<07:14, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1413/8000 [01:29<07:05, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1417/8000 [01:29<06:27, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1420/8000 [01:30<07:52, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1426/8000 [01:30<06:38, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▏                            | 1428/8000 [01:30<07:02, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1430/8000 [01:30<07:33, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1433/8000 [01:31<08:46, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1440/8000 [01:31<05:51, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1444/8000 [01:31<05:31, 19.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1448/8000 [01:31<06:22, 17.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▎                            | 1454/8000 [01:32<05:04, 21.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1459/8000 [01:32<04:36, 23.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1462/8000 [01:32<08:24, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1468/8000 [01:33<06:25, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1473/8000 [01:33<06:24, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|██████▍                            | 1478/8000 [01:33<05:35, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                            | 1482/8000 [01:33<06:37, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▍                            | 1485/8000 [01:34<06:23, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1488/8000 [01:34<07:40, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1490/8000 [01:34<08:05, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1492/8000 [01:34<08:17, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1494/8000 [01:34<08:33, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1501/8000 [01:35<06:23, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1503/8000 [01:35<08:28, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1506/8000 [01:35<07:54, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▌                            | 1513/8000 [01:36<06:35, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1516/8000 [01:36<07:31, 14.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1519/8000 [01:36<07:09, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1524/8000 [01:36<05:56, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1526/8000 [01:36<06:20, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1530/8000 [01:36<05:50, 18.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1533/8000 [01:37<06:04, 17.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1536/8000 [01:37<05:53, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▋                            | 1540/8000 [01:37<06:36, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1544/8000 [01:37<07:19, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1547/8000 [01:38<08:34, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1550/8000 [01:38<07:51, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1555/8000 [01:38<05:54, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|██████▊                            | 1558/8000 [01:38<05:55, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                            | 1561/8000 [01:39<07:25, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                            | 1565/8000 [01:39<06:22, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▊                            | 1568/8000 [01:39<06:17, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1574/8000 [01:39<04:59, 21.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1577/8000 [01:39<04:53, 21.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1582/8000 [01:40<05:48, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1587/8000 [01:40<05:08, 20.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1590/8000 [01:40<06:38, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1596/8000 [01:40<06:03, 17.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|██████▉                            | 1599/8000 [01:41<05:58, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1602/8000 [01:41<05:56, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1604/8000 [01:41<06:04, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1607/8000 [01:41<06:05, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1609/8000 [01:41<06:40, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1612/8000 [01:41<06:26, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1616/8000 [01:41<05:39, 18.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1618/8000 [01:42<08:00, 13.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1621/8000 [01:42<08:24, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████                            | 1626/8000 [01:42<06:21, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1629/8000 [01:42<06:02, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1633/8000 [01:43<05:20, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|███████▏                           | 1640/8000 [01:43<06:03, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1642/8000 [01:43<09:12, 11.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1645/8000 [01:44<08:30, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1647/8000 [01:44<08:15, 12.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1651/8000 [01:44<06:49, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1654/8000 [01:44<06:30, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▏                           | 1656/8000 [01:44<06:52, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1659/8000 [01:44<06:48, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1663/8000 [01:45<05:54, 17.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1671/8000 [01:45<04:53, 21.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1674/8000 [01:45<06:29, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1676/8000 [01:45<06:51, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1678/8000 [01:46<09:05, 11.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1682/8000 [01:46<07:23, 14.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▎                           | 1685/8000 [01:46<08:41, 12.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1689/8000 [01:46<07:13, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1692/8000 [01:47<09:52, 10.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1695/8000 [01:47<09:35, 10.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1699/8000 [01:47<07:51, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1706/8000 [01:48<05:22, 19.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▍                           | 1712/8000 [01:48<05:35, 18.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|███████▌                           | 1718/8000 [01:48<05:39, 18.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1722/8000 [01:48<05:06, 20.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1726/8000 [01:49<04:54, 21.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1730/8000 [01:49<04:54, 21.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1733/8000 [01:49<06:26, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1737/8000 [01:49<05:45, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▌                           | 1740/8000 [01:49<05:44, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1746/8000 [01:50<04:17, 24.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1750/8000 [01:50<05:27, 19.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1753/8000 [01:50<07:00, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1757/8000 [01:50<06:17, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1760/8000 [01:51<06:09, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1762/8000 [01:51<06:09, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1764/8000 [01:51<06:39, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1767/8000 [01:51<06:25, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▋                           | 1769/8000 [01:51<06:55, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1775/8000 [01:51<06:16, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1779/8000 [01:52<08:09, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1783/8000 [01:52<06:51, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1785/8000 [01:52<06:57, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1789/8000 [01:53<07:06, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1794/8000 [01:53<05:38, 18.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|███████▊                           | 1797/8000 [01:53<06:32, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1801/8000 [01:53<05:47, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1803/8000 [01:53<06:18, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1805/8000 [01:54<08:20, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1807/8000 [01:54<10:18, 10.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1811/8000 [01:55<12:23,  8.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1813/8000 [01:55<16:04,  6.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1814/8000 [01:55<15:44,  6.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1815/8000 [01:55<15:56,  6.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1817/8000 [01:56<13:38,  7.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1819/8000 [01:56<11:58,  8.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1821/8000 [01:56<11:06,  9.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1824/8000 [01:56<08:55, 11.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|███████▉                           | 1828/8000 [01:56<06:58, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1831/8000 [01:56<06:17, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1835/8000 [01:57<05:38, 18.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1837/8000 [01:57<06:31, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1843/8000 [01:57<04:44, 21.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1848/8000 [01:57<05:21, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1853/8000 [01:57<04:39, 22.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████                           | 1857/8000 [01:58<04:17, 23.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1861/8000 [01:58<05:29, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1864/8000 [01:58<07:04, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1868/8000 [01:58<06:11, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1872/8000 [01:59<05:31, 18.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|████████▏                          | 1875/8000 [01:59<06:36, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▏                          | 1880/8000 [01:59<05:29, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1886/8000 [01:59<04:24, 23.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1894/8000 [02:00<04:24, 23.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1897/8000 [02:00<04:38, 21.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1900/8000 [02:00<06:04, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1902/8000 [02:00<06:32, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1904/8000 [02:01<08:34, 11.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1908/8000 [02:01<07:03, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1911/8000 [02:01<06:14, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▎                          | 1914/8000 [02:01<05:39, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1918/8000 [02:01<06:29, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1923/8000 [02:02<06:45, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1926/8000 [02:02<06:35, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1928/8000 [02:02<06:55, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1930/8000 [02:02<06:46, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1932/8000 [02:02<07:16, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1934/8000 [02:02<07:33, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1937/8000 [02:03<06:48, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▍                          | 1941/8000 [02:03<05:43, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1947/8000 [02:03<04:27, 22.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|████████▌                          | 1954/8000 [02:03<04:24, 22.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1961/8000 [02:04<05:26, 18.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1965/8000 [02:04<06:12, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1967/8000 [02:04<06:31, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▌                          | 1971/8000 [02:04<05:33, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1974/8000 [02:05<06:37, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1979/8000 [02:05<05:19, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1982/8000 [02:05<06:28, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1985/8000 [02:05<07:49, 12.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1988/8000 [02:06<07:16, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1991/8000 [02:06<06:40, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1993/8000 [02:06<06:56, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1995/8000 [02:06<08:25, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▋                          | 1999/8000 [02:07<08:01, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2002/8000 [02:07<06:45, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2005/8000 [02:07<06:31, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2011/8000 [02:07<04:47, 20.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2014/8000 [02:07<04:59, 19.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2018/8000 [02:07<05:58, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2022/8000 [02:08<05:31, 18.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2025/8000 [02:08<05:12, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▊                          | 2028/8000 [02:08<06:51, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2030/8000 [02:08<07:10, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2032/8000 [02:08<07:30, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|████████▉                          | 2035/8000 [02:09<09:55, 10.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2040/8000 [02:09<08:36, 11.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2044/8000 [02:09<07:05, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2048/8000 [02:10<07:41, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2053/8000 [02:10<06:52, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|████████▉                          | 2056/8000 [02:10<06:34, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2059/8000 [02:10<06:24, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2062/8000 [02:11<06:18, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2067/8000 [02:11<05:06, 19.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2072/8000 [02:11<04:25, 22.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2075/8000 [02:11<04:42, 20.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2079/8000 [02:11<05:45, 17.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2081/8000 [02:12<07:47, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2083/8000 [02:12<07:56, 12.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████                          | 2085/8000 [02:12<07:59, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2090/8000 [02:12<07:17, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2092/8000 [02:13<09:21, 10.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2094/8000 [02:13<08:38, 11.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2099/8000 [02:13<07:41, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2101/8000 [02:13<07:45, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2104/8000 [02:14<07:04, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▏                         | 2108/8000 [02:14<07:35, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|█████████▎                         | 2116/8000 [02:14<06:20, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2121/8000 [02:15<06:01, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2125/8000 [02:15<05:32, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2128/8000 [02:15<05:33, 17.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2131/8000 [02:15<05:34, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2136/8000 [02:15<04:44, 20.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▎                         | 2139/8000 [02:15<04:55, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2144/8000 [02:16<06:18, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2148/8000 [02:16<06:34, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2150/8000 [02:17<08:59, 10.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2153/8000 [02:17<07:57, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2155/8000 [02:17<08:04, 12.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2159/8000 [02:17<08:02, 12.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2161/8000 [02:17<08:12, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2164/8000 [02:18<07:24, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▍                         | 2168/8000 [02:18<07:38, 12.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2172/8000 [02:18<06:23, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2175/8000 [02:19<07:40, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2179/8000 [02:19<06:06, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2182/8000 [02:19<06:01, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2185/8000 [02:19<05:55, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2187/8000 [02:19<06:15, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2190/8000 [02:19<06:09, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2194/8000 [02:20<07:58, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2196/8000 [02:20<07:58, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|█████████▌                         | 2199/8000 [02:20<07:08, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2201/8000 [02:20<07:16, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2204/8000 [02:20<06:42, 14.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2207/8000 [02:21<06:07, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2211/8000 [02:21<05:17, 18.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2213/8000 [02:21<05:49, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2215/8000 [02:21<06:15, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2219/8000 [02:21<05:21, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2221/8000 [02:21<05:56, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▋                         | 2227/8000 [02:22<04:18, 22.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2232/8000 [02:22<06:12, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2235/8000 [02:22<07:24, 12.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2239/8000 [02:23<06:20, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2244/8000 [02:23<06:25, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2251/8000 [02:23<06:15, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▊                         | 2256/8000 [02:24<06:19, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2262/8000 [02:24<05:04, 18.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2265/8000 [02:24<05:10, 18.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2268/8000 [02:24<06:02, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2273/8000 [02:25<05:03, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2276/8000 [02:25<06:02, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2278/8000 [02:25<06:04, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|█████████▉                         | 2280/8000 [02:25<06:25, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|█████████▉                         | 2282/8000 [02:25<06:41, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2286/8000 [02:26<07:15, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2288/8000 [02:26<07:22, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2293/8000 [02:26<05:34, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2295/8000 [02:26<05:43, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2297/8000 [02:26<06:10, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2301/8000 [02:26<05:14, 18.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2304/8000 [02:27<05:24, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2307/8000 [02:27<05:21, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2309/8000 [02:27<06:01, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████                         | 2312/8000 [02:27<05:46, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2318/8000 [02:27<04:02, 23.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2322/8000 [02:28<06:28, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2326/8000 [02:28<05:39, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2331/8000 [02:28<05:48, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2335/8000 [02:28<05:10, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▏                        | 2339/8000 [02:29<05:52, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2343/8000 [02:29<05:06, 18.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2350/8000 [02:29<03:40, 25.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2354/8000 [02:29<04:49, 19.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|██████████▎                        | 2357/8000 [02:29<04:49, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2361/8000 [02:30<05:51, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2363/8000 [02:30<06:06, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2366/8000 [02:30<06:55, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▎                        | 2368/8000 [02:30<07:09, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2372/8000 [02:31<05:55, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2377/8000 [02:31<04:44, 19.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2380/8000 [02:31<04:55, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2386/8000 [02:31<05:56, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2390/8000 [02:32<05:25, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▍                        | 2396/8000 [02:32<04:20, 21.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2400/8000 [02:32<05:16, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2404/8000 [02:32<05:43, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2409/8000 [02:33<05:44, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2412/8000 [02:33<05:26, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2414/8000 [02:33<06:46, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2417/8000 [02:33<07:24, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2419/8000 [02:34<07:26, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2421/8000 [02:34<07:30, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2423/8000 [02:34<07:26, 12.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▌                        | 2428/8000 [02:34<05:23, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2434/8000 [02:34<04:11, 22.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2437/8000 [02:35<05:30, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|██████████▋                        | 2440/8000 [02:35<05:03, 18.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2445/8000 [02:35<05:29, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2447/8000 [02:35<07:03, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2449/8000 [02:36<08:51, 10.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2453/8000 [02:36<08:20, 11.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▋                        | 2456/8000 [02:36<07:20, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2460/8000 [02:36<05:50, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2463/8000 [02:36<05:22, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2467/8000 [02:37<06:01, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2470/8000 [02:37<05:45, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2472/8000 [02:37<07:49, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2474/8000 [02:38<09:11, 10.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2476/8000 [02:38<08:43, 10.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2478/8000 [02:38<08:25, 10.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2483/8000 [02:38<05:55, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▊                        | 2485/8000 [02:38<06:12, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2492/8000 [02:39<05:16, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2495/8000 [02:39<05:03, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2498/8000 [02:39<06:14, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2507/8000 [02:39<04:34, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2509/8000 [02:39<05:02, 18.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|██████████▉                        | 2512/8000 [02:40<06:11, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|███████████                        | 2516/8000 [02:40<05:28, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|███████████                        | 2518/8000 [02:40<05:49, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2522/8000 [02:40<05:56, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2527/8000 [02:41<04:49, 18.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2531/8000 [02:41<04:24, 20.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2535/8000 [02:41<05:15, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2537/8000 [02:41<05:39, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████                        | 2539/8000 [02:42<07:30, 12.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2543/8000 [02:42<06:03, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2545/8000 [02:42<07:47, 11.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2550/8000 [02:42<05:30, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2553/8000 [02:42<05:20, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2556/8000 [02:42<05:16, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2558/8000 [02:43<05:41, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2561/8000 [02:43<05:26, 16.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2563/8000 [02:43<05:58, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▏                       | 2568/8000 [02:43<05:42, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2573/8000 [02:43<04:38, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2576/8000 [02:44<04:39, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2581/8000 [02:44<04:57, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2585/8000 [02:44<04:34, 19.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2589/8000 [02:44<05:25, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2591/8000 [02:45<05:59, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|███████████▎                       | 2597/8000 [02:45<04:20, 20.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2601/8000 [02:45<04:03, 22.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2606/8000 [02:45<05:31, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2610/8000 [02:45<04:58, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2613/8000 [02:46<04:57, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2616/8000 [02:46<06:12, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2620/8000 [02:46<05:16, 16.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2623/8000 [02:46<06:23, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▍                       | 2626/8000 [02:47<05:39, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2629/8000 [02:47<05:05, 17.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2634/8000 [02:47<04:13, 21.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2639/8000 [02:47<04:33, 19.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2644/8000 [02:47<04:44, 18.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2650/8000 [02:48<03:54, 22.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2653/8000 [02:48<04:06, 21.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▌                       | 2657/8000 [02:48<03:59, 22.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2660/8000 [02:48<04:11, 21.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2665/8000 [02:48<03:42, 23.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2669/8000 [02:48<03:41, 24.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2673/8000 [02:49<03:55, 22.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|███████████▋                       | 2677/8000 [02:49<03:51, 23.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▋                       | 2683/8000 [02:49<04:19, 20.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2686/8000 [02:49<05:19, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2688/8000 [02:50<05:24, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2691/8000 [02:50<06:30, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2695/8000 [02:50<05:35, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2697/8000 [02:50<05:55, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2699/8000 [02:51<07:55, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2703/8000 [02:51<07:20, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2705/8000 [02:51<07:15, 12.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2708/8000 [02:51<06:16, 14.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▊                       | 2714/8000 [02:51<04:30, 19.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2717/8000 [02:51<04:30, 19.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2720/8000 [02:52<04:37, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2722/8000 [02:52<05:13, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2726/8000 [02:52<05:56, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2728/8000 [02:53<08:35, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2731/8000 [02:53<07:20, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2733/8000 [02:53<08:16, 10.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2737/8000 [02:53<06:24, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|███████████▉                       | 2742/8000 [02:53<04:56, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2745/8000 [02:54<06:11, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2749/8000 [02:54<05:25, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2752/8000 [02:54<06:13, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2755/8000 [02:54<05:50, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2757/8000 [02:54<05:54, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████████                       | 2759/8000 [02:55<06:01, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                       | 2761/8000 [02:55<05:52, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                       | 2763/8000 [02:55<07:58, 10.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████                       | 2768/8000 [02:55<05:32, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2773/8000 [02:56<05:42, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2777/8000 [02:56<06:09, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2782/8000 [02:56<04:57, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2784/8000 [02:56<05:19, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2787/8000 [02:56<05:09, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2789/8000 [02:57<05:34, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2791/8000 [02:57<06:00, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2794/8000 [02:57<09:43,  8.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2796/8000 [02:57<08:53,  9.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▏                      | 2798/8000 [02:58<07:59, 10.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2803/8000 [02:58<06:52, 12.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2807/8000 [02:58<05:46, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2811/8000 [02:58<05:08, 16.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2815/8000 [02:59<05:47, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2823/8000 [02:59<03:38, 23.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▎                      | 2827/8000 [02:59<04:31, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2830/8000 [02:59<04:20, 19.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2833/8000 [02:59<04:19, 19.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████████▍                      | 2837/8000 [03:00<05:09, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2841/8000 [03:00<04:36, 18.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2844/8000 [03:00<04:42, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2848/8000 [03:00<05:33, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2852/8000 [03:00<04:41, 18.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▍                      | 2856/8000 [03:01<05:12, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2858/8000 [03:01<05:20, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2860/8000 [03:01<05:27, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2862/8000 [03:01<05:47, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2866/8000 [03:01<04:51, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2869/8000 [03:01<04:50, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2872/8000 [03:02<04:48, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2875/8000 [03:02<07:23, 11.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2877/8000 [03:02<07:15, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2881/8000 [03:02<05:43, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▌                      | 2884/8000 [03:03<06:12, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2886/8000 [03:03<06:24, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2888/8000 [03:03<06:25, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2892/8000 [03:03<05:00, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2895/8000 [03:03<04:27, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2898/8000 [03:04<05:58, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2900/8000 [03:04<06:14, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2902/8000 [03:04<06:32, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2904/8000 [03:04<06:41, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▋                      | 2911/8000 [03:04<04:03, 20.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▊                      | 2915/8000 [03:05<05:48, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|████████████▊                      | 2920/8000 [03:05<05:19, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2924/8000 [03:05<04:55, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2927/8000 [03:05<04:54, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2930/8000 [03:06<04:49, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2932/8000 [03:06<05:13, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2934/8000 [03:06<05:32, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▊                      | 2939/8000 [03:06<05:14, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2943/8000 [03:06<05:51, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2946/8000 [03:07<05:38, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2950/8000 [03:07<05:20, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2953/8000 [03:08<08:47,  9.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2957/8000 [03:08<09:01,  9.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2963/8000 [03:08<08:08, 10.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2965/8000 [03:09<08:00, 10.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2968/8000 [03:09<07:10, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|████████████▉                      | 2971/8000 [03:09<07:29, 11.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2976/8000 [03:09<05:32, 15.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2979/8000 [03:09<05:08, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2982/8000 [03:10<05:06, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2987/8000 [03:10<05:12, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2989/8000 [03:10<05:39, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2996/8000 [03:11<05:34, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████████                      | 2998/8000 [03:11<07:32, 11.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3000/8000 [03:11<08:16, 10.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3002/8000 [03:11<08:03, 10.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3005/8000 [03:12<07:04, 11.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3007/8000 [03:12<08:28,  9.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3009/8000 [03:12<08:00, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3013/8000 [03:12<06:06, 13.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3019/8000 [03:13<05:16, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3022/8000 [03:13<05:06, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▏                     | 3024/8000 [03:13<05:07, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3029/8000 [03:13<05:19, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3031/8000 [03:13<05:39, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3035/8000 [03:14<04:56, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3037/8000 [03:14<05:23, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3040/8000 [03:14<06:36, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3042/8000 [03:14<06:36, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3044/8000 [03:14<06:57, 11.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3047/8000 [03:15<06:04, 13.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3051/8000 [03:15<06:01, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▎                     | 3056/8000 [03:15<04:44, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3060/8000 [03:15<04:19, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3064/8000 [03:15<04:06, 19.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3068/8000 [03:16<04:57, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3073/8000 [03:16<03:53, 21.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████████▍                     | 3078/8000 [03:16<03:30, 23.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▍                     | 3082/8000 [03:16<04:54, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3086/8000 [03:17<05:16, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3088/8000 [03:17<06:17, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3093/8000 [03:17<04:56, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3096/8000 [03:17<04:52, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3099/8000 [03:18<06:01, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3103/8000 [03:18<05:09, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3106/8000 [03:18<04:57, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▌                     | 3109/8000 [03:18<04:30, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3115/8000 [03:18<04:23, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3120/8000 [03:19<04:21, 18.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3125/8000 [03:19<03:46, 21.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3128/8000 [03:19<03:54, 20.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3131/8000 [03:19<04:36, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3134/8000 [03:20<05:27, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3136/8000 [03:20<05:38, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3138/8000 [03:20<05:25, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▋                     | 3141/8000 [03:20<05:09, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3146/8000 [03:20<03:59, 20.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3149/8000 [03:20<04:56, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3153/8000 [03:21<04:26, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|█████████████▊                     | 3155/8000 [03:21<05:00, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3161/8000 [03:21<04:53, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3163/8000 [03:21<05:09, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3167/8000 [03:21<04:24, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▊                     | 3170/8000 [03:22<04:05, 19.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3174/8000 [03:22<03:53, 20.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3179/8000 [03:22<04:25, 18.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3182/8000 [03:22<04:34, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3185/8000 [03:22<04:36, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3187/8000 [03:23<04:57, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3193/8000 [03:23<03:27, 23.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3196/8000 [03:23<03:41, 21.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|█████████████▉                     | 3199/8000 [03:23<05:02, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3201/8000 [03:23<05:29, 14.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3204/8000 [03:24<06:31, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3206/8000 [03:24<07:46, 10.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3208/8000 [03:24<07:30, 10.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3211/8000 [03:24<06:23, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3215/8000 [03:25<05:19, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3222/8000 [03:25<03:34, 22.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████                     | 3227/8000 [03:25<03:16, 24.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3232/8000 [03:25<04:31, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████████▏                    | 3239/8000 [03:25<03:19, 23.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3245/8000 [03:26<03:32, 22.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3249/8000 [03:26<04:12, 18.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3254/8000 [03:26<03:44, 21.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▏                    | 3257/8000 [03:26<03:53, 20.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3260/8000 [03:27<04:00, 19.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3263/8000 [03:27<03:52, 20.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3270/8000 [03:27<03:45, 20.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3274/8000 [03:27<04:06, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3277/8000 [03:28<04:14, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3280/8000 [03:28<04:19, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▎                    | 3284/8000 [03:28<03:58, 19.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3287/8000 [03:28<06:10, 12.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3290/8000 [03:29<05:38, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3294/8000 [03:29<05:51, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3297/8000 [03:29<06:46, 11.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3300/8000 [03:30<07:20, 10.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3303/8000 [03:30<06:10, 12.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3307/8000 [03:30<06:20, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▍                    | 3310/8000 [03:30<05:46, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▌                    | 3315/8000 [03:30<04:33, 17.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████████▌                    | 3318/8000 [03:30<04:30, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3321/8000 [03:31<04:28, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3323/8000 [03:31<04:32, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3327/8000 [03:31<04:07, 18.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3330/8000 [03:31<04:03, 19.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3334/8000 [03:31<03:40, 21.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3337/8000 [03:31<03:51, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▌                    | 3341/8000 [03:32<04:41, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3343/8000 [03:32<04:58, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3345/8000 [03:32<05:23, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3350/8000 [03:32<05:09, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3356/8000 [03:33<04:26, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3363/8000 [03:33<04:00, 19.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3367/8000 [03:33<03:34, 21.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▋                    | 3370/8000 [03:33<03:40, 20.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3375/8000 [03:33<03:12, 24.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3379/8000 [03:34<03:07, 24.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3382/8000 [03:34<04:02, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3386/8000 [03:34<04:34, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3388/8000 [03:34<05:47, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3392/8000 [03:35<04:52, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████████▊                    | 3395/8000 [03:35<05:24, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3401/8000 [03:35<04:54, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3403/8000 [03:35<05:14, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3408/8000 [03:36<05:06, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3412/8000 [03:36<05:12, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3415/8000 [03:36<04:58, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3417/8000 [03:36<05:11, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3419/8000 [03:36<05:05, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3424/8000 [03:37<03:58, 19.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|██████████████▉                    | 3427/8000 [03:37<04:01, 18.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3430/8000 [03:37<05:23, 14.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3432/8000 [03:37<05:38, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3434/8000 [03:37<05:38, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3436/8000 [03:38<06:45, 11.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3441/8000 [03:38<04:43, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3446/8000 [03:38<03:42, 20.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████                    | 3451/8000 [03:38<03:11, 23.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3459/8000 [03:38<02:15, 33.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3464/8000 [03:39<03:35, 21.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3469/8000 [03:39<04:24, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3472/8000 [03:39<04:55, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3475/8000 [03:40<04:44, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████████▏                   | 3477/8000 [03:40<05:49, 12.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▏                   | 3481/8000 [03:40<05:39, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▏                   | 3483/8000 [03:41<07:33,  9.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▏                   | 3485/8000 [03:41<07:16, 10.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3487/8000 [03:41<08:04,  9.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3489/8000 [03:41<07:14, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3494/8000 [03:42<06:14, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3498/8000 [03:42<05:06, 14.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3500/8000 [03:42<05:18, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3502/8000 [03:42<05:26, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3505/8000 [03:42<04:58, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3507/8000 [03:42<05:10, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3509/8000 [03:42<05:23, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▎                   | 3514/8000 [03:43<03:54, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3516/8000 [03:43<04:16, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3518/8000 [03:43<07:19, 10.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3522/8000 [03:43<05:35, 13.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3525/8000 [03:44<05:16, 14.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3528/8000 [03:44<04:51, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3532/8000 [03:44<05:07, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3535/8000 [03:44<04:52, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3539/8000 [03:45<05:06, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▍                   | 3542/8000 [03:45<05:35, 13.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3546/8000 [03:45<05:34, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3548/8000 [03:45<05:30, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3550/8000 [03:46<06:48, 10.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3555/8000 [03:46<05:51, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3557/8000 [03:46<05:58, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████████▌                   | 3559/8000 [03:46<06:03, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3564/8000 [03:46<04:20, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▌                   | 3571/8000 [03:47<03:49, 19.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3574/8000 [03:47<03:51, 19.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3579/8000 [03:47<04:41, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3583/8000 [03:47<04:08, 17.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3586/8000 [03:48<04:36, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3588/8000 [03:48<04:47, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3592/8000 [03:48<04:08, 17.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▋                   | 3597/8000 [03:48<04:13, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3602/8000 [03:48<03:31, 20.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3606/8000 [03:49<03:17, 22.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3610/8000 [03:49<03:51, 18.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3613/8000 [03:49<03:49, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3619/8000 [03:49<03:30, 20.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3623/8000 [03:50<04:47, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▊                   | 3627/8000 [03:50<04:55, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3629/8000 [03:50<05:55, 12.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3632/8000 [03:51<06:14, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3636/8000 [03:51<05:08, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████████▉                   | 3638/8000 [03:51<05:19, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3640/8000 [03:51<05:30, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3642/8000 [03:51<05:33, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3645/8000 [03:51<05:01, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3649/8000 [03:52<04:09, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3651/8000 [03:52<05:42, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3653/8000 [03:52<06:51, 10.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████████▉                   | 3656/8000 [03:52<05:47, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3659/8000 [03:52<05:10, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3663/8000 [03:53<04:08, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3666/8000 [03:53<04:05, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3668/8000 [03:53<04:24, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3672/8000 [03:53<03:47, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3675/8000 [03:53<03:47, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3677/8000 [03:54<05:37, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3679/8000 [03:54<06:39, 10.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3682/8000 [03:54<08:57,  8.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████                   | 3684/8000 [03:55<08:15,  8.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3688/8000 [03:55<06:00, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3693/8000 [03:55<04:22, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3698/8000 [03:55<03:22, 21.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3703/8000 [03:55<03:47, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3707/8000 [03:56<03:58, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▏                  | 3711/8000 [03:56<03:40, 19.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▎                  | 3715/8000 [03:56<03:24, 20.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████████▎                  | 3718/8000 [03:56<04:05, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3721/8000 [03:56<04:09, 17.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3726/8000 [03:57<03:30, 20.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3733/8000 [03:57<04:07, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3736/8000 [03:57<04:05, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▎                  | 3740/8000 [03:57<03:36, 19.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3744/8000 [03:57<03:23, 20.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3747/8000 [03:58<04:14, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3750/8000 [03:58<04:58, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3754/8000 [03:58<04:19, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3757/8000 [03:58<04:11, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3762/8000 [03:59<05:09, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3766/8000 [03:59<04:22, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3768/8000 [03:59<04:42, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▍                  | 3771/8000 [03:59<05:17, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3774/8000 [04:00<04:37, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3777/8000 [04:00<04:23, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3781/8000 [04:00<03:53, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3785/8000 [04:00<03:35, 19.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3794/8000 [04:00<03:01, 23.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████████▌                  | 3797/8000 [04:01<03:07, 22.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3800/8000 [04:01<03:16, 21.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3803/8000 [04:01<03:28, 20.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3808/8000 [04:01<03:53, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3811/8000 [04:02<04:45, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3814/8000 [04:02<06:17, 11.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3816/8000 [04:02<06:08, 11.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3820/8000 [04:02<04:58, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▋                  | 3823/8000 [04:02<04:19, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3829/8000 [04:03<03:17, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3837/8000 [04:03<02:57, 23.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3844/8000 [04:03<02:54, 23.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3848/8000 [04:03<02:53, 23.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3852/8000 [04:04<03:36, 19.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▊                  | 3855/8000 [04:04<04:19, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3858/8000 [04:04<04:14, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3861/8000 [04:04<04:03, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3863/8000 [04:05<04:16, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3866/8000 [04:05<04:49, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3869/8000 [04:05<04:30, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3871/8000 [04:05<05:45, 11.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3874/8000 [04:05<04:48, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3876/8000 [04:06<05:01, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████████▉                  | 3878/8000 [04:06<05:14, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▉                  | 3883/8000 [04:06<04:43, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████████▉                  | 3885/8000 [04:06<04:54, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3889/8000 [04:06<04:52, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3894/8000 [04:07<04:40, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3897/8000 [04:07<04:30, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3900/8000 [04:07<04:07, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3902/8000 [04:07<04:27, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3905/8000 [04:08<05:09, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3910/8000 [04:08<04:37, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3912/8000 [04:08<04:48, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████                  | 3914/8000 [04:08<04:41, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3916/8000 [04:08<04:51, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3920/8000 [04:08<04:06, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3922/8000 [04:09<04:26, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3924/8000 [04:09<04:39, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3927/8000 [04:09<04:18, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3930/8000 [04:09<05:19, 12.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3934/8000 [04:09<04:02, 16.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3936/8000 [04:10<04:21, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3938/8000 [04:10<04:27, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▏                 | 3941/8000 [04:10<04:10, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3945/8000 [04:10<03:18, 20.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3950/8000 [04:10<03:38, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3954/8000 [04:11<03:58, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3956/8000 [04:11<04:14, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████████▎                 | 3959/8000 [04:11<03:58, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3962/8000 [04:11<03:46, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3965/8000 [04:11<04:23, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3968/8000 [04:11<04:04, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▎                 | 3970/8000 [04:12<05:19, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3974/8000 [04:12<05:16, 12.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3977/8000 [04:12<04:38, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3981/8000 [04:12<04:36, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3984/8000 [04:13<04:15, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3990/8000 [04:13<03:41, 18.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3992/8000 [04:13<04:47, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3995/8000 [04:13<04:28, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▍                 | 3998/8000 [04:13<04:02, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4001/8000 [04:14<03:52, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4003/8000 [04:14<04:13, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4005/8000 [04:14<04:36, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4008/8000 [04:14<04:14, 15.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4011/8000 [04:14<05:00, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4014/8000 [04:15<05:37, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4017/8000 [04:15<04:57, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4019/8000 [04:15<05:00, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4021/8000 [04:15<06:04, 10.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▌                 | 4026/8000 [04:15<04:11, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4029/8000 [04:16<03:57, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4031/8000 [04:16<03:56, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4034/8000 [04:16<03:49, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4037/8000 [04:16<04:42, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████████▋                 | 4040/8000 [04:16<04:00, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4042/8000 [04:16<04:18, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4045/8000 [04:17<03:55, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4049/8000 [04:17<03:25, 19.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▋                 | 4053/8000 [04:17<05:34, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4058/8000 [04:18<04:11, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4062/8000 [04:18<03:42, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4066/8000 [04:18<03:28, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4069/8000 [04:18<04:21, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4071/8000 [04:18<04:31, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4073/8000 [04:19<04:39, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4076/8000 [04:19<04:03, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4080/8000 [04:19<04:32, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▊                 | 4084/8000 [04:19<03:43, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4087/8000 [04:19<03:24, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4090/8000 [04:20<04:22, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4092/8000 [04:20<05:14, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4096/8000 [04:20<04:14, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4099/8000 [04:20<04:04, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4103/8000 [04:20<03:25, 18.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4107/8000 [04:20<02:54, 22.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████████▉                 | 4113/8000 [04:21<02:27, 26.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████████                 | 4116/8000 [04:21<02:39, 24.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████████                 | 4119/8000 [04:21<02:59, 21.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4122/8000 [04:21<03:09, 20.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4125/8000 [04:22<05:04, 12.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4127/8000 [04:22<05:06, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4129/8000 [04:22<06:56,  9.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4132/8000 [04:23<07:50,  8.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4135/8000 [04:23<06:12, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████                 | 4138/8000 [04:23<05:22, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4143/8000 [04:23<03:52, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4148/8000 [04:23<03:45, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4154/8000 [04:24<03:35, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4158/8000 [04:24<03:14, 19.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4161/8000 [04:24<03:05, 20.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4164/8000 [04:24<03:10, 20.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▏                | 4170/8000 [04:24<03:08, 20.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4173/8000 [04:24<03:00, 21.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4176/8000 [04:25<03:07, 20.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4180/8000 [04:25<02:50, 22.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4183/8000 [04:25<05:13, 12.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4186/8000 [04:25<04:42, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4192/8000 [04:26<03:16, 19.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████████▎                | 4195/8000 [04:26<04:43, 13.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4201/8000 [04:26<04:02, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4204/8000 [04:26<03:55, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4207/8000 [04:27<04:31, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4209/8000 [04:27<04:38, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4213/8000 [04:27<03:54, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4218/8000 [04:27<03:08, 20.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4221/8000 [04:27<03:12, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▍                | 4225/8000 [04:28<03:03, 20.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4230/8000 [04:28<03:12, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4233/8000 [04:28<03:58, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4236/8000 [04:28<03:45, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4239/8000 [04:28<03:23, 18.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4243/8000 [04:29<03:05, 20.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4247/8000 [04:29<02:49, 22.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4252/8000 [04:29<02:30, 24.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▌                | 4256/8000 [04:29<03:02, 20.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4262/8000 [04:29<02:29, 24.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4265/8000 [04:30<02:35, 24.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4270/8000 [04:30<02:56, 21.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4273/8000 [04:30<04:11, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4276/8000 [04:30<04:00, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████████▋                | 4278/8000 [04:31<04:11, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▋                | 4282/8000 [04:31<03:41, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4287/8000 [04:31<03:05, 20.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4292/8000 [04:31<04:01, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4296/8000 [04:32<03:35, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4301/8000 [04:32<03:00, 20.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4307/8000 [04:32<02:55, 21.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▊                | 4311/8000 [04:32<03:29, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4316/8000 [04:32<03:02, 20.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4321/8000 [04:33<02:43, 22.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4324/8000 [04:33<02:53, 21.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4329/8000 [04:33<02:33, 23.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4333/8000 [04:33<02:30, 24.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████████▉                | 4338/8000 [04:33<03:02, 20.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4344/8000 [04:34<03:46, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4346/8000 [04:34<04:03, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4351/8000 [04:34<03:09, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4354/8000 [04:34<03:11, 19.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████████                | 4358/8000 [04:35<03:35, 16.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4361/8000 [04:35<03:16, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4365/8000 [04:35<03:49, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████                | 4368/8000 [04:35<03:39, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4374/8000 [04:35<02:49, 21.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4378/8000 [04:36<03:25, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4384/8000 [04:36<03:24, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4386/8000 [04:36<03:32, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4388/8000 [04:36<03:43, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4390/8000 [04:37<03:41, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4393/8000 [04:37<04:23, 13.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4395/8000 [04:37<04:29, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▏               | 4399/8000 [04:37<03:23, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4403/8000 [04:37<03:03, 19.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4406/8000 [04:38<04:29, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4409/8000 [04:38<04:13, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4413/8000 [04:38<04:15, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4416/8000 [04:38<03:47, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4418/8000 [04:38<03:58, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4421/8000 [04:39<03:42, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4423/8000 [04:39<04:32, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▎               | 4427/8000 [04:39<03:47, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▍               | 4430/8000 [04:39<03:40, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▍               | 4435/8000 [04:39<02:58, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████████▍               | 4438/8000 [04:40<03:01, 19.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4444/8000 [04:40<03:10, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4447/8000 [04:40<03:02, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4450/8000 [04:40<03:07, 18.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4452/8000 [04:40<04:13, 13.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▍               | 4454/8000 [04:41<04:04, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4458/8000 [04:41<03:26, 17.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4461/8000 [04:41<04:07, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4463/8000 [04:41<04:06, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4465/8000 [04:41<04:12, 13.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4467/8000 [04:42<05:15, 11.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4474/8000 [04:42<03:02, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4477/8000 [04:42<02:58, 19.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▌               | 4481/8000 [04:42<02:44, 21.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4488/8000 [04:43<04:05, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4492/8000 [04:43<03:29, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4495/8000 [04:43<05:11, 11.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4498/8000 [04:44<05:21, 10.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4500/8000 [04:44<05:11, 11.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4502/8000 [04:44<04:53, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4504/8000 [04:44<04:54, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4506/8000 [04:44<04:50, 12.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4508/8000 [04:45<06:03,  9.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4510/8000 [04:45<06:52,  8.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▋               | 4514/8000 [04:45<04:53, 11.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▊               | 4517/8000 [04:45<04:21, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████████▊               | 4519/8000 [04:46<04:24, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4524/8000 [04:46<03:50, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4528/8000 [04:46<04:05, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4530/8000 [04:46<04:09, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4533/8000 [04:46<03:36, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4535/8000 [04:47<03:52, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▊               | 4537/8000 [04:47<05:18, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4543/8000 [04:47<03:23, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4546/8000 [04:47<03:17, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4551/8000 [04:48<03:21, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4554/8000 [04:48<04:04, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4557/8000 [04:48<04:25, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4559/8000 [04:48<04:29, 12.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4563/8000 [04:48<03:40, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████████▉               | 4570/8000 [04:49<03:33, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4573/8000 [04:49<03:30, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4576/8000 [04:49<03:25, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4578/8000 [04:49<03:42, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4581/8000 [04:50<04:25, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4583/8000 [04:50<06:09,  9.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4586/8000 [04:50<05:52,  9.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4588/8000 [04:51<05:33, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████               | 4595/8000 [04:51<03:12, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████████▏              | 4600/8000 [04:51<03:11, 17.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4607/8000 [04:51<02:58, 18.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4610/8000 [04:51<02:54, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4613/8000 [04:52<02:56, 19.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4618/8000 [04:52<02:33, 22.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4622/8000 [04:52<02:33, 22.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▏              | 4628/8000 [04:52<02:42, 20.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4633/8000 [04:53<02:56, 19.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4637/8000 [04:53<03:10, 17.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4642/8000 [04:53<03:15, 17.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4645/8000 [04:53<03:00, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4649/8000 [04:54<03:22, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4651/8000 [04:54<04:53, 11.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▎              | 4656/8000 [04:54<03:40, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4659/8000 [04:54<03:25, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4663/8000 [04:55<04:09, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4666/8000 [04:55<03:52, 14.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4670/8000 [04:55<03:55, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4675/8000 [04:56<03:40, 15.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████████▍              | 4679/8000 [04:56<03:17, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▍              | 4683/8000 [04:56<02:58, 18.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4688/8000 [04:56<02:37, 21.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4692/8000 [04:56<03:12, 17.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4695/8000 [04:57<03:13, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4698/8000 [04:57<03:11, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4700/8000 [04:57<03:27, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4702/8000 [04:57<05:30,  9.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4705/8000 [04:58<04:42, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4708/8000 [04:58<04:08, 13.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4710/8000 [04:58<04:48, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▌              | 4713/8000 [04:58<04:10, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4716/8000 [04:58<04:38, 11.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4720/8000 [04:59<04:21, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4723/8000 [04:59<04:57, 11.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4727/8000 [04:59<04:15, 12.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4730/8000 [04:59<03:58, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4738/8000 [05:00<02:45, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▋              | 4742/8000 [05:00<04:36, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4747/8000 [05:01<04:57, 10.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4752/8000 [05:01<04:26, 12.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4754/8000 [05:01<04:23, 12.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4756/8000 [05:02<05:10, 10.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████████▊              | 4759/8000 [05:02<04:57, 10.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▊              | 4765/8000 [05:02<03:59, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▊              | 4769/8000 [05:02<03:24, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4773/8000 [05:03<02:50, 18.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4779/8000 [05:03<02:19, 23.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4782/8000 [05:03<02:57, 18.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4786/8000 [05:03<02:32, 21.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4789/8000 [05:03<02:40, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4792/8000 [05:03<02:41, 19.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4795/8000 [05:04<03:11, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████████▉              | 4797/8000 [05:04<03:26, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4800/8000 [05:04<03:14, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4804/8000 [05:04<02:50, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4806/8000 [05:04<03:07, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4808/8000 [05:05<04:23, 12.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4810/8000 [05:05<04:18, 12.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4813/8000 [05:05<03:45, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4816/8000 [05:05<03:31, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4820/8000 [05:05<02:58, 17.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4822/8000 [05:05<03:02, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4825/8000 [05:06<03:52, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████              | 4828/8000 [05:06<03:30, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4830/8000 [05:06<03:44, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4833/8000 [05:06<03:23, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4835/8000 [05:06<03:37, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████████▏             | 4839/8000 [05:07<03:05, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4841/8000 [05:07<03:23, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4844/8000 [05:07<03:05, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4846/8000 [05:07<03:20, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4851/8000 [05:07<02:22, 22.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▏             | 4857/8000 [05:08<03:34, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4859/8000 [05:08<04:16, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4861/8000 [05:09<06:17,  8.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4864/8000 [05:09<05:19,  9.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4866/8000 [05:09<04:52, 10.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4871/8000 [05:09<03:29, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4873/8000 [05:09<03:38, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4875/8000 [05:09<04:15, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4878/8000 [05:10<03:48, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4882/8000 [05:10<03:03, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▎             | 4885/8000 [05:10<02:54, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4890/8000 [05:10<02:53, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4897/8000 [05:10<02:35, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4900/8000 [05:11<02:36, 19.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4904/8000 [05:11<03:10, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4908/8000 [05:11<03:28, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4911/8000 [05:11<03:16, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▍             | 4914/8000 [05:12<03:08, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████████▌             | 4916/8000 [05:12<03:59, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4920/8000 [05:12<03:21, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4925/8000 [05:12<02:41, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4929/8000 [05:12<02:30, 20.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4932/8000 [05:13<03:54, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4934/8000 [05:13<03:56, 12.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4936/8000 [05:13<03:52, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▌             | 4940/8000 [05:13<03:45, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4943/8000 [05:14<03:28, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4946/8000 [05:14<04:07, 12.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4949/8000 [05:14<03:42, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4954/8000 [05:14<02:53, 17.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4957/8000 [05:14<02:48, 18.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4959/8000 [05:15<03:02, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4963/8000 [05:15<02:41, 18.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4966/8000 [05:15<03:21, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4968/8000 [05:15<03:16, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▋             | 4971/8000 [05:15<03:06, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4973/8000 [05:16<03:16, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4975/8000 [05:16<03:21, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4977/8000 [05:16<03:14, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4980/8000 [05:16<03:05, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4986/8000 [05:16<02:08, 23.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4989/8000 [05:16<02:14, 22.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4994/8000 [05:17<02:34, 19.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████████▊             | 4998/8000 [05:17<03:07, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5001/8000 [05:17<03:01, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5003/8000 [05:17<03:13, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5006/8000 [05:18<04:16, 11.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5011/8000 [05:18<04:19, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5014/8000 [05:18<03:54, 12.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5016/8000 [05:18<03:54, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5018/8000 [05:19<03:49, 13.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5021/8000 [05:19<03:18, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████████▉             | 5026/8000 [05:19<02:32, 19.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5035/8000 [05:19<02:09, 22.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5040/8000 [05:20<02:27, 20.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5044/8000 [05:20<02:49, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5050/8000 [05:20<02:43, 18.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5053/8000 [05:20<02:41, 18.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5055/8000 [05:20<02:44, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████             | 5057/8000 [05:21<03:49, 12.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5060/8000 [05:21<04:54, 10.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5062/8000 [05:21<04:44, 10.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5065/8000 [05:22<04:03, 12.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5069/8000 [05:22<03:20, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5073/8000 [05:22<02:52, 16.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████████▏            | 5079/8000 [05:22<02:44, 17.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▏            | 5083/8000 [05:22<02:32, 19.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5086/8000 [05:23<02:36, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5091/8000 [05:23<02:11, 22.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5095/8000 [05:23<02:07, 22.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5099/8000 [05:23<02:59, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5102/8000 [05:23<02:57, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5105/8000 [05:24<02:47, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5109/8000 [05:24<02:27, 19.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▎            | 5114/8000 [05:24<02:33, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5117/8000 [05:24<02:29, 19.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5120/8000 [05:24<02:56, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5124/8000 [05:25<03:08, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5128/8000 [05:25<03:14, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5132/8000 [05:25<03:22, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5135/8000 [05:25<03:05, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5137/8000 [05:26<03:03, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5140/8000 [05:26<02:56, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▍            | 5142/8000 [05:26<03:47, 12.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5146/8000 [05:26<03:00, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5149/8000 [05:26<02:50, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5151/8000 [05:27<03:05, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5154/8000 [05:27<02:55, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5156/8000 [05:27<03:07, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████████▌            | 5160/8000 [05:27<02:41, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▌            | 5163/8000 [05:27<02:40, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▌            | 5170/8000 [05:27<01:45, 26.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5173/8000 [05:27<01:56, 24.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5178/8000 [05:28<02:16, 20.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5181/8000 [05:28<03:17, 14.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5186/8000 [05:28<02:37, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5190/8000 [05:29<03:14, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5192/8000 [05:29<03:21, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5194/8000 [05:29<03:14, 14.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5196/8000 [05:29<04:11, 11.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▋            | 5199/8000 [05:30<03:33, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5202/8000 [05:30<03:46, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5204/8000 [05:30<03:44, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5207/8000 [05:30<03:16, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5212/8000 [05:30<03:02, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5214/8000 [05:31<03:20, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5216/8000 [05:31<03:27, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5218/8000 [05:31<03:29, 13.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5222/8000 [05:31<02:50, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▊            | 5226/8000 [05:31<02:28, 18.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5230/8000 [05:32<02:46, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5232/8000 [05:32<02:53, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5235/8000 [05:32<02:44, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████████▉            | 5238/8000 [05:32<03:14, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5243/8000 [05:32<02:33, 17.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5245/8000 [05:32<02:49, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5247/8000 [05:33<03:50, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5251/8000 [05:33<03:05, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5255/8000 [05:33<03:58, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████████▉            | 5257/8000 [05:34<03:53, 11.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5260/8000 [05:34<03:26, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5262/8000 [05:34<03:27, 13.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5264/8000 [05:34<03:31, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5267/8000 [05:34<04:01, 11.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5272/8000 [05:35<02:49, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5278/8000 [05:35<02:04, 21.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5281/8000 [05:35<02:03, 22.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████            | 5284/8000 [05:35<03:14, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5287/8000 [05:35<02:50, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5292/8000 [05:36<03:20, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5294/8000 [05:36<03:12, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5296/8000 [05:36<04:00, 11.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5301/8000 [05:36<02:52, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5306/8000 [05:37<02:45, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5308/8000 [05:37<03:36, 12.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▏           | 5310/8000 [05:37<03:36, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▎           | 5315/8000 [05:38<03:53, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████████▎           | 5319/8000 [05:38<03:12, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5325/8000 [05:38<02:20, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5328/8000 [05:38<02:20, 19.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5333/8000 [05:38<02:24, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5337/8000 [05:39<02:47, 15.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5340/8000 [05:39<02:44, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▎           | 5342/8000 [05:39<03:39, 12.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5346/8000 [05:40<03:58, 11.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5348/8000 [05:40<04:26,  9.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5354/8000 [05:40<02:59, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5356/8000 [05:40<03:08, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5358/8000 [05:41<03:12, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5360/8000 [05:41<03:21, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5363/8000 [05:41<04:30,  9.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5367/8000 [05:42<04:07, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▍           | 5371/8000 [05:42<03:11, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5374/8000 [05:42<02:53, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5377/8000 [05:42<02:43, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5379/8000 [05:42<03:38, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5382/8000 [05:43<03:57, 11.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5385/8000 [05:43<03:29, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5390/8000 [05:43<02:33, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████████▌           | 5393/8000 [05:43<03:03, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5401/8000 [05:44<02:16, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5404/8000 [05:44<02:47, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5409/8000 [05:44<02:14, 19.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5414/8000 [05:44<02:00, 21.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5419/8000 [05:44<02:19, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5422/8000 [05:45<02:19, 18.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▋           | 5428/8000 [05:45<02:17, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5434/8000 [05:45<02:33, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5440/8000 [05:46<02:23, 17.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5442/8000 [05:46<03:01, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5445/8000 [05:46<02:51, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5447/8000 [05:46<02:53, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5449/8000 [05:46<03:02, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5452/8000 [05:47<03:34, 11.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▊           | 5456/8000 [05:47<02:44, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5460/8000 [05:47<02:23, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5464/8000 [05:47<02:07, 19.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5467/8000 [05:47<02:07, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5471/8000 [05:48<02:24, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5474/8000 [05:48<02:21, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5476/8000 [05:48<02:27, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████████▉           | 5478/8000 [05:48<03:21, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▉           | 5481/8000 [05:48<03:00, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▉           | 5483/8000 [05:49<03:47, 11.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████████▉           | 5485/8000 [05:49<03:35, 11.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5488/8000 [05:49<03:06, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5492/8000 [05:49<02:31, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5494/8000 [05:50<03:30, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5499/8000 [05:50<02:32, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5503/8000 [05:50<02:05, 19.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5509/8000 [05:50<01:43, 24.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████           | 5513/8000 [05:50<01:41, 24.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5517/8000 [05:50<01:55, 21.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5523/8000 [05:51<02:01, 20.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5526/8000 [05:51<02:46, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5530/8000 [05:51<02:25, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5535/8000 [05:51<02:04, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▏          | 5538/8000 [05:52<02:30, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5543/8000 [05:52<02:27, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5547/8000 [05:52<02:05, 19.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5551/8000 [05:52<01:58, 20.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5554/8000 [05:52<02:00, 20.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████████▎          | 5558/8000 [05:53<01:50, 22.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5562/8000 [05:53<01:37, 25.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5566/8000 [05:53<02:03, 19.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▎          | 5570/8000 [05:53<01:55, 20.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5573/8000 [05:53<01:49, 22.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5576/8000 [05:54<02:30, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5580/8000 [05:54<02:41, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5582/8000 [05:54<02:50, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5585/8000 [05:54<02:43, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5589/8000 [05:54<02:20, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5593/8000 [05:55<03:03, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▍          | 5596/8000 [05:55<03:11, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5602/8000 [05:56<02:43, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5606/8000 [05:56<02:48, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5611/8000 [05:56<02:14, 17.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5614/8000 [05:56<02:32, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5616/8000 [05:57<04:06,  9.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5618/8000 [05:57<03:57, 10.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5620/8000 [05:57<03:44, 10.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5624/8000 [05:57<02:51, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▌          | 5627/8000 [05:57<02:40, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5631/8000 [05:58<03:18, 11.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5634/8000 [05:58<02:58, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████████▋          | 5639/8000 [05:58<02:49, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5642/8000 [05:59<03:09, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5644/8000 [05:59<03:39, 10.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5646/8000 [05:59<03:21, 11.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5649/8000 [05:59<02:57, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5652/8000 [05:59<02:39, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▋          | 5656/8000 [06:00<02:11, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5663/8000 [06:00<01:27, 26.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5669/8000 [06:00<02:00, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5675/8000 [06:00<01:34, 24.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▊          | 5682/8000 [06:01<01:57, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5686/8000 [06:01<02:12, 17.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5690/8000 [06:01<02:26, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5693/8000 [06:02<02:22, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5696/8000 [06:02<02:16, 16.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5699/8000 [06:02<02:14, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5701/8000 [06:02<02:13, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5704/8000 [06:02<03:16, 11.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5709/8000 [06:03<02:18, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████████▉          | 5713/8000 [06:03<02:34, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|█████████████████████████          | 5716/8000 [06:03<02:22, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|█████████████████████████          | 5719/8000 [06:03<02:07, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5722/8000 [06:03<02:38, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5725/8000 [06:04<02:50, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5731/8000 [06:04<01:59, 18.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5736/8000 [06:04<02:05, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5740/8000 [06:04<02:13, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████          | 5742/8000 [06:05<02:18, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5745/8000 [06:05<02:13, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5748/8000 [06:05<02:00, 18.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5754/8000 [06:05<02:00, 18.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5758/8000 [06:05<02:08, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5760/8000 [06:06<02:17, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5765/8000 [06:06<01:49, 20.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5768/8000 [06:06<01:49, 20.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▏         | 5771/8000 [06:06<01:43, 21.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5775/8000 [06:06<02:08, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5778/8000 [06:07<02:02, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5781/8000 [06:07<02:25, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5783/8000 [06:07<03:03, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5787/8000 [06:08<03:18, 11.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5790/8000 [06:08<03:20, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5794/8000 [06:08<02:43, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5797/8000 [06:08<02:27, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████████▎         | 5799/8000 [06:08<02:31, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5802/8000 [06:08<02:13, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5805/8000 [06:09<02:10, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5808/8000 [06:09<02:04, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5811/8000 [06:09<01:55, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5813/8000 [06:09<01:58, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5815/8000 [06:09<02:14, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5817/8000 [06:09<02:24, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5821/8000 [06:09<02:04, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▍         | 5824/8000 [06:10<02:01, 17.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5830/8000 [06:10<01:33, 23.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5834/8000 [06:10<02:20, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5837/8000 [06:10<02:12, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5839/8000 [06:11<02:20, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5841/8000 [06:11<02:19, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5844/8000 [06:11<02:44, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5847/8000 [06:11<02:27, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5852/8000 [06:11<02:16, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5854/8000 [06:12<02:19, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▌         | 5857/8000 [06:12<02:10, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5860/8000 [06:12<01:56, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5866/8000 [06:12<01:55, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5868/8000 [06:12<02:00, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5870/8000 [06:12<02:04, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5872/8000 [06:13<02:15, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5874/8000 [06:13<02:23, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5877/8000 [06:13<02:16, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████████▋         | 5879/8000 [06:13<02:28, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▋         | 5883/8000 [06:13<02:01, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▋         | 5885/8000 [06:13<02:13, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5888/8000 [06:14<02:42, 12.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5890/8000 [06:14<04:23,  8.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5892/8000 [06:15<04:37,  7.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5894/8000 [06:15<03:57,  8.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5896/8000 [06:15<03:40,  9.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5900/8000 [06:15<02:44, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5903/8000 [06:15<02:27, 14.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5906/8000 [06:15<02:18, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5908/8000 [06:16<02:26, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5910/8000 [06:16<02:33, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▊         | 5912/8000 [06:16<02:31, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5916/8000 [06:16<02:02, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5919/8000 [06:16<01:57, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5924/8000 [06:16<01:32, 22.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5927/8000 [06:16<01:31, 22.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5930/8000 [06:17<02:42, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5933/8000 [06:17<02:30, 13.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5936/8000 [06:17<02:19, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5940/8000 [06:17<01:59, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████████▉         | 5942/8000 [06:18<03:04, 11.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5945/8000 [06:18<02:42, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5948/8000 [06:18<02:16, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5951/8000 [06:18<02:07, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5953/8000 [06:18<02:13, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5957/8000 [06:19<02:16, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|██████████████████████████         | 5959/8000 [06:19<02:23, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5964/8000 [06:19<01:50, 18.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5966/8000 [06:19<02:02, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████         | 5968/8000 [06:19<02:12, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5974/8000 [06:20<02:04, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5981/8000 [06:20<01:47, 18.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5986/8000 [06:20<01:31, 21.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5989/8000 [06:20<01:33, 21.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5992/8000 [06:20<01:30, 22.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5996/8000 [06:21<01:49, 18.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▏        | 5998/8000 [06:21<01:57, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6001/8000 [06:21<01:49, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6006/8000 [06:21<01:31, 21.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6011/8000 [06:21<01:23, 23.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6014/8000 [06:22<01:27, 22.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6017/8000 [06:22<01:34, 21.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6020/8000 [06:22<01:36, 20.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6024/8000 [06:22<01:29, 22.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▎        | 6027/8000 [06:22<02:00, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6032/8000 [06:23<02:00, 16.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6036/8000 [06:23<02:05, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|██████████████████████████▍        | 6039/8000 [06:23<02:22, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6041/8000 [06:23<02:18, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6043/8000 [06:24<02:22, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6045/8000 [06:24<02:24, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6047/8000 [06:24<02:21, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6049/8000 [06:24<02:52, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6051/8000 [06:24<02:44, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▍        | 6054/8000 [06:25<03:27,  9.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6059/8000 [06:25<02:19, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6061/8000 [06:25<02:20, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6065/8000 [06:25<02:40, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6069/8000 [06:26<02:10, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6072/8000 [06:26<02:27, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6075/8000 [06:26<02:38, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6077/8000 [06:26<02:34, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▌        | 6081/8000 [06:26<02:10, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6088/8000 [06:27<01:26, 21.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6092/8000 [06:27<01:21, 23.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6095/8000 [06:27<01:46, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6098/8000 [06:27<01:46, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6104/8000 [06:28<01:45, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6108/8000 [06:28<01:57, 16.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▋        | 6112/8000 [06:28<01:39, 18.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6116/8000 [06:28<01:31, 20.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████████▊        | 6120/8000 [06:28<01:25, 22.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6123/8000 [06:29<01:44, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6128/8000 [06:29<01:50, 16.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6130/8000 [06:29<01:53, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6136/8000 [06:29<01:44, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▊        | 6139/8000 [06:29<01:44, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6143/8000 [06:30<02:00, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6145/8000 [06:30<02:04, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6147/8000 [06:30<02:37, 11.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6150/8000 [06:31<02:45, 11.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6154/8000 [06:31<02:11, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6157/8000 [06:31<01:55, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6159/8000 [06:31<02:55, 10.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6161/8000 [06:31<02:44, 11.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6163/8000 [06:32<02:40, 11.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6166/8000 [06:32<02:47, 10.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6168/8000 [06:32<03:08,  9.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████████▉        | 6171/8000 [06:32<02:36, 11.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6174/8000 [06:32<02:13, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6177/8000 [06:33<02:34, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6179/8000 [06:33<03:02,  9.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6181/8000 [06:33<02:54, 10.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6185/8000 [06:34<02:47, 10.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6189/8000 [06:34<02:06, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6191/8000 [06:34<02:12, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6195/8000 [06:34<02:12, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████████████        | 6197/8000 [06:34<02:15, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6201/8000 [06:35<01:53, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6204/8000 [06:35<01:47, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6207/8000 [06:35<01:43, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6209/8000 [06:35<01:55, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6211/8000 [06:35<02:34, 11.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6214/8000 [06:35<02:06, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6217/8000 [06:36<01:58, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6219/8000 [06:36<02:04, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6221/8000 [06:36<02:12, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6223/8000 [06:36<02:14, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▏       | 6227/8000 [06:36<01:48, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6229/8000 [06:37<01:59, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6235/8000 [06:37<01:43, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6238/8000 [06:37<02:02, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6241/8000 [06:37<02:15, 13.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6244/8000 [06:38<02:02, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6246/8000 [06:38<02:01, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6248/8000 [06:38<02:35, 11.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▎       | 6255/8000 [06:38<01:55, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6258/8000 [06:39<02:12, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6260/8000 [06:39<02:13, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6262/8000 [06:39<02:14, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6264/8000 [06:39<02:17, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6269/8000 [06:39<01:38, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6273/8000 [06:39<01:27, 19.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6276/8000 [06:40<02:19, 12.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|███████████████████████████▍       | 6279/8000 [06:40<02:34, 11.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6281/8000 [06:41<02:56,  9.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6283/8000 [06:41<02:40, 10.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▍       | 6285/8000 [06:41<02:33, 11.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6289/8000 [06:41<01:56, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6293/8000 [06:41<01:36, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6299/8000 [06:41<01:28, 19.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6303/8000 [06:42<01:20, 20.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6308/8000 [06:42<01:10, 24.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▌       | 6312/8000 [06:42<01:24, 19.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6321/8000 [06:42<01:12, 23.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6325/8000 [06:42<01:10, 23.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6328/8000 [06:43<01:30, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6331/8000 [06:43<01:50, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6336/8000 [06:43<01:47, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▋       | 6338/8000 [06:44<01:48, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6344/8000 [06:44<01:21, 20.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6350/8000 [06:44<01:34, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6353/8000 [06:44<01:34, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6356/8000 [06:44<01:33, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████████▊       | 6359/8000 [06:45<01:29, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6361/8000 [06:45<02:03, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6365/8000 [06:45<02:06, 12.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▊       | 6369/8000 [06:45<01:47, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6374/8000 [06:46<01:44, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6377/8000 [06:46<01:55, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6379/8000 [06:46<01:58, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6382/8000 [06:46<02:04, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6384/8000 [06:47<02:39, 10.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6388/8000 [06:47<02:01, 13.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6390/8000 [06:47<02:02, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6392/8000 [06:47<02:33, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6395/8000 [06:48<02:11, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6397/8000 [06:48<02:11, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████████▉       | 6399/8000 [06:48<02:08, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6401/8000 [06:48<02:05, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6404/8000 [06:48<01:42, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6406/8000 [06:48<02:19, 11.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6408/8000 [06:49<02:13, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6411/8000 [06:49<01:49, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6415/8000 [06:49<01:33, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6420/8000 [06:49<01:29, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6422/8000 [06:49<01:56, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6424/8000 [06:50<01:58, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6426/8000 [06:50<02:26, 10.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████       | 6428/8000 [06:50<02:23, 10.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6430/8000 [06:50<02:22, 11.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6433/8000 [06:50<02:01, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████████████▏      | 6437/8000 [06:51<01:38, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6441/8000 [06:51<01:22, 18.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6444/8000 [06:51<01:22, 18.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6447/8000 [06:51<01:24, 18.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6449/8000 [06:51<02:04, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▏      | 6455/8000 [06:52<01:25, 18.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6460/8000 [06:52<01:30, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6464/8000 [06:52<01:32, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6467/8000 [06:53<01:50, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6469/8000 [06:53<01:50, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6472/8000 [06:53<01:42, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6475/8000 [06:53<01:37, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6481/8000 [06:53<01:12, 20.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▎      | 6484/8000 [06:53<01:14, 20.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6489/8000 [06:54<01:23, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6494/8000 [06:54<01:23, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6496/8000 [06:54<01:29, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6499/8000 [06:54<01:28, 16.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6501/8000 [06:54<01:29, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6505/8000 [06:55<01:21, 18.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6507/8000 [06:55<01:34, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6511/8000 [06:55<01:25, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▍      | 6513/8000 [06:55<02:22, 10.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████████████▌      | 6517/8000 [06:56<02:02, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6520/8000 [06:56<02:02, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6523/8000 [06:56<01:59, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6527/8000 [06:56<01:42, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6529/8000 [06:57<01:43, 14.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6536/8000 [06:57<01:07, 21.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▌      | 6541/8000 [06:57<00:57, 25.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6546/8000 [06:57<00:53, 27.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6549/8000 [06:57<01:13, 19.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6555/8000 [06:58<01:11, 20.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6558/8000 [06:58<01:13, 19.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6564/8000 [06:58<01:23, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6566/8000 [06:58<01:41, 14.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▋      | 6571/8000 [06:59<01:17, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6575/8000 [06:59<01:11, 20.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6578/8000 [06:59<01:40, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6581/8000 [06:59<01:35, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6585/8000 [06:59<01:21, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6591/8000 [07:00<01:12, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|████████████████████████████▊      | 6596/8000 [07:00<01:03, 22.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6601/8000 [07:00<01:09, 20.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6606/8000 [07:01<01:23, 16.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6608/8000 [07:01<01:27, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6612/8000 [07:01<01:14, 18.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6617/8000 [07:01<01:18, 17.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6623/8000 [07:02<01:37, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████████▉      | 6625/8000 [07:02<01:39, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6630/8000 [07:02<01:18, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6634/8000 [07:02<01:11, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6638/8000 [07:02<01:06, 20.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6641/8000 [07:03<01:27, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6646/8000 [07:03<01:28, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6648/8000 [07:03<01:44, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6651/8000 [07:03<01:34, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6653/8000 [07:04<01:51, 12.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6655/8000 [07:04<01:52, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████      | 6657/8000 [07:04<02:31,  8.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6661/8000 [07:04<01:52, 11.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6664/8000 [07:05<01:37, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6668/8000 [07:05<01:33, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6671/8000 [07:05<01:46, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6673/8000 [07:05<01:44, 12.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6676/8000 [07:06<01:51, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████████████▏     | 6679/8000 [07:06<02:03, 10.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6682/8000 [07:06<01:47, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▏     | 6685/8000 [07:06<01:35, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6688/8000 [07:06<01:27, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6693/8000 [07:07<01:07, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6697/8000 [07:07<01:03, 20.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6701/8000 [07:07<01:14, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6706/8000 [07:07<01:14, 17.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6708/8000 [07:08<01:19, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▎     | 6711/8000 [07:08<01:14, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6715/8000 [07:08<01:07, 19.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6718/8000 [07:08<01:44, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6722/8000 [07:08<01:26, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6725/8000 [07:09<01:39, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6727/8000 [07:09<01:55, 11.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6729/8000 [07:09<01:50, 11.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6733/8000 [07:09<01:25, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6735/8000 [07:10<01:23, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▍     | 6737/8000 [07:10<01:28, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6743/8000 [07:10<01:26, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6748/8000 [07:10<01:09, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6754/8000 [07:10<00:53, 23.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████████████▌     | 6760/8000 [07:11<00:53, 23.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6763/8000 [07:11<01:09, 17.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▌     | 6767/8000 [07:11<01:02, 19.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6772/8000 [07:11<01:04, 19.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6777/8000 [07:12<00:54, 22.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6781/8000 [07:12<00:52, 23.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6786/8000 [07:12<00:45, 26.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6791/8000 [07:12<00:56, 21.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▋     | 6799/8000 [07:13<01:09, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6803/8000 [07:13<01:04, 18.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6807/8000 [07:13<01:13, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6810/8000 [07:13<01:10, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6813/8000 [07:14<01:21, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6816/8000 [07:14<01:16, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6818/8000 [07:14<01:18, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6824/8000 [07:14<01:08, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▊     | 6828/8000 [07:15<01:14, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6831/8000 [07:15<01:11, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6833/8000 [07:15<01:16, 15.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6835/8000 [07:15<01:21, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████████████▉     | 6838/8000 [07:15<01:14, 15.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6840/8000 [07:15<01:18, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6844/8000 [07:16<01:05, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6849/8000 [07:16<01:06, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6851/8000 [07:16<01:10, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6853/8000 [07:16<01:12, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████████▉     | 6856/8000 [07:17<01:41, 11.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6858/8000 [07:17<01:57,  9.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6861/8000 [07:17<02:11,  8.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6863/8000 [07:17<02:00,  9.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6866/8000 [07:18<01:34, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6868/8000 [07:18<01:35, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6872/8000 [07:18<01:14, 15.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6875/8000 [07:18<01:11, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6878/8000 [07:19<01:45, 10.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6880/8000 [07:19<01:40, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████     | 6882/8000 [07:19<01:39, 11.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6886/8000 [07:19<01:15, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6888/8000 [07:19<01:16, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6892/8000 [07:19<00:58, 18.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6898/8000 [07:20<00:58, 18.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6901/8000 [07:20<00:56, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6904/8000 [07:20<01:07, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6908/8000 [07:20<00:57, 18.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▏    | 6914/8000 [07:20<00:46, 23.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6917/8000 [07:21<01:10, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████████████▎    | 6920/8000 [07:21<01:17, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6922/8000 [07:21<01:34, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6924/8000 [07:22<01:44, 10.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6928/8000 [07:22<01:20, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6930/8000 [07:22<01:21, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6932/8000 [07:22<01:34, 11.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6937/8000 [07:22<01:06, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▎    | 6939/8000 [07:23<01:08, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6943/8000 [07:23<01:37, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6947/8000 [07:23<01:18, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6949/8000 [07:23<01:29, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6954/8000 [07:24<01:05, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6960/8000 [07:24<00:48, 21.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6964/8000 [07:24<00:45, 22.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▍    | 6969/8000 [07:24<00:40, 25.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6973/8000 [07:24<00:39, 25.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6976/8000 [07:25<01:04, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6980/8000 [07:25<01:07, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6983/8000 [07:25<01:01, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6988/8000 [07:25<01:01, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6990/8000 [07:26<01:14, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████████████▌    | 6997/8000 [07:26<00:50, 19.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7001/8000 [07:26<00:47, 21.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7005/8000 [07:26<01:01, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7008/8000 [07:27<01:11, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7010/8000 [07:27<01:08, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7014/8000 [07:27<00:59, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7016/8000 [07:27<01:15, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7018/8000 [07:27<01:11, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7020/8000 [07:28<01:17, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7023/8000 [07:28<01:09, 14.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▋    | 7027/8000 [07:28<00:54, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7030/8000 [07:28<00:50, 19.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7035/8000 [07:28<00:39, 24.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7038/8000 [07:28<00:40, 23.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7042/8000 [07:29<00:49, 19.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7048/8000 [07:29<00:39, 24.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▊    | 7051/8000 [07:29<00:39, 23.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7058/8000 [07:29<00:39, 24.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7064/8000 [07:29<00:41, 22.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7070/8000 [07:30<00:41, 22.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7074/8000 [07:30<00:40, 22.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7077/8000 [07:30<00:43, 21.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████████████▉    | 7080/8000 [07:30<00:45, 20.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|██████████████████████████████▉    | 7085/8000 [07:30<00:39, 23.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7090/8000 [07:31<00:45, 20.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7094/8000 [07:31<00:52, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7096/8000 [07:31<00:54, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7098/8000 [07:31<00:54, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7102/8000 [07:31<00:48, 18.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7105/8000 [07:32<00:58, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7110/8000 [07:32<00:54, 16.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████    | 7113/8000 [07:32<00:52, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7117/8000 [07:32<00:55, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7119/8000 [07:33<00:58, 15.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7121/8000 [07:33<01:16, 11.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7123/8000 [07:33<01:15, 11.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7129/8000 [07:33<01:01, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7132/8000 [07:34<00:55, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7136/8000 [07:34<00:48, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▏   | 7140/8000 [07:34<00:42, 20.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7144/8000 [07:34<00:47, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7147/8000 [07:34<00:47, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7149/8000 [07:35<01:03, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7154/8000 [07:35<00:45, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████████████▎   | 7159/8000 [07:35<00:38, 21.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7162/8000 [07:35<00:40, 20.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▎   | 7168/8000 [07:35<00:37, 22.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7172/8000 [07:36<00:53, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7174/8000 [07:36<00:52, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7177/8000 [07:36<00:50, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7180/8000 [07:36<00:58, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7185/8000 [07:37<00:54, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7188/8000 [07:37<00:50, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7191/8000 [07:37<01:01, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7195/8000 [07:37<00:51, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▍   | 7198/8000 [07:37<00:49, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7200/8000 [07:38<00:51, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7202/8000 [07:38<00:55, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7205/8000 [07:38<01:02, 12.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7208/8000 [07:38<00:52, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7211/8000 [07:38<00:49, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7216/8000 [07:39<00:38, 20.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7219/8000 [07:39<00:37, 20.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7222/8000 [07:39<00:47, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▌   | 7225/8000 [07:39<00:44, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7231/8000 [07:39<00:39, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7234/8000 [07:40<00:39, 19.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████████████▋   | 7239/8000 [07:40<00:33, 22.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7243/8000 [07:40<00:31, 23.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7248/8000 [07:40<00:27, 27.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7253/8000 [07:40<00:26, 28.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▋   | 7256/8000 [07:40<00:29, 25.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7260/8000 [07:41<00:37, 19.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7263/8000 [07:41<00:38, 19.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7267/8000 [07:41<00:45, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7272/8000 [07:41<00:37, 19.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7277/8000 [07:42<00:39, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▊   | 7281/8000 [07:42<00:34, 21.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7287/8000 [07:42<00:28, 24.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7291/8000 [07:42<00:36, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7296/8000 [07:42<00:31, 22.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7299/8000 [07:43<00:33, 20.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7302/8000 [07:43<00:43, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7306/8000 [07:43<00:49, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7308/8000 [07:44<01:00, 11.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7310/8000 [07:44<01:00, 11.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████████████▉   | 7314/8000 [07:44<00:48, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████████████   | 7316/8000 [07:44<00:47, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7321/8000 [07:44<00:52, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7323/8000 [07:45<00:50, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7326/8000 [07:45<00:46, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7328/8000 [07:45<01:01, 11.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7333/8000 [07:45<00:43, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7337/8000 [07:45<00:38, 17.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████   | 7339/8000 [07:46<00:41, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7344/8000 [07:46<00:32, 20.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7347/8000 [07:46<00:33, 19.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7350/8000 [07:46<00:34, 19.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7356/8000 [07:46<00:34, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7358/8000 [07:47<00:47, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7360/8000 [07:47<00:46, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7364/8000 [07:47<00:39, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7366/8000 [07:47<00:41, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7369/8000 [07:47<00:46, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▏  | 7371/8000 [07:48<00:47, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7375/8000 [07:48<00:38, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7378/8000 [07:48<00:45, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7380/8000 [07:48<00:55, 11.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7386/8000 [07:49<00:36, 16.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7391/8000 [07:49<00:30, 19.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7395/8000 [07:49<00:35, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████████████▎  | 7398/8000 [07:49<00:33, 18.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7404/8000 [07:49<00:26, 22.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7408/8000 [07:50<00:32, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7411/8000 [07:50<00:46, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7414/8000 [07:50<00:42, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7419/8000 [07:50<00:33, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7422/8000 [07:51<00:32, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▍  | 7425/8000 [07:51<00:37, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7429/8000 [07:51<00:32, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7431/8000 [07:51<00:48, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7434/8000 [07:52<00:43, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7438/8000 [07:52<00:35, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7443/8000 [07:52<00:27, 19.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7446/8000 [07:52<00:35, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7452/8000 [07:53<00:34, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▌  | 7456/8000 [07:53<00:30, 17.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7459/8000 [07:53<00:29, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7462/8000 [07:53<00:28, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7465/8000 [07:53<00:35, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7472/8000 [07:54<00:24, 21.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7475/8000 [07:54<00:24, 21.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████████████▋  | 7479/8000 [07:54<00:35, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▋  | 7482/8000 [07:54<00:33, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7488/8000 [07:55<00:25, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7492/8000 [07:55<00:29, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7496/8000 [07:55<00:30, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7501/8000 [07:56<00:34, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7506/8000 [07:56<00:41, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▊  | 7511/8000 [07:56<00:32, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7518/8000 [07:56<00:23, 20.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7523/8000 [07:57<00:21, 22.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7526/8000 [07:57<00:21, 22.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7530/8000 [07:57<00:20, 22.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7533/8000 [07:57<00:21, 21.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7537/8000 [07:57<00:26, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████████████▉  | 7542/8000 [07:58<00:21, 20.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7546/8000 [07:58<00:25, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7550/8000 [07:58<00:23, 19.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7553/8000 [07:58<00:28, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████████████  | 7557/8000 [07:58<00:23, 18.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7562/8000 [07:59<00:25, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7564/8000 [07:59<00:25, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7566/8000 [07:59<00:32, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████  | 7570/8000 [07:59<00:31, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7574/8000 [08:00<00:30, 13.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7576/8000 [08:00<00:31, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7580/8000 [08:00<00:25, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7582/8000 [08:00<00:30, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7587/8000 [08:01<00:28, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7590/8000 [08:01<00:25, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7592/8000 [08:01<00:26, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7594/8000 [08:01<00:27, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7596/8000 [08:01<00:28, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▏ | 7599/8000 [08:01<00:23, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7604/8000 [08:01<00:18, 21.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7607/8000 [08:02<00:24, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7610/8000 [08:02<00:21, 17.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7614/8000 [08:02<00:19, 20.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7618/8000 [08:02<00:25, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7620/8000 [08:03<00:32, 11.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▎ | 7627/8000 [08:03<00:20, 18.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7630/8000 [08:03<00:19, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7633/8000 [08:03<00:19, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████████████▍ | 7637/8000 [08:03<00:17, 20.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7640/8000 [08:04<00:18, 19.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7645/8000 [08:04<00:15, 22.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7648/8000 [08:04<00:16, 21.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▍ | 7654/8000 [08:04<00:16, 20.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7659/8000 [08:04<00:14, 23.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7663/8000 [08:05<00:19, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7666/8000 [08:05<00:19, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7669/8000 [08:05<00:18, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7671/8000 [08:05<00:19, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7673/8000 [08:05<00:19, 16.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7675/8000 [08:06<00:26, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7679/8000 [08:06<00:20, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▌ | 7684/8000 [08:06<00:14, 21.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7689/8000 [08:06<00:16, 18.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7695/8000 [08:06<00:15, 20.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7698/8000 [08:07<00:15, 19.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7703/8000 [08:07<00:15, 18.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7708/8000 [08:07<00:12, 22.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▋ | 7712/8000 [08:07<00:15, 18.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7715/8000 [08:08<00:14, 19.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████████████▊ | 7718/8000 [08:08<00:18, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7724/8000 [08:08<00:14, 19.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7728/8000 [08:08<00:12, 21.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7733/8000 [08:09<00:14, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▊ | 7738/8000 [08:09<00:14, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7743/8000 [08:09<00:12, 20.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7746/8000 [08:09<00:14, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7748/8000 [08:10<00:18, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7750/8000 [08:10<00:18, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7753/8000 [08:10<00:16, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7759/8000 [08:10<00:11, 20.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7764/8000 [08:10<00:15, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████████████▉ | 7769/8000 [08:11<00:12, 18.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7774/8000 [08:11<00:14, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7776/8000 [08:11<00:15, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7778/8000 [08:11<00:15, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7784/8000 [08:12<00:11, 19.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7790/8000 [08:12<00:11, 18.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7793/8000 [08:13<00:18, 11.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████████████ | 7797/8000 [08:13<00:16, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7801/8000 [08:13<00:16, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7806/8000 [08:13<00:12, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7813/8000 [08:14<00:09, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7817/8000 [08:14<00:13, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7820/8000 [08:14<00:12, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7823/8000 [08:14<00:11, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7825/8000 [08:15<00:11, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▏| 7827/8000 [08:15<00:11, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7830/8000 [08:15<00:10, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7832/8000 [08:15<00:10, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7836/8000 [08:15<00:08, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7839/8000 [08:16<00:13, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7842/8000 [08:16<00:11, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7844/8000 [08:16<00:11, 13.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7847/8000 [08:16<00:13, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7849/8000 [08:16<00:12, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▎| 7853/8000 [08:17<00:11, 12.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7858/8000 [08:17<00:10, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7860/8000 [08:17<00:09, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7862/8000 [08:17<00:09, 14.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7865/8000 [08:17<00:08, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7869/8000 [08:18<00:07, 18.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7872/8000 [08:18<00:08, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7875/8000 [08:18<00:07, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████████████▍| 7879/8000 [08:18<00:06, 19.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7886/8000 [08:18<00:05, 21.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7892/8000 [08:19<00:04, 26.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7897/8000 [08:19<00:05, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7900/8000 [08:19<00:05, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7903/8000 [08:19<00:06, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7906/8000 [08:20<00:07, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▌| 7912/8000 [08:20<00:04, 18.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7915/8000 [08:20<00:04, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7919/8000 [08:20<00:05, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7921/8000 [08:21<00:05, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7924/8000 [08:21<00:04, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7927/8000 [08:21<00:05, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7929/8000 [08:21<00:05, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7931/8000 [08:21<00:05, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7933/8000 [08:22<00:05, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7936/8000 [08:22<00:05, 11.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7938/8000 [08:22<00:04, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▋| 7940/8000 [08:22<00:05, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7943/8000 [08:22<00:04, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7948/8000 [08:23<00:04, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7951/8000 [08:23<00:03, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7955/8000 [08:23<00:02, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████████████▊| 7958/8000 [08:23<00:02, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7962/8000 [08:24<00:02, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7967/8000 [08:24<00:01, 21.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▊| 7971/8000 [08:24<00:01, 22.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7974/8000 [08:24<00:01, 20.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7977/8000 [08:24<00:01, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7984/8000 [08:24<00:00, 28.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7989/8000 [08:25<00:00, 22.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████████████▉| 7992/8000 [08:25<00:00, 20.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|███████████████████████████████████| 8000/8000 [08:25<00:00, 15.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                                | 1/200 [00:06<20:11,  6.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                                | 2/200 [00:07<11:12,  3.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                                | 3/200 [00:09<08:22,  2.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                | 4/200 [00:10<07:05,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▏                                               | 5/200 [00:12<06:17,  1.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▍                                               | 6/200 [00:13<05:46,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                               | 7/200 [00:15<05:26,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                               | 8/200 [00:16<05:11,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▏                                              | 9/200 [00:18<05:04,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██▍                                             | 10/200 [00:19<04:57,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                             | 11/200 [00:21<04:52,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▉                                             | 12/200 [00:22<04:49,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███                                             | 13/200 [00:24<04:51,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|███▎                                            | 14/200 [00:25<04:50,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                            | 15/200 [00:27<04:47,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▊                                            | 16/200 [00:28<04:42,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████                                            | 17/200 [00:30<04:40,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|████▎                                           | 18/200 [00:32<04:38,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▌                                           | 19/200 [00:33<04:39,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▊                                           | 20/200 [00:35<04:37,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|█████                                           | 21/200 [00:36<04:33,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|█████▎                                          | 22/200 [00:38<04:30,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▌                                          | 23/200 [00:39<04:27,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▊                                          | 24/200 [00:41<04:24,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|██████                                          | 25/200 [00:42<04:25,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|██████▏                                         | 26/200 [00:44<04:24,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▍                                         | 27/200 [00:45<04:25,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▋                                         | 28/200 [00:47<04:22,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████▉                                         | 29/200 [00:48<04:20,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|███████▏                                        | 30/200 [00:50<04:19,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▍                                        | 31/200 [00:51<04:24,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▋                                        | 32/200 [00:53<04:17,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|███████▉                                        | 33/200 [00:54<04:17,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|████████▏                                       | 34/200 [00:56<04:15,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▍                                       | 35/200 [00:57<04:10,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▋                                       | 36/200 [00:59<04:06,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|████████▉                                       | 37/200 [01:00<04:04,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|█████████                                       | 38/200 [01:02<04:05,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▎                                      | 39/200 [01:04<04:03,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▌                                      | 40/200 [01:05<04:02,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████▊                                      | 41/200 [01:07<04:01,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|██████████                                      | 42/200 [01:08<03:58,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▎                                     | 43/200 [01:10<04:00,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████▌                                     | 44/200 [01:13<04:19,  1.66s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.45it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 37it [00:00, 53.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 71it [00:01, 102.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 114it [00:01, 166.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 149it [00:01, 206.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 186it [00:01, 243.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 223it [00:01, 275.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 260it [00:01, 298.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 297it [00:01, 316.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 334it [00:01, 330.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 371it [00:01, 338.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 408it [00:01, 345.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 445it [00:02, 350.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 483it [00:02, 358.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 520it [00:02, 359.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 557it [00:02, 360.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 601it [00:02, 382.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 640it [00:02, 376.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 678it [00:02, 367.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 715it [00:02, 365.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 752it [00:02, 364.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 264.79it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                     | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   6%|▊            | 6/100 [00:00<00:01, 51.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  12%|█▍          | 12/100 [00:00<00:01, 45.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  17%|██          | 17/100 [00:00<00:01, 45.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  22%|██▋         | 22/100 [00:00<00:01, 44.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  27%|███▏        | 27/100 [00:00<00:01, 44.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|███▉        | 33/100 [00:00<00:01, 46.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|████▌       | 38/100 [00:00<00:01, 45.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█████▏      | 43/100 [00:00<00:01, 45.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  48%|█████▊      | 48/100 [00:01<00:01, 44.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  53%|██████▎     | 53/100 [00:01<00:01, 43.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  59%|███████     | 59/100 [00:01<00:00, 46.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  64%|███████▋    | 64/100 [00:01<00:00, 44.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  69%|████████▎   | 69/100 [00:01<00:00, 44.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  74%|████████▉   | 74/100 [00:01<00:00, 43.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  79%|█████████▍  | 79/100 [00:01<00:00, 43.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  85%|██████████▏ | 85/100 [00:01<00:00, 46.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  90%|██████████▊ | 90/100 [00:01<00:00, 45.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  95%|███████████▍| 95/100 [00:02<00:00, 45.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████| 100/100 [00:02<00:00, 44.89it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████████████████████| 4/4 [47:21<00:00, 844.03s/it]\u001b[A\n",
+      "Replicates:  80%|██████████████████▍    | 12/15 [7:38:34<1:29:14, 1784.89s/it]\n",
+      "Nyquist Samples:   0%|                                                    | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.848656022681968\n",
+      "2999: -5.9197044\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<16:08,  4.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:06,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:10<05:14,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<04:08,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<04:01,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:15<03:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:17<03:50,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:18<03:46,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:19<03:46,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:20<03:47,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:21<03:44,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:23<03:41,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:24<03:38,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:25<03:33,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:26<03:31,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:27<03:31,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:29<03:34,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:30<03:33,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:31<03:33,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:32<03:42,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:34<03:36,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:35<03:32,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:36<03:26,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:37<03:24,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:38<03:23,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:39<03:21,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:40<03:18,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:42<03:14,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:43<03:12,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:44<03:15,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:45<03:20,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:47<03:19,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:48<03:14,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:49<03:24,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:50<03:16,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:51<03:14,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:53<03:12,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:54<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:55<03:25,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:57<03:28,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [00:58<03:30,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:00<03:31,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:01<03:31,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:02<03:31,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:04<03:30,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:05<03:30,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:07<03:28,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:08<03:27,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:10<03:26,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:11<03:24,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:12<03:23,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:14<03:22,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:15<03:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:16<03:16,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:18<03:17,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:19<03:17,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:21<03:13,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:22<03:10,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:23<03:05,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:25<03:06,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:26<03:07,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:27<02:58,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:29<03:22,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:31<03:25,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▊                          | 69/200 [01:32<03:08,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████                          | 70/200 [01:33<02:55,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▏                         | 71/200 [01:34<02:50,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▍                         | 72/200 [01:36<02:43,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▌                         | 73/200 [01:37<02:39,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|██████████████▊                         | 74/200 [01:39<02:50,  1.35s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 21.98it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 42it [00:00, 58.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 78it [00:01, 108.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 114it [00:01, 155.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 151it [00:01, 200.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 189it [00:01, 241.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 226it [00:01, 271.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 263it [00:01, 295.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 305it [00:01, 327.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 343it [00:01, 337.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 380it [00:01, 344.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 417it [00:02, 349.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 454it [00:02, 346.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 491it [00:02, 351.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 527it [00:02, 351.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 569it [00:02, 370.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 607it [00:02, 367.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 645it [00:02, 366.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 682it [00:02, 364.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 719it [00:02, 364.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 756it [00:02, 361.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 262.16it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 43.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:02, 43.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:01, 43.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 43.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  27%|█   | 27/100 [00:00<00:01, 50.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|█▎  | 33/100 [00:00<00:01, 47.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|█▌  | 38/100 [00:00<00:01, 47.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█▋  | 43/100 [00:00<00:01, 45.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  48%|█▉  | 48/100 [00:01<00:01, 44.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██▏ | 55/100 [00:01<00:00, 49.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  60%|██▍ | 60/100 [00:01<00:00, 47.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  65%|██▌ | 65/100 [00:01<00:00, 45.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  70%|██▊ | 70/100 [00:01<00:00, 44.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  75%|███ | 75/100 [00:01<00:00, 44.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  84%|███▎| 84/100 [00:01<00:00, 55.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  90%|███▌| 90/100 [00:01<00:00, 50.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 47.10it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 168/8000 [00:00<00:09, 796.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                         | 349/8000 [00:00<00:08, 879.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 544/8000 [00:00<00:07, 932.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                        | 722/8000 [00:00<00:07, 938.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 898/8000 [00:00<00:07, 939.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                      | 1077/8000 [00:01<00:07, 944.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████                      | 1250/8000 [00:01<00:07, 941.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1426/8000 [00:01<00:06, 941.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▏                    | 1600/8000 [00:01<00:06, 923.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                    | 1775/8000 [00:01<00:06, 958.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▏                  | 1965/8000 [00:02<00:05, 1030.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|██████▉                   | 2135/8000 [00:02<00:05, 993.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▌                  | 2310/8000 [00:02<00:05, 976.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████                  | 2483/8000 [00:02<00:05, 968.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▎                | 2659/8000 [00:02<00:05, 1017.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▏                | 2821/8000 [00:02<00:05, 963.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▋                | 2997/8000 [00:03<00:05, 955.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▎               | 3164/8000 [00:03<00:05, 930.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▊               | 3338/8000 [00:03<00:04, 958.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▍              | 3525/8000 [00:03<00:04, 962.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▉              | 3690/8000 [00:03<00:04, 911.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▌             | 3855/8000 [00:04<00:04, 903.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4036/8000 [00:04<00:04, 911.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▋            | 4199/8000 [00:04<00:04, 874.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▏           | 4375/8000 [00:04<00:04, 903.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▊           | 4555/8000 [00:04<00:03, 897.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4730/8000 [00:05<00:03, 907.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▉          | 4910/8000 [00:05<00:03, 922.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▍         | 5072/8000 [00:05<00:03, 898.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████         | 5248/8000 [00:05<00:03, 893.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▌        | 5414/8000 [00:05<00:02, 889.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5583/8000 [00:06<00:02, 880.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▋       | 5764/8000 [00:06<00:02, 897.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▎      | 5927/8000 [00:06<00:02, 898.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▊      | 6096/8000 [00:06<00:02, 899.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▍     | 6273/8000 [00:06<00:01, 923.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████▉     | 6444/8000 [00:06<00:01, 903.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▍    | 6607/8000 [00:07<00:01, 881.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████    | 6774/8000 [00:07<00:01, 856.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6934/8000 [00:07<00:01, 835.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████   | 7095/8000 [00:07<00:01, 838.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▋  | 7279/8000 [00:07<00:00, 873.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▏ | 7455/8000 [00:08<00:00, 908.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▊ | 7624/8000 [00:08<00:00, 910.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▎| 7797/8000 [00:08<00:00, 909.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7971/8000 [00:08<00:00, 916.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8145it [00:08, 917.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<18:58,  5.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:07<10:33,  3.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:08<07:47,  2.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:09<06:31,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:11<05:49,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:12<05:19,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:14<05:04,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:15<04:52,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:17<04:48,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:18<04:38,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:19<04:22,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:20<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:21<03:33,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:22<03:21,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:23<03:19,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:24<03:11,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:25<03:03,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:26<02:58,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:27<02:55,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:28<02:51,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:29<02:59,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:30<02:52,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:31<02:49,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:32<02:48,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:33<02:48,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:34<02:55,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:35<02:56,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:36<02:49,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:37<02:50,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:38<02:44,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:38<02:37,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:39<02:35,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:40<02:33,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:41<02:37,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:42<02:33,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:43<02:28,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:44<02:24,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:45<02:22,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:46<02:21,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:46<02:21,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:47<02:19,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:48<02:16,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:49<02:15,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:50<02:17,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [00:51<02:15,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [00:52<02:12,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [00:52<02:11,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [00:54<02:19,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [00:55<02:23,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [00:55<02:20,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [00:56<02:15,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [00:57<02:10,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [00:58<02:04,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [00:59<02:10,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:00<02:12,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:01<02:06,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:02<02:07,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:02<02:05,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:03<02:01,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:04<02:05,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:05<02:02,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:06<02:07,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:07<02:10,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:08<02:12,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:09<02:17,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:10<02:17,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:11<02:22,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:13<02:22,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▊                          | 69/200 [01:14<02:18,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████                          | 70/200 [01:15<02:16,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▏                         | 71/200 [01:16<02:17,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▍                         | 72/200 [01:17<02:17,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▌                         | 73/200 [01:19<02:18,  1.09s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.89it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 55it [00:01, 68.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 116it [00:01, 149.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 161it [00:01, 191.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 207it [00:01, 242.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 312.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 314it [00:01, 356.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 362it [00:01, 369.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 409it [00:01, 394.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 455it [00:01, 399.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 500it [00:02, 405.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 544it [00:02, 411.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 598it [00:02, 445.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 645it [00:02, 412.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 708it [00:02, 470.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:02, 301.66it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  29%|▊  | 29/100 [00:00<00:00, 283.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  58%|█▋ | 58/100 [00:00<00:00, 274.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 281.03it/s]\n",
+      "\n",
+      "Nyquist Samples:  25%|██████████▊                                | 1/4 [08:47<26:23, 527.76s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.864399080845921\n",
+      "2999: -6.2208033\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<18:59,  5.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:46,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:54,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:35,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:10<04:49,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<04:19,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<03:58,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<03:43,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<03:38,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:15<03:33,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:16<03:31,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:17<03:26,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:18<03:26,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:19<03:21,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:20<03:16,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:21<03:12,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:22<03:13,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:23<03:11,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:24<03:09,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:25<03:13,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:26<03:11,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:28<03:16,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:29<03:13,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:30<03:11,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:31<03:11,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:32<03:09,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:33<03:06,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:34<03:05,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:35<03:04,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:36<03:03,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:37<03:02,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:38<02:59,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:39<02:55,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:40<02:56,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:42<02:56,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:43<02:57,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:44<02:57,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:45<02:55,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:46<02:53,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:47<02:51,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:48<02:54,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:49<02:56,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:50<02:50,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:51<02:48,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [00:52<02:45,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [00:54<02:48,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [00:55<02:50,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [00:56<02:50,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [00:57<02:51,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [00:58<02:51,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [00:59<02:49,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:00<02:40,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:01<02:34,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:02<02:33,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:03<02:27,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:04<02:27,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:05<02:23,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:06<02:20,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:07<02:20,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:08<02:21,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:09<02:20,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:10<02:12,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:11<02:10,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:12<02:16,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:13<02:14,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:14<02:22,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:15<02:19,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:16<02:11,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▊                          | 69/200 [01:17<02:08,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████                          | 70/200 [01:18<02:06,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▏                         | 71/200 [01:19<02:02,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▍                         | 72/200 [01:20<02:06,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▌                         | 73/200 [01:21<02:12,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|██████████████▊                         | 74/200 [01:23<02:22,  1.13s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 23.06it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 73it [00:01, 89.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 146it [00:01, 184.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 219it [00:01, 280.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 283it [00:01, 352.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 351it [00:01, 424.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 419it [00:01, 485.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 485it [00:01, 525.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 551it [00:01, 546.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 623it [00:01, 593.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 690it [00:02, 544.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:02, 352.54it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  31%|▉  | 31/100 [00:00<00:00, 308.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  64%|█▉ | 64/100 [00:00<00:00, 317.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 320.27it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 87/8000 [00:00<00:12, 638.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 183/8000 [00:00<00:11, 682.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|▉                          | 291/8000 [00:00<00:10, 736.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                         | 385/8000 [00:00<00:10, 715.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                         | 482/8000 [00:00<00:10, 712.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                         | 579/8000 [00:00<00:10, 717.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                        | 676/8000 [00:00<00:10, 724.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                        | 790/8000 [00:01<00:09, 794.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 895/8000 [00:01<00:11, 632.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                       | 990/8000 [00:01<00:11, 618.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▌                      | 1096/8000 [00:01<00:12, 536.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                      | 1199/8000 [00:01<00:13, 501.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                     | 1289/8000 [00:02<00:16, 401.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▎                     | 1344/8000 [00:02<00:16, 402.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                     | 1398/8000 [00:02<00:16, 398.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1443/8000 [00:02<00:16, 402.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▊                     | 1497/8000 [00:02<00:16, 395.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                     | 1544/8000 [00:02<00:15, 406.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▏                    | 1596/8000 [00:03<00:17, 357.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▎                    | 1647/8000 [00:03<00:18, 350.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▍                    | 1692/8000 [00:03<00:18, 335.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                    | 1784/8000 [00:03<00:15, 395.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                    | 1878/8000 [00:03<00:12, 485.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▍                   | 1976/8000 [00:03<00:10, 582.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▋                   | 2072/8000 [00:03<00:09, 642.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████                   | 2163/8000 [00:04<00:08, 681.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▎                  | 2262/8000 [00:04<00:08, 709.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                  | 2347/8000 [00:04<00:08, 682.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                  | 2439/8000 [00:04<00:08, 682.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▏                 | 2535/8000 [00:04<00:07, 683.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▌                 | 2637/8000 [00:04<00:07, 702.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▊                 | 2729/8000 [00:04<00:07, 722.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▏                | 2816/8000 [00:04<00:07, 714.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▍                | 2919/8000 [00:05<00:06, 740.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3017/8000 [00:05<00:06, 751.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████                | 3107/8000 [00:05<00:07, 658.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▍               | 3207/8000 [00:05<00:06, 697.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3294/8000 [00:05<00:06, 673.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████               | 3389/8000 [00:05<00:08, 560.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▎              | 3488/8000 [00:06<00:07, 571.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▋              | 3604/8000 [00:06<00:06, 683.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████              | 3707/8000 [00:06<00:05, 736.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▎             | 3807/8000 [00:06<00:05, 752.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▋             | 3902/8000 [00:06<00:05, 741.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████▉             | 3993/8000 [00:06<00:05, 734.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▎            | 4083/8000 [00:06<00:05, 750.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▌            | 4173/8000 [00:06<00:04, 775.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|█████████████▉            | 4280/8000 [00:07<00:04, 800.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▎           | 4388/8000 [00:07<00:04, 833.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▌           | 4487/8000 [00:07<00:04, 815.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▉           | 4592/8000 [00:07<00:04, 844.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4703/8000 [00:07<00:03, 861.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▌          | 4803/8000 [00:07<00:03, 866.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▉          | 4911/8000 [00:07<00:03, 887.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▎         | 5016/8000 [00:07<00:03, 809.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▌         | 5106/8000 [00:08<00:04, 694.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▉         | 5195/8000 [00:08<00:04, 692.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▏        | 5287/8000 [00:08<00:03, 694.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▌        | 5385/8000 [00:08<00:03, 732.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▉        | 5507/8000 [00:08<00:03, 669.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5608/8000 [00:08<00:04, 513.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▌       | 5708/8000 [00:09<00:04, 523.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▊       | 5798/8000 [00:09<00:03, 569.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████       | 5878/8000 [00:09<00:03, 597.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▍      | 5975/8000 [00:09<00:03, 647.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6072/8000 [00:09<00:02, 708.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████      | 6158/8000 [00:09<00:02, 700.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▎     | 6258/8000 [00:10<00:03, 547.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▋     | 6359/8000 [00:10<00:03, 465.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████▉     | 6453/8000 [00:10<00:03, 420.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▏    | 6507/8000 [00:10<00:03, 410.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▍    | 6607/8000 [00:10<00:03, 406.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6651/8000 [00:11<00:03, 370.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▊    | 6700/8000 [00:11<00:03, 347.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▉    | 6756/8000 [00:11<00:03, 352.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████    | 6798/8000 [00:11<00:03, 331.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▎   | 6847/8000 [00:11<00:03, 323.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▍   | 6899/8000 [00:11<00:03, 326.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6949/8000 [00:12<00:03, 325.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▋   | 6999/8000 [00:12<00:03, 320.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▉   | 7062/8000 [00:12<00:02, 347.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████   | 7105/8000 [00:12<00:02, 344.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▎  | 7155/8000 [00:12<00:02, 363.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▍  | 7209/8000 [00:12<00:02, 356.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▌  | 7257/8000 [00:12<00:02, 340.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▋  | 7302/8000 [00:13<00:02, 346.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7354/8000 [00:13<00:01, 375.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████  | 7399/8000 [00:13<00:01, 346.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▏ | 7444/8000 [00:13<00:01, 339.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▎ | 7498/8000 [00:13<00:01, 341.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▌ | 7542/8000 [00:13<00:01, 347.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▋ | 7594/8000 [00:13<00:01, 339.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▊ | 7645/8000 [00:14<00:01, 336.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▉ | 7684/8000 [00:14<00:00, 327.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████ | 7725/8000 [00:14<00:00, 303.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▏| 7769/8000 [00:14<00:00, 296.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▍| 7823/8000 [00:14<00:00, 301.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▌| 7879/8000 [00:14<00:00, 326.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▊| 7933/8000 [00:15<00:00, 331.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7984/8000 [00:15<00:00, 330.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8037it [00:15, 523.82it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<16:18,  4.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:06,  2.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:42,  2.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:36,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:09<05:03,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<04:29,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<04:20,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<04:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:16<04:07,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:17<04:16,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:18<04:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:20<04:03,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:21<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:22<03:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:26<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:28<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:31<03:39,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:32<03:39,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:34<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:36<03:34,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:37<03:35,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:38<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:39<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:41<03:31,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:42<03:30,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:43<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:45<03:40,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:46<03:35,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:47<03:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:48<03:28,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:50<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:51<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:52<03:23,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:53<03:19,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:54<03:17,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:56<03:12,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:57<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:58<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:00<03:21,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:01<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:02<03:22,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:04<03:21,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:05<03:20,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:08<03:24,  1.37s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 21.31it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 37it [00:01, 47.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 73it [00:01, 96.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 104it [00:01, 134.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 140it [00:01, 180.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 176it [00:01, 219.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 213it [00:01, 254.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 248it [00:01, 279.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 285it [00:01, 301.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 321it [00:01, 316.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 356it [00:01, 322.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 393it [00:02, 333.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 430it [00:02, 341.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 467it [00:02, 347.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 506it [00:02, 357.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 543it [00:02, 356.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 580it [00:02, 357.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 617it [00:02, 356.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 654it [00:02, 357.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 691it [00:02, 358.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 728it [00:03, 361.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 249.76it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 44.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:02, 43.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  17%|▋   | 17/100 [00:00<00:01, 51.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  23%|▉   | 23/100 [00:00<00:01, 47.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  28%|█   | 28/100 [00:00<00:01, 46.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|█▎  | 33/100 [00:00<00:01, 45.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|█▌  | 38/100 [00:00<00:01, 44.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|█▊  | 45/100 [00:00<00:01, 49.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|██  | 50/100 [00:01<00:01, 47.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██▏ | 55/100 [00:01<00:00, 45.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  60%|██▍ | 60/100 [00:01<00:00, 45.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  65%|██▌ | 65/100 [00:01<00:00, 44.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  74%|██▉ | 74/100 [00:01<00:00, 55.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:01<00:00, 51.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  86%|███▍| 86/100 [00:01<00:00, 48.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  91%|███▋| 91/100 [00:01<00:00, 47.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 47.97it/s]\n",
+      "\n",
+      "Nyquist Samples:  50%|█████████████████████▌                     | 2/4 [13:20<12:35, 377.65s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.4940408703457666\n",
+      "2999: -6.1495859\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<16:36,  5.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:39,  2.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<07:13,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:09<06:41,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:11<06:21,  1.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:12<05:33,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:13<05:00,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:15<04:42,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:16<04:29,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:17<04:22,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:19<04:15,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:20<04:06,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:21<04:00,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:22<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:23<03:53,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:25<03:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:26<04:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:28<04:04,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:29<04:02,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:30<03:57,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:32<03:55,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:33<03:57,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:34<04:02,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:36<03:51,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:37<03:48,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:38<03:48,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:39<03:43,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:41<03:39,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:42<03:36,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:43<03:35,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:44<03:33,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:46<03:30,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:47<03:30,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:48<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:49<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:51<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:52<03:25,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:53<03:22,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:54<03:22,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:56<03:21,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:57<03:22,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:58<03:24,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:00<03:23,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:01<03:19,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:02<03:17,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:04<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:05<03:15,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:06<03:19,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:07<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:09<03:13,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:10<03:11,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:11<03:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:13<03:12,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:14<03:13,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:15<03:11,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:17<03:10,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:18<03:10,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:19<03:08,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:21<03:08,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:22<03:04,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:23<03:03,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:25<03:02,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:26<03:02,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:27<03:00,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:29<02:59,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:30<03:01,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:31<02:59,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:33<02:59,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▊                          | 69/200 [01:34<02:59,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████                          | 70/200 [01:36<02:59,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▏                         | 71/200 [01:37<02:57,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▍                         | 72/200 [01:38<02:53,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▌                         | 73/200 [01:39<02:49,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|██████████████▊                         | 74/200 [01:41<02:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|███████████████                         | 75/200 [01:42<02:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|███████████████▏                        | 76/200 [01:43<02:43,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|███████████████▍                        | 77/200 [01:45<02:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|███████████████▌                        | 78/200 [01:46<02:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|███████████████▊                        | 79/200 [01:47<02:39,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|████████████████                        | 80/200 [01:49<02:43,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|████████████████▏                       | 81/200 [01:50<02:43,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|████████████████▍                       | 82/200 [01:52<02:40,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████▌                       | 83/200 [01:53<02:42,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████▊                       | 84/200 [01:54<02:39,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|█████████████████                       | 85/200 [01:56<02:36,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|█████████████████▏                      | 86/200 [01:57<02:34,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████▍                      | 87/200 [01:59<02:38,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████▌                      | 88/200 [02:00<02:40,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████▊                      | 89/200 [02:01<02:40,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|██████████████████                      | 90/200 [02:03<02:41,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████▏                     | 91/200 [02:05<02:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████▍                     | 92/200 [02:06<02:41,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████▌                     | 93/200 [02:08<02:39,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████▊                     | 94/200 [02:09<02:37,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████                     | 95/200 [02:10<02:36,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████▏                    | 96/200 [02:12<02:36,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████▍                    | 97/200 [02:13<02:31,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████▌                    | 98/200 [02:15<02:25,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████▊                    | 99/200 [02:16<02:23,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████▌                   | 100/200 [02:18<02:23,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████▋                   | 101/200 [02:19<02:25,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|███████████████████▉                   | 102/200 [02:22<02:16,  1.40s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.48it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 49.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 80it [00:01, 108.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 116it [00:01, 154.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 152it [00:01, 196.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 189it [00:01, 234.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 225it [00:01, 264.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 262it [00:01, 290.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 299it [00:01, 309.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 341it [00:01, 339.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 379it [00:01, 346.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 417it [00:02, 344.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 455it [00:02, 351.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 492it [00:02, 350.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 529it [00:02, 353.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 570it [00:02, 368.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 608it [00:02, 344.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 644it [00:02, 298.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 680it [00:02, 313.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 717it [00:02, 326.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 754it [00:03, 336.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 250.78it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 43.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:02, 44.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:01, 43.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  23%|▉   | 23/100 [00:00<00:01, 53.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  29%|█▏  | 29/100 [00:00<00:01, 49.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|█▎  | 34/100 [00:00<00:01, 47.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  39%|█▌  | 39/100 [00:00<00:01, 46.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  44%|█▊  | 44/100 [00:00<00:01, 45.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  51%|██  | 51/100 [00:01<00:00, 51.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  57%|██▎ | 57/100 [00:01<00:00, 48.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  62%|██▍ | 62/100 [00:01<00:00, 48.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  67%|██▋ | 67/100 [00:01<00:00, 46.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  72%|██▉ | 72/100 [00:01<00:00, 45.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  79%|███▏| 79/100 [00:01<00:00, 51.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  85%|███▍| 85/100 [00:01<00:00, 48.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  90%|███▌| 90/100 [00:01<00:00, 47.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  95%|███▊| 95/100 [00:01<00:00, 46.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 47.31it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                            | 18/8000 [00:00<01:07, 117.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                            | 34/8000 [00:00<01:15, 105.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 50/8000 [00:00<01:21, 97.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                           | 71/8000 [00:00<01:11, 111.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 93/8000 [00:00<01:02, 125.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 106/8000 [00:01<01:19, 99.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                          | 131/8000 [00:01<01:11, 109.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 148/8000 [00:01<01:18, 99.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 176/8000 [00:01<01:11, 109.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▋                          | 203/8000 [00:01<01:11, 108.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 223/8000 [00:02<01:26, 90.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 243/8000 [00:02<01:32, 83.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 253/8000 [00:02<01:50, 70.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 279/8000 [00:02<01:34, 81.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 304/8000 [00:03<01:24, 91.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 323/8000 [00:03<01:26, 89.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 340/8000 [00:03<01:37, 78.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 353/8000 [00:03<01:46, 71.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 375/8000 [00:04<01:32, 82.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 392/8000 [00:04<01:28, 86.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 415/8000 [00:04<01:16, 98.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                         | 435/8000 [00:04<01:12, 103.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                         | 452/8000 [00:04<01:13, 103.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                         | 474/8000 [00:04<01:08, 109.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                         | 494/8000 [00:05<01:05, 114.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                         | 515/8000 [00:05<01:06, 111.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 530/8000 [00:05<01:09, 106.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 548/8000 [00:05<01:07, 111.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                         | 564/8000 [00:05<01:07, 109.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                         | 583/8000 [00:05<01:08, 108.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██                         | 606/8000 [00:06<01:04, 115.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██                         | 627/8000 [00:06<01:02, 117.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                        | 647/8000 [00:06<01:01, 118.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                        | 664/8000 [00:06<01:04, 113.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▎                        | 681/8000 [00:06<01:04, 112.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▎                        | 700/8000 [00:06<01:05, 110.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                        | 714/8000 [00:07<01:11, 102.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                        | 728/8000 [00:07<01:12, 100.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                        | 746/8000 [00:07<01:06, 109.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▌                        | 767/8000 [00:07<01:02, 115.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                        | 785/8000 [00:07<01:03, 112.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                        | 804/8000 [00:07<01:05, 109.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                        | 824/8000 [00:08<01:03, 112.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▊                        | 843/8000 [00:08<01:03, 112.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                        | 868/8000 [00:08<00:54, 130.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 894/8000 [00:08<00:47, 149.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 911/8000 [00:08<00:52, 133.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                       | 934/8000 [00:08<00:52, 133.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                       | 960/8000 [00:09<00:49, 142.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                       | 979/8000 [00:09<00:53, 131.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▎                      | 1004/8000 [00:09<01:04, 108.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▎                      | 1022/8000 [00:09<01:00, 115.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                      | 1043/8000 [00:09<00:58, 119.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                      | 1066/8000 [00:10<00:56, 123.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▌                      | 1085/8000 [00:10<00:57, 120.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▌                      | 1102/8000 [00:10<01:00, 113.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▌                      | 1114/8000 [00:10<01:07, 102.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                      | 1135/8000 [00:10<01:00, 114.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                      | 1160/8000 [00:10<00:51, 133.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▊                      | 1177/8000 [00:10<00:55, 122.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                      | 1196/8000 [00:11<00:57, 118.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                      | 1220/8000 [00:11<00:54, 124.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                      | 1234/8000 [00:11<01:01, 110.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████                      | 1250/8000 [00:11<01:02, 108.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                     | 1272/8000 [00:11<00:54, 122.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                     | 1295/8000 [00:11<00:52, 126.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                     | 1314/8000 [00:12<00:54, 123.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▎                     | 1336/8000 [00:12<00:52, 126.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                     | 1358/8000 [00:12<00:52, 126.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                     | 1379/8000 [00:12<00:51, 127.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▌                     | 1402/8000 [00:12<00:51, 129.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1424/8000 [00:12<00:51, 127.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1440/8000 [00:13<00:56, 116.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1458/8000 [00:13<00:57, 112.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                     | 1474/8000 [00:13<01:02, 104.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▊                     | 1496/8000 [00:13<00:58, 111.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▉                     | 1511/8000 [00:13<01:02, 103.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1545/8000 [00:14<01:08, 94.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████                     | 1567/8000 [00:14<01:01, 104.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▏                    | 1588/8000 [00:14<00:57, 111.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▏                    | 1601/8000 [00:14<00:59, 106.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1615/8000 [00:14<01:04, 98.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1631/8000 [00:15<01:06, 95.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1649/8000 [00:15<01:04, 98.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1661/8000 [00:15<01:09, 91.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▍                    | 1683/8000 [00:15<01:02, 101.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                    | 1704/8000 [00:15<00:59, 106.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▌                    | 1722/8000 [00:15<00:59, 105.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▋                    | 1737/8000 [00:16<01:01, 102.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▋                    | 1761/8000 [00:16<00:52, 119.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                    | 1793/8000 [00:16<00:54, 112.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|█████▉                    | 1808/8000 [00:16<00:58, 104.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|█████▉                    | 1836/8000 [00:16<00:50, 121.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                    | 1857/8000 [00:17<00:52, 118.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                    | 1875/8000 [00:17<00:54, 112.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                    | 1888/8000 [00:17<01:01, 99.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▏                   | 1912/8000 [00:17<00:54, 110.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                   | 1936/8000 [00:17<00:50, 120.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                   | 1960/8000 [00:17<00:48, 124.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▍                   | 1985/8000 [00:18<00:46, 128.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▌                   | 2003/8000 [00:18<00:49, 120.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▌                   | 2027/8000 [00:18<00:45, 131.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▋                   | 2059/8000 [00:18<00:39, 149.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▊                   | 2081/8000 [00:18<00:41, 141.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▊                   | 2104/8000 [00:18<00:43, 134.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|██████▉                   | 2122/8000 [00:19<00:44, 133.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|██████▉                   | 2138/8000 [00:19<00:47, 124.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████                   | 2154/8000 [00:19<00:51, 114.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████                   | 2187/8000 [00:19<00:43, 133.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▏                  | 2205/8000 [00:19<00:46, 125.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▏                  | 2228/8000 [00:19<00:44, 130.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▎                  | 2254/8000 [00:20<00:41, 139.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▍                  | 2284/8000 [00:20<00:50, 113.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▍                  | 2307/8000 [00:20<00:48, 118.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▌                  | 2330/8000 [00:20<00:43, 130.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▌                  | 2345/8000 [00:20<00:46, 121.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▋                  | 2364/8000 [00:21<00:48, 116.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▋                  | 2379/8000 [00:21<00:52, 107.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▊                  | 2395/8000 [00:21<00:54, 103.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▊                  | 2417/8000 [00:21<00:49, 113.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                  | 2433/8000 [00:21<00:49, 112.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|███████▉                  | 2453/8000 [00:21<00:44, 125.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████                  | 2470/8000 [00:22<00:46, 119.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▏                 | 2500/8000 [00:22<00:41, 132.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▏                 | 2518/8000 [00:22<00:44, 123.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▏                 | 2538/8000 [00:22<00:44, 123.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▎                 | 2558/8000 [00:22<00:44, 121.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▍                 | 2579/8000 [00:22<00:42, 128.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▍                 | 2601/8000 [00:23<00:42, 128.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▍                 | 2615/8000 [00:23<00:48, 112.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2629/8000 [00:23<00:53, 99.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▌                 | 2651/8000 [00:23<00:46, 114.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▋                 | 2677/8000 [00:23<00:43, 123.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▋                 | 2691/8000 [00:23<00:48, 109.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▊                 | 2708/8000 [00:24<00:49, 106.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▊                 | 2726/8000 [00:24<00:49, 106.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▉                 | 2744/8000 [00:24<00:50, 104.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2763/8000 [00:24<00:57, 90.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2779/8000 [00:24<00:57, 90.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2800/8000 [00:25<00:52, 99.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▏                | 2829/8000 [00:25<00:43, 118.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▎                | 2860/8000 [00:25<00:49, 104.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▎                | 2881/8000 [00:25<00:46, 109.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▍                | 2900/8000 [00:25<00:47, 108.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2929/8000 [00:26<00:39, 126.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▋                | 2963/8000 [00:26<00:39, 125.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▋                | 2985/8000 [00:26<00:39, 125.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3004/8000 [00:26<00:41, 121.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3020/8000 [00:26<00:43, 113.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3035/8000 [00:27<00:46, 106.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▉                | 3068/8000 [00:27<00:47, 103.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████                | 3089/8000 [00:27<00:44, 109.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████                | 3102/8000 [00:27<00:48, 101.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3117/8000 [00:27<00:49, 98.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▏               | 3135/8000 [00:28<00:44, 108.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▎               | 3155/8000 [00:28<00:43, 111.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▎               | 3180/8000 [00:28<00:39, 122.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▍               | 3199/8000 [00:28<00:40, 118.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▍               | 3221/8000 [00:28<00:38, 122.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▌               | 3236/8000 [00:28<00:42, 111.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▌               | 3255/8000 [00:29<00:40, 117.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3271/8000 [00:29<00:41, 113.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3289/8000 [00:29<00:42, 111.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▊               | 3311/8000 [00:29<00:39, 117.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▊               | 3337/8000 [00:29<00:37, 125.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▉               | 3373/8000 [00:30<00:39, 116.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████               | 3387/8000 [00:30<00:43, 106.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3399/8000 [00:30<00:47, 96.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████               | 3418/8000 [00:30<00:44, 102.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3435/8000 [00:30<00:46, 98.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▏              | 3460/8000 [00:30<00:40, 111.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▎              | 3479/8000 [00:31<00:41, 109.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▎              | 3496/8000 [00:31<00:40, 111.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▍              | 3519/8000 [00:31<00:35, 127.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▍              | 3538/8000 [00:31<00:35, 124.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▌              | 3557/8000 [00:31<00:37, 119.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▌              | 3575/8000 [00:31<00:38, 114.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▋              | 3598/8000 [00:32<00:36, 121.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▊              | 3622/8000 [00:32<00:33, 129.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▊              | 3637/8000 [00:32<00:35, 122.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▉              | 3656/8000 [00:32<00:35, 123.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▉              | 3676/8000 [00:32<00:36, 118.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████              | 3696/8000 [00:32<00:37, 114.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████              | 3710/8000 [00:33<00:39, 107.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3724/8000 [00:33<00:43, 99.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▏             | 3746/8000 [00:33<00:39, 106.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▏             | 3764/8000 [00:33<00:39, 106.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▎             | 3789/8000 [00:33<00:35, 118.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▍             | 3812/8000 [00:33<00:33, 124.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▍             | 3832/8000 [00:34<00:34, 121.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▌             | 3847/8000 [00:34<00:37, 110.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▌             | 3864/8000 [00:34<00:38, 106.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▋             | 3887/8000 [00:34<00:34, 117.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▋             | 3912/8000 [00:34<00:29, 136.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▊             | 3945/8000 [00:35<00:34, 117.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████▉             | 3977/8000 [00:35<00:37, 106.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████▉             | 3992/8000 [00:35<00:39, 102.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4012/8000 [00:35<00:38, 104.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4032/8000 [00:35<00:36, 107.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▏            | 4046/8000 [00:36<00:39, 100.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▏            | 4066/8000 [00:36<00:36, 108.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▎            | 4084/8000 [00:36<00:34, 114.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▎            | 4105/8000 [00:36<00:33, 117.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▍            | 4121/8000 [00:36<00:35, 110.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▍            | 4145/8000 [00:36<00:32, 117.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▌            | 4163/8000 [00:37<00:34, 112.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▌            | 4180/8000 [00:37<00:34, 111.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▋            | 4211/8000 [00:37<00:28, 131.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▊            | 4231/8000 [00:37<00:30, 125.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▊            | 4256/8000 [00:37<00:28, 130.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▉            | 4277/8000 [00:37<00:27, 133.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|█████████████▉            | 4300/8000 [00:38<00:27, 134.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████            | 4321/8000 [00:38<00:28, 131.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████            | 4342/8000 [00:38<00:28, 127.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▏           | 4359/8000 [00:38<00:30, 121.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▏           | 4377/8000 [00:38<00:30, 118.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▎           | 4394/8000 [00:38<00:31, 112.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4407/8000 [00:39<00:36, 98.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4424/8000 [00:39<00:36, 98.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4439/8000 [00:39<00:36, 96.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▍           | 4461/8000 [00:39<00:34, 103.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4476/8000 [00:39<00:35, 99.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4490/8000 [00:39<00:38, 92.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4506/8000 [00:40<00:36, 95.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▋           | 4530/8000 [00:40<00:29, 116.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▊           | 4551/8000 [00:40<00:28, 120.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▉           | 4580/8000 [00:40<00:25, 135.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▉           | 4598/8000 [00:40<00:27, 125.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████           | 4629/8000 [00:41<00:30, 112.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████           | 4647/8000 [00:41<00:28, 115.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▏          | 4663/8000 [00:41<00:28, 115.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▏          | 4679/8000 [00:41<00:30, 108.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4699/8000 [00:41<00:29, 111.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4714/8000 [00:41<00:31, 104.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▍          | 4736/8000 [00:42<00:29, 111.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▍          | 4755/8000 [00:42<00:28, 112.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▌          | 4774/8000 [00:42<00:28, 112.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▌          | 4797/8000 [00:42<00:27, 117.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▋          | 4812/8000 [00:42<00:29, 107.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▋          | 4831/8000 [00:42<00:28, 112.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4852/8000 [00:43<00:25, 123.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4870/8000 [00:43<00:26, 119.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4884/8000 [00:43<00:29, 107.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4899/8000 [00:43<00:31, 98.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4913/8000 [00:43<00:33, 91.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4927/8000 [00:43<00:35, 86.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4948/8000 [00:44<00:31, 96.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4962/8000 [00:44<00:33, 91.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▏         | 4988/8000 [00:44<00:27, 111.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▎         | 5003/8000 [00:44<00:28, 103.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▎         | 5019/8000 [00:44<00:29, 101.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▍         | 5042/8000 [00:44<00:26, 110.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▍         | 5059/8000 [00:45<00:26, 109.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▌         | 5079/8000 [00:45<00:24, 118.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▌         | 5093/8000 [00:45<00:26, 109.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▌         | 5115/8000 [00:45<00:25, 115.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▋         | 5136/8000 [00:45<00:24, 117.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▊         | 5159/8000 [00:45<00:23, 122.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▊         | 5179/8000 [00:46<00:22, 123.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▉         | 5211/8000 [00:46<00:23, 120.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████         | 5245/8000 [00:46<00:24, 112.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████         | 5259/8000 [00:46<00:26, 103.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▏        | 5283/8000 [00:47<00:24, 111.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▏        | 5306/8000 [00:47<00:22, 119.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▎        | 5336/8000 [00:47<00:19, 133.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▍        | 5355/8000 [00:47<00:20, 127.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▍        | 5371/8000 [00:47<00:22, 118.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▌        | 5388/8000 [00:47<00:22, 114.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▌        | 5410/8000 [00:48<00:21, 122.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▌        | 5423/8000 [00:48<00:25, 102.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▋        | 5442/8000 [00:48<00:24, 106.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▊        | 5462/8000 [00:48<00:22, 112.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▊        | 5477/8000 [00:48<00:24, 103.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▊        | 5497/8000 [00:48<00:24, 103.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▉        | 5516/8000 [00:49<00:23, 106.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▉        | 5537/8000 [00:49<00:22, 111.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████        | 5554/8000 [00:49<00:23, 101.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████        | 5571/8000 [00:49<00:23, 101.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5588/8000 [00:49<00:23, 101.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5605/8000 [00:49<00:23, 102.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▎       | 5621/8000 [00:50<00:22, 106.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▎       | 5637/8000 [00:50<00:21, 110.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▎       | 5652/8000 [00:50<00:22, 103.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▍       | 5675/8000 [00:50<00:20, 112.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▍       | 5692/8000 [00:50<00:21, 107.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▌       | 5712/8000 [00:50<00:20, 109.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▋       | 5738/8000 [00:51<00:18, 124.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▋       | 5759/8000 [00:51<00:18, 123.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▊       | 5778/8000 [00:51<00:18, 117.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|██████████████████▊       | 5801/8000 [00:51<00:17, 122.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|██████████████████▉       | 5814/8000 [00:51<00:19, 112.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|██████████████████▉       | 5833/8000 [00:51<00:18, 119.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████       | 5848/8000 [00:52<00:19, 110.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████       | 5869/8000 [00:52<00:18, 115.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▏      | 5888/8000 [00:52<00:18, 111.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▏      | 5904/8000 [00:52<00:20, 104.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▎      | 5933/8000 [00:52<00:16, 122.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▎      | 5950/8000 [00:52<00:17, 117.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▍      | 5964/8000 [00:53<00:19, 104.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▍      | 5981/8000 [00:53<00:19, 104.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▍      | 5999/8000 [00:53<00:18, 108.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▌      | 6015/8000 [00:53<00:18, 106.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▌      | 6033/8000 [00:53<00:18, 106.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6055/8000 [00:53<00:17, 113.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6076/8000 [00:54<00:16, 115.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▊      | 6095/8000 [00:54<00:16, 115.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▊      | 6113/8000 [00:54<00:16, 112.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6125/8000 [00:54<00:19, 98.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████▉      | 6148/8000 [00:54<00:16, 110.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████      | 6161/8000 [00:54<00:18, 101.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6175/8000 [00:55<00:18, 98.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████      | 6192/8000 [00:55<00:16, 106.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6206/8000 [00:55<00:18, 98.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6233/8000 [00:55<00:19, 89.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▎     | 6258/8000 [00:55<00:16, 105.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▍     | 6288/8000 [00:56<00:16, 102.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▌     | 6313/8000 [00:56<00:14, 120.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▌     | 6332/8000 [00:56<00:13, 119.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▋     | 6352/8000 [00:56<00:13, 119.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▋     | 6367/8000 [00:56<00:14, 114.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▊     | 6394/8000 [00:57<00:12, 125.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▊     | 6417/8000 [00:57<00:12, 127.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▉     | 6433/8000 [00:57<00:12, 121.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6466/8000 [00:57<00:13, 115.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6486/8000 [00:57<00:12, 116.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6499/8000 [00:57<00:14, 102.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▏    | 6521/8000 [00:58<00:13, 111.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▏    | 6535/8000 [00:58<00:14, 102.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▎    | 6551/8000 [00:58<00:14, 100.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▎    | 6570/8000 [00:58<00:14, 100.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▍    | 6587/8000 [00:58<00:13, 101.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▍    | 6606/8000 [00:58<00:12, 108.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6624/8000 [00:59<00:11, 116.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6647/8000 [00:59<00:10, 124.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▋    | 6667/8000 [00:59<00:10, 123.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▋    | 6685/8000 [00:59<00:10, 121.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▊    | 6709/8000 [00:59<00:10, 126.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▊    | 6724/8000 [00:59<00:11, 114.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▉    | 6749/8000 [01:00<00:09, 127.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████▉    | 6765/8000 [01:00<00:09, 127.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6784/8000 [01:00<00:14, 86.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6801/8000 [01:00<00:13, 91.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6815/8000 [01:00<00:13, 88.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6831/8000 [01:01<00:13, 89.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6852/8000 [01:01<00:11, 99.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▎   | 6871/8000 [01:01<00:10, 105.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▍   | 6897/8000 [01:01<00:09, 121.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▍   | 6919/8000 [01:01<00:08, 126.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6951/8000 [01:02<00:08, 119.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▋   | 6965/8000 [01:02<00:09, 108.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6990/8000 [01:02<00:10, 92.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7006/8000 [01:02<00:10, 95.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7022/8000 [01:02<00:09, 98.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▉   | 7043/8000 [01:03<00:08, 112.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▉   | 7063/8000 [01:03<00:07, 117.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████   | 7079/8000 [01:03<00:08, 110.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████   | 7100/8000 [01:03<00:07, 117.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▏  | 7116/8000 [01:03<00:08, 109.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7128/8000 [01:03<00:08, 98.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7144/8000 [01:04<00:08, 97.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▎  | 7164/8000 [01:04<00:07, 113.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▎  | 7181/8000 [01:04<00:07, 109.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▍  | 7198/8000 [01:04<00:07, 109.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▍  | 7216/8000 [01:04<00:07, 109.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▌  | 7231/8000 [01:04<00:07, 105.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▌  | 7251/8000 [01:04<00:06, 108.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▌  | 7266/8000 [01:05<00:06, 105.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▋  | 7287/8000 [01:05<00:06, 116.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▊  | 7315/8000 [01:05<00:05, 133.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▊  | 7345/8000 [01:05<00:05, 114.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7363/8000 [01:05<00:05, 112.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7379/8000 [01:06<00:05, 109.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████  | 7395/8000 [01:06<00:05, 105.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████  | 7414/8000 [01:06<00:04, 117.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▏ | 7431/8000 [01:06<00:05, 112.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▏ | 7453/8000 [01:06<00:04, 111.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▎ | 7475/8000 [01:06<00:04, 117.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▎ | 7488/8000 [01:07<00:04, 106.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▍ | 7505/8000 [01:07<00:04, 105.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7521/8000 [01:07<00:04, 99.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7536/8000 [01:07<00:04, 98.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7552/8000 [01:07<00:04, 98.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7566/8000 [01:07<00:04, 93.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7576/8000 [01:08<00:04, 90.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7593/8000 [01:08<00:04, 94.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▋ | 7611/8000 [01:08<00:03, 102.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▊ | 7628/8000 [01:08<00:03, 108.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▊ | 7647/8000 [01:08<00:03, 115.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▉ | 7665/8000 [01:08<00:03, 108.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▉ | 7684/8000 [01:08<00:03, 102.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████ | 7708/8000 [01:09<00:02, 123.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████ | 7723/8000 [01:09<00:02, 114.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▏| 7740/8000 [01:09<00:02, 111.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▏| 7759/8000 [01:09<00:02, 111.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▎| 7774/8000 [01:09<00:02, 106.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▎| 7796/8000 [01:09<00:01, 114.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▍| 7815/8000 [01:10<00:01, 115.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▍| 7833/8000 [01:10<00:01, 120.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▌| 7853/8000 [01:10<00:01, 121.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▌| 7873/8000 [01:10<00:01, 118.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▋| 7894/8000 [01:10<00:00, 123.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▋| 7923/8000 [01:11<00:00, 108.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▊| 7946/8000 [01:11<00:00, 116.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7965/8000 [01:11<00:00, 123.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7982/8000 [01:11<00:00, 117.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8004it [01:11, 111.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<16:33,  4.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:10,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:41,  2.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:26,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:09<04:47,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:10<04:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<04:16,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<04:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:15<03:58,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:17<04:01,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:18<03:54,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:19<03:49,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:20<03:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:22<03:59,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:23<04:06,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:24<04:08,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:26<04:13,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:27<04:13,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:29<04:15,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:30<04:15,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:32<04:14,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:33<04:13,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:35<04:12,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:36<04:11,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:37<04:02,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:39<04:03,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:40<03:58,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:42<04:02,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:43<03:53,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:44<03:57,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:46<04:03,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:47<04:00,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:49<04:02,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:50<03:59,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:52<04:23,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:54<04:32,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:55<04:09,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:57<03:54,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:58<03:40,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:59<03:33,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [01:00<03:33,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:02<03:31,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:03<03:29,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:04<03:27,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:06<03:22,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:07<03:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:08<03:17,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:09<03:15,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:11<03:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:12<03:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:13<03:10,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:16<03:31,  1.44s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.87it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 46.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 95.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 112it [00:01, 142.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 149it [00:01, 187.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 190it [00:01, 236.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 227it [00:01, 267.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 291.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 300it [00:01, 308.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 337it [00:01, 323.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 374it [00:02, 334.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 411it [00:02, 342.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 454it [00:02, 365.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 492it [00:02, 359.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 529it [00:02, 359.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 566it [00:02, 359.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 603it [00:02, 360.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 640it [00:02, 360.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 683it [00:02, 378.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 722it [00:02, 373.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 760it [00:03, 369.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 251.56it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 42.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  12%|▍   | 12/100 [00:00<00:01, 52.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  18%|▋   | 18/100 [00:00<00:01, 46.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  23%|▉   | 23/100 [00:00<00:01, 45.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  28%|█   | 28/100 [00:00<00:01, 44.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|█▎  | 33/100 [00:00<00:01, 44.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  39%|█▌  | 39/100 [00:00<00:01, 47.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  44%|█▊  | 44/100 [00:00<00:01, 45.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  49%|█▉  | 49/100 [00:01<00:01, 45.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  54%|██▏ | 54/100 [00:01<00:01, 44.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  59%|██▎ | 59/100 [00:01<00:00, 43.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  65%|██▌ | 65/100 [00:01<00:00, 46.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  70%|██▊ | 70/100 [00:01<00:00, 44.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  75%|███ | 75/100 [00:01<00:00, 44.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:01<00:00, 44.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  85%|███▍| 85/100 [00:01<00:00, 43.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  90%|███▌| 90/100 [00:01<00:00, 44.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  95%|███▊| 95/100 [00:02<00:00, 44.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 45.07it/s]\n",
+      "\n",
+      "Nyquist Samples:  75%|████████████████████████████████▎          | 3/4 [25:50<09:07, 547.67s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.7148088189775343\n",
+      "2999: -5.8343610\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<18:24,  5.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:07<10:40,  3.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:08<08:12,  2.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:10<07:04,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:12<06:30,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:13<06:10,  1.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:15<06:03,  1.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:17<05:43,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:18<05:32,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:20<05:24,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:22<05:22,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:23<05:16,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:25<05:08,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:27<05:03,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:28<05:03,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:30<05:03,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:32<05:00,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:33<05:04,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:35<04:55,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:36<04:55,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:38<04:57,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:40<05:01,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:42<05:04,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:44<05:06,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:45<05:08,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:47<05:09,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:49<05:08,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:51<05:06,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:52<05:05,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:54<05:02,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:56<04:56,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:58<04:54,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:59<04:53,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [01:01<04:54,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [01:03<04:54,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [01:05<04:51,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [01:07<04:50,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [01:08<04:50,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [01:10<04:55,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [01:12<04:47,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [01:14<05:12,  1.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [01:16<04:58,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:18<04:47,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:20<04:37,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:21<04:30,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:23<04:24,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:25<04:21,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:26<04:18,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:28<04:13,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:30<04:11,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:31<04:09,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:33<04:07,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:36<04:28,  1.82s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.77it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 36it [00:01, 46.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 72it [00:01, 96.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 108it [00:01, 144.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 145it [00:01, 190.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 180it [00:01, 226.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 214it [00:01, 253.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 248it [00:01, 265.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 283it [00:01, 286.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 319it [00:01, 305.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 355it [00:01, 319.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 393it [00:02, 334.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 429it [00:02, 338.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 466it [00:02, 345.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 502it [00:02, 332.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 538it [00:02, 335.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 573it [00:02, 339.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 608it [00:02, 332.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 645it [00:02, 341.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 681it [00:02, 345.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 718it [00:03, 350.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 754it [00:03, 352.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 246.46it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 41.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:02, 40.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:02, 40.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 41.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  26%|█   | 26/100 [00:00<00:01, 46.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  31%|█▏  | 31/100 [00:00<00:01, 44.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  36%|█▍  | 36/100 [00:00<00:01, 44.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  41%|█▋  | 41/100 [00:00<00:01, 43.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  46%|█▊  | 46/100 [00:01<00:01, 43.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  51%|██  | 51/100 [00:01<00:01, 43.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  56%|██▏ | 56/100 [00:01<00:01, 42.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  61%|██▍ | 61/100 [00:01<00:00, 42.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  66%|██▋ | 66/100 [00:01<00:00, 42.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  71%|██▊ | 71/100 [00:01<00:00, 42.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  77%|███ | 77/100 [00:01<00:00, 45.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  82%|███▎| 82/100 [00:01<00:00, 44.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  87%|███▍| 87/100 [00:02<00:00, 43.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  92%|███▋| 92/100 [00:02<00:00, 44.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 43.18it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 2/8000 [00:00<11:13, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 7/8000 [00:00<09:26, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 9/8000 [00:00<09:17, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 11/8000 [00:00<09:16, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 15/8000 [00:00<07:14, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 17/8000 [00:01<08:18, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 22/8000 [00:01<08:24, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 26/8000 [00:01<07:24, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 30/8000 [00:01<06:42, 19.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                            | 35/8000 [00:02<07:28, 17.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                            | 39/8000 [00:02<06:51, 19.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 42/8000 [00:02<08:52, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 46/8000 [00:02<07:56, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 48/8000 [00:02<08:37, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 51/8000 [00:03<09:55, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 55/8000 [00:03<10:17, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 57/8000 [00:03<10:30, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 62/8000 [00:03<08:09, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 64/8000 [00:04<08:40, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 68/8000 [00:04<07:01, 18.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 71/8000 [00:04<08:55, 14.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 74/8000 [00:04<08:09, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 76/8000 [00:04<08:10, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 78/8000 [00:04<08:52, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 80/8000 [00:05<09:34, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 82/8000 [00:05<09:48, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 87/8000 [00:05<07:12, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 89/8000 [00:05<08:05, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 92/8000 [00:05<08:02, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 97/8000 [00:06<07:53, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 101/8000 [00:06<08:31, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 103/8000 [00:06<09:01, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 106/8000 [00:06<08:49, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 108/8000 [00:06<09:27, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 110/8000 [00:07<09:31, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 112/8000 [00:07<10:03, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 114/8000 [00:07<10:29, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 116/8000 [00:07<10:30, 12.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 120/8000 [00:07<08:31, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 123/8000 [00:07<08:13, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 125/8000 [00:08<08:41, 15.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 130/8000 [00:08<06:06, 21.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 134/8000 [00:08<07:32, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 138/8000 [00:08<06:39, 19.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 141/8000 [00:09<09:48, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 146/8000 [00:09<07:31, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 152/8000 [00:09<07:04, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 155/8000 [00:09<08:14, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 157/8000 [00:10<10:09, 12.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 161/8000 [00:10<08:53, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 166/8000 [00:10<08:58, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 168/8000 [00:10<09:19, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 176/8000 [00:11<05:50, 22.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 180/8000 [00:11<06:52, 18.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 186/8000 [00:11<07:11, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 191/8000 [00:11<06:21, 20.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 196/8000 [00:12<07:55, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 200/8000 [00:12<08:23, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▋                           | 206/8000 [00:12<08:02, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▋                           | 214/8000 [00:13<06:59, 18.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 220/8000 [00:13<06:01, 21.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 223/8000 [00:13<07:32, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 227/8000 [00:14<08:11, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 229/8000 [00:14<08:40, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 232/8000 [00:14<09:58, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 234/8000 [00:14<11:49, 10.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 240/8000 [00:15<07:56, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 243/8000 [00:15<07:16, 17.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 246/8000 [00:15<11:00, 11.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 251/8000 [00:15<08:32, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 254/8000 [00:16<08:16, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 257/8000 [00:16<07:55, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 259/8000 [00:16<08:00, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 264/8000 [00:16<06:19, 20.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 269/8000 [00:16<07:12, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 272/8000 [00:16<07:10, 17.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 275/8000 [00:17<07:08, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 277/8000 [00:17<09:43, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|▉                           | 282/8000 [00:17<07:27, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|▉                           | 285/8000 [00:17<07:27, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 289/8000 [00:17<06:40, 19.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 293/8000 [00:18<06:15, 20.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 296/8000 [00:18<08:24, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 299/8000 [00:18<09:26, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 302/8000 [00:18<08:42, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 307/8000 [00:19<06:38, 19.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 310/8000 [00:19<07:56, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 312/8000 [00:19<08:17, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 314/8000 [00:19<09:13, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 319/8000 [00:19<07:03, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 326/8000 [00:20<06:34, 19.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 331/8000 [00:20<06:52, 18.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 333/8000 [00:20<07:29, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 338/8000 [00:20<07:56, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 341/8000 [00:21<07:41, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 343/8000 [00:21<08:16, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 345/8000 [00:21<08:16, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 349/8000 [00:21<07:20, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 351/8000 [00:21<09:28, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 354/8000 [00:22<08:43, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 357/8000 [00:22<10:16, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                          | 359/8000 [00:22<10:35, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 361/8000 [00:22<13:01,  9.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 364/8000 [00:23<13:42,  9.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 367/8000 [00:23<11:40, 10.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 370/8000 [00:23<11:44, 10.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 372/8000 [00:23<11:30, 11.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 375/8000 [00:24<09:55, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 377/8000 [00:24<09:49, 12.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 379/8000 [00:24<13:05,  9.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 385/8000 [00:24<08:13, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 388/8000 [00:24<07:42, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 391/8000 [00:25<11:22, 11.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 393/8000 [00:25<11:23, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 397/8000 [00:25<12:24, 10.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 402/8000 [00:26<09:14, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 405/8000 [00:26<09:12, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 409/8000 [00:26<07:54, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 412/8000 [00:26<09:30, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 416/8000 [00:27<07:43, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 420/8000 [00:27<08:38, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 422/8000 [00:27<10:43, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 425/8000 [00:27<09:53, 12.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 428/8000 [00:27<08:49, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                          | 431/8000 [00:28<08:02, 15.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                          | 435/8000 [00:28<08:31, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                          | 439/8000 [00:28<07:19, 17.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 443/8000 [00:28<08:08, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 448/8000 [00:29<06:40, 18.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 451/8000 [00:29<09:41, 12.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 454/8000 [00:29<10:22, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 457/8000 [00:29<09:06, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 459/8000 [00:30<09:26, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 462/8000 [00:30<08:37, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 465/8000 [00:30<08:15, 15.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 472/8000 [00:30<05:48, 21.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 480/8000 [00:30<05:29, 22.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 486/8000 [00:31<05:42, 21.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 492/8000 [00:31<04:55, 25.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 495/8000 [00:31<05:17, 23.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 500/8000 [00:31<06:18, 19.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 504/8000 [00:32<07:03, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 507/8000 [00:32<07:06, 17.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 516/8000 [00:32<06:55, 18.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 520/8000 [00:33<06:33, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 522/8000 [00:33<07:06, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 526/8000 [00:33<06:16, 19.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 531/8000 [00:33<05:28, 22.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 535/8000 [00:33<05:16, 23.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 539/8000 [00:33<06:10, 20.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 542/8000 [00:34<06:15, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 546/8000 [00:34<07:34, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 548/8000 [00:34<08:01, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 552/8000 [00:34<07:06, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 554/8000 [00:34<07:41, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 557/8000 [00:35<08:51, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 563/8000 [00:35<07:38, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 570/8000 [00:35<06:31, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 573/8000 [00:35<06:28, 19.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 576/8000 [00:36<06:12, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 581/8000 [00:36<05:08, 24.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 585/8000 [00:36<05:07, 24.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 590/8000 [00:36<06:08, 20.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 594/8000 [00:37<07:24, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 599/8000 [00:37<06:14, 19.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██                          | 603/8000 [00:37<05:30, 22.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██                          | 606/8000 [00:37<05:51, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 611/8000 [00:37<07:18, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 614/8000 [00:38<07:12, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 621/8000 [00:38<06:16, 19.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 627/8000 [00:38<07:22, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 631/8000 [00:39<06:45, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 637/8000 [00:39<07:41, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 640/8000 [00:39<07:31, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 644/8000 [00:40<09:16, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 647/8000 [00:40<08:19, 14.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 654/8000 [00:40<05:56, 20.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 657/8000 [00:40<06:09, 19.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 660/8000 [00:40<06:16, 19.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 667/8000 [00:41<05:52, 20.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 672/8000 [00:41<06:26, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 675/8000 [00:41<09:03, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 678/8000 [00:41<08:26, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 681/8000 [00:42<08:10, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 685/8000 [00:42<07:16, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 688/8000 [00:42<07:08, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 691/8000 [00:42<06:56, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 693/8000 [00:42<09:04, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 696/8000 [00:43<07:52, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 700/8000 [00:43<06:54, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 703/8000 [00:43<06:43, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 705/8000 [00:43<09:42, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 709/8000 [00:43<07:52, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 712/8000 [00:44<07:25, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 714/8000 [00:44<07:42, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 717/8000 [00:44<09:02, 13.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 720/8000 [00:44<11:28, 10.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 722/8000 [00:45<10:58, 11.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 728/8000 [00:45<07:10, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 731/8000 [00:45<06:32, 18.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 736/8000 [00:45<07:09, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 741/8000 [00:45<05:41, 21.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 749/8000 [00:46<06:09, 19.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                         | 752/8000 [00:46<06:03, 19.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                         | 755/8000 [00:46<06:13, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                         | 758/8000 [00:46<07:32, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 762/8000 [00:47<06:44, 17.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 768/8000 [00:47<05:26, 22.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 775/8000 [00:47<06:17, 19.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 779/8000 [00:48<08:01, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 783/8000 [00:48<08:10, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 785/8000 [00:48<08:18, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 787/8000 [00:48<08:14, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 789/8000 [00:48<07:59, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 791/8000 [00:48<08:27, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 793/8000 [00:49<08:49, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 795/8000 [00:49<09:09, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 797/8000 [00:49<12:07,  9.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 799/8000 [00:49<11:27, 10.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 803/8000 [00:49<08:10, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 807/8000 [00:50<06:51, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 809/8000 [00:50<09:10, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 814/8000 [00:50<08:10, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 820/8000 [00:50<06:06, 19.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 823/8000 [00:50<06:16, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 828/8000 [00:51<08:03, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 831/8000 [00:51<07:34, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 833/8000 [00:51<09:37, 12.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 836/8000 [00:52<10:04, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 838/8000 [00:52<10:07, 11.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 841/8000 [00:52<12:54,  9.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 844/8000 [00:52<10:58, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 848/8000 [00:53<10:20, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 853/8000 [00:53<10:52, 10.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 855/8000 [00:53<10:34, 11.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 859/8000 [00:54<08:42, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 864/8000 [00:54<08:07, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 868/8000 [00:54<07:08, 16.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 871/8000 [00:54<08:38, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 874/8000 [00:55<09:41, 12.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 879/8000 [00:55<07:29, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 881/8000 [00:55<09:31, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 883/8000 [00:55<09:41, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 887/8000 [00:56<09:40, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 889/8000 [00:56<09:42, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 891/8000 [00:56<09:37, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 893/8000 [00:56<09:23, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 896/8000 [00:56<08:20, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 899/8000 [00:57<07:30, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 901/8000 [00:57<07:40, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 903/8000 [00:57<07:25, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 905/8000 [00:57<08:02, 14.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 908/8000 [00:57<09:44, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 912/8000 [00:58<09:51, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 916/8000 [00:58<11:03, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 918/8000 [00:58<14:02,  8.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                        | 920/8000 [00:59<13:02,  9.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                        | 923/8000 [00:59<13:04,  9.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                        | 925/8000 [00:59<12:25,  9.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 929/8000 [00:59<09:22, 12.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 932/8000 [01:00<09:58, 11.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 934/8000 [01:00<09:58, 11.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 937/8000 [01:00<08:54, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 940/8000 [01:00<09:57, 11.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 942/8000 [01:00<09:54, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 945/8000 [01:01<08:35, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 947/8000 [01:01<08:54, 13.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 952/8000 [01:01<06:36, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 955/8000 [01:01<06:25, 18.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 958/8000 [01:01<06:06, 19.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 962/8000 [01:01<05:14, 22.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 966/8000 [01:02<06:45, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 969/8000 [01:02<06:11, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 974/8000 [01:02<06:34, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 979/8000 [01:02<05:34, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 982/8000 [01:02<05:51, 19.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 985/8000 [01:03<06:00, 19.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 991/8000 [01:03<04:43, 24.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 995/8000 [01:03<05:54, 19.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                       | 1000/8000 [01:03<05:06, 22.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1003/8000 [01:04<08:02, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1005/8000 [01:04<08:26, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1008/8000 [01:04<11:07, 10.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1014/8000 [01:04<07:31, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1017/8000 [01:05<09:39, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1019/8000 [01:05<09:32, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1022/8000 [01:05<09:49, 11.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1026/8000 [01:05<08:06, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1031/8000 [01:06<07:57, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1034/8000 [01:06<07:29, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1040/8000 [01:06<05:36, 20.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1046/8000 [01:07<06:54, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1049/8000 [01:07<06:38, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1053/8000 [01:07<05:57, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1056/8000 [01:07<05:38, 20.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1060/8000 [01:07<06:55, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1062/8000 [01:08<07:23, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1066/8000 [01:08<06:36, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1070/8000 [01:08<07:17, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1073/8000 [01:08<06:59, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                       | 1076/8000 [01:08<08:10, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                       | 1079/8000 [01:09<08:49, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1081/8000 [01:09<08:56, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1087/8000 [01:09<07:15, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1091/8000 [01:09<06:09, 18.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1095/8000 [01:09<05:42, 20.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1101/8000 [01:10<05:55, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1104/8000 [01:10<05:59, 19.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1107/8000 [01:10<07:20, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1110/8000 [01:11<08:36, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1116/8000 [01:11<06:08, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1120/8000 [01:11<05:38, 20.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1125/8000 [01:11<05:55, 19.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1130/8000 [01:11<06:28, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1132/8000 [01:12<08:34, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1135/8000 [01:12<10:39, 10.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1137/8000 [01:12<10:19, 11.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1139/8000 [01:13<11:12, 10.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1144/8000 [01:13<09:36, 11.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1148/8000 [01:13<08:01, 14.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                       | 1152/8000 [01:14<09:46, 11.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                       | 1156/8000 [01:14<08:10, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                       | 1158/8000 [01:14<08:27, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1162/8000 [01:14<07:09, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1164/8000 [01:14<09:18, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1167/8000 [01:15<08:29, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1170/8000 [01:15<07:56, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1173/8000 [01:15<09:03, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1175/8000 [01:15<10:51, 10.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1179/8000 [01:16<10:07, 11.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1182/8000 [01:16<09:03, 12.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1188/8000 [01:16<07:49, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1191/8000 [01:16<07:31, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1193/8000 [01:17<07:50, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1197/8000 [01:17<06:47, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1199/8000 [01:17<10:36, 10.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1201/8000 [01:17<10:23, 10.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1203/8000 [01:18<10:02, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1208/8000 [01:18<06:46, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1211/8000 [01:18<06:54, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1214/8000 [01:18<08:34, 13.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1216/8000 [01:19<10:38, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1219/8000 [01:19<09:16, 12.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1224/8000 [01:19<06:54, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1227/8000 [01:19<08:31, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1232/8000 [01:19<07:44, 14.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1235/8000 [01:20<07:22, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1237/8000 [01:20<07:46, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1240/8000 [01:20<07:23, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1242/8000 [01:20<07:53, 14.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1244/8000 [01:20<08:21, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1246/8000 [01:21<08:33, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1249/8000 [01:21<07:10, 15.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1251/8000 [01:21<07:45, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1254/8000 [01:21<07:15, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1256/8000 [01:21<07:51, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1259/8000 [01:21<07:28, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1263/8000 [01:22<06:27, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1269/8000 [01:22<05:51, 19.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1271/8000 [01:22<06:29, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1274/8000 [01:22<06:27, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1278/8000 [01:22<07:30, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1281/8000 [01:23<08:56, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1283/8000 [01:23<08:36, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1286/8000 [01:23<07:47, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1290/8000 [01:23<06:34, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1292/8000 [01:24<08:21, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1294/8000 [01:24<08:33, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1298/8000 [01:24<08:35, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1303/8000 [01:24<08:01, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1305/8000 [01:25<08:26, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1308/8000 [01:25<09:21, 11.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1311/8000 [01:25<08:26, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1314/8000 [01:25<07:38, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1317/8000 [01:25<07:15, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                      | 1326/8000 [01:26<06:28, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                      | 1328/8000 [01:26<08:02, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                      | 1331/8000 [01:26<07:20, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1334/8000 [01:26<06:41, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1336/8000 [01:27<11:38,  9.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1339/8000 [01:27<11:45,  9.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1341/8000 [01:27<11:09,  9.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1345/8000 [01:28<08:54, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1348/8000 [01:28<08:06, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1351/8000 [01:28<08:44, 12.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1353/8000 [01:28<08:48, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1355/8000 [01:28<09:02, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1360/8000 [01:29<08:02, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1364/8000 [01:29<06:57, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1367/8000 [01:29<06:48, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1369/8000 [01:29<06:59, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1373/8000 [01:29<07:24, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1377/8000 [01:30<07:26, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1379/8000 [01:30<07:50, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1381/8000 [01:30<08:21, 13.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1385/8000 [01:30<08:37, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1387/8000 [01:31<08:54, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1390/8000 [01:31<07:50, 14.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1392/8000 [01:31<07:46, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1394/8000 [01:31<08:17, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1397/8000 [01:31<07:37, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                      | 1400/8000 [01:32<09:19, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                      | 1402/8000 [01:32<09:13, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                      | 1404/8000 [01:32<08:49, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                      | 1406/8000 [01:32<08:18, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1408/8000 [01:32<11:11,  9.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1413/8000 [01:33<09:20, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1417/8000 [01:33<09:09, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1421/8000 [01:33<07:12, 15.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1425/8000 [01:33<06:15, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1431/8000 [01:34<05:57, 18.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1434/8000 [01:34<05:46, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1437/8000 [01:34<07:17, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1439/8000 [01:34<07:29, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1442/8000 [01:34<07:02, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1444/8000 [01:35<07:24, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1447/8000 [01:35<06:58, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1449/8000 [01:35<07:05, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1451/8000 [01:35<09:28, 11.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1453/8000 [01:35<10:52, 10.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1455/8000 [01:36<12:04,  9.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1461/8000 [01:36<07:11, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1464/8000 [01:36<09:26, 11.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1467/8000 [01:36<08:19, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1469/8000 [01:37<08:16, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1471/8000 [01:37<08:24, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1475/8000 [01:37<06:51, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1478/8000 [01:37<08:23, 12.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1480/8000 [01:38<10:29, 10.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1482/8000 [01:38<14:15,  7.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1483/8000 [01:38<14:47,  7.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1486/8000 [01:38<11:34,  9.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1490/8000 [01:39<08:31, 12.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1492/8000 [01:39<09:58, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1495/8000 [01:39<08:44, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1500/8000 [01:39<06:37, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1507/8000 [01:40<06:02, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1513/8000 [01:40<06:31, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1517/8000 [01:40<05:56, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1520/8000 [01:40<07:03, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1522/8000 [01:41<07:06, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1525/8000 [01:41<08:15, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1527/8000 [01:41<08:27, 12.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1529/8000 [01:41<10:37, 10.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1533/8000 [01:42<08:17, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1535/8000 [01:42<07:52, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1539/8000 [01:42<06:45, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1542/8000 [01:42<06:23, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1547/8000 [01:42<05:07, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1550/8000 [01:43<08:15, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1554/8000 [01:43<06:54, 15.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▎                     | 1556/8000 [01:43<07:09, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1560/8000 [01:43<07:35, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1564/8000 [01:43<06:07, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1571/8000 [01:44<04:29, 23.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1574/8000 [01:44<05:02, 21.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1578/8000 [01:44<06:59, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1583/8000 [01:44<06:16, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1586/8000 [01:45<06:47, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1590/8000 [01:45<06:46, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1592/8000 [01:45<08:03, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1598/8000 [01:45<06:11, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1600/8000 [01:46<06:36, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1602/8000 [01:46<06:36, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1604/8000 [01:46<08:59, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1606/8000 [01:46<08:55, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1608/8000 [01:47<10:39,  9.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1611/8000 [01:47<09:04, 11.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1613/8000 [01:47<09:03, 11.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1617/8000 [01:47<07:09, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1622/8000 [01:47<06:41, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1624/8000 [01:48<08:18, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1626/8000 [01:48<08:07, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1630/8000 [01:48<07:56, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1632/8000 [01:48<08:02, 13.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1634/8000 [01:48<08:13, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1636/8000 [01:49<09:26, 11.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1639/8000 [01:49<08:10, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1643/8000 [01:49<06:31, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1646/8000 [01:49<05:59, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1652/8000 [01:49<04:16, 24.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1657/8000 [01:49<05:09, 20.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1661/8000 [01:50<06:13, 16.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1666/8000 [01:50<06:37, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1671/8000 [01:50<05:18, 19.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1674/8000 [01:51<07:40, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1677/8000 [01:51<06:46, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1682/8000 [01:51<05:33, 18.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1686/8000 [01:51<05:00, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1691/8000 [01:51<05:12, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1695/8000 [01:52<07:10, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1700/8000 [01:52<06:32, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1703/8000 [01:52<07:34, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1707/8000 [01:53<06:17, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1712/8000 [01:53<07:24, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1714/8000 [01:53<07:16, 14.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1716/8000 [01:53<07:26, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1718/8000 [01:53<07:39, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1721/8000 [01:54<06:46, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1725/8000 [01:54<05:53, 17.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1728/8000 [01:54<07:25, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1730/8000 [01:54<07:34, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1732/8000 [01:54<07:47, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1734/8000 [01:55<07:50, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1738/8000 [01:55<06:18, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1740/8000 [01:55<06:22, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1742/8000 [01:55<06:50, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1744/8000 [01:55<07:14, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1749/8000 [01:55<05:05, 20.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1753/8000 [01:56<07:41, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1755/8000 [01:56<07:53, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1757/8000 [01:56<08:12, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1759/8000 [01:56<08:16, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1763/8000 [01:56<06:32, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1765/8000 [01:57<06:46, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1769/8000 [01:57<05:36, 18.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1772/8000 [01:57<06:57, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1776/8000 [01:57<05:58, 17.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1781/8000 [01:57<04:56, 20.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1785/8000 [01:58<07:10, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1790/8000 [01:58<05:51, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1793/8000 [01:58<05:48, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1796/8000 [01:58<05:24, 19.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1799/8000 [01:59<08:05, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1801/8000 [01:59<07:43, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1803/8000 [01:59<07:50, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1808/8000 [01:59<05:49, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1812/8000 [01:59<04:56, 20.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1817/8000 [02:00<05:18, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1825/8000 [02:00<04:50, 21.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1828/8000 [02:00<06:00, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1832/8000 [02:00<05:32, 18.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1835/8000 [02:01<06:50, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1837/8000 [02:01<08:23, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1839/8000 [02:01<08:23, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1843/8000 [02:01<06:45, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1845/8000 [02:02<07:14, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1847/8000 [02:02<07:33, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1849/8000 [02:02<07:49, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1852/8000 [02:02<06:44, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1856/8000 [02:02<06:47, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1858/8000 [02:02<07:06, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1862/8000 [02:03<05:48, 17.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1866/8000 [02:03<05:07, 19.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1870/8000 [02:03<04:33, 22.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1873/8000 [02:03<04:40, 21.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1877/8000 [02:03<05:58, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1879/8000 [02:04<06:30, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                    | 1881/8000 [02:04<06:53, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                    | 1883/8000 [02:04<08:48, 11.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                    | 1885/8000 [02:04<08:52, 11.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1889/8000 [02:04<07:06, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1891/8000 [02:05<07:38, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1893/8000 [02:05<07:57, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1895/8000 [02:05<07:54, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1902/8000 [02:05<04:43, 21.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1907/8000 [02:05<05:04, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1911/8000 [02:06<06:04, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1915/8000 [02:06<05:28, 18.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1918/8000 [02:06<06:59, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1921/8000 [02:07<08:50, 11.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1923/8000 [02:07<08:37, 11.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1927/8000 [02:07<06:52, 14.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1930/8000 [02:07<06:21, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1936/8000 [02:07<04:49, 20.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1940/8000 [02:08<05:51, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1942/8000 [02:08<06:11, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1945/8000 [02:08<05:57, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1947/8000 [02:08<06:17, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1950/8000 [02:08<07:28, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1953/8000 [02:08<06:41, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1958/8000 [02:09<04:53, 20.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▌                    | 1962/8000 [02:09<04:36, 21.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1968/8000 [02:09<03:43, 26.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1973/8000 [02:09<04:14, 23.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1979/8000 [02:10<05:25, 18.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1982/8000 [02:10<05:06, 19.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1986/8000 [02:10<06:54, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1990/8000 [02:10<05:56, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1993/8000 [02:11<07:05, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1997/8000 [02:11<06:05, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2000/8000 [02:11<07:16, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2004/8000 [02:11<06:24, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2009/8000 [02:12<06:02, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2013/8000 [02:12<06:31, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2017/8000 [02:12<05:27, 18.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2021/8000 [02:12<05:03, 19.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2025/8000 [02:13<06:03, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2029/8000 [02:13<06:43, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2032/8000 [02:13<07:29, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2035/8000 [02:14<09:19, 10.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2041/8000 [02:14<06:22, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2045/8000 [02:14<05:41, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2049/8000 [02:14<05:09, 19.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2052/8000 [02:14<04:59, 19.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2055/8000 [02:14<04:55, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2058/8000 [02:14<04:57, 19.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2061/8000 [02:15<05:10, 19.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2066/8000 [02:15<07:00, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2069/8000 [02:15<06:36, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2071/8000 [02:16<08:06, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2073/8000 [02:16<08:09, 12.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2076/8000 [02:16<07:22, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2082/8000 [02:16<05:05, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2085/8000 [02:16<05:03, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2088/8000 [02:17<07:58, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2090/8000 [02:17<07:57, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2094/8000 [02:17<09:38, 10.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2098/8000 [02:18<07:48, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2100/8000 [02:18<07:31, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2105/8000 [02:18<07:05, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2107/8000 [02:18<07:08, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2110/8000 [02:19<08:27, 11.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▏                   | 2113/8000 [02:19<07:47, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▏                   | 2117/8000 [02:19<07:37, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2121/8000 [02:19<06:23, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2123/8000 [02:19<06:27, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2128/8000 [02:20<07:05, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2134/8000 [02:20<05:14, 18.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2138/8000 [02:20<05:45, 16.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2143/8000 [02:21<06:00, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2147/8000 [02:21<05:31, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2149/8000 [02:21<06:01, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2153/8000 [02:21<05:25, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2155/8000 [02:21<05:29, 17.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2159/8000 [02:21<04:57, 19.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2165/8000 [02:22<05:09, 18.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2173/8000 [02:22<04:40, 20.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2176/8000 [02:22<05:40, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2180/8000 [02:23<06:09, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2183/8000 [02:23<06:40, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2187/8000 [02:23<06:54, 14.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2189/8000 [02:23<07:00, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2191/8000 [02:23<06:48, 14.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2193/8000 [02:24<08:41, 11.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2195/8000 [02:24<08:02, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2199/8000 [02:24<06:02, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2204/8000 [02:24<04:48, 20.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2210/8000 [02:25<05:01, 19.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2213/8000 [02:25<07:28, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2216/8000 [02:25<08:02, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2219/8000 [02:26<08:26, 11.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2221/8000 [02:26<07:58, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2225/8000 [02:26<06:26, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2229/8000 [02:26<05:23, 17.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2235/8000 [02:27<06:57, 13.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2237/8000 [02:27<06:59, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2240/8000 [02:27<06:16, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2243/8000 [02:27<05:56, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2245/8000 [02:27<06:19, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2248/8000 [02:27<06:02, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2252/8000 [02:28<05:13, 18.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2256/8000 [02:28<04:45, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2259/8000 [02:28<06:11, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2262/8000 [02:28<05:50, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2265/8000 [02:28<06:57, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2270/8000 [02:29<05:02, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2275/8000 [02:29<06:22, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2277/8000 [02:29<07:25, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2281/8000 [02:30<07:17, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2283/8000 [02:30<07:08, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2288/8000 [02:30<06:26, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2292/8000 [02:30<06:31, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2298/8000 [02:31<06:49, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2300/8000 [02:31<06:38, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2306/8000 [02:31<04:59, 19.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2309/8000 [02:31<05:05, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2313/8000 [02:31<04:43, 20.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2316/8000 [02:32<04:46, 19.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2319/8000 [02:32<05:59, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2322/8000 [02:32<05:46, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2325/8000 [02:32<05:40, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2328/8000 [02:32<05:31, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2330/8000 [02:32<05:43, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2332/8000 [02:33<05:38, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2335/8000 [02:33<07:05, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2338/8000 [02:33<06:17, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2341/8000 [02:33<05:33, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2344/8000 [02:33<05:24, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2347/8000 [02:34<05:23, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2350/8000 [02:34<05:14, 17.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2352/8000 [02:34<05:51, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2355/8000 [02:34<05:36, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2358/8000 [02:34<05:27, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                   | 2361/8000 [02:34<05:06, 18.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                   | 2364/8000 [02:34<05:01, 18.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                   | 2366/8000 [02:35<05:25, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                   | 2368/8000 [02:35<07:15, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2371/8000 [02:35<06:37, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2374/8000 [02:35<06:14, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2380/8000 [02:35<04:23, 21.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2385/8000 [02:36<03:58, 23.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2391/8000 [02:36<03:24, 27.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2394/8000 [02:36<03:44, 24.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2397/8000 [02:36<03:52, 24.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2402/8000 [02:36<04:44, 19.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2408/8000 [02:37<04:32, 20.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2415/8000 [02:37<04:32, 20.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2419/8000 [02:37<04:25, 20.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2422/8000 [02:37<05:35, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2425/8000 [02:38<05:26, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2427/8000 [02:38<05:45, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2429/8000 [02:38<06:11, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2432/8000 [02:38<05:55, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2434/8000 [02:38<06:10, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2437/8000 [02:39<07:23, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▏                  | 2441/8000 [02:39<07:04, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▏                  | 2444/8000 [02:39<06:33, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2448/8000 [02:39<05:36, 16.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2451/8000 [02:39<05:27, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2456/8000 [02:40<04:31, 20.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2460/8000 [02:40<04:15, 21.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2464/8000 [02:40<04:04, 22.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2468/8000 [02:40<03:51, 23.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2473/8000 [02:40<03:31, 26.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2476/8000 [02:41<06:17, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2479/8000 [02:41<06:04, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2484/8000 [02:41<04:57, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2487/8000 [02:41<04:47, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2490/8000 [02:41<04:52, 18.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2494/8000 [02:42<05:48, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2497/8000 [02:42<06:46, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2499/8000 [02:42<06:53, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2501/8000 [02:42<06:59, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2503/8000 [02:42<07:05, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2507/8000 [02:43<05:31, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2510/8000 [02:43<05:14, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2513/8000 [02:43<04:41, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▌                  | 2519/8000 [02:43<04:38, 19.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2523/8000 [02:43<04:19, 21.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2526/8000 [02:44<05:25, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2529/8000 [02:44<05:16, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2533/8000 [02:44<05:57, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2537/8000 [02:44<06:23, 14.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2539/8000 [02:45<06:37, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2542/8000 [02:45<06:08, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2546/8000 [02:45<05:09, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2548/8000 [02:45<05:27, 16.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2551/8000 [02:45<04:56, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2553/8000 [02:45<05:30, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2555/8000 [02:46<05:54, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2557/8000 [02:46<06:23, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2559/8000 [02:46<06:33, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2561/8000 [02:46<06:48, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2564/8000 [02:46<06:03, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2567/8000 [02:46<05:38, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2571/8000 [02:47<06:15, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2575/8000 [02:47<06:36, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2579/8000 [02:47<06:36, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2582/8000 [02:48<08:45, 10.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2584/8000 [02:48<08:25, 10.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2586/8000 [02:48<07:57, 11.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2590/8000 [02:48<06:19, 14.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▊                  | 2596/8000 [02:49<05:44, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▊                  | 2599/8000 [02:49<06:52, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2603/8000 [02:49<05:49, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2608/8000 [02:49<04:31, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2612/8000 [02:49<04:16, 21.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2616/8000 [02:50<04:59, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2622/8000 [02:50<05:33, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2626/8000 [02:50<04:55, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2630/8000 [02:51<06:22, 14.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2633/8000 [02:51<07:03, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2636/8000 [02:51<06:30, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2638/8000 [02:51<06:41, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2644/8000 [02:52<05:42, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2650/8000 [02:52<04:20, 20.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2653/8000 [02:52<05:11, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2657/8000 [02:52<05:49, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2660/8000 [02:53<05:21, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2663/8000 [02:53<06:06, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2665/8000 [02:53<06:21, 13.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2667/8000 [02:53<06:07, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2669/8000 [02:53<05:58, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2671/8000 [02:53<06:19, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2673/8000 [02:54<06:37, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2676/8000 [02:54<07:52, 11.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2679/8000 [02:54<06:54, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2684/8000 [02:54<05:10, 17.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2686/8000 [02:54<05:09, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2688/8000 [02:54<05:35, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2691/8000 [02:55<05:15, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2693/8000 [02:55<05:33, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2698/8000 [02:55<05:10, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2701/8000 [02:55<04:55, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2704/8000 [02:55<04:49, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2707/8000 [02:55<04:26, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2710/8000 [02:56<05:41, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2714/8000 [02:56<05:49, 15.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2716/8000 [02:56<06:04, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2719/8000 [02:56<05:35, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2722/8000 [02:57<05:10, 16.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2725/8000 [02:57<04:39, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2729/8000 [02:57<05:27, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2731/8000 [02:57<05:33, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2733/8000 [02:57<05:33, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2735/8000 [02:57<05:55, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2739/8000 [02:58<04:54, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2741/8000 [02:58<05:04, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2744/8000 [02:58<04:53, 17.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2746/8000 [02:58<05:18, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2751/8000 [02:58<05:23, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2755/8000 [02:59<06:09, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2759/8000 [02:59<06:12, 14.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2761/8000 [02:59<06:22, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2765/8000 [02:59<06:22, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2771/8000 [03:00<05:23, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2774/8000 [03:00<05:12, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2781/8000 [03:00<03:37, 24.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2786/8000 [03:00<03:26, 25.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2791/8000 [03:00<04:08, 20.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2795/8000 [03:01<04:04, 21.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2798/8000 [03:01<06:05, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2801/8000 [03:01<06:25, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2806/8000 [03:02<06:02, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2810/8000 [03:02<05:01, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2814/8000 [03:02<07:08, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2817/8000 [03:03<06:37, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2819/8000 [03:03<06:42, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2822/8000 [03:03<06:14, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2824/8000 [03:03<06:26, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2829/8000 [03:03<04:50, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2833/8000 [03:03<04:26, 19.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2840/8000 [03:04<04:53, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2842/8000 [03:04<05:07, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2844/8000 [03:04<05:09, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2848/8000 [03:04<04:33, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2850/8000 [03:04<05:05, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2852/8000 [03:05<05:32, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2855/8000 [03:05<05:16, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2860/8000 [03:05<06:26, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2862/8000 [03:05<06:41, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2864/8000 [03:06<06:50, 12.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2871/8000 [03:06<04:01, 21.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2874/8000 [03:06<04:13, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2877/8000 [03:06<05:44, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2883/8000 [03:06<04:11, 20.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2887/8000 [03:07<05:46, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2894/8000 [03:07<03:58, 21.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2898/8000 [03:07<04:46, 17.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2903/8000 [03:07<04:08, 20.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2907/8000 [03:08<03:55, 21.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2910/8000 [03:08<04:06, 20.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2916/8000 [03:08<03:28, 24.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▊                 | 2921/8000 [03:08<03:20, 25.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▊                 | 2924/8000 [03:08<03:29, 24.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2928/8000 [03:08<03:25, 24.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2931/8000 [03:09<03:40, 22.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2935/8000 [03:09<03:32, 23.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2938/8000 [03:09<05:03, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2941/8000 [03:10<07:04, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2949/8000 [03:10<04:18, 19.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2953/8000 [03:10<03:49, 21.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2957/8000 [03:10<04:43, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2961/8000 [03:10<04:50, 17.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2964/8000 [03:11<06:32, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2968/8000 [03:11<05:20, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2971/8000 [03:11<06:00, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2975/8000 [03:11<05:07, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2983/8000 [03:12<04:05, 20.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2987/8000 [03:12<03:54, 21.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2990/8000 [03:12<03:49, 21.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2993/8000 [03:12<03:56, 21.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2997/8000 [03:12<04:53, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2999/8000 [03:13<05:18, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3002/8000 [03:13<06:20, 13.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3006/8000 [03:13<05:13, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3009/8000 [03:13<05:59, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3012/8000 [03:14<05:42, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3016/8000 [03:14<04:49, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3020/8000 [03:14<05:22, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3029/8000 [03:14<03:22, 24.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3035/8000 [03:14<03:43, 22.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3039/8000 [03:15<03:33, 23.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3042/8000 [03:15<03:39, 22.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3045/8000 [03:15<03:46, 21.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3049/8000 [03:15<03:36, 22.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3054/8000 [03:15<03:18, 24.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3059/8000 [03:16<04:01, 20.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3062/8000 [03:16<04:10, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3067/8000 [03:16<04:16, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3070/8000 [03:16<04:21, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3072/8000 [03:16<04:45, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3074/8000 [03:16<04:50, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▍                | 3076/8000 [03:17<05:10, 15.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▍                | 3078/8000 [03:17<05:39, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3084/8000 [03:17<05:14, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3087/8000 [03:17<05:01, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3092/8000 [03:18<04:08, 19.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3096/8000 [03:18<03:55, 20.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3100/8000 [03:18<03:41, 22.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3104/8000 [03:18<04:28, 18.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3106/8000 [03:18<04:54, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3109/8000 [03:18<04:43, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3113/8000 [03:19<05:09, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3115/8000 [03:19<05:08, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3117/8000 [03:19<05:25, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3122/8000 [03:19<04:09, 19.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3125/8000 [03:19<04:19, 18.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3130/8000 [03:20<04:41, 17.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3132/8000 [03:20<05:02, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3135/8000 [03:20<04:45, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3137/8000 [03:20<05:10, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3140/8000 [03:20<05:04, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3144/8000 [03:21<06:35, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3147/8000 [03:21<05:58, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3151/8000 [03:21<06:10, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3153/8000 [03:21<06:07, 13.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3155/8000 [03:22<07:05, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3157/8000 [03:22<06:59, 11.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3159/8000 [03:22<06:39, 12.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3161/8000 [03:22<06:27, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3164/8000 [03:22<06:51, 11.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3166/8000 [03:23<06:41, 12.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3173/8000 [03:23<03:45, 21.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3180/8000 [03:23<03:31, 22.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3184/8000 [03:23<03:23, 23.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3188/8000 [03:23<03:27, 23.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3191/8000 [03:24<03:40, 21.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3194/8000 [03:24<03:51, 20.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3198/8000 [03:24<05:25, 14.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3201/8000 [03:24<06:04, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3204/8000 [03:25<05:15, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3207/8000 [03:25<05:00, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3209/8000 [03:25<05:13, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3213/8000 [03:25<05:12, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3215/8000 [03:25<05:25, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3219/8000 [03:26<05:54, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3225/8000 [03:26<05:18, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3227/8000 [03:26<05:26, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3231/8000 [03:26<04:30, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3234/8000 [03:26<04:32, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3238/8000 [03:27<04:04, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3242/8000 [03:27<05:28, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3249/8000 [03:27<03:53, 20.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3252/8000 [03:27<03:57, 19.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3258/8000 [03:28<04:08, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3262/8000 [03:28<03:50, 20.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3266/8000 [03:28<04:20, 18.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3269/8000 [03:28<05:26, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3272/8000 [03:29<05:12, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3276/8000 [03:29<04:30, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3279/8000 [03:29<04:25, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3283/8000 [03:29<04:47, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3288/8000 [03:29<03:52, 20.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3291/8000 [03:30<03:54, 20.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3295/8000 [03:30<04:24, 17.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3297/8000 [03:30<04:45, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3303/8000 [03:30<04:43, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3305/8000 [03:30<05:00, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3311/8000 [03:31<04:24, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3313/8000 [03:31<04:46, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3315/8000 [03:31<05:09, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▏               | 3320/8000 [03:31<04:04, 19.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▏               | 3324/8000 [03:31<03:51, 20.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▏               | 3330/8000 [03:32<03:56, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3336/8000 [03:32<03:53, 20.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3339/8000 [03:32<03:51, 20.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3342/8000 [03:32<04:36, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3345/8000 [03:33<04:30, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3347/8000 [03:33<04:52, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3349/8000 [03:33<06:19, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3351/8000 [03:33<06:19, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3353/8000 [03:34<07:49,  9.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3355/8000 [03:34<09:02,  8.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3356/8000 [03:34<09:30,  8.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3361/8000 [03:34<07:07, 10.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3364/8000 [03:35<07:30, 10.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3367/8000 [03:35<06:25, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3371/8000 [03:35<05:10, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3376/8000 [03:35<03:55, 19.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3380/8000 [03:35<04:24, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3384/8000 [03:36<04:06, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3391/8000 [03:36<03:50, 19.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3394/8000 [03:36<03:59, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3399/8000 [03:36<03:27, 22.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▍               | 3404/8000 [03:36<02:57, 25.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3408/8000 [03:37<02:58, 25.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3414/8000 [03:37<03:31, 21.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3420/8000 [03:37<03:44, 20.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3426/8000 [03:38<03:40, 20.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3431/8000 [03:38<04:41, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3433/8000 [03:38<04:52, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3438/8000 [03:38<04:06, 18.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3441/8000 [03:38<04:10, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3446/8000 [03:39<03:28, 21.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3452/8000 [03:39<02:55, 25.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3455/8000 [03:39<03:07, 24.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3460/8000 [03:39<04:27, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3466/8000 [03:40<04:20, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3473/8000 [03:40<03:19, 22.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3477/8000 [03:40<03:11, 23.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▋               | 3480/8000 [03:40<03:20, 22.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3487/8000 [03:40<03:13, 23.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3490/8000 [03:41<03:24, 22.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3496/8000 [03:41<03:39, 20.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3499/8000 [03:41<04:35, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3501/8000 [03:41<04:37, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3503/8000 [03:42<04:51, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3509/8000 [03:42<03:30, 21.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3512/8000 [03:42<06:03, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3515/8000 [03:42<05:27, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3519/8000 [03:43<05:30, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3523/8000 [03:43<05:48, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3527/8000 [03:43<05:00, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3529/8000 [03:44<06:10, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3533/8000 [03:44<04:51, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3535/8000 [03:44<06:31, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3539/8000 [03:44<05:19, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3543/8000 [03:44<04:31, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3545/8000 [03:45<05:52, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3547/8000 [03:45<05:47, 12.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3554/8000 [03:45<03:38, 20.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████               | 3557/8000 [03:45<03:47, 19.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████               | 3560/8000 [03:45<03:54, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3563/8000 [03:46<03:57, 18.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3565/8000 [03:46<04:18, 17.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3569/8000 [03:46<03:46, 19.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3574/8000 [03:46<03:01, 24.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3578/8000 [03:46<03:02, 24.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3587/8000 [03:46<02:13, 32.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3592/8000 [03:46<02:12, 33.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3599/8000 [03:47<02:03, 35.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3603/8000 [03:47<02:59, 24.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3607/8000 [03:47<03:33, 20.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3610/8000 [03:47<03:39, 20.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3613/8000 [03:48<05:30, 13.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3615/8000 [03:48<05:40, 12.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3621/8000 [03:48<03:56, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3627/8000 [03:49<03:49, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▎              | 3632/8000 [03:49<03:17, 22.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▎              | 3635/8000 [03:49<03:11, 22.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3641/8000 [03:49<03:23, 21.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3644/8000 [03:49<03:24, 21.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3649/8000 [03:49<02:51, 25.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3658/8000 [03:50<02:41, 26.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3662/8000 [03:50<02:42, 26.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3665/8000 [03:50<03:01, 23.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3670/8000 [03:50<02:50, 25.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3673/8000 [03:50<03:49, 18.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3676/8000 [03:51<03:52, 18.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3682/8000 [03:51<04:26, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3684/8000 [03:51<05:37, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3688/8000 [03:52<05:51, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3690/8000 [03:52<05:51, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3693/8000 [03:52<05:57, 12.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3695/8000 [03:52<05:55, 12.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3697/8000 [03:53<05:58, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3701/8000 [03:53<04:43, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3703/8000 [03:53<04:56, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3705/8000 [03:53<05:15, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3711/8000 [03:53<04:24, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3714/8000 [03:53<04:16, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3718/8000 [03:54<03:48, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3720/8000 [03:54<05:18, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3724/8000 [03:54<04:32, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3729/8000 [03:54<03:40, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3734/8000 [03:54<03:02, 23.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3742/8000 [03:55<02:56, 24.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3745/8000 [03:55<04:00, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3750/8000 [03:55<04:10, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3752/8000 [03:56<05:06, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3754/8000 [03:56<05:13, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3758/8000 [03:56<04:19, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3762/8000 [03:56<03:39, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3765/8000 [03:57<04:45, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3767/8000 [03:57<04:54, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3771/8000 [03:57<04:16, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3774/8000 [03:57<05:15, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3776/8000 [03:57<05:05, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3780/8000 [03:57<04:15, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3784/8000 [03:58<03:56, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3786/8000 [03:58<05:23, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3789/8000 [03:58<04:53, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3793/8000 [03:58<04:09, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3795/8000 [03:58<04:11, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3802/8000 [03:59<02:56, 23.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3805/8000 [03:59<03:01, 23.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3808/8000 [03:59<03:48, 18.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3812/8000 [03:59<03:27, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3817/8000 [03:59<02:56, 23.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3820/8000 [04:00<04:00, 17.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3823/8000 [04:00<05:09, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3826/8000 [04:00<04:58, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3828/8000 [04:01<06:06, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3832/8000 [04:01<07:00,  9.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3836/8000 [04:01<05:40, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3838/8000 [04:02<07:04,  9.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3841/8000 [04:02<06:15, 11.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3843/8000 [04:02<05:56, 11.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3845/8000 [04:02<05:47, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3847/8000 [04:02<06:51, 10.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3850/8000 [04:02<05:28, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3852/8000 [04:03<05:31, 12.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3854/8000 [04:03<05:28, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3856/8000 [04:03<05:11, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3860/8000 [04:03<03:49, 18.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3863/8000 [04:03<03:49, 18.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3868/8000 [04:03<03:01, 22.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3871/8000 [04:03<03:06, 22.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3875/8000 [04:04<02:44, 25.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3879/8000 [04:04<03:40, 18.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████              | 3882/8000 [04:04<03:26, 19.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████              | 3885/8000 [04:04<04:17, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████              | 3887/8000 [04:04<04:35, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3893/8000 [04:05<03:07, 21.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3896/8000 [04:05<04:52, 14.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3899/8000 [04:05<04:21, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3906/8000 [04:05<03:36, 18.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3909/8000 [04:06<03:32, 19.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3913/8000 [04:06<03:12, 21.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3917/8000 [04:06<02:54, 23.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3922/8000 [04:06<03:22, 20.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3928/8000 [04:07<03:27, 19.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3933/8000 [04:07<03:49, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3938/8000 [04:07<03:57, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3944/8000 [04:07<03:10, 21.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3947/8000 [04:08<04:28, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3952/8000 [04:08<03:43, 18.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3955/8000 [04:08<04:08, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▎             | 3960/8000 [04:08<03:32, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3963/8000 [04:09<04:42, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3965/8000 [04:09<04:50, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3967/8000 [04:09<04:59, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3971/8000 [04:09<03:59, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3974/8000 [04:09<03:37, 18.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3978/8000 [04:09<03:15, 20.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3981/8000 [04:10<03:16, 20.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3985/8000 [04:10<03:01, 22.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3989/8000 [04:10<03:29, 19.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3993/8000 [04:10<04:01, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3998/8000 [04:10<03:08, 21.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4001/8000 [04:11<03:17, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4005/8000 [04:11<03:06, 21.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4009/8000 [04:11<03:26, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4013/8000 [04:11<03:57, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4016/8000 [04:12<03:45, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4018/8000 [04:12<04:45, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4021/8000 [04:12<04:24, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4023/8000 [04:12<04:28, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4030/8000 [04:12<02:48, 23.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4033/8000 [04:12<03:01, 21.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▋             | 4040/8000 [04:13<03:36, 18.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4047/8000 [04:13<03:59, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4053/8000 [04:14<03:48, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4059/8000 [04:14<03:30, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4062/8000 [04:14<04:02, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4066/8000 [04:15<04:12, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4069/8000 [04:15<04:05, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4074/8000 [04:15<04:00, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4081/8000 [04:15<02:49, 23.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4085/8000 [04:15<03:22, 19.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4088/8000 [04:16<03:16, 19.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4091/8000 [04:16<03:07, 20.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4095/8000 [04:16<03:46, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4098/8000 [04:16<04:35, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4100/8000 [04:16<04:45, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4104/8000 [04:17<03:59, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4108/8000 [04:17<04:21, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4110/8000 [04:17<04:31, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▉             | 4112/8000 [04:17<04:37, 14.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▉             | 4115/8000 [04:17<03:59, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4120/8000 [04:18<03:11, 20.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4124/8000 [04:18<03:02, 21.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4129/8000 [04:18<02:39, 24.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4134/8000 [04:18<03:51, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4139/8000 [04:19<03:14, 19.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4143/8000 [04:19<03:03, 20.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4147/8000 [04:19<03:40, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4150/8000 [04:19<04:15, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4154/8000 [04:19<03:43, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4156/8000 [04:20<03:49, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4160/8000 [04:20<03:14, 19.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4165/8000 [04:20<03:34, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4171/8000 [04:20<03:33, 17.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4173/8000 [04:21<04:37, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4175/8000 [04:21<04:36, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4180/8000 [04:21<03:39, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4182/8000 [04:21<03:55, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4184/8000 [04:21<03:57, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▏            | 4188/8000 [04:22<04:03, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▏            | 4193/8000 [04:22<03:11, 19.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▏            | 4196/8000 [04:22<03:14, 19.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▏            | 4199/8000 [04:22<03:57, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4203/8000 [04:22<04:19, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4206/8000 [04:23<04:05, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4209/8000 [04:23<03:57, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4217/8000 [04:23<03:46, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4220/8000 [04:24<04:26, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4222/8000 [04:24<05:18, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4225/8000 [04:24<04:50, 13.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4228/8000 [04:24<04:23, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4233/8000 [04:24<03:30, 17.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4237/8000 [04:25<03:12, 19.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4242/8000 [04:25<04:12, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4244/8000 [04:25<04:21, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4246/8000 [04:25<05:19, 11.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4248/8000 [04:26<05:15, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4250/8000 [04:26<06:25,  9.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4252/8000 [04:26<06:12, 10.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4254/8000 [04:26<05:49, 10.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4257/8000 [04:26<04:53, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4260/8000 [04:27<04:05, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4263/8000 [04:27<03:52, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4265/8000 [04:27<05:08, 12.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4267/8000 [04:27<05:52, 10.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4271/8000 [04:27<04:23, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4273/8000 [04:28<04:30, 13.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4277/8000 [04:28<03:27, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4281/8000 [04:28<03:54, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4284/8000 [04:28<03:44, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4286/8000 [04:28<03:43, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4291/8000 [04:28<02:59, 20.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4295/8000 [04:29<03:31, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4298/8000 [04:29<03:16, 18.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4302/8000 [04:29<03:51, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4304/8000 [04:29<04:07, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4308/8000 [04:30<03:33, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4310/8000 [04:30<03:53, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4313/8000 [04:30<03:40, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4315/8000 [04:30<03:51, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4317/8000 [04:30<04:08, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4319/8000 [04:30<04:25, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4322/8000 [04:31<04:40, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4324/8000 [04:31<05:51, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4329/8000 [04:31<03:58, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4332/8000 [04:31<04:25, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4335/8000 [04:32<04:00, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4339/8000 [04:32<03:25, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4341/8000 [04:32<03:26, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4343/8000 [04:32<03:47, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4345/8000 [04:32<05:58, 10.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4348/8000 [04:33<05:07, 11.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4352/8000 [04:33<04:08, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4354/8000 [04:33<04:50, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4357/8000 [04:33<05:18, 11.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▋            | 4362/8000 [04:34<04:22, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▋            | 4364/8000 [04:34<04:33, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▋            | 4367/8000 [04:34<04:08, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▋            | 4369/8000 [04:34<04:05, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4371/8000 [04:34<04:16, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4377/8000 [04:34<03:47, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4381/8000 [04:35<03:22, 17.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4384/8000 [04:35<03:23, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4386/8000 [04:35<03:36, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4389/8000 [04:35<03:28, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4391/8000 [04:35<03:30, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4396/8000 [04:36<03:36, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4399/8000 [04:36<04:06, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4405/8000 [04:36<03:01, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4410/8000 [04:36<03:12, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4414/8000 [04:37<03:37, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4417/8000 [04:37<04:18, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4420/8000 [04:37<04:00, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4423/8000 [04:37<03:49, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4428/8000 [04:38<03:42, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4431/8000 [04:38<03:36, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4433/8000 [04:38<03:47, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4435/8000 [04:38<03:55, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4438/8000 [04:38<04:25, 13.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▉            | 4440/8000 [04:38<04:21, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▉            | 4444/8000 [04:39<03:29, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4447/8000 [04:39<03:07, 18.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4453/8000 [04:39<03:06, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4456/8000 [04:39<03:38, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4458/8000 [04:39<03:51, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4461/8000 [04:40<03:40, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4465/8000 [04:40<03:06, 18.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4468/8000 [04:40<03:09, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4470/8000 [04:40<03:28, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4473/8000 [04:40<04:24, 13.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4476/8000 [04:41<03:59, 14.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4479/8000 [04:41<03:42, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4481/8000 [04:41<04:00, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4484/8000 [04:41<03:29, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4486/8000 [04:41<03:43, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4488/8000 [04:41<03:57, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4491/8000 [04:41<03:32, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4493/8000 [04:42<04:40, 12.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4496/8000 [04:42<04:02, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4500/8000 [04:42<03:18, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4504/8000 [04:42<02:42, 21.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4507/8000 [04:42<02:55, 19.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4510/8000 [04:43<03:00, 19.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4514/8000 [04:43<02:48, 20.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▎           | 4520/8000 [04:43<03:03, 18.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4523/8000 [04:44<04:26, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4525/8000 [04:44<04:26, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4529/8000 [04:44<03:35, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4533/8000 [04:44<02:57, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4538/8000 [04:44<02:31, 22.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4541/8000 [04:44<03:18, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4545/8000 [04:45<04:13, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4549/8000 [04:45<03:34, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4553/8000 [04:45<03:45, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4557/8000 [04:45<03:21, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4560/8000 [04:46<03:24, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4562/8000 [04:46<03:37, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4567/8000 [04:46<03:40, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4571/8000 [04:46<03:05, 18.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4576/8000 [04:47<03:13, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4581/8000 [04:47<02:42, 21.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4585/8000 [04:47<02:28, 23.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4588/8000 [04:47<03:19, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4592/8000 [04:47<03:23, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▌           | 4594/8000 [04:48<03:37, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▌           | 4596/8000 [04:48<04:35, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4602/8000 [04:48<03:00, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4605/8000 [04:48<03:44, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4607/8000 [04:49<04:53, 11.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4611/8000 [04:49<03:53, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4613/8000 [04:49<04:51, 11.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4618/8000 [04:49<03:34, 15.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4622/8000 [04:50<03:51, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4626/8000 [04:50<03:07, 18.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4629/8000 [04:50<03:09, 17.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4633/8000 [04:50<02:45, 20.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4638/8000 [04:50<02:51, 19.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4641/8000 [04:51<03:31, 15.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4644/8000 [04:51<03:56, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4647/8000 [04:51<03:43, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4652/8000 [04:51<02:59, 18.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4657/8000 [04:52<03:18, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4659/8000 [04:52<03:34, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4661/8000 [04:52<03:46, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4664/8000 [04:52<04:05, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▊           | 4669/8000 [04:52<03:50, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▊           | 4674/8000 [04:53<03:29, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▊           | 4676/8000 [04:53<03:36, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▊           | 4678/8000 [04:53<03:49, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4681/8000 [04:53<04:07, 13.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4684/8000 [04:54<04:30, 12.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4686/8000 [04:54<04:22, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4688/8000 [04:54<04:23, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4690/8000 [04:54<04:22, 12.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4693/8000 [04:54<03:42, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4697/8000 [04:54<02:51, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4700/8000 [04:54<02:55, 18.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4704/8000 [04:55<04:17, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4708/8000 [04:55<03:34, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4712/8000 [04:55<03:47, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4714/8000 [04:56<04:45, 11.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4717/8000 [04:56<04:15, 12.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4720/8000 [04:56<04:36, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4725/8000 [04:56<03:28, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4729/8000 [04:57<03:03, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4732/8000 [04:57<02:59, 18.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4736/8000 [04:57<02:47, 19.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4741/8000 [04:57<03:00, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4745/8000 [04:57<02:45, 19.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4750/8000 [04:58<02:55, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4753/8000 [04:58<02:44, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4757/8000 [04:58<02:38, 20.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4760/8000 [04:58<02:43, 19.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4763/8000 [04:58<02:34, 20.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4766/8000 [04:59<03:27, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4769/8000 [04:59<03:20, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4774/8000 [04:59<04:01, 13.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4777/8000 [04:59<04:22, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4781/8000 [05:00<03:37, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4785/8000 [05:00<03:05, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4789/8000 [05:00<02:45, 19.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4794/8000 [05:00<02:11, 24.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4798/8000 [05:00<02:46, 19.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4801/8000 [05:01<03:16, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4808/8000 [05:01<02:20, 22.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4814/8000 [05:01<02:27, 21.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4817/8000 [05:01<02:59, 17.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4821/8000 [05:02<03:24, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4825/8000 [05:02<03:06, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4829/8000 [05:02<03:27, 15.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4835/8000 [05:03<03:11, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4838/8000 [05:03<03:34, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4840/8000 [05:03<03:42, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▎          | 4844/8000 [05:03<03:11, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▎          | 4846/8000 [05:03<04:09, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▎          | 4848/8000 [05:04<04:10, 12.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4854/8000 [05:04<02:48, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4859/8000 [05:04<02:57, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4862/8000 [05:04<02:57, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4867/8000 [05:04<02:29, 20.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4871/8000 [05:05<02:21, 22.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4875/8000 [05:05<02:55, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4878/8000 [05:05<03:30, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4882/8000 [05:06<03:44, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4886/8000 [05:06<03:12, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4892/8000 [05:06<02:20, 22.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4897/8000 [05:06<02:38, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4902/8000 [05:06<02:38, 19.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4906/8000 [05:07<03:02, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4910/8000 [05:07<02:41, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4915/8000 [05:07<02:45, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4918/8000 [05:07<03:15, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▌          | 4920/8000 [05:08<03:15, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▌          | 4922/8000 [05:08<03:26, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4926/8000 [05:08<02:58, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4931/8000 [05:09<04:20, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4933/8000 [05:09<04:56, 10.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4936/8000 [05:09<04:19, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4940/8000 [05:09<03:31, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4942/8000 [05:09<03:31, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4945/8000 [05:09<03:17, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4947/8000 [05:10<03:29, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4951/8000 [05:10<02:47, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4953/8000 [05:10<02:53, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4959/8000 [05:10<02:08, 23.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4962/8000 [05:10<03:03, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4967/8000 [05:11<02:32, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4973/8000 [05:11<03:40, 13.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4976/8000 [05:11<03:23, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4979/8000 [05:12<03:36, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4981/8000 [05:12<03:45, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4983/8000 [05:12<03:47, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4985/8000 [05:12<03:41, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4987/8000 [05:12<04:25, 11.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4990/8000 [05:12<03:42, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4992/8000 [05:13<03:45, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4995/8000 [05:13<03:09, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4999/8000 [05:13<03:28, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5001/8000 [05:13<03:34, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5005/8000 [05:13<02:56, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5007/8000 [05:14<03:09, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5009/8000 [05:14<03:26, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5013/8000 [05:14<02:56, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5016/8000 [05:14<02:53, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5021/8000 [05:14<02:54, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5024/8000 [05:14<02:42, 18.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5027/8000 [05:15<03:23, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5031/8000 [05:15<02:46, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5034/8000 [05:15<02:32, 19.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5037/8000 [05:15<03:15, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5039/8000 [05:16<03:20, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5043/8000 [05:16<02:36, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5046/8000 [05:16<03:20, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5050/8000 [05:16<02:48, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5054/8000 [05:16<02:19, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5058/8000 [05:16<02:15, 21.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5063/8000 [05:17<01:56, 25.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5068/8000 [05:17<02:08, 22.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5073/8000 [05:17<02:26, 19.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████▏         | 5078/8000 [05:17<02:31, 19.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5081/8000 [05:18<02:33, 19.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5084/8000 [05:18<03:14, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5086/8000 [05:18<03:21, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5088/8000 [05:18<03:30, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5092/8000 [05:19<04:59,  9.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5094/8000 [05:19<04:43, 10.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5100/8000 [05:19<03:30, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5103/8000 [05:20<03:49, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5105/8000 [05:20<03:37, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5107/8000 [05:20<03:38, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5111/8000 [05:20<03:03, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5113/8000 [05:20<03:05, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5119/8000 [05:20<02:40, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5121/8000 [05:21<02:53, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5125/8000 [05:21<02:57, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5127/8000 [05:21<03:09, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5131/8000 [05:21<02:44, 17.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5135/8000 [05:21<03:10, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5138/8000 [05:22<03:02, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5141/8000 [05:22<03:31, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5144/8000 [05:22<03:14, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5147/8000 [05:22<02:59, 15.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▍         | 5154/8000 [05:23<02:25, 19.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▍         | 5156/8000 [05:23<02:39, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▍         | 5159/8000 [05:23<02:34, 18.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5161/8000 [05:23<02:44, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5163/8000 [05:23<02:45, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5165/8000 [05:23<03:46, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5169/8000 [05:24<03:01, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5171/8000 [05:24<03:57, 11.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5177/8000 [05:24<03:16, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5179/8000 [05:25<04:08, 11.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5182/8000 [05:25<03:40, 12.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5188/8000 [05:25<03:06, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5193/8000 [05:25<02:31, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5196/8000 [05:25<02:26, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5201/8000 [05:26<02:56, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5203/8000 [05:26<03:31, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5205/8000 [05:26<03:33, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5207/8000 [05:27<04:52,  9.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5209/8000 [05:27<04:39,  9.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5211/8000 [05:27<04:26, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5216/8000 [05:27<02:56, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5218/8000 [05:27<02:51, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5221/8000 [05:27<02:49, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5225/8000 [05:27<02:27, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5234/8000 [05:28<01:47, 25.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5237/8000 [05:28<02:51, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5239/8000 [05:28<02:53, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5242/8000 [05:28<02:48, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5247/8000 [05:29<02:44, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5250/8000 [05:29<02:38, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5255/8000 [05:29<02:12, 20.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5260/8000 [05:29<02:25, 18.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5263/8000 [05:30<03:28, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5266/8000 [05:30<03:12, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5268/8000 [05:31<04:39,  9.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5273/8000 [05:31<03:13, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5277/8000 [05:31<03:23, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5279/8000 [05:31<03:28, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5281/8000 [05:31<04:20, 10.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5284/8000 [05:32<04:25, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5289/8000 [05:32<04:16, 10.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5292/8000 [05:32<03:39, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5294/8000 [05:33<03:39, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5297/8000 [05:33<03:16, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5301/8000 [05:33<03:04, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5304/8000 [05:33<04:09, 10.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5307/8000 [05:34<03:28, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5310/8000 [05:34<03:09, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5312/8000 [05:34<03:14, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5316/8000 [05:34<02:34, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5320/8000 [05:35<03:54, 11.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5323/8000 [05:35<03:58, 11.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5328/8000 [05:35<03:55, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5332/8000 [05:35<03:16, 13.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5334/8000 [05:36<03:21, 13.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5339/8000 [05:36<03:13, 13.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5341/8000 [05:36<03:17, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5344/8000 [05:36<03:02, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5347/8000 [05:36<02:40, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5350/8000 [05:37<02:33, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5352/8000 [05:37<03:29, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5354/8000 [05:37<04:01, 10.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5360/8000 [05:37<03:09, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5362/8000 [05:38<03:01, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5366/8000 [05:38<02:34, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5369/8000 [05:38<02:26, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5371/8000 [05:38<02:38, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5373/8000 [05:38<02:41, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5375/8000 [05:38<02:55, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5378/8000 [05:38<02:44, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5380/8000 [05:39<02:58, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5382/8000 [05:39<03:11, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5386/8000 [05:39<02:32, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5389/8000 [05:39<02:31, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5393/8000 [05:39<02:10, 20.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5398/8000 [05:40<02:23, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▏        | 5402/8000 [05:40<02:30, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▏        | 5407/8000 [05:40<02:40, 16.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5410/8000 [05:40<03:09, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5412/8000 [05:41<04:21,  9.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5414/8000 [05:41<04:50,  8.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5418/8000 [05:41<03:40, 11.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5425/8000 [05:42<02:18, 18.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5428/8000 [05:42<02:18, 18.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5432/8000 [05:42<02:40, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5434/8000 [05:42<03:17, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5437/8000 [05:43<03:03, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5442/8000 [05:43<02:26, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5446/8000 [05:43<02:13, 19.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5449/8000 [05:43<02:11, 19.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5457/8000 [05:43<01:48, 23.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5461/8000 [05:44<02:14, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5465/8000 [05:44<02:19, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5469/8000 [05:44<02:35, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5472/8000 [05:44<02:27, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5476/8000 [05:45<02:34, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5478/8000 [05:45<02:42, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▍        | 5481/8000 [05:45<02:59, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5483/8000 [05:45<03:42, 11.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5485/8000 [05:46<04:30,  9.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5487/8000 [05:46<04:16,  9.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5491/8000 [05:46<03:14, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5494/8000 [05:46<03:33, 11.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5498/8000 [05:46<02:49, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5501/8000 [05:47<03:04, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5505/8000 [05:47<02:36, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5507/8000 [05:47<03:15, 12.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5509/8000 [05:47<03:09, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5512/8000 [05:47<02:52, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5516/8000 [05:48<03:08, 13.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5521/8000 [05:48<02:26, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5523/8000 [05:48<02:35, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5525/8000 [05:48<02:43, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5528/8000 [05:48<02:24, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5530/8000 [05:49<03:22, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5533/8000 [05:49<03:04, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5535/8000 [05:49<03:08, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5537/8000 [05:49<03:14, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5541/8000 [05:49<02:35, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5546/8000 [05:50<02:31, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5548/8000 [05:50<02:37, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5551/8000 [05:50<02:24, 16.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▊        | 5558/8000 [05:50<02:03, 19.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5560/8000 [05:50<02:18, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5563/8000 [05:51<02:15, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5565/8000 [05:51<02:27, 16.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5568/8000 [05:51<02:23, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5573/8000 [05:51<01:56, 20.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5576/8000 [05:51<02:31, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5579/8000 [05:52<02:25, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5582/8000 [05:52<02:16, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5584/8000 [05:52<02:21, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5586/8000 [05:52<02:32, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5588/8000 [05:52<02:43, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5590/8000 [05:52<02:41, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5594/8000 [05:52<02:04, 19.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5599/8000 [05:53<02:53, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5601/8000 [05:53<03:03, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5603/8000 [05:53<03:05, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5607/8000 [05:53<02:32, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5613/8000 [05:54<01:47, 22.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5617/8000 [05:54<01:43, 23.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5622/8000 [05:54<01:34, 25.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5626/8000 [05:54<01:50, 21.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████        | 5630/8000 [05:55<02:14, 17.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████        | 5634/8000 [05:55<02:03, 19.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5641/8000 [05:55<01:58, 19.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5646/8000 [05:55<01:46, 22.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5649/8000 [05:55<02:13, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5651/8000 [05:56<02:26, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5653/8000 [05:56<02:32, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5655/8000 [05:56<02:44, 14.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5659/8000 [05:56<02:18, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5662/8000 [05:56<02:48, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5664/8000 [05:57<02:54, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5670/8000 [05:57<02:01, 19.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5673/8000 [05:57<02:36, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5676/8000 [05:57<02:28, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5678/8000 [05:57<02:39, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5683/8000 [05:58<01:55, 20.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5687/8000 [05:58<02:18, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5692/8000 [05:58<01:50, 20.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5697/8000 [05:58<01:31, 25.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5701/8000 [05:58<01:30, 25.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5708/8000 [05:59<01:33, 24.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5712/8000 [05:59<01:26, 26.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5715/8000 [05:59<01:59, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5718/8000 [05:59<01:56, 19.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5723/8000 [05:59<01:35, 23.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5728/8000 [06:00<01:54, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5731/8000 [06:00<01:56, 19.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5735/8000 [06:00<02:23, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5739/8000 [06:00<02:31, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5741/8000 [06:01<03:09, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5744/8000 [06:01<03:24, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5747/8000 [06:01<03:03, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5750/8000 [06:02<03:14, 11.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5756/8000 [06:02<02:14, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5758/8000 [06:02<02:48, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5762/8000 [06:02<02:13, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5765/8000 [06:02<02:12, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5769/8000 [06:03<02:42, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5771/8000 [06:03<02:48, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5773/8000 [06:03<02:47, 13.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5775/8000 [06:03<03:21, 11.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5777/8000 [06:04<03:47,  9.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5782/8000 [06:04<03:27, 10.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5784/8000 [06:04<03:17, 11.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5786/8000 [06:04<03:13, 11.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5788/8000 [06:04<03:00, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5790/8000 [06:05<03:01, 12.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5792/8000 [06:05<04:59,  7.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5795/8000 [06:05<03:48,  9.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5799/8000 [06:06<03:20, 10.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5805/8000 [06:06<02:12, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5808/8000 [06:06<02:52, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5812/8000 [06:07<02:49, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5817/8000 [06:07<02:29, 14.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5820/8000 [06:07<02:47, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5822/8000 [06:07<02:47, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5826/8000 [06:08<02:41, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5831/8000 [06:08<02:02, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5837/8000 [06:08<01:54, 18.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5841/8000 [06:09<02:45, 13.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5846/8000 [06:09<02:14, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5850/8000 [06:09<02:01, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5854/8000 [06:09<02:04, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5857/8000 [06:09<02:02, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5861/8000 [06:09<01:50, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5866/8000 [06:10<02:02, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5869/8000 [06:10<02:01, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5875/8000 [06:10<01:56, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5877/8000 [06:10<02:07, 16.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▊       | 5882/8000 [06:11<02:29, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▊       | 5886/8000 [06:11<02:17, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▊       | 5888/8000 [06:11<02:42, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5891/8000 [06:12<02:39, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5896/8000 [06:12<02:51, 12.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5900/8000 [06:12<02:35, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5902/8000 [06:12<03:02, 11.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5904/8000 [06:13<03:01, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5908/8000 [06:13<02:26, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5911/8000 [06:13<02:53, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5913/8000 [06:13<03:26, 10.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5916/8000 [06:14<03:25, 10.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5923/8000 [06:14<02:26, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5927/8000 [06:14<02:26, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5931/8000 [06:15<02:05, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5933/8000 [06:15<02:09, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5937/8000 [06:15<02:10, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5941/8000 [06:15<02:19, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5945/8000 [06:15<01:56, 17.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5947/8000 [06:16<02:06, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5950/8000 [06:16<02:25, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5952/8000 [06:16<02:20, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5955/8000 [06:16<02:44, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5958/8000 [06:16<02:22, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5960/8000 [06:17<02:32, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5963/8000 [06:17<02:19, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5965/8000 [06:17<02:27, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5967/8000 [06:17<02:29, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5969/8000 [06:17<02:36, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5974/8000 [06:18<02:18, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5976/8000 [06:18<02:13, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5979/8000 [06:18<02:08, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5983/8000 [06:18<01:50, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5990/8000 [06:18<01:31, 21.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5993/8000 [06:18<01:35, 20.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5999/8000 [06:19<01:51, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6004/8000 [06:19<01:34, 21.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6008/8000 [06:19<01:29, 22.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6012/8000 [06:19<01:43, 19.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6017/8000 [06:20<01:31, 21.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6021/8000 [06:20<02:31, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6024/8000 [06:20<02:21, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6028/8000 [06:21<02:02, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6031/8000 [06:21<01:57, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6035/8000 [06:21<02:28, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6037/8000 [06:21<02:30, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6041/8000 [06:22<02:27, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6044/8000 [06:22<02:40, 12.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6048/8000 [06:22<02:14, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6052/8000 [06:22<02:23, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6054/8000 [06:23<02:28, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6057/8000 [06:23<02:16, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6064/8000 [06:23<01:25, 22.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6068/8000 [06:23<01:45, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6077/8000 [06:23<01:21, 23.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6080/8000 [06:24<01:44, 18.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6084/8000 [06:24<01:47, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6087/8000 [06:24<01:46, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6094/8000 [06:24<01:18, 24.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6097/8000 [06:25<01:38, 19.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6103/8000 [06:25<01:43, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6106/8000 [06:25<02:07, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6111/8000 [06:25<01:43, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▋      | 6114/8000 [06:26<01:59, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▋      | 6119/8000 [06:26<01:40, 18.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6122/8000 [06:26<01:58, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6125/8000 [06:26<01:52, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6129/8000 [06:26<01:42, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6132/8000 [06:27<01:41, 18.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6136/8000 [06:27<01:35, 19.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6140/8000 [06:27<01:52, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6146/8000 [06:27<01:26, 21.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6149/8000 [06:27<01:23, 22.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6155/8000 [06:28<01:28, 20.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6160/8000 [06:28<01:31, 20.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6163/8000 [06:28<01:56, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6169/8000 [06:29<01:29, 20.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6172/8000 [06:29<01:33, 19.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6175/8000 [06:29<02:15, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6177/8000 [06:29<02:42, 11.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6179/8000 [06:30<02:38, 11.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6181/8000 [06:30<02:59, 10.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6184/8000 [06:30<02:31, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6186/8000 [06:30<02:26, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6188/8000 [06:30<02:16, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6190/8000 [06:30<02:19, 13.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6192/8000 [06:31<02:19, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6194/8000 [06:31<02:44, 10.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6197/8000 [06:31<02:17, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6202/8000 [06:31<02:02, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6204/8000 [06:32<02:31, 11.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6208/8000 [06:32<02:00, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6210/8000 [06:32<02:01, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6212/8000 [06:32<03:01,  9.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6214/8000 [06:33<03:32,  8.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6219/8000 [06:33<02:19, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6222/8000 [06:33<02:05, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6226/8000 [06:33<02:07, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6229/8000 [06:33<01:57, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6233/8000 [06:34<01:42, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6235/8000 [06:34<01:42, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6238/8000 [06:34<01:40, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6241/8000 [06:34<01:36, 18.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6243/8000 [06:34<01:45, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6245/8000 [06:34<02:20, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6247/8000 [06:35<02:23, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6249/8000 [06:35<02:23, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6251/8000 [06:35<03:03,  9.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6258/8000 [06:36<02:25, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6260/8000 [06:36<02:23, 12.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6263/8000 [06:36<02:05, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6266/8000 [06:36<01:47, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6270/8000 [06:36<01:59, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6275/8000 [06:37<01:47, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6278/8000 [06:37<01:43, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6282/8000 [06:37<01:50, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6287/8000 [06:37<01:46, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6289/8000 [06:38<01:50, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6291/8000 [06:38<01:50, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6294/8000 [06:38<01:36, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6296/8000 [06:38<01:44, 16.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6300/8000 [06:38<01:27, 19.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6304/8000 [06:38<01:20, 21.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6307/8000 [06:39<01:43, 16.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6315/8000 [06:39<01:21, 20.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6320/8000 [06:39<01:07, 24.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6325/8000 [06:39<01:19, 21.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6330/8000 [06:39<01:10, 23.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6335/8000 [06:40<01:00, 27.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6341/8000 [06:40<01:10, 23.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6346/8000 [06:40<01:02, 26.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6352/8000 [06:40<01:10, 23.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6357/8000 [06:41<01:19, 20.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▍     | 6361/8000 [06:41<01:17, 21.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▍     | 6364/8000 [06:41<01:38, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▍     | 6367/8000 [06:41<01:31, 17.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6371/8000 [06:41<01:22, 19.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6374/8000 [06:42<01:21, 19.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6379/8000 [06:42<01:24, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6382/8000 [06:42<01:25, 18.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6384/8000 [06:42<01:32, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6389/8000 [06:42<01:12, 22.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6395/8000 [06:43<01:14, 21.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6403/8000 [06:43<01:22, 19.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6408/8000 [06:43<01:29, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6414/8000 [06:44<01:13, 21.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6417/8000 [06:44<01:30, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6422/8000 [06:44<01:30, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6424/8000 [06:44<01:37, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6428/8000 [06:44<01:27, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6430/8000 [06:45<01:33, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6436/8000 [06:45<01:11, 21.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6439/8000 [06:45<01:54, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▋     | 6441/8000 [06:45<01:55, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▋     | 6444/8000 [06:46<01:46, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6446/8000 [06:46<01:47, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6451/8000 [06:46<01:38, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6453/8000 [06:46<02:04, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6455/8000 [06:46<01:58, 13.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6459/8000 [06:47<02:01, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6466/8000 [06:47<01:50, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6468/8000 [06:47<01:51, 13.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6470/8000 [06:48<02:19, 10.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6473/8000 [06:48<02:20, 10.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6475/8000 [06:48<02:15, 11.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6481/8000 [06:48<01:46, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6483/8000 [06:49<01:49, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6486/8000 [06:49<01:35, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6489/8000 [06:49<01:52, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6491/8000 [06:49<01:52, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6495/8000 [06:49<01:27, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6497/8000 [06:50<01:59, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6500/8000 [06:50<01:42, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6503/8000 [06:50<01:28, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6506/8000 [06:50<01:47, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6508/8000 [06:50<01:49, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6511/8000 [06:50<01:32, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6513/8000 [06:51<01:38, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6518/8000 [06:51<01:33, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6521/8000 [06:51<01:24, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6525/8000 [06:51<01:16, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6528/8000 [06:51<01:15, 19.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6532/8000 [06:51<01:07, 21.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6535/8000 [06:52<01:04, 22.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6541/8000 [06:52<01:36, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6544/8000 [06:52<01:32, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6546/8000 [06:52<01:36, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6548/8000 [06:53<01:54, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6551/8000 [06:53<01:44, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6554/8000 [06:53<01:33, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6558/8000 [06:53<01:18, 18.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6562/8000 [06:54<01:44, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6565/8000 [06:54<01:33, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6568/8000 [06:54<02:03, 11.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6570/8000 [06:54<02:00, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6575/8000 [06:54<01:22, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6581/8000 [06:55<01:18, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6585/8000 [06:55<01:23, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6588/8000 [06:55<01:22, 17.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6591/8000 [06:55<01:19, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▎    | 6594/8000 [06:56<01:15, 18.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▎    | 6597/8000 [06:56<01:09, 20.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▎    | 6600/8000 [06:56<01:29, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6603/8000 [06:56<01:25, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6605/8000 [06:56<01:24, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6607/8000 [06:56<01:31, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6611/8000 [06:57<01:36, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6614/8000 [06:57<01:45, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6616/8000 [06:57<01:45, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6620/8000 [06:57<01:24, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6622/8000 [06:57<01:23, 16.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6625/8000 [06:58<01:19, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6627/8000 [06:58<01:46, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6629/8000 [06:58<02:32,  8.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6635/8000 [06:58<01:32, 14.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6637/8000 [06:59<01:30, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6640/8000 [06:59<01:43, 13.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6642/8000 [06:59<01:44, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6645/8000 [06:59<01:51, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6647/8000 [07:00<02:13, 10.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6652/8000 [07:00<01:26, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6657/8000 [07:00<01:09, 19.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6660/8000 [07:00<01:10, 19.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6665/8000 [07:00<01:13, 18.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▌    | 6673/8000 [07:01<01:05, 20.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▌    | 6677/8000 [07:01<01:28, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6680/8000 [07:01<01:24, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6682/8000 [07:01<01:22, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6684/8000 [07:02<01:45, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6686/8000 [07:02<01:45, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6688/8000 [07:02<01:37, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6692/8000 [07:02<01:40, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6694/8000 [07:02<01:40, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6696/8000 [07:03<02:00, 10.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6698/8000 [07:03<01:55, 11.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6700/8000 [07:03<02:10, 10.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6705/8000 [07:03<01:26, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6709/8000 [07:04<01:29, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6712/8000 [07:04<01:18, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6716/8000 [07:04<01:26, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6719/8000 [07:04<01:19, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6725/8000 [07:04<01:11, 17.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6727/8000 [07:05<01:17, 16.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6730/8000 [07:05<01:16, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6733/8000 [07:05<01:16, 16.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6736/8000 [07:05<01:14, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6739/8000 [07:05<01:12, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6743/8000 [07:06<01:18, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6746/8000 [07:06<01:13, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6750/8000 [07:06<01:03, 19.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6753/8000 [07:06<01:00, 20.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6758/8000 [07:06<00:52, 23.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6765/8000 [07:06<00:52, 23.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6768/8000 [07:07<01:18, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6770/8000 [07:07<01:20, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6772/8000 [07:07<01:36, 12.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6774/8000 [07:07<01:36, 12.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6777/8000 [07:08<01:24, 14.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6779/8000 [07:08<01:21, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6783/8000 [07:08<01:09, 17.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6788/8000 [07:08<01:09, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6792/8000 [07:08<00:58, 20.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6795/8000 [07:09<01:00, 19.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6799/8000 [07:09<01:21, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6802/8000 [07:09<01:17, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6806/8000 [07:09<01:20, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6809/8000 [07:10<01:31, 12.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6814/8000 [07:10<01:11, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6818/8000 [07:10<01:03, 18.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6821/8000 [07:10<01:05, 18.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6825/8000 [07:10<00:58, 20.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6828/8000 [07:11<00:58, 19.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6833/8000 [07:11<00:49, 23.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6837/8000 [07:11<00:49, 23.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6840/8000 [07:11<00:51, 22.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6844/8000 [07:11<00:52, 22.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6848/8000 [07:11<01:04, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6850/8000 [07:12<01:11, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6852/8000 [07:12<01:14, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6856/8000 [07:12<01:02, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6860/8000 [07:12<00:55, 20.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6864/8000 [07:12<01:02, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6866/8000 [07:13<01:09, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6873/8000 [07:13<00:48, 23.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6878/8000 [07:13<00:50, 22.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6881/8000 [07:13<00:52, 21.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6884/8000 [07:14<01:25, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6888/8000 [07:14<01:12, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6890/8000 [07:14<01:28, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6892/8000 [07:14<01:47, 10.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6895/8000 [07:15<01:29, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6899/8000 [07:15<01:25, 12.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6903/8000 [07:15<01:12, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6905/8000 [07:15<01:11, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6909/8000 [07:15<01:01, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6911/8000 [07:15<01:06, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6913/8000 [07:16<01:11, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▎   | 6922/8000 [07:16<00:40, 26.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6926/8000 [07:16<00:52, 20.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6933/8000 [07:16<00:49, 21.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6938/8000 [07:17<00:44, 23.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6942/8000 [07:17<00:55, 19.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6947/8000 [07:17<00:49, 21.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6951/8000 [07:17<00:59, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6954/8000 [07:18<00:56, 18.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6961/8000 [07:18<00:51, 20.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6966/8000 [07:18<00:53, 19.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6970/8000 [07:18<00:50, 20.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6974/8000 [07:18<00:46, 21.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6978/8000 [07:19<00:52, 19.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6982/8000 [07:19<00:49, 20.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6985/8000 [07:19<00:49, 20.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6988/8000 [07:19<00:49, 20.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6992/8000 [07:19<00:56, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6995/8000 [07:20<00:55, 18.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6997/8000 [07:20<00:57, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7000/8000 [07:20<00:52, 18.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7002/8000 [07:20<00:59, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7004/8000 [07:20<01:03, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7009/8000 [07:20<00:46, 21.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7015/8000 [07:21<00:46, 20.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7018/8000 [07:21<00:57, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7022/8000 [07:21<01:02, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7030/8000 [07:21<00:48, 19.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7033/8000 [07:22<00:47, 20.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7037/8000 [07:22<00:54, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7041/8000 [07:22<01:06, 14.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7047/8000 [07:23<00:59, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7051/8000 [07:23<00:50, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7057/8000 [07:23<00:57, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7060/8000 [07:23<00:53, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7066/8000 [07:24<00:52, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7069/8000 [07:24<00:52, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7071/8000 [07:24<00:57, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▉   | 7075/8000 [07:24<00:50, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▉   | 7079/8000 [07:24<00:45, 20.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7085/8000 [07:25<00:47, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7088/8000 [07:25<01:04, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7090/8000 [07:25<01:07, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7093/8000 [07:25<01:01, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7096/8000 [07:26<01:06, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7099/8000 [07:26<00:59, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7102/8000 [07:26<00:55, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7106/8000 [07:26<00:57, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7110/8000 [07:27<01:02, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7112/8000 [07:27<01:37,  9.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7115/8000 [07:27<01:31,  9.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7120/8000 [07:28<01:06, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7123/8000 [07:28<01:00, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7125/8000 [07:28<01:01, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7127/8000 [07:28<00:58, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7131/8000 [07:28<01:02, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7134/8000 [07:28<00:56, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7139/8000 [07:29<00:44, 19.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7142/8000 [07:29<00:58, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7145/8000 [07:29<00:55, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████▏  | 7150/8000 [07:29<00:44, 18.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████▏  | 7153/8000 [07:30<00:54, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████▏  | 7156/8000 [07:30<01:01, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████▏  | 7158/8000 [07:30<01:02, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7160/8000 [07:30<01:01, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7162/8000 [07:30<00:57, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7164/8000 [07:30<00:59, 14.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7170/8000 [07:31<00:39, 21.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7173/8000 [07:31<00:39, 20.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7177/8000 [07:31<00:45, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7179/8000 [07:31<00:48, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7182/8000 [07:31<00:46, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7185/8000 [07:31<00:42, 19.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7188/8000 [07:32<00:43, 18.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7190/8000 [07:32<01:04, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7192/8000 [07:32<01:06, 12.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7197/8000 [07:32<00:47, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7199/8000 [07:32<00:51, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7201/8000 [07:33<01:05, 12.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7205/8000 [07:33<01:03, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7210/8000 [07:33<00:55, 14.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7213/8000 [07:34<01:00, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7217/8000 [07:34<00:47, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7220/8000 [07:34<00:46, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7222/8000 [07:34<00:49, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7225/8000 [07:34<00:54, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7227/8000 [07:34<00:55, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7233/8000 [07:35<00:38, 20.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7236/8000 [07:35<00:37, 20.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7241/8000 [07:35<00:30, 24.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7244/8000 [07:35<00:32, 22.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7247/8000 [07:35<00:34, 21.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7250/8000 [07:35<00:34, 22.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7253/8000 [07:36<00:57, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7256/8000 [07:36<01:01, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7258/8000 [07:36<01:02, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7261/8000 [07:37<01:07, 10.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7265/8000 [07:37<01:03, 11.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7271/8000 [07:37<00:43, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7274/8000 [07:37<00:49, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7280/8000 [07:38<00:36, 19.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7283/8000 [07:38<00:35, 20.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7289/8000 [07:38<00:40, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7292/8000 [07:38<00:46, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7294/8000 [07:39<00:58, 12.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7297/8000 [07:39<00:57, 12.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7299/8000 [07:39<00:57, 12.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7301/8000 [07:39<00:56, 12.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7303/8000 [07:39<00:55, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7308/8000 [07:40<00:37, 18.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7312/8000 [07:40<00:34, 20.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7316/8000 [07:40<00:41, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7319/8000 [07:40<00:40, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7323/8000 [07:40<00:35, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7326/8000 [07:40<00:35, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7330/8000 [07:41<00:32, 20.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7333/8000 [07:41<00:33, 20.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7337/8000 [07:41<00:30, 22.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7340/8000 [07:41<00:31, 21.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7343/8000 [07:42<00:50, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7345/8000 [07:42<01:13,  8.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7347/8000 [07:42<01:09,  9.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7350/8000 [07:42<00:56, 11.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7353/8000 [07:43<01:00, 10.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7356/8000 [07:43<00:51, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7360/8000 [07:43<00:39, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7363/8000 [07:43<00:48, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7365/8000 [07:43<00:49, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7369/8000 [07:44<00:39, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7371/8000 [07:44<00:42, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7373/8000 [07:44<00:44, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7377/8000 [07:44<00:46, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7380/8000 [07:45<00:57, 10.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7382/8000 [07:45<01:05,  9.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7387/8000 [07:45<00:50, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7390/8000 [07:46<00:53, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7393/8000 [07:46<00:55, 10.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7396/8000 [07:46<00:49, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7399/8000 [07:46<00:44, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▉  | 7403/8000 [07:46<00:37, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▉  | 7406/8000 [07:47<00:36, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7411/8000 [07:47<00:29, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7415/8000 [07:47<00:27, 21.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7418/8000 [07:47<00:34, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7420/8000 [07:47<00:36, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7423/8000 [07:47<00:33, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7426/8000 [07:48<00:31, 18.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7428/8000 [07:48<00:34, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7430/8000 [07:48<00:36, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7434/8000 [07:48<00:40, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7437/8000 [07:48<00:37, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7440/8000 [07:49<00:34, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7443/8000 [07:49<00:33, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7445/8000 [07:49<00:36, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7447/8000 [07:49<00:38, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7452/8000 [07:49<00:27, 20.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7457/8000 [07:50<00:34, 15.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7461/8000 [07:50<00:34, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7465/8000 [07:50<00:37, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7467/8000 [07:50<00:37, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7474/8000 [07:51<00:24, 21.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7477/8000 [07:51<00:25, 20.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▏ | 7480/8000 [07:51<00:39, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7484/8000 [07:52<00:45, 11.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7486/8000 [07:52<00:44, 11.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7490/8000 [07:52<00:35, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7493/8000 [07:52<00:38, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7496/8000 [07:53<00:48, 10.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7498/8000 [07:53<00:45, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7500/8000 [07:53<00:44, 11.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7504/8000 [07:53<00:34, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7507/8000 [07:53<00:31, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7509/8000 [07:54<00:40, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7512/8000 [07:54<00:35, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7516/8000 [07:54<00:36, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7521/8000 [07:54<00:27, 17.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7527/8000 [07:55<00:25, 18.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7530/8000 [07:55<00:31, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7535/8000 [07:55<00:24, 18.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7542/8000 [07:55<00:21, 20.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7546/8000 [07:55<00:20, 22.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7549/8000 [07:56<00:30, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7551/8000 [07:56<00:37, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7555/8000 [07:57<00:36, 12.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▌ | 7558/8000 [07:57<00:33, 13.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7562/8000 [07:57<00:33, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7565/8000 [07:57<00:30, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7570/8000 [07:57<00:27, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7575/8000 [07:58<00:26, 15.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7580/8000 [07:58<00:25, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7582/8000 [07:58<00:36, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7586/8000 [07:59<00:34, 12.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7589/8000 [07:59<00:36, 11.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7591/8000 [07:59<00:36, 11.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7594/8000 [07:59<00:32, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7598/8000 [08:00<00:26, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7601/8000 [08:00<00:23, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7605/8000 [08:00<00:20, 18.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7611/8000 [08:00<00:20, 19.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7614/8000 [08:00<00:19, 20.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7618/8000 [08:01<00:22, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7620/8000 [08:01<00:24, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7622/8000 [08:01<00:25, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7626/8000 [08:01<00:21, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▊ | 7630/8000 [08:01<00:19, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▊ | 7633/8000 [08:02<00:24, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▊ | 7636/8000 [08:02<00:22, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▊ | 7638/8000 [08:02<00:24, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7641/8000 [08:02<00:23, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7644/8000 [08:02<00:21, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7646/8000 [08:03<00:29, 11.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7648/8000 [08:03<00:29, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7651/8000 [08:03<00:25, 13.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7653/8000 [08:03<00:25, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7655/8000 [08:03<00:33, 10.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7658/8000 [08:04<00:33, 10.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7660/8000 [08:04<00:32, 10.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7662/8000 [08:04<00:30, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7665/8000 [08:04<00:26, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7669/8000 [08:04<00:19, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7671/8000 [08:05<00:26, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7674/8000 [08:05<00:22, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7676/8000 [08:05<00:22, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7681/8000 [08:05<00:16, 19.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7684/8000 [08:05<00:16, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7691/8000 [08:06<00:19, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7694/8000 [08:06<00:18, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7697/8000 [08:06<00:17, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7699/8000 [08:06<00:19, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7701/8000 [08:06<00:20, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7703/8000 [08:07<00:21, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7707/8000 [08:07<00:22, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7710/8000 [08:07<00:20, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7713/8000 [08:07<00:18, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7717/8000 [08:08<00:19, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7720/8000 [08:08<00:17, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7723/8000 [08:08<00:19, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7725/8000 [08:08<00:20, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7729/8000 [08:08<00:15, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7734/8000 [08:08<00:12, 20.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7739/8000 [08:09<00:11, 22.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7742/8000 [08:09<00:15, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7746/8000 [08:09<00:17, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7748/8000 [08:09<00:18, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7752/8000 [08:10<00:15, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7754/8000 [08:10<00:15, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7758/8000 [08:10<00:16, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7761/8000 [08:10<00:15, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7764/8000 [08:10<00:14, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7767/8000 [08:11<00:16, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7769/8000 [08:11<00:19, 11.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7772/8000 [08:12<00:26,  8.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7776/8000 [08:12<00:22,  9.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7779/8000 [08:12<00:22,  9.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7785/8000 [08:12<00:14, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7788/8000 [08:12<00:13, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7792/8000 [08:13<00:14, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7794/8000 [08:13<00:15, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7798/8000 [08:13<00:12, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7800/8000 [08:13<00:13, 15.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7802/8000 [08:13<00:13, 14.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7804/8000 [08:14<00:18, 10.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7807/8000 [08:14<00:18, 10.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7813/8000 [08:14<00:12, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7817/8000 [08:14<00:10, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7819/8000 [08:15<00:10, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7821/8000 [08:15<00:10, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7824/8000 [08:15<00:10, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7828/8000 [08:15<00:13, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7831/8000 [08:16<00:12, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7835/8000 [08:16<00:10, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7837/8000 [08:16<00:10, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7839/8000 [08:16<00:11, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7841/8000 [08:16<00:14, 11.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7845/8000 [08:16<00:10, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7848/8000 [08:17<00:09, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7852/8000 [08:17<00:08, 17.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7857/8000 [08:17<00:06, 21.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7863/8000 [08:17<00:07, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7866/8000 [08:17<00:07, 19.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7870/8000 [08:18<00:07, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7875/8000 [08:18<00:06, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7880/8000 [08:18<00:05, 22.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▌| 7885/8000 [08:18<00:05, 19.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7889/8000 [08:19<00:05, 20.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7893/8000 [08:19<00:06, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7899/8000 [08:19<00:04, 22.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7903/8000 [08:19<00:04, 23.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7906/8000 [08:20<00:06, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7909/8000 [08:20<00:07, 12.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7914/8000 [08:20<00:05, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7917/8000 [08:21<00:06, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7922/8000 [08:21<00:05, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7926/8000 [08:21<00:04, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7929/8000 [08:21<00:05, 13.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7934/8000 [08:22<00:04, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7936/8000 [08:22<00:04, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7939/8000 [08:22<00:04, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7942/8000 [08:22<00:05, 11.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7945/8000 [08:23<00:05, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7947/8000 [08:23<00:05,  9.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7950/8000 [08:24<00:06,  8.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7956/8000 [08:24<00:03, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7958/8000 [08:24<00:03, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▊| 7960/8000 [08:24<00:03, 12.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7963/8000 [08:24<00:02, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7968/8000 [08:24<00:01, 18.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7972/8000 [08:24<00:01, 22.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7978/8000 [08:25<00:01, 21.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7983/8000 [08:25<00:00, 20.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7988/8000 [08:25<00:00, 18.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7994/8000 [08:26<00:00, 19.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7997/8000 [08:26<00:00, 19.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8002it [08:26, 15.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<18:06,  5.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:07<10:31,  3.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:08<08:00,  2.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:10<06:45,  2.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:11<06:00,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:13<05:44,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:14<05:29,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:16<05:13,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:17<05:08,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:19<05:01,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:20<04:59,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:22<04:53,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:23<04:48,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:25<04:45,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:27<04:47,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:28<04:45,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:30<04:40,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:31<04:36,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:33<04:33,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:34<04:31,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:36<04:28,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:37<04:30,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:39<04:27,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:40<04:27,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:42<04:27,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:43<04:25,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:45<04:24,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:46<04:23,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:48<04:20,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:49<04:20,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:51<04:20,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:52<04:20,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:54<04:24,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:56<04:18,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:57<04:13,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:59<04:09,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [01:00<04:07,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [01:02<04:05,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [01:03<04:03,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [01:05<03:59,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [01:06<04:05,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [01:08<04:07,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:09<04:01,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:11<04:07,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:14<04:16,  1.66s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.70it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 46.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 74it [00:01, 93.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 111it [00:01, 141.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 148it [00:01, 186.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 185it [00:01, 227.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 229it [00:01, 278.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 267it [00:01, 300.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 305it [00:01, 315.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 343it [00:01, 330.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 380it [00:02, 337.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 417it [00:02, 344.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 454it [00:02, 349.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 495it [00:02, 365.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 533it [00:02, 362.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 570it [00:02, 357.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 607it [00:02, 358.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 644it [00:02, 359.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 681it [00:02, 360.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 718it [00:02, 361.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 755it [00:03, 361.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 250.13it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 41.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:02, 41.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:01, 44.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 44.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|█   | 25/100 [00:00<00:01, 44.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|█▏  | 30/100 [00:00<00:01, 44.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  35%|█▍  | 35/100 [00:00<00:01, 43.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:01, 45.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|█▊  | 45/100 [00:01<00:01, 44.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|██  | 50/100 [00:01<00:01, 43.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██▏ | 55/100 [00:01<00:01, 44.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  60%|██▍ | 60/100 [00:01<00:00, 44.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  66%|██▋ | 66/100 [00:01<00:00, 47.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  71%|██▊ | 71/100 [00:01<00:00, 46.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  76%|███ | 76/100 [00:01<00:00, 45.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  81%|███▏| 81/100 [00:01<00:00, 45.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  86%|███▍| 86/100 [00:01<00:00, 44.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  91%|███▋| 91/100 [00:02<00:00, 45.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 44.73it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████████████████████| 4/4 [47:07<00:00, 835.67s/it]\u001b[A\n",
+      "Replicates:  87%|███████████████████▉   | 13/15 [8:25:42<1:10:01, 2100.78s/it]\n",
+      "Nyquist Samples:   0%|                                            | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.925622322909265\n",
+      "2920: -5.9220400\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<15:59,  4.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:00,  2.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:36,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:09<04:56,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:10<04:33,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<04:16,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<04:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<03:58,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:15<03:53,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:16<03:44,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:17<03:45,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:19<03:43,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:20<03:39,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:21<03:41,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:22<03:41,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:23<03:41,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:25<03:39,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:26<03:36,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:27<03:34,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:28<03:31,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:29<03:31,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:31<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:32<03:31,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:33<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:34<03:28,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:35<03:21,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:36<03:18,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:38<03:15,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:39<03:15,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:40<03:12,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:41<03:12,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:42<03:14,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:43<03:13,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:45<03:12,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:46<03:11,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:47<03:09,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:48<03:07,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:49<03:08,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:50<03:06,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:52<03:04,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:53<03:05,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:54<03:04,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:55<03:03,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [00:56<03:02,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [00:58<03:03,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [00:59<03:02,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:00<02:59,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:01<02:59,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:02<02:56,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:03<02:57,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:05<02:57,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:06<02:54,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:07<02:51,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:08<02:50,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:09<02:48,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:11<02:59,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:12<03:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:13<02:55,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:15<02:56,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:16<02:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:17<02:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:18<02:50,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:20<02:53,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:21<02:56,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:22<03:01,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:24<03:03,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:25<03:04,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▊                          | 69/200 [01:27<03:04,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████                          | 70/200 [01:28<03:04,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▏                         | 71/200 [01:30<03:03,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▍                         | 72/200 [01:31<03:03,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▌                         | 73/200 [01:33<03:01,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|██████████████▊                         | 74/200 [01:34<03:00,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|███████████████                         | 75/200 [01:35<02:58,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|███████████████▏                        | 76/200 [01:37<02:56,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|███████████████▍                        | 77/200 [01:38<02:49,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|███████████████▌                        | 78/200 [01:39<02:45,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|███████████████▊                        | 79/200 [01:41<02:42,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|████████████████                        | 80/200 [01:42<02:40,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|████████████████▏                       | 81/200 [01:43<02:38,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|████████████████▍                       | 82/200 [01:45<02:34,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████▌                       | 83/200 [01:46<02:38,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|████████████████▊                       | 84/200 [01:47<02:35,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|█████████████████                       | 85/200 [01:49<02:37,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|█████████████████▏                      | 86/200 [01:50<02:33,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████▍                      | 87/200 [01:51<02:30,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████▌                      | 88/200 [01:53<02:27,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|█████████████████▊                      | 89/200 [01:54<02:41,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|██████████████████                      | 90/200 [01:56<02:37,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████▏                     | 91/200 [01:57<02:27,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████▍                     | 92/200 [01:58<02:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|██████████████████▌                     | 93/200 [01:59<02:13,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████▊                     | 94/200 [02:00<02:10,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████                     | 95/200 [02:02<02:08,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████▏                    | 96/200 [02:03<02:05,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████▍                    | 97/200 [02:04<02:05,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████▌                    | 98/200 [02:05<02:02,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████▊                    | 99/200 [02:06<01:59,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████▌                   | 100/200 [02:08<01:56,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████▋                   | 101/200 [02:09<01:56,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|███████████████████▉                   | 102/200 [02:10<01:54,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████                   | 103/200 [02:11<01:55,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████▎                  | 104/200 [02:12<01:54,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████▍                  | 105/200 [02:13<01:52,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|████████████████████▋                  | 106/200 [02:15<01:51,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|████████████████████▊                  | 107/200 [02:16<01:50,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████                  | 108/200 [02:17<01:50,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|█████████████████████▎                 | 109/200 [02:18<01:49,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|█████████████████████▍                 | 110/200 [02:19<01:46,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|█████████████████████▋                 | 111/200 [02:21<01:45,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|█████████████████████▊                 | 112/200 [02:22<01:42,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████                 | 113/200 [02:23<01:40,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|██████████████████████▏                | 114/200 [02:25<01:49,  1.28s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.61it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 39it [00:00, 54.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 76it [00:01, 106.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 113it [00:01, 156.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 151it [00:01, 204.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 188it [00:01, 242.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 225it [00:01, 272.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 261it [00:01, 294.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 299it [00:01, 315.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 336it [00:01, 330.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 373it [00:01, 339.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 417it [00:01, 367.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 456it [00:02, 366.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 494it [00:02, 365.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 532it [00:02, 366.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 570it [00:02, 365.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 607it [00:02, 364.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 649it [00:02, 379.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 688it [00:02, 373.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 726it [00:02, 370.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 264.34it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 43.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  12%|▍   | 12/100 [00:00<00:01, 56.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  18%|▋   | 18/100 [00:00<00:01, 49.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  24%|▉   | 24/100 [00:00<00:01, 47.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  29%|█▏  | 29/100 [00:00<00:01, 45.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|█▎  | 34/100 [00:00<00:01, 46.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:01, 50.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  46%|█▊  | 46/100 [00:00<00:01, 47.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  51%|██  | 51/100 [00:01<00:01, 47.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  56%|██▏ | 56/100 [00:01<00:00, 46.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  61%|██▍ | 61/100 [00:01<00:00, 45.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  70%|██▊ | 70/100 [00:01<00:00, 54.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  76%|███ | 76/100 [00:01<00:00, 50.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  82%|███▎| 82/100 [00:01<00:00, 48.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  87%|███▍| 87/100 [00:01<00:00, 46.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 49.66it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 185/8000 [00:00<00:08, 950.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                         | 351/8000 [00:00<00:08, 908.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 522/8000 [00:00<00:07, 938.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▎                       | 697/8000 [00:00<00:07, 1035.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                        | 861/8000 [00:00<00:07, 980.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▎                      | 1036/8000 [00:01<00:07, 955.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                      | 1204/8000 [00:01<00:07, 910.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                     | 1382/8000 [00:01<00:07, 943.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████                     | 1560/8000 [00:01<00:06, 941.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▌                    | 1720/8000 [00:01<00:06, 920.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▏                   | 1899/8000 [00:02<00:06, 907.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▋                   | 2070/8000 [00:02<00:06, 911.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▎                  | 2236/8000 [00:02<00:06, 939.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                  | 2424/8000 [00:02<00:06, 908.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▍                 | 2591/8000 [00:02<00:06, 897.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▉                 | 2757/8000 [00:02<00:05, 891.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2932/8000 [00:03<00:05, 896.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████                | 3112/8000 [00:03<00:05, 920.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3301/8000 [00:03<00:04, 947.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▎              | 3470/8000 [00:03<00:04, 950.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▉              | 3657/8000 [00:03<00:04, 968.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████             | 3843/8000 [00:04<00:04, 1001.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4021/8000 [00:04<00:04, 980.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▋            | 4199/8000 [00:04<00:03, 990.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|█████████████▋           | 4367/8000 [00:04<00:03, 1010.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▊           | 4544/8000 [00:04<00:03, 997.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4729/8000 [00:04<00:03, 996.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▉          | 4910/8000 [00:05<00:03, 987.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|███████████████▉         | 5084/8000 [00:05<00:02, 1017.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|████████████████▍        | 5263/8000 [00:05<00:02, 1007.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▋        | 5433/8000 [00:05<00:02, 965.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5611/8000 [00:05<00:02, 968.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▊       | 5787/8000 [00:06<00:02, 948.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▎      | 5961/8000 [00:06<00:02, 935.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████▉      | 6127/8000 [00:06<00:02, 918.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▍     | 6304/8000 [00:06<00:01, 905.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6480/8000 [00:06<00:01, 895.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6649/8000 [00:07<00:01, 889.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▏   | 6821/8000 [00:07<00:01, 879.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▊   | 7008/8000 [00:07<00:01, 910.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▍  | 7193/8000 [00:07<00:00, 927.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7370/8000 [00:07<00:00, 915.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▌ | 7552/8000 [00:08<00:00, 919.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████ | 7728/8000 [00:08<00:00, 920.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▋| 7914/8000 [00:08<00:00, 948.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8080it [00:08, 940.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<16:04,  4.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<08:52,  2.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:34,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:31,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:09<04:54,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:10<04:22,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:11<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<04:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<03:58,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:15<03:51,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:16<03:47,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:17<03:43,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:18<03:41,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:20<03:37,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:21<03:32,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:22<03:30,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:23<03:39,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:24<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:26<03:42,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:27<03:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:29<03:58,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:30<03:53,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:31<03:52,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:32<03:56,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:34<03:49,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:35<03:49,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:36<03:47,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:38<03:55,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:39<03:51,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:41<03:52,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:42<03:52,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:43<03:49,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:45<03:47,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:46<03:44,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:47<03:41,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:49<03:40,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:50<03:41,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:52<04:25,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:54<04:10,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:55<03:53,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:56<03:42,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:57<03:33,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:59<03:28,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:00<03:27,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:03<03:19,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:04<03:17,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:05<03:14,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:06<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:08<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:09<03:08,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:10<03:06,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:11<03:05,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:13<03:03,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:14<03:01,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:15<03:04,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:17<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:18<03:02,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:19<02:59,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:20<02:54,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:22<02:54,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:23<02:52,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:24<02:51,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:25<02:51,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:27<02:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:28<02:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:29<02:49,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:32<02:58,  1.35s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.91it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 36it [00:00, 51.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 73it [00:01, 105.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 117it [00:01, 170.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 154it [00:01, 212.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 191it [00:01, 248.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 228it [00:01, 277.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 296.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 301it [00:01, 314.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 338it [00:01, 328.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 380it [00:01, 353.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 418it [00:01, 353.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 455it [00:02, 356.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 493it [00:02, 360.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 530it [00:02, 360.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 567it [00:02, 361.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 610it [00:02, 379.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 649it [00:02, 373.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 687it [00:02, 368.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 724it [00:02, 366.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 761it [00:02, 364.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 265.13it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   7%|▎    | 7/100 [00:00<00:01, 65.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  14%|▌   | 14/100 [00:00<00:01, 50.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 48.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|█   | 25/100 [00:00<00:01, 46.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|█▏  | 30/100 [00:00<00:01, 46.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  37%|█▍  | 37/100 [00:00<00:01, 50.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█▋  | 43/100 [00:00<00:01, 47.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  48%|█▉  | 48/100 [00:00<00:01, 47.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  53%|██  | 53/100 [00:01<00:01, 46.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  58%|██▎ | 58/100 [00:01<00:00, 46.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  64%|██▌ | 64/100 [00:01<00:00, 49.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  70%|██▊ | 70/100 [00:01<00:00, 47.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  75%|███ | 75/100 [00:01<00:00, 47.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:01<00:00, 46.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  85%|███▍| 85/100 [00:01<00:00, 45.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  92%|███▋| 92/100 [00:01<00:00, 50.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 47.85it/s]\n",
+      "\n",
+      "Nyquist Samples:  25%|████████▊                          | 1/4 [09:39<28:57, 579.23s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.017837426709235\n",
+      "2999: -6.1903695\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<15:59,  4.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:10,  2.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:54,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:44,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:09<05:03,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<04:11,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<04:15,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:16<04:05,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:17<04:00,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:18<03:58,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:19<03:49,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:20<03:46,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:22<03:41,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:23<03:41,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:24<03:40,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:25<03:41,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:26<03:37,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:28<03:37,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:29<03:36,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:30<03:35,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:31<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:33<03:35,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:34<03:30,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:35<03:30,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:36<03:29,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:37<03:29,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:39<03:25,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:40<03:23,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:41<03:35,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:42<03:34,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:44<03:31,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:45<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:46<03:24,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:47<03:23,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:49<03:19,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:50<03:15,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:51<03:13,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:52<03:13,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:54<03:21,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:55<03:20,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:56<03:16,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:57<03:15,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [00:59<03:22,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:00<03:17,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:01<03:10,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:02<03:15,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:04<03:15,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:05<03:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:06<03:17,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:08<03:15,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:09<03:19,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:11<03:15,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:12<03:13,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:13<03:11,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:14<03:09,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:16<03:34,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:18<03:31,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:19<03:17,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:20<03:08,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:21<02:59,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:23<02:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:24<02:49,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:25<02:48,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:26<02:44,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:27<02:41,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:29<02:39,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▊                          | 69/200 [01:31<02:53,  1.33s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 23.78it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 36it [00:00, 51.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 74it [00:01, 107.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 111it [00:01, 158.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 148it [00:01, 203.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 185it [00:01, 242.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 220it [00:01, 268.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 257it [00:01, 293.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 299it [00:01, 326.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 336it [00:01, 336.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 373it [00:01, 337.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 410it [00:01, 343.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 446it [00:02, 346.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 483it [00:02, 350.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 519it [00:02, 351.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 561it [00:02, 369.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 599it [00:02, 366.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 636it [00:02, 364.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 673it [00:02, 361.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 710it [00:02, 361.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 747it [00:02, 356.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 263.16it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 43.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:01, 45.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:01, 43.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 43.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  27%|█   | 27/100 [00:00<00:01, 51.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|█▎  | 33/100 [00:00<00:01, 48.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|█▌  | 38/100 [00:00<00:01, 47.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█▋  | 43/100 [00:00<00:01, 45.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  48%|█▉  | 48/100 [00:01<00:01, 44.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  60%|██▍ | 60/100 [00:01<00:00, 65.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  67%|██▋ | 67/100 [00:01<00:00, 56.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  74%|██▉ | 74/100 [00:01<00:00, 53.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  80%|███▏| 80/100 [00:01<00:00, 50.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  89%|███▌| 89/100 [00:01<00:00, 57.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:01<00:00, 50.78it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                           | 41/8000 [00:00<00:29, 267.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 95/8000 [00:00<00:26, 302.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                          | 139/8000 [00:00<00:26, 293.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                          | 190/8000 [00:00<00:24, 324.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                          | 238/8000 [00:00<00:24, 313.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|▉                          | 293/8000 [00:00<00:22, 341.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                         | 344/8000 [00:01<00:22, 339.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                         | 396/8000 [00:01<00:21, 361.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                         | 449/8000 [00:01<00:21, 349.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                         | 500/8000 [00:01<00:21, 356.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 546/8000 [00:01<00:22, 333.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                         | 590/8000 [00:01<00:21, 344.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                        | 630/8000 [00:01<00:23, 312.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                        | 679/8000 [00:02<00:22, 321.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                        | 724/8000 [00:02<00:23, 315.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▌                        | 774/8000 [00:02<00:21, 343.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                        | 822/8000 [00:02<00:21, 334.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                        | 867/8000 [00:02<00:22, 322.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 919/8000 [00:02<00:21, 325.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                       | 964/8000 [00:02<00:20, 341.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▎                      | 1019/8000 [00:03<00:20, 332.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                      | 1071/8000 [00:03<00:20, 344.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                      | 1119/8000 [00:03<00:20, 334.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▊                      | 1168/8000 [00:03<00:19, 355.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                      | 1218/8000 [00:03<00:20, 332.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████                      | 1258/8000 [00:03<00:21, 314.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                     | 1311/8000 [00:03<00:20, 328.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                     | 1356/8000 [00:04<00:19, 345.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▌                     | 1411/8000 [00:04<00:19, 342.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1454/8000 [00:04<00:20, 319.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▊                     | 1497/8000 [00:04<00:20, 314.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                     | 1552/8000 [00:04<00:18, 357.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▏                    | 1600/8000 [00:04<00:18, 336.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▎                    | 1651/8000 [00:04<00:18, 338.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                    | 1703/8000 [00:05<00:18, 341.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▋                    | 1751/8000 [00:05<00:17, 362.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                    | 1798/8000 [00:05<00:18, 344.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                    | 1847/8000 [00:05<00:18, 340.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▏                   | 1895/8000 [00:05<00:18, 326.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                   | 1945/8000 [00:05<00:17, 351.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▍                   | 1992/8000 [00:05<00:18, 333.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▌                   | 2035/8000 [00:06<00:18, 324.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▊                   | 2087/8000 [00:06<00:18, 325.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|██████▉                   | 2142/8000 [00:06<00:15, 367.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                  | 2193/8000 [00:06<00:16, 357.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▎                  | 2250/8000 [00:06<00:15, 370.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▌                  | 2338/8000 [00:06<00:16, 340.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▊                  | 2385/8000 [00:07<00:17, 325.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                  | 2439/8000 [00:07<00:16, 347.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████                  | 2487/8000 [00:07<00:16, 329.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▏                 | 2527/8000 [00:07<00:16, 332.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▎                 | 2573/8000 [00:07<00:17, 313.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▌                 | 2626/8000 [00:07<00:16, 323.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▋                 | 2681/8000 [00:07<00:15, 339.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▊                 | 2724/8000 [00:08<00:16, 323.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████                 | 2773/8000 [00:08<00:16, 326.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▏                | 2829/8000 [00:08<00:15, 332.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▎                | 2868/8000 [00:08<00:16, 305.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2927/8000 [00:08<00:15, 328.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2961/8000 [00:08<00:16, 298.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3002/8000 [00:09<00:17, 286.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▉                | 3051/8000 [00:09<00:17, 288.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████                | 3097/8000 [00:09<00:16, 289.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▏               | 3145/8000 [00:09<00:16, 295.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▍               | 3198/8000 [00:09<00:15, 305.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▌               | 3251/8000 [00:09<00:13, 342.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3293/8000 [00:09<00:14, 320.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▊               | 3343/8000 [00:10<00:14, 329.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████               | 3397/8000 [00:10<00:13, 330.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▏              | 3443/8000 [00:10<00:13, 342.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▎              | 3495/8000 [00:10<00:14, 317.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▍              | 3538/8000 [00:10<00:14, 305.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▋              | 3595/8000 [00:10<00:13, 320.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▊              | 3648/8000 [00:11<00:13, 326.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████              | 3693/8000 [00:11<00:13, 311.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▏             | 3747/8000 [00:11<00:13, 322.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▎             | 3804/8000 [00:11<00:13, 316.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▌             | 3855/8000 [00:11<00:13, 317.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▋             | 3899/8000 [00:11<00:13, 305.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▊             | 3935/8000 [00:12<00:13, 296.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████▉             | 3991/8000 [00:12<00:11, 348.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4031/8000 [00:12<00:12, 321.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▏            | 4075/8000 [00:12<00:12, 314.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▍            | 4132/8000 [00:12<00:11, 331.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▌            | 4179/8000 [00:12<00:11, 343.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▋            | 4229/8000 [00:12<00:11, 329.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▉            | 4277/8000 [00:13<00:11, 332.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████            | 4319/8000 [00:13<00:11, 314.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▏           | 4370/8000 [00:13<00:10, 347.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▍           | 4428/8000 [00:13<00:10, 356.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▌           | 4467/8000 [00:13<00:10, 324.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▋           | 4513/8000 [00:13<00:11, 316.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▊           | 4554/8000 [00:13<00:10, 325.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|██████████████▉           | 4613/8000 [00:14<00:11, 295.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▏          | 4661/8000 [00:14<00:11, 291.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4714/8000 [00:14<00:10, 299.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▍          | 4762/8000 [00:14<00:10, 299.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▌          | 4805/8000 [00:14<00:10, 303.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4861/8000 [00:14<00:09, 322.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▉          | 4903/8000 [00:15<00:09, 324.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████          | 4951/8000 [00:15<00:09, 318.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▏         | 4998/8000 [00:15<00:09, 321.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▍         | 5047/8000 [00:15<00:09, 316.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▌         | 5095/8000 [00:15<00:08, 334.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▋         | 5148/8000 [00:15<00:08, 336.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▉         | 5201/8000 [00:15<00:07, 352.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████         | 5245/8000 [00:16<00:08, 330.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▏        | 5295/8000 [00:16<00:07, 352.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▍        | 5347/8000 [00:16<00:07, 344.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▌        | 5387/8000 [00:16<00:08, 317.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▋        | 5432/8000 [00:16<00:08, 309.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▊        | 5482/8000 [00:16<00:07, 340.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████        | 5539/8000 [00:16<00:07, 349.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5581/8000 [00:17<00:07, 325.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▎       | 5632/8000 [00:17<00:07, 326.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▌       | 5695/8000 [00:17<00:06, 375.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▋       | 5743/8000 [00:17<00:06, 346.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▊       | 5794/8000 [00:17<00:06, 344.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|██████████████████▉       | 5844/8000 [00:17<00:06, 347.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▏      | 5892/8000 [00:17<00:05, 363.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▎      | 5938/8000 [00:18<00:06, 342.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▍      | 5984/8000 [00:18<00:05, 339.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▌      | 6028/8000 [00:18<00:06, 323.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6063/8000 [00:18<00:06, 307.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▊      | 6112/8000 [00:18<00:06, 308.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▏     | 6195/8000 [00:19<00:08, 205.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▎     | 6249/8000 [00:19<00:07, 233.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▍     | 6297/8000 [00:19<00:06, 259.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▋     | 6348/8000 [00:19<00:06, 270.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▊     | 6395/8000 [00:19<00:05, 285.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████▉     | 6444/8000 [00:19<00:05, 293.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6499/8000 [00:20<00:04, 312.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▎    | 6542/8000 [00:20<00:04, 303.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▍    | 6599/8000 [00:20<00:04, 329.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6644/8000 [00:20<00:04, 315.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▋    | 6691/8000 [00:20<00:04, 310.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▉    | 6733/8000 [00:20<00:04, 291.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████    | 6779/8000 [00:21<00:04, 295.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▏   | 6826/8000 [00:21<00:03, 300.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▎   | 6874/8000 [00:21<00:03, 311.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6924/8000 [00:21<00:03, 314.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▋   | 6969/8000 [00:21<00:03, 311.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▊   | 7017/8000 [00:21<00:03, 310.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████   | 7077/8000 [00:21<00:02, 366.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▏  | 7134/8000 [00:22<00:02, 364.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▎  | 7179/8000 [00:22<00:02, 344.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▍  | 7224/8000 [00:22<00:02, 324.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▌  | 7269/8000 [00:22<00:02, 334.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▊  | 7323/8000 [00:22<00:02, 336.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7359/8000 [00:22<00:02, 304.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████  | 7414/8000 [00:22<00:01, 327.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▏ | 7454/8000 [00:23<00:01, 327.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▍ | 7505/8000 [00:23<00:01, 330.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▌ | 7546/8000 [00:23<00:01, 321.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▋ | 7594/8000 [00:23<00:01, 312.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▊ | 7648/8000 [00:23<00:01, 349.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████ | 7697/8000 [00:23<00:00, 338.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▏| 7750/8000 [00:23<00:00, 350.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▎| 7803/8000 [00:24<00:00, 344.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▌| 7847/8000 [00:24<00:00, 353.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▋| 7887/8000 [00:24<00:00, 320.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▊| 7950/8000 [00:24<00:00, 354.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8005it [00:24, 324.25it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<16:14,  4.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<08:57,  2.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<04:33,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:13<04:21,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:14<04:10,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:16<04:03,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:19<03:50,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:20<03:46,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:22<03:44,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:23<03:42,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:24<03:42,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:25<03:48,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:27<03:50,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:28<03:43,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:29<03:52,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:31<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:32<03:43,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:33<03:38,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:34<03:51,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:36<03:58,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:37<04:01,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:39<04:05,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:40<04:05,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:42<04:06,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:43<04:06,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:45<04:05,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:46<04:03,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:48<04:02,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:49<04:02,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:51<04:00,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:52<03:46,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:53<03:47,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:55<03:43,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:56<03:39,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:57<03:33,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:59<03:33,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:00<03:28,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:01<03:26,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:03<03:26,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:04<03:31,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:05<03:27,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:07<03:41,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:09<03:45,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:10<03:28,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:11<03:19,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:12<03:12,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:13<03:09,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:15<03:06,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:16<02:51,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:16<02:33,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:17<02:19,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:18<02:10,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:19<02:02,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:20<01:59,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:21<03:06,  1.34s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.42it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 74it [00:01, 100.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 151it [00:01, 209.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 217it [00:01, 294.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 287it [00:01, 380.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 361it [00:01, 462.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 434it [00:01, 528.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 514it [00:01, 598.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 587it [00:01, 623.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 659it [00:01, 636.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:02, 397.80it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|█  | 34/100 [00:00<00:00, 339.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|██| 100/100 [00:00<00:00, 346.04it/s]\n",
+      "\n",
+      "Nyquist Samples:  50%|█████████████████▌                 | 2/4 [19:03<19:00, 570.30s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.8539181768752377\n",
+      "2999: -6.2139578\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<16:13,  4.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:05<08:04,  2.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:06<05:28,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:07<04:18,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:07<03:35,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:08<03:08,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:09<02:56,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:10<02:46,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:10<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:11<02:37,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:12<02:34,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:13<02:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:14<02:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:14<02:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:15<02:24,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:16<02:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:17<02:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:17<02:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:18<02:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:19<02:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:19<02:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:20<02:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:21<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:22<02:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:23<02:23,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:24<02:21,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:25<02:34,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:26<02:49,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:27<02:56,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:28<03:03,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:29<03:10,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:31<03:13,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:32<03:11,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:33<03:11,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:34<03:11,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:35<03:07,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:36<03:05,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:37<03:04,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:39<03:02,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:40<03:00,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:41<02:58,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:42<03:00,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:43<03:01,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:44<02:59,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [00:46<02:59,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [00:47<03:10,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [00:48<03:08,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [00:49<03:06,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [00:51<03:14,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [00:52<03:19,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [00:54<03:17,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [00:55<03:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [00:56<03:02,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [00:57<02:56,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [00:58<03:03,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:00<03:02,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:01<02:55,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:02<02:49,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:03<02:45,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:04<02:43,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:05<02:41,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:07<02:43,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:08<02:44,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:09<02:43,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:10<02:43,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:13<02:29,  1.11s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.41it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 44it [00:00, 62.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 81it [00:01, 113.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 118it [00:01, 162.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 155it [00:01, 206.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 193it [00:01, 246.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 230it [00:01, 275.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 266it [00:01, 296.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 303it [00:01, 315.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 340it [00:01, 328.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 376it [00:01, 335.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 412it [00:01, 339.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 448it [00:02, 343.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 485it [00:02, 348.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 522it [00:02, 352.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 564it [00:02, 369.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 602it [00:02, 366.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 639it [00:02, 359.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 677it [00:02, 364.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 714it [00:02, 362.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 751it [00:02, 361.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 263.20it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 42.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:01, 45.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:01, 44.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 43.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  27%|█   | 27/100 [00:00<00:01, 50.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  33%|█▎  | 33/100 [00:00<00:01, 47.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  38%|█▌  | 38/100 [00:00<00:01, 47.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  43%|█▋  | 43/100 [00:00<00:01, 45.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  48%|█▉  | 48/100 [00:01<00:01, 44.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  54%|██▏ | 54/100 [00:01<00:00, 47.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  59%|██▎ | 59/100 [00:01<00:00, 45.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  64%|██▌ | 64/100 [00:01<00:00, 45.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  69%|██▊ | 69/100 [00:01<00:00, 44.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  74%|██▉ | 74/100 [00:01<00:00, 43.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  81%|███▏| 81/100 [00:01<00:00, 49.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  86%|███▍| 86/100 [00:01<00:00, 47.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  91%|███▋| 91/100 [00:01<00:00, 47.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 46.09it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 15/8000 [00:00<01:34, 84.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 29/8000 [00:00<01:32, 86.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 46/8000 [00:00<01:26, 92.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 58/8000 [00:00<01:33, 85.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 73/8000 [00:00<01:32, 85.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 99/8000 [00:01<01:12, 108.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                          | 118/8000 [00:01<01:12, 109.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                          | 137/8000 [00:01<01:12, 107.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 156/8000 [00:01<01:06, 118.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 172/8000 [00:01<01:10, 111.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                          | 190/8000 [00:01<01:10, 110.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▋                          | 209/8000 [00:01<01:04, 121.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                          | 225/8000 [00:02<01:08, 113.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                          | 248/8000 [00:02<01:05, 117.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                          | 261/8000 [00:02<01:13, 105.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                          | 278/8000 [00:02<01:14, 103.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                          | 304/8000 [00:02<01:04, 119.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                          | 323/8000 [00:02<01:02, 122.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                         | 337/8000 [00:03<01:06, 114.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                         | 357/8000 [00:03<01:00, 126.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                         | 376/8000 [00:03<01:02, 121.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                         | 390/8000 [00:03<01:10, 108.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                         | 414/8000 [00:03<01:02, 120.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▍                         | 444/8000 [00:04<01:11, 105.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 457/8000 [00:04<01:17, 96.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                         | 471/8000 [00:04<01:14, 100.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                         | 492/8000 [00:04<01:09, 107.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                         | 513/8000 [00:04<01:05, 114.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 529/8000 [00:04<01:04, 115.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 544/8000 [00:04<01:05, 114.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                         | 563/8000 [00:05<01:06, 112.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                         | 587/8000 [00:05<01:01, 120.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██                         | 612/8000 [00:05<00:57, 128.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                        | 633/8000 [00:05<00:56, 129.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                        | 658/8000 [00:05<00:55, 132.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                        | 677/8000 [00:05<00:56, 129.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▎                        | 700/8000 [00:06<00:51, 142.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                        | 715/8000 [00:06<00:58, 125.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                        | 731/8000 [00:06<01:02, 116.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                        | 751/8000 [00:06<01:02, 116.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▌                        | 770/8000 [00:06<01:01, 117.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                        | 792/8000 [00:06<00:58, 122.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                        | 806/8000 [00:07<01:05, 110.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                        | 828/8000 [00:07<00:59, 119.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▊                        | 848/8000 [00:07<00:59, 119.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                        | 870/8000 [00:07<00:56, 126.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 895/8000 [00:07<01:02, 113.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 911/8000 [00:07<01:04, 109.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                       | 930/8000 [00:08<01:03, 111.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                       | 944/8000 [00:08<01:03, 110.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                       | 959/8000 [00:08<01:06, 106.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                       | 983/8000 [00:08<00:58, 120.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                      | 1000/8000 [00:08<01:01, 113.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▎                      | 1016/8000 [00:08<01:06, 105.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▎                      | 1033/8000 [00:09<01:08, 102.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                      | 1053/8000 [00:09<01:03, 109.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                      | 1068/8000 [00:09<01:04, 108.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▌                      | 1088/8000 [00:09<00:57, 121.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▌                      | 1102/8000 [00:09<01:03, 109.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                      | 1120/8000 [00:09<01:05, 105.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                      | 1138/8000 [00:10<01:03, 107.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                      | 1160/8000 [00:10<01:01, 111.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▊                      | 1180/8000 [00:10<01:01, 110.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                      | 1201/8000 [00:10<00:57, 118.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                      | 1214/8000 [00:10<00:59, 114.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                      | 1235/8000 [00:10<00:57, 118.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████                      | 1249/8000 [00:11<01:03, 106.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████                      | 1265/8000 [00:11<01:04, 104.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                     | 1282/8000 [00:11<01:04, 103.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1295/8000 [00:11<01:11, 93.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▎                     | 1321/8000 [00:11<00:59, 111.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▎                     | 1342/8000 [00:11<00:53, 123.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                     | 1362/8000 [00:12<00:53, 122.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                     | 1382/8000 [00:12<00:54, 121.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                     | 1399/8000 [00:12<00:57, 115.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▌                     | 1416/8000 [00:12<01:00, 109.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1428/8000 [00:12<01:08, 96.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1450/8000 [00:12<01:01, 106.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                     | 1467/8000 [00:13<00:57, 113.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▊                     | 1492/8000 [00:13<00:51, 125.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▉                     | 1506/8000 [00:13<00:57, 113.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▉                     | 1526/8000 [00:13<00:56, 114.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                     | 1539/8000 [00:13<01:03, 102.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                     | 1556/8000 [00:13<01:02, 103.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████                     | 1575/8000 [00:14<01:02, 103.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▏                    | 1596/8000 [00:14<00:54, 117.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                    | 1617/8000 [00:14<00:52, 120.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                    | 1640/8000 [00:14<00:49, 128.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▍                    | 1662/8000 [00:14<00:45, 138.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                    | 1696/8000 [00:14<00:50, 124.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                    | 1715/8000 [00:15<00:51, 121.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▋                    | 1738/8000 [00:15<00:48, 129.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▋                    | 1756/8000 [00:15<00:50, 124.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                    | 1774/8000 [00:15<00:51, 120.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                    | 1797/8000 [00:15<00:50, 121.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|█████▉                    | 1814/8000 [00:15<00:49, 123.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|█████▉                    | 1836/8000 [00:16<00:48, 126.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                    | 1855/8000 [00:16<00:49, 123.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                    | 1877/8000 [00:16<00:45, 135.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▏                   | 1899/8000 [00:16<00:43, 140.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▏                   | 1914/8000 [00:16<00:48, 125.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                   | 1930/8000 [00:16<00:51, 117.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                   | 1949/8000 [00:16<00:51, 117.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▍                   | 1967/8000 [00:17<00:52, 115.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▍                   | 1990/8000 [00:17<00:48, 124.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▌                   | 2011/8000 [00:17<00:47, 124.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▌                   | 2033/8000 [00:17<00:44, 132.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▋                   | 2050/8000 [00:17<00:48, 121.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▋                   | 2072/8000 [00:17<00:47, 124.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▊                   | 2089/8000 [00:18<00:47, 124.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▊                   | 2104/8000 [00:18<00:51, 114.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                   | 2119/8000 [00:18<00:54, 107.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|██████▉                   | 2138/8000 [00:18<00:54, 107.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2163/8000 [00:18<01:04, 91.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2176/8000 [00:19<01:06, 88.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2195/8000 [00:19<01:00, 95.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2210/8000 [00:19<01:03, 91.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2230/8000 [00:19<01:01, 93.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▎                  | 2251/8000 [00:19<00:54, 105.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                  | 2273/8000 [00:19<00:47, 120.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▍                  | 2290/8000 [00:20<00:49, 116.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▌                  | 2310/8000 [00:20<00:48, 117.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▌                  | 2329/8000 [00:20<00:48, 116.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                  | 2347/8000 [00:20<00:50, 113.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▋                  | 2361/8000 [00:20<00:55, 102.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▋                  | 2381/8000 [00:20<00:52, 106.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▊                  | 2405/8000 [00:21<00:44, 124.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                  | 2426/8000 [00:21<00:44, 125.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|███████▉                  | 2458/8000 [00:21<00:46, 119.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████                  | 2472/8000 [00:21<00:46, 118.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████                  | 2493/8000 [00:21<00:45, 121.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▏                 | 2509/8000 [00:21<00:48, 112.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▏                 | 2532/8000 [00:22<00:45, 121.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▎                 | 2556/8000 [00:22<00:43, 125.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▍                 | 2580/8000 [00:22<00:43, 123.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▍                 | 2606/8000 [00:22<00:40, 134.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▌                 | 2625/8000 [00:22<00:44, 120.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▌                 | 2649/8000 [00:23<00:44, 121.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▋                 | 2668/8000 [00:23<00:44, 119.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▋                 | 2683/8000 [00:23<00:47, 111.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▊                 | 2704/8000 [00:23<00:48, 108.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▊                 | 2719/8000 [00:23<00:51, 101.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▉                 | 2740/8000 [00:23<00:49, 105.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2754/8000 [00:24<00:53, 97.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2774/8000 [00:24<00:53, 98.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████                 | 2796/8000 [00:24<00:49, 105.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2808/8000 [00:24<00:55, 93.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2821/8000 [00:24<00:58, 87.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2831/8000 [00:25<01:04, 80.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2856/8000 [00:25<00:52, 97.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2871/8000 [00:25<00:55, 93.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▍                | 2891/8000 [00:25<00:50, 101.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▍                | 2909/8000 [00:25<00:49, 101.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2926/8000 [00:25<00:50, 101.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2943/8000 [00:26<00:49, 101.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▋                | 2962/8000 [00:26<00:43, 115.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▋                | 2982/8000 [00:26<00:42, 119.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▋                | 2995/8000 [00:26<00:47, 105.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3020/8000 [00:26<00:39, 125.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3038/8000 [00:26<00:38, 128.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▉                | 3053/8000 [00:26<00:42, 117.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▉                | 3075/8000 [00:27<00:42, 115.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████                | 3104/8000 [00:27<00:37, 131.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▏               | 3123/8000 [00:27<00:38, 125.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▏               | 3142/8000 [00:27<00:39, 124.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▎               | 3162/8000 [00:27<00:38, 124.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▎               | 3183/8000 [00:27<00:36, 132.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▍               | 3206/8000 [00:28<00:35, 133.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▍               | 3222/8000 [00:28<00:40, 117.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▌               | 3240/8000 [00:28<00:42, 112.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▌               | 3258/8000 [00:28<00:42, 110.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3289/8000 [00:28<00:34, 136.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▊               | 3311/8000 [00:28<00:34, 136.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▊               | 3332/8000 [00:29<00:32, 144.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▉               | 3350/8000 [00:29<00:34, 132.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▉               | 3367/8000 [00:29<00:38, 121.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████               | 3386/8000 [00:29<00:35, 130.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████               | 3404/8000 [00:29<00:34, 135.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▏              | 3437/8000 [00:30<00:38, 117.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▎              | 3470/8000 [00:30<00:41, 110.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▎              | 3494/8000 [00:30<00:42, 105.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▍              | 3515/8000 [00:30<00:43, 104.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3528/8000 [00:31<00:50, 88.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3546/8000 [00:31<00:51, 86.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3567/8000 [00:31<00:50, 88.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3582/8000 [00:31<00:53, 82.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3600/8000 [00:31<00:52, 83.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3611/8000 [00:32<00:58, 75.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▎              | 3632/8000 [00:32<00:48, 90.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▉              | 3657/8000 [00:32<00:40, 108.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▉              | 3679/8000 [00:32<00:34, 124.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████              | 3696/8000 [00:32<00:36, 118.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████              | 3713/8000 [00:32<00:36, 116.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▏             | 3732/8000 [00:32<00:35, 120.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▏             | 3765/8000 [00:33<00:35, 117.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▎             | 3781/8000 [00:33<00:36, 116.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▎             | 3803/8000 [00:33<00:33, 124.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▍             | 3833/8000 [00:33<00:35, 117.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▌             | 3852/8000 [00:34<00:34, 121.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▌             | 3871/8000 [00:34<00:34, 121.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▋             | 3890/8000 [00:34<00:32, 127.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▋             | 3904/8000 [00:34<00:35, 115.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▋             | 3922/8000 [00:34<00:36, 113.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▊             | 3945/8000 [00:34<00:30, 131.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████▉             | 3965/8000 [00:34<00:30, 134.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████▉             | 3980/8000 [00:35<00:32, 125.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4006/8000 [00:35<00:28, 137.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4021/8000 [00:35<00:30, 129.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▏            | 4039/8000 [00:35<00:32, 121.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▏            | 4061/8000 [00:35<00:31, 125.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▎            | 4084/8000 [00:35<00:30, 129.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▎            | 4103/8000 [00:35<00:31, 122.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▍            | 4128/8000 [00:36<00:30, 127.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▍            | 4142/8000 [00:36<00:33, 113.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▌            | 4164/8000 [00:36<00:31, 121.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▌            | 4181/8000 [00:36<00:33, 113.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▋            | 4194/8000 [00:36<00:36, 103.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4207/8000 [00:37<00:39, 95.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4223/8000 [00:37<00:38, 98.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▊            | 4244/8000 [00:37<00:35, 107.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▊            | 4264/8000 [00:37<00:34, 109.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|█████████████▉            | 4282/8000 [00:37<00:31, 118.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|█████████████▉            | 4299/8000 [00:37<00:32, 112.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████            | 4317/8000 [00:37<00:32, 112.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████            | 4340/8000 [00:38<00:30, 120.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▏           | 4370/8000 [00:38<00:34, 105.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▎           | 4385/8000 [00:38<00:34, 104.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4399/8000 [00:38<00:36, 99.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▎           | 4421/8000 [00:38<00:30, 119.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▍           | 4440/8000 [00:39<00:29, 120.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▍           | 4453/8000 [00:39<00:31, 110.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▌           | 4472/8000 [00:39<00:31, 112.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▌           | 4493/8000 [00:39<00:28, 124.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▋           | 4518/8000 [00:39<00:25, 136.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▋           | 4533/8000 [00:39<00:28, 123.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▊           | 4552/8000 [00:39<00:26, 129.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▊           | 4573/8000 [00:40<00:24, 140.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▉           | 4588/8000 [00:40<00:27, 123.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|██████████████▉           | 4605/8000 [00:40<00:29, 115.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████           | 4627/8000 [00:40<00:27, 121.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████           | 4643/8000 [00:40<00:29, 113.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▏          | 4656/8000 [00:40<00:32, 102.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▏          | 4679/8000 [00:41<00:30, 109.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4698/8000 [00:41<00:28, 117.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4718/8000 [00:41<00:27, 118.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▍          | 4734/8000 [00:41<00:28, 113.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▍          | 4749/8000 [00:41<00:28, 115.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▌          | 4772/8000 [00:41<00:24, 133.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▌          | 4792/8000 [00:41<00:24, 128.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▋          | 4818/8000 [00:42<00:22, 138.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▋          | 4836/8000 [00:42<00:22, 139.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4851/8000 [00:42<00:23, 135.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4878/8000 [00:42<00:21, 143.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▉          | 4897/8000 [00:42<00:23, 132.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|███████████████▉          | 4923/8000 [00:42<00:20, 150.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████          | 4941/8000 [00:42<00:22, 135.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▏         | 4962/8000 [00:43<00:22, 135.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▏         | 4985/8000 [00:43<00:21, 139.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▎         | 5001/8000 [00:43<00:23, 127.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▎         | 5016/8000 [00:43<00:26, 110.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▍         | 5040/8000 [00:43<00:24, 123.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▍         | 5065/8000 [00:43<00:21, 135.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▌         | 5096/8000 [00:44<00:23, 124.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▋         | 5119/8000 [00:44<00:21, 132.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▋         | 5136/8000 [00:44<00:22, 127.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▊         | 5167/8000 [00:44<00:18, 155.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▊         | 5186/8000 [00:44<00:19, 144.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▉         | 5222/8000 [00:45<00:21, 129.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████         | 5242/8000 [00:45<00:21, 128.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████         | 5267/8000 [00:45<00:20, 133.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▏        | 5289/8000 [00:45<00:21, 128.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▎        | 5314/8000 [00:45<00:19, 140.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▎        | 5336/8000 [00:45<00:19, 139.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▍        | 5356/8000 [00:46<00:19, 138.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▍        | 5371/8000 [00:46<00:20, 127.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▌        | 5397/8000 [00:46<00:17, 149.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▋        | 5431/8000 [00:46<00:19, 129.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▋        | 5448/8000 [00:46<00:20, 126.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▊        | 5469/8000 [00:46<00:19, 130.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▊        | 5495/8000 [00:47<00:18, 135.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▉        | 5515/8000 [00:47<00:19, 128.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▉        | 5533/8000 [00:47<00:21, 116.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████        | 5556/8000 [00:47<00:19, 125.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████        | 5576/8000 [00:47<00:19, 125.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5601/8000 [00:47<00:17, 138.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▎       | 5623/8000 [00:48<00:16, 147.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▎       | 5640/8000 [00:48<00:17, 134.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▍       | 5658/8000 [00:48<00:18, 127.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5680/8000 [00:48<00:23, 97.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▌       | 5700/8000 [00:48<00:21, 104.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5716/8000 [00:49<00:22, 99.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▋       | 5738/8000 [00:49<00:20, 109.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▋       | 5758/8000 [00:49<00:19, 113.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▊       | 5775/8000 [00:49<00:20, 111.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▊       | 5789/8000 [00:49<00:21, 104.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|██████████████████▉       | 5811/8000 [00:49<00:19, 112.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|██████████████████▉       | 5834/8000 [00:50<00:18, 119.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████       | 5855/8000 [00:50<00:17, 122.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████       | 5877/8000 [00:50<00:16, 126.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▏      | 5898/8000 [00:50<00:16, 126.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▏      | 5918/8000 [00:50<00:16, 124.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▎      | 5934/8000 [00:50<00:16, 123.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▎      | 5955/8000 [00:51<00:16, 124.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▍      | 5981/8000 [00:51<00:15, 133.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▌      | 6003/8000 [00:51<00:15, 131.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▌      | 6020/8000 [00:51<00:16, 119.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6042/8000 [00:51<00:15, 123.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6067/8000 [00:51<00:14, 136.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▊      | 6086/8000 [00:52<00:14, 134.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████▉      | 6125/8000 [00:52<00:13, 137.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|███████████████████▉      | 6143/8000 [00:52<00:13, 133.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████      | 6163/8000 [00:52<00:13, 132.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████      | 6185/8000 [00:52<00:12, 144.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▏     | 6207/8000 [00:52<00:12, 142.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▏     | 6226/8000 [00:53<00:13, 136.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▎     | 6251/8000 [00:53<00:11, 147.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▍     | 6270/8000 [00:53<00:11, 148.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▍     | 6297/8000 [00:53<00:11, 153.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▌     | 6331/8000 [00:53<00:13, 127.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▋     | 6348/8000 [00:53<00:13, 120.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▋     | 6366/8000 [00:54<00:13, 116.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▋     | 6383/8000 [00:54<00:14, 114.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▊     | 6405/8000 [00:54<00:12, 124.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▉     | 6425/8000 [00:54<00:12, 122.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|████████████████████▉     | 6457/8000 [00:54<00:14, 110.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6475/8000 [00:55<00:14, 108.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6495/8000 [00:55<00:13, 109.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▏    | 6512/8000 [00:55<00:13, 108.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▏    | 6530/8000 [00:55<00:12, 114.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▎    | 6547/8000 [00:55<00:12, 112.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▎    | 6568/8000 [00:55<00:11, 120.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▍    | 6601/8000 [00:56<00:11, 121.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6617/8000 [00:56<00:11, 116.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6635/8000 [00:56<00:12, 113.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▌    | 6651/8000 [00:56<00:12, 112.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▋    | 6665/8000 [00:56<00:12, 103.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▋    | 6686/8000 [00:56<00:11, 112.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▊    | 6706/8000 [00:57<00:11, 110.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▊    | 6726/8000 [00:57<00:11, 113.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▉    | 6746/8000 [00:57<00:10, 117.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████▉    | 6765/8000 [00:57<00:10, 122.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████    | 6783/8000 [00:57<00:10, 120.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████    | 6801/8000 [00:57<00:09, 126.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▏   | 6822/8000 [00:58<00:09, 127.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▏   | 6845/8000 [00:58<00:08, 131.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▎   | 6870/8000 [00:58<00:07, 148.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▍   | 6901/8000 [00:58<00:08, 129.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6925/8000 [00:58<00:08, 134.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6941/8000 [00:58<00:07, 132.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6956/8000 [00:59<00:08, 127.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▋   | 6977/8000 [00:59<00:07, 128.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▋   | 6994/8000 [00:59<00:08, 122.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▊   | 7007/8000 [00:59<00:09, 106.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▊   | 7026/8000 [00:59<00:08, 109.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▉   | 7039/8000 [00:59<00:09, 100.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▉   | 7063/8000 [01:00<00:08, 112.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████   | 7087/8000 [01:00<00:07, 130.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████   | 7110/8000 [01:00<00:06, 133.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▏  | 7139/8000 [01:00<00:05, 150.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▎  | 7163/8000 [01:00<00:05, 153.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▎  | 7181/8000 [01:00<00:05, 150.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▍  | 7199/8000 [01:00<00:05, 138.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▍  | 7223/8000 [01:01<00:06, 113.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▌  | 7236/8000 [01:01<00:06, 113.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▌  | 7260/8000 [01:01<00:06, 123.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▋  | 7277/8000 [01:01<00:06, 119.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▋  | 7294/8000 [01:01<00:05, 119.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▊  | 7316/8000 [01:01<00:05, 133.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▊  | 7333/8000 [01:02<00:05, 124.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7358/8000 [01:02<00:04, 131.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7377/8000 [01:02<00:04, 125.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████  | 7396/8000 [01:02<00:04, 121.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████  | 7414/8000 [01:02<00:04, 119.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▏ | 7439/8000 [01:03<00:05, 101.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7453/8000 [01:03<00:05, 99.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▎ | 7470/8000 [01:03<00:05, 104.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▎ | 7487/8000 [01:03<00:04, 104.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▍ | 7504/8000 [01:03<00:04, 111.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▍ | 7524/8000 [01:03<00:04, 114.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▍ | 7538/8000 [01:04<00:04, 103.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▌ | 7557/8000 [01:04<00:04, 107.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▌ | 7574/8000 [01:04<00:04, 105.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▋ | 7596/8000 [01:04<00:03, 114.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▋ | 7609/8000 [01:04<00:03, 104.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▊ | 7633/8000 [01:04<00:03, 120.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▊ | 7646/8000 [01:04<00:03, 108.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▉ | 7661/8000 [01:05<00:03, 105.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▉ | 7678/8000 [01:05<00:02, 108.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████ | 7693/8000 [01:05<00:02, 111.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7720/8000 [01:05<00:02, 98.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▏| 7740/8000 [01:05<00:02, 109.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▏| 7761/8000 [01:05<00:01, 124.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▎| 7782/8000 [01:06<00:01, 126.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▎| 7800/8000 [01:06<00:01, 125.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▍| 7822/8000 [01:06<00:01, 131.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▌| 7847/8000 [01:06<00:01, 150.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▌| 7880/8000 [01:06<00:00, 127.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▋| 7896/8000 [01:06<00:00, 124.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▋| 7917/8000 [01:07<00:00, 134.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▊| 7947/8000 [01:07<00:00, 117.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7966/8000 [01:07<00:00, 121.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7982/8000 [01:07<00:00, 124.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8011it [01:08, 117.75it/s]                                          \n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:04<16:07,  4.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:26,  2.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<07:07,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:09<06:08,  1.88s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:10<05:31,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<05:02,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:12<04:36,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:14<04:15,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:15<03:59,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:16<03:51,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:17<03:46,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:18<03:41,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:19<03:38,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:20<03:37,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:22<03:41,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:23<03:34,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:24<03:33,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:25<03:30,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:26<03:25,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:27<03:24,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:28<03:24,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:30<03:23,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:31<03:18,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:32<03:15,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:33<03:19,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:34<03:25,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:36<03:30,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:37<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:38<03:25,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:39<03:40,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:41<03:34,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:42<03:27,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:43<03:22,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:44<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:46<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:47<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:48<03:26,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:50<03:34,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:51<03:34,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:53<03:44,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:54<03:56,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:55<03:39,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [00:57<03:37,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [00:58<03:38,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [00:59<03:26,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:01<03:18,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:02<03:11,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:03<03:04,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:04<02:59,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:05<02:54,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:06<02:48,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:07<02:50,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:08<02:46,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:10<02:46,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:11<02:45,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:12<02:50,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:15<03:08,  1.32s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.62it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 37it [00:00, 51.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 74it [00:01, 104.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 110it [00:01, 153.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 146it [00:01, 196.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 182it [00:01, 235.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 217it [00:01, 263.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 253it [00:01, 289.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 290it [00:01, 309.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 326it [00:01, 322.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 362it [00:01, 333.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 398it [00:02, 290.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 441it [00:02, 324.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 477it [00:02, 333.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 514it [00:02, 341.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 551it [00:02, 348.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 588it [00:02, 352.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 624it [00:02, 352.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 660it [00:02, 352.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 698it [00:02, 360.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 735it [00:02, 360.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 254.33it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 43.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  13%|▌   | 13/100 [00:00<00:01, 61.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 51.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  26%|█   | 26/100 [00:00<00:01, 49.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  32%|█▎  | 32/100 [00:00<00:01, 46.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  37%|█▍  | 37/100 [00:00<00:01, 45.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  44%|█▊  | 44/100 [00:00<00:01, 50.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|██  | 50/100 [00:01<00:01, 47.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██▏ | 55/100 [00:01<00:00, 47.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  60%|██▍ | 60/100 [00:01<00:00, 46.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  65%|██▌ | 65/100 [00:01<00:00, 45.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  72%|██▉ | 72/100 [00:01<00:00, 50.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  78%|███ | 78/100 [00:01<00:00, 47.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|███▎| 83/100 [00:01<00:00, 48.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  88%|███▌| 88/100 [00:01<00:00, 46.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 48.92it/s]\n",
+      "\n",
+      "Nyquist Samples:  75%|██████████████████████████▎        | 3/4 [25:11<07:58, 478.19s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.277461564322273\n",
+      "2999: -5.8360089\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<18:01,  5.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:07<10:46,  3.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:08<08:10,  2.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:10<07:03,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:12<06:29,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:13<06:08,  1.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:15<05:54,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:17<05:41,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:18<05:28,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:20<05:21,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:22<05:20,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:23<05:20,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:25<05:16,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:27<05:14,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:28<05:10,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:30<05:13,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:32<05:09,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:33<05:04,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:35<04:59,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:37<04:57,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:38<04:57,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:40<04:54,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:42<04:52,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:43<04:55,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:45<04:52,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:47<04:48,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:48<04:49,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:50<04:43,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:52<04:39,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:53<04:37,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:55<04:35,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:56<04:35,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:58<04:38,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [01:00<04:37,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [01:02<04:39,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [01:03<04:36,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [01:05<04:36,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [01:07<04:31,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [01:08<04:29,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [01:10<04:25,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [01:12<04:27,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [01:14<04:33,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:15<04:29,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:17<04:29,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:19<04:26,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:20<04:23,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:22<04:23,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:24<04:25,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:26<04:28,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:28<04:28,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:29<04:26,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:31<04:24,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:33<04:23,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:35<04:21,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:37<04:21,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:40<04:18,  1.80s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 12it [00:01, 11.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 16.51it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 48.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 98.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 112it [00:01, 147.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 149it [00:01, 192.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 192it [00:01, 245.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 229it [00:01, 274.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 266it [00:01, 297.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 303it [00:01, 309.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 340it [00:01, 323.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 377it [00:01, 334.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 414it [00:02, 342.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 455it [00:02, 359.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 493it [00:02, 360.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 530it [00:02, 312.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 563it [00:02, 277.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 599it [00:02, 296.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 634it [00:02, 310.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 672it [00:02, 327.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 709it [00:03, 336.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 746it [00:03, 344.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 245.04it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   4%|▏    | 4/100 [00:00<00:02, 39.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   9%|▍    | 9/100 [00:00<00:02, 42.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  14%|▌   | 14/100 [00:00<00:02, 42.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  19%|▊   | 19/100 [00:00<00:01, 44.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  24%|▉   | 24/100 [00:00<00:01, 43.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  29%|█▏  | 29/100 [00:00<00:01, 43.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  35%|█▍  | 35/100 [00:00<00:01, 45.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:01, 44.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|█▊  | 45/100 [00:01<00:01, 45.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|██  | 50/100 [00:01<00:01, 44.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██▏ | 55/100 [00:01<00:01, 43.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  61%|██▍ | 61/100 [00:01<00:00, 46.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  66%|██▋ | 66/100 [00:01<00:00, 44.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  71%|██▊ | 71/100 [00:01<00:00, 43.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  76%|███ | 76/100 [00:01<00:00, 42.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  81%|███▏| 81/100 [00:01<00:00, 42.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  86%|███▍| 86/100 [00:01<00:00, 43.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  91%|███▋| 91/100 [00:02<00:00, 42.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 43.67it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 1/8000 [00:00<22:44,  5.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 2/8000 [00:00<20:22,  6.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 4/8000 [00:00<12:53, 10.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 7/8000 [00:00<09:51, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 10/8000 [00:00<08:47, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 12/8000 [00:00<09:34, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 15/8000 [00:01<08:55, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 18/8000 [00:01<11:01, 12.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 20/8000 [00:01<13:26,  9.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 25/8000 [00:02<13:11, 10.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 29/8000 [00:02<12:30, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                             | 32/8000 [00:02<10:43, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                            | 36/8000 [00:02<09:01, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                            | 38/8000 [00:03<09:25, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                            | 40/8000 [00:03<09:50, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 43/8000 [00:03<09:14, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 48/8000 [00:03<09:12, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 54/8000 [00:04<12:02, 11.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 59/8000 [00:04<11:04, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 62/8000 [00:05<11:27, 11.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                            | 65/8000 [00:05<10:56, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 69/8000 [00:05<12:26, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 71/8000 [00:06<12:49, 10.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 73/8000 [00:06<13:00, 10.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 76/8000 [00:06<11:23, 11.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 80/8000 [00:06<09:17, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 82/8000 [00:06<09:08, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 90/8000 [00:06<05:44, 22.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 95/8000 [00:07<05:23, 24.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                            | 98/8000 [00:07<08:18, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 101/8000 [00:07<10:35, 12.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 104/8000 [00:08<11:04, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 108/8000 [00:08<11:46, 11.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 110/8000 [00:08<12:05, 10.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 113/8000 [00:09<11:30, 11.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                           | 115/8000 [00:09<12:25, 10.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 121/8000 [00:09<08:32, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 124/8000 [00:09<09:01, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 127/8000 [00:09<08:38, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 132/8000 [00:10<08:07, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 136/8000 [00:10<08:57, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 138/8000 [00:10<08:56, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                           | 140/8000 [00:10<08:42, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 143/8000 [00:10<08:17, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 146/8000 [00:11<08:11, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 149/8000 [00:11<11:59, 10.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 151/8000 [00:11<14:05,  9.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 155/8000 [00:12<16:00,  8.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 157/8000 [00:12<14:55,  8.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 159/8000 [00:12<14:05,  9.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 162/8000 [00:12<11:43, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 168/8000 [00:13<07:49, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 174/8000 [00:13<06:09, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                           | 177/8000 [00:13<06:23, 20.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 180/8000 [00:13<08:02, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 182/8000 [00:13<08:35, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 188/8000 [00:14<07:57, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 191/8000 [00:14<07:48, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                           | 197/8000 [00:14<06:10, 21.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▋                           | 202/8000 [00:14<05:16, 24.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▋                           | 207/8000 [00:14<05:01, 25.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▋                           | 213/8000 [00:15<04:20, 29.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 218/8000 [00:15<05:05, 25.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 222/8000 [00:15<07:33, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 228/8000 [00:16<07:03, 18.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 232/8000 [00:16<07:43, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 237/8000 [00:16<06:17, 20.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 241/8000 [00:16<07:14, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                           | 248/8000 [00:16<05:29, 23.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 251/8000 [00:17<05:31, 23.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 255/8000 [00:17<05:23, 23.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 258/8000 [00:17<07:00, 18.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 263/8000 [00:17<08:12, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 268/8000 [00:18<06:49, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 273/8000 [00:18<07:06, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 276/8000 [00:18<08:14, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                           | 279/8000 [00:19<09:12, 13.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|▉                           | 283/8000 [00:19<08:01, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 286/8000 [00:19<07:30, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 290/8000 [00:19<08:45, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 294/8000 [00:19<07:35, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 296/8000 [00:19<08:01, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 299/8000 [00:20<07:49, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 302/8000 [00:20<07:26, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 304/8000 [00:20<08:21, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 307/8000 [00:20<08:06, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 312/8000 [00:20<06:18, 20.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 316/8000 [00:21<07:37, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                           | 318/8000 [00:21<08:20, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 323/8000 [00:21<08:07, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 327/8000 [00:22<10:28, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 331/8000 [00:22<10:38, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 334/8000 [00:22<09:32, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 337/8000 [00:23<11:42, 10.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 344/8000 [00:23<07:33, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 348/8000 [00:23<07:58, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 350/8000 [00:23<08:24, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 352/8000 [00:23<08:58, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                          | 357/8000 [00:23<07:00, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                          | 360/8000 [00:24<08:53, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 365/8000 [00:24<07:10, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 368/8000 [00:24<06:46, 18.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 374/8000 [00:25<07:57, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 378/8000 [00:25<09:24, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 382/8000 [00:25<08:10, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 385/8000 [00:25<07:32, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                          | 389/8000 [00:25<06:47, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 393/8000 [00:26<06:21, 19.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 397/8000 [00:26<06:03, 20.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 403/8000 [00:26<06:25, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 408/8000 [00:27<07:57, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 411/8000 [00:27<09:04, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 413/8000 [00:27<08:56, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 415/8000 [00:27<09:11, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 421/8000 [00:27<06:33, 19.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 424/8000 [00:27<06:23, 19.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                          | 427/8000 [00:28<06:04, 20.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                          | 433/8000 [00:28<06:35, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                          | 436/8000 [00:28<06:37, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 443/8000 [00:29<08:45, 14.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 447/8000 [00:29<07:43, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 450/8000 [00:29<07:20, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 455/8000 [00:29<07:10, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 458/8000 [00:29<07:13, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 460/8000 [00:30<09:09, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                          | 462/8000 [00:30<10:59, 11.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 467/8000 [00:31<11:35, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 470/8000 [00:31<12:16, 10.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 474/8000 [00:31<12:50,  9.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 480/8000 [00:31<08:55, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 482/8000 [00:32<08:43, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 484/8000 [00:32<09:01, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 488/8000 [00:32<07:38, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 493/8000 [00:32<06:14, 20.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                          | 496/8000 [00:32<08:09, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 503/8000 [00:33<05:44, 21.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 507/8000 [00:33<05:34, 22.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 510/8000 [00:33<05:53, 21.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 514/8000 [00:33<08:40, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 516/8000 [00:34<08:49, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                          | 518/8000 [00:34<11:21, 10.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 521/8000 [00:34<09:59, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 525/8000 [00:34<08:04, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 528/8000 [00:34<08:52, 14.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                          | 532/8000 [00:35<07:32, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 536/8000 [00:35<08:07, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 538/8000 [00:35<07:55, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 544/8000 [00:35<05:53, 21.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 548/8000 [00:35<05:30, 22.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 551/8000 [00:36<05:41, 21.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 555/8000 [00:36<05:15, 23.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 561/8000 [00:36<04:33, 27.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 564/8000 [00:36<06:36, 18.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 567/8000 [00:36<06:41, 18.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                          | 570/8000 [00:37<06:37, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 575/8000 [00:37<06:54, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 579/8000 [00:37<07:41, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 583/8000 [00:37<07:57, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 586/8000 [00:38<07:43, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 589/8000 [00:38<10:57, 11.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 592/8000 [00:38<09:49, 12.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 596/8000 [00:38<08:21, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                          | 599/8000 [00:39<07:46, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██                          | 603/8000 [00:39<08:37, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 608/8000 [00:39<06:50, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 611/8000 [00:39<08:28, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 615/8000 [00:40<07:20, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 617/8000 [00:40<07:50, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 619/8000 [00:40<08:21, 14.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 621/8000 [00:40<08:35, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 624/8000 [00:40<07:46, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 628/8000 [00:40<06:15, 19.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 632/8000 [00:41<09:27, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 634/8000 [00:41<11:17, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 636/8000 [00:41<11:02, 11.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                         | 641/8000 [00:42<09:58, 12.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 645/8000 [00:42<08:14, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 647/8000 [00:42<08:12, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 650/8000 [00:42<07:14, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 652/8000 [00:42<07:48, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 654/8000 [00:42<08:06, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 661/8000 [00:43<06:10, 19.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 663/8000 [00:43<07:00, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 669/8000 [00:43<05:19, 22.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                         | 673/8000 [00:43<06:04, 20.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                         | 679/8000 [00:44<06:37, 18.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 685/8000 [00:44<06:44, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 687/8000 [00:44<07:06, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 689/8000 [00:44<07:32, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 692/8000 [00:44<08:37, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 695/8000 [00:45<09:32, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 700/8000 [00:45<09:46, 12.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 702/8000 [00:45<09:43, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 706/8000 [00:45<07:41, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 710/8000 [00:46<06:55, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                         | 713/8000 [00:46<08:40, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 715/8000 [00:46<08:55, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 718/8000 [00:46<08:22, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 721/8000 [00:47<07:55, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 723/8000 [00:47<07:44, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 728/8000 [00:47<07:49, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 731/8000 [00:47<07:11, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 734/8000 [00:47<06:39, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 737/8000 [00:47<06:33, 18.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 742/8000 [00:48<07:14, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 744/8000 [00:48<07:40, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                         | 746/8000 [00:48<08:26, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                         | 751/8000 [00:48<06:29, 18.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                         | 753/8000 [00:48<06:46, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                         | 757/8000 [00:49<07:59, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 761/8000 [00:49<06:55, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 764/8000 [00:49<08:31, 14.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 769/8000 [00:49<06:53, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 771/8000 [00:49<07:10, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 773/8000 [00:50<07:43, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 776/8000 [00:50<10:20, 11.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 780/8000 [00:50<08:16, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                         | 782/8000 [00:50<08:23, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 786/8000 [00:51<08:20, 14.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 792/8000 [00:51<07:33, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 796/8000 [00:51<07:58, 15.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 798/8000 [00:51<08:16, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 801/8000 [00:52<07:59, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 806/8000 [00:52<08:06, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 810/8000 [00:52<07:09, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 812/8000 [00:52<09:28, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 815/8000 [00:53<10:27, 11.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                         | 821/8000 [00:53<08:29, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 824/8000 [00:53<08:01, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 828/8000 [00:53<06:51, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 831/8000 [00:54<06:26, 18.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                         | 838/8000 [00:54<06:02, 19.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 844/8000 [00:54<06:12, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 846/8000 [00:54<06:41, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 848/8000 [00:55<07:14, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 850/8000 [00:55<09:27, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 853/8000 [00:55<08:27, 14.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                         | 857/8000 [00:55<08:16, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 859/8000 [00:55<08:37, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 861/8000 [00:56<11:11, 10.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 864/8000 [00:56<09:52, 12.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 869/8000 [00:56<07:17, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 872/8000 [00:56<06:56, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 876/8000 [00:57<07:28, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 879/8000 [00:57<07:14, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 882/8000 [00:57<06:43, 17.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 884/8000 [00:57<06:50, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 887/8000 [00:57<06:41, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                         | 890/8000 [00:57<06:45, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 893/8000 [00:58<07:42, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 895/8000 [00:58<08:03, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 897/8000 [00:58<10:53, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 900/8000 [00:58<09:40, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 902/8000 [00:58<09:34, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 905/8000 [00:59<10:20, 11.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 907/8000 [00:59<10:15, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 910/8000 [00:59<09:04, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 913/8000 [00:59<12:17,  9.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 915/8000 [01:00<11:57,  9.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                        | 919/8000 [01:00<08:35, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                        | 921/8000 [01:00<09:03, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                        | 923/8000 [01:00<09:19, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                        | 925/8000 [01:00<11:43, 10.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 929/8000 [01:01<10:44, 10.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 936/8000 [01:01<07:46, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 939/8000 [01:01<08:46, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 942/8000 [01:02<09:25, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 944/8000 [01:02<09:33, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 948/8000 [01:02<09:35, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 950/8000 [01:02<09:48, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 957/8000 [01:03<07:16, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 959/8000 [01:03<07:40, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 961/8000 [01:03<09:29, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                        | 964/8000 [01:03<08:05, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 967/8000 [01:03<07:37, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 969/8000 [01:04<09:41, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 974/8000 [01:04<07:07, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 979/8000 [01:04<05:54, 19.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 982/8000 [01:04<06:07, 19.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 986/8000 [01:05<07:13, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 989/8000 [01:05<06:51, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 992/8000 [01:05<07:53, 14.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 994/8000 [01:05<08:11, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                        | 996/8000 [01:05<08:16, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                       | 1000/8000 [01:06<08:03, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1005/8000 [01:06<06:18, 18.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1010/8000 [01:06<06:55, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1012/8000 [01:06<07:28, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1014/8000 [01:06<08:01, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1016/8000 [01:07<08:21, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1019/8000 [01:07<09:13, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1023/8000 [01:07<07:21, 15.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1026/8000 [01:07<06:47, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1029/8000 [01:07<08:00, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1032/8000 [01:08<07:13, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1034/8000 [01:08<09:06, 12.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                       | 1036/8000 [01:08<11:06, 10.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1038/8000 [01:08<10:27, 11.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1041/8000 [01:08<08:27, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1043/8000 [01:09<08:43, 13.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1045/8000 [01:09<08:49, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1049/8000 [01:09<06:41, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1052/8000 [01:09<06:22, 18.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1056/8000 [01:09<05:33, 20.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1060/8000 [01:09<06:56, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1063/8000 [01:10<08:19, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1065/8000 [01:10<08:35, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1070/8000 [01:10<09:28, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                       | 1074/8000 [01:11<07:47, 14.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                       | 1076/8000 [01:11<07:35, 15.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1080/8000 [01:11<06:37, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1082/8000 [01:11<07:18, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1086/8000 [01:11<06:20, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1090/8000 [01:11<07:23, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1096/8000 [01:12<06:49, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1098/8000 [01:12<07:13, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1101/8000 [01:12<07:00, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1104/8000 [01:12<08:29, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1107/8000 [01:13<09:19, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                       | 1111/8000 [01:13<07:52, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1118/8000 [01:13<05:29, 20.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1121/8000 [01:13<05:39, 20.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1124/8000 [01:14<08:16, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1127/8000 [01:14<07:41, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1130/8000 [01:14<07:17, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1133/8000 [01:14<07:06, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1139/8000 [01:14<05:25, 21.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1142/8000 [01:15<06:48, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                       | 1145/8000 [01:15<08:11, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                       | 1149/8000 [01:15<08:29, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                       | 1151/8000 [01:15<08:29, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                       | 1155/8000 [01:16<07:17, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                       | 1160/8000 [01:16<05:57, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1165/8000 [01:16<06:23, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1172/8000 [01:16<04:39, 24.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1177/8000 [01:16<04:02, 28.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▉                       | 1185/8000 [01:17<04:14, 26.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1191/8000 [01:17<04:26, 25.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1196/8000 [01:17<04:13, 26.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1199/8000 [01:17<05:31, 20.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1205/8000 [01:18<05:29, 20.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1210/8000 [01:18<06:06, 18.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1214/8000 [01:18<05:48, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1217/8000 [01:19<08:19, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████                       | 1219/8000 [01:19<10:02, 11.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1223/8000 [01:19<08:17, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1225/8000 [01:19<08:26, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1230/8000 [01:19<06:33, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1234/8000 [01:20<07:14, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1237/8000 [01:20<06:54, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                      | 1239/8000 [01:20<07:14, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1242/8000 [01:20<06:40, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1247/8000 [01:20<05:09, 21.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1250/8000 [01:21<05:19, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                      | 1256/8000 [01:21<05:46, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1260/8000 [01:21<06:39, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1262/8000 [01:22<08:29, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1265/8000 [01:22<07:53, 14.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1268/8000 [01:22<08:46, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1271/8000 [01:22<07:40, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1273/8000 [01:22<10:12, 10.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1277/8000 [01:23<08:10, 13.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1279/8000 [01:23<08:31, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1282/8000 [01:23<07:32, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1287/8000 [01:23<07:05, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1290/8000 [01:23<06:48, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                      | 1294/8000 [01:24<06:00, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1297/8000 [01:24<06:54, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1299/8000 [01:24<07:20, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1301/8000 [01:24<09:53, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1303/8000 [01:24<09:43, 11.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1306/8000 [01:25<12:25,  8.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1311/8000 [01:25<09:48, 11.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1314/8000 [01:26<09:55, 11.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                      | 1320/8000 [01:26<08:10, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                      | 1324/8000 [01:26<06:51, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                      | 1327/8000 [01:26<08:11, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                      | 1330/8000 [01:26<07:45, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▍                      | 1333/8000 [01:27<10:17, 10.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1335/8000 [01:27<10:05, 11.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1341/8000 [01:27<06:31, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1344/8000 [01:28<07:51, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1348/8000 [01:28<08:25, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1350/8000 [01:28<08:41, 12.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1354/8000 [01:28<07:12, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1360/8000 [01:28<05:08, 21.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1363/8000 [01:29<08:31, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                      | 1368/8000 [01:29<08:07, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1375/8000 [01:30<06:25, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1379/8000 [01:30<05:55, 18.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1384/8000 [01:30<06:05, 18.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1388/8000 [01:30<05:25, 20.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1391/8000 [01:30<06:46, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1394/8000 [01:31<08:15, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▋                      | 1398/8000 [01:31<08:27, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                      | 1401/8000 [01:31<07:34, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                      | 1405/8000 [01:31<06:32, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1410/8000 [01:32<05:19, 20.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1414/8000 [01:32<04:57, 22.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1417/8000 [01:32<04:57, 22.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1423/8000 [01:32<04:14, 25.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1426/8000 [01:32<04:39, 23.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1430/8000 [01:33<06:07, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1434/8000 [01:33<07:50, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1436/8000 [01:33<09:23, 11.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1438/8000 [01:33<09:07, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1440/8000 [01:34<08:27, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                      | 1442/8000 [01:34<08:33, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1446/8000 [01:34<06:44, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1451/8000 [01:34<05:12, 20.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1455/8000 [01:34<06:07, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1459/8000 [01:35<06:37, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1463/8000 [01:35<05:55, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1468/8000 [01:35<05:08, 21.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1471/8000 [01:35<05:20, 20.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1474/8000 [01:35<05:35, 19.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1477/8000 [01:35<05:47, 18.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                      | 1480/8000 [01:36<05:36, 19.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1485/8000 [01:36<04:40, 23.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1488/8000 [01:36<08:43, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1490/8000 [01:36<08:12, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1496/8000 [01:37<05:49, 18.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1500/8000 [01:37<05:18, 20.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1504/8000 [01:37<05:49, 18.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1507/8000 [01:37<07:14, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1510/8000 [01:37<06:38, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1512/8000 [01:38<06:41, 16.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████                      | 1518/8000 [01:38<06:10, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1521/8000 [01:38<05:50, 18.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1524/8000 [01:38<05:30, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1527/8000 [01:38<05:36, 19.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1532/8000 [01:39<06:16, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1534/8000 [01:39<06:43, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1540/8000 [01:39<07:30, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1545/8000 [01:39<06:07, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1547/8000 [01:40<06:36, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1550/8000 [01:40<06:27, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1553/8000 [01:40<06:15, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                     | 1555/8000 [01:40<06:48, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▎                     | 1558/8000 [01:40<06:37, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1560/8000 [01:40<06:41, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1566/8000 [01:41<07:24, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1569/8000 [01:41<06:39, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1571/8000 [01:41<06:58, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1573/8000 [01:41<07:08, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1577/8000 [01:41<05:56, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1581/8000 [01:42<06:32, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1583/8000 [01:42<07:06, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1589/8000 [01:42<05:25, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▎                     | 1592/8000 [01:42<05:40, 18.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1595/8000 [01:42<05:40, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1598/8000 [01:43<05:29, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1604/8000 [01:43<05:20, 19.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1607/8000 [01:43<06:35, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1609/8000 [01:43<07:00, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1614/8000 [01:43<05:33, 19.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1619/8000 [01:44<06:07, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1622/8000 [01:44<08:33, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1625/8000 [01:44<07:27, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                     | 1628/8000 [01:45<08:27, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1631/8000 [01:45<07:44, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1636/8000 [01:45<07:34, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                     | 1640/8000 [01:45<06:44, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1642/8000 [01:46<06:48, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1645/8000 [01:46<07:50, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1650/8000 [01:46<07:00, 15.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1654/8000 [01:46<06:12, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1656/8000 [01:46<06:38, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1661/8000 [01:47<05:03, 20.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                     | 1665/8000 [01:47<06:20, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1667/8000 [01:47<06:58, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1670/8000 [01:47<08:46, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1673/8000 [01:48<08:04, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1677/8000 [01:48<09:29, 11.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1681/8000 [01:48<07:46, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1685/8000 [01:48<06:17, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1688/8000 [01:49<06:09, 17.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1693/8000 [01:49<06:31, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1695/8000 [01:49<06:52, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1697/8000 [01:49<07:10, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1700/8000 [01:49<06:45, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▋                     | 1702/8000 [01:50<06:55, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1704/8000 [01:50<07:25, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1707/8000 [01:50<08:25, 12.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1709/8000 [01:50<10:17, 10.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1712/8000 [01:50<08:53, 11.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1715/8000 [01:51<09:51, 10.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1717/8000 [01:51<09:29, 11.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                     | 1719/8000 [01:51<09:11, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1723/8000 [01:51<06:41, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1727/8000 [01:52<07:29, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1729/8000 [01:52<07:59, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1731/8000 [01:52<08:19, 12.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1733/8000 [01:52<10:17, 10.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                     | 1737/8000 [01:53<09:10, 11.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1742/8000 [01:53<06:45, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1744/8000 [01:53<07:09, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1746/8000 [01:53<07:36, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1749/8000 [01:53<06:58, 14.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1753/8000 [01:53<06:01, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1756/8000 [01:54<07:11, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1760/8000 [01:54<06:12, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1762/8000 [01:54<06:38, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1765/8000 [01:54<06:22, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1769/8000 [01:54<05:41, 18.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1771/8000 [01:55<07:56, 13.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▉                     | 1776/8000 [01:55<07:35, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1781/8000 [01:55<06:15, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1784/8000 [01:55<06:13, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1788/8000 [01:56<05:30, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1791/8000 [01:56<07:56, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1796/8000 [01:56<06:15, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                     | 1799/8000 [01:57<07:35, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1802/8000 [01:57<07:08, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1804/8000 [01:57<07:24, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1807/8000 [01:57<08:11, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1810/8000 [01:57<08:49, 11.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████                     | 1812/8000 [01:58<08:27, 12.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1816/8000 [01:58<06:52, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1818/8000 [01:58<09:31, 10.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1821/8000 [01:58<08:24, 12.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1827/8000 [01:58<05:53, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1830/8000 [01:59<05:44, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1836/8000 [01:59<06:45, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1838/8000 [01:59<06:59, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1844/8000 [01:59<05:17, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1848/8000 [02:00<07:25, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▏                    | 1850/8000 [02:00<07:36, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1853/8000 [02:00<07:07, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1856/8000 [02:00<06:29, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1861/8000 [02:01<05:21, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1864/8000 [02:01<05:27, 18.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1867/8000 [02:01<05:28, 18.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1869/8000 [02:01<06:05, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1872/8000 [02:01<05:54, 17.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1874/8000 [02:01<06:46, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                    | 1878/8000 [02:02<06:57, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                    | 1881/8000 [02:02<06:35, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                    | 1883/8000 [02:02<08:48, 11.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                    | 1887/8000 [02:03<08:46, 11.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1889/8000 [02:03<08:30, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1891/8000 [02:03<08:42, 11.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1894/8000 [02:03<07:46, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1897/8000 [02:03<07:05, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1902/8000 [02:03<05:31, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1907/8000 [02:04<04:37, 21.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1912/8000 [02:04<04:59, 20.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1916/8000 [02:04<06:52, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1920/8000 [02:04<05:52, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▍                    | 1925/8000 [02:05<08:28, 11.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1928/8000 [02:05<07:49, 12.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1930/8000 [02:05<08:00, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1932/8000 [02:06<07:41, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1936/8000 [02:06<06:21, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1939/8000 [02:06<06:10, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1943/8000 [02:06<05:30, 18.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1945/8000 [02:06<06:10, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1949/8000 [02:06<05:28, 18.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1953/8000 [02:07<05:02, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                    | 1960/8000 [02:07<04:44, 21.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1965/8000 [02:07<05:20, 18.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1969/8000 [02:07<05:05, 19.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1973/8000 [02:08<06:02, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1976/8000 [02:08<05:40, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1979/8000 [02:08<05:45, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1982/8000 [02:08<05:46, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1984/8000 [02:08<06:32, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1986/8000 [02:09<07:52, 12.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1989/8000 [02:09<07:31, 13.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1992/8000 [02:09<08:26, 11.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▋                    | 1998/8000 [02:09<05:53, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2001/8000 [02:10<07:49, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2003/8000 [02:10<07:58, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2006/8000 [02:10<07:25, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2008/8000 [02:11<11:23,  8.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2012/8000 [02:11<08:51, 11.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2015/8000 [02:11<08:11, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2017/8000 [02:12<12:38,  7.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2020/8000 [02:12<10:28,  9.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2023/8000 [02:12<09:11, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2026/8000 [02:12<08:25, 11.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2028/8000 [02:12<09:26, 10.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2030/8000 [02:13<11:32,  8.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2032/8000 [02:13<14:37,  6.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▊                    | 2035/8000 [02:13<11:17,  8.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▉                    | 2038/8000 [02:14<09:00, 11.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2041/8000 [02:14<07:42, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2043/8000 [02:14<07:52, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2047/8000 [02:14<06:24, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2050/8000 [02:14<06:12, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2054/8000 [02:14<05:24, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2057/8000 [02:15<06:45, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2060/8000 [02:15<07:37, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2063/8000 [02:15<08:02, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2066/8000 [02:15<07:10, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2069/8000 [02:16<06:37, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▉                    | 2073/8000 [02:16<05:57, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2077/8000 [02:16<05:40, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2079/8000 [02:16<06:16, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2081/8000 [02:17<09:47, 10.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2083/8000 [02:17<09:46, 10.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2085/8000 [02:17<09:35, 10.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2088/8000 [02:17<08:30, 11.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2090/8000 [02:17<09:21, 10.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2093/8000 [02:18<08:27, 11.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2095/8000 [02:18<11:19,  8.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2098/8000 [02:19<12:41,  7.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2101/8000 [02:19<10:48,  9.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2105/8000 [02:19<10:24,  9.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████                    | 2110/8000 [02:19<08:28, 11.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▏                   | 2112/8000 [02:20<10:39,  9.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▏                   | 2117/8000 [02:20<09:04, 10.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▏                   | 2119/8000 [02:20<09:00, 10.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2121/8000 [02:21<09:01, 10.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2124/8000 [02:21<07:50, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2126/8000 [02:21<11:13,  8.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2129/8000 [02:21<09:47,  9.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2132/8000 [02:22<08:26, 11.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2138/8000 [02:22<09:02, 10.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2141/8000 [02:22<08:25, 11.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2143/8000 [02:23<08:49, 11.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▏                   | 2148/8000 [02:23<06:21, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2150/8000 [02:23<08:00, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2153/8000 [02:23<07:00, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2155/8000 [02:23<06:47, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2158/8000 [02:23<06:24, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2160/8000 [02:24<07:08, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2164/8000 [02:24<07:23, 13.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2169/8000 [02:24<07:57, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2172/8000 [02:25<07:18, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▎                   | 2177/8000 [02:25<07:15, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2186/8000 [02:25<04:34, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2191/8000 [02:25<04:12, 23.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2196/8000 [02:25<04:00, 24.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                   | 2199/8000 [02:26<04:18, 22.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2204/8000 [02:26<04:53, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2211/8000 [02:26<03:47, 25.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2216/8000 [02:26<04:25, 21.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▍                   | 2220/8000 [02:27<05:41, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2226/8000 [02:27<05:29, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2230/8000 [02:27<05:01, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2235/8000 [02:28<04:37, 20.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2238/8000 [02:28<04:43, 20.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2241/8000 [02:28<06:13, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2248/8000 [02:28<05:43, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2250/8000 [02:29<06:04, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2256/8000 [02:29<04:55, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▌                   | 2259/8000 [02:29<05:05, 18.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2261/8000 [02:29<05:55, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2265/8000 [02:30<07:42, 12.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2268/8000 [02:30<10:53,  8.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2271/8000 [02:31<14:10,  6.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2275/8000 [02:31<11:17,  8.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2278/8000 [02:32<14:11,  6.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▋                   | 2279/8000 [02:32<16:36,  5.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2282/8000 [02:33<15:05,  6.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2284/8000 [02:33<15:36,  6.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2287/8000 [02:33<13:47,  6.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2288/8000 [02:34<14:51,  6.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2292/8000 [02:34<10:20,  9.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▋                   | 2296/8000 [02:34<07:42, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2298/8000 [02:34<07:42, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2300/8000 [02:34<10:30,  9.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2302/8000 [02:35<09:52,  9.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2306/8000 [02:35<08:36, 11.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2309/8000 [02:35<08:52, 10.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2312/8000 [02:35<07:55, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2315/8000 [02:36<10:11,  9.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2320/8000 [02:36<07:11, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2324/8000 [02:36<05:59, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2326/8000 [02:36<06:10, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2328/8000 [02:37<06:17, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2330/8000 [02:37<06:27, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▊                   | 2332/8000 [02:37<06:48, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2335/8000 [02:37<06:21, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2340/8000 [02:37<04:58, 18.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2343/8000 [02:37<05:02, 18.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2349/8000 [02:38<03:54, 24.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2352/8000 [02:38<05:17, 17.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2357/8000 [02:38<06:42, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▉                   | 2359/8000 [02:38<06:53, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                   | 2362/8000 [02:39<06:25, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                   | 2365/8000 [02:39<05:56, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                   | 2368/8000 [02:39<05:48, 16.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2372/8000 [02:39<05:16, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2374/8000 [02:39<05:48, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2376/8000 [02:39<06:15, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2380/8000 [02:40<05:23, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2384/8000 [02:40<04:50, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2387/8000 [02:40<04:32, 20.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2391/8000 [02:40<07:32, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2397/8000 [02:41<06:37, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2400/8000 [02:41<07:22, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████                   | 2404/8000 [02:41<06:28, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2408/8000 [02:42<05:44, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2410/8000 [02:42<05:48, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2418/8000 [02:42<04:50, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2420/8000 [02:42<07:31, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2424/8000 [02:43<06:25, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2426/8000 [02:43<06:34, 14.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2428/8000 [02:43<09:58,  9.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2431/8000 [02:44<10:11,  9.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2435/8000 [02:44<07:50, 11.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▏                  | 2438/8000 [02:44<06:44, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▏                  | 2443/8000 [02:44<05:13, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2448/8000 [02:44<05:38, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2455/8000 [02:45<04:10, 22.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2458/8000 [02:45<05:28, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2464/8000 [02:45<05:10, 17.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2468/8000 [02:46<06:17, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2476/8000 [02:46<04:25, 20.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▎                  | 2481/8000 [02:46<04:04, 22.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2484/8000 [02:46<04:15, 21.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2487/8000 [02:46<04:23, 20.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2492/8000 [02:47<05:52, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2495/8000 [02:47<05:39, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2499/8000 [02:47<04:54, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2502/8000 [02:47<04:51, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2506/8000 [02:47<04:13, 21.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2509/8000 [02:48<05:57, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2513/8000 [02:48<05:14, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▍                  | 2516/8000 [02:48<05:17, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▌                  | 2519/8000 [02:48<05:12, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2524/8000 [02:49<05:06, 17.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2528/8000 [02:49<04:41, 19.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2531/8000 [02:49<04:49, 18.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2534/8000 [02:49<04:55, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2538/8000 [02:49<05:54, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2543/8000 [02:50<05:38, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2545/8000 [02:50<05:56, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2547/8000 [02:50<07:49, 11.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2550/8000 [02:50<07:00, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▌                  | 2552/8000 [02:51<07:06, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2556/8000 [02:51<05:47, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2560/8000 [02:51<04:48, 18.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2567/8000 [02:51<03:37, 24.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2571/8000 [02:51<04:44, 19.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2578/8000 [02:52<05:22, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2580/8000 [02:52<06:42, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2582/8000 [02:52<06:52, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▋                  | 2589/8000 [02:52<04:24, 20.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▊                  | 2593/8000 [02:53<03:59, 22.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▊                  | 2597/8000 [02:53<04:59, 18.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2602/8000 [02:53<05:18, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2606/8000 [02:54<05:59, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2608/8000 [02:54<05:56, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2612/8000 [02:54<05:15, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2615/8000 [02:54<05:16, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2617/8000 [02:54<07:19, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2622/8000 [02:55<06:37, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▊                  | 2626/8000 [02:55<05:30, 16.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2630/8000 [02:55<06:17, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2633/8000 [02:56<08:13, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2635/8000 [02:56<08:11, 10.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2641/8000 [02:56<05:21, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2645/8000 [02:56<06:01, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2647/8000 [02:57<07:37, 11.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2652/8000 [02:57<07:01, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2656/8000 [02:57<05:43, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2659/8000 [02:57<05:29, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2661/8000 [02:58<07:17, 12.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▉                  | 2665/8000 [02:58<07:12, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2669/8000 [02:58<07:13, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2672/8000 [02:59<07:39, 11.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2674/8000 [02:59<07:29, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████                  | 2677/8000 [02:59<06:22, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2680/8000 [02:59<07:11, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2682/8000 [02:59<07:00, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2686/8000 [02:59<05:24, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2691/8000 [03:00<05:27, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2694/8000 [03:00<05:02, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2698/8000 [03:00<04:18, 20.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████                  | 2701/8000 [03:00<04:24, 20.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2704/8000 [03:00<04:25, 19.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2707/8000 [03:01<05:16, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2712/8000 [03:01<04:23, 20.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2717/8000 [03:01<03:47, 23.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2721/8000 [03:01<04:18, 20.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2727/8000 [03:01<04:26, 19.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2730/8000 [03:02<04:38, 18.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2734/8000 [03:02<04:18, 20.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▏                 | 2737/8000 [03:02<04:26, 19.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2742/8000 [03:02<04:46, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2745/8000 [03:03<06:01, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2748/8000 [03:03<05:41, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2750/8000 [03:03<05:59, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2753/8000 [03:03<07:15, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2755/8000 [03:04<07:32, 11.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▎                 | 2757/8000 [03:04<07:30, 11.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2762/8000 [03:04<05:25, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2765/8000 [03:04<05:04, 17.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2768/8000 [03:04<05:11, 16.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2771/8000 [03:05<06:23, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▎                 | 2773/8000 [03:05<06:44, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2783/8000 [03:05<04:24, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2786/8000 [03:05<05:13, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2788/8000 [03:05<05:34, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2792/8000 [03:06<04:57, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2797/8000 [03:06<04:17, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2805/8000 [03:06<03:58, 21.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2808/8000 [03:06<04:44, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▍                 | 2812/8000 [03:07<04:26, 19.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2815/8000 [03:07<04:24, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2818/8000 [03:07<05:27, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2821/8000 [03:07<05:19, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2824/8000 [03:08<06:37, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2826/8000 [03:08<06:43, 12.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2830/8000 [03:08<06:29, 13.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2833/8000 [03:08<07:35, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▌                 | 2838/8000 [03:09<05:45, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2841/8000 [03:09<06:51, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2844/8000 [03:09<07:44, 11.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2848/8000 [03:09<06:18, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▌                 | 2850/8000 [03:10<07:37, 11.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2853/8000 [03:10<06:46, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2856/8000 [03:10<06:14, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2858/8000 [03:10<06:26, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2862/8000 [03:10<05:19, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2864/8000 [03:11<05:41, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2868/8000 [03:11<05:57, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2874/8000 [03:11<04:24, 19.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2877/8000 [03:11<04:30, 18.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2881/8000 [03:12<05:28, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2884/8000 [03:12<05:21, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▋                 | 2886/8000 [03:12<05:35, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2890/8000 [03:12<04:33, 18.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2896/8000 [03:12<05:20, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2899/8000 [03:13<04:54, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2903/8000 [03:13<04:26, 19.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2907/8000 [03:13<04:09, 20.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2911/8000 [03:13<06:02, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2913/8000 [03:14<06:07, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▊                 | 2918/8000 [03:14<04:50, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▊                 | 2922/8000 [03:14<05:23, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2928/8000 [03:14<04:05, 20.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2938/8000 [03:15<03:18, 25.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2941/8000 [03:15<04:06, 20.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2948/8000 [03:15<04:24, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2951/8000 [03:15<04:27, 18.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2953/8000 [03:15<04:31, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2955/8000 [03:16<04:57, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2957/8000 [03:16<05:34, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▉                 | 2962/8000 [03:16<04:26, 18.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2964/8000 [03:16<04:52, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2968/8000 [03:16<04:20, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2971/8000 [03:17<06:24, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2973/8000 [03:17<07:48, 10.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2976/8000 [03:17<08:01, 10.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2981/8000 [03:18<07:07, 11.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2983/8000 [03:18<07:01, 11.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2985/8000 [03:18<07:04, 11.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2988/8000 [03:18<06:18, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2991/8000 [03:18<05:25, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2995/8000 [03:19<05:53, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████                 | 2998/8000 [03:19<05:36, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3000/8000 [03:19<07:20, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3004/8000 [03:19<06:00, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3007/8000 [03:20<05:23, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3009/8000 [03:20<05:41, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3012/8000 [03:20<05:27, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3016/8000 [03:20<06:08, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3020/8000 [03:21<06:25, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3022/8000 [03:21<06:10, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3025/8000 [03:21<06:51, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3028/8000 [03:21<05:56, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3030/8000 [03:21<05:44, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3032/8000 [03:21<05:57, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3034/8000 [03:22<06:08, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▏                | 3037/8000 [03:22<05:16, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3040/8000 [03:22<04:38, 17.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3043/8000 [03:22<04:37, 17.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3046/8000 [03:22<04:33, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3050/8000 [03:22<03:54, 21.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3053/8000 [03:22<04:10, 19.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3056/8000 [03:23<05:23, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3058/8000 [03:23<05:46, 14.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3060/8000 [03:23<06:00, 13.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3062/8000 [03:24<09:04,  9.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3066/8000 [03:24<06:41, 12.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▎                | 3072/8000 [03:24<04:38, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▍                | 3075/8000 [03:24<05:44, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▍                | 3078/8000 [03:24<05:27, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▍                | 3080/8000 [03:25<05:41, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3082/8000 [03:25<07:20, 11.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3084/8000 [03:25<07:04, 11.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3086/8000 [03:25<06:45, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3089/8000 [03:25<05:59, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3092/8000 [03:25<05:28, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3094/8000 [03:26<07:03, 11.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3096/8000 [03:26<08:36,  9.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3102/8000 [03:27<07:29, 10.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3104/8000 [03:27<07:16, 11.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3106/8000 [03:27<06:59, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3109/8000 [03:27<07:13, 11.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▍                | 3111/8000 [03:27<06:55, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3113/8000 [03:27<06:43, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3117/8000 [03:28<05:00, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3119/8000 [03:28<05:18, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3122/8000 [03:28<05:00, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3124/8000 [03:28<05:27, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3126/8000 [03:28<05:46, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3129/8000 [03:28<05:21, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3131/8000 [03:29<07:11, 11.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3133/8000 [03:29<07:12, 11.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3136/8000 [03:29<06:34, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3142/8000 [03:29<05:32, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▌                | 3146/8000 [03:30<04:44, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3149/8000 [03:30<05:10, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3154/8000 [03:30<04:13, 19.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▋                | 3159/8000 [03:30<03:35, 22.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3163/8000 [03:30<03:28, 23.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3168/8000 [03:30<03:00, 26.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3174/8000 [03:31<02:41, 29.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▋                | 3181/8000 [03:31<03:56, 20.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3186/8000 [03:31<04:19, 18.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3189/8000 [03:32<04:08, 19.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3192/8000 [03:32<05:05, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3195/8000 [03:32<04:49, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3197/8000 [03:32<04:49, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3201/8000 [03:32<04:13, 18.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3204/8000 [03:32<04:06, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3208/8000 [03:33<04:36, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3212/8000 [03:33<04:11, 19.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3216/8000 [03:33<04:42, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▊                | 3221/8000 [03:33<03:43, 21.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3224/8000 [03:34<04:56, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3228/8000 [03:34<04:28, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3231/8000 [03:34<04:26, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3234/8000 [03:34<04:21, 18.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3237/8000 [03:34<04:22, 18.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▉                | 3239/8000 [03:34<04:41, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3241/8000 [03:35<06:33, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3244/8000 [03:35<05:34, 14.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3247/8000 [03:35<04:52, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3249/8000 [03:35<05:13, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3252/8000 [03:35<05:06, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3256/8000 [03:36<04:15, 18.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▉                | 3259/8000 [03:36<06:48, 11.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3262/8000 [03:36<05:56, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3267/8000 [03:36<04:34, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3271/8000 [03:37<04:58, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3275/8000 [03:37<05:06, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3279/8000 [03:37<04:26, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3288/8000 [03:38<04:05, 19.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3291/8000 [03:38<04:45, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████                | 3294/8000 [03:38<04:23, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3297/8000 [03:38<04:23, 17.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3300/8000 [03:38<04:25, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3303/8000 [03:38<04:25, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3309/8000 [03:39<03:26, 22.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3313/8000 [03:39<03:20, 23.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3316/8000 [03:39<04:30, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▏               | 3319/8000 [03:39<05:38, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▏               | 3321/8000 [03:40<05:50, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▏               | 3328/8000 [03:40<04:42, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▏               | 3331/8000 [03:40<04:37, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3338/8000 [03:41<04:46, 16.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3343/8000 [03:41<04:02, 19.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3350/8000 [03:41<03:41, 21.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3354/8000 [03:41<03:31, 21.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3360/8000 [03:41<03:30, 22.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▎               | 3366/8000 [03:42<03:02, 25.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3373/8000 [03:42<04:12, 18.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3378/8000 [03:43<04:43, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3380/8000 [03:43<04:55, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3385/8000 [03:43<04:09, 18.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3392/8000 [03:43<03:07, 24.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▍               | 3397/8000 [03:43<03:28, 22.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▍               | 3402/8000 [03:44<05:20, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▍               | 3406/8000 [03:44<04:45, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3409/8000 [03:44<04:39, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3416/8000 [03:45<04:11, 18.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3419/8000 [03:45<05:07, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3422/8000 [03:45<05:44, 13.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3425/8000 [03:46<06:28, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3427/8000 [03:46<06:18, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3431/8000 [03:46<05:06, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3434/8000 [03:46<04:32, 16.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3436/8000 [03:46<04:56, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▌               | 3442/8000 [03:46<03:37, 20.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3448/8000 [03:47<05:19, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3453/8000 [03:47<04:23, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3460/8000 [03:47<03:21, 22.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3465/8000 [03:47<03:06, 24.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3471/8000 [03:48<03:56, 19.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3474/8000 [03:48<04:00, 18.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3477/8000 [03:48<05:20, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▋               | 3479/8000 [03:49<06:32, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3482/8000 [03:49<05:51, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3487/8000 [03:49<06:19, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3489/8000 [03:50<06:24, 11.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3493/8000 [03:50<05:11, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3497/8000 [03:50<06:02, 12.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3502/8000 [03:51<05:30, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3505/8000 [03:51<04:55, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3507/8000 [03:51<05:04, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3511/8000 [03:51<04:18, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3514/8000 [03:51<04:58, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▊               | 3516/8000 [03:51<05:12, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3520/8000 [03:52<05:21, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3522/8000 [03:52<06:47, 10.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3527/8000 [03:52<04:56, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3530/8000 [03:52<04:39, 15.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3532/8000 [03:53<05:00, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3538/8000 [03:53<03:31, 21.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3541/8000 [03:53<03:39, 20.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3544/8000 [03:53<03:31, 21.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3550/8000 [03:53<02:54, 25.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▉               | 3554/8000 [03:53<03:40, 20.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████               | 3559/8000 [03:54<04:09, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3562/8000 [03:54<05:04, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3567/8000 [03:54<04:53, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3571/8000 [03:55<04:19, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3576/8000 [03:55<03:28, 21.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3580/8000 [03:55<04:06, 17.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3583/8000 [03:55<03:58, 18.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3586/8000 [03:55<04:44, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████               | 3591/8000 [03:56<04:34, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3594/8000 [03:56<04:22, 16.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3600/8000 [03:56<03:29, 21.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3605/8000 [03:57<04:35, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3607/8000 [03:57<05:46, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3610/8000 [03:57<05:04, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3613/8000 [03:57<05:43, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3615/8000 [03:57<05:44, 12.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3617/8000 [03:58<05:37, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3619/8000 [03:58<05:38, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3624/8000 [03:58<04:36, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▏              | 3628/8000 [03:58<05:12, 13.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▎              | 3632/8000 [03:58<04:30, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▎              | 3634/8000 [03:59<05:00, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▎              | 3638/8000 [03:59<05:57, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3646/8000 [04:00<05:11, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3651/8000 [04:00<04:20, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3655/8000 [04:00<04:46, 15.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3658/8000 [04:00<05:24, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3660/8000 [04:01<06:29, 11.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▎              | 3664/8000 [04:01<05:08, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3670/8000 [04:01<04:43, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3674/8000 [04:01<04:14, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3676/8000 [04:02<04:34, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3678/8000 [04:02<05:13, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3685/8000 [04:02<03:29, 20.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3690/8000 [04:02<04:36, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3694/8000 [04:03<04:49, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3698/8000 [04:03<04:17, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3700/8000 [04:03<05:32, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▍              | 3702/8000 [04:03<05:35, 12.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3705/8000 [04:04<05:08, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3707/8000 [04:04<06:29, 11.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3709/8000 [04:04<06:20, 11.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3712/8000 [04:04<05:29, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▌              | 3717/8000 [04:04<03:54, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3722/8000 [04:05<03:59, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3726/8000 [04:05<03:43, 19.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3732/8000 [04:05<03:01, 23.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3736/8000 [04:05<02:58, 23.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▌              | 3740/8000 [04:05<03:49, 18.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3748/8000 [04:06<03:21, 21.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3751/8000 [04:06<04:00, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3755/8000 [04:06<03:31, 20.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3760/8000 [04:07<03:54, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3766/8000 [04:07<03:54, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3770/8000 [04:07<04:26, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▋              | 3775/8000 [04:07<03:39, 19.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3780/8000 [04:08<04:01, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3785/8000 [04:08<04:11, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3789/8000 [04:08<03:58, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3792/8000 [04:09<05:19, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▊              | 3795/8000 [04:09<05:47, 12.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3803/8000 [04:09<03:35, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3809/8000 [04:10<04:19, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3812/8000 [04:10<05:00, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▊              | 3814/8000 [04:10<05:09, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3823/8000 [04:10<03:44, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3830/8000 [04:11<03:25, 20.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3834/8000 [04:11<03:06, 22.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3837/8000 [04:11<03:15, 21.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3841/8000 [04:11<03:48, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3844/8000 [04:11<03:46, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3846/8000 [04:12<04:04, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▉              | 3851/8000 [04:12<03:22, 20.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3857/8000 [04:12<02:48, 24.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3860/8000 [04:12<04:06, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3866/8000 [04:13<04:31, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3870/8000 [04:13<04:15, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3872/8000 [04:13<04:25, 15.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3875/8000 [04:13<04:12, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████              | 3878/8000 [04:13<04:08, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████              | 3885/8000 [04:14<03:46, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3890/8000 [04:14<03:19, 20.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3894/8000 [04:14<03:10, 21.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3898/8000 [04:14<03:57, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3901/8000 [04:15<04:55, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3907/8000 [04:15<04:32, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3911/8000 [04:15<04:06, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3915/8000 [04:16<03:49, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3920/8000 [04:16<03:16, 20.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▏             | 3923/8000 [04:16<03:17, 20.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3928/8000 [04:16<03:30, 19.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3931/8000 [04:16<03:33, 19.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3933/8000 [04:17<04:40, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3935/8000 [04:17<04:44, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3942/8000 [04:17<04:11, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3945/8000 [04:17<04:19, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3947/8000 [04:18<04:44, 14.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3951/8000 [04:18<06:18, 10.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3954/8000 [04:18<05:55, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▎             | 3958/8000 [04:19<06:10, 10.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▎             | 3960/8000 [04:19<05:56, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▎             | 3962/8000 [04:19<05:52, 11.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3965/8000 [04:19<05:13, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3967/8000 [04:19<05:15, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3970/8000 [04:20<06:13, 10.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3972/8000 [04:20<06:04, 11.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3974/8000 [04:20<05:36, 11.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3976/8000 [04:20<05:34, 12.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3979/8000 [04:20<04:59, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3982/8000 [04:21<06:53,  9.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3984/8000 [04:21<06:45,  9.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3986/8000 [04:21<06:19, 10.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3988/8000 [04:21<06:04, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3990/8000 [04:22<07:29,  8.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▍             | 3995/8000 [04:22<06:16, 10.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4000/8000 [04:22<05:08, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4005/8000 [04:22<04:08, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4009/8000 [04:23<03:46, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4011/8000 [04:23<04:07, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4013/8000 [04:23<04:30, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4016/8000 [04:23<05:19, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4019/8000 [04:24<04:52, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4021/8000 [04:24<04:55, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4024/8000 [04:24<04:38, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4026/8000 [04:24<04:54, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4029/8000 [04:24<05:33, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4032/8000 [04:24<04:44, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▌             | 4036/8000 [04:25<03:53, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▋             | 4040/8000 [04:25<03:33, 18.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4042/8000 [04:25<05:06, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4046/8000 [04:25<05:11, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4049/8000 [04:26<05:26, 12.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4051/8000 [04:26<05:25, 12.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4054/8000 [04:26<04:52, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4057/8000 [04:26<04:37, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4061/8000 [04:27<04:55, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4066/8000 [04:27<03:45, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4068/8000 [04:27<03:56, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4070/8000 [04:27<04:10, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▋             | 4073/8000 [04:27<04:05, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4077/8000 [04:27<03:37, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4079/8000 [04:28<04:09, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4083/8000 [04:28<03:39, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4087/8000 [04:28<04:22, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4089/8000 [04:28<05:36, 11.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4093/8000 [04:29<04:21, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4095/8000 [04:29<04:40, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4100/8000 [04:29<05:42, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▊             | 4107/8000 [04:30<04:50, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▉             | 4114/8000 [04:30<04:06, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▉             | 4118/8000 [04:30<04:27, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4121/8000 [04:31<04:19, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4123/8000 [04:31<04:29, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4126/8000 [04:31<04:03, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4128/8000 [04:31<04:15, 15.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4130/8000 [04:31<04:34, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4137/8000 [04:32<03:42, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4139/8000 [04:32<04:02, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4142/8000 [04:32<03:54, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4146/8000 [04:32<04:10, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▉             | 4148/8000 [04:32<04:28, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4153/8000 [04:33<04:25, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4156/8000 [04:33<04:19, 14.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4161/8000 [04:33<03:22, 18.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4165/8000 [04:33<03:43, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4168/8000 [04:34<04:55, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4171/8000 [04:34<04:34, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4173/8000 [04:34<04:43, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4176/8000 [04:34<05:22, 11.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4179/8000 [04:35<05:57, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4181/8000 [04:35<06:43,  9.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4183/8000 [04:35<08:11,  7.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████             | 4185/8000 [04:36<08:12,  7.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▏            | 4188/8000 [04:36<07:16,  8.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▏            | 4192/8000 [04:36<05:40, 11.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▏            | 4196/8000 [04:37<06:15, 10.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4201/8000 [04:37<06:14, 10.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4207/8000 [04:37<04:37, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4210/8000 [04:37<04:28, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4214/8000 [04:38<04:42, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4217/8000 [04:38<05:05, 12.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4220/8000 [04:38<05:20, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▏            | 4222/8000 [04:39<05:18, 11.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4225/8000 [04:39<04:42, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4227/8000 [04:39<06:08, 10.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4229/8000 [04:39<06:56,  9.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4232/8000 [04:40<05:48, 10.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4236/8000 [04:40<05:17, 11.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4238/8000 [04:40<05:18, 11.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4241/8000 [04:40<04:47, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4246/8000 [04:40<03:40, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4252/8000 [04:41<03:32, 17.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4254/8000 [04:41<03:52, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▎            | 4257/8000 [04:41<04:49, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4260/8000 [04:42<05:11, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4264/8000 [04:42<04:02, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4266/8000 [04:42<04:26, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4268/8000 [04:42<04:45, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4270/8000 [04:42<06:13,  9.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4274/8000 [04:43<06:00, 10.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▍            | 4278/8000 [04:43<05:39, 10.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4280/8000 [04:43<06:10, 10.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4282/8000 [04:44<05:55, 10.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4286/8000 [04:44<04:34, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4289/8000 [04:44<04:15, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4291/8000 [04:44<05:33, 11.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4293/8000 [04:44<05:28, 11.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▍            | 4295/8000 [04:44<05:16, 11.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4298/8000 [04:45<04:35, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4301/8000 [04:45<04:12, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4303/8000 [04:45<04:22, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4307/8000 [04:45<04:41, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4309/8000 [04:45<04:50, 12.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4312/8000 [04:46<04:03, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4316/8000 [04:46<04:19, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4319/8000 [04:46<03:55, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4321/8000 [04:46<04:06, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4329/8000 [04:47<03:14, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▌            | 4331/8000 [04:47<03:34, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4334/8000 [04:47<03:32, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4336/8000 [04:47<03:42, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4339/8000 [04:47<03:30, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4342/8000 [04:47<03:12, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4345/8000 [04:48<04:01, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4349/8000 [04:48<03:22, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4352/8000 [04:48<03:06, 19.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4356/8000 [04:48<03:44, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████▋            | 4359/8000 [04:48<03:25, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▋            | 4365/8000 [04:49<04:02, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▋            | 4367/8000 [04:49<04:09, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▋            | 4370/8000 [04:49<04:49, 12.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4377/8000 [04:49<03:11, 18.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4380/8000 [04:50<03:43, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4387/8000 [04:50<02:40, 22.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4393/8000 [04:50<02:53, 20.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4397/8000 [04:51<03:25, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4400/8000 [04:51<04:43, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4403/8000 [04:51<04:27, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▊            | 4405/8000 [04:51<04:36, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4408/8000 [04:52<04:17, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4410/8000 [04:52<04:21, 13.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4413/8000 [04:52<04:46, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4417/8000 [04:52<04:01, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4422/8000 [04:52<03:16, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4425/8000 [04:53<03:14, 18.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4428/8000 [04:53<03:12, 18.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4431/8000 [04:53<03:13, 18.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4435/8000 [04:53<02:46, 21.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▉            | 4438/8000 [04:53<02:52, 20.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▉            | 4442/8000 [04:53<03:34, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4449/8000 [04:54<03:18, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4451/8000 [04:54<03:33, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4454/8000 [04:54<03:35, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4456/8000 [04:54<03:58, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4460/8000 [04:55<03:27, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4465/8000 [04:55<02:45, 21.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4469/8000 [04:55<03:24, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4473/8000 [04:55<03:02, 19.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4478/8000 [04:56<04:03, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████            | 4480/8000 [04:56<03:58, 14.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4482/8000 [04:56<04:58, 11.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4486/8000 [04:56<04:09, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4494/8000 [04:57<03:22, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4496/8000 [04:57<03:39, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4499/8000 [04:57<03:45, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4502/8000 [04:58<05:07, 11.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4506/8000 [04:58<05:17, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4510/8000 [04:58<04:29, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4513/8000 [04:58<04:42, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4516/8000 [04:59<05:43, 10.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▏           | 4518/8000 [04:59<05:43, 10.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▎           | 4520/8000 [04:59<05:22, 10.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4523/8000 [04:59<04:36, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4526/8000 [04:59<04:07, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4529/8000 [05:00<04:59, 11.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4532/8000 [05:00<05:18, 10.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4536/8000 [05:00<04:03, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4538/8000 [05:01<05:14, 10.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4540/8000 [05:01<05:04, 11.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4544/8000 [05:01<05:00, 11.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4546/8000 [05:01<04:55, 11.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4548/8000 [05:01<04:36, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4551/8000 [05:02<05:12, 11.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▎           | 4553/8000 [05:02<05:04, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4559/8000 [05:02<03:21, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4562/8000 [05:02<03:16, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4568/8000 [05:03<03:02, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4570/8000 [05:03<03:19, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4573/8000 [05:03<03:19, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4575/8000 [05:03<04:38, 12.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4579/8000 [05:03<03:45, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4582/8000 [05:04<03:31, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4584/8000 [05:04<03:33, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4586/8000 [05:04<03:52, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4588/8000 [05:04<04:14, 13.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▍           | 4591/8000 [05:04<03:53, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▌           | 4595/8000 [05:05<04:16, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▌           | 4598/8000 [05:05<04:48, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4604/8000 [05:05<04:39, 12.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4608/8000 [05:05<03:56, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4614/8000 [05:06<03:03, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4618/8000 [05:06<03:33, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4621/8000 [05:06<04:04, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▌           | 4625/8000 [05:06<03:23, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4632/8000 [05:07<03:35, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4635/8000 [05:07<03:33, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4639/8000 [05:07<03:12, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4643/8000 [05:08<03:24, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4645/8000 [05:08<04:19, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4648/8000 [05:08<04:02, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4651/8000 [05:08<04:43, 11.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4655/8000 [05:09<03:51, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4658/8000 [05:09<04:20, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4663/8000 [05:09<03:25, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▋           | 4666/8000 [05:09<04:10, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▊           | 4670/8000 [05:10<03:30, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▊           | 4675/8000 [05:10<02:47, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████▊           | 4678/8000 [05:10<02:42, 20.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4683/8000 [05:10<02:23, 23.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4686/8000 [05:10<02:31, 21.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4689/8000 [05:10<02:37, 21.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4693/8000 [05:11<04:00, 13.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4697/8000 [05:11<03:18, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▊           | 4701/8000 [05:11<03:42, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4704/8000 [05:12<04:21, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4708/8000 [05:12<04:21, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4710/8000 [05:12<04:15, 12.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4712/8000 [05:12<04:16, 12.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4717/8000 [05:13<04:02, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4720/8000 [05:13<03:48, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4723/8000 [05:13<04:15, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4726/8000 [05:13<03:42, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4730/8000 [05:13<03:11, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4735/8000 [05:14<03:27, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▉           | 4738/8000 [05:14<03:22, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4743/8000 [05:14<02:46, 19.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4747/8000 [05:14<03:00, 18.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4750/8000 [05:14<02:58, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████           | 4754/8000 [05:15<02:44, 19.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4761/8000 [05:15<02:39, 20.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4765/8000 [05:15<03:09, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4771/8000 [05:15<02:25, 22.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████           | 4775/8000 [05:16<03:19, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4781/8000 [05:16<03:02, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4784/8000 [05:16<03:03, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4787/8000 [05:17<03:43, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4794/8000 [05:17<03:11, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4797/8000 [05:17<03:01, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4801/8000 [05:17<03:26, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4804/8000 [05:18<03:21, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4806/8000 [05:18<03:40, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4810/8000 [05:18<03:09, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▏          | 4812/8000 [05:18<03:15, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4818/8000 [05:18<03:04, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4823/8000 [05:19<03:21, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4827/8000 [05:19<03:05, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4832/8000 [05:19<03:17, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4836/8000 [05:20<02:58, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▎          | 4840/8000 [05:20<02:45, 19.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▎          | 4844/8000 [05:20<02:34, 20.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▎          | 4849/8000 [05:20<03:02, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4852/8000 [05:21<03:46, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4854/8000 [05:21<04:38, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4856/8000 [05:21<05:12, 10.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4860/8000 [05:21<04:02, 12.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4865/8000 [05:22<03:28, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4868/8000 [05:22<03:20, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4871/8000 [05:22<03:23, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4873/8000 [05:22<03:34, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4879/8000 [05:22<03:12, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4882/8000 [05:23<03:03, 16.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4885/8000 [05:23<02:50, 18.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▍          | 4888/8000 [05:23<02:47, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4891/8000 [05:23<02:49, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4897/8000 [05:23<02:11, 23.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4900/8000 [05:23<02:23, 21.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4903/8000 [05:24<03:13, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4907/8000 [05:24<02:40, 19.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4911/8000 [05:24<03:40, 14.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4914/8000 [05:24<03:13, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|████████████████▌          | 4918/8000 [05:25<04:05, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▌          | 4921/8000 [05:25<03:33, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▌          | 4923/8000 [05:25<04:20, 11.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4930/8000 [05:26<03:09, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4934/8000 [05:26<02:50, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4939/8000 [05:26<02:28, 20.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4949/8000 [05:26<02:03, 24.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4954/8000 [05:27<03:08, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▋          | 4959/8000 [05:27<02:43, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4964/8000 [05:27<02:20, 21.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4971/8000 [05:27<02:13, 22.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4975/8000 [05:28<02:40, 18.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4978/8000 [05:28<02:41, 18.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4981/8000 [05:28<02:44, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4984/8000 [05:28<02:45, 18.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4987/8000 [05:28<02:34, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4990/8000 [05:29<02:38, 18.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4992/8000 [05:29<03:45, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4995/8000 [05:29<05:06,  9.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4997/8000 [05:30<04:42, 10.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▊          | 4999/8000 [05:30<04:35, 10.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5001/8000 [05:30<04:32, 10.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5003/8000 [05:30<04:27, 11.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5006/8000 [05:31<05:47,  8.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5010/8000 [05:31<04:54, 10.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5012/8000 [05:31<04:45, 10.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5015/8000 [05:31<04:04, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5017/8000 [05:31<04:08, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5020/8000 [05:31<03:41, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5023/8000 [05:32<03:21, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5027/8000 [05:32<03:24, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5030/8000 [05:32<03:14, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▉          | 5036/8000 [05:32<02:21, 20.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5039/8000 [05:33<03:03, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5041/8000 [05:33<03:11, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5044/8000 [05:33<03:02, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5047/8000 [05:33<03:42, 13.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5049/8000 [05:33<03:50, 12.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5053/8000 [05:34<03:53, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5055/8000 [05:34<04:00, 12.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5059/8000 [05:34<03:48, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5065/8000 [05:34<03:08, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5067/8000 [05:35<04:00, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5069/8000 [05:35<04:02, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████          | 5073/8000 [05:35<03:19, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████▏         | 5076/8000 [05:35<04:00, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████▏         | 5078/8000 [05:36<04:02, 12.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5080/8000 [05:36<04:08, 11.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5082/8000 [05:36<04:03, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5084/8000 [05:36<03:53, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5087/8000 [05:36<03:19, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5090/8000 [05:36<03:10, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5092/8000 [05:37<03:23, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5095/8000 [05:37<04:02, 12.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5097/8000 [05:37<04:00, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5099/8000 [05:37<03:51, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5104/8000 [05:38<03:19, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5108/8000 [05:38<02:54, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▏         | 5111/8000 [05:38<03:31, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5118/8000 [05:39<03:48, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5121/8000 [05:39<03:28, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5123/8000 [05:39<03:28, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5126/8000 [05:39<04:20, 11.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5130/8000 [05:40<03:58, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5133/8000 [05:40<03:35, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5135/8000 [05:40<03:43, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5140/8000 [05:40<03:27, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5142/8000 [05:40<03:31, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▎         | 5147/8000 [05:41<03:12, 14.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▍         | 5149/8000 [05:41<03:20, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▍         | 5153/8000 [05:41<02:51, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▍         | 5155/8000 [05:41<03:04, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▍         | 5160/8000 [05:42<03:00, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5162/8000 [05:42<03:09, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5166/8000 [05:42<03:23, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5168/8000 [05:42<03:34, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5171/8000 [05:42<03:19, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5175/8000 [05:43<02:48, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5177/8000 [05:43<02:56, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5180/8000 [05:43<02:50, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5182/8000 [05:43<02:51, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▍         | 5184/8000 [05:43<03:59, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5186/8000 [05:43<03:56, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5189/8000 [05:44<03:27, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5191/8000 [05:44<03:31, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5193/8000 [05:44<03:38, 12.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5195/8000 [05:44<03:37, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5197/8000 [05:44<03:39, 12.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5199/8000 [05:44<03:45, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5201/8000 [05:45<03:46, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5206/8000 [05:45<02:38, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5212/8000 [05:45<02:00, 23.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5216/8000 [05:45<02:25, 19.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▌         | 5219/8000 [05:45<02:26, 18.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5223/8000 [05:46<02:12, 20.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5226/8000 [05:46<02:22, 19.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5230/8000 [05:46<02:13, 20.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5233/8000 [05:46<02:18, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5236/8000 [05:47<03:37, 12.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████▋         | 5238/8000 [05:47<03:39, 12.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5240/8000 [05:47<03:41, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5244/8000 [05:47<03:45, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5247/8000 [05:47<03:25, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5251/8000 [05:48<02:41, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5254/8000 [05:48<03:08, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▋         | 5257/8000 [05:48<02:56, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5260/8000 [05:48<03:20, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5262/8000 [05:48<03:29, 13.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5266/8000 [05:49<04:12, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5269/8000 [05:49<04:22, 10.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5271/8000 [05:49<04:13, 10.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5274/8000 [05:50<04:28, 10.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5278/8000 [05:50<04:16, 10.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5281/8000 [05:50<03:43, 12.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5283/8000 [05:50<03:30, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5285/8000 [05:50<03:33, 12.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5288/8000 [05:51<04:02, 11.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5291/8000 [05:51<03:32, 12.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▊         | 5295/8000 [05:51<02:53, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5299/8000 [05:51<02:31, 17.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5303/8000 [05:52<02:46, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5305/8000 [05:52<02:52, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5308/8000 [05:52<02:38, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5313/8000 [05:52<02:02, 21.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5316/8000 [05:52<03:10, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5318/8000 [05:53<03:48, 11.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▉         | 5320/8000 [05:53<04:34,  9.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5323/8000 [05:53<03:54, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5325/8000 [05:53<03:54, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5327/8000 [05:54<03:50, 11.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5331/8000 [05:54<02:52, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▉         | 5333/8000 [05:54<03:04, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5337/8000 [05:54<02:38, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5340/8000 [05:54<03:20, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5343/8000 [05:55<03:06, 14.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5346/8000 [05:55<02:45, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5349/8000 [05:55<02:29, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5351/8000 [05:55<02:41, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5353/8000 [05:55<02:55, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5356/8000 [05:55<03:34, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5362/8000 [05:56<02:24, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5366/8000 [05:56<02:43, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████         | 5369/8000 [05:56<02:35, 16.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5371/8000 [05:56<03:29, 12.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5374/8000 [05:57<03:11, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5377/8000 [05:57<02:58, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5381/8000 [05:57<02:31, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5383/8000 [05:57<02:33, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5387/8000 [05:57<02:19, 18.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5390/8000 [05:58<03:02, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5392/8000 [05:58<03:08, 13.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5395/8000 [05:58<02:53, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5397/8000 [05:58<03:03, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▏        | 5399/8000 [05:58<02:59, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▏        | 5402/8000 [05:59<03:30, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▏        | 5405/8000 [05:59<03:37, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5410/8000 [05:59<02:40, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5412/8000 [05:59<02:49, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5414/8000 [05:59<03:45, 11.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5418/8000 [06:00<04:11, 10.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5420/8000 [06:00<05:21,  8.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5422/8000 [06:00<04:44,  9.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5424/8000 [06:01<04:25,  9.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5428/8000 [06:01<04:01, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5430/8000 [06:01<03:55, 10.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5435/8000 [06:01<03:24, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5438/8000 [06:02<03:00, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▎        | 5440/8000 [06:02<03:05, 13.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5446/8000 [06:02<02:11, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5451/8000 [06:02<02:59, 14.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5455/8000 [06:03<02:35, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5461/8000 [06:03<02:21, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5464/8000 [06:03<02:58, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5466/8000 [06:03<03:06, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5469/8000 [06:04<02:52, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5471/8000 [06:04<03:03, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5474/8000 [06:04<02:52, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▍        | 5477/8000 [06:04<02:48, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▍        | 5481/8000 [06:04<02:33, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5486/8000 [06:04<02:07, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5490/8000 [06:05<02:30, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5493/8000 [06:05<02:20, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5496/8000 [06:05<02:48, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5498/8000 [06:05<02:51, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5501/8000 [06:06<02:32, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5503/8000 [06:06<02:42, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5505/8000 [06:06<02:54, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5509/8000 [06:06<03:06, 13.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5513/8000 [06:06<02:35, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▌        | 5515/8000 [06:07<02:47, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5520/8000 [06:07<02:36, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5522/8000 [06:07<02:49, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5525/8000 [06:07<03:18, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5528/8000 [06:08<03:37, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5533/8000 [06:08<02:35, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5537/8000 [06:08<02:17, 17.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5541/8000 [06:08<03:11, 12.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5547/8000 [06:09<02:48, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5549/8000 [06:09<02:44, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5551/8000 [06:09<02:52, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5553/8000 [06:09<02:55, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▋        | 5555/8000 [06:10<03:44, 10.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|██████████████████▊        | 5559/8000 [06:10<03:42, 10.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5561/8000 [06:10<04:50,  8.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5566/8000 [06:11<03:56, 10.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5568/8000 [06:11<03:47, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5573/8000 [06:11<03:50, 10.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5575/8000 [06:11<03:45, 10.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5578/8000 [06:12<03:18, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5582/8000 [06:12<03:18, 12.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5585/8000 [06:12<03:00, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5590/8000 [06:12<02:45, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▊        | 5592/8000 [06:13<03:29, 11.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5596/8000 [06:13<02:54, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5598/8000 [06:13<03:00, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5601/8000 [06:13<02:43, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5603/8000 [06:13<02:53, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5606/8000 [06:14<02:37, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5609/8000 [06:14<02:59, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5611/8000 [06:14<03:04, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5613/8000 [06:14<03:53, 10.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5615/8000 [06:15<03:34, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5620/8000 [06:15<02:26, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5623/8000 [06:15<02:51, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▉        | 5628/8000 [06:15<02:45, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████        | 5630/8000 [06:15<02:50, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████        | 5632/8000 [06:16<02:55, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████        | 5635/8000 [06:16<02:41, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████        | 5639/8000 [06:16<02:15, 17.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5641/8000 [06:16<02:22, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5644/8000 [06:16<02:06, 18.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5646/8000 [06:16<02:22, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5652/8000 [06:17<01:44, 22.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5655/8000 [06:17<01:50, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5659/8000 [06:17<01:48, 21.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5663/8000 [06:17<02:16, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████        | 5665/8000 [06:18<02:52, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5669/8000 [06:18<02:32, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5674/8000 [06:18<02:07, 18.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5679/8000 [06:18<01:51, 20.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5682/8000 [06:18<01:58, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5685/8000 [06:18<02:06, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5689/8000 [06:19<01:58, 19.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5693/8000 [06:19<02:29, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5695/8000 [06:19<03:12, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▏       | 5699/8000 [06:20<03:18, 11.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5704/8000 [06:20<03:12, 11.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5709/8000 [06:20<02:38, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5713/8000 [06:21<02:41, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5716/8000 [06:21<03:02, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▎       | 5719/8000 [06:21<03:23, 11.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5722/8000 [06:21<03:03, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5725/8000 [06:22<02:46, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5731/8000 [06:22<02:30, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5734/8000 [06:22<02:24, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▎       | 5737/8000 [06:22<02:45, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5741/8000 [06:23<02:21, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5745/8000 [06:23<02:09, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5747/8000 [06:23<02:24, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5750/8000 [06:23<02:21, 15.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5752/8000 [06:24<03:13, 11.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5754/8000 [06:24<04:08,  9.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5757/8000 [06:24<03:30, 10.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5761/8000 [06:24<02:54, 12.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5764/8000 [06:24<02:39, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5766/8000 [06:25<02:41, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5768/8000 [06:25<02:50, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5770/8000 [06:25<03:10, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5773/8000 [06:25<02:53, 12.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▍       | 5777/8000 [06:25<02:22, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5780/8000 [06:26<03:21, 11.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5782/8000 [06:26<03:17, 11.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5785/8000 [06:26<02:57, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5788/8000 [06:26<02:42, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5790/8000 [06:27<02:51, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5796/8000 [06:27<01:55, 19.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|███████████████████▌       | 5799/8000 [06:27<02:03, 17.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5801/8000 [06:27<03:00, 12.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5805/8000 [06:28<03:06, 11.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5807/8000 [06:28<03:10, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5809/8000 [06:28<03:06, 11.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5811/8000 [06:28<03:47,  9.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▌       | 5813/8000 [06:28<03:39,  9.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5815/8000 [06:29<03:34, 10.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5818/8000 [06:29<03:04, 11.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5820/8000 [06:29<03:10, 11.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5823/8000 [06:29<02:51, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5828/8000 [06:30<02:42, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5830/8000 [06:30<04:04,  8.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5833/8000 [06:30<03:22, 10.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5835/8000 [06:31<03:42,  9.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5838/8000 [06:31<03:40,  9.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5841/8000 [06:31<02:56, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5846/8000 [06:31<02:11, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▋       | 5849/8000 [06:31<02:08, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5853/8000 [06:31<01:55, 18.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5856/8000 [06:32<01:58, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5859/8000 [06:32<01:57, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5866/8000 [06:32<01:26, 24.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5869/8000 [06:32<01:54, 18.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████▊       | 5872/8000 [06:33<02:22, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▊       | 5881/8000 [06:33<01:50, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▊       | 5884/8000 [06:33<01:52, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▊       | 5886/8000 [06:33<02:19, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▊       | 5888/8000 [06:33<02:21, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5893/8000 [06:34<01:52, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5895/8000 [06:34<01:55, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5898/8000 [06:34<01:54, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5905/8000 [06:34<01:23, 25.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5909/8000 [06:34<01:50, 18.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5914/8000 [06:35<02:18, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5916/8000 [06:35<02:23, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▉       | 5923/8000 [06:35<01:40, 20.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5926/8000 [06:35<01:38, 21.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5929/8000 [06:36<01:41, 20.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5933/8000 [06:36<02:00, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5935/8000 [06:36<02:07, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5941/8000 [06:36<02:03, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5945/8000 [06:37<02:10, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5948/8000 [06:37<02:27, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5952/8000 [06:37<02:04, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5954/8000 [06:37<02:34, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████       | 5960/8000 [06:38<01:50, 18.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5964/8000 [06:38<02:25, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5967/8000 [06:38<02:17, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5971/8000 [06:38<02:01, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5977/8000 [06:39<01:55, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5979/8000 [06:39<02:33, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5981/8000 [06:39<02:41, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5984/8000 [06:39<02:56, 11.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5988/8000 [06:40<02:26, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5991/8000 [06:40<02:19, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5993/8000 [06:40<02:26, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▏      | 5998/8000 [06:40<02:17, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6000/8000 [06:41<02:22, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6003/8000 [06:41<02:32, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6005/8000 [06:41<03:19, 10.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6007/8000 [06:41<03:13, 10.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6011/8000 [06:41<02:28, 13.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6013/8000 [06:42<02:31, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6015/8000 [06:42<02:25, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6018/8000 [06:42<02:13, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6020/8000 [06:42<03:20,  9.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6023/8000 [06:43<03:25,  9.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6027/8000 [06:43<03:04, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6030/8000 [06:43<02:41, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6032/8000 [06:43<02:44, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▎      | 6034/8000 [06:44<02:44, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▍      | 6038/8000 [06:44<02:14, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6041/8000 [06:44<02:11, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6045/8000 [06:44<02:57, 11.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6047/8000 [06:45<03:03, 10.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6049/8000 [06:45<03:01, 10.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6054/8000 [06:45<02:46, 11.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6057/8000 [06:46<02:58, 10.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6060/8000 [06:46<02:35, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6066/8000 [06:46<01:44, 18.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▍      | 6073/8000 [06:46<01:18, 24.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6076/8000 [06:46<02:10, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6081/8000 [06:47<02:10, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6086/8000 [06:47<02:23, 13.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6089/8000 [06:47<02:15, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6094/8000 [06:48<01:52, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6099/8000 [06:48<02:15, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6102/8000 [06:48<02:08, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6106/8000 [06:48<01:56, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▌      | 6110/8000 [06:49<01:48, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▋      | 6112/8000 [06:49<02:01, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▋      | 6116/8000 [06:49<02:14, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|████████████████████▋      | 6120/8000 [06:49<02:20, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6123/8000 [06:50<02:13, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6127/8000 [06:50<01:51, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6129/8000 [06:50<02:00, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6132/8000 [06:50<01:56, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6135/8000 [06:50<02:21, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6140/8000 [06:51<01:49, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6143/8000 [06:51<01:49, 16.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▋      | 6145/8000 [06:51<01:51, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6150/8000 [06:51<01:29, 20.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6153/8000 [06:51<01:34, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6157/8000 [06:52<01:54, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6162/8000 [06:52<01:58, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6166/8000 [06:52<01:41, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6170/8000 [06:52<02:02, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6172/8000 [06:53<02:08, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6175/8000 [06:53<02:01, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6177/8000 [06:53<02:04, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6179/8000 [06:53<03:08,  9.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▊      | 6185/8000 [06:54<02:29, 12.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6187/8000 [06:54<02:31, 11.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6193/8000 [06:54<01:45, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████▉      | 6199/8000 [06:54<01:21, 22.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6206/8000 [06:55<01:22, 21.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6212/8000 [06:55<01:14, 24.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6216/8000 [06:55<01:17, 23.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▉      | 6222/8000 [06:55<01:09, 25.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6225/8000 [06:55<01:16, 23.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6230/8000 [06:56<01:13, 24.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6233/8000 [06:56<01:55, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6235/8000 [06:56<02:01, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6238/8000 [06:57<02:28, 11.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6240/8000 [06:57<02:22, 12.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6243/8000 [06:57<02:10, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6245/8000 [06:57<02:16, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6247/8000 [06:57<02:16, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6251/8000 [06:58<02:29, 11.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6254/8000 [06:58<02:16, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████      | 6258/8000 [06:58<02:50, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6260/8000 [06:59<03:15,  8.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6263/8000 [06:59<02:47, 10.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6265/8000 [06:59<02:48, 10.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6267/8000 [06:59<02:48, 10.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6269/8000 [06:59<02:43, 10.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6274/8000 [07:00<01:50, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6277/8000 [07:00<01:48, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▏     | 6280/8000 [07:00<01:45, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6282/8000 [07:00<01:48, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6285/8000 [07:00<01:48, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6287/8000 [07:00<01:57, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6290/8000 [07:01<02:21, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▏     | 6295/8000 [07:01<01:47, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6297/8000 [07:01<01:55, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6303/8000 [07:01<01:47, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6305/8000 [07:02<01:54, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6309/8000 [07:02<02:31, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6313/8000 [07:02<02:03, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6316/8000 [07:02<01:49, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6319/8000 [07:03<02:12, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6321/8000 [07:03<03:09,  8.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6324/8000 [07:03<02:40, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6326/8000 [07:04<02:31, 11.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▎     | 6329/8000 [07:04<02:12, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6336/8000 [07:04<01:40, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6339/8000 [07:04<01:37, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6345/8000 [07:04<01:15, 21.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6355/8000 [07:05<00:51, 31.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▍     | 6359/8000 [07:05<01:00, 26.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▍     | 6364/8000 [07:05<01:21, 20.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▍     | 6369/8000 [07:06<02:09, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6372/8000 [07:06<02:19, 11.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6376/8000 [07:07<02:04, 13.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6379/8000 [07:07<02:08, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6381/8000 [07:07<02:27, 10.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6386/8000 [07:07<02:12, 12.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6389/8000 [07:08<02:16, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6391/8000 [07:08<02:36, 10.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6394/8000 [07:08<02:41,  9.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6397/8000 [07:09<02:30, 10.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6404/8000 [07:09<02:05, 12.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▌     | 6407/8000 [07:09<01:58, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6410/8000 [07:09<02:08, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6412/8000 [07:10<02:28, 10.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6415/8000 [07:10<02:06, 12.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6419/8000 [07:10<02:07, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6423/8000 [07:11<02:09, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6427/8000 [07:11<02:10, 12.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6430/8000 [07:11<01:53, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6435/8000 [07:12<02:09, 12.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6437/8000 [07:12<02:55,  8.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6439/8000 [07:13<03:36,  7.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|█████████████████████▋     | 6440/8000 [07:13<03:36,  7.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▋     | 6442/8000 [07:13<03:15,  7.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6445/8000 [07:13<02:37,  9.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6448/8000 [07:13<02:42,  9.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6452/8000 [07:13<02:03, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6455/8000 [07:14<01:51, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6457/8000 [07:14<02:18, 11.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6460/8000 [07:14<02:30, 10.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6463/8000 [07:15<02:42,  9.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6465/8000 [07:15<03:06,  8.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6467/8000 [07:15<02:53,  8.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6469/8000 [07:15<02:37,  9.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6471/8000 [07:16<02:27, 10.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6474/8000 [07:16<01:58, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▊     | 6478/8000 [07:16<01:33, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6482/8000 [07:16<01:22, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6490/8000 [07:16<00:55, 27.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6493/8000 [07:16<01:21, 18.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6496/8000 [07:17<02:01, 12.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6499/8000 [07:17<01:56, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6501/8000 [07:18<02:23, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6506/8000 [07:18<01:43, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6508/8000 [07:18<01:44, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6511/8000 [07:18<01:39, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▉     | 6516/8000 [07:18<01:38, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6521/8000 [07:19<01:22, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6523/8000 [07:19<01:29, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6527/8000 [07:19<01:45, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6530/8000 [07:19<02:02, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6533/8000 [07:20<02:03, 11.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6538/8000 [07:20<02:07, 11.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6540/8000 [07:20<02:00, 12.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6542/8000 [07:20<02:01, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6546/8000 [07:21<01:40, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6548/8000 [07:21<02:08, 11.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████     | 6552/8000 [07:21<01:43, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6556/8000 [07:21<01:28, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6560/8000 [07:21<01:14, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6563/8000 [07:22<02:18, 10.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6568/8000 [07:22<02:04, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6572/8000 [07:23<02:29,  9.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6577/8000 [07:23<01:49, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6583/8000 [07:23<01:39, 14.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6586/8000 [07:24<01:34, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▏    | 6590/8000 [07:24<01:25, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▎    | 6593/8000 [07:24<01:40, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▎    | 6595/8000 [07:24<01:39, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▎    | 6598/8000 [07:25<01:53, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6602/8000 [07:25<01:30, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6606/8000 [07:25<01:20, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6610/8000 [07:25<01:13, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6617/8000 [07:26<01:23, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6620/8000 [07:26<01:42, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6623/8000 [07:26<01:41, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6625/8000 [07:26<01:48, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▎    | 6628/8000 [07:27<01:46, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6630/8000 [07:27<02:02, 11.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6633/8000 [07:27<02:01, 11.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6636/8000 [07:28<02:26,  9.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6638/8000 [07:28<02:20,  9.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6643/8000 [07:28<01:47, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6647/8000 [07:28<01:30, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6649/8000 [07:28<01:29, 15.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6652/8000 [07:28<01:24, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6658/8000 [07:29<01:03, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6661/8000 [07:29<01:06, 20.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▍    | 6664/8000 [07:29<01:27, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▌    | 6667/8000 [07:29<01:25, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▌    | 6670/8000 [07:29<01:17, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|██████████████████████▌    | 6676/8000 [07:30<00:59, 22.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6680/8000 [07:30<01:14, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6684/8000 [07:30<01:09, 18.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6690/8000 [07:31<01:21, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6693/8000 [07:31<01:20, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6698/8000 [07:31<01:26, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6700/8000 [07:31<01:33, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▌    | 6703/8000 [07:32<01:50, 11.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6707/8000 [07:32<01:35, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6709/8000 [07:32<01:38, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6711/8000 [07:32<01:39, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6716/8000 [07:32<01:16, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6720/8000 [07:33<01:08, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6722/8000 [07:33<01:16, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6724/8000 [07:33<01:19, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6726/8000 [07:33<01:29, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6729/8000 [07:33<01:25, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6732/8000 [07:33<01:21, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6737/8000 [07:34<01:20, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▋    | 6740/8000 [07:34<01:15, 16.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6743/8000 [07:34<01:09, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6745/8000 [07:35<02:01, 10.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6749/8000 [07:35<01:36, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6752/8000 [07:35<01:49, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|██████████████████████▊    | 6759/8000 [07:35<01:08, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6762/8000 [07:36<01:25, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6765/8000 [07:36<01:21, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6767/8000 [07:36<01:25, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6770/8000 [07:36<01:19, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6772/8000 [07:36<01:23, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6774/8000 [07:36<01:23, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▊    | 6777/8000 [07:37<01:16, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6781/8000 [07:37<01:06, 18.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6785/8000 [07:37<01:00, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6788/8000 [07:37<01:42, 11.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6792/8000 [07:38<01:21, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6797/8000 [07:38<01:22, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6800/8000 [07:38<01:36, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6804/8000 [07:38<01:20, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6809/8000 [07:39<01:04, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▉    | 6812/8000 [07:39<01:00, 19.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6815/8000 [07:39<01:34, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6818/8000 [07:39<01:26, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6825/8000 [07:40<00:59, 19.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6828/8000 [07:40<01:13, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████    | 6834/8000 [07:40<01:34, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6840/8000 [07:41<01:33, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6842/8000 [07:41<01:34, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6845/8000 [07:41<01:28, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████    | 6850/8000 [07:42<01:34, 12.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6854/8000 [07:42<01:20, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6856/8000 [07:42<01:26, 13.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6859/8000 [07:42<01:19, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6863/8000 [07:42<01:08, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6867/8000 [07:43<01:05, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6872/8000 [07:43<01:11, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6876/8000 [07:43<01:05, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6879/8000 [07:43<01:04, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6881/8000 [07:44<01:24, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6886/8000 [07:44<01:17, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▏   | 6888/8000 [07:44<01:33, 11.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6894/8000 [07:44<01:02, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6899/8000 [07:45<01:07, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6901/8000 [07:45<01:12, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6903/8000 [07:45<01:17, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6906/8000 [07:45<01:26, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6909/8000 [07:46<01:16, 14.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6914/8000 [07:46<00:59, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6917/8000 [07:46<01:01, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|███████████████████████▎   | 6920/8000 [07:46<01:01, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▎   | 6924/8000 [07:46<00:56, 19.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6929/8000 [07:47<00:59, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6932/8000 [07:47<00:59, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6942/8000 [07:47<00:37, 28.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6946/8000 [07:47<00:39, 26.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6952/8000 [07:47<00:35, 29.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6955/8000 [07:47<00:40, 25.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▍   | 6960/8000 [07:48<00:39, 26.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6965/8000 [07:48<00:48, 21.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6970/8000 [07:48<00:55, 18.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6973/8000 [07:48<00:52, 19.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6976/8000 [07:49<01:07, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6981/8000 [07:49<01:18, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6987/8000 [07:49<00:59, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|███████████████████████▌   | 6990/8000 [07:50<00:57, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7000/8000 [07:50<00:45, 22.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7004/8000 [07:50<00:42, 23.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7007/8000 [07:50<00:45, 22.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7010/8000 [07:50<00:47, 20.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7013/8000 [07:51<00:50, 19.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7016/8000 [07:51<00:50, 19.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7019/8000 [07:51<00:51, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7023/8000 [07:51<00:48, 20.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7026/8000 [07:51<00:49, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7029/8000 [07:52<01:19, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▋   | 7032/8000 [07:52<01:12, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7038/8000 [07:52<00:51, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7041/8000 [07:52<00:52, 18.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7047/8000 [07:52<00:41, 23.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7050/8000 [07:53<01:16, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7052/8000 [07:53<01:31, 10.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7054/8000 [07:54<01:26, 10.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7059/8000 [07:54<01:12, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7062/8000 [07:54<01:35,  9.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7064/8000 [07:55<01:32, 10.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7070/8000 [07:55<01:01, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▊   | 7072/8000 [07:55<01:01, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▉   | 7076/8000 [07:55<01:07, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████▉   | 7078/8000 [07:56<01:25, 10.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7081/8000 [07:56<01:41,  9.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7083/8000 [07:56<01:53,  8.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7084/8000 [07:56<01:57,  7.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7087/8000 [07:57<01:33,  9.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7090/8000 [07:57<01:50,  8.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7099/8000 [07:57<00:55, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7103/8000 [07:58<01:14, 12.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7107/8000 [07:58<01:03, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▉   | 7109/8000 [07:58<01:06, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7112/8000 [07:59<01:16, 11.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7114/8000 [07:59<01:26, 10.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7119/8000 [07:59<01:02, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7125/8000 [07:59<00:57, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7129/8000 [08:00<01:02, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7132/8000 [08:00<01:07, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7135/8000 [08:00<01:02, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7138/8000 [08:00<00:58, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7142/8000 [08:00<00:50, 16.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████   | 7147/8000 [08:01<00:52, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████▏  | 7151/8000 [08:01<00:47, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████▏  | 7154/8000 [08:01<00:46, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7160/8000 [08:01<00:46, 18.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7164/8000 [08:02<00:42, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7167/8000 [08:02<00:43, 18.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7170/8000 [08:02<00:44, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7173/8000 [08:02<00:54, 15.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7175/8000 [08:02<00:57, 14.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7179/8000 [08:03<00:58, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▏  | 7182/8000 [08:03<00:51, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7187/8000 [08:03<00:50, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7192/8000 [08:03<00:41, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7195/8000 [08:04<00:44, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7198/8000 [08:04<00:43, 18.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7201/8000 [08:04<00:54, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7203/8000 [08:04<00:56, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7206/8000 [08:04<00:52, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7209/8000 [08:04<00:48, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7212/8000 [08:05<00:46, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7216/8000 [08:05<00:53, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▎  | 7219/8000 [08:05<01:14, 10.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7224/8000 [08:06<00:56, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7228/8000 [08:06<01:00, 12.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7230/8000 [08:06<01:09, 11.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7232/8000 [08:07<01:09, 11.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7234/8000 [08:07<01:08, 11.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7236/8000 [08:07<01:07, 11.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|████████████████████████▍  | 7238/8000 [08:07<01:07, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7244/8000 [08:07<01:00, 12.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7246/8000 [08:08<01:00, 12.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7249/8000 [08:08<01:08, 11.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7251/8000 [08:08<01:07, 11.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7256/8000 [08:09<00:59, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▍  | 7259/8000 [08:09<01:02, 11.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7261/8000 [08:09<01:02, 11.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7263/8000 [08:09<00:58, 12.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7266/8000 [08:09<01:02, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7271/8000 [08:10<00:45, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7274/8000 [08:10<00:44, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7278/8000 [08:10<00:39, 18.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7282/8000 [08:10<00:36, 19.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7285/8000 [08:10<00:37, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7287/8000 [08:10<00:38, 18.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7291/8000 [08:11<00:35, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▌  | 7294/8000 [08:11<00:56, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7297/8000 [08:11<00:51, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7302/8000 [08:11<00:41, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7307/8000 [08:12<00:33, 20.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7310/8000 [08:12<00:42, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7312/8000 [08:12<00:44, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7314/8000 [08:12<00:44, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|████████████████████████▋  | 7317/8000 [08:12<00:41, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7322/8000 [08:12<00:33, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7328/8000 [08:13<00:35, 19.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7330/8000 [08:13<00:38, 17.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▋  | 7332/8000 [08:13<00:42, 15.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7335/8000 [08:13<00:38, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7337/8000 [08:13<00:42, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7344/8000 [08:14<00:37, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7346/8000 [08:14<00:40, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7350/8000 [08:14<00:44, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7352/8000 [08:14<00:43, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7355/8000 [08:15<00:41, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7357/8000 [08:15<00:47, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7364/8000 [08:15<00:30, 20.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7367/8000 [08:15<00:39, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▊  | 7369/8000 [08:15<00:40, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7373/8000 [08:16<00:32, 19.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7376/8000 [08:16<00:32, 18.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7380/8000 [08:16<00:37, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7384/8000 [08:16<00:30, 19.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7388/8000 [08:16<00:29, 20.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7393/8000 [08:17<00:36, 16.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7396/8000 [08:17<00:35, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|████████████████████████▉  | 7399/8000 [08:17<00:36, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▉  | 7402/8000 [08:17<00:36, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▉  | 7407/8000 [08:18<00:43, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7414/8000 [08:18<00:29, 19.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7417/8000 [08:18<00:36, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7423/8000 [08:18<00:29, 19.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7426/8000 [08:19<00:36, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7429/8000 [08:19<00:35, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7432/8000 [08:19<00:32, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7436/8000 [08:19<00:29, 18.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7439/8000 [08:19<00:30, 18.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████  | 7442/8000 [08:20<00:31, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7445/8000 [08:20<00:31, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7447/8000 [08:20<00:34, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7451/8000 [08:20<00:29, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7459/8000 [08:20<00:24, 22.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7463/8000 [08:21<00:29, 17.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7465/8000 [08:21<00:32, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7468/8000 [08:21<00:32, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7470/8000 [08:21<00:34, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7472/8000 [08:21<00:34, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▏ | 7475/8000 [08:22<00:41, 12.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▏ | 7480/8000 [08:22<00:38, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7485/8000 [08:22<00:37, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7488/8000 [08:23<00:35, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7492/8000 [08:23<00:30, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7494/8000 [08:23<00:33, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7498/8000 [08:23<00:36, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7500/8000 [08:23<00:38, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7504/8000 [08:24<00:31, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7508/8000 [08:24<00:28, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7510/8000 [08:24<00:38, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7513/8000 [08:24<00:33, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▎ | 7518/8000 [08:24<00:27, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7520/8000 [08:25<00:31, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7526/8000 [08:25<00:28, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7528/8000 [08:25<00:30, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7530/8000 [08:25<00:33, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7532/8000 [08:25<00:34, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7537/8000 [08:26<00:25, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7541/8000 [08:26<00:23, 19.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7544/8000 [08:26<00:22, 20.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7548/8000 [08:26<00:22, 20.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7552/8000 [08:26<00:28, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|█████████████████████████▍ | 7555/8000 [08:27<00:27, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7561/8000 [08:27<00:26, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7564/8000 [08:27<00:36, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7567/8000 [08:28<00:31, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7570/8000 [08:28<00:36, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7572/8000 [08:28<00:36, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7575/8000 [08:28<00:33, 12.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7578/8000 [08:29<00:37, 11.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7580/8000 [08:29<00:35, 11.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7584/8000 [08:29<00:28, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▌ | 7588/8000 [08:29<00:24, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7593/8000 [08:29<00:25, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7596/8000 [08:30<00:29, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7599/8000 [08:30<00:26, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7601/8000 [08:30<00:27, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7603/8000 [08:30<00:29, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7608/8000 [08:30<00:22, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7613/8000 [08:31<00:18, 21.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7617/8000 [08:31<00:26, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7621/8000 [08:31<00:23, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7623/8000 [08:31<00:24, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7626/8000 [08:32<00:23, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▋ | 7628/8000 [08:32<00:25, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▊ | 7630/8000 [08:32<00:26, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▊ | 7633/8000 [08:32<00:24, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|█████████████████████████▊ | 7638/8000 [08:32<00:22, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7642/8000 [08:33<00:20, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7644/8000 [08:33<00:21, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7647/8000 [08:33<00:23, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7649/8000 [08:33<00:24, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7651/8000 [08:33<00:32, 10.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7657/8000 [08:34<00:21, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▊ | 7662/8000 [08:34<00:17, 19.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7667/8000 [08:34<00:14, 23.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7670/8000 [08:34<00:15, 21.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7674/8000 [08:34<00:14, 22.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7677/8000 [08:34<00:15, 21.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7680/8000 [08:35<00:21, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7682/8000 [08:35<00:22, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7688/8000 [08:35<00:14, 20.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7692/8000 [08:35<00:18, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7696/8000 [08:36<00:22, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████▉ | 7699/8000 [08:36<00:24, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7704/8000 [08:36<00:19, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7706/8000 [08:37<00:23, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7708/8000 [08:37<00:30,  9.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7711/8000 [08:37<00:29,  9.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████ | 7717/8000 [08:38<00:23, 12.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7721/8000 [08:38<00:29,  9.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7724/8000 [08:39<00:26, 10.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7726/8000 [08:39<00:31,  8.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7729/8000 [08:39<00:30,  8.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7730/8000 [08:39<00:32,  8.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7731/8000 [08:40<00:34,  7.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7736/8000 [08:40<00:21, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████ | 7739/8000 [08:40<00:18, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7744/8000 [08:40<00:15, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7746/8000 [08:40<00:17, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7750/8000 [08:41<00:20, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7752/8000 [08:41<00:26,  9.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7756/8000 [08:42<00:23, 10.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7760/8000 [08:42<00:22, 10.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7762/8000 [08:42<00:23, 10.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7766/8000 [08:42<00:21, 11.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7768/8000 [08:43<00:21, 10.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7771/8000 [08:43<00:25,  8.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7774/8000 [08:43<00:21, 10.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▏| 7777/8000 [08:43<00:18, 12.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7781/8000 [08:44<00:14, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7783/8000 [08:44<00:14, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7787/8000 [08:44<00:12, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7791/8000 [08:44<00:11, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|██████████████████████████▎| 7797/8000 [08:45<00:15, 13.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7802/8000 [08:45<00:11, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7807/8000 [08:45<00:11, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▎| 7813/8000 [08:45<00:09, 20.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7817/8000 [08:46<00:10, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7821/8000 [08:46<00:11, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7824/8000 [08:46<00:10, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7827/8000 [08:46<00:10, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7830/8000 [08:47<00:10, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7834/8000 [08:47<00:09, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7837/8000 [08:47<00:08, 18.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7840/8000 [08:47<00:08, 18.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7845/8000 [08:47<00:08, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▍| 7848/8000 [08:48<00:08, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7852/8000 [08:48<00:08, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7855/8000 [08:48<00:08, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7857/8000 [08:48<00:08, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7862/8000 [08:48<00:08, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7868/8000 [08:49<00:05, 22.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7871/8000 [08:49<00:06, 20.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7874/8000 [08:49<00:06, 20.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|██████████████████████████▌| 7877/8000 [08:49<00:08, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▌| 7881/8000 [08:49<00:06, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▌| 7884/8000 [08:50<00:06, 18.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▌| 7887/8000 [08:50<00:06, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7889/8000 [08:50<00:06, 17.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7894/8000 [08:50<00:05, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7897/8000 [08:50<00:05, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7901/8000 [08:50<00:04, 20.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7905/8000 [08:51<00:06, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7908/8000 [08:51<00:07, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7913/8000 [08:51<00:04, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7916/8000 [08:52<00:05, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7922/8000 [08:52<00:03, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▋| 7925/8000 [08:52<00:04, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7928/8000 [08:52<00:04, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7932/8000 [08:52<00:03, 19.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7938/8000 [08:52<00:02, 23.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7941/8000 [08:53<00:02, 21.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7945/8000 [08:53<00:03, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7950/8000 [08:53<00:02, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7954/8000 [08:54<00:02, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|██████████████████████████▊| 7957/8000 [08:54<00:02, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▊| 7960/8000 [08:54<00:02, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▊| 7962/8000 [08:54<00:02, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7964/8000 [08:54<00:02, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7969/8000 [08:55<00:01, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7972/8000 [08:55<00:01, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7974/8000 [08:55<00:01, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7977/8000 [08:55<00:01, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7981/8000 [08:55<00:01, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7983/8000 [08:56<00:01, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7985/8000 [08:56<00:01, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7987/8000 [08:56<00:00, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7990/8000 [08:56<00:00, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7993/8000 [08:56<00:00, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|██████████████████████████▉| 7999/8000 [08:57<00:00, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8004it [08:57, 14.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:06<22:50,  6.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:08<12:53,  3.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:10<09:43,  2.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:12<08:15,  2.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:14<07:25,  2.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:16<07:39,  2.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:18<07:20,  2.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:20<06:56,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:22<06:45,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:24<06:40,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:27<06:55,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:29<06:26,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:30<06:04,  1.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:32<05:50,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:34<05:43,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:35<05:33,  1.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:37<05:24,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:39<05:20,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:41<05:15,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:42<05:12,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:44<05:04,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:46<05:04,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:47<04:58,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:49<04:57,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:51<04:59,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:53<05:00,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:54<04:58,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:56<04:57,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:58<04:50,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:59<04:51,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [01:01<04:52,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [01:03<04:50,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [01:05<04:45,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [01:06<04:42,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [01:08<04:37,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [01:10<04:41,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [01:11<04:38,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [01:13<04:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [01:15<04:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [01:17<04:36,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [01:18<04:31,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [01:20<04:29,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:22<04:31,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:23<04:25,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:25<04:21,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:27<04:19,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:28<04:14,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:30<04:20,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:32<04:16,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:34<04:16,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:35<04:14,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:37<04:13,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:39<04:10,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:40<04:06,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:42<04:06,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:44<04:03,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:45<04:00,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:49<04:27,  1.89s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 17.59it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 48.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 76it [00:01, 100.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 113it [00:01, 149.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 150it [00:01, 193.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 187it [00:01, 232.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 232it [00:01, 284.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 270it [00:01, 305.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 308it [00:01, 320.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 346it [00:01, 336.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 384it [00:01, 343.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 421it [00:02, 349.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 459it [00:02, 357.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 497it [00:02, 362.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 535it [00:02, 362.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 572it [00:02, 362.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 609it [00:02, 363.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 646it [00:02, 363.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 683it [00:02, 363.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 726it [00:02, 380.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 257.36it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 43.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:02, 43.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  16%|▋   | 16/100 [00:00<00:01, 48.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  21%|▊   | 21/100 [00:00<00:01, 45.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  26%|█   | 26/100 [00:00<00:01, 44.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  31%|█▏  | 31/100 [00:00<00:01, 44.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  36%|█▍  | 36/100 [00:00<00:01, 44.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  41%|█▋  | 41/100 [00:00<00:01, 44.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  46%|█▊  | 46/100 [00:01<00:01, 43.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  51%|██  | 51/100 [00:01<00:01, 43.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  56%|██▏ | 56/100 [00:01<00:00, 44.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  61%|██▍ | 61/100 [00:01<00:00, 43.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  67%|██▋ | 67/100 [00:01<00:00, 47.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  72%|██▉ | 72/100 [00:01<00:00, 46.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  77%|███ | 77/100 [00:01<00:00, 45.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  82%|███▎| 82/100 [00:01<00:00, 44.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  87%|███▍| 87/100 [00:01<00:00, 43.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  92%|███▋| 92/100 [00:02<00:00, 43.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 44.81it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████████████| 4/4 [47:50<00:00, 825.63s/it]\u001b[A\n",
+      "Replicates:  93%|███████████████████████▎ | 14/15 [9:13:32<38:53, 2333.22s/it]\n",
+      "Nyquist Samples:   0%|                                            | 0/4 [00:00<?, ?it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.469277397238549\n",
+      "2999: -5.9200860\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<17:28,  5.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<10:05,  3.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:08<07:38,  2.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:09<06:23,  1.96s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:10<05:40,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:12<05:28,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:13<05:03,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:16<05:38,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:17<05:08,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:18<04:44,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:19<04:29,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:21<04:17,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:22<04:13,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:23<04:06,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:24<04:02,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:26<03:56,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:27<03:54,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:28<03:52,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:30<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:31<03:51,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:32<03:48,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:33<03:44,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:35<03:43,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:36<03:42,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:37<03:39,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:38<03:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:40<03:38,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:41<03:46,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:42<03:45,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:44<03:44,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:45<03:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:46<03:40,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:48<03:36,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:49<03:36,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:50<03:31,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:51<03:27,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:53<03:27,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:54<03:28,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:55<03:33,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:57<03:31,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:58<03:27,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:59<03:28,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:01<03:32,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:02<03:29,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:03<03:27,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:05<03:29,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:06<03:32,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:08<03:34,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:09<03:38,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:11<03:37,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:12<03:34,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:14<03:33,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:15<03:32,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:16<03:29,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:18<03:28,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:19<03:26,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:21<03:23,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:22<03:20,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:23<03:18,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:25<03:16,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:26<03:21,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:28<03:15,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:29<03:10,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:31<03:10,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████                           | 65/200 [01:32<03:06,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▏                          | 66/200 [01:33<03:04,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▍                          | 67/200 [01:35<03:06,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▌                          | 68/200 [01:36<03:01,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████▊                          | 69/200 [01:38<03:27,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████                          | 70/200 [01:41<03:08,  1.45s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.46it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 49.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 99.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 112it [00:01, 148.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 152it [00:01, 200.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 189it [00:01, 237.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 226it [00:01, 268.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 297.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 301it [00:01, 315.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 338it [00:01, 328.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 375it [00:01, 338.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 419it [00:02, 365.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 458it [00:02, 364.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 496it [00:02, 364.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 534it [00:02, 368.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 572it [00:02, 366.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 610it [00:02, 362.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 650it [00:02, 372.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 688it [00:02, 369.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 726it [00:02, 367.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 257.97it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 42.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  12%|▍   | 12/100 [00:00<00:01, 56.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  18%|▋   | 18/100 [00:00<00:01, 49.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  24%|▉   | 24/100 [00:00<00:01, 47.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  29%|█▏  | 29/100 [00:00<00:01, 46.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|█▎  | 34/100 [00:00<00:01, 45.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:01, 49.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|█▊  | 45/100 [00:00<00:01, 47.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|██  | 50/100 [00:01<00:01, 47.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|██▏ | 55/100 [00:01<00:00, 46.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  60%|██▍ | 60/100 [00:01<00:00, 45.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  68%|██▋ | 68/100 [00:01<00:00, 51.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  74%|██▉ | 74/100 [00:01<00:00, 48.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  79%|███▏| 79/100 [00:01<00:00, 47.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  84%|███▎| 84/100 [00:01<00:00, 46.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  89%|███▌| 89/100 [00:01<00:00, 45.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 47.65it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 183/8000 [00:00<00:08, 899.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                         | 358/8000 [00:00<00:08, 920.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▊                         | 522/8000 [00:00<00:07, 990.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▎                        | 692/8000 [00:00<00:07, 946.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                        | 863/8000 [00:00<00:07, 926.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▍                      | 1045/8000 [00:01<00:07, 945.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▊                     | 1225/8000 [00:01<00:06, 1016.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▍                    | 1403/8000 [00:01<00:06, 1018.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████                     | 1569/8000 [00:01<00:06, 977.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▋                    | 1739/8000 [00:01<00:06, 966.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▏                   | 1908/8000 [00:01<00:06, 942.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▊                   | 2087/8000 [00:02<00:06, 925.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▎                  | 2269/8000 [00:02<00:06, 936.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                  | 2430/8000 [00:02<00:06, 903.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▍                 | 2608/8000 [00:02<00:06, 897.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████                 | 2781/8000 [00:02<00:05, 909.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2954/8000 [00:03<00:05, 922.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▏               | 3124/8000 [00:03<00:05, 901.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3297/8000 [00:03<00:05, 935.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▎              | 3473/8000 [00:03<00:04, 949.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▊              | 3645/8000 [00:03<00:04, 964.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▍             | 3826/8000 [00:04<00:04, 910.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4015/8000 [00:04<00:04, 898.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▋            | 4201/8000 [00:04<00:04, 858.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▏           | 4360/8000 [00:04<00:04, 809.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▋           | 4528/8000 [00:05<00:04, 773.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▏          | 4682/8000 [00:05<00:04, 753.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4849/8000 [00:05<00:04, 781.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▎         | 5029/8000 [00:05<00:03, 833.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▉         | 5196/8000 [00:05<00:03, 881.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▍        | 5362/8000 [00:05<00:03, 859.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▉        | 5535/8000 [00:06<00:02, 867.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▌       | 5697/8000 [00:06<00:02, 941.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████       | 5875/8000 [00:06<00:02, 930.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6055/8000 [00:06<00:02, 911.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▏     | 6221/8000 [00:06<00:01, 933.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▊     | 6396/8000 [00:07<00:01, 924.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▎    | 6573/8000 [00:07<00:01, 900.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|█████████████████████▉    | 6763/8000 [00:07<00:01, 950.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6945/8000 [00:07<00:01, 940.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▏  | 7120/8000 [00:07<00:00, 902.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▋  | 7293/8000 [00:08<00:00, 952.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▎ | 7469/8000 [00:08<00:00, 991.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▊ | 7648/8000 [00:08<00:00, 963.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▍| 7817/8000 [00:08<00:00, 937.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7992/8000 [00:08<00:00, 957.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8170it [00:08, 915.80it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:06<21:33,  6.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:07<11:22,  3.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:09<08:03,  2.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:10<06:30,  1.99s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:11<05:37,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:12<05:07,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:14<04:47,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:15<04:34,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:16<04:21,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:18<04:12,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:19<04:11,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:20<04:05,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:21<04:02,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:23<03:57,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:24<03:54,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:25<03:49,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:26<03:51,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:28<03:49,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:29<03:48,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:30<03:47,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:31<03:46,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:33<03:44,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:34<03:42,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:35<03:40,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:36<03:38,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:38<03:36,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:39<03:40,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:40<03:39,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:42<03:38,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:43<03:34,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:44<03:44,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:45<03:38,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:47<03:33,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:48<03:30,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:49<03:28,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:50<03:26,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:52<03:28,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:53<03:27,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:54<03:27,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:56<03:24,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [00:57<03:24,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [00:58<03:31,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:00<03:30,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:01<03:27,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:02<03:30,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:04<03:27,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:05<03:23,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:06<03:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:08<03:23,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:09<03:25,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:11<03:26,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:12<03:28,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:14<03:27,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:15<03:28,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:17<03:31,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:19<03:25,  1.43s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 18.92it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 44.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 91.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 112it [00:01, 138.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 152it [00:01, 189.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 189it [00:01, 227.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 226it [00:01, 259.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 263it [00:01, 285.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 299it [00:01, 300.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 335it [00:01, 309.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 377it [00:02, 336.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 414it [00:02, 343.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 451it [00:02, 347.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 488it [00:02, 349.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 525it [00:02, 354.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 562it [00:02, 350.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 598it [00:02, 343.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 635it [00:02, 349.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 671it [00:02, 340.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 708it [00:03, 346.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 746it [00:03, 355.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 243.25it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   7%|▎    | 7/100 [00:00<00:01, 60.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  14%|▌   | 14/100 [00:00<00:01, 48.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 47.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|█   | 25/100 [00:00<00:01, 45.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|█▏  | 30/100 [00:00<00:01, 46.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  36%|█▍  | 36/100 [00:00<00:01, 47.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  41%|█▋  | 41/100 [00:00<00:01, 44.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  46%|█▊  | 46/100 [00:00<00:01, 46.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  51%|██  | 51/100 [00:01<00:01, 45.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  56%|██▏ | 56/100 [00:01<00:00, 44.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  63%|██▌ | 63/100 [00:01<00:00, 48.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  68%|██▋ | 68/100 [00:01<00:00, 46.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  73%|██▉ | 73/100 [00:01<00:00, 46.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  78%|███ | 78/100 [00:01<00:00, 45.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|███▎| 83/100 [00:01<00:00, 44.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  90%|███▌| 90/100 [00:01<00:00, 49.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 46.95it/s]\n",
+      "\n",
+      "Nyquist Samples:  25%|████████▊                          | 1/4 [08:53<26:39, 533.16s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -5.0146951911536775\n",
+      "2999: -6.1850217\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<17:03,  5.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:38,  2.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:07<07:12,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:09<06:01,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:10<05:21,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<04:58,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:13<04:44,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:14<04:35,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:15<04:27,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:17<04:19,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:18<04:23,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:19<04:17,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:21<04:15,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:22<04:17,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:23<04:10,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:25<04:09,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:26<04:08,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:27<04:02,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:29<04:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:30<04:00,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:31<03:58,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:33<03:56,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:34<04:03,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:36<04:07,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:37<04:06,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:39<04:04,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:40<04:02,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:41<04:03,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:43<03:58,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:44<03:56,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:45<03:53,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:47<03:56,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:49<04:05,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:50<04:03,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:51<04:00,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:53<04:05,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:54<03:58,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:56<03:53,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [00:57<03:51,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [00:59<03:53,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [01:00<03:48,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [01:02<03:52,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:03<03:54,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:05<03:54,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:06<03:51,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:08<03:53,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:09<03:47,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:11<03:44,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:12<03:43,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:14<04:16,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:16<04:05,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:17<03:56,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:19<03:49,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:20<03:45,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:22<03:41,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:23<03:42,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:25<03:37,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:26<03:33,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:28<03:31,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:29<03:27,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:32<03:31,  1.52s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.59it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:00, 52.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 81it [00:01, 114.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 118it [00:01, 162.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 152it [00:01, 186.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 183it [00:01, 202.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 213it [00:01, 224.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 246it [00:01, 250.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 289it [00:01, 296.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 326it [00:01, 314.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 362it [00:01, 325.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 398it [00:02, 335.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 435it [00:02, 342.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 472it [00:02, 348.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 509it [00:02, 352.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 552it [00:02, 374.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 590it [00:02, 370.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 628it [00:02, 367.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 666it [00:02, 367.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 703it [00:02, 365.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 740it [00:02, 364.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 253.78it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|             | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎    | 5/100 [00:00<00:02, 43.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▍   | 10/100 [00:00<00:01, 45.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▌   | 15/100 [00:00<00:02, 41.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|▊   | 20/100 [00:00<00:01, 40.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|█   | 25/100 [00:00<00:01, 38.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|█▏  | 30/100 [00:00<00:01, 39.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  35%|█▍  | 35/100 [00:00<00:01, 40.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|█▌  | 40/100 [00:00<00:01, 41.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  47%|█▉  | 47/100 [00:01<00:01, 47.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  52%|██  | 52/100 [00:01<00:01, 46.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  57%|██▎ | 57/100 [00:01<00:00, 44.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  62%|██▍ | 62/100 [00:01<00:00, 43.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  67%|██▋ | 67/100 [00:01<00:00, 42.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  75%|███ | 75/100 [00:01<00:00, 51.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  81%|███▏| 81/100 [00:01<00:00, 48.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  87%|███▍| 87/100 [00:01<00:00, 48.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  92%|███▋| 92/100 [00:02<00:00, 45.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|███| 100/100 [00:02<00:00, 45.05it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                      | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                           | 45/8000 [00:00<00:30, 262.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                           | 97/8000 [00:00<00:27, 285.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                          | 136/8000 [00:00<00:31, 251.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                          | 185/8000 [00:00<00:30, 260.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                          | 244/8000 [00:00<00:26, 290.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                          | 307/8000 [00:01<00:23, 333.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                         | 360/8000 [00:01<00:22, 332.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                         | 408/8000 [00:01<00:21, 346.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▌                         | 458/8000 [00:01<00:22, 332.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                         | 506/8000 [00:01<00:22, 336.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                         | 558/8000 [00:01<00:22, 335.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██                         | 616/8000 [00:01<00:19, 378.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                        | 667/8000 [00:02<00:20, 355.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▍                        | 709/8000 [00:02<00:21, 342.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                        | 754/8000 [00:02<00:22, 321.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▋                        | 806/8000 [00:02<00:20, 352.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|██▉                        | 855/8000 [00:02<00:21, 335.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                        | 898/8000 [00:02<00:22, 322.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▏                       | 945/8000 [00:02<00:22, 317.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▎                       | 982/8000 [00:03<00:21, 319.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▎                      | 1031/8000 [00:03<00:21, 320.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▌                      | 1087/8000 [00:03<00:20, 333.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▋                      | 1132/8000 [00:03<00:21, 319.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|███▊                      | 1186/8000 [00:03<00:19, 353.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████                      | 1241/8000 [00:03<00:19, 352.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▏                     | 1285/8000 [00:03<00:19, 350.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▎                     | 1332/8000 [00:04<00:20, 331.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▌                     | 1385/8000 [00:04<00:18, 356.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▋                     | 1435/8000 [00:04<00:18, 346.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▊                     | 1479/8000 [00:04<00:19, 335.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|████▉                     | 1528/8000 [00:04<00:19, 327.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████                     | 1570/8000 [00:04<00:20, 311.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▏                    | 1615/8000 [00:04<00:21, 298.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▍                    | 1663/8000 [00:05<00:22, 281.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▌                    | 1695/8000 [00:05<00:26, 242.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▋                    | 1741/8000 [00:05<00:26, 239.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|█████▊                    | 1792/8000 [00:05<00:27, 224.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|█████▉                    | 1841/8000 [00:05<00:25, 237.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▏                   | 1889/8000 [00:06<00:24, 245.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▎                   | 1940/8000 [00:06<00:25, 242.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▍                   | 1992/8000 [00:06<00:22, 271.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▋                   | 2043/8000 [00:06<00:22, 265.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|██████▊                   | 2082/8000 [00:06<00:25, 234.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|██████▉                   | 2145/8000 [00:07<00:25, 228.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████                   | 2192/8000 [00:07<00:27, 209.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▎                  | 2247/8000 [00:07<00:26, 218.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▍                  | 2293/8000 [00:07<00:26, 219.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|███████▌                  | 2332/8000 [00:08<00:26, 217.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▊                  | 2388/8000 [00:08<00:22, 246.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|███████▉                  | 2438/8000 [00:08<00:21, 259.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████                  | 2478/8000 [00:08<00:21, 256.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▏                 | 2520/8000 [00:08<00:20, 269.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▎                 | 2565/8000 [00:08<00:20, 267.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▌                 | 2621/8000 [00:09<00:18, 296.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|████████▋                 | 2670/8000 [00:09<00:17, 309.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|████████▊                 | 2714/8000 [00:09<00:17, 310.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|████████▉                 | 2766/8000 [00:09<00:17, 307.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▏                | 2821/8000 [00:09<00:16, 309.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|█████████▎                | 2875/8000 [00:09<00:16, 302.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▌                | 2931/8000 [00:10<00:16, 310.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|█████████▋                | 2982/8000 [00:10<00:16, 311.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|█████████▊                | 3038/8000 [00:10<00:15, 330.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████                | 3092/8000 [00:10<00:14, 350.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▏               | 3140/8000 [00:10<00:15, 321.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▍               | 3194/8000 [00:10<00:14, 322.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|██████████▌               | 3240/8000 [00:11<00:14, 318.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|██████████▋               | 3286/8000 [00:11<00:15, 310.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▊               | 3335/8000 [00:11<00:15, 308.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|██████████▉               | 3384/8000 [00:11<00:14, 313.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▏              | 3431/8000 [00:11<00:14, 307.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▎              | 3475/8000 [00:11<00:15, 298.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|███████████▍              | 3516/8000 [00:11<00:15, 297.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▌              | 3573/8000 [00:12<00:14, 310.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|███████████▊              | 3624/8000 [00:12<00:13, 333.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|███████████▉              | 3672/8000 [00:12<00:13, 318.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████              | 3719/8000 [00:12<00:13, 311.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|████████████▏             | 3767/8000 [00:12<00:12, 327.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▍             | 3815/8000 [00:12<00:12, 344.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|████████████▌             | 3864/8000 [00:12<00:12, 323.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|████████████▊             | 3924/8000 [00:13<00:12, 329.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|████████████▉             | 3973/8000 [00:13<00:12, 325.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████             | 4016/8000 [00:13<00:13, 303.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|█████████████▏            | 4075/8000 [00:13<00:12, 327.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▍            | 4128/8000 [00:13<00:11, 332.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|█████████████▌            | 4185/8000 [00:13<00:11, 344.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|█████████████▊            | 4235/8000 [00:14<00:11, 338.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|█████████████▉            | 4292/8000 [00:14<00:10, 356.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|██████████████            | 4338/8000 [00:14<00:11, 328.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▎           | 4387/8000 [00:14<00:10, 340.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|██████████████▍           | 4437/8000 [00:14<00:10, 332.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|██████████████▌           | 4491/8000 [00:14<00:10, 346.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▊           | 4542/8000 [00:14<00:10, 342.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|██████████████▉           | 4588/8000 [00:15<00:09, 343.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|███████████████           | 4640/8000 [00:15<00:09, 344.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▏          | 4686/8000 [00:15<00:09, 335.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|███████████████▎          | 4728/8000 [00:15<00:10, 318.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▌          | 4771/8000 [00:15<00:10, 315.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|███████████████▋          | 4825/8000 [00:15<00:09, 325.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|███████████████▊          | 4879/8000 [00:16<00:09, 331.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████          | 4936/8000 [00:16<00:08, 346.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|████████████████▏         | 4979/8000 [00:16<00:09, 322.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▎         | 5031/8000 [00:16<00:09, 326.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|████████████████▍         | 5070/8000 [00:16<00:09, 299.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▌         | 5115/8000 [00:16<00:10, 288.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|████████████████▊         | 5155/8000 [00:16<00:10, 277.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|████████████████▉         | 5196/8000 [00:17<00:10, 270.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|█████████████████         | 5237/8000 [00:17<00:10, 264.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|█████████████████▏        | 5277/8000 [00:17<00:09, 275.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▎        | 5329/8000 [00:17<00:09, 294.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|█████████████████▍        | 5371/8000 [00:17<00:08, 295.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▌        | 5407/8000 [00:17<00:09, 274.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|█████████████████▋        | 5449/8000 [00:18<00:08, 285.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|█████████████████▉        | 5511/8000 [00:18<00:07, 320.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████        | 5560/8000 [00:18<00:07, 308.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|██████████████████▏       | 5610/8000 [00:18<00:07, 308.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|██████████████████▎       | 5652/8000 [00:18<00:07, 298.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▌       | 5721/8000 [00:19<00:16, 135.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|██████████████████▋       | 5760/8000 [00:19<00:14, 150.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|██████████████████▊       | 5806/8000 [00:19<00:12, 173.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|███████████████████       | 5849/8000 [00:20<00:11, 190.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▏      | 5896/8000 [00:20<00:09, 224.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|███████████████████▎      | 5942/8000 [00:20<00:08, 234.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▍      | 5982/8000 [00:20<00:08, 241.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|███████████████████▌      | 6031/8000 [00:20<00:07, 270.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▋      | 6066/8000 [00:20<00:06, 277.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|███████████████████▉      | 6118/8000 [00:20<00:06, 286.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|████████████████████      | 6160/8000 [00:21<00:06, 275.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▏     | 6226/8000 [00:21<00:05, 307.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|████████████████████▍     | 6275/8000 [00:21<00:05, 309.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|████████████████████▌     | 6319/8000 [00:21<00:05, 296.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▋     | 6375/8000 [00:21<00:05, 308.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|████████████████████▊     | 6413/8000 [00:21<00:05, 290.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████     | 6470/8000 [00:22<00:04, 309.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|█████████████████████▏    | 6518/8000 [00:22<00:04, 308.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|█████████████████████▎    | 6563/8000 [00:22<00:04, 312.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▍    | 6611/8000 [00:22<00:04, 341.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|█████████████████████▋    | 6670/8000 [00:22<00:03, 351.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▊    | 6706/8000 [00:22<00:04, 322.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|█████████████████████▉    | 6753/8000 [00:22<00:04, 311.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|██████████████████████▏   | 6809/8000 [00:23<00:03, 351.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▏   | 6846/8000 [00:23<00:03, 316.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|██████████████████████▍   | 6893/8000 [00:23<00:03, 320.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▌   | 6945/8000 [00:23<00:03, 323.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|██████████████████████▋   | 6988/8000 [00:23<00:03, 331.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|██████████████████████▉   | 7041/8000 [00:23<00:02, 320.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|███████████████████████   | 7080/8000 [00:24<00:03, 296.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|███████████████████████▏  | 7141/8000 [00:24<00:02, 341.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▎  | 7189/8000 [00:24<00:02, 322.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|███████████████████████▌  | 7232/8000 [00:24<00:02, 305.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▋  | 7276/8000 [00:24<00:02, 293.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|███████████████████████▊  | 7317/8000 [00:24<00:02, 281.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|███████████████████████▉  | 7366/8000 [00:24<00:02, 286.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████  | 7410/8000 [00:25<00:02, 288.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|████████████████████████▎ | 7468/8000 [00:25<00:01, 307.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|████████████████████████▍ | 7510/8000 [00:25<00:01, 307.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▌ | 7570/8000 [00:25<00:01, 326.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|████████████████████████▋ | 7611/8000 [00:25<00:01, 308.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|████████████████████████▉ | 7657/8000 [00:25<00:01, 317.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|█████████████████████████ | 7708/8000 [00:26<00:00, 340.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▏| 7751/8000 [00:26<00:00, 316.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|█████████████████████████▎| 7792/8000 [00:26<00:00, 303.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|█████████████████████████▍| 7840/8000 [00:26<00:00, 316.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▋| 7887/8000 [00:26<00:00, 337.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|█████████████████████████▊| 7935/8000 [00:26<00:00, 332.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|█████████████████████████▉| 7985/8000 [00:26<00:00, 328.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8034it [00:27, 297.17it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                        | 1/200 [00:05<17:26,  5.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                        | 2/200 [00:06<09:50,  2.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                        | 3/200 [00:08<07:21,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                        | 4/200 [00:09<06:05,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                        | 5/200 [00:10<05:23,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▏                                       | 6/200 [00:11<05:00,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                       | 7/200 [00:13<04:47,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                       | 8/200 [00:14<04:43,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▊                                       | 9/200 [00:16<04:37,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                      | 10/200 [00:17<04:29,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                     | 11/200 [00:19<04:33,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▍                                     | 12/200 [00:20<04:34,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                     | 13/200 [00:21<04:29,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▊                                     | 14/200 [00:23<04:30,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                     | 15/200 [00:24<04:30,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                    | 16/200 [00:26<04:27,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▍                                    | 17/200 [00:27<04:28,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▌                                    | 18/200 [00:29<04:23,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                    | 19/200 [00:30<04:17,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████                                    | 20/200 [00:31<04:15,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                   | 21/200 [00:33<04:17,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▍                                   | 22/200 [00:34<04:17,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                                   | 23/200 [00:36<04:20,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                   | 24/200 [00:37<04:16,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                   | 25/200 [00:39<04:19,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▏                                  | 26/200 [00:40<04:16,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▍                                  | 27/200 [00:42<04:17,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▌                                  | 28/200 [00:43<04:14,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▊                                  | 29/200 [00:45<04:14,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████                                  | 30/200 [00:46<04:15,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▏                                 | 31/200 [00:48<04:17,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▍                                 | 32/200 [00:50<04:21,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                 | 33/200 [00:51<04:15,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████▊                                 | 34/200 [00:53<04:15,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████                                 | 35/200 [00:54<04:12,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▏                                | 36/200 [00:56<04:15,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▍                                | 37/200 [00:57<04:17,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▌                                | 38/200 [00:59<04:14,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▊                                | 39/200 [01:00<04:08,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████                                | 40/200 [01:02<04:04,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                               | 41/200 [01:03<04:02,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▍                               | 42/200 [01:05<04:05,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▌                               | 43/200 [01:07<04:00,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████▊                               | 44/200 [01:08<03:57,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                               | 45/200 [01:09<03:49,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▏                              | 46/200 [01:11<03:54,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▍                              | 47/200 [01:13<03:55,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▌                              | 48/200 [01:14<03:52,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                              | 49/200 [01:16<03:50,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████                              | 50/200 [01:17<04:01,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▏                             | 51/200 [01:19<03:57,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▍                             | 52/200 [01:20<03:51,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▌                             | 53/200 [01:22<04:04,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|██████████▊                             | 54/200 [01:25<04:49,  1.98s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████                             | 55/200 [01:27<04:31,  1.87s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▏                            | 56/200 [01:28<04:19,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▍                            | 57/200 [01:30<04:12,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|███████████▌                            | 58/200 [01:32<04:05,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████▊                            | 59/200 [01:33<04:05,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████                            | 60/200 [01:35<03:59,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▏                           | 61/200 [01:37<03:52,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|████████████▍                           | 62/200 [01:39<03:58,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▌                           | 63/200 [01:40<03:53,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████▊                           | 64/200 [01:44<03:41,  1.63s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 18.79it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 27it [00:01, 28.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 60it [00:01, 68.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 86it [00:01, 98.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 119it [00:01, 140.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 153it [00:01, 181.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 183it [00:01, 191.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 210it [00:01, 207.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 248it [00:02, 249.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 284it [00:02, 277.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 321it [00:02, 300.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 358it [00:02, 318.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 395it [00:02, 330.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 430it [00:02, 331.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 472it [00:02, 356.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 509it [00:02, 355.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 546it [00:02, 354.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 583it [00:02, 356.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 620it [00:03, 358.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 657it [00:03, 346.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 693it [00:03, 347.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 731it [00:03, 355.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 223.45it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|               | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎      | 5/100 [00:00<00:02, 43.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▌     | 10/100 [00:00<00:02, 42.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  18%|█     | 18/100 [00:00<00:01, 55.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  24%|█▍    | 24/100 [00:00<00:01, 50.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|█▊    | 30/100 [00:00<00:01, 48.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  35%|██    | 35/100 [00:00<00:01, 46.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|██▍   | 40/100 [00:00<00:01, 45.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  47%|██▊   | 47/100 [00:00<00:01, 50.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  53%|███▏  | 53/100 [00:01<00:00, 47.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  58%|███▍  | 58/100 [00:01<00:00, 47.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  63%|███▊  | 63/100 [00:01<00:00, 45.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  68%|████  | 68/100 [00:01<00:00, 44.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  76%|████▌ | 76/100 [00:01<00:00, 52.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  82%|████▉ | 82/100 [00:01<00:00, 49.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  88%|█████▎| 88/100 [00:01<00:00, 48.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  93%|█████▌| 93/100 [00:01<00:00, 47.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|█████| 100/100 [00:02<00:00, 48.60it/s]\n",
+      "\n",
+      "Nyquist Samples:  50%|█████████████████▌                 | 2/4 [18:47<18:58, 569.30s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -4.5397086129072\n",
+      "2999: -6.1658687\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                          | 1/200 [00:05<18:21,  5.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                          | 2/200 [00:07<10:24,  3.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                          | 3/200 [00:08<07:39,  2.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                          | 4/200 [00:09<06:21,  1.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                          | 5/200 [00:11<05:39,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                         | 6/200 [00:12<05:10,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▌                                         | 7/200 [00:13<04:53,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                         | 8/200 [00:15<04:38,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                         | 9/200 [00:16<04:30,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                        | 10/200 [00:17<04:25,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                       | 11/200 [00:19<04:20,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                       | 12/200 [00:20<04:23,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                       | 13/200 [00:21<04:18,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▉                                       | 14/200 [00:23<04:20,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                      | 15/200 [00:24<04:26,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▎                                      | 16/200 [00:26<04:58,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                      | 17/200 [00:28<04:32,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▊                                      | 18/200 [00:29<04:15,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▉                                      | 19/200 [00:30<04:00,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                     | 20/200 [00:31<03:50,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▍                                     | 21/200 [00:32<03:45,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▌                                     | 22/200 [00:34<03:40,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                     | 23/200 [00:35<03:37,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                     | 24/200 [00:36<03:37,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▎                                    | 25/200 [00:37<03:35,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▍                                    | 26/200 [00:38<03:33,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▋                                    | 27/200 [00:40<03:32,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▉                                    | 28/200 [00:41<03:32,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████                                    | 29/200 [00:42<03:37,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████▎                                   | 30/200 [00:44<03:38,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                   | 31/200 [00:45<03:41,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▋                                   | 32/200 [00:46<03:42,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▉                                   | 33/200 [00:48<03:42,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|███████▏                                  | 34/200 [00:49<03:43,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▎                                  | 35/200 [00:50<03:42,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▌                                  | 36/200 [00:52<03:41,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▊                                  | 37/200 [00:53<03:41,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▉                                  | 38/200 [00:55<03:46,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                                 | 39/200 [00:56<03:40,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▍                                 | 40/200 [00:57<03:35,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▌                                 | 41/200 [00:59<03:31,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▊                                 | 42/200 [01:00<03:29,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                                 | 43/200 [01:01<03:29,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▏                                | 44/200 [01:02<03:26,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▍                                | 45/200 [01:04<03:23,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▋                                | 46/200 [01:05<03:20,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                                | 47/200 [01:06<03:21,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████                                | 48/200 [01:08<03:41,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████▎                               | 49/200 [01:10<03:45,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████▌                               | 50/200 [01:11<03:44,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▋                               | 51/200 [01:13<03:35,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▉                               | 52/200 [01:14<03:37,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|███████████▏                              | 53/200 [01:15<03:29,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|███████████▎                              | 54/200 [01:17<03:23,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▌                              | 55/200 [01:18<03:20,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▊                              | 56/200 [01:20<03:29,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▉                              | 57/200 [01:21<03:32,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|████████████▏                             | 58/200 [01:23<03:34,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▍                             | 59/200 [01:24<03:32,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▌                             | 60/200 [01:26<03:29,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▊                             | 61/200 [01:27<03:25,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|█████████████                             | 62/200 [01:29<03:28,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████▏                            | 63/200 [01:30<03:29,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████▍                            | 64/200 [01:32<03:26,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████▋                            | 65/200 [01:33<03:22,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████▊                            | 66/200 [01:35<03:18,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████                            | 67/200 [01:36<03:16,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████▎                           | 68/200 [01:38<03:08,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████▍                           | 69/200 [01:39<03:07,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████▋                           | 70/200 [01:40<03:05,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████▉                           | 71/200 [01:42<03:01,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████                           | 72/200 [01:43<02:58,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████▎                          | 73/200 [01:46<03:05,  1.46s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.75it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 40.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 83.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 112it [00:01, 128.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 154it [00:01, 181.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 191it [00:01, 219.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 228it [00:01, 252.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 278.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 300it [00:02, 277.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 337it [00:02, 299.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 374it [00:02, 315.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 412it [00:02, 332.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 448it [00:02, 340.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 484it [00:02, 345.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 520it [00:02, 342.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 556it [00:02, 343.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 593it [00:02, 348.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 630it [00:02, 353.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 666it [00:03, 352.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 702it [00:03, 347.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 738it [00:03, 351.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 229.91it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|               | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   6%|▍      | 6/100 [00:00<00:01, 59.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  12%|▋     | 12/100 [00:00<00:01, 48.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  17%|█     | 17/100 [00:00<00:01, 48.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  22%|█▎    | 22/100 [00:00<00:01, 46.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  27%|█▌    | 27/100 [00:00<00:01, 44.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  34%|██    | 34/100 [00:00<00:01, 50.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|██▍   | 40/100 [00:00<00:01, 47.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  45%|██▋   | 45/100 [00:00<00:01, 47.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  50%|███   | 50/100 [00:01<00:01, 45.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  55%|███▎  | 55/100 [00:01<00:01, 44.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  62%|███▋  | 62/100 [00:01<00:00, 51.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  68%|████  | 68/100 [00:01<00:00, 48.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  73%|████▍ | 73/100 [00:01<00:00, 47.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  78%|████▋ | 78/100 [00:01<00:00, 46.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|████▉ | 83/100 [00:01<00:00, 45.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  89%|█████▎| 89/100 [00:01<00:00, 48.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  94%|█████▋| 94/100 [00:01<00:00, 46.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|█████| 100/100 [00:02<00:00, 47.33it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                        | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                              | 22/8000 [00:00<01:04, 124.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                             | 43/8000 [00:00<01:09, 115.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                             | 73/8000 [00:00<00:55, 141.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                             | 91/8000 [00:00<01:03, 124.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                            | 115/8000 [00:00<00:58, 133.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                            | 149/8000 [00:01<01:11, 109.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                            | 170/8000 [00:01<01:10, 110.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                            | 190/8000 [00:01<01:08, 114.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                            | 215/8000 [00:01<01:03, 122.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                            | 231/8000 [00:01<01:11, 109.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                            | 253/8000 [00:02<01:09, 112.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                            | 274/8000 [00:02<01:08, 112.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                            | 295/8000 [00:02<01:14, 102.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                           | 313/8000 [00:02<01:14, 102.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 327/8000 [00:03<01:31, 84.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                            | 348/8000 [00:03<01:41, 75.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                            | 363/8000 [00:03<01:55, 66.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 378/8000 [00:03<01:57, 64.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 401/8000 [00:04<01:42, 74.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 420/8000 [00:04<01:38, 76.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 441/8000 [00:04<01:37, 77.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 464/8000 [00:04<01:30, 83.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 489/8000 [00:05<01:23, 90.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 509/8000 [00:05<01:23, 90.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                           | 536/8000 [00:05<01:11, 104.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 550/8000 [00:05<01:15, 98.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                           | 570/8000 [00:05<01:11, 103.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                          | 587/8000 [00:06<01:14, 100.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▏                          | 606/8000 [00:06<01:12, 101.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                          | 637/8000 [00:06<00:59, 124.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                          | 660/8000 [00:06<00:57, 126.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                          | 677/8000 [00:06<00:59, 122.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                          | 697/8000 [00:06<00:55, 131.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                          | 713/8000 [00:07<01:00, 120.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                          | 735/8000 [00:07<00:58, 123.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                          | 755/8000 [00:07<00:59, 122.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                          | 771/8000 [00:07<01:03, 113.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                          | 793/8000 [00:07<01:02, 115.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                          | 812/8000 [00:07<01:01, 115.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                          | 837/8000 [00:08<00:52, 135.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███                          | 856/8000 [00:08<00:57, 124.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                         | 870/8000 [00:08<01:04, 110.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                         | 888/8000 [00:08<01:07, 105.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 903/8000 [00:08<01:11, 99.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 916/8000 [00:08<01:15, 94.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                         | 940/8000 [00:09<01:06, 106.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                         | 967/8000 [00:09<00:57, 122.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                         | 986/8000 [00:09<00:58, 119.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                        | 1008/8000 [00:09<00:57, 121.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▌                        | 1033/8000 [00:09<00:50, 137.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                        | 1050/8000 [00:09<00:55, 124.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                        | 1067/8000 [00:10<00:59, 115.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                        | 1080/8000 [00:10<01:01, 112.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▊                        | 1096/8000 [00:10<01:01, 112.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                        | 1124/8000 [00:10<00:53, 129.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                        | 1140/8000 [00:10<00:59, 115.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                        | 1155/8000 [00:10<01:04, 105.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                        | 1168/8000 [00:11<01:11, 95.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                       | 1190/8000 [00:11<01:02, 108.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                       | 1212/8000 [00:11<00:56, 121.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                       | 1226/8000 [00:11<00:58, 115.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▎                       | 1245/8000 [00:11<00:58, 115.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1273/8000 [00:11<01:08, 98.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                       | 1294/8000 [00:12<01:04, 104.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                       | 1317/8000 [00:12<00:58, 114.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1339/8000 [00:12<01:07, 98.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                       | 1358/8000 [00:12<01:04, 102.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                       | 1376/8000 [00:12<01:04, 103.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                       | 1396/8000 [00:13<01:00, 109.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|████▉                       | 1418/8000 [00:13<00:57, 114.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████                       | 1446/8000 [00:13<00:52, 124.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                      | 1465/8000 [00:13<00:52, 124.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▏                      | 1495/8000 [00:13<00:44, 145.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▎                      | 1531/8000 [00:13<00:37, 170.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▍                      | 1567/8000 [00:14<00:34, 187.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▌                      | 1596/8000 [00:14<00:31, 205.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                      | 1629/8000 [00:14<00:32, 198.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▊                      | 1660/8000 [00:14<00:32, 197.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▉                      | 1692/8000 [00:14<00:31, 200.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████                      | 1731/8000 [00:14<00:30, 206.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▏                     | 1764/8000 [00:15<00:30, 206.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▎                     | 1805/8000 [00:15<00:28, 220.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▍                     | 1846/8000 [00:15<00:26, 228.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▌                     | 1883/8000 [00:15<00:28, 211.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▋                     | 1928/8000 [00:15<00:26, 233.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▉                     | 1973/8000 [00:15<00:24, 248.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|██████▉                     | 1999/8000 [00:15<00:24, 249.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████                     | 2032/8000 [00:16<00:26, 224.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▎                    | 2080/8000 [00:16<00:24, 238.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▍                    | 2121/8000 [00:16<00:25, 233.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▌                    | 2157/8000 [00:16<00:26, 223.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▋                    | 2199/8000 [00:16<00:24, 233.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▊                    | 2236/8000 [00:17<00:24, 235.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▉                    | 2273/8000 [00:17<00:24, 237.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████                    | 2309/8000 [00:17<00:24, 229.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▏                   | 2341/8000 [00:17<00:25, 225.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▎                   | 2371/8000 [00:17<00:26, 215.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▍                   | 2417/8000 [00:17<00:23, 241.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▌                   | 2457/8000 [00:17<00:22, 247.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▊                   | 2500/8000 [00:18<00:22, 248.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|████████▊                   | 2532/8000 [00:18<00:24, 221.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████                   | 2574/8000 [00:18<00:25, 213.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▏                  | 2615/8000 [00:18<00:24, 223.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▎                  | 2643/8000 [00:18<00:25, 207.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▍                  | 2683/8000 [00:19<00:23, 222.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▍                  | 2711/8000 [00:19<00:25, 205.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▋                  | 2754/8000 [00:19<00:23, 227.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▊                  | 2798/8000 [00:19<00:21, 241.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|█████████▉                  | 2838/8000 [00:19<00:21, 243.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████                  | 2874/8000 [00:19<00:21, 235.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▏                 | 2904/8000 [00:19<00:23, 221.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▎                 | 2932/8000 [00:20<00:24, 210.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▍                 | 2974/8000 [00:20<00:22, 222.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▌                 | 3006/8000 [00:20<00:23, 209.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▋                 | 3046/8000 [00:20<00:22, 220.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▊                 | 3084/8000 [00:20<00:21, 225.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|██████████▉                 | 3126/8000 [00:20<00:20, 233.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████                 | 3163/8000 [00:21<00:20, 235.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▏                | 3201/8000 [00:21<00:20, 235.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▎                | 3239/8000 [00:21<00:23, 202.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▍                | 3264/8000 [00:21<00:23, 205.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▌                | 3287/8000 [00:21<00:22, 205.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▋                | 3328/8000 [00:21<00:22, 206.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▊                | 3360/8000 [00:22<00:24, 186.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|███████████▉                | 3400/8000 [00:22<00:23, 197.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|███████████▉                | 3426/8000 [00:22<00:26, 174.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████                | 3459/8000 [00:22<00:26, 172.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▎               | 3504/8000 [00:22<00:23, 187.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▍               | 3549/8000 [00:23<00:22, 194.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▌               | 3588/8000 [00:23<00:23, 190.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▋               | 3613/8000 [00:23<00:22, 197.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▊               | 3652/8000 [00:23<00:23, 185.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|████████████▉               | 3690/8000 [00:23<00:22, 193.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████               | 3727/8000 [00:24<00:26, 163.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▏              | 3765/8000 [00:24<00:24, 175.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▎              | 3800/8000 [00:24<00:22, 183.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▍              | 3835/8000 [00:24<00:21, 190.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▌              | 3884/8000 [00:24<00:19, 207.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|█████████████▊              | 3931/8000 [00:25<00:18, 224.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|█████████████▉              | 3972/8000 [00:25<00:17, 226.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████              | 4008/8000 [00:25<00:18, 221.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▏             | 4046/8000 [00:25<00:17, 220.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▎             | 4092/8000 [00:25<00:17, 225.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▍             | 4134/8000 [00:25<00:16, 236.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▌             | 4172/8000 [00:26<00:16, 233.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▋             | 4203/8000 [00:26<00:18, 206.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▊             | 4238/8000 [00:26<00:19, 195.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|██████████████▉             | 4266/8000 [00:26<00:18, 205.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████             | 4292/8000 [00:26<00:17, 215.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▏            | 4325/8000 [00:26<00:17, 207.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▎            | 4363/8000 [00:27<00:17, 206.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▎            | 4392/8000 [00:27<00:16, 220.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▍            | 4427/8000 [00:27<00:18, 195.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▌            | 4463/8000 [00:27<00:18, 195.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|███████████████▋            | 4496/8000 [00:27<00:18, 186.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|███████████████▊            | 4533/8000 [00:28<00:17, 194.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████            | 4577/8000 [00:28<00:16, 205.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▏           | 4619/8000 [00:28<00:16, 199.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▎           | 4650/8000 [00:28<00:17, 192.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████▍           | 4684/8000 [00:28<00:17, 189.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████▍           | 4713/8000 [00:28<00:18, 178.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▋           | 4760/8000 [00:29<00:16, 202.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▊           | 4800/8000 [00:29<00:15, 202.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|████████████████▉           | 4836/8000 [00:29<00:16, 197.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████           | 4873/8000 [00:29<00:16, 193.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▏          | 4908/8000 [00:29<00:16, 184.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▎          | 4954/8000 [00:30<00:14, 206.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▍          | 4998/8000 [00:30<00:13, 223.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████▋          | 5041/8000 [00:30<00:13, 224.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|█████████████████▊          | 5074/8000 [00:30<00:14, 208.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|█████████████████▉          | 5117/8000 [00:30<00:12, 224.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████          | 5153/8000 [00:31<00:15, 180.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▏         | 5186/8000 [00:31<00:14, 187.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▎         | 5215/8000 [00:31<00:14, 188.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████▍         | 5259/8000 [00:31<00:12, 211.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|██████████████████▌         | 5300/8000 [00:31<00:11, 228.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▋         | 5329/8000 [00:31<00:13, 201.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|██████████████████▊         | 5374/8000 [00:32<00:11, 225.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|██████████████████▉         | 5420/8000 [00:32<00:10, 241.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████         | 5460/8000 [00:32<00:10, 242.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▏        | 5494/8000 [00:32<00:10, 236.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▎        | 5530/8000 [00:32<00:10, 231.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████▍        | 5566/8000 [00:32<00:11, 211.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████▌        | 5598/8000 [00:33<00:11, 206.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|███████████████████▋        | 5630/8000 [00:33<00:11, 208.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▊        | 5674/8000 [00:33<00:09, 239.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|███████████████████▉        | 5713/8000 [00:33<00:08, 256.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▏       | 5752/8000 [00:33<00:08, 261.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▎       | 5794/8000 [00:33<00:08, 252.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████▍       | 5837/8000 [00:34<00:08, 253.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|████████████████████▌       | 5873/8000 [00:34<00:08, 247.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████▋       | 5906/8000 [00:34<00:09, 229.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|████████████████████▊       | 5950/8000 [00:34<00:08, 248.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|████████████████████▉       | 5990/8000 [00:34<00:08, 245.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████       | 6029/8000 [00:34<00:07, 251.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▏      | 6065/8000 [00:34<00:08, 239.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▎      | 6092/8000 [00:35<00:08, 227.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|█████████████████████▍      | 6138/8000 [00:35<00:07, 254.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|█████████████████████▌      | 6168/8000 [00:35<00:07, 237.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▋      | 6211/8000 [00:35<00:07, 243.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|█████████████████████▊      | 6242/8000 [00:35<00:07, 242.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|█████████████████████▉      | 6283/8000 [00:35<00:07, 227.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████      | 6311/8000 [00:36<00:07, 212.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▏     | 6348/8000 [00:36<00:07, 218.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|██████████████████████▎     | 6379/8000 [00:36<00:07, 204.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|██████████████████████▍     | 6418/8000 [00:36<00:07, 212.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|██████████████████████▌     | 6453/8000 [00:36<00:07, 215.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|██████████████████████▊     | 6502/8000 [00:36<00:06, 241.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▊     | 6534/8000 [00:37<00:06, 229.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|██████████████████████▉     | 6562/8000 [00:37<00:06, 211.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████     | 6601/8000 [00:37<00:06, 213.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████▏    | 6626/8000 [00:37<00:07, 195.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████▎    | 6674/8000 [00:37<00:06, 217.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|███████████████████████▌    | 6715/8000 [00:37<00:05, 223.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|███████████████████████▋    | 6753/8000 [00:38<00:05, 225.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████▊    | 6799/8000 [00:38<00:05, 236.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|███████████████████████▉    | 6834/8000 [00:38<00:05, 231.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████    | 6872/8000 [00:38<00:05, 225.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▏   | 6895/8000 [00:38<00:04, 225.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|████████████████████████▎   | 6929/8000 [00:38<00:04, 217.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|████████████████████████▎   | 6960/8000 [00:39<00:05, 191.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|████████████████████████▍   | 6997/8000 [00:39<00:05, 198.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|████████████████████████▋   | 7040/8000 [00:39<00:04, 213.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|████████████████████████▊   | 7079/8000 [00:39<00:04, 219.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|████████████████████████▉   | 7118/8000 [00:39<00:03, 224.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████   | 7156/8000 [00:39<00:03, 223.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|█████████████████████████▏  | 7193/8000 [00:40<00:03, 224.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|█████████████████████████▎  | 7224/8000 [00:40<00:03, 213.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|█████████████████████████▍  | 7264/8000 [00:40<00:03, 215.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|█████████████████████████▌  | 7306/8000 [00:40<00:03, 225.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|█████████████████████████▋  | 7342/8000 [00:40<00:03, 213.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|█████████████████████████▊  | 7373/8000 [00:40<00:03, 206.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|█████████████████████████▉  | 7410/8000 [00:41<00:02, 210.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████  | 7445/8000 [00:41<00:02, 204.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|██████████████████████████▏ | 7481/8000 [00:41<00:02, 192.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|██████████████████████████▎ | 7527/8000 [00:41<00:02, 218.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|██████████████████████████▍ | 7563/8000 [00:41<00:02, 212.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|██████████████████████████▌ | 7594/8000 [00:42<00:02, 202.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|██████████████████████████▋ | 7628/8000 [00:42<00:01, 203.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████▊ | 7673/8000 [00:42<00:01, 218.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|██████████████████████████▉ | 7712/8000 [00:42<00:01, 220.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|███████████████████████████▏| 7752/8000 [00:42<00:01, 227.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|███████████████████████████▎| 7790/8000 [00:42<00:00, 225.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|███████████████████████████▍| 7828/8000 [00:43<00:00, 216.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|███████████████████████████▌| 7873/8000 [00:43<00:00, 230.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|███████████████████████████▋| 7908/8000 [00:43<00:00, 223.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|███████████████████████████▊| 7943/8000 [00:43<00:00, 213.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|███████████████████████████▉| 7972/8000 [00:43<00:00, 201.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8018it [00:43, 182.41it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                          | 1/200 [00:05<18:39,  5.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                          | 2/200 [00:06<09:53,  3.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                          | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                          | 4/200 [00:09<05:51,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                          | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                         | 6/200 [00:11<04:33,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▌                                         | 7/200 [00:12<04:06,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                         | 8/200 [00:13<03:57,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                         | 9/200 [00:15<04:25,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                        | 10/200 [00:16<04:41,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                       | 11/200 [00:17<04:08,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                       | 12/200 [00:18<03:48,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                       | 13/200 [00:19<03:35,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▉                                       | 14/200 [00:20<03:28,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                      | 15/200 [00:22<03:23,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▎                                      | 16/200 [00:22<03:13,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                      | 17/200 [00:23<03:05,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▊                                      | 18/200 [00:24<03:03,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▉                                      | 19/200 [00:25<03:00,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                     | 20/200 [00:26<02:55,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▍                                     | 21/200 [00:27<02:55,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▌                                     | 22/200 [00:28<02:56,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                     | 23/200 [00:29<02:55,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                     | 24/200 [00:30<03:00,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▎                                    | 25/200 [00:31<03:02,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▍                                    | 26/200 [00:32<02:58,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▋                                    | 27/200 [00:33<02:55,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▉                                    | 28/200 [00:34<02:51,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████                                    | 29/200 [00:35<02:47,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████▎                                   | 30/200 [00:36<02:45,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                   | 31/200 [00:37<02:45,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▋                                   | 32/200 [00:38<02:43,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▉                                   | 33/200 [00:39<02:45,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|███████▏                                  | 34/200 [00:40<02:41,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▎                                  | 35/200 [00:41<02:39,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▌                                  | 36/200 [00:42<02:44,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▊                                  | 37/200 [00:43<02:46,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▉                                  | 38/200 [00:44<02:47,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                                 | 39/200 [00:45<02:46,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▍                                 | 40/200 [00:46<02:45,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▌                                 | 41/200 [00:47<02:41,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▊                                 | 42/200 [00:48<02:37,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                                 | 43/200 [00:49<02:33,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▏                                | 44/200 [00:50<02:34,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▍                                | 45/200 [00:51<02:30,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▋                                | 46/200 [00:52<02:28,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                                | 47/200 [00:53<02:32,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████                                | 48/200 [00:54<02:33,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████▎                               | 49/200 [00:55<02:19,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████▌                               | 50/200 [00:56<02:17,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▋                               | 51/200 [00:57<02:17,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▉                               | 52/200 [00:58<02:10,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|███████████▏                              | 53/200 [00:58<02:03,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|███████████▎                              | 54/200 [00:59<01:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▌                              | 55/200 [01:00<01:58,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▊                              | 56/200 [01:01<01:55,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▉                              | 57/200 [01:02<01:54,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|████████████▏                             | 58/200 [01:03<02:35,  1.10s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 22.85it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 63it [00:01, 79.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 135it [00:01, 177.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 203it [00:01, 266.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 272it [00:01, 353.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 343it [00:01, 432.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 421it [00:01, 515.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 492it [00:01, 564.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 565it [00:01, 608.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 641it [00:01, 649.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 714it [00:01, 664.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:02, 378.47it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|               | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|█▌   | 30/100 [00:00<00:00, 293.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  61%|███  | 61/100 [00:00<00:00, 302.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|████| 100/100 [00:00<00:00, 276.05it/s]\n",
+      "\n",
+      "Nyquist Samples:  75%|██████████████████████████▎        | 3/4 [29:38<10:06, 606.47s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initial loss -3.799317079632776\n",
+      "2999: -5.7792895\r"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                          | 1/200 [00:05<19:34,  5.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                          | 2/200 [00:07<11:14,  3.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                          | 3/200 [00:09<08:26,  2.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                          | 4/200 [00:10<07:03,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                          | 5/200 [00:12<06:20,  1.95s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                         | 6/200 [00:13<05:56,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▌                                         | 7/200 [00:15<05:39,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                         | 8/200 [00:17<05:33,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                         | 9/200 [00:18<05:30,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                        | 10/200 [00:20<05:17,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                       | 11/200 [00:21<05:11,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                       | 12/200 [00:23<05:09,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                       | 13/200 [00:25<05:05,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▉                                       | 14/200 [00:26<05:02,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                      | 15/200 [00:28<04:55,  1.60s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▎                                      | 16/200 [00:30<05:01,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                      | 17/200 [00:31<05:03,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▊                                      | 18/200 [00:33<05:08,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▉                                      | 19/200 [00:35<05:04,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                     | 20/200 [00:37<05:06,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▍                                     | 21/200 [00:38<05:05,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▌                                     | 22/200 [00:40<04:59,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                     | 23/200 [00:42<05:00,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                     | 24/200 [00:43<05:05,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▎                                    | 25/200 [00:45<05:09,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▍                                    | 26/200 [00:47<05:09,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▋                                    | 27/200 [00:49<05:06,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▉                                    | 28/200 [00:51<05:04,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████                                    | 29/200 [00:52<05:01,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████▎                                   | 30/200 [00:54<05:01,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                   | 31/200 [00:56<04:59,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▋                                   | 32/200 [00:58<04:51,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▉                                   | 33/200 [00:59<04:48,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|███████▏                                  | 34/200 [01:01<04:47,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▎                                  | 35/200 [01:03<04:48,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▌                                  | 36/200 [01:05<04:49,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▊                                  | 37/200 [01:06<04:47,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▉                                  | 38/200 [01:08<04:45,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                                 | 39/200 [01:10<04:49,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▍                                 | 40/200 [01:12<04:55,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▌                                 | 41/200 [01:14<04:52,  1.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▊                                 | 42/200 [01:16<04:47,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                                 | 43/200 [01:18<04:52,  1.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▏                                | 44/200 [01:20<05:27,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▍                                | 45/200 [01:22<05:09,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▋                                | 46/200 [01:24<04:57,  1.93s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                                | 47/200 [01:26<04:49,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████                                | 48/200 [01:27<04:41,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████▎                               | 49/200 [01:29<04:36,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████▌                               | 50/200 [01:31<04:32,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▋                               | 51/200 [01:33<04:28,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|██████████▉                               | 52/200 [01:34<04:29,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|███████████▏                              | 53/200 [01:36<04:28,  1.83s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|███████████▎                              | 54/200 [01:38<04:25,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▌                              | 55/200 [01:40<04:19,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▊                              | 56/200 [01:42<04:17,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|███████████▉                              | 57/200 [01:43<04:14,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|████████████▏                             | 58/200 [01:45<04:14,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▍                             | 59/200 [01:47<04:13,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▌                             | 60/200 [01:49<04:10,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|████████████▊                             | 61/200 [01:51<04:09,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|█████████████                             | 62/200 [01:52<04:05,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████▏                            | 63/200 [01:54<04:00,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|█████████████▍                            | 64/200 [01:58<04:11,  1.85s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 16.82it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 40it [00:01, 49.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 77it [00:01, 98.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 114it [00:01, 145.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 155it [00:01, 199.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 192it [00:01, 236.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 228it [00:01, 263.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 264it [00:01, 287.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 303it [00:01, 313.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 340it [00:01, 326.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 377it [00:02, 336.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 417it [00:02, 352.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 455it [00:02, 353.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 492it [00:02, 346.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 528it [00:02, 350.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 565it [00:02, 353.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 601it [00:02, 351.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 638it [00:02, 354.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 682it [00:02, 378.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 721it [00:02, 373.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 759it [00:03, 370.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 252.82it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|               | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎      | 5/100 [00:00<00:02, 43.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  11%|▋     | 11/100 [00:00<00:01, 48.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  16%|▉     | 16/100 [00:00<00:01, 45.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  21%|█▎    | 21/100 [00:00<00:01, 44.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  26%|█▌    | 26/100 [00:00<00:01, 43.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  31%|█▊    | 31/100 [00:00<00:01, 43.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  37%|██▏   | 37/100 [00:00<00:01, 46.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  42%|██▌   | 42/100 [00:00<00:01, 45.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  47%|██▊   | 47/100 [00:01<00:01, 44.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  52%|███   | 52/100 [00:01<00:01, 45.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  57%|███▍  | 57/100 [00:01<00:00, 44.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  63%|███▊  | 63/100 [00:01<00:00, 47.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  68%|████  | 68/100 [00:01<00:00, 46.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  73%|████▍ | 73/100 [00:01<00:00, 45.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  78%|████▋ | 78/100 [00:01<00:00, 45.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  83%|████▉ | 83/100 [00:01<00:00, 44.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  88%|█████▎| 88/100 [00:01<00:00, 45.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  93%|█████▌| 93/100 [00:02<00:00, 46.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|█████| 100/100 [00:02<00:00, 45.22it/s]\n",
+      "\n",
+      "\n",
+      "Generating signals:   0%|                                        | 0/8000 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                | 3/8000 [00:00<08:14, 16.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                | 6/8000 [00:00<07:21, 18.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                                | 9/8000 [00:00<09:43, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                               | 11/8000 [00:00<09:58, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                               | 15/8000 [00:00<07:34, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                               | 17/8000 [00:01<08:13, 16.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                               | 20/8000 [00:01<07:11, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                               | 22/8000 [00:01<11:36, 11.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                               | 28/8000 [00:02<11:02, 12.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|                               | 32/8000 [00:02<10:39, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                              | 35/8000 [00:02<09:51, 13.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                              | 37/8000 [00:02<10:27, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   0%|▏                              | 40/8000 [00:02<09:25, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                              | 44/8000 [00:03<07:58, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                              | 47/8000 [00:03<07:50, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                              | 50/8000 [00:03<07:36, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                              | 52/8000 [00:03<08:06, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                              | 57/8000 [00:03<06:30, 20.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▏                              | 64/8000 [00:04<07:54, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 66/8000 [00:04<08:14, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 71/8000 [00:04<06:43, 19.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 74/8000 [00:04<08:22, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 77/8000 [00:04<07:55, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 79/8000 [00:05<08:23, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 82/8000 [00:05<09:54, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 84/8000 [00:05<10:11, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 87/8000 [00:05<11:26, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 90/8000 [00:06<10:01, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▎                              | 92/8000 [00:06<10:15, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                              | 98/8000 [00:06<06:54, 19.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                             | 101/8000 [00:06<08:38, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                             | 105/8000 [00:06<07:31, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                             | 108/8000 [00:06<07:01, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                             | 112/8000 [00:07<06:25, 20.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   1%|▍                             | 118/8000 [00:07<06:26, 20.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                             | 126/8000 [00:07<04:55, 26.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▍                             | 129/8000 [00:07<06:49, 19.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                             | 134/8000 [00:08<07:21, 17.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                             | 140/8000 [00:08<05:51, 22.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                             | 143/8000 [00:08<06:58, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                             | 148/8000 [00:09<07:16, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                             | 152/8000 [00:09<06:49, 19.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                             | 157/8000 [00:09<05:37, 23.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▌                             | 162/8000 [00:09<06:38, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 168/8000 [00:09<06:41, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 172/8000 [00:10<07:42, 16.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 177/8000 [00:10<06:37, 19.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 181/8000 [00:10<06:17, 20.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 186/8000 [00:10<05:35, 23.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 190/8000 [00:10<05:28, 23.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 194/8000 [00:11<05:05, 25.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   2%|▋                             | 198/8000 [00:11<06:37, 19.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 201/8000 [00:11<06:25, 20.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 205/8000 [00:11<05:36, 23.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 208/8000 [00:11<05:59, 21.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 212/8000 [00:12<08:28, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 214/8000 [00:12<08:50, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 216/8000 [00:12<09:14, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 221/8000 [00:12<06:43, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 225/8000 [00:12<06:21, 20.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▊                             | 231/8000 [00:12<05:10, 25.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 237/8000 [00:13<05:54, 21.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 243/8000 [00:13<06:14, 20.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 246/8000 [00:13<06:23, 20.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 250/8000 [00:13<05:46, 22.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 254/8000 [00:14<08:13, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 257/8000 [00:14<09:09, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 259/8000 [00:14<09:20, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 261/8000 [00:15<10:54, 11.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|▉                             | 265/8000 [00:15<08:47, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                             | 267/8000 [00:15<09:16, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                             | 269/8000 [00:15<14:01,  9.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   3%|█                             | 276/8000 [00:16<08:07, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 280/8000 [00:16<07:14, 17.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 283/8000 [00:16<09:07, 14.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 286/8000 [00:16<08:48, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 288/8000 [00:16<09:13, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 291/8000 [00:17<08:35, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 294/8000 [00:17<08:12, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 297/8000 [00:17<07:31, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█                             | 299/8000 [00:17<10:10, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 302/8000 [00:17<10:38, 12.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 306/8000 [00:18<08:42, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 308/8000 [00:18<09:05, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 310/8000 [00:18<11:47, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 312/8000 [00:18<11:26, 11.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 316/8000 [00:19<13:00,  9.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 321/8000 [00:19<09:04, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 323/8000 [00:19<09:27, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 329/8000 [00:19<06:19, 20.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▏                            | 333/8000 [00:19<05:52, 21.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                            | 336/8000 [00:20<05:56, 21.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                            | 340/8000 [00:20<08:18, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                            | 345/8000 [00:20<06:47, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                            | 351/8000 [00:20<06:24, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                            | 355/8000 [00:21<06:00, 21.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   4%|█▎                            | 359/8000 [00:21<05:43, 22.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                            | 363/8000 [00:21<05:27, 23.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▎                            | 366/8000 [00:21<06:47, 18.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 369/8000 [00:21<06:45, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 375/8000 [00:22<07:42, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 378/8000 [00:22<08:52, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 380/8000 [00:22<10:44, 11.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 386/8000 [00:22<07:29, 16.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 390/8000 [00:23<07:37, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 392/8000 [00:23<08:10, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▍                            | 397/8000 [00:23<06:43, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 401/8000 [00:23<09:08, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 404/8000 [00:24<08:28, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 409/8000 [00:24<08:25, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 412/8000 [00:24<08:05, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 415/8000 [00:24<07:48, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 418/8000 [00:24<07:05, 17.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 421/8000 [00:25<08:29, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 425/8000 [00:25<08:39, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 430/8000 [00:25<07:01, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▌                            | 433/8000 [00:25<07:06, 17.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   5%|█▋                            | 438/8000 [00:26<07:24, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 440/8000 [00:26<07:52, 16.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 443/8000 [00:26<09:24, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 446/8000 [00:26<08:50, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 449/8000 [00:26<08:01, 15.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 454/8000 [00:27<07:35, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 456/8000 [00:27<08:08, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 458/8000 [00:27<08:37, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 460/8000 [00:27<08:26, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 462/8000 [00:27<08:17, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▋                            | 466/8000 [00:27<06:53, 18.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 469/8000 [00:28<06:48, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 472/8000 [00:28<06:22, 19.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 474/8000 [00:28<07:08, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 480/8000 [00:28<05:05, 24.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 485/8000 [00:28<05:45, 21.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 489/8000 [00:29<07:05, 17.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 491/8000 [00:29<09:21, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 493/8000 [00:29<09:29, 13.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 496/8000 [00:29<08:45, 14.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▊                            | 499/8000 [00:29<08:05, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 503/8000 [00:30<08:38, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 505/8000 [00:30<08:59, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 507/8000 [00:30<09:05, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 509/8000 [00:30<12:05, 10.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 513/8000 [00:31<09:07, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 515/8000 [00:31<09:09, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   6%|█▉                            | 517/8000 [00:31<09:31, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                            | 523/8000 [00:31<06:16, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                            | 526/8000 [00:31<07:35, 16.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|█▉                            | 530/8000 [00:32<06:40, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 536/8000 [00:32<06:22, 19.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 539/8000 [00:32<07:35, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 541/8000 [00:32<07:58, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 543/8000 [00:32<08:01, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 545/8000 [00:32<07:59, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 549/8000 [00:33<06:44, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 551/8000 [00:33<07:27, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 554/8000 [00:33<06:42, 18.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 556/8000 [00:33<07:07, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 559/8000 [00:33<06:46, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 561/8000 [00:33<07:31, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 563/8000 [00:34<15:14,  8.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██                            | 566/8000 [00:34<14:54,  8.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                           | 571/8000 [00:34<09:56, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                           | 576/8000 [00:35<07:31, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                           | 579/8000 [00:35<06:49, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                           | 583/8000 [00:35<06:19, 19.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                           | 586/8000 [00:35<06:21, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                           | 590/8000 [00:35<07:44, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   7%|██▏                           | 595/8000 [00:36<06:24, 19.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 601/8000 [00:36<07:30, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 603/8000 [00:36<07:45, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 607/8000 [00:36<06:53, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 610/8000 [00:37<07:55, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 613/8000 [00:37<09:07, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 617/8000 [00:37<07:22, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 621/8000 [00:38<10:41, 11.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 623/8000 [00:38<10:31, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 625/8000 [00:38<10:28, 11.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 627/8000 [00:38<10:20, 11.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 629/8000 [00:38<10:09, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▎                           | 633/8000 [00:38<07:58, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 635/8000 [00:39<08:38, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 637/8000 [00:39<11:16, 10.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 640/8000 [00:39<09:52, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 644/8000 [00:39<08:01, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 646/8000 [00:39<08:50, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 650/8000 [00:40<07:18, 16.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 652/8000 [00:40<07:50, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 656/8000 [00:40<06:29, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 659/8000 [00:40<08:24, 14.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▍                           | 663/8000 [00:40<07:23, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▌                           | 670/8000 [00:41<05:17, 23.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▌                           | 673/8000 [00:41<05:29, 22.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   8%|██▌                           | 676/8000 [00:41<05:48, 21.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                           | 683/8000 [00:41<06:22, 19.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                           | 686/8000 [00:41<06:24, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                           | 690/8000 [00:42<06:59, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                           | 692/8000 [00:42<07:18, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                           | 695/8000 [00:42<07:02, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▌                           | 699/8000 [00:42<07:39, 15.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                           | 704/8000 [00:43<06:18, 19.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                           | 710/8000 [00:43<05:03, 23.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                           | 714/8000 [00:43<06:17, 19.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                           | 719/8000 [00:43<06:55, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                           | 725/8000 [00:44<05:40, 21.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▋                           | 729/8000 [00:44<05:25, 22.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▊                           | 734/8000 [00:44<05:32, 21.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▊                           | 738/8000 [00:44<06:28, 18.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▊                           | 745/8000 [00:45<06:59, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▊                           | 748/8000 [00:45<06:53, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▊                           | 752/8000 [00:45<06:02, 19.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▊                           | 755/8000 [00:45<07:21, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:   9%|██▊                           | 759/8000 [00:45<06:31, 18.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▊                           | 764/8000 [00:46<06:23, 18.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 767/8000 [00:46<06:24, 18.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 771/8000 [00:46<06:04, 19.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 774/8000 [00:46<06:08, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 781/8000 [00:47<07:14, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 784/8000 [00:47<09:39, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 787/8000 [00:47<08:39, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 790/8000 [00:47<07:45, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 794/8000 [00:48<09:31, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 796/8000 [00:48<09:01, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|██▉                           | 798/8000 [00:48<09:05, 13.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 800/8000 [00:48<09:09, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 804/8000 [00:48<08:35, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 808/8000 [00:49<07:20, 16.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 812/8000 [00:49<06:39, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 814/8000 [00:49<07:12, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 816/8000 [00:49<07:59, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 819/8000 [00:49<07:27, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 821/8000 [00:50<08:01, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 823/8000 [00:50<07:58, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 826/8000 [00:50<09:16, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███                           | 830/8000 [00:50<07:32, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▏                          | 834/8000 [00:50<06:09, 19.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  10%|███▏                          | 839/8000 [00:51<06:44, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 844/8000 [00:51<07:06, 16.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 846/8000 [00:51<09:20, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 848/8000 [00:51<09:15, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 851/8000 [00:52<08:22, 14.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 855/8000 [00:52<08:34, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 858/8000 [00:52<07:38, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 861/8000 [00:52<07:16, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▏                          | 864/8000 [00:52<07:08, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 867/8000 [00:52<06:55, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 869/8000 [00:53<07:45, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 873/8000 [00:53<06:48, 17.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 875/8000 [00:53<07:16, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 880/8000 [00:53<07:10, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 882/8000 [00:53<07:46, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 885/8000 [00:54<07:40, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 889/8000 [00:54<06:49, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 894/8000 [00:54<06:57, 17.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▎                          | 898/8000 [00:54<05:56, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 902/8000 [00:54<05:35, 21.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 905/8000 [00:55<07:16, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 908/8000 [00:55<08:32, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 912/8000 [00:55<09:03, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 914/8000 [00:56<10:56, 10.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  11%|███▍                          | 916/8000 [00:56<12:08,  9.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                          | 921/8000 [00:56<08:27, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                          | 925/8000 [00:56<07:21, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                          | 929/8000 [00:56<06:35, 17.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▍                          | 932/8000 [00:57<06:30, 18.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 935/8000 [00:57<06:29, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 937/8000 [00:57<08:41, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 941/8000 [00:57<07:13, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 944/8000 [00:58<08:32, 13.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 946/8000 [00:58<08:17, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 954/8000 [00:58<05:06, 22.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 959/8000 [00:58<06:11, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▌                          | 964/8000 [00:58<05:31, 21.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 968/8000 [00:59<07:32, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 972/8000 [00:59<06:47, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 975/8000 [00:59<08:08, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 978/8000 [01:00<09:30, 12.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 980/8000 [01:00<09:28, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 982/8000 [01:00<09:03, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 985/8000 [01:00<13:02,  8.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 987/8000 [01:01<14:25,  8.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 990/8000 [01:01<11:14, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 992/8000 [01:01<10:18, 11.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 994/8000 [01:01<10:03, 11.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                          | 997/8000 [01:01<08:53, 13.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  12%|███▋                         | 1000/8000 [01:02<12:23,  9.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1004/8000 [01:02<09:30, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1006/8000 [01:02<10:58, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1008/8000 [01:03<14:19,  8.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1014/8000 [01:03<08:38, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1016/8000 [01:03<08:30, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1018/8000 [01:03<08:37, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1021/8000 [01:03<07:28, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1023/8000 [01:04<11:32, 10.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1025/8000 [01:04<10:36, 10.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1028/8000 [01:04<09:09, 12.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1030/8000 [01:04<09:15, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▋                         | 1034/8000 [01:05<09:32, 12.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1037/8000 [01:05<12:01,  9.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1040/8000 [01:05<10:21, 11.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1042/8000 [01:05<10:22, 11.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1044/8000 [01:06<10:08, 11.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1046/8000 [01:06<10:01, 11.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1049/8000 [01:06<11:05, 10.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1053/8000 [01:06<08:12, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1062/8000 [01:07<05:47, 19.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▊                         | 1067/8000 [01:07<06:27, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▉                         | 1073/8000 [01:07<06:35, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  13%|███▉                         | 1078/8000 [01:07<05:28, 21.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                         | 1081/8000 [01:08<06:57, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                         | 1087/8000 [01:08<07:41, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                         | 1091/8000 [01:08<06:55, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                         | 1096/8000 [01:08<05:57, 19.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|███▉                         | 1100/8000 [01:09<05:36, 20.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1104/8000 [01:09<06:23, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1107/8000 [01:09<06:04, 18.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1110/8000 [01:09<07:38, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1114/8000 [01:10<06:43, 17.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1116/8000 [01:10<07:25, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1119/8000 [01:10<08:49, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1123/8000 [01:10<07:06, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1125/8000 [01:10<07:31, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1127/8000 [01:11<07:54, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1131/8000 [01:11<06:47, 16.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1133/8000 [01:11<07:27, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████                         | 1136/8000 [01:11<07:14, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▏                        | 1139/8000 [01:11<06:56, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▏                        | 1144/8000 [01:11<05:15, 21.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▏                        | 1149/8000 [01:12<04:42, 24.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▏                        | 1152/8000 [01:12<05:11, 21.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  14%|████▏                        | 1158/8000 [01:12<04:19, 26.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                        | 1165/8000 [01:12<05:58, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                        | 1169/8000 [01:12<05:21, 21.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▏                        | 1172/8000 [01:13<05:32, 20.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1175/8000 [01:13<07:18, 15.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1178/8000 [01:13<07:08, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1180/8000 [01:13<07:41, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1182/8000 [01:13<07:56, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1187/8000 [01:14<07:21, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1190/8000 [01:14<07:02, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1195/8000 [01:14<05:50, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▎                        | 1202/8000 [01:15<06:44, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1207/8000 [01:15<06:42, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1213/8000 [01:15<05:20, 21.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1217/8000 [01:15<05:09, 21.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1221/8000 [01:16<07:00, 16.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1225/8000 [01:16<06:10, 18.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1228/8000 [01:16<05:59, 18.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1231/8000 [01:16<06:16, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1237/8000 [01:16<06:22, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  15%|████▍                        | 1239/8000 [01:17<06:57, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▍                        | 1241/8000 [01:17<07:34, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1246/8000 [01:17<09:20, 12.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1248/8000 [01:18<09:03, 12.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1250/8000 [01:18<09:03, 12.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1254/8000 [01:18<07:28, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1258/8000 [01:18<06:35, 17.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1262/8000 [01:18<07:31, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1265/8000 [01:19<07:06, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1267/8000 [01:19<08:34, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1271/8000 [01:19<07:08, 15.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1273/8000 [01:19<07:41, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▌                        | 1275/8000 [01:19<07:54, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1279/8000 [01:19<06:37, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1281/8000 [01:20<07:19, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1283/8000 [01:20<07:48, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1287/8000 [01:20<06:13, 17.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1292/8000 [01:20<06:35, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1296/8000 [01:20<05:38, 19.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1301/8000 [01:21<05:50, 19.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1304/8000 [01:21<05:53, 18.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▋                        | 1308/8000 [01:21<05:37, 19.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▊                        | 1311/8000 [01:21<05:55, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  16%|████▊                        | 1315/8000 [01:21<05:32, 20.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1321/8000 [01:22<05:33, 20.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1327/8000 [01:22<04:34, 24.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1330/8000 [01:22<04:58, 22.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1334/8000 [01:22<06:04, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1336/8000 [01:23<07:54, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1338/8000 [01:23<08:06, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1341/8000 [01:23<07:27, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▊                        | 1343/8000 [01:23<07:44, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1345/8000 [01:23<07:54, 14.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1349/8000 [01:23<06:05, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1352/8000 [01:24<06:06, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1355/8000 [01:24<06:03, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1358/8000 [01:24<05:42, 19.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1364/8000 [01:24<07:30, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1366/8000 [01:25<09:00, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1373/8000 [01:25<05:47, 19.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|████▉                        | 1377/8000 [01:25<06:27, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████                        | 1380/8000 [01:25<07:41, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████                        | 1382/8000 [01:26<07:34, 14.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████                        | 1387/8000 [01:26<08:25, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████                        | 1392/8000 [01:26<06:51, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████                        | 1394/8000 [01:26<07:19, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  17%|█████                        | 1396/8000 [01:26<07:39, 14.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████                        | 1400/8000 [01:27<06:37, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████                        | 1403/8000 [01:27<06:02, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████                        | 1405/8000 [01:27<06:39, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████                        | 1409/8000 [01:27<07:07, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████                        | 1411/8000 [01:27<07:01, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1414/8000 [01:28<06:44, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1420/8000 [01:28<05:07, 21.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1423/8000 [01:28<06:39, 16.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1428/8000 [01:28<05:29, 19.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1433/8000 [01:28<06:01, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1437/8000 [01:29<05:31, 19.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1445/8000 [01:29<04:50, 22.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▏                       | 1448/8000 [01:29<04:54, 22.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1452/8000 [01:29<04:56, 22.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1456/8000 [01:30<07:45, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1458/8000 [01:30<08:15, 13.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1461/8000 [01:30<07:53, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1467/8000 [01:31<07:00, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1471/8000 [01:31<07:53, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1474/8000 [01:31<07:28, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  18%|█████▎                       | 1476/8000 [01:31<07:39, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▎                       | 1482/8000 [01:32<06:46, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1486/8000 [01:32<06:05, 17.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1489/8000 [01:32<06:04, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1491/8000 [01:32<06:39, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1493/8000 [01:32<06:58, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1495/8000 [01:32<07:23, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1498/8000 [01:32<06:29, 16.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1500/8000 [01:33<07:19, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1503/8000 [01:33<06:51, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1505/8000 [01:33<07:47, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1508/8000 [01:33<07:25, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1510/8000 [01:34<11:11,  9.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1512/8000 [01:34<10:34, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1515/8000 [01:34<10:33, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▍                       | 1517/8000 [01:34<10:24, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1519/8000 [01:34<10:10, 10.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1521/8000 [01:35<09:53, 10.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1524/8000 [01:35<10:45, 10.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1528/8000 [01:35<08:32, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1531/8000 [01:35<09:37, 11.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1536/8000 [01:36<06:57, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1541/8000 [01:36<07:21, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1543/8000 [01:36<07:48, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1548/8000 [01:37<07:22, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▌                       | 1551/8000 [01:37<07:12, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▋                       | 1556/8000 [01:37<05:57, 18.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  19%|█████▋                       | 1559/8000 [01:37<05:55, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                       | 1564/8000 [01:37<05:06, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                       | 1570/8000 [01:38<05:23, 19.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                       | 1573/8000 [01:38<05:41, 18.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                       | 1576/8000 [01:38<07:12, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                       | 1578/8000 [01:38<09:56, 10.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                       | 1580/8000 [01:39<10:56,  9.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▋                       | 1584/8000 [01:39<10:32, 10.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1589/8000 [01:40<10:48,  9.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1591/8000 [01:40<11:30,  9.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1592/8000 [01:40<13:17,  8.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1594/8000 [01:40<12:23,  8.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1596/8000 [01:41<11:24,  9.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1597/8000 [01:41<12:34,  8.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1600/8000 [01:41<09:55, 10.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1602/8000 [01:41<09:02, 11.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1605/8000 [01:41<07:47, 13.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1607/8000 [01:41<07:51, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1610/8000 [01:41<06:54, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1613/8000 [01:42<06:01, 17.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1615/8000 [01:42<06:40, 15.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▊                       | 1620/8000 [01:42<05:14, 20.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▉                       | 1623/8000 [01:42<07:09, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▉                       | 1625/8000 [01:42<07:39, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▉                       | 1627/8000 [01:43<07:55, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▉                       | 1631/8000 [01:43<06:07, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  20%|█████▉                       | 1639/8000 [01:43<03:57, 26.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▉                       | 1644/8000 [01:43<04:38, 22.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▉                       | 1647/8000 [01:43<05:56, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▉                       | 1650/8000 [01:44<06:00, 17.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|█████▉                       | 1653/8000 [01:44<08:21, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████                       | 1659/8000 [01:44<07:12, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████                       | 1668/8000 [01:45<04:44, 22.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████                       | 1675/8000 [01:45<03:55, 26.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████                       | 1679/8000 [01:45<03:56, 26.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████                       | 1685/8000 [01:45<04:58, 21.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████                       | 1689/8000 [01:45<04:47, 21.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1692/8000 [01:46<04:45, 22.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1695/8000 [01:46<04:58, 21.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1699/8000 [01:46<06:04, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1701/8000 [01:46<06:44, 15.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1703/8000 [01:46<07:12, 14.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1707/8000 [01:47<06:02, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1711/8000 [01:47<06:35, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1714/8000 [01:47<06:21, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  21%|██████▏                      | 1718/8000 [01:47<05:40, 18.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▏                      | 1722/8000 [01:47<05:16, 19.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▎                      | 1727/8000 [01:48<06:53, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▎                      | 1729/8000 [01:48<07:16, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▎                      | 1735/8000 [01:48<05:20, 19.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▎                      | 1738/8000 [01:48<05:41, 18.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▎                      | 1748/8000 [01:49<04:26, 23.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▎                      | 1755/8000 [01:49<04:22, 23.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▎                      | 1758/8000 [01:49<05:41, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1761/8000 [01:50<06:47, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1764/8000 [01:50<06:41, 15.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1767/8000 [01:50<07:35, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1769/8000 [01:50<07:53, 13.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1771/8000 [01:50<08:17, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1775/8000 [01:51<06:52, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1778/8000 [01:51<06:32, 15.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1780/8000 [01:51<06:43, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1784/8000 [01:51<06:49, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1790/8000 [01:51<05:00, 20.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▍                      | 1793/8000 [01:52<06:51, 15.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  22%|██████▌                      | 1797/8000 [01:52<07:12, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1801/8000 [01:52<06:15, 16.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1803/8000 [01:52<06:24, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1806/8000 [01:53<06:06, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1808/8000 [01:53<06:23, 16.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1811/8000 [01:53<05:54, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1813/8000 [01:53<07:53, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1816/8000 [01:53<09:04, 11.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1819/8000 [01:54<09:45, 10.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1821/8000 [01:54<09:22, 10.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1825/8000 [01:54<07:01, 14.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▌                      | 1827/8000 [01:54<07:30, 13.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1829/8000 [01:54<07:55, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1831/8000 [01:55<17:21,  5.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1833/8000 [01:55<15:19,  6.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1836/8000 [01:56<12:21,  8.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1838/8000 [01:56<12:39,  8.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1842/8000 [01:56<11:22,  9.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1847/8000 [01:57<10:39,  9.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1851/8000 [01:57<08:42, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1854/8000 [01:57<07:53, 12.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1858/8000 [01:57<07:33, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▋                      | 1862/8000 [01:58<06:25, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▊                      | 1864/8000 [01:58<06:46, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▊                      | 1867/8000 [01:58<06:28, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▊                      | 1870/8000 [01:58<06:11, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▊                      | 1872/8000 [01:58<06:37, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  23%|██████▊                      | 1877/8000 [01:59<06:28, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▊                      | 1881/8000 [01:59<05:42, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▊                      | 1884/8000 [01:59<05:37, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▊                      | 1886/8000 [01:59<06:08, 16.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▊                      | 1888/8000 [01:59<06:42, 15.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1900/8000 [02:00<04:36, 22.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1904/8000 [02:00<05:27, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1906/8000 [02:00<05:51, 17.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1909/8000 [02:00<05:26, 18.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1915/8000 [02:01<05:29, 18.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1919/8000 [02:01<04:56, 20.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1922/8000 [02:01<05:02, 20.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1925/8000 [02:01<06:19, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|██████▉                      | 1930/8000 [02:01<05:18, 19.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1934/8000 [02:02<05:04, 19.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1941/8000 [02:02<04:39, 21.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1944/8000 [02:02<04:34, 22.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1948/8000 [02:02<05:30, 18.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1951/8000 [02:02<05:31, 18.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1955/8000 [02:03<05:08, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1958/8000 [02:03<05:15, 19.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  24%|███████                      | 1960/8000 [02:03<05:55, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████                      | 1963/8000 [02:03<05:24, 18.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1969/8000 [02:03<04:12, 23.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1972/8000 [02:03<04:32, 22.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1977/8000 [02:04<05:05, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1980/8000 [02:04<06:28, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1982/8000 [02:04<06:45, 14.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1984/8000 [02:04<07:03, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1989/8000 [02:04<05:23, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1994/8000 [02:05<05:45, 17.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▏                     | 1997/8000 [02:05<05:42, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2000/8000 [02:05<05:33, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2002/8000 [02:05<06:05, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2005/8000 [02:06<07:14, 13.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2009/8000 [02:06<05:55, 16.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2012/8000 [02:06<05:26, 18.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2018/8000 [02:06<06:32, 15.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2020/8000 [02:06<06:52, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2022/8000 [02:07<07:20, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2025/8000 [02:07<06:46, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2027/8000 [02:07<06:38, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▎                     | 2031/8000 [02:07<07:02, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▍                     | 2035/8000 [02:07<05:45, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  25%|███████▍                     | 2038/8000 [02:08<07:11, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▍                     | 2042/8000 [02:08<06:08, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▍                     | 2047/8000 [02:08<06:13, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▍                     | 2050/8000 [02:08<05:51, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▍                     | 2053/8000 [02:09<06:47, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▍                     | 2060/8000 [02:09<04:40, 21.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▍                     | 2063/8000 [02:09<05:42, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2070/8000 [02:09<04:13, 23.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2073/8000 [02:09<04:19, 22.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2076/8000 [02:10<04:37, 21.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2081/8000 [02:10<04:06, 23.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2084/8000 [02:10<04:29, 21.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2091/8000 [02:10<04:19, 22.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2095/8000 [02:10<04:05, 24.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▌                     | 2099/8000 [02:11<05:03, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▋                     | 2104/8000 [02:11<04:40, 21.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▋                     | 2107/8000 [02:11<06:06, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▋                     | 2112/8000 [02:11<05:47, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▋                     | 2114/8000 [02:12<06:12, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▋                     | 2116/8000 [02:12<06:39, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  26%|███████▋                     | 2120/8000 [02:12<05:47, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▋                     | 2123/8000 [02:12<05:40, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▋                     | 2127/8000 [02:12<06:27, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▋                     | 2129/8000 [02:13<06:18, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▋                     | 2132/8000 [02:13<07:37, 12.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▋                     | 2137/8000 [02:13<05:33, 17.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2140/8000 [02:13<06:48, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2143/8000 [02:14<06:22, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2147/8000 [02:14<05:34, 17.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2149/8000 [02:14<06:11, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2151/8000 [02:14<06:25, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2153/8000 [02:14<07:47, 12.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2156/8000 [02:14<07:03, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2158/8000 [02:15<09:15, 10.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2161/8000 [02:15<07:54, 12.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2165/8000 [02:15<06:20, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2167/8000 [02:15<08:13, 11.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▊                     | 2171/8000 [02:16<06:30, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2174/8000 [02:16<06:05, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2177/8000 [02:16<06:48, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2180/8000 [02:16<07:44, 12.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2182/8000 [02:17<08:39, 11.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2186/8000 [02:17<06:50, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2188/8000 [02:17<07:14, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2190/8000 [02:17<07:16, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2193/8000 [02:17<08:27, 11.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2195/8000 [02:18<08:17, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  27%|███████▉                     | 2199/8000 [02:18<07:49, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|███████▉                     | 2205/8000 [02:18<05:22, 17.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2209/8000 [02:18<04:40, 20.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2212/8000 [02:18<04:58, 19.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2216/8000 [02:18<04:33, 21.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2219/8000 [02:19<06:02, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2221/8000 [02:19<06:26, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2223/8000 [02:19<06:49, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2228/8000 [02:19<04:53, 19.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2235/8000 [02:19<03:20, 28.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████                     | 2240/8000 [02:20<04:08, 23.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2244/8000 [02:20<03:57, 24.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2248/8000 [02:20<03:39, 26.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2252/8000 [02:20<03:44, 25.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2255/8000 [02:20<03:59, 23.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2258/8000 [02:20<04:03, 23.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2261/8000 [02:21<05:20, 17.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2266/8000 [02:21<05:45, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2269/8000 [02:21<05:36, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2272/8000 [02:21<05:26, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▏                    | 2275/8000 [02:21<05:18, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  28%|████████▎                    | 2279/8000 [02:22<06:05, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▎                    | 2282/8000 [02:22<05:48, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▎                    | 2288/8000 [02:22<04:28, 21.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▎                    | 2292/8000 [02:22<04:20, 21.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▎                    | 2297/8000 [02:23<05:04, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▎                    | 2301/8000 [02:23<04:28, 21.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▎                    | 2305/8000 [02:23<04:19, 21.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▎                    | 2310/8000 [02:23<04:59, 19.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2315/8000 [02:24<07:16, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2317/8000 [02:24<07:22, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2320/8000 [02:24<07:49, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2322/8000 [02:24<07:27, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2324/8000 [02:25<07:28, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2327/8000 [02:25<06:42, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2330/8000 [02:25<07:42, 12.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2333/8000 [02:25<06:56, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2335/8000 [02:26<08:34, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▍                    | 2338/8000 [02:26<07:25, 12.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▌                    | 2345/8000 [02:26<05:45, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▌                    | 2349/8000 [02:26<04:57, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▌                    | 2354/8000 [02:27<06:26, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  29%|████████▌                    | 2356/8000 [02:27<07:02, 13.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▌                    | 2360/8000 [02:27<06:09, 15.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▌                    | 2363/8000 [02:27<06:19, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▌                    | 2367/8000 [02:27<05:35, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▌                    | 2370/8000 [02:28<07:24, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▌                    | 2373/8000 [02:28<06:56, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▌                    | 2376/8000 [02:28<06:34, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2381/8000 [02:28<05:20, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2384/8000 [02:29<06:45, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2386/8000 [02:29<06:38, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2390/8000 [02:29<08:53, 10.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2393/8000 [02:30<07:40, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2395/8000 [02:30<09:29,  9.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2399/8000 [02:30<08:48, 10.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2407/8000 [02:31<06:11, 15.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▋                    | 2409/8000 [02:31<06:23, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▊                    | 2417/8000 [02:31<05:12, 17.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▊                    | 2420/8000 [02:31<05:11, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▊                    | 2424/8000 [02:31<04:47, 19.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▊                    | 2429/8000 [02:32<04:09, 22.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▊                    | 2432/8000 [02:32<05:11, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  30%|████████▊                    | 2437/8000 [02:32<04:41, 19.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▊                    | 2443/8000 [02:32<04:49, 19.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▊                    | 2446/8000 [02:33<04:55, 18.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▉                    | 2450/8000 [02:33<05:32, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▉                    | 2452/8000 [02:33<05:33, 16.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▉                    | 2459/8000 [02:33<04:57, 18.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▉                    | 2465/8000 [02:34<04:35, 20.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▉                    | 2469/8000 [02:34<04:19, 21.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▉                    | 2473/8000 [02:34<04:08, 22.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|████████▉                    | 2478/8000 [02:34<03:34, 25.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2483/8000 [02:34<04:04, 22.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2486/8000 [02:34<04:20, 21.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2489/8000 [02:35<05:45, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2491/8000 [02:35<06:08, 14.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2495/8000 [02:35<06:19, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2500/8000 [02:35<05:08, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2503/8000 [02:36<05:11, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2507/8000 [02:36<05:51, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2512/8000 [02:36<04:54, 18.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2515/8000 [02:37<06:59, 13.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  31%|█████████                    | 2517/8000 [02:37<06:58, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▏                   | 2521/8000 [02:37<06:35, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▏                   | 2525/8000 [02:37<05:37, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▏                   | 2531/8000 [02:37<04:18, 21.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▏                   | 2538/8000 [02:38<03:53, 23.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▏                   | 2546/8000 [02:38<03:06, 29.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2554/8000 [02:38<04:06, 22.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2557/8000 [02:39<05:06, 17.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2564/8000 [02:39<04:36, 19.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2568/8000 [02:39<04:27, 20.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2572/8000 [02:39<04:15, 21.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2576/8000 [02:39<04:07, 21.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2580/8000 [02:40<05:02, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2584/8000 [02:40<06:22, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▎                   | 2586/8000 [02:40<07:23, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▍                   | 2588/8000 [02:41<07:25, 12.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▍                   | 2592/8000 [02:41<06:11, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▍                   | 2594/8000 [02:41<07:52, 11.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▍                   | 2596/8000 [02:41<07:49, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  32%|█████████▍                   | 2599/8000 [02:41<07:00, 12.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2601/8000 [02:42<06:39, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2605/8000 [02:42<07:02, 12.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2608/8000 [02:42<09:19,  9.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2611/8000 [02:43<09:22,  9.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2613/8000 [02:43<08:57, 10.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2615/8000 [02:43<08:32, 10.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2618/8000 [02:43<09:06,  9.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▍                   | 2620/8000 [02:44<08:59,  9.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2623/8000 [02:44<07:34, 11.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2627/8000 [02:44<05:40, 15.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2630/8000 [02:44<05:26, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2634/8000 [02:44<04:55, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2637/8000 [02:44<04:52, 18.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2639/8000 [02:45<05:27, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2641/8000 [02:45<05:46, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2643/8000 [02:45<07:46, 11.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2646/8000 [02:45<06:44, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2649/8000 [02:45<07:56, 11.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▌                   | 2652/8000 [02:46<08:41, 10.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▋                   | 2657/8000 [02:46<06:10, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▋                   | 2659/8000 [02:46<06:07, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▋                   | 2661/8000 [02:46<06:24, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▋                   | 2666/8000 [02:46<04:50, 18.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▋                   | 2672/8000 [02:47<03:34, 24.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▋                   | 2675/8000 [02:47<03:42, 23.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  33%|█████████▋                   | 2679/8000 [02:47<03:33, 24.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▋                   | 2682/8000 [02:47<03:57, 22.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▋                   | 2686/8000 [02:47<05:03, 17.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▋                   | 2689/8000 [02:48<04:59, 17.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2691/8000 [02:48<05:25, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2696/8000 [02:48<04:15, 20.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2699/8000 [02:48<04:24, 20.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2703/8000 [02:48<04:11, 21.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2707/8000 [02:49<05:09, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2710/8000 [02:49<06:19, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2714/8000 [02:49<05:19, 16.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2719/8000 [02:49<04:29, 19.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▊                   | 2722/8000 [02:49<04:42, 18.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2725/8000 [02:50<05:58, 14.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2729/8000 [02:50<05:11, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2733/8000 [02:50<05:42, 15.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2737/8000 [02:50<05:03, 17.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2742/8000 [02:50<04:17, 20.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2745/8000 [02:51<04:10, 20.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2748/8000 [02:51<04:20, 20.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2752/8000 [02:51<05:10, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|█████████▉                   | 2756/8000 [02:51<05:50, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  34%|██████████                   | 2759/8000 [02:52<05:32, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████                   | 2765/8000 [02:52<04:10, 20.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████                   | 2770/8000 [02:52<06:44, 12.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████                   | 2775/8000 [02:53<06:19, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████                   | 2777/8000 [02:53<06:19, 13.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████                   | 2784/8000 [02:53<04:13, 20.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████                   | 2788/8000 [02:53<04:02, 21.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████                   | 2791/8000 [02:53<04:10, 20.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2794/8000 [02:54<04:52, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2799/8000 [02:54<05:12, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2801/8000 [02:55<09:08,  9.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2805/8000 [02:55<07:22, 11.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2807/8000 [02:55<07:15, 11.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2809/8000 [02:55<07:12, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2812/8000 [02:55<06:35, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2814/8000 [02:55<06:45, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2818/8000 [02:56<06:58, 12.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2820/8000 [02:56<06:38, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2822/8000 [02:56<06:41, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▏                  | 2824/8000 [02:56<06:36, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▎                  | 2831/8000 [02:56<04:44, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▎                  | 2835/8000 [02:57<05:24, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  35%|██████████▎                  | 2837/8000 [02:57<06:23, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▎                  | 2844/8000 [02:57<04:17, 20.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▎                  | 2847/8000 [02:57<04:21, 19.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▎                  | 2850/8000 [02:57<04:16, 20.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▎                  | 2853/8000 [02:58<04:05, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▎                  | 2860/8000 [02:58<04:45, 17.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2863/8000 [02:58<04:29, 19.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2867/8000 [02:59<05:13, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2873/8000 [02:59<04:01, 21.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2879/8000 [02:59<04:22, 19.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2883/8000 [02:59<04:59, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2887/8000 [03:00<04:36, 18.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2891/8000 [03:00<04:10, 20.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▍                  | 2895/8000 [03:00<05:39, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2899/8000 [03:00<06:02, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2902/8000 [03:01<06:46, 12.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2904/8000 [03:01<06:43, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2908/8000 [03:01<05:40, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2910/8000 [03:01<06:00, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2913/8000 [03:01<05:42, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2916/8000 [03:02<06:51, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2918/8000 [03:02<06:56, 12.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  36%|██████████▌                  | 2920/8000 [03:02<06:52, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▌                  | 2925/8000 [03:03<07:05, 11.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2933/8000 [03:03<04:58, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2935/8000 [03:03<05:15, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2939/8000 [03:03<04:37, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2941/8000 [03:03<04:54, 17.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2944/8000 [03:03<04:26, 18.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2952/8000 [03:04<03:02, 27.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2956/8000 [03:04<03:07, 26.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2959/8000 [03:04<04:05, 20.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2962/8000 [03:04<05:02, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▋                  | 2965/8000 [03:05<05:47, 14.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2969/8000 [03:05<04:58, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2971/8000 [03:05<05:18, 15.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2973/8000 [03:05<05:18, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2978/8000 [03:06<06:43, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2981/8000 [03:06<07:24, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2986/8000 [03:06<05:32, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2992/8000 [03:06<04:03, 20.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2996/8000 [03:06<03:45, 22.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  37%|██████████▊                  | 2999/8000 [03:06<03:59, 20.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3003/8000 [03:07<04:03, 20.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3006/8000 [03:07<04:08, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3010/8000 [03:07<03:58, 20.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3015/8000 [03:07<03:25, 24.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3019/8000 [03:07<03:20, 24.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3022/8000 [03:07<03:19, 24.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3029/8000 [03:08<04:14, 19.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|██████████▉                  | 3032/8000 [03:08<04:02, 20.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3036/8000 [03:08<03:56, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3045/8000 [03:09<03:39, 22.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3049/8000 [03:09<04:21, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3052/8000 [03:09<04:24, 18.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3054/8000 [03:09<04:31, 18.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3059/8000 [03:09<04:46, 17.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3062/8000 [03:10<05:32, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3065/8000 [03:10<05:22, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████                  | 3067/8000 [03:10<05:35, 14.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████▏                 | 3071/8000 [03:10<04:47, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  38%|███████████▏                 | 3075/8000 [03:11<05:25, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▏                 | 3081/8000 [03:11<04:05, 20.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▏                 | 3085/8000 [03:11<03:37, 22.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▏                 | 3088/8000 [03:11<03:51, 21.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▏                 | 3092/8000 [03:11<04:28, 18.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▏                 | 3096/8000 [03:12<04:54, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▏                 | 3100/8000 [03:12<04:31, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3104/8000 [03:12<04:09, 19.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3112/8000 [03:12<03:48, 21.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3117/8000 [03:13<04:09, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3121/8000 [03:13<05:25, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3123/8000 [03:13<05:22, 15.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3126/8000 [03:13<05:08, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3128/8000 [03:14<06:26, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3130/8000 [03:14<06:07, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▎                 | 3134/8000 [03:14<05:04, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▍                 | 3138/8000 [03:14<05:21, 15.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▍                 | 3141/8000 [03:14<05:58, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▍                 | 3143/8000 [03:15<06:02, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▍                 | 3148/8000 [03:15<04:25, 18.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▍                 | 3153/8000 [03:15<05:15, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  39%|███████████▍                 | 3158/8000 [03:15<04:18, 18.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▍                 | 3161/8000 [03:15<04:02, 19.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▍                 | 3167/8000 [03:16<03:16, 24.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▍                 | 3171/8000 [03:16<03:16, 24.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3174/8000 [03:16<04:07, 19.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3180/8000 [03:16<04:07, 19.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3184/8000 [03:17<03:48, 21.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3187/8000 [03:17<04:38, 17.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3189/8000 [03:17<04:52, 16.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3192/8000 [03:17<04:37, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3194/8000 [03:17<04:42, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3196/8000 [03:17<05:06, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3200/8000 [03:18<04:24, 18.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3203/8000 [03:18<04:24, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▌                 | 3205/8000 [03:18<05:03, 15.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3208/8000 [03:18<07:17, 10.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3213/8000 [03:19<05:14, 15.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3218/8000 [03:19<04:08, 19.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3221/8000 [03:19<04:06, 19.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3225/8000 [03:19<04:37, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3230/8000 [03:19<03:54, 20.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3234/8000 [03:19<03:29, 22.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3237/8000 [03:20<03:28, 22.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  40%|███████████▋                 | 3240/8000 [03:20<03:38, 21.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3244/8000 [03:20<03:28, 22.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3249/8000 [03:20<02:57, 26.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3254/8000 [03:20<02:37, 30.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3258/8000 [03:21<04:41, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3261/8000 [03:21<05:50, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3266/8000 [03:21<05:23, 14.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3269/8000 [03:21<05:28, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3272/8000 [03:22<05:23, 14.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▊                 | 3274/8000 [03:22<05:25, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3278/8000 [03:22<04:39, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3281/8000 [03:22<05:47, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3284/8000 [03:23<08:50,  8.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3286/8000 [03:23<08:15,  9.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3291/8000 [03:24<08:36,  9.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3297/8000 [03:24<05:44, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3300/8000 [03:24<05:23, 14.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3303/8000 [03:24<05:59, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3306/8000 [03:25<06:43, 11.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|███████████▉                 | 3309/8000 [03:25<07:20, 10.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|████████████                 | 3314/8000 [03:25<05:25, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  41%|████████████                 | 3318/8000 [03:25<04:27, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████                 | 3321/8000 [03:25<04:25, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████                 | 3327/8000 [03:26<04:09, 18.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████                 | 3330/8000 [03:26<03:55, 19.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████                 | 3333/8000 [03:26<04:46, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████                 | 3335/8000 [03:26<04:59, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████                 | 3337/8000 [03:26<05:03, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████                 | 3340/8000 [03:27<06:07, 12.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3345/8000 [03:27<05:13, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3347/8000 [03:27<05:27, 14.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3351/8000 [03:27<04:44, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3356/8000 [03:27<03:50, 20.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3360/8000 [03:28<03:38, 21.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3365/8000 [03:28<03:14, 23.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3370/8000 [03:28<03:01, 25.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3374/8000 [03:28<02:59, 25.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▏                | 3377/8000 [03:28<03:19, 23.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▎                | 3380/8000 [03:28<03:27, 22.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▎                | 3385/8000 [03:29<03:38, 21.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▎                | 3390/8000 [03:29<03:16, 23.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  42%|████████████▎                | 3394/8000 [03:29<04:11, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▎                | 3401/8000 [03:30<04:46, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▎                | 3403/8000 [03:30<04:50, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▎                | 3405/8000 [03:30<05:05, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▎                | 3407/8000 [03:30<05:23, 14.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▎                | 3411/8000 [03:31<05:52, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▎                | 3413/8000 [03:31<05:56, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▍                | 3418/8000 [03:31<05:22, 14.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▍                | 3426/8000 [03:31<04:12, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▍                | 3431/8000 [03:31<03:33, 21.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▍                | 3436/8000 [03:32<04:33, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▍                | 3439/8000 [03:32<04:32, 16.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▍                | 3441/8000 [03:32<06:01, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▍                | 3447/8000 [03:33<04:24, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3450/8000 [03:33<05:07, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3453/8000 [03:33<04:58, 15.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3456/8000 [03:33<04:42, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3460/8000 [03:34<06:21, 11.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3463/8000 [03:34<05:32, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3467/8000 [03:34<05:35, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3469/8000 [03:35<07:37,  9.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3472/8000 [03:35<06:44, 11.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3476/8000 [03:35<06:57, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  43%|████████████▌                | 3478/8000 [03:36<07:46,  9.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▌                | 3480/8000 [03:36<08:19,  9.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▌                | 3481/8000 [03:36<08:35,  8.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3484/8000 [03:36<06:58, 10.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3488/8000 [03:36<05:02, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3491/8000 [03:37<06:06, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3495/8000 [03:37<06:22, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3498/8000 [03:37<05:43, 13.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3500/8000 [03:37<05:48, 12.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3503/8000 [03:37<05:00, 14.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3505/8000 [03:38<05:16, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3507/8000 [03:38<08:22,  8.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3510/8000 [03:38<06:47, 11.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3512/8000 [03:38<06:37, 11.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▋                | 3515/8000 [03:39<07:13, 10.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3520/8000 [03:39<06:01, 12.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3524/8000 [03:39<04:45, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3528/8000 [03:39<05:10, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3533/8000 [03:40<06:06, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3535/8000 [03:40<06:12, 11.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3537/8000 [03:40<05:53, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3542/8000 [03:40<04:30, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3547/8000 [03:41<03:47, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▊                | 3551/8000 [03:41<04:38, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▉                | 3554/8000 [03:41<05:26, 13.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  44%|████████████▉                | 3556/8000 [03:41<05:20, 13.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3561/8000 [03:42<04:11, 17.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3563/8000 [03:42<05:42, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3569/8000 [03:42<04:58, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3572/8000 [03:42<04:47, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3574/8000 [03:43<06:53, 10.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3579/8000 [03:43<05:03, 14.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3582/8000 [03:43<04:30, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|████████████▉                | 3586/8000 [03:44<05:48, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3591/8000 [03:44<04:17, 17.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3596/8000 [03:44<03:40, 19.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3599/8000 [03:44<03:42, 19.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3603/8000 [03:44<03:20, 21.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3606/8000 [03:44<03:16, 22.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3609/8000 [03:44<03:26, 21.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3615/8000 [03:45<03:44, 19.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████                | 3619/8000 [03:45<04:29, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████▏               | 3622/8000 [03:45<05:12, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████▏               | 3625/8000 [03:46<04:58, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████▏               | 3627/8000 [03:46<05:25, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████▏               | 3629/8000 [03:46<05:49, 12.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████▏               | 3633/8000 [03:46<04:47, 15.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  45%|█████████████▏               | 3636/8000 [03:47<05:44, 12.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▏               | 3642/8000 [03:47<04:50, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▏               | 3644/8000 [03:47<05:03, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▏               | 3648/8000 [03:47<04:14, 17.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▏               | 3650/8000 [03:47<04:34, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▏               | 3654/8000 [03:47<03:53, 18.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3656/8000 [03:48<04:11, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3663/8000 [03:48<03:35, 20.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3667/8000 [03:48<03:29, 20.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3670/8000 [03:49<05:31, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3674/8000 [03:49<04:47, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3680/8000 [03:49<03:32, 20.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3684/8000 [03:49<04:14, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3687/8000 [03:50<05:08, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▎               | 3689/8000 [03:50<06:19, 11.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3691/8000 [03:50<05:52, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3695/8000 [03:50<06:00, 11.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3697/8000 [03:51<07:15,  9.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3699/8000 [03:51<06:59, 10.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3701/8000 [03:51<07:59,  8.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3705/8000 [03:51<05:52, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3708/8000 [03:52<05:15, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3710/8000 [03:52<05:10, 13.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3713/8000 [03:52<04:32, 15.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3717/8000 [03:52<05:14, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  46%|█████████████▍               | 3720/8000 [03:52<04:51, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▍               | 3723/8000 [03:53<04:44, 15.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3725/8000 [03:53<05:04, 14.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3728/8000 [03:53<04:27, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3730/8000 [03:53<05:42, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3732/8000 [03:53<05:35, 12.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3736/8000 [03:53<04:17, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3741/8000 [03:53<03:09, 22.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3746/8000 [03:54<02:49, 25.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3749/8000 [03:54<03:01, 23.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▌               | 3753/8000 [03:54<03:31, 20.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3759/8000 [03:55<04:50, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3763/8000 [03:55<04:19, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3767/8000 [03:55<03:57, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3770/8000 [03:55<04:47, 14.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3772/8000 [03:55<04:53, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3778/8000 [03:56<03:39, 19.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3783/8000 [03:56<03:10, 22.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3786/8000 [03:56<03:21, 20.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▋               | 3790/8000 [03:56<03:52, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  47%|█████████████▊               | 3796/8000 [03:56<03:10, 22.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3800/8000 [03:57<03:03, 22.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3804/8000 [03:57<02:58, 23.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3807/8000 [03:57<04:50, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3809/8000 [03:57<05:04, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3812/8000 [03:58<04:28, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3815/8000 [03:58<04:18, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3818/8000 [03:58<04:07, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3821/8000 [03:58<03:49, 18.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3824/8000 [03:58<03:33, 19.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▊               | 3827/8000 [03:58<03:34, 19.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3833/8000 [03:59<04:58, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3835/8000 [03:59<04:59, 13.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3837/8000 [03:59<05:02, 13.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3839/8000 [03:59<04:58, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3843/8000 [04:00<07:07,  9.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3847/8000 [04:00<06:26, 10.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3852/8000 [04:01<06:28, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3857/8000 [04:01<05:05, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|█████████████▉               | 3861/8000 [04:02<07:50,  8.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|██████████████               | 3864/8000 [04:02<06:47, 10.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|██████████████               | 3866/8000 [04:02<06:27, 10.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|██████████████               | 3869/8000 [04:02<05:24, 12.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|██████████████               | 3874/8000 [04:02<04:48, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  48%|██████████████               | 3878/8000 [04:03<04:10, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████               | 3883/8000 [04:03<03:17, 20.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████               | 3889/8000 [04:03<04:07, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████               | 3892/8000 [04:04<05:37, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3899/8000 [04:04<04:30, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3901/8000 [04:04<04:40, 14.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3904/8000 [04:04<05:09, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3909/8000 [04:05<04:07, 16.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3914/8000 [04:05<04:50, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3916/8000 [04:05<05:00, 13.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3918/8000 [04:05<05:04, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3922/8000 [04:06<04:18, 15.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▏              | 3928/8000 [04:06<04:04, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3933/8000 [04:06<03:55, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3935/8000 [04:06<05:04, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3937/8000 [04:07<04:59, 13.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3939/8000 [04:07<04:53, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3942/8000 [04:07<04:30, 14.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3944/8000 [04:07<05:01, 13.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3948/8000 [04:07<05:26, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3953/8000 [04:08<04:19, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3955/8000 [04:08<05:08, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  49%|██████████████▎              | 3958/8000 [04:08<05:08, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▎              | 3960/8000 [04:09<07:23,  9.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▎              | 3962/8000 [04:09<07:52,  8.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3966/8000 [04:09<05:46, 11.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3968/8000 [04:09<05:46, 11.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3971/8000 [04:09<05:05, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3973/8000 [04:10<05:12, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3976/8000 [04:10<04:42, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3980/8000 [04:10<03:51, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3985/8000 [04:10<03:10, 21.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3989/8000 [04:10<02:50, 23.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3992/8000 [04:11<04:42, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▍              | 3999/8000 [04:11<03:05, 21.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4004/8000 [04:11<04:01, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4007/8000 [04:12<05:17, 12.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4013/8000 [04:12<04:43, 14.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4016/8000 [04:12<04:27, 14.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4018/8000 [04:12<04:36, 14.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4020/8000 [04:12<04:44, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4023/8000 [04:13<04:26, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4027/8000 [04:13<04:35, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4029/8000 [04:13<04:35, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4031/8000 [04:13<04:41, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▌              | 4034/8000 [04:13<04:15, 15.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  50%|██████████████▋              | 4038/8000 [04:14<03:39, 18.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▋              | 4041/8000 [04:14<03:36, 18.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▋              | 4045/8000 [04:14<03:18, 19.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▋              | 4048/8000 [04:14<03:28, 18.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▋              | 4057/8000 [04:14<02:47, 23.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▋              | 4061/8000 [04:15<03:19, 19.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▋              | 4065/8000 [04:15<03:01, 21.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4069/8000 [04:15<04:41, 13.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4072/8000 [04:16<04:27, 14.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4078/8000 [04:16<03:20, 19.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4082/8000 [04:16<03:04, 21.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4087/8000 [04:16<03:19, 19.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4091/8000 [04:16<03:57, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4096/8000 [04:17<04:07, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▊              | 4102/8000 [04:17<03:13, 20.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▉              | 4105/8000 [04:17<03:51, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▉              | 4109/8000 [04:18<04:50, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▉              | 4111/8000 [04:18<04:54, 13.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▉              | 4113/8000 [04:18<04:56, 13.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▉              | 4115/8000 [04:18<05:00, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▉              | 4117/8000 [04:18<05:05, 12.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  51%|██████████████▉              | 4119/8000 [04:19<06:33,  9.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▉              | 4123/8000 [04:19<04:37, 13.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▉              | 4131/8000 [04:19<03:30, 18.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|██████████████▉              | 4136/8000 [04:19<02:56, 21.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4140/8000 [04:20<03:24, 18.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4145/8000 [04:20<03:42, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4149/8000 [04:20<03:22, 19.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4152/8000 [04:20<04:14, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4154/8000 [04:21<04:20, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4157/8000 [04:21<04:52, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4159/8000 [04:21<04:57, 12.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4163/8000 [04:21<04:40, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4166/8000 [04:22<05:13, 12.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4170/8000 [04:22<05:00, 12.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████              | 4172/8000 [04:22<05:03, 12.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4174/8000 [04:22<05:09, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4176/8000 [04:23<06:30,  9.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4181/8000 [04:23<05:19, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4186/8000 [04:23<04:50, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4188/8000 [04:23<05:44, 11.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4190/8000 [04:24<05:37, 11.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4194/8000 [04:24<04:28, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4196/8000 [04:24<05:43, 11.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  52%|███████████████▏             | 4199/8000 [04:24<04:55, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▏             | 4203/8000 [04:24<04:02, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▏             | 4206/8000 [04:25<04:36, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4209/8000 [04:25<04:14, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4212/8000 [04:25<04:05, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4217/8000 [04:25<04:05, 15.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4220/8000 [04:26<04:43, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4223/8000 [04:26<04:22, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4228/8000 [04:26<03:28, 18.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4234/8000 [04:26<02:39, 23.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4237/8000 [04:26<02:38, 23.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▎             | 4240/8000 [04:26<02:49, 22.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4243/8000 [04:27<03:56, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4245/8000 [04:27<04:09, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4250/8000 [04:27<03:59, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4253/8000 [04:27<03:51, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4257/8000 [04:28<04:59, 12.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4260/8000 [04:28<05:12, 11.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4263/8000 [04:28<04:41, 13.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4267/8000 [04:29<04:29, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4269/8000 [04:29<04:33, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4273/8000 [04:29<04:33, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▍             | 4275/8000 [04:29<05:24, 11.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▌             | 4277/8000 [04:29<05:12, 11.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  53%|███████████████▌             | 4279/8000 [04:30<05:05, 12.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4286/8000 [04:30<02:51, 21.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4291/8000 [04:30<03:13, 19.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4294/8000 [04:30<03:46, 16.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4296/8000 [04:30<04:02, 15.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4298/8000 [04:31<04:18, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4301/8000 [04:31<04:00, 15.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4304/8000 [04:31<03:50, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▌             | 4308/8000 [04:31<04:18, 14.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4311/8000 [04:31<03:51, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4314/8000 [04:32<03:41, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4316/8000 [04:32<03:50, 16.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4320/8000 [04:32<04:02, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4322/8000 [04:32<04:12, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4325/8000 [04:32<03:55, 15.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4328/8000 [04:33<03:31, 17.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4333/8000 [04:33<02:38, 23.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4339/8000 [04:33<02:54, 20.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▋             | 4342/8000 [04:33<02:55, 20.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▊             | 4345/8000 [04:33<02:49, 21.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▊             | 4348/8000 [04:34<03:48, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▊             | 4350/8000 [04:34<03:53, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▊             | 4354/8000 [04:34<03:22, 18.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  54%|███████████████▊             | 4357/8000 [04:34<03:22, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▊             | 4360/8000 [04:34<04:25, 13.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▊             | 4366/8000 [04:35<03:10, 19.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▊             | 4369/8000 [04:35<03:06, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▊             | 4374/8000 [04:35<03:10, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▊             | 4377/8000 [04:35<03:14, 18.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▊             | 4379/8000 [04:35<03:33, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4382/8000 [04:35<03:33, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4384/8000 [04:36<03:52, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4386/8000 [04:36<04:15, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4389/8000 [04:36<04:47, 12.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4393/8000 [04:36<04:49, 12.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4395/8000 [04:37<04:39, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4397/8000 [04:37<04:20, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4400/8000 [04:37<03:57, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4405/8000 [04:37<02:59, 19.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4409/8000 [04:37<03:21, 17.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|███████████████▉             | 4411/8000 [04:37<03:39, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|████████████████             | 4419/8000 [04:38<02:24, 24.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|████████████████             | 4423/8000 [04:38<02:27, 24.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|████████████████             | 4426/8000 [04:38<03:19, 17.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|████████████████             | 4430/8000 [04:38<03:44, 15.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|████████████████             | 4434/8000 [04:39<03:21, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|████████████████             | 4437/8000 [04:39<03:18, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  55%|████████████████             | 4439/8000 [04:39<03:26, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████             | 4442/8000 [04:39<03:09, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████             | 4444/8000 [04:39<03:29, 16.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████             | 4447/8000 [04:39<03:15, 18.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4450/8000 [04:39<03:11, 18.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4452/8000 [04:40<03:17, 18.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4456/8000 [04:40<02:57, 19.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4460/8000 [04:40<02:47, 21.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4463/8000 [04:40<03:49, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4465/8000 [04:40<04:01, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4468/8000 [04:41<04:39, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4473/8000 [04:41<04:11, 14.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▏            | 4478/8000 [04:41<03:15, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4484/8000 [04:41<03:04, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4488/8000 [04:42<04:23, 13.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4491/8000 [04:42<04:08, 14.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4494/8000 [04:42<03:51, 15.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4497/8000 [04:42<03:25, 17.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4503/8000 [04:43<04:06, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4506/8000 [04:43<05:16, 11.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4509/8000 [04:44<04:42, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  56%|████████████████▎            | 4513/8000 [04:44<04:37, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4521/8000 [04:44<03:24, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4523/8000 [04:44<03:35, 16.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4529/8000 [04:44<02:46, 20.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4533/8000 [04:45<03:05, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4539/8000 [04:45<03:08, 18.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4542/8000 [04:46<04:26, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4544/8000 [04:46<05:11, 11.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▍            | 4550/8000 [04:46<03:43, 15.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4552/8000 [04:46<03:54, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4558/8000 [04:46<02:57, 19.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4562/8000 [04:47<02:48, 20.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4565/8000 [04:47<03:33, 16.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4567/8000 [04:47<03:45, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4571/8000 [04:47<03:17, 17.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4574/8000 [04:47<03:15, 17.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4579/8000 [04:48<02:45, 20.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▌            | 4582/8000 [04:48<02:54, 19.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▋            | 4587/8000 [04:48<03:14, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▋            | 4590/8000 [04:48<03:42, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▋            | 4592/8000 [04:49<04:38, 12.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▋            | 4596/8000 [04:49<03:37, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▋            | 4598/8000 [04:49<03:48, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  57%|████████████████▋            | 4600/8000 [04:49<04:49, 11.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▋            | 4602/8000 [04:49<04:27, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▋            | 4607/8000 [04:49<03:13, 17.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▋            | 4610/8000 [04:50<03:05, 18.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▋            | 4613/8000 [04:50<03:00, 18.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▋            | 4618/8000 [04:50<03:03, 18.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▋            | 4620/8000 [04:50<03:16, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4623/8000 [04:50<03:48, 14.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4625/8000 [04:51<03:58, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4627/8000 [04:51<05:18, 10.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4630/8000 [04:51<04:33, 12.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4635/8000 [04:52<04:15, 13.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4639/8000 [04:52<04:12, 13.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4642/8000 [04:52<03:53, 14.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4644/8000 [04:52<03:50, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4647/8000 [04:52<04:12, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4649/8000 [04:53<05:47,  9.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4651/8000 [04:53<06:23,  8.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4652/8000 [04:53<06:47,  8.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▊            | 4654/8000 [04:53<05:48,  9.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▉            | 4656/8000 [04:54<05:24, 10.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▉            | 4660/8000 [04:54<04:49, 11.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▉            | 4665/8000 [04:54<03:17, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▉            | 4669/8000 [04:54<02:55, 19.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▉            | 4674/8000 [04:54<02:33, 21.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  58%|████████████████▉            | 4678/8000 [04:55<03:08, 17.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████▉            | 4682/8000 [04:55<03:36, 15.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████▉            | 4685/8000 [04:55<04:07, 13.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|████████████████▉            | 4687/8000 [04:55<04:10, 13.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4692/8000 [04:56<03:53, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4694/8000 [04:56<03:58, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4698/8000 [04:56<03:23, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4703/8000 [04:56<03:32, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4708/8000 [04:57<02:50, 19.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4711/8000 [04:57<02:49, 19.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4715/8000 [04:57<03:09, 17.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4718/8000 [04:57<03:52, 14.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4720/8000 [04:57<04:01, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████            | 4723/8000 [04:58<03:46, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4727/8000 [04:58<03:15, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4730/8000 [04:58<04:25, 12.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4732/8000 [04:58<04:26, 12.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4735/8000 [04:59<03:45, 14.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4737/8000 [04:59<03:57, 13.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4741/8000 [04:59<03:19, 16.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4744/8000 [04:59<04:04, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4746/8000 [04:59<04:10, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4750/8000 [05:00<03:22, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4755/8000 [05:00<04:03, 13.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  59%|█████████████████▏           | 4757/8000 [05:00<04:04, 13.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4762/8000 [05:00<03:40, 14.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4764/8000 [05:01<03:47, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4768/8000 [05:01<03:06, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4774/8000 [05:01<02:23, 22.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4779/8000 [05:01<02:12, 24.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4782/8000 [05:01<03:06, 17.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4787/8000 [05:02<03:46, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▎           | 4791/8000 [05:02<03:07, 17.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4795/8000 [05:02<02:51, 18.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4798/8000 [05:02<03:27, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4800/8000 [05:03<03:23, 15.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4802/8000 [05:03<03:37, 14.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4809/8000 [05:03<02:20, 22.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4812/8000 [05:03<02:23, 22.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4815/8000 [05:03<02:23, 22.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4820/8000 [05:03<02:11, 24.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4823/8000 [05:04<02:21, 22.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▍           | 4826/8000 [05:04<03:52, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▌           | 4831/8000 [05:04<03:03, 17.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▌           | 4834/8000 [05:04<03:41, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▌           | 4838/8000 [05:05<03:47, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  60%|█████████████████▌           | 4840/8000 [05:05<03:41, 14.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▌           | 4842/8000 [05:05<03:48, 13.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▌           | 4844/8000 [05:05<03:47, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▌           | 4847/8000 [05:05<03:25, 15.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▌           | 4849/8000 [05:05<03:24, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▌           | 4852/8000 [05:06<03:12, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▌           | 4855/8000 [05:06<03:10, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▌           | 4860/8000 [05:06<02:34, 20.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4864/8000 [05:06<02:27, 21.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4868/8000 [05:06<03:00, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4872/8000 [05:07<03:16, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4876/8000 [05:07<02:56, 17.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4881/8000 [05:07<02:31, 20.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4884/8000 [05:07<03:14, 16.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4886/8000 [05:08<03:24, 15.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4889/8000 [05:08<03:11, 16.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4893/8000 [05:08<02:50, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▋           | 4895/8000 [05:08<03:47, 13.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4897/8000 [05:08<03:38, 14.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4900/8000 [05:09<04:05, 12.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4903/8000 [05:09<04:19, 11.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4905/8000 [05:09<04:21, 11.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4908/8000 [05:09<03:52, 13.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4911/8000 [05:10<04:30, 11.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4914/8000 [05:10<03:54, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  61%|█████████████████▊           | 4916/8000 [05:10<04:00, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▊           | 4922/8000 [05:10<03:22, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▊           | 4927/8000 [05:11<03:18, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▊           | 4929/8000 [05:11<03:27, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4932/8000 [05:11<04:01, 12.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4937/8000 [05:11<03:03, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4940/8000 [05:11<02:58, 17.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4945/8000 [05:12<02:28, 20.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4948/8000 [05:12<02:32, 19.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4951/8000 [05:12<02:23, 21.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4955/8000 [05:12<02:19, 21.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4958/8000 [05:12<02:21, 21.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4961/8000 [05:12<02:22, 21.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|█████████████████▉           | 4965/8000 [05:13<02:49, 17.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4967/8000 [05:13<03:04, 16.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4973/8000 [05:13<02:19, 21.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4976/8000 [05:13<02:27, 20.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4980/8000 [05:13<02:19, 21.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4983/8000 [05:13<02:25, 20.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4986/8000 [05:14<03:11, 15.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4990/8000 [05:14<02:49, 17.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4994/8000 [05:14<02:35, 19.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  62%|██████████████████           | 4998/8000 [05:14<03:08, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5002/8000 [05:15<02:49, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5007/8000 [05:15<02:50, 17.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5009/8000 [05:15<03:03, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5012/8000 [05:15<02:48, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5015/8000 [05:15<02:50, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5018/8000 [05:15<02:47, 17.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5022/8000 [05:16<02:31, 19.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5026/8000 [05:16<03:00, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5028/8000 [05:16<03:55, 12.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5030/8000 [05:17<04:36, 10.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▏          | 5034/8000 [05:17<03:32, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5038/8000 [05:17<02:58, 16.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5040/8000 [05:17<02:58, 16.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5042/8000 [05:17<03:13, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5044/8000 [05:17<03:26, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5047/8000 [05:17<03:11, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5055/8000 [05:18<02:30, 19.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5059/8000 [05:18<02:22, 20.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5062/8000 [05:18<02:20, 20.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▎          | 5066/8000 [05:18<02:46, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▍          | 5069/8000 [05:19<02:44, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▍          | 5071/8000 [05:19<03:31, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▍          | 5074/8000 [05:19<03:55, 12.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  63%|██████████████████▍          | 5076/8000 [05:19<03:44, 13.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5081/8000 [05:19<02:47, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5083/8000 [05:20<03:06, 15.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5085/8000 [05:20<03:18, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5089/8000 [05:20<02:45, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5092/8000 [05:20<02:43, 17.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5096/8000 [05:20<03:07, 15.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5098/8000 [05:21<03:19, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5101/8000 [05:21<03:05, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▍          | 5103/8000 [05:21<03:11, 15.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5106/8000 [05:21<03:57, 12.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5109/8000 [05:21<03:31, 13.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5111/8000 [05:22<03:33, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5113/8000 [05:22<03:43, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5118/8000 [05:22<02:41, 17.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5121/8000 [05:22<02:31, 18.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5123/8000 [05:22<02:36, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5125/8000 [05:22<02:55, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5127/8000 [05:23<03:11, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5131/8000 [05:23<03:35, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▌          | 5135/8000 [05:23<02:55, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▋          | 5138/8000 [05:23<02:48, 16.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▋          | 5142/8000 [05:23<03:09, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▋          | 5147/8000 [05:24<03:00, 15.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▋          | 5152/8000 [05:24<02:56, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▋          | 5155/8000 [05:24<02:51, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  64%|██████████████████▋          | 5158/8000 [05:24<02:38, 17.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▋          | 5161/8000 [05:25<02:30, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▋          | 5164/8000 [05:25<02:31, 18.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▋          | 5167/8000 [05:25<02:34, 18.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▋          | 5169/8000 [05:25<03:55, 12.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5176/8000 [05:25<02:28, 19.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5181/8000 [05:26<03:05, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5184/8000 [05:26<03:00, 15.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5186/8000 [05:26<03:15, 14.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5188/8000 [05:26<03:24, 13.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5191/8000 [05:27<03:44, 12.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5193/8000 [05:27<03:29, 13.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5195/8000 [05:27<03:34, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▊          | 5203/8000 [05:27<02:00, 23.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5207/8000 [05:27<02:20, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5210/8000 [05:28<02:24, 19.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5213/8000 [05:28<02:26, 18.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5216/8000 [05:28<03:44, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5218/8000 [05:29<04:35, 10.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5221/8000 [05:29<03:59, 11.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5227/8000 [05:29<02:41, 17.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5231/8000 [05:29<02:21, 19.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5234/8000 [05:29<02:15, 20.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  65%|██████████████████▉          | 5238/8000 [05:29<02:47, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████          | 5244/8000 [05:30<02:10, 21.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████          | 5250/8000 [05:30<02:49, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████          | 5257/8000 [05:30<02:27, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████          | 5262/8000 [05:31<02:31, 18.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████          | 5269/8000 [05:31<02:38, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████          | 5272/8000 [05:31<02:51, 15.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████          | 5274/8000 [05:32<02:57, 15.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5276/8000 [05:32<03:00, 15.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5280/8000 [05:32<02:36, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5285/8000 [05:32<02:34, 17.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5289/8000 [05:32<02:48, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5293/8000 [05:33<02:52, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5296/8000 [05:33<02:42, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5301/8000 [05:33<02:14, 20.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5305/8000 [05:33<02:32, 17.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5307/8000 [05:33<02:45, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▏         | 5310/8000 [05:34<02:39, 16.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▎         | 5313/8000 [05:34<02:36, 17.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▎         | 5316/8000 [05:34<02:34, 17.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  66%|███████████████████▎         | 5319/8000 [05:34<02:34, 17.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▎         | 5322/8000 [05:34<02:25, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▎         | 5324/8000 [05:34<02:41, 16.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▎         | 5327/8000 [05:35<02:35, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▎         | 5331/8000 [05:35<02:12, 20.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▎         | 5336/8000 [05:35<01:45, 25.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▎         | 5340/8000 [05:35<01:46, 24.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▎         | 5343/8000 [05:35<02:36, 16.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▍         | 5348/8000 [05:36<02:47, 15.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▍         | 5352/8000 [05:36<02:30, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▍         | 5361/8000 [05:36<01:58, 22.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▍         | 5366/8000 [05:36<01:51, 23.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▍         | 5370/8000 [05:37<01:50, 23.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▍         | 5375/8000 [05:37<02:11, 19.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▍         | 5379/8000 [05:37<02:06, 20.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▌         | 5383/8000 [05:37<01:55, 22.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▌         | 5386/8000 [05:37<02:01, 21.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▌         | 5391/8000 [05:38<02:12, 19.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▌         | 5394/8000 [05:38<03:25, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  67%|███████████████████▌         | 5397/8000 [05:38<03:08, 13.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▌         | 5400/8000 [05:38<02:55, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▌         | 5405/8000 [05:39<02:18, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▌         | 5410/8000 [05:39<02:20, 18.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5415/8000 [05:39<02:25, 17.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5418/8000 [05:39<02:22, 18.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5421/8000 [05:40<02:14, 19.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5424/8000 [05:40<02:15, 19.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5432/8000 [05:40<02:02, 21.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5436/8000 [05:40<01:58, 21.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5440/8000 [05:41<02:24, 17.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5442/8000 [05:41<02:30, 17.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5446/8000 [05:41<02:44, 15.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▋         | 5448/8000 [05:41<02:49, 15.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▊         | 5455/8000 [05:41<01:49, 23.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▊         | 5459/8000 [05:41<01:46, 23.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▊         | 5462/8000 [05:42<01:50, 23.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▊         | 5466/8000 [05:42<01:45, 24.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▊         | 5471/8000 [05:42<02:27, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▊         | 5476/8000 [05:43<02:47, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  68%|███████████████████▊         | 5480/8000 [05:43<02:26, 17.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5487/8000 [05:43<02:06, 19.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5491/8000 [05:43<02:26, 17.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5493/8000 [05:43<02:37, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5496/8000 [05:44<02:35, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5500/8000 [05:44<02:53, 14.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5503/8000 [05:44<02:35, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5505/8000 [05:44<02:43, 15.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5509/8000 [05:45<02:52, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5513/8000 [05:45<02:54, 14.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|███████████████████▉         | 5515/8000 [05:45<03:03, 13.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5521/8000 [05:45<02:18, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5526/8000 [05:45<02:05, 19.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5529/8000 [05:46<02:10, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5531/8000 [05:46<03:03, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5534/8000 [05:46<02:46, 14.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5537/8000 [05:46<02:36, 15.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5541/8000 [05:46<02:09, 19.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5546/8000 [05:47<01:51, 22.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████         | 5550/8000 [05:47<02:13, 18.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████▏        | 5555/8000 [05:47<02:28, 16.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  69%|████████████████████▏        | 5557/8000 [05:47<02:37, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5560/8000 [05:48<02:31, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5563/8000 [05:48<02:57, 13.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5568/8000 [05:48<02:41, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5574/8000 [05:49<02:36, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5579/8000 [05:49<02:12, 18.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5581/8000 [05:49<02:25, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5583/8000 [05:49<02:33, 15.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▏        | 5585/8000 [05:49<02:36, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5591/8000 [05:49<02:14, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5596/8000 [05:50<01:51, 21.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5599/8000 [05:50<01:55, 20.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5604/8000 [05:50<02:03, 19.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5608/8000 [05:50<01:58, 20.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5613/8000 [05:51<03:00, 13.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5615/8000 [05:51<03:04, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▎        | 5618/8000 [05:51<03:23, 11.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▍        | 5624/8000 [05:52<02:40, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▍        | 5627/8000 [05:52<02:34, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▍        | 5633/8000 [05:52<02:00, 19.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▍        | 5637/8000 [05:52<01:53, 20.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  70%|████████████████████▍        | 5640/8000 [05:53<03:00, 13.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▍        | 5644/8000 [05:53<02:29, 15.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▍        | 5647/8000 [05:53<03:18, 11.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▍        | 5650/8000 [05:53<02:53, 13.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▍        | 5653/8000 [05:54<02:41, 14.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5656/8000 [05:54<03:08, 12.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5661/8000 [05:54<03:01, 12.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5663/8000 [05:54<03:02, 12.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5666/8000 [05:55<02:37, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5668/8000 [05:55<02:44, 14.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5670/8000 [05:55<02:51, 13.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5673/8000 [05:55<02:40, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5677/8000 [05:55<02:17, 16.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5682/8000 [05:56<02:24, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▌        | 5686/8000 [05:56<02:02, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▋        | 5692/8000 [05:56<02:02, 18.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▋        | 5699/8000 [05:57<02:23, 16.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▋        | 5702/8000 [05:57<02:19, 16.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▋        | 5707/8000 [05:57<01:54, 19.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▋        | 5712/8000 [05:57<02:24, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  71%|████████████████████▋        | 5715/8000 [05:57<02:12, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▋        | 5722/8000 [05:58<01:59, 18.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5725/8000 [05:58<02:16, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5727/8000 [05:58<03:17, 11.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5729/8000 [05:59<03:11, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5731/8000 [05:59<03:49,  9.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5733/8000 [05:59<03:38, 10.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5735/8000 [05:59<03:28, 10.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5739/8000 [06:00<03:21, 11.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5742/8000 [06:00<03:31, 10.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5746/8000 [06:00<02:44, 13.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5748/8000 [06:00<02:55, 12.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5750/8000 [06:00<02:57, 12.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5753/8000 [06:01<02:37, 14.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5755/8000 [06:01<03:29, 10.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▊        | 5758/8000 [06:01<03:42, 10.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5760/8000 [06:01<03:34, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5763/8000 [06:02<03:02, 12.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5765/8000 [06:02<03:05, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5768/8000 [06:02<02:45, 13.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5773/8000 [06:02<02:33, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5778/8000 [06:02<02:03, 17.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5781/8000 [06:03<01:58, 18.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5786/8000 [06:03<01:44, 21.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5789/8000 [06:03<03:10, 11.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|████████████████████▉        | 5793/8000 [06:04<03:24, 10.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████        | 5797/8000 [06:04<02:48, 13.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  72%|█████████████████████        | 5799/8000 [06:04<03:26, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5803/8000 [06:05<03:16, 11.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5807/8000 [06:05<02:41, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5809/8000 [06:05<02:43, 13.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5813/8000 [06:05<02:52, 12.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5817/8000 [06:05<02:24, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5821/8000 [06:06<02:42, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5823/8000 [06:06<02:53, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5825/8000 [06:06<02:59, 12.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████        | 5827/8000 [06:06<03:00, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5830/8000 [06:07<03:08, 11.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5832/8000 [06:07<03:06, 11.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5835/8000 [06:07<03:15, 11.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5839/8000 [06:07<02:25, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5841/8000 [06:07<03:03, 11.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5844/8000 [06:08<02:39, 13.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5847/8000 [06:08<02:27, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5850/8000 [06:08<02:24, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5853/8000 [06:08<02:20, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5856/8000 [06:09<02:48, 12.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▏       | 5860/8000 [06:09<02:58, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▎       | 5863/8000 [06:09<02:42, 13.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▎       | 5866/8000 [06:09<02:27, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▎       | 5868/8000 [06:09<02:30, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▎       | 5871/8000 [06:09<02:10, 16.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▎       | 5873/8000 [06:10<02:21, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  73%|█████████████████████▎       | 5875/8000 [06:10<03:15, 10.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▎       | 5881/8000 [06:10<02:34, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▎       | 5883/8000 [06:11<02:41, 13.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▎       | 5886/8000 [06:11<02:22, 14.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▎       | 5889/8000 [06:11<02:12, 15.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▎       | 5891/8000 [06:11<02:17, 15.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▎       | 5893/8000 [06:11<02:21, 14.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▎       | 5896/8000 [06:11<02:04, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5900/8000 [06:11<01:50, 19.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5902/8000 [06:12<02:20, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5905/8000 [06:12<02:16, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5910/8000 [06:12<01:45, 19.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5913/8000 [06:12<02:20, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5917/8000 [06:13<02:33, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5919/8000 [06:13<02:35, 13.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5923/8000 [06:13<02:35, 13.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5925/8000 [06:14<03:28,  9.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▍       | 5928/8000 [06:14<02:59, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▌       | 5938/8000 [06:14<01:49, 18.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▌       | 5941/8000 [06:14<01:44, 19.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▌       | 5945/8000 [06:15<02:27, 13.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▌       | 5948/8000 [06:15<02:18, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▌       | 5950/8000 [06:15<03:21, 10.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▌       | 5952/8000 [06:16<03:44,  9.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  74%|█████████████████████▌       | 5956/8000 [06:16<03:44,  9.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5967/8000 [06:16<02:08, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5970/8000 [06:17<02:06, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5972/8000 [06:17<02:18, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5976/8000 [06:17<02:17, 14.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5983/8000 [06:17<01:57, 17.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5986/8000 [06:18<02:20, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5991/8000 [06:18<01:56, 17.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5993/8000 [06:18<02:06, 15.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▋       | 5996/8000 [06:18<02:27, 13.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6000/8000 [06:18<02:08, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6007/8000 [06:19<01:31, 21.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6012/8000 [06:19<01:49, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6017/8000 [06:19<02:08, 15.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6019/8000 [06:20<02:25, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6022/8000 [06:20<02:25, 13.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6025/8000 [06:20<03:21,  9.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6028/8000 [06:21<03:58,  8.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6030/8000 [06:21<04:16,  7.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▊       | 6034/8000 [06:21<03:08, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  75%|█████████████████████▉       | 6036/8000 [06:22<03:26,  9.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6041/8000 [06:22<03:05, 10.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6043/8000 [06:22<03:03, 10.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6045/8000 [06:22<02:57, 11.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6048/8000 [06:23<02:27, 13.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6050/8000 [06:23<02:51, 11.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6053/8000 [06:23<02:28, 13.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6056/8000 [06:23<02:15, 14.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6059/8000 [06:23<01:59, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|█████████████████████▉       | 6065/8000 [06:24<01:27, 22.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6069/8000 [06:24<01:24, 22.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6073/8000 [06:24<01:17, 24.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6076/8000 [06:24<02:02, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6079/8000 [06:24<01:58, 16.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6082/8000 [06:25<02:16, 14.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6085/8000 [06:25<02:28, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6088/8000 [06:25<02:18, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6090/8000 [06:25<02:32, 12.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6092/8000 [06:26<02:41, 11.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6095/8000 [06:26<02:45, 11.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6100/8000 [06:26<02:59, 10.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████       | 6103/8000 [06:27<02:52, 11.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████▏      | 6107/8000 [06:27<02:47, 11.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████▏      | 6110/8000 [06:27<02:27, 12.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████▏      | 6113/8000 [06:27<02:16, 13.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████▏      | 6116/8000 [06:27<01:57, 15.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  76%|██████████████████████▏      | 6119/8000 [06:27<01:52, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▏      | 6124/8000 [06:28<01:32, 20.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▏      | 6127/8000 [06:28<01:39, 18.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▏      | 6131/8000 [06:28<02:25, 12.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▏      | 6134/8000 [06:29<02:42, 11.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▏      | 6136/8000 [06:29<03:02, 10.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6140/8000 [06:29<02:32, 12.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6142/8000 [06:30<03:32,  8.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6144/8000 [06:30<03:28,  8.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6147/8000 [06:30<03:18,  9.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6149/8000 [06:31<03:40,  8.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6151/8000 [06:31<03:19,  9.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6153/8000 [06:31<02:53, 10.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6155/8000 [06:31<03:23,  9.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6157/8000 [06:31<03:06,  9.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6159/8000 [06:31<02:47, 10.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6162/8000 [06:32<02:21, 12.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6165/8000 [06:32<02:44, 11.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▎      | 6172/8000 [06:32<02:04, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6174/8000 [06:32<02:10, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6178/8000 [06:33<02:08, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6181/8000 [06:33<02:21, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6184/8000 [06:33<02:06, 14.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6187/8000 [06:33<01:57, 15.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6191/8000 [06:34<02:02, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6195/8000 [06:34<01:40, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  77%|██████████████████████▍      | 6198/8000 [06:34<01:39, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▍      | 6201/8000 [06:34<01:38, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▍      | 6205/8000 [06:34<01:24, 21.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6212/8000 [06:35<01:39, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6217/8000 [06:35<01:44, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6219/8000 [06:35<01:51, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6224/8000 [06:35<01:34, 18.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6228/8000 [06:36<02:05, 14.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6230/8000 [06:36<02:04, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6232/8000 [06:36<02:00, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6236/8000 [06:36<01:43, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6238/8000 [06:36<01:48, 16.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▌      | 6240/8000 [06:36<01:54, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6243/8000 [06:37<02:15, 12.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6246/8000 [06:37<02:02, 14.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6248/8000 [06:37<02:43, 10.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6250/8000 [06:37<02:38, 11.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6252/8000 [06:38<03:04,  9.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6254/8000 [06:38<02:53, 10.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6256/8000 [06:38<02:39, 10.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6259/8000 [06:38<02:12, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6261/8000 [06:38<02:04, 13.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6263/8000 [06:38<02:11, 13.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6266/8000 [06:39<02:35, 11.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▋      | 6273/8000 [06:39<01:32, 18.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▊      | 6276/8000 [06:39<01:34, 18.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  78%|██████████████████████▊      | 6279/8000 [06:39<01:32, 18.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6281/8000 [06:39<01:39, 17.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6283/8000 [06:40<01:48, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6286/8000 [06:40<01:41, 16.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6288/8000 [06:40<01:46, 16.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6292/8000 [06:40<01:25, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6295/8000 [06:40<01:26, 19.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6298/8000 [06:40<01:30, 18.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6303/8000 [06:41<01:37, 17.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6305/8000 [06:41<01:47, 15.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▊      | 6307/8000 [06:41<02:21, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6311/8000 [06:42<02:27, 11.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6313/8000 [06:42<02:25, 11.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6316/8000 [06:42<02:15, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6320/8000 [06:42<01:52, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6322/8000 [06:42<01:58, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6325/8000 [06:42<01:48, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6333/8000 [06:43<01:29, 18.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6335/8000 [06:43<01:33, 17.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6339/8000 [06:43<01:24, 19.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|██████████████████████▉      | 6342/8000 [06:43<01:27, 18.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████      | 6346/8000 [06:43<01:21, 20.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████      | 6351/8000 [06:44<01:11, 22.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████      | 6356/8000 [06:44<01:26, 18.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  79%|███████████████████████      | 6359/8000 [06:44<01:24, 19.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████      | 6362/8000 [06:44<01:24, 19.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████      | 6365/8000 [06:45<01:55, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████      | 6368/8000 [06:45<01:49, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████      | 6372/8000 [06:45<01:34, 17.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████      | 6377/8000 [06:45<01:17, 20.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6382/8000 [06:45<01:23, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6385/8000 [06:46<01:25, 18.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6390/8000 [06:46<01:33, 17.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6393/8000 [06:46<01:30, 17.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6395/8000 [06:46<02:01, 13.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6400/8000 [06:47<01:52, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6402/8000 [06:47<02:17, 11.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6407/8000 [06:47<02:15, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6411/8000 [06:48<02:08, 12.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▏     | 6413/8000 [06:48<02:09, 12.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6419/8000 [06:48<01:32, 17.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6421/8000 [06:48<01:37, 16.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6424/8000 [06:48<01:38, 15.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6426/8000 [06:49<01:45, 14.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6429/8000 [06:49<01:34, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6431/8000 [06:49<01:41, 15.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6434/8000 [06:49<02:05, 12.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  80%|███████████████████████▎     | 6437/8000 [06:49<01:54, 13.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▎     | 6442/8000 [06:50<01:49, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▎     | 6445/8000 [06:50<01:39, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▎     | 6447/8000 [06:50<02:08, 12.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6450/8000 [06:50<01:51, 13.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6456/8000 [06:50<01:14, 20.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6460/8000 [06:51<01:10, 21.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6464/8000 [06:51<01:06, 23.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6468/8000 [06:51<01:16, 19.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6471/8000 [06:51<01:39, 15.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6474/8000 [06:52<01:54, 13.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6478/8000 [06:52<01:58, 12.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▍     | 6481/8000 [06:52<01:49, 13.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6484/8000 [06:52<01:42, 14.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6488/8000 [06:52<01:27, 17.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6490/8000 [06:53<01:32, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6496/8000 [06:53<01:04, 23.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6501/8000 [06:53<00:59, 25.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6505/8000 [06:53<01:17, 19.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6508/8000 [06:53<01:18, 18.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  81%|███████████████████████▌     | 6512/8000 [06:54<01:12, 20.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▋     | 6521/8000 [06:54<01:29, 16.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▋     | 6524/8000 [06:54<01:24, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▋     | 6530/8000 [06:54<01:05, 22.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▋     | 6535/8000 [06:55<01:00, 24.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▋     | 6538/8000 [06:55<01:21, 18.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▋     | 6542/8000 [06:55<01:15, 19.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▋     | 6548/8000 [06:55<01:16, 18.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6552/8000 [06:56<01:24, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6554/8000 [06:56<01:28, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6556/8000 [06:56<01:49, 13.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6559/8000 [06:56<01:41, 14.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6561/8000 [06:57<01:45, 13.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6564/8000 [06:57<01:37, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6567/8000 [06:57<01:56, 12.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6570/8000 [06:57<01:44, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6576/8000 [06:57<01:11, 19.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6580/8000 [06:58<01:24, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6583/8000 [06:58<01:20, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▊     | 6585/8000 [06:58<01:21, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▉     | 6588/8000 [06:58<01:20, 17.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▉     | 6590/8000 [06:58<01:29, 15.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▉     | 6595/8000 [06:59<01:25, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  82%|███████████████████████▉     | 6598/8000 [06:59<01:23, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████▉     | 6603/8000 [06:59<01:08, 20.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████▉     | 6607/8000 [06:59<01:08, 20.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████▉     | 6611/8000 [06:59<01:23, 16.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████▉     | 6618/8000 [07:00<01:27, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|███████████████████████▉     | 6620/8000 [07:00<01:31, 15.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6626/8000 [07:00<01:26, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6628/8000 [07:01<01:45, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6632/8000 [07:01<01:47, 12.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6635/8000 [07:01<01:39, 13.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6638/8000 [07:01<01:34, 14.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6643/8000 [07:02<01:13, 18.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6649/8000 [07:02<00:57, 23.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████     | 6653/8000 [07:02<00:56, 23.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6656/8000 [07:02<00:56, 23.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6659/8000 [07:02<01:00, 22.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6662/8000 [07:02<01:04, 20.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6666/8000 [07:03<01:18, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6669/8000 [07:03<01:17, 17.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6671/8000 [07:03<01:22, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6674/8000 [07:03<01:42, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  83%|████████████████████████▏    | 6679/8000 [07:03<01:19, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▏    | 6682/8000 [07:04<01:17, 17.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▏    | 6686/8000 [07:04<01:30, 14.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▏    | 6689/8000 [07:04<01:25, 15.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▎    | 6695/8000 [07:04<01:15, 17.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▎    | 6700/8000 [07:05<01:17, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▎    | 6702/8000 [07:05<01:24, 15.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▎    | 6704/8000 [07:05<01:30, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▎    | 6710/8000 [07:05<01:06, 19.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▎    | 6716/8000 [07:06<01:04, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▎    | 6723/8000 [07:06<01:16, 16.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6725/8000 [07:06<01:21, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6727/8000 [07:06<01:24, 14.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6731/8000 [07:07<01:26, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6736/8000 [07:07<01:32, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6742/8000 [07:07<01:23, 15.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6744/8000 [07:08<01:26, 14.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6748/8000 [07:08<01:33, 13.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6752/8000 [07:08<01:22, 15.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6754/8000 [07:08<01:23, 14.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▍    | 6757/8000 [07:08<01:16, 16.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  84%|████████████████████████▌    | 6760/8000 [07:09<01:13, 16.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6763/8000 [07:09<01:07, 18.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6765/8000 [07:09<01:09, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6769/8000 [07:09<01:20, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6772/8000 [07:09<01:14, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6777/8000 [07:10<01:18, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6780/8000 [07:10<01:17, 15.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6784/8000 [07:10<01:22, 14.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6786/8000 [07:11<01:41, 11.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6788/8000 [07:11<01:35, 12.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6790/8000 [07:11<01:37, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▌    | 6792/8000 [07:11<01:35, 12.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  85%|████████████████████████▋    | 6797/8000 [07:11<01:05, 18.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:   8%| | 8/100 [00:00<00:01, 77.5\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▋    | 6803/8000 [07:11<01:01, 19.32it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:01, 66.\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▋    | 6810/8000 [07:12<01:00, 19.79it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  26%|▎| 26/100 [00:00<00:00, 76.\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▋    | 6815/8000 [07:12<01:14, 15.86it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 77.\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▋    | 6818/8000 [07:12<01:12, 16.29it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00<00:00, 90.\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▋    | 6821/8000 [07:13<01:11, 16.56it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:00<00:00, 97.\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▋    | 6823/8000 [07:13<01:13, 16.00it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  69%|▋| 69/100 [00:00<00:00, 93.\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▋    | 6827/8000 [07:13<01:16, 15.24it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval:  79%|▊| 79/100 [00:00<00:00, 88.\u001b[A\u001b[A\n",
+      "Generating signals:  85%|████████████████████████▊    | 6830/8000 [07:13<01:14, 15.74it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:01<00:00, 89\n",
+      "Generating signals:  85%|████████████████████████▊    | 6833/8000 [07:13<01:11, 16.23it/s]\u001b[A\u001b[A\n",
       "\n",
-      "Nyquist Samples: 100%|█████████████████████████| 4/4 [33:46<00:00, 585.49s/it]\u001b[A\n",
-      "Replicates: 100%|███████████████████████████| 8/8 [4:38:11<00:00, 2086.46s/it]\n"
+      "Generating signals:  85%|████████████████████████▊    | 6836/8000 [07:13<01:09, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6840/8000 [07:14<01:20, 14.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6845/8000 [07:14<01:15, 15.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6847/8000 [07:14<01:18, 14.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6850/8000 [07:15<01:28, 12.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6852/8000 [07:15<01:44, 10.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6856/8000 [07:15<01:22, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6858/8000 [07:15<01:20, 14.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6860/8000 [07:15<01:19, 14.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▊    | 6862/8000 [07:15<01:23, 13.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6867/8000 [07:16<01:19, 14.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6870/8000 [07:16<01:33, 12.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6873/8000 [07:16<01:37, 11.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6876/8000 [07:17<01:27, 12.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6878/8000 [07:17<01:44, 10.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6882/8000 [07:17<01:16, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6886/8000 [07:17<01:06, 16.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6889/8000 [07:17<01:03, 17.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|████████████████████████▉    | 6894/8000 [07:18<01:01, 17.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████    | 6899/8000 [07:18<01:06, 16.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████    | 6901/8000 [07:18<01:25, 12.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████    | 6906/8000 [07:18<01:05, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████    | 6909/8000 [07:19<01:06, 16.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████    | 6912/8000 [07:19<01:02, 17.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████    | 6915/8000 [07:19<01:01, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  86%|█████████████████████████    | 6920/8000 [07:19<01:01, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████    | 6922/8000 [07:19<01:02, 17.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████    | 6926/8000 [07:20<01:10, 15.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████    | 6931/8000 [07:20<00:58, 18.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▏   | 6934/8000 [07:20<00:58, 18.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▏   | 6938/8000 [07:20<00:53, 19.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▏   | 6942/8000 [07:20<00:49, 21.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▏   | 6945/8000 [07:20<00:51, 20.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▏   | 6953/8000 [07:21<00:45, 23.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▏   | 6958/8000 [07:21<00:48, 21.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▏   | 6964/8000 [07:22<00:59, 17.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6966/8000 [07:22<00:59, 17.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6968/8000 [07:22<01:04, 16.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6973/8000 [07:22<00:53, 19.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6979/8000 [07:22<00:43, 23.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6984/8000 [07:22<00:50, 20.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6987/8000 [07:23<01:12, 13.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6990/8000 [07:23<01:20, 12.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  87%|█████████████████████████▎   | 6995/8000 [07:24<01:21, 12.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7000/8000 [07:24<01:13, 13.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7005/8000 [07:24<00:59, 16.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7008/8000 [07:24<00:59, 16.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7011/8000 [07:25<01:03, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7013/8000 [07:25<01:06, 14.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7015/8000 [07:25<01:11, 13.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7018/8000 [07:25<01:20, 12.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7020/8000 [07:25<01:20, 12.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7022/8000 [07:26<01:21, 11.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7028/8000 [07:26<01:06, 14.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▍   | 7034/8000 [07:26<00:49, 19.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7038/8000 [07:26<00:57, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7042/8000 [07:27<00:52, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7049/8000 [07:27<00:48, 19.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7054/8000 [07:27<00:59, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7056/8000 [07:27<00:59, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7059/8000 [07:28<00:57, 16.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7064/8000 [07:28<01:09, 13.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▌   | 7068/8000 [07:28<00:59, 15.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▋   | 7071/8000 [07:28<00:57, 16.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▋   | 7075/8000 [07:29<00:50, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  88%|█████████████████████████▋   | 7078/8000 [07:29<00:50, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7081/8000 [07:29<00:49, 18.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7084/8000 [07:29<01:12, 12.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7086/8000 [07:29<01:10, 12.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7088/8000 [07:30<01:10, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7092/8000 [07:30<00:53, 16.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7097/8000 [07:30<00:55, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7100/8000 [07:30<00:54, 16.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▋   | 7102/8000 [07:30<00:58, 15.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7106/8000 [07:31<01:05, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7110/8000 [07:31<01:04, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7112/8000 [07:31<01:04, 13.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7114/8000 [07:31<01:05, 13.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7117/8000 [07:31<00:55, 16.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7120/8000 [07:32<01:06, 13.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7124/8000 [07:32<00:53, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7127/8000 [07:32<01:02, 14.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7129/8000 [07:33<01:20, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7131/8000 [07:33<01:35,  9.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7134/8000 [07:33<01:20, 10.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▊   | 7137/8000 [07:33<01:22, 10.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▉   | 7142/8000 [07:34<00:59, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▉   | 7147/8000 [07:34<00:47, 18.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▉   | 7151/8000 [07:34<00:54, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▉   | 7153/8000 [07:34<00:58, 14.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  89%|█████████████████████████▉   | 7157/8000 [07:34<00:47, 17.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|█████████████████████████▉   | 7161/8000 [07:35<00:57, 14.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|█████████████████████████▉   | 7163/8000 [07:35<01:00, 13.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|█████████████████████████▉   | 7166/8000 [07:35<00:55, 15.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|█████████████████████████▉   | 7168/8000 [07:35<01:12, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|█████████████████████████▉   | 7171/8000 [07:35<01:00, 13.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7173/8000 [07:36<01:03, 12.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7176/8000 [07:36<01:12, 11.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7181/8000 [07:36<00:52, 15.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7183/8000 [07:36<00:58, 13.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7186/8000 [07:37<00:52, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7191/8000 [07:37<00:39, 20.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7194/8000 [07:37<01:00, 13.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7197/8000 [07:37<01:03, 12.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7201/8000 [07:38<00:53, 15.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████   | 7205/8000 [07:38<00:48, 16.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7207/8000 [07:38<01:03, 12.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7209/8000 [07:38<01:05, 12.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7212/8000 [07:38<00:55, 14.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7215/8000 [07:39<00:52, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7218/8000 [07:39<00:49, 15.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7223/8000 [07:39<00:39, 19.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7227/8000 [07:39<00:37, 20.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7232/8000 [07:39<00:33, 22.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7235/8000 [07:39<00:35, 21.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  90%|██████████████████████████▏  | 7238/8000 [07:40<00:44, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▏  | 7241/8000 [07:40<00:52, 14.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▎  | 7243/8000 [07:40<01:04, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▎  | 7250/8000 [07:40<00:40, 18.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▎  | 7259/8000 [07:41<00:40, 18.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▎  | 7262/8000 [07:41<00:40, 18.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▎  | 7267/8000 [07:41<00:42, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▎  | 7270/8000 [07:42<00:42, 17.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▎  | 7273/8000 [07:42<00:45, 15.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7276/8000 [07:42<00:43, 16.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7278/8000 [07:42<00:45, 15.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7281/8000 [07:42<00:42, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7283/8000 [07:43<00:55, 12.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7286/8000 [07:43<00:50, 14.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7290/8000 [07:43<00:42, 16.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7294/8000 [07:43<00:38, 18.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7297/8000 [07:43<00:38, 18.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7301/8000 [07:44<00:43, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7303/8000 [07:44<00:45, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7306/8000 [07:44<00:43, 16.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▍  | 7309/8000 [07:44<00:39, 17.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▌  | 7311/8000 [07:44<00:39, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  91%|██████████████████████████▌  | 7314/8000 [07:44<00:39, 17.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▌  | 7321/8000 [07:44<00:27, 25.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▌  | 7324/8000 [07:45<00:28, 23.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▌  | 7327/8000 [07:45<00:30, 22.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▌  | 7334/8000 [07:45<00:29, 22.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▌  | 7341/8000 [07:45<00:24, 27.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▌  | 7344/8000 [07:46<00:32, 19.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7348/8000 [07:46<00:31, 21.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7354/8000 [07:46<00:31, 20.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7357/8000 [07:46<00:32, 20.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7361/8000 [07:46<00:30, 20.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7364/8000 [07:47<00:53, 11.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7366/8000 [07:47<00:52, 12.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7369/8000 [07:47<00:47, 13.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7372/8000 [07:47<00:43, 14.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7374/8000 [07:48<00:53, 11.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▋  | 7378/8000 [07:48<00:41, 14.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▊  | 7381/8000 [07:48<00:38, 16.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▊  | 7383/8000 [07:48<00:37, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▊  | 7388/8000 [07:48<00:29, 20.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▊  | 7394/8000 [07:48<00:23, 26.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  92%|██████████████████████████▊  | 7397/8000 [07:49<00:30, 19.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▊  | 7401/8000 [07:49<00:29, 20.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▊  | 7405/8000 [07:49<00:28, 20.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▊  | 7408/8000 [07:49<00:38, 15.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▊  | 7411/8000 [07:50<00:36, 16.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▊  | 7413/8000 [07:50<00:45, 12.91it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7416/8000 [07:50<00:41, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7418/8000 [07:50<00:41, 14.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7420/8000 [07:50<00:41, 13.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7423/8000 [07:50<00:35, 16.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7425/8000 [07:51<00:38, 15.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7427/8000 [07:51<00:53, 10.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7430/8000 [07:51<00:45, 12.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7433/8000 [07:52<00:52, 10.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7436/8000 [07:52<00:43, 13.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7440/8000 [07:52<00:35, 15.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7442/8000 [07:52<00:48, 11.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7445/8000 [07:52<00:42, 12.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|██████████████████████████▉  | 7447/8000 [07:53<00:44, 12.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7449/8000 [07:53<00:44, 12.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7455/8000 [07:53<00:35, 15.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7459/8000 [07:54<00:49, 11.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7461/8000 [07:54<00:48, 11.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7466/8000 [07:54<00:35, 14.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7470/8000 [07:54<00:31, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7473/8000 [07:54<00:30, 17.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7475/8000 [07:55<00:40, 13.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7477/8000 [07:55<00:50, 10.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  93%|███████████████████████████  | 7479/8000 [07:55<00:47, 10.99it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████  | 7482/8000 [07:55<00:41, 12.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7485/8000 [07:55<00:36, 14.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7489/8000 [07:56<00:30, 16.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7492/8000 [07:56<00:30, 16.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7495/8000 [07:56<00:29, 16.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7497/8000 [07:56<00:49, 10.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7499/8000 [07:57<00:48, 10.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7501/8000 [07:57<00:47, 10.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7504/8000 [07:57<00:38, 12.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7506/8000 [07:57<00:47, 10.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7514/8000 [07:57<00:24, 19.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▏ | 7517/8000 [07:57<00:23, 20.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7522/8000 [07:58<00:20, 23.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7525/8000 [07:58<00:22, 21.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7528/8000 [07:58<00:30, 15.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7530/8000 [07:58<00:32, 14.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7532/8000 [07:58<00:35, 13.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7536/8000 [07:59<00:29, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7539/8000 [07:59<00:28, 16.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7543/8000 [07:59<00:24, 18.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7545/8000 [07:59<00:26, 17.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▎ | 7548/8000 [07:59<00:26, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▍ | 7552/8000 [07:59<00:23, 19.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▍ | 7554/8000 [08:00<00:26, 17.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  94%|███████████████████████████▍ | 7557/8000 [08:00<00:25, 17.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▍ | 7563/8000 [08:00<00:19, 21.97it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▍ | 7566/8000 [08:00<00:25, 16.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▍ | 7569/8000 [08:00<00:25, 16.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▍ | 7574/8000 [08:01<00:27, 15.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▍ | 7578/8000 [08:01<00:23, 17.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▍ | 7581/8000 [08:01<00:23, 18.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7587/8000 [08:01<00:21, 18.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7589/8000 [08:02<00:29, 13.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7593/8000 [08:02<00:30, 13.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7598/8000 [08:02<00:23, 16.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7604/8000 [08:03<00:22, 17.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7608/8000 [08:03<00:25, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7610/8000 [08:03<00:34, 11.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7612/8000 [08:04<00:34, 11.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7616/8000 [08:04<00:32, 11.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▌ | 7618/8000 [08:04<00:32, 11.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▋ | 7621/8000 [08:04<00:28, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▋ | 7626/8000 [08:05<00:26, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▋ | 7630/8000 [08:05<00:26, 14.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▋ | 7634/8000 [08:05<00:21, 16.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▋ | 7636/8000 [08:05<00:23, 15.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  95%|███████████████████████████▋ | 7638/8000 [08:05<00:24, 15.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▋ | 7640/8000 [08:05<00:24, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▋ | 7643/8000 [08:06<00:27, 13.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▋ | 7645/8000 [08:06<00:27, 12.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▋ | 7648/8000 [08:06<00:24, 14.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▋ | 7652/8000 [08:06<00:21, 16.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▋ | 7655/8000 [08:06<00:20, 16.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▊ | 7659/8000 [08:07<00:18, 18.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▊ | 7662/8000 [08:07<00:22, 15.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▊ | 7665/8000 [08:07<00:28, 11.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▊ | 7670/8000 [08:08<00:32, 10.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▊ | 7674/8000 [08:08<00:27, 11.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▊ | 7678/8000 [08:08<00:27, 11.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▊ | 7685/8000 [08:09<00:19, 16.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7693/8000 [08:09<00:16, 18.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7701/8000 [08:09<00:14, 20.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7705/8000 [08:10<00:20, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7707/8000 [08:10<00:20, 14.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7709/8000 [08:10<00:19, 14.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7711/8000 [08:10<00:20, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7714/8000 [08:10<00:18, 15.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7717/8000 [08:11<00:17, 16.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  96%|███████████████████████████▉ | 7719/8000 [08:11<00:21, 12.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7725/8000 [08:11<00:14, 19.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7728/8000 [08:11<00:16, 16.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7730/8000 [08:11<00:17, 15.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7735/8000 [08:12<00:17, 14.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7737/8000 [08:12<00:18, 14.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7739/8000 [08:12<00:19, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7742/8000 [08:12<00:17, 14.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7745/8000 [08:12<00:16, 15.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7748/8000 [08:13<00:15, 16.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7753/8000 [08:13<00:15, 15.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████ | 7756/8000 [08:13<00:15, 16.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7759/8000 [08:13<00:17, 13.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7763/8000 [08:14<00:17, 13.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7767/8000 [08:14<00:14, 16.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7770/8000 [08:14<00:13, 16.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7773/8000 [08:14<00:12, 17.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7775/8000 [08:14<00:13, 16.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7780/8000 [08:15<00:13, 15.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7784/8000 [08:15<00:12, 17.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7787/8000 [08:15<00:11, 17.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7789/8000 [08:15<00:12, 16.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▏| 7792/8000 [08:15<00:11, 17.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  97%|████████████████████████████▎| 7798/8000 [08:15<00:09, 22.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▎| 7802/8000 [08:16<00:08, 23.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▎| 7807/8000 [08:16<00:07, 24.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▎| 7812/8000 [08:16<00:09, 19.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▎| 7816/8000 [08:17<00:12, 14.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▎| 7819/8000 [08:17<00:11, 15.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▎| 7823/8000 [08:17<00:11, 15.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▎| 7825/8000 [08:17<00:11, 14.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7828/8000 [08:17<00:11, 15.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7831/8000 [08:18<00:09, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7834/8000 [08:18<00:09, 18.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7836/8000 [08:18<00:09, 16.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7838/8000 [08:18<00:10, 15.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7840/8000 [08:18<00:10, 15.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7846/8000 [08:18<00:09, 16.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7850/8000 [08:19<00:08, 18.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7853/8000 [08:19<00:10, 14.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7856/8000 [08:19<00:09, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7858/8000 [08:19<00:09, 14.96it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▍| 7861/8000 [08:19<00:08, 17.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▌| 7864/8000 [08:20<00:07, 17.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▌| 7866/8000 [08:20<00:08, 16.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▌| 7870/8000 [08:20<00:08, 15.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  98%|████████████████████████████▌| 7875/8000 [08:20<00:08, 13.95it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▌| 7881/8000 [08:21<00:06, 18.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▌| 7888/8000 [08:21<00:04, 23.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▌| 7892/8000 [08:21<00:04, 22.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7897/8000 [08:21<00:05, 19.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7904/8000 [08:22<00:05, 16.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7908/8000 [08:22<00:05, 17.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7915/8000 [08:22<00:05, 16.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7917/8000 [08:23<00:05, 15.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7921/8000 [08:23<00:04, 16.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7923/8000 [08:23<00:05, 14.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7927/8000 [08:23<00:04, 16.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7929/8000 [08:23<00:04, 14.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▋| 7931/8000 [08:24<00:06, 10.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▊| 7937/8000 [08:24<00:04, 15.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▊| 7939/8000 [08:24<00:04, 13.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▊| 7947/8000 [08:25<00:04, 12.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▊| 7951/8000 [08:25<00:03, 14.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▊| 7955/8000 [08:25<00:03, 13.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals:  99%|████████████████████████████▊| 7958/8000 [08:26<00:02, 14.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▊| 7960/8000 [08:26<00:02, 14.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▊| 7964/8000 [08:26<00:02, 16.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7967/8000 [08:26<00:01, 16.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7971/8000 [08:26<00:01, 18.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7974/8000 [08:27<00:01, 14.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7977/8000 [08:27<00:01, 15.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7979/8000 [08:27<00:01, 14.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7985/8000 [08:27<00:00, 15.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7991/8000 [08:27<00:00, 19.90it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7994/8000 [08:28<00:00, 14.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 100%|████████████████████████████▉| 7998/8000 [08:28<00:00, 16.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Generating signals: 8004it [08:28, 15.73it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "concatenating...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "training:   0%|                                                   | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                          | 1/200 [00:06<20:12,  6.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▍                                          | 2/200 [00:07<11:32,  3.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                          | 3/200 [00:09<08:48,  2.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▊                                          | 4/200 [00:11<07:36,  2.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█                                          | 5/200 [00:13<06:55,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▎                                         | 6/200 [00:14<06:27,  2.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▌                                         | 7/200 [00:16<06:09,  1.91s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                         | 8/200 [00:18<06:02,  1.89s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▉                                         | 9/200 [00:20<05:53,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|██                                        | 10/200 [00:21<05:45,  1.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                       | 11/200 [00:23<05:38,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▌                                       | 12/200 [00:25<05:33,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▋                                       | 13/200 [00:27<05:33,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▉                                       | 14/200 [00:28<05:29,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▏                                      | 15/200 [00:30<05:26,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▎                                      | 16/200 [00:32<05:20,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███▌                                      | 17/200 [00:34<05:15,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▊                                      | 18/200 [00:35<05:13,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▉                                      | 19/200 [00:37<05:11,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▏                                     | 20/200 [00:39<05:10,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|████▍                                     | 21/200 [00:40<05:09,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|████▌                                     | 22/200 [00:42<05:07,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▊                                     | 23/200 [00:44<05:05,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████                                     | 24/200 [00:46<05:03,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|█████▎                                    | 25/200 [00:47<05:00,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|█████▍                                    | 26/200 [00:49<05:00,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▋                                    | 27/200 [00:51<04:59,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▉                                    | 28/200 [00:53<04:59,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|██████                                    | 29/200 [00:54<04:57,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|██████▎                                   | 30/200 [00:56<04:54,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▌                                   | 31/200 [00:58<04:57,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▋                                   | 32/200 [01:00<04:58,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████▉                                   | 33/200 [01:01<04:58,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|███████▏                                  | 34/200 [01:03<04:56,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▎                                  | 35/200 [01:05<04:54,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▌                                  | 36/200 [01:07<04:51,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████▊                                  | 37/200 [01:09<04:50,  1.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|███████▉                                  | 38/200 [01:10<04:45,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▏                                 | 39/200 [01:12<04:40,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▍                                 | 40/200 [01:14<04:36,  1.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████▌                                 | 41/200 [01:15<04:30,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|████████▊                                 | 42/200 [01:17<04:28,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████                                 | 43/200 [01:19<04:25,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▏                                | 44/200 [01:20<04:27,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████▍                                | 45/200 [01:22<04:24,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|█████████▋                                | 46/200 [01:24<04:20,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|█████████▊                                | 47/200 [01:25<04:16,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████                                | 48/200 [01:27<04:14,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|██████████▎                               | 49/200 [01:29<04:15,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|██████████▌                               | 50/200 [01:32<04:37,  1.85s/it]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 25it [00:01, 20.03it/s]\n",
+      "\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 1it [00:01,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 38it [00:01, 42.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 75it [00:01, 87.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 112it [00:01, 133.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 151it [00:01, 182.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 187it [00:01, 219.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 223it [00:01, 251.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 258it [00:01, 273.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 293it [00:01, 289.92it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 329it [00:02, 307.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 367it [00:02, 326.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 404it [00:02, 336.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 442it [00:02, 346.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 479it [00:02, 352.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 519it [00:02, 365.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 557it [00:02, 364.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 595it [00:02, 364.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 633it [00:02, 366.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 670it [00:02, 365.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 708it [00:03, 367.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 751it [00:03, 384.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Evaluating NLL: 800it [00:03, 242.28it/s]\n",
+      "\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|               | 0/100 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   5%|▎      | 5/100 [00:00<00:01, 48.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  10%|▌     | 10/100 [00:00<00:02, 44.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  15%|▉     | 15/100 [00:00<00:01, 44.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  20%|█▏    | 20/100 [00:00<00:01, 42.94it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  25%|█▌    | 25/100 [00:00<00:01, 42.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  30%|█▊    | 30/100 [00:00<00:01, 44.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  35%|██    | 35/100 [00:00<00:01, 43.98it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  40%|██▍   | 40/100 [00:00<00:01, 44.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  46%|██▊   | 46/100 [00:01<00:01, 46.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  51%|███   | 51/100 [00:01<00:01, 44.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  56%|███▎  | 56/100 [00:01<00:01, 42.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  61%|███▋  | 61/100 [00:01<00:00, 42.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  67%|████  | 67/100 [00:01<00:00, 44.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  72%|████▎ | 72/100 [00:01<00:00, 43.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  77%|████▌ | 77/100 [00:01<00:00, 43.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  82%|████▉ | 82/100 [00:01<00:00, 44.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  87%|█████▏| 87/100 [00:01<00:00, 43.87it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:  93%|█████▌| 93/100 [00:02<00:00, 46.93it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Bootstrapping to compute confidence interval: 100%|█████| 100/100 [00:02<00:00, 44.40it/s]\n",
+      "\n",
+      "Nyquist Samples: 100%|███████████████████████████████████| 4/4 [50:33<00:00, 862.70s/it]\u001b[A\n",
+      "Replicates: 100%|████████████████████████| 15/15 [10:04:06<00:00, 2416.45s/it]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAGCCAYAAAA46BDEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC6tUlEQVR4nOzddXhUR9vA4d9KsnGBEIcQIBCCFyvuUGjRUhwSKMWL1SjuFIpDixanUFrko/D2RVookuIaXBIkAgTint35/kizL0uEsCQsMvd15WozO3vmObtknz1zRhRCCIEkSZIkSa+U0tQBSJIkSdK7SCZgSZIkSTIBmYAlSZIkyQRkApYkSZIkE5AJWJIkSZJMQCZgSZIkSTIBmYAlSZIkyQRkApYkSZIkE5AJOJ9Uq1YNT09PqlWrZupQJEmSpDeA2tQBvC0iIiIIDQ01dRiSJEnSG0JeAUuSJEmSCcgELEmSJEkmIBOwJEmSJJmATMCSJEmSZAIyAUuSJEmSCcgELEmSJEkmIBOwJEmSJJmAnAdsYmlpaWi1WlOHIUnSO0alUmFmZmbqMN5pMgGbSGxsLJGRkaSkpJg6FEmS3lEajQYnJyfs7OxMHYrJ6YSWK08CiU55gIPGhbKFaqNUqAq0TZmATSA2NpbQ0FBsbGxwcnLCzMwMhUJh6rAkSXpHCCFIS0sjJiZGv4Lfu5yET0TsZM3lUTxJDtOXFbJwJ8DvO2q4timwdhVCCFFgR3+HeHp6EhoaioeHB/fv38+17u3btzEzM8PT01MmXkmSTEYIwf3790lLS6NEiRKmDsckTkTsZO4Zf+DZVJjx2TzyvbUFloTlIKxXLC0tjZSUFOzt7WXylSTJpBQKBfb29qSkpJCWlmbqcF45ndCy5vIosiZf9GVrL3+LThTMOB2ZgF+xzAFXcvCDJEmvg8zPondxMOiVJ4EG3c5ZCR4nh3LlSWCBtC8TsInIq19Jkl4H7/JnUXTKg3yt96JkApYkSZLeOU+Swzh0f3Oe6jpoXAokBjkKWpIkSXpnxKZE8tuN7/jr3lrSxfPueysobOFO2UK1CyQWmYAlSZKkt15CWjQ7bs1jT8gyUnXJAFip7ShiWYw7cUFkjHp+ejBWRte8v9+MApsPLLugpVcqICAAhUJh8KNUKilcuDCNGzdm+/btryyWGzduvLK2TKlhw4bv9H0+6d2WnB7P1hszGfxXOX6/vYBUXTIWKhvKFarHJz6jGf7eWkZUWUchCzeD5xW2cC/QKUggr4DfWlqd4NiDNB4maXG2VPG+ixkq5evzITx69GjKli0LZEzNevToEb/88gsdOnRg1apV9O7du0DbHzp0KLt37+bWrVsF2o4kSaaRqk1mT8hydtyaS0J6NAAalRXF7SpR1bkl7zk3x8OmDAqFAjfrklR3/VCuhCW9vN0hyYw9Hkd4ok5f5malZGpNWz4sbmHCyP6nWbNmNGzY0KBsyJAhlClThm+//VZ/pVxQdu7cWWDHliTJdNJ1afx1bx2/3ZhBbGokAGZKC7xsy1PFuQVVXVpQzLY8SoVhB7BSoaJc4XqvNNaXTsCPHz/mwIED3L59m4cPHxIVFYWlpSUuLi74+vpSt25d3Nzcnn8gKV/sDkmm74GYLNPKIxJ19D0Qw8pGvDZJ+FlWVlbUqVOHX375hUePHuHs7GzqkCRJekPohJYjoVvYfH2Kfm6vWmlOUZuyVCnSnKouLfG2r1TgV7Uvwqh7wE+ePOH777+natWquLi40LlzZ7799lvmzZvH6tWrWbJkCRMnTqRLly54enpSpUoV5s2bR1xcXH7H/9YQQpCQ9nI/sSk6xhyPy2VNFxh7PI7YFJ3RbRT0yqXBwcEULlyYwoULG5Rfu3aNrl274uzsjEajwcfHh3HjxpGUlGRQ7/bt23Tu3BkvLy80Gg1FixalT58+3L17F4CQkBAUCgV37tzhzp07KBQKJk6cmGtMycnJTJkyBV9fX/3i9R07diQoKMigXkBAADY2NoSEhNC5c2cKFy6MpaUltWvXZu/evVmOe+PGDfz9/XF3d8fKygo/Pz9mzpxJamqqQb0//viDJk2aYGdnh6WlJZUrV2bhwoXodDqDelqtlu+++47SpUtjYWFB+fLl2bRpU7bnpNPpWLRoEZUrV8bS0hIHBwdatmzJ0aNHDepNnDgRhULBH3/8QdmyZdFoNDRo0CDX10uSXiUhBMfDdzLi72r8eGEgT5LDUCnMKGrrR0uvgQSUm0UHn68p6fDea5V84QWvgGNjY5kxYwaLFy8mMTFR/2Hs5uZGmTJlcHBwwNramqioKCIjI7l//z5hYWGcP3+eL7/8kqlTpzJixAiGDh36Ti/8nZ3EdCi54WGBtiGA8EQdpX9+ZPQxbvVwxjofFvGKiYkhMjKje0ir1RIZGclPP/3EiRMnWLZsGSrV//5QTpw4QdOmTbGzs2Pw4ME4Ozvzzz//MG3aNPbv38+BAwewsLAgOjqaRo0aodPpGDhwIC4uLgQFBfHjjz9y4MABLl++TJEiRVi/fj0jRowAYN68eVSsWDHHOFNTU2nevDn//PMPvXr1YsSIEYSGhrJ06VJq1qzJvn37qF37f1MU0tLSqFu3LpUrV2bKlCk8fvyYOXPm0KpVK65cuYKPjw8A58+fp169evpYS5UqxV9//cWoUaO4cOECGzduBGDOnDl8+eWXlCpVim+++QYbGxu2b9/OsGHDOHDgAFu3bkWpzPge3aNHDzZv3kyrVq0YNmwY169fJyAgAHNz8yzn1b17dzZv3kzHjh3p168fT548YfXq1TRo0EBf/rROnTrx6aefUrp0aTQajTFvuSTlKyEE5x7tZ+PVcdyPvwpkdCO7WpWkUpEmVHNpRWmHGpipXuN/ryKPNm/eLFxdXYVCoRCFCxcWAwcOFP/3f/8nHj9+nOvzQkJCxIYNG0S3bt2EpaWlUCgUwtnZWWzfvj2vTb8RPDw8BCA8PDxyrZeUlCQuX74skpKSDMrjU3XCZVXEa/8Tn6p7qdfJ399fkPFdINuf9u3bi8TERH19nU4nypUrJ4oWLZrl39rKlSsFIGbOnCmEEOLXX38VgPjll18M6s2dO1dUqFBBnDp1Sl/m5eUlvLy8nhvvrFmzBCC2bNliUB4eHi6cnJyEn59flnMbOHCgQd3169cLQIwePVpfVr9+fWFubi7Onz9vULdr164CEEFBQeLWrVtCrVYLPz8/ER8fb/Ca9OjRQwBizZo1QgghDh48KADRrVs3g+Pt3r1b/9pm2rJliwDErFmzDOrGxcUJX19f4eTkJBISEoQQQkyYMEEA4pNPPnnuayW9mXL6THqdXYo8LL493EB03m0vOu+2F112O4qhB6qIlRe+EOcf/iWS0xNMHWKe5CkBd+/eXSgUCuHr6yvWr18vUlJSjGosKipKzJs3T3h7ewulUin8/f2NOs7r6GUTsE6nE/GpL/ez/25ynpLo/rvJRreh0+VPAp49e7bYt2+f2Ldvn9izZ4/45ZdfxJAhQ4SZmZkoX768ePTokRBCiPPnzwtADBgwQDx69MjgJyIiQlhYWIiaNWsKIYQ4duyYAETFihXFzp07DZLWs/KagKtUqSIcHByytP3o0SPRs2dPAYgrV64YnNvRo0cNjnH16lUBiD59+gghhHj06JFQKBSibdu2WdqLiIgQFy9eFMnJyWL27NkCEOvXr89S7969ewIQLVu2FEIIMXToUAGI06dPZ6lbsWJFgwTcvn17AYhbt25lOacxY8YIQPzxxx9CiP8l4OxikN4Ob1ICvvHklBgf+MFTiddBDPmrglh2fog482CPSEyLNXWILyRPXdB79uzhxx9/pF+/fvruLmM4ODgwfPhwBg8ezMKFC5k+fbrRx3rbKBSKl+7abehhjpuVkohEXbb3gRWAm7WShh7mJp+SVLVq1SyjoDt16oSvry9Dhgxh6tSpzJ8/n2vXrgGwdOlSli5dmu2xQkJCAKhZsyZjx45lxowZtGnTBjMzM95//31atmypv9f6oq5du0ZiYiJFihTJsU5ISAi+vr76311dXQ0ez+yyzVzs/s6dOwgh9NOwnubi4oKLS8ayd5lTpMqVK5elnqenJ/b29gQHBxvULVWqVJa65cqV48KFCwbnBFCyZMlcz+lpz56TJL1K9+Ius/7KWC5E/vVvSeYKVXWp4dqacoXrYm3mYMoQjZKnBHzjxg0cHBzyrVEzMzO++OIL+vTp88LPPX78OLVr1+bPP//M8gF+9+5dxo4dy/79+4mJiaFChQqMHj2aNm2yTqS+dOkSY8aMITAwkKSkJGrUqMHkyZOpU6eOsadlciqlgqk1bel7ICaHNV1gSg1bkyff3PTo0YMhQ4Zw8OBB4H9Ja/DgwbRr1y7b5zy9s9SUKVMYPHgwu3btYt++fRw8eJDDhw/r7xe///77LxSPVqulVKlSLFmyJMc6lSpVMvj9eV9SM7d9e940K/HvGIuc6ul0uiz3Y58dmJVdmVarxdbWlm3btuXYdpkyZQx+V6vljEXp1YtIuM3Gq+M5+WA3mZ9ojhpXfAvVprrLR5R3aoCdeeHcD/Iay9NfVX4m36c5Ojq+UP0bN27Qvn37bD9kIiIiqF+/Pk+ePGHo0KF4eHjw008/0bZtWzZu3Ei3bt30da9cuULdunWxtLRk6NCh2Nra8sMPP9CoUSP27dv3Ro/y/LC4BSsbkXUesLWSKTVen3nAOcl8bzMHYXl7e+sfa9q0aZa6W7du1W8kHhERQVBQEPXr16dv37707dsXIQQbN26kZ8+ezJ49m99+++2F4vH29iYiIoKGDRtmSUKBgYEkJCRgZWX1wseE/12JPu3ChQvMmDGD/v37669Qg4KCqFy5skG9u3fvEhcXR7FixQD0g7uuXLlCrVq1DOo+u+KXt7c3165do3Llyjg5ORk8du7cOcLDw7G2tn6hc5Kk/BSZdI/N16ZwNOw3BBmfCXbmRSjj+D7VXT6kglNDHC3egl6ZgurbvnDhgjh79qxIS0vLl+Nt27ZNODo66geUHDhwwODxAQMGCIVCYXD/LSkpSVSqVEk4OTkZ3A/84IMPhKWlpbh165a+LDIyUri7uws/Pz+j7nO+7D3g/Jau1YkjYSli261EcSQsRaRrX+7ebX7JvE/67PuXad68eQIQ33zzjRBCiPT0dFG8eHFhbW0tbt68aVB3yZIlAhCDBg0SQggxbdo0AYjNmzcb1Lt9+7YAROfOnfVlJUqUEEWLFn1uvGPHjhWAmDFjhkH5/fv3RaFChYSTk5N+0FjmuQUHBxvUDQ4OFoDBmIf3339fWFpaimvXrhnU7du3rwDEmTNnxM2bN4VKpRJly5bNMgirV69eAhBr164VQggRGBgoAPHhhx8KrVarr/vnn39mGYSVOXitf//+Bm3HxMQIHx8fYWZmJkJDQ4UQ/7sHnNP7Jb35Xqd7wFHJD8TyC8NEt/8U1t/n/XSvt5h5orP4885a8SjxnqlDzFcvlYDj4+PFzJkzxcqVK/Vl9+/fF1WqVBFKpVIolUpRvHhx8ffff79UkK1atRKA8PPz048SffoDIT09XdjY2IjatWtnee6qVasMPpQjIiKyHS0qhBDjx48XgDh27NgLx/i6JeDXVWaSGj16tFi/fr3+Z8WKFaJr165CpVKJYsWKicjISP1z9u3bJzQajbC3txfffPONWL58ufjss8+ESqUSJUuWFOHh4UIIIR48eCCKFi0qrK2txfDhw8XKlSvFd999J0qUKCHMzc0NvpzVqlVLqFQqMWfOHPHPP//kGG9cXJyoVKmSAESHDh3EDz/8IKZOnSo8PT2FUqkUmzZtynJueUnAp06dEjY2NsLe3l6MHj1aLF26VHTq1CnLKOoZM2YIQJQqVUpMmzZNLFiwQDRu3FgA4qOPPjJItoMHDxaAaNCggVi0aJH4+uuvhZWVlShSpIhBAk5PTxctWrQQgGjSpIlYsGCBmD17tvD19c0yOlom4Lff6/CZFJfyRKy99K3o/oezPvH23lNMTD/+sfhv8HIRkRD80gNAX0dGJ+C4uDhRvnx5oVQqRYcOHfTlrVu3FgqFwuDHxsZG3Ltn/DeXEiVKiOnTp4vk5ORsPxAyR8qOHDkyy3MvXbokADFixAghhBD/93//JwCxcOHCLHUzp2wsWLDghWOUCThvcpqGZGVlJcqWLSuGDx+uT6hPO3v2rOjYsaMoUqSIMDc3FyVKlBCff/55lrq3b98Wffr0EcWLFxcajUYULlxYtG3bVhw/ftyg3v79+0XJkiWFmZmZfnRyTuLi4sTo0aNF6dKlhbm5uXB2dhYtWrQQf/31V7bnlpcELIQQly9fFp988okoXLiwsLS0FBUrVhQ//vijSE9PN6i3Y8cOUb9+fWFjYyOsra1F1apVxeLFiw2Sb6Yff/xRlCtXTmg0GlGyZEmxbNky0b17d4MELIQQqampYtasWaJChQrCwsJCFCpUSNStW1ds3brVoJ5MwG8/U34mJabFis1XJ4ue/3XVJ96A/3qKyf+0Fr/fXCRC466/lYk3k9EJ+LvvvtPP6c28Ar5//77+yveXX34Rjx8/Fv7+/kKhUIjhw4cbHWRycrL+/7P7QMhMnPPnz8/y3JiYGAGIdu3aCSGE+OGHHwQgduzYkaVuZiI3JlaZgCVJehOZ4jMpJT1RbL8xRwTs8dQn3l7/dRPjAz8Q22/MFXdigt7qxJvJ6KGNO3bsQKlUsmfPHv0Akd27dyOEoFq1anTq1AmAH374ga1bt7Jnzx5jm3ruyjsxMTEA2NjYZHksc4BMQkLCC9fNzty5c5k7d26W8vDw8FxjlCRJetel61LZd2cVW2/OIj7tCQDmSguK2ZanSpHmvOfSHC+7Cq/dkpEFxegEfO3aNUqVKmUwOnPv3r0oFApatGihL7O2tqZkyZIFuu2b+He6RuZ/s3ssc1Tti9TNTmxsLKGhoS8XsCRJ0jtEq0vn7/s/s+XGNKJTHgBgptTgaeNLpSLNqOrcghL2VVAp363pbkafbXJyssF6zkIIDhw4AJBlGo9WqyU9Pd3Ypp7L1tYWgMTExCyPZZbZ29u/cN3s2NnZ4eHhkaU8PDw82+lRkiRJ7yqd0PFP2DY2XZ9EZNI9AFQKMzysS1OxSCOqurTCx6EaamXW9crfBUYn4GLFiulX9FEoFBw/fly/FWG9ev/bUzEqKopbt25lm7TyS+a8yvv372d5LLOsaNGiL1w3OyNHjmTkyJFZyj09PeWVsSRJEhkXZKcf/peNV8cSnpDR+6lSqHG1KklFp4ZUdWlFaceamKte73UJCprR60pWrlyZyMhI5s2bR2xsLFOnTkWhUNCsWTP9Pdu0tDQGDRpESkoKdevWzbegn+Xr64u9vT3Hjx/P8lhmWeaONdWrV0epVOapriRJkvRiLkb+zTdH6jL7dFfCE26hJGOHokaevejpN43OZcZT3qnBO5984SUS8IgRI1CpVHz11Vc4Ojryxx9/AOivDk+cOIG7uztbtmzB3Nw826vG/KJWq+ncuTOHDx8mMDBQX56cnMyCBQtwcXGhZcuWQMZau02bNuW3337j9u3b+rqPHz9m5cqVVKpUiSpVqhRYrJIkSW+ja0+OMzawKdNOtOVu3CUUKCli6UV9j6708J1CN98JVC7SFAu1XGUtk9Fd0DVr1mTz5s0MGTKEiIgIHB0dmTlzJvXr1wcy7pU+fvwYJycnfv3111z3XM0PkyZNYufOnbRs2ZKRI0fi4uLCTz/9xMWLF9m8eTMWFv/7tjVnzhxq1apF3bp1GTFiBBqNhh9++IEnT56wZcuWAo1TkiTpbRIcc4H1V8Zw+clhABQoKGThQdlCdajm0oryhetjY/5iyw6/K15qyFmHDh1o3749jx49wsnJyWAR+pIlS7Jjxw5atmxpsFh+QXF1dSUwMJBRo0axYMEC0tLSqFChArt27aJVq1YGdcuXL8/hw4cZPXo0U6ZMQalUUq1aNdauXfvCi/VLkiS9i0Ljr7PhyljOPtqrL3PUuOHrWIuqLi2p4NQQe03Ou4hJoBDZzcfJg0OHDmFvb59lJ5js7N27l5s3bzJo0CBjmnojZA7C8vDwyHaAV6bk5GSCg4Px9vY2uCqXJEkyhRf9THqYGMLPVydyPOL/EP/uUGRv7kwZh5q85/IBFYs0opDFi2/9+S4y+gq4YcOG1KtXj7///vu5dceOHcuNGzfe6gQsSZL0NnuSHM4v16ZwOOwXdCJjm1Bbs8KUdqxOlSItqFikMc5WXiaO8s2SpwQcFxfHo0ePspQnJSUZDGR6lhCCO3fucPXqVTlHVpIk6Q0Um/qY367P4M97a9GKjL2srdUOlHKoShXn5lQq0hRXqxLP3d9ayipPCTg2NpYKFSqQnJysL1MoFJw+fVq/D+nzPLtHqSRJkvT6SkyLYfvNOfz3znLSdBmf/ZZqO0rYVaGyc1OqFGmGh00ZmXhfQp4SsIeHB1988QVTp07VlykUimyXc8yOl5cXCxcuNC7Cd8z3Z+NNHUKOvqqSdf1sYzx+/JiJEyeyc+dOHj58SOnSpRk2bBh9+vTJ0/OTkpKYOnUqP//8M+Hh4Xh5edG9e3e++uorLC0tDepu2LCBnj17Znscf39/1qxZo/990aJFzJgxg6SkJFq0aMHixYuzbFj/448/MmHCBG7dumWwEtzrLCgoiH79+nH27FksLS3ZtWsXderUoUGDBhw8eDDX5x48eJBGjRoxYcIEJk6c+EriLUghISF4e3tnee9fRExMDKmpqRQp8noPMJo4cSKTJk3iwIEDNGzYMM/PS05PYFfwInbdXkSyNmNdfAuVNV52Fank1IT3XJpTzLY8SoXRs1ilf+X5HvC4ceP49NNPgYyu5RIlSlC9evVcp+0olUpsbGxwdJRD0KUMCQkJNG/enIsXLzJ48GB8fX359ddf+fTTT4mIiGD06NG5Pj8tLY0WLVpw+PBhGjVqxMiRIwkJCWH69Ons2bOHP//802AgyYULFwBYsWJFlgEmJUuW1P//0aNHGTp0KN26daNGjRpMnz6dgIAAdu3apa8THx/P5MmTGTNmzBuTfAECAgI4c+YMY8eOpWjRopQrV47169fj4uJi6tBeuSJFirB+/XqD9/5F7N27lx49erBly5YXSmpvgjRtKvtuL2fHzbkkpEcDYK60xMuuPBULN+Y9lxZ421d6ZzZKeBXynIDNzMzw8vrfDfb69etTqVIlgzJJep7Fixdz5swZNm3aRJcuXQDo168frVq1YtKkSfTs2TPXpUCXL1/O4cOH6dq1Kxs3btR3fzVp0oQPP/yQWbNmMX78eH398+fP4+zsTN++fXONa+3atbi6urJu3TpUKhVqtZohQ4bw4MEDfaKaPXs2lpaWb9xgwgsXLlCpUiUmT56sL+vRo4cJIzIda2vrlzr3wMDAbMfDvMmE0JGUHs+U4x9xNznjC6uZ0iJjowSnxlRxaUFJ+/dQKwt+Oum7xug+hIMHD7JgwYL8jEV6B6xduxYPDw998oWM2xlff/01qamp/Pzzz7k+f9u2bQDMmjXL4N5Tq1atqFy5MsuWLTOof+HCBcqXL//cuO7du4e3t7d+J6zMsQ137twB4MGDB8yZM4epU6dibv5mLRyflpaW6wYj0rtJCEFc6mPCEm4Sn/aE+LQo1ApzitmWo7lXX3qWncrHPt9QxrGmTL4FJE8JWKfTZRnFnFn2Ij/Suy0mJoarV69Ss2bNLI9llmW3RvfT7t27R6FChfD09MzymI+PD2FhYYSFhQHw8OFDIiIi9Ak4NTWVlJSUbI/r4uJCVFSU/vfIyEgAnJ2dgYyV1kqVKkW3bt2ed5rZOnXqFB9//DHOzs7Y2NhQuXJlli9fnmUcxcaNG6lVqxbW1tZYW1tTq1YtNmzYYFDn4MGDKBQKNm/ezPTp0ylVqhQajQZvb2/Gjx+v33ls4sSJ+i8pf//9NwqFgoCAACDjS8+zXajnzp2jdevWODo64uDgQK9evXj48GG253Pt2jW6du2Ks7MzGo0GHx8fxo0bR1JSkkE9hULBkCFD2Lp1K9WqVcPS0pIiRYoQEBBAREREluNu2bKF+vXrY29vT+HChWnSpAl//fWXQR2dTseiRYuoXLkylpaWODg40LJlS44ePZr7m0DGPeCnXwfImFJZvnx5Lly4QKtWrbC3t8fGxobmzZtz8uRJg3qTJk0CoFGjRhQvXlz/WHJyMlOmTMHX1xeNRoOTkxMdO3YkKCjIoP2AgABsbGz4z3/+g5eXF5aWlnTv3h1nZ2fKlCmTbcwVKlTAxcWFtLSMEcgPHjxgxIgRlClTBktLSywtLfHz82PKlCl52nVOCEF8ajT34i/zMOkuWpGOAgUult40KdqbHr5T6VR6NH6F62Kmyn0vdunl5KkLWq1Wo1QquXz5MqVLlwZ44dWtFApFgW5JKL3+QkNDEUJQrFixLI9ZWVnh6OhIcHBwrsewsbHh7t27aLXaLPs2ZybNsLAw3N3dOX/+PAB3796lWrVqnDt3Dp1OR7Vq1fjuu+9o3Lix/rnNmjVj7dq1rFmzhtq1a7No0SL8/PwoVqwY169fZ8WKFezatcuoEZ979uyhTZs22NnZMXDgQNzd3dm2bRv9+/fnzp07TJs2DYDPP/+cxYsX89577+kHPG3atImePXty8uTJLD1Oo0ePRqfT0b9/fxwdHVm9ejVTpkxBoVAwadIkOnToQKlSpejZsye+vr6MGTMmx3ufZ86coX79+mg0GoYNG4aDgwMbNmxgx44dWeqeOHGCpk2bYmdnx+DBg3F2duaff/5h2rRp7N+/nwMHDhjcb9+9ezdr166lf//+9O/fn7/++ou1a9cSHBxssI7AhAkTmDx5MpUqVWLMmDFoNBp+/PFHmjdvzu+//65fz7179+5s3ryZjh070q9fP548ecLq1atp0KCBvvxFRUREUL9+ff1tjODgYObNm0fTpk25e/cu9vb2jBkzhkKFCrF9+3ZGjx5N9erVgYwvds2bN+eff/6hV69ejBgxgtDQUJYuXUrNmjXZt2+fwQYvycnJdOnShZEjR+Lg4ICXlxcuLi7MmzePEydOUKNGDX3ds2fPEhQUxBdffIGZmRkxMTHUrFmTqKgoBg4ciI+PD5GRkaxbt47x48ejUqlyHEchhCApPY7HyaGk/juqWYECtdIMjcqa1iWHUtalJpZq2xd+/SQjiTxQKBRCqVSKa9euGZS9yI9SqcxLU28sDw8PAQgPD49c6yUlJYnLly+LpKSkbB+fdSbutf15WYGBgQIQY8eOzfE19PHxyfUYQ4YMEYDYsmWLQfndu3eFpaWlAMShQ4eEEELMnj1bAMLJyUl89913YufOnWL27NnCxcVFqFQqsXPnTv3ztVqt6NmzpwAEIDw9PcXJkyeFEEJ06NBBNGnSxKhz1ul0onjx4qJw4cLi/v37Bu3VrVtXaDQa8fjxY3Ho0CEBiCZNmojU1FR9vdTUVNGoUSMBiIMHDwohhDhw4IAAhJubm4iKitLXjY+PF/b29sLd3d0gBkA0aNAg17KGDRsKtVotLl26pC9LTk4WdevWFYCYMGGC/nzKlSsnihYtKh4/fmxwzJUrVwpAzJw506AdQBw7dsygbpMmTQQgrl+/LoQQ4saNG0KlUokGDRqIlJQUfb0HDx4Ie3t7Ua1aNSGEEFu2bBGAmDVrlsHx4uLihK+vr3BychIJCQkiJ8HBwQIQ/v7++rIGDRpkiVsIIaZMmSIAsXz5cn3ZhAkTBCAOHDigL5s1a1a2/ybDw8OFk5OT8PPz05f5+/sLQHz11VcGdYOCggQghgwZYlA+fPhwAejflwULFghAbN261aBeVFSUMDc3FxUqVMg21sS0OHEv9oq4GX1G3Iw+I25FnxF3YoLEg4QQ8Tj2gQi6FJTjZ5JUcPJ0BXzgwAEAgyuXzDJJyivxb3eryGH6mhAiy1Xts7744gvWrVvHZ599RkxMDE2aNOH27duMGDECKysrkpKS9L0zNWrUYMyYMQQEBFCqVCn9MTp27Ej58uUZNGgQH374IUqlEqVSybp165gyZQqRkZGUL18ejUbDsWPH2L59u74rcs2aNcyePZvHjx/TtGlT5s2bl2Wq0tPOnj1LSEgIw4YNM9gTW6lUsmHDBpKTk7Gzs9PPJpg4caJB75KZmRmTJ0+mXr16/PLLLzRo0ED/2EcffYSDg4P+d2tra8qWLWvQbZoXjx8/5tChQ3z44Yf4+fnpyzUaDSNHjuTIkSP6sosXL3Lp0iUGDBiATqfT9zpkxmNhYcG2bdv4+uuv9eU+Pj5ZbjtUr16dP//8k4iICHx8fNi5cydarZbhw4cb3GN3dnbmyJEj+lHnmzZtAuDjjz82aDuzbNq0aRw6dIgPPvjghV4DyDowLfMKN7uu8qdt2rQJBwcHGjVqZBCTWq2mZcuWrF+/nqtXr+Lr66t/rE2bNgbHKFeuHNWrV2fz5s3MnTsXMzMz0tPT2bRpEzVr1tS/L0OHDqVLly5Z/s1FRkZib29PfHzWaYyPku4RlpAxBkABqBXmWKitsVLbY6m2JS01HaXi8XNeHakg5CkBP/1Hn1uZJOXG1jajaysxMTHbxxMTE3MdAQ1QvHhx9u3bR8+ePfnss88AMDc3Z9CgQTg4ODBx4kQKFSoEQL169ahXr16WY3h5edG+fXvWr1/P5cuXDQZpeXl5GYzs//rrr+nUqRNVq1bl77//pnfv3syaNYvatWszYMAAunfvzp49e3KMN7NLvWzZstnGkenWrYxNy8uVK5elXmZ8z3bPu7q6Zqmr0WjQarU5xpNTjDqdzuBLSqZn47l27RoAS5cuZenSpdkeLyQkJE9xAvpYc3udnn5/MtvPbRrRs+3n1bNxPhtjTq5du0ZiYmKu84JDQkIMEnB2r0mfPn0YOHAg//3vf2ndujV79uzhwYMHBqPXAVQqFbNnz+bEiRMEBwdz8+ZNYmNjgYxbOQAp2iTiUp8AkKrN6G5WK8ywUFljZZaReDMHVqUhbw2aykvthvSs2NhYEhMTcXBwkBsNSFl4e3ujUCiy3awiISGB6Ojo5yZgyLiyvXr1KkFBQcTGxuLn54ejoyP+/v6o1eo8TY3LnFoUFxeXY52dO3dy7Ngxrly5AsC6desoUaIEX331FQDffPMNPXv2JDw8HDc3t2yPkTlw5nn3jnPqFYD/JYDMhJDp6d3H8kN2MTw7eDIzlsGDB9OuXbtsj/Ps+JC8xJnX10mr1WJra6sfDZ+dnAYzPY+xr6dWq6VUqVIsWbIkxzrPblqjVmf96O3atSsjR45k/fr1tG7dmnXr1mFlZWUwYyAoKIgGDRqQnJxMo0aNaNasGSNGjNAvrAKCiITbJKTHkKbLGHCoUqixVttjpbbD0swOM+WbNYr/bfbSCfjatWvMmTOH3bt3G3TVlCxZkrZt2zJy5MgcP5ykd4uNjQ1ly5blxIkTWR7LHP389GCV7Jw5c4YTJ07Qs2dPKlSooC/XarXs27ePWrVq6RNVu3btCAoK4uLFi1lWyLp8+TKQ85WUVqvl22+/pX///vo6YWFhBotXZF7x3Lt3L8d/497e3sD/rtyetm/fPtatW8fXX3+tb+PSpUvUrVvXoN6lS5cAsh28lh9KlCihH2T5rBs3bhj8nnk+AE2bNjV4TKfTsXXrVkqUKPHCMTz9OmUO9Mw0b948Ll++zMKFC/H29ubatWtUrlw5SzfsuXPnCA8Px9r61W747u3tTUREBA0bNsySWAMDA0lISNBfmebG3t6eDh06sG3bNiIiIvj999/p2LGjwaIvw4cPJzo6mqCgIIPegsSUBCIjH1HIyZGE9BgA/UpVNmaOFLbwQK00l8tGvmZe6iv02rVrqVy5Mj/99BPh4eEIIfQ/N2/eZO7cuVSsWDFPOyZJ74YePXpw584dNm/erC8TQvD999+j0WgMvu1n5/z58wwcOJBffvnFoHzGjBmEh4fzxRdf6Mvc3Ny4desWy5cvN6h78OBB/vjjD1q1aqWfZvSs1atXc/fuXcaNG6cv8/T05M6dO/qrwsxu4+ymRGWqWrUqnp6ebNy40WABh8xz3rRpE25ubvqRuxMnTjSYLZCenq6f+mLM6N68KFSoEM2aNWPfvn0cO3ZMX67Vapk7d65B3WrVqlG8eHHWrFmjP/9My5cvp1OnTqxateqFY2jbti0KhYLFixcbnP+TJ0+YOXMmJ06cwNLSUv8ajB071uD5sbGxdOrUibZt2xqsWZ/fMscoPN0z0LFjR6Kjo5k9e7ZB3dDQUFq3bk23bt3yfHXdp08fkpKSGDRoEElJSVmWZ42MjMTa2lr/JSddl8ajxLtM/X4cSUnJpGu1qBQqrNR2WKgylo61NrPHTKWRyfc1ZPQV8OnTp/n000/R6XTUrVuXzz77jIoVK2Jra0tMTAxnzpxhyZIlnDlzhrZt23L+/Hm5apbE8OHD2bBhA/7+/pw+fZrSpUuzZcsW9u/fz/fff29wJXn79m0CAwMpWbKkfjOPTp06MXv2bIYOHcqNGzcoWbIkBw4c4OeffyYgIIC2bdvqnz9x4kT+85//8MUXX3DhwgVq1KjB5cuXWbp0Ke7u7vzwww/ZxpiYmMiECRP48ssvDRJ09+7dWblyJT179qRmzZpMnz6dZs2a4e6e896narWapUuX0q5dOypXrsyAAQNwcnJi+/bt7Nu3j5kzZ+Lk5ETDhg3p378/y5Yto2bNmnTt2hWAzZs3c/r0aQYNGkT9+vVf6rXPzcKFC6lVqxZNmzbl888/x93dnS1btnD9+nWDeiqVihUrVvDRRx9RtWpVBgwYQMmSJTl58iSrVq2iZMmSBl9a8srX15dRo0YxY8YM6tSpQ5cuXdDpdKxYsYKoqCh+++03IGMe7a+//sqyZcu4efMmbdq0IS0tjZUrV3Ljxg1mzZqV6/vxsjJ7QJYsWUJYWBg9evTgm2++4ffff+fbb7/l5MmTNGnShKioKJYuXUp0dDQbN27M0gOTk0aNGuHt7c327dspUaJElrE2bdu2ZfLkyTRr3ozWHT4gMS2Ov/cf4cCeI1hYWhAfm4CDuQuWZnZoVBlX3TLxvr6MTsCzZs1Cp9MxePBgFi1alOXxKlWq0Lt3b3r27MmmTZuYPn16llWKpHePpaUlBw8eZPTo0axbt464uDjKlCnDunXrsmyacOjQIXr37o2/v78+AVtbW7N//37GjRvHhg0bePLkCT4+Pvz444/079/f4PkuLi6cOHGCCRMmsGvXLtatW4ezszP+/v5MnDgxxw/q+fPno9VqDa6mIWMhhhUrVjBjxgx2795NixYtsv23/6wPP/yQw4cPM2XKFObMmYNWq6Vs2bIGy3FCxsCmGjVqsHTpUsaPH49araZSpUps3LjR6AVA8qp06dIcP36cMWPGsHz5clJSUmjWrBlTpkyhUaNGBnWbNm3KsWPHmDZtGqtWrSImJgZPT08GDRrE6NGjsx1glBfTp0+nbNmyLFq0iDFjxmBtbU316tXZsGED1apVAzK+APz+++/Mnz+f9evX880332BlZYWfnx9bt26lQ4cOL/1a5KZLly5s376dXbt2sX//ftq3b4+NjQ1HjhxhxowZ/Pbbb+zatQsHBweqVKnCunXrsrx+uVEoFPTu3Zvx48fTu3fvLMlzzNjRpJHIz+t/Yew3k3BwtKNEqeL89PMPXDp3jbmzFnLx5HXq1asnE+8bQCFyG/2RC1dXV1JTU4mIiMh1ab74+Hjc3NxwcHDg3r17Rgf6uvP09CQ0NBQPD49sBxllSk5OJjg4GG9vbzlQTZKkPNEJHTEpD4lOeYiOjIFwSpSYqyyxVNtipc644jUm6crPJNMx+go4JiaGihUrPndd3MyBNxcvXjS2KUmSpHeSEDpiUyOJSnmAVmTcG1eixExpgaXaBiszeyxUVijk1oBvJKMTcMmSJQkODs52ScCnCSG4f/9+nqaXSJIkSf/bKCEqJYJ08e8ULRSYKy2wUNtgrbZHo7aWe/K+4Yx+9/r3709kZCRTpkzJtd6SJUuIiIgwWPxckiRJykoIQXzqE+7FXeZR8j3SRZo+8dqZF6aQhTuFLNywNLOVyfctkKcr4Nu3b2cpa9myJVu3bmXKlCncvn2bwYMH89577+mXULt69SqrVq1i4cKFtGnThm+++Sbfg5ckSXobCCFITI/lcXKofgENBQrMlOZYqDK7mm1QKXNfqlV6s+RpENbz1ud9mpmZmX5Vm0zm5uYolUoSEhJePMI3hByEJUmSMRLT4niSHEqKLmMrRwWKZ9ZrtkGlzNdFCw3IzyTTydO7+iIDpVNTU7OU5bQHqyRJ0rsqOT2ex8mhJGsz1kbP3ChBo7bGWm2HxVPrNUtvpzwl4Oft0SpJkiTlTUp6Io+TQ0nSZuxcpABUCrN/u5pt/90oQa7X/C7IUwKWK1hJkiS9nFRtMo+TQ0lMj9WXqRVmaFRWGRslqG0xU2lyOYL0tslTAo6JicHe3j7fG4+OjjbYz1SSJOltk6ZN4UlyGAnp0WTezFMr1GhUVlhmJl6lXKv5XZSnceylS5dm2bJlWbYmM1Z6ejpz587Ndv9RSZKkt0G6LpWHiXe4F3+F+H+Tr0qhxkpth73GmUIW7tiZO2GuspDJ9x2VpwTcuHFjBg4cSPny5fn555+zHWiVF1FRUSxcuBBfX1++/PJLWrZsadRxJEmSXldaXTqPku5xN+4ycWlPEAhUChWWKlvszYtQyMINe3NnzFWWMvG+4/KUgDdt2sTGjRuJioqiZ8+euLu7M2jQIHbt2kV0dHSuzw0JCWHDhg10794dDw8PRowYQWxsLL/99hvr16/Pj3OQJEkyOa0uncdJodyNu0RsaiQCgRIVliob7P5NvA4aF6PXbJbePi+0GUNsbCzTp09n8eLFJCYm6v8Rubm5UapUKRwcHLCysiI6OprIyEju37/PgwcPgIypTA4ODgwbNozhw4cXyD1lU5LzgCXp3aQTWqJTHhKT8hAdGbfp9BslqGywMsvcKOH1XLlKfiaZzgv9i7Czs+O7777jzp07TJ8+nUqVKgEQFhbGoUOH2LlzJ5s3b+a///0vp06dIiIiAiEEFSpU4PvvvyckJIQJEya8dclXMs6+ffv45JNP8PT0RKPR4OnpSfPmzdm0aZPBpuzGiomJ4dGjR/rfJ06ciEKh4ODBgy99bGMoFAoaNmyYL8cy9blkJyAgAIVCQUhIiFHPf/acQkJCUCgURi9jGx4enqfFf7Jrp2HDhs+9StUJHdEpD7gbe4molAh06FCiRKO0ws7cKeOK18IVC7WNQfK9efOmwXHy0pb0djJqeZXChQszatQoRo0axePHjzl48CDBwcE8fPiQqKgoLCwscHV1pUyZMtSpU8dgk3VJSklJoW/fvmzYsAEfHx969+5N8eLFefjwIf/3f/9Ht27d+OGHH/j111+N/rezd+9eevTowZYtW/RJr0OHDpQqVYqyZcvm49mYxtt0LjkpUqQI69evp2TJki/83PXr1zNo0CAuXryItbV1vrYjhPh3h6II/Q5FCpSYKzVYqG2wUtthobZGqci6guCsWbMYN26cweJEY8aMoW/fvi9wdtLb4qXXNytcuDAff/xxfsQi5SOd0HLlSSDRKQ9w0LhQtlDtbD8QTGHQoEFs2LCBL774gpkzZxosdfrtt9/y008/MWDAAD766COOHTuGmdmLrwYUGBhocPULULFiRSpWrPjS8b8O3qZzyYm1tTU9evQw6rl//vkn8fHx+dqOEIL4tCc8SQ432KHITKnJ2BpQbYeF2ibXv7P//Oc/WQaxNmvWLE9xSm+fgltgVDKZExE7WXN5FE+Sw/RlhSzcCfD7jhqubUwYGRw7doxVq1bx0UcfMXv27GzrfPrpp9y4cYOZM2eycOFCvvjii1ccpST9jxCChLQYnqSEkqbLSJ6ZiddCZY2Vmd2/GyXIj1PpxbyeowIko52I2MncM/4GyRfgSXI4c8/4cyJip4kiy7BixQqA5ybVr7/+GrVazerVq/VlAQEBqNVqbt26RatWrbCxsaFIkSJ069bN4L5jw4YNmTRpEgCNGjWiePHiQM73GL///nsWLVpEmTJlsLCwwMfHh2XLlgGwdOlSfH19sbKyws/PL9uR+9u3b6d58+Y4OTlhZmaGk5MTrVu35vTp00a9Rn/++SdNmjTB2dkZCwsLypYty7hx40hKStLXye4esBCCRYsWUa5cOSwtLSlRogSzZs1iypQpBvdm16xZg0Kh4O+//+arr76iaNGiaDQafH19WbBgQZZ4bt26xWeffYa3tzcajQZra2uqVKnCDz/8YNT5AWzbto2aNWtibW2Np6cnEyZMyHLfP7t7s4mJiYwcORJfX18sLS0pXLgwH330EUePHtXXKV68OGvXrgXA29tbfwsiICAAGxsb/vOf/+Dl5YWlpSU9evTI9V7z0eN/U6teDZzsXXmvZH1GD51C1KNYbMwccdS4UsjCHRtzR5o0bprluc++R5mveeb/Z7aX3T3g5ORkpkyZgq+vLxqNhkKFCtG6dWuOHTuWbRtXr17ls88+w9XVFQsLCypXrszPP//83PdBMi35le01IYQg5d9F2Y2lE1pWX/oGyG5guwAUrLk0ivKFGxrdHf2yUygOHz6MWq3m/fffz7VeoUKFqFq1KsePH+fhw4c4OzsDGa9T48aNKVasGDNmzCA4OJgffviBv/76i1OnTuHp6cmYMWMoVKgQ27dvZ/To0VSvXj3XthYtWoQQgsGDB2NlZcXs2bMZMGAAu3fv5uzZs/ryuXPn4u/vj4+Pjz7+BQsWMHz4cBo2bMiECRMwNzfn1KlTrF27lsDAQO7cuYONjU2eX59jx47x4YcfUrlyZcaOHYuFhQV79+5l6tSpXL9+nV9++SXH5w4ZMoQff/yRRo0aMXDgQO7evcukSZOwtLTMtn7v3r2xtrZm+PDhmJmZ8eOPPzJ8+HDs7Ozo3bs3kJEEq1evjkajYcCAAXh6ehIWFsaKFSsYMmQIjo6OdOvWLc/nB7Bs2TIGDBhA+fLlmTJlCgkJCfzwww8kJj7/33/Xrl3Zu3cvQ4YMwdfXl4iICBYvXkyjRo04efIklSpVYv78+cydO5fDhw8zb948ypUrp39+cnIyXbp0YeTIkTg4OOS4zK5OaAFo0bQVterXYPTUkVy7dJPNa7dy4vBZTp0+iY2V4wud9/r165k2bRpXr17N9Z5zYmIijRs35vjx47Rr147PP/+cBw8esGzZMurVq8fPP//MJ598YvCcVq1a4e7uzujRo0lJSWH+/Pl0794dNzc3GjVq9EJxSq+OTMCviRRtIgF7PQq4FcGTlDD67Ctm9BHWNA/FQp37oJbchIWF4ejomKfpDp6enhw/fpzQ0FB9AtbpdPj5+bFr1y79veMGDRrQrl07xo0bx+rVq2nWrBlHjx5l+/btNGvW7Lkjjx89esTVq1f1H8ZeXl60a9eOP//8k6tXr1K0aFEgY0W4li1bsnv3bt5//320Wi1Tp06lSpUq7N+/Xx9P//79cXR05Pvvv2fv3r106NAhz6/Phg0bSElJYefOnfpz7tevH126dOHu3bukpKSg0WRdL/jkyZP8+OOPtG3blu3bt+u/JLVt25Z69epl25adnR0nTpzA3Dxj4f/27dvj5eXFypUr9Ql44cKFREVFcfr0ad577z39cz/++GPKlSvH5s2bXygBx8XF8dVXX1GqVCmOHz+OlZUVkPFlIHNWRU4iIyPZuXMnAwcO5Pvvv9eXN23alJ49e+oTcLt27dixYweHDx+mXbt2+h4QAK1Wy4ABA5g4caK+7Onek+T0BB4nh5Kizeht6ND1I6bOHo9GZY2l2obqlWsxYthIFsxbyOTJk/N83gA9evRg5cqVXL16Ndd7znPmzOH48eOMHz9e35MDGWMnKlSoQL9+/WjevLnBbJKyZcuya9cu/fteq1Yt6tWrx8qVK2UCfo3JLmjplRJC5HlQlVqt1j/naRMmTDAYuNW2bVv8/PzYvn37C22dmal27doGV0J+fn4A1KlTR598Af3SqaGhoUDGPtmhoaH8+eefBvEkJCTozzGvA4EyZbY3aNAgAgMD0WozrsQ2b95MYGBgtskXYMuWLQCMHj3aoIeiTp06NG2atXsUoFOnTvrkm9m2i4sLERER+rI5c+YQERFhkHx1Op2+u/hFz2///v3ExcUxcOBAffKFjC9bzxsIZWtri729PVu2bGHFihX6OGvWrMn169fzPJK4TZvsx0EkpcUSmnCdZO3/pi59PfrLf5eNdMPWvDCDBw7B3t6ebdu25aktY2zZsgVra2u+/fZbg3JXV1eGDRtGdHQ0e/bsMXise/fuBu97Zq/P0++l9PqRV8CvCY3KijXNQ1/qGFeeBDLz1CfPrfdNtV8pW6i2UW1oVFbPr5SLokWLEhISQlpa2nMTcWai8/T0NCivUKFClrplypTh8uXLREZGUqRIkReKydXV1eD3zLienQKV+YXg6TXRzc3NOXLkCFu2bOHmzZvcvn2bO3fu6L8IvOj66Z9//jmHDx9m69atbN26FXt7exo0aEDr1q3p1q2bQdJ62rVr1wDw9fXN8pifnx/79u3LUv7seQNoNBp90oeMe5VpaWlMnDiRc+fOERwczM2bN/XdxS96frdu3QLIdh34p7uKs6PRaFizZg29e/emX79+AJQvX54WLVrQvXt3qlSpkqcYnj7vNG0KjxLvApD+75QitcIMlUKNU5HClC5awWCjBDMzM7y9vbly5Uqe2jLGrVu38PHxybaXqHz58kDWLWKffS8zv6g9/V5Krx95BfyaUCgUWKitX+qnUpHGFLJwJ2OH0WxbobCFB5WKNDa6jZddMKBRo0akpKQQGBiYa73Y2FhOnz6Nn5+fvis283XKLnFnXpFlJskXkdMXgbyc64ABA2jSpAmHDx/Gy8uLIUOGsG/fPqMHKFlZWbFr1y4uXbrEzJkzqVmzJvv37+ezzz6jUqVKPHnyJNvnZU5tye4KOafufqXy+X/+f/31Fz4+PixYsACVSkXbtm1ZvXp1rqu95UV2PRV5Sebt2rUjNDSUrVu30r9/f1JSUpgzZw5Vq1Zl4cKFeWpbrVaTrkvlQWII9+Ivk5geB2S831ZqO+zNi6BWmqNSqrPdKEGn0+Xp35mxi8kIIXL8t5eZUJ99n/PyXkqvH/muvUWUChUBft/9+9uzf8AZv/v7zTDpfOC+ffuiUCiYPn16rt3F8+fPJykpiT59+hiUCyH0V1FPu3btGkWKFMHR8cUGxryMI0eOsGzZMrp160ZQUBA//fQTI0eOpHHjxjkmyue5fv06R44cwc/Pj6+//po9e/bw+PFjBg0axM2bN3Mc2Vq6dGkArl69muWxzKtjY/Tr1w9LS0uuXLnC1q1bmTx5Mp06dTL6eD4+PgBcvnw5y2M3btzI9blxcXEcPXqU6OhoOnTowNKlS7l+/Trnzp3D0dExz/dkM9Zrvkx8WtS/OxRl/D2YKy0opHHDXuOMUqEkMjKS2NhYg+empKQQHBysf70h41bE0yPUMxnb/VuyZElu3rxJcnJylscuXboEQLFixo/jkF4f+ZKAExMTCQ8P5969e9y9ezfHH6ng1XBtw8j31lLIwrD7tLCFOyPfW2vyecBVq1Zl+PDh7N27l2HDhmV7lbBx40amTJlC1apVGTp0aJbHnx6AA/DLL79w/fp1unTpoi/LvCebX1toZicyMhLI6BZ8+oolMjKSn376CXjxq6AhQ4bQpEkTg78XCwsLqlWrBuR8hd+5c2cA5s2bZ1B+6dIl/vjjjxeK4WmRkZE4Ozvj4uJiUD5z5kzgxc+vWbNmODo6snjxYoONXCIjI1mzZk2uz7148SJ169ZlypQpBuXlypXDwcHB4LV59v3X6tL193bj06Izdij6d6MEG/NCAJirLNGo/zfKX6vVsnjxYoO25s+fT1xcnMGXEHd3d65evWpwP/zJkyfs2rUryznk5d9lx44dSUhIYMaMGQblDx8+ZNGiRdja2tKiRYscny+9OV7qHvAff/zBmDFjOH/+/HPrKhSKfFnfV3q+Gq5tqOby4Wu7EtasWbNIS0tj0aJF7Nmzh65du1KsWDH9h9bff/9NnTp1+OWXX7LtHt60aRORkZF88MEHXL16lSVLllCyZEmDEaOZCWPJkiWEhYUZvaJSburWrUvhwoWZNm0a8fHxlChRgtu3b7Nq1SpiYmIAnrtb2LPGjh3LwYMHqVu3Lv369cPNzY0bN27w448/UrRoUX2ifVadOnUICAhgzZo1hIWF0bZtW8LCwli8eLG+e9KY2wdt27Zl3bp1tG/fnlatWpGYmMjWrVs5evQoGo3mhc/PysqKJUuW0K1bN6pWraq/l7tkyZLnduvWrl2bFi1asHTpUqKiomjYsCHp6en89ttv3L5922Bhl8z3f9asWdRpVIO6H1QlTZux/KMSJRYq638X0bAnxTz7vwsrKyv9VLfq1atz9OhR1q1bR7Vq1Rg+fLi+Xp8+fZg0aRJNmjShd+/eREdHs3z5cgoVKqTfjObZuCZMmECDBg2yHSD31Vdf8fvvvzN58mQuXrxIkyZNePToEcuWLSMqKor169c/d3lN6Q0hjHT48GGhVquFUqkUCoUiTz9vMw8PDwEIDw+PXOslJSWJy5cvi6SkpFcU2evryJEjolu3bqJEiRJCo9EINzc30axZM7F+/XqRlpaWpb6/v78AxP79+0WTJk2EhYWFcHd3F4MHDxaRkZEGdWNiYsQHH3wgLCwshIODg4iPjxcTJkwQgDhw4IAQQojg4GABCH9/f4Pnvkj56dOnRfPmzUWhQoWEtbW1KFOmjBgyZIgIDg4W5ubmokmTJvq6gGjQoMFzX5dDhw6Jli1bCjc3N2Fubi6KFi0q+vfvL+7fv6+v8+y5CCFEWlqamDZtmihZsqQwMzMT3t7eYt68eaJTp04CEOHh4UIIIVavXi0AsXr16ixte3l5CS8vL/3vcXFxYsSIEaJYsWJCo9EIT09P0bJlS3Hw4EHRrVs3oVQqxZ07d4QQ/3t/goODn3uO+/fvF/Xr1xfW1taiSJEiYvjw4WLx4sXPfX/i4uLEhAkThK+vr7C2thY2NjaiTp06YuPGjQbHD7kTIqrXrCbMzc1E8ZLFxM3oM+Ljrq0FIM5c+UckpsYKrU6bYzsNGjQQHh4e4siRI6JGjRpCo9EId3d3MWLECBEXF2fQVlpampgwYYLw9vYW5ubmolSpUmL27Nli69atWd6jc+fOifLlywszMzPRtGlTfVvPfhTHx8eLsWPHCh8fH2Fubi6KFCki2rdvL/755x+Detn9O8iU139v8jPJdF5oO8KntWnThl27dlGxYkUmTJhA2bJlc5zwnymnSe9vA7kdYcELCAhg7dq1BAcHG8ztlDIGralUqmyvjJo3b86BAwdISkoyapDam0QI3b8bJTzQb5SgRJmxbORzNkp4V8nPJNMx+q8xMDAQCwsL9uzZk+X+kCRJr9Yff/xBly5dWLt2Lb169dKX3717l8OHD1OtWrW3IvkKIUjWxqPVpaFSmmGhskGhUCCEIC71MVEpEQYbJZgrLdAYrNcsE6/0+jD6LzIhIYFy5cq9dsn3+vXrjB07lr/++ou4uDhKlChB//79GTp0qMFQ/bt37zJ27Fj2799PTEwMFSpUYPTo0TlO0pek19kHH3yAm5sbn3/+OZcvX8bHx4ewsDCWL1+OECLHjS/eJAlp0UQm3dcnWMiYs2tt5kBiWgxpIutGCZZqOyzVcqME6fVk9L9KLy+vLAMMTC0kJIRatWqRmJjIkCFDKFGiBNu2bWPEiBFcu3aNJUuWABnTA+rXr8+TJ08YOnQoHh4e/PTTT7Rt25aNGze+8Nq2kmRq9vb2/PPPP0yfPp1ffvmF8PBwHBwcqFevHmPGjKFy5cqmDvGlJKRFE5EYnKU8XaQRk5qx7WRG4jXPuOJV22KhtkWtfPGtLCXpVTH6HvC4ceOYPn06e/fupUmTJvkdl1E+//xzFi9ezObNmw1GizZp0oS//vqLK1eu4Ovry8CBA1m2bBlHjhyhdu2MFaGSk5N5//33CQ0NJSQk5IVHGcp7wJJUMIQQ3I27ZHDl+ywFCqzVDliZ2WKptkWtNM+xrmRIfiaZjtHzgL/99lvKlStHr169+L//+z9SUlLyMy6jXL9+HYCPPvrIoLxdu3YAnDt3Dq1Wy4YNG6hVq5Y++ULGXMthw4YRGRmZ7fw9SZJMI1kbn2vyBRAIrM3ssDUvLJOv9MYwOgH37dsXT09PwsPD6dChA7a2tri6ulKsWLFsf17FCOjMdXAzV4vJlLkSkIeHB5cuXSI+Pj7b7fBq1qwJwPHjxws4UkmS8kqryz35StKbyuh7wJs3b9b/vxCC9PR0Hj58mGP9l11DOC9GjRrFnj17CAgIYPHixZQoUYJdu3axbNkymjRpQt26dfWrAmW3lFvmov/PLnT+tLlz5zJ37tws5eHh4fl0FpIkPU0r8raamUre75XeMEYn4NWrV+dnHPnCzc2NqVOn0qdPH4P70rVr12bHjh0oFAr9CkXZbZKeudNMQkJClscyxcbG6nfpkSSp4GRMLYrkcfLz/97UiowpSZL0JjE6Afv7++dnHPniu+++49tvv6VUqVLMmjULFxcXDh8+zOLFi2ncuDF79+7VbwCQ3dizzLKn93Z9lp2dHR4eHlnKw8PDC3TdYUl6l+iEjkdJd4lPiwIyFtPQkfPfl5Ol5yvpZZOk/JSvk+OioqKIi4vD1tb2le5KAxlXppMnT8bd3Z0TJ07o22/fvj3vvfcePXv2ZNq0adSvXx9Av5/p0zLL7O3tc2xn5MiRjBw5Mkt55ihoSZJeTpo2hYjE26TqMnYDMlNqsDFzRK1QE5XyIMs8YCdLT6zNHEwUrSQZ76V3Q7pz5w79+vXD1dUVJycnvL29cXJywsnJiV69emW7dVxBuH79OklJSbRv3z5L8u/WrRvW1tbs378fb29vgGynCmWWFS1atOADliQpi4S0GO7HXyVVl4wCBRYqaxzMnbE3d8ZOU4RituVwty6Fi6UX7talKGZbTiZf6Y31UlfAf//9N+3btycmJiZLl+6TJ0/YuHEjO3fu5Lfffst214/8lLlBdeaG1U8TQqDT6RBC4Ovri729fbYjnTPLnp6eJElSwRNC8CQ5nOjUjMV9lKiwUttia14IS7WdvntZoVBgqbY1ZaiSlG+MTsARERF06NCB6OhoKlSowOeff07VqlWxs7MjKiqKU6dO8eOPPxIUFETXrl25ePEirq6u+Rm7gXLlyuHl5cWWLVsYO3aswX3alStXkpSURPPmzVGr1XTu3JkVK1YQGBhosBDHggULcHFxoWXLlgUW5/MkpsWQos26uffrQKOyxMos5+55STKGVpfOg8RgkrQZ++lmLi9pZ14Yc1XuG7xI0pvM6AQ8Z84coqKiaNOmDb/++muWfVurVavGp59+SseOHdm1axdLliwx2K81vymVSlasWMFHH31EtWrVGDBgAK6urgQGBrJ+/XrKli3L6NGjAZg0aRI7d+6kZcuWjBw5EhcXF3766ScuXrzI5s2bTbYaTGJaDFtvfk9c6mOTtP88tuaF+bjUVy+dhB8/fszEiRPZuXMnDx8+pHTp0gwbNow+ffrk6flJSUlMnTqVn3/+mfDwcLy8vOjevTtfffVVlh25NmzYQM+ePbM9jr+/v8Em8IsWLWLGjBkkJSXRokULFi9ejJOTk8FzfvzxRyZMmMCtW7ews7N7sRM3kaCgIPr168fZs2extLRk165d1KlThwYNGnDw4MFcn3vw4EEaNWrEhAkTmDhxYr7HlpKeSETibdJFGgrAXGmJjbkjNmaFCmQZyZCQELy9vbO89y8iJiaG1NRUihQpkr/B5bOJEycyadIkDhw4QMOGDU0djpQNoxPw7t27MTMzY8WKFdlumg7oHy9atCg7duwo0AQM0KxZM/755x+mTJnCwoULiYuLw9PTk5EjRzJu3Dj94KrMxDxq1CgWLFhAWloaFSpUYNeuXbRq1apAY8xNijaJuNTHmCstMFdZmSyO7KRqE4lLfUyKNumlEnBCQgLNmzfn4sWLDB48GF9fX3799Vc+/fRTIiIi9F+ScpKWlkaLFi04fPgwjRo1YuTIkYSEhDB9+nT27NnDn3/+afAF6sKFCwCsWLEiyxerkiVL6v//6NGjDB06lG7dulGjRg2mT59OQECAwapo8fHxTJ48mTFjxrwxyRcytnE8c+YMY8eOpWjRopQrV47169ebfCOV2JRIIpPvIxAoUGKptsHWrBBWZvYoFS89PCVbRYoUYf369Qbv/YvYu3cvPXr0YMuWLTKpSS/N6AR8584dypcv/9xvgc7OzpQvX56bN28a29QLee+999i+fftz63l7e/PLL7+8gohenLnKCkv16zenMXNU6stYvHgxZ86cYdOmTXTp0gWAfv360apVKyZNmkTPnj1zHQS3fPlyDh8+TNeuXdm4caP+3mCTJk348MMPmTVrFuPHj9fXP3/+PM7OzvTt2zfXuNauXYurqyvr1q1DpVKhVqsZMmQIDx480Ceq2bNnY2lpyaBBg172ZXilLly4QKVKlZg8ebK+rEePHiaLRyd0RCbdIy7tCQAqhRprtT225oXRqKwKdDqRtbX1S517YGAgjx49yseIpHeZ0V8zlUolaWl5WyIuLS1NzpGVgIxE5+HhoU++kDGw5uuvvyY1NZWff/451+dv27YNgFmzZhl8ULdq1YrKlSuzbNkyg/oXLlygfPnyz43r3r17eHt76+eA+/j4ABlfNAEePHjAnDlzmDp1Kubmb9Zaw2lpablOrXuV0rQphMZf0ydfM6UGO3MnHCxcsFBby7m80jvF6ATs4+PDlStX9B9QOQkJCeHy5cuUKlXK2Kakt0RMTAxXr17Vr7n9tLyuw33v3j0KFSqkXzb0aZl74IaFhQHw8OFDIiIi9Ak4NTU1x01DXFxciIqK0v8eGRkJZPTgQMa4gVKlShm9VeWpU6f4+OOPcXZ2xsbGhsqVK+v36n3axo0bqVWrFtbW1lhbW1OrVi02bNhgUOfgwYMoFAo2b97M9OnTKVWqFBqNBm9vb8aPH096ejqQcQ8wM6H9/fffKBQKAgICgIwvPc92oZ47d47WrVvj6OiIg4MDvXr1ynF52WvXrtG1a1ecnZ3RaDT4+Pgwbtw4kpIMBxAqFAqGDBnC1q1bea9qFWxt7KnkXZuvB04gLjIRB3NnHDTOmCkzZjFs2bKF+vXrY29vT+HChfU7mT1Np9OxaNEiKleujKWlJQ4ODrRs2ZKjR48+930ICQkxeB0AGjZsSPny5blw4QKtWrXC3t4eGxsbmjdvzsmTJw3qZd5Ga9SoEcWLF9c/lpyczJQpU/D19UWj0eDk5ETHjh0JCgoyaD8gIAAbGxv+85//4OXlhaWlJd27d8fZ2ZkyZcpkG3OFChVwcXHRX/A8ePCAESNGUKZMGSwtLbG0tMTPz48pU6bo33vpzWB0Am7bti1arZYePXrol3d8VkxMDN27d0cIQdu2bY0OUno7hIaGIoTIdh1uKysrHB0dc12HGzKWEE1ISMh2ullm0sxMwOfPnwfg7t27VKtWDSsrKywtLalRo0aWD/VmzZpx9epV1qxZw/Xr11m0aBF+fn4UK1aM69evs2LFCr777jujrtD27NlDnTp1OHToEAMGDGD27Nk4OzvTv39/xo4dq6/3+eef06NHD1JTU5k4cSITJ04kJSWFnj17MmzYsCzHHT16NMuXL+fTTz9lwYIFODs7M2XKFKZMmQJAhw4dWL9+PZCxUcn69evp379/tjGeOXOGunXrEhgYyLBhw5g4cSKXL1/Otuv+xIkTVK9encOHDzN48GDmz59PrVq1mDZtGo0bNyY52fBWxe7du/EP8Oe92hUYN/Mr6jR8n22bfufz3t9ga14YpSKj12HChAl07tyZ2NhYxowZw/jx47l//z7NmzfXr+EO0L17d4YOHYqPjw9z5szhyy+/5Pr16zRo0IDffvvtBd+dDJl7hDs6OjJr1iyGDBnC33//TdOmTfWfb2PGjKF9+/b6137+/PlAxhe75s2bM3nyZOrUqcPChQsZNGgQhw4dombNmgQGBhq0lZycTJcuXejduzczZsygY8eO9OjRg+vXr3PixAmDumfPniUoKIiePXtiZmZGTEwMNWvWZNWqVbRv357Fixfrv2iNHz+eWbNmGXX+kokII0VFRQl3d3ehVCqFm5ubGDVqlPj111/F3r17xa+//ipGjRol3NzchEKhEB4eHiIqKsrYpt4IHh4eAhAeHh651ktKShKXL18WSUlJWR57khQufjg3QKy9NFpsuTb9tfpZe2m0+OHcAPEkKdzo1ygwMFAAYuzYsTm+hj4+PrkeY8iQIQIQW7ZsMSi/e/eusLS0FIA4dOiQEEKI2bNnC0A4OTmJ7777TuzcuVPMnj1buLi4CJVKJXbu3Kl/vlarFT179hSAAISnp6c4efKkEEKIDh06iCZNmhh1zjqdThQvXlwULlxY3L9/36C9unXrCo1GIx4/fiwOHTokANGkSRORmpqqr5eamioaNWokAHHw4EEhhBAHDhwQgHBzczP4u4qPjxf29vbC3d3dIAZANGjQINeyhg0bCrVaLS5duqQvS05OFnXr1hWAmDBhgv58ypUrJ4oWLSoeP35scMyVK1cKQMycOdOgHUD8tn+tuBl9RoTEXBSPEu+LRo0zzun69etCCCFu3LghVCqVaNCggUhJSdE//8GDB8Le3l5Uq1ZNCCHEli1bBCBmzZpl0HZcXJzw9fUVTk5OIiEhIdv3QgghgoODBSD8/f31ZQ0aNMgStxBCTJkyRQBi+fLl+rIJEyYIQBw4cEBfNmvWrGz/TYaHhwsnJyfh5+enL/P39xeA+OqrrwzqBgUFCUAMGTLEoHz48OEC0L8vCxYsEIDYunWrQb2oqChhbm4uKlSokGus2cntM0kqWEZfATs4OPDf//4XV1dXIiIimDVrFp07d+aDDz6gc+fOzJo1i4iICDw8PNi1axcODg7GNiW9JUQu63Bnlue2DjfAF198gZ2dHZ999hkrV64kODiYP//8kw8//FC/mUbmqPwaNWowZswY/vnnH7755htat27NF198wfHjx/WDqTLHJiiVStatW0dISAinTp3i5s2bVKtWjWPHjrF9+3ZmzpwJwJo1ayhfvjxubm707NlTf9Wdk7NnzxISEkKPHj0M5qYrlUo2bNjA+fPnsbOzY8uWLUBGt/HTswrMzMz0g6eeHTT40UcfGfxdWVtbU7ZsWR48eJBrTM96/Pgxhw4domXLlvj5+enLNRpNlmVXL168yKVLl/jwww/R6XRERkbqfz766CMsLCz09+lTtBlLuxYvWYwq1SqgUVpir3HGUeNCzRoZtxwiIiIA2LlzJ1qtluHDhxvcY3d2dubIkSNs3boVgE2bNgHw8ccfG7SdnJysLzt06NALnX+mZwdnVa9e3SDGnGzatAkHBwcaNWpkEJNaraZly5ZcvnyZq1evGjynTZs2Br+XK1eO6tWrs3nzZn1Xc3p6Ops2baJmzZr692Xo0KE8ePBAv8d5psjISOzt7YmPj3/h85ZM56VWwqpQoQLXrl1j8eLF7Nq1i6tXrxIbG4utrS2+vr60bt2agQMHvjYDQCTTsrXNWMEou3W4M8uftwxo8eLF2bdvHz179uSzzz4DwNzcnEGDBuHg4MDEiRMpVKgQAPXq1aNevXpZjuHl5UX79u1Zv349ly9fNhik5eXlZbB39ddff02nTp2oWrUqf//9N71792bWrFnUrl2bAQMG0L17d/bs2ZNjvJld6mXLls02jkyZS7aWK1cuS73M+J7tns9uYRuNRpNt93xugoOD0el02Y7TeDaezL21ly5dytKlS7M9XkhICLEpj4lMvgdAEefCWKrtsDUrhLWZPQqFMsvKdbm9Tk+/P5nt5zaNKCQkJMfHcvPs65nb6npPu3btGomJibnOCAkJCdHvV55dWwB9+vRh4MCB/Pe//6V169bs2bOHBw8eGIxeh4zNYmbPns2JEycIDg7m5s2bxMbGAv/b0U16M7z0Zgw2NjaMGjWKUaNG5Uc80lvM29sbhUKR7TrcCQkJREdH52kd7ho1anD16lWCgoKIjY3Fz88PR0dH/P39UavVBoktJ5lTi+Li4nKss3PnTo4dO8aVK1cAWLduHSVKlOCrr74C4JtvvqFnz56Eh4fj5uaW7TEyr2aed+84p14B+F8CyEwImZTK/J0rm10Mz85eyIxl8ODBWa7CIGOKUaIuikfJd/VlZmoNjhrXXKcY5fV10mq12Nra6q+ys5PTYKbnMfb11Gq1lCpViiVLluRYp1KlSga/q9VZP3q7du3KyJEjWb9+Pa1bt2bdunVYWVkZzBgICgqiQYMGJCcn06hRI5o1a8aIESP0C6tIb5Z83Q1JknJjY2ND2bJlsww0gbyvw33mzBlOnDhBz549qVChgr5cq9Wyb98+atWqpU9U7dq1IygoiIsXL2ZZIevy5ctAzldSWq2Wb7/9lv79++vrhIWFGSxekXnFc+/evRwTcObmH5lXbk/bt28f69at4+uvv9a3cenSJerWrWtQ79KlSwDZDl7LDyVKlECpVOpfk6fduHHD4PfM8wGyrO+ekp7M6p+X4uaV8bpkjmxWK82xUFvnGsPTr1Pp0qUNHps3bx6XL19m4cKFeHt7c+3aNSpXrpxllbJz584RHh6OtXXubeU3b29vIiIiaNiwYZbEGhgYSEJCQp6uTO3t7enQoQPbtm0jIiKC33//nY4dOxos+jJ8+HCio6MJCgoy6C1IS0sjMjJSP2pfejPk6SvfqlWrWLVqlcHVQmbZi/xIUo8ePbhz5w6bN2/Wlwkh+P7779FoNAbf9rNz/vx5Bg4cmOV+6IwZMwgPD+eLL77Ql7m5uXHr1i2WL19uUPfgwYP88ccftGrVKscPrNWrV3P37l3GjRunL/P09OTOnTv6q8LMbuPspkRlqlq1Kp6enmzcuNFgAYfMc960aRNubm507NgRyLgH/PRUkvT0dP3Ul8w6+a1QoUI0a9aMffv2cezYMX25Vqtl7ty5BnWrVatG8eLFWbNmjcFOZ4lpscxeNJWB/iP4bcNOLFXWOGgyvqwoeP7I8bZt26JQKFi8eLHB+T958oSZM2dy4sQJLC0t9a/B06PHIWM70k6dOtG2bdsso7DzU+YYhad7Bjp27Eh0dDSzZ882qBsaGkrr1q3p1q1bnq+u+/TpQ1JSEoMGDSIpKSnL8qyRkZFYW1tTokQJg/KFCxeSlJQkpyG9YfJ0Bdy3b18UCgV169bV38fLLHsReV3r912Xqs3+Hqkp5VdMw4cPZ8OGDfj7+3P69GlKly7Nli1b2L9/P99//73BleTt27cJDAykZMmS1KpVC4BOnToxe/Zshg4dyo0bNyhZsiQHDhzg559/JiAgwGC628SJE/nPf/7DF198wYULF6hRowaXL19m6dKluLu788MPP2QbY2JiIhMmTODLL780SNDdu3dn5cqV9OzZk5o1azJ9+nSaNWuGu7t7juerVqtZunQp7dq1o3LlygwYMAAnJye2b9/Ovn37mDlzJk5OTjRs2JD+/fuzbNkyatasSdeuXQHYvHkzp0+fZtCgQfq9rAvCwoULqVWrFk2bNuXzzz/H3d2dLVu2cP36dYN6KpVKv+Z61apV6d+/P25eRTh2IpDfNuzEy7soX3/7JQ4atxdazc3X15dRo0YxY8YM6tSpQ5cuXdDpdKxYsYKoqCj99KKAgAB+/fVXli1bxs2bN2nTpg1paWmsXLmSGzduMGvWrFzfj5eV2QOyZMkSwsLC6NGjB9988w2///473377LSdPnqRJkyZERUWxdOlSoqOj2bhxY5YemJw0atQIb29vtm/fTokSJbJ0K7dt25bJkyfTvHlz/Wv0xx9/sHv3biwtLXOcEiq9nvKUgIsVK4ZCoTAYnZlZJuUfjcoSW/PCxKU+zpdlH/NbxlKBL7c7jaWlJQcPHmT06NGsW7eOuLg4ypQpw7p167JsmnDo0CF69+6Nv7+/PgFn7us8btw4NmzYwJMnT/Dx8eHHH3/MMsfVxcWFEydOMGHCBHbt2sW6detwdnbG39+fiRMn5vhBPX/+fLRarcHVNGQsxLBixQpmzJjB7t27adGiBYsWLXruOX/44YccPnyYKVOmMGfOHLRaLWXLljVYjhMyBjbVqFGDpUuXMn78eNRqNZUqVWLjxo1GLwCSV6VLl+b48eOMGTOG5cuXk5KSQrNmzZgyZQqNGjUyqNu0aVOOHTvG1KlT+WnVSmJj43B1d6Fn38589c2XlCpWFnPVi29oMn36dMqWLcuiRYsYM2YM1tbWVK9enQ0bNlCtWjUg4wvA77//zvz581m/fj3ffPMNVlZW+Pn5sXXrVjp06JAvr0dOunTpwvbt29m1axf79++nffv22NjYcOTIEWbMmMFvv/2mn/VRpUoV1q1bl+X1y41CoaB3796MHz+e3r17Z/mMHTduHGq1mrVr1zJixAgKFSpEmTJl2L59OydPnmT69OkcOXIky20M6fWkELmN/pDyzNPTk9DQUDw8PLIdZJQpOTmZ4OBgvL29s911SW5HKL0JUrRJRCTcMtjFyNa8EDZmhVAp5dCSN8nzPpOkgvNK/lKioqIICQmhSpUqr6K5N5qVmb1MctJrLS71MY+S7v27i5ECS7UtduaFsVLboSigXYwk6W1k9F+LSqXK87D35s2bm3SbP0mSXp5O6HiUeJeHSXcRCFQKFbZmhSikccNKbS+TryS9IKOvgIUQuc5dzJSQkEBYWBjR0dHGNiVJkoml61KJSLhNii7j9oiZUoOtWSFszQuhVr5Zu0NJ0usiTwn48uXLtGzZMkvCPXnyZK5zE4UQREVFkZSUlGVunyRJb4bEtDgeJgajRYsCBRYqa2zNC2Ft5qDfSEGSpBeXpwTs5+dHnTp1DOZuAqSkpOQ64CiTUqnMMm9PkqTXmxCC6JQHRKWEIwAlSqzM7LEzL4yFykbOgpCkl5TnLui5c+fSokULIOMPs0+fPpQuXZpvv/02x+colUpsbGyoWLFirmu3SpL0etHq0nmYdIfE9Iw1htUKM2zMHLEzd8JMpXnOsyVJyos8J2BXV1f8/f31v/fp00c/p1J6cXL2l/S6StEm8SDhNmki9akpRoWxMXOUU4zeQvKzyHSM/mt6dpF2KW8yl7JLS0vL8+o4kvSqxKU+4dG/o5z/N8XI6d8pRrLL+W2UuRHG87YClfLfK5s38M8//7yqpl5rZmZmaDQaYmJi5DdP6bUhhI5HSfd4mHTnqSlGhSlk4fbvFoIy+b6NhBDExMSg0WgMVjqUXo2X6k8KCwtjwYIFXLx4kcTExCxXxenp6SQmJhIWFsaTJ0/kQuH/cnJyIjQ0lPv372Nvb4+ZmZn8gJNMJl2XSmRSKKn/TjFSK82xVNtjiQMiTUly2uu3LKr0coQQpKWlERMTQ3x8PB4eHqYO6Z1kdAIODw+natWqPHz4UH8lp1AoDK7qMpOKEEIucfaUzO3FIiMjCQ0NNXE00rssTZtMbOpjdP9OMVIrNViorElWKXmiiDd1eFIB02g0eHh4GGx5KL06RifgOXPm8ODBA/2G0TY2NixcuJB69epRr1497t+/z65du4iKiqJZs2bs2LEjH8N+89nZ2WFnZ0daWpp+k3NJelWEEPx1by3/ubsEgQ5zlSV+jnWp4/oJJezLyR6Zd4BKpZLdziZmdALes2cPCoWC7du306xZMwDWr1+PWq1m6tSpADx48ICmTZvy559/cu7cOf2ONtL/mJmZyT8C6ZVKTItl8fl+nHn4XwAcNa7U8OhIk2L+uFgVN21wkvQOMXoQ1t27d3F1ddUnX4AqVapw4sQJfTe0i4sLK1asQKfTsXjx4pePVpKkl3Iv7gqjjtTnzMP/okBBcdsKtCv5Je1KjpDJV5JeMaMTcHJyMp6engZlvr6+JCYmcuvWLX3Z+++/j4eHhxwFLUkmFhi2ldFHG/EwKQS10pzKRZrTqfRYmnn1xspM3gOUpFfN6C5oR0fHLBsseHt7A3DlyhVKlSqlL3dzcyMoKMjYpiRJegnpujTWXxnDnjvLAbBWO1DLrQPNvT6lmF05E0cnSe8uo6+AK1asyK1bt7h9+7a+rHTp0gghOH36tEHd0NBQNBq5fJ0kvWpRyRFM/OcDffJ1sfKmdYmhfFL6W5l8JcnEjE7Abdu2RafT0apVK/744w8go7vZzMyMH374QZ+YFy5cSHh4OCVKlMifiCVJypMrT47y1eHa3Iw5jVKhwtexNp1Kj+FD78HYa4qYOjxJeucphJHLMSUnJ1OjRg2CgoJQqVQkJCRgbm5Ojx49+PnnnzEzM8PW1paoqCgAZs6cyZdffpmvwb9OPD09CQ0NxcPDI087RElSQRFCsCt4MT9fnYBAh0ZlRVXnlrQo3o/SDjXkFCNJek0YnYAhYyGJL7/8kiNHjnDz5k0gY+pRo0aNuHr1qr5enTp12L9//1vdDS0TsPQ6SEqP44fzAzj1YDcADhpXGnp0o4lXAEUsc967W5KkV++lEnAmrVZrsJB3SkoKO3bsIDg4GF9fX9q0aYNS+cqWnTYJmYAlUwuNv8asU114kBgMKPCyLU/Tor2p6/kJlmpbU4cnSdIz8mVvsWd30dBoNHTu3Dk/Di1JUh4cC9vBjxcGkqpLQq0wp4JTIz4o3o8KTg1RKuQuN5L0OpKbe0rSG0yrS2fD1XH8EbIEyJhiVNu9Ay28+uFp62vi6CRJys1LJeC9e/cyc+ZMzpw5Q2xsbK51FQqF3A1JkvJRdMoD5pzuyY3oE0DGFKMmRQNoWLQHduaFTRydJEnPY3QC/uOPP2jdujVCCLmvrSS9Ylef/MPcMz2JTY1EqVBR2qEmLbw+o7rrh6iV5qYOT5KkPDA6AU+bNg2dTkeVKlUYNmwY7u7uclMBSSpgQgj+E7KEjVfHoRNa/RSjlt4DKWVfVU4xkqQ3iNEJ+Ny5c9jY2LB//34cHR3zMyZJkrKRnB7PkguDOR7xfwA4aFxo5NmTJsUCcLL0fM6zJUl63RidgFUqFaVLl5bJV5JegbD4G8w61YWIxFtkTjFq7tWXOu4dsVBbmzo8SZKMYHQCrlSpEpcuXcrPWCRJysbx8J38eGEAKdrEf6cYNaaV9wDKFa6PUvF2z6+XpLeZ0X+9w4cPJyoqivnz5+djOJIkZdLq0ll/ZSzzzvYiRZuItdqBRp496VF28r/ze2XylaQ3mdFXwB06dGDUqFF8+eWXXLhwgZYtW1KkSJFcV7yqX7++sc1J0jslOuUh88704lrUMQBcLL1p5tWHhp49sDGXt30k6W2QLwtxrF27lrVr1+ZaR84DlqS8uR51gtmnuxOb+kg/xahV8YG85/IBaqWcaSBJbwujE/C8efOYOXNmnucAy7nCkpQ7IQT/vbOc9VfGoBPp/04xasWH3oMpYV9ZTjGSpLeM0TeRVq5cCUBAQADXrl0jJSUFnU6X648kSdlLTk9g4blPWXv5G3QiHQeNC62KD6JH2cmUdKgik68kvYWMTsDBwcG4urqyatUqfHx8XptFOHQ6HYsXL6ZSpUpYWlpStGhRAgICCA0NNah39+5devXqhbu7O9bW1rz//vvs3LnTRFFL77LwhFuMPtqQf8K3kTnFqLPPONqVGkkhC3dThydJUgExOgHb29vj4uKSn7Hki4CAAD7//HO8vb2ZP38+n3zyCZs2baJ+/fpER0cDEBERQf369dmxYwd9+vRh9uzZpKen07ZtW37++WfTnoD0Tjn1YDejjtQnLOEGaoU5VYo0p5ffdBoU7YZGZWXq8CRJKkjCSN26dRMWFhbi4cOHxh4i323fvl0AYtCgQQbla9asEYCYMWOGEEKIAQMGCIVCIY4ePaqvk5SUJCpVqiScnJxEfHz8C7ft4eEhAOHh4fFyJyG9E9K1aWLD5fGi82570Xm3vei9p5hYeXGkCIu/aerQJEl6RYy+Ap40aRIqlYpPPvmE8PDwfPtC8DKWLFmCra0t3333nUF5ly5dGDVqFKVLl0ar1bJhwwZq1apF7dq19XUsLCwYNmwYkZGR7Nq161WHLr1DYlIeMfVEG34PXgBk7GLU0ecbOpcZh5t1SRNHJ0nSq2L0KOhDhw7RuXNnVq9eTfHixalUqRJFixbF2jr7ZfEUCsVzpyq9DK1Wy6FDh2jatCm2trYAJCUloVKp0Gg0zJgxA4ALFy4QHx/P+++/n+UYNWvWBOD48eN07ty5wGKV3l03ok8x53R3olMeoERFaceafOQ9hCrOzVEp5fbckvQuMfovvm/fvvqRmWlpaZw6dYpTp05lqadQKBBCFHgCDg4OJjk5GW9vb7Zu3cqkSZO4ePEiKpWKJk2asHDhQsqUKcP9+/cBKFasWJZjeHp66o8lSflJCMG+Oz+x5sqojClGSiuqubTioxKf421fydThSZJkAkYn4F69er1WUyOioqIA2LdvH8uXL2fkyJFMnjyZ8+fPM3PmTGrXrs2pU6eIiYkBwMbGJssxrKwyBr0kJCTk2M7cuXOZO3dulvLXpRteev2kaBNZfnEYR8N+BcDe3JmmRQNo6tUHRwtXE0cnSZKpGJ2AV61aleuyk69aSkoKAFevXmXr1q106NABgHbt2vHee+/Rpk0bxo0bR6tWrYDsFwbJLFOpVDm2Exsbm2VKkyTlJCIhmNmnu3I//ioAXrblaVV8MLXc22GusjRxdJIkmZLRCbh58+a4ubmxaNEiHBwc8jEk42Tee/bw8NAn30ytW7emaNGi7Nu3T39vNzExMcsxMsvs7e1zbMfOzg4PD48s5eHh4XKxEcnA6Qd/sOjcZyRr41ErzKjo1JjWJYfh61jrteo9kiTJNIxOwKdOncLa2vq1SL4ARYsWBcDVNfsuPVdXVy5cuIC3tzeA/l7w0zLLMo+VnZEjRzJy5Mgs5Z6envLKWAJAJ7RsuT6NHbcyblVYqe2p4/4JH5UYgotVcdMGJ0nSa8PoBKzVanF2ds7PWF6Kk5MTJUuW5Pr16yQnJ2NhYaF/TKfTERwcjLe3N76+vtjb23P8+PEsx8gse3p6kiS9iNjUxyw4G8Clx4eBjF2MWhTvT0PPrliZ5dyzIknSu8fom7ht27bl4sWLHD16ND/jeSl9+vQhLi6O77//3qB8xYoVREZG0qVLF9RqNZ07d+bw4cMEBgbq6yQnJ7NgwQJcXFxo2bLlqw5degvcij7DN4frcunxYZQo8XWsTS+/GbTw6iuTryRJWShEdqOR8uDhw4d8/PHHnD59mk6dOlG3bl3c3NywtMx5YEnjxo2NDjQvUlJSaNy4MYGBgXTr1o0GDRpw9uxZli9fjp+fH8ePH8fKyoqIiAiqVKlCYmIiI0eOxMXFhZ9++onTp0+zefNmOnXq9MJtZ3ZBe3h4ZNu9Lb29hBD8eXcNqy9/jVakYa60pLrLh7QpORwvu/KmDk+SpNeU0Qk4t5HC2Tb0ivYDTkxMZObMmWzcuJF79+7h7OxMhw4dmDJlCnZ2dvp6wcHBjBo1in379pGWlkaFChUYO3asfpT0i5IJ+N2Uqk1iRdAIDoduBv6dYlSsN828PsVB8/rcopEk6fVjdAI2ZgrS2zxKWCbgd8+DxBBmn+rGvfjLQMYUo9YlhlHDtTXmKovnPFuSpHed0YOw3uZkKknPc/bhXhae+5Sk9Lh/pxg1oW3J4ZR2rCmnGEmSlCdy8VlJegE6oeW3G9+x7WbGQD8rtT113T/hoxKf42zlZeLoJEl6k+RLAk5MTOTgwYNcu3aNuLg4bG1t8fHxoUGDBvqNESTpTReX+oSFZz/l4uMDADhbFedDr8HUL9oFS7X8dy5J0ot56QS8YMECJk2apF9j+WlWVlaMHz+er7766mWbkSSTuh1zjtmnu/EkOQwFSso4vk+7UiOp6NQIpeLFBiRKkiTBSybgL774gvnz5yOEwNzcnDJlymBnZ0dUVBTXr18nISGBUaNGERoayvz58/MpZEl6tf66u45Vl74gPXOKketHtCs5kqK2ZU0dmiRJbzCjF+I4cOAA8+bNQ6VSMWPGDJ48ecL58+c5fPgwQUFBPH78mKlTp6JSqVi0aBGHDh3Kz7glqcClapNZcmEwy4OGki7SsDd3pk2JYfiXnSGTryRJL83oK+AffvgBhULBggULGDhwYJbHbW1tGT16NI6OjgwePJjly5dTv379lwpWkl6Vh4l3mH26O3fjgoCMKUZtS4yghltr1EpzE0cnSdLbwOh5wO7u7mi1WiIiInKddiGEwMXFBWtr67d6o3s5D/jtce7Rfhae/ZTE9BhUCjMqOTWhfakvKOVQTU4xkiQp3xh9Bfz48WOqVKny3A8khUJBiRIlOH/+vLFNSdIroRM6tt34nt9ufgcIrNR21HHvRNuSw3CyzHmHLEmSJGMYnYDt7e3zfKV3//59OR1Jeq3Fp0Wz6OynnI/8EwBny+K09v6cep6dsVDbmDg6SZLeRkYPwqpatSrh4eFs374913pbt24lLCyMqlWrGtuUJBWo4JjzfHO4Lucj/0Tx7y5Gn1WYTxOv3jL5SpJUYIxOwH379kUIQUBAAFu2bMm2zi+//ELv3r1RKBR8+umnRgcpSQXl4L0NjPunGY+T72OutKS228d8VmE+FZwaolQY/echSZL0XEYPwgLo0KEDO3bsQKFQ4OrqSpUqVbC3tycmJoazZ88SERGBEIJ27dqxbdu2/Iz7tSMHYb1Z0rQprL70FX/dXweAvXkRmnl9RguvvtiaFzJxdJIkvQteKgGnp6czYsQIli1blu1Wg2q1mn79+jF37lzMzd/uqRsyAb85IpPuMed0D4JjMwYGetmWp33JL6nm+iFqpZmJo5Mk6V2RpwR86dIlvL29sbKyyvbx0NBQ/vjjD65evUpsbCy2trb4+vrSsmVLPD098z3o15FMwK8XndBy5Ukg0SkPcNC4ULZQbZQKFRceHWDB2d4kpEfrpxh97PM1JeyfP6JfkiQpP+UpAXt5eWFra0tQUJC+bN26dbi4uNCiRYsCDfBNIRPw6+NExE7WXB7Fk+QwfVkhC3d8HWsRGL6Np6cYtS81kkIW7qYLVpKkd1aeErClpSXly5fn5MmT+jKlUkndunXlEpP/kgn49XAiYidzz/gDOf+zdrb0onWJ4dT37IxGlX2vjiRJUkHL0zBPa2trrly5wvXr1ws6Hkkymk5oWXN5FLklX7XCPGOKUTF/mXwlSTKpPC3EUadOHX7//Xf8/PxwcXFBo9EAcOrUKUqUKJGnhhQKBbdu3TI+Ukl6jitPAg26nbOTLlJRKtRyipEkSSaXpwT8/fffc+rUKcLDwwkPD9eXJycnExISkqeG5AAXqaBFpzzI13qSJEkFKU8JuHTp0ly/fp3Dhw8TGRlJeno6ffr0oXTp0nz77bcFHaMk5YmDxiVf60mSJBWkPK8FbW1tzQcffKD/vU+fPjg7O+Pv718ggUnSi/JxqIa50pJUXVIONRQUtnCnbKHarzQuSZKk7Bi9GcOBAwewt7fPz1gkyWhJ6XHMPt091+QL4O83A6VC9eoCkyRJyoHRCbhBgwb5GYckGS065QEzTnzMnbgglAoVxW0r8jg5lJjUh/o6hS3c8febQQ3XNiaMVJIk6X+MTsAACQkJ/PTTTxw9epTo6GjS09PJaVqxQqHgzz//fJnmJCmLsPibTD/Znsike6gV5tR0a0u3MhNxtHDNdiUsSZKk14XRCTgyMpI6depw8+ZNgBwTbyY5ClrKbzeiTzHz5CfEp0WhUVlRx70TXcuM12+mUK5wPRNHKEmSlDOjE/D06dO5ceMGKpWKVq1aUbZsWSwtLfMzNknK0ZmHe5h/xp9UXTJWans+8PqMNiWHy/17JUl6YxidgP/v//4PhULB9u3b+eijj/IzJknK1YF761lxcTg6tNibO9O+5Bc0KRaAmUpj6tAkSZLyzOgEHBoaSsmSJWXylV4ZIQTbbs7m1xvTAChi6UWXMuOo5dZe3t+VJOmNY3QCdnBw0C9JKUkFTSe0rAr6kv33VgNQ1MaPnmWnUcGpoRxfIEnSG8noBXHr16/P9evXefjw4fMrS9JLSNUmMfd0T33yLe1Qk/4VF1GxSCOZfCVJemMZnYDHjh0LwGeffUZqamq+BSRJT4tPjWLK8TacevgfFCip5NSUfhUWUMqhqqlDkyRJeilGd0FHREQwcOBAFi5ciJeXF02bNsXDwwNzc/McnzN58mRjm5PeQY+S7jL9RAfCE26iUqip7vIR3ctOpohlMVOHJkmS9NIU4nkTeHOgVCpRKBT6+b+5dQUKIVAoFGi1WuOifAN4enoSGhqKh4cH9+/fN3U4b7w7sUHMOPkx0SkPMFNaUNvtY7r5TsReU8TUoUmSJOULo6+A69evL++/SQXi0uPDfH+qK8naeCxVtjQq2ouOPt9gZWZn6tAkSZLyjdEJ+ODBg/kYhiRl+Cd8O4vP9UMr0rA1K8xHJYbQsvhAzFUWpg5NkiQpX73UWtCSlJ/+E7yE9VdGIxAUsvCgk88Y6nl0QqWU/0wlSXr7yE82yeR0QsfPVyewK3gRAO7WPnT3ncJ7zi3kbQ5Jkt5aeUrAxYoVQ6FQcPDgQby9vfVlL0KhUHDnzp0Xj1B6q6XrUllyYTBHw34FoIRdFXr5zaCMY02ZfCVJeqvlKQHfv38fhUJBWlqaQdmLkB+m0rOS0uOYc7onQY8PAgrKFaqHv98MitmVM3VokiRJBS5PCXj16owViNzc3LKUSZIxolMe8N3JjoTEXkSpUPFekQ/o6TcNF6vipg5NkiTplTB6HrBkSM4Dzruw+JtMP9meyKR7qBXmvO/Wjh5lp+CgcTF1aJIkSa+MHIQlvVI3ok8x8+QnxKdFoVFZUde9M918J2Bt5mDq0CRJkl4pmYClV+bMwz3MPxNAqi4Ja7UDLbz60bbUcDQqK1OHJkmS9MrJBCy9EgfurWfFxeHo0GKvcebjkl/TuJg/aqWZqUOTJEkyCZmApQIlhGD7rdlsuT4NAGfL4nT1nUhN1zYoFUZvxiVJkvTGkwlYKjA6oWVV0Jf6fXyL2ZbHv+wM/ArXldPSJEl6573VlyBarZZ69epl+2F/9+5devXqhbu7O9bW1rz//vvs3LnTBFG+nVK1Scw900uffMs4vs/Aij9Qzin790OSJOld81Yn4OnTp3PkyJEs5REREdSvX58dO3bQp08fZs+eTXp6Om3btuXnn382QaRvl/jUKKYeb8upB7tRoKSyUzMGVPwBb/tKpg5NkiTptfHWzgM+ceIEderUQaVSkZKSwtOnOXDgQJYtW8aRI0eoXbs2AMnJybz//vuEhoYSEhKCtbX1C7Un5wFniEy6x/QTHQhLuIFKoaa6S2t6+U2jkIW7qUOTJEl6reQpAf/111/50ljjxo3z5TjPEx8fT5UqVfD19SUuLo6///5bn4C1Wi0ODg5UrFiRo0ePGjxv9erV9OnTh82bN9O5c+cXalMmYLgbe4npJzsQnfIAM6UFddw70sN3CjbmjqYOTZIk6bWTp0FYTZs2fen7dgqFgvT09Jc6Rl4NGzaMmJgYVq5cmSWRXrp0ifj4eN5///0sz6tZsyYAx48ff+EE/K679Pgw35/qSrI2Hku1LY09/fmk9Cgs1DamDk2SJOm1lOdR0C/bU/2qerq3bdvGqlWr2LFjBy4uWZc2zLw6zW43J09PTwCCg4NzPP7cuXOZO3dulvLw8HBjQ37j/RO+ncXn+qEVadiZO/GR9+e08h6IWmlu6tAkSZJeW3lKwDqdrqDjyBehoaF89tlnfPrpp7Rt2zbbOjExMQDY2GS9MrOyyliRKSEhIcc2YmNjCQ0NzYdo3w7/CV7C+iujEQgKW3jQufQ46np8glKhMnVokiRJr7W3Zh6wEAJ/f38cHByYP39+rvWe/m92j6lUOScPOzs7PDw8spSHh4e/MV9U8oNO6Nh0bSK/314IgLu1Dz3LTqdykZe/XSFJkvQueGUJ+P79+/ou3oIwd+5c/vrrL3bs2EFycjLJyckA+j2MIyMjUalU2NraApCYmJjlGJll9vb2ObYzcuRIRo4cmaU8cxDWuyBdl8qSC4M5GvYrACXt3yPAbxY+jtVMHJkkSdKb46UScGJiImvXruXixYskJiZmuQJMT08nMTGR+/fvc+HCBVJTU18q2Nz8/vvvCCFy7HouUqQIXl5e7Nq1CyDbkcqZZUWLFi2wON90SelxzD3dk4uPDwIKyheuT+9ys/CwKWPq0CRJkt4oRifg6OhoateuzbVr17I8JoQw6IZ8FQOw5syZQ1RUVJbyL774ggsXLrBv3z4sLS3x9fXF3t6e48ePZ6mbWZY5N1gyFJ3ygO9OfkJI7AWUChXvFWlJQLkZOFnKLyySJEkvyugEvGDBAq5evYpSqaRhw4bY2dmxY8cOKleujJ+fH/fv3+eff/4hPT2dRo0a8dNPP+Vn3FlUrVo123JHx4w5qE2bNtWXde7cmRUrVhAYGGiwEMeCBQtwcXGhZcuWBRrrmyg84RbTT7TnUdJd1EoNtVzb0bPsNOw0TqYOTZIk6Y1kdAL+/fffUSgUrF27lu7du6PVanF0dMTd3Z0NGzYAcPnyZT744AOOHj2a7T1XU5k0aRI7d+6kZcuWjBw5EhcXF3766ScuXrzI5s2bsbCwMHWIr5Wb0af57uQnxKc9QaOypq57J7r7TsLKzM7UoUmSJL2xjF4L+tatWxQuXJju3bsDGSOHK1eubLC6lJ+fH8uXLyc1NTXXkcmvmqurK4GBgXzwwQcsWLCAr776CjMzM3bt2kWnTp1MHd5r5ezDvUw+9hHxaU+wNnPgI+8h+PvNkMlXkiTpJRl9BZyQkEClSoaL65ctW5ajR49y584dvLy8APjggw9wdnbm77//frlIjXTw4MFsy729vfnll19ebTBvmIP3NrD84jB0aHHQuPJxqa9pXLQXKuVbM3tNkiTJZIy+Ara3t8/SrVyiRAkArl69alBerFixd3Z95DeREIJtN79n6cUh6NDiYulNn3Lf07RYb5l8JUmS8onRCdjPz4+bN2/y8OFDfVmpUqUQQnD27FmDuo8ePUKpfKt3Pnxr6ISWVZe+ZMv1aQB42ZZnQMUfqOHaWi6wIUmSlI+MzootW7YkLS2NDh06cOXKFSBjMwOlUsmyZcv0U4K2bdvGnTt38Pb2zp+IpQKTqk1i7ple7LubMWLd17EWgysto2xhOS1LkiQpvxmdgAcOHIinpyeBgYFUqFCBlJQUPD09adWqFXfu3KF06dJUq1aNzp07o1AoaN26dX7GLeWz+NQopp5ox6kHu1GgpHKR5gyqtIRiduVMHZokSdJbKU/7Aefk5s2b9O3bl8uXL+u7oq9fv069evV49OiRvp6Pjw8nT57Ezu7tHTn7Ju8HHJl0j+knPiYs4ToqhRk1XFrjX24GDpqsu0lJkiRJ+eOlEnCmR48eUaRIEYPfV61aRXBwML6+vnz66af6NZjfVm9qAr4be4npJzsQnfIAc6Ulddw70qPsFKzNHEwdmiRJ0lstXxKw9GYm4EuPDzP7VFeStPFYqu1o7NmLzmXGYK6yNHVokiRJbz05p+QddSx8B4vPfUa6SMPO3Ik23sP4wHsAaqWZqUOTJEl6JxidgBs3bvxC9RUKBX/++aexzUn56I+Qpay7/C0CgZNFUbqUHkdtj44oFXKqmCRJ0qtidALOaYWpp2XOG312dyTJNHRCx6Zrk/j99gIAPKzL4O83gwpOjeT7I0mS9IoZnYAnTJiQ42MJCQmEhYWxf/9+Hj16xNixY2nQoIGxTUn5IF2XytILQzgStgWAUvZV6VNuNiUcqpg4MkmSpHdTgQ7CSkhI4OOPP+bIkSOcPXsWHx+fgmrK5F7nQVhJ6XHMO+PPhci/AAXlCzfg0/JzcLMuaerQJEmS3lkFetPP2tqa1atXk5aWxuTJkwuyKSkH0SkPmHTsQy5E/oVSoaK6y0cMqvSjTL6SJEkmVuCjoN3c3PDz85MDsEwgPOEW00+051HSXcyUGt53bY+/3wxszB1NHZokSdI775VMQ4qPj9evDS29GjejT/PdyU+IT3uChcqauh6d6eE7BQu1talDkyRJkngFCXj79u3cunXrrb7/+7o5+3Av8874k6pLwsbMkRZe/WlfaiRqpbmpQ5MkSZL+ZXQCHj9+fI6PCSFISUnh6tWr/Pe//0WhUNC+fXtjm5JewMF7G1geNAyd0OKoceXjUt/QuFgvlAqVqUOTJEmSnmL0KGilUvncuaOZh/b19eWff/7B3t7emKbeCKYeBS2EYPut2fp9fF2sStDTdypVXVrKOb6SJEmvIaOvgOvXr5/rB7tarcbJyYm6desSEBCAtbW891hQdELL6ktf6/fxLW5XgT5+cyhdqIaJI5MkSZJyUqArYUkFL1WbxKJzn3HywS4AfB3r8FmFuXjYlDFxZJIkSVJujJ4HfPfuXf0ewM8TFBTE77//bmxTUg7iU6OYeqIdJx/sQoGS94q0YEjlZTL5SpIkvQGMvgIuXrw49erV4++//35u3T59+hAcHMyjR4+MbU56RmTSPaaf+JiwhOuoFGbUcG1D73KzsDMvbOrQJEmSpDzIcwLW6XT6/88cXCWE0P9kRwjBnTt3uH37NomJiS8ZqpTpbuwlZpzsSFRKOOZKS+q4d6SX33Qs1bamDk2SJEnKozwl4KtXr1KxYkW0Wq2+TKFQcPToUdTqvOXwSpUqGRehZODS48PMPt2NpPQ4rNR2NPLsRecyYzFXWZg6NEmSJOkF5OkesK+vL/369ctyxfv077n9WFlZ8d133xXoibwLjoXvYMaJDiSlx2FnXoQOJb+ie9lJMvlKkiS9gfI8DzguLo4zZ84AGYm3cePGVKhQgYULF+b4HKVSiY2NDaVLl37rpyEV9DzgP0KWsu7ytwgEThbF6Oo7ntpuH8s5vpIkSW+oPN8DtrW1NdjTt1ixYvj6+sp9fguYTujYdG0Sv99eAICnjS8BfjMp7yRfd0mSpDeZ0aOgQ0JC8jEMCTIW1LjyJJDolAc4aFzwcajG8ovDOBK2BYBSDtXoW24uxe0rmjhSSZIk6WW9kt2QpOc7EbGTNZdH8SQ5TF9mptSQpktBgYLyhRvyWYV5OFsVN12QkiRJUr4xOgGXKFHiheorFApu3bplbHNvtRMRO5l7xh8wvB2fpksBoKR9VQZXXoaDxtkE0UmSJEkFocC7oBUKBUIIOVgoBzqhZc3lUTybfJ/2JDlULrAhSZL0ljE6Aa9evTrHxxISEggLC2Pnzp1cunSJyZMn07VrV2ObeqtdeRJo0O2cnScp4Vx5Eki5wvVeUVSSJElSQTM6Afv7+z+3zuTJk+nduzeTJ0/mgw8+MLapt1p0yoN8rSdJkiS9GYzejCFPB1cqWbBgAWZmZkybNq0gm3pjOWhc8rWeJEmS9GYo0AQM4ODggK+vL0eOHCnopt5IZQvVppCFO5DTPXIFhS08KFuo9qsMS5IkSSpgBZ6AASIjI0lISHgVTb1xlAoVAX6Zy3Q+m4Qzfvf3m4FSoXqlcUmSJEkFq8AT8KJFi7h37x6lSpUq6KbeWDVc2zDyvbUUsnAzKC9s4c7I99ZSw7WNiSKTJEmSCorRg7B69eqV42NCCFJSUrh69SqXLl1CoVDIUdDPUcO1DdVcPjRYCatsodryyleSJOktlefNGJ6lVCr1c3yfp169euzduxeNRmNMU2+Egt6MQZIkSXq7vNQVcG6La6jVapycnKhbty6tWrWSC3FIkiRJ0lOMTsBr1qzJxzAkSZIkyXS0OsGxB2k8TNLibKnifRczVMqCvXCUmzFIkiRJ77TdIcmMPR5HeKJOX+ZmpWRqTVs+LG5RYO2+kmlIkiRJkvQ62h2STN8DMQbJFyAiUUffAzHsDkkusLbzdAVcrFixl25IoVBw586dlz6OJOWFKbqTJEl6s2h1grHH47LdCkeQsRLDuBNxfFBMUyCfH3lKwPfv38/ziOecyEFY0qtiqu4kSZIKjhCCdAEpWkFyuiAxXZCQJkjSZvx/UjokpetISodkrSApXZCsFaSkC5K1GWUpWkGKln//K3iYpMty5WvQJhCWoOPYgzTquJnn+zm90D1ghUJBlSpV6PT/7Z15WFRVH8e/dxYGGFbZZRECATd2FNyXXNL3NQwl07IytcwSl8q10rRXX9PKNFvUxNQiy0wtc8slU0AWRcE9xAWV5UUUWYeZ8/4x3MMMszDowACdz/PwMHOWe39z7jnne/YbFwcXF3Y2MaPlwQ8n1W8q8sNJGwaAiXArho1smAYFqROu6lpxK6tR/i+v/ausASrkitr/SpGslNf+1QBVCqUYVivqBLBaAVTLCarlgEyh/C5TEMhq/9cogBoFgYwAcoW+l7Y2LQUV8ia5rkECvGLFCmzfvh1paWnIyMhAZmYm+vfvj7FjxyI2NhZ2dnZNYtyjcO7cOSxevBjHjh3D/fv30b59e8TExGDx4sWwtbWl4W7cuIGFCxfi0KFDuH//Prp164b58+dj5Eh26lRrxdTDSYym5Z84skGIUoyqFcpenFLACMpre358D7CiRil4FTUEFXKgsoagSqEUvkq5Qq3XVyVXil5VrfjxwseLYI2KCNYQ5Xe5qZRPDwIOEHGAkAOEAk75WQCIOA4iASAW1P0XCwAzAWAm5CAWcJAIle6l1Qr8eUfW4L2cLZrmQKRGHcRx7do1JCYmYvv27cjMzATHcRCLxRgyZAjGjh2Lp59+GlKptEkMNYRLly4hPDwcIpEI06ZNg5eXF5KSkrBlyxZ06dIFSUlJkEqluHv3LqKiolBcXIzp06fD3d0dGzduRHp6OrZt24Zx48Y1+t7sIA7Tc+JONWL33WswXA9nMZwsBRBCWXCFHCAS8AWag1BQW6jVCnZtODV3ZQHn44tUriXklAVcwHF11+I4CPjwHOr8BFALJ6r3Xb/fP2N6R9fIBv/LNwywNboIyxV1IlUlVwog/7lcpfdXKed7gES991crgGrCpyD0u6y2J8j39qrltb09vvdHgBoT9vr0IaL5nKsVPeVnPu8La0VPTP8rP5sJAImKCJoJOUiEHMyFSnflZ6WfhYiDuYiDuYCDVAyYiwSwEAIWYgEsa+MKBRzEKrbw9ze0TMgVBBE/FuFuuUJrOnMA3KQCpI52bJJG+yOfhHX58mUkJibihx9+wIULF8BxHMzNzfGvf/0LY8eOxfDhw5v95KuhQ4fiyJEjyMjIQNeuXan7Z599hvj4eKxYsQJvv/02pk6diq+++gp//fUXevZUvmWosrISUVFRyMvLQ25ubqMbEkyATUN5DUHW/2RIyZdh17UKZBU3zVBRS4aDUojV/sCpfeeFvM6fd+eokAsEynhCgdJfKKjnrxKebzgIa6/JNzCEKg0MoYCjjRqBSsOEb6ioVd4qPZe6RoyykcOBYE5yKe5V6a6qbM04vN7VAlVyjs71VdbO/VXJlb3BqtqhTl70quV1Q57VCkAmV4oe3/tTtEDlU017EceppRWfljR9BYCY4yAW1omfqvCZ1X7mhU8igFLwakXPXMTBQqgifCIOlmJlOJFQUCfCtY1U8SOKoKnhG3eAemOnKRt39B6PKsCqZGVl0Z7x1atXwXEcrKysEBMTg2effRZDhw6FUNi0ZxpXV1fDzs4O0dHR+OOPP9T8SkpKYG9vjxEjRmDXrl2ws7NDUFAQTpw4oRZu06ZNmDhxIhITE/Hss8826v5MgJseuYLgyn050guqkZQvQ0ahDLml8kZXlC/4m8PTWgS5gkBOlMNrNbWfa1R6H3L6XzkUJ1egdi6qLl79ayh4d6J0V6iE4z8r+D9ocSMEhAAK1Lm1QB34x8EBOnt6ar1BLY0JsYCDmVAphmbCOhGUCDmY1fb+zGoFz1wImAkFsBQBFkIOkloRtBAJav3qen1q91b5LGqFImhqtE1vtJcKsKR7005vGOUgjq5du2Lp0qVYunQpTp8+TcV4y5Yt2Lp1K+zt7REbG4uvvvrKGLfTikgkQnZ2NhQKzRVt+fn5AAChUIjs7Gw8fPgQUVFRGuF69OgBAEhJSWm0ADOMz+0yOU4XypCcX43UAhku3qtBpZYOroM5hzBHMcKcxNhwvhzFVUTvcNLyaJtWNQdMiKqwK0WaLk6pnR/kv1fXfpcpgBrao1P68f780GaNgqCmdo5RTr+r+ikbEjWKukYI3yChjYza6ylqGxxKP5XGR+13tYYG6hoZRKWRUedGqJuCKEc57lc33AxxteBgby7QOiyqnAPkant+/NCn8r85FUQBzEWAuQCQiASwEAEWQuV/Sa3waQqedhEUclxtj5CJYGtghLc5hnlJWv9JWKGhoQgNDcV///tffP7555g/fz6Ki4uxYcOGJhVggUAAHx8frX4rV64EAAwYMID2TrXtbfbw8ACgnOtmNC+l1QpkFtXgVEE1UvJlOPc/GYq1DDmaC4FuDmJ0dxYj1EmMUEcx2ksFtJLztxNh0pH74KB9OGlJd+tWJb6AsgIXckDdGFLrsr8+6mKt/F6/gaE6epBWUI1pfz5o8LpLo6wR5SpRE0FRrRAzEWQ0hFDANclWI30YXYBTUlLw448/4qeffsLNmzfp3mFvb29j38ogtmzZgg0bNsDT0xOTJk3Cnj17AABWVlYaYS0tLQEAZWVlOq/38ccf4+OPP9Zwv3PnjpEsbvvIFAQXimuQXliN5HwZThfW4OZDuUavVcABHW2FiHQWI8zJDKGOIvjbifQK6Ahvc2wYAM3Vss0wnMQwDH4uWkzP4dMvjh5ScyxNe9jgQpmnvMxbXeOK8c/GKAKcnJxMRffWrVtUdDt06IDRo0cjLi4OkZGRxrhVo9i8eTNeeeUVSKVS7NixA1ZWVtQ2bVPfvJu++eoHDx4gLy+vaQxugxBCcOOhHBmFyoVSaQUyXCqpgUzL3nc3SwHCncQId1YOJ3dtJ4ZU3PgK1VTDSYymQSjgsLSHdZsb2WAwHlmAk5KS8OOPP2LHjh1qouvp6UlFl59TNQVLlizBe++9B1tbW/z666+0AWBtbQ0AKC8v14jDu6nuF66PjY0N3N3dNdzv3Lmjdf75n8b/KhU4UyRDeoFy7vbc/2pQKtNs7FiJOYQ4ihDpbFY7lCyCkxH32pliOInRdLCRDUZbpFECfPLkSSq6eXl5VHTd3d2p6EZHRzeJoYYik8kwZcoUJCQkwN3dHXv37kVQUBD15+eJta1U5t08PT11Xn/WrFmYNWuWhju/CvqfREUNQXaxcjVycr4MZwpluK3lWDexAAi0E6G7S9287RM2QjYvx2gUbGSD0dYwSIBnzJiBHTt24Pbt21R03dzcEBsbi7i4OPTu3btJjTQUuVyO5557Djt27EBQUBD27t2r0VsNDAyEra0tUlJSNOLzbvzeYEYdCkJw9b5yKDm1QIa0gmpcvS/XekKOlxU/b6v869xOBImQVZKMx4eNbDDaEgbtAxYIlKtMRSIRBg4ciLi4OPTp0wcCQePeZvjEE088sqGGMH/+fCxbtgzdu3fHgQMHdA4lv/rqq1i/fr3GQRw9evRAfn4+cnNzYW7euCGttrYPOL9cKbbphTKcypchq1iG8hrNcPYS5RagiFrBDXYUw07C3nLJYDAYDdEoAX6sG3Ecamq01OBG4saNG/D19YVcLseyZcu0ztO6uLhg8ODBuHv3LkJDQ1FeXo5Zs2bBxcWFHkWZmJiIuLi4Rt+/NQtwmUyBM0U1OF0kQ8rdapwpqkFhpeZQsrkQ6NJOZQuQkxge0sfPGwwGg/FPxOA54Mc9MMsIB27p5ejRo1Tg586dqzVMv379MHjwYLi6uuLkyZOYO3cuVq9eDZlMhm7duuHXX3/F8OHDm9ROU1OjILh4Tym2qfkypBXKkPtAjvpyKwDwRO0WoPBasQ2wE0HE5tsYDAbDKBjUA75+/bpRbtahQwejXKcl0hJ7wIQQ3HyowOkiGdILlKdJnS+uQZWWxdouFgKEOdUNJQc5iCAVs6FkBoPBaCoM6gG3ZeFsSTzuu05LqpRie7p23jbzfzKtB9hLRRyCa7cAhTmJEOIohotl057VzWAwGAx1jH4SFuPRaOy7TqvkBNnFNcqFUgXVSCuU4eZDza6tiAMC7ESIdFH2bEMdxfC1FULA5m0ZDAbDpDABbgHoetfp3XIFJh25j/UDCALtxThduyo5rfbFBDVaJg88rASIcBIjwtlMuQXIXgRzERNbBoPBaGkwATYxcgXBwpRSrWfc8m6TjzzQ6m9rxiHUUUx7tyGOYtizLUAMBoPRKmACbGKS82Vqw87aIFAOJXdzqD1NylF5fKOXFdsCxGAwGK0VJsAmpqBCywtutfBJbxuM8bNoYmsYDAaD0Vyw8UoT42zgCwjaS9kqZQaDwWhLMAE2MVEuYrhZCnS+EZUD0F4qQJSLuDnNYjAYDEYTwwTYxPDvOgU0X0vO3nXKYDAYbRcmwC0A5btObeFqqf443KQCbBhgy951ymAwGG0QtgirhcDedcpgMBj/LJgAtyDYu04ZDAbjnwMbgmYwGAwGwwQwAWYwGAwGwwQwAWYwGAwGwwQwAWYwGAwGwwQwAWYwGAwGwwQwAWYwGAwGwwQwAWYwGAwGwwRwhBBtr5plNBIzMzPIZDIIBAK4ubmZ2hwGg8FgmBBXV1ekpaXpDcMO4jAScrnytYIKhQJ5eXkmtobBYDAYLR0mwEbC3NwclZWVEAqFcHZ2Npkdd+7cgUKhYD1xI8HS07iw9DQuLD2NizHT09XVtcEwTICNRFlZmalNAAB4eHggLy8Pbm5uuHXrlqnNafWw9DQuLD2NC0tP49Lc6ckWYTEYDAaDYQKYADMYDAaDYQKYADMYDAaDYQKYADMYDAaDYQLYIqw2xqxZs/DgwQPY2NiY2pQ2AUtP48LS07iw9DQuzZ2e7CAOBoPBYDBMABuCZjAYDAbDBDABZjAYDAbDBDABZjAYDAbDBDABbkNMmjQJHMdp/UtISDC1eS2elJQUCIVCHD16VMPvxo0bmDBhAtq3bw+pVIqoqCjs3r27+Y1sRehLzyeffFJnXtUW/p/MuXPnMHr0aDg5OcHMzAze3t6YMWMG7t+/rxaO5VHDMDQ9myOPslXQbYizZ8/C29sbS5Ys0fDr2bOnCSxqPVy5cgWjRo2CQqHQ8Lt79y769u2L4uJiTJ8+He7u7ti4cSOefvppbNu2DePGjTOBxS0bfekJKPNqREQE4uPjNfw6derU1Oa1Gi5duoTo6GiIRCJMmzYNXl5eSEpKwpo1a3D48GEkJSVBKpWyPGoghqYn0Ex5lDDaBHK5nFhYWJC4uDhTm9Lq+Pnnn4m9vT0BQACQI0eOqPm/9tprhOM4cuLECepWUVFBgoODiaOjI3n48GEzW9yyaSg9b9++TQCQd955xzQGtiKGDBlCxGIxOXfunJr76tWrCQCyYsUKQgjLo4ZiaHo2Vx5lQ9BthCtXrqCiogJdu3Y1tSmtihEjRuCZZ56Bm5sbnnvuOQ1/uVyOrVu3Ijo6Wm0UwdzcHPHx8SgqKsKvv/7anCa3aBpKT0DZswDA8moDVFdX4/jx4+jTp49GWk2YMAEAcOzYMZZHDcTQ9ASaL48yAW4jZGZmAqjLMOXl5fQdxQzdXLx4Ef/5z3+QkZEBf39/Df/s7Gw8fPgQUVFRGn49evQAoJzrZChpKD0BzbxaVlamc6j6n4xIJEJ2dja+/vprDb/8/HwAgFAoZHnUQAxNT6D58igT4DYCn2H27dsHb29vSKVSWFpaIiYmBjk5OSa2ruVy/vx5zJs3DxKJRKs//0oyLy8vDT8PDw8AwLVr15rOwFZGQ+kJ1OXVzZs3w9XVFVZWVrC2tsaECRNQWFjYXKa2eAQCAXx8fODr66vht3LlSgDAgAEDWB41EEPTE2i+PMoWYbUR+CGT5ORkLFy4EI6Ojjh58iRWr16NkydP4tSpU/D29jatkS0QfUIBgK6MtLKy0vCztLQE0HLeBd0SaCg9gbq8mpGRgZUrV8Lc3BwHDx7E+vXrkZKSgpSUFNjZ2TWxpa2XLVu2YMOGDfD09MSkSZOwZ88eACyPPir10xNovjzKBLiNMHbsWISFhWHu3LmwsLAAAMTExCAqKgqxsbFYsGABtm3bZmIrWx+k9qRWouXEVt6NH7ZiGMZrr72G0tJSvPPOOxAIlINwo0ePRkBAAGbPno2PPvoIH374oYmtbJls3rwZr7zyCqRSKXbs2AErKyuWRx8DbekJNF8eZUPQbYTx48dj8eLFVHx5nnnmGXh6emL//v0msqx1Y21tDUA5p14f3s3W1rZZbWrtTJs2DXPnzqUVG88bb7wBoVDI8qoOlixZgpdeeglWVlbYt28fIiMjAbA8+qjoSk+g+fIo6wH/A3BxcaFDKozG4ePjA6BuLlgV3s3T07NZbWqrmJmZwd7eHqWlpaY2pUUhk8kwZcoUJCQkwN3dHXv37kVQUBD1Z3m0cTSUnvowdh5lPeA2QFFREYKCgvDMM89o+MlkMly5cgV+fn4msKz1ExgYCFtbW62rSHk3dsiJ4Zw7dw5dunTB9OnTNfwKCgpQVFTE8qoKcrkczz33HBISEhAUFISUlBQNsWB51HAMSc9mzaNNusuY0Wx06tSJiEQikpaWpua+ePFitQ3mDN28//77Wg+OmDJlitZDDoKCgoiLiwupqKhoZktbB9rSs7y8nNjb2xNbW1ty/fp1tfATJ04kAMj27dub2dKWy7x58wgA0r17d1JSUqIzHMujhmFIejZnHmVD0G2EdevWYdiwYRg0aBCmTZsGd3d3HD58GDt27ED//v0xY8YMU5vYalm8eDF2796Np556CrNmzYKLiws2btyIc+fOITExEebm5qY2sdVgYWGBtWvX4vnnn0dUVBRef/112NraYteuXfjjjz8wfvx4jBkzxtRmtghu3LiBjz76CBzH4ZlnnqGrnVVxcXHB4MGDWR41gMakZ7PlUaPIOKNFkJ6eTkaOHEns7e2JmZkZCQgIIEuWLGGtXwPR1QMmhJCcnBwSFxdH7O3tiZWVFYmOjia//fZb8xvZitCXnocOHSKDBg0iVlZWxNzcnAQHB5O1a9cSuVze/Ia2UDZv3kyP89T1169fPxqe5VH9NDY9myOPcoRoWbvOYDAYDAajSWGLsBgMBoPBMAFMgBkMBoPBMAFMgBkMBoPBMAFMgBkMBoPBMAFMgBkMBoPBMAFMgBkMBoPBMAFMgBkMBoPBMAFMgBkMBoPBMAFNIsBnz57F9OnT0aVLF9jZ2cHc3Byenp546qmnsHbtWlRUVGiN99JLL4HjODz//PNNYVaTsmjRInAch969exscx9vbGxzHYcOGDU1oWdPDcRw4jsOhQ4eMfu2jR4/S69fU1BgcLyEhARzHwcPDQ6v/rVu38ODBAzW3ps5/crkcffr0QadOnSCTyZrkHqagNZfbxlBaWkqPeZVIJHBzc2v1ZfdReZT6Th/l5eXIzc01yrVaE0YX4Pfffx+hoaFYs2YNbt26hSeeeAIhISEQCATYt28f3nzzTQQEBCAjI8PYt2YwGqS6uhrvvfce/P39UVBQ0Kz3XrFiBf766y+sWrUKYrG4We/NeHzGjRuHdevW4c6dOwgICICTkxO8vb1NbVar57vvvoO/v3+TNOBbOkZ9GcOmTZvwwQcfQCqVIiEhAaNGjYJQKKT+Fy5cwMSJE5GcnIyhQ4fi/PnzcHJyov7Lli3D3Llz2cujGZTu3bvjwoULAACR6PGz6+3bt7FkyZLHvk5juXbtGpYsWYKBAwdi+PDhzX5/xuNRVlaG3377DQDwxRdf4NVXXzWxRabljTfewNixY2FpafnY15o/fz7y8vKMYFXrw6g94A8//BAAsHLlSowePVpNfAGgU6dO2L17N5ydnVFUVITPPvtMzd/NzQ2BgYFwc3MzplmMVoylpSUCAwMRGBhoalMei3nz5qGiogKLFi0ytSmMR6C4uBj8sfn9+/c3rTEtAEdHRwQGBsLLy8vUprRqjCbAJSUl+PvvvwEAPXr00BnOyckJMTExAKD1BdIMRlsjOzsb27dvR+fOndGnTx9Tm8N4BORyOf0skUhMaAmjLWE0AVad0/r111/1hl28eDGys7Px/fffq7nrW8xRXFyMRYsWoUuXLpBKpXB2dsa4ceNw+fJluiBAtXfBL96JioqCTCbDqlWrEBwcDEtLS9jZ2WHQoEHYtWuXThuPHTuGF154Ab6+vpBKpZBIJHB3d0dsbCz++OMPA1PFcPbv349+/frBysoKdnZ2GDhwILZv364z/J07d7BgwQJ0794d7dq1g1gsRrt27dCzZ0+sWrVKY6Fbbm4uOI6Dq6srCCHYuHEjevToAWtra1hbWyM6OhqbNm2CrpdjXb16FVOmTIGPjw/Mzc3RsWNHLF26FNXV1Rphx48fD47jMGXKFA2/vLw8uqhq7dq1Gv67d+8Gx3Ho1q0bgIYXYe3cuRODBg2Co6MjrK2t0b9/fxw4cEDrb+jfvz98fHzo944dO4LjOBw9elQj7PXr1zFlyhR4enrSZ//SSy/hypUrWq+tjzVr1oAQojVfP2o+bWiRGf+8OY5TW9zCl5WFCxfi7t27mDp1Kjw8PGBubg5fX18sXLiQPtOjR49i2LBhsLe3h4WFBcLCwvDtt9/q/a2XL19GXFwcHBwcYGlpiZCQEKxYsQKVlZU64/z5558YPXo03NzcYGZmBhcXF8TExODw4cNaw/OLF8+ePYv4+HjY29vDysoK4eHhKC4u1mufqp1Tp06Fn58fJBIJbG1tERUVhU8++USj7HAcp5ZvfHx8wHEcXnrppQbvw9dpX375JXJzczFx4kR4eHhAIpHAw8MDkyZN0lh8FB0dDY7jMH36dJ3XXbp0KTiOw1NPPaXmfuXKFbz66qtq5fSDDz5AdXU1evfurbFY8nHzkbZFWImJiRg6dCg6dOgAiUQCZ2dnDBs2DNu2bYNCodC4xvXr1wEAkydP1qjH9ZGXl4fp06cjODgYNjY2sLa2RufOnREfH69zQVdJSQmWL1+Ovn37wtHREWKxGHZ2dggPD8eiRYtw7949jTiq9U9iYiJ69uwJa2tr2NvbY8iQIUhKSgKgnKZYsGABzVMuLi6YMGECbt++rTVN7ezsjPs+4F69ehEAhOM4MmHCBHLs2DFSU1NjcPwXX3yRACDjx49Xc8/JySF+fn4EABEKhSQ4OJgEBAQQAEQqlZIhQ4YQAOT999+ncY4cOUIAkNDQUDJo0CACgDg6OpKwsDAilUrp+x+/+OILDTvmzp1L/Z2cnEh4eDjx8/MjIpGIun/11Vdqcfh3n/bq1cvg39uhQwcCgPTp04cAIObm5iQ8PJy4u7vT+7z88ssa8ZKSkoidnR2N06VLFxISEkJsbW1pvD59+qil/bVr1wgA4uzsTF544QUCgNjZ2ZGwsDB6LQBkzpw5Gvc7dOgQsba2pukdERFB2rdvTwCQAQMG0LgHDx4khBCSmJhIABAvLy+Na23atImGHzVqlIb/5MmTCQCyYMECQkjdcwRAZDKZWtjXX3+d+nl6epLw8HBiYWFBAJD+/fsTAMTd3Z2Gf+ONN0hERASNEx4eTnr16kUyMjIIIXX5r1u3bsTOzo5wHEc6depEAgMDiVAoJACIlZUVyc7ONuTxEkIIkcvlpF27dgQAyczM1PB/1HzKp6Pq71OFf94AyLVr16g7n0/j4uKIs7MzEQgEpFu3bsTLy4uGHzt2LPniiy8Ix3HEysqKhIaGquWRL7/8Uu1efLqFhIQQGxsbwnEc6dq1K+nUqRONExYWRoqLizXsnDNnDg1jb29PwsPDiaurK3V75513NOLw5Yavbzp37kw6dOhAoqOjDXkkZOvWrUQikRAAxMLCgoSHhxN/f396z27dupGbN2/S8L169VLLNxEREaRXr17kww8/bPBefNpMnjyZ2NjYEIFAQDp27Eg6d+6sVsfcuHGDxvn666+pe/08z8Pb+8MPP1C3gwcPEhsbGwKAWFpakoiICJqWAwcOJN27d1crp4Q8fj6qX9/NnDmThu/QoYNaXQGAvPDCCzTsxo0bSa9eveiz8PPzI7169SIbN25sMF2vXr1KnJ2daZ3UrVs30q1bN3otGxsbWq55Ll++TDw9PQkAIhKJSGBgIAkLCyMODg7UvoCAAFJaWqoWj/d744036HMJCQmhdY1EIiGHDx8mXbp0IRzHER8fH9KlSxcaz9fXl5SVlWmkqa2tLTGqAGdkZBArKyu1Fxzb2NiQ4cOHk2XLlpHk5GS9LzPWJsAKhYIWtIiICJKTk0P9kpOT1R6uNgHmK81t27ZRv5KSElrZOTg4qGVyPp5AICDffPONmr03b96kFbuLi4ua3+MIMAAyYsQIUlRURP02btxIBV81Q9bU1BBfX18CgMTExKhVatXV1WT58uX0mr/++iv1Uy1IQqGQrF69mgp0RUUFef7552nGLCgooPHu3btHnJycaMX84MED6peQkEDEYrGGAN+/f5+6X7x4Ue03P/fcczR8u3btNPID3/hITU1Vex71BXjr1q0EADEzM1N7tvfu3SNjxoyhcepXLKrpcOXKFTU/Pv/xgqhq+5kzZ4ijoyMVL0NJS0uj5UAbj5pPH7fiBEA6depELl++TP3ee+896icQCMisWbNIZWUlIYSQsrIyMmzYMFqx6ko3Pz8/cu7cOeqXnJxM3NzcNCpfQgj58ssvCaBsCG7dupW6KxQKkpiYSBsgGzZsUIunWm4SExOpe2Fhoda0UCU5OZmWq8mTJ5P79+9Tv9OnT1NhCwsLU0tvXenZEKppExUVRS5dukT9Tp48SRu28fHx1P3BgwfE0tKSACB79uzRuObJkydpg4V/PkVFRbScxsbGkpKSEkKIsgG4Zs0aIhAINMopIcYV4PPnzxNA2Sk4cuSI2nU2b95MbUhKSlLz45/n+vXr9SemCs8++ywBQEaPHq0mmHfv3iU9e/YkAMjQoUPV4vTr148+h9u3b1N3hUJBvv32W2rf2rVr1eKp6tny5ctpvrh58ybVH4FAQHx8fMipU6dovAMHDtCGe0JCAnVXTVOjCjAhhGRnZ5PevXurGa365+zsTBYsWKDWIuDRJsB79+6lldOdO3c04iQnJzcowKtWrdKIl56eTv2zsrKo+5w5c4hEIiGxsbFaf9+ff/5J46na8zgC7OvrSyoqKjT8Fy5cSCs1Vbvt7OyIRCJRE2xVnnjiCQKALFu2jLqpPvQ333xTI05RURHhOE5DuHlB9/f3J9XV1RrxFi9erLVgP/nkkwQAWb16NXVTKBTE2dmZ2NjYkJCQEAJArZWakZFBABAPDw/qpkuAAwMDCQCyaNEiDZuqqqroCMmjCLBIJCLXr1/XuO6HH35IABA3NzcNP1189NFHBIDO3tmj5lNjCHBycrJanLKyMlph9O3bV+OaR48epXFVG358ugmFQnL+/HmNeIcOHaKVVG5uLiFE+YxcXFwIAPLzzz9r/Q3r1q2jv1H12auOHDWWoUOHEgBkyJAhWv1zcnJoz2bLli3U/XEF2MzMTGv99eabbxJA2blQZcKECTobe6+++ioBQKZNm0bd+Lzp6+tLRVmV+fPnN7kA8yNfwcHBWq81bdo08txzz5GjR4+quT+KAPPlf+fOnRp+qampZNiwYWTmzJnULT8/nzagVRuIqgwcOJAAIK+++qqaO//7hw0bphFn3rx51P/w4cMa/nyHbfr06dRNNU2Nvg+4c+fOOH78OE6fPo33338fPXv2VJsfLigowIcffoigoCDcunWrwevt3LkTADBq1Ci4urpq+Pfo0QPR0dF6r/Hvf/9bw61Tp070c0lJCf28fPlyVFRUYOvWrVqvpbrsvry8XO99DeWVV16Bubm5hvvkyZMBKOdfL126BAAICwvDvXv3cO/ePTg4OGjEqaqqQrt27fTapy09HBwc6JYw1fTgt16MGzdO697V1157Tes9Ro4cCQBq87GZmZkoKChAv3790LdvXwBQm+fj78XH1UVOTg4uXrwIAFrn4czMzPDKK6/ovYY+IiIitK7uDAoKAgAUFRUZfK1r164BAPz8/BoM25h8+rg4ODhoLJa0tLSEs7MzAGjdKtW+fXv6uf4hJgAwaNAgNXtV3X18fKBQKLBv3z4AwMmTJ5Gfnw9ra2s8/fTTWm0cP348BAIB8vLytJ4b0NhDIMrKynDkyBEAwIwZM7SG8fHxwahRowAAv/zyS6Our4+IiAit9RefXvWf7cSJEwEo10Tcv3+fuldVVeGHH34AALz88svUfffu3QCA559/XusisZkzZ4LjuMf7EQ3QsWNHAMpy/tZbb2msl1i7di2+++479OvXz2j3mjt3Lnbt2qU2bx8REYHff/8dH3/8MXVzdnZGYWEhysvL0bVrV43ryeVy2NjYANBdb44YMULDjd8HbmlpqfV38WVGW3kBmvAoypCQECxatAgnTpxASUkJDhw4gNmzZ9MC/vfff2PMmDENXicrKwsAEBwcrDNMRESE3mu4u7truFlYWNDP9Rf3cBwHoVCI48eP46uvvsKcOXMQGxuLjh07qt1LdUHB4xAWFqbV3cvLi+6J5vfCqtp/5coVfP/991i6dCkmTpyIHj16wM7ODmlpaXrt05Ye/DUB9fTghV9bpgWUGVu1YubhRfTo0aN0UQ8vxoMGDaKrgVUFmF+8x6+S1wVvk7W1NTp06KA1TEhIiN5r6ENX+lhZWQEAZDKZwady8Yd92NnZPdJ99eXTx8HT01Oru5mZGQCo7c/nUW2AES2L9XTlY6Cu8cLnY75cV1dXo2/fvujdu7fG3/Dhw+lWRr7BpUpjtyvm5OTQvBgeHq4zHO/H5zNj0JgyBwD9+vWDn58fKisr8eOPP1L33bt3o6SkBN26dVP7DfwOFH7xYn0cHR3h6+v7WL+hIcLCwjB+/HgAwKpVq+Dv7w8fHx9MmjQJP/74I0pLS412rw8++ABWVla4dOkSYmJiYG9vj4EDB2LZsmXIzMzUGc/CwgI3b97ETz/9hOXLl2PKlCno06cP7OzsaINLV72prczw5cXBwQECgaac8mVGW3nhOK55zoK2tLTE4MGDsXLlSuTm5mLs2LEAgOTk5AZPxOJ7G3zlpw2+5aILPpF0oZo4hBB8+umn8Pb2Rt++ffHaa69hxYoV2LlzJ0QiEV544QW913oUrK2tG/RTbZWlpKSgf//+8Pf3x7hx4/Duu+9i06ZNyMnJwfDhw9VWbGqjMenBrwrUl/729vYabh06dEBwcDDKyspw4sQJAMDBgwcBKAV44MCBEAgEOH78OGpqalBYWIjU1FTY2to2uM+St0kqlTbKJkPRNhrxqPC2GnJgQWOey+OiL+0AaK1MGqIx+Zjv1VVVVeHEiRM6//gjO7X1/lUbJ4ag2gvRd9gPX58YUzAe5dnyoztbtmyhbps3bwag3vsF6tJTXznVNmJmbLZs2YKNGzciKiqKrpzeuHEj4uLi4OzsjPj4eK07JxpLSEgIMjMzMWnSJDg4OKCqqgpHjhzB/PnzERISgqCgIPz1119qcS5duoSRI0fC29sbY8aMwbx587B+/XqcPXsWffr00dvJA/SXmUcpL1Kp1HgC/Nprr6Fjx470MA5dWFhY4Ouvv6YZsqFWJv+jdXXhAeMWlA8++AAzZ87E7du38eyzzyIhIQGpqal48OABLly4gHfffddo9+J5+PChTj++YPGCcuHCBQwYMADHjh1D586dsXLlShw8eBC3bt1CYWEhduzYYdSDTPhCqy/9dZ3trToMXVlZib/++gsuLi7o2rUr2rVrh5CQEJSWliItLQ179+6FQqHA8OHDGzymkbdJ33PXZVNzw4u5MYePVdElymVlZU1yP300Jh/z5To8PByEkAb/9G3JMRTVBoLqsG59+EaTvgZFc/DSSy/RRuqNGzdQWFiI/fv3QywWa2xp4xsU+sqpvikzY+UjjuMwceJEJCUlIT8/H9u3b8frr7+ODh06oLKyEp999hneeuutRl1TF0888QTWr1+PgoICnDp1CitWrMDQoUMhFotx7tw5DB06FDdv3gSgHInq27cv9uzZAw8PDyxduhR79+5FTk4OSkpKsHfvXnTp0sUodhlKRUWF8QS4oqICV69eNWjexNramrbUtA11qcIPqZw9e1ZnGH1DDo1BJpNh5cqVAID33nsPiYmJePHFFxEREUHtNWTeurFoG14DlHO/vMjwQ8CrV69GRUUFAgMDkZqaitmzZ+PJJ59UG+Iypo0BAQEAgNOnT2v1f/jwId3HVx9egPfv34+kpCRUVlZi0KBB1P/JJ58EoByGNnT4WdWmsrIyXL58WWuY7OzsBq/THPDzfo2ZNzYE/ljOqqoqrf719x42B7ryMSGE5h++PPPP8PLlyzqH1gkhOHLkCK5cuWKUXpOvry9t3KWnp+sMx0/h8POMpsLd3R1DhgwBIQS//PIL9uzZg5qaGowYMUKj3uTTVVc5raqq0rqH3Zj5qLS0FOnp6bRT5eTkhDFjxuDzzz9HTk4Opk6dCkC9R/8oEEKQm5tLR9QEAgEiIyPx9ttvY9++fcjKyoKNjQ3Ky8vx888/AwC++eYbFBQUoF27dkhPT8eCBQvw1FNP0T3dQNPU7fqQy+XGE2C+RZaWloaEhAS9YQ8cOIDi4mK0a9cOUVFResM+88wzAJRzH9oqsQsXLuD48eOPZnQ9ioqKaCte1xyR6ttPjDUn9+2336qdtMOzZs0aAEBoaCidf+AX9XTq1EnrsObBgwdx48YNo9kXGxsLQFlotPVwNm3apNV2QJmG7u7uOHPmDD1URJsA79+/HwcOHICZmZnGwQLa8Pb2pvONX3zxhYa/QqHAN998ozWu6lCRMYd0dcELjbELt6OjIwDlATXaXirBL15sTvbv36+1wv7ll19w69YtSCQSDBkyBADQt29f2NraorS0FJs2bdJ6ve+++w4DBw5EYGAg7ck8DlKpFAMGDAAAfPrpp1rD5OTk0AVNhuTFpoZfTLhz5056IEv94Weg4XK6ZcsWrYehGDMfvffee4iIiMDs2bM1/AQCAS379esLvkwaWh6Li4vRsWNHDBkyhDaWVPH396eLKPl78fVmhw4d6G9W5fz58/RADWOutWgIownw4MGDaSaYNGkSZsyYoXEaSWVlJTZt2oS4uDgAytNc9M1ZAMqVoeHh4Xjw4AFiYmLUCmJWVhZiYmKMthjKycmJriD+5JNP1E5FKSwsxOuvv47vvvuOuhlrFXR6ejpeeeUVOtyjUCjwySefUAFeunQpDcufiXzgwAG1OY6amhp8//33ePbZZ41q36RJk+Dn54dbt24hNjZWrZD+/PPPmDt3rs64HMfhX//6Fz15C1AX4N69e0MikeDPP//EgwcPMHDgQIOH/ZYtWwYA+Oyzz/Dpp5/SPFBeXo4pU6YgNTVVazzV/Kar525MevXqBUDZM9F3GlRj6dGjB8RiMQghmDFjBh1yl8lkWL16Nb7++muj3ctQysvLMXLkSLV0PXToEBWR+Ph4ughTKpVi3rx51H3Tpk1q5XjXrl10hX1cXJzRFhAtWrQIIpEIBw4cwJQpU9SmMTIzMzF8+HBUVlYiODgYEyZMMMo9H4eRI0fC0dERx48fx4EDB+Di4qJ1hfrLL78Mf39/Wk7z8/Op3549ezBz5kyt1zdmPnr++efBcRx+++03rFixQu2Vmzdu3KDTk/Xt58ukoeXRwcGBNo5efvlltZEXhUKBdevWISsrCxzHYdiwYQDq6s3MzEzs2LGDhieEYN++fRg2bBi111j1ukFo3RD1iFRVVZEXX3yR7icFlKchRUZGkq5du9JTSszMzNT2qPLoOgnrypUrdMOzSCQiISEh9NQRe3t70rFjRwKALFmyhMbRd4ISD++vummc33sIKE+TCQkJIZ06daKb90NDQ+l+sl27dtF4j7MPePTo0QQAsba2JhEREXR/JMdxZMWKFWpxrl+/Tu8PgHTs2JGEh4cTe3t7gtr90tHR0QRQP2lK3/7X+vZs2rRJzf3MmTP0MAWJRELCw8Np2MjISOLh4aGxv5Dnt99+o/f19fXV8Fc9Sav+CUuE6H+O//3vf2lec3FxIZGRkfRgg1GjRunc38jbbmVlRSIiIsjvv/9OCNGd/wyxRRcymYyetHPs2LFHuqa2fEoIIQsWLKB+/ClS/L2mTp1KDzUx5AQjHl15gBDde0L5dPv3v/9NJBIJEYlEJDg4mJ5exz+P+vvIFQoFPfkMUJ4AFhkZqXa4Tq9evcjDhw+12tiYfaOqbN68mZiZmRGg7iQsft84oDwJS/XAH32/vSEaylP8Ptz6h5uoEh8fT+89e/ZsneEuXrxITzQzMzMjYWFhxMfHh9Zb/DXql1Nj5iN+PzKgPGAlNDSUBAQE0PrT19eX5OXlqcXh9zyLRCISGhqqVo/r4vbt2/S3CgQC4uvrS8LDw9XqxuXLl9Pw9+/fV8uP/Cld/GlaYrGY7tkNDQ1Vu5eudCOk4efHP/8XX3yRujXZPmAzMzMkJCTg1KlTmD17NsLCwlBVVYUzZ87g1q1bCAgIwNtvv42zZ8/q7TnVx8/PD5mZmZgxYwa8vLxw4cIFFBQUYPz48UhPT6erfo3xaqypU6fi0KFDGDx4MOzs7JCVlYWCggJERUXh888/R0pKCm3B7dmz57HvBwCzZ8/GDz/8gICAAGRnZ6OqqgojRozAsWPH8Pbbb6uF9fLywtmzZzF16lT4+/vj5s2buHjxIlxdXfHmm2/i7NmztKV55MgRoyzGCQ4ORkZGBmbPng1PT09kZWVBoVBg5syZ+OOPP/QeTj9o0CDawlXt/fLww9AcxzW4/7c+77zzDo4cOUL3z2ZlZSEgIADff/89Zs2apTPeTz/9hOjoaMjlcly+fBlXr15t1H0bg0gkoqv+f//9d6Nee+nSpdi2bRt69+4NmUyGS5cuwd/fH1u3bsW6deuMei9D6NmzJ06ePInBgwfj2rVruH37Nnr06IFvvvkGP/30k8biOo7j8PXXX2P//v0YNWoURCIRTp8+jdLSUkRFReGzzz7D4cOHG1yx3VgmTJiAzMxMTJ48Ga6ursjKykJRURF69eqFtWvX4tSpUw3uJGhOVPe6axt+5gkICMCZM2cwZ84cWk5ramrw1ltv4dixYzrjGTMfzZ8/Hzt37sTw4cMhkUhw7tw53LlzB6GhofjPf/6DzMxMjW2LK1euRGxsLKRSKS5evKix5VIbbm5uSE1Nxdtvv43OnTvjzp07OHv2LMzNzTF27FicOHECc+bMoeFtbGyQmpqKuXPnokuXLigsLKRzxRMnTkR6ejqdtsrMzKTTeE0NV6vwrZrIyEikpaVh27ZtGDdunKnNYTDU+PvvvxEYGAgnJyfcuHHDKO81Zvxz2LNnD0aOHInIyEicOnXqka/DLzY6ePAgbfgymp/c3FzawGuWfcCPwzfffIOAgADEx8dr9c/Ly6OroPUdBMBgmApfX1+MHz8ed+7cafBNYQxGfdavXw+g7mQ8RtuhxQtwREQELl++jM8//xzfffed2kq53NxcjBkzBjKZjK6WZDBaIu+++y4kEgnd5sZg6EIulyMjIwO5ublYtGgR9uzZA2dnZ62vs2S0blq8AAcFBSE+Ph5yuRzjx4+Hq6srIiMj4e/vD19fXyQlJaFz5870hBgGoyXi6+uLpUuX4sSJE2qrMBmM+ggEAvTs2RM+Pj5YvHgxAOWujMae/MVo+bR4AQaUe/YOHTqEmJgYWFlZISsrC/fu3UNkZCRWrVqF1NRUnS+UZjBaCrNmzULfvn0xd+5ctS0aDIYqHMdhwIABsLS0hJ+fHzZu3MjWtrRR2sQiLAaDwWAwWhutogfMYDAYDEZbgwkwg8FgMBgmgAkwg8FgMBgmgAkwg8FgMBgmgAkwg8FgMBgmgAkwg8FgMBgmgAkwg8FgMBgmgAkwg8FgMBgmgAkwg8FgMBgm4P+1K7G4Z7cUMwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56732,7 +98532,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56742,7 +98542,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56752,7 +98552,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56762,7 +98562,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAGCCAYAAAA46BDEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC50ElEQVR4nOzddXRUx9vA8e9K3F0hBA3Bi7sEKS7lh0uQ4sVquBYoFCmuxSmUFuhLoS1SoFhxDcEJFhIgEPfszvtHmi1LhLAkJMB8ztkDmZ2997m7yT73zh1RCCEEkiRJkiS9Vcq8DkCSJEmSPkQyAUuSJElSHpAJWJIkSZLygEzAkiRJkpQHZAKWJEmSpDwgE7AkSZIk5QGZgCVJkiQpD8gELEmSJEl5QCbgHFKpUiU8PT2pVKlSXociSZIkvQPUeR3A+yI0NJTg4OC8DkOSJEl6R8grYEmSJEnKAzIBS5IkSVIekAlYkiRJkvKATMCSJEmSlAdkApYkSZKkPCATsCRJkiTlAZmAJUmSJCkPyHHAeSw5ORmNRpPXYUiS9IFRqVQYGRnldRgfNJmA80hUVBRhYWEkJibmdSiSJH2gTExMcHR0xNraOq9D+SDJBJwHoqKiCA4OxtLSEkdHR4yMjFAoFHkdliRJHwghBMnJyURGRupm8PvQk7BWaLj6/DgRiY+xNXGhpH0NlApVru5TJuA8EBYWhqWlJZ6enjLxSpKUJ8zMzLCysuLhw4eEhYV90An4VOhO1gaO4nnCI12Zvak7/r7fUsW1Va7tV3bCesuSk5NJTEzExsZGJl9JkvKUQqHAxsaGxMREkpOT8zqcPHEqdCdzz/XUS74AzxNCmHuuJ6dCd+bavmUCfsvSOlzJzg+SJOUHad9FH2JnUK3QsDZwFCAyeDa1bF3gaLQid94bmYDziLz6lSQpP/iQv4uuPj+e7spXn+BZQjBXnx/Plf3LBCxJkiR9kB5F38xWvYjEx7myf9kJS5IkSfqgaLQp/Hp7Djtuzc5WfVsTl1yJQyZgSZIk6YNx/sk+fggYQVjCQwAUKBAZ3gNOfdbB1J2S9jVyJRbZBC29Vf7+/igUCr2HUqnEwcGBBg0asGPHjrcWy82b2Wt+etfVq1fvg77PJ0kAT+Pu883J1sw88z/CEh6iVhjhZVWGUvZ1/q3x8t9I6s89fWfk2nhgeQX8ntJoBSceJ/MkXoOzmYpqLkaolPnnS3jMmDGULFkSSB2a9fTpU3766SfatWvH6tWr6dWrV67uf+jQoezevZvbt2/n6n4kScpbyZpEfrrxDX/eXUaKSB1q5WzmhZd1WUo71KaiSzPuRJ5LNw7YwdSdnr4zcnUcsEzA76HddxMYdzKakDitrszNXMk3Va1oXsg0DyP7T6NGjahXr55e2ZAhQyhRogSjR4/WXSnnlp07c29snyRJ+cPxR9tZFziKyKQnAFga2eNlVZri9lWo4tKCQtblUCgUOJp5Usml+bs3E9azZ884ePAgd+7c4cmTJ4SHh2NmZoaLiws+Pj7UqlULNze3nIhVyobddxPoezAy3R2N0DgtfQ9Gsqo++SYJv8zc3JyaNWvy008/8fTpU5ydnfM6JEmS3kHB0ddZdnkINyNOA2CkNKWApQ+FbT6ioktTSjnUwlhlpvcapUJFKYfabzVOg+4BP3/+nO+++46KFSvi4uJCx44dGT16NPPmzWPNmjUsXbqUSZMm0alTJzw9PalQoQLz5s0jOjo6p+N/bwghiE1+s0dUopaxJ6OzGFIO405GE5WoNXgfQmTWWSFnBAUF4eDggIODg1759evX6dy5M87OzpiYmFCsWDHGjx9PfHy8Xr07d+7QsWNHvLy8MDExoUCBAvTu3Zv79+8DcPfuXRQKBffu3ePevXsoFAomTZqUZUwJCQlMnToVHx8f3eT17du3JyAgQK+ev78/lpaW3L17l44dO+Lg4ICZmRk1atRg79696bZ78+ZNevbsibu7O+bm5vj6+jJz5kySkpL06v3xxx/4+flhbW2NmZkZ5cuXZ8GCBWi1Wr16Go2Gb7/9luLFi2Nqakrp0qXZvHlzhsek1WpZuHAh5cuXx8zMDFtbW5o2bcqxY8f06k2aNAmFQsEff/xByZIlMTExoW7dulm+X5KUVxJSYlh1eQRfHq3BzYjTKFDgal6YSs7NaFiwN+2Lj6KCc6N0yTevvNYVcFRUFDNmzGDRokXExcXpvozd3NwoUaIEtra2WFhYEB4eTlhYGA8fPuTRo0dcvHiRL774gm+++YYRI0YwdOjQD3re0YzEpUCRjU9ydR8CCInTUvzHpwZv43Y3ZyxyYBKvyMhIwsLCgNTEERYWxg8//MCpU6dYvnw5KtV/TT+nTp2iYcOGWFtbM3jwYJydnfnnn3+YNm0a+/fv5+DBg5iamhIREUH9+vXRarUMHDgQFxcXAgICWLJkCQcPHiQwMBAnJyc2bNjAiBEjAJg3bx5ly5bNNM6kpCQaN27MP//8Q48ePRgxYgTBwcEsW7aMqlWrsm/fPmrU+K+HZHJyMrVq1aJ8+fJMnTqVZ8+eMWfOHJo1a8bVq1cpVqwYABcvXqR27dq6WIsWLcqBAwcYNWoUly5dYtOmTQDMmTOHL774gqJFi/L1119jaWnJjh07GDZsGAcPHmTbtm0olann0d26dWPLli00a9aMYcOGcePGDfz9/TE2Nk53XF27dmXLli20b9+efv368fz5c9asWUPdunV15S/q0KEDffr0oXjx4piYmBjykUtSrhFCcPDBBjZdn0BscgQANsZOeFmXwceuOpVdW+Bp6ZP/OiOKbNqyZYtwdXUVCoVCODg4iIEDB4r/+7//E8+ePcvydXfv3hUbN24UXbp0EWZmZkKhUAhnZ2exY8eO7O76neDh4SEA4eHhkWW9+Ph4ERgYKOLj4/XKY5K0wmV1aL5/xCRp3+h96tmzpyD1XCDDR9u2bUVcXJyuvlarFaVKlRIFChRI97u2atUqAYiZM2cKIYT4+eefBSB++uknvXpz584VZcqUEWfOnNGVeXl5CS8vr1fGO2vWLAGIrVu36pWHhIQIR0dH4evrm+7YBg4cqFd3w4YNAhBjxozRldWpU0cYGxuLixcv6tXt3LmzAERAQIC4ffu2UKvVwtfXV8TExOi9J926dROAWLt2rRBCiEOHDglAdOnSRW97u3fv1r23abZu3SoAMWvWLL260dHRwsfHRzg6OorY2FghhBATJ04UgPjf//73yvdKejdl9p30rrgdcV58ebim6LjbRnTcbSN6/OkmxhxtIFZf/kpcfnpIJGsS8zrETGUrAXft2lUoFArh4+MjNmzYIBITDTug8PBwMW/ePOHt7S2USqXo2bOnQdvJj940AWu1WhGT9GaP/fcTspVE999PMHgfWm3OJODZs2eLffv2iX379ok9e/aIn376SQwZMkQYGRmJ0qVLi6dPnwohhLh48aIAxIABA8TTp0/1HqGhocLU1FRUrVpVCCHEiRMnBCDKli0rdu7cqZe0XpbdBFyhQgVha2ubbt9Pnz4V3bt3F4C4evWq3rEdO3ZMbxvXrl0TgOjdu7cQQoinT58KhUIhWrdunW5/oaGh4vLlyyIhIUHMnj1bAGLDhg3p6j148EAAomnTpkIIIYYOHSoAcfbs2XR1y5Ytq5eA27ZtKwBx+/btdMc0duxYAYg//vhDCPFfAs4oBun98K4m4KjE52LB+b6i025b0XG3jei0206MOFRZLDzfTxx5+JOITnye1yG+UraaoPfs2cOSJUvo16+frrnLELa2tgwfPpzBgwezYMECpk+fbvC23jcKheKNm3breRjjZq4kNE6b4X1gBeBmoaSeh3GeD0mqWLFiul7QHTp0wMfHhyFDhvDNN9/w/fffc/36dQCWLVvGsmXLMtzW3bt3AahatSrjxo1jxowZtGrVCiMjI6pVq0bTpk1191pf1/Xr14mLi8PJySnTOnfv3sXHx0f3s6urq97zaU22aZPd37t3DyGEbhjWi1xcXHBxSZ11J22IVKlSpdLV8/T0xMbGhqCgIL26RYsWTVe3VKlSXLp0Se+YAIoUKZLlMb3o5WOSpLyiFVr+CFrKzzenk6CJBcDOxA1v67L42NegimtLXC0K53GU2ZOtBHzz5k1sbW1zbKdGRkZ8/vnn9O7dO8e2KYFKqeCbqlb0PRiJAv31PdLS7dQqVnmefLPSrVs3hgwZwqFDh4D/ktbgwYNp06ZNhq95cWWpqVOnMnjwYHbt2sW+ffs4dOgQR44c0d0vrlat2mvFo9FoKFq0KEuXLs20Trly5fR+ftVJatqyb6+6HyX+7WORWT2tVpvufuzLHbMyKtNoNFhZWbF9+/ZM912iRAm9n9VqOWJRyntXnx1n+eXPCI1LPeE0VVniZVWaYnaVqezSnGJ2lXN96FBOytZfVU4m3xfZ2dnlynY/ZM0LmbKqPunHAVsomVol/4wDzkxaskjrhOXt7a17rmHDhunqbtu2jcKFU892Q0NDCQgIoE6dOvTt25e+ffsihGDTpk10796d2bNn88svv7xWPN7e3oSGhlKvXr10Sej48ePExsZibm7+2tuE/65EX3Tp0iVmzJhB//79dVeoAQEBlC9fXq/e/fv3iY6OpmDBggC6zl1Xr16levXqenVfnvHL29ub69evU758eRwdHfWeu3DhAiEhIVhYWLzWMUlSbgpPeMyqgBGcffI7ACqFGjeLYhS1qUh550aUc2qAmdoqj6N8fbk2FeXly5e5cOECKSkpubULKRPNC5ly5n+ObPvYjqV1rdn2sR2n2zvm++QLsG7dOiB1og6ASpUqUahQIdauXZtu1qoVK1bQoUMHVq9eDcDq1atp1KiR3nSWCoWCmjVrAvpXcSqVKsOrxZe1b9+eiIgIZs/Wn7Q9ODiYli1b0qVLl9e+LePi4kK1atX4448/uHHjht5zCxcuZMuWLdjY2NC2bVtUKhXTp08nNjZWV0cIwfjx43XxQWrzPcC0adP0juvAgQOcO3cu3TEBjBs3Tq88KiqKDh060Lp1axISEl7rmCQpN6Rok/nl5rcMPVROl3wdTT2p4NSEhgX9aV/8a6q5tX4nky+84UQcsbGxLF68GAcHB/r06QP898V08eJFAAoWLMi6deuoU6dOVpuScphKqaCmW/rhJ/nFvn37ePjwoe7nhIQEDhw4wNatWylYsCBffvklkJooV65cSYsWLahYsSIDBgygSJEinD59mtWrV1OkSBFdMurbty/Lli2jT58+nDhxgtKlSxMWFsaKFSswNjZm6NChuv25uLhw6tQp5s6dS40aNTJtmv7666/57bffGD16NKdPn8bPz4/w8HCWLVtGREQEmzZtwszs9ccULlq0iHr16lGlShUGDx5MwYIFdcc/cOBAKlSoAMA333zD6NGjKV++PL169cLS0pL/+7//48CBA7Ro0YJu3boBUL16dQYPHszixYtp0KAB7du358GDByxatAgnJyeePv1v6Jm/vz8///wzy5cv59atW7Rq1Yrk5GRWrVrFzZs3mTVrlkH3yyUpJ51/spdVASN4lhAMgLnaBi+r0vjYV6eSS3O8bcqhVLzjyxkY2nsrOjpalC5dWiiVStGuXTtdecuWLYVCodB7WFpaigcPHrxxj7H87E17QX8oMhuGZG5uLkqWLCmGDx8uQkJC0r3u/Pnzon379sLJyUkYGxuLwoULi88++yxd3Tt37ojevXuLQoUKCRMTE+Hg4CBat24tTp48qVdv//79okiRIsLIyEjXOzkz0dHRYsyYMaJ48eLC2NhYODs7iyZNmogDBw5keGxBQUF65UFBQQJI1+s/MDBQ/O9//xMODg7CzMxMlC1bVixZskSkpKTo1fv1119FnTp1hKWlpbCwsBAVK1YUixYtEhqNJl2sS5YsEaVKlRImJiaiSJEiYvny5aJr167i5T/1pKQkMWvWLFGmTBlhamoq7O3tRa1atcS2bdv06qX1gj548GCW75H07spv30mPY++JKSda6oYVdf3dUXx1uJZYcWmYOPd4j0hMiXv1Rt4RCiEMm9po5syZjB49GicnJ6ZPn06fPn0IDg7W3ZPavHkzDRs2ZOTIkaxfv55hw4Yxb968HDhlyJ88PT0JDg7Gw8ND78ruZQkJCQQFBeHt7Y2paf5vEpYk6f2WX76TkjQJ/y6asBzNC4smFLb5iDKO9ajo8nGurcubVwxugv71119RKpXs2bNH10Fk9+7dCCGoVKmS7p7U4sWL2bZtG3v27MmRgCVJkqT3hxCC44+2sf7qaCKTUm+VWBrZU8i6DCXta1LZpTkFrHzz3yxWOcDgBHz9+nWKFi2q1ztz7969KBQKmjRpoiuzsLCgSJEictk3SZIkSU9wzHWWXhzMrcgzQOqiCZ6WPhS3rUIl1+aUtK+OWpl/+7K8KYMTcEJCgt58zkIIDh48CJBusnaNRiN7Q0uSJEkAxKdEs+HqOA492IgWDQoUuJh7U9SmImWd/PjIuQmWxu//MFWDE3DBggV1M/ooFApOnjypW4qwdu3/lnQKDw/n9u3beHh45EjAkiRJ0rtJCMGBB+v58dpEYlMiALA2dsLbuhy+DrWo4toSN4vMZ2h73xicgMuXL8/PP//MvHnz6Nu3L9988w0KhYJGjRrpZudJTk5m0KBBJCYmUqtWrRwLWpIkSXq33I44z/LLg7kfHQiAicqcglalKG5blcquzSlmWxmV8sOacc3gXtAnT56kdu3auqkC0xw8eJA6depw6tQpmjdvzvPnzzE2NubkyZNZLvv2rpO9oCVJehfl9ndSdOJz1gR+yT8h2xEIlChxtShKUdtKfOTcmLKODTA3+jCXpzX4dKNq1aps2bKFIUOGEBoaip2dHTNnztRNuGFtbc2zZ89wdHTk559/fq+TryRJkqRPKzTsvrOEbbe+1V80waYcpR3qUtm1OU5mBfM4yrz1Rtf77dq1o23btjx9+hRHR0e9KfmKFCnCr7/+StOmTfUmy5ckSZLeb1eeHWXl5aGExt0B/l00wbo0Je1rUNmlBd425d/9WaxygMHvwOHDh7l48SIKhQJnZ+d08+EaGRnploTbu3cvS5YseeNgIbXpW6VS6VbLedH9+/fp0aMH7u7uWFhYUK1aNXbu3Jnhdq5cuUKbNm1wdnbGysoKPz8/jh07liMxSpIkfYjCE0KZdaYjU0+2IDTuDiqFGk/LktRw/4RWhYfRtugXFLH9SCbffxl8BVyvXj1q167N33///cq648aN4+bNmwwaNMjQ3QGpq7q0bds2w0n0Q0NDqVOnDs+fP2fo0KF4eHjwww8/0Lp1azZt2kSXLl10da9evUqtWrUwMzNj6NChWFlZsXjxYurXr8++ffvSDaOSJEmSMpeiTWbHre/YeWc+ydpEABxMPSli8xFlHetT0aUpdqZyTemXZSsBR0dH603mniY+Pp47d+5k+johBPfu3ePatWvZWnkmKzt27KBPnz6Eh4dn+PzkyZO5f/8+R48epUaNGgD06tWLatWqMWzYMFq3bq1bYm3kyJEkJiZy9uxZ3VJ23bp1o2zZsgwaNIiAgID3ctYVSZKknHb28R/8EPA5zxMfAamLJhSyLouvQy0quzSnoFUp+X2aiWwl4KioKMqUKaO3RJlCoeDs2bO6dUhf5eU1Sl9H8+bN+f333/H19eXjjz9m8+bNes9rNBo2btxI9erVdckXwNTUlGHDhtG7d2927dpFx44defz4MX/++SddunTRJV8ABwcH+vbty5QpUzh16hRVq1Y1OF5JkqT33ePYu6y4/BlXnh8BQK0wxsOyBD521ano0pSS9jUwUpnkcZT5W7YSsIeHB59//jnffPONrkyhUJDdEUxeXl4sWLDAsAiBa9euMX36dEaOHMmMGTPSPX/lyhViYmIyXFIuLZGePHmSjh07cvLkSYBX1s2rBPzd+Zg82W92fFnBMke28+zZMyZNmsTOnTt58uQJxYsX150oZUd8fDzffPMNP/74IyEhIXh5edG1a1e+/PLLdEsDbty4ke7du2e4nZ49e7J27VrdzwsXLmTGjBnEx8fTpEkTFi1alG7B+iVLljBx4kRu376tNxNcfhYQEEC/fv04f/48ZmZm7Nq1i5o1a1K3bt0M+1K86NChQ9SvX5+JEycyadKktxJvbrp79y7e3t7pPvvXERkZSVJSEk5OTjkbXA6bNGkSkydP5uDBg9SrVy/HtpukiWfL9ansubcCjUgBFDibFaSobSXKOflRwbkJ1sYOOba/91m27wGPHz9et+avEILChQtTuXJltm7dmulrlEollpaW2Nm92ZRigYGBusk9MpI27jZtJaYXeXp6AhAUFPTadaWcFxsbS+PGjbl8+TKDBw/Gx8eHn3/+mT59+hAaGsqYMWOyfH1ycjJNmjThyJEj1K9fn5EjR3L37l2mT5/Onj17+Ouvv/TGMl66dAmAlStXphvjWKTIfzPuHDt2jKFDh9KlSxeqVKnC9OnT8ff3Z9euXbo6MTExTJkyhbFjx74zyRdS1/89d+4c48aNo0CBApQqVYoNGzbg4vJ+rSyTHU5OTmzYsEHvs38de/fupVu3bmzdujVHk9q7QAjBsUc/s/7qGKKSwoDURRMKW5ejlGMdKru0wM2iqGxufg3ZTsBGRkZ4eXnpfq5Tpw7lypXTK8stWSVfSD0jBbC0TH+FZm5uDqR+8b9u3YzMnTuXuXPnpisPCQnJMkYp1aJFizh37hybN2+mU6dOAPTr149mzZoxefJkunfvToECBTJ9/YoVKzhy5AidO3dm06ZNuj92Pz8/mjdvzqxZs5gwYYKu/sWLF3F2dqZv375ZxrVu3TpcXV1Zv349KpUKtVrNkCFDePz4sS5RzZ49GzMzszfuTPi2Xbp0iXLlyjFlyhRdWbdu3fIworxjYWHxRsd+/PjxDPvDvO8eRF9l2aXB3I48B6QumlDAsiQ+9tWp7NqC4rZVPrhZrHKCwe/Yq5qu3qa0pvCMmsTTylQq1WvXzUhUVBTBwcFvFvAHbN26dXh4eOiSL6Tezvjqq6/4888/+fHHH/n6668zff327dsBmDVrlt6ZdrNmzShfvjzLly/XS8CXLl2idOnSr4zrwYMHeHt76z77tL4N9+7dw8XFhcePHzNnzhyWLVuGsfG7tTpLcnIyNjY2eR2G9A6KS45iw9WxHHq4CYH230UTClPctjIVnJtQzqkB5kbyd8tQ2RqMpdVq0/ViTit7nUdusbKyAiAuLi7dc2llaV9Ar1M3I9bW1nh4eKR7vDwOWkovMjKSa9euZXh//cX771l58OAB9vb2utsFLypWrBiPHj3i0aPU3phPnjwhNDRUl4CTkpJITEzMcLsuLi56PezDwlKb2JydnYHUXvZFixbVG872Os6cOcMnn3yCs7MzlpaWlC9fnhUrVqQ7Edy0aRPVq1fHwsICCwsLqlevzsaNG/XqHDp0CIVCwZYtW5g+fTpFixbFxMQEb29vJkyYoFt5bNKkSbqTlL///huFQoG/vz+QetLzchPqhQsXaNmyJXZ2dtja2tKjRw+ePHmS4fFcv36dzp074+zsjImJCcWKFWP8+PHEx8fr1VMoFAwZMoRt27ZRqVIlzMzMcHJywt/fn9DQ0HTb3bp1K3Xq1MHGxgYHBwf8/Pw4cOCAXh2tVsvChQspX748ZmZm2Nra0rRp02yN4797967e+wCpQypLly7NpUuXaNasGTY2NlhaWtK4cWNOnz6tV2/y5MkA1K9fn0KFCumeS0hIYOrUqfj4+GBiYoKjoyPt27cnICBAb//+/v5YWlry+++/4+XlhZmZGV27dsXZ2ZkSJUpkGHOZMmVwcXEhOTl1kfrHjx8zYsQISpQogZmZGWZmZvj6+jJ16tQcW3VOCMG+e6sZcrAMBx9uQKDFxtiJ8k6NaOzVh/bFR1Pdva1Mvm8oW1fAarUapVJJYGAgxYsXB3jt2a0UCkWuLUno7e0NkOEczGllac2ar1M3IyNHjmTkyJHpytPmgpYyFxwcjBAiw/vv5ubm2NnZvfL+u6WlJffv30ej0aRrqUhLmo8ePcLd3Z2LFy8CqRO0VKpUiQsXLqDVaqlUqRLffvstDRo00L22UaNGrFu3jrVr11KjRg0WLlyIr68vBQsW5MaNG6xcuZJdu3YZdH9rz549tGrVCmtrawYOHIi7uzvbt2+nf//+3Lt3j2nTpgHw2WefsWjRIj766CNdh6fNmzfTvXt3Tp8+zfz58/W2O2bMGLRaLf3798fOzo41a9YwdepUFAoFkydPpl27dhQtWpTu3bvj4+PD2LFjM733ee7cOerUqYOJiQnDhg3D1taWjRs38uuvv6are+rUKRo2bIi1tTWDBw/G2dmZf/75h2nTprF//34OHjyod7999+7drFu3jv79+9O/f38OHDjAunXrCAoK0ptHYOLEiUyZMoVy5coxduxYTExMWLJkCY0bN+a3336jadOmAHTt2pUtW7bQvn17+vXrx/Pnz1mzZg1169bVlb+utHkE0m5jBAUFMW/ePBo2bMj9+/exsbFh7Nix2Nvbs2PHDsaMGUPlypWB1BO7xo0b888//9CjRw9GjBhBcHAwy5Yto2rVquzbt09vdEZCQgKdOnVi5MiR2Nra4uXlhYuLC/PmzePUqVNUqVJFV/f8+fMEBATw+eefY2RkRGRkJFWrViU8PJyBAwdSrFgxwsLCWL9+PRMmTEClUr2yH8Wr3Io4y7JLg3kYcw34b9EEX/taVHZtQWGbCnIijZwiskGhUAilUimuX7+uV/Y6D6VSmZ1dvdLEiRMFIA4ePKgrS05OFjY2NqJOnTrp6v/www8CENu3bxdCCBEaGiqUSqXo0aNHurrjx48XgDh37txrx+Xh4SEA4eHhkWW9+Ph4ERgYKOLj4zN8fta56Hz7eFPHjx8XgBg3blym72GxYsWy3MaQIUMEILZu3apXfv/+fWFmZiYAcfjwYSGEELNnzxaAcHR0FN9++63YuXOnmD17tnBxcREqlUrs3LlT93qNRiO6d+8uAAEIT09Pcfr0aSGEEO3atRN+fn4GHbNWqxWFChUSDg4O4uHDh3r7q1WrljAxMRHPnj0Thw8fFoDw8/MTSUlJunpJSUmifv36AhCHDh0SQghx8OBBAQg3NzcRHh6uqxsTEyNsbGyEu7u7XgyAqFu3bpZl9erVE2q1Wly5ckVXlpCQIGrVqiUAMXHiRN3xlCpVShQoUEA8e/ZMb5urVq0SgJg5c6befgBx4sQJvbp+fn4CEDdu3BBCCHHz5k2hUqlE3bp1RWJioq7e48ePhY2NjahUqZIQQoitW7cKQMyaNUtve9HR0cLHx0c4OjqK2NhYkZmgoCABiJ49e+rK6tatmy5uIYSYOnWqAMSKFSt0ZRl9/8yaNSvD38mQkBDh6OgofH19dWU9e/YUgPjyyy/16gYEBAhADBkyRK98+PDhAtB9LvPnzxeA2LZtm1698PBwYWxsLMqUKZNlrBlJ+056GvlIfH+ul+i420Z03G0jOu+2FyMPVRbLLn4mTofsFvHJMVluR3p92boCPnjwIKDfczitLD9Qq9V07NiRlStXcvz4cd3ZZkJCAvPnz8fFxUV39uzi4kLDhg355ZdfmDhxom4s8LNnz1i1ahXlypWjQoUKeXYs7zORxf33tPKs7r8DfP7556xfv55PP/2UyMhI/Pz8uHPnDiNGjMDc3Jz4+Hhd60yVKlUYO3Ys/v7+FC1aVLeN9u3bU7p0aQYNGkTz5s1RKpUolUrWr1/P1KlTCQsLo3Tp0piYmHDixAl27Niha4pcu3Yts2fP5tmzZzRs2JB58+alG6r0ovPnz3P37l2GDRumtya2Uqlk48aNJCQkYG1trRtNMGnSJL3WJSMjI6ZMmULt2rX56aef9GZpa9GiBba2trqfLSwsKFmypF6zaXY8e/aMw4cP07x5c3x9fXXlJiYmjBw5kqNHj+rKLl++zJUrVxgwYABarVbX6pAWj6mpKdu3b+err77SlRcrVizdbYfKlSvz119/ERoaSrFixdi5cycajYbhw4fr3WN3dnbm6NGjul7naXMAfPLJJ3r7TiubNm0ahw8f5uOPP36t9wDSd0xLu8LNqKn8RZs3b8bW1pb69evrxaRWq2natCkbNmzg2rVr+Pj46J5r1aqV3jZKlSpF5cqV2bJlC3PnzsXIyIiUlBQ2b95M1apVdZ/L0KFD6dSpU7rfubCwMGxsbIiJef1hjEII4lKi+OZUT0KTbgBgZ+JKEduKlHP0o6JLU+xN3V57u9KrZSsBZzQ1Y36brnHy5Mns3LmTpk2bMnLkSFxcXPjhhx+4fPkyW7Zs0WsSmzNnDtWrV6dWrVqMGDECExMTFi9ezPPnz7McViW9mazuv6eVZ9X8D1CoUCH27dtH9+7d+fTTTwEwNjZm0KBB2NraMmnSJOzt7QGoXbs2tWvXTrcNLy8v2rZty4YNGwgMDNTrpOXl5aXXs/+rr76iQ4cOVKxYkb///ptevXoxa9YsatSowYABA+jatSt79uzJNN60JvWSJUtmGEea27dvA6lfxC9Li+/l5nlX1/RT+5mYmKRbIvRVgoKC0Gq1eicpaV6O5/r16wAsW7aMZcuWZbi9u3fvZitOQBdrVu/Ti59P2v6zGkb08v6z6+U4X44xM9evXycuLi7LccF3797VS8AZvSe9e/dm4MCB/Pnnn7Rs2ZI9e/bw+PFjvd7rkNpJdPbs2Zw6dYqgoCBu3bpFVFQU8N9IjuyKT44mJO4esckRJGniMVVZUsi6LKUd6lDZtTkFrUrLYUW5KEf7jUdFRREXF4etre1bX+vW1dWV48ePM2rUKObPn09ycjJlypRh165dNGvWTK9u6dKlOXLkCGPGjGHq1KkolUoqVarEunXrMpygQ8oZ3t7eKBSKDO+/x8bGEhER8coEDKlXtteuXSMgIICoqCh8fX2xs7OjZ8+eqNXqbA2NSxtaFB0dnWmdnTt3cuLECa5evQrA+vXrKVy4MF9++SUAX3/9Nd27dyckJAQ3t4yvENI6zrzqSyyzVgH4LwG8PBwvpzv+ZRTDy50n02IZPHgwbdq0yXA7L/cPyU6c2X2fNBoNVlZWut7wGcmsM9OrGPp+ajQaihYtytKlSzOtU65cOb2f1er0X72dO3dm5MiRbNiwgZYtW7J+/XrMzc31RgwEBARQt25dEhISqF+/Po0aNWLEiBG6iVWyK0WbxNP4B8SlRKHRChQocDUvirttYSq7NsfXvqacxeoteOMEfP36debMmcPu3bv1mmqKFClC69atGTlyZKZfToaYNGlSpjPyeHt789NPP2VrO+XLl+f333/PsbikV7O0tKRkyZKcOnUq3XNpvZ9f7KySkXPnznHq1Cm6d+9OmTJldOUajYZ9+/ZRvXp1XaJq06YNAQEBXL58Od0MWYGBgUDmV1IajYbRo0fTv39/XZ1Hjx7pTV6RdsXz4MGDTH/H0zr9pV25vWjfvn2sX7+er776SrePK1euUKtWLb16V65cATKePCYnFC5cWNfJ8mU3b97U+znteAAaNmyo95xWq2Xbtm16U7xm14vvU1pHzzTz5s0jMDCQBQsW4O3tzfXr1ylfvny6ZtgLFy4QEhKim/P9bfH29iY0NJR69eqlS6zHjx8nNjY2W1emNjY2tGvXju3btxMaGspvv/1G+/bt9SZ9GT58OBEREQQEBOi1FiQnJxMWFqbrtZ8ZIbSEJz4mIvExgn+HXSrUGKvMqOfZhfIeDeQsVm/RG51Cr1u3jvLly/PDDz8QEhKCEEL3uHXrFnPnzqVs2bLZWjFJ+jB069aNe/fusWXLFl2ZEILvvvsOExMTvbP9jFy8eJGBAwemO9GaMWMGISEhfP7557oyNzc3bt++zYoVK/TqHjp0iD/++INmzZpl+oW1Zs0a7t+/z/jx43Vlnp6e3Lt3T3dVmNZsnNGQqDQVK1bE09OTTZs26U3gkHbMmzdvxs3NTddzd9KkSXqjBVJSUnRDXwzp3Zsd9vb2NGrUiH379nHixAlduUajSTfpTKVKlShUqBBr167VHX+aFStW0KFDB1avXv3aMbRu3RqFQsGiRYv0jv/58+fMnDmTU6dOYWZmpnsPxo0bp/f6qKgoOnToQOvWrfXmrM9paX0UXmwZaN++PREREcyePVuvbnBwMC1btqRLly7Zvrru3bs38fHxDBo0iPj4+HTTs4aFhWFhYZHuJGfBggXEx8dnOdIkNjmS+9GBhCeGIhCoUGGmssLSyA5ztQ2VXVvI5PuWGXwFfPbsWfr06YNWq6VWrVp8+umnlC1bFisrKyIjIzl37hxLly7l3LlztG7dmosXL76VWbOk/G348OFs3LiRnj17cvbsWYoXL87WrVvZv38/3333nd6V5J07dzh+/DhFihTRLebRoUMHZs+ezdChQ7l58yZFihTh4MGD/Pjjj/j7+9O6dWvd6ydNmsTvv//O559/zqVLl6hSpQqBgYEsW7YMd3d3Fi9enGGMcXFxTJw4kS+++EIvQXft2pVVq1bRvXt3qlatyvTp02nUqBHu7u6ZHq9arWbZsmW0adOG8uXLM2DAABwdHdmxYwf79u1j5syZODo6Uq9ePfr378/y5cupWrUqnTt3BmDLli2cPXuWQYMGUadOnTd677OyYMECqlevTsOGDfnss89wd3dn69at3LhxQ6+eSqVi5cqVtGjRgooVKzJgwACKFCnC6dOnWb16NUWKFNE7ackuHx8fRo0axYwZM6hZsyadOnVCq9WycuVKwsPD+eWXX4DUcbQ///wzy5cv59atW7Rq1Yrk5GRWrVrFzZs3mTVrVpafx5tKawFZunQpjx49olu3bnz99df89ttvjB49mtOnT+Pn50d4eDjLli0jIiKCTZs2pWuByUz9+vXx9vZmx44dFC5cOF2zcuvWrZkyZQqNGzfWvUd//PEHu3fvxszMTDfT34tSNEk8irlJvCa1g5YCBcZKU0zVllgY2UCyiufKGHmvNw8YnIBnzZqFVqtl8ODBLFy4MN3zFSpUoFevXnTv3p3Nmzczffp0li9f/kbBSu8+MzMzDh06xJgxY1i/fj3R0dGUKFGC9evXp1s04fDhw/Tq1YuePXvqErCFhQX79+9n/PjxbNy4kefPn1OsWDGWLFlC//799V7v4uLCqVOnmDhxIrt27WL9+vU4OzvTs2dPJk2alOkX9ffff49Go9G7mobUiRhWrlzJjBkz2L17N02aNMnwd/9lzZs358iRI0ydOpU5c+ag0WgoWbKk3nSckNqxqUqVKixbtowJEyagVqspV65cuvWsc0Px4sU5efIkY8eOZcWKFSQmJtKoUSOmTp1K/fr19eo2bNiQEydOMG3aNFavXk1kZCSenp4MGjSIMWPGZNjBKDumT59OyZIlWbhwIWPHjsXCwoLKlSuzceNGKlWqBKSeAPz22298//33bNiwga+//hpzc3N8fX3Ztm0b7dq1e+P3IiudOnVix44d7Nq1i/3799O2bVssLS05evQoM2bM4JdffmHXrl3Y2tpSoUIF1q9fn+79y4pCoaBXr15MmDCBXr16pUuK48ePR61Ws27dOkaMGIG9vT0lSpTQ9dSfPn06R48epVatWgiRepX+JP4+3prU2yVGCmNM1RaYq20wU1uhUqpJSMm9FgMpawqRVe+PLLi6upKUlERoaGiWU/PFxMTg5uaGra0tDx48MDjQ/C5tIg4PD48MOxmlSUhIICgoCG9v77feUU2SpPefEIKY5HCeJQT/u1pR6n1eE5U5ZmorLNQ2eh2s5HdS3jH4CjgyMpKyZcu+cl7ctI43ly9fNnRXkiRJUjYkauJ5Gn+fRE3qUD8FSkxUppiqLLEwssVEZS6bmvMRgxNwkSJFCAoKynBKwBcJIXj48GG2hpdIkiRJr0+jTeFZQjAxyc8RgAJQK03+TbzWmKmtUCqynuRGevsM7gXdv39/wsLCmDp1apb1li5dSmhoqN7k55IkSdKbE0IQmfiUB9GBRP+bfNUKNeZqG2yMnbA3dcXCyFYm33wqW1fAd+7cSVfWtGlTtm3bxtSpU7lz5w6DBw/mo48+0k2hdu3aNVavXs2CBQto1apVlkvMSZIkSa8nISWWp/H3SdKmdqJSosRYZZZ6n9fIBmOlmWxuzuey1QnrVfPzvsjIyEg3q00aY2NjlEpllgvdv+tkJyxJkt6GFG0yz+IfEpMSAaQOKzLSNTfbYKq2fK3ViuR3Ut7J1hXw63SUTkpKSleW2RqskiRJUvakNjc/ITwxFC2pQ4zUCiNMVBaYG1ljobZGpXy9ZWKlvJWtBPyqNVolSZKk3BOXHE1Y/H2SReoFjhLlf8OKjGwxUprI5uZ3ULYSsJzBSpIk6e17cdEESGtuNsXs31msTFUWKF6juVnKX7KVgCMjI7GxscnxnUdEROitZypJkiSBVmiJSAwlIvGJbtEEtcIYU5XFv8OKrFEpc3QxOykPZOvUqXjx4ixfvjzd0mSGSklJYe7cuRmuPypJkvQhi02O4EF0IOH/rlikQoW52gobE0fsTd2wNLaXyfc9ka0E3KBBAwYOHEjp0qX58ccfM+xolR3h4eEsWLAAHx8fvvjiC5o2bWrQdiRJkt43SZoEHsXcJDQuiBSRjAIFJkozrIwdsDNxw8bYWa7R+57JVgLevHkzmzZtIjw8nO7du+Pu7s6gQYPYtWsXERERWb727t27bNy4ka5du+Lh4cGIESOIioril19+YcOGDTlxDJIkSe8srdAQFv+QhzHXdCsWGSmMsTSyw87UFTtTV0zVFrKT1XvotRZjiIqKYvr06SxatIi4uDjdL4SbmxtFixbF1tYWc3NzIiIiCAsL4+HDhzx+/BhI7UJva2vLsGHDGD58eK7cU85LchywJEmvI6tFE8zV1lgY2aBWZj3Xfk6Q30l5SBggLCxMzJgxQ1SoUEEolUqhUCiyfJQrV07Mnj1bREZGGrK7d4KHh4cAhIeHR5b14uPjRWBgoIiPj39LkeVfe/fuFe3btxceHh7C2NhYeHh4iEaNGokff/xRJCcnv/H2IyIixJMnT3Q/T5w4UQDi4MGDb7xtQwCibt26ObKtvD6WjPTs2VMAIigoyKDXv3xMQUFBAhA9e/Y0aHuPHj0SMTExr6yX0X7q1q0rDPx6fKWbN2+KhJQ48SDqqrgVcU5UqVlRAOJh9DXxLD5YJCTHCq1Wmyv7zoj8Tso7Bt3Jd3BwYNSoUYwaNYpnz55x6NAhgoKCePLkCeHh4ZiamuLq6kqJEiWoWbOm3iLrkpSYmEjfvn3ZuHEjxYoVo1evXhQqVIgnT57wf//3f3Tp0oXFixfz888/G/y7s3fvXrp168bWrVupV68eAO3ataNo0aKULFkyB48mb7xPx5IZJycnNmzYQJEiRV77tRs2bGDQoEFcvnwZCwuLXNvP6/p25rdMnDCRq09O6BZNGPrlACJ7xWBn4vbas1hJ77Y37krn4ODAJ598khOxSDlIKzRcfX6ciMTH2Jq4UNK+Rr6ZkH3QoEFs3LiRzz//nJkzZ+pNdTp69Gh++OEHBgwYQIsWLThx4gRGRq8/u8/x48d5+vSpXlnZsmUpW7bsG8efH7xPx5IZCwsLunXrZtBr//rrL2JiYnJ9P9klhCAqKYxff9tGUlKSbtEEE5UFLZu2wVxtg1rOYvXBkX3Z30OnQneyNnAUzxMe6crsTd3x9/2WKq6t8jAyOHHiBKtXr6ZFixbMnj07wzp9+vTh5s2bzJw5kwULFvD555+/5SglKee8uGhC2pheU5WF7j6vkdJUdrD6QMm2jvfMqdCdzD3XUy/5AjxPCGHuuZ6cCt2ZR5GlWrlyJcArk+pXX32FWq1mzZo1ujJ/f3/UajW3b9+mWbNmWFpa4uTkRJcuXbh7966uXr169Zg8eTIA9evXp1ChQgBMmjQJhULBoUOHgNQe+gqFgu+++46FCxdSokQJTE1NKVasGMuXLwdg2bJl+Pj4YG5ujq+vb4Y993fs2EHjxo1xdHTEyMgIR0dHWrZsydmzZw16j/766y/8/PxwdnbG1NSUkiVLMn78eOLj43V1Xj4WSL3KWrhwIaVKlcLMzIzChQsza9Yspk6dikKh0L1Ha9euRaFQ8Pfff/Pll19SoEABTExM8PHxYf78+eniuX37Np9++ine3t6YmJhgYWFBhQoVWLx4sUHHB7B9+3aqVq2KhYUFnp6eTJw4kZSUFL06aZ/Pi0uZxsXFMXLkSHx8fDAzM8PBwYEWLVpw7NgxXZ1ChQqxbt06ALy9vXW3IPz9/bG0tOT333/Hy8sLMzMzunXrluF+0pw9e5a6detiZmaGi4sLn376KaGhoXp1FAqFbh8vmjBxPAqFgm1//kiSNoGith9x6ljq74SnVQlGDPgaY5UZ9evXT5eAExISmDp1Kj4+PpiYmGBvb0/Lli05ceKEXr2034Nr167x6aef4urqiqmpKeXLl+fHH3/M8jOQ8p68As4nhBAkauLeaBtaoWHNla+BjDq2p95xWntlFKUd6hncHG2iMn+js/UjR46gVqupVq1alvXs7e2pWLEiJ0+e5MmTJzg7OwOp71ODBg0oWLAgM2bMICgoiMWLF3PgwAHOnDmDp6cnY8eOxd7enh07djBmzBgqV66c5b4WLlyIEILBgwdjbm7O7NmzGTBgALt37+b8+fO68rlz59KzZ0+KFSumi3/+/PkMHz6cevXqMXHiRIyNjTlz5gzr1q3j+PHj3Lt3D0tLy2y/PydOnKB58+aUL1+ecePGYWpqyt69e/nmm2+4ceMGP/30U6avHTJkCEuWLKF+/foMHDiQ+/fvM3nyZMzMzDKs36tXLywsLBg+fDhGRkYsWbKE4cOHY21tTa9evYDUJFi5cmVMTEwYMGAAnp6ePHr0iJUrVzJkyBDs7Ozo0qVLto8PYPny5QwYMIDSpUszdepUYmNjWbx4MXFxr/7979y5M3v37mXIkCH4+PgQGhrKokWLqF+/PqdPn6ZcuXJ8//33zJ07lyNHjjBv3jxKlSqle31CQgKdOnVi5MiR2NravnKa3Xr16uHn58ecOXO4ePEiq1at4sCBA5w7dy7TkRxCCCISHxOZ+N8tELXCiEWr5rLgu6XcuH4zy3vOcXFxNGjQgJMnT9KmTRs+++wzHj9+zPLly6lduzY//vgj//vf//Re06xZM9zd3RkzZgyJiYl8//33dO3aFTc3N+rXr//K91XKGzIB5xOJmjj893rk8l4EzxMf0XtfQYO3sLZxMKbqrDu1ZOXRo0fY2dlla7iDp6cnJ0+eJDg4WJeAtVotvr6+7Nq1S3fvuG7durRp04bx48ezZs0aGjVqxLFjx9ixYweNGjXK8OrkRU+fPuXatWu6L2MvLy/atGnDX3/9xbVr1yhQoACQOiNc06ZN2b17N9WqVUOj0fDNN99QoUIF9u/fr4unf//+2NnZ8d1337F3717atWuX7fdn48aNJCYmsnPnTt0x9+vXj06dOnH//n0SExMxMUk/GcPp06dZsmQJrVu3ZseOHbqTpNatW1O7du0M92Vtbc2pU6cwNk4d6tK2bVu8vLxYtWqVLgEvWLCA8PBwzp49y0cffaR77SeffEKpUqXYsmXLayXg6OhovvzyS4oWLcrJkycxNzcHUk8GypUrl+Vrw8LC2LlzJwMHDuS7777TlTds2JDu3bvrEnCbNm349ddfOXLkCG3atNG1gABoNBoGDBjApEmTdGUvtp68rGfPnixatEj3c+nSpRk6dChz5sxhypQp6erHJUcRFv+AZJGka242UZlhY+xEP/9B/Lzh/7hx/WaW95znzJnDyZMnmTBhgq4lB1L7TpQpU4Z+/frRuHFjvROAkiVLsmvXLt3nXr16dWrXrs2qVatkAs7HZBO09FYJIbLdqUqtVute86KJEyfqddxq3bo1vr6+7Nix47WWzkxTo0YNvSshX19fAGrWrKlLvoBu6tTg4GAgdZ3s4OBg/vrrL714YmNjdceY3Y5AadL2N2jQII4fP45GowFgy5YtHD9+PMPkC7B161YAxowZo9dCUbNmTRo2bJjhazp06KBLvmn7dnFx0WtinTNnDqGhoXrJV6vV6pqLX/f49u/fT3R0NAMHDtQlX0g92XpVRygrKytsbGzYunUrK1eu1MVZtWpVbty4Qd++fbMVQ6tW2e8HMXHiRL2fBwwYgI2NDdu3b9crF0IQEnuLkLjbJIskFChQK1J/B6yNHbAxyf4sVlu3bsXCwoLRo0frlbu6ujJs2DAiIiLYs2eP3nNdu3bV+9zTWn1ebi6X8hd5BZxPmKjMWds4+I22cfX5cWae+d8r631d6WdK2tcwaB8mKvNXV8pCgQIFuHv3LsnJya9MxGmJztPTU6+8TJky6eqWKFGCwMBAwsLCcHJyeq2YXF1d9X5Oi+vlIVBpJwQvzolubGzM0aNH2bp1K7du3eLOnTvcu3dPdyLwuvOnf/bZZxw5coRt27axbds2bGxsqFu3Li1btqRLly56SetF169fB8DHxyfdc76+vuzbty9d+cvHDWBiYqJL+pB6fzM5OZlJkyZx4cIFgoKCuHXrlq65+HWP7/bt2wAZzgP/YlNxRkxMTFi7di29evWiX79+QOoVaZMmTejatSsVKlTIVgwZHXdGnJyc0v0uGRkZ4e3tzdWrV4HURRMAEjWxxKVEA6mLJpipLTBVp956MFaZvdZtm9u3b1OsWLEMW4lKly4NpF8i9uVjSjtRe/GzlPIfeQWcTygUCkzVFm/0KOfUAHtTd1JHF2a4FxxMPSjn1MDgfbxpb8369euTmJjI8ePHs6wXFRXF2bNn8fX11TXFpr1PGSXutCuytCT5OjI7EcjOsQ4YMAA/Pz+OHDmCl5cXQ4YMYd++fQZ3UDI3N2fXrl1cuXKFmTNnUrVqVfbv38+nn35KuXLleP78eYavS5ufPaMr5Mya+5XKV//5HzhwgGLFijF//nxUKhWtW7dmzZo1Wc72lh0ZtVRkJ5m3adOG4OBgtm3bRv/+/UlMTGTOnDlUrFiRBQsWZGvf2f0dyez90Wq1qNVqYpJSF02A1B4WaYsm2Jo4Y2fqjtKwaRYQQmT6u5eWUF/+nLPzWUr5j/zU3iNKhQp/32///enlP+DUn3v6zsjT8cB9+/ZFoVAwffr0LJuLv//+e+Lj4+ndu7deuRBCdxX1ouvXr+Pk5ISdnV2Ox5yZo0ePsnz5crp06UJAQAA//PADI0eOpEGDBpkmyle5ceMGR48exdfXl6+++oo9e/bw7NkzBg0axK1btzLt2Vq8eHEArl27lu65tKtjQ/Tr1w8zMzOuXr3Ktm3bmDJlCh06dDB4e8WKFQMgMDAw3XM3b97M8rXR0dEcO3aMiIgI2rVrx7Jly7hx4wYXLlzAzs4uw3uybyIsLIyoqCi9ssTExNRpG4t68Tg+ddEElUpFUkIy1saO2Ju4Y23siJHS2ODm3yJFinDr1i0SEhLSPXflyhUAChY0vB+HlH/kSAKOi4sjJCSEBw8ecP/+/UwfUu6r4tqKkR+tw95Uv/nUwdSdkR+ty/NxwBUrVmT48OHs3buXYcOGpRt6ArBp0yamTp1KxYoVGTp0aLrnX+yAA/DTTz9x48YNOnXqpCtLuyebU0toZiQsLAxIbRZ88YolLCyMH374ASDD48vKkCFD8PPz0/t7MTU1pVKlSkDmV28dO3YEYN68eXrlV65c4Y8//nitGF4UFhaGs7MzLi4ueuUzZ84EXv/4GjVqhJ2dHYsWLdJbyCUsLIy1a9dm+drLly9Tq1Ytpk6dqldeqlQpbG1t9d6bnPj8NRqNXgcsrdAw7btJREdH83EbPxSkLprg6ubCnZt3UadYYqJOHSXw/Plzdu3alW6b2Ymrffv2xMbGMmPGDL3yJ0+esHDhQqysrGjSpInBxyXlH290D/iPP/5g7NixXLx48ZV1FQrFa/+xSoap4tqKSi7N8+1MWLNmzSI5OZmFCxeyZ88eOnfuTMGCBXVfWn///Tc1a9bkp59+yrB5ePPmzYSFhfHxxx9z7do1li5dSpEiRfR6jKYljKVLl/Lo0aNcmemoVq1aODg4MG3aNGJiYihcuDB37txh9erVREZGArxytbCXjRs3jkOHDlGrVi369euHm5sbN2/eZMmSJRQoUECXaF9Ws2ZN/P39Wbt2LY8ePaJ169Y8evSIRYsW6ZonDbl90Lp1a9avX0/btm1p1qwZcXFxbNu2jWPHjmFiYvLax2dubs7SpUvp0qULFStW1N3LXbp06SubhmvUqEGTJk1YtmwZ4eHh1KtXj5SUFH755Rfu3LmjN7FL2uf/3Xff0aRJE9q0afNacabFOmPGDO7cuUPZj3z5++ghtm/+jTIVfOk7qAfmahvM1db07t2bqVO+oVHDRvTq1YuIiAhWrFiBvb29bjGal+OaOHEidevWzbCD3Jdffslvv/3GlClTuHz5Mn5+fjx9+pTly5cTHh7Ohg0bXjm9pvSOMHQS6SNHjgi1Wp2txRjSHu8zuRjD6zt69Kjo0qWLKFy4sDAxMRFubm6iUaNGYsOGDRkuxpA22f/+/fuFn5+fMDU1Fe7u7mLw4MEiLCxMr25kZKT4+OOPhampqbC1tRUxMTHZnuz/dcrPnj0rGjduLOzt7YWFhYUoUaKEGDJkiAgKChLGxsbCz89PV5dsLsZw+PBh0bRpU+Hm5iaMjY1FgQIFRP/+/cXDhw91dTJajCE5OVlMmzZNFClSRBgZGQlvb28xb9480aFDBwGIkJAQIYQQa9asEYBYs2ZNun17eXkJLy8v3c/R0dFixIgRomDBgsLExER4enqKpk2bikOHDokuXboIpVIp7t27J4R4vcUY9u/fL+rUqSMsLCyEk5OTGD58uFi0aNErP5/o6GgxceJE4ePjIywsLISlpaWoWbOm2LRpk97279+/L6pXry6MjY1FsWLFsowvs8UYPDw8xIFD+0X5imWFsYmxcHFzEr0HdRPXQ86JZ/GPRGJKnNBqtSI5OVlMnDhReHt7C2NjY1G0aFExe/ZssW3btnSf0YULF0Tp0qWFkZGRaNiwoW5fL38Vx8TEiHHjxolixYoJY2Nj4eTkJNq2bSv++ecfvXpZLcqR3d83+Z2Ud15rOcIXtWrVil27dlG2bFkmTpxIyZIlMx3wn+ZVg97fZXI5wtzn7+/PunXrCAoK0hvbKaV2WlOpVBleGTVu3JiDBw8SHx9vUCe1D5FGm8KzhGBikp/rFk0wUppgqrLCwsgWM7UFivdk0QT5nZR3DP5rPH78OKampuzZsyfd/SFJkt6uP/74g06dOrFu3Tp69OihK79//z5HjhyhUqVKMvlmgxCCqKSnPE8IQUvqfVq1Qo2pyhJzo9QmZ5VSvo9SzjD4Nyk2NpZSpUrJ5CtJ+cDHH3+Mm5sbn332GYGBgRQrVoxHjx6xYsUKhBCZLnwh/SchJebfRRMSAVCixERljrnaGnMjG4xV8upQylkGJ2AvL690HQwkScobNjY2/PPPP0yfPp2ffvqJkJAQbG1tqV27NmPHjqV8+fJ5HWK+laJNJiz+AbEpqR3nFCgwUppgpk5tbjZVvfn4d0nKiMH3gMePH8/06dPZu3cvfn5+OR3XO0feA5akd4sQWiISnxCR+PiF5mYjTNWWWKhtMTeyyjcjB3KT/E7KOwb3Ihg9ejSlSpWiR48e/N///R+JiYk5GZckSVKuiUuO4kH0VZ4npt7rVaLEXJU6i5W9qTuWxrYfRPKV8pbBTdB9+/bF09OTgIAA2rVrh0qlwt7eXm9y9xcpFAru3btncKCSJElvKlmTSFj8A+I0qfM2K1BgrDTFTG2NhZEtJq85b7MkvQmDE/CWLVt0/xdCkJKSwpMnTzKtL3+pJUnKK1qhJTwhlMikJ7plAlMXTbD8d1iRpbzild46gxPwmjVrcjIOSZKkHCeEIDY5gmcJwaSIZCB10QQTtQUWahvMjWxQK7O3PKYk5TSDE3DPnj1zMg5JkqQclaRJ4Gn8fRI0sUBac7MZ5kapzc3GSlPZMiflqRwdUR4eHk50dDRWVlZvdVUaSZKkNFqh4VnCI6KTniEQKEhrbrZ+72axkt5tb/xbeO/ePfr164erqyuOjo54e3vj6OiIo6MjPXr0yHDpOEmSpJyWOotVGPejrxCVFIZAoFKoMVfbYGfqir2pG+ZGVjL5SvnGG10B//3337Rt25bIyMh0a7s+f/6cTZs2sXPnTn755ZcMV/2QJEl6HUIIEjQxaLTJqJRGmKosUSgUJKbE8TT+PonaeAAUKDFRmWGutsFCzmIl5VMGJ+DQ0FDatWtHREQEZcqU4bPPPqNixYpYW1sTHh7OmTNnWLJkCQEBAXTu3JnLly/j6uqak7Fn6MaNG4wbN44DBw4QHR1N4cKF6d+/P0OHDtUtywapc+SOGzeO/fv3ExkZSZkyZRgzZgytWuXterlxyZEkauLzNIbMmKjMMDeyyeswpA9UbHIEYfEPdZ2pIHXiDCOlCQmaGL1FE9Kam+UsVlJ+ZnACnjNnDuHh4bRq1Yqff/453bqtlSpVok+fPrRv355du3axdOlSvfVac8Pdu3epXr06cXFxDBkyhMKFC7N9+3ZGjBjB9evXWbp0KZB68lCnTh2eP3/O0KFD8fDw4IcffqB169Zs2rSJLl265GqcmYlLjmTbre+ITnqWJ/t/FStjBz4p+uUbJ+Fnz54xadIkdu7cyZMnTyhevDjDhg2jd+/e2Xp9fHw833zzDT/++CMhISF4eXnRtWtXvvzyy3Qrcm3cuJHu3btnuJ2ePXvqLQK/cOFCZsyYQXx8PE2aNGHRokU4OjrqvWbJkiVMnDiR27dvY21t/XoHnkcCAgLo168f58+fx8zMjF27dlGzZk3q1q3LoUOHsnztoUOHqF+/PhMnTmTSpElvJd6MxCZHEBoXlK48RSSToklNyKmLJlhhYWSDmdoalTL9sKK7d+/i7e2d7rN/HZGRkSQlJeHk5GTQ69+WSZMmMXnyZA4ePEi9evXyOhwpAwYn4N27d2NkZMTKlSszXDQd0D1foEABfv3111xPwHPmzOH58+ds2bJFt3D5wIED8fPzY9myZQwbNgwfHx8mT57M/fv3OXr0KDVq1ACgV69eVKtWjWHDhtG6des8WfA6URNPdNIzjJWmGKvM3/r+s5KkiSM66RmJmvg3SsCxsbE0btyYy5cvM3jwYHx8fPj555/p06cPoaGhjBkzJsvXJycn06RJE44cOUL9+vUZOXIkd+/eZfr06ezZs4e//vpLbzq9S5cuAbBy5cp00+wVKVJE9/9jx44xdOhQunTpQpUqVZg+fTr+/v7s2rVLVycmJoYpU6YwduzYdyb5QuoyjufOnWPcuHEUKFCAUqVKsWHDhndmIRUhBGHxmU/vCqk9nG1MXLBQ22CkMsm0npOTExs2bND77F/H3r176datG1u3bpVJTXpjBifge/fuUbp06VeeBTo7O1O6dGlu3bpl6K6y7caNGwC0aNFCr7xNmzYcOHCACxcuUKxYMTZu3Ej16tV1yRfA1NRUdxW2a9cuXQLPC8Yqc8zUlnm2/8wkaRPeeBuLFi3i3LlzbN68mU6dOgHQr18/mjVrxuTJk+nevTsFChTI9PUrVqzgyJEjdO7cmU2bNumaF/38/GjevDmzZs1iwoQJuvoXL17E2dmZvn37ZhnXunXrcHV1Zf369ahUKtRqNUOGDOHx48e6RDV79mzMzMwYNGjQm74Nb9WlS5coV64cU6ZM0ZV169YtDyN6PQmaGL1m54wIBCZKsyyTL4CFhcUbHfvx48d5+vSpwa+XpBcZ3B1QqVSSnJz1H0Wa5ORktFqtobvKNh8fHwCuXLmiV379+nUAPDw8uHLlCjExMVSrVi3d66tWrQrAyZMncznSD9e6devw8PDQJV9InSXtq6++IikpiR9//DHL12/fvh2AWbNm6d3ba9asGeXLl2f58uV69S9dukTp0qVfGdeDBw/w9vZGpUpttixWrBiAbvrUx48fM2fOHL755ptMp1vNr5KTk7GxeXfv3Wu02fue0bwiSUtSfmNwAi5WrBhXr1595fzOd+/eJTAwkKJFixq6q2wbNWoUJUqUwN/fnwMHDnD37l0WLVrE8uXL8fPzo1atWrqVigoWLJju9Z6engAEBaW/1yS9ucjISK5du6Y70XlRdk9+Hjx4gL29ve6zelHaGriPHj0C4MmTJ4SGhuoScFJSUqaLhri4uBAeHq77OSwsDEhtwQGYPHkyRYsWNbh/wJkzZ/jkk09wdnbG0tKS8uXL69bqfdGmTZuoXr06FhYWWFhYUL16dTZu3KhX59ChQygUCrZs2cL06dMpWrQoJiYmeHt7M2HCBFJSUoDUe4BpJyl///03CoUCf39/IPWk5+Um1AsXLtCyZUvs7OywtbWlR48emU4ve/36dTp37oyzszMmJiYUK1aM8ePHEx+v34FQoVAwZMgQtm3bRqVKlTAzM8PJyQl/f39CQ0PTbXfr1q3UqVMHGxsbHBwc8PPz468DB/TqaLVa1i/fQstanSjlWp0KBevQu/0QTv1zNusPgdTvoxffB4B69epRunRpLl26RLNmzbCxscHS0pLGjRtz+vRpvXppt9Hq169PoUKFdM8lJCQwdepUfHx8MDExwdHRkfbt2xMQEKC3f39/fywtLfn999/x8vLCzMyMrl274uzsTIkSJTKMuUyZMri4uOgueB4/fsyIESMoUaIEZmZmmJmZ4evry9SpU3WfvfRuMDgBt27dGo1GQ7du3YiMjMywTmRkJF27dkUIQevWrQ0OMrvc3Nz45ptvePjwIX5+fnh7e/PZZ59RpUoVfv31VxQKhS5WS8v0Tbzm5qn3XWNjYzPdx9y5c/H09Ez3CAkJyZ2Deo8EBwcjhMjw5Mfc3Bw7O7tXnvxYWloSGxuLRqNJ91xa0kxLwBcvXgRSe7xXqlQJc3NzzMzMqFKlCgde+lJv1KgR165dY+3atdy4cYOFCxfi6+tLwYIFuXHjBitXruTbb781qEftnj17qFmzJocPH2bAgAHMnj0bZ2dn+vfvz7hx43T1PvvsM7p160ZSUhKTJk1i0qRJJCYm0r17d4YNG5Zuu2PGjGHFihX06dOH+fPn4+zszNSpU5k6dSoA7dq1Y8OGDUBq69CGDRvo379/hjGeO3eOWrVqcfz4cYYNG8akSZMIDAzMsOn+1KlTVK5cmSNHjjB48GC+//57qlevzrRp02jQoAEJCfq3Knbv3o2/vz/16tVjwYIFNGzYkHXr1qW7zTNx4kQ6duxIVFQUY8eOZfz48dx7cJdPWnTi733HdPVGfjqWKV/PwqtIQUZ/M4K+n/Xg7q37NPZryi+//JLNT0VfWsdMOzs7Zs2axZAhQ/j7779p2LCh7jtj7NixtG3bVvfef//990DqiV3jxo2ZMmUKNWvWZMGCBQwaNIjDhw9TtWpVjh8/rrevhIQEOnXqRK9evZgxYwbt27enW7du3Lhxg1OnTunVPX/+PAEBAXTv3h0jIyMiIyOpWrUqq1evpm3btixatEh3ojVhwgRmzZpl0PFLeUQYKDw8XLi7uwulUinc3NzEqFGjxM8//yz27t0rfv75ZzFq1Cjh5uYmFAqF8PDwEOHh4YbuKttmzJghAFG0aFGxdOlSsX37djFixAhhZGQkKleuLMLDw8WmTZsEIFauXJnu9UlJSQIQH3/8cab7mDhxogAyfXh4eGQZY3x8vAgMDBTx8fHpnnseHyIWXxgg1l0ZI7Zen56vHuuujBGLLwwQz+NDXv+D+dfx48cFIMaNG5fh8x4eHqJYsWJZbmPIkCECEFu3btUrv3//vjAzMxOAOHz4sBBCiNmzZwtAODo6im+//Vbs3LlTzJ49W7i4uAiVSiV27type71GoxHdu3fXfY6enp7i9OnTQggh2rVrJ/z8/Aw6Zq1WKwoVKiQcHBzEw4cP9fZXq1YtYWJiIp49eyYOHz4sAOHn5yeSkpJ09ZKSkkT9+vUFIA4dOiSEEOLgwYMCEG5ubnp/VzExMcLGxka4u7vrxQCIunXrZllWr149oVarxZUrV3RlCQkJolatWgIQEydO1B1PqVKlRIECBcSzZ8/0trlq1SoBiJkzZ+rtBxAnTpzQq+vn5ycAcePGDSGEEDdv3hQqlUrUrVtXJCYmiiRNgngQdVWcvLlfWFlbirIVSolbEefEgrUzBSC+njJM3Io4p3uEPn8gfHx8hKOjo4iNjc308wgKChKA6Nmzp66sbt266eIWQoipU6cKQKxYsUJXlvb3f/DgQV3ZrFmzMvydDAkJEY6OjsLX11dX1rNnTwGIL7/8Uq9uQECAAMSQIUP0yocPHy4A3ecyf/58AYht27bp1QsPDxfGxsaiTJkyWcaakay+k6TcZfAVsK2tLX/++Seurq6EhoYya9YsOnbsyMcff0zHjh2ZNWsWoaGheHh4sGvXLmxtbQ3dVbZERUUxZcoU3N3dOXXqFAMGDKBt27bMnTuX1atXc/r0aaZNm4aVlRUAcXFx6baRVpbV/TJra2s8PDzSPV4cYyxlTPzb3CpeanZ98fm0e7CZ+fzzz7G2tubTTz9l1apVBAUF8ddff9G8eXNdC0Zar/wqVaowduxY/vnnH77++mtatmzJ559/zsmTJ3WdqdL6JiiVStavX8/du3c5c+YMt27dolKlSpw4cYIdO3Ywc+ZMANauXUvp0qVxc3Oje/fuuqvuzJw/f567d+/SrVs3PDw8dOVKpZKNGzdy8eJFrK2t2bp1K5DabPziqAIjIyNd56mffvpJb9stWrTQ+7uysLCgZMmSPH78OMuYXvbs2TMOHz5M06ZN8fX11ZWbmJgwcuRIvbqXL1/mypUrNG/eHK1WS1hYmO7RokULTE1Ndffp0xQrVizdbYfKlSsD6Jqhd+7ciUajYfjw4SQRx8PoayRq43F0cmDHvs38+NNGnM282P3LXgCatPLj+bNwop7HoI6zRqUx5ZNPPiEsLIzDhw+/1vGneblz1ssxZmbz5s3Y2tpSv359vfdDrVbTtGlTAgMDuXbtmt5rXp5voFSpUlSuXJktW7bomppTUlLYvHkzVatW1X0uQ4cO5fHjx7Rp00bv9WFhYdjY2BATE/Paxy3lnTeaCatMmTJcv36dRYsWsWvXLq5du0ZUVBRWVlb4+PjQsmVLBg4c+FY6gNy4cYP4+Hh69+6dbh7qLl26MGDAAPbv369bRCLtXvCL0sqy6oU7cuTIdF9KkHr/ODg4+E0O4b2X1clPWnlW7z1AoUKF2LdvH927d+fTTz8FwNjYmEGDBmFra8ukSZOwt7cHoHbt2tSuXTvdNry8vGjbti0bNmwgMDBQr5OWl5cXXl5eup+/+uorOnToQMWKFfn777/p1asXs2bNokaNGgwYMICuXbuyZ8+eTONNa1IvWbJkhnGkSZuytVSpUunqpcX3cvN8RhPbmJiYZNg8n5WgoCC0Wm2G/TRejietQ+OyZctYtmxZhtu7e/dutuIEdLGmHZtLIRsex6f+X4kKcyNranxURDfj1YM7qbd6GpTPfMKcl/efXS/H+XKMmbl+/TpxcXFZjgi5e/eurpNoRvsC6N27NwMHDuTPP/+kZcuW7Nmzh8ePH+v1XgdQqVTMnj2bU6dOERQUxK1bt4iKigL+u40mvRveeDEGS0tLRo0axahRo3IiHoNl9ccihECr1SKEwMfHBxsbmww7+6SVvTg8Sco53t7eKBSKDE9+YmNjiYiIeGUChtQr22vXrhEQEEBUVBS+vr7Y2dnRs2dP1Gq1XmLLTNrQoujo6Ezr7Ny5kxMnTnD16lUA1q9fT+HChfnyyy8B+Prrr+nevTshISG4ublluI20q5lX3TvOrFUA/vudTvsdT5PTrS4ZxfDy6IW0WAYPHpzuKizNy/MCZCfOxKTU+8axmkjAFiOFMZbG9lgZ2esNLdJoNFhZWaW7yn5RZp2ZXsXQ91Oj0VC0aFHdRD8ZKVeunN7PanX6r97OnTszcuRINmzYQMuWLVm/fj3m5uZ6IwYCAgKoW7cuCQkJ1K9fn0aNGjFixAjdxCrSuyVHV0PKS6VKlcLLy4utW7cybtw4vea+VatWER8fT+PGjVGr1XTs2JGVK1dy/PhxXbJNSEhg/vz5uLi40LRp07w6jPeapaUlJUuWTNfRBLJ/8nPu3DlOnTpF9+7dKVOmjK5co9Gwb98+qlevrktUbdq0ISAggMuXL6ebISswMBAg0wkZNBoNo0ePpn///ro6jx490pu8Iu2K58GDB5kmYG9vb+C/K8cX7du3j/Xr1/PVV1/p9nHlyhVq1aqlVy9tWF1GnddyQuHChVEqlbr35EU3b97U+znteIB087trtVq2bdtG4cKFX2v/0UnPsXNPnfgm6OY9fEuUwsrYHgsjW5QKFfPmzSMwMJAFCxbg7e3N9evXKV++fLpZyi5cuEBISMhbn0TH29ub0NBQ6tWrly6xHj9+nNjY2GxdmdrY2NCuXTu2b99OaGgov/32G+3bt9eb9GX48OFEREQQEBCg16qSnJxMWFiYrte+9G7I1inf6tWrWb16td7VQlrZ6zxyk1KpZOXKlcTExFCpUiUmT57M8uXL6dmzJwMHDqRkyZK6WZYmT56sS7STJ09m2bJl1K5dm8uXL7NgwYJ0MyZJOadbt27cu3ePLVu26MqEEHz33XeYmJjone1n5OLFiwwcODDd/dAZM2YQEhLC559/ritzc3Pj9u3brFixQq/uoUOH+OOPP2jWrFmmX1hr1qzh/v37jB8/Xlfm6enJvXv3dFeFac3GGQ2JSlOxYkU8PT3ZtGmT3gQOace8efNm3NzcaN++PZB6D/jFoSQpKSm6oS9pdXKavb09jRo1Yt++fZw4cUJXrtFomDt3rl7dSpUqUahQIdauXZtupbMVK1bQoUOH1/pbD08I5Un8Pfya1UGhUPDjqm1Yq52wNLJHqVDx/PlzZs6cyalTpzAzM9O9By/2HofUPiAdOnSgdevW6Xph56S0Pgovtgy0b9+eiIgIZs+erVc3ODiYli1b0qVLl2xfXffu3Zv4+HgGDRqku6X2orCwMCwsLNKd5CxYsID4+Hg5DOkdk60r4L59+6JQKKhVq5buPl5a2evI7ly/hmrUqBH//PMPU6dOZcGCBURHR+Pp6cnIkSMZP3687l60q6srx48fZ9SoUcyfP5/k5GTKlCnDrl27aNasWa7GmB1JmozvkealnIpp+PDhbNy4kZ49e3L27FmKFy/O1q1b2b9/P999953eleSdO3c4fvw4RYoUoXr16gB06NCB2bNnM3ToUG7evEmRIkU4ePAgP/74I/7+/nrD3SZNmsTvv//O559/zqVLl6hSpQqBgYEsW7YMd3d3Fi9enGGMcXFxTJw4kS+++EIvQXft2pVVq1bRvXt3qlatyvTp02nUqBHu7u6ZHq9arWbZsmW0adOG8uXLM2DAABwdHdmxYwf79u1j5syZODo6Uq9ePfr378/y5cupWrUqnTt3BmDLli2cPXuWQYMGUadOnTd677OyYMECqlevTsOGDfnss89wd3dn69atutnl0qhUKlauXEmLFi2oWLEiAwYMoEiRIpw+fZrVq1dTpEgRvZOWzKRNrhGXknpS7+Pjw/AvhjDvu4X41W1Cp06d0Gq1rFy5kvDwcN3wIn9/f37++WeWL1/OrVu3aNWqFcnJyaxatYqbN28ya9asLD+PN5XWArJ06VIePXpEt27d+Prrr/ntt98YPXo0p0+fxs/Pj/DwcJYtW0ZERASbNm1K1wKTmfr16+Pt7c2OHTsoXLhwumbl1q1bM2XKFBo3bqx7j/744w92796NmZlZpkNCpfwpWwm4YMGCKBQKvXs7aWX5zUcffcSOHTteWc/b2zvdVVReM1GZYWXsQHTSsxyZ9jGnWRk7YKLK3hdJZszMzDh06BBjxoxh/fr1REdHU6JECdavX59u0YTDhw/Tq1cvevbsqUvAFhYW7N+/n/Hjx7Nx40aeP39OsWLFWLJkSboxri4uLpw6dYqJEyeya9cu1q9fj7OzMz179mTSpEmZflF///33aDQavatpSJ2IYeXKlcyYMYPdu3fTpEkTFi5c+Mpjbt68OUeOHGHq1KnMmTMHjUZDyZIl9abjhNSOTVWqVGHZsmVMmDABtVpNuXLl3soCIcWLF+fkyZOMHTuWFStWkJiYSKNGjZg6dSr169fXq9uwYUNOnDjBtGnTWL16NZGRkXh6ejJo0CDGjBnzylXPopLCiPp3wREFCsxUllgbOzB75vdUKFOZhQsXMnbsWCwsLKhcuTIbN26kUqVKQOoJwG+//cb333/Phg0b+PrrrzE3N8fX15dt27bRrl273HmD/tWpUyd27NjBrl272L9/P23btsXS0pKjR48yY8YMfvnlF92ojwoVKrB+/fp0719WFAoFvXr1YsKECfTq1Svdd+z48eNRq9WsW7eOESNGYG9vT4kSJdixYwenT59m+vTpHD16NN1tDCl/Uoisen9I2ZbWC9rDwyPDTkZpEhISCAoKwtvbO8OmbrkcofS+0goNT+PuE5MSAYBKocJCbfvviZ15vjyh/xC86jtJyj1vpRNWeHg4d+/epUKFCm9jd+80cyMbmeSk905iShyP44JIFkkAGCtNsTKyx9LYHrUy49XUJOl9Z/A4BpVKle1u740bN84X91YlSXq7hBBEJj4lOPYGySIJBQrM1VbYmbphY+Ikk6/0QTP4ClgIkeXYxTSxsbE8evSIiIgIQ3clSdI7SKPV8DT+HrEpqR2DVAo1lkb/NTlL0ocuWwk4MDCQpk2bpku4p0+fznJsohCC8PBw4uPjKV68+JtFKknSOyMhJY7HcXd06/gaK02xMnbAysgelfK9mX5Akt5Itv4SfH19qVmzpt7YTYDExMQsOxylUSqV6cbtSZL0/hFCEJn0lOcJjxAIXZOzlbEj5mpr2dFKkl6Q7VPRuXPn0qRJEyD1j6x3794UL16c0aNHZ/oapVKJpaUlZcuWzXTGIUmS3g8abQpP4u/qxvamNjnbYW3siLFK9q6VpJdlOwG7urrqFjKA1Ek10sZUSq9Pjv6S3icJKTE8jgsiRaSgAIyVqWPaLY3sZJNzPie/i/KOwX8ZL0/SLmVP2lR2ycnJ2Z4dR5LyKyEEEYmPCU8MQQBKlJiprbA2dsJMbSmbnN8BaQuGvGopUCnnvbVFbP/555+3tat8zcjICBMTEyIjI+WZp/ROS9EmExJ7i+f/Jl+1wghrY0ccTD0wN7KSyfcdIIQgMjISExOTdKtYSbnvjdqGHj16xPz587l8+TJxcXHpropTUlKIi4vj0aNHPH/+XE4U/i9HR0eCg4N5+PAhNjY2GBkZyS8r6Z2SkBzLs8RgNP82ORspzbAwssUMazTJAk1y/ptKVfqPEILk5GQiIyOJiYnRWz1OensMTsAhISFUrFiRJ0+e6K7kFAqF3lVdWlIRQsgpzl6QtrxYWFgYwcHBeRyNJGWfQBCXHPlvRyuBAiXGKlPM1ErilOEoiMjrEKXXYGJigoeHh96Sh9LbY3ACnjNnDo8fP9YtGG1pacmCBQuoXbs2tWvX5uHDh+zatYvw8HAaNWrEr7/+moNhv/usra2xtrYmOTlZt8i5JOVnEYlPWB84mrtRlwCwMXGmsksLqrm1xt4091YgknKHSqWSzc55zOAEvGfPHhQKBTt27KBRo0YAbNiwAbVazTfffAPA48ePadiwIX/99RcXLlzQrWgj/cfIyEj+EUj53oUn+1lwoQ9xKZEoUVLEpiKNvPpQ1a2VnNVKkgxkcCes+/fv4+rqqku+ABUqVODUqVO6ZmgXFxdWrlyJVqtl0aJFbx6tJElvVYo2mQ1Xx/HtmfbEpURiqrKguvsn9PCdTm2PjjL5StIbMDgBJyQk4OnpqVfm4+NDXFwct2/f1pVVq1YNDw8P2Qtakt4xYfEPGH+8EbuDUk+eHc0K0rLwMLr6TKGYXWXZcVCS3pDBTdB2dnbpFljw9vYG4OrVqxQtWlRX7ubmRkBAgKG7kiTpLTsdupvFF/uToIlBqVBR3LYqTbw+paJLUzmrlSTlEIOvgMuWLcvt27e5c+eOrqx48eIIITh79qxe3eDgYExMTAyPUpKktyJFm8TqgC+Yc64rCZoYzFRW1HbvRE/f6VRzayOTryTlIIMTcOvWrdFqtTRr1ow//vgDSG1uNjIyYvHixbrEvGDBAkJCQihcuHDORCxJUq4IjQ1i9LF67L2/CgAXM2/aFP2cLj6T8LYpL5ucJSmHKYSB0zElJCRQpUoVAgICUKlUxMbGYmxsTLdu3fjxxx8xMjLCysqK8PBwAGbOnMkXX3yRo8HnJ56engQHB+Ph4ZGtFaIkKT85/mgbyy9/RqImDpVCjY9dDT4u1J8Kzo1RK2UvfUnKDQYnYEidSOKLL77g6NGj3Lp1C0gdelS/fn2uXbumq1ezZk3279//XjdDywQsvYuSNPGsufIlBx9uBMBcbUMNt3Y0KfQpBax88zg6SXq/vVECTqPRaPQm8k5MTOTXX38lKCgIHx8fWrVqhVL51qadzhMyAUvvmuCYG8w525VHsTcBcLMoSuOCfajt0QlLY7s8jk6S3n85sk7Yy6tomJiY0LFjx5zYtCRJueDvh5tZFTCCZG0CKoURpR3q0NR7IGUc6snlAyXpLZF/aZL0AUlIiWVlwHCOPfoZAEsjO2q5d6CxV1/cLYvlcXSS9GF5owS8d+9eZs6cyblz54iKisqyrkKhkKshSVIeuh91hdlnu/Ik/i4AnpY+NC74KbU82mNuZJO3wUnSB8jgBPzHH3/QsmVLhBByXVtJyseEEPz1YC1rr3xNikhCrTCmrGN9mnkPwtehNkrF+90/Q5LyK4MT8LRp09BqtVSoUIFhw4bh7u4uFxWQpHwmLjmKZZcGc+rxbwBYGTlQz7MrDb1642JeKG+Dk6QPnMEJ+MKFC1haWrJ//37s7GSPSUnKb4IiLzD7bDeeJTwEFHhZleJjr/5Ud2+Lqdoyr8OTpA+ewQlYpVJRvHhxmXwlKZ8RQvDH3WVsujYejUjBSGlCeadGNPceTAm7anJGK0nKJwxOwOXKlePKlSs5GYskSW8oJjmCxRf6cf7pXgBsjJ2p59mdRl69cDTzfMWrJUl6mwzufTF8+HDCw8P5/vvvczAcSZIMdTP8NF8ersb5p3tRoKCwdQW6l5zGJ8W+kMlXkvIhg6+A27Vrx6hRo/jiiy+4dOkSTZs2xcnJKcsZr+rUqWPo7iRJyoRWaPntznx+uv4NWjQYK834yLkJLQp/RhGbj2STsyTlUzkyEce6detYt25dlnXkOGBJynlRiWEsuNCHgGd/A2Bn4kbDAv74eflja+KSx9FJkpQVgxPwvHnzmDlzZrbHAMuxwpKUswKfHeX78/5EJYWhQElR24o0LzSESq5NUSuN8zo8SZJeweB7wKtWpa4Z6u/vz/Xr10lMTESr1Wb5kCTpzWmFhp9vfMvUky2JSgrDRGVODbd29Ck9h2rurWXylaR3hMFXwEFBQbi6urJ69eqcjEeSpCyEJ4Qy/3wvroX/A4CjqSeNvPpSv0B3rI0d8jg6SZJeh8EJ2MbGBhcXeY9Jkt6WS08PsuBCb2KSw1GiorhdFVoWHkY5Jz/USjkLnSS9awxOwA0aNGD79u08ffoUJyennIxJkqQXaLQp/HTjG3bemQ8ITFWWVHVpRcsiQ/G08snr8CRJMpDB94AnT56MSqXif//7HyEhITkZkyRJ/wqLf8iEf5qw8873gMDZrBAdio+je6npMvlK0jvO4Cvgw4cP07FjR9asWUOhQoUoV64cBQoUwMLCIsP6CoXilUOVJEn6z5nQ31l8aQDxKVEoFSpK2tWgdZGRlHasg1KhyuvwJEl6Qwph4PggpVKJQqF45fCitDoKhQKNRmNQkO8CT09PgoOD8fDw4OHDh3kdjvQOS9EmsfHqeP68txwAM7UVNd3b09x7CG4WRfI4OkmScorBV8A9evSQM+xIUg57HHeXuee6cy/qMgBu5kVo5j2IWh4dMFNb5XF0kiTlJIMT8OrVq7OcdlKSpNdz4tGvLL08mERNLCqFmlIOdWhTZCQ+9jVQKuTfmiS9bwz+q27cuDHdu3cnIiIiB8N5c1qtlkWLFlGuXDnMzMwoUKAA/v7+BAcH69W7f/8+PXr0wN3dHQsLC6pVq8bOnTvzKGrpQ5akSWDl5eF8f8GfRE0sFmpbGhf8lE/LfI+vQy2ZfCXpPWXwPWBbW1ssLCzSJba81qNHDzZs2EDr1q1p2rQp169fZ/HixXh6enL27FlsbW0JDQ2lWrVqPH/+nKFDh+Lh4cEPP/zA2bNn2bRpE126dHnt/cp7wJIhHsXcZO657jyMuQaAh0UJWhQeSg33tpiozPM4OkmScpPBTdAajQZnZ+ecjOWN/frrr2zYsIFBgwaxePFiXXm5cuXw9/dn2bJljBo1ismTJ3P//n2OHj1KjRo1AOjVqxfVqlVj2LBhtG7dOtPe3JKUUw4/3MKqgOEkaRNQK4wo69iANkU/p5htZdm/QpI+AAa3bbVu3ZrLly9z7NixnIznjSxduhQrKyu+/fZbvfJOnToxatQoihcvjkajYePGjVSvXl2XfAFMTU0ZNmwYYWFh7Nq1622HLn1AElJiWXxxAEsuDSBJm4ClkT1NvQfRt8w8ittVkclXkj4QBl8Bz507l3v37tGoUSM6dOhArVq1cHNzw8zMLNPXNGjQwNDdvZJGo+Hw4cM0bNgQK6vU3qLx8fGoVCpMTEyYMWMGAJcuXSImJoZq1aql20bVqlUBOHnyJB07dsy1WKUP14PoQOac7UZo3B0AClqVolXhEVRxbYGxyjSPo5Mk6W0yOAG7ubnp/r9hwwY2bNiQZf3cXg84KCiIhIQEvL292bZtG5MnT+by5cuoVCr8/PxYsGABJUqU0N2fLViwYLpteHp66rYlSTlJCMGBB+tZc+VLUkQSaqUx5Z0a067oF3hbl5NXvZL0ATI4Ab9u363cXg84PDwcgH379rFixQpGjhzJlClTuHjxIjNnzqRGjRqcOXOGyMhIACwtLdNtw9w8tdNLbGxspvuZO3cuc+fOTVcup+OUMhOXHMWKy8M4EboDAGtjJxoW8KdxoU+xNclf/SgkSXp7DE7A+W1938TERACuXbvGtm3baNeuHQBt2rTho48+olWrVowfP55mzZoBGZ8QpJWpVJlP8xcVFZXven5L+VdQ5EXmnuvO0/j7gAJv63K0LfI5H7k0kev2StIHzuAEnN+k9Vr28PDQJd80LVu2pECBAuzbt093bzcuLi7dNtLKbGxsMt2PtbU1Hh4e6cpDQkLy3UmJlHeEEOy9t5L1V8eiEckYKU2p6NyUdkW/oKB1qbwOT5KkfCBHEnBcXByHDh3i+vXrREdHY2VlRbFixahbt66uQ1RuK1CgAACurq4ZPu/q6sqlS5fw9vYGyHCsblpZ2rYyMnLkSEaOHJmuPG0csCTFJkew5OIgzj75HQBbExeaePWjUcHeWBrb5XF0kiTlF2+cgOfPn8/kyZN191ZfZG5uzoQJE/jyyy/fdDev5OjoSJEiRbhx4wYJCQmYmv7Xo1Sr1RIUFIS3tzc+Pj7Y2Nhw8uTJdNtIK3txeJIkvY6bEWeYd64HzxMeoUBBEZuKtCv6FeWcGqBSvjcNTpIk5YA3muPu888/Z+TIkURERGBkZETp0qWpUaMGJUuWRKVSERsby6hRoxg+fHgOhZu13r17Ex0dzXfffadXvnLlSsLCwujUqRNqtZqOHTty5MgRjh8/rquTkJDA/PnzcXFxoWnTpm8lXun9oRVafru9gIn/NOF5wiOMlWZUd2vPoHJL+cilsUy+kiSlY/BUlAcPHsTPzw+1Ws3UqVP57LPPdL2IAaKjo1m4cCGTJk1Co9Fw8OBB6tSpk2OBZyQxMZEGDRpw/PhxunTpQt26dTl//jwrVqzA19eXkydPYm5uTmhoKBUqVCAuLo6RI0fi4uKim4pyy5YtdOjQ4bX3Laei/HBFJT1j0YVPuRR2AAB7U3eaFxpE/QLdMTfKvD+BJEkfOGGgTz75RCiVSrFkyZIs6y1ZskQoFArRtWtXQ3f1WmJjY8WECRNEkSJFhLGxsfD09BRDhw4VkZGRevXu3LkjOnToIOzs7ISlpaWoXr262L17t8H79fDwEIDw8PB400OQ3iGBz46KfvuKiY67bUSn3XZiwvEm4tKTg0Kj1eR1aJIk5XMGXwG7u7uj0WgIDQ3NchIBIQQuLi5YWFi81xNcyCvgD4tWaNhxaw6/3PwWgRYTlQVVXVrySfFRuJgXyuvwJEl6Bxh8Y+rZs2dUqFDhlTP4KBQKChcuzMWLFw3dlSTlKxGJj1lwvg+Bz48C4GhWkFbew6jj2RFTdfoJXiRJkjJicAK2sbHJ9pXew4cP39pwJEnKTZfDDjH/fG9ikp+jREVJ+xr8r/gYSthVk9NJSpL0WgzuBV2xYkVCQkLYsWNHlvW2bdvGo0ePqFixoqG7kqQ8p9GmsOX6VKadaktM8nNMVZbU9+zGwHJL8bGvLpOvJEmvzeAr4L59+7Jnzx78/f1JTk7OsOfwTz/9xKeffopCoaBPnz5vFKgk5ZVn8cF8f96fmxGnAXA1L0ybIiOp4f4JxqrMV/+SJEnKisGdsADatWvHr7/+ikKhwNXVlQoVKmBjY0NkZCTnz58nNDQUIQRt2rRh+/btORl3viM7Yb2fzj3Zw+IL/YhNiUSpUFHKvi4dS4yliM1H8qpXkqQ38kYJOCUlhREjRrB8+fIMlxpUq9X069ePuXPnYmz8fk88LxPw+yVFm8Tm65PZHbQYAHO1NXXcO9O66AjsTDOe7lSSJOl1ZCsBX7lyBW9vb72JNl4UHBzMH3/8wbVr14iKisLKygofHx+aNm2qW2P3fScT8PvjSdxd5p3zJyjqAgDuFsVpV/Qrqrq2xEhlkrfBSZL03shWAvby8sLKyoqAgABd2fr163FxcaFJkya5GuC7Qibg98PJkP9j6aXBJGhiUCnUlHVsQMcS4/CyKiObnCVJylHZ6oT15MkTnJ31Fw739/enVq1aMgFL74UkTQIbro5l3/0fALAwsqOBZ3daFh6KtYljHkcnSdL7KFsJ2MLCgqtXr3Ljxg2KFy+e2zFJ0lv1KOYW88734EF0IAAFLH35X/HRfOT8MWqlUR5HJ0nS+ypbCbhmzZr89ttv+Pr64uLigolJ6n2wM2fOULhw4WztSKFQcPv2bcMjlaRccDT4Z1YGDCNRE4daYUx5p0Z0LDGOAlYl8zo0SZLec9m6B3zjxg3q169PSEiI4TtSKNBoNAa/Pr+T94DfLYmaONZc+ZJDDzcBYGXkQOOCfWhaeBCWRrZ5G5wkSR+EbF0BFy9enBs3bnDkyBHCwsJISUmhd+/eFC9enNGjR+d2jJKUox5EX2XeuR48ir0JQCGrsnQsMZ5yTg1QKlR5HJ0kSR8Kg8cBK5VKatWqxeHDh3M6pneSvALOX7RCw9Xnx4lIfIytiQsl7WugQMmhhxtZfeULkrWJGClN+Mi5KZ1KjMfNokhehyxJ0gfG4KkoDx48iI2NXGxcyn9Ohe5kbeAonic80pXZmbjhbObF9YgTANgYO9O00AAae/XF3Mg6r0KVJOkDZnACrlu3bk7GIUk54lToTuae6wnoN+yEJ4YQnpjah6GITUU6l5iAr0NtlAqD1yORJEl6IwYnYIDY2Fh++OEHjh07RkREBCkpKWTWoq1QKPjrr7/eZHeSlCWt0LA2cBQvJ98XGSvN+Kz8Slwtstd7X5IkKbcYnIDDwsKoWbMmt27dAsg08aaRswhJue3q8+N6zc4ZSdLG8ywhWCZgSZLynMEJePr06dy8eROVSkWzZs0oWbIkZmZyaTYp70QkPs7RepIkSbnJ4AT8f//3fygUCnbs2EGLFi1yMiZJMoitiUuO1pMkScpNBvdACQ4OpkiRIjL5SvmGEFoUZHWrQ4GDqQcl7Wu8tZgkSZIyY3ACtrW11U1JKUl57e+HPzLj9CeITDtgpSbmnr4z5GQbkiTlCwYn4Dp16nDjxg2ePHmSk/FI0mvRCi1brk9l6aVBaEQKjqYFaFpoMPam7nr1HEzdGfnROqq4tsqjSCVJkvQZPBPWpUuXqFy5Mh9//DE///wzxsbGOR3bO0XOhPX2JWniWXJpECdCdgDgZVUG/1Kz8LGrhkCbbiYseeUrSVJ+YnAnrNDQUAYOHMiCBQvw8vKiYcOGeHh4ZJmIp0yZYujuJElPZOJTZp3pxO3IsyhQUMqhDr1KfYeHZepymQpUlHKoncdRSpIkZe6N5oJWKBS68b9ZjfMVQsjVkKQc8zD6Gt+ebk9YwkNUCiMqu7Sgh+907E3d8jo0SZKkbDP4CrhOnTpycg3prbv09CBzz3UnQRODicqCOh6d6FxiAuZGcl5ySZLeLQYn4EOHDuVgGJL0an/dX8cPASPRosHKyIHWhYfTpFA/jFSyN74kSe+eN5oLWpLeBq3Q8uO1CewKWgSAs5kXXUpMoYpbS7mYgiRJ7yyZgKV8LVETx6ILn3L68W4ACluXx7/UdxS3q5zHkUmSJL2ZbCXgggULolAoOHToEN7e3rqy16FQKLh3797rRyh9sMITQpl1piNBURdRoKSMYz38fWfhblk0r0OTJEl6Y9lKwA8fPkShUJCcnKxX9jpkhy3pddyLCmDm6Q48T3yEWmFMFddW9PCdJudxliTpvZGtBLxmzRoA3Nzc0pVJUk47/2Qv35/3J1ETh6nKknqe3ehYYixmaqu8Dk2SJCnHGDwOWNInxwHnjD13V7I28GsEWqyNnWhb5HMaefVGrfywZ1qTJOn9IzthSfmCVmhYHziGP+8tB8DVvDDdfL7hI5ePZU9nSZLeSzIBS3kuPiWa+ed7c+HpPgCK2lTE33cWRe0q5nFkkiRJuUcmYClPPYsPZuaZDtyPvoICJeWcGuLv+y2uFoXzOjRJkqRcJROwlGfuRF5g5ukORCY9Qa00oZprG7qX/AYbE6e8Dk2SJCnXyQQs5Ykzj3ez4HxfkrTxmKmt8CvQk/bFRmGqtszr0CRJkt4KmYClt0oIwe93l7Dx6jgEAltjF9oW/QK/gv6olUZ5HZ4kSdJbIxOw9NZotCmsufIl+x/8O67cvCjdS06jgnNjOVGLJEkfHJmApbciLjmSeef8ufzsIADFbavg7zuTwrYV8jgySZKkvJGtBHzgwIEc2VmDBg1yZDvSu+VJ3D1mnulAcMx1lAoV5R0b0bPUt7iYF8rr0CRJkvJMthJww4YN37iJUKFQkJKS8kbbkN49NyPOMOtMR6KTnmGkNKW6Wxu6+XyDtYljXocmSZKUp7I9xZAQ4o0eWq02N48jQxqNhtq1a2d48nD//n169OiBu7s7FhYWVKtWjZ07d771GN9nJ0L+jyknmhOd9AxztQ1NvQbQu9QcmXwlSZLI5hVwXiTPnDB9+nSOHj2arjw0NJQ6derw/Plzhg4dioeHBz/88AOtW7dm06ZNdOnSJQ+ifX8IIdh553s2X58MgJ2JG58U/Zr6BbqhUspuB5IkSfAeL8Zw6tQpatasiUqlIjExkRcPc+DAgSxfvpyjR49So0YNABISEqhWrRrBwcHcvXsXCwuL19qfXIwhVYo2iVUBIzn0cCMAnpY+dC85jbKODWRPZ0mSpBe8tVnu32ZSiomJoWvXrnz88cdUq1ZN7zmNRsPGjRupXr26LvkCmJqaMmzYMMLCwti1a9dbi/V9EpMcwfRT7f5Nvgp87KrzWfmVlHPyk8lXkiTpJW/UHhgXF8e6deu4fPkycXFx6ZqqU1JSiIuL4+HDh1y6dImkpKQ3Cja7hg0bRmRkJKtWraJjx456z125coWYmJh0iRmgatWqAJw8eTLd66SshcYGMfNMe0Jib6NUqPnIqQk9fKfjbO6V16FJkiTlSwYn4IiICGrUqMH169fTPSeE0LvieZut3Nu3b2f16tX8+uuvuLi4pHs+7Uq8YMGC6Z7z9PQEICgoKNPtz507l7lz56YrDwkJMTTkd9715yeYdbYzscnhGCvNqO7Wjm4lp2JlbJ/XoUmSJOVbBifg+fPnc+3aNZRKJfXq1cPa2ppff/2V8uXL4+vry8OHD/nnn39ISUmhfv36/PDDDzkZd4aCg4P59NNP6dOnD61bt86wTmRkJACWlunnHDY3NwcgNjY2031ERUURHBycA9G+H44G/8zSS4PQiGQsjGxpXLAvbYqOxERlntehSZIk5WsGJ+DffvsNhULBunXr6Nq1KxqNBjs7O9zd3dm4MbUDTmBgIB9//DHHjh0jLi4ux4LOiBCCnj17Ymtry/fff59lvRf/zeg5lUqV6eutra3x8PBIVx4SEvLO9hY3hBCC7bdm8fPNGQA4mHrQvuho6nh2kj2dJUmSssHgTli3b9/GwcGBrl27AqlJq3z58hw7dkxXx9fXlxUrVpCUlJRlUswJc+fO5cCBA8ybN4+EhATCwsIICwsjOTkZgLCwMMLDw7GysgLI8IQgrczGxibT/YwcOZKHDx+me7i5ueXCUeVPyZpEFl/sr0u+BSx9GVh2MfUKdJXJV5IkKZsM/raMjY2lXLlyemUlS5bk2LFj3Lt3Dy+v1M43H3/8Mc7Ozvz9999vFukr/PbbbwghMm16dnJywsvLS9fDOaNe2WllBQoUyL1A33FRSc+Yc7Yr18NPAAp87Wvi7zuTgtal8jo0SZKkd4rBCdjGxibdVWThwoUBuHbtmi4BQ2qHpytXrhi6q2yZM2cO4eHh6co///xzLl26xL59+zAzM8PHxwcbGxtOnjyZrm5a2YvDk6T/PIq5yben/8eT+LuoFGoqujSjR8lpOJrJExZJkqTXZXAC9vX15cSJEzx58gRnZ2cAihYtihCC8+fP06RJE13dp0+folTm7pDjihUrZlhuZ2cHpM5nnaZjx46sXLmS48eP603EMX/+fFxcXGjatGmuxvouuvLsCHPOdiMuJRITlTk13NrT1WcylsZ2eR2aJEnSO8ngrNi0aVOSk5Np164dV69eBVLH0SqVSpYvX667Gt2+fTv37t3D29s7ZyLOAZMnT9Yl2smTJ7Ns2TJq167N5cuXWbBgAaampnkdYr5y6OEmpp9qS1xKJJZG9rTwHkqvUjNl8pUkSXoDBl8BDxw4kCVLlnD8+HHKlClDbGwsnp6eNGvWjF27dlG8eHG8vLy4ePEiCoWCli1b5mTcb8TV1ZXjx48zatQo5s+fT3JyMmXKlGHXrl00a9Ysr8PLN7RCy9Yb0/j19hwAHE0L0KH4WGp5/A+lIvOe4pIkSdKrvdFc0Ldu3aJv374EBgby5MkTAG7cuEHt2rV5+vSprl6xYsU4ffo01tbWbx5xPvW+zQWdpIlnycVBnAjdAYCXdRl6+EzD1yHj1aUkSZKk15MjizE8ffoUJycnvZ9Xr15NUFAQPj4+9OnTRzf85331PiXgyMSnfHemM7ciz6BASSmHOvT0nUEBq5J5HZokSdJ7471dDelte18S8MPoa3x7+n+EJTxApTCisksLupf8Bgez9JOPSJIkSYaTsyZIOpeeHmTuue4kaGIwVVlQ0+1/dCk5CQsj27wOTZIk6b1jcAJu0KDBa9VXKBT89ddfhu5OymV/3V/HDwEj0aLB2tiRj70G0KLwEIxVske4JElSbjA4AR86dOiVddI667y8OpKUf2iFlh+vTWRX0EIAnM0K0aH4OGq4t5U9nSVJknKRwQl44sSJmT4XGxvLo0eP2L9/P0+fPmXcuHHUrVvX0F1JuSQhJZZFF/tx5vFuAApbV6B7yWn42FeXJ0ySJEm5LFc7YcXGxvLJJ59w9OhRzp8/T7FixXJrV3nuXeuE9TwhhO/OdCIo6iIKlJRxrE9P3xl4WBbP69AkSZI+CLk6P6SFhQVr1qwhOTmZKVOm5OaupNdwL+oy4441ICjqImqlMdXd2jKg7CKZfCVJkt6iXO8F7ebmhq+vr+yAlU+cf7KX78/3IlETi5nKipru/6OLz0TMjTJfglGSJEnKeW9lGFJMTEyGKxVJb9efd1ewLnAUAi02xs408x5Es0IDMVKZ5HVokiRJH5xcT8A7duzg9u3b7/X93/xOKzSsDxzNn/dWAOBqXpiOxSdQ1a0VSkXurlIlSZIkZczgBDxhwoRMnxNCkJiYyLVr1/jzzz9RKBS0bdvW0F1JbyA+JZoF5/tw/uleAIraVKR7yemUsK+ax5FJkiR92AzuBa1UKl85VCVt0z4+Pvzzzz/Y2Ly/9xnzYy/oZ/HBzDzTgfvRV1CioqxjA3r4zsDdsmhehyZJkvTBM/gKuE6dOlkmYLVajaOjI7Vq1cLf3x8LCwtDdyUZ4E7kBWad6UhE4mOMlCZUdW1Nt5JTsTVxyevQJEmSJHJ5Jiwpb5x5vJsF5/uSpI3HTG1NbfcOdPaZiJn6/V6RSpIk6V1icA+c+/fv69YAfpWAgAB+++03Q3clZZMQgt13FjHnbDeStPHYmrjSvuhX9PCdLpOvJElSPmPwFXChQoWoXbs2f//99yvr9u7dm6CgIJ4+fWro7qRXSNEms/bKV+x/sAYAd4tidC4xkYouzWRPZ0mSpHwo2wlYq9Xq/p/WuUoIoXtkRAjBvXv3uHPnDnFxcW8YqpSZuORIvj/fi0thBwAobluV7iWnUcyuUh5HJkmSJGUmWwn42rVrlC1bFo1GoytTKBQcO3YMtTp7ObxcuXKGRShl6UncPWad6cjDmGsoFSrKOzWiR8npuFoUzuvQJEmSpCxkq23Sx8eHfv36pbviffHnrB7m5uZ8++23uXogH6KbEWcYd9yPhzHXMFaaUtOtA/3KLJDJV5Ik6R2Q7XHA0dHRnDt3DkhNvA0aNKBMmTIsWLAg09colUosLS0pXrz4ez8M6W2PAz4R8iuLL/YnWZuIhdqG2p6d6VR8HKZqy1zftyRJkvTmsn0P2MrKSm9N34IFC+Lj4yPX+X3LhBD83+15bLmRurqUvYk7rQoPo6FXb9RKozyOTpIkScoug3tB3717NwfDkLIjRZvEqoCRHHq4EQBPSx86l5jER85NXjkrmSRJkpS/vJXVkKQ3F5Mcwdyz3Ql8fgRQ4GNXjR6+MyhsUz6vQ5MkSZIMYHACLlz49Tr6KBQKbt++bejuPmihsUHMPPM/QmJvoVKoqeDUhO6+03AxL5TXoUmSJEkGyvUmaIVCgRBCNpFmg1ZouPr8OBGJj7E1caGkfQ1uhJ9i9tkuxCSHY6w0o7pbW7r6TMHaxDGvw5UkSZLegMEJeM2aNZk+Fxsby6NHj9i5cydXrlxhypQpdO7c2dBdfRBOhe5kbeAonic80pVZGtkRlxyFFg2WRnbU9ejK/4qPxlT9fvcolyRJ+hAYvBxhdmi1Wnr16sXmzZs5fvw4lSq9vzMzvckwpFOhO5l7rieQ8UdhaeRAh+Jj8CvQE5VS3raXJEl6H+TqJMFKpZL58+djZGTEtGnTcnNX7yyt0LA2cBSZJV8ABcjkK0mS9J7J9Vn6bW1t8fHx4ejRo7m9q3fS1efH9ZqdMxKd/Ixr4f+8pYgkSZKkt+GtLJMTFhZGbGzs29jVOyci8XGO1pMkSZLeDbmegBcuXMiDBw8oWrRobu/qnWRr4pKj9SRJkqR3g8E3FXv06JHpc0IIEhMTuXbtGleuXEGhUMhe0JkoaV8De1N3nieEkPF9YAUOpu6UtK/xtkOTJEmScpHBvaCVSqVujO+r1K5dm71792JiYmLIrt4JOdMLGvSTcOrY6ZEfraOKa6ucCVSSJEnKF97oCjiryTXUajWOjo7UqlWLZs2ayYk4slDFtRUjP1qXbhywg6k7PX1nyOQrSZL0HsrVccAfkpxYjjCjmbCUClUORypJkiTlB3JgaT6iVKgo5VA7r8OQpP9v77zDojrav/89u0vdpQlSpAekWOgoiN3Yn8egKBo1mtgSYyKWGGsSjfroz2gSS6oxYiwhJsaoiVE0lhgFGwbFbhAVRJFXUTrL7rx/LGfcZQsLLCyQ+VzXXixTztw7Z+Z8z8yZuQ+DwWgEGmUbEoPBYDAYDFX0GgF7eHjUuyCO43Dnzp16H4fBYDAYjJaAXgKcnZ2t94pnbbBFWAwGg8FgPKdWz4A5jkNoaCji4+Ph5MQcQzAYDAajZSCTE6Q+lCKvVAZHCyGinEwgFDTswFEvAV61ahV27tyJc+fOIS0tDenp6ejZsydGjRqFuLg42NraNqiRDAaDwWA0FL9llWHR6ULklshpmIulAMs6W2Gwl3mDlVurbUi3b99GUlISdu7cifT0dHAcBxMTE/Tr1w+jRo3CSy+9BLH43/muWkNsQ2IwGLoxxiiF0bL5LasMk44+VfNDyLeqb3rZNJgI13kf8I0bN5CUlIQffvgBV69eBcdxMDc3x3/+8x+MGjUKgwYNMornq0uXLmHJkiU4fvw4nj59ijZt2iA2NhZLliyBjY0NTXf37l0sWrQIhw8fxtOnT9GxY0csWLAAQ4bUzekFE2AGo2Ex1iiFYTxkcgI5AJkckBGAgEAmhyKMAHKleDkAOakWTxRhcqL6P/+9Qk7w5rGn+H/lmmWQA+AiFuDscIcGudEziCOOjIwMOjK+desWOI6DRCJBbGwsRo4cif79+0MobHiHEtevX0d4eDhEIhGmTZsGDw8PpKSkYOvWrWjfvj1SUlIgFovx4MEDREVF4fHjx5g+fTpcXV2xadMmnD9/Htu3b8fo0aNrXTYTYAaj4TDmKKU5okt0NAmTjCh8+KumUT9GpZygQg5I5UClHJDK5ZBW/a8II5DKCSoJIJUp/q8kmv4qPnKiGq4QWoJKXnAJIAepKl/xIVC1X46qdPynKj3RI31ZJcFTac0SuGuAHWJcTA1+ngzuCevChQtUjO/cuQOO42BnZ4e4uDh89dVXhixKjf79++Po0aNIS0tDhw4daPi6deuQkJCAVatWYc6cOZg6dSq++uor/PXXX+jSRfGSg7KyMkRFRSEnJwdZWVm1nkpnAsxgNAwyOUHEj/kqI19lDD1K0TbqkhGCcjkgrSSQEoIKGSAlgFSmEB1twqT4v0qYlISqkhAaXylXiJ9U6fvzv1D5X/Eh1D5ZlXDK5KpCqxgRPhcfovI/L0RE5X8qXCDVRE3x99/KFz2sMfQFC4Mft0FdUX722WdYsGABCgsLwXEcZDJZQxWFiooK2NraIjo6Gn/88YdKXEFBAezs7DB48GDs2bMHtra2CAoKwsmTJ1XSbd68GRMmTEBSUhJGjhxZq/KZADMYtUNOCMpkQFGFHM+kBMVSOQorCIqkBMWVBMVVf689rsQP/5TVeLxAOyEkJoJq4vRcrORV35VHdcojvedCQ6qJ1PORFEMzAg4Qcs//CjlO5btQwH8HhAIOQg4QKcWJqsIVfwGRUrhIwEFUFSaqFiasCjNRjhdwMOHD6HfV9Pzxrz+R4sNzNb+rvqFGwAZ3RXn69Gn8+OOP+Omnn3Dv3j26d9jLy8vQRakgEolw+fJlyOXqd8kPHypeZi8UCnH58mUUFRUhKipKLV3nzp0BKH5DbQWY0bRgi3XqjlROUFZJUFpJUCojKKogKJQSFEnlKJICxZVyFEkV8cVSgpJKxaeskqBEBpRVylEmU0zvlcsUI8NymeK7VA5UVP2tNLCiXX0iA9BwN/na4PBcXAQCQIBqgqMiQFXiI9AiPhrERVSVXllg+DBeWJ6HVRcf1fJEyuVVK1ukK221cOV4Add8/Tz0bGOKjVdK8aBEruVlsIrZlSgnkwYp3yACnJqaSkU3Ozubiq6npyeGDx+O+Ph4REZGGqIorQgEAnh7e2uMW716NQCgV69edHSqybuXm5sbAMVqb0bzpSUu1iFVo8UyWZUwVhL6vUhKUFhBqoQRKJbKqSiW8EJaCZqnTEaqxBEol1eJpAx0GtUYU40iDjAVAmZCDuZCruovYC7iUCEjuPKkZmEd62cOXxsRFR+hFvEQaRA+baKjEq9RiABBMxUfhuKcLutshUlHn4KDppfBAks7WTXYzXudBTglJQU//vgjdu3apSK67u7uVHT5EaUx2bp1K7755hu4u7tj0qRJ2LdvHwBAIpGopbW0tAQAFBdrn5L4+OOP8fHHH6uF5+bmGshiRn3QtljnQYkck44+xTe9YFARrpQTJVEESqu+l1WNHnmBLK6aYi2uVIwgS6RAaaUcJUp5yqvylMuejxgrlEaMxpgCNRUoRNFMSRzNhRzMRYC5kIOliIO5SPGX/4hNOIir/lqKOFhUpbEQqn+3qDqOSMcFjn8GXNMo5f+irdksB6PWDPYyxze9oH7TLhZgaaeGvWmvlQCfOnWKim5OTg4VXVdXVyq60dHRDWJoXdiyZQsmTpwIsViMXbt2QSKRUJs1Pfrmw3St2H727BlycnIaxmBGvZDJCRadLtR4kebD3k15BpEAKFcaTfICWVKpeOZYIlUaOSqNGMsqFYtwqDjKDD+Nqg/CqtGiqeD5SJGKo0jxv4XoufhRcawSRisTDpYmAkVclZhaVImieVUeiyrRbQpTi8YepTBaPoO9zDHAw6xpesKaMWMGdu3ahfv371ORcnFxQVxcHOLj49G1a9cGNbIuLF26FO+//z5sbGzw66+/0ilwKysrAEBJSYlaHj5Meb9wdaytreHq6qoWnpubq/H5M6NhkMoJHpbIcb9YhvvFir+nH1ZoXSnL8//KCMb/8bRBbDKpGi2aCgBTOpUKKoy8wFmKAAuRAJYi0BGjpYiDRCSAxEQhlBb8yFHIi+jz45j8C4XGmKMUxr8DoYBrkIVWutBLgNetWweO4yASidC7d2/Ex8ejW7duEAgUbzPMzMzUq7AXXnih7pbqiVQqxZQpU5CYmAhXV1fs378fQUFBNJ5/TqxppTIf5u7urvX4s2bNwqxZs9TC+VXQjPpTISN4UCJHbokM94tlyCmS4U6hHNnFMuQWy5BXKseTclLnKVlHCw725gKl6VSlqVQhYMmLo4mgajoVkJhUiaNIXRwtqkadTWG02JIx1iiFwWgoajUFXVlZieTkZCQnJ9e6II7jUFlZWet8tUEmk+Hll1/Grl27EBQUhP3796uNVgMCAmBjY4PTp0+r5efD+L3BDMNTLiN4UKIYteYWy5BTLENWoQw5RQrBzSuVo0BPcRVyQCszDg4WQjhZCiDiCA5nS2vM90UP20a/02UYBmOMUhiMhkJvAa7vduEG3G5Mee+997Br1y506tQJycnJGqeSRSIRRo4ciY0bN+LUqVMqjjjWrl0LJycnDBw4sMFtbYmUVSrENadYIabZRTLcLZThXpEcD6rE9WmFfu1AxAH25gK0thDA2VKANmIhPK2E8LISwk0iRBuxAPbmApUVqPou1mmoLQUMBoNRG/RyxHHnzh2DFObp6WmQ42ji7t278PHxgUwmw4oVKzQ+p3VyckLfvn3x4MEDhIaGoqSkBLNmzYKTkxN1RZmUlIT4+Phal9/SHXGUVBI8KJbhftVz1+wiGe4Uyugz2EelCmcK+mAiUIirk4UALmIBXMVCeEiE8LIWoo1YCBexEPZmXJ2mdPlV0IDmxTrMZSGDwWgqNKgnrMbku+++w/jx43Wm6dGjB44dOwZAsdd33rx5OHToEKRSKTp27IhFixZh0KBBdSrfEAJsLOcRxVKC3BLF89X7xXLcLZLhbpEM94tkyC2RI69U4XhBH0wFQGsLARwtFKNWN4li5OomEcJVLICLpRB2dRRXfdG0D7gNW6zDYDCaGC1GgI1NfQW4oZxHFEvliinhqtHrvcLnI9cHJXI8KtNfXM2EgKOFAE4WiilgDyvFyLWNWPF/G7EQNqYNK676wjxhMRiMpo7BXVEyak9dnUcUVsjplHBusbzqeWslsovkeFCqmBYu0XOjqoUQcLQUwtlSMSXsLlEIrIulYkq4jaUA1k1EXPWBLdZhMBhNHSbARkYf5xGzTz5D2qMK5BTLFauHS2TIL5WjVE+3t2IRB0cLAZzFAriLhXC3EsJVLIRL1ai1jaUAVqYCQ/0kBoPBYOgBE2Ajk/pQWqPziIIKgs8ySjXGWZkoxNVFLIS7WAB3q+cjVpeqqWGJCRNXBoPBaGowATYyeXoOY2OcTdDVxZQ+b3URC+FiKYCYiSuDwWA0S5gAGxlHC+1+p5WZFSJhzzQZDAajBcGGT0YmyskELpYCaFvaxEGxhYY5j2AwGIyWBRNgI8O/6QWAmgizN70wGAxGy4UJcBNA8aYXGzhbqp4OF7GAeW5iMBiMFgp7BtxEYG96YTAYjH8XTICbEMx5BIPBYPx7YFPQDAaDwWAYASbADAaDwWAYASbADAaDwWAYASbADAaDwWAYASbADAaDwWAYASbADAaDwWAYASbADAaDwWAYAY4Qot8b2xk6MTU1hVQqhUAggIuLi7HNYTAYDIYRcXZ2xrlz53SmYY44DIRMpnitoFwuR05OjpGtYTAYDEZThwmwgTA3N0dZWRmEQiEcHR2NZkdubi7kcjkbiRsIVp+GhdWnYWH1aVgMWZ/Ozs41pmECbCCKi4uNbQIAwM3NDTk5OXBxcUF2draxzWn2sPo0LKw+DQurT8PS2PXJFmExGAwGg2EEmAAzGAwGg2EEmAAzGAwGg2EEmAAzGAwGg2EE2CKsFsasWbPw7NkzWFtbG9uUFgGrT8PC6tOwsPo0LI1dn8wRB4PBYDAYRoBNQTMYDAaDYQSYADMYDAaDYQSYADMYDAaDYQSYALcgJk2aBI7jNH4SExONbV6T5/Tp0xAKhTh27Jha3N27dzFu3Di0adMGYrEYUVFR2Lt3b+Mb2YzQVZ8vvvii1raqKf2/mUuXLmH48OFo3bo1TE1N4eXlhRkzZuDp06cq6Vgb1Q9967Mx2ihbBd2CuHjxIry8vLB06VK1uC5duhjBoubDzZs3MXToUMjlcrW4Bw8eoHv37nj8+DGmT58OV1dXbNq0CS+99BK2b9+O0aNHG8Hipo2u+gQUbTUiIgIJCQlqcYGBgQ1tXrPh+vXriI6OhkgkwrRp0+Dh4YGUlBSsX78eR44cQUpKCsRiMWujeqJvfQKN1EYJo0Ugk8mIhYUFiY+PN7YpzY6ff/6Z2NnZEQAEADl69KhK/BtvvEE4jiMnT56kYaWlpSQ4OJg4ODiQoqKiRra4aVNTfd6/f58AIO+++65xDGxG9OvXj5iYmJBLly6phK9du5YAIKtWrSKEsDaqL/rWZ2O1UTYF3UK4efMmSktL0aFDB2Ob0qwYPHgwhg0bBhcXF7z88stq8TKZDNu2bUN0dLTKLIK5uTkSEhKQn5+PX3/9tTFNbtLUVJ+AYmQBgLXVGqioqMCJEyfQrVs3tboaN24cAOD48eOsjeqJvvUJNF4bZQLcQkhPTwfwvMGUlJTQdxQztHPt2jX873//Q1paGvz8/NTiL1++jKKiIkRFRanFde7cGYDiWSdDQU31Cai31eLiYq1T1f9mRCIRLl++jK+//lot7uHDhwAAoVDI2qie6FufQOO1USbALQS+wRw4cABeXl4Qi8WwtLREbGwsMjMzjWxd0+XKlSuYP38+zMzMNMbzryTz8PBQi3NzcwMA3L59u+EMbGbUVJ/A87a6ZcsWODs7QyKRwMrKCuPGjcOjR48ay9Qmj0AggLe3N3x8fNTiVq9eDQDo1asXa6N6om99Ao3XRtkirBYCP2WSmpqKRYsWwcHBAadOncLatWtx6tQpnDlzBl5eXsY1sgmiSygA0JWREolELc7S0hJA03kXdFOgpvoEnrfVtLQ0rF69Gubm5jh06BA2btyI06dP4/Tp07C1tW1gS5svW7duxTfffAN3d3dMmjQJ+/btA8DaaF2pXp9A47VRJsAthFGjRiEsLAzz5s2DhYUFACA2NhZRUVGIi4vDwoULsX37diNb2fwgVZ5aiQaPrXwYP23F0I833ngDhYWFePfddyEQKCbhhg8fDn9/f8yePRsfffQRli9fbmQrmyZbtmzBxIkTIRaLsWvXLkgkEtZG64Gm+gQar42yKegWwpgxY7BkyRIqvjzDhg2Du7s7Dh48aCTLmjdWVlYAFM/Uq8OH2djYNKpNzZ1p06Zh3rx59MLG89Zbb0EoFLK2qoWlS5fi1VdfhUQiwYEDBxAZGQmAtdG6oq0+gcZro2wE/C/AycmJTqkwaoe3tzeA58+CleHD3N3dG9WmloqpqSns7OxQWFhobFOaFFKpFFOmTEFiYiJcXV2xf/9+BAUF0XjWRmtHTfWpC0O3UTYCbgHk5+cjKCgIw4YNU4uTSqW4efMmfH19jWBZ8ycgIAA2NjYaV5HyYczJif5cunQJ7du3x/Tp09Xi8vLykJ+fz9qqEjKZDC+//DISExMRFBSE06dPq4kFa6P6o099NmobbdBdxoxGIzAwkIhEInLu3DmV8CVLlqhsMGdo54MPPtDoOGLKlCkanRwEBQURJycnUlpa2siWNg801WdJSQmxs7MjNjY25M6dOyrpJ0yYQACQnTt3NrKlTZf58+cTAKRTp06koKBAazrWRvVDn/pszDbKpqBbCJ9//jkGDBiAPn36YNq0aXB1dcWRI0ewa9cu9OzZEzNmzDC2ic2WJUuWYO/evRg4cCBmzZoFJycnbNq0CZcuXUJSUhLMzc2NbWKzwcLCAhs2bMDYsWMRFRWFN998EzY2NtizZw/++OMPjBkzBiNGjDC2mU2Cu3fv4qOPPgLHcRg2bBhd7ayMk5MT+vbty9qoHtSmPhutjRpExhlNgvPnz5MhQ4YQOzs7YmpqSvz9/cnSpUvZ3a+eaBsBE0JIZmYmiY+PJ3Z2dkQikZDo6Gjy22+/Nb6RzQhd9Xn48GHSp08fIpFIiLm5OQkODiYbNmwgMpms8Q1tomzZsoW689T26dGjB03P2qhualufjdFGOUI0rF1nMBgMBoPRoLBFWAwGg8FgGAEmwAwGg8FgGAEmwAwGg8FgGAEmwAwGg8FgGAEmwAwGg8FgGAEmwAwGg8FgGAEmwAwGg8FgGAEmwAwGg8FgGIEGEeCLFy9i+vTpaN++PWxtbWFubg53d3cMHDgQGzZsQGlpqcZ8r776KjiOw9ixYxvCrAZl8eLF4DgOXbt21TuPl5cXOI7DN99804CWNTwcx4HjOBw+fNjgxz527Bg9fmVlpd75EhMTwXEc3NzcNMZnZ2fj2bNnKmEN3f5kMhm6deuGwMBASKXSBinDGDTnflsbCgsLqZtXMzMzuLi4NPu+W1fqcr3TRUlJCbKysgxyrOaEwQX4gw8+QGhoKNavX4/s7Gy88MILCAkJgUAgwIEDB/D222/D398faWlphi6awaiRiooKvP/++/Dz80NeXl6jlr1q1Sr89ddfWLNmDUxMTBq1bEb9GT16ND7//HPk5ubC398frVu3hpeXl7HNavbs2LEDfn5+DXID39Qx6MsYNm/ejA8//BBisRiJiYkYOnQohEIhjb969SomTJiA1NRU9O/fH1euXEHr1q1p/IoVKzBv3jz28mgGpVOnTrh69SoAQCSqf3O9f/8+li5dWu/j1Jbbt29j6dKl6N27NwYNGtTo5TPqR3FxMX777TcAwBdffIHXX3/dyBYZl7feegujRo2CpaVlvY+1YMEC5OTkGMCq5odBR8DLly8HAKxevRrDhw9XEV8ACAwMxN69e+Ho6Ij8/HysW7dOJd7FxQUBAQFwcXExpFmMZoylpSUCAgIQEBBgbFPqxfz581FaWorFixcb2xRGHXj8+DF4t/k9e/Y0rjFNAAcHBwQEBMDDw8PYpjRrDCbABQUF+OeffwAAnTt31pqudevWiI2NBQCNL5BmMFoaly9fxs6dO9GuXTt069bN2OYw6oBMJqPfzczMjGgJoyVhMAFWfqb166+/6ky7ZMkSXL58Gd9//71KuK7FHI8fP8bixYvRvn17iMViODo6YvTo0bhx4wZdEKA8uuAX70RFRUEqlWLNmjUIDg6GpaUlbG1t0adPH+zZs0erjcePH8crr7wCHx8fiMVimJmZwdXVFXFxcfjjjz/0rBX9OXjwIHr06AGJRAJbW1v07t0bO3fu1Jo+NzcXCxcuRKdOndCqVSuYmJigVatW6NKlC9asWaO20C0rKwscx8HZ2RmEEGzatAmdO3eGlZUVrKysEB0djc2bN0Pby7Fu3bqFKVOmwNvbG+bm5mjbti2WLVuGiooKtbRjxowBx3GYMmWKWlxOTg5dVLVhwwa1+L1794LjOHTs2BFAzYuwdu/ejT59+sDBwQFWVlbo2bMnkpOTNf6Gnj17wtvbm/7ftm1bcByHY8eOqaW9c+cOpkyZAnd3d3ruX331Vdy8eVPjsXWxfv16EEI0tuu6ttOaFpnx55vjOJXFLXxfWbRoER48eICpU6fCzc0N5ubm8PHxwaJFi+g5PXbsGAYMGAA7OztYWFggLCwM3333nc7feuPGDcTHx8Pe3h6WlpYICQnBqlWrUFZWpjXPn3/+ieHDh8PFxQWmpqZwcnJCbGwsjhw5ojE9v3jx4sWLSEhIgJ2dHSQSCcLDw/H48WOd9inbOXXqVPj6+sLMzAw2NjaIiorCJ598otZ3OI5TaTfe3t7gOA6vvvpqjeXw17Qvv/wSWVlZmDBhAtzc3GBmZgY3NzdMmjRJbfFRdHQ0OI7D9OnTtR532bJl4DgOAwcOVAm/efMmXn/9dZV++uGHH6KiogJdu3ZVWyxZ33akaRFWUlIS+vfvD09PT5iZmcHR0REDBgzA9u3bIZfL1Y5x584dAMDkyZPVruO6yMnJwfTp0xEcHAxra2tYWVmhXbt2SEhI0Lqgq6CgACtXrkT37t3h4OAAExMT2NraIjw8HIsXL8aTJ0/U8ihff5KSktClSxdYWVnBzs4O/fr1Q0pKCgDFY4qFCxfSNuXk5IRx48bh/v37GuvU1tbWsO8DjomJIQAIx3Fk3Lhx5Pjx46SyslLv/OPHjycAyJgxY1TCMzMzia+vLwFAhEIhCQ4OJv7+/gQAEYvFpF+/fgQA+eCDD2ieo0ePEgAkNDSU9OnThwAgDg4OJCwsjIjFYvr+xy+++ELNjnnz5tH41q1bk/DwcOLr60tEIhEN/+qrr1Ty8O8+jYmJ0fv3enp6EgCkW7duBAAxNzcn4eHhxNXVlZbz2muvqeVLSUkhtra2NE/79u1JSEgIsbGxofm6deumUve3b98mAIijoyN55ZVXCABia2tLwsLC6LEAkLlz56qVd/jwYWJlZUXrOyIigrRp04YAIL169aJ5Dx06RAghJCkpiQAgHh4easfavHkzTT906FC1+MmTJxMAZOHChYSQ5+cRAJFKpSpp33zzTRrn7u5OwsPDiYWFBQFAevbsSQAQV1dXmv6tt94iERERNE94eDiJiYkhaWlphJDn7a9jx47E1taWcBxHAgMDSUBAABEKhQQAkUgk5PLly/qcXkIIITKZjLRq1YoAIOnp6WrxdW2nfD0q/z5l+PMNgNy+fZuG8+00Pj6eODo6EoFAQDp27Eg8PDxo+lGjRpEvvviCcBxHJBIJCQ0NVWkjX375pUpZfL2FhIQQa2trwnEc6dChAwkMDKR5wsLCyOPHj9XsnDt3Lk1jZ2dHwsPDibOzMw1799131fLw/Ya/3rRr1454enqS6OhofU4J2bZtGzEzMyMAiIWFBQkPDyd+fn60zI4dO5J79+7R9DExMSrtJiIigsTExJDly5fXWBZfN5MnTybW1tZEIBCQtm3bknbt2qlcY+7evUvzfP311zS8epvn4e394YcfaNihQ4eItbU1AUAsLS1JREQErcvevXuTTp06qfRTQurfjqpf72bOnEnTe3p6qlwrAJBXXnmFpt20aROJiYmh58LX15fExMSQTZs21Vivt27dIo6OjvSa1LFjR9KxY0d6LGtra9qveW7cuEHc3d0JACISiUhAQAAJCwsj9vb21D5/f39SWFioko+Pe+utt+h5CQkJodcaMzMzcuTIEdK+fXvCcRzx9vYm7du3p/l8fHxIcXGxWp3a2NgQgwpwWloakUgkKi84tra2JoMGDSIrVqwgqampOl9mrEmA5XI57WgREREkMzOTxqWmpqqcXE0CzF80t2/fTuMKCgroxc7e3l6lkfP5BAIB+fbbb1XsvXfvHr2wOzk5qcTVR4ABkMGDB5P8/Hwat2nTJir4yg2ysrKS+Pj4EAAkNjZW5aJWUVFBVq5cSY/566+/0jjljiQUCsnatWupQJeWlpKxY8fShpmXl0fzPXnyhLRu3ZpemJ89e0bjEhMTiYmJiZoAP336lIZfu3ZN5Te//PLLNH2rVq3U2gN/83H27FmV81FdgLdt20YAEFNTU5Vz++TJEzJixAiap/qFRbkebt68qRLHtz9eEJVt//vvv4mDgwMVL305d+4c7QeaqGs7re+FEwAJDAwkN27coHHvv/8+jRMIBGTWrFmkrKyMEEJIcXExGTBgAL2waqs3X19fcunSJRqXmppKXFxc1C6+hBDy5ZdfEkBxI7ht2zYaLpfLSVJSEr0B+eabb1TyKfebpKQkGv7o0SONdaFMamoq7VeTJ08mT58+pXEXLlygwhYWFqZS39rqsyaU6yYqKopcv36dxp06dYre2CYkJNDwZ8+eEUtLSwKA7Nu3T+2Yp06dojcs/PnJz8+n/TQuLo4UFBQQQhQ3gOvXrycCgUCtnxJiWAG+cuUKARSDgqNHj6ocZ8uWLdSGlJQUlTj+fG7cuFF3ZSoxcuRIAoAMHz5cRTAfPHhAunTpQgCQ/v37q+Tp0aMHPQ/379+n4XK5nHz33XfUvg0bNqjkU9azlStX0nZx7949qj8CgYB4e3uTM2fO0HzJycn0xj0xMZGGK9epQQWYEEIuX75MunbtqmK08sfR0ZEsXLhQ5Y6AR5MA79+/n16ccnNz1fKkpqbWKMBr1qxRy3f+/Hkan5GRQcPnzp1LzMzMSFxcnMbf9+eff9J8yvbUR4B9fHxIaWmpWvyiRYvoRU3ZbltbW2JmZqYi2Mq88MILBABZsWIFDVM+6W+//bZanvz8fMJxnJpw84Lu5+dHKioq1PItWbJEY8d+8cUXCQCydu1aGiaXy4mjoyOxtrYmISEhBIDKXWpaWhoBQNzc3GiYNgEOCAggAMjixYvVbCovL6czJHURYJFIRO7cuaN23OXLlxMAxMXFRS1OGx999BEBoHV0Vtd2aggBTk1NVclTXFxMLxjdu3dXO+axY8doXuUbP77ehEIhuXLlilq+w4cP04tUVlYWIURxjpycnAgA8vPPP2v8DZ9//jn9jcrnXnnmqLb079+fACD9+vXTGJ+ZmUlHNlu3bqXh9RVgU1NTjdevt99+mwCKwYUy48aN03qz9/rrrxMAZNq0aTSMb5s+Pj5UlJVZsGBBgwswP/MVHBys8VjTpk0jL7/8Mjl27JhKeF0EmO//u3fvVos7e/YsGTBgAJk5cyYNe/jwIb2BVr5BVKZ3794EAHn99ddVwvnfP2DAALU88+fPp/FHjhxRi+cHbNOnT6dhynVq8H3A7dq1w4kTJ3DhwgV88MEH6NKli8rz4by8PCxfvhxBQUHIzs6u8Xi7d+8GAAwdOhTOzs5q8Z07d0Z0dLTOY/z3v/9VCwsMDKTfCwoK6PeVK1eitLQU27Zt03gs5WX3JSUlOsvVl4kTJ8Lc3FwtfPLkyQAUz1+vX78OAAgLC8OTJ0/w5MkT2Nvbq+UpLy9Hq1atdNqnqT7s7e3pljDl+uC3XowePVrj3tU33nhDYxlDhgwBAJXnsenp6cjLy0OPHj3QvXt3AFB5zseXxefVRmZmJq5duwYAGp/DmZqaYuLEiTqPoYuIiAiNqzuDgoIAAPn5+Xof6/bt2wAAX1/fGtPWpp3WF3t7e7XFkpaWlnB0dAQAjVul2rRpQ79Xd2ICAH369FGxVznc29sbcrkcBw4cAACcOnUKDx8+hJWVFV566SWNNo4ZMwYCgQA5OTka/QbU1glEcXExjh49CgCYMWOGxjTe3t4YOnQoAOCXX36p1fF1ERERofH6xddX9XM7YcIEAIo1EU+fPqXh5eXl+OGHHwAAr732Gg3fu3cvAGDs2LEaF4nNnDkTHMfV70fUQNu2bQEo+vk777yjtl5iw4YN2LFjB3r06GGwsubNm4c9e/aoPLePiIjA77//jo8//piGOTo64tGjRygpKUGHDh3UjieTyWBtbQ1A+3Vz8ODBamH8PnBLS0uNv4vvM5r6C9CArihDQkKwePFinDx5EgUFBUhOTsbs2bNpB//nn38wYsSIGo+TkZEBAAgODtaaJiIiQucxXF1d1cIsLCzo9+qLeziOg1AoxIkTJ/DVV19h7ty5iIuLQ9u2bVXKUl5QUB/CwsI0hnt4eNA90fxeWGX7b968ie+//x7Lli3DhAkT0LlzZ9ja2uLcuXM67dNUH/wxAdX64IVfU6MFFA1b+cLMw4vosWPH6KIeXoz79OlDVwMrCzC/eI9fJa8N3iYrKyt4enpqTBMSEqLzGLrQVj8SiQQAIJVK9fbKxTv7sLW1rVO5utppfXB3d9cYbmpqCgAq+/N5lG/AiIbFetraMfD85oVvx3y/rqioQPfu3dG1a1e1z6BBg+hWRv6GS5nablfMzMykbTE8PFxrOj6Ob2eGoDZ9DgB69OgBX19flJWV4ccff6The/fuRUFBATp27KjyG/gdKPzixeo4ODjAx8enXr+hJsLCwjBmzBgAwJo1a+Dn5wdvb29MmjQJP/74IwoLCw1W1ocffgiJRILr168jNjYWdnZ26N27N1asWIH09HSt+SwsLHDv3j389NNPWLlyJaZMmYJu3brB1taW3nBpu25q6jN8f7G3t4dAoC6nfJ/R1F84jmscX9CWlpbo27cvVq9ejaysLIwaNQoAkJqaWqNHLH60wV/8NMHfuWiDryRtKFcOIQSffvopvLy80L17d7zxxhtYtWoVdu/eDZFIhFdeeUXnseqClZVVjXHKd2WnT59Gz5494efnh9GjR+O9997D5s2bkZmZiUGDBqms2NREbeqDXxWoq/7t7OzUwjw9PREcHIzi4mKcPHkSAHDo0CEACgHu3bs3BAIBTpw4gcrKSjx69Ahnz56FjY1NjfsseZvEYnGtbNIXTbMRdYW3VR+HBbU5L/VFV90B0HgxqYnatGN+VFdeXo6TJ09q/fAuOzWN/pVvTvRBeRSiy9kPfz0xpGDU5dzysztbt26lYVu2bAGgOvoFntenrn6qacbM0GzduhWbNm1CVFQUXTm9adMmxMfHw9HREQkJCRp3TtSWkJAQpKenY9KkSbC3t0d5eTmOHj2KBQsWICQkBEFBQfjrr79U8ly/fh1DhgyBl5cXRowYgfnz52Pjxo24ePEiunXrpnOQB+juM3XpL2Kx2HAC/MYbb6Bt27bUGYc2LCws8PXXX9MGWdNdJv+jtQ3hAcN2lA8//BAzZ87E/fv3MXLkSCQmJuLs2bN49uwZrl69ivfee89gZfEUFRVpjeM7Fi8oV69eRa9evXD8+HG0a9cOq1evxqFDh5CdnY1Hjx5h165dBnVkwndaXfWvzbe38jR0WVkZ/vrrLzg5OaFDhw5o1aoVQkJCUFhYiHPnzmH//v2Qy+UYNGhQjW4aeZt0nXdtNjU2vJgbcvpYGW2iXFxc3CDl6aI27Zjv1+Hh4SCE1PjRtSVHX5RvEJSndavD3zTpuqFoDF599VV6k3r37l08evQIBw8ehImJidqWNv6GQlc/1fXIzFDtiOM4TJgwASkpKXj48CF27tyJN998E56enigrK8O6devwzjvv1OqY2njhhRewceNG5OXl4cyZM1i1ahX69+8PExMTXLp0Cf3798e9e/cAKGaiunfvjn379sHNzQ3Lli3D/v37kZmZiYKCAuzfvx/t27c3iF36UlpaajgBLi0txa1bt/R6bmJlZUXv1DRNdSnDT6lcvHhRaxpdUw61QSqVYvXq1QCA999/H0lJSRg/fjwiIiKovfo8t64tmqbXAMWzX15k+CngtWvXorS0FAEBATh79ixmz56NF198UWWKy5A2+vv7AwAuXLigMb6oqIju46sOL8AHDx5ESkoKysrK0KdPHxr/4osvAlBMQ+s7/axsU3FxMW7cuKExzeXLl2s8TmPAP/erzXNjfeDdcpaXl2uMr773sDHQ1o4JIbT98P2ZP4c3btzQOrVOCMHRo0dx8+ZNg4yafHx86M3d+fPntabjH+HwzxmNhaurK/r16wdCCH755Rfs27cPlZWVGDx4sNp1k69Xbf20vLxc4x52Q7ajwsJCnD9/ng6qWrdujREjRuCzzz5DZmYmpk6dCkB1RF8XCCHIysqiM2oCgQCRkZGYM2cODhw4gIyMDFhbW6OkpAQ///wzAODbb79FXl4eWrVqhfPnz2PhwoUYOHAg3dMNNMy1XRcymcxwAszfkZ07dw6JiYk60yYnJ+Px48do1aoVoqKidKYdNmwYAMWzD00XsatXr+LEiRN1M7oa+fn59C5e2zMi5befGOqZ3HfffafiaYdn/fr1AIDQ0FD6/IFf1BMYGKhxWvPQoUO4e/euweyLi4sDoOg0mkY4mzdv1mg7oKhDV1dX/P3339SpiCYBPnjwIJKTk2FqaqrmWEATXl5e9HnjF198oRYvl8vx7bffasyrPFVkyCldbfBCY+jO7eDgAEDhoEbTSyX4xYuNycGDBzVesH/55RdkZ2fDzMwM/fr1AwB0794dNjY2KCwsxObNmzUeb8eOHejduzcCAgLoSKY+iMVi9OrVCwDw6aefakyTmZlJFzTp0xYbGn4x4e7du6lDlurTz0DN/XTr1q0anaEYsh29//77iIiIwOzZs9XiBAIB7fvVrxd8n9S3Pz5+/Bht27ZFv3796M2SMn5+fnQRJV8Wf9309PSkv1mZK1euUIcahlxrURMGE+C+ffvSRjBp0iTMmDFDzRtJWVkZNm/ejPj4eAAKby66nlkAipWh4eHhePbsGWJjY1U6YkZGBmJjYw22GKp169Z0BfEnn3yi4hXl0aNHePPNN7Fjxw4aZqhV0OfPn8fEiRPpdI9cLscnn3xCBXjZsmU0Le8TOTk5WeUZR2VlJb7//nuMHDnSoPZNmjQJvr6+yM7ORlxcnEon/fnnnzFv3jyteTmOw3/+8x/qeQtQFeCuXbvCzMwMf/75J549e4bevXvrPe23YsUKAMC6devw6aef0jZQUlKCKVOm4OzZsxrzKbc3bSN3QxITEwNAMTLR5Q2qtnTu3BkmJiYghGDGjBl0yl0qlWLt2rX4+uuvDVaWvpSUlGDIkCEq9Xr48GEqIgkJCXQRplgsxvz582n45s2bVfrxnj176Ar7+Ph4gy0gWrx4MUQiEZKTkzFlyhSVxxjp6ekYNGgQysrKEBwcjHHjxhmkzPowZMgQODg44MSJE0hOToaTk5PGFeqvvfYa/Pz8aD99+PAhjdu3bx9mzpyp8fiGbEdjx44Fx3H47bffsGrVKpVXbt69e5c+nqxuP98n9e2P9vb29ObotddeU5l5kcvl+Pzzz5GRkQGO4zBgwAAAz6+b6enp2LVrF01PCMGBAwcwYMAAaq+hrut6oXFDVB0pLy8n48ePp/tJAYU3pMjISNKhQwfqpcTU1FRljyqPNk9YN2/epBueRSIRCQkJoV5H7OzsSNu2bQkAsnTpUppHlwclHj5eedM4v/cQUHiTCQkJIYGBgXTzfmhoKN1PtmfPHpqvPvuAhw8fTgAQKysrEhERQfdHchxHVq1apZLnzp07tHwApG3btiQ8PJzY2dkRVO2Xjo6OJoCqpyld+1+r27N582aV8L///ps6UzAzMyPh4eE0bWRkJHFzc1PbX8jz22+/0XJ9fHzU4pU9aVX3sESI7vP4f//3f7StOTk5kcjISOrYYOjQoVr3N/K2SyQSEhERQX7//XdCiPb2p48t2pBKpdTTzvHjx+t0TE3tlBBCFi5cSON4L1J8WVOnTqVOTfTxYMSjrQ0Qon1PKF9v//3vf4mZmRkRiUQkODiYeq/jz0f1feRyuZx6PgMUHsAiIyNVnOvExMSQoqIijTbWZt+oMlu2bCGmpqYEeO4Ji983Dig8YSk7/NH122uipjbF78Ot7txEmYSEBFr27Nmztaa7du0a9WhmampKwsLCiLe3N71u8ceo3k8N2Y74/ciAwsFKaGgo8ff3p9dPHx8fkpOTo5KH3/MsEolIaGioynVcG/fv36e/VSAQEB8fHxIeHq5ybVy5ciVN//TpU5X2yHvp4r1pmZiY0D27oaGhKmVpqzdCaj5//PkfP348DWuwfcCmpqZITEzEmTNnMHv2bISFhaG8vBx///03srOz4e/vjzlz5uDixYs6R07V8fX1RXp6OmbMmAEPDw9cvXoVeXl5GDNmDM6fP09X/Rri1VhTp07F4cOH0bdvX9ja2iIjIwN5eXmIiorCZ599htOnT9M7uH379tW7PACYPXs2fvjhB/j7++Py5csoLy/H4MGDcfz4ccyZM0clrYeHBy5evIipU6fCz88P9+7dw7Vr1+Ds7Iy3334bFy9epHeaR48eNchinODgYKSlpWH27Nlwd3dHRkYG5HI5Zs6ciT/++EOnc/o+ffrQO1zl0S8PPw3NcVyN+3+r8+677+Lo0aN0/2xGRgb8/f3x/fffY9asWVrz/fTTT4iOjoZMJsONGzdw69atWpVbG0QiEV31//vvvxv02MuWLcP27dvRtWtXSKVSXL9+HX5+fti2bRs+//xzg5alD126dMGpU6fQt29f3L59G/fv30fnzp3x7bff4qefflJbXMdxHL7++mscPHgQQ4cOhUgkwoULF1BYWIioqCisW7cOR44cqXHFdm0ZN24c0tPTMXnyZDg7OyMjIwP5+fmIiYnBhg0bcObMmRp3EjQmynvdNU0/8/j7++Pvv//G3LlzaT+trKzEO++8g+PHj2vNZ8h2tGDBAuzevRuDBg2CmZkZLl26hNzcXISGhuJ///sf0tPT1bYtrl69GnFxcRCLxbh27ZralktNuLi44OzZs5gzZw7atWuH3NxcXLx4Eebm5hg1ahROnjyJuXPn0vTW1tY4e/Ys5s2bh/bt2+PRo0f0WfGECRNw/vx5+tgqPT2dPsZraLgqhW/WREZG4ty5c9i+fTtGjx5tbHMYDBX++ecfBAQEoHXr1rh7965B3mvM+Pewb98+DBkyBJGRkThz5kydj8MvNjp06BC98WU0PllZWfQGr1H2AdeHb7/9Fv7+/khISNAYn5OTQ1dB63IEwGAYCx8fH4wZMwa5ubk1vimMwajOxo0bATz3jMdoOTR5AY6IiMCNGzfw2WefYceOHSor5bKysjBixAhIpVK6WpLBaIq89957MDMzo9vcGAxtyGQypKWlISsrC4sXL8a+ffvg6Oio8XWWjOZNkxfgoKAgJCQkQCaTYcyYMXB2dkZkZCT8/Pzg4+ODlJQUtGvXjnqIYTCaIj4+Pli2bBlOnjypsgqTwaiOQCBAly5d4O3tjSVLlgBQ7MqorecvRtOnyQswoNizd/jwYcTGxkIikSAjIwNPnjxBZGQk1qxZg7Nnz2p9oTSD0VSYNWsWunfvjnnz5qls0WAwlOE4Dr169YKlpSV8fX2xadMmtralhdIiFmExGAwGg9HcaBYjYAaDwWAwWhpMgBkMBoPBMAJMgBkMBoPBMAJMgBkMBoPBMAJMgBkMBoPBMAJMgBkMBoPBMAJMgBkMBoPBMAJMgBkMBoPBMAJMgBkMBoPBMAL/H/Fshr5jR/4oAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56772,7 +98572,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAGCCAYAAAA46BDEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC350lEQVR4nOzdd3zM9x/A8dddxmUnshcRMSJ27b0pRVC1VYzatbrM2lSKmrU3pVqlSlVp7dRWBDETIxKE7J27z++PNPdzMsRJJPg8H4882nzuc9/v+3sX977P5/sZCiGEQJIkSZKk10pZ0AFIkiRJ0rtIJmBJkiRJKgAyAUuSJElSAZAJWJIkSZIKgEzAkiRJklQAZAKWJEmSpAIgE7AkSZIkFQCZgCVJkiSpAMgEnEeqVauGu7s71apVK+hQJEmSpDeAYUEH8LYIDw8nNDS0oMOQJEmS3hCyBSxJkiRJBUAmYEmSJEkqADIBS5IkSVIBkAlYkiRJkgqATMCSJEmSVABkApYkSZKkAiATsCRJkiQVADkPuIClpqaiVqsLOgxJkt4xBgYGGBkZFXQY7zSZgAtITEwMERERJCcnF3QokiS9o1QqFfb29lhZWRV0KAVOI9RcfRpAVPJDbFROlLWtg1JhkK/nlAm4AMTExBAaGoqFhQX29vYYGRmhUCgKOixJkt4RQghSU1OJjo7WruD3LifhU+G7WHdlDE+THmjLbE1c8fP5hhrO7fLtvAohhMi3o79D3N3dCQ0Nxc3Njfv37+dY9/bt2xgZGeHu7i4TryRJBUYIwf3790lNTaVEiRIFHU6BOBW+i3nnegPPp8L0z+bR763PtyQsB2G9ZqmpqSQnJ2NtbS2TryRJBUqhUGBtbU1ycjKpqakFHc5rpxFq1l0ZQ+bki7Zs/ZWxaET+jNORCfg1yxhwJQc/SJJUGGR8Fr2Lg0GvPg3Q6XbOTPAkKZSrTwPy5fwyARcQ2fqVJKkweJc/i6KSH+ZpvZclE7AkSZL0TjI1sMxVPRuVU76cX46CliRJkt455x/tZ9nFoS+opcDOxJWytnXyJQaZgCVJkqR3RkJqNGsvf8nRBz8CYKAwQi2yGoCW3jXf22dWvs0Hll3Q0mvl5+eHQqHQ+VEqldjZ2dGkSRN27Njx2mK5cePGaztXQWrUqNE7fZ9PkjKcf7SfkYerapOvnYkbDd160KnUWGxNXHXq2pm45usUJJAt4LeWWiM48TCVR4lqHE0NqOVkhIGy8HwIjxs3jrJlywLpU7MeP37Mjz/+SMeOHVmzZg19+vTJ1/MPHz6cPXv2cOvWrXw9jyRJBS8hNZo1l7/g2INtABgpTShpU40azm2o7dIBG5UTHUt+LlfCkl7dnpAkJpyMJSxBoy1zMVMyvaYlHxQ3KcDI/q958+Y0atRIp2zYsGGUKVOGsWPHalvK+WXXrl35dmxJkgqP84/+ZOnFocSkPAbAzsSd9xxaUMftQ8oUqaVNskqFAeXs6r/W2F45AT958oSDBw9y+/ZtHj16RGRkJKampjg5OeHt7U29evVwcXHJi1ilXNgTkkT/g9GZppWHJ2jofzCaVY0pNEn4eWZmZtStW5cff/yRx48f4+joWNAhSZL0hopPjWJN4BccD/sJSG/1lrKpRg3nttRy6YCNquA/X/S6B/z06VO+/fZbqlatipOTE126dGHs2LF89913rF27lqVLlzJ58mS6du2Ku7s7VapU4bvvviM2Njav439rCCGIT321n5hkDeNPxuawpgtMOBlLTLJG73Pk98qlwcHB2NnZYWdnp1N+7do1unXrhqOjIyqVilKlSjFx4kQSExN16t2+fZsuXbrg4eGBSqWiaNGi9O3bl7t37wIQEhKCQqHgzp073LlzB4VCweTJk3OMKSkpiWnTpuHt7a1dvL5Tp04EBgbq1PPz88PCwoKQkBC6dOmCnZ0dpqam1KlThz///DPTcW/cuEHv3r1xdXXFzMwMHx8fZs+eTUpKik69vXv30rRpU6ysrDA1NaVy5cosXLgQjUajU0+tVvPNN99QunRpTExMKF++PFu2bMnymjQaDYsWLaJy5cqYmppiY2NDq1atOH78uE69yZMno1Ao2Lt3L2XLlkWlUtGwYcMcXy9JKmjnHu5j1OFq2uRrZ+JOI7fufFR6HC08+heK5Asv2QKOiYlh1qxZLF68mISEBO2HsYuLC2XKlMHGxgZzc3MiIyOJiIjg/v37PHjwgAsXLvD5558zffp0Ro0axfDhw9/phb+zkpAGXpse5es5BBCWoKH0D4/1Psatno6Y58EiXtHR0URERADpiSMiIoLVq1dz6tQpli9fjoHB/++9nDp1imbNmmFlZcXQoUNxdHTkn3/+YcaMGRw4cICDBw9iYmJCVFQUjRs3RqPRMHjwYJycnAgMDOT777/n4MGDXLlyBQcHBzZu3MioUaMA+O6776hYsWK2caakpNCiRQv++ecfPv74Y0aNGkVoaCjLli2jZs2a7N+/nzp1/j9FITU1lXr16lG5cmWmTZvGkydPmDt3Lq1bt+bq1auUKlUKgAsXLlC/fn1trCVLluTvv/9mzJgxXLx4kc2bNwMwd+5cPv/8c0qWLMlXX32FhYUFO3bsYMSIERw8eJDt27ejVKZ/j+7Zsydbt26ldevWjBgxguvXr+Pn54exsXGm6+rRowdbt26lU6dODBgwgKdPn7J27VoaNmyoLX9W586d6devH6VLl0alUunzlktSvotPjWJ14OcEhP0MgLHShJI21anp3I5aLu2xVjkUcITPEbm0detW4ezsLBQKhbCzsxODBw8Wv/76q3jy5EmOzwsJCRGbNm0S3bt3F6ampkKhUAhHR0exY8eO3J76jeDm5iYA4ebmlmO9xMREceXKFZGYmKhTHpeiEU5rwgv9T1yK5pVep969ewvSvwtk+dOhQweRkJCgra/RaES5cuVE0aJFM/2trVq1SgBi9uzZQgghfvrpJwGIH3/8UafevHnzRIUKFcSZM2e0ZR4eHsLDw+OF8fr7+wtAbNu2Tac8LCxM2NvbCx8fn0zXNnjwYJ26GzduFIAYN26ctqxBgwbC2NhYXLhwQadut27dBCACAwPFrVu3hKGhofDx8RFxcXE6r0nPnj0FINatWyeEEOLQoUMCEN27d9c53p49e7SvbYZt27YJQPj7++vUjY2NFd7e3sLe3l7Ex8cLIYSYNGmSAMRHH330wtdKejNl95n0pjkTvlf0319CdNljLbrssRZD/yovVl/6TFx9EiDUGnVBh5elXCXgHj16CIVCIby9vcXGjRtFcnKyXieLjIwU3333nfD09BRKpVL07t1br+MURq+agDUajYhLebWfA3eTcpVED9xN0vscGk3eJOA5c+aI/fv3i/3794t9+/aJH3/8UQwbNkwYGRmJ8uXLi8ePHwshhLhw4YIAxKBBg8Tjx491fsLDw4WJiYmoWbOmEEKIEydOCEBUrFhR7Nq1SydpPS+3CbhKlSrCxsYm07kfP34sevXqJQBx9epVnWs7fvy4zjGCgoIEIPr27SuEEOLx48dCoVAIX1/fTOcLDw8Xly5dEklJSWLOnDkCEBs3bsxU7969ewIQrVq1EkIIMXz4cAGIs2fPZqpbsWJFnQTcoUMHAYhbt25luqbx48cLQOzdu1cI8f8EnFUM0tvhTU/AsSmRYsG5vtrE22uvk5h2op3YF7JKRCc9LujwcpSrLuh9+/bx/fffM2DAAG13lz5sbGwYOXIkQ4cOZeHChcycOVPvY71tFArFK3ftNnIzxsVMSXiCJsv7wArAxVxJIzfjAp+SVLVq1UyjoDt37oy3tzfDhg1j+vTpzJ8/n2vXrgGwbNkyli1bluWxQkJCAKhZsyYTJkxg1qxZtGvXDiMjI2rVqkWrVq2091pf1rVr10hISMDBIfuuq5CQELy9vbW/Ozs76zye0WWbsdj9nTt3EEJop2E9y8nJCSen9GXvMqZIlStXLlM9d3d3rK2tCQ4O1qlbsmTJTHXLlSvHxYsXda4JwMvLK8dretbz1yRJhcHZh3+w7NJQYlOeAGBvUpSqjq2o4/YhpWyqo1QU7qUucpWAb9y4gY2NTZ6d1MjIiM8++4y+ffvm2TElMFAqmF7Tkv4Ho1Ggu8FWRrqdVsOywJNvTnr27MmwYcM4dOgQ8P+kNXToUNq3b5/lc57dWWratGkMHTqU3bt3s3//fg4dOsTRo0e194tr1ar1UvGo1WpKlizJ0qVLs61TqVIlnd9f9CU1Y9u3F02zEv+NsciunkajyXQ/9vmBWVmVqdVqLC0t+eWXX7I9d5kyZXR+NzSUMxalwiMuNYrVgaP5Jyz9b9hYaULpIjWp6exLTRdfrIztXnCEwiFX/6ryMvk+q0iRIvly3HfZB8VNWNWYzPOAzZVMq1F45gFnJyNZZAzC8vT01D7WrFmzTHW3b9+u3Ug8PDycwMBAGjRoQP/+/enfvz9CCDZv3kyvXr2YM2cOP//880vF4+npSXh4OI0aNcqUhAICAoiPj8fMzOyljwn/b4k+6+LFi8yaNYuBAwdqW6iBgYFUrlxZp97du3eJjY2lWLFiANrBXVevXqV27do6dZ9f8cvT05Nr165RuXJl7O3tdR77999/CQsLw9zc/KWuSZJelzPhv7P80qfEpv7X6jUtRlWH96nr1omSNtUKfav3WfkW6aVLl/j3339JS0vLr1NI2figuAlnPrJn+/tFWNrQiu3vF+F0J/tCn3wB1q9fD6Qv1AFQrVo1ihcvzrp16zKtWrVixQo6d+7MmjVrAFizZg3NmzfXWc5SoVBQt25dQLcVZ2BgkGVr8XmdOnUiKiqKOXPm6JSHhobStm1bunfv/tK3ZZycnKhVqxZ79+7l+vXrOo8tWrSIrVu3Ym1tTYcOHTAwMGDmzJnEx8dr6wghmDhxojY+SO++B5gxY4bOdf3999+cO3cu0zUBTJgwQac8JiaGzp074+vrS1JS0ktdkyTlt7iUSBac78ucc92JTX2CsdKUCnaNaF9iFJ1Kj6F0kRpvVPKFV1yIIz4+niVLlmBnZ0e/fv2A/38wXbhwAYBixYqxfv16GjRo8OrRSrlmoFRQ1yXz9JPCYv/+/dy/f1/7e1JSEn///Tfbtm2jWLFifPHFF0B6oly5ciVt2rShatWqDBo0CC8vL06fPs2aNWvw8vLSJqP+/fuzbNky+vXrx4kTJyhfvjwRERGsWLECY2Njhg8frj2fk5MTp06dYt68edSpUyfbrumvvvqK3377jbFjx3L69GmaNm1KZGQky5YtIyoqis2bN2NqavrS17948WIaNWpEjRo1GDp0KMWKFdNe/+DBg6lSpQoA06dPZ+zYsVSuXJk+ffpgYWHBr7/+yt9//02bNm3o2bMnALVr12bo0KEsWbKEJk2a0KlTJ+7du8fixYtxcHDg8eP/Tz3z8/Pjp59+Yvny5dy8eZN27dqRmprKqlWruHHjBv7+/nrdL5ek/HI6fA/LL31KXOpTABxMi1HVsRV13T6ipHXVN3etc31Hb8XGxory5csLpVIpOnbsqC1v27atUCgUOj8WFhbi3r17rzxirDB71VHQ74rspiGZmZmJsmXLipEjR4qwsLBMzzt//rzo1KmTcHBwEMbGxqJEiRLi008/zVT39u3bom/fvqJ48eJCpVIJOzs74evrK06ePKlT78CBA8LLy0sYGRlpRydnJzY2VowbN06ULl1aGBsbC0dHR9GyZUvx999/Z3ltwcHBOuXBwcECyDTq/8qVK+Kjjz4SdnZ2wtTUVFSsWFF8//33Ii0tTafezp07RYMGDYSFhYUwNzcXVatWFYsXLxZqdeapFd9//70oV66cUKlUwsvLSyxfvlz06NFDPP9PPSUlRfj7+4sKFSoIExMTYWtrK+rVqye2b9+uUy9jFPTBgwdzfI2kN1dh/kyKTX4q5p3t/cwIZ2cx/UR7ceDOOhGb/LSgw3tlCiH0W9po9uzZjB07FgcHB2bOnEm/fv0IDQ3V3pPasmULzZo1Y/To0WzYsIERI0bw3Xff5cFXhsLJ3d2d0NBQ3NzcdFp2z0tKSiI4OBhPT09MTAp/l7AkSW+3wvqZlN7qHUZcaiQADqYeVHNsTV23TnhZv/fmtnqfoXcX9M6dO1Eqlezbt087QGTPnj0IIahWrZr2ntSSJUvYvn07+/bty5OAJUmSpLdXbMpTVgWO4mT4rwAYK03xLlKLWq4dqOHcDgsjm4INMA/pnYCvXbtGyZIldUZn/vnnnygUClq2bKktMzc3x8vLS277JkmSJOXoVPhuVlz6VNvqdTT1oKpja+q5daaEdeW3otX7LL0TcFJSks56zkIIDh48CJBpsXa1Wi1HQ0uSJElZSm/1juRkePo2ocZKU7xt61DbpQM1nNtg/ha1ep+ldwIuVqyYdkUfhULByZMntVsR1q///z0VIyMjuXXrFm5ubnkSsCRJkvT2OBX2G8sDhxOvbfUWp5rTB9Rz+whPq0pvXav3WXpPmqpcuTIRERF89913xMTEMH36dBQKBc2bN9euzpOamsqQIUNITk6mXr16eRa0JEmS9GaLSXnCvLO9mHe+F/GpkagMzKhk34yOJb+gU6kv38ou5+fpPQr65MmT1K9fX7tUYIaDBw/SoEEDTp06xQcffMDTp08xNjbm5MmTOW779qaTo6AlSXoTFcRn0smwXawIHPFfq1eBo6kH1Zw/oL5rZ4pbVXzrE28GvVvANWvWZOvWrTg5OSGEwMbGhuXLl2sX3LCysuLJkyfY2dnxxx9/vNXJV5IkSXqxmOQI5p7tyXfnP9Zp9XYq9RWdSn6Fp/Xb3eX8vFdaCatjx4506NCBx48fY29vr7Mkn5eXFzt37qRVq1Y6i+VLkiRJ754TD3ayMnAk8WlRgAIn0+JUd/6Aem6d8bCs8E4l3gx6J+AjR45gbW1NpUqVcHR0zPS4kZER7dq1A9KnJ928eZMhQ4boH6kkSZL0xolJjmBF4AjOPNwDgMrAjLJF6lLH9UOqObXGzMjqBUd4e+mdgBs1akT9+vU5fPjwC+tOmDCBGzduyAQsSZL0DvnnwQ5WBo4kIS0aUOBkVpzqTm2o79aZYpbl38lW77NylYBjY2N1FnPPkJiYyO3bt7N9nhCCO3fuEBQUlKudZyRJkqQ3X3TyY1YGjtRp9foUqUddt05UdWqFqaFlAUdYOOQqAcfExFChQgWdLcoUCgVnz57V7kP6Is/vUSpJkiS9fQIe/MKqwFHPtHo9qencjnquH1HU0uedb/U+K1cJ2M3Njc8++4zp06dryxQKBbmdweTh4cHChQv1i/Ad8+35uIIOIVtfVLHIk+M8efKEyZMns2vXLh49ekTp0qUZMWIEffv2zdXzExMTmT59Oj/88ANhYWF4eHjQo0cPvvjii0xbA27atIlevXpleZzevXuzbt067e+LFi1i1qxZJCYm0rJlSxYvXpxpw/rvv/+eSZMmcevWLZ2V4AqzwMBABgwYwPnz5zE1NWX37t3UrVuXhg0bcujQoRyfe+jQIRo3bsykSZOYPHnya4k3P4WEhODp6ZnpvX8Z0dHRpKSk4ODgkLfB5bHJkyczZcoUDh48SKNGjfL9fNHJj1l5aQRnHv0OgMrAHB/butRz60xVx/cxMcybz4+3Sa7vAU+cOFG7568QghIlSlC9enW2bduW7XOUSiUWFhYUKVLk1SP9z8mTJ6lTpw5//fVXpj+qu3fvMmHCBA4cOEB0dDQVKlRg3Lhx2sFgz7p8+TLjx48nICCAxMREatSowdSpU7Wbt0v5Iz4+nhYtWnDp0iWGDh2Kt7c3P/30E/369SM8PJxx48bl+PzU1FRatmzJ0aNHady4MaNHjyYkJISZM2eyb98+/vrrL525jBcvXgRg5cqVmeY4enl5af//+PHjDB8+nO7du1OjRg1mzpyJn58fu3fv1taJi4tj6tSpjB8//o1JvpC+/++5c+eYMGECRYsWpVy5cmzcuBEnJ6eCDu21c3BwYOPGjTrv/cv4888/6dmzJ9u2bXstSe1NIITgn7CMVm8MoMDZzJOaLr7Uc/0Id4uystWbjVwnYCMjIzw8PLS/N2jQgEqVKumU5bcbN27QoUOHLO8nh4eH06BBA54+fcrw4cNxc3Nj9erV+Pr6snnzZrp3766te/XqVerVq4epqSnDhw/H0tKSJUuW0LhxY/bv359pLWsp7yxevJhz586xZcsWunbtCsCAAQNo3bo1U6ZMoVevXhQtWjTb569YsYKjR4/SrVs3Nm/erP2H3bRpUz744AP8/f35+uuvtfUvXLiAo6Mj/fv3zzGu9evX4+zszIYNGzAwMMDQ0JBhw4bx8OFDbaKaM2cOpqamb9xgwosXL1KpUiWmTp2qLevZs2cBRlRwzM3NX+naAwICshwP866KSn7EykvDOfvoDyC91VvOth713DrznuP7mBiaF3CEhZveo6Bf1HWV13bs2EG/fv2IjIzM8vEpU6Zw9+5djh07Rp06dQDo06cPtWrVYsSIEfj6+mJunv7HMHr0aJKTkzl79iwlSpQA0j+QKlasyJAhQwgMDJTf2PLJ+vXrcXNz0yZfSL+d8eWXX/LHH3/www8/8NVXX2X7/F9++QUAf39/nfeodevWVK5cmeXLl+sk4IsXL1K+fPkXxnXv3j08PT0xMDAA0I5tuHPnDk5OTjx8+JC5c+eybNkyjI2NX+6iC1hqairW1tYFHYb0FhFCEPBgO6svj36m1VuCWi7tqevaiaKWZQs6xDdCrlbC0mg0mVqdGWUv86OvDz74gI4dO+Li4kK3bt0yPa5Wq9m0aRO1a9fWJl8AExMTRowYQUREhLYr8eHDh/zxxx906NBBm3wB7Ozs6N+/P1euXOHUqVN6xyplLzo6mqCgIGrWrJnpsYyykydP5niMe/fuYWtri7u7e6bHSpUqxYMHD3jw4AEAjx49Ijw8XJuAU1JSSE5OzvK4Tk5OOl/uIiIiALRz3KdMmULJkiV1elJexpkzZ/jwww9xdHTEwsKCypUrs2LFikzjKDZv3kzt2rUxNzfH3Nyc2rVrs2nTJp06hw4dQqFQsHXrVmbOnEnJkiVRqVR4enry9ddfa3cemzx5svZLyuHDh1EoFPj5+QHpX3qe70L9999/adu2LUWKFMHGxoaPP/6YR48eZXk9165do1u3bjg6OqJSqShVqhQTJ04kMTFRp55CoWDYsGFs376datWqYWpqioODA35+foSHh2c67rZt22jQoAHW1tbY2dnRtGlT/v77b506Go2GRYsWUblyZUxNTbGxsaFVq1YcP3485zeB9HvAz74OkD6lsnz58ly8eJHWrVtjbW2NhYUFLVq04PTp0zr1pkyZAkDjxo0pXry49rGkpCSmTZuGt7c3KpUKe3t7OnXqRGBgoM75/fz8sLCw4Pfff8fDwwNTU1N69OiBo6MjZcqUyTLmChUq4OTkRGpqKpD+GTZq1CjKlCmDqakppqam+Pj4MG3atNey61xU8kO+PduNRRf6k5AWg4mBOVUd3qdL6Qm09xotk+9LyFUCNjQ0xNjYmOvXr2vLjIyMXurnVVoNQUFBzJw5k3PnzlG6dOlMj1++fJm4uDhq1aqV6bHnP9gz/pubulLeCg0NRQhBsWLFMj1mZmZGkSJFCA4OzvEYFhYWxMfHZ1qDHP6fNDMS8IULF4D0sQHVqlXDzMwMU1NTatSokelDvXnz5gQFBbFu3TquX7/OokWL8PHxoVixYly/fp2VK1fyzTff6NUzsm/fPurWrcuRI0cYNGgQc+bMwdHRkYEDBzJhwgRtvU8//ZSePXuSkpLC5MmTmTx5MsnJyfTq1YsRI0ZkOu64ceNYsWIF/fr1Y8GCBTg6OjJt2jSmTZsGpK9Ut3HjRgC8vb3ZuHEjAwcOzDLGc+fOUa9ePQICAhgxYgSTJ0/mypUrWXbdnzp1iurVq3P06FGGDh3K/PnzqV27NjNmzKBJkyY6syUA9uzZg5+fH40aNWLhwoU0a9aM9evX06VLF516kyZNokuXLsTExDB+/Hi+/vpr7t+/T4sWLdi7d6+2Xo8ePRg+fDilSpVi7ty5fP7551y/fp2GDRvy888/5/Jd0ZVxC6tIkSL4+/szbNgwDh8+TLNmzYiOjgZg/PjxdOjQQfvaz58/H0j/YteiRQvtGJKFCxcyZMgQjhw5Qs2aNQkICNA5V1JSEl27dqVPnz7MmjWLTp060bNnT65fv57py//58+cJDAykV69eGBkZER0dTc2aNVmzZg0dOnRg8eLF2i9aX3/9Nf7+/npdf24IITgWuo1Rh6tz7tEfpLd6vXi/+CC6eU+itmsH2eX8skQuKBQKoVQqxbVr13TKXuZHqVTm5lRZSkpK0v7/pEmTBCAOHjyoLduzZ48AxPz58zM9Nzo6WgCiffv2QgghlixZIgCxc+fOTHUvXLggADFy5MiXjtHNzU0Aws3NLcd6iYmJ4sqVKyIxMTHLx/3PxRban1cVEBAgADFhwoRsX8NSpUrleIxhw4YJQGzbtk2n/O7du8LU1FQA4siRI0IIIebMmSMAYW9vL7755huxa9cuMWfOHOHk5CQMDAzErl27tM9Xq9WiV69eAhCAcHd3F6dPnxZCCNGxY0fRtGlTva5Zo9GI4sWLCzs7O3H//n2d89WrV0+oVCrx5MkTceTIEQGIpk2bipSUFG29lJQU0bhxYwGIQ4cOCSGEOHjwoACEi4uLiIyM1NaNi4sT1tbWwtXVVScGQDRs2DDHskaNGglDQ0Nx+fJlbVlSUpKoV6+eAMSkSZO011OuXDlRtGhR8eTJE51jrlq1SgBi9uzZOucBxIkTJ3TqNm3aVADi+vXrQgghbty4IQwMDETDhg1FcnKytt7Dhw+FtbW1qFatmhBCiG3btglA+Pv76xwvNjZWeHt7C3t7exEfHy+yExwcLADRu3dvbVnDhg0zxS2EENOmTROAWLFihbYsq88ff3//LP8mw8LChL29vfDx8dGW9e7dWwDiiy++0KkbGBgoADFs2DCd8pEjRwpA+74sWLBAAGL79u069SIjI4WxsbGoUKFCjrFm5UWfSUIIEZkULr451Vl02WMtuuyxFr3/cBX+p7uKfx7sEElp2b/eUs5ydQ/44MGDADotl4yy1yFje8PsZHxDtbDIPMzdzMwMSB99+7J1szJv3jzmzZuXqTwsLCzHGCW03a0im+lrQgjtPdjsfPbZZ2zYsIFPPvmE6OhomjZtyu3btxk1ahRmZmYkJiZq1x6vUaMG48ePx8/Pj5IlS2qP0alTJ8qXL8+QIUP44IMPUCqVKJVKNmzYwLRp04iIiKB8+fKoVCpOnDjBjh07tF2R69atY86cOTx58oRmzZrx3XffZZqq9Kzz588TEhLCiBEjdPbEViqVbNq0iaSkJKysrLSzCSZPnqyzdrqRkRFTp06lfv36/PjjjzoDBNu0aYONjY32d3Nzc8qWLavTbZobT5484ciRI3zwwQf4+Phoy1UqFaNHj+bYsWPaskuXLnH58mUGDRqERqPR9jpkxGNiYsIvv/zCl19+qS0vVapUptsO1atX56+//iI8PJxSpUqxa9cu1Go1I0eO1Oktc3R05NixY9pR51u2bAHgww8/1Dl3RtmMGTM4cuQI77///ku9BpB5YFr16tUBsuwqf9aWLVuwsbGhcePGOjEZGhrSqlUrNm7cSFBQEN7e3trHnp+ZUa5cOapXr87WrVuZN28eRkZGpKWlsWXLFmrWrKl9X4YPH07Xrl0z/c1FRERgbW1NXFzeTmMUQnD8wU+svvw5iWkxKP5r9dZ26UBdt49ws8jcIynlXq4ScFajggvTSOGcPtgzyjI+2F+mblZiYmIIDQ19tYDfUZaW6avfJCQkZPl4QkJCjiOgAYoXL87+/fvp1asXn3zyCQDGxsYMGTIEGxsbJk+ejK2tLQD169enfv36mY7h4eFBhw4d2LhxI1euXNEZpOXh4aEzsv/LL7+kc+fOVK1alcOHD9OnTx/8/f2pU6cOgwYNokePHuzbty/beDO61MuWzXxf7Nnz3Lp1C0j/IH5eRnzPd887OztnqqtSqbLsns9JcHAwGo1G50tKhufjuXbtGgDLli1j2bJlWR4vJCQkV3EC2lhzep2efX8yzp/TNKLnz59bz8f5fIzZuXbtGgkJCTnOCw4JCdFJwFm9Jn379mXw4MH88ccftG3bln379vHw4UOd0euQ/vk0Z84cTp06RXBwMDdv3iQmJgb4fyMiL0QmhbPi0nDOP/4TABMDC8rbNaC+W1eqODbH2MD0BUeQXuSVdkN6XkxMDAkJCdjY2LzWvW5z+mDPKMsYBfoydbNiZWWl05LJEBYWJpfbfAFPT08UCkWW+yXHx8cTFRX1wgQM6S3boKAgAgMDiYmJwcfHhyJFitC7d28MDQ1zNTUuY2pRbGxstnV27drFiRMnuHr1KgAbNmygRIkSfPHFFwB89dVX9OrVi7CwMFxcXLI8RsbAmRfdO86uVwD+nwCe7wl6dvexvJBVDM//TWfEMnToUNq3b5/lcZ7f/Sw3ceb2dVKr1VhaWmpHw2clu8FML6Lv66lWqylZsiRLly7Ntk6lSpV0fjc0zPzR261bN0aPHs3GjRtp27YtGzZswMzMTGfGQGBgIA0bNiQpKYnGjRvTvHlzRo0apV1YJS8IITga+iNrr3xBYlpseqvXvCS1XTpQz/UjXC1yt/qh9GKvnICvXbvG3Llz2bNnj05XjZeXF76+vowePTrbD6e84unpCZDlB3tGWcYH+8vUzcro0aMZPXp0pnJ3d3fZMn4BCwsLypYtm+Uo84yBb8+OYs/KuXPnOHXqFL169aJChQracrVazf79+6ldu7Y2UbVv357AwEAuXbqUaYWsK1euANm3pNRqNWPHjmXgwIHaOg8ePNBZvCKjxXPv3r1s/8Yz/t4yWm7P2r9/Pxs2bODLL7/UnuPy5cvUq1dPp97ly5cBshy8lhdKlCiBUqnUvibPunHjhs7vGdcD0KxZM53HNBoN27dv15ldkFvPvk7PD7T87rvvuHLlCgsXLsTT05Nr165RuXLlTN2w//77L2FhYdrphq+Lp6cn4eHhNGrUKFNiDQgIID4+PlctU2trazp27Mgvv/xCeHg4v/32G506ddJZ9GXkyJFERUURGBio01uQmppKREREljvTvYzIpHCWX/qUfx/vB/5r9do3pIFbNyo7NJWt3jz2Sl+h169fT+XKlVm9ejVhYWEIIbQ/N2/eZN68eVSsWDFXOya9Cm9vb6ytrbMcvfz8B3v16tVRKpW5qivlvZ49e3Lnzh22bt2qLRNC8O2336JSqXS+7WflwoULDB48mB9//FGnfNasWYSFhfHZZ59py1xcXLh16xYrVqzQqXvo0CH27t1L69ats/3AWrt2LXfv3mXixInaMnd3d+7cuaNtFWZ0G2c1JSpD1apVcXd3Z/PmzToLOGRc85YtW3BxcaFTp05A+j3gZ6eSpKWlaae+ZNTJa7a2tjRv3pz9+/dz4sQJbblarc403qFatWoUL16cdevWaa8/w4oVK+jcuTNr1qx56Rh8fX1RKBQsXrxY5/qfPn3K7NmzOXXqFKamptrX4NnR45De+9a5c2d8fX0zjcLOSxm3p57tGejUqRNRUVHMmTNHp25oaCht27ale/fuuW5d9+3bl8TERIYMGUJiYmKm5VkjIiIwNzfP9CVn4cKFJCYmvtI0pNPhuxl1uBr/Pt6PAgWu5qX4wHMoPbynUsO5jUy++UDvFvDZs2fp168fGo2GevXq8cknn1CxYkUsLS2Jjo7m3LlzLF26lHPnzuHr68uFCxfybdUsQ0NDunTpwsqVKwkICNAm0KSkJBYsWICTkxOtWrUC0rsemzVrxs8//8ykSZO0f8hPnjxh1apVVKpUiSpVquRLnFL6N/hNmzbRu3dvzp49S+nSpdm2bRsHDhzg22+/1WlJ3r59m4CAALy8vLSbeXTu3Jk5c+YwfPhwbty4gZeXFwcPHuSHH37Az88PX19f7fMnT57M77//zmeffcbFixepUaMGV65cYdmyZbi6urJkyZIsY0xISGDSpEl8/vnnOgm6R48erFq1il69elGzZk1mzpxJ8+bNcXV1zfZ6DQ0NWbZsGe3bt6dy5coMGjQIe3t7duzYwf79+5k9ezb29vY0atSIgQMHsnz5cmrWrKmd775161bOnj3LkCFDaNCgwSu99jlZuHAhtWvXplmzZnz66ae4urqybds2namHkJ6AVq5cSZs2bahatSqDBg3Cy8uL06dPs2bNGry8vHS+tOSWt7c3Y8aMYdasWdStW5euXbui0WhYuXIlkZGR2ulFfn5+/PTTTyxfvpybN2/Srl07UlNTWbVqFTdu3MDf3z/H9+NVZfSALF26lAcPHtCzZ0+++uorfvvtN8aOHcvp06dp2rQpkZGRLFu2jKioKDZv3pypByY7jRs3xtPTkx07dlCiRIlM3cq+vr5MnTqVFi1aaF+jvXv3smfPHkxNTbWDTF9GmiaF6OTHbLk7hSR1HCYGFlSwb0QDt25UcmiKscHru534ztF3+HTnzp2FQqHINGz+WWq1WnTv3l0oFAoxYMAAfU+lI7uh9WFhYcLZ2VlYWVmJyZMni6VLl4pq1aoJhUIhfvzxR526ly5dEhYWFsLFxUX4+/uLBQsWiNKlSwuVSiWOHj2qV1xyGlLuPXr0SPTv3184OjoKU1NTUblyZbFhw4ZM9dauXZtpyogQQjx48ED069dPuLu7CzMzM1GpUiXx/fffC7VanekY4eHhYuDAgcLNzU0YGhoKV1dX8cknn4jQ0NBs45sxY4ZwcnISsbGZr3nlypWiRIkSwtraWnTu3Fk8fPgwV9f8zz//iNatWwtra2thYWEhqlevLrZs2ZKp3urVq0X16tWFqampsLS0FPXq1RObN2/WqZMxDSljatCzMqbUPItcTEMSIn0qUOfOnYWtra0wNzcX7du3z/Zc58+fF506dRIODg7C2NhYlChRQnz66aciLCzshecRIvt/xxs2bNBev729vWjVqpV2OliGlJQU4e/vLypUqCBMTEyEra2tqFevXqapOVnJaRrS87K69ujoaPH+++8LExMTYWNjI+Li4oQQ6dOgxo0bJ0qXLi2MjY2Fo6OjaNmypfj77791jpkxDSk4ODjbGKdOnSoAMW3atEyPpaamiqlTpwovLy+hUqmEi4uLaNSokdixY4cYN26cALSfYS+ahqTRaERMcoS48ei8OHz2DzHkjwpi1KHq4qdr34iwuFvZxiflHYUQudzS6DnOzs6kpKQQHh6e4yIbcXFxuLi4YGNjw7179/Q5lY6cdvgIDg5mzJgx7N+/n9TUVCpUqMCECRNo3bp1puP8+++/jBs3jmPHjqFUKqlWrRrTp0/PcoGO3Mi4B+zm5pbl/eUMSUlJBAcH4+np+VoHqkmSJGVI06TwOPEuCWmxqFMED+89IVDsorpbKyo5NMXIIOepn1Le0DsBm5qaUrFixVytGlWjRg0uXbqUaZm6t4lMwJIkFXZCCOJSnxKReB8NGhSAIs2Yp6GxOLrbU9Q281Q0Kf/ofQ/Yy8uL4OBg1Gp1jvNmhRDcv38/V9NLJEmSpPzxbKsXQIkBpoaWGCstSDRU4GCW/WBCKX/oPQp64MCBREREaNedzc7SpUsJDw/XWfxckiRJej2EEMSmPOFe7FUS0mJRAMZKE2xUjtiZuGJmZCl3fysguWoB3759O1NZq1at2L59O9OmTeP27dsMHTqU9957T7uEWlBQEGvWrGHhwoW0a9cuxy3mJEmSpLyXVavXzNASC2NbzAwtUSiUqFPzb9qWlLNc3QN+0fq8zzIyMtKuapPB2NgYpVKZ4xrLbzp5D1iSpMIivdX7lCdJ/7/Xa6Q0wcKoCBZGRXQGWcnPpIKTqxbwy4zTSklJyVSW3R6skiRJUt5K1aQQkXCXBLVuq9fS2A5TQwsUirxdwlTSX64S8Iv2aJUkSZIKVsa93idJoZlbvcZFMFLKqUWFTa4ScH6tYCVJkiS9ulRNCo8T7pIoW71vlFwl4Ojo6Bx3CNJXVFSUzn6mkiRJUu5l3+q1/a/Vm/0iSVLBy9XXotKlS7N8+fI8224vLS2NefPmZbn/qCRJkvRiqZoUwuJv8TjpHho0GGCAuaENdiZu2KgcZPJ9A+QqATdp0oTBgwdTvnx5fvjhhywHWuVGZGQkCxcuxNvbm88//1y7QYIkSZKUO0IIYpIjuB97lUT1s/N6nbA1dcPMyEp2Ob8hcvUubdmyhc2bNxMZGUmvXr1wdXVlyJAh7N69m6ioqByfGxISwqZNm+jRowdubm6MGjWKmJgYfv75ZzZu3JgX1yBJkvROSNUkExZ/87lWbxHsTNywlq3eN85LrQUdExPDzJkzWbx4MQkJCdrVU1xcXChZsiQ2NjaYmZkRFRVFREQE9+/f5+HDh0D6tzYbGxtGjBjByJEj8+WeckGS84AlScovWd3rNVaaakc4G75C4pWfSQXnpfoprKys+Oabb7hz5w4zZ86kUqVKADx48IAjR46wa9cutm7dyh9//MGZM2cIDw9HCEGFChX49ttvCQkJYdKkSW9d8pX0s3//fj766CPc3d1RqVS4u7vTokULtmzZ8kobi2eIjo7m8ePH2t8nT56MQqHg0KFDr3xsfSgUikw7eOmroK8lK35+figUCkJCQvR6/vPXFBISgkKh0HsZ27CwsFwt/pPVeRo1apRvyzPevHlT5/cXnSu7Vq+tiRvWKsdXSr5SwdJrMwY7OzvGjBnDmDFjePLkCYcOHSI4OJhHjx4RGRmJiYkJzs7OlClThrp16+pssi5JycnJ9O/fn02bNlGqVCn69OlD8eLFefToEb/++ivdu3dnyZIl/PTTT3r/7fz555/07NmTbdu2aZNex44dKVmyJGXLls3DqykYb9O1ZMfBwYGNGzfi5eX10s/duHEjQ4YM4dKlS5ibm+fbeV6Wv78/EydO1FmcaPz48fTv3z9T3fxs9UqFg967IWWws7Pjww8/zItYpDykEWquPg0gKvkhNionytrWQanI/ZKi+WnIkCFs2rSJzz77jNmzZ+ssdTp27FhWr17NoEGDaNOmDSdOnMDIyOilzxEQEKDT+gWoWLEiFStWfOX4C4O36VqyY25uTs+ePfV67l9//UVcXFy+n+dl/f7775kGsTZv3jxTvVR1Mo8T75KoTr8GAwwwNbLCysgOE0MLuXnCW+KVE7BU+JwK38W6K2N4mvRAW2Zr4oqfzzfUcG5XgJHBiRMnWLNmDW3atGHOnDlZ1unXrx83btxg9uzZLFy4kM8+++w1RylJBUMIQUxKBE+THui2eo1tsTAqgqHy5b+MSoWXHKv+ljkVvot553rrJF+Ap0lhzDvXm1PhuwoosnQrV64EeGFS/fLLLzE0NGTt2rXaMj8/PwwNDbl16xatW7fGwsICBwcHunfvrnPfsVGjRkyZMgWAxo0bU7x4cSD7e4zffvstixYtokyZMpiYmFCqVCmWL18OwLJly/D29sbMzAwfH58sR+7v2LGDFi1aYG9vj5GREfb29rRt25azZ8/q9Rr99ddfNG3aFEdHR0xMTChbtiwTJ04kMTFRWyere8BCCBYtWkS5cuUwNTWlRIkS+Pv7M23aNJ17s+vWrUOhUHD48GG++OILihYtikqlwtvbmwULFmSK59atW3zyySd4enqiUqkwNzenSpUqLFmyRK/rA/jll1+oWbMm5ubmuLu7M2nSpEz3/bO6N5uQkMDo0aPx9vbG1NQUOzs72rRpw/Hjx7V1ihcvzvr16wHw9PTU3oLw8/PDwsKC33//HQ8PD0xNTenZs2eO95rPnj1Lw4YNMTU1xcnJiU8++YTw8HCdOtnd23/+Pcp4zTP+P+N8GfeAU9Xp93ojku6TmJTIEv9VtKzRCS/7SpRw8aaDb0dOnDiR5TmCgoL45JNPcHZ2xsTEhMqVK/PDDz+86G2QCphsARcSQgiS1QmvdAyNULP28ldAVgPbBaBg3eUxlLdrpHd3tMrA7JW6v44ePYqhoSG1atXKsZ6trS1Vq1bl5MmTPHr0CEdHRyD9dWrSpAnFihVj1qxZBAcHs2TJEv7++2/OnDmDu7s748ePx9bWlh07djBu3DiqV6+e47kWLVqEEIKhQ4diZmbGnDlzGDRoEHv27OH8+fPa8nnz5tG7d29KlSqljX/BggWMHDmSRo0aMWnSJIyNjTlz5gzr168nICCAO3fuYGFhkevX58SJE3zwwQdUrlyZCRMmYGJiwp9//sn06dO5fv06P/74Y7bPHTZsGN9//z2NGzdm8ODB3L17lylTpmBqappl/T59+mBubs7IkSMxMjLi+++/Z+TIkVhZWdGnTx8gPQlWr14dlUrFoEGDcHd358GDB6xcuZJhw4ZRpEgRunfvnuvrA1i+fDmDBg2ifPnyTJs2jfj4eJYsWUJCwov//rt168aff/7JsGHD8Pb2Jjw8nMWLF9O4cWNOnz5NpUqVmD9/PvPmzePo0aN89913lCtXTvv8pKQkunbtyujRo7GxsXnhMruNGjWiadOmzJ07lwsXLrBq1Sr+/vtvzp0799KDSTdu3MiMGTMICgrK8p7z/bggNGhISkikV7vBnD9zEV/fdowcPpqHDx+yfPly6tevzw8//MBHH32k89zWrVvj6urKuHHjSE5OZv78+fTo0QMXFxcaN278UnFKr49MwIVEsjoBvz/d8vksgqfJD+i7v5jeR1jXIhQTw5wHteTkwYMHFClSJFfTHdzd3Tl58iShoaHaBKzRaPDx8WH37t3ae8cNGzakffv2TJw4kbVr19K8eXOOHz/Ojh07aN68+QtHHj9+/JigoCDth7GHhwft27fnr7/+IigoiKJFiwLpK8K1atWKPXv2UKtWLdRqNdOnT6dKlSocOHBAG8/AgQMpUqQI3377LX/++ScdO3bM9euzadMmkpOT2bVrl/aaBwwYQNeuXbl79y7JycmoVJkX1T99+jTff/89vr6+7NixQ/slydfXl/r162d5LisrK06dOoWxcfpgng4dOuDh4cGqVau0CXjhwoVERkZy9uxZ3nvvPe1zP/zwQ8qVK8fWrVtfKgHHxsbyxRdfULJkSU6ePImZmRmQ/mUgY1ZFdiIiIti1axeDBw/m22+/1ZY3a9aMXr16aRNw+/bt2blzJ0ePHqV9+/baHhAAtVrNoEGDmDx5srYsp1HbvXv3ZvHixdrfy5cvz/Dhw5k7dy5Tp07N9XUD9OzZk1WrVhEUFKS955yqTiZFnd6zkTHCeePSnzl/5iITJ07UOceQIUOoUKECAwYMoEWLFjpfAMqWLcvu3bu173vt2rWpX78+q1atkgm4EJNd0NJrJYTI9aAqQ0ND7XOeNWnSJJ2BW76+vvj4+LBjx46X2jozQ506dXRaQj4+PgDUrVtXm3wB7dKpoaGhQPo+2aGhofz111868cTHx2uvMbcDgTJknG/IkCEEBASgVqsB2Lp1KwEBAVkmX4Bt27YBMG7cOJ0eirp169KsWbMsn9O5c2dt8s04t5OTk04X69y5cwkPD9dJvhqNRttd/LLXd+DAAWJjYxk8eLA2+UL6l60XDYSytLTE2tqabdu2sXLlSm2cNWvW5Pr161mOJM5Ku3a5HwcxadIknd8HDRqEtbU1v/zyS66PkRUhBNHJj7kfF4RapL/HKqUpRUyc2b3jT8zNzRk3bpzOc5ydnRkxYgRRUVHs27dP57EePXrovO8ZvT7Pd5dLhYtsARcSKgMz1rUIfaVjXH0awOwzH72w3lfVfqKsbR29zqEyMHtxpRwULVqUkJAQUlNTX5iIMxKdu7u7TnmFChUy1S1TpgxXrlwhIiICBweHl4rJ2dlZ5/eMuJ6fApXxheDZNdGNjY05duwY27Zt4+bNm9y+fZs7d+5ovwi87Prpn376KUePHmX79u1s374da2trGjZsSNu2benevbtO0nrWtWvXAPD29s70mI+PD/v3789U/vx1A6hUKm3Sh/R7lampqUyePJl///2X4OBgbt68qe0uftnru3XrFkCW68A/21WcFZVKxbp16+jTpw8DBgwA0lukLVu2pEePHlSpUiVXMWR13VlxcHDI9LdkZGSEp6cnV69ezdUxshMWf4NEdfocZQXpidPO1B0TA3Nu37pNqVKlsuwlKl++PJB5i9jnrynji9qz76VU+MgWcCGhUCgwMTR/pZ9KDk2wNXEFsrtHq8DOxI1KDk30PserTn9o3LgxycnJBAQE5FgvJiaGs2fP4uPjo+2KzXidskrcGS2yjCT5MrL7IpCbax00aBBNmzbl6NGjeHh4MGzYMPbv36/3ACUzMzN2797N5cuXmT17NjVr1uTAgQN88sknVKpUiadPn2b5vIypLVm1kLPr7lcqX/zP/++//6ZUqVIsWLAAAwMDfH19Wbt2bY6rveVGVj0VuUnm7du3JzQ0lO3btzNw4ECSk5OZO3cuVatWZeHChbk6d27/RrJ7fTQaTa6O8fygMiEEaZpUABLV8ShQoFKaaufzmv43vUgIke3fXkZCff59zs17KRU+8l17iygVBvj5fPPfb8//A07/vbfPrAKdD9y/f38UCgUzZ87Msbt4/vz5JCYm0rdvX51yIYS2FfWsa9eu4eDgQJEiRfI85uwcO3aM5cuX0717dwIDA1m9ejWjR4+mSZMm2SbKF7l+/TrHjh3Dx8eHL7/8kn379vHkyROGDBnCzZs3sx3ZWrp0aQCCgoIyPZbROtbHgAEDMDU15erVq2zfvp2pU6fSuXNnvY9XqlQpAK5cuZLpsRs3buT43NjYWI4fP05UVBQdO3Zk2bJlXL9+nX///ZciRYq89D3ZF4mIiCAmJkanLDk5meDgYO3rDem3Ip4doZ7h2e7fVHUyD+JvkKpJX4DDAAMsjIpgZ+qeaWqRl5cXN2/eJCkpKdMxL1++DECxYvqP45AKjzxJwAkJCYSFhXHv3j3u3r2b7Y+U/2o4t2P0e+uxNdHtPrUzcWX0e+sLfB5w1apVGTlyJH/++ScjRozIcsnJzZs3M23aNKpWrcrw4cMzPf7sAByAH3/8kevXr9O1a1dtWcY92bzaQjMrERERQHq34LMtloiICFavXg1kbgW9yLBhw2jatKnOvxcTExOqVasGZN9669KlCwDfffedTvnly5fZu3fvS8XwrIiICBwdHXFyctIpnz17NvDy19e8eXOKFCnC4sWLdTZyiYiIYN26dTk+99KlS9SrV49p06bplJcrVw4bGxud1yYv3n+1Wq0zAAvSvxjGxsbqfAlxdXUlKChI537406dP2b17NwBxKVHcjwsiSR2vjcva2Ak7E1dMDTOPkO/UqRPx8fHMmjVLp/zRo0csWrQIS0tLWrZsqfd1SYXHK90D3rt3L+PHj+fChQsvrKtQKPJkfV/pxWo4t6Oa0weFdiUsf39/UlNTWbRoEfv27aNbt24UK1ZM+6F1+PBh6taty48//phl9/CWLVuIiIjg/fffJygoiKVLl+Ll5aWd+wtoE8bSpUt58OBBvqx0VK9ePezs7JgxYwZxcXGUKFGC27dvs2bNGqKjowFeuFvY8yZMmMChQ4eoV68eAwYMwMXFhRs3bvD9999TtGhRbaJ9Xt26dfHz82PdunU8ePAAX19fHjx4wOLFi7Xdk/rcPvD19WXDhg106NCB1q1bk5CQwPbt2zl+/Dgqleqlr8/MzIylS5fSvXt3qlatqr2Xu3Tp0hd269apU4eWLVuybNkyIiMjadSoEWlpafz888/cvn1bZ2GXjPf/22+/pWXLlrRv3/6l4syINWOqW/Xq1Tl+/DgbNmygWrVqjBw5Uluvb9++TJkyhaZNm9KnTx+ioqJYsWIFRWyL8PDhQ6JTHqPBAwOFAc5O6fdq585YRKNGjbIcIPfFF1/w22+/MXXqVC5dukTTpk15/Pgxy5cvJzIyko0bN75weU3pDSH0dPToUWFoaCiUSqVQKBS5+nmbubm5CUC4ubnlWC8xMVFcuXJFJCYmvqbICq9jx46J7t27ixIlSgiVSiVcXFxE8+bNxcaNG0Vqamqm+r179xaAOHDggGjatKkwMTERrq6uYujQoSIiIkKnbnR0tHj//feFiYmJsLGxEXFxcWLSpEkCEAcPHhRCCBEcHCwA0bt3b53nvkz52bNnRYsWLYStra0wNzcXZcqUEcOGDRPBwcHC2NhYNG3aVFsXEA0bNnzh63LkyBHRqlUr4eLiIoyNjUXRokXFwIEDxf3797V1nr8WIYRITU0VM2bMEF5eXsLIyEh4enqK7777TnTu3FkAIiwsTAghxNq1awUg1q5dm+ncHh4ewsPDQ/t7bGysGDVqlChWrJhQqVTC3d1dtGrVShw6dEh0795dKJVKcefOHSHE/9+f4ODgF17jgQMHRIMGDYS5ublwcHAQI0eOFIsXL37h+xMbGysmTZokvL29hbm5ubCwsBB169YVmzdv1jn+3bt3Re3atYWxsbEoVapUjvFldZ6GDRsKNzc3cezYMVGjRg2hUqmEq6urGDVqlIiNjdV5fmpqqpg0aZLw9PQUxsbGomTJkmLaN5PF9xvmCEBs/m2luBcTJKKSHomz586I8uXLCyMjI9GsWTPtuZ7/KI6LixMTJkwQpUqVEsbGxsLBwUF06NBB/PPPPzr1svo7yJDbvzf5mVRwXmo7wme1a9eO3bt3U7FiRSZNmkTZsmWznfCf4UWT3t9kcjvC/Ofn58f69esJDg7WmdsppQ9aMzAwyLJl1KJFCw4ePEhiYqJeg9Sk3EtVJ/Mo8Q5J/41wNlAYYGZojZWx/SsvYpNf5GdSwdH7X2NAQAAmJibs27cv0/0hSZJer71799K1a1fWr1/Pxx9/rC2/e/cuR48epVq1ajL55iMhBDEpj3mS9ACBQIECY6UpVsa2mBsVwUApX3spM73/KuLj4ylXrpxMvpJUCLz//vu4uLjw6aefcuXKFUqVKsWDBw9YsWIFQohsN76QXl2KOonHiXffqFavVDjonYA9PDx4+PBhXsYiSZKerK2t+eeff5g5cyY//vgjYWFh2NjYUL9+fcaPH0/lypULOsS3jhCC6JTHPM3U6rXD3MhGtnqlF9L7HvDEiROZOXMmf/75J02bNs3ruN448h6wJL07Mrd6DTEztHojW73yM6ng6D0PeOzYsZQrV46PP/6YX3/9leTk5LyMS5IkqdARQhCV/Eg7r1eBAhMDM2xVLtiZuOXJanHSu0PvPpL+/fvj7u5OYGAgHTt2xMDAAFtbW53F3Z+lUCi4c+eO3oFKkiQVpLep1SsVDnon4K1bt2r/XwhBWloajx49yra+/OOUJOlNlH6v9xFPk8K093pVBqZYGsl7vdKr0fsvZ+3atXkZhyRJUqGT3uq9Q5I6ffen9FavNdbG9hgbmMqGhfRK9E7AvXv3zss4JEmSCo3sW732mBtZy1avlCfy9K8oMjKS2NhYLC0tX+uuNJIkSXlFtnql1+WVd0O6c+cOAwYMwNnZGXt7ezw9PbG3t8fe3p6PP/44y63jJEmSChshBFFJD/8b4ZygHeFsZ+KKnYkrKkM50ErKW6+UgA8fPkyVKlVYvXo1jx49Qgih/Xn69CmbN2+matWqHDhwIK/ilSRJynMp6iQexF/nSXL6ohoGCkMsjWyxNy2KhZGt7HKW8oXef1Xh4eF07NiRqKgoKlSowKeffkrVqlWxsrIiMjKSM2fO8P333xMYGEi3bt24dOkSzs7OeRn7WykhNZpkdebNvQsDlYEpZkbWBR2GJOUZIQTRyY94mvzsvV4zrIztMDO0wUBZOLbwlN5OeifguXPnEhkZSbt27fjpp58y7dtarVo1+vXrR6dOndi9ezdLly7V2a9VyiwhNZrtN78lNuVJQYeSJUtjOz4s+cUrJ+EnT54wefJkdu3axaNHjyhdujQjRoygb9++uXp+YmIi06dP54cffiAsLAwPDw969OjBF198kWlHrk2bNtGrV68sj9O7d2+dTeAXLVrErFmzSExMpGXLlixevBh7e3ud53z//fdMmjSJW7duYWVl9XIXXkACAwMZMGAA58+fx9TUlN27d1O3bl0aNmzIoUOHcnzuoUOHaNy4MZMmTWLy5MmvJd78FBISgqenJ71792bF6qU8SrxL8jP3es0NrbFS2WOszP5eb3R0NCkpKTg4OLzO0F/a5MmTmTJlCgcPHqRRo0YFHY6UBb0T8J49ezAyMmLlypVZbpoOaB8vWrQoO3fulAn4BZLVicSmPMFYaYKxgVlBh6MjRZ1AbMoTktWJr5SA4+PjadGiBZcuXWLo0KF4e3vz008/0a9fP8LDwxk3blyOz09NTaVly5YcPXqUxo0bM3r0aEJCQpg5cyb79u3jr7/+0llO7+LFiwCsXLky0zJ7Xl5e2v8/fvw4w4cPp3v37tSoUYOZM2fi5+fH7t27tXXi4uKYOnUq48ePf2OSL6Rv43ju3DkmTJhA0aJFKVeuHBs3bnwnN1JxcHBgw4YNOBW15X7ctedavekjnJWK7Fu9f/75Jz179mTbtm0yqUmvTO8EfOfOHcqXL//Cb4GOjo6UL1+emzdv6nuql3L9+nUmTJjA33//TWxsLCVKlGDgwIEMHz4cpfL/t7zv3r3LhAkTOHDgANHR0VSoUIFx48bRrl271xJnTowNzDA1tCjoMDJJ0SS98jEWL17MuXPn2LJlC127dgVgwIABtG7dmilTptCrVy+KFi2a7fNXrFjB0aNH6datG5s3b9a2Upo2bcoHH3yAv78/X3/9tbb+hQsXcHR0pH///jnGtX79epydndmwYQMGBgYYGhoybNgwHj58qE1Uc+bMwdTUlCFDhrzqy/BaXbx4kUqVKjF16lRtWc+ePQswooJjZKKkcfsaJGsStPd601u9DhgrTV44yCogIIDHjx+/pmilt53eg7CUSiWpqam5qpuamopGo9H3VLkWEhJC7dq1+e233+jTpw/z58/H3d2dUaNGMXToUG298PBwGjRowM6dO+nbty9z5swhLS0NX19ffvjhh3yP8122fv163NzctMkX0ldJ+/LLL0lJSXnh6//LL78A4O/vr/Nh2bp1aypXrszy5ct16l+8eJHy5cu/MK579+7h6emJgUF666dUqVIA2uVTHz58yNy5c5k+fXq2y60WVqmpqVhbv9v37oUQRCaFcz/uGsmajBHO5tiZuGFn6oZKTi+SCoDeCbhUqVJcvXr1hes7h4SEcOXKFUqWLKnvqXJt7ty5PH36lHXr1vHtt98yePBg9u/fT5MmTVi2bBlBQUEATJkyhbt37/LHH38wffp0Bg8ezLFjx6hUqRIjRowgPj4+32N9F0VHRxMUFETNmjUzPZZRdvLkyRyPce/ePWxtbXF3d8/0WMYeuA8ePADg0aNHhIeHaxNwSkpKtpuGODk5ERkZqf09IiICSO/BgfS/mZIlS9K9e/cXXWaWzpw5w4cffoijoyMWFhZUrlxZu1fvszZv3kzt2rUxNzfH3Nyc2rVrs2nTJp06hw4dQqFQsHXrVmbOnEnJkiVRqVR4enry9ddfk5aWBqTfA8xIKocPH0ahUODn5wekf+l5vgv133//pW3bthQpUgQbGxs+/vjjbJeXvXbtGt26dcPR0RGVSkWpUqWYOHEiiYm6AwgVCgXDhg1j+/btVKtWDVNTUxwcHPDz8yM8PDzTcbdt20aDBg2wtrbGzs6Opk2b8vfff+vU0Wg0LFq0iMqVK2NqaoqNjQ2tWrXi+PHjWcaaok4kNO4aT5PDuHcnlJI27zFu6AwcTItiYVSEJo2bUr58eS5evEjr1q2xtrbGwsKCFi1acPr0ae1xGjVqpL2N1rhxY4oXL659LCkpiWnTpuHt7Y1KpcLe3p5OnToRGBioE4ufnx8WFhb8/vvveHh4YGpqSo8ePXB0dKRMmTJZxl+hQgWcnJy0DZ6HDx8yatQoypQpg6mpKaampvj4+DBt2jTtey+9GfROwL6+vqjVanr27El0dHSWdaKjo+nRowdCCHx9ffUOMreuX78OQJs2bXTK27dvD6R/wKjVajZt2kTt2rWpU6eOto6JiQkjRowgIiJC576flHdCQ0MRQlCsWLFMj5mZmVGkSBGCg4NzPIaFhQXx8fGo1epMj2UkzYwEfOHCBSD9dkO1atUwMzPD1NSUGjVqZPpQb968OUFBQaxbt47r16+zaNEifHx8KFasGNevX2flypV88803erWS9u3bR926dTly5AiDBg1izpw5ODo6MnDgQCZMmKCt9+mnn9KzZ09SUlKYPHkykydPJjk5mV69ejFixIhMxx03bhwrVqygX79+LFiwAEdHR6ZNm8a0adMA6NixIxs3bgTA29ubjRs3MnDgwCxjPHfuHPXq1SMgIIARI0YwefJkrly5kmXX/alTp6hevTpHjx5l6NChzJ8/n9q1azNjxgyaNGlCUpLurYo9e/bg5+dHo0aNWLhwIc2aNWP9+vV06dJFp96kSZPo0qULMTExjB8/nq+//pr79+/TokUL9u7dq63Xo0cPhg8fTqlSpZg7dy6ff/45169fp2HDhvz888/aerqt3kTtalYAKkMznUU1MnrFihQpgr+/P8OGDePw4cM0a9ZM+/k2fvx4OnTooH3t58+fD6R/sWvRogVTp06lbt26LFy4kCFDhnDkyBFq1qxJQECAznUmJSXRtWtX+vTpw6xZs+jUqRM9e/bk+vXrnDp1Sqfu+fPnCQwMpFevXhgZGREdHU3NmjVZs2YNHTp0YPHixdovWl9//TX+/v5Zvr9SISX0FBkZKVxdXYVSqRQuLi5izJgx4qeffhJ//vmn+Omnn8SYMWOEi4uLUCgUws3NTURGRup7qlwbPny4AMTJkyd1yocOHSoAceTIEXHhwgUBiNGjR2d6/uXLlwUgRo0a9dLndnNzE4Bwc3PLsV5iYqK4cuWKSExMzPTY08QwseTfQWL95XFi27WZhepn/eVxYsm/g8TTxLCXfm0yBAQECEBMmDAh29ewVKlSOR5j2LBhAhDbtm3TKb97964wNTXVvs9CCDFnzhwBCHt7e/HNN9+IXbt2iTlz5ggnJydhYGAgdu3apX2+Wq0WvXr1EoAAhLu7uzh9+rQQQoiOHTuKpk2b6nXNGo1GFC9eXNjZ2Yn79+/rnK9evXpCpVKJJ0+eiCNHjghANG3aVKSkpGjrpaSkiMaNGwtAHDp0SAghxMGDBwUgXFxcdP5dxcXFCWtra+Hq6qoTAyAaNmyYY1mjRo2EoaGhuHz5srYsKSlJ1KtXTwBi0qRJ2uspV66cKFq0qHjy5InOMVetWiUAMXv2bJ3zAOLEiRM6dZs2bSoAcf36dSGEEDdu3BAGBgaiYcOGIjk5WVvv4cOHwtraWlSrVk0IIcS2bdsEIPz9/XWOFxsbK7y9vYW9vb2Ij48XSWkJ4l7MVXEz6py4GXVOhERfFI/i74hrN68KQPTu3Vv73IYNG2aKWwghpk2bJgCxYsUKbdmkSZMEIA4ePKgt8/f3z/JvMiwsTNjb2wsfHx9tWe/evQUgvvjiC526gYGBAhDDhg3TKR85cqQAtO/LggULBCC2b9+uUy8yMlIYGxuLChUq5BhrVnL6TJLyl94tYBsbG/744w+cnZ0JDw/H39+fLl268P7779OlSxf8/f0JDw/Hzc2N3bt3Y2Njo++pcm3MmDGUKVMGPz8//v77b0JCQli8eDHLly+nadOm1KtXj/v37wNk2QrL6NZ8UStM0o/4r7tVPNft+uzjGfdgs/PZZ59hZWXFJ598wqpVqwgODuavv/7igw8+wMwsfeR4xqj8GjVqMH78eP755x+++uor2rZty2effcbJkye1g6kyxiYolUo2bNhASEgIZ86c4ebNm1SrVo0TJ06wY8cOZs+eDcC6desoX748Li4u9OrVS9vqzs758+cJCQmhZ8+euLm5acuVSiWbNm3iwoULWFlZsW3bNiC92/jZWQVGRkbawVM//vijzrHbtGmj8+/K3NycsmXL8vDhwxxjet6TJ084cuQIrVq1wsfHR1uuUqkYPXq0Tt1Lly5x+fJlPvjgAzQaDREREdqfNm3aYGJior1Pn6FUqVKZbjtUr14dQNsNvWvXLtRqNSNHjtS5x+7o6MixY8fYvn07AFu2bAHgww8/1Dl3UlKStmzPgZ2EPtPqTb/X646dqRvGBtlvOP/8wLTnY8zOli1bsLGxoXHjxjoxGRoa0qpVK65cuaK9/ZXh+cGe5cqVo3r16mzdulXb1ZyWlsaWLVuoWbOm9n0ZPnw4Dx8+1PbqZYiIiMDa2pq4uLgcY5UKl1da3qVChQpcu3aNxYsXs3v3boKCgoiJicHS0hJvb2/atm3L4MGDX9sAEBcXF6ZPn07fvn1p2rSptrxOnTrs3LkThUKh7U6ysMg8yjjjAzyne8Dz5s1j3rx5mcrDwsJeNfy3nqWlJQAJCQlZPp6QkJDjCGiA4sWLs3//fnr16sUnn3wCgLGxMUOGDMHGxobJkydja2sLQP369alfv36mY3h4eNChQwc2btzIlStXdAZpeXh44OHhof39yy+/pHPnzlStWpXDhw/Tp08f/P39qVOnDoMGDaJHjx7s27cv23gzvsyVLVs2yzgyZCzZWq5cuUz1MuJ7/othVgvbqFSqLLvncxIcHIxGo8lynMbz8Vy7dg2AZcuWsWzZsiyPFxISkqs4AW2sOb1Oz74/Ged/dgrZ84JuXea9hmUxVBhiZmiDtSp9DecXeT7O52PMzrVr10hISMhxRkhISAje3t7Zngugb9++DB48mD/++IO2bduyb98+Hj58qDN6HcDAwIA5c+Zw6tQpgoODuXnzJjExMcD/P8OkN8Mrr69mYWHBmDFjGDNmTF7E80q++eYbxo4dS8mSJfH398fJyYmjR4+yePFimjRpwp9//pljKyyjLKdWWExMDKGhoflzAW85T09PFAqFthfiWfHx8URFRb0wAUN6yzYoKIjAwEBiYmLw8fGhSJEi9O7dG0NDQ53Elp2MqUWxsbHZ1tm1axcnTpzg6tWrAGzYsIESJUrwxRdfAPDVV1/Rq1cvwsLCcHFxyfIYGa2ZF907zq5XAP6fADISQoZnp9XlhaxieH72QkYsQ4cOzdQKy/D8ugC5iTO3r5NarcbS0pLNP65DqTTEWGkKCOJSo4hLfYoASpQsjqmBOVbGDpgZWeU4r/dl48wuppIlS7J06dJs61SqVEnnd0PDzB+93bp1Y/To0WzcuJG2bduyYcMGzMzMdGYMBAYG0rBhQ5KSkmjcuDHNmzdn1KhR2oVVpDfLW7PAaUxMDFOnTsXV1ZVTp05pd2Pq0KED7733Hr169WLGjBk0aNAAyLoVllGWU4vdyspKpysxQ1hY2GuZavUms7CwoGzZspkGmsD/Rz8/OzAuK+fOnePUqVP06tWLChUqaMvVajX79++ndu3a2kTVvn17AgMDuXTpUqYVsq5cuQJk35JSq9WMHTuWgQMHaus8ePBAZ/GKjBbPvXv3sk3Anp6ewP9bbs/av38/GzZs4Msvv9Se4/Lly9SrV0+n3uXLl4Gsb5vkhRIlSqBUKrWvybNu3Lih83vG9QA0a9ZM5zGNRsP27dspUaLES8fw7OtUunRpnce+++47rly5wqy5U3Euas+1a9dwLmODrV0RDBWGKFCSKhy4cvEaTx49xdHaDXvTYjl2N+clT09PwsPDadSoUabEGhAQQHx8fK5aptbW1nTs2JFffvmF8PBwfvvtNzp16qSz6MvIkSOJiooiMDBQp7cgNTWViIgI7ah96c2Qq698a9asYc2aNTqthYyyl/nJT9evXycxMZEOHTpk2gqxe/fumJubc+DAAe0/9KxaYRllObXCRo8ezf379zP9ZPcBLOnq2bMnd+7cYevWrdoyIQTffvstKpVK59t+Vi5cuMDgwYMz3Q+dNWsWYWFhfPbZZ9oyFxcXbt26xYoVK3TqHjp0iL1799K6detsP7DWrl3L3bt3mThxorbM3d2dO3fuaL9oZXQbZzUlKkPVqlVxd3dn8+bNOgs4ZFzzli1bcHFxoVOnTkD6PeBnp5KkpaVpp75k1Mlrtra2NG/enP3793PixAltuVqtznS7pVq1ahQvXpx169Zl2ulsxYoVdO7cWa9/676+vigUChYvXqxz/U+fPmX27NmcOPkP0SKclu3Sby19N/17ANJEGqkihbiYeEb0GcuAbiMxU9jkW/LN6B179st2p06diIqKYs6cOTp1Q0NDadu2Ld27d89167pv374kJiYyZMgQEhMTMy3PGhERgbm5eaYvOQsXLiQxMVFOQ3rD5KoF3L9/fxQKBfXq1dPex8soexm5XetXHzndrxFCoNFoEELg7e2NtbV1lvNNc9sKy28p6qzvkRakvIpp5MiRbNq0id69e3P27FlKly7Ntm3bOHDgAN9++63OF5nbt28TEBCAl5cXtWvXBqBz587MmTOH4cOHc+PGDby8vDh48CA//PADfn5+OtPdJk+ezO+//85nn33GxYsXqVGjBleuXGHZsmW4urqyZMmSLGNMSEhg0qRJfP755zoJukePHqxatYpevXpRs2ZNZs6cSfPmzXF1dc32eg0NDVm2bBnt27encuXKDBo0CHt7e3bs2MH+/fuZPXs29vb2NGrUiIEDB7J8+XJq1qxJt27dANi6dStnz55lyJAh2t6b/LBw4UJq165Ns2bN+PTTT3F1dWXbtm3aqX0ZDAwMWLlyJW3atKFq1aoMGjQILy8vTp8+zZo1a/Dy8tL50pJb3t7ejBkzhlmzZlG3bl26du2KRqNh5cqVREZGsnh9+vSaD3u0Ze+v+9mydjt3bt+jaauGpKam8dPGnQTfCsHf3x93txffxtBXRg/I0qVLefDgAT179uSrr77it99+Y+zYsZw+fZqmTZsSGRnJsmXLiIqKYvPmzZl6YLLTuHFjPD092bFjByVKlMjUrezr68vUqVNp0aKF9jXau3cve/bswdTUNNspoVLhlKsEXKxYMRQKhc69nYyywqJcuXJ4eHiwbds2JkyYoNNNvGrVKhITE2nRogWGhoZ06dKFlStXEhAQoE22SUlJLFiwACcnJ1q1alUg16AyMMXS2I7YlCd5suxjXrM0ttPOo9SXqakphw4dYty4cWzYsIHY2FjKlCnDhg0bMm2acOTIEfr06UPv3r21CTijJ2PixIls2rSJp0+fUqpUKb7//vtMc1ydnJw4deoUkyZNYvfu3WzYsAFHR0d69+7N5MmTs02c8+fPR61W67SmIX0hhpUrVzJr1iz27NlDy5YtWbRo0Quv+YMPPuDo0aNMmzaNuXPnolarKVu2rM5ynJA+sKlGjRosW7aMr7/+GkNDQypVqsTmzZv1XgAkt0qXLs3JkycZP348K1asIDk5mebNmzNt2jQaN26sU7dZs2acOHGCGTNmsGbNGqKjo3F3d2fIkCGMGzdO713PZs6cSdmyZVm0aBHjx4/H3Nyc6tWrs2rdclzKpnfDGhgYsGLrfNYt/YGdW/fgP3khpqYmlPT25Idtm+j2UY9Xfi1y0rVrV3bs2MHu3bs5cOAAHTp0wMLCgmPHjjFr1ix+/vln7ayPKlWqsGHDhkyvX04UCgV9+vTh66+/pk+fPpk+YydOnIihoSHr169n1KhR2NraUqZMGXbs2MHp06eZOXMmx44dy3QbQyqcFCKn0R9vmP3799OmTRtsbW0ZNGgQzs7OBAQEsHHjRry9vQkICMDGxobw8HCqVKlCQkICo0ePxsnJidWrV3P27Fm2bt1K586dX/rc7u7uhIaG4ubmlmX3doakpCSCg4Px9PTMtDkAyO0IJel5cSlPeZiY84p7AE6mHlgY276GiN4uL/pMkvLPaxmEFRkZSUhICFWqVMnX8zRv3px//vmHadOmsXDhQmJjY3F3d2f06NFMnDhRO7gqIzGPGTOGBQsWkJqaSoUKFdi9ezetW7fO1xhfxMzIWiY5SfqPWpNGbGrkiysCBsqsd2WTpMJK7wRsYGBAvXr1OHz48AvrtmjRgvv377+WubLvvfceO3bseGE9T0/PTAN5JEkqHIQQxKdGE5F0D7V48cAiQ4URJgaFbwcxScqJ3glYCJHj3MUM8fHxPHjwgKioKH1PJUnSOyRNk0JE4n3i09IHFClRYqw0IUmT/UBAe1P3QjUmRZJyI1cJ+MqVK7Rq1SpTwj19+nSOcxOFEERGRpKYmJhpbp8kSdKzhBDEpETwNOkBGjQoACOlCZZGtlgY25Ksjici8T5p4v/boBoqjLA3dcfcyKbA4pYkfeUqAfv4+FC3bl2duZsAycnJOQ44yqBUKnV2fZEkSXpWijqRx4n3SFKnLwNrgAFmRlZYGTugMjBDoVBgqLTBzNCaJHUcak0qBsr0bmfZ8pXeVLnugp43bx4tW7YE0r+p9u3bl9KlSzN27Nhsn6NUKrGwsKBixYo5rt0qSdK7SSM0RCU/JCr5IQKh3TLQ0tgec0NrDJS6H1EKhQJTQ8sCilaS8lauE7CzszO9e/fW/t63b1/tnErp5b1Fs78kSS+JaXFEJN4lRZMM8N/mCdZYqxwwUprIlu1rIj+LCo7eg7Dkusf6yVjKLjU1Nder40jS20StSeNpUhgxqelbOWZsGWht7ICpkRVKRd5uMiHlLGMjjBdtBSrlvdf2l/7PP/+8rlMVakZGRqhUKqKjo+U3T+mdE58axf24IG3yNVIYY23sgINpMcyNbWTyfc2EEERHR6NSqTLtYiXlv1daiOPBgwcsWLCAS5cukZCQkKlVnJaWRkJCAg8ePODp06dyofD/2NvbExoayv3797G2tsbIyEh2t0lvtTRNCpFJD0lUp2/ookSJysAcCyNbTDBFnSpQpxa+5VffVkIIUlNTiY6OJi4uLssd3qT8p3cCDgsLo2rVqjx69EjbklMoFDqtuoykIoSQS5w9I2N7sYiICLm3sPRWEwiS0+KJS41CoAEUGCoMURmYY2JoSJziUUGH+E5TqVS4ubnpbHkovT56J+C5c+fy8OFD7YbRFhYWLFy4kPr161O/fn3u37/P7t27iYyMpHnz5uzcuTMPw37zWVlZYWVlRWpqapY7OEnSm+5RfAg/Xp9BcMwFAEwMLPGxq0M9t64Us/SR3c0FzMDAQHY7FzC9E/C+fftQKBTs2LGD5s2bA7Bx40YMDQ2ZPn06AA8fPqRZs2b89ddf/Pvvv9odbaT/MzIykv8IpLdKmiaFXbcXsP2GP2qRihIDiltVpLlHP2o6t5VrnUvSf/ROwHfv3sXZ2VmbfAGqVKnCyZMnEUKgUChwcnJi5cqV1KlTh8WLF8sELElvuRuRp1l+6VPuxwUBYGXsQDXH1rTyHIS7hbcc6yBJz9A7ASclJeHt7a1T5u3tzcGDB7l16xYlS5YEoFatWri5uclR0JL0FktMi2XrtWn8eWclAoGhwpjSRWrwvscgKjs2xfgV95GWpLeR3gm4SJEimTZY8PT0BODq1avaBAzg4uJCYGCgvqeSJKkQO/vwD1ZfHs3TpAcA2Jm4U9ulPS08PsHRzKOAo5OkwkvvURAVK1bk1q1b3L59W1tWunRphBCcPXtWp25oaCgqlUr/KCVJKnSikh8x/3wfvj3bladJDzBWmvKew/sMqriErmUmyeQrSS+gdwL29fVFo9HQunVr9u7dC6R3NxsZGbFkyRJtYl64cCFhYWGUKFEibyKWJKlACSE4eG8jow9X50RY+t7brual+LDUGAZUXEgF+4YYKuXAQkl6EYXQczmmpKQkatSoQWBgIAYGBsTHx2NsbEzPnj354YcfMDIywtLSksjISABmz57N559/nqfBFybu7u6Ehobi5uaWqx2iJOlNFB5/mxWXRnDl6VEATA2tqOLQnFaeg/Gyfk9OLZKkl6B3Aob0hSQ+//xzjh07xs2bN4H0qUeNGzcmKChIW69u3bocOHDgre6GlglYepulaVLZE7yYn298Q6omGSVKPKwq0LxYf2q5tJNTiyRJD6+UgDOo1WqdhbyTk5PZuXMnwcHBeHt7065dO5TKt/ubsUzA0tvqVtQ5ll8axt3YKwBYGtlTzak1rT0Hy6lFkvQKXmkt6AzP76KhUqno0qVLXhxakqQCkpQWz7brM9gbsgyBBgOFEaVtatCq+GA5tUiS8kCeJGBJkt4u/z4+wMpLI3iSlL5WuZ2JG7Wc29Oy+AA5ulmS8sgrJeA///yT2bNnc+7cOWJiYnKsq1Ao5G5IklTIxSRHsP7qWI4/+AkAY6UJ5ewa0NpzKGVt68jRzZKUh/ROwHv37qVt27YIIeS+tpL0hhNCcPTBj6y/Mob41CgAXMxL0si9Bw3de2CjcizYACXpLaR3Ap4xYwYajYYqVaowYsQIXF1d5aYCkvQGepgQwspLIwl8cggAUwNLKjs2o7XnUDm1SJLykd4J+N9//8XCwoIDBw5QpEiRvIxJkqTXQK1J4/eQpWy7PoNUTRIKlHhYlqe5xyfUllOLJCnf6Z2ADQwMKF26tEy+kvQGCo6+wLJLw7gTcwkASyNbqjp9wAeeg3G3KCunFknSa6B3Aq5UqRKXL1/Oy1gkScpnyeoEfro+i9+Dv0eDGgOFISVtqvNB8aFyapEkvWZ639wZOXIkkZGRzJ8/Pw/DkSQpv1yKOMTnR2qxO3gRGtTYmrjyvscAhlZaRg2XNjL5StJrpncLuGPHjowZM4bPP/+cixcv0qpVKxwcHHJc8apBgwb6nk6SJD3Fpjxl49XxHAndAoCR0oRytvX5oMQwObVIkgpQnizEsX79etavX59jHTkPWJJeLyEExx/8zPorXxGb+hQAZzMvGrn3oFHRnnJqkSQVML0T8Hfffcfs2bNzPQdYzhWWpNfnceJdVl0axYWIvwAwMbCgkkMz2pQYJqcWSVIhoXcCXrVqFQB+fn6MHTuW4sWLy3nAklTANELNHyEr2HptKimaRBQoKWbpQ4tiA6jt6iunFklSIaJ3Ag4ODsbZ2Zk1a9bkZTySJOnpTkwgyy99yu3o8wBYGBWhmmNrPigxVE4tkqRCSO8EbG1tjZOTU17GIkmSHlLUSfxy059dtxegEelTi7ysq9HGcyiVHZvJ0c2SVEjpnYCbNGnCL7/8wuPHj3FwcMjLmCRJyqXLT46y4tJwHiYEA1BE5UItl/a0Kj5I7lokSYWcQug5OurmzZtUrlyZatWqsWXLFlxcXPI6tjeKu7s7oaGhuLm5cf/+/YIOR3rLxaVGsfnqRA7e3wiAkVKFj2092pQYLqcWSdIbQu8W8JEjR+jSpQtr166lePHiVKpUiaJFi2Jubp5lfYVC8cKpSpIk5UwIwcnwX1lz+XNiUiIAcDLzpLF7TxoV7SWnFknSG0TvFrBSqUShULxwelFGHYVCgVqt1ivIN4FsAUv57UliKKsCR3P+8T4ATAzMqWjfjLZen8qpRZL0BtK7Bfzxxx/LUZWS9BpohIb9d1bzw7XJJKvjUaCgqIUPLT0GUNu1vZxaJElvKL0T8Jo1a3JcdlKSpFd3L/Yqyy99ys2oMwCYG9lQzbE1bUoMk1OLJOkNp3cCbtGiBS4uLixatAgbG5s8DEmSpFR1MjtuzeXXW/NQizSUCgO8rKvSxvNTqsipRZL0VtA7AZ85cwZzc3OZfCUpjwU9/Yfll4YRFn8LABuVM7Wc29PacxCOZsULNjhJkvKM3n3IarUaR8fCN+JSo9GwePFiKlWqhKmpKUWLFsXPz4/Q0FCdenfv3uXjjz/G1dUVc3NzatWqxa5duwooakmChNRoVl4axeQTrQiLv4Wh0piK9k0YUnEZPctOk8lXkt4yeidgX19fLl26xPHjx/Mynlfm5+fHp59+iqenJ/Pnz+ejjz5iy5YtNGjQgKioKADCw8Np0KABO3fupG/fvsyZM4e0tDR8fX354YcfCvYCpHfSqfDfGHWkOn/dWwuAo2lxOnp9yZBKy6jo0EjO65Wkt5De05AePXrEhx9+yNmzZ+ncuTP16tXDxcUFU9Ps7001adJE70BzY+fOnXTo0IEhQ4awZMkSbfn69evx8/Nj1qxZjBkzhsGDB7N8+XKOHTtGnTp1AEhKSqJWrVqEhoYSEhKS7Xzm7MhpSJI+niaFsfby55x+uAcAlYEZFe2b0s5rhJxaJElvOb0TsIGBwcud6DXsB9yyZUv++ecfQkNDsbS01JYnJyczefJkqlevjq+vLzY2NlSsWDFT633t2rX07duXrVu30qVLl5c6t0zA0svQCA1/31vP5qsTSVTHoUCBu4U3LT0GUse1g5xaJEnvAL0HYb1s3s7v/YDVajVHjhyhWbNm2uSbmJiIgYEBKpWKWbNmAXDx4kXi4uKoVatWpmPUrFkTgJMnT750Apak3AqNu86KS59yLfIkAGaG1lRzak3bEp/KqUWS9A7Ru39Lo9G89E9+Cg4OJikpCU9PT7Zv307FihUxMzPDzMyMli1bcu3aNQBt67RYsWKZjuHu7q49VnbmzZuHu7t7pp+wsLB8uCrpbZKmSWH7DX++PFqXa5EnUSoMKGldjYEVFtO//DyKWvrI5CtJ7xC9W8CFTWRkJAD79+9nxYoVjB49mqlTp3LhwgVmz55NnTp1OHPmDNHR0QBYWFhkOoaZmRkA8fHx2Z4nJiYm04hqSXqR65GnWH5xGKHx1wGwNnaktksHWnsOlqObJekdlScJOCEhgUOHDnHt2jViY2OxtLSkVKlSNGzYUOdebH5KTk4GICgoiO3bt9OxY0cA2rdvz3vvvUe7du2YOHEirVu3BrLuEs8oy+n+tpWVFW5ubpnKw8LC8r2VL715EtNi2XJtKvvvrEIgMFQY421bh3YlRuJjV1eObpakd9grJ+AFCxYwZcoUbcvyWWZmZnz99dd88cUXr3qaF8oYtezm5qZNvhnatm1L0aJF2b9/v/bebkJCQqZjZJRZW2c/AGb06NGMHj06U3nGICxJynD24R+sChxJZHI4AA6mHjR270mTYr3lrkWSJL1aAv7ss8+YP38+QgiMjY0pU6YMVlZWREZGcv36deLj4xkzZgyhoaHMnz8/j0LOWtGiRQFwdnbO8nFnZ2cuXryIp6cnQJYjlTPKMo4lSfqISn7E2stfcDL8VwCMlaZUtG+Cb8lRcmqRJElaen8SHDx4kO+++w4DAwNmzZrF06dPuXDhAkePHiUwMJAnT54wffp0DAwMWLRoEUeOHMnLuDOxt7fHy8uL69evk5SUpPOYRqMhODgYT09PvL29sba25uTJk5mOkVGWMTdYkl6GEIKD9zYy+nC1/5KvAndzb3qVncWQSt9TyqaaTL6SJGnp/WmwZMkSFAoFCxYs4KuvvtIOYMpgaWnJuHHjWLBgAUIIVqxY8crBvkjfvn2JjY3l22+/1SlfuXIlERERdO3aFUNDQ7p06cLRo0cJCAjQ1klKSmLBggU4OTnRqlWrfI9VeruExd9i6sk2LL/0KQlpMZgZWlHftTMj3ltDs2K95bxeSZIy0XshDldXV9RqNeHh4TlOnRBC4OTkhLm5eY7Te/JCcnIyTZo0ISAggO7du9OwYUPOnz/PihUr8PHx4eTJk5iZmREeHk6VKlVISEhg9OjRODk5sXr1as6ePcvWrVvp3LnzS59bLsTxbkrTpLL79iJ+vvkNaZoUlCjxtK5C2xLDec+xhdy1SJKkbOmdgFUqFVWqVOHEiRMvrFurVi0uXLhAYmKiPqd6KQkJCcyePZvNmzdz7949HB0d6dixI9OmTcPKykpbLzg4mDFjxrB//35SU1OpUKECEyZM0I6SflkyAb97bkWdY9nFYdyLuwKAlbEDtZzb06bEUDm1SJKkF9I7ATs6OmJsbJyrZOPu7k5KSgqPHj3S51RvBJmA3x1JaXH8eH06f4SsQKDBQGGEd5Ha+HqNllOLJEnKNb3vAVetWpWwsDB27NiRY73t27fz4MEDqlatqu+pJKnQ+PfxAUYfqcnekGUINNibFKWj1xd8WmWV3LVIkqSXovc0pP79+7Nv3z78/PxITU3N8r7pjz/+yCeffIJCoaBfv36vFKgkFaSY5AjWXfmKgLDtQPrUogr2jWlfcrScWiRJkl707oIG6NixIzt37kShUODs7EyVKlWwtrYmOjqa8+fPEx4ejhCC9u3b88svv+Rl3IWO7IJ+OwkhOBq6lfVXxhKfFgWAm3kZ3vcYQF23TnJ0syRJenulBJyWlsaoUaNYvnx5llsNGhoaMmDAAObNm4exsfErBVrYyQT89nmYEMLKS8MJfJI+h93U0JKqjq3w9Ropdy2SJOmV5SoBX758GU9Pz0xzfTOEhoayd+9egoKCiImJwdLSEm9vb1q1aqXdYehtJxPw20OtSeP34O/ZdmMGqZpkFCjxtKpEW68RVHVsKacWSZKUJ3KVgD08PLC0tCQwMFBbtmHDBpycnGjZsmW+BvimkAn47RAc/S/LLg7jTmz637qVsT01ndrT1muYnFokSVKeytUgrEePHuHoqLt4vJ+fH/Xq1ZMJWHorJKsT+On6LH4PXoIGDQYKQ8oUqU17r8/k1CJJkvJFrhKwubk5V69e5fr165QuXTq/Y5Kk1+pSxCGWX/qUiMR7ANiZuNPYvSfNPPrKXYskSco3uUrAdevW5bfffsPHxwcnJydUKhUAZ86coUSJErk6kUKh4NatW/pHKkl5LDblKRuujOPog60AGClNqGDXmA6lPpNTiyRJyne5ugd8/fp1GjduTFhYmP4nUihQq9V6P7+wk/eA3xxCCI4/+Jl1V74kLjUSABfzUrTyGEQ9ObVIkqTXJFct4NKlS3P9+nWOHj1KREQEaWlp9O3bl9KlSzN27Nj8jlGSXppGqLn6NICo5IfYqJwoa1sHpcKAx4l3WXlpJBcj/gbA1MCS9xxb0r7kaDm1SJKk10rvecBKpZJ69erl+z6/bwrZAi48ToXvYt2VMTxNeqAtszVxpYJdI/4J20GKJhEFSopbVaRdiZFUdZJTiyRJev30Xory4MGDWFvLrjqpcDkVvot553oDut8rnyY94HDoDwBYGtlR09mXdl7D5dQiSZIKjN4JuGHDhnkZhyS9Mo1Qs+7KGJ5Pvs8yVBgztNJyyts3lFOLJEkqUHonYID4+HhWr17N8ePHiYqKIi0tjex6tBUKBX/99dernE6ScnT1aYBOt3NW0kQKRgYqmXwlSSpweifgiIgI6taty82bNwGyTbwZ5OAWKb9FJT/M03qSJEn5Se8EPHPmTG7cuIGBgQGtW7embNmymJrKgSxSwbFROeVpPUmSpPykdwL+9ddfUSgU7NixgzZt2uRlTJKkl7sxV15QQ4GdiStlbeu8lngkSZJyoncCDg0NxcvLSyZfqcCpNWlsuDqOfXdW5FAr/RZIb59ZKBUGrycwSZKkHOi91p6NjY12SUpJKiiJabH4n+2qTb4lravStfTX2Jq46tSzM3Fl9HvrqeHcriDClCRJykTvFnCDBg349ddfs9wpSZJeh4jEe3xz+iPuxwWhxIBKDk3pVXYmrhYlaec1IsuVsCRJkgoLvVfCunjxItWrV+f999/np59+wtjYOK9je6PIlbBer5tRZ/E/04WYlAiMlCpqObenR9lpcvciSZLeGHq3gMPDwxk8eDALFy7Ew8ODZs2a4ebmlmMinjp1qr6nkyStE2E7WXJhIKmaZMwMrWhatA8flvoCE0OLgg5NkiQp115pLWiFQqGd/5vTPF8hhNwNSXplQgh23prLj9enA1BE5cKHJcfQqGh3ubCGJElvnFe6BywX15Bel1R1MisCR3A0NH3vXncLb3qVnUlF+8by71CSpDeS3gn40KFDeRiGJGUvJuUJc8/24FrkCUBB2SJ18Cv3DR5WFQo6NEmSJL290lrQkpTfQuOuM/v0RzxKvIOBwpCqTq3pVXY6DqbFCjo0SZKkVyITsFRoXYo4zLxzvUhMi0FlYEYdl0708J6ChXGRgg5NkiTpleUqARcrVgyFQsGhQ4fw9PTUlr0MhULBnTt3Xj5C6Z30970NrAochUaosTSypbXnUD7wHIKxgVxvXJKkt0OuEvD9+/dRKBSkpqbqlL0MOVBGyg2NUPND0CR2By8GwMHUg26lv6aWa3u5kIYkSW+VXCXgtWvXAuDi4pKpTJLySlJaPIv+7c/ZR3sB8LSqTG+fbyhTpKb8AidJ0ltH73nAki45D/jVPE16wOzTnbkTG4gCJRXtm9DbZxauFqUKOjRJkqR8IQdhSQUuOPpfZp/pQlTyQwyVxtR08qWXz3S5b68kSW81mYClAnU6fDeL/v2EFE0ipoaWNHbvyUelx2FqaFnQoUmSJOUrmYClAiGEYHfwIn4ImoRAYKNyor3XZzQr1kcuKylJ0jtBJmDptUvTpLI68DMO3t8AgKt5aXqVnU5lh+ZysJUkSe8MmYCl1youNYrvzvbi8tOjgALvIrXo7fMNntaVCjo0SZKk10omYOm1CY+/zTenPyI84RZKhSHvObTkY5+ZOJp5FHRokiRJr51MwNJrcfXpceac6UF8WhTGSlNqu3SkZ9lpWBrbFnRokiRJBUImYCnfHbm/heWXPkUt0rAwKkJLjwH4eo2Uy0pKkvROy1UC/vvvv/PkZE2aNMmT40hvBo3QsO36DHbemguAvUlRupSeQF23TnJZSUmS3nm5SsDNmjV75dGpCoWCtLS0VzrGy1Kr1TRq1Ihjx47x/IJfd+/eZcKECRw4cIDo6GgqVKjAuHHjaNeu3WuN8W2VrE5gyYVBnArfBUBxqwp8XHYWZW3rypHOkiRJvEQX9KuuWFkQK17OnDmTY8eOZSoPDw+nQYMGPH36lOHDh+Pm5sbq1avx9fVl8+bNdO/e/bXH+jaJSn6I/5mu3I4+jwIF5e0a4VduNm4WpQs6NEmSpELjrV0L+tSpU9StWxcDAwOSk5N1vgAMHjyY5cuXc+zYMerUqQNAUlIStWrVIjQ0lJCQEMzNzV/qfHIt6HR3YgKZfaYzT5MeYKgwprpzWz4uO4MiJs4FHZokSVKhoizoAPJDXFwcPXr04P3336dWrVo6j6nVajZt2kTt2rW1yRfAxMSEESNGEBERwe7du193yG+F84/+5Ot/WvA06QEmBhY0LtqLT8p/J5OvJElSFl5bAn6drcIRI0YQHR3NqlWrMj12+fJl4uLiMiVmgJo1awJw8uTJfI/xbSKEYG/IMvzPdCVZnYC1sSOdS4+jt88szIysCjo8SZKkQumVpiElJCSwfv16Ll26REJCAhqNRufxtLQ0EhISuH//PhcvXiQlJeWVgs2NX375hTVr1rBz506cnDLvppPxRaBYsWKZHnN3dwcgODg4f4N8i6g1aay78hX7764GwMW8JD3KTKWqUys52EqSJCkHeifgqKgo6tSpw7Vr1zI9JoTQ+fB9XbeZQ0ND+eSTT+jXrx++vr5Z1omOjgbAwsIi02NmZmYAxMfHZ3uOefPmMW/evEzlYWFh+oT8RktIjWb++T5cjEifplbKpgZ+PrPxsqlSwJFJkiQVfnon4AULFhAUFIRSqaRRo0ZYWVmxc+dOKleujI+PD/fv3+eff/4hLS2Nxo0bs3r16ryMOxMhBL1798bGxob58+fnWO/Z/2b1mIFB9nNUY2JiCA0NfbVg3wKPEkKYfbozofHXUSoMqGzfHL9y3+BoVrygQ5MkSXoj6J2Af/vtNxQKBevXr6dHjx6o1WqKFCmCq6srmzZtAuDKlSu8//77HD9+nISEhDwLOivz5s3j77//ZufOnSQlJZGUlARAamoqABERERgYGGBpmb7PbFbxZJRZW1tnex4rKyvc3NwylYeFhWXqgn9bXY88xbdnuhGb+gQjpYl2WUkrY7uCDk2SJOmNofcgrFu3bmFnZ0ePHj2A9FZj5cqVOX78uLaOj48PK1asICUlJcdWaV747bffEELg6+uLg4OD9icgIAAABwcHqlSpgqenJ5D1oLCMsqJFi2Z7ntGjR3P//v1MPy4uLvlwVYXP8Qc/M/VkG2JTn2BuaEPr4oPpV36OTL6SJEkvSe8WcHx8PJUq6W4hV7ZsWY4fP86dO3fw8Ejf4eb999/H0dGRw4cPv1qkLzB37lwiIyMzlX/22WdcvHiR/fv3Y2pqire3N9bW1lmOdM4oe3Z6kpROCMH2m7P5+cY3ANiZuNGp5FgaFu0ml5WUJEnSg94J2NraOlM3bokSJQAICgrSJmBIH3F8+fJlfU+VK1WrVs2yvEiRIkD6cpoZunTpwsqVKwkICNBZiGPBggU4OTnRqlWrfI31TZOiTmL5xWEcD/sZgGKW5fi47EzK2TWQI50lSZL0pHcXtI+PDzdv3uTRo0faspIlSyKE4Pz58zp1Hz9+jFJZeNb8mDJlijbRTpkyhWXLllG/fn0uXbrEwoULMTExKegQC42Y5AimnWzH8bCfUaCgnG0DhldeTXn7hjL5SpIkvQK9s2KrVq1ITU2lY8eOXL16FUhfyEKpVLJ8+XJtd/Avv/zCnTt3tPdeCwNnZ2cCAgJ4//33WbBgAV988QVGRkbs3r2bzp07F3R4hcb92CDGBzThRtQpDBRG1HT2ZWjlZbhbehd0aJIkSW88vdeCzthB6P79+yiVSuLj41GpVLRr147du3djZ2eHh4cHFy5cQKPRMGbMGGbMmJHX8Rcab9ta0BcfH2TeuV4kqeMwMTCnrutH9PCegplR9iPEJUmSpNx7pc0Ybt68Sf/+/bly5Yq2K/r69evUr1+fx48fa+uVKlWK06dPY2X19i5L+DYl4P131rD28hdoUGNlbE/r4kNpU2Iohkrjgg5NkiTprZEnuyE9fvwYBwcHnd/XrFlDcHAw3t7e9OvXTzv/9m31NiRgjVCz8eoE9oYsBcDJrATdynxNDed2KBWF5x6+JEnS2+Ct3Y7wdXvTE3BiWiyLzvfn3ON9AJS0rkpvn9mUKlKtgCOTJEl6O73SZgzS2yEi8T7+ZzpzN/YKSgyo5NAUv3L+OMllJSVJkvKN3gm4SZMmL1VfoVDw119/6Xs6KZ/cijqH/5muRKc8wkipopZze3qVnYGVyr6gQ5MkSXqr6Z2ADx069MI6GfNEn98dSSocTob9yuILA0nVJGFmaE1j9x50LjMBlYFZQYcmSZL01tM7AU+aNCnbx+Lj43nw4AEHDhzg8ePHTJgwgYYNG+p7KimPCSH49dZ3bL0+FQBblSvtvUbTtJgfBkp5V0KSJOl1yNdBWPHx8Xz44YccO3aM8+fPU6pUqfw6VYF7UwZhpWlSWHlpJIdDfwDA3aIsvcpOp6J9E9lLIUmS9Brl69wSc3Nz1q5dS2pqKlOnTs3PU0m5EJvylOkn2/+XfBWUta3LiCqrqeTQVCZfSZKk1yzf+xtdXFzw8fGRA7AK2IO4m8w+8xEPE4IxUBhR1bEVvX1mYWeaeW9jSZIkKf+9lht+cXFxWW4VKL0el58cYe7ZXiSkRaMyMKOOSyd6lp2KuZFNQYcmSZL0zsr3BLxjxw5u3br1Vt//LcwO3tvIysBRaEQalkZ2tPQYgK/XSIwMVAUdmiRJ0jtN7wT89ddfZ/uYEILk5GSCgoL4448/UCgUdOjQQd9TSXrQCA1brk3ht9sLAHA0LU6X0uOp7fqhXFZSkiSpENB7FLRSqXzhwJ2MQ3t7e/PPP/9gbf327qRTmEZBJ6XFs+TCQE4/3A1ACasq9Pb5hjK2NQs0LkmSJOn/9G4BN2jQIMcEbGhoiL29PfXq1cPPzw9zc3N9TyW9hKdJYXx7pivBMRdQoKSCfWP6lvsWZ/MSBR2aJEmS9Ix8XQlLer2Coy/gf6YrkclhGCpV1HRux8dlZ2KtcnjxkyVJkqTXSu+bgXfv3tXuAfwigYGB/Pbbb/qeSsqFMw9/Z/KJVkQmh2FqaEUT9158Un6+TL6SJEmFlN4t4OLFi1O/fn0OHz78wrp9+/YlODiYx48f63s6KRtCCPYEL2Zz0NcIBEVUzrQtMZyWHgPkspKSJEmFWK4/oTUajfb/MwZXCSG0P1kRQnDnzh1u375NQkLCK4YqPS9Nk8ray1/w1711ALiZl6aH9zSqOLaQK1tJkiQVcrlKwEFBQVSsWBG1Wq0tUygUHD9+HEPD3OXwSpUq6RehlKW41Cjmn+tN4JP0HogyRWrTx2c2xa0rFnBkkiRJUm7k6h6wt7c3AwYMyNTiffb3nH7MzMz45ptv8vVC3iXh8cF8HdCcwCeHUSoMqeb0AZ9WXimTryRJ0hsk1/OAY2NjOXfuHJCeeJs0aUKFChVYuHBhts9RKpVYWFhQunTpt34a0uuaBxz09B/mnO1OXGokxkpT6rh8SC+f6XJZSUmSpDdMru8BW1pa6uzpW6xYMby9veU+v6/R0dAfWX5xGGkiFQsjW5oV86NjyS8xNjAp6NAkSZKkl6T3MNmQkJA8DEPKiUZo+OnGTHbcnAOAg2kxPiz5FQ3cu8llJSVJkt5Qcp5KIZeiTuT7i0M4EbYDgOJWFfm47Ex87OoVcGSSJEnSq9A7AZco8XJLGyoUCm7duqXv6d4JGqHm6tMAopIfYqNywsW8JPPO9uRm9FkUKClv15A+5b7F1aJkQYcqSZIkvaJ874JWKBQIIeS81Bc4Fb6LdVfG8DTpgbZMiQEa1BgqjKnu1Jbe5WZho3IswCglSZKkvKJ3Al67dm22j8XHx/PgwQN27drF5cuXmTp1Kt26ddP3VG+9U+G7mHeuN6A7IF1D+rxrH9t6DKy4ABNDiwKITpIkScoPem9HmBsajYY+ffqwZcsWAgICqFatWn6dqsDpOw1JI9QMO1hBp+X7PFsTNxY3vohSYZAXoUqSJEmFQL4OoVUqlSxYsAAjIyNmzJiRn6d6Y119GpBj8gV4mhTK1acBrykiSZIk6XXI9zksNjY2eHt7c+zYsfw+1RspKvlhntaTJEmS3gyvZRJpREQE8fHxr+NUbxwblVOe1pMkSZLeDPmegBctWsS9e/coWVJOnclKWds62Jq4AtmNEldgZ+JGWds6rzMsSZIkKZ/pPQr6448/zvYxIQTJyckEBQVx+fJlFAqFHAWdDaXCAD+fb/4bBa1AdyR0elLu7TNLDsCSJEnKR2qN4MTDVB4lqnE0NaCWkxEGyvydPqv3KGilUqmd4/si9evX588//0SlUulzqjfCq27GkNU8YDsTN3r7zKKGc7u8DFWSJEl6xp6QJCacjCUs4f/73ruYKZle05IPiuffWvt6J2A/P78cF9cwNDTE3t6eevXq0bp167d+IY682A3p+ZWwytrWkS1fSZKkfLQnJIn+B6N5PhFmZKxVja3zLQnn6zzgd8nr2o5QkiRJyhtqjaDaTxE6Ld9nKQAXcyWnO9nnS3e03IxBkiRJequpNYLYVEF0sobHSRqeJGl4mqTh/OPUbJMvpI/IeRCv4cTDVOq6GOd5XDIBS5IkSYVa2rMJNDE9gT5J1vA0SRCVrCEqRUNMiiA6RRCXqiEuVRCfKkhIS/9JVr/a+R8lvuIBspGrBFysWLFXPpFCoeDOnTuvfBxJkiTpzZKqEcSkCKKS1TxOEjxJ0hCZpCEyWUNkcnoSjUkRxKQKYlP+S6BpgoT/kmhK9o3Ul2KsBDNDBWZGCiyMFCAE16NffHBH0/wZi5OrBHz//v1cj3jOzts+CEuSJOltlaJOT45RyWoeJ4j01mdyRhJNb5lGp6S3UmOfa4Em5kMCNf8vgVoYKbEyVmBlpMBapcRGpcBWpcTWRImdiRJblRJrYwVWKiVWRgqMDXTzUMY94PAETaZBWPD/e8C1nIzy5gKe81Jd0AqFgipVqtC5c2ecnOTKTJIkSW+CZLUgJiX9vmdEkuBp0jMJNOWZFmiKhtj/kmdGCzRRLUjNowSqUoLpfwnU3EiBpZESSyMF1ioF1sbpCbSISkkRlRJ7EyV2JgpsVEqsjNMTrVEeD4QyUCqYXtOS/gejs1mFAabVsMy3+cC5GgU9Z84ctm3bxpkzZwAwMDCgUaNGdO3alQ8//BAbG5t8CU4fly5dYsqUKRw+fJjo6GhcXV1p3749U6ZMwdraWlvv7t27TJgwgQMHDhAdHU2FChUYN24c7drpN+dWjoL+X3vnHRbVsb/x9ywLS2eXTgCBgBQLHQWxG0v0/gyKYo0xdmMilhhRUzTq1Ws0iSUmURGMJcTEGEuMLZYYBbsoWNAgFqxcRJAOO78/ljPusoVFFha483ke7yVTzvnunJl5z7TvYTAY9UVJRZWAlkqRUywT0WdVo9C8UoLnZVLkV62DvignSiPQCh2ddTE2kAmoqZAfgcpE1NJINgKVGMkE07pKPO2MBZAYC2gaYT07tnhVVJ0Dfs1MgIXtGtE54Nu3byMpKQnbt29HamoqOI6DoaEhevXqhaFDh+Ktt96CmZlZvRlbEzdu3EBISAiEQiGmTJmCFi1aIDk5GZs3b0br1q2RnJwMMzMzPHr0COHh4cjNzcXUqVPh7OyM+Ph4nD9/Hlu3bsXw4cNrfW8mwI0LfXi1YdQ/TfG5EkJQXAnkl1VtHqK7cGUimlcmm8LNL5NWrYG+HIEW15OAmslN41IBrRJPsUgmntYi2TSurYkBrIw4WBhyjb6s60KT8oSVkZGBpKQk/PTTT7h27Ro4joOxsTH+9a9/YejQoejbt2+De77q3bs3jh49igsXLqBNmzY0fNWqVYiNjcWyZcswa9YsTJ48Gd9//z3+/vtvdOgg87FcUlKC8PBwZGdnIysrq9YvEkyAGw/68mrDqF/09VwJISiqUBTQnKpjLHlVG4ielxE8L5eiQH4EWjWFW1JJUKlDATWtGoGaVa2BWhhyVQIqgJWIg6RqGtemah3UzlgmqubNXECbIjpxxJGWlkZHxrdu3QLHcTA3N0dUVBSGDBmC3r17w8Cgfj06lZWVQSwWIyIiAn/++adCXF5eHiQSCfr164ddu3ZBLBbD398fJ0+eVEiXkJCAMWPGICkpCUOGDKnV/ZkANw706dWGUX/U5bkSIhPD56Wkag20agRaKtuFK5vCJXJTuFK8KAeKq46wFFcSSHUgoBwUp3CrbySyMhJAXDUKlRhzstFn1T9rY5mACthm1maFTs4Bt2nTBosWLcKiRYtw8eJFKsabN2/Gli1bIJFIEB0dje+//14Xt1OJUChEeno6pFLl3QKPH8u+pWtgYID09HS8ePEC4eHhSunat28PADh9+nStBZihfyqlBB+fLlC5m5FA1gF+cqYAfVqImvxIgBACAoAQ2W9T+LtaGECU4qXq0mlxPSKfTlV6/m9CIAUglVblASAlQCV5mVZKQMVNWvXf/JhACgKpFKgkBLNOqX+uAPD+X8+xNaMIL8rx8hxo1fRtSYXs2nWFA2AsBEwMVAioIQdLEQexkWy0KRFxdARqayyAnYksHTsNwpBH5444goKCEBQUhP/85z/45ptvMHfuXOTm5mLDhg31KsACgQAeHh4q45YvXw4A6NatGx2dqjrb7OLiAkC21s1oeiQ/KtPKq03YzzkwFnIqREbWncsLCZRESrUYQU0a+fzy1wZejqrUpVN1HcilYbykuBI4kl2uMY0ALwW0+k5cS34aV/RSQOkaqLEA9iYcLIyYgDJ0i84F+PTp0/j555/xyy+/4N69e/Rt1t3dXde30orNmzdjw4YNcHV1xbhx47Bnzx4AgLm5uVJaU1NTAEBhYaHa63355Zf48ssvlcIfPnyoI4sZNSElBNmFUtx4VoHL/y1HWm4FbuZV4Ha+dt5qHmgQaYYMTu7/ec3hqv6Hq56GU87zMh2nkE7tdTVcp6wSKCiv+bWjg6MhAmyEdBeu7CiLbCRqV7WRiAkoozGhEwFOSUmhonv//n0qum5ubhg0aBBiYmIQFhami1vVik2bNmHs2LEwMzPDjh07YG5uTm1TtfTNh2lar87Pz0d2dnb9GMxQgPBCm1eBK/8tx5X/yoT27otKlNTBM9xHQWbwkwipCHAADKr+kA8TQFEUBAKZnMinUUjPyfKA/jenMp1iHrk0VYojUJWnWhigzbU1pFMZ1jjF6eTDMkTvf1ZjupmB5vXir5fBqC9eWYCTk5Px888/Y8eOHQqi6+rqSkWXX1PVBwsXLsSnn34KKysr7N27l74AWFhYAACKioqU8vBh8ueFq2NpaQlnZ2el8IcPH6pcf2bUDC+0GXkVSMuViW1GXgXuFKgXWgNOdk7P00qIVhIhAmyE8JEIMexgXo1ebWL9zZr8GvD/EuEOhnAyFejNWxGDUV/USoBPnTpFRTc7O5uKrrOzMxXdiIiIejFUW8rLyzFhwgQkJibC2dkZ+/btg7+/P43n14lV7VTmw1xdXdVef8aMGZgxY4ZSOL8LmqEeQggeFEqR8bwC6f+VTR9n5FUgSwuh9bISwk8iRICtEH4SQ3hYGqj0iqNPrzaM+kHf3ooYjPpCKwGeNm0aduzYgQcPHlDRdXJyQnR0NGJiYtCxY8d6NVJbKisrMWzYMOzYsQP+/v7Yt2+f0mjV19cXVlZWOH36tFJ+Pow/G8x4NQgheFgkmzq+lisT2htVQltcoTqPvNC2qhJaXw1Cq45+7sbY0A3K50UbwKsNo/5gz5XRHNHqHLBAINv9JxQK0b17d8TExKBTp04QCAS1utnrr7/+yoZqw9y5c7FkyRK0a9cOBw8eVDuVPHHiRKxfv17JEUf79u3x+PFjZGVlwdi4dg36f/EcMCEEj6qE9mpuBa7kluPGs0pkFVSgqAahbWklRCtrIfxtDOEjEcLDwkDJUXpdaIoekxg1w54rozlRKwGu0404DhUVanplHXD37l14enqisrISS5YsUblO6+DggJ49e+LRo0cICgpCUVERZsyYAQcHB+qKMikpCTExMbW+f3MWYEIIHhfLdh1feyZbo72eV4nb+eqFVsABzlVC29paiDY2hvARC/G6pW6FlsFgMJoqWq8B19Vhlg4cbmnk2LFjVODj4uJUpunSpQt69uwJR0dHnDp1CnFxcVi5ciXKy8vRtm1b7N27F3379q1XOxszhBA8KZafOq7AjarjPYVqnNHKC20bGyFaSWQjWk8mtAwGg6ERrUbAd+7c0cnN3NzcdHKdxkhTGgETQvCUF9pnFbjy3wpcf1aB2wWVeKHmvCUvtN5VQusnMYSP2ACvWwkhYkLLYDAYtUarEXBzFs7GhK7XtwghyCmR4sazSlzPk52jvfZMNqJV59hAAMDZXABvsRBtrIXwlQjhIxbCkwktg8Fg6BSde8JivBp1+dKLTGgJbuRV4Mazl0KbqYXQ+ohlm6F8xcKqqWMhjIVMaBkMBqO+YQLcCFD3pZdHRVKMO/ocG7qBirBsRCtbm037bzmuPqtA5vNK5KsRWg4yofWtElof8csRrQkTWgaDwdAbTID1TE1f8AGAD048x5rLL/BPgexzaargIFuj9ZXIHFb4VE0dezGhZTAYjEYJE2A9k/K4XOMXfACgqAK4+F+Zqyh5oeXXZ33EQniJhTBlQstgMBhNBibAeuZJsXZfFXjHxwTDvU3Qkgktg8FgNAuYAOsZexP1X16Sp7+HMQJsmbN5BoPBaC7UzpckQ+fwX3pRN6blIHPdyL70wmAwGM0LJsB6hv/SCwAlEWZfemEwGIzmCxPgRoDsSy9WcDRVfBxOZgJs6GbFvvTCYDAYzRC2BtxI6OdujD4tROxLLwwGg/E/AhPgRoSBgEOkk5G+zWAwGAxGA8CmoBkMBoPB0ANMgBkMBoPB0ANMgBkMBoPB0ANMgBkMBoPB0ANMgBkMBoPB0ANMgBkMBoPB0ANMgBkMBoPB0AMcIUT1B2YZtcLIyAjl5eUQCARwcnLStzkMBoPB0COOjo44d+6cxjTMEYeOqKyUfVZQKpUiOztbz9YwGAwGo7HDBFhHGBsbo6SkBAYGBrC3t9ebHQ8fPoRUKmUjcR3BylO3sPLULaw8dYsuy9PR0bHGNEyAdURhYaG+TQAAuLi4IDs7G05OTrh//76+zWnysPLULaw8dQsrT93S0OXJNmExGAwGg6EHmAAzGAwGg6EHmAAzGAwGg6EHmAAzGAwGg6EH2CasZsaMGTOQn58PS0tLfZvSLGDlqVtYeeoWVp66paHLkzniYDAYDAZDD7ApaAaDwWAw9AATYAaDwWAw9AATYAaDwWAw9AAT4GbEuHHjwHGcyn+JiYn6Nq/Rc/r0aRgYGODYsWNKcXfv3sWoUaPw2muvwczMDOHh4di9e3fDG9mE0FSeb7zxhtq6qir9/zJXrlzBoEGDYGdnByMjI7i7u2PatGl4/vy5QjpWR7VD2/JsiDrKdkE3Iy5fvgx3d3csXLhQKa5Dhw56sKjpcPPmTQwYMABSqVQp7tGjR+jcuTNyc3MxdepUODs7Iz4+Hm+99Ra2bt2K4cOH68Hixo2m8gRkdTU0NBSxsbFKcX5+fvVtXpPhxo0biIiIgFAoxJQpU9CiRQskJydj9erVOHLkCJKTk2FmZsbqqJZoW55AA9VRwmgWVFZWEhMTExITE6NvU5ocv/76K5FIJAQAAUCOHj2qED9p0iTCcRw5efIkDSsuLiYBAQHE1taWvHjxooEtbtzUVJ4PHjwgAMhHH32kHwObEL169SKGhobkypUrCuErV64kAMiyZcsIIayOaou25dlQdZRNQTcTbt68ieLiYrRp00bfpjQp+vXrh4EDB8LJyQnDhg1Tiq+srMSWLVsQERGhMItgbGyM2NhY5OTkYO/evQ1pcqOmpvIEZCMLAKyu1kBZWRlOnDiBTp06KZXVqFGjAADHjx9ndVRLtC1PoOHqKBPgZkJqaiqAlxWmqKiIfqOYoZ7r16/j3//+Ny5cuABvb2+l+PT0dLx48QLh4eFKce3btwcgW+tkyKipPAHlulpYWKh2qvp/GaFQiPT0dKxbt04p7vHjxwAAAwMDVke1RNvyBBqujjIBbibwFWb//v1wd3eHmZkZTE1NERUVhczMTD1b13i5evUq5syZA5FIpDKe/yRZixYtlOJcXFwAALdv364/A5sYNZUn8LKubtq0CY6OjjA3N4eFhQVGjRqFp0+fNpSpjR6BQAAPDw94enoqxS1fvhwA0K1bN1ZHtUTb8gQaro6yTVjNBH7KJCUlBR9//DFsbW1x6tQprFy5EqdOncKZM2fg7u6uXyMbIZqEAgDdGWlubq4UZ2pqCqDxfAu6MVBTeQIv6+qFCxewfPlyGBsb49ChQ1i/fj1Onz6N06dPQywW17OlTZfNmzdjw4YNcHV1xbhx47Bnzx4ArI6+KtXLE2i4OsoEuJkwdOhQBAcHIy4uDiYmJgCAqKgohIeHIzo6GvPmzcPWrVv1bGXTg1R5aiUqPLbyYfy0FUM7Jk2ahIKCAnz00UcQCGSTcIMGDYKPjw9mzpyJL774AosXL9azlY2TTZs2YezYsTAzM8OOHTtgbm7O6mgdUFWeQMPVUTYF3UwYMWIEFixYQMWXZ+DAgXB1dcWBAwf0ZFnTxsLCAoBsTb06fJiVlVWD2tTUmTJlCuLi4mjHxvP+++/DwMCA1VU1LFy4EKNHj4a5uTn279+PsLAwAKyOvirqyhNouDrKRsD/Azg4ONApFUbt8PDwAPByLVgePszV1bVBbWquGBkZQSKRoKCgQN+mNCrKy8sxYcIEJCYmwtnZGfv27YO/vz+NZ3W0dtRUnprQdR1lI+BmQE5ODvz9/TFw4ECluPLycty8eRNeXl56sKzp4+vrCysrK5W7SPkw5uREe65cuYLWrVtj6tSpSnFPnjxBTk4Oq6tyVFZWYtiwYUhMTIS/vz9Onz6tJBasjmqPNuXZoHW0Xk8ZMxoMPz8/IhQKyblz5xTCFyxYoHDAnKGezz77TKXjiAkTJqh0cuDv708cHBxIcXFxA1vaNFBVnkVFRUQikRArKyty584dhfRjxowhAMj27dsb2NLGy5w5cwgA0q5dO5KXl6c2Hauj2qFNeTZkHWVT0M2EtWvXok+fPujRowemTJkCZ2dnHDlyBDt27EDXrl0xbdo0fZvYZFmwYAF2796NN998EzNmzICDgwPi4+Nx5coVJCUlwdjYWN8mNhlMTEywZs0ajBw5EuHh4XjvvfdgZWWFXbt24c8//8SIESMwePBgfZvZKLh79y6++OILcByHgQMH0t3O8jg4OKBnz56sjmpBbcqzweqoTmSc0Sg4f/486d+/P5FIJMTIyIj4+PiQhQsXsrdfLVE3AiaEkMzMTBITE0MkEgkxNzcnERER5Pfff294I5sQmsrz8OHDpEePHsTc3JwYGxuTgIAAsmbNGlJZWdnwhjZSNm3aRN15qvvXpUsXmp7VUc3Utjwboo5yhKjYu85gMBgMBqNeYZuwGAwGg8HQA0yAGQwGg8HQA0yAGQwGg8HQA0yAGQwGg8HQA0yAGQwGg8HQA0yAGQwGg8HQA0yAGQwGg8HQA0yAGQwGg8HQA/UiwJcvX8bUqVPRunVriMViGBsbw9XVFW+++SbWrFmD4uJilflGjx4NjuMwcuTI+jCrXpk/fz44jkPHjh21zuPu7g6O47Bhw4Z6tKz+4TgOHMfh8OHDOr/2sWPH6PUrKiq0zpeYmAiO4+Di4qIy/v79+8jPz1cIq+/6V1lZiU6dOsHPzw/l5eX1cg990JTbbW0oKCigbl5FIhGcnJyafNt9VV6lv9NEUVERsrKydHKtpoTOBfizzz5DUFAQVq9ejfv37+P1119HYGAgBAIB9u/fjw8++AA+Pj64cOGCrm/NYNRIWVkZPv30U3h7e+PJkycNeu9ly5bh77//xooVK2BoaNig92bUneHDh2Pt2rV4+PAhfHx8YGdnB3d3d32b1eTZtm0bvL296+UFvrGj048xJCQk4PPPP4eZmRkSExMxYMAAGBgY0Phr165hzJgxSElJQe/evXH16lXY2dnR+CVLliAuLo59PJpBadeuHa5duwYAEArrXl0fPHiAhQsX1vk6teX27dtYuHAhunfvjr59+zb4/Rl1o7CwEL///jsA4Ntvv8XEiRP1bJF+ef/99zF06FCYmprW+Vpz585Fdna2Dqxqeuh0BLx48WIAwPLlyzFo0CAF8QUAPz8/7N69G/b29sjJycGqVasU4p2cnODr6wsnJyddmsVowpiamsLX1xe+vr76NqVOzJkzB8XFxZg/f76+TWG8Arm5ueDd5nft2lW/xjQCbG1t4evrixYtWujblCaNzgQ4Ly8P//zzDwCgffv2atPZ2dkhKioKAFR+QJrBaG6kp6dj+/btaNWqFTp16qRvcxivQGVlJf1bJBLp0RJGc0JnAiy/prV3716NaRcsWID09HT8+OOPCuGaNnPk5uZi/vz5aN26NczMzGBvb4/hw4cjIyODbgiQH13wm3fCw8NRXl6OFStWICAgAKamphCLxejRowd27dql1sbjx4/j7bffhqenJ8zMzCASieDs7Izo6Gj8+eefWpaK9hw4cABdunSBubk5xGIxunfvju3bt6tN//DhQ8ybNw/t2rWDtbU1DA0NYW1tjQ4dOmDFihVKG92ysrLAcRwcHR1BCEF8fDzat28PCwsLWFhYICIiAgkJCVD3caxbt25hwoQJ8PDwgLGxMVq2bIlFixahrKxMKe2IESPAcRwmTJigFJednU03Va1Zs0Ypfvfu3eA4Dm3btgVQ8yasnTt3okePHrC1tYWFhQW6du2KgwcPqvwNXbt2hYeHB/3vli1bguM4HDt2TCntnTt3MGHCBLi6utJnP3r0aNy8eVPltTWxevVqEEJU1utXrac1bTLjnzfHcQqbW/i28vHHH+PRo0eYPHkyXFxcYGxsDE9PT3z88cf0mR47dgx9+vSBRCKBiYkJgoOD8cMPP2j8rRkZGYiJiYGNjQ1MTU0RGBiIZcuWoaSkRG2ev/76C4MGDYKTkxOMjIzg4OCAqKgoHDlyRGV6fvPi5cuXERsbC4lEAnNzc4SEhCA3N1ejffJ2Tp48GV5eXhCJRLCyskJ4eDi++uorpbbDcZxCvfHw8ADHcRg9enSN9+H7tO+++w5ZWVkYM2YMXFxcIBKJ4OLignHjxiltPoqIiADHcZg6dara6y5atAgcx+HNN99UCL958yYmTpyo0E4///xzlJWVoWPHjkqbJetaj1RtwkpKSkLv3r3h5uYGkUgEe3t79OnTB1u3boVUKlW6xp07dwAA48ePV+rHNZGdnY2pU6ciICAAlpaWsLCwQKtWrRAbG6t2Q1deXh6WLl2Kzp07w9bWFoaGhhCLxQgJCcH8+fPx7NkzpTzy/U9SUhI6dOgACwsLSCQS9OrVC8nJyQBkyxTz5s2jdcrBwQGjRo3CgwcPVJapWCzW7feAIyMjCQDCcRwZNWoUOX78OKmoqNA6/zvvvEMAkBEjRiiEZ2ZmEi8vLwKAGBgYkICAAOLj40MAEDMzM9KrVy8CgHz22Wc0z9GjRwkAEhQURHr06EEAEFtbWxIcHEzMzMzo9x+//fZbJTvi4uJovJ2dHQkJCSFeXl5EKBTS8O+//14hD//t08jISK1/r5ubGwFAOnXqRAAQY2NjEhISQpydnel93n33XaV8ycnJRCwW0zytW7cmgYGBxMrKiubr1KmTQtnfvn2bACD29vbk7bffJgCIWCwmwcHB9FoAyOzZs5Xud/jwYWJhYUHLOzQ0lLz22msEAOnWrRvNe+jQIUIIIUlJSQQAadGihdK1EhISaPoBAwYoxY8fP54AIPPmzSOEvHyOAEh5eblC2vfee4/Gubq6kpCQEGJiYkIAkK5duxIAxNnZmaZ///33SWhoKM0TEhJCIiMjyYULFwghL+tf27ZtiVgsJhzHET8/P+Lr60sMDAwIAGJubk7S09O1ebyEEEIqKyuJtbU1AUBSU1OV4l+1nvLlKP/75OGfNwBy+/ZtGs7X05iYGGJvb08EAgFp27YtadGiBU0/dOhQ8u233xKO44i5uTkJCgpSqCPfffedwr34cgsMDCSWlpaE4zjSpk0b4ufnR/MEBweT3NxcJTtnz55N00gkEhISEkIcHR1p2EcffaSUh283fH/TqlUr4ubmRiIiIrR5JGTLli1EJBIRAMTExISEhIQQb29ves+2bduSe/fu0fSRkZEK9SY0NJRERkaSxYsX13gvvmzGjx9PLC0tiUAgIC1btiStWrVS6GPu3r1L86xbt46GV6/zPLy9P/30Ew07dOgQsbS0JACIqakpCQ0NpWXZvXt30q5dO4V2Skjd61H1/m769Ok0vZubm0JfAYC8/fbbNG18fDyJjIykz8LLy4tERkaS+Pj4Gsv11q1bxN7envZJbdu2JW3btqXXsrS0pO2aJyMjg7i6uhIARCgUEl9fXxIcHExsbGyofT4+PqSgoEAhHx/3/vvv0+cSGBhI+xqRSESOHDlCWrduTTiOIx4eHqR169Y0n6enJyksLFQqUysrK6JTAb5w4QIxNzdX+MCxpaUl6du3L1myZAlJSUnR+DFjVQIslUppQwsNDSWZmZk0LiUlReHhqhJgvtPcunUrjcvLy6OdnY2NjUIl5/MJBAKyceNGBXvv3btHO3YHBweFuLoIMADSr18/kpOTQ+Pi4+Op4MtXyIqKCuLp6UkAkKioKIVOraysjCxdupRec+/evTROviEZGBiQlStXUoEuLi4mI0eOpBXzyZMnNN+zZ8+InZ0d7Zjz8/NpXGJiIjE0NFQS4OfPn9Pw69evK/zmYcOG0fTW1tZK9YF/+Th79qzC86guwFu2bCEAiJGRkcKzffbsGRk8eDDNU71jkS+HmzdvKsTx9Y8XRHnbL126RGxtbal4acu5c+doO1DFq9bTunacAIifnx/JyMigcZ9++imNEwgEZMaMGaSkpIQQQkhhYSHp06cP7VjVlZuXlxe5cuUKjUtJSSFOTk5KnS8hhHz33XcEkL0IbtmyhYZLpVKSlJREX0A2bNigkE++3SQlJdHwp0+fqiwLeVJSUmi7Gj9+PHn+/DmNu3jxIhW24OBghfJWV541IV824eHh5MaNGzTu1KlT9MU2NjaWhufn5xNTU1MCgOzZs0fpmqdOnaIvLPzzycnJoe00Ojqa5OXlEUJkL4CrV68mAoFAqZ0SolsBvnr1KgFkg4KjR48qXGfTpk3UhuTkZIU4/nmuX79ec2HKMWTIEAKADBo0SEEwHz16RDp06EAAkN69eyvk6dKlC30ODx48oOFSqZT88MMP1L41a9Yo5JPXs6VLl9J6ce/ePao/AoGAeHh4kDNnztB8Bw8epC/uiYmJNFy+THUqwIQQkp6eTjp27KhgtPw/e3t7Mm/ePIU3Ah5VArxv3z7aOT18+FApT0pKSo0CvGLFCqV858+fp/FpaWk0fPbs2UQkEpHo6GiVv++vv/6i+eTtqYsAe3p6kuLiYqX4jz/+mHZq8naLxWIiEokUBFue119/nQAgS5YsoWHyD/2DDz5QypOTk0M4jlMSbl7Qvb29SVlZmVK+BQsWqGzYb7zxBgFAVq5cScOkUimxt7cnlpaWJDAwkABQeEu9cOECAUBcXFxomDoB9vX1JQDI/PnzlWwqLS2lMySvIsBCoZDcuXNH6bqLFy8mAIiTk5NSnDq++OILAkDt6OxV66kuBDglJUUhT2FhIe0wOnfurHTNY8eO0bzyL358uRkYGJCrV68q5Tt8+DDtpLKysgghsmfk4OBAAJBff/1V5W9Yu3Yt/Y3yz15+5qi29O7dmwAgvXr1UhmfmZlJRzabN2+m4XUVYCMjI5X91wcffEAA2eBCnlGjRql92Zs4cSIBQKZMmULD+Lrp6elJRVmeuXPn1rsA8zNfAQEBKq81ZcoUMmzYMHLs2DGF8FcRYL7979y5Uynu7NmzpE+fPmT69Ok07PHjx/QFWv4FUZ7u3bsTAGTixIkK4fzv79Onj1KeOXPm0PgjR44oxfMDtqlTp9Iw+TLV+TngVq1a4cSJE7h48SI+++wzdOjQQWF9+MmTJ1i8eDH8/f1x//79Gq+3c+dOAMCAAQPg6OioFN++fXtERERovMb//d//KYX5+fnRv/Py8ujfS5cuRXFxMbZs2aLyWvLb7ouKijTeV1vGjh0LY2NjpfDx48cDkK2/3rhxAwAQHByMZ8+e4dmzZ7CxsVHKU1paCmtra432qSoPGxsbeiRMvjz4oxfDhw9XeXZ10qRJKu/Rv39/AFBYj01NTcWTJ0/QpUsXdO7cGQAU1vn4e/F51ZGZmYnr168DgMp1OCMjI4wdO1bjNTQRGhqqcnenv78/ACAnJ0fra92+fRsA4OXlVWPa2tTTumJjY6O0WdLU1BT29vYAoPKo1GuvvUb/ru7EBAB69OihYK98uIeHB6RSKfbv3w8AOHXqFB4/fgwLCwu89dZbKm0cMWIEBAIBsrOzVfoNqK0TiMLCQhw9ehQAMG3aNJVpPDw8MGDAAADAb7/9VqvrayI0NFRl/8WXV/VnO2bMGACyPRHPnz+n4aWlpfjpp58AAO+++y4N3717NwBg5MiRKjeJTZ8+HRzH1e1H1EDLli0ByNr5hx9+qLRfYs2aNdi2bRu6dOmis3vFxcVh165dCuv2oaGh+OOPP/Dll1/SMHt7ezx9+hRFRUVo06aN0vUqKythaWkJQH2/2a9fP6Uw/hy4qampyt/FtxlV7QWoR1eUgYGBmD9/Pk6ePIm8vDwcPHgQM2fOpA38n3/+weDBg2u8TlpaGgAgICBAbZrQ0FCN13B2dlYKMzExoX9X39zDcRwMDAxw4sQJfP/995g9ezaio6PRsmVLhXvJbyioC8HBwSrDW7RoQc9E82dh5e2/efMmfvzxRyxatAhjxoxB+/btIRaLce7cOY32qSoP/pqAYnnwwq+q0gKyii3fMfPwInrs2DG6qYcX4x49etDdwPICzG/e43fJq4O3ycLCAm5ubirTBAYGaryGJtSVj7m5OQCgvLxca69cvLMPsVj8SvfVVE/rgqurq8pwIyMjAFA4n88j/wJGVGzWU1ePgZcvL3w95tt1WVkZOnfujI4dOyr969u3Lz3KyL9wyVPb44qZmZm0LoaEhKhNx8fx9UwX1KbNAUCXLl3g5eWFkpIS/PzzzzR89+7dyMvLQ9u2bRV+A38Chd+8WB1bW1t4enrW6TfURHBwMEaMGAEAWLFiBby9veHh4YFx48bh559/RkFBgc7u9fnnn8Pc3Bw3btxAVFQUJBIJunfvjiVLliA1NVVtPhMTE9y7dw+//PILli5digkTJqBTp04Qi8X0hUtdv6mqzfDtxcbGBgKBspzybUZVe+E4rmF8QZuamqJnz55Yvnw5srKyMHToUABASkpKjR6x+NEG3/mpgn9zUQdfSOqQLxxCCL7++mu4u7ujc+fOmDRpEpYtW4adO3dCKBTi7bff1nitV8HCwqLGOPm3stOnT6Nr167w9vbG8OHD8cknnyAhIQGZmZno27evwo5NVdSmPPhdgZrKXyKRKIW5ubkhICAAhYWFOHnyJADg0KFDAGQC3L17dwgEApw4cQIVFRV4+vQpzp49CysrqxrPWfI2mZmZ1combVE1G/Gq8LZq47CgNs+lrmgqOwAqO5OaqE095kd1paWlOHnypNp/vMtOVaN/+ZcTbZAfhWhy9sP3J7oUjFd5tvzszubNm2nYpk2bACiOfoGX5ampnaqaMdM1mzdvRnx8PMLDw+nO6fj4eMTExMDe3h6xsbEqT07UlsDAQKSmpmLcuHGwsbFBaWkpjh49irlz5yIwMBD+/v74+++/FfLcuHED/fv3h7u7OwYPHow5c+Zg/fr1uHz5Mjp16qRxkAdobjOv0l7MzMx0J8CTJk1Cy5YtqTMOdZiYmGDdunW0Qtb0lsn/aHVDeEC3DeXzzz/H9OnT8eDBAwwZMgSJiYk4e/Ys8vPzce3aNXzyySc6uxfPixcv1MbxDYsXlGvXrqFbt244fvw4WrVqheXLl+PQoUO4f/8+nj59ih07dujUkQnfaDWVvzrf3vLT0CUlJfj777/h4OCANm3awNraGoGBgSgoKMC5c+ewb98+SKVS9O3bt0Y3jbxNmp67OpsaGl7MdTl9LI86US4sLKyX+2miNvWYb9chISEghNT4T9ORHG2Rf0GQn9atDv/SpOmFoiEYPXo0fUm9e/cunj59igMHDsDQ0FDpSBv/QqGpnWpaMtNVPeI4DmPGjEFycjIeP36M7du347333oObmxtKSkqwatUqfPjhh7W6pjpef/11rF+/Hk+ePMGZM2ewbNky9O7dG4aGhrhy5Qp69+6Ne/fuAZDNRHXu3Bl79uyBi4sLFi1ahH379iEzMxN5eXnYt28fWrdurRO7tKW4uFh3AlxcXIxbt25ptW5iYWFB39RUTXXJw0+pXL58WW0aTVMOtaG8vBzLly8HAHz66adISkrCO++8g9DQUGqvNuvWtUXV9BogW/vlRYafAl65ciWKi4vh6+uLs2fPYubMmXjjjTcUprh0aaOPjw8A4OLFiyrjX7x4Qc/xVYcX4AMHDiA5ORklJSXo0aMHjX/jjTcAyKahtZ1+lrepsLAQGRkZKtOkp6fXeJ2GgF/3q826sTbwbjlLS0tVxlc/e9gQqKvHhBBaf/j2zD/DjIwMtVPrhBAcPXoUN2/e1MmoydPTk77cnT9/Xm06fgmHX2fUF87OzujVqxcIIfjtt9+wZ88eVFRUoF+/fkr9Jl+u6tppaWmpyjPsuqxHBQUFOH/+PB1U2dnZYfDgwfjmm2+QmZmJyZMnA1Ac0b8KhBBkZWXRGTWBQICwsDDMmjUL+/fvR1paGiwtLVFUVIRff/0VALBx40Y8efIE1tbWOH/+PObNm4c333yTnukG6qdv10RlZaXuBJh/Izt37hwSExM1pj148CByc3NhbW2N8PBwjWkHDhwIQLb2oaoTu3btGk6cOPFqRlcjJyeHvsWrWyOS//qJrtbkfvjhBwVPOzyrV68GAAQFBdH1B35Tj5+fn8ppzUOHDuHu3bs6sy86OhqArNGoGuEkJCSotB2QlaGzszMuXbpEnYqoEuADBw7g4MGDMDIyUnIsoAp3d3e63vjtt98qxUulUmzcuFFlXvmpIl1O6aqDFxpdN25bW1sAMgc1qj4qwW9ebEgOHDigssP+7bffcP/+fYhEIvTq1QsA0LlzZ1hZWaGgoAAJCQkqr7dt2zZ0794dvr6+dCRTF8zMzNCtWzcAwNdff60yTWZmJt3QpE1drG/4zYQ7d+6kDlmqTz8DNbfTzZs3q3SGost69OmnnyI0NBQzZ85UihMIBLTtV+8v+DapbXvMzc1Fy5Yt0atXL/qyJI+3tzfdRMnfi+833dzc6G+W5+rVq9Shhi73WtSEzgS4Z8+etBKMGzcO06ZNU/JGUlJSgoSEBMTExACQeXPRtGYByHaGhoSEID8/H1FRUQoNMS0tDVFRUTrbDGVnZ0d3EH/11VcKXlGePn2K9957D9u2baNhutoFff78eYwdO5ZO90ilUnz11VdUgBctWkTT8j6RDx48qLDGUVFRgR9//BFDhgzRqX3jxo2Dl5cX7t+/j+joaIVG+uuvvyIuLk5tXo7j8K9//Yt63gIUBbhjx44QiUT466+/kJ+fj+7du2s97bdkyRIAwKpVq/D111/TOlBUVIQJEybg7NmzKvPJ1zd1I3ddEhkZCUA2MtHkDaq2tG/fHoaGhiCEYNq0aXTKvby8HCtXrsS6det0di9tKSoqQv/+/RXK9fDhw1REYmNj6SZMMzMzzJkzh4YnJCQotONdu3bRHfYxMTE620A0f/58CIVCHDx4EBMmTFBYxkhNTUXfvn1RUlKCgIAAjBo1Sif3rAv9+/eHra0tTpw4gYMHD8LBwUHlDvV3330X3t7etJ0+fvyYxu3ZswfTp09XeX1d1qORI0eC4zj8/vvvWLZsmcInN+/evUuXJ6vbz7dJbdujjY0NfTl69913FWZepFIp1q5di7S0NHAchz59+gB42W+mpqZix44dND0hBPv370efPn2ovbrq17VC5YGoV6S0tJS888479DwpIPOGFBYWRtq0aUO9lBgZGSmcUeVR5wnr5s2b9MCzUCgkgYGB1OuIRCIhLVu2JADIwoULaR5NHpR4+Hj5Q+P82UNA5k0mMDCQ+Pn50cP7QUFB9DzZrl27aL66nAMeNGgQAUAsLCxIaGgoPR/JcRxZtmyZQp47d+7Q+wMgLVu2JCEhIUQikRBUnZeOiIgggKKnKU3nX6vbk5CQoBB+6dIl6kxBJBKRkJAQmjYsLIy4uLgonS/k+f333+l9PT09leLlPWlV97BEiObn+J///IfWNQcHBxIWFkYdGwwYMEDt+UbednNzcxIaGkr++OMPQoj6+qeNLeooLy+nnnaOHz/+StdUVU8JIWTevHk0jvcixd9r8uTJ1KmJNh6MeNTVAULUnwnly+3//u//iEgkIkKhkAQEBFDvdfzzqH6OXCqVUs9ngMwDWFhYmIJzncjISPLixQuVNtbm3Kg8mzZtIkZGRgR46QmLPzcOyDxhyTv80fTba6KmOsWfw63u3ESe2NhYeu+ZM2eqTXf9+nXq0czIyIgEBwcTDw8P2m/x16jeTnVZj/jzyIDMwUpQUBDx8fGh/aenpyfJzs5WyMOfeRYKhSQoKEihH1fHgwcP6G8VCATE09OThISEKPSNS5cupemfP3+uUB95L128Ny1DQ0N6ZjcoKEjhXurKjZCanx///N955x0aVm/ngI2MjJCYmIgzZ85g5syZCA4ORmlpKS5duoT79+/Dx8cHs2bNwuXLlzWOnKrj5eWF1NRUTJs2DS1atMC1a9fw5MkTjBgxAufPn6e7fnXxaazJkyfj8OHD6NmzJ8RiMdLS0vDkyROEh4fjm2++wenTp+kb3J49e+p8PwCYOXMmfvrpJ/j4+CA9PR2lpaXo168fjh8/jlmzZimkbdGiBS5fvozJkyfD29sb9+7dw/Xr1+Ho6IgPPvgAly9fpm+aR48e1clmnICAAFy4cAEzZ86Eq6sr0tLSIJVKMX36dPz5558andP36NGDvuHKj355+GlojuNqPP9bnY8++ghHjx6l52fT0tLg4+ODH3/8ETNmzFCb75dffkFERAQqKyuRkZGBW7du1eq+tUEoFNJd/3/88YdOr71o0SJs3boVHTt2RHl5OW7cuAFvb29s2bIFa9eu1em9tKFDhw44deoUevbsidu3b+PBgwdo3749Nm7ciF9++UVpcx3HcVi3bh0OHDiAAQMGQCgU4uLFiygoKEB4eDhWrVqFI0eO1Lhju7aMGjUKqampGD9+PBwdHZGWloacnBxERkZizZo1OHPmTI0nCRoS+bPuqqafeXx8fHDp0iXMnj2bttOKigp8+OGHOH78uNp8uqxHc+fOxc6dO9G3b1+IRCJcuXIFDx8+RFBQEP79738jNTVV6dji8uXLER0dDTMzM1y/fl3pyKUqnJyccPbsWcyaNQutWrXCw4cPcfnyZRgbG2Po0KE4efIkZs+eTdNbWlri7NmziIuLQ+vWrfH06VO6VjxmzBicP3+eLlulpqbSZbz6hqtS+CZNWFgYzp07h61bt2L48OH6NofBUOCff/6Br68v7OzscPfuXZ1815jxv8OePXvQv39/hIWF4cyZM698HX6z0aFDh+iLL6PhycrKoi94DXIOuC5s3LgRPj4+iI2NVRmfnZ1Nd0FrcgTAYOgLT09PjBgxAg8fPqzxS2EMRnXWr18P4KVnPEbzodELcGhoKDIyMvDNN99g27ZtCjvlsrKyMHjwYJSXl9PdkgxGY+STTz6BSCSix9wYDHVUVlbiwoULyMrKwvz587Fnzx7Y29ur/Jwlo2nT6AXY398fsbGxqKysxIgRI+Do6IiwsDB4e3vD09MTycnJaNWqFfUQw2A0Rjw9PbFo0SKcPHlSYRcmg1EdgUCADh06wMPDAwsWLAAgO5VRW89fjMZPoxdgQHZm7/Dhw4iKioK5uTnS0tLw7NkzhIWFYcWKFTh79qzaD0ozGI2FGTNmoHPnzoiLi1M4osFgyMNxHLp16wZTU1N4eXkhPj6e7W1ppjSLTVgMBoPBYDQ1msQImMFgMBiM5gYTYAaDwWAw9AATYAaDwWAw9AATYAaDwWAw9AATYAaDwWAw9AATYAaDwWAw9AATYAaDwWAw9AATYAaDwWAw9AATYAaDwWAw9MD/A2a9NKvj0vX0AAAAAElFTkSuQmCC",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56782,7 +98582,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56792,7 +98592,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56855,16 +98655,16 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T07:42:18.044998Z",
-     "iopub.status.busy": "2024-10-14T07:42:18.044385Z",
-     "iopub.status.idle": "2024-10-14T07:42:20.283754Z",
-     "shell.execute_reply": "2024-10-14T07:42:20.282862Z"
+     "iopub.execute_input": "2024-10-15T00:15:17.319501Z",
+     "iopub.status.busy": "2024-10-15T00:15:17.318818Z",
+     "iopub.status.idle": "2024-10-15T00:15:21.136275Z",
+     "shell.execute_reply": "2024-10-15T00:15:21.135635Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 1 Axes>"
       ]
@@ -56906,10 +98706,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T07:42:20.287415Z",
-     "iopub.status.busy": "2024-10-14T07:42:20.287028Z",
-     "iopub.status.idle": "2024-10-14T07:53:15.509064Z",
-     "shell.execute_reply": "2024-10-14T07:53:15.508221Z"
+     "iopub.execute_input": "2024-10-15T00:15:21.139771Z",
+     "iopub.status.busy": "2024-10-15T00:15:21.139524Z",
+     "iopub.status.idle": "2024-10-15T00:34:06.830902Z",
+     "shell.execute_reply": "2024-10-15T00:34:06.830311Z"
     }
    },
    "outputs": [
@@ -56917,15 +98717,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -5.135809792908661\n",
-      "999: -5.9002794\r"
+      "initial loss -5.652240467076812\n",
+      "999: -5.8371744\r"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Generating signals: 50112it [01:10, 710.10it/s]\n"
+      "Generating signals: 50001it [01:14, 669.41it/s]\n"
      ]
     },
     {
@@ -56976,16 +98776,16 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-14T07:53:15.513850Z",
-     "iopub.status.busy": "2024-10-14T07:53:15.513436Z",
-     "iopub.status.idle": "2024-10-14T07:53:21.486596Z",
-     "shell.execute_reply": "2024-10-14T07:53:21.485538Z"
+     "iopub.execute_input": "2024-10-15T00:34:06.835690Z",
+     "iopub.status.busy": "2024-10-15T00:34:06.835418Z",
+     "iopub.status.idle": "2024-10-15T00:34:12.570550Z",
+     "shell.execute_reply": "2024-10-15T00:34:12.569914Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -56995,7 +98795,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAG1CAYAAAAcOq4qAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADTQElEQVR4nOzdeXydZZ3//9d932fPvjUlSTe6UBGa1rL6tSBFWxQGLSNURQQSnEGZflFHRX7guIyI6LhgRdSpMB39KsVRBFwoTgtSBaxU2rAF0tItTZtmT85+n/u+f39c55ycpEmapknOSfN5Ph6xJGe7ksZe9/t8rutzaY7jOAghhBBCCCGEOCF6tgcghBBCCCGEEFORhCkhhBBCCCGEGAMJU0IIIYQQQggxBhKmhBBCCCGEEGIMJEwJIYQQQgghxBhImBJCCCGEEEKIMZAwJYQQQgghhBBjIGFKCCGEEEIIIcZAwpQQQgghhBBCjMGUC1N//etfMQyDp59+OttDEUIIIWReEkKIaWxKhammpibWrFmDbdvZHooQQggh85IQQkxzUyZMPfLII5x//vkcPnw420MRQgghZF4SQggxNcLU5ZdfzlVXXcVpp53Ghz70oWwPRwghxDQn85IQQgiYImGqsbGRr33ta/z9739n0aJF2R6OEEKIaU7mJSGEEACubA9gNF599VW8Xu8JPebb3/423/72t4/5+tGjRwkEAixYsIAXXnhhvIYohBBiGhnLvAQyNwkhxKlmSoSpsUxYvb29HDp0aMjbenp6aGhooKenh6KiopMdnhBCiGlmLPMSHH9uamxsxHEcNE07meEJIYSYJFNimd9YFBYWUl1dfcyHrqtv2TRNVq1aRU9PT5ZHKoQQYro43twUCoW49dZbcRwnyyMVQggxGqdsmPr0pz9Nc3PzMR+nnXYaAJqmsX37dglUQgghJs3x5iaA9evXS6ASQogp4pQNU8dTUVFBaWmpBCohhBA5o6SkBJBAJYQQU8W0DVNut5utW7dKoBJCCJEzAoEAGzZsACRQCSHEVDBtwxRAbW1tOlAdOXKE7u7ubA9JCCHENFdfX58OVHv27ME0zSyPSAghxHCmRDe/iZQKVIWFhcyZMyfbwxFCCCGor6+nurqaSy65BI/Hk+3hCCGEGMa0rkyl1NbWMm/evPTnW7dulSqVEEKIrLrsssvSLdgdx+E3v/mNLPkTQogcM+XC1Je+9CUcx+Gd73znhDz/448/zmWXXcbq1aslUAkhhDiuiZ6XAD772c+yZs0a2UMlhBA5ZsqFqYk2e/ZsCgsL2b59uwQqIYQQOeHMM89E0zRpSiGEEDlGwtQgtbW1bNmyhbKyMglUQgghckJdXR0bNmyQQCWEEDlGwtQQJFAJIYTINRKohBAi90iYGsZQgSoYDGZ7WEIIIaaxwYHq05/+dLaHJIQQ05qEqRFkBqqzzjqLQCCQ7SEJIYSY5lKByuVysXz58mwPRwghprVpf87U8dTW1vLCCy8we/ZsdF2ypxBCiOyrq6vjne98J6effnq2hyKEENOapINRmDt3bjpIJRIJvvSlL8keKiGEEFmVGaQOHz7M3XffLXuohBBikkll6gStW7eOH/7wh/zhD39g8+bNFBcXZ3tIQgghprFYLMall17Ka6+9xuHDh7n33nvRNC3bwxJCiGlBKlMn6Oabb6a0tFS6/AkhhMgJXq+Xz3zmM9LlTwghskDC1Amqra1l69atEqiEEELkDGmbLoQQ2SFhagwkUAkhhMg1EqiEEGLySZgao8GB6v3vf79MWkIIIbJqcKD62te+lu0hCSHEKU3C1ElIBarq6mr+v//v/5MNv0IIIbIuFagWLVrE9ddfn+3hCCHEKU1zplk5paamhkOHDlFdXU1zc/O4PGc0GsXn843LcwkhhJh+ZG4SQoipSSpT4yBzsmpsbOR973uf7KESQgiRVZlz00MPPcSnPvUpWY4uhBDjTM6ZGke2bXP11Vfz8ssvs3r1ajmHSgghRNbt27eP6667jkQigWVZcg6VEEKMI6lMjSNd1/nZz34mXf6EEELkjLlz5/LDH/4QQLr8CSHEOJMwNc6kbboQQohcU19fz4YNGwAJVEIIMZ4kTE0ACVRCCCFyjQQqIYQYfxKmJsjgQHXbbbdle0hCCCGmucGB6te//nWWRySEEFObNKCYQKlAdfvtt3PPPfdkezhCCCEE9fX1AOzatYurrroqy6MRQoipTcLUBKutreX3v//9gK+Zponb7c7SiIQQQkx3qUCVYpomLpdLuvwJIcQJkmV+k+w73/kOK1asoKenJ9tDEUIIIYjH43zgAx+QPVRCCDEGUpmaRB0dHdx11110dHSwatUqnnzySYqKirI9LCGEENPY008/zWOPPZb+XM6hEkKI0ZPK1CQqKytjy5Yt6aYUq1atkgqVEEKIrFq1apV0+RNCiDGSMDXJBnf5k0AlhBAi26RtuhBCjI2EqSyQQCWEECLXSKASQogTJ2EqSwYHqt/85jfZHpIQQohpLjNQPfDAA+zevTvLIxJCiNwmDSiyKBWonn76aa6//vpsD0cIIYSgvr4eXdeZP38+CxcuzPZwhBAip0mYyrLa2lpqa2vTn/f29mLbNsXFxdkblBBCiGntxhtvHPB5c3Mz1dXV0uVPCCEGkWV+OaS3t5f3vOc9rF69mu7u7mwPRwghhKChoYFly5bJHiohhBiChKkc0tzczOuvv8727dslUAkhhMgJu3btoqOjQ5pSCCHEECRM5ZAzzzyTLVu2UFZWJoFKCCFETrjuuuvYsGEDmqZJoBJCiEEkTOWY2tpaCVRCCCFySl1dnQQqIYQYgoSpHCSBSgghRK6RQCWEEMeSMJWjMgPV3r17OXLkSLaHJIQQYprLDFQvvvgi0Wg020MSQoisktboOSwVqFwuF4sXL872cIQQQgjq6uooLy9n5cqV+P3+bA9HCCGySipTOa62tpa3vvWt6c+ff/55WfInhBAiq6688kry8/PTnz/55JOy5E8IMS1JmJpCnn76aVauXCl7qIQQQuSMr33ta6xevVr2UAkhpiUJU1NISUkJfr9fmlIIIYTIGTNnzpSmFEKIaUvC1BRSW1vL1q1bKS0tlUAlhBAiJ0iXPyHEdCZhaoqRQCWEECLXSKASQkxXEqamoKECVW9vb7aHJYQQYhobHKg++clPZntIQggx4SRMTVGZgaq6ulra0wohhMi6zEC1YMGCbA9HCCEmnJwzNYXV1tby17/+lTlz5uB2u7M9HCGEEIK6ujouvPBC3vKWt2R7KEIIMeGkMjXFLViwIB2kbNvmP/7jP2QPlRBCiKzKDFKdnZ1861vfkj1UQohTkoSpU8jtt9/OZz/7WWlKIYQQIickEglWr17NZz7zGWlKIYQ4JUmYOoV8+MMfli5/QgghcobL5eLmm28GkC5/QohTkoSpU4i0TRdCCJFr6uvr2bBhAyCBSghx6pEwdYqRQCWEECLXSKASQpyqJEydggYHqssvvxzbtrM9LCGEENPY4ED15S9/OcsjEkKIkydh6hSVClSVlZV88pOfRNflr1oIIUR2pQJVTU0NH/7wh7M9HCGEOGmaM83q7DU1NRw6dIjq6mqam5uzPZwJFwwGyc/Pz/YwhBBCjEDmJiGEmJqkXHGKy5ysDhw4wAc/+EF6enqyOCIhhBDTXebc9MQTT/CZz3xG9lAJIaYkV7YHICaH4zhcffXVbN++nb179/Lkk09SVFSU7WEJIYSYxg4fPsyaNWuIRqPE43HuvfdeNE3L9rCEEGLUpDI1TWiaxo9//ON0U4pVq1ZJhUoIIURWnXbaaXz/+98HpMufEGJqkjA1jQzu8ieBSgghRLZJ23QhxFQmYWqakUAlhBAi10igEkJMVRKmpqHBgWrdunXZHpIQQohpbnCg+tnPfpblEQkhxPFJA4ppKhWo1q1bxze+8Y1sD0cIIYSgvr4egKefflrOoRJCTAlyztQ05zjOgM5JlmVhGEYWRySEENOPzE0DZc5NlmWh67p0+RNC5CRZ5jfNZU5OGzdu5B3veAfd3d3ZG5AQQohpLzNI1dfXyx4qIUTOkmV+AoDe3l4++9nP0tbWxurVq9m8eTPFxcXZHpYQQohp7M9//jP//d//nQ5Scg6VECLXSGVKAFBYWMgf//hHysrK2L59O6tXr5YKlRBCiKy6+OKL2bBhA5qmSZc/IUROkjAl0mpra9myZYsEKiGEEDmjrq5OApUQImdJmBIDSKASQgiRayRQCSFylYQpcYzBgUrO+hBCCJFtmYHqRz/6ES+//HK2hySEENKAQgwtFageffRRbrnllmwPRwghhKCurg6AGTNmcPbZZ2d5NEIIIWFKjKC2tpba2tr059FolFgsRlFRURZHJYQQYjpLBaqUo0ePUlFRIV3+hBBZIcv8xKhEo1He//73s2rVKtlDJYQQIifs2bOHc845R/ZQCSGyRsKUGJX9+/fzt7/9TZpSCCGEyBl//etfaW5ulqYUQoiskTAlRuWMM85g69atlJaWSqASQgiREz784Q9Llz8hRFZJmBKjVltbK4FKCCFETpG26UKIbJIwJU6IBCohhBC5RgKVECJbJEyJE5YZqBobG9m7d2+2hySEEGKaywxUzzzzDMFgMNtDEkJMA9IaXYxJKlBFo1GWLVuW7eEIIYQQ1NXVUVRUxDvf+U4KCgqyPRwhxDQglSkxZrW1tZx//vnpzxsaGmTJnxBCiKz6x3/8R8rKytKfb9u2TZb8CSEmjIQpMS527NjBxRdfLHuohBBC5Iz777+fiy66SPZQCSEmjIQpMS5cLhe6rktTCiGEEDnD4/EASFMKIcSEkTAlxoV0+RNCCJFr6uvr2bBhAyCBSggxMSRMiXEjgUoIIUSukUAlhJhIEqbEuBoqUPX09GR7WEIIIaYxCVRCiIkiYUqMu8xAlZ+fj9vtzvaQhBBCTHOZgaq0tBRN07I8IiHEqUDOmRITora2lr/85S/Mnj2bQCCQ7eEIIYQQ1NfXs2zZMt72trdleyhCiFOEVKbEhFm8eHE6SDmOww9/+ENZ8ieEECKrMoNUKBTivvvukyV/QogxkzAlJsXdd9/Nxz/+cVatWiWBSgghRNbZts0//MM/8C//8i+yh0oIMWYSpsSkuPzyy9NNKSRQCSGEyDZd17n22msBaUohhBg7CVNiUgzu8ieBSgghRLZJlz8hxMmSMCUmjQQqIYQQuUYClRDiZEiYEpNqcKC67LLLSCQS2R6WEEKIaWxwoLrzzjuzPCIhxFQhYUpMulSgKi8vp66uDpdLOvQLIYTIrlSgKi8v5wMf+EC2hyOEmCI0Z5rVsmtqajh06BDV1dU0NzdnezjTWnd3N8XFxdkehhBCZJ3MTblD5iYhxImQypTImszJqq2tjRtvvJHu7u6sjUcIIYTInJueffZZPv/5z8seKiHEsGR9lcgJa9eu5amnnuLVV19l8+bN8q6gEEKIrGpvb+c973kPvb29hMNh7r33XjRNy/awhBA5RipTIid85zvfoaysjO3bt7N69WqpUAkhhMiq8vJyvvOd76BpmnT5E0IMS8KUyAm1tbVs2bJFApUQQoicUVdXx4YNGyRQCSGGJWFK5AwJVEIIIXKNBCohxEgkTImcMjhQfexjH8v2kIQQQkxzgwNV6kwqIYSQBhQi56QCVX19Pd/85jezPRwhhBCCuro6AH7zm9/w0Y9+NMujEULkijGdM9XT04NlWZSWlo7q/kePHiUajTJ79uwTHuB4k7M8pg7HcQZ0TrJtG12XYqoQYmgyN4nJkDk3pS6hpMufENPXCV2Z/ud//ieLFi2itLSUiooKqqqq+PznP3/cfS3/+I//yOmnn34y4xTTUObk9Oijj3LxxRfLHiohxDFkbhKTKTNI/eu//qvsoRJimht1mFq3bh0333wzu3fvxnEcHMfhyJEjfPOb32TJkiX89a9/HfHx8g+NGKtwOMzNN9/Mn//8Z2lKIYQYQOYmkS3bt2/nu9/9rjSlEGKaG1WY+u1vf8t9992HYRjccccd/O1vf2P79u185jOfwePx0NzczMqVK3nyyScnerxiGgoEAjzxxBOUlpZKlz8hRJrMTSKbzj//fOnyJ4QYXZi6//770TSN73//+/z7v/87y5cv55xzzuEb3/gGf/vb35g7dy6RSIQ1a9bw5z//eaLHLKah2tpatm7dKoFKCJEmc5PINmmbLoQYVZj629/+RnFxMf/0T/90zG1nnXUWf/rTn5g3bx6RSIQrr7ySxsbGcR+oEBKohBCZZG4SuUAClRDT26jCVHd3N3Pnzh329lmzZvHHP/6RmTNn0t3dzeWXX87Ro0fHa4xCpA0OVD/84Q+zPSQhRJbI3CRyRWaguu+++3jhhReyPSQhxCQZVZgqLi5m//79I97n9NNP59FHH8Xv97Nv3z6uuOIKIpHIuAxSiEypQPXpT3+az33uc9kejhAiS2RuErkkFag2btzIueeem+3hCCEmyajC1LJly+jq6uLnP//5iPc799xz+e///m80TWPHjh1ceeWVhMPhcRmoEJlqa2v51re+lT53KpFI0NfXl+VRCSEmk8xNItfU1dXxkY98JP15d3e3LPkT4hQ3qjBVX1+P4zjcfPPN3HvvvbS2tg5733/8x3/km9/8Jo7jsHXrVs4991wOHz48bgMWYrBEIsFHPvIR3vWud9HT05Pt4QghJonMTSKXHT58mPPPP1/2UAlxihtVmLrmmmu4+uqrCQaDfPrTn6aqqoof/ehHw97/U5/6FPfccw+O49DY2Mibb745bgMWYrD9+/fzxz/+ke3bt7Nq1SoJVEJMEzI3iVz2zDPP8MYbb0hTCiFOcaM+tPcXv/gF//7v/05paSmO41BVVTXi/T/72c/yy1/+kpKSkpMepBAjmT9//oCmFBKohJg+ZG4SuWrt2rVs2LABQAKVEKcwzTnB/2dblsVLL73E3LlzKS4uPu79e3t7+fGPf8wzzzzDY489NtZxjpuamhoOHTpEdXU1zc3N2R7OiOY+2Ep0nJ7ryI2V4/RMuWvXrl2sXLmSzs5OzjvvPJ588kmKioqyPSwhxCSQuUnkqp/85CfcdNNNAKxbt457770XTdOyPCohxHg54TA1Vj09PTlxYZvLE9bMB4df7z/eTtVwJYFKCHEiZG4Sk0EClRCnrlEv8xvs5ptvJhodXd1k69atnH322WN9qVPezAdbJzVIZb5mNl57ImWeQ9XQ0MBLL72U7SEJISaRzE0iF9XX16eX/P3hD3+gq6sryyMSQoyXMVemdF3nrLPO4qGHHuLMM88c8j7xeJzbb7+de++9F8dxsCzrpAY7HnLp3b9cDDEzPNBw7dSvWu3atYu2tjbe9a53ZXsoQohJJHOTyGUPPfQQ73jHO6ipqcn2UIQQ42TMlamKigpefvllzj33XH784x8fc/tLL73EOeecw3e/+11s2+byyy8/qYGeSmblcDXoaLy/alWdo2Mcjdra2gFBqqmpSZpSCDENyNwkctkHP/jBAUHqhRdekKYUQkxxYw5TL7/8Mu95z3uIRCJ8/OMf5wMf+ADd3d0AfOtb3+K8887j5Zdfpri4mI0bN+bEBt9cMPPBVswxPO7IjZUn/HGyLPqD1elTOFg1NjZy0UUXSZc/IaYBmZvEVPHQQw/JOVRCnAJOugHF+vXrue2224jFYsyaNYs5c+bw5z//GcdxeN/73scPf/hDKitzZ9lYNpdSjLYaFQDenKAGESdbEdOAw1OsecWuXbu49NJL6ejokKYUQkwTMjdNvp3tJttaYqyo8rK03J3t4eS8Bx54gJtuugnHcaQphRBT2Lh083vttdd4//vfT1NTE5qmoes6//Vf/8W11147HmMcV9mYsEYbYLLRYW+s4coDHJhCoUq6/Akx/cjcNLnWNwTZ2hxnZY2HdUvyj7ldwtaxJFAJMfWNeZlfSjgc5sc//jF79+4FwHEcbNvm29/+tnRSY3RhZbyW5Y3FWJcFxlHf25wpsvwvs8ufHOwrxKlP5qbJt6LKy8oaDyuqvEPevq0lxtbmONtaYpM8stxVV1fHhg0b0DRNDvYVYoo6qTCVaiv7ve99j0QiwU033cTjjz9OVVUVL774Iueeey5f+cpXcqJTUjZUjTJI5YrMYOUf5WNiqFA1dwqEKglUQkwPMjdlx9JyNyuqvGxqCnP7c73sbB+4Q/h4YWu6kkAlxNQ25jD1z//8z7z73e9m7969lJeX85vf/IYf//jHXH755bz00ktcc801xONxvvzlL3PuuefS0NAwnuOeEuwRbjPIrSA12N4TrFZF6W9WkcvVqsxA5TiOTFhCnGJkbsqubS0xthyMs6U5dkwFamm5m3VL8mWJ3xAyA5Vtj3T1IITINSd1zhTAe97zHh588EFmzJhxzH1+9rOfsW7dOnp6evB4PKM+SHEiTda69JGW9+VyiBpJzYOtJE7g/rn8fb7yyitUV1dTXFyc7aEIIcaRzE2TL3MvFMCmpjCgsXahX4LTCXr22We58MILZd+UEFPImCtTfr+f++67j9/97ndDTlYAH/nIR2hoaOCiiy7CNMfSEHxqOhWDFEDzCVarZj7YyuwcrVK99a1vHRCkfv7zn6fbJwshpi6ZmyZf5l6opeVu7r6wiLsvLJQgNQZvf/vb00EqHo/zk5/8RFZQCJHjXGN94N///nfOOOOM495v1qxZPPXUU/zHf/zHWF9qSjlVg1Sm1PcxmuYaqUYVPmBfjn7/P/jBD7jllls477zz2Lx5s1SrhJjCZG6afKmK1Eh7oaST34lxHIe1a9fym9/8hl27dkmXPyFy2Li0Rp9KJnIpxUjL4E6VIDWUE2mvrgMtOfazGHwOlQQqIcRkm8rL/IaTGaBS1avh2qaLY0nbdCGmhpNujS76TccgBf1dAEfzXqPNyR8cPN5qa2vZsmULZWVlbN++ndWrV8uSPyGEGMbOdpP1DUEe3h1hfUPwmK59KdtaYjy2N8o9O/qoDBjSye8ESZc/IaYGCVPjZLiA4JvkcWTTwWSoGs0vVarzX66QQCWEEKOTqjI9sicy4rlRK6q8lPt02qM2L7bJ3rSxkEAlRO6TMDXBcnWf0ERqOYFGFbkUqoYKVPF4PNvDEkKInJI6L2rNfP+I1aal5W5uW17AlfN8gCMH9o7R4ED1+c9/PttDEkJkGHMDCtFvuDBwqi/vO57U9z/7wVaOF0lSP8Ns/8xSgerSSy/lqquuwuPxZHU8QgiRa5aWu0dsIjG4VXpLyKI96rC4xJBlfmNUV1cHwCc/+UmuuOKKLI9GCJFJwpSYcAdOoPvfzAdbcyJQvfbaa1RUVGR1HEIIMVXsbDfT50uBQ2OXRUvIYl+vxa52Ewu46DRvujIlHf1OXF1dHf/wD/8gc5MQOUaW+Z0kqUqN3pEbK5kxikJPLiz9y5ysent7+cQnPiF7qIQQYhjbWmJsORhnS3MM0FhZ46E96vBqVwJDA5+h0RJOpJf6pZpYDNW8YqTbprvMuamhoYE777xT9lAJkWVjrkx95StfYfbs2dxwww3Hve9dd91FY2MjP/3pT8f6cuIU0XDtiVWpIPvB9CMf+QiPP/44O3bskLbpQuQ4mZuyY0WVl5aQBWisXehnabmb25/rwatr1M7wsKTcRWXA4MW2OC0hm01NYRq7rPTjM8+gSjW4aAnZcjbVMHp6enj3u9/N0aNH6e3tlbbpQmTRmM+Z0nWdd7zjHTzzzDPHve/y5ct5/fXXCQaDY3mpcTWeZ3mc9mArQ/3wsn3xP5WMtgKVzZ/prl27WLlyJZ2dnXIOlRA5TuamiTF4H9RoQs5QB/WubwiytTnO4hIXVXn6kGdQpZYMNnSYxC24cp5PzqYagpxDJURuGFVlav/+/WzZsuWYr7e2tvLAAw8M+zjHcdi/fz8vvfQS+fmn3j+EUlg/eUdGuZ8qm3upamtr2bp1KytXrkx3+ZNAJUT2ydw0eVKBJyX13yOFqaEaVaTC2FBBLHWbqk4ZvHDUpNynS9OKYaSaUtx0002sX78eQAKVEFkwqspUJBJh0aJFtLS0jOlFHMfhAx/4AA8//PCYHj+exvPdv6ECgFSlxm40Xf+kQiWESJG5afKkqkwxC546FKUq4OKWJXnDhqnjVbIyG1aklgUO93hZ4jcyqVAJkV2jqkz5/X7uuece7rjjjvTX9u/fj9frZebMmcM+Ttd18vPzWbZsGf/xH/9x8qPNIdlukHAqGk3Xv2zuoxpcobruuut4/PHHJ30cQghF5qbJk6oyfWhzJ290WxS6Vf+q9Q3BdOAZ2NEPGrsS6ccPrmSlGlagQVWefkxgOl77ddFvcIVq4cKFrFu3LsujEmL6mJQ9U7lkvN79ky5+E2s0YTVbP+tdu3bxoQ99iF/+8pe89a1vzcoYhBBDm+5z00T7jxeD/HpPhKvm+/EaDNjrtL4hyE8bI6DBpTUeqvKMIStTKnRFaI9YlPsN1i70H3MfceIeeOABNm7cyO9+9ztZvirEJBpzN78vfvGLzJ49ezzHMuVJkBo/R26sHNU+qgDw5iT/3Gtra3nppZcwDCP9NcdxZFmFEDlA5qaJ5TWgMmCk//QZELNUhaoyYFBb7qIlbFPmM9Jd+9YuDKQbSDy8O8LXd/QRNB0WFhncsiSfpeXudGMKUFWph3dHeGRPhDXz/VyzwJ/Nb3nKqKur4/rrr5e5SYhJdlJharqSJX6TYzTNKcJkpzlF5mT19NNP85WvfIVHHnmEoqKiSR2HEGKg6Tw3TYbMBhLbWmJEk3uo2sIOl87ysKTcTXtznKcORdndbWHosK/X4rblBSwtd/PInghtEQfbgeZk6/Ol5e4Bz7uz3eS7O4McCdsAEqZOQObc9NWvfpWjR4/KHiohJtiYw1RKT08Pr7/+OuFwGNu2B9yWSCQIh8M0Nzfz+OOPs3nz5pN9uZwl/0xNnNFWqTTg8CSHqlgsxkc/+lEOHjzIqlWrePLJJyVQCZEDZG6aeDELWsMWhR4tOQlq6VD0TEuchGOh2bCrI8Edz/dw1wVFrJnv53DYJpJwWFLmGtDBD9RSv5aQjabBzIDOmvl+aUYxBjt37uQLX/hC+nMJVEJMnJMKU1/4whf45je/iWnKKeWTfRE/3YymSuUw+VUqr9fL448/nm5KIYFKiOyTuWniZLZI33E0TlvUpsRncN0Z/gFh51d7Ilg2uAyIWQ7NfaoKtW5JPq1hi63NcZYkm0ykwlJLyKKxy2JxicFFVR7aow4vtsV5sS3OC0dNnj0cT1e4xMiWLl3Khg0bpG26EJNgzGHqV7/6FXfdddeo7rtgwQI+/OEPj/WlckqNLPHLqiM3VlL9YCvWCPeZ7I5/g7v8SaASInum69w00VKd+tojDotLVEWpMqCWlC2f4aGhPcETB6JcvziP1rBFV8zBciBuw5wCgwtnDlzKl/nnpqYIW5pj1Ja7WVnjSS8h3NIcAQdqK9yETIdwwkovCxTHV19fD8g5VEJMNH2sD0wdiLh27VoOHDhAW1sbuq7zT//0T8Tjcd58801uv/12dF3HcRw+97nPjdugsylx/LuICXboxspRBaXJ3NuWClSlpaXpQNXT0zNpry+EUKbr3DTRUq3Mtx+Ns69XzYTXLPDzi9WldERtnjgQ4+9tFl/f0UdlwOC9c3zML9TxuyDPDcsqPGxribGz3WRpuZt1ycYTigMOlPu09NdjFiRshzkFBuU+DU0DxyEd4MTo1NfXs2HDBgDWr1/PrbfeyhibOAshhjHmMLVjxw58Ph/3338/NTU1lJWVsXjxYv73f/8Xl8vF3Llzueuuu7jzzjt58803ue+++8Zz3DlFuvhlR64Hqm9+85uT9tpCCEXmpvG1s91kfUOQmAUBN9g2HAiqClFKe8TCRi21bo04/PFAlLsvLOQTS/Ip9uh0xxwe2RNha3OcbS0xHt4d4UObO3l4dwSAtQsDXDpLVake3h1hfUOQpw7F6DMdXLq6fXa+QZ5bozU80roEMZTBgWqqHRsgRK4bc5jq6upi3rx5FBcXp7929tlns3fv3gHvyP/rv/4rXq+XX/3qVyc1UCGGcmQUVaqZD7ZOWqhKBar6+nrpKiZEFsjcNL42NUX46esRnjoUJWyCrsPsfCPddW99QxA0jXKfRsBQFxUtyS58rWGLPLeGW9c4HLZwadASstnYGOKFNpNH9kTSr7OvN8ELR8106KoK6JxT4WbNfD9Ly93ctryAK+f5Brzuzna1J25wOBPHSgWq733ve1x88cXZHo4Qp5Qx75ny+Xz4/QPblc6fPx+A1157jQsuuACAgoICFi5cyBtvvHESwxRiZEdurGTOg63ERrjPZDWnqK2tTb8LCGDbNuFwWA5RFGISyNw03tQSvIBLp8JvU5Xv5t2zvMc0jLjpzAD7+yyePRznkuqBe6J+tSfCnh6blpBNd9zG0MClQXW+WrK3rSVGe9Sm3Ke697WGrWM69y1NNqsAuP25HrYcjNMSslhaXsQjeyK80KaCVaqNeupgYHBYuzAg+6zo30OVEgwGycvLkz1UQpykMVemqqur2bdvH5bVX3JPTVgvv/zyMfcPhUJjfamcIedL5bb9OValAhWkbrnlFi699FLZQyXEJJiOc9NEWlbhYX6RQbFHI25DMG7zx4Mxfvp6hPaoakYBai/ToaCqRHmT25pSe6Mq/Aa2A5YN5T4dQ4OEA691Jbj9uR4aOhLMLTCYW+hiUbFr0H4qjqlEQX8rdlANMCp8OstneNKPUQ0s1D6vzCWJQuns7OTiiy+WPVRCjIMxh6l3vOMddHV1DdgXctZZZ+E4Dr/5zW/SX2tpaeH111+nqqrqpAYqxGjl0l6q5uZmfvnLX0pTCiEmicxN46s1bBG1oNxvUO7TORC0eO5wjI6IDY5DVZ5OY5fFxsYQr3YlMG21lC+192lnu8mCIoNyv8biEoPblhdw/VvyWFTsIhS3eXRvlGfSVa7EkMEn1Y49ddvahX6uO8PPsgo36xuCdEQtKgNGOsSBqopdWuPl0lmedIVM9Hv66ad58cUXpSmFEONgzGHqlltuQdM07rjjDs4//3xisRjnnnsuCxYs4A9/+AN1dXV8//vfZ/Xq1Zimydve9rbxHHfOkHPZc1OuNAWZPXs2W7ZskS5/QkwSmZvGz852k4Z2k7jtsKxC7VuanW8QSUDUgu6Yw4oqLytrPFQFXHh1DXDY0hxjY2OI/2oM86HNnZg2vHeOjyXJatM1C/wsKXPRFXcwAJ+hUZXvSrdFT7327c/1cvtzPVQGDBaXuGgJWQO6AabOqwJtwGNBVcXuvrCQuy8skiV+Q7jqqqvYsGEDmqZJoBLiJGnOSfy/5/777+fWW2/F4/EQDAYB+NnPfsZHP/rR9Bpcx3EwDIO//e1vLF26dFwGfTJqamo4dOgQ1dXVNDc3n9Bjh6pm5MpFuxjeSFWoyfr727VrFytXrqSzs5PzzjtPzqESYgJNt7lpIuxsN7lnRx+vdqk26GeWuLhteQEAdVu6aIuoqlS5X6cqoPPu2T5+2RRhR5uJS4eLq7w83RKjz4Rij9oj1WfCvAKd715UnD5bak6BgUtTS/W8Bum9Uusbgvz0dXXO1HWL1duWW5vjLC5JlZ80llW4h9xfJUbvgQce4KabbsJxHNatWyfnUAkxBmOuTAF8/OMf59VXX+Ub3/hG+msf+chHePDBB1m0aBFut5slS5bwm9/8JicmKzE9jRSYZj7YStUkLPmTc6iEmDwyN528VFOImjydmnyD9qidXmY3w6/OjwqaNjvbEmxtjvNiW5yXO03CFoRMOKPExfxCAwNIWNARg5gN+4PqedYu9FNb5mZPT4IDQYunDql9WKpphNqDVeHXqC1Xh/2mKmCgseVgnC3NMV5sM4cd/7H7rMRQ6urqpEIlxEk6qcrUVCSVqenrePukJuPvMlWh6unp4fe//z2rVq2a8NcUQuS+XKtMPbw7wsbXQlgOHAlZWI7G6jleDgUtnm81iVtQ4IGYBXkuuHCml7+3xzkccnCA0wIayys8bD8ax2do9MRsIlZ/ZQpUhas14lDq0Qi4oD3msLLawy1L8rlnRx8Hghaz89U+q1TlSXXpC9MedWgJJojbcOU8H+uWDOyWur4hyNbmOCtrPMfcJo6VqlCddtpp7Nixg5kzZ2Z7SEJMGWNujT7dTEb1QkysIzdWjhioJqN1eqpCtWfPHglSQoic1Rq2aIs4HArZqL6IDs8ejpPn1shLXjn4DQ2X5hBJwDMtMUq8OiVeh64YBE0HNDA0aI/YBFxqCd+7Z3vZ1BTmuSMm7VG1Z6rMr3E0YpOwAU1jU1OEV7sS6rHJilgqTKk/A9yzo4/uuDPgzKttLTEqAwatYdWQIrWPKnWbLAccXl1dHV6vl3PPPVeClBAn6KSW+U0ndrYHIMbFaFqnT7Ta2lquuuqq9OfNzc10d3dP+OsKIcRoVQYMKgIambtneuI2puVww1vyuKDSTcxWwSlkQa8Jh8M2UQv8BiwsNij3aQRNh1BymV/QtGkNW2w5GKc5aOHW4Oxygyvm+vEbGnluwIHnjsQJmg4z/DrlPp3KQH+bvtRersFVq1THv9Shv61hK91ifXA3QDG0a6+9lkWLFqU/f+WVV2TJnxCjIGFKTDujCVSnTVIl8sCBA1x88cWsXr1aApUQIme0hi08uk5NgY4BGEB3TAUmrwG3LS9gXqELl64uJDw6vKXE4G0Vbmbl61g2/GF/lFByy5INdERtGtoTaJpDuV9nZY2Huy4oUi3NNXAcOBhMcDBogQNdMZvnWk3+7a+9fGxrV7rC1NRj0Rm1qc432NYS4+HdEVpCFotLXKyZ7z+ms19qv1VlwBhyH5XsrzrWE088wfLly2UPlRCjIMv8ToLsl5q6Un93w1WiHCZn2V93dzc9PT28+eabrF69ms2bN1NcXDyhrymEEEPJXA6XCiMxC367L0pbxCJuQ7FH4+9tJr94I8IZJS7KfBodUQeXDrPyXQRNm6MRm544WIOe/5VOi+aQRU8MHBxm5TvpZXe/eCNCl+VwNGKT79aoydcJmRCz1DlXWw/FCZp9rJnvx6NDpwXPHo6zp8fAZ6hW7StrPFyz4NgDS5aWu9MdArc2x2kJ2QOW/aUqV6n7Cjh8+DCxWIz169cDSJc/IUYglSkxrWV72d+SJUsGnEMlFSohRLZsa4nx2N4o9+zoA1RFZ8fROF0xm1BCdek7HHb434Nx3uyz2dIcx+/SKEwuz2vqMTkQtDAdcBuqmpV5+e13qTOlbFSlasdRk4d3qw5+hR6NYq/G3AKDPJfGJdU+PnF2HmcU68wt0JmVpw4MfmRPhKvm+zm/0s1VySrUUNWooaQqVO0Ri582RtjUFB7wdTnct9+NN97Ihg0bAKTLnxDHIWFKTHvZDlSD26ZLoBJCZENlwCBkOrzRk+CeHX3c1xDk1c4EJV4dn6ECkOmAnbym1oHZ+QYXzPSS79HojjkcDtnYNpR6NWbmaeS7wK1BvgsKPRrzC12UJjOLpsHGxjC/3hPhtS6LjqjDG90J2qI2O47GuWaBn0+cnc+8AoMr5vkp9urs6jD57b4ocwsN3jXLy7ol+SwqHnqRzeDle6nDfsv9RjLlaQO+LlWpgerr6yVQCTEKEqaEQAWqkabRmQ+2MmsCQ5UEKiFEtrWGLfLcGl5doz1q0xK28bo0Lpzp4exSFwaqDXqFX8OrQ4FbY/kMD+BgaNAVcwhbELfVIbyLilzp4OXWVYe/v7aaxJJNKir8Ou0R1TI9YYNpq+V6/c8LG18L8ZcjJk81R6kKqKV/+/tUE4tNTRHWNwTZ1BQZssHEUI0nUsHq0hoPaxceuyRQDCSBSojjG3OYuvzyy/nVr36FacqGTXFqOHhj5YhVKpOJrVJlBqq+vj5iMek8JcSJkrlp7FZUeTlnhofqPAOPAZdUe7m0xkN71KYt6uBzgcfQiCYcYrY6F+r+l0JsaY7TFhl4gV3k0ch362r5ngcumOkhYUPcgWACdA3KfBp9po1lq2WBHl193aVrqikFUJXvwm9AwK3TErbwGuA1IODW2N2dYMOrYbY2x4jbzoCuf6nvZ/DyvU1NEbY0xwBNKlGjlBmoOjs7sW3pbyxEpjE3oPjDH/7AE088QUlJCddeey033HADy5YtG8+xCZEV2TyPqra2lqeffpoZM2ZQWSkNToQ4UTI3jV2qGcOWZotYwmHH0ThzCw12tZvELIdZ+Tq9cYdU0zsH1RZdQ32k4lTCgd/ti6JpGiHTIc8Nu9pNzOQ1uI4KTTP8Bgd6bTTNQQfyVJGLgEtLB6N3z/ISjNvku3VaQhaGBpYNh8MWPTGb9oiDS1cHBbeGVcuLh3dHeGRPhDXz/UMc2OuAo86+Wt8QTAetzIYUg8+skvOpVKA6/fTTueiiizAM4/gPEGIaGXNl6itf+QoLFy6ks7OT9evXc84557Bs2TK+973v0dHRMZ5jFGLSZXMf1dlnnz0gSD322GOy5E+IUZK5aWxS+4sqAwaX1nipKVANH546FCds2pwW0Mlz62gaBFyQl2wwAepCIs8NXh3ykxWmmAU6KkgZqCWANuo+Lh1iCXj+SJyY7eA4av9UNHle1cGgxYttcR7eHeG7O4McCFqU+zWKvTqWo8Ka40Bn8jn9Blxa4023Pt/YGOaFNpNH9kTS39vtz/Vw+3O9LKvwcN1iP+V+Lb0EcPBywMFnVm1riUn7dOCSSy5JBynLsvj5z38uS/6E4CTC1J133kljYyPPPvss//RP/0RRURG7du3iU5/6FNXV1XzgAx/gd7/7nZSDxZSV7cYUAD/72c94//vfL3uohBglmZvGJhUgWsMWd19YyPWL83AcaA2rNudv9lo0dSfoijpEEirQuHTVXKLADfkuDQvwudQyPNOGfLfO/5npIeDWsW3w6yr4ZHb4S9hgaGpPlWmDlWxw0dBh8oOXghwM2kQSDmsXBlhS5kJLvka5T8ebfK6IBbu7Tf54IMaGV8O0hy2q8wwSDtz+XI9a2ncwzpbmWPow37ULA+klgIOXA6Y+z+wSKAf/9nMch4997GNce+21sodKCEBzxun/BfF4nEcffZSNGzfy5JNPkkgk0DSNyspKrrvuOm644Qbe8pa3jMdLnZSamhoOHTpEdXU1zc3No3rMcBfNcs7U9DFScJrI34Ndu3axcuVKOjs7Oe+88+QcKiFO0Kk8N42nwUvbWkIWLxw1OdBn0WP2V6Gg//woHSjyqH1UvXGHmKWCka6psJVnQL5HozPmYCZvMzTVoAJNBatIAjyGOr/qaNTBo4HXBbatnsd04G0VbhYUGbRHVUmq3G+wrMLNi20m/7MnQp+pKl5nlRns7rbwuTTOLHHxaleC3phDvhsWl7hYUOxm7UL/sEv2Ms/ZGnyfkW6bjh544AFuuukmHMdh3bp1cg6VmNbGLUxlam9v5/HHH+fRRx/lySefTG+kP++889LvZni92TnPQcKUGKuRApUHODBBvw8SqIQYH6fa3DSeUmGhod1kV3uC2nI35X6Nn70exXRUcCr2quYTEUvtj9JRVakSr06fadOZLNqklry4Mta+GJpqre4kl+glMq48CtyqKhWx+u+rAQUeqC1zM7fQYMvBOGhw3Rn+dKWoMmDwy90RGtpNSrwaH1gQoCNqAw7LKjz84KUQb3RbaMBFVW5+sbp0xJ9B6lDflTWeIfZaicEkUAmhTEhr9Gg0SigUoq+vD9M0cRwHx3H461//ysc+9jHmzZvHpk2bJuKlJ40EqelnpL/zOBO37E/apgsxPqbD3DRWqWVsLWE73VHiuSPxdOgpcMNbS11U+HU8ySsHG+gx4WDQpjeuvmZo/SEqYasPl6aqT2aybbrlqPs59AenWDKgpb42v1Dn/8z0MrfQxbIKD7Xl6rX391l84uluHmqKsLExxBvdCfLcGgUetezv7gsLWbswQGvYYmGxi2IvVAY01swfvg16ak9VQ3uCxSUuObx3lOrq6tiwYQOapknbdDGtjVuY6uvr44EHHuCSSy5h7ty53HrrrTz11FOUlJRw66238uKLL/LYY49x+eWXc+TIET784Q+zcePG8Xp5ISZFtvZRDRWowuHwhLyWEKcSmZtGltl4wqXDgd4EcwoMuqMWr3fb6Q594QS81GFyMGgTS243M1ANJyzUcjy3BjMDOvMKDUq8/aHKRgWowUULHbU/KmwNXDpY5tP4xJJ80ODXeyL88WCMcr9BW8Rma3OMlpBNZ9QmYOhEEw5Ry6Hcpw/ozLe1OU65T+N98/ysnu095mDfzIYS21pibDkYZ1eHSVWeLsv4TsDgQPWZz3wm20MSYtKNuTU6gG3bPPHEE/z0pz/lscceIxqN4jgOuq6zevVq6uvrufLKK3G71T9MtbW1XHHFFXzpS1/iK1/5CnfffTfXX3/9uHwjQkyWbLVOTwWqlStXcvHFF+P3y4GTQgxF5qbRSwWPxSUudhw1CVtAZ4LZhUY6SLmSVaRkV3FAhZ5ZBTrFHo2XOlVZqcADsYRDwrKxgZl+jR7TIWrB/EKDQ0GLjpgKTk6yOpVw1P6oFENTweuPB2LsaIsTSkBLMEG5zw0OzPDrRBIWAbfGgmIXC4oNQEsfwJsKhkM1jlha7ubh3aqiFTJV0wtQDSdaQhagSVVqDOrq6gC45ZZbuOSSS7I8GiEm35j3TH3yk5/koYceoq2tLV3WXbBgATfccAM33HADVVVVwz52//79zJs3j7y8PPr6+sY28jEarz1TssxPzHqwlZGa5E7U78ihQ4eoqqqStelCDGE6zU3jYWe7yaamMA0dJg3tFqYDxR646cw8fvBSiLClGkksLjWY4TfYdiiO6UC+G/pMFX5Se50yaUCpF3rjqpHEaXk6LUGbePKKw6OrkBa2khUqVKWrwg99cfC7wOdSSwqvmu/n9e4EDW1xwgnwuGBRkYvblhekq0g7203u2dHHgaDF7HyD5TM87Dgapzpfhbg18/0sKnbxiae72den0tuiYoPvrihKh6zU2VTXLBj4RpU0nxid1O+vENPNmCtT3/ve9wAIBAJ84AMfoK6ujosuumhUj+3s7MTj8Yz6/kLkooPJsDRclarqwVZaJiBQZU5W0WiUL3zhC9x5550UFRWN+2sJMdXI3HRilpa72dSk0dhlYTtq2Z5bhw2vhNKH7DpAoVtnb2+CiKVak+e7VQe/yDAd5h2gO6ZCkgvY32cPqGq5NJjh1+iJqwN3Ay6N5RVu0DSeORQjbkOhDp9cms+LbXG2NseIJJKhKwaXVKv+gqlK1MbGEE3dFo4DUcvhhTaTaAIq/AnKfDqP7Ikwt9BFzFajsJOjTIWjR/ZEeKFNvT02OExta4nx2N4ozx6ODwhwYqDMuWnPnj387Gc/49/+7d/kjT9xyhtzmLrggguoq6vjgx/8IPn5J9b1pra2lmg0OtaXFiKnDLfsbzJOsamvr+fnP/85zzzzDE8++aQEKjHtydw0Fg7hRH8DiJCpKkYppg2vdCboSZaVfAb0xB1sR1Wghlre4tZU9SluH3ufArf699F2oDJgEEnY9JkOLWGL6xfnUe7TaWiPE7ehNayW30HGWVTAo3ujvNZp0hpxCJk2lqOaWHgNCJtOeumgW9fw6NAetZlb6LCoyEVf3CRmwcKi/lCUalCR+jOzGrWiysuzh+O0R+30ckExvFAoxMqVKzlw4AAdHR3S5U+c8sYcpp599tkxv6iuT0gTQSGyZrhAlfraRC35+9znPscTTzzB9u3bWbVqlQQqMe3J3HRidrabgEa+C/oSKkQNDkcOEDSddOOJjmQLdJcGlT6NzqiDmbGfygAuqHQDDnt6reT5UOq2gAF+l8bRiEPIdDA0C59Ldf17tdNiY2OIuy4oYlmFm0f2RKgMGMl9TA6gYdoOj+6NEk3Aq12q7XnUUkFrZkDH74I+08HvgkKPjluHJeUeqvJUg4o3uhP0mjYBl065X2Nnu8nScjfXLFDLALe1xNJBamuzalG4bkk+ty0vSIerzJ+dLP87Vl5eHv/2b//GTTfdxPr16wEkUIlT2kk1oBBCjM5kNKWQQCWEOFHbWmI0diV4Z7WXLc2xARUpDch3wfwig3191oDbQO2X8hlg6BDPuM0B3uxLsKjIRZlPpztmEU8+n0uH3riTbqFuO6pluoNqPLG3x2Jbi0pr7VGbH7wUJM+lcf1b8gC1HG9+ocFLHRZRS+3Lshy1L6svbmPaGoVuDbehcc4MN1V5Rjrs7Gw32dgYornPpiYfGrtIv9a2lhgNHQl2tZu0hOx0Q4tUeFpa7h4QmFJ7tNqjdvp20a++vh5AApWYFsb8NpxhGKP+8Hg8FBcXs3jxYq699lq2bds2nt+DEDkhV9qmr1q1ip6engl5LSFyncxNJyZmwf6+BN0xC3/G26upA3nz3RpvdFv0xY99rGnD4bCDhqpGpdhAd9ThcNim2KOlu+a5NFWBitsqiPldKgSZNuklgzOSlagVVV7KfTr7+2xebLfY2BhmY2OYPx821UG8yWvycKJ/vL0mtEcd8jw6V87zsXZhgHVL8tNBZ1tLjOagTY/pcKDPTrdTT5+xFUz0ty0cwuB26u1RG4+h0RKykhU+kam+vp4NGzYAyDlU4pQ25jCVOuxwNB+JRILe3l7eeOMNfvGLX3DJJZfw3e9+dxy/DSFyQ64EqquvvnpCXkeIXCdz04nZcTROe9Th7+2J9MG7oJbN9ZlwOOIMOAcK1IVD6uIhYUPMVm3R/UZ/qApb8Hq3xUsdCfqSOSPhgGWrx/pd6k+vAXluCLhUpaotbPGBP3Tywc2dVOcbBFwqnPXFbaoCOnrykN9iD5R7VVUrZqvXS/aWwLKdZKvzgQFoRZWX987xUerV6DMdXulUA6sMGPgMuKTGx3WL/axdGEgHrE1NkQEBKtVmfUWVlyvn+agK6Gw5GGdTk5z7N5TBgepb3/pWlkckxPgbc5iyLIurrroKgPe+971s3ryZjo4OTNOks7OTp556irVr1wJqQ/Cf//xnfvvb3/Lxj38cTdP47Gc/y9/+9rfx+S6EyCFHbqzEN8LtEx2oTj/9dL7yla9MyGsIketkbjq+zICxZr6f0wsNAsbA+8Tt4eoziqElm0igLiTOLnUxp0BPh65UM4tuU92eWtxlOhB3oCeuPlQjCIOLqrx4dOiIQzAB3XF4eHc0HfCaemyKvDqrZnko8moUeXWumOfj9CI9He7culp2uLfP4vf7Y9zxXA93PN/LY3ujbGqKsK0lxtqFfj6/vICqPB1Ng01NYR7ZE6E9auM1SFeyVlR5WVnjARweaorwiae7iVmwssZDZcBIB6pyv5H85mT52nBSgWrZsmXceOON2R6OEONuzHum7r//fh555BHWrVvHvffeO+C24uJiLr74Yi6++GLOOOMM/v3f/53Gxkbq6up473vfS21tLTfffDP3338/55577kl/E0Lkmn3HaZs+kXuoGhsb04eRCjHdyNx0fIObK7SGLR5qimDaNr2mCkiq3cPAQKUlP1KhpScZlCr8GvkenedbE0O+npXxPKk/U938TEs1jACHgFsd8psSz2iJ6gCP7Y3yj/N9lPl0yn06yyo8PHkg1t851VZjcwGa49DYbeE1YIbf4PG9EeIWPHEgyl0XFPHJpfnJEOXQHu1f8peS2iO1s93kT4fiHAnb7Dga5xerS1nfEEz//NYu9KebW4jh1dfX89GPflTmJnFKGvOhvcuWLWPfvn0cOXIEr3f4f0QSiQSVlZWcfvrp6Xf7LMuisrKS4uJidu/ePbaRj5Ec2ism20iVqIn+PXrhhRe488472bRpkzSlENPCdJqbxiqzCx3AfQ1BdrSZRC2HvriqHg2mowJUJNkswgUkUOEqYKh9T/ETuJrIDGreZDgLJdRSwMH3Sf1Z6IZ/nK/CizpbKszLHYl0FU1LjlPX1FlYbh3mFboAh4Z2dY5WuV/jpjMDAGxtjrO4xBjQpGKon9Eb3Qk2NoaoCri4ZYlqhDGaLn7S7W94P/jBD3j99df57ne/K00pxJQ35mV+b7zxBosWLRpxsgJwuVwsXLiQV155Jf01wzCYO3cuhw8fHuvLCzFljBSYJmrJH6iLxQ996ENs3rxZmlKIaUPmpuNbWu5OL2fb1hJjV3sCy1GtxIdjM7BteqoG5QAha2AIguMvevPo6iwqDbXvqsfsr3ylHl/oVt36Kn1Q4dWoLXezdqGfyoDB13f08XKHahjhSj7I0KDYC5qm/ru2zM1dFxRy/eI85hcZVPg1Srwa+/ssnjgQo9ynsazCA8Ab3Yn00kdgwP6oaxb4uWy2j319Ce7Z0ccb3YkhHzNY5nOIfo2Njaxbt47vfe970pRCnBLGHKaKioo4ePDgqO574MAB/P6BJ4pHIhHy8vLG+vJCTCnZCFQul4v/+Z//kS5/YlqRuenErKjyMqdAx9DAscd+UTs4PGmoipPfOPbrGioAFXsgz9Xf3MJ0VHhKPe60PJ33zfNR7DPoMR3aIqpt+sbGEK0RB9Mm3YFQA2rydeYWGLg0cGsah8MW9zWEWFTs4sKZHoKmw74+m1/vibKrPUFTd4KNr4XY8GqYja+FBgSfFVVeFpcYtITsdPMKjw6vdiXS9934WoifNkaGbT6R2nclSwAHWrx4Mf/5n/+JpmnS5U+cEsYcppYtW0Zrays/+MEPRrzfhg0bOHLkCMuXL09/7ciRI+zevZs5c+aM9eWFmHKyEaikbbqYbmRuOr7MBhRvdCd4uTNBe9QZsF/JYHQtFYrc4NGgKNnNL/WY1J6ozE1XXg3m5GucUWwwq0BH1zWig86u6oypDn8A+/tsdvdY9MRt4jbs6bHZ2BghZEKpRyPfrQZpOepixrEdLEcFNNN22NNjs/VQjDue7+W5wzEsR52HFbHAcQBNozlkE004VOW7BgSf1LK8LQdj3NcQZFtLjKp8F15DS9+3Kt81oPlE5s819RyZrdmH+vlPV3V1dWzYsEEClTgljDlMfepTn8JxHG699VY+//nPs2/fvgG37927lzvvvJNbbrkFTdNYt24dADt37uTDH/4wiUSCNWvWnNTghZhqJFAJMbFkbhpZ6rDZh5oi3LOjj2+92EdXXDV76MnYL5XZNGIkcVst8euJq/A0J1+n1AvFbrVvKZLsDqGh7tcacdjfZxFJwGkBPb3PKbVUL7XsL2apj8ZOk7aIGonpwNGwTUvIosynUeLV023XAfaHHHZ2WHTEVGDKc6mAt7c3wdGoTV5yHxWoduxXzPVx3gwPC4oNziju78e1s93k9ud6eao5RmfMpqnHYmtznHKfxnVn+Lnl7DzWLcnn3bO8zC80WFbRf47VaJb1yfI/RQKVOFWMuQEFwFe/+lX+7d/+Lb15MD8/n/z8fHp7ewmHVdnbcRw+97nP8fWvfx2At7/97Tz//PPMmDGD1157jZKSknH4NkZPGlCIXJCNphS7du1i5cqVdHZ28qlPfYpvf/vbE/I6QmTbdJmbxmJ9Q5DH9kY5GLQImSoAWcd/2Kh5dTir1CDg1vnrEXPYphSpM6YiVv/j9OShvpqmzqPK7CqoofZBGboKWbqmGl+EElDoUffvy2gm6NZgSblB3AKPoVpYNAdtTBuiCQfbgXfP8rCk3M3W5jg+A6LJ1ucAP309QkfExmXARad5WVLuOqaRRKqr38oaD+uW5I+64YQ0phjogQce4KabbsJxHH7/+9/znve8J9tDEuKEnFSYAvjjH//InXfeyQsvvHDMOwq1tbV8+ctf5sorr0x/7ayzzuKMM87g61//OgsXLjyZlx4TCVMiV2QrUH3ta1/jwQcfJBAITMhrCJELpsPcNBY7203uawjxu/0ZLcXHmVtTFaCoxbCvodN/FlXqMamqWOpcqsyQN9OvqmAG0BVTDTDcmnp8dUAjZjscifTf36PD/12SR0fUAjTKfDpPNUepynfR0G5yMGhT5tX4wnkFtIYtYpY6wHjNfD+Lil1saorQHrEo9xusXehPh55UEKoMGLzYZgIOaxcGJBSdpAceeICDBw/yxS9+MdtDEeKEjTlMdXd3U1xcnP68paWFV155hfb2dvLy8jj77LOZN2/eeI1z3EiYErkkm23TQb07H41Gj9mEL8RUNZ3mprH60OZOnmpR6+MGnyU1HJ3hg9FonyNTkbu/FXqq8pR6/tS+q8znrApouHTV0Q9U8AmaDjELir1qyV9Tt4WVfHyFT+Njb1VvGG1sjNAesfG7wO/S6Ik5hCzVjv1fl6ole5lVphVV3mRTCY1lFe4BoSm1RM9nkD6f6rblBceELak6nZxoNIrX65W26WJKGPOhve9617vw+/08+uijlJaWUlVVRVVV1XiOTYhpbaIO9k1xHIcvfOEL/PGPf2Tz5s0DLkCFmKpkbjq+5TM8PHvEJGYPDCwGQy/5M1Bd88KJ4QPViQqapIOPSzv2bKvMT+fma3xgQYCOqFr4V+YzaAklCBg6aHCgz6I7ZjHDr6WXCea7NSoDqpNFe8QmaoMZh964k/4eHQd+tTtCQ0eCM4pdxG2bhvYELSGLLQfjoMG+3gR7elVP+NR5VKCWGv56T4RwQnUYTAWnzAORJUyNTSgU4vLLL2fJkiXce++9EqhEzhtzmHr99depqKigtLR0PMcjxLRy5MbKEatTExmojhw5wg9/+EM6OjpYvXq1BCpxSpC56fg6oja6dmxFabi9UxYQTAz8moHat2Q6A59j8HOmluJZyfullgCm9ko5DAxSqS6CqZfzGXB2mZvf7ovSEbEo9Oo4DrRFbc6p0Hn7aR6+uzNEyAK35nDZbNUCvT1q82KbSUN7HF2DAhe8o8rDrnaT9qiDhmrD/mafzeFwjJaQRWOXxetdFpfUeKktd9PUY5JwoLbcTblPG1BtuuO5HtqjNgUZoQ1Ih60VVV4e3h3hkT0Rls/wpJcbZi4ZFEPbunUrf/rTn/jTn/4EIIFK5Lwxd/Nzu93T6iwOISbKkRsrs9Ll77TTTmPLli3pLn+rV6+mu7t7Ql5LiMkic9NoOBn/OzYWqko1+CJi8HOajqoAaagW6tV5GgGXajgRMKDMqxpL6KivufSB1a88Fzx/JM7r3RZtMdUu/YwSF+dUuFkz38+KKi/lftUV0HZgR5vJ8hkezpnh5rkjMV7psnCARSUubq3N54FLS/g/M93U5OsUeQ1KveDRNfriNtGE2ueFA0vKXYRN2N9nUe7TqMoz0of0bmoK0xyyiSUPK24NW+l250C6HfojeyK80Gby6z0RthyMs6U5Nu07+I3GP/zDP7BhwwYA6fInpoQxh6nrrruOV199lf/5n/8Zz/EIMW3lQtt0CVRiqpO5aXiq5XcPu3ss8pOBxjjOY0aqB1jO8Zf9eXWoydco9sLphTohU50lZdrq8aCW+aXCUGLQE3bH1P1Tl9IJB7YcjONzaTyyJ8Ib3QmWlLvJc6nugO1Rhx+9EuKp5hj7+2w0IN+lsbcnwR3P9/C/B2P0mg4JG7piNlELOmIOR8M2PkM9R7lfVaFqy11U+HVebDO5d1eIf3u+lw2vhmmPOLx3jpfzKlUoqwwYQ7Y7XzPfzzkVbq6a7+fSWR4urfEOOMBXzpsaXn19vQQqMWWMeZnfTTfdxN///nfWrl3LihUreMc73sFpp5024kb2urq6sb6cENPCSMv+JmrJXypQrVy5Mh2oZMmfmKpkbhretpYYv98fozPqoGtQ6NYIJRzCI/RGP9nL14QNS8rU6bpNPQl6TRsHFcIsG2IZhRqXpvZmxSzVuS/V7S9VAXNpEHfUx1PNcdwG9Jo2IdPBcaAmX2dfn02fCX2mg0uD6jwdTYNDQYembos3e0LEkmdQhRPqdUC97vx8g6qAK92dr9xvsKsjQdi0CSdSSxgdyv06axf6uWdHH3ELXmyLAxqLSwwqAwbrG4KsqPJyzQI/1ywY/vdO9leNrL6+HlD/n16/fj0gS/5EbhpzNz/DUO9nOY4z6l9syxrP0yzGRrr5iakg2+dQ/epXv+Kqq66akNcRYiJNp7npRO1sN7njuR5e7bRIOFDgAc2BjviEvFxaoVs1hOiKqcsNrw695rFVrXwDygM6rWG1hC51ceKgWp0XuiGSUJ/X5OtqaY2m0RJShwAXeVQ4CiYLPR5D7bkKmioI+VxqyWHChhl+jdaIg0uHSr9OwnZoDjlUBjQevLSEpeVurv5DB9uPJphToFPg1rAcdc7VJdU+dhyNcyBoMTvfYG6hixeOxin36eS7dXZ1mFxa4+XuCwtH/LlI57/R+clPfsJNN91EaWkpO3fuZNasWdkekhADjLkyNWvWrGn97kD1g60ckkAlJkg2K1QvvviiBCkxZU33uWkk/3swxoE+myIvzAwYHAradMadMbU2PxGJ5EG5iWS1aXCbdSP5edCCSJ+NS1fL7aIZgcq0oc+E0wI6ZX6dI2GLrqhqjQ79e62q8gyiCYeopfZA9cX7G2toif6liUfCDpoGfgOumu9nfUMIG2gNO+nufG1RB9OGrqjD9y4qZlNThC3NMZ46FCVuwex8g9uWF/C/B2Ps67PY32dR6lWt19sj1jFhKfNzQILUKNXX1+NyuaitrZUgJXLSmMPUvn37xnEYU0/238cUp7rjdfqbCLW1tdTW1qY/b2trw+12y5I/MWVM97lpJL/eE6Et5mBo0Bm1iCeTyolGz6HCl4v+DnyDFXk0Cjwanck1dYmMCVRHVZlaQjamo+ZW3VFNKTI5qEAVMm0ilkNk0PJEF2DbEE3YRCyIZizhS4lnDNp01GMiFjx1KEahR+3PKvTAM4fiPNPSSXNQNa8IJxzu2dFHfrI1YVXAxZJyVzoI3bOjj2jymw/p4HOpk7PueL6X5qDFMy1xXBrke3TaI/2DkiV+o3f99dcP+HzPnj2cfvrp8saJyAljbkAxnciSPpEtw/3uTUbIamtr49JLL5WmFEKcAna2m1Tn6RR7+vcepZxoVWqo+w8OUpmXuG0Rhz09Q7eqcIDZBQbu5NVIqqtf6iDfTIYGrmTKqi13D3g32HZU5aot4lCTrzOv0MBODjTVZEOj/6LHp0OeW90WMm0uqPSwpNzApWs0dJo8f8QkmFCvGXBp7Ggzef5InNpyN++e7aUlZLGpKcLOdpM18/0sKTeYX2RQ6NU4s8RFuV+nOWgRTTi82ZPghTaTlmAifSjwiipv+r/FiXn22WdZunSpNKUQOWPMlalMtm3z97//ncbGRrq7u/mXf/kXTNOkubk5J0+aF2IqCQDhIb4+0Yf6tra20tLSIudQiSlL5qZ+21piJByNZeVuXu406Yj2L7UbvOxuPOj0r+AYHLQyDwfWgbaIPSA4RW11FlWBW+13Ukf1qiV6vTGHxaUG51d6eO6ImU52pqParJ9Z4gJNY8dRdSixBhS5YW6hQVfM4WhEdfCzUedkWRrs67Np6lZVIr9L7e+KWw5WslHF4hKD7UcThBMQNG1awxa/3x8jmlAvfveFhVyzwM/DuyNsbAyR79ZZVpGqNqmlgs8ejnNJjY/KgME9O/pYM9/PuiX54/kjnzbeeOMNQqGQNKUQOeOkK1MPPvggc+fO5fzzz+f666/n1ltvBWD//v0sWrSIj3zkI0QikZMeqBDT1ZtZaJkOcNZZZ7FlyxbKysqkbbqYcmRu6rez3aQlZLO4xGD5DA9RCzRNnfOk0R+kxvNydKSl8Jm32cC+PgvT7v884aiOfj2mum9mI4qEA+0Rhx+9HCIxqCjRZ0Jb1OHvbebAQ4Ft6I07tEXUEkAHVZ2bV+hCQ+3NspLjCiWgI+pgO/3LGcMWnDfDTW25QXW+wRMHovgMDUOHJw5Eedumo/zHi0FawxZtYYddHSatYYu7Lyzk7guLmFOguvx5DdJnTz2yZ3r87k2EG264gQ0bNqBpmrRNFznhpMLU7bffzk033URzczOapuFy9Re6mpubsSyLX/ziF1x22WUkEsOtphZCHE82zqACtYdKApWYamRuGmhbS4zGrgTtEYf/ei1En6mqPBFr4JK9zGVww9Ey/jQGfT3PGNtFRdQauOwQhq6UOUDMhv1Bm74h/triNjR2W9iO6uyXGkvEgj7TJp6R4ir8OlUBA13r/55cye/DcsCtqaYWMQv29iZYUOzid1eU81qnyUsdKuL5DY0jYYeWsMOv90SoDBhUBDRqy9zpFukP746kg+yKKm/67Kk18/3DnjO1s93kY091c/nj7Ty8W0LXUOrq6iRQiZwx5jD11FNPcc899xAIBLj//vvp6urivPPOS9/+zne+k5/+9Kfk5eXx5z//mR//+MfjMmAhpisJVEIcn8xNx6oMGPgMeKYlRnvGuU6DLz1Hs9TPoX8/U2aFyUCFosxwwqDbh3u+sXAnX8etqT1W3oyrmagFK6q8LCkz0hc5CRtOL9JxaVDgUudFPXkwRndcXQjNydeoztfT31t5QMfnSi4FtNVId7abhJL7qMImdMccnOT39vbTPPyyKczLHRYHgwk2vhZiw6thNjaGaOxKUJVnsLTczTUL/Ny2vIDWsMWmpjCP7Y1yz46+AYFqW0uMZw7FeKnTkgrWCCRQiVwx5jD1ve99D03TeOCBB/jnf/5nCgoKjrnPtddey09/+lMcx+H//b//d1IDFULkRqBqbW2lp6dnwl5LiJMhc9OxWsMWUUsdUns8IwWqVCByhrhfAhWuEs7QAWmoZX8jLSvUUEvxhpLvUnuZUuMo82nkucGj9Y8Px6HEq+PW1fNYjlrqN8OvUeLVOBB0iCW/CUODcr9OX/JAYY+hzrWyHajwaRR7dZ5qjnHz090cjViUeDU8LrV8UEsO9oXWODvaEsRseKPb4tUu1bo94NJZXOKiJWSlA1P/Yb0a5T6d9qjNpqZIRiXLYkm5m7NLDdbMH/7QXzEwUL355puYpnn8BwkxzsbcgOK5555j5syZXH311SPe733vex9VVVW88sorY30pIUSGbLRMh/5zqAoLC5kzZ86kv74QoyFz07EqAwZdUYuAa2BDhxNlaOqQ3/FaGDnS+VYeHd5SYjCrwEVTd4K9vRYxWwWjZRVuDvRZBBM2RR5w6xqm5VDkVVUpDWgJW1xS7aOxK8HRiEMkoVqcZ+6FAij2QJFHJ2BozPCr1hgz/DoH+uxk63UH23EIJdR/GxrMLtXpjTsUeR21RNGCPb02hq66BNbk6xwK2ugGLCgyqMrTeWxvlH29fdy2vIDKgEHcsmmP2qyZ76c1bNESstjaHMeXPF9rcYlBVZ6HRcXj0ifslFZXV0dVVRWXXHIJHo8n28MR09CYK1NdXV1UV1eP6r7V1dXTZqOvEJNhpJbpExm0lixZwty5c9Ofb9myRapUIqfI3HSs1rDF4bBqvlDgGfvSuqEqUsczUvVppHF4DRWauqM2e3stEnb/0r7nj5g0B20MDSIJtYeqLQYhE95xmod8t8beXouOqE3UcrBIniulqfGnqmR+AxWcgjbPHklQ4dP4+Fl5XDjTk17uV+LRBnwPjgNRyyHPrXFWqZu7LihkXqGBSwefAUVejTOK3Vyz0M81C3wsq/DQ0JGgM2pzIGixrSVGa9iiOWTzzKEYL7aZ6fboi0tcLJ/hwWeoJhtbm+Nsa4khju+yyy7D61U/R8dxeOSRR2TJn5g0Yw5TZWVlvPnmm8e9n+M47N27l/Ly8rG+lBBiCIERbpuMytXjjz/OZZddxqpVqyRQiZwhc9NAO9tNGtpNAi6NgEuFj+MZ7sLAdE48TKX2FJ2ooAn/7/Uo246oFucWatmdmfywUM0oMg/ujVjw/JE4QdMhZEJ7xOKCmf2ViuigwSdsaA6qpX0WsLvH4okDMf5yOI6Das1eEVCjTzXn0AGfoXHlPF+6qrSwyKDCp+MzNLpjDt0xm7svLGTtwgCP7Inwl8MxuuMOxR4tfcZUTZ6ePNzXYVNTmC0H44CD14D2qE1LOMHiEpecQzUGn/nMZ7jqqqtkD5WYNGMOU29/+9vp6urioYceGvF+GzdupL29nQsvvHCsLyWEGMJILdNh4gPV7NmzKSwsZPv27RKoRM6QuWmg+xqCPHkwTnfcSbcJz+Qb4ipgvM+cGq5N+kiXuTYDO/xlnls1HAcVkBxUZetvR83k3iRlyMYYWv/zd8Yc/t6W4I2eZBXPrXH94gCX1Hh5W7nBDL9a4vhyp8WPXw7xy90RHtsbpanHIuCGaHIZYVh1rGBTU5hXOxMYqLOrlpR7WFquzp9aUu7hvXO8rF0YUCNLbr5aUeWl3KcnOw86bGuJHdPtL9NwHQGnszPPPBNAmlKISTPmMPV//+//xXEcbrnlFh577LFjbrdtm5/85CfccsstaJrGzTfffFIDFUIc63iH9k50U4qtW7dSWloqgUrkDJmbBmoJ21iOChenBXRKBhU6BldrxkNqidyJ8ozwoOFuGnwRE7PAb2gYGhyN9DeZAFhYpLNippsyb3/3v76EWj64qFjHa/SPXQNOL3KxqNhFd9TiQJ+dbjiRcKAtBo2dJqYNTd0Wr3fbxG2o8GtUBVzJcKPhdWlcMNPLTWcGWLtQNZNIt6qPOtzxfA+7exJcWuNJ357v0TFth+eOmDzUFDmm21+mVDMLWQ7Yr76+ng0bNgASqMTkGHOYuuiii/jc5z5HV1cXa9asobi4mB07dgBw3nnnUVpayj/90z8RiUT42Mc+xrve9a5xG7QQot+RGyvH9bDNEyGBSuQamZsGun5xgBWnuTm71E1XzDm5wyVHyWFs+7L0Ef4hy6xKaaj9TkPd3QFsxyFuHVth29+n0tDcAoM8l1ouCOrP7pgz4AwtB7XX7J4dfTzfmqAt5tAbhyJ3//NV56vnyTxY2K3B9qNxNjWFWbvQT225m6aeBA3JMKQOULZUh79ggoZ2i+2tJqnv5p4dfWxvjXM4bNMVtXEctexvuLC0osrLyhqPLAccRAKVmEwn9e/q17/+de6//34qKiro7e0lGo3iOA4vvPACvb29FBYW8vWvf50f/vCH4zVeIcQQDt9YOWJTiok0VKAKBoMT+ppCjETmpn7XLPCzZr4/3dWuLUsFjNFcbByvSmagzogqcKv26DrHBibTgc4YhIZYExizYdthk4YOS50XRX8gOxJx6El2Okx1O2wO2rzalaDcp1Hh1XhrqcHMPD3dCKMr5vCWUjfFblXpcunQHnMImQ4NHSo8tYQsmrotth5S1aNtLTFeOGrS0BEn4NLJc4OmQXtEBab2qE2JV+O0gMF5lW4+uTSfK+f5hg1LS8vdrFuSn14+KPoNDlSf/vSnszwicao66Z6b//zP/0xdXR3PPvssL7/8Mj09PeTl5XHGGWdw0UUXEQiMtE1eCDEZZj7YetwlgScjFahWrlzJWWedJf+/F1k33eemne0mm5oitEdsnj8Sy1qIShmP1YQWEEyoqpTXUOHFGuKJj/daCQc8qEpYvgE9QzTlMFAtzqMJuHSWh2UVHr6+o4+g6aBrqqvfoZDNa10mhV4d3bTxGRrzCw3ClqqM3dcQ5EBvQh1k7MCv9kQ5Z4abjqhNe9TBrVnMKjAImw7lfi0dmFpCNi8cVY00FhW7uGaBWv738O4IGxvDVAV0bkkGqJ3tJttaYqyo8kqgGkJ9fT0AN998M8uXL8/yaMSpSnOmWd2zpqaGQ4cOUV1dTXNz86gfN9S7+xN5cSrEWI1UiZro39l9+/Yxe/ZsdH0yFhMJceoY69w0lJ3tJnc830tjZ0I1crCO37xhKhnpfKrR3NdFf5v3VGMLDXW2VWqPlUuD6jydcp/GJTU+/uu1EO0xFbJcGfebnadRETAIxW3cyS5/K6q8bGuJ8as9Ufb0WPiTB/zGLagMaFgOdEUddB3mFugsTK4dLPfrLKtw82JbnIYOk+6Yw+x8g9uWF7C03M2HNnfylyMmbh3Om+HmtuUF6T1TK2s8rFuSfzI/1lPam2++yemnn57tYYhTlFzxnIRsHJwqxPGMFJhmTfDv7Ny5c9NBKpFI8MUvfpHu7u4JfU0hxEDbWmI0B9XptXkudfE/VQ019KGCVOb9/BmfDL6vVwdd79/npGkqIHkz+rfrqC5/zUGbxm6L3+6N0JVsCqhrqmNgyuGwQ1vEZmGJm3NmeGhIVgRXVHlZWOQi4Ibacjdz8nV8LnWuVU2+wfmVbt5aauDWNYKmza52k9/vj/LdnUFeOGqypMzD7HyD9qjNpqYw6xuCLJ/h4ewyF7Py9PQ+qhVVXhaXGLSEbOnoN4LMINXS0sLdd98te6jEuDmpZX6HDh3iW9/6Fn/5y1/o7u4mkUgM+8upaRp79uw5mZcTQozSkRsrhwz7kznV/su//As/+tGPeOKJJ9i8eTPFxcWT+OpiOpvuc9OKKi8tIbXzp8xnsPG1UNaX+Y2VoUGlT+NwZOQL31S+cWkQcENkmO+31KsRNB3ituoeqGkQc8BOlu50YFGxATg0ddskbOiIOuS5VPgq9GhEEw4dyefXgKBp09SdoMWlAhia2hNVFTC46nQ/axf6eaM7wSN7IuS7VRBaUGyQqpEtq/DwxwMxdrTF6YzZdMWgttxhbqGLhG3y270xLBzeN8/P764oO2Zp37YWI93RT5b6jSwWi3HppZfS2NjI4cOHuffee9G0bLVwEqeKMYep/fv3c/7559PW1jaqdD/Vf1mHuzgVIlcN9zs70funUj7+8Y/zy1/+ku3bt7N69WoJVGJSTLe5aShLy93p/TR3PN9D1xQNUik98YF/j6nCk6GBz1DtzVMKPVDhN+iKWUPuneqJO0SsZOvzQp2o5XAwqJ7fQi31W1hkgKbRFYtjJAOXmXwnqsKn0xaxcWsOhgZlfo14wmF3j4WuwemFBnlujeagRXPQ5swS9dytYYuoBXMLdZaUu2joSLCrPc6lNV6uWaAO/93XlyAStIhZ8FRzDEPTiCQcOmLqtdojFrc/10sqgKU6/KX2WklHv+Pzer189rOf5aabbmL9+vUAEqjESRtzmPrqV7/K0aNHKSgo4IYbbuAtb3kLfr9/PMcmhDhJBkPvlZiMQJXZlEIClZgsMjcpD++O8N2dQVrCNkP0V5gyTAfMQf+IGRpcNsdLU3eCsGkTTKgDiT0a+AwNe4QQHU4+V6Ebrpjn57f7ongMi0jy63Eb9vVZzC0wKPFo9JgOZkK1RbeBlzqs9EaseYU6/+c0L1ubY7TFbDQHKnwaC4pdVOUZNHUn2NFmct2TXSwudVHuUxfsqnJoJdcgOumvAezvs3j2cJxCj0ZnzCHgVmP2GfB0SxzbUcs2GzpMPMkl1Znd/KQhxfHV1dUBSKAS42bMYeqJJ55A0zSefPJJzj///PEckxBinBwaoaIqgUqcimRuUhfU/769l7Zkw4RTUVO3SVO3nW5j7tbArUNL+PinXGlAnktjwysh+sxj33DqjTtsaY6ng5c74xrbov/p9/ba6FqcuK3OqNKBtqhDostiZY2Hcp/Gfzda9OLQ22pSnaeT57aoytNZuzBAVZ5xTDWpJ6aWFs7KN1hWYdAesSn3a2w+EKMz5uDRId+lDgZeUu6iMmCwviGYseRPNaQAJEyNQAKVGE9jDlNtbW2ceeaZ03ayEmKqGGmJajYC1fvf/36eeuopmbTEhJC5STWg6E0uS/MZqtpinkJ77U0H2iLqAF7dUQFnqArWcBwYdg+WS1PPHc54ruF+dg5wMGihJR+nGn04LC7pD0m/3RulLaa6/7WEbN5a5koHn9RSzNuf66WhI053zKEnZierZBpVeXqyRbrOyhovzx6O8/bTPMwp6H/+e3b00Z48oGtpuVuW/J2AwYGqsrKSO+64I8ujElPRmMNURUUFpimdY4SYCrK95y8VqK644gruuOMOCVJiwkznuSm1xGt/n0oCqUNpczlInUib80wdseGXMae4NHWmlI7aC3W8Q4FBVbdG+69TiQcumOlNfhMOLWGbuKW+G3XGl9pHlWLasKTMxRvdCe7Z0cea+Wqv1O/3R+mJO6pNu6PGXe7XqQwYdERt3uyxKOpO8PnlBVyzwJ/+e24JWbRHbcp9erpCVRk4VWuREyMVqO655x6uv/76LI9GTFVjbpj67ne/mz179pxyXZCEOFUNV4GarJBVW1tLU1MT7373uyfl9cT0NJ3nptQSr2cPq701ugauHLi2HimcnEzOO14hKhWkLqgc/fvGGqpjX8CARUU6/iF+fhoqyOW5dcr9GrecncctS/JZUuZibqGLhg6TR/dGefJgnNZo/+Nm+DXKfAbf3RnkuSMm9+zo45mWOFZymaCD+jsLuDTaIxYvtplELYewBa0Rh0f2RAAylvKpc61uW15Aa9hia3OcR/ZE0p39xOjU1dWxa9cuampqsj0UMUWNOUx98YtfJC8vj+uvv562trbxHJMQYoIMd1EzWYHK5/Ol/7uxsZH3ve99cg6VGFfTeW5KnTlUnW9QlaeT51ZL39xZLgRPZGFMZ+QLGRv4e1sC0x5dxcnvgu6YCjBv9NhDvoZHhwI3HArZ/HdjlOue7OSuv/Wx5WCclmCC7phDyHRI2P0/e7cGbkNjx9E4mqZep8902N5qYgNnlrp4V42HM0td6Drsak/QHrHwGSrYVfo1ls/wpKtPK2s8rF3oTzefWFHlZWWNhzXz/ays8RyzzG9nu8n6hqCcRTWMzLnpoYce4lOf+pScQyVGbczL/DZv3szatWv5z//8T2bPns0555xDdXU1Ho9nyPtrmsbGjRvHPFAhxMk7nOX9Uym2bXP11Vfz8ssvS1MKMa6m89yUOnOoscvi9EKDXe1q74/G2JfT5ToHtS8sMkKZykadPRUcRY6IWgOXRUYstezOzvia7TCgcUVbDCJtJhELWsLg09XBvg4wv0jnwple2iMW5X4D03Z4qcPEZ6i/kUgCogl4S4kLt672YNk2zCnSKfcblPkszijWuW15AZuawjy6N4rf0LhteQHAgOYTIzWc2NQUYUtzjJaQLY0pRrBv3z6uu+46EokElmVJUwoxKmMOUzfffHP6FywWi/GXv/xlyPtpmobjOKfUhCXEVJbt/VMAuq7zs5/9TLr8iXE3neemne0mLSGLcr8ODswI6PTE7eMuh5vKHCB+nG8wboPf6A+TOqq9esJRQcx2VIMIgGACitzQkxG8Mi+lh9uDZtHf6S9oqdco88IVc/14DVhW4aY1bPHEgSidMXXHPJd6vrgNzx6OEzId2qPqyQ+FbK5emAo96mvtEYeeGPSglvy9/TTPCXTucwa0YhdDmzt3Lj/84Q+ly584IWMOUx/96Efll0uIKSrbB/qCtE0XE2M6z03bWmI0dln4DFVhuXCml0gixv7gKDov5KDRVtNGExYzw5FbV80gDE0tn+uMqXOq4skfk9+AeYUGDR3q4N+E0z+OzPGkKn5uXS39i1sDx3LFPB+vdyV45nCM0wKqKtURsQm4wLIhnFBjKPdpFHo0+uI2PgN0XR0U3BpWbdRTe6DK/TpFXg2fActneGgJWSwucY2qc99wrdjFserr6wFpmy5Gb8xh6r/+67/GcRhT12RefAoxGSRQialsOs9NqQvlyoDBHw/GeO5wjPbI1AxSMPoayoksYdTor0JpDjhoRBIOltP/PL0m9PVYaMkvDA5QHl0t/bMcFcry3VDs1fHoDm0xde/CZKHopU6T3jhEExYFblX58uqqMUjEVs/RGnHoTpbXfIZaJmg58ExLnGKPxuISdZ5US8jiffN8rF3oTwfnxSVGutnESNWp1G2jua+QQCVOzJgbUExHOdAUSYhxM1JgmsxlgKlAVVpayvbt2/n85z8/aa8txKmmJaS6wLUEE7zRYxM6ldf4JQ0XpHSObTqR+bkDBE0byyF9+C+ofVLxhApM+a7+FvNeXVWt/C542ww3cVtVorpiEEk4GLqDgWpOcXaZmy0H40RNldxMG/LcGjP8GhUBHb+h4dX7xxG3wHGgOw6tUWgJ2mxvNdl+1AQcNjaG+P2+GOCwtNxNZcDAZ8CLbSbfbwjx+Wd7uPy3HXxsaxc7280hG06kugCmApU0pRhZfX09GzZsAGD9+vU88sgjWR6RyFVjrkydiM2bN9Pa2spHP/rRyXi5CXMoB/aaCDGecmH/FPQHqttvv52vf/3r2R6OmCZOlbkpZVNTmF/viWLoMMNvoKNCwqnAramKUGY4PN73N9RtmV9zgN64akee2WDCAUxAs1VwMpJVqHiyqUTYgp1tJn5DjccGjoQdvIZ6rmgC9vcl6DEdXFr/Hi0NjTKfxjkz3DR0JAh1JrCTr/3WUoPdPVb6+yrza8Qsh5o8FQmbgzbBhENDR4Kd7SatYYuoBYeCFn0mvNKpHrvbBUvKVVh6bG+UJw5EWVLmZlnFscsC+1usS6VqOKkKVUNDA2vWrMnyaESuGlVlqrS0lCuuuGLY25955hl27do17O1f/epXufHGG098dEKICZft86dSamtr+f3vfz9giV88Hp/UMYipReamwTSM5H4g03a48DT3oFunLkMD16ArlpMJijow06/x1lKDfNfQF0MOEDIhngxaqT1SoAKQ21Cf66gQFUl2AjQdOBhSrdHDCVW9ijtwJGzjMdT+pSVlLvI9GkUeKPdruA0t/f05QGfUwatrVOW5MG0HQ4MCFzQHLTY1hdOVqep8I71nq8KvcVGVaou+ospLuU+nOWiz5aA6f6qxS+3BSgWnVDv14fZRSeVKqa+vH7DEzzRNaZsuBhhVZaq7u5ve3t5hb3/nO9/JihUr+NOf/jRuAxNCTJ5yNww1X859sJV9WdoT+J3vfIeHHnpI9lCJYcncNNCyCjebD0RJ2A5uXaPYo+HW+rvPTeXLv6itPsaLDcnOeTbBxNDBzECFJJL7qUq8Kjj1qPNyKXKr/VZeAxYWu9jZlkg3oHCASAK8GfsDTFs1nrhnRx/V+QZnlrhI2A77+2yqAjoh0yDUbWGh9nUdjThsPxonbqkW6m4DAi4AjRfbTPb0WtSWq6oTOKxdGEgHpZ3tJnMLDfI9OuU+jWUVHlrD1oDglLrvpqYwm5q0dMfBVKt1qVwdKx6Pc/XVVzNnzhzZQyXSxm2Zn6R0Iaaulz8y9HK/aBbGAtDR0cFdd91FR0eHNKUQJ2U6zU1q6ZdDyISmbovdPRbW9Pn2B/DqqjFER2z4+5gOHI4M/QNyaWoPUywjZQVN1VhCT1b/qvMNyv0OVfkuijwar3cmiNtQ4FGva6P2QrmTS/l0DQ4Fbfb0WrzencBywO/SVEDSNCp8Gk3J1zKA6jwNy4HUt+AzoCZPpz1q09AWpyPmgONw94WFx4w/1aBiZY2HdUvyh/0ZbGuJseVgHDTY15sgmkyDqYOAAekAmOHpp5/m8ccfT/+7IoFKgDSgEEIk5cpyP4CysjK2bNlCWVlZustfd3f3pI9DiKlkRZWXeYUubNTSssyW3tNNzFaNIcbCQC2bG9y7I2ZDewxilto/9XxrgoYOi+6ozRP7owQttcfqynl+3Mnrawv1d+A1VLvzYEIt/zsadmiLOBwO2ezptXmqOUZb1MGtqwuzIi8E3DqdMYe4pfZQnVXqpirfxTOHYhyJqOdB0wYsx9vZbnL7c700tCco92m0hGwe3h0ZcrmeOpfMprbcxaU1XtbM9w9Y9re03J0OYrLcT1m1ahUbNmxA0zTWr1/PrbfeOq3esBFDkzAlhDiubASq2tpaCVRCnICl5W5m+PVpG6AGG+uqQAu1/ynzAinznKnUf9uo6tae3kT6a70mPNMSozpPLbEs80BtuYtFxQYJWz1vxIJE8v4eXYVe04aFRQZvLXNR7IV5hS66o5bah2XDoiIXty0voNyn4XNp+I3kHjLHGdClb1NTmF+/GWF7a5yg6dDYlWBjY4ifNkbY1BQe8H2q6lWCJeVu7r6wkEXFarHSG92JAeFpcBfA6a6urk4ClRhAwpQQIi1X2qWnSKASYnRS1Yk/t0jTlrEYvFBL7aYaur16Jh348KIAFX49/TjHgU8vK+AjZ/h4e5WXgKGW9xmaWj5Y5IaAAUvKDEq9GhpqCV+53+D6xQE+flYe1y8OpPeI+QyYW2jwRreKYO+d42NFlZcyn0653xjUSELDZ2jUFOjpSlPA0OkxbdqTSxpTvyuVASP9uJ3tJvfs6OOxvVEe2RMZEJ6O16hiOpJAJTJNSmt0IcTUMVK79GwcUp0KVJdeeinbt2/nscceO2VaWQsxXu57KcRTzdOjcnC8Q3pH2xLeoH8pn86xy/pIPk+ewYjndT34aoi4DWVeKHDrvLXUzYE+iz8ditMdd+iJOViofVznVbrZ3ZOgPeIQTqgP1Y1P47f7IvzmzQgXzPSwqz1MMK72W1Xn6bxw1GTzgRiWo8LULUvy2NYSSzeLyGwQUZWnUxkw0s0kWkI2+4MW5X49HZraozZXzvMNWMbXHrUp96kQltmsYvDzC6Wurg5QB/s+8MADrFu3joULF2Z5VCIbJEwJIY6RK+dPpaQC1Z/+9CcJUkIMoanbJJg4/v1OBcd7/380QWpweBrpbOPMIDU4yNlAR7IY6LHAtG2eb43x5MEYMbv/jCnLUU0owgmb1rCDDezrtdE09ZweFxwJqXE8cSCOkxzjsjKDS2p8/PyNMG3JylJ7xEoHnNQeqd3dCcKWzSXVPrwGvNgW54WjJs8ejrN8hof5hQbLKlSHvvaoatHeErLZ2W4e02wiFZwe3h3hnh19rJnv55oF/lH8VKefuro6dF1n/vz5EqSmMQlTQoghDReoslGdAhWoamtr05/39vbiOA5FRUWTPhYhcona26Idt2Ij+o11P1W+SzWgsBz13z0ZATbuQNwET6L/bCob0FL/7cDrXSqZuTWYXaBzIGijATMDBj3RBMFkcNMAtw4Bl85Th2IETQfLAY+hbl3fEGRFlZdtLTEe3RuhM1mUPNAbwu/WqS13U+5Tnf92HI0TtRhQqWpoj/PC0Xj63Kmhqk+P7InwQptJb9we0DJdDHTDDTcM+Ly5uZnq6mrp8jeNyJ6pcZBL7+ALMZ4KhpkLarL8O9/b28tll13GqlWr6OnpyepYhMi2bS0x3MlW4Mbx737KcWvHP5BYR+1TGqvU8/clg1LqIF6D/sN8teRY5hbqeJNXVwb9h/takD7TStdUZci0VZfA/b0JlpS7KHTDDF+yMYUNL7SZNActvLqGW1dLBZt6EjzUpKpG6vBeLT1G04aYpVqmzy10cc4MN8tnePAZUBkwWFrupipPpzvuEDIdKgNGeg/V4K5/a+b7OadCdRCUBhSj09DQwNKlS2UP1TQz6srU0aNH+e///u8x3X706NETH1mOGu1abCFOBU03DF2dyvZqoubmZl5//XU6OztZtWoVTz75pFSopimZm9TSrGda4jg9Fn6XOix2pGVrp5qRWsAbqJbkHl1VdcJj/MGkDj/OfJ241X89kPq63wVnFLvRMTkUsola4NNV44nUMkwDVaWyM56vKwYvdSSwge54//lWXl3tkdrdk2B7q0nchq6YTZ5L40DQ4pE9EVbWeNnaHMOjaxg6zM5Xfdi3NMe4tMaL1yBdmQIVqhxH/VxawxatYYutzXF8yfuB2id1zQK1vG9nu5nenyVGtnPnTjo6Oli/fj0g51BNF5oziuis6/q4/DJYVvb/ea+pqeHQoUNUV1fT3Nw8pucY6uIyG8uehJgsw1Vfs/l7v2vXLlauXElnZyfnnXeeBKppaLrPTZkXuZ94ups3+2xZ6jcMHdUVb6xhCo7dL5X6zXMyPi/zQrlfZ3+vTdRWtxmog3x74+rzfDfMLzQ4FLSJ2Q4lXo2emEOfmWx3Tn+YOs2v8eC7SrivIcjW5jgFHo3qfJ1Lqn08dSjG3t4EPkOjzKczt8BFS9giYMCBoE17xOZtFS4WFLsBh7ULAywtd7O+IchDTREcBz65NJ9FxS62tcQGNK2Q5Xxj95Of/ISbbroJgHXr1kmgmgZGXZk62XKl/CIJMXV5gaEWeMx9sJV9WQpUtbW1bN26lZUrV7J9+3apUE1T03VuyuzKBnDVfD8bXwvRFVeVGjGQjTrf6USlVqMMFVK9ulpWZwOlXphb6KIqYPCnlhgRu7+tugWEE2oPlJOsSB0KWsRs9RwlXp3DIQsLMBxV3UqFqc6YQ/2WLty6htellgceCdv84o0wluPQF4eEy+G0ALSEE+zttehLhjZfMlT9vT3CrHyDtcn+CCuqvDx7OE57VO2FumaB/4TDU2aQl+A1UH19PaC6/EmFanoY1Z4p27ZP+iMX3vkTQozN/mECU3SSxzFYKlCVlpamA5XsoZo+pvPctK0lxoGgRch0iFnwerc6ONaWIDWssf5o3MNcA6eCVJEH3jPHR6Fbo8irkRhiL0Bqb1TcgVACOmNqOWZHDHZ2WOmGFaYDQbVlKd2E4kjYoTm5vyphQ0fEoSXscCSiwtXCYhdLyj3ELfAZaqmfS4czSgz8Logm4GDQYltLjJ3tJpuaIuS7Nc6ZcezZUan9U6l9U8ORg3xHVl9fz4YNGwDkHKppQLr5CSFGZbjufqc/2MqbWVzul1mh2rt3L4cPH5bqlDjlZVYXnjoUY1d7QipSJ0gHZuXrtIbt9AG5g6W68Q3+0aYaTjioPU6/2hMlZoHNwBBiaCrgDm7FbgMVPo32qJMOwAaQ51b37Uk+TblfJxi36Y6DbYOpOfhcEI8nlxX6NO66oJA3uhM0dJjM8OsUehwq/AYLigzKfAb/7/UwPXGHv7epVumvdiXw6hrXLe5vr55a5rfxtRDNIZuWkMXS8v5/RwdXojJbqYuhZVaoXnzxRaLRKH6/tJg/FUmYEkKclHC2B0B/oHK5XCxevDjbwxFiwi0td3Pb8gK2tcT43b6oBKkxsAGvobrtDRemYOhmHirIQGuyPD94CaELmBHQmFtgsK/PImw6BE31XKm/KstWVa2OZHHHpcPphQbdcYeEbWMBZ5e6KPfr/H5/DJcGPpdGNOFgOw4Fbo3blhcAqo15c9AiGHdAg/aIzRvdCd47x4ffpdEacXj+SJxSn45tOwS8OjELbn+ul+cOx+iKO9Tk6eztswiZsLvbSrdfX1ruZlNThC3NMRo6Eiwpc7Giyps+8FcMr76+noqKClauXClB6hQmrdGFEKM2XMOJXDgeoLa2lre+9a3pz5977jm6u7uzNyAhJkhqKRbAuiX5GLrsxRirN3ps+kZe0TYkm/4gBapbYLGnvzV9AmgJO7zencDvUr3bS7xw7SIf/zDXS1VAI2Y76fOhQO1129NrcaBPVcrOLHVxy5J8llV4OLPExYcWBfjgQj+nF7nQgNOLXLSGLTY1RWiP2pR4dQIuVQkLJaA35vDkgSj5bg2XDgVuDbemmnAcjVj8ek+ER/dG2NNr0xt3CCVghl+n2KsRtpxBy/hUea6py+SnjRE2NeXC22hTw5VXXkl+fn/w3Lx5syz5O8VImBJCjItsnz2V6emnn+bSSy9l9erVEqjEKWfwfpXrFwco9x7/rCUxcRLJ/UzeQWdZdcegM2rTE1cVqD+3xMCBt1W4yXNr6SqVV4dKv0aJR/0t6kBVQGdTU5iNjSHaozYdUYtnDsXZcdTEAdoiFj99PcLungQeQwPHodCrc2apwbtqvJQklxG+0W1hOSrc7e2zSdgqcHXH1RJDV7J1+8E+iwq/wU1nBrik2ovPUAcUr28IsqzCw3WL/SwscROzHRo6EsfdVyWO9dWvfpXLLrtM9lCdYmSZnxDihAy3dyrbZ09lKikpwe/3s337dlavXs3mzZspLi7O9rCEGBeZ+1V2tpu0hi0unOnl9/tj0+p8qVxio86RGhxo893g1tXuKhs4EHQ4FFIh2My4li7xQplPpyNq43epYLbtcBzbUc0r8lzw3BHY02NhOlBiqEDUHLRpC9vougpytWXu9NK/+xpgR1sc03aIWhA2IZY8aLjIreGgEXBphBMQTtj0mhA2bdYtyedjW7v5e7vJ4bCqeK2s8bBuST47202CcZv2qM22lph08jtBVVVVaJomXf5OMRKmhBAnbLhANfPB1pw4c21w23QJVOJUkrqAva8hxLaWGDFbNTOQIDWx3BpYTv9BvcejofZS2Y4zoIHFUM8RteBIyCJmqQ59cRticVWxSjhgxiFiWegaFLuh/sw8Nr4WUi3fbdBtKHRr5Ht07vpbL41dFhYO/2emB9Bo6jEJmw6Hww4eA2YXuggnbCxbNb2YEfDQ0G4ScOvsbDdpCSeIJAAcVtZ40sF9W0uMNfP96fOoMkm79OOrq6sDpG36qUbC1DiZ9WArB3PgIlKIbKt5sJXmHPj/ggQqcSpKtbZuaI/T2G0RSpaEm0OyZGi8GKgzmkKD0ql5nB9x5rlSoBpNxG3VaCLFnTybKpOGClOh5PK7+UU6R8NqL5dbByf5HKmlhC4NOqI2JT6djpg6qNkGWiMOWw/GiFrqc7cGLWEbHIdDIRuXpp6v0q9T7NFoaLdI2KoiVuhxMDSN/X1qD1ZVngscuP4teVyzQDVOWJ88ODgVrlLLTN/oTvDIngj5bj197pmEqeFJoDr1yJ6pMRgqgcrKYTHdDFeByqXlfoPPoVq9ejW9vb3ZHpYQY7atJcaW5hjNIZtZ+TpeXV2Me2U2HzcWqknDiZrhV/uPhno+SLY+d6l26ZlcWjIsJStWRyM2bh08hqpSZd7dAfpM+P3+KEHTocKv8faZbry6emw4WdnKc0GhBw70JTjQZ1Odp3PBTA/FXo3qfAM0DZ9LY1aBwaWzPKyZ7+fSWR4urfECDvt6ExR6dA70WXxocycP746woso7IEil9u09sifCC22qmrW4xKAlZMt+quOoq6tjw4YN6SV/n/zkJ7M9JHESpDI1Bs3DLHESYrrJ9eV+MLBCVVNTI+1pxZRWGTAIuDQ0R50l5DM0jkZs5hYY/LU1IUv9xslY6nxHIiPfrmswI6ATD9qYGe86Da549cVVAMs3VJfAoJlcFmirgOUzwLId3C6dM4oNbltewP8ejPGjV0KETTV2vwFel057xCbhOCQchwqfTU/c4cU2k0uqvdx0ZiC9VG9bS4y1CwPpc6f29fbRHrX5xRth2qIOr3Ul0DWYX6Taomfu26sMqK4bqeV/qZA1VHVKVVbDgMbahf5pXcHKrFAtWLAgy6MRJ0PClBDilFdbW8tf//pX5syZg9s9fSdvMfW1hi26YjbdMYeOmImhQZFX59VOCVKjZTD02VGZdEa3N6rEA3MKDHZ1WCMGMA3VICKaILkXaXip1w1a6iOl2AteQyNqOWr5oGlTne9hU1OY3T0WPh1ierJTXww008ajg5lQAW1nRwLLBkOHcr+WPidqfUOQx/ZGeeJAjKqADppGvlsj3+OiLx7H0KAj6mA60BE1k8sA9XSgerEtztxCF4uK1QcMf5jvtpYYv98XI2o7gDPgYODpqK6ujgsvvJC3vOUt2R6KOAkSpoQQJ2UqVKeAAe/82bbNt7/9bW666SbZQyWmlBVVXp44EMW0LAo8Gn6Xhs/QaA5me2RTx2hCkoMKQB5d7XsaKigVe1S4aQ3b+I2hlwa6SDaUcMBxoDtmHzfIaRz7ehqqQhUyHXRNhb2IDZv3Rwkn+g8N9hkqKMYc0JJdAIvyNMKmg1vXKHDD8go3yyo8rG8IUhkwaAlZeAyN5qBFY2eChKMeV+zVMXSN0wIafaZNzILacjegzqBK2XIwnl6LmApZw1WcUr+/zUGb9qgz4GDg6SozSHV2dvLggw/y6U9/WvZQTSESpoQQE+a0B1s5nEOBKuX222/nG9/4Br/85S+lKYWYcqoCauquynOxrzfBG93WqDvMidEt4UvdZ7ggpaOqPd3HeTZPRsgKWeC2+6tegxtWjDQ+B4jZ/a/tM9RzuwyNSEy1XXdBuvkEqL1Yl8/1cShosXyGhx1H46pBhKbx3Z1BYraDV9fQkl0KYwkHTVP773wu9fVUW/cLSr3csiQvvQxwW0uMyoDBi21xasvdlPt1UiGrJWSxrcWgMmCku/6lwtLScjd3XVDEtpYYLSErHcqmc5hKSSQSrF69mhdeeIH9+/dLU4opRLasCiFO2nAVqFztL/bhD394QFMKOdhX5Lqd7SbrG4Jsagqz/Wic3d0W3TGbjqidrkqI8Tfcv2E2x18qCMmGEBmfmxlt0Z1hnuN4/27aqI6A5T6dIo+GRn81KzNU2w78+XA8eeCvzdxCA0ODP+yPsbfPpivq0B23iSQcDgVtuk11VlaqAjU73wBUJ8CgafNGd4LLH2/nvpdCrKjy0hq2aOyyWFLu4u4LC1m7MMDKGg/tEYefNkbY+FpowOHSKUvL3axbkp++/3BLAqcbl8vFzTffDMD69evlYN8pRMKUEGJcDBeocrFZy1Bd/iRQiVyW6p4GGjV5Ogkbnj1sciQsF1u5rsQLniEKDCfzN5dw4EjYZk+Pik+pMJX5Mhawr8+mJWTx+N4Ij+6N8lqXRcJR93XrEDJhhl/HnXE1GLPh9a5Ess25xqIigzXz/TyyJ8JLnRbPHIqlz5PK7N6XCknlftVisirfNSAspd4QSHX6S91fqlL96uvr2bBhAyCBaiqRMCWEmHBVEqiEOCmpttRrF/q568IidF1dLNvkbgVY9IcbQ1PnPmWGncwLMGOIxxqopXoGKoxl3selqeqU6fT/HmhAmVd18svUG4euGPRkFIh01H4wBzB0jU+cnZdur28A4YSDaavAddkcH9cs8LNmvp+zSw0uqvaml+5V5Rk80xLjE0938/Bu1c5w7UI/153h55az8waEpcx26mJ4EqimHtkzJYQYN8M1o8jV/RyDD/a94ooreOaZZ9B1eZ9J5Jal5e70RenOdpNSr07f4NNfxaQrdEHvCN35HFRnPZeuGlHoTv/SvhKvOjMqbg+/ZDCRvIZ2af3nUYEKQqnbUr8GXgO8Lo2+ZK91Pfn6qXOtPLo6e6otCvkuOKfSw+tdCd5S4qIjarNqlpemHpM9PTYdUYdir9qX1dCeYGe7yTUL/OkDfEH9HjZ0JGiL2IQT8MW/qjP8rlkwdMvzzHbqYmT19fVA/8G+paWlfOlLX8ruoMSwJEwJIcaVHzjOcSs5JRWoVq9ezac+9SkJUiLnbWuJYTlDd30Tk0eDUe1XswCGCEzdsZH3XWXelrBVGEoJWaqC5DcgnvzztDydnpiNaauxzfBrRBIO4USy4UWyM6HtqADW0G6ScOC1LpPmoE3QdNBQtxd4YGGRwa72BM3BOMEdNrctLxgQkra1xNjVbuLSVdALJ2BjYyjddCJ1n8xGFKl27ENJNbaY7t39UlKB6stf/jLXXnttlkcjRiJhahzlWitoIbJh7xRplZ6ptraWPXv2kJeXl+2hCDGine0mLSGLoGlLkMoyh2MP3B3OUKEp82tubeTnsujv5pf5+iGrf/9TsUfjaFgFJ5cGC4tdtEVsemI2CVuFqg6z/7ERy8GyVXfIkJmgK2phJfdTzS80ePdsHy2hEO1R2NVhcsdzPVz/lrx0MFpR5aUlpBaalvkMnjoUI2TaPNQU4dnDceYWGjR2WfgM1WUQRu7a178vULr7pdTX1/PBD35Q5qYcJ2/BCiEEDJisDhw4wAc/+EHZQyVyRmY3vxeOmkSPc/CrmDpcqDOoBn9t8AVaal9Uai+VW++vTJo2NHZZhBPqcSUeFa66YjbdcUc1p8jYsFXk0fg/Mz1cNd/Pu2d7yXOBzwUFbpiVr1OV5+LFtjgeQ6fcr2HZ0Byy2dgY4qevR9jUpNYfVOXprF0Y4DPL8rlsthfThs6ozYGgBWisrPGwfIYHnwGVgaF2himpNwkWl7hkGeAgmXPTE088wWc+8xnZQ5VjpDI1RqM5QV2I6WqqHOQ7FMdxuPrqq9m+fTt79+6Vc6hETtjUFOb3+2KU+HQipk08uZSrxAOd8eM+XOQoHbV3qn1QTwa/Wy3r640PrFg5yfvPKTSwbNjVYeGg9jaZttqbBdAdh6cOxTFtVdGKWGopYCqgxWwImg63LPGzrSVGd9zB0CDg1jA0eLXLpDiss6TMxbIKP388GKMlmMCyoSdms7s7wT07+miP2jS0J2gJJQi4dYq9GqAzO99g7UK1vyp1v9ahTjVGBanUfa6c55Oq1DAOHz7MVVddRSQSIR6PyzlUOUQqU2N0KMcvCIUQY6NpGj/+8Y8pKyuTLn8ih2gEEw7NQYujUSfdxa9bgtSUkurOl7oEtoG2IZrbhRMQSaimFZl04C2lbrpjDkcjNpV+DY8Gs/MNFhcbzCs00JJLBoOJY5cG6hoEDNUO/UDQ4pPbevjJq+HkniloCzt0xmw6ozbdMZuqPINrFvhZUubCY+hELXXA70udJrs6THrjDk+3xNjZbtHQbrKkzM0HF6rOf9taYmxqitAetSn36cNWnLa1xDgQtAiZzojVq+nutNNO4/vf/z6apkmXvxwjYUoIMSGm0rlTg9XW1rJlyxYJVCJnLKtwU+zR8BiqQUCK9PObOlKH61qoDnvejCuwwZfElqMO/B3csFHXVOOIvb02R8IObREH04E9PRa7eywqfBrnznAPaI+uA8Ue9eEk90RdOFMdynswaHEk4hBLtllPOBA21esXe7R0AKoMGPgMCJs2wQT0xCGWANN2iFsQcMFF1V7WLgywbkk+rWEruf/J4cp5vmOaV2RaUeVldr5BnlsbtnollLq6OjZs2CCBKsfIMj8hhBjC/9/evcfHVdf5H3+dc+aeya1Nmja90wstbpvWcoeiFgW5KBdXqqICbXF/UhDW9QGC/vbHrrrIsuuqCAtSRSoChf0BwioXaeFHtUC3hRJutRd6IU2bJmmuc59zvr8/vjOTSTJJkzTp5PJ5Ph55pJkzc+Y7Ycj3vOf7/X6+6UB17rnnZgKVTPkTx1u6wll1Q5K2hCLp6IvihqhMNR9psgtWJFVHafPedA3LSQUtic63megqfSEbdrXodUfZn5QH3TA1aDGj0EV1Y4JwwmG8z2JxuYcPW5PE2vVIZ6kXvJaRqepXWeBiY22MHc1JntodYX8qeKVfy9SgyckVHjYdjHP5LD+fnurN7CGVDl+Lyz2dyqnnsqjMzS1LCjOV/ETvVqxYAXSUTQdkyl+eyciUEGLIjOTRKeg+QvWtb30r300SY0y6wlltOEk4qUcr6iVI5Y17GFyvpke40hw6gllDVPF2Y5Iij0EgtUYqbusRJFAkHEUoCVsPx3mrPk5zTGGmilmcOdHDd5cUMqfYYmGZm50tSf7z3RA/3trG/nYblRqt8phQ5IYzJnmZXqhHlLYejrNuZySzKe9b9Ql2t9o8sSvCl184ktnQtyeLytydNvjtKl2AZVtDIufxsabrCNXvfve7fDdpTJORKSGE6EU6UH3rW9/irrvuyndzxBiTPc3qH99opTkuU/uGk657fXkNiB3DrCsLXUSi61qnbD2d3kCPfEWSipmlFuGkTdiGqAO7WxwOR+IkHb257+SgRXVjEsvUlf1ijmJrfYKP2m22N9uY2LhMvW4rbCpmeEzmlLgo8xksLvd02ktq08E4DVGHGUWKZVM8LK30ck91iMaoQ0NEv5DWhNNt/6n0flJ92V9KyqZ3lx6heuWVV/jyl7+c59aMbRKmhBBDqqfKfjMerGPvCCnkUlVVxSuvvNJpGoVt21iWLJYWQ2tRmTtz8fin/TH+uC8mo1J5kmuTZJPOo4THEqTIcf7081pAT9XwzdTjTHTQ9lp6zVMyK5AlFERTZdOjCv57TxTThEK3fnQoAg0RRX3EzkxFLDKhzAdNMWiM2LhNKPN1BKl0EJpR5CLo1qXQ02GpNpTUe1Y5MN5vUBlwZcIQwDN7ojy/L8rCMg+g2N6kf4u9ravK/i60FStWcM0112T6Jtu2MU1TpvwdZxKmhBB5Ec13A/opu3P6zW9+wy9/+Uuee+45iouL89gqMdplf2q/eqHeb+YP+2IyOpUHJt3XOdmAz9SjP/2RK5iBDkPdKvAdrV2GLixR4tVVANvisK2xc+ROj3iFk/o1xB1dKt1nKQ5HFEpBwK0LXiRtXZa90GOgFBiGoj0JHsugIeKwviaW2qy3o+x5mc+kIZrMrJmKOzDOa2ArXTlwa32cWcV6D6kdzUkaow5NMUVNKMqF032ZEa2eZH+oIDrLDlIrVqyguLhY1lAdZxKmhBBDbiTvO9VVS0sLN998M/X19Zx33nm8+OKLEqjEkEjvv7O/3eb5/VEqC1yU+Q3On+bhT/vjPY5UiKHR04hg9qhQX/V1ACtd/a83dqpCXzQJMTt3Oxx0QEoYHYGw1GuwbIqXJ3dHsRXMKrI4HHGI2nDWJA+1IZs9rUkKXGCZBgvHuzKNb4jYfO/1Vva0JplZZLFkgoeXD0SpbkjymWlePj/TR0XAoi5s8+AHYQ6FFT5Lv5KHPgjRFNMBbkrQZPkcf6egtK0hwbqdYcDodkz0bOPGjaxduzbzswSq40fClBBC9ENxcTF/+tOfWLZsGZs3b5ZAJYbMxtoYDVEHpWBPi011g43HhMlBU4LUMNLb+qb0pexAZ/91fVz2iJaFDlrpn0O9pC4FtCd0Ww30qFRVmZtNB+O4THADlgnjfSYJR1HdkKAu7JBwoNwHF0z3ZTbhrSywqA05bD6cwHZg4Xg3Xgv2tNq8mxoRe2BZSea5YzY8uTvCmZM83Lm1jZ0tNrYDJxSb/Oh0/Xfz7ur2zMjUnVvbeP9IEq/LoLLAPGqY6suaq7Hgk5/8JGvWrJEqf3kgYUoIIfqpqqqKDRs2SKASQyq7+MS977TT0qxHDXa0yCS/kcAACiyI2L2PLqVDUV+kR8F8FkT7+CAz1Zbs/cmSDrx2KE5bXE8TLPHA/FI3bhOe3ROlIWsjYf1vlVkjBXrfs3SLls8JAPDgB2Ha4op3jiT48gtHuGyWnytm65LpjVGH6kZdPTDoNnCZUJAqjZhdXAKgIeowpdBk4fjep/6lSXGKDitXrgSkbPrxJmFKCHFcjKapfiCBSgyd7E/ab1gYBOBPH8X4sCWGLftzjhgmUOY32dfec/g16AhGNj1PF/SaejQplIAEukR+18vj9LqouNN5RKvUC8UekxNLXbx/JMlH7Q5xBR6n4/71MXhiV5TzpnqIp0av0mu1DOCF/THG+9rZelhX7jt5gofKApOKgMXG2hgVAYsl5R5qw0lCccWW+gQHwzZP7Y4Q9Ji83ZAgllScNM7FZbP8PLU7QkPUYd3OCKCYV+rqFJz6M8qU3tOqIiAFgUACVT7IPlODbMoI2T9HCHHs0oFq3LhxbN68mYcffjjfTRKjQPqT9vRifoDmmIOtpCz6SOIA9REnE2y6XnCl94uK2uCxoMQNhT3kh4QDoSSdElTXXG2n7md1uWaOJaEx5uAyDE4o6ggcXgsmBszMeWIOPL8/TszW7bhwupcF4y2SCmrDirurQ7zdmMRj6cmGG2riPLU7wjN7ovx0WzvvNyUocptcPNPPyeVuwGBLfYLa9iTnTvFy4QwvSyZ4eOiDEEkFJ0/wsKs5wZO7ozRE7EyRid72m8qlLmwTtfV3oa1cuZI1a9YAcP/99/Puu+/muUWjm4xMDTKZxy5Ez0bb6BR0BKrf//73XHfddflujhgFcpWBrg/bUhJ9BMpex+SgN/1NT7fLXu8UsfVXNo+hS5qr1P0SfRiVdKBbygrZoGx4/VCMkys8mTzWGIOWeOd4nkg9mcuA5rjD+0fsTmHLZSsWjnexfE4gMyL10AchDsQdfJZBQ9TBa8Gj54/j395q55EdYdoSiurGOJ+a7OPJ3RH2tzv4LJv6iM2eVoe4AztbBv7ulrLpua1cuRLDMJgwYQILFizId3NGNQlTx6CnC0MhxNhSVVVFVVVV5udoNEosFpMpf2JAcpWB9rlkis5I4zK6B6CE6rksejaLjip99HD/AgvK/AaGYRBNODTF9ZS9rtKPtYG/NiU7jW46CoIuXTLdm5puqNABbNPBBHr3KP3lNmFeqQ5S2e/Rt+oT1EdjVI13s7CsY7qe1wKXabCvzcFR8GFLiAK3SYlHn29fKkiZQLnP4O7q9kwFwP5M85Oy6T1Lb+ybdvjwYcrLy2XK3yCTaX5CiONqpI5A9VU0GuXSSy/lvPPOo7m5Od/NESPYtoYEd1e3s60hgWXKxc9IlOu/Wm9BKr1Br8PRi1L4XNASV0QTDnNK3fgtfe5cU0FL3PCxcW7CSQe/pZ8DdOCrLDAp8uhg5ctadpR+/qkFBnNLTCYGTCoL9B0e3xXhyy8c4fFdERaXu5lVZFHsNdh0MM6O5iTbGhJUNyQJuGB+qUWJR+85VREw+dxMPxMLLCYETMq8cOZEN7NL3Jlpg12nuIrBsWvXLpYsWcKNN96IUrL4cjDJyJQQYlgYyVP9su3bt48tW7bQ2NjI+eefzwsvvEBJSUm+myVGoPTaqdqQTWWBxYH2JIci+W6V6KuEgmI3tCX6vtZN0ffKfo1ZeePQwUSP9zOBi2f6eLkmRl1EByiXoUe+4kpXh3Sl9p9Kh7nsQhaHwoqkUrhNUA0J7qkO8eeDMUJJ+GtzkkkFFnFbsa/Npj61e/GZkzxsPhwnaisuP8HP4nLFH/fGCCUcGiI2Mwpd1IaTNJsmLlNXB0wXtKgL21QErEy59K57UEkZ9IF54403OHDggBSlGAIyMiWEEIPoxBNPZP369ZmiFOeff76MUIl+29aQoDbkMK/UoiGq2FwXpzV+9MeJ4aWlH0FqIJe1fbmIswxdqe9gWGGi9ynzd8khJrpiYDrMJRwIWHoqYTxV+CSZqvL3+qEY7ansdiSm2NNq47GgyGNQ6DZYMkGXNPe7DMIJ2NWSZPmcACeNc9EUU2w+nOC9IwmaY4pIUvF+U5K36uPcsDDI3BL9Gf9b9fFOI1TbGhLc+lor33u9hWf2RGXkagCuvPJK1qxZg2EY3H333TJCNYgkTAkhxCDrWuVPApXor421MbY3JfW0KgVHoqpbgQIxuvT3sja9juloHKWLRzhARcDgvk+WsHSSt1N4SyrIvq420BUGPz7BTZFbB60Cty5KEXMg6IaTSi0Clp4aWBmwOBJTuAwDb2qqYCiuSCpd0XBRmZtblhRy4XQfUwpMDAOmBXU5da+VXpUFP9rSxr++GeL5/THmlVqZEap1OyOsr4lR0+ZQ5jM7FZvIng4rerdixQoJVENApvkJIY67sVC8pes+VDLlT/TH0kovtSGb2pADKDyWrshm0LeqbmL085h6ul5vYzReA4IeXWACwG0avPRRjDK/yTgvHInpT9VNQ49ApRV69FS/7UeSlHpNSr0GTTEHMPC7DNwmLC73UOpNsr/dproxQSQBpT6D/7s7wv/dHSXmKHTdFMW2hgSLytzsaE5S3RjnpFI3qxcWALBuZxjQoWj7kSQJBYfDir2tNg2RGG83JKgqc3HuFC/pTYKzp/jJpr39ky5KIftQDR4JU0KIYWO0rJtKyw5U27dvZ8+ePSxevDjfzRLDXHpdCBhsORwn4UCZz6A5pmiTD9/HNJ+pg0+xx2B2sYt3jiR6XWQVUxCP6dEjR0FNu8MvqkMUeqAp1jEaFnRDU9Y00lBCj0S1xhVNcUVNu75v0K0o81kEXCbVjQkm+E0OhhzCtp5OmFSK1rgeBStw6a/DEYd1O8MsKivmqd0RPjhi0xRVvPRRjJdrooSSeoStssBkXqlFc12SAhc0RB0Ohm1aEjrE3XFGEdAxEpVeNyWb9vZfdqB69dVXaW9vp7CwMM+tGrkkTAkh8sJkbGxAmg5U0WhUgpTok/Qn7fNKLTyWwa6WJHG774UJxPAzkL93uR6TSBWF8CQV1Y0JWvoQrhV6vdPMIpMPWx2iDkSjHcfLffDpaT4e3tFxY0JBczxVxl11vPeSji5Mcag1yaGw6vSe9JpQ7jdpT+gNpsOpkKSS8NqhBP/2VjsHwzYBFxgGPLk7Qk3IwWPCiaUuakM2X5wT4JzJdqYQRXVjknAyQZm/Yz5jR2EWh421MWpDsmnvQKxYsYLi4mI++clPSpA6RhKmhBB5UTsGpvqlZe9BBVBdXc20adNkyp/ISU/x09P7Apa+gJXLxJFtIB8c5Zp0lX4fNPdzhDKRGpXympDMejNZgMLgg6YEPhOiTud9sBR67VTc1q+h2AMlHpM9rXa3IDWt0KQppvC7wEkV3nAUWCY0xRwe2RHmcERR6jE4p9LDeJ/FyzVRKoMuynwG25tsKgtsblgYzJw3u3pf+ufakM28UhegUh86uJiXCmPp6YSib77whS90+nnjxo2cffbZMuWvn6QAhRBiWBntAWvLli184hOfkKIUokeLynSZ6O1NNmEbxvkMgvLR55jT1wDtMTr2jUrLNeEtbOuvYndHULOBxpiiusHObKA7zttxceg2YGrQZJzX0FMFMdjdmiSU1OcosGBG0KDUa7C3Ve9hNafYwjRSlQEVzAianFrhwVEKW0FrQgEG31kc5A+fK2P1ggLAYF6p1amwBOj/F25YGMwEJF2YxaaywGT5nADLpnhYPsef+f9l3c6wFKMYoHvvvZdzzjlHilIMgIQpIYQ4jtxuN6ZpSpU/0aP0hqdxx+FTk70dFdDy3TAxLMWVDi7ZASo7iBVkHUgHnEmBjndTUqWq+aGn5ZmA39JhKalgX5tDW1JR4jHwWQb1YYWOQzBvnItPTfHSllDEU1MA549z42Rdixd4DMp8JvHUqJdppFuiZVeuTIemdCn0W19r6RSM0uujYjad9ptaWull2RQPYMimvwPk9eogK1X++k/ClBBCHEdSNl0czcZaXcGsPqLwWtAQsdnX7vS7dLYYfMPloqlrsHbIPZXQRK+zytaehBmFVqfRTp8FhanZcUdiELJ13FHogBSzoTGqqA05pKONAexpTfLaoQSTC/Tmu0kHNh2MZ96rlgHNMX2mmYUWJV6YXqjTXTokpYNQ9lS+O7e28cd9UdZ/1DkY1YVtGqIOT+6OdNpvKj2CtXyOH5cJv/kgzL+91d7n36eAlStXsmbNGkACVX8Nl78LQogxaDRV7usPCVSiNxUBi3K/wfRCi1dr47ywPy57TA0T+SqaY9B55CnXJW5Pt+Uqpb+tIck4r5kqXa5DkOPo/aiy32oGHReKttJfaUE3NMdgT6tNe0Lpsv2O4sRSVybsJRW0xBx2tdhUFlicNdFLgRterY3zvddauPW1VoDMOqlbX2vle6+1sL/dZkrQ4typOmSlK/hVBCzKfHqfqq77TYEOVQfabQ6GFY/ukCl//SWBamBkFrYQQuRBrn2oXnzxRYqLi/PdNJFndWGb5rjiw9YEUbvz/j9ibFIcPcjlqv7nMyGS44G2A/GsuXgJBYkcgd1tpjbzTU0DLHDpUazxfotIUtGacEg4MMFv4rMUhgHRpN4XLZIa3WpLQHVDAp/LwGVAxFaYwP42eLMhwnP7opwywU1t2KGmXTfipFIXtywpzEz7u/W1VtbXxKgqczOjyMWMIqvTflOP74rw1O4Il83yc/ksP0/ujjA5aMn+UwOwcuVKQPah6g8ZmRJCDDujvQhFWvYIVVFREW63dPhCj0wdiTq0JXQ1NLmEEZB75MlEr4kqcYM3R9WJXEEK9P5ThyKKZC9BfVqBwQmFJpUFJqahy5lbqYp/dWGbcMIh6NKBK2rDTYuCnFPpIeg2Kfd1vGsL3XDOZC9TCkwitsJ2IOg2SKQC2uGI4tXaOHtabSwDTp3g7hSk0q8+ZivePBxny+F4p/VVAE/tjrClPsFTuyN8Z3GQTX9bzvdOLuw0fVD0XfYI1fjx4yVIHYWMTAkhRB5VVVXxl7/8hWnTphEIBPLdHJFnyS/9K3+/sRWvBX6XLmNtIqXRRW6WAVMLLXa32Dmn8/VHdkl00Bv22uiRqY+Ns4jasL9NV5gEPe1wnFdP5dvVYnPvO+2cMdHL3rYEhyMdZxrnMynzGXxmagFv1ccBg7fq4xjtNgqYUmBQVeZmZ0uSpqh+XPY6qXveCbGzOUmp10htYG1SEbAyG/eCDmdzS1xcNsufedyiMreMSB2DlStX8vGPf1z2R+wDGZkSQog8mzdvXiZIKaW47777ZA3VWDX7DGx0CesCl4GDBCnRM7cJkaRzzEHKpHs59bYE1EcUtWHFO402h0I2ZqoMu8cAlwmTgxYuQ6+l+qhN743m6XJluaPF4Y97Y9SFbZbPCVBZoPejSje5PGCyemEQMAglFTtbbB7bGWHF+iZu2tjMho9ifNhqAwbTghaXzfJTF7Z5Zk+UO7e2sW5nmIao4rPTvFwx209P0uuuZA1V32UHqVAoxD333CNrqHKQMCWEyKuxWoSiJ3fccQff/OY3pSjFWLXrNSz0WpeWuOpWiU2INF3wAQyMbhdzJt0v8LJ/9nc56ADJrJ+9JpT7jU77UTUn9FQ/t6XDU9ANdWGHmKM38/14uYuGiCKU1NMBs/lcevrqTRtb+Mm2EIez5h/aDnzv9Vb2tuqRqjnFFkrpIPdRu8PUQosF4yzmFOvRsbqwzdJKL2U+k4aoAxhHnc6XrhCYXQFQ9J3jOHzuc5/j+uuvl6IUOUiYEkKIYeSiiy5i/PjxUuVvjHI9djN/+Nw4vjzXx/RCC0uWKogeKPQUu6itmFLQ+Y2Sq1S6kfXV21opC5gYMImmSvel14N4TZhVZBGz05v9wsGI3l/Kb4HLNPjLoRh7W23cpt7wN63Mb1EXttnXZhOxdal1twEeE3a32rx3JEncgUIXNMcVtlKU+QymBk2uW1CgN/ddGGTZFA8VAYuNtTEum+Xn8zN9LJ/jpyJgcefWNh7fFck5ArWxNkZD1MlZAVAcnWmafPWrX8UwDKnyl4OEKSGEGEaqqqpYv369BKoxrrLA4roFBZT7JU2NJempdiZQ7D76RZpCF5I4EOp+Ydv1nWMARW4dYJxeroNtYF+7Q3Ncl0p3mbqIxJJyFwfanZxtOhyBd48kiCb1vlQtcf0cXhMqAwZXzQuwtNLL9EITt6HXepX5DQIWhBN6dMoywDQN3qxPUNOuaEso9rQ6/Pr9EHdX6z2jblgY5K36OL/dHuFP+6OZ588uQLFuZ5g174f5XtaGv0srvXx+pi9HYQvRVytWrGDNmjUSqHKQMCWEEMOMBKqxyTWjCvvsq1i3M8KGmjh1YZtlU+RT9LEkvT7OAVoSfd/XKte6uq4XeEkgnOy+lxTooOU1c18URh29fmrL4SQNMYVDR1DL7EEFhBJ6+p9eOaW/Ct2wZIKH/W02d25t4+IZfsr9BraCtriixKcrBXpMMA2IJRUuQ58jlGrr+016fdSP/qeNj687zHP7YsQcRW3YYUNNPDNKNbfYIugxaYgqQgnF9iabdTvDffwNir6QQJWbhCkhhBiGugaqz372sySTyaM/UIxYnvlno+aeCajMdKZNB+P5bpYYoXTJBj3alR7x6ml6X8CCEq+B2+xeiCItrvTokcfsXqbdAIo8nW+zDB3C/nIwzv3vhdh4MMHLNVGWlLvxW1DmM4gmHGyl96SKO9AUJxPWXKnnchvgsQx2tyapDSvqIoq2uMJ2FPNKLSoCegrhwjIPDREHlKLIY+B1pVump/mlg1c2KUrRf10D1fe///18NynvJEwJIcQwlQ5U5eXlrFq1CpdLdrMYzeIf/BljxyYWl3uoDdk8tD1Me0J1WnsiRH8o9DQ9v6VDUk9jCDrA6KNmD+83A71hb1HWLDmvpUOTZYDt6DCWllB6ZMlKPdYEQklFsdckkFob1ZT6rKDIAxMDBkVuKPUYjPPC6RPdnD/Vw3ifSWXAZFaRRcDS54rYsKfVprJABym9Oa9iXqmL2rCNaRiUeEwWl+vGLq30ZopUPL4rwpdfOMLjuyKZkLVuZ0RCVT+kA1VZWRlf/OIX892cvJOeWQghhrGqqip27NhBSUlJvpsihlhy79tYiQbqwrey/qM47UkHpaDEC43Rvk/5EmOPSe73h4EONDF63/w5YkM00hG20ntOpaf+RRw9UnRiqYtPTfby6I4wEVtR6DY4HNab/7YlFL5UaLOzzjM5aGKZ0BBxaIoqXq6J0RjT7fWaMK3QZME4F2V+i/U1MeojDkkH6iMOp1X4aE8omuOKfe0O5071glJUNybxuwwqAhZzS/Sl7NJKLxtrY2w5rEe0CtwGb9UneKs+ASiWzwmwqMzNnVvb2FKvQ9MtSwoBqA2lAxmypqqPVqxYweWXXy59ExKmhBBi2MvurOrr67n55pv56U9/SnFxcf4aJYbM0kovtSGb6sYkzTGH1pjC6XFMQQg9mpSrqITq4d+55LpvLCuhOQoaow6HwjaFHpNCpSgPWDTHEqhUepoQMIm260VZZmovqqgNbqXLuGNAud+kKWYTszvC2vtNSUpCNlVlbl47qMPW7hab/94bJZxUBFwGKD018I4zSri7uj2zrvCK2f5uASg99a82ZLO+Jg5KF3VZVObObOx72Sx/ZmPfbQ0JNtbGpNJfP2X3TZs2beKZZ57hjjvuwDDG1nC6hCkhhBghlFJcccUVvPLKK7z//vu8+OKLEqhGLYPKgEllwOL1QzFMOhb1C9FV9lqo9JS63jZ7To889YcN7G1zOByOklR66mC538RjGrQnFDZwOOzgNiBKqqgE0BzT0+4MA6YUWKAUBans4wANUYdCt0HcZbBwvIsTS1zc/15IT+dLOrTEFOO8FpMCFuN9FndXt7OvzaYurANZth3NSTYdjHPZLD83LAympu3pV7u00su2hgR1YbtbVb90qBID09DQwAUXXEBrayvhcJif/exnYypQSZgSQogRwjAMfvrTn7Js2TI2b97MeeedJ4FqFNpYG+OP+6K0xxUlXgOvy2SGR3/Sv6tVJvuJ3il6D1Lp+6RlT8vLNt4LzTF9LF2UwkZX7Ct2g99l8JdDyU7TC0NJKPHo7xZ63VRTDExDUeCChrBNW1KRsMlsSO2xoNBjcPIET2aq3oxCizKfSVJBcyxB1NYjXFsPxzMb99ZHHbYejncaVUqXSG9NqMzmvnecUZRpX3pEC2Q632AqKyvjP/7jP1i1ahV33303wJgKVFKAQgghRpCqqio2bNjAuHHjMoGqpaUl380Sg2hppZcpQQvD0OtQPCZcPsvPvjYJUkLrqeJeLrkuZ11GxwhWTwUnWuOdS7UXpD5+jzlQH4P9IdVtnZat9PkKXFDg1lMDTUMHp6YYHAgr4jZMKjBxmxBw6RGutoRib6uuVloRsPCYEPSYtMUdorYufLFsioclEzz4LL2psMuAyUGrU6W+y2b5ObncTWXAzFm9L7sQhRhcY7lsuoQpIYQYYSRQjW6Lytx8arKXIo+B7eipVQ9+EOqxrLUYvnKFnmO98PKZRx956tSGHGHJUXp0ykGPHuWSfbsCWnvZmcFr6nDmAHVRaEvqsugKfbvbSD2f0l+x1MhUW0J/NUUVW+sTfO/1Vp7YGaa60ebF/TE+aLJJOBC1FTcsDOK19AjVobBDUsGBdj36NK/UojbkMLfExaPnj2P1wmDO0LSozM0NC4MyKjVExmqgkjAlhMiriQ/W5bsJI1LXQLVq1ap8N0kMoq2H47QlFBFbX6A2xmS91EiUK/Qc6/hitJ8nSCrwdAlUgzHGmb6ANADDgGlBs1MZ/2KPwQS/wTifyfQiU49UocOQ3wXlfl1K3Ult9BtJwl+bkmxvskmmyqo7CtwmzCnuXOL88tQIVLqIRGWBxfamZGYkqqfQJPtKDb2ugWrNmjX5btKQkzVTQggxQqUD1YoVK7jrrrvy3RwxSLY1JEgqPQLhWBDqzzCEEDkooMQNlgntic5V+rK5U9P/4j0kd1dqTym/CxK2fm8q9EjTtEKLpZUentwdxQQCboNzKj1UFljUhmye2BXFAIJuuHiGn782JdjZYlPuNynxmOxsSdAUU8wtcfFhS5LGqMI0YMkEN6sXFvD4rghP7Y5w2Sw/V8z2Z9ZKbWtIUBGw8Fl6imBv0lMCQdZMDaUVK1YA8PTTT/P1r389z60ZehKmhBDDzqFrKvLdhBGjqqqKLVu2dFro6zgOpikTD0aqjbUx9rXZxB0IegzKLIMD7Q69zLISolcJpb9M9BqluJN7pNNWupy5oboft0iVMjd0eIpkhXwFfNRms7slmZk+eDjisKs5yfI5AXY0J3n5QBx/3OGq+QU0Rm1erY3jcxl8YZYvU3kvuzz5da80cyjs4DLotj/UFbP9bKyN8cyeKJsOxplRZGUKU/QmfW5ZMzX0VqxYwTXXXJPpm9LT/UZjUQrpbYUQYoTL7pyefvppzjnnHJqbm/PXIHFMllZ6qSpzE3AbxJKKlrgEKXHswkk4EoPmRM9TRh1y71cF+jFRW1fqsx0osFIFLIByr4FCcTBVYAIglIA3G/TUu7qwTSSpCNvw8oEoDVGFz2Xgdxk8vz/K47si3Z5vctCi1GuwZIIHIFNcIr1P1NJKL2U+k4ao3sCqL4UlZM3U8ZUdpP7+7/9+1K6hkpEpIUTeyHqpwRUOh/nmN7/JoUOHOP/883nhhRdkd/oRaFGZm4XjXbx6IEZbArxWz+Wrheirvl7C9lToJHtmYFzpUapgqvDEeL+Jz4JDqZEhv6ULTCQd+MPeKIVuA8dR2Apq2hwWjjdYdVKA5/dF2dFs89D2EEXudDCC2pBDdUMCn8vAm5q5d8VsPb0vbVGZm1uWFGZGsyQgDV+vv/46P/vZzzI/j7ay6TIyJYQQo0QgEOD555/PFKU4//zzZYRqhKoIWJT6TAIWTPCbzCru6K5HzyWIyDcLHXyK3Xo9VFe9vdccR48+tSZge7PNrhYbv6XXYx3Ry5KIOVDdaPNmQxIbGOc1OLXCzfI5AZZWeqkscDG3xKIy4GJ/u00ooVLrnvTI1ZQCs9fRJhlpGhnOOOOMTCGK0VjlT8KUECIvehqVkvVSx6ZrlT8JVCOLqpzPra+1cO87IerCDnEH2hIOR6IOXlNf+I6eSxDRV/3ZV6o/bDqq6/lSI6DZAWqcFwrd3UPV3GKTqYUWBe6O92N7suPB6TLopG6aWmAys8iF32XQnlDsaE5y59Y23m9KUOQ2+cw0L9OCFgVug7qwzfI5AVadFOBHZxRLUBolVq5cOWoDlYQpIYQYZSRQjVzOCaey/qM4H7XZJB095epIDBpiQKpMtBh7DIZuRFIBhyI6DNldnieUAOWAx4QKH1QGDJZOcuNNbV7l7fJ+9FkGJW59u8vU4awiYPDTc0r40elFTAtaNEQdntodoSHqoBQ0RB3qwja3LCnk8zN9mSl7MuI0+ozWQCV/loUQx52slRp6XQPVfffdl+8miT4wP9zMuVM9LJvq5bPTPJ02XLWkxx6zkujQ0zW8DAWDjovDhAPttp6ul1Q6LNmOoqbdZm+rrUN+igkciSnijl7nN6nAZE6Jxawii3uqQ6zbqcuaf36mL/P9pkVBTp7goTZV/18C1OiXHajuuecetmzZkucWHTspQCGEGDZkit/gSgeqtWvXcvPNN+e7OaKPKgssls/Rn86f82Q9O1ocDKDMZ3AoMvI/xRUDF3N02Bnou8BEP763YibZo1PZ9zsSg6aYQ23IwTQ6pvG5DJhcYBJOODTFIGakiqYY0Bp3+KhNgZEk6DbY25rkliWFnQJTXdhmQ02cjbUxCVJjxMqVKwHwer2ccsopeW7NsZMwJYQ4rnoalZI/RkOjqqqKf//3f8/8nEwmCYfDFBUV5bFVoifOCaeyoSZOdWOS2vYkbXGVubCNpEpSi7HtWOK0CZT7DQ5HVKeg5DP13lPpt1eu53AZesPd+aUuvJaBz2XwdkOCCX4Ty4Q6ZRJ0HNymwfnTvRxot9l8OEHEhgl+g8mpKX5dQ5Ps/TQ2pQNVWlNTEyUlJSOyyp9MGjgGMlVJiP7p7f+ZGhmVGnLJZJIrr7ySz3zmM7KGapgyP9zMvFKLrYfjbGuwORRRKPTFbVNMMbnA6NZxS0cu+ioJ3YIU6Ol8R8vplQUG80rdnFjq4sxJHlyGoUudt9vsabFxlMI0Ie4o3KbBLUsKKfYYmAZMDpr86PSizJqobLI+StTW1nL66aeP2DVU8mGwEOK46C1IyfS+42Pfvn2sX7+exsZG2YdqmDJqPwB0xTMDPRqQVDpMJYGakOp20SuDVaI/+rNfmYUuJFHohi/ODuC1oLohyR/3hnGUIpzU7z+PBUsmeKhtT1ITcgDFojI3311SyFO79VqpRWVuCUwip40bN7Jjxw527NgBjLx9qCRMDTK5KBSifwL5bsAYMmvWLNavX8+yZcsyVf4kUA1HCsOAgBuCboOg2+CjdoeILcFJDEx69DLX+8eFDurZLPS6p09M9hBNKhqiDl5LF4i49bVWoo7CMgymFuozzym2WL2gACCziS5032hXiFyWL19Oe3s7q1at4u677wZGVqCSMCWEGHK9jUp9KB9AHFfpohQSqIYnVTmfhqhiUsAimlREbEXM1ov9TSRMidx8pt4namKBxcwiiw01cWKpN4vb0PtItSZyPzZXkCpwgd9tEE0qLpvlpy5sZwLS8jl+9FipLoWxvclmYdaok4w+iYFIr6EaiYFKwpQQYkjJ9L7hRwLV8OWccCpvNyRA6UIBNSHFobCTqeImRC4eCyYETA6FbZpjNvNLLXY224RsSCiws4JUgaU36s2e7mcAE/0GhgGGARMDuu5feg+oGxYGM/fV0/WKAdjWkOg0EiXEsRipgUrClBBiSMx8sI5IL8clSOVXdqCqrq7mvffe46yzzsp3s8Y888PNnDvFS0PEBsMglEhwJKoXZI+8ZdnieGlLQGtLx7hlXaTzyigFeAy9V1mx1yDgKI5EdaAygbklFj9dqgNSdjg6WlCSdVBisGUHqueee47bb7+dcePG5blVvZMwNUAnSCU/IXp0tEqXEqSGh3SgamhokCA1TBi1H3DHGUXcXd3Ohpo4BR4Tj+XgJPtXOECMbl2nfHYN2l1/Lnbr0uO14STNMYVPGYBD1IZF491875TCnNP0JCiJfFi5ciUFBQUsXbp02AcpkDA1YOF8N0CIYWjGg3VEj3IfCVLDS1VVVaefd+7cSXl5uUz5y7OllV6qG5PsbE4yucCkKaqIO4qWrOlax7J5qxiZDMBvQaSHZG2h10dFUguhfJYue37GJA8PLCvJTMurCFg8tTtCQ9ThnMkeCU1i2PnSl77U6ectW7awZMmSYTnlT8KUEGJQ9GXfNQlSw9v27dv51Kc+xbRp02QNVZ4tKnPTHnfY32aTdMDOkZokSI09CojZnf/b+y0o8UDSMTi5ws3hiI3tgGUa7G+1aYwp/tqk01X2tLy5JS5Z7yRGhMcee4wrr7yS1atXD8s1VBKmBtFQXSgOZHNgF7IJqhh60x6sI97H+0qQGv5isRiJREKKUgwTSyZ42NaQIGb3PsXPBCxDFxoQo0v26KOJHmmyFSin43a/y2DFSQFuWBhMTQ9V+Ny6yMS8UhcHQjaXz+penlzWO4mRIhwOo5QatkUpJEwNwEDCzbE8biCSg/x8hQbsvFouhoXWl+l8aSZQK0FqRKiqqmL9+vWce+65EqiGAa8FU4IW+9rsHstag14740iQGpVchg5Pehtc8LlgZpGLyoCpy+4pRZnfyowupb9XBKxMOXMJTGKkW7FiBTB8q/xJmBoClQ/Wjbq9QNrUwMKZjEaMLv19D8h//5FHAtXwURGwKPOZVJW5eWZPlLaETO0bSzym3u/JMqA5rr/PLHJx1bxAj0FJRpvEaDWcA5WEqUF0PEeeRor+/E7kwnv4OZb3tPz3HLkkUOXftoZEpkDAmZM8/PD0Im57rZX2VGEBi9xT/3q6XYwM6Sp9XlOPSlUETK5bEOSt+jhgsHyOn421MTbU6AnWEpzEWDJcA5WEqX6SwDR0ZNQj/wbj/S3/XUaH7EAljr91OyO83ZjAZxnEbKgL2yRSUx4MoMQLjbHuj5MgNbxllzQvcEEitamuAZR6deW9oNvAYxnUhhwK3CZXzPZzxezua56kcIQYi7ID1XAhYWqE6cuF6lgJfDLqdewG870iv+PRp6qqiv/3//4fkydPllGp404RsyGWVLx8IEpzTGXWRbkMCCfz2zrRIVeJ+ly3mcDsYpP6iMP8Uhdey2By0OJAu03QY7K5Lk5SKc6f5mNxuZundke4TApHCNHNihUrmD9/PqeffnreR6VAwlS/jJQLz8E+92gIZ2N5vVd/Ku7112j5HYmefexjH+v08+9+9zsuvvhiiouL89SisWH5nADVDQn2tNmEEhBNKgygyA1VZW7eP5Loca8hMTQMYGqBwaGwIp6VlLKr7VmGLgYywQ/FXou9bTYJG0wDZhaZnFji4qxJFqDY3mQzvdDi384qZltDgjKfCSiWz/GzqMydczRKCKGdccYZmX/H43F++9vfsmLFiryEKwlTfTTQQDEaLjYH8hrGagDLZTDfA5MfrMv7NJ4gsGsUvK9F/91zzz1cf/31nHrqqbz44osSqIbYwjIPoUSMwxGbQrdJkUfhALOLXexuSQIKffkthSmOBxOoCSn8lp6el/07t9CV9pZN9tKecGiIOpw8wc0ZE91UNyaJ24oyn8nbDUkwkpw7xcuyKZ7MVD0ZbRJiYJRSLF++nKeffpq33347L2uoJEz1QX8uqmV/J60/AWI0BK/ejJbXNxo+GBDH5uyzz2bcuHFs3ryZ8847TwLVENpYG2PL4TitCUUkCTHbYWrQoinqUN0Yp6rMTUNNHBM97a899SmL14CYJKsB662AR/r2UOofbgMK3HokapzXZLzP4DPTvJ02w11U5mZbQ4KNtTEqAlanQhISnoQ4doZh8LnPfY7f//73eStKIWFqEHiBfXKhOWD9vUgfLeFkuJPwJLqqqqpiw4YNLFu2TALVEKsIWIQSCsOAUq+B7cCcYov2gMmOliQftti4Db0xqzJ1CFCQe7GO6DOvpb+HU4HJRWofL6DCBxGbTIn6WcUmP11awrqd4czoU13Y5orZnYNS9qiTTN0TYvDlu8qfhKljJBecx19/fuezH6yjfQjbMlrI+1j0lQSq46MubFPgNig1DSqDLsp8BsvnBAC47pVmDsb1lD8TvR/ROK9BS1xlQoAB+FLBIG53bPo6lnlNPYqUyPpFGKmvtDKfgWEY7G93UEC6zofbgFMrPKxeGORH/9PK9iabcp/FjuYke1ttmmMO04KWVNgTIk/yGagkTB0DuQAd/gaytme0jnzJ+1UMllyBauPGjXg8nnw3bdRIX5RXNyR5uz7BuVM97GhOcm91OxG7Iw04QCgJjlIsmeBm62FdmGJ60CDuQG1YF64IWHq0pWug8hh0KqYw0vlN/RqjTvdjMQdKPBBOdLxmjwlJBwxDh62WuMIyFQFLj0IpOgb7drboaHXOZC/72iLsa7cze4FNC1rcsqRQpu4JkUddA5Xf7+fOO+8c8ueVMNUHh66p6HaBLRemo9dg/bed+mAdiUE5U3cGcFDegyKP0oHq3HPP5fLLL5cgNcjSU8Nufa0VDGiIKH66rZ0P23RKKMi62FfooLTpYIKPjbMY7zNJOvB6XcdfoFxBCkZmkDLRfwNzrW2KObqiXi4GMDGgS5HHkzo8jfPC4Yh+TJnPYFqRi3DCoTmuUEqHrEhS0RJXNEVVZi1UbcgGDBaXu6kL25n1UUKI/EoHqptuuomLL774uDynoZQagX9KB27KlCkcOHCAyZMnU1NTk+/mCCHEiFZfX095eXm+mzHiTZkyhTp3GWWnXcRzv7gdIFO0oC5sUxty+MPeKIejussu8cB4n8nBkIOtdIgAPdI0o8hkR0uOoZlRorcw5bcgZndsjJsuKGECZ01y88XZfr67qZWwrT9NvmC6l/eOJGiOK4Iug6/N87O00tvpd68LRyTQZcsDEpqEGAGOZ98kI1NCCCEGLLuzam1t5bvf/S7/8i//Ipv8DoBn/tmouWeysTYGwIaaOMumeLhhYZBtDQle2B8FdDCYUWjRGtdT+GzVERoMgz4HKbfRef3QseitCt6xSg80pZvqtzoq6pno0aX5pS5ml7gZ7zN5+UCUgMukxGPw+qEEjTGF3wXnVHq4Yrafn2xrZ2+bQ9ADqxcWALBuZwRQmRGmroFJCkcIMbJk901vv/02TzzxBD/4wQ+GZA2VhCkhhBCD4sorr+S///u/2bp1Ky+88IIEqn6Kf/BnjKJillael7ktu6BBkcekKWYzvdBkcbmHV2tjmKlNYtPxKdZDjsoVdgYrSJF1bpOOtvSkL/cBGO+FSFJXLCzywES/SdRWfNSuG24Ac0tMrlsQpC5sE7Ph5ZoolUEXqxcUsG5nmJij8Fnw8TJX5nf57UVBntod4bJZHVX3ZLRJiNGppaWFz3zmM9TX19Pa2jokRSkkTAkhhBgUP/zhD9m0aRObN2/m/PPPl0DVT8m9b2OWl7NuZ5iuexFtrI3hNuH0Cje3LCkEYG+rLohwMOQQsfV+U+naFOO84DYNjsQUyVRyKfcaJBxFc2oplUVHlb+uFdX7Gni66u0x6efoS9iaGjRoiOi2m+hQFbGhMaowU8Ui5pZYLC5381Z9gu1NSerCNjUhh10tNgvHuwADw4BCj8E5k72dypPLSJMQY0NxcTF33HHHkFb5MwftTEOosbGRG264genTp+P3+6mqquLXv/51vpslhBAiS7ooRXpj3/PPP5/m5uZ8N2vIDEXf5JxwKus/irO+JpaZ7gd6HVAooVgywZOZhnbLkkK+NMfPdQsK+GSlm+VzfMwtsZhbbHLGJC9uy6DADQEXuEwo9RlMLDBxoTt/l9kRoBQ6jJE6VnCUj1qPdhniNvSXJxV8XIY+r5k61huXAaVek5CtC2QkgYQD9REHjwUT/AaTCkxa44oth/VapmVTPFw+y8+CcRbnTPaytNLL8jl+Lj/Bx4XTfVKyXIgxbOXKlaxZswaAu+++mxtvvJHBLBkx7EemQqEQ5513Hu+88w6rV69m3rx5PPHEE6xcuZJDhw5x22235buJQgghUrqWTR+tI1RD1TeZH27m3KkewMgEgG0NCR7dEeZQWPHojjCfnqpvT1eWS4+43F3dzu4Wm2VTPCyt9PKjLW1sq3eIOzqM7GtzKHBB0AMovUFtXbTjuX2mLinuMmFWscU7jXa3qYFFLlAGLBjn4v0jSVoTUBHQo0jpaYMm8LFxFitOKuChD0JUN9rYCorcek1XW7zjfOl9ntJVCUGPrtVHHEx0lb2ZRSbtqQ2M5xa7WDLBw5O7I8QdmO4zOxWF+M7iYKf2LiqT/c+EEDpQwdDsQzXsq/ndeeedfPe73+XRRx/lS1/6EgBKKS688EI2bNjArl27mDp1ap/PJ9X8hBBi6L399tssW7aMI0eOcPHFF/Pss8/mu0mD6nj0TdsaEmysjVEbsvn9nigtMSj2wjf/RhdNyC5QkX3/dMD68gtHeKMuQYELAm69Ga3PgqaYIppUYOgNfePpYg6GDlLTgyYFHpM365OZgGOgg83SSW4ePX8cALe+1sL6j+KcO9XDeJ/FQx+EaUso4g64TfjKXB9g8Lu/RkgqmBgwCCUUbelphoZeC2UrUAraE3rt1SS/wZUnBth6OM5ls/SUvOzXtrE2xjN7opT5TNnbSQjRL7/61a9YtWoVAD//+c+54YYbjvmcw35k6qGHHmLy5MmZzgrAMAxuvvlmnn/+eR555BFuueWWPLZQCCFEV+kRqq985Sv8+Mc/zndzBt3x6Js21sbYUBNnXqmLS2b6aIgqynxGqlS3vj179KrrSNVls/yZ73Vhm2f2RPGYMKfETXPUpj6qKPebfNiS5FBY4Sgo9RrMKXXz/pEEQbcOWkG3HtkKuo3MOQGWzwmQHlf69FQv31kc5PFdEX68tY2oDel9mF47FCOShGmFFgDhhMOnpvjwWvD8/hjvNCbxu2BRuUV9RHHuFG+OEabuFfZkbychRH+lR6jWrl3LNddcMyjnHNZhqqWlhe3bt3PZZZd1O3baaacB8MYbbxzvZgkhhOiDqqoqqqursSwrc5tSakhK0x5Px6tvSgel9PfskZntTXo6XzpMrNsZYX1NjNqQk7PQwraGBM/vj1HTbrOwzGTheBcbauKcU+nhi7P9PLQ9RCihR5TKfAbTghYBl8HnZ/qoDdms/yhOVZmLurDNtoZEJtys2xlm/Ud63t6isuLM8z21O5LZ0LbUazEpoKvyZY+kAVQELB7aHqIy4OIz07yZDXB7kytYCSFEX61cuZKrr766U990LIZ1mDpw4ABKKaZNm9btWCAQoLS0lD179uShZUIIIfoiu7N6+eWX+ed//mdefvnlPLbo2B2vvik7NNxd3c6GGh1auoYsTaUWHeWeub+ozM3C8S7qw7p+X/qx6Y1pf3S6XluU3qwWEswo6rhfZYFFbcjJtKEjzBipahQdAbkubBO16RSM0s/TNShJZT0hRD5k900/+MEP+N//+38P+FzDOky1tLQAEAwGcx4PBAKEQqGcx37yk5/wk5/8pNvtBw4cAODgwYNMmTJlkFoqhBDiaA4dOoTH42HKlClMnDiRLVu25LtJA5KPvklVzsc54VQ2f7iZO2s/AODOHMfXfriZ36aO93SO7PvYZ1+Fmnsmxo5NWH9+qNttv03d1lMbcp0z1/3S7kQIIYaPRCLB4cOHuf/++wfcLw3rMJWujdFTjQylVI9DdK2trZnOKRfHcXo9LoQQYvBFIhEOHDhAe3t7vpsyYHnpmw4cgP95qedGHe14D/dxvfEHPK0txD/4M8nU8+a6rcfn6OttQggxjB1LvzSsw1Rhod6YMBwO5zweDod7rJZUVFTE5MmTu92e3UnlOi6EEGLoHDx4EMdxevy7PhKMqr4p0QDVT6efuOfbhBBilDrWfmlYh6mZM2diGEbOEuahUIjm5uYeO6xvf/vbfPvb3+52u5RGF0KI/En/DZ4wYUK+mzJg0jcJIcTocaz9kjnI7RlUwWCQ+fPns3nz5m7H0pWSzjzzzOPdLCGEEGOY9E1CCCHShnWYAvjqV7/Kvn37eOyxxzK3KaW466678Hq9nfb4EEIIIY4H6ZuEEELAMJ/mB3DTTTfx8MMPc9VVV7F161bmzp3L448/zksvvcRdd93FpEmT8t1EIYQQY4z0TUIIIWAEhCm/388rr7zCbbfdxtq1a2lra+PEE09k7dq1fO1rX8t384QQQoxB0jcJIYSAERCmAMrLy3nggQd44IEHjvlc3/72t2ltbaWoqGgQWiaEEKI/RtPfYOmbhBBi5DvWv7+G6mmjDCGEEEIIIYQQPRr2BSiEEEIIIYQQYjiSMCWEEEIIIYQQAyBhSgghhBBCCCEGYEyFqcbGRm644QamT5+O3++nqqqKX//61/lulhBCjBlvvPEGlmXxyiuv5Lspw4L0S0IIkX/H0jeNiGp+gyEUCnHeeefxzjvvsHr1aubNm8cTTzzBypUrOXToELfddlu+myiEEKPazp07ueyyy3AcJ99NGRakXxJCiPw71r5pzIxM/eIXv+DNN99k7dq1/Md//Ad/93d/x5/+9Cc++9nP8k//9E989NFH+W6iEEKMWk899RSnnXYaBw8ezHdThg3pl4QQIr8Go28aM2HqoYceYvLkyXzpS1/K3GYYBjfffDPxeJxHHnkkj60TQojR66KLLuLyyy9n0qRJfPnLX853c4YN6ZeEECJ/BqtvGhNhqqWlhe3bt3Paaad1O5a+7Y033jjezRJCiDFh+/bt/Mu//Atvvvkmc+fOzXdzhgXpl4QQIr8Gq28aE2umDhw4gFKKadOmdTsWCAQoLS1lz549eWiZEEKMfu+//z5erzffzRhWpF8SQoj8Gqy+acyMTAEEg8GcxwOBAKFQ6Hg2SQghxgwJUt1JvySEEPk1WH3TmAhTSqlO33MdtyzreDZJCCHEGCb9khBCjA5jIkwVFhYCEA6Hcx4Ph8MUFxcfzyYJIYQYw6RfEkKI0WFMhKmZM2diGAY1NTXdjoVCIZqbm5k6dWoeWiaEEGIskn5JCCFGhzERpoLBIPPnz2fz5s3djqWrJZ155pnHu1lCCCHGKOmXhBBidBgTYQrgq1/9Kvv27eOxxx7L3KaU4q677sLr9Xba50MIIYQYatIvCSHEyDcmSqMD3HTTTTz88MNcddVVbN26lblz5/L444/z0ksvcddddzFp0qR8N1EIIcQYIv2SEEKMfGMmTPn9fl555RVuu+021q5dS1tbGyeeeCJr167la1/7Wr6bJ4QQYoyRfkkIIUY+Q/VUl1UIIYQQQgghRI/GzJopIYQQQgghhBhMEqaEEEIIIYQQYgAkTAkhhBBCCCHEAEiYEkIIIYQQQogBkDAlhBBCCCGEEAMgYUoIIYQQQgghBkDClBBCCCGEEEIMgIQpIYQQQgghhBgACVNCCCGEEEIIMQASpsSwZxhGv75KSkry3eRRLxwOs3fv3rw876xZs5gyZcpxf24hhMgmfdPwI32TyAdXvhsgRF/NmTOHCRMmHPV+hYWFx6E1Y9cjjzzCzTffzO23386qVauO2/M6jsO1117Lhx9+yOTJk4/b8wohRG+kbxoepG8S+SJhSowYt912G1dffXW+mzHm3XbbbRw4cOC4PmckEmHVqlU88sgjx/V5hRDiaKRvGh6kbxL5ImFKCDGsbd26lWuuuYZ33nkn300RQgghAOmbRAdZMyWEGLZuvfVWTjnlFN555x0+9rGP8b3vfS/fTRJCCDHGSd8kskmYEqPe1VdfjWEY3Hfffezdu5cVK1YwZcoUvF4vU6ZMYdWqVb0uWH311Vf527/9WyZNmoTH46GiooJLL72UDRs25Lz/jBkzMAyD6upqbrzxRkpLSwkGgyxZsoQjR45k7vfSSy9x0UUXMWnSJAKBAIsWLeKee+7BcZzMguW0M844A8Mw+Na3vtVjO3/4wx9iGAYXXHBBn38327Zt4xvf+Abz58+nqKgo8/ouvPBC/uu//qvTfW+//XYMw2Dfvn0AXHvttRiGwe23397rc2zfvp1AIIBhGFx77bXdjh8+fJiKigoMw+Ab3/hGp2OvvfYagUCA//N//g9bt25l9uzZfX5tQggxnEnf1DPpm8SIooQY5gAFqAcffHBAj7/qqqsUoK699lpVVFSkTNNUc+bMUSeddFLm3OXl5Wr//v3dHnvLLbdk7lNaWqqWLFmiJk6cmLnt5ptv7vaY6dOnK0CdddZZClAnnXSSmj59ujrjjDMy9/nBD36QOUdFRYU6+eSTVVFRkQLU5ZdfnjmW9stf/jLTzkQikfN1zp07VwFq3bp1ffq93Hvvvco0zcxrW7x4sZo3b57yer2Z57/tttsy9//Vr36lzjrrrMzx2bNnq7POOkv96le/Oupz/eIXv8ic809/+lOnYxdeeGHm9xQKhTode/TRR9WhQ4cyPz/44IMKUJMnT+7TaxRCiKEifZP0TWnSN41tEqbEsDdYHRagTj/9dPXXv/41c2zTpk2qsLBQAerGG2/s9Lj77rtPAaqkpEQ9/PDDmdsdx1GPPfaYKigoUIBas2ZNp8elOyxAPfbYY5nb6+vrlVJKvfjiiwpQpmmqn//858q2baWUUuFwWF1//fWZx2Z3WK2trSoQCChAPfvss91e46ZNmzIdTzQaPervZMeOHcrtditA/fCHP1TxeDxzrLGxUV1xxRUKUG63Wx05ciTn63vggQeO+jzZLrjgAgWoGTNmqLa2NqWUUj//+c8VoHw+n6qurj7qOaTDEkIMF9I3Sd+UJn3T2CZhSgx72X/A+/L18ssvd3p8usPyeDzq4MGD3c5/ww03KECdfPLJmdtisZiqqKhQgHryySdztuvee+/N/PHM/kQu/Qd96dKlOR936qmnKkB95zvfyXk8/WlY14Hjr3/96wpQV1xxRbfH/N3f/Z0C1OrVq3Oes6tf/OIXyu/3qyVLluQ8vn///kwbXnvttU7HBtphHTp0SJWXlytAfetb31Lvvfee8vl8ClD33ntvn84hHZYQYriQvkmTvkn6prFOqvmJEaOve3kUFxfnvP3kk09m4sSJ3W6fP38+AM3NzZnbNm3aRF1dHYWFhVxyySU5z3fllVdy/fXXc+DAAd58801OPfXUTsfPPvvsbo85cOAA//M//wPAN7/5zZznvfHGG/njH//Y7fYVK1awdu1annnmGVpaWjKvMxaLsW7dOgCuueaanOfsavXq1axevZpIJJLzeCAQyPw7HA736ZxHU1FRwQMPPMCll17KPffcw3PPPUc0GuXSSy/t8XchhBDDnfRN0jeJsU3ClBgxjnUvj5420/P7/QAkk8nMbe+++y4A8Xicc845p8dzWpaF4zhs3769W4c1adKkbvd/7733UEoRDAY54YQTcp7z5JNPznn7Jz7xCWbPns2uXbt44oknMpsSPvPMMzQ3N7NgwQKWLFnSY1tz8fl8bN68mXfffZfdu3eze/du3nnnHbZv3565j+M4/Tpnby655BJWrVrFmjVr2LlzJ1OnTuVXv/rVoJ1fCCGON+mbpG8SY5uEKTFmeDyeXo8rpTL/bmlpAfQna3/5y1+Oeu7sTw7T0h1htoaGBgCCwWCP5yoqKurx2NVXX833v/99fvvb32Y6rIceegjo+yd/aQ8//DD//M//zM6dOzvdPnPmTFauXMkDDzzQr/P11SWXXMKaNWsAmD59OiUlJUPyPEIIMRJI39SZ9E1ipJHS6ELkUFBQAMCSJUtQem1hr1+9lYXNdd7W1tYe79PW1tbjsauvvhrTNNm4cSP79++nvr6eF154AbfbzVe/+tU+v76HHnqIr33ta+zcuZPPfvaz3H///fzlL3/hyJEjfPjhh9xzzz19Pld/NDU1ZaZNmKbJn//8Z/793/99SJ5LCCFGG+mbpG8Sw4+EKSFyOPHEEwHYsWNHpykW2ZRSvPzyy+zcuZN4PN6n8y5YsADQc713796d8z5vv/12j4+fPHky5513Hkopnn76aZ599lmSySQXXXQR5eXlfWoDwB133AHA17/+dZ577jm+8Y1vcOaZZ1JaWgpATU1Nn8/VH9/85jepqamhqqqK3/zmNwB8//vf7/U1CyGE0KRvkr5JDD8SpoTI4ZxzzqG4uJi2tjYefPDBnPd55JFHWLZsGfPmzeOjjz7q03lPOOEEqqqqAHqcj33//ff3eo6VK1cC8NRTT/H73/8e6P80ij179gD0OI89PdUB6NZhm6b+s5E99aQvHn74YdatW4fb7eY3v/kNX/va17jkkkuIx+NceeWVRKPRfp1PCCHGGumbpG8Sw4+EKSFyKCgo4NZbbwV0BaMHH3yw02LX3//+9/yv//W/ALjiiiuYNWtWn8/9T//0TwDcddddPPDAA5k//IlEgttvv53HHnus18d//vOfp6ysjI0bN/Liiy9mdoXvj3nz5gG6czxw4EDm9tbWVm6//XZ+/OMfZ27rWjEpPac+vdt8X+zfv5/rr78egFtvvZVFixYBcN9991FaWsp7772X+X0LIYTITfom6ZvEMHQ86q8LcSxI7SkxZ84cddZZZ/Xp649//GPm8em9PK688sqc50/vDzF9+vROtzuOo6699trM85eVlalTTjlFVVZWZm4766yzVHt7e6fH9WWvi5tvvjlzjokTJ6pTTz1VlZaWKkCddtppClCWZfX4+BtvvDHz+H/4h3/ow2+xs2effTazw7zH41ELFixQCxYsyOytccIJJ6hZs2YpQP3sZz/r9Nj0niIul0stXrxY/eAHP+j1uWzbVp/4xCcUoBYuXNhpE0allHrooYcUoAzDUC+99FKv55K9PIQQw4X0Td1J3yR901gkYUoMe+k/zP35yt6RfqAdVtoLL7ygLrvsMjVx4kTlcrlUYWGhOv3009XPf/5zFYvFut2/rxsHPv300+rcc89VJSUlyuv1qsWLF6tf/vKX6s9//rMCVGFhYY+PfeuttzKv9d133+31eXqydetWdemll6pp06Ypl8uliouL1SmnnKLuuOMO1dbWpv7xH/9RAerTn/50p8cdPnxYfeELX1DFxcXK7/err3zlK70+z5133pnp4N58882c97nooosyHVHXXe2zSYclhBgupG/qTvom6ZvGIkOpfk4uFUIMqT/84Q9cfPHFzJkzhx07duS8z7PPPsvnP/95TjnlFDZv3nycWyiEEGKskb5JiNxkzZQQx9nf/M3fcMYZZ/Dmm2/mPJ7eYf7jH/94j+dI77Nx7bXXDn4DhRBCjDnSNwkxMBKmhDjO5s6dy+uvv863v/1tDh48mLk9mUzyy1/+kvvvvx/DMDJ7XgDYts2bb77J3r17uf3223n22WeZMGFCv/bvEEIIIXoifZMQAyPT/IQ4znbs2MHZZ59NfX09breb2bNn4/f72bt3L0eOHME0Tf71X/+Vf/iHf8g8RimF3+8nFotlbvvd737HV77ylXy8BCGEEKOM9E1CDIyEKSHyoLGxkf/8z//kqaeeYt++fYRCISZNmsTSpUu57rrrOO2007o95oILLuDVV1+lsrKSW2+9lRUrVuSh5UIIIUYr6ZuE6D8JU0IIIYQQQggxALJmSgghhBBCCCEGQMKUEEIIIYQQQgyAhCkhhBBCCCGEGAAJU0IIIYQQQggxABKmhBBCCCGEEGIAJEwJIYQQQgghxABImBJCCCGEEEKIAZAwJYQQQgghhBAD8P8BuJgQIX2PG54AAAAASUVORK5CYII=",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1000x500 with 2 Axes>"
       ]
@@ -57005,7 +98805,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAAGTCAYAAAAcBI40AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADH9klEQVR4nOy9eZQk2VXf/73vRWZtPTM9PdOlnp5pSSPNImEJW0aLzWGzD8IGccBik41YhNiPwDJgMBgM/Fh8AAEHC2RhYYOEsIXBHCSQDwjbYEDGYIGwkYQ007NP90xPz9ZbVWVmxHv398d998WLrKxeqyqzsu5nlKpcIiNeZlVX1De/934vMTPDMAzDMAzDMAzDMPYobtoLMAzDMAzDMAzDMIxrwYStYRiGYRiGYRiGsacxYWsYhmEYhmEYhmHsaUzYGoZhGIZhGIZhGHsaE7aGYRiGYRiGYRjGnsaErWEYhmEYhmEYhrGnMWFrGIZhGIZhGIZh7GlM2BqGYRiGYRiGYRh7GhO2hmEYhmEYhmEYxp7GhO0E/uzP/gzee/zP//k/p70UwzAMwzB2ADvXG4ZhzBcmbMc4fvw4Xvva1yLGOO2lGIZhGIaxA9i53jAMY/4wYVvwm7/5m3jVq16Fxx9/fNpLMQzDMAxjB7BzvWEYxnxiwjbxmte8Bl/0RV+EW265Bf/kn/yTaS/HMAzDMIxtxs71hmEY84sJ28QnPvEJ/Ot//a/x4Q9/GHfddde0l2MYhmEYxjZj53rDMIz5pZr2AmaFv/7rv8bCwsIVP++nf/qn8dM//dOb7j99+jSWl5dxxx134M///M+3Y4mGYRiGYVwDdq43DMOYX0zYJq7mRAcA586dw8mTJyc+dvbs2S0fMwzDMAxjd9mpc/2JEycQY4RzVghnGIYxLUzYXiPXX389br311k33P/7444gxIsaIkydPTtzGMAzDMIzZ51LnembG8ePHceedd5q4NQzDmBL22/ca+fZv/3acOHFi0+WWW27J25w6dcqcW8MwDMPYo1zOuf7ChQs4fvy4jRAyDMOYEiZsdxjvPQATt4ZhGIYxr1RVBe+9iVvDMIwpYsJ2h3HO4dixYwBE3J46dWrKKzIMwzAMYzshItx5551Z3N53333TXpJhGMa+w4TtLrC6uopjx46h1+vhhhtumPZyDMMwDMPYZlZWVnDnnXeiqircfPPN016OYRjGvsPCo3aJ1dVV3HTTTbk02TAMwzCM+WJlZQUveclL7FxvGIYxBcyx3UXKE9358+fx2GOPTXE1hmEYhmFsN+W5vq5rPPTQQ9ZzaxiGsQsQM/O0FzGP3HbbbXnMz4kTJzqP1XWNj370o4gx4siRIzYKyDAMwzD2IBc71wPAJz7xCaytreHAgQM2CsgwDGOHsd+wU6DX62Uxa2nJhmEYhjGfHDt2zNKSDcMwdgkTtlNCA6UAE7eGYRiGMY9ooJSJW8MwjJ3HhO0UMXFrGIZhGPONiVvDMIzdwYTtlBkXt88888yUV2QYhmEYxnYyLm4feeSRaS/JMAxj7rBxPzPA6uoqAODChQu48cYbp7wawzAMwzC2GxW3jzzyCI4ePTrt5RiGYcwdJmxnhNXV1SxwDcMwDMOYP1ZWVvDiF7942sswDMOYS6wUeUZ55JFHrOfWMAzDMOaYZ599Fvfee6/13BqGYWwDJmxnkPPnz+PJJ5+0QCnDMAzDmFNCCHjkkUdw/vx5C5QyDMPYBkzYziDXXXedpSUbhmEYxhzjvccdd9xhacmGYRjbhAnbGcVGARmGYRjGfGOjgAzDMLYPE7YzjIlbwzAMw5hvTNwahmFsDyZsZ5xS3D7xxBPY2NiY8ooMwzAMw9hOxsXtU089Ne0lGYZh7Dls3M8eYHV1FUSEqqqwtLQ07eUYhmEYhrHNqLg9c+aMjf8zDMO4CkzY7hEOHz7cuR1CgPd+SqsxDMMwDGO7WVlZwcrKSr7NzGBmOGcFdoZhGJfCflPuQUajET7+8Y9bz61hGIZhzCnMjAceeMB6bg3DMC4TE7Z7kPPnz2M4HFqglGEYhmHMKcPhEOfPn7dAKcMwjMvEhO0e5KabbrK0ZMMwDMOYYxYXFy0t2TAM4wowYbtHsVFAhmEYhjHf2CggwzCMy8eE7R7GxK1hGIZhzDcmbg3DMC4PE7Z7nFLcnjlzBiGEKa/IMAzDMIztpBS36+vrGAwG016SYRjGzGHjfuaA1dVVVFWF6667zkYAGYZhGMYcouKWmbG8vDzt5RiGYcwcJmznhEOHDnVur6+v24nPMAzDMOaIcsYtAAwGA/T7fZtzaxiGAStFnkuefvppm3NrGIZhGHPMxsYG7rnnHuu5NQzDSJiwnUP0BGeBUoZhGIYxn8QYwcwWKGUYhpEwYTuHHD582NKSDcMwDGOOsbRkwzCMLiZs5xQbBWQYhmEY842JW8MwjBYTtnOMiVvDMAzDmG9M3BqGYQgmbOecUtwahmEYhjF/lOIWAJh5yisyDMPYfWzczz5gdXUVy8vLOHDgwLSXYhiGYRjGDrCysoK7774b/X7fZtobhrEvMcd2n1CK2hgjnn766SmuxjAMwzCM7WZpaakjap9++mkrSzYMY99gwnYfcv/99+Ohhx6ynlvDMAzDmFOeeOIJPPTQQ9ZzaxjGvsGE7T7khhtuAGCBUoZhGIYxrxw4cMACpQzD2FeYsN2HWFqyYRiGYcw3lpZsGMZ+w4TtPsXErWEYhmHMNyZuDcPYT5iw3ceMi9vHH398yisyDMMwDGM7mSRubRyQYRjziAnbfY6KW+89rr/++mkvxzAMwzCMbaYUtzfeeCOIaNpLMgzD2HZsjq2B1dVVHDp0CFVlPw6GYRiGMY+srKzgJS95iZ3rDcOYW8yxNQCgc6JbX1+3smTDMAzDmDPKc30IAY888oj13BqGMTfYx3ZGh6ZpcO+99yKEgBgjbr311mkvyTAMwzCMbeb+++/H+fPnsbGxgTvvvBPOmddhGMbexn6LGR2qqsLRo0cBWFqyYRiGYcwrt956q6UlG4YxV5iwNTZho4AMwzAMY76xUUCGYcwbJmyNiZi4NQzDMIz5xsStYRjzhAlbY0vGxe1TTz015RUZhmEYhrGdjIvbhx56aNpLMgzDuCosPMq4KKurqwCAs2fP4tChQ1NejWEYhmEY242K24ceeshCIw3D2LOYsDUuyerqKg4fPmwD3Q3DMAxjTllZWcEnfdIn2bneMIw9i5UiG5dFeaI7efKk9dwahmEYxpxRnuvPnTtnPbeGYewpTNgaV8Ta2hpOnTplgVKGYRiGMafEGPHQQw+ZuDUMY09hwta4IlZWViwt2TAMwzDmGOccXvjCF1pasmEYewoTtsYVY6OADMMwDGO+sVFAhmHsNUzYGleFiVvDMAzDmG9M3BqGsZcwYWtcNePidm1tbcorMgzDMAxjOxkXt6dPn572kgzDMCZi436Ma0Ln3DrnsLKyMuXVGIZhGIax3ai4ffrpp/Gc5zxn2ssxDMOYiAlb45pRcauEEOC9n9JqDMMwDMPYblZWVjofYDMzmBnOWfGfYRizgf02MraVpmlwzz33WM+tYRiGYcwpzIyHH37Yem4Nw5gpTNga28q5c+ewsbFhgVKGYRiGMaeMRiOcOXPGAqUMw5gpTNga28qhQ4csLdkwDMMw5piFhQVLSzYMY+YwYWtsOzYKyDAMwzDmGxsFZBjGrGHC1tgRTNwahmEYxnxj4tYwjFliZoXt2bNn8cwzz1z29qdPn8YjjzyygysyrpRS3D7zzDNommbKKzIMwzBmDTvf721Kcbu2tob19fVpL8kwjH3KzAnbX/iFX8Bdd92FQ4cO4fDhwzh69Ci++7u/G2fOnLno8774i78YL3jBC3ZnkcZls7q6iuc///m4++67UVU2XcowDMMQ7Hw/P6i4feELX4gDBw5MezmGYexTZkrYfuu3fiu+6Zu+Cffdd1+ej3bq1Cm85S1vwSd/8ifjz/7szy76fGbepZUaV8JNN92Efr+fb29sbExxNYZhGMa0sfP9/LGysoIbbrgh3x6NRlaWbBjGrjIzwvb9738/3va2t8F7j+/93u/Fhz70Ifyf//N/8M//+T9Hv9/HiRMn8Pf//t/H7/3e7017qcY1cObMGXz84x+3nlvDMIx9ip3v55/hcIh77rnHem4Nw9hVZkbYvv3tbwcR4ed+7ufwwz/8w/iUT/kUvPzlL8dP/MRP4EMf+hCe//znY2NjA6997WvxwQ9+cNrLNa6Suq7zJ/Mmbg3DMPYfdr6ff5qmQQjBAqUMw9hVZkbYfuhDH8LBgwfxDd/wDZsee8lLXoI//MM/xO23346NjQ18wRd8AT7xiU9MYZXGtXL48GFLSzYMw9jH2Pl+/rG0ZMMwpsHMCNszZ87g+c9//paPHzt2DP/tv/03HDlyBGfOnMFrXvManD59evcWaGwbNgrIMAxj/2Ln+/2BiVvDMHabmRG2Bw8exMMPP3zRbV7wghfgfe97H5aWlvDQQw/h8z//8y2IaI9i4tYwDGN/Yuf7/YOJW8MwdpOZEbYve9nL8Oyzz+I//af/dNHtXvGKV+CXf/mXQUT4i7/4C3zBF3yBzUzbo5Ti1mbcGoZh7A/sfL+/KMVtCMGErWEYO8bMCNuv/dqvBTPjm77pm/Bv/s2/wRNPPLHltl/8xV+Mt7zlLWBm/P7v/z5e8YpX4PHHH9/F1RrbxerqKu666y4873nPm/ZSDMMwjF3Azvf7j5WVFdx111246667bKa9YRg7xswI2y/7si/Dl37pl+LChQv49m//dhw9ehT/7t/9uy23/7Zv+zb8+I//OJgZn/jEJ/DAAw/s4mqN7eS6667L15kZzzzzzBRXYxiGYewkdr7fnywvL3dE7ZkzZ8y9NQxjW5kZYQsA73nPe/DDP/zDOHToEJgZR48evej23/md34lf//Vfx4033rhLKzR2mgcffBAPPvig9dwahmHMMXa+39889dRTuP/++63n1jCMbYWYmae9iHFCCPjIRz6C5z//+Th48OAltz937hze8Y534I/+6I/wW7/1Wzu/wMvgtttuw8mTJ3HrrbfixIkT017OnuH06dN49NFHAQBHjhzBrbfeOuUVGYZhGDvFXj/f27n+6lhbW8Px48cRQsCBAwdw5513wrmZ8loMw9iDzKSwvVrOnj2LG264YdrLAGAnu2vBxK1hGIZxMWblfG/n+qvHxK1hGNvNTP8G+aZv+iYMBoPL2vb3f//38dKXvnSHV2TsBjYKyDAMY39h5/v9h40CMgxju5lpYfuOd7wDr3zlK/HXf/3XW24zGo3wHd/xHficz/kcE0BzxLi4feyxx6a8IsMwDGOnsPP9/mSSuJ2jQkLDMHaZmRa2hw8fxkc/+lG84hWvwDve8Y5Nj3/kIx/By1/+cvzMz/wMYox4zWteM4VVGjuFilvnXCc52TAMw5gv7Hy/fynF7XXXXQcimvaSDMPYo8y0sP3oRz+Kz/3cz8XGxga++Zu/GV/yJV+CM2fOAAB+6qd+Cq985Svx0Y9+FAcPHsS73vWumQiSMLaX1dVVvOQlLzFhaxiGMcfY+X5/s7Kygr/xN/7GJdOxDcMwLsaeCI/62Z/9WfyLf/EvMBwOcezYMTzvec/DBz/4QTAzvvALvxA///M/j+c85znTXmYHC5TYGQaDAc6cOYMjR45MeymGYRjGNrPXzvd2rt8ZYox47LHHcPToUQuUMgzjstkTwhYAPv7xj+Mf/aN/hOPHj4OI4JzDO9/5Trz+9a+f9tImYie77SeEgI997GOo69rSkg3DMOaUvXS+t3P9znD//ffjzJkzlpZsGMYVsSd+U6yvr+Md73gHHnzwQQAAMyPGiJ/+6Z/GRz7ykSmvbm8QmS962Qt477NTa2nJhmEY84ed7w1ARv1ZWrJhGFfKzAtbjfV/61vfiqZp8HVf93X47d/+bRw9ehR/+Zd/iVe84hX4oR/6IYQQpr3UmaEUrE3UC9BEYBS6lyYCkYEm7g2ha6OADMMw5hM73xuKjQIyDONqmGlh+43f+I149atfjQcffBA333wz3vve9+Id73gHXvOa1+AjH/kIvuzLvgyj0Qj/3//3/+EVr3gF/uqv/mraS54KpYgdBc4CdhCAUQSGgTEMjI2GsREYa41cNvS+hjGKrdCVC8+s2DVxaxiGMV/Y+d4Yx8StYRhXykz32GpPxed+7ufil37pl7C6urppm1/5lV/Bt37rt+Ls2bPo9/uXPeB9p9mNvhsVm+q6BmZxX1luR5YyLr2tj8f0HXcEeCJUDqgI8I7gSD7tcCQXIurcdqTPnX4c/+nTp/Hoo48CAJ773Ofi8OHDU16RYRiGcTXs1fO99djuPGtrazh+/DhCCDh48CBe+MIXTntJhmHMKDPt2C4tLeFtb3sb/ut//a8TT3IA8BVf8RX4q7/6K3zGZ3wG6rre5RVOh9ahFZd1qC5szTg7YpwbRZwZyuXZEefrZ4eMZ4ft7WeHsu3ZEeN8zbhQR5xP13V/Gw1nx7fr6k7fyVXndmVlBYcOHZraOgzDMIxrw873xlaoc7uwsIBbbrll2ssxDGOGqaa9gIvx4Q9/GHffffcltzt27Bj+4A/+AD/5kz+5C6uaLlFd2SgO7CCgIzzr0Dq0nLYFgIjWqW0dW0YdgYoYQ0eoSO4j0utA5Ti7up7QcXer7OBy2t/uu7irq6s4fPiwDXQ3DMPYw9j53rgYOufWzvWGYVyMmS5F3stsd3lSWXbcsAjYYZA+2fVGxG0pakvKEuJxtMxYTxZaiqzXtTy5SqLWE6HvgJ6ntnwZs1Oq/MQTT6BpGhsFZBiGYew4Voo8HS5cuIBTp07hBS94gY0CMgwjM9OOrSGULm0dxZnV0CctFR6lPlsGg5DEqBORWrqrpcAdF8Dj90UGYmA4AgIBo0ioiBG8iGsRtq3oLZ3eabi46+vrnT8sTNwahmEYxnzBzHjwwQcxGo1w/Phxm3NrGEbGhO2MU4ra4ZhLq6K2ia2g7TlxVFXM6ld1WycJW0Y3VCpyW7pcGvrMjAYEBFlXQwQXWfYdCZVjhCmWKS8vL+PYsWN49NFHcerUKQAmbg3DMIz9R2RJD3Y0f4KPiPCCF7wAx48fz2nJJm4NwwBM2M4046JWx/Os1SJoB6EVoypoFzyh55O4LRKPKV0vf+3HfBxqe3LRJipLknJxu3heE4FIyc1l6b8NDDREqLgVuA4iqndL4GroiIlbwzAMYz+iora8Pm8CVwOlTNwahlFiwnaGGRe1a3VKKg6MUZpPXzlkQauXvu+K2nJsD9CWI5fBUuLMUkfEynVqxwjFze5uE3WfjMAApTX1nAjcioCI3RW4Jm4NwzCM/YgKWU5fKQnaUuzOi8g1cWsYxjgmbGcUdWvrmMqPk1O71jDqpCp7jrBUpUsStAtbhDoBWwdIye5oc38tNFmZ2lm4sXVsy7m4kWUUkArcJk4WuLtVojwubm+44QYcOHBgR45lGIZhGLPIuMAFROTOq7g9deoUjh49Ou1lGYYxJUzYziA6o7aOUm6sbu1GEFEbuRW11/Vacdsv+mivJKG4dXC7aVKl4O1loZuSmZ2I3Ia7IlefF5nBkMf6fnOJ8m4IXBW3MUYTtYZhGMa+gAt3tryP5kTMjqPi9vTp0zbn1jD2OTP9W+6HfuiH8M53vvOytv3RH/1RfOVXfuXOLmiXiCyCcRCQndqNpi0/XvCElZ6I2ut6hJXk2PacuLaVAypHcERXJBh1e71Urk1X7nspee45KXfOTnFxWfCpjzcdUgWvhl5pX/AwCfRRTL26vFlUbxerq6s4cuRIvh3j5hO+YRiGMV326/l+O4mTBC3a+yYJ3nlhZWUFt99+e2fOrZ3vDWP/MdNzbJ1z+LRP+zT80R/90SW3/ZRP+RTcc889uHDhwi6s7NJc7Wy7yCJgh0HKjs/XrbCNLAJzqRC2Kihbl3Znw5lUgGr5cZNCp3S+bojtLN3SxQXSXNzU+6vzcMu05srpdjvzGkIIOH78OK677jrruTUMw5gh9ur5flbm2JaithSwpbDVpA0iNzelyFvx6KOPYn193XpuDWOfMTOlyA8//DD+x//4H5vuf+KJJ/CLv/iLWz6PmfHwww/jIx/5yJ4vN9USZHU5VdAOg6hDFbXX9ZNL23FJd2debHscSUR2KWDKE9BD6sFlFbltqbKGTgGMGCj34vpI6DsZGRSZcsDUTryec+fOYW1tDWtrawAsUMowDGMa2Pl+e7kcUau3VdzOU5/tOKPRCE8//XT+MNvErWHsH2bGsd3Y2MBdd92Fxx577Kqez8z4ki/5Evzar/3aNq/s6riaT3GbVJ670TAu1BFnh9JXq0FMk5za3RS1WzHu4mrwlLq3dei6uAqhdWrH5+/ulAN9+vRpPProowCAI0eOmLg1DMPYZebpfD8Lju14ErLCiJvKkx05ENzcu7Zra2s4fvw4Qgg4cOCAiVvD2CfMjGO7tLSEH//xH8f3fu/35vsefvhhLCwsdHokx3HO4cCBA3jZy16Gn/zJn9yNpe4ImoIcUgryeiPlyE0Ugad9rStJ1PbcbIhaoF2Do67IbUDwkJFDbchU23srl+I+B/Q8wMUM3u0Ol7JRQIZhGNNlv5/vt5NJfbXAWG9tcT1yysEoZtPPo8C1UUCGsT+ZGcd2ElfSczNrXOmnuKVbe24UcXbU9tUuemC55yQoqidBURoQNat0BG6c7OCqwFVUzKp72/Pt/NvLTXi+XMy5NQzDmB326vl+2o7tpDJkFbKRIxixM/KH4LJrq/dNYl7Erjm3hrG/mBnHdhI/8AM/gOc+97nTXsaOoyJQ3dpBEPEXWUTdgicsV/JVA5dmWNMC6Pbi9n3r4DpmOBA8y8zbJlIeYaQjgiLLWKEITJx/ux3itnRun3rqKayurqLX613zfg3DMIwrZ7+c73eK8TLkcVGbt6HCtQW0KArAfM66HXduz58/jxtuuGHayzIMY4eYacd2L3Mln+JqEvJGYJwfMc4MIzaSsF3waaxPCoyalb7aK0VLrfUSWBxqdW9bcdtNT9aS63JG73a+/qeeegoHDhzA4uLituzPMAzD2D9My7HdKjAKaHtrx4Ut0IpXdW71+ji63TyIW0Cc2+FwiEOHDk17KYZh7CAz7dgqZ8+exT333IP19fVNc8mapsH6+jpOnDiB3/7t38YHPvCBKa3y6tGwpSaKwGvSRw0VSRnyQppR6x3tSKDSbuCSKFWBi0hY8DJI2ScxOwri4qrw5agiWBKT+47hHQFRttuO9+Lmm2/u3B4Oh1hYWLimfRqGYRhXx7yf77eT8dLjK3ouYh7HN14BRnBgjluWKe9FVlZWsLKykm/XdQ3vvZUlG8acMfPC9l/9q3+Ft7zlLajretpL2RHK0Ch1MdVE7yVB2/fiWLbluHsXEaI63gdwnuCilBc7MBqmjnsr7w/lr33mTeFS2yX0z58/j/vuuw+rq6vWc2sYhrHLzPv5fruIHDuiVt1bdVfVrR1nXKjq+J/xsuN5HwtU1zXuvfdeVFVlPbeGMWfMtLD9jd/4Dfzoj/7oZW17xx134Mu//Mt3eEU7g4q4URCBG1mczIpkdu1ed2vHGXdvHaWeW3TdW519G5iBKIJYem/FvUXcXnE7GAwQY7S0ZMMwjF1mv5zvt5Oy5BgAAosg1dubSpQnlCWXInbT/ufMtVXqukZd1xgMBpaWbBhzxkz/S9ZB7a973evwyCOP4Mknn4RzDt/wDd+A0WiEBx54AN/zPd8D5xyYGd/1Xd815RVfObkMOQk4LUP2jrJjW5F8o/a6WzuOK3pm+ykkS0uvF1XQp20YMvpI+3JHWrYdW2f3Wjl8+DCOHTsGADh16hROnjx5zfs0DMMwLs1+ON9vN6Wo5eTiRm7AHBFig8gRgRsEluvlRZ9T7qv82h0RdOWlzrPM8vIy7rzzTnjv8yig8bJ3wzD2JjMtbP/iL/4Ci4uLePvb347bbrsNN910E170ohfhv//3/46qqvD85z8fP/qjP4rv+77vwwMPPIC3ve1t017yFaFijJllFE4SaUBya4vwpGqmv1NXj4jbVuD2HOWZvQteyrA9EQiUHdxRlBm/Km5HMY0UYr5mgbu6umri1jAMY5eZ9/P9TsJjojVwk9zcgMABzIzIQcKkilFAWtJc3td5rAifmjdxq2nJJm4NY76Yabn07LPP4vbbb8fBgwfzfS996Uvx4IMP4uzZs/m+7/iO78DCwgJ+4zd+YwqrvDa0DLlJF2buurWpnxSYjzLkrXBEqIoE5J4T13bJdwUu0M7GrWM7Gknn4m6He2vi1jAMY3fZD+f77WRSD20pZuWS3FtuEBHBzHLB5h5cvS9yI67vhGRlE7eGYcw6My1sFxcXsbS01LnvhS98IQDg4x//eL7vuuuuw5133ol77713V9e3HWRRm0KjiCi7tWVg1LyVIW/FJPdWZ/iqwHXU9iSrYzsMvK2lyaW4HQ6HsKlYhmEYO8d+ON9vB+PBUdxxVzkJ2JjFqbq3zBFxi/RkLWMunV8pZW72lbit6xohhGkvyTCMa2Cmhe2tt96Khx56qPOLRk90H/3oRzdtv7a2tmtr2w60vzZyG5Skoq5y0mc7T6FRl0vp3pYCV/tv+0Vptjq3dUjubVGa3MRrK01eXV3FHXfcgdtvvx20j95/wzCM3Wbez/fbQVk6HHlrUauPl6XFpbgtP6id2HtbCOCtnNt5ErgrKyu46667cPfdd6PX6017OYZhXAMzLWw/7dM+Dc8++yze8pa35Pte8pKXgJnx3ve+N9/32GOP4Z577sHRo0ensMqrQxOBx/trHVJw1ByHRl0uk8qTNWRKxyA5QhsqVZQmb5d7e8MNN3RE7ZkzZ7bhlRmGYRgl83y+305a0dp0RO3445fcTypJnkSZktwR0mjDqYD5ErjLy8sdUXvu3DkrSzaMPchMC9s3velNICJ87/d+L171qldhOBziFa94Be644w78zu/8Dt74xjfi537u5/AP/sE/QF3X+Nt/+29Pe8lXTOnaAsmxpf1XgnwxxsuTe0Vq8iT3drgD7i0APPLII7j//vut59YwDGOb2Q/n+2uhdGhjTjkO2amNhaO65fOTMO303E7ot3Xk8uzactzPeKjUuMCdF5H77LPP4vjx49Zzaxh7EOIZbx58+9vfjje/+c3o9/u4cOECAOBXfuVX8FVf9VXZSWNmeO/xoQ99CH/rb/2tKa625bbbbsPJkydx66234sSJE5sej8wYBekNXWsY52vGsGFUDljpEZYrh5WeCLf9Vop8MdTpbqKMR9LxPw23QVwqgj21bq9ev5b38vTp03j00UcBAEeOHLE5t4ZhGNvIXjzfX+pcv12ooA3cpP7XsEnMXq5b68iB4EDk4KAClkCYLGhL1M3VfeT7qb1/r7O2tobjx48jhIADBw7YnFvD2EPM/L/Ub/7mb8Zf//Vf4yd+4ifyfV/xFV+BX/qlX8Jdd92FXq+HT/7kT8Z73/vemTjJXS7q0JZuLaAnlyTC0n0malvG3duyNFkd7jJYquy9vdbSZEtLNgzD2Dnm9Xy/neTS4KKfNhSCtwyC2uoy3nM7zlaiFtjcf1uuC5iPcClLSzaMvcvMO7Z7lUt9itukMtmNhrFWi2sbIqPnCdf1CCtVmQJswnYcdW41VbpOc20Dd+cBj4tgHaFkzq1hGIZxreyGY6tlyIEbNHGURvqk1OPYALhKt3bMuS1d24uJ246zO6euLWDOrWHsRexf6BRQx5A1QArtZwsaFmX9tRenk5xM6PTdqnAFWqGr/bY74dw+9thj2/WyDMMwDGMT2gs77tAGbtBwjcBBypMvcpHpC4VbqzNu0/605zZu4fTuN8y5NYy9RzXtBexX1G3kJLzUOHf7PAn5ShGBKwlczhEAhnyGLEI2MuWeXH1M/kSQ1OnKcd7PlbC6ugoAOHHixKbZi4ZhGIax3YhrGwoxGrJABbApBGpcjDqqspvqSB4nOMR0O3AjbqxI4I4bC8jfJhctU+YIIofIcW5cWxW3x48fx/Lysjm2hjHjmLCdEmUZrV4H5ORCRKDUS2pcGhW38h4SHDFGRCAShzb3MzMQImMEQsUMeBJBTADAVyVub7jhBiwsLGzzKzIMwzAMYdxdjdygiXV2X3WbcWFbPi4f90Zw+rOPwSAQgChSltuSZIbLAhdADu7SkYQgDWp0mwRwKW6B+ShLXllZwYtf/GI71xvGHsCE7RTR4Cj92vZ+tttYf+3lIe+TpEojEvqOk/NNMic4iduGgRgZMW0PT8U/gisXt+WJbjQa4dlnn8VznvOca349hmEYhlG6sdK+1Lq242K27LFty4215SbAO58fJ257aQMH0FYClxzAkPRkiogM+DGxOkncApgrgVue65kZjz/+OI4cOWIOrmHMGCZsp0DkrW8TyMqRr5JS3DoWx9ZFRkMESsFSuSzZMUZxe8QtAMQYce+992I4HKJpGguUMgzDMLaF8fThfNkiMEq3jzqnVreJAJwIYE+VCNckcBnq4kZ4VLk8OZcrE+DYgah1ZbdaI5Ac4jkUuADw8MMP4+mnn8b58+ctUMowZgz71zgDqEur5cf2Tbl6ylApTUFe8G0isr7X43NwrzVUyjmX+25tFJBhGIZxrWiIE4CJo3k6bm55KYSvPi8izcGNIYdFxSI4qnOs9DyZl1uUQKMV05dKYda16NrnicOHD1uglGHMKKahZoTxJGRza6+Nct6tpibncT/FvFsVt+WoIBG4Vy5ubc6tYRiGsd1E3lpEtiI05EtkboOlCqHbClNOorYQwlmwcrvfJIa1xzcHVmFrxzjmbbuBVjbn1jCM3WCmhe1rXvMa/MZv/Abqup72UnYUN/7VRO22MMm9XfQictW9VXFbB8YgpHFAqVy5iXzFAtfErWEYxpWzX873V8q4E7vp8dRzGwsHVsWoQlT0zXJX3IbCvZ1EZyRQbNOYyx7fcTFbPldfg65V79/rAtfErWHMJjMtbH/nd34HX/ZlX4ZbbrkFb37zm/GXf/mX017StqMiVlMHTdRuP6V7O16aXFGbTl3HJG5TabKmVV+ruD19+vROvCzDMIy5YT+c76+VTnkvYkdklpei+Biu+C/vpxC3WppcJijHQoBy+i8fZ4K4jdxsKoeetPZ87DkRuOPi9oEHHpj2kgxj3zPTwvaHfuiHcOedd+KZZ57Bz/7sz+LlL385Xvayl+Gtb30rnn766Wkvb1sog6NM1O4cW5Ume9eOVSpLk5uIbRG3S0tLuPHGG3fgFRmGYcwP++F8f6WU/bU66751UJMwLZzT8R5bRR3b8X2rIG1Lk0W46nE2rSfNt9V+3I6ojnK5WD/teAiWMg/ittfr4ciRI9NejmHse4j5KpoJd5k//dM/xTvf+U782q/9Gs6cOQMiQq/Xw+d//ufja77ma/C5n/u5M5dKd9ttt+HkyZO49dZbceLEic5jTRTxNAyMtZqx0Uj5a98BKz3CSiWuooz/MbW73UTtpdW5tlG+6tglR4AnEb7VWOjUlX4/Yowz97NpGIYxq+y18/3FzvXXQiseGzRxhIZr1GGAOg5RxwFGYZhLicd7XR3JHFotP1ZROymACkDepucWULk+KtdPM26Re24VAsFTBe+qzjEAqTwjtGOEdATQeBJyORpI17bX05LtXG8Ys8GeELbKaDTC+973PrzrXe/C7/3e76FpGhARnvOc5+Arv/Ir8YY3vAEvfvGLp71MAFcubCOSsK0IKz3pATVhu3OMi1vtqw3pn4ODuLlVcnk9UTFn+Oq+J0899RSGw6GNAjIMw7gEe+V8v5PCVoVrE0eo4xDDsJHE7QBNrLNbCmCTQ6vi1pMMs4tlKfCE0TyeKlSuh55bFGFLDgTqCFsZR1iI5uI6oAJZhG8pbIGucKUshCc/vtfZ2NjAY489httvv93ErmHsMntK2JY89dRT+O3f/m28733vw+/93u9hOBwCAF75ylfi67/+6/H617++M1B7t9nqZBfTLNVhCitaqxnDIKeZRQ8sVw7LFbDg6ZpElHFpVNxG7oZGlf8ktkvcDgYDfOxjHwMAHDlyxMStYRjGZTLL5/udELbjbq26tMOwgVFY74jadkRPV4CWwlPZakSPIwdHFfpuYZOw1X3r9UnCNh83ubeOfHZtZT1dV3aehS0z42Mf+xiGwyEOHDhgc24NY5fZs//aBoMB1tbWcP78edR1DWYGM+PP/uzP8PVf//W4/fbb8Z//83+e9jInon21nf5a6KehZZCUidqdxCWhquXG/ZSe7ItmZy1VLscBXU3P7eLioqUlG4ZhXAV7+Xx/tWg4VMM1mjjCKAw3ObWlqNUgppCCnSIH6XtNpcraByu9tWFTH67b4s/BdvzP5HPe+LxamZcbOscbD6WaxF7usy0hItx+++2WlmwYU6Ka9gKuhPPnz+PXf/3X8e53vxt//Md/nE9uN998M17/+tfjDW94Ax599FG84x3vwPvf/358+Zd/OQaDAb76q7962kvfRGT5ZE9PFuUcWwcLktot5MMDRuUARAIcAxGAo9x3C2ZwAABC3zEQRRADfEUfPqyurgIAHn30UZw6dQoAzLk1DMOYwDyd768EdWvjmGM7iuuowxANj3LAk/79UM6jVYGoLqwHEIr5tOXj6sK2DmyVg6bKMuQsgMkhcpS/T9KpUveVy5tZT6UOoCi5FXKQjrur+y1dW9n3nvVbMhoodfz48Sxuzbk1jN1h5kuRY4z43d/9Xbz73e/Gb/3Wb2EwGICZ4ZzD53zO5+Brv/Zr8QVf8AXo9Xqd5/3gD/4gfuiHfgh33XUXPvGJT+z6ui9WijwKUoq8EVJwVBQhu1S1wVF9b47tbqIl4nn0T+BO/y0wlqZ8DWXJp0+fxqOPPgrAypINwzCUvXi+3+5SZBW2DY9y6fFGcx6DZg2jOEDksMndLEWtzqMlkr5ZR1VnOwA54MmRlyCosf5aRz5tz5vEsoresgxZy5IVvV96bTcHSo2XMOs+lHkQtwCwtraG48ePI4RgZcmGsUvMtLD9Z//sn+FXf/VX8eSTT+a+xzvuuANveMMb8IY3vAFHjx7d8rkPP/wwbr/9dqysrOD8+fO7teTM1QhbS0SeLpPEbVmC7GhnxO2dd96J66+/fidekmEYxp5gr57vd0LYxuTUDsMGNprz2GjOYRDWMAwbWWi6MadTvsoMW3VCVbSWqciluKxcD44qVNRH5XqbEpFVRJdhU12RujlMqtxuUs+tPr9kUkoyMB8CtxS3z3nOc3DbbbdNe0mGMdfMdCnyW9/6VgDA8vIyvuRLvgRvfOMb8Rmf8RmX9dxnnnkG/X7/srffLcq+2jwjFSKYjOmiZcmA/MPgdE4dRUIkEb0NAATGdpUl13VtotYwjH3PPJ7vrwUVlNoz20TptdUUZMebRZ+KWqBNP46IHW+0FbcpywMuzXlvxS/IZQGd5+IWbm/k1hEGAH8FAlR6h7uitZPSnP5Golz2vLfFrZYlP/7447jlllumvRzDmHtmWtj+nb/zd/DGN74R//gf/2McOHDgip77N//m38RgMNihlV07FiUwm5TitucIolVFyNbpU4kIcXMBQkXy1RFQuasTtwoz5+AwwzCM/cQ8n++vhLL0V79qWJQEMtVyX9peRSpNEIAREeAGjO5MWRGSvt2OuTM2qHR4x4OfGI24rmPJxuOJx53XhLbXVrffKiwq54swJr6mvcjKygruuOOOzn12vjeMnWGmhe2f/MmfXPVz91ofQ5zZgvD9h4pbRxAV61XsEkIqVwaJuI0pRcM7AuKVi1slxoj7778fy8vL1nNrGMa+Yz+d77eiFHtl0rB+jRzQxNGmcmOCg3dVFricwqKYAxixFbVwYDh4VAgIQATIOfF1g0NDdSoVpk1zaLl0gxGzWGaOYGrLo8vjbcX4HN3ue4BNZdZ73bUd5/HHH8e5c+es59YwdoCZFrbzirm1s4+K00pTkpO4HUX5qiXkMTIiE/rMss1Vittz587lC2BpyYZhGPudrqiNCLFJ5citYI3USOATO4AckEVtd2atI4dIDsQEpphaaADEEYCu8+q0NxcO3vnOmiKzHJsZ5BoQO7nAFX/bJDGaEpJ94Q6rIOctHFuQilvMlWur1HWNJ554AiEES0s2jB1gpv81ee8v+9Lv93Hw4EG86EUvwutf/3r88R//8bSXb8wBedZtERrVc5TLpSJLuNQoioNbR511e2UW/MGDB23OrWEY+xY73ws6F5aTgMzObZprGzig4RGaOJKv3Pbe8piojdzki86VjRwQU+9unpPLNeo4RB2HuZe3iaO2vzaPIOJOz23ed5qXq7c5ifD2+d3zIRel1uVlKy71+F6i1+vhzjvvtDm3hrFDzLSw1bl1l3Npmgbnzp3Dvffei/e85z34e3/v7+FnfuZnpv0SjDngUuK2ia24bSKuWtyurq6auDUMY1+y38/3nfm1scmhUTrLtuyzVeGpl5BKlFXcKqXbWfbL8vix0oVZBW8oRgjp/NtW1Kow1vWJyA3pdkDDdRbSGmAl28Tu60XsiPfLeY/mQeBqoJSJW8PYfmZa2IYQ8EVf9EUAgM/7vM/DBz7wATz99NOo6xrPPPMM/uAP/gCve93rAEjwxAc/+EG8//3vxzd/8zeDiPCd3/md+NCHPjTNl2DMCaW49Y5E5CZx66jr3Jq4NQzDuDLsfN+6suKgDlCHAeowFKEYawQOrSOahWSDhkf5ugjTNsGYqL1MOl732LHrpmZBKucxFbWli8usTnLTEckxrSdy17VVcTpJzOZty3VNELImbg3D2IqZFrZvf/vb8Zu/+Zv41m/9Vrz//e/Hq1/9atx4443w3uPgwYP4zM/8TLznPe/B93//9+PP/uzP8IlPfAKf93mfh7e97W1429vehhAC3v72t0/7ZWxi0pvuLBxv5tnKua0mlCU3UWbhXqu4ffLJJzEajbb7pRiGYcwU83q+vxxU0JWO7CgMMQwbaX7tOkZxkF3ZiFZoatlv5AYxOb3jc2cvxni5M4C2rLkQmhOFaBK9sRC4MTu4bVl06dperkNbit+txO1eF7jj4vbs2bPTXpJh7HmIx5sfZoiXvexleOihh3Dq1CksLCxsuV3TNHjOc56DF7zgBfkTWx2GffDgQdx33327teTMVkPbm+TkbQTGRiOXhoG+A1YqwlJFWPAioK4mXdfYeSJzFq4hipANUb6PgHxI4SkJXy/C92q+n08++SSWl5exsrKyA6/CMAxjdtir5/utzvVXgvbDNnGUxexGcw4bzXms1Wex0ZzHoFlDncVt6MyVrVwPnipUro+KenCuyjNmga4wdFTBuwqOfB7ZU6JJy5Xro3I9VNSHS/vSUmQdDUTkUJFs58ZSlNv99NowKnKbjqkjb8rnaoDV+AihSa7zPCQmr62tYX19HYcPH572UgxjzzPTvxHuvfde3HXXXRc9yQFAVVW488478bGPfSzf573H85//fDz++OM7vcyrQt94R91vgjm3s894WXLfteXJwOZAqat1bg8fPtwRtebcGoYxr8zz+f5yUAey7aGtMQwbEuoUBh1RW8KInTLkwAExdvtyY/Ff2zsb8mPlduNpykQy/scRwZNvRwyNidRxZ7gNsCpcX+3hTf+Nv45JpdHj+5xHVlZWOqI2hGBlyYZxlcz0uJ8bbrgBjz766GVt+8gjj2Bpaalz38bGxsy5XaVwNRG7dxFxm2b+OEIfjFGaU9CwziVmNExwkeEc5fuuxolfX1/Hvffei8OHD9soIMMw5o55PN9fKWUKsQZD1WGAURymFOS6O+s2jc4BkoMaG8ABjEpG8JCDQ3JEiz7byBE+XW9n0zLAEZEASqJy3IEFAA8gwoHSuCHvfMc1LZ1kDZWSg6pIdp0xPsycXVt9fmTk9ckcW7T7zK95Pgkh4N5774VzzkYBGcZVMNP/Yl72spfhiSeewL/9t//2otv9+3//73Hq1Cl8yqd8Sr7v1KlTuO+++/C85z1vp5d5TWj4UDk+xtgbXI5zW6dS5TZM6sqdW0BKlUIIFihlGMZcsh/O91vRzqvl5MB2E5DrKOK2DgOEOMqXctxOeT2PAkoOr/bkisx1WYiy9uqOfc3rUpGayow1jErdW0fUEb/jjmvr2mqQVNOmIXPhIheJ1+V7ovsrA6f0ucpe77MdZzgcYjgcWqCUYVwlMy1sv+3bvg3MjDe/+c347u/+bjz00EOdxx988EF83/d9H970pjeBiPCt3/qtAID/+3//L778y78cTdPgta997RRWfmnGS5BjMv+MvcVW4laTkiO3/beBr17cHj582NKSDcOYW+b5fH8xYnZMWxFXlgvLnNo6i9tRmjWbR+vEupglG/Jc23YUkLi5nqpOfy2l/wDk6+3tsTFBhcjcKmlZS4fLSzvrVl5LO1s3dtzpsjRZxX2nhLl4b8p1zSPLy8uWlmwY18BMh0cBwI/8yI/g+7//+3OpyoEDB3DgwAGcO3cO6+vrAOQX4Xd913fhx37sxwAAn/qpn4o//dM/xerqKj7+8Y/jxhtv3PV1bxUoocFDw8AYBGCjYdSR4QhY8ISVFB7V9xYetZcYD5QahFbIOuomKXvScLAr/x6fPn06l+sdOXLEypINw5gb9uL5/lrDo8rgqFEcYqM5j/X6DC7UZ3B2+CTOjZ7EhfpZbDTnOyIVaEWmpyo5qFVb7gsJiuq7BfT9Mhb8EirXhyMPoCsMVaA6Fa1wOTxKg6S0v1bR50+6r9yvpyq5xFUKhfLt+ou1ZjcYrhMolR8vtiuDqvLa56w8eW1tDcePH0cIAQcOHLCyZMO4TGb+X8n3fd/34QMf+ABe/vKXAwDOnz+Pxx9/HGtra2BmfPInfzLe+9735pMcAJw7dw6vfe1r8cd//MdTEbWXw6QS5NK1tZLkvcVE5zbdB8j3tUljgK7FubU5t4ZhzCvzer6/HFTgqvOqpbtSjlynfltxa0dhUMysLUKj0u0mjory5HL0z9YfpKqoVbQ/NnTWUCPEgBBDGvPD+bY+nrfj0Blh1Dq1nEcStWXU3fLkXJqcyrLL+brtKKHuKKB5K0m2ObeGcXXMdHjUmTNncPDgQbz61a/Gq1/9ajz22GP42Mc+hqeeegorKyt46Utfittvv33T8z760Y9OYbWXjyOJuHfEnQCpcXFr7C06gVKeADBGUb4CmpRMiAD6joGowvfKAqVWV1cBAI8++ijW19c3hW8YhmHsNeb1fH8puCjbVTGqvbUqWLNwRQQ4wsGBI8OTB1CBUrlugAQ/AUmosuuW/HIAFTNwgeR4cgTGHM+QgqgAoIkjcVm56+4qmqbcKVdWR5UZ3iG7zRwjPFWyFnJ6qtQcRoCBUOxHjiUilzg5uKnVx9F8B0qpuD1+/DiGwyHqur5karhh7HdmWth+9md/NpaWlvC+970Phw4dwtGjR3H06NFpL+uacESbXDpH4uQxGJEpBSjIdlaOvLeYJG4bJoQop/zAImiBaxe3/X4f119/vYlawzD2PPN4vr8UMbuNRX8sl/2x4no6OHjyWRyqwAVEaHZCm9LfF5EiHMeOa6rJxCUeFUAOEREu/WnCEKGo4jZyRCRJNyZQLiFW4Vn2xOqaHAoBHAFHInw9qixEPSoE5FMlwphA1bCrSIBjB5AkJjtZSEfc6vs5byXJKm6rqjJRaxiXwUwL23vuuQeHDx/GoUOHpr2UbcfpZUyTtKLWxgHtVcbFrYvtKKDtFLcHDx7s3D537hyuv/767XoZhmEYu8Y8n+8vRhu6JGW5dRiiTgFRTZTZ5SIkK1QU0XC9yaEcnyFLRFlYRjAiN6jjEIHCxPE/xC6NCSr2m53hduxP2e8qYVQy6qdbKpzGBiGNDSIg8ggujR9iakUtgCxuCSJKsziGy0LXo+qI2/EuOkbMbnJZkjwvInd8jNWFCxewvLxsPbeGMYGZFra9Xm/Pz6WbxFaC1fpq54eLzbndTnGrnDx5EqdOnbJAKcMw9iTzer6/FFqG3I71GWAY1lHHAZo4SmKTULm+lBVHRkCT3ExKwVEVnPOd8uB2Tmws3NqmDV9icYGZpDRYS4S1VBkAIgKIqS0/TuLUo0JEBOXC580wx7xOAOCiPFlm4UYRvohwkIYdLU8GWqELBphEuEYCPLw4s7JRMgHakutS5Ova5kXgAvIB9n333ZedXBO3htFlpv9FfOVXfiX++q//Gv/lv/yXaS9lVzGBOx+MB0rpV/1HpyFSo2sMlALkj0LAAqUMw9ib7MfzvfallqN76jhsXVuupRwYSOXIFSrXQ0U9OPKSXEx9OCdJw1uN4VH3t3PhUR4l1BkPFJvORcb0hBzstBXdY6b/xkb/tL2+3M6kHRsT1D1u+3gbFsWd4KhNz09l1+PzbucF7z2ccxYoZRhbMNOO7dd93dfhwx/+MF73utfh0z/90/Fpn/ZpuOWWW7C0tLTlc974xjfu4gq3D/vVNJ9s6rkNqcZ8C+fWMV1VCXoZKHXq1CkAMOfWMIw9w3463wPF/NqiDDn31/IIozDI17Vv1lGFyBHeVXkkTxa1Rb9rSYhN7sOl5LjKeabKZcGMCq74c7AUqW1PqxYxI63Fdcb1aOKxitAy+IlAm8KpWqHcTCildp0P+AkSbsVFyJX206q4He8dzu1cuXBqpn2cy6YMlFJxa86tYbTM9Bxb73Xe2uWnvoYQdnJJl83FZttFZoyCzLLdCIyNRty6ygErFWEpzbKVWafWaLvXkbEIcqKtY9el1ZOvQzvntueu/ntvc24Nw9iL7NXz/dXOsdW+2lEYYBDWsF6fxfn6aZwbPY210bO4UD+LQVhDHYbZKR1PHlYH91KzZDu34bIw1tm00i9b5f2p+zv+HJ1Bq7NttccWQB5TpGN9VPiOH0f3rfeN9+/q8fJzXTuj16VeY511Oy6Iy/20x+7O9p0XbM6tYUxmph3bY8eO7cvEVwuNmi9EoMrnR54IcPKnCYd2vq0jycRwAIgARKByV95va86tYRh7kf14vm9LbZvOvFYZ7cO5rFbTkDWoSa5Ln2rgkDtdS58iv5ccN90fQyOOratkJBBFuHQCItYeV9eZbSuimmS9qfxXe2x1zdoTLNumPl5Xpf7a9JoR4dHuUx8IqT9W+2XL0ULttq74yilIqkveD9rEaIKbu8Rkc24NYzIzLWwfeuihaS9hVzFBO7+ouK0cJCiKGNERYuy6uXKWZzhPqRTr2sStjQcwDGMvsN/O9+q+MnMhcFM/axK7MYYkeJtNwoyTWIs0uZQ3pxCP98ay9Os6F3PiMbsKTmt2ATBVKVgqpRenkCYil0V2E9EK42IGrx7LpdPZ+PHb/cm5LURx3R1RJwxqEpEjfBpNJMfYvB1REt8MUEpQ1hTp9kOB+RB/pbjt9/v77oMhw5jETAvbeUeFrCMTtfsBFbcuhUj10QpZDY6qo5zsXRK5V5uUvLq6iuuvvx6Li4vb/joMwzCMq6ccSSPCNSAkERu46QQ81XEoYUjFjFjF5ZJdOT/k8l92nb5boFvGzHCIMYKJRcBGcWw5zZqNVIFdP/fdaklvE0dprmyAowYNt/NqJbCJ0/aUEoxFTFKaP0sgOUYWmm0QVGCAmMU5RgW/hfhUNxcAAia8Jyp203EiYu611e3mSeCurKzgRS96ERYWFkzYGgb2kLCNMeLDH/4wPvGJT+DMmTP4lm/5FtR1jRMnTuD222+f9vKuijIcwZKQ9wfjY4AqZkQiRBJxCwAhMhoiOJYwKXnelR+rFLVN0+CZZ57Jbq5hGMasMo/n+3HU5ZRUZAmOqsMAdRhglJKRR+m2BkhNEi4qXolccmIrVNQDnLij4z22emwwENCkfXR7eAOJyK5cH0x9ePJ5hoaDy+5tRziXvb+u6gRNlaIaEXC+6hwzO7BwACoQFduP7SedOjfdr2sJKUjKsTi7KqpV3Ob3jeanPLk81zMznnjiCayurlpZsrEv2RPC9pd+6ZfwAz/wA50xJt/yLd+Chx9+GC9+8Yvxute9Dr/wC79w0fTEWUZFrYnb/cGmpOTCuY0MNNztt63Qlm1dDcyMe++9FxsbG6jr2npuDcOYWeb9fK9sml8bNjAMGxiENQybCxiECxiENYzCRnZs8zxZuE55r95H5FBxD5H64p4mceupAlIJ73i4lNx2WeRq/210le4YQD/NwXVoUiky0J2Vq7cr18/rKV+rPC69t6XQjansGhAnGYB8oDsuXlOvrIrbcTpiNw0bIBBCbOBdlf6+KoQsz5e4VR599FE8+eSTOHv2rPXcGvuSmf+J/57v+R583dd9HU6cOCFDyqtWi584cQIhBLznPe/BP/yH/xBN01xkT3sHE7jzTznjtkxD1pNzYEbDyb1lpFLlq/vBICLcfPPNAGzOrWEYs8t+ON9rQJTOr21ijToMMWguYKM5j7X6LNaas1irz2KjOS8Ct1mT+baxHQEUU8lyHQeooyQr67bDuCFObwpzApBTiSvXz+nE3XAoBjPLmnTsUJpxKyXSIYdaSbBVO3e34Vpm7qZ5t1sx/pgKbZ1fq8FUWz5fj87tpXxf8wglFCFcHHL/r868LbeZN2666SZ4723OrbFvmWlh+wd/8Af48R//cSwvL+Ptb387nn32Wbzyla/Mj3/WZ30W3v3ud2NlZQUf/OAH8Y53vGOKq7024thXY/65mLiNSdSOonzVgKmrFberq6s4duwYABO3hmHMHvvpfK9lyCpMB2FNRG1zFuvNWWzU50WoNmsYhg1xdOMAdRiiiXXuxZX9hCxAVWhq+bKIzTbQiYhARHDO54veB2i6cRtaJaI2ZHE7LihVKOb1pLLisry4LCku+4HLx3V/MX/tnufiuMt8kb+UVOB206ZT/3Lsilt9zeP738tooJSJW2O/MtPC9q1vfSuICL/4i7+Ib/zGb8R11123aZvXv/71ePe73w1mxn/8j/9xCqvcHmb6G2HsGJPErf4sNIW41fm3Jm4Nw5hH9tP5vixDHiW3dr05j43mHAbNmsyvjSJOY2w6zmnkpjO+J+8zu5QaQFVvcl2BlIgM13FtJ+0jxpBd4cit4zkJHnNB4wQB7Iqe3HY7FZucg6cu9p7lkuair3ir9YhLq864OM7tKKJW/Jb7nxdM3Br7mZnWU//7f/9vHDlyBF/6pV960e2+8Au/EEePHsXHPvaxXVrZtaNJyA6WiLzfKcVt5UTgVulnoixHDrz94vaJJ57YrpdhGIZx1czz+b5ExZ6WydZxgGHYwCisJ4d2XYRoGoOjxFT627qjcg5QweipyoFObe/uEMOwLuXCSeQGDpvWpOvKI4gKMSgjfLgrLCcFUhWCNiaXVB1jLlxUBme3WtejM3y7rzcWY4RCcZH7c78xudwjOy6mVeC25cuc15jXPaclyePi9v7775/2kgxjV5hpYfvss89edtDNrbfeio2NjR1e0fbiKJUGoe2tbEtOp7s2Y/dxBHgSUeuLftuGgTqksuRtFLf9fh833njjNr4CwzCMq2Pez/dA6wqqmFLxqeJW+1XVXVWxCqBNTk4idZK4zY4oMwI3krKcxO0olTS3Lm7ditdUeqxOZtfpbDq9urL/za5nu862jFkEep3XHmLaH0vJ9ChIf3ApPkt4bB1aVlyK01gI6vzejpVBT3Jjx0uSt9puL6Pitqoqm4hg7BtmOhX5pptuwgMPPHDJ7ZgZDz74YA7I2QtcbIZt7rdlc3P3CzrjtnJARDsGqExKRkizbenaZtwCIm5vvvlmS0w0DGMmmOfzfUkbbNQUFwlskhLgJpfu6lgdoHAjHUCxDX0iuI5rqUR1ZsMgPzciIpCkBHva/OdfKVRD4dA6ODQ6FzeHCk92OjUUiznCuUq+UgXvKjQYgaPuf2w2bwS8l+01EVnHEm1VAu3QOtS6/s7jW406muDplKnT88TKygpe+tKX2rne2DfM9E/6p37qp+LZZ5/Fr/7qr150u3e961146qmn8Hf/7t/dpZVtD0TUliRT161txa1Zt/sFl34etCS5KvptVdyqc1v23F718YoT3ZkzZ/DYY49d2wswDMO4Sub9fF/SSUaObd9sjE3qc+VNPaBt72vTcUTbgKQ44dImHNdxiGHq3x00a1hvzmPQrGEUBnlf2enkmIOoRmFd5urGQTeZOaclby4jFhHNea3aSxtTEJU6ueocx9T/2pYraznz+AcATb6fUzJzmdjMxX9XwnivbVl2PQ+U5/rhcIgHH3zQem6NuWWmhe0//af/FMyMN73pTfit3/qtTY/HGPEf/sN/wJve9CYQEb7pm75pCqu8ctRhc9j8DVBhOykcwph/VNx6SinJRb9tZGRRq2XJ11KSrAyHQzzwwAN4/PHHLVDKMIypMK/n+3FK8dbp9eRWVOZtOLbisOx9LcqJVSjqCCANecpCkRkNjzAM6xiGdWw057FejhIKaxjFYXZZdUyOiudRErfDXMrcjvZphWmzyb1VcTt+H4M7vbKd8T2dlOXQvp4JfbEhNqlXt87XQ9y8js57f5HS5PJ7UH6v5o37778fzzzzjAVKGXPLTJcif8ZnfAa+67u+Cz/xEz+B1772tbjuuuswGo0AAK985Stx77334vz582BmfMM3fAM++7M/e8orvnzKEuOyv5bBEJ1CVoq8T3HJsUWUsuPoCBUYo1RZ1kRAS5CJAMft7athYWEBt912Gx599FGcOnUKAC67180wDGM7mOfzvdKKVw1j4k6vp4pXDX/Ko2pyMjHDkQg/RCBSg0BV22NLlGfTeqoAkpJi1Zc1hgCkfLlyPVSuj8r15UGHPIpHBXVMpcAODo5G0vPbmX3blu8SN+l6m1jsyUugVVHiW/bClrQzbUXQbto/HFyac0uQcuVxIUqpfNsBIPL5/oklyankWf7O2ry+dn9x4vP3Ks973vNw/PjxnJZ85513WpmyMVfM/E/zj/3Yj+Htb387Dh8+jHPnzmEwGICZ8ed//uc4d+4crr/+evzYj/0Yfv7nf37aS70mtPxYS0stPGr/4ooSde9SmFS6T8qvpAy5Dow6XHtJMmCjgAzDmD775XwPpA+xy/EzxaicNlQqBT/FYXZGQwy5J7eOg+SmrkvJcNjI25azZzVIapiSl0djYVLZFZ3gsAIiOrXkWUKgRllsh7EZt4AIQgfKAlv7gEvyyJ5CKGsJsJZnl/Nxu+9dzM70eJlyW9LMeS3d18QTe3a5+FChLMnWdc0LNgrImHdm2rFVvvEbvxFvfOMb8Sd/8if46Ec/irNnz2JlZQV33303PuMzPgPLy8vTXuK2UfbXGvsXGQEkPwwsuRoIDMRArbhlgmegYoZjSp/mX73Fr6mJ5twahjEt5v18X4pYDXQqhVssyoBz6W8U51rH+Ti41IbSPk8CpCp48qhcH+z6cDHCwRUjdcQJDVSJ80kOFfU7rU8qQJ2rNv0xkkfmUMzPISI47alNwlQmPrgc7jTJ8dRwLHGbKc/TFWHpEKI4wB4VIkFcWvjOPsqSbXmujgBq/7QVZ9blIKpIgGPxfh2QXdvytes6CG4uQ6VU3Jpza8wje0LYAkCv18NnfuZn4jM/8zOnvRTD2BXEueXUb8uILCnJoyRumwhUxGi2qSQZ2CxuV1ZWcPDgwe14OYZhGJfFPJ7vSxGqzuz4+Jo23VdClkZBwpp09qyDg3M+CzMVmkAaHZiEqvbL+lRmrKXNKmwr6sOR2+QWKy4JShU6ufQ4OaSOSwHkwJTWQkXPLAiRpGx4vKJIBaR3FSg5u5SSnfPIIjDAEQFNFr3yWAP90zX3IevYI8j2pMLYEfxYOTFzRJRTKRgRHlVOYS7X6ZIY1vXkhOg5Ebnj4vbEiRN47nOfO+1lGcY1s2eErWHsR8p+276TMT+RGU2UM3HDAEXAEaO5xhFAiorbjY0NE7WGYRjbRKf0uCizzbNgCxHVxDqVFg+yIHVUwXPV6XEN+TERhZFCLi2uUt+rBkLpfuAAx1Wn1Dcm0UogEbbkESEfomrvrYi7BpzKh13q6y1fW4hNanLrY9JkHRGpVRaIWq6sM3u7rimDdWwRmuSxVgCazSXGiIjsWnFLDpGD7C9VwuXMksK5DWhAnI6bHidsFsO61ssZKbRXUHF78uRJHD16dNrLMYxtYeaF7cmTJ/FTP/VT+F//63/hzJkzaJoGWyUGExHuv//+XV7h1VPOsHXpE0QLizJKyvm2ctbl/LWJEiTliDGK4u46Tonb1/hzND7MnZnzHzCGYRg7wTyf7xXtIVW3tomjLG7LkTfaz1rHAWJIjqRvEJMA1PmtbR+oCC9GzCXEgRt4qvL+tKyWopPgKSfvryQt1/IkJ4KvMz8XSGnGsS15RgUUoi5yREASoUnQMipQkPt0fz6FOjnqb3pv2r5b6vT7xuRAw+kfrRUcNpcIq7glkg8OKJVh+3TqFHE7WYhGjil0quvUdvpxx34US6G7VwXuysoK7rrrrs59dr439jIzLWwffvhhvOpVr8KTTz55WeNv9uo/RIckcovbhqFMErdalqzitnVpGT137f22JcyMBx98EAsLC9ZzaxjGjjDv53stP5bZsjojdiDjdOKwO+ImFvNamcER6cKIrga5VHo89teCgwQ9BUhqsnOxdWnTY6nwJ8/LzXNmNWk5CcJSNOZS4OSuVi71yCLCpb5XTmXDjAgHBsdURlw4swQHuD6InIhuVGBQLh8WQdr23ZbiVoV168aKO6uo0M/vNSIcaR8zZfMgAvATBHF2adGK8M2hUa2Lq/23KLafB5588kk888wz1nNr7FlmWtj+yI/8CE6fPo3rrrsOb3jDG/DiF78YS0tL017WtiIhC/IbN4/9meqKjFmkFLcRhD63ZcmRk3Ob3FwiABGo3PaI23PnzuHZZ5/Nt03cGoax3czz+V4FUnZio86VXcOguYBR2MCgWUMdBu2Yn+IvAY4AByA2ABFAHnCeQT5kgT8ucoE2OCm7pXBtyS/G3NaiJFnR0UG6vSMPplbMibjj3AdLRalwDl3SYCZQJ9RJD68usPTn9mVMEaosbuV94+J6zE504O7rHn8P9PV0RPoWIrTtb0YOlZrk2rZBU91jzsNYoKZpcPLkSYQQLFDK2LPMtLD93d/9XRARfu/3fg+vetWrpr2cHYVymakgiYc6Y2166zJmhyxuCYAnRDACA6MgPyejKH/0ODDgaVvCpADghhtuwLFjxywt2TCMHWPez/cMKUHWtONR2MB6cx4bzXlsNOcwCGsYhLXk3qawKHL5g28RtqK6XCCgBxAD5Dg5uMg9qp4qCZmClA23c26dzD/PgpU6Pb9NcpS1b5fg4J1HRf3k1PY7JcrtLFmHSOIGc5LGjlqXWvEUuiIxua+eqlb0Zh1V5bJlFbWACEhCESpVnN4miftLfl/GqwPUNZYdbt6+dHcnzPPdy1RVZWnJxp5npoXtk08+iU/6pE+ay5Mc0Lq1jgq3Nrlv0bfO7XaWlRp7m01jgFjDpORnp47ys+JiGya1HR+M2CggwzB2knk+34+XIQ/DhgjZ5gIGYS0J3HN5Hq323bY7AGJkxFRVzJ7BDLiK4HppmzQJh1KgkwYy6QigkrbUVkQsRYcAKYMWx1iEtSePivuAT/2kiHngzqT5sCDksTqhGMWjLnCAQ+B+ej8aEcosbq1zbW8wnPa6upyarO+jljE76OMaYuUK11XKmrtOa+yUIOd05AI3wfGd9L51tin6cecBGwVk7HVmWtgePnwYdV1Pexk7giNqI+rHfrmquNURL+bYGuM4Qh4DFKIkPAaWn5cQZQSQh7r+2/PBiIlbwzB2ink934+XIWfXNgwwihsYNBcwbNYwDBsYhQ3UcYiGR2hincqDuS1FTtlMDgBTuvg2w0nFrKcqzY8VUeuLEuAsMlNPbb4OEacNjxCTgvbJoa24n47bishyf75IR9bXqknM5fxcgkOlQVCJ7NRGgH2VkpBbQezJb5rzS2mqbe6bJQAcW2FKADN1+myJfA7n0l7Zcr+SKC3BWerayvq6I4/2AyZujb3MTP+UvvrVr8b999+/J5MPLxenl8KxlV/slw7PMPYnMt9WxgB5R+h5cWYJJB+KpA9GQuSipH17fp5WV1dx7NgxAMDp06cxHA63Zb+GYexv5vl8X475CUm0juIwObQbGMZ11GlmbRNbURtiAw4iajflGBWQ05mwLs+zdansWHtqS9czJrFZxwEGzQVsNOcxbNYwCBfEMeZRp9d2PKhLhWcZ1lQifcQ16jhIZddy0dsNjzCKwzxbV3qKW0ELlB8IcP4adOZv2jYWrrCOPtJLZC7m3MYcPqWjkEJsupeix5jTc8t15PfiIn82bw6b2ruouPXe48KFC52cDcOYZWZa2P7AD/wAVlZW8NVf/dV48sknp72cbad0YsvgKP2kcHyouWEoWdymiyedYSs/N4EZDauLu73HXl1dxXOf+1zccccdWFhY2N6dG4axL5n383055kf7bOswxDBuoA5DjMI6RmGIJtZZeEYO2aUFUoaCB1wlAVISIgW45MqWbq1eOmtII4VCDGlO7hCDsIaN5jzWmrMYNGvp+AwH19lfuQ+Zedt1Y5XADWIMyXUepf2FVPYcivtEzJbiVMW0fs3CudwOcbK41ZFGWcy2M4K1vzknKxcCuHzOVuIW2CzexxkX5POAittbb70VN91007SXYxiXxUyXIn/gAx/A6173OvzCL/wCnvvc5+LlL385br31VvT7m+efAfKp4rve9a5dXuW1Uc6yVSJbMrJxeTgS17bP0nMF14aOSVIytj0lGZCywZK6rtHr9bbY2jAM4+LM4/k+cusYRg6tqGQpRdae2kGQUmR1MzUZmQMATnNFk7YUYUsiansibku3FkAWc5xEqEsfbspa2hE/TayTiIspxMmj5xZROYJzUsZcOr1NHOXr7NL3xSUXMwJMPOachm7SMsdUWdTkNbCWGhdhU2VHcFnKrYi4RS5TjmiFZeAI76rO8wmy/xCbFMjVFfyBAwhRxg9RzOXNZVlyLl2mi7u2esy9npCsrKysYGVlJd+OMc3ttbJkY0YhnuGaV+c0te/iS9RtiAghhItuu1vcdtttOHnyJG699VacOHFi4jYa+jMMjLWGsVaLy1YRsNIjLHnCgif0PbZNkBjzhf4M1ZExSj9LKmwrB/QcoZ++apDUdv8sDQYD3Hvvvbjpppus59YwjKtir57vL3auV2Hb8AjDsIH1+izOjp7Ek+uP4NT6A3h87T48vXECZ4ZPFI7pSF4Xy3ifWEtwFGt/bdWGRpEDvPfwrsrJxVqKXIZH6e98GQ/XusZNrNGwiFWCQ98tou+XsFitYNEfQM8vtK5tSlPWdOTK9dBzi+i5BVSun/t4y17iyKFT0gxIH3DPLWDBL6Pvl2Rf1EPfL6KiHnq+u08aC3wC2lFD4yOB5HUQvJPX3XcLqc+42vS8Ej0GoS3d7iRJa4m3HpNaN7yca1vua16EbUmMEffddx+Y2XpujZllph3br/qqr9pzQ9gNYzdxKVXbE6EiRnSEOrazbZFm2+rX7UpJLrlw4QLqurZAKcMwrpp5Pd+XpbFlGfIohUXpmJ9Bs4ZR2EAYQURtADhwGxrF4s4CbVhUuf8G4vKqIGuozsFR5fzYLDy5Hb3DyeUkoiRa+/l7oe6r4qlCjxsAy1noxlTOCyCX/I7PxM3HL94LKbd2YPJp7VV6vBg5xFsLxbLsd/zxMuG4TC5Wt9dNEMwoE5e5FbVt+rKUJ3tU2bnNc3izK50SpOfItVWGwyHW19dtzq0x08y0sH3nO9857SXsKprwZynIxpWgQVIRhIoZId1XiltHBMcMl8YDbadre/PNNyPGaGnJhmFcNfN2vu+WIeulQR2HqFNwlPTZDlKP7QbCEAgjEbPMRRIyM5ByFTiJXpUTEQx2dVuGnNxsmUFbuLZlqXLhinsSkVZRP5Uh97LDKWOHOPehEhwq1wMn0eapyuXJSpuw3M52HS/9jamsOTupSTh6atAUM3iY4mZxSa1A7e4THTe1DMzK+yvWNC48I0dJm4a6vwHEhYNbrAEQwa3HCxwLBxdzK26XlpYsLdmYeWZa2O4n3NhXwASucXmISGVUlGbbghADJ1GrvbZpIiABbgc+PLFRQIZhGJtpA4+0/HeEURhLP+YGHFPZcSo/5sgiatVM9AwmGXODyIgOcI5yiBSgjbTyXHIBNQW4agjnqZhpWxXlsvInoEuC1ScBHHO/apOTi/UD0RD7QAVUsY+GRnDk8xq1X1dFJ6nrya3IjDqiJ6UyA2hLfqOTP4JiGiOU1hvJwcEndzhtUvTetoK3gkt9weN9sKWjWrqtJRpEBUbeJ4Ph4TsOrrxnaVyjPrmYbFGK23nDRgEZs87cCNsPfOADeOKJJ/BVX/VV016KYew64yXJTbodmBEYGMUkaokRaftdW8DErWEYu8NeOd9357kWqcBcyyWOuoILOtqHU29tuy/n5H6AoK3F0XGRkEwiCFP6JKfSZecIrseIVQ1XNWDXh0fV6R91rg2JiohAHKFGlB7cJGyBJIQ94GMfozhA5Xqg6MAuZhdYxS2ALGojERzaXldNVSZuQHEETz6VJ/u29DgymCICNUl498EUs7jNa01U6Hc+sJX3ngBqJ0zkkmFOgniC8ORCMDMcPCoEAF4FOQCgAahq95Pk7iSHWFc4T86tiVtjlpmZn8JDhw7h8z//87d8/I/+6I/w//7f/9vy8R/5kR/B13zN1+zE0nYc/WU8KR3ZMC6XcrZt5dCZbRuYc8BUs82zbUvKObfnz5/PCYqGYRjKvJ/vN5chS5hSDmyKbfJxiKnXVc//JDNrmdNX/RWag4UZsWGEIaMZMpohEEZAHMn9sYbcN2Q0G0jbsGzTcOfYbd+vOMcNj9q+3zzXdiM/Ls6zBFDF2GCUZu/WYZj3G2OT58rGPB+Ws2BMUlpei47ISSN8AoexkUhDKdPOpdtDNKxrrRGiXGScUDEiqUhlDjHkHmB5zW1qsz4nz+WdkMCsrm5nLJG+vmIskN7XjiNqty/3My+Uc24HgwFGo9Gln2QYu8DMOLZnzpzBuXPntnz8sz7rs/Dpn/7p+MM//MNdXNXOo2JWemLEUQO6ojbuQOmoMX9sKklO/bQc5WeojiziNwAOkra9E6yurqLX6+H666+3T3ANw9jEfjjftyXIKtYKMZlEm6YSB26yYzu2E/kSAbhW5MZG+mzFBNSAwOKxRvp0iVhKYh3JOaGR7dnXIGo6Kb+RG1Qk5cUA8qgeQJzIinptABNHNFyDUilxdLFIT2630VFDkSIcfEob9puShXX7/L5lgdj2wmqwlPT3tgnMAJKr2ojbm94jRxGRJJDKscvp0KVTG/T1jSUl5z7aInnZja03h0wVwVJAcos6LnH7fYxondtxobsXHV0Vt0SExcXFaS/HMADMkLC9HGZ4MtE1QalsVNFxLRHtPNudKB015hNHyCXJgYAmpSJrmFTjgIoZTaRtnW1bcuONN3ZuX7hwAQcOHNj24xiGMZ/s5fO9unrqPobCGS1d2zpKr22IjaQgM6e5tSJcY0hiyZPMKR97S1j/SGAGhxQMpUnKDUTvOsAFIHoGxbSfIDNnwQEBASDA+wbRxU3CyyVRq2N+fIpmDtwApTZzIoA92n5aN7ZeVwRaqUj0ZWIzR4B8xx0NHJKwlrVpqXIpoAOa9BxGIHmv257iRsb/pDRjgkOenZQJ8M53BG4e+VOmJxcp0oQUGqV9t1TlCimHttRZjQmC65Qm59eLvR00Vc64BYD19XUsLi7ah9rG1NhTwnbecNrrCJ0vKversG0i0HeMyGSOrXFZqGurZckNS1IyR/l5kpJkoCKCp92pBnjiiSdw4sQJHDlyxHpuDcOYa0onruvatv21oziQEt44QB2HCE1ITqukIoeRlBJzQLIAGeQp+7Kl5h+/rqJYl6HJym68EpbbcmdyEIGLESrXz2snOFQk4396bhE+BTNFRMQ4AlMrzDwmlwBFRBBLqbXO162oDyLXjtkp3qvs1oJzCTeRg3OUy331jcgurwrhmAQn1XDFqKOK+6hczD265doAEdwcYxLtFfwlRgvps/W5QAVKPbmgdpyQrjVwzGKeN+2nTWveq+JW0X7b5eVl67k1poYJ2ymT+2uL6xFIc+CknNQwroQySEo/GGmilLk3UUIwRgRxa3dg/M84OmLCAqUMw9gvcOq/zD2jPEo9rIPCta0xCoMsZJsNRjMAmg1GqOUDSefl96er0HVIiwpkjuk6s/4BIe5vBGJDIGJET7KPXvs8rZLlCJSnABFXUr5buV4St73cGxtTynPum00iUlOV9f48+mdsNI+M4hE7WXpxZX4RaSoy2j7lcgZt5/0t+nOzWCxEfha1rtc6rRTBhcIvxaqnCnBJcKeAqYh2BND49uNu7jjZ8UbpyKJbfq3p0el92OspykQEIrJAKWOqmLCdAUrHTPsymghE37q3hnElaAWAd+LY9j0wDMhBUoGBJraurc663QksLdkwjP2ECpbkO0rZcRhgFDYwihsYhQ25HQcIISDUKQxqANTrcj3WyY3ti1vLAeBkipahUhEqSsf+UEgBVLGWGbg0kgRlF0XgEslYuKylCKmX1eW5t5XroXKtqFWXVUqtU01zHKWS31EazdMtZ3bJ4R0PXlJRm2fkBodIAY59R0BLmW+3NFjvz+OFWI5QuqGBAioX23FJcIDryygkUJpV266x46S6VoDyWNBVy2Rnt/z+y/67fbfj4naesLRkYxYwYTuDpER/E7TGVaOurWOgIqAhLWMT15Ygzq26tjtdjmzi1jCM/YQKLhnxU2MYNiRpuFnL6b4xphLkkSQZN0NNO5ZeWSC5YJ4RGwmBAkSsauC89LFSLgSOSfTG1H8rexHHVsuddUSQ81JAKwFTKh4r+KKM100IhCrTfpkopxCP4gAV9Tvvg/auMnMniVlH5cg4oRQC5Sp4rrKwJnKFu9wVR+OiNqbE43xcVIixQXQOjCo93oY9pXclucJyH3EbQKW9xK44btl3XLqtk9zb3COupcmyMWQCrrjQ5Wva626tYuLWmDb2kzYjdOavMXcCpAATucaVM2n8j6PWtd2N8T8l5SigU6dO4eTJkzt6PMMwjGmS3do4wChuYBAu5EsdBmh4lIKe0tzaEcSpDdz2yDJyGFQe3aPb6qVhhAYIDfL+1LFlFpHbpiVzGidU/L4nncwgQUht8FKaO1uUUotA7J4rxIkNiLGRQKxi2xBDDs/KQr8oyx7FoTjYcYhh2MAoDPI+Aje5D7YMbto0nmesV7f8mteXxv3kflxsfj7raKY0Kkiv69im8ZLq9u0r051Z/obD5nFA8l5efOzPPIwFKkcBqbi10X/GbmGOrWHMKTlICuLaVgTU6QMU7bmtQnJ0d3D8T0np3Hq/Cwc0DMPYZdr5tSKUtBRZXdth2MAoDkVopaAnDpzFJhEBjnM+QQwARpzSkTk/B1DxSuDAIEpCtmkfVyOQY0pabkQ8E6Vk5IugvaaBu0KRkpvqkhhWt1HH++QwJALADRg+9xlT7I7MEcdWHFyCAzsRyewiKgfE6KQ3NpUti9MpHqsck7vrLdZYfj9cDqWKue91k8sKRuSYgqBkS1+YDh2HFTIGKCdAl8cr59cS4Nil703bR1v2Do+vY6+HSAFd59Z7n3+WDWOnMWE7o8QtrhvGlVI5IILQ88BIQ6SK0T8NAx4732urrK6u4sCBA1heXt7R4xiGYew25TxWdfwkCXnYJiGHdsyPOHzyXCKC66lQK8M3kjOr83Nim4YsycZp3E9KgyrHBBGJiM0CVx1dEKj8bJEZTTVKI3Uoj6rBmLsJQEKZqA9K7u74TNrOe0EOIQZ4EheU4igLvLY0WWbmeqrQxJHUEmZh7pJjXOdwKY9KpO0ViD8uBPCk9eq82vYtj50txufcjgvPcpAPl+4uA8ilyy7P5y3HCk1iXsTt3XffjcXFRRO2xq4xU8L29OnT+OVf/uWrevz06dM7tSzD2LOMu7Z9B9QpNbNhoI5d11bLlXeaUtSGEPDMM8/g8OHDO39gwzBmgnk838dC1AYOckljfmKeY1vnMtfIQUb6qIjzAJUj2Fjd3LZntj1Y2iSkx8fcV/IAu1bAkk8ure4PDDTF7ogQ+wG8sJ7vy6XIuZ+1cJHZ5fE/4tpS7ostR/lEiAOq7ixccitZ+3Z5bFvKacrEDi69b2WfLzsZQQRuheFWJbx6fL0+LlZLMVtSOrDjonaz09sV/mUvrh7XQ+bzbhk4Nbb+eRj/AwBLS0ud208++SRuuukm67k1doyZErbHjx/H13zN10x8jIgu+rhhGFtTura9CDRRPj9uooz+8RHwru213WnXVmFmHD9+HGtraxiNRhYoZRj7hHk93zMiQmzyDFbtFc39nUVJrM6YjcmBJQJclWL+0geQsZHgKO2NlZCntA1zKlMuFuAA5wEPktE+TsewtJvEACDkBaf9Mlzq0eWlNaCSUmXnfBa1MZUm81iPbc9LOFSpEcsE5YAGFB0IIxGMDrnEl4jgt/hTlFl6lJ0M2s2vj9iBWNziTn9trr9GLiVGbOCczpRtLepJorZMXi5LrfPjW7jSYYKo3sqJbefwbpW2PL+cPHkSp06dwjPPPGOBUsaOMVPCdvyX5ZWy10sdSqfM/rkb24W6thUBIbm2wQOjcvxPBEIEnKM8kmA3ICIcOnQIa2trlpZsGPuIeTzf6zibyOLSBh6lkuM63W7SDFh1QNH22HJbXgyIgI0Ntw5rrccAnBfnND/WFCnKXgQtecBN0EzM4grrMTVoCgBcT49NGNI64AEXq85oHR3PM4oOleuj70YIHNB3Dbzrp9Jkys8BpMdU0ol9xzV1yRFG52+fbq8qwbW9tdF10oSZYsfJLWfHyrEbgCp5fhLDEY2sB3FLx9ZpSvIEZ7Y7h5azu9x5Prlcgjz+/MiAnzDXdrw/eB7m2o5z8OBBPPnkk5aWbOwoMyNsLTFNcNQ9YVsasrEd6Pgf78S1lR5bSd8uXVsZEUS76traKCDD2F/M8/le3djAAaMwRB2HGMUB6ijXtSw5pDJkFbfQ2bTaRhskJCrUQGg4lyK7CkDRGyvPZ4Tk2rqezL31eaSCOrupJDldB5IYLtziHDBVA8EzhlhHlcRqG4RV53WT28DAeyz4DSxWK+i5xTwqiIoeUk+VrJ0qVCQzaDV1WcudkZdb9PIWrmaAilPKQjxQyCFSXSpInXWVgqCkvJmiAznKZdBA1fa6pv2o0Czd2rLMWcVo5CTQx8Rn3s+E8T9E1JYtF3NtL8Y8lCMrNgrI2A1mRtjuZxylC9pfchGtqJVPtndXbBjzh0vJyOyAxhFqx5tc24YI/jJPuNuJiVvDMPY6eS4qt+XIdRhgFDYwDOsYhvUkcCU8KgZkm7YNgRJx2RkD1HTDopQcCFW4vZ3KVk5lx40I15jMReeLnaRRsc5LGbTzSQjXUukT3RDkhuLwRqT1yDrhAFcFhIXzqOMAfb+MyvVE2Op8VzhUroe+X4ZzFfpYSIfVsUK+I9xkrE4R9JQnsKuwlhLpyE1nxq4eS3p+26AmLUkmjoiQ5wPi9jqK8K5qxSy47YtlBlHsOKoqaCWRIs3E5aJ8GZTXIWvvJkaD5XWrSPeuQuCuCzxJxJq4NYzLx4TtlHFE6Reo9MA44iwoJHpegxp2V2gY80d2bYlQOUbPEZrYJiSPSPpsK9591xbYLG6dc7jlllt27fiGYRjXis475RSYFPK4H5nXOmjWMGjWMAobIlxDW5atJcehToI2ubQqesm1/bfkk4iNY2K2QMSoJCZHkuczpf0mHUEk5cuuklJkSlVjzEAYtYnKbdkz57FBgIhkDkBcqNH0z6JyvezGilvr0eNFAEDlep3ZtCXak+tSmW4pbsv3Fki9ylSBKLRlz1lE98GgTnmzOL4tTZRgLYcIjjGLcIYDRQI5EaQhAo6KtarxoH+Psd7XilpHVUeEakmx9tKGJJAduHPsMqlZRXRZjjzP4va+++7DXXfdNe1lGXOCCdsZQkpc9Ldl66RFJitJNrYFrQ7QubZ6ctaftToCVXJtp4GK21OnTuHGG2+cziIMwzCuAi1ZzT20aPtt6zjAMKxjEC5gENbQbMgIn1yGrEFSDbJLq2N7StSdDck1VfeUA9qAqCjPpYayKAYIREVKcio/hm/Fq4paoO3ZBVEqPebOGmXuLiSZWMcKOaCpajiKiNSkWbbyZ6aM8qnTZQRPXsqTXZXmvHbFYCYJXd8ReMh9t44dmBgEGRckvaqSQFyNlTlrSTMAcJTtI4U8j9ZT1Rk1JPtzhaPbpl7r/R4VQOiI2vGQqXIElN7W5zuxMPJ+dBTQJHE7T6i4ve+++3DzzTdPeznGHGHCdgYoy5Bd8YlgeTGM7aDstfUpmVIn7DVRbjdOwjR3a67tOKurq7jpppvgvb/0xoZhGDNELkeGBklFRDACBzSxzmXJoWaEkSQey0Wu5/sC535bdQpVoGqLcmyktzbUIjrJAxSoELUspcWBUq2yjn8T59Z5pJTj1pFl1/2DQ3pDx0qgCR0/lVMfL8cUaOULSzjtI3CDEEdZ5Ds4BBfgoowK0rJdNyYKnatQUW/i7NhJRI6IxBMDOCMYTvt10bqo6payEzFJzoF05JB0BKe5u0UyMgd41w190u0nMb5d+3Mi4jig2VLczisrKyt4yUteYud6Y1sxYTtDlL++uHBqdXydlSMb24F+kFIR0PdAYGAUNyckNyD0p3S+KU9058+fx/nz53H06NHpLMYwDOMyELHChajVebVSjtyklOSydzaMGM2Q0WwA9YARR2mET9Kh5JBVpI7l0euxAeKIERrZhmLrviKN3yFHcBWDmQBKc2gdw3lxc6EzbSHOLDlkIU2unZBDKZ/BQc4V5AnEUt5cjiAi1/aWaq+tvjchhU/VYSjbxrYPNzumaIOXPHlUEWCf7p+UIEzFDN0J4rfsc9WgqTx2KTcpIwVkOURuwKgQOaTnSImyzsxV4S3lxZRTri/2MzHOeBhVYBG1mhh9OTNsu/vYu+K3PNfXdY2TJ0/iuc99rvXcGleNCdsZYZJgVTFrjq2x3VROBK2PtMm1bZykJntM/8OUuq5x3333IUb5g9ECpQzDmGViFrVJzKZL4AYxpnE/ORxK+libAVCvM5qB9tam0T1j82czqVw2NiJqc0pxBFQRx1yazO0YH6IUEgVAe3QdRNxGTjNvkzh1mu6LVK4sa4lgGSUkj8r/fLokLeIKUavjfwCIa80j+NiKGXVLpZS3DZIiSBkzeYeKN7uiKjKzqKUJAjb1Om/poiY33YEQSd31mO5vy4bLtWYhXgrc9D131B0JNEnUlseW90QCrGS0EUHPxhdzaseF9Lz0395///1YW1vDcDi0QCnjqrGfmhlFBe2kAAXDuBa0tFhCpETkliXwdWTUgRHSOKB4jfMmr4Ver5fF7KlTp3Dy5MmprcUwDONiqBMYU9lxE0cYxSHqMESINQIHxBi65b+RC9eWUW+IexuGkPuKS6g5lSxzDnFCGvfDQe4LtTwv6KVmhAEQhvoczscGWuc35JLolMqcEprzr38nF+ekR9dVyIFTrqI2oTkil2CP980yR8TYpLLsVJYcBjkluo5DjMJQ3jsWhztymBg2BbTObhm8NC4Itd8ZUGd66+/dOJFjEuO1XIoe4ZiCwUJUVz50emm3ErWlKM0hYylBWyr15ncU1qU4duwYvPc5LXmex4IZO4c5tjOIilqXx/y0I38MYzvYKkSqYcAVrm1vBj5XsVFAhmHsFXLJLY/QcI06DjBKl4bFuS21C5fubQPEWgRracARAXDitpKT4CegEMdcuLZpNi16yU0lAF5m12YhzHmxuT9WjkPZjY1oP/AsnWMpRyZwWgR5WRfQiuXoVOw5gCQcKqCBUzGo/at5p6nXNfWzxlT2CydlutqzPO5kSrCS3xSwJJNrRTC67OJKuJMct52Pe7Hvo24zXvZL5MCRUTkgwiGwh+MGjiUQq1zL+Cgj3ae6vADgU+lxJHFttwqMmnfRa6OAjO3AflpmDBWxVoZs7CQSIoUUIkXwmoTJ7fifJnbnKU+T1dVVHDt2DIA5t4ZhzCbqVOocW3EjZczPKGygDoMk1AAwZ0G5CW7Frl44cBaxnU3Lbeuu6zouYjcdRkOfggpsbp+TNJSI6rbf1vl021Mraqk9hq5FnU3tLQ6p37h0M3nCexDRlgPn0ClukqspHxpo8FJMPc0x7VMFo+y7HbmUS8GToN70fbuIkxuzCG0Kl3b8a0jH484sYxXFsRDHk8R0Z83JtVXhW+5vP6Di1pxb42oxYTujlGJiy5OfYVwD42nc+ssgh0jxbJQjK+Pi9plnnpnyigzDMJQU6sQ6v7Ytt1XHto5D1HHQzq8NKRAqJRSLaBSntS1V7jqzQHu/liHHPIaHs+OrQjij7q2apKzly10BHeuxEmS0z9NgKSounXmxEZ3+YRW3Kiql/5iLNGLKPbL63rXlyk1b8hu1FLgoAS6EZowhC14t6w0c8iUWwjiC2+MU10tRLG9X1xmOxVziVkRzFup6bDlmK+CjOs4TxKmjyUnP48nanWNOELjz0F9bMi5uH3nkkWkvydhDWCnyDDILDpmxP8jlyKnPlkhGRDSRxLWdkXJkRcuSL1y4YHNuDcOYKbLzpg4fGE2sO0FSsWHEkYRGxSL4qSMYXSFc01cgiVVqP4XM4riYZysjf1ohTID0xhbhTkh9tfpVRwuRB5jlHAAiuCBpypqWzKErsImKtY0Jb1Balw9w7DoCmIry4PGSW059reAApohRqFBRHy54GceTQqMceQldSsFJLlZg4jwf17uqFYFUgcZ6XrOohfbfui3FZ9doSIJzLA1Z1tGWP0cOxWsLKRgKnXAsPXYZNhURc+k2sculz50gx/z+l7N951PcPvLII9Z6ZFwRJmxnkHKeLVD0oRjGNqIzbR1pkBTnsT9Ncm2bSIh++unIJaurq1ngGoZhzApaOtuWoDZZqGmglI74CUMJjZJAqFbgqosaRqk0GEns6ozYwHkET+uQcsf9lVJnpN4m6vwNoW4qoA4v8lxcIsBVDN8XxSqjfMT5dQ3a5lsgB0mNF/O0qcpIYp1SmrAI1Mr14KmCJ99JMpb3L+ayYhWNDY8wjOuyXg7i8FKVxvOENCbIg6hBpApMfTgXwTG26clUiEeOiORSabF8j7JARE88WI4g6qYhIzm0sl3MQllLz7WnmHQ8ENAp/9bkZ0YS35g885Y5IqReYpcc4a3m227VizsvrKys4MUvfvG0l2HsMUzYzhjqmgE8M0LCmG9U1PokdAOQXVudaxuJEJlzovIs8fDDD6OqKvtU1zCMqSFtqSJ+cvlrLpVtr0sCsqQeh6E6tym9eASEGggD7vTJggDnOYlJ7W9Nxx0rG9brMcjz4LRcmRBrIPjk7nJbdhx1/q0HfCARytl9JbiRiGn99a+9tey57cGltC7Xjv5xnlC5Pirqo+8W0PcL6LlFVNRD5fp5zTITNiIS4NghQmbIBm5AsS1VbngERxUcHCrXF9eWKgQn9wVqEF1EhX52bx2Sc0utW1qK2sDijMJBvk/UIFIDx5vd5PY9bsuPVYzH9KEGpZm344iLmwKsHOALsd0R9xNG/USW+bYqbmV/xQ8eNOxrfkUuADzzzDN46qmncMcdd1iglLElJmxnFP0na+LW2EnK/trKSVlyDQmOIgJ6AVjws1WOXHL+/Hk89dRT+baJW8MwpoWGRkkqcp2DkxpOo23iIAlaGe3TDLkNfaq7DqqWCGeh2shs2ZhcVVch/4GQx/ekr7FJajiFVAVPcCOgqQrBGtL+YyFK1eUFASMAkPWpqJXwKAJ5Ob6UN6ck5V6ao6vbOuQ5tr0kaPt+GQt+CZXrd3tYEbMrCqjgawOmAjdAAAIFOKrhyCNwg8r1pTw5NPBUwbkqO8pMEUwVKgcwqhwkpSXA2gudy5Ej0FANxyN4rkDsRPCiDXwiUGcEY4gNvK9S2bkDIuCoW8qsaNKzT8caF7c6HxfAJkHtJghfdbTVjRaBPb/iNoSARx99FE3TWFqycVFM2M4gmooMWL+tsfO41GPrY1uarOncgTGT5cjKddddh2PHjtkoIMMwpk4ZGBRigybWqIPMZq3DEHVdy0zaQVmCXLqjE3aqI3RYjiBOrXwa6cBFKrK4r3AAWFzX6AEXCCARojQkcPqjQkuXgSRYiaVazKWe2yaJ5sA5OIocwfWSqM49o0X77JjOULHmqULleqion8qQq5znMCktuAxxyq4oRYAbsIo6BxGeEfBpjA/SqDpEgF0rWCfNuRWhGzrHlcCqGp5Gaf0+u8Wbvy1SjqyuMjndr5tYYpzf9CT4xZkm+DEhqj3EWj6+laurj0l/bX5gbsWt9x533HGHjQIyLokJ2xlDhayN+jF2A5dKjCmVIXsieAc0OQ1ZRzIAszpH2ebcGoYxfTgn8jaxzi6tpCEPMIzruQQ51qlUuJxnO5ZCTB6gFOSURagKYC/P5RTQJD27qXeWgFgxXA/wlbQ1xYYQRgB5Boex3+PdXKeiP7cNt9JyY+fFzaV0XBHR7X2y+MnvjvTTUhZoIYaxsT3qooY85oYmiETtKy2TgwOKMT4RiM5lVxQAKHbdUAfaFB4VGKA4Sr2t8iI8VeDUC9wV3px6btMaKJUhk4NLzmzZthOZi5E+BKaY+njbftn229Fe175cfd3leynr0D7f9NwU5jWv4tbm3BqXg/00zBBy3uJN4rYc9zMLY1eM+SKXIms6MlpnNnI7y3ZWxv5MwubcGoYxbRquMQpDjMIgObUyv3YQ1jBo1nJgFIf2OXlmbBKUXMyeVW2k433aEUGlUyt9uo2WN6+LIxxHQGg471t6alO5s17SL3gVzjGFUCFd18disc0l0dcQ2xmw7UzX0BH7dZAxSJIYXWMUh2h41I7IQXcEj7wnhdhDW04s43+C5hWn97Ydl5NHA6VRQFkUx3YebRNr1HGAURxiGDbyukKs02ifzee/i82ondSjG4s5tbq+/LVYe7n/8evj44PaSoH5n/dqc26NS2GO7YxQurMqZuMMBvUY80nutaXNoraJe6N6oHRun3jiCRw6dAhLS0tTXpVhGPsBZkYdkmCLAwzDOoZxHcOwjlFYxyhsIKSe2ixmkRzXOiUkj9qS4twDq6KzbitnnApcEvEaGyCOtLcWgCPEasy5iAyOhKC/y7UN1zMoArEhuAqInuGZ4LT02UkiM8pxQfk1F729QbZnamuTo0uzfKmPYdgAADRutNkBZR250x2346kCOYdIEcQR2LKkuIFHhUgRPr887uw/cABxA0f9/LxSYIKlL3ecysUcJNXph9X0ruIYk54//hpLtzmm3uLAjWQgpxArhktJyOomj5ceu4n71dc1z64tsNm5feqpp2xSgpExYWsYxkQigBAZkSm7trPO6uoqiAhVVZmoNQxjV2l4hKE6tGENG/X5wq0Vx1TnvTK3YVFhlBKSa3FaVdDG5MjGWrYDIGFQjgAnicSxbvt00/jZNrwplRADqfQ5ib2y8CagLDWGiNsGqBakbNZVgN/iL0V1jcNIRLPryTGIJTU5RICrGsCFlBzcSKoxuY67OEmAERzYxc54ISkjbkcglgFKUeKbOkFPsppCvI7tX3euLmnumWUHiqP8vHakkEtjhia7sbovQpQEZHWOi+PI2dTn+0OaveRIZvNqT3C7xqI8mdrS43kVrZeLitszZ86YqDU6mLCdccad3FkL7zHmBy1JBuRnLURGAxn5E3nv/OAdPny4c7tpGlSV/aozDGPnYLAERMUBRmEDw2Yti9ph2Mhpx1yU/+YS4oEkJMv4n1bUcmCEIjEZADxLGBQlZ1SFssygTQnFVbr41A+bSplD4RLr88pXQAT4PlA1ACKhghyHPWehCCRRHiQmKUTRW5LFkAKPUl8wEYkwr2rU1VmMqu6YH0VH+JTzdokcPFcIFNBLwtDBgX0lzmbhjkYWUapjfTqvSsfxUNzkbjq4iR/YynOa1Mcc4ahCpHJUz+bzSZ5/q+XTBETuurPMZV9ucomz+BWxz47h4bOglrm1aR4vu46oHxe3XDQWz3tKMiDidmVlJd+Wn0G2ntt9jv21N6NEBvylNzOMa8al4KgSdWgbllLkECXiYq99uDIajXDPPffg0KFDFihlGMaOEpPjV/aRjsI66jhoS4xT/2qeXTtkhIGI2kbn16ZtpX9WSpS1dBhJbDZgOJ9SjjmFTUEEre+L09rOumUElrh7dVlzGbHsMovjoMFWAJBG+eiFkzDnQGCC9Og6EbCyL1kbSdVwd20eCL0arld3SprJSfqwd1U3RCkJyYZG0kfrRcg5rlLAU7mTthdXU4UB7fFtk5c9VfApRdiBwEhiVXeTwppU+Mq8W4CoScnOQIwE53TubnKJEZO4phxkVa5xvMRa1yYTCFJQFicRG2VMkYwG8uBUBu3gAKrgkqifVJZc7nc/jAAqYWbcf//9CCFYoNQ+x4TtjKF9jXodaEWGYew08mmw/LBFAI4ZTSpFZs5/We0Zzp8/j9FoZGnJhmHsCm2QkQQS5ZRfLrdJZcZ1KjXWcuOi7Dg2ImjFsRVBqnNjo0+fMBY7lXE8gOtRdmv1WOXMWg2F2rxuEbbMBCJGUwG+B8Q+4HvUBknJ1lJ67CElzC6dG9SRBrWjjJJIJp/2tZCcZYd8OmEfEChs6uF15LPD68ihin0ZweNcZ6yOFP12X5T22AZOiclpLFDlemAW1e+T8ndF6a+WHOv3MrYNxEl8VqmsOsIVzq53FQLrCKYI7ypw0Qc8iU64FmK7PYtLLG6xjhyqpMSZIIIXY/NsgUKQ7z9xOxwOceHCBYQQLC15n2PCdobolB1DHFvLejN2Cx35UzlJSK7RBpk1kTrJyG6PBJvddNNNebC7iVvDMHYSndHqk+OmQklcQIzN1Ulfyq8qeENX8Ia6fZpLj1NKK87tl9QNd9KAqlyGXIRTAalMOZUta1+u7kvXktONI0AhjfVJ9zvPAMs+OOVFtS4wF/3Bcg5xXkYXIX14msUtCoe4fG9IBC9XA7jU21rHQS5bdq7KjiinMubyAwWPJEBTD2vjAM8jNLEPl74/DpS/T3pdv3fMaYwQdxeXE4lJUp4zEW3ZMFz+463sxZ1UOiyJyPpD0M65FaFbusll+bK83k3zbOWGLIexSdxOWsO8sLi4aKOADAAmbGeOVjzsTYfM2Jtof60jwCeBK2VS3ctexObcGoax0xAIleuh5xfRc4tYcMtY8Mvo+2X03CJcVcN5Tn2vLKJShWUq1YUKTNlhPv3nubFciM4gCcS5i8QXQrSRvx+0FDnWUuYss25Tr26vdU99P5Ucp/m52qsL6LGASGmfIZUZpxm5DiRzdXVzLXXOjnQqwfYial2FFDJVvE4Uxyo+zY+Q2bs6sifEgEAjNOTgYswi1LHMxfUkI3sAIKQS41wCHIFADRoa5Q8bkLYpRW0uTabk0BbisSQLX30ZJP26Xuf0chChywEEwqSwKRWw8tJjFqGgtm94fEavOMMi3NsdFaFSxfs6Lm6pCO2aR4Frc24NwITtTMLMcyEojL1FR9zqpHekD1nSaXevYuLWMIydpqI+em4Bi9UKlnrXYam6DsuVfF1bPN+GOvVIynwXAN8Afgj4USo5DuriplJjlt/FuR02je1hBiiVBAOt+I1IwU0+lQoDeRxQGJbuL8MlQesXAL8g26vA1cdEKEsZM6WABfKATyXHIIaLbdkzxbbUWsJ8ijeI00a6/jK9WZ/vW3HrvFYSqVhs0PAIFF1boswOnB4fBhHAVeyLSHWVlBan8CVOZ7LAARRHcCTiOJIrxgTFjogtBWkeMaT9tZzSmMdKmsdhcPqcwm0SuOIcpxTlsUqoci269sgRnlxKgdb1pDeZYicxWXt/cy6GbkaT+3PnARO3hglbwzAy48FQUS/a772HP2Qpxe2ZM2dw5MgReG8RbYZhXDsiwCpUro++X8KiX8FSErWL1Qr6/R5GSyO4NUa1qAFO0osaFliEbYMsZJ2HjPWBpA1n8ZJEIznIbNmiBJkZ4CbdF5PAdSR9teVaXVt+7CuCXyD0lgm+r8KWcmkys6Q368GJkuMaCeQYvq+iVkKlQtQ1tv21UPFKGp4FuFyq2x6zs8YU5OSSi6rzX0MMIIxyCbEKzIZlkU0cgWgDFfWx4JcAv4ieq/LIHllrzL23uR82lRerY1uOCCpnxJY9u3BVx8cdn3XbvpaLfygsa+u+7nGHOH8H0jq3ehxALkvu9tzuH2FXitv19XUMBgMsLy9Pe1nGLmHCdsbYy8LB2PuMVwrMW8XA6uoqqqrCddddZ6LWMIxtxbsKFfWTc7uIxWpFLv4AFv0BDBafRW9Zg5XEfY0N0AwpC1vtQ5WZt/I4FY4bJ+EIjDmeeVZbKmNNApRcCpLqMTwTyKce24rgem2fbbUAVEtSKgwUoVOpnFj7YMkBPkg5dQyUe4KRwqL0uZ2AENeuL/cSR4bT8UBEuQxYy4vlaS4/pqKxnEsrAs/n4ChxiOs8A9eROLsOrrMPJYc2xRTWhAgq+ma1D1fLg5s4yvtmVKgiZPxQng8s23VCnbZ0cVtxWorh0tUtU5oJlB/Lz5vwYUDez0V6e/cDKm6Z2UTtPsOErWEYANpEbp1hO6/BZYcOHercXl9ftxOfYRjbhkvuoCcRuj0v5cnV0piwDUAYAr7P8H0pF+YoohGcHFx0g6XGwpCzqCWtXFbx69qSXvKA9+KwxkbKgDU0ql10KpPuUy5rbiIjRu4kM5NPjnKPOgFXbYJUWxpLlIQzpetFWBQl0QySPlpQm1pcitAysVjPSpE5hS7FJPbE0dZSXXmPVOxqjyx1RCMzIyCJWA55pE8rGp0EUGU7XAVvQAwN2PVBzoGS2CVIL4+O7dF9aGlw+VZHZundLe4sRW3ndSdRm/uJqRj/g27vb/c5Xee4fF3l/fNMOeMWAAaDAfr9vpUlzzkmbGeQcsyPYewGpaiVuX/tY5UGSmFvzbC9HJ566ik8/PDDOHLkiPXcGoaxbWSB4ir03WLuvW0W19LsWiD00tzZnpQBx0UATsQuM4NSCbD01aYda0VyLI91sYUgu7AqNDmw9Mum0mCOyL29WhqsYVWxLmbpAnAVI3pxlGMt9xNJuTMVopuSYNW+0ayjogZYaY5DWny/llJu9Np0Yjh454s+05hT+aXHNM2v5XJcjwhd56rsaMrQHhW+TU4uLmfLEjkwcQqW2vynsbq7sg4H4gbEIyAmsVikIGuwk0eVXp7b5KJu/jZtLWqzmNXS7CTmJzm0lxK05WP7iY2NDdx77705PdnE7fxiwnZGUWFhycjGbsHMCJHRRKDhNlGxvMwbMcpfIxYoZRjGtdIRP3m+qIMjn9KSF+B6ayIqHSe3EvBpvmt2a0Hi5uZSYiC69OkjpRLlQKAgt50bE7fcBkwpmnLsAETWtCZk8aqjeWIFUOqR7czZTe4xJSc4RnF/4whoUk8wEXX6aTvjh9LaQ0z3y6eo4Ah4ll7ghkaoXB9eS3rzyCRxKwM3MvZGS5E5IiK2YlbFITlU1O+EPTVRlDmTpAOjeG8AEaEODkhzbH0xEiiObdwGUDXJtXXpjZVydP2+57Wlv+EcVLi2Y4aAVpiOi9r8vcNmUauUz823x/pv95NLuxUxRjCzBUrtA0zYzhAX62Wcpz5HY7aIRQp3w8AoplJkTm6tI1SuFLjzo3AtLdkwjO0mFqFDKpLa+bbdbZ2n5NoCiBIU5QJLqbCTPlkdkaPiUgWh3BYhrAnIHLV0WcKcOHASzOnx4m8JVgHbAM1AjqNlyhx0VA+Pj3Jt+28bIDhZc/SAczrGiJBNTy2PDt2E5EgEaoAqpSuTB8gFNC6N4+EqlTC3YtWjyiFRDBGrVdImFbVOrytKmuX7EbKwDWkboA11cillOFLbF6thUx1Xtyz7BWUHOLIDs5c5s7ottSFQVHwtRe2k8uPWeaXOWi7m1I4HTl3MGd6vWFry/sGE7YyhKbRSDprKaEzUGjuMitomiVp1bIF2tq2kfk53nTuBiVvDMHaCste2G1oEKWEVgxCuD/gAcCC4ivPYHZ1z6/tADNrAquXDnL5qc63sO4aiHRTiqpJHW8KcQqsAAEStQB0CIzCqAPgkcCXMCsk9RXc0T2zDpQCWUao9gnMa4kRJZIvAjqnvN/dYOZb5s07WJyOFCJEbBA7whWAsi9Y0GbkMX6ocELl1Wb3zHZGoScZ1HEhIVHJt1eGNHEEUOyJWu2JlTm5sLW8g3z8eRDWOilu9Xj5vvI9Yy4715wZoy5N9ErVO5++OreFyRO1+dmsVE7f7AxO2M0L5K3FeE2mN2UT7a5sI1JExinIdEFFbqVM71VXuLCZuDcPYLhxJb205qkbLR7Nr6aT81lUMHwixB7gew2mgUipTdlVydPuiMGPDOUxKEpIluCm2OUh5vI9L6cXkWrGrzwVEqMYsbFWMMmIP8ks/iWfnpY1Vy6KzO6wCNiTBq5/MFxow9++Wb5CMvwWThmipuywiVMStuLbl2J3ADUIMqOMgz3EN3IjIJYeKe8UhumcsKWN2smaKMgeXusKUKSZRHRGJ4Thmt1j3OS4QSzdWQ6S05Lj8eSjXlBOgXZV7aMvHy9JhFcGOfC5zHt93+TwTsBfHxO38Y8J2xpj3cSvGbKFlyCFyFrV15Dy+QE6U89tjW1KKW8MwjKuhdNlyMnIqi3VUFeKQ4HqchZ9z6DijcCJoXcXi6DLl3lkZtSPHi404sKpnYpNShwkAM4KXkCjXa5OTgXQ9lQFHDZPKoU4AOdmn7teRJCKTy5uUL1ouaaxPKeK1X7fE+e4O8oje5PBGjjnlOMQAcg3AQOCAhkdoYp1d2Mr18vvuqYLjCp6rPOtVhWnuySUp85XrER4uVcjJjFqXhHVkB1CVxe2kcUHyvrSuvHdV/j67QuhO+vnQ55W39XH5/rQjj1TUThTWJmqvmFLcGvOHCdsZQj79bPsdORXbGMZOomXIdWTUQcKjIkv5cZmIvB9YXV3F8vIyDhw4MO2lGIaxR1GRVbm+zFEtemy993D9Bn4huaV1CpJKglfG7hB8n1PZMKWQJ7lNdVvCXCYk6wihGEQsi7bU3tXUx6qlxE73QXnObdZEUdxTSonJsibp4S110/h15+U54z3E4xW6lEqVxwXfhEre7KI2Uu+MwA2aOEritu21BcQFrcOgIzx7zomoh0McW1dEBHWSjiHl4Rjl0nFyDpFcNqD1+9p5Pcmdl++zb0XtRUQmJbEqqc9VFrcA2jE+qZe27fetNolqPT5gpcZXysrKCu6++24sLCyYWztnmLCdAcYdWQYjsPTYlgLXnFtjO4nMufx4GBiDAAyD/OwB0rfU84S+RxEeNf8ftJSiNsaIZ599FjfddNMUV2QYxl5CRsZ4VNRH5XpZ4Fauh75fxKi3Bt8DQgU4z1kQkif4ngYttT2qMQAu9co6L66tQzsCSAOjYiPjd/Kc2kaSjYOT2l9XEainvbta4izHdFUrdLWdNLu1SdRO0k6tw4wkoosH46bwYVkbtWtWx1pDpgANRdKMEalv1rAodWtjUZ7suEIdB1nkafkyEcGzjNyRIKixdGOOOYwKAJyrgIiUzOwROI3umdBfC0gfbjnOST/EaN3XIigqpSG77NaKwzuedLx53E83ZGocE7RXz9LSUuf2008/jRtvvNGE7h7HhO0MoXNErQTZ2A3KFORhYAybtr+2ckA/XXqOxLWdf027ifvvvx/nzp3DYDCwnlvDMLaEWURYntuaSk0r10ffLaDnFtD3y+i5BfiFNbhB69BK+jHBq9vq2rE/YMhIHeI28RhJhCa9FWsGQgqOKubd6oza2LCU/1ZJ1Ka5ub5P8AsyR1dLlTN63bVzaMc/1yRqnd4sztPIn1xerH/H6JxXj9YjZQnEIpd6hMdc24iIyKNURsy5n3bzey9jf9TFlee1JcQOFTzkw4byOQGxsz8XI5xzUoqMHlzRdysCsz2mT2OBKurDux56fhEV9XIfrLyFLodUVa6XnPt+Li12qVx9KzFb7kO+FSa4doonnngCJ06cwFNPPWU9t3scE7YzRiloI8Pm2Bo7QnZrA2OjYazVjI0gfbaAlB8veLn03fz3127FDTfcgHPnzlmglGEYl0B+d0bmTuJuReLY9vwi+m4BC34Zveo8ml4t/bNVEpqRRSh6IKQAKWZGGKbrgREbTiXH3LqrDtBQqXHdk28Xbqjer+6qG3da07adX/laYpz6Z9X53CR0c49t2lVKf2ZWwQ4gJCc2b0AIxPA9IC5JMnTwDSi2vbGloM3zgansZa7S0mX8Th1kmyr20MQaPRdBVIz/SY5u5IA4JpTHb0/CJSe2oj56Xr6nC34JFYlj29mW2m11DFFbnu439e5OSjg2QbvzHDhwAN57C5SaA0zYzgi5tzbdbtP496miMHYMFbXDwFhr0qWWcuTI4tYuVYSlSoStd7RvypDHsbRkwzCuFg0uclRlx3bRr2DRH0C99CxC3SYKu56MzvF9oBnK8+NIwqOAto821pKK7H1bIqzTaCiFOBG1pcUa7KQjgmIguDSmx/k0C3c8NRnJ2aViv6n3F5ASW/1jRZKZ29fskriFlhpz4TSXU3MiZAQQRKSHRtYUG6D2QwSScChGlPApHTuUxHXug01lvGUScS5JTkFUep9zUvYbERFjI98fbtq1Uxv+1BnHg7KE2OfScu966LtFLPgl9N1yZ3Zu+TOQU42L8uOKemmf1UR3VtZjwmq3sLTk+cGE7YxQlh83xfzQkv3qmhnbRylqNwLjfM24MIrYSKFRjoAFT1nYLvp23M9+xcStYRhXgrqKmsTrUuhQ3ydxW61g1B8grmyICO2LiA11ErUkQVFhEXBrBOe7FmqeKau9r0F7dJOgdYBLvbOyTUopToI2pDLnBtK/6xtdt4hf56VEmVJ6ce7J1TE/Re8sF24wecqOLTtJRE65Vy1FgFV+v/JMXE6inhB9yKJZU57JyeuDZxn3Q6VwTOFczqcPEhZFaDqft+mUCvtUwswxTwHw5NFLQrXnFlOvtIjRsl9a+2l12wW/hL5fRuX6nVCssjc2z9gtXNoyEMrc2elj4nY+MGE7ZXTcStRU2sgIse36cCma3v5ZGdeC/pxpWNRawzg/ElG71jBGoRW1K5VclipCbx+7tSXj4tY5h1tuuWXKqzIMY3ZQV09LbVO5q3iOrXPrF7DoV9BUI/BSxMgP4fpAswHQUAQnBxG6vgdUC4xqiVAtEqrF9mgyCigJz4rhegTXAOqA+p700KrOUr3EkREb6W3NPbx1O3YIkHm6IOnDlTtaN3jTqy5n7mqyMwEISO6x9gunEUdl6bOK1cJ5VuHdjgwSh9q5sp+X8mgd7XX16b2tXD+XCPfdIhac9DX3Uhl45frZ4VV0Jq5LSdZtEJTLJcPq0sq+FtH3Cx0R3HMLm+bMtiXp3YTjSYFQJmhng0ni9q677rLqyT2ECdsZYDzEZ1S0eKRxdvvaMTOunsja94Xs1A6DlB6fr3myqO2JqO27Ng3ZaMXtY489huuvv37KqzEMY5YgakuPVaxEcHZvARlJ03eLqP0yFlMar6c1bNAasshstO8W8AtAtUzoDYGFGyQl2fWQS3rJqxgl+F4rikEiarPIVMdUe19j685G7XlN/buIgE/H0ccAdFORS5c2jQ1yvl2TwqyCNwlwJ/t1VSu4nRfHuuzzlX1zu4/CjfZVUQqsgjMJ2Z6TPuaeF6HZd4tYqFZSD6z0wfb84pahTer8Vq6XemK1lFwTrvtZ0JYCV9dQJhsD6AjZfNsc2pmnFLc33nijido9hgnbKaN9tXWUVNpBkOu542KCqI2dup7tZb87c/PCuKANzPmDkyxqaxG1gCQfr/TSJZUgm1u7mdXVVRw6dAhVZb86DcPoQqCOWFK0FLlyveQcLiEUvZ2xF8FxI/XUJhHXI1QLAK+IeOUkMv2CuJr5uQ3aObU+JQ3TWOkwS+BUXqfT0l7qBE5xSlWmRo+pwR/jKVHI+xbnGG2YFVEbNgWWkl8NjirX5VrB6vvyWv2CjCDSZGU9MpH0Hvd6PfT9MvpuoVN6LMJ2EX2/hL5rv0pf8xIWqwO5XFifq8K4DKAqw75UkPoiwbgiEbae2tFNVZFyLOvd3CM7LmbLx4zZZGVlBS95yUvsXL8Hse/YFNHy0BBFYAxTQu2wYfR8d25t7r8dj8Pn7XPUHF27aC7XYoJo9ykFrVYC1KEVtRtFWFRghqNC1Ca3dsFbCfLFKE90a2trOHfunJUlG4aR55MSXC6T9eThnE+idhHBB5nDmhqOYhpj0/RGiAsBYSQiTv4AaAWepDcxfI8Rak7CEwgjznNqVfySK1xRBmKQx7TUOM+cLebTcpRk5RLphaXubSSnODupNDEdWR/r3O/bcCkdFeQqEbTVgvQFu6oVzeXztIR7QUcmUQWXypEr6mWXVh9f8Mvo+6Xiq1xX4Sqlxj594NCmFIvgTfNlU6Jx6+R2XWIVtGVpcff9MyG7lynP9U3T4LHHHsNtt91mPbczjgnbKRNTCXIZ5LMegGWI8GiSmG0YQOAkPtNzi/1czj+zSwngiz1+OaUYugZ1mbcSyVsdx0TU1VGKWUB+XiLSByapp3YYGOsN5IOTsLWoXUojfqwE+dI0TYPjx48jhIAYowVKGca+huDGQoFUNFUkQUPspe8WlaT9SnhRg8gNmlgjLJxH1QDM1Jbf9rrzbn0PaAZAGAFxlMb+eIarKDuj+lyZDyuitkxUVtGZ3VaXFDCLuJU5tyR9u6mUWIRwK2JzKTK1jnAM1DrOmo6MVsRqT60+T9OgqwWgWpLAqsr1OoJQ30cRtgdyT6smEItI7bV9tcmV7afS40W/kq+X/bPlc9X91X12U5Gpk2asJcwqaDUFmcZdehOyc8UDDzyA8+fPY2NjwwKlZhwTtlMiu7UsQuPCKOLMMOLZkdzmHuFAECGy5uUMUjl9bppxm3pQNIXvSoTrpG31n+kkEetos0jdlNpc3O9oi/1cZD0q0C5XUO1XIVx+YFDOPNafJ/0gpC4qAfQyiiJ8CYS+B5Z8W4K85Am9QtTu1/f3cqmqCkePHrW0ZMMwALQ9lC6Lpn6eYRu5ESHro4ybccsILqB2w7Y8udcAy5KUHPoqXovy5NRnWq8TmqHMuHVDEbsxQj7RzLNp2yTkUOuoIO2tlX355JDqKB9XiQh2PUJvCagWpfTZ9fVxdIRtO6dWkot9+rRdBCvlUUG69o61ywxyJMI2hWMt+gNZXFJRxqvCtucWsVit5DJjFag6dkddcS07XqxWsOCkFFm3L4WwGxezrmqTkNMayvCncXfW+mT3D7feequlJe8RTNhOCXVq1xvg7Ijx1CDisfWIx9cCmgiMFhwWfMRiKskZhrbIJQKdMmVHnEOmJlHer2Jzkiht+3l54r4uJaD1+Z60jHULx3ZsXaUI3kr4TjpW5Mnr3Lzt3hZoWwlZ/Ro08bjspw2tUzuKUo7cJPFbOaBfzKpdSfNqe14Co0zUXj42CsgwDEXFj3c+9Wb2UojRCNFL+TEjInJAcHUaTSPlsCLaIjxVGFbrCI2UJYehlugyyLG4s32G30jO7QIQG8o9tFo+3DqpMs4njJDLlwEUYlZEZwxAtUiSQFwBvWVCb5lQLbW9r75qS6NjLWFTDHTKx3KJcxLheVRQEudElJKOKfXOiqhdqq5P7upCCmJqg5fUTS3LisW17ReCtdf22rqF7NQuVm35sm43Xlo87sA68hb8ZHSwUUB7BxO2U0BniW40jDPDiJMXAo6fDfjEsw1OrctstiOhFW3rDbBciWAcpxSTert8bNO2mOyKUhKjFU1+rjyenrvF69K1OGKJ0Cj2UfYC5+2K46qYvpSw1fskrn/r7cr7r9QJvhjbKfgu1c88qcda/34IkfNtFbM6yqf82qQe7gh5/X0n5ccqahd8GxRlTu3VYeLWMAwAuXSV4DolyI2rEbhBiA0iBVRuBKepvHBwrkLFPRAdkCRfv4RRNUTdH2C0MIQbFCKxl8qRF4BmSIgjEZgxULc/Ca2wDY04r+quylrbcuR2FFDbn6uCVscGZWHq0sxZl+bgpmPp2nQMUfm1WiC4vvQOi7Btj6W9s4v+AJZ61+U0Y0f6AYGENjlX5d7ZcsROFrVjY37G+2s11Vg/SNDS4kuN49Hvq2Jidv9i4nZvYMJ2l4nJTdsIjGcGEQ+fD/jI0zU+dLrGXz3d4Mww4oY+Ya2uspjZaDiLkMqJ+POuFaLeEZwKw4sIWxWb2k5TbueYwQREdVvHNJcjeRyQbSZSitd0gLHzbMcZdtRmLZZr36rEGSgd51YIX/Q1jznB1yJur6ZceivGndfx+/NtyBy/Mgyq49LGbvlxiK0zy8VaPYkb20tCdiH10i54QkVtmbuJ2qtjXNwuLCzg5ptvnvKqDMPYbTRASkbDLKLhUb70fEDkBi7qDNYUgMQV2PXzH2TMEQ2PMAwbGNAaBv6CCEcdedMjhCGjGTLiSPpnQzGuJ2oCMovg9cmp5XQSL/t3JTyqvR+pj9f3ZY4tJce17JvlIE4ynJRKA2k00SJJr2wa6SMiXETyQl8EpvakyvFcLh1e9CtYrA50Eo3LHli9Xs6O7Rflxb7ouVVx3C/6cXPZ8gRH1tKLjctlXNw+9NBDeMELXjDtZRkFJmx3GZ0lqqL2Q6dr/MHJIf7ygcfRPPpRAMDwOS9AvO35eZ5o1fk0qBUjvUKUeKKLCjygKwLH+19LkTipP7YVrJdGW31UXKlYcyQz270juCyCLz9gSrcvBfqkdZVrLwVwfp0X0W7jwnJ8LZOc4s3jmC69/1KwTnpOLLZXx7UUsSpyWW+jK2ZlgL2stUou7YKXvtqeo+zcmku7fai4PXv2LA4dOjTl1RiGMQ3aHtteFljB1YhOHNvG9dF3ixi5jewyRmrgvE/uoZxhmlijon7u9RwtrYN8ncbjSGmxOrahbvtxQ83ZvWWWD6nz6B50Q6Ncj1I/blp7uu576tBSdmnLtGKOQDME3CAJW5IAqN4BKSsWQdy6tUvV9VipbsBitYKeW8znaBWiuSfWr2TRqjNnO2FPKdG4vF/va/tk25mz5XNVzEracStkLfTJuFJU3D700ENWnTWDmLDdRWJKOV5rGI+tRfy/p2v87iMDfPQv/g8Gf/5+hCcfhr/pViBGnF19HkJkVCTumibXliWkKk5ccm2Bi5TlltcnlCjr14v10W4lnMcDjAAJthIBRpvSmyeJy/FSW9W7k08xvElUdvfHndcxSQCX4pcnlATHCcfWcuzxDwE2PfcSwlZFaOm8ls8r30MAHSFbliCPC1lAfg6qtC4VrlnIpp8Z/RBERK0J2u1kdXUVhw8ftoHuhrFPIXId17bnFhB8g8ANFjikHtsGi7yCJo7AHOGd3/S7vIl1vh45iuhdWIerhtJXOyJUNSOMknu7ATQVQEMJitJeWmZGTHNwNYXY9wh+sRWw4+N3VNRK4BNyn2wuRY4ioMNAUpTJSdlyb1mEbc8tdvpil3s3YKW6AUvVdVloyvm06syd7Scxu+CXsOBE7PZSz613bXJxRV0nN/c2F/2yLgnhUsxaj6yxXaysrOCTPumT7Fw/g5iw3UVimil6dsQ4uRbwl0/W+Ni992Pjg+/B4MO/C9QD9F/8aQCAG/uE5yx73LLicXTF4WDfZQdX+yI9FY4bLnMkzyX6US/2a/5ynM5WtElAxHjQ1aT9aEktgy8qCjcdi7v77gpveSDwVuueIGYvceyyd7jsR9b3Xf8wGS+/3mrdrfjvBoKNC+1S9F/MlVVRqw6+itrx+9oPA+wX8k5Q/js8efIkAOu5NYz9gMtBQ751bf0iAgcE30x+jqvQD0uIOdUJMteWpMZXzqNRBFr00rPrR2gWRghNQDOUBGVxYIGmB4RhmnMbpfzYhXburO9LyXCeHdsDQGPjhSoRtdmt9ZTDmvQ1NjxCE0cI3MBThb5fxnJ1HfpprI6Kz8VqBUvV9ViqrsOiX+kI2zLsqecWO/2xZcmx9tqWacUXSy8eF7NWWmzsBOW5/uzZszh9+jRe+MIXWs/tlDFhu4uo66ahUSfWIsKp+9E88RD4/FOgA4fgDz8P1bFPwt+4qYcX3Vjhzhs8VpccDvRdDvlRJ06dua0E7dX807qW3tHxMluANrmQE51abBbDQHf9ZWlufh63ruY4hO6BStE8ScBOmg08ibKXuVZhi3YdFzvG+P3j5doXK4PO5dQk/6cuvQPQLz7kyG5sErPyx8N4D7MJ2t3gwoULOUwKMHFrGPsBTxUiNemrBB/1fRmzpCFTIr56bhF1HLThUhzQxBFqqvKsW4Y4tpXrIcQgwpdHqP0Qw2odTT+IC9tn+CEQhjq/tg2Q0hE/rg/0Ui+sjvEhKsqNq67jqonNPb+Q3VGgncMLAI4q6ZOtVrDgWpdV+ozl/kXfjtxR4VnOn5X3qR3Z00tit+8XNonU8RE8AC7aK2ti1thJYox46KGH8mx7C5SaLiZsp4CKjJWK4K6/GdXRuwCOqFafj4VPeQ1e+oJjeMWqCNvbVhyu7zvptR3rp71UT+20EIFGl3RA223LntOLv5DxUt5LBAvnfUem/BxPmwXxeALxRY+/Sbxy9/Gx/WzZ7wyIUCUCMeB1b8z5w4rNolS+lgK2LD/WoKjx55iY3X0OHDiAY8eOWVqyYewTWsewAlFIgmyUy2LZLcp2EMexcn2M3AIartHEEZooX+s4AAUn826zePTo8SICN4hRSptrN8wJyqP+OurlWkRtw2kEEFIZcttfWy2ktOMkalsHtMoiU1OEveuLm1r0tLoxB5TIoSJxpqVHdrlIKZb7tay4nFGbk6OLRON8veidrVyvk1is75+8J5OTi/Uxw9gNnHO44447LC15RjBhu4uoEFmqCIeXPO68weO+u/4WngIQz56GX70dd999Nz77WB9/8+Yennedx419KT/WXtrNgnb2BIumB1+u2G6F4qWfUArHcVf4Us9pxbA8Lz9+2evb3P868VjpsmXKszrt2Or1bu4PLkcj6f5Kx36859fc2dnARgEZxv6AkFxDcOqx9QhJ5HoKYNfP5w1XCDVPVS7rbWKNOg7gQ5XnuAIi0lyoEFkELbuIwAF9bhC5yc8N/QbNUiuQmzhCCAFgSr2ylNKEF3OPqopDDV3S2a8afNXzC53ROt3SX7eppFgFrPbF6vN6biGnElNxTLmvn3tkxdGV61p+XArY/H5bebExQ9gooNnBhO0uooE91/UIR1cc/ubNPQDAvdd/CtYDcHTZ4WWHe1nU3rzYdWplH3tDqFzJOndaCANlefTk511SHGNM2F4kxXjS/sZLitv5vZMpe55LwVqOL+o6suVz98bPyH7BxK1h7C8I7RgZTxXYpZJi1/0YVQWijyrwRq24C0nYQRzRijakXJmbVAbMiKlMWUuCI2Jyc0NHLEdukkMqDmpOCS7cTk9VmikrI3cW/Qr6fnHTvNhSbI4LUO2RdUUZc/k8T/22XLjoky33ZT2yxl7FxO1sYMJ2l+k5wkoFHFl2ACrc0Cd80qEKTQQOLjgcO5DCohYcVqr9M45lWkL4csql87YoR/RoWuXk7bZiXJBeLPDrYmOFTMjuLcbF7cGDB7GysjLlVRmGsZ04cmDEthdUQ6QAwAMUk1ADiWsZXQokrFBxg4Z6CE5m34qYbUt0e34RTRwl17YNmir7TYlc7skNHFCHARoeIcQARy47qc5VWdQC+kFrm1A83hNbzot1hZD1rspl1W0ScX9T2JM+R7fPTnRae+ni6vvY2c7ErLFHGBe3p0+fxpEjR6a9rH2FCduCp59+Gj/4gz+I3/qt38Lp06dx11134c1vfjPe+MY3bsv+HREqx1jwhIN9B0+EgwsOa7Woo6WKcF1PLjpz1ETLZHZSCI/T7b+lifdves74Gias6XLWs7mM2X4e9ioqbp1zJmoNY4rs5Lm+W45cAQ6I7EDsWhFIFTyPcmlu5cSJ7fGizLrlUQpsWsQwLKIfllDHYe6/FaeWO8nAlet3PiiNMWAUBzJSKI3A05E5pWhkjq1j6xYkAMov5xE8Po/X6XdcWC0lzv2vWnY9NnZHXnOVBSyAy+6VNUFr7EVU3D799NN4znOeM+3l7DtM2CbW1tbwOZ/zOfjIRz6CN73pTXjRi16EX//1X8fXfu3X4tSpU/iX//Jfbstx/v/27j06yvrO4/hnBkJIyIWssCQGCAYIF7mFq5J6ge1aARVQy+kCCipYXa1Wu2ph3QqlR2rZtVurHgVdhFJE7alWvKyaCscLVCqgIDWAyDUbxIKQkAghme/+EeZhJvPMZBImlwnv1zkckt99npnMN9/M8zw/r8ejdm3sdJJbc1pyVc39JNTWU7PXqP/UY5KY2DmbY+lPLqNJjuveMih260L88Se3ftXV1WrTpk2Y1gBirTFjvce/yatq7hQsr+Sxmk9RvVaT0FZbVc0NpXxt1dZTpSpvgqpPnz5s8p2+M3LV6RtK1eznWtW25prZytPJrc+qasYM2CvXf/2rX/Xp62+rT7f1r8l/ba+/zK/mE9uaa2QTT+8n678utmbswOtfT5/KHJCg+vfv9Z4+fTpw650zd4GOfK1szTpIZhH/OnToEPQHbDOTmXFachPwWO1NM89RjzzyiH7605/q+eef1w9+8ANJNS/E8ePH691339UXX3yhbt26RT1e165dVVxcrOzsbB04cCCk3udc7xl899xz4bRjAFJVVZV27Nih9PR0rrkFmkjjxfrztWvvTpn5TieTNfvP+hNIO32HY/+pxP4E9kwCWu0koTVl1WduAFXr+yo7JUnOtaz+T1YDE1tnHQE3OnTbBs9fXnN343ZOUtvOmxiQxJ75JNZt79gz44cmsv5yElicq8xMe/fu1cmTJ7nmtgmQ2J7Wv39/lZaWhiSha9as0dixY/XLX/5SDzzwQNTj1ZXYAji3HTlyRLt375YkZWZmktwCTaAxY/2Xe79wPnU1l7stBCa7tZNYf4LrvyFUta/aSXzPtK12PrGV5Hxi69/r1Z/YBt5MKvD7wNOPnTUFXBPsPx3Zfz1tpFOKa59OXLMe931kA+uAc83Jkyf1+eefq7q6WikpKSS3jYxTkSUdO3ZMRUVFmjx5ckjdqFGjJEkfffRRUy8LQCv2D//wD6qqquJuyUATaexY7/V4a/ZK97YNOdVXkrPLnM98aqsEJ9H1WZWTDNd8qlstn7em3F9mTkJc7VwX67/TceCdiv18bvMHqJ14+xPYwFOOOaUYOHuJiYkhd0vu06dPcy+r1SKxlVRcXCwzU/fu3UPqkpOTlZGR4XyyAgCxUvtuySS2QONp7Fhfk/SdvrzIE1weyOs5k1ia+eSzBPk81c6nuU4ye/oTXenMacOBCbP/bsP+61s98rp+Uhzt2v1b8NROZGvmcj+tmAQWqFvtuyWj8ZDYquavuJKUkpLiWp+cnKzy8nLXukcffVSPPvpoSHlxcbEkqaSkRF27do3RSgG0Rj6fT9XV1UpISFBmZqY+/vjj5l4S0Oo0dqzP6d4joKb2VV7h7p1hLq1rfedywVjorThic28Oj8tXAGLDzFRVVUWsb0Qktqp5oQX+71Yf7s6lpaWlTmBz4/P5ItYDQCD+mgs0DmI9gJaCWN84SGwlpaamSpIqKipc6ysqKsLeJTEtLc319MHAAMfphQDqUlJSIp/PF/Z9CMDZIdYDaG7E+sZFYivpggsukMfjcb17cXl5uY4ePRo22N1777269957Q8q5KzKA+vC/Z9Te6xZAbBDrATQ3Yn3j4qp/1Vxv069fP23YsCGkzn+HxNGjRzf1sgAAQIwQ6wGgdSOxPW369Onau3evVq1a5ZSZmRYtWqTExERnI3cAABCfiPUA0HpxKvJpP/7xj7VixQrNmDFDGzduVF5enl588UUVFhZq0aJFysrKau4lAgCAs0CsB4DWi8T2tKSkJK1du1Zz587V8uXLVVZWpj59+mj58uW64YYbmnt5AADgLBHrAaD1IrEN0LlzZy1ZskRLliw567HuvfdelZaWKi0tLQYrA9Da8Z4BNA1iPYDmwntG4/JYuA3dAAAAAACIA9w8CgAAAAAQ10hsAQAAAABxjcQWAAAAABDXSGxj7PDhw/rRj36knJwcJSUlafDgwfqf//mf5l4WgDjw0UcfqU2bNlq7dm1zLwVABMR6AA1FrG883BU5hsrLy3XFFVdo69atuuOOO9S3b1+99NJLuuWWW3Tw4EHNnTu3uZcIoIXauXOnJk+eLJ/P19xLARABsR5AQxHrGxef2MbQ448/rk2bNmn58uX69a9/rR/+8Id65513dOWVV2r+/Pnav39/cy8RQAv08ssva9SoUSopKWnupQCoA7EeQEMQ6xsfiW0MLVu2TNnZ2frBD37glHk8Ht1///2qrKzUypUrm3F1AFqiCRMm6Nprr1VWVpb+5V/+pbmXA6AOxHoA9UWsbxoktjFy7NgxFRUVadSoUSF1/rKPPvqoqZcFoIUrKirSww8/rE2bNikvL6+5lwMgAmI9gIYg1jcNrrGNkeLiYpmZunfvHlKXnJysjIwM7d69uxlWBqAl+9vf/qbExMTmXgaAKBDrATQEsb5p8IltjBw7dkySlJKS4lqfnJys8vLyplwSgDhAoAPiB7EeQEMQ65sGiW2MmFnQ/271bdq0acolAQCAGCLWA0DLRWIbI6mpqZKkiooK1/qKigqlp6c35ZIAAEAMEesBoOUisY2RCy64QB6PRwcOHAipKy8v19GjR9WtW7dmWBkAAIgFYj0AtFwktjGSkpKifv36acOGDSF1/jskjh49uqmXBQAAYoRYDwAtF4ltDE2fPl179+7VqlWrnDIz06JFi5SYmBi05x0AAIg/xHoAaJnY7ieGfvzjH2vFihWaMWOGNm7cqLy8PL344osqLCzUokWLlJWV1dxLBAAAZ4FYDwAtE4ltDCUlJWnt2rWaO3euli9frrKyMvXp00fLly/XDTfc0NzLAwAAZ4lYDwAtk8fC3bMeAAAAAIA4wDW2AAAAAIC4RmILAAAAAIhrJLYAAAAAgLhGYgsAAAAAiGsktgAAAACAuEZiCwAAAACIayS2AAAAAIC4RmILAAAAAIhrJLYAAAAAgLhGYotG5/F46vWvY8eOzb3kVq+iokJ79uxplnl79uyprl27NvncAIDGQ6xveYj1ONe0be4F4NzRu3dv/eM//mOd7VJTU5tgNeeulStX6v7779e8efM0a9asJpvX5/Np9uzZ+vLLL5Wdnd1k8wIAmg6xvmUg1uNcRGKLJjN37lzNnDmzuZdxzps7d66Ki4ubdM5vv/1Ws2bN0sqVK5t0XgBA0yLWtwzEepyLSGwBNKqNGzfqpptu0tatW5t7KQAAoBEQ69EScI0tgEYzZ84cjRgxQlu3btWFF16of//3f2/uJQEAgBgi1qOlILFFizdz5kx5PB499dRT2rNnj26++WZ17dpViYmJ6tq1q2bNmhXx5gjvvfeerr/+emVlZaldu3bq0qWLJk2apHfffde1fY8ePeTxeLRlyxbdfffdysjIUEpKioYNG6YjR4447QoLCzVhwgRlZWUpOTlZQ4YM0RNPPCGfz+fcHMPv4osvlsfj0V133RV2nb/4xS/k8Xg0bty4qI/NJ598oltvvVX9+vVTWlqa8/jGjx+vP/zhD0Ft582bJ4/Ho71790qSZs+eLY/Ho3nz5kWco6ioSMnJyfJ4PJo9e3ZI/aFDh9SlSxd5PB7deuutQXXr169XcnKyHnroIW3cuFG9evWK+rEBAM4dxPrwiPVAlAxoZJJMki1durRB/WfMmGGSbPbs2ZaWlmZer9d69+5t/fv3d8bu3Lmz7du3L6TvAw884LTJyMiwYcOGWWZmplN2//33h/TJyckxSVZQUGCSrH///paTk2MXX3yx02bBggXOGF26dLHhw4dbWlqaSbJrr73WqfNbvHixs85Tp065Ps68vDyTZC+88EJUx+XJJ580r9frPLb8/Hzr27evJSYmOvPPnTvXaf/ss89aQUGBU9+rVy8rKCiwZ599ts65Hn/8cWfMd955J6hu/PjxznEqLy8Pqnv++eft4MGDzvdLly41SZadnR3VYwQAxAdiPbHej1iP5kJii0YXq2AnyS666CLbvn27U7du3TpLTU01SXb33XcH9XvqqadMknXs2NFWrFjhlPt8Plu1apV16NDBJNkzzzwT1M8f7CTZqlWrnPKvv/7azMzefvttk2Rer9cee+wxq66uNjOziooKu/POO52+gcGutLTUkpOTTZKtXr065DGuW7fOCVonTpyo85js2LHDEhISTJL94he/sMrKSqfu8OHDNmXKFJNkCQkJduTIEdfHt2TJkjrnCTRu3DiTZD169LCysjIzM3vsscdMkrVv3962bNlS5xgEOwBonYj1xHo/Yj2aC4ktGl3gm380/9asWRPU3x/s2rVrZyUlJSHj/+hHPzJJNnz4cKfs5MmT1qVLF5Nkf/zjH13X9eSTTzpvvIF/WfUHg0suucS138iRI02S/du//Ztrvf+vmrVPiLjxxhtNkk2ZMiWkzw9/+EOTZHfccYfrmLU9/vjjlpSUZMOGDXOt37dvn7OG9evXB9U1NNgdPHjQOnfubJLsrrvusm3btln79u1Nkj355JNRjUGwA4DWiVhfg1hPrEfz4a7IaDLR7m2Xnp7uWj58+HBlZmaGlPfr10+SdPToUads3bp1+uqrr5SamqqJEye6jjdt2jTdeeedKi4u1qZNmzRy5Mig+u985zshfYqLi/XXv/5VknT77be7jnv33XfrjTfeCCm/+eabtXz5cr366qs6duyY8zhPnjypF154QZJ00003uY5Z2x133KE77rhD3377rWt9cnKy83VFRUVUY9alS5cuWrJkiSZNmqQnnnhCb775pk6cOKFJkyaFPRYAgHMLsZ5YDzQXEls0mbPd2y7cRt9JSUmSpKqqKqfss88+kyRVVlbq0ksvDTtmmzZt5PP5VFRUFBLssrKyQtpv27ZNZqaUlBTl5ua6jjl8+HDX8ssuu0y9evXSF198oZdeesnZMP3VV1/V0aNHNXDgQA0bNizsWt20b99eGzZs0GeffaZdu3Zp165d2rp1q4qKipw2Pp+vXmNGMnHiRM2aNUvPPPOMdu7cqW7duunZZ5+N2fgAgPhGrCfWA82FxBZxo127dhHrzcz5+tixY5Jq/kL64Ycf1jl24F+A/fxBNNDf//53SVJKSkrYsdLS0sLWzZw5Uw8++KB+97vfOcFu2bJlkqL/C67fihUr9POf/1w7d+4MKr/gggt0yy23aMmSJfUaL1oTJ07UM888I0nKyclRx44dG2UeAMC5h1gfjFgPRI/tftAqdejQQZI0bNgwWc215BH/Rbo1v9u4paWlYduUlZWFrZs5c6a8Xq/ef/997du3T19//bXeeustJSQkaPr06VE/vmXLlumGG27Qzp07deWVV+rpp5/Whx9+qCNHjujLL7/UE088EfVY9fHNN984pyJ5vV598MEH+q//+q9GmQsAgEiI9cR6IBCJLVqlPn36SJJ27NgRdNpSIDPTmjVrtHPnTlVWVkY17sCBAyXVXMuya9cu1zaffvpp2P7Z2dm64oorZGZ65ZVXtHr1alVVVWnChAnq3LlzVGuQpIULF0qSbrzxRr355pu69dZbNXr0aGVkZEiSDhw4EPVY9XH77bfrwIEDGjx4sJ577jlJ0oMPPhjxMQMA0BiI9cR6IBCJLVqlSy+9VOnp6SorK9PSpUtd26xcuVJjx45V3759tX///qjGzc3N1eDBgyUp7PUmTz/9dMQxbrnlFknSyy+/rD/96U+S6n9q0u7duyUp7HU6/tOHJIUEe6+35sc+8HSuaKxYsUIvvPCCEhIS9Nxzz+mGG27QxIkTVVlZqWnTpunEiRP1Gg8AgLNBrCfWA4FIbNEqdejQQXPmzJFUc+fCpUuXBt1Y4U9/+pNuu+02SdKUKVPUs2fPqMeeP3++JGnRokVasmSJEzROnTqlefPmadWqVRH7X3PNNerUqZPef/99vf322+rSpYvGjx9fr8fXt29fSTWBtbi42CkvLS3VvHnz9Mtf/tIpq32nRP81Q3v37o16vn379unOO++UJM2ZM0dDhgyRJD311FPKyMjQtm3bnOMNAEBTINYT64EgTbGnEM5tOr3HWu/eva2goCCqf2+88YbT37+33bRp01zH9++XlpOTE1Tu8/ls9uzZzvydOnWyESNG2Pnnn++UFRQU2PHjx4P6RbP32/333++MkZmZaSNHjrSMjAyTZKNGjTJJ1qZNm7D97777bqf/T37ykyiOYrDVq1eb1+t19vwbOHCgDRw40NlrLjc313r27GmS7De/+U1QX/8ee23btrX8/HxbsGBBxLmqq6vtsssuM0k2aNCgoA3izcyWLVtmkszj8VhhYWHEsdjbDgBaJ2J9KGI9sR5Ni8QWjc7/pl6ff0uXLnX6NzTY+b311ls2efJky8zMtLZt21pqaqpddNFF9thjj9nJkydD2ke7qfkrr7xi//RP/2QdO3a0xMREy8/Pt8WLF9sHH3xgkiw1NTVs382bNzuP9bPPPos4TzgbN260SZMmWffu3a1t27aWnp5uI0aMsIULF1pZWZn97Gc/M0n23e9+N6jfoUOH7LrrrrP09HRLSkqyqVOnRpznkUcecYLjpk2bXNtMmDDBCWJHjhwJOxbBDgBaJ2J9KGI9sR5Ny2NWz5PvAUT0+uuv66qrrlLv3r21Y8cO1zarV6/WNddcoxEjRmjDhg1NvEIAAHA2iPVAy8M1tkA9DRgwQBdffLE2bdrkWv/GG29IkoYOHRp2DP++c7Nnz479AgEAwFkh1gPxJ6pPbIcPH66DBw82xXqAFu/w4cM6ceKE2rVrp/POO8+586AklZeXOxvAd+rUSYmJiU7dqVOn5PV6VV5errKyMnm9XmVmZsrj8TT1QwBalczMTH388cfNvYy4R6wHziDWAy1LNLE+qsS2a9euQXdjAwCgpcjOzm60/RzPJcR6AEBLFU2sb1ufAb1er7KyslzrorlQt64UOly1a7lF0aYec/gr6hrHInwXVBJhoPrNEVrYoP5ubZpxjaFtQjuf7eOMxXPh2s6lU8PWWtcDjWaiKB5BVJfR13X86zqIUR6BiM3CVEbzRLrV1+dY1XeOejxnoYc/ijkacpycqiif7/rOUdcPgVvjRp0DjSV8rDeXr9zrQ2rq8dqM3CpC3D0zUQNHr2PsKF7Xda2+XsfQ9S2t7mMYfg3hf+aiHTvy+O5j1OcY1n38IrUKHwOijmcB9VG/xhtwDN2Ga+jbfl1t6/MrRrTjxmStYQ9lA37JirJ9Q36FiqZdg0JXtGPG6LG7VsdorU6zs3idxkq9EtusrCzt239APueHuOYLn8kpc/4P+Nr/obDPaspr93Hq5TJOQJ+gcQLm8ZcF1rvNF9Iu1vOFGS9wvnDHy2TBa2jgfGbm+phrj2dmdT72SM9ptPM16DXi8tgbdT6X8aJ5znxW93xur0nnJAnzOf87Zb4zZYH1Nf9ZSFlQO5/PdeygMf1rCuhT7/l8AeO4rKF2WbPM569zOU5h6+s5X8ixDjOe+Vyek7qeswbOZ2Yhz7fbeEHPictrpNHnqzVe0Fz1fY0g5rKysrR//z5Jkk8+mf/4n35H9Jk5X5v55KtVb+YLaHvmOQ4pCxjbF9g3pMylr9scQePZmXUH1LutNXBu/3whZYF9Xccz1/FcH1+tsWsfz/DHPbDMzpRF6us6nrk+lkhr9Zm5lEX7PLr3rf34go+dRXnc3V6H7s9F5PHOzBf5uAeU1Vqr23NhZqr9VqqA3wOCyvxvbVbzfWB94FuoAvo6iURguPKXBfW14HYWPHfk8c78UhMSrsKs1f9Lj1lgmYLLfGH61jo24foGrtVqzRe0VrcyX4S+tZ+LsH3NfT2Bxz2a5yLq59HCjBe+r+tzEfa41y6z0OMe9fNo7usJ+hmoXWYRj3u0uHkUAAAAACCukdgCAAAAAOIaiS0AAAAAIK6R2AIAAAAA4hqJLQAAAAAgrpHYAgAAAADiGoktAAAAACCukdgCAAAAAOIaiS0AAAAAIK6R2AIAAAAA4hqJLQAAAAAgrpHYAgAAAADiGoktAAAAACCukdgCAAAAAOIaiS0AAAAAIK6R2AIAAAAA4hqJLQAAAAAgrrWtT+OSkhJ179bVtc6i6G91NApX7VpuUbSpxxz+irrGsQjfBZVEGKh+c4QWNqi/W5tmXGNom9DOZ/s4Y/FcuLZz6dSwtdb1QKOZKIpHUNcPXu1xXJdV10GM8ghEbBamMpon0q2+PseqvnPU4zkLPfxRzNGQ4+RURfl813eOun4I3Bo36hxoLCUlJerWrbtLjbl85V4fUlOP12bkVhHi7pmJGjh6HWNH8bqua/X1Ooaub2l1H8Pwawj/Mxft2JHHdx+jPsew7uMXqVX4GBB1PAuoj/o13oBj6DZcQ9/262pbn18xoh03JmsNeygb8EtWlO0b8itUNO0aFLqiHTNGj921OkZrdZqdxes0VuqV2Pp8PhUXFzfOSgAAQLMj1gMA4lFUiW1mZmZjrwMAgAYhRsUGxxEA0FJFE6M8ZvX64BgAAAAAgBaFm0cBAAAAAOIaiS0AAAAAIK6R2AIAAAAA4hqJLQAAAAAgrpHYosXZs2ePPB5P2H+JiYnq3LmzCgoK9PDDD6u0tLRJ1jVv3jx5PB595zvfaZL5Lr/8cnk8Hj344IP16tejRw95PB4988wzTtnatWud41dVVeWUz5w5Ux6PR9OnT3cda9u2bQ1bPAAAERDraxDrgdip1z62QFMbMGCA0tPTg8oqKyt16NAhrVu3TuvWrdPTTz+tP//5z+rVq1czrbL12bFjh+666y4dP35cH3zwQXMvBwDQihHrmwexHq0NiS1atN/+9re6/PLLXevWrl2riRMnat++fZoxY4Y+/PDDpl1cC/XnP/9Zp06dUlZWVp1tFy5cqJ/+9Kchv1CsXLlSb731lgoKChprmQAASCLWNwSxHgjFqciIW5dffrkWLlwoSVq3bp02btzYzCtqGXr27Km+ffuGBDA3WVlZ6tu3b1SBEQCApkasd0esB0KR2CKuTZ482fn6L3/5SzOuBAAANAZiPYBokNgirgX+pbKsrEzSmRsxvPnmm1qwYIG6dOmi5ORkDRgwQEVFRU774uJi3Xffferfv7+Sk5OVkpKiIUOGaP78+Tp69GjEeUtKSnTLLbcoMzNT7du3V79+/fTggw+G7VdVVaXf/e53uvrqq5Wdna327dsrJSVFeXl5uu2227Rjx46I8/31r3/VuHHjlJ6ertTUVF100UV6+umnVV1dHdLW7YYS4dS+oYT/Zh7z58+XJH344YfyeDzq0aOHjh49qqSkJHk8Hv3xj38MO+Z3v/tdeTwePfLII3XODwBAXYj1xHogKga0MLt37zZJJsnWrFkTse0nn3zitF2xYoWZmV122WUmyQoKCkyS9ezZ0/Ly8qxbt25WVVVlZmaFhYWWnp5ukiwhIcGGDBliF154oXm9XpNk3bp1sy1btgTN9dBDDznjZWdnmyTLy8uzQYMGOf169Ohhe/bsCepXUVFhY8aMcdbZo0cPGz58uHXr1s0p69Chg23atCmon/9xXHTRRZaQkGBt27a1/Px8y83Ndfp973vfs5MnTwb1y8nJMUm2ZMkSp2zNmjVOn1OnTjnlM2bMMEk2bdo0MzMrKSmxgoICZ21paWlWUFBg119/vZmZTZ061STZpEmTXJ+P/fv3m9frtTZt2lhxcXHE5w4AcO4i1lvQ4yDWA2ePxBYtTn2C3Y033miSrF27dnbw4EEzOxMkJNkjjzzitD106JCZme3Zs8dSUlJMkl1zzTVOPzOzXbt22cUXX2ySrHv37nb06FGnzh/sJFnnzp3tvffec+q2b99u/fr1M0l2ySWXBK3R369Tp062YcOGoLoNGzZYVlaWSXICil/g4xg5cmRQEH3ttdcsNTXVJNl//Md/BPU7m2BXe80FBQVB5YWFhc7xPnz4sNX28MMPmyQbP358SB0AAH7Eegt5HMR64OxwKjLizrfffqvNmzfr9ttv1/LlyyVJ99xzj7p06RLULicnR/fdd5/zfefOnSXV3B3w+PHjGjBggF566aWgfrm5uXr99deVmZmpffv26be//a3rGn7/+9/rkksucb7Py8vTyy+/rDZt2uj9998Pum1+YWGhvF6vHnroIY0YMSJonBEjRuj222+XJG3dutV1ro4dO+q1115TTk6OUzZhwgT9+te/liT95je/UXl5eZijFVtjx45Vjx49VFlZqRdeeCGk3v983HTTTU2yHgBA60SsJ9YD9UViixZtzJgxIZu2Jycna+jQoXrqqackSbNmzdKCBQtC+o4ePVoejyek/LXXXpMk/eu//qvatWsXUp+RkaGbb75ZkvTKK6+E1Ofl5emf//mfQ8r79OmjSy+9VJL0+uuvO+UffPCBTpw4odtuu831MSYnJ0uSKioqXOunTJniBOpA06dPV1JSkkpLS5ts/zmPx6OZM2dKOhPY/D766CMVFRXpvPPO0zXXXNMk6wEAxD9iPbEeiAX2sUWLVnvTdo/Ho/bt2+u8887ToEGDNGnSJPXv39+1r9tt7cvKylRcXCxJGjZsWNh5/XXbt28PqRs6dGjYfoMGDdKaNWv0+eefB5UnJCTo2LFjWrdunXbs2KEvv/xSO3bs0ObNm/XVV19Jknw+n+uY4eZLTExUXl6ePv30U33++ef63ve+F3ZdsXTTTTfp5z//uf7yl79o586d6t27t6QzwW/q1Kmuv0QAAOCGWE+sB2KBxBYtWqRN2+uSlJQUUlZaWup8HWnvt7S0NEnS8ePHZWZBfw1OTU0N289fF/gX2bKyMs2ZM0fPPfdc0GlE7dq109ChQ5Wfn6///d//rXPMaOdrbN27d9fYsWNVWFioFStWaP78+aqsrNSqVaskcWoSAKB+iPXEeiAWOBUZ55TAwHHs2LGw7b755htJUkpKSsgpTsePHw/bzz9mRkaGUzZx4kQ98cQT8vl8uvfee/Xiiy9q27ZtOn78uNavX69rr7024prrO19T8J++9fvf/15SzelYR44c0eDBg5Wfn9+kawEAIBCxPjaI9Yg3fGKLc0paWprOP/98/d///Z82btyokSNHurb7+OOPJck59SZQ4P54tW3atEmSNHDgQEk1G8mvWbNGUk1AGDNmTEifAwcORFxzuPnKy8ud06f88zWVyZMnKyMjQ7t27dKWLVv08ssvS+IvuACA5kesjw1iPeINn9jinHPVVVdJkp588klVVlaG1H/zzTdatmyZJGncuHEh9Z988ok2b94cUr5p0yatW7dOkpwbKuzevdupd7vOp6KiQs8//7ykmo3d3bz44ouuf8ldvHixKisrlZmZGTZoN5TXW/PWYGau9e3bt9fUqVMlSS+99JJef/11JSQkaNq0aTFdBwAADUGsrxuxHq0NiS3OOQ888IBSU1P12Wef6fvf/74OHTrk1O3evVsTJkzQV199pezsbN1zzz0h/c1M1113nbZs2eKUbd68WZMnT5aZacqUKRo0aJAkqW/fvk6bBQsWBAW0v/3tbxo3bpx27twpKfy1M8XFxbr++uv197//3Sl7/vnnNWfOHEnSz372s5jfwCElJcWZO1wQ9p+i9N///d86cuSIrr76anXq1Cmm6wAAoCGI9XUj1qO1IbHFOSc3N1d/+MMflJaWpldffVVdu3ZVfn6+Bg4cqF69emn9+vXq3r27Xn31Vdc377Fjx+rYsWMaMmSIBgwYoAEDBmjo0KHat2+fCgoKtHjxYqdtfn6+pkyZIkn6z//8T2VlZWnEiBHKzc3VhRdeqPfee8/ZTqCsrCzohhd+1113nd5++21169ZNw4YNU/fu3TV16lSdPHlSd955p7M3Xiz5r53Zu3evevfurdGjR4f8RXfo0KEaPHiw8xdmTk0CALQUxPq6EevR2pDY4px0xRVXaNu2bbrnnnuUm5ur7du3a//+/crPz9fChQv16aefhr31fu/evbVhwwZ9//vfV0lJiXbt2qXBgwfr0Ucf1bvvvhtyB8aVK1dq8eLFGjFihKqrq/Xpp5/q5MmTuvrqq/Xaa6/p7bffdjZkX716dch8119/vd555x2NHDlS27dv19GjRzVmzBi98sorYTeVP1tjxozRokWLlJOTo+LiYu3evdvZqiCQf5+7zMxMXXnllY2yFgAAGoJYHxmxHq2Nx8KdWA8AdfjJT36iRx99VPfdd59+9atfNfdyAABAjBHrES9IbAE0yIkTJ5STk6Ovv/5aRUVFysvLa+4lAQCAGCLWI56w3Q+AqB0+fFhff/21zExz5szRoUOHdNVVVxHoAABoJYj1iFd8YgsgauvXr9fo0aOd75OTk/XJJ5+47gEIAADiD7Ee8YqbRwGIWm5urnr37q2kpCSNGjVKhYWFBDoAAFoRYj3iFZ/YAgAAAADiGp/YAgAAAADiGoktAAAAACCukdgCAAAAAOIaiS0AAAAAIK6R2AIAAAAA4hqJLQAAAAAgrpHYAgAAAADiGoktAAAAACCu/T96zbpVVLTMuwAAAABJRU5ErkJggg==",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAAGTCAYAAAAcBI40AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADHD0lEQVR4nOy9e5Rc2VXf/93n3Kp+STMazaitkUb2jD0P2xmbOHjshAU2YWESMD+IeZlgHsa8zHIcBwivQAIxkGVjYIGBmJgEbExiAmFhG7LAToIDOATHYBL8mBlpHpqRNKPRaPRqdXdV3XvO/v1xzj733NvVLanV3fXo/RmXu7vq1q1T1a0+/a299/dLzMxQFEVRFEVRFEVRlAnFjHoBiqIoiqIoiqIoinI9qLBVFEVRFEVRFEVRJhoVtoqiKIqiKIqiKMpEo8JWURRFURRFURRFmWhU2CqKoiiKoiiKoigTjQpbRVEURVEURVEUZaJRYasoiqIoiqIoiqJMNCpsFUVRFEVRFEVRlIlGha2iKIqiKIqiKIoy0aiwHcLHP/5xWGvxP//n/xz1UhRFURRF2QZ0r1cURZkuVNi2OHbsGF7zmtfAez/qpSiKoiiKsg3oXq8oijJ9qLDN+L3f+z28/OUvx5NPPjnqpSiKoiiKsg3oXq8oijKdqLCNvPrVr8ZXfdVX4dZbb8U//sf/eNTLURRFURRli9G9XlEUZXpRYRt54IEH8G/+zb/BJz/5Sdx9992jXo6iKIqiKFuM7vWKoijTSzHqBYwLn/3sZzEzM3PN9/u5n/s5/NzP/dya68+cOYP5+Xnceeed+Mu//MutWKKiKIqiKNeB7vWKoijTiwrbyGY2OgC4dOkSTp06NfS2ixcvrnuboiiKoig7y3bt9SdPnoT3HsZoI5yiKMqoUGF7ndxwww04fPjwmuuffPJJeO/hvcepU6eGHqMoiqIoyvhzpb2emXHs2DHcddddKm4VRVFGhP72vU6+93u/FydPnlxzufXWW9Mxp0+f1sqtoiiKokwoV7PXX758GceOHdMIIUVRlBGhwnabsdYCUHGrKIqiKNNKURSw1qq4VRRFGSEqbLcZYwyOHDkCIIjb06dPj3hFiqIoiqJsJUSEu+66K4nbhx56aNRLUhRF2XWosN0BFhcXceTIEXQ6Hdx4442jXo6iKIqiKFvMwsIC7rrrLhRFgVtuuWXUy1EURdl1qHnUDrG4uIibb745tSYriqIoijJdLCws4N5779W9XlEUZQRoxXYHyTe6paUlPPHEEyNcjaIoiqIoW02+15dliePHj+vMraIoyg5AzMyjXsQ0ctttt6WYn5MnTzZuK8sSn/70p+G9x8GDBzUKSFEURVEmkI32egB44IEHsLy8jD179mgUkKIoyjajv2FHQKfTSWJW3ZIVRVEUZTo5cuSIuiUriqLsECpsR4QYSgEqbhVFURRlGhFDKRW3iqIo248K2xGi4lZRFEVRphsVt4qiKDuDCtsR0xa3586dG/GKFEVRFEXZStri9vHHHx/1khRFUaYOjfsZAxYXFwEAly9fxk033TTi1SiKoiiKstWIuH388cdx6NChUS9HURRl6lBhOyYsLi4mgasoiqIoyvSxsLCAF7zgBaNehqIoylSirchjyuOPP64zt4qiKIoyxZw/fx5Hjx7VmVtFUZQtQIXtGLK0tISnn35aDaUURVEUZUpxzuHxxx/H0tKSGkopiqJsASpsx5C9e/eqW7KiKIqiTDHWWtx5553qlqwoirJFqLAdUzQKSFEURVGmG40CUhRF2TpU2I4xKm4VRVEUZbpRcasoirI1qLAdc3Jx+9RTT2F1dXXEK1IURVEUZStpi9uzZ8+OekmKoigTh8b9TACLi4sgIhRFgbm5uVEvR1EURVGULUbE7YULFzT+T1EUZROosJ0QDhw40PjaOQdr7YhWoyiKoijKVrOwsICFhYX0NTODmWGMNtgpiqJcCf1NOYEMBgPcf//9OnOrKIqiKFMKM+ORRx7RmVtFUZSrRIXtBLK0tIR+v6+GUoqiKIoypfT7fSwtLamhlKIoylWiwnYCufnmm9UtWVEURVGmmNnZWXVLVhRFuQZU2E4oGgWkKIqiKNONRgEpiqJcPSpsJxgVt4qiKIoy3ai4VRRFuTpU2E44ubi9cOECnHMjXpGiKIqiKFtJLm5XVlbQ6/VGvSRFUZSxQ+N+poDFxUUURYG9e/dqBJCiKIqiTCEibpkZ8/Pzo16OoijK2KHCdkrYv39/4+uVlRXd+BRFURRlisgzbgGg1+uh2+1qzq2iKAq0FXkqeeaZZzTnVlEURVGmmNXVVTz44IM6c6soihJRYTuFyAanhlKKoiiKMp1478HMaiilKIoSUWE7hRw4cEDdkhVFURRlilG3ZEVRlCYqbKcUjQJSFEVRlOlGxa2iKEqNCtspRsWtoiiKokw3Km4VRVECKmynnFzcKoqiKIoyfeTiFgCYecQrUhRF2Xk07mcXsLi4iPn5eezZs2fUS1EURVEUZRtYWFjAPffcg263q5n2iqLsSrRiu0vIRa33Hs8888wIV6MoiqIoylYzNzfXELXPPPOMtiUrirJrUGG7C3n44Ydx/PhxnblVFEVRlCnlqaeewvHjx3XmVlGUXYMK213IjTfeCEANpRRFURRlWtmzZ48aSimKsqtQYbsLUbdkRVEURZlu1C1ZUZTdhgrbXYqKW0VRFEWZblTcKoqym1Bhu4tpi9snn3xyxCtSFEVRFGUrGSZuNQ5IUZRpRIXtLkfErbUWN9xww6iXoyiKoijKFpOL25tuuglENOolKYqibDmaY6tgcXER+/fvR1Hoj4OiKIqiTCMLCwu49957da9XFGVq0YqtAgCNjW5lZUXbkhVFURRlysj3euccHn/8cZ25VRRlatC37ZQGVVXh6NGjcM7Be4/Dhw+PekmKoiiKomwxDz/8MJaWlrC6uoq77roLxmitQ1GUyUZ/iykNiqLAoUOHAKhbsqIoiqJMK4cPH1a3ZEVRpgoVtsoaNApIURRFUaYbjQJSFGXaUGGrDEXFraIoiqJMNypuFUWZJlTYKuvSFrdnz54d8YoURVEURdlK2uL2+PHjo16SoijKplDzKGVDFhcXAQAXL17E/v37R7waRVEURVG2GhG3x48fV9NIRVEmFhW2yhVZXFzEgQMHNNBdURRFUaaUhYUFvPCFL9S9XlGUiUVbkZWrIt/oTp06pTO3iqIoijJl5Hv9pUuXdOZWUZSJQoWtck0sLy/j9OnTaiilKIqiKFOK9x7Hjx9XcasoykShwla5JhYWFtQtWVEURVGmGGMMnve856lbsqIoE4UKW+Wa0SggRVEURZluNApIUZRJQ4WtsilU3CqKoijKdKPiVlGUSUKFrbJp2uJ2eXl5xCtSFEVRFGUraYvbM2fOjHpJiqIoQ9G4H+W6kJxbYwwWFhZGvBpFURRFUbYaEbfPPPMMnvWsZ416OYqiKENRYatcNyJuBeccrLUjWo2iKIqiKFvNwsJC4w1sZgYzwxht/lMUZTzQ30bKllJVFR588EGduVUURVGUKYWZ8dhjj+nMraIoY4UKW2VLuXTpElZXV9VQSlEURVGmlMFggAsXLqihlKIoY4UKW2VL2b9/v7olK4qiKMoUMzMzo27JiqKMHSpslS1Ho4AURVEUZbrRKCBFUcYNFbbKtqDiVlEURVGmGxW3iqKME2MrbC9evIhz585d9fFnzpzB448/vo0rUq6VXNyeO3cOVVWNeEWKoijKuKH7/WSTi9vl5WWsrKyMekmKouxSxk7Y/uqv/iruvvtu7N+/HwcOHMChQ4fwQz/0Q7hw4cKG9/vqr/5qPPe5z92ZRSpXzeLiIm6//Xbcc889KApNl1IURVECut9PDyJun/e852HPnj2jXo6iKLuUsRK2b37zm/HGN74RDz30UMpHO336NN7xjnfgxS9+MT7+8Y9veH9m3qGVKtfCzTffjG63m75eXV0d4WoURVGUUaP7/fSxsLCAG2+8MX09GAy0LVlRlB1lbITtH/zBH+CXf/mXYa3Fj/zIj+ATn/gE/s//+T/45//8n6Pb7eLkyZP4oi/6InzkIx8Z9VKV6+DChQu4//77deZWURRll6L7/fTT7/fx4IMP6sytoig7ytgI23e9610gIvzSL/0SfuInfgKf+7mfi5e+9KX46Z/+aXziE5/A7bffjtXVVbzmNa/Bxz72sVEvV9kkZVmmd+ZV3CqKouw+dL+ffqqqgnNODaUURdlRxkbYfuITn8C+ffvwnd/5nWtuu/fee/Enf/InuOOOO7C6uoqv+IqvwAMPPDCCVSrXy4EDB9QtWVEUZRej+/30o27JiqKMgrERthcuXMDtt9++7u1HjhzBf/tv/w0HDx7EhQsX8OpXvxpnzpzZuQUqW4ZGASmKouxedL/fHai4VRRlpxkbYbtv3z489thjGx7z3Oc+Fx/84AcxNzeH48eP48u//MvViGhCUXGrKIqyO9H9fveg4lZRlJ1kbITtS17yEpw/fx7/6T/9pw2Pu++++/Abv/EbICL81V/9Fb7iK75CM9MmlFzcasatoijK7kD3+91FLm6dcypsFUXZNsZG2H7bt30bmBlvfOMb8Qu/8At46qmn1j32q7/6q/GOd7wDzIw//uM/xn333Ycnn3xyB1erbBWLi4u4++678ZznPGfUS1EURVF2AN3vdx8LCwu4++67cffdd2umvaIo28bYCNuv+7qvw9d+7dfi8uXL+N7v/V4cOnQI/+7f/bt1j/+e7/kevP3tbwcz44EHHsAjjzyyg6tVtpK9e/emz5kZ586dG+FqFEVRlO1E9/vdyfz8fEPUXrhwQau3iqJsKWMjbAHg/e9/P37iJ34C+/fvBzPj0KFDGx7//d///fid3/kd3HTTTTu0QmW7efTRR/Hoo4/qzK2iKMoUo/v97ubs2bN4+OGHdeZWUZQthZiZR72INs45fOpTn8Ltt9+Offv2XfH4S5cu4d3vfjf+9E//FB/60Ie2f4FXwW233YZTp07h8OHDOHny5KiXMzGcOXMGJ06cAAAcPHgQhw8fHvGKFEVRlO1i0vd73es3x/LyMo4dOwbnHPbs2YO77roLxoxVrUVRlAlkLIXtZrl48SJuvPHGUS8DgG5214OKW0VRFGUjxmW/171+86i4VRRlqxnr3yBvfOMb0ev1rurYP/7jP8aLXvSibV6RshNoFJCiKMruQvf73YdGASmKstWMtbB997vfjZe97GX47Gc/u+4xg8EA3/d934cv+ZIvUQE0RbTF7RNPPDHiFSmKoijbhe73u5Nh4naKGgkVRdlhxlrYHjhwAJ/+9Kdx33334d3vfvea2z/1qU/hpS99KX7+538e3nu8+tWvHsEqle1CxK0xpuGcrCiKokwXut/vXnJxu3fvXhDRqJekKMqEMtbC9tOf/jS+9Eu/FKurq/ju7/5ufM3XfA0uXLgAAPjZn/1ZvOxlL8OnP/1p7Nu3D+9973vHwkhC2VoWFxdx7733qrBVFEWZYnS/390sLCzgb/2tv3VFd2xFUZSNmAjzqF/8xV/ED/7gD6Lf7+PIkSN4znOeg4997GNgZnzlV34lfuVXfgXPetazRr3MBmoosT30ej1cuHABBw8eHPVSFEVRlC1m0vZ73eu3B+89nnjiCRw6dEgNpRRFuWomQtgCwP33349/9I/+EY4dOwYigjEG73nPe/C6171u1Esbim52W49zDp/5zGdQlqW6JSuKokwpk7Tf616/PTz88MO4cOGCuiUrinJNTMRvipWVFbz73e/Go48+CgBgZnjv8XM/93P41Kc+NeLVjT+eecPLpGCtTZVadUtWFEWZPnS/V4AQ9aduyYqiXCtjL2zF1v+d73wnqqrCt3/7t+P3f//3cejQIfz1X/817rvvPrz1rW+Fc27USx0bctFaeYZnoPLhMnD1Ra6rvBw3/oJXo4AURVGmE93vFUGjgBRF2QxjLWy/67u+C6961avw6KOP4pZbbsEHPvABvPvd78arX/1qfOpTn8LXfd3XYTAY4F//63+N++67D3/zN38z6iWPhFzEhksQrj0H9B1jtQqX5Yqx6rJLxei7cBn44YJ3HEWuiltFUZTpQvd7pY2KW0VRrpWxnrGVmYov/dIvxa//+q9jcXFxzTG/+Zu/iTe/+c24ePEiut3uVQe8bzc7MXcjgrPygGfAcazOcvharstvFwwBBEJhwueWCIbC5ybebgggIhTxc7lf+Dh6O/4zZ87gxIkTAIBnP/vZOHDgwIhXpCiKomyGSd3vdcZ2+1leXsaxY8fgnMO+ffvwvOc9b9RLUhRlTBnriu3c3Bx++Zd/Gf/1v/7XoZscAHzjN34j/uZv/gaveMUrUJblDq9wNNQV2roqu1wxlkvGxQHj0sDjQt/jXN/jfJ9xoR++Pt/nxuXSwOPigLEU7yOXiwPGUhnOJ9VeqeqOUzVXKrcLCwvYv3//yNahKIqiXB+63yvrIZXbmZkZ3HrrraNejqIoY0wx6gVsxCc/+Uncc889VzzuyJEj+OhHP4qf+Zmf2YFVjRbP9cxs6YPYzNuJS8eoGHBxttYjVGuRfZRqrCOAfFaFjY9RGAYRoWvC5x0TqrbWAAUBhQmV3LqyyyOr5C4uLuLAgQMa6K4oijLB6H6vbITk3OperyjKRox1K/Iks9XtSSJoPTcF7WrF6DlgtWKUvm47zsnbiPNW43yDaP8Y2CRmKYnZjgntyu3rmyJXLqPZfJ566ilUVaVRQIqiKMq2o63Io+Hy5cs4ffo0nvvc52oUkKIoibGu2CqBdpVWhGxuADVwACMc156fFSFqUAvRHGZG5alR3YV87hlMQMWEyofKbGEAcuFju5Jbz+ryjgvclZWVxh8WKm4VRVEUZbpgZjz66KMYDAY4duyY5twqipJQYTvm5KJWWo5XXT3/2nO1aZShUFWV6moSm/nnUXgKlQcYtQFVW9zmVAwgmlMZMEofzxk/dphQEI9M4M7Pz+PIkSM4ceIETp8+DUDFraIoiqJME0SE5z73uTh27FhyS1ZxqygKoMJ2rGmLWhG0y2X4vO+agrZrgBlL6FiZjw0CM//Y/rUfhCyFmVzUbsoibqUKLBdxXHYc5nArBgpiuLjOIHQJXTMagSumIypuFUVRFGU6EUMpFbeKouSosB1j1hO1y3Ge1nMQjrM2CNqZInzeMZQqt3n7sfy6l4ptMpUCwC1BW4tcCq3KIlwlPoioFrsAKhcEbukBS4Czayu4hQFCfXh7Ba6KW0VRFEWZblTcKorSRoXtmCJxPqXfWNTOF8B8x2C+iNXaWLnNs2mB2tRp+GMBAK1xTw6Cl+GZkrB16WOzkutRtyobxAquAbomzOd2DcOjnvPdboHbFrc33ngj9uzZsy2PpSiKoijKztMWt6dPn8ahQ4dGvSxFUUaECtsxpCFqY45sW9R2DGGuIOzthI9zRRC0Mk/bFrIbCci6gtscrs0Fb4cBZ3KRS0nc5kK3iiJ34MP5nNzXAx0bTKY86jUCvO3i1nuvolZRFEVRphARt2fOnNGcW0XZ5Yx1v8Zb3/pWvOc977mqY3/qp34K3/RN37S9C9oBfCYQBx61UZQLohYIldmFDuHGLmFvtxa3M9lsbWEIhurL1ZAfb4jiOcL5utLubAlzNlSKF4paVMvjz0bxCsTn4EIrtTg59+LXEk1UeXnO25M6tbi4iIMHD6avvffb8jiKoijK5tmN+72ydSwsLOCOO+5opD7ofq8ou4+xzrE1xuDzP//z8ad/+qdXPPZzP/dz8eCDD+Ly5cs7sLIrs9lsO88hukcE7aWBx3IZBCEQROZCEYStCNo0T7sD5kwivMPntZGUj0LcxYxdx81MXakgD3Vtpp1Zv3MOx44dw969e3XmVlEUZYyY1P1+XHNsPdeizpAZelv7+mnixIkTWFlZ0ZlbRdlljE0r8mOPPYb/8T/+x5rrn3rqKfzar/3auvdjZjz22GP41Kc+NfHtpnkLct+F1uPVKghFIFRN52yo0i5kVdIgCncmL9Zkc7tSZfUc2pWtiS3HDJQuzNi253FLXzsot+dvi212T7506RKWl5exvLwMQA2lFEVRRoHu99tDLmbb1w8TsdMqcAeDAZ555pn0ZraKW0XZPYxNxXZ1dRV33303nnjiiU3dn5nxNV/zNfjt3/7tLV7Z5tjMu7hVrHauVqFSe3EQhK3E6LQrtTstajfCt8ykpGKbV29dVu01BBDC+gsCOjYI2zxzNxy3tc/tzJkzOHHiBADg4MGDKm4VRVF2mGna78ehYjtM0HK8jjLRKgJ2PQHcPm6SWV5exrFjx+Ccw549e1TcKsouYWwqtnNzc3j729+OH/mRH0nXPfbYY5iZmWnMSLYxxmDPnj14yUtegp/5mZ/ZiaVuCyIMXazWrlbhY+WDCJyxQcwujKGoBepKrjwPwwRLQGEYlSdUBijTTG3Ix2WEeKAKhIo5tCczg02oAot78lY+R40CUhRFGS27fb/fTrglWpl9ErdtQds+FghCeL0K7yShUUCKsjsZm4rtMK5l5mbcuNZ3cWW2djmr1i6X4VvTNcCersHeThS2UdQW6+X3jAHrVXCrKN4lPogRhTDVs7YSWdR2eN5KgauVW0VRlPFhUvf7carYDhOqQLNqmx/HaB4vafdy/KSLW0Art4qy2xibiu0wfuzHfgzPfvazR72MbSdVazm4BfccULogaguD5Dacux6PsaYFsEEFlxkVESyHedrKEwBpUWZ4J18TurF6K3m8hdm67Nu8cnv27FksLi6i0+lc93kVRVGUa2e37PdbzTBRK4JVhOowwSvHNE2m6vtMC+3K7dLSEm688cZRL0tRlG1irCu2k8y1vIsrplGrFePiIDohV0HsSbTP3k5tGDVOLchXS17BbVdtJfqHEX4U89lbu83V27Nnz2LPnj2YnZ3dkvMpiqIou4dRVmzborZdgb2q+8InMSsVWoIBkZmKiq2wvLyMfr+P/fv3j3opiqJsI2NdsRUuXryIBx98ECsrK2tyyaqqwsrKCk6ePInf//3fx4c//OERrXLziNirpGU3vtVQUGhDlmptx9KORPpsB2HN0fGYw1ytBVBQmKEtiFExoYrf3soDnoAiVm+LIdXbrXgdbrnllsbX/X4fMzMz131eRVEU5dqZ9v1+q8hF7bDq65XIRbCI2/Zs7TS5Ji8sLGBhYSF9XZYlrLXalqwoU8bYC9t/+S//Jd7xjnegLMtRL2VbkMgcjm3Ig2iuZBCcgmfipRsrmOPegrwRtRBlFCY+T0OhJZkI5KU9OcvHdYTCMJiDoVTXMKwhwG99NNDS0hIeeughLC4u6sytoijKDjPt+/1W0Ra1fhMV27w9mcg0KrfDHm8axK1QliWOHj2Koih05lZRpoyxFra/+7u/i5/6qZ+6qmPvvPNOfMM3fMM2r2h78LElt/KhRZc5CLauCdm1HUOwZnKrtW2Gzd9SzLCtmGAgAp+Sc7InStVbmb3tmFoob8Xr0uv14L1Xt2RFUZQdZrfs91uFiFrHVfo6Z73qbTvyx5BpOCfn5wewxlF5GgRuWZYoyxK9Xk/dkhVlyhjrf8kS1P7a174Wjz/+OJ5++mkYY/Cd3/mdGAwGeOSRR/DDP/zDMMaAmfEDP/ADI17xtZO3IcusKRBmSq2hEIFD4Rs1ydXaYZjUVhxEvLghS6W6MGHeFgjV24ELr1HfBeFb+tx5+fpHxQ8cOIAjR44AAE6fPo1Tp05d9zkVRVGUK7Mb9vutwLNviFpmD+crePbp4riKlVyXLoz6PrnobQtgn7U2A1FAt47PL5PI/Pw87rrrLlhrUxRQu+1dUZTJZKyF7V/91V9hdnYW73rXu3Dbbbfh5ptvxvOf/3z89//+31EUBW6//Xb81E/9FH70R38UjzzyCH75l3951Eu+JqRimbchV1y3IovYK6IT8jQSxG0tcDumbr2esYSuDXO1BKneRlGbiVtpXd4Kcbu4uKjiVlEUZYeZ9v1+K5H2Y47iUkSriFlmhmMHx1V2cXDxtlzoAk2xnL7GWoE7jEkVt+KWrOJWUaaLsZZL58+fxx133IF9+/al6170ohfh0UcfxcWLF9N13/d934eZmRn87u/+7ghWeX3kbcilYzhfx/xIrquNbbbT0Ia8HoYIRRTxImrFNGto9TYTt5KRq+JWURRlMtkN+/1WkDsgh6ppEKsiZn38j1uCVyqvjqt4e7jPMHG73tzueuJ2UlFxqyjTx1gL29nZWczNzTWue97zngcAuP/++9N1e/fuxV133YWjR4/u6Pqul6hh6zZkBjxCy7E1deSNmXDTqGthWPU2Zfhm1VvPSOK2F1uUB17eJODrFri5uO33+9BULEVRlO1j2vf7rUDE5poKay5m2cOnVuS17ckicuv7cEPMtoWzfD6t5OK2LEs450a9JEVRroOxFraHDx/G8ePHG79oZKP79Kc/veb45eXlHVvbVuEhlcZa6EpmaycZRo10iTtOu3rbMUHczolDtK1bs+VNgZ4LIldak0MF9/oE7uLiIu68807ccccdoCmuliuKooya3bDfbwe5oM0F6rAWYZ+3LnMtcKVFOZ/bbSPitj1zK+edZBYWFnD33XfjnnvuQafTGfVyFEW5DsZa2H7+538+zp8/j3e84x3punvvvRfMjA984APpuieeeAIPPvggDh06NIJVbo58vpbFHZgyAyWze9qQ16Ndve1k1Vt5faSVW0ylpHpbMTBw19+efOONNzZE7YULF7bgmSmKoig507zf7wQiaJ2vaqHLTTFaV3T9GqEqc7jh75FaKOeiNZ+7nba25Pn5+YaovXTpkrYlK8oEMtbC9k1vehOICD/yIz+Cl7/85ej3+7jvvvtw55134g//8A/xhje8Ab/0S7+Ef/AP/gHKssTf+Tt/Z9RLvmZ8bD8GwjdDKrVdu/vakIcxrHor5lIibkN0ULN623e85e3Jjz/+OB5++GGduVUURdlidsN+vxU024a5IUCHVlo3MH1qzOBmLcrt1uO2C/IwcTvJLsltzp8/j2PHjunMraJMIMRjPjz4rne9C295y1vQ7XZx+fJlAMBv/uZv4pu/+ZtTJY2ZYa3FJz7xCfztv/23R7jamttuuw2nTp3C4cOHcfLkyTW3+xhf03eM5YqxVDJKF/JrFzqE+cJgoRME3LTk114vUuUWEetiZbZ0nNykgfrNAEvh9cuNuK7ntTxz5gxOnDgBADh48KDm3CqKomwhk7jfX2mv3yp8Vln1mdOxj/E9+cwtxZoFxYxaAENva2PIwFABSxYEA0OmcZzc15C54nkmmeXlZRw7dgzOOezZs0dzbhVlghj7f6nf/d3fjc9+9rP46Z/+6XTdN37jN+LXf/3Xcffdd6PT6eDFL34xPvCBD4zFJne1iAjz2ecAQNEcqTD1N0dFbWBY9VZyb6V6C6xtT96qWCB1S1YURdk+pnW/3yryaq2I2pBhW61pG97oHMDa9uT1KruNY6bYRCpH3ZIVZXIZ+4rtpHKld3Gr2Ca7WjEulx7LZRBeHUvY2wkXMUpSYbuWvHororUd/QOgMaPbNbUxV6jeauVWURRF2Tw7UbHNq7WVH8CzQ8UDOB8qtwDACJsegVJFVSq2eaRPXm3NhSrBwJqiUbFtIxXd/PzrMelVW0Art4oyiei/0BGQG0e53MU33i4ztbt5tvZKiLGUGG6JsZQI2GHVW4kHqrh2Td4M7crtE088sVVPS1EURVHWkM/C5qLWs4NnV5tGod7X2jOw8pGHzNFu9HgN06h1cm3bVd9pmLfVyq2iTB4qbEeIzIpKfm3ekqyi9srkrckFNVuT66rs9opbIlqTvagoiqIoW4U4GQcxG0RsxSUqX6Yqbh7V03A9FmOp+B+wVnS2q6+S1uDYpUv4esj5W4J2msXt/Py8VmwVZcwpRr2A3UqeXeuHGB8pV08QuBxfQ4JhhgGBstZkIAhZGMbAEwAGLAEeKAyn81wLi4uLuPHGGzEzM7OVT0dRFEVRADTFYRCp0TjKV3A8SLcbhPZgDw8iE9uO5X5cC04yIKzd65g9EFuXXUsUGzJgGFgU8fw+RRQKeeuyPNZGrcqTxMLCAl7wghfoXq8oE8B0/NaZMNrGUTLmbChG/EAF7rWyXuatVHHltayi2F1bud1c9Tbf6AaDAZ566qmtekqKoiiKksXxcIzVcUng+swlOY/tAZBMpqTKWrsnM9rxPQyf2pulCizGVPn1qe0Z4tBcV3CHtThPQ9UWaO71zIwnnnhC25IVZQzRiu2IGPbrUHJrraEUbaDGUVdPeK1CZBI8YAzBEGNABDhGhbotuV25rf8h8KZec+89jh49in6/j6qq1FBKURRF2TJSxqy0JXMF54PABQBDBcgYEDxAtZmUmE7VsT8GxHWUj2OXHoPgYWK9oxa93LieYsXXeXlcEyvESF8zPAih+ltXj6enjvLYY4/hmWeewdLSkhpKKcqYof8axwAiqkWtmCJBK7abIVRu49ytqbNsZ2IkUF65zWdurzcOyBiDxcVFABoFpCiKolw/UmFtVlvrS6raxs/z2B/nQ6U1uCjnFde6wlv5QbqEY8t6phYMn6q9LluL5Oe6VLWVCm5OPoMrz2VaOHDggBpKKcqYosJ2RNQZtfXFGhW1W8Ww1mTJvs0dk/Os2zwqaDPiVnNuFUVRlK2kFrEiJIPYdN6FCq6IWnj4aPAUZm/Dx4oHSdzmAje/To6VduTcECqv3ErrcV09DiZT0v7cztId1ho9DahbsqKML2MtbF/96lfjd3/3d1GW5aiXsq2kmVrobO1WssY1OZu7bTgmc6jaDrIqrjgmX6vAVXGrKIpy7eyW/f5ayN2QPXyqrDYqr17mXOVrF4QrDzBwfVS+HHrxMkebKrlNd+Oc/LY8F3c9fFZlTvO3Km4VRdkBxlrY/uEf/iG+7uu+Drfeeive8pa34K//+q9HvaQtR0RsPVM7wsVMKdKaLBXbWRtak4vsTYSGuL1OU6m2uD1z5sxWPyVFUZSpYjfs99dCbsjk2UUR6uoqa1YdTaJRcm7XEa95O3I4F68rZsXROJ+NXS8j96qfy5SIWqEtbh955JFRL0lRdj1jLWzf+ta34q677sK5c+fwi7/4i3jpS1+Kl7zkJXjnO9+JZ555ZtTLu27aL76K2u0jb01ed+6W64pt/zrnbkXczs3N4aabbtqGZ6QoijI9TPt+f600s2uzeVgepHZkIRg6mXQ/MYwSp+TcMflqEFHbFrcSE+QzIS1cSyV2Wqq2QC1uO50ODh48OOrlKMquh5g3MUy4w/zFX/wF3vOe9+C3f/u3ceHCBRAROp0OvvzLvxzf+q3fii/90i8dO1e62267DadOncLhw4dx8uTJxm1VFEyrjrFaMZbLUCHsGmChQ1goKFQUjboibzWhvXhtCzJz+B4AdVu4jVFB8jG0iV/b98N7P3Y/m4qiKOPKpO33G+31m0Vaix1XKF0ffbeCvltF6XuhGhtnaAWCaWTG5u3CuYg0ZGCoCJm3UQwbIhAZWCri10VyOm5DoHCcHE8mnotAMLCmaOTZ5phMfA+rBk86utcryngwEcJWGAwG+OAHP4j3vve9+MhHPoKqqkBEeNaznoVv+qZvwutf/3q84AUvGPUyAVy9sF0ug7hVYbtzDBO3FQPOc8oWBmqX6q5BMp/ajLgVzp49i36/r1FAiqIoV2BS9vvtErZSoS19Hz23jNL1UPpedC2uknhti8W2iZNUdonMNQnb/JxSqRVxeq3Ctq74Ns877JhpYHV1FU888QTuuOMOFbuKssNMlLDNOXv2LH7/938fH/zgB/GRj3wE/X4fAPCyl70M3/Ed34HXve51jUDtneZKwlbaXdvCdm+HsNChzOBIhe124aMLsojaXOSKuJXW5bbp1LV+X3q9Hj7zmc8AAA4ePKjiVlEU5SoZ5/1+q4WtGDVVPGhUawd+JcbxVOvOqoqgHXa7CFtCLUpzYRs+r4Vr+jrm1rYrrk2RTI1zCrlYbQveYeK2fZ9JhJnxmc98Bv1+H3v27NGcW0XZYSb2X1uv18Py8jKWlpZQlmWynf/4xz+O7/iO78Add9yB//yf//Ool7kGmdXkrGoIrHVD1nnb7Sfl3VIUrdE5uWEqFWOAxDF5szO3s7Oz6pasKIqyCSZ1v98sYrSUx/k47xoxPI1jUVdpfcyglTxayaSVmV2gORs77LHrGCCJ8eFkADWM9dqPN3yOG7gwTzJEhDvuuEPdkhVlRBSjXsC1sLS0hN/5nd/B+973PvzZn/1Z2txuueUWvO51r8PrX/96nDhxAu9+97vxB3/wB/iGb/gG9Ho9fMu3fMuol95AxKzn4DIov/IMhV+KtUOyqtvtJohbjt+TmP9jCXCMCrFd2QMAh9vjx5CFy9f0PVpcXAQAnDhxAqdPnwYArdwqiqIMYVr2++shmEC5WrhmwjKfoU1RPOsIUM+xEsoeTB7gcP/GXC58/Jskti7DxMctYFEAZODZh79TWkI23GdzdRLm5jrCY0xszQVAbSh17NixJG61cqsoO8PYtyJ77/FHf/RHeN/73ocPfehD6PV6YGYYY/AlX/Il+LZv+zZ8xVd8BTqdTuN+P/7jP463vvWtuPvuu/HAAw/s+LrXa0+S9te+GEdVoRpoEFx693YJc3G+ttCy7Y4hc7eNCm3WlmwoVHbFSErclDfTLn7mzBmcOHECgLYlK4qiCJO4329HK7LnCgPXS/O1veoyBm41uRvLcXk1FcgqvS3DqLy12JBtGkBl7cjhvvWfhNaEY008LszQrjWQsmRbrctNAbeeUM2Pa7cmT7q4BYDl5WUcO3YMzjltS1aUHWKshe0/+2f/DL/1W7+Fp59+GrLMO++8E69//evx+te/HocOHVr3vo899hjuuOMOLCwsYGlpaaeWnNhI2A5cFLZR3A58EE1zRW0c1bVasd1phonbKrYgDxO3NrYvX6+4veuuu3DDDTdsx1NSFEWZCCZ1v99OYTvwq+hVy+i5yyhdPwlbEbWpmsu12AXQqNjmgjOIVDvURKp+/JCRK7cVprMpcZtzLU7J7dsnnVzcPutZz8Jtt9026iUpylQz1q3I73znOwEA8/Pz+Jqv+Rq84Q1vwCte8Yqruu+5c+fQ7Xav+vhxQOZstVA7GoI45SBUOXwuXo4ibisA3od25C6i2vVAYTbXllyWpYpaRVF2Pbttv1+Pxiws1yJW5m2dr1KVVuZgG/dH7YZMLfMmcTfOhSazR7w65d+GFmQDiwLOu9RlbKiA8xWsKeC4goFJj8NUwFL4k7ItRxtCu9F2jNTa3F6vPP9JF7fSlvzkk0/i1ltvHfVyFGXqGWth+3f/7t/FG97wBnz913899uzZc033/ZzP+Rz0er1tWtnm8bz2cwMVs+OCiFsgtBsHrRqErIjb8H2Ls7YcZ3KvQ9wKzJzmqxVFUXYT07jfXyu5SJVKLCNUZj1XqHzZELR5li2A1E6ck6qzsZIqQpHZw8t2k0UDuXjO8J6tB8GDmdLnIMD5Kp2byICYwuyuqdcu4nmN4RTXVWSxtdhI3E4DCwsLuPPOOxvX6X6vKNvDWAvbP//zP9/0fSdxjkEF7nhgKMzPembAIwjXlrgVQykvQniT4lbw3uPhhx/G/Py8ztwqirLr2G37/XokF+SYV1v5QfxYps/rymp4E7ZdiTWp16gWs9KCvKZai1p8SjWYgoRNQlaOYdTGU0D95jyRAZu4X5ooXOPahjkwm2ibGUypdoe4zXnyySdx6dIlnblVlG1grIXtbkXF7Xggjsltceu4jv0BGAPfFLeG+JqNvy5dupQugLolK4qi7DakDbmuykqldpAuLl2/tjJLEKWJOBfbnKsFmhVUn1Vqw22c2n9TMZfDxK0laQ1u3hcIQroSY2QPWFOkQZ52xZZg4AkwbOIsb92eLJXeXNxOQztyTlmWeOqpp+CcU7dkRdkGxvpfk7X2qi/dbhf79u3D85//fLzuda/Dn/3Zn416+RuyUapZ3q6sjJZG1q2tHZGL+C/H83AX5WvNud23b5/m3CqKsmuZ5v3+asiNnzw7OC/VWhe+5goVl6lyW3G4SOZsEr4+CN+8JbkpmMNFjstzbyUqqHZczmd7XZaV22yZTmZWXtYYBLhLLdP1ZW1Gb/1Rzidrzl+b9uNOKp1OB3fddZfm3CrKNjHWwlZy667mUlUVLl26hKNHj+L9738//v7f//v4+Z//+VE/hSvieWORq4yedcVtZvTVFrehontt4nZxcVHFraIou5LdsN9fCRF9ImLzVmTHVRSjUTD6cEm3JSFZJeHo47lEHMul8mVqdZZLajVOa+H0eJUfZDO+VeM4mZeV+9ciOqwjF6jSCk1kUrs0gFpEt42wRGBz0/l50gWuGEqpuFWUrWesha1zDl/1VV8FAPiyL/syfPjDH8YzzzyDsixx7tw5fPSjH8VrX/taAMF44mMf+xj+4A/+AN/93d8NIsL3f//34xOf+MQon8JVIwJXq7XjiYhbEbVdE0SuzeZpVdwqiqJsjt203w9DBKEIzcoPUPp+swqLTKhGKRkEaqjQeh+ieoIILlH6HkrXQ+l76LuVECHkeo3qrlzaplM+ZuS6KJ4rrmd9pXoL1JE8Mrsr99ssUsFNr0smuNtV3ElGxa2ibA9jPWP7rne9C7/3e7+HN7/5zfiFX/iFxm379u3DK1/5Srzyla/EPffcg5/4iZ/AAw88gDe84Q34si/7MnzO53wO3vjGN+Jd73oX7rvvvhE9g+Hor67JJI8DAghGRKsPc7cyc1sxAS7M3Jp43Wbckk+cOIGnn34aBw4cQLfb3dLnoiiKMk5M635/NeRtuI5dY6a29H2UvpcEZqpgxr8kkqAUgckeDsG12HGFCiWAzMEY9V4kc6yhelrArhO3wyKcKXxdANEssSlq1943M7Rar1oLH8yjwonWdVNOX/PwzNtJRMTtsWPHcPnyZVy8eBE33XTTqJelKBMNMV9jSWkHeclLXoLjx4/j9OnTmJmZWfe4qqrwrGc9C8997nPTO7YShr1v3z489NBDO7XkxHqh7ZUPVb2+Y6xW4TLwoRK40CEsFIQZG6qDm3HXVbYfz7Uzcl6llYq7oayqa+R7ee3fz6effhrz8/NYWFjYnieiKIoyJkzqfr/eXn8tSOtu5Qfou1WsVktYLi9ipbqIXnUZfbca5ml9FeqXMZInN1aqRSM1hF9uxCSikqLIrCOALGzMoaWWc7JpmTgRCNYUKKiLwnTqKCHUj1GfhxqCVq7PhS0RNdaSC+H89WlXhpMonwKRu7y8jJWVFRw4cGDUS1GUiWesK7ZHjx7Fvffeu+EmBwBFUeCuu+7C3/zN36TrrLW4/fbbcf/992/3Mq+Z9X4NaxvyZNCo3HpCQQwf8oGSuC19cFBOebcGuNbKbXuTGwwGWrlVFGUqmdb9/lqQVuRkDuUHwYwpE7XMvlXh9VEYyqxq7Y6cKru8VvgSGRgOGbfyRwmJ5KQsazbDcRWEpQfIGBguQOTTG7pxUcntGGRqJ2VGyLplpPxcAwNmBqhZtQ2vxfDXKM/InRYWFhYab2A750BE6pasKJtgrIXtjTfeiBMnTlzVsY8//jjm5uYa162uro5ttav960p+iau4nQxE3BoCrCF0wRiA4GPO7VaJW2FlZQVHjx7FgQMHNApIUZSpY5r3+ytRGy652hwqczoOJlG1K7GYNKX7w6d4HYKBjLiuMXmCScLXsAdMAXAVhSrBowJQNCJ4CNSYmZXHkrxdwwQQGgI7VJQLQFqZwbAUcnLzii2jFti5uKVoRjWsepu/ZtPSkpzjnMPRo0dhjNEoIEXZBGP9L+YlL3kJnnrqKfzbf/tvNzzu3//7f4/Tp0/jcz/3c9N1p0+fxkMPPYTnPOc5273MayKPNzVUXwAVt5NG7pZso0uyJYobe2hVLmPreenr7NtrNZQCQquSc04NpRRFmUqmcb+/FiRax6Xs2jKr3pbJvMlHd+S2G3I9kxvMonJzqI1ch+vYHVefP36U46WleBieOcvBzeN7qmYUUH5+X60R6MzcOP5qmXQTqTb9fh/9fl8NpRRlk4y1sP2e7/keMDPe8pa34Id+6Idw/Pjxxu2PPvoofvRHfxRvetObQER485vfDAD4v//3/+IbvuEbUFUVXvOa14xg5RuTBG3r+uSMDBW3k8KwKCARt0Ccw/XBJdnx5sXtgQMH1C1ZUZSpZVr3+yvRyHZNotXVDsTRTKrOtRXx6WKmbbgEk6nmpeKyIVCHIdVTqRhLzFAdAVTvVXkLsM9EMYCh4jY9pyRmXXpueUu1x3Bx2zaQEoa5JE8L8/Pz6pasKNfBWJtHAcBP/uRP4l/9q38Fiu2be/bswZ49e3Dp0iWsrKwACL8Mf+AHfgBve9vbAACf93mfh7/4i7/A4uIi7r///pG4zK1nKOGjuCk9Y6VCMJBy4Vswa4H5wmChEwRSYa69ZVUZDfJ9rTgYSfVj5I/MHrWF72YNpc6cOZPa9Q4ePKhtyYqiTA2TuN9fr3mUGEcNXA89t4yV8iIul+fTZbVaQul68JnAFLGai0mgKfSCKVSBwnRRUAfWBHMoQwUMCMYUyUiqTTjOomNmUZguTJyVzZ2YC9NBYbrBEAqmsZc1zKlaZk9iIFVQZ6ih1DAzqfWqxZStfxpMpHKWl5dx7NgxOOewZ88ebUtWlKtk7P+V/OiP/ig+/OEP46UvfSkAYGlpCU8++SSWl5fBzHjxi1+MD3zgA2mTA4BLly7hNa95Df7sz/5sLK3TDYVf3qJb5ZvguQ6pD1+P9XsOSou8ctuxoTU5tSVHwTvw11e51ZxbRVGmlWnc768WqXBKVm1oLS7rVmQ/iIJWqp9xDje2KEu+rWtVRdvRQG1RS6jFJ2fmVTLv67PqrRyTV2PrNbuYGMCN1ue26M6rxyJer+t1G3LeaUBzbhVlc4y1edSFCxewb98+vOpVr8KrXvUqPPHEE/jMZz6Ds2fPYmFhAS960Ytwxx13rLnfpz/96RGs9uowREnMtOdr8xnbhsugMvbIu9WFif3kNn7zHKNCFLfxa4DQNcEacjOGUnnO7crKCpg5VTgURVEmkWnc76+WUH3lTNRKe3EvtBW7XjguCtEkLOGRN901W3MNmNZv523H+oioZfZgMiA2MGxBHD+PVdtwZsDBgdjAocqMqYKhk2cDg6szdxpmAiXZtpZM+nwYkpMr58hjgaaBPOe23++jLMsruoYrym5nrIXtF3/xF2Nubg4f/OAHsX//fhw6dAiHDh0a9bK2BPnV2xav+n7cZBNmbjcWtxS/ydcrbrvdLm644QYVtYqiTDzTvN9vRC5GHTs471IltnQ9lK6Hge8DkDZeyu67VvHJvKrhEM9juKgrtxSCeIg9HKpUtRWRnK+JuTaUEjflWkAixvy4dJ3Jqr+hrThEAaElnnMRm0cBORHCHOd+ycP5KgjVIVscoY4S2g3itigKFbWKchWMtbB98MEHceDAAezfv3/US1GUqyYXtyzBfFHcAqEVGT6+V34d4nbfvn2Nry9duoQbbrhhK56CoijKjrIb9/s8j9a3zJYk4kfakRkePs3Irm3hTRXX2AJsyKDwXXiq4LgAcQXi+j6Gg5SV87Tnc/PzegIgt7MPQtcHoRzWUzsjW1OkGB9mCpm2cVvLq80p0za8EvHciPFAIpBDFVsqxvnapBIt3W1tcdtmksVuO8bq8uXLmJ+f15lbRRnCWAvbTqczsbl0m0GdkKeHJG6zHFuK87VA+DhIKfUcsgABbDbn9tSpUzh9+rQaSimKMpHstv1eaLsH+9iOnHJrY3SOZxGSQbQaFLWjMer7i9uwNdHUiS0MV7Bs4XyVCqi+XgCA2ojJgEKbchKQDGa3Zs2NNuZ8TtczCtOB42rI7R4WRYo1Ivj0HATPYS1JIMPAkghYs0aAS6uyqbfQNUxTJffSpUt46KGHUiVXxa2iNBnrfxHf9E3fhM9+9rP4L//lv4x6KdtC+8XXmdrpw8Rs265BHQUUb2MOZlKSc3s9b2x0Oh0AaiilKMpkMu37/Xr4WGkVQygxbmpnujI4ZdzmebN5hq3M5np2Ib92neGm/H4D14sGVYO6rRjN+ds2DE5tyrWYrqvFYQ2ZoZV3jetqA6za7ErWXkcdNaON1svhzY2qUsxQ+zJFxlLWWhhj1FBKUdZhrCu23/7t345PfvKTeO1rX4sv+IIvwOd//ufj1ltvxdzc3Lr3ecMb3rCDK9wcbQErMybKdBEqrxzajD2FtmMAA08wHP/kyNqSiQD4YEB1rVXb3FDq9OnTAKCVW0VRJoZp3e83IncUFufjypcxy7YaKsg8+2jeVKXW3lokhvsYU6CgDgyFiB+J1LGmaDx2LUoRjrVhHjefaZU1tNuTAYBBqaocrvCplRgeMBSqzCmShw0MMdh4FIiVXG62FkvbcZ2fW4DgwznJp8ptw1053p/IwHGzmjwNVdqc3FBKxK1WbhWlZqxzbK21AHBNrq/OuSsftANslG3nmTFwQN+FDNvlMmSedg0wVxAWCsKMlaxTLeNOOiECAaiyqB+J/ZEWKqnqStbtZjJuAc25VRRlMpnU/X6zObaSX1v5AXpuGcsxv3Zp8AyWBs/gcnkeK+VF9NwyBq6XCT2AovIUgZdXTwGgMF3M2DnM2j3o2jl0zQwK023M0yZRG89rY25tfmyeJyuPy9k8bTiGknCu12dSK7Ql28iwlSxda2zKwBWkxVke20aBHj52U8Ztfnz+mPKa1GvLTK2mLO9Wc24VZThjXbE9cuTIrnB8NXUxDwbakjxtDKvcegDswpxT5QGYMHNriFFhc2ZSgFZuFUWZTHbLfp8jbcjSSlxKW3BsyfVZ+21evWUgViddEqkiVPNWYiJakxUrM7gianOn4lB9Df9vW63IIqaDuI3GV3GW13HVrJJGcylLRcP8idmnGSzDlKrOOaa2pahNpoC4Jpu9BnXUUfB65nQ9yeQuhdW0X/NpELdauVWU4Yy1sD1+/Piol7CjqKCdXtritiCGNwTv62quiFvZ1bdC3Go8gKIok8Bu2+/zNuQ0Y8olSt9HxfXMbDCOcsk1uT5B81xXeiyHKt0nF7XDjvW+CgJJTJsAENnmcbFaWvlBrLxy4zYRrHVrsUlCV8yjLLAmyicZQUVxK7OzhmK8EYnQ58ZrInFBhuuWZbuOgE15vBMucHNx2+12d90bQ4oyjLEWtooyTYi4NQRYQyiY4YngKRO3Ucga3nwMEBDE7Q033IDZ2dmtfyKKoijKpkkmR1k7cul7Kbe2dD2UvhcNlFzWNjxciAJrnYrDbZyckNuzrEKjtTcK4FANrmBRNNyRpRW5rvjW1eS6XVnaf6XFucC1+JSKy7FNTsY2mUSBTKNKK28O5OtnmLjuKz/WNFRvFxYW8PznPx8zMzMqbBUFEyRsvff45Cc/iQceeAAXLlzAP/kn/wRlWeLkyZO44447Rr28LUHjfqafPOMWtn5bWlyRPQOlC9d1rzMGKBe1VVXh3LlzqZqrKIoyruyG/R5AckNOrci+j0G8SH5t5QdJRHISxLW4y4m2TY34H2ICD3HOTfE+cRvKp5U9h2ZmTx7gCkARW3vruJ20HvZgql2UTVyFRQEGpYpsELw2tgnHmCJmGNTzusOQ2eKGSRXqinfbKdmSgYdPrdP58WtegymJAcr3embGU089hcXFRW1LVnYlE/FT/+u//uu4/fbb8fKXvxzf8i3fgre85S0AgMceewx33303vvEbvxGrq6sjXuXWoQJ3ujFEMAQUBBQmXii0X3kGqihuKx8+v96fB2bG0aNHceLECY0CUhRlrNkt+32oenJ0Qy4zV+RYvfU9VFw25mcb90doGZbZXJnP9ezgfRXbm0M7c+lEKEukThVbnKvkwCyxOmnml9vHivtyXSWVlmaZ9ZV1CbWBUwFLtuHQXL8OnM515dcruwwRtQDWVHA3OiateYrigGSf1yggZbcy9sL2h3/4h/Ht3/7tOHnyJIgIRVEXmU+ePAnnHN7//vfjH/7Df4iqqjY402Sgv4Z2ByYK2Y6p3ZCL+AaziNuBjwI3tin7TRqYExFuueUWAJpzqyjK+LJb9vt6vjZWbONM7cD3Ufo+SieXHnx0OiYKM6oFdWFNJ6t6xvvzAAPfw8D34jl6SSjnBlWOK3jvWhVgn1WOy3R8LnYlp5azSq04KueY7M9KE9uSLVkUpptErVR35Xz53tZ2Ypbj8uPXy+cd+loPybVtXD9FohYAbr75ZlhrNedW2bWMtbD96Ec/ire//e2Yn5/Hu971Lpw/fx4ve9nL0u1f+IVfiPe9731YWFjAxz72Mbz73e8e4WqvHTWL2r1I1bYtbrvxX6TnEAe0VeJ2cXERR44cAaDiVlGU8WPa93shdznm2DLsc2dk36vNozi4IzOCw3DHzqJjZ9E1s0HcxlxaZg6VWd/HwK2i71aCuPX9eJ4gaNNcLzy8d2tFa8PQqq7kNlqhh8z6SqRPivKhIrQdU6zWmizmJzOhSufjCp6zGKEsukdaiqX6ml82gjmbBc6Ob5+jnnW+8jknATGUUnGr7FbGWti+853vBBHh137t1/Bd3/Vd2Lt375pjXve61+F973sfmBn/8T/+xxGscnOIqB0W7+O3oP1UGX82ErfSluw4iFrnObUlq7hVFGXamOb9vo200UobcKiWlmkuVqq43lfwvkoztQRCYTrx0oUlC0MibuvooNL3o7jtRXGbnTu1HVepepu3Esu5pIrrMwdl+S+ZOQEwoCRq82prO0dWsmuD2A1/9PhYtfbIxG1LNCezqtbjbvj6ZoZakhecty7n1dv83MB0tCWruFV2M2MtbP/3//7fOHjwIL72a792w+O+8iu/EocOHcJnPvOZHVrZ1mAIMXCckpDxXLcjq7idfnJxaymIWmuabcmlD5Vb5zlWbbdO3D711FNb9VQURVE2zbTv921qYVWh4lp4eu/g2aU4G5eJ0BzJqjUxr1Zad+U+FQ9CO7Ovs3FzQ6p6jneQzi0V1kaubTZ/m8+4XvfzF/GcZnpjZdnXLtC56Jc55LyKvBE+mmwl8cqtVuTWrO60tSW3xe3DDz886iUpyo4w1sL2/PnzOHz48FUde/jw4YkylBBRK6JGSMIlfr1ZAaNMDslMyjTFrcnErYvitszE7WYRcdvtdnHTTTdtzZNQFEW5DqZ5v2/j2dfZtQ2x2a8rqY3W4CoJ4DADKy3ELjoA1yLXwMRqq4stzSVKmb11vVjNXQ3zvK6fZmk9gjuwiWcD6vZfEZq5qJX5WqnmNirBvlobxZNVXF1cdxC1ZfN1iGuWduzS9+B4kAS6Z3fVQnSouG0J87a4nSZE3BZFoYkIyq5hrON+br75ZjzyyCNXPI6Z8eijjyaDnHHHECXBmgtbz9J2Q7H1SIdwdwuScVsYwCPLuOWYcQsAnlHFjFvD4bZrjQASFhcXccstt2gcgKIoY8G07vdt8tlaEXKl76PvVzMhV0Vn41hJhUflSxB68FQEURiFoLTZAoChoiHOHFcgPwAAWGI4IGWdEkzMmA2VWplJ9VS7FBMZgCsYNnBhF1ozIwsAlczGRqMoSwU8mySMk0EV6pZpx1UQ8r5ef2G64fUxHp6bRlPhDWDfKMfYzGBKInuSMZUIcAIMG4BCPq5pODIHMR8ycgHENwU8msdNMgsLC3jRi16ke72yaxjrn/TP+7zPw/nz5/Fbv/VbGx733ve+F2fPnsXf+3t/b4dWtjUY1N+AvGqbMuq0WLurWC8GCECK/sljgK7HTApAY6O7cOECnnjiiet9CoqiKJti2vd7AEnkOV/P0fbF7Mn1UiXVRWfiIIDrCJ6BW0XPLSeDqIHvhQqud2vbbOFTW6/cP6+Q+swgqm77bUYHhfu4hoCWhl55jIrrtubSh+eQ3JezOda8pVqq03VLdL+OLYqmV4PsnKFd2mWt0fWsLLBW1ApyvJNW6jRz66civ/Zqyff6fr+PRx99VGdulallrP9V/9N/+k/BzHjTm96ED33oQ2tu997jP/yH/4A3velNICK88Y1vHMEqN0cuZPPPRcyqqN2diLi1FOZs83b1PAbIea5nsq+zXb3f7+ORRx7Bk08+qYZSiqKMhGne79tIe3HImO2hdP0kCPNYHuddFGEuxfn03Qp67nIQwW41CcBcOCbBms3xNq7PnI3TPC37hrNyEJi9JG7zyCCfCVSJKQoCfTVzc67bl33r/nlmb92KXa65Xsy12i3EUVqnda+XVZsbdPlM3LbJK8PTzsMPP4xz586poZQytYx1K/IrXvEK/MAP/AB++qd/Gq95zWuwd+9eDAahreZlL3sZjh49iqWlJTAzvvM7vxNf/MVfPOIVXxvt+VpFAZDmbT0IXWZwbEUWIRtigELrMkAoDABsvi15ZmYGt912G06cOIHTp08DwFXPuimKomwF077fC8352iDy8ogfn1U6ARHBHuCsvRY+OCbHGVJB2oyZGUQEpjB/CwNYKkJ7MQxAYRbXUhHNp0yqvorYMdFICgbJ9IPJw1CodIrbsIje1LpsAMtFQ9AaLhrPPxe8aVaWYpsyKhDXIrOQamw72za2Ntu0FYbnQDBrDaPYg9mkUo6lUN3dTVVb4TnPeQ6OHTuW3JLvuusubVNWpoqx/2l+29vehne96104cOAALl26hF6vB2bGX/7lX+LSpUu44YYb8La3vQ2/8iu/Muqlbpqx/yYoO0q7JbljQ/W2bSZVRZG7FfFQGgWkKMqomfb9PhdaeXatuCHnVUgxPRIRGCqjvaxS269Nlrie1a0rrWVyR3bepa85VWopuiCHmVkfH0MqtgOXuSnHduNBPH8/VnSlwirOyT6rrrpo8rQmXoglQqiuFkrFtC1eTS5q419KPp2/aszwSjRRW9S6zGyqFtXNWKG20ZU8znrZt5OceatRQMq0Q8yTYbtbliX+/M//HJ/+9Kdx8eJFLCws4J577sErXvEKzM/Pj3p5a7jttttw6tQpHD58GCdPnlxzu+cwK9l3jNWKsVyFrwsDLBSEuYIwY0M1brOVOGVySaZRXiq0cb42ilgRvpJ72zFb87Ny5swZnDhxAgBw8OBBrdwqirLjTNJ+f6W9XpAK58D10HPLWBo8gwv9MzjffxKX+k9jaXAOl8vz6LnL6FXLyR24jsQpU8sxEOJ+hj6OzJ3CwJouCtMJGbMxa7YwHXTMLGbsHLp2Hl0zk8RkEr/s07GGCth4EXOoXGjm7sREBh0zi46ZwYydx2yxgI6ZCZVhqqu2tdNznZ9roumUrDM8roU1BQoKz6OdjZvyc2VdMI3W6iDk6wxgSwWsCedOMUl59m52bnk+GzHJ1d7l5WUcO3YMzjns2bNHK7fK1DDWrcg5nU4Hr3zlK/HKV75y1EvZcvS9MqVN7pIMT+ia6JLtOLUlDxgAGAYEIsDI19chbiUSQNqSFxYWsG/fvut8NoqiKFfPNO73eXWxXeEUKKtQQvw24JOo7btVeK6Co7GxDWdjEbS5GzD8ILQYo4Qhg4I6AKQtmdOaDFBXV2OLM4DUEixC1JCBSyKX1ojrNeZNsd1Y3JXbEAgUhbKIZ0NFFJeURCiRgWeGJZmdjR1MDDhUsCiabcmojaVq0d1yc45O0MktOSwonZuiQ/J6EE12K7NUbqUt+eTJk3j2s5896mUpynUzMcJ2N+BbH5XdTVvcFsTwhuAz46iBB2QnN1swbwvU4nZ1dVVFraIoynWSu/jm+bWe2+249e9tZq4Nl6J78sCthAgfMii4i8J0Qztx/A+IglYeFx7O9dLsacfOAgjCtqJBakO2MUJIIoYoRuC4lvA2ZFGYbpijRQFPHgYmZOBibQuxj+2/1DJtyquodSW5G4WtHRrdI2uKN8TxGx9FbRC3HtSIAJLXdej3JK5ZxG0+FBYE69C7pXWlmeIJRsTtqVOncOjQoVEvR1G2hLEXtqdOncLP/uzP4n/9r/+FCxcuoKoqrNc9TUR4+OGHd3iFW4O6ISvDaIhbhM89EzwxHDMcAwMfK7ZUm0ldrylZO8xdzEgURVG2i2ne7+s50Oa86bCqIGfVU3FOFvdhqdiykRZeAmJrLdBsUZZ25soPkkEUABgfKq4eHgWXKZs2/Z5nqfhW8KhFoDEFDBswFwA1H8vDg1DPpKb5YArPW14DQdp+rbQVwzREbTpe1sIVgAKGKMudDVm9QdT69BzblWMCramsMvuQcZvN7so2K8ZSwzDrPMaksrCwgLvvvrtxne73yiQz1sL2sccew8tf/nI8/fTT625uOZP+D9FzqNbWMS6jXpEyDuTiNndK9kQoPQNglF5MpxiGCZ6vr2qbw8x49NFHMTMzozO3iqJsC9O83681OcrELTjE0CQXYZcckgfRCCqP3vGxogofq5EOKEz4fZ/PmAKA8y7EAbl+qnhK+zKzhzMOhRmsmVktTCe2+YpAZTBZENeCT15/cWZm9oAByA9gySY3ZOIqVXVDm3P43tpY/TVsk0gNMUT154KsKxA/sgsiN+9xi4VXEdhSGc6RaqxUXT0Bhg3yH6dhkUDA7nBSfvrpp3Hu3DmduVUmlrEWtj/5kz+JM2fOYO/evXj961+PF7zgBZibmxv1srYdEbjyuUYCKUncEgBL8V30ULEVkylDjIK2bt5WuHTpEs6fP5++VnGrKMpWsxv2e3HjFeMkzy7Lgu0hz3AN7sQrjbzaKs7MkkTXOMCTS2JXRKuI2IoHKF0QxdKyXPo+rCviejwqrs2hCtNptOcycxSuzXlVeS551ZTZw7kKbDi5G+dVzVSFjrO20kZdoJvydh0DyCOAIil2KCLi3bG0K2fzu0M0qTUFPHvY+Lp4ZH9XxYowgepZ2xbJkGqKKrXDqKoKp06dgnNOo4CUiWWshe0f/dEfgYjwkY98BC9/+ctHvZxtwVD4RZqiXJBVbEe5MGXsMEQoTPjBYIPQksxA6UNbcuUJg9iSXG1RSzIA3HjjjThy5Ijm3CqKsm1M+34vVUDPPsbvBDE7cCsxXmcVfb8aTaJW0HcrWK2W0HPLGLgenHMAADIAYnsvk0dFBhUXWcW1dheW+VzHIRvWkVSJY2QNeZAYWsXr6uoppyorAHjyMV+X0/nCcXX8TjgwVHNNSwxKlbryZXodDBUoqEJBnRS5kyrbmfuzofjXkGgsrsWmgQFnDs1WjK8yZ2PnK1hTJAMsZH9f1SZUgAGvEa+Ghs/TTvp87TCKomgYSqm4VSaRsRa2Tz/9NF74whdO5SYH1KKDpIVUNpQ4Rxnasba2rVSZfAwBloJTsvNAhfDz42OubcUEw1vbktx2SwZU3CqKsnVM+34vcBRPlR8EMetW0fcrsWW4FyusffSqZfTcMlYHy/AlwB4gCxgLwDIYLjoOEyoMolNyAU8FHDkYmFRJrQ2SasFnMidiouCybKmAMWH+lpmTmRQAwAOebJjXjerXUxS9WQ4sw8N7h4oGDZOlUB0O875C4bvwZmaNUJaYntAqHBqZRYl6cg3TKR87lBwAYgaTb0T3IAYKiBNyMoZKrtPh/+OjgNik6jZRrOIOEbfTYB41jLZbsopbZdIYa2F74MABlGU56mVsC4aC6BBRG6ZjOFVsa1GrrchKTXvetmNDS/LAERihamtjF8BWtySruFUUZbuY1v0+NyGStl7HFSouMXC9JG4HbjW0H4vAjZVb1wd8FV2EOfwez7wEwxwpAWQc2Pu6qklFEpvUmr81qGdq86gdmWMVR2afVU4BgHzdXuzZrTF7kufoKdyP/ABsilAtjm3IUrHNq7jSki0COLRpSx5v7GRDFUdu4poR5oStKWLonThDF0GiRudkcUmWVmT5vB3p47IsXgMTDLnYpDig/HXPzzuNqLhVJpmx/il91atehYcffniinA+vFWlDHiZe1TxKGUYwiQIKAroG6JjQdiwzQqFqCzjPqLy0tm/ND9Pi4iKOHDkCADhz5gz6/f6WnFdRlN3NbtjvxURKZmlL30ffryQhO0hGUX303SpcyfAVw1cAO4Bd+DxdXLiwDxd5jPxjEq6miG29BrVFE6LApUa0Tj3rO6hngblec77WypdwmRBNz9OH2V/nq2R6FY6tUttxMNBySdw6XzZEbWpNlmpvXIOc07ELLdnxDQOfMoKH73fhcevbZLZX1pLOw/X5ZC46X0u6Xzx2GhFxa63F5cuXGz4bijLOjLWw/bEf+zEsLCzgW77lW/D000+PejlbjojZXNzqfK1ytUhLchFFrvw8VT6K2ihyt/oNksXFRTz72c/GnXfeiZmZma09uaIou5Jp3u9T9A08XIzgETErrccD10fpxDBqJYjBMgpaH4SsGwCuz6j6DD9ALXg9QvU2CrrkXBzNpExWjRVxJnFCeQux5ObmAlsErvO1uK18CRdjhJoCuBZ9odrrotCsGpm4ubDOhbQ4Rcs5hCQ0kwiuso9B3LYzd4dRC1MOM8bZRa7Lv1ciboNrNafHr9ulp/svNRG3hw8fxs033zzq5SjKVTHWrcgf/vCH8drXvha/+qu/imc/+9l46UtfisOHD6Pb7Q49nojw3ve+d4dXeX0kcbtOxVartsowpCXZENCxdbjBIO6zVRypkpZk+DoSYis4cOBA4+uyLNHpdLbk3Iqi7D6mfb8PplFVJh6jkI0txwO3gp67HGZu3Spc5UK11ol45VSVJRMuphPnbqVFmYLJE1FTGEqcT1hHqJxaGsBRAcMVLBdRbPrUFpwLRZIqr++CTBUMDMnDmNDubPI/JeNtRcPFmJBHOEkrtAhEma8VUyjJls2zdx0HN2gxgZLsWnFEtgiPK3m6zFTfP87ZhucvLcVr6zoibu2QWVyHMFNrOLwWoUU6PJaPr/E0srCwgIWFhfS197FFXNuSlTGF+GoC40aEMWbNL8RhyDFElJwDR81tt92GU6dO4fDhwzh58uTQYzyHVtG+YyxXjOUyCJOuARY6hDlLmLGErsWWCRJlupCfodIzei58rOLfM0VsU87blUNnwNb+LPV6PRw9ehQ333yzztwqirIpJnW/v9JeL2J24FaxUl3CpcFZnF09gadXH8fTq4/jfO80lgbP4HJ5HsvlRaxWS1itllCuhOqsGyC1I/syVGWNJZgCMAVgu/I5wXTjddamWVQAyUgpVG4tOmYWXTuDrp3HjJlDx84ml49UkRVX4hQjFHNnqaiNpii2N5twXkOt6KDW7K5gTFyDmUHHzqKgbhS0lOZ+zZCGQmmrNmTT52KaRTAoTCc+dhc2HpNMpIDGnHE7ticdE52lGzPJrfuL87ShIr2u+XHTivceDz30EJhZZ26VsWWsK7bf/M3fPFEh7FuBGkUp10rukuyZ4KNDchC44a1qQ8EleTt+vi5fvoyyLNVQSlGUTTON+31jVjSrhsoMbbisoOeWkwtyz11GtRpFbRS2boAwbxu9tYxlmC5gY3qCcQTLWVmSHbx16fUMwrIAUAX3X67gfAFPVXBQ5iq5KEv7cXIzju3MHpQMpdgXcHCwpgrCkoMbszEOniowhSp7s2pbG1e1RWuqwJKBkXblIT8KIe1OXI/F6RgpzicYWpkkyi1kjlYq1r6uBmdV4fC9io+BYMxoopMywYRqMZnMcIrjOjyQOT/n87bTKHL7/T5WVlY051YZa8Za2L7nPe8Z9RJGhrYgK1dD3pJsDaFgBjMwiG3slQ/ZfAOiLXdJFm655RZ479UtWVGUTTOt+31tMlQ1ZlJdZiBVul6I/HErqHqMqscoVzg4IpcMVwJuwKktFhys7+tf4yJq48cOQPF3PRHAtowis4BDBWKCiXOqwb04xvdkM6QAGoKNYOBQxVbcIBzJm1QhdVQlgcuG0xytiFtpP0ZWBRUXZIpitHYj9sPjdIY4MJv4lJ2vkoVyQ1DDAOxgSB7H1yJXHj9Wq8Nzjdm3HGKFRMwO/d7GGdxcBANIr9m0idu5uTl1S1bGnrEWtoqiXBlDhCK8lQ02gGdCAcbARXEb520LYlQIre1bjUYBKYqiDKcxX8slKh8uYoJUiaFUVaLqM6pVrqu2sVIrHdqmCHO1ImrZxxlcYrAjeMtBqIrojWLPk2907HA0RXJ+EERdFHe5AVMjtzVuMBXK9JwMGTgKVdyCusmOlNiAmFJmbWGQVX7XGi7JHG14Q78Cp6pq/Q6/pebGlWJ64gwtkQF7D6YhgpoMPEtSLZKIbYvcPPM2VKu5EQ20ds0muiWn73R9nvCyT5241SggZdyZmp/ED3/4w/iN3/iNUS/juskNo3x2naJcidwlWZy2Q7ZtmL0d+BgF5Lcu/icnjwI6ffo0Tp06teWPoSiKMin7fXIfjpXQKjoJp5Ze5ljN5eAeXAKuB1Q91FXbXnA/BgBj4yxtnKklG42kMsFKlJlLWflIIe4nm7sVN+JBrBiXvtdoQTamaMyo5sLOcZUqzp7XzjmH5+2kATu6Cq8fjcNR+KeKNg+SyZaXeKBYHZXjOf4ncUS5S7OLMUM+vs7rPaZU0lOUTyvuJ72mrfZlwWfnaJ9HHsNv8LwnlXYU0LFjx5KplKKMmrERtvv378eXf/mXr3v7n/7pn+L//b//t+7tP/mTP4lv/dZv3Y6lbTsmEyKCxv4o14Jk2xqKplG2zrYVcdvMtd2edeTidmlpSTc7RVHWsBv2+1woiagtU/ZrLcBEGDmuQi5txalq6/phtpY5CFbTIdhuFLedKHQNQluyjUKXQrWWTHRMtoTCdFFQuEjlU9bTc5exWi2hVy2jdP2mG3ImaolozQy0HGdgkqFUo8rLPkQKXUHceWnV5jIJbRG1EqvjYxlUxKPzVcNlWl5jEbV5Xm39OJwuIYoofC7xPnlcUXj+lCquwyqvch+fieF8zXkF3E+ZyM3Fba/Xw2AwGPWSFAXAGLUiX7hwAZcuXVr39i/8wi/EF3zBF+BP/uRPdnBV24+IWaLaV95D8kcpZqcRPG/tXKQyfQRxy7Fqy6hii3Llo7hlCi3JJhhJbdfP1OLiIjqdDm644QZtT1IUZQ27ab/3sTLquJ6nHfjVWuByLcZy92NfAa4KfxNYW1dhpUqbQ1RfsjHWcFtsAQ6iM/y+d9HEykeTJSKDgrro2BkwZlP7chCpRWzFrVuB02wppKJbBOFsuul8w1yN82zY/DwpjzZzYjZgWNgU6yNzrCA0K6Lxc8PN1mkRkfn98vXLeqRNGexgTYwDam2LlMT98OeVP6YIYBGw683fyvOcZETcEhFmZ2dHvRxFATBGwvZqGONkok1hKIgLygSJIQlYRxK1inK1SMXWg9CNRlKeACdGUsQY+O0zkhJuuummxteXL1/Gnj17tvxxFEWZTiZ9v2+3IQ9cHwPXS07Iof23X7cmswO7IGiDwGWkLt+syMcO8AhztKkq29JHIfc2CtwiPD7BJJEYTKt6KSeXDFB1Bo3sW0cuRuZUKdoGQF2RZJ+ci6XFuZlBW+8rXmZ42cOhgvUFKhqAjIEHp7ZoEdqeTIinAzUEaR7VI07TOXmrdf598FQ7KjeOj2tCcjv2ydBKHndN+3HmrLwe+f2T23LrMcJtk28wlWfcAsDKygpmZ2f1TW1lZEyUsJ1G8jZk+bzSGVtlk4hLckHRSCpE1cM7aUkOfxIUBFSos223k6eeegonT57EwYMH1VBKUZSpZ1gbcsVBTAZR20ffr2LgZba1hK+iaHUcP4avYYLIZ09gH4QvOQAmOB6HzmKCsQxmAjuGz7JyuANgpg/YunW28gP4KkQJyWMAjAF6dTYtefhYqfQx1gdAbOENArQwneSsLLTblT3ioo2YMrmGgJbzStUWCDE9nj18fKPfUKoNrzVxGuaenN3GFMylXNZiLI/rCevm5a45V2ZSFdyYzYbxVLUBVRD1uQlVavWeouotgDRvOz8/r4ZSyshQYTsm5DO2hjIDKRW1yjUyrCXZEMNxaHEfeIKN1dvtbEkWZPNXt2RFUXYL0gobhN+grtBynLV1PQzcKgauH6qnJVKVlrkWucSURK6vuNlmTMFIigjwBWBceEOcKFRiAcBUCKJ4pg9rY0WUXX1OzyAGfEnwhYsC0wZBykH4lbEtWVp/67nRWRTUTTPDlorkaAwAjPAgngjk6rZnx910LhsrwflsL0wXxBWYiyT8UkU4ikExf6pfi+GtwuH+a4+18djaIIvS+eWYXGyKME2ju2LCBb9GbKfn3xK37ePb1VtgsgWuzGGrW7IySlTYjgFSrZXPKw+AVNQqm0d+piTb1jHgHcExA2CUHiiIYNPP2faJW40CUhRlN1GbEbnUuuuSM3J0IpZ5W7eKQdmvY32kUgsADLAP8yTeh0pt43bDsByqtr4KUT8mhuQE4crwNsu37TqQDW3K4f5otjn7IDANFzDMDREbltM0P2JmWCrQsbMofDfkwlK3bucdApGB8yXYhnPJXG5t2GTifC+haFVZAWA986VcXJokOhkcX6xcOFLrfiJqCfll7X4oItnE3F2LMLd8NeJW7m/aQrdVcZ7k9mSNAlLGARW2Y0TbFRlotiNvd8uoMj1Itq13CG3HWdW2iuM/lmojqe3+2VJxqyjKbiAXXhJ143wQuJUvU0zOwPfQc8vo+5XgftwDXIVUac1HjL0HjGN4onC9D6KSLKVqrbQvix5jJzO6DF8RyDLIECg7LxFgmuG2WQSOiVm3TVfjXIAaMqHF2vVQUCe2La8vzOrs2yI+xyD+RQgHcRlmsqwv4KiCoSKJbZvPrmaCV6q0huwakShrlvN7Dm7HQ3RrfE2yqm0mPnMce1hTXLW4lTVQo5pdv455a/Kko+JWGTUqbMeMvEqrFVvlehEjKanauhj141hE7vYbSQkqbhVF2Q2IkMqzUl3MOg0xO30MoonUYNCH63Oo2FYM9lyL2lxz+lit5dhm7AHywXzSV6HaawoCU2hXzoWx3NeXcZw2b6mNekPu47gCcZytTTFEXB8f729s3WpdcahCh+c+m9qLh+HZwVB0VCaC97UZU2gRLpIxlOTDOl/BUhU8IkwtinPaYjT/XoT4Hw9iA2sKALZ2VoaHZwODtcZR7RbiRrXY10I0OE9brEfeklybSdXnba97kqu2gIpbZbSosB0j5BceS2idolwHuZGUi1Xb8FPF8AwMXF219TtQtQWa4tba9f8QUBRFmXSk4ilOxFKpHbgV9N0K+m4VboB0EQOpIJoAyn4pM0djKUaawzWIDsolwXfCOQAxlAJyjceStOBo6J8Xotk8MaoizMt69vAuRBAhE9vG1hVbAHDewdEAFRnAAc4Ua2ZdcxFIREFAewNLoRItrs2I8Tgyg+t8hYpKGG9AxiRBmVc5G9m58ECsNoe1VXWebDy3gwOxgUMVLalMmg2WGV4xdgqeJyKCuSGo5TlaFHBAGu0Zmnk7xExKHq99zDSQi1tr7YZGW4qylaiwHVOmI8JbGQfyWduuXVu13Yn4n5zFxUXs2bMH8/Pz2/o4iqIoo0LMo6RiW/kSg1SpXY3GUauNaq0vY3U1llsll9aY7L1uj9SO7IEgJEuA+uE+zKFya2ydeStaiRkgHxzz64XKejkTHwwUVVhHsGUIsUDRhZksYE3Irg35uCa2XVdpRpYzgUZkAI6VWQKYDZh8clMOFc8hr6F38CbMJctkLJsuTPzTNV0XRTCl4WOAuK6ISju1tP0WJozkMIWWYs7dlv0gzR6HedhazIrbtcBZGTuJ3A38UQywrnAVwcuZqAYm20xqYWEB99xzD2ZnZ1XYKjvGWAnbM2fO4Dd+4zc2dfuZM2e2a1kjR0Wuslna8T+VF8OoVtU2vmu+U3Pcuah1zuHcuXM4cODAzjy4oigjZ5r3+/XifgZuBT13GavVElaqpTBbG6u1IXuWUwYtkInIIsydcqzY1uYbocoblGdoSS4qgukybIdguwjZOciOBzfmbOuZ3eCmzIyoYNsOzEEgGxtmWQvqojAdFNRtvBnKMbU2CVnUbcNMtYAkru9jEd2Us/3HcRUdlB3gBxhkT8NQcFP2ZGBiNq48P6YQ8SMOyT6aeHHsx2ayUbR6sCnAnkEI5lHhvjGvl2rH5mFmWA6hZRsmfL8tFSnSKIj9vNrO6bWRqnRe8QbWtlCnb9mEtyXPzc01vn766adx8803a1uysm0Qj0kKujEbZ4JdLc65Kx+0A9x22204deoUDh8+jJMnT657nGdG5YG+YyxXjKUBo/SMjiHMFYSFDmHGEroGKNQ9StkEScTGn7PVKlw8hxncGRt+1uTnLDgq78zPGjPjwQcfxPLysubcKsouYZr2+3yvf/zE40nMDtwqeu4ylgbP4EL/DJ5efRynlx/BE8tH8eTyQ3hq5Th65z0GS4yyx0Hk9jkKXYZMJJlOiPQBgvh1A04ty/LXW6rsFkEE2xmgmCMUs4RiBjDdUMFFFKf5S+8dGhVZ0wGK2SCKxUE5Ce1Yse3aOXTNLLp2DoXpwpKFpQLGFEHUwQwVduEchMJ0kzC2FO5TmC6s6cTzFbBkQ6YuGRgqUJgOOmYWXTODwnRh4v3COUPCrbQl2+w2oBkllG6Pj12YbpqRFeMoOXfbMTmf481bq4FQwSZQXFs4R5uQD2zT7evNBcs60/0mWNjmnDp1CqdPn8aePXt05lbZNsaqYnu9GnsaWh0MTcfzUMaHNVVbQygNY+BCy1Tlw2Wnq7ZA+Fnfv38/lpeX1VBKUXYR07zfi/lRiPhxYbbW9dJcba9aRtVjVP3QRuwHcVY2ilWSVtgOwXaC2ARCZVeEKRsAUuUFgApgF6q2YZ5W2pqBggEukFyUc50UhC2HYxHmekU0EwBjCWykNZqS8Lya1z+vVKa249gibIwJpk0ShQMPwx7eV6nySeRAIFhTwMf7h3WYWM2tQrswR49kBgwV8KjgTJGtwzeEbWo1NgbOVyhMN0UDeXYgVKhaIjeYQxVrRKasi31tNCXVW6BlCkXRkRlXrtQ2H2Oyq7bCvn378PTTT6uhlLKtjI2w9X73NtxK5qjJPgeC6Gi6JG///KMynZgY90MxBqhjCJXnpkMyEyzv3KytoG7JirK7mOb9PndEdr6KrsElKi7r7Frfhy8BP+BUgZUMW8lwpU6owIY5WQJiqzBFQwRiADYcGwylGOxCXm0wkeLQssxB7NpOiPwxtq7wEqHZ2mzqGV/29e9/qTJK5bL9XJE5AovhElhcgH3jNQnzoyZF+Hj28ORB7OERK/Ayo8tBiHrnwMYnkenYgXwVBW+9Fnl8SwW8l5nj2v2YYsovxVlgDw8DcTxunodYhG1obYbpxipq06wqfx0MFfBcP4dQAQ5fXo2AbZxzioykBHVLVnYC/WkaMSIeiAjWrK2WeXEyHIuGcWWSMRQybbsmfOxkP29StXV+ND9ri4uLOHLkCADg9OnTOHXq1M4vQlEU5TqQvFeJwQmmUYN0cVzBxXlP5rqy6mPmbCpiR9Ep7y2y5+yYWD21QfimS5dgilCRDS3LQNVnlCuMaqX+WK3GSvEgVIvZtZ9DjAaqokszcx2NE0WqPA+XVVHT/aOYrXiQXgPvm63AeYROLnplHjZvHV7vdc7Xkxs6AYhzzfF153BpGz/l58nXLm9I1BFNrrFGYaOZWAY3hPZGtNfUXl9uXHWlc00CIm6ttUncTvMbXcrOo8J2DMirtZZIumTgmJOoVWGrXC8mthlbInQsoTB1d0Co2jIqBiqWboGd/aFri9snn3xyRx9fURTleuEoznzMd618idL14PwAzpd1RZDWxu4k1+EoakVguh5Q9YJQFaOpFAkUBa7NKryQTNoy3KfqA1UvuC+7MrQ95yZVAJKYBqKoHTBcD3D9cHxZlcEEKxPq3BKLeW6v91US8x5NUUtxbjV/zVy8j8/OJwZQhmzdAh0rprnY3uh7kfKE4/HipCxITq6PYnqYePRRdLfPndaJ0LIsc7gpWK/lpixvaMjXdYyQ3/jSEriTTlvcPvTQQ6NekjJFjE0rslKLW8HHTiIVtcpWIa3u3ThrW/kgamXWdhQOyTnSlnz69GncdNNNO78ARVGUTbLGCdmXKH1oPR74fqx0xllTm1VdLQGdMGMLILkUs0PKkZXPwyBsNJWKLsUkWTk2dniVgPdIrslEDGMI62qieJ7wJGJrtCMYxzAeMC43n2KQcTAUqrZrInCiCMzFJCFkDolxU55xyxwmkg0hRvbE1l8Es6UiGkols6lsdlZmT6Vtt11FbVdY5fbk1pw8nH3Kn02GTtn50hxwFKWEuk04N5EK97drsmnjt7R+jqCUq+skhmidyq2UOkzemj0FM7cibh966CHccssto16OMkWosB0ThrUgg2pxK9epMbJyPbRnbbsW6Lt6njtUbetZ21GJ25tvvhnW2isfrCiKMibkbciVHwRR61YxcCsYuBWUvle35JqYN1sAphs3fArC1Ydkmlih5dQWHNSRiNBwn0aVtwC8J5BnOVUDMrWgJos0b5tXjmMqDoDwuNYR0GVwFNAigCtfojChKm19ATJVymEN1cj60Ydl1AJZ/mwc+hWx11izOBIbm6qict9Q0Yy3AylGJ7+viN40I0xZGzR7MCiISkISt43HB6W54fC8whsTJhPK+fnl58BQS6ymv+UyE6w4x7tRO7K8HunvvykTt/fee6/u9cqWosJ2jBgmbqVaO/nNJ8q4ILO2joAS4tIYW5Fjzm3XMDzTyAzL8o1uaWkJS0tLOHTo0I6vQ1EU5VoQ8RMif/oYZBXbypf1XCpJRi3gC8RNvnY1dgOGi23EflBXc8kAXFB6NCICGV5fPWZI+7OJAlVMpBrrd4CrkCrD8ontxvZoCtezByo/QEFdVDRIZknpPNlMrbhE5w8VRGzmGMweNgpRxw6G6sqviDuO1dwgSKVSyjDwAIp1xW27kuvhYRHEIZFJXw+DwWGHFMEOA6CqBfUaQ63wR5vLhpfDHiu5vgUoCnKQ37AjL69GD2MaxG2+15dliVOnTuHZz362Gkopm0aF7QSgrcjKViJVW2sI1gDGB1fkhkNyFLij7hIoyxIPPfQQvA+zSeqWrCjKuMKNiJ8wj1q6PgZuFaXrofKDMMuJumobZltJ7OjhXZhpDfOxIds2tAaHx5BqLREl4clMMIZD7mx0OW5bJHA0omSmFAOUzKm4ntv1Pjy+VIfJEIyN92MGmJLeFZMo8sEoRCqQMgcq7bMeJoi5KEw9KAjL2AbMHEReigXygMvahokMyJvo2txqL47tzR4MCwuLYk3Vt34NQoU2r+QOd3Wuq8ftv7+CCC5AzLAmrDDdb70fjJbg5yjaLWxya74WcrE7DeJWePjhh7G8vIx+v69uycqmUWE7pjCHOUcVtcp2IIZlBQGFQRK2KdfWADE6caQxU51OB4cPH9YoIEVRxp7kputDi27peyh9L7rzBqHrvBtuAORrd2QnubaVRAFxnUvrEKN4QiWVoxCWfNtc1JGJ/TguzuiWkoPLIEPwCN3PAnMdO8Qc9gj29QVoti0D0RiJOcT3wLQiCkPF1KKAIUrmTDJTKsLSx2qmQ1WLPI81hk3h6qwSSsGIiqnbmL2lLB8XEKOnIs64mhAtRABF9Z7mgZOZom+dp4ivj0+PY6kAMyVxLu3B7bZiaZOu24frSCRm3nQec14RnxZxe+TIEY0CUq4bFbaKsksRIylLycOxnrX1DOcxMhOpHM25VRRl/AlWRCG7VpyDyzpyxg9SRI7nKmXXhvnZ4FjsB1ibaytnbwjMKGo916ZPEbKURQExyFKKFEJZC2KAQzyQSV9G4cxDq71gqfpG/ZcbXQYVGtuEOQlSEx2QQ9etgbE+tg3LeUM7suTIGhgwXHIDNj4/T23SlJtSFaabKrlF1EAmm71tCk1pA6Y17b0eHoabYjqsJwhHa+J8r8w5Iwwsi6Bc16VZOp8AUFYZZvYgYzes1g6rHrdzcets4MkXt5pzq2wFKmwVZReStyMbz0nkepY/z2isYqZU3CqKMu5I5TK5InOo1Jau38hUrXyZxKzrMVzKlZXPoxOyaCRDgGtH9ASBGrJmkX5ZkwWKmdjenP2Fxw5B0slMLkJrsYnt0KLnpEU5mFJR6qL1DiAHMDGYCBji9yMZvTJHbMjAoABxFSucwyqwQVA22n45PI6hWMmlCia2KIc11tXKRBTPMIiilgG0QnoRKq1rvm9R1TsRp3FOVtqETRS0yTGZDTzV8TsyGyz3Eda4NGd2XuvNzbbvn9q6gUzcNsW9iltFqdGfFEXZpbTbkeWd4MrHC9dRQDudaTuMds7tuXPnRrwiRVGUAAOxGtucr+27FQx8D6Xvo3Q9lK4fRGzMiK360f04GkVJ5TZVTtuuxVy3FrNjsGP4snZOZpcbTVHD9Zg5tjiXob3ZD5Du57O5W7IxTqiT3deFDN26TRrw7BpmWZLd6znLsI35sC7OFrfFrYkZsJaKRvVTjvdZlmvlB+i7lXSRPN2Qm+sajyvXy0cxc2pHE7mUY+vA8l/K43XweZW9kUdbOz/nmbPijJ2ub2XWbvwzJFVfDpfWefNz+SFCelpo59w+/vjjo16SMkFoxVZRdjEmilrro29j3KhLzyg8UFB0SR4TN36p3F6+fFlzbhVFGStEkEnUT+mDkE2i1vdR+l5oOS4lyqduPfalZNBmmbaI0UBWMmkR2oBF7BoCMUNMhiWSh9pRPlkZI7QmIwzYEoUPtj7OFPU5AIQ2Y4iQBiBGUiBgph8qqkNmTGV+1rEDxSq2oQKGqxA5R0WYwY0tuRwfSMSv8w5FbG+u/AAMn5ylTTana7mAoQKOHSwAx4NYwZR1GJAxa8SlVItTbFA2m5uOjW3ItdGVD1XrKOgrD9hWP/i1mEE1Xy9O64o31pm/0UHZrlfp3cA9eRIRcfv4449rd5ZyTaiwHSPyaB/PTVMHRdlq8kxbk/2wVTFH0TqgE02kRu2OnLO4uJgErqIoyrgQKmnV0PnaisvUoixiNohE1C7GXLsaC0RIDayiW4xUVC2CwC0ojc7aAjDdOm9Wmm0ozobKdSnJJ/s7IwjhWtQaG+duESu6PpyHmWF96Iv1jmCsA1mXzoEYKVS/LkEA5uRRNhKb47h2Rmb2MMbD+DBfa2DC68fBgMsaCyKDgqpGW3QtCusHtFSsqWwm8Zy19FoqhmbZtkl5xTGTln1sWaZ6dvdKbcF5ZJGsu13NligiAxONpmQuuW5TlrZkEdPT0I4sLCws4AUveMGol6FMGCpsxwyZa/QMGA4TGT5/d1RRthBpR04ztojGUQjXdy3QjSZSo3RH3ojHHnsMRVHou7qKoowUcf117FL7ay5WmD3YI4naNOFhopAsCMYx2IUqbNJiIk4pOzYaRGV+RGF+Nl4PRMMoF1uSbXQ5liquRxKEjZnaWOnNi5Cp/dnXH+FjlXiQrye0L5sCmYlUFd+0H8BzN70eoQXZokittqFCG6rd/SDQuIT3oc1ZKrqVH4DZo4NZOFM1Xv/cYViqn43rIILUxRncGhtvD07LplEZpSF/e/mstZnYJPdnG+7QzLaNYjRUsClUfqmeRZbzNJ4LNs6wHcY0zdoO49y5czh79izuvPNOnblV1kWF7ZjhY9ZcxSHWTr5WUatsJyJugfjGCgDLwMABMxbojGn3wNLSEs6ePZu+VnGrKMpoqNtTZTa0jYeHr4I5lFy8C0LSFqECapkgw7WpTdkhfcz1ilRYk+Cl+vbcfIosB7HbIZisapsj+q+th8QJufVUQwV3EL4gQhC0neA7DHmj1IQZVVmHs2FOlSjM1RpfNMycwgztamjXjqKvoC4qLmGigm9H5EjF11JRt+0CydzJRHfk5CKczeymc8DAxXMFk+i1rsmmdY7UYccOhkJ+Lqf6ai1IpZoqbdmUXKA9MORnBBheMb7aWCB5XrXv2HQIQOccTpw4gaqq1FBK2RAVtmOCiAkGo/Iibps5tmLiM45VM2VykWqtodDR1kcW+cNA5Ql+TGZs2+zduxdHjhxRt2RFUUbOMIOgvDW18iX8IBpGRTfkhAnCMBg8BaHpXZi7daXE/4SZkDzyx3CM7UFdWYUPc7opmoaju3GsuHpiGENpzpYswXvAGA4ZuabRyVs/F9ERVLco50illh2Bs3OkczHDd6LRk6ngfYWS+kn0Vb5Mc8lSzXTk4OFRmC4sFcml2EbDqSCSTRT4dcSPRR0PZE1RC82shTjHooD3BLZF/HaYdaultYlT0904PH4ohefiF/BxBrlK3xNZW/hO1q3EbUEc7u1h2DRNxLLb5eu0jilzSgYAay3uvPNOdUtWrogK2zFFZmxlbxqX2BVlupA5W8mzNcQoTHg33mcdA8yhJX5c5mxzNApIUZRxgMHJgE+EkTjoumgoVfUZVY9RrYbKrRg+hdlUAkW/A+9DtbbqA66fx/+E7Fo/APwsYGcIVvJso3iVCi8grcoMYykI2ii90lAu6tvrPzYotNQa1Fm61LwLZcKVTN0SLRVeX9ZfA1iTt+ujI7GJAlbmkz2vFZ3B9TgYQBXGojBdGLJp9jY8DtdrW5Nf23zcYaqdycc3AQpYjoI5E675DGt4xeJsLoeIIQLVgjdWbWU9Mk8r5zBsgixdR6jmYjRvR67zfptvmLSFa3rucf3TIm41Cki5GlTYjhlJyCLMfEyfkbsyjhARCsOw0tYWt8ZqQt5YUXGrKMqoGZbTCjTdksX5OM3Y+uacrFRd3SAIWqns+jK8wSjux6YACpnVnQFsN6gkqfKyj7OyRfj97lMLMoc82ujdwQ7wA0rtyrZLKEKQbC1mW+QmVikeqKBa3LrgD5KMqwiAlRblIrXVSkuwRPoA4XaYKBgR3nyl+CaBodhyLPFAMNF1OQhkYkpCN6+keo6OxrFNetj3i2NWbcqtBcBk66+zudm8lZljNVWckuU6z/W7AmEON+byJqFZf9NTVm18vYe2MRPWVG3b92/TPt80oOJWuRIqbMcEeedOzHyAaL4Q25HHXVgok0sykEKdZxvf+IfnkGlbm5qNbyt8Lm6feuop7N+/H3NzcyNelaIouwkRN+Jym2eOClLhpGxmNkTwZNE/A4brMapVjtmxCLOtLlaECzGVBAACxz8S2NdtyxSdiU0nZM8yU+Z2HFqGvdgjG8AYoJgLa7FVy5jKNp2OgeGiNkUDcV0YNZ36eRuqRWrjXFE8WhRJSErtMzyWaQjcRmuxr0AYhMeKx0hdOsjSIpo1ARRrp2Gp2R9WUYQSD0A+CGZLBQrTDYJ8iNVJvr7GddxsDa6PK+DZww7ZQkO13ycxWs/Itoyosl7mdjtyffu1RQ5NGm1xe/bsWU1KUBIqbMcIMS0wxI1fSbVJwShWpewG8jnb/GdP3lyZFBYXF0P1uShU1CqKsqOkNtXYfhyMiuqNm2BgCg/bYdhuuEaqtsxcG0uV8fMStdCtOLkSA1G4mti6bADjKF0vxxAzTCfM5HoHGHB8PAqxPT60QovTMdloCsUEnuHokBwrrR1AWpalagxpoxZxLnO3maiV2V4gGl1lO0xDqMb2YR8dg4HmXmRjpTcIVxGmoVpLbKKgltidWgQncyoPWFME0dhu3U3RPwg93gjtzwV1wBzmezkaROUkEZ21BRN8ihBqZNQOmdmt25o5OiebkMWbVVpzsYvscYbl2a7Xfj2NiLi9cOGCilqlgQrbMaIhLtJsRqzajnZpyi5gTfSPhNvHPxcm5Y2VAwcONL6uqgpFob/qFEXZfjjOjjp2cL5KookomBiZogo5s50gLF1ZV2rdIFZsowOyCFnvalErlVCDOKNLQfWkTNs440oE5N2ZSYg2HYhSW7SvOClJYxDEr42OyjHDhih2KFusbYmN6+JWDzIBGFY8zKvYyTgJJu09w6qR7eoop9zgMCPr2cVqLcFE5+UwO9sN52dTi8T1vncA2PXAppuclQGEyi3EOblZRc7vXwWr6PiSxMghEKwpsjc7mm94SGVfZmdzMdtoqYaHxfpOjtNcpR3GwsICFhYW0tfBD4S1LXmXo3/tjQG5gU9hAGsIzb2nFrchPHxkS1WmEPn5A+pZ29QOxVk7sp28n7/BYIAHH3wQ+/fv15lbRVF2hLxiK/Oj4uRrCoItglgMIiqK2wGj6oeP3jVNm4iiFjOIubT5LC7DO4KxmXlSnME1UYymduF2LFB6Az1GCUVHZlcCMGEW11gCiiBwOeveYQbIh0+8CxFFSdiiblOW9afHkvnU1j5CRPDs4Lmet2231eYRSnIOJ3Os7OGoalRRi2gIFXJmCcQVgGLDPUyMnuAHMCRt5eE7WVA3VH2pjv+R6nDbVCqf3zVUhKrykPne9eZj07nE3HpINu56s7PS5p1XxKcdZsbDDz8M55zO3O5yVNiOEUQES0BBjIKCcY8wKdUyZXKhKHAJmYFUFLXijLzmr5ExZ2lpCYPBQA2lFEXZEXz8L1RtQ6QNMzdEmSAmUdVqMIhKFdsya1+O7b7SRpxibhE+uhKgikMOq4mitkN1VZWiyZOtPwJ1xdWjOesL1PO3Yc41vJtpYowQM4M4tjcDQBTB6WRAEJMmPJ6J7cwpX5d92E8yw6jwXDiJWu9rZ2SiOG/LQag5rkL+rQ2vsUUBh9BmTfFVDkLWRJdjA+YiPaZHBebhoqcWivJmRDbsGp+XjLjmYjGZYOWiPKvUh69tmrteL3JoWGlbxC3EYRu1gZSI++S2nEcP7bLqbb/fx+XLl+GcU0OpXY4K2zEhbwNtGPhkxlHMQ9wLFGULaLchm+yPnuSOnGa9x9dAqs3NN9+cgt1V3CqKshOkaq13yQ258iUcR5HrAVcyyh6jXGFUK7FaW0WX4kpmaEPlFUDIhpV239iaLJXdZCGVZmGHtAoDqXoq8ULMce7WxupregKxYmwBOIBNqMp6F0SwR2yBZsD5ZpQPyWMYaWVuti5LFbvxeuVuwr5q3G7Sk0PItvWxHdgbdEwd6+OzFmMTaq5JbKY5XADrDXZJBVYEoWMPT2HeN7UPswdsjOuhME/rGQDXt7efm+FoJCZztMjEav6t2aCqGlyWw1ythwfF0aAUCdRq225n4k5L3M9GzM7OqluyAkCF7diRt8jEOLmUJSqidpKEhTI55OJW8Aw433TnnqRWZECjgBRF2TmCqGU4X6HiASouo7ANFzGDCnE+QLXCGCzHSB8HwHNqMwZCK3DIjBXjJ8BXFHNqOZo9NR2KTRHvF8WeGyDm44Zzydxr+FUeo36YgEykMgNc1YKUrFSSKWbmct1uHK8TAUs2F7XUcFdOr1FmtOXRFIaSZUux9Ns43njAiXETxcpqEaNw5ELpc44uEZ7reeccmZllYsSnUsf6SGXVI4la8qGlPMURxcgh+b7HVzSdG2hWUj1n7seyhiHV1bxF2chrQBL/E/rA25m2651Lzjft4lajgBRAhe1Yw8yoRFAAEysslMmhUa2NorYiguPwnvCkouJWUZTtJq/sSZXN+2Ak5bkKJlAuVFx9FXJqyx5QrYbPvUMyfxJkHpbiL2aO7cCh9ziKR4SPtkuwM4DtUHBdNqHKin6Yj7UuilviVLVFvB8ZTk7G3gFwHFtgAZMydwnGhbxbWZPpIJ1HRK20IJuWX4h3DIcK1hahXds7GKoFp5huyfyoIZ+ickQ8OlcFcYtQCe2aWRgK0TwWRTJ0yo2mKl/CoVobMUQmGkqFSCAigh1iUMUUVpHaiLkC+dDq7OEagllEpgjm9DUoPidquEDLzw1YYoHQ7AsHkhAW12XJtA1l9g0qva12aBW3ym5Ahe2Ykcx64kfKRK2i7ATyMxjeWAE6mI7IqVzcXrhwAQcPHoS16ztMKoqiXC3MWFMRNK0KYn4sfN1W7KsodGMrch6VI/mx0jos4jiExdZztXaGUMzWwjbNtFbBkAp9oJgR0UwoiGG7BGOi0zFRnaNb1W3RQKgCy9fWRSFsEQyrQDCmaSxVm1LFoWBXd6CBHQboJfEpbbTyRkAyjGKDwnSHvpHvuILhChRCeFFQN4ldMWlKlVipCGciT0SvIRuFXm0oRVgby5PMq7IZYG8M2PuGQCWYRgxPnr27EbVwrcAwKVYozefGSnBetd2IPONXPl5pDdNELm5XVlbQ6/UwPz8/6mUpO4QK2zEkF7fwdY7oJIsKZTJoi9p2K/Kks7i4iKIosHfvXhW1iqJsGzKvWZgOCtMNjrrWgmwVHIot1626VLcfh/naxolg4rHskcylfBlvlvZjG+ZxbYfSnK3k37o+wN7D9WMLczICDDm3af7VhxhXVzLcIEYAIZxX1sVMYA6iODw+w1cEKkNMEJlgPMXRwZm5uXEYQ/BdB565HMyfYs5sbiRlqEBhOrBcCzybC8gk3BjeV6gMwL4ADGC5qNuaqa6wtoVt3iZs4nV5lXeNs3AaBfPwxMm4irFWNObi+UpIy3Ee8eNQvy5hDc3W5vr6PCZprYim2MK8m0StIOKWmVXU7jJU2I4RHrH1U1pA47yLZNkqynYiLe+Ow89eblyWtyhPOvv37298vbKyohufoijXSeZkHMWSpSKINOomgWs7DrbLsN3YOtzhWtxKbm1VR+cQhag1oig8yyA862qqfKQY1VPP0rJj+AFQ9erW54Zo9gTTDSZVct5qNbgzy1pA4TwyiytCOpg4Zc+eGa6qxS5zJpgl25ZCLJGJVWHXXW2aXFGoThemCwMDRxU6mKnzYuOxQYzaJB5lFpa8QUUDGC5AcTZWZnaTwGYHJhuqolRXVY2xzczYddp2ZV7XgODJ15m2SVxSHTmUrY+Jo5kVI28tzgW9jJrlFVkbB5QNFUm8ynyx/KzJ4+di3bfakOX63USecQsAvV4P3W5X25KnHBW2Y4JUyXJxyzLggukSFsr4IaK28vESY34AxBigtcZS08DZs2fx2GOP4eDBgzpzqyjKdSHttACSeZGlAtbYULU1XZjOaqiszgRRa7sE02EpoIZ24DLOuUYxJvOw3gVx6sooNimIY4lj8w4oSsDPyJviob1ZKre+yqKCfKj8mlh5FeHrBtGROZlGBR9h42IEkMwAm2z216QXAK4Ko59USfts3VptLMNbwPqwZp+bU1Fob+YOgzoVvMnmVmMGsBxnqRhaheQYs1T5AWDqKrDM7abvS8svol1hbVc5Q/XXAFwFR2SEqq0ckURtfkFTJLvYOk1kAA/YzEo6tbCLq1c6LzWyaOV1IKJGldYMEa+7TcReidXVVRw9ejS5J6u4nV5U2I4Rkhkq1TKBaMrUhDJWhHeQwxsqjuuOAQbDEqGgsMFOm6gFAO/DHxRqKKUoylaT3HkhItdmzsVR1Bac5mSZ6/lWMZICAcRBaPoKqPqxtThWc8OMLoUM3AHDzYQ522ImrMFXwVHZO1kTo1wN5/VVqPiGxw3VXVcxjI3OykVYQ1sDSKSPKULbs9wu+oylBdnXQpoIQCfIPbaxXTkbSTaWYUBgi4YBF2dVTaK6GmqpaIjHNKvLDM9VyF+PAjifNQ3HhrieHA/fMI4Ks7nZm7kMeIo5unmbMDWFqKxLhKXPHtvBgUR0ezmuubGKQRSontEOFeoiVWrbcT7yuYrZ9fE+5CerodT0o8J2DBBh4Rm1sIi7Qcq2zfJFNepH2Q7kjZXShZ9BoZGtPGU/f+qWrCjKVpFHtABIc5v5/GNdoUSKxEkROzE4POXTcjN+R66X6quIXVOF87gBUAyAohLRGiusrbZhcGxL9qEF2juZqY2GvB1OLswplzaP8InXSS4uTBCduZiV9adqbSdWcmPGLTLBKM8vjzUM19WiNjgH1+3IMr+ct92mTFv2AEJkkMQAheeykYNwiAXKzx/mc6V662P7sm20DwtSqQVqQRp+BuJcLnsQefjokA0Algp4borb/L7p61b7sVyfHvsq53l3M+qWvHvQ7+iYIGZRlReznnreoiB553d6BIUyXsjPn7yxIl0DhgBrKP0MTiOLi4s4cuQIgFC5PXXq1IhXpCjKpJPPPDZmLVtRPiYZSNVVW2n3zcWjXJJodEgOxq7PcH1G1QPKFUa5wnWEUHyXUhyWycbYIA6tyOUqo1yJc7hxdpfFbTkT3yTxPQaN1mNZC0e3Zi9zwmlWmJN4JRsEbrpkgj69J8C1yAzzsdxwm/ZRVIrpU0FddM1sMujKHYw9V+n48HrXrbz5fK5k3ObuyUKegcvDbm99nd7AgLQJF7AmXNJziLFB7ZigXKDmX7dzaoe1HitXRsSttTaJW+naUqYH/RcxRiRxm5n2pPnGZJowuvUp00mekyxvrEi8QJG9sTKNM7aCiltFUbaKZktq0309iDiuBW4Ujykb1tZfm460LVMQv9LyG6u33iHN4zbycQecxK4vo2iU1uGirrr6JI7j/K2MeuaZtEW4n5X1ZNFDbsCpNbrqhYpxmOeNz9HXz9GYmG0b12BkNjcT676qn4s4GbeFY/N1jq26pgjGXFQ0XJZ9dn+btfKabEZXqqmOK3jvkhBuZBK3TJ7S97JVKc6r85ZsuhTUSWsLYz2mUeElqudnTXYctaq16wlf5epRcTv9aCvymOAh7se1eU83ExG5P4OibBX5fG3pGYP4swdk1VpTtyJPM3lbsqIoymYQN2RLwTDKmiIKFlGl4YNUQ02HYLvBxMl2GcVMrKi6rGJqkKJ+jM3aipudu+GqTCSSDbdbW1eHjUXsj433j8dLp2+jvdgixQiJIJU/RNgBThyWDdftyVLpTeeiVJWuRTU11sBO3koNzsveElxRgc21ZczlQrAdTyfCsB33E17GpnAV8yip4gJozN+ueVyYJERN/N5bs3YGWEjzsjCwxiZBG74u0hsjjRzkrFKbnudV5uQqTfK2ZGX6UGE7BuS/gEMLDjcifqZdUCijRToFSs9hvja2IXdiC7J83A0sLi5ifn4ee/bsGfVSFEWZQPIqYqjWRTdkCnEyQahSFLWMYgYoZgjFHNDphzlVsrWxkohadpzuR5ZhbKhuivGUtBmbKIQB1CLaxBZk+T0uc7zRhZml9VnuxqiFL+rzi+gVt+WUu+vD2sRwKplPdVBXpDvZuuIa2IWqr7g1M4fzeAO4jkNlB3BcBcMmLmApi/fhCsQhdijPfJXvgfzdlLsU163Itbhdr5V3vUrxMBFZv5lRi9ra7Klec36OtqgNH21aX/3RNlyX6+dn1qxBuXoWFhZwzz33YGZmRudspwwVtmOEtIPmF82wVbYLH/NqS8/oO0bPAX3HybisMEDH0tQaR61HLmq99zh//jxuvvnmEa5IUZRJwGQiylKRIn6s6aBrZtE1s7Adgp3lEMtTEXwJFLNAZ57hB+H3q+sgzdmyDwLSlSEWyBQx4ifG/ZCJbcwiJmO7b8OYimpRKW7LPpuLTcVEH9uSS4Ivwgyu7+Qim+rjPOAHjKov1WVOs7O2CxQIVVlTZBVmRDELTnFDVR9pDph9yLkN7dGEqhig9D0UPuTahv+ZtAZmD0cuCcTaoMsAXM/U2vimQlvU5u2+67X15jm1lH0OIFVoDdmQVxwr9HU7cWYMFVvSPTNMdMsuqBvvF1uVTZEJ8Kaglcc0NGwNKsw2w9zcXOPrZ555BjfddJMK3QlHhe2YIOHheSuyRV3NbbfVKMr1IpXagQdWK8ZqFcRt5aOoNYRZGz5ams64nyvx8MMP49KlS+j1euqWrCjKVZG3jEpLcmG66No5zNo9qGaXGgZQnT5Q9Ql+IegfX8WZ2dh+7Abhb4S6LRgwXQI5huS/hgppLm5j+3A3Ct2YkyumUalKGtuJ2SPE0MS5XWcZpqA0e5sMosR8yoV1uX6IEiICbHwTnkx9/5CxS+m8obobPq/6gIuGVY253/i6lFUJQ6upLbjgKla/C3hyqFDCko3V3CgQUaQZWtOeW6VmXm2e+ZrPvK79fkYDqCg20/VReMr3tzCdJFZFvAJN12IjMT4w6T4iik2cyb4aMdt+Dsr18dRTT+HkyZM4e/asuiVPOCpsxwgRGlK5dT4ks6moVbaaVK11nETtahXmbAHAEqEbxW13F8zXrseNN96IS5cuaRSQoihXBbNf43prYjRNx8xitlhAOdsDuzIIyAHg5hidfojmMZ0oACXTdhCqoRLfY6JgtL4WmcngqZsJ2mQ81RSNXNVuynm7c9OYKmTjisD2DvCe4R2BHILhk8sMsNBqM0Z9LkDOEQeCK4SKcVatJYOQYVsw4KmONCoJfawGIyjj0LVzYPhGm6+jApYd2IQcWzIGBL9GqOaiVqq1w753RLZ2Xs4qwCYK5NqkykbB20miOrSdd+Kxw6vAdRtygYI66Xwmm8vdSMgCKma3gz179jQMpVTcTi4qbMcIaT12Plw8qahVtp68BXnVMZYrxlIZhG3FQNcAsxaYKwgzlmCNOHPvPnWrObeKolwrbQddEUYibrt2HtXcxVCNLQHXJ3QXwrGmU1dsfQVURRCIRRUiecKsLCchGVqRg6i1M1Hc5pXbTnRTZgBVaAHOdZHMz0r0Tlgv1phSxScWBK2kNFgAnVo8S6W4kXWLzNAq/kETHJVjHJCrH9PYzCXTB4EPAkrTD4LPWBhvUBgAVMAw4FHBiNETV2DEnFmOzsjkYdjD+wosIpUBiq3BV3IWTm3lUYTmlVn52DEz6JpZdOxMVrEdXgEWYyhpWS6o0xC0w8ygVMhuP5pzOz2osB0TZKa2yqq2irLVNERtxVguw6XvgjtyQcCMJcwV4dI14brdWrEFVNwqinJ1NDJMs/ZWETMiirpmBqWdgZvpozMfBCwQRK0bUKxWAlU/ClkOIs92uHYcrh80tSIncdupxW3In42HlqEfmb18lPtSihuSKm6o+MaZ2U49t5sEL9Vt0elrS9laKBpGkYyXNiq4QF0prlumo+tznpDEsRXa1hXwOgLHJDfhvELr2cNQiAyCByoTRK5BAYvwRE1yMM7Nmqhh+CS3d8wMunYWHTODjplNM7GFCaK2Y4Oo7ZhwjJwzJ6/CymNJm3Rb0KqQHQ0qbqcDFbYjRuJWxJW28kiutIDm1ypbg/yceQ4GUatVq1Lrw8/YjCUsFISFTqjWduzurdbmtMWtMQa33nrriFelKMr4QEkISXupOCLnGatBDM1ixpRwM1VSeaYA7CrBlcFEyg04isPov1EyypXwdfvXsWTOipFUqtiKqZQIxfT3eYjUYQ7CspitRbDM9pIJc7y2E0SqjbO6ax93bcxPaovuUBLIw+4X4n2CILYd1O3TawywKL1+aX42VjlTG3B800BMm+KzTOIWJlZnKa+gx9ZhmHTeXJTKfPSMmUPHzqJr57LvaUvcmll07Uy4jboh0mjtT0hjflYF7fgxTNzefffdQ7+fyniiwnYMEMOoga9daT3X2bXFdf6u87x+P/NuFyy7gfTmSV6prRhLg3qu1sRYn4VOuMzZYBy126u1OSJun3jiCdxwww0jXo2iKOMEkcS+2Bj50omtqp1M/ISLxNgAQG/uMsiWMAVQzERDpUGYra16UQB6wA9CJFBoN44zsI5DlTSJyuYv6zzCR6J6TAFYF52kfBCfdiaKWyvmUuEujfbmoq68SkuZVHQpzvpK+7GJFeP8fm3Nlh+b4o9ixq4xsdob71dXNoMpk8zKiqiVKmrXzKbKrcw4+zhva2OUTmG6mLFzdXU1uRjbLEe2dlPuxErtjJ0PwjYKV2tsZgDVRUEddOwsCuqsMX/KySu3KmjHk1zc3nTTTSpqJwwVtiPGM+A4iNm+i5eK0z8kawiU/XoMldwrD97ms7kbzeka4uzz9Y7Rf9SThs9ctlNHgAsztatR1C5nZlHSfrxXhG0RnJBD1I9+/4XFxUXs378fRaG/OhVFaWKSoVCo1oqQ6sZ21dliD7xUaMWx11gUZhX9zgqqPsP2xW0YSdwxh7if7gqjWiG4QXAb9mVtHpW3CXtxS44mT741MEsWsAgVW1MAtkMoZoIYBUJ1GBBxmlVOTZyXNfV5JGYotUhTy7yqszbyJ7Qg57dRo+IrgtZYSkIymDI1BSARRbfpGXTtfJhXjZm2HnXrcqqsxnbhXNhKNT0Xv/I9NGRDTJOdw4ydbxyf3IujuBWRvZFxVFizOhpPAgsLC7j33nt1r59A9Ds2QvJKWt4e2nNA10YHQTk2XqRlNNx/g3PHjxwfYxi5kA2zJWuvN7RxxfdK51VRtLMME7TOc+oGkLnaVbdW1EoL8pwldIyK2vXIN7rl5WVcunRJ25IVRUlIm6lUGTt2FrN2AZUfNJySjSlgnU1ty32zgrLbh+sDVaeeNQ3ROoTuXoOq7+EdwVhC1Q+/w4niTGs2m8oxq5YYICezTaj/OKBQIbUxd9Z0QjswTBC6zFEAx6qrMeE2EiflOFMrc7GSlZsLVpnPbbRDZ9m7InzTNiO3RYfg+s2BTor5oUa1Nho32XnM2oVUgSUYcBS20l4sQrZjZ8MbDZnZk4hYaUkO0T51NbjdaizV1jCPS6nSm2fptlERO3nke31VVXjiiSdw22236cztmKPCdsSI+FipgKWScbEfxK0HYeCDKHHMKKPZQmYYuOE5xWFZooNy0txu/JqIYIhT6/Owud6racXI/6kbujpR3K4Sq5C6dtpiVroAJA+59KEToOeAfhVE7cAFd8auBeZsXaWdi3O1xS6O+LlaqqrCsWPH4JyD914NpRRll8MQoRnEjTW1+JotFsBZDA0RgSqZtYzzuL6DPq2iND2YogRReIObfTSVGgDsCMYaDGYkLqeeiU1uxCbLrXVhTxCn49S23JFW4Hoe17TEccNYKlZRmcJb7rmbcnjcrLJb5PfLxG/8I8HE1mMRzVJlFREqDtIyz2rJZkLSpNd0xs5httiDuWIvZu0CurEluVkVzarmmZiVj/n5cyOp/PHTDC91GzmzQDY3m0XzqIidPh555BEsLS1hdXVVDaXGHBW2I0KqtTLzeHngcaHvca7vsVoxnA9toasuVNgAwDoRi2EDzQVrLkJygbOesBUBG+4XRK0lyq5rCl85pv1Yw8jPLYL4Sr8CciF8tYJqN4vgXMzKxyq+mTHw9Txt6YOILWPVthT34+h2LO7HqVJr69za3fz6Xg1FUeDQoUPqlqwoCtrv4QbBY9E1M/B2fo2jr3xOZGBcnOn0oZW58B0YWgWwCoBj9E+oosIY2BlGMc+oVuosWHa1iVPCx+xaFyS3kVnY2EJsu7FiO0OwucC1WWtwEq/1fRGWUc/BUlC7uahNorUrbcz1HxZpDrcgdMxsMoaS1mJpPe7Y2dT2G+7aFLbzxQ2Y79yIuWIvZuwcunY+tRPLjGvtRF23HueRPaFCaxvV1pQrmxuBRcGcRHj2Quus7PRz+PBhdUueEFTYjgjPwSxqtWJcGnicWfU4ddnhyWWHngMGswYz1mPGEgCPvgt5onJfbu2i7Ypq3oIs1V0RnCQCFs3qLFEtetG4LYhNAq1pUx6GnCOvBK9H/ljWDG+HHnZ+4NpEcLjfZAq1vOqdC1n5KGZjMkc78E0hK+3IVbxPEV/ruYIwX4SK7Uy8FCpqrwmNAlIUJcfEymJuVtThqvHXFsOnGVDJvJX5XEMFrMvjYlbDXKt0ExdAZ5ZQzDHKlXCpVuvIIKA2avIlgDKONXEtPCWKR9yObaeO25HryFIjizYXzLXgrQ2liKLYLepzrmseFSvHoZKatwTbWE2drc2YTCdVRcPrFF7TWbuAuWIv5ou9mCn2YNYuNNyKRZyuNXeqH6duO7b1mwxREOdztMMieeR7rewONApoclBhOwIkS3S1YpwfME4uezx0scJnz1c4dTn0HK9WNgnP0puUKTqsJTgcF80ehuiRXJQWBjAcAtY9UcOHqmEk1bp/EJ7N29qPlUcUhQs3xPCw2eB0LML5i9axw54LsP5M8Eb3uVYhvP75rv8kVzO3PEzEAuGNCs7EbOWDuG1XZkvPqWoPyGscfo7aWbX1TK2K2mtFxa2iKEDYZ6W12JoCBXfrSm0mgphDDI33VcPgyNKgrl66OrKGuQ/4epa2mmHYGYRLFyi7wXCKXW3gBASxa6IZFTzXVdqZaBbVrduEydbtwXaGUuV26JvoVLcWy3YhbcvFjJw/z6at9xRmTpXZWbuAWbsnmDlFMZubPIkgTRVYMiiog26cWZ4r9mI2ilpxL5Y3E0SUioCtI4FC63GeYZvPyrbjeML3VR2MFRW3k4IK2x3GRwGyXDHO9jyOX6rwN89U+PhTJf7f2RKXVnqYmZnBclVvJgMP7I25ol3LqWUYWNtWnLf+ilARd9si3t6OcMkFU7vKK+cyXAtDHx9/mPOybIJpHSK4N3hNRHBbn7dHD79PqjYTX1N191qE8EZca7v0MNpCdb3bgaaIzVvLpQIrorbKqrRym5xHKrQiaGdilI/k1BZUR0qpqN0cbXE7MzODW265ZcSrUhRlp5C4n1D1ozg32wGbUJ0t2IONR2UG6NhZzPgSzlZwXIHIoKReEreyfzJ7sPXADAD0g5CMc6ww2bysCS7K7FC3EJtQsXUzUfQy1WZRMyHix3SiKZQ8B5tVXGcouiDTmucJ06zWArFa2w3CtpgjFLOhzbhrZmHi4G6I4AnCtmNmMVtEcWr3oJvE7NrZ1jofuJNcjcUwarZYqI2dkntykSqtYuxUUCdF+5jYJj5MxAJozMsCKmaVmra4PX78OJ773OeOellKhgrbHUYckC/0g6j9xJkS//3kAJ859ijKE58BvEf1rDvw2HNegBu6Bnu7hNkCKMhEAUjoGKBr6tbdukJaV2YNNQWNNbWgNUOEbeVr0dR2Ul6vfVlu87HTyXMQvXmrtJgx+iGt0kAUwFGEDWtbbn9N2fV5tXc95A+EwlydEN6Itki+Ult2W7i2K67tY9qCFgjfF+ZaqMp8deWbrsfyMa/QtgVt19bCtmvqNzy0Srs1iLi9ePEi9u/fP+LVKIqy09QtrAUsOXhTRD+MIGo9V0Gw+UGY+7SzYDCsC6KtdH1UpgNyBswMtj5WbBnc9WBfyiYAdgR2QCHGkh2EX/4GSax6H8Str8SxuG41bufS+lQRziqurb8QpQU5uRxT3WJMNsYGzQGzxUJDdIrQBADPHoZMMtSatc2Kaz7/mldXpdoaRG2I3xkW2yOzseKODCDN7+Y5tcOqsYIKWWUjRNweP35cu7PGEBW2O4iPAmWpZDyx7PE3z1T4w8f6uP8v/xd6/+dDcE8/BnvzYXQBrB66G0AHMzbEsOztEvZ0TDL56drQPjp8VpaSsC0yAdM2hwprajooB8E0pN2Zmo+x9rk1hZdnSl/n55BjMURIXSl7V4yu2msYVn2ubwvt0LnL79U4S6957PhxmIP0RkK5/ZzkzYP8NUd2Xft++fxs+02HtjkYEcES0Gm9qdExtahVQbu9LC4u4sCBAxrorii7kLzCJy3JhjysKeA5VguzCCAPH8WwReWDaVLfrwKo25U7XMHbsFP4mUtg5+AdYDsM3wF4NohMX8ZFGCTRyS4I4Px6cSvON0LvAI4bSci0JRSzQQQ3nJRtLYilxTiPNurGKuysXcBMsZDEZ0FdmEwlW7KNyqsIVKnIiqDNM2VlRja4Ic+nCJ40U2vkDQXbaCkG6gps271YrlOUa2VhYQEvfOELda8fQ1TY7iA+mvsslYxTyw7/72yJB48dQ+9//TZ6f/kH4LKP7t96Bcha7J8rcHjB4sgei0MLBvtnDPbGSBapvm3sYhxbkSHzsdQ4prEuxMy7IbOc7VnbKz2/ILQoVA+vcGz789TOjCGqdp37DLt/jrQtO7exAL7S9fm5cqF8pS2xLVxzR+u2QM0fP6/o5rentcSPBSHMO1FdwZc3MeRnpGuab3DUb1DoL+TtIN/oTp06BUBnbhVl+gn/7uvWVoLhILAsFXBiVORjRit34M1suk9hKgycTYLLR2Ert0vVkefPh/26CuM7MAzfifu7qWdlAYRuqNg2Ja3DxqDRQswcI4Fc6J6ynbqV2HSR4nwaRkytimoQliFSR4TqbLGArp1PMTv1nCylXNm1gjafg62rrxRnZeX2vEKbm0vllWFq7c5q+qRsNflef/HiRZw5cwbPe97zdOZ2xKiw3UGk8tZ3wQn59IqHe/oxuGdOgFcvgRb2oVi8HcVzXowX3dzBC/cXuOtGi8U5gz1dg7k4G9luK17PUAq4cqVV1iWb8kbC7mqfowjlK52vPc9bi721hhXDjhsmhIdFG61Z3zpi8YqIORYYZWseeM1aNzB+ytfYbkle7/XKq/I2q9S356jFIKoYInZV0O4sly9fTmZSgIpbRZl2QkWQsqxaA2ITR4TCRxsza8PsbaySUoHKl2tmPevbZlDZEkUVImeW8AySmVQn7Ld5NVaEay5YZTaX5G+G/GF8vReZIszfdjszmDHzmdjsREfhIGCNKRpiU+J0pE1YDKC6UYTmZk0Sv2Nb87SSQyvHJjfi+NqJmE2iN2sr1kqsMiq89zh+/HjKtldDqdGiwnYEGAQhsrdDsDfdiuK2FwJkYG85gpmX/n940XOP4L7FDp5/U4HbFgxu6NauyO252jXnHnrdxkJmq12DRZxejUhui9ta0A6/f1sghvbc1jEbPFYej5Of72oYNjO73hxtvo51xSohtWTbeJwdcsywOercDEzazvM3Otr3CedSQbuT7NmzB0eOHFG3ZEXZJeQiy1IBJg+XiVxx7PWonZItWTh2MBTmbiXqR26bsfOo/AAVD9C1cylH9fLe8zBdB19RahO2XaBjgzOwZ4/KD+CcC2/KppbietY0z24FAAOTqqedzMypKw7F8bZQoe0k1+KunY0GTnOhQhu/riu7dQasCNYkmKmO6JHHaMfr5K7FbTGrlVhl1BhjcOedd6pb8pigwnYHEQEyVxAW5w3u3lfg4TtfiCfwj+EuPAV74Dn4W3c/D198pIvPuaWD5+y12D9jMF802463S6hs1fk24xxci7+NBfFwA6aNH6gthp3fWPxe8TzrtEwHA6wh56L684abNTWzgYdVm+W2XMTmYjdvMR9WnVcxO1o0CkhRdg/ShmxNAfYeRFUSuJ4c2HTTcaES20FFXVQ8iHO2ZRJrBYXKqGMHxxUqP0C/Wg7C0IR51n5nBY6De1Re8SQYMDwqX2Lge43IIRNnf6WNWKqpoZXXpuqriNJOchvuNsSoMVlOb2w3TmZOrfvksTphZja2L1M35chKlTbPj9UZWWVS0Cig8UGF7Q5iCOjYYAR1aN7gxTeHl//oDS/CcnUvDs4bfM4tHbzo5g7u2Guxf7au1E6aULnWdYoYls834mpFcPPYWgx7c+1t18NMnpibt633uG1EmOZCtX2/9tx0uxqbX5cfHz6fjJ+R3YKKW0XZHZhM1DIV8FHUMhVgk4lLH8yQnJlB5UuUvoeKujHyJ7TYOj+DGZ6DYwdmj4oH6KcW3xnM2gWUvp9chkWUShXWRzFccQnP9S5lYhasiGOpkEr1NZ+jlbbpwnRgswzYwnRTVTqv8EplN3c3zqvC9Sxs07k4F7PrzclqZVYZd1TcjgcqbHeYgoA5SzgwF5pOb5gxeP5NBSoGbuwSDi9YHJw3QdTa3eVee7XP8VpEsFBXWzc/S5y3S19Nu/UwwTtMqK5HO55oPSfo3fCzMem0xe2+ffuwsLAw4lUpirKVEFGcqRXx1k2/yImDaPNcwVEFBqdKrPUFSteLGbgFCq7gjUT9hFncyodW5E40aFpwN6JKVshomC0BCOfmEt5XqfUZCOLbRJfhYWZQHTOTXIjFkdhS83hDttFa3BazBXVTu3Gq1rbiddbLktX2YmWSaYvbM2fO4ODBg6Ne1q5ChW3GM888gx//8R/Hhz70IZw5cwZ333033vKWt+ANb3jDlpzfEKEwjLkiCJGutbiha7C6JzgIzxVh7nYhcz5W0TKczVaENzNDvHaO9urFsfw50d6ar2To1T6u/lp/HiYVEbfGGBW1ijJCtmOvDwb1cTbWFClWwIjQ5QqWLTwH46ggWMMcrHWxYukKVKYD74MbsphLScW28sGYad7tReXLhmuykapo3COYgxh2XIFjrBCAVBWVautG1VppqzZkmy7IMVonby1ui9l2/I6QBG5LzMp1+TGKMomIuH3mmWfwrGc9a9TL2XWosI0sLy/jS77kS/CpT30Kb3rTm/D85z8fv/M7v4Nv+7Zvw+nTp/Ev/sW/2JLHycVtxxLmLKfZzyK2Knd3UZV2J9ns69lsFd5YHF9J7KpQ3d2IuBWcc7C2bRmmKMp2sd17vYEBqABMqNJ6rsLMbIzvkXlXhg8GTxRja3wXA+qFim4UrAwOAphD9TZUeMvGMUIuEuXcnqs1bcjNluACBtRwOO6Y2YZBU2obzoRw27E4n5GVr3OjJ3ns9noBFbPKdLKwsNB4A5uZwczalrwDEA/LVdmFvP3tb8cP/dAP4f3vfz++/uu/HkD4QfyyL/sy/PEf/zEeeughHDly5KrPd9ttt+HUqVM4fPgwTp48ueZ238opBXIXWxU7ijLtVFWFo0eP4sYbb9SZW0XZIbZvrz+Eh44/GAWlg49txCIy888FnxlDlb5fV1jZx7xzH4+rkriVSi9n58krtkJ+HokgAqLwRl0xBVC3G2dtxIaoNsOKQlZalKW1+GraiuWxhqFiVtkNMDMee+wx9Pt9nbndAVTYRl74whfi0qVLa0ToRz/6UXzRF30R3va2t+EHf/AHr/p8VxK2iqLsbs6dO4dHH30UAHDw4EEVt4qyA2znXv/wY0cbFVYgejKkz2sxmldVHTs4X6VKbBKwzOuK2Zy2aAXQOH6Y8G3cHyY5FeeV1zp/16xpLR7WUiznaqxJxauyy+n3+7j//vvhnMOePXtU3G4z2ooM4OLFi3jggQfwmte8Zs1tL3/5ywEAH//4x3d6WYqiTDH79+9HVVXqlqwoO8R27/WWCnh4ENVC1sPDxIRypqYwFdHL7OFM1azwttqQc5E6jHyOFUAS1mtFaJzBbcXVEShrJa7jecL91mbIth9XhayiDGdmZmaNW/I999wz6mVNLSpsAZw6dQrMjGc/+9lrbpufn8dNN92UKiuKoihbRdstWYWtomwf273Xh7bdesY1mDmtrbKmyishtBsTUKBTZ7Pj/2/vzqOjKu8/jn8mEEJCFnKEksgSDBAW2cIOcYNaFVAWRU7LIiCgUlAUKxbKT0E8UkuLdYHDZhFEZPEIsmjFKBxlUZQdlKXImgZBEAiJEGCe3x90LjOZO5NJmCwT3q9zOCTP+r33TuY7z8yde71PXTb/G88lzBHmcVqzq0yS9/dq3T6tdS1W836KfO1TXce1U4ndTifOe1Vju3kB2Mt7tWQUHRa2uvouriRFR0fb1kdFRSk7O9u2bsqUKZoyZYpXeUZGhiQpMzNTNWrUCFKkAMoip9OpK1euKDw8XAkJCfruu+9KOiSgzCnqXJ9Uq7ZNz8C/7WXf0q4073U4/M1RsGt2OGx+AhAcxhhdvnyZXF+EWNhK1rukvr5ubIzxeeXSc+fOWYnNjtPp9FsPAO54NxcoGuR6AKUFub5osLCVFBMTI0nKycmxrc/JyfF5lcTY2Fjb0wfdExynFwLIT2ZmppxOp8/nIQDXh1wPoKSR64sWC1tJt9xyixwOh+3Vi7Ozs3XmzBmfyW7UqFEaNWqUVzlXRQZQEK7njLz3ugUQHOR6ACWNXF+0+Ma/rn7fpmHDhtq0aZNXnesKiR06dCjusAAAQJCQ6wGgbGNh+z/9+vXT4cOHtXDhQqvMGKPJkycrIiLCupE7AAAITeR6ACi7OBX5f55++mnNnz9fAwYM0ObNm5WSkqLFixcrPT1dkydPVmJiYkmHCAAArgO5HgDKLha2/xMZGam1a9dq7NixmjdvnrKyslS/fn3NmzdP/fv3L+nwAADAdSLXA0DZxcLWTdWqVTVr1izNmjXruscaNWqUzp07p9jY2CBEBqCs4zkDKB7kegAlheeMouUwvm7oBgAAAABACODiUQAAAACAkMbCFgAAAAAQ0ljYAgAAAABCGgvbIDt16pSefPJJJSUlKTIyUs2aNdO//vWvkg4LQAj45ptvVK5cOa1du7akQwHgB7keQGGR64sOV0UOouzsbN1zzz3auXOnhg8frgYNGmjJkiUaPHiwjh8/rrFjx5Z0iABKqf3796tnz55yOp0lHQoAP8j1AAqLXF+0+MQ2iN566y1t2bJF8+bN02uvvabHH39cn332me677z5NmDBBR48eLekQAZRCS5cuVdu2bZWZmVnSoQDIB7keQGGQ64seC9sgmjt3rqpXr67f//73VpnD4dDo0aOVm5urBQsWlGB0AEqjrl276sEHH1RiYqL+8Ic/lHQ4APJBrgdQUOT64sHCNkjOnj2rPXv2qG3btl51rrJvvvmmuMMCUMrt2bNHr7zyirZs2aKUlJSSDgeAH+R6AIVBri8efMc2SDIyMmSMUa1atbzqoqKiFB8fr4MHD5ZAZABKs++//14RERElHQaAAJDrARQGub548IltkJw9e1aSFB0dbVsfFRWl7Ozs4gwJQAgg0QGhg1wPoDDI9cWDhW2QGGM8/rerL1euXHGGBAAAgohcDwClFwvbIImJiZEk5eTk2Nbn5OQoLi6uOEMCAABBRK4HgNKLhW2Q3HLLLXI4HDp27JhXXXZ2ts6cOaOaNWuWQGQAACAYyPUAUHqxsA2S6OhoNWzYUJs2bfKqc10hsUOHDsUdFgAACBJyPQCUXixsg6hfv346fPiwFi5caJUZYzR58mRFRER43PMOAACEHnI9AJRO3O4niJ5++mnNnz9fAwYM0ObNm5WSkqLFixcrPT1dkydPVmJiYkmHCAAArgO5HgBKJxa2QRQZGam1a9dq7NixmjdvnrKyslS/fn3NmzdP/fv3L+nwAADAdSLXA0Dp5DC+rlkPAAAAAEAI4Du2AAAAAICQxsIWAAAAABDSWNgCAAAAAEIaC1sAAAAAQEhjYQsAAAAACGksbAEAAAAAIY2FLQAAAAAgpLGwBQAAAACENBa2AAAAAICQxsIWRc7hcBToX+XKlUs65DIvJydHhw4dKpF569Spoxo1ahT73ACAokOuL33I9bjRlC/pAHDjqFevnn7zm9/k2y4mJqYYorlxLViwQKNHj9b48eM1ZMiQYpvX6XRq6NCh+vHHH1W9evVimxcAUHzI9aUDuR43Iha2KDZjx47VwIEDSzqMG97YsWOVkZFRrHP++uuvGjJkiBYsWFCs8wIAihe5vnQg1+NGxMIWQJHavHmzBg0apJ07d5Z0KAAAoAiQ61Ea8B1bAEVmzJgxat26tXbu3Klbb71Vf/nLX0o6JAAAEETkepQWLGxR6g0cOFAOh0PTp0/XoUOH9Oijj6pGjRqKiIhQjRo1NGTIEL8XR/jyyy/Vq1cvJSYmqkKFCqpWrZp69OihL774wrZ97dq15XA4tGPHDo0cOVLx8fGKjo5Wy5Ytdfr0aatdenq6unbtqsTEREVFRal58+aaOnWqnE6ndXEMl/bt28vhcOipp57yGefLL78sh8Ohzp07B7xvtm3bpscee0wNGzZUbGystX1dunTRBx984NF2/PjxcjgcOnz4sCRp6NChcjgcGj9+vN859uzZo6ioKDkcDg0dOtSr/sSJE6pWrZocDocee+wxj7qNGzcqKipKL774ojZv3qy6desGvG0AgBsHud43cj0QIAMUMUlGkpkzZ06h+g8YMMBIMkOHDjWxsbEmLCzM1KtXzzRq1Mgau2rVqubIkSNefZ9//nmrTXx8vGnZsqVJSEiwykaPHu3VJykpyUgyaWlpRpJp1KiRSUpKMu3bt7faTJw40RqjWrVqplWrViY2NtZIMg8++KBV5zJz5kwrzkuXLtluZ0pKipFkFi1aFNB+mTZtmgkLC7O2LTU11TRo0MBERERY848dO9Zq//bbb5u0tDSrvm7duiYtLc28/fbb+c711ltvWWN+9tlnHnVdunSx9lN2drZH3fvvv2+OHz9u/T5nzhwjyVSvXj2gbQQAhAZyPbnehVyPksLCFkUuWMlOkmnXrp3Zu3evVbdhwwYTExNjJJmRI0d69Js+fbqRZCpXrmzmz59vlTudTrNw4UJTqVIlI8nMnj3bo58r2UkyCxcutMpPnjxpjDFm9erVRpIJCwszb7zxhrly5YoxxpicnBwzYsQIq697sjt37pyJiooyksyKFSu8tnHDhg1W0rpw4UK++2Tfvn0mPDzcSDIvv/yyyc3NtepOnTplevfubSSZ8PBwc/r0advtmzVrVr7zuOvcubORZGrXrm2ysrKMMca88cYbRpKpWLGi2bFjR75jkOwAoGwi15PrXcj1KCksbFHk3J/8A/m3Zs0aj/6uZFehQgWTmZnpNf6TTz5pJJlWrVpZZRcvXjTVqlUzksyHH35oG9e0adOsJ173d1ZdyeD222+37demTRsjyfzpT3+yrXe9q5n3hIhHHnnESDK9e/f26vP4448bSWb48OG2Y+b11ltvmcjISNOyZUvb+iNHjlgxbNy40aOusMnu+PHjpmrVqkaSeeqpp8zu3btNxYoVjSQzbdq0gMYg2QFA2USuv4pcT65HyeGqyCg2gd7bLi4uzra8VatWSkhI8Cpv2LChJOnMmTNW2YYNG/TTTz8pJiZG3bt3tx2vb9++GjFihDIyMrRlyxa1adPGo/62227z6pORkaFvv/1WkjRs2DDbcUeOHKmPP/7Yq/zRRx/VvHnztHz5cp09e9bazosXL2rRokWSpEGDBtmOmdfw4cM1fPhw/frrr7b1UVFR1s85OTkBjZmfatWqadasWerRo4emTp2qTz75RBcuXFCPHj187gsAwI2FXE+uB0oKC1sUm+u9t52vG31HRkZKki5fvmyV7dq1S5KUm5urO+64w+eY5cqVk9Pp1J49e7ySXWJiolf73bt3yxij6OhoJScn247ZqlUr2/I777xTdevW1X/+8x8tWbLEumH68uXLdebMGTVp0kQtW7b0GaudihUratOmTdq1a5cOHDigAwcOaOfOndqzZ4/Vxul0FmhMf7p3764hQ4Zo9uzZ2r9/v2rWrKm33347aOMDAEIbuZ5cD5QUFrYIGRUqVPBbb4yxfj579qykq++Qrl+/Pt+x3d8BdnElUXc///yzJCk6OtrnWLGxsT7rBg4cqHHjxundd9+1kt3cuXMlBf4Orsv8+fP10ksvaf/+/R7lt9xyiwYPHqxZs2YVaLxAde/eXbNnz5YkJSUlqXLlykUyDwDgxkOu90SuBwLH7X5QJlWqVEmS1LJlS5mr3yX3+8/fpfntxj137pzPNllZWT7rBg4cqLCwMH311Vc6cuSITp48qU8//VTh4eHq169fwNs3d+5c9e/fX/v379d9992nGTNmaP369Tp9+rR+/PFHTZ06NeCxCuKXX36xTkUKCwvTunXr9I9//KNI5gIAwB9yPbkecMfCFmVS/fr1JUn79u3zOG3JnTFGa9as0f79+5WbmxvQuE2aNJF09bssBw4csG2zfft2n/2rV6+ue+65R8YYLVu2TCtWrNDly5fVtWtXVa1aNaAYJGnSpEmSpEceeUSffPKJHnvsMXXo0EHx8fGSpGPHjgU8VkEMGzZMx44dU7NmzfTOO+9IksaNG+d3mwEAKArkenI94I6FLcqkO+64Q3FxccrKytKcOXNs2yxYsECdOnVSgwYNdPTo0YDGTU5OVrNmzSTJ5/dNZsyY4XeMwYMHS5KWLl2qjz76SFLBT006ePCgJPn8no7r9CFJXsk+LOzqn7376VyBmD9/vhYtWqTw8HC988476t+/v7p3767c3Fz17dtXFy5cKNB4AABcD3I9uR5wx8IWZVKlSpU0ZswYSVevXDhnzhyPCyt89NFHeuKJJyRJvXv3Vp06dQIee8KECZKkyZMna9asWVbSuHTpksaPH6+FCxf67d+tWzdVqVJFX331lVavXq1q1aqpS5cuBdq+Bg0aSLqaWDMyMqzyc+fOafz48frrX/9qleW9UqLrO0OHDx8OeL4jR45oxIgRkqQxY8aoefPmkqTp06crPj5eu3fvtvY3AADFgVxPrgc8FMc9hXBj0//usVavXj2TlpYW0L+PP/7Y6u+6t13fvn1tx3fdLy0pKcmj3Ol0mqFDh1rzV6lSxbRu3drcfPPNVllaWpo5f/68R79A7v02evRoa4yEhATTpk0bEx8fbySZtm3bGkmmXLlyPvuPHDnS6v/ss88GsBc9rVixwoSFhVn3/GvSpIlp0qSJda+55ORkU6dOHSPJvP766x59XffYK1++vElNTTUTJ070O9eVK1fMnXfeaSSZpk2betwg3hhj5s6dayQZh8Nh0tPT/Y7Fve0AoGwi13sj15PrUbxY2KLIuZ7UC/Jvzpw5Vv/CJjuXTz/91PTs2dMkJCSY8uXLm5iYGNOuXTvzxhtvmIsXL3q1D/Sm5suWLTO//e1vTeXKlU1ERIRJTU01M2fONOvWrTOSTExMjM++W7dutbZ1165dfufxZfPmzaZHjx6mVq1apnz58iYuLs60bt3aTJo0yWRlZZkXXnjBSDJ33323R78TJ06Yhx56yMTFxZnIyEjTp08fv/O8+uqrVnLcsmWLbZuuXbtaSez06dM+xyLZAUDZRK73Rq4n16N4OYwp4Mn3APxatWqV7r//ftWrV0/79u2zbbNixQp169ZNrVu31qZNm4o5QgAAcD3I9UDpw3dsgQJq3Lix2rdvry1bttjWf/zxx5KkFi1a+BzDdd+5oUOHBj9AAABwXcj1QOgJ6BPbVq1a6fjx48URD1DqnTp1ShcuXFCFChV00003WVcelKTs7GzrBvBVqlRRRESEVXfp0iWFhYUpOztbWVlZCgsLU0JCghwOR3FvAlCmJCQk6LvvvivpMEIeuR64hlwPlC6B5PqAFrY1atTwuBobAAClRfXq1Yvsfo43EnI9AKC0CiTXly/IgGFhYUpMTLStC+SLuvktoX1V25abANoUYA5XRX7jGD+/eZT4Gahgc3gXFqq/XZsSjNG7jXfn693OYBwL23Y2nQoXa34bGshEAWxBQF+jz2//57cTA9wDfpv5qAzkQNrVF2RfFXSOAhwz790fwByF2U9WVYDHu6Bz5PdHYNe4SOdAUfGd643NT/b1XjUFeGz6b+Un716bqJCj5zN2AI/r/KIv0D60fUrLfx/6jsH331ygY/sf336MguzD/Pefv1a+c0DA+cytPuDHeCH2od1whX3az69tQV5iBDpuUGL1uSsL8SIrwPaFeQkVSLtCpa5AxwzStttWBylWq9l1PE6DpUAL28TERB05ekxO64/46g9OI6vM+t/tZ9eHwk5ztTxvH6teNuO49fEYx20eV5l7vd18Xu2CPZ+P8dzn87W/jIxnDIWczxhju815xzPG5Lvt/o5poPMV6jFis+1FOp/NeIEcM6fJfz67x6R1koRxWv9bZc5rZe71V/8zXmUe7ZxO27E9xnTF5NanwPM53caxiSFvWYnM56qz2U8+6ws4n9e+9jGecdock/yOWSHnM8Z4HW+78TyOic1jpMjnyzOex1wFfYwg6BITE3X06BFJklNOGdf+/98zotMY62djnHLmqTfG6db22jH2KnMb2+ne16vMpq/dHB7jmWtxu9Xbxeo+t2s+rzL3vrbjGdvxbLcvz9h596fv/e5eZq6V+etrO56x3RZ/sTqNsSkL9Dja9827fZ77zgS43+0eh/bHwv941+bzv9/dyvLEancsjDHK+1Qqt9cBHmWupzZz9Xf3evenULn1tRYS7unKVebR13i2M55z+x/v2osar3TlI1bXix5j3MvkWeb00TfPvvHV1z1Wk2c+j1jtypx++uY9Fj77Gvt43Pd7IMci4ONofIznu6/tsfC53/OWGe/9HvBxNPbxePwN5C0zfvd7oLh4FAAAAAAgpLGwBQAAAACENBa2AAAAAICQxsIWAAAAABDSWNgCAAAAAEIaC1sAAAAAQEhjYQsAAAAACGksbAEAAAAAIY2FLQAAAAAgpLGwBQAAAACENBa2AAAAAICQxsIWAAAAABDSWNgCAAAAAEIaC1sAAAAAQEhjYQsAAAAACGksbAEAAAAAIY2FLQAAAAAgpJUvSOPMzEzVqlnDts4E0N/k08hXtW25CaBNAeZwVeQ3jvHzm0eJn4EKNod3YaH627UpwRi923h3vt7tDMaxsG1n06lwsea3oYFMFMAW5PeHl3cc27Dy24kB7gG/zXxUBnIg7eoLsq8KOkcBjpn37g9gjsLsJ6sqwONd0Dny+yOwa1ykc6CoZGZmqmbNWjY1xuYn+3qvmgI8Nv238pN3r01UyNHzGTuAx3V+0RdoH9o+peW/D33H4PtvLtCx/Y9vP0ZB9mH++89fK985IOB85lYf8GO8EPvQbrjCPu3n17YgLzECHTcosfrclYV4kRVg+8K8hAqkXaFSV6BjBmnbbauDFKvV7Doep8FSoIWt0+lURkZG0UQCAABKHLkeABCKAlrYJiQkFHUcAAAUCjkqONiPAIDSKpAc5TCmQB8cAwAAAABQqnDxKAAAAABASGNhCwAAAAAIaSxsAQAAAAAhjYUtAAAAACCksbBFqXPo0CE5HA6f/yIiIlS1alWlpaXplVde0blz54olrvHjx8vhcOi2224rlvnuuusuORwOjRs3rkD9ateuLYfDodmzZ1tla9eutfbf5cuXrfKBAwfK4XCoX79+tmPt3r27cMEDAOAHuf4qcj0QPAW6jy1Q3Bo3bqy4uDiPstzcXJ04cUIbNmzQhg0bNGPGDH3++eeqW7duCUVZ9uzbt09PPfWUzp8/r3Xr1pV0OACAMoxcXzLI9ShrWNiiVHvzzTd111132datXbtW3bt315EjRzRgwACtX7++eIMrpT7//HNdunRJiYmJ+badNGmS/vznP3u9oFiwYIE+/fRTpaWlFVWYAABIItcXBrke8MapyAhZd911lyZNmiRJ2rBhgzZv3lzCEZUOderUUYMGDbwSmJ3ExEQ1aNAgoMQIAEBxI9fbI9cD3ljYIqT17NnT+vnrr78uwUgAAEBRINcDCAQLW4Q093cqs7KyJF27EMMnn3yiiRMnqlq1aoqKilLjxo21Z88eq31GRoaee+45NWrUSFFRUYqOjlbz5s01YcIEnTlzxu+8mZmZGjx4sBISElSxYkU1bNhQ48aN89nv8uXLevfdd/XAAw+oevXqqlixoqKjo5WSkqInnnhC+/bt8zvft99+q86dOysuLk4xMTFq166dZsyYoStXrni1tbughC95LyjhupjHhAkTJEnr16+Xw+FQ7dq1debMGUVGRsrhcOjDDz/0Oebdd98th8OhV199Nd/5AQDID7meXA8ExAClzMGDB40kI8msWbPGb9tt27ZZbefPn2+MMebOO+80kkxaWpqRZOrUqWNSUlJMzZo1zeXLl40xxqSnp5u4uDgjyYSHh5vmzZubW2+91YSFhRlJpmbNmmbHjh0ec7344ovWeNWrVzeSTEpKimnatKnVr3bt2ubQoUMe/XJyckzHjh2tOGvXrm1atWplatasaZVVqlTJbNmyxaOfazvatWtnwsPDTfny5U1qaqpJTk62+t17773m4sWLHv2SkpKMJDNr1iyrbM2aNVafS5cuWeUDBgwwkkzfvn2NMcZkZmaatLQ0K7bY2FiTlpZmevXqZYwxpk+fPkaS6dGjh+3xOHr0qAkLCzPlypUzGRkZfo8dAODGRa43HttBrgeuHwtblDoFSXaPPPKIkWQqVKhgjh8/boy5liQkmVdffdVqe+LECWOMMYcOHTLR0dFGkunWrZvVzxhjDhw4YNq3b28kmVq1apkzZ85Yda5kJ8lUrVrVfPnll1bd3r17TcOGDY0kc/vtt3vE6OpXpUoVs2nTJo+6TZs2mcTERCPJSigu7tvRpk0bjyS6cuVKExMTYySZ//u///Podz3JLm/MaWlpHuXp6enW/j516pTJ65VXXjGSTJcuXbzqAABwIdcbr+0g1wPXh1OREXJ+/fVXbd26VcOGDdO8efMkSc8884yqVavm0S4pKUnPPfec9XvVqlUlXb064Pnz59W4cWMtWbLEo19ycrJWrVqlhIQEHTlyRG+++aZtDO+9955uv/126/eUlBQtXbpU5cqV01dffeVx2fz09HSFhYXpxRdfVOvWrT3Gad26tYYNGyZJ2rlzp+1clStX1sqVK5WUlGSVde3aVa+99pok6fXXX1d2draPvRVcnTp1Uu3atZWbm6tFixZ51buOx6BBg4olHgBA2USuJ9cDBcXCFqVax44dvW7aHhUVpRYtWmj69OmSpCFDhmjixIlefTt06CCHw+FVvnLlSknSH//4R1WoUMGrPj4+Xo8++qgkadmyZV71KSkp+t3vfudVXr9+fd1xxx2SpFWrVlnl69at04ULF/TEE0/YbmNUVJQkKScnx7a+d+/eVqJ2169fP0VGRurcuXPFdv85h8OhgQMHSrqW2Fy++eYb7dmzRzfddJO6detWLPEAAEIfuZ5cDwQD97FFqZb3pu0Oh0MVK1bUTTfdpKZNm6pHjx5q1KiRbV+7y9pnZWUpIyNDktSyZUuf87rq9u7d61XXokULn/2aNm2qNWvW6IcffvAoDw8P19mzZ7Vhwwbt27dPP/74o/bt26etW7fqp59+kiQ5nU7bMX3NFxERoZSUFG3fvl0//PCD7r33Xp9xBdOgQYP00ksv6euvv9b+/ftVr149SdeSX58+fWxfRAAAYIdcT64HgoGFLUo1fzdtz09kZKRX2blz56yf/d37LTY2VpJ0/vx5GWM83g2OiYnx2c9V5/6ObFZWlsaMGaN33nnH4zSiChUqqEWLFkpNTdW///3vfMcMdL6iVqtWLXXq1Enp6emaP3++JkyYoNzcXC1cuFASpyYBAAqGXE+uB4KBU5FxQ3FPHGfPnvXZ7pdffpEkRUdHe53idP78eZ/9XGPGx8dbZd27d9fUqVPldDo1atQoLV68WLt379b58+e1ceNGPfjgg35jLuh8xcF1+tZ7770n6erpWKdPn1azZs2UmpparLEAAOCOXB8c5HqEGj6xxQ0lNjZWN998s/773/9q8+bNatOmjW277777TpKsU2/cud8fL68tW7ZIkpo0aSLp6o3k16xZI+lqQujYsaNXn2PHjvmN2dd82dnZ1ulTrvmKS8+ePRUfH68DBw5ox44dWrp0qSTewQUAlDxyfXCQ6xFq+MQWN5z7779fkjRt2jTl5uZ61f/yyy+aO3euJKlz585e9du2bdPWrVu9yrds2aINGzZIknVBhYMHD1r1dt/zycnJ0fvvvy/p6o3d7SxevNj2ndyZM2cqNzdXCQkJPpN2YYWFXX1qMMbY1lesWFF9+vSRJC1ZskSrVq1SeHi4+vbtG9Q4AAAoDHJ9/sj1KGtY2OKG8/zzzysmJka7du3Sww8/rBMnTlh1Bw8eVNeuXfXTTz+pevXqeuaZZ7z6G2P00EMPaceOHVbZ1q1b1bNnTxlj1Lt3bzVt2lSS1KBBA6vNxIkTPRLa999/r86dO2v//v2SfH93JiMjQ7169dLPP/9slb3//vsaM2aMJOmFF14I+gUcoqOjrbl9JWHXKUr//Oc/dfr0aT3wwAOqUqVKUOMAAKAwyPX5I9ejrGFhixtOcnKyPvjgA8XGxmr58uWqUaOGUlNT1aRJE9WtW1cbN25UrVq1tHz5ctsn706dOuns2bNq3ry5GjdurMaNG6tFixY6cuSI0tLSNHPmTKttamqqevfuLUn6+9//rsTERLVu3VrJycm69dZb9eWXX1q3E8jKyvK44IXLQw89pNWrV6tmzZpq2bKlatWqpT59+ujixYsaMWKEdW+8YHJ9d+bw4cOqV6+eOnTo4PWObosWLdSsWTPrHWZOTQIAlBbk+vyR61HWsLDFDemee+7R7t279cwzzyg5OVl79+7V0aNHlZqaqkmTJmn79u0+L71fr149bdq0SQ8//LAyMzN14MABNWvWTFOmTNEXX3zhdQXGBQsWaObMmWrdurWuXLmi7du36+LFi3rggQe0cuVKrV692roh+4oVK7zm69Wrlz777DO1adNGe/fu1ZkzZ9SxY0ctW7bM503lr1fHjh01efJkJSUlKSMjQwcPHrRuVeDOdZ+7hIQE3XfffUUSCwAAhUGu949cj7LGYXydWA8A+Xj22Wc1ZcoUPffcc/rb3/5W0uEAAIAgI9cjVLCwBVAoFy5cUFJSkk6ePKk9e/YoJSWlpEMCAABBRK5HKOF2PwACdurUKZ08eVLGGI0ZM0YnTpzQ/fffT6IDAKCMINcjVPGJLYCAbdy4UR06dLB+j4qK0rZt22zvAQgAAEIPuR6hiotHAQhYcnKy6tWrp8jISLVt21bp6ekkOgAAyhByPUIVn9gCAAAAAEIan9gCAAAAAEIaC1sAAAAAQEhjYQsAAAAACGksbAEAAAAAIY2FLQAAAAAgpLGwBQAAAACENBa2AAAAAICQxsIWAAAAABDS/h8qjukU8wBfRgAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 1200x500 with 4 Axes>"
       ]
diff --git a/simulations_1d/mi_vs_sampling_density_SNRs.ipynb b/simulations_1d/mi_vs_sampling_density_SNRs.ipynb
index df9881b..7d41534 100644
--- a/simulations_1d/mi_vs_sampling_density_SNRs.ipynb
+++ b/simulations_1d/mi_vs_sampling_density_SNRs.ipynb
@@ -5,10 +5,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-24T07:39:42.438537Z",
-     "iopub.status.busy": "2023-11-24T07:39:42.438133Z",
-     "iopub.status.idle": "2023-11-24T07:39:43.555494Z",
-     "shell.execute_reply": "2023-11-24T07:39:43.554866Z"
+     "iopub.execute_input": "2024-10-17T16:28:35.909622Z",
+     "iopub.status.busy": "2024-10-17T16:28:35.908854Z",
+     "iopub.status.idle": "2024-10-17T16:28:38.883998Z",
+     "shell.execute_reply": "2024-10-17T16:28:38.883334Z"
     }
    },
    "outputs": [
@@ -16,10 +16,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-10-10 13:28:20.104794: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-10 13:28:20.936306: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-10 13:28:20.936390: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-10 13:28:20.936398: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+      "2024-10-17 09:28:37.291123: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-17 09:28:37.942782: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-17 09:28:37.942864: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-17 09:28:37.942873: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
      ]
     }
    ],
@@ -32,9 +32,9 @@
     "\n",
     "import os\n",
     "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '3'\n",
     "from encoding_information.gpu_utils import limit_gpu_memory_growth\n",
-    "# limit_gpu_memory_growth()\n",
+    "limit_gpu_memory_growth()\n",
     "\n",
     "from cleanplots import *\n",
     "from tqdm import tqdm\n",
@@ -53,10 +53,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-25T05:09:55.515295Z",
-     "iopub.status.busy": "2023-11-25T05:09:55.514603Z",
-     "iopub.status.idle": "2023-11-25T16:07:36.061865Z",
-     "shell.execute_reply": "2023-11-25T16:07:36.061285Z"
+     "iopub.execute_input": "2024-10-17T16:28:38.889743Z",
+     "iopub.status.busy": "2024-10-17T16:28:38.889017Z",
+     "iopub.status.idle": "2024-10-17T21:33:12.900035Z",
+     "shell.execute_reply": "2024-10-17T21:33:12.899259Z"
     }
    },
    "outputs": [
@@ -68,46 +68,51071 @@
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [2], line 36\u001b[0m\n\u001b[1;32m     32\u001b[0m objects_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m : np\u001b[38;5;241m.\u001b[39marray([generate_random_object(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdelta\u001b[39m\u001b[38;5;124m'\u001b[39m, num_deltas\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m, object_size\u001b[38;5;241m=\u001b[39mupsampled_signal_length) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(N_objects)])\n\u001b[1;32m     35\u001b[0m \u001b[38;5;66;03m# Find an optimal PSF for this object\u001b[39;00m\n\u001b[0;32m---> 36\u001b[0m initial_kernel, initial_params, optimized_params, objects, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43moptimize_PSF_and_estimate_mi\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     37\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobjects_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise_sigma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mupsampled_signal_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mupsampled_signal_length\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;66;03m# compute the information with this psf when integrating over pixels of different sizes \u001b[39;00m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m num_samples \u001b[38;5;129;01min\u001b[39;00m tqdm(num_samples_list):\n",
-      "File \u001b[0;32m/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/1d_simulations/signal_utils_1D.py:526\u001b[0m, in \u001b[0;36moptimize_PSF_and_estimate_mi\u001b[0;34m(objects_fn, noise_sigma, initial_kernel, learning_rate, learning_rate_decay, verbose, estimate_with_pixel_cnn, loss_improvement_patience, max_epochs, num_nyquist_samples, upsampled_signal_length)\u001b[0m\n\u001b[1;32m    521\u001b[0m initial_params \u001b[38;5;241m=\u001b[39m params_from_signal(initial_kernel, num_nyquist_samples\u001b[38;5;241m=\u001b[39mnum_nyquist_samples)\n\u001b[1;32m    523\u001b[0m loss_fn \u001b[38;5;241m=\u001b[39m make_convolutional_forward_model_with_mi_loss(\n\u001b[1;32m    524\u001b[0m     objects, noise_sigma\u001b[38;5;241m=\u001b[39mnoise_sigma,  num_nyquist_samples\u001b[38;5;241m=\u001b[39mnum_nyquist_samples,\n\u001b[1;32m    525\u001b[0m     upsampled_signal_length\u001b[38;5;241m=\u001b[39mupsampled_signal_length)\n\u001b[0;32m--> 526\u001b[0m optimized_params \u001b[38;5;241m=\u001b[39m \u001b[43mrun_optimzation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msignal_prox_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    527\u001b[0m \u001b[43m                        \u001b[49m\u001b[43minitial_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate_decay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    528\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mloss_improvement_patience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mloss_improvement_patience\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    529\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPRNGKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43monp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m100000\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    530\u001b[0m test_objects \u001b[38;5;241m=\u001b[39m objects_fn()   \n\u001b[1;32m    532\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m estimate_with_pixel_cnn:\n",
-      "File \u001b[0;32m/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/1d_simulations/signal_utils_1D.py:244\u001b[0m, in \u001b[0;36mrun_optimzation\u001b[0;34m(loss_fn, prox_fn, parameters, learning_rate, verbose, tolerance, momentum, loss_improvement_patience, max_epochs, learning_rate_decay, transition_begin, key)\u001b[0m\n\u001b[1;32m    241\u001b[0m opt_state \u001b[38;5;241m=\u001b[39m optimizer\u001b[38;5;241m.\u001b[39minit(parameters)\n\u001b[1;32m    243\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m verbose:\n\u001b[0;32m--> 244\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minitial loss\u001b[39m\u001b[38;5;124m'\u001b[39m, loss_fn(parameters) \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[43mloss_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    246\u001b[0m \u001b[38;5;129m@jit\u001b[39m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtolerance_check\u001b[39m(loss, loss_history):\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39mabs(loss \u001b[38;5;241m-\u001b[39m loss_history\u001b[38;5;241m.\u001b[39mmax()) \u001b[38;5;241m<\u001b[39m tolerance\n",
-      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/pjit.py:256\u001b[0m, in \u001b[0;36m_cpp_pjit.<locals>.cache_miss\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;129m@api_boundary\u001b[39m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcache_miss\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 256\u001b[0m   outs, out_flat, out_tree, args_flat, jaxpr \u001b[38;5;241m=\u001b[39m \u001b[43m_python_pjit_helper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[43m      \u001b[49m\u001b[43mfun\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minfer_params_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    258\u001b[0m   executable \u001b[38;5;241m=\u001b[39m _read_most_recent_pjit_call_executable(jaxpr)\n\u001b[1;32m    259\u001b[0m   fastpath_data \u001b[38;5;241m=\u001b[39m _get_fastpath_data(executable, out_tree, args_flat, out_flat)\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/pjit.py:167\u001b[0m, in \u001b[0;36m_python_pjit_helper\u001b[0;34m(fun, infer_params_fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    165\u001b[0m   dispatch\u001b[38;5;241m.\u001b[39mcheck_arg(arg)\n\u001b[1;32m    166\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 167\u001b[0m   out_flat \u001b[38;5;241m=\u001b[39m \u001b[43mpjit_p\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbind\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs_flat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    168\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pxla\u001b[38;5;241m.\u001b[39mDeviceAssignmentMismatchError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    169\u001b[0m   fails, \u001b[38;5;241m=\u001b[39m e\u001b[38;5;241m.\u001b[39margs\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/core.py:2657\u001b[0m, in \u001b[0;36mAxisPrimitive.bind\u001b[0;34m(self, *args, **params)\u001b[0m\n\u001b[1;32m   2653\u001b[0m axis_main \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m((axis_frame(a)\u001b[38;5;241m.\u001b[39mmain_trace \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m used_axis_names(\u001b[38;5;28mself\u001b[39m, params)),\n\u001b[1;32m   2654\u001b[0m                 default\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mlambda\u001b[39;00m t: \u001b[38;5;28mgetattr\u001b[39m(t, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlevel\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m   2655\u001b[0m top_trace \u001b[38;5;241m=\u001b[39m (top_trace \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis_main \u001b[38;5;129;01mor\u001b[39;00m axis_main\u001b[38;5;241m.\u001b[39mlevel \u001b[38;5;241m<\u001b[39m top_trace\u001b[38;5;241m.\u001b[39mlevel\n\u001b[1;32m   2656\u001b[0m              \u001b[38;5;28;01melse\u001b[39;00m axis_main\u001b[38;5;241m.\u001b[39mwith_cur_sublevel())\n\u001b[0;32m-> 2657\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbind_with_trace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtop_trace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/core.py:389\u001b[0m, in \u001b[0;36mPrimitive.bind_with_trace\u001b[0;34m(self, trace, args, params)\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbind_with_trace\u001b[39m(\u001b[38;5;28mself\u001b[39m, trace, args, params):\n\u001b[0;32m--> 389\u001b[0m   out \u001b[38;5;241m=\u001b[39m \u001b[43mtrace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess_primitive\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfull_raise\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    390\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mmap\u001b[39m(full_lower, out) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmultiple_results \u001b[38;5;28;01melse\u001b[39;00m full_lower(out)\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/core.py:869\u001b[0m, in \u001b[0;36mEvalTrace.process_primitive\u001b[0;34m(self, primitive, tracers, params)\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprocess_primitive\u001b[39m(\u001b[38;5;28mself\u001b[39m, primitive, tracers, params):\n\u001b[0;32m--> 869\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprimitive\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimpl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtracers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/pjit.py:1212\u001b[0m, in \u001b[0;36m_pjit_call_impl\u001b[0;34m(jaxpr, in_shardings, out_shardings, resource_env, donated_invars, name, keep_unused, inline, *args)\u001b[0m\n\u001b[1;32m   1209\u001b[0m donated_argnums \u001b[38;5;241m=\u001b[39m [i \u001b[38;5;28;01mfor\u001b[39;00m i, d \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(donated_invars) \u001b[38;5;28;01mif\u001b[39;00m d]\n\u001b[1;32m   1210\u001b[0m has_explicit_sharding \u001b[38;5;241m=\u001b[39m _pjit_explicit_sharding(\n\u001b[1;32m   1211\u001b[0m     in_shardings, out_shardings, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m-> 1212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mxc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_xla\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpjit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcall_impl_cache_miss\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdonated_argnums\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1213\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mtree_util\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch_registry\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1214\u001b[0m \u001b[43m                    \u001b[49m\u001b[43m_get_cpp_global_cache\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhas_explicit_sharding\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/pjit.py:1196\u001b[0m, in \u001b[0;36m_pjit_call_impl.<locals>.call_impl_cache_miss\u001b[0;34m(*args_, **kwargs_)\u001b[0m\n\u001b[1;32m   1195\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_impl_cache_miss\u001b[39m(\u001b[38;5;241m*\u001b[39margs_, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs_):\n\u001b[0;32m-> 1196\u001b[0m   out_flat, compiled \u001b[38;5;241m=\u001b[39m \u001b[43m_pjit_call_impl_python\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1197\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjaxpr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjaxpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_shardings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43min_shardings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1198\u001b[0m \u001b[43m      \u001b[49m\u001b[43mout_shardings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout_shardings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresource_env\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresource_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1199\u001b[0m \u001b[43m      \u001b[49m\u001b[43mdonated_invars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdonated_invars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeep_unused\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_unused\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1200\u001b[0m \u001b[43m      \u001b[49m\u001b[43minline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minline\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1201\u001b[0m   fastpath_data \u001b[38;5;241m=\u001b[39m _get_fastpath_data(\n\u001b[1;32m   1202\u001b[0m       compiled, tree_structure(out_flat), args, out_flat)\n\u001b[1;32m   1203\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m out_flat, fastpath_data\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/pjit.py:1132\u001b[0m, in \u001b[0;36m_pjit_call_impl_python\u001b[0;34m(jaxpr, in_shardings, out_shardings, resource_env, donated_invars, name, keep_unused, inline, *args)\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[38;5;28;01mglobal\u001b[39;00m _most_recent_pjit_call_executable\n\u001b[1;32m   1125\u001b[0m in_shardings \u001b[38;5;241m=\u001b[39m _resolve_in_shardings(\n\u001b[1;32m   1126\u001b[0m     args, in_shardings, out_shardings,\n\u001b[1;32m   1127\u001b[0m     resource_env\u001b[38;5;241m.\u001b[39mphysical_mesh \u001b[38;5;28;01mif\u001b[39;00m resource_env \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m   1129\u001b[0m compiled \u001b[38;5;241m=\u001b[39m \u001b[43m_pjit_lower\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1130\u001b[0m \u001b[43m    \u001b[49m\u001b[43mjaxpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43min_shardings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_shardings\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresource_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1131\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdonated_invars\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeep_unused\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minline\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m-> 1132\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlowering_parameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmlir\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mLoweringParameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1133\u001b[0m _most_recent_pjit_call_executable\u001b[38;5;241m.\u001b[39mweak_key_dict[jaxpr] \u001b[38;5;241m=\u001b[39m compiled\n\u001b[1;32m   1134\u001b[0m \u001b[38;5;66;03m# This check is expensive so only do it if enable_checks is on.\u001b[39;00m\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py:2276\u001b[0m, in \u001b[0;36mMeshComputation.compile\u001b[0;34m(self, compiler_options)\u001b[0m\n\u001b[1;32m   2274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompile\u001b[39m(\u001b[38;5;28mself\u001b[39m, compiler_options\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m MeshExecutable:\n\u001b[1;32m   2275\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executable \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m compiler_options \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 2276\u001b[0m     executable \u001b[38;5;241m=\u001b[39m \u001b[43mUnloadedMeshExecutable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_hlo\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2277\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_hlo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2278\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcompiler_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompiler_options\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2279\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compiler_options \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2280\u001b[0m       \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executable \u001b[38;5;241m=\u001b[39m executable\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py:2624\u001b[0m, in \u001b[0;36mUnloadedMeshExecutable.from_hlo\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   2621\u001b[0m       mesh \u001b[38;5;241m=\u001b[39m i\u001b[38;5;241m.\u001b[39mmesh  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m   2622\u001b[0m       \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m-> 2624\u001b[0m xla_executable, compile_options \u001b[38;5;241m=\u001b[39m \u001b[43m_cached_compilation\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2625\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhlo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmesh\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspmd_lowering\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2626\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtuple_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauto_spmd_lowering\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_prop_to_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2627\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mhost_callbacks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mda\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpmap_nreps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2628\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompiler_options_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompiler_options_values\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2630\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(backend, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompile_replicated\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m   2631\u001b[0m   semantics_in_shardings \u001b[38;5;241m=\u001b[39m SemanticallyEqualShardings(in_shardings)  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/interpreters/pxla.py:2531\u001b[0m, in \u001b[0;36m_cached_compilation\u001b[0;34m(computation, name, mesh, spmd_lowering, tuple_args, auto_spmd_lowering, _allow_propagation_to_outputs, host_callbacks, backend, da, pmap_nreps, compiler_options_keys, compiler_options_values)\u001b[0m\n\u001b[1;32m   2526\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, compile_options\n\u001b[1;32m   2528\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m dispatch\u001b[38;5;241m.\u001b[39mlog_elapsed_time(\n\u001b[1;32m   2529\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFinished XLA compilation of \u001b[39m\u001b[38;5;132;01m{fun_name}\u001b[39;00m\u001b[38;5;124m in \u001b[39m\u001b[38;5;132;01m{elapsed_time}\u001b[39;00m\u001b[38;5;124m sec\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2530\u001b[0m     fun_name\u001b[38;5;241m=\u001b[39mname, event\u001b[38;5;241m=\u001b[39mdispatch\u001b[38;5;241m.\u001b[39mBACKEND_COMPILE_EVENT):\n\u001b[0;32m-> 2531\u001b[0m   xla_executable \u001b[38;5;241m=\u001b[39m \u001b[43mcompiler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile_or_get_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2532\u001b[0m \u001b[43m      \u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomputation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdev\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompile_options\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhost_callbacks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2533\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xla_executable, compile_options\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/compiler.py:294\u001b[0m, in \u001b[0;36mcompile_or_get_cached\u001b[0;34m(backend, computation, devices, compile_options, host_callbacks)\u001b[0m\n\u001b[1;32m    290\u001b[0m use_compilation_cache \u001b[38;5;241m=\u001b[39m (compilation_cache\u001b[38;5;241m.\u001b[39mis_initialized() \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m    291\u001b[0m                          backend\u001b[38;5;241m.\u001b[39mplatform \u001b[38;5;129;01min\u001b[39;00m supported_platforms)\n\u001b[1;32m    293\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m use_compilation_cache:\n\u001b[0;32m--> 294\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackend_compile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbackend\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcomputation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompile_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    295\u001b[0m \u001b[43m                         \u001b[49m\u001b[43mhost_callbacks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    297\u001b[0m \u001b[38;5;66;03m# TODO(b/293308239) Instrument a metric to track the adoption of the new cache\u001b[39;00m\n\u001b[1;32m    298\u001b[0m \u001b[38;5;66;03m# key implementation after it is enabled.\u001b[39;00m\n\u001b[1;32m    299\u001b[0m \u001b[38;5;28;01mglobal\u001b[39;00m _cache_used\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/profiler.py:314\u001b[0m, in \u001b[0;36mannotate_function.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(func)\n\u001b[1;32m    312\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    313\u001b[0m   \u001b[38;5;28;01mwith\u001b[39;00m TraceAnnotation(name, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecorator_kwargs):\n\u001b[0;32m--> 314\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    315\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m wrapper\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/compiler.py:256\u001b[0m, in \u001b[0;36mbackend_compile\u001b[0;34m(backend, module, options, host_callbacks)\u001b[0m\n\u001b[1;32m    251\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m backend\u001b[38;5;241m.\u001b[39mcompile(built_c, compile_options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[1;32m    252\u001b[0m                          host_callbacks\u001b[38;5;241m=\u001b[39mhost_callbacks)\n\u001b[1;32m    253\u001b[0m \u001b[38;5;66;03m# Some backends don't have `host_callbacks` option yet\u001b[39;00m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;66;03m# TODO(sharadmv): remove this fallback when all backends allow `compile`\u001b[39;00m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;66;03m# to take in `host_callbacks`\u001b[39;00m\n\u001b[0;32m--> 256\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuilt_c\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcompile_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-10-17 09:29:48.357860: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.74s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 181.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 202.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 210.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 219.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 361.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 344.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 180.83it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 213.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 369.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 291.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 118.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 211.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 176.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 312.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 345.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 259.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 352.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 374.52it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 274.41it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 317.92it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 230.54it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 328.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 172.04it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 368.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 293.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 284.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 314.39it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 150.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 175.73it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 221.65it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 207.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 33: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 294.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 33: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 34: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 332.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 34: validation NLL: 10.36\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "precomputing masks and variances: 100%|███████████████████████████████████████████| 4/4 [00:00<00:00, 26.44it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "computing log likelihoods: 100%|█████████████████████████████████████████████████| 4/4 [00:00<00:00, 490.23it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "precomputing masks and variances: 100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 436.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "computing log likelihoods: 100%|█████████████████████████████████████████████████| 4/4 [00:00<00:00, 430.63it/s]\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████████████| 100/100 [00:01<00:00, 77.85it/s]\n",
+      "  0%|                                                                                     | 0/6 [00:00<?, ?it/s]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:06<20:45,  6.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:06<09:35,  2.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:07<06:04,  1.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:23,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:09<03:05,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:04,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<01:58,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<01:54,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<01:52,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<01:51,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<01:46,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<01:46,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<01:46,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<01:45,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:44,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<01:45,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:45,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:42,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:42,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:41,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:40,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:38,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:35,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:35,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:33,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:22<01:34,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:34,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:35,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:34,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:35,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:25<01:35,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:26<01:35,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:26<01:33,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:27<01:33,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:33,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:28<01:33,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:29<01:32,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:29<01:30,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:30<01:30,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:31<01:30,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:31<01:29,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:32<01:27,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:32<01:25,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:33<01:25,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:33<01:26,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:34<01:25,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:34<01:23,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:35<01:23,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:36<01:24,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:36<01:23,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:37<01:28,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:38<01:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:38<01:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:39<01:27,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:39<01:25,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:40<01:24,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:41<01:22,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:41<01:22,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:42<01:20,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:42<01:18,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:43<01:17,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:43<01:18,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:44<01:17,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:45<01:15,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:45<01:15,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:46<01:15,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:46<01:13,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:47<01:12,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:47<01:12,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:48<01:10,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:49<01:10,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:49<01:09,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:50<01:10,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:50<01:10,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:51<01:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:52<01:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:52<01:14,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:53<01:14,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:54<01:10,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:54<01:08,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:55<01:07,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:55<01:07,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:56<01:08,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:57<01:09,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:57<01:06,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [00:58<01:04,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [00:58<01:05,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [00:59<01:03,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [00:59<01:03,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:00<01:03,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:01<01:03,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:01<01:02,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:02<01:00,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:02<01:01,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:03<01:01,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:04<00:59,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:04<00:56,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:05<01:00,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:06<01:01,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:06<00:59,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:07<01:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:08<01:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:08<01:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:09<01:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:10<01:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:10<01:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:11<00:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:12<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:13<01:07,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:14<00:56,  1.53it/s]\n",
+      "\n",
+      "Training models:  20%|████████████                                                | 1/5 [01:24<05:38, 84.56s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:33,  4.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:41,  2.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:28,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:05<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<02:52,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:07<02:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:08<02:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<01:57,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:09<01:54,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:10<01:52,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:49,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:11<01:50,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:12<01:49,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:12<01:46,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:13<01:46,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:13<01:45,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:14<01:46,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:15<01:45,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:15<01:44,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:16<01:43,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:16<01:44,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:17<01:43,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:18<01:44,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:18<01:42,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:19<01:41,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:19<01:43,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:20<01:43,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:21<01:42,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:21<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:22<01:39,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:22<01:38,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:23<01:36,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:23<01:35,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:24<01:34,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:25<01:33,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:25<01:33,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:26<01:33,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:26<01:33,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:27<01:30,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:27<01:29,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:28<01:29,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:29<01:30,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:29<01:28,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:30<01:26,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:30<01:26,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:31<01:28,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:32<01:29,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:32<01:29,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:33<01:28,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:33<01:27,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:34<01:26,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:34<01:23,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:35<01:23,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:36<01:20,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:36<01:19,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:37<01:20,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:37<01:21,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:38<01:20,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:38<01:18,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:39<01:18,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:40<01:17,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:40<01:16,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:41<01:15,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:41<01:14,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:42<01:15,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:42<01:17,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:43<01:15,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:44<01:14,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:44<01:14,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:45<01:14,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:45<01:14,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:46<01:14,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:47<01:13,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:47<01:13,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:48<01:12,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:48<01:10,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:49<01:10,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:49<01:09,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:50<01:12,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:51<01:10,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:51<01:09,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:52<01:08,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:52<01:06,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:53<01:06,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:54<01:07,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:54<01:05,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:55<01:04,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [00:55<01:01,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [00:56<01:01,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [00:56<00:59,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [00:57<00:58,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [00:57<00:57,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [00:58<00:57,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [00:59<00:56,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [00:59<00:56,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:00<00:58,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:00<00:57,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:01<00:57,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:01<00:56,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:02<00:56,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:03<00:58,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:03<00:56,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:04<00:55,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:04<00:55,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:05<00:54,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:06<00:55,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:06<00:54,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:07<00:53,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:07<00:52,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:08<00:51,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:09<00:51,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:09<00:49,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:10<00:48,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:10<00:48,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:11<00:46,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:11<00:46,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:12<00:46,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:13<00:46,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:13<00:45,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:14<00:45,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:14<00:44,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:15<00:44,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:15<00:43,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:16<00:43,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:17<00:42,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:17<00:42,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:18<00:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:18<00:42,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:19<00:42,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:20<00:40,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:20<00:39,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:21<00:38,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:21<00:38,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:22<00:37,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:23<00:35,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:23<00:35,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:24<00:34,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:24<00:34,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:25<00:34,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:25<00:33,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:26<00:33,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:27<00:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:27<00:32,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:28<00:31,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:28<00:30,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:29<00:29,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:29<00:28,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:30<00:27,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:31<00:27,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:31<00:26,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:32<00:29,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:33<00:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:33<00:27,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:34<00:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:34<00:26,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:35<00:26,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:36<00:24,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:36<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:37<00:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:38<00:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:38<00:23,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:39<00:21,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:39<00:21,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:40<00:20,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:41<00:20,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:41<00:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:42<00:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:43<00:20,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:43<00:20,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:44<00:19,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:45<00:19,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:46<00:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:46<00:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [01:47<00:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [01:48<00:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [01:49<00:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [01:49<00:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [01:50<00:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [01:51<00:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [01:52<00:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [01:52<00:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [01:53<00:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [01:54<00:10,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [01:54<00:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [01:55<00:09,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [01:56<00:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [01:57<00:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [01:57<00:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [01:58<00:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [01:59<00:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [01:59<00:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:00<00:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:01<00:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:02<00:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:02<00:02,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:03<00:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:03<00:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:04<00:00,  1.60it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [03:31<05:28, 109.44s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:29,  4.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:27,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:04,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:55,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:13,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:51,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:42,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:40,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<01:55,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:51,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:49,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<01:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:47,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:46,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:44,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:42,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:43,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:43,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:41,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:42,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:42,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:39,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:36,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:36,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:25<01:37,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:26<01:36,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:36,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:27<01:36,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:36,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:28<01:35,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:29<01:34,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:34,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:30<01:34,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:31<01:34,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:31<01:32,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:32<01:31,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:33<01:30,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:33<01:30,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:34<01:29,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:34<01:28,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:35<01:28,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:36<01:27,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:36<01:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:37<01:25,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:37<01:26,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:38<01:23,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:39<01:23,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:39<01:23,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:40<01:23,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:40<01:22,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:41<01:21,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:41<01:22,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:42<01:20,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:43<01:20,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:43<01:20,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:44<01:17,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:44<01:17,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:45<01:16,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:46<01:15,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:46<01:16,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:47<01:15,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:47<01:15,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:48<01:13,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:48<01:13,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:49<01:12,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:50<01:12,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:50<01:12,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:51<01:12,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:51<01:10,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:52<01:09,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:53<01:08,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:53<01:07,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:54<01:07,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:54<01:06,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:55<01:06,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:55<01:07,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:56<01:07,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:57<01:06,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:57<01:05,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [00:58<01:04,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [00:58<01:05,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [00:59<01:03,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:00<01:02,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:00<01:03,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:01<01:03,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:01<01:01,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:02<01:00,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:03<01:00,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:03<01:01,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:04<01:01,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:04<00:59,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:05<00:58,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:06<00:58,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:06<00:56,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:07<00:55,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:07<00:55,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:08<00:54,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:08<00:52,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:09<00:51,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:10<00:50,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:10<00:49,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:11<00:47,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:11<00:47,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:12<00:48,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:12<00:48,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:13<00:46,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:13<00:46,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:14<00:45,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:15<00:45,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:15<00:44,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:16<00:43,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:16<00:42,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:17<00:41,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:17<00:40,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:18<00:40,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:18<00:40,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:19<00:41,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:20<00:40,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:20<00:39,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:21<00:39,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:21<00:38,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:22<00:40,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:22<00:38,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:23<00:37,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:24<00:36,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:24<00:36,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:25<00:36,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:25<00:36,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:26<00:35,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:27<00:35,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:27<00:34,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:28<00:33,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:28<00:33,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:29<00:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:29<00:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:30<00:31,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:31<00:30,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:31<00:31,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:32<00:32,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:33<00:31,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:33<00:29,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:34<00:28,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:34<00:28,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:35<00:27,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:35<00:26,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:36<00:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:37<00:24,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:37<00:24,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:38<00:23,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:38<00:23,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:39<00:22,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:40<00:22,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:40<00:21,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:41<00:20,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:41<00:20,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:42<00:19,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:42<00:19,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:43<00:18,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:44<00:17,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:44<00:17,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:45<00:18,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:46<00:17,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:46<00:16,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:47<00:16,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:47<00:15,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [01:48<00:14,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [01:49<00:13,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [01:49<00:13,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [01:50<00:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [01:51<00:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [01:51<00:11,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [01:52<00:11,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [01:52<00:10,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [01:53<00:09,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [01:53<00:08,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [01:54<00:08,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [01:55<00:08,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [01:55<00:07,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [01:56<00:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [01:57<00:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [01:58<00:06,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [01:58<00:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [01:59<00:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:00<00:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:01<00:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:01<00:02,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:02<00:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:03<00:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:04<00:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:04<00:00,  1.60it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [05:38<03:55, 117.58s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:05<16:47,  5.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:18,  2.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:38,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:19,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:38,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<01:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<01:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<01:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<01:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<01:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:18,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<01:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<01:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<01:43,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:42,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<01:40,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<01:40,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:39,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<01:38,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:36,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:34,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:32,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:31,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:31,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:28,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<01:28,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:26,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:26,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:25,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:25,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:23,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:24,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:26,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:27,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:24,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:23,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:24,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:22,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:22,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:21,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:21,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:43<01:21,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:21,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:22,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:45<01:19,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:46<01:18,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:46<01:17,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:47<01:18,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:48<01:19,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:48<01:17,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:49<01:18,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:49<01:16,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:50<01:15,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:51<01:16,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:51<01:13,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:52<01:11,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:52<01:10,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:53<01:09,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:53<01:08,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:54<01:07,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:54<01:07,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:55<01:07,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:56<01:06,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:56<01:07,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:57<01:07,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:57<01:06,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:58<01:06,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:59<01:05,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:59<01:04,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:00<01:04,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:00<01:02,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:01<01:02,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:01<01:01,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:02<00:59,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:03<00:59,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:03<01:00,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:04<00:59,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:04<00:58,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:05<00:58,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:05<00:57,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:06<00:57,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:07<00:57,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:07<00:56,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:08<00:55,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:08<00:53,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:09<00:53,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:10<00:53,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:10<00:54,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:11<00:52,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:11<00:52,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:12<00:51,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:12<00:51,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:13<00:50,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:14<00:50,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:14<00:50,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:15<00:50,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:15<00:48,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:16<00:49,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:17<00:49,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:17<00:49,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:18<00:46,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:18<00:46,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:19<00:44,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:20<00:44,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:20<00:43,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:21<00:43,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:21<00:41,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:22<00:42,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:23<00:41,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:23<00:41,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:24<00:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:24<00:40,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:25<00:39,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:25<00:37,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:26<00:37,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:27<00:37,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:27<00:36,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:28<00:34,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:28<00:34,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:29<00:33,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:29<00:32,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:30<00:32,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:31<00:32,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:31<00:31,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:32<00:31,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:32<00:30,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:33<00:30,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:33<00:30,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:34<00:29,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:35<00:29,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:35<00:28,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:36<00:27,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:36<00:27,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:37<00:26,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:37<00:25,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:38<00:24,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:39<00:24,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:39<00:23,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:40<00:23,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:40<00:22,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:41<00:22,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:41<00:22,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:42<00:21,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:43<00:20,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:43<00:20,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:44<00:19,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:44<00:19,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:45<00:19,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:46<00:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:46<00:19,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:47<00:18,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:48<00:17,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:48<00:16,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:49<00:15,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:49<00:15,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [01:50<00:14,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [01:51<00:13,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [01:51<00:12,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [01:52<00:12,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [01:52<00:11,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [01:53<00:11,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [01:54<00:11,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [01:54<00:10,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [01:55<00:09,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [01:55<00:08,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [01:56<00:08,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [01:57<00:07,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [01:57<00:07,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [01:58<00:06,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [01:58<00:05,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [01:59<00:05,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:00<00:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:00<00:04,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:01<00:03,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:02<00:03,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:02<00:02,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:03<00:01,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:03<00:01,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:04<00:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:05<00:00,  1.60it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [07:46<02:01, 121.59s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:31,  4.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:50,  2.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:21,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:10,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:29,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:07,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:51,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:42,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:06,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<01:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<01:59,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<01:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<01:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<01:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<01:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<01:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:36<01:37,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<01:35,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:35,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:33,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:40<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<02:03,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<02:05,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:42<01:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:43<01:42,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:44<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:45<01:27,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:46<01:24,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:22,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:47<01:21,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:20,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:21,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:49<01:20,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:18,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:17,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:16,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:15,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:15,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:15,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:15,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:13,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:13,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:11,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:11,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:15,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:13,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:11,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:09,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:07,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:07,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:08,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:00<01:07,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:07,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:01<01:06,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:02<01:06,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:05,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:05,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:05,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:04,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:05,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:05,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:02,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:06<01:01,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:07<01:00,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:00,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:08<00:59,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:09<00:58,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:09<00:58,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:10<00:57,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:11<00:56,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:11<00:56,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:12<00:55,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:12<00:54,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:13<00:53,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:13<00:53,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:14<00:52,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:15<00:51,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:15<00:51,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:16<00:51,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:16<00:50,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:17<00:50,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:18<00:50,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:19<00:57,  1.46it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [09:07<00:00, 109.57s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.43s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  5.72it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.93it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.17it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.07s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.41it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.80it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.32s/it]\u001b[A\n",
+      "Evaluating NLL: 82it [00:01, 79.79it/s]\u001b[A\n",
+      "Evaluating NLL: 174it [00:01, 182.47it/s]\u001b[A\n",
+      "Evaluating NLL: 255it [00:01, 275.61it/s]\u001b[A\n",
+      "Evaluating NLL: 339it [00:01, 373.76it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 261.07it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   3%|▊                            | 3/100 [00:00<00:03, 29.96it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|███████████▌               | 43/100 [00:00<00:00, 244.89it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 299.93it/s]\n",
+      " 17%|████████████▋                                                               | 1/6 [09:22<46:51, 562.37s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:06<21:10,  6.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:07<09:56,  3.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:07<06:14,  1.90s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:08<04:33,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:36,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:09<03:02,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:10<02:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<01:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<01:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<01:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<01:57,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<01:56,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<01:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<01:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<01:59,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<01:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:53,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<01:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<01:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<01:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:41,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<01:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:46,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<01:46,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:44,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<01:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:41<01:43,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:42<01:42,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:45<01:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:34,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:47<01:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:49<01:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:33,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:33,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:32,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:53<01:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:55<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:57<01:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:58<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:59<01:47,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:00<01:41,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:00<01:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:01<01:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:02<01:23,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:02<01:20,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:03<01:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:04<01:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:04<01:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:05<01:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:06<01:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:06<01:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:07<01:09,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:08,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:08<01:07,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:09<01:06,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:06,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:10<01:06,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:11<01:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:03,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:12<01:02,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:14<01:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:14<01:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:15<01:00,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:16<00:58,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<00:58,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:17<00:58,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:18<00:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<00:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:19<00:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<00:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<00:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:21<00:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:22<00:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<00:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:23<00:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:24<00:52,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:25<00:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:25<00:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:27<00:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:28<00:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:46,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:46,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:32<00:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:33<00:43,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:40,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:35<00:39,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:38,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:38,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:37<00:38,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:37,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:39<00:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:40<00:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:40<00:35,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:41<00:34,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:42<00:33,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:42<00:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:43<00:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:44<00:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:44<00:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:45<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:46<00:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:46<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:47<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:48<00:29,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:48<00:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:49<00:27,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:50<00:27,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:50<00:27,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:51<00:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:52<00:26,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:52<00:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:53<00:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:54<00:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:54<00:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:55<00:21,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:55<00:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:56<00:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:57<00:19,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:57<00:19,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:58<00:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:59<00:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:59<00:17,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:00<00:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:01<00:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:01<00:15,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:02<00:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:03<00:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:03<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:04<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:05<00:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:05<00:11,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:06<00:11,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:07<00:10,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:07<00:09,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:08<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:08<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:09<00:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:10<00:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:10<00:06,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:11<00:05,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:12<00:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:12<00:04,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:13<00:03,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:14<00:03,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:14<00:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:15<00:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:15<00:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:16<00:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:17<00:00,  1.46it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:24<09:37, 144.49s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:09,  4.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:14,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:51,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:43,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<02:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:55,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<02:01,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<02:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<02:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<02:11,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<02:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<02:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<02:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<02:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<02:11,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<02:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<02:07,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<02:05,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<02:03,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<02:04,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<02:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<02:01,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:36<02:00,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:38<01:58,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:39<01:58,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:56,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:45,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:46<01:46,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:47<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:36,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:50<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:31,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:52<01:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:32,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:54<01:41,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:30,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:58<01:26,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:04<01:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:15,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:06<01:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:07<01:11,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:08<01:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:09<01:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:09<01:10,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:10<01:09,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:11<01:08,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:11<01:08,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:12<01:07,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:13<01:06,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:13<01:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:14<01:04,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:15<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:15<01:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:16<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:17<01:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:17<01:03,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:18<01:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:19<01:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:19<01:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:20<00:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:20<00:58,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:21<00:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:22<00:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<00:55,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:23<00:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:24<00:54,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:24<00:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:25<00:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:26<00:52,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:26<00:52,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:27<00:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:27<00:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:28<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:29<00:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:29<00:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:30<00:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:31<00:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:31<00:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:32<00:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:33<00:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:33<00:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:34<00:44,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:34<00:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:35<00:43,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:36<00:42,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:36<00:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:37<00:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:38<00:40,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:38<00:39,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:39<00:38,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:39<00:37,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:40<00:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:41<00:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:41<00:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:42<00:35,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:43<00:34,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:43<00:34,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:44<00:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:45<00:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:45<00:32,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:46<00:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:46<00:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:47<00:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:48<00:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:48<00:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:49<00:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:50<00:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:50<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:51<00:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:52<00:25,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:52<00:24,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:53<00:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:53<00:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:54<00:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:55<00:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:55<00:24,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:56<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:57<00:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:57<00:21,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:58<00:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:59<00:19,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:59<00:18,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:00<00:17,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:00<00:17,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:01<00:16,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:02<00:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:02<00:15,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:03<00:14,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:03<00:14,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:04<00:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:05<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:05<00:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:06<00:12,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:07<00:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:07<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:08<00:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:09<00:09,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:09<00:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:10<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:11<00:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:11<00:07,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:12<00:06,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:13<00:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:13<00:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:14<00:04,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:14<00:03,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:15<00:03,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:16<00:02,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:16<00:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:17<00:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:18<00:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:18<00:00,  1.44it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:45<07:07, 142.59s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:11,  4.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:15,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:49,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:11,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:56,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:59,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:52,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:52,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:59,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:58,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:53,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:52,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:54,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:52,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:49,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:50,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:48,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:46,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:44,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:44,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<02:08,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<02:08,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:56,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:44,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:29,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:26,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:25,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:23,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:24,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:23,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:23,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:23,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:21,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:20,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:19,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:18,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:17,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:17,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:53<01:16,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:16,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:16,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:55<01:15,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:15,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:56<01:13,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:57<01:12,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:15,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:58<01:14,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:00<01:13,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:01<01:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:02<01:11,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:10,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:09,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:09,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:08,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:07,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:06,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:05,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:07<01:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:04,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:08<01:04,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:09<01:04,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:10<01:04,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:10<01:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:11<01:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:12<01:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:12<01:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:13<01:00,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:14<00:58,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:14<00:57,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:15<00:57,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:15<00:56,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:16<00:56,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:17<00:55,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:17<00:54,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:18<00:53,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:19<00:53,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:19<00:51,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:20<00:50,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:20<00:50,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:21<00:50,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:22<00:49,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:22<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:23<00:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:24<00:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:24<00:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:25<00:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:26<00:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:26<00:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:27<00:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:27<00:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:28<00:43,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:29<00:43,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:29<00:43,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:30<00:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:31<00:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:31<00:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:32<00:41,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:33<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:33<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:34<00:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:35<00:37,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:35<00:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:36<00:35,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:36<00:35,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:37<00:34,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:38<00:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:38<00:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:39<00:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:40<00:32,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:40<00:31,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:41<00:31,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:41<00:30,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:42<00:30,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:43<00:29,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:43<00:28,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:44<00:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:45<00:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:45<00:27,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:46<00:26,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:46<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:47<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:48<00:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:49<00:25,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:49<00:23,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:50<00:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:50<00:21,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:51<00:20,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:52<00:20,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:52<00:19,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:53<00:19,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:53<00:18,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:54<00:18,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:55<00:17,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:55<00:17,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:56<00:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:57<00:15,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [01:57<00:14,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [01:58<00:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [01:59<00:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [01:59<00:14,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:00<00:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:01<00:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:01<00:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:02<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:03<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:03<00:09,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:04<00:08,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:04<00:07,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:05<00:07,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:06<00:06,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:06<00:06,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:07<00:05,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:07<00:04,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:08<00:04,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:09<00:03,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:09<00:03,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:10<00:02,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:10<00:01,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:11<00:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:12<00:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:13<00:00,  1.50it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [07:01<04:38, 139.33s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:04,  4.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:25,  2.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:04,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:59,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:25,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:59,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:12,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:10,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:22,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:11,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:38,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:31,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:18,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<02:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<01:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<01:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<01:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<01:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<01:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:40,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:39,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<01:39,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:38,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:37,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<01:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:35,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:29,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:27,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:23,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:21,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:19,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:18,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:19,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:18,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:14,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:12,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:11,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:00<01:09,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:01<01:08,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:01<01:09,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:02<01:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:03<01:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:04<01:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:08,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:06<01:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:08<01:07,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:10<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:04,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:11<01:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:12<01:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:13<01:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:14<01:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:15<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:16<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:17<00:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:18<00:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<00:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:19<00:54,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:20<00:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<00:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:21<00:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:22<00:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:22<00:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:23<00:49,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:24<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:24<00:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:25<00:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:47,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:26<00:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:27<00:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:28<00:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:29<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:30<00:45,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:31<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:32<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:32<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:33<00:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:34<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:34<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:35<00:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:35<00:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:36<00:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:37<00:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:38<00:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:38<00:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:39<00:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:40<00:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:40<00:38,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:41<00:38,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:42<00:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:42<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:43<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:44<00:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:44<00:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:45<00:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:46<00:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:46<00:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:47<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:48<00:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:48<00:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:49<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:49<00:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:50<00:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:51<00:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:51<00:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:52<00:22,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:53<00:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:53<00:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:54<00:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:55<00:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:55<00:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:56<00:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:57<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:58<00:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:58<00:19,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:59<00:18,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:00<00:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:00<00:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:01<00:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:02<00:14,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:02<00:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:03<00:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:04<00:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:04<00:12,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:05<00:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:06<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:07<00:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:07<00:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:08<00:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:09<00:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:09<00:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:10<00:07,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:11<00:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:11<00:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:12<00:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:13<00:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:14<00:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:14<00:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:15<00:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:16<00:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:16<00:00,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:17<00:00,  1.45it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [09:21<02:19, 139.64s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<16:22,  4.94s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:09,  2.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:33,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:59,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:39,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<01:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<01:57,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<01:56,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:56,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<01:53,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:55,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:53,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<01:52,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:52,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:50,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:50,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:52,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:37,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:34,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:33,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:30,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:29,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:26,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:26,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:45<01:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:46<01:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:47<01:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:48<01:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:49<01:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:50<01:19,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:51<01:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:52<01:20,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:53<01:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:53<01:19,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:54<01:26,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:55<01:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:55<01:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:56<01:19,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:57<01:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:57<01:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:58<01:15,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:59<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:11,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:00<01:11,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:01<01:10,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:02<01:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:02<01:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:03<01:09,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:04<01:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:04<01:08,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:05<01:06,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:06<01:05,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:06<01:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:07<01:04,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:07<01:03,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:08<01:02,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:09<01:02,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:09<01:01,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:10<01:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:11<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:11<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:12<01:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:13<00:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:13<00:57,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:14<00:56,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:14<00:55,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:15<00:54,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:16<00:53,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:16<00:52,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:17<00:51,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:17<00:52,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:18<00:51,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:19<00:50,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:19<00:49,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:20<00:48,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:20<00:50,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:21<00:49,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:22<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:23<00:53,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:23<00:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:24<00:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:25<00:49,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:25<00:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:26<00:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:26<00:45,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:27<00:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:28<00:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:28<00:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:29<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:30<00:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:30<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:31<00:40,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:32<00:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:32<00:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:33<00:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:33<00:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:34<00:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:35<00:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:36<00:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:36<00:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:37<00:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:38<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:38<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:39<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:40<00:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:40<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:41<00:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:42<00:33,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:42<00:31,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:43<00:30,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:44<00:30,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:44<00:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:45<00:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:46<00:29,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:46<00:29,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:47<00:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:48<00:29,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:49<00:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:50<00:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:50<00:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:51<00:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:52<00:26,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:53<00:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:54<00:25,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:54<00:24,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:55<00:23,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:56<00:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:57<00:22,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:58<00:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:58<00:20,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:59<00:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:00<00:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:01<00:18,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:02<00:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:02<00:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:03<00:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:04<00:14,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:05<00:14,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:05<00:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:06<00:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:07<00:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:08<00:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:08<00:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:09<00:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:10<00:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:10<00:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:11<00:06,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:12<00:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:12<00:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:13<00:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:14<00:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:15<00:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:15<00:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:16<00:01,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:17<00:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:18<00:00,  1.45it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [11:41<00:00, 140.35s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.64s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:03,  4.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.71it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.93it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.29it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.09it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.47s/it]\u001b[A\n",
+      "Evaluating NLL: 82it [00:01, 72.17it/s]\u001b[A\n",
+      "Evaluating NLL: 143it [00:01, 132.33it/s]\u001b[A\n",
+      "Evaluating NLL: 212it [00:01, 208.97it/s]\u001b[A\n",
+      "Evaluating NLL: 297it [00:01, 314.14it/s]\u001b[A\n",
+      "Evaluating NLL: 378it [00:01, 408.23it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 233.64it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%|██                           | 7/100 [00:00<00:01, 68.85it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██████████████             | 52/100 [00:00<00:00, 291.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 318.86it/s]\n",
+      " 33%|█████████████████████████▎                                                  | 2/6 [21:19<43:33, 653.35s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:06<22:37,  6.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:07<10:38,  3.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:08<06:45,  2.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:08<04:54,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:09<03:53,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:10<03:17,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:10<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:11<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:12<02:31,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:13<02:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:14<02:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:16<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:17<02:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<01:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:23<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<01:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<01:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<01:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<01:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<01:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<01:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:51,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<01:50,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:47,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<01:45,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<01:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:44<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:46<01:37,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:37,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:47<01:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:34,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:49<01:33,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:50<01:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:51<01:32,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:52<01:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:53<01:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:54<01:26,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:55<01:24,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:55<01:24,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:56<01:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:57<01:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:57<01:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:58<01:19,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:59<01:17,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:59<01:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:00<01:18,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:01<01:18,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:01<01:23,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:02<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:03<01:23,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:04<01:22,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:04<01:20,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:05<01:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:06<01:16,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:06<01:15,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:07<01:15,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:08<01:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:08<01:13,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:09<01:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:10<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:10<01:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:11<01:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:12<01:07,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:12<01:06,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:13<01:06,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:14<01:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:14<01:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:15<01:08,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:16<01:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:16<01:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:17<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:18<01:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:19<01:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:19<01:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:20<01:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:21<01:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:21<01:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:23<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:23<01:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:24<01:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:25<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:26<01:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:27<01:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:27<01:04,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:28<01:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:29<01:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:30<01:04,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<01:03,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<01:02,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:32<01:01,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:33<01:00,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:59,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:58,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:36<00:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:56,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:38<00:55,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:39<00:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:40<00:54,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:41<00:53,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:41<00:52,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:42<00:51,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:43<00:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:44<00:48,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:44<00:46,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:45<00:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:46<00:43,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:47<00:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:47<00:41,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:48<00:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:49<00:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:50<00:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:50<00:38,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:51<00:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:52<00:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:52<00:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:53<00:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:54<00:35,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:55<00:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:55<00:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:56<00:32,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:57<00:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:58<00:31,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:58<00:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:59<00:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:00<00:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:01<00:27,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:02<00:29,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:03<00:31,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:03<00:27,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:04<00:24,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:04<00:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:05<00:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:06<00:19,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:06<00:18,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:07<00:17,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:07<00:16,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:08<00:16,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:09<00:16,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:09<00:15,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:10<00:14,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:10<00:13,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:11<00:13,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:12<00:12,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:12<00:11,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:13<00:11,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:13<00:11,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:14<00:10,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:15<00:09,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:15<00:08,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:16<00:08,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:16<00:07,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:17<00:07,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:18<00:06,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:18<00:06,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:19<00:05,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:19<00:04,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:20<00:04,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:21<00:03,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:21<00:03,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:22<00:02,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:23<00:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:23<00:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:24<00:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:25<00:00,  1.38it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:33<10:12, 153.15s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:34,  4.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:43,  2.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:37,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:40,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<02:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<02:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<02:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:59,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:59,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:58,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:44,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:41,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:38,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:35,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:31,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:29,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:29,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:26,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:25,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:23,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:22,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:21,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:21,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:18,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:04<01:13,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:12,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:06<01:11,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:10,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:08<01:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:09,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:09,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<01:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<01:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<01:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<01:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<01:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:21<01:08,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:22<01:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<01:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:23<01:07,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:24<01:07,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:25<01:06,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:26<01:05,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:27<01:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:27<01:02,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:28<01:02,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:29<01:01,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:30<01:01,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:31<01:01,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:31<00:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:32<00:58,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:33<00:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:34<00:58,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:35<00:57,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:35<00:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:36<00:54,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:37<00:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:38<00:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:51,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:39<00:50,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:40<00:50,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:47,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:45,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:45,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:43<00:46,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:46,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:44,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:48<00:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:49<00:39,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:37,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:50<00:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:51<00:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:52<00:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:53<00:34,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:54<00:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:55<00:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:55<00:31,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:56<00:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:57<00:38,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:58<00:35,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:59<00:31,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:59<00:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:00<00:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:01<00:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:01<00:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:02<00:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:03<00:23,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:04<00:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:04<00:21,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:05<00:20,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:06<00:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:06<00:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:07<00:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:08<00:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:08<00:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:09<00:16,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:10<00:15,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:10<00:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:11<00:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:12<00:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:13<00:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:13<00:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:14<00:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:15<00:11,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:15<00:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:16<00:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:17<00:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:17<00:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:18<00:07,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:19<00:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:20<00:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:20<00:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:21<00:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:22<00:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:23<00:03,  1.36it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:58<07:26, 148.78s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:35,  4.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:20,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:59,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:53,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:15,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:53,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:39,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:06,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<02:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<02:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<01:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<02:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<01:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:54,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:50,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<01:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:38,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:41,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:40,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:37,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:37,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:33,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:30,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:25,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:24,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:24,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:20,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:17,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:18,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:05<01:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:06<01:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:08<01:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:15,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:10<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:11<01:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:13<01:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:14<01:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:15<01:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:16<01:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:17<01:16,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:17<01:16,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:18<01:16,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:19<01:15,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:20<01:14,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:21<01:13,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:22<01:13,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<01:11,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:23<01:10,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:24<01:09,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:25<01:08,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:26<01:08,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:26<01:08,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:27<01:06,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:28<01:05,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<01:05,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<01:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:31<01:03,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<01:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<01:02,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<01:01,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:34<01:00,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:35<00:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:56,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:36<00:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:37<00:56,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:38<00:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:39<00:53,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:39<00:51,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:40<00:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:41<00:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:41<00:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:42<00:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:43<00:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:44<00:45,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:45<00:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:45<00:46,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:46<00:44,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:47<00:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:48<00:43,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:48<00:42,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:49<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:50<00:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:51<00:44,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:52<00:46,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:53<00:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:53<00:38,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:54<00:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:55<00:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:55<00:32,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:56<00:31,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:57<00:30,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:57<00:30,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:58<00:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:59<00:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:00<00:28,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:00<00:27,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:01<00:26,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:02<00:25,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:02<00:24,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:03<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:04<00:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:04<00:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:05<00:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:06<00:21,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:07<00:22,  1.33it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [07:08<04:40, 140.12s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:13,  4.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:13,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:02,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:03,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:32,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:08,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:50,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:05,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<01:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<01:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:53,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<01:54,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:53,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:52,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:50,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<01:49,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:47,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<01:41,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:27,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:26,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:25,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:26,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:26,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:24,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:20,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:16,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:04<01:15,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:11<01:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<01:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<01:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<01:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<01:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<01:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<01:01,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<00:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<01:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:23<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<01:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:24<00:57,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:25<00:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:26<00:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:27<00:57,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:28<00:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:29<00:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:29<00:58,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:30<00:59,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:31<00:58,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:32<00:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:33<00:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:34<00:56,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:34<00:55,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:35<00:54,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:36<00:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:37<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:38<00:52,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:38<00:52,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:39<00:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:40<00:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:41<00:49,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:48,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:42<00:47,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:47,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:46,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:45,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:43,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:42,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:47<00:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:40,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:49<00:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:52<00:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:32,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:54<00:32,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:55<00:31,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:56<00:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:57<00:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:58<00:29,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:58<00:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:59<00:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:00<00:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:01<00:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:01<00:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:02<00:24,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:03<00:23,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:04<00:22,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:04<00:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:05<00:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:06<00:20,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:07<00:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:08<00:23,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:09<00:21,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:09<00:19,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:10<00:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:11<00:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:11<00:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:12<00:13,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:12<00:12,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:13<00:11,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:14<00:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:14<00:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:15<00:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:16<00:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:17<00:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:17<00:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:18<00:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:19<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:19<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:20<00:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:21<00:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:21<00:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:23<00:03,  1.36it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [09:34<02:22, 142.19s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:35,  4.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:15,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:58,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:51,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:14,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:50,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:07,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<01:54,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:55,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:54,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<01:47,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:54,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:50,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:48,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:45,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:45,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<01:43,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:41,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:37,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:33,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:27,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:26,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:24,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:23,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:24,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:23,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:21,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:18,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:17,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:16,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:17,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:22,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:19,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:16,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:11<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:14<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<01:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<01:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<01:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<01:06,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<01:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<01:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<01:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:21<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<01:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<01:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:23<00:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:24<00:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:25<00:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:26<00:56,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:27<00:54,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:28<00:53,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:29<00:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:29<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:30<00:55,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:31<00:54,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:32<00:54,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:32<00:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:33<00:52,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:34<00:51,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:35<00:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:35<00:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:36<00:49,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:37<00:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:37<00:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:38<00:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:39<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:40<00:50,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:41<00:56,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:42<00:50,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:46,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:43<00:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:44<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:45<00:38,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:46<00:37,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:47<00:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:48<00:34,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:33,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:49<00:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:50<00:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:51<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:52<00:29,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:53<00:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:54<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:55<00:26,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:56<00:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:57<00:25,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:57<00:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:58<00:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:59<00:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:00<00:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:01<00:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:02<00:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:03<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:04<00:18,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:04<00:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:05<00:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:06<00:16,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:06<00:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:07<00:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:08<00:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:08<00:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:09<00:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:10<00:12,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:10<00:11,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:11<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:12<00:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:12<00:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:13<00:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:14<00:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:14<00:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:15<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:16<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:16<00:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:17<00:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:18<00:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:19<00:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:19<00:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:20<00:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:20<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:21<00:00,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:22<00:00,  1.41it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [11:58<00:00, 143.72s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.08s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:03,  4.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.86it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.02it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.01it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.74s/it]\u001b[A\n",
+      "Evaluating NLL: 86it [00:01, 65.12it/s]\u001b[A\n",
+      "Evaluating NLL: 173it [00:01, 143.80it/s]\u001b[A\n",
+      "Evaluating NLL: 260it [00:02, 232.55it/s]\u001b[A\n",
+      "Evaluating NLL: 346it [00:02, 325.74it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 215.25it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%|▎                            | 1/100 [00:00<00:10,  9.83it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|████████████▋              | 47/100 [00:00<00:00, 270.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 303.38it/s]\n",
+      " 50%|██████████████████████████████████████                                      | 3/6 [33:33<34:30, 690.06s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:06<21:46,  6.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:07<10:12,  3.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:07<06:27,  1.97s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:08<04:43,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:09<03:47,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:09<03:11,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:10<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:11<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:13<02:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:00,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<01:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<01:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:51,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<01:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:50,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:48,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<01:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:45,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:48,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:41<01:50,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:54,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:54,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:53,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:52,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:49<01:51,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:50,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:48,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:52<01:48,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:53<01:49,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:54<01:48,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:54<01:48,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:55<01:48,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:56<01:47,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:57<01:46,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:58<01:44,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:58<01:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:59<01:41,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:00<01:40,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:01<01:38,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:02<01:38,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:02<01:37,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:03<01:35,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:04<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:05<01:34,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:06<01:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:06<01:29,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:07<01:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:08<01:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:08<01:25,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:09<01:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:10<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:11<01:22,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:11<01:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:12<01:27,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:13<01:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:14<01:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:15<01:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:15<01:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:16<01:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:17<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:18<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:18<01:12,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:19<01:20,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:20<01:27,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:21<01:20,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:22<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:22<01:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:23<01:06,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:24<01:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:24<01:05,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:25<01:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:26<01:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:26<01:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:27<01:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:28<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:28<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:29<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:30<00:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:30<00:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:31<00:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:32<00:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:32<00:52,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:33<00:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:34<00:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:34<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:35<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:36<00:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:36<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:37<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:38<00:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:38<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:39<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:40<00:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:40<00:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:41<00:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:42<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:42<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:43<00:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:44<00:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:44<00:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:45<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:46<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:46<00:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:47<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:48<00:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:48<00:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:49<00:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:49<00:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:50<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:51<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:51<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:52<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:53<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:54<00:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:54<00:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:55<00:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:56<00:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:56<00:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:57<00:29,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:58<00:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:58<00:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:59<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:00<00:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:00<00:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:01<00:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:02<00:24,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:02<00:24,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:03<00:23,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:04<00:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:04<00:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:05<00:21,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:06<00:21,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:06<00:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:07<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:08<00:18,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:08<00:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:09<00:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:10<00:16,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:10<00:15,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:12<00:17,  1.34it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:19<09:19, 139.87s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:06,  4.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:03,  2.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:45,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:09,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:45,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:13,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:54,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:54,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:59,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<02:03,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:57,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:55,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:44,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:43,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:42,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:39,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:38,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:34,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:38,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:40,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:41,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:41,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:40,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:39,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:39,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:37,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:38,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:59<01:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:00<01:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:02<01:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:03<01:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:04<01:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:04<01:31,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:05<01:30,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:06<01:29,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:07<01:28,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:08<01:27,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:08<01:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:09<01:25,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:10<01:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:11<01:23,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:12<01:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:12<01:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:13<01:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:14<01:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:15<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:15<01:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:16<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:17<01:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:17<01:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:18<01:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:19<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:20<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:20<01:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:21<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:22<01:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:23<01:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:23<01:04,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:24<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:25<01:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:26<01:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:26<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:28<01:12,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:29<01:15,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:29<01:07,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:30<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:31<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:32<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<00:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:34<00:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:36<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:38<00:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:40<00:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:42<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:40,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:44<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:46<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:48<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:49<00:38,  1.35it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:11<06:09, 123.22s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:07,  4.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:05,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:49,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:13,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:54,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:54,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:49,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:53,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:37,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:37,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:30,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:30,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:50<01:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:52<01:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:53<01:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:54<01:18,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:55<01:17,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:56<01:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:57<01:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:29,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:00<01:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:01<01:20,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:01<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:02<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:03<01:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:04<01:12,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:12,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:06<01:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:08<01:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:07,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:10<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:11<01:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:12<01:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:14<01:05,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<01:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<01:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:16<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<01:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<01:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:18<01:01,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<01:00,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<00:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:59,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<00:59,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<01:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:58,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:23<00:57,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:56,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<01:00,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:33<01:03,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:34<00:56,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:35<00:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:35<00:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:36<00:46,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:37<00:45,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:37<00:43,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:38<00:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:39<00:41,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:39<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:40<00:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:41<00:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:42<00:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:43<00:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:43<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:44<00:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:45<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:45<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:47<00:35,  1.40it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [06:00<03:53, 116.88s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:21,  4.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:11,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:47,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:52,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:59,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:50,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:45,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:49,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:31,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:31,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:30,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:46<01:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:27,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:48<01:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:50<01:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:50<01:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:52<01:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:52<01:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:53<01:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:54<01:21,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:54<01:20,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:55<01:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:56<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:56<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:57<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:58<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:58<01:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:13,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:01<01:16,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:07<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:09<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:10<01:04,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:11<01:02,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:11<01:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:12<01:00,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:12<00:59,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:13<00:59,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:14<01:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:14<01:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:15<01:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:16<01:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:17<01:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:17<01:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:18<01:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:19<00:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:19<00:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:20<00:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:21<00:58,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:22<00:57,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:22<00:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:23<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:24<00:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:24<00:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:25<00:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:53,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:26<00:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:27<00:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:29<00:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:30<00:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:55,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:54,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:33<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:53,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:53,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:35<00:52,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:51,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:50,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:39<00:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:48,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:47,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:42<00:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:43<00:43,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:43<00:42,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:44<00:42,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:45<00:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:46<00:40,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:47<00:39,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:47<00:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:48<00:37,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:49<00:35,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:50<00:34,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:50<00:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:51<00:32,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:52<00:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:52<00:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:53<00:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:54<00:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:55<00:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:55<00:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:56<00:27,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:57<00:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:58<00:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:59<00:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:00<00:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:01<00:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:02<00:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:03<00:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:04<00:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:04<00:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:06<00:22,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:06<00:21,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:08<00:16,  1.38it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [08:11<02:02, 122.29s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:18,  4.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:12,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:50,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:47,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<02:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:56,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:50,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:42,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:40,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:40,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:40,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:40,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:40,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:36,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:35,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:37,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:34,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:33,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:30,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:31,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:29,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:25,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:25,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:24,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:22,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:21,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:01<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:18,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:03<01:17,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:04<01:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:11,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:06<01:11,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:10,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:08<01:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<01:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<01:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<01:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<01:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<00:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<00:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<00:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<00:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<00:53,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:53,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:52,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:29<00:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:50,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:31<00:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:32<00:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:33<00:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:46,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:36<00:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:42,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:41,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:42,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:40,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:41<00:39,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:42<00:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:43<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:43<00:37,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:44<00:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:46<00:36,  1.40it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [09:59<00:00, 119.94s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.94s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:04,  3.95it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.50s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  5.74it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.30it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.03it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.69s/it]\u001b[A\n",
+      "Evaluating NLL: 76it [00:01, 58.88it/s]\u001b[A\n",
+      "Evaluating NLL: 158it [00:01, 134.93it/s]\u001b[A\n",
+      "Evaluating NLL: 242it [00:01, 222.78it/s]\u001b[A\n",
+      "Evaluating NLL: 329it [00:02, 320.40it/s]\u001b[A\n",
+      "Evaluating NLL: 418it [00:02, 420.94it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 217.50it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|██████████▌                | 39/100 [00:00<00:00, 387.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 405.08it/s]\n",
+      " 67%|██████████████████████████████████████████████████▋                         | 4/6 [43:48<22:01, 660.53s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:06<22:37,  6.82s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:07<10:45,  3.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:08<06:58,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:09<05:10,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:09<04:09,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:10<03:35,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:11<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:12<02:55,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:12<02:45,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:13<02:39,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:14<02:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:15<02:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:15<02:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:16<02:26,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:17<02:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:18<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:18<02:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:19<02:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:20<02:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:21<02:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:21<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:22<02:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:23<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:24<02:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:24<02:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:25<02:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:26<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:27<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:28<02:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:28<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:29<02:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:30<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:31<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:31<02:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:32<02:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:33<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:34<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:35<02:07,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:35<02:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:36<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:37<02:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:38<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:38<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:39<01:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:40<01:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:41<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:41<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:42<01:56,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:43<01:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:44<01:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:44<01:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:45<01:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:46<01:49,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:47<01:47,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:47<01:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:48<01:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:49<01:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:50<01:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:51<01:49,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:51<01:47,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:52<01:45,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:53<01:43,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:53<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:54<01:39,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:55<01:41,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:56<01:40,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:56<01:38,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:57<01:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:58<01:41,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:59<01:40,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [01:00<01:39,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:00<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:01<01:38,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:02<01:42,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:03<01:44,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:04<01:40,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:04<01:37,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:05<01:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:06<01:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:07<01:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:08<01:31,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:08<01:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:09<01:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:10<01:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:11<01:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:11<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:12<01:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:13<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:14<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:14<01:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:15<01:27,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:16<01:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:17<01:25,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:18<01:22,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:18<01:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:19<01:23,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:20<01:23,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:21<01:21,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:22<01:20,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:22<01:18,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:23<01:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:24<01:19,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:25<01:21,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:26<01:22,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:27<01:22,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:28<01:21,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:29<01:21,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:29<01:21,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:30<01:21,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:31<01:20,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:32<01:20,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:33<01:19,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:34<01:18,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:35<01:17,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:36<01:17,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:37<01:16,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:38<01:15,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:38<01:13,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:39<01:12,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:40<01:11,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:41<01:10,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:42<01:10,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:43<01:09,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:44<01:07,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:45<01:06,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:46<01:05,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:46<01:04,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:47<01:03,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:48<00:59,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:49<01:00,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:50<01:00,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:51<00:59,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:52<00:57,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:53<00:56,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:53<00:55,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:54<00:53,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:55<00:53,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:56<00:51,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:57<00:50,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:57<00:49,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:58<00:49,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:59<00:48,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [02:00<00:48,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [02:01<00:46,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [02:02<00:46,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [02:03<00:46,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [02:03<00:43,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [02:04<00:43,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [02:05<00:49,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [02:06<00:47,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [02:07<00:43,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [02:08<00:40,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:09<00:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:09<00:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:10<00:35,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:11<00:34,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:12<00:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [02:12<00:32,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [02:13<00:31,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:15<00:33,  1.18it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:23<09:33, 143.30s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:18,  4.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:16,  2.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:03,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:02,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:31,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:09,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:57,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:45,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:37,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:22,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:22,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:19,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<02:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<02:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:33<02:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<02:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<02:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<02:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<01:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:55,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:41<01:58,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<02:00,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:56,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:50,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:48<01:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:48<01:44,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:43,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:50<01:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:51<01:43,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:52<01:41,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:53<01:41,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:54<01:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:54<01:38,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:55<01:38,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:56<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:57<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:57<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:58<01:39,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:59<01:36,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:00<01:34,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:00<01:33,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:01<01:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:02<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:02<01:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:03<01:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:04<01:29,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:05<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:05<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:06<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:07<01:28,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:08<01:27,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:09<01:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:09<01:31,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:10<01:30,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:11<01:28,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:12<01:27,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:13<01:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:13<01:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:14<01:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:15<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:16<01:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:16<01:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:17<01:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:18<01:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:19<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:19<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:20<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:21<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:22<01:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:22<01:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:23<01:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:24<01:13,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:25<01:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:26<01:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:26<01:12,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:27<01:11,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:28<01:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:29<01:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:29<01:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:30<01:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:31<01:07,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:32<01:09,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:33<01:10,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:34<01:09,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:35<01:09,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:36<01:09,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:36<01:08,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:37<01:07,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:38<01:06,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:39<01:06,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:40<01:05,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:41<01:05,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:42<01:05,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:43<01:04,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:43<01:02,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:44<01:02,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:45<01:01,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:46<01:00,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:47<00:59,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:48<00:58,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:49<00:57,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:50<00:56,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:51<00:56,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:52<00:50,  1.22it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:18<06:19, 126.66s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:35,  4.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:07,  2.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:39,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:31,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:50,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:24,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:57,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:52,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:13<02:51,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:14<02:43,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:37,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:16<03:00,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:17<03:03,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:17<02:48,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:18<02:40,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:19<02:29,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:20<02:24,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:20<02:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:21<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:22<02:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:23<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:23<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:24<02:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:25<02:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:26<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:26<02:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:27<02:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:28<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:29<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:29<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:30<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:31<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:32<02:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:32<02:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:33<02:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:34<02:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:35<02:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:35<02:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:36<02:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:37<02:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:38<02:01,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:39<02:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:39<01:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:40<01:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:41<01:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:42<01:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:42<01:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:43<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:44<01:53,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:45<01:52,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:45<01:51,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:46<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:47<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:48<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:48<01:48,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:49<01:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:50<01:49,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:51<01:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:52<01:48,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:52<01:46,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:53<01:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:54<01:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:55<01:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:55<01:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:56<01:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:57<01:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:58<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:58<01:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:59<01:36,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:00<01:35,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:01<01:35,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:01<01:35,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:02<01:34,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:03<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:04<01:34,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:04<01:32,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:05<01:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:06<01:33,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:07<01:31,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:08<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:08<01:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:09<01:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:10<01:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:11<01:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:12<01:30,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:12<01:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:13<01:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:14<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:15<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:15<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:16<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:17<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:18<01:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:19<01:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:20<01:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:21<01:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:22<01:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:23<01:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:24<01:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:25<01:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:26<01:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:27<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:28<01:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:29<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:30<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:31<01:09,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:31<01:07,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:33<01:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:34<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:34<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:35<01:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:36<00:59,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:37<01:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:38<01:02,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:38<01:01,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:39<00:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:40<00:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:41<00:58,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:41<00:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:42<00:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:43<00:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:44<00:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:44<00:52,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:45<00:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:46<00:51,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:47<00:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:48<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:48<00:52,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:49<00:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:50<00:50,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:51<00:48,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:52<00:48,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:52<00:48,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:53<00:48,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:54<00:45,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:55<00:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:55<00:43,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:56<00:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:57<00:42,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:58<00:43,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:59<00:43,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [02:00<00:43,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [02:01<00:43,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [02:01<00:42,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [02:02<00:41,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:03<00:41,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:04<00:40,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:05<00:39,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:06<00:38,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:07<00:37,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [02:08<00:37,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [02:08<00:36,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:09<00:35,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:10<00:35,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:11<00:33,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:12<00:32,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:13<00:31,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:14<00:31,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:15<00:30,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:16<00:29,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:16<00:28,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:17<00:27,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:18<00:26,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:20<00:23,  1.22it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [06:41<04:28, 134.02s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<16:05,  4.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:19,  2.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:44,  1.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:32,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:52,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:09<03:27,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:12,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<03:01,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:52,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:47,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:13<02:42,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:14<02:38,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:43,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:41,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:16<02:36,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:17<02:58,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:19<03:06,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:19<02:48,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:20<02:38,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:21<02:31,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:22<02:27,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:22<02:21,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:23<02:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:24<02:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:25<02:18,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:25<02:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:26<02:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:27<02:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:28<02:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:28<02:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:29<02:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:30<02:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:31<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:31<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:32<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:33<02:07,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:34<02:07,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:35<02:06,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:35<02:05,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:36<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:37<02:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:38<02:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:39<02:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:39<02:02,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:40<02:03,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:41<02:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:42<02:01,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:42<01:59,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:43<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:44<01:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:45<01:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:46<01:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:46<01:50,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:47<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:48<01:48,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:49<01:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:49<01:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:50<01:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:51<01:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:52<01:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:52<01:48,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:53<01:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:54<01:42,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:55<01:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:55<01:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:56<01:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:57<01:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:58<01:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:58<01:40,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:59<01:41,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [01:00<01:40,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:01<01:40,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:02<01:39,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:02<01:40,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:03<01:38,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:04<01:37,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:05<01:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:06<01:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:06<01:34,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:07<01:33,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:08<01:33,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:09<01:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:10<01:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:10<01:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:11<01:31,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:12<01:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:13<01:27,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:13<01:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:14<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:15<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:16<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:16<01:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:17<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:18<01:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:19<01:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:20<01:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:20<01:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:21<01:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:22<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:23<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:23<01:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:24<01:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:25<01:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:26<01:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:26<01:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:27<01:13,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:28<01:12,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:29<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:30<01:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:30<01:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:31<01:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:32<01:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:33<01:07,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:33<01:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:34<01:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:35<01:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:36<01:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:37<01:06,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:37<01:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:38<01:03,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:39<01:02,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:40<01:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:41<00:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:41<00:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:42<00:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:43<00:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:44<00:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:44<00:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:45<00:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:46<00:52,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:47<00:51,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:47<00:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:48<00:50,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:49<00:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:50<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:51<00:51,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:51<00:51,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:52<00:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:53<00:48,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:54<00:47,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:55<00:47,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:55<00:47,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:56<00:45,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:57<00:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:58<00:42,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:58<00:42,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:59<00:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [02:00<00:43,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [02:01<00:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [02:02<00:42,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [02:03<00:42,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [02:04<00:41,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:05<00:41,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:05<00:40,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:06<00:40,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:07<00:39,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:09<00:35,  1.21it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [08:52<02:13, 133.16s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:05,  4.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:23,  2.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:53,  1.79s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:43,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<04:04,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:09<03:38,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:10<03:21,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:11<03:12,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:12<03:03,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:51,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:48,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:14<02:45,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:15<02:41,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:16<02:37,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:35,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:17<02:33,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:18<02:35,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:19<02:32,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:20<02:30,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:21<02:33,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:22<02:31,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:22<02:31,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:23<02:33,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:24<02:33,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:25<02:30,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:26<02:28,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:27<02:26,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:28<02:23,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:29<02:47,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:30<02:47,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:31<02:34,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:31<02:25,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:32<02:19,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:33<02:15,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:34<02:13,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:34<02:10,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:35<02:08,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:36<02:06,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:37<02:05,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:38<02:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:38<02:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:39<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:40<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:41<01:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:41<01:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:42<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:43<01:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:44<01:58,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:44<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:45<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:46<01:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:47<01:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:48<01:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:48<01:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:49<01:51,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:50<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:51<01:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:51<01:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:52<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:53<01:48,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:54<01:46,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:54<01:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:55<01:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:56<01:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:57<01:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:57<01:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:58<01:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:59<01:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:00<01:40,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:01<01:40,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:01<01:39,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:02<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:03<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:04<01:38,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:04<01:38,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:05<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:06<01:34,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:07<01:32,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:07<01:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:08<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:09<01:29,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:10<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:11<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:11<01:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:12<01:27,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:13<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:14<01:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:14<01:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:15<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:16<01:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:17<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:17<01:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:18<01:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:19<01:23,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:20<01:24,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:21<01:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:21<01:20,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:22<01:19,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:23<01:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:24<01:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:24<01:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:25<01:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:26<01:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:27<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:28<01:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:28<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:29<01:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:30<01:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:31<01:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:31<01:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:32<01:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:33<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:34<01:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:34<01:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:35<01:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:36<01:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:37<01:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:38<01:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:38<01:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:39<01:00,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:40<01:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:41<00:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:41<00:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:42<00:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:43<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:44<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:44<00:55,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:45<00:54,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:46<00:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:47<00:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:47<00:54,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:48<00:56,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:49<00:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:50<00:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:51<00:51,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:51<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:52<00:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:53<00:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:54<00:46,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:54<00:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:55<00:44,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:56<00:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:57<00:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:58<00:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:58<00:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:59<00:40,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [02:00<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [02:01<00:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [02:01<00:40,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [02:02<00:39,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [02:03<00:39,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [02:04<00:38,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [02:05<00:37,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [02:05<00:36,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [02:06<00:37,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:07<00:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:08<00:34,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:09<00:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:09<00:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:10<00:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:12<00:32,  1.22it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [11:07<00:00, 133.51s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.92s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:03,  4.19it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.88it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.00it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.40it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.20s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.19it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.84s/it]\u001b[A\n",
+      "Evaluating NLL: 90it [00:01, 64.59it/s]\u001b[A\n",
+      "Evaluating NLL: 181it [00:02, 143.40it/s]\u001b[A\n",
+      "Evaluating NLL: 261it [00:02, 220.19it/s]\u001b[A\n",
+      "Evaluating NLL: 349it [00:02, 314.85it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 203.61it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%| | 5/100 [00:00<00:01, 49.51it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|▌| 52/100 [00:00<00:00, 295.80it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 327.37it\n",
+      " 83%|███████████████████████████████████████████████████████████████▎            | 5/6 [55:12<11:09, 669.05s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:06<21:37,  6.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:07<10:26,  3.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:08<06:44,  2.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:08<04:58,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<04:01,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:10<03:25,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:10<03:04,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:11<02:49,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:12<02:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:13<02:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:14<02:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:15<02:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:16<02:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:17<02:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:18<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:18<02:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:19<02:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:20<02:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:21<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:23<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:24<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:24<02:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:06,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:26<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:28<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:29<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:31<01:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:32<01:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:33<02:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:34<02:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:35<02:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<01:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:36<01:57,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:37<01:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:38<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:52,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:39<01:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:40<01:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:41<01:56,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:42<01:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:43<01:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:44<01:49,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:45<01:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:46<01:46,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:47<01:46,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:48<01:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<01:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:51<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:52<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:54<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:55<01:36,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:57<01:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:58<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:59<01:33,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:00<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:00<01:30,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:01<01:28,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:03<01:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:03<01:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:04<01:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:05<01:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:06<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:06<01:29,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:08<01:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:09<01:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:10<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:11<01:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:12<01:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:12<01:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:14<01:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:15<01:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:15<01:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:17<01:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:18<01:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:18<01:12,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:19<01:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:20<01:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:21<01:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:21<01:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:22<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:23<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:24<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:24<01:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:25<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:26<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:27<01:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:27<01:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:28<01:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:29<01:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:30<01:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:30<01:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:31<01:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:32<01:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:33<01:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:34<01:04,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:34<01:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:35<01:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:36<01:01,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:37<01:01,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:38<01:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:38<01:00,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:39<00:59,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:40<00:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:41<00:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:42<00:56,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:43<01:06,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:44<01:02,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:44<00:57,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:45<00:54,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:46<00:52,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:47<00:50,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:47<00:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:48<00:47,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:49<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:49<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:50<00:44,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:51<00:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:52<00:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:52<00:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:53<00:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:54<00:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:55<00:38,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:55<00:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:56<00:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:57<00:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:58<00:39,  1.26it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:06<08:27, 126.96s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:44,  4.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:51,  2.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:19,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:32,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:56,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:50,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:41,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:26,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:24,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:25,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:22,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:23<02:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<02:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<02:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:33<02:10,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:34<02:10,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<02:05,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<02:03,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:37<02:01,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<01:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<01:54,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:39<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:52,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:43<01:51,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:46<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:50,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:48<01:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:49<01:48,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:46,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:47,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:51<01:47,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:52<01:48,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:53<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:53<01:44,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:54<01:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:55<01:42,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:56<01:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:56<01:41,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:57<01:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:58<01:38,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:59<01:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:59<01:37,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:00<01:42,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:01<01:44,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:02<01:42,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:03<01:37,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:04<01:36,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:04<01:34,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:05<01:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:06<01:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:07<01:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:07<01:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:08<01:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:09<01:28,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:10<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:10<01:25,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:11<01:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:12<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:13<01:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:13<01:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:14<01:27,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:15<01:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:16<01:26,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:17<01:24,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:22,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:18<01:24,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:19<01:21,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:20<01:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:21<01:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:22<01:16,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:23<01:17,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:16,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:25<01:14,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:25<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:26<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:12,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:28<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:29<01:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:30<01:09,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:31<01:09,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:32<01:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:33<01:07,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:34<01:06,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:35<01:05,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:36<01:03,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:36<01:03,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:37<01:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:38<01:02,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:39<01:01,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:40<01:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:40<00:58,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:41<00:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:42<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:43<00:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:44<00:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:45<01:06,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:46<01:03,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:46<00:58,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:47<00:55,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:48<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:49<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:49<00:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:50<00:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:51<00:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:52<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:52<00:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:53<00:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:54<00:43,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:55<00:42,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:56<00:45,  1.24it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:05<06:06, 122.10s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:40,  4.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:26,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:06,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:02,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:25,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:03,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:40,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:10,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<02:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<02:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<02:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<02:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<01:57,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:33<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:57,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:35<01:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:36<01:57,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:36<01:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:37<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:38<01:56,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:39<01:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:39<01:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:40<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:41<01:48,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:42<01:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:42<01:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:43<01:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:44<01:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:44<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:45<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:46<01:42,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:47<01:41,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:47<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:48<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:49<01:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:50<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:50<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:51<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:52<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:52<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:53<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:54<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:55<01:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:55<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:56<01:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:57<01:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:58<01:33,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:58<01:31,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:59<01:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:00<01:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:01<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:01<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:02<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:03<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:04<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:04<01:26,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:05<01:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:06<01:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:07<01:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:07<01:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:08<01:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:09<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:10<01:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:10<01:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:11<01:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:12<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:13<01:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:13<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:14<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:15<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:16<01:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:16<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:17<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:18<01:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:19<01:16,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:19<01:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:20<01:14,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:21<01:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:22<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:22<01:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:23<01:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:24<01:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:25<01:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:25<01:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:26<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:27<01:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:28<01:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:28<01:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:29<01:06,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:30<01:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:31<01:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:32<01:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:32<01:03,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:33<01:02,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:34<01:01,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:35<01:01,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:36<00:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:36<00:58,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:37<00:58,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:38<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:39<00:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:40<00:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:40<00:55,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:41<00:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:42<00:56,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:43<00:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:44<00:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:44<00:52,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:45<00:51,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:46<00:49,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:47<00:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:48<00:47,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:48<00:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:50<00:53,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:50<00:52,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:51<00:48,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:52<00:45,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:53<00:43,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:53<00:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:54<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:55<00:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:56<00:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:57<00:39,  1.28it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [06:05<04:01, 120.96s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:21,  4.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:21,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:02,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:58,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:21,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<03:01,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:47,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<02:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:06,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<02:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<02:02,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<02:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:55,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<01:54,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:53,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:52,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:48,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:51,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:46<01:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:37,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:54<01:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:32,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:26,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:24,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:04<01:23,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:07<01:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:08<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:09<01:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:10<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:11<01:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:11<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:12<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:13<01:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:13<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:14<01:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:15<01:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:16<01:12,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:16<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:17<01:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:18<01:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:18<01:11,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:19<01:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:20<01:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:21<01:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:21<01:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:22<01:11,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:23<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:24<01:10,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:25<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:25<01:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:26<01:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:27<01:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:28<01:06,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:28<01:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:29<01:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:30<01:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:31<01:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:32<01:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:32<01:03,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:33<01:04,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:34<01:03,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:35<01:03,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:36<01:03,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:37<01:02,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:38<01:01,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:38<01:01,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:39<01:00,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:40<00:59,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:41<00:58,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:42<00:58,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:43<00:56,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:43<00:56,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:44<00:55,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:45<00:54,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:46<00:54,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:47<00:53,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:48<00:52,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:49<00:51,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:50<00:50,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:50<00:49,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:51<00:49,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:52<00:48,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:53<00:45,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:54<00:43,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:54<00:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:55<00:42,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:56<00:42,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:57<00:43,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:58<00:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:59<00:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:00<00:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:00<00:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:01<00:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:02<00:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:03<00:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [02:03<00:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [02:04<00:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:05<00:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:06<00:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:07<00:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:08<00:33,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:09<00:34,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:10<00:31,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:10<00:28,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:11<00:26,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:12<00:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:13<00:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:13<00:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:14<00:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:15<00:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:15<00:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:16<00:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:17<00:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:18<00:18,  1.27it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [08:26<02:08, 128.86s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:22,  4.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:18,  2.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:03,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:58,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:21,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:59,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<02:00,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<02:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:57,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:57,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<01:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:53,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:52,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:47,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:45,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:43,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:42,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:46<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:50<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:36,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:35,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:54<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:31,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:30,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:58<01:28,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:27,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:01<01:27,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:27,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:25,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:25,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:04<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:07<01:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:08<01:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:09<01:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:10<01:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:10<01:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:11<01:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:12<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:12<01:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:13<01:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:14<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:15<01:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:15<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:16<01:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:17<01:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:18<01:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:18<01:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:19<01:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:20<01:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:21<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:21<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:22<01:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:23<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:24<01:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:24<01:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:25<01:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:26<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:27<01:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:27<01:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:28<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:29<01:05,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:30<01:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:30<01:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:31<01:01,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:32<01:02,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:33<01:02,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:34<01:02,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:35<01:05,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:36<01:03,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:36<01:00,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:37<00:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:38<00:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:39<00:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:39<00:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:40<00:54,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:41<00:53,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:42<00:52,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:43<00:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:43<00:51,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:44<00:50,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:45<00:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:46<00:47,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:47<00:57,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:48<00:54,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:49<00:49,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:49<00:46,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:50<00:44,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:51<00:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:51<00:41,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:52<00:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:53<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:54<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:54<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:55<00:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:57<00:38,  1.29it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [10:25<00:00, 125.13s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.82s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:03,  4.37it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.66it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.77it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.68s/it]\u001b[A\n",
+      "Evaluating NLL: 76it [00:01, 59.23it/s]\u001b[A\n",
+      "Evaluating NLL: 157it [00:01, 134.56it/s]\u001b[A\n",
+      "Evaluating NLL: 233it [00:01, 212.95it/s]\u001b[A\n",
+      "Evaluating NLL: 317it [00:02, 307.96it/s]\u001b[A\n",
+      "Evaluating NLL: 400it [00:02, 400.95it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:02, 217.15it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%|▎                            | 1/100 [00:00<00:10,  9.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  34%|█████████▏                 | 34/100 [00:00<00:00, 190.97it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 247.54it/s]\n",
+      "100%|██████████████████████████████████████████████████████████████████████████| 6/6 [1:05:53<00:00, 658.95s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise sigma 0.00625\n",
+      "Initial validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.51s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 111.06it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 168.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 224.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 265.80it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 218.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 250.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 279.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 217.73it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 197.93it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 201.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 205.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 169.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 234.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 365.61it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 195.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 179.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 134.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 349.29it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 186.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 143.46it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 173.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 314.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 275.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 150.41it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 132.09it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 156.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 149.19it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 158.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 297.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 225.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 234.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 33: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 254.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 33: validation NLL: 9.56\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "precomputing masks and variances: 100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 484.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods: 100%|█████████████████████████████████████████████████| 4/4 [00:00<00:00, 380.96it/s]\n",
+      "precomputing masks and variances: 100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 391.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "computing log likelihoods: 100%|█████████████████████████████████████████████████| 4/4 [00:00<00:00, 328.64it/s]\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 205.01it/s]\n",
+      "  0%|                                                                                     | 0/6 [00:00<?, ?it/s]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:48,  4.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:40,  2.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:13,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:00,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:19,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:56,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:39,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:43,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:24,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:12,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:52,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<01:49,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<01:47,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:47,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:47,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:47,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:46,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:44,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:44,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:43,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:43,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:39,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:38,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:37,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:37,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:39,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:38,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:36,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:36,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:35,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:35,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:34,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:31<01:33,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:32<01:33,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:32,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:33<01:29,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:34<01:29,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:34<01:28,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:35<01:28,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:29,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:36<01:26,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:37<01:26,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:37<01:26,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:38<01:27,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:39<01:26,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:39<01:25,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:40<01:24,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:40<01:24,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:41<01:23,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:41<01:21,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:42<01:19,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:43<01:18,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:43<01:17,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:44<01:15,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:44<01:14,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:45<01:14,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:45<01:15,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:46<01:14,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:47<01:13,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:47<01:12,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:48<01:13,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:48<01:15,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:49<01:16,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:50<01:15,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:50<01:14,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:51<01:14,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:51<01:13,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:52<01:13,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:53<01:12,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:53<01:11,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:54<01:11,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:54<01:10,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:55<01:08,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:56<01:08,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:56<01:08,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:57<01:07,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:57<01:06,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:58<01:08,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [00:59<01:06,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [00:59<01:06,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:00<01:06,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:00<01:05,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:01<01:04,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:02<01:03,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:02<01:04,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:03<01:03,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:03<01:01,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:04<01:00,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:05<01:00,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:05<01:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:06<01:02,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:07<01:00,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:07<00:59,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:08<00:56,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:08<00:55,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:09<00:55,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:10<00:56,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:10<00:55,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:11<00:58,  1.53it/s]\n",
+      "\n",
+      "Training models:  20%|████████████                                                | 1/5 [01:14<04:56, 74.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:53,  4.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:51,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:37,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:05<03:30,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<02:57,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:38,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:07<02:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<02:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<01:59,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:10<01:55,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:53,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:11<01:50,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:12<01:51,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:49,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:13<01:48,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:14<01:46,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:14<01:47,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:15<01:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:16<01:46,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:16<01:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:17<01:47,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:17<01:45,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:18<01:45,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:19<01:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:20<01:51,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:21<01:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:21<01:45,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:22<01:44,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:22<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:23<01:40,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:24<01:39,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:24<01:37,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:25<01:37,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:25<01:37,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:26<01:36,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:27<01:35,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:27<01:35,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:28<01:33,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:28<01:32,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:29<01:30,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:29<01:28,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:30<01:28,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:31<01:29,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:31<01:27,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:32<01:36,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:33<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:33<01:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:34<01:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:35<01:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:35<01:30,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:36<01:28,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:36<01:29,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:37<01:29,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:38<01:29,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:38<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:39<01:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:40<01:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:40<01:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:41<01:26,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:41<01:25,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:42<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:43<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:44<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:44<01:35,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:45<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:46<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:47<01:36,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:47<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:48<01:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:49<01:38,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:50<01:36,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:51<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:51<01:33,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:52<01:34,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:53<01:34,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:54<01:34,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:54<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:55<01:31,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:56<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:57<01:29,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:57<01:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:58<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:59<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:00<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:01<01:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:02<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:03<01:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:04<01:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:06<01:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:07<01:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:09<01:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:10<01:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:11<01:10,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:12<01:08,  1.42it/s]\n",
+      "\n",
+      "Training models:  40%|████████████████████████                                    | 2/5 [02:29<03:43, 74.66s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:05<16:52,  5.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:05,  2.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:13,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:56,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:11,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:44,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:04,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:00,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<01:57,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<01:54,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:52,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:50,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:49,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:48,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:48,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:46,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:16<01:45,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:43,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:47,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:18<01:45,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:43,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:19<01:41,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:20<01:42,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:21<01:42,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:22<01:40,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:22<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:23<01:43,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:41,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:24<01:40,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:25<01:39,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:25<01:37,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:26<01:37,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:27<01:37,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:27<01:35,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:28<01:35,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:28<01:36,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:29<01:34,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:30<01:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:30<01:33,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:31<01:32,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:31<01:32,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:32<01:31,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:33<01:30,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:33<01:30,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:34<01:28,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:34<01:28,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:35<01:26,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:36<01:25,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:36<01:24,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:37<01:25,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:37<01:25,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:38<01:23,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:39<01:23,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:39<01:22,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:40<01:21,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:40<01:21,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:41<01:19,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:41<01:18,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:42<01:18,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:43<01:17,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:43<01:18,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:44<01:18,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:44<01:16,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:45<01:16,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:45<01:14,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:46<01:15,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:47<01:14,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:47<01:12,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:48<01:12,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:48<01:15,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:49<01:17,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:50<01:16,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:50<01:14,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:51<01:12,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:51<01:10,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:52<01:10,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:53<01:09,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:53<01:08,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:54<01:07,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:54<01:08,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:55<01:06,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:56<01:07,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:56<01:11,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:57<01:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [00:58<01:07,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [00:58<01:04,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [00:59<01:04,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [00:59<01:03,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:00<01:02,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:00<01:01,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:01<00:59,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:02<00:59,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:02<00:59,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:03<00:57,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:03<00:56,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:04<00:55,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:04<00:56,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:05<00:53,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:05<00:52,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:06<00:50,  1.89it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:06<00:50,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:07<00:51,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:08<00:51,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:08<00:51,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:09<00:51,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:09<00:51,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:10<00:49,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:10<00:48,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:11<00:48,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:12<00:47,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:12<00:46,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:13<00:52,  1.59it/s]\n",
+      "\n",
+      "Training models:  60%|████████████████████████████████████                        | 3/5 [03:45<02:30, 75.43s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:01,  4.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:54,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:40,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:35,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:02,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:40,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:15,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:55,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:52,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:51,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:50,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:14<01:49,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:47,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:16<01:47,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:45,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:17<01:44,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:18<01:44,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:18<01:44,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:19<01:42,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:20<01:42,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:20<01:41,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:21<01:44,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:22<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:22<01:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:23<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:24<01:44,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:25<01:42,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:25<01:42,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:26<01:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:27<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:27<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:28<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:29<01:39,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:29<01:36,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:30<01:34,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:31<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:31<01:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:32<01:47,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:33<01:49,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:34<01:49,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:34<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:35<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:49,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:37<01:48,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:38<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:48,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:46,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:46,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:43,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:39,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:39,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:36,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:31,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:27,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:53<01:24,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:19,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:57<01:18,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:00<01:16,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:01<01:15,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:01<01:14,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:02<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:03<01:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:04<01:09,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:11,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:06<01:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:08<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:10<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:23,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:16,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<00:59,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<00:56,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:16<00:53,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<00:51,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:17<00:51,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:18<00:52,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<00:51,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:19<00:50,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:20<00:49,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:20<00:49,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:21<00:50,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:22<00:49,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:22<00:49,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:23<00:49,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:23<00:48,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:24<00:47,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:25<00:46,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:25<00:45,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:26<00:46,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:26<00:44,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:27<00:44,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:28<00:44,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:28<00:43,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:29<00:43,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:30<00:42,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:31<00:46,  1.45it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████████████████████████████            | 4/5 [05:18<01:22, 82.52s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:06,  4.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:23,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:52,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:42,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:02,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:40,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<02:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<01:59,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<01:58,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:57,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:55,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:54,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:51,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:14<01:49,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:49,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:15<01:46,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:16<01:45,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:44,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:17<01:42,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:18<01:41,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:18<01:39,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:19<01:40,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:20<01:41,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:20<01:41,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:21<01:40,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:21<01:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:22<01:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:23<01:40,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:23<01:42,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:24<01:39,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:24<01:37,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:25<01:38,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:26<01:38,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:26<01:37,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:27<01:36,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:27<01:34,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:28<01:34,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:29<01:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:29<01:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:30<01:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:30<01:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:31<01:35,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:32<01:31,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:32<01:30,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:33<01:29,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:33<01:29,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:34<01:28,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:35<01:26,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:35<01:26,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:36<01:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:36<01:26,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:37<01:23,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:37<01:23,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:38<01:24,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:39<01:23,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:39<01:22,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:40<01:22,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:40<01:21,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:41<01:21,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:42<01:21,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:42<01:20,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:43<01:20,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:43<01:20,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:44<01:19,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:45<01:19,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:45<01:18,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:46<01:17,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:46<01:16,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:47<01:16,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:48<01:15,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:48<01:15,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:49<01:14,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:49<01:13,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:50<01:14,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:51<01:13,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:51<01:11,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:52<01:10,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:52<01:10,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:53<01:07,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:54<01:05,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:54<01:04,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:55<01:04,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:55<01:04,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:56<01:03,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:56<01:03,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [00:57<01:03,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [00:58<01:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [00:59<01:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [00:59<01:09,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:00<01:07,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:00<01:05,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:01<01:04,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:02<01:03,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:02<01:01,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:03<01:00,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:03<01:00,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:04<01:00,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:05<00:58,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:05<00:59,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:06<00:58,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:06<00:57,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:07<00:56,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:08<00:55,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:08<00:54,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:09<00:52,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:09<00:52,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:10<00:52,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:10<00:51,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:11<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:12<00:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:13<00:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:13<00:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:14<00:52,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:14<00:50,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:15<00:50,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:16<00:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:17<00:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:17<00:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:18<00:47,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:18<00:46,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:19<00:45,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:19<00:44,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:20<00:44,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:21<00:46,  1.56it/s]\n",
+      "\n",
+      "Training models: 100%|████████████████████████████████████████████████████████████| 5/5 [06:43<00:00, 80.61s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.56it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.57it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.22it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.56it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 82it [00:01, 107.60it/s]\u001b[A\n",
+      "Evaluating NLL: 165it [00:01, 221.63it/s]\u001b[A\n",
+      "Evaluating NLL: 237it [00:01, 313.41it/s]\u001b[A\n",
+      "Evaluating NLL: 319it [00:01, 418.73it/s]\u001b[A\n",
+      "Evaluating NLL: 401it [00:01, 510.33it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 318.75it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|███████████▎               | 42/100 [00:00<00:00, 416.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 410.40it/s]\n",
+      " 17%|████████████▋                                                               | 1/6 [06:55<34:37, 415.57s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:10,  4.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:58,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:40,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:36,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:03,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:46,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:22,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<02:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<02:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:59,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:26<01:45,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:40,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:32<01:37,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:33<01:36,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:34<01:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:35<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:38<01:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:39<01:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:32,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:41<01:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:43<01:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:45<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:46<01:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:47<01:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:48<01:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:49<01:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:50<01:24,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:51<01:21,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:52<01:20,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:52<01:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:53<01:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:54<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:54<01:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:55<01:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:56<01:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:56<01:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:57<01:15,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:58<01:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:58<01:13,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:59<01:12,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:00<01:11,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:01<01:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:01<01:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:02<01:09,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:03<01:09,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:03<01:08,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:04<01:08,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:05<01:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:05<01:06,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:06<01:05,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:07<01:04,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:07<01:04,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:08<01:05,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:09<01:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:09<01:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:10<01:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:11<01:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:11<01:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:12<01:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:13<01:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:13<01:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:14<01:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:15<01:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:15<01:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:16<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:17<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:17<00:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:18<00:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:19<00:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:19<00:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:20<00:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:21<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:21<00:53,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:22<00:52,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:23<00:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:23<00:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:24<00:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:25<00:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:25<00:52,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:26<00:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:27<00:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:28<00:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:28<00:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:29<00:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:30<00:46,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:30<00:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:31<00:46,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:32<00:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:32<00:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:33<00:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:34<00:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:35<00:43,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:35<00:45,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:36<00:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:37<00:45,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:38<00:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:39<00:44,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:39<00:43,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:40<00:42,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:41<00:42,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:42<00:41,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:43<00:41,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:43<00:39,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:44<00:39,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:45<00:38,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:46<00:38,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:47<00:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:47<00:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:48<00:35,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:49<00:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:50<00:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:51<00:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:52<00:32,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:52<00:31,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:53<00:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:54<00:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:55<00:28,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:55<00:27,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:56<00:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:57<00:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:58<00:22,  1.41it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:00<08:03, 120.96s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<16:03,  4.84s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:07,  2.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:24,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:13,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:29,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:02,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:54,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:54,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:35,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:00,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<01:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<01:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<01:58,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<01:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<01:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:36<01:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:38<01:40,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:39<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<01:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:40<01:34,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<01:34,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:41<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:42<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:43<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:44<01:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:45<01:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:46<01:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:47<01:31,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:30,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:49<01:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:49<01:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:21,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:21,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:20,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:18,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:17,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:15,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:14,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:17,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:14,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:10<01:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:11<01:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:12<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:04,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:14<01:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<01:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<01:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<01:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<00:56,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<00:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:24<00:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<00:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:25<00:53,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:53,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:28<00:50,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:32<00:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:33<00:46,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:36<00:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:40,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:40<00:36,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:41<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:42<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:42<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:43<00:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:43<00:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:44<00:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:45<00:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:45<00:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:46<00:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:47<00:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:48<00:31,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:48<00:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:49<00:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:49<00:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:50<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:51<00:26,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:51<00:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:52<00:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:53<00:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:54<00:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:54<00:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:55<00:23,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:56<00:22,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:56<00:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:57<00:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:58<00:21,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:58<00:21,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:59<00:20,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:00<00:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:00<00:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:01<00:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:02<00:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:03<00:17,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:03<00:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:04<00:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:05<00:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:05<00:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:06<00:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:07<00:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:08<00:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:08<00:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:10<00:10,  1.42it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:13<06:23, 127.91s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:05<17:57,  5.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:06<08:37,  2.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:41,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:29,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:58,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:40,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:11,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<01:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<01:54,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<01:50,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:48,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<01:49,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<01:50,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:51,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:51,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:48,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:52,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:39,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:36,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:34,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:34,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:32,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:31,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:29,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:30,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:28,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:26,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:25,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:26,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:24,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:46<01:24,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:23,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:47<01:22,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:48<01:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:49<01:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:50<01:24,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:51<01:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:52<01:20,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:53<01:18,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:53<01:18,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:54<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:55<01:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:55<01:15,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:56<01:15,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:57<01:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:57<01:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:58<01:22,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:59<01:17,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:01<01:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:01<01:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:02<01:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:10,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:03<01:08,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:04<01:06,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:04<01:05,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:05<01:05,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:06<01:05,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:06<01:04,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:07<01:03,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:08<01:02,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:08<01:02,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:09<01:02,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:10<01:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:10<01:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:11<01:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:11<00:59,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:12<00:58,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:13<00:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:13<00:58,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:14<00:57,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:15<00:56,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:15<00:56,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:16<00:55,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:16<00:55,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:17<00:54,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:18<00:53,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:18<00:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:19<00:52,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:20<00:51,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:20<00:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:21<00:50,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:22<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:22<00:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:23<00:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:23<00:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:24<00:46,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:25<00:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:26<00:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:26<00:50,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:27<00:49,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:28<00:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:28<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:29<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:30<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:30<00:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:31<00:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:31<00:40,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:32<00:39,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:33<00:37,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:33<00:36,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:34<00:35,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:34<00:34,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:35<00:34,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:36<00:35,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:36<00:33,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:37<00:32,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:37<00:32,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:38<00:37,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:39<00:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:40<00:35,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:40<00:34,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:41<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:42<00:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:42<00:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:43<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:44<00:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:44<00:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:45<00:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:46<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:46<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:47<00:25,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:48<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:48<00:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:49<00:26,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:50<00:26,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:51<00:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:51<00:25,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:52<00:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:53<00:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:54<00:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:55<00:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:55<00:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:56<00:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:57<00:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:58<00:20,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:59<00:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [01:59<00:18,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:00<00:18,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:02<00:15,  1.46it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [06:18<04:12, 126.49s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:05<16:44,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:32,  2.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:47,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:26,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:36,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:00,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:10,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:06,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:10,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:31,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:30,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<02:17,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<01:59,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<01:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<01:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<01:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:42,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:42,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:36<01:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:36,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:38<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:39<01:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:40<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<01:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:42<01:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:43<01:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:32,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:44<01:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:45<01:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:46<01:25,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:47<01:25,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:47<01:26,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:27,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:25,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:49<01:25,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:25,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:19,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:19,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:16,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:16,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:17,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:15,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:15,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:15,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:11,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:10,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:09,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:08,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:04<01:09,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:08,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:06<01:08,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:06,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:08<01:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:06,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:05,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<01:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<01:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<01:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<00:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<00:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<00:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<00:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:52,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:23<00:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:24<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:25<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:48,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:46,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:27<00:46,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:28<00:45,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:45,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:29<00:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:29<00:44,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:44,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:43,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:31<00:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:32<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:33<00:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:33<00:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:34<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:35<00:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:35<00:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:36<00:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:36<00:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:37<00:37,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:38<00:36,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:38<00:35,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:39<00:35,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:40<00:34,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:40<00:33,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:41<00:33,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:41<00:32,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:42<00:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:43<00:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:43<00:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:44<00:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:45<00:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:46<00:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:46<00:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:47<00:31,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:48<00:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:48<00:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:49<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:50<00:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:50<00:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:51<00:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:51<00:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:52<00:23,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:53<00:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:53<00:21,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:54<00:21,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:55<00:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:55<00:19,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:56<00:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:57<00:17,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:57<00:17,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:58<00:16,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:58<00:16,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:59<00:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:00<00:16,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:01<00:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:01<00:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:02<00:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:03<00:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:03<00:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:04<00:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:05<00:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:05<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:06<00:09,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:07<00:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:07<00:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:08<00:07,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:09<00:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:09<00:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:10<00:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:11<00:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:12<00:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:13<00:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:13<00:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:14<00:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:15<00:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:16<00:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:17<00:00,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:17<00:00,  1.45it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [08:38<02:11, 131.92s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:49,  4.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:09,  2.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:33,  1.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:21,  1.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:40,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:17,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:01,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:49,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:06,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:26,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:20,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<01:57,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<01:53,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<01:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:41,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:40,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:39<01:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:34,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<01:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<01:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:43<01:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:44<01:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:46<01:29,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:46<01:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:47<01:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:48<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:49<01:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:50<01:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:26,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:51<01:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:52<01:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:53<01:22,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:54<01:21,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:55<01:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:17,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:17,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:57<01:17,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:16,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:16,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:59<01:15,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:14,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:13,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:12,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:11,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:11,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:07,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:06,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:06,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:05,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:04,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:02,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:02,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:01,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:01,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:02,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:01,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:00,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:00,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<00:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<00:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<00:59,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<00:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<00:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<00:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<00:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<00:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<00:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<00:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:52,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:23<00:51,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:51,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:51,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:25<00:50,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:27<00:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:28<00:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:29<00:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:30<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:32<00:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:33<00:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:34<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:34<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:35<00:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:36<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:36<00:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:37<00:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:37<00:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:38<00:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:39<00:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:39<00:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:40<00:34,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:41<00:34,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:41<00:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:42<00:32,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:42<00:31,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:43<00:31,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:44<00:30,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:44<00:30,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:45<00:29,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:46<00:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:46<00:28,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:47<00:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:48<00:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:48<00:27,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:49<00:27,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:50<00:27,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:50<00:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:51<00:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:51<00:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:52<00:22,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:53<00:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:53<00:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:54<00:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:55<00:20,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:55<00:19,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:56<00:18,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:57<00:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:57<00:17,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:58<00:16,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:58<00:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:59<00:15,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:00<00:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:00<00:14,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:01<00:13,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:02<00:13,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:02<00:12,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:03<00:12,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:04<00:12,  1.46it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [10:46<00:00, 129.21s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.25it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.07it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.32it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.99it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 74it [00:01, 96.75it/s]\u001b[A\n",
+      "Evaluating NLL: 149it [00:01, 199.49it/s]\u001b[A\n",
+      "Evaluating NLL: 224it [00:01, 300.54it/s]\u001b[A\n",
+      "Evaluating NLL: 297it [00:01, 389.56it/s]\u001b[A\n",
+      "Evaluating NLL: 367it [00:01, 460.27it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 305.84it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|█████████▋                 | 36/100 [00:00<00:00, 353.57it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 366.29it/s]\n",
+      " 33%|█████████████████████████▎                                                  | 2/6 [17:53<37:12, 558.13s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:18,  4.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:53,  2.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:29,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:20,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:41,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:18,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:04,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:53,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:45,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:41,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:40,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:35,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:33,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:31,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:29,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:21,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:25,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:19,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:12,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:23<02:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:24<02:18,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:25<02:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:27<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:28<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<02:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:30<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:31<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<02:11,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:34<02:35,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:34<02:20,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:35<02:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:36<02:00,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:37<01:50,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<01:46,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<01:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:39<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:44<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:46<01:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:47<01:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:48<01:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:34,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:49<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:50<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:30,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:52<01:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:54<01:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:29,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:56<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:57<01:28,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:59<01:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:24,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:00<01:23,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:01<01:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:03<01:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:18,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:05<01:18,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:18,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:07<01:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:08<01:16,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:09<01:16,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:10<01:15,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:10<01:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:11<01:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:12<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:12<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:13<01:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:14<01:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:14<01:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:15<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:16<01:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:16<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:17<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:18<01:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:18<01:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:19<01:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:20<01:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:20<01:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:21<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:22<01:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:22<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:23<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:24<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:24<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:25<00:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:26<00:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:27<00:59,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:27<00:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:28<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:29<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<00:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:30<00:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<00:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:32<00:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:33<00:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:44,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:43,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:43<00:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:47<00:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:34,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:50<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:52<00:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:54<00:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:54<00:30,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:31,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:56<00:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:57<00:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:57<00:27,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:58<00:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:59<00:25,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:59<00:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:00<00:23,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:01<00:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:01<00:22,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:02<00:21,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:03<00:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:03<00:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:04<00:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:05<00:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:05<00:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:06<00:17,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:07<00:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:07<00:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:08<00:16,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:09<00:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:10<00:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:10<00:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:11<00:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:12<00:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:12<00:11,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:13<00:11,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:14<00:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:15<00:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:15<00:09,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:16<00:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:17<00:07,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:17<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:18<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:19<00:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:19<00:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:20<00:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:21<00:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:22<00:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:23<00:02,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:24<00:01,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:24<00:00,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:25<00:00,  1.37it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:28<09:53, 148.32s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:28,  4.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:00,  2.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:36,  1.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:25,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:47,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:24,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:08,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:58,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:50,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:43,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:40,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:35,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:23<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:24<02:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:27<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:22,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<02:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:30<02:19,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:31<02:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:32<02:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<01:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:33<01:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:34<01:53,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<01:52,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<01:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<01:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<01:48,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<01:47,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:44,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:40<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:41<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:41,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:42<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:43<01:42,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:44<01:42,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:41,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:39,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:46<01:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:47<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:36,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:49<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:33,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:54<01:33,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:32,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:31,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:56<01:30,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:27,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:26,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:00<01:24,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:20,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:04<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:18,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:17,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:06<01:16,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:08<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:09<01:14,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:09<01:14,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:10<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:11<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:11<01:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:12<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:13<01:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:13<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:14<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:15<01:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:15<01:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:16<01:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:17<01:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:17<01:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:18<01:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:19<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:19<01:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:20<01:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:21<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:22<01:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:22<01:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:23<01:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:24<01:01,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:24<01:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:25<00:59,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:26<00:59,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:26<00:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:27<00:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:28<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:29<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:31<00:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<00:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:33<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:41,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:43<00:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:38,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:47<00:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:50<00:35,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:52<00:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:31,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:54<00:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:54<00:29,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:29,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:56<00:28,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:57<00:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:57<00:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:58<00:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:59<00:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:59<00:24,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:00<00:23,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:01<00:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:01<00:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:02<00:22,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:03<00:22,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:04<00:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:04<00:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:05<00:19,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:06<00:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:06<00:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:07<00:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:08<00:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:09<00:16,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:09<00:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:10<00:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:11<00:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:12<00:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:12<00:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:13<00:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:14<00:12,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:15<00:12,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:16<00:11,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:16<00:10,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:17<00:09,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:18<00:09,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:19<00:08,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:20<00:07,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:21<00:06,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:21<00:05,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:22<00:04,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:23<00:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:24<00:03,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:25<00:02,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:26<00:01,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:26<00:00,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:27<00:00,  1.35it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:58<07:27, 149.33s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:35,  4.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:15,  2.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:34,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:18,  1.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:35,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:24,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:03,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:40,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:35,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:44,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:22<02:34,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:22,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:23<02:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:24<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<01:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<01:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<01:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<01:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<01:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<01:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<01:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:37<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:43,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:41<01:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:41,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:40,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:40,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:40,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:39,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:46<01:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:37,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:50<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:34,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:52<01:31,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:54<01:29,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:55<01:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:27,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:57<01:28,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:58<01:27,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:59<01:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:00<01:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:01<01:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:01<01:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:02<01:15,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:03<01:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:03<01:15,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:04<01:13,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:04<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:05<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:06<01:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:06<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:07<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:08<01:14,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:09<01:13,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:09<01:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:10<01:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:11<01:13,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:11<01:13,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:12<01:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:13<01:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:13<01:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:14<01:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:15<01:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:16<01:08,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:16<01:07,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:17<01:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:18<01:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:18<01:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:19<01:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:20<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:20<01:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:21<01:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:22<01:00,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<01:00,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:23<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:24<00:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:24<00:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:25<00:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:26<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:27<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:27<00:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:28<00:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:29<00:55,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:29<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:30<00:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:31<00:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:31<00:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:32<00:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:33<00:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:33<00:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:34<00:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:35<00:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:35<00:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:36<00:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:37<00:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:37<00:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:38<00:44,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:39<00:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:40<00:43,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:40<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:41<00:41,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:42<00:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:39,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:44<00:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:46<00:36,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:49<00:34,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:34,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:50<00:33,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:51<00:33,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:31,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:31,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:53<00:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:54<00:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:55<00:27,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:57<00:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:58<00:27,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:58<00:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:59<00:24,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:00<00:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:01<00:23,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:01<00:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:02<00:22,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:03<00:21,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:03<00:20,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:04<00:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:05<00:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:06<00:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:06<00:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:07<00:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:08<00:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:09<00:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:09<00:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:10<00:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:11<00:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:12<00:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:12<00:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:13<00:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:14<00:11,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:15<00:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:16<00:12,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:17<00:10,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:17<00:09,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:18<00:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:19<00:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:19<00:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:20<00:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:21<00:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:21<00:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:22<00:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:23<00:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:24<00:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:24<00:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:25<00:00,  1.38it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [07:26<04:57, 148.57s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:11,  4.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:09,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:57,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:48,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:13,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:50,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<02:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:47,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:46,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:45,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<01:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:43,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:31,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:29,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:27,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:27,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:25,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:25,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:24,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:23,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:22,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:21,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:21,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:18,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:00<01:15,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:01<01:15,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:02<01:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:03<01:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:04<01:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:04<01:12,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:12,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:06<01:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:08<01:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:11,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<01:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<01:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<00:59,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<00:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<00:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<01:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:59,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<01:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:57,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<00:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:59,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:53,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:52,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:53,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:29<00:54,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:33<00:53,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:33<00:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:52,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:35<00:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:36<00:50,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:37<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:37<00:50,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:38<00:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:39<00:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:40<00:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:41<00:48,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:41<00:47,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:42<00:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:43<00:45,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:44<00:44,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:45<00:43,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:45<00:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:46<00:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:47<00:41,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:48<00:40,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:39,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:49<00:38,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:50<00:37,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:36,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:34,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:52<00:32,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:54<00:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:55<00:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:56<00:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:29,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:57<00:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:58<00:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:59<00:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:59<00:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:00<00:27,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:01<00:26,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:02<00:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:03<00:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:04<00:26,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:05<00:27,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:05<00:24,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:06<00:21,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:07<00:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:07<00:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:09<00:17,  1.36it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [09:37<02:21, 141.81s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:04,  4.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:05,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:48,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:44,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:11,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:15,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:44,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:36,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:41,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:34,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:33,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:20,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:18,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:21,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:21,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:19,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:13,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:00<01:11,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:01<01:10,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:01<01:10,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:02<01:09,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:03<01:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:04<01:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:09,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:06<01:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:10,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:08<01:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:10<01:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:12<01:11,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:14<01:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<01:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<01:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:16<01:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<01:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<01:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<01:01,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<01:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<01:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:58,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<00:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<00:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:23<00:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:25<00:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:54,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:52,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:28<00:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:29<00:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:30<00:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:31<00:49,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:32<00:48,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:33<00:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:35<00:48,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:48,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:39<00:48,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:40<00:47,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:46,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:45,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:42<00:45,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:43<00:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:44<00:43,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:44<00:43,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:45<00:42,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:46<00:41,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:47<00:40,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:48<00:39,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:48<00:38,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:49<00:38,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:50<00:37,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:51<00:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:52<00:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:52<00:34,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:53<00:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:54<00:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:56<00:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:57<00:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:58<00:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:59<00:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:25,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:00<00:24,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:01<00:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:22,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:02<00:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:03<00:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:04<00:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:05<00:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:05<00:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:06<00:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:07<00:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:07<00:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:08<00:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:09<00:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:10<00:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:10<00:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:11<00:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:12<00:14,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:13<00:14,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:14<00:12,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:15<00:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:15<00:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:16<00:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:17<00:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:17<00:07,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:18<00:06,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:19<00:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:19<00:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:20<00:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:21<00:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:21<00:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:22<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:23<00:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:23<00:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:24<00:00,  1.38it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [12:04<00:00, 144.83s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.47it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.12s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.37it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 90it [00:00, 125.76it/s]\u001b[A\n",
+      "Evaluating NLL: 179it [00:01, 253.07it/s]\u001b[A\n",
+      "Evaluating NLL: 259it [00:01, 358.23it/s]\u001b[A\n",
+      "Evaluating NLL: 350it [00:01, 477.10it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 346.35it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|███████████▉               | 44/100 [00:00<00:00, 438.63it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 414.74it/s]\n",
+      " 50%|██████████████████████████████████████                                      | 3/6 [30:08<31:56, 638.94s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:53,  4.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:58,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:44,  1.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:40,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<02:00,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:50,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:29,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:25,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:25,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:25,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:48<01:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:50<01:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:50<01:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:52<01:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:52<01:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:53<01:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:54<01:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:55<01:25,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:55<01:24,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:56<01:22,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:57<01:20,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:57<01:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:58<01:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:16,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:15,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:13,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:12,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:01<01:10,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:02<01:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:10,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:04<01:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:08,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:06<01:06,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:07<01:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:08<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:09<01:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:10<01:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:10<01:04,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:11<01:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:12<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:12<01:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:13<01:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:14<01:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:14<01:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:15<00:58,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:16<00:56,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:16<00:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:17<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:18<01:00,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:18<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:19<00:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:20<00:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:20<00:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:21<00:58,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:22<00:57,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:23<00:56,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:23<00:55,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:24<00:54,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:25<00:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:26<00:55,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:27<00:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:52,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:28<00:51,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:29<00:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:49,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:49,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:58,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:33<00:56,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:33<00:51,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:34<00:48,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:34<00:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:35<00:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:36<00:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:36<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:37<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:38<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:38<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:39<00:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:40<00:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:40<00:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:41<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:42<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:42<00:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:43<00:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:44<00:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:44<00:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:45<00:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:46<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:46<00:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:47<00:29,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:48<00:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:48<00:27,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:49<00:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:49<00:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:50<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:51<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:51<00:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:52<00:23,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:53<00:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:53<00:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:54<00:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:55<00:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:55<00:20,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:56<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:57<00:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:57<00:18,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:58<00:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:59<00:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:59<00:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:00<00:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:00<00:15,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:01<00:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:02<00:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:02<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:03<00:13,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:04<00:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:04<00:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:05<00:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:06<00:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:06<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:07<00:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:08<00:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:08<00:07,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:09<00:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:09<00:06,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:10<00:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:11<00:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:11<00:04,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:12<00:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:13<00:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:13<00:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:14<00:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:15<00:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:15<00:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:16<00:00,  1.47it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:18<09:14, 138.61s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:43,  4.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:41,  2.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:07,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:55,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:15,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:50,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:48,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:47,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:47,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:47,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:48,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:50,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:51,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:50,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:47,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:44,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:29,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:29,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:31,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:50<01:31,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:26,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:52<01:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:53<01:24,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:54<01:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:55<01:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:31,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:57<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:58<01:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:59<01:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:01<01:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:28,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:25,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:24,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:23,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:10<01:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:13<01:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:18,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:14<01:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:15<01:16,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:16<01:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:16<01:13,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:17<01:14,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:18<01:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:19<01:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:19<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:20<01:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:21<01:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<01:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:22<01:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:23<01:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:24<01:00,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:25<01:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:25<01:05,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:26<01:05,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:27<01:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:28<01:00,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:28<00:59,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:29<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:30<00:57,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:31<00:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:31<00:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:32<00:53,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:33<01:02,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:34<01:04,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:58,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:36<00:51,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:48,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:46,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:38<00:44,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:39<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:40<00:39,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:38,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:41<00:37,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:36,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:42<00:37,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:44<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:46<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:48<00:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:50<00:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:50<00:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:52<00:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:27,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:54<00:27,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:54<00:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:56<00:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:57<00:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:57<00:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:58<00:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:59<00:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:00<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:20,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:01<00:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:02<00:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:03<00:18,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:03<00:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:04<00:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:05<00:16,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:05<00:15,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:06<00:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:07<00:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:07<00:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:08<00:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:09<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:09<00:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:10<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:11<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:11<00:09,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:12<00:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:12<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:13<00:07,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:14<00:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:14<00:06,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:15<00:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:16<00:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:16<00:04,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:17<00:03,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:18<00:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:18<00:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:19<00:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:20<00:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:20<00:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:21<00:00,  1.41it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:42<07:04, 141.61s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:02,  4.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:08,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:43,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:44,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:50,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:49,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:38<01:33,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:31,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:28,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:27,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:29,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:30,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:36,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:35,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:32,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:31,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:50<01:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:35,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:37,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:37,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:53<01:38,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:37,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:38,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:38,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:36,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:31,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:05<01:28,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:27,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:26,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:08<01:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:09<01:25,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:24,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:10<01:23,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:11<01:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:12<01:19,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:13<01:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:14<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:15<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:15<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:16<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:17<01:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:18<01:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:18<01:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:19<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:20<01:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:20<01:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:21<01:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<01:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:23<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:23<01:03,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:24<01:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:25<01:10,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:26<01:15,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:27<01:07,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:28<01:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:28<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:29<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:30<00:54,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:30<00:53,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:31<00:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:32<00:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:32<00:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:33<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:34<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:34<00:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:35<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:36<00:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:36<00:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:37<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:38<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:38<00:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:39<00:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:40<00:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:40<00:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:41<00:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:42<00:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:38,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:44<00:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:46<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:48<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:50<00:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:50<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:29,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:52<00:29,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:54<00:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:54<00:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:56<00:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:57<00:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:58<00:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:58<00:23,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:00<00:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:00<00:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:20,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:02<00:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:02<00:19,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:03<00:18,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:03<00:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:04<00:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:05<00:16,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:05<00:15,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:06<00:14,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:07<00:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:07<00:13,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:08<00:12,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:08<00:11,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:09<00:10,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:10<00:10,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:10<00:09,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:11<00:09,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:12<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:12<00:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:13<00:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:14<00:07,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:14<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:15<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:16<00:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:16<00:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:17<00:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:18<00:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:19<00:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:19<00:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:20<00:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:21<00:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:21<00:00,  1.41it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [07:06<04:45, 142.63s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:58,  4.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:59,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:44,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:06,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:46,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:56,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:55,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<02:01,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<02:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:48,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:45,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:43,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:40,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:30,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:32,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:31,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:32,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:31,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:36,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:25,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:20,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:19,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:30,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:33,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:06,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:06,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:04,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:11<01:04,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<00:59,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<00:58,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<00:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<00:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<00:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<00:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:25<00:52,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:28<00:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:29<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:31<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:32<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:33<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:34<00:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:35<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:36<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:37<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:38<00:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:39<00:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:40<00:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:40<00:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:41<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:42<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:42<00:33,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:43<00:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:44<00:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:44<00:32,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:45<00:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:46<00:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:46<00:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:47<00:29,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:47<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:48<00:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:49<00:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:49<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:50<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:51<00:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:51<00:25,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:52<00:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:53<00:24,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:53<00:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:54<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:55<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:55<00:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:56<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:57<00:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:57<00:20,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:58<00:19,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:59<00:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:59<00:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:00<00:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:01<00:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:01<00:15,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:02<00:15,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:03<00:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:03<00:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:04<00:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:05<00:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:05<00:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:06<00:10,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:07<00:11,  1.44it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [09:15<02:17, 137.55s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:47,  4.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:19,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:55,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:11,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:57,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:07,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:47,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:48,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:46,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:44,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:44,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:42,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:42,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:43,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:41,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:43,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:42,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:54,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:53,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:51,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:50,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:48,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:44,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:42,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:40,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:42,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:39,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:39,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:42,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:49,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:56,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:46,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:39,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:22,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:22,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:14,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:12,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:12,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:13,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:12,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:09,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:10<01:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:06,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:11<01:06,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:12<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:14<01:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<01:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<01:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<00:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:25<00:53,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:29<00:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:31<00:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:32<00:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:33<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:33<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:35<00:41,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:37<00:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:39<00:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:40<00:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:41<00:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:41<00:36,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:42<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:43<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:43<00:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:44<00:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:45<00:34,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:45<00:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:46<00:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:47<00:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:47<00:29,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:48<00:27,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:49<00:27,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:49<00:26,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:50<00:25,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:50<00:24,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:51<00:24,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:52<00:23,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:52<00:23,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:53<00:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:53<00:22,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:54<00:21,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:55<00:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:55<00:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:56<00:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:57<00:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:57<00:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:58<00:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:59<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:59<00:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:00<00:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:01<00:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:02<00:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:02<00:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:03<00:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:03<00:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:04<00:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:05<00:12,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:05<00:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:06<00:11,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:07<00:10,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:07<00:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:08<00:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:09<00:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:09<00:07,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:10<00:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:11<00:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:12<00:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:12<00:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:13<00:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:14<00:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:14<00:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:15<00:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:16<00:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:16<00:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:17<00:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:18<00:00,  1.45it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [11:36<00:00, 139.27s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.20it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.15it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.19it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.28it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 86it [00:00, 120.72it/s]\u001b[A\n",
+      "Evaluating NLL: 171it [00:01, 242.68it/s]\u001b[A\n",
+      "Evaluating NLL: 250it [00:01, 347.49it/s]\u001b[A\n",
+      "Evaluating NLL: 332it [00:01, 448.69it/s]\u001b[A\n",
+      "Evaluating NLL: 407it [00:01, 501.59it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 316.30it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|████████████▏              | 45/100 [00:00<00:00, 446.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 435.44it/s]\n",
+      " 67%|██████████████████████████████████████████████████▋                         | 4/6 [41:56<22:12, 666.09s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:31,  4.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:03,  2.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:42,  1.74s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:36,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:59,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:09<03:37,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:10<03:21,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<03:11,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<03:04,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:59,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:13<02:55,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:14<02:51,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:15<02:47,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:16<02:46,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:17<02:44,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:18<02:41,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:18<02:39,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:19<02:39,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:20<02:39,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:21<02:37,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:22<02:37,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:23<02:34,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:24<02:32,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:24<02:28,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:25<02:31,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:26<02:37,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:27<02:29,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:28<02:23,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:29<02:23,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:30<02:22,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:30<02:19,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:31<02:21,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:32<02:24,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:33<02:28,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:34<02:24,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:35<02:16,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:36<02:31,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:37<02:37,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:38<02:22,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:38<02:12,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:39<02:06,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:40<02:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:41<02:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:41<02:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:42<01:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:43<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:44<01:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:44<01:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:45<01:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:46<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:47<01:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:47<01:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:48<01:51,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:49<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:50<01:51,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:50<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:51<01:48,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:52<01:50,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:53<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:54<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:54<01:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:55<01:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:56<01:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:57<01:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:57<01:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:58<01:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:59<01:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [01:00<01:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [01:00<01:39,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [01:01<01:38,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [01:02<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:03<01:37,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:03<01:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:04<01:36,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:05<01:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:06<01:33,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:06<01:32,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:07<01:31,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:08<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:09<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:09<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:10<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:11<01:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:12<01:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:13<01:28,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:13<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:14<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:15<01:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:16<01:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:16<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:17<01:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:18<01:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:19<01:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:19<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:20<01:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:21<01:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:22<01:19,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:22<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:23<01:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:24<01:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:25<01:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:26<01:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:26<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:27<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:28<01:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:29<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:29<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:30<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:31<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:32<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:32<01:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:33<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:34<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:35<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:35<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:36<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:37<01:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:38<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:38<01:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:39<00:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:40<00:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:41<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:41<00:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:42<00:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:43<00:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:44<00:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:44<00:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:45<00:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:46<00:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:47<00:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:47<00:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:48<00:51,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:49<00:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:50<00:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:50<00:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:51<00:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:52<00:47,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:53<00:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:54<00:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:54<00:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:55<00:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:56<00:46,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:57<00:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:58<00:43,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:58<00:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:59<00:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [02:00<00:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [02:00<00:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [02:01<00:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [02:02<00:35,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [02:03<00:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [02:03<00:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:04<00:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:05<00:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:05<00:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:06<00:33,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:07<00:32,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [02:08<00:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [02:09<00:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:09<00:31,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:10<00:30,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:11<00:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:12<00:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:12<00:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:13<00:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:14<00:27,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:15<00:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:16<00:24,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:16<00:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:17<00:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:18<00:22,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:19<00:22,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:20<00:22,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:21<00:22,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:22<00:21,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:22<00:20,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:23<00:19,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:24<00:19,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:25<00:18,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:26<00:17,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:27<00:16,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:28<00:15,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:29<00:14,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:29<00:14,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:30<00:13,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:31<00:12,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:33<00:10,  1.22it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:35<10:22, 155.71s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:55,  4.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:12,  2.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:35,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:25,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:52,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:09<03:30,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:11,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:57,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:50,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:43,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:13<02:39,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:40,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:45,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:40,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:16<02:37,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:17<02:34,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:18<02:31,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:28,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:20<02:42,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:21<02:50,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:21<02:38,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:22<02:30,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:23<02:23,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:24<02:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:24<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:25<02:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:26<02:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:27<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:27<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:28<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:29<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:30<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:30<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:31<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:32<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:33<02:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:33<02:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:34<02:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:35<02:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:36<02:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:36<02:00,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:37<02:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:38<01:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:39<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:39<02:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:40<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:41<01:56,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:42<01:54,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:42<01:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:43<01:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:44<01:51,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:45<01:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:45<01:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:46<01:47,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:47<01:46,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:48<01:45,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:48<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:49<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:50<01:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:50<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:51<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:52<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:53<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:53<01:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:54<01:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:55<01:39,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:56<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:56<01:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:57<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:58<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:59<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:59<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:00<01:36,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:01<01:36,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:02<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:03<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:03<01:34,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:04<01:33,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:05<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:06<01:34,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:06<01:33,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:07<01:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:08<01:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:09<01:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:10<01:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:10<01:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:11<01:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:12<01:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:13<01:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:13<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:14<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:15<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:16<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:16<01:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:17<01:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:19<01:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:19<01:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:20<01:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:22<01:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:22<01:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:23<01:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:24<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:25<01:06,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:26<01:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:27<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:28<01:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:29<01:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:30<01:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:30<01:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:31<01:03,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:32<01:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:33<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:33<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:34<01:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:35<01:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:36<01:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:36<00:59,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:37<00:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:38<00:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:39<00:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:39<00:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:40<00:58,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:41<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:42<00:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:43<00:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:43<00:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:45<00:55,  1.24it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:23<06:22, 127.59s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:27,  4.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:23,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:13,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:40,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:15,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:58,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:45,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:43,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:36,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:27,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:31,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:34,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:34,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:35,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:36,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:35,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:22<02:34,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:23<02:33,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:24<02:32,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:25<02:32,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:25<02:32,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:26<02:33,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:27<02:31,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:28<02:31,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:29<02:30,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:30<02:27,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:31<02:25,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:32<02:25,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:32<02:24,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:33<02:24,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:34<02:23,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:35<02:22,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:36<02:19,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:37<02:12,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:38<02:14,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:38<02:14,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:39<02:20,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:40<02:20,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:41<02:18,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:42<02:17,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:43<02:12,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:44<02:10,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:45<02:14,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:46<02:13,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:46<02:08,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:47<02:01,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:48<02:10,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:49<02:24,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:50<02:12,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:51<02:03,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:52<01:57,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:52<01:52,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:53<01:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:54<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:55<01:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:55<01:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:56<01:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:57<01:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:58<01:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:58<01:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:59<01:40,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [01:00<01:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [01:01<01:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [01:01<01:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [01:02<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:03<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:04<01:37,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:04<01:37,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:05<01:35,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:06<01:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:07<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:07<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:08<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:09<01:27,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:09<01:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:10<01:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:11<01:26,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:12<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:12<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:13<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:14<01:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:15<01:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:16<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:16<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:17<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:18<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:19<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:19<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:20<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:21<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:22<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:22<01:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:23<01:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:24<01:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:25<01:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:25<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:26<01:12,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:27<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:28<01:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:28<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:30<01:18,  1.19it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [05:56<03:43, 111.53s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:03,  4.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:41,  2.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:19,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:35,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:18,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:59,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:47,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:40,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<02:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<02:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:33<02:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<02:10,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<02:07,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<02:03,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<02:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<01:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:56,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:44<01:48,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:48,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:47,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:48<01:51,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:48<01:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:51,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:50,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:51<01:48,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:51<01:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:52<01:46,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:53<01:48,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:54<01:46,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:55<01:43,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:55<01:43,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:56<01:42,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:57<01:42,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:58<01:43,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:59<01:44,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:59<01:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:00<01:39,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:01<01:43,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:02<01:39,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:03<01:42,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:04<01:39,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:04<01:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:05<01:39,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:06<01:39,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:07<01:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:08<01:35,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:09<01:35,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:09<01:34,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:11<01:47,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:11<01:44,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:12<01:37,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:13<01:33,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:14<01:29,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:14<01:26,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:15<01:24,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:16<01:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:17<01:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:17<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:19<01:19,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:20<01:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:21<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:22<01:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:23<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:24<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:25<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:26<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:27<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:29<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:30<01:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:30<01:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:31<01:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:33<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:33<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:35<01:07,  1.23it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [07:33<01:46, 106.03s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:23,  4.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:21,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:05,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:01,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:04,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:51,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:30,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:19,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<02:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<02:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<02:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<02:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<02:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<01:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<01:57,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:57,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<01:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:37<01:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<01:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<01:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:40<01:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:41<01:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:51,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:42<01:50,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:43<01:51,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:44<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:46<01:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:47<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:49<01:43,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:50<01:44,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:43,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:52<01:49,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:53<01:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:54<01:45,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:54<01:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:55<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:56<01:36,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:57<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:57<01:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:58<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:59<01:34,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:00<01:33,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:00<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:01<01:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:02<01:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:03<01:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:03<01:29,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:04<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:05<01:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:06<01:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:07<01:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:07<01:31,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:08<01:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:09<01:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:10<01:30,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:11<01:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:11<01:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:12<01:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:13<01:24,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:14<01:25,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:15<01:27,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:16<01:29,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:16<01:29,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:17<01:31,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:18<01:30,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:19<01:29,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:20<01:28,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:21<01:28,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:22<01:27,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:23<01:26,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:25,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:24<01:24,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:25<01:24,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:26<01:23,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:22,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:20,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:29<01:19,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:30<01:18,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:31<01:17,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:32<01:17,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:16,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:33<01:15,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:34<01:13,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:35<01:12,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:36<01:09,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:37<01:06,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:37<01:05,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:39<01:05,  1.22it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [09:15<00:00, 111.07s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.21it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.11s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.85it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.47it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.25s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  5.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 80it [00:00, 116.95it/s]\u001b[A\n",
+      "Evaluating NLL: 162it [00:01, 238.22it/s]\u001b[A\n",
+      "Evaluating NLL: 245it [00:01, 354.40it/s]\u001b[A\n",
+      "Evaluating NLL: 328it [00:01, 458.12it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 350.41it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|███████████▌               | 43/100 [00:00<00:00, 426.65it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 422.67it/s]\n",
+      " 83%|███████████████████████████████████████████████████████████████▎            | 5/6 [51:24<10:30, 630.77s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:30,  4.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:27,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:09,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:03,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:02,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:50,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:09,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:10,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<02:06,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<02:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<01:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:49,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:48,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:47,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:49,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:46<01:46,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:38,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:37,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:37,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:54<01:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:25,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:05<01:25,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:22,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:07<01:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:08<01:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:09<01:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:10<01:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:10<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:11<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:12<01:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:13<01:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:14<01:24,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:14<01:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:15<01:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:16<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:17<01:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:17<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:18<01:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:19<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:20<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:20<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:21<01:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:22<01:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:23<01:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:23<01:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:24<01:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:25<01:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:25<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:26<01:07,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:27<01:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:28<01:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:29<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:29<01:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:30<01:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:31<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:32<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:33<01:01,  1.29it/s]\n",
+      "\n",
+      "Training models:  20%|████████████                                                | 1/5 [01:36<06:24, 96.06s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:55,  4.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:53,  2.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:23,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:36,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:22,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:07,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:53,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:49,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:40,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:36,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:37,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:39,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:34,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:30,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:28,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:18<02:36,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:19<02:53,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:20<02:40,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:21<02:29,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:22,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:18,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:23<02:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:23<02:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:24<02:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:26<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:28<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:29<02:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:31<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:32<01:59,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<01:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:33<01:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:34<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:36<01:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:37<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:52,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:39<01:52,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:40<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:42<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:43<01:49,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:45,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:45<01:43,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:42,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:40,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:40,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:42,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<01:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:50<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:42,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:39,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:58<01:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:59<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:59<01:31,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:00<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:01<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:01<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:02<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:03<01:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:04<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:04<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:05<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:06<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:07<01:25,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:08<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:09<01:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:10<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:10<01:20,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:11<01:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:12<01:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:13<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:14<01:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:15<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:16<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:17<01:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:18<01:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:18<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:19<01:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:20<01:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:21<01:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:21<01:09,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:22<01:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:23<01:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:24<01:13,  1.26it/s]\n",
+      "\n",
+      "Training models:  40%|████████████████████████                                    | 2/5 [03:02<04:32, 90.68s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:56,  4.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:56,  2.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:20,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:34,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:11,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:58,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:47,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:28,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<02:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<02:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<02:07,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<02:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<02:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<01:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<01:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<01:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:57,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:55,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:58,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:42<01:59,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<02:02,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<02:01,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:45<01:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:46<01:54,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:55,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:48<01:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:49,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:50<02:03,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:51<02:12,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:52<02:01,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:52<01:52,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:53<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:54<01:46,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:55<01:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:55<01:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:56<01:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:57<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:58<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:58<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:59<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:00<01:33,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:00<01:32,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:01<01:31,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:02<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:03<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:03<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:04<01:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:05<01:31,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:06<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:06<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:07<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:08<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:09<01:25,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:24,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:10<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:11<01:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:12<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:12<01:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:13<01:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:14<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:15<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:15<01:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:16<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:17<01:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:18<01:23,  1.23it/s]\n",
+      "\n",
+      "Training models:  60%|████████████████████████████████████                        | 3/5 [04:23<02:52, 86.21s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:46,  4.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:28,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:06,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:00,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:23,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:00,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:31,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:09,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:10,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:03,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<02:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<02:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:56,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<01:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<01:50,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<01:49,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:48,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:47,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:41<01:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:47,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:45,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:45,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:41,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:45,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:31,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:30,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:00<01:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:27,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:03<01:26,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:29,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:05<01:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:06<01:27,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:07<01:23,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:08<01:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:09<01:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:10<01:26,  1.27it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████████████████████████████            | 4/5 [05:36<01:20, 80.99s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:48,  4.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:58,  2.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:31,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:24,  1.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:52,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:23,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:05,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:57,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:12<02:43,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:39,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:37,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:40,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:16<03:10,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:16<02:55,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:17<02:41,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:18<02:31,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:19<02:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:20<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:21<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:22<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:23<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:24<02:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:24<02:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:25<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:26<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:27<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:27<02:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:28<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:29<02:00,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<02:00,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:30<02:02,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:31<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:32<01:57,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<01:56,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:33<01:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:34<01:54,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<01:54,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<01:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:37<01:53,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<01:52,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<01:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:39<01:52,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:40<01:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:43,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:43,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:43,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:47<01:41,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:48<01:41,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:50<01:41,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:51<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:52<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:53<01:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:53<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:54<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:55<01:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:55<01:33,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:56<01:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:57<01:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:58<01:32,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:58<01:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:59<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:00<01:29,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:00<01:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:01<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:02<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:03<01:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:03<01:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:05<01:36,  1.24it/s]\n",
+      "\n",
+      "Training models: 100%|████████████████████████████████████████████████████████████| 5/5 [06:44<00:00, 80.88s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.79it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.70it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.12s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.07it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.73it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\n",
+      "Evaluating NLL: 79it [00:00, 115.57it/s]\u001b[A\n",
+      "Evaluating NLL: 163it [00:01, 240.83it/s]\u001b[A\n",
+      "Evaluating NLL: 248it [00:01, 360.84it/s]\u001b[A\n",
+      "Evaluating NLL: 332it [00:01, 465.64it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 349.58it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|██████████▊                | 40/100 [00:00<00:00, 394.38it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 397.17it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████████████| 6/6 [58:20<00:00, 583.39s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise sigma 0.000625\n",
+      "Initial validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|████████████████████████████████████████████████████████████████████| 1/1 [00:01<00:00,  1.56s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 141.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 339.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 285.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 365.90it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 168.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 187.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 174.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: 100%|███████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 240.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 304.16it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 163.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 166.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 222.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 278.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 274.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 162.97it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 202.72it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 176.59it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 275.45it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 331.02it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 316.03it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 315.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 334.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 336.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 172.20it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 147.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 266.66it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: 100%|██████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 243.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "precomputing masks and variances: 100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 498.09it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods: 100%|█████████████████████████████████████████████████| 4/4 [00:00<00:00, 419.61it/s]\n",
+      "precomputing masks and variances: 100%|██████████████████████████████████████████| 4/4 [00:00<00:00, 424.95it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "computing log likelihoods: 100%|█████████████████████████████████████████████████| 4/4 [00:00<00:00, 456.93it/s]\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 193.95it/s]\n",
+      "  0%|                                                                                     | 0/6 [00:00<?, ?it/s]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:22,  4.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:00,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:42,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:35,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:02,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:38,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:07<02:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:16,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<02:04,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:00,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:10<01:54,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:53,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:50,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:12<01:49,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:48,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:13<01:47,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:14<01:48,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:48,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:15<01:46,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:16<01:45,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:16<01:43,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:17<01:43,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:17<01:43,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:18<01:41,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:19<01:41,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:19<01:43,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:20<01:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:20<01:42,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:21<01:42,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:22<01:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:22<01:40,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:23<01:41,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:23<01:40,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:24<01:40,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:25<01:40,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:25<01:39,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:26<01:37,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:27<01:37,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:27<01:37,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:28<01:37,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:28<01:37,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:29<01:35,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:30<01:34,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:30<01:32,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:31<01:30,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:31<01:28,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:32<01:28,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:32<01:29,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:33<01:29,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:34<01:27,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:34<01:27,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:35<01:26,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:35<01:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:36<01:25,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:37<01:24,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:37<01:25,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:38<01:23,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:38<01:21,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:39<01:21,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:39<01:20,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:40<01:19,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:41<01:19,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:41<01:18,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:42<01:18,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:42<01:17,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:43<01:17,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:44<01:16,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:44<01:17,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:45<01:15,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:45<01:16,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:46<01:16,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:47<01:16,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:47<01:16,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:48<01:14,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:48<01:13,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:49<01:14,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:50<01:12,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:50<01:12,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:51<01:10,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:51<01:10,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:52<01:08,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:52<01:07,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:53<01:06,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:54<01:10,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:54<01:11,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:56<01:12,  1.55it/s]\n",
+      "\n",
+      "Training models:  20%|████████████                                                | 1/5 [00:58<03:54, 58.67s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:02,  4.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:18,  2.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:46,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:39,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:02,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:39,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<01:59,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<01:54,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:10<01:52,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:50,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:49,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:12<01:49,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:47,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:13<01:44,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:14<01:42,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:14<01:40,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:15<01:39,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:15<01:38,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:16<01:38,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:17<01:37,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:17<01:36,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:18<01:36,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:18<01:36,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:19<01:34,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:19<01:34,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:20<01:33,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:20<01:34,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:21<01:34,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:22<01:34,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:22<01:32,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:23<01:31,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:23<01:36,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:24<01:42,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:25<01:42,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:25<01:37,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:26<01:33,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:26<01:31,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:27<01:31,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:27<01:29,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:28<01:27,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:28<01:27,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:29<01:26,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:30<01:25,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:30<01:25,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:31<01:24,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:31<01:24,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:32<01:23,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:32<01:22,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:33<01:24,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:34<01:25,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:34<01:25,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:35<01:26,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:35<01:25,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:36<01:23,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:37<01:22,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:37<01:25,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:38<01:28,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:39<01:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:39<01:27,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:40<01:26,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:40<01:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:41<01:22,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:42<01:23,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:42<01:27,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:43<01:22,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:43<01:17,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:44<01:15,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:45<01:14,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:45<01:13,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:46<01:12,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:46<01:15,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:47<01:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:48<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:48<01:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:49<01:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:50<01:17,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:50<01:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:51<01:20,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:52<01:18,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:52<01:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:53<01:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:54<01:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:55<01:18,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [00:55<01:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [00:56<01:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [00:57<01:15,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [00:57<01:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [00:58<01:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [00:59<01:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [00:59<01:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:00<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:01<01:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:01<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:02<01:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:03<01:28,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:04<01:22,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:05<01:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:05<01:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:06<01:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:06<01:01,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:07<01:00,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:08<00:59,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:08<00:57,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:09<00:56,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:09<00:55,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:10<00:55,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:11<00:53,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:11<00:53,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:12<00:52,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:12<00:51,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:13<00:51,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:14<00:50,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:14<00:49,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:15<00:49,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:15<00:49,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:16<00:48,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:17<00:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:17<00:48,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:18<00:46,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:18<00:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:19<00:45,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:20<00:43,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:20<00:43,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:21<00:42,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:21<00:41,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:22<00:42,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:23<00:41,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:24<00:44,  1.56it/s]\n",
+      "\n",
+      "Training models:  40%|████████████████████████                                    | 2/5 [02:25<03:45, 75.04s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:00,  4.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:52,  2.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:33,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:05<03:28,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<02:53,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:07<02:17,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:08<02:00,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<01:56,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:09<01:52,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:10<01:49,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:48,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:11<01:45,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:12<01:43,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:12<01:43,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:13<01:43,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:13<01:39,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:14<01:40,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:14<01:38,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:15<01:37,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:15<01:36,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:16<01:38,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:17<01:37,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:17<01:36,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:18<01:36,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:18<01:35,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:19<01:36,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:19<01:35,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:20<01:34,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:20<01:34,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:21<01:33,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:22<01:36,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:22<01:35,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:23<01:34,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:23<01:33,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:24<01:31,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:24<01:31,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:25<01:31,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:26<01:31,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:26<01:31,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:27<01:28,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:27<01:31,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:28<01:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:29<01:31,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:29<01:28,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:30<01:26,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:30<01:25,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:31<01:24,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:31<01:23,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:32<01:22,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:32<01:20,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:33<01:34,  1.56it/s]\n",
+      "\n",
+      "Training models:  60%|████████████████████████████████████                        | 3/5 [03:01<01:54, 57.30s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:53,  4.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:54,  2.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:35,  1.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:05<03:30,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<02:54,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:07<02:23,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<02:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<01:59,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:10<01:56,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:55,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:11<01:51,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:12<01:48,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:45,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:13<01:44,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:14<01:45,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:14<01:41,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:15<01:38,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:15<01:42,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:16<01:44,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:17<01:44,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:17<01:42,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:18<01:40,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:18<01:44,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:19<01:48,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:20<01:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:20<01:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:21<01:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:22<01:45,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:22<01:43,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:23<01:41,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:23<01:41,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:24<01:38,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:25<01:38,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:25<01:37,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:26<01:36,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:26<01:36,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:27<01:36,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:28<01:35,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:28<01:33,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:29<01:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:29<01:31,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:30<01:30,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:31<01:30,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:31<01:30,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:32<01:33,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:33<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:33<01:33,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:34<01:41,  1.46it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████████████████████████████            | 4/5 [03:38<00:49, 49.31s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:20,  4.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:23,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:06,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:03,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:06,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:53,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:45,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<01:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<01:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<01:55,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<01:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<01:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:53,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<01:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:46,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:36<01:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:38<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:39<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:40<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<01:42,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<02:05,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:43<01:55,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:44<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:46<01:27,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:47<01:24,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:47<01:21,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:48<01:20,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:22,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:49<01:20,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:50<01:19,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:19,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:51<01:19,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:17,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:16,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:15,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:13,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:13,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:12,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:11,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:11,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:13,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:16,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:13,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:13,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:12,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:10,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:08,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:24,  1.37it/s]\n",
+      "\n",
+      "Training models: 100%|████████████████████████████████████████████████████████████| 5/5 [04:42<00:00, 56.47s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.30it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.68it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.29it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.01it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 89it [00:01, 114.06it/s]\u001b[A\n",
+      "Evaluating NLL: 184it [00:01, 243.73it/s]\u001b[A\n",
+      "Evaluating NLL: 262it [00:01, 340.41it/s]\u001b[A\n",
+      "Evaluating NLL: 342it [00:01, 434.86it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 321.78it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|███████████▌               | 43/100 [00:00<00:00, 426.10it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 416.15it/s]\n",
+      " 17%|████████████▋                                                               | 1/6 [04:53<24:27, 293.59s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:56,  4.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:36,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:02,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:52,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:13,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:59,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:34,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:36,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:30,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:25,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:25,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:50<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:29,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:26,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:52<01:23,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:53<01:22,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:53<01:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:54<01:20,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:55<01:19,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:55<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:56<01:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:57<01:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:57<01:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:58<01:15,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:15,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:01<01:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:01<01:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:16,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:11,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:09,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:06,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:07<01:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:06,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:09<01:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:10<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:11<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:11<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:12<01:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:13<01:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:13<01:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:14<01:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:15<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:16<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:17<01:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:18<01:08,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<01:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<01:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<01:08,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<01:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<01:07,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<01:06,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<01:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:25<01:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<01:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<01:02,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<01:01,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:28<00:59,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:29<00:59,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:58,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:57,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:31<00:56,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:32<00:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:33<00:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:34<00:49,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:35<00:46,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:36<00:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:37<00:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:38<00:49,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:38<00:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:39<00:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:40<00:47,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:41<00:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:42<00:43,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:42<00:42,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:43<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:44<00:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:44<00:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:45<00:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:46<00:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:47<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:48<00:45,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:41,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:49<00:37,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:50<00:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:51<00:31,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:52<00:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:52<00:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:53<00:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:54<00:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:54<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:55<00:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:56<00:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:56<00:24,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:57<00:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:58<00:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:58<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:59<00:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:00<00:20,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:00<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:01<00:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:02<00:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:02<00:18,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:03<00:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:04<00:17,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:04<00:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:05<00:15,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:06<00:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:06<00:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:07<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:08<00:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:08<00:12,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:09<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:09<00:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:10<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:11<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:11<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:12<00:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:13<00:07,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:13<00:07,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:14<00:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:15<00:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:15<00:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:16<00:04,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:17<00:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:17<00:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:18<00:02,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:18<00:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:19<00:01,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:20<00:00,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:20<00:00,  1.42it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:23<09:32, 143.05s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:20,  4.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:09,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:48,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:43,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:06,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:45,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<02:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:56,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:52,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:55,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:50,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:18<01:49,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:48,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:20<01:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:22<01:47,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:26<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:44,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:29<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:31<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:32<01:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:33<01:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:34<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:35<01:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:36<01:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:37<01:33,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:38<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:38<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:39<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:40<01:30,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:41<01:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:42<01:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:43<01:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:29,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:44<01:34,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:45<01:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:46<01:27,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:46<01:26,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:47<01:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:47<01:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:48<01:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:49<01:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:49<01:22,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:50<01:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:51<01:24,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:51<01:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:52<01:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:53<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:53<01:21,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:54<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:55<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:55<01:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:56<01:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:57<01:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:58<01:20,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:58<01:18,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [00:59<01:19,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:00<01:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:01<01:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:02<01:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:02<01:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:03<01:13,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:04<01:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:04<01:08,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:05<01:06,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:05<01:06,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:06<01:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:07<01:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:08<01:11,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:08<01:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:09<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:10<01:08,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:10<01:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:11<01:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:12<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:13<01:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:13<01:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:14<01:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:15<01:11,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:16<01:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:16<01:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:17<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:18<01:17,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:19<01:12,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:20<01:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:20<01:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:21<00:58,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:22<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:22<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:23<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:24<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:24<00:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:25<00:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:26<00:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:27<00:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:27<00:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:28<00:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:29<00:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:29<00:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:30<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:31<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:31<00:42,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:32<00:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:33<00:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:33<00:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:34<00:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:35<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:35<00:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:36<00:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:36<00:37,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:37<00:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:38<00:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:38<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:39<00:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:40<00:34,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:40<00:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:41<00:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:42<00:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:42<00:32,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:43<00:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:43<00:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:44<00:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:45<00:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:45<00:28,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:46<00:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:47<00:26,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:47<00:26,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:48<00:26,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:49<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:49<00:24,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:50<00:23,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:50<00:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:51<00:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:52<00:21,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:52<00:21,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:53<00:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:54<00:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:54<00:20,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:55<00:19,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:56<00:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:56<00:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:57<00:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:57<00:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:58<00:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [01:59<00:15,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [01:59<00:14,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:00<00:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:01<00:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:01<00:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:02<00:12,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:03<00:12,  1.47it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:29<06:39, 133.08s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:07,  4.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:22,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:54,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<01:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<02:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:56,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:54,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:52,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:50,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:48,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:16<01:46,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:45,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:45,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:18<01:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:47,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:19<01:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:20<01:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:21<01:46,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:22<01:48,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:46,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:23<01:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:44,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:25<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:26<01:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:27<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:28<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:29<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:39,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:30<01:37,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:31<01:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:32<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:32<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:33<01:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:34<01:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:34<01:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:35<01:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:36<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:37<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:37<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:38<01:33,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:39<01:32,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:39<01:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:40<01:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:41<01:29,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:41<01:26,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:42<01:24,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:42<01:23,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:43<01:24,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:44<01:24,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:45<01:33,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:45<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:46<01:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:47<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:47<01:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:48<01:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:48<01:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:49<01:31,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:50<01:27,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:51<01:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:51<01:23,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:52<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:53<01:20,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:53<01:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:54<01:26,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:55<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:56<01:29,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:56<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:57<01:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:58<01:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:01<01:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:26,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:04<01:26,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:06<01:23,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:07<01:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:08<01:20,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:20,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:18,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<01:06,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<01:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<01:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<01:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<01:02,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<01:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<01:01,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:59,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:59,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:25<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:55,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:53,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:28<00:54,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:52,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:33<00:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:34<00:59,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:35<00:49,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:36<00:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:36<00:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:37<00:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:39<00:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:41<00:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:42<00:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:43<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:43<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:44<00:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:45<00:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:45<00:31,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:46<00:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:46<00:30,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:47<00:29,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:48<00:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:48<00:27,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:49<00:27,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:50<00:26,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:50<00:26,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:51<00:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:51<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:52<00:25,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:53<00:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:53<00:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:54<00:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:55<00:21,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:55<00:20,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:56<00:20,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:56<00:19,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:57<00:19,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:58<00:18,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:58<00:17,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:59<00:17,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:59<00:16,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:00<00:16,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:01<00:15,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:01<00:15,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:02<00:14,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:03<00:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:03<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:04<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:05<00:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:05<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:06<00:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:07<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:07<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:08<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:08<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:09<00:07,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:10<00:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:10<00:06,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:11<00:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:12<00:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:12<00:04,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:13<00:03,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:14<00:03,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:14<00:02,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:15<00:01,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:15<00:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:16<00:00,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:17<00:00,  1.46it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [06:48<04:31, 136.00s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:49,  4.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:06,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:59,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:17,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:51,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:34,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:00,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:59,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:55,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:52,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:32<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:37,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:52,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:38<01:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:27,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:26,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:45<01:24,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:46<01:24,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:23,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:47<01:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:48<01:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:49<01:27,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:50<01:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:51<01:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:52<01:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:53<01:28,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:54<01:23,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:54<01:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:55<01:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:55<01:18,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:56<01:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:57<01:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:58<01:21,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:58<01:22,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:19,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:17,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:01<01:18,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:18,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:02<01:16,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:08<01:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:10<01:10,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:10<01:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:11<01:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:12<01:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:12<01:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:13<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:14<01:14,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<01:13,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:16<01:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<01:19,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<01:16,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<01:09,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<01:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<00:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:52,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:23<00:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:49,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:48,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:25<00:46,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:46,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:47,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:27<00:47,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:28<00:46,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:29<00:45,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:30<00:44,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:44,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:43,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:31<00:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:32<00:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:33<00:42,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:33<00:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:34<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:35<00:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:35<00:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:36<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:37<00:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:37<00:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:38<00:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:39<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:39<00:36,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:40<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:41<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:41<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:42<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:43<00:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:43<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:44<00:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:45<00:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:45<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:46<00:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:46<00:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:47<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:48<00:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:48<00:26,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:49<00:26,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:50<00:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:50<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:51<00:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:52<00:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:52<00:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:53<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:54<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:54<00:21,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:55<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:56<00:20,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:56<00:19,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:57<00:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:57<00:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:58<00:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [01:59<00:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [01:59<00:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:00<00:15,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:01<00:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:01<00:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:02<00:14,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:03<00:13,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:03<00:12,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:04<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:05<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:05<00:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:06<00:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:07<00:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:07<00:08,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:08<00:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:09<00:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:09<00:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:10<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:11<00:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:11<00:04,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:12<00:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:12<00:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:13<00:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:14<00:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:14<00:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:15<00:00,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:16<00:00,  1.47it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [09:07<02:17, 137.09s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:24,  4.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:16,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:55,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:47,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:09,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:45,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:04,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<01:59,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:52,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:18<01:52,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:52,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:22<01:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:26<01:45,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:44,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:40,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:39,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:29<01:39,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:38,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:37,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:31<01:36,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:32<01:35,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:36,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:33<01:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:34<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:35<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:37<01:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:38<01:45,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:41,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:39<01:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:40<01:35,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:41<01:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:42<01:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:35,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:44<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:42,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:44,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:42,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:41,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:40,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:39,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:37,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:35,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:34,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:35,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:35,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:34,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:33,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:18,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:17,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:22,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:10<01:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:10<01:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:12<01:10,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:09,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:14<01:10,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:15<01:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:15<01:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:16<01:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:17<01:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:17<01:06,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:18<01:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:19<01:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:20<01:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:21<01:16,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<01:15,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:22<01:08,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:23<01:03,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:23<01:00,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:24<00:58,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:25<00:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:25<00:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:26<00:52,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:27<00:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:27<00:51,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:28<00:50,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:29<00:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:29<00:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:30<00:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:31<00:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:31<00:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:32<00:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:32<00:45,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:33<00:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:34<00:43,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:34<00:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:35<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:36<00:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:36<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:37<00:40,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:38<00:39,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:38<00:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:39<00:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:39<00:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:40<00:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:41<00:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:42<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:43<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:43<00:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:44<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:45<00:33,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:45<00:32,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:46<00:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:47<00:30,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:47<00:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:48<00:29,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:48<00:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:49<00:27,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:50<00:26,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:50<00:26,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:51<00:26,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:52<00:25,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:52<00:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:53<00:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:54<00:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:54<00:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:55<00:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:55<00:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:56<00:21,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:57<00:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:57<00:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:58<00:19,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:59<00:18,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:00<00:20,  1.42it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [11:09<00:00, 133.98s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.74it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.90it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.95it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.67it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 94it [00:00, 132.53it/s]\u001b[A\n",
+      "Evaluating NLL: 182it [00:01, 258.12it/s]\u001b[A\n",
+      "Evaluating NLL: 271it [00:01, 379.68it/s]\u001b[A\n",
+      "Evaluating NLL: 360it [00:01, 488.98it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 348.67it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|███████████▌               | 43/100 [00:00<00:00, 425.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 441.41it/s]\n",
+      " 33%|█████████████████████████▎                                                  | 2/6 [16:15<34:47, 521.76s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:02,  4.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:04,  2.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:44,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:06,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:52,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:52,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:52,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:59,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:56,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:55,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:45,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:43,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:42,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:37,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:33,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:34,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:37,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:37,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:31,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:59<01:36,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:00<01:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:02<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:03<01:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:04<01:48,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:05<01:37,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:05<01:30,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:06<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:07<01:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:07<01:16,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:08<01:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:09<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:09<01:12,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:10<01:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:11<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:11<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:12<01:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:13<01:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:13<01:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:14<01:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:15<01:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:15<01:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:16<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:17<01:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:17<01:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:18<01:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:19<01:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:19<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:20<01:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:21<01:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<00:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:22<00:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:23<00:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:24<00:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:24<00:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:25<00:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:26<00:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:26<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:27<00:53,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:28<00:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:28<00:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:29<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:30<00:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:30<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:31<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:32<00:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:32<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:33<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:34<00:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:34<00:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:35<00:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:36<00:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:36<00:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:37<00:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:38<00:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:38<00:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:39<00:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:40<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:40<00:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:41<00:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:42<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:42<00:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:43<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:44<00:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:44<00:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:45<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:46<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:46<00:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:47<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:48<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:48<00:31,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:49<00:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:49<00:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:50<00:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:51<00:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:51<00:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:52<00:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:53<00:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:53<00:25,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:54<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:55<00:25,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:55<00:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:56<00:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:57<00:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:58<00:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:58<00:23,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:59<00:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:00<00:21,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:00<00:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:01<00:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:02<00:18,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:02<00:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:03<00:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:03<00:16,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:04<00:15,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:05<00:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:06<00:14,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:06<00:14,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:07<00:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:08<00:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:08<00:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:09<00:11,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:10<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:10<00:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:11<00:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:12<00:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:12<00:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:13<00:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:14<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:14<00:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:15<00:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:16<00:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:16<00:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:17<00:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:18<00:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:18<00:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:19<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:20<00:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:20<00:00,  1.42it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:23<09:32, 143.18s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:05,  4.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:30,  2.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:04,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:56,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:18,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:40,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<02:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<02:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<02:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<02:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<02:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<02:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<02:11,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<02:12,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<02:12,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<02:11,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<02:11,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<02:11,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<02:10,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<02:11,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<02:09,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<02:09,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<02:08,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:37<02:08,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<02:07,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<02:06,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<02:06,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:40<02:05,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:41<02:04,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<02:04,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<02:02,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<02:01,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:44<01:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:59,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:47<01:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:48<01:46,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:44,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:50<01:42,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:51<01:42,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:52<01:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:53<01:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:53<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:54<01:37,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:55<01:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:56<01:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:56<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:57<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:58<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:58<01:31,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:59<01:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:00<01:32,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:01<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:02<01:34,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:02<01:31,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:03<01:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:04<01:26,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:04<01:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:05<01:23,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:07<01:35,  1.22it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [03:33<05:00, 100.12s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:09,  4.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:39,  2.32s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:11,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:02,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:21,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:58,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:41,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:09,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:08,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<02:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:00,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:59,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<01:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:56,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<01:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:47,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<01:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:41<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:41,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:41,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:40,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:39,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:45<01:36,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:47<01:34,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:49<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:33,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:32,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:53<01:30,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:29,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:28,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:55<01:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:26,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:57<01:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:22,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:59<01:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:21,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:21,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:02<01:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:18,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:03<01:18,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:04<01:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:05<01:17,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:06<01:17,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:08<01:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:10<01:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:11<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:13<01:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:14<01:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:15<01:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:15<01:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:16<01:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:17<01:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:18<01:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:18<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:19<01:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:20<01:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:20<01:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:21<01:01,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:22<01:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:23<01:00,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:23<00:59,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:24<00:58,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:25<00:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:25<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:26<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:27<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:27<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:28<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:29<00:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:29<00:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:30<00:53,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:31<00:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:32<00:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:32<00:50,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:33<00:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:34<00:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:35<00:51,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:35<00:48,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:36<00:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:36<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:37<00:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:38<00:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:39<00:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:39<00:45,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:40<00:43,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:41<00:42,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:42<00:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:42<00:43,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:43<00:45,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:44<00:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:44,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:44,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:46<00:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:40,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:44,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:49<00:46,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:42,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:38,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:51<00:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:34,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:54<00:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:54<00:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:56<00:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:56<00:27,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:57<00:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:58<00:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:58<00:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:59<00:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:00<00:23,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:00<00:23,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:01<00:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:02<00:21,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:02<00:21,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:03<00:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:04<00:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:05<00:19,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:05<00:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:06<00:18,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:07<00:17,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:07<00:16,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:08<00:15,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:09<00:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:09<00:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:10<00:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:11<00:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:12<00:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:12<00:11,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:13<00:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:14<00:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:14<00:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:15<00:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:16<00:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:16<00:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:17<00:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:18<00:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:18<00:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:19<00:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:20<00:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:20<00:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:21<00:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:22<00:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:23<00:01,  1.38it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [05:59<04:01, 121.00s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:16,  4.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:09,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:52,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:51,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:14,  1.00it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:08,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:03,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<01:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<01:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<01:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<01:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<01:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:48,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:45,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:45<01:39,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:47<01:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:34,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:49<01:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:29,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:27,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:24,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:25,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:29,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:25,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:24,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:23,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:22,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:22,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:18,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:03<01:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:05<01:15,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:16,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:08<01:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:21,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:10<01:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:11<01:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:13<01:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:14<01:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:14<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:15<01:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:16<01:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:17<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:17<01:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:18<01:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:19<01:17,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:20<01:23,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:21<01:15,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:22<01:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:22<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:23<01:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:24<01:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:24<01:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:25<00:59,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:26<00:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:26<00:56,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:27<00:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:28<00:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:29<00:55,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:29<00:54,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:30<00:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:31<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:31<00:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:32<00:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:33<00:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:33<00:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:34<00:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:35<00:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:35<00:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:36<00:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:37<00:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:37<00:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:38<00:44,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:39<00:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:39<00:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:40<00:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:41<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:41<00:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:42<00:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:44<00:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:46<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:48<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:50<00:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:50<00:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:31,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:52<00:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:30,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:54<00:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:55<00:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:57<00:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:57<00:25,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:58<00:24,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:59<00:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:00<00:22,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:01<00:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:21,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:02<00:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:03<00:19,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:04<00:19,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:04<00:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:05<00:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:06<00:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:06<00:16,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:07<00:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:08<00:15,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:08<00:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:09<00:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:10<00:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:10<00:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:11<00:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:12<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:13<00:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:13<00:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:14<00:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:15<00:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:15<00:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:16<00:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:17<00:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:17<00:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:18<00:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:19<00:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:19<00:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:21<00:02,  1.39it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [08:22<02:09, 129.92s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:56,  4.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:33,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:07,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:58,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:20,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:56,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:10,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:06,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<01:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<01:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:53,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:53,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<01:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:56,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:54,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:54,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:52,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:51,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:52,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<01:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:52,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:50,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<01:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:52,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<01:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:40<01:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<01:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<01:56,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:42<01:58,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:43<01:58,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:44<01:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:46<01:55,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:55,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:47<01:55,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:54,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:53,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:52,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:52,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:50,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:49,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:48,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:47,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:56<01:45,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:44,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:43,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:42,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:59<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:33,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:00<01:36,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:01<01:33,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:02<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:29,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:03<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:04<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:05<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:05<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:06<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:07<01:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:08<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:09<01:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:09<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:10<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:11<01:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:12<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:12<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:13<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:14<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:15<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:15<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:16<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:17<01:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:18<01:23,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:19<01:31,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:20<01:23,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:20<01:17,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:21<01:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:22<01:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:22<01:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:23<01:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:24<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:25<01:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:25<01:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:26<01:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:27<01:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:27<01:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:28<01:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:29<00:58,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:29<00:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:30<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:31<00:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:31<00:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:32<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:33<00:54,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:33<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:34<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:35<00:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:36<00:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:36<00:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:37<00:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:38<00:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:38<00:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:39<00:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:40<00:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:40<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:41<00:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:42<00:46,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:42<00:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:43<00:44,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:44<00:43,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:44<00:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:45<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:46<00:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:46<00:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:47<00:38,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:48<00:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:48<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:49<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:50<00:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:50<00:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:51<00:35,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:52<00:35,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:53<00:34,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:53<00:33,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:54<00:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:55<00:31,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:55<00:30,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:56<00:29,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:56<00:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:57<00:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:58<00:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:58<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:59<00:25,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:00<00:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:00<00:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:01<00:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:02<00:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:02<00:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:03<00:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:04<00:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:04<00:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:05<00:21,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:06<00:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:06<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:07<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:08<00:18,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:08<00:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:09<00:17,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:10<00:17,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:11<00:16,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:11<00:15,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:12<00:14,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:13<00:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:13<00:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:14<00:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:15<00:11,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:15<00:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:16<00:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:17<00:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:17<00:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:18<00:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:19<00:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:20<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:20<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:21<00:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:22<00:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:23<00:04,  1.35it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [10:48<00:00, 129.64s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.79it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.21it/s]\u001b[A\n",
+      "Evaluating NLL: 84it [00:00, 123.11it/s]\u001b[A\n",
+      "Evaluating NLL: 169it [00:01, 248.96it/s]\u001b[A\n",
+      "Evaluating NLL: 255it [00:01, 369.65it/s]\u001b[A\n",
+      "Evaluating NLL: 342it [00:01, 479.91it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 354.00it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|███████████▎               | 42/100 [00:00<00:00, 417.45it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 407.96it/s]\n",
+      " 50%|██████████████████████████████████████                                      | 3/6 [27:14<29:13, 584.65s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:54,  4.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:20,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:57,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:51,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:52,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:08,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<02:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<02:00,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<01:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<02:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<02:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<02:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<02:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<02:09,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<02:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<02:10,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<02:09,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<02:09,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<02:10,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<02:09,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<02:08,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<02:08,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<02:08,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<02:08,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<02:07,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<02:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<02:03,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<02:03,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<02:01,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:41<02:00,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:42<01:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:43<01:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:56,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:55,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:46<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:38,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:41,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:38,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:54<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:34,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:56<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:30,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:30,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:59<01:34,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:00<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:01<01:51,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:02<01:47,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:03<01:36,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:03<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:04<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:05<01:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:06<01:22,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:06<01:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:07<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:08<01:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:08<01:16,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:09<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:10<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:10<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:11<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:12<01:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:12<01:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:13<01:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:14<01:06,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:14<01:06,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:15<01:06,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:15<01:05,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:16<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:17<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:18<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:18<01:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:19<01:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:20<01:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:20<01:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:21<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:22<01:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:22<01:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:23<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:24<00:59,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:24<00:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:25<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:26<00:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:26<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:27<00:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:28<00:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:28<00:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:29<00:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:30<00:53,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:31<00:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:32<00:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:34<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:36<00:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:45,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:38<00:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:40<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:42<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:44<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:46<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:48<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:50<00:36,  1.35it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [01:52<07:31, 112.95s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:35,  4.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:37,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:06,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:53,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:43,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:52,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:36,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:34,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:33,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:32,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:32,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:30,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:29,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:32,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:40,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:34,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:35,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:33,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:52<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:53<01:29,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:53<01:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:54<01:28,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:55<01:31,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:56<01:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:28,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:24,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:36,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:41,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:33,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:05<01:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:16,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:12,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:10<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:11<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:12<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:04,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:14<01:02,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:14<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<00:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:16<00:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<00:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<00:59,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:18<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<00:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<00:57,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:20<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:22<00:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:24<00:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:24<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:25<00:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:26<00:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:28<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:28<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:30<00:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:32<00:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:33<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:36<00:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:38,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:40<00:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:41<00:36,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:42<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:42<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:43<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:44<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:44<00:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:45<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:46<00:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:46<00:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:47<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:48<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:48<00:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:49<00:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:50<00:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:50<00:27,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:51<00:27,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:52<00:26,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:52<00:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:53<00:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:54<00:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:54<00:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:55<00:24,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:56<00:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:56<00:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:57<00:21,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:58<00:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:58<00:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:59<00:19,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:00<00:18,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:00<00:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:02<00:18,  1.42it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [03:57<05:58, 119.64s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:57,  4.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:00,  2.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:42,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:04,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:43,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:27,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:09<02:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:11<02:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:58,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<02:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<02:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<02:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<02:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<02:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:53,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:52,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:49,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:47,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:48,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:44,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:38<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:39<01:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:40<01:42,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:43,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:41<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:42<01:39,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:43<01:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:40,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:50,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:44,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:49<01:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:57,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:51<01:59,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:51<01:48,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:41,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:53<01:36,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:53<01:31,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:28,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:55<01:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:55<01:24,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:23,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:57<01:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:59<01:22,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:21,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:18,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:01<01:17,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:16,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:03<01:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:03<01:15,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:05<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:06<01:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:12,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:07<01:11,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:10,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:09,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:09<01:08,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:10<01:07,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:07,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:11<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:12<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:13<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:14<01:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:15<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:16<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<01:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:17<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:18<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<01:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:19<01:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:20<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<00:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:21<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<00:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:23<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:24<00:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:24<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:25<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:26<00:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:27<00:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:28<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:28<00:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:29<00:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:30<00:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:31<00:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:33<00:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:34<00:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:35<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:36<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:37<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:40,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:40,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:39<00:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:41<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:42<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:43<00:38,  1.40it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [05:43<03:46, 113.45s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:48,  4.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:57,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:45,  1.45s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:55,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:55,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:52,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:57,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:36<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:37,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:44,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:40<01:42,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:42<01:38,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:36,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:44<01:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:45<01:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:34,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:47<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:32,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:39,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:39,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:39,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:39,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:39,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:38,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:37,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:36,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:35,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:58<01:34,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:34,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:34,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:02<01:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:03<01:30,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:04<01:31,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:05<01:30,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:06<01:29,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:07<01:29,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:07<01:28,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:08<01:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:09<01:26,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:10<01:25,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:11<01:23,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:11<01:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:12<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:13<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:13<01:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:14<01:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:15<01:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:16<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:17<01:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:17<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:18<01:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:19<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:19<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:20<01:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:21<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:22<01:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:22<01:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:23<01:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:24<01:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:25<01:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:25<01:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:26<01:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:27<00:59,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:28<01:05,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:29<01:12,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<01:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:59,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:31<00:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<00:53,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:33<00:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:38<00:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:40<00:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:39,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:43<00:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:36,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:47<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:49<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:50<00:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:51<00:31,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:29,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:53<00:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:54<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:55<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:26,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:57<00:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:57<00:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:58<00:23,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:59<00:22,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:00<00:20,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:00<00:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:19,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:02<00:19,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:02<00:18,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:03<00:17,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:04<00:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:04<00:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:05<00:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:06<00:15,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:06<00:14,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:07<00:13,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:07<00:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:08<00:12,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:09<00:11,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:09<00:11,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:10<00:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:11<00:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:11<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:12<00:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:13<00:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:13<00:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:14<00:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:15<00:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:15<00:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:16<00:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:17<00:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:17<00:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:18<00:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:19<00:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:19<00:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:20<00:01,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:21<00:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:21<00:00,  1.41it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [08:07<02:05, 125.56s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:52,  4.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:56,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:41,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:42,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:05,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:44,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:15,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:11,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<01:56,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<01:53,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:13<01:53,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<01:52,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:51,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:15<01:48,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:46,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:16<01:46,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:17<01:46,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:17<01:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:18<01:46,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:19<01:46,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:19<01:46,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:20<01:48,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:21<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:21<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:22<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:23<01:58,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:24<01:59,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:25<01:59,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:56,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:26<01:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:28<01:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:30<01:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:32<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:34<01:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:43,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:36<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:44,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:38<01:43,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:41,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:36,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:34,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:46<01:41,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:44,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:48<01:43,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:49<01:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:44,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:50<01:44,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:51<01:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:42,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:41,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:40,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:40,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:40,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:39,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:57<01:38,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:37,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:59<01:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:59<01:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:02<01:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:03<01:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:03<01:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:04<01:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:05<01:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:06<01:28,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:07<01:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:07<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:08<01:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:09<01:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:09<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:10<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:11<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:12<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:12<01:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:13<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:14<01:20,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:15<01:22,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:16<01:18,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:16<01:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:17<01:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:18<01:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:19<01:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:19<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:20<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:21<01:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:22<01:21,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:23<01:16,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:24<01:10,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:24<01:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:25<01:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:25<00:59,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:26<00:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:27<00:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:27<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:28<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<00:54,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:30<00:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<00:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:32<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:33<00:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:33<00:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:34<00:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:45,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:35<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:36<00:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:37<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:38<00:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:39<00:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:40<00:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:41<00:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:43<00:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:37,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:47<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:49<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:49<00:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:50<00:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:51<00:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:51<00:29,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:53<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:53<00:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:54<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:55<00:27,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:55<00:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:56<00:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:56<00:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:57<00:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:58<00:23,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:58<00:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:59<00:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:00<00:20,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:00<00:19,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:01<00:19,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:01<00:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:02<00:18,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:03<00:17,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:03<00:16,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:04<00:15,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:05<00:15,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:05<00:14,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:06<00:14,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:06<00:13,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:07<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:08<00:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:08<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:09<00:11,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:10<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:10<00:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:11<00:09,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:12<00:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:12<00:08,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:13<00:07,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:14<00:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:14<00:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:15<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:16<00:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:16<00:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:17<00:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:18<00:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:18<00:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:19<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:20<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:20<00:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:21<00:00,  1.41it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [10:31<00:00, 126.29s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.95it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.03it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.81it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.16it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\n",
+      "Evaluating NLL: 66it [00:00, 97.43it/s]\u001b[A\n",
+      "Evaluating NLL: 134it [00:01, 199.21it/s]\u001b[A\n",
+      "Evaluating NLL: 205it [00:01, 301.13it/s]\u001b[A\n",
+      "Evaluating NLL: 277it [00:01, 393.23it/s]\u001b[A\n",
+      "Evaluating NLL: 343it [00:01, 456.11it/s]\u001b[A\n",
+      "Evaluating NLL: 408it [00:01, 504.74it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 319.24it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 418.90it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 406.42it\n",
+      " 67%|██████████████████████████████████████████████████▋                         | 4/6 [37:57<20:15, 607.60s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:22,  4.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:49,  2.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:39,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:32,  1.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<03:47,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:21,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:02,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:50,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:43,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:37,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:25,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:25,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:20<02:26,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:22,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:23<02:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:24<02:17,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:19,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:26<02:17,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:28<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:29<02:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:12,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:13,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:31<02:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:32<02:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:33<02:11,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:33<02:12,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:34<02:10,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:35<02:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:36<02:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:37<02:09,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:37<02:07,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:38<02:06,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:39<02:08,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:40<02:13,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:41<02:14,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:42<02:10,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:43<02:05,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:44<02:11,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:44<02:10,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:45<02:06,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:46<02:17,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:48<02:26,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:48<02:14,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:49<02:06,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:50<02:01,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:51<01:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:51<01:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:52<01:50,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:53<01:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:54<01:46,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:54<01:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:55<01:46,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:56<01:44,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:57<01:44,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:58<01:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:58<01:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:59<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:00<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:00<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:01<01:39,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:02<01:39,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:03<01:38,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:04<01:37,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:04<01:37,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:05<01:35,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:06<01:34,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:07<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:07<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:08<01:31,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:09<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:10<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:11<01:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:11<01:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:12<01:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:13<01:27,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:14<01:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:14<01:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:15<01:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:16<01:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:17<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:17<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:18<01:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:19<01:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:20<01:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:20<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:21<01:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:22<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:22<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:23<01:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:24<01:10,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:25<01:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:25<01:10,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:26<01:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:27<01:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:28<01:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:28<01:11,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:29<01:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:30<01:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:31<01:10,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:32<01:09,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:32<01:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:33<01:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:34<01:06,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:35<01:05,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:36<01:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:36<01:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:37<01:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:38<01:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:39<01:02,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:40<01:01,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:40<00:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:42<01:02,  1.21it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [01:44<06:58, 104.54s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:27,  4.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:25,  2.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:07,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:03,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:27,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:05,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:53,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:43,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:37,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:30,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:25,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:20,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:16,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:14,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<02:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<02:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<02:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<02:11,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<02:08,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:36<02:08,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<02:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<02:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:39<02:08,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:40<02:03,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<02:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:58,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:42<01:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:43<01:57,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:44<01:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:45<01:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:46<01:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:47<01:59,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:48<01:56,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:48<01:54,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:50<01:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:51<01:53,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:52<01:53,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:53<01:52,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:53<01:50,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:54<01:49,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:55<01:47,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:56<01:47,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:57<01:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:57<01:45,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:58<01:45,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:59<01:48,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:00<01:49,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:01<01:47,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:02<01:46,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:02<01:43,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:03<01:41,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:04<01:54,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:05<01:58,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:06<01:48,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:07<01:41,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:08<01:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:08<01:33,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:09<01:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:10<01:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:11<01:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:11<01:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:12<01:27,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:13<01:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:14<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:14<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:15<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:16<01:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:17<01:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:17<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:18<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:19<01:19,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:20<01:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:21<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:21<01:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:22<01:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:23<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:23<01:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:24<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:25<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:26<01:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:27<01:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:27<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:28<01:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:29<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:30<01:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:30<01:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:31<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:32<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:33<01:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:33<01:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:34<01:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:35<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:36<01:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:36<01:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:37<01:00,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:38<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:39<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:39<00:57,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:40<00:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:41<00:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:42<00:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:42<00:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:43<00:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:44<00:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:45<00:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:45<00:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:46<00:50,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:47<00:50,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:48<00:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:48<00:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:49<00:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:50<00:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:51<00:46,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:51<00:45,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:52<00:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:53<00:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:53<00:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:54<00:40,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:55<00:39,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:56<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:57<00:43,  1.24it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [03:44<05:40, 113.57s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:03,  4.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:10,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:57,  1.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:55,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<03:05,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:52,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:19,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:17,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:20,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<02:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<02:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:31<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<02:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:33<01:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:34<01:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<01:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:35<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:36<01:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:37<02:02,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<02:00,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:39<01:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:40<01:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:56,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:43<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:54,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:57,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:59,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<02:00,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<02:02,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<02:01,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:51<02:00,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:52<01:59,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:53<01:58,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:54<01:58,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:55<01:58,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:55<01:58,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:56<01:56,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:57<01:56,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:58<01:56,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:59<01:55,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:00<01:53,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:01<01:52,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:02<01:51,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:03<01:50,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:03<01:49,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:04<01:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:05<01:47,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:06<01:45,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:07<01:45,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:08<01:42,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:09<01:41,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:09<01:39,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:10<01:35,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:11<01:35,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:12<01:35,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:13<01:36,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:14<01:39,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:15<01:42,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:16<01:37,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:16<01:33,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:17<01:33,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:18<01:31,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:19<01:30,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:20<01:27,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:21<01:25,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:21<01:24,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:22<01:22,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:23<01:21,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:24<01:31,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:25<01:32,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:26<01:27,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:27<01:21,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:27<01:18,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:28<01:16,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:29<01:14,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:30<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:30<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:31<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:33<01:16,  1.18it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [05:19<03:30, 105.23s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:07,  4.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:19,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:03,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:58,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:24,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<03:01,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:47,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:27,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:16,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<02:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<02:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<02:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:31<02:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<02:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<02:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:33<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:34<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<02:00,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:35<01:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:36<01:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:37<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:38<01:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:39<02:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:40<02:01,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<01:58,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:42<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:43<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:53,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:51,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:46<01:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:49<01:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:50<01:46,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:46,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:45,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:41,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:40,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:40,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:58<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:59<01:37,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:59<01:36,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:00<01:35,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:01<01:32,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:32,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:02<01:32,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:03<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:04<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:05<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:05<01:29,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:06<01:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:35,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:08<01:34,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:10<01:29,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:10<01:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:11<01:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:12<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:13<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:14<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:15<01:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:16<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:17<01:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:18<01:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:19<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:19<01:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:20<01:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:21<01:17,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:22<01:16,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:23<01:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:23<01:13,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:24<01:18,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:25<01:15,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:26<01:13,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:27<01:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:27<01:10,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:28<01:09,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:29<01:12,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:30<01:13,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:31<01:13,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:32<01:13,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:33<01:13,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:34<01:12,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:34<01:12,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:35<01:12,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:36<01:11,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:37<01:10,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:38<01:09,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:39<01:09,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:40<01:08,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:41<01:07,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:42<01:06,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:43<01:05,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:43<01:04,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:44<01:03,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:45<01:03,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:46<01:02,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:47<01:01,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:48<01:00,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:49<00:58,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:50<00:58,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:51<00:56,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:51<00:53,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:52<00:51,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:53<00:50,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:54<00:50,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:55<00:48,  1.22it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [07:18<01:50, 110.45s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<17:00,  5.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:35,  2.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:49,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:59,  1.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:09<04:27,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:09<03:45,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:10<03:16,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:11<03:01,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:12<02:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:40,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:33,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:14<02:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:15<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:17<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:18<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:18<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:19<02:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:20<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:21<02:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:23<02:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:24<02:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:24<02:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:26<02:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:27<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:28<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:29<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:30<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:31<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:32<02:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:33<02:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:33<02:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:34<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:35<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:36<01:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:36<01:57,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:37<01:57,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:38<01:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:39<01:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:39<01:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:40<01:54,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:41<01:52,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:42<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:42<01:48,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:43<01:47,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:44<01:47,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:45,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:45<01:43,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:46<01:41,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:48<01:40,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:39,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<01:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:51<01:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:39,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:54<01:40,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:38,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:57<01:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:38,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:58<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:59<01:38,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:00<01:37,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:01<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:01<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:34,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:03<01:32,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:04<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:04<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:05<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:06<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:07<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:08<01:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:10<01:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:10<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:11<01:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:12<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:13<01:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:14<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:15<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:16<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:17<01:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:18<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:19<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:19<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:20<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:21<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:22<01:12,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:22<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:23<01:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:24<01:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:25<01:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:26<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:26<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:27<01:12,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:28<01:13,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:29<01:10,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:30<01:09,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:30<01:07,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:31<01:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:32<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:33<01:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:33<01:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:34<01:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:35<01:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:36<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:36<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:37<00:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:38<00:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:39<00:55,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:39<00:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:40<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:41<00:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:42<00:57,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:43<00:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:43<00:53,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:44<00:53,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:45<00:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:46<00:52,  1.25it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [09:07<00:00, 109.49s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.06s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.01it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  6.99it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.79it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 74it [00:01, 93.99it/s]\u001b[A\n",
+      "Evaluating NLL: 151it [00:01, 197.81it/s]\u001b[A\n",
+      "Evaluating NLL: 219it [00:01, 284.39it/s]\u001b[A\n",
+      "Evaluating NLL: 295it [00:01, 381.07it/s]\u001b[A\n",
+      "Evaluating NLL: 369it [00:01, 460.80it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 297.12it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|▎| 29/100 [00:00<00:00, 288.25it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:00<00:00, 319.57it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 327.39it\n",
+      " 83%|███████████████████████████████████████████████████████████████▎            | 5/6 [47:17<09:50, 590.60s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:58,  4.81s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:17,  2.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:47,  1.76s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:36,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<03:55,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:09<03:30,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:14,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<03:03,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:52,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:42,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:36,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:33,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:27,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:26,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:17<02:28,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:29,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:18<02:37,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:19<02:30,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:20<02:26,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:21<02:28,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:22<02:29,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:24,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:23<02:20,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:24<02:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:25<02:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:27<02:34,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:28<02:39,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:28<02:25,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:29<02:18,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:30<02:13,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:31<02:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:31<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:32<02:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:33<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:33<02:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:34<01:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:35<01:57,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:36<01:55,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:36<01:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:37<01:52,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:38<01:51,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:38<01:50,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:39<01:48,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:40<01:47,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:41<01:47,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:41<01:48,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:42<01:46,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:43<01:47,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:43<01:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:44<01:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:45<01:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:46<01:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:46<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:47<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:48<01:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:49<01:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:50<01:44,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:51<01:43,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:52<01:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:52<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:53<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:54<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:54<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:55<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:56<01:34,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:57<01:34,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:57<01:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:58<01:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:59<01:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:00<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:00<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:01<01:29,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:02<01:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:03<01:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:04<01:26,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:05<01:26,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:05<01:25,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:06<01:25,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:07<01:23,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:22,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:08<01:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:21,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:10<01:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:10<01:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:11<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:12<01:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:12<01:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:19,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:14<01:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:15<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:15<01:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:17<01:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:18<01:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:18<01:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:19<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:20<01:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:21<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:21<01:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:22<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:23<01:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:24<01:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:24<01:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:25<01:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:26<01:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:26<01:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:27<01:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:28<01:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:29<01:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:29<01:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:30<01:00,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:31<01:00,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:31<00:59,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:32<00:59,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:33<00:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:34<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:34<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:35<00:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:36<00:55,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:36<00:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:37<00:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:38<00:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:39<00:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:39<00:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:40<00:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:41<00:50,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:42<00:49,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:42<00:48,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:43<00:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:44<00:51,  1.28it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [01:47<07:09, 107.34s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:32,  4.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:54,  2.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:21,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:30,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:05,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:51,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:19,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:14,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:17,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:18,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:20,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:20,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:20,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:21,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:31<02:21,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<02:20,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<02:19,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:33<02:18,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:34<02:17,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:35<02:16,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:36<02:15,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:36<02:14,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:37<02:14,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:38<02:11,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:39<02:10,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:40<02:09,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:41<02:09,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:42<02:08,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:42<02:06,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:43<02:05,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:44<02:04,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:45<02:03,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:46<02:03,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:46<01:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:47<01:58,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:48<01:59,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:49<01:54,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:50<01:54,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:50<01:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:51<01:51,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:52<01:50,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:53<01:51,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:54<01:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:54<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:55<01:52,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:56<01:50,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:57<01:47,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:58<01:46,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:59<01:45,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:59<01:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:00<01:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:01<01:40,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:02<01:48,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:03<02:01,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:04<01:51,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:05<01:44,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:05<01:39,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:06<01:34,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:07<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:07<01:29,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:08<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:09<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:10<01:25,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:10<01:24,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:11<01:24,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:12<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:13<01:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:13<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:14<01:20,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:15<01:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:15<01:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:16<01:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:17<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:18<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:18<01:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:20<01:27,  1.20it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▊                   | 2/5 [03:09<04:38, 92.73s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:41,  4.73s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:57,  2.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:24,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:11,  1.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:30,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:05,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:40,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:24,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:21,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:13,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:12,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:12,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:09,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<02:07,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<02:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<02:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<02:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<01:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<01:57,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<01:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<01:52,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:49,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<01:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:43<01:45,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:45,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:46<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:49<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:50<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:38,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:51<01:37,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:52<01:36,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:35,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:54<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:55<01:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:56<01:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:57<01:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:58<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:59<01:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:59<01:28,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:00<01:29,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:01<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:02<01:27,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:02<01:26,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:03<01:24,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:04<01:23,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:04<01:23,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:05<01:23,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:06<01:23,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:07<01:23,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:07<01:22,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:08<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:09<01:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:09<01:22,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:10<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:11<01:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:12<01:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:13<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:14<01:26,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:14<01:26,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:15<01:25,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:16<01:22,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:17<01:19,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:17<01:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:18<01:18,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:19<01:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:20<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:20<01:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:21<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:22<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:23<01:13,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:24<01:14,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:25<01:15,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:25<01:15,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:26<01:14,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:27<01:14,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:28<01:14,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:29<01:14,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:30<01:13,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:30<01:12,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:31<01:11,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:32<01:10,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:33<01:10,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:34<01:09,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:35<01:08,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:36<01:07,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:37<01:06,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:37<01:06,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:38<01:05,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:39<01:04,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:40<01:04,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:41<01:03,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:42<01:01,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:43<01:00,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:43<00:59,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:44<00:56,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:45<00:55,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:46<00:55,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:47<00:53,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:47<00:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:48<00:52,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:49<00:52,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:50<00:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:51<00:48,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:51<00:48,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:52<00:46,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:53<00:45,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:54<00:45,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:55<00:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:55<00:42,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:56<00:42,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:57<00:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:58<00:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:58<00:40,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:59<00:39,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [02:00<00:38,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [02:01<00:36,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [02:02<00:37,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [02:03<00:43,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [02:04<00:40,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:04<00:36,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:05<00:34,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:06<00:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:07<00:31,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:07<00:29,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:08<00:28,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:09<00:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:09<00:26,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:10<00:26,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:11<00:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:12<00:24,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:12<00:24,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:13<00:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:14<00:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:15<00:21,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:15<00:20,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:17<00:22,  1.25it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [05:29<03:48, 114.06s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:48,  4.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:29,  2.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:12,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:32,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:09,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:52,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:39,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<02:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:21<02:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:23<02:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:24<02:03,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:26<02:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<02:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<02:02,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<02:01,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:29<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<01:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<01:57,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:32<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:34<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:36<02:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:37<01:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:38<01:49,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:39<01:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:40<01:45,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:40<01:44,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:41<01:42,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:42<01:41,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:42<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:43<01:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:44<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:44<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:45<01:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:46<01:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:46<01:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:47<01:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:48<01:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:49<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:49<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:50<01:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:51<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:52<01:36,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:52<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:53<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:54<01:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:55<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:55<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:56<01:31,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:57<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:57<01:30,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:58<01:28,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:59<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:00<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:00<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:01<01:27,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:02<01:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:03<01:25,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:03<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:04<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:05<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:06<01:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:06<01:22,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:07<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:08<01:20,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:08<01:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:09<01:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:10<01:17,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:11<01:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:11<01:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:12<01:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:13<01:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:13<01:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:14<01:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:15<01:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:16<01:11,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:16<01:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:17<01:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:18<01:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:19<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:19<01:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:20<01:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:21<01:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:22<01:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:22<01:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:23<01:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:24<01:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:25<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:25<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:27<01:05,  1.31it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [06:58<01:44, 104.37s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:18,  4.92s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:15,  2.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:31,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:27,  1.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<03:49,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:21,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:06,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<03:22,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<03:09,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:53,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:42,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:14<02:36,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:26,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:17<02:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:18<02:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:19<02:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:20<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:23<02:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:24<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:01,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<01:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<01:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<01:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<01:58,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<01:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<01:56,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<01:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<01:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<01:50,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:50,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:49,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:51,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:50,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:48,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<01:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:50<01:42,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:43,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:58<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:59<01:35,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:59<01:35,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:00<01:35,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:01<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:37,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:03<01:37,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:03<01:35,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:04<01:33,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:05<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:06<01:29,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:06<01:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:08<01:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:24,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:09<01:23,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:10<01:23,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:11<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:11<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:12<01:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:14<01:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:14<01:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:15<01:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:17<01:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:18<01:19,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:18<01:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:19<01:18,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:20<01:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:21<01:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:22<01:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:22<01:14,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:23<01:13,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:24<01:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:25<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:25<01:11,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:26<01:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:27<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:28<01:11,  1.25it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [08:30<00:00, 102.00s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.38it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.45it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.74it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 83it [00:00, 117.84it/s]\u001b[A\n",
+      "Evaluating NLL: 155it [00:01, 219.50it/s]\u001b[A\n",
+      "Evaluating NLL: 242it [00:01, 345.35it/s]\u001b[A\n",
+      "Evaluating NLL: 327it [00:01, 454.74it/s]\u001b[A\n",
+      "Evaluating NLL: 404it [00:01, 527.84it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 335.57it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 344.54it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 338.81it\n",
+      "100%|████████████████████████████████████████████████████████████████████████████| 6/6 [55:59<00:00, 559.94s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise sigma 6.25e-05\n",
+      "Initial validation NLL: 767.07\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|████████████████████████████████████████| 1/1 [00:01<00:00,  1.50s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: validation NLL: 10.75\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 123.14it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: validation NLL: 10.06\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 155.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: validation NLL: 9.83\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 149.09it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: validation NLL: 9.72\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 171.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: validation NLL: 9.66\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 345.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: validation NLL: 9.62\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 324.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: validation NLL: 9.59\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 323.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: validation NLL: 9.57\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 182.79it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: validation NLL: 9.55\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 293.27it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: validation NLL: 9.54\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 200.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: validation NLL: 9.53\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 231.13it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: validation NLL: 9.52\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 287.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 197.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: validation NLL: 9.51\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 157.36it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: validation NLL: 9.50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 369.93it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: validation NLL: 9.50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 343.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: validation NLL: 9.50\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 226.17it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: validation NLL: 9.49\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 196.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: validation NLL: 9.49\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 202.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: validation NLL: 9.49\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 374.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: validation NLL: 9.49\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 353.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: validation NLL: 9.49\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 151.51it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 195.83it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 24: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 195.34it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 297.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 26: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 159.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 27: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 187.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 28: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 209.91it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 29: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 346.58it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 183.05it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 31: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 346.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 32: validation NLL: 9.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 33: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 305.13it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 33: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 34: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 179.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 34: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 35: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 282.56it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 35: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 36: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 189.88it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 36: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 37: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 165.42it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 37: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 38: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 165.51it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 38: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 39: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 276.76it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 39: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 40: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 248.35it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 40: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 41: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 356.51it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 41: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 42: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 167.78it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 42: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 43: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 167.28it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 43: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 44: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 270.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 44: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 45: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 183.57it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 45: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 46: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 171.06it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 46: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 47: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 246.14it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 47: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 48: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 231.38it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 48: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 49: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 160.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 49: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 50: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 196.09it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 50: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 51: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 323.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 51: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 52: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 162.54it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 52: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 53: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 199.61it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 53: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 54: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 339.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 54: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 55: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 151.67it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 55: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 56: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 204.51it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 56: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 57: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 257.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 57: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 58: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 202.71it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 58: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 59: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 168.62it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 59: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 60: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 334.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 60: validation NLL: 9.47\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "precomputing masks and variances: 100%|██████████████| 4/4 [00:00<00:00, 354.59it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods: 100%|█████████████████████| 4/4 [00:00<00:00, 421.50it/s]\n",
+      "precomputing masks and variances: 100%|██████████████| 4/4 [00:00<00:00, 397.58it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "computing log likelihoods: 100%|█████████████████████| 4/4 [00:00<00:00, 375.01it/s]\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 239.14it\n",
+      "  0%|                                                         | 0/6 [00:00<?, ?it/s]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:51,  4.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:21,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:49,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:36,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<02:57,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:07<02:17,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<01:58,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<01:53,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<01:49,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:10<01:49,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:46,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:11<01:45,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:12<01:43,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:12<01:41,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:13<01:39,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:13<01:40,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:14<01:40,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:15<01:38,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:15<01:36,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:16<01:37,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:16<01:36,  1.83it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:17<01:37,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:17<01:36,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:18<01:37,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:18<01:38,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:19<01:37,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:20<01:37,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:20<01:42,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:21<01:42,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:21<01:38,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:22<01:38,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:23<01:42,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:23<01:41,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:24<01:41,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:25<01:40,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:25<01:36,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:26<01:34,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:26<01:34,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:27<01:34,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:27<01:33,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:28<01:32,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:29<01:31,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:29<01:30,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:30<01:30,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:30<01:30,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:31<01:29,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:32<01:27,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:32<01:28,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:33<01:25,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:33<01:25,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:34<01:36,  1.52it/s]\n",
+      "\n",
+      "Training models:  20%|██████▍                         | 1/5 [00:37<02:28, 37.17s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<13:44,  4.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<06:42,  2.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:31,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:05<03:29,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<02:54,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:34,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:07<02:22,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:08<02:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<02:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:00,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:10<01:54,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:55,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:11<01:57,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:12<01:58,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:55,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:13<01:52,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:14<01:50,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:14<01:49,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:15<01:51,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:16<01:49,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:16<01:48,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:17<01:47,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:18<01:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:18<01:46,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:19<01:45,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:19<01:43,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:20<01:40,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:20<01:40,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:21<01:38,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:22<01:36,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:22<01:36,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:23<01:36,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:23<01:35,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:24<01:35,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:25<01:36,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:25<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:26<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:27<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:27<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:28<01:38,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:28<01:36,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:29<01:35,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:30<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:30<01:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:32<01:47,  1.43it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▊                   | 2/5 [01:11<01:46, 35.50s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:47,  4.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:11,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:51,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:50,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:16,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:42,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<01:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<01:52,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:49,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:48,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<01:45,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:45,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:45,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:44,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:43,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:43,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:42,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:42,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:20<01:43,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:43,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:41,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:22<01:39,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:39,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:23<01:38,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<01:37,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:24<01:39,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:25<01:37,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:36,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:26<01:35,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:27<01:35,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:27<01:32,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:28<01:30,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:29<01:54,  1.39it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████▏            | 3/5 [01:43<01:07, 33.84s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:03,  4.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:20,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:50,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:39,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<02:58,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:34,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:07<02:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:00,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<01:55,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<01:52,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:10<01:50,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:51,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:11<01:49,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:12<01:47,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:45,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:13<01:45,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:14<01:46,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:14<01:45,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:15<01:43,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:15<01:42,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:16<01:40,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:17<01:39,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:17<01:39,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:18<01:40,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:18<01:40,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:19<01:38,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:19<01:37,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:20<01:37,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:21<01:36,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:21<01:36,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:22<01:37,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:22<01:36,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:23<01:36,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:24<01:37,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:24<01:35,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:25<01:34,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:25<01:32,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:26<01:31,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:26<01:30,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:27<01:30,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:28<01:30,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:28<01:30,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:29<01:30,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:29<01:31,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:30<01:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:31<01:33,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:31<01:31,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:32<01:29,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:32<01:27,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:33<01:26,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:33<01:26,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:34<01:24,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:35<01:23,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:35<01:23,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:36<01:22,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:36<01:20,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:37<01:20,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:37<01:20,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:38<01:19,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:39<01:19,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:39<01:19,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:40<01:19,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:40<01:19,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:41<01:19,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:42<01:18,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:42<01:17,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:43<01:17,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:43<01:16,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:44<01:15,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:44<01:15,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:45<01:15,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:46<01:13,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:46<01:14,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:47<01:13,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:47<01:11,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:48<01:11,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:48<01:10,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:49<01:09,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:50<01:10,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:50<01:09,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:51<01:09,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:51<01:08,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:52<01:07,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [00:53<01:06,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [00:53<01:07,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [00:54<01:06,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [00:54<01:06,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [00:56<01:09,  1.59it/s]\n",
+      "\n",
+      "Training models:  80%|█████████████████████████▌      | 4/5 [02:41<00:43, 43.48s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:28,  4.37s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:03,  2.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:42,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:35,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<02:59,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:07<02:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<01:56,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<01:53,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:10<01:51,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:47,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:11<01:47,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:12<01:47,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:13<01:56,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:14<01:54,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:15<01:48,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:16<01:44,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:16<01:40,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:17<01:41,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:18<01:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:18<01:48,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:19<01:41,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:19<01:39,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:20<01:37,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:20<01:35,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:21<01:34,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:21<01:33,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:22<01:37,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:23<01:39,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:23<01:36,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:24<01:33,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:24<01:30,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:25<01:37,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:26<01:40,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:26<01:36,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:27<01:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:28<01:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:28<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:29<01:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:30<01:37,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:30<01:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:31<02:06,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:32<02:02,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:33<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:33<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:34<01:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:34<01:31,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:35<01:28,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:36<01:26,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:36<01:24,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:37<01:39,  1.46it/s]\n",
+      "\n",
+      "Training models: 100%|████████████████████████████████| 5/5 [03:21<00:00, 40.33s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.84it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.56it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 87it [00:01, 116.76it/s]\u001b[A\n",
+      "Evaluating NLL: 176it [00:01, 241.26it/s]\u001b[A\n",
+      "Evaluating NLL: 262it [00:01, 356.43it/s]\u001b[A\n",
+      "Evaluating NLL: 349it [00:01, 464.60it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 331.58it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 455.14it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 440.03it\n",
+      " 17%|████████                                        | 1/6 [03:33<17:45, 213.13s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<13:59,  4.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:06,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:44,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:45,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<02:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<01:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<02:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<02:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<02:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:56,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:16<01:54,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:52,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:18<01:49,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:20<01:51,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:50,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:22<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:46,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:23<01:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<01:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:25<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<01:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:27<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:28<01:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:29<01:45,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:29<01:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:30<01:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:31<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:32<01:49,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:32<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:33<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:33<01:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:34<01:40,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:35<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:35<01:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:36<01:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:37<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:37<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:38<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:39<01:32,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:39<01:29,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:40<01:28,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:40<01:27,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:41<01:26,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:42<01:25,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:42<01:25,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:43<01:25,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:44<01:25,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:44<01:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:45<01:26,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:46<01:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:46<01:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:47<01:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:47<01:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:48<01:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:49<01:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:49<01:21,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:50<01:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:51<01:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:51<01:19,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:52<01:19,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:53<01:19,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:53<01:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:54<01:16,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:54<01:14,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:55<01:14,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:56<01:14,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:56<01:14,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:57<01:13,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [00:58<01:12,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [00:58<01:12,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [00:59<01:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:00<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:00<01:17,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:01<01:16,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:02<01:14,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:02<01:11,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:03<01:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:04<01:08,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:04<01:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:05<01:07,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:06<01:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:06<01:06,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:07<01:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:08<01:03,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:08<01:02,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:09<01:01,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:09<00:59,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:10<00:58,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:11<00:57,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:11<00:59,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:12<01:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:13<01:03,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:13<01:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:14<01:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:15<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:15<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:16<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:17<01:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:18<00:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:18<00:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:19<00:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:19<00:51,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:20<00:50,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:21<00:48,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:21<00:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:22<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:23<00:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:23<00:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:24<00:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:25<00:53,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:25<00:50,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:26<00:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:27<00:49,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:27<00:49,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:28<00:49,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:29<00:48,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:30<00:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:31<00:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:31<00:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:32<00:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:33<00:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:34<00:52,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:35<00:54,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:35<00:48,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:36<00:44,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:37<00:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:37<00:39,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:38<00:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:39<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:39<00:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:40<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:40<00:33,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:41<00:33,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:42<00:32,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:42<00:31,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:43<00:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:44<00:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:44<00:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:45<00:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:46<00:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:46<00:26,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:47<00:26,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:47<00:25,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:48<00:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:49<00:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:49<00:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:50<00:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:51<00:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:51<00:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:52<00:21,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:53<00:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:53<00:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:54<00:19,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [01:54<00:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [01:55<00:18,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [01:56<00:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [01:56<00:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [01:57<00:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [01:58<00:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [01:58<00:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [01:59<00:14,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:00<00:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:00<00:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:01<00:13,  1.48it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:04<08:17, 124.45s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<13:59,  4.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:12,  2.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:44,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:06,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<02:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<01:59,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<01:57,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<02:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:57,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:56,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:53,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:52,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:20<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:23<01:55,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:24<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<01:50,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:26<01:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:28<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:28<01:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:29<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:30<01:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:30<01:42,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:31<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:32<01:41,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:32<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:33<01:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:34<01:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:34<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:35<01:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:35<01:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:36<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:37<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:38<01:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:39<01:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:39<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:40<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:41<01:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:41<01:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:42<01:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:43<01:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:43<01:27,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:44<01:26,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:44<01:24,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:45<01:24,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:46<01:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:46<01:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:47<01:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:48<01:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:48<01:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:49<01:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:50<01:31,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:51<01:30,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:51<01:27,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:52<01:25,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:52<01:22,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:53<01:20,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:54<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:54<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:55<01:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:56<01:17,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:56<01:18,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:57<01:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:58<01:16,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [00:58<01:14,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [00:59<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:00<01:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:00<01:11,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:01<01:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:01<01:09,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:02<01:09,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:03<01:14,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:04<01:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:04<01:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:05<01:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:06<01:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:06<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:07<01:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:08<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:08<01:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:09<01:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:10<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:10<01:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:11<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:12<01:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:13<01:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:13<01:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:14<01:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:15<01:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:16<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:17<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:17<01:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:18<01:07,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:19<01:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:20<01:06,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:20<01:06,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:21<01:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:22<01:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:23<01:03,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:24<01:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:24<01:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:25<01:00,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:26<01:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:27<00:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:28<00:59,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:28<00:58,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:29<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:30<00:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:31<00:56,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:31<00:55,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:32<00:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:33<00:50,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:34<00:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:34<00:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:35<00:47,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:36<00:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:36<00:44,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:37<00:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:38<00:46,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:39<00:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:39<00:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:40<00:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:41<00:40,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:42<00:39,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:42<00:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:43<00:38,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:44<00:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:44<00:36,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:45<00:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:46<00:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:47<00:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:47<00:34,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:48<00:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:49<00:40,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:50<00:36,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:51<00:33,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:51<00:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:52<00:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:53<00:27,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:53<00:26,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:54<00:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:54<00:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:55<00:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:56<00:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:56<00:22,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:57<00:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:58<00:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:58<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:59<00:20,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:00<00:19,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:00<00:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:01<00:17,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:02<00:17,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:02<00:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:03<00:16,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:04<00:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:04<00:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:05<00:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:05<00:13,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:06<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:07<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:07<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:08<00:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:09<00:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:09<00:09,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:10<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:11<00:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:11<00:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:12<00:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:13<00:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:13<00:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:14<00:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:15<00:04,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:15<00:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:16<00:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:17<00:02,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:17<00:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:18<00:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:19<00:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:19<00:00,  1.43it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:26<06:44, 134.67s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:11,  4.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:35,  2.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:58,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:49,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:08,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:46,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<02:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<01:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:52,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<02:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:21<01:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:23<01:47,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:24<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:25<01:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:26<01:44,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:41,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<01:40,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:28<01:40,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:28<01:41,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:29<01:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:30<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:30<01:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:31<01:39,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:32<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:32<01:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:33<01:37,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:33<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:34<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:35<01:37,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:35<01:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:36<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:37<01:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:38<01:33,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:39<01:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:39<01:30,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:40<01:29,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:40<01:29,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:41<01:28,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:42<01:28,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:42<01:28,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:43<01:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:44<01:27,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:44<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:45<01:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:46<01:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:46<01:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:47<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:48<01:34,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:49<01:30,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:49<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:50<01:25,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:51<01:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:51<01:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:52<01:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:52<01:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:53<01:19,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:54<01:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:54<01:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:55<01:15,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:56<01:14,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:56<01:12,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:57<01:14,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:57<01:13,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [00:58<01:11,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [00:59<01:11,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [00:59<01:09,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:00<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:01<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:01<01:15,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:02<01:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:03<01:10,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:03<01:08,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:04<01:07,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:05<01:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:06<01:13,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:06<01:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:07<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:07<01:05,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:08<01:04,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:09<01:04,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:10<01:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:10<01:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:11<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:12<01:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:13<01:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:14<01:12,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:14<01:12,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:15<01:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:16<01:11,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:17<01:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:18<01:09,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:18<01:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:19<01:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:20<01:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:22<00:59,  1.41it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [05:50<03:43, 111.66s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<16:07,  4.86s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:11,  2.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:39,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:26,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:37,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:05,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:00,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:50,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:47,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:18,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:29,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:16,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<01:58,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<01:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:31<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:42,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<01:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:33<01:40,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:35<01:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:36<01:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:37<01:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:35,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:38<01:34,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:38<01:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:39<01:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:40<01:33,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:40<01:32,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:41<01:32,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:42<01:30,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:42<01:31,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:43<01:30,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:43<01:29,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:44<01:27,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:45<01:27,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:45<01:27,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:46<01:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:47<01:27,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:47<01:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:48<01:27,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:49<01:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:49<01:27,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:50<01:25,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:50<01:24,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:51<01:23,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:52<01:22,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:52<01:22,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:53<01:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:54<01:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:54<01:19,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:55<01:18,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:56<01:19,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:56<01:17,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:57<01:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:57<01:17,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:58<01:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:59<01:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:59<01:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:00<01:14,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:01<01:16,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:01<01:14,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:02<01:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:03<01:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:03<01:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:04<01:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:05<01:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:05<01:07,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:06<01:06,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:06<01:05,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:07<01:05,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:08<01:04,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:08<01:04,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:09<01:04,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:09<01:03,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:10<01:02,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:11<01:02,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:11<01:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:12<01:01,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:13<01:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:13<01:03,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:14<01:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:15<01:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:15<01:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:16<00:58,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:17<00:56,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:17<00:55,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:18<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:18<00:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:19<00:54,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:20<00:53,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:20<00:53,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:21<00:52,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:22<00:51,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:22<00:50,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:23<00:48,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:23<00:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:24<00:47,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:24<00:45,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:25<00:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:26<00:45,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:26<00:45,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:27<00:44,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:28<00:43,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:28<00:42,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:29<00:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:29<00:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:30<00:41,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:31<00:41,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:31<00:40,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:32<00:39,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:32<00:39,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:33<00:39,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:34<00:38,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:34<00:38,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:35<00:38,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:36<00:38,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:36<00:37,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:37<00:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:37<00:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:38<00:35,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:39<00:34,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:39<00:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:40<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:41<00:32,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:41<00:31,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:42<00:30,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:42<00:30,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:43<00:29,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:44<00:29,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:44<00:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:45<00:31,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:46<00:30,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:47<00:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:47<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:48<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:49<00:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:49<00:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:50<00:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:50<00:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:51<00:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:52<00:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:52<00:21,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:53<00:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:54<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:54<00:19,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [01:55<00:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [01:55<00:18,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [01:57<00:19,  1.47it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [07:50<01:54, 114.83s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:01,  4.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:35,  2.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:09,  1.57s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:59,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:18,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<02:56,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:47,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:42,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:40,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:37,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:34,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:31,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:30,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:29,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:28,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:27,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:24,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:22,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:20,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:20,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:18,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:23<02:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:16,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:16,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:16,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:14,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:13,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:15,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:13,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:31<02:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<02:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<01:58,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:33<02:06,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:34<02:13,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<01:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:36<01:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:37<01:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:51,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:39<01:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:48,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:48,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<01:46,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:46,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:47,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:46,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:43,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<02:07,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:51<01:56,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:39,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:35,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:55<01:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:24,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:57<01:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:58<01:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:58<01:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:59<01:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:00<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:00<01:19,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:01<01:20,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:02<01:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:02<01:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:03<01:15,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:04<01:13,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:04<01:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:05<01:13,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:06<01:13,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:06<01:13,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:07<01:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:07<01:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:08<01:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:09<01:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:09<01:09,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:10<01:09,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:11<01:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:11<01:08,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:12<01:08,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:13<01:08,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:13<01:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:14<01:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:15<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:15<01:05,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:16<01:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:17<01:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:17<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:18<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:19<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:19<01:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:20<01:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:21<00:59,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:21<00:59,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:22<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:23<00:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:23<00:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:24<00:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:24<00:54,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:25<00:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:26<00:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:26<00:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:27<00:52,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:28<00:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:28<00:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:29<00:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:30<00:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:30<00:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:31<00:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:31<00:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:32<00:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:33<00:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:33<00:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:34<00:45,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:35<00:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:35<00:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:36<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:37<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:37<00:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:38<00:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:39<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:39<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:40<00:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:41<00:38,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:41<00:37,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:42<00:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:43<00:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:43<00:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:44<00:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:45<00:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:45<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:46<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:47<00:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:47<00:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:48<00:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:48<00:30,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:49<00:29,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:50<00:28,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:50<00:28,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:51<00:27,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:51<00:26,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:52<00:26,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:53<00:25,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:53<00:25,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:54<00:24,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:55<00:24,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:55<00:23,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:56<00:22,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:56<00:22,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:57<00:21,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:58<00:20,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:58<00:19,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:59<00:19,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:59<00:18,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:00<00:17,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:00<00:17,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:01<00:17,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:02<00:16,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:02<00:15,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:03<00:14,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:04<00:14,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:04<00:13,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:05<00:13,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:05<00:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:06<00:12,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:07<00:11,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:07<00:11,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:08<00:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:09<00:10,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:09<00:09,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:10<00:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:11<00:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:12<00:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:12<00:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:13<00:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:14<00:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:14<00:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:15<00:04,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:15<00:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:16<00:03,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:17<00:02,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:17<00:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:18<00:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:19<00:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:19<00:00,  1.43it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [10:12<00:00, 122.50s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.57it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.89it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.21s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.31it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.69it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 77it [00:00, 110.54it/s]\u001b[A\n",
+      "Evaluating NLL: 168it [00:01, 246.86it/s]\u001b[A\n",
+      "Evaluating NLL: 255it [00:01, 368.24it/s]\u001b[A\n",
+      "Evaluating NLL: 330it [00:01, 451.61it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 347.04it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 428.14it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 413.20it\n",
+      " 33%|████████████████                                | 2/6 [13:57<30:20, 455.16s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:24,  4.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:50,  2.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:21,  1.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:34,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:56,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:30,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:38,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:51,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:36,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:01,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<01:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<01:53,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<01:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<01:54,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<01:53,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:32<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:34<01:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:36<01:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:36<01:44,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:44,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:38<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:38<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:39<01:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:40<01:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:40<01:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:41<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:42<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:43<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:43<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:44<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:45<01:35,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:45<01:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:46<01:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:47<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:47<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:48<01:34,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:49<01:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:49<01:32,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:50<01:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:51<01:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:51<01:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:52<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:53<01:27,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:53<01:27,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:54<01:25,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:55<01:25,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:55<01:25,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:56<01:24,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:57<01:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:57<01:24,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:58<01:24,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:59<01:25,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:00<01:24,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:00<01:23,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:01<01:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:02<01:21,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:02<01:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:03<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:04<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:04<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:05<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:06<01:16,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:07<01:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:07<01:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:08<01:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:09<01:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:09<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:10<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:11<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:11<01:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:12<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:13<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:13<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:14<01:04,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:15<01:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:15<01:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:16<01:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:17<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:17<01:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:18<01:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:19<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:19<01:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:20<01:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:21<01:00,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:21<01:00,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:22<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:23<00:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:23<00:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:24<00:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:25<00:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:25<00:52,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:26<00:50,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:27<00:50,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:27<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:28<00:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:28<00:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:29<00:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:30<00:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:30<00:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:31<00:45,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:32<00:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:32<00:44,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:33<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:34<00:47,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:34<00:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:35<00:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:36<00:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:36<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:37<00:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:38<00:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:38<00:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:39<00:38,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:40<00:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:40<00:37,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:41<00:36,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:41<00:35,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:42<00:34,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:43<00:34,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:43<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:44<00:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:45<00:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:45<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:46<00:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:47<00:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:48<00:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:48<00:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:49<00:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:50<00:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:51<00:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:52<00:33,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:52<00:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:53<00:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:54<00:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:54<00:27,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:55<00:26,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:56<00:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:57<00:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:57<00:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:58<00:26,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:59<00:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:00<00:25,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:01<00:25,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:02<00:24,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:02<00:24,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:03<00:23,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:04<00:22,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:05<00:21,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:06<00:20,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:07<00:19,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:07<00:19,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:08<00:18,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:09<00:17,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:10<00:16,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:11<00:15,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:12<00:14,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:12<00:14,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:13<00:13,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:14<00:12,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:15<00:11,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:16<00:10,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:17<00:09,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:17<00:09,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:18<00:08,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:19<00:07,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:20<00:06,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:20<00:05,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:21<00:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:22<00:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:23<00:03,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:23<00:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:24<00:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:25<00:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:26<00:00,  1.37it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:28<09:54, 148.67s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:56,  4.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:35,  2.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:48,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<05:10,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<04:13,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:09<03:32,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:05,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:11,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:01,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:02,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:01,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:24<01:58,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<01:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<01:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<01:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<01:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:29<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:31<01:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:32<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:33<01:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:34<01:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:35<01:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:36<01:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:37<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:38<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:39<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:39<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:40<01:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:41<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:41<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:42<01:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:43<01:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:43<01:38,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:44<01:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:45<01:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:45<01:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:46<01:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:47<01:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:47<01:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:48<01:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:49<01:27,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:49<01:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:50<01:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:51<01:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:51<01:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:52<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:53<01:29,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:54<01:30,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:54<01:27,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:55<01:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:56<01:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:56<01:25,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:57<01:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:58<01:24,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:58<01:23,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:59<01:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:00<01:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:01<01:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:01<01:22,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:02<01:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:03<01:18,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:03<01:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:04<01:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:05<01:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:05<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:06<01:13,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:07<01:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:07<01:14,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:08<01:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:09<01:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:09<01:10,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:10<01:08,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:11<01:05,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:11<01:04,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:12<01:03,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:12<01:03,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:13<01:03,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:14<01:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:14<01:02,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:15<01:00,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:16<00:59,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:16<00:58,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:17<00:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:17<00:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:18<00:58,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:19<00:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:19<00:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:20<00:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:21<00:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:21<00:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:22<00:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:23<00:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:23<00:51,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:24<00:51,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:24<00:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:25<00:50,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:26<00:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:26<00:50,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:27<00:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:28<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:29<00:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:29<00:52,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:30<00:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:31<00:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:31<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:32<00:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:33<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:33<00:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:34<00:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:35<00:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:35<00:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:36<00:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:36<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:37<00:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:38<00:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:39<00:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:39<00:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:40<00:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:41<00:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:41<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:42<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:43<00:39,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:44<00:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:44<00:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:45<00:35,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:46<00:36,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:47<00:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:47<00:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:48<00:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:49<00:32,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:49<00:31,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:50<00:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:51<00:30,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:51<00:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:52<00:29,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:53<00:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:54<00:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:54<00:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:55<00:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:56<00:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:57<00:27,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:58<00:27,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:59<00:27,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:59<00:26,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:00<00:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:01<00:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:02<00:25,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:03<00:25,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:04<00:22,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:04<00:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:05<00:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:06<00:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:06<00:16,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:07<00:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:08<00:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:08<00:13,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:09<00:13,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:10<00:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:10<00:11,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:11<00:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:12<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:12<00:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:13<00:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:14<00:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:14<00:07,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:15<00:06,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:16<00:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:16<00:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:17<00:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:18<00:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:18<00:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:19<00:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:20<00:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:20<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:21<00:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:22<00:00,  1.41it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:53<07:18, 146.23s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:22,  4.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:36,  2.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:08,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:58,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:20,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:41,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<02:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<01:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:21<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<01:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:23<01:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<01:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:25<01:56,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<01:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:27<01:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:28<01:57,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:28<01:55,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:29<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:30<01:53,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:30<01:55,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:31<01:53,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:32<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:32<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:33<01:49,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:34<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:35<01:47,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:35<01:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:36<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:36<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:37<01:40,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:38<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:39<01:41,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:39<01:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:40<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:41<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:41<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:42<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:43<01:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:43<01:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:44<01:36,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:45<01:36,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:45<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:46<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:47<01:33,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:47<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:48<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:49<01:32,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:49<01:31,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:50<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:51<01:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:51<01:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:52<01:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:53<01:27,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:54<01:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:54<01:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:55<01:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:56<01:25,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:56<01:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:57<01:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:58<01:26,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:59<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:59<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:00<01:25,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:01<01:23,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:01<01:22,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:02<01:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:03<01:20,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:03<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:04<01:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:05<01:16,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:06<01:16,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:06<01:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:07<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:08<01:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:08<01:11,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:09<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:10<01:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:10<01:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:11<01:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:12<01:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:13<01:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:13<01:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:14<01:10,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:15<01:08,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:15<01:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:16<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:17<01:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:18<01:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:18<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:19<01:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:20<01:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:20<01:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:21<01:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:22<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:23<01:07,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:24<01:07,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:25<01:08,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:25<01:07,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:26<01:07,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:27<01:06,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:28<01:05,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:29<01:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:30<01:03,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:30<01:03,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:31<01:02,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:32<01:01,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:33<01:00,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:34<00:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:34<00:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:35<00:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:36<00:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:37<00:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:38<00:56,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:39<00:55,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:39<00:54,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:40<00:53,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:41<00:52,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:42<00:51,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:43<00:50,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:43<00:48,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:44<00:48,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:45<00:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:46<00:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:46<00:42,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:47<00:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:48<00:41,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:49<00:43,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:50<00:42,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:51<00:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:51<00:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:52<00:40,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:53<00:39,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:54<00:37,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:54<00:35,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:55<00:34,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:56<00:33,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:56<00:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:57<00:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:59<00:39,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:59<00:35,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:00<00:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:01<00:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:01<00:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:02<00:27,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:03<00:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:04<00:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:04<00:24,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:05<00:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:06<00:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:06<00:21,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:07<00:21,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:08<00:20,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:09<00:19,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:09<00:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:10<00:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:11<00:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:11<00:16,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:12<00:15,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:13<00:15,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:13<00:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:14<00:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:15<00:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:15<00:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:16<00:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:17<00:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:18<00:11,  1.33it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [07:14<04:47, 143.83s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:17,  4.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:13,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:55,  1.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:49,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:50,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<02:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:07,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<02:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<02:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<02:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<01:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<02:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<02:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<02:00,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:58,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<01:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:54,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:28<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:29<01:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:30<01:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:31<01:53,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:31<01:50,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:32<01:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:33<01:49,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:33<01:49,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:34<01:47,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:35<01:44,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:36<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:37<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:37<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:38<01:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:39<01:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:39<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:40<01:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:41<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:41<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:42<01:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:43<01:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:44<01:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:44<01:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:45<01:36,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:46<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:46<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:47<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:48<01:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:48<01:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:49<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:50<01:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:50<01:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:51<01:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:52<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:52<01:26,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:53<01:26,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:54<01:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:54<01:24,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:55<01:23,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:56<01:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:57<01:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:57<01:23,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:58<01:24,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:59<01:28,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:00<01:29,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:00<01:25,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:01<01:22,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:02<01:20,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:02<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:03<01:16,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:04<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:04<01:14,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:05<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:06<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:06<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:07<01:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:08<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:08<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:09<01:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:10<01:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:10<01:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:11<01:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:12<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:13<01:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:13<01:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:14<01:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:15<01:07,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:15<01:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:16<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:17<01:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:18<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:18<01:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:19<01:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:20<01:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:21<01:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:21<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:22<01:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:23<01:06,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:24<01:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:25<01:07,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:25<01:06,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:26<01:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:27<01:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:28<01:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:29<01:03,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:30<01:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:30<01:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:31<01:00,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:32<01:00,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:33<00:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:34<00:59,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:34<00:59,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:35<00:58,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:36<00:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:37<00:55,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:38<00:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:39<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:39<00:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:40<00:51,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:41<00:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:42<00:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:43<00:48,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:43<00:44,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:44<00:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:44<00:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:45<00:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:46<00:39,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:47<00:38,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:47<00:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:48<00:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:49<00:37,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:49<00:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:50<00:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:51<00:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:52<00:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:52<00:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:53<00:34,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:54<00:34,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:55<00:33,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:56<00:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:57<00:38,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:58<00:37,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:58<00:33,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:59<00:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:00<00:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:00<00:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:01<00:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:02<00:24,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:02<00:23,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:03<00:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:04<00:21,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:05<00:21,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:05<00:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:06<00:20,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:07<00:19,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:07<00:18,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:08<00:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:09<00:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:09<00:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:10<00:15,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:11<00:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:11<00:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:12<00:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:13<00:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:14<00:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:14<00:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:15<00:11,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:16<00:10,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:16<00:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:17<00:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:18<00:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:18<00:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:19<00:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:20<00:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:20<00:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:21<00:04,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:22<00:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:22<00:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:23<00:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:24<00:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:24<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:25<00:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:26<00:00,  1.37it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [09:42<02:25, 145.68s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:11,  4.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:30,  2.28s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:05,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:55,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:19,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<02:58,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:19,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:11,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:09,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<02:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<02:04,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<02:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:21<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<02:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:23<02:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:24<02:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<02:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:25<01:59,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:26<01:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<01:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:27<01:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:28<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:29<01:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:29<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:30<01:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:31<01:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:31<01:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:32<01:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:33<01:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:33<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:34<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:35<01:47,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:36<01:48,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:36<01:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:37<01:46,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:38<01:46,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:38<01:45,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:39<01:43,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:40<01:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:40<01:43,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:41<01:41,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:42<01:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:43<01:39,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:43<01:40,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:44<01:39,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:45<01:39,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:45<01:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:46<01:37,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:47<01:36,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:47<01:34,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:48<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:49<01:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:50<01:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:50<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:51<01:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:52<01:33,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:53<01:31,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:53<01:31,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:54<01:31,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:55<01:30,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:55<01:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:56<01:27,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:57<01:26,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:57<01:26,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:58<01:24,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:59<01:22,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:00<01:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:00<01:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:01<01:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:02<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:03<01:25,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:03<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:04<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:05<01:21,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:05<01:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:06<01:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:07<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:08<01:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:08<01:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:09<01:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:10<01:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:11<01:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:11<01:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:12<01:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:13<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:14<01:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:14<01:11,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:15<01:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:16<01:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:17<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:17<01:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:18<01:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:19<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:20<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:20<01:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:21<01:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:22<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:23<01:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:23<01:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:24<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:25<01:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:26<01:10,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:27<01:18,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:28<01:11,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:28<01:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:29<01:03,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:30<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:30<00:57,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:31<00:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:32<00:54,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:32<00:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:33<00:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:34<00:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:34<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:35<00:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:36<00:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:36<00:45,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:37<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:38<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:38<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:39<00:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:40<00:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:40<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:41<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:42<00:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:42<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:43<00:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:44<00:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:44<00:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:45<00:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:46<00:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:46<00:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:47<00:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:48<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:48<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:49<00:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:50<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:50<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:51<00:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:51<00:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:52<00:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:53<00:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:53<00:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:54<00:27,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:55<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:55<00:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:56<00:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:57<00:26,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:58<00:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:58<00:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:59<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:00<00:24,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:00<00:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:01<00:23,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:02<00:22,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:02<00:21,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:03<00:20,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:04<00:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:05<00:19,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:05<00:19,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:06<00:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:07<00:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:07<00:16,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:08<00:16,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:09<00:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:09<00:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:10<00:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:11<00:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:12<00:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:12<00:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:13<00:11,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:14<00:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:14<00:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:15<00:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:16<00:08,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:17<00:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:17<00:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:18<00:06,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:19<00:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:19<00:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:20<00:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:21<00:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:22<00:02,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:22<00:02,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:23<00:01,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:24<00:00,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:24<00:00,  1.38it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [12:09<00:00, 145.94s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.88it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.94it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.03it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.89it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.94it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 81it [00:00, 118.11it/s]\u001b[A\n",
+      "Evaluating NLL: 160it [00:01, 233.73it/s]\u001b[A\n",
+      "Evaluating NLL: 241it [00:01, 347.57it/s]\u001b[A\n",
+      "Evaluating NLL: 330it [00:01, 465.87it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 349.45it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|▍| 43/100 [00:00<00:00, 425.76it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 422.31it\n",
+      " 50%|████████████████████████                        | 3/6 [26:18<29:16, 585.62s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:04,  4.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:05,  2.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:51,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:45,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:08,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:56,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<02:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<02:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<02:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<01:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<02:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:21<02:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<02:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<02:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:28<01:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:30<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:31<01:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:32<02:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:32<02:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:33<02:03,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:34<02:03,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:35<02:04,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:36<02:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:36<02:02,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:37<02:03,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:38<02:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:39<02:01,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:40<01:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:40<01:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:41<01:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:42<01:58,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:43<01:58,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:44<01:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:44<01:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:45<01:55,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:46<01:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:47<01:52,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:48<01:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:48<01:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:49<01:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:50<01:47,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:51<01:46,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:52<01:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:52<01:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:53<01:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:54<01:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:54<01:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:55<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:56<01:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:57<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:57<01:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:58<01:30,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:59<01:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:00<01:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:00<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:01<01:28,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:02<01:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:03<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:03<01:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:04<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:05<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:05<01:24,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:06<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:07<01:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:08<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:08<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:10<01:36,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:11<01:36,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:11<01:29,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:12<01:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:13<01:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:13<01:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:14<01:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:15<01:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:15<01:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:16<01:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:17<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:17<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:18<01:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:19<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:19<01:02,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:20<01:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:21<01:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:21<01:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:22<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:23<01:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:23<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:24<01:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:25<00:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:25<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:26<00:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:27<00:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:27<00:53,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:28<00:51,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:28<00:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:29<00:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:30<00:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:30<00:50,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:31<00:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:32<00:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:32<00:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:33<00:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:34<00:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:34<00:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:35<00:45,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:35<00:44,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:36<00:43,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:37<00:42,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:37<00:40,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:38<00:40,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:38<00:40,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:39<00:39,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:40<00:38,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:40<00:37,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:41<00:37,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:41<00:36,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:42<00:36,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:43<00:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:43<00:36,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:44<00:36,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:45<00:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:45<00:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:46<00:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:47<00:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:47<00:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:48<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:49<00:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:49<00:31,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:50<00:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:51<00:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:51<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:52<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:53<00:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:53<00:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:54<00:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:55<00:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:55<00:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:56<00:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:57<00:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:58<00:26,  1.37it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:00<08:03, 120.78s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:16,  4.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:34,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:04,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:54,  1.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:13,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:51,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:15,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<02:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<02:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<02:01,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<02:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:59,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<01:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<01:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<01:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<01:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:28<01:49,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:55,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:30<01:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:30<01:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:31<01:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:32<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:32<01:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:33<01:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:34<01:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:34<01:44,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:35<01:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:36<01:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:36<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:37<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:38<01:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:39<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:39<01:37,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:40<01:37,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:41<01:37,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:41<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:42<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:43<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:43<01:40,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:44<01:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:45<01:35,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:46<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:46<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:47<01:35,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:48<01:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:48<01:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:49<01:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:50<01:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:50<01:32,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:51<01:35,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:52<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:53<01:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:54<01:39,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:54<01:38,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:55<01:38,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:56<01:38,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:57<01:37,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:58<01:37,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:58<01:36,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:59<01:37,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:00<01:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:01<01:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:02<01:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:03<01:34,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:03<01:34,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:04<01:33,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:05<01:32,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:06<01:31,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:07<01:31,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:07<01:30,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:08<01:29,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:09<01:27,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:10<01:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:11<01:25,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:11<01:23,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:12<01:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:13<01:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:14<01:20,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:14<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:15<01:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:16<01:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:17<01:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:17<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:18<01:20,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:19<01:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:20<01:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:21<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:21<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:22<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:23<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:24<01:11,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:25<01:11,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:25<01:09,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:26<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:27<01:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:28<01:19,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:29<01:12,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:30<01:06,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:30<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:31<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:32<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:32<00:55,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:33<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:34<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:34<00:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:35<00:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:36<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:36<00:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:37<00:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:38<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:38<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:39<00:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:39<00:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:40<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:41<00:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:41<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:42<00:43,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:43<00:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:44<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:44<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:45<00:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:45<00:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:46<00:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:47<00:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:47<00:34,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:48<00:33,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:49<00:33,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:49<00:32,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:50<00:31,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:50<00:31,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:51<00:30,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:52<00:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:52<00:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:53<00:28,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:54<00:28,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:54<00:27,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:55<00:26,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:55<00:26,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:56<00:25,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:57<00:24,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:57<00:24,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:58<00:22,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:58<00:22,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:59<00:21,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:00<00:21,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:00<00:21,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:01<00:20,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:02<00:19,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:02<00:19,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:03<00:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:03<00:18,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:04<00:17,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:05<00:16,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:05<00:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:06<00:15,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:07<00:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:07<00:14,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:08<00:13,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:09<00:13,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:09<00:13,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:10<00:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:11<00:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:11<00:11,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:12<00:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:13<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:13<00:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:14<00:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:15<00:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:15<00:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:16<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:17<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:17<00:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:18<00:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:19<00:04,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:20<00:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:20<00:02,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:21<00:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:22<00:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:22<00:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:23<00:00,  1.39it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:26<06:46, 135.34s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:00,  4.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:01,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:44,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:09,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:14,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<02:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<02:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<01:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:56,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<01:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:21<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<02:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:23<02:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<01:58,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:25<01:54,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:53,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:27<01:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:29<01:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:30<01:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:31<01:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:31<01:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:32<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:33<01:41,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:33<01:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:34<01:40,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:35<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:36<01:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:37<01:45,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:38<01:47,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:38<01:44,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:39<01:42,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:40<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:40<01:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:41<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:42<01:41,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:42<01:39,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:43<01:36,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:44<01:35,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:45<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:45<01:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:46<01:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:47<01:46,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:48<01:47,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:49<01:47,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:49<01:47,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:50<01:46,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:51<01:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:52<01:46,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:53<01:45,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:54<01:45,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:54<01:44,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:55<01:44,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:56<01:43,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:57<01:41,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:58<01:41,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:59<01:40,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:59<01:39,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:00<01:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:01<01:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:02<01:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:03<01:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:03<01:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:04<01:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:05<01:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:06<01:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:06<01:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:07<01:22,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:08<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:09<01:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:09<01:19,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:10<01:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:11<01:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:12<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:12<01:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:13<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:14<01:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:14<01:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:15<01:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:16<01:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:17<01:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:17<01:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:18<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:19<01:14,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:20<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:20<01:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:21<01:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:22<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:23<01:11,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:24<01:23,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:25<01:16,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:25<01:09,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:26<01:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:27<01:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:27<00:59,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:28<00:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:29<00:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:29<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:30<00:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:31<00:54,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:31<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:32<00:52,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:33<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:33<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:34<00:49,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:35<00:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:36<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:36<00:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:37<00:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:38<00:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:38<00:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:39<00:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:40<00:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:40<00:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:41<00:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:42<00:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:42<00:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:43<00:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:44<00:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:44<00:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:45<00:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:46<00:37,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:46<00:37,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:47<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:48<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:48<00:35,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:49<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:50<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:50<00:34,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:51<00:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:52<00:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:52<00:30,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:53<00:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:53<00:28,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:54<00:27,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:55<00:26,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:55<00:26,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:56<00:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:57<00:24,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:57<00:24,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:58<00:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:58<00:23,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:59<00:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:00<00:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:00<00:21,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:01<00:20,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:02<00:19,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:02<00:19,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:03<00:18,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:03<00:17,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:04<00:17,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:05<00:16,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:05<00:15,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:06<00:15,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:07<00:14,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:07<00:14,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:08<00:13,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:08<00:13,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:09<00:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:10<00:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:10<00:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:11<00:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:12<00:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:12<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:13<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:14<00:08,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:14<00:07,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:15<00:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:16<00:06,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:17<00:06,  1.39it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [06:45<04:34, 137.26s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:01,  4.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:02,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:50,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:47,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:13,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:51,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:10,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<02:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<02:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:21<01:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<02:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<02:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<01:55,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:53,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:27<01:49,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:29<01:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:30<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:31<01:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:31<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:33<01:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:33<01:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:34<01:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:35<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:36<01:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:36<01:48,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:37<01:46,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:38<01:44,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:38<01:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:39<01:41,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:40<01:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:40<01:40,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:41<01:41,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:42<01:45,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:43<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:43<01:39,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:44<01:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:45<01:36,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:45<01:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:46<01:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:47<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:48<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:48<01:34,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:49<01:34,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:50<01:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:51<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:51<01:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:52<01:34,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:53<01:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:54<01:38,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:54<01:39,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:55<01:38,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:56<01:44,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:57<01:42,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:58<01:38,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:59<01:39,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:59<01:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:00<01:33,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:01<01:34,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:02<01:51,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:03<01:43,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:04<01:35,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:04<01:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:05<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:06<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:06<01:19,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:07<01:17,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:08<01:15,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:08<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:09<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:10<01:12,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:10<01:11,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:11<01:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:12<01:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:13<01:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:13<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:14<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:14<01:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:15<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:16<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:16<01:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:17<01:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:18<01:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:18<01:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:19<01:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:20<00:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:20<00:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:21<00:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:22<00:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:22<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:23<00:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:24<00:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:24<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:25<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:26<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:27<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:27<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:28<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:29<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:29<00:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:30<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:31<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:31<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:32<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:33<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:33<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:34<00:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:35<00:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:35<00:46,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:36<00:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:37<00:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:37<00:45,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:38<00:44,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:39<00:44,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:40<00:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:40<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:41<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:41<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:42<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:43<00:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:43<00:36,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:44<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:45<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:45<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:46<00:35,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:47<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:47<00:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:48<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:49<00:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:50<00:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:50<00:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:51<00:31,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:52<00:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:52<00:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:53<00:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:54<00:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:54<00:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:55<00:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:56<00:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:56<00:25,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:57<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:58<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:59<00:24,  1.39it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [08:47<02:11, 131.18s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:56,  4.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:27,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:00,  1.52s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:50,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:11,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:51,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<02:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:00,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:56,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<01:54,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<01:53,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<01:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<01:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<02:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<02:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<02:04,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<02:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<01:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:25<01:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:44,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:27<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:29<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:30<01:41,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:30<01:40,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:31<01:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:32<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:32<01:37,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:33<01:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:34<01:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:34<01:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:35<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:36<01:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:36<01:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:37<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:38<01:43,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:39<01:42,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:39<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:40<01:37,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:40<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:41<01:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:42<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:42<01:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:43<01:37,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:44<01:36,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:45<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:45<01:37,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:46<01:41,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:47<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:48<01:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:48<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:49<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:50<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:50<01:32,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:51<01:31,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:52<01:48,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:53<01:52,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:54<01:43,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:55<01:37,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:55<01:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:56<01:29,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:57<01:26,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:57<01:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:58<01:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:59<01:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:59<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:00<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:01<01:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:01<01:17,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:02<01:17,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:03<01:16,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:03<01:15,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:04<01:15,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:05<01:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:05<01:11,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:06<01:10,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:07<01:10,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:07<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:08<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:09<01:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:09<01:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:10<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:11<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:12<01:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:12<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:13<01:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:14<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:14<01:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:15<01:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:16<01:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:16<01:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:17<01:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:18<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:18<01:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:19<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:19<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:20<00:56,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:21<00:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:21<00:54,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:22<00:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:23<00:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:23<00:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:24<00:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:25<00:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:25<00:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:26<00:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:27<00:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:27<00:50,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:28<00:49,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:29<00:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:29<00:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:30<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:31<00:47,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:31<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:32<00:46,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:33<00:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:33<00:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:34<00:45,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:35<00:44,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:36<00:43,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:36<00:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:37<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:38<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:38<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:39<00:40,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:40<00:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:40<00:38,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:41<00:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:42<00:37,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:42<00:36,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:43<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:44<00:34,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:44<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:45<00:34,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:46<00:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:46<00:33,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:47<00:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:48<00:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:48<00:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:49<00:29,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:50<00:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:50<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:51<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:52<00:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:52<00:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:53<00:25,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:54<00:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:55<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:55<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:56<00:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:57<00:22,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:57<00:21,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:59<00:21,  1.42it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [10:48<00:00, 129.79s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.94it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.86it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.96it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 83it [00:00, 119.61it/s]\u001b[A\n",
+      "Evaluating NLL: 165it [00:01, 239.17it/s]\u001b[A\n",
+      "Evaluating NLL: 245it [00:01, 348.56it/s]\u001b[A\n",
+      "Evaluating NLL: 326it [00:01, 448.98it/s]\u001b[A\n",
+      "Evaluating NLL: 407it [00:01, 534.02it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 343.70it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  46%|▍| 46/100 [00:00<00:00, 459.56it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 428.37it\n",
+      " 67%|████████████████████████████████                | 4/6 [37:18<20:30, 615.09s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:38,  4.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:56,  2.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:27,  1.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:16,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:33,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:55,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:35,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:36,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:32,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:28,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:26,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:28,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:19,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:20<02:20,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:28,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:29,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:23<02:30,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:24<02:30,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:29,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:26<02:28,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:27<02:28,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:28<02:28,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:29<02:27,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:29<02:26,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:30<02:25,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:31<02:24,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:32<02:24,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:33<02:23,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:34<02:22,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:35<02:23,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:36<02:23,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:36<02:22,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:37<02:21,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:38<02:19,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:39<02:18,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:40<02:17,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:41<02:16,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:42<02:16,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:43<02:11,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:43<02:06,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:44<02:03,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:45<02:01,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:46<02:03,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:47<02:06,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:48<02:04,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:48<02:02,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:49<02:01,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:50<02:04,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:51<02:12,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:52<02:12,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:53<02:05,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:54<02:01,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:55<02:00,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:55<01:55,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:57<02:07,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:58<02:15,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:58<02:04,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:59<01:57,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [01:00<01:52,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:01<01:48,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:01<01:45,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:02<01:43,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:03<01:41,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:04<01:40,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:05<01:39,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:05<01:39,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:06<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:07<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:08<01:33,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:08<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:09<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:10<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:11<01:29,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:11<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:12<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:13<01:28,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:14<01:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:14<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:15<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:16<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:17<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:18<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:18<01:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:19<01:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:20<01:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:21<01:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:21<01:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:22<01:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:23<01:19,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:24<01:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:24<01:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:25<01:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:26<01:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:27<01:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:27<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:28<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:29<01:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:30<01:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:31<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:31<01:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:32<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:33<01:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:34<01:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:34<01:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:35<01:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:36<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:37<01:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:37<01:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:38<01:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:39<01:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:40<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:41<01:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:41<01:01,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:42<01:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:43<00:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:44<00:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:44<00:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:45<00:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:46<00:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:47<00:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:47<00:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:48<00:54,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:49<00:56,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:50<00:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:51<00:52,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:51<00:51,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:52<00:50,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:53<00:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:54<00:47,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:54<00:46,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:55<00:45,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:56<00:44,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:57<00:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:57<00:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:58<00:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:59<00:42,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [02:00<00:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [02:00<00:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [02:01<00:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [02:02<00:39,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [02:03<00:39,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [02:03<00:38,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [02:04<00:37,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [02:05<00:36,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [02:06<00:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [02:07<00:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [02:07<00:34,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:08<00:33,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:09<00:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:10<00:31,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:10<00:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:11<00:29,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:12<00:29,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:13<00:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:13<00:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:14<00:27,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:15<00:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:16<00:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:16<00:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:17<00:25,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:18<00:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:19<00:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:20<00:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:20<00:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:21<00:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:22<00:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:22<00:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:23<00:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:24<00:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:25<00:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:25<00:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:26<00:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:28<00:15,  1.22it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:30<10:01, 150.42s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:14,  4.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:03,  2.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:29,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:38,  1.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:14,  1.00s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:07,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:59,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:56,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:53,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:50,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:45,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:46,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:45,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:45,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:17<02:46,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:18<02:46,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:19<02:43,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:20<02:40,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:21<02:40,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:21<02:40,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:22<02:38,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:23<02:36,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:24<02:35,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:25<02:31,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:26<02:31,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:27<02:31,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:28<02:31,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:28<02:28,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:29<02:28,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:30<02:26,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:31<02:23,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:32<02:16,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:33<02:16,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:33<02:14,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:34<02:13,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:35<02:10,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:36<02:09,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:37<02:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:37<02:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:38<02:06,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:39<02:05,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:40<02:04,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:40<02:04,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:41<02:06,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:42<02:04,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:43<02:07,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:44<02:05,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:45<02:03,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:45<02:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:46<02:02,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:47<02:02,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:48<02:02,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:49<01:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:50<02:02,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:51<02:27,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:52<02:15,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:53<02:06,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:53<01:59,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:54<01:54,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:55<01:51,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:56<01:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:56<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:57<01:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:58<01:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:59<01:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:59<01:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [01:00<01:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [01:01<01:40,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [01:02<01:40,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [01:03<01:38,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [01:03<01:37,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:04<01:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:05<01:35,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:06<01:36,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:06<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:07<01:34,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:08<01:32,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:09<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:09<01:29,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:10<01:29,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:11<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:12<01:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:12<01:28,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:13<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:14<01:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:15<01:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:15<01:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:16<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:17<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:18<01:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:18<01:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:19<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:20<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:21<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:21<01:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:22<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:23<01:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:24<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:24<01:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:25<01:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:26<01:16,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:27<01:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:28<01:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:28<01:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:29<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:30<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:30<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:31<01:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:32<01:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:33<01:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:33<01:04,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:34<01:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:35<01:03,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:36<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:36<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:37<01:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:38<01:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:39<01:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:39<00:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:40<00:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:41<00:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:42<00:56,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:42<00:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:43<00:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:44<00:55,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:45<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:46<01:00,  1.20it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:19<06:18, 126.08s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:00,  4.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:13,  2.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:02,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:01,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:04,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:50,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:40,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:29,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:17,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<02:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:31<02:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:33<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:34<01:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:56,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:35<01:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:36<02:04,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:37<02:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<02:02,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:38<01:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:39<01:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:40<01:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<02:00,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:58,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:42<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:43<01:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:45<01:52,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:46<01:54,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:57,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:56,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<01:57,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:50<01:57,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:57,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:58,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:57,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:54<01:57,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:55<01:57,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:56<01:56,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:57<01:54,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:58<01:53,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:58<01:52,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:59<01:51,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:00<01:49,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:01<01:48,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:02<01:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:03<01:45,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:04<01:45,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:04<01:43,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:05<01:41,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:06<01:35,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:07<01:38,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:08<01:38,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:08<01:37,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:09<01:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:10<01:33,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:11<01:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:12<01:31,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:12<01:30,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:13<01:28,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:14<01:29,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:15<01:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:16<01:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:16<01:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:17<01:22,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:18<01:21,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:19<01:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:20<01:22,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:20<01:23,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:22<01:37,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:23<01:31,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:23<01:24,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:24<01:23,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:25<01:18,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:26<01:17,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:26<01:14,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:27<01:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:28<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:29<01:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:29<01:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:30<01:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:31<01:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:32<01:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:32<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:33<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:34<01:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:35<01:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:35<01:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:36<01:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:37<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:38<01:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:38<00:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:39<00:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:40<00:57,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:41<00:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:41<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:42<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:43<00:55,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:44<00:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:44<00:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:45<00:51,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:46<00:51,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:47<00:51,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:47<00:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:48<00:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:49<00:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:50<00:47,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:50<00:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:51<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:52<00:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:53<00:44,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:53<00:44,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:54<00:43,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:55<00:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:56<00:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:56<00:41,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:57<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:58<00:40,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:59<00:39,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:59<00:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [02:00<00:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [02:01<00:36,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [02:02<00:35,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [02:02<00:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [02:03<00:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [02:04<00:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:05<00:35,  1.24it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [06:27<04:13, 126.94s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:20,  4.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:57,  2.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:30,  1.68s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:36,  1.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:58,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:47,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:31<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<02:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<02:09,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<02:10,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<02:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<02:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<02:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<01:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:56,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:55,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:42<01:52,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:51,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:54,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:48<01:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:50,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:47,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<01:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:51<01:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:52<01:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:45,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:54<01:44,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:55<01:43,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:56<01:45,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:57<01:47,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:58<01:48,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:59<01:49,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [01:00<01:50,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:01<01:50,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:02<01:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:03<01:49,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:04<01:49,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:04<01:48,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:05<01:47,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:06<01:46,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:07<01:46,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:08<01:45,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:09<01:46,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:10<01:45,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:11<01:43,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:12<01:41,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:13<01:40,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:13<01:39,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:14<01:38,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:15<01:37,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:16<01:36,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:17<01:35,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:18<01:30,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:18<01:27,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:19<01:26,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:20<01:26,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:21<01:24,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:22<01:22,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:23<01:21,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:23<01:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:24<01:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:25<01:20,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:26<01:21,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:27<01:18,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:27<01:16,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:28<01:18,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:29<01:17,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:30<01:16,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:31<01:15,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:31<01:12,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:32<01:11,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:33<01:20,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:34<01:19,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:35<01:12,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:36<01:08,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:37<01:05,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:37<01:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:38<01:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:39<01:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:39<00:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:40<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:41<00:58,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:42<00:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:42<00:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:43<00:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:44<00:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:45<00:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:46<00:55,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:46<00:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:47<00:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:48<00:53,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:49<00:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:49<00:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:50<00:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:51<00:50,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:52<00:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:53<00:48,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:53<00:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:54<00:46,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:55<00:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:56<00:45,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:56<00:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:57<00:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:58<00:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:59<00:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:59<00:41,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [02:00<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [02:01<00:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [02:02<00:38,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [02:02<00:38,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [02:03<00:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [02:04<00:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [02:05<00:35,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [02:05<00:35,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [02:06<00:34,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:07<00:33,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:08<00:33,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:09<00:32,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:09<00:31,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:11<00:32,  1.22it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [08:40<02:09, 129.56s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:15,  4.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:23,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:07,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:03,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:08,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:53,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:43,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:38,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:26<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<02:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<02:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:31<02:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<02:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<02:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:33<01:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:34<01:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<01:58,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<01:57,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:36<01:57,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:37<01:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<02:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<02:05,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<02:01,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:40<01:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<01:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:52,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:43<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:49,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:50,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:46<01:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:57,  1.20it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [09:32<00:00, 114.48s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.55it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.14s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.76it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.48it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.17s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.42it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.65it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 86it [00:00, 117.73it/s]\u001b[A\n",
+      "Evaluating NLL: 172it [00:01, 239.35it/s]\u001b[A\n",
+      "Evaluating NLL: 261it [00:01, 362.12it/s]\u001b[A\n",
+      "Evaluating NLL: 350it [00:01, 474.12it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 340.91it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|▎| 37/100 [00:00<00:00, 367.42it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 390.98it\n",
+      " 83%|████████████████████████████████████████        | 5/6 [47:03<10:04, 604.28s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:05<16:45,  5.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:32,  2.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:48,  1.77s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:31,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<03:43,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:09<03:29,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:10<03:39,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:11<03:13,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:55,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:42,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:33,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:14<02:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:17<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:18<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:19<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:20<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:23<02:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:24<02:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:25<02:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:30<02:01,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<01:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<01:58,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:33<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<01:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:38<01:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:50,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:41<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:43<01:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:45,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:45,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:46<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:40,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:48<01:38,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:49<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:50<01:38,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:38,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:51<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:52<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:54<01:34,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:55<01:36,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:56<01:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:57<01:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:58<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:59<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:59<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:00<01:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:01<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:02<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:02<01:32,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:03<01:30,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:04<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:05<01:27,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:05<01:25,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:06<01:26,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:07<01:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:08<01:24,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:08<01:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:09<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:10<01:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:11<01:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:11<01:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:12<01:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:13<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:13<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:14<01:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:15<01:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:16<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:16<01:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:17<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:18<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:19<01:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:19<01:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:20<01:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:21<01:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:21<01:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:22<01:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:23<01:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:24<01:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:24<01:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:25<01:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:26<01:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:27<01:03,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:27<01:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:28<01:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:29<01:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:30<01:04,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:30<01:06,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:31<01:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:32<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:33<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:33<00:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:34<00:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:35<00:56,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:36<00:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:36<00:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:37<00:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:38<00:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:38<00:52,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:39<00:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:40<00:50,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:41<00:49,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:41<00:48,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:42<00:50,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:43<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:44<00:51,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:45<00:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:45<00:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:46<00:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:47<00:46,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:48<00:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:48<00:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:49<00:43,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:50<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:51<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:51<00:41,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:52<00:43,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:53<00:40,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:54<00:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:54<00:38,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:55<00:38,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:56<00:38,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:57<00:37,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:58<00:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:58<00:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:59<00:36,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:00<00:36,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:01<00:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:02<00:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:02<00:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:03<00:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:04<00:32,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:05<00:36,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:06<00:33,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:07<00:30,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:08<00:28,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:08<00:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:09<00:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:10<00:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:10<00:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:11<00:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:12<00:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:13<00:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:13<00:19,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:14<00:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:15<00:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:16<00:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:16<00:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:17<00:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:18<00:15,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:18<00:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:19<00:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:20<00:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:21<00:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:21<00:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:22<00:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:23<00:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:24<00:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:24<00:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:25<00:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:26<00:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:26<00:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:27<00:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:28<00:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:29<00:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:29<00:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:30<00:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:31<00:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:32<00:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:32<00:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:33<00:00,  1.30it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:35<10:22, 155.64s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:23,  4.34s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:30,  2.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:17,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:06,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:26,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:01,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:34,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<02:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:21<02:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:24<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<02:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:26<02:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<02:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<01:59,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<01:57,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:29<01:57,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<01:57,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:31<01:54,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:32<01:53,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:53,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:53,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:34<01:52,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:52,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:36<01:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:36<01:50,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:38<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:39<01:49,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:39<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:40<01:48,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:41<01:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:42<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:42<01:45,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:43<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:44<01:42,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:44<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:45<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:46<01:40,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:47<01:40,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:47<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:48<01:38,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:49<01:38,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:50<01:42,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:50<01:48,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:51<01:43,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:52<01:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:53<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:53<01:33,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:54<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:55<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:55<01:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:56<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:57<01:33,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:58<01:31,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:58<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:59<01:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:00<01:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:00<01:25,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:01<01:26,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:02<01:24,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:03<01:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:03<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:04<01:31,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:05<01:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:06<01:25,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:07<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:07<01:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:08<01:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:09<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:10<01:21,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:10<01:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:11<01:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:12<01:21,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:13<01:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:14<01:23,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:14<01:24,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:15<01:24,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:16<01:25,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:17<01:24,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:18<01:24,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:19<01:22,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:20<01:22,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:20<01:21,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:21<01:20,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:22<01:19,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:23<01:18,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:24<01:18,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:25<01:17,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:26<01:16,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:26<01:16,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:27<01:16,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:28<01:14,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:29<01:13,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:30<01:12,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:31<01:11,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:31<01:10,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:32<01:09,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:33<01:08,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:34<01:08,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:35<01:05,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:36<01:02,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:37<01:06,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:37<01:03,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:39<00:59,  1.26it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:17<06:11, 123.81s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:44,  4.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:01,  2.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:25,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:30,  1.38s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<04:20,  1.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:09<03:39,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<03:11,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<02:53,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:41,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:18<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:19<02:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:20<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:21<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:22<02:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:23<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:24<02:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:25<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:26<02:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:27<02:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:27<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:28<02:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:29<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:29<02:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:30<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:31<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:32<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:32<01:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:33<01:57,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:34<02:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:35<02:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:35<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:36<01:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:37<01:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:38<01:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:39<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:39<01:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:40<01:56,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:41<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:42<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:42<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:43<01:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:44<01:54,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:45<01:53,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:45<01:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:46<01:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:47<01:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:48<01:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:48<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:49<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:50<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:51<01:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:53<01:39,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:32,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:58<01:32,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:58<01:30,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:59<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:00<01:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:01<01:34,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:32,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:02<01:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:03<01:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:04<01:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:04<01:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:05<01:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:06<01:23,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:22,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:07<01:21,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:08<01:21,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:09<01:20,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:09<01:20,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:10<01:19,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:11<01:19,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:12<01:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:12<01:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:13<01:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:14<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:15<01:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:15<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:16<01:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:17<01:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:18<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:18<01:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:19<01:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:20<01:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:20<01:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:21<01:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:22<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:23<01:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:23<01:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:24<01:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:25<01:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:25<01:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:26<01:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:27<01:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:28<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:28<01:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:29<01:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:30<01:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:31<01:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:31<00:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:32<00:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:33<00:58,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:33<00:57,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:34<00:57,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:35<00:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:36<00:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:36<00:53,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:37<00:52,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:38<00:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:38<00:50,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:39<00:50,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:40<00:50,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:41<00:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:41<00:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:42<00:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:43<00:48,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:44<00:47,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:44<00:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:45<00:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:46<00:46,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:47<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:47<00:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:48<00:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:49<00:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:50<00:43,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:50<00:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:51<00:44,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:52<00:44,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:53<00:43,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:54<00:43,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:56<00:39,  1.28it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [06:15<04:02, 121.30s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:51,  4.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<08:20,  2.53s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:54,  1.80s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:07<04:38,  1.42s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:08<03:59,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:09<03:32,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:10<03:14,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:10<03:01,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:11<02:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:12<02:40,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:13<02:33,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:13<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:14<02:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:15<02:29,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:16<02:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:16<02:24,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:17<02:27,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:18<02:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:19<02:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:20<02:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:20<02:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:21<02:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:22<02:21,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:23<02:21,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:24<02:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:25<02:25,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:26<02:44,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:27<02:32,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:27<02:22,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:28<02:15,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:29<02:12,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:29<02:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:30<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:31<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:32<02:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:32<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:33<02:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:34<02:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:35<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:35<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:36<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:37<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:38<01:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:38<01:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:39<01:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:40<01:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:40<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:41<01:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:42<01:51,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:43<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:43<01:48,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:44<01:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:45<01:45,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:46<01:44,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:46<01:44,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:47<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:48<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:48<01:44,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:49<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:50<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:51<01:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:51<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:52<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:53<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:54<01:36,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:54<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:55<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:56<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:33,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:57<01:33,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:58<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:59<01:33,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:59<01:31,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [01:00<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:01<01:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:01<01:28,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:03<01:26,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:04<01:26,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:04<01:24,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:05<01:24,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:06<01:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:06<01:23,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:07<01:22,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:08<01:22,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:22,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:09<01:22,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:10<01:20,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:11<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:11<01:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:12<01:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:14<01:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:14<01:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:15<01:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:17<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:17<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:18<01:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:19<01:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:20<01:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:20<01:12,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:21<01:11,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:22<01:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:23<01:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:23<01:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:24<01:06,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:25<01:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:25<01:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:26<01:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:27<01:03,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:28<01:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:28<01:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:29<01:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:30<01:00,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:30<00:59,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:31<00:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:32<00:58,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:33<00:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:33<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:34<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:35<01:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:36<01:00,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:37<01:00,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:37<00:59,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:38<00:58,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:39<00:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:40<00:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:40<00:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:41<00:52,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:42<00:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:42<00:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:43<00:49,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:44<00:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:45<00:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:46<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:46<00:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:47<00:47,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:48<00:45,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:49<00:44,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:49<00:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:50<00:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:51<00:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:52<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:52<00:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:53<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:54<00:38,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:54<00:37,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:55<00:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:56<00:35,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:56<00:34,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:57<00:34,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:58<00:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:59<00:34,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:59<00:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:00<00:33,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:01<00:32,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:02<00:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:03<00:31,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:03<00:32,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:04<00:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:05<00:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:06<00:28,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:06<00:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:07<00:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:08<00:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:09<00:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:10<00:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:10<00:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:11<00:23,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:12<00:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:13<00:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:13<00:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:14<00:20,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:15<00:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:16<00:18,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:17<00:18,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:18<00:18,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:18<00:17,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:19<00:16,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:20<00:15,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:21<00:14,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:22<00:13,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:22<00:12,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:23<00:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:24<00:10,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:25<00:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:26<00:11,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:27<00:10,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:28<00:08,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:28<00:07,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:29<00:06,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:30<00:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:30<00:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:31<00:03,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:32<00:02,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:33<00:02,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:33<00:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:34<00:00,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:35<00:00,  1.29it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [08:53<02:15, 135.77s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<13:56,  4.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:23,  2.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:03,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:56,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:21,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:58,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:27,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<02:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<02:06,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:21<02:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:23<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:24<02:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<02:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:25<02:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:26<02:00,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<01:59,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<01:58,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:28<01:56,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:29<01:56,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<01:56,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:30<01:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:31<01:55,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:32<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:33<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:34<01:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:36<01:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:36<01:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:38<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:39<01:50,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:39<01:48,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:40<01:47,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:41<01:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:42<01:51,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:42<01:49,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:43<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:44<01:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:44<01:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:45<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:46<01:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:47<01:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:47<01:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:48<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:49<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:50<01:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:50<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:51<01:36,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:52<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:52<01:34,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:53<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:54<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:55<01:32,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:55<01:32,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:56<01:30,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:57<01:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:57<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:58<01:28,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:59<01:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:00<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:00<01:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:01<01:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:02<01:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:02<01:23,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:03<01:22,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:04<01:21,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:05<01:21,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:05<01:21,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:06<01:20,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:07<01:19,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:07<01:18,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:08<01:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:09<01:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:10<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:10<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:11<01:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:12<01:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:13<01:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:13<01:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:14<01:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:15<01:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:15<01:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:16<01:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:17<01:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:18<01:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:18<01:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:19<01:08,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:20<01:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:20<01:06,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:21<01:06,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:22<01:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:23<01:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:24<01:07,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:24<01:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:25<01:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:26<01:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:27<01:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:27<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:28<01:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:29<01:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:29<00:58,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:30<00:57,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:31<00:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:32<01:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:33<01:00,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:33<01:01,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:34<01:01,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:35<01:01,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:36<01:01,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:37<00:59,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:38<00:58,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:39<00:58,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:39<00:57,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:40<00:56,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:41<00:55,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:42<00:55,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:43<00:55,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:44<00:54,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:44<00:53,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:45<00:51,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:46<00:51,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:47<00:50,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:48<00:49,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:49<00:49,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:49<00:48,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:50<00:47,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:51<00:46,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:52<00:44,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:53<00:43,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:53<00:41,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:54<00:41,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:55<00:40,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:56<00:39,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:57<00:38,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:58<00:37,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:58<00:36,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:59<00:35,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:00<00:34,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:01<00:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:01<00:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:02<00:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:03<00:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:04<00:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:05<00:32,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:06<00:31,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:06<00:30,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:07<00:28,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:08<00:27,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:09<00:31,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:10<00:30,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:11<00:26,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:12<00:24,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:12<00:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:13<00:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:14<00:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:14<00:19,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:15<00:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:16<00:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:17<00:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:17<00:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:18<00:15,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:19<00:14,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:19<00:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:20<00:12,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:21<00:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:22<00:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:22<00:10,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:23<00:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:24<00:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:24<00:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:25<00:07,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:26<00:07,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:27<00:06,  1.29it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [11:23<00:00, 136.69s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.90it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.56it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.81it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.18s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.66it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.69it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.24it/s]\u001b[A\n",
+      "Evaluating NLL: 85it [00:00, 127.45it/s]\u001b[A\n",
+      "Evaluating NLL: 165it [00:01, 246.36it/s]\u001b[A\n",
+      "Evaluating NLL: 249it [00:01, 365.70it/s]\u001b[A\n",
+      "Evaluating NLL: 332it [00:01, 468.92it/s]\u001b[A\n",
+      "Evaluating NLL: 414it [00:01, 553.55it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 354.39it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:00, 385.14it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 375.55it\n",
+      "100%|████████████████████████████████████████████████| 6/6 [58:39<00:00, 586.53s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "noise sigma 6.25e-06\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:55: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 0.001\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/gaussian_process.py:55: UserWarning: Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.Try raising the eigenvalue floor than the current value of 0.01\n",
+      "  warnings.warn('Covariance matrix is not positive definite even after applying eigenvalue floor. This indicates numerical error.' +\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "trying eigenvalue floor of 0.01\n",
+      "trying eigenvalue floor of 0.1\n",
+      "Initial validation NLL: 42.48\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: 100%|████████████████████████████████████████| 1/1 [00:01<00:00,  1.47s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: validation NLL: 9.44\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 255.07it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 2: validation NLL: 9.37\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 156.08it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 3: validation NLL: 9.35\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 267.56it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 4: validation NLL: 9.35\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 308.54it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 205.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 6: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 278.69it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 7: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 291.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 8: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: 100%|███████████████████████████████████████| 1/1 [00:00<00:00, 327.99it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 9: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 363.68it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 226.16it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 11: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 267.89it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 12: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 310.69it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 13: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 236.18it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 14: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 291.21it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 309.61it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 16: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 192.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 17: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 359.93it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 18: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 156.87it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 19: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 265.46it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 229.15it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 21: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 357.30it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 22: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: 100%|██████████████████████████████████████| 1/1 [00:00<00:00, 356.75it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 23: validation NLL: 9.34\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "precomputing masks and variances: 100%|██████████████| 4/4 [00:00<00:00, 409.78it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "computing log likelihoods: 100%|█████████████████████| 4/4 [00:00<00:00, 395.79it/s]\n",
+      "precomputing masks and variances: 100%|██████████████| 4/4 [00:00<00:00, 386.98it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating likelihood\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "computing log likelihoods: 100%|█████████████████████| 4/4 [00:00<00:00, 375.46it/s]\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 219.67it\n",
+      "  0%|                                                         | 0/6 [00:00<?, ?it/s]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:16,  4.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:37,  2.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:17,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:09,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:32,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:08,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:52,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:42,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:12<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:15<02:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:10,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<01:57,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<01:56,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<01:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<01:50,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:29<01:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<02:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<02:22,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<02:07,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:32<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:34<01:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:38,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:35<01:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:36<01:33,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:32,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:37<01:30,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:38<01:29,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:38<01:28,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:39<01:28,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:40<01:27,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:40<01:26,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:41<01:26,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:41<01:26,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:43<01:50,  1.30it/s]\n",
+      "\n",
+      "Training models:  20%|██████▍                         | 1/5 [00:45<03:01, 45.44s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:55,  4.50s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:19,  2.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:51,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:41,  1.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:03,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:39,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:14,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<02:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<01:56,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<01:53,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:50,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<01:48,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:12<01:45,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:44,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:13<01:43,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:14<01:43,  1.76it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:14<01:42,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:15<01:41,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:16<01:40,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:16<01:38,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:17<01:38,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:17<01:37,  1.80it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:18<01:37,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:18<01:37,  1.79it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:19<01:35,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:19<01:36,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:20<01:36,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:21<01:36,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:21<01:36,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:22<01:37,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:22<01:34,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:23<01:31,  1.81it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:23<01:29,  1.85it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:24<01:30,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:24<01:27,  1.86it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:25<01:26,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:26<01:25,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:26<01:25,  1.88it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:27<01:26,  1.84it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:27<01:26,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:28<01:28,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:28<01:25,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:29<01:24,  1.82it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:29<01:28,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:30<01:27,  1.74it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:31<01:25,  1.77it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:31<01:25,  1.78it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:32<01:38,  1.52it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▊                   | 2/5 [01:20<01:57, 39.32s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:21,  4.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:01,  2.13s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:41,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:35,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<02:59,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:07<02:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<01:58,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<01:56,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:10<01:54,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:51,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:11<01:49,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:12<01:48,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:48,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:13<01:49,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:14<01:47,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:14<01:46,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:15<01:44,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:16<01:44,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:16<01:43,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:17<01:43,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:17<01:43,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:18<01:44,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:18<01:43,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:19<01:42,  1.68it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:20<01:43,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:20<01:43,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:21<01:40,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:21<01:38,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:22<01:37,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:23<01:36,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:23<01:34,  1.75it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:24<01:35,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:24<01:35,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:25<01:34,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:26<01:35,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:26<01:35,  1.69it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:27<01:33,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:27<01:32,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:28<01:32,  1.71it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:28<01:30,  1.73it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:29<01:34,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:30<01:37,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:31<01:45,  1.47it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████▏            | 3/5 [01:54<01:13, 36.72s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:04,  4.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<06:54,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:36,  1.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:05<03:31,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<02:57,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:36,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:07<02:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<02:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<02:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<01:56,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:12<01:52,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:53,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:55,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:14<01:54,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:54,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:16<01:52,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:49,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:17<01:46,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:18<01:43,  1.70it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:45,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:19<01:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:20<01:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:59,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<02:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:22<02:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<02:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:24<02:00,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<02:00,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:59,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:26<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:27<01:58,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:28<01:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:29<01:57,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:30<01:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:30<01:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:31<01:57,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:32<01:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:33<01:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:33<01:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:34<01:53,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:35<01:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:35<01:53,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:36<01:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:37<01:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:38<01:53,  1.31it/s]\n",
+      "\n",
+      "Training models:  80%|█████████████████████████▌      | 4/5 [02:35<00:38, 38.49s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:52,  4.78s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:44,  2.35s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:06,  1.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:56,  1.21s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:27,  1.06s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:53,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:09,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:36,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:26,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<02:03,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<01:51,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:21<01:48,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<01:47,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<01:46,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:23<01:45,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<01:44,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:41,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:25<01:42,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:41,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<01:41,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:41,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:39,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:28<01:39,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:37,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:34,  1.72it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:30<02:05,  1.29it/s]\n",
+      "\n",
+      "Training models: 100%|████████████████████████████████| 5/5 [03:08<00:00, 37.62s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.67it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.85it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.86it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.69it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 72it [00:01, 97.05it/s]\u001b[A\n",
+      "Evaluating NLL: 157it [00:01, 218.82it/s]\u001b[A\n",
+      "Evaluating NLL: 246it [00:01, 344.55it/s]\u001b[A\n",
+      "Evaluating NLL: 336it [00:01, 461.01it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 331.10it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 416.97it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 419.31it\n",
+      " 17%|████████                                        | 1/6 [03:19<16:37, 199.57s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:09,  4.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:10,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:49,  1.47s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:06,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:43,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:09<02:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:00,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:02,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:59,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<02:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:53,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:54,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:16<01:54,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:55,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:18<01:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:20<01:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:22<01:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:46,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:24<01:53,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<01:57,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:26<01:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<01:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:28<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:28<01:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:29<01:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:30<01:40,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:30<01:39,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:31<01:39,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:31<01:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:32<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:33<01:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:33<01:38,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:34<01:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:35<01:35,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:35<01:35,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:36<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:37<01:34,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:37<01:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:38<01:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:39<01:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:39<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:40<01:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:40<01:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:41<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:42<01:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:42<01:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:43<01:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:44<01:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:44<01:25,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:45<01:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:46<01:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:46<01:23,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:47<01:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:47<01:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:48<01:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:49<01:23,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:49<01:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:50<01:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:51<01:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:51<01:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:52<01:26,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:53<01:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:54<01:25,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:54<01:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:55<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:56<01:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:56<01:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:57<01:15,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:57<01:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [00:58<01:13,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [00:59<01:11,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [00:59<01:11,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:00<01:10,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:01<01:09,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:01<01:09,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:02<01:09,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:02<01:07,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:03<01:06,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:04<01:05,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:04<01:05,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:05<01:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:06<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:06<01:06,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:07<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:08<01:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:08<01:06,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:09<01:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:10<01:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:11<01:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:11<01:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:12<01:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:13<01:01,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:13<01:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:14<00:57,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:14<00:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:15<00:59,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:16<01:00,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:16<00:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:17<00:55,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:18<00:55,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:18<00:56,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:19<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:20<00:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:21<00:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:21<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:22<00:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:23<00:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:24<01:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:25<01:10,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:26<01:11,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:27<01:02,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:27<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:28<00:53,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:29<00:50,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:29<00:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:30<00:45,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:30<00:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:31<00:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:32<00:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:32<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:33<00:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:34<00:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:34<00:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:35<00:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:36<00:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:36<00:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:37<00:36,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:37<00:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:38<00:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:39<00:34,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:39<00:34,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:40<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:41<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:41<00:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:42<00:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:43<00:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:43<00:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:44<00:30,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:45<00:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:45<00:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:46<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:47<00:27,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:47<00:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:48<00:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:48<00:25,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:49<00:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:50<00:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:50<00:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:51<00:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:52<00:22,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:52<00:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:53<00:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:54<00:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:54<00:19,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [01:55<00:18,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [01:55<00:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [01:56<00:17,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [01:57<00:17,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [01:57<00:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [01:58<00:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [01:59<00:15,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [01:59<00:14,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:00<00:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:01<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:01<00:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:02<00:12,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:03<00:11,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:03<00:10,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:04<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:04<00:09,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:05<00:08,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:06<00:07,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:06<00:07,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:07<00:06,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:08<00:06,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:08<00:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:09<00:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:09<00:04,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:10<00:03,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:11<00:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:11<00:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:12<00:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:13<00:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:13<00:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:14<00:00,  1.49it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:16<09:06, 136.72s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:37,  4.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:21,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:04,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:51,  1.18s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:44,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<01:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:55,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<01:55,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:59,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:59,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:55,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:19<01:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:52,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:21<01:51,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:23<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:24<01:48,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<01:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:26<01:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:28<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:28<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:29<01:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:30<01:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:30<01:49,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:31<01:51,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:32<01:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:32<01:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:33<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:34<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:34<01:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:35<01:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:36<01:36,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:36<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:37<01:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:38<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:38<01:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:39<01:33,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:40<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:40<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:41<01:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:41<01:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:42<01:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:43<01:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:44<01:38,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:44<01:35,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:45<01:36,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:46<01:34,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:46<01:32,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:47<01:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:48<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:49<01:33,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:49<01:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:50<01:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:50<01:24,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:51<01:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:52<01:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:52<01:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:53<01:29,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:54<01:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:55<01:31,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:56<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:56<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:57<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:58<01:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:59<01:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:00<01:30,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:00<01:29,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:01<01:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:02<01:28,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:03<01:27,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:03<01:26,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:04<01:25,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:05<01:25,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:06<01:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:07<01:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:07<01:23,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:08<01:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:09<01:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:10<01:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:11<01:20,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:11<01:19,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:12<01:19,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:13<01:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:14<01:16,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:14<01:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:15<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:16<01:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:16<01:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:17<01:06,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:18<01:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:19<01:04,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:19<01:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:20<01:03,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:21<01:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:22<01:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:22<01:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:23<01:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:24<01:00,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:24<00:58,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:25<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:26<01:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:27<00:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:27<00:57,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:28<00:58,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:29<00:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:30<00:57,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:30<00:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:32<01:01,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:33<01:12,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:34<01:06,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:34<01:00,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:35<00:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:36<00:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:36<00:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:37<00:46,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:37<00:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:38<00:42,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:39<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:39<00:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:40<00:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:41<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:41<00:39,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:42<00:39,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:43<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:43<00:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:44<00:35,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:45<00:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:45<00:34,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:46<00:33,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:46<00:33,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:47<00:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:48<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:48<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:49<00:30,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:50<00:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:50<00:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:51<00:28,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:52<00:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:52<00:27,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:53<00:26,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:54<00:26,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:54<00:25,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:55<00:24,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:55<00:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:56<00:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:57<00:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:57<00:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:58<00:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:59<00:21,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:59<00:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:01<00:22,  1.40it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:20<06:27, 129.03s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<13:55,  4.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:04<07:09,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:52,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:46,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:49,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:12,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:11<01:59,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:13<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:58,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:56,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:53,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:51,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:18<01:51,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:52,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:20<01:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:48,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:22<01:44,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:46,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:24<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<01:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:25<01:44,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<01:49,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:28<01:46,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:28<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:29<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:29<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:30<01:39,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:31<01:36,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:31<01:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:32<01:36,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:33<01:37,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:33<01:37,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:34<01:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:34<01:35,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:35<01:34,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:36<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:36<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:37<01:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:38<01:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:38<01:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:39<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:40<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:40<01:31,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:41<01:30,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:42<01:29,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:42<01:27,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:43<01:27,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:43<01:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:44<01:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:45<01:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:45<01:24,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:46<01:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:47<01:24,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:47<01:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:48<01:23,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:49<01:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:49<01:22,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:50<01:21,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:50<01:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:51<01:19,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:52<01:20,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:52<01:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:53<01:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:54<01:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:54<01:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:55<01:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:56<01:15,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:56<01:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:57<01:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [00:58<01:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [00:58<01:14,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [00:59<01:13,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:00<01:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:00<01:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:01<01:12,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:02<01:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:02<01:10,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:03<01:10,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:04<01:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:04<01:07,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:05<01:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:05<01:04,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:06<01:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:07<01:11,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:08<01:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:08<01:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:09<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:10<01:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:10<01:03,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:11<01:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:12<01:06,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:12<01:05,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:13<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:14<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:14<00:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:15<00:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:16<00:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:16<01:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:17<01:02,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:18<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:19<01:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:20<01:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:20<01:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:21<01:03,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:22<01:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:23<01:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:23<01:00,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:24<00:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:25<00:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:26<00:58,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:27<00:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:27<00:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:28<00:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:29<00:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:30<00:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:30<00:53,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:31<00:52,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:32<00:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:33<00:51,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:34<00:51,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:34<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:35<00:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:36<00:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:37<00:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:37<00:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:38<00:44,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:39<00:44,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:40<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:40<00:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:41<00:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:42<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:43<00:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:43<00:38,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:44<00:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:45<00:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:45<00:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:46<00:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:47<00:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:48<00:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:48<00:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:49<00:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:50<00:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:51<00:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:51<00:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:52<00:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:53<00:27,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:53<00:25,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:54<00:26,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:55<00:31,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:56<00:30,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:57<00:27,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:57<00:24,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:58<00:22,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:59<00:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [01:59<00:19,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:00<00:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:00<00:18,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:01<00:17,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:02<00:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:02<00:16,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:03<00:15,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:04<00:15,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:04<00:14,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:05<00:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:06<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:06<00:12,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:07<00:11,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:08<00:10,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:08<00:10,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:09<00:09,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:09<00:09,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:10<00:08,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:11<00:08,  1.43it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [06:34<04:22, 131.33s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:23,  4.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:34,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:03,  1.54s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:52,  1.19s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:18,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<02:57,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:40,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:31,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:12,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:11,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:09,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:06,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<02:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<01:55,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<01:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<01:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:47,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<01:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:27<01:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:29<01:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:30<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:31<01:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:32<01:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:33<01:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:34<01:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:34<01:48,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:35<01:48,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:36<01:45,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:36<01:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:37<01:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:38<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:38<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:39<01:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:40<01:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:40<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:41<01:32,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:42<01:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:42<01:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:43<01:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:44<01:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:44<01:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:45<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:46<01:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:46<01:31,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:47<01:30,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:48<01:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:48<01:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:49<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:50<01:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:50<01:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:51<01:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:52<01:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:52<01:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:53<01:20,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:53<01:17,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:54<01:15,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:55<01:15,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:55<01:15,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:56<01:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:57<01:16,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:57<01:16,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:58<01:15,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:59<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:59<01:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:00<01:19,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:01<01:23,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:02<01:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:02<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:03<01:19,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:04<01:17,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:04<01:14,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:05<01:14,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:06<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:06<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:07<01:11,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:08<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:08<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:09<01:05,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:10<01:04,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:10<01:03,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:11<01:02,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:12<01:00,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:12<00:59,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:13<01:00,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:13<01:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:14<01:05,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:15<01:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:16<01:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:16<01:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:17<01:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:18<01:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:19<01:04,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:19<01:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:20<01:00,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:21<00:59,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:21<00:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:22<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:23<00:57,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:23<00:58,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:24<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:25<01:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:26<01:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:27<00:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:27<00:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:28<00:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:29<00:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:30<00:58,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:31<00:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:31<00:57,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:32<00:56,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:33<00:54,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:34<00:53,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:35<00:52,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:35<00:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:36<00:50,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:37<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:38<00:49,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:39<00:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:39<00:48,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:40<00:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:41<00:46,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:42<00:46,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:42<00:45,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:43<00:44,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:44<00:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:45<00:43,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:46<00:41,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:47<00:42,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:47<00:40,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:48<00:39,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:49<00:37,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:50<00:37,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:50<00:37,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:51<00:35,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:52<00:34,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:53<00:33,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:53<00:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:54<00:31,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:55<00:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:56<00:32,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:57<00:30,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:57<00:29,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:58<00:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:59<00:29,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:00<00:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:00<00:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:02<00:30,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:03<00:29,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:03<00:26,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:04<00:23,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:05<00:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:05<00:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:06<00:19,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:07<00:18,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:07<00:17,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:08<00:16,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:09<00:16,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:09<00:15,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:10<00:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:11<00:14,  1.36it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [08:48<02:12, 132.42s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:26,  4.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:15,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<04:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:48,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:06<03:07,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:47,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:08<02:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:09<02:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:10<02:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:11<02:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:01,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:12<02:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:13<01:59,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<01:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:14<01:56,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:15<01:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:15<01:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:16<01:54,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:17<01:53,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:17<01:50,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:18<01:48,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:18<01:48,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:19<01:49,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:20<01:49,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:20<01:49,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:21<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:22<01:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:22<01:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:23<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:24<01:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:24<01:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:25<01:47,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:26<01:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:26<01:44,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:27<01:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:27<01:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:28<01:42,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:29<01:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:29<01:43,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:30<01:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:31<01:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:31<01:39,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:32<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:33<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:33<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:34<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:35<01:37,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:35<01:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:36<01:35,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:36<01:33,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:37<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:38<01:32,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:38<01:31,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:39<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:40<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:40<01:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:41<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:42<01:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:42<01:29,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:43<01:29,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:44<01:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:44<01:26,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:45<01:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:45<01:24,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:46<01:23,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:47<01:23,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:47<01:21,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:48<01:21,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:49<01:22,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:49<01:22,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:50<01:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:51<01:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:51<01:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:52<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:53<01:19,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:53<01:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:54<01:17,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:55<01:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:55<01:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:56<01:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [00:56<01:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [00:57<01:15,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [00:58<01:14,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [00:58<01:13,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [00:59<01:12,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:00<01:11,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:00<01:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:01<01:12,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:02<01:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:02<01:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:03<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:04<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:04<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:05<01:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:06<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:06<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:07<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:08<01:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:09<01:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:09<01:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:10<01:03,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:10<01:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:11<01:00,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:12<01:00,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:12<00:59,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:13<00:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:14<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:14<00:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:15<00:56,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:16<00:56,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:16<00:55,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:17<00:52,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:17<00:51,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:18<00:50,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:19<00:49,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:19<00:51,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:20<00:50,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:21<00:57,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:21<00:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:22<00:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:23<00:55,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:24<00:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:24<00:50,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:25<00:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:26<00:52,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:26<00:50,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:27<00:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:28<00:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:28<00:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:29<00:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:30<00:45,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:30<00:46,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:31<00:46,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:32<00:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:32<00:42,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:33<00:42,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:34<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:35<00:41,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:35<00:41,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:36<00:39,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:37<00:40,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:38<00:42,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:38<00:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:39<00:40,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:40<00:38,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:40<00:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:41<00:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:42<00:35,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:43<00:40,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:44<00:43,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:45<00:38,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:45<00:34,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:46<00:32,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:46<00:30,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:47<00:29,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:48<00:28,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:48<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:49<00:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:50<00:25,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:50<00:24,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:51<00:23,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:52<00:23,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:52<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:53<00:22,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:54<00:21,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [01:54<00:20,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [01:55<00:20,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [01:56<00:19,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [01:56<00:18,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [01:57<00:17,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [01:57<00:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [01:58<00:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [01:59<00:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [01:59<00:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:00<00:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:01<00:14,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:01<00:13,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:02<00:12,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:03<00:12,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:03<00:11,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:04<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:05<00:10,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:05<00:09,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:06<00:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:07<00:08,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:07<00:07,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:08<00:07,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:08<00:06,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:09<00:05,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:10<00:05,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:10<00:04,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:11<00:03,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:12<00:03,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:12<00:02,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:13<00:01,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:13<00:01,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:14<00:00,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:15<00:00,  1.48it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [11:06<00:00, 133.21s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.68it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.56it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.20it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.81it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 88it [00:00, 126.12it/s]\u001b[A\n",
+      "Evaluating NLL: 168it [00:01, 240.77it/s]\u001b[A\n",
+      "Evaluating NLL: 250it [00:01, 353.57it/s]\u001b[A\n",
+      "Evaluating NLL: 337it [00:01, 465.65it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 349.61it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|▍| 44/100 [00:00<00:00, 436.59it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 424.24it\n",
+      " 33%|████████████████                                | 2/6 [14:37<32:02, 480.68s/it]\n",
+      "Training models:   0%|                                        | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:06,  4.56s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:33,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:05,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:04,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:00,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:14,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:12<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:14<02:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:16<02:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:17<01:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<01:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:18<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:19<01:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:20<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:20<01:57,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:21<01:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:22<02:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:22<01:58,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:23<01:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:24<01:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:24<01:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:25<01:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:26<01:46,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:26<01:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:27<01:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:28<01:44,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:28<01:42,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:29<01:42,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:29<01:45,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:30<01:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:31<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:31<01:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:32<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:33<01:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:33<01:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:34<01:49,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:35<01:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:36<01:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:37<01:49,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:37<01:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:38<01:44,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:39<01:42,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:39<01:40,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:40<01:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:41<01:38,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:41<01:37,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:42<01:36,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:43<01:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:43<01:35,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:44<01:34,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:45<01:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:45<01:32,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:46<01:29,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:46<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:47<01:26,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:48<01:32,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:49<01:32,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:49<01:34,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:50<01:32,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:51<01:30,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:51<01:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:52<01:30,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:53<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:54<01:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:54<01:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:55<01:28,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:56<01:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [00:56<01:26,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [00:57<01:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [00:58<01:28,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [00:59<01:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [00:59<01:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:00<01:23,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:01<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:02<01:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:02<01:23,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:03<01:22,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:04<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:05<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:05<01:20,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:06<01:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:07<01:19,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:07<01:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:08<01:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:09<01:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:10<01:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:11<01:23,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:11<01:20,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:12<01:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:13<01:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:14<01:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:14<01:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:15<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:16<01:25,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:17<01:24,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:18<01:17,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:19<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:19<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:20<01:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:21<01:03,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:21<01:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:22<01:01,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:23<01:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:23<01:00,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:24<01:00,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:25<00:59,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:25<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:26<00:56,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:27<00:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:27<00:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:28<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:29<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:29<00:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:30<00:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:31<00:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:32<00:51,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:32<00:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:33<00:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:34<00:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:34<00:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:35<00:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:35<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:36<00:43,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:37<00:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:38<00:44,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:38<00:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:39<00:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:40<00:44,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:40<00:42,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:41<00:41,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:42<00:41,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:42<00:40,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:43<00:39,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:44<00:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:44<00:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:45<00:36,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:46<00:35,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:46<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:47<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:48<00:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:48<00:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:49<00:31,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:50<00:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:50<00:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:51<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:52<00:29,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:52<00:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [01:53<00:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [01:54<00:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [01:54<00:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [01:55<00:26,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [01:56<00:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [01:57<00:25,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [01:57<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [01:58<00:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [01:59<00:23,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [01:59<00:22,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:00<00:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:01<00:21,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:01<00:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:02<00:20,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:03<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:03<00:18,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:04<00:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:05<00:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:05<00:16,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:06<00:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:07<00:15,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:07<00:14,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:08<00:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:09<00:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:10<00:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:10<00:11,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:11<00:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:12<00:10,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:12<00:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:13<00:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:14<00:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:14<00:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:15<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:16<00:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:16<00:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:17<00:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:18<00:03,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:18<00:03,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:19<00:02,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:20<00:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:20<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:21<00:00,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:22<00:00,  1.41it/s]\n",
+      "\n",
+      "Training models:  20%|██████▏                        | 1/5 [02:24<09:37, 144.40s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:09,  4.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:10,  2.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:52,  1.48s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:46,  1.15s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:49,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:35,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:15,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:09,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<01:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:59,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:50,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:46,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:50,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:54,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:53,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:52,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:51,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:47,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<01:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:34<01:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:35<01:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:36<01:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:51,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:37<01:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:38<01:54,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:41<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:53,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:54,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:44<01:54,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:45<01:52,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:46<01:50,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:47<01:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:48<01:49,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:49<01:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:49<01:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:50<01:47,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:51<01:46,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:52<01:45,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:44,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:53<01:42,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:54<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:55<01:37,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:41,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:43,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:57<01:42,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:58<01:41,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:40,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:40,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:35,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:32,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:05<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:41,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:07<01:39,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:08<01:31,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:09<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:09<01:18,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:10<01:16,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:11<01:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:11<01:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:12<01:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:13<01:12,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:13<01:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:14<01:09,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:15<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:15<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:16<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:17<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:17<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:18<01:05,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:19<01:04,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:19<01:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:20<01:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:21<01:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:21<01:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:22<01:00,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:23<01:00,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:23<01:01,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:24<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:25<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:25<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:26<00:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:27<00:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:27<00:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:28<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:29<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<00:54,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:53,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:31<00:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<00:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:51,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<00:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:34<00:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:44,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:43,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:42,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:41,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:41,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:43<00:39,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:38,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:47<00:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:34,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:49<00:33,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:32,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:51<00:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:30,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:31,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:53<00:30,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:54<00:29,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:55<00:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:56<00:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:57<00:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:58<00:25,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:58<00:25,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:59<00:25,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:00<00:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:00<00:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:01<00:23,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:02<00:22,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:02<00:21,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:03<00:20,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:04<00:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:04<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:05<00:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:06<00:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:07<00:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:07<00:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:08<00:15,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:09<00:15,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:09<00:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:10<00:13,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:11<00:12,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:11<00:11,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:12<00:11,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:13<00:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:13<00:09,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:14<00:09,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:15<00:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:15<00:07,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:16<00:07,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:17<00:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:17<00:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:18<00:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:19<00:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:19<00:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:20<00:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:21<00:02,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:21<00:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:22<00:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:23<00:00,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:23<00:00,  1.39it/s]\n",
+      "\n",
+      "Training models:  40%|████████████▍                  | 2/5 [04:50<07:16, 145.37s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:35,  4.40s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:15,  2.20s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:53,  1.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:48,  1.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:12,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:09,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:05,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:02,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<01:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:11,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:09,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:06,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<02:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<02:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:03,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<02:05,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<01:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<01:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:24<02:00,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:26<02:10,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<02:09,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<02:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<02:10,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<02:09,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<02:09,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<02:08,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<02:08,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<02:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<02:06,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:34<02:06,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<02:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<02:03,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:36<02:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<02:02,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<02:02,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<02:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<02:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<02:01,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:59,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:57,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:44<01:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:54,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:52,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:50,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:44,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:48,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:45,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:38,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:37,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:56<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:57<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:35,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:59<01:34,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:00<01:40,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:01<01:41,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:01<01:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:02<01:32,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:03<01:49,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:04<01:49,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:05<01:39,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:06<01:32,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:06<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:07<01:23,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:08<01:20,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:08<01:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:09<01:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:10<01:16,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:10<01:13,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:11<01:12,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:11<01:11,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:12<01:11,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:13<01:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:13<01:09,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:14<01:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:15<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:15<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:16<01:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:17<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:18<01:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:18<01:06,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:19<01:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:20<01:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:20<01:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:21<01:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:22<01:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:22<01:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:23<01:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:24<01:00,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:24<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:25<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:26<00:58,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:26<00:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:27<00:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:28<00:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:28<00:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:29<00:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:30<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:30<00:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:31<00:51,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:32<00:49,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:32<00:49,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:33<00:48,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<00:47,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:34<00:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:35<00:48,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:47,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:36<00:47,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:37<00:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:46,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:38<00:45,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:39<00:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:40<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:41<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:40,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:42<00:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:43<00:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:38,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:44<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:45<00:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:46<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:47<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:48<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:49<00:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:34,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:50<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:51<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:52<00:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:53<00:30,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:29,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:54<00:29,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:55<00:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:29,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:56<00:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:57<00:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:58<00:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:58<00:26,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:59<00:25,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:00<00:24,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:00<00:23,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:01<00:22,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:02<00:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:02<00:21,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:03<00:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:04<00:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:04<00:19,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:05<00:18,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:06<00:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:06<00:16,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:07<00:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:07<00:15,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:08<00:15,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:09<00:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:10<00:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:10<00:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:11<00:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:12<00:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:12<00:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:13<00:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:14<00:10,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:14<00:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:15<00:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:16<00:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:16<00:07,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:17<00:06,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:18<00:05,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:18<00:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:19<00:04,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:20<00:03,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:20<00:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:21<00:02,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:21<00:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:22<00:01,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:23<00:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:23<00:00,  1.39it/s]\n",
+      "\n",
+      "Training models:  60%|██████████████████▌            | 3/5 [07:16<04:51, 145.76s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:27,  4.66s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:41,  2.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:12,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:00,  1.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:21,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<02:58,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:09,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:05,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:02,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:04,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<02:05,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<02:04,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<02:10,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<02:11,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<02:07,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<02:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:31<02:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<02:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<02:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<01:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<02:15,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<02:23,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<02:10,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:37<02:02,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<01:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<01:54,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:40<01:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:46,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:41<01:44,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:42<01:43,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:42<01:42,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:41,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:42,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:40,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:45<01:39,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:39,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:36,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:47<01:34,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:34,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:50<01:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:29,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:29,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:54<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:28,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:55<01:28,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:27,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:58<01:26,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:00<01:23,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:01<01:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:02<01:21,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:03<01:20,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:04<01:19,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:04<01:19,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:05<01:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:06<01:17,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:06<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:07<01:16,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:08<01:15,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:09<01:14,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:09<01:13,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:10<01:13,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:11<01:13,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:11<01:10,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:12<01:09,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:13<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:13<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:14<01:08,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:15<01:08,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:15<01:07,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:16<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:17<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:17<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:18<01:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:19<01:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:19<01:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:20<01:03,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:21<01:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:22<01:02,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:22<01:01,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:23<01:01,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:24<01:00,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:24<00:59,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:25<00:58,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:26<00:57,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:26<00:58,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:27<00:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:28<00:57,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<00:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:29<00:55,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:30<00:54,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<00:54,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:31<00:53,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:32<00:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:33<00:49,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:33<00:49,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:34<00:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:46,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:35<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:36<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:37<00:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:38<00:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:43,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:39<00:43,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:40<00:43,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:42,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:41<00:41,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:42<00:40,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:43<00:40,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:43<00:39,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:44<00:39,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:45<00:38,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:45<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:46<00:36,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:47<00:36,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:47<00:35,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:48<00:34,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:49<00:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:50<00:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:51<00:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:31,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:52<00:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:53<00:30,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:54<00:31,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:31,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:55<00:30,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:56<00:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:57<00:27,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:57<00:26,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:58<00:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:59<00:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:59<00:24,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:00<00:23,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:01<00:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:01<00:22,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:02<00:21,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:03<00:20,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:04<00:19,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:04<00:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:05<00:18,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:06<00:17,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:06<00:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:07<00:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:08<00:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:09<00:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:09<00:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:10<00:14,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:11<00:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:11<00:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:12<00:12,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:13<00:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:14<00:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:14<00:10,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:15<00:09,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:16<00:08,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:16<00:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:17<00:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:18<00:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:19<00:06,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:20<00:05,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:20<00:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:21<00:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:22<00:03,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████████████████████████████████ | 197/200 [02:23<00:02,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████████████████████████████████▎| 198/200 [02:24<00:01,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████████████████████████████████▋| 199/200 [02:25<00:00,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████████████████████████████████| 200/200 [02:25<00:00,  1.37it/s]\n",
+      "\n",
+      "Training models:  80%|████████████████████████▊      | 4/5 [09:44<02:26, 146.73s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:37,  4.71s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:03,  2.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:35,  1.70s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:27,  1.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:08<03:47,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:22,  1.04s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:06,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:48,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:11<02:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:31,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:34,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:42,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:33,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:27,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:29,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:36,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:19<02:28,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:20<02:24,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:21,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:22<02:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:23<02:47,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:24<02:35,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:25<02:24,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:25<02:17,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:26<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:27<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:27<02:03,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:28<01:58,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:29<01:58,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<01:57,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:30<01:56,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:31<01:55,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:32<01:54,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<01:52,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:33<01:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:34<01:52,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<01:50,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<01:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<01:48,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<01:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<01:48,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<01:49,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<01:47,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:45,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:40<01:41,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:41<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:42<01:35,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:43<01:36,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:44<01:35,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:46<01:37,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:38,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:49<01:37,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:35,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:31,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:29,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:27,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:28,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:27,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:56<01:27,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:28,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:27,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:26,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:25,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:23,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:23,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:01<01:21,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:21,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:20,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:19,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:04<01:18,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:18,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:18,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:06<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:17,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:08<01:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:08<01:18,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:09<01:16,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:10<01:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:10<01:14,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:11<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:12<01:12,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:13<01:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:13<01:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:14<01:11,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:15<01:11,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:15<01:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:16<01:09,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:17<01:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:17<01:06,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:18<01:05,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:19<01:05,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:19<01:04,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:20<01:03,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:21<01:02,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:22<01:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:22<01:01,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:23<01:00,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:24<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:24<00:59,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:25<00:58,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:26<00:57,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:26<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:27<00:56,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:28<00:55,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:28<00:55,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:29<00:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:30<00:53,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:31<00:52,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:51,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:32<00:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:33<00:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:34<00:49,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:35<00:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:47,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:46,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:37<00:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:46,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:45,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:39<00:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:44,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:41<00:43,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:42,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:44,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:44<00:44,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:46<00:39,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:38,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:37,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:48<00:35,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:34,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:50<00:33,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:33,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:33,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:52<00:32,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:31,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:30,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:54<00:29,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:54<00:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:27,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:56<00:28,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:57<00:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:57<00:28,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:58<00:27,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:59<00:25,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:59<00:24,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:00<00:24,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:01<00:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:02<00:24,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:02<00:23,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:03<00:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:04<00:21,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:04<00:20,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:05<00:19,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:06<00:19,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:07<00:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:07<00:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:08<00:17,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:09<00:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:10<00:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:10<00:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:11<00:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:12<00:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:14<00:13,  1.35it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████| 5/5 [12:01<00:00, 144.32s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.66it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.41it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.81it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.14s/it]\u001b[A\n",
+      "Evaluating NLL: 88it [00:01, 97.75it/s]\u001b[A\n",
+      "Evaluating NLL: 168it [00:01, 194.17it/s]\u001b[A\n",
+      "Evaluating NLL: 252it [00:01, 299.57it/s]\u001b[A\n",
+      "Evaluating NLL: 337it [00:01, 403.83it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 287.21it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|███████████▉               | 44/100 [00:00<00:00, 436.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 445.21it/s]\n",
+      " 50%|████████████████████████                        | 3/6 [26:51<29:49, 596.36s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:39,  4.12s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:57,  2.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:46,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:43,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:13,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:50,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:37,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:10<02:13,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:09,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:07,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:12<02:05,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:04,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:14<02:01,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<01:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<01:58,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:16<01:55,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:52,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:22<01:52,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:53,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<01:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:24<01:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:49,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:49,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:26<01:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:27<01:48,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:27<01:47,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:28<01:48,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:29<01:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:29<01:45,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:30<01:45,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:31<01:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:31<01:42,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:32<01:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:33<01:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:33<01:39,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:34<01:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:35<01:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:35<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:36<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:37<01:36,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:37<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:38<01:32,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:39<01:33,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:39<01:34,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:40<01:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:41<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:41<01:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:42<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:43<01:32,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:43<01:34,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:44<01:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:45<01:31,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:45<01:30,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:46<01:29,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:47<01:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:47<01:28,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:48<01:28,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:49<01:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:49<01:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:50<01:24,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:51<01:23,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:51<01:22,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:52<01:22,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:52<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:53<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:54<01:21,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:54<01:20,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:55<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:56<01:19,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:56<01:18,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:57<01:19,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [00:58<01:17,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [00:59<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [00:59<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:00<01:14,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:00<01:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:01<01:14,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:02<01:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:03<01:14,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:03<01:13,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:04<01:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:05<01:12,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:05<01:10,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:06<01:09,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:07<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:07<01:12,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:08<01:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:09<01:10,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:09<01:08,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:10<01:06,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:11<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:11<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:12<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:13<01:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:13<01:04,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:14<01:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:15<01:01,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:15<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:16<00:59,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:17<00:59,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:17<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:18<00:57,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:19<00:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:19<00:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:20<00:59,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:21<00:58,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:22<00:58,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:22<00:56,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:23<00:55,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:24<00:54,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:24<00:53,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:25<00:54,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:54,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:26<00:52,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:27<00:51,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:50,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:29<00:49,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:29<00:48,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:49,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:50,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:51,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:32<00:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:33<00:52,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:34<00:51,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:35<00:51,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:51,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:50,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:37<00:50,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:48,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:40<00:47,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:41<00:47,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:41<00:46,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:42<00:45,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:43<00:43,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:44<00:43,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:45<00:42,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:46<00:41,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:46<00:40,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:47<00:40,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:48<00:39,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:49<00:38,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:50<00:33,  1.39it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [01:53<07:32, 113.10s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:47,  4.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:35,  2.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:18,  1.62s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:04,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:29,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:52,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:43,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:36,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:29,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:28,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:50,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:38,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:24,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:07,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:05,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:02,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<01:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<01:55,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:22<01:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<01:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<01:50,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<01:47,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<01:44,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<01:44,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<01:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<01:45,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:27<01:43,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<01:43,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:29<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<01:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:31<01:46,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:44,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<01:44,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<01:44,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:43,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<01:42,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<01:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:39,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:38,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:41<01:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:34,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:33,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:28,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:27,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:26,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:23,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:22,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:21,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:20,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:19,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:19,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:18,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:18,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:18,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:19,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:16,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:16,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:13,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:13,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:03<01:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:04<01:11,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:11,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:05<01:10,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:06<01:08,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:09,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:13,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:08<01:11,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:09,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:09<01:08,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:11<01:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:05,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:01,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<00:59,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<00:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<00:58,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<00:58,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<00:57,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<00:57,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:18<00:57,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<00:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<00:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<00:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<00:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:54,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:23<00:54,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:24<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:25<00:50,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:26<00:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:27<00:48,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:28<00:47,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:28<00:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:29<00:46,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:30<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:44,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:42,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:32<00:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:33<00:42,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:33<00:41,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:34<00:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:35<00:41,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:36<00:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:36<00:40,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:37<00:39,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:37<00:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:38<00:36,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:39<00:35,  1.60it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:39<00:34,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:40<00:33,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:40<00:33,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:41<00:32,  1.63it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:42<00:32,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:42<00:32,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:43<00:33,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:44<00:32,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:44<00:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:45<00:30,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:46<00:29,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:46<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:47<00:28,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:48<00:28,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:49<00:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:49<00:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:50<00:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:51<00:27,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [01:51<00:26,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [01:52<00:25,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [01:53<00:25,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [01:53<00:24,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [01:54<00:23,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [01:55<00:22,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [01:55<00:21,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [01:56<00:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [01:57<00:20,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [01:58<00:20,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [01:58<00:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [01:59<00:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:00<00:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:01<00:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:01<00:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:02<00:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:03<00:17,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:04<00:16,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:05<00:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:05<00:14,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:06<00:14,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:07<00:13,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:08<00:12,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:09<00:11,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:09<00:11,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:10<00:10,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:11<00:09,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:12<00:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:13<00:07,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:13<00:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▍  | 192/200 [02:14<00:06,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▊  | 193/200 [02:15<00:05,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████████████████████████████████  | 194/200 [02:16<00:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▍ | 195/200 [02:16<00:03,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|█████████████████████████████████████████████████████████████▋ | 196/200 [02:18<00:02,  1.41it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:13<06:28, 129.44s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:44,  4.75s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:12,  2.49s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:38,  1.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:33,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:11,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:43,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:30,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:32,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:48,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:32,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:21,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:04,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<01:58,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<01:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<01:56,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<01:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<01:55,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<01:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<01:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<01:49,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<01:46,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<01:43,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<01:43,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<01:43,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<01:45,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<01:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:43,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:32<01:41,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:33<01:41,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:33<01:40,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:34<01:40,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:35<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<01:40,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<01:40,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<01:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<01:38,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<01:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<01:37,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:39<01:36,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:40<01:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:41<01:37,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:41<01:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:42<01:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:43<01:33,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:43<01:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:44<01:29,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:44<01:26,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:45<01:26,  1.61it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:46<01:26,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:46<01:27,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:47<01:25,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:48<01:25,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:48<01:26,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:49<01:27,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:50<01:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:50<01:27,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:51<01:26,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:52<01:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:52<01:25,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:53<01:23,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:54<01:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:54<01:21,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:55<01:20,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:56<01:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:56<01:18,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:57<01:17,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:57<01:17,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [00:58<01:17,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [00:59<01:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:00<01:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:00<01:18,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:01<01:17,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:02<01:16,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:02<01:16,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:03<01:15,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:04<01:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:04<01:13,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:05<01:12,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:06<01:11,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:06<01:15,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:07<01:14,  1.43it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:08<01:11,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:08<01:09,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:09<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:10<01:08,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:10<01:07,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:11<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:12<01:04,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:12<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:13<01:02,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:13<01:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:14<01:01,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:15<01:01,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:15<01:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:16<01:00,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:17<00:59,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:17<00:58,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:18<00:58,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:19<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:19<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:20<00:56,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:21<00:55,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:21<00:54,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:22<00:53,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:23<00:53,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:23<00:52,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:24<00:52,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:25<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:25<00:51,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:26<00:51,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:27<00:50,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:27<00:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:28<00:48,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:29<00:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:29<00:46,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:30<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:30<00:45,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:31<00:44,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:32<00:44,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:32<00:43,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:33<00:42,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:34<00:45,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:35<00:47,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:35<00:44,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:36<00:42,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:37<00:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:42,  1.42it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [05:54<03:52, 116.41s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:17,  4.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<07:07,  2.16s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:47,  1.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:49,  1.17s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:19,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:42,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:08,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:06,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:16<02:08,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:09,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:18<02:08,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:19<02:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:19<02:07,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:20<02:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:21<02:05,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:21<02:03,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:22<02:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:23<02:17,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:24<02:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:25<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:25<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:26<02:08,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:27<02:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:28<02:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:28<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:29<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:30<01:59,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:30<01:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:31<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:32<02:15,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:33<02:24,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<02:13,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<02:02,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:35<01:55,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:36<01:51,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:46,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:37<01:42,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:38<01:40,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:39<01:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:39<01:37,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:40<01:36,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:41<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<01:35,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:42<01:35,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:42<01:35,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:43<01:34,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:44<01:33,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:44<01:32,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:31,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:46<01:31,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:46<01:31,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:47<01:31,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:48<01:32,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:48<01:30,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:49<01:29,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:50<01:29,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:50<01:26,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:51<01:26,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:52<01:25,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:52<01:25,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:53<01:24,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:54<01:25,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:54<01:24,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:55<01:23,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:56<01:22,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:56<01:21,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:57<01:22,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [00:58<01:21,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [00:58<01:20,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [00:59<01:20,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:00<01:19,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:00<01:17,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:01<01:16,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:02<01:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:02<01:15,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:03<01:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:04<01:14,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:04<01:14,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:05<01:13,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:06<01:12,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:06<01:11,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:07<01:11,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:08<01:10,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:08<01:10,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:09<01:10,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:10<01:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:10<01:08,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:11<01:07,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:12<01:06,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:12<01:06,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:13<01:05,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:14<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:14<01:03,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:15<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:15<01:02,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:16<01:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:17<01:00,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:17<00:59,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:18<01:00,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:19<00:59,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:19<00:58,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:20<00:57,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:21<00:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:21<00:57,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:22<00:56,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:23<00:55,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:23<00:55,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:24<00:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:25<00:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:26<00:53,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:26<00:51,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:27<00:51,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:27<00:50,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:28<00:49,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:29<00:47,  1.58it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:29<00:47,  1.56it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:30<00:47,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:31<00:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:31<00:45,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:32<00:45,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:33<00:43,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:33<00:42,  1.62it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:34<00:40,  1.65it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:34<00:39,  1.66it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:35<00:38,  1.67it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:36<00:39,  1.64it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:36<00:39,  1.59it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:37<00:39,  1.57it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:38<00:39,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:38<00:38,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:39<00:41,  1.41it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [07:36<01:50, 110.76s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<13:38,  4.11s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:04<06:52,  2.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<04:42,  1.43s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<03:43,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:06<03:09,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:07<02:45,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:32,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:08<02:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:09<02:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:10<02:27,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:11<02:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:12<02:13,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:13<02:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:13<02:07,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:14<02:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:15<02:03,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:15<02:02,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:16<02:01,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:17<02:00,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:17<01:57,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:18<01:56,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:18<01:55,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:19<01:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:20<01:53,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:20<01:52,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:21<01:52,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:22<01:54,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:23<02:00,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:23<01:59,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:24<02:00,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:25<01:59,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:25<01:56,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:26<01:52,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:27<01:56,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:28<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:28<01:56,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:29<01:53,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:30<01:51,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:30<01:48,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:31<01:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:32<01:51,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:32<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:33<01:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:34<02:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:35<02:01,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:36<02:00,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:37<02:01,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:37<02:01,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:38<02:01,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:39<02:00,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:40<01:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:41<01:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:41<01:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:42<01:57,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:43<01:54,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:44<01:54,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:45<01:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:45<01:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:46<01:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:47<01:50,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:48<01:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:49<01:48,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:49<01:47,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:50<01:47,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:51<01:46,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:52<01:45,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:53<01:44,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:53<01:43,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:54<01:41,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:55<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:55<01:31,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:56<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:57<01:33,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:58<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:58<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [00:59<01:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:00<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:01<01:31,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:01<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:02<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:03<01:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:04<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:05<01:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:06<01:23,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:07<01:20,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:07<01:17,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:08<01:18,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:09<01:18,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:09<01:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:10<01:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:11<01:15,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:12<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:13<01:34,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:14<01:30,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:14<01:23,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:15<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:16<01:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:16<01:10,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:17<01:08,  1.44it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:18<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:18<01:06,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:19<01:05,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:20<01:04,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:20<01:03,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:21<01:03,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:22<01:03,  1.45it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:22<01:02,  1.46it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:23<01:00,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:24<00:59,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:24<00:59,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:25<00:58,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:26<00:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:26<00:57,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:27<00:56,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:28<00:54,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:28<00:54,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:29<00:53,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:30<00:53,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:30<00:52,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:31<00:52,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:32<00:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:32<00:51,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:33<00:51,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:34<00:50,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:34<00:49,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:35<00:48,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:36<00:48,  1.47it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:36<00:47,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:37<00:46,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:38<00:45,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:38<00:44,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:39<00:43,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:40<00:43,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:40<00:42,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:41<00:41,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:42<00:41,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:42<00:40,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:43<00:39,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:44<00:38,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:44<00:38,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:45<00:37,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:46<00:36,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:46<00:36,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:47<00:35,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:48<00:35,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:48<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:49<00:34,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:50<00:33,  1.48it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:50<00:32,  1.49it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:51<00:31,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:52<00:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:52<00:30,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [01:53<00:29,  1.54it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [01:54<00:28,  1.55it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [01:54<00:28,  1.52it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [01:55<00:27,  1.51it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [01:56<00:26,  1.53it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [01:56<00:26,  1.50it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [01:58<00:28,  1.36it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [09:37<00:00, 115.44s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.70it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.18it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.15s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.73it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.64it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.24it/s]\u001b[A\n",
+      "Evaluating NLL: 71it [00:00, 106.65it/s]\u001b[A\n",
+      "Evaluating NLL: 155it [00:01, 236.54it/s]\u001b[A\n",
+      "Evaluating NLL: 239it [00:01, 358.29it/s]\u001b[A\n",
+      "Evaluating NLL: 325it [00:01, 469.95it/s]\u001b[A\n",
+      "Evaluating NLL: 410it [00:01, 561.79it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 351.36it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|████████████▏              | 45/100 [00:00<00:00, 446.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 452.56it/s]\n",
+      " 67%|████████████████████████████████                | 4/6 [36:39<19:46, 593.37s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:27,  4.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:27,  2.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:10,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:04,  1.25s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:07,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:51,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:42,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:33,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:19,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:20,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:16,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:16,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:15,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:10,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:09,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:07,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<01:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:33<02:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:07,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<02:07,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<02:04,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<01:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:37<02:04,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<02:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<02:00,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:59,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:59,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:41<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:55,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:58,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:55,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:48<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:49<01:52,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:53,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:52,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:51<01:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:52<01:55,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:53<01:53,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:54<01:50,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:54<01:52,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:55<01:55,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:56<01:55,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:57<01:49,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:58<01:58,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:59<02:09,  1.01s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:00<02:00,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:01<01:52,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:02<01:47,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:02<01:42,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:03<01:37,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:04<01:35,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:05<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:05<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:06<01:32,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:07<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:08<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:08<01:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:09<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:10<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:11<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:11<01:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:12<01:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:13<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:14<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:14<01:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:15<01:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:16<01:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:17<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:17<01:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:19<01:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:20<01:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:20<01:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:22<01:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:23<01:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:23<01:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:25<01:13,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:26<01:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:27<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:29<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:30<01:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:30<01:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:31<01:06,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:33<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:33<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:34<01:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:35<01:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:36<00:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:36<00:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:37<00:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:38<00:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:38<00:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:39<00:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:40<00:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:41<00:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:41<00:54,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:42<00:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:43<00:53,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:44<00:53,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:45<00:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:45<00:51,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:46<00:50,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:47<00:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:48<00:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:48<00:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:49<00:47,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:50<00:46,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:51<00:49,  1.24it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [01:54<07:36, 114.16s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:33,  4.69s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:47,  2.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:22,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:32,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:07,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:54,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:44,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:38,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:35,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:14,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:17,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:19,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:10,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:06,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<02:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<02:02,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:33<02:02,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<01:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<01:58,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<01:56,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<02:03,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<02:03,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:39<02:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<02:00,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:59,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:56,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:57,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:53,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:48,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:48<01:51,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:49<01:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:50,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:49,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:51<01:55,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:52<01:51,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:53<01:50,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:54<01:53,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:54<01:50,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:55<01:50,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:56<01:52,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:57<01:52,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:58<01:47,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:59<01:54,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:00<02:06,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:01<01:57,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:02<01:49,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:02<01:44,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:03<01:40,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:04<01:38,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:05<01:36,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:05<01:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:06<01:32,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:07<01:31,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:08<01:31,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:08<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:09<01:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:10<01:28,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:11<01:27,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:11<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:12<01:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:13<01:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:14<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:15<01:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:15<01:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:16<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:17<01:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:18<01:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:19<01:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:20<01:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:21<01:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:22<01:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:23<01:15,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:24<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:25<01:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:26<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:27<01:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:29<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:30<01:07,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:30<01:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:31<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:33<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:33<01:03,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:34<01:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:35<01:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:36<01:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:36<00:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:37<00:59,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:38<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:39<00:57,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:39<00:57,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:40<00:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:41<00:55,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:42<00:55,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:42<00:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:43<00:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:44<00:53,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:45<00:53,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:46<00:52,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:46<00:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:47<00:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:48<00:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:49<00:48,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:49<00:47,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:50<00:47,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:51<00:48,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:52<00:47,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:53<00:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:53<00:44,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:54<00:43,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:55<00:41,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:55<00:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:56<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:57<00:39,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:58<00:38,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:58<00:37,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [02:00<00:40,  1.25it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [03:56<05:57, 119.15s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:44,  4.44s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:33,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:13,  1.59s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:31,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:55,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:44,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:38,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:27,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:30,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:25,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:16,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:11,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:08,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:08,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<02:09,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<02:07,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:33<02:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:02,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<02:01,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<02:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<02:05,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<02:03,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<02:01,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:38<01:59,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:39<01:57,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:57,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:58,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:59,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:59,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:46<01:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:58,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:57,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:48<01:56,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:49<01:54,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:50<01:55,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:51<01:54,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:51<01:54,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:52<01:52,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:53<01:51,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:54<02:03,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:55<02:11,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:56<02:00,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:57<01:52,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:58<01:47,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:58<01:43,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:59<01:39,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:00<01:38,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:01<01:38,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:01<01:37,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:02<01:39,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:03<01:36,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:04<01:34,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:04<01:32,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:05<01:30,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:06<01:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:07<01:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:07<01:27,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:08<01:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:09<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:10<01:25,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:10<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:11<01:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:12<01:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:13<01:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:13<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:14<01:22,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:15<01:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:16<01:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:17<01:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:17<01:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:19<01:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:20<01:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:20<01:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:22<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:23<01:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:23<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:25<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:26<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:26<01:09,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:09,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:29<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:29<01:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:30<01:07,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:31<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:04,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:32<01:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:33<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:34<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:35<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:35<01:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:36<01:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:37<01:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:38<00:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:38<00:59,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:39<00:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:40<00:58,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:41<00:57,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:42<00:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:42<00:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:43<00:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:44<00:53,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:45<00:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:45<00:52,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:46<00:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:47<00:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:48<00:51,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:49<00:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:49<00:49,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:50<00:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:51<00:47,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:52<00:45,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:52<00:44,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:53<00:43,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:54<00:42,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:55<00:42,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:55<00:41,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:56<00:40,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:57<00:39,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:58<00:38,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:58<00:38,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:59<00:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [02:00<00:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [02:01<00:35,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:02<00:37,  1.25it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [06:01<04:03, 121.73s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:13,  4.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:20,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:12,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:07,  1.26s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:29,  1.08s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:06,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:54,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:52,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:45,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:38,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:34,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:20,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:21,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:20,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:19,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:17,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<02:13,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:16,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:16,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:12,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<02:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:08,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:05,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:07,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<02:08,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:33<02:09,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:33<02:06,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:04,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<02:08,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<02:08,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:37<02:06,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<02:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<02:05,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:39<02:05,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:40<02:05,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:41<02:03,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:42<02:02,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<02:00,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:43<01:59,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:44<01:58,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:45<01:58,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:46<01:56,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:46<01:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:47<01:55,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:48<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:49<01:53,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:50<01:52,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:50<01:57,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:52<02:16,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:53<02:06,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:53<01:57,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:54<01:52,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:55<01:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:56<01:45,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:56<01:43,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:57<01:41,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:58<01:39,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:59<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:59<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:00<01:36,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:01<01:35,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:02<01:35,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:02<01:33,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:03<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:04<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:04<01:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:05<01:29,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:06<01:28,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:07<01:30,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:08<01:29,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:08<01:28,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:09<01:26,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:10<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:11<01:26,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:11<01:25,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:12<01:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:13<01:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:14<01:22,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:14<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:15<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:16<01:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:17<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:17<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:18<01:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:19<01:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:20<01:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:20<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:21<01:14,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:22<01:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:22<01:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:23<01:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:24<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:25<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:26<01:10,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:26<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:27<01:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:28<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:28<01:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:29<01:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:30<01:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:31<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:32<01:04,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:33<01:03,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:34<01:02,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:35<01:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:35<01:01,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:36<01:00,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:37<01:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:38<00:59,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:38<00:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:39<00:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:40<00:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:41<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:41<00:54,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:42<00:54,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:44<00:57,  1.24it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [07:47<01:55, 115.66s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<15:39,  4.72s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:54,  2.39s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:24,  1.65s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:12,  1.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:33,  1.09s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:56,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:49,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:34,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:29,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:20,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:19,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:15,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:19<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:15,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:22<02:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:14,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:16,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<02:17,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:16,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:15,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:13,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<02:11,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:09,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:08,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:06,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<02:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:33<02:05,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:33<02:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<02:04,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<02:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<02:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:36<02:00,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<02:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<02:06,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:39<02:04,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:40<02:05,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<02:01,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:59,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:57,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:43<02:03,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:44<01:58,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:54,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:46<01:51,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:47<01:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:48<01:52,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:48<01:52,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:49<01:53,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:50<01:51,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:51<01:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:52<01:57,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:53<01:55,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:54<01:58,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:54<01:54,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:55<01:53,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:56<01:55,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:57<01:50,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:58<01:49,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:59<02:08,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [01:00<02:02,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [01:01<01:53,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [01:01<01:48,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [01:02<01:43,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:03<01:40,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:04<01:37,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:04<01:36,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:05<01:35,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:06<01:34,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:07<01:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:08<01:33,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:08<01:31,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:09<01:29,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:10<01:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:11<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:11<01:25,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:12<01:24,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:13<01:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:14<01:24,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:14<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:15<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:16<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:17<01:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:17<01:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:18<01:19,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:19<01:18,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:20<01:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:20<01:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:21<01:17,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:22<01:16,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:23<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:23<01:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:24<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:25<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:26<01:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:26<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:27<01:11,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:28<01:11,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:29<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:30<01:08,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:30<01:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:31<01:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:32<01:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:32<01:02,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:33<01:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:34<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:35<01:02,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:35<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:36<01:00,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:37<01:01,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:38<00:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:38<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:39<00:58,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:40<00:57,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:41<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:42<00:56,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:42<00:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:43<00:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:44<00:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:45<00:55,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:45<00:54,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:46<00:52,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:47<00:52,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:48<00:51,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:49<00:50,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:49<00:49,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:50<00:48,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:51<00:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:52<00:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:53<00:47,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:53<00:46,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:54<00:45,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:55<00:44,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:56<00:43,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:56<00:42,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:57<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:58<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:59<00:39,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:59<00:39,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [02:00<00:38,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [02:01<00:38,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [02:02<00:38,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:03<00:36,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:03<00:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:04<00:34,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:05<00:34,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:06<00:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [02:06<00:32,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [02:07<00:31,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:08<00:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:09<00:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:10<00:29,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:10<00:28,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:11<00:27,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:12<00:26,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:13<00:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:13<00:25,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:14<00:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:15<00:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:16<00:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:16<00:22,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:17<00:21,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:18<00:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:19<00:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:20<00:20,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:21<00:19,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:21<00:18,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:22<00:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:23<00:16,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:24<00:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:25<00:15,  1.24it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [10:15<00:00, 123.17s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.33it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.51it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.65it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:02,  7.96it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.12it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 92it [00:00, 134.00it/s]\u001b[A\n",
+      "Evaluating NLL: 179it [00:01, 261.02it/s]\u001b[A\n",
+      "Evaluating NLL: 267it [00:01, 383.09it/s]\u001b[A\n",
+      "Evaluating NLL: 352it [00:01, 485.09it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 347.09it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                                     | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|█████████▍                 | 35/100 [00:00<00:00, 346.59it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████████████| 100/100 [00:00<00:00, 310.65it/s]\n",
+      " 83%|████████████████████████████████████████        | 5/6 [47:07<10:05, 605.81s/it]\n",
+      "Training models:   0%|                                                                    | 0/5 [00:00<?, ?it/s]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:57,  4.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:41,  2.33s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:23,  1.64s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:17,  1.31s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:42,  1.14s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:18,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<03:02,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:55,  1.09it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:44,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:39,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:43,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:38,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:14<02:35,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:15<02:39,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:16<03:02,  1.01it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:17<02:47,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:17<02:35,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:18<02:27,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:19<02:23,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:20<02:18,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:20<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:21<02:14,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:22<02:14,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:23<02:12,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:23<02:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:24<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:25<02:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:25<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:26<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:27<02:04,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:28<02:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:28<02:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:29<02:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:30<02:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:31<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:31<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:32<02:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:33<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:34<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:34<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:35<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:36<01:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:37<01:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:37<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:38<01:54,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:39<01:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:40<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:40<01:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:41<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:42<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:42<01:47,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:43<01:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:44<01:45,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:45<01:45,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:45<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:46<01:43,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:47<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:47<01:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:48<01:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:49<01:42,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:50<01:42,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:50<01:41,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:51<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:52<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:53<01:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:53<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:54<01:36,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:55<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:56<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:56<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:57<01:33,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:58<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:58<01:33,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:59<01:32,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [01:00<01:31,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:01<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:01<01:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:02<01:30,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:03<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:04<01:28,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:04<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:05<01:28,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:06<01:30,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:07<01:30,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:07<01:27,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:08<01:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:09<01:23,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:10<01:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:10<01:22,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:11<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:12<01:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:13<01:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:13<01:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:14<01:16,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:15<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:16<01:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:16<01:17,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:17<01:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:18<01:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:19<01:15,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:19<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:20<01:14,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:21<01:13,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:22<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:22<01:13,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:23<01:11,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:24<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:25<01:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:25<01:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:26<01:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:27<01:03,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:27<01:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:28<01:01,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:29<01:01,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:30<01:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:30<01:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:31<01:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:32<01:01,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:33<00:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:33<01:02,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:34<01:02,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:35<01:00,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:36<00:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:36<00:57,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:37<00:56,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:38<00:54,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:39<00:53,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:39<00:53,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:40<00:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:41<00:51,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:42<00:50,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:42<00:50,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:43<00:49,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:44<00:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:45<00:47,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:45<00:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:46<00:45,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:47<00:48,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:48<00:46,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:48<00:47,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:49<00:45,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:50<00:44,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:51<00:43,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:52<00:43,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:52<00:42,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:53<00:40,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:54<00:39,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:54<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:55<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:56<00:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:57<00:37,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:58<00:37,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [01:58<00:36,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [01:59<00:35,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:00<00:35,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:01<00:34,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:02<00:33,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [02:02<00:33,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [02:03<00:33,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:04<00:34,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:05<00:33,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:06<00:30,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:07<00:31,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:08<00:34,  1.03it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:09<00:31,  1.10it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:09<00:29,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:10<00:27,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:11<00:25,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:12<00:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:12<00:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:13<00:22,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:14<00:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:15<00:20,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:15<00:19,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:16<00:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:17<00:17,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:18<00:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:18<00:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:19<00:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:20<00:14,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:20<00:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:21<00:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:22<00:12,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:23<00:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:23<00:11,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:24<00:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:25<00:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:26<00:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▌   | 189/200 [02:26<00:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|███████████████████████████████████████████████████████████▊   | 190/200 [02:27<00:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|████████████████████████████████████████████████████████████▏  | 191/200 [02:29<00:07,  1.28it/s]\n",
+      "\n",
+      "Training models:  20%|███████████▊                                               | 1/5 [02:31<10:04, 151.19s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<16:05,  4.85s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<08:07,  2.46s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:06<05:29,  1.67s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:07<04:14,  1.30s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:34,  1.10s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:10,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:09<02:52,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:10<02:41,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:35,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:31,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:12<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:13<02:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:21,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:20,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:15<02:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:16<02:17,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:16,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:18<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:13,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:11,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:10,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:21<02:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:06,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:24<02:05,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:05,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:27<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:05,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:30<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<01:59,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<02:00,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<01:58,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:58,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:58,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<01:56,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:35<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:53,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<01:50,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:37<01:48,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:48,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<01:47,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<01:47,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:40<01:49,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:47,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:41<01:46,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:42<01:49,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:43<01:49,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:49,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:49,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:47,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:46<01:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:45,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:48<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:40,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:39,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:37,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:54<01:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:56<01:37,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:57<01:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:35,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:34,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:59<01:32,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [01:00<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:00<01:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:01<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:02<01:29,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:29,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:03<01:28,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:04<01:27,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:05<01:26,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:05<01:25,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:06<01:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:07<01:21,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:08<01:21,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:08<01:21,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:09<01:23,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:10<01:23,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:11<01:23,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:11<01:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:12<01:21,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:13<01:19,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:14<01:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:15<01:21,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:15<01:19,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:16<01:19,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:17<01:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:18<01:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:18<01:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:19<01:19,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:20<01:20,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:21<01:19,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:22<01:20,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:23<01:18,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:24<01:18,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:24<01:18,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:25<01:18,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:26<01:17,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:27<01:16,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:28<01:15,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:29<01:14,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:30<01:13,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:30<01:13,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:31<01:12,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:32<01:11,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:33<01:10,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:34<01:09,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:35<01:08,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:36<01:07,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:36<01:06,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:37<01:05,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:38<01:04,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:39<01:02,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:40<00:59,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:41<01:01,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:41<00:59,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:42<00:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:43<00:57,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:44<00:55,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:45<00:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:45<00:55,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:46<00:55,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:47<00:53,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:48<00:52,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:49<00:51,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:50<00:50,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:50<00:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:51<00:50,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:52<00:49,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:53<00:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:54<00:51,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:55<00:54,  1.02it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:56<00:49,  1.11it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:57<00:45,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:57<00:43,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:58<00:41,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:59<00:39,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [02:00<00:38,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [02:00<00:37,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [02:01<00:36,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:02<00:35,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:03<00:36,  1.24it/s]\n",
+      "\n",
+      "Training models:  40%|███████████████████████▌                                   | 2/5 [04:37<06:48, 136.33s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                                                         | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▎                                                                | 1/200 [00:04<14:57,  4.51s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▋                                                                | 2/200 [00:05<07:33,  2.29s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                                                | 3/200 [00:05<05:10,  1.58s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▎                                                               | 4/200 [00:06<04:03,  1.24s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|█▋                                                               | 5/200 [00:07<03:25,  1.05s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█▉                                                               | 6/200 [00:08<03:01,  1.07it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▎                                                              | 7/200 [00:08<02:45,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▌                                                              | 8/200 [00:09<02:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|██▉                                                              | 9/200 [00:10<02:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|███▏                                                            | 10/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▌                                                            | 11/200 [00:11<02:26,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|███▊                                                            | 12/200 [00:12<02:30,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|████▏                                                           | 13/200 [00:13<02:28,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|████▍                                                           | 14/200 [00:14<02:24,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|████▊                                                           | 15/200 [00:14<02:23,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████                                                           | 16/200 [00:15<02:22,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|█████▍                                                          | 17/200 [00:16<02:20,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|█████▊                                                          | 18/200 [00:17<02:17,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████                                                          | 19/200 [00:17<02:17,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▍                                                         | 20/200 [00:18<02:15,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|██████▋                                                         | 21/200 [00:19<02:13,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███████                                                         | 22/200 [00:20<02:12,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▎                                                        | 23/200 [00:20<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|███████▋                                                        | 24/200 [00:21<02:08,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████████                                                        | 25/200 [00:22<02:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████████▎                                                       | 26/200 [00:23<02:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▋                                                       | 27/200 [00:23<02:10,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████████▉                                                       | 28/200 [00:24<02:08,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████████▎                                                      | 29/200 [00:25<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████████▌                                                      | 30/200 [00:26<02:05,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████████▉                                                      | 31/200 [00:26<02:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▏                                                     | 32/200 [00:27<02:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|██████████▌                                                     | 33/200 [00:28<02:03,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████████▉                                                     | 34/200 [00:29<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▏                                                    | 35/200 [00:29<02:02,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▌                                                    | 36/200 [00:30<02:00,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|███████████▊                                                    | 37/200 [00:31<02:00,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|████████████▏                                                   | 38/200 [00:32<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▍                                                   | 39/200 [00:32<01:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|████████████▊                                                   | 40/200 [00:33<01:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|█████████████                                                   | 41/200 [00:34<01:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|█████████████▍                                                  | 42/200 [00:34<01:55,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|█████████████▊                                                  | 43/200 [00:35<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████                                                  | 44/200 [00:36<01:54,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|██████████████▍                                                 | 45/200 [00:37<01:54,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|██████████████▋                                                 | 46/200 [00:37<01:55,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████                                                 | 47/200 [00:38<01:53,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▎                                                | 48/200 [00:39<01:56,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|███████████████▋                                                | 49/200 [00:40<01:55,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|████████████████                                                | 50/200 [00:40<01:53,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▎                                               | 51/200 [00:41<01:51,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▋                                               | 52/200 [00:42<01:50,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|████████████████▉                                               | 53/200 [00:43<01:49,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████████████▎                                              | 54/200 [00:43<01:48,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▌                                              | 55/200 [00:44<01:47,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████████████▉                                              | 56/200 [00:45<01:45,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████████████▏                                             | 57/200 [00:46<01:46,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████████████▌                                             | 58/200 [00:46<01:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████████████▉                                             | 59/200 [00:47<01:43,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▏                                            | 60/200 [00:48<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|███████████████████▌                                            | 61/200 [00:49<01:41,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████████████▊                                            | 62/200 [00:49<01:40,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▏                                           | 63/200 [00:50<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▍                                           | 64/200 [00:51<01:39,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|████████████████████▊                                           | 65/200 [00:51<01:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|█████████████████████                                           | 66/200 [00:52<01:37,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▍                                          | 67/200 [00:53<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|█████████████████████▊                                          | 68/200 [00:54<01:35,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|██████████████████████                                          | 69/200 [00:54<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|██████████████████████▍                                         | 70/200 [00:55<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|██████████████████████▋                                         | 71/200 [00:56<01:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████                                         | 72/200 [00:57<01:32,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|███████████████████████▎                                        | 73/200 [00:57<01:31,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|███████████████████████▋                                        | 74/200 [00:58<01:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████                                        | 75/200 [00:59<01:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▎                                       | 76/200 [00:59<01:28,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|████████████████████████▋                                       | 77/200 [01:00<01:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|████████████████████████▉                                       | 78/200 [01:01<01:27,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▎                                      | 79/200 [01:02<01:26,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▌                                      | 80/200 [01:02<01:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|█████████████████████████▉                                      | 81/200 [01:03<01:25,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████████████████▏                                     | 82/200 [01:04<01:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▌                                     | 83/200 [01:04<01:24,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████████████████▉                                     | 84/200 [01:05<01:24,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████████████████▏                                    | 85/200 [01:06<01:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████████████████▌                                    | 86/200 [01:07<01:27,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████████████████▊                                    | 87/200 [01:08<01:29,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▏                                   | 88/200 [01:08<01:25,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████████████████▍                                   | 89/200 [01:09<01:23,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████████████████▊                                   | 90/200 [01:10<01:22,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████                                   | 91/200 [01:10<01:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▍                                  | 92/200 [01:11<01:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|█████████████████████████████▊                                  | 93/200 [01:12<01:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|██████████████████████████████                                  | 94/200 [01:13<01:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▍                                 | 95/200 [01:13<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|██████████████████████████████▋                                 | 96/200 [01:14<01:16,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|███████████████████████████████                                 | 97/200 [01:15<01:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|███████████████████████████████▎                                | 98/200 [01:16<01:13,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▋                                | 99/200 [01:16<01:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▌                               | 100/200 [01:17<01:11,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|███████████████████████████████▊                               | 101/200 [01:18<01:10,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|████████████████████████████████▏                              | 102/200 [01:18<01:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▍                              | 103/200 [01:19<01:11,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|████████████████████████████████▊                              | 104/200 [01:20<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|█████████████████████████████████                              | 105/200 [01:21<01:12,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|█████████████████████████████████▍                             | 106/200 [01:22<01:12,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|█████████████████████████████████▋                             | 107/200 [01:22<01:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████████████████████                             | 108/200 [01:23<01:07,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▎                            | 109/200 [01:24<01:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|██████████████████████████████████▋                            | 110/200 [01:25<01:09,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|██████████████████████████████████▉                            | 111/200 [01:25<01:06,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▎                           | 112/200 [01:26<01:05,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████████████████████▌                           | 113/200 [01:27<01:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████████████████████▉                           | 114/200 [01:28<01:04,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████████████████████▏                          | 115/200 [01:28<01:04,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▌                          | 116/200 [01:29<01:06,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████████████████████▊                          | 117/200 [01:30<01:06,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|█████████████████████████████████████▏                         | 118/200 [01:31<01:07,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▍                         | 119/200 [01:32<01:07,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████████████████████▊                         | 120/200 [01:33<01:08,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|██████████████████████████████████████                         | 121/200 [01:33<01:07,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|██████████████████████████████████████▍                        | 122/200 [01:34<01:06,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|██████████████████████████████████████▋                        | 123/200 [01:35<01:06,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████                        | 124/200 [01:36<01:05,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|███████████████████████████████████████▍                       | 125/200 [01:37<01:04,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|███████████████████████████████████████▋                       | 126/200 [01:38<01:03,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████                       | 127/200 [01:39<01:02,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▎                      | 128/200 [01:40<01:01,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|████████████████████████████████████████▋                      | 129/200 [01:40<01:00,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|████████████████████████████████████████▉                      | 130/200 [01:41<00:59,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▎                     | 131/200 [01:42<00:59,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▌                     | 132/200 [01:43<00:58,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|█████████████████████████████████████████▉                     | 133/200 [01:44<00:57,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|██████████████████████████████████████████▏                    | 134/200 [01:45<00:56,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▌                    | 135/200 [01:45<00:55,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|██████████████████████████████████████████▊                    | 136/200 [01:46<00:54,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████████████████████████▏                   | 137/200 [01:47<00:53,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|███████████████████████████████████████████▍                   | 138/200 [01:48<00:52,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|███████████████████████████████████████████▊                   | 139/200 [01:49<00:51,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████                   | 140/200 [01:50<00:50,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████████████████████████▍                  | 141/200 [01:51<00:49,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████████████████████████▋                  | 142/200 [01:51<00:46,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████                  | 143/200 [01:52<00:46,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▎                 | 144/200 [01:53<00:45,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████████████████████████▋                 | 145/200 [01:54<00:44,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████████████████████████▉                 | 146/200 [01:54<00:42,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▎                | 147/200 [01:55<00:42,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▌                | 148/200 [01:56<00:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████████████████████████▉                | 149/200 [01:57<00:40,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|███████████████████████████████████████████████▎               | 150/200 [01:58<00:40,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▌               | 151/200 [01:58<00:39,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|███████████████████████████████████████████████▉               | 152/200 [01:59<00:39,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|████████████████████████████████████████████████▏              | 153/200 [02:00<00:38,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|████████████████████████████████████████████████▌              | 154/200 [02:01<00:37,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|████████████████████████████████████████████████▊              | 155/200 [02:02<00:38,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▏             | 156/200 [02:03<00:37,  1.18it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|█████████████████████████████████████████████████▍             | 157/200 [02:04<00:36,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|█████████████████████████████████████████████████▊             | 158/200 [02:04<00:35,  1.19it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████             | 159/200 [02:05<00:33,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▍            | 160/200 [02:06<00:34,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|██████████████████████████████████████████████████▋            | 161/200 [02:08<00:39,  1.02s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|███████████████████████████████████████████████████            | 162/200 [02:08<00:35,  1.06it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▎           | 163/200 [02:09<00:33,  1.12it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▋           | 164/200 [02:10<00:30,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|███████████████████████████████████████████████████▉           | 165/200 [02:11<00:29,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|████████████████████████████████████████████████████▎          | 166/200 [02:11<00:27,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▌          | 167/200 [02:12<00:26,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|████████████████████████████████████████████████████▉          | 168/200 [02:13<00:25,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████████████████████████████▏         | 169/200 [02:14<00:24,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████████████████████████████▌         | 170/200 [02:15<00:23,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████████████████████████████▊         | 171/200 [02:15<00:22,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▏        | 172/200 [02:16<00:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████████████████████████████▍        | 173/200 [02:17<00:21,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████████████████████████████▊        | 174/200 [02:18<00:20,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▏       | 175/200 [02:18<00:19,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▍       | 176/200 [02:19<00:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|███████████████████████████████████████████████████████▊       | 177/200 [02:20<00:17,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|████████████████████████████████████████████████████████       | 178/200 [02:21<00:17,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▍      | 179/200 [02:22<00:16,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|████████████████████████████████████████████████████████▋      | 180/200 [02:22<00:15,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|█████████████████████████████████████████████████████████      | 181/200 [02:23<00:15,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|█████████████████████████████████████████████████████████▎     | 182/200 [02:24<00:14,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▋     | 183/200 [02:25<00:13,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|█████████████████████████████████████████████████████████▉     | 184/200 [02:26<00:12,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|██████████████████████████████████████████████████████████▎    | 185/200 [02:26<00:11,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|██████████████████████████████████████████████████████████▌    | 186/200 [02:27<00:10,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|██████████████████████████████████████████████████████████▉    | 187/200 [02:28<00:10,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|███████████████████████████████████████████████████████████▏   | 188/200 [02:29<00:09,  1.25it/s]\n",
+      "\n",
+      "Training models:  60%|███████████████████████████████████▍                       | 3/5 [07:09<04:47, 143.52s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<14:37,  4.41s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:21,  2.23s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:05<05:04,  1.55s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<03:59,  1.22s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:21,  1.03s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:07<02:59,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:08<02:45,  1.16it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:34,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:10<02:26,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:19,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:19,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:13<02:16,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:15,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:13,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:15<02:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:16<02:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:18<02:11,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:19<02:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:09,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:21<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:22<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:24<02:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<02:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:26<02:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:27<02:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<02:03,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<02:01,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:29<01:59,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:30<01:59,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:58,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<01:56,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:32<01:54,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:33<01:55,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:54,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:35<01:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:35<01:53,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:36<01:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:37<01:52,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:37<01:51,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:38<01:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:39<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:40<01:50,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:40<01:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:41<01:48,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:42<01:47,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:43<01:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:43<01:46,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:44<01:44,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:45<01:43,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:45<01:42,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:46<01:41,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:47<01:40,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:48<01:39,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:48<01:39,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:49<01:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:50<01:37,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:50<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:51<01:37,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:52<01:36,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:53<01:35,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:53<01:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:54<01:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:55<01:38,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:56<01:35,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:56<01:34,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:57<01:32,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [00:58<01:31,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [00:59<01:30,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [00:59<01:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:00<01:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:01<01:27,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:01<01:27,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:02<01:26,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:03<01:25,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:04<01:25,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:04<01:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:05<01:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:06<01:22,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:07<01:22,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:07<01:21,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:08<01:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:09<01:25,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:10<01:24,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:10<01:20,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:11<01:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:12<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:13<01:18,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:13<01:20,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:14<01:18,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:15<01:16,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:16<01:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:16<01:13,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:17<01:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:18<01:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:19<01:15,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:19<01:13,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:20<01:12,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:21<01:11,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:22<01:12,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:23<01:12,  1.27it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:23<01:10,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:24<01:14,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:25<01:13,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:26<01:11,  1.23it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:27<01:09,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:27<01:07,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:28<01:05,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:29<01:03,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:30<01:13,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:31<01:16,  1.08it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:32<01:09,  1.17it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:33<01:06,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:33<01:03,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:34<01:00,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:35<00:58,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:35<00:57,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:36<00:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:37<00:54,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:38<00:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:38<00:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:39<00:51,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:40<00:51,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:41<00:50,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:41<00:49,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:42<00:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:43<00:47,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:43<00:46,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:44<00:46,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:45<00:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:46<00:45,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:46<00:44,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:47<00:43,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:48<00:42,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:49<00:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:49<00:41,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:50<00:40,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:51<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:51<00:39,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:52<00:38,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:53<00:38,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:54<00:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:54<00:36,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:55<00:35,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:56<00:34,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:57<00:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:57<00:33,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [01:58<00:31,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [01:59<00:31,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [01:59<00:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:00<00:30,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:01<00:29,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:02<00:28,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:02<00:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  81%|████████████████████████████▎      | 162/200 [02:03<00:27,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▌      | 163/200 [02:04<00:26,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▋      | 164/200 [02:04<00:26,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  82%|████████████████████████████▉      | 165/200 [02:05<00:25,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  83%|█████████████████████████████      | 166/200 [02:06<00:24,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▏     | 167/200 [02:07<00:24,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▍     | 168/200 [02:07<00:23,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  84%|█████████████████████████████▌     | 169/200 [02:08<00:22,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  85%|█████████████████████████████▊     | 170/200 [02:09<00:21,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|█████████████████████████████▉     | 171/200 [02:09<00:20,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████     | 172/200 [02:10<00:20,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  86%|██████████████████████████████▎    | 173/200 [02:11<00:19,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  87%|██████████████████████████████▍    | 174/200 [02:12<00:18,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▋    | 175/200 [02:12<00:18,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▊    | 176/200 [02:13<00:17,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  88%|██████████████████████████████▉    | 177/200 [02:14<00:16,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  89%|███████████████████████████████▏   | 178/200 [02:15<00:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▎   | 179/200 [02:15<00:15,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▌   | 180/200 [02:16<00:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  90%|███████████████████████████████▋   | 181/200 [02:17<00:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  91%|███████████████████████████████▊   | 182/200 [02:17<00:13,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████   | 183/200 [02:18<00:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▏  | 184/200 [02:19<00:11,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  92%|████████████████████████████████▍  | 185/200 [02:20<00:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  93%|████████████████████████████████▌  | 186/200 [02:20<00:10,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▋  | 187/200 [02:21<00:09,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|████████████████████████████████▉  | 188/200 [02:22<00:08,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  94%|█████████████████████████████████  | 189/200 [02:23<00:08,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  95%|█████████████████████████████████▎ | 190/200 [02:23<00:07,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▍ | 191/200 [02:24<00:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▌ | 192/200 [02:25<00:05,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  96%|█████████████████████████████████▊ | 193/200 [02:26<00:05,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  97%|█████████████████████████████████▉ | 194/200 [02:26<00:04,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▏| 195/200 [02:27<00:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▎| 196/200 [02:28<00:02,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  98%|██████████████████████████████████▍| 197/200 [02:28<00:02,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  99%|██████████████████████████████████▋| 198/200 [02:29<00:01,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|██████████████████████████████████▊| 199/200 [02:30<00:00,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training: 100%|███████████████████████████████████| 200/200 [02:31<00:00,  1.32it/s]\n",
+      "\n",
+      "Training models:  80%|███████████████████████████████████████████████▏           | 4/5 [09:42<02:27, 147.33s/it]\u001b[A\n",
+      "\n",
+      "training:   0%|                                             | 0/200 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   0%|▏                                    | 1/200 [00:04<15:20,  4.63s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   1%|▎                                    | 2/200 [00:05<07:47,  2.36s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▌                                    | 3/200 [00:06<05:17,  1.61s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▋                                    | 4/200 [00:06<04:08,  1.27s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   2%|▉                                    | 5/200 [00:07<03:28,  1.07s/it]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   3%|█                                    | 6/200 [00:08<03:05,  1.05it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▎                                   | 7/200 [00:09<02:49,  1.14it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▍                                   | 8/200 [00:09<02:39,  1.20it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   4%|█▋                                   | 9/200 [00:10<02:32,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   5%|█▊                                  | 10/200 [00:11<02:28,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|█▉                                  | 11/200 [00:11<02:24,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▏                                 | 12/200 [00:12<02:21,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   6%|██▎                                 | 13/200 [00:13<02:18,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   7%|██▌                                 | 14/200 [00:14<02:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▋                                 | 15/200 [00:14<02:16,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|██▉                                 | 16/200 [00:15<02:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   8%|███                                 | 17/200 [00:16<02:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:   9%|███▏                                | 18/200 [00:17<02:14,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▍                                | 19/200 [00:17<02:15,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▌                                | 20/200 [00:18<02:16,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  10%|███▊                                | 21/200 [00:19<02:18,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  11%|███▉                                | 22/200 [00:20<02:13,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▏                               | 23/200 [00:20<02:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▎                               | 24/200 [00:21<02:09,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  12%|████▌                               | 25/200 [00:22<02:08,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  13%|████▋                               | 26/200 [00:23<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|████▊                               | 27/200 [00:23<02:07,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████                               | 28/200 [00:24<02:06,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  14%|█████▏                              | 29/200 [00:25<02:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  15%|█████▍                              | 30/200 [00:25<02:04,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▌                              | 31/200 [00:26<02:04,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▊                              | 32/200 [00:27<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  16%|█████▉                              | 33/200 [00:28<02:01,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  17%|██████                              | 34/200 [00:28<02:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▎                             | 35/200 [00:29<01:59,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▍                             | 36/200 [00:30<02:01,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  18%|██████▋                             | 37/200 [00:31<01:58,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  19%|██████▊                             | 38/200 [00:31<01:56,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████                             | 39/200 [00:32<02:01,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▏                            | 40/200 [00:33<01:59,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  20%|███████▍                            | 41/200 [00:34<01:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  21%|███████▌                            | 42/200 [00:34<01:56,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▋                            | 43/200 [00:35<01:53,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|███████▉                            | 44/200 [00:36<01:52,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  22%|████████                            | 45/200 [00:37<01:54,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  23%|████████▎                           | 46/200 [00:37<01:55,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▍                           | 47/200 [00:38<01:58,  1.30it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▋                           | 48/200 [00:39<01:59,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  24%|████████▊                           | 49/200 [00:40<01:56,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  25%|█████████                           | 50/200 [00:40<01:54,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▏                          | 51/200 [00:41<01:52,  1.32it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▎                          | 52/200 [00:42<01:55,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  26%|█████████▌                          | 53/200 [00:43<01:57,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  27%|█████████▋                          | 54/200 [00:44<01:53,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|█████████▉                          | 55/200 [00:44<01:50,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████                          | 56/200 [00:45<01:49,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  28%|██████████▎                         | 57/200 [00:46<01:51,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  29%|██████████▍                         | 58/200 [00:47<01:53,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▌                         | 59/200 [00:47<01:50,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▊                         | 60/200 [00:48<01:49,  1.28it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  30%|██████████▉                         | 61/200 [00:49<01:51,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  31%|███████████▏                        | 62/200 [00:50<01:50,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▎                        | 63/200 [00:51<01:50,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▌                        | 64/200 [00:52<01:49,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  32%|███████████▋                        | 65/200 [00:52<01:47,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  33%|███████████▉                        | 66/200 [00:53<01:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████                        | 67/200 [00:54<01:49,  1.22it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▏                       | 68/200 [00:55<01:46,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  34%|████████████▍                       | 69/200 [00:56<01:44,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  35%|████████████▌                       | 70/200 [00:56<01:45,  1.24it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▊                       | 71/200 [00:57<01:43,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|████████████▉                       | 72/200 [00:58<01:41,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  36%|█████████████▏                      | 73/200 [00:59<01:40,  1.26it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  37%|█████████████▎                      | 74/200 [00:59<01:38,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▌                      | 75/200 [01:01<01:48,  1.15it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▋                      | 76/200 [01:02<01:59,  1.04it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  38%|█████████████▊                      | 77/200 [01:02<01:48,  1.13it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  39%|██████████████                      | 78/200 [01:03<01:40,  1.21it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▏                     | 79/200 [01:04<01:36,  1.25it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▍                     | 80/200 [01:05<01:33,  1.29it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  40%|██████████████▌                     | 81/200 [01:05<01:30,  1.31it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  41%|██████████████▊                     | 82/200 [01:06<01:28,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|██████████████▉                     | 83/200 [01:07<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████                     | 84/200 [01:08<01:27,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  42%|███████████████▎                    | 85/200 [01:08<01:26,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  43%|███████████████▍                    | 86/200 [01:09<01:25,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▋                    | 87/200 [01:10<01:24,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|███████████████▊                    | 88/200 [01:11<01:23,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  44%|████████████████                    | 89/200 [01:11<01:21,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  45%|████████████████▏                   | 90/200 [01:12<01:21,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▍                   | 91/200 [01:13<01:20,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▌                   | 92/200 [01:13<01:18,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  46%|████████████████▋                   | 93/200 [01:14<01:17,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  47%|████████████████▉                   | 94/200 [01:15<01:18,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████                   | 95/200 [01:16<01:18,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▎                  | 96/200 [01:16<01:17,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  48%|█████████████████▍                  | 97/200 [01:17<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  49%|█████████████████▋                  | 98/200 [01:18<01:15,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▊                  | 99/200 [01:19<01:14,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▌                 | 100/200 [01:19<01:12,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  50%|█████████████████▋                 | 101/200 [01:20<01:12,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  51%|█████████████████▊                 | 102/200 [01:21<01:12,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████                 | 103/200 [01:22<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▏                | 104/200 [01:22<01:11,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  52%|██████████████████▍                | 105/200 [01:23<01:10,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  53%|██████████████████▌                | 106/200 [01:24<01:10,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▋                | 107/200 [01:25<01:09,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  54%|██████████████████▉                | 108/200 [01:25<01:08,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████                | 109/200 [01:26<01:07,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  55%|███████████████████▎               | 110/200 [01:27<01:06,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▍               | 111/200 [01:28<01:06,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▌               | 112/200 [01:28<01:04,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  56%|███████████████████▊               | 113/200 [01:29<01:03,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|███████████████████▉               | 114/200 [01:30<01:02,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  57%|████████████████████▏              | 115/200 [01:30<01:01,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▎              | 116/200 [01:31<01:00,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  58%|████████████████████▍              | 117/200 [01:32<00:59,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  59%|████████████████████▋              | 118/200 [01:33<00:59,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|████████████████████▊              | 119/200 [01:33<00:59,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████              | 120/200 [01:34<00:59,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  60%|█████████████████████▏             | 121/200 [01:35<00:58,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  61%|█████████████████████▎             | 122/200 [01:36<00:57,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▌             | 123/200 [01:36<00:56,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▋             | 124/200 [01:37<00:56,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  62%|█████████████████████▉             | 125/200 [01:38<00:55,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  63%|██████████████████████             | 126/200 [01:38<00:54,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▏            | 127/200 [01:39<00:53,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▍            | 128/200 [01:40<00:52,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  64%|██████████████████████▌            | 129/200 [01:41<00:50,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  65%|██████████████████████▊            | 130/200 [01:41<00:49,  1.40it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|██████████████████████▉            | 131/200 [01:42<00:49,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████            | 132/200 [01:43<00:49,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  66%|███████████████████████▎           | 133/200 [01:44<00:48,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  67%|███████████████████████▍           | 134/200 [01:44<00:48,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▋           | 135/200 [01:45<00:47,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▊           | 136/200 [01:46<00:46,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  68%|███████████████████████▉           | 137/200 [01:46<00:46,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  69%|████████████████████████▏          | 138/200 [01:47<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▎          | 139/200 [01:48<00:45,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▌          | 140/200 [01:49<00:45,  1.33it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  70%|████████████████████████▋          | 141/200 [01:49<00:43,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  71%|████████████████████████▊          | 142/200 [01:50<00:42,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████          | 143/200 [01:51<00:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▏         | 144/200 [01:52<00:40,  1.39it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  72%|█████████████████████████▍         | 145/200 [01:52<00:39,  1.41it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  73%|█████████████████████████▌         | 146/200 [01:53<00:38,  1.42it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▋         | 147/200 [01:54<00:39,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|█████████████████████████▉         | 148/200 [01:55<00:38,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  74%|██████████████████████████         | 149/200 [01:55<00:37,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  75%|██████████████████████████▎        | 150/200 [01:56<00:37,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▍        | 151/200 [01:57<00:36,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▌        | 152/200 [01:58<00:35,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  76%|██████████████████████████▊        | 153/200 [01:58<00:34,  1.37it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  77%|██████████████████████████▉        | 154/200 [01:59<00:34,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▏       | 155/200 [02:00<00:33,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▎       | 156/200 [02:00<00:32,  1.34it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  78%|███████████████████████████▍       | 157/200 [02:01<00:31,  1.35it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  79%|███████████████████████████▋       | 158/200 [02:02<00:30,  1.36it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|███████████████████████████▊       | 159/200 [02:03<00:29,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████       | 160/200 [02:03<00:28,  1.38it/s]\u001b[A\u001b[A\n",
+      "\n",
+      "training:  80%|████████████████████████████▏      | 161/200 [02:05<00:30,  1.28it/s]\n",
+      "\n",
+      "Training models: 100%|███████████████████████████████████████████████████████████| 5/5 [11:49<00:00, 141.95s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.68it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.89it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  8.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 16it [00:01,  9.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.23it/s]\u001b[A\n",
+      "Evaluating NLL: 92it [00:00, 136.65it/s]\u001b[A\n",
+      "Evaluating NLL: 176it [00:01, 259.64it/s]\u001b[A\n",
+      "Evaluating NLL: 255it [00:01, 365.64it/s]\u001b[A\n",
+      "Evaluating NLL: 327it [00:01, 442.08it/s]\u001b[A\n",
+      "Evaluating NLL: 500it [00:01, 352.97it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|         | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|▎| 36/100 [00:00<00:00, 349.91it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 351.41it\n",
+      "100%|████████████████████████████████████████████████| 6/6 [59:08<00:00, 591.49s/it]\n"
      ]
     }
    ],
    "source": [
     "from scipy.signal import resample\n",
     "import jax.numpy as np\n",
+    "from encoding_information.models import PixelCNN, PoissonNoiseModel, AnalyticGaussianNoiseModel, FullGaussianProcess, StationaryGaussianProcess\n",
+    "from encoding_information import estimate_information\n",
+    "import time\n",
+    "\n",
     "\n",
     "N_objects = 5000\n",
     "noise_sigma = 1e-3\n",
     "num_samples_list = [1, 4, 9, 16, 25, 36]\n",
     "upsampling_factor = 16\n",
     "max_epochs = 5000\n",
-    "confidence = 95\n",
+    "confidence = .95\n",
     "num_nyquist_samples = 16\n",
     "noise_sigmas_list = 1 / num_nyquist_samples / np.array([1e0, 1e1, 1e2, 1e3, 1e4])\n",
+    "test_set_fraction = 0.1\n",
+    "num_models_to_train = 5\n",
     "\n",
     "\n",
     "upsampled_signal_length = upsampling_factor*num_nyquist_samples\n",
@@ -129,7 +51154,7 @@
     "\n",
     "\n",
     "  # Find an optimal PSF for this object\n",
-    "  initial_kernel, initial_params, optimized_params, objects, _, _ = optimize_PSF_and_estimate_mi(\n",
+    "  initial_kernel, initial_params, optimized_params, objects, _, _, _, _ = optimize_PSF_and_estimate_mi(\n",
     "      objects_fn, noise_sigma, num_nyquist_samples=num_nyquist_samples, upsampled_signal_length=upsampled_signal_length,\n",
     "      max_epochs=max_epochs, )\n",
     "\n",
@@ -158,66 +51183,41 @@
     "    noisy_pixelated_signals = pixelated_signal + jax.random.normal(jax.random.PRNGKey(onp.random.randint(10000)), pixelated_signal.shape) * np.sqrt(pixel_variance)\n",
     "\n",
     "    fake_images = noisy_pixelated_signals.reshape(-1, int(np.sqrt(num_samples)), int(np.sqrt(num_samples))) * scale_factor\n",
-    "    mi_estimate = estimate_mutual_information(fake_images, gaussian_noise_sigma=float(np.sqrt(pixel_variance) * scale_factor), verbose=False, entropy_model='pixelcnn')                                                      \n",
-    "    print(mi_estimate)\n",
-    "    runs.append(mi_estimate)\n",
     "\n",
     "\n",
-    "    mutual_information.append(np.median(runs))\n",
-    "    mi_lo.append(np.percentile(np.array(runs), 100-confidence))\n",
-    "    mi_hi.append(np.percentile(np.array(runs), confidence))\n",
+    "    train_patches = fake_images[:int(fake_images.shape[0] * (1 - test_set_fraction))]\n",
+    "    test_patches = fake_images[int(fake_images.shape[0] * (1 - test_set_fraction)):]\n",
+    "    \n",
+    "\n",
+    "    pixel_cnns = []\n",
+    "    stationary_gaussians = []\n",
+    "    full_gaussians = []\n",
+    "    for i in tqdm(range(num_models_to_train), desc='Training models'):\n",
+    "        pixelcnn = PixelCNN()\n",
+    "        pixelcnn.fit(train_patches, verbose=False)\n",
+    "        pixel_cnns.append(pixelcnn)\n",
+    "    \n",
+    "    noise_model = AnalyticGaussianNoiseModel(float(np.sqrt(pixel_variance) * scale_factor))\n",
+    "\n",
+    "    estimate, lower, upper = estimate_information(pixel_cnns, noise_model, train_patches, test_patches, confidence_interval=confidence)\n",
+    "\n",
+    "    mutual_information.append(estimate)\n",
+    "    mi_lo.append(lower)\n",
+    "    mi_hi.append(upper)\n",
     "  mi_by_snr_median[noise_sigma] = mutual_information\n",
     "  mi_by_snr_hi[noise_sigma] = mi_hi\n",
     "  mi_by_snr_lo[noise_sigma] = mi_lo"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# save the results\n",
-    "\n",
-    "# make results dir\n",
-    "if not os.path.exists('results'):\n",
-    "    os.makedirs('results')\n",
-    "\n",
-    "import pickle\n",
-    "with open('results/mi_by_snr_median.pickle', 'wb') as handle:\n",
-    "    pickle.dump(mi_by_snr_median, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
-    "with open('results/mi_by_snr_hi.pickle', 'wb') as handle:\n",
-    "    pickle.dump(mi_by_snr_hi, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
-    "with open('results/mi_by_snr_lo.pickle', 'wb') as handle:\n",
-    "    pickle.dump(mi_by_snr_lo, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load the results\n",
-    "import pickle\n",
-    "with open('results/mi_by_snr_median.pickle', 'rb') as handle:\n",
-    "    mi_by_snr_median = pickle.load(handle)\n",
-    "with open('results/mi_by_snr_hi.pickle', 'rb') as handle:\n",
-    "    mi_by_snr_hi = pickle.load(handle)\n",
-    "with open('results/mi_by_snr_lo.pickle', 'rb') as handle:\n",
-    "    mi_by_snr_lo = pickle.load(handle)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-11-25T16:07:36.068215Z",
-     "iopub.status.busy": "2023-11-25T16:07:36.067974Z",
-     "iopub.status.idle": "2023-11-25T16:07:36.602188Z",
-     "shell.execute_reply": "2023-11-25T16:07:36.601651Z"
+     "iopub.execute_input": "2024-10-17T21:33:12.902826Z",
+     "iopub.status.busy": "2024-10-17T21:33:12.902569Z",
+     "iopub.status.idle": "2024-10-17T21:33:14.394800Z",
+     "shell.execute_reply": "2024-10-17T21:33:14.394297Z"
     }
    },
    "outputs": [
@@ -225,27 +51225,19 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_11003/1087950307.py:7: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
+      "/tmp/ipykernel_5140/1087950307.py:7: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
       "  base_colormap = plt.cm.get_cmap('inferno')\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x400 with 2 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "ename": "",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
-     ]
     }
    ],
    "source": [
diff --git a/simulations_1d/mi_vs_signal_bandwidth.ipynb b/simulations_1d/mi_vs_signal_bandwidth.ipynb
index 19451a7..505cf45 100644
--- a/simulations_1d/mi_vs_signal_bandwidth.ipynb
+++ b/simulations_1d/mi_vs_signal_bandwidth.ipynb
@@ -5,10 +5,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-13T05:01:27.541380Z",
-     "iopub.status.busy": "2024-10-13T05:01:27.540985Z",
-     "iopub.status.idle": "2024-10-13T05:01:31.145911Z",
-     "shell.execute_reply": "2024-10-13T05:01:31.145083Z"
+     "iopub.execute_input": "2024-10-16T23:33:20.465368Z",
+     "iopub.status.busy": "2024-10-16T23:33:20.465035Z",
+     "iopub.status.idle": "2024-10-16T23:33:23.759732Z",
+     "shell.execute_reply": "2024-10-16T23:33:23.759143Z"
     }
    },
    "outputs": [
@@ -16,10 +16,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-10-14 09:35:49.324540: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-14 09:35:50.442741: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-14 09:35:50.442878: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-14 09:35:50.442892: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+      "2024-10-16 16:33:21.986572: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-16 16:33:22.798982: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-16 16:33:22.799064: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-16 16:33:22.799073: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
      ]
     }
    ],
@@ -32,7 +32,7 @@
     "\n",
     "import os\n",
     "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" \n",
-    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '3'\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n",
     "\n",
     "\n",
     "\n",
@@ -47,33 +47,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [13], line 35\u001b[0m\n\u001b[1;32m     31\u001b[0m delta8_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m :np\u001b[38;5;241m.\u001b[39marray([generate_random_object(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdelta\u001b[39m\u001b[38;5;124m'\u001b[39m, num_deltas\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8\u001b[39m, object_size\u001b[38;5;241m=\u001b[39mUPSAMPLED_SIGNAL_LENGTH) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(N_objects)])\n\u001b[1;32m     34\u001b[0m \u001b[38;5;66;03m# plot an example of each object\u001b[39;00m\n\u001b[0;32m---> 35\u001b[0m wn \u001b[38;5;241m=\u001b[39m \u001b[43mwhite_noise_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     36\u001b[0m delta \u001b[38;5;241m=\u001b[39m delta_fn()[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     37\u001b[0m delta8 \u001b[38;5;241m=\u001b[39m delta8_fn()[\u001b[38;5;241m0\u001b[39m]\n",
-      "Cell \u001b[0;32mIn [13], line 19\u001b[0m, in \u001b[0;36mwhite_noise_fn\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m objects \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(N_objects):  \n\u001b[0;32m---> 19\u001b[0m   obj \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_random_object\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mwhite_noise\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobject_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnum_nyquist_samples_list\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m   \u001b[38;5;66;03m# upsample it\u001b[39;00m\n\u001b[1;32m     21\u001b[0m   obj \u001b[38;5;241m=\u001b[39m resample(obj, UPSAMPLED_SIGNAL_LENGTH)\n",
-      "File \u001b[0;32m/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/simulations_1d/signal_utils_1D.py:197\u001b[0m, in \u001b[0;36mgenerate_random_object\u001b[0;34m(type, seed, object_size, num_deltas, sin_freq_range, gaussian_mixture_position, num_mixture_components, gaussian_mixture_seed)\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;241m=\u001b[39m onp\u001b[38;5;241m.\u001b[39mfft\u001b[38;5;241m.\u001b[39mirfft( random_phase) \n\u001b[1;32m    196\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;28mobject\u001b[39m) \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 197\u001b[0m     \u001b[38;5;28mobject\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmin(\u001b[38;5;28mobject\u001b[39m)\n\u001b[1;32m    198\u001b[0m \u001b[38;5;66;03m# return object \u001b[39;00m\n\u001b[1;32m    199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mobject\u001b[39m \u001b[38;5;241m/\u001b[39m onp\u001b[38;5;241m.\u001b[39msum(\u001b[38;5;28mobject\u001b[39m)\n",
-      "File \u001b[0;32m~/mambaforge/envs/phenotypes/lib/python3.10/site-packages/jax/_src/numpy/array_methods.py:256\u001b[0m, in \u001b[0;36m_defer_to_unrecognized_arg.<locals>.deferring_binary_op\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    254\u001b[0m args \u001b[38;5;241m=\u001b[39m (other, \u001b[38;5;28mself\u001b[39m) \u001b[38;5;28;01mif\u001b[39;00m swap \u001b[38;5;28;01melse\u001b[39;00m (\u001b[38;5;28mself\u001b[39m, other)\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, _accepted_binop_types):\n\u001b[0;32m--> 256\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbinary_op\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[38;5;66;03m# Note: don't use isinstance here, because we don't want to raise for\u001b[39;00m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;66;03m# subclasses, e.g. NamedTuple objects that may override operators.\u001b[39;00m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(other) \u001b[38;5;129;01min\u001b[39;00m _rejected_binop_types:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-10-16T23:33:23.762730Z",
+     "iopub.status.busy": "2024-10-16T23:33:23.762375Z",
+     "iopub.status.idle": "2024-10-16T23:33:51.983068Z",
+     "shell.execute_reply": "2024-10-16T23:33:51.982508Z"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "from scipy.signal import resample\n",
     "\n",
     "N_objects = 5000\n",
     "noise_sigma = 1e-3\n",
     "num_nyquist_samples_list = [4, 9, 16, 25, 36]\n",
-    "max_epochs = 5000\n",
+    "max_epochs = 2000\n",
     "confidence = .95\n",
-    "num_replicates = 2\n",
+    "num_mi_models = 5\n",
+    "\n",
+    "upsampled_signal_length = 3600 # LCM of 4, 9, 16, 25, 36\n",
     "\n",
     "object_names = ['white_noise', 'delta_uniform', '8_deltas_uniform']\n",
     "\n",
@@ -86,7 +80,7 @@
     "  for i in range(N_objects):  \n",
     "    obj = generate_random_object('white_noise', object_size=2*num_nyquist_samples_list[-1])\n",
     "    # upsample it\n",
-    "    obj = resample(obj, UPSAMPLED_SIGNAL_LENGTH)\n",
+    "    obj = resample(obj, upsampled_signal_length)\n",
     "    # make positive and sum to 1\n",
     "    obj -= np.min(obj)\n",
     "    obj /= np.sum(obj)\n",
@@ -94,9 +88,9 @@
     "  # print(objects[0].shape)\n",
     "  return np.array(objects) \n",
     "\n",
-    "delta_fn = lambda : np.array([generate_random_object('delta', num_deltas=1, object_size=UPSAMPLED_SIGNAL_LENGTH) for i in range(N_objects)])\n",
+    "delta_fn = lambda : np.array([generate_random_object('delta', num_deltas=1, object_size=upsampled_signal_length) for i in range(N_objects)])\n",
     "\n",
-    "delta8_fn = lambda :np.array([generate_random_object('delta', num_deltas=8, object_size=UPSAMPLED_SIGNAL_LENGTH) for i in range(N_objects)])\n",
+    "delta8_fn = lambda :np.array([generate_random_object('delta', num_deltas=8, object_size=upsampled_signal_length) for i in range(N_objects)])\n",
     "\n",
     "\n",
     "# plot an example of each object\n",
@@ -107,13 +101,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-13T05:01:31.151282Z",
-     "iopub.status.busy": "2024-10-13T05:01:31.150636Z",
-     "iopub.status.idle": "2024-10-14T14:32:24.811447Z",
-     "shell.execute_reply": "2024-10-14T14:32:24.806973Z"
+     "iopub.execute_input": "2024-10-16T23:33:51.986909Z",
+     "iopub.status.busy": "2024-10-16T23:33:51.986621Z",
+     "iopub.status.idle": "2024-10-17T07:53:15.287742Z",
+     "shell.execute_reply": "2024-10-17T07:53:15.287016Z"
     }
    },
    "outputs": [
@@ -128,97 +122,10053 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/5 [00:00<?, ?it/s]"
+      "  0%|                                                                             | 0/5 [00:00<?, ?it/s]2024-10-16 16:56:47.218173: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:09<30:28,  9.19s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:10<14:20,  4.35s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:11<09:08,  2.79s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:11<06:38,  2.03s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:12<05:18,  1.63s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:13<04:25,  1.37s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:14<03:58,  1.23s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<03:32,  1.11s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<03:14,  1.02s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<03:09,  1.00it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<03:05,  1.02it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<02:57,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<02:51,  1.09it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<02:51,  1.09it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:21<02:45,  1.12it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:22<02:45,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:23<02:46,  1.10it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:24<02:43,  1.11it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:25<02:39,  1.13it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:26<02:39,  1.13it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:27<02:35,  1.15it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:27<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:28<02:35,  1.14it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:29<02:31,  1.16it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:30<02:30,  1.16it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:31<02:37,  1.11it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:32<02:35,  1.12it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:33<02:30,  1.14it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:34<02:45,  1.03it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:35<03:10,  1.12s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:36<02:54,  1.03s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:37<02:43,  1.03it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:38<02:35,  1.08it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:39<02:31,  1.10it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:40<02:28,  1.11it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:40<02:25,  1.13it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:41<02:22,  1.14it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:42<02:21,  1.14it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:43<02:23,  1.12it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:44<02:22,  1.12it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:45<02:19,  1.14it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:46<02:21,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:47<02:18,  1.13it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:47<02:17,  1.13it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:48<02:18,  1.12it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:49<02:15,  1.13it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:50<02:15,  1.13it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:51<02:13,  1.14it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:52<02:12,  1.14it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:53<02:11,  1.14it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:54<02:11,  1.14it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:54<02:08,  1.15it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:55<02:08,  1.14it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:56<02:10,  1.12it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:57<02:07,  1.14it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:58<02:04,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:59<02:04,  1.15it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:00<02:02,  1.16it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:01<02:01,  1.16it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:01<01:59,  1.18it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:02<01:57,  1.18it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:03<01:56,  1.19it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:04<01:55,  1.19it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:05<01:55,  1.18it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:06<01:53,  1.19it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:06<01:52,  1.19it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:07<01:51,  1.20it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:08<01:49,  1.21it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:09<01:50,  1.19it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:10<01:48,  1.20it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:11<01:47,  1.20it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:11<01:47,  1.19it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:12<01:47,  1.18it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:13<01:46,  1.18it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:14<01:45,  1.19it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:15<01:44,  1.18it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:16<01:43,  1.19it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:17<01:42,  1.19it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:17<01:42,  1.18it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:18<01:41,  1.19it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:19<01:40,  1.18it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:20<01:38,  1.20it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:21<01:36,  1.21it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:21<01:35,  1.22it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:22<01:33,  1.22it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:23<01:35,  1.19it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:24<01:35,  1.18it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:25<01:35,  1.17it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:26<01:34,  1.17it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:27<01:34,  1.17it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:27<01:33,  1.17it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:28<01:32,  1.17it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:29<01:32,  1.16it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:30<01:34,  1.12it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:31<01:31,  1.15it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:32<01:31,  1.14it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:33<01:29,  1.15it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:35<01:38,  1.03it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:02,  4.84s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:27,  2.57s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:56,  1.81s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:38,  1.42s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:55,  1.21s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:29,  1.08s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:11,  1.01it/s]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:10<02:59,  1.07it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:56,  1.08it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:53,  1.10it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:46,  1.14it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:42,  1.15it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:40,  1.16it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:15<02:41,  1.15it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:16<02:37,  1.18it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:17<02:36,  1.18it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:18<02:35,  1.18it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:36,  1.16it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:20<02:34,  1.17it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:32,  1.18it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:21<02:31,  1.19it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:22<02:37,  1.13it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:23<02:41,  1.10it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:24<02:35,  1.13it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:25<02:33,  1.14it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:26<02:33,  1.13it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:30,  1.15it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<02:28,  1.16it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:28<02:26,  1.16it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:29<02:24,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:30<02:23,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:31<02:21,  1.19it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:32<02:19,  1.20it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:33<02:16,  1.22it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:33<02:16,  1.21it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:34<02:15,  1.21it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:35<02:12,  1.23it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:06,  1.29it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:36<02:02,  1.31it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:37<02:11,  1.22it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:38<02:02,  1.30it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:39<02:01,  1.30it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:39<01:55,  1.36it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:40<01:54,  1.37it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:41<01:50,  1.40it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:42<01:56,  1.32it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:43<02:02,  1.25it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:43<02:02,  1.24it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:44<02:02,  1.23it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:45<02:04,  1.20it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:46<02:04,  1.20it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:47<02:06,  1.17it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:48<02:07,  1.15it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:49<02:08,  1.13it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:50<02:09,  1.12it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:51<02:09,  1.11it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:51<02:09,  1.11it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:52<02:10,  1.09it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:53<02:12,  1.07it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:54<02:10,  1.07it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:55<02:11,  1.06it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:56<02:08,  1.07it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:57<02:05,  1.09it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [00:58<02:04,  1.09it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [00:59<02:05,  1.08it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:00<02:05,  1.07it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:01<02:03,  1.08it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:02<02:03,  1.07it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:03<02:01,  1.08it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:04<01:58,  1.10it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:05<01:59,  1.08it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:05<01:58,  1.08it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:06<01:53,  1.12it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:07<01:52,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:08<01:55,  1.08it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:09<01:52,  1.10it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:10<01:48,  1.13it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:11<01:49,  1.11it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:12<01:52,  1.08it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:13<01:53,  1.05it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:14<01:50,  1.07it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:15<01:47,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:15<01:48,  1.08it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:16<01:45,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:17<01:42,  1.12it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:18<01:44,  1.10it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:19<01:45,  1.07it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:20<01:41,  1.10it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:21<01:39,  1.11it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:22<01:39,  1.10it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:23<01:39,  1.09it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:24<01:56,  1.08s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:26<02:10,  1.22s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:27<01:59,  1.13s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:28<01:50,  1.05s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:28<01:42,  1.01it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:29<01:38,  1.05it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:31<01:35,  1.07it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:59,  5.42s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:16,  2.81s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:28,  1.97s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:05,  1.56s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:17,  1.32s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<03:47,  1.17s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:26,  1.07s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:15,  1.02s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:07,  1.02it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:01,  1.05it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:58,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:53,  1.08it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:16<02:49,  1.10it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:17<02:46,  1.12it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:18<02:43,  1.13it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:43,  1.13it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:42,  1.13it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:42,  1.12it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:44,  1.10it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:44,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:39,  1.12it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:37,  1.13it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:25<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:31,  1.16it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:30,  1.17it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:27,  1.18it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:25,  1.19it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:29<02:24,  1.19it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:30<02:24,  1.18it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:31<02:24,  1.17it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:31<02:23,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:32<02:22,  1.18it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:19,  1.19it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:34<02:18,  1.20it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:35<02:17,  1.20it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:36<02:20,  1.17it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:36<02:20,  1.16it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:37<02:22,  1.14it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:38<02:24,  1.12it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:39<02:22,  1.12it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:40<02:22,  1.11it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:41<02:21,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:42<02:21,  1.11it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:43<02:17,  1.14it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:44<02:14,  1.16it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:44<02:11,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:45<02:10,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:46<02:10,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:47<02:15,  1.12it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:48<02:11,  1.14it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:49<02:09,  1.15it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:50<02:07,  1.16it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:51<02:07,  1.16it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:05,  1.16it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:05,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:53<02:05,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:04,  1.15it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:55<02:03,  1.15it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:56<02:03,  1.14it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:57<02:04,  1.13it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:58<02:04,  1.12it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:59<02:05,  1.10it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:59<02:04,  1.10it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:00<02:05,  1.08it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:01<02:06,  1.06it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:02<02:05,  1.07it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:03<02:01,  1.09it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:04<02:00,  1.10it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:05<01:58,  1.11it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:06<01:56,  1.11it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:07<01:54,  1.12it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:08<01:53,  1.13it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:08<01:52,  1.13it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:09<01:51,  1.13it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:10<01:49,  1.14it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:11<01:47,  1.15it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:12<01:46,  1.15it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:13<01:46,  1.15it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:14<01:45,  1.15it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:15<01:46,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:15<01:43,  1.15it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:16<01:46,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:18<01:51,  1.05it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:18<01:48,  1.07it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:19<01:47,  1.07it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:20<01:46,  1.07it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:21<01:40,  1.13it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:22<01:33,  1.20it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:23<01:30,  1.22it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:23<01:25,  1.28it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:24<01:23,  1.31it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:25<01:20,  1.35it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:25<01:20,  1.33it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:26<01:17,  1.37it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:27<01:16,  1.37it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:28<01:20,  1.29it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:29<01:22,  1.24it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:29<01:25,  1.20it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:31<01:31,  1.11it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:31<01:30,  1.11it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:32<01:30,  1.09it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:33<01:29,  1.09it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:34<01:28,  1.10it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:35<01:26,  1.10it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:36<01:25,  1.11it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:37<01:25,  1.09it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:38<01:24,  1.10it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:39<01:22,  1.11it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:40<01:22,  1.11it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:41<01:22,  1.09it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:42<01:23,  1.07it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:43<01:24,  1.05it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:44<01:24,  1.03it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:45<01:23,  1.03it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:46<01:23,  1.02it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:46<01:22,  1.02it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:47<01:20,  1.03it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:48<01:19,  1.04it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:49<01:18,  1.03it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:50<01:17,  1.03it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:51<01:16,  1.03it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:52<01:16,  1.02it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:53<01:15,  1.02it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:54<01:14,  1.03it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:55<01:13,  1.02it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:56<01:12,  1.02it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:57<01:11,  1.02it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:58<01:09,  1.03it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:59<01:08,  1.03it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:00<01:08,  1.03it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:01<01:05,  1.05it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:02<01:05,  1.04it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:03<01:05,  1.03it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:04<01:02,  1.05it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:05<01:00,  1.08it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:07<00:59,  1.07it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:59,  5.42s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:05,  2.76s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:11,  1.89s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:14,  1.60s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:16,  1.32s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:43,  1.16s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:24,  1.06s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:11,  1.00s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:00,  1.05it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:52,  1.09it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:48,  1.12it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:16<02:44,  1.14it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:17<02:42,  1.15it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:18<02:38,  1.16it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:19<02:38,  1.16it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:20<02:38,  1.15it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:39,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:38,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:38,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:35,  1.15it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:35,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:25<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:26<02:33,  1.15it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:31,  1.16it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:32,  1.14it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:31,  1.14it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:29<02:29,  1.15it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:30<02:27,  1.16it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:31<02:24,  1.17it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:32<02:23,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:33<02:24,  1.16it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:23,  1.16it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:34<02:23,  1.15it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:35<02:26,  1.13it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:36<02:28,  1.11it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:37<02:26,  1.11it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:38<02:24,  1.12it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:39<02:21,  1.14it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:40<02:15,  1.18it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:40<02:14,  1.18it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:41<02:12,  1.19it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:42<02:09,  1.21it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:43<02:05,  1.24it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:43<02:00,  1.29it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:44<02:01,  1.27it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:45<02:00,  1.27it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:46<02:02,  1.24it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:47<02:03,  1.23it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:48<02:05,  1.19it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:48<02:05,  1.19it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:49<02:05,  1.18it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:50<02:05,  1.17it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:03,  1.18it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:03,  1.17it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:53<02:04,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:04,  1.15it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:55<02:04,  1.14it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:55<02:02,  1.15it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:56<02:01,  1.15it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:57<02:00,  1.16it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:58<02:00,  1.15it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:59<01:58,  1.15it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:00<01:59,  1.14it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:01<01:56,  1.16it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:01<01:54,  1.18it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:02<01:53,  1.17it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:03<01:51,  1.18it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:04<01:50,  1.18it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:05<01:49,  1.19it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:06<01:49,  1.17it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:07<01:48,  1.18it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:07<01:47,  1.18it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:08<01:46,  1.18it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:09<01:46,  1.17it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:10<01:47,  1.15it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:11<01:47,  1.14it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:12<01:46,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:14<01:53,  1.07it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:31,  5.28s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:50,  2.68s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:06,  1.86s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:51,  1.49s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:08,  1.28s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:42,  1.15s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:27,  1.07s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:10,  1.01it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<02:58,  1.07it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<02:51,  1.11it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:49,  1.11it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:44,  1.14it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:42,  1.15it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:40,  1.16it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:38,  1.17it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:43,  1.13it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:48,  1.08it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:43,  1.12it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:40,  1.13it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:39,  1.13it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:22<02:35,  1.15it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:23<02:33,  1.16it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:32,  1.16it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:31,  1.16it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:30,  1.15it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:28,  1.16it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<02:27,  1.17it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:29<02:25,  1.18it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:30<02:24,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:31<02:22,  1.19it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:32<02:22,  1.18it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:25,  1.15it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:34<02:27,  1.12it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:34<02:29,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:35<02:27,  1.11it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:36<02:23,  1.14it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:37<02:23,  1.13it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:38<02:29,  1.08it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:39<02:24,  1.10it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:40<02:22,  1.12it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:41<02:19,  1.14it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:42<02:16,  1.15it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:42<02:18,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:43<02:19,  1.11it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:44<02:19,  1.10it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:45<02:18,  1.10it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:46<02:18,  1.10it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:47<02:18,  1.09it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:48<02:19,  1.08it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:49<02:19,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:50<02:18,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:51<02:16,  1.08it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:52<02:16,  1.07it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:53<02:14,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:54<02:12,  1.09it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:12,  1.08it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:55<02:12,  1.07it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:56<02:12,  1.06it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:57<02:13,  1.05it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:58<02:12,  1.05it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:59<02:13,  1.04it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:00<02:10,  1.05it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:01<02:13,  1.02it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:02<02:12,  1.02it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:03<02:10,  1.03it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:04<02:06,  1.05it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:05<02:04,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:06<02:01,  1.08it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:07<02:01,  1.07it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:08<01:59,  1.08it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:09<01:57,  1.09it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:10<01:57,  1.08it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:11<01:55,  1.09it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:12<01:57,  1.06it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:12<01:52,  1.10it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:13<01:51,  1.11it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:14<01:50,  1.10it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:15<01:55,  1.05it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:16<01:54,  1.04it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:17<01:51,  1.07it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:18<01:51,  1.06it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:19<02:04,  1.06s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:21<01:53,  1.02it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.45s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:04,  1.42it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.07s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.46it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.64it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.66it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.56it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.42s/it]\u001b[A\n",
+      "Evaluating NLL: 64it [00:02, 35.51it/s]\u001b[A\n",
+      "Evaluating NLL: 126it [00:02, 78.49it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 72.93it/s] \n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%|█▍                   | 7/100 [00:00<00:01, 68.48it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|███████            | 37/100 [00:00<00:00, 198.83it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|████████████▌      | 66/100 [00:00<00:00, 235.75it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████| 100/100 [00:00<00:00, 229.12it/s]\n",
+      " 20%|█████████████                                                    | 1/5 [31:28<2:05:54, 1888.66s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:07<25:42,  7.75s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:08<12:13,  3.70s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:09<07:56,  2.42s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:10<06:01,  1.85s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:12<05:40,  1.74s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:13<04:50,  1.50s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:08,  1.29s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<03:41,  1.15s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<03:23,  1.07s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<03:12,  1.01s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:04,  1.02it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<02:58,  1.05it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<02:52,  1.08it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<02:47,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:20<02:44,  1.13it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:21<02:42,  1.14it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:22<02:39,  1.15it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:23<02:38,  1.15it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:24<02:38,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:25<02:36,  1.15it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:26<02:36,  1.15it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:26<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:27<02:32,  1.16it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:28<02:32,  1.15it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:29<02:30,  1.16it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:30<02:31,  1.15it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:31<02:29,  1.16it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:32<02:30,  1.14it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:33<02:30,  1.13it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:33<02:28,  1.15it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:34<02:27,  1.14it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:35<02:26,  1.15it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:36<02:24,  1.15it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:37<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:38<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:39<02:23,  1.14it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:40<02:22,  1.14it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:40<02:22,  1.14it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:41<02:21,  1.13it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:42<02:23,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:43<02:26,  1.09it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:44<02:25,  1.09it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:45<02:27,  1.07it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:46<02:24,  1.08it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:47<02:20,  1.10it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:48<02:15,  1.14it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:48<02:11,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:49<02:09,  1.18it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:50<02:06,  1.19it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:51<02:03,  1.22it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:52<02:03,  1.21it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:53<02:01,  1.22it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:53<02:03,  1.19it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:54<02:07,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:55<02:10,  1.11it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:56<02:11,  1.10it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:57<02:12,  1.08it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:58<02:15,  1.05it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:59<02:13,  1.05it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:00<02:14,  1.04it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:01<02:11,  1.05it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:02<02:08,  1.07it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:03<02:09,  1.06it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:04<02:07,  1.07it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:05<02:06,  1.06it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:06<02:05,  1.06it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:07<02:07,  1.04it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:08<02:04,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:09<02:03,  1.06it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:10<02:01,  1.07it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:11<02:00,  1.07it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:11<02:00,  1.07it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:12<01:58,  1.07it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:13<01:57,  1.08it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:14<01:55,  1.08it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:16<02:05,  1.01s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:36,  5.01s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:33,  2.59s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<06:02,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:48,  1.47s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:07,  1.27s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:41,  1.14s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:23,  1.06s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:13,  1.01s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:06,  1.03it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:01,  1.05it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:58,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:56,  1.07it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:56,  1.06it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:58,  1.04it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<03:00,  1.02it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:59,  1.02it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:54,  1.05it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:52,  1.06it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:50,  1.06it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:49,  1.06it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:47,  1.07it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:50,  1.05it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:25<02:46,  1.06it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:26<02:46,  1.06it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:27<02:45,  1.06it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:28<02:40,  1.08it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:29<02:37,  1.10it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:30<02:37,  1.09it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:30<02:36,  1.09it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:32<02:42,  1.05it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:32<02:39,  1.06it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:33<02:38,  1.06it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:34<02:36,  1.07it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:35<02:31,  1.09it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:36<02:31,  1.09it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:37<02:31,  1.08it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:38<02:30,  1.08it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:39<02:28,  1.09it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:40<02:26,  1.10it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:41<02:24,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:42<02:25,  1.09it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:43<02:24,  1.09it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:43<02:24,  1.09it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:44<02:23,  1.08it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:45<02:23,  1.08it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:46<02:23,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:47<02:21,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:48<02:17,  1.10it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:49<02:13,  1.13it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:50<02:10,  1.15it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:51<02:09,  1.15it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:51<02:06,  1.17it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:52<02:05,  1.17it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:53<02:06,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:54<02:03,  1.17it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:55<02:05,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:56<02:06,  1.13it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:57<02:08,  1.10it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:58<02:10,  1.08it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:59<02:12,  1.06it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:00<02:10,  1.07it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:01<02:11,  1.05it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:02<02:10,  1.05it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:02<02:06,  1.07it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:03<02:04,  1.08it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:04<02:05,  1.07it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:06<02:12,  1.01it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:06<19:57,  6.02s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:07<11:11,  3.39s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:25,  2.26s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:39,  1.73s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<04:40,  1.44s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:05,  1.26s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<03:42,  1.15s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<03:28,  1.08s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<03:19,  1.05s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:15<03:16,  1.03s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:16<03:13,  1.02s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:17<03:09,  1.01s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<03:03,  1.02it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<03:01,  1.03it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:19<03:01,  1.02it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:20<02:58,  1.03it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:21<02:56,  1.04it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:22<02:57,  1.03it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:23<02:51,  1.06it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:24<02:50,  1.06it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:25<02:46,  1.07it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:26<02:44,  1.08it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:27<02:41,  1.09it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:28<02:38,  1.11it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:29<02:37,  1.11it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:29<02:35,  1.12it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:30<02:39,  1.09it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:31<02:37,  1.09it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:32<02:38,  1.08it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:33<02:35,  1.09it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:34<02:34,  1.10it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:35<02:31,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:36<02:28,  1.13it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:37<02:27,  1.12it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:38<02:27,  1.12it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:39<02:25,  1.13it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:39<02:27,  1.10it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:40<02:27,  1.10it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:41<02:26,  1.10it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:42<02:23,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:43<02:23,  1.11it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:44<02:22,  1.11it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:45<02:21,  1.11it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:46<02:20,  1.11it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:47<02:20,  1.11it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:48<02:21,  1.09it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:49<02:21,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:50<02:20,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:50<02:20,  1.08it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:51<02:19,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:52<02:17,  1.08it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:53<02:16,  1.09it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:54<02:14,  1.09it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:55<02:14,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:56<02:14,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:57<02:13,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:58<02:13,  1.07it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:59<02:11,  1.08it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:00<02:11,  1.08it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:01<02:09,  1.08it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:02<02:14,  1.04it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:03<02:11,  1.05it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:03<02:07,  1.07it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:04<02:04,  1.09it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:05<02:03,  1.09it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:06<02:03,  1.09it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:07<02:00,  1.10it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:08<01:59,  1.10it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:09<01:57,  1.11it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:10<01:56,  1.12it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:11<01:54,  1.12it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:12<01:53,  1.13it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:12<01:53,  1.12it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:13<01:53,  1.11it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:14<01:53,  1.10it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:15<01:51,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:16<01:49,  1.13it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:17<01:46,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:18<01:46,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:19<01:45,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:19<01:43,  1.15it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:20<01:44,  1.13it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:21<01:42,  1.14it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:22<01:41,  1.15it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:23<01:41,  1.13it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:24<01:41,  1.12it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:25<01:40,  1.12it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:26<01:40,  1.12it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:27<01:40,  1.10it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:28<01:39,  1.11it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:28<01:38,  1.11it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:29<01:37,  1.11it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:30<01:36,  1.11it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:31<01:40,  1.06it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:32<01:40,  1.04it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:33<01:39,  1.05it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:34<01:36,  1.06it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:35<01:34,  1.07it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:36<01:31,  1.10it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:37<01:31,  1.10it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:38<01:30,  1.10it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:39<01:28,  1.11it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:40<01:28,  1.10it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:40<01:27,  1.10it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:41<01:26,  1.10it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:42<01:25,  1.10it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:43<01:25,  1.09it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:44<01:24,  1.09it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:45<01:24,  1.07it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:46<01:26,  1.05it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:48<01:26,  1.02it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:37,  5.31s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:13,  2.79s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:27,  1.97s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:14,  1.60s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:26,  1.37s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<03:57,  1.22s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:39,  1.14s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:28,  1.09s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:18,  1.04s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:13,  1.02s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<03:11,  1.01s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<03:09,  1.01s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<03:05,  1.01it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<03:02,  1.02it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:18<02:59,  1.03it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:19<02:59,  1.02it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:20<02:59,  1.02it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:21<02:58,  1.02it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:22<02:54,  1.04it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:23<02:54,  1.03it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:24<02:58,  1.00it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:25<02:54,  1.02it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:26<02:48,  1.05it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:27<02:46,  1.06it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:28<02:44,  1.06it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:29<02:44,  1.06it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:30<02:48,  1.03it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:31<02:49,  1.01it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:32<02:44,  1.04it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:33<02:41,  1.06it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:34<02:37,  1.08it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:35<02:30,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:35<02:26,  1.14it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:36<02:32,  1.08it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:37<02:27,  1.12it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:38<02:23,  1.14it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:39<02:37,  1.04it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:41<02:59,  1.11s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:42<03:05,  1.15s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:43<02:52,  1.08s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:44<02:44,  1.03s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:45<02:37,  1.01it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:46<02:32,  1.03it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:47<02:30,  1.04it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:47<02:24,  1.08it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:48<02:21,  1.09it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:49<02:20,  1.09it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:50<02:20,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:51<02:19,  1.08it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:52<02:17,  1.09it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:53<02:14,  1.11it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:54<02:12,  1.12it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:55<02:13,  1.10it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:56<02:12,  1.10it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:57<02:11,  1.10it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:57<02:09,  1.11it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:58<02:07,  1.12it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:59<02:06,  1.12it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:00<02:05,  1.12it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:01<02:03,  1.14it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:02<02:01,  1.14it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:03<02:00,  1.14it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:04<02:00,  1.14it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:04<02:00,  1.13it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:05<01:59,  1.13it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:06<01:59,  1.12it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:07<01:57,  1.13it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:08<01:57,  1.12it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:09<01:58,  1.10it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:10<01:58,  1.10it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:11<01:57,  1.10it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:12<01:55,  1.11it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:13<01:53,  1.12it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:13<01:52,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:14<01:51,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:15<01:50,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:17<02:03,  1.01s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:42,  5.04s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:37,  2.61s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<06:01,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:47,  1.47s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:08,  1.28s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:45,  1.16s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:30,  1.09s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:21,  1.05s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:11,  1.00s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:06,  1.02it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<03:03,  1.03it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:59,  1.05it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:16<02:55,  1.07it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:17<02:52,  1.08it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:18<02:54,  1.06it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:53,  1.06it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:53,  1.06it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:50,  1.07it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:47,  1.08it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:46,  1.08it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:45,  1.08it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:42,  1.10it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:25<02:42,  1.09it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:26<02:38,  1.11it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:27<02:35,  1.12it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:28<02:34,  1.12it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:35,  1.11it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:29<02:34,  1.11it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:30<02:34,  1.11it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:31<02:31,  1.12it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:32<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:33<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:34<02:30,  1.11it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:35<02:29,  1.11it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:36<02:28,  1.11it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:37<02:26,  1.12it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:37<02:26,  1.11it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:38<02:26,  1.11it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:39<02:24,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:40<02:24,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:41<02:23,  1.11it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:42<02:21,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:43<02:21,  1.11it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:44<02:19,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:45<02:18,  1.12it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:46<02:18,  1.11it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:46<02:19,  1.10it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:47<02:21,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:48<02:22,  1.06it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:49<02:19,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:50<02:17,  1.08it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:51<02:17,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:52<02:17,  1.07it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:53<02:16,  1.07it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:54<02:15,  1.07it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:55<02:13,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:56<02:12,  1.08it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:57<02:10,  1.09it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:58<02:10,  1.08it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:59<02:08,  1.09it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:59<02:07,  1.09it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:00<02:08,  1.07it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:01<02:08,  1.07it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:02<02:09,  1.05it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:03<02:06,  1.07it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:04<02:07,  1.05it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:05<02:04,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:06<02:04,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:07<02:04,  1.06it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:08<02:04,  1.05it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:09<02:02,  1.06it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:10<01:58,  1.08it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:11<01:57,  1.08it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:12<01:55,  1.09it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:13<01:56,  1.07it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:14<01:56,  1.06it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:15<01:55,  1.06it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:16<01:57,  1.04it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:16<01:55,  1.05it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:17<01:53,  1.05it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:18<01:52,  1.06it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:19<01:52,  1.04it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:20<01:50,  1.06it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:21<01:49,  1.06it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:22<01:50,  1.04it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:23<01:48,  1.05it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:24<01:45,  1.07it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:25<01:45,  1.06it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:26<01:44,  1.06it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:27<01:45,  1.04it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:28<01:44,  1.04it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:29<01:44,  1.04it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:30<01:43,  1.04it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:31<01:41,  1.05it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:32<01:40,  1.04it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:33<01:37,  1.06it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:34<01:38,  1.05it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:35<01:37,  1.05it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:35<01:34,  1.07it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:36<01:32,  1.08it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:37<01:33,  1.06it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:38<01:32,  1.06it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:39<01:29,  1.08it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:40<01:29,  1.07it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:41<01:27,  1.08it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:42<01:26,  1.08it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:43<01:30,  1.03it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:44<01:28,  1.04it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:45<01:26,  1.05it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:46<01:26,  1.04it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:47<01:25,  1.04it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:48<01:23,  1.05it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:49<01:24,  1.03it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:50<01:37,  1.14s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:51<01:32,  1.09s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:52<01:26,  1.03s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:53<01:21,  1.02it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:54<01:17,  1.06it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:55<01:17,  1.05it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:56<01:16,  1.05it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:57<01:14,  1.06it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:58<01:14,  1.05it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:59<01:12,  1.07it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:00<01:09,  1.09it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:00<01:06,  1.13it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:01<01:03,  1.17it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:02<01:00,  1.20it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:03<00:58,  1.23it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:03<00:57,  1.23it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:04<00:56,  1.24it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:05<00:56,  1.23it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:06<00:57,  1.18it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:07<00:57,  1.16it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:09<01:03,  1.04it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.92s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:05,  1.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.60it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.70it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.06s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.43it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.67it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.64s/it]\u001b[A\n",
+      "Evaluating NLL: 66it [00:02, 33.68it/s]\u001b[A\n",
+      "Evaluating NLL: 116it [00:02, 65.46it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 67.17it/s] \n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|██                  | 10/100 [00:00<00:00, 97.39it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|███████▌           | 40/100 [00:00<00:00, 210.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████| 100/100 [00:00<00:00, 247.34it/s]\n",
+      " 40%|█████████████████████████▏                                     | 2/5 [1:02:49<1:34:11, 1883.88s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:09<31:39,  9.54s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:10<15:09,  4.59s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:11<09:43,  2.96s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:12<07:08,  2.19s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:13<05:51,  1.80s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:14<04:56,  1.53s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:15<04:22,  1.36s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:16<04:02,  1.27s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:17<03:47,  1.19s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:18<03:34,  1.13s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<03:28,  1.11s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<03:20,  1.07s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<03:14,  1.04s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<03:09,  1.02s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<03:18,  1.07s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<03:47,  1.24s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<03:47,  1.24s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:32,  1.16s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:19,  1.10s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<02:59,  1.00it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<02:45,  1.08it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<02:33,  1.16it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<02:24,  1.22it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:32<02:18,  1.27it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:33<02:12,  1.32it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:34<02:14,  1.29it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:34<02:10,  1.32it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:35<02:07,  1.35it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:36<02:15,  1.26it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:37<02:20,  1.21it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:38<02:26,  1.15it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:39<02:30,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:40<02:31,  1.10it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:41<02:34,  1.07it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:42<02:37,  1.05it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:43<02:38,  1.04it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:44<02:37,  1.04it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:45<02:33,  1.06it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:45<02:32,  1.06it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:46<02:33,  1.04it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:47<02:32,  1.04it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:48<02:32,  1.03it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:49<02:31,  1.04it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:50<02:27,  1.06it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:51<02:26,  1.06it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:52<02:27,  1.04it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:53<02:27,  1.04it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:54<02:26,  1.04it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:55<02:24,  1.04it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:56<02:22,  1.05it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:57<02:21,  1.06it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:58<02:19,  1.06it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:59<02:20,  1.05it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:00<02:20,  1.04it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:01<02:16,  1.06it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:02<02:14,  1.07it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:03<02:15,  1.06it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:04<02:13,  1.06it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:04<02:12,  1.07it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:06<02:14,  1.04it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:07<02:16,  1.02it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:08<02:16,  1.01it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:10<02:32,  1.11s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:07,  5.47s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:26,  2.86s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:37,  2.02s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:18,  1.63s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:39,  1.43s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:13,  1.31s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:54,  1.22s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:44,  1.17s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:34,  1.12s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:28,  1.10s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<03:23,  1.08s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<03:15,  1.04s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<03:17,  1.06s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<03:12,  1.04s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:19<03:08,  1.02s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:20<03:04,  1.00s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:21<03:07,  1.02s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:22<03:04,  1.01s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:23<03:01,  1.00s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:24<03:03,  1.02s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:25<03:02,  1.02s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:27<03:01,  1.02s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:28<03:05,  1.05s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:29<03:05,  1.06s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:30<02:59,  1.03s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:31<02:57,  1.02s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:32<02:58,  1.03s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:33<03:01,  1.06s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:34<03:01,  1.06s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:35<03:01,  1.07s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:36<03:01,  1.07s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:37<03:04,  1.10s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:38<03:04,  1.10s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:39<03:01,  1.09s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:41<03:04,  1.12s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:42<03:06,  1.14s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:43<03:03,  1.13s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:44<03:01,  1.12s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:45<02:58,  1.11s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:46<02:57,  1.11s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:47<02:54,  1.10s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:48<02:51,  1.09s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:49<02:52,  1.10s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:51<02:49,  1.09s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:52<02:47,  1.08s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:53<02:45,  1.07s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:54<02:42,  1.06s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:55<02:46,  1.10s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:56<02:46,  1.10s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:57<02:44,  1.10s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:58<02:44,  1.10s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:59<02:40,  1.08s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:00<02:45,  1.12s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:01<02:42,  1.11s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:03<02:39,  1.10s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:04<02:38,  1.10s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:05<02:37,  1.10s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:06<02:39,  1.12s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:07<02:36,  1.11s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:08<02:32,  1.09s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:09<02:31,  1.09s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:10<02:33,  1.11s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:12<02:35,  1.13s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:13<02:35,  1.14s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:14<02:31,  1.12s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:15<02:30,  1.12s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:17<02:34,  1.16s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<16:43,  5.04s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:04,  2.75s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:41,  2.04s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:31,  1.69s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:50,  1.49s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<04:14,  1.31s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:11<03:49,  1.19s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:12<03:32,  1.10s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:13<03:26,  1.08s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:14<03:20,  1.05s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:15<03:15,  1.04s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:16<03:12,  1.03s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:17<03:09,  1.01s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:18<03:08,  1.01s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:19<03:06,  1.01s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:20<03:05,  1.01s/it]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:21<03:03,  1.00s/it]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:22<03:01,  1.00it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:23<03:00,  1.00it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:24<03:00,  1.00s/it]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:25<03:01,  1.02s/it]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:26<02:59,  1.01s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:27<02:59,  1.01s/it]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:28<02:57,  1.01s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:29<02:55,  1.00s/it]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:30<02:53,  1.00it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:31<02:52,  1.00it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:32<02:49,  1.02it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:33<02:47,  1.02it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:34<02:47,  1.02it/s]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:35<02:45,  1.02it/s]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:36<02:43,  1.03it/s]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:37<02:44,  1.02it/s]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:38<02:40,  1.03it/s]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:39<02:39,  1.03it/s]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:40<02:39,  1.03it/s]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:41<02:39,  1.02it/s]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:42<02:37,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:43<02:37,  1.02it/s]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:44<02:35,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:45<02:33,  1.04it/s]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:46<02:34,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:47<02:33,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:48<02:33,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:49<02:32,  1.02it/s]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:50<02:32,  1.01it/s]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:51<02:32,  1.00it/s]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:52<02:29,  1.02it/s]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:53<02:30,  1.00it/s]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:54<02:32,  1.02s/it]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [00:55<02:31,  1.01s/it]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [00:56<02:28,  1.00s/it]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [00:57<02:28,  1.01s/it]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [00:58<02:26,  1.00s/it]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [00:59<02:25,  1.00s/it]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [01:00<02:28,  1.03s/it]\u001b[A\n",
+      "training:  28%|█████████▋                        | 57/200 [01:01<02:27,  1.03s/it]\u001b[A\n",
+      "training:  29%|█████████▊                        | 58/200 [01:02<02:26,  1.03s/it]\u001b[A\n",
+      "training:  30%|██████████                        | 59/200 [01:03<02:24,  1.02s/it]\u001b[A\n",
+      "training:  30%|██████████▏                       | 60/200 [01:04<02:26,  1.05s/it]\u001b[A\n",
+      "training:  30%|██████████▎                       | 61/200 [01:05<02:24,  1.04s/it]\u001b[A\n",
+      "training:  31%|██████████▌                       | 62/200 [01:07<02:30,  1.09s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:04<15:07,  4.56s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:05<07:46,  2.36s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:06<05:18,  1.62s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:06<04:06,  1.26s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:07<03:33,  1.10s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:08<03:08,  1.03it/s]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:09<02:52,  1.12it/s]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:09<02:41,  1.19it/s]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:10<02:35,  1.23it/s]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:11<02:32,  1.25it/s]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:12<02:29,  1.27it/s]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:12<02:31,  1.24it/s]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:13<02:32,  1.23it/s]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:14<02:29,  1.24it/s]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:15<02:26,  1.26it/s]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:16<02:23,  1.28it/s]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:16<02:21,  1.29it/s]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:17<02:21,  1.29it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:18<02:19,  1.29it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:19<02:19,  1.29it/s]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:19<02:20,  1.27it/s]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:20<02:25,  1.22it/s]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:21<02:29,  1.18it/s]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:22<02:31,  1.16it/s]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:23<02:32,  1.15it/s]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:24<02:31,  1.15it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:25<02:34,  1.12it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:26<02:32,  1.13it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:27<02:40,  1.06it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:28<02:39,  1.07it/s]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:29<02:38,  1.07it/s]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:30<02:33,  1.09it/s]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:30<02:30,  1.11it/s]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:31<02:30,  1.10it/s]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:32<02:29,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:33<02:31,  1.08it/s]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:34<02:27,  1.10it/s]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:35<02:27,  1.10it/s]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:36<02:24,  1.11it/s]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:37<02:23,  1.11it/s]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:38<02:26,  1.09it/s]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:39<02:29,  1.06it/s]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:40<02:30,  1.04it/s]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:41<02:30,  1.03it/s]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:42<02:30,  1.03it/s]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:43<02:30,  1.02it/s]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:44<02:29,  1.03it/s]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:45<02:27,  1.03it/s]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:46<02:27,  1.02it/s]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:47<02:28,  1.01it/s]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [00:48<02:28,  1.01it/s]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [00:49<02:26,  1.01it/s]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [00:50<02:26,  1.00it/s]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [00:51<02:25,  1.00it/s]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [00:52<02:24,  1.01it/s]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [00:53<02:22,  1.01it/s]\u001b[A\n",
+      "training:  28%|█████████▋                        | 57/200 [00:54<02:22,  1.00it/s]\u001b[A\n",
+      "training:  29%|█████████▊                        | 58/200 [00:55<02:19,  1.02it/s]\u001b[A\n",
+      "training:  30%|██████████                        | 59/200 [00:56<02:21,  1.00s/it]\u001b[A\n",
+      "training:  30%|██████████▏                       | 60/200 [00:57<02:16,  1.03it/s]\u001b[A\n",
+      "training:  30%|██████████▎                       | 61/200 [00:58<02:18,  1.00it/s]\u001b[A\n",
+      "training:  31%|██████████▌                       | 62/200 [00:59<02:19,  1.01s/it]\u001b[A\n",
+      "training:  32%|██████████▋                       | 63/200 [01:00<02:15,  1.01it/s]\u001b[A\n",
+      "training:  32%|██████████▉                       | 64/200 [01:00<02:12,  1.03it/s]\u001b[A\n",
+      "training:  32%|███████████                       | 65/200 [01:01<02:10,  1.04it/s]\u001b[A\n",
+      "training:  33%|███████████▏                      | 66/200 [01:02<02:06,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████▍                      | 67/200 [01:04<02:08,  1.04it/s]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<17:59,  5.43s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:48,  2.97s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:08<07:22,  2.25s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:27,  1.67s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:18,  1.33s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<03:36,  1.11s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:11<03:14,  1.01s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:11<03:02,  1.05it/s]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:12<02:49,  1.13it/s]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:13<02:53,  1.10it/s]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:14<02:55,  1.08it/s]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:15<02:58,  1.05it/s]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:16<02:59,  1.04it/s]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:17<03:01,  1.03it/s]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:18<03:03,  1.01it/s]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:19<03:02,  1.01it/s]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:20<03:01,  1.01it/s]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:21<03:04,  1.01s/it]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:22<03:04,  1.02s/it]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:23<03:03,  1.02s/it]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:24<03:03,  1.03s/it]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:25<03:02,  1.02s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:26<03:01,  1.02s/it]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:27<03:00,  1.03s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:28<03:00,  1.03s/it]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:29<02:58,  1.02s/it]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:30<02:57,  1.02s/it]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:31<02:56,  1.02s/it]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:32<02:56,  1.03s/it]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:33<02:53,  1.02s/it]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:34<02:50,  1.01s/it]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:35<02:48,  1.00s/it]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:36<02:47,  1.00s/it]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:37<02:45,  1.00it/s]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:38<02:44,  1.00it/s]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:39<02:42,  1.01it/s]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:40<02:39,  1.02it/s]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:41<02:37,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:42<02:38,  1.01it/s]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:43<02:38,  1.01it/s]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:44<02:38,  1.00it/s]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:45<02:35,  1.01it/s]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:46<02:35,  1.01it/s]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:47<02:33,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:48<02:31,  1.02it/s]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:49<02:29,  1.03it/s]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:50<02:29,  1.02it/s]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:51<02:27,  1.03it/s]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:52<02:25,  1.04it/s]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:53<02:25,  1.03it/s]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [00:54<02:25,  1.03it/s]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [00:55<02:24,  1.02it/s]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [00:56<02:24,  1.02it/s]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [00:57<02:23,  1.02it/s]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [00:58<02:23,  1.01it/s]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [00:59<02:23,  1.00it/s]\u001b[A\n",
+      "training:  28%|█████████▋                        | 57/200 [01:00<02:22,  1.01it/s]\u001b[A\n",
+      "training:  29%|█████████▊                        | 58/200 [01:01<02:16,  1.04it/s]\u001b[A\n",
+      "training:  30%|██████████                        | 59/200 [01:02<02:10,  1.08it/s]\u001b[A\n",
+      "training:  30%|██████████▏                       | 60/200 [01:02<02:01,  1.15it/s]\u001b[A\n",
+      "training:  30%|██████████▎                       | 61/200 [01:03<01:56,  1.19it/s]\u001b[A\n",
+      "training:  31%|██████████▌                       | 62/200 [01:04<01:50,  1.25it/s]\u001b[A\n",
+      "training:  32%|██████████▋                       | 63/200 [01:05<01:50,  1.24it/s]\u001b[A\n",
+      "training:  32%|██████████▉                       | 64/200 [01:05<01:45,  1.29it/s]\u001b[A\n",
+      "training:  32%|███████████                       | 65/200 [01:07<02:20,  1.04s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.89s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:05,  1.23it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.68it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.68it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.19it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.61s/it]\u001b[A\n",
+      "Evaluating NLL: 61it [00:02, 31.49it/s]\u001b[A\n",
+      "Evaluating NLL: 116it [00:02, 67.25it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 66.77it/s] \n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%| | 7/100 [00:00<00:01, 68.58it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|▍| 40/100 [00:00<00:00, 219.85i\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|▋| 65/100 [00:00<00:00, 232.76i\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 223.46\n",
+      " 60%|█████████████████████████████████████▊                         | 3/5 [1:32:22<1:01:06, 1833.23s/it]\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:09<30:58,  9.34s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:10<14:57,  4.53s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:11<09:40,  2.95s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:12<07:10,  2.20s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:13<05:50,  1.80s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:14<05:03,  1.56s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:15<04:32,  1.41s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:16<04:11,  1.31s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:18<03:55,  1.23s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:19<03:45,  1.18s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:20<03:39,  1.16s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:21<03:31,  1.13s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:22<03:27,  1.11s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:23<03:25,  1.10s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:24<03:23,  1.10s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:25<03:21,  1.10s/it]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:26<03:22,  1.10s/it]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:27<03:20,  1.10s/it]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:28<03:18,  1.10s/it]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:30<03:16,  1.09s/it]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:31<03:16,  1.10s/it]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:32<03:13,  1.09s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:33<03:11,  1.08s/it]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:34<03:08,  1.07s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:35<02:58,  1.02s/it]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:36<02:49,  1.03it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:36<02:42,  1.06it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:37<02:37,  1.09it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:38<02:34,  1.11it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:39<02:34,  1.10it/s]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:40<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:41<02:38,  1.06it/s]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:42<02:45,  1.01it/s]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:43<02:48,  1.02s/it]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:44<02:53,  1.05s/it]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:45<02:56,  1.08s/it]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:47<02:57,  1.09s/it]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:48<02:59,  1.11s/it]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:49<02:59,  1.11s/it]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:50<02:58,  1.12s/it]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:51<02:57,  1.12s/it]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:52<02:55,  1.11s/it]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:53<02:53,  1.10s/it]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:54<02:50,  1.09s/it]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:55<02:50,  1.10s/it]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:57<02:50,  1.11s/it]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:58<02:50,  1.12s/it]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:59<02:50,  1.12s/it]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [01:00<02:50,  1.13s/it]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [01:01<02:48,  1.13s/it]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [01:02<02:47,  1.12s/it]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [01:05<03:05,  1.25s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<17:11,  5.18s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:11,  2.78s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:38,  2.02s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:25,  1.66s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:51,  1.50s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<04:26,  1.37s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:12<04:13,  1.31s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:13<03:59,  1.25s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:14<03:52,  1.22s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:15<03:48,  1.20s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:16<03:42,  1.18s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:17<03:40,  1.17s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:18<03:39,  1.17s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:20<03:36,  1.16s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:21<03:32,  1.15s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:22<03:30,  1.14s/it]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:23<03:29,  1.15s/it]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:24<03:27,  1.14s/it]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:25<03:27,  1.14s/it]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:26<03:20,  1.11s/it]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:27<03:11,  1.07s/it]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:28<03:04,  1.04s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:29<02:57,  1.00s/it]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:30<02:51,  1.03it/s]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:31<02:47,  1.04it/s]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:32<02:47,  1.04it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:33<02:44,  1.05it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:34<02:50,  1.01it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:35<02:55,  1.02s/it]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:36<02:59,  1.06s/it]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:37<03:00,  1.07s/it]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:38<03:01,  1.08s/it]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:40<03:04,  1.10s/it]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:41<03:02,  1.10s/it]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:42<03:01,  1.10s/it]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:43<03:02,  1.11s/it]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:44<02:59,  1.10s/it]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:45<03:04,  1.14s/it]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:46<03:07,  1.16s/it]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:48<03:04,  1.15s/it]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:49<03:00,  1.14s/it]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:50<02:59,  1.13s/it]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:51<02:55,  1.12s/it]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:52<02:53,  1.11s/it]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:53<02:52,  1.11s/it]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:55<03:12,  1.25s/it]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:56<03:16,  1.29s/it]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:57<03:06,  1.23s/it]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:58<02:59,  1.19s/it]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:59<02:52,  1.15s/it]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [01:00<02:49,  1.14s/it]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [01:01<02:46,  1.12s/it]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [01:02<02:42,  1.11s/it]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [01:04<02:40,  1.10s/it]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [01:05<02:39,  1.10s/it]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [01:06<02:37,  1.09s/it]\u001b[A\n",
+      "training:  28%|█████████▋                        | 57/200 [01:07<02:38,  1.11s/it]\u001b[A\n",
+      "training:  29%|█████████▊                        | 58/200 [01:08<02:37,  1.11s/it]\u001b[A\n",
+      "training:  30%|██████████                        | 59/200 [01:09<02:36,  1.11s/it]\u001b[A\n",
+      "training:  30%|██████████▏                       | 60/200 [01:11<02:47,  1.20s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<17:33,  5.30s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:21,  2.84s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:43,  2.05s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:29,  1.68s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:46,  1.47s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<04:20,  1.34s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:11<04:04,  1.27s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:13<03:54,  1.22s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:14<03:47,  1.19s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:15<03:42,  1.17s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:16<03:38,  1.16s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:17<03:35,  1.15s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:18<03:31,  1.13s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:19<03:27,  1.12s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:20<03:11,  1.04s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:21<03:04,  1.00s/it]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:22<02:54,  1.05it/s]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:23<02:48,  1.08it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:24<02:42,  1.11it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:24<02:38,  1.13it/s]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:25<02:37,  1.14it/s]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:26<02:35,  1.14it/s]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:27<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:28<02:32,  1.15it/s]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:29<02:31,  1.15it/s]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:30<02:30,  1.16it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:30<02:27,  1.17it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:31<02:31,  1.14it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:32<02:31,  1.13it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:33<02:31,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:34<02:31,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:35<02:35,  1.08it/s]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:36<02:37,  1.06it/s]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:37<02:36,  1.06it/s]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:38<02:39,  1.03it/s]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:39<02:49,  1.03s/it]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:40<02:53,  1.06s/it]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:42<02:58,  1.10s/it]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:43<02:59,  1.12s/it]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:44<02:59,  1.12s/it]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:45<03:00,  1.13s/it]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:46<02:58,  1.13s/it]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:47<02:58,  1.14s/it]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:48<02:52,  1.11s/it]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:49<02:50,  1.10s/it]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:50<02:48,  1.09s/it]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:51<02:45,  1.08s/it]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:53<02:42,  1.07s/it]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:54<02:41,  1.07s/it]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:55<02:38,  1.06s/it]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [00:56<02:35,  1.04s/it]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [00:57<02:36,  1.05s/it]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [00:58<02:34,  1.05s/it]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [00:59<02:36,  1.07s/it]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [01:00<02:33,  1.06s/it]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [01:02<02:40,  1.12s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<17:35,  5.30s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:16,  2.81s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:32,  1.99s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:13,  1.60s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:32,  1.40s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<04:05,  1.26s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:11<03:48,  1.18s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:12<03:40,  1.15s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:13<03:32,  1.11s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:14<03:33,  1.12s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:15<03:33,  1.13s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:16<03:31,  1.13s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:17<03:25,  1.10s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:19<03:23,  1.10s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:20<03:33,  1.15s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:21<03:26,  1.12s/it]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:22<03:21,  1.10s/it]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:23<03:17,  1.08s/it]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:24<03:17,  1.09s/it]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:25<03:08,  1.05s/it]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:26<03:04,  1.03s/it]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:27<03:00,  1.01s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:28<02:59,  1.01s/it]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:29<02:57,  1.01s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:30<02:56,  1.01s/it]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:31<02:51,  1.02it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:32<02:46,  1.04it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:33<02:45,  1.04it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:34<02:40,  1.06it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:35<02:37,  1.08it/s]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:36<02:40,  1.05it/s]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:37<02:37,  1.06it/s]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:38<02:45,  1.01it/s]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:39<02:50,  1.03s/it]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:40<02:55,  1.07s/it]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:41<03:01,  1.10s/it]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:42<03:04,  1.13s/it]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:43<03:01,  1.12s/it]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:45<03:12,  1.19s/it]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:46<03:23,  1.27s/it]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:47<03:13,  1.22s/it]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:48<03:05,  1.18s/it]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:49<02:59,  1.14s/it]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:51<02:54,  1.12s/it]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:52<02:51,  1.11s/it]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:53<02:50,  1.11s/it]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:54<02:50,  1.11s/it]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:55<02:46,  1.10s/it]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:56<02:45,  1.09s/it]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:57<02:41,  1.08s/it]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [00:58<02:41,  1.08s/it]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [00:59<02:40,  1.08s/it]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [01:00<02:41,  1.10s/it]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [01:01<02:40,  1.10s/it]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [01:03<02:38,  1.10s/it]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [01:04<02:37,  1.09s/it]\u001b[A\n",
+      "training:  28%|█████████▋                        | 57/200 [01:06<02:46,  1.16s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<18:00,  5.43s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:33,  2.90s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:50,  2.09s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:38,  1.73s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:54,  1.51s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:11<04:25,  1.37s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:12<04:09,  1.29s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:13<03:57,  1.24s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:14<03:51,  1.21s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:15<03:42,  1.17s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:16<03:39,  1.16s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:17<03:34,  1.14s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:18<03:33,  1.14s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:19<03:22,  1.09s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:20<03:09,  1.03s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:21<03:02,  1.01it/s]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:22<02:54,  1.05it/s]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:23<02:50,  1.07it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:24<02:49,  1.07it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:25<02:48,  1.07it/s]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:26<02:52,  1.04it/s]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:27<02:59,  1.01s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:28<03:03,  1.04s/it]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:29<03:06,  1.06s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:30<03:11,  1.09s/it]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:31<03:11,  1.10s/it]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:33<03:09,  1.10s/it]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:34<03:10,  1.11s/it]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:35<03:11,  1.12s/it]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:36<03:10,  1.12s/it]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:37<03:09,  1.12s/it]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:38<03:06,  1.11s/it]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:39<03:04,  1.11s/it]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:40<03:06,  1.12s/it]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:42<03:06,  1.13s/it]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:43<03:03,  1.12s/it]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:44<03:01,  1.11s/it]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:45<03:00,  1.11s/it]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:46<02:58,  1.11s/it]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:47<02:59,  1.12s/it]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:48<02:57,  1.11s/it]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:49<02:57,  1.12s/it]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:50<02:55,  1.12s/it]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:52<02:54,  1.12s/it]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:53<02:53,  1.12s/it]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:54<02:52,  1.12s/it]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:55<02:52,  1.13s/it]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:57<03:02,  1.20s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:03,  3.04s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:05,  1.19it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.49it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.34s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.05it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.43it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.11s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.29it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.60s/it]\u001b[A\n",
+      "Evaluating NLL: 57it [00:02, 29.47it/s]\u001b[A\n",
+      "Evaluating NLL: 111it [00:02, 64.74it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:02, 67.91it/s] \n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|▏| 14/100 [00:00<00:00, 137.52i\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|▌| 53/100 [00:00<00:00, 280.06i\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 305.13\n",
+      " 80%|████████████████████████████████████████████████████             | 4/5 [2:00:38<29:39, 1779.24s/it]\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:09<31:36,  9.53s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:10<15:10,  4.60s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:11<09:48,  2.99s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:12<07:14,  2.22s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:13<05:51,  1.80s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:14<04:55,  1.52s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:15<04:24,  1.37s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:16<03:58,  1.24s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:17<03:43,  1.17s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:18<03:32,  1.12s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:19<03:21,  1.07s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:20<03:20,  1.07s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:21<03:15,  1.04s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:22<03:11,  1.03s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:23<03:11,  1.03s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:24<03:05,  1.01s/it]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:25<03:05,  1.02s/it]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:26<03:03,  1.01s/it]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:27<03:02,  1.01s/it]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:28<03:00,  1.00s/it]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:29<03:00,  1.01s/it]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:30<02:59,  1.01s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:31<02:58,  1.01s/it]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:33<03:17,  1.12s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:34<03:23,  1.16s/it]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:35<03:13,  1.11s/it]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:36<03:05,  1.08s/it]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:37<03:02,  1.06s/it]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:38<02:58,  1.04s/it]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:39<02:57,  1.04s/it]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:40<02:51,  1.01s/it]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:41<02:50,  1.01s/it]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:42<02:49,  1.02s/it]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:43<02:47,  1.01s/it]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:44<02:45,  1.01s/it]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:45<02:43,  1.00it/s]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:46<02:41,  1.01it/s]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:47<02:38,  1.02it/s]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:48<02:40,  1.01it/s]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:49<02:39,  1.00it/s]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:50<02:38,  1.01it/s]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:51<02:35,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:52<02:34,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:53<02:33,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:54<02:32,  1.02it/s]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:55<02:30,  1.02it/s]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:56<02:29,  1.02it/s]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:57<02:28,  1.02it/s]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:59<03:02,  1.21s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<17:39,  5.33s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:35,  2.01s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:14,  1.61s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:26,  1.37s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<04:00,  1.24s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:11<03:42,  1.15s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:12<03:30,  1.10s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:13<03:20,  1.05s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:14<03:16,  1.03s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:15<03:15,  1.03s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:16<03:12,  1.02s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:17<03:07,  1.00s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:18<03:05,  1.00it/s]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:19<03:06,  1.01s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:20<03:03,  1.00it/s]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:21<02:58,  1.03it/s]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:22<02:57,  1.03it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:23<02:54,  1.04it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:24<02:55,  1.03it/s]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:25<02:53,  1.03it/s]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:26<02:53,  1.03it/s]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:27<02:54,  1.02it/s]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:28<02:56,  1.01s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:29<02:53,  1.01it/s]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:30<02:53,  1.00it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:31<02:52,  1.00it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:32<02:50,  1.01it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:32<02:47,  1.02it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:33<02:46,  1.02it/s]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:34<02:45,  1.02it/s]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:35<02:44,  1.02it/s]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:36<02:44,  1.02it/s]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:37<02:42,  1.02it/s]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:38<02:41,  1.02it/s]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:39<02:40,  1.02it/s]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:40<02:38,  1.03it/s]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:41<02:37,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:42<02:35,  1.04it/s]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:43<02:35,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:44<02:36,  1.01it/s]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:45<02:33,  1.03it/s]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:46<02:32,  1.03it/s]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:47<02:31,  1.03it/s]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:48<02:33,  1.01it/s]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:49<02:31,  1.02it/s]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:50<02:28,  1.03it/s]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:51<02:26,  1.03it/s]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:53<02:44,  1.09s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<17:33,  5.29s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:11,  2.78s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:29,  1.98s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:17,  1.62s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:31,  1.39s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<04:05,  1.27s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:11<03:46,  1.17s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:12<03:32,  1.11s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:13<03:23,  1.07s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:14<03:24,  1.08s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:15<03:17,  1.04s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:16<03:16,  1.04s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:17<03:10,  1.02s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:18<03:07,  1.01s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:19<03:10,  1.03s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:20<03:10,  1.03s/it]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:21<03:04,  1.01s/it]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:22<03:00,  1.01it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:23<02:59,  1.01it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:24<02:59,  1.00it/s]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:25<02:56,  1.02it/s]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:26<02:58,  1.00s/it]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:27<02:56,  1.00it/s]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:28<02:56,  1.00s/it]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:29<02:56,  1.01s/it]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:30<02:55,  1.01s/it]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:31<02:53,  1.00s/it]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:32<02:53,  1.01s/it]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:33<02:51,  1.00s/it]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:34<02:52,  1.01s/it]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:35<02:50,  1.01s/it]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:36<02:49,  1.01s/it]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:37<02:48,  1.01s/it]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:38<02:49,  1.02s/it]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:39<02:47,  1.01s/it]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:40<02:46,  1.02s/it]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:41<02:47,  1.03s/it]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:42<02:36,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:43<02:30,  1.07it/s]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:44<02:26,  1.09it/s]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:45<02:24,  1.10it/s]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:45<02:21,  1.12it/s]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:46<02:17,  1.14it/s]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:47<02:16,  1.14it/s]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:48<02:21,  1.09it/s]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:50<02:50,  1.10s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:06<20:48,  6.27s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:07<10:24,  3.15s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:08<07:07,  2.17s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:09<05:31,  1.69s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:10<04:36,  1.42s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:11<04:06,  1.27s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:12<03:47,  1.18s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:13<03:33,  1.11s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:14<03:22,  1.06s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:14<03:15,  1.03s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:15<03:10,  1.01s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:16<03:04,  1.02it/s]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:17<03:05,  1.01it/s]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:18<03:04,  1.01it/s]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:19<03:02,  1.01it/s]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:20<02:59,  1.03it/s]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:21<03:00,  1.01it/s]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:22<02:59,  1.01it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:23<02:56,  1.03it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:24<02:55,  1.03it/s]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:25<02:53,  1.03it/s]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:26<02:53,  1.02it/s]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:27<02:52,  1.03it/s]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:28<02:51,  1.03it/s]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:29<02:50,  1.03it/s]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:30<02:51,  1.02it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:31<02:48,  1.03it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:32<02:47,  1.03it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:33<02:45,  1.03it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:34<02:44,  1.03it/s]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:35<02:42,  1.04it/s]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:36<02:41,  1.04it/s]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:37<02:38,  1.06it/s]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:38<02:39,  1.04it/s]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:39<02:37,  1.05it/s]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:40<02:36,  1.05it/s]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:41<02:37,  1.04it/s]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:42<02:36,  1.04it/s]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:43<02:35,  1.04it/s]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:44<02:35,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:45<02:35,  1.02it/s]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:46<02:35,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:47<02:35,  1.01it/s]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:48<02:33,  1.02it/s]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:49<02:33,  1.01it/s]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:50<02:36,  1.02s/it]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:51<02:37,  1.03s/it]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:52<02:37,  1.04s/it]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:53<02:35,  1.03s/it]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:54<02:34,  1.03s/it]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [00:55<02:32,  1.03s/it]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [00:56<02:31,  1.02s/it]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [00:57<02:30,  1.02s/it]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [00:58<02:29,  1.02s/it]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [00:59<02:32,  1.05s/it]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [01:00<02:33,  1.06s/it]\u001b[A\n",
+      "training:  28%|█████████▋                        | 57/200 [01:01<02:31,  1.06s/it]\u001b[A\n",
+      "training:  29%|█████████▊                        | 58/200 [01:02<02:28,  1.05s/it]\u001b[A\n",
+      "training:  30%|██████████                        | 59/200 [01:03<02:26,  1.04s/it]\u001b[A\n",
+      "training:  30%|██████████▏                       | 60/200 [01:04<02:25,  1.04s/it]\u001b[A\n",
+      "training:  30%|██████████▎                       | 61/200 [01:05<02:23,  1.03s/it]\u001b[A\n",
+      "training:  31%|██████████▌                       | 62/200 [01:06<02:21,  1.03s/it]\u001b[A\n",
+      "training:  32%|██████████▋                       | 63/200 [01:07<02:20,  1.03s/it]\u001b[A\n",
+      "training:  32%|██████████▉                       | 64/200 [01:08<02:19,  1.03s/it]\u001b[A\n",
+      "training:  32%|███████████                       | 65/200 [01:09<02:18,  1.02s/it]\u001b[A\n",
+      "training:  33%|███████████▏                      | 66/200 [01:10<02:17,  1.03s/it]\u001b[A\n",
+      "training:  34%|███████████▍                      | 67/200 [01:11<02:17,  1.03s/it]\u001b[A\n",
+      "training:  34%|███████████▌                      | 68/200 [01:12<02:15,  1.03s/it]\u001b[A\n",
+      "training:  34%|███████████▋                      | 69/200 [01:13<02:14,  1.03s/it]\u001b[A\n",
+      "training:  35%|███████████▉                      | 70/200 [01:14<02:13,  1.03s/it]\u001b[A\n",
+      "training:  36%|████████████                      | 71/200 [01:15<02:11,  1.02s/it]\u001b[A\n",
+      "training:  36%|████████████▏                     | 72/200 [01:16<02:10,  1.02s/it]\u001b[A\n",
+      "training:  36%|████████████▍                     | 73/200 [01:17<02:09,  1.02s/it]\u001b[A\n",
+      "training:  37%|████████████▌                     | 74/200 [01:18<02:08,  1.02s/it]\u001b[A\n",
+      "training:  38%|████████████▊                     | 75/200 [01:19<02:06,  1.01s/it]\u001b[A\n",
+      "training:  38%|████████████▉                     | 76/200 [01:20<02:04,  1.01s/it]\u001b[A\n",
+      "training:  38%|█████████████                     | 77/200 [01:22<02:05,  1.02s/it]\u001b[A\n",
+      "training:  39%|█████████████▎                    | 78/200 [01:23<02:04,  1.02s/it]\u001b[A\n",
+      "training:  40%|█████████████▍                    | 79/200 [01:25<02:10,  1.08s/it]\n",
+      "\n",
+      "training:   0%|                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                  | 1/200 [00:05<17:51,  5.38s/it]\u001b[A\n",
+      "training:   1%|▎                                  | 2/200 [00:06<09:09,  2.77s/it]\u001b[A\n",
+      "training:   2%|▌                                  | 3/200 [00:07<06:23,  1.95s/it]\u001b[A\n",
+      "training:   2%|▋                                  | 4/200 [00:08<05:08,  1.57s/it]\u001b[A\n",
+      "training:   2%|▉                                  | 5/200 [00:09<04:27,  1.37s/it]\u001b[A\n",
+      "training:   3%|█                                  | 6/200 [00:10<04:01,  1.24s/it]\u001b[A\n",
+      "training:   4%|█▏                                 | 7/200 [00:11<03:41,  1.15s/it]\u001b[A\n",
+      "training:   4%|█▍                                 | 8/200 [00:12<03:31,  1.10s/it]\u001b[A\n",
+      "training:   4%|█▌                                 | 9/200 [00:13<03:26,  1.08s/it]\u001b[A\n",
+      "training:   5%|█▋                                | 10/200 [00:14<03:20,  1.06s/it]\u001b[A\n",
+      "training:   6%|█▊                                | 11/200 [00:15<03:18,  1.05s/it]\u001b[A\n",
+      "training:   6%|██                                | 12/200 [00:16<03:16,  1.04s/it]\u001b[A\n",
+      "training:   6%|██▏                               | 13/200 [00:17<03:12,  1.03s/it]\u001b[A\n",
+      "training:   7%|██▍                               | 14/200 [00:18<03:09,  1.02s/it]\u001b[A\n",
+      "training:   8%|██▌                               | 15/200 [00:19<03:05,  1.00s/it]\u001b[A\n",
+      "training:   8%|██▋                               | 16/200 [00:20<03:00,  1.02it/s]\u001b[A\n",
+      "training:   8%|██▉                               | 17/200 [00:21<02:52,  1.06it/s]\u001b[A\n",
+      "training:   9%|███                               | 18/200 [00:21<02:45,  1.10it/s]\u001b[A\n",
+      "training:  10%|███▏                              | 19/200 [00:22<02:41,  1.12it/s]\u001b[A\n",
+      "training:  10%|███▍                              | 20/200 [00:23<02:33,  1.17it/s]\u001b[A\n",
+      "training:  10%|███▌                              | 21/200 [00:24<02:27,  1.21it/s]\u001b[A\n",
+      "training:  11%|███▋                              | 22/200 [00:25<02:23,  1.24it/s]\u001b[A\n",
+      "training:  12%|███▉                              | 23/200 [00:25<02:22,  1.24it/s]\u001b[A\n",
+      "training:  12%|████                              | 24/200 [00:26<02:27,  1.20it/s]\u001b[A\n",
+      "training:  12%|████▎                             | 25/200 [00:27<02:34,  1.13it/s]\u001b[A\n",
+      "training:  13%|████▍                             | 26/200 [00:28<02:39,  1.09it/s]\u001b[A\n",
+      "training:  14%|████▌                             | 27/200 [00:29<02:43,  1.06it/s]\u001b[A\n",
+      "training:  14%|████▊                             | 28/200 [00:30<02:45,  1.04it/s]\u001b[A\n",
+      "training:  14%|████▉                             | 29/200 [00:31<02:48,  1.02it/s]\u001b[A\n",
+      "training:  15%|█████                             | 30/200 [00:32<02:50,  1.00s/it]\u001b[A\n",
+      "training:  16%|█████▎                            | 31/200 [00:33<02:49,  1.01s/it]\u001b[A\n",
+      "training:  16%|█████▍                            | 32/200 [00:34<02:50,  1.02s/it]\u001b[A\n",
+      "training:  16%|█████▌                            | 33/200 [00:35<02:49,  1.01s/it]\u001b[A\n",
+      "training:  17%|█████▊                            | 34/200 [00:36<02:48,  1.02s/it]\u001b[A\n",
+      "training:  18%|█████▉                            | 35/200 [00:37<02:47,  1.01s/it]\u001b[A\n",
+      "training:  18%|██████                            | 36/200 [00:38<02:45,  1.01s/it]\u001b[A\n",
+      "training:  18%|██████▎                           | 37/200 [00:39<02:45,  1.01s/it]\u001b[A\n",
+      "training:  19%|██████▍                           | 38/200 [00:40<02:44,  1.01s/it]\u001b[A\n",
+      "training:  20%|██████▋                           | 39/200 [00:41<02:43,  1.01s/it]\u001b[A\n",
+      "training:  20%|██████▊                           | 40/200 [00:42<02:41,  1.01s/it]\u001b[A\n",
+      "training:  20%|██████▉                           | 41/200 [00:44<02:41,  1.02s/it]\u001b[A\n",
+      "training:  21%|███████▏                          | 42/200 [00:45<02:40,  1.02s/it]\u001b[A\n",
+      "training:  22%|███████▎                          | 43/200 [00:46<02:37,  1.00s/it]\u001b[A\n",
+      "training:  22%|███████▍                          | 44/200 [00:47<02:42,  1.04s/it]\u001b[A\n",
+      "training:  22%|███████▋                          | 45/200 [00:48<02:36,  1.01s/it]\u001b[A\n",
+      "training:  23%|███████▊                          | 46/200 [00:49<02:34,  1.00s/it]\u001b[A\n",
+      "training:  24%|███████▉                          | 47/200 [00:50<02:34,  1.01s/it]\u001b[A\n",
+      "training:  24%|████████▏                         | 48/200 [00:51<02:30,  1.01it/s]\u001b[A\n",
+      "training:  24%|████████▎                         | 49/200 [00:52<02:30,  1.00it/s]\u001b[A\n",
+      "training:  25%|████████▌                         | 50/200 [00:53<02:35,  1.04s/it]\u001b[A\n",
+      "training:  26%|████████▋                         | 51/200 [00:54<02:37,  1.06s/it]\u001b[A\n",
+      "training:  26%|████████▊                         | 52/200 [00:55<02:33,  1.04s/it]\u001b[A\n",
+      "training:  26%|█████████                         | 53/200 [00:56<02:30,  1.02s/it]\u001b[A\n",
+      "training:  27%|█████████▏                        | 54/200 [00:57<02:33,  1.05s/it]\u001b[A\n",
+      "training:  28%|█████████▎                        | 55/200 [00:58<02:30,  1.04s/it]\u001b[A\n",
+      "training:  28%|█████████▌                        | 56/200 [01:00<02:35,  1.08s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:03,  3.45s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:06,  1.11it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.07s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.39it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.05s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.02it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.05s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.44it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.53it/s]\n",
+      "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
+      "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:02,  2.71s/it]\u001b[A\n",
+      "Evaluating NLL: 59it [00:02, 29.33it/s]\u001b[A\n",
+      "Evaluating NLL: 119it [00:02, 67.35it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:03, 65.45it/s] \n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   6%| | 6/100 [00:00<00:01, 59.42it/\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  34%|▎| 34/100 [00:00<00:00, 186.77i\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|▋| 64/100 [00:00<00:00, 237.77i\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 235.69\n",
+      "100%|█████████████████████████████████████████████████████████████████| 5/5 [2:28:42<00:00, 1784.54s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -1.9429441095615134\n",
-      "2557: -2.5876572\r"
+      "delta_uniform\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/5 [02:17<?, ?it/s]\n"
+      "  0%|                                                       | 0/5 [00:00<?, ?it/s]\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\u001b[A\n",
+      "training:  86%|▊| 173/2\u001b[A\n",
+      "training:  87%|▊| 174/2\u001b[A\n",
+      "training:  88%|▉| 175/2\u001b[A\n",
+      "training:  88%|▉| 176/2\u001b[A\n",
+      "training:  88%|▉| 177/2\u001b[A\n",
+      "training:  89%|▉| 178/2\u001b[A\n",
+      "training:  90%|▉| 179/2\u001b[A\n",
+      "training:  90%|▉| 180/2\u001b[A\n",
+      "training:  90%|▉| 181/2\u001b[A\n",
+      "training:  91%|▉| 182/2\u001b[A\n",
+      "training:  92%|▉| 183/2\u001b[A\n",
+      "training:  92%|▉| 184/2\u001b[A\n",
+      "training:  92%|▉| 185/2\u001b[A\n",
+      "training:  93%|▉| 186/2\u001b[A\n",
+      "training:  94%|▉| 187/2\u001b[A\n",
+      "training:  94%|▉| 188/2\u001b[A\n",
+      "training:  94%|▉| 189/2\u001b[A\n",
+      "training:  95%|▉| 190/2\u001b[A\n",
+      "training:  96%|▉| 191/2\u001b[A\n",
+      "training:  96%|▉| 192/2\u001b[A\n",
+      "training:  96%|▉| 193/2\u001b[A\n",
+      "training:  97%|▉| 194/2\u001b[A\n",
+      "training:  98%|▉| 195/2\u001b[A\n",
+      "training:  98%|▉| 196/2\u001b[A\n",
+      "training:  98%|▉| 197/2\u001b[A\n",
+      "training:  99%|▉| 198/2\u001b[A\n",
+      "training: 100%|▉| 199/2\u001b[A\n",
+      "training: 100%|█| 200/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.51it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.53it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.17s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.48it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.00it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.49it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 49it [00:01, 63.20it/s]\u001b[A\n",
+      "Evaluating NLL: 109it [00:01, 146.89it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 151.84it/s]\n",
+      "\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\n",
+      " 20%|████████▌                                  | 1/5 [31:30<2:06:02, 1890.55s/it]\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\u001b[A\n",
+      "training:  86%|▊| 173/2\u001b[A\n",
+      "training:  87%|▊| 174/2\u001b[A\n",
+      "training:  88%|▉| 175/2\u001b[A\n",
+      "training:  88%|▉| 176/2\u001b[A\n",
+      "training:  88%|▉| 177/2\u001b[A\n",
+      "training:  89%|▉| 178/2\u001b[A\n",
+      "training:  90%|▉| 179/2\u001b[A\n",
+      "training:  90%|▉| 180/2\u001b[A\n",
+      "training:  90%|▉| 181/2\u001b[A\n",
+      "training:  91%|▉| 182/2\u001b[A\n",
+      "training:  92%|▉| 183/2\u001b[A\n",
+      "training:  92%|▉| 184/2\u001b[A\n",
+      "training:  92%|▉| 185/2\u001b[A\n",
+      "training:  93%|▉| 186/2\u001b[A\n",
+      "training:  94%|▉| 187/2\u001b[A\n",
+      "training:  94%|▉| 188/2\u001b[A\n",
+      "training:  94%|▉| 189/2\u001b[A\n",
+      "training:  95%|▉| 190/2\u001b[A\n",
+      "training:  96%|▉| 191/2\u001b[A\n",
+      "training:  96%|▉| 192/2\u001b[A\n",
+      "training:  96%|▉| 193/2\u001b[A\n",
+      "training:  97%|▉| 194/2\u001b[A\n",
+      "training:  98%|▉| 195/2\u001b[A\n",
+      "training:  98%|▉| 196/2\u001b[A\n",
+      "training:  98%|▉| 197/2\u001b[A\n",
+      "training:  99%|▉| 198/2\u001b[A\n",
+      "training: 100%|▉| 199/2\u001b[A\n",
+      "training: 100%|█| 200/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.55it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.52it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.08s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  2.95it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.39it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.54it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 51it [00:01, 66.70it/s]\u001b[A\n",
+      "Evaluating NLL: 110it [00:01, 149.20it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 153.58it/s]\n",
+      "\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\n",
+      " 40%|████████████████▍                        | 2/5 [1:03:10<1:34:48, 1896.11s/it]\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\u001b[A\n",
+      "training:  86%|▊| 173/2\u001b[A\n",
+      "training:  87%|▊| 174/2\u001b[A\n",
+      "training:  88%|▉| 175/2\u001b[A\n",
+      "training:  88%|▉| 176/2\u001b[A\n",
+      "training:  88%|▉| 177/2\u001b[A\n",
+      "training:  89%|▉| 178/2\u001b[A\n",
+      "training:  90%|▉| 179/2\u001b[A\n",
+      "training:  90%|▉| 180/2\u001b[A\n",
+      "training:  90%|▉| 181/2\u001b[A\n",
+      "training:  91%|▉| 182/2\u001b[A\n",
+      "training:  92%|▉| 183/2\u001b[A\n",
+      "training:  92%|▉| 184/2\u001b[A\n",
+      "training:  92%|▉| 185/2\u001b[A\n",
+      "training:  93%|▉| 186/2\u001b[A\n",
+      "training:  94%|▉| 187/2\u001b[A\n",
+      "training:  94%|▉| 188/2\u001b[A\n",
+      "training:  94%|▉| 189/2\u001b[A\n",
+      "training:  95%|▉| 190/2\u001b[A\n",
+      "training:  96%|▉| 191/2\u001b[A\n",
+      "training:  96%|▉| 192/2\u001b[A\n",
+      "training:  96%|▉| 193/2\u001b[A\n",
+      "training:  97%|▉| 194/2\u001b[A\n",
+      "training:  98%|▉| 195/2\u001b[A\n",
+      "training:  98%|▉| 196/2\u001b[A\n",
+      "training:  98%|▉| 197/2\u001b[A\n",
+      "training:  99%|▉| 198/2\u001b[A\n",
+      "training: 100%|▉| 199/2\u001b[A\n",
+      "training: 100%|█| 200/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\u001b[A\n",
+      "training:  86%|▊| 173/2\u001b[A\n",
+      "training:  87%|▊| 174/2\u001b[A\n",
+      "training:  88%|▉| 175/2\u001b[A\n",
+      "training:  88%|▉| 176/2\u001b[A\n",
+      "training:  88%|▉| 177/2\u001b[A\n",
+      "training:  89%|▉| 178/2\u001b[A\n",
+      "training:  90%|▉| 179/2\u001b[A\n",
+      "training:  90%|▉| 180/2\u001b[A\n",
+      "training:  90%|▉| 181/2\u001b[A\n",
+      "training:  91%|▉| 182/2\u001b[A\n",
+      "training:  92%|▉| 183/2\u001b[A\n",
+      "training:  92%|▉| 184/2\u001b[A\n",
+      "training:  92%|▉| 185/2\u001b[A\n",
+      "training:  93%|▉| 186/2\u001b[A\n",
+      "training:  94%|▉| 187/2\u001b[A\n",
+      "training:  94%|▉| 188/2\u001b[A\n",
+      "training:  94%|▉| 189/2\u001b[A\n",
+      "training:  95%|▉| 190/2\u001b[A\n",
+      "training:  96%|▉| 191/2\u001b[A\n",
+      "training:  96%|▉| 192/2\u001b[A\n",
+      "training:  96%|▉| 193/2\u001b[A\n",
+      "training:  97%|▉| 194/2\u001b[A\n",
+      "training:  98%|▉| 195/2\u001b[A\n",
+      "training:  98%|▉| 196/2\u001b[A\n",
+      "training:  98%|▉| 197/2\u001b[A\n",
+      "training:  99%|▉| 198/2\u001b[A\n",
+      "training: 100%|▉| 199/2\u001b[A\n",
+      "training: 100%|█| 200/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\u001b[A\n",
+      "training:  86%|▊| 173/2\u001b[A\n",
+      "training:  87%|▊| 174/2\u001b[A\n",
+      "training:  88%|▉| 175/2\u001b[A\n",
+      "training:  88%|▉| 176/2\u001b[A\n",
+      "training:  88%|▉| 177/2\u001b[A\n",
+      "training:  89%|▉| 178/2\u001b[A\n",
+      "training:  90%|▉| 179/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\u001b[A\n",
+      "training:  86%|▊| 173/2\u001b[A\n",
+      "training:  87%|▊| 174/2\u001b[A\n",
+      "training:  88%|▉| 175/2\u001b[A\n",
+      "training:  88%|▉| 176/2\u001b[A\n",
+      "training:  88%|▉| 177/2\u001b[A\n",
+      "training:  89%|▉| 178/2\u001b[A\n",
+      "training:  90%|▉| 179/2\u001b[A\n",
+      "training:  90%|▉| 180/2\u001b[A\n",
+      "training:  90%|▉| 181/2\u001b[A\n",
+      "training:  91%|▉| 182/2\u001b[A\n",
+      "training:  92%|▉| 183/2\u001b[A\n",
+      "training:  92%|▉| 184/2\u001b[A\n",
+      "training:  92%|▉| 185/2\u001b[A\n",
+      "training:  93%|▉| 186/2\u001b[A\n",
+      "training:  94%|▉| 187/2\u001b[A\n",
+      "training:  94%|▉| 188/2\u001b[A\n",
+      "training:  94%|▉| 189/2\u001b[A\n",
+      "training:  95%|▉| 190/2\u001b[A\n",
+      "training:  96%|▉| 191/2\u001b[A\n",
+      "training:  96%|▉| 192/2\u001b[A\n",
+      "training:  96%|▉| 193/2\u001b[A\n",
+      "training:  97%|▉| 194/2\u001b[A\n",
+      "training:  98%|▉| 195/2\u001b[A\n",
+      "training:  98%|▉| 196/2\u001b[A\n",
+      "training:  98%|▉| 197/2\u001b[A\n",
+      "training:  99%|▉| 198/2\u001b[A\n",
+      "training: 100%|▉| 199/2\u001b[A\n",
+      "training: 100%|█| 200/2\n",
+      "\n",
+      "training:   0%| | 0/200\u001b[A\n",
+      "training:   0%| | 1/200\u001b[A\n",
+      "training:   1%| | 2/200\u001b[A\n",
+      "training:   2%| | 3/200\u001b[A\n",
+      "training:   2%| | 4/200\u001b[A\n",
+      "training:   2%| | 5/200\u001b[A\n",
+      "training:   3%| | 6/200\u001b[A\n",
+      "training:   4%| | 7/200\u001b[A\n",
+      "training:   4%| | 8/200\u001b[A\n",
+      "training:   4%| | 9/200\u001b[A\n",
+      "training:   5%| | 10/20\u001b[A\n",
+      "training:   6%| | 11/20\u001b[A\n",
+      "training:   6%| | 12/20\u001b[A\n",
+      "training:   6%| | 13/20\u001b[A\n",
+      "training:   7%| | 14/20\u001b[A\n",
+      "training:   8%| | 15/20\u001b[A\n",
+      "training:   8%| | 16/20\u001b[A\n",
+      "training:   8%| | 17/20\u001b[A\n",
+      "training:   9%| | 18/20\u001b[A\n",
+      "training:  10%| | 19/20\u001b[A\n",
+      "training:  10%| | 20/20\u001b[A\n",
+      "training:  10%| | 21/20\u001b[A\n",
+      "training:  11%| | 22/20\u001b[A\n",
+      "training:  12%| | 23/20\u001b[A\n",
+      "training:  12%| | 24/20\u001b[A\n",
+      "training:  12%|▏| 25/20\u001b[A\n",
+      "training:  13%|▏| 26/20\u001b[A\n",
+      "training:  14%|▏| 27/20\u001b[A\n",
+      "training:  14%|▏| 28/20\u001b[A\n",
+      "training:  14%|▏| 29/20\u001b[A\n",
+      "training:  15%|▏| 30/20\u001b[A\n",
+      "training:  16%|▏| 31/20\u001b[A\n",
+      "training:  16%|▏| 32/20\u001b[A\n",
+      "training:  16%|▏| 33/20\u001b[A\n",
+      "training:  17%|▏| 34/20\u001b[A\n",
+      "training:  18%|▏| 35/20\u001b[A\n",
+      "training:  18%|▏| 36/20\u001b[A\n",
+      "training:  18%|▏| 37/20\u001b[A\n",
+      "training:  19%|▏| 38/20\u001b[A\n",
+      "training:  20%|▏| 39/20\u001b[A\n",
+      "training:  20%|▏| 40/20\u001b[A\n",
+      "training:  20%|▏| 41/20\u001b[A\n",
+      "training:  21%|▏| 42/20\u001b[A\n",
+      "training:  22%|▏| 43/20\u001b[A\n",
+      "training:  22%|▏| 44/20\u001b[A\n",
+      "training:  22%|▏| 45/20\u001b[A\n",
+      "training:  23%|▏| 46/20\u001b[A\n",
+      "training:  24%|▏| 47/20\u001b[A\n",
+      "training:  24%|▏| 48/20\u001b[A\n",
+      "training:  24%|▏| 49/20\u001b[A\n",
+      "training:  25%|▎| 50/20\u001b[A\n",
+      "training:  26%|▎| 51/20\u001b[A\n",
+      "training:  26%|▎| 52/20\u001b[A\n",
+      "training:  26%|▎| 53/20\u001b[A\n",
+      "training:  27%|▎| 54/20\u001b[A\n",
+      "training:  28%|▎| 55/20\u001b[A\n",
+      "training:  28%|▎| 56/20\u001b[A\n",
+      "training:  28%|▎| 57/20\u001b[A\n",
+      "training:  29%|▎| 58/20\u001b[A\n",
+      "training:  30%|▎| 59/20\u001b[A\n",
+      "training:  30%|▎| 60/20\u001b[A\n",
+      "training:  30%|▎| 61/20\u001b[A\n",
+      "training:  31%|▎| 62/20\u001b[A\n",
+      "training:  32%|▎| 63/20\u001b[A\n",
+      "training:  32%|▎| 64/20\u001b[A\n",
+      "training:  32%|▎| 65/20\u001b[A\n",
+      "training:  33%|▎| 66/20\u001b[A\n",
+      "training:  34%|▎| 67/20\u001b[A\n",
+      "training:  34%|▎| 68/20\u001b[A\n",
+      "training:  34%|▎| 69/20\u001b[A\n",
+      "training:  35%|▎| 70/20\u001b[A\n",
+      "training:  36%|▎| 71/20\u001b[A\n",
+      "training:  36%|▎| 72/20\u001b[A\n",
+      "training:  36%|▎| 73/20\u001b[A\n",
+      "training:  37%|▎| 74/20\u001b[A\n",
+      "training:  38%|▍| 75/20\u001b[A\n",
+      "training:  38%|▍| 76/20\u001b[A\n",
+      "training:  38%|▍| 77/20\u001b[A\n",
+      "training:  39%|▍| 78/20\u001b[A\n",
+      "training:  40%|▍| 79/20\u001b[A\n",
+      "training:  40%|▍| 80/20\u001b[A\n",
+      "training:  40%|▍| 81/20\u001b[A\n",
+      "training:  41%|▍| 82/20\u001b[A\n",
+      "training:  42%|▍| 83/20\u001b[A\n",
+      "training:  42%|▍| 84/20\u001b[A\n",
+      "training:  42%|▍| 85/20\u001b[A\n",
+      "training:  43%|▍| 86/20\u001b[A\n",
+      "training:  44%|▍| 87/20\u001b[A\n",
+      "training:  44%|▍| 88/20\u001b[A\n",
+      "training:  44%|▍| 89/20\u001b[A\n",
+      "training:  45%|▍| 90/20\u001b[A\n",
+      "training:  46%|▍| 91/20\u001b[A\n",
+      "training:  46%|▍| 92/20\u001b[A\n",
+      "training:  46%|▍| 93/20\u001b[A\n",
+      "training:  47%|▍| 94/20\u001b[A\n",
+      "training:  48%|▍| 95/20\u001b[A\n",
+      "training:  48%|▍| 96/20\u001b[A\n",
+      "training:  48%|▍| 97/20\u001b[A\n",
+      "training:  49%|▍| 98/20\u001b[A\n",
+      "training:  50%|▍| 99/20\u001b[A\n",
+      "training:  50%|▌| 100/2\u001b[A\n",
+      "training:  50%|▌| 101/2\u001b[A\n",
+      "training:  51%|▌| 102/2\u001b[A\n",
+      "training:  52%|▌| 103/2\u001b[A\n",
+      "training:  52%|▌| 104/2\u001b[A\n",
+      "training:  52%|▌| 105/2\u001b[A\n",
+      "training:  53%|▌| 106/2\u001b[A\n",
+      "training:  54%|▌| 107/2\u001b[A\n",
+      "training:  54%|▌| 108/2\u001b[A\n",
+      "training:  55%|▌| 109/2\u001b[A\n",
+      "training:  55%|▌| 110/2\u001b[A\n",
+      "training:  56%|▌| 111/2\u001b[A\n",
+      "training:  56%|▌| 112/2\u001b[A\n",
+      "training:  56%|▌| 113/2\u001b[A\n",
+      "training:  57%|▌| 114/2\u001b[A\n",
+      "training:  57%|▌| 115/2\u001b[A\n",
+      "training:  58%|▌| 116/2\u001b[A\n",
+      "training:  58%|▌| 117/2\u001b[A\n",
+      "training:  59%|▌| 118/2\u001b[A\n",
+      "training:  60%|▌| 119/2\u001b[A\n",
+      "training:  60%|▌| 120/2\u001b[A\n",
+      "training:  60%|▌| 121/2\u001b[A\n",
+      "training:  61%|▌| 122/2\u001b[A\n",
+      "training:  62%|▌| 123/2\u001b[A\n",
+      "training:  62%|▌| 124/2\u001b[A\n",
+      "training:  62%|▋| 125/2\u001b[A\n",
+      "training:  63%|▋| 126/2\u001b[A\n",
+      "training:  64%|▋| 127/2\u001b[A\n",
+      "training:  64%|▋| 128/2\u001b[A\n",
+      "training:  64%|▋| 129/2\u001b[A\n",
+      "training:  65%|▋| 130/2\u001b[A\n",
+      "training:  66%|▋| 131/2\u001b[A\n",
+      "training:  66%|▋| 132/2\u001b[A\n",
+      "training:  66%|▋| 133/2\u001b[A\n",
+      "training:  67%|▋| 134/2\u001b[A\n",
+      "training:  68%|▋| 135/2\u001b[A\n",
+      "training:  68%|▋| 136/2\u001b[A\n",
+      "training:  68%|▋| 137/2\u001b[A\n",
+      "training:  69%|▋| 138/2\u001b[A\n",
+      "training:  70%|▋| 139/2\u001b[A\n",
+      "training:  70%|▋| 140/2\u001b[A\n",
+      "training:  70%|▋| 141/2\u001b[A\n",
+      "training:  71%|▋| 142/2\u001b[A\n",
+      "training:  72%|▋| 143/2\u001b[A\n",
+      "training:  72%|▋| 144/2\u001b[A\n",
+      "training:  72%|▋| 145/2\u001b[A\n",
+      "training:  73%|▋| 146/2\u001b[A\n",
+      "training:  74%|▋| 147/2\u001b[A\n",
+      "training:  74%|▋| 148/2\u001b[A\n",
+      "training:  74%|▋| 149/2\u001b[A\n",
+      "training:  75%|▊| 150/2\u001b[A\n",
+      "training:  76%|▊| 151/2\u001b[A\n",
+      "training:  76%|▊| 152/2\u001b[A\n",
+      "training:  76%|▊| 153/2\u001b[A\n",
+      "training:  77%|▊| 154/2\u001b[A\n",
+      "training:  78%|▊| 155/2\u001b[A\n",
+      "training:  78%|▊| 156/2\u001b[A\n",
+      "training:  78%|▊| 157/2\u001b[A\n",
+      "training:  79%|▊| 158/2\u001b[A\n",
+      "training:  80%|▊| 159/2\u001b[A\n",
+      "training:  80%|▊| 160/2\u001b[A\n",
+      "training:  80%|▊| 161/2\u001b[A\n",
+      "training:  81%|▊| 162/2\u001b[A\n",
+      "training:  82%|▊| 163/2\u001b[A\n",
+      "training:  82%|▊| 164/2\u001b[A\n",
+      "training:  82%|▊| 165/2\u001b[A\n",
+      "training:  83%|▊| 166/2\u001b[A\n",
+      "training:  84%|▊| 167/2\u001b[A\n",
+      "training:  84%|▊| 168/2\u001b[A\n",
+      "training:  84%|▊| 169/2\u001b[A\n",
+      "training:  85%|▊| 170/2\u001b[A\n",
+      "training:  86%|▊| 171/2\u001b[A\n",
+      "training:  86%|▊| 172/2\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.73it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.72it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.12s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  2.69it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  4.05it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  4.03it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.22it/s]\u001b[A\n",
+      "Evaluating NLL: 84it [00:00, 123.77it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 182.11it/s]\n",
+      "\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\u001b[A\n",
+      "Bootstrapping to comput\n",
+      " 60%|████████████████████████▌                | 3/5 [1:35:39<1:04:00, 1920.17s/it]\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<16:04,  4.85s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:22,  2.54s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:44,  1.75s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:29,  1.37s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:08<03:48,  1.17s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:09<03:23,  1.05s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<03:07,  1.03it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:56,  1.09it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:11<02:48,  1.13it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:12<02:44,  1.16it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:13<02:39,  1.19it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:35,  1.21it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:14<02:35,  1.20it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:15<02:33,  1.21it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:16<02:32,  1.21it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:17<02:31,  1.22it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:17<02:30,  1.22it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:18<02:33,  1.19it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:19<02:36,  1.16it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:20<02:39,  1.13it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:21<02:38,  1.13it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:22<02:37,  1.13it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:23<02:37,  1.12it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:24<02:35,  1.13it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:25<02:33,  1.14it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:25<02:32,  1.14it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:26<02:32,  1.14it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:27<02:29,  1.15it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:28<02:31,  1.13it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:29<02:31,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:30<02:32,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:31<02:31,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:32<02:27,  1.13it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:33<02:31,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:34<02:27,  1.12it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:34<02:23,  1.15it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:35<02:19,  1.17it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:36<02:16,  1.18it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:37<02:14,  1.20it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:38<02:13,  1.20it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:38<02:11,  1.21it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:39<02:09,  1.22it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:40<02:08,  1.22it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:41<02:09,  1.20it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:42<02:06,  1.22it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:43<02:05,  1.22it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:43<02:03,  1.24it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:44<02:02,  1.24it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:45<02:02,  1.23it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:46<02:06,  1.18it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:47<02:04,  1.19it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:48<02:06,  1.17it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:48<02:04,  1.18it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:49<02:01,  1.20it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:50<02:04,  1.17it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:51<02:03,  1.16it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:52<02:00,  1.19it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:53<01:58,  1.20it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:53<01:58,  1.19it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:54<01:58,  1.18it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:55<02:00,  1.15it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:56<02:01,  1.14it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:57<02:02,  1.11it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:58<02:04,  1.10it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:59<02:05,  1.08it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:00<02:05,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:01<02:05,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:02<02:05,  1.05it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:03<02:04,  1.05it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:04<02:04,  1.04it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:05<02:02,  1.05it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:06<02:01,  1.05it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:07<02:00,  1.05it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:08<01:59,  1.05it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:09<01:58,  1.05it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:10<01:58,  1.05it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:10<01:57,  1.05it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:11<01:56,  1.04it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:12<01:55,  1.05it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:13<01:54,  1.05it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:14<01:52,  1.06it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:15<01:50,  1.07it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:16<01:48,  1.07it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:17<01:43,  1.12it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:18<01:40,  1.14it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:19<01:38,  1.16it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:19<01:38,  1.15it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:20<01:38,  1.14it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:21<01:39,  1.12it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:22<01:36,  1.14it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:23<01:36,  1.13it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:24<01:33,  1.15it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:25<01:31,  1.17it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:26<01:31,  1.15it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:26<01:29,  1.17it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:27<01:28,  1.18it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:28<01:28,  1.16it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:29<01:27,  1.16it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:30<01:27,  1.15it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:31<01:26,  1.16it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:32<01:24,  1.17it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:32<01:24,  1.16it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:33<01:22,  1.18it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:34<01:24,  1.14it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:36<01:41,  1.07s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:37<01:33,  1.01it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:37<01:26,  1.08it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:38<01:22,  1.12it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:39<01:19,  1.15it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:40<01:16,  1.18it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:41<01:15,  1.18it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:41<01:13,  1.19it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:42<01:12,  1.21it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:43<01:11,  1.21it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:44<01:10,  1.21it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:45<01:08,  1.22it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:45<01:07,  1.23it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:46<01:06,  1.23it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:47<01:05,  1.24it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:48<01:05,  1.23it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:49<01:04,  1.23it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:50<01:03,  1.22it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:50<01:02,  1.22it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:51<01:02,  1.22it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:52<01:01,  1.23it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:53<01:00,  1.23it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:54<00:58,  1.24it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:54<00:58,  1.24it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:55<00:57,  1.23it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:56<00:57,  1.22it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:57<00:56,  1.22it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:58<00:56,  1.21it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:58<00:54,  1.22it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:59<00:55,  1.19it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:00<00:54,  1.18it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:01<00:54,  1.18it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:02<00:53,  1.18it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:03<00:53,  1.16it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:04<00:52,  1.15it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:05<00:52,  1.15it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:05<00:51,  1.14it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:06<00:50,  1.15it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:07<00:49,  1.15it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:08<00:49,  1.14it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:09<00:48,  1.14it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:10<00:47,  1.14it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:11<00:46,  1.14it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:12<00:45,  1.14it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:12<00:44,  1.15it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:13<00:43,  1.14it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:14<00:42,  1.15it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:15<00:41,  1.16it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:16<00:39,  1.18it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:17<00:38,  1.20it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:18<00:37,  1.19it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:18<00:37,  1.19it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:19<00:36,  1.19it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:20<00:35,  1.17it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:21<00:34,  1.18it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:22<00:33,  1.19it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:23<00:32,  1.20it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:23<00:31,  1.21it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:24<00:30,  1.21it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:25<00:29,  1.22it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:26<00:28,  1.22it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:27<00:27,  1.23it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:28<00:28,  1.18it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:28<00:26,  1.19it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:29<00:26,  1.19it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:30<00:25,  1.19it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:31<00:24,  1.19it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:32<00:23,  1.19it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:33<00:22,  1.18it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:33<00:22,  1.17it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:34<00:21,  1.19it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:36<00:21,  1.12it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:58,  4.82s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:29,  2.57s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<06:08,  1.87s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:52,  1.49s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:08<04:11,  1.29s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:09<03:45,  1.16s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:10<03:27,  1.07s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:11<03:11,  1.00it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:12<03:01,  1.05it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:13<02:54,  1.09it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:13<02:48,  1.12it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:14<02:44,  1.14it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:15<02:43,  1.15it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:16<02:42,  1.15it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:17<02:42,  1.14it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:18<02:41,  1.14it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:19<02:38,  1.16it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:19<02:35,  1.17it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:20<02:36,  1.15it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:21<02:35,  1.16it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:22<02:33,  1.17it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:23<02:31,  1.17it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:24<02:31,  1.17it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:24<02:28,  1.18it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:25<02:28,  1.18it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:26<02:27,  1.18it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:27<02:25,  1.19it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:28<02:22,  1.21it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:29<02:21,  1.21it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:30<02:23,  1.18it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:31<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:31<02:27,  1.14it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:32<02:27,  1.13it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:33<02:23,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:34<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:35<02:31,  1.08it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:36<02:26,  1.11it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:37<02:22,  1.14it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:38<02:20,  1.15it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:38<02:17,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:39<02:20,  1.13it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:40<02:22,  1.11it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:41<02:24,  1.09it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:42<02:24,  1.08it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:43<02:24,  1.07it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:44<02:26,  1.05it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:45<02:25,  1.05it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:46<02:25,  1.04it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:47<02:25,  1.04it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:48<02:23,  1.04it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:49<02:22,  1.05it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:50<02:21,  1.05it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:51<02:20,  1.05it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:52<02:19,  1.05it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:53<02:18,  1.05it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:54<02:16,  1.05it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:55<02:17,  1.04it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:56<02:17,  1.03it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:57<02:16,  1.03it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:58<02:15,  1.03it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:59<02:16,  1.02it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:59<02:12,  1.04it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:00<02:10,  1.05it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:01<02:07,  1.07it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:02<02:07,  1.06it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:03<02:05,  1.06it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:04<02:03,  1.07it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:05<02:05,  1.05it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:06<02:08,  1.02it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:07<02:05,  1.04it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:08<02:04,  1.03it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:09<02:04,  1.03it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:10<02:04,  1.02it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:11<02:01,  1.04it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:12<02:01,  1.03it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:13<02:02,  1.01it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:14<01:55,  1.06it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:15<02:01,  1.00it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:16<02:16,  1.13s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:17<02:05,  1.05s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:18<01:58,  1.01it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:19<01:51,  1.06it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:20<01:47,  1.09it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:21<01:43,  1.12it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:21<01:41,  1.13it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:22<01:39,  1.15it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:23<01:37,  1.16it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:24<01:35,  1.17it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:25<01:34,  1.18it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:26<01:33,  1.18it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:27<01:31,  1.19it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:27<01:30,  1.20it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:28<01:28,  1.21it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:29<01:27,  1.21it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:30<01:28,  1.19it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:31<01:28,  1.18it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:32<01:27,  1.18it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:32<01:27,  1.16it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:33<01:26,  1.17it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:34<01:26,  1.16it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:35<01:26,  1.14it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:36<01:26,  1.13it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:37<01:26,  1.12it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:38<01:26,  1.11it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:39<01:25,  1.11it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:40<01:26,  1.09it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:41<01:25,  1.09it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:42<01:24,  1.10it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:42<01:22,  1.11it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:43<01:21,  1.10it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:44<01:21,  1.09it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:45<01:19,  1.11it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:46<01:18,  1.11it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:47<01:17,  1.11it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:48<01:17,  1.09it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:49<01:16,  1.09it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:50<01:15,  1.11it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:50<01:12,  1.13it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:51<01:10,  1.15it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:52<01:09,  1.15it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:53<01:07,  1.17it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:54<01:06,  1.17it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:55<01:05,  1.18it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:56<01:04,  1.18it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:56<01:03,  1.19it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:57<01:01,  1.20it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:58<01:01,  1.19it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:59<01:00,  1.19it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:00<00:59,  1.20it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:01<00:58,  1.19it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:01<00:57,  1.20it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:02<00:56,  1.19it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:03<00:56,  1.20it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:04<00:55,  1.19it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:05<00:54,  1.20it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:06<00:53,  1.20it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:06<00:52,  1.20it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:07<00:54,  1.13it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:08<00:53,  1.13it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:09<00:52,  1.15it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:10<00:50,  1.16it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:11<00:49,  1.16it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:12<00:49,  1.16it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:13<00:48,  1.15it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:13<00:46,  1.17it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:14<00:46,  1.17it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:15<00:44,  1.19it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:16<00:43,  1.19it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:17<00:42,  1.21it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:17<00:40,  1.22it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:18<00:40,  1.22it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:19<00:39,  1.21it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:21<00:43,  1.08it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<14:48,  4.46s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<07:46,  2.35s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:23,  1.64s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:06<04:15,  1.30s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:38,  1.12s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<03:15,  1.01s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<03:02,  1.06it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:56,  1.09it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:11<02:58,  1.07it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:12<03:00,  1.05it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:12<02:53,  1.09it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:48,  1.12it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:14<02:48,  1.11it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:15<02:47,  1.11it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:16<02:43,  1.13it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:17<02:43,  1.12it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:18<02:42,  1.13it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:19<02:40,  1.13it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:20<02:39,  1.14it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:20<02:36,  1.15it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:21<02:36,  1.15it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:22<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:23<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:24<02:39,  1.11it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:25<02:40,  1.09it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:26<02:37,  1.11it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:27<02:32,  1.13it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:27<02:27,  1.16it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:28<02:25,  1.18it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:29<02:24,  1.17it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:30<02:22,  1.19it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:31<02:25,  1.15it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:32<02:22,  1.17it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:32<02:19,  1.19it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:33<02:18,  1.19it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:34<02:19,  1.18it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:35<02:21,  1.15it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:36<02:23,  1.13it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:37<02:26,  1.10it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:38<02:24,  1.10it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:39<02:24,  1.10it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:40<02:25,  1.09it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:41<02:24,  1.09it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:42<02:23,  1.09it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:43<02:22,  1.09it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:43<02:22,  1.08it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:44<02:20,  1.09it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:45<02:19,  1.09it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:46<02:19,  1.08it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:47<02:18,  1.08it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:48<02:17,  1.08it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:49<02:16,  1.08it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:50<02:15,  1.08it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:52<02:21,  1.03it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<18:39,  5.62s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:27,  2.87s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:33,  2.00s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:06,  1.57s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<04:20,  1.33s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:10<03:51,  1.19s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:11<03:35,  1.12s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:12<03:22,  1.06s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:13<03:12,  1.01s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:14<03:08,  1.01it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:14<03:02,  1.04it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:15<02:58,  1.05it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:16<02:52,  1.09it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:17<02:48,  1.10it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:18<03:12,  1.04s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:20<03:34,  1.17s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:21<03:15,  1.07s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:22<03:00,  1.01it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:22<02:49,  1.07it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:23<02:41,  1.11it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:24<02:35,  1.15it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:25<02:31,  1.18it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:26<02:29,  1.18it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:26<02:28,  1.19it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:27<02:25,  1.20it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:28<02:23,  1.21it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:29<02:23,  1.20it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:30<02:22,  1.21it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:31<02:21,  1.21it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:31<02:18,  1.23it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:32<02:16,  1.24it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:33<02:15,  1.24it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:34<02:19,  1.19it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:35<02:21,  1.17it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:36<02:19,  1.18it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:36<02:16,  1.20it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:37<02:14,  1.21it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:38<02:13,  1.21it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:39<02:11,  1.22it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:40<02:09,  1.23it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:40<02:07,  1.25it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:41<02:06,  1.25it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:42<02:05,  1.25it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:43<02:05,  1.24it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:44<02:05,  1.24it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:44<02:04,  1.24it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:45<02:03,  1.24it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:46<02:03,  1.23it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:47<02:02,  1.23it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:48<02:01,  1.23it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:48<02:00,  1.24it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:49<02:00,  1.23it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:50<02:00,  1.22it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:51<01:58,  1.23it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:52<01:57,  1.24it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:52<01:56,  1.24it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:53<01:57,  1.22it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:54<01:56,  1.22it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:55<01:55,  1.22it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:56<01:54,  1.22it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:57<01:52,  1.23it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:57<01:52,  1.23it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:58<01:50,  1.23it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:59<01:49,  1.24it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:00<01:49,  1.23it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:01<01:49,  1.23it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:02<01:50,  1.21it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:02<01:51,  1.19it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:03<01:51,  1.18it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:04<01:53,  1.15it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:05<01:54,  1.13it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:06<01:55,  1.11it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:07<01:54,  1.11it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:08<01:55,  1.10it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:09<01:52,  1.11it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:10<01:51,  1.11it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:11<01:50,  1.11it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:11<01:49,  1.12it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:12<01:52,  1.08it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:13<01:53,  1.06it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:14<01:47,  1.11it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:15<01:41,  1.16it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:16<01:39,  1.17it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:17<01:39,  1.16it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:18<01:37,  1.17it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:18<01:36,  1.19it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:19<01:36,  1.17it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:20<01:34,  1.18it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:21<01:33,  1.19it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:22<01:32,  1.19it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:23<01:32,  1.18it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:23<01:32,  1.17it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:24<01:29,  1.19it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:25<01:29,  1.18it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:26<01:27,  1.19it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:27<01:26,  1.20it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:28<01:24,  1.22it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:28<01:23,  1.22it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:29<01:23,  1.21it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:30<01:23,  1.19it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:31<01:22,  1.20it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:32<01:21,  1.20it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:33<01:20,  1.21it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:33<01:19,  1.21it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:34<01:17,  1.23it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:35<01:16,  1.23it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:36<01:16,  1.22it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:37<01:15,  1.21it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:37<01:15,  1.21it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:38<01:14,  1.20it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:39<01:14,  1.19it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:40<01:14,  1.18it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:41<01:17,  1.13it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:42<01:17,  1.11it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:43<01:16,  1.12it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:44<01:14,  1.13it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:45<01:12,  1.14it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:45<01:12,  1.14it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:46<01:10,  1.15it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:47<01:10,  1.14it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:48<01:09,  1.13it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:49<01:08,  1.14it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:50<01:08,  1.13it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:51<01:08,  1.12it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:52<01:07,  1.10it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:53<01:07,  1.09it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:54<01:06,  1.10it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:55<01:10,  1.02it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:56<01:06,  1.06it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:56<01:05,  1.07it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:57<01:02,  1.10it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:58<01:00,  1.12it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:59<01:00,  1.11it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:00<01:00,  1.10it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:01<00:58,  1.12it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:02<00:55,  1.15it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:03<00:53,  1.17it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:03<00:52,  1.19it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:04<00:51,  1.18it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:05<00:51,  1.15it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:06<00:50,  1.17it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:07<00:48,  1.19it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:08<00:47,  1.19it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:08<00:47,  1.19it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:09<00:47,  1.16it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:10<00:45,  1.17it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:11<00:45,  1.18it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:12<00:43,  1.18it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:13<00:45,  1.12it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:14<00:46,  1.07it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:15<00:44,  1.09it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:16<00:42,  1.12it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:16<00:42,  1.12it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:17<00:40,  1.13it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:18<00:39,  1.15it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:19<00:39,  1.12it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:20<00:38,  1.11it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:21<00:36,  1.14it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:22<00:39,  1.03it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:23<00:42,  1.07s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:24<00:38,  1.00it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:25<00:35,  1.07it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:26<00:33,  1.12it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:27<00:31,  1.14it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:27<00:30,  1.16it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:28<00:28,  1.18it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:29<00:27,  1.20it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:30<00:26,  1.21it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:31<00:25,  1.21it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:32<00:24,  1.21it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:32<00:23,  1.22it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:33<00:22,  1.22it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:34<00:22,  1.23it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:36<00:23,  1.11it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:38,  4.72s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:14,  2.50s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:54,  1.80s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:42,  1.44s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:08<04:00,  1.23s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:09<03:35,  1.11s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:10<03:19,  1.03s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<03:07,  1.02it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:11<03:00,  1.06it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:12<02:54,  1.09it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:13<02:49,  1.11it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:14<02:46,  1.13it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:15<02:43,  1.14it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:16<02:40,  1.16it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:16<02:39,  1.16it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:17<02:40,  1.15it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:18<02:38,  1.15it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:19<02:34,  1.18it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:20<02:31,  1.19it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:21<02:29,  1.20it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:21<02:28,  1.20it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:22<02:28,  1.20it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:23<02:25,  1.21it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:24<02:25,  1.21it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:25<02:24,  1.21it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:26<02:21,  1.23it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:26<02:20,  1.23it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:27<02:19,  1.23it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:28<02:19,  1.22it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:29<02:18,  1.23it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:30<02:17,  1.23it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:30<02:17,  1.22it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:31<02:17,  1.21it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:32<02:16,  1.22it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:33<02:14,  1.23it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:34<02:16,  1.20it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:35<02:14,  1.21it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:35<02:14,  1.21it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:36<02:12,  1.22it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:37<02:18,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:38<02:15,  1.18it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:39<02:13,  1.18it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:40<02:12,  1.19it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:41<02:10,  1.20it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:41<02:09,  1.20it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:42<02:08,  1.20it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:43<02:09,  1.18it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:44<02:11,  1.15it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:45<02:12,  1.14it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:46<02:13,  1.12it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:47<02:14,  1.11it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:48<02:13,  1.10it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:49<02:13,  1.10it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:49<02:12,  1.10it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:50<02:11,  1.11it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:51<02:11,  1.10it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:52<02:10,  1.09it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:53<02:11,  1.08it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:54<02:09,  1.09it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:55<02:07,  1.10it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:56<02:04,  1.11it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:57<02:01,  1.14it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:57<01:58,  1.16it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:58<01:54,  1.18it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:59<01:52,  1.20it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:00<01:50,  1.21it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:01<01:48,  1.22it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:02<01:48,  1.22it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:02<01:47,  1.22it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:03<01:46,  1.22it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:04<01:45,  1.23it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:05<01:44,  1.23it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:06<01:44,  1.21it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:06<01:45,  1.19it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:07<01:43,  1.20it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:08<01:43,  1.19it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:09<01:40,  1.22it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:10<01:40,  1.21it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:11<01:39,  1.22it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:11<01:36,  1.24it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:12<01:36,  1.24it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:13<01:34,  1.25it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:14<01:33,  1.25it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:15<01:31,  1.26it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:15<01:30,  1.27it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:16<01:29,  1.27it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:17<01:28,  1.27it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:18<01:28,  1.27it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:18<01:27,  1.26it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:19<01:30,  1.21it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:20<01:30,  1.21it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:21<01:30,  1.19it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:22<01:27,  1.22it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:23<01:26,  1.23it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:24<01:28,  1.18it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:24<01:30,  1.15it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:25<01:28,  1.17it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:26<01:26,  1.18it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:27<01:25,  1.18it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:28<01:23,  1.20it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:29<01:24,  1.17it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:30<01:27,  1.12it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:31<01:27,  1.11it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:31<01:26,  1.11it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:32<01:25,  1.11it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:33<01:25,  1.10it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:34<01:24,  1.10it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:35<01:24,  1.09it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:36<01:23,  1.09it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:37<01:23,  1.08it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:38<01:22,  1.08it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:39<01:20,  1.09it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:40<01:20,  1.08it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:41<01:19,  1.08it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:42<01:19,  1.07it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:43<01:18,  1.06it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:44<01:18,  1.06it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:45<01:16,  1.07it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:45<01:15,  1.07it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:46<01:15,  1.06it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:47<01:15,  1.05it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:48<01:14,  1.05it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:49<01:12,  1.07it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:50<01:10,  1.08it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:51<01:11,  1.05it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:52<01:08,  1.08it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:53<01:06,  1.10it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:54<01:05,  1.09it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:55<01:04,  1.10it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:56<01:02,  1.13it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:56<01:00,  1.13it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:57<00:59,  1.14it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:58<01:00,  1.11it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:59<01:00,  1.09it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:00<00:58,  1.12it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:01<00:56,  1.13it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:02<00:55,  1.13it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:03<00:53,  1.17it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:04<00:56,  1.09it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:05<01:02,  1.05s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:06<00:58,  1.01it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:07<00:55,  1.05it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:08<00:52,  1.08it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:08<00:49,  1.12it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:09<00:47,  1.15it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:10<00:46,  1.17it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:11<00:45,  1.16it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:12<00:44,  1.18it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:13<00:42,  1.19it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:13<00:41,  1.19it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:14<00:40,  1.20it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:15<00:39,  1.21it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:16<00:38,  1.21it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:17<00:37,  1.21it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:17<00:37,  1.21it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:18<00:36,  1.22it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:19<00:35,  1.20it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:20<00:34,  1.20it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:21<00:34,  1.20it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:22<00:33,  1.20it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:23<00:32,  1.18it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:23<00:32,  1.18it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:24<00:31,  1.16it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:25<00:31,  1.16it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:26<00:30,  1.16it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:27<00:29,  1.16it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:28<00:28,  1.16it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:29<00:27,  1.16it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:29<00:26,  1.17it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:30<00:25,  1.16it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:31<00:24,  1.16it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:32<00:24,  1.16it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:33<00:23,  1.16it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:34<00:22,  1.15it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:35<00:21,  1.16it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:35<00:20,  1.16it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:36<00:20,  1.15it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:37<00:19,  1.16it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:38<00:17,  1.18it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:39<00:16,  1.20it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:40<00:15,  1.21it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [02:40<00:14,  1.22it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [02:41<00:13,  1.23it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [02:42<00:13,  1.22it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [02:43<00:12,  1.23it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [02:44<00:11,  1.24it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [02:44<00:10,  1.25it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [02:45<00:09,  1.25it/s]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [02:46<00:08,  1.24it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [02:47<00:08,  1.23it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [02:48<00:07,  1.24it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [02:48<00:06,  1.25it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [02:49<00:05,  1.24it/s]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [02:50<00:04,  1.24it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [02:51<00:04,  1.23it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [02:52<00:03,  1.23it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [02:53<00:02,  1.23it/s]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [02:53<00:01,  1.22it/s]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [02:54<00:00,  1.22it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [02:55<00:00,  1.14it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.77it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.42it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 74it [00:00, 106.39it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 178.34it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|        | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|▍| 42/100 [00:00<00:00, 414.63it\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 428.86i\n",
+      " 80%|██████████████████████████████████▍        | 4/5 [2:04:54<30:55, 1855.13s/it]\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:41,  4.73s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:17,  2.51s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:49,  1.77s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:34,  1.40s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:08<03:50,  1.18s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:09<03:23,  1.05s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<03:08,  1.03it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:56,  1.09it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:11<02:49,  1.13it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:12<02:46,  1.14it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:13<02:41,  1.17it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:34,  1.21it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:14<02:30,  1.24it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:15<02:26,  1.27it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:16<02:24,  1.28it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:16<02:20,  1.31it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:17<02:19,  1.31it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:18<02:18,  1.31it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:19<02:18,  1.31it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:19<02:16,  1.32it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:20<02:15,  1.32it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:21<02:13,  1.33it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:22<02:13,  1.33it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:22<02:11,  1.33it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:23<02:10,  1.34it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:24<02:08,  1.35it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:25<02:10,  1.33it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:25<02:09,  1.33it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:26<02:07,  1.34it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:27<02:06,  1.34it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:28<02:07,  1.33it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:28<02:07,  1.32it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:29<02:05,  1.33it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:30<02:05,  1.33it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:31<02:04,  1.33it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:31<02:02,  1.34it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:32<02:01,  1.34it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:33<02:00,  1.35it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:34<01:59,  1.35it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:34<01:59,  1.34it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:35<01:59,  1.33it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:36<01:58,  1.33it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:37<01:58,  1.33it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:37<01:57,  1.32it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:38<01:56,  1.33it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:39<01:56,  1.32it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:40<01:56,  1.32it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:40<01:55,  1.31it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:41<01:54,  1.32it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:42<01:52,  1.33it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:43<01:52,  1.33it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:44<01:55,  1.29it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:44<01:55,  1.28it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:45<01:55,  1.26it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:46<01:56,  1.25it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:47<01:56,  1.24it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:48<01:57,  1.22it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:48<01:55,  1.23it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:49<01:54,  1.23it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:50<01:53,  1.24it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:51<01:52,  1.24it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:52<01:51,  1.23it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:52<01:50,  1.24it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:53<01:50,  1.23it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:54<01:49,  1.24it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:55<01:48,  1.24it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:56<01:47,  1.23it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:56<01:46,  1.24it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:57<01:45,  1.24it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:58<01:44,  1.25it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:59<01:40,  1.29it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:00<01:37,  1.31it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:00<01:36,  1.32it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:01<01:34,  1.33it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:02<01:34,  1.33it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:03<01:32,  1.34it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:03<01:32,  1.33it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:04<01:32,  1.32it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:05<01:32,  1.31it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:06<01:30,  1.33it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:06<01:29,  1.34it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:07<01:28,  1.34it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:08<01:26,  1.35it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:08<01:25,  1.36it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:09<01:24,  1.36it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:10<01:23,  1.36it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:11<01:23,  1.36it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:11<01:23,  1.34it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:12<01:22,  1.35it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:13<01:21,  1.35it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:14<01:21,  1.34it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:14<01:22,  1.30it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:15<01:27,  1.22it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:16<01:26,  1.23it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:17<01:22,  1.28it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:18<01:20,  1.29it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:18<01:18,  1.31it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:19<01:17,  1.32it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:20<01:15,  1.34it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:21<01:14,  1.34it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:21<01:13,  1.34it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:22<01:13,  1.34it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:23<01:12,  1.33it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:24<01:12,  1.33it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:24<01:10,  1.35it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:25<01:09,  1.35it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:26<01:08,  1.36it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:27<01:07,  1.37it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:27<01:06,  1.36it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:28<01:05,  1.36it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:29<01:10,  1.26it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:30<01:08,  1.28it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:31<01:08,  1.27it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:31<01:06,  1.29it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:32<01:05,  1.30it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:33<01:04,  1.31it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:34<01:08,  1.21it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:34<01:05,  1.25it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:35<01:03,  1.27it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:36<01:02,  1.28it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:37<01:00,  1.30it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:38<01:01,  1.27it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:38<01:02,  1.23it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:39<01:03,  1.20it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:40<01:03,  1.18it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:41<01:02,  1.18it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:42<01:01,  1.18it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:43<01:01,  1.17it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:44<01:01,  1.16it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:45<01:01,  1.15it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:45<01:00,  1.14it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:46<01:00,  1.13it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:47<00:59,  1.13it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:48<00:58,  1.13it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:49<00:57,  1.13it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:50<00:56,  1.13it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:51<00:56,  1.12it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:52<00:55,  1.12it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:53<00:54,  1.11it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:54<00:53,  1.11it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:54<00:53,  1.11it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:55<00:52,  1.11it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:56<00:51,  1.12it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:57<00:50,  1.12it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:58<00:49,  1.12it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:59<00:48,  1.12it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:00<00:47,  1.12it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:01<00:44,  1.18it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:01<00:42,  1.20it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:02<00:41,  1.21it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:03<00:40,  1.21it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:04<00:39,  1.23it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:05<00:37,  1.24it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:05<00:37,  1.22it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:06<00:36,  1.23it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:07<00:35,  1.23it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:08<00:37,  1.16it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:09<00:35,  1.17it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:10<00:33,  1.21it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:10<00:32,  1.23it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:11<00:31,  1.24it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:12<00:30,  1.24it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:13<00:29,  1.25it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:14<00:28,  1.25it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:14<00:27,  1.26it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:16<00:31,  1.08it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:17<00:32,  1.01it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:17<00:29,  1.09it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:18<00:26,  1.17it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:19<00:24,  1.21it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:20<00:23,  1.25it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:20<00:21,  1.28it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:21<00:20,  1.30it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:22<00:19,  1.31it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:23<00:19,  1.29it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:23<00:18,  1.29it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:24<00:17,  1.28it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:25<00:17,  1.26it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:26<00:16,  1.26it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:27<00:16,  1.24it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:28<00:15,  1.22it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:45,  4.75s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:03,  2.44s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:36,  1.71s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:23,  1.34s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:42,  1.14s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<03:13,  1.00it/s]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<02:57,  1.09it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:46,  1.15it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:10<02:37,  1.21it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:11<02:31,  1.25it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:12<02:28,  1.28it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:24,  1.31it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:13<02:23,  1.30it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:14<02:21,  1.32it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:15<02:20,  1.32it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:16<02:21,  1.30it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:16<02:19,  1.31it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:17<02:18,  1.31it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:18<02:17,  1.32it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:19<02:13,  1.34it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:19<02:12,  1.36it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:20<02:09,  1.37it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:21<02:09,  1.37it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:22<02:08,  1.37it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:22<02:06,  1.39it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:23<02:04,  1.40it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:24<02:03,  1.40it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:24<02:04,  1.39it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:25<02:03,  1.38it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:26<02:03,  1.38it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:27<02:03,  1.37it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:27<02:03,  1.36it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:28<02:04,  1.35it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:29<02:02,  1.36it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:30<02:02,  1.34it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:30<02:00,  1.36it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:31<01:59,  1.36it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:32<01:58,  1.36it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:33<01:58,  1.36it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:33<01:55,  1.38it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:34<01:55,  1.37it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:35<01:54,  1.38it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:35<01:53,  1.39it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:36<01:59,  1.31it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:37<01:58,  1.31it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:38<01:54,  1.34it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:38<01:53,  1.35it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:39<01:51,  1.36it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:40<01:50,  1.36it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:41<01:51,  1.35it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:41<01:51,  1.34it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:42<01:50,  1.34it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:43<01:48,  1.36it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:44<01:46,  1.37it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:44<01:50,  1.32it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:45<01:47,  1.33it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:46<01:46,  1.34it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:47<01:45,  1.34it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:47<01:43,  1.36it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:48<01:43,  1.36it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:49<01:42,  1.36it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:50<01:44,  1.32it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:50<01:45,  1.30it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:51<01:46,  1.27it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:52<01:47,  1.25it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:53<01:49,  1.23it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:54<01:48,  1.23it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:55<01:46,  1.24it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:55<01:46,  1.23it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:56<01:45,  1.23it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:57<01:44,  1.24it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [00:58<01:43,  1.23it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [00:59<01:44,  1.22it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [00:59<01:43,  1.22it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:00<01:44,  1.20it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:01<01:42,  1.21it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:02<01:40,  1.22it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:03<01:39,  1.23it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:04<01:39,  1.21it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:05<01:44,  1.15it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:05<01:42,  1.17it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:06<01:37,  1.21it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:07<01:33,  1.25it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:08<01:31,  1.27it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:08<01:30,  1.27it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:09<01:29,  1.28it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:10<01:27,  1.29it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:11<01:25,  1.30it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:11<01:24,  1.31it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:12<01:23,  1.33it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:13<01:22,  1.32it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:14<01:22,  1.30it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:15<01:21,  1.31it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:15<01:19,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:16<01:18,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:17<01:17,  1.34it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:18<01:23,  1.23it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:18<01:21,  1.25it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:19<01:22,  1.23it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:20<01:19,  1.26it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:21<01:18,  1.27it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:22<01:17,  1.27it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:23<01:19,  1.21it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:23<01:16,  1.26it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:24<01:15,  1.27it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:25<01:12,  1.29it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:26<01:11,  1.30it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:26<01:11,  1.29it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:27<01:11,  1.27it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:28<01:12,  1.25it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:29<01:12,  1.23it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:30<01:12,  1.21it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:31<01:12,  1.20it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:31<01:12,  1.19it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:32<01:12,  1.17it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:33<01:11,  1.17it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:34<01:10,  1.17it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:35<01:10,  1.16it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:36<01:10,  1.15it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:37<01:10,  1.14it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:38<01:09,  1.13it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:38<01:09,  1.12it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:39<01:08,  1.13it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:40<01:07,  1.13it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:41<01:06,  1.14it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:42<01:04,  1.14it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:43<01:04,  1.14it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:44<01:03,  1.13it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:45<01:02,  1.13it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:45<01:01,  1.13it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:46<01:00,  1.14it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:47<01:00,  1.13it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:48<00:57,  1.17it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:49<00:55,  1.18it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:50<00:54,  1.19it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:51<00:54,  1.17it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:51<00:52,  1.20it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:52<00:51,  1.21it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:53<00:52,  1.17it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:54<00:50,  1.19it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:55<00:48,  1.22it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:56<00:48,  1.20it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:56<00:49,  1.16it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:57<00:47,  1.19it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:58<00:46,  1.18it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:59<00:45,  1.20it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:00<00:43,  1.23it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:00<00:41,  1.24it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:01<00:40,  1.25it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:02<00:40,  1.25it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:03<00:46,  1.06it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:04<00:46,  1.04it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:05<00:41,  1.13it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:06<00:38,  1.18it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:07<00:36,  1.22it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:07<00:35,  1.25it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:08<00:33,  1.29it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:09<00:32,  1.31it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:10<00:30,  1.32it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:10<00:29,  1.35it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:11<00:28,  1.35it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:12<00:28,  1.35it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:12<00:27,  1.35it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:13<00:26,  1.36it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:14<00:25,  1.37it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:15<00:24,  1.38it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:15<00:23,  1.39it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:16<00:22,  1.40it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:17<00:22,  1.41it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:17<00:21,  1.39it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:18<00:21,  1.35it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:19<00:20,  1.35it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:20<00:19,  1.35it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:20<00:19,  1.36it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:21<00:18,  1.36it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:22<00:17,  1.36it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:23<00:16,  1.36it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:23<00:16,  1.37it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:24<00:15,  1.37it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:25<00:14,  1.37it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:26<00:13,  1.36it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [02:26<00:13,  1.37it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [02:27<00:12,  1.37it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [02:28<00:11,  1.37it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [02:28<00:10,  1.37it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [02:29<00:10,  1.37it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [02:30<00:09,  1.37it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [02:31<00:08,  1.35it/s]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [02:31<00:08,  1.36it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [02:32<00:07,  1.35it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [02:33<00:06,  1.35it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [02:34<00:05,  1.35it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [02:34<00:05,  1.35it/s]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [02:35<00:04,  1.34it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [02:36<00:03,  1.33it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [02:38<00:03,  1.24it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<16:44,  5.05s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:26,  2.56s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:48,  1.77s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:30,  1.38s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:08<03:48,  1.17s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:09<03:27,  1.07s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<03:10,  1.01it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:58,  1.07it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:11<02:51,  1.12it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:12<02:43,  1.16it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:13<02:35,  1.21it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:30,  1.25it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:14<02:27,  1.27it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:15<02:23,  1.29it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:16<02:22,  1.30it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:17<02:31,  1.21it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:17<02:26,  1.25it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:18<02:21,  1.28it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:19<02:20,  1.28it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:20<02:18,  1.30it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:20<02:16,  1.31it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:21<02:15,  1.32it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:22<02:14,  1.32it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:23<02:13,  1.32it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:23<02:12,  1.32it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:24<02:11,  1.33it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:25<02:10,  1.33it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:26<02:10,  1.32it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:26<02:10,  1.31it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:27<02:10,  1.31it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:28<02:10,  1.29it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:29<02:10,  1.29it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:30<02:09,  1.29it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:30<02:08,  1.29it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:31<02:09,  1.28it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:32<02:08,  1.28it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:33<02:09,  1.26it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:34<02:11,  1.23it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:34<02:12,  1.22it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:35<02:12,  1.21it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:36<02:12,  1.20it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:37<02:10,  1.21it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:38<02:08,  1.22it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:38<02:05,  1.25it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:39<02:04,  1.25it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:40<02:02,  1.26it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:41<02:01,  1.25it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:42<02:01,  1.25it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:42<02:00,  1.25it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:43<02:00,  1.25it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:44<02:00,  1.23it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:45<02:04,  1.19it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:46<02:05,  1.17it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:47<02:01,  1.20it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:47<01:58,  1.22it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:48<01:54,  1.26it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:49<01:50,  1.30it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:50<01:47,  1.32it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:50<01:44,  1.34it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:51<01:43,  1.35it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:52<01:41,  1.36it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:53<01:41,  1.36it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:53<01:40,  1.37it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:54<01:39,  1.37it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:55<01:38,  1.37it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:55<01:40,  1.34it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:56<01:40,  1.33it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:57<01:38,  1.34it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:58<01:39,  1.32it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:59<01:46,  1.22it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:59<01:43,  1.25it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:00<01:43,  1.24it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:01<01:40,  1.27it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:02<01:37,  1.30it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:03<01:40,  1.24it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:04<01:42,  1.21it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:04<01:39,  1.24it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:05<01:36,  1.27it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:06<01:32,  1.30it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:06<01:30,  1.33it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:07<01:33,  1.28it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:08<01:36,  1.23it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:09<01:37,  1.20it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:10<01:37,  1.19it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:11<01:37,  1.18it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:12<01:37,  1.17it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:13<01:36,  1.17it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:13<01:36,  1.16it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:14<01:35,  1.16it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:15<01:35,  1.16it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:16<01:33,  1.16it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:17<01:33,  1.16it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:18<01:32,  1.16it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:19<01:31,  1.16it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:19<01:30,  1.17it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:20<01:29,  1.17it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:21<01:28,  1.16it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:22<01:27,  1.16it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:23<01:27,  1.16it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:24<01:26,  1.16it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:25<01:25,  1.16it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:25<01:24,  1.16it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:26<01:24,  1.15it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:27<01:19,  1.21it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:28<01:16,  1.24it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:29<01:14,  1.26it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:29<01:13,  1.26it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:30<01:14,  1.23it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:31<01:12,  1.25it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:32<01:12,  1.24it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:33<01:12,  1.23it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:34<01:12,  1.21it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:34<01:12,  1.20it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:35<01:12,  1.18it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:36<01:11,  1.19it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:37<01:11,  1.18it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:38<01:09,  1.19it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:39<01:08,  1.19it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:40<01:09,  1.17it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:40<01:07,  1.18it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:41<01:06,  1.18it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:42<01:06,  1.17it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:43<01:05,  1.17it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:44<01:04,  1.18it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:45<01:04,  1.17it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:45<01:03,  1.17it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:47<01:12,  1.01it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:48<01:14,  1.03s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:49<01:07,  1.06it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:49<01:01,  1.14it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:50<00:57,  1.19it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:51<00:55,  1.22it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:52<00:53,  1.26it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:52<00:52,  1.26it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:53<00:51,  1.27it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:54<00:50,  1.27it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:55<00:48,  1.29it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:56<00:48,  1.28it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:56<00:47,  1.28it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:57<00:46,  1.29it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:58<00:45,  1.31it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:59<00:43,  1.32it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:59<00:42,  1.34it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:00<00:42,  1.32it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:01<00:41,  1.32it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:02<00:41,  1.31it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:02<00:40,  1.32it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:03<00:39,  1.33it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:04<00:38,  1.33it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:05<00:37,  1.34it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:05<00:36,  1.33it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:06<00:36,  1.30it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:07<00:36,  1.28it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:08<00:36,  1.26it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:09<00:35,  1.26it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:10<00:36,  1.19it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:45,  4.75s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:01,  2.43s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:33,  1.69s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:22,  1.34s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:43,  1.15s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<03:17,  1.02s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<02:58,  1.08it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:46,  1.15it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:10<02:36,  1.22it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:11<02:30,  1.26it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:12<02:25,  1.30it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:23,  1.31it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:13<02:21,  1.33it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:14<02:19,  1.34it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:15<02:18,  1.33it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:16<02:16,  1.35it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:16<02:14,  1.36it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:17<02:14,  1.35it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:18<02:14,  1.34it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:19<02:13,  1.35it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:19<02:13,  1.34it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:20<02:13,  1.33it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:21<02:12,  1.33it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:22<02:11,  1.34it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:22<02:10,  1.34it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:23<02:09,  1.34it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:24<02:08,  1.35it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:25<02:08,  1.34it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:25<02:07,  1.34it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:26<02:14,  1.26it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:27<02:11,  1.28it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:28<02:08,  1.30it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:28<02:06,  1.32it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:29<02:05,  1.32it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:30<02:04,  1.32it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:31<02:03,  1.33it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:31<02:01,  1.34it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:32<01:59,  1.36it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:33<01:58,  1.35it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:34<01:57,  1.36it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:34<01:56,  1.37it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:35<01:54,  1.37it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:36<01:54,  1.38it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:36<01:54,  1.37it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:37<01:53,  1.36it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:38<01:52,  1.37it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:39<01:53,  1.35it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:39<01:53,  1.34it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:40<01:51,  1.35it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:41<01:50,  1.36it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:42<01:51,  1.34it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:42<01:50,  1.34it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:43<01:49,  1.34it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:44<01:48,  1.35it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:45<01:48,  1.34it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:45<01:48,  1.33it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:46<01:48,  1.32it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:47<01:46,  1.33it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:48<01:44,  1.34it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:48<01:42,  1.36it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:49<01:41,  1.36it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:50<01:41,  1.35it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:51<01:41,  1.35it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:51<01:40,  1.35it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:52<01:43,  1.30it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:53<01:44,  1.29it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:54<01:47,  1.24it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:55<01:51,  1.18it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:56<01:51,  1.18it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:56<01:49,  1.19it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:57<01:46,  1.21it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [00:58<01:45,  1.22it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [00:59<01:43,  1.22it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:00<01:44,  1.21it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:01<01:44,  1.19it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:01<01:43,  1.20it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:02<01:42,  1.20it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:03<01:43,  1.18it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:04<01:41,  1.19it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:05<01:40,  1.20it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:06<01:40,  1.19it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:06<01:36,  1.22it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:07<01:34,  1.24it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:08<01:38,  1.18it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:09<01:36,  1.19it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:10<01:35,  1.19it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:11<01:32,  1.22it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:11<01:29,  1.25it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:12<01:26,  1.28it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:13<01:31,  1.20it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:14<01:29,  1.22it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:15<01:26,  1.25it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:15<01:24,  1.27it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:16<01:22,  1.29it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:17<01:20,  1.30it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:18<01:22,  1.26it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:19<01:24,  1.22it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:19<01:21,  1.25it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:20<01:19,  1.27it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:21<01:20,  1.24it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:22<01:20,  1.22it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:23<01:21,  1.20it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:24<01:25,  1.14it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:25<01:25,  1.12it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:25<01:23,  1.14it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:27<01:31,  1.02it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:28<01:36,  1.04s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:29<01:28,  1.03it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:29<01:23,  1.08it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:30<01:20,  1.12it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:31<01:18,  1.14it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:32<01:16,  1.16it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:33<01:14,  1.16it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:34<01:13,  1.18it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:34<01:10,  1.20it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:35<01:08,  1.23it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:36<01:05,  1.26it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:37<01:04,  1.28it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:37<01:02,  1.30it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:38<01:00,  1.32it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:39<01:00,  1.31it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:40<00:58,  1.32it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:40<00:57,  1.34it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:41<00:56,  1.34it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:42<00:56,  1.32it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:43<00:56,  1.32it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:43<00:54,  1.33it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:44<00:53,  1.35it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:45<00:52,  1.35it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:46<00:51,  1.35it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:46<00:51,  1.33it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:47<00:51,  1.33it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:48<00:51,  1.29it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:49<00:51,  1.29it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:49<00:49,  1.30it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:50<00:48,  1.31it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:51<00:47,  1.34it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:52<00:46,  1.35it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:52<00:45,  1.34it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:53<00:44,  1.34it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:54<00:43,  1.35it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:55<00:42,  1.37it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:55<00:41,  1.39it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:56<00:40,  1.39it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:57<00:39,  1.39it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:57<00:38,  1.38it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [01:58<00:38,  1.38it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [01:59<00:37,  1.39it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:00<00:37,  1.37it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:00<00:36,  1.38it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:01<00:35,  1.37it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:02<00:34,  1.38it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:03<00:34,  1.38it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:03<00:33,  1.39it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:04<00:32,  1.39it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:05<00:31,  1.40it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:05<00:30,  1.41it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:06<00:30,  1.39it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:07<00:29,  1.39it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:08<00:28,  1.39it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:08<00:27,  1.39it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:09<00:27,  1.40it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:10<00:26,  1.40it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:10<00:25,  1.39it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:11<00:25,  1.39it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:12<00:24,  1.38it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:13<00:24,  1.37it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:13<00:23,  1.38it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:14<00:22,  1.38it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:15<00:21,  1.40it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:15<00:20,  1.41it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:16<00:19,  1.41it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:17<00:19,  1.37it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:18<00:19,  1.33it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:19<00:19,  1.29it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:19<00:18,  1.27it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:20<00:18,  1.27it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:21<00:17,  1.27it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:22<00:16,  1.27it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:22<00:15,  1.27it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:23<00:14,  1.28it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [02:24<00:14,  1.26it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [02:25<00:13,  1.26it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [02:26<00:12,  1.25it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<16:18,  4.92s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:10,  2.47s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:32,  1.69s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:19,  1.33s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:39,  1.12s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<03:15,  1.01s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<02:57,  1.09it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:48,  1.14it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:11<02:39,  1.20it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:11<02:33,  1.24it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:12<02:29,  1.27it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:26,  1.28it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:14<02:23,  1.30it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:14<02:24,  1.29it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:15<02:22,  1.30it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:16<02:21,  1.30it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:17<02:20,  1.31it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:17<02:21,  1.28it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:18<02:21,  1.28it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:19<02:25,  1.23it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:20<02:22,  1.26it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:21<02:21,  1.26it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:21<02:18,  1.28it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:22<02:16,  1.29it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:23<02:14,  1.30it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:24<02:12,  1.31it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:24<02:13,  1.29it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:25<02:14,  1.28it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:26<02:15,  1.26it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:27<02:16,  1.25it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:28<02:16,  1.24it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:28<02:14,  1.25it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:29<02:13,  1.25it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:30<02:13,  1.24it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:31<02:11,  1.25it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:32<02:10,  1.25it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:33<02:17,  1.19it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:33<02:15,  1.20it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:34<02:13,  1.20it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:35<02:12,  1.21it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:36<02:09,  1.22it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:37<02:08,  1.23it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:38<02:13,  1.18it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:39<02:15,  1.15it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:39<02:11,  1.18it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:40<02:09,  1.19it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:41<02:06,  1.21it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:42<02:01,  1.25it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:42<02:01,  1.25it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:43<02:01,  1.23it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:44<01:59,  1.24it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:45<01:56,  1.27it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:46<01:56,  1.26it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:47<02:00,  1.21it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:47<01:56,  1.25it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:48<01:54,  1.26it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:49<01:52,  1.27it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:50<01:53,  1.25it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:50<01:51,  1.26it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:51<01:50,  1.27it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:52<01:51,  1.24it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:53<01:49,  1.26it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:54<01:48,  1.26it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:54<01:48,  1.25it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:55<01:47,  1.25it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:56<01:48,  1.23it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:57<01:46,  1.25it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:58<01:43,  1.27it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:59<01:46,  1.23it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:59<01:49,  1.19it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:00<01:45,  1.22it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:01<01:42,  1.25it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:02<02:02,  1.04it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:03<02:03,  1.02it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:04<01:54,  1.09it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:05<01:46,  1.16it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:06<01:41,  1.22it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:06<01:37,  1.25it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:07<01:35,  1.27it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:08<01:33,  1.28it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:09<01:32,  1.29it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:09<01:31,  1.29it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:10<01:29,  1.31it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:11<01:29,  1.30it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:12<01:28,  1.31it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:12<01:26,  1.32it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:13<01:25,  1.32it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:14<01:23,  1.34it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:15<01:22,  1.35it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:15<01:20,  1.36it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:16<01:21,  1.34it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:17<01:21,  1.32it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:18<01:20,  1.33it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:18<01:19,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:19<01:19,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:20<01:17,  1.34it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:21<01:17,  1.34it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:21<01:16,  1.33it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:22<01:15,  1.34it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:23<01:14,  1.35it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:24<01:13,  1.35it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:24<01:13,  1.34it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:25<01:13,  1.32it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:26<01:14,  1.28it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:27<01:15,  1.26it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:27<01:14,  1.26it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:28<01:14,  1.25it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:29<01:13,  1.25it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:30<01:13,  1.25it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:31<01:12,  1.24it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:32<01:11,  1.24it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:32<01:10,  1.24it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:33<01:10,  1.24it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:34<01:08,  1.25it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:35<01:08,  1.25it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:36<01:07,  1.24it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:36<01:06,  1.24it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:37<01:05,  1.24it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:38<01:05,  1.24it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:39<01:04,  1.23it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:40<01:04,  1.23it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:40<01:03,  1.24it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:41<01:01,  1.25it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:42<01:00,  1.26it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:43<00:59,  1.27it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:44<00:57,  1.28it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:44<00:56,  1.29it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:45<00:54,  1.31it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:46<00:54,  1.31it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:47<00:53,  1.30it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:47<00:53,  1.29it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:48<00:52,  1.31it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:49<00:51,  1.30it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:50<00:49,  1.32it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:50<00:49,  1.31it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:51<00:48,  1.32it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:52<00:47,  1.34it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:53<00:46,  1.32it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:53<00:47,  1.29it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:54<00:48,  1.24it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:55<00:47,  1.24it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:56<00:46,  1.25it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:57<00:44,  1.27it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:57<00:44,  1.27it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:58<00:42,  1.28it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:59<00:41,  1.31it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:00<00:40,  1.32it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:00<00:38,  1.33it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:01<00:38,  1.34it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:02<00:37,  1.33it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:03<00:37,  1.32it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:03<00:36,  1.31it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:04<00:35,  1.32it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:05<00:35,  1.29it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:06<00:35,  1.28it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:07<00:33,  1.30it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:07<00:33,  1.30it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:08<00:32,  1.31it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:09<00:31,  1.32it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:10<00:30,  1.31it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:10<00:29,  1.32it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:11<00:29,  1.31it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:12<00:28,  1.28it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:13<00:28,  1.26it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:14<00:28,  1.24it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:14<00:27,  1.24it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:15<00:26,  1.24it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:16<00:25,  1.24it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:17<00:24,  1.26it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:18<00:23,  1.25it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:18<00:23,  1.25it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:19<00:22,  1.25it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:20<00:21,  1.25it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:21<00:20,  1.25it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:22<00:20,  1.20it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:23<00:20,  1.15it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:24<00:19,  1.17it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:24<00:18,  1.18it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:25<00:17,  1.19it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:26<00:16,  1.21it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:27<00:15,  1.24it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [02:27<00:14,  1.28it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [02:28<00:13,  1.29it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [02:29<00:12,  1.31it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [02:30<00:11,  1.32it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [02:30<00:10,  1.33it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [02:31<00:09,  1.33it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [02:32<00:09,  1.33it/s]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [02:33<00:08,  1.32it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [02:33<00:07,  1.33it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [02:34<00:06,  1.33it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [02:35<00:06,  1.30it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [02:36<00:05,  1.26it/s]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [02:37<00:04,  1.26it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [02:37<00:03,  1.28it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [02:38<00:03,  1.31it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [02:39<00:02,  1.34it/s]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [02:40<00:01,  1.28it/s]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [02:40<00:00,  1.26it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [02:41<00:00,  1.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.00it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.56it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.51it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.52it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.55it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 58it [00:01, 74.19it/s]\u001b[A\n",
+      "Evaluating NLL: 114it [00:01, 149.37it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 150.36it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|        | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|▍| 39/100 [00:00<00:00, 389.85it\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 390.34i\n",
+      "100%|███████████████████████████████████████████| 5/5 [2:35:12<00:00, 1862.45s/it]\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [12], line 21\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     18\u001b[0m   objects_fn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m : white_noise_fn()\n\u001b[0;32m---> 21\u001b[0m initial_kernel, initial_params, optimized_params, objects, initial_mi, optimized_mi, optimized_mi_lower, optimized_mi_upper \u001b[38;5;241m=\u001b[39m \u001b[43moptimize_PSF_and_estimate_mi\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m      \u001b[49m\u001b[43mobjects_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnoise_sigma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mupsampled_signal_length\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mUPSAMPLED_SIGNAL_LENGTH\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     23\u001b[0m \n\u001b[1;32m     24\u001b[0m \u001b[43m      \u001b[49m\u001b[43mestimate_with_pixel_cnn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     25\u001b[0m \n\u001b[1;32m     26\u001b[0m \u001b[43m      \u001b[49m\u001b[43mmax_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfidence\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconfidence\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     28\u001b[0m mutual_information\u001b[38;5;241m.\u001b[39mappend(optimized_mi)\n\u001b[1;32m     29\u001b[0m mi_lo\u001b[38;5;241m.\u001b[39mappend(optimized_mi_lower)\n",
-      "File \u001b[0;32m/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/simulations_1d/signal_utils_1D.py:534\u001b[0m, in \u001b[0;36moptimize_PSF_and_estimate_mi\u001b[0;34m(objects_fn, noise_sigma, initial_kernel, learning_rate, learning_rate_decay, verbose, estimate_with_pixel_cnn, loss_improvement_patience, max_epochs, num_nyquist_samples, upsampled_signal_length, test_fraction, confidence)\u001b[0m\n\u001b[1;32m    529\u001b[0m initial_params \u001b[38;5;241m=\u001b[39m params_from_signal(initial_kernel, num_nyquist_samples\u001b[38;5;241m=\u001b[39mnum_nyquist_samples)\n\u001b[1;32m    531\u001b[0m loss_fn \u001b[38;5;241m=\u001b[39m make_convolutional_forward_model_with_mi_loss(\n\u001b[1;32m    532\u001b[0m     objects, noise_sigma\u001b[38;5;241m=\u001b[39mnoise_sigma,  num_nyquist_samples\u001b[38;5;241m=\u001b[39mnum_nyquist_samples,\n\u001b[1;32m    533\u001b[0m     upsampled_signal_length\u001b[38;5;241m=\u001b[39mupsampled_signal_length)\n\u001b[0;32m--> 534\u001b[0m optimized_params \u001b[38;5;241m=\u001b[39m \u001b[43mrun_optimzation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43msignal_prox_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_nyquist_samples\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    535\u001b[0m \u001b[43m                        \u001b[49m\u001b[43minitial_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlearning_rate_decay\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlearning_rate_decay\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    536\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mloss_improvement_patience\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mloss_improvement_patience\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_epochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    537\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjax\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPRNGKey\u001b[49m\u001b[43m(\u001b[49m\u001b[43monp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m100000\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    538\u001b[0m test_objects \u001b[38;5;241m=\u001b[39m objects_fn()   \n\u001b[1;32m    540\u001b[0m scale_factor \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m100000\u001b[39m \u001b[38;5;66;03m# because these signals are 0-1 but pixel cnn is designed for photon counts\u001b[39;00m\n",
-      "File \u001b[0;32m/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/simulations_1d/signal_utils_1D.py:261\u001b[0m, in \u001b[0;36mrun_optimzation\u001b[0;34m(loss_fn, prox_fn, parameters, learning_rate, verbose, tolerance, momentum, loss_improvement_patience, max_epochs, learning_rate_decay, transition_begin, return_param_history, key)\u001b[0m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    260\u001b[0m     key, subkey \u001b[38;5;241m=\u001b[39m jax\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39msplit(key)\n\u001b[0;32m--> 261\u001b[0m     loss, gradient \u001b[38;5;241m=\u001b[39m \u001b[43mgrad_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    263\u001b[0m     loss, gradient \u001b[38;5;241m=\u001b[39m grad_fn(parameters)\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8_deltas_uniform\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  0%|                                                        | 0/5 [00:00<?, ?it/s]\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<13:39,  4.12s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:04<06:49,  2.07s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:05<04:38,  1.41s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:06<03:38,  1.12s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:06<03:06,  1.05it/s]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:07<02:43,  1.18it/s]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:07<02:30,  1.29it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:08<02:20,  1.37it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:09<02:14,  1.42it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:09<02:09,  1.47it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:10<02:05,  1.51it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:11<02:02,  1.53it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:11<01:59,  1.56it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:12<01:58,  1.57it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:12<01:56,  1.59it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:13<01:54,  1.60it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:14<01:54,  1.60it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:14<01:53,  1.61it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:15<01:52,  1.61it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:16<01:52,  1.60it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:16<01:53,  1.57it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:17<01:52,  1.58it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:18<01:51,  1.58it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:18<01:52,  1.56it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:19<01:50,  1.58it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:19<01:49,  1.58it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:20<01:47,  1.60it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:21<01:46,  1.62it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:21<01:47,  1.59it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:22<01:46,  1.60it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:23<01:45,  1.61it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:23<01:47,  1.56it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:24<01:46,  1.56it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:25<01:47,  1.54it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:25<01:47,  1.53it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:26<01:47,  1.53it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:26<01:45,  1.54it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:27<01:46,  1.52it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:28<01:44,  1.54it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:28<01:42,  1.56it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:29<01:41,  1.56it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:30<01:40,  1.58it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:30<01:37,  1.60it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:31<01:46,  1.47it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:32<01:44,  1.49it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:32<01:42,  1.50it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:33<01:40,  1.53it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:34<01:37,  1.56it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:34<01:37,  1.54it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:35<01:35,  1.57it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:36<01:34,  1.57it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:36<01:33,  1.58it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:37<01:33,  1.58it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:37<01:33,  1.56it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:38<01:38,  1.48it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:39<01:40,  1.43it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:40<01:41,  1.41it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:40<01:42,  1.39it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:41<01:43,  1.37it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:42<01:42,  1.36it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:43<01:42,  1.35it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:43<01:42,  1.34it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:44<01:42,  1.33it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:45<01:42,  1.32it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:46<01:42,  1.32it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:46<01:40,  1.33it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:47<01:39,  1.33it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:48<01:39,  1.33it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:49<01:38,  1.33it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:49<01:36,  1.35it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:50<01:33,  1.39it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [00:51<01:28,  1.45it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [00:51<01:26,  1.47it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [00:52<01:24,  1.49it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [00:53<01:23,  1.49it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [00:53<01:23,  1.48it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [00:54<01:22,  1.50it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [00:55<01:19,  1.53it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [00:55<01:18,  1.54it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [00:56<01:18,  1.53it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [00:57<01:18,  1.52it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [00:57<01:16,  1.54it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [00:58<01:20,  1.45it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [00:59<01:25,  1.35it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:00<01:21,  1.40it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:00<01:18,  1.45it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:01<01:17,  1.46it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:02<01:16,  1.47it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:02<01:15,  1.46it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:03<01:13,  1.49it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:04<01:14,  1.47it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:04<01:15,  1.44it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:05<01:17,  1.38it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:06<01:17,  1.36it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:07<01:18,  1.34it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:07<01:17,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:08<01:17,  1.33it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:09<01:16,  1.33it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:10<01:15,  1.33it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:10<01:14,  1.34it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:11<01:19,  1.25it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:12<01:17,  1.27it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:13<01:18,  1.24it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:14<01:15,  1.27it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:14<01:13,  1.30it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:15<01:11,  1.31it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:16<01:12,  1.29it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:17<01:10,  1.31it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:17<01:06,  1.37it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:18<01:04,  1.40it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:19<01:02,  1.43it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:19<00:59,  1.47it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:20<00:59,  1.47it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:21<00:59,  1.44it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:21<00:59,  1.43it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:22<00:56,  1.49it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:23<00:54,  1.51it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:23<00:56,  1.46it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:24<00:56,  1.44it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:25<00:56,  1.42it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:26<00:55,  1.43it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:26<00:55,  1.41it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:27<00:55,  1.39it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:28<00:54,  1.40it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:28<00:54,  1.37it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:29<00:53,  1.39it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:30<00:52,  1.40it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:31<00:51,  1.39it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:31<00:50,  1.42it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:32<00:50,  1.40it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:33<00:51,  1.35it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:34<00:52,  1.29it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:34<00:49,  1.35it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:35<00:48,  1.36it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:36<00:46,  1.39it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:36<00:46,  1.39it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:37<00:44,  1.43it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:38<00:44,  1.39it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:39<00:52,  1.16it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:40<00:50,  1.18it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:41<00:45,  1.28it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:41<00:42,  1.36it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:42<00:39,  1.43it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:42<00:38,  1.47it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:43<00:36,  1.52it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:44<00:34,  1.56it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [01:44<00:33,  1.58it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [01:45<00:33,  1.57it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [01:45<00:32,  1.57it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [01:46<00:31,  1.58it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [01:47<00:31,  1.55it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [01:47<00:31,  1.53it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [01:48<00:30,  1.52it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [01:49<00:30,  1.51it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [01:49<00:29,  1.54it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [01:50<00:28,  1.55it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [01:51<00:27,  1.56it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [01:51<00:26,  1.57it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [01:52<00:25,  1.59it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [01:53<00:25,  1.56it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [01:53<00:25,  1.56it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [01:54<00:24,  1.57it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [01:54<00:23,  1.58it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [01:55<00:22,  1.57it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [01:56<00:22,  1.57it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [01:56<00:21,  1.57it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [01:57<00:20,  1.58it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [01:58<00:20,  1.54it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [01:58<00:21,  1.47it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [01:59<00:21,  1.41it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:00<00:20,  1.38it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:01<00:20,  1.37it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:02<00:20,  1.34it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:02<00:19,  1.34it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:03<00:18,  1.34it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:04<00:18,  1.33it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:05<00:17,  1.32it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:05<00:16,  1.32it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:06<00:15,  1.33it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:07<00:15,  1.33it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:08<00:14,  1.32it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [02:08<00:13,  1.32it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [02:09<00:12,  1.32it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [02:10<00:11,  1.35it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [02:10<00:10,  1.39it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [02:11<00:09,  1.44it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [02:12<00:09,  1.44it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [02:12<00:08,  1.48it/s]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [02:13<00:07,  1.48it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [02:14<00:06,  1.50it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [02:14<00:05,  1.53it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [02:15<00:05,  1.54it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [02:16<00:04,  1.53it/s]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [02:16<00:03,  1.52it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [02:17<00:03,  1.53it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [02:18<00:02,  1.52it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [02:18<00:01,  1.54it/s]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [02:19<00:01,  1.52it/s]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [02:20<00:00,  1.54it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [02:20<00:00,  1.42it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:10,  4.58s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<07:43,  2.34s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:17,  1.61s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:06<04:16,  1.31s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:35,  1.11s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<03:09,  1.02it/s]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<02:55,  1.10it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:09<02:45,  1.16it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:10<02:37,  1.21it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:11<02:33,  1.24it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:12<02:29,  1.26it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:12<02:20,  1.34it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:13<02:11,  1.42it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:14<02:05,  1.48it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:14<02:02,  1.51it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:15<02:02,  1.51it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:15<01:58,  1.54it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:16<01:58,  1.53it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:17<01:55,  1.57it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:17<01:53,  1.59it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:18<01:55,  1.55it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:19<01:55,  1.54it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:19<01:53,  1.56it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:20<01:52,  1.56it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:21<01:50,  1.58it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:21<01:50,  1.57it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:22<01:50,  1.57it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:22<01:49,  1.57it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:23<01:47,  1.59it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:24<01:46,  1.60it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:24<01:47,  1.58it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:25<01:45,  1.59it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:26<01:45,  1.58it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:26<01:44,  1.58it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:27<01:43,  1.59it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:28<01:43,  1.59it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:28<01:41,  1.60it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:29<01:40,  1.61it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:29<01:42,  1.58it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:30<01:42,  1.56it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:31<01:42,  1.55it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:31<01:42,  1.53it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:32<01:40,  1.56it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:33<01:38,  1.59it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:33<01:37,  1.58it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:34<01:42,  1.51it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:35<01:48,  1.41it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:35<01:44,  1.45it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:36<01:41,  1.48it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:37<01:38,  1.52it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:37<01:35,  1.56it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:38<01:34,  1.57it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:39<01:31,  1.60it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:39<01:32,  1.58it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:40<01:30,  1.60it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:40<01:29,  1.61it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:41<01:29,  1.60it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:42<01:30,  1.56it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:42<01:30,  1.57it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:43<01:28,  1.59it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:44<01:26,  1.62it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:44<01:24,  1.63it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:45<01:23,  1.65it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:45<01:21,  1.66it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:46<01:22,  1.63it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:47<01:22,  1.62it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:47<01:21,  1.62it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:48<01:31,  1.45it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:49<01:28,  1.48it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:49<01:28,  1.46it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:50<01:25,  1.51it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [00:51<01:22,  1.55it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [00:51<01:21,  1.56it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [00:52<01:20,  1.56it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [00:53<01:22,  1.51it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [00:53<01:25,  1.46it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [00:54<01:28,  1.39it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [00:55<01:29,  1.36it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [00:56<01:29,  1.35it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [00:56<01:29,  1.35it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [00:57<01:31,  1.31it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [00:58<01:32,  1.28it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [00:59<01:32,  1.26it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:00<01:33,  1.24it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:01<01:33,  1.22it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:01<01:33,  1.21it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:02<01:32,  1.22it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:03<01:32,  1.21it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:04<01:32,  1.20it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:05<01:30,  1.21it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:06<01:29,  1.21it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:06<01:30,  1.19it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:07<01:28,  1.21it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:08<01:27,  1.22it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:09<01:25,  1.22it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:10<01:24,  1.23it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:10<01:22,  1.24it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:11<01:21,  1.25it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:12<01:20,  1.26it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:13<01:19,  1.26it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:14<01:18,  1.26it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:14<01:17,  1.27it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:15<01:18,  1.24it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:16<01:17,  1.25it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:17<01:16,  1.24it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:18<01:14,  1.25it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:18<01:13,  1.27it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:19<01:12,  1.28it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:20<01:09,  1.31it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:21<01:06,  1.36it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:21<01:05,  1.35it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:22<01:05,  1.35it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:23<01:03,  1.37it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:23<01:01,  1.39it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:24<01:02,  1.36it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:25<01:02,  1.35it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:26<01:02,  1.33it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:27<01:03,  1.29it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:27<01:02,  1.29it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:28<01:01,  1.30it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:29<01:01,  1.27it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:30<01:00,  1.28it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:30<01:00,  1.28it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:31<01:00,  1.26it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:32<00:58,  1.28it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:33<00:57,  1.28it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:34<00:57,  1.26it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:34<00:56,  1.27it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:35<00:55,  1.28it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:36<00:54,  1.29it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:37<01:03,  1.09it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:38<01:01,  1.11it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:39<00:54,  1.23it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:39<00:49,  1.34it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:40<00:46,  1.41it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:40<00:43,  1.46it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:41<00:41,  1.52it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:42<00:39,  1.56it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:42<00:39,  1.56it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:43<00:38,  1.57it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:44<00:38,  1.53it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:44<00:36,  1.57it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:45<00:35,  1.59it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:46<00:35,  1.56it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:46<00:34,  1.59it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:47<00:34,  1.59it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [01:47<00:33,  1.58it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [01:48<00:32,  1.60it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [01:49<00:31,  1.61it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [01:49<00:30,  1.63it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [01:50<00:30,  1.61it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [01:50<00:29,  1.63it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [01:51<00:28,  1.63it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [01:52<00:28,  1.59it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [01:52<00:28,  1.57it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [01:53<00:28,  1.55it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [01:54<00:27,  1.57it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [01:54<00:26,  1.60it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [01:55<00:25,  1.61it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [01:55<00:24,  1.61it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [01:56<00:24,  1.59it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [01:57<00:23,  1.60it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [01:57<00:23,  1.61it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [01:58<00:22,  1.61it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [01:59<00:21,  1.61it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [01:59<00:21,  1.59it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:00<00:21,  1.56it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:01<00:20,  1.54it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:01<00:20,  1.55it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:02<00:19,  1.54it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:03<00:18,  1.55it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:03<00:17,  1.59it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:04<00:16,  1.59it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:04<00:16,  1.60it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:05<00:15,  1.61it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:06<00:14,  1.62it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:06<00:14,  1.64it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:07<00:13,  1.63it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:07<00:12,  1.65it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:08<00:12,  1.65it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:09<00:11,  1.62it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [02:09<00:11,  1.61it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [02:10<00:10,  1.61it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [02:11<00:09,  1.61it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [02:11<00:09,  1.60it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [02:12<00:08,  1.61it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [02:12<00:08,  1.62it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [02:13<00:07,  1.59it/s]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [02:14<00:06,  1.60it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [02:14<00:06,  1.60it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [02:15<00:05,  1.59it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [02:16<00:05,  1.57it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [02:16<00:04,  1.60it/s]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [02:17<00:03,  1.60it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [02:17<00:03,  1.60it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [02:18<00:02,  1.55it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [02:19<00:02,  1.49it/s]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [02:20<00:01,  1.43it/s]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [02:20<00:00,  1.41it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [02:21<00:00,  1.41it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:09,  4.57s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<07:51,  2.38s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:28,  1.67s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:06<04:07,  1.26s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:26,  1.06s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<02:59,  1.08it/s]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:08<02:44,  1.17it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:09<02:31,  1.27it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:10<02:22,  1.34it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:10<02:17,  1.38it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:11<02:12,  1.43it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:12<02:06,  1.48it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:12<02:03,  1.51it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:13<02:03,  1.51it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:14<02:03,  1.50it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:14<02:03,  1.49it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:15<02:03,  1.48it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:16<02:02,  1.48it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:16<02:01,  1.49it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:17<01:58,  1.52it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:18<01:57,  1.52it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:18<01:57,  1.51it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:19<01:57,  1.50it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:20<01:59,  1.47it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:20<01:58,  1.47it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:21<01:59,  1.46it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:22<01:58,  1.46it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:22<01:56,  1.47it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:23<01:57,  1.45it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:24<02:01,  1.40it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:25<02:03,  1.37it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:25<02:03,  1.36it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:26<02:03,  1.35it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:27<02:03,  1.34it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:28<02:02,  1.35it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:28<02:03,  1.33it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:29<02:03,  1.32it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:30<02:03,  1.31it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:31<02:02,  1.32it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:32<02:01,  1.32it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:32<01:59,  1.33it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:33<02:03,  1.28it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:34<02:05,  1.25it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:35<02:05,  1.24it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:35<01:58,  1.31it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:36<01:52,  1.37it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:37<01:48,  1.41it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:37<01:42,  1.48it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:38<01:40,  1.50it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:39<01:38,  1.53it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:39<01:36,  1.54it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:40<01:37,  1.51it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:41<01:37,  1.51it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:41<01:35,  1.53it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:42<01:35,  1.52it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:43<01:34,  1.52it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:43<01:33,  1.53it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:44<01:32,  1.54it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:44<01:29,  1.58it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:45<01:27,  1.60it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:46<01:26,  1.61it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:46<01:33,  1.47it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:47<01:35,  1.44it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:48<01:34,  1.45it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:49<01:32,  1.46it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:49<01:29,  1.49it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:50<01:29,  1.49it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:51<01:29,  1.47it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:51<01:29,  1.46it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:52<01:29,  1.44it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:53<01:31,  1.41it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [00:53<01:28,  1.45it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [00:54<01:25,  1.48it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [00:55<01:23,  1.51it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [00:55<01:22,  1.52it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [00:56<01:20,  1.55it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [00:57<01:20,  1.53it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [00:57<01:21,  1.49it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [00:58<01:23,  1.45it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [00:59<01:21,  1.47it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [00:59<01:19,  1.50it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:00<01:19,  1.48it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:01<01:21,  1.44it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:01<01:22,  1.41it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:02<01:21,  1.42it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:03<01:26,  1.33it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:04<01:24,  1.33it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:04<01:20,  1.39it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:05<01:19,  1.40it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:06<01:18,  1.40it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:07<01:23,  1.30it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:07<01:21,  1.32it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:08<01:18,  1.36it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:09<01:16,  1.39it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:10<01:16,  1.37it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:10<01:14,  1.39it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:11<01:13,  1.40it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:12<01:12,  1.40it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:12<01:11,  1.40it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:13<01:10,  1.41it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:14<01:10,  1.41it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:15<01:09,  1.41it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:15<01:07,  1.45it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:16<01:06,  1.45it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:17<01:19,  1.20it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:18<01:32,  1.02it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:19<01:25,  1.08it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:20<01:19,  1.16it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:21<01:16,  1.20it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:21<01:12,  1.25it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:22<01:09,  1.28it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:23<01:07,  1.31it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:24<01:06,  1.31it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:24<01:05,  1.31it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:25<01:04,  1.32it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:26<01:03,  1.33it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:27<01:03,  1.31it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:27<01:01,  1.32it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:28<01:02,  1.30it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:29<01:01,  1.31it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:30<00:58,  1.35it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:30<00:56,  1.37it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:31<00:54,  1.41it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:32<00:52,  1.45it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:32<00:51,  1.44it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:33<00:51,  1.44it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:34<00:50,  1.45it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:34<00:48,  1.47it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:35<00:47,  1.49it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:36<00:46,  1.51it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:36<00:45,  1.51it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:37<00:44,  1.52it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:38<00:49,  1.35it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:08,  4.57s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<07:31,  2.28s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:05<05:04,  1.55s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:06<03:59,  1.22s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:27,  1.06s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<03:05,  1.04it/s]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:08<02:53,  1.11it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:09<02:44,  1.17it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:10<02:40,  1.19it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:11<02:37,  1.21it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:12<02:32,  1.24it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:12<02:30,  1.25it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:13<02:27,  1.27it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:14<02:30,  1.24it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:15<02:30,  1.23it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:16<02:25,  1.26it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:16<02:22,  1.28it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:17<02:20,  1.29it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:18<02:12,  1.37it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:18<02:05,  1.44it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:19<02:01,  1.47it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:20<01:59,  1.49it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:20<01:56,  1.52it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:21<01:55,  1.52it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:22<01:55,  1.52it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:22<01:53,  1.53it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:23<01:56,  1.49it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:24<01:55,  1.49it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:24<01:53,  1.51it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:25<01:52,  1.51it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:26<01:49,  1.54it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:26<01:50,  1.52it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:27<01:48,  1.54it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:27<01:48,  1.53it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:28<01:46,  1.55it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:29<01:45,  1.55it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:29<01:44,  1.56it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:30<01:43,  1.56it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:31<01:44,  1.53it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:31<01:43,  1.55it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:32<01:42,  1.55it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:33<01:42,  1.54it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:33<01:41,  1.55it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:34<01:39,  1.57it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:34<01:37,  1.59it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:35<01:36,  1.59it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:36<01:34,  1.61it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:36<01:34,  1.61it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:37<01:34,  1.59it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:38<01:35,  1.58it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:38<01:35,  1.57it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:39<01:35,  1.54it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:40<01:34,  1.55it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:40<01:33,  1.55it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:41<01:32,  1.56it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:42<01:36,  1.50it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:42<01:43,  1.38it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:43<01:38,  1.44it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:44<01:36,  1.46it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:44<01:34,  1.49it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:45<01:31,  1.51it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:46<01:30,  1.52it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:46<01:29,  1.53it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:47<01:27,  1.55it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:48<01:26,  1.57it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:48<01:25,  1.56it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:49<01:24,  1.57it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:49<01:24,  1.55it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:50<01:24,  1.55it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:51<01:22,  1.57it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:51<01:21,  1.59it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [00:52<01:20,  1.59it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [00:53<01:19,  1.61it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [00:53<01:19,  1.58it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [00:54<01:19,  1.58it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [00:55<01:19,  1.56it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [00:55<01:18,  1.56it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [00:56<01:17,  1.57it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [00:56<01:17,  1.55it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [00:57<01:16,  1.56it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [00:58<01:17,  1.55it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [00:58<01:15,  1.57it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [00:59<01:19,  1.48it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:00<01:22,  1.41it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:01<01:24,  1.36it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:02<01:26,  1.31it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:02<01:28,  1.28it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:03<01:27,  1.28it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:04<01:26,  1.28it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:05<01:24,  1.30it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:05<01:23,  1.31it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:06<01:22,  1.31it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:07<01:22,  1.30it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:08<01:20,  1.31it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:09<01:20,  1.30it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:09<01:20,  1.30it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:10<01:18,  1.31it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:11<01:18,  1.30it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:12<01:22,  1.23it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:12<01:17,  1.29it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:13<01:14,  1.32it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:14<01:11,  1.37it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:14<01:08,  1.42it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:15<01:05,  1.46it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:16<01:06,  1.44it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:17<01:07,  1.40it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:17<01:06,  1.40it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:18<01:05,  1.41it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:19<01:03,  1.43it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:19<01:01,  1.45it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:20<01:01,  1.44it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:21<01:03,  1.39it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:22<01:04,  1.34it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:22<01:05,  1.31it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:23<01:05,  1.29it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:24<01:05,  1.28it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:25<01:05,  1.26it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:26<01:06,  1.23it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:27<01:06,  1.22it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:27<01:04,  1.23it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:28<01:05,  1.22it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:29<01:03,  1.22it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:30<01:03,  1.20it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:31<01:03,  1.19it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:32<01:03,  1.18it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:32<01:03,  1.17it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:33<01:01,  1.18it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:34<01:01,  1.17it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:35<01:00,  1.17it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:36<00:59,  1.18it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:37<00:58,  1.18it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:38<00:57,  1.19it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:38<00:56,  1.18it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:39<00:55,  1.18it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:40<00:55,  1.18it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:41<00:54,  1.18it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:42<00:52,  1.20it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:43<00:51,  1.21it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:43<00:49,  1.22it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:44<00:47,  1.25it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:45<00:44,  1.33it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:46<00:46,  1.25it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:46<00:45,  1.25it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:47<00:43,  1.28it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:48<00:42,  1.28it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:49<00:41,  1.30it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [01:51<00:40,  1.32it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<17:53,  5.40s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<08:41,  2.64s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:52,  1.79s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:09,  1.58s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<04:55,  1.51s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:10<03:59,  1.23s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:10<03:23,  1.05s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:11<03:00,  1.06it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:12<02:46,  1.15it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:13<02:36,  1.22it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:13<02:30,  1.26it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:14<02:24,  1.30it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:15<02:19,  1.34it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:15<02:17,  1.36it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:16<02:14,  1.37it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:17<02:13,  1.38it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:18<02:13,  1.37it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:18<02:13,  1.37it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:19<02:14,  1.34it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:20<02:10,  1.38it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:20<02:08,  1.39it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:21<02:08,  1.39it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:22<02:07,  1.39it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:23<02:10,  1.35it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:23<02:07,  1.37it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:24<02:06,  1.37it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:25<02:06,  1.37it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:26<02:02,  1.40it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:26<02:01,  1.40it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:27<02:02,  1.39it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:28<02:01,  1.39it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:28<02:01,  1.38it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:29<01:59,  1.40it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:30<01:59,  1.39it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:31<01:57,  1.40it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:31<01:57,  1.39it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:32<01:59,  1.37it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:33<02:03,  1.31it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:34<02:03,  1.30it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:34<02:02,  1.31it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:35<02:02,  1.30it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:36<02:01,  1.30it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:37<02:01,  1.29it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:37<01:59,  1.31it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:38<02:02,  1.27it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:39<02:00,  1.28it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:40<01:57,  1.30it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:41<01:58,  1.29it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:41<01:56,  1.29it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:42<01:56,  1.29it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:43<01:55,  1.29it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:44<01:49,  1.35it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:44<01:44,  1.40it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:45<01:43,  1.41it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:46<01:41,  1.42it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:46<01:39,  1.45it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:47<01:37,  1.47it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:48<01:36,  1.48it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:48<01:37,  1.45it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:49<01:40,  1.40it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:50<01:39,  1.40it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:51<01:39,  1.39it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:51<01:39,  1.38it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:52<01:38,  1.38it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:53<01:37,  1.38it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [00:54<01:38,  1.35it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [00:54<01:38,  1.35it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [00:55<01:38,  1.34it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [00:56<01:38,  1.34it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [00:57<01:39,  1.31it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [00:57<01:37,  1.32it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [00:58<01:34,  1.35it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [00:59<01:32,  1.38it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [00:59<01:30,  1.40it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:00<01:28,  1.41it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:01<01:27,  1.42it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:02<01:28,  1.38it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:02<01:31,  1.33it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:03<01:34,  1.28it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:04<01:34,  1.28it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:05<01:33,  1.27it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:06<01:35,  1.23it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:07<01:36,  1.22it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:07<01:35,  1.21it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:08<01:35,  1.21it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:09<01:34,  1.21it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:10<01:33,  1.21it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:11<01:33,  1.20it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:12<01:32,  1.20it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:12<01:31,  1.21it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:13<01:29,  1.22it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:14<01:26,  1.25it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:15<01:22,  1.29it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:15<01:20,  1.31it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:16<01:19,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:17<01:18,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:18<01:20,  1.28it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:18<01:18,  1.30it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:19<01:16,  1.32it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:20<01:14,  1.34it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:21<01:13,  1.34it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:21<01:13,  1.34it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:22<01:12,  1.33it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:23<01:11,  1.35it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:24<01:09,  1.36it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:24<01:07,  1.39it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:25<01:09,  1.34it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:26<01:09,  1.33it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:27<01:07,  1.34it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:27<01:06,  1.36it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:28<01:05,  1.36it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:29<01:04,  1.36it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:30<01:04,  1.35it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:30<01:03,  1.36it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:31<01:02,  1.36it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:32<01:02,  1.34it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:32<01:02,  1.34it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:33<01:01,  1.34it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:34<01:01,  1.33it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:35<00:59,  1.35it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:35<00:57,  1.37it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:36<00:56,  1.38it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:37<00:57,  1.34it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:38<01:00,  1.25it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:39<01:01,  1.22it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:39<00:58,  1.27it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:40<00:56,  1.28it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:41<00:54,  1.32it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:42<00:54,  1.31it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [01:42<00:52,  1.34it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [01:43<00:50,  1.37it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [01:44<00:49,  1.37it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [01:45<00:48,  1.38it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [01:45<00:47,  1.39it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [01:46<00:46,  1.40it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [01:47<00:46,  1.38it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [01:47<00:45,  1.37it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [01:48<00:46,  1.33it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [01:49<00:48,  1.26it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [01:50<00:47,  1.27it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [01:51<00:46,  1.27it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [01:51<00:44,  1.31it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [01:52<00:43,  1.31it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [01:53<00:42,  1.32it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [01:54<00:42,  1.30it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [01:55<00:42,  1.27it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [01:55<00:42,  1.26it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [01:56<00:40,  1.29it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [01:57<00:40,  1.27it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [01:58<00:40,  1.24it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [01:59<00:40,  1.20it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [01:59<00:40,  1.19it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:00<00:40,  1.17it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:01<00:39,  1.16it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:02<00:38,  1.15it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:03<00:37,  1.16it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:04<00:37,  1.15it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:05<00:36,  1.15it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:06<00:35,  1.14it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:07<00:35,  1.14it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:07<00:34,  1.12it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:08<00:34,  1.11it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:09<00:33,  1.10it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:10<00:32,  1.11it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:11<00:31,  1.13it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:12<00:30,  1.13it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:13<00:28,  1.15it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:14<00:27,  1.16it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:14<00:26,  1.17it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:15<00:25,  1.18it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:16<00:24,  1.17it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:17<00:24,  1.17it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:18<00:23,  1.17it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [02:19<00:22,  1.14it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [02:20<00:21,  1.17it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [02:20<00:19,  1.22it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [02:21<00:19,  1.17it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [02:22<00:18,  1.19it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [02:23<00:18,  1.14it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [02:24<00:17,  1.14it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [02:25<00:16,  1.16it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [02:26<00:15,  1.16it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [02:27<00:15,  1.13it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [02:27<00:14,  1.13it/s]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [02:28<00:13,  1.12it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [02:29<00:12,  1.15it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [02:30<00:11,  1.17it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [02:31<00:10,  1.13it/s]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [02:32<00:09,  1.15it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [02:33<00:08,  1.15it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [02:34<00:08,  1.12it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [02:34<00:07,  1.11it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [02:35<00:06,  1.14it/s]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [02:36<00:05,  1.13it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [02:38<00:05,  1.06s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [02:39<00:04,  1.11s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [02:40<00:02,  1.01it/s]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [02:40<00:01,  1.10it/s]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [02:41<00:00,  1.17it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [02:42<00:00,  1.23it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.73it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.71it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 68it [00:00, 94.48it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 172.17it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|        | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|▍| 38/100 [00:00<00:00, 373.68it\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 357.96i\n",
+      " 20%|████████▊                                   | 1/5 [28:44<1:54:57, 1724.43s/it]\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:41,  4.73s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:39,  2.63s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:27,  1.97s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:26,  1.66s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<04:45,  1.47s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:10<04:16,  1.32s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:11<03:58,  1.23s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:12<03:47,  1.19s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:13<03:40,  1.15s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:14<03:37,  1.14s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:15<03:33,  1.13s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:16<03:29,  1.11s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:18<03:25,  1.10s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:19<03:24,  1.10s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:20<03:24,  1.11s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:21<03:29,  1.14s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:22<03:29,  1.15s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:23<03:25,  1.13s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:24<03:23,  1.13s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:26<03:27,  1.15s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:27<03:27,  1.16s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:28<03:25,  1.16s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:29<03:22,  1.15s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:30<03:24,  1.16s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:31<03:26,  1.18s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:33<03:23,  1.17s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:34<03:24,  1.18s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:35<03:25,  1.20s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:36<03:24,  1.20s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:37<03:26,  1.22s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:39<03:22,  1.20s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:40<03:38,  1.30s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:41<03:34,  1.28s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:43<03:26,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:44<03:20,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:45<03:15,  1.19s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:46<03:12,  1.18s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:47<03:09,  1.17s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:48<03:05,  1.15s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:49<03:04,  1.15s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:51<03:02,  1.15s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:52<02:59,  1.13s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:53<02:56,  1.12s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:54<02:55,  1.12s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:55<02:53,  1.12s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:56<02:53,  1.13s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:57<02:51,  1.12s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:58<02:53,  1.14s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:00<02:52,  1.14s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:01<02:51,  1.14s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:02<02:50,  1.15s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:03<02:49,  1.14s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:04<02:48,  1.15s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:05<02:46,  1.14s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:06<02:44,  1.13s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:07<02:43,  1.13s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:09<02:42,  1.14s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:10<02:39,  1.12s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:11<02:38,  1.12s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:12<02:37,  1.13s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:13<02:37,  1.13s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:14<02:34,  1.12s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:15<02:34,  1.13s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:16<02:29,  1.10s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:17<02:10,  1.04it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:18<01:56,  1.15it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:18<01:47,  1.24it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:19<01:43,  1.28it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:20<01:38,  1.33it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:20<01:34,  1.37it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:21<01:35,  1.35it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:22<01:31,  1.39it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:23<01:29,  1.42it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:23<01:27,  1.43it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:24<01:31,  1.37it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:25<01:41,  1.22it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:26<01:46,  1.16it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:27<01:50,  1.11it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:28<01:50,  1.09it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:29<01:46,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:29<01:38,  1.21it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:30<01:31,  1.28it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:31<01:27,  1.33it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:32<01:27,  1.33it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:32<01:28,  1.30it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:33<01:24,  1.36it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:34<01:21,  1.39it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:34<01:21,  1.38it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:36<01:32,  1.20it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:37<01:38,  1.12it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:38<01:41,  1.07it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:39<01:42,  1.06it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:40<01:42,  1.04it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:41<01:45,  1.00it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:42<01:44,  1.00it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:43<01:45,  1.01s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:44<01:46,  1.03s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:45<01:44,  1.03s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:46<01:44,  1.04s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:47<01:43,  1.03s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:48<01:43,  1.04s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:49<01:42,  1.05s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:50<01:42,  1.05s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:51<01:41,  1.05s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:52<01:39,  1.04s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:53<01:38,  1.05s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:54<01:37,  1.05s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:55<01:38,  1.07s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:56<01:38,  1.08s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:58<01:37,  1.08s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:59<01:35,  1.07s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:00<01:39,  1.13s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:01<01:36,  1.11s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:02<01:33,  1.09s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:03<01:31,  1.07s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:04<01:28,  1.05s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:05<01:27,  1.05s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:06<01:25,  1.05s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:07<01:25,  1.06s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:08<01:24,  1.05s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:09<01:22,  1.04s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:10<01:20,  1.04s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:11<01:20,  1.04s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:12<01:21,  1.07s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:14<01:21,  1.09s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:15<01:23,  1.12s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:16<01:19,  1.09s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:17<01:19,  1.10s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:18<01:19,  1.12s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:19<01:17,  1.11s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:20<01:15,  1.09s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:21<01:15,  1.12s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:23<01:17,  1.16s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:24<01:17,  1.17s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:25<01:17,  1.18s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:26<01:16,  1.20s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:28<01:16,  1.21s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:29<01:15,  1.22s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:30<01:15,  1.24s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:31<01:14,  1.24s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:33<01:12,  1.24s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:35<01:03,  1.10s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<16:56,  5.11s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:31,  2.89s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:57,  2.12s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:10<05:03,  1.55s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:22,  1.36s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:11,  1.31s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<04:04,  1.28s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:16<03:59,  1.26s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:17<03:53,  1.23s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:18<03:57,  1.26s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:19<03:59,  1.28s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:21<03:52,  1.25s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:22<04:00,  1.30s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:23<03:58,  1.30s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:24<03:42,  1.22s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:25<03:35,  1.18s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:27<03:29,  1.16s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:28<03:25,  1.14s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:29<03:21,  1.13s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:30<03:20,  1.13s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:31<03:17,  1.12s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:32<03:15,  1.11s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:33<02:58,  1.02s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:34<02:41,  1.08it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:34<02:27,  1.17it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:35<02:17,  1.25it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:36<02:11,  1.30it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:36<02:10,  1.30it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:37<02:07,  1.32it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:38<02:03,  1.36it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:39<02:04,  1.34it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:39<02:01,  1.37it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:40<01:59,  1.38it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:41<01:58,  1.38it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:41<01:57,  1.39it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:42<01:55,  1.40it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:43<01:55,  1.40it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:44<01:54,  1.40it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:44<01:51,  1.43it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:45<01:52,  1.40it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:46<01:50,  1.42it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:46<01:50,  1.41it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:47<01:49,  1.41it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:48<01:46,  1.44it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:48<01:45,  1.45it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:49<01:45,  1.45it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:50<01:46,  1.42it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:51<01:45,  1.42it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:51<01:44,  1.43it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:52<01:43,  1.43it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:53<01:45,  1.40it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:53<01:43,  1.41it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:54<01:42,  1.42it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:55<01:40,  1.43it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:55<01:38,  1.45it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:56<01:38,  1.45it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:57<01:36,  1.46it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:57<01:36,  1.45it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:58<01:36,  1.45it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:59<01:34,  1.46it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:59<01:33,  1.47it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:00<01:32,  1.47it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:01<01:31,  1.48it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:02<01:31,  1.47it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:02<01:29,  1.48it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:03<01:29,  1.48it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:04<01:28,  1.48it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:04<01:27,  1.49it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:05<01:27,  1.47it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:06<01:26,  1.47it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:06<01:26,  1.46it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:07<01:27,  1.45it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:08<01:25,  1.46it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:08<01:27,  1.42it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:09<01:26,  1.43it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:10<01:24,  1.44it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:10<01:24,  1.44it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:11<01:30,  1.33it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:12<01:39,  1.20it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:13<01:41,  1.17it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:14<01:47,  1.09it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:15<01:41,  1.15it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:16<01:33,  1.23it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:17<01:28,  1.28it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:17<01:26,  1.31it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:18<01:23,  1.35it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:19<01:19,  1.40it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:19<01:16,  1.44it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:20<01:14,  1.47it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:21<01:14,  1.46it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:21<01:13,  1.46it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:22<01:11,  1.48it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:23<01:19,  1.31it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:24<01:26,  1.20it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:25<01:30,  1.14it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:26<01:33,  1.09it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:27<01:36,  1.05it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:28<01:38,  1.01it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:29<01:39,  1.01s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:30<01:39,  1.02s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:31<01:40,  1.03s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:32<01:40,  1.04s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:33<01:38,  1.04s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:34<01:37,  1.03s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:35<01:36,  1.04s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:36<01:36,  1.05s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:37<01:35,  1.05s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:38<01:34,  1.05s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:39<01:32,  1.04s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:41<01:31,  1.04s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:42<01:32,  1.07s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:43<01:32,  1.08s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:44<01:31,  1.08s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:45<01:29,  1.07s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:46<01:28,  1.06s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:47<01:26,  1.06s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [01:48<01:25,  1.06s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [01:49<01:23,  1.05s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [01:50<01:23,  1.06s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [01:51<01:21,  1.05s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [01:52<01:19,  1.03s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [01:53<01:18,  1.03s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [01:54<01:18,  1.05s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [01:55<01:18,  1.06s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [01:56<01:18,  1.08s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [01:58<01:16,  1.07s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [01:59<01:16,  1.07s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:00<01:15,  1.08s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:01<01:13,  1.07s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:02<01:12,  1.07s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:03<01:12,  1.08s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:04<01:13,  1.11s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:05<01:11,  1.11s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:06<01:11,  1.11s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:08<01:11,  1.13s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:09<01:11,  1.15s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:10<01:11,  1.18s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:11<01:11,  1.20s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:12<01:11,  1.21s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:14<01:11,  1.23s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:15<01:09,  1.22s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:17<01:15,  1.34s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:18<01:10,  1.28s/it]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:19<01:06,  1.22s/it]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:20<01:03,  1.19s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:21<01:00,  1.17s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:22<00:59,  1.17s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:23<00:58,  1.16s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:24<00:56,  1.16s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:26<00:55,  1.15s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:27<00:53,  1.14s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:28<00:52,  1.14s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:29<00:50,  1.13s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:30<00:50,  1.14s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:31<00:49,  1.14s/it]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:32<00:48,  1.15s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:34<00:47,  1.16s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:36<00:39,  1.02it/s]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<17:02,  5.14s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:13,  2.79s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:49,  2.08s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:38,  1.73s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:35,  1.42s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:17,  1.33s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:07,  1.29s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<03:59,  1.25s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<03:50,  1.21s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:16<03:46,  1.20s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:18<03:40,  1.17s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:19<03:37,  1.16s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:19<03:09,  1.02s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:20<02:51,  1.08it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:21<02:37,  1.16it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:22<02:30,  1.22it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:22<02:22,  1.28it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:23<02:16,  1.32it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:24<02:15,  1.32it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:24<02:10,  1.37it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:25<02:07,  1.40it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:26<02:07,  1.39it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:27<02:13,  1.32it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:28<02:33,  1.14it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:29<02:37,  1.11it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:30<02:41,  1.07it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:30<02:27,  1.16it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:31<02:18,  1.23it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:32<02:10,  1.30it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:32<02:04,  1.35it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:33<02:01,  1.38it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:34<02:01,  1.37it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:35<01:58,  1.40it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:35<01:56,  1.41it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:36<02:10,  1.26it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:37<02:21,  1.16it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:38<02:27,  1.10it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:39<02:32,  1.05it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:40<02:35,  1.03it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:41<02:40,  1.01s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:42<02:42,  1.03s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:44<02:44,  1.05s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:45<02:44,  1.05s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:46<02:44,  1.06s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:47<02:43,  1.06s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:48<02:41,  1.05s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:49<02:40,  1.06s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:50<02:40,  1.06s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:51<02:39,  1.06s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:52<02:38,  1.06s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:53<02:36,  1.06s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:54<02:37,  1.07s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:55<02:42,  1.11s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:57<02:39,  1.10s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:58<02:37,  1.09s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:59<02:33,  1.08s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:00<02:33,  1.08s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:01<02:30,  1.07s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:02<02:29,  1.07s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:03<02:27,  1.06s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:04<02:27,  1.07s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:05<02:24,  1.05s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:06<02:23,  1.06s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:07<02:23,  1.06s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:08<02:26,  1.09s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:09<02:25,  1.10s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:11<02:26,  1.11s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:12<02:21,  1.08s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:13<02:23,  1.11s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:14<02:23,  1.11s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:15<02:20,  1.10s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:16<02:18,  1.09s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:17<02:18,  1.10s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:18<02:20,  1.13s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:19<02:21,  1.14s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:21<02:18,  1.12s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:22<02:21,  1.16s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:23<02:20,  1.16s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:24<02:23,  1.19s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:25<02:23,  1.21s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:27<02:22,  1.21s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:28<02:20,  1.20s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:29<02:18,  1.19s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:30<02:16,  1.19s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:31<02:15,  1.19s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:33<02:20,  1.24s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:34<02:23,  1.28s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:35<02:15,  1.22s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:36<02:09,  1.17s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:37<02:05,  1.15s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:38<02:03,  1.14s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:40<02:01,  1.13s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:41<01:59,  1.12s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:42<01:56,  1.11s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:43<01:54,  1.10s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:44<01:53,  1.10s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:45<01:52,  1.10s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:46<01:50,  1.09s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:47<01:49,  1.10s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:48<01:48,  1.09s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:49<01:48,  1.10s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:51<01:46,  1.10s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:52<01:44,  1.09s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:53<01:43,  1.08s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:54<01:42,  1.09s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:55<01:42,  1.10s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:56<01:40,  1.10s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:57<01:41,  1.11s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:58<01:39,  1.10s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:59<01:37,  1.09s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:00<01:36,  1.10s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:02<01:35,  1.10s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:03<01:33,  1.09s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:04<01:32,  1.09s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:05<01:32,  1.10s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:06<01:31,  1.11s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:07<01:31,  1.11s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:08<01:29,  1.11s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:09<01:29,  1.12s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:10<01:28,  1.12s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:12<01:26,  1.12s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:13<01:24,  1.09s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:14<01:23,  1.10s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:15<01:21,  1.09s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:16<01:20,  1.09s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:17<01:19,  1.09s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:18<01:19,  1.10s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:19<01:17,  1.09s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:20<01:17,  1.11s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:21<01:15,  1.10s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:22<01:14,  1.10s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:24<01:13,  1.09s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:25<01:12,  1.10s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:26<01:11,  1.10s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:27<01:09,  1.08s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:28<01:10,  1.12s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:29<01:09,  1.12s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:30<01:06,  1.09s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:31<00:58,  1.02it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:32<00:52,  1.13it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:32<00:47,  1.23it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:33<00:44,  1.29it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:34<00:41,  1.34it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:34<00:39,  1.38it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:35<00:39,  1.38it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:36<00:38,  1.38it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:36<00:36,  1.41it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:37<00:35,  1.42it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:38<00:35,  1.41it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:39<00:38,  1.28it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:40<00:40,  1.19it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:41<00:41,  1.14it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:42<00:40,  1.13it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:42<00:37,  1.21it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:43<00:34,  1.27it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:44<00:32,  1.31it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:44<00:31,  1.35it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:45<00:29,  1.37it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:46<00:29,  1.36it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:46<00:28,  1.37it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:47<00:27,  1.41it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:48<00:29,  1.25it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:49<00:31,  1.15it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:50<00:32,  1.07it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:51<00:33,  1.00it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:53<00:34,  1.04s/it]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:54<00:33,  1.05s/it]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:55<00:32,  1.06s/it]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:56<00:32,  1.09s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:57<00:32,  1.12s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:58<00:30,  1.10s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:59<00:29,  1.11s/it]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [03:00<00:28,  1.11s/it]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [03:01<00:27,  1.09s/it]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [03:02<00:26,  1.09s/it]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [03:04<00:25,  1.09s/it]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [03:05<00:23,  1.09s/it]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [03:06<00:23,  1.10s/it]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [03:07<00:21,  1.09s/it]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [03:08<00:20,  1.08s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [03:09<00:19,  1.09s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [03:10<00:19,  1.13s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [03:11<00:18,  1.13s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [03:12<00:16,  1.12s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [03:14<00:15,  1.12s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [03:15<00:15,  1.16s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [03:16<00:13,  1.16s/it]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [03:17<00:12,  1.14s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [03:18<00:11,  1.11s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [03:19<00:09,  1.10s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [03:20<00:09,  1.14s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [03:22<00:07,  1.13s/it]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [03:23<00:06,  1.15s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [03:24<00:05,  1.17s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [03:25<00:04,  1.18s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [03:26<00:03,  1.19s/it]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [03:28<00:02,  1.20s/it]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [03:29<00:01,  1.22s/it]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [03:30<00:00,  1.05s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<17:05,  5.15s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<10:04,  3.05s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<07:04,  2.15s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:10<04:59,  1.53s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:30,  1.39s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:17,  1.33s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:04,  1.27s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<03:58,  1.25s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<03:52,  1.22s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:17<03:46,  1.20s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:18<03:43,  1.19s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:19<03:40,  1.18s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:20<03:38,  1.18s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:21<03:35,  1.17s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:22<03:33,  1.16s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:23<03:31,  1.16s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:25<03:30,  1.16s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:26<03:28,  1.15s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:27<03:30,  1.17s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:28<03:30,  1.18s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:29<03:29,  1.18s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:31<03:26,  1.17s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:32<03:27,  1.18s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:33<03:26,  1.18s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:34<03:23,  1.17s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:35<03:20,  1.16s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:36<03:15,  1.14s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:37<03:14,  1.14s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:39<03:15,  1.15s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:40<03:15,  1.16s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:41<03:12,  1.15s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:42<03:09,  1.13s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:43<03:06,  1.13s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:44<03:09,  1.15s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:45<02:50,  1.04s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:46<02:29,  1.09it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:46<02:17,  1.18it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:47<02:09,  1.25it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:48<02:04,  1.29it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:48<01:59,  1.33it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:49<01:58,  1.34it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:50<01:56,  1.35it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:51<01:51,  1.40it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:51<01:49,  1.41it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:52<01:51,  1.38it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:53<02:02,  1.25it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:54<02:10,  1.16it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:55<02:17,  1.10it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:56<02:15,  1.11it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:57<02:05,  1.19it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:57<01:57,  1.26it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:58<01:51,  1.32it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:59<01:47,  1.36it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:59<01:48,  1.34it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:00<01:43,  1.39it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:01<01:41,  1.41it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:02<01:52,  1.27it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:03<02:01,  1.16it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:04<02:06,  1.11it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:05<02:11,  1.06it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:06<02:15,  1.02it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:07<02:17,  1.00s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:08<02:18,  1.02s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:09<02:21,  1.05s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:10<02:21,  1.06s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:11<02:22,  1.07s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:12<02:23,  1.09s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:14<02:24,  1.10s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:15<02:24,  1.11s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:16<02:22,  1.10s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:17<02:20,  1.10s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:18<02:20,  1.11s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:19<02:17,  1.10s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:20<02:17,  1.10s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:21<02:14,  1.08s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:22<02:19,  1.13s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:24<02:19,  1.14s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:25<02:15,  1.12s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:26<02:14,  1.12s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:27<02:11,  1.10s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:28<02:10,  1.11s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:29<02:11,  1.12s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:30<02:07,  1.10s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:31<02:03,  1.08s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:32<02:02,  1.07s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:33<02:01,  1.08s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:35<02:01,  1.09s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:36<02:02,  1.10s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:37<02:03,  1.13s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:38<02:01,  1.11s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:39<02:03,  1.14s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:40<02:03,  1.15s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:41<02:01,  1.15s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:43<01:58,  1.13s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:44<01:57,  1.13s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:45<01:58,  1.15s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:46<01:56,  1.14s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:47<01:53,  1.13s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:48<01:56,  1.17s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:50<01:58,  1.19s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:51<01:57,  1.20s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:52<01:57,  1.21s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:53<01:55,  1.20s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:54<01:56,  1.22s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:56<01:58,  1.26s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:57<01:57,  1.27s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:58<01:57,  1.27s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:00<01:55,  1.27s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:01<01:53,  1.26s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:02<01:51,  1.26s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:03<01:51,  1.27s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:05<01:51,  1.29s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:06<01:56,  1.35s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:07<01:48,  1.28s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:09<01:43,  1.23s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:10<01:39,  1.20s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:11<01:36,  1.17s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:12<01:34,  1.17s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:13<01:33,  1.16s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:14<01:30,  1.15s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:15<01:30,  1.16s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:16<01:28,  1.14s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:18<01:25,  1.12s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:20<01:24,  1.12s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:46,  4.75s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<08:53,  2.69s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:32,  1.99s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:23,  1.65s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<04:45,  1.46s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:10<04:24,  1.36s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:11<04:10,  1.30s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:12<04:02,  1.26s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<03:52,  1.22s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<03:47,  1.20s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:16<03:47,  1.20s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:17<03:42,  1.18s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:18<03:39,  1.17s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:19<03:35,  1.16s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:20<03:20,  1.09s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:21<02:58,  1.03it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:22<02:40,  1.14it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:22<02:27,  1.23it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:23<02:21,  1.28it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:24<02:15,  1.33it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:24<02:12,  1.35it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:25<02:12,  1.34it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:26<02:09,  1.36it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:27<02:06,  1.39it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:27<02:02,  1.43it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:28<02:07,  1.36it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:29<02:18,  1.25it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:30<02:25,  1.18it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:31<02:31,  1.13it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:32<02:22,  1.19it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:32<02:14,  1.25it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:33<02:07,  1.32it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:34<02:02,  1.36it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:34<01:59,  1.39it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:35<02:00,  1.37it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:36<01:58,  1.38it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:37<01:56,  1.40it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:37<02:07,  1.27it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:38<02:16,  1.18it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:39<02:22,  1.12it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:41<02:32,  1.05it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:42<02:38,  1.00s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:43<02:42,  1.03s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:44<02:42,  1.04s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:45<02:50,  1.10s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:46<02:48,  1.10s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:47<02:48,  1.10s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:48<02:44,  1.08s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:49<02:46,  1.10s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:51<02:44,  1.10s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:52<02:42,  1.09s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:53<02:39,  1.08s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:54<02:38,  1.08s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:55<02:36,  1.07s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:56<02:36,  1.08s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:57<02:36,  1.09s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:58<02:34,  1.08s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:59<02:32,  1.08s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:00<02:32,  1.08s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:01<02:30,  1.08s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:02<02:31,  1.09s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:04<02:30,  1.09s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:05<02:28,  1.08s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:06<02:29,  1.10s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:07<02:32,  1.13s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:08<02:30,  1.12s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:09<02:26,  1.10s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:10<02:23,  1.09s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:11<02:21,  1.08s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:12<02:21,  1.09s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:13<02:24,  1.12s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:15<02:23,  1.12s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:16<02:21,  1.12s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:17<02:18,  1.10s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:18<02:22,  1.14s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:19<02:19,  1.12s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:20<02:18,  1.12s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:21<02:15,  1.11s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:22<02:16,  1.13s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:24<02:19,  1.16s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:25<02:15,  1.14s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:26<02:15,  1.15s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:27<02:15,  1.16s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:28<02:16,  1.18s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:30<02:18,  1.21s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:31<02:17,  1.21s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:32<02:18,  1.22s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:33<02:17,  1.22s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:35<02:16,  1.23s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:36<02:16,  1.24s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:37<02:15,  1.24s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:38<02:15,  1.26s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:40<02:16,  1.27s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:41<02:14,  1.27s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:43<02:22,  1.36s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:44<02:12,  1.28s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:45<02:06,  1.23s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:46<02:02,  1.20s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:47<02:00,  1.19s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:48<01:57,  1.17s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:49<01:54,  1.15s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:50<01:53,  1.15s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:52<01:51,  1.15s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:53<01:51,  1.16s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:54<01:49,  1.16s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:55<01:47,  1.14s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:56<01:47,  1.16s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:57<01:45,  1.15s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:58<01:43,  1.14s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:00<01:41,  1.13s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:01<01:41,  1.15s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:02<01:39,  1.13s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:03<01:38,  1.13s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:04<01:38,  1.15s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:05<01:37,  1.15s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:07<01:38,  1.17s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:08<01:36,  1.16s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:09<01:33,  1.14s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:10<01:31,  1.13s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:11<01:30,  1.13s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:12<01:29,  1.13s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:13<01:28,  1.13s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:14<01:26,  1.12s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:16<01:26,  1.14s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:17<01:24,  1.13s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:18<01:23,  1.13s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:19<01:21,  1.12s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:20<01:20,  1.12s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:21<01:19,  1.12s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:22<01:18,  1.12s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:23<01:16,  1.11s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:24<01:16,  1.12s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:26<01:16,  1.14s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:27<01:16,  1.16s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:28<01:15,  1.16s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:29<01:10,  1.10s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:30<01:01,  1.03it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:30<00:54,  1.14it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:31<00:49,  1.23it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:32<00:46,  1.28it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:32<00:44,  1.33it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:33<00:45,  1.26it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:34<00:44,  1.28it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:35<00:42,  1.32it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:35<00:40,  1.37it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:36<00:38,  1.41it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:37<00:38,  1.37it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:38<00:41,  1.27it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:40<00:54,  1.07s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.95it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.37it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.85it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.99it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.88it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 47it [00:00, 66.96it/s]\u001b[A\n",
+      "Evaluating NLL: 94it [00:01, 135.46it/s]\u001b[A\n",
+      "Evaluating NLL: 137it [00:01, 192.82it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 154.74it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|        | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|▏| 18/100 [00:00<00:00, 166.16it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|▎| 35/100 [00:00<00:00, 151.27it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  55%|▌| 55/100 [00:00<00:00, 166.36it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|▋| 72/100 [00:00<00:00, 158.89it\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 153.21i\n",
+      " 40%|████████████████▊                         | 2/5 [1:10:14<1:48:45, 2175.04s/it]\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<16:13,  4.89s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:01,  2.73s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:44,  2.05s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<05:10,  1.59s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:46,  1.48s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:30,  1.40s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:24,  1.38s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:15<04:16,  1.34s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:16<04:10,  1.32s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:17<04:03,  1.29s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:20<03:56,  1.26s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:21<03:51,  1.25s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:22<03:54,  1.27s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:23<03:52,  1.26s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:25<03:52,  1.27s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:26<03:52,  1.28s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:27<03:25,  1.13s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:27<02:56,  1.02it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:28<02:38,  1.13it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:29<02:23,  1.24it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:29<02:19,  1.27it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:30<02:12,  1.33it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:31<02:06,  1.38it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:31<02:05,  1.38it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:32<02:02,  1.41it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:33<01:58,  1.45it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:33<01:57,  1.45it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:34<02:02,  1.39it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:35<02:16,  1.24it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:36<02:21,  1.19it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:37<02:23,  1.17it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:38<02:19,  1.19it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:38<02:11,  1.26it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:39<02:04,  1.32it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:40<01:56,  1.40it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:40<01:50,  1.46it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:41<02:00,  1.34it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:42<01:56,  1.38it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:43<01:52,  1.41it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:43<01:58,  1.33it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:45<02:24,  1.09it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:46<02:33,  1.01it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:47<02:41,  1.04s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:48<02:44,  1.07s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:50<02:53,  1.14s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:51<02:58,  1.18s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:52<03:01,  1.20s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:53<03:03,  1.23s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:55<03:01,  1.22s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:56<03:03,  1.24s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:57<03:03,  1.25s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:58<03:03,  1.25s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:00<03:02,  1.26s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:01<03:03,  1.27s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:02<02:58,  1.25s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:03<02:55,  1.23s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:05<02:53,  1.23s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:06<02:54,  1.24s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:07<02:52,  1.24s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:08<02:53,  1.26s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:10<02:53,  1.27s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:11<02:52,  1.27s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:12<02:51,  1.27s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:13<02:51,  1.28s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:15<02:48,  1.27s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:16<02:49,  1.28s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:17<02:45,  1.26s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:18<02:41,  1.24s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:20<02:41,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:21<02:38,  1.24s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:22<02:41,  1.28s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:24<02:40,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:25<02:36,  1.25s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:26<02:39,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:27<02:35,  1.27s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:29<02:34,  1.27s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:30<02:32,  1.26s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:31<02:33,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:32<02:32,  1.28s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:34<02:31,  1.28s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:35<02:31,  1.30s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:36<02:32,  1.32s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:38<02:31,  1.32s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:39<02:31,  1.33s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:40<02:29,  1.33s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:42<02:36,  1.40s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:44<02:44,  1.48s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:45<02:36,  1.42s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:46<02:27,  1.35s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:47<02:23,  1.33s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:49<02:20,  1.31s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:50<02:17,  1.30s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:51<02:16,  1.30s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:53<02:14,  1.29s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:54<02:10,  1.27s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:55<02:06,  1.24s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:56<02:04,  1.24s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:57<02:03,  1.24s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:59<02:03,  1.25s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:00<02:03,  1.26s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:01<02:01,  1.26s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:03<02:01,  1.27s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:04<02:01,  1.28s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:05<02:00,  1.29s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:06<01:57,  1.26s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:08<01:57,  1.27s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:09<01:54,  1.26s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:10<01:52,  1.25s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:11<01:50,  1.24s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:13<01:48,  1.23s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:14<01:48,  1.24s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:15<01:43,  1.21s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:16<01:30,  1.06s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:16<01:19,  1.06it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:17<01:10,  1.18it/s]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:18<01:06,  1.24it/s]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:18<01:03,  1.28it/s]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:19<00:59,  1.33it/s]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:20<00:57,  1.37it/s]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:20<00:56,  1.37it/s]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:21<00:54,  1.42it/s]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:22<00:52,  1.45it/s]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:23<00:52,  1.42it/s]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:23<00:56,  1.30it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:24<01:00,  1.21it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:25<01:01,  1.17it/s]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:26<01:01,  1.15it/s]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:27<00:57,  1.21it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:28<00:54,  1.27it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:28<00:51,  1.33it/s]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:29<00:48,  1.37it/s]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:30<00:48,  1.37it/s]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:30<00:46,  1.40it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:31<00:45,  1.41it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:32<00:44,  1.42it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:33<00:54,  1.13it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:34<01:01,  1.01s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:36<01:03,  1.06s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:37<01:05,  1.11s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:38<01:06,  1.14s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:39<01:05,  1.15s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:40<01:04,  1.15s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:42<01:04,  1.17s/it]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:43<01:04,  1.19s/it]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:44<01:04,  1.21s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:45<01:02,  1.21s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:46<01:01,  1.20s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:48<01:02,  1.24s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:49<01:01,  1.25s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:50<01:00,  1.26s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:51<00:58,  1.24s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:53<00:57,  1.24s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:54<00:55,  1.24s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:55<00:53,  1.22s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:56<00:52,  1.23s/it]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:58<00:51,  1.24s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:59<00:51,  1.24s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [03:00<00:49,  1.25s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [03:02<00:50,  1.29s/it]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [03:03<00:48,  1.27s/it]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [03:04<00:46,  1.26s/it]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [03:05<00:45,  1.26s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [03:07<00:43,  1.25s/it]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [03:08<00:41,  1.23s/it]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [03:09<00:40,  1.22s/it]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [03:10<00:39,  1.23s/it]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [03:11<00:38,  1.23s/it]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [03:13<00:37,  1.24s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [03:14<00:36,  1.27s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [03:15<00:35,  1.28s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [03:17<00:34,  1.29s/it]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [03:18<00:34,  1.32s/it]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [03:19<00:32,  1.29s/it]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [03:20<00:30,  1.28s/it]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [03:22<00:29,  1.28s/it]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [03:23<00:28,  1.28s/it]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [03:24<00:27,  1.29s/it]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [03:26<00:26,  1.30s/it]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [03:27<00:24,  1.31s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [03:28<00:23,  1.32s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [03:30<00:22,  1.32s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [03:31<00:21,  1.34s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [03:32<00:20,  1.37s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [03:34<00:19,  1.38s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [03:35<00:17,  1.36s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [03:37<00:16,  1.35s/it]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [03:38<00:15,  1.37s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [03:40<00:14,  1.43s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [03:41<00:12,  1.35s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [03:42<00:10,  1.31s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [03:43<00:09,  1.29s/it]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [03:44<00:07,  1.30s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [03:46<00:06,  1.29s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [03:47<00:05,  1.28s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [03:48<00:03,  1.27s/it]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [03:49<00:02,  1.26s/it]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [03:51<00:01,  1.26s/it]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [03:52<00:00,  1.16s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<17:36,  5.31s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:49,  2.98s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<07:10,  2.19s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<05:56,  1.82s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<04:45,  1.46s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:10<03:50,  1.19s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:11<03:15,  1.01s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:11<02:52,  1.11it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:12<02:42,  1.18it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:13<02:30,  1.27it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:14<02:23,  1.32it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:14<02:22,  1.32it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:15<02:16,  1.36it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:16<02:11,  1.42it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:16<02:10,  1.42it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:17<02:21,  1.30it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:18<02:28,  1.23it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:19<02:35,  1.17it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:20<02:38,  1.14it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:21<02:29,  1.21it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:21<02:21,  1.27it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:22<02:11,  1.35it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:23<02:05,  1.41it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:23<02:01,  1.45it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:24<02:02,  1.43it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:25<01:59,  1.45it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:25<01:57,  1.47it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:27<02:19,  1.24it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:28<02:42,  1.05it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:29<02:52,  1.02s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:30<03:05,  1.10s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:32<03:15,  1.16s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:33<03:18,  1.19s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:34<03:21,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:35<03:27,  1.26s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:37<03:25,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:38<03:27,  1.27s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:39<03:27,  1.28s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:41<03:22,  1.26s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:42<03:19,  1.25s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:43<03:15,  1.23s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:44<03:15,  1.24s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:45<03:15,  1.25s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:47<03:13,  1.24s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:48<03:17,  1.27s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:49<03:14,  1.26s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:51<03:14,  1.27s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:52<03:13,  1.27s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:53<03:12,  1.27s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:54<03:08,  1.26s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:56<03:08,  1.27s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:57<03:04,  1.25s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:58<03:05,  1.26s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:59<03:04,  1.26s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:01<03:04,  1.27s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:02<03:03,  1.27s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:03<03:07,  1.31s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:05<03:02,  1.28s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:06<02:59,  1.28s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:07<02:58,  1.28s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:08<02:58,  1.28s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:10<02:54,  1.27s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:11<02:52,  1.26s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:12<02:47,  1.23s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:13<02:46,  1.23s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:15<02:49,  1.27s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:16<02:50,  1.28s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:17<02:50,  1.30s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:19<02:50,  1.30s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:20<02:51,  1.32s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:21<02:47,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:22<02:42,  1.27s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:24<02:44,  1.30s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:25<02:44,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:26<02:39,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:28<02:41,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:29<02:37,  1.28s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:30<02:39,  1.31s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:32<02:40,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:33<02:40,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:34<02:39,  1.34s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:36<02:49,  1.44s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:37<02:41,  1.38s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:38<02:36,  1.35s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:40<02:33,  1.33s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:41<02:28,  1.30s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:42<02:24,  1.28s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:43<02:22,  1.27s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:45<02:20,  1.26s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:46<02:19,  1.27s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:47<02:18,  1.27s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:49<02:15,  1.25s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:50<02:15,  1.27s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:51<02:15,  1.27s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:52<02:11,  1.26s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:54<02:11,  1.26s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:55<02:11,  1.27s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:56<02:08,  1.26s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:57<02:08,  1.27s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:59<02:08,  1.28s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:00<02:07,  1.28s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:01<02:03,  1.26s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:02<02:02,  1.26s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:04<01:59,  1.24s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:05<01:58,  1.25s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:06<01:57,  1.25s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:07<01:54,  1.23s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:09<01:53,  1.23s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:10<01:51,  1.23s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:11<01:49,  1.22s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:12<01:49,  1.23s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:14<01:49,  1.24s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:15<01:47,  1.23s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:16<01:39,  1.16s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:16<01:26,  1.02s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:17<01:16,  1.09it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:18<01:38,  1.19s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:00,  4.53s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<08:25,  2.55s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:53,  1.80s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<04:28,  1.37s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:07<03:36,  1.11s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:08<03:05,  1.05it/s]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:09<02:47,  1.15it/s]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:10<02:38,  1.21it/s]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:10<02:27,  1.29it/s]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:11<02:22,  1.33it/s]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:12<02:37,  1.20it/s]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:13<02:52,  1.09it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:14<03:05,  1.01it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:15<03:21,  1.08s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:17<03:29,  1.13s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:18<03:36,  1.18s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:19<03:41,  1.21s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:21<03:45,  1.24s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:22<03:44,  1.24s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:23<03:47,  1.27s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:24<03:43,  1.25s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:26<03:43,  1.26s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:27<03:46,  1.28s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:28<03:44,  1.28s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:29<03:39,  1.26s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:31<03:42,  1.28s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:32<03:47,  1.32s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:33<03:44,  1.31s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:35<03:38,  1.28s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:36<03:34,  1.26s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:37<03:32,  1.26s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:38<03:29,  1.24s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:40<03:28,  1.25s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:41<03:28,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:42<03:24,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:43<03:28,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:45<03:28,  1.28s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:46<03:28,  1.29s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:47<03:27,  1.29s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:49<03:30,  1.32s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:50<03:26,  1.30s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:51<03:18,  1.26s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:52<03:19,  1.27s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:54<03:21,  1.29s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:55<03:20,  1.29s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:56<03:22,  1.31s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:58<03:19,  1.31s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:59<03:21,  1.33s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:00<03:22,  1.34s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:02<03:21,  1.34s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:03<03:20,  1.35s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:05<03:19,  1.35s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:06<03:18,  1.35s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:07<03:15,  1.34s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:09<03:12,  1.33s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:10<03:09,  1.32s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:11<03:19,  1.40s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:13<03:18,  1.40s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:14<03:12,  1.36s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:15<03:06,  1.33s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:17<03:01,  1.31s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:18<02:59,  1.30s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:19<02:56,  1.29s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:20<02:52,  1.27s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:22<02:49,  1.25s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:23<02:50,  1.27s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:24<02:45,  1.24s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:25<02:47,  1.27s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:27<02:43,  1.25s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:28<02:42,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:29<02:42,  1.26s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:30<02:41,  1.26s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:32<02:39,  1.25s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:33<02:39,  1.26s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:34<02:39,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:36<02:38,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:37<02:38,  1.29s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:38<02:35,  1.27s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:39<02:31,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:41<02:31,  1.26s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:42<02:28,  1.25s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:43<02:29,  1.26s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:44<02:26,  1.25s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:45<02:05,  1.09s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:46<01:48,  1.06it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:46<01:37,  1.17it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:47<01:28,  1.27it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:48<01:24,  1.33it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:48<01:20,  1.37it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:49<01:18,  1.40it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:50<01:17,  1.40it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:50<01:15,  1.44it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:51<01:12,  1.48it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:52<02:07,  1.20s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<14:28,  4.37s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:05<07:27,  2.26s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:06<05:48,  1.77s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:07<05:00,  1.53s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:08<04:34,  1.41s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:09<04:23,  1.36s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:11<04:14,  1.32s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:12<04:11,  1.31s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:13<04:10,  1.31s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<04:07,  1.30s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:16<04:05,  1.30s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:17<04:02,  1.29s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:18<04:01,  1.29s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:20<03:59,  1.29s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:21<03:55,  1.27s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:22<03:53,  1.27s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:23<03:50,  1.26s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:26<03:44,  1.24s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:27<03:42,  1.24s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:28<03:42,  1.24s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:30<03:43,  1.26s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:31<03:45,  1.27s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:32<03:48,  1.30s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:34<03:51,  1.32s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:35<03:47,  1.31s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:36<03:43,  1.29s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:37<03:41,  1.29s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:39<03:36,  1.26s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:40<03:37,  1.28s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:41<03:33,  1.26s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:42<03:30,  1.26s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:44<03:32,  1.27s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:45<03:33,  1.29s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:46<03:30,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:48<03:30,  1.28s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:49<03:30,  1.29s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:50<03:30,  1.30s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:52<03:29,  1.30s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:53<03:24,  1.28s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:54<03:26,  1.30s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:55<03:24,  1.29s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:57<03:24,  1.30s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:58<03:23,  1.30s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:59<03:23,  1.31s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [01:01<03:25,  1.33s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [01:02<03:25,  1.34s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [01:04<03:25,  1.35s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:05<03:24,  1.35s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:06<03:26,  1.37s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:08<03:22,  1.36s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:09<03:22,  1.37s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:10<03:24,  1.39s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:12<03:32,  1.45s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:13<03:22,  1.40s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:15<03:15,  1.36s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:16<03:06,  1.30s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:17<03:04,  1.30s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:18<02:58,  1.26s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:20<02:57,  1.27s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:21<02:53,  1.25s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:22<02:49,  1.23s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:23<02:49,  1.24s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:24<02:49,  1.24s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:26<02:50,  1.26s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:27<02:50,  1.27s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:28<02:45,  1.25s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:29<02:43,  1.24s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:31<02:39,  1.22s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:32<02:37,  1.21s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:33<02:40,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:34<02:41,  1.26s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:36<02:41,  1.27s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:37<02:37,  1.25s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:38<02:36,  1.26s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:39<02:16,  1.10s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:40<01:58,  1.04it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:40<01:47,  1.14it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:41<01:39,  1.22it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:42<01:33,  1.28it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:42<01:29,  1.33it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:43<01:26,  1.36it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:44<01:24,  1.38it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:44<01:22,  1.41it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:45<01:19,  1.45it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:46<01:17,  1.46it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:46<01:21,  1.39it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:47<01:26,  1.29it/s]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:48<01:29,  1.24it/s]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:49<01:32,  1.19it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:50<01:31,  1.19it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:51<01:25,  1.26it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:51<01:20,  1.33it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:52<01:15,  1.41it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:53<01:12,  1.44it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:53<01:10,  1.47it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:54<01:11,  1.44it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:55<01:11,  1.43it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:55<01:11,  1.42it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:57<01:24,  1.18it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:58<01:36,  1.03it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:59<01:44,  1.07s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:00<01:47,  1.10s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:02<01:53,  1.18s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:03<01:57,  1.23s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:04<01:55,  1.23s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:06<01:54,  1.24s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:07<01:51,  1.21s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:08<01:49,  1.20s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:09<01:50,  1.23s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:10<01:48,  1.22s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:12<01:47,  1.22s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:13<01:47,  1.24s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:14<01:47,  1.26s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:15<01:47,  1.26s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:17<01:45,  1.26s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:18<01:43,  1.24s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:19<01:42,  1.25s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:20<01:42,  1.26s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:22<01:39,  1.24s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:23<01:36,  1.23s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:24<01:37,  1.25s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:25<01:37,  1.26s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:27<01:34,  1.24s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:28<01:33,  1.25s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:29<01:35,  1.29s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:31<01:33,  1.28s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:32<01:32,  1.28s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:33<01:30,  1.27s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:34<01:29,  1.28s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:36<01:27,  1.27s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:37<01:24,  1.24s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:38<01:23,  1.25s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:39<01:23,  1.26s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:41<01:23,  1.28s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:42<01:22,  1.29s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:43<01:19,  1.27s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:44<01:18,  1.27s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:46<01:18,  1.29s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:47<01:16,  1.28s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:48<01:15,  1.27s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:50<01:13,  1.28s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:51<01:14,  1.31s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:52<01:14,  1.33s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:54<01:14,  1.36s/it]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:55<01:14,  1.38s/it]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:57<01:13,  1.39s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:58<01:12,  1.39s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:59<01:11,  1.40s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [03:01<01:09,  1.39s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [03:04<00:59,  1.22s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<18:42,  5.64s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:07<10:26,  3.16s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:08<07:43,  2.35s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<06:26,  1.97s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:11<05:41,  1.75s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:12<05:15,  1.63s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:13<04:57,  1.54s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:15<04:38,  1.45s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:16<04:30,  1.42s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:17<04:25,  1.40s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:19<04:21,  1.38s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:20<04:19,  1.38s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:21<03:48,  1.22s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:22<03:29,  1.13s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:23<03:42,  1.21s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:24<03:12,  1.05s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:25<02:52,  1.06it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:25<02:39,  1.14it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:26<02:28,  1.22it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:27<02:21,  1.28it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:27<02:16,  1.31it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:28<02:15,  1.32it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:29<02:10,  1.35it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:30<02:08,  1.37it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:30<02:07,  1.38it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:31<02:05,  1.39it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:32<02:02,  1.42it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:32<01:59,  1.43it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:33<01:58,  1.45it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:34<01:57,  1.45it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:34<01:56,  1.45it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:35<01:54,  1.46it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:36<01:52,  1.48it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:36<01:50,  1.50it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:37<01:54,  1.44it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:38<01:54,  1.43it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:39<01:53,  1.43it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:39<01:54,  1.42it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:40<01:51,  1.45it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:41<01:50,  1.45it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:41<01:48,  1.46it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:42<01:50,  1.43it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:43<01:49,  1.44it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:43<01:48,  1.44it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:44<01:49,  1.41it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:45<01:50,  1.40it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:46<01:49,  1.39it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:46<01:47,  1.41it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:47<01:47,  1.40it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:48<01:48,  1.39it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:49<01:48,  1.38it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:49<01:47,  1.37it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:50<01:43,  1.41it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:51<01:42,  1.43it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [00:51<01:41,  1.42it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [00:52<01:40,  1.43it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [00:53<01:38,  1.45it/s]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [00:53<01:37,  1.45it/s]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [00:54<01:37,  1.44it/s]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [00:55<01:39,  1.40it/s]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [00:56<01:39,  1.40it/s]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [00:56<01:38,  1.40it/s]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [00:57<01:39,  1.38it/s]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [00:58<01:48,  1.25it/s]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [00:59<01:53,  1.19it/s]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:00<01:45,  1.27it/s]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:00<01:39,  1.33it/s]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:01<01:34,  1.40it/s]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:02<01:33,  1.41it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:02<01:33,  1.39it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:03<01:33,  1.38it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:04<01:30,  1.42it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:04<01:28,  1.44it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:05<01:27,  1.44it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:06<01:30,  1.38it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:07<01:27,  1.41it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:07<01:27,  1.41it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:09<01:49,  1.12it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:10<01:58,  1.02it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:11<02:07,  1.06s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:12<02:14,  1.13s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:14<02:17,  1.17s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:15<02:23,  1.22s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:16<02:22,  1.23s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:17<02:23,  1.25s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:19<02:22,  1.25s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:20<02:21,  1.25s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:21<02:17,  1.23s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:22<02:18,  1.25s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:24<02:18,  1.26s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:25<02:17,  1.26s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:26<02:16,  1.26s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:28<02:15,  1.26s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:29<02:13,  1.26s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:30<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:31<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:33<02:08,  1.25s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:34<02:09,  1.27s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:35<02:05,  1.24s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:36<02:05,  1.26s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:38<02:05,  1.27s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [01:39<02:04,  1.27s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [01:40<02:02,  1.27s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [01:41<02:03,  1.29s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [01:43<02:03,  1.30s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [01:44<02:00,  1.28s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [01:45<01:56,  1.25s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [01:46<01:53,  1.24s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [01:48<01:52,  1.24s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [01:49<01:52,  1.25s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [01:50<01:50,  1.24s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [01:51<01:48,  1.23s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [01:53<01:46,  1.23s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [01:54<01:49,  1.27s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [01:55<01:49,  1.29s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [01:57<01:48,  1.29s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [01:58<01:45,  1.27s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [01:59<01:46,  1.30s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:00<01:44,  1.29s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:02<01:42,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:03<01:41,  1.28s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:04<01:41,  1.30s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:06<01:40,  1.31s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:07<01:40,  1.32s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:08<01:37,  1.30s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:10<01:35,  1.29s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:11<01:36,  1.33s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:12<01:35,  1.33s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:14<01:35,  1.34s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:15<01:33,  1.34s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:17<01:39,  1.45s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:18<01:34,  1.39s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:19<01:31,  1.36s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:21<01:27,  1.33s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:22<01:25,  1.31s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:23<01:22,  1.29s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:24<01:21,  1.29s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:26<01:19,  1.29s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:27<01:17,  1.27s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:28<01:15,  1.26s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:29<01:13,  1.25s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:30<01:11,  1.24s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:32<01:10,  1.24s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:33<01:09,  1.25s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:34<01:08,  1.24s/it]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:35<01:07,  1.25s/it]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:37<01:07,  1.27s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:38<01:06,  1.28s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:39<01:05,  1.28s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:41<01:03,  1.26s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:42<01:01,  1.26s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:43<00:59,  1.24s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:44<00:58,  1.24s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:45<00:53,  1.15s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [02:46<00:45,  1.01s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [02:47<00:40,  1.10it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [02:47<00:35,  1.20it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [02:48<00:33,  1.26it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [02:49<00:31,  1.30it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [02:49<00:29,  1.34it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [02:50<00:28,  1.36it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [02:51<00:27,  1.37it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [02:51<00:26,  1.40it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [02:52<00:25,  1.42it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [02:53<00:25,  1.40it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [02:54<00:26,  1.27it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [02:55<00:27,  1.22it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [02:56<00:27,  1.17it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [02:56<00:25,  1.22it/s]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [02:57<00:23,  1.28it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [02:58<00:21,  1.35it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [02:58<00:20,  1.37it/s]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [02:59<00:19,  1.41it/s]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [03:00<00:18,  1.37it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [03:01<00:18,  1.38it/s]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [03:01<00:17,  1.41it/s]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [03:02<00:16,  1.36it/s]\u001b[A\n",
+      "training:  89%|██████████████████████████████▎   | 178/200 [03:03<00:18,  1.16it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▍   | 179/200 [03:04<00:20,  1.05it/s]\u001b[A\n",
+      "training:  90%|██████████████████████████████▌   | 180/200 [03:06<00:20,  1.02s/it]\u001b[A\n",
+      "training:  90%|██████████████████████████████▊   | 181/200 [03:07<00:20,  1.08s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████▉   | 182/200 [03:08<00:21,  1.17s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████   | 183/200 [03:10<00:20,  1.22s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████▎  | 184/200 [03:11<00:20,  1.25s/it]\u001b[A\n",
+      "training:  92%|███████████████████████████████▍  | 185/200 [03:12<00:18,  1.27s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████▌  | 186/200 [03:13<00:17,  1.26s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████▊  | 187/200 [03:15<00:16,  1.26s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████▉  | 188/200 [03:16<00:15,  1.27s/it]\u001b[A\n",
+      "training:  94%|████████████████████████████████▏ | 189/200 [03:17<00:14,  1.27s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████▎ | 190/200 [03:19<00:12,  1.28s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▍ | 191/200 [03:20<00:11,  1.28s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▋ | 192/200 [03:21<00:10,  1.25s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████▊ | 193/200 [03:22<00:08,  1.26s/it]\u001b[A\n",
+      "training:  97%|████████████████████████████████▉ | 194/200 [03:24<00:07,  1.26s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▏| 195/200 [03:25<00:06,  1.28s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▎| 196/200 [03:26<00:04,  1.24s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████▍| 197/200 [03:27<00:03,  1.22s/it]\u001b[A\n",
+      "training:  99%|█████████████████████████████████▋| 198/200 [03:28<00:02,  1.24s/it]\u001b[A\n",
+      "training: 100%|█████████████████████████████████▊| 199/200 [03:30<00:01,  1.24s/it]\u001b[A\n",
+      "training: 100%|██████████████████████████████████| 200/200 [03:31<00:00,  1.06s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 37it [00:00, 50.63it/s]\u001b[A\n",
+      "Evaluating NLL: 72it [00:01, 99.91it/s]\u001b[A\n",
+      "Evaluating NLL: 106it [00:01, 145.29it/s]\u001b[A\n",
+      "Evaluating NLL: 143it [00:01, 192.43it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 136.32it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|        | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|▏| 16/100 [00:00<00:00, 154.93it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|▎| 32/100 [00:00<00:00, 133.71it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|▍| 47/100 [00:00<00:00, 135.20it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:00<00:00, 136.88it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|▊| 75/100 [00:00<00:00, 134.30it\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 134.87i\n",
+      " 60%|█████████████████████████▏                | 3/5 [1:52:49<1:18:16, 2348.48s/it]\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<17:45,  5.35s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:55,  3.01s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:08<07:18,  2.23s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<06:08,  1.88s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:10<05:25,  1.67s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:58,  1.54s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:13<04:40,  1.45s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:14<04:33,  1.43s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:15<04:28,  1.41s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:17<04:22,  1.38s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:18<04:18,  1.37s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:19<04:13,  1.35s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:21<04:12,  1.35s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:22<04:08,  1.33s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:23<04:07,  1.34s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:25<04:07,  1.35s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:26<04:05,  1.34s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:27<04:04,  1.34s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:29<04:04,  1.35s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:30<04:01,  1.34s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:31<04:00,  1.34s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:33<03:57,  1.33s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:34<03:58,  1.35s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:36<03:55,  1.34s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:37<03:55,  1.34s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:38<03:47,  1.31s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:39<03:50,  1.33s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:41<03:46,  1.32s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:42<03:43,  1.31s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:43<03:45,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:45<03:43,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:46<03:41,  1.32s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:47<03:39,  1.32s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:49<03:39,  1.32s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:50<03:40,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:51<03:39,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:53<03:36,  1.33s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:54<03:35,  1.33s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:55<03:35,  1.34s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:57<03:34,  1.34s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:58<03:32,  1.34s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:59<03:32,  1.34s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [01:01<03:35,  1.38s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [01:02<03:33,  1.37s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [01:04<03:32,  1.37s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [01:05<03:30,  1.36s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [01:06<03:26,  1.35s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [01:08<03:23,  1.34s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:09<03:21,  1.33s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:10<03:18,  1.33s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:12<03:20,  1.34s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:13<03:24,  1.38s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:14<03:22,  1.38s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:16<03:17,  1.35s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:17<03:18,  1.37s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:19<03:17,  1.37s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:20<03:13,  1.36s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:21<03:14,  1.37s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:23<03:17,  1.40s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:24<03:20,  1.43s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:26<03:20,  1.44s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:27<03:20,  1.46s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:29<03:19,  1.45s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:30<03:18,  1.46s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:32<03:17,  1.46s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:33<03:15,  1.46s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:34<03:12,  1.45s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:36<03:12,  1.46s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:37<03:10,  1.45s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:39<03:09,  1.45s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:40<03:07,  1.46s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:42<03:06,  1.46s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:43<03:03,  1.44s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:45<02:58,  1.42s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:46<02:57,  1.42s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:47<02:56,  1.42s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:49<02:56,  1.43s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:50<02:53,  1.42s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:52<02:52,  1.42s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:53<02:50,  1.42s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:54<02:48,  1.42s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:56<02:47,  1.42s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:57<02:45,  1.42s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:59<02:44,  1.42s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [02:00<02:55,  1.53s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [02:02<02:46,  1.46s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [02:03<02:40,  1.42s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [02:04<02:36,  1.40s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [02:06<02:33,  1.38s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [02:07<02:31,  1.37s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [02:09<02:29,  1.37s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [02:10<02:27,  1.36s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [02:11<02:24,  1.35s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [02:12<02:21,  1.33s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [02:13<02:03,  1.17s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [02:14<01:51,  1.07s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [02:15<01:40,  1.02it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [02:16<01:34,  1.08it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [02:16<01:29,  1.13it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [02:17<01:25,  1.17it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:18<01:24,  1.17it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:19<01:21,  1.20it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:20<01:19,  1.22it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:21<01:18,  1.22it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:22<01:29,  1.06it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:23<01:37,  1.04s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:24<01:41,  1.09s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:25<01:33,  1.02s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:26<01:26,  1.05it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:27<01:20,  1.12it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:27<01:15,  1.18it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:28<01:14,  1.19it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:29<01:11,  1.22it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:30<01:09,  1.25it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:31<01:15,  1.12it/s]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:32<01:23,  1.01it/s]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:33<01:28,  1.07s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:35<01:33,  1.14s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:36<01:37,  1.20s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:37<01:38,  1.23s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:39<01:38,  1.25s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:40<01:38,  1.27s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:41<01:39,  1.29s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:42<01:38,  1.29s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:45<01:39,  1.33s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<16:43,  5.04s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:43,  2.94s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<07:09,  2.18s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<05:57,  1.83s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:10<05:18,  1.63s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:54,  1.52s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:39,  1.45s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:14<04:27,  1.39s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:15<04:23,  1.38s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:16<04:18,  1.36s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:18<04:15,  1.35s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:19<04:13,  1.35s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:20<04:11,  1.35s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:22<04:07,  1.33s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:23<04:04,  1.32s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:24<04:02,  1.32s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:26<04:05,  1.34s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:27<04:02,  1.33s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:28<03:56,  1.31s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:30<03:57,  1.32s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:31<03:59,  1.34s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:32<04:00,  1.35s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:34<03:58,  1.35s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:35<03:56,  1.34s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:36<03:51,  1.32s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:38<03:49,  1.32s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:39<03:47,  1.31s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:40<03:44,  1.30s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:42<03:43,  1.31s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:43<03:46,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:44<03:46,  1.34s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:46<03:45,  1.34s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:47<03:43,  1.34s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:48<03:44,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:50<03:41,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:51<03:40,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:52<03:42,  1.37s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:54<03:42,  1.37s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:55<03:38,  1.35s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:57<03:37,  1.36s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:58<03:39,  1.38s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:59<03:39,  1.39s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [01:01<03:39,  1.40s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [01:02<03:37,  1.40s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [01:04<03:48,  1.47s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [01:05<03:44,  1.46s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [01:07<03:36,  1.42s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [01:08<03:30,  1.38s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:09<03:27,  1.37s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:11<03:23,  1.36s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:12<03:22,  1.36s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:13<03:20,  1.35s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:15<03:16,  1.34s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:16<03:13,  1.32s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:17<03:11,  1.32s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:18<03:08,  1.31s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:20<03:07,  1.31s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:21<03:06,  1.31s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:22<03:07,  1.33s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:24<03:05,  1.33s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:25<03:06,  1.34s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:26<03:06,  1.35s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:28<03:02,  1.33s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:29<03:02,  1.34s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:30<03:00,  1.34s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:32<02:49,  1.26s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:32<02:30,  1.13s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:33<02:14,  1.02s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:34<02:03,  1.06it/s]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:35<01:57,  1.10it/s]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:36<01:52,  1.15it/s]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:36<01:49,  1.17it/s]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:37<01:47,  1.18it/s]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:38<01:43,  1.22it/s]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:39<01:41,  1.23it/s]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:40<01:47,  1.16it/s]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:41<01:59,  1.03it/s]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:42<02:07,  1.05s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:43<02:01,  1.00s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:44<01:52,  1.07it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:45<01:44,  1.14it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:45<01:40,  1.18it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:46<01:36,  1.21it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:47<01:37,  1.19it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:48<01:35,  1.21it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:49<01:35,  1.19it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:50<01:50,  1.03it/s]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:51<01:59,  1.07s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:53<02:06,  1.14s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:54<02:11,  1.20s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:55<02:14,  1.24s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:56<02:14,  1.25s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:58<02:15,  1.26s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:59<02:12,  1.25s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [02:00<02:11,  1.26s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [02:02<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [02:03<02:11,  1.27s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [02:04<02:09,  1.27s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [02:05<02:08,  1.27s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [02:07<02:06,  1.27s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:08<02:05,  1.27s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:09<02:04,  1.27s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:10<02:03,  1.27s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:12<02:02,  1.28s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:13<02:01,  1.28s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:14<02:00,  1.28s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:16<01:58,  1.28s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:17<01:57,  1.28s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:18<01:57,  1.29s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:19<01:55,  1.28s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:21<01:55,  1.29s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:22<01:53,  1.29s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:23<01:52,  1.29s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:25<01:50,  1.29s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:26<01:48,  1.28s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:27<01:47,  1.28s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:28<01:46,  1.28s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:30<01:45,  1.29s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:31<01:43,  1.27s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:32<01:42,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:34<01:43,  1.31s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:35<01:41,  1.31s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:36<01:40,  1.30s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:38<01:38,  1.30s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:39<01:38,  1.31s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:40<01:36,  1.30s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:41<01:33,  1.29s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:43<01:33,  1.29s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:44<01:34,  1.33s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:46<01:34,  1.34s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:47<01:33,  1.36s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:48<01:33,  1.37s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:50<01:32,  1.38s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:51<01:31,  1.38s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:52<01:30,  1.39s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:54<01:28,  1.39s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:55<01:27,  1.39s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:57<01:26,  1.39s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:58<01:24,  1.38s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [03:01<01:17,  1.29s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<16:52,  5.09s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:48,  2.97s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<07:17,  2.22s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<06:06,  1.87s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:10<05:28,  1.69s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<05:04,  1.57s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:13<04:49,  1.50s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:14<04:36,  1.44s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:15<04:30,  1.41s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:17<04:24,  1.39s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:18<04:18,  1.37s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:20<04:23,  1.40s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:21<04:30,  1.44s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:22<04:19,  1.39s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:24<04:11,  1.36s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:25<04:05,  1.33s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:26<04:02,  1.33s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:28<03:57,  1.31s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:29<03:55,  1.30s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:30<03:52,  1.29s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:31<03:50,  1.29s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:33<03:48,  1.28s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:34<03:47,  1.28s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:35<03:46,  1.29s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:37<03:45,  1.29s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:38<03:43,  1.28s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:39<03:34,  1.24s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:40<03:11,  1.11s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:40<02:51,  1.00s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:41<02:37,  1.08it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:42<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:43<02:24,  1.16it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:44<02:20,  1.19it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:44<02:19,  1.19it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:45<02:14,  1.23it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:46<02:10,  1.26it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:47<02:10,  1.25it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:48<02:30,  1.08it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:49<02:43,  1.01s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:51<02:55,  1.10s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:52<02:52,  1.08s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:52<02:37,  1.00it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:53<02:26,  1.07it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:54<02:17,  1.14it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:55<02:11,  1.18it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:55<02:08,  1.20it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:56<02:05,  1.22it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:57<02:13,  1.14it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:59<02:31,  1.00s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:00<02:44,  1.10s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:01<02:52,  1.16s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:02<02:57,  1.20s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:04<03:03,  1.25s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:05<03:06,  1.28s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:07<03:07,  1.30s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:08<03:07,  1.30s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:09<03:05,  1.29s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:11<03:08,  1.33s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:12<03:05,  1.31s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:13<03:06,  1.33s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:14<03:04,  1.33s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:16<03:03,  1.33s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:17<03:02,  1.33s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:18<02:59,  1.32s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:20<02:57,  1.32s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:21<02:57,  1.32s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:22<02:54,  1.31s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:24<02:52,  1.31s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:25<02:53,  1.32s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:26<02:51,  1.32s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:28<02:47,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:29<02:47,  1.31s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:30<02:45,  1.30s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:32<02:43,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:33<02:42,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:34<02:41,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:35<02:40,  1.31s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:37<02:43,  1.34s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:38<02:44,  1.36s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:40<02:40,  1.34s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:41<02:38,  1.34s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:42<02:37,  1.33s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:44<02:35,  1.33s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:45<02:34,  1.33s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:46<02:31,  1.32s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:47<02:30,  1.32s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:49<02:27,  1.31s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:50<02:28,  1.33s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:52<02:29,  1.35s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:53<02:25,  1.32s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:54<02:25,  1.33s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:55<02:24,  1.34s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:57<02:22,  1.33s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:58<02:21,  1.34s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [02:00<02:23,  1.37s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [02:01<02:24,  1.39s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [02:02<02:24,  1.41s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [02:04<02:24,  1.41s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [02:05<02:23,  1.42s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [02:07<02:22,  1.43s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:08<02:21,  1.43s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:10<02:20,  1.44s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:11<02:19,  1.44s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:13<02:19,  1.45s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:14<02:17,  1.45s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:15<02:15,  1.44s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:17<02:14,  1.44s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:18<02:12,  1.44s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:20<02:12,  1.45s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:21<02:11,  1.47s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:23<02:10,  1.46s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:24<02:06,  1.43s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:26<02:06,  1.45s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:27<02:04,  1.45s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:29<02:02,  1.45s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:30<02:00,  1.44s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:31<01:59,  1.44s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:33<01:57,  1.43s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:34<01:55,  1.43s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:36<01:53,  1.42s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:37<01:50,  1.40s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:39<01:58,  1.52s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:40<01:51,  1.45s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:41<01:46,  1.40s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:43<01:41,  1.35s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:44<01:39,  1.35s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:45<01:37,  1.33s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:47<01:36,  1.33s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:48<01:34,  1.33s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:49<01:32,  1.32s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:50<01:30,  1.31s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:52<01:30,  1.33s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:53<01:19,  1.19s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:54<01:10,  1.08s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:54<01:04,  1.01it/s]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:55<00:59,  1.07it/s]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:56<00:55,  1.13it/s]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:57<00:52,  1.18it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:57<00:51,  1.18it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:58<00:49,  1.21it/s]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:59<00:47,  1.24it/s]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [03:00<00:46,  1.25it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [03:01<00:52,  1.09it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [03:02<00:56,  1.02s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [03:03<00:54,  1.00it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [03:04<00:50,  1.06it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [03:05<00:47,  1.12it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [03:06<00:44,  1.18it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [03:06<00:41,  1.22it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [03:07<00:41,  1.21it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [03:08<00:40,  1.22it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [03:09<00:41,  1.17it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [03:10<00:45,  1.03it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [03:11<00:48,  1.06s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [03:13<00:50,  1.12s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [03:14<00:51,  1.17s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [03:15<00:51,  1.20s/it]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [03:16<00:51,  1.22s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [03:18<00:50,  1.22s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [03:19<00:49,  1.24s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [03:20<00:48,  1.25s/it]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [03:22<00:47,  1.26s/it]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [03:23<00:47,  1.27s/it]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [03:24<00:45,  1.26s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [03:27<00:43,  1.26s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<18:01,  5.43s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:49,  2.98s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:08<07:19,  2.23s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<06:08,  1.88s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:10<05:26,  1.67s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:59,  1.54s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:13<04:46,  1.48s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:14<04:35,  1.43s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:15<04:25,  1.39s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:17<04:17,  1.36s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:18<04:11,  1.33s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:19<04:06,  1.31s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:21<04:02,  1.30s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:22<04:02,  1.30s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:23<04:02,  1.31s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:25<04:03,  1.32s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:26<04:05,  1.34s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:27<04:01,  1.32s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:28<03:55,  1.30s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:30<03:51,  1.29s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:31<03:47,  1.27s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:32<03:47,  1.28s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:33<03:44,  1.27s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:35<03:44,  1.27s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:36<03:42,  1.27s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:37<03:46,  1.30s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:39<03:51,  1.34s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:40<03:48,  1.33s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:41<03:43,  1.31s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:43<03:48,  1.34s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:44<03:44,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:45<03:40,  1.31s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:47<03:42,  1.33s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:48<03:44,  1.36s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:50<03:46,  1.37s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:51<03:46,  1.38s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:52<03:47,  1.40s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:54<03:46,  1.40s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:55<03:43,  1.39s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:57<03:44,  1.40s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:58<03:41,  1.39s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:59<03:40,  1.40s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [01:01<03:39,  1.40s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [01:02<03:36,  1.39s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [01:04<03:35,  1.39s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [01:05<03:33,  1.39s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [01:06<03:32,  1.39s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [01:08<03:31,  1.39s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:09<03:32,  1.40s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:10<03:25,  1.37s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:12<03:25,  1.38s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:13<03:21,  1.36s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:15<03:19,  1.35s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:16<03:17,  1.35s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:17<03:15,  1.35s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:19<03:14,  1.35s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:20<03:12,  1.34s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:21<03:10,  1.34s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:23<03:09,  1.34s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:24<03:08,  1.34s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:25<03:04,  1.33s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:27<03:16,  1.42s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:28<03:14,  1.42s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:30<03:07,  1.38s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:31<03:02,  1.35s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:32<02:58,  1.33s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:33<02:55,  1.32s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:35<02:52,  1.30s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:36<02:51,  1.31s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:37<02:48,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:39<02:48,  1.31s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:40<02:44,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:41<02:43,  1.29s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:42<02:42,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:44<02:42,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:45<02:26,  1.18s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:45<02:10,  1.06s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:46<01:59,  1.02it/s]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:47<01:51,  1.09it/s]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:48<01:45,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:49<01:41,  1.17it/s]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:49<01:39,  1.19it/s]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:50<01:37,  1.20it/s]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:51<01:33,  1.24it/s]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:52<01:33,  1.23it/s]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:53<01:43,  1.10it/s]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:54<01:54,  1.01s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:55<02:00,  1.07s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:56<01:54,  1.03s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:57<01:46,  1.03it/s]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:58<01:38,  1.10it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:59<01:35,  1.13it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [02:00<01:35,  1.12it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [02:00<01:30,  1.17it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [02:01<01:27,  1.19it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [02:02<01:34,  1.10it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [02:04<01:43,  1.00s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [02:05<01:50,  1.08s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [02:06<01:55,  1.14s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [02:07<01:57,  1.17s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:09<01:59,  1.21s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:10<01:59,  1.22s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:11<02:01,  1.25s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:12<02:00,  1.26s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:14<01:59,  1.26s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:15<01:59,  1.27s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:16<01:59,  1.29s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:18<01:59,  1.30s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:19<01:57,  1.29s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:20<01:55,  1.28s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:21<01:53,  1.28s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:23<01:52,  1.28s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:24<01:50,  1.27s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:25<01:48,  1.27s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:26<01:47,  1.26s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:28<01:47,  1.28s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:29<01:45,  1.27s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:30<01:44,  1.27s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:32<01:42,  1.27s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:33<01:42,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:34<01:41,  1.29s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:35<01:39,  1.28s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:37<01:38,  1.28s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:38<01:37,  1.28s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:39<01:36,  1.28s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:41<01:34,  1.28s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:42<01:33,  1.28s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:43<01:33,  1.29s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:45<01:32,  1.30s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:46<01:30,  1.29s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:47<01:29,  1.30s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:48<01:27,  1.28s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:50<01:26,  1.29s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:51<01:24,  1.28s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:52<01:23,  1.28s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:53<01:21,  1.28s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:55<01:20,  1.27s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:56<01:18,  1.27s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:57<01:18,  1.29s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:59<01:17,  1.29s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [03:00<01:16,  1.29s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [03:01<01:16,  1.32s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [03:03<01:14,  1.31s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [03:04<01:12,  1.30s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [03:05<01:11,  1.30s/it]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [03:06<01:10,  1.31s/it]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [03:08<01:09,  1.30s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [03:09<01:09,  1.34s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [03:11<01:07,  1.33s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [03:12<01:06,  1.34s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [03:15<01:03,  1.29s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<18:44,  5.65s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<10:07,  3.07s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:08<07:21,  2.24s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<06:08,  1.88s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:10<05:26,  1.67s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:12<04:58,  1.54s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:13<04:39,  1.45s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:14<04:26,  1.39s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:15<04:19,  1.36s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:17<04:14,  1.34s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:18<04:08,  1.32s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:19<04:04,  1.30s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:20<04:02,  1.29s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:22<04:00,  1.29s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:23<03:59,  1.30s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:24<03:57,  1.29s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:26<03:56,  1.29s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:27<03:54,  1.29s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:28<03:54,  1.30s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:30<03:51,  1.29s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:31<03:49,  1.28s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:32<03:53,  1.31s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:34<03:54,  1.32s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:35<03:51,  1.32s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:36<03:48,  1.31s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:37<03:44,  1.29s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:39<03:44,  1.30s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:40<03:42,  1.30s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:41<03:44,  1.31s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:43<03:45,  1.32s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:44<03:42,  1.32s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:45<03:41,  1.32s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:47<03:39,  1.32s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:48<03:38,  1.32s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:49<03:34,  1.30s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:50<03:07,  1.15s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:51<02:48,  1.04s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:52<02:39,  1.01it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:52<02:33,  1.05it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:53<02:26,  1.10it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:54<02:19,  1.14it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:55<02:16,  1.16it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:56<02:09,  1.21it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:56<02:06,  1.24it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:57<02:05,  1.24it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:58<02:23,  1.08it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [01:00<02:36,  1.02s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [01:01<02:46,  1.10s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:02<02:37,  1.04s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:03<02:26,  1.02it/s]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:03<02:16,  1.09it/s]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:04<02:09,  1.14it/s]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:05<02:05,  1.17it/s]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:06<02:05,  1.16it/s]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:07<02:02,  1.18it/s]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:08<02:09,  1.11it/s]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:09<02:24,  1.01s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:10<02:33,  1.08s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:12<02:40,  1.14s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:13<02:44,  1.17s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:14<02:47,  1.21s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:15<02:51,  1.24s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:17<02:51,  1.25s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:18<02:54,  1.28s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:19<02:52,  1.28s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:21<02:50,  1.27s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:22<02:50,  1.28s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:23<02:49,  1.29s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:24<02:48,  1.29s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:26<02:47,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:27<02:44,  1.27s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:28<02:43,  1.28s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:30<02:43,  1.29s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:31<02:44,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:32<02:43,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:34<02:41,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:35<02:42,  1.32s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:36<02:39,  1.31s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:38<02:37,  1.30s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:39<02:39,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:40<02:42,  1.37s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:42<02:44,  1.40s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:43<02:44,  1.41s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:45<02:44,  1.42s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:46<02:44,  1.43s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:48<02:43,  1.44s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:49<02:42,  1.44s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:50<02:39,  1.42s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:52<02:36,  1.41s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:53<02:34,  1.41s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:55<02:32,  1.40s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:56<02:31,  1.40s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:57<02:27,  1.38s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:59<02:27,  1.39s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [02:00<02:26,  1.39s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [02:01<02:23,  1.38s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [02:03<02:21,  1.37s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [02:04<02:16,  1.34s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [02:05<02:15,  1.34s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [02:07<02:14,  1.34s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:08<02:13,  1.35s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:09<02:11,  1.34s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:11<02:10,  1.34s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:12<02:09,  1.34s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:14<02:07,  1.34s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:15<02:05,  1.34s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:16<02:04,  1.34s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:18<02:03,  1.34s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:19<02:00,  1.33s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:21<02:09,  1.44s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:22<02:03,  1.39s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:23<01:58,  1.35s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:24<01:56,  1.33s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:26<01:53,  1.32s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:27<01:50,  1.31s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:28<01:49,  1.30s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:29<01:47,  1.29s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:31<01:46,  1.29s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:32<01:43,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:33<01:42,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:35<01:41,  1.29s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:36<01:40,  1.29s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:37<01:38,  1.28s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:38<01:37,  1.28s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:40<01:35,  1.28s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:41<01:34,  1.28s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:42<01:33,  1.28s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:44<01:32,  1.28s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:45<01:30,  1.28s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:46<01:29,  1.27s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:47<01:27,  1.27s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:49<01:26,  1.27s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:50<01:24,  1.26s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:51<01:23,  1.27s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:52<01:22,  1.27s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:54<01:21,  1.27s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:55<01:20,  1.28s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:56<01:18,  1.27s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:58<01:17,  1.27s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:59<01:16,  1.27s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [03:00<01:15,  1.27s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [03:01<01:14,  1.28s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [03:03<01:12,  1.28s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [03:04<01:11,  1.28s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [03:05<01:10,  1.28s/it]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [03:06<01:08,  1.28s/it]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [03:08<01:06,  1.25s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [03:08<00:58,  1.12s/it]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [03:09<00:51,  1.01s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [03:10<00:46,  1.07it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [03:11<00:44,  1.11it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [03:12<00:42,  1.13it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [03:12<00:40,  1.17it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [03:13<00:39,  1.17it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [03:14<00:37,  1.21it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [03:15<00:35,  1.24it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [03:16<00:34,  1.24it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [03:17<00:39,  1.08it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [03:18<00:41,  1.01s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [03:19<00:40,  1.00s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [03:20<00:36,  1.06it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [03:21<00:33,  1.13it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [03:21<00:31,  1.18it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [03:22<00:30,  1.20it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [03:23<00:29,  1.19it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [03:24<00:27,  1.22it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [03:25<00:29,  1.14it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▌     | 168/200 [03:26<00:31,  1.00it/s]\u001b[A\n",
+      "training:  84%|████████████████████████████▋     | 169/200 [03:27<00:33,  1.08s/it]\u001b[A\n",
+      "training:  85%|████████████████████████████▉     | 170/200 [03:29<00:33,  1.13s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████     | 171/200 [03:30<00:33,  1.17s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████▏    | 172/200 [03:31<00:33,  1.20s/it]\u001b[A\n",
+      "training:  86%|█████████████████████████████▍    | 173/200 [03:32<00:32,  1.22s/it]\u001b[A\n",
+      "training:  87%|█████████████████████████████▌    | 174/200 [03:34<00:32,  1.24s/it]\u001b[A\n",
+      "training:  88%|█████████████████████████████▊    | 175/200 [03:35<00:31,  1.25s/it]\u001b[A\n",
+      "training:  88%|█████████████████████████████▉    | 176/200 [03:36<00:30,  1.26s/it]\u001b[A\n",
+      "training:  88%|██████████████████████████████    | 177/200 [03:39<00:28,  1.24s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.84it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.64it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.19s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.33it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.15it/s]\u001b[A\n",
+      "Evaluating NLL: 45it [00:00, 62.99it/s]\u001b[A\n",
+      "Evaluating NLL: 91it [00:01, 129.16it/s]\u001b[A\n",
+      "Evaluating NLL: 139it [00:01, 196.33it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 153.48it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|        | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 147.37it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 148.25it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:00<00:00, 135.52it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|▌| 59/100 [00:00<00:00, 136.70it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|▋| 73/100 [00:00<00:00, 135.68it\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 138.42i\n",
+      " 80%|███████████████████████████████████▏        | 4/5 [2:36:47<41:02, 2462.75s/it]\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<16:00,  4.83s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<08:54,  2.70s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:40,  2.03s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:35,  1.71s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<04:59,  1.54s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:10<04:37,  1.43s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:23,  1.37s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:10,  1.30s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<04:03,  1.27s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<03:57,  1.25s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:16<03:53,  1.23s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:18<03:50,  1.23s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:19<03:48,  1.22s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:20<03:47,  1.22s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:21<03:45,  1.22s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:23<03:43,  1.21s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:24<03:39,  1.20s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:25<03:38,  1.20s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:26<03:37,  1.20s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:27<03:30,  1.17s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:28<03:04,  1.03s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:29<02:47,  1.07it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:29<02:33,  1.15it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:30<02:24,  1.22it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:31<02:20,  1.25it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:32<02:17,  1.27it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:32<02:12,  1.30it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:33<02:11,  1.31it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:34<02:08,  1.33it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:34<02:04,  1.36it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:35<02:03,  1.36it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:36<02:21,  1.19it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:38<02:43,  1.02it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:38<02:33,  1.08it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:39<02:23,  1.15it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:40<02:15,  1.21it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:41<02:10,  1.25it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:41<02:05,  1.29it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:42<02:05,  1.29it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:43<02:00,  1.32it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:43<01:58,  1.35it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:45<02:16,  1.15it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:46<02:28,  1.06it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:47<02:35,  1.01it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:48<02:43,  1.05s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:49<02:49,  1.10s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:50<02:52,  1.13s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:52<02:51,  1.13s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:53<02:55,  1.16s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:54<02:56,  1.18s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:55<02:56,  1.18s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:56<02:55,  1.19s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:58<02:53,  1.18s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:59<02:52,  1.18s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:00<02:52,  1.19s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:01<02:50,  1.19s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:02<02:51,  1.20s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:04<02:51,  1.20s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:05<02:52,  1.22s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:06<02:55,  1.25s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:07<02:53,  1.25s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:09<02:49,  1.23s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:10<02:46,  1.22s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:11<02:46,  1.22s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:12<02:44,  1.22s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:13<02:43,  1.22s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:15<02:41,  1.21s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:16<02:38,  1.20s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:17<02:37,  1.20s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:18<02:39,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:20<02:39,  1.24s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:21<02:37,  1.23s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:22<02:38,  1.25s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:23<02:39,  1.26s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:25<02:35,  1.24s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:26<02:33,  1.24s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:27<02:33,  1.25s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:28<02:36,  1.28s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:30<02:37,  1.30s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:31<02:37,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:32<02:37,  1.32s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:34<02:35,  1.32s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:35<02:36,  1.34s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:37<02:35,  1.34s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:38<02:35,  1.35s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:39<02:33,  1.35s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:41<02:32,  1.35s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:42<02:31,  1.35s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:43<02:30,  1.35s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:45<02:28,  1.35s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:46<02:27,  1.35s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:47<02:25,  1.35s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:49<02:23,  1.35s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:50<02:23,  1.35s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:51<02:20,  1.34s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:53<02:18,  1.33s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:54<02:16,  1.33s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:55<02:15,  1.33s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:57<02:14,  1.33s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [01:58<02:12,  1.32s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [01:59<02:10,  1.32s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:01<02:09,  1.32s/it]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:02<02:07,  1.31s/it]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:03<02:06,  1.32s/it]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:05<02:05,  1.32s/it]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:06<02:04,  1.33s/it]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:07<02:01,  1.30s/it]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:09<02:09,  1.41s/it]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:10<02:04,  1.37s/it]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:11<01:58,  1.32s/it]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:12<01:54,  1.29s/it]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:14<01:51,  1.27s/it]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:15<01:49,  1.26s/it]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:16<01:46,  1.24s/it]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:17<01:43,  1.22s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:19<01:42,  1.22s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:20<01:41,  1.22s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:21<01:38,  1.21s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:22<01:37,  1.21s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:23<01:37,  1.22s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:25<01:36,  1.22s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:26<01:35,  1.22s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:27<01:33,  1.22s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:28<01:32,  1.22s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:29<01:30,  1.21s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:31<01:30,  1.22s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:32<01:29,  1.23s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:33<01:28,  1.22s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:34<01:26,  1.22s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:36<01:25,  1.22s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:37<01:24,  1.22s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:38<01:22,  1.22s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:39<01:21,  1.21s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:40<01:19,  1.20s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:42<01:18,  1.21s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:43<01:17,  1.21s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:44<01:15,  1.20s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:45<01:13,  1.19s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:46<01:12,  1.18s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:48<01:10,  1.18s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:48<01:02,  1.06s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:49<00:55,  1.04it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:50<00:50,  1.14it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:50<00:46,  1.21it/s]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:51<00:44,  1.25it/s]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:52<00:42,  1.27it/s]\u001b[A\n",
+      "training:  74%|████████████████████████▉         | 147/200 [02:53<00:40,  1.29it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▏        | 148/200 [02:53<00:40,  1.30it/s]\u001b[A\n",
+      "training:  74%|█████████████████████████▎        | 149/200 [02:54<00:38,  1.33it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████▌        | 150/200 [02:55<00:36,  1.38it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▋        | 151/200 [02:56<00:36,  1.36it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████▊        | 152/200 [02:57<00:39,  1.22it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████        | 153/200 [02:58<00:42,  1.10it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████▏       | 154/200 [02:59<00:45,  1.02it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████▎       | 155/200 [03:00<00:49,  1.09s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▌       | 156/200 [03:01<00:47,  1.08s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████▋       | 157/200 [03:02<00:41,  1.02it/s]\u001b[A\n",
+      "training:  79%|██████████████████████████▊       | 158/200 [03:03<00:38,  1.10it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████       | 159/200 [03:03<00:34,  1.18it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▏      | 160/200 [03:04<00:31,  1.25it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████▎      | 161/200 [03:05<00:30,  1.26it/s]\u001b[A\n",
+      "training:  81%|███████████████████████████▌      | 162/200 [03:06<00:29,  1.30it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▋      | 163/200 [03:06<00:27,  1.33it/s]\u001b[A\n",
+      "training:  82%|███████████████████████████▉      | 164/200 [03:07<00:30,  1.18it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████      | 165/200 [03:09<00:33,  1.06it/s]\u001b[A\n",
+      "training:  83%|████████████████████████████▏     | 166/200 [03:10<00:34,  1.02s/it]\u001b[A\n",
+      "training:  84%|████████████████████████████▍     | 167/200 [03:12<00:38,  1.15s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<16:20,  4.93s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:01,  2.74s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:39,  2.03s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:34,  1.71s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<04:56,  1.52s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:10<04:36,  1.43s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:29,  1.39s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<04:00,  1.26s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:17<03:58,  1.26s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:19<03:51,  1.24s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:20<03:47,  1.22s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:21<03:44,  1.22s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:23<03:42,  1.21s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:25<03:48,  1.25s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:26<03:44,  1.24s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:28<03:42,  1.24s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:29<03:43,  1.25s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:30<03:40,  1.24s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:31<03:39,  1.24s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:33<03:42,  1.27s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:34<03:46,  1.30s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:35<03:47,  1.31s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:37<03:47,  1.31s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:38<03:49,  1.34s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:40<03:49,  1.34s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:41<03:48,  1.34s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:42<03:46,  1.34s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:44<03:47,  1.36s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:45<03:46,  1.35s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:46<03:43,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:48<03:41,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:49<03:39,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:50<03:38,  1.34s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:52<03:36,  1.33s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:53<03:32,  1.32s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:54<03:28,  1.30s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:55<03:27,  1.31s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:57<03:27,  1.31s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:58<03:25,  1.31s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:59<03:23,  1.31s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [01:01<03:23,  1.31s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [01:02<03:22,  1.31s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [01:03<03:20,  1.31s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [01:05<03:17,  1.30s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [01:06<03:16,  1.30s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [01:07<03:15,  1.30s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [01:09<03:12,  1.29s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [01:10<03:24,  1.38s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [01:11<03:21,  1.37s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [01:13<03:13,  1.33s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:14<03:07,  1.29s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:15<03:03,  1.27s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:16<02:59,  1.25s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:18<02:57,  1.25s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:19<02:55,  1.25s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:20<02:54,  1.25s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:21<02:51,  1.24s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:22<02:49,  1.23s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:24<02:48,  1.23s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:25<02:46,  1.22s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:26<02:45,  1.23s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:27<02:43,  1.22s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:29<02:42,  1.22s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:30<02:40,  1.22s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:31<02:39,  1.22s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:32<02:38,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:33<02:35,  1.21s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:35<02:35,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:36<02:34,  1.22s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:37<02:32,  1.21s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:38<02:30,  1.21s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:39<02:29,  1.20s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:41<02:27,  1.20s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:42<02:28,  1.21s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:43<02:27,  1.22s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:44<02:26,  1.22s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:46<02:26,  1.23s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:47<02:23,  1.22s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:48<02:20,  1.20s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:49<02:18,  1.19s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:50<02:17,  1.20s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:52<02:16,  1.20s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:53<02:15,  1.20s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:54<02:14,  1.20s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:55<02:14,  1.21s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:56<01:58,  1.07s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:57<01:47,  1.01it/s]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:57<01:36,  1.12it/s]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:58<01:29,  1.19it/s]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:59<01:25,  1.24it/s]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [02:00<01:21,  1.29it/s]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [02:00<01:18,  1.33it/s]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [02:01<01:17,  1.32it/s]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [02:02<01:18,  1.30it/s]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [02:02<01:14,  1.35it/s]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [02:03<01:12,  1.37it/s]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:04<01:18,  1.26it/s]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:05<01:29,  1.10it/s]\u001b[A\n",
+      "training:  52%|█████████████████▌                | 103/200 [02:06<01:30,  1.07it/s]\u001b[A\n",
+      "training:  52%|█████████████████▋                | 104/200 [02:07<01:22,  1.16it/s]\u001b[A\n",
+      "training:  52%|█████████████████▊                | 105/200 [02:08<01:18,  1.21it/s]\u001b[A\n",
+      "training:  53%|██████████████████                | 106/200 [02:08<01:14,  1.27it/s]\u001b[A\n",
+      "training:  54%|██████████████████▏               | 107/200 [02:09<01:10,  1.32it/s]\u001b[A\n",
+      "training:  54%|██████████████████▎               | 108/200 [02:10<01:07,  1.36it/s]\u001b[A\n",
+      "training:  55%|██████████████████▌               | 109/200 [02:11<01:07,  1.34it/s]\u001b[A\n",
+      "training:  55%|██████████████████▋               | 110/200 [02:11<01:05,  1.36it/s]\u001b[A\n",
+      "training:  56%|██████████████████▊               | 111/200 [02:12<01:05,  1.35it/s]\u001b[A\n",
+      "training:  56%|███████████████████               | 112/200 [02:13<01:13,  1.19it/s]\u001b[A\n",
+      "training:  56%|███████████████████▏              | 113/200 [02:14<01:20,  1.08it/s]\u001b[A\n",
+      "training:  57%|███████████████████▍              | 114/200 [02:15<01:25,  1.01it/s]\u001b[A\n",
+      "training:  57%|███████████████████▌              | 115/200 [02:17<01:28,  1.04s/it]\u001b[A\n",
+      "training:  58%|███████████████████▋              | 116/200 [02:18<01:30,  1.08s/it]\u001b[A\n",
+      "training:  58%|███████████████████▉              | 117/200 [02:19<01:33,  1.12s/it]\u001b[A\n",
+      "training:  59%|████████████████████              | 118/200 [02:20<01:34,  1.16s/it]\u001b[A\n",
+      "training:  60%|████████████████████▏             | 119/200 [02:21<01:34,  1.17s/it]\u001b[A\n",
+      "training:  60%|████████████████████▍             | 120/200 [02:23<01:34,  1.19s/it]\u001b[A\n",
+      "training:  60%|████████████████████▌             | 121/200 [02:24<01:34,  1.19s/it]\u001b[A\n",
+      "training:  61%|████████████████████▋             | 122/200 [02:25<01:32,  1.19s/it]\u001b[A\n",
+      "training:  62%|████████████████████▉             | 123/200 [02:26<01:31,  1.19s/it]\u001b[A\n",
+      "training:  62%|█████████████████████             | 124/200 [02:27<01:31,  1.20s/it]\u001b[A\n",
+      "training:  62%|█████████████████████▎            | 125/200 [02:29<01:30,  1.20s/it]\u001b[A\n",
+      "training:  63%|█████████████████████▍            | 126/200 [02:30<01:31,  1.23s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▌            | 127/200 [02:31<01:29,  1.23s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▊            | 128/200 [02:32<01:27,  1.22s/it]\u001b[A\n",
+      "training:  64%|█████████████████████▉            | 129/200 [02:33<01:25,  1.21s/it]\u001b[A\n",
+      "training:  65%|██████████████████████            | 130/200 [02:35<01:23,  1.20s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▎           | 131/200 [02:36<01:22,  1.19s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▍           | 132/200 [02:37<01:21,  1.20s/it]\u001b[A\n",
+      "training:  66%|██████████████████████▌           | 133/200 [02:38<01:20,  1.20s/it]\u001b[A\n",
+      "training:  67%|██████████████████████▊           | 134/200 [02:39<01:20,  1.21s/it]\u001b[A\n",
+      "training:  68%|██████████████████████▉           | 135/200 [02:41<01:18,  1.20s/it]\u001b[A\n",
+      "training:  68%|███████████████████████           | 136/200 [02:42<01:17,  1.21s/it]\u001b[A\n",
+      "training:  68%|███████████████████████▎          | 137/200 [02:43<01:18,  1.24s/it]\u001b[A\n",
+      "training:  69%|███████████████████████▍          | 138/200 [02:45<01:17,  1.26s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▋          | 139/200 [02:46<01:15,  1.24s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▊          | 140/200 [02:47<01:14,  1.23s/it]\u001b[A\n",
+      "training:  70%|███████████████████████▉          | 141/200 [02:48<01:14,  1.26s/it]\u001b[A\n",
+      "training:  71%|████████████████████████▏         | 142/200 [02:49<01:12,  1.25s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▎         | 143/200 [02:51<01:10,  1.24s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▍         | 144/200 [02:52<01:10,  1.26s/it]\u001b[A\n",
+      "training:  72%|████████████████████████▋         | 145/200 [02:53<01:11,  1.29s/it]\u001b[A\n",
+      "training:  73%|████████████████████████▊         | 146/200 [02:56<01:05,  1.21s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:05<18:43,  5.65s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:07<10:22,  3.14s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:08<07:43,  2.35s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:09<06:28,  1.98s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:11<05:44,  1.76s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:12<05:13,  1.62s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:13<04:53,  1.52s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:15<04:41,  1.47s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:16<04:26,  1.40s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:17<04:18,  1.36s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:19<04:13,  1.34s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:20<04:11,  1.34s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:21<04:07,  1.32s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:22<04:05,  1.32s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:24<04:04,  1.32s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:25<04:02,  1.32s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:26<04:02,  1.33s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:28<04:00,  1.32s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:29<03:58,  1.32s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:30<03:56,  1.32s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:32<03:53,  1.30s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:33<04:01,  1.36s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:35<04:04,  1.38s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:36<03:54,  1.33s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:37<03:46,  1.29s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:38<03:38,  1.26s/it]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:39<03:35,  1.24s/it]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:41<03:32,  1.24s/it]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:42<03:28,  1.22s/it]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:43<03:27,  1.22s/it]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:44<03:24,  1.21s/it]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:45<03:21,  1.20s/it]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:47<03:19,  1.19s/it]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:48<03:20,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:49<03:19,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:50<03:20,  1.22s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:51<03:17,  1.21s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:53<03:18,  1.22s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:54<03:14,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:55<03:15,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:56<03:14,  1.22s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:58<03:12,  1.22s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:59<03:10,  1.21s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [01:00<03:06,  1.19s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [01:01<03:05,  1.20s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [01:02<03:05,  1.20s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [01:05<03:32,  1.39s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<16:22,  4.94s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<09:11,  2.78s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:47,  2.07s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:36,  1.71s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<05:01,  1.55s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:35,  1.42s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:22,  1.36s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:09,  1.30s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<04:03,  1.27s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<03:46,  1.19s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:16<03:22,  1.07s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:17<02:58,  1.05it/s]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:17<02:42,  1.15it/s]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:18<02:31,  1.22it/s]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:19<02:27,  1.25it/s]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:19<02:22,  1.30it/s]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:20<02:17,  1.33it/s]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:21<02:16,  1.34it/s]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:22<02:12,  1.37it/s]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:22<02:08,  1.40it/s]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:23<02:08,  1.39it/s]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:24<02:30,  1.18it/s]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:25<02:45,  1.07it/s]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:26<02:34,  1.14it/s]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:27<02:27,  1.19it/s]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:28<02:20,  1.24it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:28<02:15,  1.28it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:29<02:13,  1.29it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:30<02:09,  1.32it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:30<02:07,  1.33it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:31<02:08,  1.31it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:32<02:26,  1.15it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:34<02:39,  1.05it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:35<02:48,  1.02s/it]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:36<02:58,  1.08s/it]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:37<03:03,  1.12s/it]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:38<03:06,  1.15s/it]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:40<03:07,  1.16s/it]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:41<03:09,  1.18s/it]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:42<03:13,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:43<03:15,  1.23s/it]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:45<03:12,  1.22s/it]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:46<03:09,  1.21s/it]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:47<03:07,  1.20s/it]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:48<03:06,  1.20s/it]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:49<03:04,  1.20s/it]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:50<03:03,  1.20s/it]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:52<03:01,  1.19s/it]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:53<02:58,  1.18s/it]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:54<02:58,  1.19s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:55<03:03,  1.23s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:57<03:06,  1.26s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:58<03:00,  1.23s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:59<03:00,  1.24s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:00<03:00,  1.24s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:02<02:58,  1.24s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:03<02:55,  1.23s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:04<02:57,  1.25s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:05<02:57,  1.26s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:07<02:55,  1.25s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:08<02:54,  1.25s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:09<02:57,  1.29s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:11<02:58,  1.31s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:12<02:56,  1.30s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:13<02:55,  1.30s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:14<02:54,  1.30s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:16<02:54,  1.32s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:17<02:53,  1.31s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:18<02:50,  1.30s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:20<02:52,  1.32s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:21<02:47,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:22<02:51,  1.34s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:24<02:52,  1.36s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:25<02:44,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:26<02:39,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:27<02:35,  1.25s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:29<02:32,  1.24s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:30<02:30,  1.24s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:31<02:28,  1.23s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:32<02:26,  1.22s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:33<02:23,  1.20s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:35<02:20,  1.19s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:36<02:19,  1.19s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:37<02:21,  1.22s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:38<02:20,  1.22s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:40<02:19,  1.22s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:41<02:16,  1.20s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:42<02:15,  1.21s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:43<02:13,  1.20s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:44<02:12,  1.20s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:46<02:12,  1.22s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:47<02:11,  1.21s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:48<02:10,  1.22s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:49<02:07,  1.20s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:50<02:05,  1.19s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:52<02:04,  1.20s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:53<02:03,  1.20s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:54<02:02,  1.20s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:56<01:59,  1.18s/it]\n",
+      "\n",
+      "training:   0%|                                            | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▏                                   | 1/200 [00:04<15:57,  4.81s/it]\u001b[A\n",
+      "training:   1%|▎                                   | 2/200 [00:06<08:58,  2.72s/it]\u001b[A\n",
+      "training:   2%|▌                                   | 3/200 [00:07<06:42,  2.04s/it]\u001b[A\n",
+      "training:   2%|▋                                   | 4/200 [00:08<05:37,  1.72s/it]\u001b[A\n",
+      "training:   2%|▉                                   | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\n",
+      "training:   3%|█                                   | 6/200 [00:11<04:40,  1.44s/it]\u001b[A\n",
+      "training:   4%|█▎                                  | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\n",
+      "training:   4%|█▍                                  | 8/200 [00:13<04:12,  1.31s/it]\u001b[A\n",
+      "training:   4%|█▌                                  | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\n",
+      "training:   5%|█▊                                 | 10/200 [00:15<04:02,  1.27s/it]\u001b[A\n",
+      "training:   6%|█▉                                 | 11/200 [00:17<03:55,  1.25s/it]\u001b[A\n",
+      "training:   6%|██                                 | 12/200 [00:18<03:51,  1.23s/it]\u001b[A\n",
+      "training:   6%|██▎                                | 13/200 [00:19<03:49,  1.23s/it]\u001b[A\n",
+      "training:   7%|██▍                                | 14/200 [00:20<03:46,  1.22s/it]\u001b[A\n",
+      "training:   8%|██▋                                | 15/200 [00:21<03:42,  1.20s/it]\u001b[A\n",
+      "training:   8%|██▊                                | 16/200 [00:23<03:40,  1.20s/it]\u001b[A\n",
+      "training:   8%|██▉                                | 17/200 [00:24<03:39,  1.20s/it]\u001b[A\n",
+      "training:   9%|███▏                               | 18/200 [00:25<03:40,  1.21s/it]\u001b[A\n",
+      "training:  10%|███▎                               | 19/200 [00:26<03:40,  1.22s/it]\u001b[A\n",
+      "training:  10%|███▌                               | 20/200 [00:27<03:38,  1.22s/it]\u001b[A\n",
+      "training:  10%|███▋                               | 21/200 [00:29<03:36,  1.21s/it]\u001b[A\n",
+      "training:  11%|███▊                               | 22/200 [00:30<03:36,  1.22s/it]\u001b[A\n",
+      "training:  12%|████                               | 23/200 [00:31<03:35,  1.22s/it]\u001b[A\n",
+      "training:  12%|████▏                              | 24/200 [00:32<03:33,  1.22s/it]\u001b[A\n",
+      "training:  12%|████▍                              | 25/200 [00:33<03:14,  1.11s/it]\u001b[A\n",
+      "training:  13%|████▌                              | 26/200 [00:34<02:52,  1.01it/s]\u001b[A\n",
+      "training:  14%|████▋                              | 27/200 [00:35<02:36,  1.11it/s]\u001b[A\n",
+      "training:  14%|████▉                              | 28/200 [00:35<02:23,  1.20it/s]\u001b[A\n",
+      "training:  14%|█████                              | 29/200 [00:36<02:14,  1.27it/s]\u001b[A\n",
+      "training:  15%|█████▎                             | 30/200 [00:37<02:11,  1.29it/s]\u001b[A\n",
+      "training:  16%|█████▍                             | 31/200 [00:37<02:08,  1.32it/s]\u001b[A\n",
+      "training:  16%|█████▌                             | 32/200 [00:38<02:04,  1.34it/s]\u001b[A\n",
+      "training:  16%|█████▊                             | 33/200 [00:39<02:05,  1.33it/s]\u001b[A\n",
+      "training:  17%|█████▉                             | 34/200 [00:40<02:01,  1.37it/s]\u001b[A\n",
+      "training:  18%|██████▏                            | 35/200 [00:40<02:00,  1.37it/s]\u001b[A\n",
+      "training:  18%|██████▎                            | 36/200 [00:41<02:01,  1.35it/s]\u001b[A\n",
+      "training:  18%|██████▍                            | 37/200 [00:42<02:24,  1.13it/s]\u001b[A\n",
+      "training:  19%|██████▋                            | 38/200 [00:43<02:34,  1.05it/s]\u001b[A\n",
+      "training:  20%|██████▊                            | 39/200 [00:44<02:23,  1.12it/s]\u001b[A\n",
+      "training:  20%|███████                            | 40/200 [00:45<02:13,  1.19it/s]\u001b[A\n",
+      "training:  20%|███████▏                           | 41/200 [00:46<02:05,  1.27it/s]\u001b[A\n",
+      "training:  21%|███████▎                           | 42/200 [00:46<02:00,  1.32it/s]\u001b[A\n",
+      "training:  22%|███████▌                           | 43/200 [00:47<01:56,  1.34it/s]\u001b[A\n",
+      "training:  22%|███████▋                           | 44/200 [00:48<01:56,  1.34it/s]\u001b[A\n",
+      "training:  22%|███████▉                           | 45/200 [00:48<01:53,  1.37it/s]\u001b[A\n",
+      "training:  23%|████████                           | 46/200 [00:49<01:51,  1.38it/s]\u001b[A\n",
+      "training:  24%|████████▏                          | 47/200 [00:50<02:14,  1.14it/s]\u001b[A\n",
+      "training:  24%|████████▍                          | 48/200 [00:51<02:25,  1.05it/s]\u001b[A\n",
+      "training:  24%|████████▌                          | 49/200 [00:53<02:30,  1.01it/s]\u001b[A\n",
+      "training:  25%|████████▊                          | 50/200 [00:54<02:38,  1.06s/it]\u001b[A\n",
+      "training:  26%|████████▉                          | 51/200 [00:55<02:51,  1.15s/it]\u001b[A\n",
+      "training:  26%|█████████                          | 52/200 [00:56<02:56,  1.19s/it]\u001b[A\n",
+      "training:  26%|█████████▎                         | 53/200 [00:58<02:56,  1.20s/it]\u001b[A\n",
+      "training:  27%|█████████▍                         | 54/200 [00:59<02:56,  1.21s/it]\u001b[A\n",
+      "training:  28%|█████████▋                         | 55/200 [01:00<02:59,  1.24s/it]\u001b[A\n",
+      "training:  28%|█████████▊                         | 56/200 [01:01<02:59,  1.25s/it]\u001b[A\n",
+      "training:  28%|█████████▉                         | 57/200 [01:03<02:59,  1.26s/it]\u001b[A\n",
+      "training:  29%|██████████▏                        | 58/200 [01:04<02:56,  1.25s/it]\u001b[A\n",
+      "training:  30%|██████████▎                        | 59/200 [01:05<03:02,  1.29s/it]\u001b[A\n",
+      "training:  30%|██████████▌                        | 60/200 [01:07<03:05,  1.33s/it]\u001b[A\n",
+      "training:  30%|██████████▋                        | 61/200 [01:08<03:08,  1.36s/it]\u001b[A\n",
+      "training:  31%|██████████▊                        | 62/200 [01:10<03:09,  1.37s/it]\u001b[A\n",
+      "training:  32%|███████████                        | 63/200 [01:11<03:08,  1.38s/it]\u001b[A\n",
+      "training:  32%|███████████▏                       | 64/200 [01:12<03:07,  1.38s/it]\u001b[A\n",
+      "training:  32%|███████████▍                       | 65/200 [01:14<03:07,  1.39s/it]\u001b[A\n",
+      "training:  33%|███████████▌                       | 66/200 [01:15<03:06,  1.39s/it]\u001b[A\n",
+      "training:  34%|███████████▋                       | 67/200 [01:17<03:05,  1.40s/it]\u001b[A\n",
+      "training:  34%|███████████▉                       | 68/200 [01:18<03:04,  1.40s/it]\u001b[A\n",
+      "training:  34%|████████████                       | 69/200 [01:19<03:02,  1.40s/it]\u001b[A\n",
+      "training:  35%|████████████▎                      | 70/200 [01:21<03:02,  1.40s/it]\u001b[A\n",
+      "training:  36%|████████████▍                      | 71/200 [01:22<02:59,  1.39s/it]\u001b[A\n",
+      "training:  36%|████████████▌                      | 72/200 [01:24<02:58,  1.40s/it]\u001b[A\n",
+      "training:  36%|████████████▊                      | 73/200 [01:25<02:57,  1.40s/it]\u001b[A\n",
+      "training:  37%|████████████▉                      | 74/200 [01:26<02:55,  1.39s/it]\u001b[A\n",
+      "training:  38%|█████████████▏                     | 75/200 [01:28<02:52,  1.38s/it]\u001b[A\n",
+      "training:  38%|█████████████▎                     | 76/200 [01:29<02:48,  1.36s/it]\u001b[A\n",
+      "training:  38%|█████████████▍                     | 77/200 [01:30<02:46,  1.36s/it]\u001b[A\n",
+      "training:  39%|█████████████▋                     | 78/200 [01:32<02:44,  1.35s/it]\u001b[A\n",
+      "training:  40%|█████████████▊                     | 79/200 [01:33<02:41,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████                     | 80/200 [01:34<02:40,  1.34s/it]\u001b[A\n",
+      "training:  40%|██████████████▏                    | 81/200 [01:36<02:37,  1.33s/it]\u001b[A\n",
+      "training:  41%|██████████████▎                    | 82/200 [01:37<02:37,  1.34s/it]\u001b[A\n",
+      "training:  42%|██████████████▌                    | 83/200 [01:38<02:35,  1.33s/it]\u001b[A\n",
+      "training:  42%|██████████████▋                    | 84/200 [01:40<02:35,  1.34s/it]\u001b[A\n",
+      "training:  42%|██████████████▉                    | 85/200 [01:41<02:34,  1.35s/it]\u001b[A\n",
+      "training:  43%|███████████████                    | 86/200 [01:42<02:34,  1.36s/it]\u001b[A\n",
+      "training:  44%|███████████████▏                   | 87/200 [01:44<02:31,  1.34s/it]\u001b[A\n",
+      "training:  44%|███████████████▍                   | 88/200 [01:45<02:41,  1.45s/it]\u001b[A\n",
+      "training:  44%|███████████████▌                   | 89/200 [01:47<02:32,  1.37s/it]\u001b[A\n",
+      "training:  45%|███████████████▊                   | 90/200 [01:48<02:26,  1.33s/it]\u001b[A\n",
+      "training:  46%|███████████████▉                   | 91/200 [01:49<02:21,  1.30s/it]\u001b[A\n",
+      "training:  46%|████████████████                   | 92/200 [01:50<02:17,  1.28s/it]\u001b[A\n",
+      "training:  46%|████████████████▎                  | 93/200 [01:52<02:15,  1.27s/it]\u001b[A\n",
+      "training:  47%|████████████████▍                  | 94/200 [01:53<02:13,  1.26s/it]\u001b[A\n",
+      "training:  48%|████████████████▋                  | 95/200 [01:54<02:11,  1.25s/it]\u001b[A\n",
+      "training:  48%|████████████████▊                  | 96/200 [01:55<02:09,  1.25s/it]\u001b[A\n",
+      "training:  48%|████████████████▉                  | 97/200 [01:56<02:07,  1.24s/it]\u001b[A\n",
+      "training:  49%|█████████████████▏                 | 98/200 [01:58<02:05,  1.24s/it]\u001b[A\n",
+      "training:  50%|█████████████████▎                 | 99/200 [01:59<02:05,  1.24s/it]\u001b[A\n",
+      "training:  50%|█████████████████                 | 100/200 [02:00<02:03,  1.24s/it]\u001b[A\n",
+      "training:  50%|█████████████████▏                | 101/200 [02:01<02:02,  1.24s/it]\u001b[A\n",
+      "training:  51%|█████████████████▎                | 102/200 [02:04<01:59,  1.22s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.21s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.27it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.83it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.80it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.85it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.14it/s]\u001b[A\n",
+      "Evaluating NLL: 48it [00:00, 66.59it/s]\u001b[A\n",
+      "Evaluating NLL: 92it [00:01, 128.59it/s]\u001b[A\n",
+      "Evaluating NLL: 131it [00:01, 177.84it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 148.85it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|        | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|▏| 15/100 [00:00<00:00, 149.25it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|▎| 30/100 [00:00<00:00, 136.76it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|▍| 45/100 [00:00<00:00, 135.25it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|▌| 61/100 [00:00<00:00, 143.65it\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|▊| 80/100 [00:00<00:00, 159.38it\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|█| 100/100 [00:00<00:00, 152.79i\n",
+      "100%|████████████████████████████████████████████| 5/5 [3:15:27<00:00, 2345.48s/it]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 350x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "\n",
+    "mi_by_object = {}\n",
+    "mi_by_object_hi = {}\n",
+    "mi_by_object_lo = {}\n",
+    "for name in object_names:\n",
+    "  print(name)\n",
+    "  mutual_information = []\n",
+    "  mi_hi = []\n",
+    "  mi_lo = []\n",
+    "  for num_nyquist_samples in tqdm(num_nyquist_samples_list):\n",
+    "    \n",
+    "    if name == 'delta_uniform':\n",
+    "      objects_fn = delta_fn\n",
+    "    elif name == '8_deltas_uniform':\n",
+    "      objects_fn = delta8_fn\n",
+    "    else:\n",
+    "      objects_fn = white_noise_fn\n",
     "\n",
-    "for replicate in range(num_replicates):\n",
-    "\n",
-    "  mi_by_object = {}\n",
-    "  mi_by_object_hi = {}\n",
-    "  mi_by_object_lo = {}\n",
-    "  for name in object_names:\n",
-    "    print(name)\n",
-    "    mutual_information = []\n",
-    "    mi_hi = []\n",
-    "    mi_lo = []\n",
-    "    for num_nyquist_samples in tqdm(num_nyquist_samples_list):\n",
-    "      \n",
-    "      if name == 'delta_uniform':\n",
-    "        objects_fn = delta_fn\n",
-    "      elif name == '8_deltas_uniform':\n",
-    "        objects_fn = delta8_fn\n",
-    "      else:\n",
-    "        objects_fn = lambda : white_noise_fn()\n",
-    "\n",
-    "      \n",
-    "      initial_kernel, initial_params, optimized_params, objects, initial_mi, optimized_mi, optimized_mi_lower, optimized_mi_upper = optimize_PSF_and_estimate_mi(\n",
-    "            objects_fn, noise_sigma, num_nyquist_samples=num_nyquist_samples, upsampled_signal_length=UPSAMPLED_SIGNAL_LENGTH,\n",
-    "\n",
-    "            estimate_with_pixel_cnn=True,\n",
-    "\n",
-    "            max_epochs=max_epochs, confidence=confidence)\n",
+    "    \n",
+    "    initial_kernel, initial_params, optimized_params, objects, initial_mi, optimized_mi, optimized_mi_lower, optimized_mi_upper = optimize_PSF_and_estimate_mi(\n",
+    "          objects_fn, noise_sigma, num_nyquist_samples=num_nyquist_samples, upsampled_signal_length=upsampled_signal_length,\n",
+    "          estimate_with_pixel_cnn=True,\n",
+    "          max_epochs=max_epochs, confidence=confidence, num_mi_models=num_mi_models)\n",
     "\n",
-    "      mutual_information.append(optimized_mi)\n",
-    "      mi_lo.append(optimized_mi_lower)\n",
-    "      mi_hi.append(optimized_mi_upper)\n",
-    "    mi_by_object[name] = mutual_information\n",
-    "    mi_by_object_hi[name] = mi_hi\n",
-    "    mi_by_object_lo[name] = mi_lo\n",
+    "    mutual_information.append(optimized_mi)\n",
+    "    mi_lo.append(optimized_mi_lower)\n",
+    "    mi_hi.append(optimized_mi_upper)\n",
+    "  mi_by_object[name] = mutual_information\n",
+    "  mi_by_object_hi[name] = mi_hi\n",
+    "  mi_by_object_lo[name] = mi_lo\n",
     "\n",
     "\n",
-    "  fig, ax = plt.subplots(1, 1, figsize=(3.5, 3.5))\n",
+    "fig, ax = plt.subplots(1, 1, figsize=(3.5, 3.5))\n",
     "\n",
-    "  for object_name, mutual_information in mi_by_object.items():\n",
+    "for object_name, mutual_information in mi_by_object.items():\n",
     "\n",
-    "      total_mi = np.array(mutual_information) * np.array(num_nyquist_samples_list)\n",
+    "    total_mi = np.array(mutual_information) * np.array(num_nyquist_samples_list)\n",
     "\n",
-    "      total_mi_hi = np.array(mi_by_object_hi[object_name]) * np.array(num_nyquist_samples_list)\n",
-    "      total_mi_lo = np.array(mi_by_object_lo[object_name]) * np.array(num_nyquist_samples_list)\n",
+    "    total_mi_hi = np.array(mi_by_object_hi[object_name]) * np.array(num_nyquist_samples_list)\n",
+    "    total_mi_lo = np.array(mi_by_object_lo[object_name]) * np.array(num_nyquist_samples_list)\n",
     "\n",
-    "      ax.plot(num_nyquist_samples_list, total_mi, 'o-', label=object_name)\n",
-    "      ax.fill_between(num_nyquist_samples_list, total_mi_lo, total_mi_hi, alpha=0.2)\n",
-    "      ax.set_xlabel('Signal bandwidth (number of Nyquist samples)')\n",
-    "      ax.set_ylabel('Mutual information (bits)')\n",
+    "    ax.plot(num_nyquist_samples_list, total_mi, 'o-', label=object_name)\n",
+    "    ax.fill_between(num_nyquist_samples_list, total_mi_lo, total_mi_hi, alpha=0.2)\n",
+    "    ax.set_xlabel('Signal bandwidth (number of Nyquist samples)')\n",
+    "    ax.set_ylabel('Mutual information (bits)')\n",
     "\n",
-    "      ax.set(ylim=(0, max(1.1 * np.max(total_mi), ax.get_ylim()[1])))\n",
-    "      # ax.set(ylim=[0, 25])\n",
+    "    ax.set(ylim=(0, max(1.1 * np.max(total_mi), ax.get_ylim()[1])))\n",
+    "    # ax.set(ylim=[0, 25])\n",
     "\n",
-    "  clear_spines(ax)\n",
-    "  ax.legend()\n",
+    "clear_spines(ax)\n",
+    "ax.legend()\n",
     "\n",
-    "  fig.savefig('/home/hpinkard_waller/figures/1d_signals/' + f'mi_vs_bandwidth_replicate{replicate}' + '.pdf', transparent=True)\n"
+    "fig.savefig('/home/hpinkard_waller/figures/1d_signals/' + f'mi_vs_bandwidth' + '.pdf', transparent=True)\n"
    ]
   }
  ],
diff --git a/simulations_1d/mi_vs_snr.ipynb b/simulations_1d/mi_vs_snr.ipynb
index 7cf562a..19194d3 100644
--- a/simulations_1d/mi_vs_snr.ipynb
+++ b/simulations_1d/mi_vs_snr.ipynb
@@ -5,10 +5,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-10T21:22:53.230846Z",
-     "iopub.status.busy": "2024-10-10T21:22:53.230485Z",
-     "iopub.status.idle": "2024-10-10T21:22:57.815182Z",
-     "shell.execute_reply": "2024-10-10T21:22:57.814554Z"
+     "iopub.execute_input": "2024-10-16T23:33:16.773268Z",
+     "iopub.status.busy": "2024-10-16T23:33:16.772589Z",
+     "iopub.status.idle": "2024-10-16T23:33:19.878591Z",
+     "shell.execute_reply": "2024-10-16T23:33:19.878083Z"
     }
    },
    "outputs": [
@@ -16,10 +16,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024-10-10 14:22:55.480230: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
-      "2024-10-10 14:22:56.588586: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-10 14:22:56.588821: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
-      "2024-10-10 14:22:56.588835: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
+      "2024-10-16 16:33:18.207282: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-10-16 16:33:18.870068: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-16 16:33:18.870148: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
+      "2024-10-16 16:33:18.870158: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
      ]
     }
    ],
@@ -46,10 +46,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-10T21:22:57.818999Z",
-     "iopub.status.busy": "2024-10-10T21:22:57.818314Z",
-     "iopub.status.idle": "2024-10-11T02:36:31.345570Z",
-     "shell.execute_reply": "2024-10-11T02:36:31.344752Z"
+     "iopub.execute_input": "2024-10-16T23:33:19.882060Z",
+     "iopub.status.busy": "2024-10-16T23:33:19.881459Z",
+     "iopub.status.idle": "2024-10-17T03:16:15.489813Z",
+     "shell.execute_reply": "2024-10-17T03:16:15.489274Z"
     }
    },
    "outputs": [
@@ -64,2545 +64,9472 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|                                                                       | 0/5 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -13.742538939657203\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2024-10-10 14:35:08.149861: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
-      "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▏                                                | 1/200 [00:48<2:42:22, 48.95s/it]\u001b[A\n",
-      "training:   1%|▍                                                | 2/200 [00:51<1:12:04, 21.84s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:54<42:40, 13.00s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:56<28:51,  8.84s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:59<21:22,  6.58s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [01:01<16:57,  5.24s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [01:04<13:53,  4.32s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [01:06<12:01,  3.76s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [01:09<10:31,  3.31s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [01:11<09:34,  3.02s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [01:14<09:22,  2.97s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [01:16<08:46,  2.80s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [01:19<08:26,  2.71s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [01:21<08:02,  2.60s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [01:24<08:28,  2.75s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [01:28<08:54,  2.90s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [01:30<08:19,  2.73s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [01:32<07:59,  2.64s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [01:35<07:44,  2.57s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [01:37<07:20,  2.45s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [01:39<07:10,  2.40s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [01:41<06:59,  2.36s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:44<06:57,  2.36s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:46<06:53,  2.35s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:48<06:44,  2.31s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:51<06:52,  2.37s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:53<06:45,  2.34s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:55<06:34,  2.29s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:58<06:33,  2.30s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [02:00<06:29,  2.29s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [02:02<06:25,  2.28s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [02:04<06:21,  2.27s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [02:07<06:14,  2.24s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [02:09<06:20,  2.29s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [02:11<06:07,  2.23s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [02:13<05:45,  2.10s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [02:15<05:43,  2.11s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [02:17<05:45,  2.13s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [02:19<05:37,  2.10s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [02:21<05:32,  2.08s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [02:23<05:30,  2.08s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [02:25<05:15,  2.00s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [02:27<05:12,  1.99s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [02:29<05:12,  2.00s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [02:31<05:07,  1.99s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [02:33<05:01,  1.96s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [02:35<05:05,  2.00s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [02:38<05:22,  2.12s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [02:40<05:15,  2.09s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [02:42<05:13,  2.09s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [02:44<05:09,  2.08s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [02:46<05:04,  2.05s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [02:48<04:59,  2.03s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [02:50<05:01,  2.06s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [02:52<04:57,  2.05s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [02:54<05:02,  2.10s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [02:56<05:01,  2.11s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [02:58<04:55,  2.08s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [03:00<04:58,  2.12s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [03:02<04:54,  2.11s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [03:05<04:53,  2.11s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [03:07<04:49,  2.10s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [03:09<04:49,  2.11s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [03:11<04:41,  2.07s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [03:13<04:40,  2.07s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [03:15<04:41,  2.10s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [03:17<04:46,  2.15s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [03:19<04:40,  2.12s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [03:22<04:40,  2.14s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [03:24<04:39,  2.15s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [03:26<04:34,  2.13s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [03:28<04:29,  2.11s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [03:30<04:26,  2.10s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [03:32<04:27,  2.12s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [03:34<04:34,  2.19s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [03:36<04:25,  2.14s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [03:39<04:20,  2.12s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [03:41<04:18,  2.12s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [03:43<04:12,  2.08s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [03:45<04:16,  2.14s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [03:47<04:16,  2.16s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [03:49<04:18,  2.19s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [03:52<04:16,  2.19s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [03:54<04:18,  2.23s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [03:56<04:20,  2.26s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [03:58<04:16,  2.25s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [04:00<04:04,  2.16s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [04:02<03:55,  2.10s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [04:04<03:53,  2.11s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [04:06<03:44,  2.04s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [04:08<03:41,  2.04s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [04:10<03:41,  2.05s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [04:12<03:36,  2.03s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [04:14<03:30,  1.99s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [04:17<03:43,  2.13s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [04:19<03:42,  2.14s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [04:21<03:41,  2.15s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [04:23<03:44,  2.21s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [04:26<03:50,  2.28s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [04:28<03:38,  2.19s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [04:30<03:26,  2.09s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [04:32<03:14,  1.98s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [04:33<03:08,  1.95s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [04:35<03:02,  1.90s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [04:37<03:01,  1.91s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [04:39<02:57,  1.88s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [04:41<02:53,  1.86s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [04:43<02:52,  1.88s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [04:44<02:50,  1.87s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [04:46<02:46,  1.85s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [04:48<02:41,  1.82s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [04:50<02:39,  1.81s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [04:52<02:37,  1.81s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [04:54<02:37,  1.84s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [04:55<02:35,  1.83s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [04:57<02:34,  1.84s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [04:59<02:28,  1.79s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [05:01<02:25,  1.77s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [05:02<02:21,  1.74s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [05:04<02:24,  1.80s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [05:06<02:22,  1.80s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [05:08<02:19,  1.79s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [05:10<02:17,  1.78s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [05:11<02:18,  1.82s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [05:13<02:13,  1.78s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [05:15<02:13,  1.81s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [05:17<02:13,  1.82s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [05:19<02:13,  1.85s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▌                 | 129/200 [05:21<02:10,  1.84s/it]\u001b[A\n",
-      "training:  65%|███████████████████████████████▊                 | 130/200 [05:22<02:07,  1.82s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████                 | 131/200 [05:24<02:06,  1.84s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▎                | 132/200 [05:26<02:06,  1.86s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▌                | 133/200 [05:28<02:05,  1.88s/it]\u001b[A\n",
-      "training:  67%|████████████████████████████████▊                | 134/200 [05:30<02:04,  1.88s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████                | 135/200 [05:32<02:01,  1.87s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▎               | 136/200 [05:34<02:00,  1.88s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▌               | 137/200 [05:36<01:56,  1.85s/it]\u001b[A\n",
-      "training:  69%|█████████████████████████████████▊               | 138/200 [05:37<01:54,  1.85s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████               | 139/200 [05:39<01:56,  1.91s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▎              | 140/200 [05:41<01:51,  1.86s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▌              | 141/200 [05:43<01:48,  1.83s/it]\u001b[A\n",
-      "training:  71%|██████████████████████████████████▊              | 142/200 [05:45<01:45,  1.82s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████              | 143/200 [05:47<01:44,  1.83s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▎             | 144/200 [05:49<01:44,  1.87s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▌             | 145/200 [05:50<01:43,  1.89s/it]\u001b[A\n",
-      "training:  73%|███████████████████████████████████▊             | 146/200 [05:52<01:41,  1.88s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████             | 147/200 [05:54<01:42,  1.93s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▎            | 148/200 [05:56<01:38,  1.89s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▌            | 149/200 [05:58<01:36,  1.90s/it]\u001b[A\n",
-      "training:  75%|████████████████████████████████████▊            | 150/200 [06:00<01:35,  1.91s/it]\u001b[A\n",
-      "training:  76%|████████████████████████████████████▉            | 151/200 [06:02<01:31,  1.87s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▏           | 152/200 [06:04<01:31,  1.90s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▍           | 153/200 [06:06<01:29,  1.90s/it]\u001b[A\n",
-      "training:  77%|█████████████████████████████████████▋           | 154/200 [06:08<01:27,  1.90s/it]\u001b[A\n",
-      "training:  78%|█████████████████████████████████████▉           | 155/200 [06:10<01:26,  1.91s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▏          | 156/200 [06:12<01:26,  1.97s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▍          | 157/200 [06:14<01:27,  2.03s/it]\u001b[A\n",
-      "training:  79%|██████████████████████████████████████▋          | 158/200 [06:16<01:25,  2.03s/it]\u001b[A\n",
-      "training:  80%|██████████████████████████████████████▉          | 159/200 [06:18<01:23,  2.03s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▏         | 160/200 [06:20<01:22,  2.05s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▍         | 161/200 [06:22<01:19,  2.03s/it]\u001b[A\n",
-      "training:  81%|███████████████████████████████████████▋         | 162/200 [06:24<01:18,  2.07s/it]\u001b[A\n",
-      "training:  82%|███████████████████████████████████████▉         | 163/200 [06:26<01:17,  2.09s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▏        | 164/200 [06:29<01:17,  2.16s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▍        | 165/200 [06:31<01:16,  2.20s/it]\u001b[A\n",
-      "training:  83%|████████████████████████████████████████▋        | 166/200 [06:34<01:19,  2.35s/it]\u001b[A\n",
-      "training:  84%|████████████████████████████████████████▉        | 167/200 [06:36<01:17,  2.34s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▏       | 168/200 [06:38<01:16,  2.39s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▍       | 169/200 [06:41<01:13,  2.36s/it]\u001b[A\n",
-      "training:  85%|█████████████████████████████████████████▋       | 170/200 [06:43<01:10,  2.34s/it]\u001b[A\n",
-      "training:  86%|█████████████████████████████████████████▉       | 171/200 [06:46<01:10,  2.42s/it]\u001b[A\n",
-      "training:  86%|██████████████████████████████████████████▏      | 172/200 [06:48<01:08,  2.43s/it]\u001b[A\n",
-      "training:  86%|██████████████████████████████████████████▍      | 173/200 [06:50<01:05,  2.43s/it]\u001b[A\n",
-      "training:  87%|██████████████████████████████████████████▋      | 174/200 [06:53<01:00,  2.34s/it]\u001b[A\n",
-      "training:  88%|██████████████████████████████████████████▉      | 175/200 [06:55<00:57,  2.31s/it]\u001b[A\n",
-      "training:  88%|███████████████████████████████████████████      | 176/200 [06:58<00:58,  2.43s/it]\u001b[A\n",
-      "training:  88%|███████████████████████████████████████████▎     | 177/200 [07:00<00:54,  2.38s/it]\u001b[A\n",
-      "training:  89%|███████████████████████████████████████████▌     | 178/200 [07:03<00:57,  2.64s/it]\u001b[A\n",
-      "training:  90%|███████████████████████████████████████████▊     | 179/200 [07:05<00:54,  2.57s/it]\u001b[A\n",
-      "training:  90%|████████████████████████████████████████████     | 180/200 [07:08<00:49,  2.47s/it]\u001b[A\n",
-      "training:  90%|████████████████████████████████████████████▎    | 181/200 [07:10<00:45,  2.38s/it]\u001b[A\n",
-      "training:  91%|████████████████████████████████████████████▌    | 182/200 [07:12<00:41,  2.31s/it]\u001b[A\n",
-      "training:  92%|████████████████████████████████████████████▊    | 183/200 [07:17<00:40,  2.39s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:17, 17.98s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:35,  5.06s/it]\n",
+      "  0%|                                                                             | 0/5 [00:00<?, ?it/s]2024-10-16 16:34:09.857694: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                      | 1/200 [00:30<1:42:24, 30.88s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:32<44:21, 13.44s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:33<25:41,  7.82s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:34<17:06,  5.24s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:35<12:24,  3.82s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:37<09:39,  2.98s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:38<07:50,  2.44s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:39<06:40,  2.09s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:41<05:54,  1.86s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:42<05:25,  1.71s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:43<05:02,  1.60s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:45<04:47,  1.53s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:46<04:36,  1.48s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:48<04:29,  1.45s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:49<04:24,  1.43s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:50<04:18,  1.40s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:52<04:14,  1.39s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:53<04:11,  1.38s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:54<04:05,  1.36s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:56<04:05,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:57<04:02,  1.35s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:58<04:02,  1.37s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [01:00<04:04,  1.38s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [01:01<04:02,  1.38s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [01:02<03:54,  1.34s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [01:04<03:51,  1.33s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [01:05<03:48,  1.32s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [01:06<03:45,  1.31s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [01:08<04:07,  1.45s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [01:10<04:07,  1.46s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [01:11<03:53,  1.38s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [01:12<03:43,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [01:13<03:35,  1.29s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [01:14<03:29,  1.26s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [01:16<03:27,  1.26s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [01:17<03:23,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [01:18<03:21,  1.24s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [01:19<03:20,  1.23s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [01:20<03:16,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [01:22<03:14,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [01:23<03:14,  1.22s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [01:24<03:12,  1.22s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:25<03:12,  1.22s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:27<03:12,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:28<03:09,  1.22s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:29<03:08,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:30<03:08,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:31<03:02,  1.20s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:33<02:57,  1.18s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:34<02:55,  1.17s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:35<02:51,  1.15s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:36<02:46,  1.12s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:37<02:43,  1.11s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:38<02:42,  1.11s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:39<02:41,  1.11s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:40<02:43,  1.13s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:42<02:45,  1.16s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:43<02:47,  1.18s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:44<02:50,  1.21s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:45<02:50,  1.21s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:47<02:48,  1.21s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:48<02:47,  1.22s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:49<02:45,  1.21s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:50<02:46,  1.22s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:51<02:44,  1.21s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:53<02:43,  1.22s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:54<02:43,  1.23s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:55<02:42,  1.23s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:56<02:39,  1.22s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:57<02:36,  1.20s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:59<02:33,  1.19s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [02:00<02:34,  1.21s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [02:01<02:30,  1.18s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [02:02<02:25,  1.15s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [02:03<02:20,  1.13s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [02:04<02:18,  1.11s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [02:05<02:21,  1.15s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [02:07<02:21,  1.16s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [02:08<02:20,  1.16s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [02:09<02:23,  1.19s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [02:10<02:20,  1.18s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [02:11<02:18,  1.17s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [02:13<02:16,  1.16s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [02:14<02:12,  1.14s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [02:15<02:13,  1.16s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [02:16<02:12,  1.16s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [02:17<02:12,  1.18s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [02:18<02:12,  1.18s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [02:20<02:12,  1.19s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:21<02:11,  1.19s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:22<02:10,  1.20s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:23<02:09,  1.20s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:24<02:09,  1.21s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:26<02:08,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:27<02:07,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:28<02:11,  1.26s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:30<02:11,  1.28s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:31<02:05,  1.23s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:32<02:00,  1.19s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:33<01:56,  1.17s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:34<01:54,  1.15s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:35<01:52,  1.14s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:36<01:49,  1.13s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:37<01:47,  1.12s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:38<01:45,  1.11s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:40<01:43,  1.11s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:41<01:43,  1.12s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:42<01:47,  1.16s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:43<01:45,  1.16s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:44<01:43,  1.15s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:45<01:41,  1.14s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:47<01:45,  1.20s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:48<01:41,  1.17s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:49<01:38,  1.14s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:50<01:36,  1.13s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:51<01:36,  1.15s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:52<01:38,  1.18s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:54<01:35,  1.16s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:55<01:35,  1.18s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:56<01:34,  1.18s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:57<01:33,  1.19s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:58<01:33,  1.20s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [03:00<01:32,  1.20s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [03:01<01:31,  1.21s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [03:02<01:30,  1.21s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [03:03<01:28,  1.20s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [03:04<01:27,  1.20s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [03:06<01:27,  1.21s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [03:07<01:25,  1.20s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [03:08<01:24,  1.20s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [03:09<01:22,  1.20s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [03:10<01:22,  1.22s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [03:12<01:25,  1.28s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [03:13<01:31,  1.39s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [03:15<01:24,  1.30s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:16<01:22,  1.29s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:17<01:19,  1.26s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:18<01:18,  1.26s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:20<01:15,  1.24s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:21<01:14,  1.24s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:22<01:12,  1.23s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:23<01:11,  1.24s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:24<01:09,  1.23s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:26<01:08,  1.23s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:27<01:07,  1.23s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:28<01:06,  1.23s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:29<01:04,  1.22s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:31<01:03,  1.23s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:32<01:01,  1.20s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:33<00:59,  1.19s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:34<00:56,  1.16s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:35<00:55,  1.16s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:36<00:54,  1.16s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:37<00:52,  1.15s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:39<00:51,  1.15s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:40<00:50,  1.16s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:41<00:49,  1.15s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:42<00:47,  1.13s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:43<00:46,  1.13s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:44<00:44,  1.12s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:45<00:43,  1.12s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:46<00:42,  1.12s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:48<00:41,  1.13s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:49<00:40,  1.12s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:50<00:39,  1.13s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:51<00:38,  1.13s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:52<00:37,  1.13s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:53<00:36,  1.13s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:54<00:35,  1.16s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:56<00:35,  1.19s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:57<00:34,  1.20s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:58<00:33,  1.20s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:59<00:32,  1.21s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [04:01<00:31,  1.21s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [04:02<00:30,  1.22s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [04:03<00:29,  1.24s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [04:04<00:28,  1.23s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [04:06<00:27,  1.26s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [04:07<00:26,  1.24s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [04:08<00:24,  1.25s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [04:09<00:23,  1.24s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [04:10<00:21,  1.20s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [04:12<00:20,  1.18s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [04:13<00:18,  1.16s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [04:14<00:17,  1.15s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [04:15<00:16,  1.16s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [04:16<00:14,  1.14s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [04:17<00:13,  1.15s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [04:18<00:12,  1.14s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [04:20<00:11,  1.16s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [04:21<00:10,  1.16s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [04:23<00:10,  1.37s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:25,  4.95s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:28,  2.87s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:55,  1.51s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:27,  1.38s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:12,  1.31s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:01,  1.26s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<03:55,  1.23s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:15<03:48,  1.20s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:16<03:43,  1.18s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:17<03:39,  1.17s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:43,  1.20s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:46,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:21<03:45,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:22<03:37,  1.18s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:23<03:42,  1.22s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:40,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:26<03:44,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:27<03:47,  1.26s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:50,  1.29s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:52,  1.31s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<03:54,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:54,  1.33s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:54,  1.34s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:35<03:54,  1.35s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:53,  1.35s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:52,  1.35s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:50,  1.35s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:50,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:50,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:48,  1.36s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:46,  1.36s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:46,  1.36s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<03:39,  1.33s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:33,  1.30s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:32,  1.30s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:27,  1.28s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:53<03:26,  1.28s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:23,  1.27s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:23,  1.28s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:22,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:21,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:20,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:23,  1.31s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:02<03:18,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:03<03:34,  1.40s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:30,  1.38s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:59,  5.12s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:32,  2.89s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:04,  2.16s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:55,  1.81s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:18,  1.63s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:44,  1.47s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:08,  1.30s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<03:59,  1.25s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<03:53,  1.23s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:48,  1.21s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:41,  1.18s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:38,  1.17s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:35,  1.16s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:21<03:32,  1.15s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:22<03:31,  1.15s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:29,  1.14s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:31,  1.16s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:26<03:28,  1.15s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:27<03:25,  1.14s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:28<03:25,  1.15s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:29<03:22,  1.14s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:30<03:20,  1.13s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:32<03:19,  1.13s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:33<03:19,  1.14s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:34<03:17,  1.14s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:35<03:16,  1.14s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:36<03:16,  1.14s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:37<03:18,  1.16s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:39<03:20,  1.18s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:40<03:17,  1.17s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:41<03:18,  1.18s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:42<03:16,  1.17s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:43<03:15,  1.18s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:45<03:20,  1.22s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:46<03:16,  1.20s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:47<03:13,  1.19s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:48<03:10,  1.18s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:49<03:08,  1.17s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:50<03:05,  1.16s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:51<03:05,  1.16s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:53<03:02,  1.16s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:54<03:01,  1.16s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:55<03:08,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:56<03:11,  1.24s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:58<03:12,  1.25s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:59<03:12,  1.26s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:00<03:12,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:02<03:15,  1.29s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:03<03:16,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:04<03:16,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:06<03:16,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:07<03:16,  1.33s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:08<03:11,  1.31s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:09<03:05,  1.28s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:11<02:57,  1.23s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:12<02:52,  1.21s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:13<02:53,  1.22s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:14<02:54,  1.24s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:16<02:53,  1.24s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:17<02:54,  1.26s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:18<02:52,  1.25s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:19<02:51,  1.25s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:21<02:47,  1.24s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:22<02:43,  1.21s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:23<02:42,  1.21s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:24<02:38,  1.19s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:25<02:36,  1.19s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:27<02:41,  1.23s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:28<02:41,  1.24s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:29<02:37,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:30<02:39,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:32<02:43,  1.29s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:33<02:42,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:34<02:41,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:36<02:42,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:37<02:43,  1.33s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:38<02:44,  1.35s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:40<02:43,  1.35s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:41<02:43,  1.37s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:43<02:43,  1.37s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:44<02:42,  1.37s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:45<02:40,  1.37s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:47<02:38,  1.37s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:48<02:36,  1.36s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:49<02:35,  1.37s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:51<02:34,  1.36s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:52<02:32,  1.36s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:53<02:30,  1.36s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:55<02:25,  1.32s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:56<02:21,  1.30s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:57<02:21,  1.31s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:58<02:16,  1.28s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:00<02:14,  1.27s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:01<02:11,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:02<02:11,  1.26s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:03<02:08,  1.25s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:05<02:11,  1.29s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:06<02:11,  1.30s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:07<02:08,  1.29s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:09<02:09,  1.31s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:10<02:04,  1.28s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:12<02:17,  1.42s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:13<02:17,  1.43s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:14<02:07,  1.34s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:15<02:00,  1.29s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:17<01:57,  1.26s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:18<01:52,  1.23s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:19<01:50,  1.21s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:20<01:47,  1.19s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:21<01:44,  1.18s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:22<01:41,  1.16s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:24<01:40,  1.16s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:25<01:38,  1.15s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:26<01:39,  1.17s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:27<01:36,  1.15s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:28<01:36,  1.16s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:29<01:34,  1.15s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:30<01:33,  1.15s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:32<01:34,  1.19s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:33<01:36,  1.22s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:34<01:37,  1.24s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:36<01:38,  1.28s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:37<01:37,  1.28s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:38<01:36,  1.29s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:40<01:35,  1.29s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:41<01:33,  1.28s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:42<01:32,  1.28s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:43<01:31,  1.28s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:45<01:30,  1.29s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:46<01:29,  1.30s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:47<01:27,  1.29s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:48<01:23,  1.25s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:50<01:20,  1.22s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:51<01:18,  1.21s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:52<01:17,  1.21s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:53<01:15,  1.20s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:54<01:14,  1.20s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:56<01:13,  1.21s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:57<01:13,  1.22s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:58<01:11,  1.21s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:59<01:10,  1.21s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:00<01:08,  1.21s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:02<01:07,  1.20s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:03<01:05,  1.19s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:04<01:05,  1.22s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:05<01:05,  1.23s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:06<01:02,  1.20s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:08<01:01,  1.21s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:09<01:00,  1.22s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:10<00:58,  1.20s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:11<00:57,  1.19s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:12<00:55,  1.18s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:14<00:54,  1.17s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:15<00:53,  1.18s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:17<00:55,  1.27s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:15,  5.21s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:40,  2.93s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:01,  2.14s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:18,  1.63s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:55,  1.52s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:35,  1.43s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:18,  1.34s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:05,  1.29s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<03:56,  1.25s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:51,  1.23s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:43,  1.19s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:39,  1.17s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:35,  1.16s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:34,  1.16s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:41,  1.20s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:41,  1.21s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:40,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:26<03:34,  1.19s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:45,  1.25s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:32,  1.19s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<03:29,  1.19s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:36,  1.23s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:41,  1.27s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:35<03:45,  1.30s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:46,  1.31s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:47,  1.33s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:39<03:49,  1.34s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:48,  1.35s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:47,  1.35s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:43<03:46,  1.35s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:46,  1.36s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:46,  1.36s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<03:44,  1.36s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:41,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:34,  1.31s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:52<03:35,  1.33s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:53<03:36,  1.34s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:34,  1.34s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:56<03:34,  1.35s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:57<03:32,  1.35s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:32,  1.35s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:00<03:31,  1.35s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:01<03:29,  1.35s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:02<03:28,  1.35s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:04<03:25,  1.35s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:05<03:24,  1.35s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:06<03:23,  1.35s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:08<03:21,  1.34s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:09<03:26,  1.39s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:40,  1.49s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:12<03:25,  1.40s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:13<03:16,  1.35s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<03:09,  1.31s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<03:03,  1.27s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:17<02:59,  1.25s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:18<02:58,  1.25s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:19<02:54,  1.24s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:21<02:52,  1.23s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:22<02:50,  1.23s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:23<02:47,  1.21s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:24<02:46,  1.22s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:25<02:43,  1.20s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:27<02:42,  1.21s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:28<02:41,  1.21s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:29<02:42,  1.22s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:30<02:39,  1.21s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:31<02:38,  1.21s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:33<02:38,  1.22s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:34<02:37,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:35<02:39,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:37<02:40,  1.27s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:38<02:40,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:39<02:40,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:40<02:39,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:42<02:37,  1.28s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:37,  1.29s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:44<02:36,  1.29s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:46<02:34,  1.29s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:47<02:33,  1.29s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:48<02:32,  1.29s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:26,  1.25s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:51<02:22,  1.23s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:52<02:18,  1.20s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:53<02:16,  1.19s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:54<02:14,  1.19s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:55<02:12,  1.18s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:56<02:11,  1.18s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:58<02:11,  1.19s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:59<02:08,  1.18s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:00<02:08,  1.19s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:01<02:07,  1.19s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:02<02:08,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:04<02:06,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:05<02:04,  1.20s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:06<02:02,  1.19s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:07<01:59,  1.17s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:08<01:56,  1.16s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:09<01:54,  1.15s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:11<01:54,  1.15s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:12<01:52,  1.15s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:13<01:51,  1.15s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:14<01:50,  1.15s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:15<01:50,  1.16s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:16<01:49,  1.17s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:18<01:50,  1.19s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:19<01:47,  1.17s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:20<01:46,  1.17s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:21<01:45,  1.17s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:22<01:43,  1.17s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:23<01:42,  1.16s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:24<01:40,  1.16s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:26<01:39,  1.16s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:27<01:38,  1.15s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:28<01:37,  1.16s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:29<01:35,  1.15s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:30<01:39,  1.21s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:32<01:37,  1.21s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:34<01:43,  1.29s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:46,  5.36s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<10:00,  3.03s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:26,  2.27s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:14,  1.91s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:36,  1.73s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:05,  1.57s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:44,  1.47s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:37,  1.44s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:30,  1.42s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:27,  1.41s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:25,  1.41s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:23,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:21,  1.40s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:17,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:15,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:13,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:12,  1.38s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<04:10,  1.38s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<04:08,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<04:08,  1.38s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<04:05,  1.37s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:34<04:03,  1.37s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:35<04:01,  1.36s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:36<04:04,  1.39s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:38<04:05,  1.40s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<04:03,  1.40s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:57,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:42<03:54,  1.36s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:43<03:52,  1.36s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:45<03:55,  1.38s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:46<03:50,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:48<04:15,  1.52s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:49<04:01,  1.45s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:50<03:50,  1.39s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:52<03:42,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:53<03:37,  1.33s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:54<03:33,  1.31s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:55<03:32,  1.31s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:57<03:30,  1.31s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:58<03:25,  1.28s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:59<03:23,  1.28s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [01:01<03:20,  1.27s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:02<03:19,  1.27s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:03<03:19,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:04<03:17,  1.27s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:06<03:16,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:07<03:15,  1.28s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:08<03:14,  1.28s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:09<03:11,  1.27s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:11<03:11,  1.28s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:12<03:10,  1.28s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:13<03:08,  1.28s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:15<03:06,  1.27s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:16<03:04,  1.26s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:17<03:03,  1.26s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:18<03:02,  1.27s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:20<03:00,  1.26s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:21<03:00,  1.27s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:22<03:01,  1.29s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:24<03:01,  1.30s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:25<03:01,  1.30s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:26<03:00,  1.31s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:29<03:14,  1.42s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:12, 12.85s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:24,  3.57s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.85it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.85it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.91it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.81it/s]\n",
       "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
       "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:15, 15.54s/it]\u001b[A\n",
-      "Evaluating NLL: 22it [00:15,  1.98it/s]\u001b[A\n",
-      "Evaluating NLL: 53it [00:15,  5.93it/s]\u001b[A\n",
-      "Evaluating NLL: 82it [00:15, 11.04it/s]\u001b[A\n",
-      "Evaluating NLL: 110it [00:15, 17.78it/s]\u001b[A\n",
-      "Evaluating NLL: 138it [00:16, 26.29it/s]\u001b[A\n",
-      "Evaluating NLL: 162it [00:16, 36.08it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:16, 12.22it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   1%|▏              | 1/100 [00:00<00:46,  2.14it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   4%|▌              | 4/100 [00:00<00:11,  8.36it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   9%|█▎             | 9/100 [00:00<00:05, 17.48it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  12%|█▋            | 12/100 [00:00<00:04, 19.47it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  48%|██████▏      | 48/100 [00:00<00:00, 104.22it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  63%|████████▊     | 63/100 [00:01<00:00, 67.66it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  74%|██████████▎   | 74/100 [00:01<00:00, 55.55it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:01<00:00, 57.99it/s]\n",
-      " 20%|███████████▊                                               | 1/5 [21:38<1:26:34, 1298.51s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -10.53949980699176\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 1it [00:11, 11.24s/it]\u001b[A\n",
+      "Evaluating NLL: 38it [00:11,  4.73it/s]\u001b[A\n",
+      "Evaluating NLL: 75it [00:11, 11.16it/s]\u001b[A\n",
+      "Evaluating NLL: 117it [00:11, 21.19it/s]\u001b[A\n",
+      "Evaluating NLL: 156it [00:11, 33.59it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:11, 16.99it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   1%|▏                    | 1/100 [00:00<00:41,  2.38it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   6%|█▎                   | 6/100 [00:00<00:06, 13.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|██                  | 10/100 [00:00<00:04, 20.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|██▊                 | 14/100 [00:00<00:03, 25.20it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  22%|████▍               | 22/100 [00:00<00:02, 38.44it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|█████▍              | 27/100 [00:00<00:01, 38.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|██████▍             | 32/100 [00:01<00:01, 39.49it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|███████▍            | 37/100 [00:01<00:01, 39.33it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|████████▍           | 42/100 [00:01<00:01, 39.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|██████████          | 50/100 [00:01<00:01, 49.17it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|███████████▏        | 56/100 [00:01<00:00, 45.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|████████████▏       | 61/100 [00:01<00:00, 44.11it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|█████████████▏      | 66/100 [00:01<00:00, 42.55it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|██████████████▍     | 72/100 [00:01<00:00, 46.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  77%|███████████████▍    | 77/100 [00:02<00:00, 46.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|████████████████▍   | 82/100 [00:02<00:00, 43.82it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|█████████████████▍  | 87/100 [00:02<00:00, 42.42it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|██████████████████▍ | 92/100 [00:02<00:00, 41.14it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 37.81it/s]\n",
+      " 20%|█████████████▏                                                    | 1/5 [15:16<1:01:07, 916.92s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:17,  5.21s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:34,  2.90s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:11,  2.19s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:05,  1.86s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:27,  1.68s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:06,  1.58s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:49,  1.50s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:38,  1.45s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:28,  1.40s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:21,  1.38s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:22,  1.39s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:21,  1.39s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:15,  1.37s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:13,  1.36s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:11,  1.36s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:07,  1.34s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<04:00,  1.31s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:53,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<03:55,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:54,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:55,  1.31s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<04:00,  1.35s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:55,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:55,  1.34s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<04:03,  1.39s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<04:01,  1.39s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:56,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:58,  1.38s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:53,  1.37s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:44<03:51,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:45<03:51,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:47<04:20,  1.55s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:48<04:08,  1.49s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:50<03:59,  1.44s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:51<03:50,  1.40s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:52<03:45,  1.37s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:54<03:43,  1.37s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:55<03:41,  1.37s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:56<03:39,  1.37s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:58<03:35,  1.35s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:59<03:35,  1.35s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [01:00<03:34,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:02<03:29,  1.33s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:03<03:23,  1.30s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:04<03:18,  1.28s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:06<03:18,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:07<03:17,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:08<03:15,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:09<03:12,  1.28s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:11<03:08,  1.25s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:12<03:02,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:13<03:01,  1.22s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:14<03:02,  1.24s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:15<03:02,  1.25s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:17<03:02,  1.26s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:18<03:01,  1.26s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:19<03:01,  1.27s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:20<02:56,  1.24s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:22<02:52,  1.22s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:23<02:49,  1.21s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:24<02:45,  1.19s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:25<02:43,  1.19s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:26<02:40,  1.17s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:27<02:37,  1.16s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:29<02:36,  1.16s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:30<02:39,  1.19s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:31<02:43,  1.23s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:32<02:39,  1.21s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:34<02:37,  1.20s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:35<02:36,  1.21s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<02:35,  1.21s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<02:36,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:38<02:36,  1.23s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:34,  1.23s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:41<02:33,  1.23s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:42<02:30,  1.21s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:43<02:27,  1.20s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:30,  1.23s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:46<02:32,  1.26s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:47<02:33,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:33,  1.29s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:50<02:35,  1.32s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:51<02:35,  1.32s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:53<02:33,  1.32s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:33,  1.33s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:55<02:32,  1.33s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:57<02:31,  1.34s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:58<02:31,  1.36s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:30,  1.35s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:01<02:26,  1.33s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:02<02:22,  1.31s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:03<02:21,  1.31s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:05<02:19,  1.30s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:06<02:18,  1.31s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:07<02:16,  1.30s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:08<02:14,  1.29s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:10<02:13,  1.30s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:11<02:11,  1.29s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:10,  1.29s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:13<02:06,  1.27s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:15<02:05,  1.27s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:16<02:03,  1.26s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:17<02:02,  1.26s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:19<02:01,  1.27s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:20<02:05,  1.32s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:21<02:04,  1.33s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:23<02:02,  1.32s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:24<02:00,  1.31s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:25<02:01,  1.34s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:27<01:59,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:28<01:56,  1.31s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:29<01:57,  1.33s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:31<01:57,  1.35s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:32<01:57,  1.37s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:33<01:56,  1.37s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:35<01:54,  1.37s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:36<01:55,  1.39s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:38<01:55,  1.41s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:39<01:57,  1.45s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:41<02:07,  1.59s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:42<01:58,  1.50s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:44<01:51,  1.43s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:45<01:48,  1.41s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:46<01:43,  1.36s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:48<01:38,  1.31s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:49<01:36,  1.31s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:50<01:35,  1.31s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:51<01:33,  1.29s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:53<01:30,  1.28s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:54<01:28,  1.27s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:55<01:29,  1.29s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:56<01:25,  1.26s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:58<01:23,  1.24s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:59<01:21,  1.23s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [03:00<01:19,  1.22s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:01<01:16,  1.20s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:02<01:16,  1.21s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:04<01:16,  1.24s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:05<01:14,  1.22s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:06<01:12,  1.21s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:07<01:11,  1.20s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:08<01:09,  1.20s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:10<01:08,  1.20s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:11<01:06,  1.18s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:12<01:04,  1.18s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:13<01:03,  1.18s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:14<01:03,  1.19s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:16<01:01,  1.19s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:17<01:00,  1.19s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:18<00:59,  1.19s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:19<00:57,  1.18s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:20<00:57,  1.20s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:21<00:55,  1.19s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:23<00:54,  1.18s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:24<00:52,  1.17s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:25<00:51,  1.18s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:26<00:52,  1.22s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:28<00:52,  1.26s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:29<00:52,  1.28s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:30<00:52,  1.30s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:32<00:51,  1.32s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:33<00:50,  1.32s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:34<00:48,  1.32s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:36<00:47,  1.32s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:37<00:46,  1.32s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:38<00:44,  1.32s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:40<00:43,  1.32s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:41<00:42,  1.32s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:42<00:40,  1.31s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:43<00:38,  1.28s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:45<00:36,  1.27s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:46<00:34,  1.25s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:47<00:33,  1.23s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:48<00:32,  1.24s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:49<00:29,  1.20s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:50<00:25,  1.08s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:51<00:22,  1.02it/s]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:52<00:20,  1.10it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:53<00:18,  1.15it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:53<00:16,  1.20it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:54<00:15,  1.24it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [03:55<00:14,  1.27it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [03:56<00:13,  1.28it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [03:56<00:12,  1.29it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [03:57<00:11,  1.30it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [03:58<00:10,  1.28it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [03:59<00:10,  1.29it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [03:59<00:09,  1.28it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [04:00<00:08,  1.24it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [04:01<00:08,  1.17it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [04:02<00:07,  1.20it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [04:03<00:06,  1.23it/s]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [04:04<00:05,  1.23it/s]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [04:04<00:04,  1.25it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [04:05<00:04,  1.24it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [04:06<00:03,  1.28it/s]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [04:07<00:02,  1.29it/s]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [04:07<00:01,  1.27it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [04:08<00:00,  1.26it/s]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [04:09<00:00,  1.25s/it]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:07<24:02,  7.25s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:09<13:49,  4.19s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:10<09:55,  3.02s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:13<08:51,  2.71s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:14<07:46,  2.39s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:16<07:02,  2.18s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:18<06:46,  2.11s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:20<06:21,  1.99s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:22<06:10,  1.94s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:24<06:18,  1.99s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:26<06:22,  2.03s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:28<06:19,  2.02s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:30<06:28,  2.08s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:32<06:36,  2.13s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:35<06:34,  2.13s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:37<06:37,  2.16s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:39<06:21,  2.09s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:41<06:14,  2.06s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:43<06:14,  2.07s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:45<06:07,  2.04s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:47<05:57,  1.99s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:50<06:41,  2.26s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [00:51<06:17,  2.13s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [00:54<07:00,  2.39s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [00:57<06:50,  2.34s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [00:59<06:31,  2.25s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:01<06:14,  2.17s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:02<05:54,  2.06s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:04<05:42,  2.00s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:06<05:49,  2.05s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:09<05:55,  2.11s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:11<06:04,  2.17s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:14<06:36,  2.37s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:17<06:57,  2.52s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:19<06:49,  2.48s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:22<06:58,  2.55s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:25<07:19,  2.69s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:27<07:13,  2.67s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:30<07:06,  2.65s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:33<07:04,  2.66s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:35<07:00,  2.64s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:38<07:03,  2.68s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:41<06:53,  2.63s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:43<06:58,  2.68s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:46<06:50,  2.65s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:49<06:50,  2.66s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [01:51<06:46,  2.66s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [01:54<06:50,  2.70s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [01:57<06:53,  2.74s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [02:00<06:54,  2.76s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [02:03<06:54,  2.78s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [02:05<06:55,  2.80s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [02:08<06:50,  2.79s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [02:11<06:52,  2.83s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [02:14<06:44,  2.79s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [02:17<06:37,  2.76s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [02:19<06:40,  2.80s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [02:22<06:37,  2.80s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [02:25<06:31,  2.78s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [02:28<06:27,  2.77s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [02:30<06:19,  2.73s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [02:33<06:19,  2.75s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [02:36<06:22,  2.79s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [02:39<06:25,  2.84s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [02:42<06:20,  2.82s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [02:45<06:21,  2.85s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [02:48<06:24,  2.89s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [02:51<06:23,  2.90s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [02:53<06:13,  2.85s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [02:56<06:14,  2.88s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [02:59<06:13,  2.90s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [03:02<06:04,  2.85s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [03:05<06:20,  3.00s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [03:08<06:10,  2.94s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [03:11<06:12,  2.98s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [03:14<05:59,  2.90s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [03:17<06:04,  2.96s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [03:20<05:58,  2.94s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [03:23<06:00,  2.98s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [03:26<05:48,  2.91s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [03:29<05:52,  2.96s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [03:32<05:57,  3.03s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [03:35<06:06,  3.13s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [03:38<06:03,  3.13s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [03:41<05:53,  3.08s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [03:44<05:42,  3.00s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [03:47<05:38,  2.99s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [03:50<05:29,  2.94s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [03:54<05:53,  3.18s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [03:57<05:40,  3.10s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [04:00<05:31,  3.04s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [04:03<05:26,  3.02s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [04:06<05:23,  3.03s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [04:08<05:10,  2.92s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [04:11<05:01,  2.87s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [04:13<04:40,  2.70s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [04:16<04:34,  2.66s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [04:19<04:32,  2.67s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [04:21<04:25,  2.63s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [04:24<04:18,  2.59s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [04:26<04:04,  2.47s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [04:28<03:54,  2.39s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [04:30<03:50,  2.37s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [04:33<03:49,  2.39s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [04:35<03:47,  2.39s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [04:37<03:40,  2.35s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [04:40<03:38,  2.35s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [04:42<03:33,  2.32s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [04:44<03:31,  2.32s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [04:47<03:26,  2.30s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [04:49<03:23,  2.29s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [04:51<03:21,  2.29s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [04:54<03:23,  2.34s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [04:56<03:28,  2.43s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [04:59<03:23,  2.39s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [05:01<03:29,  2.49s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [05:04<03:22,  2.44s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [05:06<03:15,  2.39s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [05:08<03:10,  2.35s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [05:11<03:09,  2.37s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [05:13<03:04,  2.33s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [05:15<03:00,  2.32s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [05:18<03:03,  2.38s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [05:20<03:01,  2.38s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [05:22<02:54,  2.32s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [05:24<02:50,  2.31s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [05:27<02:50,  2.33s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [05:29<02:48,  2.33s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▌                 | 129/200 [05:31<02:40,  2.26s/it]\u001b[A\n",
-      "training:  65%|███████████████████████████████▊                 | 130/200 [05:33<02:37,  2.25s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████                 | 131/200 [05:36<02:33,  2.22s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▎                | 132/200 [05:38<02:28,  2.18s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▌                | 133/200 [05:40<02:33,  2.29s/it]\u001b[A\n",
-      "training:  67%|████████████████████████████████▊                | 134/200 [05:43<02:31,  2.30s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████                | 135/200 [05:45<02:27,  2.28s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▎               | 136/200 [05:47<02:28,  2.32s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▌               | 137/200 [05:50<02:39,  2.53s/it]\u001b[A\n",
-      "training:  69%|█████████████████████████████████▊               | 138/200 [05:53<02:45,  2.67s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████               | 139/200 [05:57<02:53,  2.84s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▎              | 140/200 [06:00<02:59,  2.99s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▌              | 141/200 [06:03<03:01,  3.08s/it]\u001b[A\n",
-      "training:  71%|██████████████████████████████████▊              | 142/200 [06:07<03:04,  3.19s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████              | 143/200 [06:10<02:59,  3.14s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▎             | 144/200 [06:13<02:54,  3.12s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▌             | 145/200 [06:16<02:54,  3.18s/it]\u001b[A\n",
-      "training:  73%|███████████████████████████████████▊             | 146/200 [06:19<02:52,  3.20s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████             | 147/200 [06:22<02:48,  3.19s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▎            | 148/200 [06:26<02:50,  3.29s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▌            | 149/200 [06:30<02:53,  3.39s/it]\u001b[A\n",
-      "training:  75%|████████████████████████████████████▊            | 150/200 [06:33<02:49,  3.38s/it]\u001b[A\n",
-      "training:  76%|████████████████████████████████████▉            | 151/200 [06:37<02:56,  3.60s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▏           | 152/200 [06:41<02:56,  3.67s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▍           | 153/200 [06:45<02:55,  3.74s/it]\u001b[A\n",
-      "training:  77%|█████████████████████████████████████▋           | 154/200 [06:49<02:57,  3.87s/it]\u001b[A\n",
-      "training:  78%|█████████████████████████████████████▉           | 155/200 [06:53<02:50,  3.78s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▏          | 156/200 [06:56<02:42,  3.70s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▍          | 157/200 [06:59<02:34,  3.58s/it]\u001b[A\n",
-      "training:  79%|██████████████████████████████████████▋          | 158/200 [07:03<02:31,  3.60s/it]\u001b[A\n",
-      "training:  80%|██████████████████████████████████████▉          | 159/200 [07:06<02:25,  3.55s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▏         | 160/200 [07:10<02:22,  3.57s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▍         | 161/200 [07:13<02:17,  3.53s/it]\u001b[A\n",
-      "training:  81%|███████████████████████████████████████▋         | 162/200 [07:17<02:12,  3.48s/it]\u001b[A\n",
-      "training:  82%|███████████████████████████████████████▉         | 163/200 [07:20<02:09,  3.49s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▏        | 164/200 [07:24<02:04,  3.45s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▍        | 165/200 [07:27<02:01,  3.48s/it]\u001b[A\n",
-      "training:  83%|████████████████████████████████████████▋        | 166/200 [07:31<01:58,  3.49s/it]\u001b[A\n",
-      "training:  84%|████████████████████████████████████████▉        | 167/200 [07:35<02:00,  3.65s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▏       | 168/200 [07:38<01:56,  3.63s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▍       | 169/200 [07:46<01:25,  2.76s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.29s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.67it/s]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:27,  4.96s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:25,  2.55s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:41,  1.73s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:28,  1.37s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:53,  1.20s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:35,  1.11s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:09<03:11,  1.01it/s]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:10<02:57,  1.08it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:51,  1.12it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:41,  1.18it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:42,  1.16it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:40,  1.17it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:14<02:39,  1.17it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:15<02:40,  1.16it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:16<02:39,  1.16it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:17<02:36,  1.17it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:18<02:38,  1.15it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:38,  1.15it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:20<02:38,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:38,  1.13it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:21<02:36,  1.15it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:22<02:39,  1.12it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:23<02:34,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:24<02:35,  1.13it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:25<02:34,  1.13it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:26<02:33,  1.13it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:33,  1.13it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<02:33,  1.12it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:29<02:32,  1.12it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:29<02:32,  1.11it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:30<02:32,  1.11it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:31<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:32<02:29,  1.12it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:33<02:31,  1.10it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:34<02:25,  1.14it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:35<02:22,  1.15it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:36<02:19,  1.17it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:18,  1.17it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:37<02:18,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:38<02:15,  1.18it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:39<02:16,  1.16it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:40<02:15,  1.17it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:41<02:13,  1.17it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:42<02:16,  1.14it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:43<02:17,  1.13it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:43<02:20,  1.10it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:44<02:17,  1.12it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:45<02:15,  1.12it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:46<02:18,  1.09it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:47<02:20,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:48<02:14,  1.10it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:49<02:15,  1.09it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:50<02:40,  1.09s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:34,  1.05s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:22,  1.02it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:53<02:15,  1.06it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:10,  1.10it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:55<02:05,  1.13it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:56<02:01,  1.16it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:56<01:59,  1.17it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:57<01:58,  1.18it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:58<01:57,  1.18it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:59<01:57,  1.17it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:00<01:56,  1.17it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:01<01:56,  1.16it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:02<01:58,  1.13it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:03<01:59,  1.11it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:03<01:58,  1.11it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:04<01:58,  1.11it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:05<01:59,  1.09it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:06<01:58,  1.09it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:07<01:57,  1.09it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:08<01:57,  1.08it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:09<01:57,  1.08it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:10<01:56,  1.07it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:11<01:59,  1.04it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:12<01:55,  1.06it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:13<01:53,  1.08it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:14<01:48,  1.11it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:14<01:45,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:15<01:46,  1.12it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:16<01:43,  1.14it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:17<01:42,  1.14it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:18<01:43,  1.13it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:19<01:42,  1.12it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:20<01:40,  1.14it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:21<01:38,  1.15it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:22<01:37,  1.15it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:22<01:34,  1.18it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:23<01:31,  1.20it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:24<01:30,  1.20it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:25<01:29,  1.21it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:26<01:28,  1.21it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:26<01:27,  1.21it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:27<01:26,  1.21it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:28<01:26,  1.21it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:29<01:24,  1.22it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:30<01:23,  1.22it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:31<01:24,  1.19it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:31<01:24,  1.19it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:32<01:21,  1.21it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:33<01:21,  1.21it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:34<01:19,  1.21it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:35<01:19,  1.21it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:36<01:19,  1.20it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:36<01:18,  1.19it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:37<01:18,  1.18it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:38<01:18,  1.17it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:39<01:18,  1.16it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:40<01:18,  1.15it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:41<01:19,  1.12it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:42<01:20,  1.09it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:43<01:19,  1.10it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:44<01:17,  1.11it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:44<01:16,  1.11it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:45<01:17,  1.08it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:46<01:17,  1.07it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:47<01:15,  1.08it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:48<01:14,  1.09it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:49<01:12,  1.11it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:50<01:10,  1.12it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:51<01:09,  1.12it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:52<01:07,  1.14it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:52<01:05,  1.17it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:53<01:03,  1.19it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:54<01:01,  1.20it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:55<00:59,  1.22it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:56<00:58,  1.23it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:56<00:57,  1.25it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [01:57<00:56,  1.24it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [01:58<00:56,  1.22it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [01:59<00:56,  1.20it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:00<00:56,  1.19it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:01<00:55,  1.19it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:02<00:55,  1.17it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:02<00:54,  1.17it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:03<00:53,  1.17it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:04<00:53,  1.17it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:05<00:52,  1.16it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:06<00:51,  1.16it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:07<00:50,  1.16it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:08<00:49,  1.16it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:08<00:48,  1.18it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:09<00:47,  1.17it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:10<00:46,  1.17it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:11<00:46,  1.16it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:12<00:45,  1.17it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:13<00:44,  1.17it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:14<00:43,  1.17it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:14<00:43,  1.16it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:15<00:44,  1.11it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:16<00:44,  1.07it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:17<00:43,  1.08it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:18<00:41,  1.10it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:19<00:40,  1.12it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:20<00:39,  1.13it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:21<00:37,  1.14it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:22<00:36,  1.15it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:23<00:35,  1.14it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:23<00:35,  1.14it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:24<00:34,  1.15it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:25<00:33,  1.15it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:26<00:32,  1.14it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:28<00:32,  1.11it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:48,  5.37s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:50,  2.68s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:59,  1.83s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:40,  1.43s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:54,  1.20s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:30,  1.08s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:18,  1.03s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:11,  1.00it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:06,  1.03it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:01,  1.05it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<03:00,  1.05it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:56,  1.07it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:53,  1.08it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:55,  1.06it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:54,  1.06it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:52,  1.07it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:49,  1.08it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:47,  1.08it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:45,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:46,  1.08it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:45,  1.08it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:44,  1.08it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:25<02:42,  1.09it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:26<02:43,  1.08it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:27<02:42,  1.08it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:44,  1.06it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:43,  1.06it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:29<02:36,  1.10it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:30<02:43,  1.05it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:31<02:37,  1.08it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:32<02:33,  1.10it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:33<02:29,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:34<02:36,  1.07it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:35<02:38,  1.05it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:36<02:35,  1.06it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:37<02:29,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:38<02:23,  1.13it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:38<02:23,  1.13it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:39<02:21,  1.14it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:40<02:18,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:41<02:25,  1.09it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:42<02:23,  1.10it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:43<02:20,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:44<02:17,  1.13it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:45<02:15,  1.14it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:46<02:20,  1.09it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:47<02:47,  1.10s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:49<02:58,  1.18s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:49<02:43,  1.08s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:50<02:31,  1.01s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:51<02:22,  1.04it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:52<02:19,  1.06it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:53<02:14,  1.09it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:54<02:12,  1.10it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:55<02:10,  1.11it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:55<02:07,  1.13it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:56<02:06,  1.13it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:57<02:02,  1.16it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:58<02:00,  1.17it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:59<01:58,  1.18it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:00<01:56,  1.19it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:00<01:56,  1.18it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:01<01:57,  1.16it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:02<01:58,  1.15it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:03<01:59,  1.13it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:04<02:00,  1.11it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:05<01:58,  1.12it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:06<01:57,  1.12it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:07<01:55,  1.14it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:08<01:53,  1.14it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:08<01:52,  1.14it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:09<01:50,  1.15it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:10<01:47,  1.18it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:11<01:48,  1.16it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:12<01:47,  1.16it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:13<01:46,  1.16it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:14<01:44,  1.17it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:14<01:44,  1.17it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:15<01:42,  1.18it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:16<01:40,  1.19it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:17<01:41,  1.17it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:18<01:41,  1.16it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:19<01:39,  1.17it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:19<01:37,  1.19it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:20<01:36,  1.19it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:21<01:34,  1.21it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:22<01:32,  1.22it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:23<01:31,  1.23it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:24<01:29,  1.23it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:24<01:29,  1.24it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:25<01:28,  1.24it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:26<01:28,  1.22it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:27<01:27,  1.22it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:28<01:26,  1.23it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:28<01:25,  1.22it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:29<01:26,  1.20it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:30<01:25,  1.21it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:31<01:24,  1.20it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:32<01:25,  1.18it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:33<01:23,  1.20it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:33<01:22,  1.20it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:34<01:23,  1.18it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:35<01:23,  1.16it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:36<01:21,  1.17it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:37<01:20,  1.18it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:38<01:19,  1.19it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:39<01:18,  1.19it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:39<01:16,  1.20it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:40<01:16,  1.19it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:41<01:15,  1.20it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:42<01:13,  1.21it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:43<01:12,  1.22it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:44<01:14,  1.16it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:45<01:15,  1.14it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:45<01:13,  1.15it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:46<01:11,  1.17it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:47<01:10,  1.18it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:48<01:08,  1.20it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:49<01:06,  1.21it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:49<01:06,  1.21it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:50<01:05,  1.21it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:51<01:05,  1.19it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:52<01:05,  1.18it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:53<01:04,  1.17it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:54<01:03,  1.18it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:55<01:03,  1.16it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:56<01:03,  1.15it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:56<01:01,  1.17it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:57<01:00,  1.17it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [01:58<00:59,  1.17it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [01:59<01:00,  1.15it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:00<00:59,  1.14it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:01<00:59,  1.13it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:02<00:58,  1.13it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:03<00:57,  1.13it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:03<00:56,  1.14it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:04<00:54,  1.15it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:05<00:53,  1.16it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:06<00:52,  1.16it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:07<00:51,  1.16it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:08<00:50,  1.18it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:09<00:49,  1.17it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:09<00:47,  1.19it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:10<00:46,  1.19it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:11<00:45,  1.20it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:12<00:44,  1.22it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:13<00:44,  1.19it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:14<00:45,  1.14it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:15<00:45,  1.13it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:15<00:43,  1.15it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:17<00:44,  1.10it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<15:56,  4.81s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:10,  2.48s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:39,  1.72s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:39,  1.43s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:58,  1.22s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:34,  1.11s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:15,  1.01s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:10<03:01,  1.06it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:54,  1.10it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:47,  1.13it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:45,  1.14it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:48,  1.12it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:43,  1.14it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:15<02:43,  1.14it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:16<02:40,  1.15it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:17<02:40,  1.15it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:18<02:42,  1.13it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:42,  1.12it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:20<02:46,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:46,  1.08it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:22<02:44,  1.09it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:23<02:43,  1.09it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:42,  1.09it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:42,  1.09it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:41,  1.09it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:26<02:40,  1.08it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:39,  1.08it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<02:41,  1.06it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:29<02:41,  1.06it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:30<02:39,  1.07it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:31<02:36,  1.08it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:32<02:35,  1.08it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:35,  1.07it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:34<02:33,  1.08it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:35<02:31,  1.09it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:36<02:29,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:37<02:31,  1.08it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:38<02:26,  1.10it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:38<02:24,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:39<02:28,  1.08it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:40<02:22,  1.12it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:41<02:20,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:42<02:17,  1.14it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:43<02:15,  1.15it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:44<02:13,  1.16it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:45<02:11,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:45<02:15,  1.13it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:46<02:08,  1.18it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:47<02:03,  1.22it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:48<02:01,  1.23it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:49<02:01,  1.23it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:49<01:58,  1.25it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:50<01:59,  1.23it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:07,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:09,  1.12it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:53<02:07,  1.13it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:08,  1.12it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:55<02:32,  1.07s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:57<02:31,  1.07s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:57<02:19,  1.00it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:58<02:10,  1.06it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:59<02:04,  1.11it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:00<02:02,  1.12it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:01<01:58,  1.15it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:01<01:56,  1.16it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:02<01:53,  1.18it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:03<01:52,  1.19it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:04<01:51,  1.18it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:05<01:50,  1.18it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:06<01:48,  1.20it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:06<01:47,  1.20it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:07<01:45,  1.21it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:08<01:45,  1.21it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:09<01:42,  1.22it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:10<01:41,  1.23it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:11<01:44,  1.19it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:11<01:44,  1.18it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:12<01:43,  1.18it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:13<01:42,  1.19it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:14<01:41,  1.18it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:15<01:41,  1.18it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:16<01:41,  1.16it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:17<01:39,  1.18it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:17<01:38,  1.18it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:18<01:37,  1.18it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:19<01:35,  1.19it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:20<01:36,  1.17it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:21<01:36,  1.16it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:22<01:35,  1.16it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:23<01:35,  1.15it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:23<01:34,  1.16it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:25<01:40,  1.07it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:40,  5.03s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:42,  2.64s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<06:10,  1.88s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:50,  1.48s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:01,  1.24s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:33,  1.10s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:16,  1.02s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:04,  1.04it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:55,  1.09it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:50,  1.11it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:49,  1.11it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:47,  1.12it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:43,  1.15it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:43,  1.14it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:40,  1.15it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:39,  1.15it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:18<02:37,  1.16it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:33,  1.19it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:20<02:30,  1.20it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:27,  1.22it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:22<02:25,  1.23it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:22<02:25,  1.22it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:23<02:27,  1.20it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:24<02:24,  1.21it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:25<02:23,  1.22it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:26<02:22,  1.22it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:23,  1.20it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:27<02:23,  1.20it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:28<02:22,  1.20it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:29<02:25,  1.17it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:30<02:24,  1.17it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:31<02:29,  1.13it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:32<02:26,  1.14it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:33<02:24,  1.15it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:34<02:20,  1.17it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:34<02:22,  1.15it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:35<02:17,  1.18it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:13,  1.22it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:37<02:12,  1.21it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:38<02:12,  1.21it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:38<02:08,  1.24it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:39<02:07,  1.24it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:40<02:08,  1.23it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:41<02:08,  1.22it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:42<02:10,  1.19it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:43<02:09,  1.19it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:43<02:09,  1.18it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:44<02:07,  1.19it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:45<02:05,  1.21it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:46<02:05,  1.20it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:47<02:02,  1.21it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:48<02:00,  1.22it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:48<01:59,  1.23it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:49<01:58,  1.23it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:50<01:58,  1.22it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:51<01:57,  1.22it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:52<01:58,  1.21it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:53<02:00,  1.18it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:53<02:03,  1.14it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:54<02:02,  1.15it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:55<01:58,  1.17it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:56<01:56,  1.18it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:57<01:55,  1.19it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [00:58<01:53,  1.20it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [00:59<01:56,  1.16it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [00:59<01:53,  1.19it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:00<01:51,  1.20it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:01<01:50,  1.20it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:02<01:48,  1.20it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:03<01:48,  1.20it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:03<01:46,  1.21it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:04<01:47,  1.19it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:05<01:47,  1.19it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:06<01:45,  1.19it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:07<01:43,  1.21it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:08<01:48,  1.14it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:09<01:48,  1.13it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:10<01:48,  1.12it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:10<01:45,  1.15it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:11<01:43,  1.16it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:12<01:43,  1.15it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:13<01:46,  1.11it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:14<01:42,  1.14it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:15<01:39,  1.16it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:16<01:37,  1.18it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:16<01:37,  1.17it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:17<01:38,  1.15it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:18<01:38,  1.13it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:19<01:39,  1.12it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:20<01:37,  1.12it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:21<01:37,  1.12it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:22<01:37,  1.11it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:23<01:36,  1.10it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:24<01:35,  1.11it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:25<01:35,  1.10it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:26<01:34,  1.11it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:26<01:33,  1.11it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:27<01:31,  1.11it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:28<01:31,  1.10it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:29<01:30,  1.10it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:30<01:32,  1.08it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:31<01:30,  1.08it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:32<01:30,  1.08it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:33<01:29,  1.07it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:34<01:30,  1.05it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:35<01:27,  1.08it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:36<01:25,  1.08it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:37<01:26,  1.06it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:38<01:24,  1.08it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:39<01:22,  1.09it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:39<01:21,  1.10it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:40<01:19,  1.11it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:41<01:17,  1.12it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:42<01:17,  1.11it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:43<01:15,  1.13it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:44<01:13,  1.14it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:45<01:14,  1.12it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:46<01:13,  1.12it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:47<01:12,  1.11it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:47<01:11,  1.12it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:48<01:09,  1.14it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:49<01:10,  1.11it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:50<01:11,  1.07it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:51<01:11,  1.06it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:52<01:08,  1.10it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:53<01:07,  1.10it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:54<01:14,  1.03s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:55<01:18,  1.09s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:56<01:11,  1.00s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [01:57<01:04,  1.08it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [01:58<00:59,  1.15it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [01:59<00:57,  1.19it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [01:59<00:54,  1.22it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:00<00:52,  1.26it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:01<00:50,  1.28it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:02<00:50,  1.26it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:02<00:49,  1.26it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:03<00:48,  1.29it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:04<00:46,  1.30it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:05<00:45,  1.31it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:05<00:45,  1.31it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:06<00:43,  1.32it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:07<00:43,  1.32it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:08<00:42,  1.33it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:08<00:40,  1.34it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:09<00:41,  1.31it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:10<00:40,  1.30it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:11<00:39,  1.30it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:11<00:38,  1.31it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:12<00:37,  1.32it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:13<00:38,  1.28it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:14<00:37,  1.29it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:15<00:35,  1.31it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:15<00:35,  1.29it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:16<00:34,  1.29it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:17<00:34,  1.27it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:18<00:34,  1.24it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:19<00:34,  1.23it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:19<00:33,  1.22it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:20<00:33,  1.21it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:21<00:32,  1.21it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:22<00:31,  1.21it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:23<00:30,  1.20it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:24<00:30,  1.19it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:24<00:29,  1.20it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:25<00:27,  1.21it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:26<00:26,  1.22it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:28<00:28,  1.13it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.54it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.71it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.92it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.20s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.30it/s]\n",
       "/2tb_nvme/hpinkard_waller/GitRepos/EncodingInformation/src/encoding_information/models/conditional_entropy_models.py:41: UserWarning: The images argument is not used in the Analytic Gaussian noise model.\n",
       "  warnings.warn(\"The images argument is not used in the Analytic Gaussian noise model.\")\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.36s/it]\u001b[A\n",
-      "Evaluating NLL: 27it [00:01, 25.41it/s]\u001b[A\n",
-      "Evaluating NLL: 49it [00:01, 48.65it/s]\u001b[A\n",
-      "Evaluating NLL: 80it [00:01, 86.64it/s]\u001b[A\n",
-      "Evaluating NLL: 104it [00:01, 111.74it/s]\u001b[A\n",
-      "Evaluating NLL: 137it [00:01, 154.09it/s]\u001b[A\n",
-      "Evaluating NLL: 165it [00:01, 181.37it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:02, 94.32it/s] \n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   3%|▍              | 3/100 [00:00<00:04, 24.07it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   7%|█              | 7/100 [00:00<00:03, 28.75it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  10%|█▍            | 10/100 [00:00<00:03, 27.30it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  27%|███▊          | 27/100 [00:00<00:00, 76.36it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  37%|█████▏        | 37/100 [00:00<00:00, 77.94it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  46%|██████▍       | 46/100 [00:00<00:01, 48.66it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  53%|███████▍      | 53/100 [00:01<00:00, 47.13it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  59%|████████▎     | 59/100 [00:01<00:00, 42.60it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  64%|████████▉     | 64/100 [00:01<00:00, 38.45it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  69%|█████████▋    | 69/100 [00:01<00:00, 33.31it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  89%|████████████▍ | 89/100 [00:01<00:00, 64.74it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:02<00:00, 48.31it/s]\n",
-      " 40%|████████████████████████▍                                    | 2/5 [36:27<52:53, 1057.71s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -7.127858971006706\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 1it [00:00,  1.13it/s]\u001b[A\n",
+      "Evaluating NLL: 38it [00:00, 52.34it/s]\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 104.65it/s]\u001b[A\n",
+      "Evaluating NLL: 113it [00:01, 156.40it/s]\u001b[A\n",
+      "Evaluating NLL: 150it [00:01, 201.32it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 139.40it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:09<31:00,  9.35s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:14<22:31,  6.83s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:18<18:53,  5.75s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:22<16:33,  5.07s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:27<15:19,  4.72s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:30<14:14,  4.40s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:34<13:47,  4.29s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:38<12:55,  4.04s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:41<12:25,  3.91s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:45<12:10,  3.85s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:49<11:49,  3.75s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:52<11:21,  3.62s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:56<11:18,  3.63s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [01:00<11:25,  3.69s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [01:03<11:01,  3.57s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [01:06<10:59,  3.59s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [01:10<10:57,  3.59s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [01:14<10:51,  3.58s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [01:17<10:53,  3.61s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [01:22<11:30,  3.83s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [01:25<10:57,  3.68s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [01:29<10:54,  3.68s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:33<11:06,  3.76s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:36<11:07,  3.79s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:40<10:58,  3.76s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:44<10:51,  3.75s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:48<10:59,  3.81s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:51<10:48,  3.77s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:55<10:51,  3.81s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:59<10:45,  3.80s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [02:03<10:51,  3.86s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [02:07<10:38,  3.80s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [02:10<10:25,  3.75s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [02:14<10:23,  3.75s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [02:19<10:48,  3.93s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [02:23<10:53,  3.98s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [02:27<10:59,  4.05s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [02:32<11:28,  4.25s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [02:36<11:44,  4.37s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [02:41<12:11,  4.57s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [02:46<12:09,  4.59s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [02:50<11:52,  4.51s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [02:56<12:39,  4.84s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [03:01<12:28,  4.80s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [03:05<12:11,  4.72s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [03:10<11:55,  4.64s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [03:14<11:44,  4.61s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [03:19<11:37,  4.59s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [03:23<11:19,  4.50s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [03:27<11:16,  4.51s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [03:32<11:23,  4.58s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [03:37<11:10,  4.53s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [03:41<11:06,  4.53s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [03:46<11:01,  4.53s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [03:50<10:57,  4.53s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [03:55<10:46,  4.49s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [03:59<10:41,  4.49s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [04:04<10:45,  4.54s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [04:08<10:31,  4.48s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [04:12<10:14,  4.39s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [04:17<10:07,  4.37s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [04:20<09:33,  4.15s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [04:24<09:04,  3.98s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [04:28<08:51,  3.91s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [04:31<08:39,  3.84s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [04:35<08:23,  3.76s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [04:39<08:24,  3.79s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [04:42<08:15,  3.75s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [04:46<08:09,  3.74s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [04:50<08:09,  3.77s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [04:53<07:46,  3.62s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [04:57<07:38,  3.58s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [05:00<07:29,  3.54s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [05:03<07:05,  3.37s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [05:06<07:00,  3.36s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [05:10<06:50,  3.31s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [05:13<06:39,  3.24s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [05:16<06:29,  3.20s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [05:19<06:25,  3.18s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [05:22<06:19,  3.17s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [05:25<06:23,  3.22s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [05:28<06:10,  3.14s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [05:31<06:07,  3.14s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [05:34<05:59,  3.10s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [05:37<05:51,  3.06s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [05:40<05:45,  3.03s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [05:44<05:50,  3.11s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [05:48<06:12,  3.32s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [05:50<05:55,  3.20s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [05:53<05:43,  3.12s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [05:56<05:37,  3.09s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [05:59<05:25,  3.01s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [06:02<05:16,  2.96s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [06:05<05:16,  2.99s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [06:08<05:12,  2.97s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [06:11<05:10,  2.98s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [06:14<05:12,  3.03s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [06:17<05:12,  3.07s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [06:20<05:08,  3.05s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [06:23<05:00,  3.01s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [06:27<05:06,  3.09s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [06:33<06:17,  3.86s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.22s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.95it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.06s/it]\u001b[A\n",
-      "Evaluating NLL: 28it [00:01, 32.74it/s]\u001b[A\n",
-      "Evaluating NLL: 53it [00:01, 63.97it/s]\u001b[A\n",
-      "Evaluating NLL: 88it [00:01, 112.00it/s]\u001b[A\n",
-      "Evaluating NLL: 115it [00:01, 142.78it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 120.08it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   3%|▍              | 3/100 [00:00<00:03, 24.92it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   9%|█▎             | 9/100 [00:00<00:02, 42.14it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  14%|█▉            | 14/100 [00:00<00:02, 36.33it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  31%|████▎         | 31/100 [00:00<00:00, 79.81it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  40%|█████▌        | 40/100 [00:00<00:00, 65.17it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  48%|██████▋       | 48/100 [00:00<00:00, 56.56it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  55%|███████▋      | 55/100 [00:01<00:00, 45.90it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  75%|██████████▌   | 75/100 [00:01<00:00, 77.15it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  85%|███████████▉  | 85/100 [00:01<00:00, 59.55it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  93%|█████████████ | 93/100 [00:01<00:00, 56.01it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:01<00:00, 54.56it/s]\n",
-      " 60%|███████████████████████████████████▍                       | 3/5 [1:01:09<41:43, 1251.55s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.038737691793173\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%|█▉                   | 9/100 [00:00<00:01, 83.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|███▌                | 18/100 [00:00<00:01, 51.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|████▊               | 24/100 [00:00<00:01, 46.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|█████▊              | 29/100 [00:00<00:01, 44.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|███████▏            | 36/100 [00:00<00:01, 49.57it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|████████▍           | 42/100 [00:00<00:01, 45.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█████████▍          | 47/100 [00:00<00:01, 44.27it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██████████▍         | 52/100 [00:01<00:01, 42.67it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|███████████▊        | 59/100 [00:01<00:00, 47.86it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|████████████▊       | 64/100 [00:01<00:00, 45.10it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  69%|█████████████▊      | 69/100 [00:01<00:00, 43.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  74%|██████████████▊     | 74/100 [00:01<00:00, 42.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|███████████████▊    | 79/100 [00:01<00:00, 41.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|█████████████████▍  | 87/100 [00:01<00:00, 48.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|██████████████████▍ | 92/100 [00:01<00:00, 45.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 45.61it/s]\n",
+      " 40%|███████████████████████████▏                                        | 2/5 [32:27<49:11, 983.76s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:16,  5.21s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:40,  2.93s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:11,  2.19s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:01,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:19,  1.64s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:48,  1.49s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:29,  1.40s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:16,  1.34s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:05,  1.29s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:00,  1.26s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:55,  1.25s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<03:54,  1.26s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<03:54,  1.26s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:52,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:50,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:50,  1.26s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:47,  1.25s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:45,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:43,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:40,  1.23s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:36,  1.21s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:35,  1.22s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:32,  1.21s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:28,  1.19s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:32,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:36,  1.26s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:38,  1.28s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:39,  1.29s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:40,  1.31s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:40,  1.31s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:39,  1.32s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:38,  1.32s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<03:36,  1.31s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:36,  1.32s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:36,  1.33s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:28,  1.29s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:53<03:30,  1.31s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:25,  1.28s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:18,  1.25s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:14,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:10,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:07,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:04,  1.19s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<03:01,  1.18s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:02<02:59,  1.17s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:03<02:55,  1.16s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:04<02:54,  1.15s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:06<02:55,  1.17s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:07<03:02,  1.22s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:08<03:03,  1.24s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:09<02:58,  1.21s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:11<03:02,  1.25s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:12<03:00,  1.24s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:13<02:55,  1.22s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:14<02:55,  1.23s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:16<03:00,  1.27s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:17<03:02,  1.30s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:18<03:03,  1.31s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:20<03:04,  1.33s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:21<03:04,  1.34s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:23<03:04,  1.35s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:24<03:04,  1.36s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:25<03:02,  1.35s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:27<03:00,  1.35s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:28<02:59,  1.35s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:29<02:58,  1.35s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:31<02:58,  1.37s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:32<02:57,  1.37s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:33<02:50,  1.32s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:35<02:46,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:36<02:47,  1.32s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:37<02:47,  1.33s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:39<02:46,  1.33s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:40<02:47,  1.35s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:41<02:47,  1.36s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:47,  1.37s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:44<02:44,  1.36s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:45<02:42,  1.35s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:47<02:40,  1.35s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:48<02:39,  1.35s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:37,  1.34s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:51<02:53,  1.49s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:53<02:50,  1.49s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:54<02:41,  1.41s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:55<02:33,  1.36s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:56<02:26,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:58<02:22,  1.29s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:59<02:20,  1.27s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:17,  1.26s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:16,  1.26s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:03<02:14,  1.26s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:04<02:11,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:05<02:13,  1.27s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:06<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:08<02:12,  1.29s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:09<02:10,  1.28s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:10<02:06,  1.25s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<02:04,  1.25s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<02:02,  1.23s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:14<02:03,  1.26s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:15<02:05,  1.29s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:17<02:06,  1.32s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:18<02:06,  1.33s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:19<02:06,  1.35s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:21<02:06,  1.36s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:22<02:04,  1.35s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:24<02:02,  1.35s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:25<02:00,  1.34s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:26<01:56,  1.31s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:27<01:53,  1.29s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:28<01:48,  1.25s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:30<01:46,  1.23s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:31<01:42,  1.21s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:32<01:40,  1.20s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:33<01:39,  1.20s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:34<01:37,  1.19s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:36<01:37,  1.20s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:37<01:36,  1.21s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:38<01:34,  1.19s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:39<01:32,  1.19s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:40<01:31,  1.19s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:42<01:29,  1.18s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:43<01:29,  1.20s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:44<01:31,  1.24s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:45<01:30,  1.24s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:47<01:28,  1.22s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:48<01:25,  1.20s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:49<01:22,  1.18s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:50<01:20,  1.17s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:51<01:19,  1.17s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:52<01:18,  1.17s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:54<01:17,  1.18s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:55<01:16,  1.17s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:56<01:15,  1.18s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:57<01:14,  1.18s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:58<01:13,  1.18s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:59<01:11,  1.18s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:01<01:10,  1.17s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:02<01:09,  1.17s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:03<01:07,  1.17s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:04<01:06,  1.17s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:05<01:05,  1.17s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:06<01:04,  1.17s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:08<01:02,  1.16s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:09<01:01,  1.17s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:10<01:02,  1.21s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:11<01:03,  1.25s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:13<01:03,  1.28s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:14<01:03,  1.29s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:15<01:02,  1.29s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:17<01:01,  1.30s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:18<01:00,  1.30s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:19<00:59,  1.31s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:21<00:57,  1.31s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:22<00:56,  1.31s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:23<00:55,  1.31s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:25<00:54,  1.33s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:26<00:52,  1.32s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:27<00:51,  1.31s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:28<00:49,  1.29s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:30<00:49,  1.33s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:31<00:46,  1.29s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:32<00:44,  1.27s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:34<00:43,  1.27s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:35<00:43,  1.32s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:36<00:41,  1.30s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:38<00:40,  1.31s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:39<00:39,  1.32s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:40<00:38,  1.32s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:42<00:37,  1.34s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:43<00:35,  1.33s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:44<00:35,  1.36s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:46<00:33,  1.33s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:47<00:33,  1.39s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:49<00:34,  1.48s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:50<00:31,  1.44s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:52<00:29,  1.41s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:53<00:27,  1.38s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:54<00:25,  1.36s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [03:56<00:24,  1.36s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [03:57<00:22,  1.35s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [03:58<00:21,  1.34s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [03:59<00:19,  1.33s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [04:01<00:18,  1.32s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [04:02<00:17,  1.32s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [04:03<00:15,  1.33s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [04:05<00:14,  1.33s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [04:06<00:13,  1.31s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [04:07<00:11,  1.28s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [04:08<00:10,  1.25s/it]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [04:10<00:08,  1.24s/it]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [04:11<00:07,  1.23s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [04:12<00:06,  1.21s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [04:13<00:04,  1.21s/it]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [04:14<00:03,  1.21s/it]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [04:16<00:02,  1.21s/it]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [04:17<00:01,  1.19s/it]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [04:18<00:00,  1.29s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:01,  5.13s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:31,  2.89s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:54,  2.10s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:42,  1.75s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:03,  1.56s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:45,  1.47s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:28,  1.39s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:16,  1.33s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:07,  1.29s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:01,  1.27s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<04:02,  1.29s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<03:57,  1.27s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<03:54,  1.26s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:50,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:48,  1.24s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:45,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:50,  1.27s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:53,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:55,  1.32s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:56,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:57,  1.34s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:57,  1.35s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:58,  1.36s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:58,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:56,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:50,  1.34s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:48,  1.33s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:42,  1.31s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:39,  1.30s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:39,  1.30s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:40,  1.32s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:44,  1.35s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:41,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:36,  1.32s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:33,  1.31s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:52<03:30,  1.30s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:27,  1.29s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:55<03:29,  1.31s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:56<03:31,  1.33s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:32,  1.34s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:34,  1.37s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:36,  1.39s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:36,  1.40s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:33,  1.39s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:34,  1.40s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:30,  1.38s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:07<03:22,  1.34s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:17,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:19,  1.34s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:11,  1.30s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:13<03:13,  1.32s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<03:09,  1.30s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<03:08,  1.30s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<03:07,  1.30s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:18<03:09,  1.32s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<03:08,  1.33s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<03:07,  1.33s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:22<03:06,  1.33s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<03:05,  1.33s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<03:03,  1.33s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:26<03:03,  1.34s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:27<02:58,  1.31s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:29<03:07,  1.39s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:30<03:13,  1.45s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:31<03:00,  1.36s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:33<02:54,  1.32s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:34<02:52,  1.31s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:35<02:47,  1.29s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<02:46,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:38<02:44,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:39<02:42,  1.28s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:42,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:42<02:38,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:43<02:33,  1.24s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:44<02:33,  1.25s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:30,  1.24s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:46<02:27,  1.22s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:48<02:26,  1.22s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:24,  1.22s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:50<02:26,  1.24s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:51<02:28,  1.27s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:53<02:29,  1.29s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:29,  1.30s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:55<02:29,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:57<02:28,  1.32s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:58<02:29,  1.33s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:27,  1.33s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:01<02:26,  1.33s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:02<02:24,  1.33s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:03<02:23,  1.33s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:05<02:23,  1.34s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:06<02:20,  1.33s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:07<02:15,  1.29s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:09<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:10<02:11,  1.27s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:11<02:09,  1.27s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:09,  1.28s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:14<02:07,  1.27s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:15<02:06,  1.27s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:16<02:03,  1.26s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:17<02:02,  1.26s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:19<02:01,  1.27s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:20<02:00,  1.27s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:21<02:01,  1.30s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:23<01:59,  1.28s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:24<01:57,  1.28s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:25<01:55,  1.27s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:26<01:54,  1.27s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:28<01:51,  1.25s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:29<01:50,  1.26s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:30<01:49,  1.25s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:31<01:47,  1.26s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:33<01:49,  1.29s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:34<01:49,  1.30s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:35<01:49,  1.32s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:37<01:48,  1.32s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:38<01:47,  1.33s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:39<01:46,  1.33s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:41<01:44,  1.33s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:42<01:43,  1.33s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:43<01:42,  1.33s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:45<01:40,  1.32s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:46<01:39,  1.33s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:47<01:36,  1.30s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:49<01:33,  1.28s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:50<01:35,  1.33s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:51<01:31,  1.29s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:52<01:28,  1.26s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:54<01:25,  1.24s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:55<01:23,  1.23s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:56<01:23,  1.25s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:57<01:21,  1.23s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:58<01:19,  1.22s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:00<01:17,  1.22s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:01<01:15,  1.20s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:02<01:16,  1.23s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:03<01:16,  1.25s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:05<01:15,  1.25s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:06<01:12,  1.23s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:07<01:11,  1.24s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:08<01:12,  1.27s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:10<01:09,  1.24s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:11<01:07,  1.23s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:12<01:05,  1.21s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:13<01:06,  1.26s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:15<01:07,  1.30s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:16<01:07,  1.32s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:17<01:06,  1.33s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:19<01:05,  1.35s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:20<01:04,  1.35s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:22<01:03,  1.36s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:23<01:02,  1.37s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:24<01:01,  1.37s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:26<00:59,  1.36s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:27<00:58,  1.36s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:28<00:57,  1.36s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:30<00:55,  1.36s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:32<00:53,  1.33s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:03,  5.45s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<10:03,  3.05s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:30,  2.29s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:15,  1.92s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:33,  1.71s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:17,  1.64s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:14<05:26,  1.69s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<04:57,  1.55s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:37,  1.45s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:21,  1.38s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:13,  1.34s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:06,  1.31s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:02,  1.30s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:00,  1.29s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<03:57,  1.28s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<03:53,  1.27s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<03:51,  1.26s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:50,  1.27s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<03:48,  1.26s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:46,  1.26s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:45,  1.26s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:43,  1.26s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:41,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:40,  1.25s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:37,  1.24s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:42,  1.28s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:44,  1.30s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:46,  1.31s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:47,  1.33s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:47,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:49,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:48,  1.36s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:45,  1.35s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:41,  1.34s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<03:34,  1.30s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:22,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:17,  1.21s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:14,  1.20s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:10,  1.18s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:55<03:08,  1.18s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:56<03:07,  1.18s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:05,  1.17s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:05,  1.18s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:00<03:05,  1.19s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:01<03:04,  1.19s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:02<03:01,  1.18s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:04<03:00,  1.18s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:05<03:06,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:06<03:04,  1.22s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:07<03:01,  1.21s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:09<03:04,  1.24s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:10<03:02,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:11<02:57,  1.21s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:12<02:55,  1.20s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:13<02:53,  1.20s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:15<02:51,  1.19s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:16<02:50,  1.19s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:17<02:50,  1.20s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:18<02:51,  1.22s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:19<02:49,  1.21s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:21<02:47,  1.20s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:22<02:46,  1.20s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:23<02:44,  1.20s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:24<02:41,  1.19s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:25<02:40,  1.19s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:27<02:41,  1.20s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:28<02:39,  1.20s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:29<02:38,  1.20s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:30<02:36,  1.20s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:31<02:39,  1.23s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:33<02:44,  1.27s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:34<02:45,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:35<02:45,  1.30s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:37<02:44,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:38<02:44,  1.32s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:39<02:43,  1.32s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:41<02:41,  1.31s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:42<02:39,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:43<02:38,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:45<02:38,  1.32s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:46<02:39,  1.34s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:47<02:37,  1.34s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:32,  1.31s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:50<02:34,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:51<02:30,  1.31s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:53<02:26,  1.29s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:54<02:24,  1.28s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:55<02:26,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:26,  1.32s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:58<02:27,  1.34s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:59<02:26,  1.35s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:26,  1.36s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:02<02:24,  1.35s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:03<02:23,  1.36s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:05<02:23,  1.36s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:06<02:21,  1.36s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:07<02:20,  1.36s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:09<02:18,  1.36s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:10<02:17,  1.36s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<02:13,  1.34s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<02:15,  1.36s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:14<02:12,  1.35s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:16<02:12,  1.36s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:17<02:10,  1.36s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:18<02:09,  1.36s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:20<02:07,  1.36s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:21<02:07,  1.37s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:22<02:05,  1.37s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:24<02:04,  1.37s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:25<02:04,  1.38s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:27<02:04,  1.40s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:28<02:01,  1.38s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:29<01:59,  1.38s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:31<02:06,  1.48s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:33<02:06,  1.49s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:34<01:58,  1.41s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:35<01:50,  1.34s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:36<01:45,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:37<01:41,  1.25s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:39<01:38,  1.24s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:40<01:37,  1.23s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:41<01:35,  1.22s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:42<01:33,  1.21s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:43<01:31,  1.20s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:44<01:29,  1.19s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:46<01:29,  1.21s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:47<01:28,  1.21s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:48<01:26,  1.20s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:49<01:24,  1.19s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:50<01:23,  1.19s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:52<01:23,  1.20s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:53<01:22,  1.21s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:54<01:21,  1.22s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:55<01:21,  1.23s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:57<01:19,  1.22s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:58<01:17,  1.21s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:59<01:15,  1.20s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:00<01:14,  1.20s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:01<01:13,  1.20s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:03<01:11,  1.19s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:04<01:10,  1.19s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:05<01:08,  1.19s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:06<01:08,  1.20s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:07<01:07,  1.20s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:08<01:04,  1.18s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:10<01:04,  1.19s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:11<01:03,  1.20s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:12<01:02,  1.20s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:13<01:01,  1.20s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:15<01:00,  1.21s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:16<01:01,  1.25s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:17<01:01,  1.28s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:19<01:00,  1.29s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:20<00:59,  1.30s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:21<00:58,  1.31s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:23<00:58,  1.32s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:24<00:57,  1.34s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:25<00:55,  1.32s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:27<00:54,  1.32s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:29<00:52,  1.31s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:35,  5.31s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:37,  2.92s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:14,  2.21s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:58,  1.83s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:17,  1.63s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:51,  1.50s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:34,  1.42s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:22,  1.37s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:14,  1.33s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:07,  1.31s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:12,  1.34s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<04:06,  1.32s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:04,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<04:06,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<04:08,  1.35s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<04:09,  1.36s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<04:09,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<04:06,  1.36s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<04:02,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<04:00,  1.34s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<04:01,  1.36s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:59,  1.35s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:52,  1.32s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:54,  1.34s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:44,  1.29s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:36,  1.25s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:32,  1.24s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:39,  1.28s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:32,  1.25s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:35,  1.27s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:39,  1.31s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:41,  1.33s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:43,  1.34s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:47,  1.38s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:45,  1.37s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:42,  1.37s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:40,  1.36s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:55<03:52,  1.44s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:57<03:52,  1.45s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:58<03:37,  1.37s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:28,  1.32s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:20,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:16,  1.26s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:03<03:13,  1.25s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:04<03:11,  1.24s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:09,  1.24s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:06,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:08<03:05,  1.23s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:05,  1.24s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:03,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:02,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:12<03:01,  1.23s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<02:58,  1.22s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<02:58,  1.23s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<03:00,  1.25s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:17<02:58,  1.24s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<02:57,  1.25s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<02:59,  1.27s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:21<02:59,  1.28s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<02:59,  1.29s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<02:59,  1.30s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<02:58,  1.30s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:27<02:58,  1.31s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:28<02:58,  1.32s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<02:57,  1.32s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:31<02:56,  1.33s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:32<02:56,  1.33s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:33<02:55,  1.34s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:35<02:53,  1.34s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<02:49,  1.31s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<02:44,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:38<02:41,  1.27s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:39,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:41<02:39,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:42<02:37,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:43<02:35,  1.27s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:34,  1.26s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:46<02:32,  1.26s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:47<02:35,  1.30s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:32,  1.29s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:50<02:31,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:51<02:28,  1.27s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:52<02:26,  1.27s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:23,  1.25s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:55<02:21,  1.24s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:56<02:21,  1.25s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:57<02:20,  1.25s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:19,  1.26s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:00<02:17,  1.25s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:01<02:18,  1.27s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:02<02:19,  1.29s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:04<02:19,  1.30s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:05<02:18,  1.31s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:06<02:17,  1.31s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:08<02:18,  1.33s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:09<02:16,  1.33s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:10<02:15,  1.33s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:15,  1.34s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:13<02:14,  1.35s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:15<02:14,  1.36s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:16<02:15,  1.39s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:17<02:12,  1.36s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:19<02:08,  1.34s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:20<02:04,  1.31s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:21<02:02,  1.30s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:22<01:58,  1.27s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:24<01:54,  1.25s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:25<01:52,  1.24s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:26<01:49,  1.22s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:27<01:48,  1.22s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:29<01:51,  1.27s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:30<01:50,  1.27s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:31<01:47,  1.24s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:32<01:44,  1.23s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:34<01:45,  1.26s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:35<01:46,  1.28s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:36<01:44,  1.27s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:37<01:41,  1.26s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:39<01:42,  1.28s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:40<01:43,  1.31s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:41<01:42,  1.32s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:43<01:42,  1.33s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:44<01:41,  1.34s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:45<01:41,  1.35s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:47<01:40,  1.35s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:48<01:38,  1.35s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:50<01:37,  1.35s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:51<01:36,  1.36s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:52<01:35,  1.36s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:54<01:33,  1.36s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:55<01:32,  1.35s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:56<01:30,  1.36s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:58<01:28,  1.34s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:59<01:28,  1.36s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:00<01:26,  1.35s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:02<01:25,  1.36s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:03<01:24,  1.36s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:05<01:23,  1.37s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:06<01:22,  1.37s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:07<01:20,  1.37s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:09<01:19,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:10<01:18,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:11<01:17,  1.38s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:13<01:15,  1.38s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:14<01:14,  1.38s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:16<01:20,  1.53s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:17<01:17,  1.48s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:19<01:12,  1.42s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:20<01:08,  1.36s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:21<01:05,  1.34s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:22<01:03,  1.31s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:24<01:00,  1.29s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:25<00:58,  1.28s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:26<00:57,  1.27s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:27<00:55,  1.25s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:29<00:53,  1.24s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:30<00:52,  1.25s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:31<00:51,  1.26s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:32<00:50,  1.26s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:34<00:48,  1.25s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:35<00:47,  1.26s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:36<00:46,  1.25s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:37<00:44,  1.24s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:39<00:43,  1.26s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:40<00:42,  1.26s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:41<00:41,  1.26s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:43<00:41,  1.29s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:44<00:40,  1.31s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:45<00:40,  1.34s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:47<00:38,  1.34s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:48<00:37,  1.34s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:49<00:36,  1.34s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:51<00:34,  1.34s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:52<00:33,  1.33s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:53<00:31,  1.32s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:55<00:30,  1.31s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:56<00:27,  1.27s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:57<00:25,  1.22s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:58<00:24,  1.20s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:59<00:22,  1.20s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [04:00<00:21,  1.19s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [04:02<00:20,  1.20s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [04:03<00:19,  1.19s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [04:04<00:17,  1.18s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [04:05<00:16,  1.20s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [04:06<00:15,  1.21s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [04:09<00:15,  1.33s/it]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:07<26:27,  7.98s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:11<17:52,  5.42s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:15<14:48,  4.51s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:18<13:56,  4.27s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:22<12:45,  3.92s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:25<12:24,  3.84s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:29<12:15,  3.81s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:32<11:39,  3.65s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:36<11:18,  3.55s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:39<10:57,  3.46s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:42<10:46,  3.42s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:46<10:59,  3.51s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:49<10:49,  3.47s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:53<10:51,  3.50s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:57<10:47,  3.50s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [01:00<10:36,  3.46s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [01:03<10:27,  3.43s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [01:07<10:33,  3.48s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [01:11<10:52,  3.60s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [01:15<10:56,  3.65s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [01:18<10:53,  3.65s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [01:22<10:49,  3.65s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:25<10:40,  3.62s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:29<10:44,  3.66s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:33<10:35,  3.63s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:37<11:17,  3.89s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:41<10:57,  3.80s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:44<10:47,  3.77s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:48<10:29,  3.68s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:51<10:03,  3.55s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:55<10:14,  3.64s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:58<10:01,  3.58s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [02:02<09:49,  3.53s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [02:06<09:51,  3.56s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [02:09<09:58,  3.63s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [02:13<10:07,  3.71s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [02:17<09:47,  3.60s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [02:20<09:38,  3.57s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [02:24<09:32,  3.56s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [02:27<09:22,  3.51s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [02:30<09:14,  3.49s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [02:34<09:14,  3.51s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [02:38<09:15,  3.54s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [02:41<08:58,  3.45s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [02:44<08:48,  3.41s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [02:47<08:38,  3.37s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [02:51<08:36,  3.38s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [02:54<08:27,  3.34s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [02:58<08:32,  3.40s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [03:01<08:38,  3.46s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [03:05<08:29,  3.42s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [03:08<08:28,  3.44s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [03:11<08:10,  3.34s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [03:15<08:31,  3.51s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [03:18<08:26,  3.49s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [03:22<08:18,  3.46s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [03:25<08:16,  3.47s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [03:29<08:14,  3.48s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [03:32<08:14,  3.51s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [03:36<08:19,  3.57s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [03:40<08:22,  3.62s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [03:44<08:30,  3.70s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [03:48<08:30,  3.73s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [03:51<08:31,  3.76s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [03:55<08:21,  3.72s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [03:59<08:15,  3.70s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [04:03<08:25,  3.80s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [04:07<08:30,  3.87s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [04:11<08:46,  4.02s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [04:15<08:26,  3.89s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [04:19<08:55,  4.15s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [04:24<08:56,  4.19s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [04:27<08:34,  4.05s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [04:31<08:21,  3.98s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [04:35<08:22,  4.02s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [04:39<08:15,  3.99s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [04:43<07:52,  3.84s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [04:47<07:51,  3.86s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [04:51<07:46,  3.85s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [04:54<07:40,  3.83s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [04:58<07:33,  3.81s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [05:02<07:25,  3.77s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [05:06<07:28,  3.83s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [05:10<07:22,  3.82s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [05:13<07:12,  3.76s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [05:17<07:01,  3.69s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [05:20<06:57,  3.69s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [05:24<06:58,  3.73s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [05:28<06:57,  3.76s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [05:32<06:45,  3.68s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [05:35<06:45,  3.72s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [05:39<06:43,  3.74s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [05:43<06:42,  3.76s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [05:47<06:36,  3.74s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [05:50<06:25,  3.68s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [05:54<06:18,  3.64s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [05:58<06:29,  3.78s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [06:01<06:19,  3.72s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [06:05<06:15,  3.72s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [06:09<06:20,  3.81s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [06:13<06:12,  3.77s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [06:17<06:14,  3.82s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [06:21<06:13,  3.85s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [06:25<06:09,  3.85s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [06:28<05:57,  3.76s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [06:32<05:57,  3.80s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [06:36<05:58,  3.85s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [06:40<05:57,  3.89s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [06:44<05:54,  3.89s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [06:48<05:51,  3.90s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [06:51<05:42,  3.84s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [06:55<05:41,  3.88s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [06:59<05:34,  3.84s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [07:04<05:47,  4.04s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [07:08<05:46,  4.08s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [07:12<05:32,  3.96s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [07:15<05:22,  3.88s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [07:19<05:18,  3.88s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [07:23<05:09,  3.82s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [07:26<05:00,  3.76s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [07:30<04:47,  3.64s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [07:34<04:46,  3.67s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [07:37<04:36,  3.59s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [07:40<04:22,  3.46s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [07:43<04:15,  3.41s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [07:47<04:07,  3.34s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [07:50<04:04,  3.35s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [07:53<04:04,  3.39s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▌                 | 129/200 [07:57<03:58,  3.36s/it]\u001b[A\n",
-      "training:  65%|███████████████████████████████▊                 | 130/200 [08:00<03:54,  3.35s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████                 | 131/200 [08:03<03:49,  3.33s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▎                | 132/200 [08:07<03:47,  3.35s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▌                | 133/200 [08:10<03:43,  3.33s/it]\u001b[A\n",
-      "training:  67%|████████████████████████████████▊                | 134/200 [08:13<03:37,  3.29s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████                | 135/200 [08:20<04:00,  3.70s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:21,  4.93s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:05,  2.75s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:45,  2.06s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:35,  1.71s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:59,  1.53s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:29,  1.40s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:20,  1.36s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:10,  1.31s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:00,  1.27s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:00,  1.27s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:58,  1.27s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:54,  1.25s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:49,  1.23s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:47,  1.23s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:55,  1.28s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:51,  1.27s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:54,  1.29s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:55,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:57,  1.32s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:55,  1.31s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:54,  1.32s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:52,  1.31s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:50,  1.31s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:52,  1.33s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:56,  1.36s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:54,  1.36s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:52,  1.35s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:50,  1.35s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:49,  1.35s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:44,  1.33s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:38,  1.30s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:39,  1.31s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:40,  1.33s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<03:41,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:42,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:41,  1.36s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:52<03:39,  1.36s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:38,  1.36s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:55<03:36,  1.36s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:36,  1.36s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:35,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:34,  1.37s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:32,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:31,  1.36s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:30,  1.36s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:30,  1.38s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:28,  1.37s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:08<03:32,  1.41s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:30,  1.40s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:29,  1.40s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:12<03:25,  1.39s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:13<03:33,  1.46s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:15<03:30,  1.44s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:16<03:30,  1.45s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:18<03:48,  1.58s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:20<03:35,  1.51s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:21<03:27,  1.46s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:22<03:21,  1.43s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:23<03:12,  1.38s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:25<03:07,  1.35s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:26<03:03,  1.33s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:27<03:01,  1.32s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:29<02:58,  1.32s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:30<02:55,  1.30s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:31<02:54,  1.30s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:33<02:52,  1.30s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:34<02:51,  1.30s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:35<02:49,  1.30s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:36<02:46,  1.28s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:38<02:46,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:39<02:44,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:40<02:44,  1.29s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:42<02:43,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:43<02:41,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:44<02:40,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:45<02:37,  1.28s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:47<02:34,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:48<02:33,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:49<02:32,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:52<02:44,  1.39s/it]\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.17s/it]\u001b[A\n",
-      "Evaluating NLL: 3it [00:01,  2.90it/s]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.90it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.23s/it]\u001b[A\n",
-      "Evaluating NLL: 28it [00:01, 28.85it/s]\u001b[A\n",
-      "Evaluating NLL: 54it [00:01, 58.74it/s]\u001b[A\n",
-      "Evaluating NLL: 105it [00:01, 129.14it/s]\u001b[A\n",
-      "Evaluating NLL: 138it [00:01, 152.61it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 107.12it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   9%|█▎             | 9/100 [00:00<00:01, 87.54it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  18%|██▌           | 18/100 [00:00<00:01, 60.77it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  30%|████▏         | 30/100 [00:00<00:00, 78.84it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  39%|█████▍        | 39/100 [00:00<00:01, 60.29it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  62%|████████▋     | 62/100 [00:00<00:00, 93.97it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  73%|██████████▏   | 73/100 [00:00<00:00, 89.30it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  83%|███████████▌  | 83/100 [00:01<00:00, 71.62it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:01<00:00, 73.91it/s]\n",
-      " 80%|███████████████████████████████████████████████▏           | 4/5 [1:23:11<21:19, 1279.29s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -2.754847795797976\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.87it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:10<34:14, 10.33s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:14<23:06,  7.00s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:19<19:13,  5.85s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:24<17:35,  5.39s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:28<16:48,  5.17s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:33<16:04,  4.97s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:37<15:17,  4.75s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:42<15:12,  4.75s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:47<15:03,  4.73s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:51<14:54,  4.71s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:56<14:51,  4.72s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [01:01<14:58,  4.78s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [01:05<14:31,  4.66s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [01:10<14:10,  4.57s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [01:15<14:48,  4.80s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [01:20<14:44,  4.81s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [01:25<14:26,  4.73s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [01:29<13:43,  4.53s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [01:33<13:07,  4.35s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [01:37<12:50,  4.28s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [01:40<12:15,  4.11s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [01:44<11:44,  3.96s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:48<11:51,  4.02s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:52<11:24,  3.89s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:56<11:15,  3.86s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [02:00<11:43,  4.04s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [02:04<11:27,  3.98s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [02:07<11:04,  3.86s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [02:11<11:09,  3.91s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [02:15<10:45,  3.80s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [02:19<10:29,  3.73s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [02:23<10:41,  3.82s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [02:26<10:42,  3.85s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [02:31<11:10,  4.04s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [02:35<11:24,  4.15s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [02:39<11:11,  4.10s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [02:44<11:14,  4.14s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [02:47<10:49,  4.01s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [02:51<10:11,  3.80s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [02:54<09:59,  3.75s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [02:58<10:12,  3.85s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [03:02<10:19,  3.92s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [03:06<09:51,  3.77s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [03:09<09:32,  3.67s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [03:13<09:25,  3.65s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [03:16<09:22,  3.65s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [03:20<08:52,  3.48s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [03:23<08:49,  3.48s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [03:27<08:44,  3.47s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [03:30<08:38,  3.45s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [03:34<09:07,  3.68s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [03:38<09:07,  3.70s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [03:42<09:04,  3.70s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [03:45<08:48,  3.62s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [03:49<08:45,  3.62s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [03:52<08:43,  3.64s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [03:56<08:37,  3.62s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [03:59<08:33,  3.62s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [04:03<08:31,  3.63s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [04:07<08:23,  3.60s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [04:11<08:32,  3.69s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [04:14<08:25,  3.66s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [04:18<08:34,  3.76s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [04:23<09:16,  4.09s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [04:26<08:45,  3.89s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [04:30<08:31,  3.81s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [04:34<08:18,  3.75s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [04:37<08:00,  3.64s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [04:41<07:51,  3.60s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [04:44<07:37,  3.52s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [04:48<07:40,  3.57s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [04:51<07:34,  3.55s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [04:55<07:25,  3.51s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [04:58<07:22,  3.51s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [05:01<07:02,  3.38s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [05:05<07:09,  3.46s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [05:08<07:10,  3.50s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [05:12<07:06,  3.50s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [05:15<06:57,  3.45s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [05:19<06:53,  3.45s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [05:22<06:46,  3.42s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [05:25<06:43,  3.42s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [05:29<06:49,  3.50s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [05:33<06:56,  3.59s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [05:36<06:46,  3.53s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [05:39<06:20,  3.34s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [05:42<06:11,  3.29s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [05:45<05:48,  3.11s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [05:48<05:54,  3.19s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [05:52<05:52,  3.21s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [05:54<05:34,  3.07s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [05:58<05:34,  3.09s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [06:00<05:21,  3.00s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [06:04<05:25,  3.07s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [06:07<05:26,  3.11s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [06:10<05:23,  3.11s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [06:13<05:18,  3.09s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [06:16<05:19,  3.13s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [06:19<05:10,  3.08s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [06:22<05:15,  3.16s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [06:26<05:12,  3.15s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [06:29<05:27,  3.34s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [06:33<05:21,  3.31s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [06:36<05:30,  3.44s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [06:40<05:24,  3.41s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [06:43<05:19,  3.39s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [06:46<05:12,  3.36s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [06:50<05:19,  3.47s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [06:54<05:18,  3.50s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [06:58<05:39,  3.77s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [07:02<05:47,  3.90s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [07:06<05:52,  4.00s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [07:12<06:17,  4.34s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [07:16<06:09,  4.30s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [07:20<05:57,  4.21s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [07:24<05:48,  4.14s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [07:28<05:39,  4.09s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [07:32<05:29,  4.01s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [07:36<05:22,  3.98s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [07:39<05:18,  3.98s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [07:43<05:07,  3.89s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [07:47<05:02,  3.87s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [07:51<05:05,  3.97s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [07:55<04:57,  3.92s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [07:59<04:50,  3.87s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [08:02<04:39,  3.77s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [08:06<04:37,  3.80s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [08:10<04:36,  3.85s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▌                 | 129/200 [08:14<04:23,  3.72s/it]\u001b[A\n",
-      "training:  65%|███████████████████████████████▊                 | 130/200 [08:18<04:28,  3.84s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████                 | 131/200 [08:21<04:19,  3.76s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▎                | 132/200 [08:25<04:26,  3.91s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▌                | 133/200 [08:30<04:25,  3.96s/it]\u001b[A\n",
-      "training:  67%|████████████████████████████████▊                | 134/200 [08:34<04:21,  3.96s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████                | 135/200 [08:37<04:13,  3.90s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▎               | 136/200 [08:41<04:09,  3.90s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▌               | 137/200 [08:45<03:59,  3.81s/it]\u001b[A\n",
-      "training:  69%|█████████████████████████████████▊               | 138/200 [08:49<03:58,  3.85s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████               | 139/200 [08:53<04:00,  3.94s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▎              | 140/200 [08:57<03:56,  3.94s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▌              | 141/200 [09:01<03:49,  3.89s/it]\u001b[A\n",
-      "training:  71%|██████████████████████████████████▊              | 142/200 [09:04<03:45,  3.89s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████              | 143/200 [09:09<03:47,  4.00s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▎             | 144/200 [09:13<03:53,  4.17s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▌             | 145/200 [09:17<03:44,  4.08s/it]\u001b[A\n",
-      "training:  73%|███████████████████████████████████▊             | 146/200 [09:21<03:43,  4.14s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████             | 147/200 [09:26<03:39,  4.13s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▎            | 148/200 [09:30<03:32,  4.09s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▌            | 149/200 [09:34<03:27,  4.07s/it]\u001b[A\n",
-      "training:  75%|████████████████████████████████████▊            | 150/200 [09:39<03:38,  4.37s/it]\u001b[A\n",
-      "training:  76%|████████████████████████████████████▉            | 151/200 [09:43<03:38,  4.46s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▏           | 152/200 [09:47<03:27,  4.32s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▍           | 153/200 [09:52<03:26,  4.40s/it]\u001b[A\n",
-      "training:  77%|█████████████████████████████████████▋           | 154/200 [09:56<03:16,  4.26s/it]\u001b[A\n",
-      "training:  78%|█████████████████████████████████████▉           | 155/200 [10:01<03:17,  4.40s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▏          | 156/200 [10:05<03:09,  4.30s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▍          | 157/200 [10:09<03:04,  4.29s/it]\u001b[A\n",
-      "training:  79%|██████████████████████████████████████▋          | 158/200 [10:13<03:00,  4.31s/it]\u001b[A\n",
-      "training:  80%|██████████████████████████████████████▉          | 159/200 [10:18<02:57,  4.32s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▏         | 160/200 [10:22<02:50,  4.26s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▍         | 161/200 [10:26<02:48,  4.32s/it]\u001b[A\n",
-      "training:  81%|███████████████████████████████████████▋         | 162/200 [10:30<02:39,  4.21s/it]\u001b[A\n",
-      "training:  82%|███████████████████████████████████████▉         | 163/200 [10:34<02:36,  4.24s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▏        | 164/200 [10:39<02:34,  4.28s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▍        | 165/200 [10:43<02:29,  4.26s/it]\u001b[A\n",
-      "training:  83%|████████████████████████████████████████▋        | 166/200 [10:48<02:27,  4.34s/it]\u001b[A\n",
-      "training:  84%|████████████████████████████████████████▉        | 167/200 [10:52<02:27,  4.46s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▏       | 168/200 [10:57<02:26,  4.57s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▍       | 169/200 [11:02<02:24,  4.67s/it]\u001b[A\n",
-      "training:  85%|█████████████████████████████████████████▋       | 170/200 [11:06<02:16,  4.56s/it]\u001b[A\n",
-      "training:  86%|█████████████████████████████████████████▉       | 171/200 [11:11<02:11,  4.52s/it]\u001b[A\n",
-      "training:  86%|██████████████████████████████████████████▏      | 172/200 [11:15<02:08,  4.57s/it]\u001b[A\n",
-      "training:  86%|██████████████████████████████████████████▍      | 173/200 [11:20<02:03,  4.58s/it]\u001b[A\n",
-      "training:  87%|██████████████████████████████████████████▋      | 174/200 [11:25<02:00,  4.64s/it]\u001b[A\n",
-      "training:  88%|██████████████████████████████████████████▉      | 175/200 [11:29<01:55,  4.62s/it]\u001b[A\n",
-      "training:  88%|███████████████████████████████████████████      | 176/200 [11:34<01:48,  4.53s/it]\u001b[A\n",
-      "training:  88%|███████████████████████████████████████████▎     | 177/200 [11:38<01:45,  4.59s/it]\u001b[A\n",
-      "training:  89%|███████████████████████████████████████████▌     | 178/200 [11:43<01:39,  4.52s/it]\u001b[A\n",
-      "training:  90%|███████████████████████████████████████████▊     | 179/200 [11:47<01:35,  4.56s/it]\u001b[A\n",
-      "training:  90%|████████████████████████████████████████████     | 180/200 [11:52<01:31,  4.55s/it]\u001b[A\n",
-      "training:  90%|████████████████████████████████████████████▎    | 181/200 [11:57<01:26,  4.55s/it]\u001b[A\n",
-      "training:  91%|████████████████████████████████████████████▌    | 182/200 [12:02<01:25,  4.75s/it]\u001b[A\n",
-      "training:  92%|████████████████████████████████████████████▊    | 183/200 [12:07<01:21,  4.78s/it]\u001b[A\n",
-      "training:  92%|█████████████████████████████████████████████    | 184/200 [12:12<01:17,  4.87s/it]\u001b[A\n",
-      "training:  92%|█████████████████████████████████████████████▎   | 185/200 [12:17<01:15,  5.03s/it]\u001b[A\n",
-      "training:  93%|█████████████████████████████████████████████▌   | 186/200 [12:23<01:14,  5.31s/it]\u001b[A\n",
-      "training:  94%|█████████████████████████████████████████████▊   | 187/200 [12:28<01:05,  5.06s/it]\u001b[A\n",
-      "training:  94%|██████████████████████████████████████████████   | 188/200 [12:33<01:01,  5.12s/it]\u001b[A\n",
-      "training:  94%|██████████████████████████████████████████████▎  | 189/200 [12:37<00:53,  4.87s/it]\u001b[A\n",
-      "training:  95%|██████████████████████████████████████████████▌  | 190/200 [12:42<00:49,  4.98s/it]\u001b[A\n",
-      "training:  96%|██████████████████████████████████████████████▊  | 191/200 [12:47<00:44,  4.89s/it]\u001b[A\n",
-      "training:  96%|███████████████████████████████████████████████  | 192/200 [12:52<00:38,  4.80s/it]\u001b[A\n",
-      "training:  96%|███████████████████████████████████████████████▎ | 193/200 [12:56<00:33,  4.80s/it]\u001b[A\n",
-      "training:  97%|███████████████████████████████████████████████▌ | 194/200 [13:01<00:28,  4.82s/it]\u001b[A\n",
-      "training:  98%|███████████████████████████████████████████████▊ | 195/200 [13:06<00:24,  4.84s/it]\u001b[A\n",
-      "training:  98%|████████████████████████████████████████████████ | 196/200 [13:11<00:19,  4.81s/it]\u001b[A\n",
-      "training:  98%|████████████████████████████████████████████████▎| 197/200 [13:16<00:14,  4.80s/it]\u001b[A\n",
-      "training:  99%|████████████████████████████████████████████████▌| 198/200 [13:21<00:09,  4.83s/it]\u001b[A\n",
-      "training: 100%|████████████████████████████████████████████████▊| 199/200 [13:26<00:04,  4.92s/it]\u001b[A\n",
-      "training: 100%|█████████████████████████████████████████████████| 200/200 [13:31<00:00,  4.06s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.40s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.66it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.25s/it]\u001b[A\n",
-      "Evaluating NLL: 11it [00:01, 10.14it/s]\u001b[A\n",
-      "Evaluating NLL: 23it [00:01, 23.15it/s]\u001b[A\n",
-      "Evaluating NLL: 31it [00:01, 27.54it/s]\u001b[A\n",
-      "Evaluating NLL: 50it [00:01, 50.50it/s]\u001b[A\n",
-      "Evaluating NLL: 62it [00:01, 62.02it/s]\u001b[A\n",
-      "Evaluating NLL: 72it [00:02, 56.53it/s]\u001b[A\n",
-      "Evaluating NLL: 81it [00:02, 52.76it/s]\u001b[A\n",
-      "Evaluating NLL: 89it [00:02, 47.46it/s]\u001b[A\n",
-      "Evaluating NLL: 97it [00:02, 52.31it/s]\u001b[A\n",
-      "Evaluating NLL: 111it [00:02, 67.61it/s]\u001b[A\n",
-      "Evaluating NLL: 123it [00:03, 63.83it/s]\u001b[A\n",
-      "Evaluating NLL: 133it [00:03, 70.68it/s]\u001b[A\n",
-      "Evaluating NLL: 142it [00:03, 64.09it/s]\u001b[A\n",
-      "Evaluating NLL: 150it [00:03, 55.86it/s]\u001b[A\n",
-      "Evaluating NLL: 170it [00:03, 84.34it/s]\u001b[A\n",
-      "Evaluating NLL: 181it [00:03, 83.53it/s]\u001b[A\n",
-      "Evaluating NLL: 191it [00:03, 72.44it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:04, 47.44it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   4%|▌              | 4/100 [00:00<00:02, 37.45it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   8%|█▏             | 8/100 [00:00<00:02, 31.39it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  16%|██▏           | 16/100 [00:00<00:01, 46.08it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  21%|██▉           | 21/100 [00:00<00:01, 42.50it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  26%|███▋          | 26/100 [00:00<00:01, 44.25it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  62%|████████     | 62/100 [00:00<00:00, 138.91it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  78%|██████████▉   | 78/100 [00:01<00:00, 69.20it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  90%|████████████▌ | 90/100 [00:01<00:00, 56.46it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:01<00:00, 58.48it/s]\n",
-      "100%|███████████████████████████████████████████████████████████| 5/5 [1:49:51<00:00, 1318.36s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "white_noise\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|                                                                       | 0/5 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -7.95673775135381\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.56it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:08<27:28,  8.28s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:10<15:58,  4.84s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:13<12:23,  3.77s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:15<10:57,  3.35s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:18<10:01,  3.09s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:21<09:29,  2.93s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:23<09:16,  2.88s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:26<09:15,  2.89s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:29<09:14,  2.91s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:32<08:52,  2.80s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:35<08:43,  2.77s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:37<08:15,  2.64s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:40<08:11,  2.63s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:42<08:07,  2.62s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:45<08:10,  2.65s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:47<07:54,  2.58s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:50<07:51,  2.58s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:52<07:50,  2.58s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:55<07:43,  2.56s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:57<07:36,  2.54s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [01:00<07:37,  2.56s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [01:02<07:29,  2.53s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:05<07:21,  2.49s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:08<07:40,  2.62s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:10<07:29,  2.57s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:13<07:18,  2.52s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:15<07:16,  2.53s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:18<07:16,  2.54s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:20<07:15,  2.55s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:23<07:18,  2.58s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:26<07:29,  2.66s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:28<07:17,  2.61s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:31<07:34,  2.72s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:34<07:19,  2.65s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:37<07:21,  2.68s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:39<07:19,  2.68s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:41<06:55,  2.55s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:44<06:51,  2.54s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:46<06:45,  2.52s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:49<06:51,  2.57s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:52<06:47,  2.56s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:55<07:14,  2.75s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:57<06:54,  2.64s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [02:00<07:02,  2.71s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [02:06<07:15,  2.81s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.28s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.82it/s]\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.73it/s]\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.04s/it]\u001b[A\n",
-      "Evaluating NLL: 25it [00:01, 29.79it/s]\u001b[A\n",
-      "Evaluating NLL: 53it [00:01, 66.35it/s]\u001b[A\n",
-      "Evaluating NLL: 83it [00:01, 106.54it/s]\u001b[A\n",
-      "Evaluating NLL: 114it [00:01, 145.92it/s]\u001b[A\n",
-      "Evaluating NLL: 141it [00:01, 171.56it/s]\u001b[A\n",
-      "Evaluating NLL: 168it [00:01, 186.87it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 110.83it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   3%|▍              | 3/100 [00:00<00:03, 26.49it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   6%|▉              | 6/100 [00:00<00:03, 27.15it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  14%|█▉            | 14/100 [00:00<00:01, 48.28it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  19%|██▋           | 19/100 [00:00<00:02, 38.83it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  24%|███▎          | 24/100 [00:00<00:02, 34.75it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  37%|█████▏        | 37/100 [00:00<00:01, 57.96it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  44%|██████▏       | 44/100 [00:01<00:01, 45.92it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  53%|███████▍      | 53/100 [00:01<00:00, 55.31it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  60%|████████▍     | 60/100 [00:01<00:00, 42.28it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  66%|█████████▏    | 66/100 [00:01<00:00, 40.28it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  71%|█████████▉    | 71/100 [00:01<00:00, 37.84it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:02<00:00, 47.98it/s]\n",
-      " 20%|███████████▊                                               | 1/5 [17:35<1:10:23, 1055.84s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -4.490379101157371\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.92it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:08<28:39,  8.64s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:11<17:34,  5.33s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:14<13:43,  4.18s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:17<11:56,  3.66s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:20<11:12,  3.45s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:23<10:17,  3.18s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:25<09:58,  3.10s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:28<09:44,  3.04s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:31<09:16,  2.92s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:34<08:49,  2.78s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:36<08:52,  2.82s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:39<08:36,  2.74s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:42<08:30,  2.73s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:44<08:29,  2.74s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:47<08:38,  2.80s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:51<08:51,  2.89s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:53<08:39,  2.84s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:56<08:51,  2.92s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:59<08:59,  2.98s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [01:02<08:51,  2.95s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [01:05<08:36,  2.88s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [01:08<08:39,  2.92s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:11<08:24,  2.85s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:14<08:21,  2.85s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:17<08:22,  2.87s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:20<08:24,  2.90s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:22<08:16,  2.87s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:25<08:09,  2.85s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:28<08:04,  2.83s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:31<08:07,  2.86s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:34<08:20,  2.96s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:37<08:13,  2.94s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:40<07:56,  2.85s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:42<07:48,  2.82s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:45<07:41,  2.80s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:48<07:42,  2.82s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:51<07:36,  2.80s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:54<07:52,  2.92s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:57<07:48,  2.91s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [02:00<07:42,  2.89s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [02:02<07:35,  2.86s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [02:05<07:10,  2.72s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [02:07<06:56,  2.65s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [02:10<06:49,  2.63s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [02:16<07:50,  3.03s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.29s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.64it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.49s/it]\u001b[A\n",
-      "Evaluating NLL: 29it [00:01, 25.04it/s]\u001b[A\n",
-      "Evaluating NLL: 49it [00:01, 44.11it/s]\u001b[A\n",
-      "Evaluating NLL: 68it [00:01, 63.57it/s]\u001b[A\n",
-      "Evaluating NLL: 99it [00:01, 102.94it/s]\u001b[A\n",
-      "Evaluating NLL: 130it [00:02, 139.44it/s]\u001b[A\n",
-      "Evaluating NLL: 155it [00:02, 151.00it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:02, 85.03it/s] \n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   3%|▍              | 3/100 [00:00<00:04, 21.94it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   6%|▉              | 6/100 [00:00<00:03, 25.59it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   9%|█▎             | 9/100 [00:00<00:03, 23.55it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  12%|█▋            | 12/100 [00:00<00:03, 23.88it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  15%|██            | 15/100 [00:00<00:03, 22.93it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  18%|██▌           | 18/100 [00:00<00:03, 22.31it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  21%|██▉           | 21/100 [00:00<00:03, 21.97it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  24%|███▎          | 24/100 [00:01<00:03, 22.09it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  28%|███▉          | 28/100 [00:01<00:02, 25.43it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  31%|████▎         | 31/100 [00:01<00:02, 23.53it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  34%|████▊         | 34/100 [00:01<00:02, 24.45it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  37%|█████▏        | 37/100 [00:01<00:02, 23.17it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  40%|█████▌        | 40/100 [00:01<00:02, 23.06it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  43%|██████        | 43/100 [00:01<00:02, 22.43it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  46%|██████▍       | 46/100 [00:01<00:02, 23.49it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  49%|██████▊       | 49/100 [00:02<00:02, 23.91it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  52%|███████▎      | 52/100 [00:02<00:02, 23.73it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  55%|███████▋      | 55/100 [00:02<00:01, 22.66it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  58%|████████      | 58/100 [00:02<00:01, 22.41it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  61%|████████▌     | 61/100 [00:02<00:01, 22.40it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  65%|█████████     | 65/100 [00:02<00:01, 23.61it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  68%|█████████▌    | 68/100 [00:02<00:01, 23.15it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  71%|█████████▉    | 71/100 [00:03<00:01, 23.25it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  74%|██████████▎   | 74/100 [00:03<00:01, 22.28it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  77%|██████████▊   | 77/100 [00:03<00:01, 22.83it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  80%|███████████▏  | 80/100 [00:03<00:00, 22.51it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  83%|███████████▌  | 83/100 [00:03<00:00, 23.56it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  86%|████████████  | 86/100 [00:03<00:00, 22.90it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  89%|████████████▍ | 89/100 [00:03<00:00, 22.71it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  92%|████████████▉ | 92/100 [00:03<00:00, 22.59it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  95%|█████████████▎| 95/100 [00:04<00:00, 22.21it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:04<00:00, 23.26it/s]\n",
-      " 40%|███████████████████████▌                                   | 2/5 [40:10<1:01:34, 1231.40s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -2.3486612081402756\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.90it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.20it/s]\u001b[A\n",
+      "Evaluating NLL: 38it [00:00, 54.95it/s]\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 108.84it/s]\u001b[A\n",
+      "Evaluating NLL: 111it [00:01, 157.83it/s]\u001b[A\n",
+      "Evaluating NLL: 148it [00:01, 203.43it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 145.40it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|▊                    | 4/100 [00:00<00:02, 39.12it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|█▋                   | 8/100 [00:00<00:02, 38.94it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  13%|██▌                 | 13/100 [00:00<00:02, 40.49it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  18%|███▌                | 18/100 [00:00<00:02, 39.84it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|█████▏              | 26/100 [00:00<00:01, 51.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|██████▍             | 32/100 [00:00<00:01, 46.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|███████▍            | 37/100 [00:00<00:01, 44.96it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  42%|████████▍           | 42/100 [00:00<00:01, 42.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  47%|█████████▍          | 47/100 [00:01<00:01, 41.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|██████████▌         | 53/100 [00:01<00:01, 45.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|███████████▌        | 58/100 [00:01<00:00, 43.53it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|████████████▌       | 63/100 [00:01<00:00, 43.02it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|█████████████▌      | 68/100 [00:01<00:00, 41.75it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  73%|██████████████▌     | 73/100 [00:01<00:00, 40.88it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|███████████████▊    | 79/100 [00:01<00:00, 43.28it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  84%|████████████████▊   | 84/100 [00:01<00:00, 41.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  89%|█████████████████▊  | 89/100 [00:02<00:00, 41.92it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  94%|██████████████████▊ | 94/100 [00:02<00:00, 41.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 43.03it/s]\n",
+      " 60%|████████████████████████████████████████▏                          | 3/5 [54:16<37:44, 1132.10s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<15:54,  4.80s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:16,  2.51s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:48,  1.77s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:41,  1.44s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:29,  1.38s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:20,  1.34s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:50,  1.19s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:28,  1.09s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:14,  1.02s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:04,  1.03it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:59,  1.05it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:54,  1.08it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:16<02:50,  1.10it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:17<02:48,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:45,  1.12it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:39,  1.15it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:40,  1.14it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:36,  1.16it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:35,  1.17it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:34,  1.16it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:31,  1.18it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:23<02:34,  1.16it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:32,  1.16it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:30,  1.17it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:30,  1.16it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:29,  1.17it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:28,  1.17it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:29<02:26,  1.18it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:29<02:24,  1.19it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:30<02:23,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:31<02:21,  1.20it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:32<02:19,  1.21it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:19,  1.20it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:34<02:18,  1.20it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:34<02:16,  1.21it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:35<02:17,  1.19it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:36<02:17,  1.19it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:37<02:18,  1.17it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:38<02:19,  1.15it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:39<02:18,  1.15it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:40<02:17,  1.16it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:41<02:20,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:41<02:17,  1.14it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:42<02:20,  1.11it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:44<02:33,  1.01it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:38,  5.02s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:44,  2.65s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<06:10,  1.88s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:52,  1.49s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:09,  1.28s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:41,  1.14s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:24,  1.06s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:12,  1.00s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:02,  1.05it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<02:54,  1.09it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:48,  1.12it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:46,  1.13it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:44,  1.14it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:48,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:52,  1.07it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:47,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:42,  1.13it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:39,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:38,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:39,  1.13it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:22<02:37,  1.14it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:23<02:35,  1.15it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:36,  1.13it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:33,  1.15it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:30,  1.16it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:29,  1.16it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:23,  1.21it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<02:19,  1.23it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:29<02:16,  1.25it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:30<02:12,  1.28it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:30<02:10,  1.30it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:31<02:09,  1.29it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:32<02:09,  1.29it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:33<02:06,  1.31it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:34<02:09,  1.28it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:34<02:11,  1.25it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:35<02:11,  1.24it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:12,  1.22it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:37<02:14,  1.19it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:38<02:14,  1.19it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:39<02:13,  1.19it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:39<02:14,  1.18it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:40<02:13,  1.17it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:41<02:14,  1.16it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:42<02:16,  1.14it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:43<02:15,  1.13it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:44<02:15,  1.13it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:45<02:14,  1.13it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:46<02:16,  1.10it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:47<02:20,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:48<02:21,  1.06it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:49<02:19,  1.06it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:50<02:17,  1.07it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:15,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:51<02:11,  1.10it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:52<02:11,  1.10it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:53<02:09,  1.10it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:54<02:07,  1.11it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:55<02:05,  1.12it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:56<02:05,  1.11it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:57<02:02,  1.14it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:58<02:01,  1.13it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:58<01:59,  1.15it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [00:59<01:58,  1.15it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:00<01:59,  1.13it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:01<02:03,  1.08it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:02<01:58,  1.12it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:03<01:59,  1.10it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:04<01:57,  1.12it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:05<01:55,  1.13it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:06<02:00,  1.07it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:07<01:57,  1.09it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:08<01:54,  1.11it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:08<01:52,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:09<01:50,  1.14it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:10<01:50,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:11<01:52,  1.10it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:12<01:51,  1.10it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:13<01:51,  1.09it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:14<01:51,  1.08it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:15<01:49,  1.08it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:17<01:51,  1.06it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:45,  5.35s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:16,  2.81s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:25,  1.95s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:03,  1.55s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:19,  1.33s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<03:45,  1.16s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:38,  1.13s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:22,  1.05s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:10,  1.00it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:03,  1.03it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:58,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:56,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:16<02:58,  1.05it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:17<03:02,  1.02it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:18<02:59,  1.03it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:19<02:46,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:20<02:39,  1.15it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:21<02:43,  1.11it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:22<02:53,  1.05it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:45,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:36,  1.15it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:44,  1.08it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:26<03:14,  1.10s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:27<03:08,  1.07s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:28<02:57,  1.01s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:29<02:52,  1.01it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:30<02:47,  1.04it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:30<02:43,  1.05it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:31<02:39,  1.07it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:32<02:37,  1.08it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:33<02:36,  1.08it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:34<02:32,  1.10it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:35<02:31,  1.10it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:36<02:30,  1.11it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:37<02:27,  1.12it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:38<02:27,  1.11it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:38<02:24,  1.12it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:39<02:25,  1.11it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:40<02:28,  1.08it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:41<02:26,  1.09it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:42<02:22,  1.11it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:43<02:20,  1.13it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:44<02:17,  1.14it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:45<02:15,  1.15it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:46<02:14,  1.15it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:46<02:13,  1.15it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:47<02:13,  1.15it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:48<02:12,  1.15it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:49<02:11,  1.15it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:50<02:10,  1.15it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:51<02:08,  1.16it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:52<02:06,  1.17it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:52<02:04,  1.18it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:53<02:03,  1.18it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:54<02:03,  1.18it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:55<02:01,  1.18it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:56<02:02,  1.16it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:57<02:01,  1.17it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:58<02:03,  1.14it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:59<02:03,  1.14it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:59<02:02,  1.14it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:00<02:00,  1.14it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:01<01:59,  1.14it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:02<01:59,  1.14it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:03<01:57,  1.15it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:04<01:56,  1.15it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:05<01:55,  1.15it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:05<01:53,  1.17it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:06<01:53,  1.15it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:07<01:56,  1.12it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:08<01:54,  1.12it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:09<01:53,  1.12it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:10<01:53,  1.12it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:11<01:51,  1.13it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:12<01:52,  1.11it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:13<01:51,  1.11it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:14<01:52,  1.09it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:15<01:50,  1.10it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:15<01:48,  1.12it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:16<01:49,  1.10it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:17<01:49,  1.09it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:18<01:47,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:19<01:46,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:20<01:46,  1.09it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:21<01:49,  1.05it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:22<01:46,  1.07it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:23<01:44,  1.08it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:24<01:44,  1.07it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:25<01:40,  1.11it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:25<01:37,  1.12it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:26<01:36,  1.13it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:27<01:33,  1.16it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:28<01:31,  1.16it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:29<01:31,  1.15it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:30<01:31,  1.14it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:31<01:30,  1.15it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:31<01:28,  1.16it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:32<01:26,  1.17it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:33<01:25,  1.18it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:34<01:25,  1.17it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:35<01:26,  1.14it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:36<01:26,  1.13it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:37<01:24,  1.15it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:38<01:22,  1.17it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:38<01:21,  1.17it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:39<01:20,  1.17it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:40<01:19,  1.17it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:41<01:19,  1.16it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:42<01:19,  1.15it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:43<01:17,  1.16it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:44<01:17,  1.15it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:44<01:16,  1.14it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:45<01:16,  1.13it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:46<01:16,  1.13it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:47<01:14,  1.13it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:48<01:13,  1.14it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:49<01:12,  1.14it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:50<01:13,  1.12it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:51<01:15,  1.07it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:52<01:12,  1.10it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:53<01:10,  1.12it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:53<01:09,  1.13it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:54<01:08,  1.13it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:55<01:07,  1.13it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:56<01:06,  1.13it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:57<01:05,  1.13it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:58<01:04,  1.14it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:59<01:02,  1.14it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:00<01:01,  1.15it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:00<01:01,  1.13it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:01<01:00,  1.13it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:02<00:58,  1.15it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:03<00:59,  1.13it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:04<00:58,  1.13it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:05<01:00,  1.07it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:06<00:58,  1.09it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:07<00:57,  1.09it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:08<00:56,  1.10it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:09<00:53,  1.13it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:10<00:54,  1.10it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:10<00:53,  1.10it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:11<00:51,  1.12it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:12<00:49,  1.14it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:13<00:48,  1.15it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:14<00:47,  1.17it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:15<00:47,  1.14it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:16<00:47,  1.11it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:17<00:48,  1.08it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:18<00:48,  1.05it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:19<00:48,  1.04it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:20<00:48,  1.01it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:21<00:47,  1.01it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:22<00:45,  1.03it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:23<00:44,  1.04it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:24<00:42,  1.05it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:24<00:41,  1.05it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:25<00:40,  1.06it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:26<00:40,  1.05it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:27<00:39,  1.03it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:28<00:39,  1.02it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:29<00:37,  1.03it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:30<00:36,  1.04it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:31<00:35,  1.04it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:32<00:35,  1.02it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:33<00:33,  1.04it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:34<00:32,  1.03it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:35<00:32,  1.01it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:36<00:30,  1.04it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:37<00:29,  1.05it/s]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:38<00:28,  1.05it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:39<00:27,  1.05it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:40<00:26,  1.06it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:41<00:26,  1.01it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:43<00:24,  1.07it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:06<21:16,  6.42s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:07<10:50,  3.28s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:15,  2.21s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:31,  1.69s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<04:31,  1.39s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<03:54,  1.21s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:32,  1.10s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:19,  1.04s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:11,  1.00s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:02,  1.04it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<02:56,  1.07it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<02:52,  1.09it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<02:50,  1.10it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<02:47,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:18<02:43,  1.13it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:19<02:45,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:20<02:42,  1.13it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:21<02:42,  1.12it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:22<02:44,  1.10it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:23<02:40,  1.12it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:24<02:37,  1.13it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:25<02:36,  1.14it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:26<02:36,  1.13it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:26<02:32,  1.15it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:27<02:32,  1.15it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:28<02:31,  1.15it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:29<02:33,  1.13it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:30<02:29,  1.15it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:31<02:29,  1.15it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:32<02:28,  1.14it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:33<02:33,  1.10it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:34<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:34<02:26,  1.14it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:35<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:36<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:37<02:21,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:38<02:19,  1.17it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:39<02:17,  1.18it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:40<02:18,  1.17it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:40<02:17,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:41<02:16,  1.17it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:42<02:14,  1.18it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:43<02:12,  1.19it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:44<02:07,  1.22it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:44<02:02,  1.26it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:45<01:58,  1.30it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:46<01:55,  1.32it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:47<01:53,  1.34it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:47<01:55,  1.31it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:48<01:52,  1.34it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:49<01:54,  1.30it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:50<01:56,  1.27it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:51<01:57,  1.25it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:00,  1.22it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:03,  1.18it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:53<02:03,  1.16it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:04,  1.15it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:55<02:04,  1.14it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:56<02:05,  1.13it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:57<02:05,  1.12it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:58<02:04,  1.11it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:59<02:03,  1.12it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:00<02:04,  1.10it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:00<02:03,  1.10it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:01<02:02,  1.10it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:02<02:07,  1.05it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:03<02:06,  1.05it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:04<02:01,  1.08it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:05<02:00,  1.09it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:06<01:57,  1.10it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:07<01:55,  1.11it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:08<01:54,  1.12it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:09<01:53,  1.12it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:09<01:50,  1.14it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:10<01:49,  1.14it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:11<01:48,  1.14it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:12<01:46,  1.15it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:13<01:44,  1.16it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:14<01:43,  1.17it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:15<01:42,  1.18it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:15<01:42,  1.16it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:16<01:42,  1.15it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:17<01:40,  1.16it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:18<01:40,  1.16it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:19<01:39,  1.16it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:20<01:38,  1.16it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:21<01:37,  1.16it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:22<01:36,  1.17it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:22<01:35,  1.17it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:23<01:35,  1.15it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:24<01:33,  1.16it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:25<01:33,  1.16it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:26<01:31,  1.17it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:27<01:32,  1.15it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:28<01:30,  1.17it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:28<01:29,  1.17it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:29<01:29,  1.15it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:30<01:28,  1.16it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:31<01:26,  1.16it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:32<01:30,  1.10it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:33<01:32,  1.07it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:34<01:29,  1.10it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:35<01:26,  1.12it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:36<01:26,  1.12it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:36<01:22,  1.15it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:37<01:22,  1.14it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:38<01:23,  1.12it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:39<01:21,  1.12it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:40<01:21,  1.12it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:41<01:21,  1.11it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:42<01:20,  1.10it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:43<01:19,  1.10it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:44<01:19,  1.10it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:45<01:19,  1.08it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:46<01:17,  1.10it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:47<01:20,  1.04it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:48<01:18,  1.05it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:49<01:18,  1.04it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:49<01:16,  1.05it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:50<01:15,  1.06it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:51<01:14,  1.06it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:52<01:14,  1.05it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:53<01:11,  1.08it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:54<01:08,  1.12it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:55<01:06,  1.13it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:56<01:04,  1.14it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:57<01:05,  1.11it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:58<01:05,  1.09it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:59<01:05,  1.09it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:00<01:05,  1.07it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:00<01:04,  1.07it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:01<01:03,  1.07it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:02<01:02,  1.08it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:03<01:01,  1.07it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:04<01:01,  1.06it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:05<01:00,  1.06it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:06<00:58,  1.07it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:07<00:58,  1.06it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:08<00:58,  1.04it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:09<00:57,  1.04it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:10<00:56,  1.05it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:11<00:55,  1.05it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:12<00:53,  1.06it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:13<00:53,  1.05it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:14<00:51,  1.07it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:15<00:50,  1.07it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:16<00:49,  1.07it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:16<00:47,  1.09it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:17<00:45,  1.12it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:18<00:44,  1.13it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:19<00:44,  1.11it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:20<00:42,  1.12it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:21<00:41,  1.13it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:22<00:40,  1.14it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:23<00:40,  1.11it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:23<00:39,  1.13it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:24<00:39,  1.10it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:25<00:37,  1.12it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:26<00:36,  1.11it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:27<00:36,  1.09it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:28<00:37,  1.05it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:29<00:35,  1.06it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:30<00:35,  1.05it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:31<00:34,  1.03it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:32<00:34,  1.02it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:33<00:31,  1.08it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:34<00:34,  1.05s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:36<00:36,  1.14s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:36<00:32,  1.05s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:37<00:29,  1.02it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:38<00:27,  1.06it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:39<00:25,  1.10it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:40<00:24,  1.10it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:41<00:24,  1.08it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [02:43<00:23,  1.07it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:36,  5.31s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:15,  2.80s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:24,  1.95s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:02,  1.54s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:02,  1.24s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:28,  1.07s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:08,  1.03it/s]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<02:56,  1.09it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<02:48,  1.13it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:43,  1.16it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:40,  1.18it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:34,  1.21it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:31,  1.23it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:31,  1.23it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:16<02:29,  1.24it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:17<02:26,  1.26it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:18<02:22,  1.28it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:20,  1.29it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:19<02:22,  1.27it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:20<02:22,  1.26it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:21<02:17,  1.30it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:22<02:16,  1.31it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:22<02:14,  1.32it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:23<02:14,  1.31it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:24<02:17,  1.27it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:25<02:17,  1.27it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:26<02:16,  1.27it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:26<02:15,  1.27it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:27<02:16,  1.26it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:28<02:16,  1.24it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:29<02:13,  1.26it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:30<02:15,  1.24it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:30<02:14,  1.24it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:31<02:14,  1.23it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:32<02:16,  1.21it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:33<02:15,  1.21it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:34<02:12,  1.23it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:35<02:14,  1.21it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:35<02:10,  1.23it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:36<02:08,  1.24it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:37<02:08,  1.24it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:38<02:08,  1.23it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:39<02:09,  1.22it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:40<02:08,  1.21it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:40<02:09,  1.20it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:41<02:09,  1.19it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:42<02:11,  1.16it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:43<02:11,  1.15it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:44<02:09,  1.16it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:45<02:13,  1.13it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:46<02:12,  1.13it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:47<02:10,  1.13it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:47<02:07,  1.16it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:48<02:04,  1.17it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:49<02:02,  1.19it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:50<02:00,  1.19it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:51<02:01,  1.17it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:52<02:04,  1.14it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:53<02:07,  1.11it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:54<02:20,  1.00s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:55<02:25,  1.05s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:56<02:32,  1.10s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:57<02:35,  1.13s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [00:59<02:36,  1.15s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:00<02:41,  1.19s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:01<02:40,  1.20s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:02<02:39,  1.20s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:04<02:41,  1.22s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:05<02:38,  1.21s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:06<02:35,  1.20s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:07<02:37,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:09<02:38,  1.24s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:10<02:35,  1.23s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:11<02:33,  1.22s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:12<02:31,  1.21s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:14<02:37,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:15<02:37,  1.28s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:16<02:32,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:17<02:30,  1.24s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:18<02:26,  1.22s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:20<02:25,  1.23s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:21<02:24,  1.23s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:22<02:23,  1.22s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:23<02:20,  1.22s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:25<02:20,  1.22s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:26<02:21,  1.24s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:27<02:24,  1.28s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:28<02:23,  1.28s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:30<02:20,  1.27s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:31<02:21,  1.28s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:32<02:19,  1.28s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:34<02:16,  1.27s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:35<02:15,  1.27s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:36<02:16,  1.29s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:38<02:17,  1.31s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:39<02:18,  1.33s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:40<02:18,  1.34s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:42<02:17,  1.35s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:43<02:16,  1.35s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:44<02:16,  1.37s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:46<02:15,  1.37s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:47<02:13,  1.36s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:48<02:12,  1.36s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:50<02:11,  1.37s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:51<02:10,  1.38s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:53<02:08,  1.37s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:54<02:08,  1.38s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:55<02:06,  1.37s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:57<02:04,  1.37s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:58<02:01,  1.35s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:59<02:00,  1.35s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:01<01:58,  1.35s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:02<01:59,  1.37s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:04<01:58,  1.37s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:05<01:56,  1.37s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:06<01:54,  1.36s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:08<01:52,  1.35s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:09<01:53,  1.38s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:11<02:00,  1.49s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:12<01:53,  1.42s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:13<01:49,  1.39s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:15<01:47,  1.38s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:16<01:45,  1.37s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:17<01:44,  1.37s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:19<01:41,  1.36s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:20<01:39,  1.35s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:21<01:38,  1.34s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:23<01:36,  1.34s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:24<01:35,  1.34s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:25<01:34,  1.34s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:27<01:32,  1.34s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:28<01:31,  1.34s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:29<01:27,  1.31s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:31<01:25,  1.29s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:32<01:21,  1.26s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:33<01:20,  1.26s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:34<01:18,  1.25s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:36<01:17,  1.25s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:37<01:15,  1.23s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:38<01:13,  1.22s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:39<01:11,  1.21s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:40<01:09,  1.20s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:41<01:08,  1.20s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:43<01:08,  1.22s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:44<01:07,  1.23s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:45<01:05,  1.22s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:46<01:04,  1.21s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:48<01:03,  1.21s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:49<01:02,  1.22s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:50<01:00,  1.22s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:51<00:59,  1.22s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:52<00:58,  1.22s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:54<00:56,  1.21s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:55<00:56,  1.22s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:56<00:55,  1.24s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:57<00:54,  1.23s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:59<00:52,  1.23s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:00<00:51,  1.22s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:01<00:50,  1.23s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:02<00:51,  1.28s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:04<00:49,  1.27s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:05<00:47,  1.26s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:06<00:46,  1.25s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:07<00:44,  1.24s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:09<00:43,  1.23s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:10<00:41,  1.23s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:11<00:40,  1.23s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:12<00:39,  1.25s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:14<00:39,  1.27s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:15<00:38,  1.30s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:16<00:37,  1.31s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:18<00:36,  1.32s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:19<00:35,  1.32s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:20<00:34,  1.33s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:22<00:33,  1.33s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:23<00:31,  1.33s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:26<00:26,  1.17s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.69it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.73it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:08<27:00,  8.14s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:11<16:41,  5.06s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:13<12:48,  3.90s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:16<11:46,  3.61s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:19<10:18,  3.17s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:21<09:09,  2.83s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:23<08:31,  2.65s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:25<08:09,  2.55s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:28<08:00,  2.51s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:30<07:47,  2.46s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:33<07:43,  2.45s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:35<07:30,  2.39s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:37<07:25,  2.38s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:40<07:24,  2.39s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:42<07:09,  2.32s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:44<07:10,  2.34s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:47<07:12,  2.36s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:49<07:04,  2.33s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:51<07:08,  2.37s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:54<07:08,  2.38s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:56<07:09,  2.40s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:58<07:02,  2.37s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:01<07:04,  2.40s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:04<07:15,  2.47s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:06<07:09,  2.46s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:08<07:00,  2.42s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:11<06:57,  2.41s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:13<06:58,  2.43s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:16<06:53,  2.42s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:18<06:53,  2.43s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:21<06:57,  2.47s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:24<07:22,  2.63s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:26<07:08,  2.56s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:29<07:00,  2.54s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:31<06:47,  2.47s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:33<06:40,  2.44s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:36<06:32,  2.41s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:38<06:33,  2.43s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:41<06:40,  2.49s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:43<06:32,  2.45s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:45<06:24,  2.42s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:48<06:24,  2.43s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:50<06:21,  2.43s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:53<06:27,  2.48s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:55<06:28,  2.51s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:58<06:14,  2.43s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [02:03<06:40,  2.62s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.25s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.62it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.16s/it]\u001b[A\n",
-      "Evaluating NLL: 32it [00:01, 34.55it/s]\u001b[A\n",
-      "Evaluating NLL: 75it [00:01, 87.63it/s]\u001b[A\n",
-      "Evaluating NLL: 105it [00:01, 114.69it/s]\u001b[A\n",
-      "Evaluating NLL: 133it [00:01, 144.10it/s]\u001b[A\n",
-      "Evaluating NLL: 160it [00:01, 162.29it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 106.92it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   4%|▌              | 4/100 [00:00<00:02, 38.17it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  36%|████▋        | 36/100 [00:00<00:00, 194.02it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  56%|███████▊      | 56/100 [00:00<00:00, 61.03it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  69%|█████████▋    | 69/100 [00:00<00:00, 70.28it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  81%|███████████▎  | 81/100 [00:01<00:00, 57.24it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  90%|████████████▌ | 90/100 [00:01<00:00, 49.88it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:01<00:00, 61.59it/s]\n",
-      " 60%|███████████████████████████████████▍                       | 3/5 [1:01:59<42:14, 1267.18s/it]"
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.64it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.97it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 38it [00:00, 53.86it/s]\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 107.10it/s]\u001b[A\n",
+      "Evaluating NLL: 116it [00:01, 165.83it/s]\u001b[A\n",
+      "Evaluating NLL: 152it [00:01, 207.47it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 143.47it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|▊                    | 4/100 [00:00<00:02, 39.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|█▋                   | 8/100 [00:00<00:02, 39.21it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|███                 | 15/100 [00:00<00:01, 53.06it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|████▏               | 21/100 [00:00<00:01, 46.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|█████▏              | 26/100 [00:00<00:01, 43.43it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|██████▏             | 31/100 [00:00<00:01, 43.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|███████▏            | 36/100 [00:00<00:01, 42.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|████████▊           | 44/100 [00:00<00:01, 49.42it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|█████████▊          | 49/100 [00:01<00:01, 46.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|██████████▊         | 54/100 [00:01<00:01, 45.89it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  59%|███████████▊        | 59/100 [00:01<00:00, 43.67it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  64%|████████████▊       | 64/100 [00:01<00:00, 42.19it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  72%|██████████████▍     | 72/100 [00:01<00:00, 50.06it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  78%|███████████████▌    | 78/100 [00:01<00:00, 46.16it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|████████████████▌   | 83/100 [00:01<00:00, 45.81it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|█████████████████▌  | 88/100 [00:01<00:00, 43.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  93%|██████████████████▌ | 93/100 [00:02<00:00, 43.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 45.42it/s]\n",
+      " 80%|████████████████████████████████████████████████████▊             | 4/5 [1:06:35<16:17, 977.16s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:07,  5.16s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:30,  2.88s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:01,  2.14s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:50,  1.79s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:15,  1.62s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:47,  1.48s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:30,  1.40s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:15,  1.34s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:10,  1.32s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:01,  1.28s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<03:52,  1.24s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<03:47,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:44,  1.21s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:43,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:39,  1.20s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:38,  1.20s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:33,  1.18s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:33,  1.19s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:41,  1.24s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:46,  1.27s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:47,  1.29s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:50,  1.31s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:53,  1.34s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:53,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:52,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:51,  1.35s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:53,  1.36s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:50,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:49,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:50,  1.37s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:48,  1.37s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:47,  1.37s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:53,  1.41s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:49,  1.40s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:47,  1.39s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:50,  1.42s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:44,  1.40s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:51,  1.44s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:59,  1.50s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:43,  1.41s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:33,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:27,  1.33s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:22,  1.31s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:04<03:19,  1.30s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:16,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:16,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:07<03:15,  1.30s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:15,  1.30s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:12,  1.29s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:13,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:13<03:11,  1.30s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<03:10,  1.30s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<03:11,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:17<03:10,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:18<03:11,  1.34s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<03:11,  1.35s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:21<03:11,  1.36s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:22<03:09,  1.36s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<03:08,  1.36s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:25<03:09,  1.37s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:26<03:08,  1.38s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:28<03:08,  1.39s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:29<03:05,  1.37s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:30<03:01,  1.36s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:32<02:59,  1.35s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:33<02:54,  1.32s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:34<02:50,  1.30s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:35<02:48,  1.30s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:37<02:45,  1.28s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:38<02:45,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:39<02:39,  1.26s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:35,  1.23s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:42<02:35,  1.24s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:43<02:33,  1.24s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:44<02:32,  1.24s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:35,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:47<02:34,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:48<02:37,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:33,  1.29s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:51<02:30,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:52<02:27,  1.26s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:53<02:24,  1.24s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:20,  1.22s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:55<02:20,  1.23s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:57<02:17,  1.21s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:58<02:15,  1.21s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:14,  1.21s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:00<02:12,  1.20s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:01<02:11,  1.20s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:03<02:10,  1.21s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:04<02:08,  1.20s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:05<02:08,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:06<02:06,  1.20s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:07<02:05,  1.21s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:09<02:02,  1.19s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:10<02:02,  1.20s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:11<02:01,  1.20s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:12<02:04,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:14<02:05,  1.27s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:15<02:07,  1.30s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:17<02:10,  1.34s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:18<02:09,  1.35s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:19<02:08,  1.35s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:21<02:06,  1.35s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:22<02:05,  1.34s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:23<02:03,  1.34s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:25<02:01,  1.34s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:26<01:59,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:27<01:58,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:29<01:56,  1.32s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:30<01:56,  1.34s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:31<01:54,  1.33s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:32<01:50,  1.30s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:34<01:49,  1.30s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:35<01:50,  1.33s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:36<01:47,  1.31s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:38<01:45,  1.30s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:39<01:44,  1.30s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:40<01:44,  1.33s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:42<01:44,  1.34s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:43<01:43,  1.35s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:45<01:43,  1.37s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:46<01:42,  1.37s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:47<01:41,  1.37s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:49<01:40,  1.38s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:50<01:38,  1.37s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:51<01:37,  1.37s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:53<01:35,  1.37s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:54<01:34,  1.37s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:56<01:33,  1.38s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:57<01:32,  1.38s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:58<01:30,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [03:00<01:29,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:01<01:27,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:02<01:26,  1.37s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:04<01:25,  1.37s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:05<01:24,  1.38s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:07<01:22,  1.38s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:08<01:22,  1.39s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:09<01:20,  1.39s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:11<01:18,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:12<01:16,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:13<01:14,  1.36s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:15<01:12,  1.34s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:17<01:19,  1.50s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:18<01:13,  1.42s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:19<01:09,  1.35s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:20<01:05,  1.31s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:21<01:02,  1.27s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:23<01:00,  1.27s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:24<00:58,  1.25s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:25<00:57,  1.24s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:26<00:55,  1.23s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:27<00:53,  1.22s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:29<00:51,  1.20s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:30<00:50,  1.21s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:31<00:50,  1.24s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:32<00:49,  1.23s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:34<00:47,  1.22s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:35<00:46,  1.22s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:36<00:45,  1.23s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:37<00:43,  1.22s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:38<00:42,  1.22s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:40<00:41,  1.21s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:41<00:40,  1.21s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:42<00:38,  1.21s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:43<00:37,  1.20s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:44<00:35,  1.19s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:46<00:34,  1.20s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:47<00:33,  1.19s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:48<00:32,  1.19s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:49<00:31,  1.20s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:50<00:29,  1.20s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:52<00:28,  1.20s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:53<00:27,  1.21s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:54<00:27,  1.25s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:56<00:26,  1.28s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:57<00:26,  1.30s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:58<00:24,  1.30s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [04:00<00:23,  1.32s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [04:01<00:22,  1.34s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [04:02<00:21,  1.34s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [04:04<00:20,  1.37s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [04:05<00:19,  1.36s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [04:06<00:17,  1.35s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [04:08<00:15,  1.33s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [04:09<00:14,  1.31s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [04:10<00:13,  1.30s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [04:11<00:11,  1.29s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [04:13<00:10,  1.28s/it]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [04:14<00:08,  1.26s/it]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [04:15<00:07,  1.25s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [04:16<00:06,  1.25s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [04:18<00:04,  1.25s/it]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [04:19<00:03,  1.25s/it]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [04:20<00:02,  1.24s/it]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [04:21<00:01,  1.26s/it]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [04:23<00:00,  1.32s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:33,  4.99s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:23,  2.85s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:06,  2.16s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:04,  1.86s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:32,  1.70s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:06,  1.58s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:51,  1.51s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:37,  1.45s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:28,  1.41s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:22,  1.38s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:19,  1.37s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:14,  1.35s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:14,  1.36s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:03,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<04:06,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:03,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<03:55,  1.29s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:55,  1.29s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:58,  1.32s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:50,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:44,  1.25s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:42,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:47,  1.29s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:43,  1.27s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:44,  1.28s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:44,  1.29s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:45,  1.30s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:44,  1.30s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:44,  1.31s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:48,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:55,  1.39s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:54,  1.40s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:46,  1.36s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:52,  1.40s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<04:05,  1.49s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:48,  1.39s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:53<03:40,  1.35s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:54<03:30,  1.30s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:55<03:26,  1.28s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:22,  1.27s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:19,  1.26s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:15,  1.24s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:12,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:08,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:06,  1.20s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:06,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:04,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:04,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:07<03:05,  1.23s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:08<03:08,  1.26s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:10,  1.28s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:12,  1.30s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:12<03:13,  1.32s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<03:13,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<03:13,  1.33s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<03:09,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:18<03:08,  1.32s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<03:08,  1.32s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<03:06,  1.32s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:22<03:06,  1.33s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<03:03,  1.32s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<02:59,  1.30s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:26<02:55,  1.28s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:27<02:52,  1.27s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:28<02:49,  1.25s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<02:47,  1.25s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:46,  1.25s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:32<02:45,  1.25s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:33<02:45,  1.26s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:34<02:48,  1.30s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<02:47,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<02:47,  1.31s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:38<02:45,  1.30s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:44,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:41<02:44,  1.32s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:42<02:40,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:43<02:35,  1.27s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:36,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:46<02:38,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:47<02:39,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:41,  1.36s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:50<02:40,  1.36s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:52<02:39,  1.36s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:53<02:37,  1.35s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:34,  1.34s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:56<02:32,  1.34s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:57<02:28,  1.32s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:58<02:23,  1.28s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:18,  1.25s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:00<02:15,  1.23s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:02<02:12,  1.21s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:03<02:10,  1.21s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:04<02:08,  1.20s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:05<02:07,  1.20s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:06<02:07,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:08<02:06,  1.21s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:09<02:08,  1.25s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:10<02:09,  1.27s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:06,  1.25s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:13<02:04,  1.25s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:14<02:02,  1.23s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:15<01:59,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:16<01:57,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:18<01:57,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:19<01:55,  1.21s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:20<01:53,  1.21s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:21<01:52,  1.21s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:23<01:56,  1.27s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:24<01:57,  1.29s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:25<01:55,  1.28s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:26<01:52,  1.27s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:28<01:52,  1.28s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:29<01:50,  1.27s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:30<01:48,  1.26s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:31<01:45,  1.24s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:33<01:46,  1.27s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:34<01:47,  1.30s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:36<01:48,  1.32s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:37<01:47,  1.33s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:38<01:46,  1.34s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:40<01:47,  1.36s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:41<01:45,  1.35s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:42<01:44,  1.36s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:44<01:43,  1.37s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:45<01:42,  1.37s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:46<01:40,  1.36s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:48<01:39,  1.36s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:49<01:38,  1.36s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:51<01:37,  1.38s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:52<01:36,  1.38s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:53<01:34,  1.37s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:55<01:33,  1.38s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:56<01:32,  1.38s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:57<01:31,  1.38s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:59<01:29,  1.38s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:00<01:27,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:02<01:26,  1.37s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:03<01:24,  1.36s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:04<01:23,  1.36s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:06<01:21,  1.36s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:07<01:19,  1.34s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:09<01:24,  1.46s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:10<01:24,  1.47s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:11<01:19,  1.42s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:13<01:15,  1.38s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:14<01:13,  1.37s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:15<01:11,  1.36s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:17<01:09,  1.33s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:18<01:07,  1.32s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:19<01:05,  1.31s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:20<01:03,  1.29s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:22<01:01,  1.28s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:23<00:59,  1.28s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:24<00:57,  1.26s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:25<00:56,  1.25s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:27<00:55,  1.27s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:28<00:55,  1.29s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:29<00:55,  1.32s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:31<00:54,  1.32s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:32<00:53,  1.33s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:34<00:52,  1.33s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:35<00:50,  1.33s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:36<00:49,  1.34s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:38<00:47,  1.33s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:39<00:46,  1.33s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:40<00:45,  1.33s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:42<00:44,  1.34s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:43<00:42,  1.33s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:44<00:40,  1.30s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:45<00:39,  1.30s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:47<00:36,  1.26s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:48<00:35,  1.25s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:49<00:33,  1.22s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:50<00:31,  1.21s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:51<00:30,  1.20s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:53<00:29,  1.22s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:54<00:28,  1.23s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:55<00:27,  1.23s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:56<00:26,  1.25s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:58<00:25,  1.26s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:59<00:23,  1.25s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [04:00<00:22,  1.24s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [04:01<00:21,  1.28s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [04:03<00:19,  1.25s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [04:04<00:18,  1.24s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [04:05<00:17,  1.24s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [04:06<00:15,  1.22s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [04:07<00:14,  1.20s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [04:09<00:13,  1.20s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [04:10<00:12,  1.20s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [04:11<00:10,  1.21s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [04:13<00:10,  1.32s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:05,  5.15s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:21,  2.84s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:01,  2.14s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:57,  1.82s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:23,  1.66s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:09,  1.60s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:53,  1.52s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:40,  1.46s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:33,  1.43s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:28,  1.41s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:23,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:19,  1.38s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:15,  1.36s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:11,  1.35s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:11,  1.36s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:12,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<04:09,  1.36s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<04:05,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<04:10,  1.39s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<04:05,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<04:01,  1.35s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<04:00,  1.35s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<04:00,  1.36s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:36<03:59,  1.36s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<03:58,  1.36s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<03:59,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:57,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:55,  1.37s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:43<03:55,  1.38s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:44<03:53,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:45<03:52,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:47<03:51,  1.38s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:48<03:49,  1.38s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:50<03:50,  1.39s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:51<03:47,  1.38s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:52<03:45,  1.37s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:54<03:45,  1.38s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:55<03:42,  1.38s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:57<03:48,  1.42s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:58<03:48,  1.43s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [01:00<03:48,  1.44s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [01:01<03:43,  1.41s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:02<03:40,  1.41s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:04<03:37,  1.39s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:05<03:33,  1.38s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:07<03:45,  1.46s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:08<03:50,  1.51s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:10<03:40,  1.45s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:11<03:33,  1.41s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:12<03:21,  1.35s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:13<03:18,  1.33s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:15<03:15,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:16<03:09,  1.29s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:17<03:06,  1.27s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:18<03:05,  1.28s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:20<03:03,  1.27s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:21<03:04,  1.29s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:22<03:04,  1.30s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:24<02:59,  1.28s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:25<02:58,  1.28s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:26<02:56,  1.27s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:27<02:55,  1.27s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:29<02:54,  1.27s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:30<02:54,  1.28s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:31<02:50,  1.26s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:32<02:50,  1.27s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:34<02:47,  1.26s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:35<02:46,  1.26s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:36<02:47,  1.28s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:38<02:47,  1.29s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:39<02:45,  1.28s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:40<02:40,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:41<02:40,  1.26s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:43<02:39,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:44<02:39,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:45<02:39,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:47<02:39,  1.29s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:48<02:36,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:49<02:35,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:50<02:32,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:51<02:28,  1.25s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:53<02:30,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:54<02:32,  1.30s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:56<02:32,  1.32s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:57<02:33,  1.33s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:58<02:31,  1.33s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [02:00<02:31,  1.34s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [02:01<02:30,  1.34s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [02:02<02:30,  1.35s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:04<02:29,  1.36s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:05<02:27,  1.35s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:06<02:26,  1.35s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:08<02:24,  1.35s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:09<02:22,  1.34s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:10<02:20,  1.33s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:12<02:18,  1.33s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:13<02:15,  1.32s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:14<02:13,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:16<02:11,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:17<02:10,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:18<02:08,  1.30s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:19<02:06,  1.29s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:21<02:03,  1.27s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:22<02:03,  1.28s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:23<02:02,  1.29s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:25<02:01,  1.29s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:26<02:00,  1.30s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:27<02:02,  1.33s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:29<02:01,  1.34s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:30<01:59,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:31<01:58,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:33<01:56,  1.33s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:34<01:55,  1.33s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:35<01:55,  1.34s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:37<01:54,  1.34s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:38<01:52,  1.34s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:39<01:51,  1.34s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:41<01:51,  1.35s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:42<01:50,  1.37s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:43<01:49,  1.37s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:45<01:47,  1.37s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:46<01:50,  1.41s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:48<01:46,  1.39s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:49<01:44,  1.37s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:50<01:43,  1.38s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:52<01:42,  1.38s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:53<01:40,  1.38s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:55<01:38,  1.37s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:56<01:37,  1.37s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:57<01:35,  1.37s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:59<01:34,  1.37s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [03:00<01:33,  1.38s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [03:01<01:32,  1.37s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [03:03<01:30,  1.36s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [03:04<01:28,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:06<01:28,  1.38s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:07<01:26,  1.38s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:08<01:24,  1.36s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:10<01:21,  1.34s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:11<01:20,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:12<01:18,  1.33s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:14<01:18,  1.35s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:15<01:16,  1.35s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:16<01:16,  1.36s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:18<01:14,  1.36s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:19<01:13,  1.36s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:20<01:12,  1.36s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:22<01:12,  1.39s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:23<01:10,  1.38s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:24<01:07,  1.36s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:26<01:09,  1.41s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:28<01:12,  1.52s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:29<01:06,  1.42s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:30<01:02,  1.35s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:31<00:59,  1.32s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:33<00:56,  1.29s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:34<00:54,  1.27s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:35<00:52,  1.26s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:36<00:52,  1.28s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:38<00:51,  1.30s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:39<00:51,  1.32s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:40<00:50,  1.32s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:42<00:49,  1.33s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:43<00:48,  1.33s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:44<00:46,  1.34s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:46<00:45,  1.34s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:47<00:44,  1.36s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:49<00:43,  1.36s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:50<00:42,  1.36s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:51<00:40,  1.35s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:53<00:38,  1.33s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:54<00:37,  1.33s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:55<00:35,  1.31s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:56<00:34,  1.31s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:58<00:32,  1.30s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:59<00:31,  1.30s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [04:00<00:29,  1.28s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [04:02<00:28,  1.29s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [04:03<00:27,  1.31s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [04:04<00:26,  1.34s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [04:07<00:25,  1.37s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:12,  5.19s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:25,  2.85s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:06,  2.16s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:49,  1.79s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:07,  1.58s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:24,  1.37s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:11,  1.31s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:04,  1.28s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<03:58,  1.26s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:55,  1.25s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:51,  1.23s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:50,  1.24s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<03:49,  1.23s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:45,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:44,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:46,  1.24s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:43,  1.23s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:42,  1.23s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:38,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:36,  1.21s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:35,  1.21s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<03:33,  1.21s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:32,  1.21s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:33,  1.22s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:35<03:39,  1.26s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:39,  1.27s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:35,  1.25s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:39<03:30,  1.23s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:40<03:30,  1.24s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:41<03:27,  1.23s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:43<03:26,  1.23s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:44<03:24,  1.23s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:45<03:26,  1.24s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:46<03:23,  1.23s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:48<03:27,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:49<03:33,  1.31s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:35,  1.33s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:33,  1.33s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:33,  1.34s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:33,  1.35s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:31,  1.34s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:30,  1.34s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:31,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:30,  1.36s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<03:30,  1.36s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:03<03:28,  1.36s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:04<03:29,  1.38s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:06<03:27,  1.38s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:07<03:25,  1.37s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:08<03:24,  1.37s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:10<03:23,  1.38s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:11<03:22,  1.37s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:12<03:20,  1.37s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:14<03:19,  1.38s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:15<03:17,  1.37s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:16<03:15,  1.37s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:18<03:10,  1.34s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:19<03:10,  1.35s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:20<03:07,  1.34s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:22<03:06,  1.34s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:23<03:05,  1.35s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<03:05,  1.35s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:26<03:03,  1.35s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:27<03:01,  1.35s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<03:02,  1.36s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:59,  1.35s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:31<02:59,  1.36s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:33<02:57,  1.36s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:34<03:01,  1.39s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<03:18,  1.54s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<03:11,  1.49s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:39<03:03,  1.45s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:57,  1.40s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:41<02:53,  1.39s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:43<02:50,  1.37s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:44<02:48,  1.37s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:47,  1.37s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:47<02:44,  1.36s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:48<02:41,  1.34s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:38,  1.33s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:51<02:31,  1.29s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:52<02:27,  1.26s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:53<02:23,  1.24s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:25,  1.26s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:56<02:23,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:57<02:23,  1.27s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:58<02:23,  1.28s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:20,  1.27s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:01<02:17,  1.25s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:02<02:14,  1.24s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:03<02:13,  1.24s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:04<02:12,  1.24s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:06<02:11,  1.24s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:07<02:11,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:08<02:10,  1.25s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:09<02:07,  1.24s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:10<02:04,  1.22s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:02,  1.21s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:13<02:01,  1.21s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:14<01:59,  1.20s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:15<01:58,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:16<01:57,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:18<01:57,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:19<01:55,  1.22s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:20<01:54,  1.22s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:21<01:54,  1.23s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:23<01:52,  1.23s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:24<01:52,  1.24s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:25<01:50,  1.23s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:26<01:51,  1.26s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:28<01:50,  1.26s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:29<01:49,  1.25s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:30<01:46,  1.24s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:31<01:43,  1.22s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:33<01:42,  1.22s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:34<01:44,  1.25s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:35<01:43,  1.27s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:36<01:32,  1.14s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:37<01:23,  1.04s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:38<01:16,  1.03it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:39<01:14,  1.05it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:39<01:10,  1.09it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:40<01:08,  1.10it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:41<01:05,  1.14it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:42<01:03,  1.16it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:43<01:01,  1.18it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:44<01:02,  1.16it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:44<01:00,  1.18it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:45<00:58,  1.20it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:46<00:56,  1.21it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:47<00:56,  1.20it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:48<00:56,  1.20it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:49<00:55,  1.18it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:49<00:53,  1.21it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:50<00:51,  1.23it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:51<00:50,  1.25it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:52<00:48,  1.27it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:52<00:48,  1.25it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:53<00:47,  1.27it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:54<00:47,  1.25it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:55<00:46,  1.25it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:56<00:47,  1.21it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:57<00:48,  1.17it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:58<00:46,  1.17it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:58<00:45,  1.19it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:59<00:44,  1.20it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:00<00:43,  1.21it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:01<00:40,  1.25it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:01<00:39,  1.28it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:02<00:37,  1.30it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:03<00:37,  1.29it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:04<00:36,  1.30it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:04<00:35,  1.31it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:05<00:34,  1.30it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:06<00:33,  1.31it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:07<00:32,  1.31it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:08<00:32,  1.31it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:08<00:31,  1.30it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:09<00:30,  1.30it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:10<00:30,  1.28it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:11<00:30,  1.25it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:12<00:31,  1.18it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:13<00:30,  1.18it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:13<00:28,  1.22it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:14<00:27,  1.24it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:15<00:26,  1.23it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:16<00:25,  1.24it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:17<00:25,  1.20it/s]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:17<00:24,  1.21it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:18<00:24,  1.21it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:19<00:23,  1.20it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:20<00:22,  1.21it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:21<00:22,  1.13it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:22<00:23,  1.08it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:23<00:23,  1.02it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:24<00:22,  1.01it/s]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:25<00:22,  1.04s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:26<00:21,  1.05s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:27<00:20,  1.05s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:28<00:20,  1.06s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [03:29<00:18,  1.05s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [03:30<00:17,  1.04s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [03:32<00:16,  1.05s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [03:33<00:15,  1.03s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [03:33<00:14,  1.02s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [03:34<00:13,  1.01s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [03:36<00:13,  1.12s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [03:37<00:13,  1.20s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [03:38<00:12,  1.21s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [03:40<00:10,  1.16s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:20,  5.23s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:30,  1.98s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:09,  1.58s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:28,  1.38s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:03,  1.25s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:45,  1.17s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:31,  1.10s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:23,  1.06s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:18,  1.05s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<03:12,  1.02s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<03:09,  1.01s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<03:07,  1.00s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<03:07,  1.01s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:19<03:10,  1.03s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:20<03:08,  1.02s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:21<03:06,  1.02s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:22<03:04,  1.01s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:23<03:01,  1.01s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:24<03:00,  1.00s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:25<03:00,  1.01s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:26<02:58,  1.00s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:27<02:58,  1.01s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:28<02:56,  1.00s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:29<02:56,  1.01s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:30<02:54,  1.00s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:31<02:52,  1.00it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:32<02:52,  1.00s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:33<02:52,  1.01s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:34<02:53,  1.02s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:35<02:53,  1.03s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:36<02:53,  1.03s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:37<02:52,  1.03s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:38<02:52,  1.04s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:39<02:39,  1.03it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:40<02:33,  1.07it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:41<02:29,  1.09it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:41<02:22,  1.14it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:42<02:18,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:43<02:18,  1.15it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:44<02:17,  1.15it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:45<02:14,  1.18it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:46<02:20,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:47<02:24,  1.08it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:48<02:29,  1.04it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:49<02:30,  1.02it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:50<02:27,  1.04it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:51<02:27,  1.03it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:52<02:28,  1.02it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:53<02:27,  1.02it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:54<02:25,  1.02it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:55<02:27,  1.00it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:56<02:26,  1.01it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:57<02:24,  1.01it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:58<02:23,  1.01it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:59<02:21,  1.02it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:00<02:20,  1.02it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:01<02:20,  1.01it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:02<02:20,  1.01it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:03<02:18,  1.01it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:04<02:18,  1.00it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:05<02:17,  1.00it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:06<02:15,  1.01it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:07<02:15,  1.00it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:08<02:14,  1.00it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:09<02:11,  1.02it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:10<02:10,  1.02it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:11<02:10,  1.01it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:12<02:10,  1.00it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:13<02:08,  1.01it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:14<02:06,  1.02it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:14<02:04,  1.03it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:16<02:05,  1.01it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:17<02:05,  1.00it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:18<02:05,  1.01s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:19<02:05,  1.01s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:20<02:04,  1.01s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:21<02:03,  1.01s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:22<02:03,  1.02s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:23<02:07,  1.06s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:24<02:03,  1.04s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:25<02:01,  1.03s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:26<01:59,  1.02s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:27<02:00,  1.04s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:28<01:58,  1.03s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:29<01:57,  1.03s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:30<01:55,  1.03s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:31<01:53,  1.01s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:32<01:50,  1.00it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:33<01:48,  1.01it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:34<01:47,  1.02it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:35<01:46,  1.01it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:36<01:45,  1.01it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:37<01:46,  1.00s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:38<01:46,  1.01s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:39<01:43,  1.01it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:40<01:41,  1.02it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:41<01:43,  1.01s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:42<01:43,  1.02s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:43<01:39,  1.00it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:44<01:37,  1.01it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:45<01:35,  1.03it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:46<01:34,  1.03it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:47<01:35,  1.01it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:48<01:33,  1.02it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:49<01:31,  1.02it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:50<01:31,  1.02it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:51<01:30,  1.02it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:52<01:30,  1.01it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:53<01:30,  1.01s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:54<01:32,  1.04s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:55<01:30,  1.03s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:56<01:28,  1.02s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:57<01:29,  1.04s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:58<01:27,  1.03s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:59<01:23,  1.00it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:00<01:31,  1.10s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:01<01:32,  1.13s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:02<01:22,  1.02s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:03<01:15,  1.07it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:04<01:10,  1.13it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:04<01:07,  1.15it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:05<01:04,  1.19it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:06<01:05,  1.16it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:07<01:06,  1.13it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:08<01:07,  1.10it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:09<01:07,  1.08it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:10<01:07,  1.06it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:11<01:07,  1.06it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:12<01:07,  1.04it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:13<01:06,  1.04it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:14<01:07,  1.01it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:15<01:06,  1.01it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:16<01:05,  1.01it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:17<01:04,  1.00it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:18<01:05,  1.02s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:19<01:04,  1.02s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:20<01:03,  1.02s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:21<01:03,  1.04s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:22<01:02,  1.04s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:23<01:01,  1.04s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:24<01:01,  1.05s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:25<00:59,  1.05s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:26<00:59,  1.06s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:28<00:58,  1.06s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:29<00:56,  1.05s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:30<00:56,  1.06s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:31<00:54,  1.06s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:32<00:54,  1.06s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:33<00:52,  1.06s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:34<00:51,  1.05s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:35<00:49,  1.03s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:36<00:48,  1.02s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:37<00:46,  1.02s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:38<00:46,  1.02s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:39<00:44,  1.00s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:40<00:43,  1.01s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:41<00:41,  1.01it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:42<00:41,  1.00s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:43<00:39,  1.00it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:44<00:38,  1.01it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:45<00:37,  1.01it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:46<00:36,  1.01it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:47<00:35,  1.01it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:48<00:34,  1.01it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:49<00:33,  1.01it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:50<00:32,  1.00it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:51<00:32,  1.00s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:52<00:31,  1.00s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:53<00:30,  1.00s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:54<00:29,  1.02s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:55<00:29,  1.04s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:56<00:28,  1.05s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:57<00:26,  1.03s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [02:58<00:25,  1.03s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [02:59<00:24,  1.03s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:00<00:23,  1.03s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:01<00:22,  1.02s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:02<00:21,  1.03s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:03<00:20,  1.05s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:04<00:19,  1.04s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [03:05<00:19,  1.06s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [03:06<00:18,  1.07s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [03:07<00:16,  1.06s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [03:09<00:15,  1.06s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [03:09<00:14,  1.02s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [03:10<00:12,  1.04it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [03:11<00:10,  1.11it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [03:12<00:09,  1.17it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [03:13<00:10,  1.02s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.81it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.70it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.67it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.58it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 67it [00:01, 88.07it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 162.39it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  28%|█████▎             | 28/100 [00:00<00:00, 279.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|███████████▌       | 61/100 [00:00<00:00, 307.35it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████| 100/100 [00:00<00:00, 294.31it/s]\n",
+      "100%|█████████████████████████████████████████████████████████████████| 5/5 [1:30:34<00:00, 1086.94s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -0.09521487108452856\n",
-      "computing optimized mi\n"
+      "white_noise\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "  0%|                                                                             | 0/5 [00:00<?, ?it/s]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:09,  5.17s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:19,  2.83s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:34,  2.00s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:11,  1.59s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:36,  1.42s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:06,  1.27s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:50,  1.20s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:37,  1.13s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:32,  1.11s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:26,  1.09s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<03:22,  1.07s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<03:18,  1.06s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<03:19,  1.07s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<03:18,  1.06s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:19<03:14,  1.05s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:20<03:12,  1.05s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:21<03:10,  1.04s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:22<03:09,  1.04s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:23<03:07,  1.04s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:24<03:07,  1.04s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:26<03:04,  1.03s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:26<03:00,  1.01s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:27<02:55,  1.01it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:28<02:50,  1.03it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:29<02:51,  1.02it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:30<02:51,  1.01it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:31<02:54,  1.01s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:32<02:52,  1.00s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:33<02:51,  1.01s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:34<02:47,  1.01it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:35<02:44,  1.03it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:36<02:45,  1.02it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:37<02:42,  1.03it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:38<02:41,  1.03it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:39<02:40,  1.03it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:40<02:40,  1.02it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:41<02:40,  1.02it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:42<02:37,  1.03it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:43<02:36,  1.03it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:44<02:35,  1.03it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:45<02:34,  1.03it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:46<02:34,  1.02it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:47<02:33,  1.02it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:48<02:31,  1.03it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:49<02:29,  1.03it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:51<02:52,  1.12s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:01,  4.83s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:12,  2.49s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:33,  1.69s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:19,  1.32s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:07<03:38,  1.12s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:08<03:17,  1.02s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:09<02:59,  1.07it/s]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:10<02:47,  1.15it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:40,  1.19it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:11<02:33,  1.24it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:12<02:32,  1.24it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:13<02:28,  1.26it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:14<02:22,  1.31it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:14<02:22,  1.30it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:15<02:24,  1.28it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:16<02:24,  1.27it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:17<02:24,  1.27it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:18<02:24,  1.26it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:18<02:25,  1.25it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:19<02:22,  1.26it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:20<02:23,  1.25it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:21<02:25,  1.23it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:22<02:24,  1.23it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:22<02:26,  1.20it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:23<02:27,  1.19it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:24<02:25,  1.20it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:25<02:26,  1.18it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:26<02:23,  1.20it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:27<02:23,  1.19it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:28<02:29,  1.14it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:29<02:34,  1.10it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:29<02:29,  1.13it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:30<02:25,  1.15it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:31<02:20,  1.18it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:32<02:17,  1.20it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:33<02:16,  1.20it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:34<02:14,  1.22it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:34<02:11,  1.24it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:35<02:08,  1.25it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:36<02:06,  1.27it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:37<02:04,  1.28it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:37<02:03,  1.28it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:38<02:02,  1.28it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:39<02:01,  1.28it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:40<01:59,  1.29it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:41<02:20,  1.10it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:37,  5.01s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:01,  2.14s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:57,  1.82s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:22,  1.65s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:02,  1.56s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:47,  1.49s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:40,  1.46s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:33,  1.43s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:29,  1.42s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:25,  1.41s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:21,  1.39s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:19,  1.39s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:16,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:16,  1.39s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:13,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<04:03,  1.33s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<04:15,  1.41s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<04:22,  1.45s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<04:06,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<03:56,  1.32s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<03:46,  1.27s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:39,  1.24s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:36,  1.23s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:34,  1.23s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:32,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:29,  1.21s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:27,  1.21s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:23,  1.19s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:26,  1.22s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:23,  1.20s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:20,  1.20s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:17,  1.18s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:17,  1.19s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<03:17,  1.20s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:16,  1.20s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:16,  1.21s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:52<03:14,  1.20s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:53<03:13,  1.20s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:13,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:56<03:10,  1.20s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:57<03:07,  1.19s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:06,  1.19s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:06,  1.19s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:06,  1.20s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:31,  1.38s/it]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:06<22:26,  6.77s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:09<13:37,  4.13s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:11<10:31,  3.21s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:13<09:14,  2.83s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:15<08:22,  2.58s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:17<07:45,  2.40s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:20<08:20,  2.59s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:23<08:21,  2.61s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:25<08:13,  2.59s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:27<07:44,  2.44s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:30<07:34,  2.41s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:32<07:34,  2.42s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:35<07:35,  2.43s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:37<07:32,  2.43s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:40<07:47,  2.53s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:42<07:41,  2.51s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:45<07:47,  2.56s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:48<07:47,  2.57s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:50<07:47,  2.58s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:53<07:38,  2.54s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:56<07:58,  2.67s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:58<07:57,  2.68s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:01<07:58,  2.70s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:04<08:28,  2.89s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:08<08:56,  3.07s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:10<07:50,  2.70s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:12<07:14,  2.51s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:14<06:54,  2.41s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:16<06:28,  2.27s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:18<06:21,  2.24s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:20<06:13,  2.21s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:23<06:16,  2.24s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:25<06:19,  2.27s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:27<06:11,  2.24s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:29<06:14,  2.27s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:32<06:11,  2.26s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:34<06:07,  2.26s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:36<06:04,  2.25s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:38<06:00,  2.24s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:41<06:00,  2.25s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:43<05:52,  2.22s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:45<05:44,  2.18s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:47<05:35,  2.13s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:49<05:34,  2.15s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:51<05:37,  2.18s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:56<06:28,  2.53s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.24s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.92it/s]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:57,  5.12s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:34,  2.90s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:07,  2.17s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:00,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:25,  1.67s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:05,  1.58s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:55,  1.53s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:45,  1.49s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:36,  1.45s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:28,  1.41s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:23,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:17,  1.37s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:12,  1.35s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:09,  1.34s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<04:04,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<03:59,  1.30s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<03:59,  1.31s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:57,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<03:57,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:54,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:48,  1.28s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:39,  1.23s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:40,  1.24s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:40,  1.26s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:44,  1.28s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:50,  1.33s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:50,  1.33s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:50,  1.34s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:49,  1.34s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:47,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:45,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:45,  1.34s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:42,  1.33s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:41,  1.33s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<03:34,  1.30s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:29,  1.28s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:23,  1.25s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:17,  1.22s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:15,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:20,  1.25s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:19,  1.26s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:15,  1.24s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:15,  1.24s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:18,  1.27s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:12,  1.24s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:08,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:04<03:08,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:13,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:07<03:16,  1.30s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:08<03:17,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:17,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:18,  1.34s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:14<03:26,  1.40s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:54,  5.40s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:56,  3.01s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:22,  2.25s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:07,  1.88s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:34,  1.71s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:13,  1.62s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:56,  1.54s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:45,  1.49s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:37,  1.45s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:30,  1.43s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:52,  1.55s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:43,  1.51s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:22<04:31,  1.45s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:26,  1.43s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:25<04:19,  1.40s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:26<04:13,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:15,  1.39s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:29<04:09,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:30<04:05,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<04:00,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<03:53,  1.31s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:34<03:53,  1.31s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:35<03:48,  1.29s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:36<03:45,  1.28s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:38<03:43,  1.28s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<03:40,  1.27s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:38,  1.26s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:37,  1.26s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:43<03:36,  1.27s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:44<03:35,  1.27s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:45<03:33,  1.26s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:31,  1.26s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:48<03:28,  1.25s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:49<03:26,  1.24s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<03:26,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:26,  1.26s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:53<03:25,  1.26s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:54<03:22,  1.25s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:55<03:19,  1.24s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:16,  1.23s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:15,  1.23s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:15,  1.24s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:12,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:14,  1.25s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:04<03:42,  1.43s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.68it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.00it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.64it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.69it/s]\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
       "Evaluating NLL: 1it [00:01,  1.12s/it]\u001b[A\n",
-      "Evaluating NLL: 33it [00:01, 36.94it/s]\u001b[A\n",
-      "Evaluating NLL: 73it [00:01, 87.32it/s]\u001b[A\n",
-      "Evaluating NLL: 110it [00:01, 133.74it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 124.12it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  24%|███          | 24/100 [00:00<00:00, 214.70it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  46%|█████▉       | 46/100 [00:00<00:00, 179.29it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  66%|████████▌    | 66/100 [00:00<00:00, 178.72it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|████████████| 100/100 [00:00<00:00, 170.01it/s]\n",
-      " 80%|███████████████████████████████████████████████▏           | 4/5 [1:20:10<19:57, 1197.61s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -0.010469666610981726\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 7it [00:02,  3.32it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:07<24:20,  7.34s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:09<14:57,  4.54s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:12<11:29,  3.50s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:14<09:45,  2.99s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:16<08:52,  2.73s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:18<08:05,  2.50s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:21<08:03,  2.51s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:23<07:50,  2.45s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:25<07:39,  2.41s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:28<07:33,  2.39s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:30<07:46,  2.47s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:33<07:36,  2.43s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:35<07:48,  2.50s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:38<07:44,  2.50s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:40<07:32,  2.44s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:43<07:29,  2.44s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:45<07:22,  2.42s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:47<07:11,  2.37s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:50<07:06,  2.36s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:52<07:06,  2.37s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:54<06:56,  2.33s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:57<07:04,  2.38s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [00:59<07:03,  2.40s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:02<07:04,  2.41s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:04<07:04,  2.42s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:06<06:55,  2.39s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:09<07:09,  2.48s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:11<07:00,  2.44s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:14<07:08,  2.51s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:17<07:05,  2.50s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:19<06:59,  2.48s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:21<06:46,  2.42s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:24<06:39,  2.39s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:26<06:39,  2.41s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:28<06:29,  2.36s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:31<06:22,  2.33s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:33<06:29,  2.39s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:35<06:22,  2.36s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:38<06:26,  2.40s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:40<06:25,  2.41s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:43<06:29,  2.45s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:45<06:27,  2.45s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:48<06:14,  2.39s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:50<06:24,  2.46s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:53<06:27,  2.50s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:55<06:27,  2.52s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [01:58<06:31,  2.56s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [02:01<06:35,  2.60s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [02:04<06:53,  2.74s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [02:06<06:35,  2.64s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [02:08<06:12,  2.50s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [02:10<05:51,  2.37s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [02:13<05:44,  2.34s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [02:15<05:47,  2.38s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [02:18<05:57,  2.46s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [02:20<05:51,  2.44s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [02:25<06:03,  2.55s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.21s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.93it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.21s/it]\u001b[A\n",
-      "Evaluating NLL: 34it [00:01, 35.44it/s]\u001b[A\n",
-      "Evaluating NLL: 55it [00:01, 58.22it/s]\u001b[A\n",
-      "Evaluating NLL: 77it [00:01, 83.49it/s]\u001b[A\n",
-      "Evaluating NLL: 106it [00:01, 120.32it/s]\u001b[A\n",
-      "Evaluating NLL: 130it [00:01, 139.63it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 103.25it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   4%|▌              | 4/100 [00:00<00:02, 37.25it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   9%|█▎             | 9/100 [00:00<00:02, 38.53it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  13%|█▊            | 13/100 [00:00<00:02, 29.65it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  17%|██▍           | 17/100 [00:00<00:02, 30.53it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  33%|████▌         | 33/100 [00:00<00:01, 62.77it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  40%|█████▌        | 40/100 [00:00<00:01, 52.18it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  52%|███████▎      | 52/100 [00:00<00:00, 65.15it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  59%|████████▎     | 59/100 [00:01<00:00, 46.30it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  65%|█████████     | 65/100 [00:01<00:00, 45.43it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  71%|█████████▉    | 71/100 [00:01<00:00, 41.66it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  76%|██████████▋   | 76/100 [00:01<00:00, 39.58it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  81%|███████████▎  | 81/100 [00:01<00:00, 37.42it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  85%|███████████▉  | 85/100 [00:02<00:00, 33.72it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  89%|████████████▍ | 89/100 [00:02<00:00, 33.62it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  93%|█████████████ | 93/100 [00:02<00:00, 35.01it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:02<00:00, 39.94it/s]\n",
-      "100%|███████████████████████████████████████████████████████████| 5/5 [1:32:55<00:00, 1115.10s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8_deltas_uniform\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|                                                                       | 0/5 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -12.221227080743\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.24it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:06<23:05,  6.96s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:09<14:19,  4.34s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:11<11:26,  3.49s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:14<10:13,  3.13s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:16<09:12,  2.84s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:19<08:47,  2.72s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:21<08:08,  2.53s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:23<07:51,  2.46s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:25<07:34,  2.38s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:28<07:19,  2.31s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:30<07:24,  2.35s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:32<07:10,  2.29s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:35<07:20,  2.36s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:37<07:25,  2.40s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:39<07:07,  2.31s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:42<07:21,  2.40s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:44<07:06,  2.33s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:47<07:17,  2.40s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:49<07:18,  2.42s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:51<07:09,  2.39s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:54<07:21,  2.47s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:57<07:20,  2.47s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [00:59<07:12,  2.44s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:02<07:14,  2.47s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:04<07:04,  2.43s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:06<07:01,  2.42s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:09<06:55,  2.40s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:11<07:04,  2.47s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:14<07:12,  2.53s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:16<07:00,  2.47s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:19<06:56,  2.47s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:21<06:47,  2.42s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:23<06:40,  2.40s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:26<06:31,  2.36s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:28<06:39,  2.42s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:31<07:17,  2.67s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:34<07:13,  2.66s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:37<07:12,  2.67s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:39<06:53,  2.57s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:41<06:36,  2.48s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:44<06:26,  2.43s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:46<06:23,  2.43s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:48<06:08,  2.35s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:51<06:03,  2.33s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:53<06:07,  2.37s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:55<06:03,  2.36s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [01:58<06:00,  2.35s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [02:00<05:54,  2.33s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [02:02<05:51,  2.33s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [02:05<05:44,  2.30s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [02:07<05:43,  2.30s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [02:09<05:35,  2.27s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [02:11<05:36,  2.29s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [02:14<05:42,  2.34s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [02:16<05:37,  2.33s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [02:19<05:37,  2.35s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [02:21<05:35,  2.35s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [02:23<05:26,  2.30s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [02:25<05:15,  2.24s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [02:27<05:11,  2.22s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [02:30<05:14,  2.26s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [02:32<05:11,  2.26s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [02:34<05:10,  2.27s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [02:37<05:15,  2.32s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [02:39<05:06,  2.27s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [02:41<05:08,  2.30s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [02:44<05:05,  2.30s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [02:46<05:04,  2.31s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [02:48<05:02,  2.31s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [02:50<05:00,  2.31s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [02:53<04:55,  2.29s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [02:55<04:55,  2.31s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [02:57<04:49,  2.28s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [03:00<04:54,  2.34s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [03:02<04:47,  2.30s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [03:04<04:45,  2.30s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [03:07<04:51,  2.37s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [03:09<04:36,  2.27s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [03:11<04:39,  2.31s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [03:14<04:36,  2.31s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [03:16<04:38,  2.34s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [03:18<04:33,  2.32s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [03:20<04:28,  2.29s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [03:23<04:28,  2.32s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [03:25<04:21,  2.27s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [03:27<04:25,  2.33s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [03:30<04:29,  2.38s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [03:32<04:31,  2.42s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [03:35<04:30,  2.44s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [03:37<04:26,  2.43s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [03:40<04:21,  2.40s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [03:42<04:25,  2.46s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [03:45<04:20,  2.43s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [03:47<04:17,  2.43s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [03:49<04:14,  2.42s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [03:52<04:08,  2.39s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [03:54<04:02,  2.35s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [03:57<04:11,  2.46s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [03:59<04:11,  2.49s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [04:02<04:05,  2.46s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [04:04<04:02,  2.45s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [04:07<04:01,  2.46s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [04:09<03:57,  2.45s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [04:12<03:56,  2.47s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [04:14<03:54,  2.47s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [04:17<03:55,  2.51s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [04:19<03:48,  2.46s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [04:21<03:43,  2.43s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [04:25<04:01,  2.65s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [04:27<03:45,  2.50s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [04:29<03:36,  2.43s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [04:31<03:27,  2.35s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [04:33<03:24,  2.35s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [04:36<03:20,  2.33s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [04:38<03:18,  2.34s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [04:41<03:21,  2.40s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [04:43<03:11,  2.30s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [04:45<03:07,  2.28s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [04:47<03:09,  2.33s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [04:50<03:08,  2.35s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [04:52<03:08,  2.38s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [04:55<03:04,  2.36s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [04:57<03:03,  2.39s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [04:59<03:01,  2.38s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [05:02<02:57,  2.37s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [05:04<03:03,  2.48s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [05:07<03:01,  2.49s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [05:10<03:02,  2.54s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▌                 | 129/200 [05:12<02:59,  2.53s/it]\u001b[A\n",
-      "training:  65%|███████████████████████████████▊                 | 130/200 [05:15<02:56,  2.52s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████                 | 131/200 [05:17<02:55,  2.55s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▎                | 132/200 [05:20<02:54,  2.57s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▌                | 133/200 [05:22<02:51,  2.55s/it]\u001b[A\n",
-      "training:  67%|████████████████████████████████▊                | 134/200 [05:25<02:50,  2.58s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████                | 135/200 [05:28<02:51,  2.65s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▎               | 136/200 [05:30<02:48,  2.63s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▌               | 137/200 [05:33<02:43,  2.59s/it]\u001b[A\n",
-      "training:  69%|█████████████████████████████████▊               | 138/200 [05:35<02:39,  2.57s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████               | 139/200 [05:38<02:37,  2.58s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▎              | 140/200 [05:40<02:32,  2.54s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▌              | 141/200 [05:43<02:29,  2.54s/it]\u001b[A\n",
-      "training:  71%|██████████████████████████████████▊              | 142/200 [05:46<02:30,  2.59s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████              | 143/200 [05:48<02:30,  2.64s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▎             | 144/200 [05:51<02:32,  2.72s/it]\u001b[A\n",
-      "training:  72%|███████████████████████████████████▌             | 145/200 [05:54<02:26,  2.66s/it]\u001b[A\n",
-      "training:  73%|███████████████████████████████████▊             | 146/200 [05:57<02:25,  2.70s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████             | 147/200 [05:59<02:20,  2.66s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▎            | 148/200 [06:02<02:21,  2.71s/it]\u001b[A\n",
-      "training:  74%|████████████████████████████████████▌            | 149/200 [06:05<02:17,  2.69s/it]\u001b[A\n",
-      "training:  75%|████████████████████████████████████▊            | 150/200 [06:08<02:17,  2.76s/it]\u001b[A\n",
-      "training:  76%|████████████████████████████████████▉            | 151/200 [06:10<02:13,  2.72s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▏           | 152/200 [06:13<02:09,  2.70s/it]\u001b[A\n",
-      "training:  76%|█████████████████████████████████████▍           | 153/200 [06:16<02:05,  2.68s/it]\u001b[A\n",
-      "training:  77%|█████████████████████████████████████▋           | 154/200 [06:18<02:03,  2.69s/it]\u001b[A\n",
-      "training:  78%|█████████████████████████████████████▉           | 155/200 [06:21<01:59,  2.65s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▏          | 156/200 [06:24<02:04,  2.82s/it]\u001b[A\n",
-      "training:  78%|██████████████████████████████████████▍          | 157/200 [06:27<02:01,  2.82s/it]\u001b[A\n",
-      "training:  79%|██████████████████████████████████████▋          | 158/200 [06:30<02:00,  2.87s/it]\u001b[A\n",
-      "training:  80%|██████████████████████████████████████▉          | 159/200 [06:33<01:55,  2.82s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▏         | 160/200 [06:35<01:50,  2.77s/it]\u001b[A\n",
-      "training:  80%|███████████████████████████████████████▍         | 161/200 [06:38<01:49,  2.80s/it]\u001b[A\n",
-      "training:  81%|███████████████████████████████████████▋         | 162/200 [06:41<01:45,  2.77s/it]\u001b[A\n",
-      "training:  82%|███████████████████████████████████████▉         | 163/200 [06:44<01:44,  2.83s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▏        | 164/200 [06:47<01:46,  2.95s/it]\u001b[A\n",
-      "training:  82%|████████████████████████████████████████▍        | 165/200 [06:50<01:46,  3.05s/it]\u001b[A\n",
-      "training:  83%|████████████████████████████████████████▋        | 166/200 [06:54<01:49,  3.22s/it]\u001b[A\n",
-      "training:  84%|████████████████████████████████████████▉        | 167/200 [06:57<01:46,  3.22s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▏       | 168/200 [07:00<01:43,  3.24s/it]\u001b[A\n",
-      "training:  84%|█████████████████████████████████████████▍       | 169/200 [07:04<01:41,  3.27s/it]\u001b[A\n",
-      "training:  85%|█████████████████████████████████████████▋       | 170/200 [07:07<01:39,  3.31s/it]\u001b[A\n",
-      "training:  86%|█████████████████████████████████████████▉       | 171/200 [07:10<01:34,  3.27s/it]\u001b[A\n",
-      "training:  86%|██████████████████████████████████████████▏      | 172/200 [07:13<01:29,  3.21s/it]\u001b[A\n",
-      "training:  86%|██████████████████████████████████████████▍      | 173/200 [07:18<01:35,  3.54s/it]\u001b[A\n",
-      "training:  87%|██████████████████████████████████████████▋      | 174/200 [07:21<01:28,  3.42s/it]\u001b[A\n",
-      "training:  88%|██████████████████████████████████████████▉      | 175/200 [07:24<01:24,  3.38s/it]\u001b[A\n",
-      "training:  88%|███████████████████████████████████████████      | 176/200 [07:28<01:21,  3.40s/it]\u001b[A\n",
-      "training:  88%|███████████████████████████████████████████▎     | 177/200 [07:30<01:12,  3.15s/it]\u001b[A\n",
-      "training:  89%|███████████████████████████████████████████▌     | 178/200 [07:33<01:08,  3.11s/it]\u001b[A\n",
-      "training:  90%|███████████████████████████████████████████▊     | 179/200 [07:36<01:04,  3.07s/it]\u001b[A\n",
-      "training:  90%|████████████████████████████████████████████     | 180/200 [07:39<01:01,  3.09s/it]\u001b[A\n",
-      "training:  90%|████████████████████████████████████████████▎    | 181/200 [07:42<00:56,  2.98s/it]\u001b[A\n",
-      "training:  91%|████████████████████████████████████████████▌    | 182/200 [07:45<00:53,  2.95s/it]\u001b[A\n",
-      "training:  92%|████████████████████████████████████████████▊    | 183/200 [07:48<00:49,  2.91s/it]\u001b[A\n",
-      "training:  92%|█████████████████████████████████████████████    | 184/200 [07:50<00:45,  2.83s/it]\u001b[A\n",
-      "training:  92%|█████████████████████████████████████████████▎   | 185/200 [07:53<00:42,  2.82s/it]\u001b[A\n",
-      "training:  93%|█████████████████████████████████████████████▌   | 186/200 [07:56<00:38,  2.77s/it]\u001b[A\n",
-      "training:  94%|█████████████████████████████████████████████▊   | 187/200 [07:59<00:36,  2.84s/it]\u001b[A\n",
-      "training:  94%|██████████████████████████████████████████████   | 188/200 [08:02<00:34,  2.86s/it]\u001b[A\n",
-      "training:  94%|██████████████████████████████████████████████▎  | 189/200 [08:05<00:31,  2.89s/it]\u001b[A\n",
-      "training:  95%|██████████████████████████████████████████████▌  | 190/200 [08:08<00:28,  2.89s/it]\u001b[A\n",
-      "training:  96%|██████████████████████████████████████████████▊  | 191/200 [08:10<00:25,  2.82s/it]\u001b[A\n",
-      "training:  96%|███████████████████████████████████████████████  | 192/200 [08:13<00:22,  2.84s/it]\u001b[A\n",
-      "training:  96%|███████████████████████████████████████████████▎ | 193/200 [08:16<00:19,  2.83s/it]\u001b[A\n",
-      "training:  97%|███████████████████████████████████████████████▌ | 194/200 [08:19<00:17,  2.84s/it]\u001b[A\n",
-      "training:  98%|███████████████████████████████████████████████▊ | 195/200 [08:21<00:13,  2.79s/it]\u001b[A\n",
-      "training:  98%|████████████████████████████████████████████████ | 196/200 [08:24<00:11,  2.81s/it]\u001b[A\n",
-      "training:  98%|████████████████████████████████████████████████▎| 197/200 [08:27<00:08,  2.87s/it]\u001b[A\n",
-      "training:  99%|████████████████████████████████████████████████▌| 198/200 [08:30<00:05,  2.83s/it]\u001b[A\n",
-      "training: 100%|████████████████████████████████████████████████▊| 199/200 [08:33<00:02,  2.84s/it]\u001b[A\n",
-      "training: 100%|█████████████████████████████████████████████████| 200/200 [08:35<00:00,  2.58s/it]\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 38it [00:01, 51.46it/s]\u001b[A\n",
+      "Evaluating NLL: 76it [00:01, 104.64it/s]\u001b[A\n",
+      "Evaluating NLL: 113it [00:01, 154.07it/s]\u001b[A\n",
+      "Evaluating NLL: 156it [00:01, 211.56it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 139.61it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|▊                    | 4/100 [00:00<00:02, 39.49it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%|█▉                   | 9/100 [00:00<00:02, 42.83it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|██▊                 | 14/100 [00:00<00:02, 40.90it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  23%|████▌               | 23/100 [00:00<00:01, 54.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  29%|█████▊              | 29/100 [00:00<00:01, 47.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  34%|██████▊             | 34/100 [00:00<00:01, 46.88it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|███████▊            | 39/100 [00:00<00:01, 44.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|████████▊           | 44/100 [00:00<00:01, 42.76it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██████████▍         | 52/100 [00:01<00:00, 49.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|███████████▌        | 58/100 [00:01<00:00, 45.97it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|████████████▌       | 63/100 [00:01<00:00, 45.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|█████████████▌      | 68/100 [00:01<00:00, 43.60it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|███████████████▏    | 76/100 [00:01<00:00, 51.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|████████████████▍   | 82/100 [00:01<00:00, 47.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|█████████████████▍  | 87/100 [00:01<00:00, 45.43it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|██████████████████▍ | 92/100 [00:02<00:00, 43.54it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 45.87it/s]\n",
+      " 20%|█████████████▌                                                      | 1/5 [06:26<25:47, 386.97s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:45,  5.05s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:10,  2.78s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:50,  2.08s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:40,  1.73s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<05:02,  1.55s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:36,  1.42s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:20,  1.35s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:12,  1.32s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:07,  1.30s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:02,  1.28s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:56,  1.26s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<03:51,  1.24s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:51,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:47,  1.24s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:45,  1.23s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:41,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:38,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:38,  1.22s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:35,  1.21s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:33,  1.20s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<03:31,  1.20s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:30,  1.19s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:30,  1.20s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:35<03:29,  1.20s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:36<03:26,  1.19s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:37<03:24,  1.19s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:39<03:24,  1.19s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:40<03:21,  1.19s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:41<03:21,  1.19s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:42<03:21,  1.20s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:43<03:21,  1.21s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:45<03:22,  1.22s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:46<03:20,  1.22s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:47<03:25,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:49<03:28,  1.28s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:50<03:30,  1.30s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:51<03:32,  1.32s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:31,  1.32s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:54<03:29,  1.32s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:55<03:28,  1.32s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:27,  1.32s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:58<03:24,  1.31s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:59<03:24,  1.32s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:00<03:23,  1.32s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:02<03:22,  1.33s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:03<03:18,  1.30s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:04<03:16,  1.30s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:07<03:22,  1.35s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:51,  5.09s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:35,  2.91s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:14,  2.21s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:57,  1.82s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:14,  1.61s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:49,  1.49s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:30,  1.40s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:24,  1.38s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:20,  1.36s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:19,  1.37s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:17,  1.36s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:16,  1.36s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:21,  1.40s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:18,  1.39s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<04:14,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:13,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<04:10,  1.37s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<04:11,  1.38s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<04:03,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:56,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:58,  1.33s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<03:54,  1.32s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:51,  1.31s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:54,  1.33s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<03:52,  1.33s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:51,  1.33s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:46,  1.31s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:52,  1.35s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:55,  1.37s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:52,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:45<03:52,  1.38s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:48,  1.36s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:45,  1.35s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:49<03:43,  1.34s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<03:47,  1.38s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:41,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:53<03:55,  1.45s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:55<03:53,  1.44s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:56<03:38,  1.36s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:57<03:30,  1.31s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:58<03:20,  1.26s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:16,  1.25s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:12,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:02<03:11,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:04<03:42,  1.44s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:44,  5.05s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:33,  2.90s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:10,  2.19s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:00,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:22,  1.66s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:00,  1.55s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:47,  1.49s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:35,  1.44s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:28,  1.41s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:21,  1.38s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:18,  1.37s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:14,  1.35s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:09,  1.33s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:00,  1.29s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<03:55,  1.27s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<03:51,  1.26s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:48,  1.25s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:52,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:48,  1.26s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:46,  1.26s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:44,  1.26s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:43,  1.26s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:43,  1.26s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:41,  1.26s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:39,  1.25s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:43,  1.28s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:39,  1.27s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:37,  1.26s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:37,  1.27s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:33,  1.25s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:34,  1.27s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:36,  1.29s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:36,  1.30s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:37,  1.31s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:38,  1.32s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:37,  1.33s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:36,  1.33s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:52<03:34,  1.32s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:33,  1.33s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:55<03:31,  1.32s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:56<03:30,  1.33s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:29,  1.33s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:25,  1.31s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:00<03:19,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:01<03:14,  1.25s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:04<03:35,  1.40s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:43,  5.04s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:56,  2.11s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:49,  1.78s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:09,  1.58s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:50,  1.50s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:36,  1.43s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:19,  1.35s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:16,  1.34s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:10,  1.32s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:02,  1.28s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:57,  1.26s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<04:01,  1.29s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<04:04,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<04:06,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<04:05,  1.34s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<04:05,  1.34s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<04:04,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<04:04,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<04:03,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<04:03,  1.36s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<04:02,  1.36s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<04:00,  1.36s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<04:00,  1.36s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:59,  1.37s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:57,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<04:00,  1.39s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:58,  1.39s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:57,  1.39s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:54,  1.38s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:54,  1.39s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:52,  1.38s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:50,  1.38s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:49<03:47,  1.37s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<03:45,  1.36s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:44,  1.37s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:53<03:42,  1.37s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:54<03:43,  1.38s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:55<03:41,  1.37s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:57<04:08,  1.55s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:59<03:52,  1.46s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [01:00<03:40,  1.40s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:01<03:33,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:02<03:26,  1.32s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:05<03:45,  1.45s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:02,  5.14s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:39,  2.93s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:17,  2.22s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:11,  1.89s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:32,  1.70s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:07,  1.59s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:51,  1.51s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:41,  1.47s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:35,  1.44s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:27,  1.41s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:16,  1.35s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:02,  1.29s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<03:55,  1.26s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<03:51,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<03:51,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<03:46,  1.23s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:47,  1.24s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:45,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:41,  1.22s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:37,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:35,  1.20s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:29,  1.18s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:30,  1.19s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:33,  1.21s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:32,  1.21s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:29,  1.20s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:30,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:29,  1.22s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:25,  1.20s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:31,  1.25s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:24,  1.22s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:24,  1.23s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:23,  1.23s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:18,  1.20s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:48<03:18,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:15,  1.20s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:14,  1.20s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:12,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:11,  1.20s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:54<03:10,  1.20s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:10,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:08,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:58<03:07,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:30,  1.35s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.46it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.78it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.70it/s]\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
       "Evaluating NLL: 1it [00:01,  1.17s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.94it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.15s/it]\u001b[A\n",
-      "Evaluating NLL: 24it [00:01, 26.08it/s]\u001b[A\n",
-      "Evaluating NLL: 50it [00:01, 57.31it/s]\u001b[A\n",
-      "Evaluating NLL: 69it [00:01, 77.65it/s]\u001b[A\n",
-      "Evaluating NLL: 88it [00:01, 93.04it/s]\u001b[A\n",
-      "Evaluating NLL: 105it [00:01, 102.58it/s]\u001b[A\n",
-      "Evaluating NLL: 121it [00:01, 111.04it/s]\u001b[A\n",
-      "Evaluating NLL: 136it [00:01, 117.68it/s]\u001b[A\n",
-      "Evaluating NLL: 163it [00:02, 153.46it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:02, 89.54it/s] \n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   2%|▎              | 2/100 [00:00<00:06, 14.14it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   4%|▌              | 4/100 [00:00<00:07, 13.01it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   6%|▉              | 6/100 [00:00<00:06, 14.99it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   8%|█▏             | 8/100 [00:00<00:05, 16.38it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  10%|█▍            | 10/100 [00:00<00:05, 16.97it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  13%|█▊            | 13/100 [00:00<00:05, 16.81it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  15%|██            | 15/100 [00:00<00:05, 16.46it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  17%|██▍           | 17/100 [00:01<00:04, 16.67it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  19%|██▋           | 19/100 [00:01<00:04, 16.54it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  21%|██▉           | 21/100 [00:01<00:04, 15.83it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  23%|███▏          | 23/100 [00:01<00:04, 16.25it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  26%|███▋          | 26/100 [00:01<00:04, 18.19it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  28%|███▉          | 28/100 [00:01<00:04, 17.22it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  30%|████▏         | 30/100 [00:01<00:04, 15.91it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  32%|████▍         | 32/100 [00:01<00:04, 15.07it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  34%|████▊         | 34/100 [00:02<00:04, 15.35it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  37%|█████▏        | 37/100 [00:02<00:03, 17.19it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  39%|█████▍        | 39/100 [00:02<00:03, 17.42it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  41%|█████▋        | 41/100 [00:02<00:03, 15.96it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  43%|██████        | 43/100 [00:02<00:03, 16.06it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  45%|██████▎       | 45/100 [00:02<00:03, 16.86it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  47%|██████▌       | 47/100 [00:02<00:03, 15.75it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  49%|██████▊       | 49/100 [00:03<00:03, 15.56it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  51%|███████▏      | 51/100 [00:03<00:03, 16.20it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  54%|███████▌      | 54/100 [00:03<00:02, 17.55it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  56%|███████▊      | 56/100 [00:03<00:02, 16.57it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  58%|████████      | 58/100 [00:03<00:02, 15.83it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  60%|████████▍     | 60/100 [00:03<00:02, 14.65it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  62%|████████▋     | 62/100 [00:03<00:02, 15.51it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  65%|█████████     | 65/100 [00:03<00:01, 17.53it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  67%|█████████▍    | 67/100 [00:04<00:01, 17.00it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  69%|█████████▋    | 69/100 [00:04<00:01, 15.59it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  71%|█████████▉    | 71/100 [00:04<00:01, 16.24it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  73%|██████████▏   | 73/100 [00:04<00:01, 16.72it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  75%|██████████▌   | 75/100 [00:04<00:01, 15.26it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  77%|██████████▊   | 77/100 [00:04<00:01, 16.38it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  80%|███████████▏  | 80/100 [00:04<00:01, 19.46it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  83%|███████████▌  | 83/100 [00:04<00:00, 21.94it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  86%|████████████  | 86/100 [00:05<00:00, 19.65it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  89%|████████████▍ | 89/100 [00:05<00:00, 17.06it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  91%|████████████▋ | 91/100 [00:05<00:00, 15.70it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  94%|█████████████▏| 94/100 [00:05<00:00, 18.62it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  97%|█████████████▌| 97/100 [00:05<00:00, 20.88it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:05<00:00, 16.78it/s]\n",
-      " 20%|███████████▊                                               | 1/5 [25:07<1:40:28, 1507.06s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -8.86695870358501\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 7it [00:02,  3.08it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:08<27:01,  8.15s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:11<17:09,  5.20s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:14<13:34,  4.13s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:16<11:46,  3.60s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:19<10:52,  3.35s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:22<10:30,  3.25s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:26<10:23,  3.23s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:28<09:56,  3.11s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:31<09:38,  3.03s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:34<09:15,  2.92s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:37<09:11,  2.92s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:40<09:10,  2.93s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:43<09:19,  2.99s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:46<09:19,  3.01s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:49<09:10,  2.98s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:52<09:12,  3.00s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:55<09:23,  3.08s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:58<09:14,  3.05s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [01:01<08:39,  2.87s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [01:03<08:17,  2.76s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [01:06<08:14,  2.76s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [01:08<07:56,  2.68s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:11<07:56,  2.69s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:14<07:56,  2.71s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:17<08:00,  2.74s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:19<07:50,  2.71s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:22<07:37,  2.65s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:25<07:50,  2.73s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:27<07:38,  2.68s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:30<07:29,  2.65s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:33<07:35,  2.69s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:35<07:29,  2.68s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:38<07:30,  2.70s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:41<07:19,  2.65s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:43<07:07,  2.59s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:46<07:02,  2.57s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:48<07:13,  2.66s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:51<07:06,  2.63s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:54<07:06,  2.65s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:56<07:06,  2.66s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:59<06:56,  2.62s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [02:02<07:07,  2.71s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [02:05<07:10,  2.75s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [02:07<06:59,  2.69s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [02:10<06:41,  2.59s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [02:12<06:31,  2.54s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [02:14<06:23,  2.51s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [02:17<06:08,  2.43s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [02:19<06:07,  2.43s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [02:22<06:04,  2.43s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [02:24<06:00,  2.42s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [02:26<05:55,  2.40s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [02:29<05:51,  2.39s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [02:31<05:43,  2.36s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [02:33<05:41,  2.35s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [02:36<05:43,  2.38s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [02:38<05:53,  2.48s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [02:41<05:42,  2.41s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [02:43<05:42,  2.43s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [02:46<05:37,  2.41s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [02:48<05:42,  2.46s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [02:50<05:29,  2.39s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [02:53<05:25,  2.38s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [02:55<05:30,  2.43s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [02:58<05:31,  2.45s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [03:00<05:23,  2.42s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [03:03<05:39,  2.56s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [03:05<05:28,  2.49s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [03:08<05:28,  2.51s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [03:10<05:21,  2.47s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [03:13<05:21,  2.49s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [03:15<05:08,  2.41s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [03:17<05:05,  2.41s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [03:20<05:17,  2.52s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [03:23<05:23,  2.58s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [03:27<06:11,  3.00s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [03:29<05:49,  2.84s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [03:32<05:35,  2.75s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [03:35<05:33,  2.75s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [03:37<05:17,  2.64s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [03:40<05:18,  2.67s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [03:42<05:12,  2.65s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [03:45<05:06,  2.62s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [03:48<05:06,  2.64s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [03:50<05:02,  2.63s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [03:53<04:52,  2.56s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [03:55<04:48,  2.55s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [03:58<04:41,  2.51s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [04:00<04:35,  2.48s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [04:03<04:41,  2.56s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [04:05<04:36,  2.54s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [04:08<04:36,  2.56s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [04:10<04:29,  2.52s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [04:13<04:35,  2.60s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [04:16<04:36,  2.63s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [04:18<04:34,  2.64s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [04:21<04:28,  2.60s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [04:23<04:19,  2.54s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [04:26<04:22,  2.60s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [04:29<04:19,  2.60s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [04:31<04:19,  2.62s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [04:34<04:14,  2.59s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [04:36<04:04,  2.52s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [04:39<03:55,  2.45s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [04:41<03:51,  2.44s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [04:44<03:56,  2.51s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [04:46<03:52,  2.50s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [04:49<03:48,  2.48s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [04:51<03:55,  2.59s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [04:54<03:48,  2.54s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [04:56<03:46,  2.54s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [04:59<03:47,  2.59s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [05:02<03:44,  2.58s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [05:04<03:43,  2.60s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [05:07<03:45,  2.65s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [05:09<03:35,  2.57s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [05:12<03:33,  2.58s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [05:15<03:35,  2.63s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [05:17<03:34,  2.65s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [05:20<03:37,  2.72s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [05:23<03:30,  2.67s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [05:26<03:28,  2.67s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [05:28<03:29,  2.72s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [05:34<03:24,  2.70s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.46s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.52it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.18s/it]\u001b[A\n",
-      "Evaluating NLL: 26it [00:01, 27.61it/s]\u001b[A\n",
-      "Evaluating NLL: 49it [00:01, 54.53it/s]\u001b[A\n",
-      "Evaluating NLL: 68it [00:01, 73.64it/s]\u001b[A\n",
-      "Evaluating NLL: 86it [00:01, 92.56it/s]\u001b[A\n",
-      "Evaluating NLL: 111it [00:01, 124.13it/s]\u001b[A\n",
-      "Evaluating NLL: 136it [00:01, 150.66it/s]\u001b[A\n",
-      "Evaluating NLL: 160it [00:01, 171.55it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:02, 96.32it/s] \n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   3%|▍              | 3/100 [00:00<00:03, 26.61it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   6%|▉              | 6/100 [00:00<00:03, 25.47it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  10%|█▍            | 10/100 [00:00<00:03, 29.71it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  14%|█▉            | 14/100 [00:00<00:02, 30.94it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  18%|██▌           | 18/100 [00:00<00:02, 28.09it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  21%|██▉           | 21/100 [00:00<00:02, 27.71it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  24%|███▎          | 24/100 [00:00<00:02, 26.31it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  27%|███▊          | 27/100 [00:00<00:02, 26.18it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  31%|████▎         | 31/100 [00:01<00:02, 28.36it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  35%|████▉         | 35/100 [00:01<00:02, 30.29it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  39%|█████▍        | 39/100 [00:01<00:02, 29.06it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  42%|█████▉        | 42/100 [00:01<00:02, 27.19it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  45%|██████▎       | 45/100 [00:01<00:02, 26.74it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  49%|██████▊       | 49/100 [00:01<00:01, 29.38it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  52%|███████▎      | 52/100 [00:01<00:01, 27.89it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  56%|███████▊      | 56/100 [00:01<00:01, 29.25it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  60%|████████▍     | 60/100 [00:02<00:01, 29.71it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  63%|████████▊     | 63/100 [00:02<00:01, 28.93it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  66%|█████████▏    | 66/100 [00:02<00:01, 28.97it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  69%|█████████▋    | 69/100 [00:02<00:01, 27.85it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  72%|██████████    | 72/100 [00:02<00:01, 27.36it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  75%|██████████▌   | 75/100 [00:02<00:00, 27.26it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  79%|███████████   | 79/100 [00:02<00:00, 29.14it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  83%|███████████▌  | 83/100 [00:02<00:00, 30.50it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  87%|████████████▏ | 87/100 [00:03<00:00, 28.88it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  90%|████████████▌ | 90/100 [00:03<00:00, 28.15it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  93%|█████████████ | 93/100 [00:03<00:00, 27.47it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  96%|█████████████▍| 96/100 [00:03<00:00, 27.52it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:03<00:00, 28.25it/s]\n",
-      " 40%|███████████████████████▌                                   | 2/5 [48:46<1:12:45, 1455.27s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -5.931953954298877\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.08s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.43it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 38it [00:01, 51.03it/s]\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 102.55it/s]\u001b[A\n",
+      "Evaluating NLL: 110it [00:01, 148.89it/s]\u001b[A\n",
+      "Evaluating NLL: 147it [00:01, 194.90it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 137.60it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|▊                    | 4/100 [00:00<00:02, 39.48it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%|█▉                   | 9/100 [00:00<00:02, 42.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|██▊                 | 14/100 [00:00<00:02, 40.51it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  19%|███▊                | 19/100 [00:00<00:02, 39.86it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|█████▍              | 27/100 [00:00<00:01, 51.75it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|██████▌             | 33/100 [00:00<00:01, 46.61it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|███████▌            | 38/100 [00:00<00:01, 44.92it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|████████▌           | 43/100 [00:00<00:01, 42.77it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  50%|██████████          | 50/100 [00:01<00:01, 49.39it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  56%|███████████▏        | 56/100 [00:01<00:00, 45.64it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  61%|████████████▏       | 61/100 [00:01<00:00, 43.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|█████████████▏      | 66/100 [00:01<00:00, 42.33it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██████████████▏     | 71/100 [00:01<00:00, 41.32it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  79%|███████████████▊    | 79/100 [00:01<00:00, 50.29it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|█████████████████   | 85/100 [00:01<00:00, 46.34it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  90%|██████████████████  | 90/100 [00:02<00:00, 45.47it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  95%|███████████████████ | 95/100 [00:02<00:00, 43.51it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 44.60it/s]\n",
+      " 40%|███████████████████████████▏                                        | 2/5 [17:17<27:06, 542.08s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:26,  4.96s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:12,  2.79s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:40,  1.74s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<05:00,  1.54s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:41,  1.45s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:27,  1.39s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:18,  1.35s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:10,  1.31s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:05,  1.29s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:01,  1.28s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:57,  1.26s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:52,  1.24s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:46,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:45,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:44,  1.22s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:48,  1.25s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:53,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:56,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:55,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:56,  1.32s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:56,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:54,  1.32s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:51,  1.32s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:50,  1.31s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:48,  1.31s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:47,  1.32s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:45,  1.31s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:38,  1.28s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:36,  1.27s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:33,  1.26s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:31,  1.26s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:31,  1.27s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:24,  1.23s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:25,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:22,  1.23s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:20,  1.23s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:17,  1.22s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:14,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:10,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:08,  1.19s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:06,  1.18s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:05,  1.18s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:58<03:05,  1.19s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:01<03:30,  1.36s/it]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:07<23:33,  7.10s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:09<14:56,  4.53s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:12<11:45,  3.58s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:14<10:00,  3.06s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:16<09:11,  2.83s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:19<08:28,  2.62s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:21<08:19,  2.59s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:24<08:00,  2.50s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:26<07:51,  2.47s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:28<07:46,  2.46s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:31<07:41,  2.44s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:33<07:38,  2.44s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:36<07:33,  2.43s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:38<07:30,  2.42s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:40<07:20,  2.38s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:43<07:14,  2.36s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:45<07:17,  2.39s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:48<07:18,  2.41s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:50<07:20,  2.43s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:52<07:09,  2.39s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:55<07:05,  2.38s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:57<07:13,  2.44s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [01:00<07:12,  2.44s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [01:02<06:59,  2.39s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [01:04<06:54,  2.37s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [01:06<06:45,  2.33s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [01:09<06:47,  2.36s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:11<06:49,  2.38s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:14<06:42,  2.35s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:16<06:46,  2.39s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:18<06:41,  2.37s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:21<06:37,  2.37s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:23<06:36,  2.37s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:26<06:35,  2.38s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:28<06:28,  2.35s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:30<06:28,  2.37s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:33<06:32,  2.41s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:35<06:24,  2.38s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:37<06:21,  2.37s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:40<06:20,  2.38s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:42<06:19,  2.39s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:45<06:11,  2.35s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:47<05:52,  2.25s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:48<05:37,  2.16s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:51<05:31,  2.14s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:53<05:36,  2.18s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [01:55<05:34,  2.19s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [01:58<05:55,  2.34s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [02:00<05:49,  2.31s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [02:02<05:46,  2.31s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [02:05<05:43,  2.31s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [02:07<05:49,  2.36s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [02:10<05:49,  2.38s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [02:12<05:59,  2.46s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [02:15<06:03,  2.51s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [02:17<05:54,  2.46s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [02:20<05:50,  2.45s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [02:22<05:53,  2.49s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [02:24<05:34,  2.37s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [02:27<06:07,  2.62s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [02:30<06:01,  2.60s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [02:32<05:50,  2.54s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [02:35<05:31,  2.42s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [02:37<05:20,  2.35s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [02:39<05:14,  2.33s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [02:41<05:12,  2.33s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [02:44<05:05,  2.30s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [02:46<05:08,  2.34s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [02:48<05:08,  2.36s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [02:51<05:03,  2.34s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [02:53<04:57,  2.31s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [02:55<04:54,  2.30s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [02:57<04:46,  2.26s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [03:00<04:49,  2.30s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [03:02<04:50,  2.32s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [03:04<04:45,  2.30s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [03:07<04:41,  2.29s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [03:09<04:35,  2.26s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [03:11<04:32,  2.26s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [03:13<04:29,  2.25s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [03:15<04:25,  2.23s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [03:18<04:24,  2.24s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [03:20<04:25,  2.27s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [03:22<04:20,  2.25s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [03:25<04:24,  2.30s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [03:27<04:25,  2.33s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [03:29<04:24,  2.34s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [03:32<04:16,  2.29s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [03:34<04:15,  2.30s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [03:36<04:09,  2.27s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [03:38<04:08,  2.28s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [03:41<04:01,  2.23s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [03:43<03:56,  2.21s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [03:45<03:52,  2.20s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [03:47<03:49,  2.19s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [03:49<03:51,  2.22s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [03:52<03:50,  2.24s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [03:54<03:49,  2.25s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [03:56<03:49,  2.27s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [03:59<03:49,  2.30s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [04:01<03:44,  2.27s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [04:03<03:37,  2.22s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [04:05<03:34,  2.21s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [04:07<03:34,  2.23s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [04:10<03:31,  2.23s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [04:12<03:30,  2.24s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [04:14<03:31,  2.28s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [04:17<03:29,  2.28s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [04:19<03:31,  2.33s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [04:22<03:36,  2.40s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [04:24<03:32,  2.39s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [04:26<03:34,  2.43s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [04:29<03:28,  2.39s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [04:31<03:24,  2.38s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [04:34<03:26,  2.43s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [04:36<03:22,  2.41s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [04:39<03:22,  2.44s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [04:41<03:19,  2.43s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [04:43<03:19,  2.46s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [04:46<03:16,  2.46s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [04:49<03:17,  2.50s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [04:51<03:11,  2.45s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [04:53<03:09,  2.46s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [04:56<03:10,  2.50s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [04:58<03:02,  2.43s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [05:00<02:56,  2.39s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [05:03<03:00,  2.47s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [05:05<02:54,  2.42s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▌                 | 129/200 [05:08<02:49,  2.38s/it]\u001b[A\n",
-      "training:  65%|███████████████████████████████▊                 | 130/200 [05:10<02:41,  2.31s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████                 | 131/200 [05:13<02:48,  2.45s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▎                | 132/200 [05:15<02:42,  2.39s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▌                | 133/200 [05:17<02:33,  2.29s/it]\u001b[A\n",
-      "training:  67%|████████████████████████████████▊                | 134/200 [05:21<02:38,  2.40s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:00,  5.13s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:08,  2.18s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:07,  1.87s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:25,  1.67s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:54,  1.52s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:37,  1.44s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:21,  1.36s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:05,  1.29s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:08,  1.32s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:11,  1.34s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<04:10,  1.34s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:17,  1.39s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<04:15,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<04:10,  1.36s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<04:13,  1.39s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<04:11,  1.38s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<04:06,  1.36s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<04:03,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<04:04,  1.37s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<04:03,  1.37s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<04:02,  1.37s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<04:00,  1.36s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<04:00,  1.37s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:57,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:58,  1.38s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:57,  1.38s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:55,  1.38s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:44<03:53,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:45<03:51,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:49,  1.37s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:48<03:48,  1.37s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:49<03:46,  1.36s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<03:41,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:52<03:42,  1.36s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:53<03:39,  1.34s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:54<03:30,  1.30s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:55<03:11,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<02:52,  1.08s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<02:39,  1.00s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<02:36,  1.01it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<02:37,  1.00s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:00<02:32,  1.02it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:01<02:27,  1.05it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<02:20,  1.09it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:02<02:17,  1.11it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:03<02:14,  1.13it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:06<03:24,  1.35s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:01,  5.13s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:44,  2.65s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:52,  1.79s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:32,  1.39s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:48,  1.17s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:19,  1.03s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:09<03:04,  1.05it/s]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:10<02:51,  1.12it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:42,  1.17it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:35,  1.22it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:12<02:29,  1.27it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:13<02:29,  1.25it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:14<02:25,  1.29it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:15<02:23,  1.30it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:15<02:22,  1.30it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:16<02:25,  1.27it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:17<02:23,  1.28it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:18<02:18,  1.31it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:19<02:16,  1.33it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:19<02:15,  1.33it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:20<02:16,  1.32it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:21<02:14,  1.32it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:22<02:15,  1.31it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:22<02:14,  1.31it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:23<02:18,  1.26it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:24<02:15,  1.29it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:25<02:11,  1.32it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:26<02:24,  1.19it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:27<02:30,  1.13it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:28<02:38,  1.08it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:29<02:43,  1.03it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:30<02:46,  1.01it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:31<02:50,  1.02s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:32<02:51,  1.03s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:33<02:52,  1.04s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:34<02:51,  1.05s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:35<02:53,  1.06s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:51,  1.06s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:37<02:50,  1.06s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:38<02:49,  1.06s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:39<02:51,  1.08s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:41<02:48,  1.07s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:42<02:49,  1.08s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:43<02:48,  1.08s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:44<02:48,  1.09s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:45<02:46,  1.08s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:46<02:45,  1.08s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:47<02:43,  1.08s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:48<02:42,  1.07s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:50<02:32,  1.02s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:23,  5.25s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:20,  2.83s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:40,  2.03s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:25,  1.66s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:46,  1.47s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:22,  1.35s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<04:09,  1.29s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<03:56,  1.23s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<03:50,  1.20s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:15<03:41,  1.17s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:16<03:37,  1.15s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:17<03:31,  1.12s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:18<03:26,  1.11s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:19<03:25,  1.10s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:20<03:23,  1.10s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:21<03:22,  1.10s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:22<03:20,  1.10s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:24<03:23,  1.12s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:25<03:18,  1.10s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:26<03:18,  1.10s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:27<03:20,  1.12s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:28<03:18,  1.11s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:29<03:00,  1.02s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:30<02:47,  1.05it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:30<02:38,  1.11it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:31<02:29,  1.16it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:32<02:22,  1.21it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:33<02:15,  1.27it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:33<02:15,  1.26it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:34<02:11,  1.29it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:35<02:12,  1.27it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:36<02:23,  1.17it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:37<02:34,  1.08it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:38<02:41,  1.03it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:39<02:48,  1.02s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:40<02:55,  1.07s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:42<02:57,  1.09s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:43<02:57,  1.10s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:44<02:54,  1.08s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:45<02:54,  1.09s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:46<02:57,  1.12s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:47<02:55,  1.11s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:48<02:53,  1.11s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:49<02:50,  1.10s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:50<02:50,  1.10s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:52<02:50,  1.11s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:53<02:51,  1.12s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:54<02:51,  1.13s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:55<02:50,  1.13s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:57<02:53,  1.15s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:24,  5.55s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:39,  2.93s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:54,  2.10s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:31,  1.69s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:49,  1.49s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:23,  1.36s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:06,  1.28s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<03:54,  1.22s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<03:46,  1.18s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:15<03:38,  1.15s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:16<03:42,  1.18s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:17<03:42,  1.18s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:18<03:35,  1.15s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:28,  1.12s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:21<03:27,  1.12s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:22<03:21,  1.10s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:23<03:22,  1.11s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:24<03:21,  1.11s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:25<03:37,  1.20s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:27<03:43,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:28<03:32,  1.19s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:29<03:26,  1.16s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:30<03:20,  1.13s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:31<03:15,  1.11s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:32<03:11,  1.09s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:33<03:10,  1.09s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:34<03:08,  1.09s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:35<03:05,  1.08s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:36<03:02,  1.07s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:37<03:02,  1.07s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:38<03:02,  1.08s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:40<03:01,  1.08s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:41<03:03,  1.10s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:42<03:01,  1.10s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:43<02:53,  1.05s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:44<02:44,  1.00s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:44<02:32,  1.07it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:45<02:24,  1.12it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:46<02:19,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:47<02:14,  1.19it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:48<02:15,  1.17it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:48<02:13,  1.18it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:49<02:17,  1.14it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:50<02:23,  1.09it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:52<02:31,  1.03it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:53<02:36,  1.02s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:54<02:39,  1.04s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:55<02:39,  1.05s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:56<02:40,  1.06s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:57<02:40,  1.07s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:58<02:38,  1.06s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:59<02:37,  1.06s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:00<02:37,  1.07s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:01<02:35,  1.06s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:02<02:36,  1.08s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:04<02:47,  1.16s/it]\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
       "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.24it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.50it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.63it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.53it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.71it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 65it [00:01, 88.56it/s]\u001b[A\n",
+      "Evaluating NLL: 117it [00:01, 159.03it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 160.48it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|█████▋             | 30/100 [00:00<00:00, 295.66it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|███████████▍       | 60/100 [00:00<00:00, 268.93it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████| 100/100 [00:00<00:00, 252.88it/s]\n",
+      " 60%|████████████████████████████████████████▊                           | 3/5 [27:37<19:15, 577.80s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:39,  5.62s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:45,  2.96s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:00,  2.14s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:49,  1.78s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:31,  1.40s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:39,  1.45s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:29,  1.40s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:11,  1.32s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<03:54,  1.23s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:43,  1.18s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:37,  1.16s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:30,  1.12s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:28,  1.12s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:21<03:24,  1.10s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:22<03:20,  1.09s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:23<03:17,  1.08s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:24<03:16,  1.08s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:26<03:16,  1.09s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:27<03:14,  1.08s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:28<03:14,  1.09s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:29<03:13,  1.09s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:30<03:10,  1.08s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:31<03:12,  1.09s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:32<03:10,  1.09s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:33<03:05,  1.07s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:34<02:51,  1.01it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:35<02:39,  1.08it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:36<02:32,  1.12it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:36<02:26,  1.16it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:37<02:21,  1.19it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:38<02:23,  1.17it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:39<02:20,  1.19it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:40<02:21,  1.17it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:41<02:29,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:42<02:36,  1.05it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:43<02:42,  1.00it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:44<02:45,  1.02s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:45<02:47,  1.04s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:46<02:50,  1.06s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:47<02:51,  1.08s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:48<02:51,  1.09s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:49<02:50,  1.09s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:51<02:49,  1.08s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:52<02:47,  1.08s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:53<02:45,  1.07s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:55<02:59,  1.17s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:36,  5.01s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:51,  2.69s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:20,  1.93s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:10,  1.59s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:37,  1.42s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:09,  1.29s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:52,  1.21s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:39,  1.14s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:32,  1.11s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:27,  1.09s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<03:24,  1.08s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<03:21,  1.07s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<03:18,  1.06s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<03:19,  1.07s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:19<03:15,  1.06s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:20<03:11,  1.04s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:21<03:10,  1.04s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:22<03:09,  1.04s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:23<03:09,  1.05s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:24<03:08,  1.05s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:25<03:06,  1.04s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:26<03:05,  1.04s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:28<03:04,  1.04s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:29<03:03,  1.04s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:30<03:03,  1.05s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:31<03:02,  1.05s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:32<03:03,  1.06s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:33<03:00,  1.05s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:34<02:58,  1.04s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:35<02:56,  1.04s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:36<02:48,  1.00it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:37<02:36,  1.08it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:37<02:34,  1.08it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:38<02:32,  1.09it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:39<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:40<02:19,  1.18it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:41<02:16,  1.19it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:42<02:16,  1.19it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:42<02:13,  1.21it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:43<02:12,  1.21it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:44<02:10,  1.22it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:45<02:12,  1.19it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:46<02:12,  1.18it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:47<02:12,  1.18it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:47<02:09,  1.20it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:48<02:09,  1.19it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:49<02:09,  1.18it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:50<02:09,  1.18it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:51<02:05,  1.20it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:53<02:39,  1.06s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:23,  5.24s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:01,  2.74s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:12,  1.89s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<04:54,  1.50s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:12,  1.30s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:45,  1.16s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:26,  1.07s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:13,  1.01s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:07,  1.02it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:01,  1.05it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:55,  1.08it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:52,  1.09it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:16<02:45,  1.13it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:43,  1.14it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:40,  1.16it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:42,  1.13it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:53,  1.05it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<03:07,  1.03s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:22<03:24,  1.13s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:23<03:35,  1.20s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:25<03:42,  1.24s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:26<03:49,  1.29s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:27<03:57,  1.34s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:29<04:03,  1.38s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:30<04:05,  1.40s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:32<04:01,  1.39s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:33<04:00,  1.39s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:34<03:56,  1.38s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:36<03:54,  1.37s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:38<04:12,  1.48s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:39<04:10,  1.48s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:40<04:00,  1.43s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:42<03:48,  1.37s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:43<03:39,  1.32s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:44<03:32,  1.29s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:45<03:26,  1.26s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:46<03:22,  1.24s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:48<03:18,  1.23s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:49<03:15,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:50<03:11,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:51<03:08,  1.19s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:52<03:09,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:53<03:09,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:55<03:08,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:56<03:07,  1.21s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:57<03:06,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:58<03:06,  1.22s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:00<03:06,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:02<03:12,  1.28s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:06,  4.86s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:05,  2.75s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:43,  2.05s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:34,  1.70s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:58,  1.53s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<04:34,  1.42s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:18,  1.34s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:06,  1.28s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:00,  1.26s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:15<03:59,  1.26s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:16<03:57,  1.25s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<04:00,  1.28s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<04:02,  1.30s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<04:04,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<04:08,  1.34s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<04:05,  1.34s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<04:04,  1.34s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<04:03,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<04:02,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<04:00,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:58,  1.33s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:57,  1.34s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:56,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:48,  1.30s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:45,  1.29s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:42,  1.28s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:40,  1.27s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:37,  1.26s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:36,  1.27s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:35,  1.27s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:31,  1.25s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:31,  1.26s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:30,  1.26s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:29,  1.26s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<03:25,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:27,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:32,  1.30s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:52<03:27,  1.28s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:53<03:25,  1.27s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:21,  1.26s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:21,  1.27s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:57<03:20,  1.27s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:18,  1.26s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:16,  1.26s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:19,  1.29s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:33,  1.39s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:53,  5.39s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:48,  2.97s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:22,  2.25s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:08,  1.88s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:35,  1.72s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:10,  1.60s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:53,  1.52s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:44,  1.48s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:37,  1.45s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:32,  1.43s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:24,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:18,  1.37s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:21,  1.40s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:41,  1.52s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:20,  1.41s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:08,  1.35s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<03:57,  1.30s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<03:50,  1.26s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<03:47,  1.25s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:42,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:40,  1.23s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<03:37,  1.22s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:33,  1.21s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:31,  1.20s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:32,  1.21s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:32,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:31,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:27,  1.21s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:26,  1.21s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:26,  1.21s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:25,  1.22s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:22,  1.21s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:25,  1.23s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:23,  1.22s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:26,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:27,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:28,  1.28s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:31,  1.30s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:31,  1.31s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:55<03:31,  1.32s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:32,  1.33s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:30,  1.33s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:30,  1.34s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:28,  1.33s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:26,  1.33s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:24,  1.33s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:21,  1.32s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:15,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:08<03:31,  1.40s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.71it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.57it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
       "Evaluating NLL: 7it [00:02,  3.39it/s]\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:00,  1.00it/s]\u001b[A\n",
-      "Evaluating NLL: 31it [00:01, 38.28it/s]\u001b[A\n",
-      "Evaluating NLL: 61it [00:01, 77.40it/s]\u001b[A\n",
-      "Evaluating NLL: 84it [00:01, 99.77it/s]\u001b[A\n",
-      "Evaluating NLL: 105it [00:01, 117.60it/s]\u001b[A\n",
-      "Evaluating NLL: 125it [00:01, 134.32it/s]\u001b[A\n",
-      "Evaluating NLL: 145it [00:01, 143.62it/s]\u001b[A\n",
-      "Evaluating NLL: 169it [00:01, 166.71it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 106.38it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   2%|▎              | 2/100 [00:00<00:05, 18.90it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   4%|▌              | 4/100 [00:00<00:05, 19.02it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   6%|▉              | 6/100 [00:00<00:05, 17.97it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   8%|█▏             | 8/100 [00:00<00:04, 18.44it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  12%|█▋            | 12/100 [00:00<00:03, 23.21it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  15%|██            | 15/100 [00:00<00:03, 24.35it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  18%|██▌           | 18/100 [00:00<00:03, 21.54it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  21%|██▉           | 21/100 [00:01<00:03, 20.48it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  24%|███▎          | 24/100 [00:01<00:03, 20.52it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  28%|███▉          | 28/100 [00:01<00:03, 23.50it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  31%|████▎         | 31/100 [00:01<00:02, 23.62it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  34%|████▊         | 34/100 [00:01<00:03, 21.52it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  37%|█████▏        | 37/100 [00:01<00:03, 20.50it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  40%|█████▌        | 40/100 [00:01<00:02, 21.07it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  44%|██████▏       | 44/100 [00:02<00:02, 23.90it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  47%|██████▌       | 47/100 [00:02<00:02, 24.34it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  50%|███████       | 50/100 [00:02<00:02, 21.92it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  53%|███████▍      | 53/100 [00:02<00:02, 20.65it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  56%|███████▊      | 56/100 [00:02<00:02, 20.77it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  60%|████████▍     | 60/100 [00:02<00:01, 23.65it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  63%|████████▊     | 63/100 [00:02<00:01, 24.21it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  66%|█████████▏    | 66/100 [00:03<00:01, 21.88it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  69%|█████████▋    | 69/100 [00:03<00:01, 20.77it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  72%|██████████    | 72/100 [00:03<00:01, 20.47it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  75%|██████████▌   | 75/100 [00:03<00:01, 22.45it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  78%|██████████▉   | 78/100 [00:03<00:00, 23.32it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  81%|███████████▎  | 81/100 [00:03<00:00, 21.24it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  84%|███████████▊  | 84/100 [00:03<00:00, 20.35it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  87%|████████████▏ | 87/100 [00:04<00:00, 20.63it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  91%|████████████▋ | 91/100 [00:04<00:00, 23.54it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  94%|█████████████▏| 94/100 [00:04<00:00, 23.21it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  97%|█████████████▌| 97/100 [00:04<00:00, 21.47it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:04<00:00, 21.66it/s]\n",
-      " 60%|███████████████████████████████████▍                       | 3/5 [1:18:03<53:06, 1593.21s/it]"
+      "Evaluating NLL: 1it [00:01,  1.00s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.70it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.84it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 38it [00:00, 52.08it/s]\u001b[A\n",
+      "Evaluating NLL: 74it [00:01, 102.58it/s]\u001b[A\n",
+      "Evaluating NLL: 111it [00:01, 152.66it/s]\u001b[A\n",
+      "Evaluating NLL: 148it [00:01, 198.15it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 138.96it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|█▋                   | 8/100 [00:00<00:01, 66.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|███                 | 15/100 [00:00<00:01, 47.88it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|████▏               | 21/100 [00:00<00:01, 44.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|█████▏              | 26/100 [00:00<00:01, 42.25it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  33%|██████▌             | 33/100 [00:00<00:01, 49.90it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  39%|███████▊            | 39/100 [00:00<00:01, 45.76it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  44%|████████▊           | 44/100 [00:00<00:01, 43.77it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  49%|█████████▊          | 49/100 [00:01<00:01, 42.28it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|██████████▊         | 54/100 [00:01<00:01, 42.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|████████████        | 60/100 [00:01<00:00, 44.41it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|█████████████       | 65/100 [00:01<00:00, 42.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  70%|██████████████      | 70/100 [00:01<00:00, 41.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|███████████████     | 75/100 [00:01<00:00, 40.85it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  82%|████████████████▍   | 82/100 [00:01<00:00, 46.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  87%|█████████████████▍  | 87/100 [00:01<00:00, 44.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  92%|██████████████████▍ | 92/100 [00:02<00:00, 42.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 43.71it/s]\n",
+      " 80%|██████████████████████████████████████████████████████▍             | 4/5 [34:15<08:26, 506.64s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:57,  5.72s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:07<10:25,  3.16s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:44,  2.36s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:28,  1.98s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:11<05:46,  1.78s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:17,  1.64s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:14<05:01,  1.56s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<04:48,  1.50s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:39,  1.46s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:18<04:31,  1.43s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:28,  1.42s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:23,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:22<04:21,  1.40s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:18,  1.39s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:25<04:15,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:26<04:09,  1.35s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:07,  1.35s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:29<04:06,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:30<04:02,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<04:03,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:33<04:00,  1.35s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:34<03:56,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:35<03:56,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:37<03:56,  1.34s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:38<03:56,  1.35s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<03:54,  1.35s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:41<03:51,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:42<03:55,  1.37s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:44<04:17,  1.50s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:45<03:58,  1.40s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:46<03:44,  1.33s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:47<03:37,  1.29s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:49<03:30,  1.26s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:50<03:25,  1.24s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:51<03:23,  1.23s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:52<03:20,  1.22s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:53<03:20,  1.23s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:55<03:15,  1.21s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:56<03:11,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:57<03:10,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:58<03:10,  1.20s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:11,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:01<03:13,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:02<03:16,  1.26s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:03<03:17,  1.28s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:05<03:19,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:06<03:19,  1.30s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:07<03:18,  1.31s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:09<03:18,  1.31s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:10<03:16,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:11<03:15,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:13<03:13,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:14<03:12,  1.31s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:15<03:11,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:16<03:09,  1.31s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:18<03:06,  1.30s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:19<03:03,  1.29s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:20<03:00,  1.27s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:21<02:56,  1.25s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:23<02:55,  1.25s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:24<02:54,  1.25s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:26<03:13,  1.40s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:20,  5.23s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:48,  2.97s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:22,  2.25s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:14,  1.91s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:34,  1.71s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:09,  1.60s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:53,  1.52s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:42,  1.47s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:34,  1.44s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:28,  1.41s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:20,  1.38s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:10,  1.33s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:01,  1.29s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<03:54,  1.26s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<03:47,  1.23s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<03:42,  1.21s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<03:40,  1.21s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:40,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:39,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:37,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:40,  1.23s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:42,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:42,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:41,  1.26s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:37,  1.24s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:32,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:30,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:26,  1.20s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:26,  1.21s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:25,  1.21s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:22,  1.20s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:20,  1.19s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:17,  1.18s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:15,  1.18s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<03:25,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:25,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:18,  1.22s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:17,  1.22s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:53<03:25,  1.27s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:21,  1.26s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:17,  1.25s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:18,  1.26s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:21,  1.29s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:23,  1.30s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:24,  1.32s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:02<03:25,  1.33s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:03<03:25,  1.34s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:04<03:24,  1.35s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:06<03:23,  1.35s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:27,  1.38s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:54,  5.70s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:07<10:18,  3.13s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:31,  2.29s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:18,  1.93s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:11<05:36,  1.73s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:16,  1.63s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:58,  1.55s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<04:50,  1.51s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:39,  1.46s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:29,  1.42s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:34,  1.45s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:21<04:57,  1.58s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:22<04:40,  1.50s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:22,  1.41s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:25<04:12,  1.36s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:26<04:04,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:00,  1.31s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<03:55,  1.29s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:30<03:53,  1.29s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<03:48,  1.27s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<03:46,  1.27s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<03:45,  1.27s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:35<03:50,  1.30s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:36<03:49,  1.31s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<03:49,  1.31s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<03:51,  1.33s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:51,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:42<03:49,  1.34s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:43<03:49,  1.34s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:44<03:47,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:46<03:45,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:47<03:44,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:48<03:41,  1.33s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:49<03:36,  1.31s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:51<03:29,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:52<03:25,  1.25s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:53<03:21,  1.24s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:54<03:20,  1.24s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:56<03:19,  1.24s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:57<03:15,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:59<03:51,  1.45s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:16,  4.91s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:09,  2.77s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:43,  2.05s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:34,  1.71s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<05:07,  1.58s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:23,  1.36s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:10,  1.30s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:02,  1.27s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:15<03:52,  1.22s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:16<03:47,  1.20s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:47,  1.21s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:43,  1.20s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:40,  1.19s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:21<03:39,  1.19s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:22<03:39,  1.19s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:38,  1.19s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:38,  1.20s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:26<03:38,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:27<03:37,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:28<03:38,  1.22s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:44,  1.26s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<03:48,  1.29s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:50,  1.31s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:54,  1.34s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:35<03:54,  1.35s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:53,  1.35s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:55,  1.37s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:39<03:52,  1.36s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:50,  1.35s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:47,  1.35s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:43<03:41,  1.32s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:37,  1.30s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:39,  1.32s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:34,  1.30s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:48<03:29,  1.28s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:26,  1.27s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:30,  1.30s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:29,  1.30s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:25,  1.28s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:23,  1.28s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:26,  1.30s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:22,  1.29s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:18,  1.27s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:18,  1.28s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<03:21,  1.31s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:03<03:22,  1.32s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:05<03:28,  1.37s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:40,  5.63s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:07<10:22,  3.14s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:45,  2.36s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:29,  1.99s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:11<05:43,  1.76s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:09,  1.60s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:50,  1.51s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<04:37,  1.45s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:31,  1.42s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:25,  1.40s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:23,  1.39s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:19,  1.38s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:18,  1.38s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:15,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:13,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:08,  1.35s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:08,  1.36s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:29<04:34,  1.51s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:30<04:14,  1.41s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<04:02,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<03:52,  1.30s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:34<03:46,  1.27s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:35<03:41,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:36<03:39,  1.25s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<03:37,  1.24s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:33,  1.23s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:31,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:27,  1.21s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:30,  1.23s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:28,  1.22s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:26,  1.22s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:24,  1.22s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:23,  1.22s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:22,  1.22s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:22,  1.23s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:23,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:21,  1.24s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:17,  1.22s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:15,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:18,  1.24s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:21,  1.27s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:23,  1.29s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:25,  1.31s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:25,  1.32s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:04<03:41,  1.43s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.63it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.72it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.54it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.56it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.84it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.17it/s]\u001b[A\n",
+      "Evaluating NLL: 36it [00:00, 51.16it/s]\u001b[A\n",
+      "Evaluating NLL: 73it [00:01, 105.01it/s]\u001b[A\n",
+      "Evaluating NLL: 116it [00:01, 167.47it/s]\u001b[A\n",
+      "Evaluating NLL: 153it [00:01, 210.53it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 143.74it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   5%|█                    | 5/100 [00:00<00:02, 40.23it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  10%|██                  | 10/100 [00:00<00:02, 39.45it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|███▏                | 16/100 [00:00<00:01, 46.83it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  21%|████▏               | 21/100 [00:00<00:01, 43.53it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  26%|█████▏              | 26/100 [00:00<00:01, 41.81it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  31%|██████▏             | 31/100 [00:00<00:01, 40.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  36%|███████▏            | 36/100 [00:00<00:01, 40.31it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|████████▌           | 43/100 [00:00<00:01, 47.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|█████████▌          | 48/100 [00:01<00:01, 44.82it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|██████████▌         | 53/100 [00:01<00:01, 43.10it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|███████████▌        | 58/100 [00:01<00:00, 42.01it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|████████████▌       | 63/100 [00:01<00:00, 41.06it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██████████████▏     | 71/100 [00:01<00:00, 49.07it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|███████████████▏    | 76/100 [00:01<00:00, 45.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  81%|████████████████▏   | 81/100 [00:01<00:00, 43.92it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|█████████████████▏  | 86/100 [00:01<00:00, 42.39it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|██████████████████▏ | 91/100 [00:02<00:00, 41.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 43.34it/s]\n",
+      "100%|████████████████████████████████████████████████████████████████████| 5/5 [45:32<00:00, 546.54s/it]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "initial loss -2.5735354571893465\n",
-      "computing optimized mi\n"
+      "8_deltas_uniform\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
+      "  0%|                                                                             | 0/5 [00:00<?, ?it/s]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:45,  5.05s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:24,  2.85s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:53,  2.10s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:43,  1.76s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:20,  1.35s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:19,  1.35s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:13,  1.33s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:09,  1.31s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:59,  1.27s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:52,  1.24s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:48,  1.22s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:45,  1.21s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:44,  1.21s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:42,  1.21s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:43,  1.22s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:39,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:38,  1.21s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:36,  1.20s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:35,  1.21s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:34,  1.20s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<03:38,  1.24s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:40,  1.25s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:43,  1.27s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:35<03:45,  1.30s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:46,  1.31s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:46,  1.32s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:39<03:46,  1.32s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:46,  1.33s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:46,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:43<03:44,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:42,  1.33s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:39,  1.33s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:35,  1.30s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:31,  1.29s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:26,  1.27s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:23,  1.26s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:23,  1.27s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:27,  1.30s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:26,  1.30s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:21,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:25,  1.32s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:22,  1.31s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<03:18,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:03<03:15,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:04<03:16,  1.30s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:05<03:19,  1.32s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:07<03:21,  1.34s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:08<03:20,  1.35s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:10<03:20,  1.35s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:11<03:18,  1.35s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:12<03:17,  1.35s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:14<03:18,  1.37s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:15<03:16,  1.36s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:16<03:14,  1.36s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:18<03:13,  1.36s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:19<03:12,  1.36s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:20<03:11,  1.37s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:22<03:08,  1.36s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:23<03:10,  1.38s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<03:08,  1.38s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:26<03:06,  1.37s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:27<03:04,  1.37s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<03:04,  1.37s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<03:03,  1.38s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:31<03:01,  1.37s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:33<02:58,  1.36s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:34<02:55,  1.35s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:35<02:54,  1.36s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<02:52,  1.35s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:38<02:50,  1.34s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<03:03,  1.46s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:41<03:04,  1.48s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:43<02:52,  1.39s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:44<02:44,  1.34s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:36,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:46<02:31,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:47<02:27,  1.23s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:26,  1.23s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:50<02:25,  1.23s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:51<02:25,  1.24s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:52<02:22,  1.23s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:24,  1.25s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:55<02:24,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:56<02:22,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:57<02:20,  1.25s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:17,  1.24s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:00<02:16,  1.24s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:01<02:14,  1.23s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:02<02:13,  1.24s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:03<02:12,  1.24s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:05<02:10,  1.23s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:06<02:08,  1.22s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:07<02:06,  1.22s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:08<02:04,  1.21s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:10<02:03,  1.21s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:11<02:01,  1.20s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:12<02:00,  1.20s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<01:59,  1.21s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:14<01:59,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:16<02:01,  1.25s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:17<02:02,  1.27s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:18<02:01,  1.28s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:20<02:01,  1.30s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:21<02:01,  1.30s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:22<02:00,  1.31s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:24<01:59,  1.32s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:25<01:59,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:26<01:58,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:28<01:56,  1.32s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:29<01:55,  1.33s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:30<01:54,  1.33s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:32<01:52,  1.32s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:33<01:50,  1.31s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:34<01:47,  1.29s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:35<01:44,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:37<01:43,  1.28s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:38<01:41,  1.27s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:39<01:39,  1.26s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:40<01:38,  1.26s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:42<01:36,  1.25s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:43<01:34,  1.24s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:44<01:33,  1.25s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:45<01:33,  1.26s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:47<01:32,  1.26s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:48<01:30,  1.26s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:49<01:28,  1.25s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:50<01:26,  1.24s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:52<01:25,  1.24s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:53<01:24,  1.25s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:54<01:23,  1.25s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:55<01:21,  1.24s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:57<01:20,  1.25s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:58<01:20,  1.26s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:59<01:20,  1.28s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:02<01:21,  1.32s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:08,  5.17s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:52,  2.69s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<06:03,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:41,  1.44s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:54,  1.20s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:26,  1.06s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:09,  1.02it/s]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:07,  1.03it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:55,  1.09it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:48,  1.13it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:41,  1.17it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:32,  1.23it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:35,  1.21it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:38,  1.17it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:16<02:37,  1.17it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:17<02:31,  1.21it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:18<02:26,  1.25it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:22,  1.27it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:19<02:21,  1.28it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:20<02:23,  1.26it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:21<02:25,  1.23it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:22<02:26,  1.22it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:23<02:26,  1.20it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:24<02:29,  1.18it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:24<02:27,  1.19it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:25<02:27,  1.18it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:26<02:27,  1.17it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:27<02:28,  1.16it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:28<02:25,  1.17it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:29<02:24,  1.17it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:30<02:24,  1.17it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:30<02:23,  1.17it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:31<02:23,  1.16it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:32<02:23,  1.16it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:33<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:34<02:23,  1.14it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:35<02:22,  1.14it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:22,  1.13it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:37<02:25,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:38<02:28,  1.08it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:39<02:25,  1.09it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:40<02:27,  1.07it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:41<02:31,  1.04it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:41<02:27,  1.06it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:42<02:25,  1.07it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:43<02:24,  1.07it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:44<02:21,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:45<02:21,  1.07it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:46<02:20,  1.07it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:47<02:19,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:48<02:22,  1.05it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:49<02:20,  1.05it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:50<02:20,  1.04it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:18,  1.05it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:16,  1.06it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:53<02:16,  1.05it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:14,  1.06it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:55<02:13,  1.06it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:56<02:14,  1.05it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:57<02:13,  1.05it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:58<02:15,  1.02it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:59<02:14,  1.03it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:00<02:26,  1.07s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:01<02:38,  1.17s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:02<02:29,  1.11s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:03<02:22,  1.06s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:04<02:17,  1.03s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:05<02:14,  1.02s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:06<02:08,  1.02it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:07<02:07,  1.02it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:08<02:06,  1.02it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:09<02:04,  1.03it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:10<02:04,  1.02it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:11<02:01,  1.03it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:12<01:59,  1.04it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:13<01:58,  1.05it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:14<01:55,  1.07it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:15<01:54,  1.07it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:16<01:55,  1.05it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:17<01:53,  1.06it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:18<01:52,  1.06it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:18<01:52,  1.05it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:19<01:50,  1.06it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:20<01:49,  1.06it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:21<01:48,  1.06it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:22<01:47,  1.06it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:23<01:46,  1.07it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:24<01:45,  1.07it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:25<01:44,  1.06it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:26<01:43,  1.06it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:27<01:39,  1.09it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:28<01:36,  1.12it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:28<01:33,  1.15it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:29<01:29,  1.18it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:30<01:27,  1.20it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:31<01:25,  1.21it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:32<01:25,  1.20it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:33<01:23,  1.22it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:33<01:25,  1.18it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:34<01:26,  1.16it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:35<01:27,  1.13it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:36<01:27,  1.12it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:37<01:27,  1.11it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:38<01:27,  1.10it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:39<01:26,  1.10it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:40<01:26,  1.09it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:41<01:22,  1.12it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:42<01:20,  1.14it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:42<01:19,  1.14it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:43<01:19,  1.13it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:44<01:19,  1.12it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:45<01:20,  1.10it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:46<01:19,  1.09it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:47<01:18,  1.10it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:48<01:16,  1.11it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:49<01:17,  1.08it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:50<01:16,  1.08it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:51<01:16,  1.07it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:52<01:15,  1.07it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:53<01:13,  1.08it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:54<01:12,  1.09it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:54<01:11,  1.10it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:55<01:10,  1.10it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:56<01:11,  1.06it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:57<01:10,  1.07it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:58<01:08,  1.08it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:59<01:07,  1.08it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:00<01:06,  1.09it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:01<01:04,  1.10it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:02<01:03,  1.10it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:03<01:02,  1.11it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:04<01:00,  1.12it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:04<00:59,  1.12it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:05<00:58,  1.12it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:06<00:57,  1.12it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:07<00:57,  1.12it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:08<00:56,  1.11it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:09<00:56,  1.09it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:10<00:56,  1.09it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:11<00:55,  1.08it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:12<00:54,  1.09it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:13<00:53,  1.09it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:14<00:51,  1.11it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:14<00:50,  1.11it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:15<00:49,  1.12it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:16<00:48,  1.12it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:17<00:47,  1.12it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:18<00:45,  1.13it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:19<00:45,  1.12it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:20<00:45,  1.11it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:21<00:44,  1.10it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:22<00:43,  1.11it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:22<00:42,  1.11it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:23<00:39,  1.16it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:24<00:36,  1.22it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:25<00:36,  1.20it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:26<00:37,  1.16it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:27<00:36,  1.16it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:28<00:35,  1.15it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:28<00:34,  1.17it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:29<00:32,  1.19it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:30<00:32,  1.16it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:31<00:32,  1.13it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:32<00:32,  1.12it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:33<00:31,  1.12it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:34<00:30,  1.10it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:35<00:29,  1.10it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:36<00:29,  1.09it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:37<00:28,  1.07it/s]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:38<00:28,  1.07it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:38<00:27,  1.06it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:40<00:27,  1.00it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:41<00:26,  1.02it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:42<00:26,  1.00s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [02:43<00:24,  1.02it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [02:43<00:23,  1.03it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [02:45<00:22,  1.00it/s]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [02:45<00:21,  1.02it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [02:46<00:20,  1.04it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [02:47<00:19,  1.05it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [02:48<00:18,  1.06it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [02:49<00:17,  1.05it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [02:50<00:16,  1.06it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [02:51<00:15,  1.06it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [02:52<00:14,  1.06it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [02:53<00:13,  1.05it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [02:54<00:12,  1.04it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [02:55<00:11,  1.05it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [02:56<00:10,  1.04it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [02:57<00:09,  1.05it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [02:58<00:08,  1.04it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [02:59<00:07,  1.05it/s]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [03:00<00:06,  1.04it/s]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [03:01<00:05,  1.05it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [03:02<00:04,  1.04it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [03:03<00:03,  1.04it/s]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [03:04<00:02,  1.04it/s]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [03:05<00:01,  1.05it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [03:06<00:00,  1.04it/s]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [03:07<00:00,  1.07it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<19:15,  5.81s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:51,  2.99s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:37,  2.02s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:07,  1.57s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:24,  1.36s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<03:47,  1.17s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:23,  1.05s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:41,  1.16s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:42,  1.17s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:14,  1.02s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<03:04,  1.03it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<02:58,  1.05it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<02:55,  1.07it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<02:52,  1.08it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:18<02:51,  1.08it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:19<02:48,  1.09it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:20<02:48,  1.09it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:21<02:46,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:22<02:44,  1.10it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:23<02:44,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:24<02:42,  1.10it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:25<02:42,  1.10it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:26<02:41,  1.10it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:27<02:39,  1.11it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:28<02:43,  1.07it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:29<02:43,  1.06it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:29<02:38,  1.09it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:30<02:35,  1.10it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:31<02:32,  1.12it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:32<02:30,  1.13it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:33<02:30,  1.12it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:34<02:29,  1.12it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:35<02:30,  1.11it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:36<02:31,  1.10it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:37<02:30,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:37<02:28,  1.11it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:38<02:31,  1.08it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:39<02:27,  1.09it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:40<02:25,  1.10it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:41<02:24,  1.11it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:42<02:24,  1.10it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:43<02:24,  1.09it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:44<02:23,  1.09it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:45<02:21,  1.10it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:46<02:20,  1.10it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:47<02:19,  1.11it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:47<02:16,  1.12it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:48<02:16,  1.12it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:49<02:14,  1.12it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:50<02:14,  1.11it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:51<02:13,  1.11it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:52<02:13,  1.11it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:53<02:12,  1.11it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:54<02:10,  1.12it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:55<02:11,  1.10it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:56<02:12,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:57<02:13,  1.07it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:58<02:12,  1.07it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:58<02:09,  1.09it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:59<02:09,  1.08it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:00<02:06,  1.09it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:01<02:05,  1.10it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:02<02:05,  1.09it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:03<02:04,  1.09it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:04<02:04,  1.09it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:05<02:03,  1.09it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:06<02:02,  1.09it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:07<02:01,  1.08it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:08<02:03,  1.06it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:09<02:06,  1.03it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:10<02:02,  1.05it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:11<02:00,  1.06it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:11<01:57,  1.08it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:12<01:56,  1.08it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:13<01:57,  1.06it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:14<01:53,  1.09it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:15<01:53,  1.09it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:16<01:51,  1.09it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:17<01:48,  1.11it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:18<01:49,  1.10it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:19<01:48,  1.09it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:20<01:47,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:21<01:44,  1.12it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:21<01:43,  1.12it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:22<01:43,  1.11it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:23<01:41,  1.12it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:24<01:40,  1.12it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:25<01:40,  1.11it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:26<01:39,  1.11it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:27<01:38,  1.12it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:28<01:37,  1.12it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:29<01:37,  1.11it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:30<01:36,  1.10it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:30<01:35,  1.11it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:31<01:34,  1.11it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:32<01:33,  1.11it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:33<01:33,  1.10it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:34<01:33,  1.09it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:35<01:32,  1.09it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:36<01:26,  1.16it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:36<01:21,  1.21it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:37<01:19,  1.23it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:38<01:21,  1.19it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:39<01:23,  1.15it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:40<01:19,  1.20it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:41<01:18,  1.20it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:42<01:19,  1.17it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:42<01:20,  1.15it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:43<01:20,  1.14it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:44<01:20,  1.12it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:45<01:20,  1.11it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:46<01:21,  1.08it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:47<01:22,  1.06it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:48<01:21,  1.05it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:49<01:21,  1.04it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:50<01:21,  1.03it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:51<01:19,  1.05it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:52<01:19,  1.03it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:53<01:17,  1.05it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:54<01:16,  1.04it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:55<01:13,  1.07it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:57<01:14,  1.04it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:06<22:03,  6.65s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:08<13:24,  4.06s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:10<10:18,  3.14s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:12<08:40,  2.65s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:14<07:36,  2.34s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:16<07:06,  2.20s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:18<06:38,  2.06s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:20<06:29,  2.03s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:22<06:31,  2.05s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:24<06:25,  2.03s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:26<06:16,  1.99s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:28<06:15,  2.00s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:30<06:06,  1.96s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:32<06:06,  1.97s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:34<06:11,  2.01s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:36<06:12,  2.02s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:38<06:13,  2.04s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:40<06:14,  2.06s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:42<06:15,  2.07s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:44<06:14,  2.08s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:46<06:16,  2.10s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:49<06:18,  2.12s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [00:50<06:06,  2.07s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [00:53<06:17,  2.15s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [00:55<06:27,  2.22s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [00:57<06:22,  2.20s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [00:59<06:04,  2.11s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [01:01<06:04,  2.12s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [01:03<05:57,  2.09s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [01:06<06:27,  2.28s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [01:08<05:54,  2.10s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [01:10<05:39,  2.02s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [01:11<05:22,  1.93s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [01:13<05:22,  1.94s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:15<05:09,  1.88s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:17<05:07,  1.88s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:19<05:08,  1.89s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:21<04:56,  1.83s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:22<04:56,  1.84s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:24<04:52,  1.83s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:26<04:49,  1.82s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:28<04:53,  1.86s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:30<04:54,  1.87s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:32<04:58,  1.91s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:34<04:56,  1.91s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:36<04:50,  1.88s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [01:38<04:54,  1.93s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [01:40<04:55,  1.95s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [01:42<04:52,  1.94s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [01:43<04:41,  1.87s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [01:45<04:37,  1.86s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [01:47<04:27,  1.81s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [01:48<04:18,  1.76s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [01:50<04:26,  1.82s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [01:52<04:29,  1.86s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [01:54<04:23,  1.83s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [01:56<04:31,  1.90s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [01:58<04:29,  1.90s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [02:00<04:18,  1.84s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [02:02<04:15,  1.82s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [02:04<04:21,  1.88s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [02:05<04:13,  1.83s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [02:07<04:16,  1.87s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [02:09<04:21,  1.92s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [02:11<04:17,  1.90s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [02:13<04:07,  1.85s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [02:15<04:08,  1.87s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [02:17<04:03,  1.84s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [02:18<04:04,  1.86s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [02:20<04:01,  1.86s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [02:22<03:53,  1.81s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [02:24<03:58,  1.86s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [02:26<04:00,  1.89s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [02:28<03:57,  1.89s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [02:30<03:57,  1.90s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [02:32<04:00,  1.94s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [02:34<04:03,  1.98s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [02:36<03:52,  1.91s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [02:38<03:56,  1.96s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [02:40<03:49,  1.92s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [02:41<03:43,  1.88s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [02:43<03:49,  1.94s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [02:45<03:47,  1.94s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [02:47<03:47,  1.96s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [02:49<03:49,  1.99s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [02:51<03:36,  1.90s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [02:53<03:36,  1.92s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [02:55<03:27,  1.85s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [02:57<03:29,  1.88s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [02:59<03:34,  1.95s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [03:01<03:36,  1.99s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [03:03<03:39,  2.03s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [03:05<03:38,  2.04s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [03:07<03:37,  2.05s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [03:09<03:33,  2.04s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [03:11<03:28,  2.01s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [03:13<03:28,  2.02s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [03:15<03:27,  2.04s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [03:17<03:27,  2.06s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [03:20<03:33,  2.14s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [03:22<03:31,  2.14s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [03:24<03:25,  2.10s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [03:26<03:23,  2.10s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [03:28<03:24,  2.13s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [03:30<03:21,  2.12s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [03:32<03:20,  2.13s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [03:35<03:38,  2.35s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [03:37<03:22,  2.20s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [03:39<03:15,  2.15s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [03:41<03:09,  2.10s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [03:43<03:04,  2.08s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [03:45<03:02,  2.08s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [03:47<03:00,  2.08s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [03:49<02:52,  2.01s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [03:51<02:52,  2.03s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [03:53<02:50,  2.04s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [03:55<02:50,  2.05s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [03:57<02:50,  2.08s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [04:00<02:47,  2.07s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [04:02<02:45,  2.07s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [04:04<02:43,  2.08s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [04:06<02:36,  2.00s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [04:08<02:35,  2.01s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [04:10<02:33,  2.02s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [04:11<02:28,  1.97s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [04:14<02:29,  2.02s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [04:15<02:24,  1.98s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [04:20<02:26,  2.03s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.15s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  3.12it/s]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:52,  5.09s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:51,  2.68s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:11,  1.89s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<04:59,  1.53s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:22,  1.35s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:10<03:59,  1.23s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:11<03:45,  1.17s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:12<03:37,  1.13s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:13<03:29,  1.10s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:14<03:24,  1.08s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:15<03:16,  1.04s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:16<03:10,  1.01s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:17<03:10,  1.02s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:18<03:07,  1.01s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:19<03:05,  1.00s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:20<03:02,  1.01it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:21<03:00,  1.02it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:22<02:59,  1.01it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:22<02:54,  1.04it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:24<03:00,  1.00s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:25<02:57,  1.01it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:26<02:58,  1.00s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:27<02:59,  1.02s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:27<02:53,  1.01it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:28<02:50,  1.03it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:29<02:53,  1.00it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:31<02:56,  1.02s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:32<02:54,  1.01s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:32<02:48,  1.02it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:33<02:48,  1.01it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:35<02:50,  1.01s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:35<02:45,  1.01it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:36<02:45,  1.01it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:37<02:39,  1.04it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:38<02:29,  1.10it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:39<02:36,  1.04it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:41<03:06,  1.15s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:42<02:49,  1.05s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:42<02:35,  1.04it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:43<02:29,  1.07it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:44<02:22,  1.12it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:45<02:17,  1.15it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:46<02:13,  1.17it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:46<02:10,  1.19it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:47<02:09,  1.19it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:48<02:10,  1.18it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:49<02:08,  1.19it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:50<02:09,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:51<02:06,  1.19it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:51<02:03,  1.21it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:52<02:01,  1.23it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:53<01:59,  1.24it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:54<01:58,  1.24it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:55<01:59,  1.23it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:56<01:58,  1.22it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:56<01:57,  1.22it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:57<01:54,  1.25it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:58<01:55,  1.23it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:59<02:05,  1.12it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:00<02:22,  1.02s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:02<02:29,  1.08s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:03<02:37,  1.14s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:04<02:42,  1.18s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:05<02:45,  1.21s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:07<02:47,  1.24s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:08<02:47,  1.25s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:09<02:47,  1.26s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:11<02:44,  1.25s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:12<02:43,  1.25s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:13<02:41,  1.24s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:14<02:40,  1.24s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:15<02:39,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:17<02:37,  1.24s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:18<02:36,  1.24s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:19<02:33,  1.22s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:20<02:34,  1.25s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:22<02:35,  1.27s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:23<02:37,  1.29s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:24<02:38,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:26<02:37,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:27<02:37,  1.32s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:28<02:36,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:30<02:36,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:31<02:35,  1.34s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:32<02:33,  1.33s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:34<02:34,  1.36s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:35<02:30,  1.33s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:36<02:26,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:38<02:21,  1.27s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:39<02:15,  1.23s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:40<02:12,  1.21s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:41<02:12,  1.22s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:42<02:12,  1.24s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:44<02:12,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:45<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:46<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:48<02:11,  1.27s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:49<02:09,  1.27s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:50<02:05,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:51<02:03,  1.23s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:52<02:01,  1.22s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:54<01:58,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:55<01:56,  1.20s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:56<01:53,  1.18s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:57<01:52,  1.18s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:58<01:51,  1.19s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:00<01:51,  1.20s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:01<01:54,  1.24s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:02<01:54,  1.25s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:03<01:52,  1.25s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:05<01:48,  1.22s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:06<01:47,  1.22s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:07<01:45,  1.21s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:08<01:43,  1.21s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:09<01:42,  1.21s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:11<01:40,  1.19s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:12<01:37,  1.18s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:13<01:37,  1.19s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:14<01:39,  1.23s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:15<01:39,  1.24s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:17<01:35,  1.21s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:18<01:32,  1.19s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:19<01:36,  1.25s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:20<01:37,  1.28s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:22<01:37,  1.30s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:23<01:37,  1.32s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:25<01:37,  1.33s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:26<01:36,  1.34s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:27<01:35,  1.34s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:29<01:34,  1.35s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:30<01:33,  1.35s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:31<01:32,  1.36s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:33<01:31,  1.36s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:34<01:29,  1.36s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:35<01:28,  1.36s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:37<01:26,  1.36s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:38<01:25,  1.36s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:39<01:23,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:41<01:22,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:42<01:21,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:43<01:17,  1.31s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:45<01:16,  1.32s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:46<01:15,  1.32s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:47<01:14,  1.33s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:49<01:13,  1.33s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:50<01:12,  1.35s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:51<01:11,  1.34s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:53<01:10,  1.35s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:54<01:09,  1.35s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:56<01:07,  1.35s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:57<01:06,  1.35s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:58<01:06,  1.38s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:00<01:09,  1.48s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:01<01:03,  1.38s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:02<00:58,  1.29s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:03<00:55,  1.25s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:05<00:52,  1.22s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:06<00:50,  1.19s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:07<00:49,  1.20s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:08<00:47,  1.19s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:09<00:47,  1.22s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:11<00:46,  1.24s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:12<00:45,  1.24s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:13<00:45,  1.27s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:15<00:44,  1.28s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:16<00:44,  1.30s/it]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [03:17<00:42,  1.29s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [03:18<00:41,  1.28s/it]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [03:20<00:38,  1.25s/it]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [03:21<00:36,  1.22s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [03:22<00:35,  1.22s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [03:23<00:33,  1.21s/it]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [03:24<00:32,  1.21s/it]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [03:26<00:31,  1.20s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [03:27<00:29,  1.19s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [03:28<00:28,  1.20s/it]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [03:29<00:27,  1.21s/it]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [03:30<00:26,  1.21s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [03:32<00:26,  1.25s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [03:33<00:24,  1.24s/it]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [03:34<00:23,  1.25s/it]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [03:35<00:22,  1.23s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [03:37<00:20,  1.21s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [03:38<00:19,  1.20s/it]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [03:39<00:17,  1.19s/it]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [03:40<00:16,  1.19s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [03:41<00:15,  1.19s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [03:43<00:14,  1.23s/it]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [03:44<00:13,  1.26s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [03:45<00:12,  1.29s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [03:47<00:11,  1.30s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [03:48<00:10,  1.30s/it]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [03:49<00:09,  1.30s/it]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [03:51<00:07,  1.32s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [03:52<00:06,  1.32s/it]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [03:53<00:05,  1.32s/it]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [03:55<00:03,  1.33s/it]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [03:56<00:02,  1.33s/it]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [03:57<00:01,  1.33s/it]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [03:59<00:00,  1.20s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:17,  5.21s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:33,  2.90s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:03,  2.15s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:49,  1.78s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:06,  1.57s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:42,  1.46s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:30,  1.40s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:20,  1.36s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:15,  1.34s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:18,  1.36s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:09,  1.32s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:04,  1.30s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<03:58,  1.28s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<03:55,  1.26s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:56,  1.28s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<03:50,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:49,  1.25s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:51,  1.27s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:56,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<04:02,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<04:03,  1.36s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:59,  1.35s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<04:03,  1.38s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<04:01,  1.37s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:56,  1.35s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:53,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:52,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:53,  1.36s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:45,  1.32s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:47,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:42,  1.32s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:43,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:47,  1.36s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:46,  1.36s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:43,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:45,  1.38s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:40,  1.36s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:38,  1.35s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:55<03:36,  1.35s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:57<04:02,  1.51s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:58<03:46,  1.42s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:32,  1.35s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:27,  1.32s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:19,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:03<03:15,  1.26s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:04<03:16,  1.28s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:11,  1.25s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:12,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:08<03:08,  1.25s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:04,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:03,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:01,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:12<03:00,  1.23s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<02:59,  1.23s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<03:01,  1.25s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<02:57,  1.23s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:17<02:53,  1.22s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<02:54,  1.23s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<02:50,  1.21s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:21<02:49,  1.21s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:22<02:51,  1.23s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:23<02:47,  1.21s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<02:45,  1.21s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:26<02:43,  1.20s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:27<02:45,  1.23s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:28<02:47,  1.25s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:49,  1.27s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:31<02:49,  1.29s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:32<02:49,  1.30s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:34<02:49,  1.31s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:35<02:48,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:36<02:47,  1.31s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:38<02:46,  1.31s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:39<02:45,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:40<02:44,  1.32s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:42<02:43,  1.32s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:43<02:38,  1.29s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:44<02:36,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:45<02:35,  1.29s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:47<02:32,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:48<02:34,  1.30s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:49<02:31,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:51<02:29,  1.27s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:52<02:29,  1.29s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:53<02:28,  1.29s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:54<02:28,  1.30s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:56<02:28,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:57<02:26,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:58<02:26,  1.32s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:00<02:24,  1.32s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:01<02:23,  1.32s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:02<02:22,  1.32s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:04<02:21,  1.33s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:05<02:23,  1.36s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:07<02:22,  1.35s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:08<02:20,  1.35s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:09<02:17,  1.33s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:10<02:14,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:08,  1.28s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:13<02:05,  1.26s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:14<02:03,  1.25s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:15<02:04,  1.27s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:17<02:04,  1.28s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:18<02:01,  1.26s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:19<01:59,  1.26s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:20<01:55,  1.22s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:22<01:54,  1.23s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:23<01:54,  1.25s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:24<01:53,  1.25s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:25<01:52,  1.25s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:27<01:51,  1.25s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:28<01:52,  1.27s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:29<01:53,  1.30s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:31<01:52,  1.31s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:32<01:49,  1.29s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:33<01:50,  1.32s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:35<01:48,  1.31s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:36<01:46,  1.30s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:37<01:45,  1.31s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:39<01:47,  1.34s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:40<01:44,  1.33s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:41<01:46,  1.37s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:43<01:45,  1.37s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:44<01:46,  1.40s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:45<01:43,  1.39s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:47<01:50,  1.49s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:49<01:51,  1.52s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:50<01:44,  1.45s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:51<01:38,  1.39s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:53<01:35,  1.36s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:54<01:33,  1.36s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:55<01:31,  1.35s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:57<01:30,  1.34s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:58<01:28,  1.35s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:59<01:27,  1.34s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [03:01<01:25,  1.34s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:02<01:25,  1.35s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:03<01:24,  1.36s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:05<01:22,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:06<01:20,  1.34s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:07<01:19,  1.34s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:09<01:17,  1.34s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:10<01:15,  1.33s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:11<01:14,  1.32s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:13<01:12,  1.33s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:15<01:12,  1.34s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.66it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.00it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.65it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.79it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.09it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.77it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.07it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.82it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.18it/s]\u001b[A\n",
+      "Evaluating NLL: 38it [00:00, 54.26it/s]\u001b[A\n",
+      "Evaluating NLL: 75it [00:01, 107.82it/s]\u001b[A\n",
+      "Evaluating NLL: 110it [00:01, 154.87it/s]\u001b[A\n",
+      "Evaluating NLL: 146it [00:01, 199.10it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 143.99it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|▊                    | 4/100 [00:00<00:02, 39.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   8%|█▋                   | 8/100 [00:00<00:02, 39.43it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  12%|██▍                 | 12/100 [00:00<00:02, 39.14it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  16%|███▏                | 16/100 [00:00<00:02, 39.03it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|████▊               | 24/100 [00:00<00:01, 51.25it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|██████              | 30/100 [00:00<00:01, 46.04it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  35%|███████             | 35/100 [00:00<00:01, 44.59it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  40%|████████            | 40/100 [00:00<00:01, 42.74it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  45%|█████████           | 45/100 [00:01<00:01, 44.13it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  52%|██████████▍         | 52/100 [00:01<00:00, 50.51it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|███████████▌        | 58/100 [00:01<00:00, 46.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  63%|████████████▌       | 63/100 [00:01<00:00, 45.17it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  68%|█████████████▌      | 68/100 [00:01<00:00, 43.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  75%|███████████████     | 75/100 [00:01<00:00, 48.94it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  80%|████████████████    | 80/100 [00:01<00:00, 45.79it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  85%|█████████████████   | 85/100 [00:01<00:00, 43.67it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  90%|██████████████████  | 90/100 [00:02<00:00, 42.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 45.30it/s]\n",
+      " 20%|█████████████                                                    | 1/5 [19:44<1:18:56, 1184.02s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:43,  5.34s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:39,  2.93s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:02,  2.15s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:46,  1.77s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:13,  1.61s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:51,  1.50s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:35,  1.43s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:22,  1.37s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:08,  1.30s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<03:57,  1.25s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:50,  1.22s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:46,  1.21s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:43,  1.19s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<03:40,  1.19s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:37,  1.18s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:36,  1.18s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:35,  1.18s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:31,  1.16s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:26<03:27,  1.15s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:28<03:33,  1.18s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:38,  1.22s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:47,  1.28s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:51,  1.31s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:53,  1.32s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:54,  1.34s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:54,  1.35s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:52,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:51,  1.35s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:50,  1.35s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:48,  1.34s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:41,  1.31s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:36,  1.29s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:33,  1.28s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:30,  1.27s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:27,  1.26s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:26,  1.26s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:26,  1.26s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:25,  1.27s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:23,  1.27s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:22,  1.28s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:22,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:21,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:21,  1.29s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:19,  1.29s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<03:16,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:03<03:16,  1.28s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:04<03:13,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:05<03:08,  1.25s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:06<03:07,  1.25s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:08<03:05,  1.25s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:09<03:09,  1.28s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:10<03:12,  1.31s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:12<03:10,  1.31s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:13<03:11,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:14<03:10,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:16<03:10,  1.33s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:17<03:08,  1.33s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:18<03:07,  1.33s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:20<03:05,  1.32s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:21<03:04,  1.33s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:22<03:03,  1.33s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:24<03:04,  1.35s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:25<03:03,  1.35s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:26<02:58,  1.32s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:28<02:55,  1.31s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:29<02:57,  1.34s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:30<02:49,  1.29s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:31<02:45,  1.26s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:33<02:42,  1.25s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:34<02:44,  1.27s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:35<02:44,  1.29s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:36<02:39,  1.26s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:38<02:44,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:39<02:42,  1.30s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:41<02:42,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:42<02:44,  1.34s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:45,  1.35s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:45<02:43,  1.35s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:47<03:01,  1.51s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:48<02:48,  1.42s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:49<02:38,  1.34s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:50<02:32,  1.30s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:51<02:25,  1.26s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:52<02:22,  1.24s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:54<02:20,  1.23s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:55<02:18,  1.23s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:56<02:16,  1.22s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:14,  1.22s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:58<02:11,  1.20s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:12,  1.21s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:10,  1.21s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:02<02:09,  1.21s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:03<02:07,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:05<02:06,  1.21s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:06<02:06,  1.21s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:07<02:05,  1.22s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:08<02:04,  1.22s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:09<02:03,  1.22s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<02:03,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:12<02:05,  1.27s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:13<02:06,  1.29s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:15<02:06,  1.30s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:16<02:05,  1.31s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:17<02:04,  1.31s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:19<02:04,  1.32s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:20<02:03,  1.33s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:21<02:02,  1.33s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:23<02:01,  1.33s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:24<01:59,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:25<01:57,  1.32s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:27<01:55,  1.31s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:28<01:53,  1.30s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:29<01:51,  1.29s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:30<01:48,  1.27s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:32<01:48,  1.29s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:33<01:46,  1.28s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:34<01:44,  1.28s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:35<01:42,  1.26s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:37<01:40,  1.26s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:38<01:40,  1.27s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:39<01:39,  1.28s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:42<01:41,  1.32s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:57,  5.11s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:30,  2.88s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:10,  2.18s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:01,  1.85s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:23,  1.66s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:02,  1.56s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:46,  1.49s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:35,  1.44s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:27,  1.40s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:23,  1.39s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:20,  1.38s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:10,  1.33s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:06,  1.32s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:03,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<03:57,  1.28s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<03:53,  1.27s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:49,  1.25s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:47,  1.25s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:46,  1.25s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:50,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:44,  1.25s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:42,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:41,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:45,  1.28s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:44,  1.28s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:47,  1.31s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:42,  1.29s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:43,  1.30s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:40,  1.29s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:36,  1.27s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:30,  1.25s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:33,  1.27s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:36,  1.30s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:38,  1.31s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:39,  1.33s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:41,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:40,  1.35s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:38,  1.35s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:37,  1.35s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:55<03:36,  1.35s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:34,  1.35s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:34,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:33,  1.36s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:01<03:33,  1.37s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:29,  1.35s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:04<03:32,  1.38s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:29,  1.37s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:31,  1.39s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:08<03:30,  1.40s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:27,  1.39s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:11<03:30,  1.41s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:12<03:26,  1.40s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:13<03:24,  1.39s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:15<03:21,  1.38s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:16<03:19,  1.38s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:18<03:32,  1.48s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:19<03:35,  1.50s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:21<03:22,  1.42s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:22<03:13,  1.37s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:23<03:06,  1.33s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:24<03:02,  1.31s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:26<02:58,  1.29s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:27<02:55,  1.28s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:28<02:53,  1.27s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:29<02:51,  1.27s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:31<02:50,  1.27s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:32<02:47,  1.26s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:33<02:45,  1.25s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:35<02:45,  1.26s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:36<02:42,  1.25s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:37<02:41,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:38<02:42,  1.27s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:40<02:40,  1.26s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:41<02:39,  1.26s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:42<02:38,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:43<02:37,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:45<02:34,  1.26s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:46<02:32,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:47<02:33,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:49<02:36,  1.30s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:50<02:35,  1.31s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:51<02:36,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:53<02:35,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:54<02:34,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:55<02:33,  1.34s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:57<02:32,  1.33s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:58<02:30,  1.33s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:59<02:28,  1.33s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [02:01<02:27,  1.33s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:02<02:25,  1.33s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:03<02:22,  1.31s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:04<02:19,  1.29s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:06<02:16,  1.27s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:07<02:12,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:08<02:09,  1.23s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:09<02:06,  1.21s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:11<02:09,  1.26s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:12<02:06,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:13<02:04,  1.23s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:14<02:02,  1.23s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:15<02:01,  1.23s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:17<01:59,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:18<01:57,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:19<01:56,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:20<01:54,  1.21s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:21<01:54,  1.21s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:23<01:52,  1.21s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:24<01:51,  1.21s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:25<01:50,  1.22s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:26<01:49,  1.22s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:28<01:48,  1.22s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:29<01:47,  1.22s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:30<01:46,  1.23s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:31<01:44,  1.22s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:32<01:42,  1.20s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:34<01:42,  1.21s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:35<01:40,  1.21s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:36<01:39,  1.22s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:37<01:41,  1.25s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:39<01:42,  1.28s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:40<01:39,  1.26s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:41<01:37,  1.26s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:42<01:34,  1.23s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:44<01:32,  1.22s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:45<01:31,  1.23s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:47<01:38,  1.33s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<19:02,  5.74s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:07<10:26,  3.16s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:38,  2.33s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:21,  1.95s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:11<05:38,  1.73s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:10,  1.60s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:54,  1.53s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<04:42,  1.47s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:36,  1.45s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:18<04:31,  1.43s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:26,  1.41s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:22,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:22<04:18,  1.38s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:15,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:12,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:26<04:12,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:13,  1.39s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<04:12,  1.39s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:30<04:08,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<04:05,  1.37s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:33<04:04,  1.36s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:34<04:03,  1.37s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:35<04:01,  1.36s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:37<03:59,  1.36s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:38<03:57,  1.36s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<03:57,  1.37s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:41<03:56,  1.36s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:42<03:55,  1.37s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:44<04:10,  1.47s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:45<04:18,  1.52s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:47<04:04,  1.45s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:48<03:53,  1.39s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:49<03:44,  1.35s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:50<03:36,  1.31s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:52<03:29,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:53<03:27,  1.26s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:54<03:23,  1.25s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:55<03:20,  1.24s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:56<03:16,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:58<03:12,  1.21s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:59<03:13,  1.22s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [01:00<03:10,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:01<03:12,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:03<03:09,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:04<03:09,  1.22s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:05<03:08,  1.22s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:06<03:06,  1.22s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:07<03:05,  1.22s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:09<03:04,  1.22s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:10<03:02,  1.22s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:11<03:00,  1.21s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:12<02:59,  1.21s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:14<03:00,  1.22s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:15<02:57,  1.21s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:16<02:56,  1.22s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:17<02:55,  1.22s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:18<02:52,  1.21s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:20<02:50,  1.20s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:21<02:49,  1.20s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:22<02:48,  1.20s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<02:46,  1.20s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<02:44,  1.19s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<02:42,  1.19s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:27<02:41,  1.19s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:28<02:41,  1.20s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<02:44,  1.23s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:47,  1.26s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:32<02:50,  1.29s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:33<02:51,  1.31s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:35<02:52,  1.33s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<02:55,  1.36s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<02:53,  1.35s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:39<02:51,  1.35s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:50,  1.35s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:41<02:47,  1.34s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:43<02:45,  1.33s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:44<02:45,  1.34s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:45<02:40,  1.32s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:47<02:38,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:48<02:35,  1.30s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:49<02:33,  1.29s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:50<02:31,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:52<02:29,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:53<02:28,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:54<02:26,  1.27s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:55<02:24,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:57<02:21,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:58<02:20,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:59<02:19,  1.26s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:00<02:18,  1.26s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:02<02:17,  1.26s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:03<02:19,  1.30s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:04<02:18,  1.29s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:06<02:15,  1.28s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:07<02:14,  1.28s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:08<02:11,  1.26s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:09<02:09,  1.26s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:11<02:07,  1.25s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:08,  1.28s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:13<02:08,  1.29s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:15<02:07,  1.29s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:16<02:08,  1.31s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:17<02:08,  1.33s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:19<02:09,  1.34s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:20<02:07,  1.34s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:21<02:08,  1.37s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:23<02:05,  1.35s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:24<02:03,  1.34s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:25<02:02,  1.35s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:27<02:01,  1.35s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:28<02:00,  1.36s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:30<01:59,  1.35s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:31<01:57,  1.36s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:32<01:56,  1.36s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:34<01:55,  1.36s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:36<01:53,  1.35s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:53,  5.40s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<10:22,  3.14s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:38,  2.33s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:24,  1.96s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:11<05:38,  1.74s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:12<05:13,  1.61s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:52,  1.51s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<04:41,  1.46s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:30,  1.42s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:21,  1.37s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:45,  1.51s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:32,  1.45s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:22<04:18,  1.38s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:09,  1.34s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<03:59,  1.30s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<03:55,  1.28s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<03:49,  1.25s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<03:44,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<03:41,  1.23s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:43,  1.24s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:45,  1.26s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<03:48,  1.28s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:50,  1.30s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:51,  1.31s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<03:52,  1.33s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:53,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:52,  1.34s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:52,  1.35s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:52,  1.36s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:44<03:51,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:45<03:49,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:43,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:36,  1.30s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:49<03:28,  1.26s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:50<03:21,  1.22s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:18,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:15,  1.20s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:14,  1.20s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:55<03:12,  1.20s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:10,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:08,  1.18s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:05,  1.17s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:03,  1.17s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:00<03:08,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:10,  1.23s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:09,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:04<03:13,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:17,  1.30s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:07<03:17,  1.31s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:08<03:18,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:17,  1.33s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:16,  1.33s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:12<03:18,  1.35s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<03:16,  1.35s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<03:15,  1.35s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<03:13,  1.35s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:18<03:11,  1.34s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<03:09,  1.34s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<03:03,  1.30s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:22<02:56,  1.26s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<02:54,  1.26s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<02:51,  1.24s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<02:50,  1.24s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:26<02:47,  1.23s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:28<02:44,  1.22s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<02:42,  1.22s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:41,  1.21s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:31<02:40,  1.21s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:32<02:36,  1.19s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:34<02:34,  1.19s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:35<02:33,  1.19s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:36<02:32,  1.19s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:37<02:32,  1.20s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:38<02:31,  1.21s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:40<02:29,  1.20s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:41<02:31,  1.22s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:42<02:32,  1.24s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:29,  1.22s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:44<02:25,  1.20s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:46<02:23,  1.19s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:47<02:21,  1.19s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:48<02:19,  1.18s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:19,  1.20s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:50<02:18,  1.20s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:52<02:16,  1.18s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:53<02:14,  1.18s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:54<02:14,  1.19s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:55<02:20,  1.25s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:19,  1.26s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:58<02:17,  1.25s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:59<02:16,  1.25s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:00<02:17,  1.27s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:02<02:14,  1.26s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:03<02:11,  1.24s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:04<02:11,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:06<02:14,  1.29s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:07<02:15,  1.31s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:08<02:15,  1.33s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:10<02:15,  1.34s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<02:15,  1.35s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:12<02:14,  1.36s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:14<02:13,  1.36s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:15<02:12,  1.37s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:17<02:11,  1.37s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:18<02:10,  1.37s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:19<02:08,  1.37s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:22<02:03,  1.33s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<18:55,  5.71s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:07<10:22,  3.14s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:47,  2.38s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:21,  1.95s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:11<06:07,  1.89s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:13<05:38,  1.74s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:14<05:05,  1.58s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:15<04:43,  1.48s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:31,  1.42s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:18<04:20,  1.37s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:11,  1.33s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:09,  1.33s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:22<04:08,  1.33s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:07,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:05,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:26<04:07,  1.35s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:05,  1.34s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<04:04,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:30<04:02,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<03:59,  1.33s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<03:59,  1.34s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:34<03:57,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:35<03:55,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:36<03:47,  1.29s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<03:42,  1.27s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<03:38,  1.26s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:33,  1.23s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:41<03:31,  1.23s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:42<03:28,  1.22s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:27,  1.22s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:45<03:26,  1.22s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:46<03:19,  1.18s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:02,  1.10s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<02:44,  1.01it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:48<02:33,  1.07it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<02:24,  1.14it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<02:15,  1.21it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:50<02:10,  1.24it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:51<02:10,  1.24it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:52<02:05,  1.27it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:53<02:02,  1.30it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:53<02:07,  1.23it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:54<02:07,  1.24it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:55<02:08,  1.21it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:56<02:07,  1.21it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:57<02:07,  1.21it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:58<02:08,  1.19it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:59<02:06,  1.20it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:59<02:05,  1.20it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:00<02:04,  1.21it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:01<02:03,  1.20it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:02<02:04,  1.18it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:03<02:05,  1.17it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:04<02:06,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:05<02:05,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:05<02:02,  1.17it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:06<02:01,  1.18it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:07<02:00,  1.18it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:08<01:59,  1.18it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:09<01:58,  1.18it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:10<01:56,  1.19it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:10<01:54,  1.20it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:11<01:54,  1.19it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:12<01:54,  1.19it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:13<01:53,  1.19it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:14<01:52,  1.19it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:15<01:53,  1.17it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:15<01:51,  1.19it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:16<01:50,  1.19it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:17<01:49,  1.19it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:18<01:47,  1.20it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:19<01:47,  1.19it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:20<01:47,  1.18it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:20<01:47,  1.18it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:21<01:48,  1.15it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:22<01:49,  1.13it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:23<01:48,  1.13it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:24<01:48,  1.12it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:25<01:50,  1.10it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:26<01:49,  1.10it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:27<01:46,  1.11it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:28<01:45,  1.12it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:29<01:45,  1.11it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:30<01:45,  1.09it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:30<01:44,  1.10it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:31<01:43,  1.10it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:32<01:40,  1.12it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:33<01:43,  1.08it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:34<01:44,  1.06it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:35<01:39,  1.11it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:36<01:35,  1.14it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:37<01:33,  1.16it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:38<01:30,  1.18it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:38<01:28,  1.19it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:39<01:27,  1.20it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:40<01:25,  1.21it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:41<01:25,  1.21it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:42<01:24,  1.21it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:42<01:23,  1.21it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:43<01:21,  1.22it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:44<01:20,  1.22it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:45<01:19,  1.23it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:46<01:17,  1.26it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:46<01:15,  1.27it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:47<01:14,  1.28it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:48<01:23,  1.13it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:49<01:18,  1.18it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:50<01:16,  1.20it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:51<01:15,  1.21it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:51<01:14,  1.21it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:52<01:15,  1.18it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:53<01:17,  1.14it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:54<01:16,  1.14it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:55<01:14,  1.16it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:56<01:13,  1.16it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:57<01:13,  1.15it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:58<01:15,  1.10it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:59<01:14,  1.10it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:00<01:14,  1.09it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:01<01:14,  1.08it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:01<01:12,  1.09it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:02<01:12,  1.08it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:03<01:11,  1.08it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:04<01:10,  1.08it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:05<01:09,  1.08it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:06<01:08,  1.07it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:07<01:08,  1.07it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:08<01:08,  1.06it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:09<01:06,  1.06it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:10<01:05,  1.06it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:11<01:04,  1.06it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:12<01:03,  1.07it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:13<01:02,  1.08it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:14<01:00,  1.09it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:15<00:59,  1.09it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:16<01:00,  1.06it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:16<00:58,  1.07it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:17<00:56,  1.10it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:18<00:57,  1.07it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:19<00:55,  1.08it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:20<00:53,  1.10it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:21<00:51,  1.12it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:22<00:53,  1.07it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:23<00:55,  1.01it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:24<00:53,  1.03it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:25<00:51,  1.04it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:26<00:52,  1.01it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:27<00:49,  1.06it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:28<00:47,  1.08it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:29<00:46,  1.08it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:30<00:46,  1.06it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:30<00:44,  1.07it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:31<00:43,  1.09it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:33<00:50,  1.09s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:34<00:49,  1.09s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:35<00:44,  1.01s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:36<00:41,  1.03it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:36<00:38,  1.08it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:37<00:36,  1.12it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:38<00:35,  1.14it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:39<00:33,  1.16it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:40<00:32,  1.16it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:41<00:31,  1.17it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:42<00:30,  1.17it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:42<00:29,  1.18it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:43<00:28,  1.19it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:44<00:27,  1.19it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:45<00:27,  1.18it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:46<00:25,  1.20it/s]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:47<00:25,  1.19it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:47<00:24,  1.20it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:48<00:23,  1.18it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:49<00:22,  1.18it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:50<00:22,  1.18it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [02:51<00:20,  1.19it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [02:52<00:20,  1.18it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [02:52<00:19,  1.18it/s]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [02:53<00:18,  1.17it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [02:54<00:17,  1.18it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [02:55<00:17,  1.18it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [02:56<00:16,  1.17it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [02:57<00:15,  1.17it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [02:58<00:14,  1.17it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [02:58<00:13,  1.16it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [02:59<00:12,  1.17it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [03:00<00:12,  1.15it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [03:01<00:11,  1.15it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [03:02<00:10,  1.15it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [03:03<00:09,  1.14it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [03:04<00:08,  1.15it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [03:05<00:07,  1.14it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [03:06<00:07,  1.13it/s]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [03:06<00:06,  1.12it/s]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [03:07<00:05,  1.13it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [03:08<00:04,  1.13it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [03:09<00:03,  1.11it/s]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [03:10<00:02,  1.11it/s]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [03:11<00:01,  1.11it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [03:12<00:00,  1.11it/s]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [03:13<00:00,  1.04it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.04s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.35it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.47it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.55it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.12it/s]\u001b[A\n",
+      "Evaluating NLL: 81it [00:00, 111.12it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 175.48it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  41%|███████▊           | 41/100 [00:00<00:00, 405.46it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████| 100/100 [00:00<00:00, 367.13it/s]\n",
+      " 40%|██████████████████████████▊                                        | 2/5 [37:51<56:21, 1127.16s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:43,  5.34s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:58,  2.72s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:10,  1.88s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<04:56,  1.51s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:16,  1.31s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:41,  1.14s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:21,  1.05s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:20,  1.04s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:08,  1.02it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<02:59,  1.06it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:58,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:15<02:54,  1.08it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:16<02:53,  1.08it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:17<02:52,  1.08it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:47,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:45,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:39,  1.14it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:39,  1.14it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<03:02,  1.01s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<03:01,  1.01s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:42,  1.10it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:29,  1.19it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:19,  1.27it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:13,  1.32it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:09,  1.36it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:26<02:05,  1.39it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:01,  1.42it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<01:59,  1.43it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:28<01:58,  1.44it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:29<02:00,  1.41it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:30<02:03,  1.37it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:31<02:03,  1.36it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:31<02:02,  1.36it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:32<02:03,  1.34it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:33<02:07,  1.30it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:34<02:09,  1.27it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:35<02:10,  1.25it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:08,  1.26it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:36<02:08,  1.25it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:37<02:09,  1.24it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:38<02:07,  1.25it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:39<02:07,  1.24it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:40<02:07,  1.23it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:40<02:08,  1.21it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:41<02:10,  1.19it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:42<02:11,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:43<02:11,  1.16it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:44<02:10,  1.16it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:45<02:11,  1.15it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:46<02:08,  1.17it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:46<02:05,  1.18it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:47<02:03,  1.20it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:48<02:02,  1.20it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:49<02:01,  1.20it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:50<02:00,  1.21it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:51<01:59,  1.21it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:51<01:57,  1.21it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:52<01:57,  1.21it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:53<01:57,  1.20it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:54<01:56,  1.20it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:55<01:57,  1.18it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:56<01:57,  1.17it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:57<01:56,  1.18it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [00:57<01:55,  1.18it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [00:58<01:55,  1.17it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [00:59<01:53,  1.18it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:00<01:53,  1.17it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:01<01:50,  1.19it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:02<01:53,  1.15it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:02<01:50,  1.18it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:03<01:49,  1.18it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:04<01:49,  1.17it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:05<01:50,  1.15it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:06<01:48,  1.16it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:07<01:46,  1.18it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:08<01:45,  1.18it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:09<01:47,  1.15it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:09<01:48,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:10<01:47,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:11<01:46,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:12<01:45,  1.13it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:13<01:45,  1.12it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:14<01:44,  1.12it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:15<01:42,  1.14it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:16<01:41,  1.14it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:17<01:42,  1.11it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:17<01:40,  1.12it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:18<01:38,  1.13it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:19<01:37,  1.14it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:20<01:37,  1.13it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:21<01:36,  1.13it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:22<01:37,  1.11it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:23<01:35,  1.12it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:24<01:36,  1.09it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:25<01:35,  1.09it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:26<01:34,  1.10it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:27<01:34,  1.09it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:27<01:33,  1.09it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:28<01:31,  1.11it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:29<01:28,  1.13it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:30<01:26,  1.15it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:31<01:24,  1.15it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:32<01:22,  1.17it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:33<01:22,  1.17it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:33<01:20,  1.18it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:34<01:19,  1.18it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:35<01:19,  1.17it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:36<01:18,  1.18it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:37<01:16,  1.19it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:38<01:15,  1.19it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:38<01:14,  1.19it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:39<01:13,  1.20it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:40<01:12,  1.20it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:41<01:12,  1.19it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:42<01:13,  1.16it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:43<01:11,  1.18it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:43<01:10,  1.17it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:44<01:10,  1.17it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:45<01:09,  1.16it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:46<01:08,  1.17it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:47<01:07,  1.17it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:48<01:08,  1.14it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:49<01:06,  1.16it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:50<01:05,  1.16it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:50<01:04,  1.16it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:51<01:02,  1.18it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:52<01:02,  1.18it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:53<01:01,  1.17it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:54<00:59,  1.19it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [01:55<00:58,  1.19it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [01:55<00:58,  1.18it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [01:56<00:58,  1.17it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [01:57<00:57,  1.16it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [01:58<00:56,  1.17it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [01:59<00:55,  1.18it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:00<00:54,  1.17it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:01<00:53,  1.18it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:01<00:52,  1.17it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:02<00:54,  1.11it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:03<00:54,  1.11it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:04<00:53,  1.10it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:05<00:51,  1.12it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:06<00:49,  1.14it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:07<00:49,  1.12it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:08<00:50,  1.09it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:09<00:48,  1.11it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:10<00:48,  1.09it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:11<00:47,  1.08it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:12<00:46,  1.10it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:12<00:46,  1.09it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:13<00:45,  1.08it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:14<00:44,  1.07it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:15<00:44,  1.06it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:16<00:43,  1.07it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:17<00:42,  1.07it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:18<00:41,  1.07it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:19<00:39,  1.08it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:20<00:39,  1.07it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:21<00:37,  1.09it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:22<00:36,  1.09it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:23<00:36,  1.08it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:24<00:34,  1.10it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:24<00:32,  1.12it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:25<00:31,  1.13it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:26<00:31,  1.12it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:27<00:30,  1.12it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:28<00:29,  1.11it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:29<00:28,  1.11it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:30<00:27,  1.11it/s]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:31<00:27,  1.10it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:32<00:25,  1.12it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:32<00:24,  1.14it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:33<00:24,  1.12it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:34<00:23,  1.11it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [02:35<00:22,  1.12it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [02:36<00:21,  1.14it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [02:37<00:19,  1.16it/s]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [02:38<00:19,  1.10it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [02:39<00:19,  1.10it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [02:40<00:17,  1.13it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [02:40<00:16,  1.14it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [02:41<00:16,  1.09it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [02:42<00:16,  1.05it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [02:43<00:15,  1.06it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [02:44<00:13,  1.08it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [02:45<00:12,  1.10it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [02:46<00:11,  1.11it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [02:47<00:10,  1.11it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [02:48<00:11,  1.04s/it]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [02:50<00:11,  1.13s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [02:50<00:09,  1.04s/it]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [02:51<00:07,  1.02it/s]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [02:52<00:06,  1.08it/s]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [02:53<00:05,  1.10it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [02:54<00:04,  1.12it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [02:55<00:03,  1.13it/s]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [02:56<00:02,  1.13it/s]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [02:56<00:01,  1.15it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [02:57<00:00,  1.15it/s]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [02:58<00:00,  1.12it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:05,  5.15s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:45,  2.65s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<06:01,  1.83s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:42,  1.44s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:57,  1.22s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:30,  1.09s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:12,  1.00it/s]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:01,  1.06it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<02:52,  1.11it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:46,  1.14it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:44,  1.15it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:43,  1.15it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:40,  1.16it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:36,  1.19it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:16<02:38,  1.17it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:17<02:34,  1.19it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:18<02:32,  1.20it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:31,  1.20it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:20<02:30,  1.20it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:30,  1.20it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:21<02:29,  1.19it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:22<02:28,  1.20it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:23<02:31,  1.17it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:24<02:30,  1.17it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:25<02:32,  1.15it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:26<02:30,  1.15it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:28,  1.17it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:27<02:28,  1.15it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:28<02:25,  1.17it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:29<02:24,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:30<02:22,  1.18it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:31<02:21,  1.19it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:32<02:19,  1.20it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:32<02:18,  1.20it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:33<02:15,  1.22it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:34<02:14,  1.22it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:35<02:14,  1.22it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:36<02:16,  1.18it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:37<02:14,  1.20it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:37<02:15,  1.18it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:38<02:12,  1.20it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:39<02:10,  1.21it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:40<02:09,  1.21it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:41<02:07,  1.22it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:41<02:03,  1.25it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:42<01:59,  1.28it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:43<02:01,  1.26it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:44<02:00,  1.26it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:45<01:55,  1.30it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:45<02:01,  1.24it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:46<01:56,  1.28it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:47<01:52,  1.31it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:48<01:54,  1.28it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:49<01:55,  1.26it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:49<01:58,  1.23it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:50<01:59,  1.21it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:51<01:58,  1.21it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:52<02:00,  1.18it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:53<02:02,  1.15it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:54<02:01,  1.15it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:55<02:00,  1.15it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:55<01:59,  1.16it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:56<01:58,  1.16it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [00:57<01:58,  1.15it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [00:58<01:58,  1.14it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [00:59<01:57,  1.14it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:00<01:56,  1.14it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:01<01:56,  1.14it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:02<01:54,  1.14it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:03<01:55,  1.13it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:03<01:55,  1.12it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:04<01:53,  1.12it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:05<01:52,  1.13it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:06<01:51,  1.13it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:07<01:50,  1.13it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:08<01:48,  1.14it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:09<01:46,  1.15it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:10<01:45,  1.16it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:10<01:43,  1.17it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:11<01:41,  1.18it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:12<01:40,  1.19it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:13<01:39,  1.18it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:14<01:40,  1.17it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:15<01:41,  1.14it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:16<01:39,  1.15it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:16<01:38,  1.16it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:17<01:37,  1.16it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:18<01:34,  1.18it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:19<01:33,  1.19it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:20<01:32,  1.19it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:21<01:31,  1.19it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:21<01:31,  1.18it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:22<01:30,  1.18it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:23<01:28,  1.19it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:24<01:28,  1.19it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:25<01:27,  1.19it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:26<01:25,  1.20it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:26<01:25,  1.20it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:27<01:26,  1.17it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:28<01:29,  1.12it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:29<01:27,  1.13it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:30<01:26,  1.13it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:31<01:24,  1.15it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:32<01:22,  1.16it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:33<01:21,  1.17it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:33<01:21,  1.16it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:34<01:20,  1.15it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:35<01:19,  1.15it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:36<01:18,  1.17it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:37<01:17,  1.16it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:38<01:15,  1.17it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:39<01:15,  1.17it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:39<01:15,  1.15it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:40<01:15,  1.14it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:41<01:15,  1.13it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:42<01:16,  1.10it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:43<01:17,  1.07it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:44<01:14,  1.10it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:45<01:12,  1.11it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:46<01:13,  1.10it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:47<01:11,  1.10it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:48<01:09,  1.12it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:49<01:09,  1.10it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:49<01:08,  1.11it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:50<01:06,  1.12it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:51<01:06,  1.12it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:52<01:05,  1.12it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:54<01:14,  1.03s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:55<01:18,  1.11s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [01:56<01:11,  1.02s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [01:56<01:05,  1.05it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [01:57<01:02,  1.08it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [01:58<00:59,  1.13it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [01:59<00:58,  1.12it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:00<00:57,  1.13it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:01<00:56,  1.14it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:02<00:55,  1.14it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:02<00:54,  1.14it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:03<00:53,  1.14it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:04<00:52,  1.15it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:05<00:51,  1.15it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:06<00:50,  1.16it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:07<00:49,  1.16it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:08<00:48,  1.17it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:08<00:47,  1.15it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:09<00:47,  1.13it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:10<00:46,  1.14it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:11<00:45,  1.14it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:12<00:44,  1.14it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:13<00:44,  1.12it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:14<00:43,  1.13it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:15<00:42,  1.14it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:16<00:40,  1.16it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:16<00:40,  1.15it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:17<00:39,  1.15it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:18<00:38,  1.14it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:19<00:37,  1.15it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:20<00:36,  1.15it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:21<00:35,  1.15it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:22<00:34,  1.16it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:22<00:33,  1.17it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:23<00:32,  1.17it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:24<00:31,  1.18it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:25<00:30,  1.18it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:26<00:29,  1.18it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:27<00:28,  1.18it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:27<00:27,  1.19it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:28<00:26,  1.20it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:29<00:25,  1.20it/s]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:30<00:25,  1.18it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:31<00:24,  1.19it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:32<00:23,  1.19it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:33<00:22,  1.20it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:33<00:21,  1.19it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [02:34<00:20,  1.20it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [02:35<00:19,  1.20it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [02:36<00:19,  1.20it/s]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [02:37<00:18,  1.20it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [02:37<00:17,  1.21it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [02:38<00:16,  1.21it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [02:39<00:15,  1.20it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [02:40<00:14,  1.20it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [02:41<00:14,  1.19it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [02:42<00:13,  1.17it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [02:43<00:13,  1.15it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [02:43<00:12,  1.16it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [02:44<00:10,  1.20it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [02:45<00:09,  1.21it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [02:46<00:09,  1.21it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [02:47<00:08,  1.21it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [02:48<00:07,  1.18it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [02:48<00:06,  1.19it/s]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [02:49<00:05,  1.21it/s]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [02:50<00:04,  1.22it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [02:51<00:04,  1.22it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [02:52<00:03,  1.23it/s]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [02:53<00:02,  1.22it/s]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [02:53<00:01,  1.23it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [02:54<00:00,  1.24it/s]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [02:55<00:00,  1.14it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:09,  5.17s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:26,  2.86s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:59,  2.13s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:52,  1.80s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:14,  1.61s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:49,  1.49s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:37,  1.44s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:23,  1.37s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:16,  1.34s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:13,  1.33s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:11,  1.33s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:14,  1.35s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<04:08,  1.33s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<04:03,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<03:54,  1.26s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<03:55,  1.28s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:55,  1.29s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:56,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:55,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:56,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:53,  1.31s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:57,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:56,  1.34s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:54,  1.33s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:52,  1.33s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:54,  1.35s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:45,  1.30s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:36,  1.26s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:35,  1.26s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:42<03:40,  1.30s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:39,  1.30s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:44,  1.34s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:46,  1.36s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:44,  1.35s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:41,  1.34s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:42,  1.36s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:39,  1.35s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:37,  1.34s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:36,  1.34s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:40,  1.38s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:44,  1.41s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:59<03:37,  1.38s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:00<03:43,  1.42s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:02<03:55,  1.51s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:03<03:37,  1.40s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:04<03:25,  1.34s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:05<03:17,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:07<03:13,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:08<03:08,  1.25s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:09<03:05,  1.24s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:10<03:03,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<02:59,  1.21s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:13<02:56,  1.20s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<02:53,  1.19s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<02:53,  1.20s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<02:51,  1.19s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:17<02:50,  1.19s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<02:50,  1.20s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<02:49,  1.20s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:21<02:47,  1.20s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:22<02:51,  1.24s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<02:54,  1.27s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<02:55,  1.28s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:26<02:55,  1.29s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:28<02:55,  1.30s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<02:55,  1.31s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:54,  1.31s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:32<02:54,  1.32s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:33<02:53,  1.32s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:34<02:52,  1.33s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<02:52,  1.33s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<02:49,  1.33s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:38<02:45,  1.31s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:39<02:41,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:41<02:37,  1.26s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:42<02:34,  1.25s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:43<02:31,  1.24s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:44<02:31,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:46<02:31,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:47<02:29,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:48<02:28,  1.24s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:49<02:26,  1.24s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:51<02:26,  1.25s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:52<02:27,  1.27s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:53<02:24,  1.26s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:54<02:21,  1.24s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:56<02:19,  1.23s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:57<02:19,  1.24s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:58<02:17,  1.24s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:59<02:15,  1.23s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:01<02:15,  1.24s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:02<02:14,  1.24s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:03<02:12,  1.24s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:04<02:12,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:06<02:11,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:07<02:11,  1.27s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:08<02:12,  1.29s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:10<02:12,  1.30s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:11<02:12,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:12<02:11,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<02:10,  1.32s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:15<02:09,  1.32s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:16<02:08,  1.33s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:18<02:08,  1.34s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:19<02:06,  1.33s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:20<02:07,  1.35s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:22<02:04,  1.34s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:23<02:01,  1.33s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:24<01:57,  1.29s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:25<01:53,  1.26s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:26<01:51,  1.25s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:28<01:49,  1.24s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:29<01:46,  1.22s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:30<01:43,  1.20s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:31<01:40,  1.19s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:32<01:40,  1.20s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:34<01:43,  1.25s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:35<01:42,  1.25s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:36<01:40,  1.24s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:37<01:39,  1.24s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:39<01:39,  1.26s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:40<01:36,  1.24s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:41<01:34,  1.23s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:43<01:35,  1.26s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:44<01:36,  1.29s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:45<01:37,  1.31s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:47<01:36,  1.33s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:48<01:36,  1.34s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:49<01:35,  1.34s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:51<01:34,  1.35s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:52<01:33,  1.36s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:53<01:32,  1.36s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:55<01:31,  1.36s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:56<01:30,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:58<01:28,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:59<01:27,  1.37s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [03:00<01:22,  1.31s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:02<01:23,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:03<01:23,  1.37s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:04<01:20,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:06<01:19,  1.34s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:07<01:18,  1.35s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:08<01:17,  1.36s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:10<01:16,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:11<01:16,  1.38s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:13<01:14,  1.39s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:14<01:14,  1.40s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:15<01:12,  1.39s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:17<01:19,  1.55s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:19<01:14,  1.50s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:20<01:10,  1.44s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:21<01:07,  1.41s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:24<01:02,  1.34s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:06,  5.16s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:30,  2.88s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:00,  2.13s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:47,  1.77s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:10,  1.59s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:46,  1.47s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:28,  1.39s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:17,  1.34s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:09,  1.31s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:57,  1.26s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<04:02,  1.29s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<04:04,  1.30s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<04:02,  1.30s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<04:02,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<04:01,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<04:02,  1.32s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<04:01,  1.32s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<04:02,  1.34s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<04:02,  1.35s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<04:02,  1.35s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:58,  1.34s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:53,  1.32s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:45,  1.28s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:38,  1.25s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:32,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:32,  1.23s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:25,  1.20s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:29,  1.22s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:28,  1.23s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:43<03:24,  1.21s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:23,  1.21s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:18,  1.19s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:12,  1.16s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:10,  1.15s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:48<03:09,  1.16s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:49<03:06,  1.14s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:08,  1.16s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:10,  1.18s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:08,  1.18s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:54<03:06,  1.17s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:55<03:04,  1.17s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:09,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:58<03:09,  1.22s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:59<03:07,  1.21s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:00<03:09,  1.23s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:02<03:10,  1.24s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:03<03:11,  1.26s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:04<03:10,  1.26s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:05<03:08,  1.26s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:07<03:11,  1.28s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:08<03:15,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:10<03:11,  1.30s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:11<03:04,  1.27s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:12<02:58,  1.23s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:13<02:55,  1.22s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:14<02:54,  1.22s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:16<02:56,  1.24s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:17<02:58,  1.27s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:18<02:58,  1.27s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:19<02:58,  1.29s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:21<03:04,  1.33s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:22<03:03,  1.34s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:24<03:02,  1.34s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:25<03:00,  1.34s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:26<03:03,  1.37s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:28<03:01,  1.36s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:29<02:58,  1.36s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:30<02:59,  1.37s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:32<02:56,  1.36s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:33<02:52,  1.34s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:34<02:52,  1.35s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:36<02:53,  1.37s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:37<02:56,  1.40s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:39<02:53,  1.39s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:40<02:50,  1.37s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:41<02:51,  1.39s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:48,  1.38s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:44<02:48,  1.39s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:46<03:03,  1.53s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:47<02:50,  1.43s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:49<02:41,  1.37s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:50<02:34,  1.32s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:51<02:29,  1.29s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:52<02:26,  1.27s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:53<02:23,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:55<02:24,  1.28s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:56<02:24,  1.29s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:23,  1.29s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:59<02:23,  1.31s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:23,  1.32s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:23,  1.33s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:03<02:22,  1.33s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:04<02:20,  1.33s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:05<02:18,  1.32s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:07<02:18,  1.33s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:08<02:17,  1.33s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:09<02:14,  1.32s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:11<02:09,  1.29s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:12<02:04,  1.25s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<02:01,  1.23s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:14<01:59,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:15<01:58,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:18<02:07,  1.33s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:03,  5.14s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:28,  2.87s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:58,  2.12s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:45,  1.76s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:04,  1.56s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:38,  1.44s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:22,  1.36s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:09,  1.30s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:10,  1.31s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:02,  1.28s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<03:56,  1.25s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<03:53,  1.24s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:19<03:46,  1.21s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:20<03:44,  1.21s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<03:40,  1.19s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:23<03:37,  1.18s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:24<03:36,  1.18s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:25<03:34,  1.18s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:26<03:35,  1.19s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:27<03:33,  1.19s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:29<03:33,  1.19s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:30<03:34,  1.20s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:31<03:37,  1.23s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:33<03:41,  1.26s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:34<03:42,  1.27s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:35<03:44,  1.29s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:36<03:44,  1.30s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:44,  1.31s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:39<03:45,  1.32s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:45,  1.32s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:49,  1.36s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:43<03:48,  1.36s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:45,  1.35s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:41,  1.34s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:36,  1.31s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:48<03:31,  1.29s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:29,  1.28s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:24,  1.27s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:22,  1.26s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:20,  1.25s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:20,  1.26s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:22,  1.28s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:22,  1.29s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:18,  1.27s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:23,  1.31s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<03:21,  1.31s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:02<03:16,  1.28s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:04<03:15,  1.29s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:05<03:16,  1.30s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:06<03:13,  1.29s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:08<03:15,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:09<03:13,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:10<03:13,  1.31s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:12<03:14,  1.33s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:13<03:14,  1.34s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:14<03:13,  1.34s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:16<03:11,  1.34s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:17<03:10,  1.34s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:19<03:34,  1.52s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:20<03:25,  1.47s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:22<03:18,  1.43s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:23<03:12,  1.40s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:24<03:08,  1.38s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:26<03:04,  1.36s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:27<03:02,  1.35s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:28<03:01,  1.35s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:51,  1.29s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:31<02:47,  1.27s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:32<02:44,  1.25s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:33<02:41,  1.24s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:34<02:40,  1.24s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:36<02:38,  1.24s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:37<02:40,  1.27s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:38<02:37,  1.25s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:39<02:35,  1.24s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:41<02:30,  1.22s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:42<02:27,  1.20s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:24,  1.18s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:44<02:21,  1.17s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:45<02:20,  1.17s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:46<02:17,  1.16s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:47<02:15,  1.15s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:13,  1.14s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:50<02:11,  1.14s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:51<02:10,  1.14s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:52<02:09,  1.13s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:53<02:08,  1.14s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:54<02:08,  1.14s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:55<02:06,  1.14s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:57<02:03,  1.12s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:58<02:01,  1.12s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:59<02:02,  1.13s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:00<02:01,  1.14s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:01<02:00,  1.13s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:02<01:59,  1.14s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:03<02:02,  1.17s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:05<02:04,  1.21s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:06<02:05,  1.23s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:07<02:03,  1.22s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:09<02:03,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:10<02:06,  1.28s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:11<02:07,  1.30s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:13<02:06,  1.30s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:14<02:06,  1.32s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:15<02:04,  1.31s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:16<02:02,  1.31s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:18<02:01,  1.31s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:19<01:59,  1.30s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:20<01:59,  1.31s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:22<01:59,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:23<01:59,  1.34s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:24<01:57,  1.33s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:26<01:55,  1.33s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:27<01:53,  1.32s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:28<01:50,  1.30s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:30<01:46,  1.27s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:31<01:44,  1.26s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:32<01:41,  1.24s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:33<01:41,  1.25s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:35<01:40,  1.25s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:36<01:39,  1.25s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:37<01:40,  1.28s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:38<01:40,  1.30s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:40<01:37,  1.29s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:41<01:36,  1.28s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:42<01:33,  1.27s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:43<01:31,  1.26s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:45<01:30,  1.26s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:46<01:28,  1.25s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:47<01:27,  1.25s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:48<01:25,  1.24s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:50<01:24,  1.24s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:51<01:25,  1.28s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:52<01:25,  1.30s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:54<01:25,  1.32s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:55<01:25,  1.34s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:57<01:24,  1.35s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:58<01:23,  1.34s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:59<01:21,  1.34s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:01<01:20,  1.35s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:02<01:20,  1.36s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:03<01:19,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:05<01:17,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:06<01:16,  1.37s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:07<01:15,  1.37s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:09<01:13,  1.37s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:10<01:12,  1.36s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:11<01:10,  1.36s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:13<01:09,  1.36s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:14<01:07,  1.35s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:16<01:06,  1.36s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:17<01:05,  1.36s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:18<01:02,  1.34s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:19<01:01,  1.33s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:21<00:59,  1.33s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:22<00:58,  1.33s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:23<00:56,  1.32s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:25<00:55,  1.32s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:26<00:55,  1.35s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:28<00:53,  1.34s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:29<00:53,  1.37s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:30<00:52,  1.38s/it]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [03:32<00:49,  1.34s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [03:33<00:51,  1.44s/it]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [03:35<00:49,  1.43s/it]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [03:37<00:44,  1.31s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.10it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.87it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.08it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.78it/s]\n",
       "\n",
       "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
       "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
-      "Evaluating NLL: 25it [00:01, 31.78it/s]\u001b[A\n",
-      "Evaluating NLL: 61it [00:01, 82.14it/s]\u001b[A\n",
-      "Evaluating NLL: 85it [00:01, 110.11it/s]\u001b[A\n",
-      "Evaluating NLL: 121it [00:01, 160.53it/s]\u001b[A\n",
-      "Evaluating NLL: 154it [00:01, 198.66it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 123.79it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   3%|▍              | 3/100 [00:00<00:04, 21.61it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:   7%|█              | 7/100 [00:00<00:03, 30.48it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  12%|█▋            | 12/100 [00:00<00:02, 37.04it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  16%|██▏           | 16/100 [00:00<00:02, 37.56it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  20%|██▊           | 20/100 [00:00<00:02, 34.25it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  24%|███▎          | 24/100 [00:00<00:02, 33.79it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  28%|███▉          | 28/100 [00:00<00:02, 32.42it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  32%|████▍         | 32/100 [00:00<00:02, 31.23it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  37%|█████▏        | 37/100 [00:01<00:01, 32.81it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  41%|█████▋        | 41/100 [00:01<00:01, 33.68it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  45%|██████▎       | 45/100 [00:01<00:01, 32.86it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  49%|██████▊       | 49/100 [00:01<00:01, 33.46it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  53%|███████▍      | 53/100 [00:01<00:01, 30.75it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  57%|███████▉      | 57/100 [00:01<00:01, 32.46it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  64%|████████▉     | 64/100 [00:01<00:00, 39.51it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  68%|█████████▌    | 68/100 [00:01<00:00, 37.85it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  72%|██████████    | 72/100 [00:02<00:00, 34.83it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  76%|██████████▋   | 76/100 [00:02<00:00, 31.94it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  80%|███████████▏  | 80/100 [00:02<00:00, 31.56it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|█████████████| 100/100 [00:02<00:00, 39.32it/s]\n",
-      " 80%|███████████████████████████████████████████████▏           | 4/5 [1:38:47<24:15, 1455.53s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial loss -1.2500069404955854\n",
-      "computing optimized mi\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
+      "Evaluating NLL: 7it [00:01,  3.73it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.06it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.80it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.04it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.55it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.05it/s]\u001b[A\n",
+      "Evaluating NLL: 37it [00:01, 47.72it/s]\u001b[A\n",
+      "Evaluating NLL: 74it [00:01, 97.93it/s]\u001b[A\n",
+      "Evaluating NLL: 111it [00:01, 147.15it/s]\u001b[A\n",
+      "Evaluating NLL: 147it [00:01, 190.47it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 132.30it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   7%|█▍                   | 7/100 [00:00<00:01, 65.51it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  14%|██▊                 | 14/100 [00:00<00:01, 46.91it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  19%|███▊                | 19/100 [00:00<00:01, 45.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  24%|████▊               | 24/100 [00:00<00:01, 43.05it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  32%|██████▍             | 32/100 [00:00<00:01, 51.63it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  38%|███████▌            | 38/100 [00:00<00:01, 46.78it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|████████▌           | 43/100 [00:00<00:01, 44.94it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|█████████▌          | 48/100 [00:01<00:01, 43.06it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|██████████▌         | 53/100 [00:01<00:01, 41.80it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  60%|████████████        | 60/100 [00:01<00:00, 48.69it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  66%|█████████████▏      | 66/100 [00:01<00:00, 45.24it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██████████████▏     | 71/100 [00:01<00:00, 44.22it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|███████████████▏    | 76/100 [00:01<00:00, 42.62it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  83%|████████████████▌   | 83/100 [00:01<00:00, 48.53it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  88%|█████████████████▌  | 88/100 [00:01<00:00, 45.54it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  93%|██████████████████▌ | 93/100 [00:02<00:00, 43.50it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 45.07it/s]\n",
+      " 60%|████████████████████████████████████████▏                          | 3/5 [54:22<35:30, 1065.11s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:32,  4.99s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:21,  2.84s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:05,  2.16s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<05:59,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:24,  1.66s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:02,  1.56s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:48,  1.49s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:36,  1.44s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:30,  1.42s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:24,  1.39s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:20,  1.38s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:16,  1.36s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:12,  1.35s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:02,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<03:54,  1.27s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<03:49,  1.25s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:43,  1.22s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:37,  1.20s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:33,  1.18s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:35,  1.20s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:33,  1.19s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:32,  1.19s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:31,  1.20s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:29,  1.19s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:26,  1.18s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:23,  1.17s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:25,  1.19s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:25,  1.20s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:25,  1.20s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:23,  1.20s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:23,  1.20s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:43<03:24,  1.22s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:44<03:23,  1.22s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:23,  1.23s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:21,  1.22s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:48<03:18,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:49<03:22,  1.24s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:20,  1.24s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:15,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:14,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:54<03:12,  1.21s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:55<03:11,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:08,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:58<03:05,  1.19s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:59<03:04,  1.19s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:00<03:01,  1.18s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:01<03:01,  1.19s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:03<03:01,  1.20s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:04<03:01,  1.20s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:05<03:05,  1.23s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:06<03:09,  1.27s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:08<03:10,  1.29s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:09<03:10,  1.29s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:10<03:10,  1.30s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:12<03:09,  1.31s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:13<03:09,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:14<03:08,  1.32s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:16<03:06,  1.32s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:17<03:09,  1.34s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:18<03:10,  1.36s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:20<03:07,  1.35s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:21<03:02,  1.32s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:22<02:56,  1.29s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:23<02:52,  1.27s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:25<02:49,  1.26s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:26<02:47,  1.25s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:27<02:42,  1.23s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:28<02:39,  1.21s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:30<02:38,  1.21s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:31<02:40,  1.24s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:32<02:42,  1.26s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:33<02:44,  1.28s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:35<02:40,  1.27s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:36<02:45,  1.31s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:37<02:40,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:39<02:37,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:40<02:38,  1.29s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:41<02:40,  1.32s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:43<02:41,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:44<02:43,  1.36s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:46<02:44,  1.38s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:47<02:42,  1.38s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:48<02:42,  1.39s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:50<02:41,  1.39s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:51<02:39,  1.39s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:52<02:37,  1.38s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:54<02:39,  1.41s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:55<02:36,  1.40s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:30,  1.36s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:58<02:43,  1.49s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:37,  1.44s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:27,  1.36s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:02<02:21,  1.32s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:03<02:15,  1.28s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:04<02:12,  1.26s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:06<02:10,  1.25s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:07<02:07,  1.24s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:08<02:06,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:09<02:04,  1.23s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<02:00,  1.21s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:12<01:58,  1.20s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:13<01:58,  1.21s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:14<01:58,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:15<01:57,  1.23s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:17<01:55,  1.22s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:18<01:53,  1.21s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:19<01:52,  1.21s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:20<01:51,  1.21s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:21<01:51,  1.23s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:23<01:49,  1.22s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:24<01:47,  1.21s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:25<01:46,  1.21s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:27<01:53,  1.31s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:12,  4.89s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:08,  2.77s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:57,  2.12s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<06:00,  1.84s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:25,  1.67s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:02,  1.56s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:50,  1.50s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:43,  1.47s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:35,  1.44s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:29,  1.42s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:25,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:23,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:13,  1.36s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:08,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<04:01,  1.31s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<03:58,  1.30s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<03:55,  1.29s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:52,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:51,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:51,  1.28s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:31<03:51,  1.29s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:32<03:51,  1.30s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:48,  1.29s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:41,  1.26s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:36<03:37,  1.24s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:37<03:35,  1.24s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:38<03:39,  1.27s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:43,  1.30s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:46,  1.33s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:48,  1.35s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:47,  1.35s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:44,  1.33s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:47<03:44,  1.34s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:48<03:46,  1.36s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:44,  1.36s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:51<03:42,  1.35s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:52<03:35,  1.32s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:53<03:28,  1.29s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:54<03:22,  1.26s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:56<03:16,  1.23s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:57<03:13,  1.22s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:58<03:11,  1.21s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:59<03:08,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:00<03:14,  1.25s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:02<03:16,  1.27s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:03<03:12,  1.25s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:04<03:12,  1.26s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:06<03:13,  1.27s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:07<03:10,  1.26s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:08<03:05,  1.24s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:09<03:07,  1.26s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:11<03:10,  1.29s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:12<03:08,  1.28s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:13<03:11,  1.31s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:15<03:07,  1.30s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:16<03:07,  1.31s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:17<03:08,  1.32s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<03:10,  1.34s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<03:09,  1.35s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:21<03:09,  1.35s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<03:06,  1.34s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<03:04,  1.34s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<03:03,  1.34s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:27<03:01,  1.34s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:28<02:58,  1.32s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:29<02:54,  1.30s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:31<03:06,  1.40s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:33<03:18,  1.50s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:34<03:05,  1.42s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:35<02:54,  1.34s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:36<02:47,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:37<02:46,  1.30s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:39<02:46,  1.31s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:40<02:46,  1.32s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:42<02:46,  1.33s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:43<02:45,  1.33s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:44<02:45,  1.35s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:46<02:43,  1.34s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:47<02:42,  1.34s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:48<02:41,  1.34s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:50<02:39,  1.34s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:51<02:36,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:52<02:36,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:53<02:32,  1.31s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:55<02:28,  1.29s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:56<02:27,  1.29s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:57<02:24,  1.28s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:59<02:21,  1.27s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [02:00<02:19,  1.25s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [02:01<02:17,  1.25s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:02<02:16,  1.25s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:03<02:14,  1.24s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:05<02:13,  1.25s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:06<02:12,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:07<02:11,  1.25s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:09<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:10<02:10,  1.27s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:11<02:08,  1.26s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:12<02:06,  1.26s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:14<02:05,  1.25s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:15<02:04,  1.26s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:16<02:04,  1.27s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:17<02:04,  1.29s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:19<02:04,  1.30s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:20<02:03,  1.30s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:21<02:03,  1.31s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:23<02:03,  1.32s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:24<02:01,  1.32s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:25<02:00,  1.32s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:27<01:59,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:28<01:59,  1.34s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:29<01:57,  1.34s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:31<01:56,  1.34s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:32<01:51,  1.30s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:33<01:48,  1.28s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:34<01:44,  1.25s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:36<01:42,  1.23s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:37<01:39,  1.21s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:38<01:37,  1.21s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:39<01:35,  1.20s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:40<01:34,  1.19s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:41<01:33,  1.20s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:43<01:32,  1.20s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:44<01:30,  1.20s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:45<01:29,  1.19s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:46<01:28,  1.19s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:47<01:26,  1.19s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:49<01:24,  1.18s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:50<01:24,  1.18s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:51<01:23,  1.19s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:52<01:21,  1.19s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:53<01:20,  1.19s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:55<01:19,  1.19s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:56<01:20,  1.21s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:57<01:20,  1.23s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:58<01:17,  1.20s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:59<01:15,  1.21s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [03:01<01:14,  1.19s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [03:02<01:12,  1.19s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:03<01:10,  1.18s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:04<01:10,  1.19s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:05<01:08,  1.18s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:06<01:07,  1.18s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:08<01:06,  1.18s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:09<01:04,  1.18s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:10<01:06,  1.23s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:11<01:04,  1.23s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:13<01:04,  1.24s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:14<01:02,  1.22s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:15<01:03,  1.28s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:17<01:03,  1.29s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:18<01:02,  1.30s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:19<01:01,  1.31s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:21<01:00,  1.33s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:22<01:00,  1.34s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [03:23<00:59,  1.35s/it]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [03:25<00:58,  1.36s/it]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [03:26<00:56,  1.36s/it]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [03:27<00:55,  1.36s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [03:29<00:54,  1.37s/it]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [03:30<00:53,  1.37s/it]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [03:33<00:50,  1.32s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:12,  5.19s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:50,  2.98s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:08<07:22,  2.25s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:07,  1.88s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:27,  1.68s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:02,  1.56s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:42,  1.46s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:51,  1.52s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:57,  1.56s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:42,  1.49s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:19<04:31,  1.44s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:24,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:17,  1.38s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:23<04:13,  1.36s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:07,  1.34s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:04,  1.33s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:27<04:02,  1.32s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<03:59,  1.32s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<04:01,  1.33s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:31<03:59,  1.33s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<03:50,  1.28s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<03:42,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:41,  1.25s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:35<03:37,  1.24s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<03:34,  1.22s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:38<03:32,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:39<03:31,  1.22s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:40<03:28,  1.21s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:41<03:27,  1.21s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:43<03:25,  1.21s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:44<03:24,  1.21s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:45<03:25,  1.23s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:46<03:23,  1.22s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:47<03:21,  1.22s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:49<03:20,  1.22s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:50<03:18,  1.21s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:51<03:16,  1.21s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:52<03:14,  1.20s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:53<03:11,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:55<03:10,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:56<03:08,  1.18s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:57<03:08,  1.19s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:58<03:07,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:07,  1.20s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:01<03:06,  1.21s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:02<03:06,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:03<03:05,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:04<03:04,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:05<03:02,  1.21s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:07<02:59,  1.20s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:08<03:04,  1.24s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:09<03:00,  1.22s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:10<02:58,  1.21s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:12<02:57,  1.21s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:13<02:54,  1.20s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:14<02:52,  1.20s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:15<02:56,  1.23s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:17<02:59,  1.27s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:18<03:01,  1.29s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:19<03:02,  1.30s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:21<03:02,  1.32s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:22<03:01,  1.32s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:23<03:02,  1.33s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:25<03:01,  1.33s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:26<03:00,  1.33s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:27<02:58,  1.33s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:29<02:56,  1.33s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:30<02:53,  1.32s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:31<02:49,  1.29s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:32<02:46,  1.28s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:34<02:42,  1.26s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:35<02:41,  1.27s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:36<02:46,  1.31s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:38<02:42,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:39<02:41,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:40<02:40,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:41<02:40,  1.31s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:37,  1.29s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:44<02:33,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:45<02:28,  1.24s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:46<02:25,  1.23s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:48<02:27,  1.25s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:30,  1.29s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:50<02:33,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:52<02:34,  1.34s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:53<02:33,  1.35s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:55<02:34,  1.36s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:56<02:31,  1.36s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:29,  1.34s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:59<02:26,  1.34s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:26,  1.35s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:26,  1.36s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:03<02:23,  1.35s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:04<02:23,  1.35s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:05<02:18,  1.32s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:07<02:16,  1.31s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:08<02:17,  1.33s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:09<02:18,  1.35s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:11<02:15,  1.34s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:12<02:11,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<02:17,  1.39s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:15<02:21,  1.45s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:16<02:11,  1.36s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:17<02:04,  1.30s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:18<01:51,  1.17s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:19<01:39,  1.06s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:20<01:28,  1.05it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:20<01:21,  1.13it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:21<01:16,  1.19it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:22<01:11,  1.26it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:23<01:10,  1.26it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:23<01:09,  1.27it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:24<01:07,  1.28it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:25<01:07,  1.27it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:26<01:09,  1.23it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:27<01:09,  1.21it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:28<01:09,  1.19it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:28<01:09,  1.18it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:29<01:10,  1.16it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:30<01:09,  1.16it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:31<01:07,  1.17it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:32<01:07,  1.16it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:33<01:06,  1.16it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:34<01:05,  1.16it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:34<01:04,  1.17it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:35<01:04,  1.16it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:36<01:02,  1.16it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:37<01:01,  1.17it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:38<01:01,  1.16it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:40<01:26,  1.23s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<15:35,  4.70s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:05,  2.45s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:43,  1.74s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:30,  1.38s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<03:53,  1.20s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:35,  1.11s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:19,  1.04s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:10<03:12,  1.00s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:11<03:05,  1.03it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<03:00,  1.05it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:56,  1.07it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:53,  1.08it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:51,  1.09it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:47,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:45,  1.12it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:45,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:45,  1.11it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:47,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:20<02:45,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:43,  1.10it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:22<02:42,  1.10it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:23<02:41,  1.10it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:39,  1.11it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:34,  1.14it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:34,  1.14it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:34,  1.13it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:27<02:34,  1.12it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<02:30,  1.15it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:29<02:31,  1.13it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:30<02:29,  1.14it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:31<02:26,  1.15it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:32<02:22,  1.18it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:22,  1.17it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:33<02:18,  1.20it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:34<02:19,  1.18it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:35<02:16,  1.20it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:36<02:16,  1.19it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:37<02:17,  1.18it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:38<02:16,  1.18it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:38<02:15,  1.18it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:39<02:15,  1.17it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:40<02:15,  1.17it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:41<02:15,  1.16it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:42<02:14,  1.16it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:43<02:14,  1.15it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:44<02:12,  1.16it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:44<02:10,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:45<02:09,  1.17it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:46<02:10,  1.16it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:47<02:09,  1.16it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:48<02:08,  1.16it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:49<02:09,  1.14it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:50<02:10,  1.12it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:11,  1.11it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:10,  1.11it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:53<02:11,  1.09it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:11,  1.08it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:54<02:12,  1.07it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:55<02:08,  1.09it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:56<02:06,  1.10it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:57<02:10,  1.07it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:58<02:12,  1.04it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:59<02:10,  1.05it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:00<02:07,  1.07it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:01<02:05,  1.08it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:02<02:07,  1.05it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:03<02:04,  1.07it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:04<02:00,  1.09it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:05<01:55,  1.13it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:05<01:53,  1.15it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:06<01:51,  1.16it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:07<01:48,  1.18it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:08<01:48,  1.17it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:09<01:47,  1.17it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:10<01:45,  1.18it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:10<01:44,  1.19it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:11<01:45,  1.16it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:12<01:47,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:13<01:47,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:14<01:45,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:15<01:43,  1.15it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:16<01:42,  1.15it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:17<01:46,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:18<01:46,  1.09it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:19<01:43,  1.11it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:19<01:40,  1.14it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:20<01:37,  1.15it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:21<01:37,  1.14it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:22<01:38,  1.12it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:23<01:38,  1.12it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:24<01:36,  1.13it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:25<01:37,  1.11it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:26<01:40,  1.06it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:27<01:37,  1.09it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:28<01:36,  1.09it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:29<01:36,  1.07it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:29<01:33,  1.10it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:30<01:31,  1.12it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:31<01:31,  1.11it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:32<01:31,  1.10it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:33<01:28,  1.12it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:34<01:27,  1.12it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:35<01:25,  1.14it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:36<01:28,  1.09it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:37<01:44,  1.10s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:39<01:49,  1.16s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:39<01:38,  1.06s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:40<01:31,  1.00it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:41<01:26,  1.05it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:42<01:22,  1.09it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:43<01:19,  1.12it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:44<01:16,  1.14it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:44<01:16,  1.13it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:45<01:16,  1.13it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:46<01:15,  1.13it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:47<01:13,  1.14it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:48<01:12,  1.15it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:49<01:10,  1.16it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:50<01:10,  1.15it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:51<01:09,  1.15it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:51<01:10,  1.12it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:52<01:10,  1.11it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:53<01:12,  1.07it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:54<01:10,  1.07it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:55<01:08,  1.09it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:56<01:07,  1.09it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:57<01:05,  1.11it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:58<01:05,  1.10it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:59<01:04,  1.10it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:00<01:02,  1.11it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:01<01:01,  1.12it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:01<01:00,  1.12it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:02<00:59,  1.12it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:03<00:58,  1.13it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:04<00:56,  1.14it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:05<00:56,  1.13it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:06<00:55,  1.13it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:07<00:55,  1.12it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:08<00:53,  1.13it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:09<00:55,  1.08it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:22,  4.94s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:38,  2.62s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:59,  1.83s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:43,  1.45s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:03,  1.25s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:39,  1.13s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:19,  1.03s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:10,  1.01it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:01,  1.05it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:12<02:56,  1.07it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:53,  1.09it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:50,  1.10it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:49,  1.11it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:46,  1.11it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:43,  1.13it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:41,  1.14it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:39,  1.14it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:19<02:37,  1.16it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:20<02:35,  1.16it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:21<02:43,  1.10it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:22<02:40,  1.11it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:23<02:39,  1.11it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:38,  1.12it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:37,  1.12it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:35,  1.13it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:32,  1.14it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:33,  1.12it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:28<02:33,  1.12it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:29<02:32,  1.12it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:30<02:34,  1.10it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:31<02:33,  1.10it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:32<02:31,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:29,  1.11it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:34<02:32,  1.09it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:35<02:32,  1.08it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:36<02:33,  1.07it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:37<02:31,  1.07it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:37<02:20,  1.15it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:38<02:14,  1.20it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:39<02:08,  1.24it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:40<02:03,  1.29it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:40<02:02,  1.29it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:41<02:04,  1.26it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:42<02:04,  1.25it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:43<01:59,  1.29it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:44<02:03,  1.25it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:45<02:05,  1.22it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:45<02:08,  1.19it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:46<02:06,  1.20it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:47<02:08,  1.17it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:48<02:08,  1.16it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:49<02:05,  1.18it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:50<02:03,  1.19it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:51<02:06,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:52<02:11,  1.11it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:52<02:07,  1.13it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:53<02:04,  1.14it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:54<02:00,  1.17it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:55<02:00,  1.17it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:56<01:58,  1.18it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:57<01:57,  1.18it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [00:57<01:55,  1.19it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [00:58<01:56,  1.18it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [00:59<01:54,  1.19it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:00<01:55,  1.17it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:01<01:56,  1.15it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:02<01:55,  1.15it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:03<01:53,  1.16it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:03<01:51,  1.18it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:04<01:52,  1.15it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:05<01:54,  1.13it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:06<01:52,  1.14it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:07<01:54,  1.11it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:08<01:51,  1.13it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:09<01:48,  1.15it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:10<01:52,  1.11it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:11<01:51,  1.10it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:11<01:48,  1.12it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:12<01:44,  1.16it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:13<01:43,  1.16it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:14<01:44,  1.14it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:15<01:46,  1.11it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:16<01:45,  1.11it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:17<01:45,  1.10it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:18<01:44,  1.10it/s]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:19<01:44,  1.10it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:20<01:44,  1.09it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:21<01:43,  1.08it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:21<01:42,  1.08it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:22<01:41,  1.08it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:23<01:41,  1.07it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:24<01:41,  1.06it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:25<01:40,  1.06it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:26<01:40,  1.05it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:27<01:39,  1.06it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:28<01:38,  1.06it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:29<01:39,  1.03it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:30<01:37,  1.04it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:31<01:35,  1.05it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:32<01:34,  1.06it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:33<01:31,  1.08it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:34<01:32,  1.06it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:35<01:34,  1.02it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:36<01:31,  1.05it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:37<01:30,  1.05it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:38<01:28,  1.06it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:39<01:26,  1.07it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:40<01:27,  1.05it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:40<01:23,  1.09it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:41<01:19,  1.13it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:42<01:20,  1.11it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:43<01:15,  1.16it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:44<01:11,  1.21it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:45<01:14,  1.15it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:45<01:13,  1.16it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:46<01:10,  1.19it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:47<01:07,  1.23it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:48<01:07,  1.22it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:49<01:07,  1.20it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:50<01:07,  1.19it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:50<01:07,  1.18it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:51<01:07,  1.16it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:54<01:11,  1.08it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.40it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.03it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.60it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.61it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.55it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.02it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.62it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.14s/it]\u001b[A\n",
+      "Evaluating NLL: 64it [00:01, 70.93it/s]\u001b[A\n",
+      "Evaluating NLL: 117it [00:01, 133.71it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 132.80it/s]\n",
+      "\n",
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  27%|█████▏             | 27/100 [00:00<00:00, 266.31it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  54%|██████████▎        | 54/100 [00:00<00:00, 266.15it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|██████████████████| 100/100 [00:00<00:00, 270.94it/s]\n",
+      " 80%|████████████████████████████████████████████████████             | 4/5 [1:11:29<17:30, 1050.08s/it]\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<15:37,  4.71s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:05<08:13,  2.49s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:51,  1.78s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:40,  1.43s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:02,  1.24s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:50,  1.19s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:31,  1.10s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:17,  1.03s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:10,  1.00it/s]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:03,  1.03it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:13<02:58,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:58,  1.06it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:54,  1.07it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:55,  1.06it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<03:00,  1.02it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:54,  1.05it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:48,  1.09it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:44,  1.10it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:43,  1.10it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:45,  1.09it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:46,  1.08it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:44,  1.08it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:25<02:42,  1.09it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:38,  1.11it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:33,  1.14it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:32,  1.14it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:29,  1.16it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:29<02:27,  1.17it/s]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:30<02:26,  1.17it/s]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:31<02:29,  1.14it/s]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:32<02:34,  1.10it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:32<02:31,  1.11it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:33<02:28,  1.12it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:34<02:25,  1.14it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:35<02:22,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:36<02:30,  1.09it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:37<02:27,  1.11it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:38<02:23,  1.13it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:39<02:21,  1.13it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:39<02:18,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:40<02:18,  1.15it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:41<02:20,  1.12it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:42<02:21,  1.11it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:43<02:23,  1.09it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:44<02:21,  1.09it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:45<02:21,  1.09it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:46<02:22,  1.08it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:47<02:19,  1.09it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:48<02:19,  1.08it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:49<02:18,  1.09it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:50<02:17,  1.08it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:51<02:18,  1.07it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:52<02:16,  1.08it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:52<02:14,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:53<02:14,  1.08it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:54<02:12,  1.09it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:55<02:13,  1.07it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:56<02:12,  1.07it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:57<02:13,  1.05it/s]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:58<02:10,  1.07it/s]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:59<02:07,  1.09it/s]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:00<02:05,  1.10it/s]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:01<02:03,  1.11it/s]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:02<02:06,  1.07it/s]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:03<02:04,  1.09it/s]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:04<02:02,  1.10it/s]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:04<02:02,  1.08it/s]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:05<02:00,  1.10it/s]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:06<01:58,  1.11it/s]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:07<01:58,  1.10it/s]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:08<01:56,  1.10it/s]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:09<01:55,  1.10it/s]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:10<01:54,  1.11it/s]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:11<01:52,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:12<01:50,  1.13it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:12<01:50,  1.12it/s]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:13<01:48,  1.14it/s]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:14<01:46,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:15<01:47,  1.13it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:16<01:44,  1.14it/s]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:17<01:43,  1.15it/s]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:18<01:42,  1.16it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:19<01:40,  1.17it/s]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:20<01:57,  1.02s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:21<02:02,  1.07s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:22<01:53,  1.00it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:23<01:47,  1.06it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:24<01:42,  1.10it/s]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:24<01:39,  1.11it/s]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:25<01:35,  1.15it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:26<01:32,  1.18it/s]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:27<01:30,  1.19it/s]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:28<01:30,  1.18it/s]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:29<01:28,  1.20it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:29<01:24,  1.24it/s]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:30<01:22,  1.26it/s]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:31<01:20,  1.29it/s]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:31<01:16,  1.34it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:32<01:14,  1.36it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:33<01:12,  1.38it/s]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:34<01:12,  1.36it/s]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:34<01:11,  1.37it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:35<01:14,  1.31it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:36<01:15,  1.27it/s]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:37<01:15,  1.26it/s]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:38<01:16,  1.23it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:39<01:16,  1.22it/s]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:39<01:15,  1.22it/s]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [01:40<01:16,  1.19it/s]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [01:41<01:14,  1.20it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [01:42<01:13,  1.20it/s]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [01:43<01:13,  1.20it/s]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [01:44<01:13,  1.18it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [01:44<01:12,  1.19it/s]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [01:45<01:12,  1.18it/s]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [01:46<01:11,  1.18it/s]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [01:47<01:11,  1.17it/s]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [01:48<01:10,  1.17it/s]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [01:49<01:08,  1.18it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [01:50<01:07,  1.19it/s]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [01:50<01:06,  1.18it/s]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [01:51<01:06,  1.18it/s]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [01:52<01:05,  1.18it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [01:53<01:04,  1.19it/s]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [01:54<01:03,  1.18it/s]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [01:55<01:02,  1.19it/s]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [01:55<01:01,  1.18it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [01:56<01:00,  1.19it/s]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [01:57<01:00,  1.17it/s]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [01:58<01:01,  1.14it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [01:59<01:01,  1.12it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:00<01:01,  1.11it/s]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:01<01:01,  1.10it/s]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:02<00:59,  1.11it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:03<00:58,  1.12it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:04<00:57,  1.12it/s]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:04<00:56,  1.12it/s]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:05<00:55,  1.11it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:06<00:54,  1.11it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [02:07<00:54,  1.10it/s]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [02:08<00:54,  1.08it/s]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [02:09<00:53,  1.09it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [02:10<00:51,  1.10it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [02:11<00:50,  1.11it/s]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [02:12<00:49,  1.12it/s]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [02:13<00:47,  1.13it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [02:13<00:47,  1.11it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [02:14<00:45,  1.14it/s]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [02:15<00:46,  1.09it/s]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [02:16<00:45,  1.11it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [02:17<00:43,  1.13it/s]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [02:18<00:41,  1.14it/s]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [02:19<00:40,  1.15it/s]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [02:20<00:39,  1.16it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [02:20<00:38,  1.16it/s]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▉            | 156/200 [02:21<00:37,  1.16it/s]\u001b[A\n",
+      "training:  78%|███████████████████████████████████████████▏           | 157/200 [02:22<00:36,  1.18it/s]\u001b[A\n",
+      "training:  79%|███████████████████████████████████████████▍           | 158/200 [02:23<00:35,  1.19it/s]\u001b[A\n",
+      "training:  80%|███████████████████████████████████████████▋           | 159/200 [02:24<00:34,  1.19it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████           | 160/200 [02:25<00:34,  1.16it/s]\u001b[A\n",
+      "training:  80%|████████████████████████████████████████████▎          | 161/200 [02:26<00:33,  1.17it/s]\u001b[A\n",
+      "training:  81%|████████████████████████████████████████████▌          | 162/200 [02:26<00:32,  1.16it/s]\u001b[A\n",
+      "training:  82%|████████████████████████████████████████████▊          | 163/200 [02:27<00:32,  1.15it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████          | 164/200 [02:28<00:31,  1.15it/s]\u001b[A\n",
+      "training:  82%|█████████████████████████████████████████████▍         | 165/200 [02:29<00:29,  1.17it/s]\u001b[A\n",
+      "training:  83%|█████████████████████████████████████████████▋         | 166/200 [02:30<00:29,  1.17it/s]\u001b[A\n",
+      "training:  84%|█████████████████████████████████████████████▉         | 167/200 [02:31<00:28,  1.16it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▏        | 168/200 [02:32<00:27,  1.16it/s]\u001b[A\n",
+      "training:  84%|██████████████████████████████████████████████▍        | 169/200 [02:32<00:26,  1.17it/s]\u001b[A\n",
+      "training:  85%|██████████████████████████████████████████████▊        | 170/200 [02:33<00:25,  1.16it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████        | 171/200 [02:34<00:24,  1.16it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▎       | 172/200 [02:35<00:23,  1.17it/s]\u001b[A\n",
+      "training:  86%|███████████████████████████████████████████████▌       | 173/200 [02:36<00:23,  1.17it/s]\u001b[A\n",
+      "training:  87%|███████████████████████████████████████████████▊       | 174/200 [02:37<00:22,  1.17it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▏      | 175/200 [02:38<00:21,  1.18it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▍      | 176/200 [02:38<00:20,  1.18it/s]\u001b[A\n",
+      "training:  88%|████████████████████████████████████████████████▋      | 177/200 [02:39<00:19,  1.17it/s]\u001b[A\n",
+      "training:  89%|████████████████████████████████████████████████▉      | 178/200 [02:40<00:18,  1.20it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▏     | 179/200 [02:41<00:17,  1.23it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▌     | 180/200 [02:42<00:16,  1.23it/s]\u001b[A\n",
+      "training:  90%|█████████████████████████████████████████████████▊     | 181/200 [02:42<00:15,  1.25it/s]\u001b[A\n",
+      "training:  91%|██████████████████████████████████████████████████     | 182/200 [02:43<00:14,  1.27it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▎    | 183/200 [02:44<00:13,  1.26it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▌    | 184/200 [02:45<00:12,  1.23it/s]\u001b[A\n",
+      "training:  92%|██████████████████████████████████████████████████▉    | 185/200 [02:46<00:12,  1.23it/s]\u001b[A\n",
+      "training:  93%|███████████████████████████████████████████████████▏   | 186/200 [02:46<00:11,  1.23it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▍   | 187/200 [02:47<00:10,  1.21it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▋   | 188/200 [02:48<00:10,  1.17it/s]\u001b[A\n",
+      "training:  94%|███████████████████████████████████████████████████▉   | 189/200 [02:49<00:09,  1.14it/s]\u001b[A\n",
+      "training:  95%|████████████████████████████████████████████████████▎  | 190/200 [02:50<00:08,  1.12it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▌  | 191/200 [02:51<00:08,  1.12it/s]\u001b[A\n",
+      "training:  96%|████████████████████████████████████████████████████▊  | 192/200 [02:52<00:07,  1.12it/s]\u001b[A\n",
+      "training:  96%|█████████████████████████████████████████████████████  | 193/200 [02:53<00:06,  1.12it/s]\u001b[A\n",
+      "training:  97%|█████████████████████████████████████████████████████▎ | 194/200 [02:54<00:05,  1.11it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▋ | 195/200 [02:55<00:04,  1.13it/s]\u001b[A\n",
+      "training:  98%|█████████████████████████████████████████████████████▉ | 196/200 [02:55<00:03,  1.12it/s]\u001b[A\n",
+      "training:  98%|██████████████████████████████████████████████████████▏| 197/200 [02:56<00:02,  1.12it/s]\u001b[A\n",
+      "training:  99%|██████████████████████████████████████████████████████▍| 198/200 [02:57<00:01,  1.15it/s]\u001b[A\n",
+      "training: 100%|██████████████████████████████████████████████████████▋| 199/200 [02:58<00:00,  1.16it/s]\u001b[A\n",
+      "training: 100%|███████████████████████████████████████████████████████| 200/200 [02:59<00:00,  1.12it/s]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:01,  5.13s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<08:40,  2.63s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:06<05:59,  1.82s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:07<04:41,  1.44s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:08<04:03,  1.25s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:09<03:43,  1.15s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:10<03:23,  1.06s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:11<03:09,  1.01it/s]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:12<03:12,  1.01s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:13<03:03,  1.04it/s]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:14<02:55,  1.07it/s]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:14<02:52,  1.09it/s]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:15<02:50,  1.10it/s]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:16<02:49,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:17<02:47,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:18<02:46,  1.10it/s]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:19<02:44,  1.11it/s]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:20<02:42,  1.12it/s]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:21<02:50,  1.06it/s]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:22<02:48,  1.07it/s]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:23<02:44,  1.09it/s]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:24<02:43,  1.09it/s]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:24<02:39,  1.11it/s]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:25<02:38,  1.11it/s]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:26<02:37,  1.11it/s]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:27<02:35,  1.12it/s]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:28<02:38,  1.09it/s]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:30<03:05,  1.08s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:31<03:02,  1.07s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:31<02:51,  1.01s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:32<02:41,  1.05it/s]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:33<02:36,  1.08it/s]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:34<02:33,  1.09it/s]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:35<02:30,  1.10it/s]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:36<02:25,  1.13it/s]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:37<02:21,  1.16it/s]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:38<02:23,  1.14it/s]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:38<02:24,  1.12it/s]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:39<02:22,  1.13it/s]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:40<02:17,  1.16it/s]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:41<02:13,  1.20it/s]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:42<02:13,  1.19it/s]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:43<02:11,  1.20it/s]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:43<02:07,  1.22it/s]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:44<02:08,  1.21it/s]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [00:45<02:06,  1.22it/s]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [00:46<02:05,  1.22it/s]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [00:47<02:04,  1.22it/s]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [00:47<02:03,  1.22it/s]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [00:48<02:05,  1.20it/s]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [00:49<02:04,  1.20it/s]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [00:50<02:05,  1.18it/s]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [00:51<02:07,  1.15it/s]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [00:52<02:08,  1.14it/s]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [00:53<02:04,  1.16it/s]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [00:54<02:04,  1.15it/s]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [00:54<02:03,  1.16it/s]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [00:56<02:16,  1.04it/s]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [00:57<02:27,  1.04s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [00:58<02:32,  1.09s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [00:59<02:39,  1.15s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:01<02:43,  1.18s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:02<02:50,  1.24s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:03<02:51,  1.26s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:05<02:51,  1.27s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:06<02:50,  1.27s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:07<02:47,  1.26s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:08<02:45,  1.25s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:10<02:45,  1.26s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:11<02:41,  1.24s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:12<02:41,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:13<02:40,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:15<02:39,  1.25s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:16<02:39,  1.26s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:17<02:38,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:18<02:39,  1.29s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:20<02:39,  1.30s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:21<02:39,  1.30s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:22<02:39,  1.32s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:24<02:39,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:25<02:38,  1.33s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:26<02:36,  1.33s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:28<02:34,  1.32s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:29<02:32,  1.32s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:30<02:31,  1.32s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:32<02:30,  1.32s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:33<02:26,  1.30s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:34<02:21,  1.26s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:35<02:18,  1.25s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:37<02:16,  1.24s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [01:38<02:11,  1.21s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [01:39<02:11,  1.22s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [01:40<02:11,  1.23s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [01:41<02:09,  1.22s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [01:43<02:08,  1.22s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [01:44<02:06,  1.22s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [01:45<02:05,  1.22s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [01:46<02:03,  1.21s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [01:48<02:03,  1.22s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [01:49<02:01,  1.21s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [01:50<02:00,  1.21s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [01:51<02:02,  1.25s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [01:52<01:58,  1.23s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [01:54<01:57,  1.22s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [01:55<01:54,  1.21s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [01:56<01:52,  1.20s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [01:57<01:51,  1.20s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [01:58<01:49,  1.19s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:00<01:48,  1.19s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:02<01:40,  1.11s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<17:32,  5.29s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:29,  2.88s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<07:07,  2.17s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:09<06:02,  1.85s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:10<05:28,  1.68s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<05:05,  1.57s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:13<04:48,  1.50s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:14<04:40,  1.46s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:16<04:33,  1.43s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:17<04:30,  1.42s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:18<04:26,  1.41s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:20<04:22,  1.40s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:21<04:19,  1.39s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:22<04:16,  1.38s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:24<04:13,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:25<04:11,  1.37s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:26<04:07,  1.35s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:28<04:06,  1.36s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:29<04:00,  1.33s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:30<03:59,  1.33s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:32<03:55,  1.32s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:33<03:54,  1.32s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:34<03:54,  1.33s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:36<03:59,  1.36s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:37<04:04,  1.40s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:39<04:02,  1.39s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:40<03:54,  1.36s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:42<04:15,  1.49s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:43<04:09,  1.46s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:44<03:56,  1.39s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:46<03:51,  1.37s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:47<03:49,  1.36s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:48<03:49,  1.38s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:50<03:46,  1.37s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:51<03:43,  1.36s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:52<03:38,  1.33s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:54<03:36,  1.33s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:55<03:36,  1.33s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:56<03:37,  1.35s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:58<03:35,  1.35s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:59<03:29,  1.32s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [01:00<03:19,  1.26s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [01:01<03:13,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [01:02<03:09,  1.22s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:04<03:06,  1.21s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:05<03:04,  1.20s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:06<03:02,  1.19s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:07<03:01,  1.20s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:08<03:00,  1.19s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:10<02:58,  1.19s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:11<02:59,  1.20s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:12<02:56,  1.19s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:13<02:53,  1.18s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:14<02:52,  1.18s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:16<02:53,  1.19s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:17<02:51,  1.19s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:18<02:50,  1.19s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:19<02:47,  1.18s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:20<02:44,  1.17s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:21<02:43,  1.17s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:23<02:42,  1.17s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:24<02:41,  1.17s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:25<02:38,  1.16s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:26<02:39,  1.17s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:27<02:40,  1.19s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:28<02:38,  1.18s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:30<02:38,  1.19s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:31<02:36,  1.19s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:32<02:38,  1.21s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:33<02:39,  1.23s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:35<02:37,  1.22s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:36<02:33,  1.20s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:37<02:31,  1.19s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:38<02:29,  1.19s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:39<02:25,  1.17s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:40<02:24,  1.16s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:42<02:26,  1.19s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:43<02:30,  1.23s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:44<02:30,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:46<02:31,  1.26s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:47<02:32,  1.28s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:48<02:31,  1.28s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:31,  1.30s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:51<02:29,  1.29s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:52<02:29,  1.30s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:53<02:28,  1.30s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:55<02:27,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:56<02:26,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:24,  1.30s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:58<02:19,  1.27s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:16,  1.25s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:18,  1.28s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:02<02:17,  1.29s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:04<02:14,  1.27s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:05<02:12,  1.27s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:06<02:10,  1.25s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:07<02:08,  1.25s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:08<02:06,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:10<02:05,  1.24s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<02:01,  1.22s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:12<01:58,  1.20s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:13<01:57,  1.20s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:15<01:58,  1.23s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:16<02:01,  1.27s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:17<01:58,  1.25s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:18<01:58,  1.26s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:20<01:59,  1.28s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:21<01:58,  1.28s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:22<01:57,  1.30s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:24<01:58,  1.31s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:25<01:58,  1.33s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:26<01:58,  1.34s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:28<01:57,  1.35s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:29<01:55,  1.35s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:30<01:54,  1.35s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:32<01:53,  1.35s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:33<01:52,  1.36s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:35<01:51,  1.36s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:36<01:50,  1.37s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:37<01:48,  1.36s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:39<01:48,  1.37s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:40<01:46,  1.36s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:41<01:43,  1.34s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:43<01:41,  1.33s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:44<01:41,  1.35s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:45<01:38,  1.33s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:47<01:36,  1.32s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:48<01:33,  1.30s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:49<01:32,  1.30s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:50<01:31,  1.30s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:52<01:29,  1.30s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:53<01:30,  1.33s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:54<01:28,  1.33s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:56<01:27,  1.33s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:58<01:26,  1.33s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:05<16:34,  5.00s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:18,  2.82s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:51,  2.09s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:39,  1.73s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<04:58,  1.53s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:37,  1.43s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:21,  1.35s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:09,  1.30s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:14<04:08,  1.30s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:08,  1.31s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:08,  1.31s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:18<04:07,  1.32s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<04:06,  1.32s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<04:05,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:22<04:04,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<04:03,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<03:59,  1.31s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:26<03:58,  1.31s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:27<03:58,  1.32s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:57,  1.32s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:56,  1.32s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:50,  1.29s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:33<03:46,  1.28s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:42,  1.26s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:37,  1.24s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:33,  1.23s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:34,  1.24s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:39<03:30,  1.22s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:30,  1.23s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:29,  1.23s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:29,  1.24s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:44<03:27,  1.23s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:45<03:27,  1.24s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:26,  1.24s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:24,  1.24s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:49<03:28,  1.27s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:50<03:26,  1.27s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:51<03:23,  1.25s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:21,  1.25s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:54<03:19,  1.25s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:55<03:18,  1.25s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:56<03:15,  1.24s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:13,  1.24s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:59<03:11,  1.23s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [01:00<03:12,  1.24s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:01<03:13,  1.26s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:02<03:15,  1.28s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:04<03:17,  1.30s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:05<03:16,  1.30s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:06<03:16,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:08<03:17,  1.33s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:09<03:15,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:10<03:14,  1.32s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:12<03:12,  1.32s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:13<03:12,  1.33s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:14<03:13,  1.34s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:16<03:06,  1.30s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:17<03:06,  1.31s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:18<03:03,  1.30s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:20<02:58,  1.27s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:21<02:53,  1.25s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:22<02:50,  1.24s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:23<02:47,  1.22s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:24<02:45,  1.21s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:25<02:41,  1.20s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:27<02:40,  1.20s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:28<02:38,  1.19s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:29<02:38,  1.20s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:30<02:43,  1.25s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:32<02:45,  1.27s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:33<02:41,  1.25s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:34<02:42,  1.27s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:36<02:41,  1.28s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:37<02:39,  1.27s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:38<02:38,  1.26s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:39<02:39,  1.28s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:41<02:41,  1.31s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:42<02:33,  1.26s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:43<02:40,  1.33s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:45<02:37,  1.31s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:46<02:38,  1.33s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:47<02:37,  1.34s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:49<02:38,  1.35s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:50<02:36,  1.35s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:51<02:34,  1.34s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:53<02:49,  1.49s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:54<02:38,  1.41s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:56<02:30,  1.34s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:57<02:28,  1.34s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:58<02:26,  1.33s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:24,  1.33s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:01<02:23,  1.32s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:02<02:20,  1.32s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:04<02:19,  1.32s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:05<02:17,  1.31s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:06<02:16,  1.31s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:07<02:15,  1.31s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:09<02:14,  1.31s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:10<02:11,  1.30s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<02:10,  1.30s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<02:08,  1.30s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:14<02:07,  1.30s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:15<02:04,  1.29s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:17<02:03,  1.28s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:18<02:00,  1.27s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:19<01:58,  1.26s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:20<01:56,  1.26s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:21<01:54,  1.24s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:23<01:53,  1.25s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:24<01:51,  1.24s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:25<01:50,  1.24s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:26<01:48,  1.24s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:29<01:55,  1.32s/it]\n",
+      "\n",
+      "training:   0%|                                                                 | 0/200 [00:00<?, ?it/s]\u001b[A\n",
+      "training:   0%|▎                                                        | 1/200 [00:04<16:13,  4.89s/it]\u001b[A\n",
+      "training:   1%|▌                                                        | 2/200 [00:06<09:08,  2.77s/it]\u001b[A\n",
+      "training:   2%|▊                                                        | 3/200 [00:07<06:53,  2.10s/it]\u001b[A\n",
+      "training:   2%|█▏                                                       | 4/200 [00:08<05:45,  1.76s/it]\u001b[A\n",
+      "training:   2%|█▍                                                       | 5/200 [00:09<05:05,  1.57s/it]\u001b[A\n",
+      "training:   3%|█▋                                                       | 6/200 [00:11<04:44,  1.47s/it]\u001b[A\n",
+      "training:   4%|█▉                                                       | 7/200 [00:12<04:41,  1.46s/it]\u001b[A\n",
+      "training:   4%|██▎                                                      | 8/200 [00:13<04:32,  1.42s/it]\u001b[A\n",
+      "training:   4%|██▌                                                      | 9/200 [00:15<04:25,  1.39s/it]\u001b[A\n",
+      "training:   5%|██▊                                                     | 10/200 [00:16<04:21,  1.38s/it]\u001b[A\n",
+      "training:   6%|███                                                     | 11/200 [00:17<04:15,  1.35s/it]\u001b[A\n",
+      "training:   6%|███▎                                                    | 12/200 [00:19<04:09,  1.33s/it]\u001b[A\n",
+      "training:   6%|███▋                                                    | 13/200 [00:20<04:07,  1.32s/it]\u001b[A\n",
+      "training:   7%|███▉                                                    | 14/200 [00:21<04:05,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▏                                                   | 15/200 [00:23<04:04,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▍                                                   | 16/200 [00:24<04:02,  1.32s/it]\u001b[A\n",
+      "training:   8%|████▊                                                   | 17/200 [00:25<04:01,  1.32s/it]\u001b[A\n",
+      "training:   9%|█████                                                   | 18/200 [00:27<03:56,  1.30s/it]\u001b[A\n",
+      "training:  10%|█████▎                                                  | 19/200 [00:28<03:49,  1.27s/it]\u001b[A\n",
+      "training:  10%|█████▌                                                  | 20/200 [00:29<03:45,  1.25s/it]\u001b[A\n",
+      "training:  10%|█████▉                                                  | 21/200 [00:30<03:41,  1.24s/it]\u001b[A\n",
+      "training:  11%|██████▏                                                 | 22/200 [00:31<03:35,  1.21s/it]\u001b[A\n",
+      "training:  12%|██████▍                                                 | 23/200 [00:32<03:32,  1.20s/it]\u001b[A\n",
+      "training:  12%|██████▋                                                 | 24/200 [00:34<03:31,  1.20s/it]\u001b[A\n",
+      "training:  12%|███████                                                 | 25/200 [00:35<03:32,  1.21s/it]\u001b[A\n",
+      "training:  13%|███████▎                                                | 26/200 [00:36<03:28,  1.20s/it]\u001b[A\n",
+      "training:  14%|███████▌                                                | 27/200 [00:37<03:25,  1.19s/it]\u001b[A\n",
+      "training:  14%|███████▊                                                | 28/200 [00:38<03:23,  1.18s/it]\u001b[A\n",
+      "training:  14%|████████                                                | 29/200 [00:40<03:28,  1.22s/it]\u001b[A\n",
+      "training:  15%|████████▍                                               | 30/200 [00:41<03:30,  1.24s/it]\u001b[A\n",
+      "training:  16%|████████▋                                               | 31/200 [00:42<03:26,  1.22s/it]\u001b[A\n",
+      "training:  16%|████████▉                                               | 32/200 [00:43<03:21,  1.20s/it]\u001b[A\n",
+      "training:  16%|█████████▏                                              | 33/200 [00:44<03:17,  1.18s/it]\u001b[A\n",
+      "training:  17%|█████████▌                                              | 34/200 [00:46<03:16,  1.18s/it]\u001b[A\n",
+      "training:  18%|█████████▊                                              | 35/200 [00:47<03:17,  1.19s/it]\u001b[A\n",
+      "training:  18%|██████████                                              | 36/200 [00:48<03:16,  1.20s/it]\u001b[A\n",
+      "training:  18%|██████████▎                                             | 37/200 [00:49<03:14,  1.19s/it]\u001b[A\n",
+      "training:  19%|██████████▋                                             | 38/200 [00:50<03:12,  1.19s/it]\u001b[A\n",
+      "training:  20%|██████████▉                                             | 39/200 [00:52<03:10,  1.19s/it]\u001b[A\n",
+      "training:  20%|███████████▏                                            | 40/200 [00:53<03:14,  1.22s/it]\u001b[A\n",
+      "training:  20%|███████████▍                                            | 41/200 [00:54<03:14,  1.22s/it]\u001b[A\n",
+      "training:  21%|███████████▊                                            | 42/200 [00:55<03:16,  1.24s/it]\u001b[A\n",
+      "training:  22%|████████████                                            | 43/200 [00:57<03:10,  1.22s/it]\u001b[A\n",
+      "training:  22%|████████████▎                                           | 44/200 [00:58<03:16,  1.26s/it]\u001b[A\n",
+      "training:  22%|████████████▌                                           | 45/200 [00:59<03:12,  1.24s/it]\u001b[A\n",
+      "training:  23%|████████████▉                                           | 46/200 [01:00<03:07,  1.22s/it]\u001b[A\n",
+      "training:  24%|█████████████▏                                          | 47/200 [01:02<03:05,  1.21s/it]\u001b[A\n",
+      "training:  24%|█████████████▍                                          | 48/200 [01:03<03:09,  1.25s/it]\u001b[A\n",
+      "training:  24%|█████████████▋                                          | 49/200 [01:04<03:13,  1.28s/it]\u001b[A\n",
+      "training:  25%|██████████████                                          | 50/200 [01:06<03:15,  1.31s/it]\u001b[A\n",
+      "training:  26%|██████████████▎                                         | 51/200 [01:07<03:16,  1.32s/it]\u001b[A\n",
+      "training:  26%|██████████████▌                                         | 52/200 [01:08<03:18,  1.34s/it]\u001b[A\n",
+      "training:  26%|██████████████▊                                         | 53/200 [01:10<03:18,  1.35s/it]\u001b[A\n",
+      "training:  27%|███████████████                                         | 54/200 [01:11<03:17,  1.35s/it]\u001b[A\n",
+      "training:  28%|███████████████▍                                        | 55/200 [01:12<03:16,  1.36s/it]\u001b[A\n",
+      "training:  28%|███████████████▋                                        | 56/200 [01:14<03:17,  1.37s/it]\u001b[A\n",
+      "training:  28%|███████████████▉                                        | 57/200 [01:15<03:15,  1.37s/it]\u001b[A\n",
+      "training:  29%|████████████████▏                                       | 58/200 [01:17<03:14,  1.37s/it]\u001b[A\n",
+      "training:  30%|████████████████▌                                       | 59/200 [01:18<03:14,  1.38s/it]\u001b[A\n",
+      "training:  30%|████████████████▊                                       | 60/200 [01:19<03:11,  1.37s/it]\u001b[A\n",
+      "training:  30%|█████████████████                                       | 61/200 [01:21<03:12,  1.39s/it]\u001b[A\n",
+      "training:  31%|█████████████████▎                                      | 62/200 [01:22<03:13,  1.40s/it]\u001b[A\n",
+      "training:  32%|█████████████████▋                                      | 63/200 [01:24<03:10,  1.39s/it]\u001b[A\n",
+      "training:  32%|█████████████████▉                                      | 64/200 [01:25<03:06,  1.37s/it]\u001b[A\n",
+      "training:  32%|██████████████████▏                                     | 65/200 [01:26<03:02,  1.35s/it]\u001b[A\n",
+      "training:  33%|██████████████████▍                                     | 66/200 [01:28<03:01,  1.35s/it]\u001b[A\n",
+      "training:  34%|██████████████████▊                                     | 67/200 [01:29<02:59,  1.35s/it]\u001b[A\n",
+      "training:  34%|███████████████████                                     | 68/200 [01:30<02:56,  1.34s/it]\u001b[A\n",
+      "training:  34%|███████████████████▎                                    | 69/200 [01:32<02:54,  1.33s/it]\u001b[A\n",
+      "training:  35%|███████████████████▌                                    | 70/200 [01:33<02:54,  1.34s/it]\u001b[A\n",
+      "training:  36%|███████████████████▉                                    | 71/200 [01:34<02:50,  1.33s/it]\u001b[A\n",
+      "training:  36%|████████████████████▏                                   | 72/200 [01:35<02:49,  1.32s/it]\u001b[A\n",
+      "training:  36%|████████████████████▍                                   | 73/200 [01:37<02:47,  1.32s/it]\u001b[A\n",
+      "training:  37%|████████████████████▋                                   | 74/200 [01:39<03:02,  1.45s/it]\u001b[A\n",
+      "training:  38%|█████████████████████                                   | 75/200 [01:40<02:59,  1.44s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▎                                  | 76/200 [01:41<02:48,  1.36s/it]\u001b[A\n",
+      "training:  38%|█████████████████████▌                                  | 77/200 [01:42<02:43,  1.33s/it]\u001b[A\n",
+      "training:  39%|█████████████████████▊                                  | 78/200 [01:44<02:36,  1.28s/it]\u001b[A\n",
+      "training:  40%|██████████████████████                                  | 79/200 [01:45<02:33,  1.27s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▍                                 | 80/200 [01:46<02:29,  1.25s/it]\u001b[A\n",
+      "training:  40%|██████████████████████▋                                 | 81/200 [01:47<02:27,  1.24s/it]\u001b[A\n",
+      "training:  41%|██████████████████████▉                                 | 82/200 [01:48<02:26,  1.24s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▏                                | 83/200 [01:50<02:24,  1.23s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▌                                | 84/200 [01:51<02:26,  1.26s/it]\u001b[A\n",
+      "training:  42%|███████████████████████▊                                | 85/200 [01:52<02:28,  1.29s/it]\u001b[A\n",
+      "training:  43%|████████████████████████                                | 86/200 [01:54<02:30,  1.32s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▎                               | 87/200 [01:55<02:27,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▋                               | 88/200 [01:56<02:26,  1.31s/it]\u001b[A\n",
+      "training:  44%|████████████████████████▉                               | 89/200 [01:58<02:25,  1.31s/it]\u001b[A\n",
+      "training:  45%|█████████████████████████▏                              | 90/200 [01:59<02:24,  1.31s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▍                              | 91/200 [02:00<02:23,  1.32s/it]\u001b[A\n",
+      "training:  46%|█████████████████████████▊                              | 92/200 [02:02<02:22,  1.32s/it]\u001b[A\n",
+      "training:  46%|██████████████████████████                              | 93/200 [02:03<02:21,  1.32s/it]\u001b[A\n",
+      "training:  47%|██████████████████████████▎                             | 94/200 [02:04<02:19,  1.31s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▌                             | 95/200 [02:06<02:17,  1.31s/it]\u001b[A\n",
+      "training:  48%|██████████████████████████▉                             | 96/200 [02:07<02:11,  1.26s/it]\u001b[A\n",
+      "training:  48%|███████████████████████████▏                            | 97/200 [02:08<02:06,  1.23s/it]\u001b[A\n",
+      "training:  49%|███████████████████████████▍                            | 98/200 [02:09<02:03,  1.22s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▋                            | 99/200 [02:10<02:00,  1.20s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▌                           | 100/200 [02:11<01:59,  1.20s/it]\u001b[A\n",
+      "training:  50%|███████████████████████████▊                           | 101/200 [02:13<01:57,  1.19s/it]\u001b[A\n",
+      "training:  51%|████████████████████████████                           | 102/200 [02:14<01:56,  1.19s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▎                          | 103/200 [02:15<01:55,  1.19s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▌                          | 104/200 [02:16<01:53,  1.18s/it]\u001b[A\n",
+      "training:  52%|████████████████████████████▉                          | 105/200 [02:17<01:53,  1.19s/it]\u001b[A\n",
+      "training:  53%|█████████████████████████████▏                         | 106/200 [02:18<01:50,  1.18s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▍                         | 107/200 [02:20<01:49,  1.17s/it]\u001b[A\n",
+      "training:  54%|█████████████████████████████▋                         | 108/200 [02:21<01:47,  1.17s/it]\u001b[A\n",
+      "training:  55%|█████████████████████████████▉                         | 109/200 [02:22<01:45,  1.16s/it]\u001b[A\n",
+      "training:  55%|██████████████████████████████▎                        | 110/200 [02:23<01:45,  1.17s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▌                        | 111/200 [02:24<01:45,  1.19s/it]\u001b[A\n",
+      "training:  56%|██████████████████████████████▊                        | 112/200 [02:26<01:45,  1.20s/it]\u001b[A\n",
+      "training:  56%|███████████████████████████████                        | 113/200 [02:27<01:43,  1.19s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▎                       | 114/200 [02:28<01:40,  1.17s/it]\u001b[A\n",
+      "training:  57%|███████████████████████████████▌                       | 115/200 [02:29<01:40,  1.18s/it]\u001b[A\n",
+      "training:  58%|███████████████████████████████▉                       | 116/200 [02:30<01:40,  1.19s/it]\u001b[A\n",
+      "training:  58%|████████████████████████████████▏                      | 117/200 [02:32<01:42,  1.24s/it]\u001b[A\n",
+      "training:  59%|████████████████████████████████▍                      | 118/200 [02:33<01:40,  1.22s/it]\u001b[A\n",
+      "training:  60%|████████████████████████████████▋                      | 119/200 [02:34<01:39,  1.22s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████                      | 120/200 [02:35<01:36,  1.21s/it]\u001b[A\n",
+      "training:  60%|█████████████████████████████████▎                     | 121/200 [02:36<01:34,  1.20s/it]\u001b[A\n",
+      "training:  61%|█████████████████████████████████▌                     | 122/200 [02:38<01:31,  1.18s/it]\u001b[A\n",
+      "training:  62%|█████████████████████████████████▊                     | 123/200 [02:39<01:30,  1.17s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████                     | 124/200 [02:40<01:29,  1.18s/it]\u001b[A\n",
+      "training:  62%|██████████████████████████████████▍                    | 125/200 [02:41<01:28,  1.18s/it]\u001b[A\n",
+      "training:  63%|██████████████████████████████████▋                    | 126/200 [02:42<01:27,  1.18s/it]\u001b[A\n",
+      "training:  64%|██████████████████████████████████▉                    | 127/200 [02:43<01:25,  1.18s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▏                   | 128/200 [02:45<01:24,  1.18s/it]\u001b[A\n",
+      "training:  64%|███████████████████████████████████▍                   | 129/200 [02:46<01:23,  1.17s/it]\u001b[A\n",
+      "training:  65%|███████████████████████████████████▊                   | 130/200 [02:47<01:22,  1.18s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████                   | 131/200 [02:48<01:22,  1.19s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▎                  | 132/200 [02:49<01:21,  1.20s/it]\u001b[A\n",
+      "training:  66%|████████████████████████████████████▌                  | 133/200 [02:51<01:22,  1.23s/it]\u001b[A\n",
+      "training:  67%|████████████████████████████████████▊                  | 134/200 [02:52<01:22,  1.25s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▏                 | 135/200 [02:53<01:22,  1.27s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▍                 | 136/200 [02:55<01:21,  1.28s/it]\u001b[A\n",
+      "training:  68%|█████████████████████████████████████▋                 | 137/200 [02:56<01:21,  1.29s/it]\u001b[A\n",
+      "training:  69%|█████████████████████████████████████▉                 | 138/200 [02:57<01:20,  1.29s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▏                | 139/200 [02:58<01:18,  1.29s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▌                | 140/200 [03:00<01:18,  1.31s/it]\u001b[A\n",
+      "training:  70%|██████████████████████████████████████▊                | 141/200 [03:01<01:18,  1.33s/it]\u001b[A\n",
+      "training:  71%|███████████████████████████████████████                | 142/200 [03:03<01:17,  1.34s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▎               | 143/200 [03:04<01:15,  1.33s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▌               | 144/200 [03:05<01:14,  1.33s/it]\u001b[A\n",
+      "training:  72%|███████████████████████████████████████▉               | 145/200 [03:06<01:11,  1.30s/it]\u001b[A\n",
+      "training:  73%|████████████████████████████████████████▏              | 146/200 [03:08<01:10,  1.30s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▍              | 147/200 [03:09<01:08,  1.28s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▋              | 148/200 [03:10<01:07,  1.29s/it]\u001b[A\n",
+      "training:  74%|████████████████████████████████████████▉              | 149/200 [03:12<01:04,  1.27s/it]\u001b[A\n",
+      "training:  75%|█████████████████████████████████████████▎             | 150/200 [03:13<01:03,  1.27s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▌             | 151/200 [03:14<01:03,  1.29s/it]\u001b[A\n",
+      "training:  76%|█████████████████████████████████████████▊             | 152/200 [03:15<01:01,  1.27s/it]\u001b[A\n",
+      "training:  76%|██████████████████████████████████████████             | 153/200 [03:17<00:57,  1.23s/it]\u001b[A\n",
+      "training:  77%|██████████████████████████████████████████▎            | 154/200 [03:18<00:57,  1.25s/it]\u001b[A\n",
+      "training:  78%|██████████████████████████████████████████▋            | 155/200 [03:20<00:58,  1.30s/it]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.01it/s]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.59it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.02s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.29it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.23it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.03s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:01,  3.51it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:01,  1.01s/it]\u001b[A\n",
+      "Evaluating NLL: 7it [00:02,  3.09it/s]\n",
+      "\n",
+      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
+      "Evaluating NLL: 1it [00:00,  1.11it/s]\u001b[A\n",
+      "Evaluating NLL: 36it [00:01, 48.51it/s]\u001b[A\n",
+      "Evaluating NLL: 73it [00:01, 100.51it/s]\u001b[A\n",
+      "Evaluating NLL: 104it [00:01, 139.87it/s]\u001b[A\n",
+      "Evaluating NLL: 133it [00:01, 157.05it/s]\u001b[A\n",
+      "Evaluating NLL: 200it [00:01, 128.95it/s]\n",
       "\n",
-      "training:   0%|                                                           | 0/200 [00:00<?, ?it/s]\u001b[A\n",
-      "training:   0%|▎                                                  | 1/200 [00:06<20:24,  6.15s/it]\u001b[A\n",
-      "training:   1%|▌                                                  | 2/200 [00:07<11:13,  3.40s/it]\u001b[A\n",
-      "training:   2%|▊                                                  | 3/200 [00:09<08:12,  2.50s/it]\u001b[A\n",
-      "training:   2%|█                                                  | 4/200 [00:10<06:47,  2.08s/it]\u001b[A\n",
-      "training:   2%|█▎                                                 | 5/200 [00:12<06:19,  1.95s/it]\u001b[A\n",
-      "training:   3%|█▌                                                 | 6/200 [00:13<05:55,  1.83s/it]\u001b[A\n",
-      "training:   4%|█▊                                                 | 7/200 [00:15<05:35,  1.74s/it]\u001b[A\n",
-      "training:   4%|██                                                 | 8/200 [00:17<05:51,  1.83s/it]\u001b[A\n",
-      "training:   4%|██▎                                                | 9/200 [00:19<05:38,  1.77s/it]\u001b[A\n",
-      "training:   5%|██▌                                               | 10/200 [00:20<05:32,  1.75s/it]\u001b[A\n",
-      "training:   6%|██▊                                               | 11/200 [00:22<05:27,  1.73s/it]\u001b[A\n",
-      "training:   6%|███                                               | 12/200 [00:24<05:17,  1.69s/it]\u001b[A\n",
-      "training:   6%|███▎                                              | 13/200 [00:25<05:24,  1.73s/it]\u001b[A\n",
-      "training:   7%|███▌                                              | 14/200 [00:27<05:10,  1.67s/it]\u001b[A\n",
-      "training:   8%|███▊                                              | 15/200 [00:28<05:01,  1.63s/it]\u001b[A\n",
-      "training:   8%|████                                              | 16/200 [00:30<05:16,  1.72s/it]\u001b[A\n",
-      "training:   8%|████▎                                             | 17/200 [00:32<05:21,  1.76s/it]\u001b[A\n",
-      "training:   9%|████▌                                             | 18/200 [00:34<05:21,  1.77s/it]\u001b[A\n",
-      "training:  10%|████▊                                             | 19/200 [00:36<05:09,  1.71s/it]\u001b[A\n",
-      "training:  10%|█████                                             | 20/200 [00:37<05:11,  1.73s/it]\u001b[A\n",
-      "training:  10%|█████▎                                            | 21/200 [00:39<05:04,  1.70s/it]\u001b[A\n",
-      "training:  11%|█████▌                                            | 22/200 [00:41<04:57,  1.67s/it]\u001b[A\n",
-      "training:  12%|█████▊                                            | 23/200 [00:42<04:51,  1.65s/it]\u001b[A\n",
-      "training:  12%|██████                                            | 24/200 [00:44<04:36,  1.57s/it]\u001b[A\n",
-      "training:  12%|██████▎                                           | 25/200 [00:45<04:33,  1.56s/it]\u001b[A\n",
-      "training:  13%|██████▌                                           | 26/200 [00:47<04:26,  1.53s/it]\u001b[A\n",
-      "training:  14%|██████▊                                           | 27/200 [00:48<04:15,  1.48s/it]\u001b[A\n",
-      "training:  14%|███████                                           | 28/200 [00:49<04:04,  1.42s/it]\u001b[A\n",
-      "training:  14%|███████▏                                          | 29/200 [00:51<03:59,  1.40s/it]\u001b[A\n",
-      "training:  15%|███████▌                                          | 30/200 [00:52<04:03,  1.43s/it]\u001b[A\n",
-      "training:  16%|███████▊                                          | 31/200 [00:54<04:18,  1.53s/it]\u001b[A\n",
-      "training:  16%|████████                                          | 32/200 [00:55<04:21,  1.56s/it]\u001b[A\n",
-      "training:  16%|████████▎                                         | 33/200 [00:57<04:30,  1.62s/it]\u001b[A\n",
-      "training:  17%|████████▌                                         | 34/200 [00:59<04:36,  1.66s/it]\u001b[A\n",
-      "training:  18%|████████▊                                         | 35/200 [01:01<04:44,  1.72s/it]\u001b[A\n",
-      "training:  18%|█████████                                         | 36/200 [01:02<04:40,  1.71s/it]\u001b[A\n",
-      "training:  18%|█████████▎                                        | 37/200 [01:04<04:32,  1.67s/it]\u001b[A\n",
-      "training:  19%|█████████▌                                        | 38/200 [01:06<04:35,  1.70s/it]\u001b[A\n",
-      "training:  20%|█████████▊                                        | 39/200 [01:07<04:27,  1.66s/it]\u001b[A\n",
-      "training:  20%|██████████                                        | 40/200 [01:09<04:31,  1.70s/it]\u001b[A\n",
-      "training:  20%|██████████▎                                       | 41/200 [01:11<04:33,  1.72s/it]\u001b[A\n",
-      "training:  21%|██████████▌                                       | 42/200 [01:13<04:28,  1.70s/it]\u001b[A\n",
-      "training:  22%|██████████▊                                       | 43/200 [01:14<04:23,  1.68s/it]\u001b[A\n",
-      "training:  22%|███████████                                       | 44/200 [01:16<04:16,  1.64s/it]\u001b[A\n",
-      "training:  22%|███████████▎                                      | 45/200 [01:17<04:16,  1.66s/it]\u001b[A\n",
-      "training:  23%|███████████▌                                      | 46/200 [01:20<04:51,  1.89s/it]\u001b[A\n",
-      "training:  24%|███████████▊                                      | 47/200 [01:22<04:38,  1.82s/it]\u001b[A\n",
-      "training:  24%|████████████                                      | 48/200 [01:23<04:31,  1.79s/it]\u001b[A\n",
-      "training:  24%|████████████▎                                     | 49/200 [01:25<04:19,  1.72s/it]\u001b[A\n",
-      "training:  25%|████████████▌                                     | 50/200 [01:26<04:11,  1.68s/it]\u001b[A\n",
-      "training:  26%|████████████▊                                     | 51/200 [01:28<04:18,  1.73s/it]\u001b[A\n",
-      "training:  26%|█████████████                                     | 52/200 [01:30<04:16,  1.73s/it]\u001b[A\n",
-      "training:  26%|█████████████▎                                    | 53/200 [01:32<04:10,  1.70s/it]\u001b[A\n",
-      "training:  27%|█████████████▌                                    | 54/200 [01:33<04:07,  1.70s/it]\u001b[A\n",
-      "training:  28%|█████████████▊                                    | 55/200 [01:35<04:02,  1.68s/it]\u001b[A\n",
-      "training:  28%|██████████████                                    | 56/200 [01:37<04:12,  1.75s/it]\u001b[A\n",
-      "training:  28%|██████████████▏                                   | 57/200 [01:39<04:08,  1.74s/it]\u001b[A\n",
-      "training:  29%|██████████████▍                                   | 58/200 [01:40<04:02,  1.71s/it]\u001b[A\n",
-      "training:  30%|██████████████▊                                   | 59/200 [01:42<04:04,  1.73s/it]\u001b[A\n",
-      "training:  30%|███████████████                                   | 60/200 [01:44<04:04,  1.74s/it]\u001b[A\n",
-      "training:  30%|███████████████▎                                  | 61/200 [01:46<04:09,  1.79s/it]\u001b[A\n",
-      "training:  31%|███████████████▌                                  | 62/200 [01:47<04:04,  1.77s/it]\u001b[A\n",
-      "training:  32%|███████████████▊                                  | 63/200 [01:49<04:04,  1.78s/it]\u001b[A\n",
-      "training:  32%|████████████████                                  | 64/200 [01:51<03:57,  1.75s/it]\u001b[A\n",
-      "training:  32%|████████████████▎                                 | 65/200 [01:53<03:59,  1.78s/it]\u001b[A\n",
-      "training:  33%|████████████████▌                                 | 66/200 [01:54<03:52,  1.73s/it]\u001b[A\n",
-      "training:  34%|████████████████▊                                 | 67/200 [01:56<03:54,  1.76s/it]\u001b[A\n",
-      "training:  34%|█████████████████                                 | 68/200 [01:58<03:47,  1.73s/it]\u001b[A\n",
-      "training:  34%|█████████████████▎                                | 69/200 [01:59<03:41,  1.69s/it]\u001b[A\n",
-      "training:  35%|█████████████████▌                                | 70/200 [02:01<03:47,  1.75s/it]\u001b[A\n",
-      "training:  36%|█████████████████▊                                | 71/200 [02:03<03:41,  1.71s/it]\u001b[A\n",
-      "training:  36%|██████████████████                                | 72/200 [02:05<03:39,  1.72s/it]\u001b[A\n",
-      "training:  36%|██████████████████▎                               | 73/200 [02:07<03:44,  1.76s/it]\u001b[A\n",
-      "training:  37%|██████████████████▌                               | 74/200 [02:08<03:36,  1.72s/it]\u001b[A\n",
-      "training:  38%|██████████████████▊                               | 75/200 [02:10<03:32,  1.70s/it]\u001b[A\n",
-      "training:  38%|███████████████████                               | 76/200 [02:12<03:28,  1.68s/it]\u001b[A\n",
-      "training:  38%|███████████████████▎                              | 77/200 [02:13<03:21,  1.64s/it]\u001b[A\n",
-      "training:  39%|███████████████████▌                              | 78/200 [02:15<03:24,  1.67s/it]\u001b[A\n",
-      "training:  40%|███████████████████▊                              | 79/200 [02:16<03:21,  1.67s/it]\u001b[A\n",
-      "training:  40%|████████████████████                              | 80/200 [02:18<03:19,  1.66s/it]\u001b[A\n",
-      "training:  40%|████████████████████▎                             | 81/200 [02:20<03:17,  1.66s/it]\u001b[A\n",
-      "training:  41%|████████████████████▌                             | 82/200 [02:21<03:15,  1.65s/it]\u001b[A\n",
-      "training:  42%|████████████████████▊                             | 83/200 [02:23<03:16,  1.68s/it]\u001b[A\n",
-      "training:  42%|█████████████████████                             | 84/200 [02:25<03:16,  1.70s/it]\u001b[A\n",
-      "training:  42%|█████████████████████▎                            | 85/200 [02:27<03:15,  1.70s/it]\u001b[A\n",
-      "training:  43%|█████████████████████▌                            | 86/200 [02:28<03:14,  1.71s/it]\u001b[A\n",
-      "training:  44%|█████████████████████▊                            | 87/200 [02:30<03:08,  1.67s/it]\u001b[A\n",
-      "training:  44%|██████████████████████                            | 88/200 [02:31<03:02,  1.63s/it]\u001b[A\n",
-      "training:  44%|██████████████████████▎                           | 89/200 [02:33<03:06,  1.68s/it]\u001b[A\n",
-      "training:  45%|██████████████████████▌                           | 90/200 [02:35<03:06,  1.70s/it]\u001b[A\n",
-      "training:  46%|██████████████████████▊                           | 91/200 [02:37<03:07,  1.72s/it]\u001b[A\n",
-      "training:  46%|███████████████████████                           | 92/200 [02:38<03:02,  1.69s/it]\u001b[A\n",
-      "training:  46%|███████████████████████▎                          | 93/200 [02:40<02:57,  1.66s/it]\u001b[A\n",
-      "training:  47%|███████████████████████▌                          | 94/200 [02:42<02:59,  1.70s/it]\u001b[A\n",
-      "training:  48%|███████████████████████▊                          | 95/200 [02:43<02:54,  1.66s/it]\u001b[A\n",
-      "training:  48%|████████████████████████                          | 96/200 [02:45<02:51,  1.64s/it]\u001b[A\n",
-      "training:  48%|████████████████████████▎                         | 97/200 [02:47<02:52,  1.68s/it]\u001b[A\n",
-      "training:  49%|████████████████████████▌                         | 98/200 [02:48<02:48,  1.65s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▊                         | 99/200 [02:50<02:46,  1.64s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▌                        | 100/200 [02:52<02:48,  1.68s/it]\u001b[A\n",
-      "training:  50%|████████████████████████▋                        | 101/200 [02:53<02:43,  1.65s/it]\u001b[A\n",
-      "training:  51%|████████████████████████▉                        | 102/200 [02:55<02:37,  1.61s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▏                       | 103/200 [02:56<02:33,  1.58s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▍                       | 104/200 [02:58<02:36,  1.63s/it]\u001b[A\n",
-      "training:  52%|█████████████████████████▋                       | 105/200 [03:00<02:35,  1.64s/it]\u001b[A\n",
-      "training:  53%|█████████████████████████▉                       | 106/200 [03:01<02:29,  1.60s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▏                      | 107/200 [03:03<02:27,  1.58s/it]\u001b[A\n",
-      "training:  54%|██████████████████████████▍                      | 108/200 [03:04<02:30,  1.64s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▋                      | 109/200 [03:06<02:28,  1.63s/it]\u001b[A\n",
-      "training:  55%|██████████████████████████▉                      | 110/200 [03:08<02:32,  1.70s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▏                     | 111/200 [03:10<02:33,  1.73s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▍                     | 112/200 [03:11<02:28,  1.69s/it]\u001b[A\n",
-      "training:  56%|███████████████████████████▋                     | 113/200 [03:13<02:27,  1.70s/it]\u001b[A\n",
-      "training:  57%|███████████████████████████▉                     | 114/200 [03:15<02:23,  1.66s/it]\u001b[A\n",
-      "training:  57%|████████████████████████████▏                    | 115/200 [03:16<02:16,  1.60s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▍                    | 116/200 [03:18<02:10,  1.56s/it]\u001b[A\n",
-      "training:  58%|████████████████████████████▋                    | 117/200 [03:19<02:08,  1.55s/it]\u001b[A\n",
-      "training:  59%|████████████████████████████▉                    | 118/200 [03:21<02:14,  1.64s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▏                   | 119/200 [03:23<02:15,  1.68s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▍                   | 120/200 [03:24<02:16,  1.71s/it]\u001b[A\n",
-      "training:  60%|█████████████████████████████▋                   | 121/200 [03:26<02:13,  1.69s/it]\u001b[A\n",
-      "training:  61%|█████████████████████████████▉                   | 122/200 [03:28<02:16,  1.75s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▏                  | 123/200 [03:30<02:12,  1.72s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▍                  | 124/200 [03:32<02:17,  1.81s/it]\u001b[A\n",
-      "training:  62%|██████████████████████████████▋                  | 125/200 [03:33<02:11,  1.76s/it]\u001b[A\n",
-      "training:  63%|██████████████████████████████▊                  | 126/200 [03:35<02:14,  1.81s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████                  | 127/200 [03:38<02:34,  2.11s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▎                 | 128/200 [03:40<02:28,  2.06s/it]\u001b[A\n",
-      "training:  64%|███████████████████████████████▌                 | 129/200 [03:42<02:24,  2.04s/it]\u001b[A\n",
-      "training:  65%|███████████████████████████████▊                 | 130/200 [03:44<02:16,  1.95s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████                 | 131/200 [03:46<02:11,  1.90s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▎                | 132/200 [03:47<02:04,  1.83s/it]\u001b[A\n",
-      "training:  66%|████████████████████████████████▌                | 133/200 [03:49<01:58,  1.77s/it]\u001b[A\n",
-      "training:  67%|████████████████████████████████▊                | 134/200 [03:51<01:58,  1.79s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████                | 135/200 [03:52<01:51,  1.72s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▎               | 136/200 [03:54<01:46,  1.67s/it]\u001b[A\n",
-      "training:  68%|█████████████████████████████████▌               | 137/200 [03:55<01:44,  1.66s/it]\u001b[A\n",
-      "training:  69%|█████████████████████████████████▊               | 138/200 [03:57<01:41,  1.64s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████               | 139/200 [03:59<01:38,  1.62s/it]\u001b[A\n",
-      "training:  70%|██████████████████████████████████▎              | 140/200 [04:02<01:43,  1.73s/it]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.34s/it]\u001b[A\n",
-      "Evaluating NLL: 7it [00:02,  2.78it/s]\n",
-      "\n",
-      "Evaluating NLL: 0it [00:00, ?it/s]\u001b[A\n",
-      "Evaluating NLL: 1it [00:01,  1.07s/it]\u001b[A\n",
-      "Evaluating NLL: 21it [00:01, 24.18it/s]\u001b[A\n",
-      "Evaluating NLL: 46it [00:01, 56.10it/s]\u001b[A\n",
-      "Evaluating NLL: 77it [00:01, 97.80it/s]\u001b[A\n",
-      "Evaluating NLL: 108it [00:01, 138.03it/s]\u001b[A\n",
-      "Evaluating NLL: 133it [00:01, 126.76it/s]\u001b[A\n",
-      "Evaluating NLL: 159it [00:01, 151.29it/s]\u001b[A\n",
-      "Evaluating NLL: 200it [00:01, 100.10it/s]\n",
-      "\n",
-      "Bootstrapping to compute confidence interval:   0%|                       | 0/100 [00:00<?, ?it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  20%|██▌          | 20/100 [00:00<00:00, 192.89it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  40%|█████▏       | 40/100 [00:00<00:00, 183.97it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval:  67%|████████▋    | 67/100 [00:00<00:00, 219.68it/s]\u001b[A\n",
-      "Bootstrapping to compute confidence interval: 100%|████████████| 100/100 [00:00<00:00, 209.69it/s]\n",
-      "100%|███████████████████████████████████████████████████████████| 5/5 [1:50:46<00:00, 1329.23s/it]\n"
+      "Bootstrapping to compute confidence interval:   0%|                             | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   4%|▊                    | 4/100 [00:00<00:02, 39.42it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:   9%|█▉                   | 9/100 [00:00<00:02, 45.09it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  15%|███                 | 15/100 [00:00<00:01, 47.73it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  20%|████                | 20/100 [00:00<00:01, 43.95it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  25%|█████               | 25/100 [00:00<00:01, 43.68it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  30%|██████              | 30/100 [00:00<00:01, 41.98it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  37%|███████▍            | 37/100 [00:00<00:01, 49.32it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  43%|████████▌           | 43/100 [00:00<00:01, 45.39it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  48%|█████████▌          | 48/100 [00:01<00:01, 44.88it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  53%|██████████▌         | 53/100 [00:01<00:01, 43.18it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  58%|███████████▌        | 58/100 [00:01<00:01, 41.88it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  65%|█████████████       | 65/100 [00:01<00:00, 48.71it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  71%|██████████████▏     | 71/100 [00:01<00:00, 45.25it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  76%|███████████████▏    | 76/100 [00:01<00:00, 44.36it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  81%|████████████████▏   | 81/100 [00:01<00:00, 42.72it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  86%|█████████████████▏  | 86/100 [00:01<00:00, 44.02it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval:  91%|██████████████████▏ | 91/100 [00:02<00:00, 41.99it/s]\u001b[A\n",
+      "Bootstrapping to compute confidence interval: 100%|███████████████████| 100/100 [00:02<00:00, 43.75it/s]\n",
+      "100%|█████████████████████████████████████████████████████████████████| 5/5 [1:26:48<00:00, 1041.63s/it]\n"
      ]
     }
    ],
@@ -2623,6 +9550,7 @@
     "noise_sigma_list = [1e-6, 1e-5, 1e-4, 1e-3, 4e-3]\n",
     "\n",
     "num_nyquist_samples = 16\n",
+    "num_mi_models = 5\n",
     "\n",
     "mi_by_object = {}\n",
     "mi_by_object_lo = {}\n",
@@ -2635,7 +9563,7 @@
     "  for noise_sigma in tqdm(noise_sigma_list):\n",
     "\n",
     "    initial_kernel, initial_params, optimized_params, objects, initial_mi, optimized_mi, optimized_mi_lower, optimized_mi_upper = optimize_PSF_and_estimate_mi(\n",
-    "        objects_fn, noise_sigma, num_nyquist_samples=num_nyquist_samples, verbose=True, confidence=confidence)\n",
+    "        objects_fn, noise_sigma, num_nyquist_samples=num_nyquist_samples, verbose=False, confidence=confidence,   num_mi_models=num_mi_models,     max_epochs = 2000, estimate_with_pixel_cnn=True,)\n",
     "\n",
     "    mutual_information.append(optimized_mi)\n",
     "    mi_lo.append(optimized_mi_lower)\n",
@@ -2650,16 +9578,16 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-10-11T02:36:31.349189Z",
-     "iopub.status.busy": "2024-10-11T02:36:31.348745Z",
-     "iopub.status.idle": "2024-10-11T02:36:33.389548Z",
-     "shell.execute_reply": "2024-10-11T02:36:33.388731Z"
+     "iopub.execute_input": "2024-10-17T03:16:15.496688Z",
+     "iopub.status.busy": "2024-10-17T03:16:15.496420Z",
+     "iopub.status.idle": "2024-10-17T03:16:17.572255Z",
+     "shell.execute_reply": "2024-10-17T03:16:17.571564Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
@@ -2669,7 +9597,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 500x500 with 1 Axes>"
       ]
diff --git a/simulations_1d/signal_utils_1D.py b/simulations_1d/signal_utils_1D.py
index 931991e..bf43c1f 100644
--- a/simulations_1d/signal_utils_1D.py
+++ b/simulations_1d/signal_utils_1D.py
@@ -15,7 +15,7 @@
 
 from encoding_information.information_estimation import estimate_mutual_information, estimate_information
 from encoding_information.models import PixelCNN, AnalyticGaussianNoiseModel, StationaryGaussianProcess
-
+from encoding_information.image_utils import extract_patches
 
 NUM_NYQUIST_SAMPLES = 8
 UPSAMPLED_SIGNAL_LENGTH = 512
@@ -512,10 +512,11 @@ def conv_forward_model(parameters, objects, align_center=False, integrate_output
 
 
 def optimize_PSF_and_estimate_mi(objects_fn, noise_sigma, initial_kernel=None,
-                                 learning_rate=1e-2, learning_rate_decay=0.999, verbose=True,
-                                 estimate_with_pixel_cnn=True,
+                                 learning_rate=1e-2, learning_rate_decay=0.999, verbose=False,
+                                 estimate_with_pixel_cnn=False,
                                  loss_improvement_patience=2000, max_epochs=5000, num_nyquist_samples=NUM_NYQUIST_SAMPLES, 
-                                 upsampled_signal_length=UPSAMPLED_SIGNAL_LENGTH, test_fraction=0.1, confidence=0.95):
+                                 upsampled_signal_length=UPSAMPLED_SIGNAL_LENGTH, test_fraction=0.1, confidence=0.95,
+                                 integrate_output_signals=True, num_mi_models=1):
   if estimate_with_pixel_cnn:
       # make sure num_nyquist_samples is a perfect square and upsampled_signal_length is a multiple of it
         if not np.sqrt(num_nyquist_samples) % 1 == 0:
@@ -542,30 +543,38 @@ def optimize_PSF_and_estimate_mi(objects_fn, noise_sigma, initial_kernel=None,
   initial_mi = None
 
   output_signals = conv_forward_model(optimized_params, test_objects,
-                                            integrate_output_signals=True, 
+                                            integrate_output_signals=integrate_output_signals, 
                                             num_nyquist_samples=num_nyquist_samples,
                                             upsampled_signal_length=upsampled_signal_length)
-  noisy_output_signals = output_signals + jax.random.normal(jax.random.PRNGKey(onp.random.randint(10000)), output_signals.shape) * noise_sigma
-  fake_images = noisy_output_signals.reshape(-1, int(np.sqrt(num_nyquist_samples)), int(np.sqrt(num_nyquist_samples))) * scale_factor
-  if verbose:
-     print('computing optimized mi')
 
+  noisy_output_signals = output_signals + jax.random.normal(jax.random.PRNGKey(onp.random.randint(10000)), output_signals.shape) * noise_sigma
 
-  # optimized_mi = estimate_mutual_information(fake_images, gaussian_noise_sigma=noise_sigma * scale_factor, verbose=False)
-  train_fake_images = fake_images[:int(fake_images.shape[0] * (1 - test_fraction))]
-  test_fake_images = fake_images[int(fake_images.shape[0] * (1 - test_fraction)):]
+  fake_images = noisy_output_signals.reshape(-1, int(np.sqrt(noisy_output_signals.shape[1])), int(np.sqrt(noisy_output_signals.shape[1]))) * scale_factor
 
   if estimate_with_pixel_cnn:
-    model = PixelCNN()
-    model.fit(train_fake_images, verbose=False)
+    patches = extract_patches(fake_images, num_patches=2000, patch_size=60, strategy='cropped', crop_location=(0, 0))
+    train_fake_images = patches[:int(patches.shape[0] * (1 - test_fraction))]
+    test_fake_images = patches[int(patches.shape[0] * (1 - test_fraction)):]
+
+    models = []
+    for i in range(num_mi_models):
+        model = PixelCNN()
+        model.fit(train_fake_images, verbose=False)
+        models.append(model)
   else:
-    model = StationaryGaussianProcess(train_fake_images)
-    model.fit(train_fake_images)
+    train_fake_images = fake_images[:int(fake_images.shape[0] * (1 - test_fraction))]
+    test_fake_images = fake_images[int(fake_images.shape[0] * (1 - test_fraction)):]
+
+    models = []
+    for i in range(num_mi_models):
+        model = StationaryGaussianProcess(train_fake_images)
+        model.fit(train_fake_images)
+        models.append(model)
 
-  noise_model = AnalyticGaussianNoiseModel(noise_sigma*scale_factor)
+  noise_model = AnalyticGaussianNoiseModel(noise_sigma)
 
   info, lower, upper = estimate_information(
-        model, noise_model, train_fake_images, test_fake_images, confidence_interval=confidence)
+        models, noise_model, train_fake_images, test_fake_images, confidence_interval=confidence)
 
                                                         
   return initial_kernel, initial_params, optimized_params, objects, initial_mi, info, lower, upper
diff --git a/src/encoding_information/information_estimation.py b/src/encoding_information/information_estimation.py
index 82c4710..efdee95 100644
--- a/src/encoding_information/information_estimation.py
+++ b/src/encoding_information/information_estimation.py
@@ -62,8 +62,9 @@ def estimate_information(measurement_model, noise_model, train_set, test_set,
 
     if isinstance(measurement_model, list):
         nlls = np.array([m.compute_negative_log_likelihood(test_set) for m in measurement_model])
-        best_model_index = np.argmin(nlls)
+        best_model_index = np.nanargmin(nlls)
         measurement_model = measurement_model[best_model_index]
+        nll = nlls[best_model_index]
     else:
         nll = measurement_model.compute_negative_log_likelihood(test_set)
     
diff --git a/src/encoding_information/models/gaussian_process.py b/src/encoding_information/models/gaussian_process.py
index 87638d4..38aa2f7 100644
--- a/src/encoding_information/models/gaussian_process.py
+++ b/src/encoding_information/models/gaussian_process.py
@@ -909,6 +909,6 @@ def compute_analytic_entropy(self):
         Compute the differential entropy per pixel of the Gaussian process
         """
         D = self.cov_mat.shape[0]
-        sum_log_evs = np.sum(np.log(np.linalg.eigvals(self.cov_mat)))
+        sum_log_evs = np.sum(np.log(np.linalg.eigvalsh(self.cov_mat)))
         gaussian_entropy = 0.5 *(sum_log_evs + D * np.log(2* np.pi * np.e)) / D
         return gaussian_entropy
\ No newline at end of file
diff --git a/src/encoding_information/models/model_base_class.py b/src/encoding_information/models/model_base_class.py
index e3d89b9..361353c 100644
--- a/src/encoding_information/models/model_base_class.py
+++ b/src/encoding_information/models/model_base_class.py
@@ -457,6 +457,9 @@ def train_model(train_images, state, batch_size, num_val_samples, steps_per_epoc
     if num_val_samples >= train_images.shape[0]:
         num_val_samples = int(train_images.shape[0] * 0.1)
         warnings.warn(f'Number of validation samples must be less than the number of training samples. Using {num_val_samples} validation samples instead.')
+    if num_val_samples < 1:
+        warnings.warn('Number of validation samples must be at least 1. Using 1 validation sample instead.')
+        num_val_samples = 1
     train_ds_iterator, val_loader_maker_fn = make_dataset_generators(train_images,
                      batch_size=batch_size, num_val_samples=num_val_samples, condition_vectors=condition_vectors,
                      add_gaussian_noise=add_gaussian_noise, add_uniform_noise=add_uniform_noise, seed=seed